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# -*- coding: utf-8 -*-
"""
For the ICRA video, we made some animations of how the preference model
posteriors evolve after each iteration. This script saves the stack of images
to make such an animation for the compass-gait biped's model posterior. For
every iteration, we save an image of the model posterior from one of the CG
biped simulation runs.
"""
import numpy as np
import scipy.io as io
import os
import matplotlib.pyplot as plt
from matplotlib import rcParams
rcParams.update({'font.size': 18})
from Preference_GP_learning import feedback
# SET THE FOLLOWING FLAG TO EITHER TRUE OR FALSE, DEPENDING ON WHETHER THE
# MODEL POSTERIOR INFORMATION FOR ALL RUNS HAS ALREADY BEEN SAVED. If the
# posterior information is already computed and saved, setting this to True
# will save runtime. If you try setting this to True but the information is not
# saved, then you will get an error. If you set this to False, then all of the
# necessary information will be saved, such that you can set this to True if
# running this script ever again.
posterior_already_computed = True
# Folder for saving plots:
save_plots_folder = 'Plots/CG_biped_animation_plots/'
if not os.path.isdir(save_plots_folder):
os.makedirs(save_plots_folder)
# Folder for saving (or loading) posterior information:
save_info_folder = 'Plotting_data/CG_biped_sim_posteriors/'
if not os.path.isdir(save_info_folder):
os.makedirs(save_info_folder)
# Load data to use for plotting evolution of the posterior:
CG_sim_folder = 'Compass_biped_results/'
run_num = 0
num_samples = 2 # CoSpar parameter (n)
num_pts_sample = 24 # Number of points in input domain
data = io.loadmat(CG_sim_folder + 'Opt_' + str(num_samples) + '_samples_' \
+ str(num_pts_sample) + '_pts_run_' + str(run_num) + \
'.mat')
data_pt_idxs = data['data_pt_idxs'] # Data: points alg. selected in simulation
pref_nums = data_pt_idxs.shape[0]
# Domain over which learning occurred:
points_to_sample = np.linspace(0.08, 0.18, num_pts_sample)
# Determine dimensionality of state space:
if len(points_to_sample.shape) == 1:
state_dim = 1
else:
state_dim = points_to_sample.shape[1]
if not posterior_already_computed:
points_to_sample = points_to_sample.reshape((num_pts_sample, state_dim))
# Load preference labels and GP model hyperparameters:
labels = data['labels']
preference_noise = data['preference_noise'][0][0]
lengthscales = data['lengthscale'][0]
signal_variance = data['signal_variance'][0][0]
GP_noise_var = data['GP_noise_var'][0][0]
# Instantiate the prior covariance matrix, using a squared exponential
# kernel in each dimension of the input space:
GP_prior_cov = signal_variance * np.ones((num_pts_sample, num_pts_sample))
for i in range(num_pts_sample):
pt1 = points_to_sample[i, :]
for j in range(num_pts_sample):
pt2 = points_to_sample[j, :]
for dim in range(state_dim):
lengthscale = lengthscales[dim]
if lengthscale > 0:
GP_prior_cov[i, j] *= np.exp(-0.5 * ((pt2[dim] - pt1[dim]) / \
lengthscale)**2)
elif lengthscale == 0 and pt1[dim] != pt2[dim]:
GP_prior_cov[i, j] = 0
GP_prior_cov += GP_noise_var * np.eye(num_pts_sample)
GP_prior_cov_inv = np.linalg.inv(GP_prior_cov)
points_to_sample = points_to_sample.flatten()
# Make a plot for each iteration of the algorithm.
for pref_num in range(pref_nums + 1):
print('Iter %i of %i' % (pref_num, pref_nums))
# Get model posterior to use for this plot:
if not posterior_already_computed:
# Preference data at this iteration:
X = data_pt_idxs[: pref_num, :]
y = labels[: pref_num, 1]
# Update the Gaussian process preference model:
posterior_model = feedback(X, y, GP_prior_cov_inv, preference_noise)
# Unpack model posterior:
post_mean = posterior_model['mean']
cov_evecs = np.real(posterior_model['cov_evecs'])
cov_evals = posterior_model['cov_evals']
else:
posterior_model = io.loadmat(save_info_folder + 'Compass_biped_' + \
str(pref_num) + '_preferences.mat')
# Unpack model posterior:
post_mean = posterior_model['post_mean'].flatten()
cov_evecs = np.real(posterior_model['cov_evecs'])
cov_evals = posterior_model['cov_evals'].flatten()
# Construct posterior covariance matrix:
post_cov = cov_evecs @ np.diag(cov_evals) @ np.linalg.inv(cov_evecs)
# Posterior standard deviation at each point:
post_stdev = np.sqrt(np.diag(post_cov))
# Without title, used (8, 6). (8, 6.3) keeps the actual plot the same size
# while adding a title.
plt.figure(figsize = (8, 6.3))
# Plot posterior mean and standard deviation:
plt.plot(points_to_sample, post_mean, color = 'blue', linewidth = 3)
plt.fill_between(points_to_sample, post_mean - 2*post_stdev,
post_mean + 2*post_stdev, alpha = 0.3, color = 'blue')
plt.ylim([-0.035, 0.043])
plt.xlabel('Step length (m)')
plt.ylabel('Posterior Utility')
plt.title('Number of Trials: ' + str(pref_num * 2))
plt.xticks([0.08, 0.13, 0.18])
plt.yticks([-0.02, 0, 0.02, 0.04])
plt.tight_layout()
if not posterior_already_computed:
# Save information about posterior:
io.savemat(save_info_folder + 'Compass_biped_' + str(pref_num) + \
'_preferences.mat', {'post_mean': post_mean,
'cov_evecs': cov_evecs,
'cov_evals': cov_evals})
# Save plot:
plt.savefig(save_plots_folder + 'Compass_biped_2STD_' + str(pref_num) + \
'_preferences_titled.png')
plt.close('all')
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] | 2.200498 | 2,813 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from gym_minigrid.minigrid import *
from gym_minigrid.register import register
from gym_minigrid.wrappers import RGBImgPartialObsWrapper
from gym_minigrid.wrappers import FrameStack
from collections import deque
from gym.spaces import Box
from gym import Wrapper
import numpy as np
import random
register(
id='MiniGrid-FourRoomsMemory-v0',
entry_point='gym_minigrid.envs:FourRoomsMemoryEnv'
)
register(
id='MiniGrid-FourRoomsMemoryRGB-v0',
entry_point='gym_minigrid.envs:rgb_env'
)
register(
id='MiniGrid-FourRoomsMemoryStacked-v0',
entry_point='gym_minigrid.envs:frame_stack_env'
)
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] | 2.600791 | 253 |
# to run open terminal window in location then type 'python "scriptname.py" "filename.csv" ' - "" indicates that these portions are replaced with actual file names
# import necessary libraries
import csv
import sys
import math
# read in the file & convert "that memory" into a csv read file
f = open(sys.argv[1],'rb')
r = csv.reader(f)
# start at row 0 (first row)
i = 0
rows = []
for row in r:
#adds an array to the list of arrays (arrays = list of variables); rows = list of lists, aka a 2D array -> defining the layout of spreadsheet
rows.append([])
#look at columns, but only for the current row
for col in row:
rows[i].append(col)
# look at row 1, then row 2, then row 3....
i+=1
answers = []
m1 = float(rows[2][11])
m2 = float(rows[2][11])
Fa1A = float(rows[2][11])
Fa1B = float(rows[2][11])
Fa2A = float(rows[2][11])
Fa2B = float(rows[2][11])
#m constant for each experiment (does not change w/ dilutions - slope from linear regressions of log(Fa/Fu) v log(Dose) )
#notation: 1A = 1.1, 1B = 1.2, 2A = 2.1, 2B = 2.2
for row in xrange(2,12):
try:
m1 = float(rows[row][8])
m2 = float(rows[row][11])
Fa1A = float(rows[row][9])
Fa2A = float(rows[row][12])
Fu1A = 1-Fa1A
Fu2A = 1-Fa2A
rows[row][14] = equation_solver_wrapper(Fa1A, Fu1A, Fa2A, Fu2A, m1, m2)
rows[row][18] = 1 - rows[row][14]
rows[row][22] = math.log(rows[row][18]/rows[row][14] ,10)
except:
rows[row][14] = "DNE"
rows[row][18] = "DNE"
rows[row][22] = "DNE"
try:
m1 = float(rows[row][8])
m2 = float(rows[row][11])
Fa1A = float(rows[row][9])
Fa2B = float(rows[row][13])
Fu1A = 1-Fa1A
Fu2B = 1-Fa2B
rows[row][15] = equation_solver_wrapper(Fa1A, Fu1A, Fa2B, Fu2B, m1, m2)
rows[row][19] = 1 - rows[row][15]
rows[row][23] = math.log(rows[row][19]/rows[row][15],10)
except Exception as e:
rows[row][15] = "DNE"
rows[row][19] = "DNE"
rows[row][23] = "DNE"
try:
m1 = float(rows[row][8])
m2 = float(rows[row][11])
Fa1B = float(rows[row][10])
Fa2A = float(rows[row][12])
Fu1B = 1-Fa1B
Fu2A = 1-Fa2A
#guess Fu
rows[row][16] = equation_solver_wrapper(Fa1B, Fu1B, Fa2A, Fu2A, m1, m2)
#guess Fa
rows[row][20] = 1 - rows[row][16]
#guess inhib
rows[row][24] = math.log(rows[row][20]/rows[row][16],10)
except:
rows[row][16] = "DNE"
rows[row][20] = "DNE"
rows[row][24] = "DNE"
try:
m1 = float(rows[row][8])
m2 = float(rows[row][11])
Fa1B = float(rows[row][10])
Fa2B = float(rows[row][13])
Fu1B = 1-Fa1B
Fu2B = 1-Fa2B
rows[row][17] = equation_solver_wrapper(Fa1B, Fu1B, Fa2B, Fu2B, m1, m2)
rows[row][21] = 1 - rows[row][17]
rows[row][25] = math.log(rows[row][21]/rows[row][17],10)
except:
rows[row][17] = "DNE"
rows[row][21] = "DNE"
rows[row][25] = "DNE"
with open(sys.argv[2], "wb") as f:
writer = csv.writer(f)
writer.writerows(rows) | [
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220,
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13,
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8
] | 2.115727 | 1,348 |
#! /usr/bin/env python
from setuptools import find_packages
from setuptools import setup
with open('README.md') as readme_file:
readme = readme_file.read()
setup(
author='Beau Martinez',
classifiers=[
'Programming Language :: Python :: 2.7',
],
description='Delete your tweets and backup tweeted photos to Google Drive.',
entry_points={
'console_scripts': [
'twittercide = twittercide.__main__:main',
],
},
install_requires=[
'arrow==0.4.4',
'python-dateutil>=2.3',
'requests-foauth>=0.1.1',
'requests>=2.5.0',
],
licence='ISC',
long_description=readme,
name='twittercide',
packages=find_packages(),
url='http://github.com/beaumartinez/twittercide',
version='0.1',
)
| [
2,
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8,
198
] | 2.318841 | 345 |
from __future__ import annotations
from typing import Any, Dict, List, Literal, Optional, Union
from .event import Event
from .database import query, queryWithResult, queryWithResults
properties = [
"time",
"position",
"points",
"incomplete",
"event",
"competitor",
"type",
"course",
"id",
"name",
"ageClass",
"club",
"course",
"eventName",
]
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import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# solveData = pd.DataFrame(data = [[1,2,4,8,16,32,64,128],[1,1,2,3,4,3,2,1]], columns=[0,1,2,3,4,5,6,7])
# solveData = solveData.transpose()
# a = [[1,2,3,4],[4,3,2,1,2,3,4,3,2,1]]
# #b= np.array(a)
# c = np.array(a).T
# newData = pd.DataFrame(data = a, columns=[1,2,3,3.5,4,5,6,7,8,9])
# newData = newData.transpose()
# currentData = solveData
# currentData
# currentData = pd.concat([currentData, newData], axis=1, sort=False)
# fig = plt.figure()
# fig.suptitle("pk model")
# ax = fig.add_subplot(1,1,1)
# ax.set_xlabel("Time"+__unitsFormat("s"))
# ax.set_ylabel("Volume"+__unitsFormat("mg"))
# plt.plot(currentData)
# plt.show()
# #def updateData(solveData, newdata)
graph = plot()
graph.adddata([100,50,25,12,6,3,1,0.5],[0,2,3,4,5,6,7,9])
graph.adddata([[1,2,3,5,8,13,21],[15,4,3,2,1,2,3]],[1,2,3,4,5,6,7])
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import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
# def zigzag_indices(shape: (int, int), count):
# x_range, y_range = shape
# index_order = sorted(((x, y) for x in range(x_range) for y in range(y_range)),
# key=lambda p: (p[0] + p[1], -p[1] if (p[0] + p[1]) % 2 else p[1]))
#
# mask = np.zeros(shape)
# for r, c in index_order[:count]:
# mask[r,c] = 1
#
# return mask
def rgb2yuv(image_rgb, image_yuv_out):
""" Transform the image from rgb to yuv """
image_yuv_out[:, 0, :, :] = 0.299 * image_rgb[:, 0, :, :].clone() + 0.587 * image_rgb[:, 1, :, :].clone() + 0.114 * image_rgb[:, 2, :, :].clone()
image_yuv_out[:, 1, :, :] = -0.14713 * image_rgb[:, 0, :, :].clone() + -0.28886 * image_rgb[:, 1, :, :].clone() + 0.436 * image_rgb[:, 2, :, :].clone()
image_yuv_out[:, 2, :, :] = 0.615 * image_rgb[:, 0, :, :].clone() + -0.51499 * image_rgb[:, 1, :, :].clone() + -0.10001 * image_rgb[:, 2, :, :].clone()
def yuv2rgb(image_yuv, image_rgb_out):
""" Transform the image from yuv to rgb """
image_rgb_out[:, 0, :, :] = image_yuv[:, 0, :, :].clone() + 1.13983 * image_yuv[:, 2, :, :].clone()
image_rgb_out[:, 1, :, :] = image_yuv[:, 0, :, :].clone() + -0.39465 * image_yuv[:, 1, :, :].clone() + -0.58060 * image_yuv[:, 2, :, :].clone()
image_rgb_out[:, 2, :, :] = image_yuv[:, 0, :, :].clone() + 2.03211 * image_yuv[:, 1, :, :].clone()
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1058,
4083,
21018,
3419,
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] | 2.053446 | 711 |
# Generated by Django 2.2 on 2019-04-15 12:47
from django.db import migrations, models
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# @Time : 2018-9-10
# @Author : zxh
import os
import tensorflow as tf
import sys
from zutils.utils import relative_project_path
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] | 2.87234 | 47 |
# based on https://realpython.com/python-sqlite-sqlalchemy/#using-flat-files-for-data-storage
from datetime import datetime
import sqlalchemy
from sqlalchemy import Column, String, Boolean, Integer, Float, Date, DateTime, ForeignKey, select, func, cast
from sqlalchemy.exc import NoResultFound
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import relationship, backref, object_session
from otter.database import engine
# from otter.database import session as sess
from otter.util.cli import project_task_abbrev
from otter.util.date import datetime_to_time_string, date_string_to_date
Base = declarative_base()
# cached Odoo objects
# class BaseMixin(Base):
# @classmethod
# def create(cls, **kw):
# obj = cls(**kw)
# # TODO: That's so ugly
# session = sess()
# session.add(obj)
# session.commit()
# Otter objects
Base.metadata.create_all(engine())
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] | 2.837349 | 332 |
#!/usr/bin/python
import argparse
from bs4 import BeautifulSoup
from html.parser import HTMLParser
from lxml import etree
import json
import re
import requests
GOLFSHOT_URL = 'https://play.golfshot.com'
parser = argparse.ArgumentParser(description='Download GolfShot data')
parser.add_argument('username', help='Username for GolfShot account')
parser.add_argument('password', help='Password for GolfShot account')
parser.add_argument('profile_id', help='Profile ID for GolfShot account')
parser.add_argument('--until', help='Download rounds until specified round (by descending date)')
args = parser.parse_args()
with requests.Session() as session:
tokenRequest = session.get(f'{GOLFSHOT_URL}/signin')
parser = etree.HTMLParser()
tree = etree.fromstring(tokenRequest.text, parser)
verificationToken = tree.xpath('//form//input[@name="__RequestVerificationToken"]/@value')[0]
signin = session.post(f'{GOLFSHOT_URL}/signin',
data={'Email': args.username,
'Password': args.password,
'__RequestVerificationToken': verificationToken,
})
download_rounds(session, args.profile_id, args.until)
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] | 2.735426 | 446 |
from PyQt4 import QtCore, QtGui
try:
_fromUtf8 = QtCore.QString.fromUtf8
except AttributeError:
_fromUtf8 = lambda s: s
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## Modify Process Token
# Global Imports
import ctypes
from ctypes.wintypes import DWORD
# Grab a handle on Advapi.dll, User32.dll and Kernel32.dll
a_handle = ctypes.WinDLL("Advapi32.dll")
u_handle = ctypes.WinDLL("User32.dll")
k_handle = ctypes.WinDLL("Kernel32.dll")
# Shortcut to give "All Access" rights to the current process.
# The |'s are being used as "or" statements, as a shortcut instead of typing out every variable needed, and their values.
PROCESS_ALL_ACCESS = ( 0x000f0000 | 0x00100000 | 0xFFF)
# Also used in C# Libraries
# 2 is enabled, 0 is disabled
SE_PRIVILEGE_ENABLED = 0x00000002
SE_PRIVILEGE_DISABLED = 0x00000000
# Token Access Rights
STANDARD_RIGHTS_REQUIRED = 0x000F0000 #
STANDARD_RIGHTS_READ = 0x00020000 #
TOKEN_ASSIGN_PRIMARY = 0x0001 #
TOKEN_DUPLICATE = 0x0002 #
TOKEN_IMPERSONATION = 0x0004 #
TOKEN_QUERY = 0x0008 #
TOKEN_QUERY_SOURCE = 0x0010 #
TOKEN_ADJUST_PRIVILEGES = 0x0020 #
TOKEN_ADJUST_GROUPS = 0x0040 #
TOKEN_ADJUST_DEFAULT = 0x0080 #
TOKEN_ADJUST_SESSIONID = 0x0100 #
TOKEN_READ = (STANDARD_RIGHTS_READ | TOKEN_QUERY) #
TOKEN_ALL_ACCESS = (STANDARD_RIGHTS_REQUIRED |
TOKEN_ASSIGN_PRIMARY |
TOKEN_DUPLICATE |
TOKEN_IMPERSONATION |
TOKEN_QUERY |
TOKEN_QUERY_SOURCE |
TOKEN_ADJUST_PRIVILEGES |
TOKEN_ADJUST_GROUPS |
TOKEN_ADJUST_DEFAULT |
TOKEN_ADJUST_SESSIONID)
# Grab the Window Name from User32.dll
lpWindowName = ctypes.c_char_p(input("Enter Window name to hook into: ").encode('utf-8'))
hWnd = u_handle.FindWindowA(None, lpWindowName)
if hWnd == 0:
print("Error Code: {0} - Could not grab Process Handle! Error Code: {0}".format(k_handle.GetLastError()))
exit(1)
else:
print("Successfuly got Handle.")
lpdwProcessId = ctypes.c_ulong()
response = u_handle.GetWindowThreadProcessId(hWnd, ctypes.byref(lpdwProcessId))
if response == 0:
print("Error Code: {0} - Could not grab PID from Handle! Error Code: {0}".format(k_handle.GetlastError))
exit(1)
else:
print("Successfuly got PID!")
dwDesiredAccess = PROCESS_ALL_ACCESS
bInheritHandle = False
dwProcessId = lpdwProcessId
hProcess = k_handle.OpenProcess(dwDesiredAccess, bInheritHandle, dwProcessId)
if hProcess <= 0:
print("Error Code {0} - Could not grab Priv Handle".format(k_handle.GetLastError()))
else:
print("Successfully grabbed higher privileges!")
# Open a Handle to the Process's Token Directly
ProcessHandle = hProcess
DesiredAccess = TOKEN_ALL_ACCESS
TokenHandle = ctypes.c_void_p()
response = k_handle.OpenProcessToken(ProcessHandle, DesiredAccess, ctypes.byref(TokenHandle))
if response > 0:
print("Handle to Process Token created! Token: {0}".format(TokenHandle))
else:
print("Error: could not grab priviledged Token Handle! Error Code: {0}".format(k_handle.GetLastError()))
lpSystemName = None
lpName = "SEDebugPrivilege"
lpLuid = LUID()
response = a_handle.LookupPrivilegeValueW(lpSystemName, lpName, ctypes.byref(lpLuid))
if response > 0:
print("Successfully found the LUID!")
else:
print("Error: could not grab LUID! Error Code: {0}".format(k_handle.GetLastError()))
print("LUID VALUE HIGH: {0} \nLUID VALUE LOW: {1}".format(lpLuid.HighPart, lpLuid.LowPart))
requiredPrivileges = PRIVILEGE_SET()
requiredPrivileges.PrivilegeCount = 1
requiredPrivileges.Privileges = LUID_AND_ATTRIBUTES()
requiredPrivileges.Privileges.Luid = lpLuid
requiredPrivileges.Privileges.Attributes = SE_PRIVILEGE_ENABLED
pfResult = ctypes.c_long()
response = a_handle.PrivilegeCheck(TokenHandle, ctypes.byref(requiredPrivileges), ctypes.byref(pfResult))
if response > 0:
print("Successfully ran Privilege check!")
else:
print("Error: Was unable to check Privileges! Error Code: {0}".format(k_handle.GetLastError()))
if pfResult:
print("Privilege Enabled: {0}".format(lpName))
requiredPrivileges.Privileges.Attributes = SE_PRIVILEGE_DISABLED
else:
print("Privileges Disabled: {0}".format(lpName))
requiredPrivileges.Privileges.Attributes = SE_PRIVILEGE_ENABLED
DisableAllPrivileges = False
NewState = TOKEN_PRIVILEGES()
BufferLength = ctypes.sizeof(NewState)
PreviousState = ctypes.c_void_p()
ReturnLength =ctypes.c_void_p()
NewState.PrivilegeCount = 1
NewState.Privileges = requiredPrivileges.Privileges
response = a_handle.AdjustTokenPrivileges(
TokenHandle,
DisableAllPrivileges,
ctypes.byref(NewState),
BufferLength,
ctypes.byref(PreviousState),
ctypes.byref(ReturnLength))
if response > 0:
print("Token was successfully modified!")
else:
print("Error: Was unable to check Privileges! Error Code: {0}".format(k_handle.GetLastError()))
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] | 2.310867 | 2,181 |
from abc import ABC, abstractmethod
| [
6738,
450,
66,
1330,
9738,
11,
12531,
24396,
628
] | 4.111111 | 9 |
"""
Ejercicio 15
Dados como datos el precio final pagado por un producto y su precio de venta al pรบblico (PVP), se requiere
que calcule y muestre el porcentaje de descuento que le ha sido aplicado.
Entradas
Precio_Final_Pagado --> Float --> P_F
Precio_Venta_Publico --> Float --> P_V_P
Salidas
Porcentaje_Descuento --> Float --> P_D
"""
# Instrucciones al usuario
print("Para conocer el porcentaje de descuento, escriba lo siguiente: ")
# Entradas
P_F = float(input("Digite el precio final pagado por el producto: "))
P_V_P = float(input("Digite el precio de venta al pรบblico del producto: "))
# Caja negra
P_D = ((P_V_P-P_F)/P_V_P)*100
# Salidas
print(f"El porcentaje de descuento aplicado es de: {P_D}%")
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47,
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47,
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47,
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7,
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1658,
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25,
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47,
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35,
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4,
4943,
198
] | 2.40339 | 295 |
#brailleInput.py
#A part of NonVisual Desktop Access (NVDA)
#This file is covered by the GNU General Public License.
#See the file COPYING for more details.
#Copyright (C) 2012-2013 NV Access Limited, Rui Batista
import os.path
import louis
import braille
import config
from logHandler import log
import winUser
import inputCore
"""Framework for handling braille input from the user.
All braille input is represented by a {BrailleInputGesture}.
Normally, all that is required is to create and execute a L{BrailleInputGesture},
as there are built-in gesture bindings for braille input.
"""
#: The singleton BrailleInputHandler instance.
#: @type: L{BrailleInputHandler}
handler = None
class BrailleInputHandler(object):
"""Handles braille input.
"""
def input(self, dots):
"""Handle one cell of braille input.
"""
# liblouis requires us to set the highest bit for proper use of dotsIO.
char = unichr(dots | 0x8000)
text = louis.backTranslate(
[os.path.join(braille.TABLES_DIR, config.conf["braille"]["inputTable"]),
"braille-patterns.cti"],
char, mode=louis.dotsIO)
chars = text[0]
if len(chars) > 0:
self.sendChars(chars)
class BrailleInputGesture(inputCore.InputGesture):
"""Input (dots and/or space bar) from a braille keyboard.
This could either be as part of a braille display or a stand-alone unit.
L{dots} and L{space} should be set appropriately.
"""
#: Bitmask of pressed dots.
#: @type: int
dots = 0
#: Whether the space bar is pressed.
