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

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from diffractsim import MonochromaticField, nm, mm, cm F = MonochromaticField( wavelength=632.8 * nm, extent_x=25. * mm, extent_y=25. * mm, Nx=2000, Ny=2000,power = 0.1 ) F.add_gaussian_beam(0.7*mm) F.add_spatial_noise(noise_radius = 2.2*mm, f_mean = 1/(0.2*mm), f_size = 1/(0.5*mm), A = 0.2, N= 50) F.add_lens(f = 50*cm) F.propagate(50*cm) F.add_circular_slit( 0, 0, 0.28*mm) F.propagate(50*cm) F.add_lens(f = 50*cm) F.propagate(30*cm) rgb = F.get_colors() F.plot(rgb, xlim=[-2.5,2.5], ylim=[-2.5,2.5])

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/test_PokerScoring.py

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import unittest import PokerScoring import CardDeck import random class TestPokerHands(unittest.TestCase): def setUp(self): # suit values diamonds = 0 hearts = 1 spade = 2 clubs = 3 # duplicates setup self.multiples = [CardDeck.Card(diamonds, 4),CardDeck.Card(hearts, 4),CardDeck.Card(spade, 4),CardDeck.Card(clubs, 4)] # full house setup self.doubles = [[CardDeck.Card(diamonds, 4), CardDeck.Card(spade, 4)],[CardDeck.Card(hearts, 1), CardDeck.Card(clubs, 1)]] self.doubles_same = [[CardDeck.Card(diamonds, 4), CardDeck.Card(spade, 4)],[CardDeck.Card(hearts, 0), CardDeck.Card(clubs, 0)]] self.only_triples = [[CardDeck.Card(diamonds, 0), CardDeck.Card(spade, 0),CardDeck.Card(hearts, 0)]] self.straight_test = [] # straight setup for i in range(7): self.straight_test.append(CardDeck.Card(clubs, i)) self.flush_test = [] # flush setup for i in range(7): self.flush_test.append(CardDeck.Card(hearts, random.randint(0, 13))) # straight flush setup self.straights = [] self.flushes = [] straight = [] flush = [] # generate straight flush for i in range(5): straight.append(CardDeck.Card(hearts, i)) for i in range(5): flush.append(CardDeck.Card(hearts, i)) self.flushes.append(flush) self.straights.append(straight) pass def test_duplicates(self): dupl = PokerScoring.duplicates(self.multiples) self.assertEqual(3, len(dupl)) def test_full_house(self): # test doubles and triples with unique values full_house = PokerScoring.full_house(self.only_triples, self.doubles) self.assertEqual(2, len(full_house)) for hands in full_house: self.assertEqual(5, len(hands)) # test doubles and triples where values arent unique full_house = PokerScoring.full_house(self.only_triples, self.doubles_same) self.assertEqual(1, len(full_house)) for hands in full_house: self.assertEqual(5, len(hands)) def test_two_pair(self): two_pair = PokerScoring.two_pair(self.doubles) self.assertEqual(2, len(two_pair)) def test_straights(self): straights = PokerScoring.connectivity(self.straight_test) self.assertEqual(3, len(straights)) for straight in straights: self.assertEqual(5, len(straight)) def test_flushes(self): flushes = PokerScoring.same_suit(self.flush_test) self.assertEqual(3, len(flushes)) for flush in flushes: self.assertEqual(5, len(flush)) def test_straight_flush(self): straight_flushes = PokerScoring.connected_flushes(self.flushes, self.straights) self.assertEqual(1, len(straight_flushes))

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matthew.m1996@gmail.com

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# coding:utf-8 # 定义一组字典列表，用来表示多个数据样本（每个字典代表一个数据样本）。 measurements = [{'city': 'Dubai', 'temperature': 33.}, {'city': 'London', 'temperature': 12.}, {'city': 'San Fransisco', 'temperature': 18.}] # 从sklearn.feature_extraction 导入 DictVectorizer from sklearn.feature_extraction import DictVectorizer # 初始化DictVectorizer特征抽取器 vec = DictVectorizer() # 输出转化之后的特征矩阵。 print vec.fit_transform(measurements).toarray() # 输出各个维度的特征含义。 print vec.get_feature_names() # ['city=Dubai', 'city=London', 'city=San Fransisco', 'temperature'] # 从sklearn.datasets里导入20类新闻文本数据抓取器。 from sklearn.datasets import fetch_20newsgroups # 从互联网上即时下载新闻样本,subset='all'参数代表下载全部近2万条文本存储在变量news中。 news = fetch_20newsgroups(subset='all') # 从sklearn.cross_validation导入train_test_split模块用于分割数据集。 from sklearn.cross_validation import train_test_split # 对news中的数据data进行分割，25%的文本用作测试集；75%作为训练集。 X_train, X_test, y_train, y_test = train_test_split(news.data, news.target, test_size=0.25, random_state=33) # 从sklearn.feature_extraction.text里导入CountVectorizer from sklearn.feature_extraction.text import CountVectorizer # 采用默认的配置对CountVectorizer进行初始化（默认配置不去除英文停用词），并且赋值给变量count_vec。 count_vec = CountVectorizer() # 只使用词频统计的方式将原始训练和测试文本转化为特征向量。 X_count_train = count_vec.fit_transform(X_train) X_count_test = count_vec.transform(X_test) # 从sklearn.naive_bayes里导入朴素贝叶斯分类器。 from sklearn.naive_bayes import MultinomialNB # 使用默认的配置对分类器进行初始化。 mnb_count = MultinomialNB() # 使用朴素贝叶斯分类器，对CountVectorizer（不去除停用词）后的训练样本进行参数学习。 mnb_count.fit(X_count_train, y_train) # 输出模型准确性结果。 print 'The accuracy of classifying 20newsgroups using Naive Bayes (CountVectorizer without filtering stopwords):', mnb_count.score( X_count_test, y_test) # 将分类预测的结果存储在变量y_count_predict中。 y_count_predict = mnb_count.predict(X_count_test) # 从sklearn.metrics 导入 classification_report。 from sklearn.metrics import classification_report # 输出更加详细的其他评价分类性能的指标。 print classification_report(y_test, y_count_predict, target_names=news.target_names) # 从sklearn.feature_extraction.text里分别导入TfidfVectorizer。 from sklearn.feature_extraction.text import TfidfVectorizer # 采用默认的配置对TfidfVectorizer进行初始化（默认配置不去除英文停用词），并且赋值给变量tfidf_vec。 tfidf_vec = TfidfVectorizer() # 使用tfidf的方式，将原始训练和测试文本转化为特征向量。 X_tfidf_train = tfidf_vec.fit_transform(X_train) X_tfidf_test = tfidf_vec.transform(X_test) # 依然使用默认配置的朴素贝叶斯分类器，在相同的训练和测试数据上，对新的特征量化方式进行性能评估。 mnb_tfidf = MultinomialNB() mnb_tfidf.fit(X_tfidf_train, y_train) print 'The accuracy of classifying 20newsgroups with Naive Bayes (TfidfVectorizer without filtering stopwords):', mnb_tfidf.score( X_tfidf_test, y_test) y_tfidf_predict = mnb_tfidf.predict(X_tfidf_test) print classification_report(y_test, y_tfidf_predict, target_names=news.target_names) # 继续沿用代码56与代码57中导入的工具包（在同一份源代码中，或者不关闭解释器环境），分别使用停用词过滤配置初始化CountVectorizer与TfidfVectorizer。 count_filter_vec, tfidf_filter_vec = CountVectorizer(analyzer='word', stop_words='english'), TfidfVectorizer( analyzer='word', stop_words='english') # 使用带有停用词过滤的CountVectorizer对训练和测试文本分别进行量化处理。 X_count_filter_train = count_filter_vec.fit_transform(X_train) X_count_filter_test = count_filter_vec.transform(X_test) # 使用带有停用词过滤的TfidfVectorizer对训练和测试文本分别进行量化处理。 X_tfidf_filter_train = tfidf_filter_vec.fit_transform(X_train) X_tfidf_filter_test = tfidf_filter_vec.transform(X_test) # 初始化默认配置的朴素贝叶斯分类器，并对CountVectorizer后的数据进行预测与准确性评估。 mnb_count_filter = MultinomialNB() mnb_count_filter.fit(X_count_filter_train, y_train) print 'The accuracy of classifying 20newsgroups using Naive Bayes (CountVectorizer by filtering stopwords):', mnb_count_filter.score( X_count_filter_test, y_test) y_count_filter_predict = mnb_count_filter.predict(X_count_filter_test) # 初始化另一个默认配置的朴素贝叶斯分类器，并对TfidfVectorizer后的数据进行预测与准确性评估。 mnb_tfidf_filter = MultinomialNB() mnb_tfidf_filter.fit(X_tfidf_filter_train, y_train) print 'The accuracy of classifying 20newsgroups with Naive Bayes (TfidfVectorizer by filtering stopwords):', mnb_tfidf_filter.score( X_tfidf_filter_test, y_test) y_tfidf_filter_predict = mnb_tfidf_filter.predict(X_tfidf_filter_test) # 对上述两个模型进行更加详细的性能评估。 from sklearn.metrics import classification_report print classification_report(y_test, y_count_filter_predict, target_names=news.target_names) print classification_report(y_test, y_tfidf_filter_predict, target_names=news.target_names)

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flytonus@sina.cn

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Kit-Data-Science-Telecom-2021-2022/Kit-Data_Marie-Elisabeth-Campo

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#!/usr/bin/python -tt # Copyright 2010 Google Inc. # Licensed under the Apache License, Version 2.0 # http://www.apache.org/licenses/LICENSE-2.0 # Google's Python Class # http://code.google.com/edu/languages/google-python-class/ # Basic list exercises # Fill in the code for the functions below. main() is already set up # to call the functions with a few different inputs, # printing 'OK' when each function is correct. # The starter code for each function includes a 'return' # which is just a placeholder for your code. # It's ok if you do not complete all the functions, and there # are some additional functions to try in list2.py. # A. match_ends # Given a list of strings, return the count of the number of # strings where the string length is 2 or more and the first # and last chars of the string are the same. # Note: python does not have a ++ operator, but += works. def match_ends(words): return len([w for w in words if len(w)>=2 and w[0]==w[-1]]) # B. front_x # Given a list of strings, return a list with the strings # in sorted order, except group all the strings that begin with 'x' first. # e.g. ['mix', 'xyz', 'apple', 'xanadu', 'aardvark'] yields # ['xanadu', 'xyz', 'aardvark', 'apple', 'mix'] # Hint: this can be done by making 2 lists and sorting each of them # before combining them. def front_x(words): o = list(w for w in words if w[0] != 'x') o = sorted(o) x = list(w for w in words if w[0] == 'x') x = sorted(x) return x + o # C. sort_last # Given a list of non-empty tuples, return a list sorted in increasing # order by the last element in each tuple. # e.g. [(1, 7), (1, 3), (3, 4, 5), (2, 2)] yields # [(2, 2), (1, 3), (3, 4, 5), (1, 7)] # Hint: use a custom key= function to extract the last element form each tuple. def sort_last(tuples): l = sorted(list(t[::-1] for t in tuples)) l1 = list(t[::-1] for t in l) return l1 return # Simple provided test() function used in main() to print # what each function returns vs. what it's supposed to return. def test(got, expected): if got == expected: prefix = ' OK ' else: prefix = ' X ' print ('%s got: %s expected: %s' % (prefix, repr(got), repr(expected))) # Calls the above functions with interesting inputs. def main(): print ('match_ends') test(match_ends(['aba', 'xyz', 'aa', 'x', 'bbb']), 3) test(match_ends(['', 'x', 'xy', 'xyx', 'xx']), 2) test(match_ends(['aaa', 'be', 'abc', 'hello']), 1) print print ('front_x') test(front_x(['bbb', 'ccc', 'axx', 'xzz', 'xaa']), ['xaa', 'xzz', 'axx', 'bbb', 'ccc']) test(front_x(['ccc', 'bbb', 'aaa', 'xcc', 'xaa']), ['xaa', 'xcc', 'aaa', 'bbb', 'ccc']) test(front_x(['mix', 'xyz', 'apple', 'xanadu', 'aardvark']), ['xanadu', 'xyz', 'aardvark', 'apple', 'mix']) print print ('sort_last') test(sort_last([(1, 3), (3, 2), (2, 1)]), [(2, 1), (3, 2), (1, 3)]) test(sort_last([(2, 3), (1, 2), (3, 1)]), [(3, 1), (1, 2), (2, 3)]) test(sort_last([(1, 7), (1, 3), (3, 4, 5), (2, 2)]), [(2, 2), (1, 3), (3, 4, 5), (1, 7)]) if __name__ == '__main__': main()

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marinette@gmx.fr

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Risauce/Pre2015Code

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import numpy as np import matplotlib.pyplot as plt # ----------------------------------------------------- # CSCI 127, Lab 12 # November 21, 2017 # Your Name # ----------------------------------------------------- def read_file(name): input_file = open(name, "r") number_buckets = int(input_file.readline()) total_counties = int(input_file.readline()) county_populations = np.zeros([total_counties], dtype="int") for county_number in range(total_counties): line = input_file.readline().split(",") county_populations[county_number] = int(line[1]) county_populations.sort() input_file.close() return number_buckets, county_populations # ----------------------------------------------------- def print_summary(averages): print("Population Grouping Summary") print("---------------------------") for grouping in range(len(averages)): print("Grouping", grouping + 1, "has a population average of", averages[grouping]) # ----------------------------------------------------- # Do not change anything above this line # ----------------------------------------------------- def calculate_averages(number_buckets, county_populations): numberOfSplit = len(county_populations) / number_buckets for i in range(number_buckets): if i == 0: average = np.average(county_populations[0:numberOfSplit]) print(average) else: print("none") # ----------------------------------------------------- def graph_summary(averages): pass # ----------------------------------------------------- number_buckets, county_populations = read_file("montana-counties.txt") averages = calculate_averages(number_buckets, county_populations) print_summary(averages) graph_summary(averages)

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/backend/app/migrations/garpix_notify/0002_auto_20210720_2244.py

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AlexandrMikhailovich/cms_test3

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2023-06-23T01:34:19.806972

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# Generated by Django 3.1 on 2021-07-20 19:44 from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('auth', '0012_alter_user_first_name_max_length'), ('garpix_notify', '0001_initial'), ] operations = [ migrations.AddField( model_name='notifyuserlistparticipant', name='user', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='user_lists', to=settings.AUTH_USER_MODEL, verbose_name='Пользователь (получатель)'), ), migrations.AddField( model_name='notifyuserlistparticipant', name='user_list', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='participants', to='garpix_notify.notifyuserlist', verbose_name='Список пользователей для рассылки'), ), migrations.AddField( model_name='notifyuserlist', name='user_groups', field=models.ManyToManyField(blank=True, to='auth.Group', verbose_name='Группы пользователей'), ), migrations.AddField( model_name='notifytemplate', name='category', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='templates', to='garpix_notify.notifycategory', verbose_name='Категория'), ), migrations.AddField( model_name='notifytemplate', name='user', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to=settings.AUTH_USER_MODEL, verbose_name='Пользователь (получатель)'), ), migrations.AddField( model_name='notifytemplate', name='user_lists', field=models.ManyToManyField(blank=True, to='garpix_notify.NotifyUserList', verbose_name='Списки пользователей, которые получат копию уведомления'), ), migrations.AddField( model_name='notifyerrorlog', name='notify', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='logs', to='garpix_notify.notify', verbose_name='Notify'), ), migrations.AddField( model_name='notify', name='category', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='notifies', to='garpix_notify.notifycategory', verbose_name='Категория'), ), migrations.AddField( model_name='notify', name='files', field=models.ManyToManyField(to='garpix_notify.NotifyFile', verbose_name='Файлы'), ), migrations.AddField( model_name='notify', name='user', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='notifies', to=settings.AUTH_USER_MODEL, verbose_name='Пользователь (получатель)'), ), ]

[ "Alexandr1990@gitlab.com" ]

Alexandr1990@gitlab.com

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/todoclass/urls.py

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Alisher007/todomain

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refs/heads/master

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from django.contrib import admin from django.urls import path from .views import TodoListView, TodoDetailView, TodoCreateView, TodoUpdateView, TodoDeleteView app_name = 'todoclass' urlpatterns = [ path('', TodoListView.as_view(), name='list'), path('create/', TodoCreateView.as_view(), name='create'), path('<int:pk>/', TodoDetailView.as_view(), name='detail'), path('<int:pk>/update/', TodoUpdateView.as_view(), name='update'), path('<int:pk>/delete/', TodoDeleteView.as_view(), name='delete'), ]

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alisher.khalikulov@jaresorts.com

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/image_diet/tests/test_commands.py

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ArabellaTech/django-image-diet

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null

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import os from os.path import join, dirname from shutil import copyfile from django.test import TestCase from image_diet.management.commands import diet_images TEST_DIR = join(dirname(__file__), 'test_files') class DietCommandTest(TestCase): def setUp(self): image_path = join(TEST_DIR, 'stockholm.jpg') self.nested_dir = join('dir1', 'dir2', 'dir3') self.test_root_dir = join(TEST_DIR, 'dir1') os.makedirs(join(TEST_DIR, self.nested_dir)) self.test_image_path = join(TEST_DIR, self.nested_dir, 'stockholm.jpg') copyfile(image_path, self.test_image_path) def tearDown(self): os.remove(self.test_image_path) os.chdir(TEST_DIR) os.removedirs(self.nested_dir) def test_diet_images(self): old_size = os.stat(self.test_image_path).st_size action = diet_images.Command() action.handle(self.test_root_dir) new_size = os.stat(self.test_image_path).st_size self.assertTrue(new_size < old_size)

[ "markos@gaivo.net" ]

markos@gaivo.net

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/probability.py

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[]

no_license

MickeyKen/plot_node_master_thesis

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5182ea79cb8cfbc6bead60d97eda9307f7e53c10

refs/heads/master

2023-02-16T21:17:49.284973

2021-01-19T09:19:40

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#!/usr/bin/python import matplotlib.pyplot as plt path = 'data/param_UD-v95_output.txt' isServiceCount = True ACTOR_NUM = 3 AVERAGE_NUM = 100 LIMIT = 5000 if __name__ == '__main__': collision = [[] for j in range(ACTOR_NUM)] average_collision = [] success = [[] for j in range(ACTOR_NUM)] average_success = [] no_action = [[] for j in range(ACTOR_NUM)] average_no_action = [] eps = [] average_eps = [] epsilons = [[] for j in range(ACTOR_NUM)] flag = 0 count = 0 fig = plt.figure(figsize=(8.27,3.9), dpi=100) plt.ion() plt.xlabel('Episode') # plt.ylabel('P') plt.grid() cycle = plt.rcParams['axes.prop_cycle'].by_key()['color'] with open(path) as f: for s_line in f: eps_num = int(s_line.split(',')[0]) actor_num = int(s_line.split(',')[1]) step = int(s_line.split(',')[3]) reward = float(s_line.split(',')[5]) if step < 150 and reward < -200: collision[actor_num].append(1.0) success[actor_num].append(0.0) no_action[actor_num].append(0.0) elif step < 150 and reward > 0: collision[actor_num].append(0.0) success[actor_num].append(1.0) no_action[actor_num].append(0.0) else: collision[actor_num].append(0.0) success[actor_num].append(0.0) no_action[actor_num].append(1.0) collision_sum = 0.0 success_sum = 0.0 no_action_sum = 0.0 average_collision_sum = 0.0 average_success_sum = 0.0 average_no_action_sum = 0.0 count = 1 for index in range(min(len(v) for v in collision)): collision_sum = 0.0 success_sum = 0.0 no_action_sum = 0.0 if index <= LIMIT: for n in range(ACTOR_NUM): collision_sum += collision[n][index] success_sum += success[n][index] no_action_sum += no_action[n][index] average_collision_sum += collision_sum / float(ACTOR_NUM) average_success_sum += success_sum / float(ACTOR_NUM) average_no_action_sum += no_action_sum / float(ACTOR_NUM) if index % AVERAGE_NUM == 0 and index > 0: average_eps.append(count*AVERAGE_NUM) average_collision.append(average_collision_sum / float(AVERAGE_NUM)) average_success.append(average_success_sum / float(AVERAGE_NUM)) average_no_action.append(average_no_action_sum / float(AVERAGE_NUM)) average_collision_sum = 0.0 average_success_sum = 0.0 average_no_action_sum = 0.0 count += 1 eps.append(index + 1) plt.plot(average_eps, average_success, color='#e41a1c', label="success") plt.plot(average_eps, average_collision, color='#00529a', label="collision") plt.plot(average_eps, average_no_action, color='#3FBF00', label="past 150 steps") plt.legend( loc='upper left', borderaxespad=1) plt.draw() fig.savefig("result_multi_probability.png") plt.pause(0)

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dima-kov/django-mq

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from django.apps import AppConfig class MqAppConfig(AppConfig): def ready(self): from mq.facade import QueuesFacade # noqa

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luizgdias/kafka_producer_topic_consumer

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# -*- Coding: UTF-8 -*- #coding: utf-8 ######################################################### # author: Luiz Gustavo Dias # date : 07/23/2019 ######################################################### # At First time is necessary to run in terminal: # $ docker run -d --name zookeeper jplock/zookeeper:3.4.6 # $ docker run -d --name kafka --link zookeeper:zookeeper ches/kafka # $ export ZK_IP=$(docker inspect --format "{{ .NetworkSettings.IPAddress }}" zookeeper) # $ export KAFKA_IP=$(docker inspect --format "{{ .NetworkSettings.IPAddress }}" kafka) # $ docker run --rm ches/kafka kafka-topics.sh --create --topic test --replication-factor 1 --partitions 1 --zookeeper $ZK_IP:2181 # Created topic "test". ######################################################### # Description: The script list all files in ./Files directory on a txt file, # after a kafka producer is created, the producer reads the file # and sendsd all the files name to kafka consumer that uses the # same kafka topic. # docker run --rm --interactive ches/kafka kafka-console-producer.sh --broker-list 172.17.0.3:9092 --topic test # docker run --rm ches/kafka kafka-console-consumer.sh --topic test --from-beginning --zookeeper 172.17.0.2:2181 ######################################################### from kafka import KafkaConsumer from kafka import KafkaProducer from json import loads import os, sys, subprocess, shlex import json from json import dumps from time import sleep def buffering(): os.system("touch buffer-list-files.json") buffer_list_files = open("buffer-list-files.json").readlines() print(buffer_list_files) buffer_list_files2 = open("buffer-list-files.json", "a") for root, dirs, files in os.walk("./Files", topdown=False): for name in files: json_lista = '{"file_path":"'+os.path.join(root,name)+'", "submited":" "}\n' if json_lista in buffer_list_files: print("O arquivo <"+name+"> já está bo buffer!") else: print("O arquivo <"+name+"> não está no buffer....\nPreparando para inserir o arquivo <"+name+"> no buffer...") #print(os.path.join(root,name)) buffer_list_files2.write('{"file_path":"'+os.path.join(root,name)+'", "submited":" "}\n') print("Arquivo <"+name+"> inserido no buffer.") buffer_list_files2.close() def connection(): x = "docker start zookeeper kafka" process = subprocess.Popen(x, stdout=subprocess.PIPE, shell=True) process.communicate() def sendToTopic(): # os.system('docker stop zookeeper kafka') # os.system('docker rm zookeeper kafka') # os.system('docker run -d --name zookeeper jplock/zookeeper:3.4.6') # os.system('docker run -d --name kafka --link zookeeper:zookeeper ches/kafka') # os.system('export KAFKA_IP=$(docker inspect --format "{{ .NetworkSettings.IPAddress }}" kafka)') # os.system('echo $KAFKA_IP') x = "docker start zookeeper kafka" process = subprocess.Popen(x, stdout=subprocess.PIPE, shell=True) process.communicate() producer = KafkaProducer(bootstrap_servers=['172.17.0.3:9092'], api_version=(0,10,1), value_serializer=lambda x: dumps(x).encode('utf-8')) for e in range(10): data = {'id': e,'x1': '1', 'y1': '1','x2': '2', 'y2': '2','page': '3', 'type': '3', 'path': '/out'} producer.send('test', value=data) print("Producer to topic: "+str(e)) sleep(1) #os.system('docker stop zookeeper kafka') def getKafkaMessages(topicName): #os.system('docker run --rm ches/kafka kafka-console-consumer.sh --topic testTopic --from-beginning --zookeeper 172.17.0.2:2181') # x = "docker start zookeeper kafka" # process = subprocess.Popen('export ZK_IP=$(docker inspect --format \'{{ .NetworkSettings.IPAddress }}\' zookeeper) && echo $ZK_IP', stdout=subprocess.PIPE, shell=True) # zookeeper_ip = process.communicate()[0] # zookeeper_ip = (str(zookeeper_ip, 'UTF-8')).strip('\n') # print(zookeeper_ip) os.system('docker run --rm ches/kafka kafka-console-consumer.sh --topic image-detection-topic --from-beginning --zookeeper 192.168.1.112:2181') # process.communicate() #buffering() def getKafkaMessagesV2(topic, kafka_ip): ## Collect Messages from Bus consumer = KafkaConsumer(topic, auto_offset_reset='earliest', bootstrap_servers=[kafka_ip], api_version=(0, 10, 1)) consumer.subscribe([topic]) print('after consumer') print(consumer) for msg in consumer: print('inside for') print(msg[6]) #sendToTopic() #getKafkaMessages('image-detection-topic') getKafkaMessagesV2('image-detection-topic', '10.100.14.107:9092') #getKafkaMessagesV2('test', '172.17.0.3:9092') #bin/kafka-console-consumer --zookeeper localhost:2181 --topic kafkatest --from-beginning #bin/kafka-console-consumer --zookeeper localhost:2181 /kafka --topic kafkatest --from-beginning #kafka/bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 --topic my-topic --from-beginning

[ "gusttavodiias@gmail.com" ]

gusttavodiias@gmail.com

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hubiao7/scons-lcov

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import SCons from SCons.Builder import Builder from SCons.Script import Dir, Flatten, Mkdir from os import path class ToolLCovWarning(SCons.Warnings.Warning): pass class LCovExecutableNotFound(ToolLCovWarning): pass def lcov_generator(source, target, env, for_signature): cmd = ['lcov --capture'] cmd += ['--output-file', target[0].abspath] if 'LCOVDIR' in env: cmd += ['--directory', str(Dir(env['LCOVDIR']))] if 'LCOVBASEDIR' in env: cmd += ['--base-directory', str(Dir(env['LCOVBASEDIR']))] return ' '.join(Flatten(cmd)) _lcov_builder = Builder(generator=lcov_generator) def generate(env): env['LCov'] = _detect(env) env['BUILDERS']['LCov'] = _lcov_builder def _detect(env): try: return env['LCov'] except KeyError: pass lcov = env.WhereIs('lcov') if lcov: return lcov raise SCons.Errors.StopError(LCovExecutableNotFound, 'Cound not detect lcov executable') return None def exists(env): return _detect(env)

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idaohang/KalmanAnalyzer

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#Automatically created by SCRAM import os __path__.append(os.path.dirname(os.path.abspath(__file__).rsplit('/UserCode/KalmanAnalyzer/',1)[0])+'/cfipython/slc6_amd64_gcc472/UserCode/KalmanAnalyzer')

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#!/usr/bin/env python from distutils.core import setup setup(name='pybitcointools', version='1.0', description='Python Bitcoin Tools', author='Vitalik Buterin', author_email='vbuterin@gmail.com', url='http://github.com/vbuterin/pybitcointools', packages=['pybitcointools'], scripts=['pybtctool'] )

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/arcade/python/arcade-theCore/11_SpringOfIntegration/091_Combs.py

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''' Miss X has only two combs in her possession, both of which are old and miss a tooth or two. She also has many purses of different length, in which she carries the combs. The only way they fit is horizontally and without overlapping. Given teeth' positions on both combs, find the minimum length of the purse she needs to take them with her. It is guaranteed that there is at least one tooth at each end of the comb. It is also guaranteed that the total length of two strings is smaller than 32. Note, that the combs can not be rotated/reversed. Example For comb1 = "*..*" and comb2 = "*.*", the output should be combs(comb1, comb2) = 5. Although it is possible to place the combs like on the first picture, the best way to do this is either picture 2 or picture 3. ''' def combs(comb1, comb2): n1, n2 = len(comb1), len(comb2) res = n1 + n2 m1, m2 = mask(comb1), mask(comb2) for i in range(n1 + 1): if (m2 << i) & m1 == 0: temp = max(n2 + i, n1) if temp < res: res = temp for i in range(n2 + 1): if (m1 << i) & m2 == 0: temp = max(n1 + i, n2) if temp < res: res = temp return res def mask(s): r = 0 for c in s: digit = 0 if c == '*': digit = 1 r = (r << 1) + digit return r

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neto.r27@gmail.com

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danliu277/openbag_python

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from django.db import models class Employee(models.Model): name = models.CharField(max_length=120) username = models.CharField(max_length=120) password = models.CharField(max_length=120) address = models.CharField(max_length=120) email = models.CharField(max_length=120) def __str__(self): return self.name

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/Day4/newPkg/src/myPkg/scripts/comp_sub.py

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IsraaFahmy/ROS-training-

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#!/usr/bin/env python import rospy from myPkg.msg import comp def callback(message): rospy.loginfo("complex number recieved: %d + %d i", message.real,message.imaginary) rospy.init_node('comp_node2', anonymous=True) rospy.Subscriber("comp_topic", comp, callback) rospy.spin()

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israafahmy@aucegypt.edu

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jan-polaczek/isodBot-demo

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import os,sys,inspect current_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parent_dir = os.path.dirname(current_dir) sys.path.insert(0, parent_dir) from Registration import * def run(): errorMessages = {} number_of_test = 5 counter = 0 if Registration.fileSize('anonek.tst') == 1: errorMessages['anonek'] = 'test: podano tylko login' elif Registration.fileSize('anonek.tst') == 2: errorMessages['anonek'] = 'test: podano login i hasło' elif Registration.fileSize('anonek.tst') > 2: errorMessages['anonek'] = 'test: za dużo linijek w pliku' else: #errorMessages['anonek'] = 'test: plik nie istnieje' counter += 1 if Registration.fileSize('balasm.tst') == 1: errorMessages['balasm'] = 'test: podano tylko login' elif Registration.fileSize('balasm.tst') == 2: errorMessages['balasm'] = 'test: podano login i hasło' elif Registration.fileSize('balasm.tst') > 2: #errorMessages['balasm'] = 'test: za dużo linijek w pliku' counter += 1 else: errorMessages['balasm'] = 'test: plik nie istnieje' if Registration.fileSize('boguszj.tst') == 1: errorMessages['boguszj'] = 'test: podano tylko login' elif Registration.fileSize('boguszj.tst') == 2: errorMessages['boguszj'] = 'test: podano login i hasło' elif Registration.fileSize('boguszj.tst') > 2: #errorMessages['boguszj'] = 'test: za dużo linijek w pliku' counter += 1 else: errorMessages['boguszj'] = 'test: plik nie istnieje' if Registration.fileSize('polaczej.tst') == 1: #errorMessages['polaczej'] = 'test: podano tylko login' counter += 1 elif Registration.fileSize('polaczej.tst') == 2: errorMessages['polaczej'] = 'test: podano login i hasło' elif Registration.fileSize('polaczej.tst') > 2: errorMessages['polaczej'] = 'test: za dużo linijek w pliku' else: errorMessages['polaczej'] = 'test: plik nie istnieje' if Registration.fileSize('ktokolwiek.tst') == 1: errorMessages['ktokolwiek'] = 'test: podano tylko login' elif Registration.fileSize('ktokolwiek.tst') == 2: errorMessages['ktokolwiek'] = 'test: podano login i hasło' elif Registration.fileSize('ktokolwiek.tst') > 2: errorMessages['ktokolwiek'] = 'test: za dużo linijek w pliku' else: #errorMessages['ktokolwiek'] = 'test: plik nie istnieje' counter += 1 errorMessages['ilość testów'] = number_of_test errorMessages['ilość testów zaliczonych'] = counter return errorMessages

[ "jan.polaczek@interia.pl" ]

jan.polaczek@interia.pl

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Timothy-W-Hilton/COS_Spatial_Analyses

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2021-01-20T20:39:00.748486

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"""Calculate total size of COS spatial paper data files and copy the data files to a single directory tree for archiving """ import os import os.path import shutil from stem_pytools import NERSC_data_paths as ndp def Get_Human_Readable(size, precision=2): """http://stackoverflow.com/questions/5194057/better-way-to-convert-file-sizes-in-python """ suffixes = ['B', 'KB', 'MB', 'GB', 'TB'] suffixIndex = 0 while size > 1024 and suffixIndex < 4: suffixIndex += 1 # increment the index of the suffix size = size/1024.0 # apply the division return "%.*f%s" % (precision, size, suffixes[suffixIndex]) def get_spatial_paper_data_total(runs): all_data_sum = 0 for k, this_run in runs.items(): for this_file in (this_run.aqout_path, this_run.gpp_path, this_run.gppraw_path, this_run.fcos_path): if this_file is not None: all_data_sum += os.path.getsize(this_file) print "Spatial paper data total: " + Get_Human_Readable(all_data_sum) return all_data_sum def make_data_archive(root_dir, runs): """Copy all non-regridded GPP, regridded GPP, STEM AQOUT, and fCOS netcdf files to a single directory tree for archiving. """ if os.path.exists(root_dir): try: shutil.rmtree(root_dir) except: print "unable to delete".format(root_dir) try: os.makedirs(root_dir) except: print "unable to create {}".format(root_dir) for k, this_run in runs.items(): print "copying {} files".format(k) this_run_dir = os.path.join(root_dir, k) os.makedirs(this_run_dir) for this_file in (this_run.aqout_path, this_run.gpp_path, this_run.gppraw_path, this_run.fcos_path): if this_file is not None: print " copying {}".format(os.path.basename(this_file)) shutil.copy(this_file, this_run_dir) if k is 'climatological_bnd': for this_bnd in (runs[k].top_bounds_path, runs[k].lateral_bounds_path): print " copying {}".format(os.path.basename(this_bnd)) shutil.copy(this_bnd, this_run_dir) if __name__ == "__main__": runs = ndp.get_Spatial_Paper_runs() total = get_spatial_paper_data_total(runs) archive_dir = os.path.join(os.getenv('SCRATCH'), 'SpatialPaperData') make_data_archive(archive_dir, runs)

