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Download_test_data.py | yanhaidong1/TEmarker | 2 | 49490 | <reponame>yanhaidong1/TEmarker
#!/usr/bin/env python
##this script will download the testing data for step 1,2,3
##Also download the testing data for step 0
##BUILT-IN MODULES
import argparse
import sys
import os
import subprocess
from distutils.spawn import find_executable
##SCRIPTS
def get_parsed_args():
parser = argparse.ArgumentParser(description="Download testing data")
##############################
##parse the required arguments
parser.add_argument("-o", dest='output_dir', default="./", help="Output directory"
"Default: ./ ")
parser.add_argument('-s0', dest='s0_yes', help="If users initiate this argument, they will download testing data for step 0.")
parser.add_argument('-s123', dest='s123_yes', help="If users initiate this argument, they will download all the testing data for step123.")
##parse of parameters
args = parser.parse_args()
return args
##define a function to download testing data
def download_data_step123 (store_download_dir,name,google_drive_path):
cmd = "wget --load-cookies /tmp/cookies.txt \"https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate " + \
"\'https://docs.google.com/uc?export=download&id=" + google_drive_path + "\' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\\1\\n/p')&id=" + google_drive_path + "\" -O " + \
store_download_dir + "/" + name + ".zip " + "&& rm -rf /tmp/cookies.txt"
subprocess.call(cmd, shell=True)
cmd = 'unzip ' + store_download_dir + '/' + name + '.zip -d ' + store_download_dir + '/'
subprocess.call(cmd,shell=True)
cmd = 'rm ' + store_download_dir + '/' + name + '.zip'
subprocess.call(cmd,shell=True)
cmd = 'rm -rf ' + store_download_dir + '/__MACOSX' + ' ' + store_download_dir + '/' + name + '/Icon'
subprocess.call(cmd,shell=True)
def download_data_step0 (store_download_dir,name,google_drive_path):
cmd = "wget --load-cookies /tmp/cookies.txt \"https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate " + \
"\'https://docs.google.com/uc?export=download&id=" + google_drive_path + "\' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\\1\\n/p')&id=" + google_drive_path + "\" -O " + \
store_download_dir + "/" + name + ".tar.gz " + "&& rm -rf /tmp/cookies.txt"
subprocess.call(cmd, shell=True)
def main(argv=None):
if argv is None:
argv = sys.argv
args = get_parsed_args()
output_dir = args.output_dir
if not output_dir.endswith('/'):
output_dir = output_dir + '/'
else:
output_dir = output_dir
##if users want to download testing data for Step 1,2,3
if args.s123_yes is not None:
google_drive_path = '1N9hb1WFWdzrP0kQ4v_uxaH8VfeOjZS2n'
download_data_step123(output_dir,'Example_dir_step123',google_drive_path)
if args.s0_yes is not None:
google_drive_path = '1TwaDBGYQ2OoHIENUgGMfQF8eiO3T1aZV'
download_data_step0(output_dir, 'Example_dir_step0', google_drive_path)
if __name__ == "__main__":
main()
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trueteam101.py | VinterMute/TrueTeam_clusterer | 1 | 1608915 | <filename>trueteam101.py
#!/home/alex/anaconda3/envs/comptech2020/bin/python3
from application import app
if __name__ == '__main__':
app.run(host='0.0.0.0', port=80)
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FileIO/writing.py | zahraaliaghazadeh/python | 0 | 84067 | <reponame>zahraaliaghazadeh/python<filename>FileIO/writing.py
# cities = ["Adelaide", "Alice Springs", "Darwin", "Melbourne", "Sydney"]
#
# with open("cities.txt", 'w') as city_file:
# for city in cities:
# print(city, file=city_file)
# can add flush=True to line above
# cities = []
#
# with open("cities.txt", 'r') as city_file:
# for city in city_file:
# cities.append(city.strip('\n'))
# #
# print(cities)
# for city in cities:
# print(city)
# imelda = "<NAME>", "<NAME>", "2011", (
# (1, "Pulling the Rug"), (2,"Psycho"), (3, "Mayhem"), (4, "Kentish Town Waltz"))
#
# with open("imelda3.txt", 'w') as imelda_file:
# print(imelda, file=imelda_file)
with open("imelda3.txt", 'r') as imelda_file:
contents = imelda_file.readline()
imelda = eval(contents)
#
print(imelda)
title, artist, year, tracks = imelda
print(title)
print(artist)
print(year)
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442,
391,
29897,
13,
2158,
29898,
6360,
29897,
13,
2
] |
spark/example/count.py | aryahadii/adb | 0 | 106142 | <filename>spark/example/count.py
import pyspark
print("*****", pyspark.SparkContext().parallelize(range(0, 10)).count(), "*****")
| [
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backend/StudentVillage/migrations/0003_studentvillage_templatewashlist.py | henrikhorluck/tdt4140-washlists | 0 | 79160 | # Generated by Django 3.0.3 on 2020-02-27 22:21
import django.db.models.deletion
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
("Washlist", "0002_auto_20200227_2219"),
("StudentVillage", "0002_auto_20200227_2219"),
]
operations = [
migrations.AddField(
model_name="studentvillage",
name="templateWashList",
field=models.ForeignKey(
blank=True,
null=True,
on_delete=django.db.models.deletion.SET_NULL,
to="Washlist.TemplateWashList",
),
)
]
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9651,
10353,
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13,
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4514,
13,
2
] |
docker/user_settings.py | puckk/Wooey | 0 | 115435 | import os
from .wooey_settings import *
# Whether to allow anonymous job submissions, set False to disallow 'guest' job submissions
WOOEY_ALLOW_ANONYMOUS = True
## Celery related options
INSTALLED_APPS += (
'django_celery_results',
)
WOOEY_CELERY = True
CELERY_RESULT_BACKEND = 'django-db'
CELERY_BROKER_URL = 'amqp://guest@rabbit'
CELERY_TRACK_STARTED = True
CELERY_SEND_EVENTS = True
CELERY_IMPORTS = ('wooey.tasks',)
CELERY_TASK_SERIALIZER = 'json'
CELERY_TASK_ACKS_LATE = True
# the directory for uploads (physical directory)
MEDIA_ROOT = os.path.join(BASE_DIR, 'user_uploads')
# the url mapping
MEDIA_URL = '/uploads/'
# the directory to store our webpage assets (images, javascript, etc.)
STATIC_ROOT = os.path.join(BASE_DIR, 'static')
# the url mapping
STATIC_URL = '/static/'
## Here is a setup example for production servers
## A postgres database -- for multiple users a sqlite based database is asking for trouble
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.postgresql_psycopg2',
# for production environments, these should be stored as environment variables
# I also recommend the django-heroku-postgresify package for a super simple setup
'NAME': os.environ.get('DATABASE_NAME', 'wooey'),
'USER': os.environ.get('DATABASE_USER', 'wooey'),
'PASSWORD': os.environ.get('DATABASE_PASSWORD', '<PASSWORD>'),
'HOST': os.environ.get('DATABASE_URL', 'localhost'),
'PORT': os.environ.get('DATABASE_PORT', '5432')
}
}
ALLOWED_HOSTS = (
'localhost',
'127.0.0.1',
'2b9e22f4.ngrok.io'
)
AUTHENTICATION_BACKEND = 'django.contrib.auth.backends.ModelBackend'
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1627,
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29889,
3195,
5841,
355,
29915,
13,
2
] |
DICOMOFFIS/admin.py | pfagomez/DICOMOFFIS | 0 | 150123 | <filename>DICOMOFFIS/admin.py<gh_stars>0
from django.contrib import admin
from .models import eintrag
# Register your models here.
admin.site.register(eintrag) | [
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gamestonk_terminal/forex/oanda/oanda_api.py | minhhoang1023/GamestonkTerminal | 1 | 136150 | """Oanda context API."""
import os
from gamestonk_terminal.helper_classes import ModelsNamespace as _models
# flake8: noqa
# pylint: disable=unused-import
# Context menus
from gamestonk_terminal.forex.oanda.oanda_view import get_fx_price as price
from gamestonk_terminal.forex.oanda.oanda_view import get_account_summary as summary
from gamestonk_terminal.forex.oanda.oanda_view import get_order_book as orderbook
from gamestonk_terminal.forex.oanda.oanda_view import get_position_book as positionbook
# Renamed due to python reserved word
from gamestonk_terminal.forex.oanda.oanda_view import list_orders as listorder
from gamestonk_terminal.forex.oanda.oanda_view import create_order as order
from gamestonk_terminal.forex.oanda.oanda_view import cancel_pending_order as cancel
from gamestonk_terminal.forex.oanda.oanda_view import get_open_positions as positions
from gamestonk_terminal.forex.oanda.oanda_view import get_pending_orders as pending
from gamestonk_terminal.forex.oanda.oanda_view import get_open_trades as trades
from gamestonk_terminal.forex.oanda.oanda_view import close_trade as closetrade
from gamestonk_terminal.forex.oanda.oanda_view import show_candles as candles
from gamestonk_terminal.forex.oanda.oanda_view import calendar
# Models
models = _models(os.path.abspath(os.path.dirname(__file__)))
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tkinter/withTtk/showicon.py | terasakisatoshi/pythonCodes | 0 | 1607755 | import tkinter as tk
from tkinter import ttk
from PIL import ImageTk, Image
def main():
root=tk.Tk()
root.title('LabelwithIcon')
framettk=ttk.Frame(root)
framettk.grid()
icon = ImageTk.PhotoImage(Image.open('pen.png'))
pen_label=ttk.Label(
framettk,
image=icon)
pen_label.grid(row=1,column=1)
s=tk.StringVar()
s.set("Going Back To School")
right_label=ttk.Label(
framettk,
textvariable=s,
width=30,
anchor=tk.W,
padding=(30,10))
right_label.grid(row=1,column=2)
root.mainloop()
if __name__ == '__main__':
main() | [
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init.py | cbbing/stock | 31 | 1602407 | #coding:utf-8
__author__ = 'cbb'
import platform, os
from sqlalchemy import create_engine
from util.MyLogger import Logger
#DB_WAY:数据存储方式 'csv' # or 'mysql' or 'redis' or 'sqlite'
DB_WAY = 'mysql'
DB_USER = 'root'
DB_PWD = '<PASSWORD>' # or '<PASSWORD>' in win7
DB_NAME = 'stock'
DownloadDir = os.path.pardir + '/stockdata/' # os.path.pardir: 上级目录
# mysql Host
# if platform.system() == 'Windows':
# host_mysql = 'localhost'
# else:
# host_mysql = '192.168.3.11'
host_mysql = '127.0.0.1'
user_mysql = 'admin'
pwd_mysql = '<PASSWORD>'
db_name_mysql = 'wealth_db'
engine = create_engine('mysql+mysqldb://%s:%s@%s/%s' % (user_mysql, pwd_mysql, host_mysql, db_name_mysql), connect_args={'charset':'utf8'})
# 短均线， 长均线
AVR_SHORT = 12
AVR_LONG = 40
#买卖标记
SIGNAL_BUY = 1 #买
SIGNAL_SALE = -1 #卖
SIGNAL_DEFAULT = 0
#阈值
Threshold_Buy_Count = 3
Threshold_Sale_Count = 2
#日志设置
from util.MyLogger import Logger
#infoLogger = Logger(logname='../Log/info.log', logger='I')
#errorLogger = Logger(logname='../Log/error.log', logger='E')
#配置文件 位置
config_file_path = '../config.ini' | [
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pint_wrapper/form.py | s-avni/dimensional-analysis | 0 | 115563 | from flask_wtf import FlaskForm
from wtforms import StringField, SubmitField, FieldList, FormField
from wtforms.validators import DataRequired
MIN_VALIDATORS = 8
class VariablesForm(FlaskForm):
var_name = StringField('Name', validators=[DataRequired()])
var_dim = StringField('Dimension', validators=[DataRequired()])
class DimensionlessForm(FlaskForm):
vars = FieldList(FormField(VariablesForm), min_entries=MIN_VALIDATORS+1)
submit = SubmitField('Find combination!')
class DimensionForm(FlaskForm):
desired_dim = StringField('Desired dimension', validators=[DataRequired()])
vars = FieldList(FormField(VariablesForm), min_entries=MIN_VALIDATORS)
submit = SubmitField('Find combination!') | [
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display/__init__.py | dysfunctionals/soton-analytica-display | 1 | 110622 | from .text import Text | [
1,
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726,
1053,
3992,
2
] |
b3files/cotacoeshistoricas.py | gbonesso/b3files | 0 | 1611064 | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from datetime import datetime
import os
import zipfile
import logging
# Here we'll specify the columns width
__widths = [2, 8, 2, 12, 3, 12,
10, 3, 4, 13, 13, 13, 13, 13, 13, 13, 5, 18,
18, 13, 1, 8, 7, 13, 12, 3]
# and the columns names, based in the original specification
__column_names = [
'TIPREG', # Tipo de Registro - Fixo = "01"
'DATPRE', # Data do pregão - "AAAAMMDD"
'CODBDI', # Código BDI - Precisa de tabela auxiliar
'CODNEG', # Código de negociação do papel
'TPMERC', # Tipo de mercado - (Vista, Termo, etc...) Precisa de tabela auxiliar
'NOMRES', # Nome resumido da empresa emissora do papel
'ESPECI', # Especificação do papel - (ON, PN, LFT, etc...) Precisa de tabela auxiliar
'PRAZOT', # Prazo em dias do mercado a termo
'MODREF', # Moeda de referência usada na data do pregão
'PREABE', # Preço de abertura do papel no pregão
'PREMAX', 'PREMIN', 'PREMED', 'PREULT', 'PREOFC', 'PREOFV',
'TOTNEG', 'QUATOT', 'VOLTOT', 'PREEXE', 'INDOPC', 'DATVEN', 'FATCOT', 'PTOEXE',
'CODISI', 'DISMES']
def _convert_price(raw_price):
"""Convert the prices, adjusting for two decimals while loading the raw file.
Parameters:
raw_price (str): The price to be converted, the last two characters are the decimals.
Returns:
(float64): Converted price.
"""
return float(raw_price) / 100.0
def _convert_date(d):
"""Convert the raw date information.
The raw file uses '9999' in the year to indicate an invalid year.
Parameters:
d (str): The field format is 'YYYYMMDD'.
Returns:
dt (datetime64): Decoded date.
"""
if d.startswith('9999'):
return None
try:
dt = datetime.strptime(d, '%Y%m%d')
return dt
except ValueError:
return None
# Specify dtype while loading
__dtype_dict = {
'TOTNEG': np.int32
}
# Use the functions defined above to convert data while loading
__convert_dict = {
'DATPRE': _convert_date,
'PREABE': _convert_price, 'PREMAX': _convert_price,
'PREMIN': _convert_price,
'PREMED': _convert_price, 'PREULT': _convert_price, 'PREOFC': _convert_price,
'PREOFV': _convert_price,
'DATVEN': _convert_date,
}
def load_dir(dir_base):
"""Loads all the raw files in a given directory.
Parameters:
dir_base (str): inform the path of the directory to be loaded
Returns:
df: a dataframe with the decoded data
"""
df = pd.DataFrame() # Creates an empty Dataframe to append all the raw files
logging.debug('load_dir::dir_base: {}'.format(dir_base))
for dir_name, _, filenames in os.walk(dir_base):
for filename in filenames:
logging.debug(os.path.join(dir_name, filename))
'''z_file = zipfile.ZipFile(os.path.join(dir_name, filename))
files = z_file.namelist()
with z_file.open(files[0]) as raw_file:
# logging.debug(raw_file.readline()) # o readline() muda o ponteiro do arquivo? Estava perdendo duas linhas de dados?
# logging.debug(raw_file.readline())
df_temp = load_file(raw_file)'''
df_temp = load_zipfile(os.path.join(dir_name, filename))
logging.debug('File: {} #rows: {}'.format(
os.path.join(dir_name, filename), df_temp.shape[0])) # shape[0] = Number of rows...
df = df.append(df_temp, ignore_index=True)
logging.debug('Total df rows: {}'.format(df.shape[0]))
return df
def load_zipfile(zip_file_path):
"""Loads one zipped file
Parameters:
zip_file_path (str): inform the path of the zipped file to be loaded
Returns:
df: a dataframe with the decoded data
"""
# df = pd.DataFrame() # Creates an empty Dataframe to append all the raw files
logging.debug('load_zipfile::zip_file_path: {}'.format(zip_file_path))
z_file = zipfile.ZipFile(os.path.join(zip_file_path))
files = z_file.namelist()
with z_file.open(files[0]) as raw_file:
df = load_file(raw_file)
return df
def load_file(file_path):
"""Loads the raw file.
Parameters:
file_path (str): inform the file path of the raw file to be loaded
Returns:
df: a dataframe with the decode data
"""
df = pd.read_fwf(
file_path,
widths=__widths,
names=__column_names,
dtype=__dtype_dict,
converters=__convert_dict,
skiprows=1, # Skip the header row
skipfooter=1 # Skip the footer row
)
return df
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] |
keras_transformer/keras_transformer/training/custom_callbacks/CustomCheckpointer.py | erelcan/keras-transformer | 3 | 4531 | <gh_stars>1-10
import os
from keras.callbacks import ModelCheckpoint
from keras_transformer.training.custom_callbacks.CustomCallbackABC import CustomCallbackABC
from keras_transformer.utils.io_utils import save_to_pickle
class CustomCheckpointer(ModelCheckpoint, CustomCallbackABC):
def __init__(self, workspace_path, artifacts, callbacks, **kwargs):
super().__init__(os.path.join(workspace_path, "model-{epoch:01d}.h5"), **kwargs)
self._workspace_path = workspace_path
self._artifacts = artifacts
self._completed_epoch = 0
self._callbacks = callbacks
def on_epoch_end(self, epoch, logs=None):
super().on_epoch_end(epoch, logs)
self._completed_epoch += 1
self.update_artifacts()
should_save = False
if self.epochs_since_last_save == 0:
if self.save_best_only:
current = logs.get(self.monitor)
if current == self.best:
should_save = True
else:
should_save = True
if should_save:
save_to_pickle(self._artifacts, os.path.join(self._workspace_path, "artifacts-" + str(epoch+1) + ".pkl"))
def update_artifacts(self):
for callback in self._callbacks:
self._artifacts["callbacks"][callback.get_name()] = callback.get_artifacts()
self._artifacts["callbacks"][self.get_name()] = self.get_artifacts()
def get_name(self):
return self.__class__.__name__
def get_artifacts(self):
return {"best_score": self.best, "completed_epoch": self._completed_epoch}
def prepare_from_artifacts(self, artifacts):
self.best = artifacts["best_score"]
self._completed_epoch = artifacts["completed_epoch"]
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2
] |
functional/Decorator.py | sammffl/pythonPractice | 2 | 91490 | <gh_stars>1-10
# -*- coding: utf-8 -*-
import functools
# 装饰器
def log(func):
def wrapper(*args, **kw):
try:
print 'begin %s():' % func.__name__
return func(*args, **kw)
except Exception,e:
raise e
finally:
print 'end'
return wrapper
# 相当于 now = log(now)
@log
def now():
print '2016'
now()
# ================================
def log_high(text):
def decorator(func):
@functools.wraps(func)
def wrapper(*args, **kw):
print '%s %s():' % (text, func.__name__)
return func(*args, **kw)
return wrapper
return decorator
# now = log('execute')(now)
@log_high('execute')
def now_high():
print '2016'
now_high()
print now_high.__name__
# =================================
# begin end
def log_mixed(param):
if isinstance(param, str):
# 是字符串
def decorator(func):
@functools.wraps(func)
def wrapper(*args, **kw):
print ' %s %s ' % (param, func.__name__)
return func(*args, **kw)
return wrapper
return decorator
else:
def wrapper(*args, **kw):
print '%s' % param.__name__
return param(*args, **kw)
return wrapper
@log_mixed
def now_1():
print '2016'
return 'now_1'
@log_mixed("test")
def now_2():
print '2016'
print '======================='
ss = now_1()
now_2()
print ss
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13,
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2
] |
ckanext/os/tests/model/test_spatial_data.py | datagovuk/ckanext-os | 2 | 1602575 | <reponame>datagovuk/ckanext-os
from ckanext.os import model.spatial_data as spatial_model
| [
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AmazonScraper/AmazonScraper/spiders/Deals.py | RyMaDe/Complete-Amazon-Scraper | 0 | 178301 | <filename>AmazonScraper/AmazonScraper/spiders/Deals.py
import scrapy
from ..items import DealsItem
from selenium import webdriver
from selenium.webdriver.firefox.options import Options
import time
class DealsSpider(scrapy.Spider):
name = 'deals'
allowed_domains = ['amazon.com', 'amazon.co.uk']
#To enable there to be a limit of how many pages to crawl we need to keep a count
count = 1
def __init__(self, q, dom=None, pages = None,dep=None):
self.q = q #Loading the multiprocessing list
self.pages = pages #Page crawl limit
self.dom=dom
self.dep = dep #dep should be the same as the text used for each category on the site
options = Options()
options.headless = True
self.driver = webdriver.Firefox(options=options)
#Deciding which function to use in start_requests() below.
#If a department was selected it will go to dep_parse(), otherwise it will go to parse().
if self.dep != None:
self.noDep = self.dep_parse
else:
self.noDep = self.parse
def start_requests(self):
if self.dom == "uk":
url = "https://www.amazon.co.uk/gp/goldbox/"
else:
#usa url will be the default
url = 'https://www.amazon.com/international-sales-offers/b/?ie=UTF8&node=15529609011'
self.driver.get(url)
yield scrapy.Request(url=url, callback=self.noDep)
def dep_parse(self,response):
time.sleep(3)
#Below we are loading the names of all the categories
if self.dom == "uk":
depts = self.driver.find_elements_by_css_selector("span.a-declarative [class='a-checkbox checkbox a-spacing-micro']")
else:#USA
#Need to expand list of all departments on US site
self.driver.find_element_by_css_selector("div.a-expander-inline-container span.a-expander-prompt").click()
time.sleep(1)
depts = self.driver.find_elements_by_css_selector("div.a-expander-inline-container span.a-declarative")
for dept in depts:
department = dept.find_element_by_css_selector("label span.a-checkbox-label").text
#Click the checkbox of the department specified by the user,
#then go to the parse function to scrape.
if self.dep == department:
dept.find_element_by_css_selector("label input").click()
link=self.driver.current_url
yield scrapy.Request(link, callback=self.parse, dont_filter=True)
break
def parse(self, response):
time.sleep(10) #Makes sure all the results will show
results = self.driver.find_elements_by_css_selector("div.a-row div.a-spacing-none.tallCellView")
#A list that contains all of the sections with a product
items = DealsItem()
for result in results:
try:
items["Product"] = result.find_element_by_css_selector("a.singleCellTitle span.a-declarative").text
except:
items["Product"] = "N/A"
try:
items["Price"] = result.find_element_by_css_selector("span.dealPriceText").text
except:
items["Price"] = "N/A"
try:
items["Pre_Price"] = result.find_element_by_css_selector("span.a-text-strike").text
except:
items["Pre_Price"] = "N/A"
try:
items["Rating"] = result.find_element_by_css_selector("div.reviewStars a.touchAnchor").get_attribute("aria-label").split(",")[0]
except:
items["Rating"] = "N/A"
try:
items["No_of_Ratings"] = int(result.find_element_by_css_selector("span.a-declarative span.a-size-small").text)
except:
items["No_of_Ratings"] = "N/A"
try:
items["Timer"] = result.find_element_by_css_selector("span[role='timer']").text
except:
items["Timer"] = "N/A"
try:
items["Claimed"] = result.find_element_by_css_selector("div.a-span5 span.a-size-mini").text
except:
items["Claimed"] = "N/A"
try:
items["URL"] = result.find_element_by_css_selector("a.a-link-normal[href]").get_attribute("href")
except:
items["URL"] = "N/A"
try:
items["IMG_URL"] = result.find_element_by_css_selector("img[src]").get_attribute("src")
except:
items["IMG_URL"] = "N/A"
yield items
try:#To go to the last page
NEXT_PAGE_SELECTOR = "[class='a-text-center'] ul.a-pagination li.a-last a[href]"
next_page = self.driver.find_element_by_css_selector(NEXT_PAGE_SELECTOR)
next_link = next_page.get_attribute("href")
next_page.click()
except:
next_page = None
#This will make sure to continue looping through all pages if a page limit is not set.
#If a limit is set, it makes sure to stay within the limit.
if next_page!=None and (self.pages==None or self.count<self.pages):
self.count+=1
yield scrapy.Request(response.urljoin(next_link), callback=self.parse, dont_filter=True)
else:
self.driver.quit() | [
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] |
adapted_network.py | Hong-Ming/Adaptive_Network_Slimming | 0 | 28117 |
import sys
sys.path.append('./train_model')
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
import numpy as np
import os
import argparse
parser = argparse.ArgumentParser(description='Adaptive Network Slimming')
parser.add_argument('-net', type=str, help='pretrained pkl file')
parser.add_argument('--nonuniform', action='store_true', help='set non-uniform pruning rate')
args = parser.parse_args()
# from models import *
transform_test = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))])
testset = torchvision.datasets.CIFAR10(root='./cifar10',train=False,download=True,transform=transform_test)
testloader = torch.utils.data.DataLoader(testset,batch_size=128,shuffle=False)
classes = ('plane','car','bird','cat','deer','dog','frog','horse','ship','truck')
input_shape = (3,32,32)
if args.net == "resnet18":
START = 0.2
END = 0.81
netnum = 18
elif args.net == "resnet34":
START = 0.2
END = 0.81
netnum = 34
elif args.net == "resnet50":
START = 0.2
END = 0.8
netnum = 50
elif args.net == "resnet101":
START = 0.2
END = 0.8
netnum = 101
elif args.net == "resnet152":
START = 0.21
END = 0.79
netnum = 152
if args.nonuniform:
PRUNE_RATE = np.arange(START,END,(END-START)/(netnum-1))
FC_PRUNE_RATE = END
Model_Name = "ResNet" + str(netnum) + " (Non-uniform Pruning Rate)"
else:
PRUNE_RATE = np.zeros([netnum-1,1]) + 0.5
FC_PRUNE_RATE = 0.5
Model_Name = "ResNet" + str(netnum) + " (Uniform Pruning Rate)"
# -------------- Load Pretrained Model---------------
File_Name = "./model_pkl/" + args.net + ".pkl"
net = torch.load(File_Name, map_location= "cpu")
def RunData():
correct = 0
total = 0
with torch.no_grad():
net.eval()
net.cuda()
for (x,y) in testloader:
xa = x.cuda()
ya = y.cuda()
out = net(xa)
_,predicted = torch.max(out.data,1)
total += y.size(0)
correct += (predicted.cpu() == y).sum()
net.cpu()
Accuracy = 100*correct.cpu().numpy()/total
return Accuracy
def RunData2():
correct = 0
total = 0
for _,layer in net.named_modules():
if isinstance(layer,nn.BatchNorm2d):
layer.track_running_stats=False
with torch.no_grad():
net.eval()
net.cuda()
for (x,y) in testloader:
xa = x.cuda()
ya = y.cuda()
out = net(xa)
_,predicted = torch.max(out.data,1)
total += y.size(0)
correct += (predicted.cpu() == y).sum()
net.cpu()
Accuracy = 100*correct.cpu().numpy()/total
return Accuracy
def prune_filter(layer,PRUNE_RATE):
prune = np.sum(abs(layer),axis = (1,2,3))
sort_prune = np.sort(prune)
mask = np.ones(layer.shape)
for i in range(len(prune)):
if prune[i] < sort_prune[int(np.floor(PRUNE_RATE*len(prune)))]:
mask[i,:] = 0
return mask
def prune_weight(layer,PRUNE_RATE):
layer_flatten_sort = np.sort(abs(layer.flatten()))
mask = np.ones(layer.shape)
for i in range(layer.shape[0]):
for j in range(layer.shape[1]):
if abs(layer[i][j]) < layer_flatten_sort[int(np.floor(PRUNE_RATE*len(layer_flatten_sort)))]:
mask[i][j] = 0
return mask
def Calculate_flop():
FLOP = 0
shape = input_shape[1]
for name,layer in net.named_modules():
if isinstance(layer,nn.Conv2d) and 'shortcut' not in name:
filter_data = layer.weight.data.numpy()
skip = sum(np.sum(abs(filter_data),axis = (1,2,3)) == 0)
filter_shape = layer.weight.data.numpy().shape
padding = layer.padding
stride = layer.stride
n = filter_shape[1] * filter_shape[2] * filter_shape[3] # vector length
fpn = n + (n - 1) # n multiplication, n-1 addition
step_x = np.floor(((shape - filter_shape[2] + 2 * padding[0]) / stride[0]) + 1)
shape = step_x
step = step_x**2
fpf = fpn*step
FLOP += fpf*(filter_shape[0] - skip)
elif isinstance(layer,nn.Linear):
filter_data = layer.weight.data.numpy()
skip = sum(sum(filter_data == 0))
filter_shape = layer.weight.data.numpy().shape
FLOP += 2 * (filter_shape[0] * filter_shape[1] - skip)
return FLOP
ACC_before = RunData()
print("Model Name: " + Model_Name)
print("Accuracy : " + str(ACC_before) + "%")
FLOP_before = Calculate_flop()
if FLOP_before / 1e9 > 1: # for Giga Flops
print("FLOP : %4.2f GFLOP" % (FLOP_before / 1e9))
else:
print("FLOP : %4.2f MFLOP" % (FLOP_before / 1e6))
print(" ")
print(" Start Pruning ")
print("---------------------------------------------------")
print("|Layer| FLOP |#Filter or #Weight|Pruning |Filter|")
print("| No. | Save | before/after | Type | Size |")
print("|-----|--------|------------------|--------|------|")
# pruning
TOTAL_WEIGHT = 0
PRUNE_WEIGHT = 0
i = 0
for parname,layer in net.named_modules():
if isinstance(layer,nn.Conv2d) and 'shortcut' not in parname:
par = layer.weight.data.numpy()
par_size = par.shape
mask = prune_filter(par,PRUNE_RATE[i])
par = (par * mask)
print("| %3i" % (i+1), "|"+
" %5.2f" % float((1-(np.count_nonzero(mask)/mask.size)) * 100) + "% |"+
" %4i" % int((mask.size-np.count_nonzero(mask))/(par_size[1]*par_size[2]*par_size[2])),"/",
"%4i" % int(mask.size/(par_size[1]*par_size[2]*par_size[2])) + " | Filter |"+
" %1ix%1i |" % (par_size[2], par_size[3]))
TOTAL_WEIGHT = TOTAL_WEIGHT + (mask.size/(par_size[1]))
PRUNE_WEIGHT = PRUNE_WEIGHT + ((mask.size-np.count_nonzero(mask))/(par_size[1]))
i = i + 1
layer.weight.data = torch.from_numpy(par).type(torch.FloatTensor)
elif isinstance(layer,nn.Linear):
par = layer.weight.data.numpy()
par_size = par.shape
mask = prune_weight(par,FC_PRUNE_RATE)
par = (par * mask)
print("| %3i" % (i+1), "|"+
" %5.2f" % float((1-(np.count_nonzero(mask)/mask.size)) * 100) + "% |"+
" %5i" % int(mask.size-np.count_nonzero(mask)),"/",
"%5i" % int(mask.size) + " | Weight |" + " none |")
TOTAL_WEIGHT = TOTAL_WEIGHT + (mask.size)
PRUNE_WEIGHT = PRUNE_WEIGHT + (mask.size-np.count_nonzero(mask))
i = i + 1
layer.weight.data = torch.from_numpy(par).type(torch.FloatTensor)
print("---------------------------------------------------")
ACC_after = RunData2()
FLOP_after = Calculate_flop()
print(" ")
print(" After Pruning ")
print("Accuracy : " + str(ACC_before) + "% -> " + str(ACC_after) + "%")
if FLOP_after / 1e9 > 1: # for Giga Flops
if FLOP_before / 1e9 > 1: # for Giga Flops
print("FLOP : %4.2f GFLOP" % (FLOP_before / 1e9) + " -> %4.2f GFLOP" % (FLOP_after / 1e9))
else:
print("FLOP : %4.2f MFLOP" % (FLOP_before / 1e6) + " -> %4.2f GFLOP" % (FLOP_after / 1e9))
else:
if FLOP_before / 1e9 > 1: # for Giga Flops
print("FLOP : %4.2f GFLOP" % (FLOP_before / 1e9) + " -> %4.2f MFLOP" % (FLOP_after / 1e6))
else:
print("FLOP : %4.2f MFLOP" % (FLOP_before / 1e6) + " -> %4.2f MFLOP" % (FLOP_after / 1e6))
print("FLOP save: %5.2f" % (100*(FLOP_before - FLOP_after)/FLOP_before),"%") | [
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2
] |
tests/test_request_helpers.py | fraymio/modis-tools | 2 | 1610352 | from datetime import timedelta
import sys
import pytest
try:
import shapely
import shapely.geometry
SHAPELY_TYPES = (
shapely.geometry.GeometryCollection,
shapely.geometry.LineString,
shapely.geometry.LinearRing,
shapely.geometry.MultiPolygon,
shapely.geometry.Point,
shapely.geometry.Polygon,
)
except ImportError:
...
try:
from osgeo import ogr
except ImportError:
...
from modis_tools.request_helpers import DateParams, SpatialQuery
NEED_OSGEO = "Requires installation of osgeo"
NEED_SHAPELY = "Requires installation of shapely"
class TestDateParams:
def test_with_one_date_raises_error(self):
with pytest.raises(Exception):
DateParams()
def test_time_delta_is_correct(self):
DateParams("2005-01-01", "2005-01-02", timedelta(days=1))
def test_start_after_end_raises_error(self):
with pytest.raises(Exception):
DateParams("2005-01-01", "2005-01-02", timedelta(days=2))
def test_can_infer_time_delta(self):
d = DateParams("2005-01-01", "2005-01-02")
assert d.time_delta == timedelta(days=1)
class TestSpatialQuery:
@pytest.mark.skipif("shapely" not in sys.modules, reason=NEED_SHAPELY)
def test_can_update_shapely_types(self):
for t in SHAPELY_TYPES:
assert issubclass(
t, shapely.geometry.base.BaseGeometry
), f"Shapely no longer considers {t} as a `BaseGeometry`. `SpatialQuery()` must be updated accordingly"
def test_can_parse_nga_list_bbox(self):
bbox = [2.1448863675, 4.002583177, 15.289420717, 14.275061098]
query = SpatialQuery(spatial=None, bounding_box=bbox)
assert query.coordinates == "2.1448863675,4.002583177,15.289420717,14.275061098"
assert query.geom_type == "bounding_box"
def test_parse_mock_list_bbox(self):
bbox = [1.2, 3.4, 5.6, 7.8]
query = SpatialQuery(spatial=None, bounding_box=bbox)
assert query.coordinates == "1.2,3.4,5.6,7.8"
assert query.geom_type == "bounding_box"
def test_parse_mock_tuple_bbox(self):
bbox = (1.2, 3.4, 5.6, 7.8)
query = SpatialQuery(spatial=None, bounding_box=bbox)
assert query.coordinates == "1.2,3.4,5.6,7.8"
assert query.geom_type == "bounding_box"
def test_parse_inverted_mock_list_bbox_fails(self):
bbox = [10, 10, 0, 0]
with pytest.raises(AssertionError):
query = SpatialQuery(spatial=None, bounding_box=bbox)
@pytest.mark.skipif("osgeo" not in sys.modules, reason=NEED_OSGEO)
def test_can_parse_bbox_from_gdal_bbox(self):
bbox = ogr.Geometry(ogr.wkbLinearRing)
bbox.AddPoint(1, 1)
bbox.AddPoint(1, 2)
bbox.AddPoint(2, 2)
bbox.AddPoint(2, 1)
query = SpatialQuery(spatial=None, bounding_box=bbox)
assert query.coordinates == "1.0,1.0,2.0,2.0"
assert query.geom_type == "bounding_box"
@pytest.mark.skipif("osgeo" not in sys.modules, reason=NEED_OSGEO)
def test_can_parse_spatial_from_gdal_bbox(self):
wkt = "POLYGON ((5 30, 5 33, 2 33, 2 30, 5 30))"
spatial = ogr.CreateGeometryFromWkt(wkt)
query = SpatialQuery(spatial=spatial, bounding_box=None)
assert (
query.coordinates
== "5.0000,30.0000,5.0000,33.0000,2.0000,33.0000,2.0000,30.0000,5.0000,30.0000"
)
assert query.geom_type == "polygon"
@pytest.mark.skipif("osgeo" not in sys.modules, reason=NEED_OSGEO)
def test_can_parse_bbox_from_gdal_diamond_polygon(self):
# Create a pentagon
bbox = ogr.Geometry(ogr.wkbLinearRing)
bbox.AddPoint(1, 2)
bbox.AddPoint(2, 3)
bbox.AddPoint(3, 2)
bbox.AddPoint(2, 1)
query = SpatialQuery(spatial=None, bounding_box=bbox)
assert query.coordinates == "1.0,1.0,3.0,3.0"
assert query.geom_type == "bounding_box"
@pytest.mark.skipif("osgeo" not in sys.modules, reason=NEED_OSGEO)
def test_can_parse_spatial_from_gdal_multipoligon(self):
multipolygon = ogr.Geometry(ogr.wkbMultiPolygon)
# Create ring #1
ring1 = ogr.Geometry(ogr.wkbLinearRing)
ring1.AddPoint(12.0, 63.0)
ring1.AddPoint(12.0, 62.0)
ring1.AddPoint(13.0, 62.0)
ring1.AddPoint(13.0, 63.0)
ring1.AddPoint(12.0, 63.0)
# Create polygon #1
poly1 = ogr.Geometry(ogr.wkbPolygon)
poly1.AddGeometry(ring1)
multipolygon.AddGeometry(poly1)
# Create ring #2
ring2 = ogr.Geometry(ogr.wkbLinearRing)
ring2.AddPoint(11.0, 64.0)
ring2.AddPoint(11.0, 62.0)
ring2.AddPoint(12.5, 62.0)
ring2.AddPoint(12.5, 64.0)
ring2.AddPoint(11.0, 64.0)
# Create polygon #2
poly2 = ogr.Geometry(ogr.wkbPolygon)
poly2.AddGeometry(ring2)
multipolygon.AddGeometry(poly2)
query = SpatialQuery(spatial=multipolygon, bounding_box=None)
assert (
query.coordinates
== "11.0000,62.0000,0.0000,13.0000,62.0000,0.0000,13.0000,63.0000,0.0000,12.5000,64.0000,0.0000,11.0000,64.0000,0.0000,11.0000,62.0000,0.0000"
)
assert query.geom_type == "polygon"
@pytest.mark.skipif("shapely" not in sys.modules, reason=NEED_SHAPELY)
def test_can_parse_bbox_from_shapely_box(self):
bbox = shapely.geometry.box(2, 30, 5, 33)
assert isinstance(bbox, shapely.geometry.base.BaseGeometry)
query = SpatialQuery(spatial=None, bounding_box=bbox)
assert query.coordinates == "2.0,30.0,5.0,33.0"
assert query.geom_type == "bounding_box"
@pytest.mark.skipif("shapely" not in sys.modules, reason=NEED_SHAPELY)
def test_can_parse_bbox_from_shapely_wkt(self):
bbox = shapely.wkt.loads("POLYGON ((5 30, 5 33, 2 33, 2 30, 5 30))")
assert isinstance(bbox, shapely.geometry.base.BaseGeometry)
query = SpatialQuery(spatial=None, bounding_box=bbox)
assert query.coordinates == "2.0,30.0,5.0,33.0"
assert query.geom_type == "bounding_box"
@pytest.mark.skipif("shapely" not in sys.modules, reason=NEED_SHAPELY)
def test_can_parse_spatial_from_shapely_wkt(self):
spatial = shapely.wkt.loads("POLYGON ((5 30, 5 33, 2 33, 2 30, 5 30))")
assert isinstance(spatial, shapely.geometry.base.BaseGeometry)
query = SpatialQuery(spatial=spatial, bounding_box=None)
assert (
query.coordinates
== "5.0000,30.0000,5.0000,33.0000,2.0000,33.0000,2.0000,30.0000,5.0000,30.0000"
)
assert query.geom_type == "polygon"
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tests/resources/ok/ok.py | lleites/topyn | 10 | 31291 | <reponame>lleites/topyn
def my_function() -> str:
return "todo bien"
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TRMM/trmm_embrace_diurnal_mean.py | peterwilletts24/Python-Scripts | 4 | 1618053 | import cPickle as pickle
import numpy as np
from collections import defaultdict
from netCDF4 import Dataset
from scipy.interpolate import griddata
lon_high = 101.866
lon_low = 64.115
lat_high= 33.
lat_low=-6.79
#lon_high = 116
#lon_low = 30.5
#lat_high= 40
#lat_low=-11.25
pcp_dom, longitude_dom, latitude_dom, time_dom, time_hour = pickle.load(open('/nfs/a90/eepdw/Data/Saved_data/TRMM/trmm_emb_time_update_large.p', 'rb'))
# Load land sea mask. TRMM land sea mask is in % of water coverage so 100% is all water
nc = Dataset('/nfs/a90/eepdw/Data/Observations/Satellite/TRMM/TMPA_mask.nc')
# Regrid lsm to data grid (offset b 0.125 degrees
lsm_lons, lsm_lats = np.meshgrid(nc.variables['lon'][:],nc.variables['lat'][:])
lons_data, lats_data = np.meshgrid(longitude_dom[0], latitude_dom[0])
lsm_regrid = griddata((lsm_lats.flatten(), lsm_lons.flatten()), nc.variables['landseamask'][:].flatten(), (lats_data,lons_data), method='linear')
# Get min and max index positions for latitude and longitude - FOR LSM
# Get min and max index positions for latitude and longitude - FOR PCP
la_index_pcp = np.where((latitude_dom[0]<=lat_high) & (latitude_dom[0] >= lat_low))
lo_index_pcp = np.where((longitude_dom[0]<=lon_high) & (longitude_dom[0] >= lon_low))
la_i_max_pcp = np.max(la_index_pcp)
la_i_min_pcp = np.min(la_index_pcp)
lo_i_max_pcp = np.max(lo_index_pcp)
lo_i_min_pcp = np.min(lo_index_pcp)
print la_i_min_pcp,la_i_max_pcp, lo_i_min_pcp,lo_i_max_pcp
pcp_dom_2 = pcp_dom[:,la_i_min_pcp:la_i_max_pcp, lo_i_min_pcp:lo_i_max_pcp]
lsm= lsm_regrid[la_i_min_pcp:la_i_max_pcp, lo_i_min_pcp:lo_i_max_pcp]
print pcp_dom.shape
print pcp_dom_2.shape
print lsm.shape
####################################################
# Calculate mean for every time in the date range for entire area
mean_of_each_time = pcp_dom_2.reshape((pcp_dom_2.shape[0], -1)).mean(axis=1)
print pcp_dom_2.reshape(pcp_dom_2.shape[0], -1).shape
mean_and_hour=zip(mean_of_each_time,time_hour)
# OCEAN - Calculate mean for every time in the date range
lsm_weights=lsm/100
print pcp_dom_2.reshape(pcp_dom_2.shape[0], -1).shape
print lsm_weights.flatten().shape
mean_oc = np.ma.average(pcp_dom_2.reshape(pcp_dom_2.shape[0], -1), axis=1, weights=lsm_weights.flatten())
oc_mean_and_hour=zip(mean_oc,time_hour)
# LAND - Calculate mean for every time in the date range
lsm_weights=1-(lsm/100)
mean_la = np.ma.average(pcp_dom_2.reshape(pcp_dom_2.shape[0], -1), weights=lsm_weights.flatten(), axis=1)
la_mean_and_hour=zip(mean_la,time_hour)
#####################################################
# Sort into time of day #################
# Total
i = defaultdict(list)
for v,k in mean_and_hour:
i[k.strip()].append(v)
mean=[]
hour=[]
# Average for each time of day
for q,a in enumerate(i.items()):
#print a[1]
if a[1]:
mean.append(np.mean(a[1]))
hour.append(a[0])
print mean
print hour
# Land
i = defaultdict(list)
for v,k in la_mean_and_hour:
i[k.strip()].append(v)
mean_l=[]
hour_l=[]
# Average for each time of day
for q,a in enumerate(i.items()):
#print a[1]
if a[1]:
mean_l.append(np.mean(a[1]))
hour_l.append(a[0])
print mean_l
print hour_l
# Ocean
i = defaultdict(list)
for v,k in oc_mean_and_hour:
i[k.strip()].append(v)
mean_o=[]
hour_o=[]
# Average for each time of day
for q,a in enumerate(i.items()):
#print a[1]
if a[1]:
mean_o.append(np.mean(a[1]))
hour_o.append(a[0])
print mean_o
print hour_o
# Save
np.savez("/nfs/a90/eepdw/Data/Observations/Satellite/TRMM/Diurnal/total_trmm_diurnal_average_lat_%s_%s_lon_%s_%s" % (lat_low,lat_high, lon_low, lon_high), mean=mean, hour=hour )
np.savez("/nfs/a90/eepdw/Data/Observations/Satellite/TRMM/Diurnal/sea_trmm_diurnal_average_lat_%s_%s_lon_%s_%s" % (lat_low,lat_high, lon_low, lon_high), mean=mean_o, hour=hour_o )
np.savez("/nfs/a90/eepdw/Data/Observations/Satellite/TRMM/Diurnal/land_trmm_diurnal_average_lat_%s_%s_lon_%s_%s" % (lat_low,lat_high, lon_low, lon_high), mean=mean_l, hour=hour_l )
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easiercfg/easiercfg.py | kistee/easiercfg | 0 | 146850 | from configparser import RawConfigParser
import os
import sys
class Config(RawConfigParser):
"""
Class for easy config file usage.
"""
def __init__(self):
"""
Initialize Class.
"""
# Parameter
self.filename = 'config.ini'
# Call parent constructor
super(Config, self).__init__()
# Load Config-File
self.__LoadConfig()
def __LoadConfig(self):
"""
Load Config-File.
"""
self.read(os.path.join(sys.path[0], self.filename))
def Show(self):
"""
Print all key and value pairs.
"""
config_dict = {}
for element in self.sections():
config_dict[element] = {}
for name, value in self.items(element):
print('{:15} = {}'.format(name, value))
config_dict[element][name] = value
def __setitem__(self, key, value):
"""
Set value of key-value-pair.
"""
self.Set(key, value)
def Set(self, key, value, section="settings"):
"""
Set value of key-value-pair.
"""
self.set(section, key, value)
self.__Save()
def __getitem__(self, key):
"""
Get corresponding value of key-value-pair.
"""
return self.Get(key)
def Get(self, key, section="settings"):
"""
Get corresponding value of key-value-pair.
"""
return self.get(section, key)
def __Save(self, filename=""):
"""
Save current config to file.
"""
# If no filename is specified use the one defined in parameters.
if filename == "":
filename = self.filename
# Write config to file
with open(filename, 'w') as configfile:
self.write(configfile)
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firedetectionserver/src/inferenceserver.py | aj-ames/NSAC-NULLPointers | 1 | 47297 | <gh_stars>1-10
from maven import Maven
import numpy as np
import ast
import multiprocessing as mp
import cv2
import random
from PIL import Image
from flask import Flask, request, jsonify
flaskserver = Flask(__name__)
num_classes = 94
thresh_conf = 0.2
global maven, athena
class Athena:
''' Class to hold member methods and variables
for drawing detections. '''
def __init__(self):
''' Method called when class object is created. '''
self.path = 'classes.txt'
self.classes = self.read_class_names(self.path)
self.num_classes = len(self.classes)
self.colors = self.colorGenerator(self.num_classes)
def colorGenerator(self, num):
''' Method to generate random colors. '''
colors = dict()
for i in range(0, num):
temp = list()
b = random.randint(0, 255)
g = random.randint(0, 255)
r = random.randint(0, 255)
temp.append(b)
temp.append(g)
temp.append(r)
colors.update({i+1:temp})
return colors
def read_class_names(self, path):
''' Method to read the class names. '''
try:
names = dict()
with open(path, 'r') as data:
for ID, name in enumerate(data):
names[ID + 1] = name.strip('\n')
data.close()
return names
except:
print('Failed to read class names')
traceback.print_exc()
sys.exit(0)
def colorGenerator(self, num):
''' Method to generate random colors. '''
colors = dict()
for i in range(0, num):
temp = list()
b = random.randint(0, 255)
g = random.randint(0, 255)
r = random.randint(0, 255)
temp.append(b)
temp.append(g)
temp.append(r)
colors.update({i+1:temp})
return colors
def read_class_names(self, path):
''' Method to read the class names. '''
try:
names = dict()
with open(path, 'r') as data:
for ID, name in enumerate(data):
names[ID + 1] = name.strip('\n')
data.close()
return names
except:
print('Failed to read class names')
traceback.print_exc()
sys.exit(0)
def plotResults(self, frame, result):
''' Method to plot results. '''
class_ids = result[0][0]
confidence = result[0][1]
boundaries = result[0][2]
for counter in range(len(class_ids)):
class_id = self.classes[class_ids[counter]]
if(class_id == 'fire' or class_id == 'person'):
conf = str(confidence[counter])
x1 = int(boundaries[counter][0])
y1 = int(boundaries[counter][1])
x2 = int(boundaries[counter][2])
y2 = int(boundaries[counter][3])
color = self.colors[class_ids[counter]]
cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)
cv2.rectangle(frame, (x1, y1), (x1 + (len(class_id) + len(conf)) * 20,
y1 - 25) , color, -1, cv2.LINE_AA)
cv2.putText(frame, class_id + ':' + conf, (x1, y1),
cv2.FONT_HERSHEY_COMPLEX, 0.85, (0, 0, 0), 1, cv2.LINE_AA)
return frame
@flaskserver.route(rule='/detect_drone', methods=['POST'])
def detect_drone():
''' Method to perform detections and return the results. '''
content = request
data = ast.literal_eval(content.data.decode('utf-8'))
# Parse the Data
batch = data['Batch'] # Batch of Images
batch_len = data['BatchLength'] # Length of Batch
height = data['ImageHeight'] # Height of Image
width = data['ImageWidth'] # Width of Image
channels = data['Channels'] # Color Channels
# Get batch in NumPy
batch = np.fromstring(batch, np.uint8).reshape((batch_len, height, width, channels))
results = maven.infer(batch)
results_scrubbed = list()
for result in results.keys():
classes = list()
bbox = list()
confidence = list()
for cat in range(1, num_classes + 1):
for val in results[result][cat]:
conf = val[4]
if not conf > thresh_conf:
continue
x1 = val[0]
y1 = val[1]
x2 = val[2]
y2 = val[3]
classes.append(cat)
confidence.append(conf)
bbox.append([x1, y1, x2, y2])
results_scrubbed.append([classes, confidence, bbox])
return jsonify({'Response':str(results_scrubbed)})
@flaskserver.route(rule='/detect', methods=['POST'])
def detect():
''' Method to perform detections and return the results. '''
content = request
img = Image.open(content.files['file'].stream)
img = np.asarray(img)
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
# Parse the Data
batch = img.expand_dims(axis=0) # Batch of Images
# batch_len = batch.shape[0]# Length of Batch
# height = batch.shape[1] # Height of Image
# width = batch.shape[2] # Width of Image
# channels = batch.shape[3] # Color Channels
# # Get batch in NumPy
# batch = np.fromstring(batch, np.uint8).reshape((batch_len, height, width, channels))
results = maven.infer(batch)
results_scrubbed = list()
for result in results.keys():
classes = list()
bbox = list()
confidence = list()
for cat in range(1, num_classes + 1):
for val in results[result][cat]:
conf = val[4]
if not conf > thresh_conf:
continue
x1 = val[0]
y1 = val[1]
x2 = val[2]
y2 = val[3]
classes.append(cat)
confidence.append(conf)
bbox.append([x1, y1, x2, y2])
results_scrubbed.append([classes, confidence, bbox])
frame = batch.squeeze()
frame = athena.plotResults(frame, results_scrubbed)
cv2.namedWindow('Result', cv2.WINDOW_NORMAL)
cv2.imshow('Result', frame)
cv2.waitKey(0)
cv2.destroyAllWindows()
return jsonify({'Response':str(results_scrubbed)})
global maven, athena
maven = Maven()
athena = Athena()
if __name__ == '__main__':
flaskserver.run(host='127.0.0.1',
port=5000,
debug=True) | [
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2
] |
day03/part1.py | joeeoj/adventofcode2020 | 0 | 172184 | def find_tree(row, index):
if index > len(row):
row = row * ((index // len(row)) + 1)
if row[index] == '#':
return 1
return 0
if __name__ == '__main__':
with open('input.txt') as f:
data = [r.strip() for r in f.readlines()]
indices = range(0, len(data) * 3, 3)
tree_count = 0
for row, i in zip(data, indices):
tree_count += find_tree(row, i)
print(f'Total trees: {tree_count}')
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2
] |
tools/android/proguard_whitelister.py | brandonpollack23/bazel-buildfiles-upstream | 142 | 157456 | # Copyright 2015 Google Inc. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Checks for proguard configuration rules that cannot be combined across libs.
The only valid proguard arguments for a library are -keep, -assumenosideeffects,
and -dontnote and -dontwarn and -checkdiscard when they are provided with
arguments.
