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int64 18.2k
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175
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
b3bf37fd0d5fc6221eac493c999c50c201736d44
|
14dd622ef84b3f48c2d66d8ab873084634cfb6d4
|
/PythonLearning/Learning Matplotlib/TestMatplotlib2.py
|
34726ae3448e8c7248ac9e600a5fa700cc2f8a8e
|
[] |
no_license
|
ByronGe/Python-base-Learning
|
648cbbf1c7a8431dece3638dfb4de754623bc84e
|
7ade3250c4abc4b5e47e39080bf1ad8d53b04d78
|
refs/heads/master
| 2020-04-15T20:18:24.134950 | 2019-01-10T04:00:51 | 2019-01-10T04:00:51 | 164,987,206 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 578 |
py
|
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(-3,3,50)
y1 = 2*x+1
y2 = x**2
plt.plot(x,y1)
plt.plot(x,y2,color='red',linewidth=3,linestyle='--')
x_tick = np.linspace(-2,2,6)
plt.xlabel('i am xlabel')
plt.xticks(x_tick)
plt.yticks([0,2,4,7,8],[r'$terrible$','bad','normal','good','really good'])
ax = plt.gca()
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
ax.spines['bottom'].set_position(('data',0))
ax.spines['left'].set_position(('data',0))
plt.show()
|
[
"[email protected]"
] | |
7fefa54d9f554c5233b70cab9dea453e0c81a844
|
23d2b9692d2723ec0f1f63497f36f5b9b59b0346
|
/2_Python_Data_Structures/Week_6/example_11.py
|
a43d19f289ff4d48bc9beb7bf41ccc2a1e07c3a2
|
[] |
no_license
|
lincolnjohnny/py4e
|
1ee331d9da7dab599dee1208de978a6c266a44af
|
a18deb696cde6d55f9266f389c0ccfd6eea2c540
|
refs/heads/master
| 2022-12-26T04:23:45.651991 | 2020-10-12T15:45:53 | 2020-10-12T15:45:53 | 300,396,585 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 372 |
py
|
# The top 10 most common words in a file
fhand = open('romeo.txt')
counts = dict()
for line in fhand :
words = line.split()
for word in words :
counts[word] = counts.get(word,0) + 1
lst = list()
for key,value in counts.items() :
tmp = (value,key)
lst.append(tmp)
lst = sorted(lst, reverse=True)
for value,key in lst[:10] :
print(key, value)
|
[
"[email protected]"
] | |
3ac86f71fb5162f00646cca9ac8f61ca39c0d2c3
|
e543f59a476a8c17ca660e727b5170dadaed81f3
|
/CrossValidation.py
|
a9721254488c6598096c14f88666d26c06139430
|
[] |
no_license
|
AbhinavJhanwar/Data-Processing-Techniques
|
f49768b4f2d77ee0870ab6d2bd63f3e3a9b5557b
|
77a7f27489caa4166e836a6e4247065aee5398d1
|
refs/heads/master
| 2021-06-13T21:05:57.210284 | 2021-03-19T09:09:21 | 2021-03-19T09:09:21 | 168,096,699 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 2,862 |
py
|
'''
Created on Aug 3, 2017
@author: abhinav.jhanwar
'''
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LinearRegression
from sklearn import metrics
from sklearn.model_selection import KFold, cross_val_score, StratifiedKFold
import csv
#CLASSIFICATION MODELS
url = "iris_data.csv"
with open(url) as csvFile:
reader = csv.reader(csvFile)
names = next(reader)
data = pd.read_csv(url)
feature_cols = names[0:-1] #names will be replaced by features directly taken from user selection
X = data[feature_cols]
y = data[names[-1]] #names replaced by target taken from user selection
#kf = KFold(n_splits=5, shuffle=False)
# print the contents of each training and testing set
# ^ - forces the field to be centered within the available space
# .format() - formats the string similar to %s or %n
# enumerate(sequence, start=0) - returns an enumerate object
#print('{:^61} {}'.format('Training set observations', 'Testing set observations'))
#for train_index, test_index in kf.split(range(1,26)):
# print('{} {!s:^25}'.format(train_index, test_index))
# 10-fold cross-validation with K=5 for KNN (the n_neighbors parameter)
# k = 5 for KNeighborsClassifier
k_scores = []
max_score=0
for k in range(1,31):
knn = KNeighborsClassifier(n_neighbors=k)
# Use cross_val_score function
# We are passing the entirety of X and y, not X_train or y_train, it takes care of splitting the data
# cv=10 for 10 folds
# scoring='accuracy' for evaluation metric - although they are many
# use StratifiedKFold for imbalanced target class
kfold = StratifiedKFold(n_splits=10, random_state=7)
scores = cross_val_score(knn, X, y, scoring='accuracy', cv=kfold).mean()
#print(scores)
k_scores.append(scores)
# use average accuracy as an estimate of out-of-sample accuracy
# numpy array has a method mean()
if(k_scores[k-1]>=max_score):
#print(k_scores[k-1])
max_score = k_scores[k-1]
#print(k)
#SIMILARLY GO FOR OTHER MODELS AND SELECT MODELS GIVING BEST SCORE
#REGRESSION MODELS
url = "Advertising.csv"
with open(url) as csvFile:
reader = csv.reader(csvFile)
names = next(reader)
data = pd.read_csv(url)
feature_cols = names[0:-1] #names will be replaced by features directly taken from user selection
X = data[feature_cols]
y = data[names[-1]] #names replaced by target taken from user selection
model = LinearRegression()
# store scores in scores object
# we can't use accuracy as our evaluation metric since that's only relevant for classification problems
# RMSE is not directly available so we will use MSE
rmse_scores = np.sqrt(-cross_val_score(model, X, y, scoring='neg_mean_squared_error', cv=10)).mean()
print(rmse_scores)
|
[
"[email protected]"
] | |
5f68224654afb98c99125e28a341ed8dd9de664a
|
316c473d020f514ae81b7485b10f6556cf914fc0
|
/scrapycrawlspidertest/scrapycrawlspidertest/spiders/universal.py
|
38f44ee1edb89a378c243113f5a699a7ccc43884
|
[
"Apache-2.0"
] |
permissive
|
silianpan/seal-spider-demo
|
ca96b12d4b6fff8fe57f8e7822b7c0eb616fc7f3
|
7bdb77465a10a146c4cea8ad5d9ac589c16edd53
|
refs/heads/master
| 2023-06-20T03:47:04.572721 | 2023-05-24T06:27:13 | 2023-05-24T06:27:13 | 189,963,452 | 1 | 1 |
Apache-2.0
| 2022-12-08T03:24:54 | 2019-06-03T08:15:56 |
Python
|
UTF-8
|
Python
| false | false | 1,855 |
py
|
# -*- coding: utf-8 -*-
import scrapy
from scrapy.linkextractors import LinkExtractor
from scrapy.spiders import CrawlSpider, Rule
from scrapycrawlspidertest.utils import get_config
from scrapycrawlspidertest.items import *
from scrapycrawlspidertest.wraps import *
from scrapycrawlspidertest import urls
class UniversalSpider(CrawlSpider):
name = 'universal'
def __init__(self, name, *args, **kwargs):
config = get_config(name)
self.config = config
self.rules = eval(config.get('rules'))
start_urls = config.get('start_urls')
if start_urls:
if start_urls.get('type') == 'static':
self.start_urls = start_urls.get('value')
elif start_urls.get('type') == 'dynamic':
self.start_urls = list(eval('urls.' + start_urls.get('method'))(*start_urls.get('args', [])))
self.allowed_domains = config.get('allowed_domains')
super(UniversalSpider, self).__init__(*args, **kwargs)
def parse_item(self, response):
# 获取item配置
item = self.config.get('item')
if item:
data = eval(item.get('class') + '()')
# 动态获取属性配置
for key, value in item.get('attrs').items():
data[key] = response
for process in value:
type = process.get('type', 'chain')
if type == 'chain':
# 动态调用函数和属性
if process.get('method'):
data[key] = getattr(data[key], process.get('method'))(*process.get('args', []))
elif type == 'wrap':
args = [data[key]] + process.get('args', [])
data[key] = eval(process.get('method'))(*args)
yield data
|
[
"[email protected]"
] | |
a3a464a3725d720d8633759081deeaade26c896e
|
2b08c18c5ac84dc170eefb05d69e24800d34983e
|
/venv/Lib/site-packages/django/middleware/common.py
|
56c625910901fce91163a3d3838946257ba5a88b
|
[] |
no_license
|
wottan32/website
|
fda48f2f9c177f2aaf008c7b9bd94fbb06cb1de4
|
db05b866badab8d046ea9eeb8c061d2e66312f98
|
refs/heads/main
| 2023-06-17T00:51:28.821850 | 2021-07-14T17:50:41 | 2021-07-14T17:50:41 | 385,640,116 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 7,372 |
py
|
import re
from urllib.parse import urlparse
from django.conf import settings
from django.core.exceptions import PermissionDenied
from django.core.mail import mail_managers
from django.http import HttpResponsePermanentRedirect
from django.urls import is_valid_path
from django.utils.deprecation import MiddlewareMixin
from django.utils.http import escape_leading_slashes
class CommonMiddleware(MiddlewareMixin):
"""
"Common" middleware for taking care of some basic operations:
- Forbid access to User-Agents in settings.DISALLOWED_USER_AGENTS
- URL rewriting: Based on the APPEND_SLASH and PREPEND_WWW settings,
append missing slashes and/or prepends missing "www."s.
- If APPEND_SLASH is set and the initial URL doesn't end with a
slash, and it is not found in urlpatterns, form a new URL by
appending a slash at the end. If this new URL is found in
urlpatterns, return an HTTP redirect to this new URL; otherwise
process the initial URL as usual.
This behavior can be customized by subclassing CommonMiddleware and
overriding the response_redirect_class attribute.
"""
response_redirect_class = HttpResponsePermanentRedirect
def process_request(self, request):
"""
Check for denied User-Agents and rewrite the URL based on
settings.APPEND_SLASH and settings.PREPEND_WWW
"""
# Check for denied User-Agents
user_agent = request.META.get()
if user_agent is not None:
for user_agent_regex in settings.DISALLOWED_USER_AGENTS:
if user_agent_regex.search(user_agent):
raise PermissionDenied('Forbidden user agent')
# Check for a redirect based on settings.PREPEND_WWW
host = request.get_host()
must_prepend = settings.PREPEND_WWW and host and not host.startswith('www.')
redirect_url = ('%s://www.%s' % (request.scheme, host)) if must_prepend else ''
# Check if a slash should be appended
if self.should_redirect_with_slash(request):
path = self.get_full_path_with_slash(request)
else:
path = request.get_full_path()
# Return a redirect if necessary
if redirect_url or path != request.get_full_path():
redirect_url += path
return self.response_redirect_class(redirect_url)
def should_redirect_with_slash(self, request):
"""
Return True if settings.APPEND_SLASH is True and appending a slash to
the request path turns an invalid path into a valid one.
"""
if settings.APPEND_SLASH and not request.path_info.endswith('/'):
urlconf = getattr(request, 'urlconf', None)
if not is_valid_path(request.path_info, urlconf):
match = is_valid_path('%s/' % request.path_info, urlconf)
if match:
view = match.func
return getattr(view, 'should_append_slash', True)
return False
def get_full_path_with_slash(self, request):
"""
Return the full path of the request with a trailing slash appended.
Raise a RuntimeError if settings.DEBUG is True and request.method is
POST, PUT, or PATCH.
"""
new_path = request.get_full_path(force_append_slash=True)
# Prevent construction of scheme relative urls.
new_path = escape_leading_slashes(new_path)
if settings.DEBUG and request.method in ('POST', 'PUT', 'PATCH'):
raise RuntimeError(
"You called this URL via %(method)s, but the URL doesn't end "
"in a slash and you have APPEND_SLASH set. Django can't "
"redirect to the slash URL while maintaining %(method)s data. "
"Change your form to point to %(url)s (note the trailing "
"slash), or set APPEND_SLASH=False in your Django settings." % {
'method': request.method,
'url': request.get_host() + new_path,
}
)
return new_path
def process_response(self, request, response):
"""
When the status code of the response is 404, it may redirect to a path
with an appended slash if should_redirect_with_slash() returns True.
"""
# If the given URL is "Not Found", then check if we should redirect to
# a path with a slash appended.
if response.status_code == 404 and self.should_redirect_with_slash(request):
return self.response_redirect_class(self.get_full_path_with_slash(request))
# Add the Content-Length header to non-streaming responses if not
# already set.
if not response.streaming and not response.has_header('Content-Length'):
response.headers['Content-Length'] = str(len(response.content))
return response
class BrokenLinkEmailsMiddleware(MiddlewareMixin):
def process_response(self, request, response):
"""Send broken link emails for relevant 404 NOT FOUND responses."""
if response.status_code == 404 and not settings.DEBUG:
domain = request.get_host()
path = request.get_full_path()
referer = request.META.get()
if not self.is_ignorable_request(request, path, domain, referer):
ua = request.META.get()
ip = request.META.get()
mail_managers(
"Broken %slink on %s" % (
('INTERNAL ' if self.is_internal_request(domain, referer) else ''),
domain
),
"Referrer: %s\nRequested URL: %s\nUser agent: %s\n"
"IP address: %s\n" % (referer, path, ua, ip),
fail_silently=True,
)
return response
def is_internal_request(self, domain, referer):
"""
Return True if the referring URL is the same domain as the current
request.
"""
# Different subdomains are treated as different domains.
return bool(re.match("^https?://%s/" % re.escape(domain), referer))
def is_ignorable_request(self, request, uri, domain, referer):
"""
Return True if the given request *shouldn't* notify the site managers
according to project settings or in situations outlined by the inline
comments.
"""
# The referer is empty.
if not referer:
return True
# APPEND_SLASH is enabled and the referer is equal to the current URL
# without a trailing slash indicating an internal redirect.
if settings.APPEND_SLASH and uri.endswith('/') and referer == uri[:-1]:
return True
# A '?' in referer is identified as a search engine source.
if not self.is_internal_request(domain, referer) and '?' in referer:
return True
# The referer is equal to the current URL, ignoring the scheme (assumed
# to be a poorly implemented bot).
parsed_referer = urlparse(referer)
if parsed_referer.netloc in ['', domain] and parsed_referer.path == uri:
return True
return any(pattern.search(uri) for pattern in settings.IGNORABLE_404_URLS)
|
[
"[email protected]"
] | |
61321df20d7cbeb1a2ed9c6275bc03479378b0de
|
65e1fe550446890a874d56aa4c5b9ab516d752aa
|
/mysite/settings.py
|
fcadc07fb5f9c28765c38f3bb835deeafd40dfd5
|
[] |
no_license
|
EijianAsax/my_first_blog
|
d0e093eccd323eb446c354b53b3c2e179a3061db
|
3e48cd9dd010ece3435a09de633df22296b7bfe3
|
refs/heads/master
| 2020-09-16T19:37:32.005580 | 2019-11-29T06:59:17 | 2019-11-29T06:59:17 | 223,870,057 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 3,362 |
py
|
"""
Django settings for mysite project.
Generated by 'django-admin startproject' using Django 2.2.7.
For more information on this file, see
https://docs.djangoproject.com/en/2.2/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/2.2/ref/settings/
"""
import os
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/2.2/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = '_0^n9rmx7(zgq9@4v5cto0evbrxpu$h-u*%@g3(vc95+^7=o(&'
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
ALLOWED_HOSTS = ['127.0.0.1', '.pythonanywhere.com']
# Application definition
INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'blog.apps.BlogConfig',
]
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
ROOT_URLCONF = 'mysite.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'mysite.wsgi.application'
# Database
# https://docs.djangoproject.com/en/2.2/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),
}
}
# Password validation
# https://docs.djangoproject.com/en/2.2/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
},
]
# Login
LOGIN_REDIRECT_URL = '/'
# Internationalization
# https://docs.djangoproject.com/en/2.2/topics/i18n/
LANGUAGE_CODE = 'ja'
TIME_ZONE = 'Asia/Tokyo'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/2.2/howto/static-files/
STATIC_URL = '/static/'
STATIC_ROOT = os.path.join(BASE_DIR, "static")
|
[
"[email protected]"
] | |
1627e2f2e17f6ce0d5c676d0f49c707e37e14b3e
|
9a6fce2ab99b489ec326725080b44d8cb312ca1c
|
/main/views.py
|
aeb5caad58faddb130a531dfddd144c68afe8cbf
|
[] |
no_license
|
jeksifrost/test_simple_website
|
ba6d39e73be91341b347010999a46ccb112ad238
|
b088c60da308529df2b63e69d4158055fb452f88
|
refs/heads/master
| 2023-07-01T07:40:26.395649 | 2021-08-04T13:28:26 | 2021-08-04T13:28:26 | 384,142,806 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 377 |
py
|
from django.shortcuts import render
def index(request):
data = {
'title': 'Main page',
'values': ['Some', 'Hello', '123'],
'obj': {
'car': 'BMW',
'age': 18,
'hobby': 'Football'
}
}
return render(request, 'main/index.html', data)
def about(request):
return render(request, 'main/about.html')
|
[
"[email protected]"
] | |
2d99088ff7bd2fb53f503d19f03ef6d9e83351db
|
d23f2c0e75bd89629be12ff3e2977956f5b1add7
|
/HelloWorldHacktober.py
|
6ee42bfc40ce6e312fa4b1a11decc7eab045ff73
|
[] |
no_license
|
AdaoNeves/Hello-Hacktober
|
22e03739530f244004819b06aac94c10fc1aff80
|
0c541056b54b7e10d8dc5776e7c90f1d9c67773f
|
refs/heads/master
| 2020-08-21T18:23:14.579135 | 2019-10-19T14:21:10 | 2019-10-19T14:21:10 | 216,217,768 | 0 | 0 | null | 2019-10-19T14:19:09 | 2019-10-19T14:19:09 | null |
UTF-8
|
Python
| false | false | 31 |
py
|
print('Hello World Hacktober!')
|
[
"[email protected]"
] | |
730b513d27ee1bb307ed3b428f8a412c5135393b
|
8436711618438240714022e6868fcc3e315c75f0
|
/exam-python/shop_online/serializers.py
|
5e7119c2d34563fd25f7ddf527b9c5cc290a9657
|
[] |
no_license
|
MaximKhrenov/dip
|
5d01c8834cab1143a008f84492cddf4d215a8060
|
317072baceb6fdd17f982cd0cb2591d0d8abf44e
|
refs/heads/master
| 2021-06-16T00:40:10.948096 | 2019-02-28T14:37:39 | 2019-02-28T14:37:39 | 173,128,998 | 0 | 0 | null | 2021-04-20T17:53:20 | 2019-02-28T14:41:31 |
HTML
|
UTF-8
|
Python
| false | false | 2,855 |
py
|
from rest_framework import serializers
from shop_online.models import (Product, Category, User, Cart, Order, )
class ProductSerializers(serializers.ModelSerializer):
class Meta:
model = Product
exclude = ()
class CategorySerializers(serializers.ModelSerializer):
class Meta:
model = Category
exclude = ()
class UserSerializers(serializers.ModelSerializer):
password = serializers.CharField(write_only=True)
class Meta:
model = User
exclude = (
'is_superuser',
'is_staff',
'last_login',
'date_joined',
'is_active',
'groups',
'user_permissions',
)
def create(self, validate_date):
user = User(
email=validate_date['email'],
username=validate_date['username'],
first_name=validate_date['first_name'],
last_name=validate_date['last_name'],
)
user.set_password(validate_date['password'])
user.save()
return user
class CartSerializers(serializers.ModelSerializer):
user = serializers.CharField(read_only=True)
count_products = serializers.IntegerField(write_only=True)
price_product = serializers.IntegerField(read_only=True)
class Meta:
model = Cart
exclude = ()
fields = ('user', 'title', 'count_products', 'price_product',)
def create(self, validated_data):
user = validated_data['user']
count_products = validated_data['count_products']
title = validated_data['title']
price = Product.objects.get(title_product=title)
cart = Cart(
user=user,
title=title,
price_product=price.price_product * count_products,
count_products=count_products,
)
cart.save()
return cart
class OrderSerializers(serializers.ModelSerializer):
user = serializers.CharField(read_only=True)
class Meta:
model = Order
exclude = ()
fields = ('user', 'address', 'title', 'date_time',)
def create(self, validated_data):
user = validated_data['user']
date_time = validated_data['date_time']
address = validated_data['address']
product = validated_data['title']
order = Order(
date_time=date_time,
user=user,
address=address,
title=product
)
order.save()
return order
class HistorySerializers(serializers.ModelSerializer):
class Meta:
model = Order
exclude = ()
class ProfileSerializers(serializers.ModelSerializer):
class Meta:
model = User
|
[
"[email protected]"
] | |
148c8c1bdb66c854ea93aeb9cece58c80697e932
|
c6a8aa51d489358381c9ba51ad162169243de16c
|
/10_1-10_3/StreamNetwork.py
|
d88915ebc6281a336a1e2ee0f66688d2f3d41da7
|
[] |
no_license
|
aantonio99/FCToolbox
|
e6fa78bb2f3a5fb0eb3174e9f80baa832b174bcc
|
4c76e5923dfff1833fd944c8af4cc5ddb3c91f08
|
refs/heads/master
| 2021-07-21T07:04:17.671545 | 2017-10-31T12:38:14 | 2017-10-31T12:38:14 | 108,995,073 | 0 | 0 | null | null | null | null |
WINDOWS-1250
|
Python
| false | false | 8,528 |
py
|
# -*- coding: utf-8 -*-
'''
Created on 21 fev. 2013
Last update on 07 fev. 2014
@author: Clement Roux
@contact: [email protected]
CNRS - UMR5600 Environnement Ville Societe
15 Parvis René Descartes, BP 7000, 69342 Lyon Cedex 07, France
@note: For each use of the FluvialCorridor toolbox leading to a publication, report, presentation or any other
document, please refer the following article :
Roux, C., Alber, A., Bertrand, M., Vaudor, L., Piegay, H., submitted. "FluvialCorridor": A new ArcGIS
package for multiscale riverscape exploration. Geomorphology
@summary: Streamnetwork is an open-source python and arcPy code.
This script works with a DEM or derived raster layers and provides a related hydrographic network
layer. The module can be launched from a raw DEM (a streamburning step is then available thanks to
the ArcHydroTools ESRI package) or from derived rasters such as the flow direction or flow accumulation
rasters.
'''
# Import of required librairies
import arcpy
from arcpy.sa import *
import os
import def__UpToDateShapeLengthField as UPD_SL
# Allow the temporary outputs overwrite
arcpy.env.overwriteOutput = True
# Inputs
Version = arcpy.GetParameterAsText(0)
StepDEM = arcpy.GetParameterAsText(1)
IncludeStreamBurning = arcpy.GetParameterAsText(2)
Dir = arcpy.GetParameterAsText(3)
UserDEM = arcpy.GetParameterAsText(4)
NetworkForNurning = arcpy.GetParameterAsText(5)
NumberOfCellsForStreamBuffer = arcpy.GetParameterAsText(6)
SmoothDropInZUnits = arcpy.GetParameterAsText(7)
SharpDropinZUnits = arcpy.GetParameterAsText(8)
ThresholdDrainage = arcpy.GetParameterAsText(9)
Output = arcpy.GetParameterAsText(10)
KeepRasters = arcpy.GetParameterAsText(11)
DeleteTF = arcpy.GetParameterAsText(12)
# Dervied variable from inputs
ThresholdDrainage = float(ThresholdDrainage.replace(',', '.'))
# Number of steps
if str(StepDEM) == "Original DEM" :
nstep = 6
if str(StepDEM) == "Burned DEM" :
nstep = 6
if str(StepDEM) == "Filled DEM" :
nstep = 5
if str(StepDEM) == "Flow Dir raster" :
nstep = 4
if str(StepDEM) == "Flow Acc raster" :
nstep = 3
if str(DeleteTF) == "true" :
nstep+=1
ncurrentstep=1
#===============================================================================
# CODING
#===============================================================================
#/extraction of the flow accumulation raster ...
if str(StepDEM) == "Original DEM" :
if str(IncludeStreamBurning) == "false" :
nstep+=1
arcpy.AddMessage("Burning the DEM - Step " + str(ncurrentstep) + "/" + str(nstep))
(path, name) = os.path.split(UserDEM)
out = os.path.join(path, "BurnedDEM")
CopyNetworkForBurning = arcpy.CopyFeatures_management(NetworkForNurning, "%ScratchWorkspace%\\CopyNetworkForBurning")
FLOATUserDEM = Float(UserDEM)
FLOATUserDEM.save(str(path) + "\\FLOATUserDEM")
DEM = arcpy.gp.DEMReconditioning_archydro(FLOATUserDEM, CopyNetworkForBurning, NumberOfCellsForStreamBuffer, SmoothDropInZUnits, SharpDropinZUnits, out, "NEGATIVE_NO")
ncurrentstep+=1
elif str(IncludeStreamBurning) == "false" :
DEM = UserDEM
arcpy.AddMessage("Filling the DEM - Step " + str(ncurrentstep) + "/" + str(nstep))
FilledDEM = arcpy.gp.Fill_sa(DEM, "%ScratchWorkspace%\\FilledDEM")
ncurrentstep+=1
arcpy.AddMessage("Generating the Flow Direction raster - Step " + str(ncurrentstep) + "/" + str(nstep))
FlowDir = arcpy.gp.FlowDirection_sa(FilledDEM, "%ScratchWorkspace%\\FlowDirRaster", "NORMAL", "%ScratchWorkspace%\\FlowDirOutput")
arcpy.Delete_management("%ScratchWorkspace%\\FlowDirOutput")
ncurrentstep+=1
arcpy.AddMessage("Generating the Flow Accumulation raster - Step " + str(ncurrentstep) + "/" + str(nstep))
FlowAcc = arcpy.gp.FlowAccumulation_sa(FlowDir, "%ScratchWorkspace%\\FlowAccRaster", "", "FLOAT")
## From the Burned DEM
if str(StepDEM) == "Burned DEM" :
arcpy.AddMessage("Filling the DEM - Step " + str(ncurrentstep) + "/" + str(nstep))
FilledDEM = arcpy.gp.Fill_sa(UserDEM, "%ScratchWorkspace%\\FilledDEM")
ncurrentstep+=1
arcpy.AddMessage("Generating the Flow Direction raster - Step " + str(ncurrentstep) + "/" + str(nstep))
FlowDir = arcpy.gp.FlowDirection_sa(FilledDEM, "%ScratchWorkspace%\\FlowDirRaster", "NORMAL", "%ScratchWorkspace%\\FlowDirOutput")
arcpy.Delete_management("%ScratchWorkspace%\\FlowDirOutput")
ncurrentstep+=1
arcpy.AddMessage("Generating the Flow Accumulation raster - Step " + str(ncurrentstep) + "/" + str(nstep))
FlowAcc = arcpy.gp.FlowAccumulation_sa(FlowDir, "%ScratchWorkspace%\\FlowAccRaster", "", "FLOAT")
## From the Filled DEM
if str(StepDEM) == "Filled DEM" :
arcpy.AddMessage("Generating the Flow Direction raster - Step " + str(ncurrentstep) + "/" + str(nstep))
FlowDir = arcpy.gp.FlowDirection_sa(UserDEM, "%ScratchWorkspace%\\FlowDirRaster", "NORMAL", "%ScratchWorkspace%\\FlowDirOutput")
arcpy.Delete_management("%ScratchWorkspace%\\FlowDirOutput")
ncurrentstep+=1
arcpy.AddMessage("Generating the Flow Accumulation raster - Step " + str(ncurrentstep) + "/" + str(nstep))
FlowAcc = arcpy.gp.FlowAccumulation_sa(FlowDir, "%ScratchWorkspace%\\FlowAccRaster", "", "FLOAT")
## From the Flow Direction raster
if str(StepDEM) == "Flow Dir raster" :
arcpy.AddMessage("Generating the Flow Accumulation raster - Step " + str(ncurrentstep) + "/" + str(nstep))
FlowDir = UserDEM
FlowAcc = arcpy.gp.FlowAccumulation_sa(UserDEM, "%ScratchWorkspace%\\FlowAccRaster", "", "FLOAT")
## From the Flow Accumulation raster
if str(StepDEM) == "Flow Acc raster" :
ncurrentstep = 0
FlowDir = Dir
FlowAcc = UserDEM
#/application of a drainage area threshold
CellS = arcpy.GetRasterProperties_management(UserDEM, "CELLSIZEX")
CellA = float(str(CellS))*float(str(CellS))/1000000
nbCell = float(str(ThresholdDrainage))/CellA
Expression = "\"Value\" >= " + str(int(nbCell))
ncurrentstep+=1
arcpy.AddMessage("Thresholding : keep cells with a drainage area >= " + str(ThresholdDrainage) + "km˛ - Step " + str(ncurrentstep) + "/" + str(nstep))
ThresholdStep = arcpy.gp.Con_sa(FlowAcc, FlowAcc, "%ScratchWorkspace%\\ThresholdStep", "", Expression)
#/linking of the streamnetwork
ncurrentstep+=1
arcpy.AddMessage("Linking Stream Network - Step " + str(ncurrentstep) + "/" + str(nstep))
StreamLink = arcpy.gp.StreamLink_sa(ThresholdStep, FlowDir, "%ScratchWorkspace%\\StreamLink")
#/conversion of the raster streamnetwork into a polyline layer
ncurrentstep+=1
arcpy.AddMessage("Converting into a polyline feature - Step " + str(ncurrentstep) + "/" + str(nstep))
Network = arcpy.gp.StreamToFeature_sa(StreamLink, FlowDir, Output, "NO_SIMPLIFY")
#/updating fields
try :
UPD_SL.UpToDateShapeLengthField(Network)
except :
try :
arcpy.AddField_management(Network, "Length", "DOUBLE", "", "", "", "", "NULLABLE", "NON_REQUIRED", "")
arcpy.CalculateField_management(Network, "Length", "!shape.length!", "PYTHON_9.3", "")
except :
pass
try :
arcpy.DeleteField_management(Network, ["GRID_CODE"])
except :
pass
#===============================================================================
# DELETING TEMPORARY FILES
#===============================================================================
if str(KeepRasters) == "false" :
if str(StepDEM) == "Original DEM" :
if str(IncludeStreamBurning) == "true" :
arcpy.Delete_management(DEM)
arcpy.Delete_management(FilledDEM)
arcpy.Delete_management(FlowDir)
arcpy.Delete_management(FlowAcc)
if str(StepDEM) == "Burned DEM" :
arcpy.Delete_management(FilledDEM)
arcpy.Delete_management(FlowDir)
arcpy.Delete_management(FlowAcc)
if str(StepDEM) == "Filled DEM" :
arcpy.Delete_management(FlowDir)
arcpy.Delete_management(FlowAcc)
if str(StepDEM) == "Flow Dir raster" :
arcpy.Delete_management(FlowAcc)
if str(DeleteTF) == "true" :
ncurrentstep+=1
arcpy.AddMessage("Deleting temporary files - Step " + str(ncurrentstep) + "/" + str(nstep))
if str(StepDEM) == "Original DEM" and str(IncludeStreamBurning) == "true" :
arcpy.Delete_management(CopyNetworkForBurning)
arcpy.Delete_management(str(path) + "\\FLOATUserDEM")
arcpy.Delete_management(ThresholdStep)
arcpy.Delete_management(StreamLink)
|
[
"[email protected]"
] | |
c3b0f249410fb61263335e29c50e31b18b701255
|
30ff67c55ca0b87cf250cc9c526dde3b25d3f24e
|
/BMpro/QueAns/migrations/0001_initial.py
|
2b619dd79c39d7036c7017dcff30868996bf5053
|
[] |
no_license
|
nerajshrm/QueAnsDj
|
17ea43dad05e125e0c2df603157cacfadf953e93
|
e64cc56402e66fabffca160af1a6a17f7df7ac6b
|
refs/heads/master
| 2020-03-28T07:33:16.284237 | 2018-09-08T06:10:57 | 2018-09-08T06:10:57 | 147,909,312 | 0 | 0 | null | 2018-09-08T06:49:49 | 2018-09-08T06:49:48 | null |
UTF-8
|
Python
| false | false | 569 |
py
|
# Generated by Django 2.0.5 on 2018-09-07 06:42
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='QuesModel',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('stmt', models.CharField(max_length=50)),
('qtype', models.CharField(max_length=5)),
],
),
]
|
[
"[email protected]"
] | |
1f143695018dec07dee3da5d12049c169c34e0c8
|
65d6a5b8ed19ed65c9e6747fc737844320ad054b
|
/packages/python-ldap/Tests/newapi.py
|
bc7b7a3c3209dbef0a50ee4f7d1e6a9875b2cb76
|
[
"Python-2.0"
] |
permissive
|
mozilla/basket-lib
|
f3242c97508571516db9c86cfa4f54e469406aad
|
3978af377f5c132e30469cfc1af0e78fce356244
|
refs/heads/master
| 2023-09-03T19:49:52.871452 | 2015-12-03T21:51:42 | 2015-12-03T21:51:42 | 2,418,254 | 1 | 1 | null | 2022-01-12T19:47:40 | 2011-09-19T20:52:10 |
Python
|
UTF-8
|
Python
| false | false | 324 |
py
|
import ldap
l=ldap.initialize('ldap://localhost:1390')
print l.search_s('',0)
l.search_s('dc=stroeder,dc=com',1)
try:
l.search_s('ou=not existent,dc=stroeder,dc=com',1)
except ldap.NO_SUCH_OBJECT,e:
print str(e)
try:
l.search_s('dc=stroeder,dc=com',2,'(objectclass')
except ldap.FILTER_ERROR,e:
print str(e)
|
[
"[email protected]"
] | |
57fd2a9e1342209b3a30476890b6f91eb8a2545f
|
87094a2ba48e9d8390c7b661065889cdd0008aa3
|
/web-scraping/opt-categories.py
|
07e44b706a29072fd4fa9899390f47e815a8ec1a
|
[
"MIT"
] |
permissive
|
mponweiser/thesis-LDA-in-R
|
e25eb6263f52482aafd67ac9b26ed5e40d2ce217
|
e3be172f97e96c53c3941801e0b913d68621a1a0
|
refs/heads/master
| 2021-01-19T03:55:05.278670 | 2016-08-06T10:07:20 | 2016-08-06T10:07:20 | 46,223,108 | 4 | 3 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,285 |
py
|
#! /usr/bin/python
from csv_unicode import *
"""
Create a separate categories.csv, for deciding which categories to merge.
Not needed for a regular run of the scraper.