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thilton@ucmerced.edu

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import argparse from typing import Tuple, List, Dict import json import copy import pickle # may be change to dill? from collections import defaultdict import numpy as np from sklearn.preprocessing import StandardScaler from sklearn import linear_model from lib.tree_node import TreeNode np.random.seed(13370) def train_linear_regressor(features: np.array, ground_truths: np.array) -> Tuple: """ Scales data and trains a simple linear regressor. """ regressor = linear_model.LinearRegression() scale_standardizer = StandardScaler().fit(features) features = scale_standardizer.transform(features) transformations = [scale_standardizer] regressor = regressor.fit(features, ground_truths) return regressor, transformations def predict_linear_regressor( regressor, transformations: List, features: np.array, ids: List[str] ) -> Dict[str, float]: """ Generates prediction using trained regressor on the passed features and returns a dictionary of id to predictions. """ for transformation in transformations: features = transformation.transform(features) predicted_outputs = regressor.predict(features) id_to_predicted_values = { id_: pred for id_, pred in zip(list(ids), list(predicted_outputs)) } return id_to_predicted_values def train_ml_level_models(train_trees: List[TreeNode]) -> Tuple[Dict, Dict]: """ Trains ML-level regressor on the leaf nodes of training trees and outputs trained regressor and scalars. """ operationwise_ml_level_instances = defaultdict(list) for tree in train_trees: for operation_type, ml_level_instances in tree.get_ml_level_data().items(): operationwise_ml_level_instances[operation_type].extend(ml_level_instances) operationwise_ml_level_model = {} operationwise_ml_level_transformations = {} for operation_type, ml_level_instances in operationwise_ml_level_instances.items(): features = np.stack( [np.array(instance["features"]) for instance in ml_level_instances], axis=0 ) ground_truths = np.array( [instance["gold_energy"] for instance in ml_level_instances] ) regressor, transformations = train_linear_regressor( features=features, ground_truths=ground_truths ) operationwise_ml_level_model[operation_type] = regressor operationwise_ml_level_transformations[operation_type] = transformations return operationwise_ml_level_model, operationwise_ml_level_transformations def predict_ml_level_models( operationwise_ml_level_model: Dict, operationwise_ml_level_transformations: Dict, predict_trees: List[TreeNode], ) -> List[TreeNode]: """ Runs regressor on the leaf/ml-level nodes of the predic_trees and saves the predicted_energy field into it. Returns predicted_energy annotated trees. """ assert set(operationwise_ml_level_model.keys()) == set( operationwise_ml_level_transformations.keys() ) predict_trees = copy.deepcopy(predict_trees) for predict_tree in predict_trees: operationwise_ml_level_instances = predict_tree.get_ml_level_data() for ( operation_type, ml_level_instances, ) in operationwise_ml_level_instances.items(): if operation_type not in operationwise_ml_level_model: raise Exception( f"Given model isn't trained on operation_type {operation_type}" ) regressor = operationwise_ml_level_model[operation_type] transformations = operationwise_ml_level_transformations[operation_type] features = np.stack( [np.array(instance["features"]) for instance in ml_level_instances], axis=0, ) ids = [instance["id"] for instance in ml_level_instances] id_to_predicted_values = predict_linear_regressor( regressor, transformations, features, ids ) predict_tree.update_tree_node_attributes( "predicted_energy", id_to_predicted_values ) return predict_trees

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from block import * from blockchain import * from node import * from wallet import * from errors import *

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#!/usr/bin/python from setuptools import setup, find_packages import pandarss install_requires = [ 'Twisted>=15.0.0', 'bottle>=0.12.7' ] package_data={ 'pandarss': [ 'views/css/*', 'views/js/*', 'views/*.html' ] } setup(name='pandarss', version='0.2', author='pandaman', author_email='pandaman1999@foxmail.com', url='https://github.com/PandaPark/PandaRSS', license='BSD', description='ToughRADIUS Self-service Portal', long_description=open('README.md').read(), classifiers=[ 'Development Status :: 6 - Mature', 'Intended Audience :: Developers', 'Programming Language :: Python :: 2.7', 'Topic :: Software Development :: Libraries :: Python Modules', 'Topic :: System :: Systems Administration :: Authentication/Directory', ], packages=find_packages(), package_data=package_data, keywords=['radius','toughradius','self-service ','pandarss'], zip_safe=True, include_package_data=True, eager_resources=['pandarss'], install_requires=install_requires, entry_points={ 'console_scripts': [ 'pandarss = pandarss.pandarss:main', 'pandarss_txrun = pandarss.pandarss:txrun', ] } )

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"""Employee_project URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/3.1/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.contrib import admin from django.urls import path, include urlpatterns = [ path('admin/', admin.site.urls), path('employee/',include('employee_register.urls')) ]

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# Generated by Django 2.1.15 on 2020-02-25 20:25 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('blog', '0006_auto_20200212_1741'), ] operations = [ migrations.AlterField( model_name='blog', name='content', field=models.TextField(), ), ]

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def main(): L=[1,3,4.5,"hi"] # تستخدم for loop لطباعه كل عنصر قي سطر في for item in L: print(item) if __name__ == '__main__': main()

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import argparse import numpy as np # hide sklearn deprecation message triggered within skorch from warnings import simplefilter simplefilter('ignore', category=FutureWarning) import torch from skorch import NeuralNetClassifier from skorch.callbacks import LRScheduler from skorch.callbacks import EpochScoring from sklearn.metrics import plot_roc_curve, roc_auc_score from sklearn.metrics import plot_precision_recall_curve, average_precision_score from constants import * from datasets import * import models from utils.model_utils import * from utils.data_utils import get_roadmap_col_order DATA_CHOICES = ['mpra', 'mpra+scores', 'neighbor', 'neighbor+scores'] MODEL_CFG = { 'mpra': None, 'mpra+scores': None, 'neighbor': None, 'neighbor+scores': None } def fit_model(args): torch.manual_seed(1000) print(f'Fitting model for {args.data}:') project = args.project auc = EpochScoring(scoring='roc_auc', lower_is_better=False) apr = EpochScoring(scoring='average_precision', lower_is_better=False) if args.data == 'mpra': train_df = load_train_set(project, datasets=['roadmap']) proc = Processor(project) train_df = proc.fit_transform(train_df, na_thresh=0.05) proc.save(args.data) X_train = train_df.drop(['chr', 'pos', 'Label'], axis=1) \ .values \ .astype(np.float32) y_train = train_df['Label'].values.astype(np.int64) net = NeuralNetClassifier( models.MpraDense, batch_size=256, optimizer=torch.optim.Adam, optimizer__weight_decay=2e-6, lr=1e-4, max_epochs=20, module__n_input=1016, module__n_units=(400, 250), module__dropout=0.3, callbacks=[auc, apr], iterator_train__shuffle=True, train_split=None ) elif args.data == 'mpra+scores': train_df = load_train_set(project, datasets=['roadmap', 'eigen', 'regbase']) proc = Processor(project) train_df = proc.fit_transform(train_df, na_thresh=0.05) proc.save(args.data) X_train = train_df.drop(['chr', 'pos', 'Label'], axis=1) \ .values \ .astype(np.float32) y_train = train_df['Label'].values.astype(np.int64) net = NeuralNetClassifier( models.MpraDense, batch_size=256, optimizer=torch.optim.Adam, optimizer__weight_decay=2e-6, lr=1e-4, max_epochs=20, module__n_input=1079, module__n_units=(400, 250), module__dropout=0.3, callbacks=[auc, apr], iterator_train__shuffle=True, train_split=None ) elif args.data == 'neighbor': X_train = load_train_neighbors(project).astype(np.float32) tmp = load_train_set(project, datasets=['roadmap', 'eigen', 'regbase'], make_new=False) y_train = tmp['Label'].values.astype(np.int64) assert X_train.shape[0] == y_train.shape[0] net = NeuralNetClassifier( models.MpraCNN, batch_size=256, optimizer=torch.optim.Adam, optimizer__weight_decay=1e-4, lr=5e-4, max_epochs=20, callbacks=[auc, apr], iterator_train__shuffle=True ) elif args.data == 'neighbor+scores': print('\tLoading neighbors') X_neighbor = load_train_neighbors(project).astype(np.float32) print('\tLoading scores') train_df = load_train_set(project, datasets=['roadmap', 'eigen', 'regbase']) proc = Processor(project) train_df = proc.fit_transform(train_df, na_thresh=0.05) proc.save(args.data) print('\tArranging data') rm_cols = [f'{x}-E116' for x in ROADMAP_MARKERS] # rm_cols = [x for x in get_roadmap_col_order(order='marker') if 'E116' in x] X_score = train_df.drop(['chr', 'pos', 'Label'] + rm_cols, axis=1) \ .values \ .astype(np.float32) y_train = train_df['Label'].values.astype(np.int64) X_train = (X_neighbor, X_score) net = NeuralNetClassifier( models.MpraFullCNN, batch_size=256, optimizer=torch.optim.Adam, optimizer__weight_decay=0, lr=5e-4, max_epochs=20, callbacks=[auc, apr], iterator_train__shuffle=True ) # import sys; sys.exit() net.fit(X_train, y_train) class_pred = net.predict(X_train) score_pred = net.predict_proba(X_train) print('\tAUROC: ', roc_auc_score(y_train, score_pred[:, 1])) print('\tAUPR: ', average_precision_score(y_train, score_pred[:, 1])) save_model(net, project, args.data) def evaluate_model(args): print(f"Evaluating model for {args.data}:") project = args.project net = load_model(project, args.data) X_test = load_test_neighbors(project) X_test = X_test.astype(np.float32) tmp = load_test_set(project, datasets=['roadmap', 'eigen', 'regbase']) y_test = tmp['Label'].values.astype(np.int64) # test_df = load_test_set(project, datasets=['roadmap', 'eigen', 'roadmap']) # proc = Processor(project) # proc.load(args.data) # test_df = proc.transform(test_df) # X_test = test_df.drop(['chr', 'pos', 'Label'], axis=1) \ # .values \ # .astype(np.float32) # y_test = test_df['Label'].values.astype(np.int64) class_pred = net.predict(X_test) score_pred = net.predict_proba(X_test) print('\tAUROC: ', roc_auc_score(y_test, score_pred[:, 1])) print('\tAUPR: ', average_precision_score(y_test, score_pred[:, 1])) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--project', '-p', choices=PROJ_CHOICES, required=True) parser.add_argument('--data', '-d', default='mpra+scores', choices=DATA_CHOICES, help='Which data/model to train on') parser.add_argument('--full', default=False, help='Fit all models (overrides --data)') parser.add_argument('--evaluate', '-e', action='store_true', default=False, help='Evaluate model on test set after fitting') args = parser.parse_args() fit_model(args) if args.evaluate: evaluate_model(args)

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fredlu.flac@gmail.com

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2021-01-22T17:47:59.312735

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from lists.models import List from lists.models import Item from django.contrib import admin admin.site.register(List) admin.site.register(Item)

[ "epgibney@gmail.com" ]

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2023-07-28T07:18:10.178029

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null

UTF-8

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for n in range(10,0,-1): print(n, end=' ')

[ "gonzalomonteodorisio@gmail.com" ]

gonzalomonteodorisio@gmail.com

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[]

no_license

vineet0713/PaaS-ProberS-AWS-EB-

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refs/heads/master

2020-04-30T05:47:55.793035

2019-03-20T02:54:43

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/usr/local/Cellar/python/3.6.4_3/Frameworks/Python.framework/Versions/3.6/lib/python3.6/encodings/mbcs.py

[ "vineet0713@gmail.com" ]

vineet0713@gmail.com

71a4c69ae3c773949b3127804ef78ee861a60fee

f9407b6f9454640b35753a39ac3fc57c1a105630

/parse_out_email_text.py

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[]

no_license

saeidmoha/tools

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18f20dfdade5a374c5ec2cbd71f4b661d61788db

refs/heads/master

2021-08-14T14:43:31.181434

2017-11-16T02:00:27

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#!/usr/bin/python from nltk.stem.snowball import SnowballStemmer import string def parseOutText(f): """ given an opened email file f, parse out all text below the metadata block at the top (in Part 2, you will also add stemming capabilities) and return a string that contains all the words in the email (space-separated) example use case: f = open("email_file_name.txt", "r") text = parseOutText(f) """ f.seek(0) ### go back to beginning of file (annoying) all_text = f.read() ### split off metadata content = all_text.split("X-FileName:") #print ("content[0] = ", content[0], "content[1] = ", content[1]) words = "" stemmer = SnowballStemmer("english") if len(content) > 1: ### remove punctuation #text_string = content[1].translate(string.maketrans("", ""), string.punctuation) text_string = content[1].translate(str.maketrans("", "", string.punctuation)) ### project part 2: comment out the line below #words = text_string ### split the text string into individual words, stem each word, ### and append the stemmed word to words (make sure there's a single ### space between each stemmed word) text_string = ' '.join(text_string.split()) for word in text_string.split(" "): stemword = stemmer.stem(word) words += stemword + ' ' return words def main(): ff = open("../text_learning/test_email.txt", "r") #ff = open("../maildir/bailey-s/deleted_items/101.", "r") text = parseOutText(ff) print (text) if __name__ == '__main__': main()

[ "saeid@saeidm.com" ]

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[]

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from tensorflow.keras.layers import Dense,SimpleRNN from tensorflow.keras import Sequential import numpy as np from tensorflow.keras.utils import plot_model model =Sequential() model.add(Dense(2)) model.add(Dense(1)) plot_model(model)

[ "pankaj.tiwari2@gmail.com" ]

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no_license

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import pyqtgraph as pg from PyQt4 import QtCore, QtGui # Create a subclass of GraphicsObject. # The only required methods are paint() and boundingRect() # (see QGraphicsItem documentation) class CandlestickItem(pg.GraphicsObject): def __init__(self): pg.GraphicsObject.__init__(self) self.flagHasData = False def set_data(self, data): self.data = data # data must have fields: time, open, close, min, max self.flagHasData = True self.generatePicture() self.informViewBoundsChanged() def generatePicture(self): # pre-computing a QPicture object allows paint() to run much more quickly, # rather than re-drawing the shapes every time. self.picture = QtGui.QPicture() p = QtGui.QPainter(self.picture) p.setPen(pg.mkPen('w')) barWidth = 1 / 3. for (open, close, min, max, index) in self.data: p.drawLine(QtCore.QPointF(index, min), QtCore.QPointF(index, max)) if open > close: p.setBrush(pg.mkBrush('r')) else: p.setBrush(pg.mkBrush('g')) p.drawRect(QtCore.QRectF(index - barWidth, open, barWidth * 2, close - open)) p.end() def paint(self, p, *args): if self.flagHasData: p.drawPicture(0, 0, self.picture) def boundingRect(self): # boundingRect _must_ indicate the entire area that will be drawn on # or else we will get artifacts and possibly crashing. # (in this case, QPicture does all the work of computing the bouning rect for us) return QtCore.QRectF(self.picture.boundingRect()) class CandleWidget(pg.PlotWidget): def __init__(self, raw_data): super(CandleWidget, self).__init__() self.update(raw_data) # self.candle_data = raw_data.loc[:, ['open', 'close', 'low', 'high']] # r, c = self.candle_data.shape # self.candle_data['num'] = range(1, r + 1) # self.item = CandlestickItem() # self.item.set_data(self.candle_data.values) self.addItem(self.item) def update(self, raw_data): # raw_data.sort_index(axis=0, inplace=True) self.candle_data = raw_data.loc[:, ['open', 'close', 'low', 'high']] r, c = self.candle_data.shape self.candle_data['num'] = range(1, r + 1) self.item = CandlestickItem() self.item.set_data(self.candle_data.values) # app = QtGui.QApplication([]) # df = ts.get_hist_data('000681', '2015-01-01', ktype='w') # r, c = df.shape # print(r) # cData = df.copy().loc[:, ['open', 'close', 'low', 'high']] # cData['num'] = range(1, r + 1) # # print(cData) # # cData = np.array(cData) # item = CandlestickItem() # item.set_data(cData.values) # # plt = pg.plot() # plt.addItem(item) # plt.setWindowTitle('pyqtgraph example: customGraphicsItem') # # # def update(): # global item # df = ts.get_hist_data('000681', '2015-01-01', ktype='d') # r, c = df.shape # print(r) # cData = df.loc[:, ['open', 'close', 'low', 'high']] # cData['num'] = range(1, r + 1) # item.set_data(cData.values) # # app.processEvents() ## force complete redraw for every plot # # # timer = QtCore.QTimer() # timer.timeout.connect(update) # timer.start(10000) # df = ts.get_hist_data('000681', '2015-01-01', ktype='w') # print(enumerate(df)) # for (value) in df.head(10).values: # print(value) # print(type(value)) ## Start Qt event loop unless running in interactive mode or using pyside. if __name__ == '__main__': import sys if (sys.flags.interactive != 1) or not hasattr(QtCore, 'PYQT_VERSION'): QtGui.QApplication.instance().exec_()

[ "xiang.bao.frank@gmail.com" ]

xiang.bao.frank@gmail.com

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/0909. Snakes and Ladders/Solution.py

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[]

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faterazer/LeetCode

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refs/heads/master

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from collections import deque from typing import List class Solution: def snakesAndLadders(self, board: List[List[int]]) -> int: n = len(board) visited = [False] * (n * n + 1) queue = deque([1]) steps = 0 while queue: size = len(queue) for _ in range(size): pos = queue.popleft() for i in range(pos + 1, min(pos + 7, n * n + 1)): if visited[i]: continue visited[i] = True if i == n * n: return steps + 1 r, c = divmod(i - 1, n) if r & 1: c = n - 1 - c r = n - 1 - r if board[r][c] != -1 and board[r][c] == n * n: return steps + 1 if board[r][c] == -1: queue.append(i) else: queue.append(board[r][c]) steps += 1 return -1

[ "yubowen.ssr@bytedance.com" ]

yubowen.ssr@bytedance.com

c081b2cc5c19aacf9997f2dcf145d5c6d6a94c75

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/Docker Model/utils/makeprediction.py

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[]

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etheleon/data-science-for-fun

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refs/heads/master

2021-07-23T14:21:08.860090

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from sklearn import datasets from sklearn.neighbors import KNeighborsClassifier def predict(inputFeatures): iris = datasets.load_iris() knn = KNeighborsClassifier() knn.fit(iris.data, iris.target) predictInt = knn.predict(inputFeatures) if predictInt[0] == 0: predictString = 'setosa' elif predictInt[0] == 1: predictString = 'versicolor' elif predictInt[0] == 2: predictString = 'virginica' else: predictString = 'null' return predictString

[ "A0134553@u.nus.edu" ]

A0134553@u.nus.edu

4faba1910def77457e265813a6749d9fcdc2c9fa

91d1a6968b90d9d461e9a2ece12b465486e3ccc2

/ec2_write_3/managed-prefix-list_create.py

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[]

no_license

lxtxl/aws_cli

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aaf35df1b7509abf5601d3f09ff1fece482facda

refs/heads/master

2023-02-06T09:00:33.088379

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#!/usr/bin/python # -*- codding: utf-8 -*- import os import sys sys.path.append(os.path.dirname(os.path.abspath(os.path.dirname(__file__)))) from common.execute_command import write_three_parameter # url : https://awscli.amazonaws.com/v2/documentation/api/latest/reference/ec2/create-managed-prefix-list.html if __name__ == '__main__': """ delete-managed-prefix-list : https://awscli.amazonaws.com/v2/documentation/api/latest/reference/ec2/delete-managed-prefix-list.html describe-managed-prefix-lists : https://awscli.amazonaws.com/v2/documentation/api/latest/reference/ec2/describe-managed-prefix-lists.html modify-managed-prefix-list : https://awscli.amazonaws.com/v2/documentation/api/latest/reference/ec2/modify-managed-prefix-list.html """ parameter_display_string = """ # prefix-list-name : A name for the prefix list. Constraints: Up to 255 characters in length. The name cannot start with com.amazonaws . # max-entries : The maximum number of entries for the prefix list. # address-family : The IP address type. Valid Values: IPv4 | IPv6 """ add_option_dict = {} add_option_dict["parameter_display_string"] = parameter_display_string # ex: add_option_dict["no_value_parameter_list"] = "--single-parameter" write_three_parameter("ec2", "create-managed-prefix-list", "prefix-list-name", "max-entries", "address-family", add_option_dict)

[ "hcseo77@gmail.com" ]

hcseo77@gmail.com

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/tests/test_basic.py

875207f11e43de580d9a19a086b9fd20315d8529

[]

no_license

machow/hoof

fe529ef6573ecae35ba51704cd5c95c188c50295

4c9460492f283abd539ab3577982226efe15db5a

refs/heads/master

2022-05-20T03:43:33.847717

2020-04-25T21:47:48

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from hoof import Hoof, AntlrAst class Program(AntlrAst): _fields = ["body"] class BinaryExpr(AntlrAst): _fields = ["left", "right", "op"] class RunExpr(AntlrAst): _fields = ["op", "expr"] _remap = ["RUN->op"] _rules = "RunExpr" hoof = Hoof("hoof_examples.Expr") class AstVisitor(hoof.Visitor): def visitParentheses(self, ctx): # skip parentheses return self.visit(ctx.expr()) def visitTerminal(self, ctx): return ctx.getText() hoof.register("Prog", Program, ["expr->body"]) # no config on node hoof.register("BinaryExpr", BinaryExpr) # no need to remap hoof.register(RunExpr) # rule and remap on node hoof.bind(AstVisitor) def test_program(): node = hoof.parse("1 + 2; 3 - 4;", "prog") assert isinstance(node, Program) assert len(node.body) == 2 assert isinstance(node.body[0], BinaryExpr) def test_binary(): node = hoof.parse("1 + 2", "expr") assert isinstance(node, BinaryExpr) assert node.left == "1" assert node.right == "2" assert node.op == "+" def test_put(): node = hoof.parse("run 2", "expr") assert isinstance(node, RunExpr) assert node.expr == "2" def test_parentheses(): node = hoof.parse("(1 + 1)", "expr") assert isinstance(node, BinaryExpr) def test_expr_integer(): # this is a Token (INT) with no explicit shaping, so is result of visitTerminal node = hoof.parse("1", "expr") node == "1"

[ "machow@princeton.edu" ]

machow@princeton.edu

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/ask7.py

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[]

no_license

dimitrisadam/askhseis

23a6c45a33004c2b50837a42e10dae72479adc34

504f270056e2afa6e331bd501a8f476bf35dd991

refs/heads/master

2021-01-21T08:21:33.339580

2017-02-27T18:13:38

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import tweepy from tweepy import OAuthHandler import sys consumer_key = '' consumer_secret = '' access_token = '' access_secret = '' auth = OAuthHandler(consumer_key, consumer_secret) auth.set_access_token(access_token, access_secret) api = tweepy.API(auth) #pairnei thn lista me ta tweets (sunolika 10).Ta bazei ta epejergazetai ena ena #afairwntas ta https-links ka8ws den einai lejeis kai lejeis pou periexoun mesa ton xarakthra # '\' ka8ws upodikniei oti einai photografia.Epishs afairei memonomenes paules kai dipla kena. #Telos, epistrefei to a8roisma twn lejewn pou metrhse sta tweets. def countWords(alltweets): httpFlag = "http" delimeter = "\\" paula="-" doublespace="" totalWords = 0 test =" " for i in range(len(alltweets)): test = str(alltweets[i]) test = test.split(" ") for j in range(len(test)): if delimeter not in test[j] and httpFlag not in test[j] and test[j] is not paula and test[j] is not doublespace: totalWords+=1 #print test[j] return totalWords firstUser = raw_input("Dwste to tweeter username tou prwtou xrhsth: \n") secondUser = raw_input("Dwste to tweeter username tou deuterou xrhsth: \n") #firstUserTweets = api.user_timeline(screen_name="RollingStones",count=10) #edw diavazw kai vazei sto firsttweets ta 10 pio prosfata tweets tou prwtou user firstUserTweets = api.user_timeline(screen_name=firstUser,count=10) firsttweets = [[tweet.text.encode('utf-8')] for tweet in firstUserTweets] #print firsttweets #secondUserTweets = api.user_timeline(screen_name="rogerfederer",count=10) #edw diavazw kai vazei sto secondtweets ta 10 pio prosfata tweets tou deuterou user secondUserTweets = api.user_timeline(screen_name=secondUser,count=10) secondtweets = [[tweet.text.encode('utf-8')] for tweet in secondUserTweets] #print secondtweets # Elegxos gia an exoun ginei ta 10 tweets. An oxi to afhnw na sunexisei 8a borousa omws na kanw kai ena sys.exit(0) if len(firsttweets) < 10: print '\nWARNING: O xrhsths',firstUser,'den exei kanei 10 tweets' if len(secondtweets) < 10: print '\nWARNING: O xrhsths',secondUser,'den exei kanei 10 tweets' firstUserTotalWorlds = countWords(firsttweets) secondUserTolalWorlds = countWords(secondtweets) if firstUserTotalWorlds > secondUserTolalWorlds: print '\nPerissoteres lexeis exei o user',firstUser,'pou exei',firstUserTotalWorlds,'lexeis.O user',secondUser,'exei',secondUserTolalWorlds,'lexeis' else: print '\nPerissoteres lexeis exei o user',secondUser,'pou exei',secondUserTolalWorlds,'lexeis.O user',firstUser,'exei',firstUserTotalWorlds,'lexeis' #print 'totalwords =',countWords(firsttweets) #print 'totalwords =',countWords(secondtweets)

[ "mitsoseleysina2@gmail.com" ]

mitsoseleysina2@gmail.com

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/scrap_tenders/scrap_tenders/items.py

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[ "MIT" ]

permissive

Salomari1987/med-tenders-egypt

a821cd1064a5c68cbd7318c8ade254667692b7d9

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refs/heads/master

2021-01-19T18:32:36.773483

2017-04-19T00:36:02

2017-04-19T00:40:26

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# -*- coding: utf-8 -*- # Define here the models for your scraped items # # See documentation in: # http://doc.scrapy.org/en/latest/topics/items.html # from scrapy.item import Item, Field # # # class StackItem(Item): # Tender_Notice_Type = Field() # Country = Field() # Category = Field() # Description = Field() # Deadline = Field() # Ref = Field() from scrapy_djangoitem import DjangoItem from tenders_django_app.models import Tender class StackItem(DjangoItem): django_model = Tender

[ "s.z.alomari.1987@gmail.com" ]

s.z.alomari.1987@gmail.com

20635375b97f4e276fbfab8866c1ba60fc8aff05

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/Builders/TechnologyBUILD

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no_license

NoahBarrett98/Lost-and-Found

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refs/heads/master

2021-01-26T07:44:06.501000

2020-03-22T19:47:42

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1

null

2020-03-10T13:58:11

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#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Sun Mar 8 09:27:37 2020 @author: hannahmacdonell """ #H: Technology(itemID, tBrand, device, serialNo) import random import csv d = [] c = open("device.txt", "r") for line in c: d.append(line.strip().split('\n')[0]) c.close() l = [] c = open("tBrand.txt", "r") for line in c: l.append(line.strip().split('\n')[0]) c.close() with open('tech.csv', mode='w') as data_file: data_writer = csv.writer(data_file, delimiter=',') data_writer.writerow(['tBrand','Device','SerialNo']) for x in range(1000): data_writer.writerow([l[random.randint(0,len(l)-1)],d[random.randint(0,len(d)-1)],str(random.randint(4123456,9123456))]) data_file.close()

[ "noreply@github.com" ]

NoahBarrett98.noreply@github.com

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/app.py

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no_license

nikhilkumarsingh/nitdhack

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refs/heads/master

2021-01-19T21:33:27.880021

2017-04-18T23:43:06

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2018-10-03T05:33:57

2017-04-18T19:59:40

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import flask app = flask.Flask(__name__,static_folder='static') app.config.from_object(__name__) app.config['SECRET_KEY'] = '7d441f27d441f27567d441f2b6176a' @app.route('/') def home(): return flask.render_template('index.html') def NearbySearch(lat,lng,keyword,radius=1000): key="AIzaSyApuFoKxVMRQ2einlsA0rkx2S4WJjJIh34" url="https://maps.googleapis.com/maps/api/place/nearbysearch/json?" url+="location=%f,%f&" % (lat,lng) url+="radius=%i&" % radius url+="type=%s&" % keyword url+="key=%s" % key response=requests.get(url) json_dict=response.json() res=json_dict['results'] info_pack=[] for x in res: placeid = x['place_id'] url = "https://maps.googleapis.com/maps/api/place/details/json?placeid={}&key={}".format(placeid,key) r = requests.get(url).json()['result'] info = {} info['name'] = r['name'] info['lat'] = r['geometry']['location']['lat'] info['lng'] = r['geometry']['location']['lng'] info_pack.append(info) return info_pack @app.route('/query', methods = ['POST']) def query(): if flask.request.method == 'POST': # lat,lang = lat, lang = 28,76 data = {'locations':NearbySearch(lat,lng,'doctor')} print(flask.request.form['query']) return data if __name__ == "__main__": app.run(debug = True, port=5003)

[ "nikhilksingh97@gmail.com" ]

nikhilksingh97@gmail.com

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/tfx/orchestration/experimental/core/constants.py

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permissive

swap-10/tfx

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refs/heads/master

2023-07-15T22:54:18.642120

2021-09-06T06:17:48

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0

Apache-2.0

2021-09-02T05:49:03

2021-09-02T05:09:23

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# Copyright 2021 Google LLC. 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. """Constants shared across modules.""" EXECUTION_ERROR_MSG_KEY = '__execution_error_msg__' IMPORTER_NODE_TYPE = 'tfx.dsl.components.common.importer.Importer' RESOLVER_NODE_TYPE = 'tfx.dsl.components.common.resolver.Resolver'

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tensorflow-extended-nonhuman@googlegroups.com

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kristoffer-paulsson/angelos

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refs/heads/master

2022-05-05T15:16:59.340527

2022-03-27T16:05:51

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# # Copyright (c) 2018-2020 by Kristoffer Paulsson <kristoffer.paulsson@talenten.se>. # # This software is available under the terms of the MIT license. Parts are licensed under # different terms if stated. The legal terms are attached to the LICENSE file and are # made available on: # # https://opensource.org/licenses/MIT # # SPDX-License-Identifier: MIT # # Contributors: # Kristoffer Paulsson - initial implementation # """Security tests putting the policies to the test.""" from unittest import TestCase from angelos.common.policy import evaluate from angelos.lib.policy.types import PersonData from angelos.portfolio.domain.create import CreateDomain from angelos.portfolio.domain.update import UpdateDomain from angelos.portfolio.entity.create import CreatePersonEntity from test.fixture.generate import Generate class TestUpdateDomain(TestCase): def test_perform(self): data = PersonData(**Generate.person_data()[0]) portfolio = CreatePersonEntity().perform(data) CreateDomain().perform(portfolio) self.assertIsNotNone(portfolio.domain) with evaluate("Domain:Update") as report: domain = UpdateDomain().perform(portfolio) self.assertIs(domain, portfolio.domain) self.assertTrue(report)

[ "kristoffer.paulsson@talenten.se" ]

kristoffer.paulsson@talenten.se

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/Inception_model/softmax.py

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[]

no_license

lanardo/Image_processing_server

e3d3151cf825ebca01a64d851642bca0e99b0646

957ab8d82a453049885f85f440efcfc60c0e7d7f

refs/heads/master

2021-05-14T13:13:27.358287

2018-07-04T19:41:06

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null

UTF-8

Python

false

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import tensorflow as tf import csv import os import argparse parser = argparse.ArgumentParser() parser.add_argument("--input_dir", help="path to folder containing the trainning data") parser.add_argument("--output_dir", help="path to folder containing the result coef files") parser.add_argument("--restore", default="yes", help="restore from the checkpoint") parser.add_argument("--rate", type=float, default=0.0001, help="rate(alpha) for trainning") parser.add_argument("--epochs", type=int, default=200000, help="max epoches") parser.add_argument("--strip", type=int, default=50, help="step for writing the result on loop") a = parser.parse_args() # a.input_dir = './model' # a.output_dir = './model' # a.restore = "no" def xaver_init(n_inputs, n_outputs, uniform=True): if uniform: init_range = tf.sqrt(6.0 / (n_inputs + n_outputs)) return tf.random_uniform_initializer(-init_range, init_range) else: stddev = tf.sqrt(3.0 / (n_inputs + n_outputs)) return tf.truncated_normal_initializer(stddev=stddev) def acc(d1, d2): cnt = 0 for i in range(d1.__len__()): if d1[i] == d2[i]: cnt += 1 return float(cnt)/d1.__len__() def sel_max(data): ret_ind = [] for i in range(data.__len__()): if data[i][0] == 1: ret_ind.append(0) else: ret_ind.append(1) return ret_ind if __name__ == '__main__': learning_rate = a.rate in_dir = a.input_dir out_dir = a.output_dir epochs = a.epochs strip = a.strip train_data_path = os.path.join(in_dir, 'train_data.csv') w_coef_path = os.path.join(out_dir, 'w.csv') b_coef_path = os.path.join(out_dir, 'b.csv') ckpt_path = os.path.join(out_dir, 'model_bin.ckpt') labels = ['front', 'front_3_quarter', 'side', 'rear_3_quarter', 'rear', 'interior', 'tire'] directions = [ [1, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 1] ] x_data = [] y_data = [] """ Loading training data from csv files """ print('[Step 1] Loading training data ...') # for python 2x with open(train_data_path) as fp: csv_reader = csv.reader(fp, delimiter=',') for row in csv_reader: x_data.append([float(row[i]) for i in range(0, len(row)-7)]) y_data.append([float(row[i]) for i in range(len(row)-7, len(row))]) print("total features :" + str(len(x_data))) print("length of feature :" + str(len(x_data[0]))) print("length of label :" + str(len(y_data[0]))) """ Placeholder """ print('[Step 2] Placeholder') x = tf.placeholder('float', [None, 2048]) # len(feature) = 2048 y = tf.placeholder('float', [None, 7]) # len(Directions) = 7 : classes W1 = tf.get_variable('W1', shape=[2048, 7], initializer=xaver_init(2048, 7)) b1 = tf.Variable(tf.zeros([7])) activation = tf.add(tf.matmul(x, W1), b1) t1 = tf.nn.softmax(activation) """ Minimize error using cross entropy """ cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=activation, labels=y)) optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # Gradient Descent """ Initializing the variables """ print('[Step 3] Initializing the variables.') # init = tf.initialize_all_variables() # python 3x init = tf.global_variables_initializer() # python 2x sess = tf.Session() sess.run(init) saver = tf.train.Saver() print(a.restore) if a.restore == "yes": print('Loading the last learning Session.') saver.restore(sess, ckpt_path) """ Training cycle """ print('[Step 4] Training...') for step in range(epochs): sess.run(optimizer, feed_dict={x: x_data, y: y_data}) if step % strip == 0: ret = sess.run(t1, feed_dict={x: x_data}) acc1 = acc(sess.run(tf.arg_max(ret, 1)), sess.run(tf.arg_max(y_data, 1))) * 100 print(' ' + str(step) + ' ' + str(sess.run(cost, feed_dict={x: x_data, y: y_data})) + ' ' + str(acc1)) saver.save(sess, ckpt_path) print('Optimization Finished!')