#: @type: bool
space = False
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25,
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25,
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2,
25,
2488,
4906,
25,
20512,
198,
197,
13200,
796,
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198
] | 2.978474 | 511 |
from typing import List, Optional, Tuple
import numpy as np
from gutenTAG.anomalies import AnomalyProtocol, LabelRange, Anomaly
from gutenTAG.base_oscillations.interface import BaseOscillationInterface
| [
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# -*- coding: utf-8 -*-
"""
Created on Mon Mar 5 11:06:16 2018
@author: r.dewinter
"""
import numpy as np
#import matplotlib.pyplot as plt
#from mpl_toolkits.mplot3d import Axes3D
#rngMin = np.zeros(9)
#rngMax = np.ones(9)
#nVar = 9
#ref = np.array([1,1,1])
#parameters = np.empty((200,9))
#objectives = np.empty((200,3))
#constraints = np.empty((200,3))
#objectives[:] = 0
#constraints[:] = 0
#parameters[:]= 0
#for i in range(200):
# x = np.random.rand(nVar)*(rngMax-rngMin)+rngMin
# parameters[i] = x
# obj, cons = DTLZ8(x)
# objectives[i] = obj
# constraints[i] = cons
#
#a = np.sum(constraints<0, axis=1)==3
##sum(a)
#fig = plt.figure()
#ax = fig.add_subplot(111,projection='3d')
#ax.scatter(objectives[a][:,0], objectives[a][:,1], objectives[a][:,2])
#fig.show() | [
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18,
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] | 2.049875 | 401 |
"""
Helper script used for turning text into tf-idf vector for the knn experiment
"""
import re
import numpy
from nltk import pos_tag
from nltk.corpus import stopwords
from nltk.corpus import wordnet
from nltk.stem import SnowballStemmer
from nltk.stem import WordNetLemmatizer
from nltk.tokenize import word_tokenize
from sklearn.feature_extraction.text import TfidfVectorizer
def cleanText(text, lemmatize, stemmer):
"""Method for cleaning text. Removes numbers, punctuation, and capitalization. Stems or lemmatizes text."""
if isinstance(text, float):
text = str(text)
if isinstance(text, numpy.int64):
text = str(text)
try:
text = text.decode()
except AttributeError:
pass
text = re.sub(r"[^A-Za-z]", " ", text)
text = text.lower()
if lemmatize:
wordnet_lemmatizer = WordNetLemmatizer()
text_result = []
tokens = word_tokenize(text) # Generate list of tokens
tagged = pos_tag(tokens)
for t in tagged:
try:
text_result.append(wordnet_lemmatizer.lemmatize(t[0], get_tag(t[1][:2])))
except:
text_result.append(wordnet_lemmatizer.lemmatize(t[0]))
return text_result
if stemmer:
text_result = []
tokens = word_tokenize(text)
snowball_stemmer = SnowballStemmer('english')
for t in tokens:
text_result.append(snowball_stemmer.stem(t))
return text_result
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25,
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220,
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220,
220,
220,
220,
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220,
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8,
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220,
220,
220,
220,
220,
220,
46275,
62,
927,
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7967,
1894,
1273,
368,
647,
10786,
39126,
11537,
201,
198,
220,
220,
220,
220,
220,
220,
220,
329,
256,
287,
16326,
25,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2420,
62,
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13,
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7,
82,
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1894,
62,
927,
647,
13,
927,
7,
83,
4008,
201,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
2420,
62,
20274,
201,
198,
201,
198
] | 2.187767 | 703 |
import os.path
DATA_DIRECTORY = os.path.join(os.path.dirname(__file__), 'data')
| [
11748,
28686,
13,
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13,
15908,
3672,
7,
834,
7753,
834,
828,
705,
7890,
11537,
198
] | 2.454545 | 33 |
# -*- coding: utf-8 -*-
# test = Solution()
# # print test.lexicographical('apple', 'appld')
# print test.trySet()
test = Solution3()
print test.canFinish([[1, 0], [2, 1], [2, 0]]) | [
2,
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] | 2.472973 | 74 |
# terrascript/resource/phillbaker/elasticsearch.py
# Automatically generated by tools/makecode.py (24-Sep-2021 15:15:48 UTC)
import terrascript
__all__ = [
"elasticsearch_component_template",
"elasticsearch_composable_index_template",
"elasticsearch_index",
"elasticsearch_index_template",
"elasticsearch_ingest_pipeline",
"elasticsearch_kibana_alert",
"elasticsearch_kibana_object",
"elasticsearch_opendistro_destination",
"elasticsearch_opendistro_ism_policy",
"elasticsearch_opendistro_ism_policy_mapping",
"elasticsearch_opendistro_kibana_tenant",
"elasticsearch_opendistro_monitor",
"elasticsearch_opendistro_role",
"elasticsearch_opendistro_roles_mapping",
"elasticsearch_opendistro_user",
"elasticsearch_snapshot_repository",
"elasticsearch_xpack_index_lifecycle_policy",
"elasticsearch_xpack_license",
"elasticsearch_xpack_role",
"elasticsearch_xpack_role_mapping",
"elasticsearch_xpack_snapshot_lifecycle_policy",
"elasticsearch_xpack_user",
"elasticsearch_xpack_watch",
]
| [
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62,
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62,
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62,
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220,
366,
417,
3477,
12947,
62,
87,
8002,
62,
8340,
1600,
198,
60,
198
] | 2.492063 | 441 |
import pytest
from ...utilities import iconfont
from ...PyQt import QtGui, QtCore
| [
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] | 3.222222 | 27 |
import argparse, os
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVR
import pandas as pd
from scipy.io import arff
# to get the summary both logistic-regression and support-vector-machines have to be run once with the output errors option
if __name__ == "__main__":
parser = argparse.ArgumentParser('FSA')
parser.add_argument('--type', type=str, help='which kind of regression', choices=["logistic-regression", "support-vector-machine"])
parser.add_argument('--output-error-values', action='store_true', default=False, help='display error values instead of output')
parser.add_argument('--train', type=str, help='training dataset', default='MC2-train.arff')
parser.add_argument('--predict', type=str, help='prediction dataset', default='MC2-predict.arff')
args = parser.parse_args()
train_data_arff = arff.loadarff(args.train)
train_data = pd.DataFrame(train_data_arff[0])
train_data = train_data.fillna(0)
prediction_data = pd.DataFrame(arff.loadarff(args.predict)[0])
prediction_data = prediction_data.fillna(0)
if args.type == "logistic-regression":
model = train_log_reg(train_data)
else:
model = train_svm(train_data)
if args.output_error_values:
save_error_values(model, train_data, prediction_data, args.type)
else:
model_predict(model, prediction_data)
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13,
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25,
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220,
220,
220,
220,
220,
3613,
62,
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62,
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7,
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11,
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62,
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11,
17724,
62,
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11,
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13,
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8,
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220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
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220,
220,
2746,
62,
79,
17407,
7,
19849,
11,
17724,
62,
7890,
8,
628
] | 2.807229 | 498 |
import numpy as np
"""
Within this file, the predefined functions appearing in the main menu may be defined. If a new one is added, it must be
added to the attributes self.predef_funs_show and self.predef_funs of the PredefinedFunctions class from the MainMenu.py
file. Moreover, these functions must be added to the self.save method from the previous class.
"""
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628,
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198
] | 3.793814 | 97 |
#!/usr/bin/env python3
"""
:problem: https://www.hackerrank.com/challenges/ctci-connected-cell-in-a-grid/problem
"""
from typing import List, Set, Tuple
Cell = Tuple[int, int]
if __name__ == '__main__':
main()
| [
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] | 2.55814 | 86 |
# -*- coding: utf-8 -*-
'''
This is a fully connected neural network.
It contains data batching , using Relu activation function,
using adam optimizer and dropout for overfitting.
'''
import torch
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import sklearn
data = pd.read_csv('bike_sharing.csv', index_col=0)
'''
plt.figure(figsize=(8, 6))
#x is yr y is cnt and coloring is based on the spring
sns.barplot('yr', 'cnt', hue = 'season', data = data, ci=None)
plt.legend(loc = 'upper right', bbox_to_anchor=(1.2,0.5))
plt.xlabel('Year')
plt.ylabel('Total number of bikes rented')
plt.title('Number of bikes rented per season')
'''
'''
plt.figure(figsize=(8, 6))
sns.barplot('mnth', 'cnt', hue = 'workingday', data = data, ci=None)
plt.legend(loc = 'upper right', bbox_to_anchor=(1.2,0.5))
plt.xlabel('Year')
plt.ylabel('Total number of bikes rented')
plt.title('Number of bikes rented per month')
'''
#get the seazon field and change each attribute to one column
data = pd.get_dummies(data, columns= ['season'])
#need just these columns
columns = ['registered', 'holiday', 'weekday',
'weathersit', 'temp', 'atemp',
'season_fall', 'season_spring',
'season_summer', 'season_winter']
#features are the input(xtrain) of neural network and target is the ytrain
features=data[columns]
target=data[['cnt']]
#use sklearn for dividing our data to train and test
from sklearn.model_selection import train_test_split
#80 percent of data is for training
X_train, x_test, Y_train, y_test = train_test_split(features,
target,
test_size=0.2)
#change to tensors
X_train_tensor = torch.tensor(X_train.values, dtype = torch.float)
x_test_tensor = torch.tensor(x_test.values, dtype = torch.float)
Y_train_tensor = torch.tensor(Y_train.values, dtype = torch.float)
y_test_tensor = torch.tensor(y_test.values, dtype = torch.float)
'''
batch the data
'''
#use data utils for batching
import torch.utils.data as data_utils
#tensordataset and loader both used to load multiple samples in parallel
train_data = data_utils.TensorDataset(X_train_tensor, Y_train_tensor)
train_loader = data_utils.DataLoader(train_data, batch_size=100, shuffle=True)
features_batch, target_batch = iter(train_loader).next()
inp = X_train_tensor.shape[1]
out = 1
hid = 10
loss_fn = torch.nn.MSELoss()
#making the neural network model
model = torch.nn.Sequential(torch.nn.Linear(inp, hid),
torch.nn.ReLU(),
#dropout is good for overfitting the p is the
#probability of deleting the neuron
torch.nn.Dropout(p=0.2),
torch.nn.Linear(hid, out))
#defining the optimizer
optimizer = torch.optim.Adam(model.parameters(), lr = 0.001)
##############################################
##make epochs based on the train loader size##
##############################################
total_step = len(train_loader)
num_epochs = 10000
#train model based on every batch data
for epoch in range(num_epochs + 1):
for i, (features, target) in enumerate(train_loader):
output = model(features)
loss = loss_fn(output, target)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if epoch % 2000 == 0:
print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
.format(epoch+1, num_epochs, i+1, total_step, loss.item()))
#evaluate our model
model.eval()
#get all predicted y value for the all x test
with torch.no_grad():
y_pred_tensor = model(x_test_tensor)
y_pred = y_pred_tensor.detach().numpy()
#make a table for comparing between actual and predicted
compare_df = pd.DataFrame({'actual': np.squeeze(y_test.values), 'predicted': np.squeeze(y_pred)})
#show ten random samples of data frame
print(compare_df.sample(10))
print(sklearn.metrics.r2_score(y_test, y_pred))
'''
Pytorch allows our model to be saved. The parameters to the torch.save()
method are the model to be saved followed by the directory path where it
should be saved
'''
torch.save(model, 'my_model')
#We can load a saved model using the torch.load() method
saved_model = torch.load('my_model')
'''
#It is now used exactly how we used the model before it was saved
y_pred_tensor = saved_model(x_test_tensor)
y_pred = y_pred_tensor.detach().numpy()
'''
#comparing the predicted and actual values with plot
plt.figure(figsize=(12, 8))
plt.plot(y_pred, label='Predicted count')
plt.plot(y_test.values, label='Actual count')
plt.legend()
plt.show() | [
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] | 2.459812 | 1,916 |
import FWCore.ParameterSet.Config as cms
hltPhase2L3MuonsNoID = cms.EDProducer("MuonIdProducer",
CaloExtractorPSet = cms.PSet(
CenterConeOnCalIntersection = cms.bool(False),
ComponentName = cms.string('CaloExtractorByAssociator'),
DR_Max = cms.double(1.0),
DR_Veto_E = cms.double(0.07),
DR_Veto_H = cms.double(0.1),
DR_Veto_HO = cms.double(0.1),
DepositInstanceLabels = cms.vstring(
'ecal',
'hcal',
'ho'
),
DepositLabel = cms.untracked.string('Cal'),
NoiseTow_EB = cms.double(0.04),
NoiseTow_EE = cms.double(0.15),
Noise_EB = cms.double(0.025),
Noise_EE = cms.double(0.1),
Noise_HB = cms.double(0.2),
Noise_HE = cms.double(0.2),
Noise_HO = cms.double(0.2),
PrintTimeReport = cms.untracked.bool(False),
PropagatorName = cms.string('hltESPFastSteppingHelixPropagatorAny'),
ServiceParameters = cms.PSet(
Propagators = cms.untracked.vstring('hltESPFastSteppingHelixPropagatorAny'),
RPCLayers = cms.bool(False),
UseMuonNavigation = cms.untracked.bool(False)
),
Threshold_E = cms.double(0.2),
Threshold_H = cms.double(0.5),
Threshold_HO = cms.double(0.5),
TrackAssociatorParameters = cms.PSet(
CSCSegmentCollectionLabel = cms.InputTag("hltCscSegments"),
CaloTowerCollectionLabel = cms.InputTag("Notused"),
DTRecSegment4DCollectionLabel = cms.InputTag("hltDt4DSegments"),
EBRecHitCollectionLabel = cms.InputTag("Notused"),
EERecHitCollectionLabel = cms.InputTag("Notused"),
HBHERecHitCollectionLabel = cms.InputTag("Notused"),
HORecHitCollectionLabel = cms.InputTag("Notused"),
accountForTrajectoryChangeCalo = cms.bool(False),
dREcal = cms.double(1.0),
dREcalPreselection = cms.double(1.0),
dRHcal = cms.double(1.0),
dRHcalPreselection = cms.double(1.0),
dRMuon = cms.double(9999.0),
dRMuonPreselection = cms.double(0.2),
dRPreshowerPreselection = cms.double(0.2),
muonMaxDistanceSigmaX = cms.double(0.0),
muonMaxDistanceSigmaY = cms.double(0.0),
muonMaxDistanceX = cms.double(5.0),
muonMaxDistanceY = cms.double(5.0),
propagateAllDirections = cms.bool(True),
trajectoryUncertaintyTolerance = cms.double(-1.0),
truthMatch = cms.bool(False),
useCalo = cms.bool(True),
useEcal = cms.bool(False),
useHO = cms.bool(False),
useHcal = cms.bool(False),
useMuon = cms.bool(False),
usePreshower = cms.bool(False)
),
UseRecHitsFlag = cms.bool(False)
),
JetExtractorPSet = cms.PSet(
ComponentName = cms.string('JetExtractor'),
DR_Max = cms.double(1.0),
DR_Veto = cms.double(0.1),
ExcludeMuonVeto = cms.bool(True),
JetCollectionLabel = cms.InputTag("Notused"),
PrintTimeReport = cms.untracked.bool(False),
PropagatorName = cms.string('hltESPFastSteppingHelixPropagatorAny'),
ServiceParameters = cms.PSet(
Propagators = cms.untracked.vstring('hltESPFastSteppingHelixPropagatorAny'),
RPCLayers = cms.bool(False),
UseMuonNavigation = cms.untracked.bool(False)
),
Threshold = cms.double(5.0),
TrackAssociatorParameters = cms.PSet(
CSCSegmentCollectionLabel = cms.InputTag("hltCscSegments"),
CaloTowerCollectionLabel = cms.InputTag("Notused"),
DTRecSegment4DCollectionLabel = cms.InputTag("hltDt4DSegments"),
EBRecHitCollectionLabel = cms.InputTag("Notused"),
EERecHitCollectionLabel = cms.InputTag("Notused"),
HBHERecHitCollectionLabel = cms.InputTag("Notused"),
HORecHitCollectionLabel = cms.InputTag("Notused"),
accountForTrajectoryChangeCalo = cms.bool(False),
dREcal = cms.double(0.5),
dREcalPreselection = cms.double(0.5),