"""
def main():
categories_major = [u"Biological Sciences", u"Physical Sciences", u"Social Sciences"]
filename = "meta.csv"
reader = UnicodeReader(open(filename, "rb"))
firstline = next(reader)
field = dict(zip(firstline, range(len(firstline))))
firstline = []
for value in range(len(field)):
for key in field:
if field[key] == value:
firstline.append(key)
categories={}
for year in range(1991,2002):
categories[str(year)]=[]
for row in reader:
if row[field["category_fullpdf"]]!=u"":
categories[row[field['year']]].append(row[field["category_fullpdf"]])
if row[field["category_minor"]]!=u"":
categories[row[field['year']]].append(row[field["category_minor"]]+" ("+row[field["category"]]+")")
writer = UnicodeWriter(open("categories.csv", "wb")) #, delimiter="\t")
writer.writerow(["year", "category"])
for year in categories:
for cat in set(categories[year]):
print year,cat
writer.writerow([year, cat])
if __name__== "__main__":
main()
|
[
"[email protected]"
] | |
9a79f974209db57f80154e7407a52ed74cadadec
|
2a953e337f4321f2fcb396d8e2f93a5bccb91186
|
/SICK_rl_pub/src/nn_utils/integration_func.py
|
7959242c5ec0821f3fe8bb38eff0f864c241712f
|
[
"Apache-2.0"
] |
permissive
|
taoshen58/ReSAN
|
d9486e8189aca0457f62d073d53ce25bf11bf4da
|
f65f3fe656907be0ec14ddf18cd7d2608e7ef905
|
refs/heads/master
| 2020-03-07T14:11:35.128966 | 2018-05-02T22:06:58 | 2018-05-02T22:06:58 | 127,520,810 | 31 | 8 | null | null | null | null |
UTF-8
|
Python
| false | false | 10,077 |
py
|
from src.nn_utils.general import get_last_state, exp_mask_for_high_rank, mask_for_high_rank
from src.nn_utils.nn import linear, get_logits, pooling_with_mask, softsel, feature_combination, dropout,\
bn_dense_layer
from src.nn_utils.rnn_cell import SwitchableDropoutWrapper
from src.nn_utils.rnn import dynamic_rnn, bidirectional_dynamic_rnn
import tensorflow as tf
from src.nn_utils.general import get_last_state, add_reg_without_bias
def traditional_attention(rep_tensor, rep_mask, scope=None,
keep_prob=1., is_train=None, wd=0., activation='elu',
tensor_dict=None, name=None):
bs, sl, vec = tf.shape(rep_tensor)[0], tf.shape(rep_tensor)[1], tf.shape(rep_tensor)[2]
ivec = rep_tensor.get_shape()[2]
with tf.variable_scope(scope or 'traditional_attention'):
rep_tensor_map = bn_dense_layer(rep_tensor, ivec, True, 0., 'bn_dense_map', activation,
False, wd, keep_prob, is_train)
rep_tensor_logits = get_logits([rep_tensor_map], None, False, scope='self_attn_logits',
mask=rep_mask, input_keep_prob=keep_prob, is_train=is_train) # bs,sl
attn_res = softsel(rep_tensor, rep_tensor_logits, rep_mask) # bs,vec
# save attn
if tensor_dict is not None and name is not None:
tensor_dict[name] = tf.nn.softmax(rep_tensor_logits)
return attn_res
def multi_dimensional_attention(rep_tensor, rep_mask, scope=None,
keep_prob=1., is_train=None, wd=0., activation='elu',
tensor_dict=None, name=None):
bs, sl, vec = tf.shape(rep_tensor)[0], tf.shape(rep_tensor)[1], tf.shape(rep_tensor)[2]
ivec = rep_tensor.get_shape()[2]
with tf.variable_scope(scope or 'multi_dimensional_attention'):
map1 = bn_dense_layer(rep_tensor, ivec, True, 0., 'bn_dense_map1', activation,
False, wd, keep_prob, is_train)
map2 = bn_dense_layer(map1, ivec, True, 0., 'bn_dense_map2', 'linear',
False, wd, keep_prob, is_train)
map2_masked = exp_mask_for_high_rank(map2, rep_mask)
soft = tf.nn.softmax(map2_masked, 1) # bs,sl,vec
attn_output = tf.reduce_sum(soft * rep_tensor, 1) # bs, vec
# save attn
if tensor_dict is not None and name is not None:
tensor_dict[name] = soft
return attn_output
def directional_attention_with_dense(rep_tensor, rep_mask, direction=None, scope=None,
keep_prob=1., is_train=None, wd=0., activation='elu',
extra_mask=None,
tensor_dict=None, name=None):
def scaled_tanh(x, scale=5.):
return scale * tf.nn.tanh(1./scale * x)
bs, sl, vec = tf.shape(rep_tensor)[0], tf.shape(rep_tensor)[1], tf.shape(rep_tensor)[2]
ivec = rep_tensor.get_shape()[2]
with tf.variable_scope(scope or 'directional_attention_%s' % direction or 'diag'):
# mask generation
sl_indices = tf.range(sl, dtype=tf.int32)
sl_col, sl_row = tf.meshgrid(sl_indices, sl_indices)
if direction is None:
direct_mask = tf.cast(tf.diag(- tf.ones([sl], tf.int32)) + 1, tf.bool)
else:
if direction == 'forward':
direct_mask = tf.greater(sl_row, sl_col)
else:
direct_mask = tf.greater(sl_col, sl_row)
direct_mask_tile = tf.tile(tf.expand_dims(direct_mask, 0), [bs, 1, 1]) # bs,sl,sl
rep_mask_tile = tf.tile(tf.expand_dims(rep_mask, 1), [1, sl, 1]) # bs,sl,sl
attn_mask = tf.logical_and(direct_mask_tile, rep_mask_tile) # bs,sl,sl
if extra_mask is not None:
attn_mask = tf.logical_and(attn_mask, extra_mask)
# non-linear
rep_map = bn_dense_layer(rep_tensor, ivec, True, 0., 'bn_dense_map', activation,
False, wd, keep_prob, is_train)
rep_map_tile = tf.tile(tf.expand_dims(rep_map, 1), [1, sl, 1, 1]) # bs,sl,sl,vec
rep_map_dp = dropout(rep_map, keep_prob, is_train)
# attention
with tf.variable_scope('attention'): # bs,sl,sl,vec
f_bias = tf.get_variable('f_bias',[ivec], tf.float32, tf.constant_initializer(0.))
dependent = linear(rep_map_dp, ivec, False, scope='linear_dependent') # bs,sl,vec
dependent_etd = tf.expand_dims(dependent, 1) # bs,1,sl,vec
head = linear(rep_map_dp, ivec, False, scope='linear_head') # bs,sl,vec
head_etd = tf.expand_dims(head, 2) # bs,sl,1,vec
logits = scaled_tanh(dependent_etd + head_etd + f_bias, 5.0) # bs,sl,sl,vec
logits_masked = exp_mask_for_high_rank(logits, attn_mask)
attn_score = tf.nn.softmax(logits_masked, 2) # bs,sl,sl,vec
attn_score = mask_for_high_rank(attn_score, attn_mask)
attn_result = tf.reduce_sum(attn_score * rep_map_tile, 2) # bs,sl,vec
with tf.variable_scope('output'):
o_bias = tf.get_variable('o_bias',[ivec], tf.float32, tf.constant_initializer(0.))
# input gate
fusion_gate = tf.nn.sigmoid(
linear(rep_map, ivec, True, 0., 'linear_fusion_i', False, wd, keep_prob, is_train) +
linear(attn_result, ivec, True, 0., 'linear_fusion_a', False, wd, keep_prob, is_train) +
o_bias)
output = fusion_gate * rep_map + (1-fusion_gate) * attn_result
output = mask_for_high_rank(output, rep_mask)
# save attn
if tensor_dict is not None and name is not None:
tensor_dict[name + '_dependent'] = dependent
tensor_dict[name + '_head'] = head
tensor_dict[name] = attn_score
tensor_dict[name + '_gate'] = fusion_gate
return output
# -------------- rnn --------------
def contextual_bi_rnn(tensor_rep, mask_rep, hn, cell_type, only_final=False,
wd=0., keep_prob=1.,is_train=None, scope=None):
"""
fusing contextual information using bi-direction rnn
:param tensor_rep: [..., sl, vec]
:param mask_rep: [..., sl]
:param hn:
:param cell_type: 'gru', 'lstm', basic_lstm' and 'basic_rnn'
:param only_final: True or False
:param wd:
:param keep_prob:
:param is_train:
:param scope:
:return:
"""
with tf.variable_scope(scope or 'contextual_bi_rnn'): # correct
reuse = None if not tf.get_variable_scope().reuse else True
#print(reuse)
if cell_type == 'gru':
cell_fw = tf.contrib.rnn.GRUCell(hn, reuse=reuse)
cell_bw = tf.contrib.rnn.GRUCell(hn, reuse=reuse)
elif cell_type == 'lstm':
cell_fw = tf.contrib.rnn.LSTMCell(hn, reuse=reuse)
cell_bw = tf.contrib.rnn.LSTMCell(hn, reuse=reuse)
elif cell_type == 'basic_lstm':
cell_fw = tf.contrib.rnn.BasicLSTMCell(hn, reuse=reuse)
cell_bw = tf.contrib.rnn.BasicLSTMCell(hn, reuse=reuse)
elif cell_type == 'basic_rnn':
cell_fw = tf.contrib.rnn.BasicRNNCell(hn, reuse=reuse)
cell_bw = tf.contrib.rnn.BasicRNNCell(hn, reuse=reuse)
else:
raise AttributeError('no cell type \'%s\'' % cell_type)
cell_dp_fw = SwitchableDropoutWrapper(cell_fw,is_train,keep_prob)
cell_dp_bw = SwitchableDropoutWrapper(cell_bw,is_train,keep_prob)
tensor_len = tf.reduce_sum(tf.cast(mask_rep, tf.int32), -1) # [bs]
(outputs_fw, output_bw), _ = bidirectional_dynamic_rnn(
cell_dp_fw, cell_dp_bw, tensor_rep, tensor_len,
dtype=tf.float32)
rnn_outputs = tf.concat([outputs_fw,output_bw],-1) # [...,sl,2hn]
if wd > 0:
add_reg_without_bias()
if not only_final:
return rnn_outputs # [....,sl, 2hn]
else:
return get_last_state(rnn_outputs, mask_rep) # [...., 2hn]
# -------------- emb mat--------------
def generate_embedding_mat(dict_size, emb_len, init_mat=None, extra_mat=None,
extra_trainable=False, scope=None):
"""
generate embedding matrix for looking up
:param dict_size: indices 0 and 1 corresponding to empty and unknown token
:param emb_len:
:param init_mat: init mat matching for [dict_size, emb_len]
:param extra_mat: extra tensor [extra_dict_size, emb_len]
:param extra_trainable:
:param scope:
:return: if extra_mat is None, return[dict_size+extra_dict_size,emb_len], else [dict_size,emb_len]
"""
with tf.variable_scope(scope or 'gene_emb_mat'):
emb_mat_ept_and_unk = tf.constant(value=0, dtype=tf.float32, shape=[2, emb_len])
if init_mat is None:
emb_mat_other = tf.get_variable('emb_mat',[dict_size - 2, emb_len], tf.float32)
else:
emb_mat_other = tf.get_variable("emb_mat",[dict_size - 2, emb_len], tf.float32,
initializer=tf.constant_initializer(init_mat[2:], dtype=tf.float32,
verify_shape=True))
emb_mat = tf.concat([emb_mat_ept_and_unk, emb_mat_other], 0)
if extra_mat is not None:
if extra_trainable:
extra_mat_var = tf.get_variable("extra_emb_mat",extra_mat.shape, tf.float32,
initializer=tf.constant_initializer(extra_mat,
dtype=tf.float32,
verify_shape=True))
return tf.concat([emb_mat, extra_mat_var], 0)
else:
#with tf.device('/cpu:0'):
extra_mat_con = tf.constant(extra_mat, dtype=tf.float32)
return tf.concat([emb_mat, extra_mat_con], 0)
else:
return emb_mat
|
[
"[email protected]"
] | |
3179c6041c2a657e53e9e100decdba0a0356ce6e
|
9e774859a540a09b7e3f6b3c051516586fc9f744
|
/DesignPatterns/Observer/Observer.py
|
e00307b92a8415397bd119e6a34dcaa339a4ca12
|
[] |
no_license
|
Cat-Garfield/PythonProject
|
4895cf36eddbd7b24989039c2ccdd7a5a7ffaa48
|
5696d7bd280c095bf5fb562eb4d5b04a225e11c0
|
refs/heads/master
| 2022-08-21T23:04:18.758703 | 2020-05-28T13:19:03 | 2020-05-28T13:19:03 | 266,335,826 | 1 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,671 |
py
|
# -*- coding:utf-8 -*-
import time
class ObserverBase(object):
def __init__(self):
self._observered_list = []
def attach(self, target):
if target not in self._observered_list:
self._observered_list.append(target)
def detach(self, target):
if target in self._observered_list:
self._observered_list.remove(target)
def notify(self, data):
for item in self._observered_list:
item.update(data)
class Observer_1(ObserverBase):
def __init__(self, name):
super(Observer_1, self).__init__()
self.name = name
self._MSG = ''
@property
def msg(self):
return self._MSG
@msg.setter
def msg(self, content):
self._MSG = content
self.notify(content)
class ObserveredBase(object):
def __init__(self):
pass
def update(self, data):
pass
class Observered_1(ObserveredBase):
def __init__(self, name):
super(Observered_1, self).__init__()
self.name = name
def update(self, data):
print('{0}:{1}'.format(self.name, data))
class Observered_2(ObserveredBase):
def __init__(self, name):
super(Observered_2, self).__init__()
self.name = name
def update(self, data):
print('{0}:{1}'.format(self.name, data))
observer = Observer_1('观察者1')
observered_1 = Observered_1('被观察者1')
observered_2 = Observered_2('被观察者2')
observer.attach(observered_1)
observer.attach(observered_2)
for i in range(5):
msg = '消息：{0}'.format(i)
observer.msg = '消息：{0}'.format(i)
# observer.notify(msg)
time.sleep(1)
|
[
"[email protected]"
] | |
0e57cc74f355730d743a3e2caabe5ca9c4e7f1d2
|
66a1dd85f472cf6db1f6a34117fd37971502ff7e
|
/hotelling_server/message_box.py
|
76125cb2cb3df4c53ee4dc30c38d6bb4525ebe79
|
[] |
no_license
|
AurelienNioche/HotellingServer
|
1c6d5bde6806d14f3a44cd8c5f8191af81133b80
|
bd288ca24d4ddf7f1c1a9a149fb2916dd50c3043
|
refs/heads/master
| 2021-07-01T06:58:03.472219 | 2018-02-16T23:10:50 | 2018-02-16T23:10:50 | 96,099,287 | 0 | 2 | null | 2017-11-17T14:04:51 | 2017-07-03T10:26:47 |
Python
|
UTF-8
|
Python
| false | false | 1,755 |
py
|
from PyQt5.QtWidgets import QMessageBox
class MessageBox:
def show_question(self, msg, question="", yes="Yes", no="No", focus="No"):
"""question with customs buttons"""
msg_box = QMessageBox(self)
msg_box.setText(msg)
msg_box.setInformativeText(question)
msg_box.setIcon(QMessageBox.Question)
no_button = msg_box.addButton(no, QMessageBox.ActionRole)
yes_button = msg_box.addButton(yes, QMessageBox.ActionRole)
msg_box.setDefaultButton((yes_button, no_button)[focus == no])
msg_box.exec_()
return msg_box.clickedButton() == yes_button
def show_warning(self, msg):
button_reply = QMessageBox.warning(
self, "Warning", msg,
QMessageBox.Ok,
)
return button_reply == QMessageBox.Yes
def show_critical_and_retry(self, msg):
button_reply = QMessageBox().critical(
self, "Error", msg, # Parent, title, message
QMessageBox.Close | QMessageBox.Retry, # Buttons
QMessageBox.Retry # Default button
)
return button_reply == QMessageBox.Retry
def show_critical_and_ok(self, msg):
button_reply = QMessageBox().critical(
self, "Error", msg, # Parent, title, message
QMessageBox.Close | QMessageBox.Ok, # Buttons
QMessageBox.Ok # Default button
)
return button_reply == QMessageBox.Ok
def show_critical(self, msg):
QMessageBox().critical(
self, "Error", msg, # Parent, title, message
QMessageBox.Close
)
def show_info(self, msg):
QMessageBox().information(
self, "Info", msg,
QMessageBox.Ok
)
|
[
"[email protected]"
] | |
a85d70022988f54dc486ed93a48ae927dda3851e
|
27867726be00243dde6d9883f98553fa6b882af0
|
/src/Python/untyped_functions.py
|
d4b7ab5e90beb2f88c9c97f3c296919850ed058e
|
[] |
no_license
|
JRWu/spring2017_cs842typesystems_final
|
33d13ef5f27647cadc684237712c22cd40769f88
|
40a42e66af512f4799bf8c24229145b47b289f99
|
refs/heads/master
| 2020-06-27T18:06:13.468364 | 2017-07-25T23:45:05 | 2017-07-25T23:45:05 | 97,070,135 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,543 |
py
|
#1) Arithmetic Primitives
def addition(x, y):
n = 0
for i in range (0,10):
n += x + y
return n
def subtraction(x, y):
n = 100
for i in range (0,10):
n = n - (x + y)
return n
def multiply(x, y):
n = 2
for i in range (0,10):
n = n * x * y
return n
def divide (x, y):
n = 1000
for i in range (0,10):
n = n / (x + y)
return n
def iterate(needle, haystack):
for i in range(len(haystack)):
res = 'true' if (needle == haystack[i]) else 'false'
return res
#2) String Operations
def concatenate(x, y):
return x+y
def levenshtein(s1, s2):
if len(s1) < len(s2):
return levenshtein(s2, s1)
# len(s1) >= len(s2)
if len(s2) == 0:
return len(s1)
previous_row = range(len(s2) + 1)
for i, c1 in enumerate(s1):
current_row = [i + 1]
for j, c2 in enumerate(s2):
insertions = previous_row[j + 1] + 1 # j+1 instead of j since previous_row and current_row are one character longer
deletions = current_row[j] + 1 # than s2
substitutions = previous_row[j] + (c1 != c2)
current_row.append(min(insertions, deletions, substitutions))
previous_row = current_row
return previous_row[-1]
#3) I/O Operations
def readFile(filename):
f = open(filename, 'r')
for line in f:
# Do nothing
s = line
f.close
#4) Recursion
def fib(n):
if n == 0: return 0
elif n == 1: return 1
else: return fib(n-1)+fib(n-2)
def gcd(m, n, g):
if (((m % g) == 0) and ((n % g) == 0)): return g
else: return gcd(m, n, g - 1);
def recursiveAdd(x, y):
if (x == 0): return y
else: return recursiveAdd((x-1), (y+1))
|
[
"[email protected]"
] | |
1db9e13e933486896e69dd010f0491a3f17c6bca
|
94da86b9772ce06c9444a56d6ef4c6edb7887ba5
|
/kafka-consumer.py
|
9607545e93bca4cdc1ed33af1c6407ad0e4d583c
|
[] |
no_license
|
ThalesGabriel/projetoPsd
|
495316e6c545027a44000878a378cd71f86821b1
|
10d51770494dce3f11e751028f8875cb9c1ab7dc
|
refs/heads/master
| 2020-08-01T02:56:20.730858 | 2019-09-25T12:26:32 | 2019-09-25T12:26:32 | 210,836,998 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 401 |
py
|
from kafka import KafkaConsumer
from json import loads
consumer = KafkaConsumer(
'meu.topico.legal',
bootstrap_servers=['172.16.205.225:9092'],
auto_offset_reset='earliest',
enable_auto_commit=True,
group_id='my-group',
value_deserializer=lambda v: str(v).encode('utf-8'))
for message in consumer:
message = message.value
print('received: ' + str(message))
|
[
"[email protected]"
] | |
b158a6dd4f53ebb5d27053bc682f95514166b60a
|
468ea25f9d118bf423391f67b363bda09c3d240e
|
/core/admin.py
|
5ffb15849f41a317d17e686ee022fe18443ebb12
|
[] |
no_license
|
eferrares/codeinquero
|
569eb7ac62450935d4ae8658b20187acffbe2c11
|
cb8302e5a8b80e7c2948ff074761b45f1f4d551b
|
refs/heads/master
| 2020-03-15T18:03:22.657945 | 2018-10-26T17:08:12 | 2018-10-26T17:08:20 | 132,275,128 | 1 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 484 |
py
|
from django.contrib import admin
from .models import(
Enterprise,
Moderation,
User,
)
class UserAdmin(admin.ModelAdmin):
list_display = ('__str__', 'email')
list_filter = ('name', 'email')
search_fields = ('name', 'email')
list_per_page = 20
fields = ('name', 'email', 'password', 'is_superuser', 'is_active')
admin.site.register(User, UserAdmin)
admin.site.register(Enterprise, admin.ModelAdmin)
admin.site.register(Moderation, admin.ModelAdmin)
|
[
"[email protected]"
] | |
64a39dd67dddf05fdb4cef56c2b7f43657b17747
|
9196d8e25360e0efeb4a7bd2ec7e0d6546466738
|
/app/config.py
|
7435b88677d8613f4077dd7c357574e10a4591f4
|
[] |
no_license
|
abj/test_api
|
3e5267391ac68381183738492b5352962909386e
|
87a90195f94d5f7ade4f6a8c823de1870d70f8a3
|
refs/heads/master
| 2022-12-05T23:05:12.296666 | 2020-08-26T09:23:12 | 2020-08-26T09:23:12 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 378 |
py
|
import os
basedir = os.path.abspath(os.path.dirname(__file__))
class Config(object):
USERNAME = 'anna'
PASSWORD = 'pass'
SQLALCHEMY_DATABASE_URI = os.environ.get('DATABASE_URL') or \
'sqlite:///' + os.path.join(basedir, 'test.db')
SQLALCHEMY_TRACK_MODIFICATIONS = False
JWT_SECRET_KEY = 'sekret_key'
UPLOAD_DIRECTORY = os.path.join(basedir, '')
|
[
"[email protected]"
] | |
666c1d248b09a8c294ba16b944e4a46a9012d7c7
|
f10a7a5e92f2d657ec7f5be87cd862792cad4772
|
/widget-qt4/testmodule.py
|
fd76dfde7123b0e4afd214fce928eb71dc21fb27
|
[] |
no_license
|
shiroyasha/oldPorjects
|
88af98532bb9232c2a036346a0f2d3d77b7ca2fe
|
b062bcaceb2a1919e23a04877e06037e50f862c0
|
refs/heads/master
| 2021-01-10T21:17:10.678714 | 2012-12-05T13:44:13 | 2012-12-05T13:44:13 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,270 |
py
|
#!/usr/bin/env python
import sys
from PyQt4.QtCore import QSize, Qt
from PyQt4.QtGui import QBrush, QPainter, QWidget
print sys.version
from PyQt4.pyqtconfig import _pkg_config
print _pkg_config
class Widget(QWidget):
def __init__(self, *args):
QWidget.__init__(self, *args)
def __del__(self):
print "__del__", self
def paintEvent(self, event):
painter = QPainter()
painter.begin(self)
painter.fillRect(event.rect(), QBrush(Qt.white))
size = min(self.width(), self.height())
painter.setViewport(self.width()/2 - size/2, self.height()/2 - size/2,
size, size)
painter.setWindow(0, 0, 100, 100)
painter.drawText(10, 10, 80, 80, Qt.AlignCenter, "Python")
painter.end()
def sizeHint(self):
return QSize(200, 200)
if __name__ == "__main__":
from PyQt4.QtGui import QApplication, QGridLayout
app = QApplication(sys.argv)
parentWidget = QWidget()
layout = QGridLayout(parentWidget)
for i in range(2):
for j in range(3):
widget = Widget(parentWidget)
layout.addWidget(widget, i, j)
parentWidget.show()
sys.exit(app.exec_())
|
[
"[email protected]"
] | |
142d5cfb6d73ff3e397f0370c9bcfd309bf228e1
|
9ca1e1ee000b2aaf6f9f80be7afe73ebf20c5259
|
/modules/neuralnetwork/training.py
|
6e089faea6a3b36648e50a8276a47d08d834b8c4
|
[
"MIT"
] |
permissive
|
kfoerderer/ANN-based-surrogates
|
c8e4ad32a97184f7c27dec3fa00759f5fc73e9b3
|
aef0eca9e969858e47babfc73a15c04262285e6b
|
refs/heads/main
| 2023-08-14T21:41:35.675510 | 2021-09-14T10:17:01 | 2021-09-14T10:17:01 | 363,385,685 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 14,846 |
py
|
import traceback
import sys
import logging
import logging.handlers
import os
import shutil
import queue
import pprint
import numpy as np
import torch
import torch.nn as nn
import torch.optim
import torch.multiprocessing as mp
from ..neuralnetwork.samples import SampleCache
class TrainingCallback:
def __call__(self, epoch, **kwargs):
pass
class EarlyStoppingCallback(TrainingCallback):
"""
Callback for premature training stopping, when the results are bad or do not improve
Arguments
- stopping_scores: {epoch: max_median_score}
- improvement_window: number of epochs to wait before stopping for failing to improve
"""
def __init__(self, stopping_scores: {int: float}, improvement_window: int=100):
super().__init__()
self.stopping_scores = stopping_scores
self.improvement_window = improvement_window
def __str__(self):
return 'EarlyStoppingCallback(stopping_scores={}, improvement_window={})'.format(self.stopping_scores, self.improvement_window)
def __repr__(self):
return self.__str__()
def __call__(self, epoch, evaluation_loss_statistics, **kwargs):
max_score = self.stopping_scores.get(epoch, float('inf'))
if np.average(evaluation_loss_statistics[epoch]) > max_score:
# result is too bad
return True
best_score_epoch = np.argmin(np.average(evaluation_loss_statistics[:epoch+1], axis=1))
if best_score_epoch + self.improvement_window < epoch:
# it has been too long since the last improvement
return True
return False
class ANNTrainer(mp.Process):
"""
Process for training ANNs
Input Queue: {identifier: str, data: {}, ann: nn.Module, output_dir}
Output Queue: {identifier: str, score: float, NaN: boolean, early_stop: boolean}
"""
def __init__(self,
training_cache: SampleCache,
training_cache_cuda: torch.Tensor,
evaluation_cache: SampleCache,
evaluation_cache_cuda: torch.Tensor,
evaluation_batch_count: int,
input_queue: mp.Queue,
output_queue: mp.Queue,
log_queue=None,
print_cache_renewals=False):
super().__init__()
# assign the shared cuda Tensors (and load the torch kernels into VRAM)
if training_cache_cuda is not None:
training_cache.write_to_cuda(training_cache_cuda)
if evaluation_cache_cuda is not None:
evaluation_cache.write_to_cuda(evaluation_cache_cuda)
self.training_cache = training_cache
self.evaluation_cache = evaluation_cache
self.evaluation_batch_count = int(evaluation_batch_count)
self.input_queue = input_queue
self.output_queue = output_queue
self.log_queue = log_queue
self.print_cache_renewals = print_cache_renewals
def run(self):
#cache_data = self.training_cache.get_data() # debug
try:
debug_switch = True
if self.log_queue is not None:
logger = logging.getLogger('')
logger.addHandler(logging.handlers.QueueHandler(self.log_queue))
logger.setLevel(logging.INFO)
logger = logging.getLogger('')
logger.info('Process {} starts (PID={})'.format(self.name, os.getpid()))
# frequently needed variables/objects
training_cache = self.training_cache
evaluation_cache = self.evaluation_cache
evaluation_batch_count = self.evaluation_batch_count
print_cache_renewals = self.print_cache_renewals
training_logger = logging.getLogger('{}'.format(self.name))
file_handler = None
pp = pprint.PrettyPrinter(indent=4)
#torch.autograd.set_detect_anomaly(True)
while self.input_queue is not None:
result = None
try:
# remove old handler if existing
if file_handler is not None:
training_logger.removeHandler(file_handler)
# get next task
task = self.input_queue.get(True)
task_id = task['identifier']
task_data = task['data']
task_ann = task['ann']
task_output_dir = task['output_dir']
result = {
'identifier': task_id,
'score': float('inf'),
'NaN': False,
'early_stop': False
}
best_result_epoch = -1
# logging (if results are stored)
if task_output_dir is not None:
file_handler = logging.FileHandler(task_output_dir + '/' + '_log.txt')
file_handler.setFormatter(logger.handlers[0].formatter)
training_logger.addHandler(file_handler)
# persist data in torch format (it may contain torch Modules)
meta_data = training_cache.get_meta_data()
torch.save(meta_data, task_output_dir + '/_meta.pt')
torch.save(task_data, task_output_dir + '/_parameters.pt')
# write human readable data
with open(task_output_dir + '/_summary.txt', 'x') as file:
file.write('meta data:\n{}\n---\n'.format(pp.pformat(meta_data)))
file.write('parameters:\n{}\n---\n'.format(pp.pformat(task_data)))
file.write('neural network:\n{}\n---\n'.format(str(task_ann)))
param_count = sum(p.numel() for p in task_ann.parameters() if p.requires_grad)
file.write('parameter count:\n{}\n---\n'.format(param_count))
# learning parameters
loss_function = task_data['loss']
batch_size = task_data['batch_size']
reg_loss_function = task_data['regularization']
learning_rate = task_data['learning_rate']
epoch_count = int(task_data['epoch_count'])
batch_count = int(task_data['batch_count'])
max_grad_norm = task_data['max_grad_norm']
lr_scheduler = task_data.get('lr_scheduler', (None, None))
early_stopping_callback = task_data.get('early_stopping_callback', None)
epoch_print_digits = int(np.log(epoch_count)/np.log(10))
training_logger.info('Starting job {}'.format(task_id))
# create solver
solver = torch.optim.Adam(task_ann.parameters(), lr=learning_rate)
# create lr scheduler
if lr_scheduler[0] is None:
lr_scheduler = None
else:
# create an object of class [0] with keyword arguments [1]
lr_scheduler = lr_scheduler[0](solver, **lr_scheduler[1])
# data structures for collecting statistics
training_loss_statistics = np.zeros((epoch_count,5))
evaluation_loss_statistics = np.zeros((epoch_count,5))
for epoch in range(epoch_count):
# training
if training_cache.has_cuda_tensor():
training_cache.write_to_cuda()
task_ann.train()
batch_sequence = None
losses = torch.zeros((batch_count,3), device=next(task_ann.parameters()).device)
for batch in range(batch_count):
# get batch
batch_in, batch_target, batch_sequence = training_cache.get_batch(batch_size, batch_sequence)
# compute loss
batch_out = task_ann(batch_in)
loss = loss_function(batch_out, batch_target)
if reg_loss_function is not None:
reg_loss = reg_loss_function(task_ann)
loss += reg_loss
losses[batch][1] = reg_loss
if torch.isnan(loss).any():
logger.info('Model {}, training epoch {:d}/{:d}, stopping due to NaN'.format(task_id, epoch, epoch_count-1))
if (batch_in > 100).any():
logger.warning('There are large inputs (max={}) passed to the ANN.'.format(float(torch.max(batch_in))))
result['NaN'] = True
raise ValueError('Got NaN in loss')
# compute gradients
solver.zero_grad()
loss.backward()
# regularization using norm clipping
losses[batch][2] = torch.nn.utils.clip_grad_norm_(task_ann.parameters(), max_grad_norm)
solver.step()
# store losses
losses[batch][0] = loss
training_loss_statistics[epoch] = np.quantile(losses[:,0].cpu().detach().numpy(), [0,0.25,0.5,0.75,1])
training_logger.info('Model {}, training epoch {:d}/{:d}, {}'.format(task_id, epoch, epoch_count-1, str(training_loss_statistics[epoch])))
# write model to disk
if task_output_dir is not None:
# [!] if the filename is changed, it must also be changed further below, where a copy of the best ANN is created
torch.save(task_ann.state_dict(), task_output_dir + '/nn' + '{:0{}d}'.format(epoch, epoch_print_digits) + '.pt')
# evaluation
if evaluation_cache.has_cuda_tensor():
evaluation_cache.write_to_cuda()
task_ann.eval()
batch_sequence = np.arange(evaluation_cache.current_size.value) # fixed sequence for better comparability (as long as batch_count <= cache size)
losses = torch.zeros((evaluation_batch_count), device=next(task_ann.parameters()).device)
with torch.no_grad():
for batch in range(evaluation_batch_count):
# get batch
batch_in, batch_target, batch_sequence = evaluation_cache.get_batch(batch_size, batch_sequence)
# compute loss
batch_out = task_ann(batch_in)
loss = loss_function(batch_out, batch_target)
if debug_switch and loss > 10000:
debug_switch = False
logger.warning(batch_in.cpu())
logger.warning(batch_out.cpu())
logger.warning(batch_target.cpu())
if reg_loss_function is not None:
loss += reg_loss_function(task_ann)
# store losses
losses[batch] = loss
evaluation_loss_statistics[epoch] = np.quantile(losses.cpu().numpy(), [0,0.25,0.5,0.75,1])
score = np.average(evaluation_loss_statistics[epoch]) # average of quantiles [0, 0.25, 0.5, 0.75, 1]
if result['score'] > score:
result['score'] = score
best_result_epoch = epoch
training_logger.info('Model {}, evaluation epoch {:d}/{:d}, {}'.format(task_id, epoch, epoch_count-1, str(evaluation_loss_statistics[epoch])))
# stop early for bad scores
if early_stopping_callback is not None:
if(early_stopping_callback(epoch, evaluation_loss_statistics=evaluation_loss_statistics)):
training_logger.info('Early stop for model {} at epoch {}'.format(task_id, epoch))
result['early_stop'] = True
break
# adapt learning rate
if lr_scheduler is not None:
lr_scheduler.step()
training_logger.info('Learning rate for next epoch is {:E}'.format(lr_scheduler.get_lr()[0]))
# info
if print_cache_renewals:
training_logger.info('Number of total cache renewals is {} and {}'.format(training_cache.get_renewal_count(), evaluation_cache.get_renewal_count()))
# store statistics
if task_output_dir is not None:
np.save(task_output_dir + '/_loss_statistics_training.npy', evaluation_loss_statistics)
np.save(task_output_dir + '/_loss_statistics_evaluation.npy', evaluation_loss_statistics)
# create a copy of the best scored ann
shutil.copyfile(task_output_dir + '/nn' + '{:0{}d}'.format(best_result_epoch, epoch_print_digits) + '.pt',
task_output_dir + '/_nn.pt')
training_logger.info('Finished training of model {} with score {:f} in epoch {}'.format(task_id, result['score'], best_result_epoch))
except queue.Empty:
pass
except KeyboardInterrupt as err:
raise err # escalate error
except ValueError as err:
training_logger.exception(err)
#traceback.print_tb(err.__traceback__)
except Exception as err:
training_logger.exception(err)
#traceback.print_tb(err.__traceback__)
finally:
self.output_queue.put(result)
logger.info('Process {} exits'.format(self.name))
except KeyboardInterrupt:
traceback.print_exc(file=sys.stdout)
logger.info('Received keyboard interrupt. Closing process {}.'.format(self.name))
|
[
"[email protected]"
] | |
8309c5957a8298e9e57c2403dd478a0c8bfc3e13
|
fa7d727b59678d51656b4bb2747b24744e17d771
|
/get_color_replace.py
|
e2d4004abd7ad66956019df0f5210215e67205ee
|
[] |
no_license
|
Du-Sen-Lin/TraditionCV
|
0998cd642284404fc7de2724b56781f26e92ec12
|
3155f9dfe145cb1f882eeb4078454ae72829f761
|
refs/heads/master
| 2023-02-03T02:57:16.369407 | 2020-12-23T13:33:10 | 2020-12-23T13:33:10 | 317,179,036 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,622 |
py
|
import cv2
import os
import numpy as np
from matplotlib import pyplot as plt
import time
__all__ = ['ColorReplace']
input_path = ''
out_path = ''
class ColorReplace():
def __init__(self, input_path=None):
"""
:param input_path:images path
"""
self.path = input_path
def replace(self, img, dst_clr):
"""
replace image's pixel in specific area
:param img: input image
:param dst_clr: pixel to replace
:return: img: images : np.array
"""
for i in range(80, 340): #x1 x2
for j in range(500, 800): #y1 y2
img[j][i] = dst_clr
return img
def replace_fast(self, img, dst_clr):
"""
fast replace image's pixel in specific area
:param img:input image
:param dst_clr:pixel to replace
:return: img: images : np.array
"""
img[535:750, :290, :] = dst_clr #h(y) w(x) c
img[575:705, 900:, :] = dst_clr
return img
def read_write_img(self):
"""
write images after replace ..