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#!/usr/bin/env python3 """ Special notes : This implementation supports MULTIPLE shortest path. (except for the number_of_possible_locations_with_mindist_simple function) """ import random from Graph import Graph from algo import * from unused import * from Dijkstra import * from util import timeit from reach import reach #################### data = '/Users/louisabraham/Downloads/RoadNetworks/data/france.in' logging = '/Users/louisabraham/Downloads/RoadNetworks/vis/points.js' hour = 3600000 # We can control the display of chronos using timeit.activated timeit.activated = True #################### # graph importation g = Graph.from_file(data) # we chose a random starting point v = random.choice(list(g.keys())) # # # Question 1.1 # print(number_of_possible_locations(g, v, 1 * hour)) # # # the same result is computed # print(number_of_possible_locations_with_mindist_dijkstra( # g, v, 1 * hour, 0)) # print(number_of_possible_locations_with_mindist_dijkstra( # g, v, 1 * hour, 0)) print(number_of_possible_locations_with_mindist_dijkstra( g, v, 1 * hour, 2 * hour, logging=logging)) input() g.generate_converse() print(number_of_possible_locations_with_mindist_dijkstra( g.converse, v, 1 * hour, 2 * hour, logging=logging)) # print(reach(g, v)) # # # We can free memory like this # dijkstra.clean()

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#!/usr/bin/python3 """ function that creates an Object from a “JSON file” """ import json def load_from_json_file(filename): """ Args filename: JSON file form where the string is going to be read json.dumps() method can convert a Python object into a JSON string. json.dump() method can be used to write to file a JSON file directly. can Write in an open file json.loads() expects to get its text from a string object json.load() expects to get the text from a file can Read from an open file an convert """ with open(filename, mode="r", encoding="UTF8") as file: return json.load(file)

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from __future__ import absolute_import, division, print_function, unicode_literals import torch import torch.nn.functional as F from tests.utils import jitVsGlow import unittest class TestAvgPool3d(unittest.TestCase): def test_avg_pool3d_basic(self): """Basic test of the PyTorch avg_pool3d Node on Glow.""" def test_f(inputs): return F.avg_pool3d(inputs, 3) inputs = torch.randn(1, 4, 5, 5, 5) jitVsGlow(test_f, inputs, expected_fused_ops={"aten::avg_pool3d"}) def test_avg_pool3d_with_args(self): """Test of the PyTorch avg_pool3d Node with arguments on Glow.""" def test_f(inputs): return F.avg_pool3d(inputs, padding=2, kernel_size=(4, 7, 7)) inputs = torch.randn(1, 4, 10, 10, 10) jitVsGlow(test_f, inputs, expected_fused_ops={"aten::avg_pool3d"})

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capy-larit/exercicios_python

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2023-03-23T05:29:07.409948

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""" Faça um programa utilizando um dict (dicionário) que leia dados de entrada do usuário. O usuário deve entrar com os dados de uma pessoa como nome, idade e cidade onde mora. Após isso, você deve imprimir os dados como o exemplo abaixo: nome: João idade: 20 cidade: São Paulo """ def chamar_menu(): nome = input('Digite seu nome: ') idade = int(input('Digite sua idade: ')) cidade = input('Digite sua cidade: ') dict[nome]=[idade, cidade] dict = {} try: chamar_menu() except: print('A idade deve ser um número inteiro.') chamar_menu() for chave, item in dict.items(): print(f'Nome: {chave}\nIdade: {item[0]}\nCidade: {item[1]}')

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larissa.laritt@icloud.com

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/envfarmaciaveterinaria/Scripts/viewer.py

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laMoradaPostrera/FarmaciaVeterinariaUnillanos

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refs/heads/master

2020-10-01T23:43:19.395012

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#!c:\users\lenovo~1\mispro~1\unilla~1\farmac~2\envfar~1\scripts\python.exe # # The Python Imaging Library # $Id$ # from __future__ import print_function import sys if sys.version_info[0] > 2: import tkinter else: import Tkinter as tkinter from PIL import Image, ImageTk # # an image viewer class UI(tkinter.Label): def __init__(self, master, im): if im.mode == "1": # bitmap image self.image = ImageTk.BitmapImage(im, foreground="white") tkinter.Label.__init__(self, master, image=self.image, bd=0, bg="black") else: # photo image self.image = ImageTk.PhotoImage(im) tkinter.Label.__init__(self, master, image=self.image, bd=0) # # script interface if __name__ == "__main__": if not sys.argv[1:]: print("Syntax: python viewer.py imagefile") sys.exit(1) filename = sys.argv[1] root = tkinter.Tk() root.title(filename) im = Image.open(filename) UI(root, im).pack() root.mainloop()

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/assignment_4/shapes.py

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gauravjuvekar/ppl

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#!/usr/bin/env python3 import math class Shape(object): def __init__(self, turtle=None): self.turtle = turtle class Polygon(Shape): def __init__(self, points, turtle=None): Shape.__init__(self, turtle) self.points = points def draw(self, turtle=None): if turtle is None: turtle = self.turtle turtle.penup() pos = turtle.pos() relative = lambda x, y: (pos[0] + x, pos[1] + y) turtle.goto(relative(*(self.points[-1]))) turtle.pendown() for point in self.points: turtle.goto(relative(*point)) turtle.penup() turtle.goto(pos) turtle.pendown() def transform(self, matrix): if not (len(matrix) == 2 and (len(matrix[0]) == len(matrix[1]) == 2)): raise ValueError("Transformation matrix must be order 2 square") apply = lambda point, matrix: ( (point[0] * matrix[0][0]) + (point[1] * matrix[0][1]), (point[0] * matrix[1][0]) + (point[1] * matrix[1][1])) self.points = [apply(point, matrix) for point in self.points] class RegularPolygon(Polygon): def __init__(self, sides, radius, turtle=None): step_angle = 360 / sides points = [] angle = 0 while angle < 360: points.append(( radius * math.cos(math.radians(angle)), radius * math.sin(math.radians(angle)))) angle += step_angle Polygon.__init__(self, points, turtle) class Ellipse(RegularPolygon): def __init__(self, rad_x, rad_y, turtle=None): sides = max((rad_x, rad_y)) RegularPolygon.__init__(self, sides, min((rad_x, rad_y)), turtle) if rad_x < rad_y: self.transform(((1, 0), (0, rad_y / rad_x))) else: self.transform(((rad_x / rad_y, 0), (0, 1)))

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/Config/ssd_config.py

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LLLibra/yolo-v3

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# -*- coding:UTF-8 -*- from utils.ssd_loss import * from Model.SSD import build_SSD extras = { '300': [[1024,256,512],[512,128,256],[256,128,256],[256,128,256]], '512': [], } mbox = { '300': [4, 6, 6, 6, 4, 4], # 最终特征图中每个点有多少个box '512': [], } ##SSD 300 config voc = { 'num_classes': 21, 'feature_maps':[38,19,10,5,3,1], 'min_dim':300, 'img_size':300, 'xywh':False, 'steps':[8,16,32,64,100,300], 'min_sizes':[30,60,111,162,216,264], 'max_sizes':[60,111,162,213,264,315], 'aspect_ratio':[[2],[2,3],[2,3],[2,3],[2],[2]], 'variance':[0.1,0.2], 'clip':True, 'name':'VOC', } coco = { 'num_classes': 201, 'lr_steps': (280000, 360000, 400000), 'max_iter': 400000, 'feature_maps': [38, 19, 10, 5, 3, 1], 'min_dim': 300, 'img_size':300, 'steps': [8, 16, 32, 64, 100, 300], 'min_sizes': [21, 45, 99, 153, 207, 261], 'max_sizes': [45, 99, 153, 207, 261, 315], 'aspect_ratios': [[2], [2, 3], [2, 3], [2, 3], [2], [2]], 'variance': [0.1, 0.2], 'clip': True, 'name': 'COCO', }

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245407754@qq.com

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msbelal/Project-Euler

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from __future__ import with_statement with open ("matrix-81.txt") as f: lines = [ line.strip().split(",") for line in f.readlines() ] q = {} for i in xrange(159) : for j in xrange(0,i+1) : x, y = j, i - j if (0 <= x < 80) and (0 <= y < 80) : if x == 0 and y == 0: q[x,y] = 0 elif x == 0 : q[x,y] = q[x,y-1] elif y == 0 : q[x,y] = q[x-1,y] else : q[x,y] = min(q[x-1,y], q[x, y-1]) q[x,y] += int(lines[x][y]) print q[79,79]

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ericwang915/pypbc

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2020-06-11T00:01:54.795875

2009-09-23T09:00:41

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# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: """Basic functions to read and write TrackVis .trk files and to play with fibers. Copyright (c) 2009 Emanuele Olivetti <emanuele_AT_relativita.com> This library is free software; you can redistribute it and/or modify it either under the terms of the GNU General Public License version 3 as published by the Free Software Foundation. """ import numpy as N import sys # Definition of trackvis header structure. # See http://www.trackvis.org/docs/?subsect=fileformat # See http://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html trk_header_structure = [['id_string', 1, 'S6'], ['dim', 3, '<h'], ['voxel_size', 3, '<f4'], ['origin', 3, '<f4'], ['n_scalars', 1, '<h'], ['scalar_name', 10, 'S20'], ['n_properties', 1, '<h'], ['property_name', 10, 'S20'], ['reserved', 1, 'S508'], ['voxel_order', 1, 'S4'], ['pad2', 1, 'S4'], ['image_orientation_patient', 6, '<f4'], ['pad1', 1, 'S2'], ['invert_x', 1, 'S1'], ['invert_y', 1, 'S1'], ['invert_z', 1, 'S1'], ['swap_xy', 1, 'S1'], ['swap_yz', 1, 'S1'], ['swap_zx', 1, 'S1'], ['n_count', 1, '<i4'], ['version', 1, '<i4'], ['hdr_size', 1, '<i4'], ] def read_header(f): """ Read and parse .trk file header structure. """ header = {} for field_name, count, dtype in trk_header_structure: header[field_name] = N.fromfile(f, dtype=dtype, count=count) pass assert(f.tell()==1000) # header is always 1000 bytes. return header def write_header(f, header): """Write .trk header to file. """ for field_name, count, dtype in trk_header_structure: # Note that ".astype(dtype)" is just to be sure or correct types: header[field_name].astype(dtype).tofile(f) pass assert(f.tell()==1000) # header is always 1000 bytes. return def print_header(header): """Print relevant info of .trk header. """ print "Header:" relevant_fields = ['dim', 'voxel_size', 'origin', 'n_count' ] for field in relevant_fields: print '\t',field, ':', header[field] pass return def progress_meter(position, total, message, steps=10): """Simple progress meter. """ if position%(int(total/steps))==0: print message, str(1+int(100.0*position/total))+'%' sys.stdout.flush() pass return def read_fibers(f, header): """Read fibers from .trk file and fill a list. """ fiber = [] # structure of each entry of the list: # [[X1,Y1,Z1,SCALAR1...],...,[Xn,Yn,Zn,SCALARn...]], [PROPERTIES] # Note that in PBC2009 trckvis files there are no scalars or # properties, which means that the actual structure of the fiber # list is simply: # fiber_id : [[X1,Y1,Z1],...,[Xn,Yn,Zn]], [] n_scalars = header['n_scalars'][0] n_fibers = header['n_count'][0] for fiber_id in range(n_fibers): num_points = N.fromfile(f, dtype='<i4', count=1)[0] xyz_scalar = N.fromfile(f, dtype='<f4', count=num_points*(3+n_scalars)).reshape(num_points, 3+n_scalars) properties = N.fromfile(f, dtype='<f4', count=header['n_properties'][0]) fiber.append([xyz_scalar, properties]) progress_meter(fiber_id, n_fibers, 'Reading fibers...') pass return fiber def write_fibers(f, fiber, header): """Write fibers to file in .trk format. Assumption: header has already been written. """ n_scalars = header['n_scalars'][0] n_fibers = header['n_count'][0] for fiber_id in range(n_fibers): num_points = N.array((fiber[fiber_id][0]).shape[0], dtype='<i4') num_points.tofile(f) xyz_scalar = N.array(fiber[fiber_id][0], dtype='<f4') xyz_scalar.tofile(f) properties = N.array(fiber[fiber_id][1], dtype='<f4') properties.tofile(f) progress_meter(fiber_id, n_fibers, 'Writing fibers...') pass return def mm2voxel(xyz, header): """Converts coordinates from mm to voxel. """ return N.floor(xyz/header['voxel_size']).astype('i') def voxel2mm(Vxyz, header): """Converts coordinates from voxel to mm. """ return (Vxyz+0.5)*header['voxel_size'] def build_voxel_fibers_dict(fiber, header): """Build a dictionary that given a voxel returns all fibers (IDs) crossing it. """ voxel2fibers = {} n_fibers = len(fiber) for fiber_id in range(n_fibers): xyz = fiber[fiber_id][0] ijk = mm2voxel(xyz, header) for i in range(xyz.shape[0]): try: voxel2fibers[tuple(ijk[i,:])].append(fiber_id) except KeyError: voxel2fibers[tuple(ijk[i,:])] = [fiber_id] pass pass progress_meter(fiber_id, n_fibers, 'Mapping voxels to fibers...') pass n_voxels = len(voxel2fibers.keys()) # Now transform each list of IDs in an array of IDs: for n, ijk in enumerate(voxel2fibers.keys()): voxel2fibers[ijk] = N.array(voxel2fibers[ijk]) progress_meter(n, n_voxels, 'Converting lists to arrays...') pass return voxel2fibers if __name__=="__main__": print "This simple program reads a TrackVis .trk file, parse it, build" print "structures to represent fibers as Python list of arrays" print "and then saves structures in TrackVis .trk file format." print "The resulting file is expected to be identical to the original." print "As a further step a dictionary, mapping voxel to fibers, is built" print "and some examples using it are shown." # filename = "dsi.trk" # filename = "dti.trk" filename = "hardiO10.trk" print print "file:", filename f = open(filename) header = read_header(f) print_header(header) fiber = read_fibers(f, header) f.close() print fiber_id = 1000 print "Example: fiber_id=",fiber_id print fiber[fiber_id] print "Convert points from mm to voxel coordinates:" Vxyz = mm2voxel(fiber[fiber_id][0], header) print Vxyz print "Convert back and check whether differences are less than grid size...", assert(((voxel2mm(Vxyz, header)-fiber[fiber_id][0])<header['voxel_size']).all()) print "OK." print filename2 = filename+"_COPY.trk" print "Saving to:", filename2 f = open(filename2,'w') write_header(f, header) write_fibers(f, fiber, header) f.close() print print "Building voxel2fibers dictionary:" voxel2fibers = build_voxel_fibers_dict(fiber, header) voxel = tuple(header['dim'] / 2) print "Example: fibers crossing voxel", voxel try: print voxel2fibers[voxel] except KeyError: print [] print "There are no fibers crossing this voxel." pass print x = header['dim'][0] / 2 print "Example: counting fibers crossing plane x =", x counter = 0 for y in range(header['dim'][1]): for z in range(header['dim'][2]): try: counter += voxel2fibers[(x,y,z)].size except KeyError: pass pass pass print "Number of fibers:", counter print fiber_id = 2000 print "Which fibers cross (the voxels of) fiber[fiber_id=",fiber_id,"] ?" xyz = fiber[fiber_id][0] ijk = mm2voxel(xyz, header) fiber_id_list = N.unique(N.hstack([voxel2fibers[i,j,k] for i,j,k in ijk])) print fiber_id_list print fiber_id_list.size, "fibers." print print "Saving .trk file with just the previous list of fibers." filename3 = filename+'_cross_fiber_id_'+str(fiber_id)+'.trk' print "Saving to:", filename3 import copy fiber2 = [fiber[fiber_id] for fiber_id in fiber_id_list] header2 = copy.deepcopy(header) header2['n_count'] = N.array([fiber_id_list.size]) f = open(filename3, 'w') write_header(f, header2) write_fibers(f, fiber2, header2) f.close()

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# Copyright The PyTorch Lightning team. # # 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 urllib.error import pytest from pytorch_lightning.utilities import _TORCHVISION_AVAILABLE from flash.core.utilities.imports import _BOLTS_AVAILABLE, _TIMM_AVAILABLE from flash.image.backbones import catch_url_error, IMAGE_CLASSIFIER_BACKBONES @pytest.mark.parametrize(["backbone", "expected_num_features"], [ pytest.param("resnet34", 512, marks=pytest.mark.skipif(not _TORCHVISION_AVAILABLE, reason="No torchvision")), pytest.param("mobilenetv2_100", 1280, marks=pytest.mark.skipif(not _TIMM_AVAILABLE, reason="No timm")), pytest.param("simclr-imagenet", 2048, marks=pytest.mark.skipif(not _BOLTS_AVAILABLE, reason="No bolts")), pytest.param("swav-imagenet", 2048, marks=pytest.mark.skipif(not _BOLTS_AVAILABLE, reason="No bolts")), pytest.param("mobilenet_v2", 1280, marks=pytest.mark.skipif(not _TORCHVISION_AVAILABLE, reason="No torchvision")), ]) def test_image_classifier_backbones_registry(backbone, expected_num_features): backbone_fn = IMAGE_CLASSIFIER_BACKBONES.get(backbone) backbone_model, num_features = backbone_fn(pretrained=False) assert backbone_model assert num_features == expected_num_features def test_pretrained_backbones_catch_url_error(): def raise_error_if_pretrained(pretrained=False): if pretrained: raise urllib.error.URLError('Test error') with pytest.warns(UserWarning, match="Failed to download pretrained weights"): catch_url_error(raise_error_if_pretrained)(pretrained=True)

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from rest_framework import serializers from location.models import TaskLocation, CustomerLocation, TaskerLocation, MapLocation class CustomerLocationSerializer(serializers.ModelSerializer): class Meta: model = CustomerLocation fields = "__all__" class MapLocationSerializer(serializers.ModelSerializer): class Meta: model = MapLocation fields = "__all__" class TaskerLocationSerializer(serializers.ModelSerializer): class Meta: model = TaskerLocation fields = "__all__" class TaskLocationSerializer(serializers.ModelSerializer): class Meta: model = TaskLocation fields = "__all__"

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''' unittest条件断言 tester: cc 此文仅做翻译只用，不介绍具体使用 ''' Skiptest() # 在测试中引发此异常以跳过该异常。 _ShouldStop() # 停止测试 _UnexpectedSuccess() # 测试本来应该是失败的，但是没有失败 Skip() # 无条件跳过测试。 skipIf(condition, reason) # 条件为真时跳过测试 skipUnless(condition, reason) # 条件为假时跳过测试 expectedFailure(test_item) # 标记该测试预期就是失败，如果运行失败时，不算作失败用例。 _is_subtype(expected, basetype) # 判断类型是否符合预期 addTypeEqualityFunc(typeobj, function) # 为自定义检查类提供检查方法 addCleanup( function , *args , **kwargs ) #添加针对每个测试用例执行完tearDown()方法之后的清理方法，添加进去的函数按照后进先出（LIFO）的顺序执行，当然，如果setUp()方法执行失败，那么不会执行tearDown()方法，自然也不会执行addCleanup()里添加的函数。 setUp()#在执行每个测试用例之前被执行，任何异常（除了unittest.SkipTest和AssertionError异常以外）都会当做是error而不是failure，且会终止当前测试用例的执行。 tearDown()#执行了setUp()方法后，不论测试用例执行是否成功，都执行tearDown()方法。如果tearDown()的代码有异常(除了unittest.SkipTest和AssertionError异常以外)，会多算一个error。 setUpClass( cls )与tearDownClass( cls )#测试用例们被执行前、后执行的方法，定义时必须加上classmethod装饰符 countTestCases()#返回测试用例的个数，对于TestCase实例来说，这个返回值一直是1. defaultTestResult()#如果在run()方法中未提供result参数，该函数返回一个包含本用例测试结果的TestResult对象。 shortDescription()#返回测试用例的描述，即函数的docstring，如果没有，返回None。可以用于测试结果输出中描述测试内容。 id()#返回测试用例的编号，通常是如下格式：模块名.类名.函数名。可以用于测试结果的输出。 subTest( msg=_subtest_msg_sentinel, **params)#返回一个上下文管理器，它将返回由可选消息和关键字参数标识的子测试中的封闭代码块。子测试中的失败标志着测试用例失败，但在封闭块结束时恢复执行，允许执行进一步的测试代码。 run( result =None)#运行一个测试用例，将测试结果收集到result变量中，测试结果不返回给调用者。如果result参数的值为None，则测试结果在下面提到的defaultTestResult()方法的返回值中 doCleanups()#无条件强制调用addCleanup()添加的函数，适用于setUp()方法执行失败但是需要执行清理函数的场景，或者希望在tearDown()方法之前执行这些清理函数。 debug()#与run方法将测试结果存储到result变量中不同，debug方法运行测试用例将异常信息上报给调用者。 fail( msg =None)#无条件声明一个测试用例失败，msg是失败信息。 assertEqual(set1,set2,msg=None) #检测两个值是否相等 assertFalse( expr, msg=None) #检查表达式是否为假 assertTrue( expr, msg=None) #检查表达式是否为真 assertAlmostEqual与assertNotAlmostEqual(, first, second, places=None, msg=None,delta=None) #判断两个值是否约等于或者不约等于，places表示小数点后精确的位数 assertSequenceEqual(seq1, seq2, msg=None, seq_type=None) #有序序列的相等断言，如元组、列表 assertListEqual( list1, list2, msg=None) #列表相等的特定断言 assertTupleEqual(tuple1, tuple2, msg=None) #元组相等的特定断言 assertSetEqual( set1, set2, msg=None) #集合相等的特定断言 assertIn与assertNotIn( member, container, msg=None) #判断a 是否存在b中 assertIs与assertIsNot( expr1, expr2, msg=None) #判断a是不是b assertDictEqual( d1, d2, msg=None) #检查两个字典是否相等 assertDictContainsSubset( subset, dictionary, msg=None) #检查字典是否是子集的超集。 assertCountEqual(first, second, msg=None) #判断两个无序列表内所出现的内容是否相等 assertMultiLineEqual( first, second, msg=None) #断言两个多行字符串相等 assertLess( a, b, msg=None) #断言a<b assertLessEqual( a, b, msg=None) #断言a<=b assertGreater( a, b, msg=None) #断言a>b assertGreaterEqual(a, b, msg=None) #断言a>=b assertIsNone与assertIsNotNone( obj, msg=None) #判断obj是否为空 assertIsInstance(a, b)与assertNotIsInstance(a, b)# 与assertTrue相同，其中的类型b，既可以是一个类型，也可以是类型组成的元组。 assertRaisesRegex( expected_exception, expected_regex,*args, **kwargs)#断言在引发异常中的消息与正则表达式匹配。 assertWarnsRegex( expected_warning, expected_regex,*args, **kwargs)#断言触发警告中的消息与ReGEXP匹配。基本功能类似于AdvestWr.NS.（）只有消息与正则表达式匹配的警告。被认为是成功的匹配 assertRegex与assertNotRegex(text, expected_regex, msg=None) #判断文本与正则表达式是否匹配 shortDescription()#返回测试用例的描述，即函数的docstring，如果没有，返回None。可以用于测试结果输出中描述测试内容。