"""
import re
import sys
from third_party.py import gflags
gflags.DEFINE_string('path', None, 'Path to the proguard config to validate')
gflags.DEFINE_string('output', None, 'Where to put the validated config')
FLAGS = gflags.FLAGS
PROGUARD_COMMENTS_PATTERN = '#.*(\n|$)'
def main():
with open(FLAGS.path) as config:
config_string = config.read()
invalid_configs = Validate(config_string)
if invalid_configs:
raise RuntimeError('Invalid proguard config parameters: '
+ str(invalid_configs))
with open(FLAGS.output, 'w+') as outconfig:
config_string = ('# Merged from %s \n' % FLAGS.path) + config_string
outconfig.write(config_string)
def Validate(config):
"""Checks the config for illegal arguments."""
config = re.sub(PROGUARD_COMMENTS_PATTERN, '', config)
args = config.split('-')
invalid_configs = []
for arg in args:
arg = arg.strip()
if not arg:
continue
elif arg.startswith('checkdiscard'):
continue
elif arg.startswith('keep'):
continue
elif arg.startswith('assumenosideeffects'):
continue
elif arg.split()[0] == 'dontnote':
if len(arg.split()) > 1:
continue
elif arg.split()[0] == 'dontwarn':
if len(arg.split()) > 1:
continue
invalid_configs.append('-' + arg.split()[0])
return invalid_configs
if __name__ == '__main__':
FLAGS(sys.argv)
main()
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] |
Blasting/Start.py | Erosion2020/SpaceCore | 4 | 34481 | <filename>Blasting/Start.py
import Blasting
name = "Blasting"
input_message = f"[{name} #]"
def menu():
print("-------------------------------弱口令爆破子模块-------------------------------")
print("1、SSH弱口令爆破")
print("2、MySQL弱口令爆破")
print("输入exit退出")
print("-----------------------------------END-----------------------------------")
def start():
menu()
while True:
option = input(input_message)
if option == 'exit':
break
elif option == '1':
Blasting.SSH.ssh_blasting()
elif option == '2':
Blasting.MySQL.blasting_mysql()
elif option == 'help':
menu()
else:
print("无效的输入,请重新输入")
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] |
PythonAPI/examples/spawn_assets.py | vishal-prgmr/carla | 0 | 171237 | <filename>PythonAPI/examples/spawn_assets.py
#!/usr/bin/env python
# Copyright (c) 2019 Computer Vision Center (CVC) at the Universitat Autonoma de
# Barcelona (UAB).
#
# This work is licensed under the terms of the MIT license.
# For a copy, see <https://opensource.org/licenses/MIT>.
#
#This script is used for dropping assets along the a lane section at random locations.
import glob
import os
import sys
import pandas as pd
try:
sys.path.append(glob.glob('../carla/dist/carla-*%d.%d-%s.egg' % (
sys.version_info.major,
sys.version_info.minor,
'win-amd64' if os.name == 'nt' else 'linux-x86_64'))[0])
except IndexError:
pass
import carla
import random
import time
import random
def main():
actor_list = []
# In this tutorial script, we are going to add a vehicle to the simulation
# and let it drive in autopilot. We will also create a camera attached to
# that vehicle, and save all the images generated by the camera to disk.
try:
# First of all, we need to create the client that will send the requests
# to the simulator. Here we'll assume the simulator is accepting
# requests in the localhost at port 2000.
client = carla.Client('localhost', 2000)
client.set_timeout(2.0)
# Once we have a client we can retrieve the world that is currently
# running.
world = client.get_world()
m = world.get_map()
# The world contains the list blueprints that we can use for adding new
# actors into the simulation.
blueprint_library = world.get_blueprint_library()
cone_bp = blueprint_library.find('static.prop.trafficcone01')
barrier_np = blueprint_library.find('static.prop.streetbarrier')
bp_list = [cone_bp, barrier_np]
#Waypoints
waypoints = m.generate_waypoints(1)
#lane width is 3.5
way_points = pd.DataFrame(data=None, columns=['x', 'y', 'z','id', 'type'])
x = []
y = []
z = []
Road_type = []
Road_id = []
for w in waypoints:
Road_id.append(w.road_id)
x.append(w.transform.location.x)
y.append(w.transform.location.y)
z.append(w.transform.location.z)
Road_type.append(str(w.lane_change))
way_points.x = x
way_points.y = y
way_points.z = z
way_points.id = Road_id
way_points.type = Road_type
way_points.to_csv("D:/Carla_Lidar/{}.csv".format("Way_pointsT2"), index=None)
print("Please note the lane type ( Left or Right) and the lane ID to spawn assets along this lane type")
time.sleep(5)
for index, rows in way_points.iterrows():
if rows['type'] == "Left":
name = 'L - ' + str(rows['id'])
world.debug.draw_string(carla.Location(x=rows['x'] , y=rows['y']), name, draw_shadow=False,
color=carla.Color(r=0, g=0, b=255), life_time=200.0,
persistent_lines=True)
elif rows['type'] == "Right":
name = 'R - ' + str(rows['id'])
world.debug.draw_string(carla.Location(x=rows['x'], y=rows['y']), name, draw_shadow=False,
color=carla.Color(r=0, g=255, b=0), life_time=200.0,
persistent_lines=True)
elif rows['type'] == "NONE":
name = 'N - ' + str(rows['id'])
world.debug.draw_string(carla.Location(x=rows['x'], y=rows['y']), name, draw_shadow=False,
color=carla.Color(r=100, g=0, b=100), life_time=200.0,
persistent_lines=True)
time.sleep(5)
chosen_road_ids = []
if len(chosen_road_ids) != 0:
for elem in chosen_road_ids:
xList = []
yList = []
coord = pd.DataFrame(data=None, columns=['x', 'y'])
for index, rows in way_points.iterrows():
if rows['id'] == elem:
xList.append(rows['x'])
yList.append(rows['y'])
coord.x = xList
coord.y = yList
coord = coord.sample(n=3)
for index,rows in coord.iterrows():
transform = carla.Transform(carla.Location(x=rows['x'], y=(rows['y']+3)),
carla.Rotation(yaw=random.choice([0,45, 60,90])))
world.debug.draw_string(carla.Location(x=rows['x'], y=rows['y']+3, z=100), 'X', draw_shadow=True,
color=carla.Color(r=255, g=0, b=0), life_time=200.0,
persistent_lines=True)
assets = world.spawn_actor(random.choice(bp_list), transform)
finally:
print('destroying actors')
for actor in actor_list:
actor.destroy()
print('done.')
if __name__ == '__main__':
main()
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] |
query.py | IntimateMerger/dockerfile-aql | 0 | 47307 | <reponame>IntimateMerger/dockerfile-aql<gh_stars>0
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys
import time
import aerospike
from aerospike import predicates as p
if __name__ == "__main__":
if len(sys.argv) == 6:
as_host = str(sys.argv[1])
ns_name = str(sys.argv[2])
st_name = str(sys.argv[3])
key_name = str(sys.argv[4])
days = int(sys.argv[5])
now = int(time.time())
min_ts = now - (60 * 60 * 24 * days)
max_ts = 2147483647 # 2038-01-19 12:14:07
cl = aerospike.client({'hosts': [(as_host, 3000)]}).connect()
query = cl.query(ns_name, st_name)
query.select(key_name)
query.where(p.between("ts", min_ts, max_ts))
def echo((key, meta, bins)):
print(bins[key_name])
query.foreach(echo, {"timeout": 0})
else:
print("usage: python -u query.py [hostname] [namespace] [set] [key] [days]")
| [
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PYPI python package/multivicks/crud.py | imvickykumar999/100th-Repository-Morsetor-python-Package | 2 | 64261 | <gh_stars>1-10
# pip install imvickykumar999
# C:\Users\Vicky\anaconda3\Lib\site-packages\vicksbase
class HomeAutomation:
def __init__(self, link):
try:
from vicksbase import firebase as f
self.link = link
self.firebase_obj = f.FirebaseApplication(self.link, None)
print(self.pull(child = '/'))
except Exception as e:
print(e)
print('try: pip install imvickykumar999')
def show(self):
return self.link
def pull(self, child = 'A/B/C/Switch'):
result = self.firebase_obj.get(f'{child}', None)
return result
def push(self, data = 1, child = 'A/B/C/Switch'):
self.firebase_obj.put('/', child, data)
return self.pull(child = '/')
def remove(self, child = 'A/B/C/led2'):
data = self.firebase_obj.delete('/', child)
return self.pull(child = '/')
# link = 'https://led-blink-wifi-default-rtdb.firebaseio.com/'
# obj = HomeAutomation(link)
# f = obj.show()
# f = obj.pull()
# f = obj.push(1)
# f = obj.remove()
# print(f)
# input('Press Enter to Exit...')
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] |
rpsls.py | cmckni3-org/rpsls | 0 | 173114 | <filename>rpsls.py
#!/usr/bin/env python
import random
# Rock-paper-scissors-lizard-Spock
# The key idea of this program is to equate the strings
# "rock", "paper", "scissors", "lizard", "Spock" to numbers
# as follows:
#
# 0 - rock
# 1 - Spock
# 2 - paper
# 3 - lizard
# 4 - scissors
# helper functions
NAME_MAPPING = [(0, "rock"), (1, "Spock"), (2, "paper"), (3, "lizard"), (4, "scissors")]
def number_to_name(number):
for (key, value) in NAME_MAPPING:
if key == number:
return value
def name_to_number(name):
for (key, value) in NAME_MAPPING:
if value == name:
return key
def player_wins(player_choice):
# convert name to player_number using name_to_number
player_number = name_to_number(player_choice)
# compute random guess for comp_number using random.randrange()
comp_number = random.randrange(0, 5)
# compute difference of player_number and comp_number modulo five
difference = (player_number - comp_number) % 5
# convert comp_number to name using number_to_name
comp_name = number_to_name(comp_number)
# print results
print("Player chooses", player_choice)
print("Computer chooses", comp_name)
# use if/elif/else to determine winner
if difference == 0:
print("Player and computer tie!")
return None
elif difference > 2:
print("Computer wins!")
return False
else:
print("Player wins!")
return True
def print_game_title():
print('{:-^80}'.format(''))
print('{:^80}'.format("Rock Paper Scissors Lizard Spock"))
def print_final_score(computer, human):
print('{:-^80}'.format(''))
print('{:^80}'.format('Final Score'))
print('{:^40} {:^40}'.format('Computer', 'Human'))
print('{:^40} {:^40}'.format(computer, human))
print('{:-^80}'.format(''))
def main():
computer = 0
human = 0
play = True
print_game_title()
while play:
print('{:-^80}'.format(''))
player_choice = input("Please, enter a choice <rock, Spock, paper, lizard, scissors>: ")
if name_to_number(player_choice) != None:
winner = player_wins(player_choice)
if winner:
human += 1
elif winner == False:
computer += 1
else:
print("Invalid choice")
play_again = input("Play again? <Y, n>: ")
play = (play_again == 'Y' or play_again == 'y')
print_final_score(computer, human)
if __name__ == "__main__":
main()
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] |
tests/test_lint.py | philvarner/stac-check | 8 | 120362 | <reponame>philvarner/stac-check
from re import L
from stac_check.lint import Linter
import pytest
def test_linter_bad_asset_requests():
file = "sample_files/1.0.0/core-item.json"
linter = Linter(file, assets=True)
asset_request_errors = [
"http://cool-sat.com/catalog/20201211_223832_CS2/20201211_223832_CS2.EPH"
]
assert linter.version == "1.0.0"
assert linter.valid_stac == True
assert linter.asset_type == "ITEM"
assert linter.invalid_asset_format == []
assert linter.invalid_asset_request == asset_request_errors
def test_linter_bad_assets():
file = "sample_files/1.0.0/core-item-bad-links.json"
linter = Linter(file, assets=True)
asset_format_errors = [
"https:/storage.googleapis.com/open-cogs/stac-examples/20201211_223832_CS2.jpg"
]
asset_request_errors = [
"https:/storage.googleapis.com/open-cogs/stac-examples/20201211_223832_CS2.jpg",
"http://cool-sat.com/catalog/20201211_223832_CS2/20201211_223832_CS2.EPH"
]
assert linter.version == "1.0.0"
assert linter.valid_stac == True
assert linter.asset_type == "ITEM"
assert linter.invalid_asset_format == asset_format_errors
assert linter.invalid_asset_request == asset_request_errors
def test_linter_bad_links():
file = "sample_files/1.0.0/core-item-bad-links.json"
linter = Linter(file, links=True)
link_format_errors = ["http:/remotdata.io/catalog/20201211_223832_CS2/index.html"]
link_request_errors = [
"http://catalog/collection.json",
"http:/remotdata.io/catalog/20201211_223832_CS2/index.html"
]
assert linter.version == "1.0.0"
assert linter.valid_stac == True
assert linter.asset_type == "ITEM"
assert len(linter.invalid_link_format) > 0
assert linter.invalid_link_format == link_format_errors
assert linter.invalid_link_request == link_request_errors
def test_linter_bad_links_assets():
file = "sample_files/1.0.0/core-item-bad-links.json"
linter = Linter(file, assets=True, links=True)
asset_format_errors = [
"https:/storage.googleapis.com/open-cogs/stac-examples/20201211_223832_CS2.jpg"
]
asset_request_errors = [
"https:/storage.googleapis.com/open-cogs/stac-examples/20201211_223832_CS2.jpg",
"http://cool-sat.com/catalog/20201211_223832_CS2/20201211_223832_CS2.EPH"
]
link_format_errors = ["http:/remotdata.io/catalog/20201211_223832_CS2/index.html"]
link_request_errors = [
"http://catalog/collection.json",
"http:/remotdata.io/catalog/20201211_223832_CS2/index.html"
]
assert linter.version == "1.0.0"
assert linter.valid_stac == True
assert linter.asset_type == "ITEM"
assert len(linter.invalid_link_format) > 0
assert linter.invalid_asset_format == asset_format_errors
assert linter.invalid_asset_request == asset_request_errors
assert linter.invalid_link_format == link_format_errors
assert linter.invalid_link_request == link_request_errors
def test_linter_collection():
file = "sample_files/1.0.0/collection.json"
linter = Linter(file, assets=False, links=False)
assert linter.version == "1.0.0"
assert linter.valid_stac == True
assert linter.asset_type == "COLLECTION"
assert linter.check_summaries() == True
def test_linter_collection_no_summaries():
file = "sample_files/1.0.0/collection-no-summaries.json"
linter = Linter(file, assets=False, links=False)
assert linter.version == "1.0.0"
assert linter.valid_stac == True
assert linter.asset_type == "COLLECTION"
assert linter.check_summaries() == False
assert linter.best_practices_msg == [
"STAC Best Practices: ",
" Object should be called 'collection.json' not 'collection-no-summaries.json'",
"",
" A STAC collection should contain a summaries field",
" It is recommended to store information like eo:bands in summaries",
""
]
def test_linter_catalog():
file = "sample_files/1.0.0/catalog.json"
linter = Linter(file, assets=False, links=False)
assert linter.version == "1.0.0"
assert linter.valid_stac == True
assert linter.asset_type == "CATALOG"
assert linter.check_bloated_links() == False
def test_linter_collection_recursive_remote():
file = "https://raw.githubusercontent.com/stac-utils/pystac/main/tests/data-files/examples/0.9.0/collection-spec/examples/landsat-collection.json"
linter = Linter(file, assets=False, links=False, recursive=True)
assert linter.version == "0.9.0"
assert linter.recursive == True
assert linter.recursive_error_msg == "Exception Could not read uri https://landsat-stac.s3.amazonaws.com/landsat-8-l1/paths/catalog.json"
def test_linter_item_id_not_matching_file_name():
file = "sample_files/1.0.0/core-item.json"
linter = Linter(file)
assert linter.file_name == "core-item"
assert linter.object_id == "20201211_223832_CS2"
assert linter.file_name != linter.object_id
assert linter.check_item_id_file_name() == False
def test_linter_collection_catalog_id():
file = "sample_files/1.0.0/collection-no-title.json"
linter = Linter(file)
assert linter.check_catalog_id_file_name() == False
def test_linter_item_id_format_best_practices():
file = "sample_files/1.0.0/core-item-invalid-id.json"
linter = Linter(file)
assert linter.check_searchable_identifiers() == False
assert linter.check_percent_encoded() == True
def test_datetime_set_to_null():
file = "sample_files/1.0.0/core-item-null-datetime.json"
linter = Linter(file)
assert linter.check_datetime_null()== True
def test_unlocated_item():
file = "sample_files/1.0.0/core-item-unlocated.json"
linter = Linter(file)
assert linter.check_unlocated() == True
assert linter.check_geometry_null() == True
def test_bloated_item():
file = "sample_files/1.0.0/core-item-bloated.json"
linter = Linter(file)
assert linter.check_bloated_metadata() == True
assert len(linter.data["properties"]) > 20
assert linter.check_bloated_links() == True
assert len(linter.data["links"]) > 20
def test_small_thumbnail():
file = "sample_files/1.0.0/core-item-large-thumbnail.json"
linter = Linter(file)
assert linter.check_thumbnail() != True
file = "sample_files/1.0.0/core-item.json"
linter = Linter(file)
assert linter.check_thumbnail() == True
def test_title_field():
file = "sample_files/1.0.0/collection-no-title.json"
linter = Linter(file)
assert linter.check_links_title_field() == False
def test_self_in_links():
file = "sample_files/1.0.0/collection-no-title.json"
linter = Linter(file)
assert linter.check_links_self() == False
def test_catalog_name():
file = "sample_files/1.0.0/catalog.json"
linter = Linter(file)
assert linter.check_catalog_id_file_name()
file = "sample_files/1.0.0/collection.json"
linter = Linter(file)
assert linter.check_catalog_id_file_name()
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code/meiduo_mall/apps/users/utils.py | Coding618/Django_base | 0 | 145226 | from itsdangerous import TimedJSONWebSignatureSerializer as Serializer
from meiduo_mall import settings
def generic_emial_verify_token(user_id):
# 1. 创建实例
s = Serializer(secret_key=settings.SECRET_KEY, expires_in=3600*24)
# 2. 加密数据
data = s.dumps({'user_id': user_id})
# 3. 返回数据
return data.decode()
def check_verify_token(token):
# 1. 创建实例
s = Serializer(secret_key=settings.SECRET_KEY, expires_in=3600*24)
# 2. 解密数据 --- 可能有异常
try:
result = s.loads(token)
except Exception as e:
return "token解密异常"
# 3. 获取数据
# result = {'user_id': user_id}
return result.get('user_id')
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odoo-13-docker/addons/odoo-debrand/models/models.py | kmrul/odoo-docker-ce | 1 | 86979 | # -*- coding: utf-8 -*-
#############################################################################
#
# Cybrosys Technologies Pvt. Ltd.
#
# Copyright (C) 2019-TODAY Cybrosys Technologies(<https://www.cybrosys.com>).
# Author: <NAME>(<https://www.cybrosys.com>)
#
# You can modify it under the terms of the GNU AFFERO
# GENERAL PUBLIC LICENSE (AGPL v3), Version 3.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU AFFERO GENERAL PUBLIC LICENSE (AGPL v3) for more details.
#
# You should have received a copy of the GNU AFFERO GENERAL PUBLIC LICENSE
# (AGPL v3) along with this program.
# If not, see <http://www.gnu.org/licenses/>.
#
#############################################################################
from odoo import models, fields, api, tools
class OdooDebrand(models.Model):
_inherit = "website"
def get_company_logo(self):
self.company_logo_url ="/web/image/res.company/%s/logo"%(self.id)
def get_favicon(self):
id = self.env['website'].sudo().search([])
self.favicon_url ="/web/image/website/%s/favicon"%(id[0].id)
favicon_url = fields.Text("Url", compute='get_favicon')
company_logo_url = fields.Text("Url", compute='get_company_logo')
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pyproc/views/__init__.py | cmin764/pyproc | 0 | 41202 | <filename>pyproc/views/__init__.py
"""All views and routes exposed by the pyproc web app."""
from . import (
message,
)
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Heat_Transfer/4.3_Pipe_with_&_out_cover.py | Daz-Riza-Seriog/Transport_Phenomena | 4 | 50395 | # Code made for <NAME>
# 12 Abril 2021
# License MIT
# Transport Phenomena: Python Program-Assessment 4.3
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy.optimize import minimize
sns.set()
# Solve for Temperature of Steam at given Pressure
class enviroment_convective:
def temp_steam_sat_reg(self, Sat_pressure_1, Sat_pressure_2, Temp_from_pressure_1, Temp_from_pressure_2,
Sat_pressure_system):
p1 = Sat_pressure_1 # [kPa]
p2 = Sat_pressure_2 # [kPa]
T1 = Temp_from_pressure_1 + 273.15 # [K]
T2 = Temp_from_pressure_2 + 273.15 # [K]
P_x = Sat_pressure_system # [kPa]
m = (T2 - T1) / (p2 - p1)
T = m * P_x - (m * p1) + T1
return T
# Optimice for the maximum difference allow
class Optimice:
def objective_T(self, x, *args):
T_supp, r = args[0], args[1]
thk = 0.015
x1 = x[0] # C1
x2 = x[1] # C2
return T_supp - ((x1 * np.log(r + thk)) - x2)
def constraint_BC1_BC2(self, x):
r, T_in = (0.025, 484.8362745098039)
K, thk, h_in, T_out, h_out, e = (15.6, 0.015, 30, 25 + 273.15, 5, 0.3)
x1 = x[0] # C1
x2 = x[1] # C2
R_conv_1 = (1 / (2 * np.pi * (r)) * h_in)
h_comb = (2 * np.pi * (r + thk)) * (h_out + e * 5.670e-8 * (x1 * np.log(r + thk) + x2 - T_out)
* ((x1 * np.log(r + thk) + x2) ** 2 + T_out ** 2))
R_cond = np.log(thk) / (2 * np.pi * K)
return ((T_in - T_out) / (R_conv_1 + R_cond + (1 / h_comb))) + ((K * x1) / r)
def objective_T_II(self, x, *args):
T_supp, r = args[0], args[1]
x1 = x[0] # C1
x2 = x[1] # C2
return T_supp - ((x1 * np.log(r)) - x2)
def constraint_BC1_BC2_II(self, x):
r, T_in = (0.025, 484.8362745098039)
K, thk_1, h_in, T_out, h_out = (15.6, 0.015, 30, 25 + 273.15, 5)
K_2, thk_2, e = (0.25, 0.012, 0.8)
x1 = x[0] # C1
x2 = x[1] # C2
R_conv_1 = (1 / (2 * np.pi * r) * h_in)
R_cond = np.log(thk_1) / (2 * np.pi * K)
R_cond_2 = np.log(thk_2) / (2 * np.pi * K_2)
h_comb = (2 * np.pi * (r + thk_1 + thk_2)) * (
h_out + e * 5.670e-8 * (x1 * np.log(r + thk_1 + thk_2) + x2 - T_out)
* ((x1 * np.log(r + thk_1 + thk_2) + x2) ** 2 + T_out ** 2))
return ((T_in - T_out) / (R_conv_1 + R_cond + R_cond_2 + (1 / h_comb))) + ((K * x1) / r)
# Determine the Q flux with cover and without cover
class Q_determine:
def Q_uncover(self, r, T_in, K, thk, h_in, T_out, h_out, e, Delta_T):
T_surf = (T_int - Delta_T) + 273.15
R_conv_1 = (1 / (2 * np.pi * r) * h_in)
h_comb = (2 * np.pi * (r + thk)) * (h_out + e * 5.670e-8 * (T_surf - T_out)
* (T_surf ** 2 + T_out ** 2))
R_cond = np.log(thk) / (2 * np.pi * K)
Q = ((T_in - T_out) / (R_conv_1 + R_cond + (1 / h_comb)))
return Q
def Q_cover(self, r, T_in, K, K_2, thk_1, thk_2, h_in, T_out, h_out, e, Delta_T):
T_surf = (T_int - Delta_T) + 273.15
R_conv_1 = (1 / (2 * np.pi * r) * h_in)
R_cond = np.log(thk_1) / (2 * np.pi * K)
R_cond_2 = np.log(thk_2) / (2 * np.pi * K_2)
h_comb = (2 * np.pi * (r + thk_1 + thk_2)) * (
h_out + e * 5.670e-8 * (T_surf - T_out)
* (T_surf ** 2 + T_out ** 2))
Q = ((T_in - T_out) / (R_conv_1 + R_cond + R_cond_2 + (1 / h_comb)))
return Q
# Temperature of T in of the cylinder iron
class T_profile_iron:
def T_in_II(self, Q_tot, r, K, thk, T_surf_out):
R_cond = np.log(r - thk) / (2 * np.pi * K)
T_surf_in = (-Q_tot * R_cond) + T_surf_out
return T_surf_in
env_conv = enviroment_convective()
Opt = Optimice()
Q_s = Q_determine()
T_iron = T_profile_iron()
T_int = env_conv.temp_steam_sat_reg(1553, 2318, 200, 220, 2000)
constraint_equal1 = {'type': 'eq', 'fun': Opt.constraint_BC1_BC2}
constraint = [constraint_equal1]
# T_suppose, Radius_max, T_in
arguments = (T_int, 0.025)
x0 = [0, 0] # This initial values are extracted from a first solution given by the method
sol = minimize(Opt.objective_T, x0, method='SLSQP', args=arguments, constraints=constraint, options={'maxiter': 5})
# BIG NOTE: modify the iteration to reach values according to reality--> You need more restrictions
# In the result you find the maximum difference that the system reach between the suppose and the reality
Q_1 = Q_s.Q_uncover(0.025, T_int, 15.6, 0.015, 30, 25 + 273.15, 5, 0.3, sol.fun)
T_in_iron = T_iron.T_in_II(Q_1, 0.025, 30, 0.015, (T_int - sol.fun) + 273.15)
########################################### Case 2 #####################################################################
constraint_equal1_II = {'type': 'eq', 'fun': Opt.constraint_BC1_BC2_II}
constraint_II = [constraint_equal1_II]
# T_suppose, Radius_max
arguments_II = (T_int, 0.025 + 0.015 + 0.012)
x0 = [0, 0] # This initial values are extracted from a first solution given by the method
sol_II = minimize(Opt.objective_T, x0, method='SLSQP', args=arguments_II, constraints=constraint_II,
options={'maxiter': 5})
# BIG NOTE: modify the iteration to reach values according to reality--> You need more restrictions
# In the result you find the maximum difference that the system reach between the suppose and the reality
Q_2 = Q_s.Q_cover(0.025, T_int, 15.6, 0.25, 0.015, 0.012, 30, 25 + 273.15, 5, 0.3, sol_II.fun)
print("========================= WITH UNCOVER ==============================================\n")
print("Temperature in the convective enviro. 1: {} [K]".format(T_int))
print("Temperature at the start of the cylinder: {} [K]".format(T_in_iron))
print("Temperature at the end of the cylinder: {} [K]".format((T_int - sol.fun) + 273.15))
print("Q for meter of cylinder: {} [W/m]\n".format(Q_1))
print("================================================================================")
print("========================= WITH COVER ==============================================\n")
print("Temperature in the convective enviro. 1: {} [K]".format(T_int))
print("Temperature at the end of the cylinder: {} [K]".format((T_int - sol_II.fun) + 273.15))
print("Q for meter of cylinder: {} [W/m]\n".format(Q_2))
print("================================================================================\n") | [
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manage.py | Vanzct/xp | 0 | 38676 | <gh_stars>0
# coding=utf-8
__author__ = 'Van'
import os
import sys
from flask.ext.script import Manager, Shell
# from flask.ext.migrate import Migrate, MigrateCommand
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from app import create_app
mode = os.getenv('APP_CONFIG_MODE') or 'default'
if mode:
mode = mode.lower()
print 'current config mode %s' % mode
app = create_app(mode)
manager = Manager(app)
# manager.add_command("shell", Shell(make_context=make_shell_context))
# manager.add_command('db', MigrateCommand)
@manager.command
def test():
"""aRun the unit tests."""
import unittest
tests = unittest.TestLoader().discover('tests')
unittest.TextTestRunner(verbosity=2).run(tests)
if __name__ == '__main__':
app.debug = True
app.run(host='0.0.0.0', port=5001)
# manager.run()
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json2csv.py | glvnst/json2csv | 3 | 161571 | #!/usr/bin/env python
"""
json2csv: Convert a json file into a csv file
"""
import argparse
import sys
import json
import csv
import codecs
import cStringIO
import os
import collections
class UnicodeWriter(object):
"""
A CSV writer which will write rows to output filehandle "stream",
which is encoded in the given encoding.
Mostly from https://docs.python.org/2/library/csv.html
"""
def __init__(self, stream, dialect=csv.excel, encoding="utf-8", **kwds):
# Redirect output to a queue
self.queue = cStringIO.StringIO()
self.writer = csv.writer(self.queue, dialect=dialect, **kwds)
self.stream = stream
self.encoder = codecs.getincrementalencoder(encoding)()
def writerow(self, raw_row_values):
""" Write a row to the output file (with hackery for unicode) """
safe_row_values = list()
for value in raw_row_values:
try:
if isinstance(value, unicode):
safe_row_values.append(value.encode("utf-8"))
continue
safe_row_values.append(value)
except UnicodeDecodeError:
print "{}:{}".format(type(value).__name__, value)
raise
self.writer.writerow(safe_row_values)
# Fetch UTF-8 output from the queue ...
data = self.queue.getvalue()
data = data.decode("utf-8")
# ... and reencode it into the target encoding
data = self.encoder.encode(data)
# write to the target stream
self.stream.write(data)
# empty queue
self.queue.truncate(0)
def writerows(self, rows):
""" Calls writerow on a collection of rows """
for row in rows:
self.writerow(row)
def not_none(input_value, replacement_value=""):
""" If the given input value is None, replace it with something else """
if input_value is None:
return replacement_value
return input_value
def json_to_table(json_filename):
"""
Convert a list of dicts into a table (list of lists of values). Return the
headings in the result row
"""
with open(json_filename, "r") as json_fh:
records = json.load(json_fh, encoding="utf-8",
object_pairs_hook=collections.OrderedDict)
headings = tuple(records[0].keys())
return [headings] + [[not_none(record[heading]) for heading in headings]
for record in records]
def safe_open(path, read=True, write=False, allow_create=True,
allow_overwrite=False, file_permissions=0755):
""" Uses os.open to avoid clobbering files that already exist """
flags = 0
mode = 'r'
if read and write:
flags |= os.O_RDWR
mode = 'r+'
elif read:
flags |= os.O_RDONLY
mode = 'r'
elif write:
flags |= os.O_WRONLY
mode = 'r+'
# This hurts me.
# 'w' and 'w+' truncate
# 'a' and 'a+' ONLY append (without regard to seek) on some platforms
else:
raise RuntimeError("a file must be opened for reading or writing")
if allow_create:
flags |= os.O_CREAT
if not allow_overwrite:
if not allow_create:
raise RuntimeError("safe_open must create or overwrite")
flags |= os.O_CREAT | os.O_EXCL
try:
return os.fdopen(os.open(path, flags, file_permissions), mode)
except OSError, error:
print error
sys.exit(1)
def make_output_filename(input_name, new_extension):
"""
Return an output filename based on an input filename; using basename and
splitext
"""
return os.path.splitext(os.path.basename(input_name))[0] + new_extension
def main():
"""
primary function for command-line execution. return an exit status integer
or a bool type (where True indicates successful exection)
"""
argp = argparse.ArgumentParser(description=(
"Create a CVS file from a JSON file. The input files must contain an "
"array of objects with uniform keys. Output will be written to the "
"current working directory. For example for the input file "
"'/something/thing.json' we create './thing.csv'. By default "
"existing files will not be overwritten."))
argp.add_argument('inputfile', nargs="+", help=(
"Input JSON file(s) "))
argp.add_argument('-w', '--overwrite', action="store_true", help=(
"Overwrite the output file(s) if they exist"))
argp.add_argument('-d', '--debug', action="store_true", help=(
"Print debugging output instead of generating CSV files"))
args = argp.parse_args()
# do things
if args.debug:
import pprint
dump = pprint.PrettyPrinter(indent=4).pprint
for input_filename in args.inputfile:
table = json_to_table(input_filename)
if args.debug:
dump(table)
continue
output_filename = make_output_filename(input_filename, '.csv')
with safe_open(output_filename, write=True,
allow_overwrite=args.overwrite) as output:
writer = UnicodeWriter(output, quoting=csv.QUOTE_MINIMAL)
writer.writerows(table)
print "Wrote {} records to {}".format(len(table), output_filename)
return True
if __name__ == '__main__':
EXIT_STATUS = main()
sys.exit(int(not EXIT_STATUS if isinstance(EXIT_STATUS, bool)
else EXIT_STATUS))
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739.py | wilbertgeng/LeetCode_exercise | 0 | 79808 | """739. Daily Temperatures"""
class Solution(object):
def dailyTemperatures(self, T):
"""
:type T: List[int]
:rtype: List[int]
"""
## R2:
res = [0 for _ in range(len(T))]
stack = []
for pos, tem in enumerate(T):
while stack and T[stack[-1]] < tem:
cur = stack.pop()
res[cur] = pos - cur
stack.append(pos)
return res
## R1:
ans = [0 for _ in range(len(T))]
stack = []
for i, t in enumerate(T):
while stack and T[stack[-1]] < t:
cur = stack.pop()
ans[cur] = i - cur
stack.append(i)
return ans
| [
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] |
dateparser/data/numeral_translation_data/se.py | bazingarj/dateparser | 8 | 1608652 | <reponame>bazingarj/dateparser<gh_stars>1-10
# -*- coding: utf-8 -*-
info = {
"%spellout-cardinal": {
"0": "nolla;",
"1": "okta;",
"2": "guokte;",
"3": "golbma;",
"4": "njeallje;",
"5": "vihtta;",
"6": "guhtta;",
"7": "čieža;",
"8": "gávcci;",
"9": "ovcci;",
"10": "logi;",
"(11, 19)": ">>nuppelohkái;",
"(20, 99)": "<<logi[>>];",
"(100, 999)": "<<čuođi[>>];",
"(1000, 999999)": "<<duhát[ >>];",
"(1000000, 999999999)": "<< miljon[ >>];",
"(1000000000, 999999999999)": "<< miljard[ >>];",
"(1000000000000, 999999999999999)": "<< biljon[ >>];",
"(1000000000000000, 999999999999999999)": "<< biljard[ >>];",
"(1000000000000000000, 'inf')": "=#,##0=;"
},
"%spellout-numbering": {
"(0, 'inf')": "=%spellout-cardinal=;"
},
"%spellout-numbering-year": {
"(0, 9999)": "=%spellout-numbering=;",
"(10000, 'inf')": "=%spellout-numbering=;"
}
} | [
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face.py | 1MT3J45/DS-StockAnalysis | 0 | 36263 | <reponame>1MT3J45/DS-StockAnalysis
from kivy.uix.boxlayout import BoxLayout
from kivy.lang import Builder
from kivy.app import App
import YP03
import sys
import dfgui
import pandas as pd
Builder.load_string('''
<faceTool>:
num1: num1
result: result
orientation: 'vertical'
BoxLayout:
orientation: 'horizontal'
Label:
id: num1
text: 'Stock Data Analysis'
BoxLayout:
orientation: 'horizontal'
GridLayout:
cols: 6
Label:
id: blank1
Label:
id: blank2
Button:
text: 'Execute'
height: 10
width: 30
on_press: root.display_fun(self)
Label:
text: 'EMPTY SLOT'
height: 10
width: 30
on_press:
Button:
text: "Show XLS Sheet"
height: 10
width: 30
on_press: root.graph()
Button:
text: "Clear"
height: 10
width: 30
on_press: root.clear_screen()
BoxLayout:
orientation: 'horizontal'
Label:
id: result
GridLayout:
cols: 2
size_hint_y: None
Button:
text: "Clear"
on_press: root.clear_screen()
height: 10
width: 30
BubbleButton:
text: 'Exit'
on_press: root.exit_it()
height: 10
width: 30
''')
class face_app(App):
def build(self):
return faceTool()
class faceTool(BoxLayout):
def __init__(self, **kwargs):
super(faceTool, self).__init__(**kwargs)
def display_fun(self, instance):
'''Fuction called when numeric buttons are pressed,
if the operation button is pressed the numbers after will be
on the right hand side.
'''
DayClusterNames, length = YP03.execute()
res = ''
for i in range(len(DayClusterNames)):
res = str(DayClusterNames[i])+'\n'+res
self.result.text = str(res)
def exit_it(self):
sys.exit()
def graph(self):
# xls = pd.read_excel('Res.xls')
# df = pd.DataFrame(xls)
# dfgui.show(df)
import main
def clear_screen(self):
self.result.text = ''
face_app().run()
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Basic Text/BasicTextEditor2.0.py | PyHubs/Workspace-UX | 0 | 169523 | <filename>Basic Text/BasicTextEditor2.0.py
from tkinter import*
from tkinter import filedialog
from tkinter import font
root=Tk()
root.title("Basictext2.0")
root.geometry("1199x650")
# creating main frame
my_frame = Frame(root).pack(pady=5)
# create main text box
my_text = Text(my_frame, width=97, height=25, font=("Arial", 16), selectbackground="Lightgrey", selectforeground="Black", undo=True)
my_text.pack()
#new file def
def new_file():
my_text.delete("1.0", END)
root.title("New file - Basictext2.0")
status_bar.config(text="New file")
#open file def
def open_file():
my_text.delete("1.0", END)
text_file = filedialog.askopenfilename(title="Open file", filetypes=(("Text Files", "*txt"), ("HTML Files", "*.html"), ("Python Files", "*.py"), ("All Files", "*.*")))
text_file = open(text_file, 'r')
stuff = text_file.read()
my_text.insert(END, stuff)
text_file.close()
def save_as_file():
text_file = filedialog.asksaveasfilename(filetypes=(("Text Files", "*txt"), ("HTML Files", "*.html"), ("Python Files", "*.py"), ("All Files", "*.*")))
#Menubars
my_menu = Menu(root)
root.config(menu=my_menu)
#filemenu
file_menu = Menu(my_menu, tearoff=False)
my_menu.add_cascade(label="File", menu=file_menu)
file_menu.add_command(label="New", command=new_file)
file_menu.add_command(label="Open", command=open_file)
file_menu.add_command(label="Save")
file_menu.add_command(label="Save As", command=save_as_file)
file_menu.add_separator()
file_menu.add_command(label="Exit", command=root.destroy)
#editmenu
edit_menu = Menu(my_menu, tearoff=False)
my_menu.add_cascade(label="Edit", menu=edit_menu)
edit_menu.add_command(label="Cut")
edit_menu.add_command(label="Copy")
edit_menu.add_command(label="Undo")
edit_menu.add_command(label="Redo")
#status bar
status_bar = Label(root, text="File in edit ", anchor=E).pack(fill=X, side=BOTTOM, ipady=5)
root.mainloop()
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29945,
29897,
13,
13,
4632,
29889,
3396,
7888,
580,
13,
2
] |
scraping codes/btis.py | shreyakupadhyay/prof-raj | 2 | 63459 | <gh_stars>1-10
import requests
import json
base_url = 'http://www.btis.in/bcity_issues_transport.txt'
headers = {"User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Ubuntu Chromium/48.0.2564.82 Chrome/48.0.2564.82 Safari/537.36"}
html = requests.get(base_url,headers=headers)
htmltext = html.text
data = json.loads(htmltext)
length_data = len(data)
for i in range(0,length_data):
print data[i]['user']['name']
print data[i]['issue_no']
print data[i]['topic_id']
print data[i]['title']
print data[i]['status']
print data[i]['topic_name']
print data[i]['issue_type_name']
print data[i]['comments']
print data[i]['image_url']
print data[i]['agency_name']
print data[i]['created_at']
print data[i]['issue_type_id']
print data[i]['source']
print data[i]['desc']
print data[i]['id']
print data[i]['ward_name']
print data[i]['agency_id']
print length_data
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texion/utils/preprocess.py | AnjanaRita/texion | 1 | 99916 | """text preprocessing utility functions"""
from textacy import preprocess, preprocess_text
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setup.py | nrecoder/CardioPy | 3 | 48255 | import setuptools
with open("README.md", "r") as fh:
long_description = fh.read()
setuptools.setup(
name = 'cardiopy',
version = "1.0.0",
author = "<NAME>",
author_email = "<EMAIL>",
description = "Analysis package for single-lead clinical EKG data",
long_description = long_description,
long_description_content_type = "text/markdown",
url="https://github.com/CardioPy/CardioPy",
packages = setuptools.find_packages(),
classifiers = [
"Programming Language :: Python :: 3",
"License :: OSI Approved :: BSD License",
"Operating System :: OS Independent",
"Intended Audience :: Science/Research",
"Topic :: Scientific/Engineering :: Medical Science Apps."
],
python_requires = '>=3.6',
install_requires = [
"datetime",
"matplotlib",
"pandas",
"scipy",
"statistics",
"mne",
"numpy",
"biosignalsnotebooks"
]
)
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] |
release/stubs.min/System/ComponentModel/__init___parts/NullableConverter.py | tranconbv/ironpython-stubs | 0 | 1612823 | <reponame>tranconbv/ironpython-stubs
class NullableConverter(TypeConverter):
"""
Provides automatic conversion between a nullable type and its underlying primitive type.
NullableConverter(type: Type)
"""
def ZZZ(self):
"""hardcoded/mock instance of the class"""
return NullableConverter()
instance=ZZZ()
"""hardcoded/returns an instance of the class"""
def CanConvertFrom(self,*__args):
"""
CanConvertFrom(self: NullableConverter,context: ITypeDescriptorContext,sourceType: Type) -> bool
Returns whether this converter can convert an object of the given type to the type of this converter,using the specified context.
context: An System.ComponentModel.ITypeDescriptorContext that provides a format context.
sourceType: A System.Type that represents the type you want to convert from.
Returns: true if this converter can perform the conversion; otherwise,false.
"""
pass
def CanConvertTo(self,*__args):
"""
CanConvertTo(self: NullableConverter,context: ITypeDescriptorContext,destinationType: Type) -> bool
Returns whether this converter can convert the object to the specified type,using the specified context.
context: An System.ComponentModel.ITypeDescriptorContext that provides a format context.
destinationType: A System.Type that represents the type you want to convert to.
Returns: true if this converter can perform the conversion; otherwise,false.
"""
pass
def ConvertFrom(self,*__args):
"""
ConvertFrom(self: NullableConverter,context: ITypeDescriptorContext,culture: CultureInfo,value: object) -> object
Converts the given object to the type of this converter,using the specified context and culture information.
context: An System.ComponentModel.ITypeDescriptorContext that provides a format context.
culture: The System.Globalization.CultureInfo to use as the current culture.
value: The System.Object to convert.
Returns: An System.Object that represents the converted value.
"""
pass
def ConvertTo(self,*__args):
"""
ConvertTo(self: NullableConverter,context: ITypeDescriptorContext,culture: CultureInfo,value: object,destinationType: Type) -> object
Converts the given value object to the specified type,using the specified context and culture information.
context: An System.ComponentModel.ITypeDescriptorContext that provides a format context.
culture: The System.Globalization.CultureInfo to use as the current culture.
value: The System.Object to convert.
destinationType: The System.Type to convert the value parameter to.
Returns: An System.Object that represents the converted value.
"""
pass
def CreateInstance(self,*__args):
"""
CreateInstance(self: NullableConverter,context: ITypeDescriptorContext,propertyValues: IDictionary) -> object
context: An System.ComponentModel.ITypeDescriptorContext that provides a format context.
propertyValues: An System.Collections.IDictionary of new property values.
Returns: An System.Object representing the given System.Collections.IDictionary,or null if the object cannot be created. This method always returns null.
"""
pass
def GetCreateInstanceSupported(self,context=None):
"""
GetCreateInstanceSupported(self: NullableConverter,context: ITypeDescriptorContext) -> bool
context: An System.ComponentModel.ITypeDescriptorContext that provides a format context.
Returns: true if changing a property on this object requires a call to System.ComponentModel.TypeConverter.CreateInstance(System.Collections.IDictionary) to create a new value;
otherwise,false.
"""
pass
def GetProperties(self,*__args):
"""
GetProperties(self: NullableConverter,context: ITypeDescriptorContext,value: object,attributes: Array[Attribute]) -> PropertyDescriptorCollection
context: An System.ComponentModel.ITypeDescriptorContext that provides a format context.
value: An System.Object that specifies the type of array for which to get properties.
attributes: An array of type System.Attribute that is used as a filter.
Returns: A System.ComponentModel.PropertyDescriptorCollection with the properties that are exposed for this data type,or null if there are no properties.
"""
pass
def GetPropertiesSupported(self,context=None):
"""
GetPropertiesSupported(self: NullableConverter,context: ITypeDescriptorContext) -> bool
context: An System.ComponentModel.ITypeDescriptorContext that provides a format context.
Returns: true if System.ComponentModel.TypeConverter.GetProperties(System.Object) should be called to find the properties of this object; otherwise,false.
"""
pass
def GetStandardValues(self,context=None):
"""
GetStandardValues(self: NullableConverter,context: ITypeDescriptorContext) -> StandardValuesCollection
context: An System.ComponentModel.ITypeDescriptorContext that provides a format context that can be used to extract additional information about the environment from which this
converter is invoked. This parameter or properties of this parameter can be null.
Returns: A System.ComponentModel.TypeConverter.StandardValuesCollection that holds a standard set of valid values,or null if the data type does not support a standard set of values.
"""
pass
def GetStandardValuesExclusive(self,context=None):
"""
GetStandardValuesExclusive(self: NullableConverter,context: ITypeDescriptorContext) -> bool
context: An System.ComponentModel.ITypeDescriptorContext that provides a format context.
Returns: true if the System.ComponentModel.TypeConverter.StandardValuesCollection returned from System.ComponentModel.TypeConverter.GetStandardValues is an exhaustive list of
possible values; false if other values are possible.
"""
pass
def GetStandardValuesSupported(self,context=None):
"""
GetStandardValuesSupported(self: NullableConverter,context: ITypeDescriptorContext) -> bool
context: An System.ComponentModel.ITypeDescriptorContext that provides a format context.
Returns: true if System.ComponentModel.TypeConverter.GetStandardValues should be called to find a common set of values the object supports; otherwise,false.
"""
pass
def IsValid(self,*__args):
"""
IsValid(self: NullableConverter,context: ITypeDescriptorContext,value: object) -> bool
context: An System.ComponentModel.ITypeDescriptorContext that provides a format context.
value: The System.Object to test for validity.
"""
pass
@staticmethod
def __new__(self,type):
""" __new__(cls: type,type: Type) """
pass
NullableType=property(lambda self: object(),lambda self,v: None,lambda self: None)
"""Gets the nullable type.
Get: NullableType(self: NullableConverter) -> Type
"""
UnderlyingType=property(lambda self: object(),lambda self,v: None,lambda self: None)
"""Gets the underlying type.
Get: UnderlyingType(self: NullableConverter) -> Type
"""
UnderlyingTypeConverter=property(lambda self: object(),lambda self,v: None,lambda self: None)
"""Gets the underlying type converter.
Get: UnderlyingTypeConverter(self: NullableConverter) -> TypeConverter
"""
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] |
ihs/collector/tasks.py | la-mar/ihs-deo | 0 | 27085 | from __future__ import annotations
import logging
from datetime import date, datetime, timedelta
from typing import Dict, Generator, List, Optional, Union, Tuple
import pandas as pd
import metrics
from api.models import ( # noqa
ChangeDeleteLog,
County,
ProductionHorizontal,
ProductionMasterHorizontal,
ProductionMasterVertical,
ProductionVertical,
WellHorizontal,
WellMasterHorizontal,
WellMasterVertical,
WellVertical,
)
from collector import ExportJob # noqa
from collector import (
CDExporter,
Collector,
Endpoint,
ExportBuilder,
ExportParameter,
ExportRetriever,
ProductionList,
ProductionTransformer,
WellboreTransformer,
WellList,
XMLParser,
)
from collector.identity_list import IdentityList
from collector.task import Task
from config import ExportDataTypes, IdentityTemplates, get_active_config
from exc import CollectorError, NoIdsError
from ihs import create_app
logger = logging.getLogger(__name__)
conf = get_active_config()
endpoints = Endpoint.load_from_config(conf)
def run_endpoint_task(
endpoint_name: str, task_name: str
) -> Generator[dict, None, None]:
""" Unpack task options and assemble metadata for job configuration """
endpoint = endpoints[endpoint_name]
task = endpoint.tasks[task_name]
metrics.post(
"task.execution", 1, tags={"endpoint": endpoint_name, "task": task_name}
)
for config in task.configs:
yield config
def submit_job(job_options: dict, metadata: dict) -> Optional[ExportJob]:
endpoint_name = metadata.get("endpoint")
endpoint = endpoints[endpoint_name]
# name = metadata.get("name", None)
target_model = metadata.get("target_model", None)
task_name = metadata.get("task", None)
source_name = metadata.get("source_name", None)
try:
ep = ExportParameter(**job_options)
requestor = ExportBuilder(endpoint)
job = requestor.submit(ep, metadata=metadata or {})
return job
except CollectorError as e:
logger.warning(
f"({target_model}) Skipping job {task_name} -> {source_name}: {e}"
)
return None
def collect(job: Union[dict, ExportJob]):
if isinstance(job, dict):
job = ExportJob(**job)
is_identity_export = IdentityTemplates.has_member(job.template)
data = get_job_results(job)
if is_identity_export:
collect_identities(job, data)
else:
collect_data(job, data)
def get_job_results(job: Union[ExportJob, dict]) -> bytes:
if not isinstance(job, ExportJob):
job = ExportJob(**job)
retr = ExportRetriever(job, base_url=job.url, endpoint=endpoints[job.endpoint])
data = retr.get(auto_delete=True)
return data
def collect_data(job: ExportJob, xml: bytes):
if xml:
parser = XMLParser.load_from_config(conf.PARSER_CONFIG)
document = parser.parse(xml)
model = endpoints[job.endpoint].model
collector = Collector(model)
data: List[Dict] = []
if job.data_type == ExportDataTypes.WELL.value:
data = WellboreTransformer.extract_from_collection(document, model=model)
elif job.data_type == ExportDataTypes.PRODUCTION.value:
data = ProductionTransformer.extract_from_collection(document, model=model)
metrics.post("job.collection.success", len(data), tags=job.limited_dict())
collector.save(data, replace=True)
def collect_identities(job: ExportJob, data: bytes) -> IdentityList:
interface = None
if job.data_type == ExportDataTypes.WELL.value:
interface = WellList(job.name, job.hole_direction)
interface.ids = data
elif job.data_type == ExportDataTypes.PRODUCTION.value:
interface = ProductionList(job.name, job.hole_direction)
interface.ids = data
return interface
# def delete_job(job: ExportJob) -> bool:
# endpoint = endpoints[job.endpoint]
# requestor = ExportBuilder(endpoint)
# result = False
# if requestor.job_exists(job):
# result = requestor.delete_job(job)
# return result
def purge_remote_exports() -> bool:
eb = ExportBuilder(None)
eb.delete_all_jobs()
return True
def calc_remote_export_capacity() -> Dict[str, Union[float, int]]:
"""Calculate the amount of storage space currently consumed by job exports on IHS' servers.
Returns:
dict -- {
capacity_used: space used in KB,
njobs: number of existing completed jobs
"""
mean_doc_size_bytes = (
18000 * conf.TASK_BATCH_SIZE
) # average single entity document size
inflation_pct: float = 0.1 # over estimate the used capacity by this percentage
doc_size_bytes = mean_doc_size_bytes + (inflation_pct * mean_doc_size_bytes)
remote_capacity_bytes: int = 1000000000 # 1 GB
eb = ExportBuilder(None)
try:
njobs = len(eb.list_completed_jobs())
except CollectorError as e:
logger.exception(f"Unable to calculate export capacity -- {e}", stack_info=True)
return {}
return {
"remote.capacity.used": njobs * doc_size_bytes,
"remote.capacity.available": remote_capacity_bytes - (njobs * doc_size_bytes),
"remote.capacity.total": remote_capacity_bytes,
"remote.jobs": njobs,
}
def download_changes_and_deletes() -> int:
max_date = ChangeDeleteLog.max_date()
max_sequence = ChangeDeleteLog.max_sequence() or 0
today = datetime.now()
if max_date:
last_date = max_date - timedelta(days=1)
else:
last_date = date.today() - timedelta(days=30)
cde = CDExporter(from_date=last_date, to_date=today)
results = cde.get_all()
logger.info(f"Downloaded {len(results)} changes and deletes")
records: List[Dict] = []
for r in results:
new = {}
for k, v in r.items():
if v is not None:
if "uwi" in k:
v = str(v)
if k == "reasoncode":
k = "reason_code"
elif k == "activecode":
k = "active_code"
elif k == "referenceuwi":
k = "reference_uwi"
elif k == "newuwi":
k = "new_uwi"
new[k] = v
if new.get("sequence", 0) > max_sequence:
new["processed"] = False
records.append(new)
logger.info(
f"Found {len(records)} changes and deletes (filtered {len(results) - len(records)})"
)
collector = Collector(ChangeDeleteLog)
return collector.save(records)
# def process_changes_and_deletes():
# # reason_action_map = {
# # "no_action": [0, 6],
# # "update_to_new_uwi": [1, 5, 7, 8, 9],
# # "update_to_ref_uwi": [2],
# # "delete": [3, 4],
# # }
# reason_action_map = {
# 0: "no_action",
# 1: "update_to_new_uwi",
# 2: "update_to_ref_uwi",
# 3: "delete",
# 4: "delete",
# 5: "update_to_new_uwi",
# 6: "no_action",
# 7: "update_to_new_uwi",
# 8: "update_to_new_uwi",
# 9: "update_to_new_uwi",
# }
# objs = ChangeDeleteLog.objects(processed=False)
# obj = objs[len(objs) - 80]
# obj._data
# #! unfinished
# # for obj in objs:
# # if obj.processed is False:
# # action = reason_action_map[obj.reason_code]
# # if action == "delete":
# # document = WellHorizontal.objects(api14=obj.uwi).first()
# # document = WellVertical.objects(api14=obj.uwi).first()
def synchronize_master_lists():
county_model_name = County.__name__.split(".")[-1]
master_counties = County.as_df().index.tolist()
for model in [
WellMasterHorizontal,
WellMasterVertical,
ProductionMasterHorizontal,
ProductionMasterVertical,
]:
target_model_name = model.__name__.split(".")[-1]
model_counties = model.as_df().index.tolist()
missing_from_model = [x for x in master_counties if x not in model_counties]
# add missing counties to model
added = []
for county in missing_from_model:
i = model(name=county)
i.save()
added.append(county)
if added:
logger.info(
f"({target_model_name}) Added {len(added)} entries from {county_model_name} master: {added}" # noqa
)
missing_from_master = [x for x in model_counties if x not in master_counties]
if missing_from_master:
logger.info(
f"({target_model_name}) has {len(missing_from_master)} entries missing from {county_model_name} master" # noqa
)
logger.info(f"({target_model_name}) synchronized to {county_model_name} master")
def refresh_master_lists() -> List[Tuple[List[Dict], str, str]]:
endpoints = Endpoint.from_yaml(conf.COLLECTOR_CONFIG_PATH)
endpoints = {
k: v for k, v in endpoints.items() if "master" in v.model.__name__.lower()
}
all_endpoint_configs: List[Tuple[List[Dict], str, str]] = []
for endpoint_name, endpoint in endpoints.items():
# endpoint_name, endpoint = list(endpoints.items())[0]
target_model_name = endpoint.model.__name__.split(".")[-1]
county_record_dict = (
County.as_df().loc[:, ["county_code", "state_code"]].to_dict(orient="index")
)
task = endpoint.tasks["sync"]
task.options.matrix = county_record_dict # override the yaml defined matrix
configs = task.configs
logger.warning(f"({target_model_name}) refreshing {len(configs)} counties")
all_endpoint_configs.append((configs, endpoint_name, task.task_name))
return all_endpoint_configs
# job_options, metadata = task.configs[0].values()
# ep = ExportParameter(**job_options)
# print(ep.params["Query"])
if __name__ == "__main__":
import loggers
loggers.config(10)
logging.getLogger("collector.parser").setLevel(30)
logging.getLogger("zeep").setLevel(30)
from time import sleep
# from uuid import UUID
from ihs import create_app
logging.basicConfig(level=10)
app = create_app()
app.app_context().push()
# endpoint_name = "well_master_vertical"
# endpoint_name = "well_master_vertical"
# task_name = "sync"
# endpoint = endpoints[endpoint_name]
# task = endpoint.tasks[task_name]
# # configs =
# job_options, metadata = task.configs[0].values()
# for configs, endpoint_name, task_name in refresh_master_lists():
# for job
# ep = ExportParameter(**job_options)
# print(ep.params["Query"])
# requestor = ExportBuilder(endpoint)
# job = submit_job(job_options=job_options, metadata=metadata)
# # job.to_dict()
# sleep(5)
# if job:
# collect(job)
# xml = get_job_results(job)
# parser = XMLParser.load_from_config(conf.PARSER_CONFIG)
# document = parser.parse(xml)
# model = endpoint.model
# data = WellboreTransformer.extract_from_collection(document, model=model)
# len(data)
# [x["api14"] for x in data]
# collector = Collector(model)
# collector.save(data, replace=True)
# from api.models import County, WellMasterHorizontal
# import pandas as pd
# df = pd.DataFrame([x._data for x in County.objects.all()]).set_index("name")
# df.columns
# df = df.drop(columns=["state_code", "county_code"]).sort_values("well_h_last_run")
# df.shape
# hz_ids = (
# pd.DataFrame([x._data for x in WellMasterHorizontal.objects.all()])
# .set_index("name")
# .sort_index()
# )
# hz_ids.loc[~hz_ids.index.str.contains("County")].shape
# joined = df.join(hz_ids.ids)
# joined[joined.ids.isna()]
# # data[7]
# self = task.options
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code/advent_of_code_day3.py | erinleeryan/2020adventofcode | 0 | 3161 | <gh_stars>0
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import numpy as np
import math
# In[2]:
fileObj = open('../data/advent_of_code_input_day_three.txt', "r") #opens the file in read mode.
items = fileObj. read(). splitlines() #puts the file into an array.
# In[3]:
#print (items)
def split(line):
return list(line)
holding = []
for i, line in enumerate(items):
result = split(line)
holding.append(result)
holding = np.array(holding)
holding[holding == '.'] = 0
holding[holding == '#'] = 1
holding = holding.astype(int)
print (holding)
# In[7]:
def dup_and_count(rightstep, downstep, basedata):
needed_slope_elements = math.floor(basedata.shape[0]/downstep)
replications_needed = (needed_slope_elements* rightstep)/basedata.shape[1]
duplicated = np.tile(basedata, math.ceil(replications_needed))
right = np.arange(0,(needed_slope_elements)*rightstep, rightstep).astype(int)
down = np.arange(0,(needed_slope_elements)*downstep,downstep).astype(int)
moves = []
for ii in range(len(right)):
moves.append(duplicated[down[ii], right[ii]])
hits = np.sum(moves)
return hits
down1_right3 = dup_and_count(3,1,holding)
down1_right1 = dup_and_count(1,1,holding)
down1_right5 = dup_and_count(5,1,holding)
down1_right7 = dup_and_count(7,1,holding)
down2_right1 = dup_and_count(1,2,holding)
results = np.array([down1_right3, down1_right1, down1_right5, down1_right7, down2_right1], dtype=np.int64)
print(results)
product = np.prod(results)
print (product)
# In[ ]:
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PyOpdb/self_PyOpdb/format/handle.py | GodInLove/OPDB | 1 | 131641 | <filename>PyOpdb/self_PyOpdb/format/handle.py<gh_stars>1-10
import re
import os
import sys
import NCBI_API
def extract_Synonym(ref_path, result_path):
"""
:method: a method to change like "b0001" to "thrL"
:param ref_path: string
:param result_path: string
:return:void(some files in result_path)
"""
synonym = []
for item in os.listdir(ref_path):
if ".ptt" in item:
ptt_file = item
synonym = extract_Synonym_sub(ref_path + "/" + ptt_file, synonym)
if ".rnt" in item:
rnt_file = item
synonym = extract_Synonym_sub(ref_path + "/" + rnt_file, synonym)
# print(len(synoym))
for item in os.listdir(result_path):
if "operon" in item:
operon_file = item
f = open(result_path + "/" + operon_file, 'r')
content = f.read()
f.close()
# change like "b0001" to "thrL"
for it in synonym:
content = content.replace(it[0], it[1])
f = open(result_path + "/" + "_operon.txt", 'w')
f.write(content)
f.close()
os.system("rm " + result_path + "/*_operons.txt")
print("Operons written to file:\t" + result_path + "_operon.txt")
def extract_wig(wig_path, gff_path, result_path):
"""
:method: a method to convert wig to bigwig
:param wig_path: string
:param gff_path: string
:param result_path: string
:return: void(somefiles in result_path)
"""
# get chrome.sizes
chrome_path = result_path + "/chrome.size"
f = open(gff_path, 'r')
content = f.read()
f.close()
chrome = NCBI_API.findall_pat("\#\#sequence\-region ([NC\_0-9\.]+ 1 [0-9]+)", content)
chrome = chrome[0].split(" ")
f = open(chrome_path, 'w')
f.write(chrome[0] + "\t" + chrome[2] + "\n")
f.close()
# remove the "track_name" in wig file
f = open(wig_path, 'r')
trash = f.readline()
track = f.readline()
name = NCBI_API.findall_pat("chrom\=([NC\_0-9\.\|refgi]+)", track)
if len(name) == 1:
track = re.sub("chrom\=([NC\_0-9\.\|refgi]+)", "chrom=" + chrome[0], track)
content = f.read()
f.close()
os.system("rm " + wig_path)
f = open(wig_path, 'w')
f.write(track + "\n" + content)
f.close()
# run the bash to convert wig to bigwig
tools_path = "/home/lyd/Documents/OPDB/PyOpdb/tools/wigToBigwig"
# print(tools_path + " " + wig_path + " " + chrome_path + " " + result_path + "/operon.bw")
os.popen(tools_path + " " + wig_path + " " + chrome_path + " " + result_path + "/operon.bw")
def extract_Synonym_sub(infile, ref):
"""
:method: a sub-method
:param infile: string(path)
:param ref: string
:return:
"""
f = open(infile, 'r')
for i in range(0, 3):
f.readline()
while True:
line = f.readline().strip()
if not line:
break
li = line.split("\t")
ref.append([li[4], li[5]])
f.close()
return ref
def extract_3_4(infile, outfile):
f = open(infile, 'r')
out_f = open(outfile, 'w')
out_f.write("fwd,rev\n")
while True:
line = f.readline().strip()
if not line:
break
li = line.split()
out_f.write(li[2] + "," + li[3] + "\n")
f.close()
out_f.close()
def extract_some(infile, outfile):
f = open(infile, 'r')
out_f = open(outfile, 'w')
out_f.write("Start\tStop\tStrand\tNumber of Genes\tGenes\n")
line = f.readline()
pat = re.compile(r'\-')
while True:
line = f.readline().strip()
if not line:
break
li = line.split("\t")
li[7] = re.sub(pat, ",", li[7])
out_f.write(li[2] + "\t" + li[3] + "\t" + li[1] + "\t" + li[4] + "\t" + li[7] + "\n")
f.close()
out_f.close()
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crossvalidation_pipeline.py | ktian08/6784-drugs | 1 | 26643 | # -*- coding: utf-8 -*-
"""
<NAME>
Computational Biologist
Target Sciences
GSK
<EMAIL>
"""
import sys
import get_generalizable_features
import get_merged_features
import get_useful_features
def main(validation_rep=0, validation_fold=0):
print('VALIDATION_REP: {0!s}, VALIDATION_FOLD:{1!s}'.format(validation_rep, validation_fold), flush=True)
print('GETTING GENERALIZABLE FEATURES...', flush=True)
get_generalizable_features.main(validation_rep, validation_fold)
print('GETTING MERGED FEATURES...', flush=True)
get_merged_features.main(validation_rep, validation_fold)
print('GETTING USEFUL FEATURES...', flush=True)
get_useful_features.main(validation_rep, validation_fold)
if __name__ == '__main__':
main(validation_rep=int(sys.argv[1]), validation_fold=int(sys.argv[2]))
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utils/nth_root.py | kbrose/project_euler | 1 | 1609301 | <filename>utils/nth_root.py
def nth_root(x,n): # credit http://stackoverflow.com/questions/356090/, <NAME>
"""Finds the integer component of the n'th root of x,
an integer such that y ** n <= x < (y + 1) ** n.