dRHcal = cms.double(0.5),
dRHcalPreselection = cms.double(0.5),
dRMuon = cms.double(9999.0),
dRMuonPreselection = cms.double(0.2),
dRPreshowerPreselection = cms.double(0.2),
muonMaxDistanceSigmaX = cms.double(0.0),
muonMaxDistanceSigmaY = cms.double(0.0),
muonMaxDistanceX = cms.double(5.0),
muonMaxDistanceY = cms.double(5.0),
propagateAllDirections = cms.bool(True),
trajectoryUncertaintyTolerance = cms.double(-1.0),
truthMatch = cms.bool(False),
useCalo = cms.bool(True),
useEcal = cms.bool(False),
useHO = cms.bool(False),
useHcal = cms.bool(False),
useMuon = cms.bool(False),
usePreshower = cms.bool(False)
)
),
MuonCaloCompatibility = cms.PSet(
MuonTemplateFileName = cms.FileInPath('RecoMuon/MuonIdentification/data/MuID_templates_muons_lowPt_3_1_norm.root'),
PionTemplateFileName = cms.FileInPath('RecoMuon/MuonIdentification/data/MuID_templates_pions_lowPt_3_1_norm.root'),
allSiPMHO = cms.bool(False),
delta_eta = cms.double(0.02),
delta_phi = cms.double(0.02)
),
TimingFillerParameters = cms.PSet(
CSCTimingParameters = cms.PSet(
CSCStripError = cms.double(7.0),
CSCStripTimeOffset = cms.double(0.0),
CSCTimeOffset = cms.double(0.0),
CSCWireError = cms.double(8.6),
CSCWireTimeOffset = cms.double(0.0),
CSCsegments = cms.InputTag("hltCscSegments"),
MatchParameters = cms.PSet(
CSCsegments = cms.InputTag("hltCscSegments"),
DTradius = cms.double(0.01),
DTsegments = cms.InputTag("hltDt4DSegments"),
TightMatchCSC = cms.bool(True),
TightMatchDT = cms.bool(False)
),
PruneCut = cms.double(100.0),
ServiceParameters = cms.PSet(
Propagators = cms.untracked.vstring('hltESPFastSteppingHelixPropagatorAny'),
RPCLayers = cms.bool(True)
),
UseStripTime = cms.bool(True),
UseWireTime = cms.bool(True),
debug = cms.bool(False)
),
DTTimingParameters = cms.PSet(
DTTimeOffset = cms.double(2.7),
DTsegments = cms.InputTag("hltDt4DSegments"),
DoWireCorr = cms.bool(False),
DropTheta = cms.bool(True),
HitError = cms.double(6.0),
HitsMin = cms.int32(5),
MatchParameters = cms.PSet(
CSCsegments = cms.InputTag("hltCscSegments"),
DTradius = cms.double(0.01),
DTsegments = cms.InputTag("hltDt4DSegments"),
TightMatchCSC = cms.bool(True),
TightMatchDT = cms.bool(False)
),
PruneCut = cms.double(10000.0),
RequireBothProjections = cms.bool(False),
ServiceParameters = cms.PSet(
Propagators = cms.untracked.vstring('hltESPFastSteppingHelixPropagatorAny'),
RPCLayers = cms.bool(True)
),
UseSegmentT0 = cms.bool(False),
debug = cms.bool(False)
),
EcalEnergyCut = cms.double(0.4),
ErrorCSC = cms.double(7.4),
ErrorDT = cms.double(6.0),
ErrorEB = cms.double(2.085),
ErrorEE = cms.double(6.95),
UseCSC = cms.bool(True),
UseDT = cms.bool(True),
UseECAL = cms.bool(True)
),
TrackAssociatorParameters = cms.PSet(
CSCSegmentCollectionLabel = cms.InputTag("hltCscSegments"),
CaloTowerCollectionLabel = cms.InputTag("Notused"),
DTRecSegment4DCollectionLabel = cms.InputTag("hltDt4DSegments"),
EBRecHitCollectionLabel = cms.InputTag("Notused"),
EERecHitCollectionLabel = cms.InputTag("Notused"),
GEMSegmentCollectionLabel = cms.InputTag("hltGemSegments"),
HBHERecHitCollectionLabel = cms.InputTag("Notused"),
HORecHitCollectionLabel = cms.InputTag("Notused"),
ME0SegmentCollectionLabel = cms.InputTag("hltMe0Segments"),
accountForTrajectoryChangeCalo = cms.bool(False),
dREcal = cms.double(9999.0),
dREcalPreselection = cms.double(0.05),
dRHcal = cms.double(9999.0),
dRHcalPreselection = cms.double(0.2),
dRMuon = cms.double(9999.0),
dRMuonPreselection = cms.double(0.2),
dRPreshowerPreselection = cms.double(0.2),
muonMaxDistanceSigmaX = cms.double(0.0),
muonMaxDistanceSigmaY = cms.double(0.0),
muonMaxDistanceX = cms.double(5.0),
muonMaxDistanceY = cms.double(5.0),
propagateAllDirections = cms.bool(True),
trajectoryUncertaintyTolerance = cms.double(-1.0),
truthMatch = cms.bool(False),
useCalo = cms.bool(False),
useEcal = cms.bool(False),
useGEM = cms.bool(True),
useHO = cms.bool(False),
useHcal = cms.bool(False),
useME0 = cms.bool(False),
# useME0 = cms.bool(True), ### Thiago: in the offline RECO it is false...
useMuon = cms.bool(True),
usePreshower = cms.bool(False)
),
TrackExtractorPSet = cms.PSet(
BeamSpotLabel = cms.InputTag("offlineBeamSpot"),
BeamlineOption = cms.string('BeamSpotFromEvent'),
Chi2Ndof_Max = cms.double(1e+64),
Chi2Prob_Min = cms.double(-1.0),
ComponentName = cms.string('TrackExtractor'),
DR_Max = cms.double(1.0),
DR_Veto = cms.double(0.01),
Diff_r = cms.double(0.1),
Diff_z = cms.double(0.2),
NHits_Min = cms.uint32(0),
Pt_Min = cms.double(-1.0),
inputTrackCollection = cms.InputTag("hltPhase2L3MuonMerged")
),
TrackerKinkFinderParameters = cms.PSet(
diagonalOnly = cms.bool(False),
usePosition = cms.bool(False)
),
addExtraSoftMuons = cms.bool(False),
arbitrateTrackerMuons = cms.bool(True),
arbitrationCleanerOptions = cms.PSet(
ClusterDPhi = cms.double(0.6),
ClusterDTheta = cms.double(0.02),
Clustering = cms.bool(True),
ME1a = cms.bool(True),
Overlap = cms.bool(True),
OverlapDPhi = cms.double(0.0786),
OverlapDTheta = cms.double(0.02)
),
debugWithTruthMatching = cms.bool(False),
ecalDepositName = cms.string('ecal'),
fillCaloCompatibility = cms.bool(False),
fillEnergy = cms.bool(False),
fillGlobalTrackQuality = cms.bool(False),
fillGlobalTrackRefits = cms.bool(False),
fillIsolation = cms.bool(False),
fillMatching = cms.bool(True),
fillTrackerKink = cms.bool(False),
globalTrackQualityInputTag = cms.InputTag(""),
hcalDepositName = cms.string('hcal'),
hoDepositName = cms.string('ho'),
inputCollectionLabels = cms.VInputTag("hltPhase2L3MuonMerged", "hltPhase2L3GlbMuon", "hltL2MuonsFromL1TkMuon:UpdatedAtVtx"),
inputCollectionTypes = cms.vstring(
'inner tracks',
'links',
'outer tracks'
),
jetDepositName = cms.string('jets'),
maxAbsDx = cms.double(3.0),
maxAbsDy = cms.double(9999.0),
maxAbsEta = cms.double(3.0),
maxAbsPullX = cms.double(4.0),
maxAbsPullY = cms.double(9999.0),
minCaloCompatibility = cms.double(0.6),
minNumberOfMatches = cms.int32(1),
minP = cms.double(0.0),
minPCaloMuon = cms.double(1000000000.0),
minPt = cms.double(2.0),
ptThresholdToFillCandidateP4WithGlobalFit = cms.double(200.0),
runArbitrationCleaner = cms.bool(False),
sigmaThresholdToFillCandidateP4WithGlobalFit = cms.double(2.0),
trackDepositName = cms.string('tracker'),
writeIsoDeposits = cms.bool(False)
)
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220,
220,
220,
220,
220,
220,
220,
21380,
17,
45,
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62,
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13,
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7,
16,
68,
10,
2414,
828,
198,
220,
220,
220,
220,
220,
220,
220,
21380,
17,
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65,
62,
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13,
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32590,
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13,
15,
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220,
220,
220,
220,
220,
220,
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35100,
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62,
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220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
34352,
62,
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796,
269,
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32590,
16,
13,
15,
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198,
220,
220,
220,
220,
220,
220,
220,
5128,
24802,
36307,
796,
269,
907,
13,
20560,
24835,
7203,
71,
2528,
35645,
17,
43,
18,
33239,
261,
13102,
2004,
4943,
198,
220,
220,
220,
10612,
198,
220,
220,
220,
26885,
42,
676,
37,
5540,
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13,
3705,
316,
7,
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220,
220,
220,
220,
220,
220,
220,
40039,
10049,
796,
269,
907,
13,
30388,
7,
25101,
828,
198,
220,
220,
220,
220,
220,
220,
220,
779,
26545,
796,
269,
907,
13,
30388,
7,
25101,
8,
198,
220,
220,
220,
10612,
198,
220,
220,
220,
751,
27726,
18380,
33239,
684,
796,
269,
907,
13,
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7,
25101,
828,
198,
220,
220,
220,
9277,
4873,
35694,
33239,
684,
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269,
907,
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7,
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220,
220,
220,
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263,
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220,
220,
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13,
21,
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220,
220,
220,
220,
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35,
464,
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7,
15,
13,
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198,
220,
220,
220,
220,
220,
220,
220,
1012,
436,
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269,
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13,
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7,
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220,
220,
220,
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220,
11948,
16,
64,
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220,
220,
220,
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220,
220,
220,
220,
220,
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13,
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220,
220,
220,
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35013,
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24835,
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220,
220,
220,
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220,
220,
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220,
220,
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53,
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220,
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15,
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220,
220,
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15,
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220,
220,
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269,
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7,
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13,
15,
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198,
220,
220,
220,
949,
34,
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25901,
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269,
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13,
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7,
15,
13,
21,
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198,
220,
220,
220,
949,
15057,
5189,
19044,
2052,
796,
269,
907,
13,
600,
2624,
7,
16,
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198,
220,
220,
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47,
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7,
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47,
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7,
17,
13,
15,
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198,
220,
220,
220,
42975,
817,
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2514,
33762,
41572,
20540,
47,
19,
3152,
22289,
31805,
796,
269,
907,
13,
23352,
7,
2167,
13,
15,
828,
198,
220,
220,
220,
1057,
3163,
2545,
1358,
32657,
263,
796,
269,
907,
13,
30388,
7,
25101,
828,
198,
220,
220,
220,
264,
13495,
817,
10126,
2514,
33762,
41572,
20540,
47,
19,
3152,
22289,
31805,
796,
269,
907,
13,
23352,
7,
17,
13,
15,
828,
198,
220,
220,
220,
2610,
12156,
7434,
5376,
796,
269,
907,
13,
8841,
10786,
2213,
10735,
33809,
198,
220,
220,
220,
3551,
40,
568,
12156,
418,
896,
796,
269,
907,
13,
30388,
7,
25101,
8,
198,
8,
198
] | 1.977833 | 6,045 |
arr = [2, 4, 1, 2, 8, 3]
insertionSort(arr)
print(arr) | [
198,
198,
3258,
796,
685,
17,
11,
604,
11,
352,
11,
362,
11,
807,
11,
513,
60,
198,
28463,
295,
42758,
7,
3258,
8,
198,
4798,
7,
3258,
8
] | 1.931034 | 29 |
from unittest.mock import patch
from django.conf import settings
from django.db import IntegrityError
from django.test import TestCase
from feeds.tests.helpers import (
make_fake_feedparser_dict, make_feed_entries_list,
make_preprocessed_entries_list
)
from feeds.models import Entry, Feed
@patch('feeds.models.preprocess_feed_entry_item')
@patch('feeds.models.fetch_feedparser_dict')
| [
6738,
555,
715,
395,
13,
76,
735,
1330,
8529,
201,
198,
201,
198,
6738,
42625,
14208,
13,
10414,
1330,
6460,
201,
198,
6738,
42625,
14208,
13,
9945,
1330,
39348,
12331,
201,
198,
6738,
42625,
14208,
13,
9288,
1330,
6208,
20448,
201,
198,
201,
198,
6738,
21318,
13,
41989,
13,
16794,
364,
1330,
357,
201,
198,
220,
220,
220,
787,
62,
30706,
62,
12363,
48610,
62,
11600,
11,
787,
62,
12363,
62,
298,
1678,
62,
4868,
11,
201,
198,
220,
220,
220,
787,
62,
3866,
14681,
276,
62,
298,
1678,
62,
4868,
201,
198,
8,
201,
198,
6738,
21318,
13,
27530,
1330,
21617,
11,
18272,
201,
198,
201,
198,
201,
198,
201,
198,
201,
198,
31,
17147,
10786,
12363,
82,
13,
27530,
13,
3866,
14681,
62,
12363,
62,
13000,
62,
9186,
11537,
201,
198,
31,
17147,
10786,
12363,
82,
13,
27530,
13,
69,
7569,
62,
12363,
48610,
62,
11600,
11537,
201,
198
] | 2.754967 | 151 |
#!/usr/bin/env python
FjRNtSCJtxQIHzHCBANyvSDFfkHSAoEHzzByCQCtzEQRIPEGztHSpPBmIAjBJFF = 'RXLQksAGmIIuwhBJUptxVuytBrDBAdGQAQvkSrGtgiSFnGSZospnORAnCEZHCBz'
zmNDzvGHuIEXFHBBtGtCEpxpAQSFvzsESQMwGFYFyGQyEUBBoMCOCFPCRARREmS = 'RICsbwNkCOqPrHxHGDwjHTJCAhHPGiRZSFzrFITzFLmZFDDAuBRtAxtkQzDUuGg'
xJOxLDDlmqmAmyPHrDJSJSCFCitymFVQqvFBFDBBwPwBRrEWwWhuIHFHJqsCGBg = 'RlQqIOyQNICOIGoFHqDFIoZQFOjWRBBARHSuPBDyRvrvGqgYJtvxYSWSrSGIwqI'
tlsIviJwUFnrJqFHMMxASrlquyGJjQAPvTGFTkYdRunCGzBEAPoRExzFJxRJXJ = 'RqOFwYDPEAExFHFSmzQJjkJwExAoABQCZvXSSrCfLCalJEExvIAOzHERyvxzAVy'
JGVSPRGBFsIFoozrXsVmAuRCtkGmrxAJLHBNJQNXmywOzWCRIqdwHfHAVRNRhcH = 'CHImoEVySFSFEvkpOyAmuzBNCzwASPrsQCsCqnzOwSyGyHpRGNGJASuGjFGVQXn'
HRyrqOtsypSnHBzRBSrqSTREnxnZBuDIwHExwJRHOERJMeIAHBxAluyrvEnGyxt = 'JGBDRuMQPHYBSFQzFDEGITFTjtPFPFuEXhsHJQGRQwvQPSwxoqXECqQqVPICHwk'
if FjRNtSCJtxQIHzHCBANyvSDFfkHSAoEHzzByCQCtzEQRIPEGztHSpPBmIAjBJFF != tlsIviJwUFnrJqFHMMxASrlquyGJjQAPvTGFTkYdRunCGzBEAPoRExzFJxRJXJ:
zmNDzvGHuIEXFHBBtGtCEpxpAQSFvzsESQMwGFYFyGQyEUBBoMCOCFPCRARREmS = xJOxLDDlmqmAmyPHrDJSJSCFCitymFVQqvFBFDBBwPwBRrEWwWhuIHFHJqsCGBg
for HRyrqOtsypSnHBzRBSrqSTREnxnZBuDIwHExwJRHOERJMeIAHBxAluyrvEnGyxt in tlsIviJwUFnrJqFHMMxASrlquyGJjQAPvTGFTkYdRunCGzBEAPoRExzFJxRJXJ:
if HRyrqOtsypSnHBzRBSrqSTREnxnZBuDIwHExwJRHOERJMeIAHBxAluyrvEnGyxt != xJOxLDDlmqmAmyPHrDJSJSCFCitymFVQqvFBFDBBwPwBRrEWwWhuIHFHJqsCGBg:
zmNDzvGHuIEXFHBBtGtCEpxpAQSFvzsESQMwGFYFyGQyEUBBoMCOCFPCRARREmS = zmNDzvGHuIEXFHBBtGtCEpxpAQSFvzsESQMwGFYFyGQyEUBBoMCOCFPCRARREmS
else:
JGVSPRGBFsIFoozrXsVmAuRCtkGmrxAJLHBNJQNXmywOzWCRIqdwHfHAVRNRhcH = FjRNtSCJtxQIHzHCBANyvSDFfkHSAoEHzzByCQCtzEQRIPEGztHSpPBmIAjBJFF
else:
xJOxLDDlmqmAmyPHrDJSJSCFCitymFVQqvFBFDBBwPwBRrEWwWhuIHFHJqsCGBg = FjRNtSCJtxQIHzHCBANyvSDFfkHSAoEHzzByCQCtzEQRIPEGztHSpPBmIAjBJFF
FjRNtSCJtxQIHzHCBANyvSDFfkHSAoEHzzByCQCtzEQRIPEGztHSpPBmIAjBJFF = JGVSPRGBFsIFoozrXsVmAuRCtkGmrxAJLHBNJQNXmywOzWCRIqdwHfHAVRNRhcH
if xJOxLDDlmqmAmyPHrDJSJSCFCitymFVQqvFBFDBBwPwBRrEWwWhuIHFHJqsCGBg == FjRNtSCJtxQIHzHCBANyvSDFfkHSAoEHzzByCQCtzEQRIPEGztHSpPBmIAjBJFF:
for HRyrqOtsypSnHBzRBSrqSTREnxnZBuDIwHExwJRHOERJMeIAHBxAluyrvEnGyxt in FjRNtSCJtxQIHzHCBANyvSDFfkHSAoEHzzByCQCtzEQRIPEGztHSpPBmIAjBJFF:
if HRyrqOtsypSnHBzRBSrqSTREnxnZBuDIwHExwJRHOERJMeIAHBxAluyrvEnGyxt == xJOxLDDlmqmAmyPHrDJSJSCFCitymFVQqvFBFDBBwPwBRrEWwWhuIHFHJqsCGBg:
xJOxLDDlmqmAmyPHrDJSJSCFCitymFVQqvFBFDBBwPwBRrEWwWhuIHFHJqsCGBg = FjRNtSCJtxQIHzHCBANyvSDFfkHSAoEHzzByCQCtzEQRIPEGztHSpPBmIAjBJFF
else:
xJOxLDDlmqmAmyPHrDJSJSCFCitymFVQqvFBFDBBwPwBRrEWwWhuIHFHJqsCGBg = JGVSPRGBFsIFoozrXsVmAuRCtkGmrxAJLHBNJQNXmywOzWCRIqdwHfHAVRNRhcH
# -*- coding: utf-8 -*-
AyyJNDoAGAIUSBzoEBDRSsBuCQTuqPIbuzzIsQenGzdCJuSRAsvpkjkkHRxNHEJ = 'tpFzvBWpzIwQzkwSGKFyEHBmEHerSCRIDrDDGHAQuRvSzJtMEYIPJHiGLxrVIBF'
GlCHfIBuGpDHwRZBDAJErEJJvuQvCJoqkHqVVetCypAJSvFIRQdEESBEvnFqYxi = 'jPxxzVnpBGycGxGSBtHAPNSRiJCyTgJFuoGRDwjOFZeDAhPuMHtEzAQjnIIHqXj'
if AyyJNDoAGAIUSBzoEBDRSsBuCQTuqPIbuzzIsQenGzdCJuSRAsvpkjkkHRxNHEJ != GlCHfIBuGpDHwRZBDAJErEJJvuQvCJoqkHqVVetCypAJSvFIRQdEESBEvnFqYxi:
AyyJNDoAGAIUSBzoEBDRSsBuCQTuqPIbuzzIsQenGzdCJuSRAsvpkjkkHRxNHEJ = 'jPxxzVnpBGycGxGSBtHAPNSRiJCyTgJFuoGRDwjOFZeDAhPuMHtEzAQjnIIHqXj'