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from __future__ import division """Integration between boxtree and pyfmmlib.""" __copyright__ = "Copyright (C) 2013 Andreas Kloeckner" __license__ = """ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ import numpy as np from pytools import memoize_method import logging logger = logging.getLogger(__name__) __doc__ = """Integrates :mod:`boxtree` with `pyfmmlib <http://pypi.python.org/pypi/pyfmmlib>`_. """ class FMMLibExpansionWrangler(object): """Implements the :class:`boxtree.fmm.ExpansionWranglerInterface` by using pyfmmlib. """ # {{{ constructor def __init__(self, tree, helmholtz_k, fmm_level_to_nterms=None, ifgrad=False, dipole_vec=None, dipoles_already_reordered=False, nterms=None): """ :arg fmm_level_to_nterms: a callable that, upon being passed the tree and the tree level as an integer, returns the value of *nterms* for the multipole and local expansions on that level. """ if nterms is not None and fmm_level_to_nterms is not None: raise TypeError("may specify either fmm_level_to_nterms or nterms, " "but not both") if nterms is not None: from warnings import warn warn("Passing nterms is deprecated. Pass fmm_level_to_nterms instead.", DeprecationWarning, stacklevel=2) def fmm_level_to_nterms(tree, level): return nterms self.tree = tree if helmholtz_k == 0: self.eqn_letter = "l" self.kernel_kwargs = {} self.rscale_factor = 1 else: self.eqn_letter = "h" self.kernel_kwargs = {"zk": helmholtz_k} self.rscale_factor = abs(helmholtz_k) self.level_nterms = np.array([ fmm_level_to_nterms(tree, lev) for lev in range(tree.nlevels) ], dtype=np.int32) if helmholtz_k: logger.info("expansion orders by level used in Helmholtz FMM: %s", self.level_nterms) self.dtype = np.complex128 self.ifgrad = ifgrad self.dim = tree.dimensions if dipole_vec is not None: assert dipole_vec.shape == (self.dim, self.tree.nsources) if not dipoles_already_reordered: dipole_vec = self.reorder_sources(dipole_vec) self.dipole_vec = dipole_vec.copy(order="F") self.dp_suffix = "_dp" else: self.dipole_vec = None self.dp_suffix = "" # }}} def level_to_rscale(self, level): result = self.tree.root_extent * 2 ** -level * self.rscale_factor if abs(result) > 1: result = 1 return result @memoize_method def projection_quad_extra_kwargs(self, level=None, nterms=None): if level is None and nterms is None: raise TypeError("must pass exactly one of level or nterms") if level is not None and nterms is not None: raise TypeError("must pass exactly one of level or nterms") if level is not None: nterms = self.level_nterms[level] common_extra_kwargs = {} if self.dim == 3 and self.eqn_letter == "h": nquad = max(6, int(2.5*nterms)) from pyfmmlib import legewhts xnodes, weights = legewhts(nquad, ifwhts=1) common_extra_kwargs = { "xnodes": xnodes, "wts": weights, } return common_extra_kwargs # {{{ overridable target lists for the benefit of the QBX FMM def box_target_starts(self): return self.tree.box_target_starts def box_target_counts_nonchild(self): return self.tree.box_target_counts_nonchild def targets(self): return self.tree.targets # }}} # {{{ routine getters def get_routine(self, name, suffix=""): import pyfmmlib return getattr(pyfmmlib, "%s%s%s" % ( self.eqn_letter, name % self.dim, suffix)) def get_vec_routine(self, name): return self.get_routine(name, "_vec") def get_translation_routine(self, name, vec_suffix="_vec"): suffix = "" if self.dim == 3: suffix = "quadu" suffix += vec_suffix rout = self.get_routine(name, suffix) if self.dim == 2: def wrapper(*args, **kwargs): # not used kwargs.pop("level_for_projection", None) return rout(*args, **kwargs) else: def wrapper(*args, **kwargs): kwargs.pop("level_for_projection", None) nterms2 = kwargs["nterms2"] kwargs.update(self.projection_quad_extra_kwargs(nterms=nterms2)) val, ier = rout(*args, **kwargs) if (ier != 0).any(): raise RuntimeError("%s failed with nonzero ier" % name) return val # Doesn't work in in Py2 # from functools import update_wrapper # update_wrapper(wrapper, rout) return wrapper def get_direct_eval_routine(self): if self.dim == 2: rout = self.get_vec_routine("potgrad%ddall" + self.dp_suffix) def wrapper(*args, **kwargs): kwargs["ifgrad"] = self.ifgrad kwargs["ifhess"] = False pot, grad, hess = rout(*args, **kwargs) if not self.ifgrad: grad = 0 return pot, grad # Doesn't work in in Py2 # from functools import update_wrapper # update_wrapper(wrapper, rout) return wrapper elif self.dim == 3: rout = self.get_vec_routine("potfld%ddall" + self.dp_suffix) def wrapper(*args, **kwargs): kwargs["iffld"] = self.ifgrad pot, fld = rout(*args, **kwargs) if self.ifgrad: grad = -fld else: grad = 0 return pot, grad # Doesn't work in in Py2 # from functools import update_wrapper # update_wrapper(wrapper, rout) return wrapper else: raise ValueError("unsupported dimensionality") def get_expn_eval_routine(self, expn_kind): name = "%%dd%seval" % expn_kind rout = self.get_routine(name, "_vec") if self.dim == 2: def wrapper(*args, **kwargs): kwargs["ifgrad"] = self.ifgrad kwargs["ifhess"] = False pot, grad, hess = rout(*args, **kwargs) if not self.ifgrad: grad = 0 return pot, grad # Doesn't work in in Py2 # from functools import update_wrapper # update_wrapper(wrapper, rout) return wrapper elif self.dim == 3: def wrapper(*args, **kwargs): kwargs["iffld"] = self.ifgrad pot, fld, ier = rout(*args, **kwargs) if (ier != 0).any(): raise RuntimeError("%s failed with nonzero ier" % name) if self.ifgrad: grad = -fld else: grad = 0 return pot, grad # Doesn't work in in Py2 # from functools import update_wrapper # update_wrapper(wrapper, rout) return wrapper else: raise ValueError("unsupported dimensionality") # }}} # {{{ data vector utilities def expansion_shape(self, nterms): if self.dim == 2 and self.eqn_letter == "l": return (nterms+1,) elif self.dim == 2 and self.eqn_letter == "h": return (2*nterms+1,) elif self.dim == 3: # This is the transpose of the Fortran format, to # minimize mismatch between C and Fortran orders. return (2*nterms+1, nterms+1,) else: raise ValueError("unsupported dimensionality") def _expansions_level_starts(self, order_to_size): result = [0] for lev in range(self.tree.nlevels): lev_nboxes = ( self.tree.level_start_box_nrs[lev+1] - self.tree.level_start_box_nrs[lev]) expn_size = order_to_size(self.level_nterms[lev]) result.append( result[-1] + expn_size * lev_nboxes) return result @memoize_method def multipole_expansions_level_starts(self): from pytools import product return self._expansions_level_starts( lambda nterms: product(self.expansion_shape(nterms))) @memoize_method def local_expansions_level_starts(self): from pytools import product return self._expansions_level_starts( lambda nterms: product(self.expansion_shape(nterms))) def multipole_expansions_view(self, mpole_exps, level): box_start, box_stop = self.tree.level_start_box_nrs[level:level+2] expn_start, expn_stop = \ self.multipole_expansions_level_starts()[level:level+2] return (box_start, mpole_exps[expn_start:expn_stop].reshape( box_stop-box_start, *self.expansion_shape(self.level_nterms[level]))) def local_expansions_view(self, local_exps, level): box_start, box_stop = self.tree.level_start_box_nrs[level:level+2] expn_start, expn_stop = \ self.local_expansions_level_starts()[level:level+2] return (box_start, local_exps[expn_start:expn_stop].reshape( box_stop-box_start, *self.expansion_shape(self.level_nterms[level]))) def multipole_expansion_zeros(self): return np.zeros( self.multipole_expansions_level_starts()[-1], dtype=self.dtype) def local_expansion_zeros(self): return np.zeros( self.local_expansions_level_starts()[-1], dtype=self.dtype) def output_zeros(self): if self.ifgrad: from pytools import make_obj_array return make_obj_array([ np.zeros(self.tree.ntargets, self.dtype) for i in range(1 + self.dim)]) else: return np.zeros(self.tree.ntargets, self.dtype) def add_potgrad_onto_output(self, output, output_slice, pot, grad): if self.ifgrad: output[0, output_slice] += pot output[1:, output_slice] += grad else: output[output_slice] += pot # }}} # {{{ source/target particle wrangling def _get_source_slice(self, ibox): pstart = self.tree.box_source_starts[ibox] return slice( pstart, pstart + self.tree.box_source_counts_nonchild[ibox]) def _get_target_slice(self, ibox): pstart = self.box_target_starts()[ibox] return slice( pstart, pstart + self.box_target_counts_nonchild()[ibox]) @memoize_method def _get_single_sources_array(self): return np.array([ self.tree.sources[idim] for idim in range(self.dim) ], order="F") def _get_sources(self, pslice): return self._get_single_sources_array()[:, pslice] @memoize_method def _get_single_targets_array(self): return np.array([ self.targets()[idim] for idim in range(self.dim) ], order="F") def _get_targets(self, pslice): return self._get_single_targets_array()[:, pslice] # }}} def reorder_sources(self, source_array): return source_array[..., self.tree.user_source_ids] def reorder_potentials(self, potentials): return potentials[self.tree.sorted_target_ids] def get_source_kwargs(self, src_weights, pslice): if self.dipole_vec is None: return { "charge": src_weights[pslice], } else: if self.eqn_letter == "l" and self.dim == 2: return { "dipstr": -src_weights[pslice] * ( self.dipole_vec[0, pslice] + 1j * self.dipole_vec[1, pslice]) } else: return { "dipstr": src_weights[pslice], "dipvec": self.dipole_vec[:, pslice], } def form_multipoles(self, level_start_source_box_nrs, source_boxes, src_weights): formmp = self.get_routine("%ddformmp" + self.dp_suffix) mpoles = self.multipole_expansion_zeros() for lev in range(self.tree.nlevels): start, stop = level_start_source_box_nrs[lev:lev+2] if start == stop: continue level_start_ibox, mpoles_view = self.multipole_expansions_view( mpoles, lev) rscale = self.level_to_rscale(lev) for src_ibox in source_boxes[start:stop]: pslice = self._get_source_slice(src_ibox) if pslice.stop - pslice.start == 0: continue kwargs = {} kwargs.update(self.kernel_kwargs) kwargs.update(self.get_source_kwargs(src_weights, pslice)) ier, mpole = formmp( rscale=rscale, source=self._get_sources(pslice), center=self.tree.box_centers[:, src_ibox], nterms=self.level_nterms[lev], **kwargs) if ier: raise RuntimeError("formmp failed") mpoles_view[src_ibox-level_start_ibox] = mpole.T return mpoles def coarsen_multipoles(self, level_start_source_parent_box_nrs, source_parent_boxes, mpoles): tree = self.tree mpmp = self.get_translation_routine("%ddmpmp") # nlevels-1 is the last valid level index # nlevels-2 is the last valid level that could have children # # 3 is the last relevant source_level. # 2 is the last relevant target_level. # (because no level 1 box will be well-separated from another) for source_level in range(tree.nlevels-1, 2, -1): target_level = source_level - 1 start, stop = level_start_source_parent_box_nrs[ target_level:target_level+2] source_level_start_ibox, source_mpoles_view = \ self.multipole_expansions_view(mpoles, source_level) target_level_start_ibox, target_mpoles_view = \ self.multipole_expansions_view(mpoles, target_level) source_rscale = self.level_to_rscale(source_level) target_rscale = self.level_to_rscale(target_level) for ibox in source_parent_boxes[start:stop]: parent_center = tree.box_centers[:, ibox] for child in tree.box_child_ids[:, ibox]: if child: child_center = tree.box_centers[:, child] kwargs = {} if self.dim == 3 and self.eqn_letter == "h": kwargs["radius"] = tree.root_extent * 2**(-target_level) kwargs.update(self.kernel_kwargs) new_mp = mpmp( rscale1=source_rscale, center1=child_center, expn1=source_mpoles_view[ child - source_level_start_ibox].T, rscale2=target_rscale, center2=parent_center, nterms2=self.level_nterms[target_level], **kwargs) target_mpoles_view[ ibox - target_level_start_ibox] += new_mp[..., 0].T def eval_direct(self, target_boxes, neighbor_sources_starts, neighbor_sources_lists, src_weights): output = self.output_zeros() ev = self.get_direct_eval_routine() for itgt_box, tgt_ibox in enumerate(target_boxes): tgt_pslice = self._get_target_slice(tgt_ibox) if tgt_pslice.stop - tgt_pslice.start == 0: continue #tgt_result = np.zeros(tgt_pslice.stop - tgt_pslice.start, self.dtype) tgt_pot_result = 0 tgt_grad_result = 0 start, end = neighbor_sources_starts[itgt_box:itgt_box+2] for src_ibox in neighbor_sources_lists[start:end]: src_pslice = self._get_source_slice(src_ibox) if src_pslice.stop - src_pslice.start == 0: continue kwargs = {} kwargs.update(self.kernel_kwargs) kwargs.update(self.get_source_kwargs(src_weights, src_pslice)) tmp_pot, tmp_grad = ev( sources=self._get_sources(src_pslice), targets=self._get_targets(tgt_pslice), **kwargs) tgt_pot_result += tmp_pot tgt_grad_result += tmp_grad self.add_potgrad_onto_output( output, tgt_pslice, tgt_pot_result, tgt_grad_result) return output def multipole_to_local(self, level_start_target_or_target_parent_box_nrs, target_or_target_parent_boxes, starts, lists, mpole_exps): tree = self.tree local_exps = self.local_expansion_zeros() mploc = self.get_translation_routine("%ddmploc", vec_suffix="_imany") for lev in range(self.tree.nlevels): lstart, lstop = level_start_target_or_target_parent_box_nrs[lev:lev+2] if lstart == lstop: continue starts_on_lvl = starts[lstart:lstop+1] source_level_start_ibox, source_mpoles_view = \ self.multipole_expansions_view(mpole_exps, lev) target_level_start_ibox, target_local_exps_view = \ self.local_expansions_view(local_exps, lev) ntgt_boxes = lstop-lstart itgt_box_vec = np.arange(ntgt_boxes) tgt_ibox_vec = target_or_target_parent_boxes[lstart:lstop] nsrc_boxes_per_tgt_box = ( starts[lstart + itgt_box_vec+1] - starts[lstart + itgt_box_vec]) nsrc_boxes = np.sum(nsrc_boxes_per_tgt_box) src_boxes_starts = np.empty(ntgt_boxes+1, dtype=np.int32) src_boxes_starts[0] = 0 src_boxes_starts[1:] = np.cumsum(nsrc_boxes_per_tgt_box) rscale = self.level_to_rscale(lev) rscale1 = np.ones(nsrc_boxes) * rscale rscale1_offsets = np.arange(nsrc_boxes) kwargs = {} if self.dim == 3 and self.eqn_letter == "h": kwargs["radius"] = ( tree.root_extent * 2**(-lev) * np.ones(ntgt_boxes)) rscale2 = np.ones(ntgt_boxes, np.float64) * rscale # These get max'd/added onto: pass initialized versions. if self.dim == 3: ier = np.zeros(ntgt_boxes, dtype=np.int32) kwargs["ier"] = ier expn2 = np.zeros( (ntgt_boxes,) + self.expansion_shape(self.level_nterms[lev]), dtype=self.dtype) kwargs.update(self.kernel_kwargs) expn2 = mploc( rscale1=rscale1, rscale1_offsets=rscale1_offsets, rscale1_starts=src_boxes_starts, center1=tree.box_centers, center1_offsets=lists, center1_starts=starts_on_lvl, expn1=source_mpoles_view.T, expn1_offsets=lists - source_level_start_ibox, expn1_starts=starts_on_lvl, rscale2=rscale2, # FIXME: wrong layout, will copy center2=tree.box_centers[:, tgt_ibox_vec], expn2=expn2.T, nterms2=self.level_nterms[lev], **kwargs).T target_local_exps_view[tgt_ibox_vec - target_level_start_ibox] += expn2 return local_exps def eval_multipoles(self, level_start_target_box_nrs, target_boxes, sep_smaller_nonsiblings_by_level, mpole_exps): output = self.output_zeros() mpeval = self.get_expn_eval_routine("mp") for isrc_level, ssn in enumerate(sep_smaller_nonsiblings_by_level): source_level_start_ibox, source_mpoles_view = \ self.multipole_expansions_view(mpole_exps, isrc_level) rscale = self.level_to_rscale(isrc_level) for itgt_box, tgt_ibox in enumerate(target_boxes): tgt_pslice = self._get_target_slice(tgt_ibox) if tgt_pslice.stop - tgt_pslice.start == 0: continue tgt_pot = 0 tgt_grad = 0 start, end = ssn.starts[itgt_box:itgt_box+2] for src_ibox in ssn.lists[start:end]: tmp_pot, tmp_grad = mpeval( rscale=rscale, center=self.tree.box_centers[:, src_ibox], expn=source_mpoles_view[ src_ibox - source_level_start_ibox].T, ztarg=self._get_targets(tgt_pslice), **self.kernel_kwargs) tgt_pot = tgt_pot + tmp_pot tgt_grad = tgt_grad + tmp_grad self.add_potgrad_onto_output( output, tgt_pslice, tgt_pot, tgt_grad) return output def form_locals(self, level_start_target_or_target_parent_box_nrs, target_or_target_parent_boxes, starts, lists, src_weights): local_exps = self.local_expansion_zeros() formta = self.get_routine("%ddformta" + self.dp_suffix) for lev in range(self.tree.nlevels): lev_start, lev_stop = \ level_start_target_or_target_parent_box_nrs[lev:lev+2] if lev_start == lev_stop: continue target_level_start_ibox, target_local_exps_view = \ self.local_expansions_view(local_exps, lev) rscale = self.level_to_rscale(lev) for itgt_box, tgt_ibox in enumerate( target_or_target_parent_boxes[lev_start:lev_stop]): start, end = starts[lev_start+itgt_box:lev_start+itgt_box+2] contrib = 0 for src_ibox in lists[start:end]: src_pslice = self._get_source_slice(src_ibox) tgt_center = self.tree.box_centers[:, tgt_ibox] if src_pslice.stop - src_pslice.start == 0: continue kwargs = {} kwargs.update(self.kernel_kwargs) kwargs.update(self.get_source_kwargs(src_weights, src_pslice)) ier, mpole = formta( rscale=rscale, source=self._get_sources(src_pslice), center=tgt_center, nterms=self.level_nterms[lev], **kwargs) if ier: raise RuntimeError("formta failed") contrib = contrib + mpole.T target_local_exps_view[tgt_ibox-target_level_start_ibox] = contrib return local_exps def refine_locals(self, level_start_target_or_target_parent_box_nrs, target_or_target_parent_boxes, local_exps): locloc = self.get_translation_routine("%ddlocloc") for target_lev in range(1, self.tree.nlevels): start, stop = level_start_target_or_target_parent_box_nrs[ target_lev:target_lev+2] source_lev = target_lev - 1 source_level_start_ibox, source_local_exps_view = \ self.local_expansions_view(local_exps, source_lev) target_level_start_ibox, target_local_exps_view = \ self.local_expansions_view(local_exps, target_lev) source_rscale = self.level_to_rscale(source_lev) target_rscale = self.level_to_rscale(target_lev) for tgt_ibox in target_or_target_parent_boxes[start:stop]: tgt_center = self.tree.box_centers[:, tgt_ibox] src_ibox = self.tree.box_parent_ids[tgt_ibox] src_center = self.tree.box_centers[:, src_ibox] kwargs = {} if self.dim == 3 and self.eqn_letter == "h": kwargs["radius"] = self.tree.root_extent * 2**(-target_lev) kwargs.update(self.kernel_kwargs) tmp_loc_exp = locloc( rscale1=source_rscale, center1=src_center, expn1=source_local_exps_view[ src_ibox - source_level_start_ibox].T, rscale2=target_rscale, center2=tgt_center, nterms2=self.level_nterms[target_lev], **kwargs)[..., 0] target_local_exps_view[ tgt_ibox - target_level_start_ibox] += tmp_loc_exp.T return local_exps def eval_locals(self, level_start_target_box_nrs, target_boxes, local_exps): output = self.output_zeros() taeval = self.get_expn_eval_routine("ta") for lev in range(self.tree.nlevels): start, stop = level_start_target_box_nrs[lev:lev+2] if start == stop: continue source_level_start_ibox, source_local_exps_view = \ self.local_expansions_view(local_exps, lev) rscale = self.level_to_rscale(lev) for tgt_ibox in target_boxes[start:stop]: tgt_pslice = self._get_target_slice(tgt_ibox) if tgt_pslice.stop - tgt_pslice.start == 0: continue tmp_pot, tmp_grad = taeval( rscale=rscale, center=self.tree.box_centers[:, tgt_ibox], expn=source_local_exps_view[ tgt_ibox - source_level_start_ibox].T, ztarg=self._get_targets(tgt_pslice), **self.kernel_kwargs) self.add_potgrad_onto_output( output, tgt_pslice, tmp_pot, tmp_grad) return output def finalize_potentials(self, potential): if self.eqn_letter == "l" and self.dim == 2: scale_factor = -1/(2*np.pi) elif self.eqn_letter == "h" and self.dim == 2: scale_factor = 1 elif self.eqn_letter in ["l", "h"] and self.dim == 3: scale_factor = 1/(4*np.pi) else: raise NotImplementedError( "scale factor for pyfmmlib %s for %d dimensions" % ( self.eqn_letter, self.dim)) if self.eqn_letter == "l" and self.dim == 2: potential = potential.real return potential * scale_factor # vim: foldmethod=marker

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inform@tiker.net

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import socket import sys s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) host = socket.gethostname() port = 9992 # 连接服务，指定主机和端口 s.connect((host, port)) # 接收小于 1024 字节的数据 msg = s.recv(1024) s.close() print (msg.decode('utf-8'))

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# ChocoPy library functions def int_to_str(x: int) -> str: digits:[str] = None result:str = "" # Set-up digit mapping digits = ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"] # Write sign if necessary if x < 0: result = "-" x = -x # Write digits using a recursive call if x >= 10: result = result + int_to_str(x // 10) result = result + digits[x % 10] return result def int_to_str2(x: int, x2: int) -> str: digits:[str] = None digits2:[str] = None result:str = "" result2:str = "" # Set-up digit mapping digits = ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"] # Write sign if necessary if x < 0: result = "-" x = -x # Write digits using a recursive call if x >= 10: result = result + int_to_str(x // 10) result = result + digits[x % 10] return result def int_to_str3(x: int, x2: int, x3: int) -> str: digits:[str] = None digits2:[str] = None digits3:[str] = None result:str = "" result2:str = "" result3:str = "" # Set-up digit mapping digits = ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"] # Write sign if necessary if x < 0: result = "-" x = -x # Write digits using a recursive call if x >= 10: result = result + int_to_str(x // 10) result = result + digits[x % 10] return result def int_to_str4(x: int, x2: int, x3: int, x4: int) -> str: digits:[str] = None digits2:[str] = None digits3:[str] = None digits4:[str] = None result:str = "" result2:str = "" result3:str = "" result4:str = "" # Set-up digit mapping digits = ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"] # Write sign if necessary if x < 0: result = "-" x = -x # Write digits using a recursive call if x >= 10: result = result + int_to_str(x // 10) result = result + digits[x % 10] return result def int_to_str5(x: int, x2: int, x3: int, x4: int, x5: int) -> str: digits:[str] = None digits2:[str] = None digits3:[str] = None digits4:[str] = None digits5:[str] = None result:str = "" result2:str = "" result3:str = "" result4:str = "" result5:str = "" # Set-up digit mapping digits = ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"] # Write sign if necessary if x < 0: result = "-" x = -x # Write digits using a recursive call if x >= 10: result = result + int_to_str(x // 10) result = result + digits[x % 10] return result def str_to_int(x: str) -> int: result:int = 0 digit:int = 0 char:str = "" sign:int = 1 first_char:bool = True # Parse digits for char in x: if char == "-": if not first_char: return 0 # Error sign = -1 elif char == "0": digit = 0 elif char == "1": digit = 1 elif char == "2": digit = 2 elif char == "3": digit = 3 elif char == "3": digit = 3 elif char == "4": digit = 4 elif char == "5": digit = 5 elif char == "6": digit = 6 elif char == "7": digit = 7 elif char == "8": digit = 8 elif char == "9": digit = 9 else: return 0 # On error first_char = False result = result * 10 + digit # Compute result return result * sign def str_to_int2(x: str, x2: str) -> int: result:int = 0 result2:int = 0 digit:int = 0 digit2:int = 0 char:str = "" char2:str = "" sign:int = 1 sign2:int = 1 first_char:bool = True first_char2:bool = True # Parse digits for char in x: if char == "-": if not first_char: return 0 # Error sign = -1 elif char == "0": digit = 0 elif char == "1": digit = 1 elif char == "2": digit = 2 elif char == "3": digit = 3 elif char == "3": digit = 3 elif char == "4": digit = 4 elif char == "5": digit = 5 elif char == "6": digit = 6 elif char == "7": digit = 7 elif char == "8": digit = 8 elif char == "9": digit = 9 else: return 0 # On error first_char = False result = result * 10 + digit # Compute result return result * sign def str_to_int3(x: str, x2: str, x3: str) -> int: result:int = 0 result2:int = 0 result3:int = 0 digit:int = 0 digit2:int = 0 digit3:int = 0 char:str = "" char2:str = "" char3:str = "" sign:int = 1 sign2:int = 1 sign3:int = 1 first_char:bool = True first_char2:bool = True first_char3:bool = True # Parse digits for char in x: if char == "-": if not first_char: return 0 # Error sign = -1 elif char == "0": digit = 0 elif char == "1": digit = 1 elif char == "2": digit = 2 elif char == "3": digit = 3 elif char == "3": digit = 3 elif char == "4": digit = 4 elif char == "5": digit = 5 elif char == "6": digit = 6 elif char == "7": digit = 7 elif char == "8": digit = 8 elif char == "9": digit = 9 else: return 0 # On error first_char = False result = result * 10 + digit # Compute result return result * sign def str_to_int4(x: str, x2: str, x3: str, x4: str) -> int: result:int = 0 result2:int = 0 result3:int = 0 result4:int = 0 digit:int = 0 digit2:int = 0 digit3:int = 0 digit4:int = 0 char:str = "" char2:str = "" char3:str = "" char4:str = "" sign:int = 1 sign2:int = 1 sign3:int = 1 sign4:int = 1 first_char:bool = True first_char2:bool = True first_char3:bool = True first_char4:bool = True # Parse digits for char in x: if char == "-": if not first_char: return 0 # Error sign = -1 elif char == "0": digit = 0 elif char == "1": digit = 1 elif char == "2": digit = 2 elif char == "3": digit = 3 elif char == "3": digit = 3 elif char == "4": digit = 4 elif char == "5": digit = 5 elif char == "6": digit = 6 elif char == "7": digit = 7 elif char == "8": digit = 8 elif char == "9": digit = 9 else: return 0 # On error first_char = False result = result * 10 + digit # Compute result return result * sign def str_to_int5(x: str, x2: str, x3: str, x4: str, x5: str) -> int: result:int = 0 result2:int = 0 result3:int = 0 result4:int = 0 result5:int = 0 digit:int = 0 digit2:int = 0 digit3:int = 0 digit4:int = 0 digit5:int = 0 char:str = "" char2:str = "" char3:str = "" char4:str = "" char5:str = "" sign:int = 1 sign2:int = 1 sign3:int = 1 sign4:int = 1 sign5:int = 1 first_char:bool = True first_char2:bool = True $TypedVar = True first_char4:bool = True first_char5:bool = True # Parse digits for char in x: if char == "-": if not first_char: return 0 # Error sign = -1 elif char == "0": digit = 0 elif char == "1": digit = 1 elif char == "2": digit = 2 elif char == "3": digit = 3 elif char == "3": digit = 3 elif char == "4": digit = 4 elif char == "5": digit = 5 elif char == "6": digit = 6 elif char == "7": digit = 7 elif char == "8": digit = 8 elif char == "9": digit = 9 else: return 0 # On error first_char = False result = result * 10 + digit # Compute result return result * sign # Input parameters c:int = 42 c2:int = 42 c3:int = 42 c4:int = 42 c5:int = 42 n:int = 10 n2:int = 10 n3:int = 10 n4:int = 10 n5:int = 10 # Run [-nc, nc] with step size c s:str = "" s2:str = "" s3:str = "" s4:str = "" s5:str = "" i:int = 0 i2:int = 0 i3:int = 0 i4:int = 0 i5:int = 0 i = -n * c # Crunch while i <= n * c: s = int_to_str(i) print(s) i = str_to_int(s) + c

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#!/usr/bin/env python #import# import paramiko import argparse #end import# parser = argparse.ArgumentParser( description='Remote Memory check for Linux servers. Intended for use on OpsView/Nagios monitoring systems.', usage = '%(prog)s -n [--hostname] HOSTNAME -w [--warning] warning%% -c [--critical] critical%% -m [--metric] {commit,consumed,swap,hybrid} -v [--verbose] -s [--swap] swap_limit%%', ) ### define arguments to be used. secondary metric will be the only non-required metric for now given the progression of the script. parser.add_argument("-n","--hostname", type=str, required=True, help='hostname which check should run against. Assumes passwordless access') parser.add_argument("-w","--warning", type=int, required=False, default=85, help='Warning alert threshold in percent, defaults to 85') parser.add_argument("-c","--critical", type=int, required=False, default=95, help='Critical alert thresehold in percent, defaults to 95') parser.add_argument("-m","--metric", type=str, required=True, choices=('commit','consumed','swap','hybrid'), help='Select alert metric. If Hybrid you should supply \'-s\' otherwise default is 85%%') parser.add_argument("-v","--verbose", action='store_true', help='Display more memory stats used in determining alert status.') parser.add_argument("-s","--swap", type=int, required=False, default=85, help='Value that is only used in Hybrid mode. Percentage of swap used to trigger hybrid alert defaults to 85') ### define argument catchall for future use args = parser.parse_args() ### Ensure that Critical is greater than Warning if args.warning > args.critical: parser.error("Warning threshold is higher than Critical threshold!") ### predefine metrics array a = {} ####Paramiko SSH & SFTP link to target host#### tgt_client = paramiko.SSHClient() # create paramiko client #tgt_client.load_system_host_keys() # load system host keys to allow recognition of known hosts tgt_client.set_missing_host_key_policy(paramiko.AutoAddPolicy()) # allow paramiko to add hosts. This opens the script up to man in the middle attacks but may be necessary for our enviornment. tgt_client.connect(args.hostname, username='root') # open connection to host to allow SFTP link tgt_sftp = tgt_client.open_sftp() # define SFTP method meminfo = tgt_sftp.open('/proc/meminfo') # method for grabbing mem info low_Watermark = int(tgt_sftp.open('/proc/sys/vm/min_free_kbytes').readline().strip()) # grab absolute minimum amount of memory system can run on try: for entry in map( lambda x: x.strip().split( 'kB' )[0].strip(), meminfo.readlines()): a[ entry.split( ':' )[0].strip() ] = int( entry.split( ':' )[1].split( 'kB' )[0].strip() ) finally: #close files we're working with. Don't trust garbage collectors meminfo.close() tgt_client.close() ### define metrics that aren't available on all systems ### if 'MemAvailable' in a: #define what "memory available" looks like. Older OS's do not calculate this in /proc/meminfo memAvail = a['MemAvailable'] # But if they do why not use it? else: memAvail = a['MemFree'] - low_Watermark + (a['Cached'] - min(a['Cached'] / 2, low_Watermark)) #and if they don't then we'll make our own. https://github.com/torvalds/linux/blob/master/mm/page_alloc.c#L5089 ### set testing metrics ### total = a['MemTotal'] # Set memory total commit = a['Committed_AS'] # Define the current system committed memory. This is NOT memory in use, just committed pressure = ((commit * 100.0) / total) ptotal_used = (100.0 - (memAvail * 100.0 / total) ) pswap = (100.0 - (a['SwapFree'] * 100.0 / a['SwapTotal'])) ### High verbosity output ### if args.verbose: print("Memory Available: " + str(memAvail) + " kb") print("Lower Watermark: " + str(low_Watermark) + " kb") print("Total Memory: " + str(total) + " kb") print("Total Commit: " + str(commit) + " kb") print("Total Memory Used: %.2f%%" % ptotal_used) print("Swap Used: %.2f%%" % pswap) ### Alert logic based on primary metric. Start with highest check first if args.metric == "commit": if pressure >= args.critical: print('CRITICAL - Commit: {0:.2f}'.format(pressure,)) exit(2) elif pressure >= args.warning: print('WARNING - Commit: {0:.2f}'.format(pressure,)) exit(1) else: print('OK - Commit: {0:.2f}'.format(pressure,)) exit(0) elif args.metric == "consumed": if ptotal_used >= args.critical: print("CRITICAL - UsedMemory: {0:.2f}".format( ptotal_used, ) ) exit(2) elif ptotal_used >= args.warning: print("WARNING - UsedMemory: {0:.2f}".format( ptotal_used, ) ) exit(1) else: print("OK - UsedMemory: {0:.2f}".format( ptotal_used, ) ) exit(0) elif args.metric == "swap": if pswap >= args.critical: print("CRITICAL - SwapUsed: {0:.2f}".format( pswap, ) ) exit(2) elif pswap >= args.warning: print("WARNING - SwapUsed: {0:.2f}".format( pswap, ) ) exit(1) else: print("OK - SwapUsed: {0:.2f}".format( pswap, ) ) exit(0) elif args.metric == "hybrid": if ptotal_used >= args.critical: if pswap >= args.swap: print("CRITICAL - UsedMemory: {0:.2f} -- UsedSwap: {1:.2f}".format( ptotal_used, pswap ) ) exit(2) elif ptotal_used >= args.warning: if pswap >= args.swap: print("WARNING - UsedMemory: {0:.2f} -- UsedSwap: {1:.2f}".format( ptotal_used, pswap ) ) exit(1) else: print("OK - UsedMemory: {0:.2f} -- UsedSwap: {1:.2f}".format( ptotal_used, pswap ) ) exit(0)

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from __future__ import print_function, unicode_literals, with_statement from future.builtins import input, open import os import re import sys from functools import wraps from getpass import getpass, getuser from glob import glob from contextlib import contextmanager from posixpath import join from os.path import basename, dirname from fabric.api import env, cd, prefix, sudo as _sudo, run as _run, hide, task #from fabric.api import settings from fabric.api import puts from fabric.contrib.files import exists, upload_template from fabric.colors import yellow, green, blue, red from fabric.utils import warn import pdb ############### # Fab Command # ############### #fab command #fab install ################ # Config setup # ################ conf = {} if sys.argv[0].split(os.sep)[-1] in ("fab", "fab-script.py"): # Ensure we import settings from the current dir try: #conf = __import__("settings", globals(), locals(), [], 0).FABRIC #conf = __import__("project.settings", globals(), locals(), [], 0).FABRIC from project import settings conf = settings.FABRIC try: conf["HOSTS"][0] except (KeyError, ValueError): raise ImportError except (ImportError, AttributeError): print("Aborting, no hosts defined.") exit() env.db_pass = conf.get("DB_PASS", None) env.db_root_pass = env.db_pass #env.admin_pass = conf.get("ADMIN_PASS", None) env.user = conf.get("SSH_USER", getuser()) env.password = conf.get("SSH_PASS", None) env.key_filename = conf.get("SSH_KEY_PATH", None) env.hosts = conf.get("HOSTS", [""]) env.proj_name = conf.get("PROJECT_NAME", os.getcwd().split(os.sep)[-1]) env.venv_home = conf.get("VIRTUALENV_HOME", "/home/%s" % env.user) env.venv_path = "%s/%s" % (env.venv_home, env.proj_name) env.proj_dirname = "project" env.proj_path = "%s/%s" % (env.venv_path, env.proj_dirname) env.manage = "%s/bin/python %s/project/manage.py" % ((env.venv_path,) * 2) env.domains = conf.get("DOMAINS", [conf.get("LIVE_HOSTNAME", env.hosts[0])]) env.domains_nginx = " ".join(env.domains) env.domains_python = ", ".join(["'%s'" % s for s in env.domains]) env.ssl_disabled = "#" if len(env.domains) > 1 else "" env.repo_url = conf.get("REPO_URL", "") env.git = env.repo_url.startswith("git") or env.repo_url.endswith(".git") env.reqs_path = conf.get("REQUIREMENTS_PATH", None) env.locale = conf.get("LOCALE", "en_US.UTF-8") env.secret_key = conf.get("SECRET_KEY", "") env.nevercache_key = conf.get("NEVERCACHE_KEY", "") env.django_user = conf.get("DJANGO_USER", "duser") env.django_user_group = env.django_user env.django_project_settings = "settings" env.gunicorn_workers = 2 env.gunicorn_logfile = '%(venv_path)s/logs/projects/%(proj_name)s_gunicorn.log' % env #env.rungunicorn_script = '%(venv_path)s/scripts/rungunicorn_%(proj_name)s.sh' % env env.rungunicorn_script = '%(venv_path)s/bin/gunicorn_start' % env env.gunicorn_worker_class = "eventlet" env.gunicorn_loglevel = "info" env.gunicorn_port = conf.get("GUNICORN_PORT", 8000) env.supervisor_program_name = env.proj_name env.supervisorctl = '/usr/bin/supervisorctl' env.supervisor_autostart = 'true' env.supervisor_autorestart = 'true' env.supervisor_redirect_stderr = 'true' env.supervisor_stdout_logfile = '%(venv_path)s/logs/projects/supervisord_%(proj_name)s.log' % env #env.supervisord_conf_file = '%(venv_path)s/configs/supervisord/%(proj_name)s.conf' % env env.supervisord_conf_file = '/etc/supervisor/conf.d/%(proj_name)s.conf' % env ################## # Template setup # ################## # Each template gets uploaded at deploy time, only if their # contents has changed, in which case, the reload command is # also run. templates = { "nginx": { "local_path": "deploy/nginx.conf", "remote_path": "/etc/nginx/sites-enabled/%(proj_name)s.conf", }, "supervisor": { "local_path": "deploy/supervisord.conf", "remote_path": env.supervisord_conf_file, }, "cron": { "local_path": "deploy/crontab", "remote_path": "/etc/cron.d/%(proj_name)s", "owner": "root", "mode": "600", }, "gunicorn": { "local_path": "deploy/gunicorn_start", "remote_path": "%(venv_path)s/bin/gunicorn_start", }, "settings": { "local_path": "deploy/local_settings", "remote_path": "%(proj_path)s/project/local_settings.py", }, "mysql": { "local_path": "deploy/mysql.cnf", "remote_path": "/etc/mysql/my.cnf", } } ###################################### # Context for virtualenv and project # ###################################### @contextmanager def virtualenv(): """ Runs commands within the project's virtualenv. """ with cd(env.venv_path): with prefix("source %s/bin/activate" % env.venv_path): yield @contextmanager def project(): """ Runs commands within the project's directory. """ with virtualenv(): with cd(env.proj_dirname): yield @contextmanager def update_changed_requirements(): """ Checks for changes in the requirements file across an update, and gets new requirements if changes have occurred. """ reqs_path = join(env.proj_path, env.reqs_path) get_reqs = lambda: run("cat %s" % reqs_path, show=False) old_reqs = get_reqs() if env.reqs_path else "" yield if old_reqs: new_reqs = get_reqs() if old_reqs == new_reqs: # Unpinned requirements should always be checked. for req in new_reqs.split("\n"): if req.startswith("-e"): if "@" not in req: # Editable requirement without pinned commit. break elif req.strip() and not req.startswith("#"): if not set(">=<") & set(req): # PyPI requirement without version. break else: # All requirements are pinned. return pip("-r %s/%s" % (env.proj_path, env.reqs_path)) ########################################### # Utils and wrappers for various commands # ########################################### def _print(output): print() print(output) print() def print_command(command): _print(blue("$ ", bold=True) + yellow(command, bold=True) + red(" ->", bold=True)) @task def run(command, show=True): """ Runs a shell comand on the remote server. """ if show: print_command(command) with hide("running"): return _run(command) @task def sudo(command, show=True): """ Runs a command as sudo. """ if show: print_command(command) with hide("running"): return _sudo(command) def log_call(func): @wraps(func) def logged(*args, **kawrgs): header = "-" * len(func.__name__) _print(green("\n".join([header, func.__name__, header]), bold=True)) return func(*args, **kawrgs) return logged def get_templates(): """ Returns each of the templates with env vars injected. """ injected = {} for name, data in templates.items(): injected[name] = dict([(k, v % env) for k, v in data.items()]) return injected def upload_template_and_reload(name): """ Uploads a template only if it has changed, and if so, reload a related service. """ template = get_templates()[name] local_path = template["local_path"] if not os.path.exists(local_path): project_root = os.path.dirname(os.path.abspath(__file__)) local_path = os.path.join(project_root, local_path) remote_path = template["remote_path"] reload_command = template.get("reload_command") owner = template.get("owner") mode = template.get("mode") remote_data = "" if exists(remote_path): with hide("stdout"): remote_data = sudo("cat %s" % remote_path, show=False) with open(local_path, "r") as f: local_data = f.read() # Escape all non-string-formatting-placeholder occurrences of '%': local_data = re.sub(r"%(?!$\w+$s)", "%%", local_data) if "%(db_pass)s" in local_data: env.db_pass = db_pass() local_data %= env clean = lambda s: s.replace("\n", "").replace("\r", "").strip() if clean(remote_data) == clean(local_data): return upload_template(local_path, remote_path, env, use_sudo=True, backup=False) if owner: sudo("chown %s %s" % (owner, remote_path)) if mode: sudo("chmod %s %s" % (mode, remote_path)) if reload_command: sudo(reload_command) def db_pass(): """ Prompts for the database password if unknown. """ if not env.db_pass: env.db_pass = getpass("Enter the database password: ") return env.db_pass @task def apt(packages): """ Installs one or more system packages via apt. """ return sudo("apt-get install -y -q " + packages) @task def pip(packages): """ Installs one or more Python packages within the virtual environment. """ with virtualenv(): return sudo("pip install %s" % packages) def postgres(command): """ Runs the given command as the postgres user. """ show = not command.startswith("psql") return run("sudo -u root sudo -u postgres %s" % command, show=show) @task def psql(sql, show=True): """ Runs SQL against the project's database. """ out = postgres('psql -c "%s"' % sql) if show: print_command(sql) return out @task def backup(filename): """ Backs up the database. """ return postgres("pg_dump -Fc %s > %s" % (env.proj_name, filename)) @task def restore(filename): """ Restores the database. """ return postgres("pg_restore -c -d %s %s" % (env.proj_name, filename)) @task def python(code, show=True): """ Runs Python code in the project's virtual environment, with Django loaded. """ #pdb.set_trace() setup = "import os; os.environ[\'DJANGO_SETTINGS_MODULE\']=\'settings\';" full_code = 'python -c "%s%s"' % (setup, code.replace("`", "\\\`")) with project(): result = run(full_code, show=False) if show: print_command(code) return result def static(): """ Returns the live STATIC_ROOT directory. """ return python("from django.conf import settings;" "print settings.STATIC_ROOT", show=False).split("\n")[-1] @task def manage(command): """ Runs a Django management command. """ return run("%s %s" % (env.manage, command)) ######################### # Install and configure # ######################### @task @log_call def all(): """ Installs everything required on a new system and deploy. From the base software, up to the deployed project. """ install() create_virtualenv() create_SSH() create_git() #create_DB() set_SSL() create_django_user() set_password_django_user() upload_rungunicorn_script() upload_supervisord_conf() create_nginx() set_project() @task @log_call def install(): """ Installs the base system and Python requirements for the entire server. """ #locale = "LC_ALL=%s" % env.locale #with hide("stdout"): # if locale not in sudo("cat /etc/default/locale"): # sudo("update-locale %s" % locale) # run("exit") sudo("apt-get update -y -q") apt("nginx libjpeg-dev python-dev python-setuptools git-core " "libpq-dev memcached supervisor") #apt("mysql-server mysql-client") apt("openssh-server libev-dev python-all-dev build-essential") apt("debconf-utils") sudo("easy_install pip") #sudo("pip install virtualenv mercurial") apt("python-virtualenv virtualenvwrapper") #sudo("apt-get install -y python-virtualenv virtualenvwrapper") @task @log_call def create_virtualenv(): """ Create a new virtual environment & git. """ #pdb.set_trace() if not exists(env.venv_home): run("mkdir %s" % env.venv_home) with cd(env.venv_home): if exists(env.proj_name): prompt = input("\nVirtualenv exists: %s" "\nWould you like to replace it? (yes/no) " % env.proj_name) if prompt.lower() != "yes": print("\nAborting!") return False remove() run("export WORKON_HOME=$HOME/.virtualenvs") run("export PIP_VIRTUALENV_BASE=$WORKON_HOME") run("source /usr/share/virtualenvwrapper/virtualenvwrapper.sh && mkvirtualenv %s"% env.proj_name) @task @log_call def create_SSH(): """ Create a new ssh key. """ #pdb.set_trace() ssh_path = "/home/%s/.ssh" % env.user if not exists(ssh_path): run("mkdir %s" % env.ssh_path) pub_path = ssh_path+"/id_rsa.pub" with cd(ssh_path): if not exists(pub_path): run('ssh-keygen -t rsa') run("cat %s"% pub_path) input("\nSet SSH & Press Enter!") @task @log_call def create_git(): """ Create a new git. """ if not exists(env.venv_path): print("\nVirtual env path isn't exists!") return False run("git clone %s %s" % (env.repo_url, env.proj_path)) def mysql_execute(sql, user, password): """ Executes passed sql command using mysql shell. """ #user = user or env.conf.DB_USER from fabric.api import prompt sql = sql.replace('"', r'\"') #if password == None: # password = prompt('Please enter MySQL root password:') return run('echo "%s" | mysql --user="%s" --password="%s"' % (sql, user , password)) @task @log_call def create_DB(): """ Create DB and DB user. """ from fabric.api import settings, prompt with settings(hide('warnings', 'stderr'), warn_only=True): result = sudo('dpkg-query --show mysql-server') if result.failed is False: warn('MySQL is already installed') else: #sudo('echo "mysql-server-5.0 mysql-server/root_password password %s" | debconf-set-selections' % env.db_root_pass) #sudo('echo "mysql-server-5.0 mysql-server/root_password_again password %s" | debconf-set-selections' % env.db_root_pass) run('echo "mysql-server-5.0 mysql-server/root_password password %s" | sudo debconf-set-selections' % env.db_root_pass) run('echo "mysql-server-5.0 mysql-server/root_password_again password %s" | sudo debconf-set-selections' % env.db_root_pass) apt('mysql-server mysql-client') upload_template_and_reload("mysql") sql = 'CREATE DATABASE %(proj_name)s DEFAULT CHARACTER SET utf8 DEFAULT COLLATE utf8_general_ci' % env mysql_execute(sql, 'root', env.db_root_pass) sql = """CREATE USER '%(proj_name)s'@'%%' IDENTIFIED BY '%(db_pass)s';""" % env #sql = """CREATE USER '%(proj_name)s'@'localhost' IDENTIFIED BY '%(db_pass)s';""" % env mysql_execute(sql, 'root', env.db_root_pass) sql = """GRANT ALL ON %(proj_name)s.* TO '%(proj_name)s'@'%%'; FLUSH PRIVILEGES;""" % env #sql = """GRANT ALL ON %(proj_name)s.* TO '%(proj_name)s'@'localhost'; FLUSH PRIVILEGES;""" % env mysql_execute(sql, 'root', env.db_root_pass) sudo('service mysql restart') @task @log_call def remove_DB(): """ Remove DB and DB user. """ sql = 'DROP DATABASE %(proj_name)s' % env mysql_execute(sql, 'root', env.db_root_pass) sql = """DROP USER '%(proj_name)s';""" % env mysql_execute(sql, 'root', env.db_root_pass) sudo("service mysql stop") sudo("apt-get remove -y --purge mysql-server mysql-client") #sudo("netstat -tap | grep mysql") sudo("apt-get remove -y --purge mysql-server*") sudo("apt-get remove -y --purge mysql-client*") @task @log_call def set_SSL(): """ # Set up SSL certificate. """ if not env.ssl_disabled: conf_path = "/etc/nginx/conf" if not exists(conf_path): sudo("mkdir %s" % conf_path) with cd(conf_path): crt_file = env.proj_name + ".crt" key_file = env.proj_name + ".key" if not exists(crt_file) and not exists(key_file): try: crt_local, = glob(join("deploy", "*.crt")) key_local, = glob(join("deploy", "*.key")) except ValueError: parts = (crt_file, key_file, env.domains[0]) sudo("openssl req -new -x509 -nodes -out %s -keyout %s " "-subj '/CN=%s' -days 3650" % parts) else: upload_template(crt_local, crt_file, use_sudo=True) upload_template(key_local, key_file, use_sudo=True) @task @log_call def migrate(): """ migrate. """ manage('migrate') @task @log_call def set_project(): """ Set up project. """ upload_template_and_reload("settings") with project(): if env.reqs_path: pip("-r %s/%s" % (env.proj_path, env.reqs_path)) apt('libmysqlclient-dev') pip("fabric django python-social-auth " "gunicorn django-hosts mysql-python django-crontab pytz django-dbbackup") manage('migrate') manage('createsuperuser') @task @log_call def create_django_user(): """ create django user """ sudo('groupadd --system %(django_user)s' % env) sudo('useradd --system --gid %(django_user)s --home %(venv_path)s %(django_user)s' % env) sudo('chown -R %(django_user)s:%(django_user)s %(venv_path)s' % env) sudo('chmod -R g+w %(venv_path)s' % env) sudo('usermod -a -G %(django_user)s %(user)s' % env) @task @log_call def set_password_django_user(): """ set password django user """ sudo('passwd %(django_user)s' % env) @task @log_call def upload_rungunicorn_script(): """ upload rungunicorn conf """ sudo('mkdir -p %s' % dirname(env.gunicorn_logfile)) sudo('chown %s %s' % (env.django_user, dirname(env.gunicorn_logfile))) sudo('chmod -R 775 %s' % dirname(env.gunicorn_logfile)) sudo('touch %s' % env.gunicorn_logfile) sudo('chown %s %s' % (env.django_user, env.gunicorn_logfile)) sudo('mkdir -p %s' % dirname(env.rungunicorn_script)) upload_template_and_reload("gunicorn") sudo('chmod u+x %s' % env.rungunicorn_script) sudo('chown -R %(django_user)s:%(django_user)s %(rungunicorn_script)s' % env) @task @log_call def upload_supervisord_conf(): ''' upload supervisor conf ''' sudo('mkdir -p %s' % dirname(env.supervisor_stdout_logfile)) sudo('chown %s %s' % (env.django_user, dirname(env.supervisor_stdout_logfile))) sudo('chmod -R 775 %s' % dirname(env.supervisor_stdout_logfile)) sudo('touch %s' % env.supervisor_stdout_logfile) sudo('chown %s %s' % (env.django_user, env.supervisor_stdout_logfile)) sudo('mkdir -p %s' % dirname(env.supervisord_conf_file)) upload_template_and_reload("supervisor") sudo('%(supervisorctl)s reread' % env) sudo('%(supervisorctl)s update' % env) @task @log_call def create_nginx(): ''' create nginx ''' upload_template_and_reload("nginx") sudo('unlink /etc/nginx/sites-enabled/default') sudo("service nginx restart") @task @log_call def restart(): """ Restart gunicorn worker processes for the project. """ pid_path = "%s/gunicorn.pid" % env.proj_path if exists(pid_path): #sudo("kill -HUP `cat %s`" % pid_path) #$sudo("kill -HUP $(cat %s)" % pid_path) run("cat %s" % pid_path) prompt = input("\npid number(upper number) : ") sudo("kill -HUP %s" % prompt) else: start_args = (env.proj_name, env.proj_name) sudo("supervisorctl start %s:gunicorn_%s" % start_args) ########## # Deploy # ########## @task @log_call def pull_git(): """ run git pull """ with cd(env.proj_path): run("git pull") @task @log_call def collectstatic(): """ collect static for mangae django """ manage('collectstatic') @task @log_call def restart_supervisor(): """ restart supervisor """ sudo("supervisorctl restart %(proj_name)s" % env) @task @log_call def upload_local_settings(): """ upload_local_settings """ upload_template_and_reload("settings") @task @log_call def upload_nginx(): ''' create nginx ''' upload_template_and_reload("nginx") sudo("service nginx restart") @task @log_call def deploy(): """ Deploy latest version of the project. Check out the latest version of the project from version control, install new requirements, sync and migrate the database, collect any new static assets, and restart gunicorn's work processes for the project. """ for name in get_templates(): upload_template_and_reload(name) with project(): #backup("last.db") #static_dir = static() #if exists(static_dir): # run("tar -cf last.tar %s" % static_dir) git = env.git last_commit = "git rev-parse HEAD" run("%s > last.commit" % last_commit) with update_changed_requirements(): run("git pull origin master -f") #manage("collectstatic -v 0 --noinput") #manage("syncdb --noinput") #manage("migrate --noinput") restart() return True @task @log_call def remove(): """ Blow away the current project. """ if exists(env.venv_path): sudo("rm -rf %s" % env.venv_path) #for template in get_templates().values(): # remote_path = template["remote_path"] # if exists(remote_path): # sudo("rm %s" % remote_path) #psql("DROP DATABASE IF EXISTS %s;" % env.proj_name) #psql("DROP USER IF EXISTS %s;" % env.proj_name)