"""
high = 1
while high ** n < x:
high *= 2
low = high/2
while low < high:
mid = (low + high) / 2
if low < mid and mid**n < x:
low = mid
elif high > mid and mid**n > x:
high = mid
else:
return mid
return mid + 1
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neo/test/coretest/test_irregularysampledsignal.py | alafuzof/python-neo | 0 | 130181 | # -*- coding: utf-8 -*-
"""
Tests of the neo.core.irregularlysampledsignal.IrregularySampledSignal class
"""
import unittest
import os
import pickle
import warnings
from copy import deepcopy
import numpy as np
import quantities as pq
from numpy.testing import assert_array_equal
from neo.core.dataobject import ArrayDict
try:
from IPython.lib.pretty import pretty
except ImportError as err:
HAVE_IPYTHON = False
else:
HAVE_IPYTHON = True
from neo.core.irregularlysampledsignal import IrregularlySampledSignal
from neo.core import Segment, ChannelIndex
from neo.core.baseneo import MergeError
from neo.test.tools import (assert_arrays_almost_equal, assert_arrays_equal,
assert_neo_object_is_compliant, assert_same_sub_schema,
assert_same_attributes, assert_same_annotations,
assert_same_array_annotations)
from neo.test.generate_datasets import (get_fake_value, get_fake_values, fake_neo,
TEST_ANNOTATIONS)
class Test__generate_datasets(unittest.TestCase):
def setUp(self):
np.random.seed(0)
self.annotations = dict(
[(str(x), TEST_ANNOTATIONS[x]) for x in range(len(TEST_ANNOTATIONS))])
def test__get_fake_values(self):
self.annotations['seed'] = 0
times = get_fake_value('times', pq.Quantity, seed=0, dim=1)
signal = get_fake_value('signal', pq.Quantity, seed=1, dim=2)
name = get_fake_value('name', str, seed=2, obj=IrregularlySampledSignal)
description = get_fake_value('description', str, seed=3, obj='IrregularlySampledSignal')
file_origin = get_fake_value('file_origin', str)
arr_ann = get_fake_value('array_annotations', dict, seed=5,
obj=IrregularlySampledSignal, n=1)
attrs1 = {'name': name, 'description': description, 'file_origin': file_origin}
attrs2 = attrs1.copy()
attrs2.update(self.annotations)
attrs2['array_annotations'] = arr_ann
res11 = get_fake_values(IrregularlySampledSignal, annotate=False, seed=0)
res12 = get_fake_values('IrregularlySampledSignal', annotate=False, seed=0)
res21 = get_fake_values(IrregularlySampledSignal, annotate=True, seed=0)
res22 = get_fake_values('IrregularlySampledSignal', annotate=True, seed=0)
assert_array_equal(res11.pop('times'), times)
assert_array_equal(res12.pop('times'), times)
assert_array_equal(res21.pop('times'), times)
assert_array_equal(res22.pop('times'), times)
assert_array_equal(res11.pop('signal'), signal)
assert_array_equal(res12.pop('signal'), signal)
assert_array_equal(res21.pop('signal'), signal)
assert_array_equal(res22.pop('signal'), signal)
self.assertEqual(res11, attrs1)
self.assertEqual(res12, attrs1)
# Array annotations need to be compared separately
# because numpy arrays define equality differently
arr_ann_res21 = res21.pop('array_annotations')
arr_ann_attrs2 = attrs2.pop('array_annotations')
self.assertEqual(res21, attrs2)
assert_arrays_equal(arr_ann_res21['valid'], arr_ann_attrs2['valid'])
assert_arrays_equal(arr_ann_res21['number'], arr_ann_attrs2['number'])
arr_ann_res22 = res22.pop('array_annotations')
self.assertEqual(res22, attrs2)
assert_arrays_equal(arr_ann_res22['valid'], arr_ann_attrs2['valid'])
assert_arrays_equal(arr_ann_res22['number'], arr_ann_attrs2['number'])
def test__fake_neo__cascade(self):
self.annotations['seed'] = None
obj_type = IrregularlySampledSignal
cascade = True
res = fake_neo(obj_type=obj_type, cascade=cascade)
self.assertTrue(isinstance(res, IrregularlySampledSignal))
assert_neo_object_is_compliant(res)
self.assertEqual(res.annotations, self.annotations)
def test__fake_neo__nocascade(self):
self.annotations['seed'] = None
obj_type = 'IrregularlySampledSignal'
cascade = False
res = fake_neo(obj_type=obj_type, cascade=cascade)
self.assertTrue(isinstance(res, IrregularlySampledSignal))
assert_neo_object_is_compliant(res)
self.assertEqual(res.annotations, self.annotations)
class TestIrregularlySampledSignalConstruction(unittest.TestCase):
def test_IrregularlySampledSignal_creation_times_units_signal_units(self):
params = {'test2': 'y1', 'test3': True}
arr_ann = {'anno1': [23], 'anno2': ['A']}
sig = IrregularlySampledSignal([1.1, 1.5, 1.7] * pq.ms, signal=[20., 40., 60.] * pq.mV,
name='test', description='tester', file_origin='test.file',
test1=1, array_annotations=arr_ann, **params)
sig.annotate(test1=1.1, test0=[1, 2])
assert_neo_object_is_compliant(sig)
assert_array_equal(sig.times, [1.1, 1.5, 1.7] * pq.ms)
assert_array_equal(np.asarray(sig).flatten(), np.array([20., 40., 60.]))
self.assertEqual(sig.units, pq.mV)
self.assertEqual(sig.name, 'test')
self.assertEqual(sig.description, 'tester')
self.assertEqual(sig.file_origin, 'test.file')
self.assertEqual(sig.annotations['test0'], [1, 2])
self.assertEqual(sig.annotations['test1'], 1.1)
self.assertEqual(sig.annotations['test2'], 'y1')
self.assertTrue(sig.annotations['test3'])
assert_arrays_equal(sig.array_annotations['anno1'], np.array([23]))
assert_arrays_equal(sig.array_annotations['anno2'], np.array(['A']))
self.assertIsInstance(sig.array_annotations, ArrayDict)
def test_IrregularlySampledSignal_creation_units_arg(self):
params = {'test2': 'y1', 'test3': True}
arr_ann = {'anno1': [23], 'anno2': ['A']}
sig = IrregularlySampledSignal([1.1, 1.5, 1.7], signal=[20., 40., 60.], units=pq.V,
time_units=pq.s, name='test', description='tester',
file_origin='test.file', test1=1,
array_annotations=arr_ann, **params)
sig.annotate(test1=1.1, test0=[1, 2])
assert_neo_object_is_compliant(sig)
assert_array_equal(sig.times, [1.1, 1.5, 1.7] * pq.s)
assert_array_equal(np.asarray(sig).flatten(), np.array([20., 40., 60.]))
self.assertEqual(sig.units, pq.V)
self.assertEqual(sig.name, 'test')
self.assertEqual(sig.description, 'tester')
self.assertEqual(sig.file_origin, 'test.file')
self.assertEqual(sig.annotations['test0'], [1, 2])
self.assertEqual(sig.annotations['test1'], 1.1)
self.assertEqual(sig.annotations['test2'], 'y1')
self.assertTrue(sig.annotations['test3'])
assert_arrays_equal(sig.array_annotations['anno1'], np.array([23]))
assert_arrays_equal(sig.array_annotations['anno2'], np.array(['A']))
self.assertIsInstance(sig.array_annotations, ArrayDict)
def test_IrregularlySampledSignal_creation_units_rescale(self):
params = {'test2': 'y1', 'test3': True}
arr_ann = {'anno1': [23], 'anno2': ['A']}
sig = IrregularlySampledSignal([1.1, 1.5, 1.7] * pq.s, signal=[2., 4., 6.] * pq.V,
units=pq.mV, time_units=pq.ms, name='test',
description='tester', file_origin='test.file', test1=1,
array_annotations=arr_ann, **params)
sig.annotate(test1=1.1, test0=[1, 2])
assert_neo_object_is_compliant(sig)
assert_array_equal(sig.times, [1100, 1500, 1700] * pq.ms)
assert_array_equal(np.asarray(sig).flatten(), np.array([2000., 4000., 6000.]))
self.assertEqual(sig.units, pq.mV)
self.assertEqual(sig.name, 'test')
self.assertEqual(sig.description, 'tester')
self.assertEqual(sig.file_origin, 'test.file')
self.assertEqual(sig.annotations['test0'], [1, 2])
self.assertEqual(sig.annotations['test1'], 1.1)
self.assertEqual(sig.annotations['test2'], 'y1')
self.assertTrue(sig.annotations['test3'])
assert_arrays_equal(sig.array_annotations['anno1'], np.array([23]))
assert_arrays_equal(sig.array_annotations['anno2'], np.array(['A']))
self.assertIsInstance(sig.array_annotations, ArrayDict)
def test_IrregularlySampledSignal_different_lens_ValueError(self):
times = [1.1, 1.5, 1.7] * pq.ms
signal = [20., 40., 60., 70.] * pq.mV
self.assertRaises(ValueError, IrregularlySampledSignal, times, signal)
def test_IrregularlySampledSignal_no_signal_units_ValueError(self):
times = [1.1, 1.5, 1.7] * pq.ms
signal = [20., 40., 60.]
self.assertRaises(ValueError, IrregularlySampledSignal, times, signal)
def test_IrregularlySampledSignal_no_time_units_ValueError(self):
times = [1.1, 1.5, 1.7]
signal = [20., 40., 60.] * pq.mV
self.assertRaises(ValueError, IrregularlySampledSignal, times, signal)
class TestIrregularlySampledSignalProperties(unittest.TestCase):
def setUp(self):
self.times = [np.arange(10.0) * pq.s, np.arange(-100.0, 100.0, 10.0) * pq.ms,
np.arange(100) * pq.ns]
self.data = [np.arange(10.0) * pq.nA, np.arange(-100.0, 100.0, 10.0) * pq.mV,
np.random.uniform(size=100) * pq.uV]
self.signals = [IrregularlySampledSignal(t, signal=D, testattr='test') for D, t in
zip(self.data, self.times)]
def test__compliant(self):
for signal in self.signals:
assert_neo_object_is_compliant(signal)
def test__t_start_getter(self):
for signal, times in zip(self.signals, self.times):
self.assertAlmostEqual(signal.t_start, times[0], delta=1e-15)
def test__t_stop_getter(self):
for signal, times in zip(self.signals, self.times):
self.assertAlmostEqual(signal.t_stop, times[-1], delta=1e-15)
def test__duration_getter(self):
for signal, times in zip(self.signals, self.times):
self.assertAlmostEqual(signal.duration, times[-1] - times[0], delta=1e-15)
def test__sampling_intervals_getter(self):
for signal, times in zip(self.signals, self.times):
assert_arrays_almost_equal(signal.sampling_intervals, np.diff(times), threshold=1e-15)
def test_IrregularlySampledSignal_repr(self):
sig = IrregularlySampledSignal([1.1, 1.5, 1.7] * pq.s, signal=[2., 4., 6.] * pq.V,
name='test', description='tester', file_origin='test.file',
test1=1)
assert_neo_object_is_compliant(sig)
if np.__version__.split(".")[:2] > ['1', '13']:
# see https://github.com/numpy/numpy/blob/master/doc/release/1.14.0-notes.rst#many
# -changes-to-array-printing-disableable-with-the-new-legacy-printing-mode
targ = (
'<IrregularlySampledSignal(array([[2.],\n [4.],\n [6.]]) * V '
'' + 'at times [1.1 1.5 1.7] s)>')
else:
targ = (
'<IrregularlySampledSignal(array([[ 2.],\n [ 4.],\n [ 6.]]) '
'* V ' + 'at times [ 1.1 1.5 1.7] s)>')
res = repr(sig)
self.assertEqual(targ, res)
class TestIrregularlySampledSignalArrayMethods(unittest.TestCase):
def setUp(self):
self.data1 = np.arange(10.0)
self.data1quant = self.data1 * pq.mV
self.time1 = np.logspace(1, 5, 10)
self.time1quant = self.time1 * pq.ms
self.arr_ann = {'anno1': [23], 'anno2': ['A']}
self.signal1 = IrregularlySampledSignal(self.time1quant, signal=self.data1quant,
name='spam', description='eggs',
file_origin='testfile.txt', arg1='test',
array_annotations=self.arr_ann)
self.signal1.segment = Segment()
self.signal1.channel_index = ChannelIndex([0])
def test__compliant(self):
assert_neo_object_is_compliant(self.signal1)
self.assertEqual(self.signal1.name, 'spam')
self.assertEqual(self.signal1.description, 'eggs')
self.assertEqual(self.signal1.file_origin, 'testfile.txt')
self.assertEqual(self.signal1.annotations, {'arg1': 'test'})
assert_arrays_equal(self.signal1.array_annotations['anno1'], np.array([23]))
assert_arrays_equal(self.signal1.array_annotations['anno2'], np.array(['A']))
self.assertIsInstance(self.signal1.array_annotations, ArrayDict)
def test__slice_should_return_IrregularlySampledSignal(self):
result = self.signal1[3:8]
self.assertIsInstance(result, IrregularlySampledSignal)
assert_neo_object_is_compliant(result)
self.assertEqual(result.name, 'spam')
self.assertEqual(result.description, 'eggs')
self.assertEqual(result.file_origin, 'testfile.txt')
self.assertEqual(result.annotations, {'arg1': 'test'})
self.assertEqual(result.size, 5)
self.assertEqual(result.t_start, self.time1quant[3])
self.assertEqual(result.t_stop, self.time1quant[7])
assert_array_equal(self.time1quant[3:8], result.times)
assert_array_equal(self.data1[3:8].reshape(-1, 1), result.magnitude)
# Test other attributes were copied over (in this case, defaults)
self.assertEqual(result.file_origin, self.signal1.file_origin)
self.assertEqual(result.name, self.signal1.name)
self.assertEqual(result.description, self.signal1.description)
self.assertEqual(result.annotations, self.signal1.annotations)
assert_arrays_equal(result.array_annotations['anno1'], np.array([23]))
assert_arrays_equal(result.array_annotations['anno2'], np.array(['A']))
self.assertIsInstance(result.array_annotations, ArrayDict)
def test__getitem_should_return_single_quantity(self):
self.assertEqual(self.signal1[0], 0 * pq.mV)
self.assertEqual(self.signal1[9], 9 * pq.mV)
self.assertRaises(IndexError, self.signal1.__getitem__, 10)
def test__getitem_out_of_bounds_IndexError(self):
self.assertRaises(IndexError, self.signal1.__getitem__, 10)
def test_comparison_operators(self):
assert_array_equal(self.signal1 >= 5 * pq.mV, np.array(
[[False, False, False, False, False, True, True, True, True, True]]).T)
assert_array_equal(self.signal1 == 5 * pq.mV, np.array(
[[False, False, False, False, False, True, False, False, False, False]]).T)
assert_array_equal(self.signal1 == self.signal1, np.array(
[[True, True, True, True, True, True, True, True, True, True]]).T)
def test__comparison_as_indexing_single_trace(self):
self.assertEqual(self.signal1[self.signal1 == 5], [5 * pq.mV])
def test__comparison_as_indexing_multi_trace(self):
signal = IrregularlySampledSignal(self.time1quant, np.arange(20).reshape((-1, 2)) * pq.V)
assert_array_equal(signal[signal < 10],
np.array([[0, 2, 4, 6, 8], [1, 3, 5, 7, 9]]).T * pq.V)
def test__indexing_keeps_order_across_channels(self):
# AnalogSignals with 10 traces each having 5 samples (eg. data[0] = [0,10,20,30,40])
data = np.array([range(10), range(10, 20), range(20, 30), range(30, 40), range(40, 50)])
mask = np.full((5, 10), fill_value=False, dtype=bool)
# selecting one entry per trace
mask[[0, 1, 0, 3, 0, 2, 4, 3, 1, 4], range(10)] = True
signal = IrregularlySampledSignal(np.arange(5) * pq.s, np.array(data) * pq.V)
assert_array_equal(signal[mask], np.array([[0, 11, 2, 33, 4, 25, 46, 37, 18, 49]]) * pq.V)
def test__indexing_keeps_order_across_time(self):
# AnalogSignals with 10 traces each having 5 samples (eg. data[0] = [0,10,20,30,40])
data = np.array([range(10), range(10, 20), range(20, 30), range(30, 40), range(40, 50)])
mask = np.full((5, 10), fill_value=False, dtype=bool)
# selecting two entries per trace
temporal_ids = [0, 1, 0, 3, 1, 2, 4, 2, 1, 4] + [4, 3, 2, 1, 0, 1, 2, 3, 2, 1]
mask[temporal_ids, list(range(10)) + list(range(10))] = True
signal = IrregularlySampledSignal(np.arange(5) * pq.s, np.array(data) * pq.V)
assert_array_equal(signal[mask], np.array([[0, 11, 2, 13, 4, 15, 26, 27, 18, 19],
[40, 31, 22, 33, 14, 25, 46, 37, 28,
49]]) * pq.V)
def test__comparison_with_inconsistent_units_should_raise_Exception(self):
self.assertRaises(ValueError, self.signal1.__gt__, 5 * pq.nA)
def test_simple_statistics(self):
targmean = self.signal1[:-1] * np.diff(self.time1quant).reshape(-1, 1)
targmean = targmean.sum() / (self.time1quant[-1] - self.time1quant[0])
self.assertEqual(self.signal1.max(), 9 * pq.mV)
self.assertEqual(self.signal1.min(), 0 * pq.mV)
self.assertEqual(self.signal1.mean(), targmean)
def test_mean_interpolation_NotImplementedError(self):
self.assertRaises(NotImplementedError, self.signal1.mean, True)
def test_resample_NotImplementedError(self):
self.assertRaises(NotImplementedError, self.signal1.resample, True)
def test__rescale_same(self):
result = self.signal1.copy()
result = result.rescale(pq.mV)
self.assertIsInstance(result, IrregularlySampledSignal)
assert_neo_object_is_compliant(result)
self.assertEqual(result.name, 'spam')
self.assertEqual(result.description, 'eggs')
self.assertEqual(result.file_origin, 'testfile.txt')
self.assertEqual(result.annotations, {'arg1': 'test'})
assert_arrays_equal(result.array_annotations['anno1'], np.array([23]))
assert_arrays_equal(result.array_annotations['anno2'], np.array(['A']))
self.assertIsInstance(result.array_annotations, ArrayDict)
self.assertEqual(result.units, 1 * pq.mV)
assert_array_equal(result.magnitude, self.data1.reshape(-1, 1))
assert_array_equal(result.times, self.time1quant)
assert_same_sub_schema(result, self.signal1)
self.assertIsInstance(result.channel_index, ChannelIndex)
self.assertIsInstance(result.segment, Segment)
self.assertIs(result.channel_index, self.signal1.channel_index)
self.assertIs(result.segment, self.signal1.segment)
def test__rescale_new(self):
result = self.signal1.copy()
result = result.rescale(pq.uV)
self.assertIsInstance(result, IrregularlySampledSignal)
assert_neo_object_is_compliant(result)
self.assertEqual(result.name, 'spam')
self.assertEqual(result.description, 'eggs')
self.assertEqual(result.file_origin, 'testfile.txt')
self.assertEqual(result.annotations, {'arg1': 'test'})
assert_arrays_equal(result.array_annotations['anno1'], np.array([23]))
assert_arrays_equal(result.array_annotations['anno2'], np.array(['A']))
self.assertIsInstance(result.array_annotations, ArrayDict)
self.assertEqual(result.units, 1 * pq.uV)
assert_arrays_almost_equal(np.array(result), self.data1.reshape(-1, 1) * 1000., 1e-10)
assert_array_equal(result.times, self.time1quant)
self.assertIsInstance(result.channel_index, ChannelIndex)
self.assertIsInstance(result.segment, Segment)
self.assertIs(result.channel_index, self.signal1.channel_index)
self.assertIs(result.segment, self.signal1.segment)
def test__rescale_new_incompatible_ValueError(self):
self.assertRaises(ValueError, self.signal1.rescale, pq.nA)
def test_time_slice(self):
targdataquant = [[1.0], [2.0], [3.0]] * pq.mV
targtime = np.logspace(1, 5, 10)
targtimequant = targtime[1:4] * pq.ms
targ_signal = IrregularlySampledSignal(targtimequant, signal=targdataquant, name='spam',
description='eggs', file_origin='testfile.txt',
arg1='test')
t_start = 15
t_stop = 250
result = self.signal1.time_slice(t_start, t_stop)
assert_array_equal(result, targ_signal)
assert_array_equal(result.times, targtimequant)
self.assertEqual(result.units, 1 * pq.mV)
self.assertIsInstance(result, IrregularlySampledSignal)
assert_neo_object_is_compliant(result)
self.assertEqual(result.name, 'spam')
self.assertEqual(result.description, 'eggs')
self.assertEqual(result.file_origin, 'testfile.txt')
self.assertEqual(result.annotations, {'arg1': 'test'})
assert_arrays_equal(result.array_annotations['anno1'], np.array([23]))
assert_arrays_equal(result.array_annotations['anno2'], np.array(['A']))
self.assertIsInstance(result.array_annotations, ArrayDict)
def test__time_slice_deepcopy_annotations(self):
params1 = {'test0': 'y1', 'test1': ['deeptest'], 'test2': True}
self.signal1.annotate(**params1)
result = self.signal1.time_slice(None, None)
# Change annotations of original
params2 = {'test0': 'y2', 'test2': False}
self.signal1.annotate(**params2)
self.signal1.annotations['test1'][0] = 'shallowtest'
self.assertNotEqual(self.signal1.annotations['test0'], result.annotations['test0'])
self.assertNotEqual(self.signal1.annotations['test1'], result.annotations['test1'])
self.assertNotEqual(self.signal1.annotations['test2'], result.annotations['test2'])
# Change annotations of result
params3 = {'test0': 'y3'}
result.annotate(**params3)
result.annotations['test1'][0] = 'shallowtest2'
self.assertNotEqual(self.signal1.annotations['test0'], result.annotations['test0'])
self.assertNotEqual(self.signal1.annotations['test1'], result.annotations['test1'])
self.assertNotEqual(self.signal1.annotations['test2'], result.annotations['test2'])
def test__time_slice_deepcopy_array_annotations(self):
length = self.signal1.shape[-1]
params1 = {'test0': ['y{}'.format(i) for i in range(length)],
'test1': ['deeptest' for i in range(length)],
'test2': [(-1)**i > 0 for i in range(length)]}
self.signal1.array_annotate(**params1)
result = self.signal1.time_slice(None, None)
# Change annotations of original
params2 = {'test0': ['x{}'.format(i) for i in range(length)],
'test2': [(-1) ** (i + 1) > 0 for i in range(length)]}
self.signal1.array_annotate(**params2)
self.signal1.array_annotations['test1'][0] = 'shallowtest'
self.assertFalse(all(self.signal1.array_annotations['test0']
== result.array_annotations['test0']))
self.assertFalse(all(self.signal1.array_annotations['test1']
== result.array_annotations['test1']))
self.assertFalse(all(self.signal1.array_annotations['test2']
== result.array_annotations['test2']))
# Change annotations of result
params3 = {'test0': ['z{}'.format(i) for i in range(1, result.shape[-1]+1)]}
result.array_annotate(**params3)
result.array_annotations['test1'][0] = 'shallow2'
self.assertFalse(all(self.signal1.array_annotations['test0']
== result.array_annotations['test0']))
self.assertFalse(all(self.signal1.array_annotations['test1']
== result.array_annotations['test1']))
self.assertFalse(all(self.signal1.array_annotations['test2']
== result.array_annotations['test2']))
def test__time_slice_deepcopy_data(self):
result = self.signal1.time_slice(None, None)
# Change values of original array
self.signal1[2] = 7.3*self.signal1.units
self.assertFalse(all(self.signal1 == result))
# Change values of sliced array
result[3] = 9.5*result.units
self.assertFalse(all(self.signal1 == result))
def test_time_slice_out_of_boundries(self):
targdataquant = self.data1quant
targtimequant = self.time1quant
targ_signal = IrregularlySampledSignal(targtimequant, signal=targdataquant, name='spam',
description='eggs', file_origin='testfile.txt',
arg1='test')
t_start = 0
t_stop = 2500000
result = self.signal1.time_slice(t_start, t_stop)
assert_array_equal(result, targ_signal)
assert_array_equal(result.times, targtimequant)
self.assertEqual(result.units, 1 * pq.mV)
self.assertIsInstance(result, IrregularlySampledSignal)
assert_neo_object_is_compliant(result)
self.assertEqual(result.name, 'spam')
self.assertEqual(result.description, 'eggs')
self.assertEqual(result.file_origin, 'testfile.txt')
self.assertEqual(result.annotations, {'arg1': 'test'})
assert_arrays_equal(result.array_annotations['anno1'], np.array([23]))
assert_arrays_equal(result.array_annotations['anno2'], np.array(['A']))
self.assertIsInstance(result.array_annotations, ArrayDict)
def test_time_slice_empty(self):
targdataquant = [] * pq.mV
targtimequant = [] * pq.ms
targ_signal = IrregularlySampledSignal(targtimequant, signal=targdataquant, name='spam',
description='eggs', file_origin='testfile.txt',
arg1='test')
t_start = 15
t_stop = 250
result = targ_signal.time_slice(t_start, t_stop)
assert_array_equal(result, targ_signal)
assert_array_equal(result.times, targtimequant)
self.assertEqual(result.units, 1 * pq.mV)
self.assertIsInstance(result, IrregularlySampledSignal)
assert_neo_object_is_compliant(result)
self.assertEqual(result.name, 'spam')
self.assertEqual(result.description, 'eggs')
self.assertEqual(result.file_origin, 'testfile.txt')
self.assertEqual(result.annotations, {'arg1': 'test'})
self.assertEqual(result.array_annotations, {})
self.assertIsInstance(result.array_annotations, ArrayDict)
def test_time_slice_none_stop(self):
targdataquant = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0], [7.0], [8.0], [9.0]] * pq.mV
targtime = np.logspace(1, 5, 10)
targtimequant = targtime[1:10] * pq.ms
targ_signal = IrregularlySampledSignal(targtimequant, signal=targdataquant, name='spam',
description='eggs', file_origin='testfile.txt',
arg1='test')
t_start = 15
t_stop = None
result = self.signal1.time_slice(t_start, t_stop)
assert_array_equal(result, targ_signal)
assert_array_equal(result.times, targtimequant)
self.assertEqual(result.units, 1 * pq.mV)
self.assertIsInstance(result, IrregularlySampledSignal)
assert_neo_object_is_compliant(result)
self.assertEqual(result.name, 'spam')
self.assertEqual(result.description, 'eggs')
self.assertEqual(result.file_origin, 'testfile.txt')
self.assertEqual(result.annotations, {'arg1': 'test'})
assert_arrays_equal(result.array_annotations['anno1'], np.array([23]))
assert_arrays_equal(result.array_annotations['anno2'], np.array(['A']))
self.assertIsInstance(result.array_annotations, ArrayDict)
def test_time_slice_none_start(self):
targdataquant = [[0.0], [1.0], [2.0], [3.0]] * pq.mV
targtime = np.logspace(1, 5, 10)
targtimequant = targtime[0:4] * pq.ms
targ_signal = IrregularlySampledSignal(targtimequant, signal=targdataquant, name='spam',
description='eggs', file_origin='testfile.txt',
arg1='test')
t_start = None
t_stop = 250
result = self.signal1.time_slice(t_start, t_stop)
assert_array_equal(result, targ_signal)
assert_array_equal(result.times, targtimequant)
self.assertEqual(result.units, 1 * pq.mV)
self.assertIsInstance(result, IrregularlySampledSignal)
assert_neo_object_is_compliant(result)
self.assertEqual(result.name, 'spam')
self.assertEqual(result.description, 'eggs')
self.assertEqual(result.file_origin, 'testfile.txt')
self.assertEqual(result.annotations, {'arg1': 'test'})
assert_arrays_equal(result.array_annotations['anno1'], np.array([23]))
assert_arrays_equal(result.array_annotations['anno2'], np.array(['A']))
self.assertIsInstance(result.array_annotations, ArrayDict)
def test_time_slice_none_both(self):
targdataquant = [[0.0], [1.0], [2.0], [3.0], [4.0], [5.0], [6.0], [7.0], [8.0],
[9.0]] * pq.mV
targtime = np.logspace(1, 5, 10)
targtimequant = targtime[0:10] * pq.ms
targ_signal = IrregularlySampledSignal(targtimequant, signal=targdataquant, name='spam',
description='eggs', file_origin='testfile.txt',
arg1='test')
t_start = None
t_stop = None
result = self.signal1.time_slice(t_start, t_stop)
assert_array_equal(result, targ_signal)
assert_array_equal(result.times, targtimequant)
self.assertEqual(result.units, 1 * pq.mV)
self.assertIsInstance(result, IrregularlySampledSignal)
assert_neo_object_is_compliant(result)
self.assertEqual(result.name, 'spam')
self.assertEqual(result.description, 'eggs')
self.assertEqual(result.file_origin, 'testfile.txt')
self.assertEqual(result.annotations, {'arg1': 'test'})
assert_arrays_equal(result.array_annotations['anno1'], np.array([23]))
assert_arrays_equal(result.array_annotations['anno2'], np.array(['A']))
self.assertIsInstance(result.array_annotations, ArrayDict)
def test_time_slice_differnt_units(self):
targdataquant = [[1.0], [2.0], [3.0]] * pq.mV
targtime = np.logspace(1, 5, 10)
targtimequant = targtime[1:4] * pq.ms
targ_signal = IrregularlySampledSignal(targtimequant, signal=targdataquant, name='spam',
description='eggs', file_origin='testfile.txt',
arg1='test')
t_start = 15
t_stop = 250
t_start = 0.015 * pq.s
t_stop = .250 * pq.s
result = self.signal1.time_slice(t_start, t_stop)
assert_array_equal(result, targ_signal)
assert_array_equal(result.times, targtimequant)
self.assertEqual(result.units, 1 * pq.mV)
self.assertIsInstance(result, IrregularlySampledSignal)
assert_neo_object_is_compliant(result)
self.assertEqual(result.name, 'spam')
self.assertEqual(result.description, 'eggs')
self.assertEqual(result.file_origin, 'testfile.txt')
self.assertEqual(result.annotations, {'arg1': 'test'})
assert_arrays_equal(result.array_annotations['anno1'], np.array([23]))
assert_arrays_equal(result.array_annotations['anno2'], np.array(['A']))
self.assertIsInstance(result.array_annotations, ArrayDict)
def test__time_slice_should_set_parents_to_None(self):
# When timeslicing, a deep copy is made,
# thus the reference to parent objects should be destroyed
result = self.signal1.time_slice(1 * pq.ms, 3 * pq.ms)
self.assertEqual(result.segment, None)
self.assertEqual(result.channel_index, None)
def test__deepcopy_should_set_parents_objects_to_None(self):
# Deepcopy should destroy references to parents
result = deepcopy(self.signal1)
self.assertEqual(result.segment, None)
self.assertEqual(result.channel_index, None)
def test__time_shift_same_attributes(self):
result = self.signal1.time_shift(1 * pq.ms)
assert_same_attributes(result, self.signal1, exclude=['times', 't_start', 't_stop'])
def test__time_shift_same_annotations(self):
result = self.signal1.time_shift(1 * pq.ms)
assert_same_annotations(result, self.signal1)
def test__time_shift_same_array_annotations(self):
result = self.signal1.time_shift(1 * pq.ms)
assert_same_array_annotations(result, self.signal1)
def test__time_shift_should_set_parents_to_None(self):
# When time-shifting, a deep copy is made,
# thus the reference to parent objects should be destroyed
result = self.signal1.time_shift(1 * pq.ms)
self.assertEqual(result.segment, None)
self.assertEqual(result.channel_index, None)
def test__time_shift_by_zero(self):
shifted = self.signal1.time_shift(0 * pq.ms)
assert_arrays_equal(shifted.times, self.signal1.times)
def test__time_shift_same_units(self):
shifted = self.signal1.time_shift(10 * pq.ms)
assert_arrays_equal(shifted.times, self.signal1.times + 10 * pq.ms)
def test__time_shift_different_units(self):
shifted = self.signal1.time_shift(1 * pq.s)
assert_arrays_equal(shifted.times, self.signal1.times + 1000 * pq.ms)
def test_as_array(self):
sig_as_arr = self.signal1.as_array()
self.assertIsInstance(sig_as_arr, np.ndarray)
assert_array_equal(self.data1, sig_as_arr.flat)
def test_as_quantity(self):
sig_as_q = self.signal1.as_quantity()
self.assertIsInstance(sig_as_q, pq.Quantity)
assert_array_equal(self.data1, sig_as_q.magnitude.flat)
def test__copy_should_preserve_parent_objects(self):
result = self.signal1.copy()
self.assertIs(result.segment, self.signal1.segment)
self.assertIs(result.channel_index, self.signal1.channel_index)
class TestIrregularlySampledSignalCombination(unittest.TestCase):
def setUp(self):
self.data1 = np.arange(10.0)
self.data1quant = self.data1 * pq.mV
self.time1 = np.logspace(1, 5, 10)
self.time1quant = self.time1 * pq.ms
self.arr_ann = {'anno1': [23], 'anno2': ['A']}
self.signal1 = IrregularlySampledSignal(self.time1quant, signal=self.data1quant,
name='spam', description='eggs',
file_origin='testfile.txt', arg1='test',
array_annotations=self.arr_ann)
def test__compliant(self):
assert_neo_object_is_compliant(self.signal1)
self.assertEqual(self.signal1.name, 'spam')
self.assertEqual(self.signal1.description, 'eggs')
self.assertEqual(self.signal1.file_origin, 'testfile.txt')
self.assertEqual(self.signal1.annotations, {'arg1': 'test'})
assert_arrays_equal(self.signal1.array_annotations['anno1'], np.array([23]))
assert_arrays_equal(self.signal1.array_annotations['anno2'], np.array(['A']))
self.assertIsInstance(self.signal1.array_annotations, ArrayDict)
def test__add_const_quantity_should_preserve_data_complement(self):
result = self.signal1 + 0.065 * pq.V
self.assertIsInstance(result, IrregularlySampledSignal)
assert_neo_object_is_compliant(result)
self.assertEqual(result.name, 'spam')
self.assertEqual(result.description, 'eggs')
self.assertEqual(result.file_origin, 'testfile.txt')
self.assertEqual(result.annotations, {'arg1': 'test'})
assert_arrays_equal(result.array_annotations['anno1'], np.array([23]))
assert_arrays_equal(result.array_annotations['anno2'], np.array(['A']))
self.assertIsInstance(result.array_annotations, ArrayDict)
assert_array_equal(result.magnitude, self.data1.reshape(-1, 1) + 65)
assert_array_equal(result.times, self.time1quant)
self.assertEqual(self.signal1[9], 9 * pq.mV)
self.assertEqual(result[9], 74 * pq.mV)
def test__add_two_consistent_signals_should_preserve_data_complement(self):
data2 = np.arange(10.0, 20.0)
data2quant = data2 * pq.mV
signal2 = IrregularlySampledSignal(self.time1quant, signal=data2quant)
assert_neo_object_is_compliant(signal2)
result = self.signal1 + signal2
self.assertIsInstance(result, IrregularlySampledSignal)
assert_neo_object_is_compliant(result)
self.assertEqual(result.name, 'spam')
self.assertEqual(result.description, 'eggs')
self.assertEqual(result.file_origin, 'testfile.txt')
self.assertEqual(result.annotations, {'arg1': 'test'})
assert_arrays_equal(result.array_annotations['anno1'], np.array([23]))
assert_arrays_equal(result.array_annotations['anno2'], np.array(['A']))
self.assertIsInstance(result.array_annotations, ArrayDict)
targ = IrregularlySampledSignal(self.time1quant, signal=np.arange(10.0, 30.0, 2.0),
units="mV", name='spam', description='eggs',
file_origin='testfile.txt', arg1='test')
assert_neo_object_is_compliant(targ)
assert_array_equal(result, targ)
assert_array_equal(self.time1quant, targ.times)
assert_array_equal(result.times, targ.times)
assert_same_sub_schema(result, targ)
def test__add_signals_with_inconsistent_times_AssertionError(self):
signal2 = IrregularlySampledSignal(self.time1quant * 2., signal=np.arange(10.0),
units="mV")
assert_neo_object_is_compliant(signal2)
self.assertRaises(ValueError, self.signal1.__add__, signal2)
def test__add_signals_with_inconsistent_dimension_ValueError(self):
signal2 = np.arange(20).reshape(2, 10)
self.assertRaises(ValueError, self.signal1.__add__, signal2)
def test__subtract_const_should_preserve_data_complement(self):
result = self.signal1 - 65 * pq.mV
self.assertIsInstance(result, IrregularlySampledSignal)
assert_neo_object_is_compliant(result)
self.assertEqual(result.name, 'spam')
self.assertEqual(result.description, 'eggs')
self.assertEqual(result.file_origin, 'testfile.txt')
self.assertEqual(result.annotations, {'arg1': 'test'})
assert_arrays_equal(result.array_annotations['anno1'], np.array([23]))
assert_arrays_equal(result.array_annotations['anno2'], np.array(['A']))
self.assertIsInstance(result.array_annotations, ArrayDict)
self.assertEqual(self.signal1[9], 9 * pq.mV)
self.assertEqual(result[9], -56 * pq.mV)
assert_array_equal(result.magnitude, (self.data1 - 65).reshape(-1, 1))
assert_array_equal(result.times, self.time1quant)
def test__subtract_from_const_should_return_signal(self):
result = 10 * pq.mV - self.signal1
self.assertIsInstance(result, IrregularlySampledSignal)
assert_neo_object_is_compliant(result)
self.assertEqual(result.name, 'spam')
self.assertEqual(result.description, 'eggs')
self.assertEqual(result.file_origin, 'testfile.txt')
self.assertEqual(result.annotations, {'arg1': 'test'})
assert_arrays_equal(result.array_annotations['anno1'], np.array([23]))
assert_arrays_equal(result.array_annotations['anno2'], np.array(['A']))
self.assertIsInstance(result.array_annotations, ArrayDict)
self.assertEqual(self.signal1[9], 9 * pq.mV)
self.assertEqual(result[9], 1 * pq.mV)
assert_array_equal(result.magnitude, (10 - self.data1).reshape(-1, 1))
assert_array_equal(result.times, self.time1quant)
def test__mult_signal_by_const_float_should_preserve_data_complement(self):
result = self.signal1 * 2.
self.assertIsInstance(result, IrregularlySampledSignal)
assert_neo_object_is_compliant(result)
self.assertEqual(result.name, 'spam')
self.assertEqual(result.description, 'eggs')
self.assertEqual(result.file_origin, 'testfile.txt')
self.assertEqual(result.annotations, {'arg1': 'test'})
assert_arrays_equal(result.array_annotations['anno1'], np.array([23]))
assert_arrays_equal(result.array_annotations['anno2'], np.array(['A']))
self.assertIsInstance(result.array_annotations, ArrayDict)
self.assertEqual(self.signal1[9], 9 * pq.mV)
self.assertEqual(result[9], 18 * pq.mV)
assert_array_equal(result.magnitude, self.data1.reshape(-1, 1) * 2)
assert_array_equal(result.times, self.time1quant)
def test__mult_signal_by_const_array_should_preserve_data_complement(self):
result = self.signal1 * np.array(2.)
self.assertIsInstance(result, IrregularlySampledSignal)
assert_neo_object_is_compliant(result)
self.assertEqual(result.name, 'spam')
self.assertEqual(result.description, 'eggs')
self.assertEqual(result.file_origin, 'testfile.txt')
self.assertEqual(result.annotations, {'arg1': 'test'})
assert_arrays_equal(result.array_annotations['anno1'], np.array([23]))
assert_arrays_equal(result.array_annotations['anno2'], np.array(['A']))
self.assertIsInstance(result.array_annotations, ArrayDict)
self.assertEqual(self.signal1[9], 9 * pq.mV)
self.assertEqual(result[9], 18 * pq.mV)
assert_array_equal(result.magnitude, self.data1.reshape(-1, 1) * 2)
assert_array_equal(result.times, self.time1quant)
def test__divide_signal_by_const_should_preserve_data_complement(self):
result = self.signal1 / 0.5
self.assertIsInstance(result, IrregularlySampledSignal)
assert_neo_object_is_compliant(result)
self.assertEqual(result.name, 'spam')
self.assertEqual(result.description, 'eggs')
self.assertEqual(result.file_origin, 'testfile.txt')
self.assertEqual(result.annotations, {'arg1': 'test'})
assert_arrays_equal(result.array_annotations['anno1'], np.array([23]))
assert_arrays_equal(result.array_annotations['anno2'], np.array(['A']))
self.assertIsInstance(result.array_annotations, ArrayDict)
self.assertEqual(self.signal1[9], 9 * pq.mV)
self.assertEqual(result[9], 18 * pq.mV)
assert_array_equal(result.magnitude, self.data1.reshape(-1, 1) / 0.5)
assert_array_equal(result.times, self.time1quant)
@unittest.skipUnless(HAVE_IPYTHON, "requires IPython")
def test__pretty(self):
res = pretty(self.signal1)
signal = self.signal1
targ = (("IrregularlySampledSignal with %d channels of length %d; units %s; datatype %s \n"
"" % (signal.shape[1], signal.shape[0], signal.units.dimensionality.unicode,
signal.dtype))
+ ("name: '%s'\ndescription: '%s'\n" % (signal.name, signal.description))
+ ("annotations: %s\n" % str(signal.annotations))
+ ("sample times: %s" % (signal.times[:10],)))
self.assertEqual(res, targ)
def test__merge(self):
data1 = np.arange(1000.0, 1066.0).reshape((11, 6)) * pq.uV
data2 = np.arange(2.0, 2.033, 0.001).reshape((11, 3)) * pq.mV
times1 = np.arange(11.0) * pq.ms
times2 = np.arange(1.0, 12.0) * pq.ms
arr_ann1 = {'anno1': np.arange(6), 'anno2': ['a', 'b', 'c', 'd', 'e', 'f']}
arr_ann2 = {'anno1': np.arange(100, 103), 'anno3': []}
signal1 = IrregularlySampledSignal(times1, data1, name='signal1',
description='test signal', file_origin='testfile.txt',
array_annotations=arr_ann1)
signal2 = IrregularlySampledSignal(times1, data2, name='signal2',
description='test signal', file_origin='testfile.txt',
array_annotations=arr_ann2)
signal3 = IrregularlySampledSignal(times2, data2, name='signal3',
description='test signal', file_origin='testfile.txt')
with warnings.catch_warnings(record=True) as w:
merged12 = signal1.merge(signal2)
self.assertTrue(len(w) == 1)
self.assertEqual(w[0].category, UserWarning)
self.assertSequenceEqual(str(w[0].message), "The following array annotations were "
"omitted, because they were only present"
" in one of the merged objects: "
"['anno2'] from the one that was merged "
"into and ['anno3'] from the one that "
"was merged into the other")
target_data12 = np.hstack([data1, data2.rescale(pq.uV)])
assert_neo_object_is_compliant(signal1)
assert_neo_object_is_compliant(signal2)
assert_neo_object_is_compliant(merged12)
self.assertAlmostEqual(merged12[5, 0], 1030.0 * pq.uV, 9)
self.assertAlmostEqual(merged12[5, 6], 2015.0 * pq.uV, 9)
self.assertEqual(merged12.name, 'merge(signal1, signal2)')
self.assertEqual(merged12.file_origin, 'testfile.txt')
assert_arrays_equal(merged12.array_annotations['anno1'],
np.array([0, 1, 2, 3, 4, 5, 100, 101, 102]))
self.assertIsInstance(merged12.array_annotations, ArrayDict)
assert_arrays_equal(merged12.magnitude, target_data12)
self.assertRaises(MergeError, signal1.merge, signal3)
class TestAnalogSignalFunctions(unittest.TestCase):
def test__pickle(self):
signal1 = IrregularlySampledSignal(np.arange(10.0) / 100 * pq.s, np.arange(10.0),
units="mV")
fobj = open('./pickle', 'wb')
pickle.dump(signal1, fobj)
fobj.close()
fobj = open('./pickle', 'rb')
try:
signal2 = pickle.load(fobj)
except ValueError:
signal2 = None
assert_array_equal(signal1, signal2)
fobj.close()
os.remove('./pickle')
class TestIrregularlySampledSignalEquality(unittest.TestCase):
def test__signals_with_different_times_should_be_not_equal(self):
signal1 = IrregularlySampledSignal(np.arange(10.0) / 100 * pq.s, np.arange(10.0),
units="mV")
signal2 = IrregularlySampledSignal(np.arange(10.0) / 100 * pq.ms, np.arange(10.0),
units="mV")
self.assertNotEqual(signal1, signal2)
if __name__ == "__main__":
unittest.main()
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src/stactools/sentinel2/utils.py | constantinius/sentinel2 | 1 | 129896 | <gh_stars>1-10
def extract_gsd(image_path: str) -> float:
return float(image_path[-7:-5])
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services/scraper.py | AlperRehaYAZGAN/ytu-web-data-mining-termpaper | 0 | 109275 | from urllib.request import urlopen
from bs4 import BeautifulSoup
import re
class Scraper():
Newlines = re.compile(r'[\r\n]\s+')
def __init__(self):
pass
def scrape_url_text_content(self,url):
return self.__get_page_text_content(url)
def __get_page_text_content(self,url):
# given a url, get page content
data = urlopen(url).read()
# parse as html structured document
bs = BeautifulSoup(data,"html.parser")
# kill javascript content
for s in bs.findAll('script'):
s.replaceWith('')
# find body and extract text
txt = bs.find('body').getText('\n')
# remove multiple linebreaks and whitespace
return self.Newlines.sub('\n', txt)
#def __get_content_with_trafilatura(self,url):
# downloaded = trafilatura.fetch_url(url)
# return trafilatura.extract(downloaded)
pass
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Codigos Python/Contains_Duplicate_II.py | BrunoHarlis/Solucoes_LeetCode | 0 | 62387 | <filename>Codigos Python/Contains_Duplicate_II.py
# Fonte: https://leetcode.com/problems/contains-duplicate-ii/
# Autor: <NAME>
# Data: 21/09/2021
"""
PROBLEMA PROSPOSTO:
Dado um array inteiro nums e um inteiro k, retorna true se houver dois
índices distintos i e j no array tal nums[i] == nums[j] e abs(i - j) <= k.
Exemplo 1:
Entrada: nums = [1,2,3,1], k = 3
Saída: verdadeiro
Exemplo 2:
Entrada: nums = [1,0,1,1], k = 1
Saída: verdadeiro
Exemplo 3:
Entrada: nums = [1,2,3,1,2,3], k = 2
Saída: falso
Tempo de execução: 588 ms, mais rápido do que 90,50 % dos envios.
Uso de memória: 25,9 MB, menos de 83,66 % dos envios.
"""
def containsNearbyDuplicate(nums, k):
if len(nums) == len(set(nums)):
return False
for i in range(len(nums)):
for j in range(i+1, len(nums)):
if nums[i] == nums[j]:
if abs(i-j) <= k:
return True
else:
break
return False
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atrous_conv.py | pit-ray/Anime-Semantic-Segmentation-GAN | 36 | 89013 | # coding: utf-8
import chainer.links as L
from spectral_norms import SNConv, SNHookConv
class AtrousConv(L.DilatedConvolution2D):
def __init__(self, in_channels, out_channels, ksize=None, rate=1, initialW=None):
super().__init__(in_channels, out_channels, ksize=ksize, stride=1, pad=rate, dilate=rate, initialW=initialW)
class AtrousSNConv(SNConv):
def __init__(self, in_channels, out_channels, ksize=None, rate=1, initialW=None):
super().__init__(in_channels, out_channels, ksize=ksize, stride=1, pad=rate, dilate=rate, initialW=initialW)
class AtrousSNHookConv(SNHookConv):
def __init__(self, in_channels, out_channels, ksize=None, rate=1, initialW=None):
super().__init__(in_channels, out_channels, ksize=ksize, stride=1, pad=rate, dilate=rate, initialW=initialW)
def define_atrous_conv(opt):
if opt.conv_norm == 'original':
return AtrousConv
if opt.conv_norm == 'spectral_norm':
return AtrousSNConv
if opt.conv_norm == 'spectral_norm_hook':
return AtrousSNHookConv
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src/main.py | GiantBlargg/cbdl | 0 | 190548 | <gh_stars>0
import click
from squif import SquiCol
from sites import siteHandle
@click.command()
@click.option("--dest", "-d", type=click.Path(file_okay=False), default=".")
@click.option(
"--output", "-o", default="{{{series}}}/{{{title}}}.cbz/{{{name}}}.{{{ext}}}")
@click.argument("URL", nargs=-1, required=True)
def main(dest, output, url):
col = SquiCol(dest, output)
for u in url:
siteHandle(u, col)
col.close()
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py/tests/problems/linkedlist/partition_test.py | bmoretz/Daily-Coding-Problem | 1 | 146465 | import unittest
from dcp.problems.linkedlist.single import build_list
from dcp.problems.linkedlist.partition import partition1
class Test_Partition1(unittest.TestCase):
def setUp(self):
pass
def test_case1(self):
assert partition1(None, 0) == None
def test_case2(self):
head, partition = build_list([3, 5, 8, 5, 10, 2, 1]), 3
actual = partition1(head, partition)
expected = build_list([2, 1, 3, 5, 8, 5, 10])
assert actual == expected
def test_case3(self):
head, partition = build_list([3, 5, 8, 5, 10, 2, 1]), 5
actual = partition1(head, partition)
expected = build_list([3, 2, 1, 5, 8, 5, 10])
assert actual == expected
def test_case4(self):
head, partition = build_list([3, 5, 8, 5, 10, 2, 1]), 10
actual = partition1(head, partition)
expected = build_list([3, 5, 8, 5, 2, 1, 10])
assert actual == expected
def test_case5(self):
head, partition = build_list([3, 5, 8, 5, 10, 2, 1]), 1
actual = partition1(head, partition)
expected = build_list([3, 5, 8, 5, 10, 2, 1])
assert actual == expected | [
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spinup/algos/pytorch/dppo/test_hvp.py | XiaohanZhangCMU/spinningup | 2 | 191110 | <reponame>XiaohanZhangCMU/spinningup
# Demonstrate
# 1. Pearlmutter hvp theory works
# 2. j-towns 3 backward prop trick works
from autograd import numpy as np
import autograd.numpy.random as npr
from autograd.test_util import check_grads, check_equivalent
from autograd import (grad, elementwise_grad, jacobian, value_and_grad,
hessian_tensor_product, hessian, make_hvp,
tensor_jacobian_product, checkpoint, make_jvp,
make_ggnvp, grad_and_aux)
import torch
import timeit
npr.seed(1)
def jvp():
A = np.random.randn(2, 2)
def f(x):
return np.dot(A, x)
x = np.zeros(2)
jvp_f_x = make_jvp(f)(x)
print(jvp_f_x(np.array([1, 0]))) # f(0) and first column of f's Jacobian at 0
print(jvp_f_x(np.array([0, 1]))) # f(0) and second column of f's Jacobian at 0
def hvp():
hvp = make_hvp(fun)(a)[0]
s = hvp(u)
return s
def hessian1():
H = hessian(fun)(a)
s = np.dot(H, u)
return s
# Adapted using the trick: https://j-towns.github.io/2017/06/12/A-new-trick.html
def torchhvp():
L = torch.sum(torch.sin(x))
y, = torch.autograd.grad(L, x, create_graph=True, retain_graph=False)
w = torch.zeros(y.size(), requires_grad=True)
g = torch.autograd.grad(y, x, grad_outputs = w, create_graph = True)
r = torch.autograd.grad(g, w, grad_outputs = v, create_graph = False)
return r
class net(torch.nn.Module):
def __init__(self): #, D_in, H, D_out):
super(net, self).__init__()
def forward(self, x):
return torch.sum(torch.sin(x))
def torchwNet():
fun = net()
L = fun(x)
y, = torch.autograd.grad(L, x, create_graph=True, retain_graph=False)
w = torch.zeros(y.size(), requires_grad=True)
g = torch.autograd.grad(y, x, grad_outputs = w, create_graph = True)
r = torch.autograd.grad(g, w, grad_outputs = v, create_graph = False)
return r
fun = lambda a: np.sum(np.sin(a))
for size in range(50, 50000, 50):
x = torch.randn(size,)
v = torch.randn(size,)
x.requires_grad=True
a = x.detach().numpy()
u = v.detach().numpy()
print([timeit.timeit(hvp, number=10),timeit.timeit(hessian1, number=10), timeit.timeit(torchhvp, number=10), timeit.timeit(torchwNet, number=10)])
#print(fun(a))
#print(hvp())
#print(hessian1())
#print(torchhvp())
#print(torchwNet())
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] |
draw_many_line.py | MeetDevin/WebCrawler | 3 | 98244 | <gh_stars>1-10
# coding: utf-8
# ---
# @File: draw_many_line.py
# @description: 用 matplotlib 在一幅图上绘多条折线，从读取 tensorboard 保存的数据，其在 eager_main 中保存
# @Author: <NAME>
# @E-mail: <EMAIL>
# @Time: 1月11, 2019
# ---
from tensorboard.backend.event_processing import event_accumulator
import matplotlib.pyplot as plt
import matplotlib
matplotlib.rcParams['font.size'] = 18
matplotlib.rcParams['font.family'] = 'Times New Roman'
# 加载tensorboard日志数据
ea_09 = event_accumulator.EventAccumulator('tensor_logs/over09_f/test/events.out.tfevents.1556093081.localhost.localdomain')
ea_09.Reload()
ea_08 = event_accumulator.EventAccumulator('tensor_logs/over08/test/events.out.tfevents.1556090611.localhost.localdomain')
ea_08.Reload()
ea_07 = event_accumulator.EventAccumulator('tensor_logs/over07/test/events.out.tfevents.1556630931.DEVIN-ENTERPRIS')
ea_07.Reload()
ea_06 = event_accumulator.EventAccumulator('tensor_logs/over06/test/events.out.tfevents.1556629252.DEVIN-ENTERPRIS')
ea_06.Reload()
ea_05 = event_accumulator.EventAccumulator('tensor_logs/over05/test/events.out.tfevents.1556628878.DEVIN-ENTERPRIS')
ea_05.Reload()
print(ea_08.scalars.Keys())
line_name = 'Loss'
index_line = 'loss'
line_09 = ea_09.scalars.Items(index_line)
line_08 = ea_08.scalars.Items(index_line)
line_07 = ea_07.scalars.Items(index_line)
line_06 = ea_06.scalars.Items(index_line)
line_05 = ea_05.scalars.Items(index_line)
print(len(line_08))
# def txt_read(logs_path):
# loss_history = []
# acc_history = []
# test_loss_history = []
# test_acc_history = []
# data_m = [loss_history, acc_history, test_loss_history, test_acc_history]
#
# file = open(logs_path, 'r')
# i_line = 0
# for line in file.readlines():
# line = line.strip('\n')
# for v in line.split('\t'):
# if v != '':
# data_m[i_line].append(v)
# i_line += 1
#
# file.close()
# return data_m
#
#
# line_name = 'Accuracy'
# index_line = 3
# # over in 6
# data_0 = txt_read('tensor_logs/lines2019-06-27-18-15-39.txt')
# data_25 = txt_read('tensor_logs/lines_over25.txt')
# data_5 = txt_read('tensor_logs/lines2019-06-27-20-21-23.txt')
# len_test = len(data_0[index_line])
fig = plt.figure(figsize=(6, 4))
ax1 = fig.add_subplot(111)
# # over in txt
# ax1.plot([float(i) for i in range(len_test)], [float(i) for i in data_0[index_line]], label='0.0')
# ax1.plot([float(i) for i in range(len_test)], [float(i) for i in data_25[index_line]], label='0.25')
# ax1.plot([float(i) for i in range(len_test)], [float(i) for i in data_5[index_line]], label='0.50')
# over in tensorboard
ax1.plot([i.step for i in line_09], [i.value for i in line_09], label='0.9')
ax1.plot([i.step for i in line_08], [i.value for i in line_08], label='0.8')
ax1.plot([i.step for i in line_07], [i.value for i in line_07], label='0.7')
ax1.plot([i.step for i in line_06], [i.value for i in line_06], label='0.6')
ax1.plot([i.step for i in line_05], [i.value for i in line_05], label='0.5')
# plt.xticks(fontsize=18)
# plt.yticks(fontsize=18)
ax1.set_xlabel("Step")
ax1.set_ylabel(line_name)
plt.legend(loc='lower right')
plt.show()
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anuga/structures/tests/test_inlet_operator.py | samcom12/anuga_core | 136 | 131945 | #!/usr/bin/env python
from __future__ import division
from past.utils import old_div
import unittest
import os.path
import sys
import numpy
import anuga
from anuga.abstract_2d_finite_volumes.mesh_factory import rectangular_cross
from anuga.shallow_water.shallow_water_domain import Domain
from anuga.abstract_2d_finite_volumes.util import file_function
from anuga.utilities.system_tools import get_pathname_from_package
from anuga.structures.inlet_operator import Inlet_operator
class Test_inlet_operator(unittest.TestCase):
"""
Test the boyd box operator, in particular the discharge_routine!