GlCHfIBuGpDHwRZBDAJErEJJvuQvCJoqkHqVVetCypAJSvFIRQdEESBEvnFqYxi = AyyJNDoAGAIUSBzoEBDRSsBuCQTuqPIbuzzIsQenGzdCJuSRAsvpkjkkHRxNHEJ
AyyJNDoAGAIUSBzoEBDRSsBuCQTuqPIbuzzIsQenGzdCJuSRAsvpkjkkHRxNHEJ = 'tpFzvBWpzIwQzkwSGKFyEHBmEHerSCRIDrDDGHAQuRvSzJtMEYIPJHiGLxrVIBF'
import socket
qSHTUIGyrrzAZSuGsyvWJRiuJSlMFiursJPnAIsRRHtCAIpEFpivlUyzPPOIkHC = 'rpIyDHGDZCcJGACzGNUjOjHhzGxzGDSypkrQFFsxBjHJANOnxAHAnDGrECiwFgD'
OJnLJCRyyIqvmzsGqwOymvRASgstGSHytRrxDrDrDwAEHRrpaksBnfQRySIzPzv = 'rEFtxByMzxySSqpGPrHPARJNFXPAHIvBEyxzJWCDSFFFeQYzPRuOJvWRtGOEIkC'
GFYQPqpCIuGDxVuRVEyVPJQxSIPCWzuIRHUGGiSsSflZjOvuySsIyVqxQxoGGWj = 'XDnRxJyrSMiDmmEEQpwcRDzyVMImOCyozwxEFJBHDGnFJHSGxuvrtuPzyIJQFWz'
if qSHTUIGyrrzAZSuGsyvWJRiuJSlMFiursJPnAIsRRHtCAIpEFpivlUyzPPOIkHC == OJnLJCRyyIqvmzsGqwOymvRASgstGSHytRrxDrDrDwAEHRrpaksBnfQRySIzPzv:
GFYQPqpCIuGDxVuRVEyVPJQxSIPCWzuIRHUGGiSsSflZjOvuySsIyVqxQxoGGWj = 'XDnRxJyrSMiDmmEEQpwcRDzyVMImOCyozwxEFJBHDGnFJHSGxuvrtuPzyIJQFWz'
GFYQPqpCIuGDxVuRVEyVPJQxSIPCWzuIRHUGGiSsSflZjOvuySsIyVqxQxoGGWj = qSHTUIGyrrzAZSuGsyvWJRiuJSlMFiursJPnAIsRRHtCAIpEFpivlUyzPPOIkHC
else:
GFYQPqpCIuGDxVuRVEyVPJQxSIPCWzuIRHUGGiSsSflZjOvuySsIyVqxQxoGGWj = 'XDnRxJyrSMiDmmEEQpwcRDzyVMImOCyozwxEFJBHDGnFJHSGxuvrtuPzyIJQFWz'
GFYQPqpCIuGDxVuRVEyVPJQxSIPCWzuIRHUGGiSsSflZjOvuySsIyVqxQxoGGWj = 'rpIyDHGDZCcJGACzGNUjOjHhzGxzGDSypkrQFFsxBjHJANOnxAHAnDGrECiwFgD'
import subprocess
JSzCSDWEJIDymqnQSyOJOzgXouJzPzPGSpRfySPyCeBGPzfJEzQveMtJFuTonMq = 'iwCpAIRHOhOClFOQABQPxSWmBGItrDODGntZwJEtHOJIpXOwxxErCJvPBQupwBw'
uSouClnmuwtgPJZKVJJJDCXRrutOSiQSAwVrCwQqSDkxEkNPmGqQhRJJISnSJxP = 'uvSDQDBGEBFqkyRNnnFRqDGyOSRloHfGRbJxMJwDrPOiuuwWyCAFrICEuDCMYvx'
if JSzCSDWEJIDymqnQSyOJOzgXouJzPzPGSpRfySPyCeBGPzfJEzQveMtJFuTonMq != uSouClnmuwtgPJZKVJJJDCXRrutOSiQSAwVrCwQqSDkxEkNPmGqQhRJJISnSJxP:
JSzCSDWEJIDymqnQSyOJOzgXouJzPzPGSpRfySPyCeBGPzfJEzQveMtJFuTonMq = 'uvSDQDBGEBFqkyRNnnFRqDGyOSRloHfGRbJxMJwDrPOiuuwWyCAFrICEuDCMYvx'
uSouClnmuwtgPJZKVJJJDCXRrutOSiQSAwVrCwQqSDkxEkNPmGqQhRJJISnSJxP = JSzCSDWEJIDymqnQSyOJOzgXouJzPzPGSpRfySPyCeBGPzfJEzQveMtJFuTonMq
JSzCSDWEJIDymqnQSyOJOzgXouJzPzPGSpRfySPyCeBGPzfJEzQveMtJFuTonMq = 'iwCpAIRHOhOClFOQABQPxSWmBGItrDODGntZwJEtHOJIpXOwxxErCJvPBQupwBw'
import struct
JBSLEPRAvQAFRizRBKCEYDppGJSJFHIpHyUIUoxAHIjtXItcSzJAzczwIJqHTts = 'KCtPJwmOCDoTVmwxGQJuYSqnFAHlOStzsByoVuQjPEvFutnAvQDzIPGGIQuESxz'
oiJoINSwBDtLHCAEDHYSHJvGvPPHecAEIEDAjqkqASOyyCHxzGDSSGwHvAxGEQw = 'iwBHjuJQUflzCAkSoRayDPpACdQFbhSQZDLNHJXYImoiGoIIhRYyPBFzXuohBIR'
if JBSLEPRAvQAFRizRBKCEYDppGJSJFHIpHyUIUoxAHIjtXItcSzJAzczwIJqHTts != oiJoINSwBDtLHCAEDHYSHJvGvPPHecAEIEDAjqkqASOyyCHxzGDSSGwHvAxGEQw:
JBSLEPRAvQAFRizRBKCEYDppGJSJFHIpHyUIUoxAHIjtXItcSzJAzczwIJqHTts = 'iwBHjuJQUflzCAkSoRayDPpACdQFbhSQZDLNHJXYImoiGoIIhRYyPBFzXuohBIR'
oiJoINSwBDtLHCAEDHYSHJvGvPPHecAEIEDAjqkqASOyyCHxzGDSSGwHvAxGEQw = JBSLEPRAvQAFRizRBKCEYDppGJSJFHIpHyUIUoxAHIjtXItcSzJAzczwIJqHTts
JBSLEPRAvQAFRizRBKCEYDppGJSJFHIpHyUIUoxAHIjtXItcSzJAzczwIJqHTts = 'KCtPJwmOCDoTVmwxGQJuYSqnFAHlOStzsByoVuQjPEvFutnAvQDzIPGGIQuESxz'
import sys
oqINBvBCuqZIDHFnVsSMPpFEEqAXzNHQPCtwytSUEvkkFboHPBGQrjAGnlOrHuw = 'RRwGSASPTBCzJQruFMNCqfqFaGQfRoluUnMYNmGnOGBSEgnrOvRmRBaStwvRCPi'
GdvXfIsyXwxJuIJSuCEFfXxztCqHDBDIDVjkSTWTXGJDxRRpESlASNsRLOorjRr = 'BFzDqIrFwIEFNqGCSuFBNzmDyrHFHlBGMCEZJzOvkPBqxjVEexyzDzNSOEqcytR'
wsXsrIQHjSeCRsHvFIFnAvvIuzRxOYLFiGUCzoRqDGOgHHGjDZHuGQePrrzRGtZ = 'RrWauTNuuqNFDSlReixvOEErSzQwDtzHgsUyUAqxkBjBUdMrHmAwDvxRQzEfGQN'
if oqINBvBCuqZIDHFnVsSMPpFEEqAXzNHQPCtwytSUEvkkFboHPBGQrjAGnlOrHuw == GdvXfIsyXwxJuIJSuCEFfXxztCqHDBDIDVjkSTWTXGJDxRRpESlASNsRLOorjRr:
wsXsrIQHjSeCRsHvFIFnAvvIuzRxOYLFiGUCzoRqDGOgHHGjDZHuGQePrrzRGtZ = 'RrWauTNuuqNFDSlReixvOEErSzQwDtzHgsUyUAqxkBjBUdMrHmAwDvxRQzEfGQN'
wsXsrIQHjSeCRsHvFIFnAvvIuzRxOYLFiGUCzoRqDGOgHHGjDZHuGQePrrzRGtZ = oqINBvBCuqZIDHFnVsSMPpFEEqAXzNHQPCtwytSUEvkkFboHPBGQrjAGnlOrHuw
else:
wsXsrIQHjSeCRsHvFIFnAvvIuzRxOYLFiGUCzoRqDGOgHHGjDZHuGQePrrzRGtZ = 'RrWauTNuuqNFDSlReixvOEErSzQwDtzHgsUyUAqxkBjBUdMrHmAwDvxRQzEfGQN'
wsXsrIQHjSeCRsHvFIFnAvvIuzRxOYLFiGUCzoRqDGOgHHGjDZHuGQePrrzRGtZ = 'RRwGSASPTBCzJQruFMNCqfqFaGQfRoluUnMYNmGnOGBSEgnrOvRmRBaStwvRCPi'
try:
uEESCwtPySzsERHPvJxvtIRNgVMytIPCJuWJHPADINRQHHwzYFIHzAPyIyBmEqJ = 'AxmxiumQAFHGgWRTPNOOCWSpzJHPSFtzPJRFnnPoMZERgJCluuIvyyEmRFGJoBP'
vRVMhtvCPyzPCJDOzTqQyHyFNXBGGoPYrAyNitCwCSfwkBSkDQCxnBvzDRpJYJn = 'sFByRmIuUpJDewYrbvHxnJIsEWCJEwRJCFIvHeqCzwezLECJqepuowzhuIvNSCu'
if uEESCwtPySzsERHPvJxvtIRNgVMytIPCJuWJHPADINRQHHwzYFIHzAPyIyBmEqJ != vRVMhtvCPyzPCJDOzTqQyHyFNXBGGoPYrAyNitCwCSfwkBSkDQCxnBvzDRpJYJn:
uEESCwtPySzsERHPvJxvtIRNgVMytIPCJuWJHPADINRQHHwzYFIHzAPyIyBmEqJ = 'sFByRmIuUpJDewYrbvHxnJIsEWCJEwRJCFIvHeqCzwezLECJqepuowzhuIvNSCu'
vRVMhtvCPyzPCJDOzTqQyHyFNXBGGoPYrAyNitCwCSfwkBSkDQCxnBvzDRpJYJn = uEESCwtPySzsERHPvJxvtIRNgVMytIPCJuWJHPADINRQHHwzYFIHzAPyIyBmEqJ
uEESCwtPySzsERHPvJxvtIRNgVMytIPCJuWJHPADINRQHHwzYFIHzAPyIyBmEqJ = 'AxmxiumQAFHGgWRTPNOOCWSpzJHPSFtzPJRFnnPoMZERgJCluuIvyyEmRFGJoBP'
from core.mlRSHnORJEINxGsriAJHTYHTPRECHHXCEJyGqrsOxjXFuEYyCypFDxmBgyzHeeR import uSIByGHznHHkkvwwPIVnIDFmmvPIHMRIyINSxzREPRIJQsuHBAIBMOtBQvIltFA, SPuftCNRrBmEOHCGpJsNAREOsyuxkCNDSBrSxGriSZOARHCTADxEyFHFPgOgFtg, vtMRvNIZLQjyJnDNOMACSDDwRCSJsDWOiDOCIESrasNPSuqDRsJHTwoEvItFRqw
CPRJjOxjNwADnDSNHHxlBNuGPzABjyBkyRsJSsABRRyBANDGRLutxtkXMRGuzeH = 'DXFYzLoSNrnpOIHygzGHvQxDUmRwCAfuFRHCpnoHCzDGBGJDSRJJGMItQFePIvd'
RPuXtRSMDSrklwCrDwHQyzqwiJHytDMAtBRCFGCpCEnOuCtoHYlbOSAEAJzhVhF = 'cxIEAGnVCYFEpCztxNhVwuwyTYCArNpuzAxvppCQIxECPJJSSozNRIrlORyYFxE'
EMJJQjNERWGwHNrZpxiZtJHpOtHAkWQHlDSvDwRxJsAompDjoJIGSEkJltFoGVf = 'tSIUjAARBvfnIdruXHANvSJRgteCDICAYGIIePByDHRDOzhkFCNpeGRDJyOiUCB'
pGPkNzMBHykwZqEEFBgIFysFjTuqySGroEMxNHDHQAAuzCgCSDAsJCstJSIoSmz = 'qGjSzLmgyoqsERyIREfXlqBeGPTHZQHgSySySRGDmNyLpIHDPYOmQSuPEIBCzpw'
JZWsPIIgkJwPhAZJQCWHSdCsJFzpQxpPBIlHkQIPJZAxqDSwzwIRZkFGZszxFDS = 'JMPjSiPGFEoBSIQFsiJYMRBSdGxpGUAFhFSyBAuRCErFyDItFBFOhESIEPNPJm'
QXkyOFRuQFrIGHvGBQIzJSQthCuGIxFZxsvGvJGHYmyAuDqAWAIQhNPPOZQyADD = 'JEEqpDtqMJGMFzNISmGVBFSTlyXSDIOMWGQEHIRuNQsPIfoJhsORFwpzRGYnHws'
if CPRJjOxjNwADnDSNHHxlBNuGPzABjyBkyRsJSsABRRyBANDGRLutxtkXMRGuzeH != pGPkNzMBHykwZqEEFBgIFysFjTuqySGroEMxNHDHQAAuzCgCSDAsJCstJSIoSmz:
RPuXtRSMDSrklwCrDwHQyzqwiJHytDMAtBRCFGCpCEnOuCtoHYlbOSAEAJzhVhF = EMJJQjNERWGwHNrZpxiZtJHpOtHAkWQHlDSvDwRxJsAompDjoJIGSEkJltFoGVf
for QXkyOFRuQFrIGHvGBQIzJSQthCuGIxFZxsvGvJGHYmyAuDqAWAIQhNPPOZQyADD in pGPkNzMBHykwZqEEFBgIFysFjTuqySGroEMxNHDHQAAuzCgCSDAsJCstJSIoSmz:
if QXkyOFRuQFrIGHvGBQIzJSQthCuGIxFZxsvGvJGHYmyAuDqAWAIQhNPPOZQyADD != EMJJQjNERWGwHNrZpxiZtJHpOtHAkWQHlDSvDwRxJsAompDjoJIGSEkJltFoGVf:
RPuXtRSMDSrklwCrDwHQyzqwiJHytDMAtBRCFGCpCEnOuCtoHYlbOSAEAJzhVhF = RPuXtRSMDSrklwCrDwHQyzqwiJHytDMAtBRCFGCpCEnOuCtoHYlbOSAEAJzhVhF
else:
JZWsPIIgkJwPhAZJQCWHSdCsJFzpQxpPBIlHkQIPJZAxqDSwzwIRZkFGZszxFDS = CPRJjOxjNwADnDSNHHxlBNuGPzABjyBkyRsJSsABRRyBANDGRLutxtkXMRGuzeH
else:
EMJJQjNERWGwHNrZpxiZtJHpOtHAkWQHlDSvDwRxJsAompDjoJIGSEkJltFoGVf = CPRJjOxjNwADnDSNHHxlBNuGPzABjyBkyRsJSsABRRyBANDGRLutxtkXMRGuzeH
CPRJjOxjNwADnDSNHHxlBNuGPzABjyBkyRsJSsABRRyBANDGRLutxtkXMRGuzeH = JZWsPIIgkJwPhAZJQCWHSdCsJFzpQxpPBIlHkQIPJZAxqDSwzwIRZkFGZszxFDS
if EMJJQjNERWGwHNrZpxiZtJHpOtHAkWQHlDSvDwRxJsAompDjoJIGSEkJltFoGVf == CPRJjOxjNwADnDSNHHxlBNuGPzABjyBkyRsJSsABRRyBANDGRLutxtkXMRGuzeH:
for QXkyOFRuQFrIGHvGBQIzJSQthCuGIxFZxsvGvJGHYmyAuDqAWAIQhNPPOZQyADD in CPRJjOxjNwADnDSNHHxlBNuGPzABjyBkyRsJSsABRRyBANDGRLutxtkXMRGuzeH:
if QXkyOFRuQFrIGHvGBQIzJSQthCuGIxFZxsvGvJGHYmyAuDqAWAIQhNPPOZQyADD == EMJJQjNERWGwHNrZpxiZtJHpOtHAkWQHlDSvDwRxJsAompDjoJIGSEkJltFoGVf:
EMJJQjNERWGwHNrZpxiZtJHpOtHAkWQHlDSvDwRxJsAompDjoJIGSEkJltFoGVf = CPRJjOxjNwADnDSNHHxlBNuGPzABjyBkyRsJSsABRRyBANDGRLutxtkXMRGuzeH
else:
EMJJQjNERWGwHNrZpxiZtJHpOtHAkWQHlDSvDwRxJsAompDjoJIGSEkJltFoGVf = JZWsPIIgkJwPhAZJQCWHSdCsJFzpQxpPBIlHkQIPJZAxqDSwzwIRZkFGZszxFDS
from core.GDBuGjtzDtAHlDJHwyDFJHNFQRIzdBRqORcFFxozwRRvCDdBHFDPIFSjpTysrjO import vIRwVSrRIoGDyAzHuoJjtNEGPQATAHFuAoCIjSvXYCAHcDGzOHiuVDIDtwoJGpy, kyvylzQylABvsJoPANuHBJzJofEFAOEIIiJpCEDRxoQNolMqHtFCHxSyBSrFSCC
try:
CvxCCiGFDyGRCOvjFJgXRoIFSJujyDlzpUEtYlgsyHIjRDpnkPHykeXzPsmUyG = 'DnQxrAWDuGnETunGOyqRePnHIyoISMxNBuVYEYJAHADXtIFvIOAnuntFMSdhwHw'
LHTnGFrzzCGQJIwASGlzRhmJZNnQkLFJyzBCoGuGsDwFHsSDFuzSERtuylQzinv = 'GFBCoZBzQWRzGJAZHrqRSDtXLUSuAvrtnrJGBZzwIAvrPryfWHGDHSXtVDwxpoE'
PAPSPmADIpzpvAwjRxDBnHPICnxQzjHQuFbgQSzHxmNyYGPmGLwIzEMQtFmjSXB = 'HORmRESSBFAoIpvuDgDwJWImDSCHSGmOTGvsIlyRBzCwFCgZPuPSDwoHjRPNrGy'
IJvzRrSRlCGoHDCvRACTJRoSrnYPFPVICiyzIxHJknxrPQouDJWyRneSIqGsBrR = 'jYBPkZBwSJlOTPUdRGhUJoEjEHHHJxuOzTHwuNTTxRmHsFoxBmnrOtJuuCPGYt'
psHblWwExvuJKJKqeuMpUSKpufHIHHzzBSrDBCMzFNUjYLUVuPkDEPMwQjGDQCR = 'JqEwTQmvptDCJTAtqAtqzMufBuAREFuFIBqpvuIvFArwLBIAuSrDpQQuiyLJBJt'
ExrrtXQmJnHEuAUSJPjFJfSyoRGGSRWyGExyKRIPuJuuBFCsVIHHUEQRgIRGOzE = 'vSjqPGJzXuFxPECGAAuxIivVryxzmNxzsQGttPODSzROIJXByPXDwDGIPkDGzGo'
AjRJAJMHzKxkUHQuBxQISHDsYyMSvOFBcEQjoxqAEGtmmyCsFEBlEMADyBviGxD = [
'DnQxrAWDuGnETunGOyqRePnHIyoISMxNBuVYEYJAHADXtIFvIOAnuntFMSdhwHw',
'HORmRESSBFAoIpvuDgDwJWImDSCHSGmOTGvsIlyRBzCwFCgZPuPSDwoHjRPNrGy',
'JqEwTQmvptDCJTAtqAtqzMufBuAREFuFIBqpvuIvFArwLBIAuSrDpQQuiyLJBJt',
'FFySBiaROmFYyHMGFkNTPgRPHkxYJSEpwkYjiIBEtAZrFIQvEAvhjxQFBDSJRMl'
]
for CvxCCiGFDyGRCOvjFJgXRoIFSJujyDlzpUEtYlgsyHIjRDpnkPHykeXzPsmUyG in ExrrtXQmJnHEuAUSJPjFJfSyoRGGSRWyGExyKRIPuJuuBFCsVIHHUEQRgIRGOzE:
for LHTnGFrzzCGQJIwASGlzRhmJZNnQkLFJyzBCoGuGsDwFHsSDFuzSERtuylQzinv in PAPSPmADIpzpvAwjRxDBnHPICnxQzjHQuFbgQSzHxmNyYGPmGLwIzEMQtFmjSXB:
if IJvzRrSRlCGoHDCvRACTJRoSrnYPFPVICiyzIxHJknxrPQouDJWyRneSIqGsBrR == psHblWwExvuJKJKqeuMpUSKpufHIHHzzBSrDBCMzFNUjYLUVuPkDEPMwQjGDQCR:
LHTnGFrzzCGQJIwASGlzRhmJZNnQkLFJyzBCoGuGsDwFHsSDFuzSERtuylQzinv = CvxCCiGFDyGRCOvjFJgXRoIFSJujyDlzpUEtYlgsyHIjRDpnkPHykeXzPsmUyG
elif psHblWwExvuJKJKqeuMpUSKpufHIHHzzBSrDBCMzFNUjYLUVuPkDEPMwQjGDQCR == LHTnGFrzzCGQJIwASGlzRhmJZNnQkLFJyzBCoGuGsDwFHsSDFuzSERtuylQzinv:
LHTnGFrzzCGQJIwASGlzRhmJZNnQkLFJyzBCoGuGsDwFHsSDFuzSERtuylQzinv = ExrrtXQmJnHEuAUSJPjFJfSyoRGGSRWyGExyKRIPuJuuBFCsVIHHUEQRgIRGOzE
else:
psHblWwExvuJKJKqeuMpUSKpufHIHHzzBSrDBCMzFNUjYLUVuPkDEPMwQjGDQCR = ExrrtXQmJnHEuAUSJPjFJfSyoRGGSRWyGExyKRIPuJuuBFCsVIHHUEQRgIRGOzE
for LHTnGFrzzCGQJIwASGlzRhmJZNnQkLFJyzBCoGuGsDwFHsSDFuzSERtuylQzinv in AjRJAJMHzKxkUHQuBxQISHDsYyMSvOFBcEQjoxqAEGtmmyCsFEBlEMADyBviGxD:
PAPSPmADIpzpvAwjRxDBnHPICnxQzjHQuFbgQSzHxmNyYGPmGLwIzEMQtFmjSXB = LHTnGFrzzCGQJIwASGlzRhmJZNnQkLFJyzBCoGuGsDwFHsSDFuzSERtuylQzinv
except Exception:
pass
from core.GEnMoqyyUuvxIDthAUEBVJIqDBEBXGAIHQeiUjsBHwSGDFHmysGkCyGRwQClFDE import GuHDlyvuyMYuBAOtBAoPLYEGnaoPxOQUqfGYkfnEGXzwIHOCMmuuwFjwmqQQFVu
CuPCCFSPRNErJQytGjGoCDsCyQCXwAvxoEtGDJzIQslAAwwJSAQwHBOERZRsjyt = 'mcBwPHgEzoQYEQGzAIDDNGuzGzujRQSJQASoRrmOwQDAxBWqkwFwEmSkCHnDknC'
hlHRIBjmtrDRADIDzPvJFCFhIiDASyCRCwAOqtytMAOBRSvyzqCfBIXuAIjWzsw = 'KBSliMqNRECCTNQsGOxkFnGtyIZTsxeToJMHhSBRPIPpZWERmRNppSSRYIIvqt'
if CuPCCFSPRNErJQytGjGoCDsCyQCXwAvxoEtGDJzIQslAAwwJSAQwHBOERZRsjyt != hlHRIBjmtrDRADIDzPvJFCFhIiDASyCRCwAOqtytMAOBRSvyzqCfBIXuAIjWzsw:
CuPCCFSPRNErJQytGjGoCDsCyQCXwAvxoEtGDJzIQslAAwwJSAQwHBOERZRsjyt = 'KBSliMqNRECCTNQsGOxkFnGtyIZTsxeToJMHhSBRPIPpZWERmRNppSSRYIIvqt'
hlHRIBjmtrDRADIDzPvJFCFhIiDASyCRCwAOqtytMAOBRSvyzqCfBIXuAIjWzsw = CuPCCFSPRNErJQytGjGoCDsCyQCXwAvxoEtGDJzIQslAAwwJSAQwHBOERZRsjyt
CuPCCFSPRNErJQytGjGoCDsCyQCXwAvxoEtGDJzIQslAAwwJSAQwHBOERZRsjyt = 'mcBwPHgEzoQYEQGzAIDDNGuzGzujRQSJQASoRrmOwQDAxBWqkwFwEmSkCHnDknC'
from core.xMPjIiIPERPCuRrUryvQHyYqqrRGnXzziqxujxkWElGFIQBpyPhzJCPRRQIHQw import HCEwqSIvzuVuOsqtHkJSBDxBGyvADGEjjJGIDlvrESExqlHvSQSszJEkDPJGOBQ
nyOxEJxDBPQJwMGiHzMDTwszQGFAFuJDmBGuSOJPQizGrJvHJGPoxIwCPGuIDji = 'IPGzINsnUSQMFwPIPpHeDazBsuRiRMWouOtSeeyySTrvIsRUtHiQYBGrIZkPrBx'
zeVHIhvOkDCRIHiRyjhSIRQRcmnADSXGYICpFyPjklyIJHzVqGStzICFOzQRzzE = 'BuvJRRKHUyHIxIxDGQvRpJCwnRRpItCbAsetFYIvvYsJIBzUAvHMIynnqDOGDRF'
FGyqEbELuzvTeFztxwDvwmDPtBpmBtDGGIRtGrBRBSEoSBJGNOECVItIOytyQfH = 'ArszGIACFSzvXmMHJQCPuHFyFCOTSFBZzHCkhjCNQEHMqDjyQoJaxSGxCHCCGlS'
HTEQmsDDCtyDxCuCAISEUHGpFzIDwSqOesfAEtzysVgBpJEMFpCDqHGkwnBwxB = 'kIlxMyOGZHTzgfyvSHBFqIsVIzJBjrAFEPITyIqZIZDoERTEjwTvFICBSBHGASP'
xHpizJHFQvvTJRQTSJPQxSHDGCtOJSvISsmPnFzYvGHHKNSIvsqSrRCSQotmkMN = 'yFayRNgJgvOHtzRFFSpFsGePwxMCGjJISQWDCrHERPhuwSWJCQJDZvMwAsPruHv'
if nyOxEJxDBPQJwMGiHzMDTwszQGFAFuJDmBGuSOJPQizGrJvHJGPoxIwCPGuIDji in zeVHIhvOkDCRIHiRyjhSIRQRcmnADSXGYICpFyPjklyIJHzVqGStzICFOzQRzzE:
nyOxEJxDBPQJwMGiHzMDTwszQGFAFuJDmBGuSOJPQizGrJvHJGPoxIwCPGuIDji = xHpizJHFQvvTJRQTSJPQxSHDGCtOJSvISsmPnFzYvGHHKNSIvsqSrRCSQotmkMN
if zeVHIhvOkDCRIHiRyjhSIRQRcmnADSXGYICpFyPjklyIJHzVqGStzICFOzQRzzE in FGyqEbELuzvTeFztxwDvwmDPtBpmBtDGGIRtGrBRBSEoSBJGNOECVItIOytyQfH:
zeVHIhvOkDCRIHiRyjhSIRQRcmnADSXGYICpFyPjklyIJHzVqGStzICFOzQRzzE = HTEQmsDDCtyDxCuCAISEUHGpFzIDwSqOesfAEtzysVgBpJEMFpCDqHGkwnBwxB
elif zeVHIhvOkDCRIHiRyjhSIRQRcmnADSXGYICpFyPjklyIJHzVqGStzICFOzQRzzE in nyOxEJxDBPQJwMGiHzMDTwszQGFAFuJDmBGuSOJPQizGrJvHJGPoxIwCPGuIDji:
FGyqEbELuzvTeFztxwDvwmDPtBpmBtDGGIRtGrBRBSEoSBJGNOECVItIOytyQfH = zeVHIhvOkDCRIHiRyjhSIRQRcmnADSXGYICpFyPjklyIJHzVqGStzICFOzQRzzE
if FGyqEbELuzvTeFztxwDvwmDPtBpmBtDGGIRtGrBRBSEoSBJGNOECVItIOytyQfH in zeVHIhvOkDCRIHiRyjhSIRQRcmnADSXGYICpFyPjklyIJHzVqGStzICFOzQRzzE:
zeVHIhvOkDCRIHiRyjhSIRQRcmnADSXGYICpFyPjklyIJHzVqGStzICFOzQRzzE = xHpizJHFQvvTJRQTSJPQxSHDGCtOJSvISsmPnFzYvGHHKNSIvsqSrRCSQotmkMN
from core.GUHQyNzYwxRHRrtOBrXpxJsOZwCyrSGeJTPwVlAmmpwxqPIASTSRPRISEGtuyIP import GuHDlyvuyMYuBAOtBAoPLYEGnaoPxOQUqfGYkfnEGXzwIHOCMmuuwFjwmqQQFVu
IOEvzClwyRBCBCQeJgtqCoVBPIvnFNNBiEODtqzqxlIBMZqamFEfRsiOXDHxtPw = 'UQsvBxSGqyPtYoEHCNmCJcaePvOCHPkMJvHEztSJguuJIIuXJwOhBYHCUCRZwSl'
SFuSAQSzrIZQJmyLOFFUhnyzIxEGsiHOANGQPtkuUwvPCDEGtJTqCPODHqGMnHI = 'xCzFNtvvRYiqwfDAtwvmSElAOtJCoFSETpGMIHEJrWPiJCOCtIuSOEJHgoCmrIv'
DvUuJFClABGCETRmVGFOIGpHJERjkzrYHSNEuAeTFGCGxBJylztDEvSBRzMxclL = 'CDkozkqNQAymGCEzzQSwvSHPQwOItyDUIHDewZsvIZwCDzGDYgtmIMJjsAqvHEf'
GtAFntGyQBJQzyRJoOACIDIiAIGtRyEwpwtnDypxAyvTEpuPAjYCNsCHIutDmUx = 'YPynqCIeRqPCCRCmuxoCrAnYBmEHsBBuHFOSSJHrQYvrrCzQJtevGRQCEIFOzBn'
if SFuSAQSzrIZQJmyLOFFUhnyzIxEGsiHOANGQPtkuUwvPCDEGtJTqCPODHqGMnHI == IOEvzClwyRBCBCQeJgtqCoVBPIvnFNNBiEODtqzqxlIBMZqamFEfRsiOXDHxtPw:
for IOEvzClwyRBCBCQeJgtqCoVBPIvnFNNBiEODtqzqxlIBMZqamFEfRsiOXDHxtPw in SFuSAQSzrIZQJmyLOFFUhnyzIxEGsiHOANGQPtkuUwvPCDEGtJTqCPODHqGMnHI:
if SFuSAQSzrIZQJmyLOFFUhnyzIxEGsiHOANGQPtkuUwvPCDEGtJTqCPODHqGMnHI == SFuSAQSzrIZQJmyLOFFUhnyzIxEGsiHOANGQPtkuUwvPCDEGtJTqCPODHqGMnHI:
DvUuJFClABGCETRmVGFOIGpHJERjkzrYHSNEuAeTFGCGxBJylztDEvSBRzMxclL = 'GtAFntGyQBJQzyRJoOACIDIiAIGtRyEwpwtnDypxAyvTEpuPAjYCNsCHIutDmUx'
elif DvUuJFClABGCETRmVGFOIGpHJERjkzrYHSNEuAeTFGCGxBJylztDEvSBRzMxclL == GtAFntGyQBJQzyRJoOACIDIiAIGtRyEwpwtnDypxAyvTEpuPAjYCNsCHIutDmUx:
GtAFntGyQBJQzyRJoOACIDIiAIGtRyEwpwtnDypxAyvTEpuPAjYCNsCHIutDmUx = IOEvzClwyRBCBCQeJgtqCoVBPIvnFNNBiEODtqzqxlIBMZqamFEfRsiOXDHxtPw
else:
IOEvzClwyRBCBCQeJgtqCoVBPIvnFNNBiEODtqzqxlIBMZqamFEfRsiOXDHxtPw = SFuSAQSzrIZQJmyLOFFUhnyzIxEGsiHOANGQPtkuUwvPCDEGtJTqCPODHqGMnHI
elif DvUuJFClABGCETRmVGFOIGpHJERjkzrYHSNEuAeTFGCGxBJylztDEvSBRzMxclL == DvUuJFClABGCETRmVGFOIGpHJERjkzrYHSNEuAeTFGCGxBJylztDEvSBRzMxclL:
for DvUuJFClABGCETRmVGFOIGpHJERjkzrYHSNEuAeTFGCGxBJylztDEvSBRzMxclL in SFuSAQSzrIZQJmyLOFFUhnyzIxEGsiHOANGQPtkuUwvPCDEGtJTqCPODHqGMnHI:
if GtAFntGyQBJQzyRJoOACIDIiAIGtRyEwpwtnDypxAyvTEpuPAjYCNsCHIutDmUx == SFuSAQSzrIZQJmyLOFFUhnyzIxEGsiHOANGQPtkuUwvPCDEGtJTqCPODHqGMnHI:
DvUuJFClABGCETRmVGFOIGpHJERjkzrYHSNEuAeTFGCGxBJylztDEvSBRzMxclL = 'GtAFntGyQBJQzyRJoOACIDIiAIGtRyEwpwtnDypxAyvTEpuPAjYCNsCHIutDmUx'
elif DvUuJFClABGCETRmVGFOIGpHJERjkzrYHSNEuAeTFGCGxBJylztDEvSBRzMxclL == GtAFntGyQBJQzyRJoOACIDIiAIGtRyEwpwtnDypxAyvTEpuPAjYCNsCHIutDmUx:
GtAFntGyQBJQzyRJoOACIDIiAIGtRyEwpwtnDypxAyvTEpuPAjYCNsCHIutDmUx = IOEvzClwyRBCBCQeJgtqCoVBPIvnFNNBiEODtqzqxlIBMZqamFEfRsiOXDHxtPw
else:
IOEvzClwyRBCBCQeJgtqCoVBPIvnFNNBiEODtqzqxlIBMZqamFEfRsiOXDHxtPw = SFuSAQSzrIZQJmyLOFFUhnyzIxEGsiHOANGQPtkuUwvPCDEGtJTqCPODHqGMnHI
for DvUuJFClABGCETRmVGFOIGpHJERjkzrYHSNEuAeTFGCGxBJylztDEvSBRzMxclL in SFuSAQSzrIZQJmyLOFFUhnyzIxEGsiHOANGQPtkuUwvPCDEGtJTqCPODHqGMnHI:
if GtAFntGyQBJQzyRJoOACIDIiAIGtRyEwpwtnDypxAyvTEpuPAjYCNsCHIutDmUx == SFuSAQSzrIZQJmyLOFFUhnyzIxEGsiHOANGQPtkuUwvPCDEGtJTqCPODHqGMnHI:
DvUuJFClABGCETRmVGFOIGpHJERjkzrYHSNEuAeTFGCGxBJylztDEvSBRzMxclL = 'GtAFntGyQBJQzyRJoOACIDIiAIGtRyEwpwtnDypxAyvTEpuPAjYCNsCHIutDmUx'
elif DvUuJFClABGCETRmVGFOIGpHJERjkzrYHSNEuAeTFGCGxBJylztDEvSBRzMxclL == GtAFntGyQBJQzyRJoOACIDIiAIGtRyEwpwtnDypxAyvTEpuPAjYCNsCHIutDmUx:
GtAFntGyQBJQzyRJoOACIDIiAIGtRyEwpwtnDypxAyvTEpuPAjYCNsCHIutDmUx = IOEvzClwyRBCBCQeJgtqCoVBPIvnFNNBiEODtqzqxlIBMZqamFEfRsiOXDHxtPw
else:
IOEvzClwyRBCBCQeJgtqCoVBPIvnFNNBiEODtqzqxlIBMZqamFEfRsiOXDHxtPw = GtAFntGyQBJQzyRJoOACIDIiAIGtRyEwpwtnDypxAyvTEpuPAjYCNsCHIutDmUx