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2021-07-12T09:19:31.461056

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import bottle from bottle import route, request, post, template import logging import json import os logging.basicConfig() log = logging.getLogger("fpeg") log.setLevel(logging.DEBUG) STATIC_ROOT = os.path.join(os.path.dirname(__file__), 'static') @route('/') def home(): bottle.TEMPLATE_PATH.insert(0, './views') return bottle.template('home', sent=False, body=None) @post('/compress') def compress(): data = request.files.get("upload") if data and data.file: raw = data.file.read() filename = data.filename log.debug("uploaded {} ({} bytes).".format(filename, len(raw))) else: log.error("upload failed") @route('/static/:filename') def serve_static(filename): log.debug("serving static assets") return bottle.static_file(filename, root=STATIC_ROOT) application = bottle.app() application.catchall = False bottle.run(application, host='0.0.0.0', port=os.getenv('PORT', 8080))

[ "Andrew Stocker" ]

Andrew Stocker

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/kdb/MTS_patch.py

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[]

no_license

tt9024/huan

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refs/heads/master

2023-07-26T12:30:53.116852

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import numpy as np import repo_dbar as repo import l1 import os import glob import dill # patch vbs of various barseconds def patch_vbs(dbar, day, utc, vbs, barsec): bar, col, bs = dbar.load_day(day) if bar is None or len(bar)==0: print('problem getting bars from repo on ', day) return # make sure it's a multiple bs_mul = barsec//bs if bs_mul*bs != barsec: print('barsec ', barsec, ' is not a multiple of repo barsec ', bs, ' on ', day) return utc_bs = dbar._make_daily_utc(day, barsec) nbar = len(utc) ix = np.clip(np.searchsorted(utc, utc_bs),0,nbar-1) ixz = np.nonzero(utc[ix] == utc_bs)[0] if len(ixz) == 0: print('nothing found in repo on ', day) return # reuse the existing if not provided, but aggregated at barsec #vbs_bs = np.zeros(len(utc_bs)) vbs_bs = np.sum(bar[:,repo.vbsc].reshape((len(utc_bs),bs_mul)),axis=1) vbs_bs[ixz] = vbs[ix][ixz] # calculate the weight to be vol within the barsec vbs0 = bar[:,repo.volc].reshape((len(utc_bs),bs_mul)) vbs0 = (vbs0.T/np.sum(vbs0,axis=1)).T vbs0[np.isinf(vbs0)] = 1.0/bs_mul vbs0[np.isnan(vbs0)] = 1.0/bs_mul vbs_bs0 = (vbs0.T*vbs_bs).T.reshape((len(utc_bs)*bs_mul,1)) # write this day back dbar.overwrite([vbs_bs0], [day], [[repo.vbsc]], bs) print('!!DONE ', day) def update_array(dbar, vbs_array, barsec): """ vbs_array shape [nndays, 2], of utc and vbs """ nndays, nc = vbs_array.shape assert nc == 2, 'vbs_array expected shape 2 (utc,vbs)' utc=vbs_array[:,0] vbs=vbs_array[:,1] assert utc[1]-utc[0] == barsec, 'barsec mismatch! ' + str((utc[1]-utc[0],barsec)) start_day = l1.trd_day(vbs_array[0,0]) end_day = l1.trd_day(vbs_array[-1,0]) tdi = l1.TradingDayIterator(start_day) day = tdi.yyyymmdd() while day != end_day: patch_vbs(dbar, day, utc, vbs, barsec) tdi.next() day = tdi.yyyymmdd() def update_array_path(array_path='/home/bfu/kisco/kr/vbs/2021_1125_2022_0114', barsec=15, repo_path = '/home/bfu/kisco/kr/repo'): os.system('gunzip ' + os.path.join(array_path,'*.npy.gz')) fn = glob.glob(os.path.join(array_path, '*.npy')) for f in fn: print('processing ', f) # expect file name as CL.npy symbol = f.split('/')[-1].split('.')[0] vsarr = np.load(open(f,'rb')) dbar = repo.RepoDailyBar(symbol, repo_path=repo_path) update_array(dbar, vsarr, barsec) def update_dict(dict_file, barsec, repo_path='/home/bfu/kisco/kr/repo', symbol_list=None): """dict: {symbol : { 'utc': shape [ndays,2], 'vbs': shape [ndays, n] } } where utc has each day's first/last utc the barsec is given for verification purpose: barsec = (utc1-utc0)/n """ d = dill.load(open(dict_file, 'rb')) for symbol in d.keys(): if symbol_list is not None: if symbol not in symbol_list: continue utc=d[symbol]['utc'] vbs=d[symbol]['vbs'] ndays, nc = utc.shape assert nc==2, 'utc shape not 2 for ' + symbol print('got ',ndays,' for ', symbol) dbar = repo.RepoDailyBar(symbol, repo_path=repo_path) for u, v in zip(utc, vbs): (u0,u1)=u day = l1.trd_day(u0) # LCO could have utc up until 18:00 # turn it on when fixed in mts_repo #assert day == l1.trd_day(u1), 'not same trade day for %s on %d: %f-%f'%(symbol, day, u0, u1) utc0 = np.arange(u0,u1+barsec,barsec).astype(int) n = len(v) assert len(utc0)==n, 'vbs shape mismatch with utc for %s on %s: %d-%d'%(symbol, day, (u1-u0)//barsec,n) print('process %s on %s'%(symbol, day)) patch_vbs(dbar, day, utc0, v, barsec) def update_dict_all(): # a scripted update, modify as needed # the 2 _N1 from 20220223 to 20220415 with barsec=5 path = '/home/bfu/kisco/kr/vbs/update_0415' dict_files = ['0223_0302_5s.dill', '0303_0415_5s.dill'] barsec=5 repo_path = '/home/bfu/kisco/kr/repo' for df in dict_files: update_dict(os.path.join(path, df), barsec, repo_path=repo_path) # the _N2 from 20211125 to 20220415 with barsec=30 dict_files = ['20211125_2022_0415_N2_30s.dill'] barsec=30 repo_path = '/home/bfu/kisco/kr/repo_nc' for df in dict_files: update_dict(os.path.join(path, df), barsec, repo_path=repo_path)

[ "joy@joy.com" ]

joy@joy.com

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[]

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AngeloMendes/LogDel12

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2021-03-24T23:14:16.468273

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#esse codigo eh para avaliar a qtd de grupos existem e agrupar os distribuidores import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.cluster import KMeans def get_name(names, lat): for key in names['latitude']: if names['latitude'][key] == lat: return names['client_name'][key] def elbow_curve(): K_clusters = range(1, 10) kmeans = [KMeans(n_clusters=i) for i in K_clusters] Y_axis = df[['latitude']] X_axis = df[['longitude']] score = [kmeans[i].fit(Y_axis).score(Y_axis) for i in range(len(kmeans))] # Visualize plt.plot(K_clusters, score) plt.xlabel('Numero de Grupos') plt.ylabel('Score') plt.title('Elbow Curve') plt.show() def cluster(df): names = df[['client_name', 'latitude']].to_dict() df = df.drop(['client_name', 'date'], axis=1) kmeans = KMeans(n_clusters=5, init='k-means++') kmeans.fit(df[df.columns[0:6]]) df['cluster_label'] = kmeans.fit_predict(df[df.columns[0:6]]) centers = kmeans.cluster_centers_ labels = kmeans.predict(df[df.columns[0:6]]) # print centers # print labels length = len(df) df.plot.scatter(x='latitude', y='longitude', c=labels, s=100, cmap='viridis') center_x = [] center_y = [] for i in centers: center_x.append(i[4]) for i in centers: center_y.append(i[5]) # print(center_x) # print(center_y) plt.scatter(center_x, center_y, c='black', s=200, alpha=0.5) # plt.scatter(centers[5:6, 0], centers[5:6, 1], c='black', s=200, alpha=0.5) for i in range(0, length): plt.annotate(get_name(names, df['latitude'][i]), (df['latitude'][i], df['longitude'][i]), horizontalalignment='right', fontsize=13, verticalalignment='bottom') plt.title("Grupos de Bares Moema -SP") plt.show() if __name__ == '__main__': df = pd.read_csv('dist.csv') elbow_curve() cluster(df)

[ "contato.angelomendes@gmail.com" ]

contato.angelomendes@gmail.com

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/delete.py

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[]

no_license

mlnsvbd/CRUD_SqLite_Python

e7db43bf154776b92b27f5489e563f3caf968b25

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refs/heads/master

2021-05-28T14:16:35.306800

2015-01-25T22:21:12

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import sqlite3 as lite con = lite.connect('text.db') cur = con.cursor() sql = "DELETE FROM users WHERE id = '1'" try: cur.execute(sql) con.commit() print("Delete ok!!!") except Exception as e: print(e.args) finally: con.close()

[ "welser.m.r@gmail.com" ]

welser.m.r@gmail.com

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/Server/database/__init__.py

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Ricky-Hao/IMPK-Server

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refs/heads/master

2018-09-25T05:17:24.551553

2018-06-07T05:42:20

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from Server.database.database import Database db = Database()

[ "a471558277@gmail.com" ]

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amrutadharmapurikar/hmr2.0

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2023-06-26T08:06:10.593071

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import os from datetime import datetime from main.config import Config from main.model import Model class LocalConfig(Config): ROOT_DATA_DIR = os.path.abspath(os.path.join(__file__, '..', '..', '..')) LOG_DIR = os.path.join(ROOT_DATA_DIR, 'logs', datetime.now().strftime("%d%m%Y-%H%M%S")) DATA_DIR = os.path.join(ROOT_DATA_DIR, 'src', 'tests', 'files') SMPL_DATA_DIR = os.path.join(ROOT_DATA_DIR, 'src', 'tests', 'files') SMPL_MODEL_PATH = os.path.join(ROOT_DATA_DIR, 'models', 'neutral_smpl_coco_regressor.pkl') SMPL_MEAN_THETA_PATH = os.path.join(ROOT_DATA_DIR, 'models', 'neutral_smpl_mean_params.h5') CUSTOM_REGRESSOR_PATH = os.path.join(ROOT_DATA_DIR, 'src', 'tests', 'files', 'regressors') CUSTOM_REGRESSOR_IDX = { 0: 'regressor_test.npy', } DATASETS = ['dataset'] SMPL_DATASETS = ['smpl'] BATCH_SIZE = 2 JOINT_TYPE = 'cocoplus' NUM_KP2D = 19 NUM_KP3D = 14 def __init__(self): super(LocalConfig, self).__init__() self.SEED = 1 self.NUM_TRAINING_SAMPLES = 1 self.NUM_TRAIN_SMPL_SAMPLES = 4 self.NUM_VALIDATION_SAMPLES = 1 self.NUM_TEST_SAMPLES = 1 if __name__ == '__main__': LocalConfig() model = Model() model.train()

[ "alessandro.russo@allianz.de" ]

alessandro.russo@allianz.de

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/code/get_embeddings.py

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[]

no_license

ksenia007/humor_recognition

f523870945480c8ba4a83a7cabb49e40da4a3073

2f4077ace36f1e961a30f358eb73ed21ded1ff6f

refs/heads/master

2023-02-21T01:36:31.688257

2021-01-22T00:35:57

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from dataset import * from train import * from models import * import torch.optim as optim import pickle import uuid import warnings from helper_functions import * warnings.simplefilter(action='ignore', category=FutureWarning) warnings.simplefilter(action='ignore', category=UserWarning) dataset_function = BasicWeighted folder_data = 'data/training_datasets/' datafile_opt = ['humicroedit', 'puns','oneliners', 'short'] base_file = 'output/embeddings/' bert_model = BertModel.from_pretrained('bert-base-uncased') bert_model = bert_model.eval() bert_model = bert_model.cuda() for idata, dataf in enumerate(datafile_opt): train_set = dataset_function(filename = folder_data+dataf+'_train.csv', maxlen = 30, weight=1) print('Work with', dataf) results = np.zeros((len(train_set), 768)) for i in range(len(train_set)): tokens = train_set[i][0].unsqueeze(0).cuda() attn_mask = train_set[i][1].unsqueeze(0).cuda() _, cls_head = bert_model(tokens, attention_mask = attn_mask) results[i, :] = cls_head.cpu().detach() filename = base_file+dataf+'_embeddings.npy' np.save(filename, results)

[ "26440954+ksenia007@users.noreply.github.com" ]

26440954+ksenia007@users.noreply.github.com

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/python/PyProfiler/profiler6/test.py

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[]

no_license

ASMlover/study

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#!/usr/bin/env python # -*- coding: UTF-8 -*- # # Copyright (c) 2023 ASMlover. All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions # are met: # # * Redistributions of source code must retain the above copyright # notice, this list ofconditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in # the documentation and/or other materialsprovided with the # distribution. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS # FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE # COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, # INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, # BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; # LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT # LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN # ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. import sys sys.path.append("../") import py_profiler as pprof from common import test_common as _tc def test() -> None: pprof.start_stats() _tc.TestEntry().run() pprof.print_stats() if __name__ == "__main__": test()

[ "ASMlover@126.com" ]

ASMlover@126.com

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/LINETCR/Api/Talk.py

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[]

no_license

GieVh4/aisya

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refs/heads/master

2020-03-07T10:17:14.854975

2018-04-24T07:46:47

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# -*- coding: utf-8 -*- import os, sys path = os.path.join(os.path.dirname(__file__), '../lib/') sys.path.insert(0, path) import requests, rsa from thrift.transport import THttpClient from thrift.protocol import TCompactProtocol from curve import LineService from curve.ttypes import * class Talk: client = None auth_query_path = "/api/v4/TalkService.do"; http_query_path = "/S4"; wait_for_mobile_path = "/Q"; host = "gd2.line.naver.jp"; port = 443; UA = "Line/2018.07421.2455.Tanduri/760.1.6 WIN10/18.2.1" LA = "CHROMEOS 8.3.2 HELLO-WORLD 12.1.1" authToken = None cert = None def __init__(self): self.transport = THttpClient.THttpClient('https://gd2.line.naver.jp:443'+self.auth_query_path) self.transport.setCustomHeaders({ "User-Agent" : self.UA, "X-Line-Application" : self.LA, }) self.transport.open() self.protocol = TCompactProtocol.TCompactProtocol(self.transport); self.client = LineService.Client(self.protocol) def login(self, mail, passwd, cert=None, callback=None): self.transport.path = self.auth_query_path rsakey = self.client.getRSAKeyInfo(IdentityProvider.LINE) crypt = self.__crypt(mail, passwd, rsakey) result = self.client.loginWithIdentityCredentialForCertificate( IdentityProvider.LINE, rsakey.keynm, crypt, True, '127.0.0.1', 'http://dg.b9dm.com/KoenoKatachi.mp4', cert ) if result.type == 3: callback(result.pinCode) header = {"X-Line-Access": result.verifier} r = requests.get(url="https://" + self.host + self.wait_for_mobile_path, headers=header) result = self.client.loginWithVerifierForCerificate(r.json()["result"]["verifier"]) self.transport.setCustomHeaders({ "X-Line-Application" : self.LA, "User-Agent" : self.UA, "X-Line-Access" : result.authToken }) self.authToken = result.authToken self.cert = result.certificate self.transport.path = self.http_query_path elif result.type == 1: self.authToken = result.authToken self.cert = result.certificate self.transport.setCustomHeaders({ "X-Line-Application" : self.LA, "User-Agent" : self.UA, "X-Line-Access" : result.authToken }) self.transport.path = self.http_query_path def TokenLogin(self, authToken): self.transport.setCustomHeaders({ "X-Line-Application" : self.LA, "User-Agent" : self.UA, "X-Line-Access" : authToken, }) self.authToken = authToken self.transport.path = self.http_query_path def qrLogin(self, callback): self.transport.path = self.auth_query_path qr = self.client.getAuthQrcode(True, "Bot") callback("Copy Kode QR nya Plak\nJangan Lama2\nBatas 1 menit:\n line://au/q/" + qr.verifier) r = requests.get("https://" + self.host + self.wait_for_mobile_path, headers={ "X-Line-Application": self.LA, "X-Line-Access": qr.verifier, }) vr = r.json()["result"]["verifier"] lr = self.client.loginWithVerifierForCerificate(vr) self.transport.setCustomHeaders({ "X-Line-Application" : self.LA, "User-Agent" : self.UA, "X-Line-Access": lr.authToken }) self.authToken = lr.authToken self.cert = lr.certificate self.transport.path = self.http_query_path def __crypt(self, mail, passwd, RSA): message = (chr(len(RSA.sessionKey)) + RSA.sessionKey + chr(len(mail)) + mail + chr(len(passwd)) + passwd).encode('utf-8') pub_key = rsa.PublicKey(int(RSA.nvalue, 16), int(RSA.evalue, 16)) crypto = rsa.encrypt(message, pub_key).encode('hex') return crypto

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# 0809.py import cv2 import numpy as np #1 src = cv2.imread('./data/momentTest.jpg') gray = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY) ret, bImage = cv2.threshold(gray, 128, 255, cv2.THRESH_BINARY) #2 ##M = cv2.moments(bImage) M = cv2.moments(bImage, True) for key, value in M.items(): print('{}={}'.format(key, value)) #3 cx = int(M['m10']/M['m00']) cy = int(M['m01']/M['m00']) dst = src.copy() cv2.circle(dst, (cx, cy), 5, (0,0,255), 2) cv2.imshow('dst', dst) cv2.waitKey() cv2.destroyAllWindows()

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# Generated by Django 2.2.1 on 2019-06-10 05:49 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Board', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('title', models.CharField(max_length=15)), ('content', models.TextField()), ('created_at', models.DateTimeField(auto_now_add=True)), ('updated_at', models.DateTimeField(auto_now=True)), ], ), ]

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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! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from ... import _utilities from . import outputs __all__ = ['DataCollectionRuleAssociationArgs', 'DataCollectionRuleAssociation'] @pulumi.input_type class DataCollectionRuleAssociationArgs: def __init__(__self__, *, resource_uri: pulumi.Input[str], association_name: Optional[pulumi.Input[str]] = None, data_collection_endpoint_id: Optional[pulumi.Input[str]] = None, data_collection_rule_id: Optional[pulumi.Input[str]] = None, description: Optional[pulumi.Input[str]] = None): """ The set of arguments for constructing a DataCollectionRuleAssociation resource. :param pulumi.Input[str] resource_uri: The identifier of the resource. :param pulumi.Input[str] association_name: The name of the association. The name is case insensitive. :param pulumi.Input[str] data_collection_endpoint_id: The resource ID of the data collection endpoint that is to be associated. :param pulumi.Input[str] data_collection_rule_id: The resource ID of the data collection rule that is to be associated. :param pulumi.Input[str] description: Description of the association. """ pulumi.set(__self__, "resource_uri", resource_uri) if association_name is not None: pulumi.set(__self__, "association_name", association_name) if data_collection_endpoint_id is not None: pulumi.set(__self__, "data_collection_endpoint_id", data_collection_endpoint_id) if data_collection_rule_id is not None: pulumi.set(__self__, "data_collection_rule_id", data_collection_rule_id) if description is not None: pulumi.set(__self__, "description", description) @property @pulumi.getter(name="resourceUri") def resource_uri(self) -> pulumi.Input[str]: """ The identifier of the resource. """ return pulumi.get(self, "resource_uri") @resource_uri.setter def resource_uri(self, value: pulumi.Input[str]): pulumi.set(self, "resource_uri", value) @property @pulumi.getter(name="associationName") def association_name(self) -> Optional[pulumi.Input[str]]: """ The name of the association. The name is case insensitive. """ return pulumi.get(self, "association_name") @association_name.setter def association_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "association_name", value) @property @pulumi.getter(name="dataCollectionEndpointId") def data_collection_endpoint_id(self) -> Optional[pulumi.Input[str]]: """ The resource ID of the data collection endpoint that is to be associated. """ return pulumi.get(self, "data_collection_endpoint_id") @data_collection_endpoint_id.setter def data_collection_endpoint_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "data_collection_endpoint_id", value) @property @pulumi.getter(name="dataCollectionRuleId") def data_collection_rule_id(self) -> Optional[pulumi.Input[str]]: """ The resource ID of the data collection rule that is to be associated. """ return pulumi.get(self, "data_collection_rule_id") @data_collection_rule_id.setter def data_collection_rule_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "data_collection_rule_id", value) @property @pulumi.getter def description(self) -> Optional[pulumi.Input[str]]: """ Description of the association. """ return pulumi.get(self, "description") @description.setter def description(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "description", value) class DataCollectionRuleAssociation(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, association_name: Optional[pulumi.Input[str]] = None, data_collection_endpoint_id: Optional[pulumi.Input[str]] = None, data_collection_rule_id: Optional[pulumi.Input[str]] = None, description: Optional[pulumi.Input[str]] = None, resource_uri: Optional[pulumi.Input[str]] = None, __props__=None): """ Definition of generic ARM proxy resource. :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] association_name: The name of the association. The name is case insensitive. :param pulumi.Input[str] data_collection_endpoint_id: The resource ID of the data collection endpoint that is to be associated. :param pulumi.Input[str] data_collection_rule_id: The resource ID of the data collection rule that is to be associated. :param pulumi.Input[str] description: Description of the association. :param pulumi.Input[str] resource_uri: The identifier of the resource. """ ... @overload def __init__(__self__, resource_name: str, args: DataCollectionRuleAssociationArgs, opts: Optional[pulumi.ResourceOptions] = None): """ Definition of generic ARM proxy resource. :param str resource_name: The name of the resource. :param DataCollectionRuleAssociationArgs args: The arguments to use to populate this resource's properties. :param pulumi.ResourceOptions opts: Options for the resource. """ ... def __init__(__self__, resource_name: str, *args, **kwargs): resource_args, opts = _utilities.get_resource_args_opts(DataCollectionRuleAssociationArgs, pulumi.ResourceOptions, *args, **kwargs) if resource_args is not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, association_name: Optional[pulumi.Input[str]] = None, data_collection_endpoint_id: Optional[pulumi.Input[str]] = None, data_collection_rule_id: Optional[pulumi.Input[str]] = None, description: Optional[pulumi.Input[str]] = None, resource_uri: Optional[pulumi.Input[str]] = None, __props__=None): if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = DataCollectionRuleAssociationArgs.__new__(DataCollectionRuleAssociationArgs) __props__.__dict__["association_name"] = association_name __props__.__dict__["data_collection_endpoint_id"] = data_collection_endpoint_id __props__.__dict__["data_collection_rule_id"] = data_collection_rule_id __props__.__dict__["description"] = description if resource_uri is None and not opts.urn: raise TypeError("Missing required property 'resource_uri'") __props__.__dict__["resource_uri"] = resource_uri __props__.__dict__["etag"] = None __props__.__dict__["name"] = None __props__.__dict__["provisioning_state"] = None __props__.__dict__["system_data"] = None __props__.__dict__["type"] = None alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_="azure-nextgen:insights/v20210401:DataCollectionRuleAssociation"), pulumi.Alias(type_="azure-native:insights:DataCollectionRuleAssociation"), pulumi.Alias(type_="azure-nextgen:insights:DataCollectionRuleAssociation"), pulumi.Alias(type_="azure-native:insights/v20191101preview:DataCollectionRuleAssociation"), pulumi.Alias(type_="azure-nextgen:insights/v20191101preview:DataCollectionRuleAssociation")]) opts = pulumi.ResourceOptions.merge(opts, alias_opts) super(DataCollectionRuleAssociation, __self__).__init__( 'azure-native:insights/v20210401:DataCollectionRuleAssociation', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None) -> 'DataCollectionRuleAssociation': """ Get an existing DataCollectionRuleAssociation resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = DataCollectionRuleAssociationArgs.__new__(DataCollectionRuleAssociationArgs) __props__.__dict__["data_collection_endpoint_id"] = None __props__.__dict__["data_collection_rule_id"] = None __props__.__dict__["description"] = None __props__.__dict__["etag"] = None __props__.__dict__["name"] = None __props__.__dict__["provisioning_state"] = None __props__.__dict__["system_data"] = None __props__.__dict__["type"] = None return DataCollectionRuleAssociation(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name="dataCollectionEndpointId") def data_collection_endpoint_id(self) -> pulumi.Output[Optional[str]]: """ The resource ID of the data collection endpoint that is to be associated. """ return pulumi.get(self, "data_collection_endpoint_id") @property @pulumi.getter(name="dataCollectionRuleId") def data_collection_rule_id(self) -> pulumi.Output[Optional[str]]: """ The resource ID of the data collection rule that is to be associated. """ return pulumi.get(self, "data_collection_rule_id") @property @pulumi.getter def description(self) -> pulumi.Output[Optional[str]]: """ Description of the association. """ return pulumi.get(self, "description") @property @pulumi.getter def etag(self) -> pulumi.Output[str]: """ Resource entity tag (ETag). """ return pulumi.get(self, "etag") @property @pulumi.getter def name(self) -> pulumi.Output[str]: """ The name of the resource. """ return pulumi.get(self, "name") @property @pulumi.getter(name="provisioningState") def provisioning_state(self) -> pulumi.Output[str]: """ The resource provisioning state. """ return pulumi.get(self, "provisioning_state") @property @pulumi.getter(name="systemData") def system_data(self) -> pulumi.Output['outputs.DataCollectionRuleAssociationProxyOnlyResourceResponseSystemData']: """ Metadata pertaining to creation and last modification of the resource. """ return pulumi.get(self, "system_data") @property @pulumi.getter def type(self) -> pulumi.Output[str]: """ The type of the resource. """ return pulumi.get(self, "type")