"""
def setUp(self):
pass
def tearDown(self):
try:
os.remove('Test_Outlet_Inlet.sww')
except:
pass
def _create_domain(self,d_length,
d_width,
dx,
dy,
elevation_0,
elevation_1,
stage_0,
stage_1):
points, vertices, boundary = rectangular_cross(int(old_div(d_length,dx)), int(old_div(d_width,dy)),
len1=d_length, len2=d_width)
domain = Domain(points, vertices, boundary)
domain.set_name('Test_Outlet_Inlet') # Output name
domain.set_store()
domain.set_default_order(2)
domain.H0 = 0.01
domain.tight_slope_limiters = 1
#print 'Size', len(domain)
#------------------------------------------------------------------------------
# Setup initial conditions
#------------------------------------------------------------------------------
def elevation(x, y):
"""Set up a elevation
"""
z = numpy.zeros(x.shape,dtype='d')
z[:] = elevation_0
numpy.putmask(z, x > old_div(d_length,2), elevation_1)
return z
def stage(x,y):
"""Set up stage
"""
z = numpy.zeros(x.shape,dtype='d')
z[:] = stage_0
numpy.putmask(z, x > old_div(d_length,2), stage_1)
return z
#print 'Setting Quantities....'
domain.set_quantity('elevation', elevation) # Use function for elevation
domain.set_quantity('stage', stage) # Use function for elevation
Br = anuga.Reflective_boundary(domain)
domain.set_boundary({'left': Br, 'right': Br, 'top': Br, 'bottom': Br})
return domain
def test_inlet_constant_Q(self):
"""test_inlet_Q
This tests that the inlet operator adds the correct amount of water
"""
stage_0 = 11.0
stage_1 = 10.0
elevation_0 = 10.0
elevation_1 = 10.0
domain_length = 200.0
domain_width = 200.0
domain = self._create_domain(d_length=domain_length,
d_width=domain_width,
dx = 10.0,
dy = 10.0,
elevation_0 = elevation_0,
elevation_1 = elevation_1,
stage_0 = stage_0,
stage_1 = stage_1)
vol0 = domain.compute_total_volume()
finaltime = 3.0
line1 = [[95.0, 10.0], [105.0, 10.0]]
Q1 = 5.00
line2 = [[10.0, 90.0], [20.0, 90.0]]
Q2 = 10.0
Inlet_operator(domain, line1, Q1, logging=False)
Inlet_operator(domain, line2, Q2)
for t in domain.evolve(yieldstep = 1.0, finaltime = finaltime):
#domain.write_time()
#print domain.volumetric_balance_statistics()
pass
vol1 = domain.compute_total_volume()
assert numpy.allclose((Q1+Q2)*finaltime, vol1-vol0, rtol=1.0e-8)
assert numpy.allclose((Q1+Q2)*finaltime, domain.fractional_step_volume_integral, rtol=1.0e-8)
def test_inlet_constant_Q_polygon(self):
"""test_inlet_Q
This tests that the inlet operator adds the correct amount of water
"""
stage_0 = 11.0
stage_1 = 10.0
elevation_0 = 10.0
elevation_1 = 10.0
domain_length = 200.0
domain_width = 200.0
domain = self._create_domain(d_length=domain_length,
d_width=domain_width,
dx = 10.0,
dy = 10.0,
elevation_0 = elevation_0,
elevation_1 = elevation_1,
stage_0 = stage_0,
stage_1 = stage_1)
vol0 = domain.compute_total_volume()
finaltime = 3.0
poly1 = [[95.0, 10.0], [105.0, 10.0], [105, 20.0], [95.0, 20.0]]
Q1 = 5.00
Inlet_operator(domain, poly1, Q1, logging=False)
for t in domain.evolve(yieldstep = 1.0, finaltime = finaltime):
#domain.write_time()
#print domain.volumetric_balance_statistics()
pass
vol1 = domain.compute_total_volume()
assert numpy.allclose((Q1)*finaltime, vol1-vol0, rtol=1.0e-8)
assert numpy.allclose((Q1)*finaltime, domain.fractional_step_volume_integral, rtol=1.0e-8)
def test_inlet_variable_Q(self):
"""test_inlet_Q
This tests that the inlet operator adds the correct amount of water
"""
stage_0 = 11.0
stage_1 = 10.0
elevation_0 = 10.0
elevation_1 = 10.0
domain_length = 200.0
domain_width = 200.0
domain = self._create_domain(d_length=domain_length,
d_width=domain_width,
dx = 10.0,
dy = 10.0,
elevation_0 = elevation_0,
elevation_1 = elevation_1,
stage_0 = stage_0,
stage_1 = stage_1)
vol0 = domain.compute_total_volume()
finaltime = 3.0
#Make sure we are inthe right directory to find the
#time series data for the inlets
import os
path = get_pathname_from_package('anuga.structures')
filename1 = os.path.join(path, 'tests', 'data', 'inlet_operator_test1.tms')
filename2 = os.path.join(path, 'tests', 'data', 'inlet_operator_test2.tms')
line1 = [[95.0, 10.0], [105.0, 10.0]]
Q1 = file_function(filename=filename1, quantities=['hydrograph'])
line2 = [[10.0, 90.0], [20.0, 90.0]]
Q2 = file_function(filename=filename2, quantities=['hydrograph'])
Inlet_operator(domain, line1, Q1)
Inlet_operator(domain, line2, Q2)
for t in domain.evolve(yieldstep = 1.0, finaltime = finaltime):
#domain.write_time()
#print domain.volumetric_balance_statistics()
pass
vol1 = domain.compute_total_volume()
#print vol1-vol0
assert numpy.allclose(13.5, vol1-vol0, rtol=1.0e-8)
assert numpy.allclose(vol1-vol0, domain.fractional_step_volume_integral, rtol=1.0e-8)
def test_inlet_variable_Q_default(self):
"""test_inlet_Q
This tests that the inlet operator adds the correct amount of water
"""
stage_0 = 11.0
stage_1 = 10.0
elevation_0 = 10.0
elevation_1 = 10.0
domain_length = 200.0
domain_width = 200.0
domain = self._create_domain(d_length=domain_length,
d_width=domain_width,
dx = 10.0,
dy = 10.0,
elevation_0 = elevation_0,
elevation_1 = elevation_1,
stage_0 = stage_0,
stage_1 = stage_1)
vol0 = domain.compute_total_volume()
finaltime = 5.0
#Make sure we are inthe right directory to find the
#time series data for the inlets
import os
baseDir = os.getcwd()
path = get_pathname_from_package('anuga.structures')
filename1 = os.path.join(path, 'tests', 'data', 'inlet_operator_test1.tms')
filename2 = os.path.join(path, 'tests', 'data', 'inlet_operator_test2.tms')
line1 = [[95.0, 10.0], [105.0, 10.0]]
Q1 = file_function(filename=filename1, quantities=['hydrograph'])
line2 = [[10.0, 90.0], [20.0, 90.0]]
Q2 = file_function(filename=filename2, quantities=['hydrograph'])
os.chdir(baseDir)
import warnings
warnings.simplefilter("ignore")
Inlet_operator(domain, line1, Q1, default=6)
Inlet_operator(domain, line2, Q2, default=3)
for t in domain.evolve(yieldstep = 1.0, finaltime = finaltime):
#domain.write_time()
#print domain.volumetric_balance_statistics()
pass
warnings.simplefilter("default")
vol1 = domain.compute_total_volume()
#print vol1-vol0
assert numpy.allclose(31.5, vol1-vol0, rtol=1.0e-8)
assert numpy.allclose(vol1-vol0, domain.fractional_step_volume_integral, rtol=1.0e-8)
# =========================================================================
if __name__ == "__main__":
suite = unittest.makeSuite(Test_inlet_operator, 'test')
runner = unittest.TextTestRunner()
runner.run(suite)
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13,
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29900,
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29897,
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13,
13,
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9166,
9166,
9166,
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13,
361,
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978,
1649,
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376,
1649,
3396,
1649,
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13,
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443,
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13,
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353,
443,
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3057,
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580,
13,
1678,
28877,
29889,
3389,
29898,
13495,
29897,
13,
2
] |
gitgud/skills/basics/__init__.py | osai-mirrors/git-gud | 133 | 132803 | <gh_stars>100-1000
from gitgud.util import Skill
from gitgud.util.level_builder import BasicLevel
skill = Skill(
'Basics',
'basics',
[
BasicLevel('Introduction to Commits', 'committing', __name__),
BasicLevel('Branching in Git', 'branching', __name__),
BasicLevel('Merging in Git', 'merging', __name__),
BasicLevel('Introduction to Rebasing', 'rebasing', __name__)
]
)
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] |
django_project/spa/urls.py | aliyaandabekova/DJANGO_PROJECT | 0 | 55060 | from django.urls import path
from .views import *
urlpatterns = [
path('',homepage,name='home'),
path('services/',services, name='services'),
path('masters/',masters, name='masters'),
path('register/',register_page,name='register'),
path('login/',login_page, name='login'),
path('master_detail/<int:master_id>/',master_detail,name='master_detail'),
path('logout/',logout_page,name='logout'),
] | [
1,
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] |
python/zephyr/datasets/score_dataset.py | r-pad/zephyr | 18 | 6946 | <filename>python/zephyr/datasets/score_dataset.py
import os, copy
import cv2
from functools import partial
import numpy as np
import torch
import torchvision
from torch.utils.data import Dataset
from zephyr.data_util import to_np, vectorize, img2uint8
from zephyr.utils import torch_norm_fast
from zephyr.utils.mask_edge import getRendEdgeScore
from zephyr.utils.edges import generate_distance_image
from zephyr.normals import compute_normals
from zephyr.utils.timer import TorchTimer
try:
from zephyr.datasets.bop_raw_dataset import BopRawDataset
except ImportError:
pass
from zephyr.datasets.prep_dataset import PrepDataset
IMPORTANCE_ORDER = [
28, 27, 32, 33, 36, 35, 29, 16, 26, 22, 13, 4, 26, 21, 22
]
class ScoreDataset(Dataset):
def __init__(self, datapoints, dataset_root, dataset_name, args, mode='train', timing = False):
self.args = args
self.datapoints = datapoints
self.dataset_root = dataset_root
self.dataset_name = dataset_name
self.mode = mode
self.return_full_data = False
self.feature_size = args.feature_size
self.norm_cos_weight = args.norm_cos_weight
self.top_n_feat = args.top_n_feat
self.max_hypos = args.max_hypos
self.ppf_only = args.ppf_only
self.n_ppf_hypos = args.n_ppf_hypos
self.n_sift_hypos = args.n_sift_hypos
self.use_mask_test = args.use_mask_test
if args.raw_bop_dataset:
self.loader = BopRawDataset(
args.bop_root, self.dataset_name, args.split, args.split_name, args.ppf_result_file, no_sift=args.ppf_only, no_ppf=args.sift_only
)
else:
self.loader = PrepDataset(
self.dataset_root, self.feature_size
)
self.dim_point = 0
self.dim_render = 0
self.dim_agg = 0
# About timing
self.timing = timing
self.timing_list = []
if args.model_name == "maskconv":
print("Using Point Render dataset")
self.return_rend, self.return_points, self.return_agg = True, True, False
else:
self.return_rend = False
if args.dataset == "feat":
print("Using Agg Dataset")
self.return_points, self.return_agg = False, True
else: # Use PointNet dataset
if "mix" in args.dataset:
print("Using Mix Dataset")
self.return_points, self.return_agg = True, True
else:
print("Using PointNet Dataset")
self.return_points, self.return_agg = True, False
'''For aggregated features Data'''
if self.return_agg:
self.std = None
self.mean = None
self.feature_inliers = True
self.use_hsv = True
self.normalize = True
self.fs_thresh = 0.02
if args.selected_features is not None:
self.selected_features = args.selected_features
print("Using feature indices:", self.selected_features)
elif self.top_n_feat is not None:
self.selected_features = IMPORTANCE_ORDER[:self.top_n_feat]
print("ScoreDataset: Using top features N =", self.top_n_feat)
print("Using feature indices:", self.selected_features)
args.selected_features = self.selected_features
else:
self.selected_features = list(range(39))
print("Using all aggregated features")
args.selected_features = self.selected_features
self.dim_agg = len(self.selected_features)
self.vectorize = partial(vectorize,
use_hsv=self.use_hsv,
feature_inliers=self.feature_inliers,
norm_cos_weight=self.norm_cos_weight,
fs_thresh=self.fs_thresh
)
self.agg_cache = [None for _ in range(len(self.datapoints))]
'''For PointNet Data'''
self.point_x_labels = []
if self.return_points:
self.max_points = args.max_points
args.xyz_channel = [] # indices of point_x channels that define coordinates
args.model_channel = [] # indices of point_x channels that are specific to the object model
'''Mask channel'''
num_features = 0
# valid_proj.unsqueeze(-1).float(),
# valid_depth.unsqueeze(-1).float(),
if not self.args.no_valid_proj:
self.point_x_labels += ['valid_proj']
num_features += 1
if not self.args.no_valid_depth:
self.point_x_labels += ["valid_depth"]
num_features += 1
'''XYZ channel'''
self.uvd, self.uv = False, False
if "uvd" in args.dataset:
self.uvd = True
args.xyz_channel = list(range(num_features, num_features + 3))
num_features +=3
self.point_x_labels += ['u', 'v', 'd']
elif "uv" in args.dataset:
self.uv = True
args.xyz_channel = list(range(num_features, num_features + 2))
num_features += 2
self.point_x_labels += ['u', 'v']
else:
num_features += 0
args.model_channel += args.xyz_channel
num_non_data = num_features
'''Data channel'''
if "cos" in args.dataset:
self.point_x_labels += ['cam_norm_cos']
self.RGB, self.HSV, self.D, self.diff, self.cos, self.edge, self.ppfscore, self.norm_cos = \
False, False, False, False, False, False, False, False
if "RGB" in args.dataset:
self.RGB, self.HSV = True, False
args.model_channel += list(range(num_features, num_features + 3))
num_features += 6
self.point_x_labels += ['R_diff', 'G_diff', 'B_diff'] if "diff" in args.dataset else ["R1", "G1", "B1", "R2", "G2", "B2"]
elif "HSV" in args.dataset:
self.RGB, self.HSV = True, True
args.model_channel += list(range(num_features, num_features + 3))
num_features += 6
self.point_x_labels += ['H_diff', 'S_diff', 'V_diff'] if "diff" in args.dataset else ["H1", "S1", "V1", "H2", "S2", "V2"]
if "D" in args.dataset:
self.D = True
args.model_channel += list(range(num_features, num_features + 1))
num_features += 2
self.point_x_labels += ["D_diff"] if "diff" in args.dataset else ["D1", "D2"]
if "diff" in args.dataset:
self.diff = True
num_features = num_non_data + (num_features-num_non_data) // 2
if "cos" in args.dataset:
self.cos = True
num_features += 1
if "edge" in args.dataset:
self.edge = True
self.edgecos = "edgecos" in args.dataset
self.edgexnor = "edgexnor" in args.dataset
num_features += 1 if (self.edgecos or self.edgexnor) else 2
if self.edgecos:
self.point_x_labels += ['obs_edge_score']
elif self.edgexnor:
self.point_x_labels += ['edge_xnor']
else:
self.point_x_labels += ['obs_edge_score', "rend_edge_score"]
if "ppfscore" in args.dataset:
self.ppfscore = True
num_features += 1
self.point_x_labels += ['ppf_score']
if "norm" in args.dataset:
self.norm_cos = True
num_features += 1
self.point_x_labels += ['norm_cos']
self.seg_mask = False
if "seg" in args.dataset:
self.seg_mask = True
num_features += 1
self.point_x_labels += ['mask', "mask_edge"]
self.dim_point = num_features
'''Train/Test specific config'''
if self.mode == 'train':
print("Initializating training dataset", self.point_x_labels)
self.cojitter = args.cojitter
self.drop_ratio = args.drop_ratio
self.uv_rot = args.uv_rot
else:
print("Initializating %s dataset" % mode, self.point_x_labels)
self.cojitter = False
self.drop_ratio = 0
self.uv_rot = False
self.transform = torchvision.transforms.Compose([
torchvision.transforms.ToPILImage(),
torchvision.transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.05),
torchvision.transforms.ToTensor(),
])
if self.cojitter:
self.transform_cojitter = torchvision.transforms.Compose([
torchvision.transforms.ToPILImage(),
torchvision.transforms.ColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5),
torchvision.transforms.ToTensor(),
])
print("ScorePointnetDataset: Using cojitter")
if self.return_rend:
self.dim_render = self.dim_point - 1
def __len__(self):
return len(self.datapoints)
def setNormalization(self, var, mean):
var = torch.from_numpy(np.asarray(var))
mean = torch.from_numpy(np.asarray(mean))
self.std = torch.sqrt(var[self.selected_features]).float()
self.mean = mean[self.selected_features].float()
'''Return [n_hypo, n_features]'''
def getAggData(self, data):
x = self.vectorize(data)
x = x[:, self.selected_features]
if self.normalize:
x = (x-self.mean)/self.std
return x
'''Return [n_hypo, n_points, n_features]'''
def getPointNetData(self, data, return_uv_original=False):
with TorchTimer("Data convert 1", agg_list=self.timing_list, timing = self.timing, verbose=False):
img = data['img'].float() # float [0, 1]
depth = data['depth'].float()
if "pbr" in self.dataset_root and self.mode == "train":
# print("blur depth image")
depth = depth * (torch.ones_like(depth) + 0.003 * torch.randn_like(depth))
transforms = data['transforms'].float()
model_points = data['model_points'].float()
model_colors = data['model_colors'].float() # float [0, 1]
model_normals = data['model_normals'].float()
meta_data = data['meta_data']
with TorchTimer("Transform and project", agg_list=self.timing_list, timing = self.timing, verbose=False):
# Transform and project point cloud
trans_pts = torch.einsum('ijk,mk->imj', transforms[:,:3,:3], model_points) + transforms[:,:3,3].unsqueeze(1)
f_cam = torch.tensor([meta_data['camera_fx'], meta_data['camera_fy']])
c_cam = torch.tensor([meta_data['camera_cx'], meta_data['camera_cy']])
proj_pts = trans_pts[:,:,:2]/trans_pts[:,:,2:]*f_cam + c_cam
uv = proj_pts.long()
invalid_proj = (uv[:,:,1]>=img.shape[0]) + (uv[:,:,1]<0) \
+ (uv[:,:,0]>=img.shape[1]) + (uv[:,:,0]< 0)
uv[invalid_proj] = 0
# Projected depth
proj_depth = trans_pts[:,:,-1]
'''Jitter the color as data augmentation'''
if self.mode == "train":
img = img.permute(2, 0, 1) # (H, W, C) to (C, H, W)
img = self.transform(img)
img = img.permute(1, 2, 0) # (C, H, W) to (H, W, C)
if self.cojitter:
H, W, C = img.shape # (H, W, C)
N, _ = model_colors.shape
data_cojitter = torch.cat([
img.reshape((1, -1, 3)),
model_colors.reshape((1, -1, 3))
], dim=1)
data_cojitter = data_cojitter.permute(2, 0, 1)
cojittered = self.transform_cojitter(data_cojitter)
cojittered = cojittered.permute(1, 2, 0)
img = cojittered[0, :H*W, :].reshape((H, W, C))
model_colors = cojittered[0, H*W:, :].reshape((N, C))
# RGb to HSV
with TorchTimer("RGB to HSV", agg_list=self.timing_list, timing = self.timing, verbose=False):
if self.HSV:
with np.errstate(divide='ignore'):
img_rgb = img2uint8(to_np(img))
# img_hsv = rgb2hsv(img_rgb) # this will convert it to range [0, 1]
img_hsv = cv2.cvtColor(img_rgb,cv2.COLOR_RGB2HSV)
img_hsv = img_hsv.astype(float) / 255.0
img = torch.from_numpy(img_hsv).to(img.device).float()
model_colors_rgb = img2uint8(np.expand_dims(to_np(model_colors), 0))
# model_colors_hsv = rgb2hsv(model_colors_rgb)[0]
model_colors_hsv = cv2.cvtColor(model_colors_rgb,cv2.COLOR_RGB2HSV)[0]
model_colors_hsv = model_colors_hsv.astype(float) / 255.0
model_colors = torch.from_numpy(model_colors_hsv).to(model_colors.device).float()
# Sample the observed HSVD
with TorchTimer("Sample obvervation", agg_list=self.timing_list, timing = self.timing, verbose=False):
obs_color = img[uv[:,:,1], uv[:,:,0], :]
obs_depth = depth[uv[:,:,1], uv[:,:,0]]
with TorchTimer("Hypo Pruning", agg_list=self.timing_list, timing = self.timing, verbose=False):
if self.args.inconst_ratio_th is not None and self.mode == "test":
d_diff = proj_depth - obs_depth
n_points = model_points.shape[0]
invalid_count = (d_diff < -0.02).sum(-1).float()
invalid_ratio = invalid_count / n_points
th = self.args.inconst_ratio_th
idx = invalid_ratio < (th/100.0)
idx[-1] = True
# At least preserve some non-oracle hypos
if idx.sum() == 1:
idx[0] = True
pruning_mask = idx
transforms = transforms[idx]
trans_pts = trans_pts[idx]
obs_color = obs_color[idx]
obs_depth = obs_depth[idx]
uv = uv[idx]
invalid_proj = invalid_proj[idx]
proj_depth = proj_depth[idx]
self.SelectDataByIdx(data, idx)
uv_original = copy.deepcopy(uv)
data['uv_original'] = uv_original
# Transform normals
with TorchTimer("Transform and project 2", agg_list=self.timing_list, timing = self.timing, verbose=False):
trans_norms = torch.einsum('ijk,mk->imj', transforms[:,:3,:3], model_normals)
cam_norm_cos = (- trans_pts * trans_norms).sum(-1) / (torch_norm_fast(trans_pts, -1) * torch_norm_fast(trans_norms, -1))
valid_norm = cam_norm_cos > 0
valid_proj = valid_norm * torch.bitwise_not(invalid_proj)
data['valid_proj'] = valid_proj
# x = []
x = model_points.new_empty((len(transforms), len(model_points), self.dim_point))
idx_feat = 0
with TorchTimer("Valid proj/depth", agg_list=self.timing_list, timing = self.timing, verbose=False):
valid_depth = obs_depth > 0
'''Mask channel'''
if not self.args.no_valid_proj:
# x += [valid_proj.unsqueeze(-1).float()]
x[:, :, idx_feat] = valid_proj.float()
idx_feat += 1
if not self.args.no_valid_depth:
# x += [valid_depth.unsqueeze(-1).float()]
x[:, :, idx_feat] = valid_depth.float()
idx_feat += 1
'''XYZ channel'''
with TorchTimer("Normalize uv", agg_list=self.timing_list, timing = self.timing, verbose=False):
if self.uv or self.uvd:
uv = uv.float()
uv_mean = uv.mean(dim=1, keepdim=True)
uv_std = uv.std(dim=1, keepdim=True)
uv = (uv - uv_mean) / uv_std
if self.uv_rot:
n_hypo, n_point, n_coord = uv.shape
'''random flip'''
flip_mat = torch.rand((n_hypo, 1, n_coord)) > 0.5
flip_mat = (flip_mat.type(uv.dtype) - 0.5) * 2
uv = uv * flip_mat
'''random rotation'''
rot_mat = torch.rand((n_hypo, 1, 1)) * 2 * np.pi
rot_mat = torch.cat([
torch.cos(rot_mat), -torch.sin(rot_mat),
torch.sin(rot_mat), torch.cos(rot_mat)
], 2).reshape((-1, 1, 2, 2))
uv = uv.unsqueeze(-1)
uv = torch.matmul(rot_mat, uv)
uv = uv.squeeze()
# x += [uv]
x[:, :, idx_feat:idx_feat+2] = uv
idx_feat += 2
if self.uvd:
d_diff = proj_depth.unsqueeze(-1) - obs_depth.unsqueeze(-1)
d_diff = (d_diff - d_diff.mean(dim=1, keepdim=True)) / d_diff.std(dim=1, keepdim=True)
# x += [d_diff]
x[:, :, idx_feat:idx_feat+1] = d_diff
idx_feat += 1
'''Point data channel'''
if self.cos:
# x += [cam_norm_cos.unsqueeze(-1).float()]
x[:, :, idx_feat] = cam_norm_cos.float()
idx_feat += 1
with TorchTimer("Compute RGBD/HSVD diff", agg_list=self.timing_list, timing = self.timing, verbose=False):
if self.RGB or self.HSV:
if self.diff:
color_diff = model_colors.unsqueeze(0).expand(obs_color.shape) - obs_color
if self.HSV:
color_diff[:,:,0] = color_diff[:,:,0].abs()
color_diff[:,:,0] = np.minimum(color_diff[:,:,0], 1-color_diff[:,:,0])
# x += [color_diff]
x[:, :, idx_feat:idx_feat+3] = color_diff
idx_feat += 3
else:
# x += [model_colors.unsqueeze(0).expand(obs_color.shape), obs_color]
x[:, :, idx_feat:idx_feat+3] = model_colors.unsqueeze(0).expand(obs_color.shape)
idx_feat += 3
x[:, :, idx_feat:idx_feat+3] = obs_color
idx_feat += 3
if self.D:
if self.diff:
# x += [proj_depth.unsqueeze(-1) - obs_depth.unsqueeze(-1)]
x[:, :, idx_feat] = proj_depth - obs_depth
idx_feat += 1
else:
# x += [proj_depth.unsqueeze(-1), obs_depth.unsqueeze(-1)]
x[:, :, idx_feat] = proj_depth
idx_feat += 1
x[:, :, idx_feat] = obs_depth
idx_feat += 1
'''Edge channel'''
with TorchTimer("Edge", agg_list=self.timing_list, timing = self.timing, verbose=False):
if self.edge:
'''Observed edges'''
if "depth_for_edge" in data:
depth_for_edge = data['depth_for_edge']
# print("Using depth_for_edge", depth_for_edge.min(), depth_for_edge.max())
else:
depth_for_edge = depth
with TorchTimer("generate_distance_image", agg_list=self.timing_list, timing = self.timing, verbose=False):
edge_obs = generate_distance_image(depth_for_edge, canny_l=20, canny_h=50)[0,0]
with TorchTimer("Edge sampling", agg_list=self.timing_list, timing = self.timing, verbose=False):
uv = copy.deepcopy(uv_original) # Re-fetch the uv as it is changed before
edge_score_obs = edge_obs[uv[:,:,1], uv[:,:,0]]
edge_score_obs = torch.exp(-edge_score_obs / 24)
'''Projected edges'''
with TorchTimer("getRendEdgeScore", agg_list=self.timing_list, timing = self.timing, verbose=False):
if "edge_score_rend" in data:
edge_score_rend = data['edge_score_rend']
else:
with torch.no_grad():
edge_score_rend = getRendEdgeScore(img.to(self.args.edge_gpu), uv_original.to(self.args.edge_gpu)).to(uv_original.device)
'''Normalized edge scores'''
edge_score_rend = edge_score_rend / edge_score_rend.max(1, keepdim=True)[0]
# edge_score_obs = torch.exp(-edge_score_obs / )
if self.edgexnor:
edge_score = edge_score_rend * edge_score_obs + (1 - edge_score_rend) * (1 - edge_score_obs)
# x += [edge_score.unsqueeze(-1)]
x[:, :, idx_feat] = edge_score
idx_feat += 1
elif self.edgecos:
# x += [edge_score_obs.unsqueeze(-1)]
x[:, :, idx_feat] = edge_score_obs
idx_feat += 1
else:
# x += [edge_score_obs.unsqueeze(-1)]
# x += [edge_score_rend.unsqueeze(-1)]
x[:, :, idx_feat] = edge_score_obs
idx_feat += 1
x[:, :, idx_feat] = edge_score_rend
idx_feat += 1
if self.args.camera_scale is not None:
meta_data['camera_scale'] = self.args.camera_scale
'''Use the cos of the angle between observed and rendered normal vectors'''
with TorchTimer("Normal vector", agg_list=self.timing_list, timing = self.timing, verbose=False):
if self.norm_cos:
norm_downsample = self.args.norm_downsample
uv = uv_original # Re-fetch the uv as it is changed before
normals = compute_normals(to_np(depth)[::norm_downsample, ::norm_downsample].astype(np.double), meta_data = meta_data)
normals = torch.from_numpy(normals).float()
scene_normals_proj = normals[uv[:,:,1]//norm_downsample, uv[:,:,0]//norm_downsample]
model_normals_proj = trans_norms
norm_cos = (scene_normals_proj * model_normals_proj).sum(dim=-1) / (torch_norm_fast(scene_normals_proj, -1) * torch_norm_fast(model_normals_proj, -1))
norm_cos[norm_cos != norm_cos] = 0
# x += [norm_cos.unsqueeze(-1).float()]
x[:, :, idx_feat] = norm_cos.float()
idx_feat += 1
# with TorchTimer("torch.cat()", agg_list=self.timing_list, timing = self.timing, verbose=False):
# x = torch.cat(x, dim=-1)
# print(x.shape)
if self.args.hard_mask:
x[~valid_proj.bool()]=0
'''Sample the points'''
if self.drop_ratio >= 0 and self.mode == 'train':
n_hypo = x.shape[0]
n_point = x.shape[1]
n_point_kept = int((1.0-self.drop_ratio) * n_point)
if self.max_points is not None and n_point_kept > self.max_points:
n_point_kept = self.max_points
idx = []
for i in range(n_hypo):
idx.append(torch.randperm(n_point)[:n_point_kept].unsqueeze(0))
idx = torch.cat(idx, dim=0)
x = x[torch.arange(n_hypo).unsqueeze(1).expand(n_hypo, n_point_kept), idx]
uv_sampled = uv_original[torch.arange(n_hypo).unsqueeze(1).expand(n_hypo, n_point_kept), idx]
else:
uv_sampled = uv_original
if return_uv_original:
return x, uv_sampled
else:
return x
def getPointRenderData(self, data):
point_x, uv = self.getPointNetData(data, True)
crop_size = 96
pad_size = 2
n_hypo = uv.shape[0]
n_point = uv.shape[1]
span_min = pad_size
span_max = crop_size - pad_size
mask_index = [0]
# data_index = [0, 1] + list(range(4, point_x.shape[2]))
data_index = list(range(point_x.shape[2]))
n_feat = len(data_index)
point_mask = point_x[:, :, mask_index].bool()
point_data = point_x[:, :, data_index]
uv = uv.float()
uv_max = uv.max(dim=1, keepdim=True)[0]
uv_min = uv.min(dim=1, keepdim=True)[0]
uv_center = (uv_max + uv_min) / 2.0
uv_radius = (uv_max - uv_min).max(-1, True)[0] / 2.0
uv_norm = (uv - uv_center) / uv_radius # range in [-1, 1]
uv_resize = (uv_norm + 1) / 2 * (span_max - span_min) + span_min
uv_resize = uv_resize.long()
u = uv_resize[:, :, 0]
v = uv_resize[:, :, 1]
feature_map = torch.zeros(n_hypo, n_feat, crop_size, crop_size)
t = torch.arange(n_hypo).view(-1,1).repeat(1, n_point)
u = u.reshape(-1)[point_mask.view(-1)]
v = v.reshape(-1)[point_mask.view(-1)]
t = t.view(-1)[point_mask.view(-1)]
feature_map[t.view(-1), :, v.view(-1), u.view(-1)] = point_data.view(-1, n_feat)[point_mask.view(-1)]
mask_map = feature_map[:, 0:1, :, :]
data_map = feature_map[:, 1:, :, :]
return mask_map, data_map
def SelectDataByIdx(self, data, idx):
data['transforms'] = data['transforms'][idx]
data['pp_err'] = data['pp_err'][idx]
if "edge_score_rend" in data:
data['edge_score_rend'] = data['edge_score_rend'][idx]
return data
def __getitem__(self, idx):
dp = self.datapoints[idx]
to_return = {"object_id": dp[0], "scene_id": dp[1], "im_id": dp[2]}
obj_id = dp[0]
scene_id = dp[1]
im_id = dp[2]
'''If only used aggregated features, return the cached one'''
if self.return_agg and not self.return_points and self.agg_cache[idx] is not None:
to_return['agg_x'], to_return['pp_err'], to_return['transforms'] = self.agg_cache[idx]
return to_return
# data = loadData(*dp, feature_size = self.feature_size, base_path = self.dataset_root)
# '''Get the model data and send it into the processing function'''
# model_data = self.getModelData(dp[0])
# data.update(model_data)
data = self.loader.loadData(*dp)
assert len(data['pp_err']) == 101 or len(data['pp_err']) == 1101 or len(data['pp_err']) == 301
assert not (self.args.ppf_only and self.args.sift_only)
if self.args.ppf_only:
assert len(data['pp_err']) >= self.args.n_ppf_hypos + 1
idx = list(np.arange(self.args.n_ppf_hypos)) + [-1]
self.SelectDataByIdx(data, idx)
if self.args.sift_only:
assert len(data['pp_err']) >= self.args.n_ppf_hypos + self.args.n_sift_hypos + 1
idx = list(range(self.n_ppf_hypos, self.n_ppf_hypos+self.n_sift_hypos)) + [-1]
data = self.SelectDataByIdx(data, idx)
'''Sample the hypotheses'''
point_x = self.getPointNetData(data)
n_hypo = len(point_x)
to_return['object_id'] = to_return['object_id'].repeat(n_hypo)
to_return['scene_id'] = to_return['scene_id'].repeat(n_hypo)
to_return['im_id'] = to_return['im_id'].repeat(n_hypo)
to_return['pp_err'] = data['pp_err'].reshape(-1)
to_return['transforms'] = data['transforms']
if self.return_agg:
to_return['agg_x'] = self.getAggData(data)
self.agg_cache[idx] = (to_return['agg_x'], to_return['pp_err'], to_return['transforms'])
if self.return_points:
if self.return_rend:
to_return['rend_mask'], to_return['x_rend'] = self.getPointRenderData(data)
to_return['mask_x'] = to_return['rend_mask']
to_return['rend_x'] = to_return['x_rend']
else:
to_return['point_x'] = point_x
# print("to_return['pp_err']", to_return['pp_err'])
# print("to_return['pp_err']", to_return['pp_err'].shape)
# print("to_return['transforms']", to_return['transforms'].shape)
# print("to_return['point_x']", to_return['point_x'].shape)
to_return['dataset_i'] = 0
# For ICP post-processing
to_return['depth'] = data['depth']
to_return['meta_data'] = data['meta_data']
to_return['uv_original'] = data['uv_original']
to_return['model_points'] = data['model_points']
return to_return
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test_mini_project6.py | chubbypanda/principles-of-computing | 22 | 127729 | # unit tests for Mini-project 6 (Tic-Tac-Toe), by k., 07/25/2014
import unittest
from mini_project6 import TTTBoard
from mini_project6 import mm_move
from mini_project6 import DRAW, EMPTY, PLAYERO, PLAYERX
class TestFunction(unittest.TestCase):
def setUp(self):
pass
def test_move_it(self):
board = TTTBoard(3, False, [[PLAYERO, PLAYERX, PLAYERX], [PLAYERO, PLAYERX, PLAYERO], [PLAYERX, PLAYERO, PLAYERX]])
self.assertIsInstance(board, TTTBoard)
self.assertIs(type(mm_move(board, PLAYERX)), tuple)
score, move = mm_move(board, PLAYERX)
self.assertIs(type(score), int)
self.assertIs(type(move), tuple)
self.assertEqual(mm_move(board, PLAYERX), (1, (-1, -1)))
board = TTTBoard(3, False, [[PLAYERO, PLAYERX, PLAYERX], [PLAYERX, PLAYERO, PLAYERO], [PLAYERO, PLAYERX, PLAYERX]])
self.assertEqual(mm_move(board, DRAW), (0, (-1, -1)))
board = TTTBoard(3, False, [[PLAYERO, PLAYERX, PLAYERX], [PLAYERO, PLAYERX, PLAYERO], [PLAYERO, PLAYERO, PLAYERX]])
self.assertEqual(mm_move(board, PLAYERO), (-1, (-1, -1)))
def test_won_it(self):
board = TTTBoard(3, False, [[PLAYERX, PLAYERX, PLAYERO], [PLAYERO, PLAYERX, PLAYERX], [PLAYERO, EMPTY, PLAYERO]])
self.assertEqual(mm_move(board, PLAYERX), (1, (2, 1)))
board = TTTBoard(2, False, [[EMPTY, EMPTY], [EMPTY, EMPTY]])
self.assertEqual(mm_move(board, PLAYERX), (1, (0, 0)))
def test_draw(self):
board = TTTBoard(3, False, [[EMPTY, EMPTY, PLAYERX], [EMPTY, EMPTY, EMPTY], [EMPTY, EMPTY, EMPTY]])
self.assertEqual(mm_move(board, PLAYERO), (0, (1, 1)))
board = TTTBoard(3, False, [[PLAYERX, EMPTY, EMPTY], [PLAYERO, PLAYERO, EMPTY], [EMPTY, PLAYERX, EMPTY]])
self.assertEqual(mm_move(board, PLAYERX), (0, (1, 2)))
board = TTTBoard(3, False, [[EMPTY, EMPTY, PLAYERX], [EMPTY, EMPTY, EMPTY], [EMPTY, EMPTY, EMPTY]])
self.assertEqual(mm_move(board, PLAYERO), (0, (1, 1)))
def test_lost_it(self):
board = TTTBoard(3, False, [[PLAYERX, PLAYERX, PLAYERO], [EMPTY, PLAYERX, PLAYERX], [PLAYERO, EMPTY, PLAYERO]])
self.assertEqual(mm_move(board, PLAYERO), (-1, (2, 1)))
board = TTTBoard(3, False, [[PLAYERX, PLAYERX, PLAYERO], [EMPTY, PLAYERX, PLAYERX], [PLAYERO, EMPTY, PLAYERO]])
self.assertEqual(mm_move(board, PLAYERO), (-1, (2, 1)))
# let's run it in IDLE
if __name__ == '__main__':
unittest.main(exit=False)
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] |
datastore/shared/postgresql_backend/create_schema.py | jsangmeister/openslides-datastore-service | 0 | 181707 | import os
from datastore.shared.di import injector
from datastore.shared.postgresql_backend import ConnectionHandler
def create_schema() -> None:
"""
Helper function to write the database schema into the database.
"""
connection_handler = injector.get(ConnectionHandler)
with connection_handler.get_connection_context():
with connection_handler.get_current_connection().cursor() as cursor:
path = os.path.realpath(
os.path.join(os.getcwd(), os.path.dirname(__file__), "schema.sql")
)
cursor.execute(open(path, "r").read(), [])
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iap/example_gce_backend.py | HoleCat/echarlosperros | 0 | 98685 | # Copyright 2017 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import platform
import flask
import validate_jwt
CLOUD_PROJECT_ID = 'YOUR_PROJECT_ID'
BACKEND_SERVICE_ID = 'YOUR_BACKEND_SERVICE_ID'
app = flask.Flask(__name__)
@app.route('/')
def root():
jwt = flask.request.headers.get('x-goog-iap-jwt-assertion')
if jwt is None:
return 'Unauthorized request.'
expected_audience = '/projects/{}/global/backendServices/{}'.format(CLOUD_PROJECT_ID, BACKEND_SERVICE_ID)
user_id, user_email, error_str = (
validate_jwt.validate_iap_jwt(
jwt, expected_audience))
if error_str:
return 'Error: {}'.format(error_str)
else:
return 'Hi, {}. I am {}.'.format(user_email, platform.node())
if __name__ == '__main__':
app.run()
| [
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curious_classes.py | sebball/etsy-shop-manager-api-v3 | 1 | 185411 | import curious_functions as cf
import curious_variables as cv
import typing
import logging
import useful_functions
# import curious_variables as cv
logging.basicConfig(format='%(levelname)s:%(message)s', level=logging.CRITICAL)
log = logging.critical("Testing program:")
class Shop:
def __init__(self):
self.type_object = "shop"
self.info = Info(parent_object=self)
# if secrets:
# for this_secret in self.secrets:
# self.secrets[nesecret] = secret
# get_new_info = input("Do you want to add or change secret information? y/n: ")
# if get_new_info == "y":
#
# if
# print("You need to add a 'secret_variables_RichmondCuriosities.txt' file")
# return
# secrets = cf.get_secret_dictionary()
#
# def ask_for_secret(self, secret):
# secret_choices = []
#
#
#
# self.,my_api_key = my_api_key
# self.my_shop_id = my_shop_id
#
# class ShopSecrets(self, ):
#
# def new_secrets(self, self):
#
# def get_secret_dictionary():
# secret_path = Path("secret_variables_RichmondCuriosities.txt")
# if secret_path.is_file():
# secret_object = open("secret_variables_RichmondCuriosities.txt", "r")
# secret_dictionary = secret_object.read()
# return secret_dictionary
class Info:
"""Return information dictionary from etsy api about the class it calls
Attributes
dict: Dictionary of values returned by etsy api calls. Contents depend on the type of the parent object.
type: Small description of classes use
parent_object: the object (type of info: eg shop, listings) Info is assigned to"""
def __init__(self, parent_object, api_call=""):
self.type = "info"
self.dict = {}
self.parent_object = parent_object
if parent_object.type_object == "shop":
self.dict.update({"end_points": cv.end_points})
elif parent_object.type_object == "listings":
self.dict.update()
# class Listings:
# def __init__(self):
# self.type = "listings"
# self.parent_object = "shop"
# def get_info(type):
# self.info = Info(parent_object="listings", api_call="")
shop = Shop()
shop2 = Shop()
print(shop.info.parent_object.type_object)
# self.shoprequired = ["shop_id", "shop_name"]
# self.shop = info_dictionary
# log.critical(f"{self.shop}")
#
# # def add(self, dict_to_update):
# # log(f"add function start: self = {type(self)} {self.shop}")
# self_copy = useful_functions.dict_fresh_id(self)
# #log(f"add function self copied: self = {self}, self_copy = {self_copy}")
# for i, key in enumerate(dict_to_update):
# # log(f"add function i: {i}, key: {key}, self = {self}, self_copy:{self_copy}")
# for value in dict_to_update[key]:
# self_copy[key] = value
# self = self_copy
# del self_copy
# def check_required(info_object):
# for requirement in info_class.required:
# assert requirement in info_class
#
# curious_info = {"shop_id": "32027063", "shop_name": "RichmondCuriosities",}
# shop = Shop()
# print(type(curious_info))
# info = Info(info_dictionary=curious_info)
# # log(f"{info_dictionary.shop}")
# print(info, info.shop["shop_id"])
#
# info.add(dict_to_update={"he;;op": "rffae"})
# print(info.shop)
#
# new_info = Info(shop_info=curious_info)
# print(new_info.shop)
# new_info.add({['did', 'add'], ['it', '?']})
# print(new_info.shop) | [
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] |
Website Code/flask_backend/database/module_db.py | camillesanchez/history-movie-index2 | 0 | 99403 | from historical_periods.scraping_histo_periods_tt import get_dictionary_from_json, get_historical_dictionary, save_dictionary_as_json
import sys, os
sys.path.insert(0,"/Users/camillesanchez/Desktop/hmi/imdbpy-master/")
from imdb import IMDb
def getIMDbDataFromFields(IMDb_id, *fields):
""" Returns IMDb Datas from IMDb film ID
Note: All IMDB ID starts with tt (i.e. "tt8579674" for the film "1917"),
which need to be removed to use the IMDbPy modules. """
ia = IMDb()
film = ia.get_movie(IMDb_id[2:])
person_fields = ["director","writer","cast"]
fields_data = []
for field in fields:
try:
if field in person_fields:
data = []
for person_name in film[field]:
data.append(str(person_name))
else:
data = film.get(field)
except KeyError:
data = None
fields_data.append(data)
url = ia.get_imdbURL(film)
fields_data.append(url)
return fields_data
def getHistoricalPeriodsJSON(history_terminology_json, new_filename):
""" From history_terminology.json and the totallytimelines webscraping,
returns updated JSON file of historical_periods.json """
# Gets history_terminology dictionary:
history_terminology_path = os.path.join("./historical_periods/", history_terminology_json)
history_terminology = get_dictionary_from_json(history_terminology_path)
# Creates json file with the scraped website data & historical terminology to make periods dictionary:
historical_dictionary = get_historical_dictionary(history_terminology)
save_dictionary_as_json(historical_dictionary, "historical_periods.json")
return new_filename
####### TESTING AREA #######
# # To test getHistoricalPeriodsJSON(history_terminology_json, new_filename):
# print(getHistoricalPeriodsJSON("history_terminology.json", "historical_periods.json"))
# To test getIMDbDataFromFields(IMDb_id, field):
# print(getIMDbDataFromFields("tt8579674", "plot", "synopsis","director","writer","cast", "cover url"))
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272,
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13236,
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4384,
613,
376,
11911,
3142,
5783,
13,
13,
13,
13,
2
] |
tests/unit/dynamodb/test_layer1.py | prufrax/boto | 0 | 1611099 | #!/usr/bin/env python
import json
import mock
import boto
from mock import Mock
try:
import unittest2 as unittest
except ImportError:
import unittest
from boto.dynamodb.layer1 import Layer1
class TestDDBConnectionProviderOverride(unittest.TestCase):
def test_provider_override(self):
alt_provider = Mock(spec=boto.provider.Provider)
alt_provider.host = None
alt_provider.host_header = None
alt_provider.port = None
alt_provider.secret_key = 'alt_secret_key'
layer1 = Layer1(aws_access_key_id='aws_access_key', aws_secret_access_key='aws_secret_key', provider=alt_provider)
self.assertEqual(alt_provider, layer1.provider)
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29918,
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7546,
29896,
29889,
18121,
29897,
13,
13,
2
] |
afterglow_core/schemas/api/v1/jobs/photometry_job.py | SkynetRTN/afterglow-access-server | 2 | 71583 | <gh_stars>1-10
"""
Afterglow Core: batch photometry job schemas
"""
from typing import List as TList
from marshmallow.fields import Integer, List, Nested
from ..job import JobSchema, JobResultSchema
from ..photometry import PhotSettingsSchema, PhotometryDataSchema
from ..source_extraction import SourceExtractionDataSchema
__all__ = ['PhotometryJobResultSchema', 'PhotometryJobSchema']
class PhotometryJobResultSchema(JobResultSchema):
data: TList[PhotometryDataSchema] = List(
Nested(PhotometryDataSchema), default=[])
class PhotometryJobSchema(JobSchema):
type = 'photometry'
result: PhotometryJobResultSchema = Nested(
PhotometryJobResultSchema, default={})
file_ids: TList[int] = List(Integer(), default=[])
sources: TList[SourceExtractionDataSchema] = List(
Nested(SourceExtractionDataSchema), default=[])
settings: PhotSettingsSchema = Nested(PhotSettingsSchema, default={})
| [
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] |
cle/backends/externs/__init__.py | orion--/cle | 0 | 1616901 | import logging
from cle.backends import Backend, Symbol, Segment, SymbolType
from cle.backends.relocation import Relocation
from cle.utils import ALIGN_UP
from cle.errors import CLEOperationError, CLEError
from cle.address_translator import AT
l = logging.getLogger(name=__name__)
class ExternSegment(Segment):
def __init__(self, map_size):
super().__init__(None, 0, None, map_size)
def addr_to_offset(self, addr):
raise CLEOperationError("'offset' operations on the extern object are meaningless as it is not mapped from a file")
def offset_to_addr(self, offset):
raise CLEOperationError("'offset' operations on the extern object are meaningless as it is not mapped from a file")
def contains_offset(self, offset):
return False
is_readable = True
is_writable = True
is_executable = True
class TOCRelocation(Relocation):
@property
def value(self):
return self.resolvedby.rebased_addr
class ExternObject(Backend):
def __init__(self, loader, map_size=0, tls_size=0):
super().__init__('cle##externs', None, loader=loader)
self._next_object = None
self._delayed_writes = []
self.next_addr = 0
self.map_size = map_size
self.set_arch(loader.main_object.arch)
self.provides = 'extern-address space'
self.pic = True
self._import_symbols = {}
self._warned_data_import = False
self.tls_data_size = tls_size
self.tls_next_addr = 0
self._tls_mapped = False
def _finalize_tls(self):
if self._is_mapped or self._tls_mapped:
raise Exception("programming error")
if self.tls_data_size != 0:
self.tls_used = True
self.tls_data_start = self._allocate(self.tls_data_size, alignment=0x10)
self.tls_block_size = self.tls_data_size
self._tls_mapped = True
def rebase(self, new_base):
if self._is_mapped:
return
if not self._tls_mapped:
self._finalize_tls()
backer = bytearray(self.map_size)
for simdata in self._delayed_writes:
value = simdata.value()
start_addr = simdata.relative_addr
if simdata.type == SymbolType.TYPE_TLS_OBJECT:
start_addr += self.tls_data_size
backer[start_addr:start_addr+len(value)] = value
self.memory.add_backer(0, bytes(backer))
self.segments.append(ExternSegment(self.map_size))
super().rebase(new_base)
def make_extern(self, name, size=0, alignment=None, thumb=False, sym_type=SymbolType.TYPE_FUNCTION, point_to=None, libname=None) -> Symbol:
try:
return self._symbol_cache[name]
except KeyError:
pass
tls = sym_type == SymbolType.TYPE_TLS_OBJECT
SymbolCls = Symbol
if point_to is not None:
simdata = PointToPrecise
else:
simdata = lookup(name, libname)
if simdata is not None:
SymbolCls = simdata
size = simdata.static_size(self)
if sym_type != simdata.type:
l.warning("Symbol type mismatch between export request and response for %s. What's going on?", name)
real_size = max(size, 1)
if alignment is None:
alignment = self.arch.bytes
make_toc = getattr(self.loader.main_object, 'is_ppc64_abiv1', False) and sym_type == SymbolType.TYPE_FUNCTION
toc_symbol = None
if make_toc:
# we make two symbols, one for the func and one for the toc
# the one for the func ends up named with the #func suffix, the toc gets the normal name
# we return the one for the toc
toc_symbol = self.make_extern(name, size=0x18, alignment=8, sym_type=SymbolType.TYPE_OBJECT)
name += '#func'
if size == 0 and sym_type in (SymbolType.TYPE_NONE, SymbolType.TYPE_OBJECT, SymbolType.TYPE_TLS_OBJECT):
l.warning("Symbol was allocated without a known size; emulation may fail if it is used non-opaquely: %s", name)
self._warned_data_import = True
real_size = 8
local_addr = self._allocate(real_size, alignment=alignment, thumb=thumb, tls=tls)
if local_addr is None:
if self._next_object is None:
# we're at the end of the line. make a new extern object
# this should only be hit if we're doing this outside a loading pass
self._make_new_externs(real_size, alignment, tls)
return self._next_object.make_extern(name, size=size, alignment=alignment, sym_type=sym_type, libname=libname)
l.info("Created extern symbol for %s", name)
new_symbol = SymbolCls(self, name, local_addr, size, sym_type)
new_symbol.is_export = True
new_symbol.is_extern = True
if point_to is not None:
new_symbol.pointto_name = point_to.name
new_symbol.pointto_type = point_to.type
new_symbol.pointto_precise = point_to
self._symbol_cache[name] = new_symbol
self.symbols.add(new_symbol)
self._init_symbol(new_symbol)
if make_toc:
# write the pointer to the func into the toc
# i.e. make a relocation for it
# then if we're already mapped, apply the relocation manually
reloc = TOCRelocation(self, toc_symbol, toc_symbol.relative_addr)
reloc.resolve(new_symbol)
self.relocs.append(reloc)
if self._is_mapped:
reloc.relocate()
return toc_symbol
return new_symbol
def get_pseudo_addr(self, name) -> int:
if not self._is_mapped:
raise CLEError("Can't allocate with extern object before it is mapped")
return self.make_extern(name).rebased_addr
def allocate(self, size=1, alignment=8, thumb=False, tls=False) -> int:
if not self._is_mapped:
raise CLEError("Can't allocate with extern object before it is mapped")
result = self._allocate(size=size, alignment=alignment, thumb=thumb, tls=tls)
if result is None:
if self._next_object is None:
# we're at the end of the line. make a new extern object
# this should only be hit if we're doing this outside a loading pass
self._make_new_externs(size, alignment, tls)
result = self._next_object.allocate(size=size, alignment=alignment, thumb=thumb, tls=tls)
return result + (0 if tls else self.mapped_base)
def _make_new_externs(self, size, alignment, tls):
self._next_object = ExternObject(self.loader, map_size=max(size + alignment, 0x8000) if not tls else 0x8000, tls_size=max(size + alignment, 0x1000) if tls else 0x1000)
self._next_object._finalize_tls()
self.loader._internal_load(self._next_object)
def _allocate(self, size=1, alignment=8, thumb=False, tls=False):
if tls:
start = self.tls_next_addr
limit = self.tls_data_size
else:
start = self.next_addr
limit = self.map_size
addr = ALIGN_UP(start, alignment) | thumb
next_start = addr + size
if next_start >= limit:
if self._is_mapped:
return None
else:
if tls:
self.tls_data_size += next_start - limit
else:
self.map_size += next_start - limit
if tls:
self.tls_next_addr = next_start
return addr
else:
self.next_addr = next_start
return addr
@property
def max_addr(self):
return AT.from_rva(self.map_size, self).to_mva()
def make_import(self, name, sym_type):
if name not in self.imports:
sym = Symbol(self, name, 0, 0, sym_type)
sym.is_import = True
sym.is_extern = True
# this is kind of tricky... normally if you have an import and an export of the same name in the binary
# the two symbols are *the same symbol*, usually with a copy relocation. but we don't know ahead of time
# whether we will have the symbol here in externs, so we will not expose the import symbol to the rest of
# the world.
self._import_symbols[name] = sym
return sym
else:
sym = self._import_symbols[name]
if sym.type != sym_type:
raise CLEOperationError("Created the same extern import %s with two different types. Something isn't right!")
return sym
def _init_symbol(self, symbol):
if isinstance(symbol, SimData):
relocs = symbol.relocations()
self.relocs.extend(relocs)
if self._is_mapped:
# TODO: is this right for tls?
if symbol.type == SymbolType.TYPE_TLS_OBJECT:
self.memory.store(self.tls_block_size, symbol.value())
else:
self.memory.store(symbol.relative_addr, symbol.value())
for reloc in relocs:
reloc.relocate()
else:
self._delayed_writes.append(symbol)
class KernelObject(Backend):
def __init__(self, loader, map_size=0x8000):
super().__init__('cle##kernel', None, loader=loader)
self.map_size = map_size
self.set_arch(loader.main_object.arch)
self.memory.add_backer(0, bytes(map_size))
self.provides = 'kernel space'
self.pic = True
def add_name(self, name, addr):
self._symbol_cache[name] = Symbol(self, name, AT.from_mva(addr, self).to_rva(), 1, SymbolType.TYPE_FUNCTION)
@property
def max_addr(self):
return AT.from_rva(self.map_size, self).to_mva()
from .simdata import lookup, SimData
from .simdata.common import PointTo, SimDataSimpleRelocation
class PointToPrecise(PointTo):
pointto_precise = None
def relocations(self):
return [SimDataSimpleRelocation(
self.owner,
self.pointto_precise,
self.relative_addr,
self.addend,
preresolved=True,
)]
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29892,
13,
4706,
1723,
29962,
13,
2
] |
.frameworks/skdaccess/GLDAS_Data.py | MITeaps/codesnippets | 0 | 57365 | # Start and end date
gldas_start_date = '2010-01-01'
gldas_end_date = '2014-01-01'
# Location (Latitude and Longitude)
gldas_geo_point = AutoParam([(38, -117), (38, -118)])
# Create data fetcher
gldasdf = GLDASDF([gldas_geo_point],start_date=gldas_start_date,
end_date=gldas_end_date, resample=False)
| [
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] |
polltask/tasks/task_execute_system_opt.py | xuweiliang/Codelibrary | 0 | 62772 | #-*- coding: utf-8 -*-
from polltask.task import Task
class TaskExecuteSystemOpt(Task):
def __init__(self, status_queue):
super(TaskExecuteSystemOpt, self).__init__(status_queue,
'TaskExecuteSystemOpt',
'subwsgi',
task_desc="Execute system command")
def register(self, mapper, loader):
mapper.connect('system_opt', '/system_opt/{action}', controller=loader.load_app('system_opt'))
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] |
util/import/raze/crates.bzl | silas-enf/rules_rust | 1 | 13202 | <reponame>silas-enf/rules_rust<gh_stars>1-10
"""
@generated
cargo-raze generated Bazel file.