else:
IOEvzClwyRBCBCQeJgtqCoVBPIvnFNNBiEODtqzqxlIBMZqamFEfRsiOXDHxtPw = SFuSAQSzrIZQJmyLOFFUhnyzIxEGsiHOANGQPtkuUwvPCDEGtJTqCPODHqGMnHI
from core.zMJIDSQBjssyEayGxDrxJGHzInFeSJvxzsGGSMSsCIHSEyiEAwPOFiAvyOoavMB import BwtroWhMwUAJNCIxRHzhDBTSCJpUovQxSozAwFoJQVyMQrvQGDCJOCEBlQCVMfA, GDksjjtSttQJGqJCSHBpJxAJSRDrJIDqHDEJwJyFDxQMvxxSnWJyzVqRauBigxx
try:
luiAasqRPAqFAbHHMGxcFsJHnkwIQLzvAkEGQBAOAsSgkuCONDExjJkGOtDtFDG = 'uHPeBmEEnIPGEceIAQOsDEFFtznzoDBISIIINyGjISPBLHGGxSHAsDVrByIHxyQ'
nDRswIKkBFHAEPyEYzSyySEumHZGoHGwGovJvsEvuuDYINjiFlRRPHEEzHESYFZ = 'JHnMUIJJzDAoOsXvEokGyCvBEtBIRXrCvBtISdhHHSNmprJrPPtnHxvlITPAjw'
OxzIIHESTuPjPQLyJDzqORFAOAQFUIqSPvQBEmxGsAXJrJSQVQGvPqzLEFxwxmX = 'OWgDxmBmrpzGvpHvuASSqtEbGywwmSktXzQGGIxnQQxgOmCmrtxzFDXCApEJLfN'
qESSVPqFIOtBHtxGJssCIKuAzRueqoawFHBzwuAiMvRICEBfQYRxxkGrSjAAsEr = 'RmBujycyGGlrBhsyFzTRmQjGSqENRSpTBsUIJCJvRDZvizFFztAxJRuPjvJoyPx'
vhRiCGyAmGwiOGMGVCMLDjvJzQqFzFCtICsDAXSjuxNJrQoaYCGAITtmfBHsEBx = 'QrzFAByGkCFESIqyAPInSSvGGKZvDIVIsHuJeDEIBqkwPBJBtwPGaADxRWCwRJz'
DxwIQPONOSIHtODEIYnvOSSErtyIEwDyvAprVwuSnDvCzsvHGICFJxTJBytwRWr = 'POSmSiAvABGOYUXHQCbIMkYFOEBXIHGyIOPPjJHeHWHUyyuMirDSGUGIBDJDJEA'
GSwRDztzstSzHrHFvMCEBvGGqCNgWwOJkQzSBHIGvxvSGHGBCtEPBhSGumyuCIx = [
'uHPeBmEEnIPGEceIAQOsDEFFtznzoDBISIIINyGjISPBLHGGxSHAsDVrByIHxyQ',
'OWgDxmBmrpzGvpHvuASSqtEbGywwmSktXzQGGIxnQQxgOmCmrtxzFDXCApEJLfN',
'QrzFAByGkCFESIqyAPInSSvGGKZvDIVIsHuJeDEIBqkwPBJBtwPGaADxRWCwRJz',
'uFFuUnxDtFEyzwmPMfXUvDksUDwQyqzwSITHJBRrFQjvvYRupIFEJeAORGnOBDB'
]
for luiAasqRPAqFAbHHMGxcFsJHnkwIQLzvAkEGQBAOAsSgkuCONDExjJkGOtDtFDG in DxwIQPONOSIHtODEIYnvOSSErtyIEwDyvAprVwuSnDvCzsvHGICFJxTJBytwRWr:
for nDRswIKkBFHAEPyEYzSyySEumHZGoHGwGovJvsEvuuDYINjiFlRRPHEEzHESYFZ in OxzIIHESTuPjPQLyJDzqORFAOAQFUIqSPvQBEmxGsAXJrJSQVQGvPqzLEFxwxmX:
if qESSVPqFIOtBHtxGJssCIKuAzRueqoawFHBzwuAiMvRICEBfQYRxxkGrSjAAsEr == vhRiCGyAmGwiOGMGVCMLDjvJzQqFzFCtICsDAXSjuxNJrQoaYCGAITtmfBHsEBx:
nDRswIKkBFHAEPyEYzSyySEumHZGoHGwGovJvsEvuuDYINjiFlRRPHEEzHESYFZ = luiAasqRPAqFAbHHMGxcFsJHnkwIQLzvAkEGQBAOAsSgkuCONDExjJkGOtDtFDG
elif vhRiCGyAmGwiOGMGVCMLDjvJzQqFzFCtICsDAXSjuxNJrQoaYCGAITtmfBHsEBx == nDRswIKkBFHAEPyEYzSyySEumHZGoHGwGovJvsEvuuDYINjiFlRRPHEEzHESYFZ:
nDRswIKkBFHAEPyEYzSyySEumHZGoHGwGovJvsEvuuDYINjiFlRRPHEEzHESYFZ = DxwIQPONOSIHtODEIYnvOSSErtyIEwDyvAprVwuSnDvCzsvHGICFJxTJBytwRWr
else:
vhRiCGyAmGwiOGMGVCMLDjvJzQqFzFCtICsDAXSjuxNJrQoaYCGAITtmfBHsEBx = DxwIQPONOSIHtODEIYnvOSSErtyIEwDyvAprVwuSnDvCzsvHGICFJxTJBytwRWr
for nDRswIKkBFHAEPyEYzSyySEumHZGoHGwGovJvsEvuuDYINjiFlRRPHEEzHESYFZ in GSwRDztzstSzHrHFvMCEBvGGqCNgWwOJkQzSBHIGvxvSGHGBCtEPBhSGumyuCIx:
OxzIIHESTuPjPQLyJDzqORFAOAQFUIqSPvQBEmxGsAXJrJSQVQGvPqzLEFxwxmX = nDRswIKkBFHAEPyEYzSyySEumHZGoHGwGovJvsEvuuDYINjiFlRRPHEEzHESYFZ
except Exception:
pass
except ImportError as GRIpvHwJmDsXvKISzSHxOSmrRBkeQuFxFGmJzDJTECGqsYCSyrolqxAeSyHYvuR:
vjOHArFGWySSwRfPAPrytxyjByRHGRCSWJxFzPtpOtTAZoHijyEDBQJOJqSFwkG = 'llzLJWoQkGzyxyEMTAiGFBrYPJVeyfSyyFHsxBnoulHsqHuwvwpLwUNGIJHPEtP'
vrClsRvNzGuStQvoRvWEHRRTJpCPPwHHXyEIJuCoQowFpzOxwSnmGuWqSpzoIJT = 'tGwOEAyvIEJRHGtGfQHRyBSIIwGNezvJFHZOFtuFIswBlzqRvCQrvRSAMpnttEQ'
GwZUHGWqGPzOyGqsyYGWMGHOWDvpJHqFISJOSQObRyJGBuyAJPzSsUqoOmQVFRP = 'BSILrwBEqFQkIJQnDAIZXFptJoRqySDEAJIjJxyIvASQcCGvycjCFJwwQuINzJD'
wIFvQYsJFSRvnvAzovVFrSuAeSuJEPRJzHItlxqUHDCgvuyXSxlvyzUPJYIwHLL = 'jHBSHySpBDFYENyuHEAEaSCAfqupwwueyqnrFBDhpJvENIHJFnBAsAHWTvQXhuR'
UPOuloDFFbszKzZzQXCwDjMmOyJSuJRQAnIIFBBpODrHmOFywqUFICXxOAqHIDE = 'sEJUWOIjxCAmPgznGsySvJGRMabExBodHSEpYAibNlHGtizyCzEvDySBMynDIPJ'
if vjOHArFGWySSwRfPAPrytxyjByRHGRCSWJxFzPtpOtTAZoHijyEDBQJOJqSFwkG in vrClsRvNzGuStQvoRvWEHRRTJpCPPwHHXyEIJuCoQowFpzOxwSnmGuWqSpzoIJT:
vjOHArFGWySSwRfPAPrytxyjByRHGRCSWJxFzPtpOtTAZoHijyEDBQJOJqSFwkG = UPOuloDFFbszKzZzQXCwDjMmOyJSuJRQAnIIFBBpODrHmOFywqUFICXxOAqHIDE
if vrClsRvNzGuStQvoRvWEHRRTJpCPPwHHXyEIJuCoQowFpzOxwSnmGuWqSpzoIJT in GwZUHGWqGPzOyGqsyYGWMGHOWDvpJHqFISJOSQObRyJGBuyAJPzSsUqoOmQVFRP:
vrClsRvNzGuStQvoRvWEHRRTJpCPPwHHXyEIJuCoQowFpzOxwSnmGuWqSpzoIJT = wIFvQYsJFSRvnvAzovVFrSuAeSuJEPRJzHItlxqUHDCgvuyXSxlvyzUPJYIwHLL
elif vrClsRvNzGuStQvoRvWEHRRTJpCPPwHHXyEIJuCoQowFpzOxwSnmGuWqSpzoIJT in vjOHArFGWySSwRfPAPrytxyjByRHGRCSWJxFzPtpOtTAZoHijyEDBQJOJqSFwkG:
GwZUHGWqGPzOyGqsyYGWMGHOWDvpJHqFISJOSQObRyJGBuyAJPzSsUqoOmQVFRP = vrClsRvNzGuStQvoRvWEHRRTJpCPPwHHXyEIJuCoQowFpzOxwSnmGuWqSpzoIJT
if GwZUHGWqGPzOyGqsyYGWMGHOWDvpJHqFISJOSQObRyJGBuyAJPzSsUqoOmQVFRP in vrClsRvNzGuStQvoRvWEHRRTJpCPPwHHXyEIJuCoQowFpzOxwSnmGuWqSpzoIJT:
vrClsRvNzGuStQvoRvWEHRRTJpCPPwHHXyEIJuCoQowFpzOxwSnmGuWqSpzoIJT = UPOuloDFFbszKzZzQXCwDjMmOyJSuJRQAnIIFBBpODrHmOFywqUFICXxOAqHIDE
print(GRIpvHwJmDsXvKISzSHxOSmrRBkeQuFxFGmJzDJTECGqsYCSyrolqxAeSyHYvuR)
sys.exit(0)
nwsJDSFGtvBSQFxsVSBNEBFkDIeEvfqNEHJStBDIEFuGcCSvRuFGFBOGTCtIxyJ = sys.platform
QGRQRwouEOazIEqAwGHEGHwzFzExIJODGtGpawWzmRHyEGEJiRkzAQCxCkGxxDO = 'GSFWuEAGpAwEklfuDJOHKOwEUzwHywxQAwGyjQIvErPvEwHzmzPBIoEywBPxDnD'
JFfEzwESmwwNVAGxznDvvHJvSFEkQVtJEzHBzJRuvqJuPyEGHeOIJqToJJqLxQD = 'GDhDBJJCweBDssXFLvAuESSEyxRyhkDwpCuXRSWJqnGvMTAYRsTGRhOIEuGxPSu'
CWMvSpGxICuPwuCEkNlXpAHRAQGYOEUrrJGzFkImNADEgzFEwCWAJzvzrIEiCmG = 'IvyEABRuRwIPLBAqCquBCHnHkkIBdYDmIrCDDTpPFAxAQltxusiTmuQERlPtCEz'
ASDwRzSXVHoyOyQFmSEeAASPDsyRGHfCSpSCEHuJVIwxgwIDSvESHnKMSQmxnOC = 'wGyHInFlnxGxXIhGZPEJSyPEJrDoNpVzANsPIyGrvGyHBGNJIAKBDBPZGHrrqHI'
xTyYtiMJtuREkELjQzyQRxJtOJQHOrQzCzxCIGnQxQhfFyGUDrUgEOHwApzYCJB = 'ErBvASsGScEAQreHlMJFQJlDDmqDGsJOrHwsJVQgXSXiQSFGPHTNMGpieymHoCy'
xRrHNlADCTwxyEEHGCStgInoJvHCwUBDSURxkYByvIICujnJWUIqRNHvRnzsqPW = 'vzNxwardqUroBkGVSRCupivMiFxsBzqSgSRvsrqGyyGDrGASQUGiJHCxClIJQqR'
if QGRQRwouEOazIEqAwGHEGHwzFzExIJODGtGpawWzmRHyEGEJiRkzAQCxCkGxxDO != ASDwRzSXVHoyOyQFmSEeAASPDsyRGHfCSpSCEHuJVIwxgwIDSvESHnKMSQmxnOC:
JFfEzwESmwwNVAGxznDvvHJvSFEkQVtJEzHBzJRuvqJuPyEGHeOIJqToJJqLxQD = CWMvSpGxICuPwuCEkNlXpAHRAQGYOEUrrJGzFkImNADEgzFEwCWAJzvzrIEiCmG
for xRrHNlADCTwxyEEHGCStgInoJvHCwUBDSURxkYByvIICujnJWUIqRNHvRnzsqPW in ASDwRzSXVHoyOyQFmSEeAASPDsyRGHfCSpSCEHuJVIwxgwIDSvESHnKMSQmxnOC:
if xRrHNlADCTwxyEEHGCStgInoJvHCwUBDSURxkYByvIICujnJWUIqRNHvRnzsqPW != CWMvSpGxICuPwuCEkNlXpAHRAQGYOEUrrJGzFkImNADEgzFEwCWAJzvzrIEiCmG:
JFfEzwESmwwNVAGxznDvvHJvSFEkQVtJEzHBzJRuvqJuPyEGHeOIJqToJJqLxQD = JFfEzwESmwwNVAGxznDvvHJvSFEkQVtJEzHBzJRuvqJuPyEGHeOIJqToJJqLxQD
else:
xTyYtiMJtuREkELjQzyQRxJtOJQHOrQzCzxCIGnQxQhfFyGUDrUgEOHwApzYCJB = QGRQRwouEOazIEqAwGHEGHwzFzExIJODGtGpawWzmRHyEGEJiRkzAQCxCkGxxDO
else:
CWMvSpGxICuPwuCEkNlXpAHRAQGYOEUrrJGzFkImNADEgzFEwCWAJzvzrIEiCmG = QGRQRwouEOazIEqAwGHEGHwzFzExIJODGtGpawWzmRHyEGEJiRkzAQCxCkGxxDO
QGRQRwouEOazIEqAwGHEGHwzFzExIJODGtGpawWzmRHyEGEJiRkzAQCxCkGxxDO = xTyYtiMJtuREkELjQzyQRxJtOJQHOrQzCzxCIGnQxQhfFyGUDrUgEOHwApzYCJB
if CWMvSpGxICuPwuCEkNlXpAHRAQGYOEUrrJGzFkImNADEgzFEwCWAJzvzrIEiCmG == QGRQRwouEOazIEqAwGHEGHwzFzExIJODGtGpawWzmRHyEGEJiRkzAQCxCkGxxDO:
for xRrHNlADCTwxyEEHGCStgInoJvHCwUBDSURxkYByvIICujnJWUIqRNHvRnzsqPW in QGRQRwouEOazIEqAwGHEGHwzFzExIJODGtGpawWzmRHyEGEJiRkzAQCxCkGxxDO:
if xRrHNlADCTwxyEEHGCStgInoJvHCwUBDSURxkYByvIICujnJWUIqRNHvRnzsqPW == CWMvSpGxICuPwuCEkNlXpAHRAQGYOEUrrJGzFkImNADEgzFEwCWAJzvzrIEiCmG:
CWMvSpGxICuPwuCEkNlXpAHRAQGYOEUrrJGzFkImNADEgzFEwCWAJzvzrIEiCmG = QGRQRwouEOazIEqAwGHEGHwzFzExIJODGtGpawWzmRHyEGEJiRkzAQCxCkGxxDO
else:
CWMvSpGxICuPwuCEkNlXpAHRAQGYOEUrrJGzFkImNADEgzFEwCWAJzvzrIEiCmG = xTyYtiMJtuREkELjQzyQRxJtOJQHOrQzCzxCIGnQxQhfFyGUDrUgEOHwApzYCJB
CIqEQCPyumOPExHuxRHSGBFHzROHQjEPDCrLBtwvGPEFEzpyGLZoeyqvzPRGAHC = 'localhost'
RwTBlAQJirnnuBSxAooWFAEHJqIIPSZPAyBIJrrBQAIrYgbGoBQSgDqRIyyktGG = 'XPnqyABHjboPmIHyJynQFQEExIGMwvRjrBBEtRlHklTFoIAyAPISuzEpJHFBvnZ'
uDYJBloFYIFrEqxyGnBvIxGRBzCxHIzNJZjorRRIyCJErBnJJsDPLFxjvSSJINX = 'hDmyQJfnfPLQsvHYrBwJHnBQKyQwRrQDYJJFEEiBAxrDSDTmBRLwjvDFAzstIzF'
hkRFsqzjsQOuFoqEGACBhIRmNoutXCrtwPwISRFlyHEEPQxCqOfAJHlEoGeFJFE = 'JQtPSGuYgrxqIvOGxCGGcFCrFgFPRpXRzuHowHPHCyzGxBHSIMtwrEDBIDZzBRu'
qOyyuJtJFGUoNFCQOJtCPgQUfQmCCGIqrHoRWAvwHRStusEexJBEuBHQIMsRRxm = 'pFRPrxMyBHwIsyexesMNaHoqyHPWonFpSBIAJGtDjFCFkAHESCHrHGuTiHsGgEB'
ujoNuzDUFyPSGsSGeIABHxIeyItFUUGyIZGJEwPIEPBHAAxSGiDGJEGOyRSIRhn = 'hSYYHPnSggRzIREQDQhRFEYQjAJEnIEBFtnJCRNwEJSnJRRvwGkBzXgcADdNkHO'
rFegFIBHlIoQRFXGsOEGxIDvDzwkHiEAJLRmCOpSECGOAQQLLQUvCUFzmkHRGJv = 'qCFzkFGQiBAYDJmvrHBCyRlrxlDwexJIPQtxRuIBFRDpIozeQIRDwHpJRxJIHmH'
if hkRFsqzjsQOuFoqEGACBhIRmNoutXCrtwPwISRFlyHEEPQxCqOfAJHlEoGeFJFE == qOyyuJtJFGUoNFCQOJtCPgQUfQmCCGIqrHoRWAvwHRStusEexJBEuBHQIMsRRxm:
for rFegFIBHlIoQRFXGsOEGxIDvDzwkHiEAJLRmCOpSECGOAQQLLQUvCUFzmkHRGJv in ujoNuzDUFyPSGsSGeIABHxIeyItFUUGyIZGJEwPIEPBHAAxSGiDGJEGOyRSIRhn:
if rFegFIBHlIoQRFXGsOEGxIDvDzwkHiEAJLRmCOpSECGOAQQLLQUvCUFzmkHRGJv == qOyyuJtJFGUoNFCQOJtCPgQUfQmCCGIqrHoRWAvwHRStusEexJBEuBHQIMsRRxm:
ujoNuzDUFyPSGsSGeIABHxIeyItFUUGyIZGJEwPIEPBHAAxSGiDGJEGOyRSIRhn = RwTBlAQJirnnuBSxAooWFAEHJqIIPSZPAyBIJrrBQAIrYgbGoBQSgDqRIyyktGG
else:
qOyyuJtJFGUoNFCQOJtCPgQUfQmCCGIqrHoRWAvwHRStusEexJBEuBHQIMsRRxm = uDYJBloFYIFrEqxyGnBvIxGRBzCxHIzNJZjorRRIyCJErBnJJsDPLFxjvSSJINX
fnQOIFPteJmyFvhQGBEIESHBAwyZCyBwBCEwIvrwpHikvJwsynfTFDREwwBAEGJ = 1337
CAYEmIEyItnVCAnEzIeMvvRJQBGEwShTGytHGiTiIXBxJBxHGIDEOuRwGgKlsJT = 'EJCytTKGBPTbFAJGLRviQRRRBBJeyJGzSnRRBzDFAYHAwDSEExxGBHXyQEHFyzw'
sHoFJJGxuIROSREMRoGoJWVUlGCJPwVuwGBInyqAsFIwFxzQlNRsFDNDyFSPIt = 'IOBSEEJLxPuJSpINpGHSqFuRGDWFHMbnJXDnGvMYGRymPTAGHHQQGNHsEhGozd'
ERknxPGwQWmUBlQSjpqQtyPuvIlxifwDtrBxDSjJqNnxACSXlysFnIsJXvAOJfs = 'PFzvzDsYHMIoQGSDBsPqGADmJxAQRysqovvzRzQkjZkZqRusPBMPzvDxEEyEvxn'
if CAYEmIEyItnVCAnEzIeMvvRJQBGEwShTGytHGiTiIXBxJBxHGIDEOuRwGgKlsJT == sHoFJJGxuIROSREMRoGoJWVUlGCJPwVuwGBInyqAsFIwFxzQlNRsFDNDyFSPIt:
ERknxPGwQWmUBlQSjpqQtyPuvIlxifwDtrBxDSjJqNnxACSXlysFnIsJXvAOJfs = 'PFzvzDsYHMIoQGSDBsPqGADmJxAQRysqovvzRzQkjZkZqRusPBMPzvDxEEyEvxn'
ERknxPGwQWmUBlQSjpqQtyPuvIlxifwDtrBxDSjJqNnxACSXlysFnIsJXvAOJfs = CAYEmIEyItnVCAnEzIeMvvRJQBGEwShTGytHGiTiIXBxJBxHGIDEOuRwGgKlsJT
else:
ERknxPGwQWmUBlQSjpqQtyPuvIlxifwDtrBxDSjJqNnxACSXlysFnIsJXvAOJfs = 'PFzvzDsYHMIoQGSDBsPqGADmJxAQRysqovvzRzQkjZkZqRusPBMPzvDxEEyEvxn'
ERknxPGwQWmUBlQSjpqQtyPuvIlxifwDtrBxDSjJqNnxACSXlysFnIsJXvAOJfs = 'EJCytTKGBPTbFAJGLRviQRRRBBJeyJGzSnRRBzDFAYHAwDSEExxGBHXyQEHFyzw'
CFRGQCPCzURSYHUgHlEFsBSnFqsWRJFJCLuvAijkNAfyFExtqWWAAuIDRGEQPsj = 'b14ce95fa4c33ac2803782d18341869f'
try:
ymVTyNXPvwmnSGzusCGFwERDIlGPFQVISdvHuYRMJJQOoDIImntGCHPElvRpsYw = 'cOLwQFnGgBRAQmBIPQFyLNqcYIIWTZbfGYvOoPkDBRmORsGSCGwHHELzWETyyxG'
CFrxCBjHxSvGFuxPiAHElExIFPJFCEGzJHsmEATzyarDSIcZSNPHmRvPIyOuGCG = 'CElERpyFSPuaRGkrCjStJgQQQtuoPCjCCSMrGmjFQZSGICVpBJVDHCqyOEDCOQE'
QMVRDGssAEEYnPztpmEGJSBiAxpyBSDteQYBNQBDwHZnqRHkiGPEpJHzHQRaxT = 'GFJYPDSPICOxBUooJuEwvvHokDvDvgRFgsJGJBmyJkPJRFRIGCRgxtGHBhIQzIU'
wwGopDIoJRByVEMiBJHPvnkAHoRFHAZotOCEfxJBInxNRARJCUIXNoHlHrjRtyk = 'JBWBwJzDRqsEHuAqepJGIxDHZsPDPRpXiFHDBnHBsvzJPRHVSJAHEzsqJPQHQBx'
QtevNgSQHCJECEmGvmqaAJzNIukRvoqSFoSGvszXItRSxKBnFpoHFIDiGBHJNyr = 'AQFwRCxOQyDzBRPCJwJtquVQkIywjwJpCDugfVQwCSAzwvlDpCtynDBukAIUFyQ'
zluSGBHADmBuCxAyxMDBRHspUlHDmJuGPGpCSOEoGBQGeISLwsQvrPlBzzGuFgn = 'upyQIBBVSAnJraVqYzWvJGygDAuQkRVOIumwjGBHHmSJjAwHsqvRRICoMoRSzDi'
FXNwZiyJWIesHsmiHJzjGDQwXHvGDEQwQEoRtHPDmsQysXxHGOtOFggNrYVwIus = [
'cOLwQFnGgBRAQmBIPQFyLNqcYIIWTZbfGYvOoPkDBRmORsGSCGwHHELzWETyyxG',
'GFJYPDSPICOxBUooJuEwvvHokDvDvgRFgsJGJBmyJkPJRFRIGCRgxtGHBhIQzIU',
'AQFwRCxOQyDzBRPCJwJtquVQkIywjwJpCDugfVQwCSAzwvlDpCtynDBukAIUFyQ',
'SztutxvJpQPSSAtDNrxDfTwWvHESskJxAhJBIeFJOCODlGoQFxPADJRzUMwmDyr'
]
for ymVTyNXPvwmnSGzusCGFwERDIlGPFQVISdvHuYRMJJQOoDIImntGCHPElvRpsYw in zluSGBHADmBuCxAyxMDBRHspUlHDmJuGPGpCSOEoGBQGeISLwsQvrPlBzzGuFgn:
for CFrxCBjHxSvGFuxPiAHElExIFPJFCEGzJHsmEATzyarDSIcZSNPHmRvPIyOuGCG in QMVRDGssAEEYnPztpmEGJSBiAxpyBSDteQYBNQBDwHZnqRHkiGPEpJHzHQRaxT:
if wwGopDIoJRByVEMiBJHPvnkAHoRFHAZotOCEfxJBInxNRARJCUIXNoHlHrjRtyk == QtevNgSQHCJECEmGvmqaAJzNIukRvoqSFoSGvszXItRSxKBnFpoHFIDiGBHJNyr:
CFrxCBjHxSvGFuxPiAHElExIFPJFCEGzJHsmEATzyarDSIcZSNPHmRvPIyOuGCG = ymVTyNXPvwmnSGzusCGFwERDIlGPFQVISdvHuYRMJJQOoDIImntGCHPElvRpsYw
elif QtevNgSQHCJECEmGvmqaAJzNIukRvoqSFoSGvszXItRSxKBnFpoHFIDiGBHJNyr == CFrxCBjHxSvGFuxPiAHElExIFPJFCEGzJHsmEATzyarDSIcZSNPHmRvPIyOuGCG:
CFrxCBjHxSvGFuxPiAHElExIFPJFCEGzJHsmEATzyarDSIcZSNPHmRvPIyOuGCG = zluSGBHADmBuCxAyxMDBRHspUlHDmJuGPGpCSOEoGBQGeISLwsQvrPlBzzGuFgn
else:
QtevNgSQHCJECEmGvmqaAJzNIukRvoqSFoSGvszXItRSxKBnFpoHFIDiGBHJNyr = zluSGBHADmBuCxAyxMDBRHspUlHDmJuGPGpCSOEoGBQGeISLwsQvrPlBzzGuFgn
for CFrxCBjHxSvGFuxPiAHElExIFPJFCEGzJHsmEATzyarDSIcZSNPHmRvPIyOuGCG in FXNwZiyJWIesHsmiHJzjGDQwXHvGDEQwQEoRtHPDmsQysXxHGOtOFggNrYVwIus:
QMVRDGssAEEYnPztpmEGJSBiAxpyBSDteQYBNQBDwHZnqRHkiGPEpJHzHQRaxT = CFrxCBjHxSvGFuxPiAHElExIFPJFCEGzJHsmEATzyarDSIcZSNPHmRvPIyOuGCG
except Exception:
pass
if __name__ == '__main__':
QCWfQifxmjwHNkqhvnExHEuymnGYqHFSODFpZFQSkhpQDLDGxGEBEAyNwEivoSJ = 'BiXAsAGtySJhFpAMxEfvysvhDOHCIyqIGlSAQPJOJoCzzYrZAmxtmwBAHRzzqpE'
vGtHHqwmpzSJANPxFEpEUILStsIWvxEvILswrUQCnEHAHHEhXEyyUtrRjxxXxsx = 'oyFlgGRGBBzPuPHhPFACIQmEyDmjiGPXGqvPArPJsOlGQxSeuTvuJCCDmxAgIBu'
jHsJgDHRLRSurHIuGRvDCEtIPjxOOIOYxRyqwOHrqQiSFBGDOFtjIIBCVyGCMRC = 'tDIjJGFQBIIIIkMGysIIxQJyHiSxgEqqVxJJmAHErtuSQIsyzoHRlAvUzSBJpHp'
tFUPHnwlINDuyCIAzmPCJpAgRrbDCIiBBRIQFPSAFIBWOIQzuUzHSuvvCeGJqRA = 'JHsFpPPFvIwYxPxSJVGAZrpxCFEPZWJudnPFBulvYDDiRHPGquEBPPsEOFQtDOx'
gvEjBJHSxOCFwSJUJGBHfRFmxIPtUzquDkAyxwRRYovHJxtDDJAaqyRFzNODSMS = 'JFAXHSRHPQEuQZHBXAHBXjAxHlzTwJEHEzlExSGfxGSlsdFyFwHYzymSNttHouI'
if QCWfQifxmjwHNkqhvnExHEuymnGYqHFSODFpZFQSkhpQDLDGxGEBEAyNwEivoSJ in vGtHHqwmpzSJANPxFEpEUILStsIWvxEvILswrUQCnEHAHHEhXEyyUtrRjxxXxsx:
QCWfQifxmjwHNkqhvnExHEuymnGYqHFSODFpZFQSkhpQDLDGxGEBEAyNwEivoSJ = gvEjBJHSxOCFwSJUJGBHfRFmxIPtUzquDkAyxwRRYovHJxtDDJAaqyRFzNODSMS
if vGtHHqwmpzSJANPxFEpEUILStsIWvxEvILswrUQCnEHAHHEhXEyyUtrRjxxXxsx in jHsJgDHRLRSurHIuGRvDCEtIPjxOOIOYxRyqwOHrqQiSFBGDOFtjIIBCVyGCMRC:
vGtHHqwmpzSJANPxFEpEUILStsIWvxEvILswrUQCnEHAHHEhXEyyUtrRjxxXxsx = tFUPHnwlINDuyCIAzmPCJpAgRrbDCIiBBRIQFPSAFIBWOIQzuUzHSuvvCeGJqRA
elif vGtHHqwmpzSJANPxFEpEUILStsIWvxEvILswrUQCnEHAHHEhXEyyUtrRjxxXxsx in QCWfQifxmjwHNkqhvnExHEuymnGYqHFSODFpZFQSkhpQDLDGxGEBEAyNwEivoSJ:
jHsJgDHRLRSurHIuGRvDCEtIPjxOOIOYxRyqwOHrqQiSFBGDOFtjIIBCVyGCMRC = vGtHHqwmpzSJANPxFEpEUILStsIWvxEvILswrUQCnEHAHHEhXEyyUtrRjxxXxsx
if jHsJgDHRLRSurHIuGRvDCEtIPjxOOIOYxRyqwOHrqQiSFBGDOFtjIIBCVyGCMRC in vGtHHqwmpzSJANPxFEpEUILStsIWvxEvILswrUQCnEHAHHEhXEyyUtrRjxxXxsx:
vGtHHqwmpzSJANPxFEpEUILStsIWvxEvILswrUQCnEHAHHEhXEyyUtrRjxxXxsx = gvEjBJHSxOCFwSJUJGBHfRFmxIPtUzquDkAyxwRRYovHJxtDDJAaqyRFzNODSMS
rAFBSHNfNqQlRskEEOBCJCBrinBnGFJIUAEGRrJSOzbMDFRJPuteotGtOqQIFRF()
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# -*- coding: utf-8 -*-
"""
Created on Mon Jun 28 10:55:12 2021
@author: mikf
"""
import numpy as np
from py_wake.examples.data.ParqueFicticio import ParqueFicticio_path
from py_wake.site import WaspGridSite
from py_wake.site.xrsite import XRSite
x = np.asarray([262403., 262553., 262703., 262853., 263003., 263153., 263303.,
263453., 263603., 263753., 263903., 264053., 264203., 264353.,
264503., 264653., 264803., 264953., 265103., 265253.])
y = np.asarray([6504239., 6504389., 6504539., 6504689., 6504839., 6504989.,
6505139., 6505289., 6505439., 6505589., 6505739., 6505889.,
6506039., 6506189., 6506339., 6506489., 6506639., 6506789.,
6506939., 6507089.])
wt_x = np.asarray([264904, 264372, 263839, 264904, 264372, 263839, 263306,
264638, 264105, 263572, 263039, 264372, 263839, 263039, 264358,
263839, 263039, 263839, 263306, 262773, 263306, 262773, 263039])
wt_y = np.asarray([6505613, 6505016, 6504420, 6506063, 6505467, 6504870,
6504273, 6506215, 6505619, 6505022, 6504425, 6506368, 6505771,
6504876, 6506803, 6506221, 6505326, 6506672, 6506075, 6505478,
6506525, 6505929, 6506677])
x_min_d = x.min()
x_max_d = x.max()
y_min_d = y.min()
y_max_d = y.max()
boundary = np.asarray([[x_min_d, y_max_d], [x_max_d, y_max_d],
[x_max_d, y_min_d], [x_min_d, y_min_d]])
if __name__ == '__main__':
site = ParqueFicticioOffshore()
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# -*- coding: utf-8 -*-
"""Neural Style Pattern Transfer.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1ijYjSvGfWm1aUkw0stn6P7U8pYwhsbU0
"""
#!nvcc --version
print("Your GPU is a ")
!nvidia-smi -L
print("GPU Logs")
print("Nvidia K80 is not enough to do past A5 for high res inputs and A7 for low res inputs")
print("Nvidia P100 will do an A8 in around 23 minutes")
from psutil import virtual_memory
ram_gb = virtual_memory().total / 1e9
print('Your runtime has {:.1f} gigabytes of available RAM\n'.format(ram_gb))
if ram_gb < 20:
print('Not using a high-RAM runtime')
else:
print('You are using a high-RAM runtime!')