:return:
"""
for file in os.listdir(self.path):
filelist = input_path + file
img = cv2.imread(filelist)
dst_img = self.replace_fast(img, (0, 0, 0))
# cv2.imwrite(out_path + file[:-4] + '.jpg', re_img)
plt.subplot(121), plt.imshow(img), plt.title('initial')
plt.subplot(122), plt.imshow(dst_img), plt.title('result')
plt.show()
if __name__ == "__main__":
cr = ColorReplace(input_path=input_path)
cr.read_write_img()
|
[
"[email protected]"
] | |
811b90f182bb790b2abaffda188bf3d3ceb63adb
|
c90de8337f3a0a924ecbd6df625485c616b0d01e
|
/examples.py
|
7fdd9f938879ed4040eb8e00b42a0490b8b8d391
|
[] |
no_license
|
A-M-Simmons/BOM_Weather_Data
|
9dea735d2cf5e3475fb54a55773fdcd4f651e5f5
|
a177c2d470c44e27d4f909c58f7637759cde3168
|
refs/heads/master
| 2022-12-12T22:03:24.086230 | 2019-07-23T05:13:48 | 2019-07-23T05:13:48 | 195,521,747 | 0 | 0 | null | 2022-12-08T01:05:05 | 2019-07-06T09:28:27 |
Python
|
UTF-8
|
Python
| false | false | 1,697 |
py
|
from api import getCurrentSites
from api.database import connectToDatabase
from api.database import connectToDatabaseThreading
from api.database_models import Location
from scraper.data_scraper import getStationList
from scraper.data_scraper import parseStationList
from scraper.data_scraper import insertStation
from scraper.data_scraper import bulkInsertStations
from scraper.data_scraper import updateStationList
from scraper.stationWeatherScraper import bulkUpload
import os
from os.path import join, dirname
from dotenv import load_dotenv
# Load Database variables from enviornment
dotenv_path = join(dirname(__file__), '.env')
load_dotenv(dotenv_path)
server = os.getenv('BOM_RAINFALL_SERVER')
database = os.getenv('BOM_RAINFALL_DATABASE')
username = os.getenv('BOM_RAINFALL_USERNAME')
password = os.getenv('BOM_RAINFALL_PASSWORD')
driver = '{ODBC Driver 17 for SQL Server}'
def getStationList_Example1(nccObsCode):
session = connectToDatabaseThreading(server, database, username, password, driver)
updateStationList(session, "136")
def getRainfallData_Example1():
session = connectToDatabaseThreading(server, database, username, password, driver)
rainFallData = getCurrentSites(session)\
.filter(current=True)\
.filter(Location=Location(-27.4698, 153.0251))\
.getRainfall()
print(rainFallData)
def uploadRainfallData():
session = connectToDatabaseThreading(server, database, username, password, driver)
bulkUpload(session, "136")
if __name__ == "__main__":
#print(os.environ.keys())
#getStationList_Example1("136")
getRainfallData_Example1()
#uploadRainfallData()
|
[
"[email protected]"
] | |
a5d5b5d8d00dd3c8d9faee9c11aeea428df67616
|
fd94ec2d4cfcdb8aa41c2ecf92504a6502987b54
|
/scripts/EmuMarker.py
|
27dad104e3e1fda0c3243804da0f8a2a8f3c2f84
|
[
"LicenseRef-scancode-glut",
"BSD-3-Clause",
"LicenseRef-scancode-public-domain",
"BSD-2-Clause",
"Unlicense",
"MIT",
"SGI-B-2.0"
] |
permissive
|
greggman/regal
|
70bccfd935c42f2a532471f84f164b9992886bce
|
60d5f5f060dcbfa6ff2cdd5bf8823fd5a9cf11db
|
refs/heads/master
| 2020-12-30T19:11:25.692166 | 2012-09-12T14:39:39 | 2012-09-12T14:39:39 | 5,432,185 | 1 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 854 |
py
|
#!/usr/bin/python -B
formulae = {
'Insert' : {
'entries' : [ 'glInsertEventMarkerEXT' ],
'impl' : [ '_context->marker->InsertEventMarker( _context, ${arg0plus} );',
'RegalAssert(_context->info);',
'if (!_context->info->gl_ext_debug_marker) return;' ]
},
'Push' : {
'entries' : [ 'glPushGroupMarkerEXT' ],
'impl' : [ '_context->marker->PushGroupMarker( _context, ${arg0plus} );',
'RegalAssert(_context->info);',
'if (!_context->info->gl_ext_debug_marker) return;' ]
},
'Pop' : {
'entries' : [ 'glPopGroupMarkerEXT' ],
'impl' : [ '_context->marker->PopGroupMarker( _context );',
'RegalAssert(_context->info);',
'if (!_context->info->gl_ext_debug_marker) return;' ]
}
}
|
[
"[email protected]"
] | |
4615ba74a389ed212b9aeec0d5795e0b39da0b9e
|
882984d795866e55a7b863558eddad0e13e37894
|
/stock/mysqltoexcel.py
|
0560cf761cc49212b3d011a2e64cd9e9587aa195
|
[] |
no_license
|
fanghuiuo/pytest
|
cd5ad8bd6d8845c618c653c7d0bd91fb6eaf6a46
|
b1023e20023a006148f668397ed53fdf61f6c1df
|
refs/heads/master
| 2022-01-28T17:43:31.637723 | 2021-06-08T08:53:44 | 2021-06-08T08:53:44 | 213,566,947 | 0 | 0 | null | 2022-01-15T07:06:31 | 2019-10-08T06:40:14 |
Python
|
UTF-8
|
Python
| false | false | 387 |
py
|
import pandas as pd
from sqlalchemy import create_engine
def mysqltoexcel(strsql):
ce = create_engine('mysql+pymysql://root:[email protected]:3306/pytest')
rows = pd.read_sql_query(strsql, ce)
df = pd.DataFrame.from_records(rows)
df.to_excel('d:/movie.xlsx', encoding='utf-8')
if __name__ == '__main__':
strsql = "select * from dbmovie "
mysqltoexcel(strsql)
|
[
"[email protected]"
] | |
c36fb34eb4696a80e01c380bf976584be74eab70
|
0725317eae7710c5991ff0976a6f99032ccaad69
|
/ex_cbv/models.py
|
89150ef7c3399ef880c44e93078196df5132cbe4
|
[] |
no_license
|
rajivshankar/rs-django-training
|
536d40cdd4d30bdc01d4b011e3affbe7b504b153
|
bdf80425e317fcb48c975ac964ac74dd0c0d11d3
|
refs/heads/master
| 2021-01-10T05:39:37.883531 | 2015-11-17T10:06:54 | 2015-11-17T10:06:54 | 43,615,240 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,416 |
py
|
# myproject/ex_cbv/models.py
# -*- coding: UTF-8 -*-
from django.db import models
from django.core.urlresolvers import reverse
class Publisher(models.Model):
name = models.CharField(max_length=30)
address = models.CharField(max_length=50)
city = models.CharField(max_length=60)
state_province = models.CharField(max_length=30)
country = models.CharField(max_length=50)
website = models.URLField()
class Meta:
ordering = ["-name"]
def __unicode__(self): # __unicode__ on Python 2
return self.name
class Author(models.Model):
salutation = models.CharField(max_length=10)
name = models.CharField(max_length=200)
email = models.EmailField()
headshot = models.ImageField(upload_to='author_headshots')
def __unicode__(self): # __unicode__ on Python 2
return self.name
class Book(models.Model):
title = models.CharField(max_length=100)
authors = models.ManyToManyField('Author')
publisher = models.ForeignKey(Publisher)
publication_date = models.DateField()
def __unicode__(self):
return self.title
class Person(models.Model):
name = models.CharField(max_length=200)
def get_absolute_url(self):
return reverse('cbv:person_detail',
kwargs={'pk':self.pk}
)
def __unicode__(self):
return self.name
|
[
"[email protected]"
] | |
d22b71abe4cadc80afcd1046ec644eef0dd4ee2a
|
0e1f2d0e990940c0a61df99a9418cb92bf51d256
|
/Desktop/6.003/c11_code/wav_utils.py
|
40e10dc294b98fa16b46ddd9b8af2f5235aa3d7e
|
[] |
no_license
|
jossotriv/Cool_Stuff
|
e57e81598c99e84f8cd08b54a8f066276e2cac6a
|
13fa757be5618f5d6a0fc35180501afe8eeb5759
|
refs/heads/master
| 2020-03-23T06:53:25.572943 | 2018-07-25T19:41:40 | 2018-07-25T19:41:40 | 141,235,473 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 3,068 |
py
|
import os
import wave
import numpy
import struct
import tempfile
import matplotlib.pyplot as plt
try:
import pyaudio
except:
pyaudio = None
class Wave:
def __init__(self, samples, sample_rate):
self.sample_rate = sample_rate
self.samples = samples
def save(self, filename):
output_file = wave.open(filename, 'w')
output_file.setparams((1, 2, self.sample_rate, 0, 'NONE', 'not compressed'))
out = []
for frame in self.samples:
frame = max(-1, min(1, frame))
frame = int(frame * (2**15 - 1))
out.append(frame)
output_file.writeframes(b''.join(struct.pack('<h', frame)
for frame in out))
output_file.close()
def plot(self, show=True):
"""
Open a matplotlib window showing the contents of the given wav file
"""
xs, ys = zip(*[(float(ix)/self.sample_rate, val)
for ix, val in enumerate(self.samples)])
plt.plot(xs, ys)
if show:
plt.show()
def play(self):
"""
REQUIRES PYAUDIO TO BE INSTALLED
"""
assert pyaudio is not None, ("You need to have pyaudio installed to "
"use the play_wav function")
filename = os.path.join(tempfile.gettempdir(),
'6003_wave_%s.wav' % abs(hash(tuple(self.samples))))
self.save(filename)
f = wave.open(filename, 'r')
try:
p = pyaudio.PyAudio()
stream = p.open(format=p.get_format_from_width(f.getsampwidth()),
channels=f.getnchannels(),
rate=f.getframerate(),
output=True)
data = f.readframes(10240)
while data:
stream.write(data)
data = f.readframes(10240)
stream.stop_stream()
stream.close()
p.terminate()
finally:
f.close()
os.unlink(filename)
@classmethod
def from_file(cls, filename):
"""
Read a wave file. This will always convert to mono.
Returns a tuple with 2 elements:
* a Python list with floats [-1, 1] representing samples
* an integer containing the sample rate
"""
f = wave.open(filename, 'r')
chan, bd, sr, count, _, _ = f.getparams()
assert bd == 2, "bit depth must be 16"
data = []
for i in range(count):
frame = f.readframes(1)
if chan == 2:
l = struct.unpack('<h', frame[:2])[0]
r = struct.unpack('<h', frame[2:])[0]
datum = (l + r) / 2
else:
datum = struct.unpack('<h', frame)[0]
data.append(datum)
if chan == 2:
# if stereo, convert to mono
data = [(data[2*i]+data[2*i+1])/2 for i in range(count//2)]
return cls([i/2**15 for i in data], sr)
|
[
"[email protected]"
] | |
f4acaf7682a9a1e14d09298963943cca14536cb0
|
7b38197bb4772724f5e875f9d3b79d61050a072b
|
/BioSTEAM 1.x.x/biorefineries/cornstover/_plot_spearman.py
|
0c107226c916f5a7750fac6a89132a83827bf351
|
[
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] |
permissive
|
yalinli2/Bioindustrial-Park
|
fac6d58d82af56f5081f529c3ee0c65a70fe7bd3
|
196e2d60ec9bf0466ef804d036c995b89bc72f72
|
refs/heads/master
| 2021-09-24T11:24:26.586458 | 2021-09-09T14:05:33 | 2021-09-09T14:05:33 | 232,337,200 | 2 | 0 |
MIT
| 2021-09-09T14:05:34 | 2020-01-07T14:04:05 |
Jupyter Notebook
|
UTF-8
|
Python
| false | false | 2,749 |
py
|
# -*- coding: utf-8 -*-
"""
Created on Mon Sep 16 00:02:57 2019
@author: yoelr
"""
import pandas as pd
from biosteam import colors
from biosteam.evaluation.evaluation_tools import plot_spearman
# %% Plot Spearman correlations
# Replacement parameter labels
replacement_labels = {
'Stream-Ethanol price': 'Ethanol price',
'TEA operating days': 'Operating days',
'Stream-cornstover price': 'Cornstover price',
'Fermentation-R301 efficiency': 'Fermentation efficiency',
'Stream-cellulase price': 'Cellulase price',
'Stream-cornstover flow rate': 'Cornstover flow rate',
'TEA income tax': 'Income tax',
'Saccharification and co fermentation-R301 saccharification conversion': 'Saccharification conversion',
'Saccharification and co fermentation-R301 ethanol conversion': 'Fermentation ethanol conversion',
'Boiler turbogenerator-BT boiler efficiency': 'Boiler efficiency',
'Boiler turbogenerator boiler base cost': 'Boiler base cost',
'Boiler turbogenerator turbogenerator base cost': 'Turbogenerator base cost',
'Pretreatment reactor system base cost': 'Pretreatment reactor base cost',
'Power utility price': 'Electricity price',
'Cooling tower base cost': 'Cooling tower base cost',
'Waste water system cost waste water system base cost': 'Wastewater treatment base cost',
'Waste water system cost waste water system exponent': 'Wastewater treatment exponent'}
def replace_label_text(label_text):
"""Replace label text for graph."""
name, distribution = label_text.split(' [')
lb, mid, ub = eval('[' + distribution)
if 'efficiency' in name:
distribution = f" ({lb:.2f}, {mid:.2f}, {ub:.2f})"
else:
distribution = f" ({lb:.3g}, {mid:.3g}, {ub:.3g})"
pos = name.find(' (')
if pos != -1:
units = str(name[pos:]).replace('(', '[').replace(')', ']')
if units == ' [USD/kg]':
units = ' [$\mathrm{USD} \cdot \mathrm{kg}^{-1}$]'
elif units == ' [USD/kWhr]':
units = ' [$\mathrm{USD} \cdot \mathrm{kWhr}^{-1}$]'
elif units == ' [kg/hr]':
units = ' [$\mathrm{kg} \cdot \mathrm{hr}^{-1}$]'
name = name[:pos]
else:
units = ''
if name in replacement_labels:
name = replacement_labels[name]
return name + units + distribution
# Get data
rhos = pd.read_excel('Spearman correlation cornstover.xlsx',
header=[0], index_col=0).iloc[:, 0]
# Get only important parameters
rhos = rhos[rhos.abs()>0.055]
# Plot and fix axis labels
fig, ax = plot_spearman(rhos, top=10, name='MESP')
labels = [item.get_text() for item in ax.get_yticklabels()]
new_labels = [replace_label_text(i) for i in labels]
ax.set_yticklabels(new_labels)
|
[
"[email protected]"
] | |
92b3b0104e4a06cbb52b0414b983ceafbca9d1a4
|
58b5624341f2352c5ac7c50320a6009bd72ad3c4
|
/external/amazon_scraper/__init__.py
|
74b3f536b9b00a6784f8e290f6ea51744be80fd5
|
[
"MIT"
] |
permissive
|
MachineLearningStudyGroup/Smart_Review_Summarization
|
ca74682206db64600d3c3e55d5777c24b29f5b71
|
2f6667b504243d195cea1a3a85af72fdd950d2a8
|
refs/heads/master
| 2021-01-19T01:01:48.113683 | 2017-08-29T01:19:16 | 2017-08-29T01:24:12 | 53,262,459 | 8 | 4 | null | 2016-09-28T01:49:15 | 2016-03-06T15:44:56 |
Jupyter Notebook
|
UTF-8
|
Python
| false | false | 8,903 |
py
|
# -*- coding: utf-8 -*-
from __future__ import absolute_import
import logging
import re
try:
import urlparse
except:
import urllib.parse as urlparse
import urllib
import functools
import time
import requests
from requests.packages.urllib3.exceptions import InsecureRequestWarning
requests.packages.urllib3.disable_warnings(InsecureRequestWarning)
import warnings
from amazon.api import AmazonAPI
import dateutil.parser
from bs4 import BeautifulSoup
from fake_useragent import UserAgent
from .version import __version__ # load our version
if 'unicode' not in dir(globals()['__builtins__']):
unicode = str
# stop warnings about unused variable
__version__
log = logging.getLogger(__name__)
# fix #1
# 'html.parser' has trouble with http://www.amazon.com/product-reviews/B00008MOQA/ref=cm_cr_pr_top_sort_recent?&sortBy=bySubmissionDateDescending
# it sometimes doesn't find the asin span
html_parser = 'html.parser'
#html_parser = 'html5lib'
#user_agent = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/32.0.1700.107 Safari/537.36'
# user_agent = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/37.0.2062.94 Safari/537.36'
ua = UserAgent() #get user agent: http://www.whoishostingthis.com/tools/user-agent/
user_agent = ua.random #or generate random one: https://pypi.python.org/pypi/fake-useragent
amazon_base = 'http://www.amazon.com'
_extract_asin_regexp = re.compile(r'(/dp/|/gp/product/)(?P<asin>[^/]+)/?')
_process_rating_regexp = re.compile(r'([\d\.]+) out of [\d\.]+ stars', flags=re.I)
_extract_reviews_asin_regexp = re.compile(r'/product-reviews/(?P<asin>[^/]+)', flags=re.I)
_extract_review_id_regexp = re.compile(r'/review/(?P<id>[^/]+)', flags=re.I)
_extract_reviewer_id_regexp = re.compile(r'/member-reviews/(?P<id>[^/]+)', flags=re.I)
_price_regexp = re.compile(r'(?P<price>[$£][\d,\.]+)', flags=re.I)
def extract_asin(url):
try:
match = _extract_asin_regexp.search(url)
return str(match.group('asin'))
except:
warnings.warn('Error matching ASIN in URL {}'.format(url))
raise
def product_url(asin):
url = '{base}/dp/{asin}'
return url.format(base=amazon_base, asin=asin)
def add_affiliate(url, affiliate):
return add_query(url, tag=affiliate)
def reviews_url(asin):
url = '{base}/product-reviews/{asin}/ref=cm_cr_pr_top_sort_recent?&sortBy=helpful'
return url.format(base=amazon_base, asin=asin)
def reviews_url_recent(asin):
url = '{base}/product-reviews/{asin}/ref=cm_cr_pr_top_sort_recent?&sortBy=bySubmissionDateDescending'
return url.format(base=amazon_base, asin=asin)
def review_url(id):
url = '{base}/review/{id}'
return url.format(base=amazon_base, id=id)
def reviewer_url(id):
url = '{base}/gp/cdp/member-reviews/{id}'
return url.format(base=amazon_base, id=id)
def process_rating(text):
"""The rating normalised to 1.0
"""
try:
rating_match = _process_rating_regexp.search(text)
return float(rating_match.group(1)) / 5.0
except:
warnings.warn('Error processing rating for text "{}"'.format(text))
raise
def extract_reviews_asin(url):
try:
match = _extract_reviews_asin_regexp.search(url)
return str(match.group('asin'))
except:
warnings.warn('Error matching reviews ASIN in URL {}'.format(url))
raise
def extract_review_id(url):
try:
match = _extract_review_id_regexp.search(url)
return str(match.group('id'))
except:
warnings.warn('Error matching review ID in URL {}'.format(url))
raise
def extract_reviewer_id(url):
try:
match = _extract_reviewer_id_regexp.search(url)
return str(match.group('id'))
except:
warnings.warn('Error matching review ID in URL {}'.format(url))
raise
def extract_price(text):
try:
match = _price_regexp.search(text)
price = match.group('price')
price = re.sub(r'[$£,]', u'', price)
price = float(price)
return price
except:
warnings.warn('Error extracting price in text "{}"'.format(text))
raise
def add_query(url, **kwargs):
scheme, netloc, path, query_string, fragment = urlparse.urlsplit(url)
query_params = urlparse.parse_qs(query_string)
# remove any existing value of 'key'
keys = kwargs.keys()
query_params = dict(filter(lambda x: x[0] not in keys, query_params.iteritems()))
query_params.update(kwargs)
query_string = urllib.urlencode(query_params, doseq=True)
return urlparse.urlunsplit((scheme, netloc, path, query_string, fragment))
def get_review_date(raw_date):
string = unicode(raw_date)
# 2011-11-07T05:50:41Z
date = dateutil.parser.parse(string)
return date
def strip_html_tags(html):
if html:
soup = BeautifulSoup(html, html_parser)
text = soup.findAll(text=True)
text = u'\n'.join(text).strip()
return text
return None
def retry(retries=5, exceptions=None):
if not exceptions:
exceptions = (BaseException,)
def outer(fn):
@functools.wraps(fn)
def decorator(*args, **kwargs):
for attempt in range(1, retries + 1):
try:
if attempt > 1:
log.debug('{0}({1}, {2}) - Retry attempt {3}/{4}'.format(fn.__name__, args, kwargs, attempt, retries))
result = fn(*args, **kwargs)
return result
except BaseException as e:
if not isinstance(e, exceptions):
raise
if attempt >= retries:
log.debug('{0}({1}, {2}) - Retry limit exceeded'.format(fn.__name__, args, kwargs))
raise e
return decorator
return outer
def get(url, api):
rate_limit(api)
# verify=False ignores SSL errors
r = requests.get(url, headers={'User-Agent': user_agent}, verify=False)
r.raise_for_status()
return r
def is_property(obj, k):
# only accept @property decorated functions
# these can only be detected via the __class__ object
if hasattr(obj.__class__, k):
if isinstance(getattr(obj.__class__, k), property):
return True
return False
def dict_acceptable(obj, k, blacklist=None):
# don't store blacklisted variables
if blacklist and k in blacklist:
return False
# don't store hidden variables
if k.startswith('_'):
return False
return is_property(obj, k)
def rate_limit(api):
# apply rate limiting
# this is taken from bottlenose/api.py
bn = api.bottlenose
if bn.MaxQPS:
last_query_time = bn._last_query_time[0]
if last_query_time:
wait_time = 1 / bn.MaxQPS - (time.time() - last_query_time)
if wait_time > 0:
log.debug('Waiting %.3fs to call Amazon API' % wait_time)
time.sleep(wait_time)
bn._last_query_time[0] = time.time()
# This schema of imports is non-standard and should change. It will require some re-ordering of
# functions inside the package though.
from external.amazon_scraper.product import Product
from external.amazon_scraper.reviews import Reviews
from external.amazon_scraper.review import Review
from external.amazon_scraper.user_reviews import UserReviews
class AmazonScraper(object):
def __init__(self, access_key, secret_key, associate_tag, *args, **kwargs):
self.api = AmazonAPI(access_key, secret_key, associate_tag, *args, **kwargs)
def reviews(self, ItemId=None, URL=None):
return Reviews(self, ItemId, URL)
def review(self, Id=None, URL=None):
return Review(self, Id, URL)
def user_reviews(self, Id=None, URL=None):
return UserReviews(self, Id, URL)
def lookup(self, URL=None, **kwargs):
if URL:
kwargs['ItemId'] = extract_asin(URL)
result = self.amazon_simple_api.lookup(**kwargs)
if isinstance(result, (list, tuple)):
result = [Product(self, p) for p in result]
else:
result = Product(self, result)
return result
def similarity_lookup(self, **kwargs):
for p in self.amazon_simple_api.similarity_lookup(**kwargs):
yield Product(self, p)
def browse_node_lookup(self, **kwargs):
return self.amazon_simple_api.browse_node_lookup(**kwargs)
def search(self, **kwargs):
for p in self.amazon_simple_api.search(**kwargs):
yield Product(self, p)
def search_n(self, n, **kwargs):
for p in self.amazon_simple_api.search_n(n, **kwargs):
yield Product(self, p)
@property
def amazon_simple_api(self):
return self.api
@property
def bottlenose(self):
return self.api.api
|
[
"[email protected]"
] | |
4a160eac6a0bf3a897d8c676fcde80f0a1fd5dca
|
d6d3793eee29ffacc43a7dbf2b1165950c626efa
|
/convy.py
|
f2a1a5f36f386e8dc58002255d62c0779a17f86f
|
[] |
no_license
|
rahulkadavil/convy
|
7a32720847ad7e6270c725b51d9e40575a9eaf8c
|
1c740cf53d5569895b34792c7f3b9bd0926dd394
|
refs/heads/master
| 2020-03-11T18:52:27.012569 | 2018-04-19T18:42:46 | 2018-04-19T18:42:46 | 130,190,694 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 594 |
py
|
#!/usr/bin/env python
print "Enter the value:"
s =raw_input()
listhex = []
listdec = []
listbin = []
for i in s:
print "Converter for " +i
hexa = hex(ord(i))
listhex.append(hexa)
listhex.append(" ")
print "Hexadecimal :" + hexa
inte = int(hexa[2:],16)
listdec.append(str(inte))
listdec.append(" ")
print "Decimal :" + str(inte)
binary = bin(inte)
listbin.append(binary)
listbin.append(" ")
print "Binary :" + binary
print "Final hexadecimal- "+"".join(listhex)
print "Final decimal- "+"".join(listdec)
print "Final binary- "+"".join(listbin)
|
[
"[email protected]"
] | |
048a8174ef620c5df230102eae4c84935ea54b6d
|
f67b330c8dfb8cb21fc7f57507d196a1febcfdbb
|
/check_armstrong_num.py
|
10619c910c516ff4f2965fcc86ef0f02133fc755
|
[] |
no_license
|
harshuop/GeeksforgeeksEXE
|
0d3ffeb61c7e9c8ead7c20821b0a272096697a5f
|
fa98d4f9a23b6b531194bf1ea23748104e01a1de
|
refs/heads/master
| 2023-03-02T16:51:03.670646 | 2021-02-13T06:09:32 | 2021-02-13T06:09:32 | 333,089,666 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 122 |
py
|
def power(n):
return len(str(n))
def process(n):
x = power(n)
x = [46]
p = map(lambda x: return x, x)
print(p)
|
[
"[email protected]"
] | |
7fde49b6b4a3be2ef140b933e37e632328f9a629
|
98770c4d1a3a02e555b7ab70b176d983e7b204ca
|
/weeklyReportGather.py
|
4da353bca34decfa109e8274a7a570bb0ff9a480
|
[] |
no_license
|
zhengxiaoqing/hello-world
|
dbc8183836c87b36b15c083cd92c9946353d50d9
|
91655a486d5ebd038e141a4182f2f1d41f413f30
|
refs/heads/master
| 2021-01-21T10:33:43.224460 | 2019-10-31T06:24:37 | 2019-10-31T06:24:37 | 83,448,554 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,386 |
py
|
import glob
import openpyxl
#将所有人的周报xlsx放入固定文件夹，xls格式不可用
#获得所有周报的一个名字列表
listOfReport = glob.glob(r'D:\documents\testop\*.xlsx')
#打开汇总excel
wbAll = openpyxl.load_workbook(r'D:\documents\huizong.xlsx')
#当前汇总表的活动工作簿---sheetAll
sheetAll = wbAll.get_active_sheet()
#获得所有人员名字的列表
NameList =[]
for cellName in sheetAll['E6': 'E18']:
for rowOfCellObj in cellName:
NameList.append(rowOfCellObj.value)
#迭代打开文件夹的每个xlsx
for reportname in listOfReport:
print(reportname)
wbPerson = openpyxl.load_workbook(reportname)
sheetPerson = wbPerson.get_active_sheet()
#获得每个xlsx的个人名字（名字在D6单元格）
singleName = sheetPerson['D6'].value
for rowNum in range(6,19):
#sheetAll汇总表的第一个名字在E6（第6行，第5列）
#对比singleName和汇总表中名字
everyName = sheetAll.cell(row = rowNum, column = 5).value
if singleName == everyName:
#汇总表第6列到第9列的单元格4个
for colNum in range(5,9):
#从第6行开始到第19行
sheetAll.cell(row = rowNum , column = colNum +1).value = sheetPerson.cell(row = 6, column = colNum).value
wbAll.save('D:\\documents\\huizong.xlsx')
|
[
"[email protected]"
] | |
8704238126ae2592998c08408bf1fef66626d8c2
|
484699c7b0329e8b8bd16814e3b7729722c6b8f0
|
/api/flaskAPI/appointmentDb.py
|
fa9dd107b822672d47f594363f2551c4e1873931
|
[] |
no_license
|
Merxon22/Appointmeow_demo
|
3fa7a016ad953a5625f36925665af8af3ff6bc67
|
1308ea778157658867fd56d62fbb275d2a3fa55b
|
refs/heads/main
| 2023-07-30T12:08:31.138072 | 2021-09-20T01:24:52 | 2021-09-20T01:24:52 | 366,699,520 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 3,204 |
py
|
from eventDb import UserEventsBetweenTime
import mysql.connector
db = mysql.connector.connect(host="freedb.tech", user="freedbtech_PeterYuan", passwd="81704002_oahnauY", database="freedbtech_Appointmeow")
cursor = db.cursor()
createAppointmentTable = """
CREATE TABLE appointment(
appointmentID INT NOT NULL AUTO_INCREMENT PRIMARY KEY UNIQUE,
title VARCHAR(80) NOT NULL,
description VARCHAR(200),
startTime TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
endTime TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
userEmail1 VARCHAR(80) NOT NULL,
userEmail2 VARCHAR(80) NOT NULL,
FOREIGN KEY (userEmail1) REFERENCES user(email),
FOREIGN KEY (userEmail2) REFERENCES user(email)
)
"""
def CreateAppointment(title, description, startTime, endTime, userEmail1, userEmail2):
cmd = """INSERT INTO appointment (title, description, startTime, endTime, userEmail1, userEmail2) VALUES (%s, %s, %s, %s ,%s, %s)"""
#verify user free time
events = UserEventsBetweenTime(userEmail1, startTime, endTime)
if len(events) >= 1:
print("User '" + userEmail1 + "' already has existing events between time block '" + startTime + "' and '" + endTime + "'")
return False
events = UserEventsBetweenTime(userEmail2, startTime, endTime)
if len(events) >= 1:
print("User '" + userEmail2 + "' already has existing events between time block '" + startTime + "' and '" + endTime + "'")
return False
#Finish verifying
cursor.execute(cmd, (title, description, startTime, endTime, userEmail1, userEmail2))
db.commit()
print("Successfully created appointment")
return "Successfully created appointment"
def UserAppointmentsBetweenDate(userEmail, startDate, endDate):
return UserAppointmentsBetweenTime(userEmail, startDate + " 00:00:00", endDate + " 23:59:59")
def UserAppointmentsBetweenTime(userEmail, startTime, endTime):
cmd = """
SELECT appointment.appointmentID, appointment.title, appointment.description, appointment.startTime, appointment.endTime, appointment.userEmail1, appointment.userEmail2
FROM appointment
JOIN user ON (appointment.userEmail1 = user.email) OR (appointment.userEmail2 = user.email)
WHERE user.email = %s AND ((appointment.startTime BETWEEN %s AND %s) OR (appointment.endTime BETWEEN %s AND %s))
"""
cursor.execute(cmd, (userEmail, startTime, endTime, startTime, endTime))
results = cursor.fetchall()
return results
def AllAppointments():
cmd = """SELECT * FROM appointment"""
cursor.execute(cmd)
results = cursor.fetchall()
return results
def RemoveAppointment(appointmentID):
cmd = """DELETE FROM appointment WHERE appointmentID = %s"""
cursor.execute(cmd, (appointmentID, ))
db.commit()
print("Successfully remove appointment")
#======================Followins are command scripts===============================
print("=============================All appointments================================")
appointments = AllAppointments()
for appointment in appointments:
print(appointment)
print("=============================================================================")
|
[
"[email protected]"
] | |
fc6a828d8062bedbbd9dd0d28c43cd6242055201
|
339cbabee232b6911f9d78251411cf64e3f4cde8
|
/weddingsite/snorlax/admin.py
|
e8e2eb12c8c3b2f1abf5e0d40c83039951f1f9ca
|
[] |
no_license
|
pedroxido/wedding-site
|
b6b0000ceca2fac3ba0bd89f28d00613a832ff3e
|
e8824b5a9cebbfb9c0e83c9b7c75c7137caa75bc
|
refs/heads/master
| 2020-04-18T01:37:15.088046 | 2019-02-23T21:54:51 | 2019-02-23T21:54:51 | 167,125,706 | 1 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 116 |
py
|
from django.contrib import admin
from .models import *
admin.site.register(Person)
admin.site.register(FamilyGroup)
|
[
"[email protected]"
] | |
a20649e0095086f43d8f53bcec3275635830d3e5
|
2d4cf380c38701e1129709e9724587f1d75f9eef
|
/quickstart/serializers.py
|
d2394dc51e7bfb92b337a6fea5fab7b288033bfb
|
[] |
no_license
|
saurabh9567/drf_apis
|
62d822f8d753c23218722d8d07450a3de565150a
|
f54f58c763e967f3346cbaa148a777dfc28f6a90
|
refs/heads/master
| 2023-08-28T19:10:39.128834 | 2021-10-19T06:44:38 | 2021-10-19T06:44:38 | 418,793,119 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 360 |
py
|
from django.contrib.auth.models import User, Group
from rest_framework import serializers
class UserSerializer(serializers.ModelSerializer):
class Meta:
model = User
fields = ('id', 'username', 'email')
class GroupSerializer(serializers.HyperlinkedModelSerializer):
class Meta:
model = Group
fields = ['url', 'name']
|
[
"[email protected]"
] | |
2b519583110d810f29e0c4ea22a6de95ab38a252
|
a782c9df6d2ffdb5274a175d553f560d1a4036a9
|
/users/views.py
|
2b065c3a74d7ab06afbb29c445c629db62be0873
|
[] |
no_license
|
python-cyl/Django_BSM
|
0a36914075f6fa166e4994ceb036594bbf5e006b
|
84e674550d61f2f5be34123c36150b64cc196cd2
|
refs/heads/master
| 2020-07-02T00:53:43.570580 | 2019-08-09T01:47:41 | 2019-08-09T01:47:41 | 201,365,502 | 2 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 962 |