DO NOT EDIT! Replaced on runs of cargo-raze
"""
load("@bazel_tools//tools/build_defs/repo:git.bzl", "new_git_repository") # buildifier: disable=load
load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive") # buildifier: disable=load
load("@bazel_tools//tools/build_defs/repo:utils.bzl", "maybe") # buildifier: disable=load
def rules_rust_util_import_fetch_remote_crates():
"""This function defines a collection of repos and should be called in a WORKSPACE file"""
maybe(
http_archive,
name = "rules_rust_util_import__aho_corasick__0_7_15",
url = "https://crates.io/api/v1/crates/aho-corasick/0.7.15/download",
type = "tar.gz",
sha256 = "7404febffaa47dac81aa44dba71523c9d069b1bdc50a77db41195149e17f68e5",
strip_prefix = "aho-corasick-0.7.15",
build_file = Label("//util/import/raze/remote:BUILD.aho-corasick-0.7.15.bazel"),
)
maybe(
http_archive,
name = "rules_rust_util_import__cfg_if__1_0_0",
url = "https://crates.io/api/v1/crates/cfg-if/1.0.0/download",
type = "tar.gz",
sha256 = "baf1de4339761588bc0619e3cbc0120ee582ebb74b53b4efbf79117bd2da40fd",
strip_prefix = "cfg-if-1.0.0",
build_file = Label("//util/import/raze/remote:BUILD.cfg-if-1.0.0.bazel"),
)
maybe(
http_archive,
name = "rules_rust_util_import__env_logger__0_8_4",
url = "https://crates.io/api/v1/crates/env_logger/0.8.4/download",
type = "tar.gz",
sha256 = "a19187fea3ac7e84da7dacf48de0c45d63c6a76f9490dae389aead16c243fce3",
strip_prefix = "env_logger-0.8.4",
build_file = Label("//util/import/raze/remote:BUILD.env_logger-0.8.4.bazel"),
)
maybe(
http_archive,
name = "rules_rust_util_import__getrandom__0_2_3",
url = "https://crates.io/api/v1/crates/getrandom/0.2.3/download",
type = "tar.gz",
sha256 = "7fcd999463524c52659517fe2cea98493cfe485d10565e7b0fb07dbba7ad2753",
strip_prefix = "getrandom-0.2.3",
build_file = Label("//util/import/raze/remote:BUILD.getrandom-0.2.3.bazel"),
)
maybe(
http_archive,
name = "rules_rust_util_import__lazy_static__1_4_0",
url = "https://crates.io/api/v1/crates/lazy_static/1.4.0/download",
type = "tar.gz",
sha256 = "e2abad23fbc42b3700f2f279844dc832adb2b2eb069b2df918f455c4e18cc646",
strip_prefix = "lazy_static-1.4.0",
build_file = Label("//util/import/raze/remote:BUILD.lazy_static-1.4.0.bazel"),
)
maybe(
http_archive,
name = "rules_rust_util_import__libc__0_2_112",
url = "https://crates.io/api/v1/crates/libc/0.2.112/download",
type = "tar.gz",
sha256 = "1b03d17f364a3a042d5e5d46b053bbbf82c92c9430c592dd4c064dc6ee997125",
strip_prefix = "libc-0.2.112",
build_file = Label("//util/import/raze/remote:BUILD.libc-0.2.112.bazel"),
)
maybe(
http_archive,
name = "rules_rust_util_import__log__0_4_14",
url = "https://crates.io/api/v1/crates/log/0.4.14/download",
type = "tar.gz",
sha256 = "51b9bbe6c47d51fc3e1a9b945965946b4c44142ab8792c50835a980d362c2710",
strip_prefix = "log-0.4.14",
build_file = Label("//util/import/raze/remote:BUILD.log-0.4.14.bazel"),
)
maybe(
http_archive,
name = "rules_rust_util_import__memchr__2_4_1",
url = "https://crates.io/api/v1/crates/memchr/2.4.1/download",
type = "tar.gz",
sha256 = "308cc39be01b73d0d18f82a0e7b2a3df85245f84af96fdddc5d202d27e47b86a",
strip_prefix = "memchr-2.4.1",
build_file = Label("//util/import/raze/remote:BUILD.memchr-2.4.1.bazel"),
)
maybe(
http_archive,
name = "rules_rust_util_import__proc_macro2__1_0_33",
url = "https://crates.io/api/v1/crates/proc-macro2/1.0.33/download",
type = "tar.gz",
sha256 = "fb37d2df5df740e582f28f8560cf425f52bb267d872fe58358eadb554909f07a",
strip_prefix = "proc-macro2-1.0.33",
build_file = Label("//util/import/raze/remote:BUILD.proc-macro2-1.0.33.bazel"),
)
maybe(
http_archive,
name = "rules_rust_util_import__quickcheck__1_0_3",
url = "https://crates.io/api/v1/crates/quickcheck/1.0.3/download",
type = "tar.gz",
sha256 = "588f6378e4dd99458b60ec275b4477add41ce4fa9f64dcba6f15adccb19b50d6",
strip_prefix = "quickcheck-1.0.3",
build_file = Label("//util/import/raze/remote:BUILD.quickcheck-1.0.3.bazel"),
)
maybe(
http_archive,
name = "rules_rust_util_import__quote__1_0_10",
url = "https://crates.io/api/v1/crates/quote/1.0.10/download",
type = "tar.gz",
sha256 = "38bc8cc6a5f2e3655e0899c1b848643b2562f853f114bfec7be120678e3ace05",
strip_prefix = "quote-1.0.10",
build_file = Label("//util/import/raze/remote:BUILD.quote-1.0.10.bazel"),
)
maybe(
http_archive,
name = "rules_rust_util_import__rand__0_8_4",
url = "https://crates.io/api/v1/crates/rand/0.8.4/download",
type = "tar.gz",
sha256 = "2e7573632e6454cf6b99d7aac4ccca54be06da05aca2ef7423d22d27d4d4bcd8",
strip_prefix = "rand-0.8.4",
build_file = Label("//util/import/raze/remote:BUILD.rand-0.8.4.bazel"),
)
maybe(
http_archive,
name = "rules_rust_util_import__rand_core__0_6_3",
url = "https://crates.io/api/v1/crates/rand_core/0.6.3/download",
type = "tar.gz",
sha256 = "d34f1408f55294453790c48b2f1ebbb1c5b4b7563eb1f418bcfcfdbb06ebb4e7",
strip_prefix = "rand_core-0.6.3",
build_file = Label("//util/import/raze/remote:BUILD.rand_core-0.6.3.bazel"),
)
maybe(
http_archive,
name = "rules_rust_util_import__regex__1_4_6",
url = "https://crates.io/api/v1/crates/regex/1.4.6/download",
type = "tar.gz",
sha256 = "2a26af418b574bd56588335b3a3659a65725d4e636eb1016c2f9e3b38c7cc759",
strip_prefix = "regex-1.4.6",
build_file = Label("//util/import/raze/remote:BUILD.regex-1.4.6.bazel"),
)
maybe(
http_archive,
name = "rules_rust_util_import__regex_syntax__0_6_25",
url = "https://crates.io/api/v1/crates/regex-syntax/0.6.25/download",
type = "tar.gz",
sha256 = "f497285884f3fcff424ffc933e56d7cbca511def0c9831a7f9b5f6153e3cc89b",
strip_prefix = "regex-syntax-0.6.25",
build_file = Label("//util/import/raze/remote:BUILD.regex-syntax-0.6.25.bazel"),
)
maybe(
http_archive,
name = "rules_rust_util_import__syn__1_0_82",
url = "https://crates.io/api/v1/crates/syn/1.0.82/download",
type = "tar.gz",
sha256 = "8daf5dd0bb60cbd4137b1b587d2fc0ae729bc07cf01cd70b36a1ed5ade3b9d59",
strip_prefix = "syn-1.0.82",
build_file = Label("//util/import/raze/remote:BUILD.syn-1.0.82.bazel"),
)
maybe(
http_archive,
name = "rules_rust_util_import__unicode_xid__0_2_2",
url = "https://crates.io/api/v1/crates/unicode-xid/0.2.2/download",
type = "tar.gz",
sha256 = "8ccb82d61f80a663efe1f787a51b16b5a51e3314d6ac365b08639f52387b33f3",
strip_prefix = "unicode-xid-0.2.2",
build_file = Label("//util/import/raze/remote:BUILD.unicode-xid-0.2.2.bazel"),
)
maybe(
http_archive,
name = "rules_rust_util_import__wasi__0_10_2_wasi_snapshot_preview1",
url = "https://crates.io/api/v1/crates/wasi/0.10.2+wasi-snapshot-preview1/download",
type = "tar.gz",
sha256 = "fd6fbd9a79829dd1ad0cc20627bf1ed606756a7f77edff7b66b7064f9cb327c6",
strip_prefix = "wasi-0.10.2+wasi-snapshot-preview1",
build_file = Label("//util/import/raze/remote:BUILD.wasi-0.10.2+wasi-snapshot-preview1.bazel"),
)
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eigenface.py | lion-tohiro/MyEigenface | 0 | 39249 | from cv2 import cv2
import numpy as np
import sys
import os
from base import normalize
# some parameters of training and testing data
train_sub_count = 40
train_img_count = 5
total_face = 200
row = 70
col = 70
def eigenfaces_train(src_path):
img_list = np.empty((row*col, total_face))
count = 0
# read all the faces and flatten them
for i in range(1, train_sub_count+1):
for j in range(1, train_img_count+1):
img_path = src_path + "/s" + str(i) + "/" + str(j) + ".png"
img = cv2.imread(img_path, 0)
img_col = np.array(img).flatten()
img_list[:, count] = img_col[:]
count += 1
# compute the average of the faces
img_mean = np.sum(img_list, axis=1) / total_face
diff = np.empty((row*col, total_face))
# compute the difference matrix
for i in range(0, total_face):
diff[:, i] = img_list[:, i] - img_mean[:]
cov = np.mat(diff)*np.mat(diff.T) / total_face
eigen_values, eigen_vectors = np.linalg.eigh(cov)
# sort the eigenvalues and eigenvectors by desc
sort_index = np.argsort(-eigen_values)
eigen_values = eigen_values[sort_index]
eigen_vectors = eigen_vectors[:, sort_index]
# print(eigen_values)
'''
compute the coveriance matrix
here we don't use original algrithom to avoid computing an 10000+ * 10000+ coveriance matrix later
oringinal: cov = 1/m * A*A^T => it will be an 10000+ * 10000+ matrix
when the dimension of the image (here we mean row*col) > the total number of the training images (here we mean total_face)
(1)cov*v = A*A^T*v = e*v (e is eigenvalue of cov, v is eigenvector of cov) => original
(2)let cov'*u = A^T*A*u = e*u
thus, on both sides of the equation(2) left side multiplied by A, we can get the equation below
(3)A*A^T*A*u = A*e2*u = e2*A*u
compare (1) with (3), if u is eigenvector of cov' of eigenvalue e, we can find that A*u = v
(e is not zero, cov and cov' have the same not-zero eigenvalues, but have different number of zero eigenvalue, it can be proofed)
so we can compute A^T*A instead of A*A^T to simplify the computation (which will generate a matrix with only 200 * 200 data)
cov = np.matrix(diff.T)*np.matrix(diff) / total_face
# compute the eigen values and eigen vectors of cov
eigen_values, vectors = np.linalg.eigh(cov)
eigen_vectors = np.matrix(diff)*np.matrix(vectors)
# sort the eigenvalues and eigenvectors by desc
sort_index = np.argsort(-eigen_values)
eigen_values = eigen_values[sort_index]
eigen_vectors = eigen_vectors[:, sort_index]
print(eigen_values)
'''
# for each image we compute the y (y = A^T * x, weight) and we will compare yf(the input image) with yf, find the nearest one
eigenfaces_weight = np.matrix(eigen_vectors.T)*np.matrix(diff)
return img_mean, eigen_values, eigen_vectors, eigenfaces_weight | [
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compiler/extensions/python/runtime/src/zserio/bitfield.py | PeachOS/zserio | 2 | 26963 | <filename>compiler/extensions/python/runtime/src/zserio/bitfield.py
"""
The module provides help methods for bit fields calculation.
"""
from zserio.exception import PythonRuntimeException
def getBitFieldLowerBound(length):
"""
Gets the lower bound of a unsigned bitfield type with given length.
:param length: Length of the unsigned bitfield in bits.
:returns: The lowest value the unsigned bitfield can hold.
:raises PythonRuntimeException: If unsigned bitfield with wrong length has been specified.
"""
_checkBitFieldLength(length, MAX_UNSIGNED_BITFIELD_BITS)
return 0
def getBitFieldUpperBound(length):
"""
Gets the upper bound of a unsigned bitfield type with given length.
:param length: Length of the unsigned bitfield in bits.
:returns: The largest value the unsigned bitfield can hold.
:raises PythonRuntimeException: If unsigned bitfield with wrong length has been specified.
"""
_checkBitFieldLength(length, MAX_UNSIGNED_BITFIELD_BITS)
return (1 << length) - 1
def getSignedBitFieldLowerBound(length):
"""
Gets the lower bound of a signed bitfield type with given length.
:param length: Length of the signed bitfield in bits.
:returns: The lowest value the signed bitfield can hold.
:raises PythonRuntimeException: If signed bitfield with wrong length has been specified.
"""
_checkBitFieldLength(length, MAX_SIGNED_BITFIELD_BITS)
return -(1 << (length - 1))
def getSignedBitFieldUpperBound(length):
"""
Gets the upper bound of a signed bitfield type with given length.
:param length: Length of the signed bitfield in bits.
:returns: The largest value the signed bitfield can hold.
:raises PythonRuntimeException: If signed bitfield with wrong length has been specified.
"""
_checkBitFieldLength(length, MAX_SIGNED_BITFIELD_BITS)
return (1 << (length - 1)) - 1
def _checkBitFieldLength(length, maxBitFieldLength):
if length <= 0 or length > maxBitFieldLength:
raise PythonRuntimeException("Asking for bound of bitfield with invalid length %d!" % length)
MAX_SIGNED_BITFIELD_BITS = 64
MAX_UNSIGNED_BITFIELD_BITS = 63
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functions/dissectData/lambda_handler.py | zinedine-zeitnot/anomaly-detection | 3 | 23678 | <filename>functions/dissectData/lambda_handler.py<gh_stars>1-10
from data_dissector import DataDissector
def handler(event, _):
switchpoint_trio = DataDissector.dissect_data(data=event['data'])
return {
"switchpoint": switchpoint_trio.switchpoint,
"preSwitchAverage": switchpoint_trio.pre_switch_average,
"postSwitchAverage": switchpoint_trio.post_switch_average,
}
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VkChatBot/bot.py | valerii-chirkov/PythonToolsAndPrograms | 0 | 69954 | import requests
from pony.orm import db_session
from chatbot import handlers
import _token
from chatbot.models import UserState, Registration
try:
import settings
except ImportError:
exit('Do cp settings.py.default settings.py and set token!')
from vk_api import VkApi
from vk_api.bot_longpoll import VkBotLongPoll, VkBotEventType
import random
import logging
log = logging.getLogger("bot")
def configure_logging():
stream_handler = logging.StreamHandler()
stream_handler.setFormatter(logging.Formatter("%(levelname)s %(message)s"))
stream_handler.setLevel(logging.INFO)
log.addHandler(stream_handler)
file_handler = logging.FileHandler("bot.log")
logging.Formatter.default_time_format = '%d-%m-%Y %H:%M'
file_handler.setFormatter(logging.Formatter("%(asctime)s %(levelname)s %(message)s"))
file_handler.setLevel(logging.DEBUG)
log.addHandler(file_handler)
log.setLevel(logging.DEBUG)
class Bot:
"""
Echo bot for vk.com
Use python >3.7
"""
def __init__(self, group_id, token):
"""
:param group_id: group id
:param token: secret token
"""
self.group_id = group_id
self.token = token
self.vk = VkApi(token=self.token)
self.long_poller = VkBotLongPoll(vk=self.vk, group_id=self.group_id)
self.api = self.vk.get_api()
def run(self):
for event in self.long_poller.listen():
try:
self.on_event(event)
except Exception as ex:
log.exception('Ошибка в обработке события. %s', type(ex))
@db_session
def on_event(self, event):
if event.type != VkBotEventType.MESSAGE_NEW:
log.info('Мы пока не умеем обрабатывать события такого типа. %s', event.type)
return
user_id = event.object.peer_id
text = event.object.text
state = UserState.get(user_id=str(user_id))
if state is not None:
self.continue_scenario(text, state, user_id)
else:
# search intent
for intent in settings.INTENTS:
log.debug(f'User got {intent}')
if any(token in text.lower() for token in intent['tokens']):
# run intent
if intent['answer']:
self.send_text(intent['answer'], user_id)
else:
self.start_scenario(user_id, intent['scenario'], text)
break
else:
self.send_text(settings.DEFAULT_ANSWER, user_id)
def send_text(self, text_to_send, user_id):
self.api.messages.send(
message=text_to_send,
random_id=random.randint(0, 2 ** 20),
peer_id=user_id)
def send_image(self, image, user_id):
upload_url = self.api.photos.getMessagesUploadServer()['upload_url']
upload_data = requests.post(url=upload_url, files={'photo': ('image.png', image, 'image/png')}).json()
image_data = self.api.photos.saveMessagesPhoto(**upload_data)
owner_id = image_data[0]['owner_id']
media_id = image_data[0]['id']
attachment = f'photo{owner_id}_{media_id}'
self.api.messages.send(
attachment=attachment,
random_id=random.randint(0, 2 ** 20),
peer_id=user_id)
def send_step(self, step, user_id, text, context):
if 'text' in step:
self.send_text(step['text'].format(**context), user_id)
if 'image' in step:
handler = getattr(handlers, step['image'])
image = handler(text, context)
self.send_image(image, user_id)
def start_scenario(self, user_id, scenario_name, text):
scenario = settings.SCENARIOS[scenario_name]
first_step = scenario['first_step']
step = scenario['steps'][first_step]
self.send_step(step, user_id, text, context={})
UserState(user_id=str(user_id), scenario_name=scenario_name, step_name=first_step, context={})
def continue_scenario(self, text, state, user_id):
steps = settings.SCENARIOS[state.scenario_name]['steps']
step = steps[state.step_name]
handler = getattr(handlers, step['handler'])
if handler(text=text, context=state.context):
# next_step
next_step = steps[step['next_step']]
self.send_step(next_step, user_id, text, state.context)
if next_step['next_step']:
# switch to next step
state.step_name = step['next_step']
else:
# finish scenario
log.info('Registered {name} {email}'.format(**state.context))
Registration(name=state.context['name'], email=state.context['email'])
state.delete()
else:
# retry current step
text_to_send = step['failure_text'].format(**state.context)
self.send_text(text_to_send, user_id)
if __name__ == '__main__':
configure_logging()
bot = Bot(_token.GROUP_ID, _token.TOKEN)
bot.run()
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] |
_conf.py | rohe/dstester | 1 | 61130 | <gh_stars>1-10
from saml2 import BINDING_HTTP_REDIRECT
from saml2 import BINDING_HTTP_POST
from saml2.extension.idpdisc import BINDING_DISCO
from saml2.saml import NAME_FORMAT_URI
#BASE= "http://localhost:8087"
BASE = "https://lingon.ladok.umu.se:8087"
CONFIG = {
"entityid": "%s/sp.xml" % BASE,
"description": "My SP",
"service": {
"sp": {
"name": "DS Tester",
"endpoints": {
"assertion_consumer_service": [
("%s/acs/redirect" % BASE, BINDING_HTTP_REDIRECT),
("%s/acs/post" % BASE, BINDING_HTTP_POST)],
"single_logout_service": [
("%s/slo" % BASE, BINDING_HTTP_REDIRECT)],
"discovery_response": [
("%s/disco" % BASE, BINDING_DISCO)
]
},
}
},
"debug": 1,
"key_file": "pki/mykey.pem",
"cert_file": "pki/mycert.pem",
"attribute_map_dir": "./attributemaps",
"metadata": {"mdfile": ["./swamid2.md"]},
# -- below used by make_metadata --
"organization": {
"name": "ITS",
"display_name": [("Umea Universitet - ITS", "se"),
("Umea Universitet ITS", "en")],
"url": "http://www.its.umu.se",
},
"contact_person": [
{
"given_name": "Roland",
"sur_name": "Hedberg",
"email_address": ["<EMAIL>"],
"contact_type": "technical",
},
],
"xmlsec_binary": "/opt/local/bin/xmlsec1",
"name_form": NAME_FORMAT_URI,
"logger": {
"rotating": {
"filename": "sp.log",
"maxBytes": 1000000,
"backupCount": 5,
},
"loglevel": "debug",
}
}
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] |
import_data.py | arcyfelix/Object-Shape-Classification-Utilizing-Magnetic-Field-Disturbance-and-Supervised-Machine-Learning | 1 | 88381 | <filename>import_data.py<gh_stars>1-10
import pandas as pd
import numpy as np
from tflearn.data_utils import to_categorical
from import_data import *
def import_csv(file_path, shuffle = False):
data = pd.read_csv(file_path)
print('*' * 70)
print('Import CSV file has been successful!')
if shuffle == True:
data.reindex(np.random.permutation(data.index))
print('The data has been shuffled!')
else:
print('The data has not been shuffled!')
return data
def labels_info(output_data):
labels_names = np.unique(output_data)
number_of_labels = labels_names.shape[0]
print('*' * 70)
print("Number of uniques categories:", number_of_labels)
labels_as_numbers = np.arange(number_of_labels)
print("Categories as numbers", labels_as_numbers)
for _ in labels_as_numbers:
print('Category ' + str(_) + ' is ' + str(labels_names[_]))
return number_of_labels
def labels_as_numbers(output_data):
_, output_data_as_numbers = np.unique(output_data, return_inverse=True)
return output_data_as_numbers
# -------------------------------------------------------------------------------
# Acquiring the data
def get_data_MNIST():
folder = 'Digit Recognizer'
file_name = 'train.csv'
specific_dataset_source = folder + '/' + file_name
output_columns = ['label']
data = import_csv(specific_dataset_source, shuffle = True)
# Data split into the input and output
x_data = data
y_data = np.array(data.pop('label'))
print('Shape of the input data:', x_data.shape)
print('Shape of the output data:', y_data.shape)
# Standalization
x_data = x_data / 255
num_samples = x_data.shape[0]
input_features = x_data.shape[1]
print('Number of samples:', num_samples)
print('Number of the input features:', input_features)
y_data_as_numbers = labels_as_numbers(y_data)
# Cross validation data preparation
split_percentage = 80
split_index = int(x_data.shape[0]/(100/split_percentage))
x_train = np.array(x_data[:split_index])
x_val = np.array(x_data[split_index:])
y_train = np.array(y_data_as_numbers[:split_index])
y_val = np.array(y_data_as_numbers[split_index:])
# Information about the data
print(x_train.shape)
print(x_val.shape)
print(y_train.shape)
print(y_val.shape)
# Shaping data into the correct shape.
x_train = x_train.reshape([-1, 28, 28, 1])
x_val = x_val.reshape([-1, 28, 28, 1])
y_train = to_categorical(y_train, nb_classes = 10)
y_val = to_categorical(y_val, nb_classes = 10)
return x_train, x_val, y_train, y_val
def get_data_MNIST_test():
# Loading the test data
file_name_test = 'test.csv'
folder = 'Digit Recognizer'
source = folder + '/' + file_name_test
data = pd.read_csv(source)
test_input = data.loc[:, :]
return test_input.as_matrix()
# Oxford Flowers Dataset
def get_data_oxford_flowers():
import tflearn.datasets.oxflower17 as oxflower17
X, Y = oxflower17.load_data(one_hot = True, resize_pics = (227, 227))
split_percentage = 80
split_index = int(X.shape[0]/(100/split_percentage))
x_train = np.array(X[:split_index])
x_val = np.array(X[split_index:])
y_train = np.array(Y[:split_index])
y_val = np.array(Y[split_index:])
return x_train, x_val, y_train, y_val
def get_data_CIFAR10(dataset = 'Train + Val'):
from tflearn.datasets import cifar10
(X, Y), (X_test, Y_test) = cifar10.load_data(one_hot=True)
# Size is 32, 32, 3
split_percentage = 100
split_index = int(X.shape[0]/(100/split_percentage))
x_train = np.array(X[:split_index])
x_val = np.array(X[split_index:])
y_train = np.array(Y[:split_index])
y_val = np.array(Y[split_index:])
if dataset == 'Train + Val':
return x_train, x_val, y_train, y_val
else:
return X_test, Y_test
def get_data_MNIST_native(dataset = 'Train + Val'):
import tflearn.datasets.mnist as mnist
X, Y, X_test, Y_test = mnist.load_data(one_hot =True)
X = X.reshape([-1, 28, 28, 1])
X_test = X_test.reshape([-1, 28, 28, 1])
# Size is 28, 28, 1
split_percentage = 100
split_index = int(X.shape[0]/(100/split_percentage))
x_train = np.array(X[:split_index])
x_val = np.array(X[split_index:])
y_train = np.array(Y[:split_index])
y_val = np.array(Y[split_index:])
if dataset == 'Train + Val':
return x_train, x_val, y_train, y_val
else:
return X_test, Y_test
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pynics/binparse/castep_bin_results.py | ThatPerson/pynics | 2 | 17712 | <filename>pynics/binparse/castep_bin_results.py
# Python 2-to-3 compatibility code
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import collections
from pynics.binparse.forbinfile import RecordError
def cbin_results_parse(binfile, results_store, curr_version, params,
current_cell, tolerant=False):
# First, parse the elec results part (always present)
results_store['elec'] = collections.OrderedDict()
cbin_elec_parse(binfile, results_store[
'elec'], curr_version, params, current_cell)
# Then go for the optional stuff
cbin_optional_parse(binfile, results_store, curr_version)
def cbin_elec_parse(binfile, elec_store, curr_version, params, current_cell):
# A few informations are stored
elec_store['found_ground_state_wvfn'] = not (
binfile.read_record('i')[0] == 0) # Logical value
elec_store['found_ground_state_den'] = not (
binfile.read_record('i')[0] == 0) # Logical value
elec_store['total_energy'] = binfile.read_record('d')[0]
elec_store['fermi_energy'] = binfile.read_record('d')
# Fermi energy for both spins if we have two. This relies on param being
# already parsed
if params['nspins'] == 2:
elec_store['fermi_energy'] = (elec_store['fermi_energy'][
0], elec_store['fermi_energy'][0])
elec_store['wvfn_nbands'], elec_store[
'wvfn_nspins'] = binfile.read_record('i')
# Read occupation eigenvalues for the Kohn-Sham states. This relies on
# cell being already parsed
elec_store['occupation'] = {}
for kp_i in range(0, current_cell['nkpts']):
kp = binfile.read_record('d')
elec_store['occupation'][kp] = {'occ': [], 'nrg': []}
for ns_i in range(0, elec_store['wvfn_nspins']):
elec_store['occupation'][kp]['occ'].append(
binfile.read_record('d')) # Occupation
elec_store['occupation'][kp]['nrg'].append(
binfile.read_record('d')) # Energies
# Why is this here again? Whatever.
elec_store['found_ground_state_den'] = not (
binfile.read_record('i')[0] == 0) # Logical value
# Read the fine grid size, keep the information because it is of use for
# various other parsing operations
elec_store['model_ngx_fine'], elec_store['model_ngy_fine'], elec_store[
'model_ngz_fine'] = binfile.read_record('i')
# Finally, dummy read of density
for n in range(0, elec_store['model_ngx_fine'] *
elec_store['model_ngy_fine']):
dummy_int = binfile.read_record('i')
def cbin_optional_parse(binfile, results_store, curr_version, tolerant=False):
if (tolerant): # In this case, unknown sections will simply be ignored
def skip_optional():
while True:
header = self.binfile.read_string_record()
if header.isalpha():
self.binfile.backspace()
break
try:
while True:
header = binfile.read_string_record()
if (header == 'END'):
break
try:
castep_bin_olist[header](binfile, results_store, curr_version)
except KeyError:
if (tolerant):
print("Skipping unrecognized header " + header)
skip_optional()
else:
# The default case, doesn't account for forward
# compatibility for now
raise CastepBinError('Unknown optional section found')
except RecordError:
raise CastepBinError(
'End of file reached while parsing optional blocks')
# Utility routine
def tensor_reshape(V):
return tuple([
tuple([
tuple([V[i+j+k] for i in range(0, 3)
]) for j in range(0, 9, 3)
]) for k in range(0, len(V), 9)])
def opt_e_fermi_parse(binfile, results_store, curr_version):
# Parse Fermi energy for second spin
efermi_2 = binfile.read_record('d')[0]
results_store['elec']['fermi_energy'] = (
results_store['elec']['fermi_energy'][0], efermi_2)
def opt_oep_pot_parse(binfile, results_store, curr_version):
# Parse optimized effective potential
results_store['oep_pot'] = {}
results_store['oep_pot']['found_oep_ground_state'] = not (
binfile.read_record('i') == 0)
results_store['oep_pot']['oep_energy_difference'] = (binfile
.read_record('d')[0])
# We need nspins, we get it indirectly
nspins = len(results_store['elec']['fermi_energy'])
ngx_fine = results_store['elec']['model_ngx_fine']
ngy_fine = results_store['elec']['model_ngy_fine']
ngz_fine = results_store['elec']['model_ngz_fine']
# Sort of necessary to initialize here
results_store['oep_pot']['pot_fine'] = [[0.0 for s in range(
0, nspins)]]*ngx_fine*ngy_fine*ngz_fine
for s_i in range(0, nspins):
for nx1 in range(0, ngx_fine):
for ny1 in range(0, ngy_fine):
nx, ny, grid_charge_r, grid_charge_im = binfile.read_record(
'iidd')
# Fortran convention needs to be used
for nz in range(1, ngz_fine+1):
# Back to Python convention, arrays count from 0
igrid = (nx-1)+(ny-1)*ngx_fine+(nz-1)*ngx_fine*ngy_fine
results_store['oep_pot']['pot_fine'][igrid][
s_i] = (grid_charge_r, grid_charge_im)
def opt_de_dloge_parse(binfile, results_store, curr_version):
# Parse energy logarithmic derivative
results_store['de_dloge'] = binfile.read_record('d')[0]
def opt_forces_parse(binfile, results_store, curr_version):
# Parse forces
f = binfile.read_record('d')
# Reshape
results_store['forces'] = tuple(
[tuple([f[i+j] for i in range(0, 3)]) for j in range(0, len(f), 3)])
def opt_stress_parse(binfile, results_store, curr_version):
# Parse stress & strain tensors
results_store['stress'] = {}
stress = binfile.read_record('d')
strain = binfile.read_record('d')
results_store['stress']['stress'] = stress
results_store['stress']['strain'] = tensor_reshape(strain)
def opt_shielding_parse(binfile, results_store, curr_version):
# Parse NMR shieldings
results_store['shielding'] = {}
results_store['shielding']['ms'] = binfile.read_record('d')
results_store['shielding']['sus'] = binfile.read_record('d')
# Reshape ms as a list of tensors
results_store['shielding']['ms'] = tensor_reshape(
results_store['shielding']['ms'])
def opt_efg_parse(binfile, results_store, curr_version):
results_store['efg'] = binfile.read_record('d')
results_store['efg'] = tensor_reshape(results_store['efg'])
castep_bin_olist = {
'E_FERMI': opt_e_fermi_parse,
'OEP_POT': opt_oep_pot_parse,
'DE_DLOGE': opt_de_dloge_parse,
'FORCES': opt_forces_parse,
'STRESS': opt_stress_parse,
'SHIELDING': opt_shielding_parse,
'EFG': opt_efg_parse,
}
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common/read_fasta.py | christophfeinauer/PairwiseDistillations | 1 | 91315 | import numpy as np
from collections import defaultdict
import sys
import argparse
# we rely on ordered dictionaries here
assert sys.version_info >= (3, 6)
def read_fasta(fasta_path, alphabet='ACDEFGHIKLMNPQRSTVWY-', default_index=20):
# read all the sequences into a dictionary
seq_dict = {}
with open(fasta_path, 'r') as file_handle:
seq_id = None
for line in file_handle:
line = line.strip()
if line.startswith(">"):
seq_id = line
seq_dict[seq_id] = ""
continue
assert seq_id is not None
line = ''.join([c for c in line if c.isupper() or c == '-'])
seq_dict[seq_id] += line
aa_index = defaultdict(lambda: default_index, {alphabet[i]: i for i in range(len(alphabet))})
seq_msa = []
keys_list = []
for k in seq_dict.keys():
seq_msa.append([aa_index[s] for s in seq_dict[k]])
keys_list.append(k)
seq_msa = np.array(seq_msa, dtype=int)
# reweighting sequences
seq_weight = np.zeros(seq_msa.shape)
for j in range(seq_msa.shape[1]):
aa_type, aa_counts = np.unique(seq_msa[:, j], return_counts=True)
num_type = len(aa_type)
aa_dict = {}
for a in aa_type:
aa_dict[a] = aa_counts[list(aa_type).index(a)]
for i in range(seq_msa.shape[0]):
seq_weight[i, j] = (1.0 / num_type) * (1.0 / aa_dict[seq_msa[i, j]])
tot_weight = np.sum(seq_weight)
seq_weight = seq_weight.sum(1) / tot_weight
return seq_msa, seq_weight, len(alphabet)
def get_seq_len(fasta_path):
seq_len = 0
first_line = True
with open(fasta_path, 'r') as file_handle:
for line in file_handle:
if first_line:
if not line.startswith(">"):
raise ValueError("Expect first line to start with >")
first_line = False
continue
if (first_line is False and line.startswith(">")):
return seq_len
seq_len += len([c for c in line if c.isupper() or c == '-'])
raise ValueError("Could not determine sequence length")
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--print_seq_len', action='store_true')
parser.add_argument('--fasta_path', type=str, default=None)
args = parser.parse_args()
if args.fasta_path is None and args.print_seq_len:
raise ValueError("need fasta_path if printing seq_len")
if args.print_seq_len:
seq_len = get_seq_len(args.fasta_path)
print(seq_len)
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] |
tpi1/tree_search.py | vascoalramos/ia | 1 | 78981 | <filename>tpi1/tree_search.py<gh_stars>1-10
# Module: tree_search
#
# This module provides a set o classes for automated
# problem solving through tree search:
# SearchDomain - problem domains
# SearchProblem - concrete problems to be solved
# SearchNode - search tree nodes
# SearchTree - search tree with the necessary methods for searhing
#
# (c) <NAME>
# Introducao a Inteligencia Artificial, 2012-2019,
# Inteligência Artificial, 2014-2019
from abc import ABC, abstractmethod
# Dominios de pesquisa
# Permitem calcular
# as accoes possiveis em cada estado, etc
class SearchDomain(ABC):
# construtor
@abstractmethod
def __init__(self):
pass
# lista de accoes possiveis num estado
@abstractmethod
def actions(self, state):
pass
# resultado de uma accao num estado, ou seja, o estado seguinte
@abstractmethod
def result(self, state, action):
pass
# custo de uma accao num estado
@abstractmethod
def cost(self, state, action):
pass
# custo estimado de chegar de um estado a outro
@abstractmethod
def heuristic(self, state, goal):
pass
# test if the given "goal" is satisfied in "state"
@abstractmethod
def satisfies(self, state, goal):
pass
# Problemas concretos a resolver
# dentro de um determinado dominio
class SearchProblem:
def __init__(self, domain, initial, goal):
self.domain = domain
self.initial = initial
self.goal = goal
def goal_test(self, state):
return self.domain.satisfies(state,self.goal)
# Nos de uma arvore de pesquisa
class SearchNode:
def __init__(self,state,parent):
self.state = state
self.parent = parent
def __str__(self):
return "no(" + str(self.state) + "," + str(self.parent) + ")"
def __repr__(self):
return str(self)
# Arvores de pesquisa
class SearchTree:
# construtor
def __init__(self,problem, strategy='breadth'):
self.problem = problem
self.root = SearchNode(problem.initial, None)
self.open_nodes = [self.root]
self.strategy = strategy
# obter o caminho (sequencia de estados) da raiz ate um no
def get_path(self,node):
if node.parent == None:
return [node.state]
path = self.get_path(node.parent)
path += [node.state]
return(path)
# procurar a solucao
def search(self):
while self.open_nodes != []:
node = self.open_nodes.pop(0)
if self.problem.goal_test(node.state):
return self.get_path(node)
lnewnodes = []
for a in self.problem.domain.actions(node.state):
newstate = self.problem.domain.result(node.state,a)
if newstate not in self.get_path(node):
newnode = SearchNode(newstate,node)
lnewnodes.append(newnode)
self.add_to_open(lnewnodes)
return None
# juntar novos nos a lista de nos abertos de acordo com a estrategia
def add_to_open(self,lnewnodes):
if self.strategy == 'breadth':
self.open_nodes.extend(lnewnodes)
elif self.strategy == 'depth':
self.open_nodes[:0] = lnewnodes
elif self.strategy == 'astar':
self.astar_add_to_open(lnewnodes)
elif self.strategy == 'uniform':
pass
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] |
pbr/scene/shadowcatcher.py | vuvuzella/nubots-pbr | 1 | 173309 | #!/usr/local/blender -P
import bpy
from scene.blender_object import BlenderObject
class ShadowCatcher(BlenderObject):
def __init__(self):
bpy.ops.mesh.primitive_plane_add()
self.obj = bpy.data.objects["Plane"]
self.obj.name = "SC_Plane"
self.obj.cycles.is_shadow_catcher = True
self.obj.cycles.show_transparent = True
self.obj.scale = (50, 50, 1)
self.obj.location = (0, 0, -0.0001)
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] |
keystone/tests/unit/test_v3_assignment.py | crowdy/keystone | 0 | 101 | # Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
import datetime
import random
import uuid
import freezegun
import http.client
from testtools import matchers
from keystone.common import provider_api
import keystone.conf
from keystone import exception
from keystone.resource.backends import base as resource_base
from keystone.tests import unit
from keystone.tests.unit import test_v3
CONF = keystone.conf.CONF
PROVIDERS = provider_api.ProviderAPIs
class SystemRoleAssignmentMixin(object):
def _create_new_role(self):
"""Create a role available for use anywhere and return the ID."""
ref = unit.new_role_ref()
response = self.post('/roles', body={'role': ref})
# We only really need the role ID, so omit the rest of the response and
# return the ID of the role we just created.
return response.json_body['role']['id']
def _create_group(self):
body = {
'group': {
'domain_id': self.domain_id,
'name': uuid.uuid4().hex
}
}
response = self.post('/groups/', body=body)
return response.json_body['group']
def _create_user(self):
body = {
'user': {
'domain_id': self.domain_id,
'name': uuid.uuid4().hex
}
}
response = self.post('/users/', body=body)
return response.json_body['user']
class AssignmentTestCase(test_v3.RestfulTestCase,
test_v3.AssignmentTestMixin,
SystemRoleAssignmentMixin):
"""Test roles and role assignments."""
def setUp(self):
super(AssignmentTestCase, self).setUp()
self.group = unit.new_group_ref(domain_id=self.domain_id)
self.group = PROVIDERS.identity_api.create_group(self.group)
self.group_id = self.group['id']
# Role CRUD tests
def test_create_role(self):
"""Call ``POST /roles``."""
ref = unit.new_role_ref()
r = self.post(
'/roles',
body={'role': ref})
return self.assertValidRoleResponse(r, ref)
def test_create_role_bad_request(self):
"""Call ``POST /roles``."""
self.post('/roles', body={'role': {}},
expected_status=http.client.BAD_REQUEST)
def test_list_head_roles(self):
"""Call ``GET & HEAD /roles``."""
resource_url = '/roles'
r = self.get(resource_url)
self.assertValidRoleListResponse(r, ref=self.role,
resource_url=resource_url)
self.head(resource_url, expected_status=http.client.OK)
def test_get_head_role(self):
"""Call ``GET & HEAD /roles/{role_id}``."""
resource_url = '/roles/%(role_id)s' % {
'role_id': self.role_id}
r = self.get(resource_url)
self.assertValidRoleResponse(r, self.role)
self.head(resource_url, expected_status=http.client.OK)
def test_update_role(self):
"""Call ``PATCH /roles/{role_id}``."""
ref = unit.new_role_ref()
del ref['id']
r = self.patch('/roles/%(role_id)s' % {
'role_id': self.role_id},
body={'role': ref})
self.assertValidRoleResponse(r, ref)
def test_delete_role(self):
"""Call ``DELETE /roles/{role_id}``."""
self.delete('/roles/%(role_id)s' % {
'role_id': self.role_id})
# Role Grants tests
def test_crud_user_project_role_grants(self):
role = unit.new_role_ref()
PROVIDERS.role_api.create_role(role['id'], role)
collection_url = (
'/projects/%(project_id)s/users/%(user_id)s/roles' % {
'project_id': self.project['id'],
'user_id': self.user['id']})
member_url = '%(collection_url)s/%(role_id)s' % {
'collection_url': collection_url,
'role_id': role['id']}
# There is a role assignment for self.user on self.project
r = self.get(collection_url)
self.assertValidRoleListResponse(r, ref=self.role,
expected_length=1)
self.put(member_url)
self.head(member_url)
self.get(member_url, expected_status=http.client.NO_CONTENT)
r = self.get(collection_url)
self.assertValidRoleListResponse(r, ref=role,
resource_url=collection_url,
expected_length=2)
self.head(collection_url, expected_status=http.client.OK)
self.delete(member_url)
r = self.get(collection_url)
self.assertValidRoleListResponse(r, ref=self.role, expected_length=1)
self.assertIn(collection_url, r.result['links']['self'])
self.head(collection_url, expected_status=http.client.OK)
def test_crud_user_project_role_grants_no_user(self):
"""Grant role on a project to a user that doesn't exist.
When grant a role on a project to a user that doesn't exist, the server
returns Not Found for the user.
"""
user_id = uuid.uuid4().hex
collection_url = (
'/projects/%(project_id)s/users/%(user_id)s/roles' % {
'project_id': self.project['id'], 'user_id': user_id})
member_url = '%(collection_url)s/%(role_id)s' % {
'collection_url': collection_url,
'role_id': self.role_id}
self.put(member_url, expected_status=http.client.NOT_FOUND)
self.head(member_url, expected_status=http.client.NOT_FOUND)
self.get(member_url, expected_status=http.client.NOT_FOUND)
def test_crud_user_domain_role_grants(self):
time = datetime.datetime.utcnow()
with freezegun.freeze_time(time) as frozen_datetime:
collection_url = (
'/domains/%(domain_id)s/users/%(user_id)s/roles' % {
'domain_id': self.domain_id,
'user_id': self.user['id']})
member_url = '%(collection_url)s/%(role_id)s' % {
'collection_url': collection_url,
'role_id': self.role_id}
self.put(member_url)
self.head(member_url)
self.get(member_url, expected_status=http.client.NO_CONTENT)
r = self.get(collection_url)
self.assertValidRoleListResponse(r, ref=self.role,
resource_url=collection_url)
self.head(collection_url, expected_status=http.client.OK)
self.delete(member_url)
# NOTE(lbragstad): Make sure we wait a second before we ask for the
# roles. This ensures the token we use isn't considered revoked
# because it was issued within the same second as a revocation
# event.
frozen_datetime.tick(delta=datetime.timedelta(seconds=1))
r = self.get(collection_url)
self.assertValidRoleListResponse(r, expected_length=0,
resource_url=collection_url)
self.head(collection_url, expected_status=http.client.OK)
def test_crud_user_domain_role_grants_no_user(self):
"""Grant role on a domain to a user that doesn't exist.
When grant a role on a domain to a user that doesn't exist, the server
returns 404 Not Found for the user.
"""
user_id = uuid.uuid4().hex
collection_url = (
'/domains/%(domain_id)s/users/%(user_id)s/roles' % {
'domain_id': self.domain_id, 'user_id': user_id})
member_url = '%(collection_url)s/%(role_id)s' % {
'collection_url': collection_url,
'role_id': self.role_id}
self.put(member_url, expected_status=http.client.NOT_FOUND)
self.head(member_url, expected_status=http.client.NOT_FOUND)
self.get(member_url, expected_status=http.client.NOT_FOUND)
def test_crud_group_project_role_grants(self):
time = datetime.datetime.utcnow()
with freezegun.freeze_time(time) as frozen_datetime:
collection_url = (
'/projects/%(project_id)s/groups/%(group_id)s/roles' % {
'project_id': self.project_id,
'group_id': self.group_id})
member_url = '%(collection_url)s/%(role_id)s' % {
'collection_url': collection_url,
'role_id': self.role_id}
self.put(member_url)
self.head(member_url)
self.get(member_url, expected_status=http.client.NO_CONTENT)
r = self.get(collection_url)
self.assertValidRoleListResponse(r, ref=self.role,
resource_url=collection_url)
self.head(collection_url, expected_status=http.client.OK)
self.delete(member_url)
# NOTE(lbragstad): Make sure we wait a second before we ask for the
# roles. This ensures the token we use isn't considered revoked
# because it was issued within the same second as a revocation
# event.
frozen_datetime.tick(delta=datetime.timedelta(seconds=1))
r = self.get(collection_url)
self.assertValidRoleListResponse(r, expected_length=0,
resource_url=collection_url)
self.head(collection_url, expected_status=http.client.OK)
def test_crud_group_project_role_grants_no_group(self):
"""Grant role on a project to a group that doesn't exist.
When grant a role on a project to a group that doesn't exist, the
server returns 404 Not Found for the group.
"""
group_id = uuid.uuid4().hex
collection_url = (
'/projects/%(project_id)s/groups/%(group_id)s/roles' % {
'project_id': self.project_id,
'group_id': group_id})
member_url = '%(collection_url)s/%(role_id)s' % {
'collection_url': collection_url,
'role_id': self.role_id}
self.put(member_url, expected_status=http.client.NOT_FOUND)
self.head(member_url, expected_status=http.client.NOT_FOUND)
self.get(member_url, expected_status=http.client.NOT_FOUND)
def test_crud_group_domain_role_grants(self):
time = datetime.datetime.utcnow()
with freezegun.freeze_time(time) as frozen_datetime:
collection_url = (
'/domains/%(domain_id)s/groups/%(group_id)s/roles' % {
'domain_id': self.domain_id,
'group_id': self.group_id})
member_url = '%(collection_url)s/%(role_id)s' % {
'collection_url': collection_url,
'role_id': self.role_id}
self.put(member_url)
self.head(member_url)
self.get(member_url, expected_status=http.client.NO_CONTENT)
r = self.get(collection_url)
self.assertValidRoleListResponse(r, ref=self.role,
resource_url=collection_url)
self.head(collection_url, expected_status=http.client.OK)
self.delete(member_url)
# NOTE(lbragstad): Make sure we wait a second before we ask for the
# roles. This ensures the token we use isn't considered revoked
# because it was issued within the same second as a revocation
# event.
frozen_datetime.tick(delta=datetime.timedelta(seconds=1))
r = self.get(collection_url)
self.assertValidRoleListResponse(r, expected_length=0,
resource_url=collection_url)
self.head(collection_url, expected_status=http.client.OK)
def test_crud_group_domain_role_grants_no_group(self):
"""Grant role on a domain to a group that doesn't exist.
When grant a role on a domain to a group that doesn't exist, the server
returns 404 Not Found for the group.
"""
group_id = uuid.uuid4().hex
collection_url = (
'/domains/%(domain_id)s/groups/%(group_id)s/roles' % {
'domain_id': self.domain_id,
'group_id': group_id})
member_url = '%(collection_url)s/%(role_id)s' % {
'collection_url': collection_url,
'role_id': self.role_id}
self.put(member_url, expected_status=http.client.NOT_FOUND)
self.head(member_url, expected_status=http.client.NOT_FOUND)
self.get(member_url, expected_status=http.client.NOT_FOUND)
def _create_new_user_and_assign_role_on_project(self):
"""Create a new user and assign user a role on a project."""
# Create a new user
new_user = unit.new_user_ref(domain_id=self.domain_id)
user_ref = PROVIDERS.identity_api.create_user(new_user)
# Assign the user a role on the project
collection_url = (
'/projects/%(project_id)s/users/%(user_id)s/roles' % {
'project_id': self.project_id,
'user_id': user_ref['id']})
member_url = ('%(collection_url)s/%(role_id)s' % {
'collection_url': collection_url,
'role_id': self.role_id})
self.put(member_url)
# Check the user has the role assigned
self.head(member_url)
self.get(member_url, expected_status=http.client.NO_CONTENT)
return member_url, user_ref
def test_delete_user_before_removing_role_assignment_succeeds(self):
"""Call ``DELETE`` on the user before the role assignment."""
member_url, user = self._create_new_user_and_assign_role_on_project()
# Delete the user from identity backend
PROVIDERS.identity_api.driver.delete_user(user['id'])
# Clean up the role assignment
self.delete(member_url)
# Make sure the role is gone
self.head(member_url, expected_status=http.client.NOT_FOUND)
def test_delete_group_before_removing_role_assignment_succeeds(self):
# Disable the cache so that we perform a fresh check of the identity
# backend when attempting to remove the role assignment.
self.config_fixture.config(group='cache', enabled=False)
# Create a new group
group = unit.new_group_ref(domain_id=self.domain_id)
group_ref = PROVIDERS.identity_api.create_group(group)
# Assign the user a role on the project
collection_url = (
'/projects/%(project_id)s/groups/%(group_id)s/roles' % {
'project_id': self.project_id,
'group_id': group_ref['id']})
member_url = ('%(collection_url)s/%(role_id)s' % {
'collection_url': collection_url,
'role_id': self.role_id})
self.put(member_url)
# Check the user has the role assigned
self.head(member_url)
self.get(member_url, expected_status=http.client.NO_CONTENT)
# Simulate removing the group via LDAP by directly removing it from the
# identity backend.
PROVIDERS.identity_api.driver.delete_group(group_ref['id'])
# Ensure we can clean up the role assignment even though the group
# doesn't exist
self.delete(member_url)
def test_delete_user_before_removing_system_assignments_succeeds(self):
system_role = self._create_new_role()
user = self._create_user()
path = (
'/system/users/%(user_id)s/roles/%(role_id)s' %
{'user_id': user['id'], 'role_id': system_role}
)
self.put(path)
response = self.get('/role_assignments')
number_of_assignments = len(response.json_body['role_assignments'])
path = '/users/%(user_id)s' % {'user_id': user['id']}
self.delete(path)
# The user with the system role assignment is a new user and only has
# one role on the system. We should expect one less role assignment in
# the list.
response = self.get('/role_assignments')
self.assertValidRoleAssignmentListResponse(
response, expected_length=number_of_assignments - 1
)
def test_delete_user_and_check_role_assignment_fails(self):
"""Call ``DELETE`` on the user and check the role assignment."""
member_url, user = self._create_new_user_and_assign_role_on_project()
# Delete the user from identity backend
PROVIDERS.identity_api.delete_user(user['id'])
# We should get a 404 Not Found when looking for the user in the
# identity backend because we're not performing a delete operation on
# the role.
self.head(member_url, expected_status=http.client.NOT_FOUND)
def test_token_revoked_once_group_role_grant_revoked(self):
"""Test token invalid when direct & indirect role on user is revoked.
When a role granted to a group is revoked for a given scope,
and user direct role is revoked, then tokens created
by user will be invalid.
"""
time = datetime.datetime.utcnow()
with freezegun.freeze_time(time) as frozen_datetime:
# creates grant from group on project.
PROVIDERS.assignment_api.create_grant(
role_id=self.role['id'], project_id=self.project['id'],
group_id=self.group['id']
)
# adds user to the group.
PROVIDERS.identity_api.add_user_to_group(
user_id=self.user['id'], group_id=self.group['id']
)
# creates a token for the user
auth_body = self.build_authentication_request(
user_id=self.user['id'],
password=<PASSWORD>['password'],
project_id=self.project['id'])
token_resp = self.post('/auth/tokens', body=auth_body)
token = token_resp.headers.get('x-subject-token')
# validates the returned token; it should be valid.
self.head('/auth/tokens',
headers={'x-subject-token': token},
expected_status=http.client.OK)
frozen_datetime.tick(delta=datetime.timedelta(seconds=1))
# revokes the grant from group on project.
PROVIDERS.assignment_api.delete_grant(
role_id=self.role['id'], project_id=self.project['id'],
group_id=self.group['id'])
# revokes the direct role form user on project
PROVIDERS.assignment_api.delete_grant(
role_id=self.role['id'], project_id=self.project['id'],
user_id=self.user['id']
)
frozen_datetime.tick(delta=datetime.timedelta(seconds=1))
# validates the same token again; it should not longer be valid.
self.head('/auth/tokens', token=token,
expected_status=http.client.UNAUTHORIZED)
def test_delete_group_before_removing_system_assignments_succeeds(self):
system_role = self._create_new_role()
group = self._create_group()
path = (
'/system/groups/%(group_id)s/roles/%(role_id)s' %
{'group_id': group['id'], 'role_id': system_role}
)
self.put(path)
response = self.get('/role_assignments')
number_of_assignments = len(response.json_body['role_assignments'])
path = '/groups/%(group_id)s' % {'group_id': group['id']}
self.delete(path)
# The group with the system role assignment is a new group and only has
# one role on the system. We should expect one less role assignment in
# the list.
response = self.get('/role_assignments')
self.assertValidRoleAssignmentListResponse(
response, expected_length=number_of_assignments - 1
)
@unit.skip_if_cache_disabled('assignment')
def test_delete_grant_from_user_and_project_invalidate_cache(self):
# create a new project
new_project = unit.new_project_ref(domain_id=self.domain_id)
PROVIDERS.resource_api.create_project(new_project['id'], new_project)
collection_url = (
'/projects/%(project_id)s/users/%(user_id)s/roles' % {
'project_id': new_project['id'],
'user_id': self.user['id']})
member_url = '%(collection_url)s/%(role_id)s' % {
'collection_url': collection_url,
'role_id': self.role_id}
# create the user a grant on the new project
self.put(member_url)
# check the grant that was just created
self.head(member_url)
self.get(member_url, expected_status=http.client.NO_CONTENT)
resp = self.get(collection_url)
self.assertValidRoleListResponse(resp, ref=self.role,
resource_url=collection_url)
# delete the grant
self.delete(member_url)
# get the collection and ensure there are no roles on the project
resp = self.get(collection_url)
self.assertListEqual(resp.json_body['roles'], [])
@unit.skip_if_cache_disabled('assignment')
def test_delete_grant_from_user_and_domain_invalidates_cache(self):
# create a new domain
new_domain = unit.new_domain_ref()
PROVIDERS.resource_api.create_domain(new_domain['id'], new_domain)
collection_url = (
'/domains/%(domain_id)s/users/%(user_id)s/roles' % {
'domain_id': new_domain['id'],
'user_id': self.user['id']})
member_url = '%(collection_url)s/%(role_id)s' % {
'collection_url': collection_url,
'role_id': self.role_id}
# create the user a grant on the new domain
self.put(member_url)
# check the grant that was just created
self.head(member_url)
self.get(member_url, expected_status=http.client.NO_CONTENT)
resp = self.get(collection_url)
self.assertValidRoleListResponse(resp, ref=self.role,
resource_url=collection_url)
# delete the grant
self.delete(member_url)
# get the collection and ensure there are no roles on the domain
resp = self.get(collection_url)
self.assertListEqual(resp.json_body['roles'], [])
@unit.skip_if_cache_disabled('assignment')
def test_delete_grant_from_group_and_project_invalidates_cache(self):
# create a new project
new_project = unit.new_project_ref(domain_id=self.domain_id)
PROVIDERS.resource_api.create_project(new_project['id'], new_project)
collection_url = (
'/projects/%(project_id)s/groups/%(group_id)s/roles' % {
'project_id': new_project['id'],
'group_id': self.group['id']})
member_url = '%(collection_url)s/%(role_id)s' % {
'collection_url': collection_url,
'role_id': self.role_id}
# create the group a grant on the new project
self.put(member_url)
# check the grant that was just created
self.head(member_url)
self.get(member_url, expected_status=http.client.NO_CONTENT)
resp = self.get(collection_url)
self.assertValidRoleListResponse(resp, ref=self.role,
resource_url=collection_url)
# delete the grant
self.delete(member_url)
# get the collection and ensure there are no roles on the project
resp = self.get(collection_url)
self.assertListEqual(resp.json_body['roles'], [])
@unit.skip_if_cache_disabled('assignment')
def test_delete_grant_from_group_and_domain_invalidates_cache(self):
# create a new domain
new_domain = unit.new_domain_ref()
PROVIDERS.resource_api.create_domain(new_domain['id'], new_domain)
collection_url = (
'/domains/%(domain_id)s/groups/%(group_id)s/roles' % {
'domain_id': new_domain['id'],
'group_id': self.group['id']})
member_url = '%(collection_url)s/%(role_id)s' % {
'collection_url': collection_url,
'role_id': self.role_id}
# create the group a grant on the new domain
self.put(member_url)
# check the grant that was just created
self.head(member_url)
self.get(member_url, expected_status=http.client.NO_CONTENT)
resp = self.get(collection_url)
self.assertValidRoleListResponse(resp, ref=self.role,
resource_url=collection_url)
# delete the grant
self.delete(member_url)
# get the collection and ensure there are no roles on the domain
resp = self.get(collection_url)
self.assertListEqual(resp.json_body['roles'], [])
# Role Assignments tests
def test_get_head_role_assignments(self):
"""Call ``GET & HEAD /role_assignments``.
The sample data set up already has a user, group and project
that is part of self.domain. We use these plus a new user
we create as our data set, making sure we ignore any
role assignments that are already in existence.
Since we don't yet support a first class entity for role
assignments, we are only testing the LIST API. To create
and delete the role assignments we use the old grant APIs.
Test Plan:
- Create extra user for tests
- Get a list of all existing role assignments
- Add a new assignment for each of the four combinations, i.e.
group+domain, user+domain, group+project, user+project, using
the same role each time
- Get a new list of all role assignments, checking these four new
ones have been added
- Then delete the four we added
- Get a new list of all role assignments, checking the four have
been removed
"""
time = datetime.datetime.utcnow()
with freezegun.freeze_time(time) as frozen_datetime:
# Since the default fixtures already assign some roles to the
# user it creates, we also need a new user that will not have any
# existing assignments
user1 = unit.new_user_ref(domain_id=self.domain['id'])
user1 = PROVIDERS.identity_api.create_user(user1)
role = unit.new_role_ref()
PROVIDERS.role_api.create_role(role['id'], role)
collection_url = '/role_assignments'
r = self.get(collection_url)
self.assertValidRoleAssignmentListResponse(
r, resource_url=collection_url)
self.head(collection_url, expected_status=http.client.OK)
existing_assignments = len(r.result.get('role_assignments'))
# Now add one of each of the four types of assignment, making sure
# that we get them all back.
gd_entity = self.build_role_assignment_entity(
domain_id=self.domain_id,
group_id=self.group_id,
role_id=role['id'])
self.put(gd_entity['links']['assignment'])
r = self.get(collection_url)
self.assertValidRoleAssignmentListResponse(
r,
expected_length=existing_assignments + 1,
resource_url=collection_url)
self.assertRoleAssignmentInListResponse(r, gd_entity)
self.head(collection_url, expected_status=http.client.OK)
ud_entity = self.build_role_assignment_entity(
domain_id=self.domain_id,
user_id=user1['id'],
role_id=role['id'])
self.put(ud_entity['links']['assignment'])
r = self.get(collection_url)
self.assertValidRoleAssignmentListResponse(
r,
expected_length=existing_assignments + 2,
resource_url=collection_url)
self.assertRoleAssignmentInListResponse(r, ud_entity)
self.head(collection_url, expected_status=http.client.OK)
gp_entity = self.build_role_assignment_entity(
project_id=self.project_id, group_id=self.group_id,
role_id=role['id'])
self.put(gp_entity['links']['assignment'])
r = self.get(collection_url)
self.assertValidRoleAssignmentListResponse(
r,
expected_length=existing_assignments + 3,
resource_url=collection_url)
self.assertRoleAssignmentInListResponse(r, gp_entity)
self.head(collection_url, expected_status=http.client.OK)
up_entity = self.build_role_assignment_entity(
project_id=self.project_id, user_id=user1['id'],
role_id=role['id'])
self.put(up_entity['links']['assignment'])
r = self.get(collection_url)
self.assertValidRoleAssignmentListResponse(
r,
expected_length=existing_assignments + 4,
resource_url=collection_url)
self.assertRoleAssignmentInListResponse(r, up_entity)
self.head(collection_url, expected_status=http.client.OK)
# Now delete the four we added and make sure they are removed
# from the collection.
self.delete(gd_entity['links']['assignment'])
self.delete(ud_entity['links']['assignment'])
self.delete(gp_entity['links']['assignment'])
self.delete(up_entity['links']['assignment'])
frozen_datetime.tick(delta=datetime.timedelta(seconds=1))
r = self.get(collection_url)
self.assertValidRoleAssignmentListResponse(
r,
expected_length=existing_assignments,
resource_url=collection_url)
self.assertRoleAssignmentNotInListResponse(r, gd_entity)
self.assertRoleAssignmentNotInListResponse(r, ud_entity)
self.assertRoleAssignmentNotInListResponse(r, gp_entity)
self.assertRoleAssignmentNotInListResponse(r, up_entity)
self.head(collection_url, expected_status=http.client.OK)
def test_get_effective_role_assignments(self):
"""Call ``GET /role_assignments?effective``.