from google.colab import drive
drive.mount('/content/gdrive')
!git clone https://github.com/ProGamerGov/neural-style-pt.git
!cp -ri "/content/gdrive/My Drive/NSPT/checkpoints/channel_pruning.pth" /content/neural-style-pt/models/
!cp -ri "/content/gdrive/My Drive/NSPT/checkpoints/nin_imagenet.pth" /content/neural-style-pt/models/
!cp -ri "/content/gdrive/My Drive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth" /content/neural-style-pt/models/
!cp -ri "/content/gdrive/My Drive/NSPT/checkpoints/vgg16-00b39a1b.pth" /content/neural-style-pt/models/
!cp -ri "/content/gdrive/My Drive/NSPT/checkpoints/vgg19-d01eb7cb.pth" /content/neural-style-pt/models/
!pip install torch==1.7.1+cu101 torchvision==0.8.2+cu101 torchaudio===0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
#---------#
# STYLE 1 #
#---------#
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style1' -style_weight 1000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input1.png' -init random -learning_rate 1 -print_iter 50 -save_iter 250 -image_size 512 -num_iterations 1000 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -style_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -optimizer lbfgs -output_image '/content/gdrive/MyDrive/NSPT/output/1A1.png' -tv_weight 0.00001 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style1' -style_weight 1000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input1.png' -content_weight 5 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/1A1.png' -print_iter 50 -save_iter 250 -image_size 768 -num_iterations 600 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -style_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -optimizer lbfgs -output_imag '/content/gdrive/MyDrive/NSPT/output/1A2.png' -tv_weight 0 -gpu 0 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style1' -style_weight 1000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input1.png' -content_weight 5 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/1A2.png' -print_iter 50 -save_iter 250 -image_size 1024 -num_iterations 1000 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -style_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -optimizer lbfgs -output_image '/content/gdrive/MyDrive/NSPT/output/1A3.png' -tv_weight 0.00001 -gpu 0 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style1' -style_weight 40000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input1.png' -content_weight 5 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/1A3.png' -print_iter 50 -save_iter 250 -image_size 1400 -num_iterations 1000 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -style_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -optimizer lbfgs -output_image '/content/gdrive/MyDrive/NSPT/output/1A4.png' -tv_weight 0 -gpu 0 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style1' -style_weight 80000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input1.png' -content_weight 5 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/1A4.png' -print_iter 50 -save_iter 250 -image_size 1400 -num_iterations 1000 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu1_2,relu2_1,relu2_2,relu3_1,relu3_2,relu3_3,relu3_4,relu4_1,relu4_2,relu4_3,relu4_4,relu5_1,relu5_2,relu5_3,relu5_4 -style_layers relu1_1,relu1_2,relu2_1,relu2_2,relu3_1,relu3_2,relu3_3,relu3_4,relu4_1,relu4_2,relu4_3,relu4_4,relu5_1,relu5_2,relu5_3,relu5_4 -optimizer lbfgs -output_image '/content/gdrive/MyDrive/NSPT/output/1A5.png' -tv_weight 0 -gpu 0 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style1' -style_weight 40000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input1.png' -content_weight 0 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/1A5.png' -print_iter 50 -save_iter 100 -image_size 1800 -num_iterations 200 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu1_2 -style_layers relu1_1,relu1_2 -optimizer lbfgs -output_image '/content/gdrive/MyDrive/NSPT/output/1A6.png' -tv_weight 0 -gpu 0 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style1' -style_weight 40000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input1.png' -content_weight 15 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/1A6.png' -learning_rate 1 -print_iter 50 -save_iter 100 -image_size 3600 -num_iterations 200 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/channel_pruning.pth' -optimizer adam -output_image '/content/gdrive/MyDrive/NSPT/output/Final_1.png' -tv_weight 0 -original_colors 0 -backend cudnn
#Optional - 80MB+ 7200x7200 image (but causes grain in flat colour areas)
#!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style1' -style_weight 1500 -style_scale 0.5 -content_image '/content/gdrive/MyDrive/NSPT/input/input1.png' -content_weight 0 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/Final_1.png' -learning_rate 1 -print_iter 50 -save_iter 0 -image_size 7200 -num_iterations 10 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nin_imagenet.pth' -content_layers relu0,relu1 -style_layers relu0,relu1 -optimizer adam -output_image '/content/gdrive/MyDrive/NSPT/output/A8-FINAL-1.png' -tv_weight 0 -original_colors 0 -backend cudnn
#---------#
# STYLE 2 #
#---------#
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style2' -style_weight 1000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input2.png' -init random -learning_rate 1 -print_iter 50 -save_iter 250 -image_size 512 -num_iterations 1000 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -style_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -optimizer lbfgs -output_image '/content/gdrive/MyDrive/NSPT/output/2A1.png' -tv_weight 0.00001 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style2' -style_weight 1000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input2.png' -content_weight 5 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/2A1.png' -print_iter 50 -save_iter 250 -image_size 768 -num_iterations 600 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -style_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -optimizer lbfgs -output_imag '/content/gdrive/MyDrive/NSPT/output/2A2.png' -tv_weight 0 -gpu 0 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style2' -style_weight 1000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input2.png' -content_weight 5 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/2A2.png' -print_iter 50 -save_iter 250 -image_size 1024 -num_iterations 1000 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -style_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -optimizer lbfgs -output_image '/content/gdrive/MyDrive/NSPT/output/2A3.png' -tv_weight 0.00001 -gpu 0 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style2' -style_weight 40000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input2.png' -content_weight 5 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/2A3.png' -print_iter 50 -save_iter 250 -image_size 1400 -num_iterations 1000 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -style_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -optimizer lbfgs -output_image '/content/gdrive/MyDrive/NSPT/output/2A4.png' -tv_weight 0 -gpu 0 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style2' -style_weight 80000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input2.png' -content_weight 5 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/2A4.png' -print_iter 50 -save_iter 250 -image_size 1400 -num_iterations 1000 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu1_2,relu2_1,relu2_2,relu3_1,relu3_2,relu3_3,relu3_4,relu4_1,relu4_2,relu4_3,relu4_4,relu5_1,relu5_2,relu5_3,relu5_4 -style_layers relu1_1,relu1_2,relu2_1,relu2_2,relu3_1,relu3_2,relu3_3,relu3_4,relu4_1,relu4_2,relu4_3,relu4_4,relu5_1,relu5_2,relu5_3,relu5_4 -optimizer lbfgs -output_image '/content/gdrive/MyDrive/NSPT/output/2A5.png' -tv_weight 0 -gpu 0 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style2' -style_weight 40000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input2.png' -content_weight 0 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/2A5.png' -print_iter 50 -save_iter 100 -image_size 1800 -num_iterations 200 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu1_2 -style_layers relu1_1,relu1_2 -optimizer lbfgs -output_image '/content/gdrive/MyDrive/NSPT/output/2A6.png' -tv_weight 0 -gpu 0 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style2' -style_weight 40000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input2.png' -content_weight 15 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/2A6.png' -learning_rate 1 -print_iter 50 -save_iter 100 -image_size 3600 -num_iterations 200 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/channel_pruning.pth' -optimizer adam -output_image '/content/gdrive/MyDrive/NSPT/output/Final_2.png' -tv_weight 0 -original_colors 0 -backend cudnn
#Optional - 80MB+ 7200x7200 image (but causes grain in flat colour areas)
#!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style2' -style_weight 1500 -style_scale 0.5 -content_image '/content/gdrive/MyDrive/NSPT/input/input2.png' -content_weight 0 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/Final_2.png' -learning_rate 1 -print_iter 50 -save_iter 0 -image_size 7200 -num_iterations 10 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nin_imagenet.pth' -content_layers relu0,relu1 -style_layers relu0,relu1 -optimizer adam -output_image '/content/gdrive/MyDrive/NSPT/output/A8-FINAL-2.png' -tv_weight 0 -original_colors 0 -backend cudnn
#---------#
# STYLE 3 #
#---------#
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style3' -style_weight 1000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input3.png' -init random -learning_rate 1 -print_iter 50 -save_iter 250 -image_size 512 -num_iterations 1000 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -style_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -optimizer lbfgs -output_image '/content/gdrive/MyDrive/NSPT/output/3A1.png' -tv_weight 0.00001 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style3' -style_weight 1000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input3.png' -content_weight 5 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/3A1.png' -print_iter 50 -save_iter 250 -image_size 768 -num_iterations 600 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -style_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -optimizer lbfgs -output_imag '/content/gdrive/MyDrive/NSPT/output/3A2.png' -tv_weight 0 -gpu 0 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style3' -style_weight 1000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input3.png' -content_weight 5 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/3A2.png' -print_iter 50 -save_iter 250 -image_size 1024 -num_iterations 1000 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -style_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -optimizer lbfgs -output_image '/content/gdrive/MyDrive/NSPT/output/3A3.png' -tv_weight 0.00001 -gpu 0 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style3' -style_weight 40000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input3.png' -content_weight 5 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/3A3.png' -print_iter 50 -save_iter 250 -image_size 1400 -num_iterations 1000 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -style_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -optimizer lbfgs -output_image '/content/gdrive/MyDrive/NSPT/output/3A4.png' -tv_weight 0 -gpu 0 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style3' -style_weight 80000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input3.png' -content_weight 5 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/3A4.png' -print_iter 50 -save_iter 250 -image_size 1400 -num_iterations 1000 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu1_2,relu2_1,relu2_2,relu3_1,relu3_2,relu3_3,relu3_4,relu4_1,relu4_2,relu4_3,relu4_4,relu5_1,relu5_2,relu5_3,relu5_4 -style_layers relu1_1,relu1_2,relu2_1,relu2_2,relu3_1,relu3_2,relu3_3,relu3_4,relu4_1,relu4_2,relu4_3,relu4_4,relu5_1,relu5_2,relu5_3,relu5_4 -optimizer lbfgs -output_image '/content/gdrive/MyDrive/NSPT/output/3A5.png' -tv_weight 0 -gpu 0 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style3' -style_weight 40000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input3.png' -content_weight 0 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/3A5.png' -print_iter 50 -save_iter 100 -image_size 1800 -num_iterations 200 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu1_2 -style_layers relu1_1,relu1_2 -optimizer lbfgs -output_image '/content/gdrive/MyDrive/NSPT/output/3A6.png' -tv_weight 0 -gpu 0 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style3' -style_weight 40000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input3.png' -content_weight 15 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/3A6.png' -learning_rate 1 -print_iter 50 -save_iter 100 -image_size 3600 -num_iterations 200 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/channel_pruning.pth' -optimizer adam -output_image '/content/gdrive/MyDrive/NSPT/output/Final_3.png' -tv_weight 0 -original_colors 0 -backend cudnn
#Optional - 80MB+ 7200x7200 image (but causes grain in flat colour areas)
#!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style3' -style_weight 1500 -style_scale 0.5 -content_image '/content/gdrive/MyDrive/NSPT/input/input3.png' -content_weight 0 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/Final_3.png' -learning_rate 1 -print_iter 50 -save_iter 0 -image_size 7200 -num_iterations 10 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nin_imagenet.pth' -content_layers relu0,relu1 -style_layers relu0,relu1 -optimizer adam -output_image '/content/gdrive/MyDrive/NSPT/output/A8-FINAL-3.png' -tv_weight 0 -original_colors 0 -backend cudnn
#---------#
# STYLE 4 #
#---------#
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style4' -style_weight 1000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input4.png' -init random -learning_rate 1 -print_iter 50 -save_iter 250 -image_size 512 -num_iterations 1000 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -style_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -optimizer lbfgs -output_image '/content/gdrive/MyDrive/NSPT/output/4A1.png' -tv_weight 0.00001 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style4' -style_weight 1000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input4.png' -content_weight 5 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/4A1.png' -print_iter 50 -save_iter 250 -image_size 768 -num_iterations 600 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -style_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -optimizer lbfgs -output_imag '/content/gdrive/MyDrive/NSPT/output/4A2.png' -tv_weight 0 -gpu 0 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style4' -style_weight 1000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input4.png' -content_weight 5 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/4A2.png' -print_iter 50 -save_iter 250 -image_size 1024 -num_iterations 1000 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -style_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -optimizer lbfgs -output_image '/content/gdrive/MyDrive/NSPT/output/4A3.png' -tv_weight 0.00001 -gpu 0 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style4' -style_weight 40000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input4.png' -content_weight 5 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/4A3.png' -print_iter 50 -save_iter 250 -image_size 1400 -num_iterations 1000 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -style_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -optimizer lbfgs -output_image '/content/gdrive/MyDrive/NSPT/output/4A4.png' -tv_weight 0 -gpu 0 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style4' -style_weight 80000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input4.png' -content_weight 5 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/4A4.png' -print_iter 50 -save_iter 250 -image_size 1400 -num_iterations 1000 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu1_2,relu2_1,relu2_2,relu3_1,relu3_2,relu3_3,relu3_4,relu4_1,relu4_2,relu4_3,relu4_4,relu5_1,relu5_2,relu5_3,relu5_4 -style_layers relu1_1,relu1_2,relu2_1,relu2_2,relu3_1,relu3_2,relu3_3,relu3_4,relu4_1,relu4_2,relu4_3,relu4_4,relu5_1,relu5_2,relu5_3,relu5_4 -optimizer lbfgs -output_image '/content/gdrive/MyDrive/NSPT/output/4A5.png' -tv_weight 0 -gpu 0 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style4' -style_weight 40000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input4.png' -content_weight 0 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/4A5.png' -print_iter 50 -save_iter 100 -image_size 1800 -num_iterations 200 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu1_2 -style_layers relu1_1,relu1_2 -optimizer lbfgs -output_image '/content/gdrive/MyDrive/NSPT/output/4A6.png' -tv_weight 0 -gpu 0 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style4' -style_weight 40000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input4.png' -content_weight 15 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/4A6.png' -learning_rate 1 -print_iter 50 -save_iter 100 -image_size 3600 -num_iterations 200 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/channel_pruning.pth' -optimizer adam -output_image '/content/gdrive/MyDrive/NSPT/output/Final_4.png' -tv_weight 0 -original_colors 0 -backend cudnn
#Optional - 80MB+ 7200x7200 image (but causes grain in flat colour areas)
#!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style4' -style_weight 1500 -style_scale 0.5 -content_image '/content/gdrive/MyDrive/NSPT/input/input4.png' -content_weight 0 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/Final_4.png' -learning_rate 1 -print_iter 50 -save_iter 0 -image_size 7200 -num_iterations 10 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nin_imagenet.pth' -content_layers relu0,relu1 -style_layers relu0,relu1 -optimizer adam -output_image '/content/gdrive/MyDrive/NSPT/output/A8-FINAL-4.png' -tv_weight 0 -original_colors 0 -backend cudnn
#---------#
# STYLE 5 #
#---------#
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style5' -style_weight 1000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input5.png' -init random -learning_rate 1 -print_iter 50 -save_iter 250 -image_size 512 -num_iterations 1000 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -style_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -optimizer lbfgs -output_image '/content/gdrive/MyDrive/NSPT/output/5A1.png' -tv_weight 0.00001 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style5' -style_weight 1000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input5.png' -content_weight 5 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/5A1.png' -print_iter 50 -save_iter 250 -image_size 768 -num_iterations 600 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -style_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -optimizer lbfgs -output_imag '/content/gdrive/MyDrive/NSPT/output/5A2.png' -tv_weight 0 -gpu 0 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style5' -style_weight 1000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input5.png' -content_weight 5 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/5A2.png' -print_iter 50 -save_iter 250 -image_size 1024 -num_iterations 1000 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -style_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -optimizer lbfgs -output_image '/content/gdrive/MyDrive/NSPT/output/5A3.png' -tv_weight 0.00001 -gpu 0 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style5' -style_weight 40000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input5.png' -content_weight 5 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/5A3.png' -print_iter 50 -save_iter 250 -image_size 1400 -num_iterations 1000 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -style_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -optimizer lbfgs -output_image '/content/gdrive/MyDrive/NSPT/output/5A4.png' -tv_weight 0 -gpu 0 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style5' -style_weight 80000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input5.png' -content_weight 5 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/5A4.png' -print_iter 50 -save_iter 250 -image_size 1400 -num_iterations 1000 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu1_2,relu2_1,relu2_2,relu3_1,relu3_2,relu3_3,relu3_4,relu4_1,relu4_2,relu4_3,relu4_4,relu5_1,relu5_2,relu5_3,relu5_4 -style_layers relu1_1,relu1_2,relu2_1,relu2_2,relu3_1,relu3_2,relu3_3,relu3_4,relu4_1,relu4_2,relu4_3,relu4_4,relu5_1,relu5_2,relu5_3,relu5_4 -optimizer lbfgs -output_image '/content/gdrive/MyDrive/NSPT/output/5A5.png' -tv_weight 0 -gpu 0 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style5' -style_weight 40000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input5.png' -content_weight 0 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/5A5.png' -print_iter 50 -save_iter 100 -image_size 1800 -num_iterations 200 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu1_2 -style_layers relu1_1,relu1_2 -optimizer lbfgs -output_image '/content/gdrive/MyDrive/NSPT/output/5A6.png' -tv_weight 0 -gpu 0 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style5' -style_weight 40000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input5.png' -content_weight 15 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/5A6.png' -learning_rate 1 -print_iter 50 -save_iter 100 -image_size 3600 -num_iterations 200 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/channel_pruning.pth' -optimizer adam -output_image '/content/gdrive/MyDrive/NSPT/output/Final_5.png' -tv_weight 0 -original_colors 0 -backend cudnn
#Optional - 80MB+ 7200x7200 image (but causes grain in flat colour areas)
#!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style5' -style_weight 1500 -style_scale 0.5 -content_image '/content/gdrive/MyDrive/NSPT/input/input.png' -content_weight 0 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/Final_5.png' -learning_rate 1 -print_iter 50 -save_iter 0 -image_size 7200 -num_iterations 10 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nin_imagenet.pth' -content_layers relu0,relu1 -style_layers relu0,relu1 -optimizer adam -output_image '/content/gdrive/MyDrive/NSPT/output/A8-FINAL-5.png' -tv_weight 0 -original_colors 0 -backend cudnn
#---------#
# STYLE 6 #
#---------#
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style6' -style_weight 1000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input6.png' -init random -learning_rate 1 -print_iter 50 -save_iter 250 -image_size 512 -num_iterations 1000 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -style_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -optimizer lbfgs -output_image '/content/gdrive/MyDrive/NSPT/output/6A1.png' -tv_weight 0.00001 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style6' -style_weight 1000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input6.png' -content_weight 5 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/6A1.png' -print_iter 50 -save_iter 250 -image_size 768 -num_iterations 600 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -style_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -optimizer lbfgs -output_imag '/content/gdrive/MyDrive/NSPT/output/6A2.png' -tv_weight 0 -gpu 0 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style6' -style_weight 1000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input6.png' -content_weight 5 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/6A2.png' -print_iter 50 -save_iter 250 -image_size 1024 -num_iterations 1000 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -style_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -optimizer lbfgs -output_image '/content/gdrive/MyDrive/NSPT/output/6A3.png' -tv_weight 0.00001 -gpu 0 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style6' -style_weight 40000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input6.png' -content_weight 5 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/6A3.png' -print_iter 50 -save_iter 250 -image_size 1400 -num_iterations 1000 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -style_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -optimizer lbfgs -output_image '/content/gdrive/MyDrive/NSPT/output/6A4.png' -tv_weight 0 -gpu 0 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style6' -style_weight 80000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input6.png' -content_weight 5 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/6A4.png' -print_iter 50 -save_iter 250 -image_size 1400 -num_iterations 1000 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu1_2,relu2_1,relu2_2,relu3_1,relu3_2,relu3_3,relu3_4,relu4_1,relu4_2,relu4_3,relu4_4,relu5_1,relu5_2,relu5_3,relu5_4 -style_layers relu1_1,relu1_2,relu2_1,relu2_2,relu3_1,relu3_2,relu3_3,relu3_4,relu4_1,relu4_2,relu4_3,relu4_4,relu5_1,relu5_2,relu5_3,relu5_4 -optimizer lbfgs -output_image '/content/gdrive/MyDrive/NSPT/output/6A5.png' -tv_weight 0 -gpu 0 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style6' -style_weight 40000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input6.png' -content_weight 0 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/6A5.png' -print_iter 50 -save_iter 100 -image_size 1800 -num_iterations 200 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu1_2 -style_layers relu1_1,relu1_2 -optimizer lbfgs -output_image '/content/gdrive/MyDrive/NSPT/output/6A6.png' -tv_weight 0 -gpu 0 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style6' -style_weight 40000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input6.png' -content_weight 15 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/6A6.png' -learning_rate 1 -print_iter 50 -save_iter 100 -image_size 3600 -num_iterations 200 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/channel_pruning.pth' -optimizer adam -output_image '/content/gdrive/MyDrive/NSPT/output/Final_6.png' -tv_weight 0 -original_colors 0 -backend cudnn
#Optional - 80MB+ 7200x7200 image (but causes grain in flat colour areas)
#!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style6' -style_weight 1500 -style_scale 0.5 -content_image '/content/gdrive/MyDrive/NSPT/input/input6.png' -content_weight 0 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/Final_6.png' -learning_rate 1 -print_iter 50 -save_iter 0 -image_size 7200 -num_iterations 10 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nin_imagenet.pth' -content_layers relu0,relu1 -style_layers relu0,relu1 -optimizer adam -output_image '/content/gdrive/MyDrive/NSPT/output/A8-FINAL-6.png' -tv_weight 0 -original_colors 0 -backend cudnn
#---------#
# STYLE 7 #
#---------#
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style7' -style_weight 1000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input7.png' -init random -learning_rate 1 -print_iter 50 -save_iter 250 -image_size 512 -num_iterations 1000 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -style_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -optimizer lbfgs -output_image '/content/gdrive/MyDrive/NSPT/output/7A1.png' -tv_weight 0.00001 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style7' -style_weight 1000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input7.png' -content_weight 5 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/7A1.png' -print_iter 50 -save_iter 250 -image_size 768 -num_iterations 600 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -style_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -optimizer lbfgs -output_imag '/content/gdrive/MyDrive/NSPT/output/7A2.png' -tv_weight 0 -gpu 0 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style7' -style_weight 1000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input7.png' -content_weight 5 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/7A2.png' -print_iter 50 -save_iter 250 -image_size 1024 -num_iterations 1000 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -style_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -optimizer lbfgs -output_image '/content/gdrive/MyDrive/NSPT/output/7A3.png' -tv_weight 0.00001 -gpu 0 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style7' -style_weight 40000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input7.png' -content_weight 5 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/7A3.png' -print_iter 50 -save_iter 250 -image_size 1400 -num_iterations 1000 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -style_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -optimizer lbfgs -output_image '/content/gdrive/MyDrive/NSPT/output/7A4.png' -tv_weight 0 -gpu 0 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style7' -style_weight 80000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input7.png' -content_weight 5 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/7A4.png' -print_iter 50 -save_iter 250 -image_size 1400 -num_iterations 1000 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu1_2,relu2_1,relu2_2,relu3_1,relu3_2,relu3_3,relu3_4,relu4_1,relu4_2,relu4_3,relu4_4,relu5_1,relu5_2,relu5_3,relu5_4 -style_layers relu1_1,relu1_2,relu2_1,relu2_2,relu3_1,relu3_2,relu3_3,relu3_4,relu4_1,relu4_2,relu4_3,relu4_4,relu5_1,relu5_2,relu5_3,relu5_4 -optimizer lbfgs -output_image '/content/gdrive/MyDrive/NSPT/output/7A5.png' -tv_weight 0 -gpu 0 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style7' -style_weight 40000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input7.png' -content_weight 0 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/7A5.png' -print_iter 50 -save_iter 100 -image_size 1800 -num_iterations 200 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu1_2 -style_layers relu1_1,relu1_2 -optimizer lbfgs -output_image '/content/gdrive/MyDrive/NSPT/output/7A6.png' -tv_weight 0 -gpu 0 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style7' -style_weight 40000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input7.png' -content_weight 15 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/7A6.png' -learning_rate 1 -print_iter 50 -save_iter 100 -image_size 3600 -num_iterations 200 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/channel_pruning.pth' -optimizer adam -output_image '/content/gdrive/MyDrive/NSPT/output/Final_7.png' -tv_weight 0 -original_colors 0 -backend cudnn
#Optional - 80MB+ 7200x7200 image (but causes grain in flat colour areas)
#!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style7' -style_weight 1500 -style_scale 0.5 -content_image '/content/gdrive/MyDrive/NSPT/input/input7.png' -content_weight 0 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/Final_7.png' -learning_rate 1 -print_iter 50 -save_iter 0 -image_size 7200 -num_iterations 10 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nin_imagenet.pth' -content_layers relu0,relu1 -style_layers relu0,relu1 -optimizer adam -output_image '/content/gdrive/MyDrive/NSPT/output/A8-FINAL-7.png' -tv_weight 0 -original_colors 0 -backend cudnn
#---------#
# STYLE 8 #
#---------#
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style8' -style_weight 1000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input8.png' -init random -learning_rate 1 -print_iter 50 -save_iter 250 -image_size 512 -num_iterations 1000 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -style_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -optimizer lbfgs -output_image '/content/gdrive/MyDrive/NSPT/output/8A1.png' -tv_weight 0.00001 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style8' -style_weight 1000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input8.png' -content_weight 5 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/8A1.png' -print_iter 50 -save_iter 250 -image_size 768 -num_iterations 600 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -style_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -optimizer lbfgs -output_imag '/content/gdrive/MyDrive/NSPT/output/8A2.png' -tv_weight 0 -gpu 0 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style8' -style_weight 1000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input8.png' -content_weight 5 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/8A2.png' -print_iter 50 -save_iter 250 -image_size 1024 -num_iterations 1000 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -style_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -optimizer lbfgs -output_image '/content/gdrive/MyDrive/NSPT/output/8A3.png' -tv_weight 0.00001 -gpu 0 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style8' -style_weight 40000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input8.png' -content_weight 5 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/8A3.png' -print_iter 50 -save_iter 250 -image_size 1400 -num_iterations 1000 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -style_layers relu1_1,relu2_1,relu3_1,relu4_1,relu5_1 -optimizer lbfgs -output_image '/content/gdrive/MyDrive/NSPT/output/8A4.png' -tv_weight 0 -gpu 0 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style8' -style_weight 80000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input8.png' -content_weight 5 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/8A4.png' -print_iter 50 -save_iter 250 -image_size 1400 -num_iterations 1000 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu1_2,relu2_1,relu2_2,relu3_1,relu3_2,relu3_3,relu3_4,relu4_1,relu4_2,relu4_3,relu4_4,relu5_1,relu5_2,relu5_3,relu5_4 -style_layers relu1_1,relu1_2,relu2_1,relu2_2,relu3_1,relu3_2,relu3_3,relu3_4,relu4_1,relu4_2,relu4_3,relu4_4,relu5_1,relu5_2,relu5_3,relu5_4 -optimizer lbfgs -output_image '/content/gdrive/MyDrive/NSPT/output/8A5.png' -tv_weight 0 -gpu 0 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style8' -style_weight 40000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input8.png' -content_weight 0 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/8A5.png' -print_iter 50 -save_iter 100 -image_size 1800 -num_iterations 200 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nyud-fcn32s-color-heavy.pth' -content_layers relu1_1,relu1_2 -style_layers relu1_1,relu1_2 -optimizer lbfgs -output_image '/content/gdrive/MyDrive/NSPT/output/8A6.png' -tv_weight 0 -gpu 0 -original_colors 0 -backend cudnn
!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style8' -style_weight 40000 -style_scale 1 -content_image '/content/gdrive/MyDrive/NSPT/input/input8.png' -content_weight 15 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/8A6.png' -learning_rate 1 -print_iter 50 -save_iter 100 -image_size 3600 -num_iterations 200 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/channel_pruning.pth' -optimizer adam -output_image '/content/gdrive/MyDrive/NSPT/output/Final_8.png' -tv_weight 0 -original_colors 0 -backend cudnn
#Optional - 80MB+ 7200x7200 image (but causes grain in flat colour areas)
#!python neural-style-pt/neural_style.py -style_image '/content/gdrive/MyDrive/NSPT/style/style8' -style_weight 1500 -style_scale 0.5 -content_image '/content/gdrive/MyDrive/NSPT/input/input8.png' -content_weight 0 -init image -init_image '/content/gdrive/MyDrive/NSPT/output/Final_8.png' -learning_rate 1 -print_iter 50 -save_iter 0 -image_size 7200 -num_iterations 10 -model_file '/content/gdrive/MyDrive/NSPT/checkpoints/nin_imagenet.pth' -content_layers relu0,relu1 -style_layers relu0,relu1 -optimizer adam -output_image '/content/gdrive/MyDrive/NSPT/output/A8-FINAL-8.png' -tv_weight 0 -original_colors 0 -backend cudnn | [
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62,
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669,
657,
532,
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437,
269,
463,
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] | 2.434266 | 18,438 |
import logging
from os import path
from os.path import dirname, join
from arekit.common.utils import create_dir_if_not_exists
from arekit.contrib.experiments.cv.default import SimpleCVFolding
from arekit.contrib.experiments.cv.doc_stat.rusentrel import RuSentRelDocStatGenerator
from arekit.contrib.experiments.cv.sentence_based import SentenceBasedCVFolding
from arekit.contrib.experiments.data_io import DataIO
from arekit.contrib.experiments.neutral.annot.rusentrel_three_scale import RuSentRelThreeScaleNeutralAnnotator
from arekit.contrib.experiments.neutral.annot.rusentrel_two_scale import RuSentRelTwoScaleNeutralAnnotator
from arekit.processing.lemmatization.mystem import MystemWrapper
from arekit.source.embeddings.rusvectores import RusvectoresEmbedding
from arekit.source.rusentrel.opinions.formatter import RuSentRelOpinionCollectionFormatter
from arekit.source.rusentrel.synonyms import RuSentRelSynonymsCollection
logger = logging.getLogger(__name__)
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] | 3.284746 | 295 |
import numpy as np
from scipy.misc import imread, imresize
FACENET_MEAN = np.array([ 0.52591038, 0.40204082, 0.34178183], dtype=np.float32)
FACENET_STD = np.sqrt(np.array([3941.30175781, 2856.94287109, 2519.35791016], dtype=np.float32) / 255.**2)
def preprocess_image(img):
"""Preprocess an image for squeezenet.
Subtracts the pixel mean and divides by the standard deviation.
"""
return (img.astype(np.float32)/255.0 - FACENET_MEAN) / FACENET_STD
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] | 2.353234 | 201 |
# -*- coding: utf-8 -*-
"""PizzaCell class."""
from enum import Enum, unique
from cell import Cell
from slice import Slice
@unique
class Ingredient(Enum):
"""Ingredient enum."""
MUSHROOM = 'M'
TOMATO = 'T'
class PizzaCell(object):
"""Cell of Pizza.
:type ingredient: Ingredient or None
:type top: PizzaCell or None
:type bottom: PizzaCell or None
:type right: PizzaCell or None
:type left: PizzaCell or None
:type _cell: Cell or None
:type _slice: Slice or None
:type _has_mushroom: bool
"""
ingredient = None
top = None
bottom = None
right = None
left = None
_slice = None
_cell = None
_has_mushroom = False
def __init__(self, ingredient: str):
"""PizzaCell constructor.
:param ingredient: ingredient in the cell
:type ingredient: str
"""
try:
ingredient = ingredient.upper()
except AttributeError:
raise ValueError
if ingredient == Ingredient.MUSHROOM.value:
self.ingredient = Ingredient.MUSHROOM
self._has_mushroom = True
elif ingredient == Ingredient.TOMATO.value:
self.ingredient = Ingredient.TOMATO
else:
raise ValueError
@property
def cell(self) -> Cell:
"""Cell getter.
:return: cell
:rtype: Cell
"""
return self._cell
@cell.setter
def cell(self, cell: Cell) -> None:
"""Cell setter.
:param cell: cell
:type cell: Cell
:return:
:rtype: None
"""
self._cell = cell
@property
def slice(self) -> Slice:
"""Slice getter.
:return: slice
:rtype: Slice
"""
return self._slice
@slice.setter
def slice(self, slice: Slice) -> None:
"""Slice setter.
:param slice: slice
:type slice: Slice
:return:
:rtype: None
"""
self._slice = slice
@property
def mushroom(self) -> bool:
"""This cell has mushroom.
:return: if mushroom
:rtype: bool
"""
return self._has_mushroom
@property
def tomato(self) -> bool:
"""This cell has tomato.
:return: if mushroom
:rtype: bool
"""
return not self._has_mushroom
@property
def x(self) -> int:
"""Return X of this cell.
:return: x
:rtype: int
"""
return self._cell.x
@property
def y(self) -> int:
"""Return Y of this cell.
:return: y
:rtype: int
"""
return self._cell.y
@property
def id(self) -> int:
"""Return id of this cell.
:return: id
:rtype: int
"""
return self._cell.id
def set_neighbour(self, direction: str, next_pizza_cell) -> None:
"""Look at next cell.