py
|
from django.shortcuts import render
from django.views.generic.base import View
from users.models import UserProfile
from django.contrib.auth import authenticate, login
# Create your views here.
class UserView(View):
def get(self, request):
return render(request, 'Empt classroom.html', {})
def post(self, request):
user_name = request.POST.get("username", "")
user_password = request.POST.get("password", "")
try:
user_test = UserProfile.objects.get(username=user_name)
user = authenticate(username=user_name, password=user_password)
if user is not None:
login(request, user)
return render(request, 'Empt classroom.html', {})
else:
return render(request, 'log.html', {"msg": '用户名或密码错误'})
except UserProfile.DoesNotExist:
return render(request, 'log.html', {"msg": '登陆信息错误'})
|
[
"[email protected]"
] | |
b05d8df69188a171c3b0d96360051ce235e9c9fc
|
a085a2fec46ae5507f93f4d4e2fc379cd693c086
|
/suspicious.py
|
056347eec468cc9a5606d97422a081e05be6ebea
|
[] |
no_license
|
Eduardo359/irccat-commands
|
94822ab429979a21641fe3fa1f58f9b1e4c98adb
|
b85c8648cb7e386b2babc8619c6800dd33276fc0
|
refs/heads/master
| 2022-11-06T16:33:41.850655 | 2020-06-25T09:26:55 | 2020-06-25T09:26:55 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 339 |
py
|
#!/usr/bin/python
import random
suspicious = [
'http://i.imgur.com/cgxBjhz.jpg',
'http://goo.gl/KrrNCQ',
'http://goo.gl/AgyDB0',
'http://goo.gl/s4UpVu',
'http://goo.gl/wOjkwH',
'http://goo.gl/ewD2tO',
u'http://hack.rs/\u0ca0_\u0ca0.jpg',
]
print suspicious[random.randrange(len(suspicious))].encode('utf-8')
|
[
"[email protected]"
] | |
5cc4b052f8af56030d1a18a236cfee198c0e14a0
|
c7a6f8ed434c86b4cdae9c6144b9dd557e594f78
|
/ECE364/.PyCharm40/system/python_stubs/348993582/gst/_gst/Date.py
|
b079f8261188a978cb48855b5781e2227e2dea1e
|
[] |
no_license
|
ArbalestV/Purdue-Coursework
|
75d979bbe72106975812b1d46b7d854e16e8e15e
|
ee7f86145edb41c17aefcd442fa42353a9e1b5d1
|
refs/heads/master
| 2020-08-29T05:27:52.342264 | 2018-04-03T17:59:01 | 2018-04-03T17:59:01 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 822 |
py
|
# encoding: utf-8
# module gst._gst
# from /usr/lib64/python2.6/site-packages/gst-0.10/gst/_gst.so
# by generator 1.136
# no doc
# imports
import gobject as __gobject
import gobject._gobject as __gobject__gobject
import gst as __gst
class Date(__gobject.GBoxed):
# no doc
def __init__(self, *args, **kwargs): # real signature unknown
pass
def __repr__(self): # real signature unknown; restored from __doc__
""" x.__repr__() <==> repr(x) """
pass
day = property(lambda self: object(), lambda self, v: None, lambda self: None) # default
month = property(lambda self: object(), lambda self, v: None, lambda self: None) # default
year = property(lambda self: object(), lambda self, v: None, lambda self: None) # default
__gtype__ = None # (!) real value is ''
|
[
"[email protected]"
] | |
4a6c022fea9e0a2c4bbbba123317ce49c4d26618
|
ecf7ad944c3533dbfc60406efcacf79ccc9eac1e
|
/dota2bbq/admin.py
|
6c25b039e27aeb0abd85f912249dd5480c2c09f0
|
[] |
no_license
|
zhoutuo/dota2bbq-django
|
846322cf85d0af1c55a64c0af5bd2059e683ac8d
|
abe6caa56fa649346f54b664a02fcffde3811d28
|
refs/heads/master
| 2020-03-29T15:28:16.079590 | 2013-02-13T03:29:39 | 2013-02-13T03:29:39 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 159 |
py
|
from dota2bbq.models import Hero, Item, Skill
from django.contrib import admin
admin.site.register(Hero)
admin.site.register(Item)
admin.site.register(Skill)
|
[
"[email protected]"
] | |
941af6b87f9ce9e928a4dc4d2bfd8774c56592b0
|
1ea9a7a807ca8f8049e09bc7716fcbf0490474c5
|
/camera/pir.py
|
3a5f4af9d523c8d07e33d69fee30678f85cf69de
|
[] |
no_license
|
Funnylaksa/picam
|
232d388a2541cfaa4e9d7daddeabfaebb138c8b7
|
5f05d45a241d840b2fff97ee22043dd74f65604a
|
refs/heads/master
| 2023-07-21T22:01:21.281244 | 2021-09-05T14:34:31 | 2021-09-05T14:34:31 | 254,529,817 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 2,847 |
py
|
import RPi.GPIO as GPIO
import time
import sys
import os
from datetime import datetime
import logging
start = datetime.now()
dt_start_string = start.strftime("%Y%m%d_%H%M%S")
date_string = start.strftime("%Y%m%d")
#Create and configure logger
logging.basicConfig(filename="/home/pi/picam/log/app." + date_string + ".log",
format='%(asctime)s %(message)s',
filemode='a')
#Creating an object
logger=logging.getLogger()
logger.setLevel(logging.DEBUG)
#Use Raspberry Pi board pin numbers
GPIO.setmode(GPIO.BOARD)
GPIO.setup(11, GPIO.IN)
time.sleep(10)
global count
count = 0
triggered = False
def capture():
now = datetime.now()
dt_string = now.strftime("%Y%m%d_%H%M%S")
print("date and time =", dt_string)
logger.info("PIR Sensor triggered. Capturing! --- ")
logger.info("---")
photo = "/home/pi/picam/mega_temp/{datetime}".format(datetime = dt_string)
os.system("raspistill -awb greyworld -o {photo}.jpg -rot 180".format(photo=photo))
#vid = "/home/pi/picam/mega_temp/{datetime}".format(datetime = dt_string)
#os.system("raspivid -awb greyworld -o {vid}.h264 -rot 180 -t 8000".format(vid=vid))
#time.sleep(8)
#os.system("MP4Box -add {vid}.h264 {vid}.mp4".format(vid=vid))
#os.system("rm {vid}.h264".format(vid=vid))
count = 0
time.sleep(3)
logger.info("Capturing Ends")
# Start here
while True:
count += 1
sensor_value = GPIO.input(11)
print ("Sensor value:", sensor_value)
if count > 600:
dt = count/60
logger.info("Not triggered for past {dt} mins".format(dt=dt))
# 4 positive sensor reading in 9 counts -> capture footage
if triggered:
if sensor_value:
sensor_value_count+=1
if sensor_value_count % 5 == 0:
time.sleep(4)
if sensor_value_count % 3 == 0:
capture()
trigger_count = 1
elif trigger_count == 9:
triggered = False
trigger_count = 0
logger.info("pir sensor not triggered after 10 seconds. Reset counters"))
else:
trigger_count += 1
print('trigger_count, sensor_val_count:', trigger_count, sensor_value_count)
logger.info("trigger_count, sensor_val_count: {}, {}".format(trigger_count, sensor_value_count))
else:
logger.info("Not activated")
# init trigger for 1st positive sensor reading
if sensor_value and triggered==False:
trigger_count = 1
sensor_value_count=1
triggered = True
capture()
print('trigger_count, sensor_val_count:', trigger_count, sensor_value_count)
logger.info("trigger_count, sensor_val_count: {}, {}".format(trigger_count, sensor_value_count))
time.sleep(1)
|
[
"[email protected]"
] | |
e8cbc351c5c69afa6bf7b356ded024118c2008e9
|
09ef36e5091fe6b28740bbdf7231daccbe8e6f6d
|
/testnet/testudp/testrw/testclient1.py
|
820ae9acc4e017f14ac3420437ea13281f0e816a
|
[] |
no_license
|
baiyang0223/python
|
59fee6762a57630e48e077dca47a6607778d7628
|
95ff2bdffefe9bba36f17dd492b94ed9889d816c
|
refs/heads/master
| 2020-04-02T17:06:16.780375 | 2020-03-05T06:51:44 | 2020-03-05T06:51:44 | 154,643,881 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 886 |
py
|
import socket
def send_msg(udp_sock):
"""发送消息"""
dest_ip = input("输入对方IP：")
dest_port = int(input("输入对方端口："))
send_data = input("输入你要发送的数据：")
udp_sock.sendto(send_data.encode("utf-8"),(dest_ip,dest_port))
def recv_msg(udp_sock):
"""接收消息"""
recv_buf = udp_sock.recvfrom(0x1024)
recv_data = recv_buf[0]
send_addr = recv_buf[1]
print("接收到数据：%s-%s" % (recv_data.decode("utf-8"), str(send_addr)))
# 使用同一个套接字进行收发的服务端，先收后发
def udp_main():
udp_sock = socket.socket(socket.AF_INET,socket.SOCK_DGRAM)
udp_sock.bind(("", 9991))
# 循环处理网络数据
while True:
#发送消息
send_msg(udp_sock)
# 接收消息并显示
recv_msg(udp_sock)
if __name__ == "__main__":
udp_main()
|
[
"[email protected]"
] | |
348f4f79a100d74fc0a86c6e1e03208fa5cc6fe3
|
8280f3378c28be3126d67b45ff5abec07cb63cc5
|
/setuplutptr3.py
|
6487bd0cc96382a9c391a40364faeaf65de2873f
|
[] |
no_license
|
RoshanPasupathy/3d_localisation
|
f3522372b80fdede2b858f706063d047e261dcdd
|
26705b1acabe65374897f3a86ce2ec302166424e
|
refs/heads/master
| 2021-01-10T15:28:20.994553 | 2017-01-05T00:18:37 | 2017-01-05T00:18:37 | 45,695,585 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 149 |
py
|
from distutils.core import setup
from Cython.Build import cythonize
setup(name="LUTptr3",
ext_modules = cythonize("/home/pi/ip/LUTptr3.pyx")
)
|
[
"[email protected]"
] | |
d3735613f495321756b10b4cad78141f3669bb8e
|
9c29a853a26d57668a377e1a6e23a67c9cb541d4
|
/days_19-21_iteration/scrabble.py
|
fd14edc2af6ea7c0b23e5cae3787cec232f2df96
|
[
"MIT"
] |
permissive
|
vkoprivica/100_Days_of_Python
|
5f83deb113e1309b58913c8a286be9a17c8ae656
|
736b8a41a03673db44cdbd095433254e60d3316b
|
refs/heads/master
| 2020-11-24T18:27:12.922206 | 2020-03-23T20:19:40 | 2020-03-23T20:19:40 | 228,118,062 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,406 |
py
|
import itertools
import os
import urllib.request
# PREWORK
DICTIONARY = os.path.join('/Users/Vule/Documents/Python/PyBites/dictionary.txt')
# urllib.request.urlretrieve('http://bit.ly/2iQ3dlZ', DICTIONARY)
with open(DICTIONARY) as f:
dictionary = set([word.strip().lower() for word in f.read().split()])
letters = "G, A, R, Y, T, E, V".split(",")
def get_possible_dict_words(draw):
"""Get all possible words from a draw (list of letters) which are
valid dictionary words. Use _get_permutations_draw and provided
dictionary"""
# valid_words = [word for word in _get_permutations_draw(draw) if word in dictionary]
# return valid_words
return [word for word in _get_permutations_draw(draw) if word in dictionary]
def _get_permutations_draw(draw):
"""Helper to get all permutations of a draw (list of letters), hint:
use itertools.permutations (order of letters matters)"""
all_permutations = []
for length in range (7):
all_permutations.extend([''.join(word).lower()
for word in itertools.permutations(draw, length)])
return all_permutations
# return [("".join(letter).lower()).replace(" ", "") for letter in itertools.permutations(letters, 6)]
if __name__ == "__main__":
print(get_possible_dict_words(letters))
# print(_get_permutations_draw(letters))
# print(type(dictionary))
|
[
"[email protected]"
] | |
57ad5861651ace41207b327cb51221ca46a23310
|
5eb456b3a51b585cf7598bc4ef31f7b92ced6381
|
/P4HW2_RunningTotal_Tennant.py
|
e19cb3a6dbed4c0a91391a70a8c50ddd92c42dcb
|
[] |
no_license
|
jared10ant/cti110
|
87d4e64475936db85b081fd8819c936231f8f577
|
7db2a707afa9542c544cd1bcd846b55838e1d347
|
refs/heads/master
| 2021-01-23T16:08:12.148675 | 2018-05-15T01:21:23 | 2018-05-15T01:21:23 | 93,285,495 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 296 |
py
|
#CTI-110
#Jared Tennant
#Running Total
#3/25/18
print('Enter your numbers at the prompt. Enter a negative number to exit.')
total = 0
number = int(input('Enter a number: '))
while number >= 0:
total += number
number = int(input('Enter a number: '))
print('Total: ', total)
|
[
"[email protected]"
] | |
b7c97ebb040f1cf79b042263d6c24c17b9d3970e
|
13f6ab0c4d8fe9c102d3f4ebdf7e15b0bf9b91b6
|
/main.py
|
6ec36484f7c25e1b31abd4b88c784964e3edc0de
|
[] |
no_license
|
jrg96dev/SpaceInvaders_pygame
|
86cb0b2bf44104df1889c5a2e08dc3d4972a8a59
|
c47747252b13432d3f372c43553faed9d8cb4095
|
refs/heads/master
| 2023-01-22T16:35:40.515822 | 2020-12-09T06:22:55 | 2020-12-09T06:22:55 | 319,838,699 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 2,218 |
py
|
import pygame
from enemy import Enemy
# This has to be the first line always
FPS = 30
pygame.init()
clock = pygame.time.Clock()
# -------------- RESOURCE LOADIN ------------------
icon = pygame.image.load('ufo.png')
player_img = pygame.image.load('player.png')
enemy_img = pygame.image.load('enemy.png')
# -------------- INITIAL STATE --------------------
running = True
player_x = 370
player_y = 400
left_pressing = False
right_pressing = False
enemies = [Enemy()]
# -------------- SETTINGS -------------------------
screen = pygame.display.set_mode([800, 480])
pygame.display.set_caption('Space Invaders')
pygame.display.set_icon(icon)
# -------------- GAME FUNCTIONS -------------------
def draw_player():
screen.blit(player_img, (player_x, player_y))
def draw_enemies():
for enemy in enemies:
screen.blit(enemy.sprite, (enemy.pos_x, enemy.pos_y))
def calculate_player_position():
global player_x
global left_pressing
global right_pressing
direction = 0
movement = 5
if left_pressing:
direction = -1
elif right_pressing:
direction = +1
player_x += direction * movement
# Check boundaries and correct anything
if player_x < 0.0:
player_x = 0
if player_x > 736.0:
player_x = 736.0
def event_handling(event):
global left_pressing
global right_pressing
run = True
# Window events
if event.type == pygame.QUIT:
run = False
# Arrow key events
if event.type == pygame.KEYDOWN:
if event.key == pygame.K_LEFT:
left_pressing = True
elif event.key == pygame.K_RIGHT:
right_pressing = True
if event.type == pygame.KEYUP:
if event.key == pygame.K_LEFT:
left_pressing = False
elif event.key == pygame.K_RIGHT:
right_pressing = False
return run
# -------------- GAME LOOP ------------------------
while running:
for event in pygame.event.get():
running = event_handling(event)
# Refreshing screen
screen.fill((0, 0, 0))
calculate_player_position()
draw_player()
draw_enemies()
pygame.display.update()
# Run whole game on 30 fps
clock.tick(FPS)
|
[
"[email protected]"
] | |
dd75709a986e89e33cd45b61c6b9ac695f16dca2
|
c7344879c91c2eaf8b33c45341f27857ede67890
|
/src/predict.py
|
f2677e314216781db11cbd8ba30ffb33008920d8
|
[
"MIT"
] |
permissive
|
brooklinsantosh/CategoricalFeatureEncodingChallenge
|
74669637c12d2c93cb6cc45c46b349a1101d09f4
|
28217157e36c921bb6929b33243e042c68769ccd
|
refs/heads/master
| 2023-03-08T09:30:12.998344 | 2021-02-16T19:55:50 | 2021-02-16T19:55:50 | 338,914,650 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,313 |
py
|
import os
import pandas as pd
import numpy as np
from sklearn import ensemble
from sklearn import preprocessing
from sklearn import metrics
import joblib
import model_dispatcher
TRAINING_DATA = os.environ.get("TRAINING_DATA")
TEST_DATA = os.environ.get("TEST_DATA")
MODEL = os.environ.get("MODEL")
def predict():
df = pd.read_csv(TEST_DATA)
test_idx = df.id.values
predictions = None
for FOLD in range(5):
df = pd.read_csv(TEST_DATA)
encoders = joblib.load(os.path.join("models",f"{MODEL}_{FOLD}_label_encoder.pkl"))
cols = joblib.load(os.path.join("models",f"{MODEL}_{FOLD}_columns.pkl"))
for c in cols:
lbl = encoders[c]
df.loc[:, c] = lbl.transform(df[c].values.tolist())
clf = joblib.load(os.path.join("models",f"{MODEL}_{FOLD}.pkl"))
df = df[cols]
preds = clf.predict_proba(df)[:,1]
if FOLD == 0:
predictions = preds
else:
predictions += preds
predictions /= 5
sub = pd.DataFrame(np.column_stack((test_idx,predictions)),columns=['id','target'])
sub.id = sub.id.astype("int")
return sub
if __name__ == "__main__":
submission = predict()
submission.to_csv(f"models/sub_{MODEL}.csv",index=False)
|
[
"[email protected]"
] | |
3d789c0bfd09acd2dfc7c3933b922e0cb1c7e4c9
|
c0a83d3efcaba2bff6dbd64d285b09e44800018b
|
/Ex1/venv/bin/pip3.7
|
7e9fbef70ac576daacc01f9f20f2971609a5a4dc
|
[] |
no_license
|
murchelon/Python_Projects
|
0d9755e35abdc1d76395154c284656ef7c574fca
|
6f2c1fed9ac5f9226df7caaebe115ed98ef40726
|
refs/heads/master
| 2021-08-18T01:27:36.608781 | 2020-05-04T22:16:43 | 2020-05-04T22:16:43 | 177,201,698 | 2 | 1 | null | null | null | null |
UTF-8
|
Python
| false | false | 425 |
7
|
#!/Users/macbookpro/Documents/GITREP/Python_Projects/Ex1/venv/bin/python
# EASY-INSTALL-ENTRY-SCRIPT: 'pip==10.0.1','console_scripts','pip3.7'
__requires__ = 'pip==10.0.1'
import re
import sys
from pkg_resources import load_entry_point
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
sys.exit(
load_entry_point('pip==10.0.1', 'console_scripts', 'pip3.7')()
)
|
[
"[email protected]"
] | |
01f0b1d097130a7f1499abe7d1410fb59a691ca0
|
07f7fd8ce8de643f31721b9726b78dc215fbfbb9
|
/sara.py
|
4dce83d2b1ab798c2932fbef1efd69180378543d
|
[] |
no_license
|
sadig2/morse
|
8525224f012ad88a94663b1596c5bf316ef7a24c
|
e77ee1c4a7b344b06fcebe8fd3cc6364c523e115
|
refs/heads/master
| 2022-12-16T10:22:08.232402 | 2020-09-16T19:39:51 | 2020-09-16T19:39:51 | 296,130,854 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,814 |
py
|
import re
val = input("Enter your value: ")
word = []
valList = re.split(r"[^\S]+", val)
for letter in valList:
if letter == '.-':
word.append('a')
elif letter == '-...':
word.append('б')
elif letter == '.--':
word.append('в')
elif letter == '--.':
word.append('г')
elif letter == '-..':
word.append('д')
elif letter == '.':
word.append('е')
elif letter == '...-':
word.append('ж')
elif letter == '--..':
word.append('з')
elif letter == '..':
word.append('и')
elif letter == '.---':
word.append('й')
elif letter == '-.-':
word.append('к')
elif letter == '.-..':
word.append('л')
elif letter == '--':
word.append('м')
elif letter == '-.':
word.append('н')
elif letter == '---':
word.append('о')
elif letter == '.--.':
word.append('п')
elif letter == '.-.':
word.append('р')
elif letter == '...':
word.append('с')
elif letter == '-':
word.append('т')
elif letter == '..-':
word.append('у')
elif letter == '..-.':
word.append('ф')
elif letter == '....':
word.append('х')
elif letter == '-.-.':
word.append('ц')
elif letter == '---.':
word.append('ч')
elif letter == '----':
word.append('ш')
elif letter == '--.-':
word.append('щ')
elif letter == '.--.-.':
word.append('Ъ')
elif letter == '-...':
word.append('ы')
elif letter == '-...':
word.append('ь')
elif letter == '...-...':
word.append('э')
elif letter == '..--':
word.append('ю')
elif letter == '.-.-':
word.append('я')
print("".join(word))
|
[
"[email protected]"
] | |
507716ccdcdc0b231befe78143fdbf537dbd0212
|
64cee8c8f33ae6be8edf0daa7a3a83efee86c82c
|
/cemba_data/tools/hdf5/netndf.py
|
81594d52043eb0370f2dfeaaea81a3b7c7138127
|
[
"MIT"
] |
permissive
|
shengyongniu/cemba_data
|
52881061dac63c5dca4bbedf9bc7f1f345b13575
|
6d076ed7f19ac76650d91fe9172393cc6c10e686
|
refs/heads/master
| 2021-10-09T14:31:43.849987 | 2018-12-29T23:19:53 | 2018-12-29T23:19:53 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 8,259 |
py
|
import numpy as np
import pandas as pd
import xarray as xr
from anndata import AnnData
def _calculate_posterior_mc_rate(mc_array, cov_array, var_dim='chrom100k',
normalize_per_cell=True, clip_norm_value=10):
raw_rate = mc_array / cov_array
cell_rate_mean = raw_rate.mean(dim=var_dim)
cell_rate_var = raw_rate.var(dim=var_dim)
# based on beta distribution mean, var
# a / (a + b) = cell_rate_mean
# a * b / ((a + b) ^ 2 * (a + b + 1)) = cell_rate_var
# calculate alpha beta value for each cell
cell_a = (1 - cell_rate_mean) * (cell_rate_mean ** 2) / cell_rate_var - cell_rate_mean
cell_b = cell_a * (1 / cell_rate_mean - 1)
# cell specific posterior rate
post_rate = (mc_array + cell_a) / (cov_array + cell_a + cell_b)
if normalize_per_cell:
# this is normalize by post_rate per cell, just a mean center
post_rate = post_rate / post_rate.mean(dim='chrom100k')
if clip_norm_value is not None:
post_rate.values[np.where(post_rate.values > clip_norm_value)] = clip_norm_value
return post_rate
class MCDS(xr.Dataset):
def __init__(self, dataset):
super().__init__(data_vars=dataset.data_vars, coords=dataset.coords,
attrs=dataset.attrs, compat='broadcast_equals')
return
@classmethod
def open_dataset(cls, file_path):
return cls(xr.open_dataset(file_path))
def filter_cell_cov(self, dim, da, mc_type,
min_cov=10000, max_cov=None):
"""
Filter cell by total cov for certain mc_type along certain dimension in certain dataarray.
Parameters
----------
dim
region dimension to sum
da
dataarray to do calculation
mc_type
mc_type to sum
min_cov
minimum cov sum, suggest to plot distribution first.
max_cov
maximum cov sum, suggest ot plot distribution first.
Returns
-------
"""
if dim not in self[da].dims:
raise ValueError(f'{dim} is not a dimension of {da}')
cell_sum = self[da] \
.sel(count_type='cov', mc_type=mc_type) \
.squeeze() \
.sum(dim)
if max_cov is None:
max_cov = np.inf
cell_max = cell_sum < max_cov
cell_min = cell_sum > min_cov
cell_mask = np.all(np.vstack([cell_max.values,
cell_min.values]),
axis=0)
select_index = self.get_index('cell')[cell_mask]
return self.loc[dict(cell=select_index)]
def filter_region_cov(self, dim, da, mc_type,
min_cov=None, max_cov=None):
"""
Filter cell by total cov for certain mc_type along certain dimension in certain dataarray.
Parameters
----------
dim
region dimension to filter,
Note when filtering region, sum is always performed on cell.
da
dataarray to do calculation
mc_type
mc_type to sum
min_cov
minimum cov sum, suggest to plot distribution first.
max_cov
maximum cov sum, suggest ot plot distribution first.
Returns
-------
"""
if dim not in self[da].dims:
raise ValueError(f'{dim} is not a dimension of {da}')
region_sum = self[da] \
.sel(count_type='cov', mc_type=mc_type) \
.squeeze() \
.sum('cell')
if max_cov is None:
max_cov = np.inf
region_max = region_sum < max_cov
region_min = region_sum > min_cov
region_mask = np.all(np.vstack([region_max.values,
region_min.values]),
axis=0)
select_index = self.get_index(dim)[region_mask]
filtered_ds = self.loc[{dim: select_index}]
return MCDS(filtered_ds)
def add_mc_rate(self, dim, da,
normalize_per_cell=True,
clip_norm_value=10,
rate_da_suffix='rate'):
if da not in self.data_vars:
raise KeyError(f'{da} is not in this dataset')
if dim not in self[da].dims:
raise KeyError(f'{dim} is not a dimension of {da}')
da_mc = self[da].sel(count_type='mc')
da_cov = self[da].sel(count_type='cov')
rate = _calculate_posterior_mc_rate(mc_array=da_mc.values,
cov_array=da_cov.values,
normalize_per_cell=normalize_per_cell,
clip_norm_value=clip_norm_value)
da_rate = xr.DataArray(data=rate,
coords=da_mc.coords,
dims=da_mc.dims)
self[da + "_" + rate_da_suffix] = da_rate
return
def add_gene_rate(self, dim='gene', da='gene_da',
normalize_per_cell=True, clip_norm_value=10,
rate_da_suffix='rate'):
if da not in self.data_vars:
raise KeyError(f'{da} is not in this dataset')
if dim not in self[da].dims:
raise KeyError(f'{dim} is not a dimension of {da}')
da_mc = self[da].sel(count_type='mc')
da_cov = self[da].sel(count_type='cov')
# for gene, we just use normal rate
rate = da_mc / da_cov
if normalize_per_cell:
cell_overall = da_mc.sum(dim='gene') / da_cov.sum(dim='gene')
rate = rate / cell_overall
if clip_norm_value is not None:
rate.values[np.where(rate.values > clip_norm_value)] = clip_norm_value
self[da + "_" + rate_da_suffix] = rate
return
def to_ann(self, da, var_dim, mc_type, obs_dim='cell'):
index_dict = self[da].indexes
return AnnData(X=self[da].sel(mc_type=mc_type).values.copy(),
obs=pd.DataFrame(index=index_dict[obs_dim]),
var=pd.DataFrame(index=index_dict[var_dim]))
def add_ann_results(self, adata, var_dim, obs_dim='cell'):
# columns from AnnData.obs and AnnData.var go to da.coords
# obsm goes to new da with corresponding new dimension
obs_df = adata.obs
obs_df.index.name = obs_dim # make sure coords added with "cell" index
for col, data in obs_df.iteritems():
self.coords[col] = data
var_df = adata.var
var_df.index.name = var_dim # make sure coords added with "cell" index
for col, data in var_df.iteritems():
self.coords[col] = data
for obsm_key in adata.obsm_keys():
coord_name = obsm_key[2:] # remove 'X_'
obsm_data = adata.obsm[obsm_key]
obsm_df = pd.DataFrame(obsm_data,
index=adata.obs_names,
columns=[f'{coord_name}_{i}' for i in range(obsm_data.shape[1])])
obsm_df.index.name = obs_dim
obsm_df.columns.name = coord_name
self[coord_name + '_coord'] = obsm_df
for varm_key in adata.varm_keys():
coord_name = varm_key
varm_data = adata.varm[varm_key]
varm_df = pd.DataFrame(varm_data,
index=adata.var_names,
columns=[f'{coord_name}_{i}' for i in range(varm_data.shape[1])])
varm_df.index.name = var_dim
varm_df.columns.name = coord_name
self[coord_name + '_coord'] = varm_df
return
def add_dataframe_to_coords(self, df, index_dim):
# add columns to da.coords based on index and index_name
df.index.name = index_dim
for col, data in df.iteritems():
self.coords[col] = data
return
def add_dataframe_to_da(self, df, index_dim, col_dim, da_name):
# add columns to da.coords based on index and index_name
df.index.name = index_dim
df.columns.name = col_dim
self[da_name] = df
return
def get_plot_data(self, genes=None, coord='tsne'):
return
|
[
"[email protected]"
] | |
52b48012a0255a71d6d731f870148a4e08e2f81f
|
9ac1f1ac18037b94556a04af64929fa76b59f59b
|
/mysite/polls/migrations/0001_initial.py
|
61d419ad5c2c90f3e5f7add83ac52d39099f32b2
|
[] |
no_license
|
afizing/djangoapps
|
58c59609763f4535db43bd302c79b1496271867d
|
ad1c29b2e250770f8007c304d31c28eb9aa78d87
|
refs/heads/master
| 2021-01-17T12:38:42.375793 | 2016-09-30T12:51:26 | 2016-09-30T12:51:26 | 64,114,905 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,229 |
py
|
# -*- coding: utf-8 -*-
# Generated by Django 1.9.8 on 2016-07-22 08:30
from __future__ import unicode_literals
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='Choice',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('choice_text', models.CharField(max_length=200)),
('votes', models.IntegerField(default=0)),
],
),
migrations.CreateModel(
name='Question',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('question_text', models.CharField(max_length=200)),
('pub_date', models.DateTimeField(verbose_name='date published')),
],
),
migrations.AddField(
model_name='choice',
name='question',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='polls.Question'),
),
]
|
[
"[email protected]"
] | |
8578bce6f8dc26887e3f83b9e0df1ba00636d6df
|
81930dc35807f2a8d575642ab7bebeb5fee78cfb
|
/kaplan_plot.py
|
33d00c3423ab3bfa18a13007cd9f1952e1d76059
|
[] |
no_license
|
arvindb95/LIGO_O3_events_summary
|
fbc478e4f06ca766a6feb521b15fab5b626cd4eb
|
f54037922c6f45ffd8ad890fc77e5aff289e2a3b
|
refs/heads/master
| 2021-01-07T00:21:50.378758 | 2020-04-02T23:00:47 | 2020-04-02T23:00:47 | 241,524,766 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 7,432 |
py
|
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from astropy.table import Table
import matplotlib.transforms as transforms
from astropy.time import Time
from matplotlib.lines import Line2D
months_jd = Time(np.array(["2019-03-01T00:00:00","2019-04-01T00:00:00", "2019-05-01T00:00:00","2019-06-01T00:00:00","2019-07-01T00:00:00","2019-08-01T00:00:00","2019-09-01T00:00:00","2019-10-01T00:00:00","2019-11-01T00:00:00","2019-12-01T00:00:00","2020-01-01T00:00:00","2020-02-01T00:00:00","2020-03-01T00:00:00","2020-04-01T00:00:00"]), format="isot",scale="utc").jd
months_jd_labels = np.array(["2019-03","2019-04", "2019-05","2019-06","2019-07","2019-08","2019-09","2019-10","2019-11","2019-12","2020-01","2020-02","2020-03","2020-04"])
data_file = "superevents_data.txt"
data_tab = Table.read(data_file,format="ascii")
superevent_ids = data_tab["id"]
superevent_times = data_tab["time"]
superevent_FAR = data_tab["FAR"]
superevent_BNS_prob = data_tab["BNS"]
superevent_NSBH_prob = data_tab["NSBH"]
superevent_BBH_prob = data_tab["BBH"]
superevent_MassGap_prob = data_tab["MassGap"]
superevent_Terrestrial_prob = data_tab["Terrestrial"]
superevent_dist = data_tab["dist"]
superevent_90_per_area = data_tab["90% area"]
logscaled_superevent_FAR = 1 - ((np.log10(superevent_FAR) - np.min(np.log10(superevent_FAR)))/(np.max(np.log10(superevent_FAR)) - np.min(np.log10(superevent_FAR))))
for i in range(len(logscaled_superevent_FAR)):
if (logscaled_superevent_FAR[i] < 0.1):
logscaled_superevent_FAR[i] = 0.1
def draw_wedge(ax, drawing_origin, radius, prob1, prob2, prob3, prob4, prob5, col_alpha):
box_height = 1
box_width = 1
origin = np.array([0,0])
trans = (fig.dpi_scale_trans + transforms.ScaledTranslation(drawing_origin[0],drawing_origin[1], ax.transData))
ang1 = prob1*360
ang2 = prob2*360
ang3 = prob3*360
ang4 = prob4*360
ang5 = prob5*360
if (prob1 != 0):
wedge1 = mpatches.Wedge(origin, radius, 0, ang1, facecolor="y", edgecolor=None, transform=trans, alpha=col_alpha)
ax.add_patch(wedge1)
if (prob2 != 0):
wedge2 = mpatches.Wedge(origin, radius,ang1,ang1+ang2, facecolor="b", edgecolor=None, transform=trans, alpha=col_alpha)
ax.add_patch(wedge2)
if (prob3 != 0):
wedge3 = mpatches.Wedge(origin, radius,ang1+ang2,ang1+ang2+ang3, facecolor="g", edgecolor=None, transform=trans, alpha=col_alpha)
ax.add_patch(wedge3)
if (prob4 != 0):
wedge4 = mpatches.Wedge(origin, radius,ang1+ang2+ang3,ang1+ang2+ang3+ang4, facecolor="purple", edgecolor=None, transform=trans, alpha=col_alpha)
ax.add_patch(wedge4)
if (prob5 != 0):
wedge5 = mpatches.Wedge(origin, radius,ang1+ang2+ang3+ang4,ang1+ang2+ang3+ang4+ang5, facecolor="k", edgecolor=None, transform=trans, alpha=col_alpha)
ax.add_patch(wedge5)
return 0
def get_radius(area):
return np.sqrt(area/np.pi)
fig, ax = plt.subplots(figsize=(8,16/3))
ax.set_ylim([10, 6000])
ax.set_xlim(min(months_jd),max(months_jd))
ax.set_xticks(months_jd)
ax.set_xticklabels(months_jd_labels,rotation=30,ha="right")
ax.set_yscale('log')
ax.set_ylabel("Luminosity Distance (Mpc)")
radius_scale = 0.2*6
size_legend_x = np.sum(Time(np.array(["2019-04-01T00:00:00", "2019-05-01T00:00:00"]), format="isot",scale="utc").jd)/2.0
size_legend_y1 = 60
c1_trans = (fig.dpi_scale_trans + transforms.ScaledTranslation(size_legend_x,size_legend_y1, ax.transData))
circle1 = mpatches.Circle(np.array([0,0]), radius_scale/get_radius(100),facecolor="b",edgecolor=None, transform=c1_trans, alpha=0.5)
ax.add_patch(circle1)
ax.text(size_legend_x+20,size_legend_y1,r"100 deg$^{{2}}$", verticalalignment="center",horizontalalignment="left")
size_legend_y2 = 40
c2_trans = (fig.dpi_scale_trans + transforms.ScaledTranslation(size_legend_x,size_legend_y2, ax.transData))
circle2 = mpatches.Circle(np.array([0,0]), radius_scale/get_radius(500),facecolor="b",edgecolor=None, transform=c2_trans, alpha=0.5)
ax.add_patch(circle2)
ax.text(size_legend_x+20,size_legend_y2,r"500 deg$^{{2}}$", verticalalignment="center",horizontalalignment="left")
size_legend_y3 = 30
c3_trans = (fig.dpi_scale_trans + transforms.ScaledTranslation(size_legend_x,size_legend_y3, ax.transData))
circle3 = mpatches.Circle(np.array([0,0]), radius_scale/get_radius(1000),facecolor="b",edgecolor=None, transform=c3_trans, alpha=0.5)
ax.add_patch(circle3)
ax.text(size_legend_x+20,size_legend_y3,r"1000 deg$^{{2}}$", verticalalignment="center",horizontalalignment="left")
size_legend_y4 = 23
c4_trans = (fig.dpi_scale_trans + transforms.ScaledTranslation(size_legend_x,size_legend_y4, ax.transData))
circle4 = mpatches.Circle(np.array([0,0]), radius_scale/get_radius(5000),facecolor="b",edgecolor=None, transform=c4_trans, alpha=0.5)
ax.add_patch(circle4)
ax.text(size_legend_x+20,size_legend_y4,r"5000 deg$^{{2}}$", verticalalignment="center",horizontalalignment="left")
size_legend_y5 = 18
c5_trans = (fig.dpi_scale_trans + transforms.ScaledTranslation(size_legend_x,size_legend_y5, ax.transData))
circle5 = mpatches.Circle(np.array([0,0]), radius_scale/get_radius(10000),facecolor="b",edgecolor=None, transform=c5_trans, alpha=0.5)
ax.add_patch(circle5)
ax.text(size_legend_x+20,size_legend_y5,r"10000 deg$^{{2}}$", verticalalignment="center",horizontalalignment="left")
for i in range(len(superevent_ids)):
do = np.array([superevent_times[i],superevent_dist[i]])
r = radius_scale/get_radius(superevent_90_per_area[i])
draw_wedge(ax, do, r, superevent_BNS_prob[i], superevent_NSBH_prob[i], superevent_BBH_prob[i], superevent_MassGap_prob[i], superevent_Terrestrial_prob[i], logscaled_superevent_FAR[i])
###### GW170817 #######
gw170817_2_time = Time("2019-08-17T00:00:00", format="isot", scale="utc").jd
gw170817_dist = 40
draw_wedge(ax,np.array([gw170817_2_time,gw170817_dist]), radius_scale/get_radius(40), 1.0, 0.0, 0.0, 0.0, 0.0, 1.0)
ax.text(Time("2019-09-15T00:00:00", format="isot", scale="utc").jd,40,"GW170817+2yr", va="center")
legend_elements = [Line2D([],[], marker="o", color="y",linestyle="None", label="BNS"), Line2D([0],[0], marker="o", color="b",linestyle="None", label="NSBH"), Line2D([0],[0], marker="o", color="g",linestyle="None", label="BBH"), Line2D([0],[0], marker="o", color="purple",linestyle="None", label="MassGap"), Line2D([0],[0], marker="o", color="k",linestyle="None", label="Terrestrial")]
ax.legend(handles=legend_elements, loc="lower right")
obs_cand = ["S190814bv","S191216ap"]
for oc_id in range(len(obs_cand)):
sel_id = np.where(superevent_ids==obs_cand[oc_id])[0][0]
obs_cand_dist = superevent_dist[sel_id]
obs_cand_time = superevent_times[sel_id]
obs_cand_90_per_area = superevent_90_per_area[sel_id]
r = 1.2*radius_scale/get_radius(obs_cand_90_per_area)
circle_trans = (fig.dpi_scale_trans + transforms.ScaledTranslation(obs_cand_time,obs_cand_dist, ax.transData))
circle = mpatches.Circle(np.array([0,0]), radius=r,facecolor="none", edgecolor="k", transform=circle_trans)
ax.add_patch(circle)
if (oc_id == 0):
ax.text(obs_cand_time+30,obs_cand_dist,obs_cand[oc_id],verticalalignment="center", horizontalalignment="left", fontsize=7)
else:
ax.text(obs_cand_time+10,obs_cand_dist+100,obs_cand[oc_id],verticalalignment="center", horizontalalignment="left", fontsize=7)
plt.savefig("Kaplan_plot_O3.pdf")
|
[
"[email protected]"
] | |
f4fbc66cba4d9d7cd720b6577b123dc775e79f58
|
8642008cec13a401d3b633d907c295851b615d22
|
/check_evals.py
|
fd2d9c3095e89183f427c81d1f652e922bbca86e
|
[] |
no_license
|
yjxiao/disentrepr_wtc
|
174a397108515af027ec782bbd74785321f34b26
|
79b86e7ee1a8f32727363e70313eef5e633da041
|
refs/heads/master
| 2023-02-14T17:59:18.470431 | 2021-01-07T00:43:32 | 2021-01-07T00:43:32 | 181,963,450 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 2,834 |
py
|
import os
import numpy as np
import pickle
from colorama import Fore, Style
result_dir = 'results'
record_file = 'results/list.pkl'
template = 'M-{}.D-{}.T-{}.{}-{}.S-{}.csv'
datasets = ['dsprites', 'cars3d', 'shapes3d']
metrics = ['mig', 'factor', 'modularity', 'recon', 'wmig', 'wmod']
tasks = ['vae', 'tc', 'factor', 'wae', 'wtc', 'wtc_wae']
hparams = ['BETA', 'BETA', 'GAMMA', 'BETA', 'GAMMA', 'BETA']
values = {
'vae': (1, 4, 8, 16),
'tc': (1, 4, 8, 16),
'factor': (10, 20, 40, 80),
'wae': (1, 4, 8, 16),
'wtc': (1, 4, 8, 16),
'wtc_wae': (1, 4, 8, 16),
}
seeds = [1, 11, 42, 73, 89]
def check(result_dir, task, hparam, dataset, metric, seed):
for val in values[task]:
filename = template.format(
metric, dataset, task, hparam, val, seed
)
if not os.path.isfile(os.path.join(result_dir, filename)):
return False
return True
def wrap_green(s):
return Fore.GREEN + s + Fore.RESET
def wrap_yellow(s):
return Fore.YELLOW + s + Fore.RESET
def wrap_bright(s):
return Style.BRIGHT + s + Style.RESET_ALL
def print_table(checks, prev_checks):
metric_header = ''.join([' {:^5} |'.format(metric[:5]) for metric in metrics])
header = '|{:^10}| seed |{}'.format('', metric_header)
line = '|' + '-' * 65 + '|'
rows = [header, line]
for dataset in checks:
prev_check = prev_checks.get(dataset, np.zeros((len(seeds), len(metrics))))
for i, seed in enumerate(seeds):
check_strs = ''