Test Plan:
- Create two extra user for tests
- Add these users to a group
- Add a role assignment for the group on a domain
- Get a list of all role assignments, checking one has been added
- Then get a list of all effective role assignments - the group
assignment should have turned into assignments on the domain
for each of the group members.
"""
user1 = unit.create_user(PROVIDERS.identity_api,
domain_id=self.domain['id'])
user2 = unit.create_user(PROVIDERS.identity_api,
domain_id=self.domain['id'])
PROVIDERS.identity_api.add_user_to_group(user1['id'], self.group['id'])
PROVIDERS.identity_api.add_user_to_group(user2['id'], self.group['id'])
collection_url = '/role_assignments'
r = self.get(collection_url)
self.assertValidRoleAssignmentListResponse(r,
resource_url=collection_url)
existing_assignments = len(r.result.get('role_assignments'))
gd_entity = self.build_role_assignment_entity(domain_id=self.domain_id,
group_id=self.group_id,
role_id=self.role_id)
self.put(gd_entity['links']['assignment'])
r = self.get(collection_url)
self.assertValidRoleAssignmentListResponse(
r,
expected_length=existing_assignments + 1,
resource_url=collection_url)
self.assertRoleAssignmentInListResponse(r, gd_entity)
# Now re-read the collection asking for effective roles - this
# should mean the group assignment is translated into the two
# member user assignments
collection_url = '/role_assignments?effective'
r = self.get(collection_url)
self.assertValidRoleAssignmentListResponse(
r,
expected_length=existing_assignments + 2,
resource_url=collection_url)
ud_entity = self.build_role_assignment_entity(
link=gd_entity['links']['assignment'], domain_id=self.domain_id,
user_id=user1['id'], role_id=self.role_id)
self.assertRoleAssignmentInListResponse(r, ud_entity)
ud_entity = self.build_role_assignment_entity(
link=gd_entity['links']['assignment'], domain_id=self.domain_id,
user_id=user2['id'], role_id=self.role_id)
self.assertRoleAssignmentInListResponse(r, ud_entity)
def test_check_effective_values_for_role_assignments(self):
"""Call ``GET & HEAD /role_assignments?effective=value``.
Check the various ways of specifying the 'effective'
query parameter. If the 'effective' query parameter
is included then this should always be treated as meaning 'True'
unless it is specified as:
{url}?effective=0
This is by design to match the agreed way of handling
policy checking on query/filter parameters.
Test Plan:
- Create two extra user for tests
- Add these users to a group
- Add a role assignment for the group on a domain
- Get a list of all role assignments, checking one has been added
- Then issue various request with different ways of defining
the 'effective' query parameter. As we have tested the
correctness of the data coming back when we get effective roles
in other tests, here we just use the count of entities to
know if we are getting effective roles or not
"""
user1 = unit.create_user(PROVIDERS.identity_api,
domain_id=self.domain['id'])
user2 = unit.create_user(PROVIDERS.identity_api,
domain_id=self.domain['id'])
PROVIDERS.identity_api.add_user_to_group(user1['id'], self.group['id'])
PROVIDERS.identity_api.add_user_to_group(user2['id'], self.group['id'])
collection_url = '/role_assignments'
r = self.get(collection_url, expected_status=http.client.OK)
self.head(collection_url, expected_status=http.client.OK)
self.assertValidRoleAssignmentListResponse(r,
resource_url=collection_url)
existing_assignments = len(r.result.get('role_assignments'))
gd_entity = self.build_role_assignment_entity(domain_id=self.domain_id,
group_id=self.group_id,
role_id=self.role_id)
self.put(gd_entity['links']['assignment'])
r = self.get(collection_url, expected_status=http.client.OK)
self.head(collection_url, expected_status=http.client.OK)
self.assertValidRoleAssignmentListResponse(
r,
expected_length=existing_assignments + 1,
resource_url=collection_url)
self.assertRoleAssignmentInListResponse(r, gd_entity)
# Now re-read the collection asking for effective roles,
# using the most common way of defining "effective'. This
# should mean the group assignment is translated into the two
# member user assignments
collection_url = '/role_assignments?effective'
r = self.get(collection_url, expected_status=http.client.OK)
self.head(collection_url, expected_status=http.client.OK)
self.assertValidRoleAssignmentListResponse(
r,
expected_length=existing_assignments + 2,
resource_url=collection_url)
# Now set 'effective' to false explicitly - should get
# back the regular roles
collection_url = '/role_assignments?effective=0'
r = self.get(collection_url, expected_status=http.client.OK)
self.head(collection_url, expected_status=http.client.OK)
self.assertValidRoleAssignmentListResponse(
r,
expected_length=existing_assignments + 1,
resource_url=collection_url)
# Now try setting 'effective' to 'False' explicitly- this is
# NOT supported as a way of setting a query or filter
# parameter to false by design. Hence we should get back
# effective roles.
collection_url = '/role_assignments?effective=False'
r = self.get(collection_url, expected_status=http.client.OK)
self.head(collection_url, expected_status=http.client.OK)
self.assertValidRoleAssignmentListResponse(
r,
expected_length=existing_assignments + 2,
resource_url=collection_url)
# Now set 'effective' to True explicitly
collection_url = '/role_assignments?effective=True'
r = self.get(collection_url, expected_status=http.client.OK)
self.head(collection_url, expected_status=http.client.OK)
self.assertValidRoleAssignmentListResponse(
r,
expected_length=existing_assignments + 2,
resource_url=collection_url)
def test_filtered_role_assignments(self):
"""Call ``GET /role_assignments?filters``.
Test Plan:
- Create extra users, group, role and project for tests
- Make the following assignments:
Give group1, role1 on project1 and domain
Give user1, role2 on project1 and domain
Make User1 a member of Group1
- Test a series of single filter list calls, checking that
the correct results are obtained
- Test a multi-filtered list call
- Test listing all effective roles for a given user
- Test the equivalent of the list of roles in a project scoped
token (all effective roles for a user on a project)
"""
# Since the default fixtures already assign some roles to the
# user it creates, we also need a new user that will not have any
# existing assignments
user1 = unit.create_user(PROVIDERS.identity_api,
domain_id=self.domain['id'])
user2 = unit.create_user(PROVIDERS.identity_api,
domain_id=self.domain['id'])
group1 = unit.new_group_ref(domain_id=self.domain['id'])
group1 = PROVIDERS.identity_api.create_group(group1)
PROVIDERS.identity_api.add_user_to_group(user1['id'], group1['id'])
PROVIDERS.identity_api.add_user_to_group(user2['id'], group1['id'])
project1 = unit.new_project_ref(domain_id=self.domain['id'])
PROVIDERS.resource_api.create_project(project1['id'], project1)
self.role1 = unit.new_role_ref()
PROVIDERS.role_api.create_role(self.role1['id'], self.role1)
self.role2 = unit.new_role_ref()
PROVIDERS.role_api.create_role(self.role2['id'], self.role2)
# Now add one of each of the six types of assignment
gd_entity = self.build_role_assignment_entity(
domain_id=self.domain_id, group_id=group1['id'],
role_id=self.role1['id'])
self.put(gd_entity['links']['assignment'])
ud_entity = self.build_role_assignment_entity(domain_id=self.domain_id,
user_id=user1['id'],
role_id=self.role2['id'])
self.put(ud_entity['links']['assignment'])
gp_entity = self.build_role_assignment_entity(
project_id=project1['id'],
group_id=group1['id'],
role_id=self.role1['id'])
self.put(gp_entity['links']['assignment'])
up_entity = self.build_role_assignment_entity(
project_id=project1['id'],
user_id=user1['id'],
role_id=self.role2['id'])
self.put(up_entity['links']['assignment'])
gs_entity = self.build_role_assignment_entity(
system='all',
group_id=group1['id'],
role_id=self.role1['id'])
self.put(gs_entity['links']['assignment'])
us_entity = self.build_role_assignment_entity(
system='all',
user_id=user1['id'],
role_id=self.role2['id'])
self.put(us_entity['links']['assignment'])
us2_entity = self.build_role_assignment_entity(
system='all',
user_id=user2['id'],
role_id=self.role2['id'])
self.put(us2_entity['links']['assignment'])
# Now list by various filters to make sure we get back the right ones
collection_url = ('/role_assignments?scope.project.id=%s' %
project1['id'])
r = self.get(collection_url, expected_status=http.client.OK)
self.head(collection_url, expected_status=http.client.OK)
self.assertValidRoleAssignmentListResponse(r,
expected_length=2,
resource_url=collection_url)
self.assertRoleAssignmentInListResponse(r, up_entity)
self.assertRoleAssignmentInListResponse(r, gp_entity)
collection_url = ('/role_assignments?scope.domain.id=%s' %
self.domain['id'])
r = self.get(collection_url, expected_status=http.client.OK)
self.head(collection_url, expected_status=http.client.OK)
self.assertValidRoleAssignmentListResponse(r,
expected_length=2,
resource_url=collection_url)
self.assertRoleAssignmentInListResponse(r, ud_entity)
self.assertRoleAssignmentInListResponse(r, gd_entity)
collection_url = '/role_assignments?user.id=%s' % user1['id']
r = self.get(collection_url, expected_status=http.client.OK)
self.head(collection_url, expected_status=http.client.OK)
self.assertValidRoleAssignmentListResponse(r,
expected_length=3,
resource_url=collection_url)
self.assertRoleAssignmentInListResponse(r, up_entity)
self.assertRoleAssignmentInListResponse(r, ud_entity)
collection_url = '/role_assignments?group.id=%s' % group1['id']
r = self.get(collection_url, expected_status=http.client.OK)
self.head(collection_url, expected_status=http.client.OK)
self.assertValidRoleAssignmentListResponse(r,
expected_length=3,
resource_url=collection_url)
self.assertRoleAssignmentInListResponse(r, gd_entity)
self.assertRoleAssignmentInListResponse(r, gp_entity)
collection_url = '/role_assignments?role.id=%s' % self.role1['id']
r = self.get(collection_url, expected_status=http.client.OK)
self.head(collection_url, expected_status=http.client.OK)
self.assertValidRoleAssignmentListResponse(r,
expected_length=3,
resource_url=collection_url)
self.assertRoleAssignmentInListResponse(r, gd_entity)
self.assertRoleAssignmentInListResponse(r, gp_entity)
self.assertRoleAssignmentInListResponse(r, gs_entity)
collection_url = '/role_assignments?role.id=%s' % self.role2['id']
r = self.get(collection_url, expected_status=http.client.OK)
self.head(collection_url, expected_status=http.client.OK)
self.assertValidRoleAssignmentListResponse(r,
expected_length=4,
resource_url=collection_url)
self.assertRoleAssignmentInListResponse(r, ud_entity)
self.assertRoleAssignmentInListResponse(r, up_entity)
self.assertRoleAssignmentInListResponse(r, us_entity)
# Let's try combining two filers together....
collection_url = (
'/role_assignments?user.id=%(user_id)s'
'&scope.project.id=%(project_id)s' % {
'user_id': user1['id'],
'project_id': project1['id']})
r = self.get(collection_url, expected_status=http.client.OK)
self.head(collection_url, expected_status=http.client.OK)
self.assertValidRoleAssignmentListResponse(r,
expected_length=1,
resource_url=collection_url)
self.assertRoleAssignmentInListResponse(r, up_entity)
# Now for a harder one - filter for user with effective
# roles - this should return role assignment that were directly
# assigned as well as by virtue of group membership
collection_url = ('/role_assignments?effective&user.id=%s' %
user1['id'])
r = self.get(collection_url, expected_status=http.client.OK)
self.head(collection_url, expected_status=http.client.OK)
self.assertValidRoleAssignmentListResponse(r,
expected_length=4,
resource_url=collection_url)
# Should have the two direct roles...
self.assertRoleAssignmentInListResponse(r, up_entity)
self.assertRoleAssignmentInListResponse(r, ud_entity)
# ...and the two via group membership...
gp1_link = self.build_role_assignment_link(
project_id=project1['id'],
group_id=group1['id'],
role_id=self.role1['id'])
gd1_link = self.build_role_assignment_link(domain_id=self.domain_id,
group_id=group1['id'],
role_id=self.role1['id'])
up1_entity = self.build_role_assignment_entity(
link=gp1_link, project_id=project1['id'],
user_id=user1['id'], role_id=self.role1['id'])
ud1_entity = self.build_role_assignment_entity(
link=gd1_link, domain_id=self.domain_id, user_id=user1['id'],
role_id=self.role1['id'])
self.assertRoleAssignmentInListResponse(r, up1_entity)
self.assertRoleAssignmentInListResponse(r, ud1_entity)
# ...and for the grand-daddy of them all, simulate the request
# that would generate the list of effective roles in a project
# scoped token.
collection_url = (
'/role_assignments?effective&user.id=%(user_id)s'
'&scope.project.id=%(project_id)s' % {
'user_id': user1['id'],
'project_id': project1['id']})
r = self.get(collection_url, expected_status=http.client.OK)
self.head(collection_url, expected_status=http.client.OK)
self.assertValidRoleAssignmentListResponse(r,
expected_length=2,
resource_url=collection_url)
# Should have one direct role and one from group membership...
self.assertRoleAssignmentInListResponse(r, up_entity)
self.assertRoleAssignmentInListResponse(r, up1_entity)
def test_list_system_role_assignments(self):
# create a bunch of roles
user_system_role_id = self._create_new_role()
user_domain_role_id = self._create_new_role()
user_project_role_id = self._create_new_role()
group_system_role_id = self._create_new_role()
group_domain_role_id = self._create_new_role()
group_project_role_id = self._create_new_role()
# create a user and grant the user a role on the system, domain, and
# project
user = self._create_user()
url = '/system/users/%s/roles/%s' % (user['id'], user_system_role_id)
self.put(url)
url = '/domains/%s/users/%s/roles/%s' % (
self.domain_id, user['id'], user_domain_role_id
)
self.put(url)
url = '/projects/%s/users/%s/roles/%s' % (
self.project_id, user['id'], user_project_role_id
)
self.put(url)
# create a group and grant the group a role on the system, domain, and
# project
group = self._create_group()
url = '/system/groups/%s/roles/%s' % (
group['id'], group_system_role_id
)
self.put(url)
url = '/domains/%s/groups/%s/roles/%s' % (
self.domain_id, group['id'], group_domain_role_id
)
self.put(url)
url = '/projects/%s/groups/%s/roles/%s' % (
self.project_id, group['id'], group_project_role_id
)
self.put(url)
# /v3/role_assignments?scope.system=all should return two assignments
response = self.get('/role_assignments?scope.system=all')
self.assertValidRoleAssignmentListResponse(response, expected_length=2)
for assignment in response.json_body['role_assignments']:
self.assertTrue(assignment['scope']['system']['all'])
if assignment.get('user'):
self.assertEqual(user_system_role_id, assignment['role']['id'])
if assignment.get('group'):
self.assertEqual(
group_system_role_id,
assignment['role']['id']
)
# /v3/role_assignments?scope_system=all&user.id=$USER_ID should return
# one role assignment
url = '/role_assignments?scope.system=all&user.id=%s' % user['id']
response = self.get(url)
self.assertValidRoleAssignmentListResponse(response, expected_length=1)
self.assertEqual(
user_system_role_id,
response.json_body['role_assignments'][0]['role']['id']
)
# /v3/role_assignments?scope_system=all&group.id=$GROUP_ID should
# return one role assignment
url = '/role_assignments?scope.system=all&group.id=%s' % group['id']
response = self.get(url)
self.assertValidRoleAssignmentListResponse(response, expected_length=1)
self.assertEqual(
group_system_role_id,
response.json_body['role_assignments'][0]['role']['id']
)
# /v3/role_assignments?user.id=$USER_ID should return 3 assignments
# and system should be in that list of assignments
url = '/role_assignments?user.id=%s' % user['id']
response = self.get(url)
self.assertValidRoleAssignmentListResponse(response, expected_length=3)
for assignment in response.json_body['role_assignments']:
if 'system' in assignment['scope']:
self.assertEqual(
user_system_role_id, assignment['role']['id']
)
if 'domain' in assignment['scope']:
self.assertEqual(
user_domain_role_id, assignment['role']['id']
)
if 'project' in assignment['scope']:
self.assertEqual(
user_project_role_id, assignment['role']['id']
)
# /v3/role_assignments?group.id=$GROUP_ID should return 3 assignments
# and system should be in that list of assignments
url = '/role_assignments?group.id=%s' % group['id']
response = self.get(url)
self.assertValidRoleAssignmentListResponse(response, expected_length=3)
for assignment in response.json_body['role_assignments']:
if 'system' in assignment['scope']:
self.assertEqual(
group_system_role_id, assignment['role']['id']
)
if 'domain' in assignment['scope']:
self.assertEqual(
group_domain_role_id, assignment['role']['id']
)
if 'project' in assignment['scope']:
self.assertEqual(
group_project_role_id, assignment['role']['id']
)
class RoleAssignmentBaseTestCase(test_v3.RestfulTestCase,
test_v3.AssignmentTestMixin):
"""Base class for testing /v3/role_assignments API behavior."""
MAX_HIERARCHY_BREADTH = 3
MAX_HIERARCHY_DEPTH = CONF.max_project_tree_depth - 1
def load_sample_data(self):
"""Create sample data to be used on tests.
Created data are i) a role and ii) a domain containing: a project
hierarchy and 3 users within 3 groups.
"""
def create_project_hierarchy(parent_id, depth):
"""Create a random project hierarchy."""
if depth == 0:
return
breadth = random.randint(1, self.MAX_HIERARCHY_BREADTH)
subprojects = []
for i in range(breadth):
subprojects.append(unit.new_project_ref(
domain_id=self.domain_id, parent_id=parent_id))
PROVIDERS.resource_api.create_project(
subprojects[-1]['id'], subprojects[-1]
)
new_parent = subprojects[random.randint(0, breadth - 1)]
create_project_hierarchy(new_parent['id'], depth - 1)
super(RoleAssignmentBaseTestCase, self).load_sample_data()
# Create a domain
self.domain = unit.new_domain_ref()
self.domain_id = self.domain['id']
PROVIDERS.resource_api.create_domain(self.domain_id, self.domain)
# Create a project hierarchy
self.project = unit.new_project_ref(domain_id=self.domain_id)
self.project_id = self.project['id']
PROVIDERS.resource_api.create_project(self.project_id, self.project)
# Create a random project hierarchy
create_project_hierarchy(self.project_id,
random.randint(1, self.MAX_HIERARCHY_DEPTH))
# Create 3 users
self.user_ids = []
for i in range(3):
user = unit.new_user_ref(domain_id=self.domain_id)
user = PROVIDERS.identity_api.create_user(user)
self.user_ids.append(user['id'])
# Create 3 groups
self.group_ids = []
for i in range(3):
group = unit.new_group_ref(domain_id=self.domain_id)
group = PROVIDERS.identity_api.create_group(group)
self.group_ids.append(group['id'])
# Put 2 members on each group
PROVIDERS.identity_api.add_user_to_group(
user_id=self.user_ids[i], group_id=group['id']
)
PROVIDERS.identity_api.add_user_to_group(
user_id=self.user_ids[i % 2], group_id=group['id']
)
PROVIDERS.assignment_api.create_grant(
user_id=self.user_id, project_id=self.project_id,
role_id=self.role_id
)
# Create a role
self.role = unit.new_role_ref()
self.role_id = self.role['id']
PROVIDERS.role_api.create_role(self.role_id, self.role)
# Set default user and group to be used on tests
self.default_user_id = self.user_ids[0]
self.default_group_id = self.group_ids[0]
def get_role_assignments(self, expected_status=http.client.OK, **filters):
"""Return the result from querying role assignment API + queried URL.
Calls GET /v3/role_assignments?<params> and returns its result, where
<params> is the HTTP query parameters form of effective option plus
filters, if provided. Queried URL is returned as well.
:returns: a tuple containing the list role assignments API response and
queried URL.
"""
query_url = self._get_role_assignments_query_url(**filters)
response = self.get(query_url, expected_status=expected_status)
return (response, query_url)
def _get_role_assignments_query_url(self, **filters):
"""Return non-effective role assignments query URL from given filters.
:param filters: query parameters are created with the provided filters
on role assignments attributes. Valid filters are:
role_id, domain_id, project_id, group_id, user_id and
inherited_to_projects.
:returns: role assignments query URL.
"""
return self.build_role_assignment_query_url(**filters)
class RoleAssignmentFailureTestCase(RoleAssignmentBaseTestCase):
"""Class for testing invalid query params on /v3/role_assignments API.
Querying domain and project, or user and group results in a HTTP 400 Bad
Request, since a role assignment must contain only a single pair of (actor,
target). In addition, since filtering on role assignments applies only to
the final result, effective mode cannot be combined with i) group or ii)
domain and inherited, because it would always result in an empty list.
"""
def test_get_role_assignments_by_domain_and_project(self):
self.get_role_assignments(domain_id=self.domain_id,
project_id=self.project_id,
expected_status=http.client.BAD_REQUEST)
def test_get_role_assignments_by_user_and_group(self):
self.get_role_assignments(user_id=self.default_user_id,
group_id=self.default_group_id,
expected_status=http.client.BAD_REQUEST)
def test_get_role_assignments_by_effective_and_inherited(self):
self.get_role_assignments(domain_id=self.domain_id, effective=True,
inherited_to_projects=True,
expected_status=http.client.BAD_REQUEST)
def test_get_role_assignments_by_effective_and_group(self):
self.get_role_assignments(effective=True,
group_id=self.default_group_id,
expected_status=http.client.BAD_REQUEST)
class RoleAssignmentDirectTestCase(RoleAssignmentBaseTestCase):
"""Class for testing direct assignments on /v3/role_assignments API.
Direct assignments on a domain or project have effect on them directly,
instead of on their project hierarchy, i.e they are non-inherited. In
addition, group direct assignments are not expanded to group's users.
Tests on this class make assertions on the representation and API filtering
of direct assignments.
"""
def _test_get_role_assignments(self, **filters):
"""Generic filtering test method.
According to the provided filters, this method:
- creates a new role assignment;
- asserts that list role assignments API reponds correctly;
- deletes the created role assignment.
:param filters: filters to be considered when listing role assignments.
Valid filters are: role_id, domain_id, project_id,
group_id, user_id and inherited_to_projects.
"""
# Fills default assignment with provided filters
test_assignment = self._set_default_assignment_attributes(**filters)
# Create new role assignment for this test
PROVIDERS.assignment_api.create_grant(**test_assignment)
# Get expected role assignments
expected_assignments = self._list_expected_role_assignments(
**test_assignment)
# Get role assignments from API
response, query_url = self.get_role_assignments(**test_assignment)
self.assertValidRoleAssignmentListResponse(response,
resource_url=query_url)
self.assertEqual(len(expected_assignments),
len(response.result.get('role_assignments')))
# Assert that expected role assignments were returned by the API call
for assignment in expected_assignments:
self.assertRoleAssignmentInListResponse(response, assignment)
# Delete created role assignment
PROVIDERS.assignment_api.delete_grant(**test_assignment)
def _set_default_assignment_attributes(self, **attribs):
"""Insert default values for missing attributes of role assignment.
If no actor, target or role are provided, they will default to values
from sample data.
:param attribs: info from a role assignment entity. Valid attributes
are: role_id, domain_id, project_id, group_id, user_id
and inherited_to_projects.
"""
if not any(target in attribs
for target in ('domain_id', 'projects_id')):
attribs['project_id'] = self.project_id
if not any(actor in attribs for actor in ('user_id', 'group_id')):
attribs['user_id'] = self.default_user_id
if 'role_id' not in attribs:
attribs['role_id'] = self.role_id
return attribs
def _list_expected_role_assignments(self, **filters):
"""Given the filters, it returns expected direct role assignments.
:param filters: filters that will be considered when listing role
assignments. Valid filters are: role_id, domain_id,
project_id, group_id, user_id and
inherited_to_projects.
:returns: the list of the expected role assignments.
"""
return [self.build_role_assignment_entity(**filters)]
# Test cases below call the generic test method, providing different filter
# combinations. Filters are provided as specified in the method name, after
# 'by'. For example, test_get_role_assignments_by_project_user_and_role
# calls the generic test method with project_id, user_id and role_id.
def test_get_role_assignments_by_domain(self, **filters):
self._test_get_role_assignments(domain_id=self.domain_id, **filters)
def test_get_role_assignments_by_project(self, **filters):
self._test_get_role_assignments(project_id=self.project_id, **filters)
def test_get_role_assignments_by_user(self, **filters):
self._test_get_role_assignments(user_id=self.default_user_id,
**filters)
def test_get_role_assignments_by_group(self, **filters):
self._test_get_role_assignments(group_id=self.default_group_id,
**filters)
def test_get_role_assignments_by_role(self, **filters):
self._test_get_role_assignments(role_id=self.role_id, **filters)
def test_get_role_assignments_by_domain_and_user(self, **filters):
self.test_get_role_assignments_by_domain(user_id=self.default_user_id,
**filters)
def test_get_role_assignments_by_domain_and_group(self, **filters):
self.test_get_role_assignments_by_domain(
group_id=self.default_group_id, **filters)
def test_get_role_assignments_by_project_and_user(self, **filters):
self.test_get_role_assignments_by_project(user_id=self.default_user_id,
**filters)
def test_get_role_assignments_by_project_and_group(self, **filters):
self.test_get_role_assignments_by_project(
group_id=self.default_group_id, **filters)
def test_get_role_assignments_by_domain_user_and_role(self, **filters):
self.test_get_role_assignments_by_domain_and_user(role_id=self.role_id,
**filters)
def test_get_role_assignments_by_domain_group_and_role(self, **filters):
self.test_get_role_assignments_by_domain_and_group(
role_id=self.role_id, **filters)
def test_get_role_assignments_by_project_user_and_role(self, **filters):
self.test_get_role_assignments_by_project_and_user(
role_id=self.role_id, **filters)
def test_get_role_assignments_by_project_group_and_role(self, **filters):
self.test_get_role_assignments_by_project_and_group(
role_id=self.role_id, **filters)
class RoleAssignmentInheritedTestCase(RoleAssignmentDirectTestCase):
"""Class for testing inherited assignments on /v3/role_assignments API.
Inherited assignments on a domain or project have no effect on them
directly, but on the projects under them instead.
Tests on this class do not make assertions on the effect of inherited
assignments, but in their representation and API filtering.
"""
def _test_get_role_assignments(self, **filters):
"""Add inherited_to_project filter to expected entity in tests."""
super(RoleAssignmentInheritedTestCase,
self)._test_get_role_assignments(inherited_to_projects=True,
**filters)
class RoleAssignmentEffectiveTestCase(RoleAssignmentInheritedTestCase):
"""Class for testing inheritance effects on /v3/role_assignments API.
Inherited assignments on a domain or project have no effect on them
directly, but on the projects under them instead.
Tests on this class make assertions on the effect of inherited assignments
and API filtering.
"""
def _get_role_assignments_query_url(self, **filters):
"""Return effective role assignments query URL from given filters.
For test methods in this class, effetive will always be true. As in
effective mode, inherited_to_projects, group_id, domain_id and
project_id will always be desconsidered from provided filters.
:param filters: query parameters are created with the provided filters.
Valid filters are: role_id, domain_id, project_id,
group_id, user_id and inherited_to_projects.
:returns: role assignments query URL.
"""
query_filters = filters.copy()
query_filters.pop('inherited_to_projects')
query_filters.pop('group_id', None)
query_filters.pop('domain_id', None)
query_filters.pop('project_id', None)
return self.build_role_assignment_query_url(effective=True,
**query_filters)
def _list_expected_role_assignments(self, **filters):
"""Given the filters, it returns expected direct role assignments.
:param filters: filters that will be considered when listing role
assignments. Valid filters are: role_id, domain_id,
project_id, group_id, user_id and
inherited_to_projects.
:returns: the list of the expected role assignments.
"""
# Get assignment link, to be put on 'links': {'assignment': link}
assignment_link = self.build_role_assignment_link(**filters)
# Expand group membership
user_ids = [None]
if filters.get('group_id'):
user_ids = [user['id'] for user in
PROVIDERS.identity_api.list_users_in_group(
filters['group_id'])]
else:
user_ids = [self.default_user_id]
# Expand role inheritance
project_ids = [None]
if filters.get('domain_id'):
project_ids = [project['id'] for project in
PROVIDERS.resource_api.list_projects_in_domain(
filters.pop('domain_id'))]
else:
project_ids = [project['id'] for project in
PROVIDERS.resource_api.list_projects_in_subtree(
self.project_id)]
# Compute expected role assignments
assignments = []
for project_id in project_ids:
filters['project_id'] = project_id
for user_id in user_ids:
filters['user_id'] = user_id
assignments.append(self.build_role_assignment_entity(
link=assignment_link, **filters))
return assignments
class AssignmentInheritanceTestCase(test_v3.RestfulTestCase,
test_v3.AssignmentTestMixin):
"""Test inheritance crud and its effects."""
def test_get_token_from_inherited_user_domain_role_grants(self):
# Create a new user to ensure that no grant is loaded from sample data
user = unit.create_user(
PROVIDERS.identity_api, domain_id=self.domain_id
)
# Define domain and project authentication data
domain_auth_data = self.build_authentication_request(
user_id=user['id'],
password=<PASSWORD>['password'],
domain_id=self.domain_id)
project_auth_data = self.build_authentication_request(
user_id=user['id'],
password=<PASSWORD>['password'],
project_id=self.project_id)
# Check the user cannot get a domain nor a project token
self.v3_create_token(domain_auth_data,
expected_status=http.client.UNAUTHORIZED)
self.v3_create_token(project_auth_data,
expected_status=http.client.UNAUTHORIZED)
# Grant non-inherited role for user on domain
non_inher_ud_link = self.build_role_assignment_link(
domain_id=self.domain_id, user_id=user['id'], role_id=self.role_id)
self.put(non_inher_ud_link)
# Check the user can get only a domain token
self.v3_create_token(domain_auth_data)
self.v3_create_token(project_auth_data,
expected_status=http.client.UNAUTHORIZED)
# Create inherited role
inherited_role = unit.new_role_ref(name='inherited')
PROVIDERS.role_api.create_role(inherited_role['id'], inherited_role)
# Grant inherited role for user on domain
inher_ud_link = self.build_role_assignment_link(
domain_id=self.domain_id, user_id=user['id'],
role_id=inherited_role['id'], inherited_to_projects=True)
self.put(inher_ud_link)
# Check the user can get both a domain and a project token
self.v3_create_token(domain_auth_data)
self.v3_create_token(project_auth_data)
# Delete inherited grant
self.delete(inher_ud_link)
# Check the user can only get a domain token
self.v3_create_token(domain_auth_data)
self.v3_create_token(project_auth_data,
expected_status=http.client.UNAUTHORIZED)
# Delete non-inherited grant
self.delete(non_inher_ud_link)
# Check the user cannot get a domain token anymore
self.v3_create_token(domain_auth_data,
expected_status=http.client.UNAUTHORIZED)
def test_get_token_from_inherited_group_domain_role_grants(self):
# Create a new group and put a new user in it to
# ensure that no grant is loaded from sample data
user = unit.create_user(
PROVIDERS.identity_api, domain_id=self.domain_id
)
group = unit.new_group_ref(domain_id=self.domain['id'])
group = PROVIDERS.identity_api.create_group(group)
PROVIDERS.identity_api.add_user_to_group(user['id'], group['id'])
# Define domain and project authentication data
domain_auth_data = self.build_authentication_request(
user_id=user['id'],
password=<PASSWORD>['password'],
domain_id=self.domain_id)
project_auth_data = self.build_authentication_request(
user_id=user['id'],
password=user['password'],
project_id=self.project_id)
# Check the user cannot get a domain nor a project token
self.v3_create_token(domain_auth_data,
expected_status=http.client.UNAUTHORIZED)
self.v3_create_token(project_auth_data,
expected_status=http.client.UNAUTHORIZED)
# Grant non-inherited role for user on domain
non_inher_gd_link = self.build_role_assignment_link(
domain_id=self.domain_id, user_id=user['id'], role_id=self.role_id)
self.put(non_inher_gd_link)
# Check the user can get only a domain token
self.v3_create_token(domain_auth_data)
self.v3_create_token(project_auth_data,
expected_status=http.client.UNAUTHORIZED)
# Create inherited role
inherited_role = unit.new_role_ref(name='inherited')
PROVIDERS.role_api.create_role(inherited_role['id'], inherited_role)
# Grant inherited role for user on domain
inher_gd_link = self.build_role_assignment_link(
domain_id=self.domain_id, user_id=user['id'],
role_id=inherited_role['id'], inherited_to_projects=True)
self.put(inher_gd_link)
# Check the user can get both a domain and a project token
self.v3_create_token(domain_auth_data)
self.v3_create_token(project_auth_data)
# Delete inherited grant
self.delete(inher_gd_link)
# Check the user can only get a domain token
self.v3_create_token(domain_auth_data)
self.v3_create_token(project_auth_data,
expected_status=http.client.UNAUTHORIZED)
# Delete non-inherited grant
self.delete(non_inher_gd_link)
# Check the user cannot get a domain token anymore
self.v3_create_token(domain_auth_data,
expected_status=http.client.UNAUTHORIZED)
def _test_crud_inherited_and_direct_assignment_on_target(self, target_url):
time = datetime.datetime.utcnow()
with freezegun.freeze_time(time) as frozen_datetime:
# Create a new role to avoid assignments loaded from sample data
role = unit.new_role_ref()
PROVIDERS.role_api.create_role(role['id'], role)
# Define URLs
direct_url = '%s/users/%s/roles/%s' % (
target_url, self.user_id, role['id'])
inherited_url = ('/OS-INHERIT/%s/inherited_to_projects' %
direct_url.lstrip('/'))
# Create the direct assignment
self.put(direct_url)
# Check the direct assignment exists, but the inherited one does
# not
self.head(direct_url)
self.head(inherited_url, expected_status=http.client.NOT_FOUND)
# Now add the inherited assignment
self.put(inherited_url)
# Check both the direct and inherited assignment exist
self.head(direct_url)
self.head(inherited_url)
# Delete indirect assignment
self.delete(inherited_url)
frozen_datetime.tick(delta=datetime.timedelta(seconds=1))
# Check the direct assignment exists, but the inherited one does
# not
self.head(direct_url)
self.head(inherited_url, expected_status=http.client.NOT_FOUND)
# Now delete the inherited assignment
self.delete(direct_url)
# Check that none of them exist
self.head(direct_url, expected_status=http.client.NOT_FOUND)
self.head(inherited_url, expected_status=http.client.NOT_FOUND)
def test_crud_inherited_and_direct_assignment_on_domains(self):
self._test_crud_inherited_and_direct_assignment_on_target(
'/domains/%s' % self.domain_id)
def test_crud_inherited_and_direct_assignment_on_projects(self):
self._test_crud_inherited_and_direct_assignment_on_target(
'/projects/%s' % self.project_id)
def test_crud_user_inherited_domain_role_grants(self):
role_list = []
for _ in range(2):
role = unit.new_role_ref()
PROVIDERS.role_api.create_role(role['id'], role)
role_list.append(role)
# Create a non-inherited role as a spoiler
PROVIDERS.assignment_api.create_grant(
role_list[1]['id'], user_id=self.user['id'],
domain_id=self.domain_id)
base_collection_url = (
'/OS-INHERIT/domains/%(domain_id)s/users/%(user_id)s/roles' % {
'domain_id': self.domain_id,
'user_id': self.user['id']})
member_url = '%(collection_url)s/%(role_id)s/inherited_to_projects' % {
'collection_url': base_collection_url,
'role_id': role_list[0]['id']}
collection_url = base_collection_url + '/inherited_to_projects'
self.put(member_url)
# Check we can read it back
self.head(member_url)
self.get(member_url, expected_status=http.client.NO_CONTENT)
r = self.get(collection_url)
self.assertValidRoleListResponse(r, ref=role_list[0],
resource_url=collection_url)
# Now delete and check its gone
self.delete(member_url)
r = self.get(collection_url)
self.assertValidRoleListResponse(r, expected_length=0,
resource_url=collection_url)
def test_list_role_assignments_for_inherited_domain_grants(self):
"""Call ``GET /role_assignments with inherited domain grants``.
Test Plan:
- Create 4 roles
- Create a domain with a user and two projects
- Assign two direct roles to project1
- Assign a spoiler role to project2
- Issue the URL to add inherited role to the domain
- Issue the URL to check it is indeed on the domain
- Issue the URL to check effective roles on project1 - this
should return 3 roles.
"""
role_list = []
for _ in range(4):
role = unit.new_role_ref()
PROVIDERS.role_api.create_role(role['id'], role)
role_list.append(role)
domain = unit.new_domain_ref()
PROVIDERS.resource_api.create_domain(domain['id'], domain)
user1 = unit.create_user(
PROVIDERS.identity_api, domain_id=domain['id']
)
project1 = unit.new_project_ref(domain_id=domain['id'])
PROVIDERS.resource_api.create_project(project1['id'], project1)
project2 = unit.new_project_ref(domain_id=domain['id'])
PROVIDERS.resource_api.create_project(project2['id'], project2)
# Add some roles to the project
PROVIDERS.assignment_api.add_role_to_user_and_project(
user1['id'], project1['id'], role_list[0]['id'])
PROVIDERS.assignment_api.add_role_to_user_and_project(
user1['id'], project1['id'], role_list[1]['id'])
# ..and one on a different project as a spoiler
PROVIDERS.assignment_api.add_role_to_user_and_project(
user1['id'], project2['id'], role_list[2]['id'])
# Now create our inherited role on the domain
base_collection_url = (
'/OS-INHERIT/domains/%(domain_id)s/users/%(user_id)s/roles' % {
'domain_id': domain['id'],
'user_id': user1['id']})
member_url = '%(collection_url)s/%(role_id)s/inherited_to_projects' % {
'collection_url': base_collection_url,
'role_id': role_list[3]['id']}
collection_url = base_collection_url + '/inherited_to_projects'
self.put(member_url)
self.head(member_url)
self.get(member_url, expected_status=http.client.NO_CONTENT)
r = self.get(collection_url)
self.assertValidRoleListResponse(r, ref=role_list[3],
resource_url=collection_url)
# Now use the list domain role assignments api to check if this
# is included
collection_url = (
'/role_assignments?user.id=%(user_id)s'
'&scope.domain.id=%(domain_id)s' % {
'user_id': user1['id'],
'domain_id': domain['id']})
r = self.get(collection_url)
self.assertValidRoleAssignmentListResponse(r,
expected_length=1,
resource_url=collection_url)
ud_entity = self.build_role_assignment_entity(
domain_id=domain['id'], user_id=user1['id'],
role_id=role_list[3]['id'], inherited_to_projects=True)
self.assertRoleAssignmentInListResponse(r, ud_entity)
# Now ask for effective list role assignments - the role should
# turn into a project role, along with the two direct roles that are
# on the project
collection_url = (
'/role_assignments?effective&user.id=%(user_id)s'
'&scope.project.id=%(project_id)s' % {
'user_id': user1['id'],
'project_id': project1['id']})
r = self.get(collection_url)
self.assertValidRoleAssignmentListResponse(r,
expected_length=3,
resource_url=collection_url)
# An effective role for an inherited role will be a project
# entity, with a domain link to the inherited assignment
ud_url = self.build_role_assignment_link(
domain_id=domain['id'], user_id=user1['id'],
role_id=role_list[3]['id'], inherited_to_projects=True)
up_entity = self.build_role_assignment_entity(
link=ud_url, project_id=project1['id'],
user_id=user1['id'], role_id=role_list[3]['id'],
inherited_to_projects=True)
self.assertRoleAssignmentInListResponse(r, up_entity)
def _test_list_role_assignments_include_names(self, role1):
"""Call ``GET /role_assignments with include names``.
Test Plan:
- Create a domain with a group and a user
- Create a project with a group and a user
"""
role1 = unit.new_role_ref()
PROVIDERS.role_api.create_role(role1['id'], role1)
user1 = unit.create_user(
PROVIDERS.identity_api, domain_id=self.domain_id
)
group = unit.new_group_ref(domain_id=self.domain_id)
group = PROVIDERS.identity_api.create_group(group)
project1 = unit.new_project_ref(domain_id=self.domain_id)
PROVIDERS.resource_api.create_project(project1['id'], project1)
expected_entity1 = self.build_role_assignment_entity_include_names(
role_ref=role1,
project_ref=project1,
user_ref=user1)
self.put(expected_entity1['links']['assignment'])
expected_entity2 = self.build_role_assignment_entity_include_names(
role_ref=role1,
domain_ref=self.domain,
group_ref=group)
self.put(expected_entity2['links']['assignment'])
expected_entity3 = self.build_role_assignment_entity_include_names(
role_ref=role1,
domain_ref=self.domain,
user_ref=user1)
self.put(expected_entity3['links']['assignment'])
expected_entity4 = self.build_role_assignment_entity_include_names(
role_ref=role1,
project_ref=project1,
group_ref=group)
self.put(expected_entity4['links']['assignment'])
collection_url_domain = (
'/role_assignments?include_names&scope.domain.id=%(domain_id)s' % {
'domain_id': self.domain_id})
rs_domain = self.get(collection_url_domain)
collection_url_project = (
'/role_assignments?include_names&'
'scope.project.id=%(project_id)s' % {
'project_id': project1['id']})
rs_project = self.get(collection_url_project)
collection_url_group = (
'/role_assignments?include_names&group.id=%(group_id)s' % {
'group_id': group['id']})
rs_group = self.get(collection_url_group)
collection_url_user = (
'/role_assignments?include_names&user.id=%(user_id)s' % {
'user_id': user1['id']})
rs_user = self.get(collection_url_user)
collection_url_role = (
'/role_assignments?include_names&role.id=%(role_id)s' % {
'role_id': role1['id']})
rs_role = self.get(collection_url_role)
# Make sure all entities were created successfully
self.assertEqual(http.client.OK, rs_domain.status_int)
self.assertEqual(http.client.OK, rs_project.status_int)
self.assertEqual(http.client.OK, rs_group.status_int)
self.assertEqual(http.client.OK, rs_user.status_int)
# Make sure we can get back the correct number of entities
self.assertValidRoleAssignmentListResponse(
rs_domain,
expected_length=2,
resource_url=collection_url_domain)
self.assertValidRoleAssignmentListResponse(
rs_project,
expected_length=2,
resource_url=collection_url_project)
self.assertValidRoleAssignmentListResponse(
rs_group,
expected_length=2,
resource_url=collection_url_group)
self.assertValidRoleAssignmentListResponse(
rs_user,
expected_length=2,
resource_url=collection_url_user)
self.assertValidRoleAssignmentListResponse(
rs_role,
expected_length=4,
resource_url=collection_url_role)
# Verify all types of entities have the correct format
self.assertRoleAssignmentInListResponse(rs_domain, expected_entity2)
self.assertRoleAssignmentInListResponse(rs_project, expected_entity1)
self.assertRoleAssignmentInListResponse(rs_group, expected_entity4)
self.assertRoleAssignmentInListResponse(rs_user, expected_entity3)
self.assertRoleAssignmentInListResponse(rs_role, expected_entity1)
def test_list_role_assignments_include_names_global_role(self):
role = unit.new_role_ref()
PROVIDERS.role_api.create_role(role['id'], role)
self._test_list_role_assignments_include_names(role)
def test_list_role_assignments_include_names_domain_role(self):
role = unit.new_role_ref(domain_id=self.domain['id'])
PROVIDERS.role_api.create_role(role['id'], role)
self._test_list_role_assignments_include_names(role)
def test_remove_assignment_for_project_acting_as_domain(self):
"""Test goal: remove assignment for project acting as domain.
Ensure when we have two role assignments for the project
acting as domain, one dealing with it as a domain and other as a
project, we still able to remove those assignments later.
Test plan:
- Create a role and a domain with a user;
- Grant a role for this user in this domain;
- Grant a role for this user in the same entity as a project;
- Ensure that both assignments were created and it was valid;
- Remove the domain assignment for the user and show that the project
assignment for him still valid
"""
role = unit.new_role_ref()
PROVIDERS.role_api.create_role(role['id'], role)
domain = unit.new_domain_ref()
PROVIDERS.resource_api.create_domain(domain['id'], domain)
user = unit.create_user(PROVIDERS.identity_api, domain_id=domain['id'])
assignment_domain = self.build_role_assignment_entity(
role_id=role['id'], domain_id=domain['id'], user_id=user['id'],
inherited_to_projects=False)
assignment_project = self.build_role_assignment_entity(
role_id=role['id'], project_id=domain['id'], user_id=user['id'],
inherited_to_projects=False)
self.put(assignment_domain['links']['assignment'])
self.put(assignment_project['links']['assignment'])
collection_url = '/role_assignments?user.id=%(user_id)s' % (
{'user_id': user['id']})
result = self.get(collection_url)
# We have two role assignments based in both roles for the domain and
# project scope
self.assertValidRoleAssignmentListResponse(
result, expected_length=2, resource_url=collection_url)
self.assertRoleAssignmentInListResponse(result, assignment_domain)
domain_url = '/domains/%s/users/%s/roles/%s' % (
domain['id'], user['id'], role['id'])
self.delete(domain_url)
collection_url = '/role_assignments?user.id=%(user_id)s' % (
{'user_id': user['id']})
result = self.get(collection_url)
# Now we only have one assignment for the project scope since the
# domain scope was removed.
self.assertValidRoleAssignmentListResponse(
result, expected_length=1, resource_url=collection_url)
self.assertRoleAssignmentInListResponse(result, assignment_project)
def test_list_inherited_role_assignments_include_names(self):
"""Call ``GET /role_assignments?include_names``.
Test goal: ensure calling list role assignments including names
honors the inherited role assignments flag.
Test plan:
- Create a role and a domain with a user;
- Create a inherited role assignment;
- List role assignments for that user;
- List role assignments for that user including names.
"""
role = unit.new_role_ref()
PROVIDERS.role_api.create_role(role['id'], role)
domain = unit.new_domain_ref()
PROVIDERS.resource_api.create_domain(domain['id'], domain)
user = unit.create_user(PROVIDERS.identity_api, domain_id=domain['id'])
# Create and store expected assignment refs
assignment = self.build_role_assignment_entity(
role_id=role['id'], domain_id=domain['id'], user_id=user['id'],
inherited_to_projects=True)
assignment_names = self.build_role_assignment_entity_include_names(
role_ref=role, domain_ref=domain, user_ref=user,
inherited_assignment=True)
# Ensure expected assignment refs are inherited and have the same URL
self.assertEqual('projects',
assignment['scope']['OS-INHERIT:inherited_to'])
self.assertEqual('projects',
assignment_names['scope']['OS-INHERIT:inherited_to'])
self.assertEqual(assignment['links']['assignment'],
assignment_names['links']['assignment'])
self.put(assignment['links']['assignment'])
collection_url = '/role_assignments?user.id=%(user_id)s' % (
{'user_id': user['id']})
result = self.get(collection_url)
self.assertValidRoleAssignmentListResponse(
result, expected_length=1, resource_url=collection_url)
self.assertRoleAssignmentInListResponse(result, assignment)
collection_url = ('/role_assignments?include_names&'
'user.id=%(user_id)s' % {'user_id': user['id']})
result = self.get(collection_url)
self.assertValidRoleAssignmentListResponse(
result, expected_length=1, resource_url=collection_url)
self.assertRoleAssignmentInListResponse(result, assignment_names)
def test_list_role_assignments_for_disabled_inheritance_extension(self):
"""Call ``GET /role_assignments with inherited domain grants``.
Test Plan:
- Issue the URL to add inherited role to the domain
- Issue the URL to check effective roles on project include the
inherited role
- Disable the extension
- Re-check the effective roles, proving the inherited role no longer
shows up.
"""
role_list = []
for _ in range(4):
role = unit.new_role_ref()
PROVIDERS.role_api.create_role(role['id'], role)
role_list.append(role)
domain = unit.new_domain_ref()
PROVIDERS.resource_api.create_domain(domain['id'], domain)
user1 = unit.create_user(
PROVIDERS.identity_api, domain_id=domain['id']
)
project1 = unit.new_project_ref(domain_id=domain['id'])
PROVIDERS.resource_api.create_project(project1['id'], project1)
project2 = unit.new_project_ref(domain_id=domain['id'])
PROVIDERS.resource_api.create_project(project2['id'], project2)
# Add some roles to the project
PROVIDERS.assignment_api.add_role_to_user_and_project(
user1['id'], project1['id'], role_list[0]['id'])
PROVIDERS.assignment_api.add_role_to_user_and_project(
user1['id'], project1['id'], role_list[1]['id'])
# ..and one on a different project as a spoiler
PROVIDERS.assignment_api.add_role_to_user_and_project(
user1['id'], project2['id'], role_list[2]['id'])
# Now create our inherited role on the domain
base_collection_url = (
'/OS-INHERIT/domains/%(domain_id)s/users/%(user_id)s/roles' % {
'domain_id': domain['id'],
'user_id': user1['id']})
member_url = '%(collection_url)s/%(role_id)s/inherited_to_projects' % {
'collection_url': base_collection_url,
'role_id': role_list[3]['id']}
collection_url = base_collection_url + '/inherited_to_projects'
self.put(member_url)
self.head(member_url)
self.get(member_url, expected_status=http.client.NO_CONTENT)
r = self.get(collection_url)
self.assertValidRoleListResponse(r, ref=role_list[3],
resource_url=collection_url)
# Get effective list role assignments - the role should
# turn into a project role, along with the two direct roles that are
# on the project
collection_url = (
'/role_assignments?effective&user.id=%(user_id)s'
'&scope.project.id=%(project_id)s' % {
'user_id': user1['id'],
'project_id': project1['id']})
r = self.get(collection_url)
self.assertValidRoleAssignmentListResponse(r,
expected_length=3,
resource_url=collection_url)
ud_url = self.build_role_assignment_link(
domain_id=domain['id'], user_id=user1['id'],
role_id=role_list[3]['id'], inherited_to_projects=True)
up_entity = self.build_role_assignment_entity(
link=ud_url, project_id=project1['id'],
user_id=user1['id'], role_id=role_list[3]['id'],
inherited_to_projects=True)
self.assertRoleAssignmentInListResponse(r, up_entity)
def test_list_role_assignments_for_inherited_group_domain_grants(self):
"""Call ``GET /role_assignments with inherited group domain grants``.
Test Plan:
- Create 4 roles
- Create a domain with a user and two projects
- Assign two direct roles to project1
- Assign a spoiler role to project2
- Issue the URL to add inherited role to the domain
- Issue the URL to check it is indeed on the domain
- Issue the URL to check effective roles on project1 - this
should return 3 roles.
"""
role_list = []
for _ in range(4):
role = unit.new_role_ref()
PROVIDERS.role_api.create_role(role['id'], role)
role_list.append(role)
domain = unit.new_domain_ref()
PROVIDERS.resource_api.create_domain(domain['id'], domain)
user1 = unit.create_user(
PROVIDERS.identity_api, domain_id=domain['id']
)
user2 = unit.create_user(
PROVIDERS.identity_api, domain_id=domain['id']
)
group1 = unit.new_group_ref(domain_id=domain['id'])
group1 = PROVIDERS.identity_api.create_group(group1)
PROVIDERS.identity_api.add_user_to_group(
user1['id'], group1['id']
)
PROVIDERS.identity_api.add_user_to_group(
user2['id'], group1['id']
)
project1 = unit.new_project_ref(domain_id=domain['id'])
PROVIDERS.resource_api.create_project(project1['id'], project1)
project2 = unit.new_project_ref(domain_id=domain['id'])
PROVIDERS.resource_api.create_project(project2['id'], project2)
# Add some roles to the project
PROVIDERS.assignment_api.add_role_to_user_and_project(
user1['id'], project1['id'], role_list[0]['id'])
PROVIDERS.assignment_api.add_role_to_user_and_project(
user1['id'], project1['id'], role_list[1]['id'])
# ..and one on a different project as a spoiler
PROVIDERS.assignment_api.add_role_to_user_and_project(
user1['id'], project2['id'], role_list[2]['id'])
# Now create our inherited role on the domain
base_collection_url = (
'/OS-INHERIT/domains/%(domain_id)s/groups/%(group_id)s/roles' % {
'domain_id': domain['id'],
'group_id': group1['id']})
member_url = '%(collection_url)s/%(role_id)s/inherited_to_projects' % {
'collection_url': base_collection_url,
'role_id': role_list[3]['id']}
collection_url = base_collection_url + '/inherited_to_projects'
self.put(member_url)
self.head(member_url)
self.get(member_url, expected_status=http.client.NO_CONTENT)
r = self.get(collection_url)
self.assertValidRoleListResponse(r, ref=role_list[3],
resource_url=collection_url)
# Now use the list domain role assignments api to check if this
# is included
collection_url = (
'/role_assignments?group.id=%(group_id)s'
'&scope.domain.id=%(domain_id)s' % {
'group_id': group1['id'],
'domain_id': domain['id']})
r = self.get(collection_url)
self.assertValidRoleAssignmentListResponse(r,
expected_length=1,
resource_url=collection_url)
gd_entity = self.build_role_assignment_entity(
domain_id=domain['id'], group_id=group1['id'],
role_id=role_list[3]['id'], inherited_to_projects=True)
self.assertRoleAssignmentInListResponse(r, gd_entity)
# Now ask for effective list role assignments - the role should
# turn into a user project role, along with the two direct roles
# that are on the project
collection_url = (
'/role_assignments?effective&user.id=%(user_id)s'
'&scope.project.id=%(project_id)s' % {
'user_id': user1['id'],
'project_id': project1['id']})
r = self.get(collection_url)
self.assertValidRoleAssignmentListResponse(r,
expected_length=3,
resource_url=collection_url)
# An effective role for an inherited role will be a project
# entity, with a domain link to the inherited assignment
up_entity = self.build_role_assignment_entity(
link=gd_entity['links']['assignment'], project_id=project1['id'],
user_id=user1['id'], role_id=role_list[3]['id'],
inherited_to_projects=True)
self.assertRoleAssignmentInListResponse(r, up_entity)
def test_filtered_role_assignments_for_inherited_grants(self):
"""Call ``GET /role_assignments?scope.OS-INHERIT:inherited_to``.
Test Plan:
- Create 5 roles
- Create a domain with a user, group and two projects
- Assign three direct spoiler roles to projects
- Issue the URL to add an inherited user role to the domain
- Issue the URL to add an inherited group role to the domain
- Issue the URL to filter by inherited roles - this should
return just the 2 inherited roles.
"""
role_list = []
for _ in range(5):
role = unit.new_role_ref()
PROVIDERS.role_api.create_role(role['id'], role)
role_list.append(role)
domain = unit.new_domain_ref()
PROVIDERS.resource_api.create_domain(domain['id'], domain)
user1 = unit.create_user(
PROVIDERS.identity_api, domain_id=domain['id']
)
group1 = unit.new_group_ref(domain_id=domain['id'])
group1 = PROVIDERS.identity_api.create_group(group1)
project1 = unit.new_project_ref(domain_id=domain['id'])
PROVIDERS.resource_api.create_project(project1['id'], project1)
project2 = unit.new_project_ref(domain_id=domain['id'])
PROVIDERS.resource_api.create_project(project2['id'], project2)
# Add some spoiler roles to the projects
PROVIDERS.assignment_api.add_role_to_user_and_project(
user1['id'], project1['id'], role_list[0]['id'])
PROVIDERS.assignment_api.add_role_to_user_and_project(
user1['id'], project2['id'], role_list[1]['id'])
# Create a non-inherited role as a spoiler
PROVIDERS.assignment_api.create_grant(
role_list[2]['id'], user_id=user1['id'], domain_id=domain['id'])
# Now create two inherited roles on the domain, one for a user
# and one for a domain
base_collection_url = (
'/OS-INHERIT/domains/%(domain_id)s/users/%(user_id)s/roles' % {
'domain_id': domain['id'],
'user_id': user1['id']})
member_url = '%(collection_url)s/%(role_id)s/inherited_to_projects' % {
'collection_url': base_collection_url,
'role_id': role_list[3]['id']}
collection_url = base_collection_url + '/inherited_to_projects'
self.put(member_url)
self.head(member_url)
self.get(member_url, expected_status=http.client.NO_CONTENT)
r = self.get(collection_url)
self.assertValidRoleListResponse(r, ref=role_list[3],
resource_url=collection_url)
base_collection_url = (
'/OS-INHERIT/domains/%(domain_id)s/groups/%(group_id)s/roles' % {
'domain_id': domain['id'],
'group_id': group1['id']})
member_url = '%(collection_url)s/%(role_id)s/inherited_to_projects' % {
'collection_url': base_collection_url,
'role_id': role_list[4]['id']}
collection_url = base_collection_url + '/inherited_to_projects'
self.put(member_url)
self.head(member_url)
self.get(member_url, expected_status=http.client.NO_CONTENT)
r = self.get(collection_url)
self.assertValidRoleListResponse(r, ref=role_list[4],
resource_url=collection_url)
# Now use the list role assignments api to get a list of inherited
# roles on the domain - should get back the two roles
collection_url = (
'/role_assignments?scope.OS-INHERIT:inherited_to=projects')
r = self.get(collection_url)
self.assertValidRoleAssignmentListResponse(r,
expected_length=2,
resource_url=collection_url)
ud_entity = self.build_role_assignment_entity(
domain_id=domain['id'], user_id=user1['id'],
role_id=role_list[3]['id'], inherited_to_projects=True)
gd_entity = self.build_role_assignment_entity(
domain_id=domain['id'], group_id=group1['id'],
role_id=role_list[4]['id'], inherited_to_projects=True)
self.assertRoleAssignmentInListResponse(r, ud_entity)
self.assertRoleAssignmentInListResponse(r, gd_entity)
def _setup_hierarchical_projects_scenario(self):
"""Create basic hierarchical projects scenario.