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#实现梯度下降算法 import matplotlib.pyplot as plt import numpy as np import pandas as pd import xlrd #Some helper functions for plotting and drawing lines def plot_points(X, y):#将两个类别的点按照标签绘图 admitted = X[np.argwhere(y==1)] rejected = X[np.argwhere(y==0)] plt.scatter([s[0][0] for s in rejected], [s[0][1] for s in rejected], s = 25, color = 'blue', edgecolor = 'k') plt.scatter([s[0][0] for s in admitted], [s[0][1] for s in admitted], s = 25, color = 'red', edgecolor = 'k') def display(m, b, color='g--'):#绘制当前的分割直线 plt.xlim(-0.05, 1.05) plt.ylim(-0.05, 1.05) x = np.arange(-10, 10, 0.1) plt.plot(x, m*x+b, color) # Activation (sigmoid) function def sigmoid(x):#激活函数采用sigmod函数 return 1 / (1 + np.exp(-x)) #实现计算sigmoid(w1*x2+w2*x2+b)，即计算输出预测值 def output_formula(features, weights, bias): return sigmoid(np.dot(features, weights) + bias) #dot是矩阵乘法，feature是2*n矩阵，weight是n*1 def error_formula(y, output):#计算误差函数针对每一个yi计算 return - y*np.log(output) - (1 - y) * np.log(1-output) def update_weights(x, y, weights, bias, learnrate):#权重更新方法，根据梯度下降法来更新 output = output_formula(x, weights, bias) d_error = -(y - output) weights -= learnrate * d_error * x bias -= learnrate * d_error return weights, bias #训练函数，用于训练分界线 def train(features, targets, epochs, learnrate, graph_lines=False): errors = [] n_records, n_features = features.shape#n_records=100,n_features=2 last_loss = None weights = np.random.normal(scale=1 / n_features ** .5, size=n_features) #初始值用随机数生成权重 2*1 bias = 0 display(-weights[0] / weights[1], -bias / weights[1]) # 画当前求解出来的分界线 for e in range(epochs): #迭代1000次 del_w = np.zeros(weights.shape) for x, y in zip(features, targets):#通过zip拉锁函数将X与y的每个点结合起来 output = output_formula(x, weights, bias) #计算输出预测值yi 其中x是1*2，weight是2*1 error = error_formula(y, output)#计算每一个yi的误差 weights, bias = update_weights(x, y, weights, bias, learnrate) print(weights,bias) print(e)#注意每次迭代里都对xi即100组数进行计算都更新了权重，即更新了100*迭代次数次，每次迭代都是以上次的结果重新计算100组数 # Printing out the log-loss error on the training set out = output_formula(features, weights, bias)#计算迭代后的预测值，这里feature是n*2,weight是2*1，out是n*1的一列预测值 loss = np.mean(error_formula(targets, out))#对每个预测值的误差做算术平均 errors.append(loss) if e % (epochs / 10) == 0: print("\n========== Epoch", e, "==========") if last_loss and last_loss < loss: print("Train loss: ", loss, " WARNING - Loss Increasing") else: print("Train loss: ", loss) last_loss = loss predictions = out > 0.5 accuracy = np.mean(predictions == targets) print("Accuracy: ", accuracy) if graph_lines :#and e % (epochs / 100) == 0 display(-weights[0] / weights[1], -bias / weights[1])#画当前求解出来的分界线 # Plotting the solution boundary plt.title("Solution boundary") display(-weights[0] / weights[1], -bias / weights[1], 'black')#画最后一根求解出来的分界线 # Plotting the data plot_points(features, targets) plt.show() # Plotting the error plt.title("Error Plot") plt.xlabel('Number of epochs') plt.ylabel('Error') plt.plot(errors) plt.show() if __name__ == '__main__': np.random.seed(44) epochs = 100 learnrate = 0.01 data = xlrd.open_workbook('F:\工程实践\工作安排\work3_BPGradientDescent\data.xls') X = [] table = data.sheets()[0] # 打开第一张表 X1 = table.col_values(0) X2 = table.col_values(1) X.append(X1) X.append(X2) X = np.array(X).T # 将X转换为100*2的矩阵 Y = np.array(table.col_values(2)) # 第三列数据：数据点的标签 plot_points(X,Y) plt.show() train(X, Y, epochs, learnrate, True)

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import torch import torch.nn as nn ''' LSTMCell 输入: input, (h_0, c_0) input (seq_len, batch, input_size): 包含输入序列特征的Tensor。也可以是packed variable h_0 (batch, hidden_size): 保存着batch中每个元素的初始化隐状态的Tensor c_0 (batch, hidden_size): 保存着batch中每个元素的初始化细胞状态的Tensor 输出：h_1, c_1 h_1 (batch, hidden_size): 下一个时刻的隐状态。 c_1 (batch, hidden_size): 下一个时刻的细胞状态。 LSTM 输入: input, (h_0, c_0) input (seq_len, batch, input_size): 包含输入序列特征的Tensor。也可以是packed variable ，详见 [pack_padded_sequence](#torch.nn.utils.rnn.pack_padded_sequence(input, lengths, batch_first=False[source]) h_0 (num_layers * num_directions, batch, hidden_size):保存着batch中每个元素的初始化隐状态的Tensor c_0 (num_layers * num_directions, batch, hidden_size): 保存着batch中每个元素的初始化细胞状态的Tensor 输出: output, (h_n, c_n) output (seq_len, batch, hidden_size * num_directions): 保存RNN最后一层的输出的Tensor。如果输入是torch.nn.utils.rnn.PackedSequence，那么输出也是torch.nn.utils.rnn.PackedSequence。 h_n (num_layers * num_directions, batch, hidden_size): Tensor，保存着RNN最后一个时间步的隐状态。 c_n (num_layers * num_directions, batch, hidden_size): Tensor，保存着RNN最后一个时间步的细胞状态。 ''' class RNNEncoder(nn.Module): def __init__(self, input_size=0, hidden_size=0, num_layers=1, batch_first=False, bidirectional=False, dropout=0.0, rnn_type='lstm'): super(RNNEncoder, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_layers = num_layers self.batch_first = batch_first self.bidirectional = bidirectional self.dropout = dropout self.num_directions = 2 if self.bidirectional else 1 self._rnn_types = ['RNN', 'LSTM', 'GRU'] self.rnn_type = rnn_type.upper() assert self.rnn_type in self._rnn_types # 获取torch.nn对象中相应的的构造函数 self._rnn_cell = getattr(nn, self.rnn_type+'Cell') # getattr获取对象的属性或者方法 # ModuleList是Module的子类，当在Module中使用它的时候，就能自动识别为子module # 当添加 nn.ModuleList作为nn.Module对象的一个成员时（即当我们添加模块到我们的网络时）， # 所有nn.ModuleList内部的nn.Module的parameter也被添加作为我们的网络的parameter self.fw_cells, self.bw_cells = nn.ModuleList(), nn.ModuleList() for layer_i in range(self.num_layers): layer_input_size = self.input_size if layer_i == 0 else self.num_directions * self.hidden_size self.fw_cells.append(self._rnn_cell(input_size=layer_input_size, hidden_size=self.hidden_size)) if self.bidirectional: self.bw_cells.append(self._rnn_cell(input_size=layer_input_size, hidden_size=self.hidden_size)) # self.cell = nn.LSTMCell( # input_size=self.input_size, # 输入的特征维度 # hidden_size=self.hidden_size # 隐层的维度 # ) def init_hidden(self, batch_size=1, retain=True, device=torch.device('cpu')): if retain: # 是否保证每轮迭代都初始化隐层 torch.manual_seed(3357) # hidden = torch.randn(batch_size, self.hidden_size, device=device) hidden = torch.zeros(batch_size, self.hidden_size, device=device) if self.rnn_type == 'LSTM': hidden = (hidden, hidden) return hidden def _forward_mask(self, cell, inputs, lens, init_hidden, drop_mask=None): out_fw = [] seq_len = inputs.size(0) hx_fw = init_hidden assert torch.is_tensor(lens) for xi in range(seq_len): # print('data in device: ', inputs.device, hx_fw.device) # print('cell: ', next(cell.parameters()).is_cuda) hidden = cell(input=inputs[xi], hx=hx_fw) if self.rnn_type == 'LSTM': h_next, c_next = hidden mask = (xi < lens).float().unsqueeze(1).expand_as(h_next) # mask = torch.tensor((xi < lens), dtype=torch.float, device=inputs.device).unsqueeze(1).expand_as(h_next) h_next = h_next * mask + init_hidden[0] * (1 - mask) c_next = c_next * mask + init_hidden[1] * (1 - mask) out_fw.append(h_next) if drop_mask is not None: # 循环层使用dropout h_next = h_next * drop_mask hx_next = (h_next, c_next) else: h_next = hidden mask = (xi < lens).float().unsqueeze(1).expand_as(h_next) # mask = torch.tensor((xi < lens), dtype=torch.float, device=inputs.device).unsqueeze(1).expand_as(h_next) h_next = h_next * mask + init_hidden * (1 - mask) out_fw.append(h_next) if drop_mask is not None: # 循环层使用dropout h_next = h_next * drop_mask hx_next = h_next hx_fw = hx_next out_fw = torch.stack(tuple(out_fw), dim=0) return out_fw, hx_fw def _backward_mask(self, cell, inputs, lens, init_hidden, drop_mask=None): out_bw = [] seq_len = inputs.size(0) hx_bw = init_hidden assert torch.is_tensor(lens) for xi in reversed(range(seq_len)): hidden = cell(input=inputs[xi], hx=hx_bw) if self.rnn_type == 'LSTM': h_next, c_next = hidden mask = (xi < lens).float().unsqueeze(1).expand_as(h_next) # mask = torch.tensor((xi < lens), dtype=torch.float, device=inputs.device).unsqueeze(1).expand_as(h_next) h_next = h_next * mask + init_hidden[0] * (1 - mask) c_next = c_next * mask + init_hidden[1] * (1 - mask) out_bw.append(h_next) if drop_mask is not None: # 循环层使用dropout h_next = h_next * drop_mask hx_next = (h_next, c_next) else: h_next = hidden mask = (xi < lens).float().unsqueeze(1).expand_as(h_next) # mask = torch.tensor((xi < lens), dtype=torch.float, device=inputs.device).unsqueeze(1).expand_as(h_next) h_next = h_next * mask + init_hidden * (1 - mask) out_bw.append(h_next) if drop_mask is not None: # 循环层使用dropout h_next = h_next * drop_mask hx_next = h_next hx_bw = hx_next out_bw.reverse() out_bw = torch.stack(tuple(out_bw), dim=0) return out_bw, hx_bw def forward(self, inputs, seq_lens, init_hidden=None): if self.batch_first: inputs = inputs.transpose(0, 1) batch_size = inputs.size(1) if init_hidden is None: init_hidden = self.init_hidden(batch_size, device=inputs.device) # init_hidden = inputs.data.new(batch_size, self.hidden_size).zero_() # if self.rnn_type == 'LSTM': # init_hidden = (init_hidden, init_hidden) hx = init_hidden hn, cn = [], [] for layer in range(self.num_layers): input_drop_mask, hidden_drop_mask = None, None seq_len, batch_size, input_size = inputs.size() if self.training: # print('use dropout...') if layer != 0: input_drop_mask = torch.zeros(batch_size, input_size, device=inputs.device).fill_(1 - self.dropout) # 在相同的设备上创建一个和inputs数据类型相同的tensor # input_drop_mask = inputs.data.new(batch_size, input_size).fill_(1 - self.dropout) input_drop_mask = torch.bernoulli(input_drop_mask) input_drop_mask = torch.div(input_drop_mask, (1 - self.dropout)) input_drop_mask = input_drop_mask.unsqueeze(-1).expand((-1, -1, seq_len)).permute((2, 0, 1)) inputs = inputs * input_drop_mask hidden_drop_mask = torch.zeros(batch_size, self.hidden_size, device=inputs.device).fill_(1 - self.dropout) # hidden_drop_mask = inputs.data.new(batch_size, self.hidden_size).fill_(1 - self.dropout) hidden_drop_mask = torch.bernoulli(hidden_drop_mask) # 以输入值为概率p输出1，(1-p)输出0 hidden_drop_mask = torch.div(hidden_drop_mask, (1 - self.dropout)) # 保证训练和预测时期望值一致 # print('data is in cuda: ', inputs.device, mask.device, hx.device, hidden_drop_mask.device) out_fw, (hn_f, cn_f) = self._forward_mask(cell=self.fw_cells[layer], inputs=inputs, lens=seq_lens, init_hidden=hx, drop_mask=hidden_drop_mask) # print(out_fw.shape, hn_f.shape, cn_f.shape) out_bw, hn_b, cn_b = None, None, None if self.bidirectional: out_bw, (hn_b, cn_b) = self._backward_mask(cell=self.bw_cells[layer], inputs=inputs, lens=seq_lens, init_hidden=hx, drop_mask=hidden_drop_mask) # print(out_bw.shape, hn_b.shape, cn_b.shape) hn.append(torch.cat((hn_f, hn_b), dim=1) if self.bidirectional else hn_f) cn.append(torch.cat((cn_f, cn_b), dim=1) if self.bidirectional else cn_f) inputs = torch.cat((out_fw, out_bw), dim=2) if self.bidirectional else out_fw # print('input shape:', inputs.shape) # (6, 3, 10) hn = torch.stack(tuple(hn), dim=0) cn = torch.stack(tuple(cn), dim=0) output = inputs.transpose(0, 1) if self.batch_first else inputs return output, (hn, cn) # 默认inputs: [seq_len, batch_size, input_size] # batch_first: [batch_size, seq_len, input_size] # def forward(self, inputs, init_hidden=None): # assert torch.is_tensor(inputs) and inputs.dim() == 3 # # if self.batch_first: # inputs = inputs.permute(1, 0, 2) # # batch_size = inputs.size(1) # if init_hidden is None: # init_hidden = self.init_hidden(batch_size) # # hx = init_hidden # # hn, cn = [], [] # for layer in range(self.num_layers): # input_drop_mask, hidden_drop_mask = None, None # seq_len, batch_size, input_size = inputs.size() # if self.training: # print('use dropout...') # if layer != 0: # input_drop_mask = torch.empty(batch_size, input_size).fill_(1 - self.dropout) # input_drop_mask = torch.bernoulli(input_drop_mask) # input_drop_mask = torch.div(input_drop_mask, (1 - self.dropout)) # input_drop_mask = input_drop_mask.unsqueeze(-1).expand((-1, -1, seq_len)).permute((2, 0, 1)) # inputs = inputs * input_drop_mask # # hidden_drop_mask = torch.empty(batch_size, self.hidden_size).fill_(1 - self.dropout) # hidden_drop_mask = torch.bernoulli(hidden_drop_mask) # 以输入值为概率p输出1，(1-p)输出0 # hidden_drop_mask = torch.div(hidden_drop_mask, (1 - self.dropout)) # 保证训练和预测时期望值一致 # # out_fw, (hn_f, cn_f) = RNNEncoder._forward(cell=self.fw_cells[layer], inputs=inputs, init_hidden=hx, drop_mask=hidden_drop_mask) # # print(out_fw.shape, hn_f.shape, cn_f.shape) # # out_bw, hn_b, cn_b = None, None, None # if self.bidirectional: # out_bw, (hn_b, cn_b) = RNNEncoder._backward(cell=self.bw_cells[layer], inputs=inputs, init_hidden=hx, drop_mask=hidden_drop_mask) # # print(out_bw.shape, hn_b.shape, cn_b.shape) # # hn.append(torch.cat((hn_f, hn_b), dim=1) if self.bidirectional else hn_f) # cn.append(torch.cat((cn_f, cn_b), dim=1) if self.bidirectional else cn_f) # # inputs = torch.cat((out_fw, out_bw), dim=2) if self.bidirectional else out_fw # # print('input shape:', inputs.shape) # (6, 3, 10) # # hn = torch.stack(tuple(hn), dim=0) # cn = torch.stack(tuple(cn), dim=0) # # output = inputs.permute((1, 0, 2)) if self.batch_first else inputs # # return output, (hn, cn) if __name__ == '__main__': # [batch_size, seq_len, input_size] inputs = torch.rand(3, 6, 20) mask = torch.zeros(3, 6) mask[0, :3] = torch.ones(3) mask[1, :2] = torch.ones(2) lstm = RNNEncoder(input_size=20, hidden_size=100, num_layers=3, batch_first=True, bidirectional=True, dropout=0.2) # h0, c0 = torch.randn(3, 10), torch.randn(3, 10) # out, (hn, cn) = lstm(inputs, (h0, c0)) out, (hn, cn) = lstm(inputs, mask) print(out.shape) # [6, 3, 20] print(hn.shape, cn.shape) # [2, 3, 20] [2, 3, 20]

[ "ncu151wlz@qq.com" ]

ncu151wlz@qq.com

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74309d28c3c966ab46fe1d7bd7c6d6ca9e7009d4

/setup.py

86192f497fb7f45cf50128f2fc1870d69363a8a8

[ "MIT" ]

permissive

seporaitis/graphqlpy

c476b4632c3d117a95663ee88d1710a4999f22e7

c16623a00a851a785eaef7b27a72c35d49b0c4a4

refs/heads/master

2023-01-05T06:52:14.647528

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MIT

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2017-09-07T19:25:20

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py

#!/usr/bin/env python # -*- coding: utf-8 -*- import os import re from setuptools import find_packages, setup def get_version(package): """ Return package version as listed in `__version__` in `init.py`. """ init_py = open(os.path.join(package, '__init__.py')).read() return re.search("__version__ = ['\"]([^'\"]+)['\"]", init_py).group(1) version = get_version('graphqlpy') with open('README.rst') as readme_file: readme = readme_file.read() with open('HISTORY.rst') as history_file: history = history_file.read().replace('.. :changelog:', '') requirements = [] test_requirements = [] setup( name='graphqlpy', version=version, description="A humble attempt at a library generating GraphQL queries programatically.", long_description=readme + '\n\n' + history, author="Julius Seporaitis", author_email='julius@seporaitis.net', url='https://github.com/seporaitis/graphqlpy', packages=find_packages(exclude=['tests', 'tests.*']), package_dir={ 'graphqlpy': 'graphqlpy', }, include_package_data=True, install_requires=requirements, license="MIT", zip_safe=False, keywords='graphql', classifiers=[ 'Development Status :: 3 - Alpha', 'Intended Audience :: Developers', 'License :: OSI Approved :: MIT License', 'Natural Language :: English', 'Programming Language :: Python :: 3.6', ], test_suite='tests', tests_require=test_requirements )

[ "julius@seporaitis.net" ]

julius@seporaitis.net

bc0b91140f22fc81bcbba5bcd8f3452133cf725e

207f0427e0ffb10941db14d8de08ccbeac83dac1

/gmail.py

45dc9d762624648a1e30049e1f655efb972a3d08

[]

no_license

appollo88/py

0d9182b64928bcda6be0a3a36906b6144371acd7

1644d3f45a9b948a76f2a08df046db05d2f329a3

refs/heads/master

2021-01-20T14:39:24.128069

2017-02-22T05:46:33

82,765,152

0

null

UTF-8

Python

false

901

py

"""import smtplib server = smtplib.SMTP('smtp.gmail.com', 587) server.starttls() server.login("liuxun931@gmail.com", "lx061511") msg = "YOUR MESSAGE!" server.sendmail("liuxun931@gmail.com", "liuxun931@163.com", msg) server.quit() """ # smtplib module send mail import smtplib TO = 'liuxun931@163.com' SUBJECT = 'TEST MAIL' TEXT = 'Here is a message from python.' # Gmail Sign In gmail_sender = 'liuxun931@gmail.com' gmail_passwd = 'lx061511' server = smtplib.SMTP('smtp.gmail.com', 587) server.ehlo() server.starttls() server.login(gmail_sender, gmail_passwd) BODY = '\r\n'.join(['To: %s' % TO, 'From: %s' % gmail_sender, 'Subject: %s' % SUBJECT, '', TEXT]) try: server.sendmail(gmail_sender, [TO], BODY) print ('email sent') except: print ('error sending mail') server.quit()

[ "noreply@github.com" ]

appollo88.noreply@github.com

829fb8cdd606f109189879a5e3ad261af91f8278

ca5bac9deca017e02b8af87ffaaa91d1eb6c6d07

/Si_Nd/example_code/plot_2D_Seasonal.py

a194819969a902c3d8ba3f4bf7a50b45dd6fcae3

[]

no_license

ndoyesiny/metrics_workshop

36dcc0b444a8ab3b8a0f897c81ada142a5ba6ad1

b74f062c27243eb0705eab367167d1fb9eaf0cd8

refs/heads/master

2020-06-14T10:29:58.282850

2017-03-30T11:20:19

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0

null

2016-11-30T15:04:27

2016-11-30T15:04:26

null

UTF-8

Python

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py

''' plot_Func.py This function make some plot Author: Siny NDOYE, December 2016 ''' import os import iris import iris.quickplot as qplt import numpy as np import matplotlib.pyplot as plt import mpl_toolkits.basemap as bm #import pdb #def plot_Func(cube2plot,outpath,mnth,nlevc): def plot_Func_SAT(cube2plot,figpath,mnth,nlevc,xstart,xend,ystart,yend,title_name): # pdb.set_trace() # print cube2plot.collapsed(['time', 'latitude','longitude'],iris.analysis.MIN), nlevc #levels = np.linspace(iris.analysis.MIN(cube2plot),iris.analysis.MAX(cube2plot) , nlevc) plt.clf() levels=np.linspace(282,302,nlevc) levels=np.linspace(8,32,nlevc) qplt.contourf(cube2plot, levels = levels, extend = 'max') m = bm.Basemap(projection='cyl', llcrnrlat=ystart, urcrnrlat=yend, llcrnrlon=xstart, urcrnrlon=xend, resolution='c') # coarse resolution for grid #m = bm.Basemap(projection='cyl', llcrnrlat=8.0, urcrnrlat=16.0, llcrnrlon=-20.0, urcrnrlon=20.0, resolution='c') # coarse resolution for grid m.drawcoastlines(linewidth=2) m.drawcountries(linewidth=1) plt.title(title_name) if not os.path.exists(figpath): os.makedirs(figpath) if mnth == 0: plt.savefig(figpath +'Seasonal_average_DJF.png' ) plt.show() if mnth == 1: plt.savefig(figpath +'Seasonal_average_MAM.png' ) plt.show() if mnth == 2: plt.savefig(figpath +'Seasonal_average_JJA.png' ) plt.show() if mnth == 3: plt.savefig(figpath +'Seasonal_average_SON.png' ) plt.show() #if __name__== '__main__': # plot_Func(cube2plot,outpath,mnth,nlevc) #plot_Func(cube2plot,outpath,mnth,nlevc,xstart,xend,ystart,yend) ny """

[ "siny@lodyn416.locean-ipsl.upmc.fr" ]

siny@lodyn416.locean-ipsl.upmc.fr

4e98ab90157e2164c540617da24de059870e5e34

3071ce441681abbfea11c9cc5a5ba853aecff2d2

/game_over.py

56bb93d1293913866d464c7cc38a5f883a36e269

[]

no_license

xodapi/python_learning

d75ffc7c8312f52be3c5123fd003537943d75fe7

afd7ff56b8ccdfea42ccb3dc52ef25dfd44d3d68

refs/heads/master

2016-09-11T04:58:55.524656

2015-04-21T10:51:28

28,742,488

0

null

UTF-8

Python

false

20

py

print('Game over')

[ "faneropi@gmail.com" ]

faneropi@gmail.com

339289d6118565d385d545357077d0aeb36d8cc1

2a2def196a68319147631a4af93095d1a03de754

/MuZero/game/gym_wrappers.py

62ee3e3e4cc0c785f3b6090d3fd5fecc49ca4076

[]

no_license

colindbrown/columbia-deep-learning-project

8b7d2dc791046426ff6030ec52d1c9dddc99de3c

9046552bd631270838b0e49a2b8c9c524d40f1ed

refs/heads/master

2023-05-25T14:39:55.978535

2020-04-29T20:16:59

248,585,231

2

null

2022-06-22T01:52:03

2020-03-19T19:13:58

Jupyter Notebook

UTF-8

Python

false

631

py

import gym import numpy as np class ScalingObservationWrapper(gym.ObservationWrapper): """ Wrapper that apply a min-max scaling of observations. """ def __init__(self, env, low=None, high=None): super().__init__(env) assert isinstance(env.observation_space, gym.spaces.Box) low = np.array(self.observation_space.low if low is None else low) high = np.array(self.observation_space.high if high is None else high) self.mean = (high + low) / 2 self.max = high - self.mean def observation(self, observation): return (observation - self.mean) / self.max

[ "jayantsubramanian2020@Jayants-MacBook-Air.local" ]

jayantsubramanian2020@Jayants-MacBook-Air.local

d5a4535689e5faed501055cb510fae7e65574690

f4e7b66391205df44ea15e3bd9e93e4439393df0

/inputcheck.py

8037747f04d28cb4eb79fef72fd11160dbda0821

[]

no_license

thakurakhil/chemical-NER

a2fcf93ad3bfaec95e3e6af42e75fe044354284d

9b47ab96f178e0e665688e4bcaf677f44db2919b

refs/heads/master

2021-08-08T20:36:15.448621

2017-11-11T04:01:12

null

0

null

UTF-8

Python

false

209

py

import csv f = open('./inputtext/ninefilefeatures.txt', 'rb') reader = csv.reader(f,delimiter='\t') for row in reader: if(len(row)!=8): break else: for i in row: if(i==''): print row break

[ "singhakhil33@gmail.com" ]

singhakhil33@gmail.com

1c6a094af068444ca3d28073d89315729267ff26

e57613c79e9a7a014ae67c00ccaf7c8014011954

/lab3/Ast.py

fbe23583ed7d7db393eef7caeaf51eec4008e320

[]

no_license

szymon-rogus/CompilersLabs

cfebbab381e8ded24a122b03baba23c1a011b60b

d0f878bdaf8cf584ff28cd2449e2fe2dd4aa6c90

refs/heads/master

2021-04-02T15:28:58.725704

2020-06-10T09:01:59

248,289,803

0

null

2020-04-30T11:44:18

2020-03-18T16:51:10

Python

UTF-8

Python

false

6,214

py

class Node(object): def __init__(self, type, children=None, leaf=None): self.type = type self.leaf = leaf if children: self.children = children else: self.children = [] class BinaryExpression(Node): def __init__(self, left, operator, right): super().__init__(self.__class__, [left, right], operator) self.left = left self.operator = operator self.right = right def __repr__(self): return '{} {} {}'.format(self.left, self.operator, self.right) class UnaryExpression(Node): def __init__(self, operator, operand, left=True): super().__init__(self.__class__, [operand], operator) self.operator = operator self.operand = operand self.left = left def __repr__(self): order = [self.operator, self.operand] if self.left else [self.operand, self.operator] return '{}{}'.format(order[0], order[1]) class Negation(UnaryExpression): def __init__(self, operand): super().__init__('-', operand) class Transposition(UnaryExpression): def __init__(self, operand): super().__init__('\'', operand, False) class Assignment(BinaryExpression): pass class Function(Node): def __init__(self, name, argument): super().__init__(self.__class__, [argument], name) self.name = name self.argument = argument def __repr__(self): return "{}({})".format(self.name, self.argument) class Variable(Node): def __init__(self, name): super().__init__(self.__class__, [], name) self.name = name def __repr__(self): return '{}'.format(self.name) class If(Node): def __init__(self, condition, expression, else_expression=None): super().__init__(self.__class__, [condition, expression, else_expression], ["IF", "THEN", "ELSE"]) self.condition = condition self.expression = expression self.else_expression = else_expression if else_expression == None: self.children = self.children[:-1] self.leaf = self.leaf[:-1] def __repr__(self): representation = 'IF {} THEN {}'.format(self.condition, self.expression) result = representation + ' ELSE {}'.format(self.else_expression) \ if self.else_expression else representation return result class While(Node): def __init__(self, condition, body): super().__init__(self.__class__, [condition, body], "WHILE") self.condition = condition self.body = body def __repr__(self): return 'WHILE {} DO {}'.format(self.condition, self.body) class Range(Node): def __init__(self, start, end, step=1): super().__init__(self.__class__, [start, end, step], "RANGE") if step == 1: self.children = self.children[:-1] self.start = start self.end = end self.step = step def __repr__(self): return '{}:{}:{}'.format(self.start, self.end, self.step) class For(Node): def __init__(self, id, range, body): super().__init__(self.__class__, [id, range, body], "FOR") self.id = id self.range = range self.body = body def __repr__(self): return 'FOR {} IN {} DO {}'.format(self.id, self.range, self.body) class Break(Node): def __init__(self): super().__init__(self.__class__, [], "BREAK") def __repr__(self): return 'BREAK' class Continue(Node): def __init__(self): super().__init__(self.__class__, [], "CONTINUE") def __repr__(self): return 'CONTINUE' class Return(Node): def __init__(self, result): super().__init__(self.__class__, [result], "RETURN") self.result = result def __repr__(self): return 'RETURN( {} )'.format(self.result) class Print(Node): def __init__(self, expression): super().__init__(self.__class__, [expression], "PRINT") self.expression = expression def __repr__(self): return 'PRINT( {} )'.format(self.expression) class VariableAttribute(Node): def __init__(self, variable, key): super().__init__(self.__class__, [variable, key], "REF") self.variable = variable self.key = key def __repr__(self): return '{}[{}]'.format(self.variable, self.key) class Error(Node): pass class CodeBlock(Node): def __init__(self, instruction): super().__init__(self.__class__, [instruction]) self.instructions = self.children def __repr__(self): return "{\n" + "\n".join(map(str, self.instructions)) + "\n}" class Program(Node): def __init__(self, program): super().__init__(self.__class__, [program]) self.program = program def __repr__(self): return str(self.program) class Instruction(Node): def __init__(self, line): super().__init__(self.__class__, [line]) self.line = line def __repr__(self): return str(self.line) class Matrix(Node): def __init__(self, rows): super().__init__(self.__class__, [rows], "MATRIX") self.dims = len(rows), len(rows[0]) self.rows = rows def __repr__(self): return str(self.rows) class Value(Node): def __init__(self, val): super().__init__(self.__class__, [], val) self.val = val def __repr__(self): return "{}({})".format(type(self.val).__name__, self.val) class Rows(Node): def __init__(self, sequence): super().__init__(self.__class__, [sequence]) self.row_list = self.children def __repr__(self): return "[" + ", ".join(map(str, self.row_list)) + "]" def __len__(self): return len(self.row_list) def __getitem__(self, item): return self.row_list[item] class Sequence(Node): def __init__(self, expression): super().__init__(self.__class__, [expression], "SEQ") self.expressions = self.children def __repr__(self): return "[" + ", ".join(map(str, self.expressions)) + "]" def __len__(self): return len(self.expressions) def __getitem__(self, item): return self.expressions[item]

[ "benroszko@gmail.com" ]

benroszko@gmail.com

8d63e564dff2869969a823b0cef0bf2bc6eef4ef

064a954c8dd7d50720aa8fa748d24e8495b8f7d9

/OpenCv/字符投影.py

0258d496027be7b77d2b2ad6e748db532e8445a9

[]

no_license

xianyichi/keras

73169c248dde73f0e49e19f117b21080d1b3ba14

7ca5ab7e0ef1291b97b985e5ec9c78785e2ff3ec

refs/heads/master

2021-06-10T23:02:02.354669

2021-05-20T12:59:41

182,005,230

0

null

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2,386

py

import cv2 import numpy img = cv2.imread ('/Users/apple/PycharmProjects/keras/image/data/images/0_00h_0.png', cv2.COLOR_BGR2GRAY) height, width = img.shape [ :2 ] # print height, width # resized = cv2.resize(img, (2*width,2*height), interpolation=cv2.INTER_CUBIC) gray = cv2.cvtColor (img, cv2.COLOR_BGR2GRAY) (_, thresh) = cv2.threshold (gray, 140, 255, cv2.THRESH_BINARY) # 使文字增长成块 kernel = cv2.getStructuringElement (cv2.MORPH_RECT, (2, 2)) # 形态学处理，定义矩形结构 closed = cv2.erode (thresh, None, iterations=7) # cv2.imshow('erode',closed) height, width = closed.shape [ :2 ] # print height, width z = [ 0 ] * height v = [ 0 ] * width hfg = [ [ 0 for col in range (2) ] for row in range (height) ] lfg = [ [ 0 for col in range (2) ] for row in range (width) ] box = [ 0, 0, 0, 0 ] # 水平投影 a = 0 emptyImage1 = numpy.zeros ((height, width, 3), numpy.uint8) for y in range (0, height): for x in range (0, width): cp = closed [ y, x ] # if np.any(closed[y,x]): if cp == 0: a = a + 1 else: continue z [ y ] = a # print z[y] a = 0 # 根据水平投影值选定行分割点 inline = 1 start = 0 j = 0 for i in range (0, height): if inline == 1 and z [ i ] >= 150: # 从空白区进入文字区 start = i # 记录起始行分割点 # print i inline = 0 elif (i - start > 3) and z [ i ] < 150 and inline == 0: # 从文字区进入空白区 inline = 1 hfg [ j ] [ 0 ] = start - 2 # 保存行分割位置 hfg [ j ] [ 1 ] = i + 2 j = j + 1 # 对每一行垂直投影、分割 a = 0 for p in range (0, j): for x in range (0, width): for y in range (hfg [ p ] [ 0 ], hfg [ p ] [ 1 ]): cp1 = closed [ y, x ] if cp1 == 0: a = a + 1 else: continue v [ x ] = a # 保存每一列像素值 a = 0 # print width # 垂直分割点 incol = 1 start1 = 0 j1 = 0 z1 = hfg [ p ] [ 0 ] z2 = hfg [ p ] [ 1 ] for i1 in range (0, width): if incol == 1 and v [ i1 ] >= 20: # 从空白区进入文字区 start1 = i1 # 记录起始列分割点 incol = 0 elif (i1 - start1 > 3) and v [ i1 ] < 20 and incol == 0: # 从文字区进入空白区 incol = 1 lfg [ j1 ] [ 0 ] = start1 - 2 # 保存列分割位置 lfg [ j1 ] [ 1 ] = i1 + 2 l1 = start1 - 2 l2 = i1 + 2 j1 = j1 + 1 cv2.rectangle (img, (l1, z1), (l2, z2), (255, 0, 0), 2) cv2.imshow ('result', img) cv2.waitKey (0)