for x, prev in zip(checks[dataset][i], prev_check[i]):
if x > 0:
if prev > 0:
check_strs += ' ' + wrap_bright(wrap_green('✓')) + ' |'
else:
check_strs += ' ' + wrap_green('✓') + ' |'
else:
check_strs += ' ' + wrap_yellow('☓') + ' |'
row = '|{:^10}|{:^6}|{}'.format(dataset, seed, check_strs)
rows.append(row)
rows.append(line)
print('\n'.join(rows))
if os.path.exists(record_file):
with open(record_file, 'rb') as f:
old_record = pickle.load(f)
else:
old_record = {}
all_checks = {}
for task, hparam in zip(tasks, hparams):
all_checks[task] = {}
for dataset in datasets:
all_checks[task][dataset] = np.zeros((len(seeds), len(metrics)))
for i, metric in enumerate(metrics):
for j, seed in enumerate(seeds):
all_checks[task][dataset][j, i] = check(
result_dir, task, hparam, dataset, metric, seed)
for task in tasks:
print('\n--{:-<65}'.format(task))
print_table(all_checks[task], old_record.get(task, {}))
with open(record_file, 'wb') as f:
pickle.dump(all_checks, f)
|
[
"[email protected]"
] | |
ba6debbc3718be3a2b21ad9029cffc2a16d99d9f
|
25c3e2f3a98934556dd5d06b7aebc9f68abe60e3
|
/dojo_model/urls.py
|
48a6c6d4b21d67a764a9892a8e779622575b9329
|
[] |
no_license
|
jeremy-dardour/django-dojos
|
30d46c52c8ad5572e9b33032b89e57b627c2414e
|
5356392836a3a8849d954ea9b9c12eec611814ff
|
refs/heads/master
| 2020-03-15T04:29:04.425177 | 2018-05-03T08:42:09 | 2018-05-03T08:42:09 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 752 |
py
|
"""dojo_model URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/2.0/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
urlpatterns = [
path('admin/', admin.site.urls),
]
|
[
"[email protected]"
] | |
01be1944208fee630c220d4581bb38c37136b962
|
47085b6b92779bc076774d5f7d08bcd54e6fa41f
|
/scraping/eumPrac.py
|
0bc9bc96025941602ec1c3e23d20cfe7d05864c9
|
[] |
no_license
|
his7876/Insure_Tech-2021
|
9c6f60a91f955c235a519bb1cab3cf40a2bade57
|
29c5f8f059e2f2c979d92504c086daba28e2f863
|
refs/heads/master
| 2023-07-15T17:36:10.566018 | 2021-08-27T06:54:28 | 2021-08-27T06:54:28 | 389,842,742 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,581 |
py
|
from selenium import webdriver
from selenium.webdriver.support.ui import Select
driver = webdriver.Chrome('./chromedriver')
# 크롬을 통해서 스크래핑을 진행 크롬 드라이버 로딩
driver.get('https://www.eum.go.kr/web/am/amMain.jsp')
#addressInput = driver.find_element_by_xpath('//*[@id="recent"]/input')
#addressInput.send_keys('서울특별시 광진구 자양동 능동로 120')
#
#nextButton = driver.find_element_by_xpath('//*[@id="frm"]/fieldset/div[3]/p/span/a')
#nextButton.click()
#실습 2 지번으로 조회하는 기능 만들기
# 전라남도/ 고흥군 / 고흥읍 / 남계리 / 45/ 1
#조회후 공시지가 가지고 오기
sidoSelect = Select(driver.find_element_by_xpath('//*[@id="selSido"]'))
gun = Select(driver.find_element_by_xpath('//*[@id="selSgg"]'))
dong = Select(driver.find_element_by_xpath('//*[@id="selUmd"]'))
ri = Select(driver.find_element_by_xpath('//*[@id="selRi"]'))
bobn = driver.find_element_by_xpath('//*[@id="bobn"]')
bubn = driver.find_element_by_xpath('//*[@id="selUmd"]')
nextButton = driver.find_element_by_xpath('//*[@id="frm"]/fieldset/div[3]/p/span/a')
sidoSelect.select_by_visible_text('전라남도')
driver.implicitly_wait(1)
gun.select_by_visible_text('고흥군')
driver.implicitly_wait(1)
dong.select_by_visible_text('고흥읍')
driver.implicitly_wait(1)
ri.select_by_visible_text('남계리')
driver.implicitly_wait(1)
bobn.send_keys('45')
bubn.send_keys('1')
nextButton.click()
result = driver.find_element_by_xpath('//*[@id="appoint"]/div[2]/table/tbody/tr[3]/td')
print(result.text)
|
[
"[email protected]"
] | |
fbafc943f1395563edbc22151835c6f3584f5b7d
|
e1a68ace5a30e4ec4bdf0962a9653fba7dd2e615
|
/src/pickle_face_model.py
|
d1994f7c96c3d63ce215e85e60593c0f67ba4e37
|
[] |
no_license
|
ivychill/arcface_test
|
8d36a0ebcaeb097551b5d3544bf7a6c09cf6c5da
|
3c448382e6c6f8b2d775b37928d690fb8904ff3f
|
refs/heads/master
| 2020-06-24T10:31:13.840538 | 2019-07-26T03:41:25 | 2019-07-26T03:41:25 | 198,939,611 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 4,888 |
py
|
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from scipy import misc
import sys
import os
import pickle
import argparse
#import tensorflow as tf
import numpy as np
import mxnet as mx
import random
import cv2
import sklearn
from sklearn.decomposition import PCA
from time import sleep
from easydict import EasyDict as edict
# sys.path.append(os.path.join(os.path.dirname(__file__), 'src'))
from mtcnn_detector import MtcnnDetector
import face_image
import face_preprocess
def do_flip(data):
for idx in range(data.shape[0]):
data[idx,:,:] = np.fliplr(data[idx,:,:])
def get_model(ctx, image_size, model_str, layer):
_vec = model_str.split(',')
assert len(_vec)==2
prefix = _vec[0]
epoch = int(_vec[1])
print('---------*** Model loading ***-----------')
# sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch)
model_path=os.path.join(os.path.dirname(__file__), '..', 'model')
# # transform model to npy
print(model_path)
with open(os.path.join(model_path,'sym.pkl'), 'rb') as a_: # open file with write-mode
# picklestring = pickle.dump(sym, a_)
sym=pickle.load(a_)
with open(os.path.join(model_path,'arg_params.pkl'), 'rb') as b_: # open file with write-mode
# picklestring = pickle.dump(arg_params, b_)
arg_params=pickle.load(b_)
with open(os.path.join(model_path,'aux_params.pkl'), 'rb') as c_: # open file with write-mode
# picklestring = pickle.dump(aux_params, c_)
aux_params=pickle.load(c_)
#end
all_layers = sym.get_internals()
sym = all_layers[layer+'_output']
# print('sym',type(sym))
# print('arg_params',type(arg_params))
# print('aux_params',type(aux_params))
model = mx.mod.Module(symbol=sym, context=ctx, label_names = None)
#model.bind(data_shapes=[('data', (args.batch_size, 3, image_size[0], image_size[1]))], label_shapes=[('softmax_label', (args.batch_size,))])
# print(image_size[0])
model.bind(data_shapes=[('data', (1, 3, image_size[0], image_size[1]))])
# print(arg_params,aux_params)
model.set_params(arg_params, aux_params)
return model
class FaceModel:
def __init__(self, args):
self.args = args
ctx = mx.gpu(args.gpu)
_vec = args.image_size.split(',')
assert len(_vec)==2
image_size = (int(_vec[0]), int(_vec[1]))
self.model = None
self.ga_model = None
if len(args.model)>0:
self.model = get_model(ctx, image_size, args.model, 'fc1')
if len(args.ga_model)>0:
self.ga_model = get_model(ctx, image_size, args.ga_model, 'fc1')
# self.threshold = args.threshold
self.det_minsize = 50
self.det_threshold = [0.6,0.7,0.8]
#self.det_factor = 0.9
self.image_size = image_size
mtcnn_path = os.path.join(os.path.dirname(__file__), '..','mtcnn-model')
if args.det==0:
# detector = MtcnnDetector(model_folder=mtcnn_path, ctx=ctx, num_worker=1, accurate_landmark = True, threshold=self.det_threshold)
with open(os.path.join(mtcnn_path,'mtcnn_0.pkl'), 'rb') as d_: # open file with write-mode
# picklestring = pickle.dump(detector, d_)
detector=pickle.load(d_)
else:
# detector = MtcnnDetector(model_folder=mtcnn_path, ctx=ctx, num_worker=1, accurate_landmark = True, threshold=[0.0,0.0,0.2])
with open(os.path.join(mtcnn_path,'mtcnn_1.pkl'), 'rb') as e_: # open file with write-mode
# picklestring = pickle.dump(detector, e_)
detector=pickle.load(e_)
self.detector = detector
def get_input(self, face_img):
ret = self.detector.detect_face(face_img, det_type = self.args.det)
if ret is None:
return None
bbox, points = ret
if bbox.shape[0]==0:
return None
bbox = bbox[0,0:4]
points = points[0,:].reshape((2,5)).T
# print('bbox',bbox)
# print('points',points)
nimg = face_preprocess.preprocess(face_img, bbox, points, image_size='112,112')
nimg = cv2.cvtColor(nimg, cv2.COLOR_BGR2RGB)
aligned = np.transpose(nimg, (2,0,1))
return aligned
def get_feature(self, aligned):
input_blob = np.expand_dims(aligned, axis=0)
data = mx.nd.array(input_blob)
db = mx.io.DataBatch(data=(data,))
self.model.forward(db, is_train=False)
embedding = self.model.get_outputs()[0].asnumpy()
embedding = sklearn.preprocessing.normalize(embedding).flatten()
return embedding
def get_ga(self, aligned):
input_blob = np.expand_dims(aligned, axis=0)
data = mx.nd.array(input_blob)
db = mx.io.DataBatch(data=(data,))
self.ga_model.forward(db, is_train=False)
ret = self.ga_model.get_outputs()[0].asnumpy()
g = ret[:,0:2].flatten()
gender = np.argmax(g)
a = ret[:,2:202].reshape( (100,2) )
a = np.argmax(a, axis=1)
age = int(sum(a))
return gender, age
|
[
"[email protected]"
] | |
7896802cac5205e45b06d674a76f0d54a7d854a6
|
7f8abf3a11122387be37c889c4aa7373777234af
|
/Free_energy_day1/OpenMM/density.py
|
f586c2598862d264549f9264fa97d94581f983b0
|
[
"CC-BY-4.0"
] |
permissive
|
QCMM/workshop2017
|
625473a2e3772e2ebf7c0fa10a62ed80b3690642
|
29b9f58046e4e85daee816995fb5b6944a333106
|
refs/heads/master
| 2021-08-24T05:25:24.930948 | 2017-12-08T07:00:13 | 2017-12-08T07:00:13 | 110,884,696 | 1 | 3 |
CC-BY-4.0
| 2017-12-02T06:51:53 | 2017-11-15T20:47:00 |
HTML
|
UTF-8
|
Python
| false | false | 10,844 |
py
|
#--------------------------------------
# OpenMM Minimization, NVT, NPT, PROD
#
# Author: Dr Gaetano Calabro'
# University of California, Irvine
# ver 0.0 06/23/2016
# Adapted Nov. 2017 by David Mobley
#--------------------------------------
import numpy as np
import os, sys
import math
import simtk.openmm as mm
from simtk.openmm import app
from simtk.openmm.app import *
from simtk.openmm import Platform
from simtk.unit import *
from simtk.openmm import XmlSerializer
from pymbar import timeseries as ts
import pandas as pd
#----------------USER INFO-----------------
#------------------------------------------
#Skip equilibration? Use if we previously equilibrated, to load stored
#trajectory file and jump straight into production
skip_equilibration = False
identifier = "phenol_toluene_cyclohexane_1_10_100"
DATA_PATH = "mixtures/amber/"
RESULT_PATH = "density_simulation/"
#md_platform = 'Reference' # e.g. Reference or CUDA or OpenCL
md_platform = 'OpenCL'
# Some file names
prmtop_filename = DATA_PATH + identifier + '.prmtop'
inpcrd_filename = DATA_PATH + identifier + '.inpcrd'
xml_filename = RESULT_PATH + identifier + '.xml' # For serialized system
#--------------MINIMIZATION----------------
MIN_TIME_STEP = 0.5 * femtoseconds
MIN_STEPS = 0 # 0=Until convergence is reached
MIN_TOLERANCE = 10.0 * kilojoule_per_mole
MIN_PLATFORM = Platform.getPlatformByName('Reference')
MIN_FRICTION = 1.0 / picoseconds
#-------------------NVT--------------------
NVT_TIME_STEP = 1.0 * femtoseconds
NVT_STEPS = 50000
NVT_FRICTION = 1.0 / picoseconds
NVT_PLATFORM = Platform.getPlatformByName(md_platform)
NVT_PROPERTIES = {'DeviceIndex':1} # TEST
if md_platform == 'CUDA':
NVT_PROPERTIES = {'CudaPrecision': 'mixed'}
else: NVT_PROPERTIES={}
NVT_OUTPUT_FREQ = 10000
NVT_DATA_FREQ = 10000
#------------------NPT--------------------
NPT_TIME_STEP = 2.0 * femtoseconds
NPT_STEPS = 5000000
NPT_FRICTION = 1.0 / picoseconds
BAROSTAT_FREQUENCY = 25
NPT_PLATFORM = Platform.getPlatformByName(md_platform)
if md_platform == 'CUDA':
NPT_PROPERTIES = {'CudaPrecision': 'mixed'}
else: NPT_PROPERTIES={}
NPT_OUTPUT_FREQ = 500000
NPT_DATA_FREQ = 500000
#--------------PRODUCTION------------------
PROD_TIME_STEP = 2.0 * femtoseconds
PROD_STEPS = 50000
PROD_FRICTION = 1.0 / picoseconds
PROD_PLATFORM = Platform.getPlatformByName(md_platform)
if md_platform == 'CUDA':
PROD_PROPERTIES = {'CudaPrecision': 'mixed'}
else: PROD_PROPERTIES={}
PROD_OUTPUT_FREQ = 500
PROD_DATA_FREQ = 500
#------------GEN PARAMETERS--------------
CUTOFF = 0.95 * nanometers
TEMPERATURE = 300 * kelvin
PRESSURE = 1.0 * atmospheres
#Density STD tolerance
STD_ERROR_TOLERANCE = 0.0005 # g/mL
#---------------END USER INFO---------------
#-------------------------------------------
nvt_dcd_filename = RESULT_PATH + "nvt/" + identifier + "_nvt.dcd"
nvt_data_filename = RESULT_PATH + "nvt/" + identifier + "_nvt.csv"
npt_dcd_filename = RESULT_PATH + "npt/" + identifier + "_npt.dcd"
npt_data_filename = RESULT_PATH + "npt/" + identifier + "_npt.csv"
prod_data_filename = RESULT_PATH + "prod/" + identifier + "_prod.csv"
prod_dcd_filename = RESULT_PATH + "prod/" + identifier + "_prod.dcd"
# Load prmtop, crd
prmtop = AmberPrmtopFile( prmtop_filename )
inpcrd = AmberInpcrdFile( inpcrd_filename )
def make_path(filename):
try:
path = os.path.split(filename)[0]
os.makedirs(path)
except OSError:
pass
# Create System and store to OpenMM XML for easier reuse later
system = prmtop.createSystem(nonbondedMethod = PME, nonbondedCutoff = 1*nanometer, constraints = HBonds)
make_path(xml_filename)
serialized_system = XmlSerializer.serialize(system)
file = open(xml_filename, 'w')
file.write(serialized_system)
file.close()
def minimization():
# Load OpenMM System
file = open(xml_filename, 'r')
serialized_system = file.read()
system = XmlSerializer.deserialize(serialized_system)
# Select Integrator
integrator = mm.LangevinIntegrator(TEMPERATURE, MIN_FRICTION, MIN_TIME_STEP)
# Set Simulation
simulation = app.Simulation(prmtop.topology, system, integrator, MIN_PLATFORM)
# Set Position
simulation.context.setPositions(inpcrd.positions)
state = simulation.context.getState(getEnergy=True)
if np.isnan(state.getPotentialEnergy() / kilojoule_per_mole):
raise ValueError("The Potential Energy before minimization is NaN")
# Minimization
print('Minimizing...\n')
simulation.minimizeEnergy(tolerance=MIN_TOLERANCE, maxIterations=MIN_STEPS)
state = simulation.context.getState(getPositions=True, getEnergy=True)
if np.isnan(state.getPotentialEnergy() / kilojoule_per_mole):
raise ValueError("The Potential Energy after minimization is NaN")
coords = state.getPositions()
return coords
def nvt(coords):
# Load OpenMM System
file = open(xml_filename, 'r')
serialized_system = file.read()
system = XmlSerializer.deserialize(serialized_system)
# Select Integrator
integrator = mm.LangevinIntegrator(TEMPERATURE, NVT_FRICTION, NVT_TIME_STEP)
# Set Simulation
simulation = app.Simulation(prmtop.topology, system, integrator, NVT_PLATFORM, NVT_PROPERTIES)
# Set Position and velocities
simulation.context.setPositions(coords)
simulation.context.setVelocitiesToTemperature(TEMPERATURE)
# Set Reporter
simulation.reporters.append(app.DCDReporter(nvt_dcd_filename, NVT_OUTPUT_FREQ))
simulation.reporters.append(app.StateDataReporter(nvt_data_filename, NVT_DATA_FREQ, step=True, potentialEnergy=True, temperature=True, density=True))
state = simulation.context.getState(getEnergy=True)
if np.isnan(state.getPotentialEnergy() / kilojoule_per_mole):
raise ValueError("The Potential Energy before NVT is NaN")
print('NVT...\n')
simulation.step(NVT_STEPS)
state = simulation.context.getState(getPositions=True, getVelocities=True, getEnergy=True)
if np.isnan(state.getPotentialEnergy() / kilojoule_per_mole):
raise ValueError("The Potential Energy after NVT is NaN")
coords = state.getPositions()
velocities = state.getVelocities()
return coords, velocities
def npt(coords, velocities):
# Create OpenMM System
file = open(xml_filename, 'r')
serialized_system = file.read()
system = XmlSerializer.deserialize(serialized_system)
# Select Integrator
integrator = mm.LangevinIntegrator(TEMPERATURE, NPT_FRICTION, NPT_TIME_STEP)
# Set Barostat
system.addForce(mm.MonteCarloBarostat(PRESSURE, TEMPERATURE, BAROSTAT_FREQUENCY))
# Set Simulation
simulation = app.Simulation(prmtop.topology, system, integrator, NPT_PLATFORM, NPT_PROPERTIES)
# Set Position and velocities
simulation.context.setPositions(coords)
simulation.context.setVelocities(velocities)
# Set Reporter
simulation.reporters.append(app.DCDReporter(npt_dcd_filename, NPT_OUTPUT_FREQ))
simulation.reporters.append(app.StateDataReporter(npt_data_filename, NPT_DATA_FREQ, step=True, potentialEnergy=True, temperature=True, density=True))
state = simulation.context.getState(getEnergy=True)
if np.isnan(state.getPotentialEnergy() / kilojoule_per_mole):
raise ValueError("The Potential Energy before NPT is NaN")
print('NPT...\n')
simulation.step(NPT_STEPS)
state = simulation.context.getState(getPositions=True, getVelocities=True, getEnergy=True)
if np.isnan(state.getPotentialEnergy() / kilojoule_per_mole):
raise ValueError("The Potential Energy after NPT is NaN")
coords = state.getPositions()
velocities = state.getVelocities()
box = state.getPeriodicBoxVectors()
return coords, velocities, box
def production(coords, velocities, box):
# Create OpenMM System
file = open(xml_filename, 'r')
serialized_system = file.read()
system = XmlSerializer.deserialize(serialized_system)
# Select Integrator
integrator = mm.LangevinIntegrator(TEMPERATURE, PROD_FRICTION, PROD_TIME_STEP)
# Set Barostat
system.addForce(mm.MonteCarloBarostat(PRESSURE, TEMPERATURE, BAROSTAT_FREQUENCY))
# Set Simulation
simulation = app.Simulation(prmtop.topology, system, integrator, PROD_PLATFORM, PROD_PROPERTIES)
# Set Position and velocities
simulation.context.setPositions(coords)
if velocities is not None:
simulation.context.setVelocities(velocities)
else: #reset
simulation.context.setVelocitiesToTemperature(TEMPERATURE)
# Set Box
#box = box.in_units_of(nanometer)
simulation.context.setPeriodicBoxVectors(box[0], box[1], box[2])
# Set Reporter
simulation.reporters.append(app.DCDReporter(prod_dcd_filename, PROD_OUTPUT_FREQ))
simulation.reporters.append(app.StateDataReporter(prod_data_filename, PROD_DATA_FREQ, step=True, potentialEnergy=True, temperature=True, density=True))
state = simulation.context.getState(getEnergy=True)
if np.isnan(state.getPotentialEnergy() / kilojoule_per_mole):
raise ValueError("The Potential Energy before Production is NaN")
print('PRODUCTION...\n')
converged = False
while not converged:
simulation.step(PROD_STEPS)
d = pd.read_csv(prod_data_filename, names=["step", "U", "Temperature", "Density"], skiprows=1)
density_ts = np.array(d.Density)
[t0, g, Neff] = ts.detectEquilibration(density_ts, nskip=1000)
density_ts = density_ts[t0:]
density_mean_stderr = density_ts.std() / np.sqrt(Neff)
print("Current density mean std error = %f g/mL" % density_mean_stderr)
if density_mean_stderr < STD_ERROR_TOLERANCE:
converged = True
print("...Convergence is OK\n")
state = simulation.context.getState(getPositions=True, getVelocities=True, getEnergy=True)
if np.isnan(state.getPotentialEnergy() / kilojoule_per_mole):
raise ValueError("The Potential Energy after Production is NaN")
coords = state.getPositions()
velocities = state.getVelocities()
box = state.getPeriodicBoxVectors()
return coords, velocities, box
if __name__=='__main__':
make_path(RESULT_PATH + 'nvt/')
make_path(RESULT_PATH + 'npt/')
make_path(RESULT_PATH + 'prod/')
if not skip_equilibration:
coords = minimization()
coords, velocities = nvt(coords)
coords, velocities, box = npt(coords, velocities)
else:
import mdtraj as md
# Load dcd file with relevant INFO
traj = md.load_dcd( npt_dcd_filename, prmtop_filename )
coords = traj.xyz[-1]
#velocities = traj.velocities[-1]
box = traj.unitcell_vectors[-1]
#DCD file seems not to have velocities; set to None to trigger a reset to temperature
velocities = None
production(coords, velocities, box)
|
[
"[email protected]"
] | |
77e6c2bbf898517312d116dee4d28d6178a86068
|
a7c84f44b4d52cbc2ec8e3a1e89c5e5f2a2be07c
|
/td/migrations/0011_waregion.py
|
cc5b968f25115d416ec7fded7126698d3463031e
|
[] |
no_license
|
unfoldingWord-dev/translationDatabaseWeb
|
561351a78d8a4ef1121d766b8fb44dd5c0539079
|
b6c2521df9a7baed1eb4610581d61c75c52419ef
|
refs/heads/master
| 2023-06-09T08:55:58.981746 | 2023-06-06T12:28:28 | 2023-06-06T12:28:28 | 25,008,788 | 9 | 8 | null | 2023-06-06T12:28:29 | 2014-10-09T21:11:28 |
Python
|
UTF-8
|
Python
| false | false | 771 |
py
|
# -*- coding: utf-8 -*-
# Generated by Django 1.9 on 2016-01-28 14:56
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('td', '0010_remove_region_wa_director'),
]
operations = [
migrations.CreateModel(
name='WARegion',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(db_index=True, max_length=100)),
('slug', models.SlugField(max_length=100)),
],
options={
'ordering': ['name'],
'db_table': 'wa_region',
},
),
]
|
[
"[email protected]"
] | |
e2a134539647aff5a20fb68320c55d08b79679d4
|
af46ef78a8e680733efa0056e0388db529a523a3
|
/dictionary/del.py
|
dd0ebbdf23dc634d3de3887209157f088bdd56ec
|
[] |
no_license
|
mjhea0/python-basic-examples
|
90f3aa1cb1a1399a7fe7b1b7e07cced517433490
|
aaf4a5458f3e016703b6677033ea17b9cc901596
|
refs/heads/master
| 2021-01-15T18:31:28.455291 | 2013-09-21T03:59:23 | 2013-09-21T03:59:23 | 13,141,089 | 1 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 245 |
py
|
#coding:utf-8
d = {"server":"A", "database":"master"}
print d
del d['server']
print d
d.clear()
print d
del d
try:
print d
except:
print "Error, Not Defined."
|
[
"[email protected]"
] | |
9a1a1bdc04ee35d0744eed400f9333d11541d4ad
|
e23a4f57ce5474d468258e5e63b9e23fb6011188
|
/030_control_flow/003_for/_exercises/_templates/Python 3 Most Nessesary/4.3.Listing 4.7. Enumeration of elements of the list of tuples.py
|
8299ab5805fc0af5d024b0635257ef3563f010f0
|
[] |
no_license
|
syurskyi/Python_Topics
|
52851ecce000cb751a3b986408efe32f0b4c0835
|
be331826b490b73f0a176e6abed86ef68ff2dd2b
|
refs/heads/master
| 2023-06-08T19:29:16.214395 | 2023-05-29T17:09:11 | 2023-05-29T17:09:11 | 220,583,118 | 3 | 2 | null | 2023-02-16T03:08:10 | 2019-11-09T02:58:47 |
Python
|
UTF-8
|
Python
| false | false | 132 |
py
|
# # -*- coding: utf-8 -*-
#
# arr = [(1, 2), (3, 4)] # Список кортежей
# ___ a, b __ ?
# print(a, b)
|
[
"[email protected]"
] | |
3fe66672ed5b331e6d30d04d1e42efee28ac75ac
|
59c5c84824b610692c19663d8b56fe19b66c41f2
|
/test_scripts/v_user.py
|
e3d9022a9a79001010fcbbfe00d5ddf9f99480ea
|
[] |
no_license
|
chesarin/Multi-test
|
bc124d1c9a3cf41e533e7845899b8c815909ef99
|
e0e79978b2b1520072e97395c8adb647bed45db0
|
refs/heads/master
| 2016-09-15T19:20:12.664378 | 2012-07-24T20:22:50 | 2012-07-24T20:22:50 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 949 |
py
|
import mechanize
import random
import time
class Transaction(object):
def __init__(self):
self.custom_timers = {}
self.base_url = 'http://www.aliceandolivia.com'
# pass
def run(self):
br = mechanize.Browser()
br.set_handle_robots(False)
start_timer = time.time()
resp = br.open(self.base_url)
# resp = br.open(self.base_url + '/r/all/')
resp.read()
print br.form
latency = time.time() - start_timer
self.custom_timers['All_Items'] = latency
assert (resp.code == 200), 'Bad HTTP Response'
# assert (resp.code == 200), 'Bad Response: HTTP %s' % resp.code
# assert ('Example Web Page' in resp.get_data())
# r = random.uniform(1, 2)
# time.sleep(r)
# self.custom_timers['Example_Timer'] = r
if __name__ == '__main__':
trans = Transaction()
trans.run()
print trans.custom_timers
|
[
"[email protected]"
] | |
e45dcd9c4e995ad520f6103a5d16583c791a71be
|
243f97eb122ad4fa5c8412b93f6135fa5258ad60
|
/w5/5_2.py
|
36b83225cc61f1821ccfe9916f08792a586976ae
|
[] |
no_license
|
tj---/ba1
|
bc27d38d19aef62bcc972160a8745a3d0e666b9b
|
a484e87eb71d10b342846b689c55ff8f4e0b971a
|
refs/heads/master
| 2016-09-06T21:43:07.077190 | 2015-05-24T17:38:51 | 2015-05-24T17:38:51 | 14,373,510 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 2,539 |
py
|
import sys
def main():
lines = [line.strip() for line in open(sys.argv[1])]
reversed_pairs = reversed_pairs_map(lines)
global_results = []
first = True
while(len(reversed_pairs) > 0):
clean_pairs(reversed_pairs, global_results, first)
start = find_start(reversed_pairs)
if(-1 == start):
for key, val in reversed_pairs.items():
global_results.append(key)
else:
# nothing is left to be done anymore - this will typically be a single element with only incoming edge
global_results.append("->".join(find_cycle(start, reversed_pairs)))
first = False
formatted_print(global_results)
# remove the elements that either don't have any incoming edges. or don't have any outgoing due to the previous step
def clean_pairs(reversed_pairs, global_results, first):
for key, val in reversed_pairs.items():
if(0 == len(val)):
reversed_pairs.pop(key, None)
for key, val in reversed_pairs.items():
for element in val:
if element not in reversed_pairs:
if(first):
global_results.append(element)
val.remove(element)
def find_cycle(start, reversed_pairs):
result = []
stack = []
stack.append(start)
while(len(stack) > 0):
u = stack[len(stack) - 1]
w_list = reversed_pairs[u]
if(len(w_list) > 0):
w = w_list[0]
stack.append(w)
w_list.remove(w)
else:
result.append(str(u))
stack.pop()
return result
def reversed_pairs_map(lines):
reversed_pairs = {}
for line in lines:
F_A_T = line.split(' ')
F = F_A_T[0]
T_LIST = F_A_T[2].split(',')
for T in T_LIST:
target_list = []
if T in reversed_pairs:
target_list = reversed_pairs[T]
target_list.append(F)
reversed_pairs[T] = target_list
return reversed_pairs
def find_start(reversed_pairs):
# make a map of [node] => {[incoming], [outgoing]} ==> Choose the one where incoming is strictly less than outgoing
check_map = {}
for key,value in reversed_pairs.items():
edge_data = [0, 0]
if key in check_map:
edge_data = check_map[key]
edge_data[0] += len(value)
check_map[key] = edge_data
for element in value:
el_data = [0, 0]
if element in check_map:
el_data = check_map[element]
el_data[1] += 1
check_map[element] = el_data
for key,value in check_map.items():
if(value[0] < value[1]):
return key
return -1
def formatted_print(global_results):
result = [global_results[0]]
index = 1
while(index < len(global_results)):
result.append(global_results[index][len(global_results[index]) - 1:])
index += 1
print "".join(result)
if __name__ == '__main__':
main()
|
[
"[email protected]"
] | |
125c58858d61224fa2a38a8a70833d29d4b3427f
|
8476cfdf99e500e50f199080f7f8a93b4892b24b
|
/Lab3/influence-maximization/IC/degreeHeuristic.py
|
cb20203e3ae6b4ac84aedc67be288dc77516f6d9
|
[] |
no_license
|
liziwl/AI-Lab
|
31def6d445a740e5201ad42324301f71f08009ee
|
ec2e0b2af00210a19a60317a06761275d4c276c6
|
refs/heads/master
| 2021-03-24T13:50:44.418255 | 2018-07-10T03:28:17 | 2018-07-10T03:28:17 | 104,038,567 | 16 | 2 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,413 |
py
|
''' Implementation of degree heuristic[1] for Independent Cascade model
of influence propagation in graph G.
Takes k nodes with the largest degree.
[1] -- Wei Chen et al. Efficient influence maximization in Social Networks
'''
__author__ = 'ivanovsergey'
from priorityQueue import PriorityQueue as PQ # priority queue
def degreeHeuristic(G, k, p=.01):
''' Finds initial set of nodes to propagate in Independent Cascade model (with priority queue)
Input: G -- networkx graph object
k -- number of nodes needed
p -- propagation probability
Output:
S -- chosen k nodes
'''
S = []
d = PQ()
for u in G:
degree = sum([G[u][v]['weight'] for v in G[u]])
# degree = len(G[u])
d.add_task(u, -degree)
for i in range(k):
u, priority = d.pop_item()
S.append(u)
return S
def degreeHeuristic2(G, k, p=.01):
''' Finds initial set of nodes to propagate in Independent Cascade model (without priority queue)
Input: G -- networkx graph object
k -- number of nodes needed
p -- propagation probability
Output:
S -- chosen k nodes
'''
S = []
d = dict()
for u in G:
degree = sum([G[u][v]['weight'] for v in G[u]])
# degree = len(G[u])
d[u] = degree
for i in range(k):
u, degree = max(d.iteritems(), key=lambda (k,v): v)
d.pop(u)
S.append(u)
return S
|
[
"[email protected]"
] | |
7f1bc74cfaa62ab5a8142c71c53727d2b94f319d
|
624ac147a39a244aa2a83106a0a7142d96d27861
|
/src/tracker_registration/apps.py
|
6709e21fea4866c46c00b7a68044f8f52a89b0bc
|
[] |
no_license
|
realaravinth/tracking
|
3e7138300fe90f6e677b4d6408ff071071414f5b
|
6344dc921ea02d0869fe8b7b34da6d2134131789
|
refs/heads/master
| 2020-09-11T05:59:08.615432 | 2020-06-28T14:39:21 | 2020-06-28T14:39:21 | 221,962,382 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 114 |
py
|
from django.apps import AppConfig
class TrackerRegistrationConfig(AppConfig):
name = 'tracker_registration'
|
[
"[email protected]"
] | |
8a71bd06c462c475ee7a108466c20a6930551e9f
|
a97bf388ba8b50c7c4fb5e2bfd40df7ed72a348d
|
/python/live/spider.py
|
2c66b396b58b9041aaca9167786c44c4dec3afe9
|
[] |
no_license
|
baboy/BK
|
5bea71944b3a7731394242b17fff4772ee6a4f5c
|
1d00d014cf418c97c38ebff9c4ffcec64f05fa46
|
refs/heads/master
| 2021-01-22T02:08:25.028535 | 2015-06-08T04:36:16 | 2015-06-08T04:36:16 | 15,705,700 | 0 | 2 | null | null | null | null |
UTF-8
|
Python
| false | false | 2,009 |
py
|
# -*- coding:utf-8 -*-
import urllib2, sys
import json
import dateutil.parser
import time
from HTMLParser import HTMLParser
import re
from lxml import etree
import lxml.html as lhtml
import lxml.html.soupparser as soupparser
import StringIO
import email.utils as eut
import datetime
import DB
import md5
import json
import codecs
class Page():
def __init__(self):
self.timeoutInterval = 20
def download(self,url):
print "Page.download:",url
urllib2.socket.setdefaulttimeout(self.timeoutInterval)
response = urllib2.urlopen(url)
#print response.headers
data = response.read()
response.close()
return data
class CNTVApi( HTMLParser):
def __init__(self, url):
self.url = url
HTMLParser.__init__(self)
self.links = []
self.db = DB.DB()
self.db.appid = "BK"
self.db.module = "news"
def getLiveUrl(self, api):
page = Page()
text = page.download(api)
data = json.loads(text)
return data
def parse(self):
page = Page()
text = page.download(self.url)
data = json.loads(text)
channels = data.get("data").get("items")
for item in channels:
keyMap = {"icon":"channelImg","name":"title","reference_url":"liveUrl"}
channel = {"rate":"300","source":"CNTV"};
for k in keyMap:
k2 = keyMap[k]
channel[k] = item.get(k2)
title = item.get("title")
channel_id = item.get("channelId")
thumbnail = "http://t.live.cntv.cn/imagehd/"+channel_id+"_01.png"
print thumbnail
channel_id = self.db.getChannelId({"name":title})
print channel_id
if channel_id:
self.db.update({"thumbnail":thumbnail},{"channel_id":channel_id})
#api = self.getLiveUrl(item.get("liveUrl"))
#channel["live_url"] = api.get("iphone")
#rowid = self.db.addItem(channel);
#print rowid
#parser = CNTVApi("http://serv.cbox.cntv.cn/json/zhibo/yangshipindao/ysmc/index.json")
parser = CNTVApi("http://serv.cbox.cntv.cn/json/zhibo/weishipindao/wsmc/index.json")
#parser = CNTVApi("http://serv.cbox.cntv.cn/json/zhibo/difangpindao/dfmc/index.json")
parser.parse()
|
[
"[email protected]"
] | |
71f09344d33d23e6d19b7a1d9894d79eb5f34f8d
|
986236feac0d098977dc3f98b705d68155048233
|
/0x06-python-classes/100-singly_linked_list.py
|
1e413ac97d382295ceaf0a64d2ca75f43de9041b
|
[] |
no_license
|
Noeuclides/holbertonschool-higher_level_programming
|
1f1ec5731840f39ab988593ace190403f701ee67
|
fcf0d733b73904a848b5718266a644c4f6452166
|
refs/heads/master
| 2020-05-18T03:28:56.901071 | 2019-10-03T17:30:20 | 2019-10-03T17:30:20 | 184,145,627 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 352 |
py
|
#!/usr/bin/python3
class Node:
def __init__(self, data=0):
if type(size) is not int:
raise TypeError("size must be an integer")
elif size < 0:
raise ValueError("size must be >= 0")
else:
self.__size = size
def data(self):
return(self.__size ** 2)
def data(self, value):
|
[
"[email protected]"
] | |
c2ef80a416cc1c202f00d685ef27f6d11b3faf08
|
4fed7ad67d3bb7da502acaf347dff542971c1c4c
|
/app.py
|
24f400a3d432d02740af9391a5b196df5498a484
|
[
"MIT"
] |
permissive
|
coolsnake/WebFtp
|
b62437b895261f3083d3f7d3550b541116b30cef
|
d76bce2391d393d2eeb92be7700dd49a1663e696
|
refs/heads/master
| 2021-04-15T14:05:50.752335 | 2017-09-25T10:59:50 | 2017-09-25T10:59:50 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 818 |
py
|
#!/usr/bin/env python3
import tornado.ioloop
import tornado.web
from controllers import index
from controllers import account
settings = {
'template_path': 'template',
'static_path': 'static',
'static_url_prefix': '/static/',
'cookie_secret': '43809138f51b96f8ac24e79b3a2cb482',
'login_url': '/login',
#'xsrf_cookies': True,
'debug': True,
'autoreload': True,
}
application = tornado.web.Application([
# 主页
(r"/index", index.IndexHandler),
# Admin
(r"/admin", index.AdminHandle),
# 登录
(r"/login", account.LoginHandler),
# 登出
(r"/logout", account.LogoutHandler),
# 上传
(r"/upload", index.UploadFileNginxHandle),
], **settings)
if __name__ == '__main__':
application.listen(8000)
tornado.ioloop.IOLoop.instance().start()
|
[
"[email protected]"
] | |
928d9ad7691259312c5e90d3b9d8b07e137765ca
|
493b11ebd30b3c94470186ef4dab0d41fe74a9cf
|
/scripts/opencl/get_html.py
|
2bdfb3daeac10bb47eb7ec718d2a4b7a658471f1
|
[] |
no_license
|
stoneforestwhu/Khronos
|
a2a7d1cc681f60abb605a3e04547a0127e3a13d3
|
e735bb23c6103411e59d40dc901e122bf0bfd5fe
|
refs/heads/master
| 2020-12-29T15:25:21.984159 | 2020-02-09T03:35:17 | 2020-02-09T03:35:17 | 238,653,167 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 505 |
py
|
# -*- coding: utf-8 -*-
import os
import urllib.request
def getHtml(url):
html = urllib.request.urlopen(url).read()
return html
def saveHtml(file_name, file_content):
# 注意windows文件命名的禁用符，比如 /
with open(file_name.replace('/', '_') + ".html", "wb") as f:
# 写文件用bytes而不是str，所以要转码
f.write(file_content)
aurl = "https://www.khronos.org/registry/OpenCL/sdk/1.2/docs/man/"
html = getHtml(aurl)
saveHtml("sduview", html)
print("下载成功")
|
[
"[email protected]"
] | |
02e645c4778cad4c943a92f52a7b3ab301a2e0a9
|
b0e0c3b61cfcc3adba469019de37147c8fcc5235
|
/L2-Copy contents from one array to anoother.py
|
c06eadf0f50854524a032fd86c94a8d9ddcafad1
|
[] |
no_license
|
121910314010/10B14-Lab-Assignment
|
04ae577f9f41c6b65a3bb94c431ef49c02eda5e5
|
e9354e8a096d8e43cf30fd257412a7d6277f1061
|
refs/heads/master
| 2023-01-20T07:35:08.544442 | 2020-11-26T09:53:24 | 2020-11-26T09:53:24 | 290,730,612 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 589 |
py
|
#Copy the contents from one array to another array
#Initialize array
arr1 = [1, 2, 3, 4, 5]
#Create another array arr2 with size of arr1
arr2 = [None] * len(arr1)
length = len(arr1)
#Copying all elements of one array into another
for i in range(0, length) :
arr2[i] = arr1[i]
#Displaying elements of array arr1
print("Elements of original array :")
print(arr1)
print()
#Displaying elements of array arr2
print("Elements of new array: ")
for i in range(0, len(arr2)):
print(arr2[i])
Output:-
Elements of original array :
[1, 2, 3, 4, 5]
Elements of new array:
1
2
3
4
5
|
[
"[email protected]"
] | |
9c26acdd9f243cc659a6ae97ad61d70e3a774709
|
af3ec207381de315f4cb6dddba727d16d42d6c57
|
/dialogue-engine/src/programy/spelling/textblob_spelling.py
|
17dce9132a295389213305638b9ac113ad1c6fc2
|
[
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] |
permissive
|
mcf-yuichi/cotoba-agent-oss
|
02a5554fe81ce21517f33229101013b6487f5404
|
ce60833915f484c4cbdc54b4b8222d64be4b6c0d
|
refs/heads/master
| 2023-01-12T20:07:34.364188 | 2020-11-11T00:55:16 | 2020-11-11T00:55:16 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 2,540 |
py
|
"""
Copyright (c) 2020 COTOBA DESIGN, Inc.