This basic scenario contains a root with one leaf project and
two roles with the following names: non-inherited and inherited.
"""
# Create project hierarchy
root = unit.new_project_ref(domain_id=self.domain['id'])
leaf = unit.new_project_ref(domain_id=self.domain['id'],
parent_id=root['id'])
PROVIDERS.resource_api.create_project(root['id'], root)
PROVIDERS.resource_api.create_project(leaf['id'], leaf)
# Create 'non-inherited' and 'inherited' roles
non_inherited_role = unit.new_role_ref(name='non-inherited')
PROVIDERS.role_api.create_role(
non_inherited_role['id'], non_inherited_role
)
inherited_role = unit.new_role_ref(name='inherited')
PROVIDERS.role_api.create_role(inherited_role['id'], inherited_role)
return (root['id'], leaf['id'],
non_inherited_role['id'], inherited_role['id'])
def test_get_token_from_inherited_user_project_role_grants(self):
# Create default scenario
root_id, leaf_id, non_inherited_role_id, inherited_role_id = (
self._setup_hierarchical_projects_scenario())
# Define root and leaf projects authentication data
root_project_auth_data = self.build_authentication_request(
user_id=self.user['id'],
password=<PASSWORD>['password'],
project_id=root_id)
leaf_project_auth_data = self.build_authentication_request(
user_id=self.user['id'],
password=<PASSWORD>['password'],
project_id=leaf_id)
# Check the user cannot get a token on root nor leaf project
self.v3_create_token(root_project_auth_data,
expected_status=http.client.UNAUTHORIZED)
self.v3_create_token(leaf_project_auth_data,
expected_status=http.client.UNAUTHORIZED)
# Grant non-inherited role for user on leaf project
non_inher_up_link = self.build_role_assignment_link(
project_id=leaf_id, user_id=self.user['id'],
role_id=non_inherited_role_id)
self.put(non_inher_up_link)
# Check the user can only get a token on leaf project
self.v3_create_token(root_project_auth_data,
expected_status=http.client.UNAUTHORIZED)
self.v3_create_token(leaf_project_auth_data)
# Grant inherited role for user on root project
inher_up_link = self.build_role_assignment_link(
project_id=root_id, user_id=self.user['id'],
role_id=inherited_role_id, inherited_to_projects=True)
self.put(inher_up_link)
# Check the user still can get a token only on leaf project
self.v3_create_token(root_project_auth_data,
expected_status=http.client.UNAUTHORIZED)
self.v3_create_token(leaf_project_auth_data)
# Delete non-inherited grant
self.delete(non_inher_up_link)
# Check the inherited role still applies for leaf project
self.v3_create_token(root_project_auth_data,
expected_status=http.client.UNAUTHORIZED)
self.v3_create_token(leaf_project_auth_data)
# Delete inherited grant
self.delete(inher_up_link)
# Check the user cannot get a token on leaf project anymore
self.v3_create_token(leaf_project_auth_data,
expected_status=http.client.UNAUTHORIZED)
def test_get_token_from_inherited_group_project_role_grants(self):
# Create default scenario
root_id, leaf_id, non_inherited_role_id, inherited_role_id = (
self._setup_hierarchical_projects_scenario())
# Create group and add user to it
group = unit.new_group_ref(domain_id=self.domain['id'])
group = PROVIDERS.identity_api.create_group(group)
PROVIDERS.identity_api.add_user_to_group(self.user['id'], group['id'])
# Define root and leaf projects authentication data
root_project_auth_data = self.build_authentication_request(
user_id=self.user['id'],
password=<PASSWORD>['password'],
project_id=root_id)
leaf_project_auth_data = self.build_authentication_request(
user_id=self.user['id'],
password=<PASSWORD>['password'],
project_id=leaf_id)
# Check the user cannot get a token on root nor leaf project
self.v3_create_token(root_project_auth_data,
expected_status=http.client.UNAUTHORIZED)
self.v3_create_token(leaf_project_auth_data,
expected_status=http.client.UNAUTHORIZED)
# Grant non-inherited role for group on leaf project
non_inher_gp_link = self.build_role_assignment_link(
project_id=leaf_id, group_id=group['id'],
role_id=non_inherited_role_id)
self.put(non_inher_gp_link)
# Check the user can only get a token on leaf project
self.v3_create_token(root_project_auth_data,
expected_status=http.client.UNAUTHORIZED)
self.v3_create_token(leaf_project_auth_data)
# Grant inherited role for group on root project
inher_gp_link = self.build_role_assignment_link(
project_id=root_id, group_id=group['id'],
role_id=inherited_role_id, inherited_to_projects=True)
self.put(inher_gp_link)
# Check the user still can get a token only on leaf project
self.v3_create_token(root_project_auth_data,
expected_status=http.client.UNAUTHORIZED)
self.v3_create_token(leaf_project_auth_data)
# Delete no-inherited grant
self.delete(non_inher_gp_link)
# Check the inherited role still applies for leaf project
self.v3_create_token(leaf_project_auth_data)
# Delete inherited grant
self.delete(inher_gp_link)
# Check the user cannot get a token on leaf project anymore
self.v3_create_token(leaf_project_auth_data,
expected_status=http.client.UNAUTHORIZED)
def test_get_role_assignments_for_project_hierarchy(self):
"""Call ``GET /role_assignments``.
Test Plan:
- Create 2 roles
- Create a hierarchy of projects with one root and one leaf project
- Issue the URL to add a non-inherited user role to the root project
- Issue the URL to add an inherited user role to the root project
- Issue the URL to get all role assignments - this should return just
2 roles (non-inherited and inherited) in the root project.
"""
# Create default scenario
root_id, leaf_id, non_inherited_role_id, inherited_role_id = (
self._setup_hierarchical_projects_scenario())
# Grant non-inherited role
non_inher_up_entity = self.build_role_assignment_entity(
project_id=root_id, user_id=self.user['id'],
role_id=non_inherited_role_id)
self.put(non_inher_up_entity['links']['assignment'])
# Grant inherited role
inher_up_entity = self.build_role_assignment_entity(
project_id=root_id, user_id=self.user['id'],
role_id=inherited_role_id, inherited_to_projects=True)
self.put(inher_up_entity['links']['assignment'])
# Get role assignments
collection_url = '/role_assignments'
r = self.get(collection_url)
self.assertValidRoleAssignmentListResponse(r,
resource_url=collection_url)
# Assert that the user has non-inherited role on root project
self.assertRoleAssignmentInListResponse(r, non_inher_up_entity)
# Assert that the user has inherited role on root project
self.assertRoleAssignmentInListResponse(r, inher_up_entity)
# Assert that the user does not have non-inherited role on leaf project
non_inher_up_entity = self.build_role_assignment_entity(
project_id=leaf_id, user_id=self.user['id'],
role_id=non_inherited_role_id)
self.assertRoleAssignmentNotInListResponse(r, non_inher_up_entity)
# Assert that the user does not have inherited role on leaf project
inher_up_entity['scope']['project']['id'] = leaf_id
self.assertRoleAssignmentNotInListResponse(r, inher_up_entity)
def test_get_effective_role_assignments_for_project_hierarchy(self):
"""Call ``GET /role_assignments?effective``.
Test Plan:
- Create 2 roles
- Create a hierarchy of projects with one root and one leaf project
- Issue the URL to add a non-inherited user role to the root project
- Issue the URL to add an inherited user role to the root project
- Issue the URL to get effective role assignments - this should return
1 role (non-inherited) on the root project and 1 role (inherited) on
the leaf project.
"""
# Create default scenario
root_id, leaf_id, non_inherited_role_id, inherited_role_id = (
self._setup_hierarchical_projects_scenario())
# Grant non-inherited role
non_inher_up_entity = self.build_role_assignment_entity(
project_id=root_id, user_id=self.user['id'],
role_id=non_inherited_role_id)
self.put(non_inher_up_entity['links']['assignment'])
# Grant inherited role
inher_up_entity = self.build_role_assignment_entity(
project_id=root_id, user_id=self.user['id'],
role_id=inherited_role_id, inherited_to_projects=True)
self.put(inher_up_entity['links']['assignment'])
# Get effective role assignments
collection_url = '/role_assignments?effective'
r = self.get(collection_url)
self.assertValidRoleAssignmentListResponse(r,
resource_url=collection_url)
# Assert that the user has non-inherited role on root project
self.assertRoleAssignmentInListResponse(r, non_inher_up_entity)
# Assert that the user does not have inherited role on root project
self.assertRoleAssignmentNotInListResponse(r, inher_up_entity)
# Assert that the user does not have non-inherited role on leaf project
non_inher_up_entity = self.build_role_assignment_entity(
project_id=leaf_id, user_id=self.user['id'],
role_id=non_inherited_role_id)
self.assertRoleAssignmentNotInListResponse(r, non_inher_up_entity)
# Assert that the user has inherited role on leaf project
inher_up_entity['scope']['project']['id'] = leaf_id
self.assertRoleAssignmentInListResponse(r, inher_up_entity)
def test_project_id_specified_if_include_subtree_specified(self):
"""When using include_subtree, you must specify a project ID."""
r = self.get('/role_assignments?include_subtree=True',
expected_status=http.client.BAD_REQUEST)
error_msg = ("scope.project.id must be specified if include_subtree "
"is also specified")
self.assertEqual(error_msg, r.result['error']['message'])
r = self.get('/role_assignments?scope.project.id&'
'include_subtree=True',
expected_status=http.client.BAD_REQUEST)
self.assertEqual(error_msg, r.result['error']['message'])
def test_get_role_assignments_for_project_tree(self):
"""Get role_assignment?scope.project.id=X&include_subtree``.
Test Plan:
- Create 2 roles and a hierarchy of projects with one root and one leaf
- Issue the URL to add a non-inherited user role to the root project
and the leaf project
- Issue the URL to get role assignments for the root project but
not the subtree - this should return just the root assignment
- Issue the URL to get role assignments for the root project and
it's subtree - this should return both assignments
- Check that explicitly setting include_subtree to False is the
equivalent to not including it at all in the query.
"""
# Create default scenario
root_id, leaf_id, non_inherited_role_id, unused_role_id = (
self._setup_hierarchical_projects_scenario())
# Grant non-inherited role to root and leaf projects
non_inher_entity_root = self.build_role_assignment_entity(
project_id=root_id, user_id=self.user['id'],
role_id=non_inherited_role_id)
self.put(non_inher_entity_root['links']['assignment'])
non_inher_entity_leaf = self.build_role_assignment_entity(
project_id=leaf_id, user_id=self.user['id'],
role_id=non_inherited_role_id)
self.put(non_inher_entity_leaf['links']['assignment'])
# Without the subtree, we should get the one assignment on the
# root project
collection_url = (
'/role_assignments?scope.project.id=%(project)s' % {
'project': root_id})
r = self.get(collection_url)
self.assertValidRoleAssignmentListResponse(
r, resource_url=collection_url)
self.assertThat(r.result['role_assignments'], matchers.HasLength(1))
self.assertRoleAssignmentInListResponse(r, non_inher_entity_root)
# With the subtree, we should get both assignments
collection_url = (
'/role_assignments?scope.project.id=%(project)s'
'&include_subtree=True' % {
'project': root_id})
r = self.get(collection_url)
self.assertValidRoleAssignmentListResponse(
r, resource_url=collection_url)
self.assertThat(r.result['role_assignments'], matchers.HasLength(2))
self.assertRoleAssignmentInListResponse(r, non_inher_entity_root)
self.assertRoleAssignmentInListResponse(r, non_inher_entity_leaf)
# With subtree=0, we should also only get the one assignment on the
# root project
collection_url = (
'/role_assignments?scope.project.id=%(project)s'
'&include_subtree=0' % {
'project': root_id})
r = self.get(collection_url)
self.assertValidRoleAssignmentListResponse(
r, resource_url=collection_url)
self.assertThat(r.result['role_assignments'], matchers.HasLength(1))
self.assertRoleAssignmentInListResponse(r, non_inher_entity_root)
def test_get_effective_role_assignments_for_project_tree(self):
"""Get role_assignment ?project_id=X&include_subtree=True&effective``.
Test Plan:
- Create 2 roles and a hierarchy of projects with one root and 4 levels
of child project
- Issue the URL to add a non-inherited user role to the root project
and a level 1 project
- Issue the URL to add an inherited user role on the level 2 project
- Issue the URL to get effective role assignments for the level 1
project and it's subtree - this should return a role (non-inherited)
on the level 1 project and roles (inherited) on each of the level
2, 3 and 4 projects
"""
# Create default scenario
root_id, leaf_id, non_inherited_role_id, inherited_role_id = (
self._setup_hierarchical_projects_scenario())
# Add some extra projects to the project hierarchy
level2 = unit.new_project_ref(domain_id=self.domain['id'],
parent_id=leaf_id)
level3 = unit.new_project_ref(domain_id=self.domain['id'],
parent_id=level2['id'])
level4 = unit.new_project_ref(domain_id=self.domain['id'],
parent_id=level3['id'])
PROVIDERS.resource_api.create_project(level2['id'], level2)
PROVIDERS.resource_api.create_project(level3['id'], level3)
PROVIDERS.resource_api.create_project(level4['id'], level4)
# Grant non-inherited role to root (as a spoiler) and to
# the level 1 (leaf) project
non_inher_entity_root = self.build_role_assignment_entity(
project_id=root_id, user_id=self.user['id'],
role_id=non_inherited_role_id)
self.put(non_inher_entity_root['links']['assignment'])
non_inher_entity_leaf = self.build_role_assignment_entity(
project_id=leaf_id, user_id=self.user['id'],
role_id=non_inherited_role_id)
self.put(non_inher_entity_leaf['links']['assignment'])
# Grant inherited role to level 2
inher_entity = self.build_role_assignment_entity(
project_id=level2['id'], user_id=self.user['id'],
role_id=inherited_role_id, inherited_to_projects=True)
self.put(inher_entity['links']['assignment'])
# Get effective role assignments
collection_url = (
'/role_assignments?scope.project.id=%(project)s'
'&include_subtree=True&effective' % {
'project': leaf_id})
r = self.get(collection_url)
self.assertValidRoleAssignmentListResponse(
r, resource_url=collection_url)
# There should be three assignments returned in total
self.assertThat(r.result['role_assignments'], matchers.HasLength(3))
# Assert that the user does not non-inherited role on root project
self.assertRoleAssignmentNotInListResponse(r, non_inher_entity_root)
# Assert that the user does have non-inherited role on leaf project
self.assertRoleAssignmentInListResponse(r, non_inher_entity_leaf)
# Assert that the user has inherited role on levels 3 and 4
inher_entity['scope']['project']['id'] = level3['id']
self.assertRoleAssignmentInListResponse(r, inher_entity)
inher_entity['scope']['project']['id'] = level4['id']
self.assertRoleAssignmentInListResponse(r, inher_entity)
def test_get_inherited_role_assignments_for_project_hierarchy(self):
"""Call ``GET /role_assignments?scope.OS-INHERIT:inherited_to``.
Test Plan:
- Create 2 roles
- Create a hierarchy of projects with one root and one leaf project
- Issue the URL to add a non-inherited user role to the root project
- Issue the URL to add an inherited user role to the root project
- Issue the URL to filter inherited to projects role assignments - this
should return 1 role (inherited) on the root project.
"""
# Create default scenario
root_id, leaf_id, non_inherited_role_id, inherited_role_id = (
self._setup_hierarchical_projects_scenario())
# Grant non-inherited role
non_inher_up_entity = self.build_role_assignment_entity(
project_id=root_id, user_id=self.user['id'],
role_id=non_inherited_role_id)
self.put(non_inher_up_entity['links']['assignment'])
# Grant inherited role
inher_up_entity = self.build_role_assignment_entity(
project_id=root_id, user_id=self.user['id'],
role_id=inherited_role_id, inherited_to_projects=True)
self.put(inher_up_entity['links']['assignment'])
# Get inherited role assignments
collection_url = ('/role_assignments'
'?scope.OS-INHERIT:inherited_to=projects')
r = self.get(collection_url)
self.assertValidRoleAssignmentListResponse(r,
resource_url=collection_url)
# Assert that the user does not have non-inherited role on root project
self.assertRoleAssignmentNotInListResponse(r, non_inher_up_entity)
# Assert that the user has inherited role on root project
self.assertRoleAssignmentInListResponse(r, inher_up_entity)
# Assert that the user does not have non-inherited role on leaf project
non_inher_up_entity = self.build_role_assignment_entity(
project_id=leaf_id, user_id=self.user['id'],
role_id=non_inherited_role_id)
self.assertRoleAssignmentNotInListResponse(r, non_inher_up_entity)
# Assert that the user does not have inherited role on leaf project
inher_up_entity['scope']['project']['id'] = leaf_id
self.assertRoleAssignmentNotInListResponse(r, inher_up_entity)
class ImpliedRolesTests(test_v3.RestfulTestCase, test_v3.AssignmentTestMixin,
unit.TestCase):
def _create_role(self):
"""Call ``POST /roles``."""
ref = unit.new_role_ref()
r = self.post('/roles', body={'role': ref})
return self.assertValidRoleResponse(r, ref)
def test_list_implied_roles_none(self):
self.prior = self._create_role()
url = '/roles/%s/implies' % (self.prior['id'])
response = self.get(url).json["role_inference"]
self.head(url, expected_status=http.client.OK)
self.assertEqual(self.prior['id'], response['prior_role']['id'])
self.assertEqual(0, len(response['implies']))
def _create_implied_role(self, prior, implied):
self.put('/roles/%s/implies/%s' % (prior['id'], implied['id']),
expected_status=http.client.CREATED)
def _delete_implied_role(self, prior, implied):
self.delete('/roles/%s/implies/%s' % (prior['id'], implied['id']))
def _setup_prior_two_implied(self):
self.prior = self._create_role()
self.implied1 = self._create_role()
self._create_implied_role(self.prior, self.implied1)
self.implied2 = self._create_role()
self._create_implied_role(self.prior, self.implied2)
def _assert_expected_implied_role_response(
self, expected_prior_id, expected_implied_ids):
r = self.get('/roles/%s/implies' % expected_prior_id)
response = r.json
role_inference = response['role_inference']
self.assertEqual(expected_prior_id, role_inference['prior_role']['id'])
prior_link = '/v3/roles/' + expected_prior_id + '/implies'
self.assertThat(response['links']['self'],
matchers.EndsWith(prior_link))
actual_implied_ids = [implied['id']
for implied in role_inference['implies']]
self.assertItemsEqual(expected_implied_ids, actual_implied_ids)
self.assertIsNotNone(role_inference['prior_role']['links']['self'])
for implied in role_inference['implies']:
self.assertIsNotNone(implied['links']['self'])
def _assert_expected_role_inference_rule_response(
self, expected_prior_id, expected_implied_id):
url = '/roles/%s/implies/%s' % (expected_prior_id, expected_implied_id)
response = self.get(url).json
self.assertThat(response['links']['self'],
matchers.EndsWith('/v3%s' % url))
role_inference = response['role_inference']
prior_role = role_inference['prior_role']
self.assertEqual(expected_prior_id, prior_role['id'])
self.assertIsNotNone(prior_role['name'])
self.assertThat(prior_role['links']['self'],
matchers.EndsWith('/v3/roles/%s' % expected_prior_id))
implied_role = role_inference['implies']
self.assertEqual(expected_implied_id, implied_role['id'])
self.assertIsNotNone(implied_role['name'])
self.assertThat(implied_role['links']['self'], matchers.EndsWith(
'/v3/roles/%s' % expected_implied_id))
def _assert_two_roles_implied(self):
self._assert_expected_implied_role_response(
self.prior['id'], [self.implied1['id'], self.implied2['id']])
self._assert_expected_role_inference_rule_response(
self.prior['id'], self.implied1['id'])
self._assert_expected_role_inference_rule_response(
self.prior['id'], self.implied2['id'])
def _assert_one_role_implied(self):
self._assert_expected_implied_role_response(
self.prior['id'], [self.implied1['id']])
self.get('/roles/%s/implies/%s' %
(self.prior['id'], self.implied2['id']),
expected_status=http.client.NOT_FOUND)
def _assert_two_rules_defined(self):
r = self.get('/role_inferences/')
rules = r.result['role_inferences']
self.assertEqual(self.prior['id'], rules[0]['prior_role']['id'])
self.assertEqual(2, len(rules[0]['implies']))
implied_ids = [implied['id'] for implied in rules[0]['implies']]
implied_names = [implied['name'] for implied in rules[0]['implies']]
self.assertIn(self.implied1['id'], implied_ids)
self.assertIn(self.implied2['id'], implied_ids)
self.assertIn(self.implied1['name'], implied_names)
self.assertIn(self.implied2['name'], implied_names)
def _assert_one_rule_defined(self):
r = self.get('/role_inferences/')
rules = r.result['role_inferences']
self.assertEqual(self.prior['id'], rules[0]['prior_role']['id'])
self.assertEqual(self.implied1['id'], rules[0]['implies'][0]['id'])
self.assertEqual(self.implied1['name'], rules[0]['implies'][0]['name'])
self.assertEqual(1, len(rules[0]['implies']))
def test_list_all_rules(self):
self._setup_prior_two_implied()
self._assert_two_rules_defined()
self._delete_implied_role(self.prior, self.implied2)
self._assert_one_rule_defined()
def test_CRD_implied_roles(self):
self._setup_prior_two_implied()
self._assert_two_roles_implied()
self._delete_implied_role(self.prior, self.implied2)
self._assert_one_role_implied()
def _create_three_roles(self):
self.role_list = []
for _ in range(3):
role = unit.new_role_ref()
PROVIDERS.role_api.create_role(role['id'], role)
self.role_list.append(role)
def _create_test_domain_user_project(self):
domain = unit.new_domain_ref()
PROVIDERS.resource_api.create_domain(domain['id'], domain)
user = unit.create_user(PROVIDERS.identity_api, domain_id=domain['id'])
project = unit.new_project_ref(domain_id=domain['id'])
PROVIDERS.resource_api.create_project(project['id'], project)
return domain, user, project
def _assign_top_role_to_user_on_project(self, user, project):
PROVIDERS.assignment_api.add_role_to_user_and_project(
user['id'], project['id'], self.role_list[0]['id'])
def _build_effective_role_assignments_url(self, user):
return '/role_assignments?effective&user.id=%(user_id)s' % {
'user_id': user['id']}
def _assert_all_roles_in_assignment(self, response, user):
# Now use the list role assignments api to check that all three roles
# appear in the collection
self.assertValidRoleAssignmentListResponse(
response,
expected_length=len(self.role_list),
resource_url=self._build_effective_role_assignments_url(user))
def _assert_initial_assignment_in_effective(self, response, user, project):
# The initial assignment should be there (the link url will be
# generated and checked automatically since it matches the assignment)
entity = self.build_role_assignment_entity(
project_id=project['id'],
user_id=user['id'], role_id=self.role_list[0]['id'])
self.assertRoleAssignmentInListResponse(response, entity)
def _assert_effective_role_for_implied_has_prior_in_links(
self, response, user, project, prior_index, implied_index):
# An effective role for an implied role will have the prior role
# assignment in the links
prior_link = '/prior_roles/%(prior)s/implies/%(implied)s' % {
'prior': self.role_list[prior_index]['id'],
'implied': self.role_list[implied_index]['id']}
link = self.build_role_assignment_link(
project_id=project['id'], user_id=user['id'],
role_id=self.role_list[prior_index]['id'])
entity = self.build_role_assignment_entity(
link=link, project_id=project['id'],
user_id=user['id'], role_id=self.role_list[implied_index]['id'],
prior_link=prior_link)
self.assertRoleAssignmentInListResponse(response, entity)
def test_list_role_assignments_with_implied_roles(self):
"""Call ``GET /role_assignments`` with implied role grant.
Test Plan:
- Create a domain with a user and a project
- Create 3 roles
- Role 0 implies role 1 and role 1 implies role 2
- Assign the top role to the project
- Issue the URL to check effective roles on project - this
should return all 3 roles.
- Check the links of the 3 roles indicate the prior role where
appropriate
"""
(domain, user, project) = self._create_test_domain_user_project()
self._create_three_roles()
self._create_implied_role(self.role_list[0], self.role_list[1])
self._create_implied_role(self.role_list[1], self.role_list[2])
self._assign_top_role_to_user_on_project(user, project)
response = self.get(self._build_effective_role_assignments_url(user))
r = response
self._assert_all_roles_in_assignment(r, user)
self._assert_initial_assignment_in_effective(response, user, project)
self._assert_effective_role_for_implied_has_prior_in_links(
response, user, project, 0, 1)
self._assert_effective_role_for_implied_has_prior_in_links(
response, user, project, 1, 2)
def _create_named_role(self, name):
role = unit.new_role_ref()
role['name'] = name
PROVIDERS.role_api.create_role(role['id'], role)
return role
def test_root_role_as_implied_role_forbidden(self):
"""Test root role is forbidden to be set as an implied role.
Create 2 roles that are prohibited from being an implied role.
Create 1 additional role which should be accepted as an implied
role. Assure the prohibited role names cannot be set as an implied
role. Assure the accepted role name which is not a member of the
prohibited implied role list can be successfully set an implied
role.
"""
prohibited_name1 = 'root1'
prohibited_name2 = 'root2'
accepted_name1 = 'implied1'
prohibited_names = [prohibited_name1, prohibited_name2]
self.config_fixture.config(group='assignment',
prohibited_implied_role=prohibited_names)
prior_role = self._create_role()
prohibited_role1 = self._create_named_role(prohibited_name1)
url = '/roles/{prior_role_id}/implies/{implied_role_id}'.format(
prior_role_id=prior_role['id'],
implied_role_id=prohibited_role1['id'])
self.put(url, expected_status=http.client.FORBIDDEN)
prohibited_role2 = self._create_named_role(prohibited_name2)
url = '/roles/{prior_role_id}/implies/{implied_role_id}'.format(
prior_role_id=prior_role['id'],
implied_role_id=prohibited_role2['id'])
self.put(url, expected_status=http.client.FORBIDDEN)
accepted_role1 = self._create_named_role(accepted_name1)
url = '/roles/{prior_role_id}/implies/{implied_role_id}'.format(
prior_role_id=prior_role['id'],
implied_role_id=accepted_role1['id'])
self.put(url, expected_status=http.client.CREATED)
def test_trusts_from_implied_role(self):
self._create_three_roles()
self._create_implied_role(self.role_list[0], self.role_list[1])
self._create_implied_role(self.role_list[1], self.role_list[2])
self._assign_top_role_to_user_on_project(self.user, self.project)
# Create a trustee and assign the prior role to her
trustee = unit.create_user(
PROVIDERS.identity_api, domain_id=self.domain_id
)
ref = unit.new_trust_ref(
trustor_user_id=self.user['id'],
trustee_user_id=trustee['id'],
project_id=self.project['id'],
role_ids=[self.role_list[0]['id']])
r = self.post('/OS-TRUST/trusts', body={'trust': ref})
trust = r.result['trust']
# Only the role that was specified is in the trust, NOT implied roles
self.assertEqual(self.role_list[0]['id'], trust['roles'][0]['id'])
self.assertThat(trust['roles'], matchers.HasLength(1))
# Authenticate as the trustee
auth_data = self.build_authentication_request(
user_id=trustee['id'],
password=trustee['password'],
trust_id=trust['id'])
r = self.v3_create_token(auth_data)
token = r.result['token']
self.assertThat(token['roles'],
matchers.HasLength(len(self.role_list)))
for role in token['roles']:
self.assertIn(role, self.role_list)
for role in self.role_list:
self.assertIn(role, token['roles'])
def test_trusts_from_domain_specific_implied_role(self):
self._create_three_roles()
# Overwrite the first role with a domain specific role
role = unit.new_role_ref(domain_id=self.domain_id)
self.role_list[0] = PROVIDERS.role_api.create_role(role['id'], role)
self._create_implied_role(self.role_list[0], self.role_list[1])
self._create_implied_role(self.role_list[1], self.role_list[2])
self._assign_top_role_to_user_on_project(self.user, self.project)
# Create a trustee and assign the prior role to her
trustee = unit.create_user(
PROVIDERS.identity_api, domain_id=self.domain_id
)
ref = unit.new_trust_ref(
trustor_user_id=self.user['id'],
trustee_user_id=trustee['id'],
project_id=self.project['id'],
role_ids=[self.role_list[0]['id']])
r = self.post('/OS-TRUST/trusts', body={'trust': ref})
trust = r.result['trust']
# Only the role that was specified is in the trust, NOT implied roles
self.assertEqual(self.role_list[0]['id'], trust['roles'][0]['id'])
self.assertThat(trust['roles'], matchers.HasLength(1))
# Authenticate as the trustee
auth_data = self.build_authentication_request(
user_id=trustee['id'],
password=<PASSWORD>ee['password'],
trust_id=trust['id'])
r = self.v3_create_token(auth_data)
token = r.result['token']
# The token should have the roles implies by the domain specific role,
# but not the domain specific role itself.
self.assertThat(token['roles'],
matchers.HasLength(len(self.role_list) - 1))
for role in token['roles']:
self.assertIn(role, self.role_list)
for role in [self.role_list[1], self.role_list[2]]:
self.assertIn(role, token['roles'])
self.assertNotIn(self.role_list[0], token['roles'])
def test_global_role_cannot_imply_domain_specific_role(self):
domain = unit.new_domain_ref()
PROVIDERS.resource_api.create_domain(domain['id'], domain)
domain_role_ref = unit.new_role_ref(domain_id=domain['id'])
domain_role = PROVIDERS.role_api.create_role(
domain_role_ref['id'], domain_role_ref
)
global_role_ref = unit.new_role_ref()
global_role = PROVIDERS.role_api.create_role(
global_role_ref['id'], global_role_ref
)
self.put('/roles/%s/implies/%s' % (global_role['id'],
domain_role['id']),
expected_status=http.client.FORBIDDEN)
class DomainSpecificRoleTests(test_v3.RestfulTestCase, unit.TestCase):
def setUp(self):
def create_role(domain_id=None):
"""Call ``POST /roles``."""
ref = unit.new_role_ref(domain_id=domain_id)
r = self.post(
'/roles',
body={'role': ref})
return self.assertValidRoleResponse(r, ref)
super(DomainSpecificRoleTests, self).setUp()
self.domainA = unit.new_domain_ref()
PROVIDERS.resource_api.create_domain(self.domainA['id'], self.domainA)
self.domainB = unit.new_domain_ref()
PROVIDERS.resource_api.create_domain(self.domainB['id'], self.domainB)
self.global_role1 = create_role()
self.global_role2 = create_role()
# Since there maybe other global roles already created, let's count
# them, so we can ensure we can check subsequent list responses
# are correct
r = self.get('/roles')
self.existing_global_roles = len(r.result['roles'])
# And now create some domain specific roles
self.domainA_role1 = create_role(domain_id=self.domainA['id'])
self.domainA_role2 = create_role(domain_id=self.domainA['id'])
self.domainB_role = create_role(domain_id=self.domainB['id'])
def test_get_and_list_domain_specific_roles(self):
# Check we can get a domain specific role
r = self.get('/roles/%s' % self.domainA_role1['id'])
self.assertValidRoleResponse(r, self.domainA_role1)
# If we list without specifying a domain, we should only get global
# roles back.
r = self.get('/roles')
self.assertValidRoleListResponse(
r, expected_length=self.existing_global_roles)
self.assertRoleInListResponse(r, self.global_role1)
self.assertRoleInListResponse(r, self.global_role2)
self.assertRoleNotInListResponse(r, self.domainA_role1)
self.assertRoleNotInListResponse(r, self.domainA_role2)
self.assertRoleNotInListResponse(r, self.domainB_role)
# Now list those in domainA, making sure that's all we get back
r = self.get('/roles?domain_id=%s' % self.domainA['id'])
self.assertValidRoleListResponse(r, expected_length=2)
self.assertRoleInListResponse(r, self.domainA_role1)
self.assertRoleInListResponse(r, self.domainA_role2)
def test_update_domain_specific_roles(self):
self.domainA_role1['name'] = uuid.uuid4().hex
self.patch('/roles/%(role_id)s' % {
'role_id': self.domainA_role1['id']},
body={'role': self.domainA_role1})
r = self.get('/roles/%s' % self.domainA_role1['id'])
self.assertValidRoleResponse(r, self.domainA_role1)
def test_delete_domain_specific_roles(self):
# Check delete only removes that one domain role
self.delete('/roles/%(role_id)s' % {
'role_id': self.domainA_role1['id']})
self.get('/roles/%s' % self.domainA_role1['id'],
expected_status=http.client.NOT_FOUND)
# Now re-list those in domainA, making sure there's only one left
r = self.get('/roles?domain_id=%s' % self.domainA['id'])
self.assertValidRoleListResponse(r, expected_length=1)
self.assertRoleInListResponse(r, self.domainA_role2)
def test_same_domain_assignment(self):
user = unit.create_user(PROVIDERS.identity_api,
domain_id=self.domainA['id'])
projectA = unit.new_project_ref(domain_id=self.domainA['id'])
PROVIDERS.resource_api.create_project(projectA['id'], projectA)
PROVIDERS.assignment_api.create_grant(
self.domainA_role1['id'], user_id=user['id'],
project_id=projectA['id']
)
def test_cross_domain_assignment_valid(self):
user = unit.create_user(PROVIDERS.identity_api,
domain_id=self.domainB['id'])
projectA = unit.new_project_ref(domain_id=self.domainA['id'])
PROVIDERS.resource_api.create_project(projectA['id'], projectA)
# Positive: a role on domainA can be assigned to a user from domainB
# but only for use on a project from domainA
PROVIDERS.assignment_api.create_grant(
self.domainA_role1['id'], user_id=user['id'],
project_id=projectA['id']
)
def test_cross_domain_assignment_invalid(self):
user = unit.create_user(PROVIDERS.identity_api,
domain_id=self.domainB['id'])
projectB = unit.new_project_ref(domain_id=self.domainB['id'])
PROVIDERS.resource_api.create_project(projectB['id'], projectB)
# Negative: a role on domainA can be assigned to a user from domainB
# only for a project from domainA
self.assertRaises(exception.DomainSpecificRoleMismatch,
PROVIDERS.assignment_api.create_grant,
self.domainA_role1['id'],
user_id=user['id'],
project_id=projectB['id'])
def test_cross_domain_implied_roles_authentication(self):
# Create a user in domainB
user = unit.create_user(PROVIDERS.identity_api,
domain_id=self.domainB['id'])
# Create project in domainA
projectA = unit.new_project_ref(domain_id=self.domainA['id'])
PROVIDERS.resource_api.create_project(projectA['id'], projectA)
# Now we create an implied rule from a role in domainA to a
# role in domainB
self.put('/roles/%s/implies/%s' %
(self.domainA_role1['id'], self.domainB_role['id']),
expected_status=http.client.CREATED)
# A role in domainA can be assigned to a user from domainB
# only for a project from domainA
PROVIDERS.assignment_api.create_grant(
self.domainA_role1['id'], user_id=user['id'],
project_id=projectA['id']
)
# The role assignments should return an empty list since domain roles
# can only be used to imply another roles
assignments = PROVIDERS.assignment_api.list_role_assignments(
user_id=user['id'], effective=True)
self.assertEqual([], assignments)
# This also means we can't authenticate using the existing assignment
auth_body = self.build_authentication_request(
user_id=user['id'],
password=<PASSWORD>['password'],
project_id=projectA['id'])
self.post('/auth/tokens', body=auth_body,
expected_status=http.client.UNAUTHORIZED)
class ListUserProjectsTestCase(test_v3.RestfulTestCase):
"""Test for /users/<user>/projects."""
def load_sample_data(self):
# do not load base class's data, keep it focused on the tests
self.auths = []
self.domains = []
self.projects = []
self.roles = []
self.users = []
root_domain = unit.new_domain_ref(
id=resource_base.NULL_DOMAIN_ID,
name=resource_base.NULL_DOMAIN_ID
)
self.resource_api.create_domain(resource_base.NULL_DOMAIN_ID,
root_domain)
# Create 3 sets of domain, roles, projects, and users to demonstrate
# the right user's data is loaded and only projects they can access
# are returned.
for _ in range(3):
domain = unit.new_domain_ref()
PROVIDERS.resource_api.create_domain(domain['id'], domain)
user = unit.create_user(
PROVIDERS.identity_api, domain_id=domain['id']
)
role = unit.new_role_ref()
PROVIDERS.role_api.create_role(role['id'], role)
PROVIDERS.assignment_api.create_grant(
role['id'], user_id=user['id'], domain_id=domain['id']
)
project = unit.new_project_ref(domain_id=domain['id'])
PROVIDERS.resource_api.create_project(project['id'], project)
PROVIDERS.assignment_api.create_grant(
role['id'], user_id=user['id'], project_id=project['id']
)
auth = self.build_authentication_request(
user_id=user['id'],
password=<PASSWORD>['password'],
domain_id=domain['id'])
self.auths.append(auth)
self.domains.append(domain)
self.projects.append(project)
self.roles.append(role)
self.users.append(user)
def test_list_head_all(self):
for i in range(len(self.users)):
user = self.users[i]
auth = self.auths[i]
url = '/users/%s/projects' % user['id']
result = self.get(url, auth=auth)
projects_result = result.json['projects']
self.assertEqual(1, len(projects_result))
self.assertEqual(self.projects[i]['id'], projects_result[0]['id'])
self.head(url, auth=auth, expected_status=http.client.OK)
def test_list_enabled(self):
for i in range(len(self.users)):
user = self.users[i]
auth = self.auths[i]
# There are no disabled projects
url = '/users/%s/projects?enabled=True' % user['id']
result = self.get(url, auth=auth)
projects_result = result.json['projects']
self.assertEqual(1, len(projects_result))
self.assertEqual(self.projects[i]['id'], projects_result[0]['id'])
def test_list_disabled(self):
for i in range(len(self.users)):
user = self.users[i]
auth = self.auths[i]
project = self.projects[i]
# There are no disabled projects
url = '/users/%s/projects?enabled=False' % user['id']
result = self.get(url, auth=auth)
self.assertEqual(0, len(result.json['projects']))
# disable this one and check again
project['enabled'] = False
PROVIDERS.resource_api.update_project(project['id'], project)
result = self.get(url, auth=auth)
projects_result = result.json['projects']
self.assertEqual(1, len(projects_result))
self.assertEqual(self.projects[i]['id'], projects_result[0]['id'])
def test_list_by_domain_id(self):
for i in range(len(self.users)):
user = self.users[i]
domain = self.domains[i]
auth = self.auths[i]
# Try looking for projects with a non-existent domain_id
url = '/users/%s/projects?domain_id=%s' % (user['id'],
uuid.uuid4().hex)
result = self.get(url, auth=auth)
self.assertEqual(0, len(result.json['projects']))
# Now try a valid one
url = '/users/%s/projects?domain_id=%s' % (user['id'],
domain['id'])
result = self.get(url, auth=auth)
projects_result = result.json['projects']
self.assertEqual(1, len(projects_result))
self.assertEqual(self.projects[i]['id'], projects_result[0]['id'])
# FIXME(lbragstad): These tests contain system-level API calls, which means
# they will log a warning message if they are called with a project-scoped
# token, regardless of the role assignment on the project. We need to fix
# them by using a proper system-scoped admin token to make the call instead
# of a project scoped token.
class UserSystemRoleAssignmentTestCase(test_v3.RestfulTestCase,
SystemRoleAssignmentMixin):
def test_assign_system_role_to_user(self):
system_role_id = self._create_new_role()
# assign the user a role on the system
member_url = (
'/system/users/%(user_id)s/roles/%(role_id)s' % {
'user_id': self.user['id'],
'role_id': system_role_id
}
)
self.put(member_url)
# validate the role assignment
self.head(member_url)
# list system roles
collection_url = (
'/system/users/%(user_id)s/roles' % {'user_id': self.user['id']}
)
roles = self.get(collection_url).json_body['roles']
self.assertEqual(len(roles), 1)
self.assertEqual(roles[0]['id'], system_role_id)
self.head(collection_url, expected_status=http.client.OK)
response = self.get(
'/role_assignments?scope.system=all&user.id=%(user_id)s' % {
'user_id': self.user['id']
}
)
self.assertValidRoleAssignmentListResponse(response)
def test_list_role_assignments_for_user_returns_all_assignments(self):
system_role_id = self._create_new_role()
# assign the user a role on the system
member_url = '/system/users/%(user_id)s/roles/%(role_id)s' % {
'user_id': self.user['id'],
'role_id': system_role_id
}
self.put(member_url)
# the response should contain one role assignment for the system role
# and one for a role that was setup during setUp().
response = self.get(
'/role_assignments?user.id=%(user_id)s' % {
'user_id': self.user['id']
}
)
self.assertValidRoleAssignmentListResponse(response, expected_length=2)
def test_list_system_roles_for_user_returns_none_without_assignment(self):
# list system roles for user
collection_url = '/system/users/%(user_id)s/roles' % {
'user_id': self.user['id']
}
response = self.get(collection_url)
# assert that the user doesn't have any system role assignments, which
# is denoted by an empty list
self.assertEqual(response.json_body['roles'], [])
response = self.get(
'/role_assignments?scope.system=all&user.id=%(user_id)s' % {
'user_id': self.user['id']
}
)
self.assertEqual(len(response.json_body['role_assignments']), 0)
self.assertValidRoleAssignmentListResponse(response)
def test_list_system_roles_for_user_does_not_return_project_roles(self):
system_role_id = self._create_new_role()
# assign the user a role on the system
member_url = '/system/users/%(user_id)s/roles/%(role_id)s' % {
'user_id': self.user['id'],
'role_id': system_role_id
}
self.put(member_url)
# list project role assignments and save the role id of that
# assignment, this assignment was created during setUp
response = self.get(
'/projects/%(project_id)s/users/%(user_id)s/roles' % {
'project_id': self.project['id'],
'user_id': self.user['id']
}
)
self.assertEqual(len(response.json_body['roles']), 1)
project_role_id = response.json_body['roles'][0]['id']
# list system role assignments
collection_url = '/system/users/%(user_id)s/roles' % {
'user_id': self.user['id']
}
response = self.get(collection_url)
# assert the project role assignment is not in the system role
# assignments
for role in response.json_body['roles']:
self.assertNotEqual(role['id'], project_role_id)
# make sure the role_assignment API filters correctly based on system
# scope
response = self.get(
'/role_assignments?scope.system=all&user.id=%(user_id)s' % {
'user_id': self.user['id']
}
)
self.assertEqual(len(response.json_body['role_assignments']), 1)
system_assignment = response.json_body['role_assignments'][0]
self.assertEqual(system_assignment['role']['id'], system_role_id)
self.assertTrue(system_assignment['scope']['system']['all'])
# make sure the role_assignment API doesn't include the system role
# assignment when we filter based on project
path = (
'/role_assignments?scope.project.id=%(project_id)s&'
'user.id=%(user_id)s'
) % {'project_id': self.project['id'],
'user_id': self.user['id']}
response = self.get(path)
self.assertEqual(len(response.json_body['role_assignments']), 1)
project_assignment = response.json_body['role_assignments'][0]
self.assertEqual(project_assignment['role']['id'], project_role_id)
def test_list_system_roles_for_user_does_not_return_domain_roles(self):
system_role_id = self._create_new_role()
domain_role_id = self._create_new_role()
# assign a role to the user on a domain
domain_member_url = (
'/domains/%(domain_id)s/users/%(user_id)s/roles/%(role_id)s' % {
'domain_id': self.user['domain_id'],
'user_id': self.user['id'],
'role_id': domain_role_id
}
)
self.put(domain_member_url)
# assign the user a role on the system
member_url = '/system/users/%(user_id)s/roles/%(role_id)s' % {
'user_id': self.user['id'],
'role_id': system_role_id
}
self.put(member_url)
# list domain role assignments
response = self.get(
'/domains/%(domain_id)s/users/%(user_id)s/roles' % {
'domain_id': self.user['domain_id'],
'user_id': self.user['id']
}
)
self.assertEqual(len(response.json_body['roles']), 1)
# list system role assignments
collection_url = '/system/users/%(user_id)s/roles' % {
'user_id': self.user['id']
}
response = self.get(collection_url)
# assert the domain role assignment is not in the system role
# assignments
for role in response.json_body['roles']:
self.assertNotEqual(role['id'], domain_role_id)
# make sure the role_assignment API filters correctly based on system
# scope
response = self.get(
'/role_assignments?scope.system=all&user.id=%(user_id)s' % {
'user_id': self.user['id']
}
)
self.assertEqual(len(response.json_body['role_assignments']), 1)
system_assignment = response.json_body['role_assignments'][0]
self.assertEqual(system_assignment['role']['id'], system_role_id)
self.assertTrue(system_assignment['scope']['system']['all'])
# make sure the role_assignment API doesn't include the system role
# assignment when we filter based on domain
path = (
'/role_assignments?scope.domain.id=%(domain_id)s&'
'user.id=%(user_id)s'
) % {'domain_id': self.user['domain_id'],
'user_id': self.user['id']}
response = self.get(path)
self.assertEqual(len(response.json_body['role_assignments']), 1)
domain_assignment = response.json_body['role_assignments'][0]
self.assertEqual(domain_assignment['role']['id'], domain_role_id)
def test_check_user_has_system_role_when_assignment_exists(self):
system_role_id = self._create_new_role()
# assign the user a role on the system
member_url = '/system/users/%(user_id)s/roles/%(role_id)s' % {
'user_id': self.user['id'],
'role_id': system_role_id
}
self.put(member_url)
# check the user has the system role assignment
self.head(member_url)
def test_check_user_does_not_have_system_role_without_assignment(self):
system_role_id = self._create_new_role()
# check the user does't have the system role assignment
member_url = '/system/users/%(user_id)s/roles/%(role_id)s' % {
'user_id': self.user['id'],
'role_id': system_role_id
}
self.head(member_url, expected_status=http.client.NOT_FOUND)
response = self.get(
'/role_assignments?scope.system=all&user.id=%(user_id)s' % {
'user_id': self.user['id']
}
)
self.assertEqual(len(response.json_body['role_assignments']), 0)
self.assertValidRoleAssignmentListResponse(response)
def test_unassign_system_role_from_user(self):
system_role_id = self._create_new_role()
# assign the user a role on the system
member_url = '/system/users/%(user_id)s/roles/%(role_id)s' % {
'user_id': self.user['id'],
'role_id': system_role_id
}
self.put(member_url)
# ensure the user has the role assignment
self.head(member_url)
response = self.get(
'/role_assignments?scope.system=all&user.id=%(user_id)s' % {
'user_id': self.user['id']
}
)
self.assertEqual(len(response.json_body['role_assignments']), 1)
self.assertValidRoleAssignmentListResponse(response)
# remove the system role assignment from the user
self.delete(member_url)
# ensure the user doesn't have any system role assignments
collection_url = '/system/users/%(user_id)s/roles' % {
'user_id': self.user['id']
}
response = self.get(collection_url)
self.assertEqual(len(response.json_body['roles']), 0)
response = self.get(
'/role_assignments?scope.system=all&user.id=%(user_id)s' % {
'user_id': self.user['id']
}
)
self.assertValidRoleAssignmentListResponse(response, expected_length=0)
def test_query_for_system_scope_and_domain_scope_fails(self):
# When asking for assignments and providing query parameters, we
# shouldn't be able to ask for two different types of scope. This is
# also true for project + domain scope.
path = (
'/role_assignments?scope.system=all'
'&scope.domain.id=%(domain_id)s'
) % {'domain_id': self.domain_id}
self.get(path, expected_status=http.client.BAD_REQUEST)
def test_query_for_system_scope_and_project_scope_fails(self):
# When asking for assignments and providing query parameters, we
# shouldn't be able to ask for two different types of scope. This is
# also true for project + domain scope.
path = (
'/role_assignments?scope.system=all'
'&scope.project.id=%(project_id)s'
) % {'project_id': self.project_id}
self.get(path, expected_status=http.client.BAD_REQUEST)
def test_query_for_role_id_does_not_return_system_user_roles(self):
system_role_id = self._create_new_role()
# assign the user a role on the system
member_url = '/system/users/%(user_id)s/roles/%(role_id)s' % {
'user_id': self.user['id'],
'role_id': system_role_id
}
self.put(member_url)
# Make sure we only get one role assignment back since the system role
# assignment shouldn't be returned.
path = (
'/role_assignments?role.id=%(role_id)s&user.id=%(user_id)s'
) % {'role_id': self.role_id, 'user_id': self.user['id']}
response = self.get(path)
self.assertValidRoleAssignmentListResponse(response, expected_length=1)
# FIXME(lbragstad): These tests contain system-level API calls, which means
# they will log a warning message if they are called with a project-scoped
# token, regardless of the role assignment on the project. We need to fix
# them by using a proper system-scoped admin token to make the call instead
# of a project scoped token.
class GroupSystemRoleAssignmentTestCase(test_v3.RestfulTestCase,
SystemRoleAssignmentMixin):
def test_assign_system_role_to_group(self):
system_role_id = self._create_new_role()
group = self._create_group()
# assign the role to the group globally
member_url = '/system/groups/%(group_id)s/roles/%(role_id)s' % {
'group_id': group['id'],
'role_id': system_role_id
}
self.put(member_url)
# validate the role assignment
self.head(member_url)
# list global roles
collection_url = '/system/groups/%(group_id)s/roles' % {
'group_id': group['id']
}
roles = self.get(collection_url).json_body['roles']
self.assertEqual(len(roles), 1)
self.assertEqual(roles[0]['id'], system_role_id)
self.head(collection_url, expected_status=http.client.OK)
response = self.get(
'/role_assignments?scope.system=all&group.id=%(group_id)s' % {
'group_id': group['id']
}
)
self.assertValidRoleAssignmentListResponse(response, expected_length=1)
self.assertEqual(
response.json_body['role_assignments'][0]['role']['id'],
system_role_id
)
def test_assign_system_role_to_non_existant_group_fails(self):
system_role_id = self._create_new_role()
group_id = uuid.uuid4().hex
# assign the role to the group globally
member_url = '/system/groups/%(group_id)s/roles/%(role_id)s' % {
'group_id': group_id,
'role_id': system_role_id
}
self.put(member_url, expected_status=http.client.NOT_FOUND)
def test_list_role_assignments_for_group_returns_all_assignments(self):
system_role_id = self._create_new_role()
group = self._create_group()
# assign the role to the group globally and on a single project
member_url = '/system/groups/%(group_id)s/roles/%(role_id)s' % {
'group_id': group['id'],
'role_id': system_role_id
}
self.put(member_url)
member_url = (
'/projects/%(project_id)s/groups/%(group_id)s/'
'roles/%(role_id)s'
) % {
'project_id': self.project_id,
'group_id': group['id'],
'role_id': system_role_id
}
self.put(member_url)
# make sure both assignments exist in the response, there should be two
response = self.get(
'/role_assignments?group.id=%(group_id)s' % {
'group_id': group['id']
}
)
self.assertValidRoleAssignmentListResponse(response, expected_length=2)
def test_list_system_roles_for_group_returns_none_without_assignment(self):
group = self._create_group()
# list global roles for group
collection_url = '/system/groups/%(group_id)s/roles' % {
'group_id': group['id']
}
response = self.get(collection_url)
# assert that the group doesn't have any system role assignments, which
# is denoted by an empty list
self.assertEqual(response.json_body['roles'], [])
response = self.get(
'/role_assignments?scope.system=all&group.id=%(group_id)s' % {
'group_id': group['id']
}
)
self.assertValidRoleAssignmentListResponse(response, expected_length=0)
def test_list_system_roles_for_group_does_not_return_project_roles(self):
system_role_id = self._create_new_role()
project_role_id = self._create_new_role()
group = self._create_group()
# assign the group a role on the system and a role on a project
member_url = '/system/groups/%(group_id)s/roles/%(role_id)s' % {
'group_id': group['id'], 'role_id': system_role_id
}
self.put(member_url)
member_url = (
'/projects/%(project_id)s/groups/%(group_id)s/'
'roles/%(role_id)s'
) % {
'project_id': self.project_id,
'group_id': group['id'],
'role_id': project_role_id
}
self.put(member_url)
# list system role assignments
collection_url = '/system/groups/%(group_id)s/roles' % {
'group_id': group['id']
}
response = self.get(collection_url)
# assert the project role assignment is not in the system role
# assignments
for role in response.json_body['roles']:
self.assertNotEqual(role['id'], project_role_id)
response = self.get(
'/role_assignments?scope.system=all&group.id=%(group_id)s' % {
'group_id': group['id']
}
)
self.assertValidRoleAssignmentListResponse(response, expected_length=1)
def test_list_system_roles_for_group_does_not_return_domain_roles(self):
system_role_id = self._create_new_role()
domain_role_id = self._create_new_role()
group = self._create_group()
# assign a role to the group on a domain
domain_member_url = (
'/domains/%(domain_id)s/groups/%(group_id)s/'
'roles/%(role_id)s' % {
'domain_id': group['domain_id'],
'group_id': group['id'],
'role_id': domain_role_id
}
)
self.put(domain_member_url)
# assign the group a role on the system
member_url = '/system/groups/%(group_id)s/roles/%(role_id)s' % {
'group_id': group['id'],
'role_id': system_role_id
}
self.put(member_url)
# list domain role assignments
response = self.get(
'/domains/%(domain_id)s/groups/%(group_id)s/roles' % {
'domain_id': group['domain_id'], 'group_id': group['id']
}
)
self.assertEqual(len(response.json_body['roles']), 1)
# list system role assignments
collection_url = '/system/groups/%(group_id)s/roles' % {
'group_id': group['id']
}
response = self.get(collection_url)
# assert the domain role assignment is not in the system role
# assignments
for role in response.json_body['roles']:
self.assertNotEqual(role['id'], domain_role_id)
response = self.get(
'/role_assignments?scope.system=all&group.id=%(group_id)s' % {
'group_id': group['id']
}
)
self.assertValidRoleAssignmentListResponse(response, expected_length=1)
def test_check_group_has_system_role_when_assignment_exists(self):
system_role_id = self._create_new_role()
group = self._create_group()
# assign the group a role on the system
member_url = '/system/groups/%(group_id)s/roles/%(role_id)s' % {
'group_id': group['id'],
'role_id': system_role_id
}
self.put(member_url)
# check the group has the system role assignment
self.head(member_url)
response = self.get(
'/role_assignments?scope.system=all&group.id=%(group_id)s' % {
'group_id': group['id']
}
)
self.assertValidRoleAssignmentListResponse(response, expected_length=1)
self.assertEqual(
response.json_body['role_assignments'][0]['role']['id'],
system_role_id
)
def test_check_group_does_not_have_system_role_without_assignment(self):
system_role_id = self._create_new_role()
group = self._create_group()
# check the group does't have the system role assignment
member_url = '/system/groups/%(group_id)s/roles/%(role_id)s' % {
'group_id': group['id'],
'role_id': system_role_id
}
self.head(member_url, expected_status=http.client.NOT_FOUND)
response = self.get(
'/role_assignments?scope.system=all&group.id=%(group_id)s' % {
'group_id': group['id']
}
)
self.assertValidRoleAssignmentListResponse(response, expected_length=0)
def test_unassign_system_role_from_group(self):
system_role_id = self._create_new_role()
group = self._create_group()
# assign the group a role on the system
member_url = '/system/groups/%(group_id)s/roles/%(role_id)s' % {
'group_id': group['id'],
'role_id': system_role_id
}
self.put(member_url)
# ensure the group has the role assignment
self.head(member_url)
response = self.get(
'/role_assignments?scope.system=all&group.id=%(group_id)s' % {
'group_id': group['id']
}
)
self.assertEqual(len(response.json_body['role_assignments']), 1)
self.assertValidRoleAssignmentListResponse(response)
# remove the system role assignment from the group
self.delete(member_url)
# ensure the group doesn't have any system role assignments
collection_url = '/system/groups/%(group_id)s/roles' % {
'group_id': group['id']
}
response = self.get(collection_url)
self.assertEqual(len(response.json_body['roles']), 0)
response = self.get(
'/role_assignments?scope.system=all&group.id=%(group_id)s' % {
'group_id': group['id']
}
)
self.assertValidRoleAssignmentListResponse(response, expected_length=0)
def test_query_for_role_id_does_not_return_system_group_roles(self):
system_role_id = self._create_new_role()
group = self._create_group()
# assign the group a role on the system
member_url = '/system/groups/%(group_id)s/roles/%(role_id)s' % {
'group_id': group['id'],
'role_id': system_role_id
}
self.put(member_url)
# assign the group a role on the system
member_url = (
'/projects/%(project_id)s/groups/%(group_id)s/roles/%(role_id)s' %
{'project_id': self.project_id,
'group_id': group['id'],
'role_id': self.role_id}
)
self.put(member_url)
# Make sure we only get one role assignment back since the system role
# assignment shouldn't be returned.
path = (
'/role_assignments?role.id=%(role_id)s&group.id=%(group_id)s'
) % {'role_id': self.role_id, 'group_id': group['id']}
response = self.get(path)
self.assertValidRoleAssignmentListResponse(response, expected_length=1)
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Other/pdf2imageTest.py | Wanganator414/python | 1 | 29291 | from pdf2image import convert_from_path, convert_from_bytes
from pdf2image.exceptions import (
PDFInfoNotInstalledError,
PDFPageCountError,
PDFSyntaxError
)
images = convert_from_path('.\git_cheat_sheet.pdf')
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cmd_chess_util.py | ilius/chess-challange | 1 | 1603125 | """
contains some chess-related utility functions to use in command line
"""
from pieces import ChessPiece
from cmd_util import input_int
PIECE_SYMBOLS = 'KQBRN' # must not re-order
def input_pieces_count(row_count, col_count):
"""
ask the user the number or chess pieces of each type
return a dict { piece_symbol => count }
row_count: number of rows
col_count: number of columns
"""
cell_count = row_count * col_count
count_by_symbol = {}
total_count = 0
for symbol in PIECE_SYMBOLS:
cls = ChessPiece.class_by_symbol[symbol]
plural_name = cls.name.capitalize() + 's'
count = input_int(
'Number of %s: ' % plural_name,
default=0,
minimum=0,
maximum=cell_count-total_count,
)
count_by_symbol[symbol] = count
total_count += count
return count_by_symbol
def input_problem():
"""
get the board size and pieces count from stdin
"""
row_count = input_int('Number of rows: ', minimum=2)
col_count = input_int('Number of columns: ', minimum=2)
print()
count_by_symbol = input_pieces_count(row_count, col_count)
return row_count, col_count, count_by_symbol
def mark_board_under_attack_cells(board, row_count, col_count, symbol='.'):
"""
fill the empty cells that are under attack by other cells,
with given symbol
return a new board dict
"""
new_board = {}
for row_num in range(row_count):
for col_num in range(col_count):
try:
new_board[(row_num, col_num)] = board[(row_num, col_num)]
except KeyError:
if ChessPiece.pos_attacked_by_board(row_num, col_num, board):
new_board[(row_num, col_num)] = symbol
return new_board
# ______________________ Test Functions ______________________ #
def test_input_pieces_count(row_count, col_count):
"""test `input_pieces_count` function"""
count_by_symbol = input_pieces_count(row_count, col_count)
assert set(count_by_symbol.keys()) == \
set(ChessPiece.class_by_symbol.keys())
for count in count_by_symbol.values():
assert isinstance(count, int)
assert count >= 0
assert sum(count_by_symbol.values()) <= row_count * col_count
for symbol, count in count_by_symbol.items():
print('%s: %s' % (symbol, count))
if __name__ == '__main__':
test_input_pieces_count(7, 7)
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var/spack/repos/builtin/packages/py-flit-core/package.py | zygyz/spack | 348 | 46740 | # Copyright 2013-2021 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
import glob
import os
import zipfile
from spack import *
class PyFlitCore(PythonPackage):
"""Distribution-building parts of Flit."""
homepage = "https://github.com/takluyver/flit"
url = "https://github.com/takluyver/flit/archive/refs/tags/3.3.0.tar.gz"
maintainers = ['takluyver']
version('3.3.0', sha256='f5340b268563dd408bf8e2df6dbc8d4d08bc76cdff0d8c7f8a4be94e5f01f22f')
depends_on('[email protected]:', type=('build', 'run'))
depends_on('py-toml', type=('build', 'run'))
def build(self, spec, prefix):
with working_dir('flit_core'):
python('build_dists.py')
def install(self, spec, prefix):
wheel = glob.glob(os.path.join('flit_core', 'dist', '*.whl'))[0]
with zipfile.ZipFile(wheel) as f:
f.extractall(python_purelib)
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peekingduck/pipeline/nodes/draw/group_bbox_and_tag.py | ericleehy/PeekingDuck | 1 | 53931 | <gh_stars>1-10
# Copyright 2022 AI Singapore
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Draws large bounding boxes with tags, over identified groups of bounding boxes.