:param direction: direction of the next cell
:param next_pizza_cell: next PizzaCell
:param direction: str
:type next_pizza_cell: PizzaCell
:return:
:rtype: None
"""
setattr(self, direction, next_pizza_cell)
def is_equal(self, cell) -> bool:
"""Check if cell is equal.
:param cell: different PizzaCell
:type cell: PizzaCell
:return:
:rtype: bool
"""
return self.ingredient == cell.ingredient
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] | 2.14746 | 1,614 |
import torch.optim as optim
import tqdm
from moses.utils import Logger
import torch
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import requests
from django.core.files import File
from tempfile import mkdtemp
from shutil import copy, rmtree
import os
import yaml
import zipfile
from django.conf import settings
from docker.client import DockerClient
from docker.models.images import ImageCollection
from docker_registry_client import DockerRegistryClient
from .registry_checker import check_registry
from .models import Stack
DOCKER_COMPOSE_FILE_DEFAULT_NAME = settings.DOCKER_COMPOSE_FILE_DEFAULT_NAME
DOCKER_REGISTRY_URL = settings.DOCKER_REGISTRY_URL
CALLBACK_URL = f"http://{settings.DEPLOYER_HOSTPORT}/tasks/update/"
check_registry()
drc = DockerRegistryClient('http://' + settings.DOCKER_REGISTRY_URL)
docker_client = DockerClient()
ic = ImageCollection(docker_client)
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# Import libraries
import os
import sys
import pypsa
import numpy as np
import pandas as pd
#from sympy import latex
import time
import math
# Timer
t0 = time.time() # Start a timer
# Import functions file
sys.path.append(os.path.split(os.getcwd())[0])
from functions_file import *
# Directory of file
#directory = os.path.split(os.path.split(os.path.split(os.getcwd())[0])[0])[0] + "\\Data\\elec_only\\"
directory = os.path.split(os.path.split(os.getcwd())[0])[0] + "\\Data\\elec_only\\"
# File name
file = "postnetwork-elec_only_0.125_0.05.h5"
# Generic file name
titleFileName = file
# Figure path
#figurePath = os.path.split(os.path.split(os.path.split(os.getcwd())[0])[0])[0] + "\\Figures\\elec_only\\"
figurePath = os.path.split(os.path.split(os.getcwd())[0])[0] + "\\Figures\\elec_only\\"
##############################################################################
##############################################################################
################################# Pre Analysis ###############################
##############################################################################
##############################################################################
# ------------------- Curtailment - CO2 constraints (Elec) ------------------#
# Path to save files
path = figurePath + "Pre Analysis\\"
# List of file names
filename_CO2 = ["postnetwork-elec_only_0.125_0.6.h5",
"postnetwork-elec_only_0.125_0.5.h5",
"postnetwork-elec_only_0.125_0.4.h5",
"postnetwork-elec_only_0.125_0.3.h5",
"postnetwork-elec_only_0.125_0.2.h5",
"postnetwork-elec_only_0.125_0.1.h5",
"postnetwork-elec_only_0.125_0.05.h5"]
# List of constraints
constraints = ["40%", "50%", "60%", "70%", "80%", "90%", "95%"]
title = ""#"Electricity Curtailment - " + file[12:-14]
fig = Curtailment(directory=directory, files=filename_CO2, title=title, constraints=constraints, fontsize=14, ylim=[-1,20], figsize=[6, 4.5])
SavePlot(fig, path, title=(file[12:-14] + " - Curtailment Elec (CO2)"))
# --------------- Curtailment - Transmission constraints (Elec) --------------#
# List of file names
filename_trans = ["postnetwork-elec_only_0_0.05.h5",
"postnetwork-elec_only_0.0625_0.05.h5",
"postnetwork-elec_only_0.125_0.05.h5",
"postnetwork-elec_only_0.25_0.05.h5",
"postnetwork-elec_only_0.375_0.05.h5"]
# List of constraints
constraints = ["Zero", "Current", "2x Current", "4x Current", "6x Current"]
title = ""#"Electricity Curtailment - " + file[12:-14]
fig = Curtailment(directory=directory, files=filename_trans, title=title, constraints=constraints, fontsize=14, rotation=-17.5, ylim=[-2,60], legendLoc="upper right", figsize=[6, 4.8])
SavePlot(fig, path, title=(file[12:-14] + " - Curtailment Elec (trans)"))
##############################################################################
##############################################################################
################################### MISMATCH #################################
##############################################################################
##############################################################################
# ------- Electricity produced by technology (Elec CO2 and Trans) -----------#
# Path to save files
path = figurePath + "Mismatch\\"
# List of file names
filename_CO2 = ["postnetwork-elec_only_0.125_0.6.h5",
"postnetwork-elec_only_0.125_0.5.h5",
"postnetwork-elec_only_0.125_0.4.h5",
"postnetwork-elec_only_0.125_0.3.h5",
"postnetwork-elec_only_0.125_0.2.h5",
"postnetwork-elec_only_0.125_0.1.h5",
"postnetwork-elec_only_0.125_0.05.h5"]
# List of constraints
constraints = ["40%", "50%", "60%", "70%", "80%", "90%", "95%"]
fig = ElecProductionOvernight(directory=directory, filenames=filename_CO2, constraints=constraints, fontsize=14, figsize=[6,6])
SavePlot(fig, path, title=(file[12:-14] + " - total elec generation (CO2)"))
# List of file names
filename_trans = ["postnetwork-elec_only_0_0.05.h5",
"postnetwork-elec_only_0.0625_0.05.h5",
"postnetwork-elec_only_0.125_0.05.h5",
"postnetwork-elec_only_0.25_0.05.h5",
"postnetwork-elec_only_0.375_0.05.h5"]
# List of constraints
constraints = ["Zero", "Current", "2x Current", "4x Current", "6x Current"]
fig = ElecProductionOvernight(directory=directory, filenames=filename_trans, constraints=constraints, rotation=-25, fontsize=14, figsize=[6,6.3])
SavePlot(fig, path, title=(file[12:-14] + " - total elec generation (trans)"))
# ------------------ Map Capacity Plots (Elec) ------------------#
# Path to save files
path = figurePath + "Mismatch\\Map Capacity\\"
# --- Elec ---
# Import network
network = pypsa.Network(directory+file)
fig1, fig2, fig3 = MapCapacityOriginal(network, titleFileName, ncol=3)
SavePlot(fig1, path, title=(file[12:-3] + " - Map Capacity Elec Generator"))
SavePlot(fig2, path, title=(file[12:-3] + " - Map Capacity Elec Storage Energy"))
SavePlot(fig3, path, title=(file[12:-3] + " - Map Capacity Elec Storage Power"))
# -------------------- Map Energy Plot (Elec) -------------------#
# Path for saving file
path = figurePath + "Mismatch\\Map Energy Distribution\\"
# Import network
network = pypsa.Network(directory+file)
# --- Elec ---
figElec = MapCapacityElectricityEnergy(network, file)
SavePlot(figElec, path, title=(file[12:-3] + " - Elec Energy Production"))
# --------------------- Map PC Plot (Elec) ----------------------#
# Path to save plots
path = figurePath + "Mismatch\\Map PC\\"
# Import network
network = pypsa.Network(directory+file)
# Get the names of the data
dataNames = network.buses.index.str.slice(0,2).unique()
# Get time stamps
timeIndex = network.loads_t.p_set.index
# --- Elec ---
# Electricity load for each country
loadElec = network.loads_t.p_set[dataNames]
# Solar PV generation
generationSolar = network.generators_t.p[dataNames + " solar"]
generationSolar.columns = generationSolar.columns.str.slice(0,2)
# Onshore wind generation
generationOnwind = network.generators_t.p[[country for country in network.generators_t.p.columns if "onwind" in country]].groupby(network.generators.bus.str.slice(0,2),axis=1).sum()
# Offshore wind generation
# Because offwind is only for 21 countries, additional methods have to be implemented to make it at 8760 x 30 matrix
# Create empty array of 8760 x 30, add the offwind generation and remove 'NaN' values.
generationOffwind = pd.DataFrame(np.zeros([8760,30]),index=timeIndex, columns=dataNames)
generationOffwind += network.generators_t.p[[country for country in network.generators_t.p.columns if "offwind" in country]].groupby(network.generators.bus.str.slice(0,2),axis=1).sum()
generationOffwind = generationOffwind.replace(np.nan,0)
# RoR generations
# Because RoR is only for 27 countries, additional methods have to be implemented to make it at 8760 x 30 matrix
# Create empty array of 8760 x 30, add the RoR generation and remove 'NaN' values.
generationRoR = pd.DataFrame(np.zeros([8760,30]),index=timeIndex, columns=dataNames)
generationRoR += network.generators_t.p[[country for country in network.generators_t.p.columns if "ror" in country]].groupby(network.generators.bus.str.slice(0,2),axis=1).sum()
generationRoR = generationRoR.replace(np.nan,0)
# Combined generation for electricity
generationElec = generationSolar + generationOnwind + generationOffwind + generationRoR
# Mismatch electricity
mismatchElec = generationElec - loadElec
# PCA on mismatch for electricity
eigenValuesElec, eigenVectorsElec, varianceExplainedElec, normConstElec, TElec = PCA(mismatchElec)
# ----------------------- Map Plot (Elec) -----------------------#
# Plot map PC for mismatch electricity
titlePlotElec = "Mismatch for electricity only"
for i in np.arange(6):
fig = MAP(eigenVectorsElec, eigenValuesElec, dataNames, (i + 1))#, titlePlotElec, titleFileName)
title = (file[12:-3] + " - Map PC Elec Mismatch (lambda " + str(i+1) + ")")
SavePlot(fig, path, title)
# ----------------------- FFT Plot (Elec) -----------------------#
# Path to save FFT plots
path = figurePath + "Mismatch\\FFT\\"
# --- Elec ---
file_name = "Electricity mismatch - " + file
for i in np.arange(6):
fig = FFTPlot(TElec.T, varianceExplainedElec, PC_NO = (i+1))
title = (file[12:-3] + " - FFT Elec Mismatch (lambda " + str(i+1) + ")")
SavePlot(fig, path, title)
# -------------------- Seasonal Plot (Elec) ---------------------#
# Path to save seasonal plots
path = figurePath + "Mismatch\\Seasonal\\"
# --- Elec ---
file_name = "Electricity mismatch - " + file
for i in np.arange(6):
fig = seasonPlot(TElec, timeIndex, PC_NO=(i+1), PC_amount=6,dpi=400)
title = (file[12:-3] + " - Seasonal Plot Elec Mismatch (lambda " + str(i+1) + ")")
SavePlot(fig, path, title)
# -------------------- FFT + Seasonal Plot (Elec) ---------------------#
# Path to save seasonal plots
path = figurePath + "Mismatch\\Timeseries\\"
# --- Elec ---
file_name = "Electricity mismatch - " + file
for i in np.arange(6):
fig = FFTseasonPlot(TElec, timeIndex, varianceExplainedElec, PC_NO=(i+1), PC_amount=6,dpi=200)
title = (file[12:-3] + " - Timeseries Plot Elec Mismatch (lambda " + str(i+1) + ")")
SavePlot(fig, path, title)
# ----------------- Contribution plot (Elec) ------------------- #
# Path to save contribution plots
path = figurePath + "Mismatch\\Contribution\\"
# --- Elec ---
# Contribution
dircConElec = Contribution(network, "elec")
lambdaCollectedConElec = ConValueGenerator(normConstElec, dircConElec, eigenVectorsElec)
for i in range(6):
fig = ConPlot(eigenValuesElec,lambdaCollectedConElec,i+1,10,suptitle=("Electricity Contribution - " + file[12:-3]),dpi=200)
title = (file[12:-3] + " - Contribution Plot Elec (lambda " + str(i+1) + ")")
SavePlot(fig, path, title)
# ------------------- Response plot (Elec) -------------------- #
# Path to save contribution plots
path = figurePath + "Mismatch\\Response\\"
# --- Elec ---
# Response
dircResElec = ElecResponse(network,True)
lambdaCollectedResElec = ConValueGenerator(normConstElec, dircResElec, eigenVectorsElec)
for i in range(6):
fig = ConPlot(eigenValuesElec,lambdaCollectedResElec,i+1,10,suptitle=("Electricity Response - " + file[12:-3]),dpi=200)
title = (file[12:-3] + " - Response Plot Elec (lambda " + str(i+1) + ")")
SavePlot(fig, path, title)
# ------------------- Covariance plot (Elec) -------------------- #
# Path to save contribution plots
path = figurePath + "Mismatch\\Covariance\\"
# --- Elec ---
# Covariance
covMatrixElec = CovValueGenerator(dircConElec, dircResElec , True, normConstElec, eigenVectorsElec).T
for i in range(6):
fig = ConPlot(eigenValuesElec,covMatrixElec,i+1,10,suptitle=("Electricity Covariance - " + file[12:-3]),dpi=200)
title = (file[12:-3] + " - Covariance Plot Elec (lambda " + str(i+1) + ")")
SavePlot(fig, path, title)
# ------------------- Combined Projection plot (Elec) -------------------- #
# Path to save contribution plots
path = figurePath + "Mismatch\\Projection\\"
# --- Elec ---
for i in range(6):
fig = CombConPlot(eigenValuesElec, lambdaCollectedConElec, lambdaCollectedResElec, covMatrixElec, i+1, depth = 6) #, suptitle=("Electricity Projection - " + file[12:-3]),dpi=200)
title = (file[12:-3] + " - Projection Plot Elec (lambda " + str(i+1) + ")")
SavePlot(fig, path, title)
# ------------------- PC1 and PC2 combined plot (Elec) -------------------- #
# Path to save contribution plots
path = figurePath + "Mismatch\\Combined Plot\\"
# --- Elec ---
fig = PC1and2Plotter(TElec, timeIndex, [1,2], eigenValuesElec, lambdaCollectedConElec, lambdaCollectedResElec, covMatrixElec,PCType="withProjection")#,suptitle=("Electricity Mismatch - " + file[12:-3]),dpi=200)
title = (file[12:-3] + " - Combined Plot Elec (lambda 1 & 2)")
SavePlot(fig, path, title)
# ---------------------- Bar plot CO2 constraint --------------------------- #
# Path to save bar plots
path = figurePath + "Mismatch\\Bar\\"
# Name of file (must be in correct folder location)
filename_CO2 = ["postnetwork-elec_only_0.125_0.6.h5",
"postnetwork-elec_only_0.125_0.5.h5",
"postnetwork-elec_only_0.125_0.4.h5",
"postnetwork-elec_only_0.125_0.3.h5",
"postnetwork-elec_only_0.125_0.2.h5",
"postnetwork-elec_only_0.125_0.1.h5",
"postnetwork-elec_only_0.125_0.05.h5"]
# Variable to store mismatch PC componentns for each network
barMatrixCO2Elec = []
for file in filename_CO2:
# --------------------------- Electricity -------------------------------#
# Network
network = pypsa.Network(directory + file)
# Get the names of the data
dataNames = network.buses.index.str.slice(0,2).unique()
# Get time stamps
timeIndex = network.loads_t.p_set.index
# Electricity load for each country
loadElec = network.loads_t.p_set[dataNames]
# Solar PV generation
generationSolar = network.generators_t.p[dataNames + " solar"]
generationSolar.columns = generationSolar.columns.str.slice(0,2)
# Onshore wind generation
generationOnwind = network.generators_t.p[[country for country in network.generators_t.p.columns if "onwind" in country]].groupby(network.generators.bus.str.slice(0,2),axis=1).sum()
# Offshore wind generation
# Because offwind is only for 21 countries, additional methods have to be implemented to make it at 8760 x 30 matrix
# Create empty array of 8760 x 30, add the offwind generation and remove 'NaN' values.
generationOffwind = pd.DataFrame(np.zeros([8760,30]),index=timeIndex, columns=dataNames)
generationOffwind += network.generators_t.p[[country for country in network.generators_t.p.columns if "offwind" in country]].groupby(network.generators.bus.str.slice(0,2),axis=1).sum()
generationOffwind = generationOffwind.replace(np.nan,0)
# RoR generations
# Because RoR is only for 27 countries, additional methods have to be implemented to make it at 8760 x 30 matrix
# Create empty array of 8760 x 30, add the RoR generation and remove 'NaN' values.
generationRoR = pd.DataFrame(np.zeros([8760,30]),index=timeIndex, columns=dataNames)
generationRoR += network.generators_t.p[[country for country in network.generators_t.p.columns if "ror" in country]].groupby(network.generators.bus.str.slice(0,2),axis=1).sum()
generationRoR = generationRoR.replace(np.nan,0)
# Combined generation for electricity
generationElec = generationSolar + generationOnwind + generationOffwind + generationRoR
# Mismatch electricity
mismatchElec = generationElec - loadElec
# PCA on mismatch for electricity
eigenValuesElec, eigenVectorsElec, varianceExplainedElec, normConstElec, TElec = PCA(mismatchElec)
# Append value to matrix
barMatrixCO2Elec.append(varianceExplainedElec)
constraints = ["40%", "50%", "60%", "70%", "80%", "90%", "95%"]
title = "" #"Number of PC describing variance of network as a function of $CO_{2}$ constraint"
xlabel = "$CO_{2}$ constraint"
suptitleElec = "" #("Electricity Mismatch - " + file[12:-14])
fig = BAR(barMatrixCO2Elec, 7, filename_CO2, constraints, title, xlabel, suptitleElec, fontsize=18, figsize=[6, 3], ncol=4, bbox=(0.5,-0.28))
titleBarCO2Elec = (file[12:-14] + " - Bar CO2 Elec Mismatch")
SavePlot(fig, path, titleBarCO2Elec)
# ------------------ Bar plot Transmission constraint ----------------------- #
# Path
#path = "C:/Users/jense/OneDrive - Aarhus Universitet/Dokumenter/ร
rhus Universitet/Kandidat - Civilingeniรธr/11. Semester/Master Thesis/Shared Documents/Figures/elec_only/Bar/"
path = figurePath + "Mismatch\\Bar\\"
# Name of file (must be in correct folder location)
filename_trans = ["postnetwork-elec_only_0_0.05.h5",
"postnetwork-elec_only_0.0625_0.05.h5",
"postnetwork-elec_only_0.125_0.05.h5",
"postnetwork-elec_only_0.25_0.05.h5",
"postnetwork-elec_only_0.375_0.05.h5"]
# Variable to store mismatch PC componentns for each network
barMatrixTransmissionElec = []
for file in filename_trans:
# --------------------------- Electricity -------------------------------#
# Network
network = pypsa.Network(directory + file)
# Get the names of the data
dataNames = network.buses.index.str.slice(0,2).unique()
# Get time stamps
timeIndex = network.loads_t.p_set.index
# Electricity load for each country
loadElec = network.loads_t.p_set[dataNames]
# Solar PV generation
generationSolar = network.generators_t.p[dataNames + " solar"]
generationSolar.columns = generationSolar.columns.str.slice(0,2)
# Onshore wind generation
generationOnwind = network.generators_t.p[[country for country in network.generators_t.p.columns if "onwind" in country]].groupby(network.generators.bus.str.slice(0,2),axis=1).sum()
# Offshore wind generation
# Because offwind is only for 21 countries, additional methods have to be implemented to make it at 8760 x 30 matrix
# Create empty array of 8760 x 30, add the offwind generation and remove 'NaN' values.
generationOffwind = pd.DataFrame(np.zeros([8760,30]),index=timeIndex, columns=dataNames)
generationOffwind += network.generators_t.p[[country for country in network.generators_t.p.columns if "offwind" in country]].groupby(network.generators.bus.str.slice(0,2),axis=1).sum()
generationOffwind = generationOffwind.replace(np.nan,0)
# RoR generations
# Because RoR is only for 27 countries, additional methods have to be implemented to make it at 8760 x 30 matrix
# Create empty array of 8760 x 30, add the RoR generation and remove 'NaN' values.
generationRoR = pd.DataFrame(np.zeros([8760,30]),index=timeIndex, columns=dataNames)
generationRoR += network.generators_t.p[[country for country in network.generators_t.p.columns if "ror" in country]].groupby(network.generators.bus.str.slice(0,2),axis=1).sum()
generationRoR = generationRoR.replace(np.nan,0)
# Combined generation for electricity
generationElec = generationSolar + generationOnwind + generationOffwind + generationRoR
# Mismatch electricity
mismatchElec = generationElec - loadElec
# PCA on mismatch for electricity
eigenValuesElec, eigenVectorsElec, varianceExplainedElec, normConstElec, TElec = PCA(mismatchElec)
# Append value to matrix
barMatrixTransmissionElec.append(varianceExplainedElec)
constraints = ["Zero", "Current", "2x Current", "4x Current", "6x Current"]
title = "Number of PC describing variance of network as a function of transmission constraint"
xlabel = "Transmission constraint"
suptitleElec = ("Electricity Mismatch - " + file[12:-14])
fig = BAR(barMatrixTransmissionElec, 7, filename_trans, constraints, title, xlabel, suptitleElec, fontsize=18, figsize=[6, 3], ncol=4, rotation=-17.5, bbox=(0.5,-0.28))
titleBarTransmissionElec = (file[12:-14] + " - Bar Trans Elec Mismatch")
SavePlot(fig, path, titleBarTransmissionElec)
# ------------------ Change in contribution and response CO2 ----------------------- #
# Variable to store lambda values
lambdaContributionElec = []
lambdaResponseElec = []
lambdaCovarianceElec = []
# Name of file (must be in correct folder location)
filename_CO2 = ["postnetwork-elec_only_0.125_0.6.h5",
"postnetwork-elec_only_0.125_0.5.h5",
"postnetwork-elec_only_0.125_0.4.h5",
"postnetwork-elec_only_0.125_0.3.h5",
"postnetwork-elec_only_0.125_0.2.h5",
"postnetwork-elec_only_0.125_0.1.h5",
"postnetwork-elec_only_0.125_0.05.h5"]
for file in filename_CO2:
# --------------------------- Electricity -------------------------------#
# Network
network = pypsa.Network(directory + file)
# Get the names of the data
dataNames = network.buses.index.str.slice(0,2).unique()
# Get time stamps
timeIndex = network.loads_t.p_set.index
# Electricity load for each country
loadElec = network.loads_t.p_set[dataNames]
# Solar PV generation
generationSolar = network.generators_t.p[dataNames + " solar"]
generationSolar.columns = generationSolar.columns.str.slice(0,2)
# Onshore wind generation
generationOnwind = network.generators_t.p[[country for country in network.generators_t.p.columns if "onwind" in country]].groupby(network.generators.bus.str.slice(0,2),axis=1).sum()
# Offshore wind generation
# Because offwind is only for 21 countries, additional methods have to be implemented to make it at 8760 x 30 matrix
# Create empty array of 8760 x 30, add the offwind generation and remove 'NaN' values.
generationOffwind = pd.DataFrame(np.zeros([8760,30]),index=timeIndex, columns=dataNames)
generationOffwind += network.generators_t.p[[country for country in network.generators_t.p.columns if "offwind" in country]].groupby(network.generators.bus.str.slice(0,2),axis=1).sum()
generationOffwind = generationOffwind.replace(np.nan,0)
# RoR generations
# Because RoR is only for 27 countries, additional methods have to be implemented to make it at 8760 x 30 matrix
# Create empty array of 8760 x 30, add the RoR generation and remove 'NaN' values.
generationRoR = pd.DataFrame(np.zeros([8760,30]),index=timeIndex, columns=dataNames)
generationRoR += network.generators_t.p[[country for country in network.generators_t.p.columns if "ror" in country]].groupby(network.generators.bus.str.slice(0,2),axis=1).sum()
generationRoR = generationRoR.replace(np.nan,0)
# Combined generation for electricity
generationElec = generationSolar + generationOnwind + generationOffwind + generationRoR
# Mismatch electricity
mismatchElec = generationElec - loadElec
# PCA on mismatch for electricity
eigenValuesElec, eigenVectorsElec, varianceExplainedElec, normConstElec, TElec = PCA(mismatchElec)
# Contribution Elec
dircConElec = Contribution(network, "elec")
lambdaCollectedConElec = ConValueGenerator(normConstElec, dircConElec, eigenVectorsElec)
lambdaContributionElec.append(lambdaCollectedConElec)
# Response Elec
dircResElec = ElecResponse(network,True)
lambdaCollectedResElec = ConValueGenerator(normConstElec, dircResElec, eigenVectorsElec)
lambdaResponseElec.append(lambdaCollectedResElec)
# Covariance Elec
covMatrix = CovValueGenerator(dircConElec, dircResElec , True, normConstElec, eigenVectorsElec)
lambdaCovarianceElec.append(covMatrix.T)
#%%
from functions_file import *
# general terms
pathContibution = figurePath + "Mismatch\\Change in Contribution\\"
pathResponse = figurePath + "Mismatch\\Change in Response\\"
pathCovariance = figurePath + "Mismatch\\Change in Covariance\\"
# Plot change in elec contribution
figtitle = "Change in electricity contribution as a function of CO2 constraint"
fig = ChangeContributionElec(lambdaContributionElec, rotate=True, PC=2) #figtitle
saveTitle = file[12:-14] + " - Change in elec cont (CO2)"
SavePlot(fig, pathContibution, saveTitle)
# Plot change in elec contribution
figtitle = "Change in electricity contribution as a function of CO2 constraint"
fig = ChangeContributionElec(lambdaContributionElec, rotate=False, PC=6) #figtitle
saveTitle = file[12:-14] + " - Change in elec cont app (CO2)"
SavePlot(fig, pathContibution, saveTitle)
# Plot change in elec response
figtitle = "Change in electricity response as a function of CO2 constraint"
fig = ChangeResponseElec(lambdaResponseElec, rotate=True, PC=2) #figtitle
saveTitle = file[12:-14] + " - Change in elec response (CO2)"
SavePlot(fig, pathResponse, saveTitle)
# Plot change in elec response
figtitle = "Change in electricity response as a function of CO2 constraint"
fig = ChangeResponseElec(lambdaResponseElec, rotate=False, PC=6) #figtitle
saveTitle = file[12:-14] + " - Change in elec response app (CO2)"
SavePlot(fig, pathResponse, saveTitle)
# Plot change in elec covariance response
figtitle = "Change in electricity covariance response as a function of CO2 constraint"
fig = ChangeResponseCov(lambdaResponseElec, rotate=True, PC=2) #figtitle
saveTitle = file[12:-14] + " - Change in elec cov response (CO2)"
SavePlot(fig, pathResponse, saveTitle)
# Plot change in elec covariance response
figtitle = "Change in electricity covariance response as a function of CO2 constraint"
fig = ChangeResponseCov(lambdaResponseElec, rotate=False, PC=6) #figtitle
saveTitle = file[12:-14] + " - Change in elec cov response app (CO2)"
SavePlot(fig, pathResponse, saveTitle)
# Plot change in covariance'
figtitle = "Change in electricity covariance between mismatch and response as a function of CO2 constraint"
fig = ChangeCovariance(lambdaCovarianceElec, collectTerms=True, rotate=True, PC=2) #figtitle
saveTitle = file[12:-14] + " - Change in elec covariance (CO2)"
SavePlot(fig, pathCovariance, saveTitle)
# Plot change in covariance
figtitle = "Change in electricity covariance between mismatch and response as a function of CO2 constraint"
fig = ChangeCovariance(lambdaCovarianceElec, collectTerms=True, rotate=False, PC=6) #figtitle
saveTitle = file[12:-14] + " - Change in elec covariance app (CO2)"
SavePlot(fig, pathCovariance, saveTitle)
#%%
# ------------------ Change in contribution and response transmission ----------------------- #
# Variable to store lambda values
lambdaContributionElec = []
lambdaResponseElec = []
lambdaCovarianceElec = []
# Name of file (must be in correct folder location)
filename_trans = ["postnetwork-elec_only_0_0.05.h5",
"postnetwork-elec_only_0.0625_0.05.h5",
"postnetwork-elec_only_0.125_0.05.h5",
"postnetwork-elec_only_0.25_0.05.h5",
"postnetwork-elec_only_0.375_0.05.h5"]
for file in filename_trans:
# --------------------------- Electricity -------------------------------#
# Network
network = pypsa.Network(directory + file)
# Get the names of the data
dataNames = network.buses.index.str.slice(0,2).unique()
# Get time stamps
timeIndex = network.loads_t.p_set.index
# Electricity load for each country
loadElec = network.loads_t.p_set[dataNames]
# Solar PV generation
generationSolar = network.generators_t.p[dataNames + " solar"]
generationSolar.columns = generationSolar.columns.str.slice(0,2)
# Onshore wind generation
generationOnwind = network.generators_t.p[[country for country in network.generators_t.p.columns if "onwind" in country]].groupby(network.generators.bus.str.slice(0,2),axis=1).sum()