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.
"""
"""
Copyright (c) 2016-2019 Keith Sterling http://www.keithsterling.com
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.
"""
from textblob import TextBlob
from programy.spelling.base import SpellingChecker
class TextBlobSpellingChecker(SpellingChecker):
def __init__(self, spelling_config=None):
SpellingChecker.__init__(self, spelling_config)
def correct(self, phrase):
blob = TextBlob(phrase)
correct_blob = blob.correct()
return str(correct_blob)
|
[
"[email protected]"
] | |
d93fcc608737fe23b284d4e66f3a1b37d68278ab
|
af4940ba8a638423934a16d55253b726312fe1f5
|
/pset04/pset04.py
|
d8998ae06ed5661234ea90f91dc0df457f02d1f3
|
[] |
no_license
|
blinvarfi/ML-FuldaWS19-20
|
ff11194abdb2d0198fab59f03bfbea5278173cac
|
c3b1db2e3632d0a87db7b7226e40c412834a0f38
|
refs/heads/master
| 2022-04-12T17:21:14.934651 | 2020-01-03T04:56:26 | 2020-01-03T04:56:26 | 230,134,114 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,678 |
py
|
# -*- coding: utf-8 -*-
"""
Created on Wed Jan 1 04:28:56 2020
Problem Set 04 Solutions
@author: Blin
"""
from matplotlib import pyplot as plt
import numpy as np
traind, testd, trainl, testl = np.load('../data/mnist.npz', 'rb').values();
# customizing matplotlib
plt.style.use('dark_background')
# 3)
print('Question 3)')
def f(x):
return np.array(x[0]**2, 2*x[1]**2)
def grad_f(x):
return np.array([2*x[0], 4*x[1]])
def calc_steps(x, n, step):
for i in range(1, n):
x = x - step * grad_f(x)
print('Step ', i, ' value is ', x)
calc_steps(np.array([1, 3]), 6, 0.1)
# 4)
# BIG QUESTION MARK ABOUT THIS SOLUTION
print('Question 4)')
first_500 = traind[0:500].reshape(500, 784)
cov_matrix = np.cov(first_500, rowvar=False)
eig_val, eig_vec = np.linalg.eigh(cov_matrix)
print('Covariance matrix shape ', cov_matrix.shape)
fig, ax = plt.subplots(1, 10)
fig.suptitle('First 5 and Last 5 EigenVectors')
for i in range (0, 10):
ax[i].imshow(eig_vec[:,i-5].reshape(28, 28))
plt.show()
# 5.a)
print('Question 5.a)')
first_500 = traind[0:500].reshape(500, 784)
op_500 = np.mean(first_500[:,np.newaxis,:] * first_500[:,:,np.newaxis], axis=0)
eig_val, eig_vec = np.linalg.eigh(op_500)
transformed_500 = np.dot(first_500, eig_vec)
print('Shape of first 500 elements transformed ', transformed_500.shape)
# 5.b)
print('Question 5.b)')
transformed_500[:,0:-5] = 0
# 5.c)
print('Question 5.c)')
transformed_500_2 = np.dot(transformed_500, np.transpose(eig_vec))
print(transformed_500_2.shape)
fig, ax = plt.subplots(1, 3)
fig.suptitle('First 3 samples')
for i in range (0, 3):
ax[i].imshow(transformed_500_2[i].reshape(28,28))
plt.show()
|
[
"[email protected]"
] | |
4b3efe39bb0661581d9f7709df149ac517e2a194
|
292cec77b5003a2f80360d0aee77556d12d990f7
|
/src/bentoml/_internal/resource.py
|
b1e467fceda9f9dc881734dfe05cc85bb1bd5c39
|
[
"Apache-2.0"
] |
permissive
|
yubozhao/BentoML
|
194a6ec804cc1c6dbe7930c49948b6707cbc3c5f
|
d4bb5cbb90f9a8ad162a417103433b9c33b39c84
|
refs/heads/master
| 2022-12-17T00:18:55.555897 | 2022-12-06T00:11:39 | 2022-12-06T00:11:39 | 178,978,385 | 3 | 0 |
Apache-2.0
| 2020-12-01T18:17:15 | 2019-04-02T01:53:53 |
Python
|
UTF-8
|
Python
| false | false | 9,725 |
py
|
from __future__ import annotations
import os
import re
import math
import typing as t
import logging
import functools
from abc import ABC
from abc import abstractmethod
import psutil
from ..exceptions import BentoMLConfigException
logger = logging.getLogger(__name__)
_RESOURCE_REGISTRY: dict[str, t.Type[Resource[t.Any]]] = {}
T = t.TypeVar("T")
def get_resource(
resources: dict[str, t.Any], resource_kind: str, validate: bool = True
) -> t.Any:
if resource_kind not in _RESOURCE_REGISTRY:
raise BentoMLConfigException(f"Unknown resource kind '{resource_kind}'.")
resource: t.Type[Resource[t.Any]] = _RESOURCE_REGISTRY[resource_kind]
if resource_kind in resources:
if resources[resource_kind] == "system":
return resource.from_system()
else:
res = resource.from_spec(resources[resource_kind])
if validate:
resource.validate(res)
return res
else:
return None
def system_resources() -> dict[str, t.Any]:
res: dict[str, t.Any] = {}
for resource_kind, resource in _RESOURCE_REGISTRY.items():
res[resource_kind] = resource.from_system()
return res
class Resource(t.Generic[T], ABC):
def __init_subclass__(cls, *, resource_id: str): # pylint: disable=arguments-differ
_RESOURCE_REGISTRY[resource_id] = cls
@classmethod
@abstractmethod
def from_spec(cls, spec: t.Any) -> T:
"""
Get an instance of this resource from user input. For example, a CPU resource
might parse "10m" and return a CPU resource with 0.01 CPUs.
"""
@classmethod
@abstractmethod
def from_system(cls) -> T:
"""
Infer resource value from the system.
"""
@classmethod
@abstractmethod
def validate(cls, val: T):
"""
Validate that the resources are available on the current system.
"""
class CpuResource(Resource[float], resource_id="cpu"):
@classmethod
def from_spec(cls, spec: t.Any) -> float:
"""
Convert spec to CpuResource.
spec can be a float, int or string.
- 1.0 -> 1.0
- 1 -> 1.0
- "1" -> 1.0
- "10m" -> 0.01
"""
if not isinstance(spec, (int, float, str)):
raise TypeError("cpu must be int, float or str")
if isinstance(spec, (int, float)):
return float(spec)
milli_match = re.match("([0-9]+)m", spec)
if milli_match:
return float(milli_match[1]) / 1000.0
try:
return float(spec)
except ValueError:
raise BentoMLConfigException(f"Invalid CPU resource limit '{spec}'. ")
@classmethod
def from_system(cls) -> float:
if psutil.POSIX:
return query_cgroup_cpu_count()
else:
return float(query_os_cpu_count())
@classmethod
def validate(cls, val: float):
if val < 0:
raise BentoMLConfigException(
f"Invalid negative CPU resource limit '{val}'."
)
if not math.isclose(val, cls.from_system()) and val > cls.from_system():
raise BentoMLConfigException(
f"CPU resource limit {val} is greater than the system available: {cls.from_system()}"
)
@functools.lru_cache(maxsize=1)
def query_cgroup_cpu_count() -> float:
# Query active cpu processor count using cgroup v1 API, based on OpenJDK
# implementation for `active_processor_count` using cgroup v1:
# https://github.com/openjdk/jdk/blob/master/src/hotspot/os/linux/cgroupSubsystem_linux.cpp
# For cgroup v2, see:
# https://github.com/openjdk/jdk/blob/master/src/hotspot/os/linux/cgroupV2Subsystem_linux.cpp
# Possible supports: cpuset.cpus on kubernetes
def _read_cgroup_file(filename: str) -> float:
with open(filename, "r", encoding="utf-8") as f:
return int(f.read().strip())
cgroup_root = "/sys/fs/cgroup/"
cfs_quota_us_file = os.path.join(cgroup_root, "cpu", "cpu.cfs_quota_us")
cfs_period_us_file = os.path.join(cgroup_root, "cpu", "cpu.cfs_period_us")
cpu_max_file = os.path.join(cgroup_root, "cpu.max")
quota = None
if os.path.exists(cfs_quota_us_file) and os.path.exists(cfs_period_us_file):
try:
quota = _read_cgroup_file(cfs_quota_us_file) / _read_cgroup_file(
cfs_period_us_file
)
except FileNotFoundError as err:
logger.warning("Caught exception while calculating CPU quota: %s", err)
# reading from cpu.max for cgroup v2
elif os.path.exists(cpu_max_file):
try:
with open(cpu_max_file, "r", encoding="utf-8") as max_file:
cfs_string = max_file.read()
quota_str, period_str = cfs_string.split()
if quota_str.isnumeric() and period_str.isnumeric():
quota = float(quota_str) / float(period_str)
else:
# quota_str is "max" meaning the cpu quota is unset
quota = None
except FileNotFoundError as err:
logger.warning("Caught exception while calculating CPU quota: %s", err)
if quota is not None and quota < 0:
quota = None
elif quota == 0:
quota = 1
os_cpu_count = float(os.cpu_count() or 1.0)
limit_count = math.inf
if quota:
limit_count = quota
return float(min(limit_count, os_cpu_count))
@functools.lru_cache(maxsize=1)
def query_os_cpu_count() -> int:
cpu_count = os.cpu_count()
if cpu_count is not None:
return cpu_count
logger.warning("Failed to determine CPU count, using 1 as default.")
return 1
# class MemResource(Resource, resource_id="mem"):
# @classmethod
# def from_spec(cls, spec: t.Any):
# assert isinstance(mem, (int, str)), "mem must be int or str"
#
# if isinstance(mem, int):
# return mem
#
# unit_match = re.match("([0-9]+)([A-Za-z]{1,2})", mem)
# mem_multipliers = {
# "k": 1000,
# "M": 1000**2,
# "G": 1000**3,
# "T": 1000**4,
# "P": 1000**5,
# "E": 1000**6,
# "Ki": 1024,
# "Mi": 1024**2,
# "Gi": 1024**3,
# "Ti": 1024**4,
# "Pi": 1024**5,
# "Ei": 1024**6,
# }
# if unit_match:
# base = int(unit_match[1])
# unit = unit_match[2]
# if unit in mem_multipliers:
# return base * mem_multipliers[unit]
# raise ValueError(f"Invalid MEM resource limit '{mem}'")
class NvidiaGpuResource(Resource[t.List[int]], resource_id="nvidia.com/gpu"):
@classmethod
def from_spec(cls, spec: int | str | list[int | str]) -> list[int]:
if not isinstance(spec, (int, str, list)):
raise TypeError(
"NVidia GPU device IDs must be int, str or a list specifing the exact GPUs to use."
)
try:
if isinstance(spec, int):
if spec < -1:
raise ValueError
return list(range(spec))
elif isinstance(spec, str):
return cls.from_spec(int(spec))
else:
return [int(x) for x in spec]
except ValueError:
raise BentoMLConfigException(
f"Invalid NVidia GPU resource limit '{spec}'. "
)
@classmethod
@functools.lru_cache(maxsize=1)
def from_system(cls) -> list[int]:
"""
query nvidia gpu count, available on Windows and Linux
"""
import pynvml # type: ignore
try:
pynvml.nvmlInit()
device_count = pynvml.nvmlDeviceGetCount()
return list(range(device_count))
except (pynvml.nvml.NVMLError, OSError):
logger.debug("GPU not detected. Unable to initialize pynvml lib.")
return []
finally:
try:
pynvml.nvmlShutdown()
except Exception: # pylint: disable=broad-except
pass
@classmethod
def validate(cls, val: t.List[int]):
if any([gpu_index < 0 for gpu_index in val]):
raise BentoMLConfigException(f"Negative GPU device in {val}.")
if any([gpu_index >= len(cls.from_system()) for gpu_index in val]):
raise BentoMLConfigException(
f"GPU device index in {val} is greater than the system available: {cls.from_system()}"
)
def get_gpu_memory(dev: int) -> t.Tuple[float, float]:
"""
Return Total Memory and Free Memory in given GPU device. in MiB
"""
import pynvml.nvml # type: ignore
from pynvml.smi import nvidia_smi # type: ignore
unit_multiplier = {
"PiB": 1024.0 * 1024 * 1024,
"TiB": 1024.0 * 1024,
"GiB": 1024.0,
"MiB": 1.0,
"KiB": 1.0 / 1024,
"B": 1.0 / 1024 / 1024,
}
try:
inst = nvidia_smi.getInstance()
query: t.Dict[str, int] = inst.DeviceQuery(dev) # type: ignore
except (pynvml.nvml.NVMLError, OSError):
return 0.0, 0.0
try:
gpus: t.List[t.Dict[str, t.Any]] = query.get("gpu", []) # type: ignore
gpu = gpus[dev]
unit = gpu["fb_memory_usage"]["unit"]
total = gpu["fb_memory_usage"]["total"] * unit_multiplier[unit]
free = gpu["fb_memory_usage"]["free"] * unit_multiplier[unit]
return total, free
except IndexError:
raise ValueError(f"Invalid GPU device index {dev}")
except KeyError:
raise RuntimeError(f"unexpected nvml query result: {query}")
|
[
"[email protected]"
] | |
15d41f5280efb5554ef32689ce5240ce74d45955
|
94c9ed7d19ca70a12f3d8e9323620453879ee1f4
|
/config.py
|
03c764ab5e1e672ca869ca1c99276990bfd5f1f3
|
[] |
no_license
|
weiss00/Spider_test_jiepai
|
a31e5c3a8fe020d356f7bfe0ebd4e69725deea1a
|
f64f1861f2ae17493105625823c78cae8a3419de
|
refs/heads/master
| 2020-04-03T19:45:30.511707 | 2018-10-31T09:58:31 | 2018-10-31T09:58:31 | 155,511,983 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 130 |
py
|
MONGO_URL='localhost'
MONGO_PORT=27017
MONGO_DB='toutiao'
MONGO_TABLE='toutiao'
GROUP_START = 0
GROUP_END = 20
KEYWORD = '街拍'
|
[
"[email protected]"
] | |
73bb00c9fbe898b48b2ca8580141ce0982e1a4fc
|
478c77145d3c89b76f64447dd7abb39f6ee343db
|
/.ipynb_checkpoints/model-checkpoint.py
|
978b01939e2502bc161067479661f662fb382ffd
|
[] |
no_license
|
UsamaKhurshid3240/Facial-Recognizition
|
e9629e15e4a893ba071e0f624749979e086fca65
|
1b097fc3458d682a526af897b376968bd58ecf52
|
refs/heads/master
| 2023-03-15T07:11:08.683448 | 2021-03-14T19:29:21 | 2021-03-14T19:29:21 | 347,560,369 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 704 |
py
|
from tensorflow.keras.models import model_from_json
import numpy as np
import tensorflow as tf
class FacialExpressionModel(object):
EMOTIONS_LIST=["Angry", "Sad", "Happy", "Disgust", "Fear", "Neutral", "Surprise"]
def __init__(self, model_json_file, model_weights_file):
with open(model_json_file, "r") as f:
loaded_model_json = f.read()
self.loaded = model_from_json(loaded_model_json)
self.loaded_model.load_weights(model_weights_file)
self.loaded_model.make_predict_function()
def predict_emotion(self, img):
self.preds = self.loaded_model.predict(img)
return FacialExpressionModel.EMOTIONS_LIST(np.argmax(self.preds))
|
[
"[email protected]"
] | |
44774962f73f1f0b3db51759451f21631171633b
|
4d159501df67958bfd036890e598ca183eec04d3
|
/2019-03/03-11/11724.py
|
ab4ffb37c31fab736fbf6a0e9052ded7c55c524e
|
[] |
no_license
|
nonameP765/back-joon
|
96a413bff4e7d5b3716135981936d76880b871c9
|
4fceaa6d8dbbb65615b39c643dd809ad96104369
|
refs/heads/master
| 2020-04-21T08:01:20.666912 | 2019-03-22T04:59:18 | 2019-03-22T04:59:18 | 169,407,280 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 618 |
py
|
c, n = map(int, input().split(' '))
# 그래프 제작
graph = {i: list() for i in range(1, c + 1)}
for i in range(n):
a, b = map(int, input().split(' '))
graph[a].append(b)
graph[b].append(a)
visited = [False for i in range(c + 1)]
count = 0
# 모든 정점을 순환
for j in range(1, c + 1):
# 물론 방문하지 않은 경우만
if not visited[j]:
stack = [j]
while stack:
now = stack.pop()
visited[now] = True
for i in graph[now]:
if not visited[i]:
stack.append(i)
count += 1
print(count)
|
[
"[email protected]"
] | |
a4f282d077acf231c813e0781067964299e282f7
|
6f50d88145923deba55f5df5f88e872a46504f71
|
/siteconfig/utils.py
|
e51be1affa4f0bc68f3bca34d399fc656a2d03cf
|
[] |
no_license
|
vfxetc/siteconfig
|
ce85cff95a865a8ab6271f305b70643c364c1952
|
7124e941cf5068a70f07d0011902af797b74657e
|
refs/heads/master
| 2021-09-12T13:00:40.933138 | 2017-08-04T15:08:42 | 2017-08-04T15:08:42 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 314 |
py
|
import re
def normalize_key(input_):
input_ = re.sub(r'[^\w\[\]]+', '_', input_)
input_ = re.sub(r'^(\w+)', lambda m: m.group(1).upper(), input_)
return input_
def shell_escape(input_):
return str(input_).replace('"', '\\"')
def shell_quote(input_):
return '"%s"' % shell_escape(input_)
|
[
"[email protected]"
] | |
1da44a2aab753c615c080b5c74b624c3c6bcc490
|
564e447120baa940ba3a3e43676c14cdfaa69979
|
/Plan 8.py
|
bac522c3ebfbf5efa7d03a5414520307918c375f
|
[] |
no_license
|
agivins/Python-JBN
|
4a9329fdb099c480a70abbdafdc2e4be61baf598
|
fcbc3c06500c2fa7fcb5685baf3b96e3e0dd90e4
|
refs/heads/master
| 2023-01-31T11:39:35.907929 | 2020-12-17T20:08:57 | 2020-12-17T20:08:57 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,459 |
py
|
#!/usr/bin/env python
# coding: utf-8
# In[2]:
import matplotlib.pyplot as plt
import pandas as pd
get_ipython().run_line_magic('matplotlib', 'inline')
Location = "C:/Users/Admin/Desktop/datasets/gradedata.csv"
df = pd.read_csv(Location)
df.head()
# In[3]:
df.hist()
# In[4]:
df.hist(column="hours")
# In[5]:
df.hist(column="hours", by="gender")
# In[ ]:
# In[ ]:
# In[6]:
import pandas as pd
import matplotlib.pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline')
names = ['Bob','Jessica','Mary','John','Mel']
absences = [3,0,1,0,8]
detentions = [2,1,0,0,1]
warnings = [2,1,5,1,2]
GradeList = zip(names,absences,detentions,warnings)
columns=['Names', 'Absences', 'Detentions','Warnings']
df = pd.DataFrame(data = GradeList, columns=columns)
df
# In[16]:
df['TotalDemerits'] = df['Absences'] +df['Detentions'] + df['Warnings']df
# In[17]:
plt.pie(df['TotalDemerits'])
# In[ ]:
# In[18]:
plt.pie(df['TotalDemerits'],
labels=df['Names'],
explode=(0,0,0,0,0.15),
startangle=90,
autopct='%1.1f%%',)
plt.axis('equal')
plt.show()
# In[ ]:
# In[19]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline')
dataframe = pd.DataFrame({'Col':
np.random.normal(size=200)})
plt.scatter(dataframe.index, dataframe['Col'])
# In[ ]:
# In[ ]:
# In[ ]:
# In[ ]:
|
[
"[email protected]"
] | |
8a71537eee5a52e3e22a887e3fa309e65110a7be
|
f0bb47d3e1ef3740b4b0c819019d827578018bab
|
/svm.py
|
05e16ed0fab1742811cccd8cd3fb0cc4b375821b
|
[] |
no_license
|
s-ravichandran/classification-using-scikit-learn
|
cb618ba118b634bd42a46a3511a8bd561b43143a
|
f72a5b1988031c29d392c63a01b1af3185383bb9
|
refs/heads/master
| 2021-04-18T22:02:50.412661 | 2018-03-24T13:46:14 | 2018-03-24T13:46:14 | 64,988,755 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 7,852 |
py
|
import numpy as np
import pandas as pd
from sklearn import svm
from sklearn import metrics
from sklearn.metrics import precision_recall_curve
from sklearn.cross_validation import StratifiedShuffleSplit
import matplotlib.pyplot as plt
import random
import sys
# args : svm.py float(sampleRatio)
data_dir = './input/' # needs trailing slash
# validation split, both files with headers and the Happy column
train_file = data_dir + 'census.data.v5.csv'
test_file = data_dir + 'census.test.v5.csv'
print "Before reading files"
large_train = pd.read_csv( train_file )
large_test = pd.read_csv( test_file )
# Stratified Sampling
sample_ratio = float(sys.argv[1])
include_cols = ['Label']
print "Large Test Stats\n", large_test.Label.describe(), "\n"
##StratifiedShuffleSplit(y, n_iter=10, test_size=0.1, train_size=None, random_state=None)
sss = StratifiedShuffleSplit(large_train.Label, n_iter=1, test_size = sample_ratio, train_size=None, random_state=0)
for train_split_ind, test_split_ind in sss:
print "Sampled Train :", test_split_ind
train = large_train.iloc[test_split_ind]
sss = StratifiedShuffleSplit(large_test.Label, n_iter=1, test_size = sample_ratio, train_size=None, random_state=0)
for train_split_ind, test_split_ind in sss:
print "Sampled Test :", test_split_ind
test = large_test.iloc[test_split_ind]
del(large_train)
del(large_test)
""""
train =
test = large_test.sample(int(sys.argv[1]))
"""
# y
y_train = train.Label
y_test = test.Label
#print type(y_test) #should be series
# populate lists of numeric and cat attributes
all_cols = list(train.columns.values)
numeric_cols = ['age','wage per hour','capital gains','capital losses','dividends from stocks','num persons worked for employer','weeks worked in year',]
# remove_cols = ['Label','instance weight','migration code-change in msa','migration code-change in reg','migration code-move within reg','migration prev res in sunbelt']
remove_cols = ['Label','instance weight']
cat_cols = [x for x in all_cols if x not in numeric_cols and x not in remove_cols]
# handle numerical features
x_num_train = train[ numeric_cols ].as_matrix()
x_num_test = test[ numeric_cols ].as_matrix()
x_train_count = x_num_train.shape[0]
x_test_count = x_num_test.shape[0]
x_num_combined = np.concatenate((x_num_train,x_num_test), axis=0) # 0 -row 1 - col
# print "\nTRAIN NUM dims: ", x_num_train.shape, ", num rows: ", x_train_count
# print "TEST NUM dims: ", x_num_test.shape, ", num rows: ", x_test_count
# print "COMBINED NUM dims: ", x_num_combined.shape
# scale numeric features to <0,1>
max_num = np.amax( x_num_combined, 0 )
x_num_combined = np.true_divide(x_num_combined, max_num) # scale by max. truedivide needed for decimals
x_num_train = x_num_combined[0:x_train_count]
x_num_test = x_num_combined[x_train_count:]
# print "\nTRAIN NUM dims: ", x_num_train.shape, ", expected num rows: ", x_train_count
# print "TEST NUM dims: ", x_num_test.shape, ", expected num rows: ", x_test_count
# categorical
x_cat_train = train.drop( numeric_cols + remove_cols, axis = 1 )
x_cat_test = test.drop( numeric_cols + remove_cols, axis = 1 )
x_cat_train.fillna( 'NA', inplace = True )
x_cat_test.fillna( 'NA', inplace = True )
x_cat_combined = pd.concat((x_cat_train, x_cat_test), axis=0)
# print "\nTRAIN CAT dims: ", x_cat_train.shape, ", num rows: ", x_train_count
# print "TEST CAT dims: ", x_cat_test.shape, ", num rows: ", x_test_count
# print "COMBINED CAT dims: ", x_cat_combined.shape
# print "\nTYPES\nx_cat_train: ", type(x_cat_train)
# print "x_cat_combined: ", type(x_cat_combined)
# one-of-k handling for categorical features
# pandas.get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False, columns=None, sparse=False)
print "Before getting cat -> dummies"
vec_x_cat_combined = pd.get_dummies(x_cat_combined, columns=cat_cols, sparse=False)#.as_matrix()
vec_x_cat_train = vec_x_cat_combined[0:x_train_count]
vec_x_cat_test = vec_x_cat_combined[x_train_count:]
# print "\nExpanded TRAIN CAT dims: ", vec_x_cat_train.shape, ", expected num rows: ", x_train_count
# print "Expanded TEST CAT dims: ", vec_x_cat_test.shape, ", expected num rows: ", x_test_count
# combine numerical and categorical
del(vec_x_cat_combined)
x_train = np.hstack(( x_num_train, vec_x_cat_train.ix[:,:200] )) # returns ndarray
del(x_num_train)
x_train = np.hstack(( x_train, vec_x_cat_train.ix[:,200:] )) # returns ndarray
del(vec_x_cat_train)
x_test = np.hstack(( x_num_test, vec_x_cat_test.ix[:,:200] ))
del(x_num_test)
x_test = np.hstack(( x_test, vec_x_cat_test.ix[:,200:] ))
del(vec_x_cat_test)
# print "\nx_train: ", x_train.shape, ", ", type(x_train)
# print "x_test: ", x_test.shape, ", ", type(x_test)
# working fine upto here - Data Processing
# below - classifier specific logic
classifier_alg = "SVM"
svm_classifier = svm.SVC(probability=True)
print "Before fit"
svm_classifier.fit( x_train, y_train , sample_weight=train['instance weight'].values)
print "Before predict"
predicted = svm_classifier.predict( x_test )
predicted_train = svm_classifier.predict( x_train )
print "Train Predictions: \n" + (metrics.classification_report(y_train, predicted_train))
print "Test Predictions: \n" + metrics.classification_report(y_test, predicted)
# Data for plotting
probs = svm_classifier.predict_proba(x_test)
y_conf=[]
y_test_num=[]
y_train_conf=[]
y_train_num=[]
probs_train = svm_classifier.predict_proba(x_train)
print "Before changing y_label to num"
print "y_test", len(y_test), "\t", type(y_test)
y_test = y_test.as_matrix()
# print y_test
for i in range(len(y_test)):
# print y_test[i]
# print i
if y_test[i] == 'Pos':
y_test_num.append(1)
else:
y_test_num.append(0)
y_train = y_train.as_matrix()
for i in range(len(y_train)):
if(y_train[i] == 'Pos'):
y_train_num.append(1)
else:
y_train_num.append(0)
#Neg is 0 in probs and 0 in y_test
#pos is 1 in probs and 1 in y_test
print "Going to plot ",classifier_alg
for class_to_plot in [0,1]:
y_conf = [] # Test Set
for i in range(len(y_test)):
y_conf.append(probs[i][class_to_plot])
precision, recall, thresholds = precision_recall_curve(y_test_num, y_conf, pos_label=class_to_plot)
plt.plot(recall,precision)
y_train_conf=[] # Train Set
for i in range(len(y_train)):
y_train_conf.append(probs_train[i][class_to_plot])
precision, recall, thresholds = precision_recall_curve(y_train_num, y_train_conf, pos_label=class_to_plot)
plt.plot(recall, precision)
if(class_to_plot == 0):
plt.axis([0,1,0.8,1])
plt.yticks(np.arange(0.8, 1.05, 0.1))
else:
plt.axis([0,1,0,1])
plt.yticks(np.arange(0, 1.1, 0.1))
print "Checking class ",class_to_plot
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.grid(b=True, which='major', axis='both', color='black', linestyle='-', alpha=0.3)
plt.xticks(np.arange(0, 1.1, 0.1))
plt.title(classifier_alg+': ' + str(class_to_plot)) #+' stratified sample '+str(sample_ratio))
filename = "./plots/"+ classifier_alg + "_"+str(class_to_plot)+"_default_"+str(sample_ratio)+".png"#+str(num_estimators)+"_"
plt.savefig(filename)
plt.clf()
"""
#Learn without Instance weights
svm_classifier = svm.SVC(probability=True)
svm_classifier.fit( x_train, y_train )
predicted = svm_classifier.predict( x_test )
print "Going to plot unweighted"
for class_to_plot in [0,1]:
y_conf = []
for i in range(len(y_test)):
y_conf.append(probs[i][class_to_plot])
precision, recall, thresholds = precision_recall_curve(y_test_num, y_conf, pos_label=class_to_plot)
plt.plot(recall,precision)
if(class_to_plot == 0):
plt.axis([0,1,0.8,1])
else:
plt.axis([0,1,0,1])
print "Checking class",class_to_plot
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.title('SVM without instance weights for class ' + str(class_to_plot))
filename = "./plots/svm_unweighted_"+str(class_to_plot)+".png"
plt.savefig(filename)
plt.clf()
"""
|
[
"[email protected]"
] | |
823b24b4795ed11e530fa7bbbbd864226b91e019
|
c9ddbdb5678ba6e1c5c7e64adf2802ca16df778c
|
/cases/synthetic/coverage-big-4168.py
|
aaa834c9f7fe2dfdd97888d1a63ea8e948edcaf2
|
[] |
no_license
|
Virtlink/ccbench-chocopy
|
c3f7f6af6349aff6503196f727ef89f210a1eac8
|
c7efae43bf32696ee2b2ee781bdfe4f7730dec3f
|
refs/heads/main
| 2023-04-07T15:07:12.464038 | 2022-02-03T15:42:39 | 2022-02-03T15:42:39 | 451,969,776 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 13,348 |
py
|
count:int = 0
count2:int = 0
count3:int = 0
count4:int = 0
count5:int = 0
def foo(s: str) -> int:
return len(s)
def foo2(s: str, s2: str) -> int:
return len(s)
def foo3(s: str, s2: str, s3: str) -> int:
return len(s)
def foo4(s: str, s2: str, s3: str, s4: str) -> int:
return len(s)
def foo5(s: str, s2: str, s3: str, s4: str, s5: str) -> int:
return len(s)
class bar(object):
p: bool = True
def baz(self:"bar", xx: [int]) -> str:
global count
x:int = 0
y:int = 1
def qux(y: int) -> object:
nonlocal x
if x > y:
x = -1
for x in xx:
self.p = x == 2
qux(0) # Yay! ChocoPy
count = count + 1
while x <= 0:
if self.p:
xx[0] = xx[1]
self.p = not self.p
x = x + 1
elif foo("Long"[0]) == 1:
self.p = self is None
return "Nope"
class bar2(object):
p: bool = True
p2: bool = True
def baz(self:"bar2", xx: [int]) -> str:
global count
x:int = 0
y:int = 1
def qux(y: int) -> object:
nonlocal x
if x > y:
x = -1
for x in xx:
self.p = x == 2
qux(0) # Yay! ChocoPy
count = count + 1
while x <= 0:
if self.p:
xx[0] = xx[1]
self.p = not self.p
x = x + 1
elif foo("Long"[0]) == 1:
self.p = self is None
return "Nope"
def baz2(self:"bar2", xx: [int], xx2: [int]) -> str:
global count
x:int = 0
x2:int = 0
y:int = 1
y2:int = 1
def qux(y: int) -> object:
nonlocal x
if x > y:
x = -1
def qux2(y: int, y2: int) -> object:
nonlocal x
nonlocal x2
if x > y:
x = -1
for x in xx:
self.p = x == 2
qux(0) # Yay! ChocoPy
count = count + 1
while x <= 0:
if self.p:
xx[0] = xx[1]
self.p = not self.p
x = x + 1
elif foo("Long"[0]) == 1:
self.p = self is None
return "Nope"
class bar3(object):
p: bool = True
p2: bool = True
p3: bool = True
def baz(self:"bar3", xx: [int]) -> str:
global count
x:int = 0
y:int = 1
def qux(y: int) -> object:
nonlocal x
if x > y:
x = -1
for x in xx:
self.p = x == 2
qux(0) # Yay! ChocoPy
count = count + 1
while x <= 0:
if self.p:
xx[0] = xx[1]
self.p = not self.p
x = x + 1
elif foo("Long"[0]) == 1:
self.p = self is None
return "Nope"
def baz2(self:"bar3", xx: [int], xx2: [int]) -> str:
global count
x:int = 0
x2:int = 0
y:int = 1
y2:int = 1
def qux(y: int) -> object:
nonlocal x
if x > y:
x = -1
def qux2(y: int, y2: int) -> object:
nonlocal x
nonlocal x2
if x > y:
x = -1
for x in xx:
self.p = x == 2
qux(0) # Yay! ChocoPy
count = count + 1
while x <= 0:
if self.p:
xx[0] = xx[1]
self.p = not self.p
x = x + 1
elif foo("Long"[0]) == 1:
self.p = self is None
return "Nope"
def baz3(self:"bar3", xx: [int], xx2: [int], xx3: [int]) -> str:
global count
x:int = 0
x2:int = 0
x3:int = 0
y:int = 1
y2:int = 1
y3:int = 1
def qux(y: int) -> object:
nonlocal x
if x > y:
x = -1
def qux2(y: int, y2: int) -> object:
nonlocal x
nonlocal x2
if x > y:
x = -1
def qux3(y: int, y2: int, y3: int) -> object:
nonlocal x
nonlocal x2
nonlocal x3
if x > y:
x = -1
for x in xx:
self.p = x == 2
qux(0) # Yay! ChocoPy
count = count + 1
while x <= 0:
if self.p:
xx[0] = xx[1]
self.p = not self.p
x = x + 1
elif foo("Long"[0]) == 1:
self.p = self is None
return "Nope"
class bar4(object):
p: bool = True
p2: bool = True
p3: bool = True
p4: bool = True
def baz(self:"bar4", xx: [int]) -> str:
global count
x:int = 0
y:int = 1
def qux(y: int) -> object:
nonlocal x
if x > y:
x = -1
for x in xx:
self.p = x == 2
qux(0) # Yay! ChocoPy
count = count + 1
while x <= 0:
if self.p:
xx[0] = xx[1]
self.p = not self.p
x = x + 1
elif foo("Long"[0]) == 1:
self.p = self is None
return "Nope"
def baz2(self:"bar4", xx: [int], xx2: [int]) -> str:
global count
x:int = 0
x2:int = 0
y:int = 1
y2:int = 1
def qux(y: int) -> object:
nonlocal x
if x > y:
x = -1
def qux2(y: int, y2: int) -> object:
nonlocal x
nonlocal x2
if x > y:
x = -1
for x in xx:
self.p = x == 2
qux(0) # Yay! ChocoPy
count = count + 1
while x <= 0:
if self.p:
xx[0] = xx[1]
self.p = not self.p
x = x + 1
elif foo("Long"[0]) == 1:
self.p = self is None
return "Nope"
def baz3(self:"bar4", xx: [int], xx2: [int], xx3: [int]) -> str:
global count
x:int = 0
x2:int = 0
x3:int = 0
y:int = 1
y2:int = 1
y3:int = 1
def qux(y: int) -> object:
nonlocal x
if x > y:
x = -1
def qux2(y: int, y2: int) -> object:
nonlocal x
nonlocal x2
if x > y:
x = -1
def qux3(y: int, y2: int, y3: int) -> object:
nonlocal x
nonlocal x2
nonlocal x3
if x > y:
x = -1
for x in xx:
self.p = x == 2
qux(0) # Yay! ChocoPy
count = count + 1
while x <= 0:
if self.p:
xx[0] = xx[1]
self.p = not self.p
x = x + 1
elif foo("Long"[0]) == 1:
self.p = self is None
return "Nope"
def baz4(self:"bar4", xx: [int], xx2: [int], xx3: [int], xx4: [int]) -> str:
global count
x:int = 0
x2:int = 0
x3:int = 0
x4:int = 0
y:int = 1
y2:int = 1
y3:int = 1
y4:int = 1
def qux(y: int) -> object:
nonlocal x
if x > y:
x = -1
def qux2(y: int, y2: int) -> object:
nonlocal x
nonlocal x2
if x > y:
x = -1
def qux3(y: int, y2: int, y3: int) -> object:
nonlocal x
nonlocal x2
nonlocal x3
if x > y:
x = -1
def qux4(y: int, y2: int, y3: int, y4: int) -> object:
nonlocal x
nonlocal x2
nonlocal x3
nonlocal x4
if x > y:
x = -1
for x in xx:
self.p = x == 2
qux(0) # Yay! ChocoPy
count = count + 1
while x <= 0:
if self.p:
xx[0] = xx[1]
self.p = not self.p
x = x + 1
elif foo("Long"[0]) == 1:
self.p = self is None
return "Nope"
class bar5(object):
p: bool = True
p2: bool = True
p3: bool = True
p4: bool = True
p5: bool = True
def baz(self:"bar5", xx: [int]) -> str:
global count
x:int = 0
y:int = 1
def qux(y: int) -> object:
nonlocal x
if x > y:
x = -1
for x in xx:
self.p = x == 2
qux(0) # Yay! ChocoPy
count = count + 1
while x <= 0:
if self.p:
xx[0] = xx[1]
self.p = not self.p
x = x + 1
elif foo("Long"[0]) == 1:
self.p = self is None
return "Nope"
def baz2(self:"bar5", xx: [int], xx2: [int]) -> str:
global count
x:int = 0
x2:int = 0
y:int = 1
y2:int = 1
def qux(y: int) -> object:
nonlocal x
if x > y:
x = -1
def qux2(y: int, y2: int) -> object:
nonlocal x
nonlocal x2
if x > y:
x = -1
for x in xx:
self.p = x == 2
qux(0) # Yay! ChocoPy
count = count + 1
while x <= 0:
if self.p:
xx[0] = xx[1]
self.p = not self.p
x = x + 1
elif foo("Long"[0]) == 1:
self.p = self is None
return "Nope"
def baz3(self:"bar5", xx: [int], xx2: [int], xx3: [int]) -> str:
global count
x:int = 0
x2:int = 0
x3:int = 0
y:int = 1
y2:int = 1
y3:int = 1
def qux(y: int) -> object:
nonlocal x
if x > y:
x = -1
def qux2(y: int, y2: int) -> object:
nonlocal x
nonlocal x2
if x > y:
x = -1
def qux3(y: int, y2: int, y3: int) -> object:
nonlocal x
nonlocal x2
nonlocal x3
if x > y:
x = -1
for x in xx:
self.p = x == 2
qux(0) # Yay! ChocoPy
count = count + 1
while x <= 0:
if self.p:
xx[0] = xx[1]
self.p = not self.p
x = x + 1
elif foo("Long"[0]) == 1:
self.p = self is None
return "Nope"
def baz4(self:"bar5", xx: [int], xx2: [int], xx3: [int], $ID: [int]) -> str:
global count
x:int = 0
x2:int = 0
x3:int = 0
x4:int = 0
y:int = 1
y2:int = 1
y3:int = 1
y4:int = 1
def qux(y: int) -> object:
nonlocal x
if x > y:
x = -1
def qux2(y: int, y2: int) -> object:
nonlocal x
nonlocal x2
if x > y:
x = -1
def qux3(y: int, y2: int, y3: int) -> object:
nonlocal x
nonlocal x2
nonlocal x3
if x > y:
x = -1
def qux4(y: int, y2: int, y3: int, y4: int) -> object:
nonlocal x
nonlocal x2
nonlocal x3
nonlocal x4
if x > y:
x = -1
for x in xx:
self.p = x == 2
qux(0) # Yay! ChocoPy
count = count + 1
while x <= 0:
if self.p:
xx[0] = xx[1]
self.p = not self.p
x = x + 1
elif foo("Long"[0]) == 1:
self.p = self is None
return "Nope"
def baz5(self:"bar5", xx: [int], xx2: [int], xx3: [int], xx4: [int], xx5: [int]) -> str:
global count
x:int = 0
x2:int = 0
x3:int = 0
x4:int = 0
x5:int = 0
y:int = 1
y2:int = 1
y3:int = 1
y4:int = 1
y5:int = 1
def qux(y: int) -> object:
nonlocal x
if x > y:
x = -1
def qux2(y: int, y2: int) -> object:
nonlocal x
nonlocal x2
if x > y:
x = -1
def qux3(y: int, y2: int, y3: int) -> object:
nonlocal x
nonlocal x2
nonlocal x3
if x > y:
x = -1
def qux4(y: int, y2: int, y3: int, y4: int) -> object:
nonlocal x
nonlocal x2
nonlocal x3
nonlocal x4
if x > y:
x = -1
def qux5(y: int, y2: int, y3: int, y4: int, y5: int) -> object:
nonlocal x
nonlocal x2
nonlocal x3
nonlocal x4
nonlocal x5
if x > y:
x = -1
for x in xx:
self.p = x == 2
qux(0) # Yay! ChocoPy
count = count + 1
while x <= 0:
if self.p:
xx[0] = xx[1]
self.p = not self.p
x = x + 1
elif foo("Long"[0]) == 1:
self.p = self is None
return "Nope"
print(bar().baz([1,2]))
|
[
"[email protected]"
] | |
981f6d0a13465e0a9d5c80cd70b5ca1e9910eb1a
|
431bcf84338b97dfa767b5dceaf21d28e971b788
|
/cell_tracker/ui/__init__.py
|
dc2a1b5a7c9088460460ae84aa03e606323b8d1e
|
[] |
no_license
|
glyg/cell-tracker
|
c2812b533afa13961e3182faa08a4f6b1681e4aa
|
33528c975caa2592ae6613ad085b9d7d3951690a
|
refs/heads/master
| 2020-06-26T08:46:55.760550 | 2014-10-02T15:58:42 | 2014-10-02T15:58:42 | 18,338,940 | 6 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 436 |
py
|
# -*- coding: utf-8 -*-
try :
from .ipy_widgets import set_metadata
from .ipy_widgets import set_parameters
from .ipy_widgets import SettingsWidget
from .qt_dialogs import get_cluster, get_multiple_clusters
from .manual_tracking import ManualTracking, pick_border_cells
from .sort_ellipses import EllipsisPicker
except ImportError:
print('UI elements could not be loaded, try installing IPython and PyQt4')
|
[
"[email protected]"
] | |
1e932951935e4ac0d6611ea52ba46a22e4559273
|
98581234e58e4ae9faa297c0c8af3b18d892a705
|
/basicswap/protocols/atomic_swap_1.py
|
5f164102ae052746314419c1b6cf9688551ae7b2
|
[
"MIT"