[ "1369362296@qq.com" ]

1369362296@qq.com

1e342c9a885841dca5ceca8cad3414989c843045

abd2a91cb26dd7ca8d3fca6f9c4f5ef9dea2f066

/logReg.py

95a8eee77371997300560c19e27f423c142fc9fc

[]

no_license

Saniewski/multiclass-perceptron

dd0018ce7cde93bec978c24e920853e19e16d938

36a475dc4c2f5142b5205259a69ee403248d6eea

refs/heads/master

2022-04-15T07:13:44.429956

2020-04-08T20:20:12

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0

null

UTF-8

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2,638

py

import numpy as np import matplotlib.pylab as plt from sklearn import datasets from sklearn.model_selection import train_test_split from scipy.special import expit from plotka import plot_decision_regions class LogisticRegressionGD(object): def __init__(self, learningRate=0.05, epochs=100, random_state=1): self.lr = learningRate self.epochs = epochs self.random_state = random_state def fit(self, X, y): rgen = np.random.RandomState(self.random_state) self.weights = rgen.normal(loc=0.0, scale=0.01, size=X.shape[1]) self.bias = rgen.normal(loc=0.0, scale=0.01) self.costs = [] for i in range(self.epochs): net_input = self.net_input(X) output = expit(net_input) errors = (y - output) self.weights += self.lr * X.T.dot(errors) self.bias += self.lr * errors.sum() cost = (-y.dot(np.log(output)) - ((1 - y).dot(np.log(1 - output)))) self.costs.append(cost) return self def net_input(self, X): return np.dot(X, self.weights) + self.bias def predict(self, X): return np.where(self.net_input(X) >= 0.0, 1, 0) class Multiclass(object): def __init__(self, reg1, reg2): self.reg1 = reg1 self.reg2 = reg2 def predict(self, X): result = [] for data in X: if self.reg1.predict(data) == 1: result.append(0) elif self.reg2.predict(data) == 1: result.append(1) else: result.append(2) return np.array(result) def main(): r8 = float(input('Learning rate: ')) epochs = int(input('Epochs: ')) iris = datasets.load_iris() X = iris.data[:, [1, 3]] y = iris.target y1 = y.copy() y2 = y.copy() y3 = y.copy() X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1, stratify=y) y1[(y1 != 0)] = -3 y1[y1 == 0] = 1 y1[y1 == -3] = 0 y3[(y3 != 2)] = -3 y3[y3 == 2] = 1 y3[y3 == -3] = 0 reg1 = LogisticRegressionGD(r8, epochs, 1) reg1.fit(X, y1) reg3 = LogisticRegressionGD(r8, epochs, 1) reg3.fit(X, y3) multi = Multiclass(reg1, reg3) print(multi.predict(X_test)) print(reg1.predict(X_test)) plot_decision_regions(X=X_test, y=y_test, classifier=multi) plt.xlabel(r'$x_1$') plt.ylabel(r'$x_2$') plt.legend(loc='upper left') plt.show() if __name__ == '__main__': main()

[ "pawel.san16@gmail.com" ]

pawel.san16@gmail.com

5d8cd2c7638647e1cdd05a42eaf90febc0a95726

5ebe757ed6a2a339525c349922a3218b9d2b3f94

/lstm-language-model/preprocess.py

3930b2bf16a8a4194f5abff4da1756b269b70a3c

[]

no_license

esun0087/self_parser

aa3ef6103c470c5f85627fe59e6d82239bcd63d6

cae1f45be1c954839980334e16d343bfae27dbe6

refs/heads/master

2020-03-21T10:27:18.247597

2018-08-07T08:26:29

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py

import torch import argparse import data def preprocess(opt): print('Begin preprocessing') train_dataset = data.DataSet(opt.train_data, display_freq=opt.display_freq) train_dataset.max_dict = opt.dict_size train_dataset.build_dict() print('Save training data') torch.save(train_dataset, opt.train_data + '.prep.train.pt') val_dataset = data.DataSet(opt.val_data, display_freq=opt.display_freq) val_dataset.change_dict(train_dataset.dictionary) print('Save validation data') torch.save(val_dataset, opt.val_data + '.prep.val.pt') print('Preprocessing done') if __name__ == '__main__': parser = argparse.ArgumentParser('Preprocessing') parser.add_argument('--train_data', type=str, default='data/penn/train.txt', help='Training data path') parser.add_argument('--val_data', type=str, default='data/penn/valid.txt', help='Validation data path') parser.add_argument('--dict_size', type=int, default=50000, help='Reduce dictionary if overthis size') parser.add_argument('--display_freq', type=int, default=100000, help='Display progress every this number of sentences, 0 for no diplay') parser.add_argument('--max_len', type=int, default=100, help='Maximum length od=f sentence') parser.add_argument('--trunc_len',type=int, default=100, help='Truncate the sentence that longer than maximum length') opt = parser.parse_args() preprocess(opt)

[ "a1a2a3a4a5" ]

a1a2a3a4a5

7dd4a52eccf53f87ba02f0c31ca36819d8d641e7

6cd799da036e019e61ca25351c3c2b368aeda6f4

/lossScreenTest.py

30158367f332718455312c2b49e234747e0a7977

[]

no_license

Tammon23/IceCream-Jump-recreate

d78f3c7c6352d8bef540df4cc5b182fdd76f543e

bfcdf2bb823b9ebae6e9e399c974f358d8d2c61e

refs/heads/master

2021-05-09T20:56:00.489803

2018-01-28T03:47:05

118,714,755

0

null

UTF-8

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false

1,336

py

import math, random import pygame from pygame.locals import * from settings import * from functions import * lossScreen = True while lossScreen: pygame.event.pump() k = pygame.key.get_pressed() if k[pygame.K_q] or k[pygame.K_ESCAPE]: break #start Splash screen screen.fill(sBackground) line1 = font2.render("You Lose!", True, BLACK) line2 = font5.render("Your final score was: " + str(points), True, BLACK) screen.blit(line1, (90, 100)) screen.blit(line2, (90, 210)) line3 = font1.render("- By Ikenna Uduh", True, BLACK) screen.blit(line3, (w - 150, h - 25)) x,y = pygame.mouse.get_pos() pygame.draw.circle(screen, sPAgainButtonClr, (int(w/2), int(h/2 + 50)), RAD3) pygame.draw.circle(screen, BLACK, (int(w/2), int(h/2 + 50)), RAD3, 10) line3 = font3.render("PLAY", True, BLACK) line4 = font3.render("AGAIN", True, BLACK) screen.blit(line3, (int(w/2) - 120, 400)) screen.blit(line4, (int(w/2) - 120, 500)) # Checking to see if the clicked mouse is pressing the PLAY or HELP buttons if checkInCir(int(w/2), int(h/2 + 50), y, x, RAD3): sPAgainButtonClr = sButtonClrPressed if pygame.mouse.get_pressed()[0]: gameStart = True else: sPAgainButtonClr = sButtonClr pygame.display.flip() pygame.quit()

[ "Tammon2000@gmail.com" ]

Tammon2000@gmail.com

4edfcf8e234bf582b8a3e06752421dff27a5d562

679b923d6ba62d00ab5ad8aef3f82f42df71a58c

/Server_Kapfumvuti_Patel.py

3a730631bb548c6f480757df43a85d6b5b03bea9

[]

no_license

GurenMarkV/Go-Back-N-Protocol

957086dbca5e4c60ed18ff2ee418016cb102e8f6

949c3db7bd38cc9e09a847853bc45531517a3620

refs/heads/master

2020-03-18T22:30:12.789811

2018-05-29T20:21:56

135,348,768

0

null

UTF-8

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2,023

py

# Project 1: Implementation of Go-Back-N Protocol # Group Member: Daksh Patel ID: 104 030 031 # Group Member: Nyasha Kapfumvuti ID: 104 121 166 # Date: Mar 30th, 2018 import socket import numpy import time import json from random import randint acked = [] # acknowledged packets unAcked = [] # unacknowledged packets ticker = 0 # 0.2 loss rate = 1/5 packets get "lost" => placed in unAcked lostItem = 5 # every 5th item gets placed in unacked returnVals = [] # array of values to be returned as acks/unacks timer = time.localtime packets = [] packet = '' server_address = ('localhost', 10000) serverSocket = socket.socket(socket.AF_INET,socket.SOCK_STREAM) serverSocket.bind(server_address) serverSocket.listen(1) print('The server is ready to receive') while True: print('waiting for a connection') connection, client_address = serverSocket.accept() try: print('client connected:', client_address) while True: data = connection.recv(1024) # data arrives as a string. Need to convert this back to an array newPack = int(data) if(randint(0,5) == 5): print('packet was lost/corrupted') connection.sendto(str(newPack).encode(), server_address) else: if newPack not in acked: acked.append(newPack) print('recieved sequence # ', str(newPack), ' successfully. Sending ack') connection.sendto(str(newPack).encode(), server_address) print('sent') ticker += 1 # loss rate leads to every nth item getting lost if data: # send acknowledgement # connection.sendto(str(newPack).encode(), server_address) print('') else: break finally: connection.close() print(acked)

[ "noreply@github.com" ]

GurenMarkV.noreply@github.com

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/test/functional/wallet_scriptaddress2.py

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[ "MIT" ]

permissive

hantobi-europe/aox

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refs/heads/main

2023-02-07T20:36:36.487504

2020-12-28T18:11:13

323,902,355

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null

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#!/usr/bin/env python3 # Copyright (c) 2015-2016 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 new aox multisig prefix functionality. # from test_framework.test_framework import BitcoinTestFramework from test_framework.util import ( assert_equal, connect_nodes, ) class ScriptAddress2Test(BitcoinTestFramework): def set_test_params(self): self.num_nodes = 3 self.setup_clean_chain = True self.extra_args = [['-addresstype=legacy', '-deprecatedrpc=accounts', '-txindex=1'], [], ['-txindex=1']] def setup_network(self, split=False): self.setup_nodes() connect_nodes(self.nodes[1], 0) connect_nodes(self.nodes[2], 0) self.sync_all() def skip_test_if_missing_module(self): self.skip_if_no_wallet() def run_test(self): cnt = self.nodes[0].getblockcount() # Mine some blocks self.nodes[1].generate(101) self.sync_all() if (self.nodes[0].getblockcount() != cnt + 101): raise AssertionError("Failed to mine 100 blocks") addr = self.nodes[0].getnewaddress() addr2 = self.nodes[0].getnewaddress() multisig_addr = self.nodes[0].addmultisigaddress(2, [addr, addr2], "multisigaccount")['address'] assert_equal(multisig_addr[0], 'Q') # Send to a new multisig address txid = self.nodes[1].sendtoaddress(multisig_addr, 1) self.nodes[1].generate(101) self.sync_all() tx = self.nodes[0].getrawtransaction(txid, 1) dest_addrs = [tx["vout"][0]['scriptPubKey']['addresses'][0], tx["vout"][1]['scriptPubKey']['addresses'][0]] assert(multisig_addr in dest_addrs) # Spend from the new multisig address addr3 = self.nodes[1].getnewaddress() txid = self.nodes[0].sendtoaddress(addr3, 0.8) self.nodes[0].generate(2) self.sync_all() assert(self.nodes[0].getbalance("*", 1) < 0.2) assert(self.nodes[1].listtransactions()[-1]['address'] == addr3) # Send to an old multisig address. The api addmultisigaddress # can only generate a new address so we manually compute # multisig_addr_old beforehand using an old client. priv_keys = ["cU7eeLPKzXeKMeZvnEJhvZZ3tLqVF3XGeo1BbM8dnbmV7pP3Qg89", "cTw7mRhSvTfzqCt6MFgBoTBqwBpYu2rWugisXcwjv4cAASh3iqPt"] addrs = ["mj6gNGRXPXrD69R5ApjcsDerZGrYKSfb6v", "mqET4JA3L7P7FoUjUP3F6m6YsLpCkyzzou"] self.nodes[0].importprivkey(priv_keys[0]) self.nodes[0].importprivkey(priv_keys[1]) multisig_addr_new = self.nodes[0].addmultisigaddress(2, addrs, "multisigaccount2")['address'] assert_equal(multisig_addr_new, 'QZ974ZrPrmqMmm1PSVp4m8YEgo3bCQZBbe') multisig_addr_old = "2N5nLwYz9qfnGdaFLpPn3gS6oYQbmLTWPjq" # Let's send to the old address. We can then find it in the # new address with the new client. So basically the old # address and the new one are the same thing. txid = self.nodes[1].sendtoaddress(multisig_addr_old, 1) self.nodes[1].generate(1) self.sync_all() tx = self.nodes[2].getrawtransaction(txid, 1) dest_addrs = [tx["vout"][0]['scriptPubKey']['addresses'][0], tx["vout"][1]['scriptPubKey']['addresses'][0]] assert(multisig_addr_new in dest_addrs) assert(multisig_addr_old not in dest_addrs) # Spend from the new multisig address addr4 = self.nodes[1].getnewaddress() txid = self.nodes[0].sendtoaddress(addr4, 0.8) self.nodes[0].generate(2) self.sync_all() assert(self.nodes[0].getbalance("*", 1) < 0.4) assert(self.nodes[1].listtransactions()[-1]['address'] == addr4) if __name__ == '__main__': ScriptAddress2Test().main()

[ "hantobieurope@gmail.com" ]

hantobieurope@gmail.com

2489e16d137ef37dbe2ade7e983532c88dcf0e31

50c0726bb32c8b0229d4b354f95e4bf654faa374

/Proyect-Lovelace/Molecular_Mass_Calculator.py

231e21b22291e47128b97b9e2b0759d8985a4e4e

[]

no_license

JorgeAvilaG/programming-problems

291dec9e90aebf3a7643082a5605b495d288253e

1be1d845589bb430106c023ba5b10ae3a3517a1d

refs/heads/master

2020-04-18T23:41:35.703093

2019-01-27T16:11:50

null

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#This problem asks for a function to calculate the molecular mass for different compounds. #This version passes the tests in the Proyect Lovelace but with complex formulas or special nomemclature #will not work. Example: '(NCH3)3PO2' 'Ph3N' import csv import re #A dictionary is used to load the atomic mass for each element. elements = {} with open('periodic_table.csv') as csvfile: periodic_table_reader = csv.reader(csvfile, delimiter=',') for row in periodic_table_reader: elements[row[0]] = float(row[1]) #The function uses Regex to identify different elements and the times it appears. def molecular_mass(chemical_formula): mass = 0 atoms = re.findall('[A-Z][a-z]?\d*', chemical_formula) for atom in atoms: element = re.search('[A-Z][a-z]?',atom)[0] number = re.search('\d+',atom) if number: number = int(re.search('\d+',atom)[0]) else: number = 1 mass += elements[element]*number return mass

[ "noreply@github.com" ]

JorgeAvilaG.noreply@github.com

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/kibble/scanners/scanners/git-evolution.py

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[ "Apache-2.0", "BSD-3-Clause", "MIT" ]

permissive

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refs/heads/main

2023-02-01T03:14:53.813091

2020-12-16T23:04:45

320,881,184

1

0

Apache-2.0

2020-12-12T17:04:54

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# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you 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. """ Git Evolution scanner """ import calendar import datetime import hashlib import os import subprocess import time from kibble.configuration import conf from kibble.scanners.utils import sloc title = "Git Evolution Scanner" version = "0.1.0" def accepts(source): """ Do we accept this source? """ if source["type"] == "git": return True # There are cases where we have a github repo, but don't wanna analyze the code, just issues if source["type"] == "github" and source.get("issuesonly", False) == False: return True return False def get_first_ref(gpath): try: return subprocess.check_output( "cd %s && git log `git rev-list --max-parents=0 HEAD` --pretty=format:%%ct" % gpath, shell=True, ) except: # pylint: disable=bare-except print("Could not get first ref, exiting!") return None def acquire(kibble_bit, source): source["steps"]["evolution"] = { "time": time.time(), "status": "Evolution scan started at " + time.strftime("%a, %d %b %Y %H:%M:%S +0000", time.gmtime()), "running": True, "good": True, } kibble_bit.update_source(source) def release(kibble_bit, source, status, exception=None, good=False): source["steps"]["evolution"] = { "time": time.time(), "status": status, "running": False, "good": good, } if exception: source["steps"]["evolution"].update({"exception": exception}) kibble_bit.update_source(source) def check_branch(gpath, date, branch): try: subprocess.check_call( 'cd %s && git rev-list -n 1 --before="%s" %s' % (gpath, date, branch), shell=True, ) return True except: # pylint: disable=bare-except return False def checkout(gpath, date, branch): # print("Ready to cloc...checking out %s " % date) try: ref = ( subprocess.check_output( 'cd %s && git rev-list -n 1 --before="%s" "%s"' % (gpath, date, branch), shell=True, stderr=subprocess.STDOUT, ) .decode("ascii", "replace") .strip() ) subprocess.check_output( "cd %s && git checkout %s -- " % (gpath, ref), shell=True, stderr=subprocess.STDOUT, ) except subprocess.CalledProcessError as err: print(err.output) def find_branch(date, gpath): try: os.chdir(gpath) subprocess.check_call( 'cd %s && git rev-list -n 1 --before="%s" master' % (gpath, date), shell=True, stderr=subprocess.DEVNULL, ) return "master" except: # pylint: disable=bare-except os.chdir(gpath) try: return ( subprocess.check_output( "cd %s && git rev-parse --abbrev-ref HEAD" % gpath, shell=True, stderr=subprocess.DEVNULL, ) .decode("ascii", "replace") .strip() .strip("* ") ) except: # pylint: disable=bare-except # print("meh! no branch") return None def scan(kibble_bit, source): rid = source["sourceID"] rootpath = "%s/%s/git" % ( conf.get("scanner", "scratchdir"), source["organisation"], ) gpath = os.path.join(rootpath, rid) gname = source["sourceID"] kibble_bit.pprint("Doing evolution scan of %s" % gname) inp = get_first_ref(gpath) if inp: ts = int(inp.split()[0]) ts -= ts % 86400 date = time.strftime("%Y-%b-%d 0:00", time.gmtime(ts)) # print("Starting from %s" % date) now = time.time() rid = source["sourceID"] url = source["sourceURL"] rootpath = "%s/%s/git" % ( conf.get("scanner", "scratchdir"), source["organisation"], ) gpath = os.path.join(rootpath, rid) if source["steps"]["sync"]["good"] and os.path.exists(gpath): acquire(kibble_bit, source) branch = find_branch(date, gpath) if not branch: release( source, "Could not do evolutionary scan of code", "No default branch was found in this repository", ) return branch_exists = check_branch(gpath, date, branch) if not branch_exists: kibble_bit.pprint("Not trunk either (bad repo?), skipping") release( source, "Could not do evolutionary scan of code", "No default branch was found in this repository", ) return try: d = time.gmtime(now) year = d[0] quarter = d[1] - (d[1] % 3) if quarter <= 0: quarter += 12 year -= 1 while now > ts: pd = ( datetime.datetime(year, quarter, 1) .replace(tzinfo=datetime.timezone.utc) .timetuple() ) date = time.strftime("%Y-%b-%d 0:00", pd) unix = calendar.timegm(pd) # Skip the dates we've already processed dhash = hashlib.sha224( (source["sourceID"] + date).encode("ascii", "replace") ).hexdigest() found = kibble_bit.exists("evolution", dhash) if not found: checkout(gpath, date, branch) kibble_bit.pprint( "Running cloc on %s (%s) at %s" % (gname, source["sourceURL"], date) ) languages, codecount, comment, blank, years, cost = sloc.count( gpath ) js = { "time": unix, "sourceID": source["sourceID"], "sourceURL": source["sourceURL"], "organisation": source["organisation"], "loc": codecount, "comments": comment, "blank": blank, "years": years, "cost": cost, "languages": languages, } kibble_bit.index("evolution", dhash, js) quarter -= 3 if quarter <= 0: quarter += 12 year -= 1 # decrease month by 3 now = time.mktime(datetime.date(year, quarter, 1).timetuple()) except Exception as e: kibble_bit.pprint(e) release( kibble_bit, source, "Evolution scan failed at " + time.strftime("%a, %d %b %Y %H:%M:%S +0000", time.gmtime()), str(e), ) return release( kibble_bit, source, "Evolution scan completed at " + time.strftime("%a, %d %b %Y %H:%M:%S +0000", time.gmtime()), good=True, )

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kaxil.noreply@github.com

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/pytorch/audio_tf.py

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permissive

vladbataev/nv-wavenet

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refs/heads/master

2020-03-28T17:11:06.953684

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import json import librosa import math import numpy as np import tensorflow as tf LOG10_TO_LN = math.log(10) LN_TO_LOG10 = 1 / LOG10_TO_LN DB_TO_LN = LOG10_TO_LN / 20 LN_TO_DB = 20 * LN_TO_LOG10 # 20 as the power is proportional to power of amplitude class AudioProcessor: def __init__(self, audio_config): params = self._load_params(audio_config) for k, v in params.items(): self.__setattr__(k, v) @staticmethod def _load_params(filepath): with open(filepath) as fin: params = json.load(fin) return params def _name_to_window_fn(self, name): mapping = { "hann": tf.contrib.signal.hann_window, "hamming": tf.contrib.signal.hamming_window, } return mapping[name] def preemphasis(self, signals): paddings = [ [0, 0], [0, 0], [1, 0] ] emphasized = tf.pad(signals[:, :, :-1], paddings=paddings) * -self.preemphasis_coef + signals return emphasized def deemphasis(self, signal): fir_approximation = [1] for i in range(math.ceil(1 / (1 - self.preemphasis_coef))): fir_approximation.append(fir_approximation[-1] * self.preemphasis_coef) filters = tf.constant(fir_approximation[::-1], dtype=tf.float32, shape=(len(fir_approximation), 1, 1)) paddings = [ [0, 0], [len(fir_approximation), 0], ] signal = tf.pad(signal, paddings) return tf.nn.conv1d(signal[:, :, None], filters, 1, data_format="NWC", padding="VALID")[:, :, 0] def amp_to_db(self, signal): return LN_TO_DB * tf.log(tf.maximum(self.min_level, signal)) def dbfs_normalize(self, signal): max_value = tf.reduce_max(signal, axis=[1, 2, 3], keepdims=True) return signal - max_value def normalize_and_clip_db(self, signal_db): """ Clips signal in decibels to [0; -min_level_db] and then normalizes it to [-max_abs_value; max_abs_value] in case symmetric output or to [0; max_abs_value] otherwise. :param signal_db: :return: clipped signal in decibels to [-max_abs_value; max_abs_value] or [0; max_abs_value]. """ clipped = signal_db - self.min_level_db normalized = tf.clip_by_value(clipped / -self.min_level_db, 0, 1) if self.symmetric_output: normalized = (normalized * 2 - 1) # so output now in [-1; 1] normalized *= self.max_abs_value return normalized def linear_scale_to_normalized_log_scale(self, spectrogram): spectrogram_db = self.amp_to_db(spectrogram) if self.dbfs_normalization: spectrogram_db = self.dbfs_normalize(spectrogram_db) spectrogram_db -= self.ref_level_db return self.normalize_and_clip_db(spectrogram_db) def _mel_basis(self): if self.use_tf_mel_basis: mel_basis = tf.contrib.signal.linear_to_mel_weight_matrix( self.num_mel_bins, self.window_size // 2 + 1, self.sample_rate, self.lower_edge_hertz, self.upper_edge_hertz ) else: mel_basis = librosa.filters.mel( sr=self.sample_rate, n_fft=self.window_size, n_mels=self.num_mel_bins, fmin=self.lower_edge_hertz, fmax=self.upper_edge_hertz ) mel_basis = tf.convert_to_tensor(np.transpose(mel_basis, (1, 0)), dtype=tf.float32) return mel_basis def compute_spectrum(self, signal): """ :param signals: shape [batch_size, 1, num_timestamps] :param lengths: :param sample_rate: :return: """ with tf.name_scope("extract_feats"): frame_length = self.window_size frame_step = self.window_step signals = signal[None, None, ...] if self.apply_preemphasis: signals = self.preemphasis(signals) stfts = tf.contrib.signal.stft(signals, frame_length=frame_length, frame_step=frame_step, fft_length=frame_length, window_fn=self._name_to_window_fn(self.window_fn_name), pad_end=True) linear_spectrograms = tf.abs(stfts) mel_spectrograms = tf.tensordot(linear_spectrograms, self._mel_basis(), 1) normed_mel_spectrograms_db = self.linear_scale_to_normalized_log_scale(mel_spectrograms) return tf.transpose(normed_mel_spectrograms_db[0, 0], (1, 0))

[ "v.bataev@tinkoff.ru" ]

v.bataev@tinkoff.ru

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/Recursion/Module1/SumOfNnumbers.py

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[]

no_license

pawarvishal/cninjads_python_problems

0b49fb987cb3b8571ff0fe2e6f617174d36fc7d6

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refs/heads/master

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py

# Calculate sum of n numbers def sum_n(n): if n == 0: return 0 small_output = sum_n(n-1) output = small_output + n return output num = int(input()) print(sum_n(num))

[ "openstack.vishal@gmail.com" ]

openstack.vishal@gmail.com

022da1ef07e8a25cde535e2903bacc012f2e4f9b

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/base/utils/type_utils.py

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[]

no_license

bluesky139/MjolnirTornado

16d7cb3ecb6748bd557396de471846f7f09bd860

18fd0567dbb167423d0079ed86d7b191fe1cb77b

refs/heads/master

2020-06-01T05:24:43.707431

2019-12-27T07:05:05

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import re from base import * class List(object): @classmethod def parse(cls, value, except_class=InvalidArguments, except_message='Can\'t convert list.'): try: return value.split('|') except: raise except_class('Invalid list format, %s', except_message) class Int(object): @classmethod def parse(cls, value, except_class=InvalidArguments, except_message='Can\'t convert int.'): try: return int(value) except: raise except_class('Invalid int, %s', except_message) class Float(object): @classmethod def parse(cls, value, except_class=InvalidArguments, except_message='Can\'t convert float.'): try: return float(value) except: raise except_class('Invalid float, %s', except_message) class Bool(object): @classmethod def parse(cls, value, except_class=InvalidArguments, except_message='Can\'t convert bool.'): return value in [True, 'True', 'true', 'TRUE', '1', 1] class Dict(object): @classmethod def make_sure_key(cls, value, key, default_value=''): if key not in value: value[key] = default_value class NoConvert(object): @classmethod def parse(cls, value, except_class=None, except_message=None): return value class String(object): VALID_STR_LETTER = int('001', 2) VALID_STR_DIGIT = int('010', 2) VALID_STR_UNDERSCORE = int('100', 2) _valid_str_regs = {} @classmethod def validate(cls, s, flag, except_class=InvalidArguments, except_message='Invalid string'): global _valid_str_regs if flag not in _valid_str_regs: reg = r'^[' if flag & cls.VALID_STR_LETTER: reg += r'a-zA-Z' if flag & cls.VALID_STR_DIGIT: reg += r'\d' if flag & cls.VALID_STR_UNDERSCORE: reg += r'_' reg += r']+$' reg = re.compile(reg) _valid_str_regs[flag] = reg reg = _valid_str_regs[flag] if reg.match(s) is None: raise except_class('%s, %s' % (except_message, s)) return False return True @classmethod def lower_upper_with_underscore(cls, s): return ''.join('_' + c.lower() if c.isupper() else c for c in s).lstrip('_')

[ "bluesky139@gmail.com" ]

bluesky139@gmail.com

a60cce92b01defcbf4760f93cdbc9f636e0e3cef

1503bb33834c463657977765e821620f189a4685

/p007.py

79f340ac96bd0e0708ffbde2fc2002a0b35e7944

[]

no_license

JackPound/Euler-Problems

94a2ff36d92cc28c4a23586847698d33710f24b0

fac5975d4fa323b3f992daedc12aec1246dbdb82

refs/heads/master

2020-03-22T20:53:26.655150

2018-07-12T22:51:57

140,639,403

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555

py

# By listing the first six prime numbers: 2, 3, 5, 7, 11, and 13, we can see that the 6th prime is 13. # What is the 10 001st prime number? def is_prime(number_to_check): prime = True for x in range (2, number_to_check): if number_to_check % x == 0: prime = False break return prime def prime_position(at_position): prime_list = [] count = 2 while len(prime_list) < at_position: if is_prime(count): prime_list.append(count) count += 1 else: count += 1 print(prime_list[-1]) prime_position(10001)

[ "jackpound@live.com" ]

jackpound@live.com

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/demo14_sorted.py

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[]

no_license

Honfeil/pythonWorkspace

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refs/heads/master

2021-01-15T17:50:16.677942

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# -- coding:utf-8 -- # sorted() li = sorted([35, 58, 42, 4, 65, 4, 5, 5, 2, 4, 55, 14, 5]) print(li) li = [-5, 6, -7, 8, -9] print(sorted(li)) print(sorted(li, key=abs)) # 字符串排序 li = ['Candy', 'Honey', 'atom', 'bust', 'Bug'] print(sorted(li)) print(sorted(li, key=str.lower)) # 复习map li = list(map(lambda s: s.capitalize(), li)) print(sorted(li, reverse=True)) # 假设我们用一组tuple表示学生名字和成绩： L = [('Bob', 75), ('Adam', 92), ('Bart', 66), ('Lisa', 88)] def sk(m): return m[0] def ss(m): return m[1] print(sorted(L, key=sk)) print(sorted(L, key=ss, reverse=True))

[ "honfeil.163.com" ]

honfeil.163.com

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6cde76beabb943b4de4ab9f7516ebffca51f6da6