"""
from typing import Any, Dict, List
import numpy as np
from peekingduck.pipeline.nodes.draw.utils.bbox import draw_bboxes, draw_tags
from peekingduck.pipeline.nodes.draw.utils.constants import TOMATO
from peekingduck.pipeline.nodes.abstract_node import AbstractNode
class Node(AbstractNode):
"""Draws large bounding boxes with tags over multiple object bounding boxes
which have been identified as belonging to the same group.
The :term:`large_groups` data type from :mod:`dabble.check_large_groups`,
and the ``groups`` key of the :term:`obj_attrs` data type from
:mod:`dabble.group_nearby_objs`, are inputs for this node which identifies
the different groups, and the group associated with each bounding box.
For better understanding, refer to
the :doc:`Group Size Checking use case </use_cases/group_size_checking>`.
Inputs:
|img_data|
|bboxes_data|
|obj_attrs_data|
:mod:`draw.group_bbox_and_tag` requires the ``groups`` attribute from
:mod:`dabble.group_nearby_objs`.
|large_groups_data|
Outputs:
|none_output_data|
Configs:
tag (:obj:`str`): **default = "LARGE GROUP!"**. |br|
The string message printed when a large group is detected.
.. versionchanged:: 1.2.0
:mod:`draw.group_bbox_and_tag` used to take in ``obj_tags``
(:obj:`List[str]`) as an input data type, which has been deprecated and
now subsumed under :term:`obj_attrs`. The same attribute is accessed by
using the ``groups`` key of :term:`obj_attrs`.
"""
def __init__(self, config: Dict[str, Any] = None, **kwargs: Any) -> None:
super().__init__(config, node_path=__name__, **kwargs)
def run(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
"""Draws large bounding boxes over multiple object bounding boxes
which have been identified as belonging to large groups.
Args:
inputs (dict): Dictionary with keys "img", "bboxes", "obj_attrs",
"large_groups".
Returns:
outputs (dict): Dictionary with keys "none".
"""
group_bboxes = self._get_group_bbox_coords(
inputs["large_groups"], inputs["bboxes"], inputs["obj_attrs"]["groups"]
)
group_tags = self._get_group_tags(inputs["large_groups"], self.tag)
# show labels set to False to reduce clutter on display
draw_bboxes(inputs["img"], group_bboxes, [], False, TOMATO)
draw_tags(inputs["img"], group_bboxes, group_tags, TOMATO)
return {}
@staticmethod
def _get_group_bbox_coords(
large_groups: List[int], bboxes: List[np.ndarray], obj_groups: List[int]
) -> List[np.ndarray]:
"""For bboxes that belong to the same large group, get the coordinates of
a large bbox that combines all these individual bboxes. Repeat for all large
groups.
"""
group_bboxes = []
bboxes = np.array(bboxes)
obj_groups = np.array(obj_groups)
for group in large_groups:
# filter relevant bboxes, select top-left and bot-right corners
group_bbox = np.array([1.0, 1.0, 0.0, 0.0])
selected_bboxes = bboxes[obj_groups == group]
group_bbox[:2] = np.amin(selected_bboxes, axis=0)[:2]
group_bbox[2:] = np.amax(selected_bboxes, axis=0)[2:]
group_bboxes.append(group_bbox)
return group_bboxes
@staticmethod
def _get_group_tags(large_groups: List[int], tag: str) -> List[str]:
"""Creates a list of tags to be used for the draw_tags function."""
group_tags = [tag for _ in range(len(large_groups))]
return group_tags
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] |
inventory.py | Jongerr/vendor_receiving | 0 | 6705 | import json
import os
import random
import requests
from passlib.hash import pbkdf2_sha256 as pbk
from PyQt5.QtSql import QSqlDatabase, QSqlQuery
from pprint import pprint
ENCODING = 'utf-8'
DB_PATH = os.path.join(os.path.curdir, 'inventory.db')
def scrambleWord(word):
"""Randomize the letters in word and return the resulting string."""
word_list = list(word)
random.shuffle(word_list)
word = ''.join(word_list)
return word
def generateItems():
"""Generate a dictionary of retail products and store the data in items.json.
Pulls a list of items and artificially doubles it with scrambled item names.
Each item is given a random PLU, UPC, and department number.
Each dictionary key is the item's PLU.
"""
response = requests.get('https://www.randomlists.com/data/things.json')
json_data = response.json()
items = json_data['RandL']['items']
#double sample size by scrambling item names
scrambled_list = []
for item in items:
scrambled_item = scrambleWord(item)
scrambled_list.append(scrambled_item)
items = items + scrambled_list
data = {}
for item in items:
random.seed(item)
upc = random.randint(100000000000, 999999999999)
plu = random.randint(1000, 9999999)
department = (plu % 7) + 1
print('UPC:{0} | PLU:{1} | Item:{2} | D{3}'.format(upc, plu, item, department))
if plu in data:
print('Duplicate found: {}'.format(plu))
continue
data[plu] = {'upc':upc, 'department':department, 'model':item}
with open('items.json', 'w') as f:
json.dump(data, f)
def generatePO():
"""Create dumby Purchase Orders and store them in pos.json.
Each PO is asigned one random vendor and department number,
along with a random length list of items belonging to said department.
Returns: True if items.json successfully opens, False otherwise.
"""
try:
with open('items.json', 'r') as f:
items_dict = json.load(f)
except FileNotFoundError:
return False
vendors = ['Dyson', 'Ingrammicro', 'LKG', 'Inland', 'Sandisk', 'Seagate', 'Hasbro', 'Mattel',\
'Gear Head', 'Logitech', 'NTE', 'Dell', 'Microsoft', 'Right Stuff', 'Alliance', 'Energizer']
po_dict = {}
for i in range(50):
po_num = 24000000 + random.randint(1, 999999)
if po_num in po_dict:
continue
po_dict[po_num] = {'department': (po_num % 7) + 1, 'items': {}, 'vendor': random.choice(vendors)}
for key in items_dict:
match_found = False
loops = 0
while not match_found:
loops += 1
if loops > 200:
print('\n\nToo many loops.\n\n')
break
po, department = random.choice(list(po_dict.items()))
department = department['department']
print('PO department: {}'.format(department))
print('item plu: {} department: {}'.format(key, items_dict[key]['department']))
if items_dict[key]['department'] == department:
max_count = random.randint(1, 20)
po_dict[po]['items'][key] = max_count
match_found = True
with open('pos.json', 'w') as f:
json.dump(po_dict, f)
return True
def fillDB():
"""Create a database and populate two tables(named items and purchase_order).
The 'items' and 'purchase_order' tables are populated with the data from items.json
and pos.json respectively.
"""
with open('items.json') as f:
data = json.load(f)
db = QSqlDatabase.addDatabase('QSQLITE')
db.setDatabaseName(DB_PATH)
if not db.open():
print('DB could not be opened')
error = QSqlDatabase.lastError()
print(error.text())
return False
query = QSqlQuery()
if query.exec_("drop table items"):
print('successfully dropped table')
else:
print('unsuccessfully dropped table')
print(query.lastError().text())
if query.exec_("create table items(plu int primary key, upc varchar(12) unique, "
"model varchar(20), department int)"):
print('success')
else:
print('failure')
print(query.lastError().text())
for key in data:
if query.exec_("insert into items values({}, '{}', '{}', {})".format(key, data[key]['upc'],
data[key]['model'], data[key]['department'])):
print("values({}, {}, {}, {}) successfully inserted.".format(key, data[key]['upc'], data[key]['model'], data[key]['department']))
else:
print("values({}, {}, {}, {}) unsuccessfully inserted.".format(key, data[key]['upc'], data[key]['model'], data[key]['department']))
print(query.lastError().text())
with open('pos.json') as f:
po_dict = json.load(f)
if query.exec_("drop table purchase_order"):
print('successfully dropped table')
else:
print('unsuccessfully dropped table')
print(query.lastError().text())
if query.exec_("create table purchase_order(po int primary key, vendor varchar(30), "
"department int, items blob)"):
print('success')
else:
print('failure')
print(query.lastError().text())
for key in po_dict:
item_string = json.dumps(po_dict[key]['items'])
item_blob = item_string.encode(ENCODING)
if query.exec_("insert into purchase_order values({}, '{}', {}, '{}')"\
.format(key, po_dict[key]['vendor'], po_dict[key]['department'], item_string)):
print("values({}, {}, {}, {}) successfully inserted."\
.format(key, po_dict[key]['vendor'], po_dict[key]['department'], item_string))
else:
print("values({}, {}, {}, {}) unsuccessfully inserted."\
.format(key, po_dict[key]['vendor'], po_dict[key]['department'], item_blob))
print(query.lastError().text())
def createEmployeeTable():
db = QSqlDatabase.addDatabase('QSQLITE')
db.setDatabaseName(DB_PATH)
if not db.open():
print('DB could not be opened')
error = QSqlDatabase.lastError()
print(error.text())
return False
query = QSqlQuery()
if not query.exec_("drop table employee"):
print(query.lastError().text())
if not query.exec_("create table employee(id int primary key, first_name varchar(10), "\
"last_name varchar(10), posistion int, pass_hash varchar(200))"):
print(query.lastError().text())
if not query.exec_("insert into employee values({}, '{}', '{}', {}, '{}')".\
format(162973, 'Jon', 'Michie', 2, pbk.hash('Michie'))):
print(query.lastError().text())
query.exec_("insert into employee values({}, '{}', '{}', {}, '{}')".\
format(131901, 'Ben', 'Terry', 3, pbk.hash('Terry')))
query.exec_("insert into employee values({}, '{}', '{}', {}, '{}')".\
format(150697, 'Daniel', 'Silva', 2, pbk.hash('Silva')))
query.exec_("insert into employee values({}, '{}', '{}', {}, '{}')".\
format(68412, 'James', 'Hutchetson', 2, pbk.hash('Hutchetson')))
query.exec_("insert into employee values({}, '{}', '{}', {}, '{}')".\
format(161844, 'MacKenly', 'Gamble', 1, pbk.hash('Gamble')))
query.exec_("insert into employee values({}, '{}', '{}', {}, '{}')".\
format(141047, 'George', 'Huston', 1, pbk.hash('Huston')))
query.exec_("insert into employee values({}, '{}', '{}', {}, '{}')".\
format(46045, 'Arthur', 'Art', 1, pbk.hash('Art')))
def testHashVerification(name):
db = QSqlDatabase.addDatabase('QSQLITE')
db.setDatabaseName(DB_PATH)
if not db.open():
print('DB could not be opened')
error = QSqlDatabase.lastError()
print(error.text())
return False
query = QSqlQuery()
if not query.exec_("select pass_hash from employee where last_name = '{}'".format(name)):
print(query.lastError().text())
elif not query.next():
print('Table values not found')
else:
pass_hash = query.value(0)
if pbk.verify(name, pass_hash):
print('It\'s a match!')
else:
print('Match not found.')
if __name__ == '__main__':
generateItems()
generatePO()
fillDB()
createEmployeeTable()
testHashVerification('Terry')
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] |
empire/server/database/base.py | iNoSec2/Empire | 0 | 72646 | <filename>empire/server/database/base.py
import subprocess
from sqlalchemy import create_engine
from sqlalchemy.orm import scoped_session, sessionmaker
from empire import arguments
from empire.server.common.config import empire_config
from empire.server.database import models
from empire.server.database.defaults import (
get_default_config,
get_default_keyword_obfuscation,
get_default_user,
)
from empire.server.database.models import Base
database_config = empire_config.database
if database_config.type == "mysql":
url = database_config.url
username = database_config.username
password = database_config.password
engine = create_engine(
f"mysql+pymysql://{username}:{password}@{url}/empire", echo=False
)
else:
location = database_config.location
engine = create_engine(f"sqlite:///{location}?check_same_thread=false", echo=False)
Session = scoped_session(sessionmaker(bind=engine))
args = arguments.args
if args.reset:
choice = input(
"\x1b[1;33m[>] Would you like to reset your Empire instance? [y/N]: \x1b[0m"
)
if choice.lower() == "y":
# The reset script will delete the default db file. This will drop tables if connected to MySQL or
# a different SQLite .db file.
Base.metadata.drop_all(engine)
subprocess.call("./setup/reset.sh")
else:
pass
Base.metadata.create_all(engine)
def color(string, color=None):
"""
Change text color for the Linux terminal.
Note: this is duplicate code copied from helpers.py because it cannot be imported into this file due to a circular
reference. There are plans to refactor these circular references out, but this is the near term solution.
"""
attr = []
# bold
attr.append("1")
if color:
if color.lower() == "red":
attr.append("31")
elif color.lower() == "green":
attr.append("32")
elif color.lower() == "yellow":
attr.append("33")
elif color.lower() == "blue":
attr.append("34")
return "\x1b[%sm%s\x1b[0m" % (";".join(attr), string)
else:
if string.strip().startswith("[!]"):
attr.append("31")
return "\x1b[%sm%s\x1b[0m" % (";".join(attr), string)
elif string.strip().startswith("[+]"):
attr.append("32")
return "\x1b[%sm%s\x1b[0m" % (";".join(attr), string)
elif string.strip().startswith("[*]"):
attr.append("34")
return "\x1b[%sm%s\x1b[0m" % (";".join(attr), string)
elif string.strip().startswith("[>]"):
attr.append("33")
return "\x1b[%sm%s\x1b[0m" % (";".join(attr), string)
else:
return string
# When Empire starts up for the first time, it will create the database and create
# these default records.
if len(Session().query(models.User).all()) == 0:
print(color("[*] Setting up database."))
print(color("[*] Adding default user."))
Session().add(get_default_user())
Session().commit()
Session.remove()
if len(Session().query(models.Config).all()) == 0:
print(color("[*] Adding database config."))
Session().add(get_default_config())
Session().commit()
Session.remove()
if len(Session().query(models.Keyword).all()) == 0:
print(color('[*] Adding default keyword obfuscation functions.'))
functions = get_default_keyword_obfuscation()
for function in functions:
Session().add(function)
Session().commit()
Session.remove()
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] |
sdv/tabular/ctgan.py | joanvaquer/SDV | 0 | 16785 | <reponame>joanvaquer/SDV
"""Wrapper around CTGAN model."""
from sdv.tabular.base import BaseTabularModel
class CTGAN(BaseTabularModel):
"""Model wrapping ``CTGANSynthesizer`` model.
Args:
field_names (list[str]):
List of names of the fields that need to be modeled
and included in the generated output data. Any additional
fields found in the data will be ignored and will not be
included in the generated output.
If ``None``, all the fields found in the data are used.
field_types (dict[str, dict]):
Dictinary specifying the data types and subtypes
of the fields that will be modeled. Field types and subtypes
combinations must be compatible with the SDV Metadata Schema.
field_transformers (dict[str, str]):
Dictinary specifying which transformers to use for each field.
Available transformers are:
* ``integer``: Uses a ``NumericalTransformer`` of dtype ``int``.
* ``float``: Uses a ``NumericalTransformer`` of dtype ``float``.
* ``categorical``: Uses a ``CategoricalTransformer`` without gaussian noise.
* ``categorical_fuzzy``: Uses a ``CategoricalTransformer`` adding gaussian noise.
* ``one_hot_encoding``: Uses a ``OneHotEncodingTransformer``.
* ``label_encoding``: Uses a ``LabelEncodingTransformer``.
* ``boolean``: Uses a ``BooleanTransformer``.
* ``datetime``: Uses a ``DatetimeTransformer``.
anonymize_fields (dict[str, str]):
Dict specifying which fields to anonymize and what faker
category they belong to.
primary_key (str):
Name of the field which is the primary key of the table.
constraints (list[Constraint, dict]):
List of Constraint objects or dicts.
table_metadata (dict or metadata.Table):
Table metadata instance or dict representation.
If given alongside any other metadata-related arguments, an
exception will be raised.
If not given at all, it will be built using the other
arguments or learned from the data.
epochs (int):
Number of training epochs. Defaults to 300.
log_frequency (boolean):
Whether to use log frequency of categorical levels in conditional
sampling. Defaults to ``True``.
embedding_dim (int):
Size of the random sample passed to the Generator. Defaults to 128.
gen_dim (tuple or list of ints):
Size of the output samples for each one of the Residuals. A Resiudal Layer
will be created for each one of the values provided. Defaults to (256, 256).
dis_dim (tuple or list of ints):
Size of the output samples for each one of the Discriminator Layers. A Linear
Layer will be created for each one of the values provided. Defaults to (256, 256).
l2scale (float):
Wheight Decay for the Adam Optimizer. Defaults to 1e-6.
batch_size (int):
Number of data samples to process in each step.
"""
_CTGAN_CLASS = None
_model = None
_DTYPE_TRANSFORMERS = {
'O': 'label_encoding'
}
def __init__(self, field_names=None, field_types=None, field_transformers=None,
anonymize_fields=None, primary_key=None, constraints=None, table_metadata=None,
epochs=300, log_frequency=True, embedding_dim=128, gen_dim=(256, 256),
dis_dim=(256, 256), l2scale=1e-6, batch_size=500):
super().__init__(
field_names=field_names,
primary_key=primary_key,
field_types=field_types,
anonymize_fields=anonymize_fields,
constraints=constraints,
table_metadata=table_metadata
)
try:
from ctgan import CTGANSynthesizer # Lazy import to make dependency optional
self._CTGAN_CLASS = CTGANSynthesizer
except ImportError as ie:
ie.msg += (
'\n\nIt seems like `ctgan` is not installed.\n'
'Please install it using:\n\n pip install sdv[ctgan]'
)
raise
self._embedding_dim = embedding_dim
self._gen_dim = gen_dim
self._dis_dim = dis_dim
self._l2scale = l2scale
self._batch_size = batch_size
self._epochs = epochs
self._log_frequency = log_frequency
def _fit(self, table_data):
"""Fit the model to the table.
Args:
table_data (pandas.DataFrame):
Data to be learned.
"""
self._model = self._CTGAN_CLASS(
embedding_dim=self._embedding_dim,
gen_dim=self._gen_dim,
dis_dim=self._dis_dim,
l2scale=self._l2scale,
batch_size=self._batch_size,
)
categoricals = [
field
for field, meta in self._metadata.get_fields().items()
if meta['type'] == 'categorical'
]
self._model.fit(
table_data,
epochs=self._epochs,
discrete_columns=categoricals,
log_frequency=self._log_frequency,
)
def _sample(self, num_rows):
"""Sample the indicated number of rows from the model.
Args:
num_rows (int):
Amount of rows to sample.
Returns:
pandas.DataFrame:
Sampled data.
"""
return self._model.sample(num_rows)
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pyontutils/utils_extra.py | memartone/pyontutils | 0 | 80711 | <filename>pyontutils/utils_extra.py
"""
Reused utilties that depend on packages outside the python standard library.
"""
import hashlib
import rdflib
rdflib.plugin.register('librdfxml', rdflib.parser.Parser,
'pyontutils.librdf', 'libRdfxmlParser')
rdflib.plugin.register('libttl', rdflib.parser.Parser,
'pyontutils.librdf', 'libTurtleParser')
def check_value(v):
if isinstance(v, rdflib.Literal) or isinstance(v, rdflib.URIRef):
return v
elif isinstance(v, str) and v.startswith('http'):
return rdflib.URIRef(v)
else:
return rdflib.Literal(v)
class OrderInvariantHash:
""" WARNING VERY BROKEN DO NOT USE """
def __init__(self, cypher=hashlib.sha256, encoding='utf-8'):
self.cypher = cypher
self.encoding = encoding
def convertToBytes(self, e):
if isinstance(e, rdflib.BNode):
raise TypeError('BNode detected, please convert bnodes to '
'ints in a deterministic manner first.')
elif isinstance(e, rdflib.URIRef):
return e.encode(self.encoding)
elif isinstance(e, rdflib.Literal):
return self.makeByteTuple((str(e), e.datatype, e.language))
elif isinstance(e, int):
return str(e).encode(self.encoding)
elif isinstance(e, bytes):
return e
elif isinstance(e, str):
return e.encode(self.encoding)
else:
raise TypeError(f'Unhandled type on {e!r} {type(e)}')
def makeByteTuple(self, t):
return b'(' + b' '.join(self.convertToBytes(e)
for e in t
if e is not None) + b')'
def __call__(self, iterable):
# convert all strings bytes
# keep existing bytes as bytes
# join as follows b'(http://s http://p http://o)'
# join as follows b'(http://s http://p http://o)'
# bnodes local indexes are treated as strings and converted
# literals are treated as tuples of strings
# if lang is not present then the tuple is only 2 elements
# this is probably not the fastest way to do this but it works
#bytes_ = [makeByteTuple(t) for t in sorted(tuples)]
#embed()
m = self.cypher()
# when everything is replaced by an integer or a bytestring
# it is safe to sort last because identity is ensured
[m.update(b) for b in sorted(self.makeByteTuple(t) for t in iterable)]
return m.digest()
def currentVMSKb():
import psutil
p = psutil.Process(os.getpid())
return p.memory_info().vms
def memoryCheck(vms_max_kb):
""" Lookup vms_max using getCurrentVMSKb """
import psutil
safety_factor = 1.2
vms_max = vms_max_kb
vms_gigs = vms_max / 1024 ** 2
buffer = safety_factor * vms_max
buffer_gigs = buffer / 1024 ** 2
vm = psutil.virtual_memory()
free_gigs = vm.available / 1024 ** 2
if vm.available < buffer:
raise MemoryError('Running this requires quite a bit of memory ~ '
f'{vms_gigs:.2f}, you have {free_gigs:.2f} of the '
f'{buffer_gigs:.2f} needed')
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] |
HTBClient/models/machinedetails.py | BGrewell/HTBClient | 1 | 82127 |
class MachineDetails:
"""
Class to represent the HTB machine details
"""
def __init__(self, identifier, name, operating_system, ip, avatar, avatar_thumb, points, release, retired_date,
maker, maker2, ratings_pro, ratings_sucks, user_blood, root_blood, user_owns, root_owns):
self.identifier = identifier
self.name = name
self.operating_system = operating_system
self.ip = ip
self.avatar = avatar
self.avatar_thumb = avatar_thumb
self.points = points
self.release = release
self.retired_date = retired_date
self.maker = maker
self.maker2 = maker2
self.ratings_pro = ratings_pro
self.ratings_sucks = ratings_sucks
self.user_blood = user_blood
self.root_blood = root_blood
self.user_owns = user_owns
self.root_owns = root_owns
@staticmethod
def json_to_machinedetails(json_dict):
md = MachineDetails(
json_dict['id'],
json_dict['name'],
json_dict['os'],
json_dict['ip'],
json_dict['avatar'],
json_dict['avatar_thumb'],
json_dict['points'],
json_dict['release'],
json_dict['retired_date'],
json_dict['maker'],
json_dict['maker2'],
json_dict['ratings_pro'],
json_dict['ratings_sucks'],
json_dict['user_blood'],
json_dict['root_blood'],
json_dict['user_owns'],
json_dict['root_owns'],
)
return md
def __str__(self):
return self.name
def __repr__(self):
return self.name | [
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tests/engine/test_scheduler.py | pronovic/vplan | 0 | 110570 | # -*- coding: utf-8 -*-
# vim: set ft=python ts=4 sw=4 expandtab:
import datetime
from unittest.mock import MagicMock, patch
import pytest
from apscheduler.triggers.cron import CronTrigger
from apscheduler.triggers.date import DateTrigger
from busypie import SECOND, wait
from tzlocal import get_localzone
from vplan.engine.config import DailyJobConfig, SchedulerConfig
from vplan.engine.exception import EngineError
from vplan.engine.scheduler import (
schedule_daily_job,
schedule_immediate_job,
scheduler,
shutdown_scheduler,
start_scheduler,
unschedule_daily_job,
)
from vplan.util import now
INDICATOR = None
JOB_DELAY_SEC = 2 # if the job is intermittently unreliable, increase this value slightly
def job_function(message):
"""Job function to be scheduled; it sets INDICATOR so we can tell that the job worked."""
global INDICATOR # pylint: disable=global-statement
INDICATOR = message
def in_future(seconds, tz):
"""Return a time some number of seconds into the future"""
timestamp = now(tz=tz)
future = timestamp + datetime.timedelta(seconds=seconds)
return future.time().replace(microsecond=0)
def assert_no_jobs():
"""Assert that there are no jobs"""
assert len(scheduler().get_jobs()) == 0
def assert_daily_job_definition(job_id, kwargs):
"""Assert that the job definition matches expectations."""
jobs = scheduler().get_jobs()
assert len(jobs) > 0 # there should be at least one job
job = jobs[0]
assert job.id == job_id
assert job.func is job_function
assert job.kwargs == kwargs
assert job.misfire_grace_time == 1
assert isinstance(job.trigger, CronTrigger)
assert job.trigger.jitter == 0
# we can't confirm much about the schedule, but the actual execution behavior should prove that's ok
def assert_immediate_job_definition(job_id, kwargs):
"""Assert that the job definition matches expectations."""
jobs = scheduler().get_jobs()
assert len(jobs) > 0 # there should be at least one job
job = jobs[0]
assert job.id == job_id
assert job.func is job_function
assert job.kwargs == kwargs
assert isinstance(job.trigger, DateTrigger)
# we can't confirm much about the schedule, but the actual execution behavior should prove that's ok
class TestLifecycle:
@patch("vplan.engine.scheduler.config")
def test_scheduler_lifecycle(self, config, tmpdir):
shutdown_scheduler()
# this sets things up so the daily job is scheduled a few seconds from now, so we can check that it runs
tz = get_localzone()
time1 = in_future(seconds=JOB_DELAY_SEC, tz=tz)
time2 = in_future(seconds=JOB_DELAY_SEC * 2, tz=tz)
database_url = "sqlite+pysqlite:///%s" % tmpdir.join("jobs.sqlite").realpath()
daily = DailyJobConfig(jitter_sec=0, misfire_grace_sec=1)
scheduler_config = SchedulerConfig(database_url=database_url, daily_job=daily)
config.return_value = MagicMock(scheduler=scheduler_config)
try:
start_scheduler()
assert scheduler() is not None
# Create a daily job and make sure it executes
schedule_daily_job(
job_id="test_job", trigger_time=time1, func=job_function, kwargs={"message": "job #1"}, time_zone="%s" % tz
)
assert_daily_job_definition("test_job", {"message": "job #1"})
wait().at_most(JOB_DELAY_SEC, SECOND).until(lambda: INDICATOR == "job #1")
# Recreate the daily job and make sure updates are reflected
schedule_daily_job(
job_id="test_job", trigger_time=time2, func=job_function, kwargs={"message": "job #2"}, time_zone="%s" % tz
)
assert_daily_job_definition("test_job", {"message": "job #2"})
wait().at_most(JOB_DELAY_SEC, SECOND).until(lambda: INDICATOR == "job #2")
# Remove the daily job and make sure the change takes effect
unschedule_daily_job(job_id="test_job")
assert_no_jobs()
# Confirm that we don't get an error removing a nonexistent job
unschedule_daily_job(job_id="test_job")
# Schedule an immediate job, make sure it runs immediately, and make sure it goes away when done
schedule_immediate_job(job_id="immediate_job", func=job_function, kwargs={"message": "job #3"})
assert_immediate_job_definition("immediate_job", {"message": "job #3"})
wait().at_most(JOB_DELAY_SEC, SECOND).until(lambda: INDICATOR == "job #3")
assert_no_jobs()
finally:
shutdown_scheduler()
with pytest.raises(EngineError, match=r"Scheduler is not available"):
scheduler()
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580,
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2
] |
runoob100/022.py | GenweiWu/PythonDemo | 0 | 1603473 | <filename>runoob100/022.py
# _*_ coding:utf-8 _*_
#两个乒乓球队进行比赛，各出三人。甲队为a,b,c三人，乙队为x,y,z三人。
#已抽签决定比赛名单。有人向队员打听比赛的名单。a说他不和x比，c说他不和x,z比，
#请编程序找出三队赛手的名单。
#假设abc已经站在那儿了，就是xyz的组合了呀
for i in ['x','y','z']:
for j in ['x','y','z']:
for k in ['x','y','z']:
if i!=j and i!=k and j!=k:
if i!='x' and k!='x' and k!='z':
print "a-{} b-{} c-{}".format(i,j,k) | [
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src/apps/trainings/migrations/0010_auto_20201012_2028.py | sanderland/katago-server | 27 | 157872 | # Generated by Django 3.0.6 on 2020-10-12 20:28
from django.db import migrations, models
import src.apps.trainings.models.network
import src.contrib.validators
class Migration(migrations.Migration):
dependencies = [
('trainings', '0009_auto_20200921_0314'),
]
operations = [
migrations.AlterField(
model_name='network',
name='model_file',
field=models.FileField(blank=True, help_text='Url to download network model file.', max_length=200, upload_to=src.apps.trainings.models.network.upload_network_to, validators=[src.contrib.validators.FileValidator(content_types=['application/gzip'], max_size=1073741824)], verbose_name='model file url'),
),
migrations.AlterField(
model_name='network',
name='model_zip_file',
field=models.FileField(blank=True, help_text='Url to download zipped network model file with also tensorflow weights.', max_length=200, upload_to=src.apps.trainings.models.network.upload_network_zip_to, validators=[src.contrib.validators.FileValidator(content_types=['application/zip'], max_size=1610612736.0)], verbose_name='model zip file url'),
),
]
| [
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] |
tensorlayer/googlenet.py | yellowei/AI | 0 | 91657 | <gh_stars>0
""" GoogLeNet.
Applying 'GoogLeNet' to Oxford's 17 Category Flower Dataset classification task.
References:
- Szegedy, Christian, et al.
Going deeper with convolutions.
- 17 Category Flower Dataset. <NAME> and <NAME>.
Links:
- [GoogLeNet Paper](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Szegedy_Going_Deeper_With_2015_CVPR_paper.pdf)
- [Flower Dataset (17)](http://www.robots.ox.ac.uk/~vgg/data/flowers/17/)
"""
from __future__ import division, print_function, absolute_import
import tensorflow as tf
import tensorlayer as tl
import os
from skimage import io, transform
import numpy as np
from log import *
def rgb2gray(im):
#rgb图转灰度图
if im.ndim == 2:
return im
return np.uint8(np.dot(im[..., :3], [0.299, 0.587, 0.114]))
def ListFiles(dir,extension):
file_list = []
for path, subdirs, files in os.walk(dir):
for name in files:
if name.endswith(extension):
#将jpg图片文件全部全部存入file_list列表
file_list.extend([os.path.join(path, name)])
return file_list
def LoadImageData(folder, extension, size):
#将folder中后缀名为extension的图片文件转成大小为size的正方形矩阵
Log().info("getData, folder:" + folder)
file_list = []
for path, subdirs, files in os.walk(folder):
for name in files:
if name.endswith(extension):
#将jpg图片文件全部全部存入file_list列表
file_list.extend([os.path.join(path, name)])
#len(a):列表a长度
num = len(file_list)
data = np.zeros([num, size, size,1])
for i in range(0,num):
im = io.imread(file_list[i])
size0 = np.max(im.shape)
scale = (size-1)/size0
#print(i," , ",file_list[i],", ndim: " + str(im.ndim))
x = rgb2gray(im)
im = transform.rescale(x, scale)
data[i, 0:im.shape[0], 0:im.shape[1],0] = im[:, :]
if i%20 == 0:
Log().info("getData, file index:" + str(i) + ",total:" + str(num))
return data
def PicClassModel(x,inputSize):
#图像分类的神经网络模型
# Define the neural network structure
with tf.variable_scope("googleNet_1"):
network = tl.layers.InputLayer(x, name='input_layer')
conv1_7_7 = tl.layers.Conv2dLayer(network,
act = tf.nn.relu,
shape = [7, 7, 1, 64],
strides = [1,2,2,1],
padding='SAME',
name='conv1_7_7_s2')
pool1_3_3 = tl.layers.PoolLayer(conv1_7_7,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='SAME',
pool=tf.nn.max_pool,
name='pool1_3_3_m')
pool1_3_3 = tl.layers.LocalResponseNormLayer(pool1_3_3,depth_radius=5, bias=1.0,alpha=0.0001, beta=0.75,name='pool1_3_3')
conv2_3_3_reduce = tl.layers.Conv2dLayer(pool1_3_3,
act = tf.nn.relu,
shape = [1, 1, 64, 1],
strides = [1,1,1,1],
padding='SAME',
name='conv2_3_3_reduce')
conv2_3_3 = tl.layers.Conv2dLayer(conv2_3_3_reduce,
act = tf.nn.relu,
shape = [3, 3, 1, 192],
strides = [1,1,1,1],
padding='SAME',
name='conv2_3_3')
norm2_3_3 = tl.layers.LocalResponseNormLayer(conv2_3_3,depth_radius=5, bias=1.0,alpha=0.0001, beta=0.75,name='norm2_3_3')
pool2_3_3 = tl.layers.PoolLayer(conv2_3_3,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='SAME',
pool=tf.nn.max_pool,
name='pool2_3_3_s2')
inception_3a_1_1 = tl.layers.Conv2dLayer(pool2_3_3,
act = tf.nn.relu,
shape = [1, 1, 192, 64],
strides = [1,1,1,1],
padding='SAME',
name='inception_3a_1_1')
inception_3a_3_3_reduce = tl.layers.Conv2dLayer(pool2_3_3,
act = tf.nn.relu,
shape = [1, 1, 192, 96],
strides = [1,1,1,1],
padding='SAME',
name='inception_3a_3_3_reduce')
inception_3a_3_3 = tl.layers.Conv2dLayer(inception_3a_3_3_reduce,
act = tf.nn.relu,
shape = [3,3,96,128],
strides = [1,1,1,1],
padding='SAME',
name='inception_3a_3_3')
inception_3a_5_5_reduce = tl.layers.Conv2dLayer(pool2_3_3,
act = tf.nn.relu,
shape = [16,16,192,1],
strides = [1,1,1,1],
padding='SAME',
name='inception_3a_5_5_reduce')
inception_3a_5_5 = tl.layers.Conv2dLayer(inception_3a_5_5_reduce,
act = tf.nn.relu,
shape = [32,32,1,5],
strides = [1,1,1,1],
padding='SAME',
name='inception_3a_5_5')
inception_3a_pool = tl.layers.PoolLayer(pool2_3_3,
ksize=[1, 3, 3, 1],
strides=[1, 1, 1, 1],
padding='SAME',
pool=tf.nn.max_pool,
name='inception_3a_pool')
inception_3a_pool_1_1 = tl.layers.Conv2dLayer(inception_3a_pool,
act = tf.nn.relu,
shape = [32,32,192,1],
strides = [1,1,1,1],
padding='SAME',
name='inception_3a_pool_1_1')
# merge the inception_3a__
inception_3a_output = tl.layers.ConcatLayer([inception_3a_1_1, inception_3a_3_3, inception_3a_5_5, inception_3a_pool_1_1],concat_dim=3)
inception_3b_1_1 = tl.layers.Conv2dLayer(inception_3a_output,
act = tf.nn.relu,
shape = [128,128,198,1],
strides = [1,1,1,1],
padding='SAME',
name='inception_3b_1_1')
inception_3b_3_3_reduce = tl.layers.Conv2dLayer(inception_3a_output,
act = tf.nn.relu,
shape = [128,128,198,1],
strides = [1,1,1,1],
padding='SAME',
name='inception_3b_3_3_reduce')
inception_3b_3_3 = tl.layers.Conv2dLayer(inception_3b_3_3_reduce,
act = tf.nn.relu,
shape = [192,192,1,3],
strides = [1,1,1,1],
padding='SAME',
name='inception_3b_3_3')
inception_3b_5_5_reduce = tl.layers.Conv2dLayer(inception_3b_3_3_reduce,
act = tf.nn.relu,
shape = [32,32,1,1],
strides = [1,1,1,1],
padding='SAME',
name='inception_3b_5_5_reduce')
inception_3b_5_5 = tl.layers.Conv2dLayer(inception_3b_3_3_reduce,
act = tf.nn.relu,
shape = [96,96,1,5],
strides = [1,1,1,1],
padding='SAME',
name='inception_3b_5_5')
inception_3b_pool = tl.layers.PoolLayer(inception_3a_output,
ksize=[1, 3, 3, 1],
strides=[1, 1, 1, 1],
padding='SAME',
pool=tf.nn.max_pool,
name='inception_3b_pool')
inception_3b_pool_1_1 = tl.layers.Conv2dLayer(inception_3b_pool,
act = tf.nn.relu,
shape = [64,64,198,1],
strides = [1,1,1,1],
padding='SAME',
name='inception_3b_pool_1_1')
#merge the inception_3b_*
inception_3b_output = tl.layers.ConcatLayer([inception_3b_1_1, inception_3b_3_3, inception_3b_5_5, inception_3b_pool_1_1],concat_dim=3,name='inception_3b_output')
pool3_3_3 = tl.layers.PoolLayer(inception_3b_output,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='SAME',
pool=tf.nn.max_pool,
name='pool3_3_3')
inception_4a_1_1 = tl.layers.Conv2dLayer(pool3_3_3,
act = tf.nn.relu,
shape = [192,192,10,1],
strides = [1,1,1,1],
padding='SAME',
name='inception_4a_1_1')
inception_4a_3_3_reduce = tl.layers.Conv2dLayer(pool3_3_3,
act = tf.nn.relu,
shape = [96,96,10,1],
strides = [1,1,1,1],
padding='SAME',
name='inception_4a_3_3_reduce')
inception_4a_3_3 = tl.layers.Conv2dLayer(inception_4a_3_3_reduce,
act = tf.nn.relu,
shape = [208,208,1,3],
strides = [1,1,1,1],
padding='SAME',
name='inception_4a_3_3')
inception_4a_5_5_reduce = tl.layers.Conv2dLayer(pool3_3_3,
act = tf.nn.relu,
shape = [16,16,10,1],
strides = [1,1,1,1],
padding='SAME',
name='inception_4a_5_5_reduce')
inception_4a_5_5 = tl.layers.Conv2dLayer(inception_4a_5_5_reduce,
act = tf.nn.relu,
shape = [48,48,1,5],
strides = [1,1,1,1],
padding='SAME',
name='inception_4a_5_5')
inception_4a_pool = tl.layers.PoolLayer(pool3_3_3,
ksize=[1, 3, 3, 1],
strides=[1, 1, 1, 1],
padding='SAME',
pool=tf.nn.max_pool,
name='inception_4a_pool')
inception_4a_pool_1_1 = tl.layers.Conv2dLayer(inception_4a_pool,
act = tf.nn.relu,
shape = [64,64,10,1],
strides = [1,1,1,1],
padding='SAME',
name='inception_4a_pool_1_1')
inception_4a_output = tl.layers.ConcatLayer([inception_4a_1_1, inception_4a_3_3, inception_4a_5_5, inception_4a_pool_1_1],concat_dim=3,name='inception_4a_output')
inception_4b_1_1 = tl.layers.Conv2dLayer(inception_4a_output,
act = tf.nn.relu,
shape = [160,160,10,1],
strides = [1,1,1,1],
padding='SAME',
name='inception_4b_1_1')
inception_4b_3_3_reduce = tl.layers.Conv2dLayer(inception_4a_output,
act = tf.nn.relu,
shape = [112,112,10,1],
strides = [1,1,1,1],
padding='SAME',
name='inception_4b_3_3_reduce')
inception_4b_3_3 = tl.layers.Conv2dLayer(inception_4b_3_3_reduce,
act = tf.nn.relu,
shape = [224,224,1,3],
strides = [1,1,1,1],
padding='SAME',
name='inception_4b_3_3')
inception_4b_5_5_reduce = tl.layers.Conv2dLayer(inception_4a_output,
act = tf.nn.relu,
shape = [24,24,10,1],
strides = [1,1,1,1],
padding='SAME',
name='inception_4b_5_5_reduce')
inception_4b_5_5 = tl.layers.Conv2dLayer(inception_4b_5_5_reduce,
act = tf.nn.relu,
shape = [64,64,1,5],
strides = [1,1,1,1],
padding='SAME',
name='inception_4b_5_5')
inception_4b_pool = tl.layers.PoolLayer(inception_4a_output,
ksize=[1, 3, 3, 1],
strides=[1, 1, 1, 1],
padding='SAME',
pool=tf.nn.max_pool,
name='inception_4b_pool')
inception_4b_pool_1_1 = tl.layers.Conv2dLayer(inception_4b_pool,
act = tf.nn.relu,
shape = [64,64,10,1],
strides = [1,1,1,1],
padding='SAME',
name='inception_4b_pool_1_1')
inception_4b_output = tl.layers.ConcatLayer([inception_4b_1_1, inception_4b_3_3, inception_4b_5_5, inception_4b_pool_1_1],concat_dim=3,name='inception_4b_output')
inception_4c_1_1 = tl.layers.Conv2dLayer(inception_4b_output,
act = tf.nn.relu,
shape = [128,128,10,1],
strides = [1,1,1,1],
padding='SAME',
name='inception_4c_1_1')
inception_4c_3_3_reduce = tl.layers.Conv2dLayer(inception_4b_output,
act = tf.nn.relu,
shape = [128,128,10,1],
strides = [1,1,1,1],
padding='SAME',
name='inception_4c_3_3_reduce')
inception_4c_3_3 = tl.layers.Conv2dLayer(inception_4c_3_3_reduce,
act = tf.nn.relu,
shape = [256,256,1,3],
strides = [1,1,1,1],
padding='SAME',
name='inception_4c_3_3')
inception_4c_5_5_reduce = tl.layers.Conv2dLayer(inception_4b_output,
act = tf.nn.relu,
shape = [24,24,10,1],
strides = [1,1,1,1],
padding='SAME',
name='inception_4c_5_5_reduce')
inception_4c_5_5 = tl.layers.Conv2dLayer(inception_4c_5_5_reduce,
act = tf.nn.relu,
shape = [64,64,1,5],
strides = [1,1,1,1],
padding='SAME',
name='inception_4c_5_5')
inception_4c_pool = tl.layers.PoolLayer(inception_4b_output,
ksize=[1, 3, 3, 1],
strides=[1, 1, 1, 1],
padding='SAME',
pool=tf.nn.max_pool,
name='inception_4c_pool')
inception_4c_pool_1_1 = tl.layers.Conv2dLayer(inception_4c_pool,
act = tf.nn.relu,
shape = [64,64,10,1],
strides = [1,1,1,1],
padding='SAME',
name='inception_4c_pool_1_1')
inception_4c_output = tl.layers.ConcatLayer([inception_4c_1_1, inception_4c_3_3, inception_4c_5_5, inception_4c_pool_1_1],concat_dim=3,name='inception_4c_output')
inception_4d_1_1 = tl.layers.Conv2dLayer(inception_4c_output,
act = tf.nn.relu,
shape = [112,112,10,1],
strides = [1,1,1,1],
padding='SAME',
name='inception_4d_1_1')
inception_4d_3_3_reduce = tl.layers.Conv2dLayer(inception_4c_output,
act = tf.nn.relu,
shape = [144,144,10,1],
strides = [1,1,1,1],
padding='SAME',
name='inception_4d_3_3_reduce')
inception_4d_3_3 = tl.layers.Conv2dLayer(inception_4d_3_3_reduce,
act = tf.nn.relu,
shape = [288,288,1,3],
strides = [1,1,1,1],
padding='SAME',
name='inception_4d_3_3')
inception_4d_5_5_reduce = tl.layers.Conv2dLayer(inception_4c_output,
act = tf.nn.relu,
shape = [32,32,10,1],
strides = [1,1,1,1],
padding='SAME',
name='inception_4d_5_5_reduce')
inception_4d_5_5 = tl.layers.Conv2dLayer(inception_4d_5_5_reduce,
act = tf.nn.relu,
shape = [64,64,1,5],
strides = [1,1,1,1],
padding='SAME',
name='inception_4d_5_5')
inception_4d_pool = tl.layers.PoolLayer(inception_4c_output,
ksize=[1, 3, 3, 1],
strides=[1, 1, 1, 1],
padding='SAME',
pool=tf.nn.max_pool,
name='inception_4d_pool')
inception_4d_pool_1_1 = tl.layers.Conv2dLayer(inception_4d_pool,
act = tf.nn.relu,
shape = [64,64,10,1],
strides = [1,1,1,1],
padding='SAME',
name='inception_4d_pool_1_1')
inception_4d_output = tl.layers.ConcatLayer([inception_4d_1_1, inception_4d_3_3, inception_4d_5_5, inception_4d_pool_1_1],concat_dim=3,name='inception_4d_output')
inception_4e_1_1 = tl.layers.Conv2dLayer(inception_4d_output,
act = tf.nn.relu,
shape = [256,256,10,1],
strides = [1,1,1,1],
padding='SAME',
name='inception_4e_1_1')
inception_4e_3_3_reduce = tl.layers.Conv2dLayer(inception_4d_output,
act = tf.nn.relu,
shape = [160,160,10,1],
strides = [1,1,1,1],
padding='SAME',
name='inception_4e_3_3_reduce')
inception_4e_3_3 = tl.layers.Conv2dLayer(inception_4e_3_3_reduce,
act = tf.nn.relu,
shape = [320,320,1,3],
strides = [1,1,1,1],
padding='SAME',
name='inception_4e_3_3')
inception_4e_5_5_reduce = tl.layers.Conv2dLayer(inception_4d_output,
act = tf.nn.relu,
shape = [32,32,10,1],
strides = [1,1,1,1],
padding='SAME',
name='inception_4e_5_5_reduce')
inception_4e_5_5 = tl.layers.Conv2dLayer(inception_4e_5_5_reduce,
act = tf.nn.relu,
shape = [128,128,1,5],
strides = [1,1,1,1],
padding='SAME',
name='inception_4e_5_5')
inception_4e_pool = tl.layers.PoolLayer(inception_4d_output,
ksize=[1, 3, 3, 1],
strides=[1, 1, 1, 1],
padding='SAME',
pool=tf.nn.max_pool,
name='inception_4e_pool')
inception_4e_pool_1_1 = tl.layers.Conv2dLayer(inception_4e_pool,
act = tf.nn.relu,
shape = [128,128,10,1],
strides = [1,1,1,1],
padding='SAME',
name='inception_4e_pool_1_1')
inception_4e_output = tl.layers.ConcatLayer([inception_4e_1_1, inception_4e_3_3, inception_4e_5_5, inception_4e_pool_1_1],concat_dim=3,name='inception_4e_output')
pool4_3_3 = tl.layers.PoolLayer(inception_4e_output,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='SAME',
pool=tf.nn.max_pool,
name='pool4_3_3')
inception_5a_1_1 = tl.layers.Conv2dLayer(pool4_3_3,
act = tf.nn.relu,
shape = [256,256,10,1],
strides = [1,1,1,1],
padding='SAME',
name='inception_5a_1_1')
inception_5a_3_3_reduce = tl.layers.Conv2dLayer(pool4_3_3,
act = tf.nn.relu,
shape = [160,160,10,1],
strides = [1,1,1,1],
padding='SAME',
name='inception_5a_3_3_reduce')
inception_5a_3_3 = tl.layers.Conv2dLayer(inception_5a_3_3_reduce,
act = tf.nn.relu,
shape = [320,320,1,3],
strides = [1,1,1,1],
padding='SAME',
name='inception_5a_3_3')
inception_5a_5_5_reduce = tl.layers.Conv2dLayer(pool4_3_3,
act = tf.nn.relu,
shape = [32,32,10,1],
strides = [1,1,1,1],
padding='SAME',
name='inception_5a_5_5_reduce')
inception_5a_5_5 = tl.layers.Conv2dLayer(inception_5a_5_5_reduce,
act = tf.nn.relu,
shape = [128,128,1,5],
strides = [1,1,1,1],
padding='SAME',
name='inception_5a_5_5')
inception_5a_pool = tl.layers.PoolLayer(pool4_3_3,
ksize=[1, 3, 3, 1],
strides=[1, 1, 1, 1],
padding='SAME',
pool=tf.nn.max_pool,
name='inception_5a_pool')
inception_5a_pool_1_1 = tl.layers.Conv2dLayer(inception_5a_pool,
act = tf.nn.relu,
shape = [128,128,10,1],
strides = [1,1,1,1],
padding='SAME',
name='inception_5a_pool_1_1')
inception_5a_output = tl.layers.ConcatLayer([inception_5a_1_1, inception_5a_3_3, inception_5a_5_5, inception_5a_pool_1_1],concat_dim=3,name='inception_5a_output')
inception_5b_1_1 = tl.layers.Conv2dLayer(inception_5a_output,
act = tf.nn.relu,
shape = [384,384,10,1],
strides = [1,1,1,1],
padding='SAME',
name='inception_5b_1_1')
inception_5b_3_3_reduce = tl.layers.Conv2dLayer(inception_5a_output,
act = tf.nn.relu,
shape = [192,192,10,1],
strides = [1,1,1,1],
padding='SAME',
name='inception_5b_3_3_reduce')
inception_5b_3_3 = tl.layers.Conv2dLayer(inception_5b_3_3_reduce,
act = tf.nn.relu,
shape = [384,384,1,3],
strides = [1,1,1,1],
padding='SAME',
name='inception_5b_3_3')
inception_5b_5_5_reduce = tl.layers.Conv2dLayer(inception_5a_output,
act = tf.nn.relu,
shape = [48,48,10,1],
strides = [1,1,1,1],
padding='SAME',
name='inception_5b_5_5_reduce')
inception_5b_5_5 = tl.layers.Conv2dLayer(inception_5b_5_5_reduce,
act = tf.nn.relu,
shape = [128,128,1,5],
strides = [1,1,1,1],
padding='SAME',
name='inception_5b_5_5')
inception_5b_pool = tl.layers.PoolLayer(inception_5a_output,
ksize=[1, 3, 3, 1],
strides=[1, 1, 1, 1],
padding='SAME',
pool=tf.nn.max_pool,
name='inception_5b_pool')
inception_5b_pool_1_1 = tl.layers.Conv2dLayer(inception_5b_pool,
act = tf.nn.relu,
shape = [128,128,10,1],
strides = [1,1,1,1],
padding='SAME',
name='inception_5b_pool_1_1')
inception_5b_output = tl.layers.ConcatLayer([inception_5b_1_1, inception_5b_3_3, inception_5b_5_5, inception_5b_pool_1_1],concat_dim=3,name='inception_5b_output')
pool5_7_7 = tl.layers.PoolLayer(inception_5b_output,
ksize=[1, 7, 7, 1],
strides=[1, 1, 1, 1],
padding='SAME',
pool=tf.nn.avg_pool,
name='pool5_7_7')
#防止CNN过拟合
#当迭代次数增多的时候，可能出现网络对训练集拟合的很好（在训练集上loss很小），但是对验证集的拟合程度很差的情况
gnet = tl.layers.DropoutLayer(pool5_7_7, keep =0.4,name='dropout5_7_7')
print(gnet.outputs._shape)
#全连接 负责对网络最终输出的特征进行分类预测，得出分类结果
gnet = tl.layers.FlattenLayer(gnet, name='flatten_layer')
print(gnet.outputs._shape)
gnet = tl.layers.DenseLayer(gnet,n_units=4,act = tf.identity,name='output_layer')
print(gnet.outputs._shape)
return gnet | [
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] |
test/test_delete_contact.py | ann-drobysh/python_training | 0 | 41934 | from model.contact import Contact
import random
import allure
def test_delete_some_contact(app, db, check_ui):
if len(db.get_contacts_list()) == 0:
app.contact.add_new_contact(Contact(firstname="Testik", middlename="Midtest", lastname="Lasttest", nickname="Nickname test", title="Mrs", company="Test Company", street="5th Avenue",
homephone="15", mobilephone="111999333", workphone="12123342", fax="2345645", email="<EMAIL>", birthday_day="11",
birthday_month="July", birthday_year="1991", anniversary_day="8", anniversary_month="November", anniversary_year="1991", address2="Sec address", phone2="163434", note="testtesttest note"))
with allure.step("Given a contacts list"):
old_contacts = db.get_contacts_list()
with allure.step("Given a contact from contacts list"):
contact = random.choice(old_contacts)
with allure.step("When I delete contact %s from the list" % contact):
app.contact.delete_contact_by_id(contact.id)
with allure.step("Then the new list is equal to the old list with the removed contact"):
assert len(old_contacts) - 1 == app.contact.count()
new_contacts = db.get_contacts_list()
old_contacts.remove(contact)
assert old_contacts == new_contacts
if check_ui:
def clean(contact):
return Contact(id=contact.id, firstname=contact.firstname.strip(), lastname=contact.lastname.strip())
new_contacts = map(clean, db.get_contacts_list())
assert sorted(new_contacts, key=Contact.id_or_max) == sorted(app.contact.get_contacts_list(), key=Contact.id_or_max)
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papers/CS-F-LTR/src/convert.py | mindspore-ai/contrib | 2 | 119958 | """[summary]
Returns:
[type]: [description]
"""
import pickle
import sys
import numpy as np
def gen_label(x):
"""[summary]
Args:
x ([type]): [description]
Returns:
[type]: [description]
"""
if 1 <= x <= 10:
return 2
if 11 <= x <= 100:
return 1
return 0
def load_only(src_path):
"""[summary]
Args:
src_path ([type]): [description]
Returns:
[type]: [description]
"""
f = open(src_path)
line = f.readline()
ext_label_id_features = []
real_label_id_features = []
cnt = 0
while line:
cnt += 1
if cnt % 10000 == 0:
print(cnt)
pure_line, comm = line.split('#') # 去除行尾注释
pure_line = pure_line.strip()
comm = comm.strip()
relevance_id_features = pure_line.split(' ')
label_id_feature = [
gen_label(int(relevance_id_features[0])) \
if comm in ("rel", "non") \
else int(relevance_id_features[0])
]
for i in range(1, len(relevance_id_features)):
label_id_feature.append(float(relevance_id_features[i].split(':')[1]))
if comm in ("non", "rel"):
real_label_id_features.append(label_id_feature)
else:
ext_label_id_features.append(label_id_feature)
line = f.readline()
f.close()
return real_label_id_features, ext_label_id_features
def main():
"""[summary]
"""
try:
fed_no = int(sys.argv[1])
except ValueError:
fed_no = 0
print(fed_no)
real_label_id_features, ext_label_id_features = load_only(
"/data/icde_data/fed_with_sh_(5_30_200)ext%d.txt" % fed_no)
pickle.dump([np.array(real_label_id_features), np.array(ext_label_id_features)], open(
"/data/icde_data/fed_with_sh_(5_30_200)ext%d.pkl" % fed_no, "wb"))
if __name__ == "__main__":
main()
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] |
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