] |
permissive
|
cryptoguard/basicswap
|
d97ccc507bb4f0d5d6b517664dfb2b6e0218e57c
|
0890d643ffca1205179b92508746a267fc1393e8
|
refs/heads/master
| 2023-03-18T17:03:00.136171 | 2023-03-16T14:01:35 | 2023-03-16T14:01:35 | 203,665,859 | 0 | 0 |
MIT
| 2020-12-18T21:11:38 | 2019-08-21T21:18:06 |
Python
|
UTF-8
|
Python
| false | false | 3,097 |
py
|
# -*- coding: utf-8 -*-
# Copyright (c) 2020-2022 tecnovert
# Distributed under the MIT software license, see the accompanying
# file LICENSE or http://www.opensource.org/licenses/mit-license.php.
from basicswap.db import (
Concepts,
)
from basicswap.util import (
SerialiseNum,
)
from basicswap.script import (
OpCodes,
)
from basicswap.basicswap_util import (
SwapTypes,
EventLogTypes,
)
from . import ProtocolInterface
INITIATE_TX_TIMEOUT = 40 * 60 # TODO: make variable per coin
ABS_LOCK_TIME_LEEWAY = 10 * 60
def buildContractScript(lock_val, secret_hash, pkh_redeem, pkh_refund, op_lock=OpCodes.OP_CHECKSEQUENCEVERIFY):
script = bytearray([
OpCodes.OP_IF,
OpCodes.OP_SIZE,
0x01, 0x20, # 32
OpCodes.OP_EQUALVERIFY,
OpCodes.OP_SHA256,
0x20]) \
+ secret_hash \
+ bytearray([
OpCodes.OP_EQUALVERIFY,
OpCodes.OP_DUP,
OpCodes.OP_HASH160,
0x14]) \
+ pkh_redeem \
+ bytearray([OpCodes.OP_ELSE, ]) \
+ SerialiseNum(lock_val) \
+ bytearray([
op_lock,
OpCodes.OP_DROP,
OpCodes.OP_DUP,
OpCodes.OP_HASH160,
0x14]) \
+ pkh_refund \
+ bytearray([
OpCodes.OP_ENDIF,
OpCodes.OP_EQUALVERIFY,
OpCodes.OP_CHECKSIG])
return script
def extractScriptSecretHash(script):
return script[7:39]
def redeemITx(self, bid_id, session):
bid, offer = self.getBidAndOffer(bid_id, session)
ci_from = self.ci(offer.coin_from)
txn = self.createRedeemTxn(ci_from.coin_type(), bid, for_txn_type='initiate')
txid = ci_from.publishTx(bytes.fromhex(txn))
bid.initiate_tx.spend_txid = bytes.fromhex(txid)
self.log.debug('Submitted initiate redeem txn %s to %s chain for bid %s', txid, ci_from.coin_name(), bid_id.hex())
self.logEvent(Concepts.BID, bid_id, EventLogTypes.ITX_REDEEM_PUBLISHED, '', session)
class AtomicSwapInterface(ProtocolInterface):
swap_type = SwapTypes.SELLER_FIRST
def getFundedInitiateTxTemplate(self, ci, amount: int, sub_fee: bool) -> bytes:
addr_to = self.getMockAddrTo(ci)
funded_tx = ci.createRawFundedTransaction(addr_to, amount, sub_fee, lock_unspents=False)
return bytes.fromhex(funded_tx)
def promoteMockTx(self, ci, mock_tx: bytes, script: bytearray) -> bytearray:
mock_txo_script = self.getMockScriptScriptPubkey(ci)
real_txo_script = ci.get_p2wsh_script_pubkey(script) if ci._use_segwit else ci.get_p2sh_script_pubkey(script)
found: int = 0
ctx = ci.loadTx(mock_tx)
for txo in ctx.vout:
if txo.scriptPubKey == mock_txo_script:
txo.scriptPubKey = real_txo_script
found += 1
if found < 1:
raise ValueError('Mocked output not found')
if found > 1:
raise ValueError('Too many mocked outputs found')
ctx.nLockTime = 0
funded_tx = ctx.serialize()
return ci.signTxWithWallet(funded_tx)
|
[
"[email protected]"
] | |
1a23070fad57bfc873607928750f628128a6a1fd
|
00a22eaf2ea17eeda6151af145d63f6a2c13a985
|
/footballleagues/migrations/0008_auto_20201114_1554.py
|
bf1e53af7a000e89a0b6c22c56b05fe5781d045d
|
[
"MIT"
] |
permissive
|
RicardoSilveira23/TonicAppChallenge
|
ad20df4af38fdb8662a7befb0457c66f06ca9c9a
|
961107acbcdd93551bcd1b4b0ecd877fb4a7d813
|
refs/heads/main
| 2023-01-15T20:26:05.503585 | 2020-11-16T13:45:01 | 2020-11-16T13:45:01 | 312,053,857 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 603 |
py
|
# Generated by Django 3.1.3 on 2020-11-14 15:54
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
("footballleagues", "0007_auto_20201114_1551"),
]
operations = [
migrations.AddIndex(
model_name="team",
index=models.Index(
fields=[
"name",
"city",
"coach",
"championships_won",
"number_of_players",
],
name="team_idx",
),
),
]
|
[
"[email protected]"
] | |
ea94d8096bd583c418ffdb82662ac4b9b3264e25
|
608d79135ea65c2350ffe688dc870a8ce09424e4
|
/color_map_test.py
|
6cc205789c7cbc08a8c1013cd437754f6874f283
|
[] |
no_license
|
dips4717/rico_autoencoder
|
735620f330d9aaeb1a034ae30fe5cb8d08d19f86
|
77f4e68587a44c3fa1c1a52ef601b052012ac62c
|
refs/heads/main
| 2023-06-06T06:11:58.356203 | 2021-06-23T11:11:50 | 2021-06-23T11:11:50 | 379,320,228 | 4 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 3,736 |
py
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jan 7 10:28:33 2020
@author: dipu
"""
"""
==================
Colormap reference
==================
Reference for colormaps included with Matplotlib.
This reference example shows all colormaps included with Matplotlib. Note that
any colormap listed here can be reversed by appending "_r" (e.g., "pink_r").
These colormaps are divided into the following categories:
Sequential:
These colormaps are approximately monochromatic colormaps varying smoothly
between two color tones---usually from low saturation (e.g. white) to high
saturation (e.g. a bright blue). Sequential colormaps are ideal for
representing most scientific data since they show a clear progression from
low-to-high values.
Diverging:
These colormaps have a median value (usually light in color) and vary
smoothly to two different color tones at high and low values. Diverging
colormaps are ideal when your data has a median value that is significant
(e.g. 0, such that positive and negative values are represented by
different colors of the colormap).
Qualitative:
These colormaps vary rapidly in color. Qualitative colormaps are useful for
choosing a set of discrete colors. For example::
color_list = plt.cm.Set3(np.linspace(0, 1, 12))
gives a list of RGB colors that are good for plotting a series of lines on
a dark background.
Miscellaneous:
Colormaps that don't fit into the categories above.
"""
import numpy as np
import matplotlib.pyplot as plt
# Have colormaps separated into categories:
# http://matplotlib.org/examples/color/colormaps_reference.html
cmaps = [('Perceptually Uniform Sequential', [
'viridis', 'plasma', 'inferno', 'magma']),
('Sequential', [
'Greys', 'Purples', 'Blues', 'Greens', 'Oranges', 'Reds',
'YlOrBr', 'YlOrRd', 'OrRd', 'PuRd', 'RdPu', 'BuPu',
'GnBu', 'PuBu', 'YlGnBu', 'PuBuGn', 'BuGn', 'YlGn']),
('Sequential (2)', [
'binary', 'gist_yarg', 'gist_gray', 'gray', 'bone', 'pink',
'spring', 'summer', 'autumn', 'winter', 'cool', 'Wistia',
'hot', 'afmhot', 'gist_heat', 'copper']),
('Diverging', [
'PiYG', 'PRGn', 'BrBG', 'PuOr', 'RdGy', 'RdBu',
'RdYlBu', 'RdYlGn', 'Spectral', 'coolwarm', 'bwr', 'seismic']),
('Qualitative', [
'Pastel1', 'Pastel2', 'Paired', 'Accent',
'Dark2', 'Set1', 'Set2', 'Set3',
'tab10', 'tab20', 'tab20b', 'tab20c']),
('Miscellaneous', [
'flag', 'prism', 'ocean', 'gist_earth', 'terrain', 'gist_stern',
'gnuplot', 'gnuplot2', 'CMRmap', 'cubehelix', 'brg', 'hsv',
'gist_rainbow', 'rainbow', 'jet', 'nipy_spectral', 'gist_ncar'])]
nrows = max(len(cmap_list) for cmap_category, cmap_list in cmaps)
gradient = np.linspace(0, 1, 256)
gradient = np.vstack((gradient, gradient))
def plot_color_gradients(cmap_category, cmap_list, nrows):
fig, axes = plt.subplots(nrows=nrows)
fig.subplots_adjust(top=0.95, bottom=0.01, left=0.2, right=0.99)
axes[0].set_title(cmap_category + ' colormaps', fontsize=14)
for ax, name in zip(axes, cmap_list):
ax.imshow(gradient, aspect='auto', cmap=plt.get_cmap(name))
pos = list(ax.get_position().bounds)
x_text = pos[0] - 0.01
y_text = pos[1] + pos[3]/2.
fig.text(x_text, y_text, name, va='center', ha='right', fontsize=10)
# Turn off *all* ticks & spines, not just the ones with colormaps.
for ax in axes:
ax.set_axis_off()
for cmap_category, cmap_list in cmaps:
plot_color_gradients(cmap_category, cmap_list, nrows)
plt.show()
|
[
"[email protected]"
] | |
a20a63970a6c3e2a6a1e1b14d79d7b061637d7e9
|
f4f83a705819f64735ae591b66664f93a577bf8a
|
/Monitor/dev06/dev06/settings.py
|
14efcaeafd92c47e1489fc18e873e8c411827a96
|
[] |
no_license
|
bbhunter/CyberSecLabs
|
b0a7733121c60ca67b053cbe7d9064d43dd08ef3
|
72664f58ca10af82b515aa95b0388cf459bb19b6
|
refs/heads/master
| 2022-12-21T10:58:53.291238 | 2020-10-02T17:43:51 | 2020-10-02T17:43:51 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 3,096 |
py
|
"""
Django settings for dev06 project.
Generated by 'django-admin startproject' using Django 2.2.9.
For more information on this file, see
https://docs.djangoproject.com/en/2.2/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/2.2/ref/settings/
"""
import os
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/2.2/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = 'zd74)gqbc=z(kb6j9$4(m7*6un69!^$*gp=9k20q3qsjd(8ymq'
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
ALLOWED_HOSTS = []
# Application definition
INSTALLED_APPS = [
'app',
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
]
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
ROOT_URLCONF = 'dev06.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'dev06.wsgi.application'
# Database
# https://docs.djangoproject.com/en/2.2/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),
}
}
# Password validation
# https://docs.djangoproject.com/en/2.2/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
},
]
# Internationalization
# https://docs.djangoproject.com/en/2.2/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/2.2/howto/static-files/
STATIC_URL = '/static/'
|
[
"[email protected]"
] | |
4d489cf9efd5758aefc378379f5a79166be8b461
|
9159df50596dcfbbe3e41eaf15a0a360a95e7089
|
/textcleanup.py
|
c864bb1b297efb48e2a88fd8179d3f544ad3ecc7
|
[] |
no_license
|
stephengough/enigma
|
c1144a244e0d4d2aa35b008c8d57f6482829d162
|
6afe9e7383924cafcbf01ed301410512f4443b95
|
refs/heads/master
| 2021-01-23T13:53:05.277937 | 2015-06-13T23:45:29 | 2015-06-13T23:45:29 | 37,391,347 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 139 |
py
|
class TextCleanup:
def preprocess(self, msg):
ret = ''
for c in msg.upper():
if c >= 'A' and c <= 'Z':
ret += c
return ret
|
[
"[email protected]"
] | |
0d62359f4aa4b91e0b20b2e5dc8b3ae4daab4878
|
ca7aa979e7059467e158830b76673f5b77a0f5a3
|
/Python_codes/p03292/s123105438.py
|
9c05d46bc0b47e9e16017b20ebf76cfb8fcba1cc
|
[] |
no_license
|
Aasthaengg/IBMdataset
|
7abb6cbcc4fb03ef5ca68ac64ba460c4a64f8901
|
f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8
|
refs/heads/main
| 2023-04-22T10:22:44.763102 | 2021-05-13T17:27:22 | 2021-05-13T17:27:22 | 367,112,348 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 69 |
py
|
a,b,c = sorted(map(int,input().split()))
print(max(a,b,c)-min(a,b,c))
|
[
"[email protected]"
] | |
ca4852f3a092ee87833c2a498724083f66a0ca0e
|
99be0cd3df5a38376406ad85c106a553505ba77b
|
/astroraf/fos/__init__.py
|
fe3fac3aa4d1266a2c9a30db0e0380a098286997
|
[] |
no_license
|
justincely/astroraf
|
72da22b79b99f37808d4224f94315862a0d2ffa5
|
af7875771ff337ff4a4d1576db99ca35709be5ba
|
refs/heads/master
| 2016-09-05T18:54:04.800462 | 2015-06-19T18:58:54 | 2015-06-19T18:58:54 | 11,696,336 | 1 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 18 |
py
|
from fos import *
|
[
"[email protected]"
] | |
fec8c459674c303f15e9c9aac189788a40d17919
|
90066fb845694ccffa446950232a95709561c8e7
|
/RB3.py
|
a7058a621f094894260aa67fc2e80463d3b2f6b1
|
[] |
no_license
|
Rxdxxn/Rezolvare-If-While-For
|
9773929f50d58da754fb793592fc1c8c801500e2
|
7e20b0f216dafbc6389fe2fe2c6fc7b7bedc0e8e
|
refs/heads/main
| 2023-01-08T23:17:52.247412 | 2020-11-20T21:21:14 | 2020-11-20T21:21:14 | 311,920,227 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 321 |
py
|
m=eval(input("Dati numarul m:"))
n=eval(input("Dati numarul n:"))
puterea=''
if n>m:
for i in range (1,n+1):
if(m**i==n):
puterea='da'
if puterea=='da':
print(n,"este putere a lui ",m)
else:
print(n,"nu este putere a lui ",m)
else:
print("Eroare")
|
[
"[email protected]"
] | |
2146739f9834f0af7d112fc44b3b75d696d80c39
|
f1a51bb6cb5810a2dfac27cbbe32f5c5761bd8ec
|
/angrmanagement/data/object_container.py
|
63b931645b8e1c14d8a5902e8eb52b570ff38979
|
[
"BSD-2-Clause"
] |
permissive
|
sraboy/angr-management
|
904848408e9eec6662e16d9b69a0991b0374d3c6
|
4c4c1df7bce7083547ae38a19709f33dd10b7e22
|
refs/heads/master
| 2020-04-30T17:23:24.427321 | 2019-09-21T09:34:21 | 2019-09-21T09:34:21 | 176,977,927 | 0 | 1 |
BSD-2-Clause
| 2019-03-21T15:52:06 | 2019-03-21T15:52:06 | null |
UTF-8
|
Python
| false | false | 2,374 |
py
|
from ..utils.namegen import NameGenerator
class EventSentinel:
def __init__(self):
self.am_subscribers = []
def am_subscribe(self, listener):
if listener is not None:
self.am_subscribers.append(listener)
def am_unsubscribe(self, listener):
if listener is not None:
self.am_subscribers.remove(listener)
def am_event(self, **kwargs):
for listener in self.am_subscribers:
listener(**kwargs)
class ObjectContainer(EventSentinel):
def __init__(self, obj, name=None, notes=''):
super(ObjectContainer, self).__init__()
self._am_obj = None
self.am_obj = obj
self.am_name = name if name is not None else NameGenerator.random_name()
self.am_notes = notes
# cause events to propagate upward through nested objectcontainers
@property
def am_obj(self):
return self._am_obj
@am_obj.setter
def am_obj(self, v):
if type(self._am_obj) is ObjectContainer:
self._am_obj.am_unsubscribe(self.__forwarder)
if type(v) is ObjectContainer:
v.am_subscribe(self.__forwarder)
self._am_obj = v
def am_none(self):
return self._am_obj is None
def __forwarder(self, **kwargs):
kwargs['forwarded'] = True
self.am_event(**kwargs)
def __getattr__(self, item):
if item.startswith('am_') or item.startswith('_am_'):
return object.__getattribute__(self, item)
return getattr(self._am_obj, item)
def __setattr__(self, key, value):
if key.startswith('am_') or key.startswith('_am_'):
return object.__setattr__(self, key, value)
setattr(self._am_obj, key, value)
def __getitem__(self, item):
return self._am_obj[item]
def __setitem__(self, key, value):
self._am_obj[key] = value
def __dir__(self):
return dir(self._am_obj) + list(self.__dict__) + list(EventSentinel.__dict__) + ['am_obj', 'am_full']
def __iter__(self):
return iter(self._am_obj)
def __len__(self):
return len(self._am_obj)
def __eq__(self, other):
return self is other or self._am_obj == other
def __ne__(self, other):
return not (self == other)
def __repr__(self):
return '(container %s)%s' % (self.am_name, repr(self._am_obj))
|
[
"[email protected]"
] | |
adec90e1bcd2ef78cd772b602c1e316d2a295e83
|
5891324384c1eb96c19bde7a6d21df91890bacce
|
/Algoritmos 2016-1/1-lista(professor_fabio)/6.py
|
1163590928b44a3286c5a4f6f2c76982b3d2826b
|
[] |
no_license
|
Guilherme2020/ADS
|
f41324f48e6e2bc6ddd7e4310328e09f79b8a7a9
|
6d3ac6effca7633d6bc309ecfa9c5e8349f8e680
|
refs/heads/master
| 2021-01-17T21:06:21.874766 | 2018-01-19T21:49:40 | 2018-01-19T21:49:40 | 60,442,107 | 0 | 0 | null | 2016-06-08T13:57:25 | 2016-06-05T03:51:00 |
Python
|
UTF-8
|
Python
| false | false | 292 |
py
|
#Leia uma velocidade em km/h,
#calcule e escreva esta velocidade em m/s. (Vm/s = Vkm/h / 3.6)
velocidade_kmh = float(input("Insira a velocidade em km/h"))
velocidade_metros_segundo = velocidade_kmh/3.6
print(" %.2fKm/h equivale a %.2f m/s "%(velocidade_kmh,velocidade_metros_segundo))
|
[
"[email protected]"
] | |
96fa13be2d366e4a842b4ae5921ef5f00857a845
|
4bb958d84d8fc53f091f7b49dc26751aeff98bcc
|
/venv/bin/pip
|
6ee3e866c9ee3706f7db4e90cc2e7ba33383c826
|
[] |
no_license
|
samfeder/banter-deepspeech
|
d2856d08c906ef9627b426c8b20bc09be831d08c
|
7ab8f51772c69e41a676e51fcea1124783bc8dda
|
refs/heads/master
| 2022-12-08T12:49:31.676710 | 2020-03-28T18:23:25 | 2020-03-28T18:23:25 | 250,857,781 | 1 | 0 | null | 2022-12-08T03:54:56 | 2020-03-28T17:46:32 |
Python
|
UTF-8
|
Python
| false | false | 278 |
#!/Users/samfeder/Development/banter/banter-deepspeech/venv/bin/python3
# -*- coding: utf-8 -*-
import re
import sys
from pip._internal.cli.main import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
sys.exit(main())
|
[
"[email protected]"
] | ||
fb4130f05a417263abf2c2d20790c2ac2f668e41
|
4850f693e3b15c5871218353088eb4898b2c6cd0
|
/python_tutorial/python_tutorial/settings.py
|
662cf383ff151240f17d07a19be4e955e7b54866
|
[] |
no_license
|
SaiSwaroopPadala99/Test-
|
61ef04a39c4ce3c4301a49074c4bfac14a18492b
|
141d7e7300b7e6624a0615a1499625e9ff0985bd
|
refs/heads/master
| 2020-12-07T19:53:04.670818 | 2020-01-09T11:01:29 | 2020-01-09T11:01:29 | 232,786,638 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 3,131 |
py
|
"""
Django settings for python_tutorial project.
Generated by 'django-admin startproject' using Django 2.2.6.