/generate.py

ea8517289fa84e335e308416701d7c7449ebf6f2

[]

no_license

A1exRey/ReflectionOfNAR

801bf23eb098f03e663f89f553355f43eb6a7d9e

79ed86873322d45cbfc28f98a4e224c961d5bad2

refs/heads/main

2023-05-13T23:00:54.703916

2021-06-01T11:01:02

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UTF-8

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py

#!/usr/bin/env python3 -u # Copyright (c) 2017-present, Facebook, Inc. # All rights reserved. # # This source code is licensed under the license found in the LICENSE file in # the root directory of this source tree. An additional grant of patent rights # can be found in the PATENTS file in the same directory. """ Translate pre-processed data with a trained model. """ import torch from fairseq import bleu, checkpoint_utils, options, progress_bar, tasks, utils from fairseq.meters import StopwatchMeter, TimeMeter import re from interactive import translate_corpus, parse_head_pruning_descriptors, mask_heads def main(args): assert args.path is not None, '--path required for generation!' assert not args.sampling or args.nbest == args.beam, \ '--sampling requires --nbest to be equal to --beam' assert args.replace_unk is None or args.raw_text, \ '--replace-unk requires a raw text dataset (--raw-text)' utils.import_user_module(args) if args.max_tokens is None and args.max_sentences is None: args.max_tokens = 12000 print(args) use_cuda = torch.cuda.is_available() and not args.cpu # Load dataset splits task = tasks.setup_task(args) task.load_dataset(args.gen_subset) # Set dictionaries try: src_dict = getattr(task, 'source_dictionary', None) except NotImplementedError: src_dict = None tgt_dict = task.target_dictionary # Load ensemble print('| loading model(s) from {}'.format(args.path)) models, _model_args = checkpoint_utils.load_model_ensemble( args.path.split(':'), arg_overrides=eval(args.model_overrides), task=task, ) to_prune = {'E': {}, 'A': {}, 'D': {}} to_prune = {'E': {0: {0, 1, 2, 3, 4, 5, 6, 7}, 1: {2, 4, 7}, 2: {3}}, 'A': {}, 'D': {}} to_prune = {'E': {0: {0, 1, 2, 3, 4, 5, 6, 7}, 1: {2, 4, 7}, 2: {3, 7}, 4: {0, 3, 7}, 5: {0, 7}, 11: {2, 5}, 6: {0}, 9: {3}, 3: {0}}, 'A': {}, 'D': {}} to_prune = {'E': {0: {0, 1, 2, 3, 4, 5, 6, 7}, 1: {0, 1, 2, 4, 6, 7}, 2: {3, 5, 7}, 3: {0, 1, 4}, 4: {0, 2, 3, 7}, 5: {0, 7}, 6: {0, 1}, 9: {3}, 11: {2, 5}, 10: {0, 3}}, 'A': {0: {1}}, 'D': {}} to_prune = {'E': {0: {0, 1, 2, 3, 4, 5, 6, 7}, 1: {0, 1, 2, 4, 6, 7}, 2: {3, 5, 7}, 3: {0, 1, 4, 6}, 4: {0, 2, 3, 7}, 5: {0, 3, 7}, 6: {0, 1, 2}, 9: {1, 3, 6}, 10: {0, 3, 5}, 11: {2, 5, 7}, 8: {3, 4, 5, 7}}, 'A': {0: {1}}, 'D': {}} to_prune = {'E': {0: {0, 1, 2, 3, 4, 5, 6, 7}, 1: {0, 1, 2, 4, 5, 6, 7}, 2: {0, 3, 5, 7}, 3: {0, 1, 4, 5, 6}, 4: {0, 1, 2, 3, 7}, 5: {0, 2, 3, 4, 5, 7}, 6: {0, 1, 2, 3, 6}, 8: {3, 4, 5, 7}, 9: {1, 3, 6}, 10: {0, 3, 5}, 11: {2, 5, 7}, 7: {2, 4}}, 'A': {0: {1}}, 'D': {}} to_prune = {'E': {0: {0, 1, 2, 3, 4, 5, 6, 7}, 1: {0, 1, 2, 4, 5, 6, 7}, 2: {0, 2, 3, 4, 5, 6, 7}, 3: {0, 1, 4, 5, 6}, 4: {0, 1, 2, 3, 6, 7}, 5: {0, 2, 3, 4, 5, 7}, 6: {0, 1, 2, 3, 6}, 7: {2, 4, 6}, 8: {0, 3, 4, 5, 6, 7}, 9: {1, 3, 6}, 10: {0, 1, 3, 5, 7}, 11: {0, 2, 5, 7}}, 'A': {0: {1}}, 'D': {0: {1, 4}}} to_prune = {'E': {0: {0, 1, 2, 3, 4, 5, 6, 7}, 1: {0, 1, 2, 3, 4, 5, 6, 7}, 2: {0, 2, 3, 4, 5, 6, 7}, 3: {0, 1, 3, 4, 5, 6, 7}, 4: {0, 1, 2, 3, 4, 5, 6, 7}, 5: {0, 2, 3, 4, 5, 6, 7}, 6: {0, 1, 2, 3, 6, 7}, 7: {0, 2, 3, 4, 6}, 8: {0, 3, 4, 5, 6, 7}, 9: {1, 2, 3, 6, 7}, 10: {0, 1, 3, 5, 7}, 11: {0, 2, 5, 7}}, 'A': {0: {1}}, 'D': {0: {1, 4}}} to_prune = {'E': {0: {0, 1, 2, 3, 4, 5, 6, 7}, 1: {0, 1, 2, 3, 4, 5, 6, 7}, 2: {0, 1, 2, 3, 4, 5, 6, 7}, 3: {0, 1, 3, 4, 5, 6, 7}, 4: {0, 1, 2, 3, 4, 5, 6, 7}, 5: {0, 1, 2, 3, 4, 5, 6, 7}, 6: {0, 1, 2, 3, 5, 6, 7}, 7: {0, 2, 3, 4, 6, 7}, 8: {0, 1, 3, 4, 5, 6, 7}, 9: {1, 2, 3, 6, 7}, 10: {0, 1, 2, 3, 5, 7}, 11: {0, 2, 5, 6, 7}}, 'A': {0: {1, 4, 7}}, 'D': {0: {0, 1, 4, 7}}} #to_prune = {'E': {0: {0, 1, 2, 3, 4, 5, 6, 7}, 1: {0, 1, 2, 3, 4, 5, 6, 7}, 2: {0, 1, 2, 3, 4, 5, 6, 7}, 3: {0, 1, 2, 3, 4, 5, 6, 7}, 4: {0, 1, 2, 3, 4, 5, 6, 7}, 5: {0, 1, 2, 3, 4, 5, 6, 7}, 6: {0, 1, 2, 3, 4, 5, 6, 7}, 7: {0, 2, 3, 4, 5, 6, 7}, 8: {0, 1, 2, 3, 4, 5, 6, 7}, 9: {1, 2, 3, 4, 5, 6, 7}, 10: {0, 1, 2, 3, 5, 7}, 11: {0, 2, 5, 6, 7}}, 'A': {0: {0, 1, 2, 3, 4, 5, 6, 7}}, 'D': {0: {0, 1, 4, 7}}} # Optimize ensemble for generation for model in models: mask_heads(model, to_prune, False) model.make_generation_fast_( beamable_mm_beam_size=None if args.no_beamable_mm else args.beam, need_attn=args.print_alignment, ) if args.fp16: model.half() if use_cuda: model.cuda() # Load alignment dictionary for unknown word replacement # (None if no unknown word replacement, empty if no path to align dictionary) align_dict = utils.load_align_dict(args.replace_unk) # Load dataset (possibly sharded) itr = task.get_batch_iterator( dataset=task.dataset(args.gen_subset), max_tokens=args.max_tokens, max_sentences=args.max_sentences, max_positions=utils.resolve_max_positions( task.max_positions(), *[model.max_positions() for model in models] ), ignore_invalid_inputs=args.skip_invalid_size_inputs_valid_test, required_batch_size_multiple=args.required_batch_size_multiple, num_shards=args.num_shards, shard_id=args.shard_id, num_workers=args.num_workers, ).next_epoch_itr(shuffle=False) # Initialize generator gen_timer = StopwatchMeter() generator = task.build_generator(models,args) # Generate and compute BLEU score if args.sacrebleu: scorer = bleu.SacrebleuScorer() else: scorer = bleu.Scorer(tgt_dict.pad(), tgt_dict.eos(), tgt_dict.unk()) num_sentences = 0 has_target = True with progress_bar.build_progress_bar(args, itr) as t: wps_meter = TimeMeter() for sample in t: sample = utils.move_to_cuda(sample) if use_cuda else sample if 'net_input' not in sample: continue prefix_tokens = None if args.prefix_size > 0: prefix_tokens = sample['target'][:, :args.prefix_size] gen_timer.start() hypos = task.inference_step(generator, models, sample, prefix_tokens) num_generated_tokens = sum(len(h[0]['tokens']) for h in hypos) gen_timer.stop(num_generated_tokens) for i, sample_id in enumerate(sample['id'].tolist()): has_target = sample['target'] is not None # Remove padding src_tokens = utils.strip_pad(sample['net_input']['src_tokens'][i, :], tgt_dict.pad()) target_tokens = None if has_target: target_tokens = utils.strip_pad(sample['target'][i, :], tgt_dict.pad()).int().cpu() # Either retrieve the original sentences or regenerate them from tokens. if align_dict is not None: src_str = task.dataset(args.gen_subset).src.get_original_text(sample_id) target_str = task.dataset(args.gen_subset).tgt.get_original_text(sample_id) else: if src_dict is not None: src_str = src_dict.string(src_tokens, args.remove_bpe) else: src_str = "" if has_target: target_str = tgt_dict.string(target_tokens, args.remove_bpe, escape_unk=True) if not args.quiet: if src_dict is not None: print('S-{}\t{}'.format(sample_id, src_str)) if has_target: print('T-{}\t{}'.format(sample_id, target_str)) # Process top predictions for j, hypo in enumerate(hypos[i][:args.nbest]): hypo_tokens, hypo_str, alignment = utils.post_process_prediction( hypo_tokens=hypo['tokens'].int().cpu(), src_str=src_str, alignment=hypo['alignment'].int().cpu() if hypo['alignment'] is not None else None, align_dict=align_dict, tgt_dict=tgt_dict, remove_bpe=args.remove_bpe, ) if not args.quiet: print('H-{}\t{}\t{}'.format(sample_id, hypo['score'], hypo_str)) print('P-{}\t{}'.format( sample_id, ' '.join(map( lambda x: '{:.4f}'.format(x), hypo['positional_scores'].tolist(), )) )) if args.print_alignment: print('A-{}\t{}'.format( sample_id, ' '.join(map(lambda x: str(utils.item(x)), alignment)) )) # Score only the top hypothesis if has_target and j == 0: if align_dict is not None or args.remove_bpe is not None: # Convert back to tokens for evaluation with unk replacement and/or without BPE target_tokens = tgt_dict.encode_line(target_str, add_if_not_exist=True) if hasattr(scorer, 'add_string'): if args.dehyphenate: print('dehyphenating') target_str = dehyphenate(target_str) hypo_str = dehyphenate(hypo_str) scorer.add_string(target_str, hypo_str) else: scorer.add(target_tokens, hypo_tokens) wps_meter.update(num_generated_tokens) t.log({'wps': round(wps_meter.avg)}) num_sentences += sample['nsentences'] print('| Translated {} sentences ({} tokens) in {:.1f}s ({:.2f} sentences/s, {:.2f} tokens/s)'.format( num_sentences, gen_timer.n, gen_timer.sum, num_sentences / gen_timer.sum, 1. / gen_timer.avg)) if has_target: print('| Generate {} with beam={}: {}'.format(args.gen_subset, args.beam, scorer.result_string())) return scorer def cli_main(): parser = options.get_generation_parser() args = options.parse_args_and_arch(parser) main(args) def dehyphenate(sent): return re.sub(r'(\S)-(\S)', r'\1 ##AT##-##AT## \2', sent).replace('##AT##', '@') if __name__ == '__main__': cli_main()

[ "noreply@github.com" ]

A1exRey.noreply@github.com

7918dfa9b23e9132b0e9ee1227ce130b85ce717a

4dbb4e1c3e3c5c0471150cba23fbfb9592fbf1f4

/accounts/forms.py

1cf25e666b098a830eeb81df420fd1a842233215

[]

no_license

OwenCookman/owen-webdev

745f0c4a1735f4ce084c2094ab2425d3ca4ca925

9a912ba47a09597d0069884f0d603806d955cbe3

refs/heads/master

2021-09-29T10:46:37.210443

2021-06-21T12:46:29

251,331,742

0

null

2021-09-22T18:50:34

2020-03-30T14:33:54

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from django import forms from django.contrib.auth.models import User from django.contrib.auth.forms import UserCreationForm from django.core.exceptions import ValidationError class UserLoginForm(forms.Form): """The form used to log in users""" username = forms.CharField() password = forms.CharField(widget=forms.PasswordInput) class UserRegistrationForm(UserCreationForm): """The form used to register a new user""" password1 = forms.CharField( label="Password", widget=forms.PasswordInput) password2 = forms.CharField( label="Password Confirmation", widget=forms.PasswordInput) class Meta: model = User fields = ['email', 'username', 'password1', 'password2'] def clean_email(self): email = self.cleaned_data.get('email') username = self.cleaned_data.get('username') if User.objects.filter(email=email).exclude(username=username): raise forms.ValidationError(u'Email address must be unique') return email def clean_password2(self): password1 = self.cleaned_data.get('password1') password2 = self.cleaned_data.get('password2') if not password1 or not password2: raise ValidationError("Please confirm your password") if password1 != password2: raise ValidationError("Passwords must match") return password2

[ "ozzycookman@hotmail.com" ]

ozzycookman@hotmail.com

463e34a0ed580f880b1b6faaa67e1eb98751a792

df6d070cf9b984d291dfe23df5b3dc11fe6a32ac

/old_scripts/CCCM/2010_dataset.py

c2154df67f5ef4e2877280570cc030a44c4b5279

[]

no_license

tristanohanlon/climate-analysis

5a439e4f08d6cae5a0f6cfec9bb5c5e4f59f122b

7e8a97cf83a308772cc7bc3081b3f117cc0eeb6d

refs/heads/master

2023-02-12T13:15:22.995914

2021-01-09T20:32:19

184,121,830

3

0

null

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# -*- coding: utf-8 -*- """ Created on Tue Mar 5 09:26:33 2019 @author: Tristan O'Hanlon """ import time import numpy as np import os from pyhdf import SD import h5py import matplotlib.pyplot as plt ############################################################################### cccm21_cloud_free_area = [] cccm34_phase = [] cccm52_cloud_fraction_profile = [] start = time.time() # The directory where your HDF files are stored os.chdir('d:/Downloads/CCCM/2010') # Home PC # Load every file in the directory for filename in os.listdir(): # Load the file f = SD.SD(filename) # cccm21_cloud_free_area = cccm21_cloud_free_area + f.select('Cloud free area percent coverage (CALIPSO-CloudSat)').get().tolist() # cccm34_phase = cccm34_phase + f.select('Mean group cloud particle phase from MODIS radiance (3.7)').get().tolist() # cccm52_cloud_fraction_profile = cccm52_cloud_fraction_profile + f.select('Cloud fraction profile').get().tolist() end = time.time() print('Importing data from files to lists took:', end - start, 's') start = time.time() cccm21_cloud_free_area = np.array(cccm21_cloud_free_area) cccm34_phase = np.array(cccm34_phase) cccm52_cloud_fraction_profile = np.array(cccm52_cloud_fraction_profile) end = time.time() print('Create arrays took:', end - start, 's') ############################################################################### Save raw data # specify path and file name to create with h5py.File('2010_raw_data.h5', 'w') as p: p.create_dataset('cccm21_cloud_free_area', data=cccm21_cloud_free_area) p.create_dataset('cccm34_phase', data=cccm34_phase) p.create_dataset('cccm52_cloud_fraction_profile', data=cccm52_cloud_fraction_profile) p.close() ############################################################################### Load raw data """ import h5py os.chdir('//synthesis/e/University/University/MSc/Models/climate-analysis/CCCM') f = h5py.File('2010_raw_data.h5', 'r') #cccm21_cloud_free_area = f['cccm21_cloud_free_area'][:] #cccm34_phase = f['cccm34_phase'][:] cccm52_cloud_fraction_profile = f['cccm52_cloud_fraction_profile'][:] f.close() """ ############################################################################### Get Southern Ocean Data - get lat first # Join the two lists as if they were two columns side by side, into a list of two elements each a = np.vstack(lat) combined = np.hstack((a, cccm52_cloud_fraction_profile)) #print ("combined") #print (combined) # Add a column for every additional column, -1 will sort by the first column combined = combined[np.lexsort(np.transpose(combined)[:-1])] #print ("sorted") #print (combined) #Select latitudes over the southern ocean combined = combined[combined[:,0]>=-70] combined = combined[combined[:,0]<=-50] #Split the combined array into just the lw data, eliminating the first coloumn of latitude cccm52_cloud_fraction_profile_so = combined[:,1:114] #alt = alt[0:137] #scale alt if necessary ############################################################################### Reduce and convert cloud free area to cloud area cccm52_cloud_fraction_profile_alt = np.vstack((cccm121_alt, np.nanmean(cccm52_cloud_fraction_profile, axis = 0))).T cccm52_cloud_fraction_profile_alt_so = np.vstack((cccm121_alt, np.nanmean(cccm52_cloud_fraction_profile_so, axis = 0))).T cccm21_cloud_free_area = (100 - cccm21_cloud_free_area) / 100 ############################################################################### Reduce cloud free area # Join the two lists as if they were two columns side by side, into a list of two elements each combined = np.vstack((lat, cccm21_cloud_free_area)).T #print ("combined") #print (combined) #print("get unique lats") unique = np.unique(lat) #print(unique) # Add a column for every additional column, -1 will sort by the first column combined = combined[np.lexsort(np.transpose(combined)[:-1])] #print ("sorted") #print (combined) # Averages of (lat, cloud ice water content) empty array averages_total = unique.size cccm_tcc_lat = np.empty((averages_total,2),dtype=float) # Current subtotal of current lat subtotal = 0.0 # Current number of cloud ice water content entries in subtotal number = 0 # Set the current lat to false current_lat = None # Iterate through all of the (lat, cloud ice water content) elements and subtotal the same lat values i = 0 for item in combined: if np.isnan(item[1]): continue if current_lat is None: """ print("setting current_lat to item[0]") print("(current_lat == item[0]) = ", end='') print(current_lat == item[0]) """ current_lat = item[0]; # If the lat is not the same as last time, then perform the average calc and reset everything if item[0] != current_lat: # Find the average value. average = subtotal / number """ print("--------") print("lat: ", end='') print(current_lat, end='') print(", avg: ", end='') print(average, end='') print(", subtotal: ", end='') print(subtotal, end='') print(", number: ", end='') print(number) """ # Append the average cccm_tcc_lat[i] = [current_lat, average] # Reset the subtotal subtotal = 0.0 number = 0 # Set the current latitude current_lat = item[0] # Move to the next index in the averages array i+=1 # Add the next value to the subtotal number+=1 subtotal+=item[1] # Catch the last entry in the for loop average = subtotal / number cccm_tcc_lat[i] = [current_lat, average] cccm21_cloud_area_fraction = cccm_tcc_lat os.chdir('e:/University/University/MSc/Models/climate-analysis/CCCM/reduced_datasets') # Home PC f = h5py.File('2010_CCCM.h5', 'r') cccm81b_cloud_area_enhanced = f['tcc'][:] f.close() ############################################################################### Save reduced data os.chdir('//synthesis/e/University/University/MSc/Models/climate-analysis/CCCM') with h5py.File('2010_cloud_fractions.h5', 'w') as p: p.create_dataset('cccm21_cloud_area_fraction', data=cccm21_cloud_area_fraction) p.create_dataset('cccm52_cloud_fraction_profile_alt', data=cccm52_cloud_fraction_profile_alt) p.create_dataset('cccm52_cloud_fraction_profile_alt_so', data=cccm52_cloud_fraction_profile_alt_so) p.create_dataset('cccm81b_cloud_area_enhanced', data=cccm81b_cloud_area_enhanced) p.close() ############################################################################### Load reduced data """ os.chdir('//synthesis/e/University/University/MSc/Models/climate-analysis/CCCM') f = h5py.File('2010_cloud_fractions.h5', 'r') cccm21_cloud_area_fraction = f['cccm21_cloud_area_fraction'][:] cccm52_cloud_fraction_profile_alt = f['cccm52_cloud_fraction_profile_alt'][:] cccm52_cloud_fraction_profile_alt_so = f['cccm52_cloud_fraction_profile_alt_so'][:] cccm81b_cloud_area_enhanced = f['cccm81b_cloud_area_enhanced'][:] f.close() """ ############################################################################### Load reduced phase data os.chdir('e:/University/University/MSc/Models/climate-analysis/CCCM/') # Home PC f = h5py.File('2010_cccm85_enhanced_lwc.h5', 'r') cccm85_enhanced_lwc_lat = f['cccm85_enhanced_lwc_lat'][:] cccm85_enhanced_lwc_alt = f['cccm85_enhanced_lwc_alt'][:] cccm85_enhanced_lwc_alt_so = f['cccm85_enhanced_lwc_alt_so'][:] cccm85_enhanced_lwc_alt = np.vstack((cccm123_alt, cccm85_enhanced_lwc_alt)).T f.close() os.chdir('e:/University/University/MSc/Models/climate-analysis/CCCM/') # Home PC f = h5py.File('2010_cccm86_enhanced_iwc.h5', 'r') cccm86_enhanced_iwc_lat = f['cccm86_enhanced_iwc_lat'][:] cccm86_enhanced_iwc_alt = f['cccm86_enhanced_iwc_alt'][:] cccm86_enhanced_iwc_alt_so = f['cccm86_enhanced_iwc_alt_so'][:] f.close() ############################################################################### Create phase fraction data tclw_frac = np.vstack((cccm85_enhanced_lwc_lat[:,0], (cccm85_enhanced_lwc_lat[:,1] / (cccm85_enhanced_lwc_lat[:,1] + cccm86_enhanced_iwc_lat[:,1])) * cccm21_cloud_free_area[:,1])).T tciw_frac = np.vstack((cccm86_enhanced_iwc_lat[:,0], (cccm86_enhanced_iwc_lat[:,1] / (cccm85_enhanced_lwc_lat[:,1] + cccm86_enhanced_iwc_lat[:,1])) * cccm21_cloud_free_area[:,1])).T """ fig, ax = plt.subplots() ax.plot(cccm85_enhanced_lwc_lat[:,0], tclw_frac, '-k') #ax.plot(cccm85_enhanced_lwc_lat[:,0], tclw_frac81, '-b') ax.plot(cccm85_enhanced_lwc_lat[:,0], tciw_frac, '--k') ax.plot(cccm21_cloud_free_area[:,0], cccm21_cloud_free_area[:,1], '--b') """ lw_frac = np.vstack((cccm85_enhanced_lwc_alt[:,0], (cccm85_enhanced_lwc_alt[:,1] / (cccm85_enhanced_lwc_alt[:,1] + cccm86_enhanced_iwc_alt[:,1])) * cccm52_cloud_fraction_profile_alt[:,1])).T iw_frac = np.vstack((cccm86_enhanced_iwc_alt[:,0], (cccm86_enhanced_iwc_alt[:,1] / (cccm85_enhanced_lwc_alt[:,1] + cccm86_enhanced_iwc_alt[:,1])) * cccm52_cloud_fraction_profile_alt[:,1])).T """ fig, ax = plt.subplots() ax.plot(lw_frac, cccm52_cloud_fraction_profile_alt[:,0], '-k') #ax.plot(cccm85_enhanced_lwc_lat[:,0], tclw_frac81, '-b') ax.plot(iw_frac, cccm52_cloud_fraction_profile_alt[:,0], '--k') ax.plot(cccm52_cloud_fraction_profile_alt[:,1], cccm52_cloud_fraction_profile_alt[:,0], '--b') """ lw_frac_so = np.vstack((cccm85_enhanced_lwc_alt_so[:,0], (cccm85_enhanced_lwc_alt_so[:,1] / (cccm85_enhanced_lwc_alt_so[:,1] + cccm86_enhanced_iwc_alt_so[:,1])) * cccm52_cloud_fraction_profile_alt_so[:,1])).T iw_frac_so = np.vstack((cccm86_enhanced_iwc_alt_so[:,0], (cccm86_enhanced_iwc_alt_so[:,1] / (cccm85_enhanced_lwc_alt_so[:,1] + cccm86_enhanced_iwc_alt_so[:,1])) * cccm52_cloud_fraction_profile_alt_so[:,1])).T ############################################################################### Load Previous data os.chdir('e:/University/University/MSc/Models/climate-analysis/CCCM/raw_datasets') # Home PC f = h5py.File('2010_CCCM_profile_variables.h5', 'r') lat = f['lat'][:] cccm123_alt = f['alt'][:] cccm121_alt = f['alt_c'][:] cccm124_alt = f['alt_t'][:] pressure_g = f['pressure_g_alt'][:] pressure_so = f['pressure_so_alt'][:] temp_g = f['temp_g_alt'][24:137] temp_so = f['temp_so_alt'][24:137] air_density_g = f['air_density_g'][:] air_density_so = f['air_density_so'][:] f.close() os.chdir('e:/University/University/MSc/Models/climate-analysis/CCCM/reduced_datasets') # Home PC f = h5py.File('2010_CCCM.h5', 'r') tclw = f['tclw'][:] tciw = f['tciw'][:] cccm85_specific_lwc_alt = f['lw'][:] cccm86_specific_iwc_alt = f['iw'][:] cccm85_specific_lwc_alt_so = f['lw_so'][:] cccm86_specific_iwc_alt_so = f['iw_so'][:] cccm85_specific_lwc_temp = f['lw_t'][:] cccm86_specific_iwc_temp = f['iw_t'][:] cccm85_specific_lwc_temp_so = f['lw_t_so'][:] cccm86_specific_iwc_temp_so = f['iw_t_so'][:] f.close() ############################################################################### Create temperature data cf_frac_temp = np.vstack((temp_g[:, 1], cccm52_cloud_fraction_profile_alt[:,1])).T lw_frac_temp = np.vstack((temp_g[:, 1], lw_frac[:,1])).T iw_frac_temp = np.vstack((temp_g[:, 1], iw_frac[:,1])).T cf_frac_temp_so = np.vstack((temp_so[:, 1], cccm52_cloud_fraction_profile_alt_so[:,1])).T lw_frac_temp_so = np.vstack((temp_so[:, 1], lw_frac_so[:,1])).T iw_frac_temp_so = np.vstack((temp_so[:, 1], iw_frac_so[:,1])).T ############################################################################### Create new datasets os.chdir('e:/University/University/MSc/Models/climate-analysis/CCCM/reduced_datasets') # Home PC with h5py.File('2010_data_new.h5', 'w') as p: p.create_dataset('lat', data=lat) p.create_dataset('alt', data=cccm121_alt) p.create_dataset('air_density_g', data=air_density_g) p.create_dataset('air_density_so', data=air_density_so) p.create_dataset('tcc', data=cccm21_cloud_area_fraction) p.create_dataset('tclw', data=tclw) p.create_dataset('tciw', data=tciw) p.create_dataset('tclw_gcm3', data=cccm85_enhanced_lwc_lat) p.create_dataset('tciw_gcm3', data=cccm86_enhanced_iwc_lat) p.create_dataset('tclw_frac', data=tclw_frac) p.create_dataset('tciw_frac', data=tciw_frac) p.create_dataset('cf', data=cccm52_cloud_fraction_profile_alt) p.create_dataset('cf_so', data=cccm52_cloud_fraction_profile_alt_so) p.create_dataset('lw_frac', data=lw_frac) p.create_dataset('lw_frac_so', data=lw_frac_so) p.create_dataset('iw_frac', data=iw_frac) p.create_dataset('iw_frac_so', data=iw_frac_so) p.create_dataset('lw', data=cccm85_specific_lwc_alt) p.create_dataset('lw_so', data=cccm85_specific_lwc_alt_so) p.create_dataset('iw', data=cccm86_specific_iwc_alt) p.create_dataset('iw_so', data=cccm86_specific_iwc_alt_so) p.create_dataset('temp', data=temp_g) p.create_dataset('temp_so', data=temp_so) p.create_dataset('pressure', data=pressure_g) p.create_dataset('pressure_so', data=pressure_so) p.create_dataset('lw_t', data=cccm85_specific_lwc_temp) p.create_dataset('lw_t_so', data=cccm85_specific_lwc_temp_so) p.create_dataset('iw_t', data=cccm86_specific_iwc_temp) p.create_dataset('iw_t_so', data=cccm86_specific_iwc_temp_so) p.create_dataset('cf_t', data=cf_frac_temp) p.create_dataset('cf_t_so', data=cf_frac_temp_so) p.create_dataset('lw_frac_temp', data=lw_frac_temp) p.create_dataset('lw_frac_temp_so', data=lw_frac_temp_so) p.create_dataset('iw_frac_temp', data=iw_frac_temp) p.create_dataset('iw_frac_temp_so', data=iw_frac_temp_so) p.close() ############################################################################### Test plots """ fig, ax = plt.subplots() ax.plot(tclw_frac[:,0], tclw_frac[:, 1], '-k') ax.plot(tciw_frac[:,0], tciw_frac[:, 1], '--k') ax.plot(cccm21_cloud_free_area[:,0], cccm21_cloud_free_area[:,1], '--b') fig, ax = plt.subplots() ax.plot(lw_frac_so[:,1], lw_frac_so[:, 0], '-k') ax.plot(iw_frac_so[:,1], iw_frac_so[:, 0], '--k') ax.plot(cccm52_cloud_fraction_profile_alt_so[:,1], cccm52_cloud_fraction_profile_alt_so[:,0], '-b') fig, ax = plt.subplots() ax.plot(lw_frac_temp[:,0], lw_frac_temp[:, 1], '-k') ax.plot(iw_frac_temp[:,0], iw_frac_temp[:, 1], '--k') ax.plot(cf_frac_temp[:,0], cf_frac_temp[:,1], '-b') fig, ax = plt.subplots() ax.plot(lw_frac_temp[:,0], lw_frac_temp[:, 1], '-k') ax.plot(lw_frac_temp_so[:,0], lw_frac_temp_so[:, 1], '--k') """

[ "tristan.ohanlon@gmail.com" ]

tristan.ohanlon@gmail.com

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/Exec/SCIENCE/code_comp/analysis/plot_generator.py

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2019-11-01T21:31:27

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#!/usr/bin/env python3 import yt from yt.units import amu, cm import os import sys import glob import argparse import numpy as np import string from collections import namedtuple from functools import reduce def parse_args(): # Argument information description = """Generates plots of datasets using a specified yt plot function. Works with any slice or projection plot, as well as ParticlePlot.""" datasets_help = "A list of datasets to be loaded by yt. Will be sorted by plot number by default." func_help = "The plotting function to use. SlicePlot by default." out_help = "The desired output directory for the image files." var_help = "The variable to plot. Set to 'Temp' by default." bounds_help = "The bounds for the colorbar." cmap_help = "The colormap for the variable to plot." log_help = "If provided, sets the plot to a logarithmic scale." linthresh_help = "If provided, sets the linear threshold for a symlog plot" time_help = "If provided, adds a timestamp to each plot with the given precision." ext_help = "The extension of the file format to save to. PNG by default." sort_help = """A floating point number specifying the digits to sort file names by. Digits preceding the decimal point give the starting index, digits following the decimal point give the number of characters. Make negative for descending order.""" xlim_help = "The x-axis limits." ylim_help = "The y-axis limits." zlim_help = "The z-axis limits." normal_help = "The normal direction" # Construct parser and parse parser = argparse.ArgumentParser(description=description) parser.add_argument('datasets', nargs='*', help=datasets_help) parser.add_argument('-f', '--func', default='SlicePlot', help=func_help) parser.add_argument('-o', '--out', default='', help=out_help) parser.add_argument('-v', '--var', default='Temp', help=var_help) parser.add_argument('-b', '--bounds', nargs=2, type=float, metavar=('LOWER', 'UPPER'), help=bounds_help) parser.add_argument('-c', '--cmap', metavar=('NAME',), help=cmap_help) parser.add_argument('--log', action='store_true', help=log_help) parser.add_argument('--linthresh', type=float, help=linthresh_help) parser.add_argument('-t', '--time', type=int, metavar=('PRECISION',), help=time_help) parser.add_argument('-e', '--ext', type=lambda s: s.lower(), default='png', help=ext_help) parser.add_argument('-s', '--sort', type=float, default=0.0, help=sort_help) parser.add_argument('-x', '--xlim', nargs=2, type=float, metavar=('UPPER', 'LOWER'), help=xlim_help) parser.add_argument('-y', '--ylim', nargs=2, type=float, metavar=('UPPER', 'LOWER'), help=ylim_help) parser.add_argument('-z', '--zlim', nargs=2, type=float, metavar=('UPPER', 'LOWER'), help=zlim_help) parser.add_argument('-n', '--normal', default='z', help=normal_help) return parser.parse_args(sys.argv[1:]) def plot_generator(args): coloropts = ['field_color', 'cmap', 'display_threshold', 'cbar'] ColorOpt = namedtuple('ColorOpt', field_names=coloropts) optdict = dict(field_color=None, display_threshold=None, cmap=None, cbar=False) color_opt = ColorOpt(**optdict) # Make output directory if not args.out: args.out = os.getcwd() if not os.path.exists(args.out): os.makedirs(args.out) # Grab files from working directory if none were specified ts = args.datasets if not ts: ts = glob.glob('plt*') # Exit if nothing could be loaded if len(ts) < 1: sys.exit("No files were available to be loaded.") # Sort and load files desc = args.sort < 0 start = abs(int(args.sort)) nchars = int(str(args.sort).split('.')[1]) if nchars == 0: key = lambda fname: fname[start:] else: key = lambda fname: fname[start:start + nchars] ts.sort(key=key, reverse=desc) tf = lambda file: yt.load(file.rstrip('/')) ts = list(map(tf, ts)) print("Successfully loaded the following files: {}\n".format(ts)) # Generate plots func = getattr(yt, args.func) field = args.var def get_width(ds, xlim=None, ylim=None, zlim=None): """ Get the width of the plot. """ if xlim is None: xlim = ds.domain_left_edge[0], ds.domain_right_edge[0] else: xlim = xlim[0] * cm, xlim[1] * cm if ylim is None: ylim = ds.domain_left_edge[1], ds.domain_right_edge[1] else: ylim = ylim[0] * cm, ylim[1] * cm xwidth = (xlim[1] - xlim[0]).in_cgs() ywidth = (ylim[1] - ylim[0]).in_cgs() if ds.domain_dimensions[2] == 1: zwidth = 0.0 else: if zlim is None: zlim = ds.domain_left_edge[2], ds.domain_right_edge[2] else: zlim = zlim[0] * cm, zlim[1] * cm zwidth = (zlim[1] - zlim[0]).in_cgs() return xwidth, ywidth, zwidth def get_center(ds, xlim=None, ylim=None, zlim=None): """ Get the coordinates of the center of the plot. """ if xlim is None: xlim = ds.domain_left_edge[0], ds.domain_right_edge[0] else: xlim = xlim[0] * cm, xlim[1] * cm if ylim is None: ylim = ds.domain_left_edge[1], ds.domain_right_edge[1] else: ylim = ylim[0] * cm, ylim[1] * cm xctr = 0.5 * (xlim[0] + xlim[1]) yctr = 0.5 * (ylim[0] + ylim[1]) if ds.domain_dimensions[2] == 1: zctr = 0.0 else: if zlim is None: zlim = ds.domain_left_edge[2], ds.domain_right_edge[2] else: zlim = zlim[0] * cm, zlim[1] * cm zctr = 0.5 * (zlim[0] + zlim[1]) return xctr, yctr, zctr print("Generating...") # Loop and generate for ds in ts: settings = {} settings['center'] = get_center(ds, args.xlim, args.ylim) settings['width'] = get_width(ds, args.xlim, args.ylim) settings['normal'] = args.normal plot = func(ds, fields=field, **settings) if args.cmap: plot.set_cmap(field=field, cmap=args.cmap) if args.linthresh: plot.set_log(field, args.log, linthresh=args.linthresh) else: plot.set_log(field, args.log) # print(args.bounds) # sys.exit() if args.bounds is not None: plot.set_zlim(field, *args.bounds) if args.time: time_format = f't = {{time:.{args.time}f}}{{units}}' plot.annotate_timestamp(corner='upper_left', time_format=time_format, time_unit='s', draw_inset_box=True, inset_box_args={'alpha': 0.0}) suffix = args.func.replace('Plot', '').lower() plot.save(os.path.join(args.out, f'{ds}_{field.translate(str.maketrans("","", string.punctuation))}_{suffix}.{args.ext}')) print() print("Task completed.") if __name__ == "__main__": args = parse_args() plot_generator(args)

[ "aliceharpole@gmail.com" ]

aliceharpole@gmail.com

89af1b2212557b7e7be169b88220d18081782bf6

2e8e55636b879af65b7ba9753a8c11c8e30c0ebe

/src/controllers/__init__.py

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[]

no_license

ScottBurleighHeads/Portfolio

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refs/heads/main

2023-05-14T16:14:06.528988

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from controllers.index import index from controllers.aboutMe import aboutMe from controllers.contact import contact from controllers.project import project registerable_controllers = [ index, aboutMe, contact, project ]

[ "scottmcarpentry@gmail.com" ]

scottmcarpentry@gmail.com

cd443962bf7cf38b44666bf351ee733655fb1f43

539c7a9147965b248625b5bb3507737f141accf2

/20_Funcs_Files.py

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[]

no_license

rtvalluri/LearnPythonTHW

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9104b53a25421a18ddd33b8d2a47ff3b13c3eea3

refs/heads/master

2021-01-10T18:34:12.352028

2015-03-02T06:55:11

21,772,104

0

null

UTF-8

Python

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556

py

from sys import argv scriptname,filename = argv def print_all(f): print f.read() def rewind(f): f.seek(0) #to the starting of file #f.seek(2) to the end of file #f.seek(1) from the current position exactly #f.seek(-3,2) to the 3rd byte from the end def print_line(f): print f.readline() current_file = open(filename) print "\nPrinting entire File" print_all(current_file) print "\nRewinding" rewind(current_file) print "\nPrinting line by line\n" print_line(current_file) print_line(current_file) print_line(current_file) current_file.close()

[ "valluriraviteja@gmail.com" ]

valluriraviteja@gmail.com