For more information on this file, see
https://docs.djangoproject.com/en/2.2/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/2.2/ref/settings/
"""
import os
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/2.2/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = 'bvb$ewln340l8&pj86lbhba*)inj9iaq+y=430sd2&a-%kdyjt'
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
ALLOWED_HOSTS = []
# Application definition
INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'tutorial',
]
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
ROOT_URLCONF = 'python_tutorial.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'python_tutorial.wsgi.application'
# Database
# https://docs.djangoproject.com/en/2.2/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),
}
}
# Password validation
# https://docs.djangoproject.com/en/2.2/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
},
]
# Internationalization
# https://docs.djangoproject.com/en/2.2/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/2.2/howto/static-files/
STATIC_URL = '/static/'
|
[
"[email protected]"
] | |
85fef74f9fafbfcb48446ecb151b3d7349bc61cb
|
553987c369aa12a42553a4adac305f499aa9f2a1
|
/PyQt5/curso/jogo/teste_rc.py
|
aedb8a74f950dbfe3c575f08da214834ba97629e
|
[] |
no_license
|
barmvicente/Study
|
a7dc7df2841dfbfb19a791cd2130479afe6d7004
|
ffdfbc80cb7e58c0f142f4f4840577b18f9382ea
|
refs/heads/master
| 2021-08-16T09:41:10.435668 | 2017-11-19T13:49:59 | 2017-11-19T13:49:59 | 111,299,243 | 1 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 432,419 |
py
|
# -*- coding: utf-8 -*-
# Resource object code
#
# Created: sáb nov 18 16:17:24 2017
# by: The Resource Compiler for PyQt (Qt v5.2.1)
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore
qt_resource_data = b"\
\x00\x00\x7a\x20\
\xff\
\xd8\xff\xe0\x00\x10\x4a\x46\x49\x46\x00\x01\x02\x00\x00\x64\x00\
\x64\x00\x00\xff\xec\x00\x11\x44\x75\x63\x6b\x79\x00\x01\x00\x04\
\x00\x00\x00\x50\x00\x00\xff\xe1\x0d\x49\x68\x74\x74\x70\x3a\x2f\
\x2f\x6e\x73\x2e\x61\x64\x6f\x62\x65\x2e\x63\x6f\x6d\x2f\x78\x61\
\x70\x2f\x31\x2e\x30\x2f\x00\x3c\x3f\x78\x70\x61\x63\x6b\x65\x74\
\x20\x62\x65\x67\x69\x6e\x3d\x22\xef\xbb\xbf\x22\x20\x69\x64\x3d\
\x22\x57\x35\x4d\x30\x4d\x70\x43\x65\x68\x69\x48\x7a\x72\x65\x53\
\x7a\x4e\x54\x63\x7a\x6b\x63\x39\x64\x22\x3f\x3e\x0a\x3c\x78\x3a\
\x78\x6d\x70\x6d\x65\x74\x61\x20\x78\x6d\x6c\x6e\x73\x3a\x78\x3d\
\x22\x61\x64\x6f\x62\x65\x3a\x6e\x73\x3a\x6d\x65\x74\x61\x2f\x22\
\x20\x78\x3a\x78\x6d\x70\x74\x6b\x3d\x22\x41\x64\x6f\x62\x65\x20\
\x58\x4d\x50\x20\x43\x6f\x72\x65\x20\x34\x2e\x32\x2e\x32\x2d\x63\
\x30\x36\x33\x20\x35\x33\x2e\x33\x35\x32\x36\x32\x34\x2c\x20\x32\
\x30\x30\x38\x2f\x30\x37\x2f\x33\x30\x2d\x31\x38\x3a\x30\x35\x3a\
\x34\x31\x20\x20\x20\x20\x20\x20\x20\x20\x22\x3e\x0a\x20\x3c\x72\
\x64\x66\x3a\x52\x44\x46\x20\x78\x6d\x6c\x6e\x73\x3a\x72\x64\x66\
\x3d\x22\x68\x74\x74\x70\x3a\x2f\x2f\x77\x77\x77\x2e\x77\x33\x2e\
\x6f\x72\x67\x2f\x31\x39\x39\x39\x2f\x30\x32\x2f\x32\x32\x2d\x72\
\x64\x66\x2d\x73\x79\x6e\x74\x61\x78\x2d\x6e\x73\x23\x22\x3e\x0a\
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\x00\x2e\x00\x70\x00\x6e\x00\x67\
\x00\x05\
\x00\x7b\x51\x67\
\x00\x78\
\x00\x2e\x00\x6a\x00\x70\x00\x67\
"
qt_resource_struct = b"\
\x00\x00\x00\x00\x00\x02\x00\x00\x00\x02\x00\x00\x00\x01\
\x00\x00\x00\x00\x00\x02\x00\x00\x00\x02\x00\x00\x00\x04\
\x00\x00\x00\x0e\x00\x02\x00\x00\x00\x01\x00\x00\x00\x03\
\x00\x00\x00\x1c\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\
\x00\x00\x00\x34\x00\x00\x00\x00\x00\x01\x00\x00\x7a\x24\
\x00\x00\x00\x44\x00\x00\x00\x00\x00\x01\x00\x01\x2f\x5e\
"
def qInitResources():
QtCore.qRegisterResourceData(0x01, qt_resource_struct, qt_resource_name, qt_resource_data)
def qCleanupResources():
QtCore.qUnregisterResourceData(0x01, qt_resource_struct, qt_resource_name, qt_resource_data)
qInitResources()
|
[
"[email protected]"
] | |
1ad07b528ea8b1ff960f11a81b99eee198db74fb
|
8b31eb19740cca14c3a2a4782f713be837b43563
|
/src/__init__.py
|
db15ec81e0a677b8fc5fafba3954f5c513642832
|
[] |
no_license
|
polytopeSVM/XNODE-WAN-PDE-solver
|
aa52dd81a02ccbaabc5a1b84ad8f4bcc86b56bec
|
46a3f12bcb17e6908b160a3e20b541a6962d6216
|
refs/heads/main
| 2023-08-29T00:46:59.938572 | 2021-10-18T10:47:13 | 2021-10-18T10:47:13 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 99 |
py
|
from src.dataset import *
from src.loss import *
from src.model import *
from src.training import *
|
[
"[email protected]"
] | |
c7733e0c97c99db3361255fd039f0965f67fe4ec
|
a70697ef62978117467695fd3507e4d08e186ab4
|
/source/res/scripts/client/gui/Scaleform/locale/BOOTCAMP.py
|
4e66b561503dd6a2e8f4fe0c3d1b2e0274984d50
|
[] |
no_license
|
chipsi007/WorldOfTanks-Decompiled
|
d208678a6f2f094b02281d09ecc30f3e32725ce9
|
3b9dc21321429e4dee146c23c7250f2c62757937
|
refs/heads/master
| 2020-03-19T01:21:09.883951 | 2018-05-04T13:19:56 | 2018-05-04T13:19:56 | 135,538,885 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 19,664 |
py
|
# Python bytecode 2.7 (decompiled from Python 2.7)
# Embedded file name: scripts/client/gui/Scaleform/locale/BOOTCAMP.py
from debug_utils import LOG_WARNING
class BOOTCAMP(object):
WELLCOME_BOOTCAMP = '#bootcamp:wellcome/bootcamp'
WELLCOME_BOOTCAMP_DESCRIPTION = '#bootcamp:wellcome/bootcamp/description'
REQUEST_BOOTCAMP_RETURN = '#bootcamp:request/bootcamp/return'
REQUEST_BOOTCAMP_START = '#bootcamp:request/bootcamp/start'
REQUEST_BOOTCAMP_FINISH = '#bootcamp:request/bootcamp/finish'
BTN_SELECT = '#bootcamp:btn/select'
BTN_CONTINUE = '#bootcamp:btn/continue'
BTN_CONTINUE_PREBATTLE = '#bootcamp:btn/continue/prebattle'
GAME_MODE = '#bootcamp:game/mode'
BTN_TUTORIAL_START = '#bootcamp:btn/tutorial/start'
BTN_TUTORIAL_SKIP = '#bootcamp:btn/tutorial/skip'
PROMO_VEHICLEBUYVIEW = '#bootcamp:promo/vehicleBuyView'
AWARD_OPTIONS = '#bootcamp:award/options'
AWARD_OPTIONS_TITLE = '#bootcamp:award/options/title'
AWARD_OPTIONS_NATION_US = '#bootcamp:award/options/nation/us'
AWARD_OPTIONS_NATION_GE = '#bootcamp:award/options/nation/ge'
AWARD_OPTIONS_NATION_USSR = '#bootcamp:award/options/nation/ussr'
AWARD_OPTIONS_NAME_US = '#bootcamp:award/options/name/us'
AWARD_OPTIONS_DESCRIPTION_US = '#bootcamp:award/options/description/us'
AWARD_OPTIONS_NAME_GE = '#bootcamp:award/options/name/ge'
AWARD_OPTIONS_DESCRIPTION_GE = '#bootcamp:award/options/description/ge'
AWARD_OPTIONS_NAME_USSR = '#bootcamp:award/options/name/ussr'
AWARD_OPTIONS_DESCRIPTION_USSR = '#bootcamp:award/options/description/ussr'
HINT_CAMERA_CONTROLS = '#bootcamp:hint/camera/controls'
HINT_MOVE = '#bootcamp:hint/move'
HINT_MOVE_TURRET = '#bootcamp:hint/move/turret'
HINT_SHOOT = '#bootcamp:hint/shoot'
HINT_SNIPER = '#bootcamp:hint/sniper'
HINT_ADVANCED_SNIPER_MAIN = '#bootcamp:hint/advanced/sniper/main'
HINT_ADVANCED_SNIPER_BEFORE = '#bootcamp:hint/advanced/sniper/before'
HINT_MESSAGE_AVOID = '#bootcamp:hint/message/avoid'
HINT_REPAIR_TRACK = '#bootcamp:hint/repair/track'
HINT_USE_EXTINGUISHER = '#bootcamp:hint/use/extinguisher'
HINT_HEAL_CREW = '#bootcamp:hint/heal/crew'
HINT_SNIPER_ON_DISTANCE_MAIN = '#bootcamp:hint/sniper/on/distance/main'
HINT_SNIPER_ON_DISTANCE_EXIT = '#bootcamp:hint/sniper/on/distance/exit'
HINT_NO_MOVE = '#bootcamp:hint/no/move'
HINT_ALLY_SHOOT = '#bootcamp:hint/ally/shoot'
HINT_TARGET_UNLOCK = '#bootcamp:hint/target/unlock'
HINT_USELESS_CONSUMABLES = '#bootcamp:hint/useless/consumables'
HINT_WAIT_RELOAD = '#bootcamp:hint/wait/reload'
HINT_EXIT_GAME_AREA = '#bootcamp:hint/exit/game/area'
HINT_SHOOT_WHILE_MOVING = '#bootcamp:hint/shoot/while/moving'
HINT_SECONDARY_SNIPER = '#bootcamp:hint/secondary/sniper'
HINT_LOW_HP = '#bootcamp:hint/low/hp'
HINT_MISSION3_PLAYERDETECTED = '#bootcamp:hint/mission3/playerdetected'
HINT_MISSION3_FALLBACK = '#bootcamp:hint/mission3/fallback'
HINT_MISSION3_FLANKENEMIES = '#bootcamp:hint/mission3/flankenemies'
HINT_MISSION3_FOLIAGEINTROA = '#bootcamp:hint/mission3/foliageintroa'
HINT_MISSION3_FOLIAGEINTROB = '#bootcamp:hint/mission3/foliageintrob'
HINT_MISSION3_FLANKINGFAILS = '#bootcamp:hint/mission3/flankingfails'
HINT_MISSION3_FLANKINGFAILS2 = '#bootcamp:hint/mission3/flankingfails2'
HINT_MISSION3_FLANKINGWAIT = '#bootcamp:hint/mission3/flankingwait'
HINT_MISSION3_CAPTUREBASE = '#bootcamp:hint/mission3/capturebase'
HINT_MISSION3_CAPTURELOST = '#bootcamp:hint/mission3/capturelost'
HINT_MISSION3_CAPTURETOGETHER = '#bootcamp:hint/mission3/capturetogether'
HINT_MISSION3_CAPTUREHELP = '#bootcamp:hint/mission3/capturehelp'
HINT_MISSION3_CAPTUREINPROGRESS = '#bootcamp:hint/mission3/captureinprogress'
QUEST_TITLE = '#bootcamp:quest/title'
QUEST_NAME = '#bootcamp:quest/name'
QUEST_CONDITION = '#bootcamp:quest/condition'
QUEST_GAMEMODE = '#bootcamp:quest/gamemode'
LOADING_TIP_WASD_HEADER_1 = '#bootcamp:loading/tip/wasd/header/1'
LOADING_TIP_WASD_HEADER_2 = '#bootcamp:loading/tip/wasd/header/2'
LOADING_TIP_WASD_HEADER_3 = '#bootcamp:loading/tip/wasd/header/3'
LOADING_TIP_SNIPER_HEADER_1 = '#bootcamp:loading/tip/sniper/header/1'
LOADING_TIP_SNIPER_HEADER_2 = '#bootcamp:loading/tip/sniper/header/2'
LOADING_TIP_SNIPER_DESCRIPTION_1 = '#bootcamp:loading/tip/sniper/description/1'
LOADING_TIP_SNIPER_DESCRIPTION_2 = '#bootcamp:loading/tip/sniper/description/2'
LOADING_TIP_PENETRATION_HEADER_1 = '#bootcamp:loading/tip/penetration/header/1'
LOADING_TIP_PENETRATION_HEADER_2 = '#bootcamp:loading/tip/penetration/header/2'
LOADING_TIP_PENETRATION_DESCRIPTION_1 = '#bootcamp:loading/tip/penetration/description/1'
LOADING_TIP_PENETRATION_DESCRIPTION_2 = '#bootcamp:loading/tip/penetration/description/2'
LOADING_TIP_VISIBILITY_HEADER_1 = '#bootcamp:loading/tip/visibility/header/1'
LOADING_TIP_VISIBILITY_HEADER_2 = '#bootcamp:loading/tip/visibility/header/2'
LOADING_TIP_VISIBILITY_DESCRIPTION_1 = '#bootcamp:loading/tip/visibility/description/1'
LOADING_TIP_VISIBILITY_DESCRIPTION_2 = '#bootcamp:loading/tip/visibility/description/2'
LOADING_TIP_VISIBILITY_DESCRIPTION_3 = '#bootcamp:loading/tip/visibility/description/3'
LOADING_TIP_VISIBILITY_DESCRIPTION_4 = '#bootcamp:loading/tip/visibility/description/4'
LOADING_TIP_EQUIPMENT_HEADER_1 = '#bootcamp:loading/tip/equipment/header/1'
LOADING_TIP_EQUIPMENT_HEADER_2 = '#bootcamp:loading/tip/equipment/header/2'
LOADING_TIP_EQUIPMENT_HEADER_3 = '#bootcamp:loading/tip/equipment/header/3'
LOADING_TIP_EQUIPMENT_DESCRIPTION_1 = '#bootcamp:loading/tip/equipment/description/1'
LOADING_TIP_EQUIPMENT_DESCRIPTION_2 = '#bootcamp:loading/tip/equipment/description/2'
LOADING_TIP_EQUIPMENT_DESCRIPTION_3 = '#bootcamp:loading/tip/equipment/description/3'
LOADING_TIP_VICTORY_HEADER_1 = '#bootcamp:loading/tip/victory/header/1'
LOADING_TIP_VICTORY_HEADER_2 = '#bootcamp:loading/tip/victory/header/2'
LOADING_TIP_VICTORY_DESCRIPTION_1 = '#bootcamp:loading/tip/victory/description/1'
LOADING_TIP_VICTORY_DESCRIPTION_2 = '#bootcamp:loading/tip/victory/description/2'
LOADING_TIP_CROSSHAIR_HEADER_1 = '#bootcamp:loading/tip/crosshair/header/1'
LOADING_TIP_CROSSHAIR_HEADER_2 = '#bootcamp:loading/tip/crosshair/header/2'
LOADING_TIP_MODULES_HEADER_1 = '#bootcamp:loading/tip/modules/header/1'
LOADING_TIP_MODULES_HEADER_2 = '#bootcamp:loading/tip/modules/header/2'
LOADING_TIP_MODULES_HEADER_3 = '#bootcamp:loading/tip/modules/header/3'
LOADING_TIP_MODULES_DESCRIPTION_2 = '#bootcamp:loading/tip/modules/description/2'
LOADING_TIP_MODULES_DESCRIPTION_3 = '#bootcamp:loading/tip/modules/description/3'
PREBATTLE_HINT_SCORE = '#bootcamp:prebattle/hint/score'
PREBATTLE_HINT_HP = '#bootcamp:prebattle/hint/hp'
PREBATTLE_HINT_MODULES = '#bootcamp:prebattle/hint/modules'
PREBATTLE_HINT_CREW = '#bootcamp:prebattle/hint/crew'
PREBATTLE_HINT_MINIMAP = '#bootcamp:prebattle/hint/minimap'
PREBATTLE_HINT_CONSUMABLES = '#bootcamp:prebattle/hint/consumables'
PREBATTLE_HINT_PENETRATION_CHANCE = '#bootcamp:prebattle/hint/penetration/chance'
PREBATTLE_HINT_PENETRATION_HIGH = '#bootcamp:prebattle/hint/penetration/high'
PREBATTLE_HINT_PENETRATION_LOW = '#bootcamp:prebattle/hint/penetration/low'
MESSAGE_VEHICLE_AWARDED_LABEL = '#bootcamp:message/vehicle/awarded/label'
MESSAGE_VEHICLE_AWARDED_TEXT = '#bootcamp:message/vehicle/awarded/text'
MESSAGE_EXTRA_AWARD_OPTIONS = '#bootcamp:message/extra/award/options'
MESSAGE_CREDITS_LABEL = '#bootcamp:message/credits/label'
MESSAGE_CREDITS_TEXT = '#bootcamp:message/credits/text'
MESSAGE_EXPERIENCE_LABEL = '#bootcamp:message/experience/label'
MESSAGE_EXPERIENCE_TEXT = '#bootcamp:message/experience/text'
MESSAGE_UNLOCK_MODULE_LABEL = '#bootcamp:message/unlock/module/label'
MESSAGE_UNLOCK_MODULE_TEXT = '#bootcamp:message/unlock/module/text'
MESSAGE_NEW_MODULE_LABEL = '#bootcamp:message/new/module/label'
MESSAGE_NEW_MODULE_TEXT = '#bootcamp:message/new/module/text'
MESSAGE_UNLOCK_VEHICLE_LABEL = '#bootcamp:message/unlock/vehicle/label'
MESSAGE_UNLOCK_VEHICLE_TEXT = '#bootcamp:message/unlock/vehicle/text'
MESSAGE_SECOND_VEHICLE_TEXT_NATION_0 = '#bootcamp:message/second/vehicle/text/nation/0'
MESSAGE_SECOND_VEHICLE_TEXT_NATION_1 = '#bootcamp:message/second/vehicle/text/nation/1'
MESSAGE_SECOND_VEHICLE_TEXT_NATION_2 = '#bootcamp:message/second/vehicle/text/nation/2'
MESSAGE_SKILLS_AND_PERKS_LABEL = '#bootcamp:message/skills/and/perks/label'
MESSAGE_SKILLS_AND_PERKS_TEXT = '#bootcamp:message/skills/and/perks/text'
MESSAGE_SIX_SENSE_PERK_LABEL = '#bootcamp:message/six/sense/perk/label'
MESSAGE_SIX_SENSE_PERK_TEXT = '#bootcamp:message/six/sense/perk/text'
MESSAGE_CONSUMABLES_LABEL = '#bootcamp:message/consumables/label'
MESSAGE_CONSUMABLES_TEXT = '#bootcamp:message/consumables/text'
MESSAGE_REPAIR_KIT_LABEL = '#bootcamp:message/repair/kit/label'
MESSAGE_FIRST_AID_KIT_LABEL = '#bootcamp:message/first/aid/kit/label'
MESSAGE_FIRE_EXTINGUISHER_LABEL = '#bootcamp:message/fire/extinguisher/label'
MESSAGE_EQUIPMENT_LABEL = '#bootcamp:message/equipment/label'
MESSAGE_EQUIPMENT_TEXT = '#bootcamp:message/equipment/text'
MESSAGE_BONUS_EQUIPMENT_LABEL = '#bootcamp:message/bonus/equipment/label'
MESSAGE_BONUS_EQUIPMENT_TEXT = '#bootcamp:message/bonus/equipment/text'
MESSAGE_GOLD_LABEL = '#bootcamp:message/gold/label'
MESSAGE_GOLD_TEXT = '#bootcamp:message/gold/text'
MESSAGE_PREMIUM_LABEL = '#bootcamp:message/premium/label'
MESSAGE_PREMIUM_TEXT = '#bootcamp:message/premium/text'
MESSAGE_BONUS_PREMIUM_DAYS = '#bootcamp:message/bonus/premium/days'
MESSAGE_BONUS_PREMIUM_HOURS = '#bootcamp:message/bonus/premium/hours'
MESSAGE_MISSION_ACCOMPLISHED_LABEL = '#bootcamp:message/mission/accomplished/label'
MESSAGE_MISSION_ACCOMPLISHED_TEXT = '#bootcamp:message/mission/accomplished/text'
MESSAGE_BOOTCAMP_GRADUATE_LABEL = '#bootcamp:message/bootcamp/graduate/label'
MESSAGE_BOOTCAMP_GRADUATE_TEXT = '#bootcamp:message/bootcamp/graduate/text'
RESULTLABEL_WIN = '#bootcamp:resultlabel/win'
RESULTLABEL_LOSE = '#bootcamp:resultlabel/lose'
RESULTLABEL_TIE = '#bootcamp:resultlabel/tie'
RESULTLABEL_TECHWIN = '#bootcamp:resultlabel/techwin'
RESULTLABEL_ENDED = '#bootcamp:resultlabel/ended'
WITH_PREMIUM = '#bootcamp:with/premium'
BATTLE_RESULT_DESTROYED = '#bootcamp:battle/result/destroyed'
BATTLE_RESULT_DAMAGE = '#bootcamp:battle/result/damage'
BATTLE_RESULT_BLOCKED = '#bootcamp:battle/result/blocked'
BATTLE_RESULT_DETECTED = '#bootcamp:battle/result/detected'
BATTLE_RESULT_ASSISTED = '#bootcamp:battle/result/assisted'
BATTLE_RESULT_DESCRIPTION_DESTROYED = '#bootcamp:battle/result/description/destroyed'
BATTLE_RESULT_DESCRIPTION_DAMAGE = '#bootcamp:battle/result/description/damage'
BATTLE_RESULT_DESCRIPTION_BLOCKED = '#bootcamp:battle/result/description/blocked'
BATTLE_RESULT_DESCRIPTION_DETECTED = '#bootcamp:battle/result/description/detected'
BATTLE_RESULT_DESCRIPTION_ASSISTED = '#bootcamp:battle/result/description/assisted'
RESULT_AWARD_CADET_LABEL = '#bootcamp:result/award/cadet/label'
RESULT_AWARD_CADET_TEXT = '#bootcamp:result/award/cadet/text'
RESULT_AWARD_TANK_LABEL = '#bootcamp:result/award/tank/label'
RESULT_AWARD_TANK_TEXT = '#bootcamp:result/award/tank/text'
RESULT_AWARD_SNIPER_LABEL = '#bootcamp:result/award/sniper/label'
RESULT_AWARD_SNIPER_TEXT = '#bootcamp:result/award/sniper/text'
RESULT_AWARD_INVADER_LABEL = '#bootcamp:result/award/invader/label'
RESULT_AWARD_INVADER_TEXT = '#bootcamp:result/award/invader/text'
RESULT_AWARD_CREW_LABEL = '#bootcamp:result/award/crew/label'
RESULT_AWARD_CREW_TEXT = '#bootcamp:result/award/crew/text'
RESULT_AWARD_DUEL_LABEL = '#bootcamp:result/award/duel/label'
RESULT_AWARD_DUEL_TEXT = '#bootcamp:result/award/duel/text'
RESULT_AWARD_SHOOT_LABEL = '#bootcamp:result/award/shoot/label'
RESULT_AWARD_SHOOT_TEXT = '#bootcamp:result/award/shoot/text'
RESULT_AWARD_PREMIUM_LABEL = '#bootcamp:result/award/premium/label'
RESULT_AWARD_PREMIUM_TEXT = '#bootcamp:result/award/premium/text'
RESULT_AWARD_GOLD_LABEL = '#bootcamp:result/award/gold/label'
RESULT_AWARD_GOLD_TEXT = '#bootcamp:result/award/gold/text'
RESULT_AWARD_MISSION_LABEL = '#bootcamp:result/award/mission/label'
RESULT_AWARD_MISSION_TEXT = '#bootcamp:result/award/mission/text'
RESULT_AWARD_REPAIRKIT_LABEL = '#bootcamp:result/award/repairkit/label'
RESULT_AWARD_REPAIRKIT_TEXT = '#bootcamp:result/award/repairkit/text'
RESULT_AWARD_MEDICALKIT_LABEL = '#bootcamp:result/award/medicalkit/label'
RESULT_AWARD_MEDICALKIT_TEXT = '#bootcamp:result/award/medicalkit/text'
RESULT_AWARD_EXTINGUISHER_LABEL = '#bootcamp:result/award/extinguisher/label'
RESULT_AWARD_EXTINGUISHER_TEXT = '#bootcamp:result/award/extinguisher/text'
RESULT_AWARD_TOOLBOX_LABEL = '#bootcamp:result/award/toolbox/label'
RESULT_AWARD_TOOLBOX_TEXT = '#bootcamp:result/award/toolbox/text'
MESSAGE_INTRO_LESSON_II_LABEL = '#bootcamp:message/intro/lesson/ii/label'
MESSAGE_INTRO_LESSON_III_LABEL = '#bootcamp:message/intro/lesson/iii/label'
MESSAGE_INTRO_LESSON_III_CREW_LABEL = '#bootcamp:message/intro/lesson/iii/crew/label'
MESSAGE_INTRO_LESSON_IV_LABEL = '#bootcamp:message/intro/lesson/iv/label'
MESSAGE_INTRO_LESSON_V_LABEL = '#bootcamp:message/intro/lesson/v/label'
INVITATION_NOTE_SQUAD = '#bootcamp:invitation/note/squad'
INVITATION_NOTE_EVENT = '#bootcamp:invitation/note/event'
INVITATION_NOTE_FALLOUT = '#bootcamp:invitation/note/fallout'
INVITATION_NOTE_TRAINING = '#bootcamp:invitation/note/training'
INVITATION_NOTE_COMPANY = '#bootcamp:invitation/note/company'
INVITATION_NOTE_TOURNAMENT = '#bootcamp:invitation/note/tournament'
INVITATION_NOTE_CLAN = '#bootcamp:invitation/note/clan'
INVITATION_NOTE_UNIT = '#bootcamp:invitation/note/unit'
INVITATION_NOTE_SORTIE = '#bootcamp:invitation/note/sortie'
INVITATION_NOTE_FORT_BATTLE = '#bootcamp:invitation/note/fort/battle'
INVITATION_NOTE_CLUBS = '#bootcamp:invitation/note/clubs'
INVITATION_NOTE_EXTERNAL = '#bootcamp:invitation/note/external'
QUEUE_TITLE = '#bootcamp:queue/title'
QUEUE_QUEUE_TOO_LONG = '#bootcamp:queue/queue/too/long'
QUEUE_UNITS = '#bootcamp:queue/units'
QUEUE_MESSAGE = '#bootcamp:queue/message'
QUEUE_MORE_N_MINUTES = '#bootcamp:queue/more/n/minutes'
QUEUE_PLAYER_WAITING_TIME = '#bootcamp:queue/player-waiting-time'
QUEUE_SKIP_TUTORIAL = '#bootcamp:queue/skip/tutorial'
QUEUE_CANCEL_QUEUE = '#bootcamp:queue/cancel/queue'
TRANSITION_TITLE = '#bootcamp:transition/title'
BILL_TENSON = '#bootcamp:Bill Tenson'
BENEDIKT_DRESDNER = '#bootcamp:Benedikt Dresdner'
HEIKO_RIHTER = '#bootcamp:Heiko Rihter'
JOHN_ALBERT = '#bootcamp:John Albert'
DENIS_GORDIENKO = '#bootcamp:Denis Gordienko'
HASSO_MIRATO = '#bootcamp:Hasso Mirato'
RALF_HOFER = '#bootcamp:Ralf Hofer'
GERHARD_BRAUN = '#bootcamp:Gerhard Braun'
SAMUEL_BRONN = '#bootcamp:Samuel Bronn'
VALERIY_GAYDUCHENKO = '#bootcamp:Valeriy Gayduchenko'
MARK_LITENGEN = '#bootcamp:Mark Litengen'
ETIEN_ASIEOS = '#bootcamp:Etien Asieos'
ALEKSANDR_ANTONUK = '#bootcamp:Aleksandr Antonuk'
PETR_SERGEEV = '#bootcamp:Petr Sergeev'
PASCAL_RAYMOND = '#bootcamp:Pascal Raymond'
ALEKSEY_EGOROV = '#bootcamp:Aleksey Egorov'
OLIVER_GREENE = '#bootcamp:Oliver Greene'
JOHN_KING = '#bootcamp:John King'
MIRON_NEBALUIEV = '#bootcamp:Miron Nebaluiev'
FRIDRIH_SIMANN = '#bootcamp:Fridrih Simann'
MATT_UNDERLAY = '#bootcamp:Matt Underlay'
JAMES_BROUNGE = '#bootcamp:James Brounge'
ODA_NISURA = '#bootcamp:Oda Nisura'
GAVRIL_STOLBOV = '#bootcamp:Gavril Stolbov'
FABIAN_HAUPT = '#bootcamp:Fabian Haupt'
FRANK_DIMMELTON = '#bootcamp:Frank Dimmelton'
JOHN_DICKER = '#bootcamp:John Dicker'
KONRAD_CERSTVY = '#bootcamp:Konrad Cerstvy'
RICHARD_BOGELBER = '#bootcamp:Richard Bogelber'
KEIKO_SIMURA = '#bootcamp:Keiko Simura'
SHENG_EN = '#bootcamp:Sheng En'
SIEGWARD_EBER = '#bootcamp:Siegward Eber'
KARL_HIMMELSBERG = '#bootcamp:Karl Himmelsberg'
LEV_SHAPIRO = '#bootcamp:Lev Shapiro'
PAUL_BOUTIN = '#bootcamp:Paul Boutin'
TEODOR_SIMMERSBEE = '#bootcamp:Teodor Simmersbee'
CLAUD_GAULT = '#bootcamp:Claud Gault'
YU_DAN = '#bootcamp:Yu Dan'
ISIDZUKURI_SOMA = '#bootcamp:Isidzukuri Soma'
MITROFAN_MORDA = '#bootcamp:Mitrofan Morda'
YAKO_SIMAMURA = '#bootcamp:Yako Simamura'
LIN_SHIN = '#bootcamp:Lin Shin'
RADOSH_ZRVECKA = '#bootcamp:Radosh Zrvecka'
OTTO_VON_VALEN = '#bootcamp:Otto Von Valen'
VITALII_ROMANOV = '#bootcamp:Vitalii Romanov'
GUNTHER_FRANKE = '#bootcamp:Gunther Franke'
ALEKSANDR_FESICH = '#bootcamp:Aleksandr Fesich'
VENIAMIN_RAGOZIN = '#bootcamp:Veniamin Ragozin'
PAUL_KELLER = '#bootcamp:Paul Keller'
JING_JIE = '#bootcamp:Jing Jie'
JOHN_LAMB = '#bootcamp:John Lamb'
CORY_PRESTON = '#bootcamp:Cory Preston'
KARL_ERIK_OLOFSSON = '#bootcamp:Karl-Erik Olofsson'
ROBERT_BEASLEY = '#bootcamp:Robert Beasley'
JOHN_PAYNE = '#bootcamp:John Payne'
ELIAS_FREDRIKSSON = '#bootcamp:Elias Fredriksson'
JEAN_CHRISTOPHE_MOREL = '#bootcamp:Jean-Christophe Morel'
THOMAS_MERRITT = '#bootcamp:Thomas Merritt'
FEDOR_BELKIN = '#bootcamp:Fedor Belkin'
VLADIMIR_KAIDUN = '#bootcamp:Vladimir Kaidun'
PAUL_DAVIS = '#bootcamp:Paul Davis'
CORNELIUS_HOLST = '#bootcamp:Cornelius Holst'
AKENO_KIDO = '#bootcamp:Akeno Kido'
ANDRII_KOZYRA = '#bootcamp:Andrii Kozyra'
LEE_LIANG = '#bootcamp:Lee Liang'
NICHOLAS_WILKINSON = '#bootcamp:Nicholas Wilkinson'
IGOR_GONCHARENKO = '#bootcamp:Igor Goncharenko'
ALEKSANDR_USTINOV = '#bootcamp:Aleksandr Ustinov'
YURIY_KRILO = '#bootcamp:Yuriy Krilo'
FUDO_SUGIMOTO = '#bootcamp:Fudo Sugimoto'
ALEKSEY_KLUCHIKOV = '#bootcamp:Aleksey Kluchikov'
CHARLES_BAKER = '#bootcamp:Charles Baker'
LUDVIK_BENES = '#bootcamp:Ludvik Benes'
JURGEN_WOLF = '#bootcamp:Jurgen Wolf'
JOSEPH_ONEAL = '#bootcamp:Joseph ONeal'
BATTLE_RESULT_ENUM = (BATTLE_RESULT_DESTROYED,
BATTLE_RESULT_DAMAGE,
BATTLE_RESULT_BLOCKED,
BATTLE_RESULT_DETECTED,
BATTLE_RESULT_ASSISTED,
BATTLE_RESULT_DESCRIPTION_DESTROYED,
BATTLE_RESULT_DESCRIPTION_DAMAGE,
BATTLE_RESULT_DESCRIPTION_BLOCKED,
BATTLE_RESULT_DESCRIPTION_DETECTED,
BATTLE_RESULT_DESCRIPTION_ASSISTED)
BATTLE_RESULT_DESCRIPTION_ENUM = (BATTLE_RESULT_DESCRIPTION_DESTROYED,
BATTLE_RESULT_DESCRIPTION_DAMAGE,
BATTLE_RESULT_DESCRIPTION_BLOCKED,
BATTLE_RESULT_DESCRIPTION_DETECTED,
BATTLE_RESULT_DESCRIPTION_ASSISTED)
@classmethod
def battle_result(cls, key0):
outcome = '#bootcamp:battle/result/{}'.format(key0)
if outcome not in cls.BATTLE_RESULT_ENUM:
LOG_WARNING('Localization key "{}" not found'.format(outcome))
return None
else:
return outcome
@classmethod
def battle_result_description(cls, key0):
outcome = '#bootcamp:battle/result/description/{}'.format(key0)
if outcome not in cls.BATTLE_RESULT_DESCRIPTION_ENUM:
LOG_WARNING('Localization key "{}" not found'.format(outcome))
return None
else:
return outcome
|
[
"[email protected]"
] | |
0a8aac0ef720cad72a6d407a4e8438d1a60ba7db
|
5ad3ec206586985161547cf606a10e32d38b9a80
|
/telegramwrapper.py
|
d04b8241c931f7f4e7963245bcc70df8fb738568
|
[
"MIT"
] |
permissive
|
lorenzobenvenuti/cashbot
|
85c179731074f9bfe45968e27ffd70dae5da58fd
|
0a5d6187141af798fec1d7dfd14b6aa99572438c
|
refs/heads/master
| 2020-06-11T03:41:47.690384 | 2017-10-06T14:19:55 | 2017-10-06T14:19:55 | 76,013,039 | 1 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 2,891 |
py
|
from telegram.ext import Updater, CommandHandler, MessageHandler, Filters, RegexHandler
import logging
logger = logging.getLogger(__name__)
class Command(object):
def __init__(self, name, function, pass_args):
self._name = name
self._function = function
self._pass_args = pass_args
@property
def name(self):
return self._name
@property
def function(self):
return self._function
@property
def pass_args(self):
return self._pass_args
class App(object):
def __init__(self, token, access_checker, bootstrap_retries=-1):
self._token = token
self._access_checker = access_checker
self._bootstrap_retries = bootstrap_retries
self._commands = []
def _secure_function(self, f):
def inner(*args, **kwargs):
bot = args[0]
update = args[1]
if not self._access_checker.is_allowed(update.message.from_user):
logger.warn(
'Unauthorized user access {}'
.format(update.message.from_user)
)
bot.sendMessage(
update.message.chat_id,
text="You are not allowed to use this bot"
)
else:
try:
f(*args, **kwargs)
except BaseException as error:
logger.exception(
'Update "%s" caused error "%s"' % (update, error))
bot.sendMessage(
update.message.chat_id,
text="An error occurred: {}".format(str(error))
)
return inner
def _unknown_command(self, bot, update):
bot.sendMessage(update.message.chat_id, text="Invalid command")
def _message(self, bot, update):
bot.sendMessage(update.message.chat_id, text="Please send a command")
def _error(self, bot, update, error):
logger.warn('Update "%s" caused error "%s"' % (update, error))
def add_command(self, command):
self._commands.append(command)
def run(self):
updater = Updater(self._token)
dispatcher = updater.dispatcher
for command in self._commands:
dispatcher.add_handler(CommandHandler(
command.name,
self._secure_function(command.function),
pass_args=command.pass_args
))
dispatcher.add_handler(MessageHandler(
[Filters.text],
self._secure_function(self._message)
))
dispatcher.add_handler(RegexHandler(
'/.*',
self._secure_function(self._unknown_command)
))
dispatcher.add_error_handler(self._error)
updater.start_polling(bootstrap_retries=self._bootstrap_retries)
updater.idle()
|
[
"[email protected]"
] | |
5df22589a40000c4156181a331d14179c3df1724
|
ab36737215b5d2b1afc2116f65204d70f7166656
|
/script/进程间不同享全局变量.py
|
234f067cd629154678858e8cdd88c3966dc1dc00
|
[] |
no_license
|
kuki123456/Send_Data
|
666cd8b532e0aec24ecd23c3740caa16601bbbc4
|
f99de7a77ca918a38ee0537d4007a423185ce7c2
|
refs/heads/master
| 2023-03-12T03:55:38.878901 | 2021-03-03T16:22:36 | 2021-03-03T16:22:36 | 344,188,664 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 598 |
py
|
# -*- coding:utf-8 -*-
from multiprocessing import Process
import os
import time
nums = [11, 22]
def work1():
"""子进程要执行的代码"""
print("in process1 pid=%d ,nums=%s" % (os.getpid(), nums))
for i in range(3):
nums.append(i)
time.sleep(1)
print("in process1 pid=%d ,nums=%s" % (os.getpid(), nums))
def work2():
"""子进程要执行的代码"""
print("in process2 pid=%d ,nums=%s" % (os.getpid(), nums))
if __name__ == '__main__':
p1 = Process(target=work1)
p1.start()
p1.join()
p2 = Process(target=work2)
p2.start()
|
[
"[email protected]"
] | |
183732c08312c07a290ffa9361595cea00c05abb
|
a3bca9d835214df6db839d9b02d7a89b16cca4e7
|
/scripts/ympcli
|
998b7d73e8a3e326fa035dc6420b6712237d9065
|
[
"MIT-Modern-Variant",
"LicenseRef-scancode-warranty-disclaimer",
"LicenseRef-scancode-unknown-license-reference",
"MIT"
] |
permissive
|
michel-slm/ymp-tools
|
7eece1f9957429f66718ab114a5f4d702bf3c326
|
be6dd58afe5378b684c74a71b2c48991a0af9076
|
refs/heads/master
| 2021-01-22T14:32:54.672486 | 2012-06-05T04:14:54 | 2012-06-05T04:14:54 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,761 |
#!/usr/bin/env python
from ConfigParser import ConfigParser
from lxml import etree
import sys
from ymp.repos import *
NS = 'http://opensuse.org/Standards/One_Click_Install'
def make_path(start, *components):
pathstr = start
for c in components:
pathstr += '/{%s}%s' % (NS, c)
return pathstr
def make_finder(start, *components):
return etree.ETXPath(start, components)
def parse_entry(elem):
return dict([ (child.tag[len(NS)+2:], child.text) for child in elem ])
def parse_ymp_file(fname):
with file(fname, 'r') as f:
tree = etree.parse(f)
find_repos = etree.ETXPath(
'/{%s}metapackage/{%s}group/{%s}repositories/{%s}repository' \
% ((NS,)*4))
repos = find_repos(tree)
find_software = etree.ETXPath(
'/{%s}metapackage/{%s}group/{%s}software/{%s}item' \
% ((NS,)*4))
software_items = find_software(tree)
try:
for repo in repos:
metadata = parse_entry(repo)
metadata['recommended'] = True if (repo.get('recommended') == 'true') else False
print metadata
print "--------"
repo_obj = YumRepository(metadata)
print repo_obj
#repo_obj.persist()
print "--------"
for item in software_items:
metadata = parse_entry(item)
print metadata['name']
except IOError:
print >>sys.stderr, "No permission to create repository descriptor"
if __name__ == '__main__':
if len(sys.argv) != 2:
print >>sys.stderr, "Usage: ympcli metapackage.ymp"
sys.exit(1)
parse_ymp_file(sys.argv[1])
|
[
"[email protected]"
] | ||
a1521f4e71a967581dae1bf9a7a5d37ebd572ef0
|
76816fc8b04087b37af28364b26576c2d16e0940
|
/envname/lib/python3.7/ntpath.py
|
b08f2539aafa36f80bc1b3c8e5080d05716e7d76
|
[] |
no_license
|
logg926/AutoGen_Flask
|
6703c97759ed34e091d79997127d33887dd738f7
|
8edbba185dd6086482f6afaa5c2f00e4073a59f5
|
refs/heads/master
| 2020-04-18T17:57:16.406268 | 2019-01-26T09:16:54 | 2019-01-26T09:16:54 | 167,670,042 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 50 |
py
|
/Users/loggcheng/anaconda3/lib/python3.7/ntpath.py
|
[
"[email protected]"
] | |
6c095a481e7dfa080fa67bb206897f5e02b14490
|
7bcfed7d85c13346ace0ab4283438e74f4dce510
|
/movierater/settings.py
|
2dff8853e782837570bd072c67b39ca7b2c94e6d
|
[] |
no_license
|
dipee/MovieRaterApi
|
f41f191c11419783d584ff378eff7297863c4a72
|
0acca6650fc1422b194c853a21ea60d4afaa255a
|
refs/heads/master
| 2023-03-06T23:42:54.482959 | 2021-02-24T18:33:41 | 2021-02-24T18:33:41 | 339,626,847 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 3,457 |
py
|
"""
Django settings for movierater project.
Generated by 'django-admin startproject' using Django 3.1.6.
For more information on this file, see
https://docs.djangoproject.com/en/3.1/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/3.1/ref/settings/
"""
from pathlib import Path
# Build paths inside the project like this: BASE_DIR / 'subdir'.
BASE_DIR = Path(__file__).resolve().parent.parent
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/3.1/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = 'izwjw00!!g0e8f^8g9o2s14i(#w%9!ce#&8lmgd2+ogbqz8vb*'
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
ALLOWED_HOSTS = []
# Application definition
INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'corsheaders',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'rest_framework',
'rest_framework.authtoken',
'api'
]
MIDDLEWARE = [
'corsheaders.middleware.CorsMiddleware',
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
CORS_ALLOWED_ORIGINS = [
"http://localhost:3000",
]
# CORS_ALLOW_ALL_ORIGINS = True
ROOT_URLCONF = 'movierater.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'movierater.wsgi.application'
# Database
# https://docs.djangoproject.com/en/3.1/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': BASE_DIR / 'db.sqlite3',
}
}
#rest frmaework default permission class
REST_FRAMEWORK = {
'DEFAULT_PERMISSION_CLASSES': [
'rest_framework.permissions.IsAuthenticated',
]
}
# Password validation
# https://docs.djangoproject.com/en/3.1/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
},
]
# Internationalization
# https://docs.djangoproject.com/en/3.1/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/3.1/howto/static-files/
STATIC_URL = '/static/'
|
[
"[email protected]"
] |
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