# Offshore wind generation
# Because offwind is only for 21 countries, additional methods have to be implemented to make it at 8760 x 30 matrix
# Create empty array of 8760 x 30, add the offwind generation and remove 'NaN' values.
generationOffwind = pd.DataFrame(np.zeros([8760,30]),index=timeIndex, columns=dataNames)
generationOffwind += network.generators_t.p[[country for country in network.generators_t.p.columns if "offwind" in country]].groupby(network.generators.bus.str.slice(0,2),axis=1).sum()
generationOffwind = generationOffwind.replace(np.nan,0)
# RoR generations
# Because RoR is only for 27 countries, additional methods have to be implemented to make it at 8760 x 30 matrix
# Create empty array of 8760 x 30, add the RoR generation and remove 'NaN' values.
generationRoR = pd.DataFrame(np.zeros([8760,30]),index=timeIndex, columns=dataNames)
generationRoR += network.generators_t.p[[country for country in network.generators_t.p.columns if "ror" in country]].groupby(network.generators.bus.str.slice(0,2),axis=1).sum()
generationRoR = generationRoR.replace(np.nan,0)
# Combined generation for electricity
generationElec = generationSolar + generationOnwind + generationOffwind + generationRoR
# Mismatch electricity
mismatchElec = generationElec - loadElec
# PCA on mismatch for electricity
eigenValuesElec, eigenVectorsElec, varianceExplainedElec, normConstElec, TElec = PCA(mismatchElec)
# Contribution Elec
dircConElec = Contribution(network, "elec")
lambdaCollectedConElec = ConValueGenerator(normConstElec, dircConElec, eigenVectorsElec)
lambdaContributionElec.append(lambdaCollectedConElec)
# Response Elec
dircResElec = ElecResponse(network,True)
lambdaCollectedResElec = ConValueGenerator(normConstElec, dircResElec, eigenVectorsElec)
lambdaResponseElec.append(lambdaCollectedResElec)
# Covariance Elec
covMatrix = CovValueGenerator(dircConElec, dircResElec , True, normConstElec,eigenVectorsElec)
lambdaCovarianceElec.append(covMatrix.T)
# general terms
pathContibution = figurePath + "Mismatch\\Change in Contribution\\"
pathResponse = figurePath + "Mismatch\\Change in Response\\"
pathCovariance = figurePath + "Mismatch\\Change in Covariance\\"
# Plot change in elec contribution
figtitle = "Change in electricity contribution as a function of transmission constraint"
fig = ChangeContributionElec(lambdaContributionElec, rotate=True, PC=2) #figtitle
saveTitle = file[12:-14] + " - Change in elec cont (trans)"
SavePlot(fig, pathContibution, saveTitle)
# Plot change in elec contribution
figtitle = "Change in electricity contribution as a function of transmission constraint"
fig = ChangeContributionElec(lambdaContributionElec, rotate=False, PC=6) #figtitle
saveTitle = file[12:-14] + " - Change in elec cont app (trans)"
SavePlot(fig, pathContibution, saveTitle)
# Plot change in elec contribution
figtitle = "Change in electricity response as a function of transmission constraint"
fig = ChangeResponseElec(lambdaResponseElec, rotate=True, PC=2) #figtitle
saveTitle = file[12:-14] + " - Change in elec response (trans)"
SavePlot(fig, pathResponse, saveTitle)
# Plot change in elec contribution
figtitle = "Change in electricity response as a function of transmission constraint"
fig = ChangeResponseElec(lambdaResponseElec, rotate=False, PC=6) #figtitle
saveTitle = file[12:-14] + " - Change in elec response app (trans)"
SavePlot(fig, pathResponse, saveTitle)
# Plot change in elec covariance response
figtitle = "Change in electricity covariance response as a function of transmission constraint"
fig = ChangeResponseCov(lambdaResponseElec, rotate=True, PC=2) #figtitle
saveTitle = file[12:-14] + " - Change in elec cov response (trans)"
SavePlot(fig, pathResponse, saveTitle)
# Plot change in elec covariance response
figtitle = "Change in electricity covariance response as a function of transmission constraint"
fig = ChangeResponseCov(lambdaResponseElec, rotate=False, PC=6) #figtitle
saveTitle = file[12:-14] + " - Change in elec cov response app (trans)"
SavePlot(fig, pathResponse, saveTitle)
# Plot change in covariance
figtitle = "Change in electricity covariance as a function of transmission constraint"
fig = ChangeCovariance(lambdaCovarianceElec, collectTerms=True, rotate=True, PC=2) #figtitle
saveTitle = file[12:-14] + " - Change in elec covariance (trans)"
SavePlot(fig, pathCovariance, saveTitle)
# Plot change in covariance
figtitle = "Change in electricity covariance as a function of transmission constraint"
fig = ChangeCovariance(lambdaCovarianceElec, collectTerms=True, rotate=False, PC=6) #figtitle
saveTitle = file[12:-14] + " - Change in elec covariance app (trans)"
SavePlot(fig, pathCovariance, saveTitle)
#%%
##############################################################################
##############################################################################
################################ NODAL PRICE #################################
##############################################################################
##############################################################################
# File name
file = "postnetwork-elec_only_0.125_0.05.h5"
# Import network
network = pypsa.Network(directory+file)
# Get the names of the data
dataNames = network.buses.index.str.slice(0,2).unique()
# ----------------------- Map PC Plot (Elec + Heat) --------------------#
# Path to save plots
path = figurePath + "Nodal Price\\Map PC\\"
# --- Elec ---
# Prices for electricity for each country (restricted to 1000 โฌ/MWh)
priceElec = FilterPrice(network.buses_t.marginal_price[dataNames], 465)
# PCA on nodal prices for electricity
eigenValuesElec, eigenVectorsElec, varianceExplainedElec, normConstElec, TElec = PCA(priceElec)
# Plot map PC for electricity nodal prices
titlePlotElec = "Nodal price for electricity only"
for i in np.arange(6):
fig = MAP(eigenVectorsElec, eigenValuesElec, dataNames, (i + 1),size="medium")#, titlePlotElec, titleFileName)
title = (file[12:-3] + " - Map PC Elec NP (lambda " + str(i+1) + ")")
SavePlot(fig, path, title)
# ------------------------ FFT Plot (Elec + Heat) -----------------------#
# Path to save FFT plots
path = figurePath + "Nodal Price\\FFT\\"
# --- Elec ---
file_name = "Electricity Nodal Price - " + file
for i in np.arange(6):
fig = FFTPlot(TElec.T, varianceExplainedElec, PC_NO = i+1, title=file_name)
title = (file[12:-3] + " - FFT Elec NP (lambda " + str(i+1) + ")")
SavePlot(fig, path, title)
# ----------------------- Seasonal Plot (Elec + Heat) ------------------------#
# Path to save seasonal plots
path = figurePath + "Nodal Price\\Seasonal\\"
# --- Elec ---
file_name = "Electricity Nodal Price - " + file
for i in np.arange(6):
fig = seasonPlot(TElec, timeIndex, PC_NO=(i+1), PC_amount=6, title=file_name)
title = (file[12:-3] + " - Seasonal Plot Elec NP (lambda " + str(i+1) + ")")
SavePlot(fig, path, title)
# -------------------- FFT + Seasonal Plot (Elec) ---------------------#
# Path to save seasonal plots
path = figurePath + "Nodal Price\\Timeseries\\"
# --- Elec ---
file_name = "Electricity Nodal Price - " + file
for i in np.arange(6):
fig = FFTseasonPlot(TElec, timeIndex, varianceExplainedElec, PC_NO=(i+1), PC_amount=6,dpi=200)
title = (file[12:-3] + " - Timeseries Plot Elec NP (lambda " + str(i+1) + ")")
SavePlot(fig, path, title)
# ------------------- PC1 and PC2 combined plot (Elec) -------------------- #
# Path to save contribution plots
path = figurePath + "Nodal Price\\Combined Plot\\"
# --- Elec ---
fig = PC1and2Plotter(TElec, timeIndex, [1,2], eigenValuesElec, lambdaCollectedConElec, lambdaCollectedResElec, covMatrixElec,PCType="withoutProjection")#,suptitle=("Electricity Mismatch - " + file[12:-3]),dpi=200)
title = (file[12:-3] + " - Combined Plot Elec NP (lambda 1 & 2)")
SavePlot(fig, path, title)
#%%
# ---------------------- Bar plot CO2 constraint --------------------------- #
# Path to save bar plots
path = figurePath + "Nodal Price\\Bar\\"
# Name of file (must be in correct folder location)
filename_CO2 = ["postnetwork-elec_only_0.125_0.6.h5",
"postnetwork-elec_only_0.125_0.5.h5",
"postnetwork-elec_only_0.125_0.4.h5",
"postnetwork-elec_only_0.125_0.3.h5",
"postnetwork-elec_only_0.125_0.2.h5",
"postnetwork-elec_only_0.125_0.1.h5",
"postnetwork-elec_only_0.125_0.05.h5"]
# Variable to store nodal price PC componentns for each network
barMatrixCO2Elec = []
# Variable to store nodal price mean and standard variation
meanPriceElec = []
stdMeanPriceElec = []
quantileMeanPriceElec = []
quantileMinPriceElec = []
for file in filename_CO2:
# Network
network = pypsa.Network(directory + file)
# Get the names of the data
dataNames = network.buses.index.str.slice(0,2).unique()
# --- Elec ---
# Prices for electricity for each country (restricted to 1000 โฌ/MWh)
priceElec = FilterPrice(network.buses_t.marginal_price[dataNames], 465)
# PCA on nodal prices for electricity
eigenValuesElec, eigenVectorsElec, varianceExplainedElec, normConstElec, TElec = PCA(priceElec)
# Append value to matrix
barMatrixCO2Elec.append(varianceExplainedElec)
# ----------------------- NP Mean (Elec) --------------------#
# --- Elec ---
# Mean price for country
minPrice = priceElec.min().mean()
meanPrice = priceElec.mean().mean()
# append min, max and mean to matrix
meanPriceElec.append([minPrice, meanPrice])
# ----------------------- NP Quantile (Elec) --------------------#
# --- Elec ---
# Mean price for country
quantileMinPrice = np.quantile(priceElec.min(),[0.05,0.25,0.75,0.95])
quantileMeanPrice = np.quantile(priceElec.mean(),[0.05,0.25,0.75,0.95])
# append min, max and mean to matrix
quantileMeanPriceElec.append(quantileMeanPrice)
quantileMinPriceElec.append(quantileMinPrice)
constraints = ["40%", "50%", "60%", "70%", "80%", "90%", "95%"]
title = "" #"Number of PC describing variance of network as a function of $CO_{2}$ constraint"
xlabel = "" #"$CO_{2}$ constraint"
suptitleElec = "" #("Electricity Nodal Price - " + file[12:-14])
fig = BAR(barMatrixCO2Elec, 7, filename_CO2, constraints, title, xlabel, suptitleElec, fontsize=18, figsize=[6, 3], ncol=4, bbox=(0.5,-0.28))
titleBarCO2Elec = (file[12:-14] + " - Bar CO2 Elec NP")
SavePlot(fig, path, titleBarCO2Elec)
# ----------------------- Price evalution (Elec) --------------------#
path = figurePath + "Nodal Price\\Price Evolution\\"
title = "" #("Electricity Nodal Price Evalution - " + file[12:-14])
fig = PriceEvolution(meanPriceElec,quantileMeanPriceElec, quantileMinPriceElec, networktype="green", title=title, figsize=[6,3], fontsize=16)
title = (file[12:-14] + " - Elec NP CO2 Evolution")
SavePlot(fig, path, title)
# ------------------ Bar plot Transmission constraint ----------------------- #
# Path
path = figurePath + "Nodal Price\\Bar\\"
# Name of file (must be in correct folder location)
filename_trans = ["postnetwork-elec_only_0_0.05.h5",
"postnetwork-elec_only_0.0625_0.05.h5",
"postnetwork-elec_only_0.125_0.05.h5",
"postnetwork-elec_only_0.25_0.05.h5",
"postnetwork-elec_only_0.375_0.05.h5"]
# Variable to store nodal price PC componentns for each network
barMatrixTransmissionElec = []
# Variable to store nodal price mean and standard variation
meanPriceElec = []
quantileMeanPriceElec = []
quantileMinPriceElec = []
for file in filename_trans:
# Network
network = pypsa.Network(directory + file)
# Get the names of the data
dataNames = network.buses.index.str.slice(0,2).unique()
# --- Elec ---
# Prices for electricity for each country (restricted to 1000 โฌ/MWh)
priceElec = FilterPrice(network.buses_t.marginal_price[dataNames], 465)
# PCA on nodal prices for electricity
eigenValuesElec, eigenVectorsElec, varianceExplainedElec, normConstElec, TElec = PCA(priceElec)
# Append value to matrix
barMatrixTransmissionElec.append(varianceExplainedElec)
# ----------------------- NP Mean (Elec) --------------------#
# --- Elec ---
# Mean price for country
minPrice = priceElec.min().mean()
meanPrice = priceElec.mean().mean()
# append min, max and mean to matrix
meanPriceElec.append([minPrice, meanPrice])
# ----------------------- NP Quantile (Elec) --------------------#
# --- Elec ---
# Mean price for country
quantileMinPrice = np.quantile(priceElec.min(),[0.05,0.25,0.75,0.95])
quantileMeanPrice = np.quantile(priceElec.mean(),[0.05,0.25,0.75,0.95])
# append min, max and mean to matrix
quantileMeanPriceElec.append(quantileMeanPrice)
quantileMinPriceElec.append(quantileMinPrice)
# ----------------------- Bar plot (Elec) --------------------#
constraints = ["Zero", "Current", "2x Current", "4x Current", "6x Current"]
title = "" #"Number of PC describing variance of network as a function of transmission constraint"
xlabel = "" #"Transmission constraint"
suptitleElec = "" #("Electricity Nodal Price - " + file[12:-14])
fig = BAR(barMatrixTransmissionElec, 7, filename_trans, constraints, title, xlabel, suptitleElec, fontsize=18, figsize=[6, 3], ncol=4, rotation=-17.5, bbox=(0.5,-0.28))
titleBarTransmissionElec = (file[12:-14] + " - Bar Trans Elec NP")
SavePlot(fig, path, titleBarTransmissionElec)
# ----------------------- Price evalution (Elec) --------------------#
path = figurePath + "Nodal Price\\Price Evolution\\"
title = "" #("Electricity Nodal Price Evalution - " + file[12:-14])
fig = PriceEvolution(meanPriceElec,quantileMeanPriceElec, quantileMinPriceElec, networktype="green", title=title, figsize=[6,3.2], fontsize=16)
title = (file[12:-14] + " - Elec NP Trans Evolution")
SavePlot(fig, path, title)
#%%
##############################################################################
##############################################################################
################################# Coherence ##################################
##############################################################################
##############################################################################
# -------------------- Coherence Plot (Elec + Heat) ---------------------#
# File name
file = "postnetwork-elec_only_0.125_0.05.h5"
# Import network
network = pypsa.Network(directory+file)
# Get the names of the data
dataNames = network.buses.index.str.slice(0,2).unique()
# Get time stamps
timeIndex = network.loads_t.p_set.index
# Path to save contribution plots
path = figurePath + "Coherence\\"
# --- Elec ---
# Electricity load for each country
loadElec = network.loads_t.p_set[dataNames]
# Solar PV generation
generationSolar = network.generators_t.p[dataNames + " solar"]
generationSolar.columns = generationSolar.columns.str.slice(0,2)
# Onshore wind generation
generationOnwind = network.generators_t.p[[country for country in network.generators_t.p.columns if "onwind" in country]].groupby(network.generators.bus.str.slice(0,2),axis=1).sum()
# Offshore wind generation
# Because offwind is only for 21 countries, additional methods have to be implemented to make it at 8760 x 30 matrix
# Create empty array of 8760 x 30, add the offwind generation and remove 'NaN' values.
generationOffwind = pd.DataFrame(np.zeros([8760,30]),index=timeIndex, columns=dataNames)
generationOffwind += network.generators_t.p[[country for country in network.generators_t.p.columns if "offwind" in country]].groupby(network.generators.bus.str.slice(0,2),axis=1).sum()
generationOffwind = generationOffwind.replace(np.nan,0)
# RoR generations
# Because RoR is only for 27 countries, additional methods have to be implemented to make it at 8760 x 30 matrix
# Create empty array of 8760 x 30, add the RoR generation and remove 'NaN' values.
generationRoR = pd.DataFrame(np.zeros([8760,30]),index=timeIndex, columns=dataNames)
generationRoR += network.generators_t.p[[country for country in network.generators_t.p.columns if "ror" in country]].groupby(network.generators.bus.str.slice(0,2),axis=1).sum()
generationRoR = generationRoR.replace(np.nan,0)
# Combined generation for electricity
generationElec = generationSolar + generationOnwind + generationOffwind + generationRoR
# Mismatch electricity
mismatchElec = generationElec - loadElec
# Prices for each country
priceElec = FilterPrice(network.buses_t.marginal_price[dataNames], 465)
# Coherence between prices and mismatch
c1Elec, c2Elec, c3Elec = Coherence(mismatchElec, priceElec)
# Plot properties
title1 = "" #"Coherence 1: Electricity mismatch and nodal price"
title2 = "" #"Coherence 2: Electricity mismatch and nodal price"
title3 = "" #"Coherence 3: Electricity mismatch and nodal price"
xlabel = "Electricity Mismatch"
ylabel="Electricity Prices"
noX = 6
noY = 6
fig1 = CoherencePlot(dataMatrix=c1Elec.T, รผbertitle="", title=title1, xlabel=xlabel, ylabel=ylabel, noX=noX, noY=noY, dataRange=[0,1])
fig2 = CoherencePlot(dataMatrix=c2Elec.T, รผbertitle="", title=title2, xlabel=xlabel, ylabel=ylabel, noX=noX, noY=noY, dataRange=[0,1])
fig3 = CoherencePlot(dataMatrix=c3Elec.T, รผbertitle="", title=title3, xlabel=xlabel, ylabel=ylabel, noX=noX, noY=noY, dataRange=[-1,1])
SavePlot(fig1, path, title = (file[12:-3] + " - C1 elec mismatch and ENP"))
SavePlot(fig2, path, title = (file[12:-3] + " - C2 elec mismatch and ENP"))
SavePlot(fig3, path, title = (file[12:-3] + " - C3 elec mismatch and ENP"))
# Combined Plot
fig = CoherencePlotCombined(c1Elec.T, c2Elec.T, c3Elec.T, xlabel=xlabel, ylabel=ylabel)
SavePlot(fig, path, title = (file[12:-3] + " - C123 combined elec mismatch and ENP"))
# Finish timer
t1 = time.time() # End timer
total_time = round(t1-t0)
total_time_min = math.floor(total_time/60)
total_time_sec = round(total_time-(total_time_min*60))
print("\n \nThe code is now done running. It took %s min and %s sec." %(total_time_min,total_time_sec))
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1980,
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1980,
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1352,
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66,
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288,
1980,
4965,
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66,
837,
6407,
11,
2593,
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1,
198,
198,
2,
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287,
9766,
66,
10156,
198,
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7839,
796,
366,
19400,
287,
8744,
10156,
355,
257,
2163,
286,
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32315,
1,
198,
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4264,
3890,
28827,
66,
7,
50033,
4264,
3890,
28827,
66,
11,
23064,
28,
17821,
11,
4217,
28,
17,
8,
1303,
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7839,
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2393,
58,
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1415,
60,
1343,
366,
532,
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287,
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] | 2.855261 | 15,870 |
# Similar to scratch3, but with the BAN channel
from sys_simulator.channels import BANChannel
from sys_simulator import general as gen
from sys_simulator.pathloss import pathloss_bs_users
from sys_simulator.plots import plot_positions_actions_pie
from sys_simulator.q_learning.environments.completeEnvironment5 \
import CompleteEnvironment5
from sys_simulator.q_learning.agents.agent import Agent
from sys_simulator.q_learning.rewards import dis_reward_tensor2
from sys_simulator.parameters.parameters \
import EnvironmentParameters, TrainingParameters, DQNAgentParameters
from matplotlib import pyplot as plt
import os
import torch
import numpy as np
import math
n_mues = 1 # number of mues
n_d2d = 2 # number of d2d pairs
n_rb = n_mues # number of RBs
bs_radius = 500 # bs radius in m
rb_bandwidth = 180*1e3 # rb bandwidth in Hz
d2d_pair_distance = 50 # d2d pair distance in m
p_max = 23 # max tx power in dBm
noise_power = -116 # noise power per RB in dBm
bs_gain = 17 # macro bs antenna gain in dBi
user_gain = 4 # user antenna gain in dBi
sinr_threshold_mue = 6 # true mue sinr threshold in dB
mue_margin = .5e4
# conversions from dB to pow
p_max = p_max - 30
p_max = gen.db_to_power(p_max)
noise_power = noise_power - 30
noise_power = gen.db_to_power(noise_power)
bs_gain = gen.db_to_power(bs_gain)
user_gain = gen.db_to_power(user_gain)
sinr_threshold_mue = gen.db_to_power(sinr_threshold_mue)
# q-learning parameters
STEPS_PER_EPISODE = 4000
EPSILON_MIN = 0.01
EPSILON_DECAY = 100 * EPSILON_MIN / STEPS_PER_EPISODE
MAX_NUM_EPISODES = int(1.2/EPSILON_DECAY)
MAX_NUMBER_OF_AGENTS = 20
ALPHA = 0.05 # Learning rate
GAMMA = 0.98 # Discount factor
C = 80 # C constant for the improved reward function
TARGET_UPDATE = 10
REPLAY_MEMORY_SIZE = 10000
BATCH_SIZE = 128
# more parameters
cwd = os.getcwd()
# params objects
env_params = EnvironmentParameters(
rb_bandwidth, d2d_pair_distance, p_max, noise_power,
bs_gain, user_gain, sinr_threshold_mue, n_mues,
n_d2d, n_rb, bs_radius, c_param=C, mue_margin=mue_margin
)
train_params = TrainingParameters(MAX_NUM_EPISODES, STEPS_PER_EPISODE)
agent_params = DQNAgentParameters(
EPSILON_MIN, EPSILON_DECAY, 1,
REPLAY_MEMORY_SIZE, BATCH_SIZE, GAMMA
)
# actions, rewards, environment, agent
actions = torch.tensor([i*0.82*p_max/5/1000 for i in range(5)])
channel = BANChannel()
env = CompleteEnvironment5(env_params, dis_reward_tensor2, channel)
pairs_positions = [
(250, 0),
(-250, 0),
(0, 250),
(0, -250)
]
mue_position = (500 / math.sqrt(2), 500 / math.sqrt(2))
tx_powers_indexes = [
4, 4, 4, 4
]
# actions = [i*0.82*p_max/5/1000 for i in range(5)] # best result
actions = [i for i in range(5)] # best result
n_agents = len(pairs_positions)
| [
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77,
62,
49638,
796,
18896,
7,
79,
3468,
62,
1930,
1756,
8,
628
] | 2.580524 | 1,068 |
import autograd.numpy as np
import scipy as sc
from scipy import optimize
from scipy import special
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import Ridge
import copy
| [
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] | 3.5 | 60 |
import torch
import sys
import os
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from utilities.losses import kld_loss
test_kld_loss() | [
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number = int(input('Digite um nรบmero de atรฉ 4 algarismos: '))
print(f'Analisando o nรบmero {number} ...')
u = number // 1 % 10
d = number // 10 % 10
c = number // 100 % 10
m = number // 1000 % 10
print(f'Unidade: {u}\nDezena: {d}\nCentena: {c}\nMilhar: {m}') | [
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] | 2.357798 | 109 |
#!/usr/bin/python
'''
Simple implementation for Linux lockfile
'''
import os
import time
def LockFile(target, retry=30, timeout=1):
'''
Use this method if you want to make sure only one process opens
the "target" file. The "target" path should be a path to a file
in an existing folder.
Create a tmp folder in the same directory as target
If the foler is created, we consider the target "locked"
@return True: succeed; False: failed
'''
targetDir = os.path.dirname(os.path.realpath(target))
if os.path.exists(targetDir) is False:
return False
lockFolder = os.path.join(targetDir, ".lock-" + os.path.basename(target))
tryCnt = 0
while tryCnt < retry:
tryCnt += 1
try:
os.mkdir(lockFolder)
return True
except OSError:
time.sleep(timeout)
return False
def ReleaseFile(target):
'''
Release the target by trying to remove the tmp dir
@return True: succeed; False: failed
'''
targetDir = os.path.dirname(os.path.realpath(target))
if os.path.exists(targetDir) is False:
return True
lockFolder = os.path.join(targetDir, ".lock-" + os.path.basename(target))
try:
os.rmdir(lockFolder)
except OSError:
return False if os.path.exists(lockFolder) else True
return True
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] | 2.525234 | 535 |
#!/usr/bin/env python
__author__ = 'Vahid Jalili'
from urllib.parse import parse_qs
import json
import os
import re
import sys
import traceback
import argparse
import importlib
import logging
from mako.lookup import TemplateLookup
from oic import rndstr
from oic.oic.provider import AuthorizationEndpoint
from oic.oic.provider import EndSessionEndpoint
from oic.oic.provider import Provider
from oic.oic.provider import RegistrationEndpoint
from oic.oic.provider import TokenEndpoint
from oic.oic.provider import UserinfoEndpoint
from oic.utils import shelve_wrapper
from oic.utils.authn.authn_context import AuthnBroker
from oic.utils.authn.authn_context import make_auth_verify
from oic.utils.authn.client import verify_client
from oic.utils.authn.multi_auth import AuthnIndexedEndpointWrapper
from oic.utils.authn.user import UsernamePasswordMako
from oic.utils.authz import AuthzHandling
from oic.utils.http_util import *
from oic.utils.keyio import keyjar_init
from oic.utils.userinfo import UserInfo
from oic.utils.webfinger import OIC_ISSUER
from oic.utils.webfinger import WebFinger
from cherrypy import wsgiserver
from cherrypy.wsgiserver.ssl_builtin import BuiltinSSLAdapter
from oic.utils.sdb import create_session_db
LOGGER = logging.getLogger("")
LOGFILE_NAME = 'oc.log'
hdlr = logging.FileHandler(LOGFILE_NAME)
base_formatter = logging.Formatter(
"%(asctime)s %(name)s:%(levelname)s %(message)s")
CPC = ('%(asctime)s %(name)s:%(levelname)s '
'[%(client)s,%(path)s,%(cid)s] %(message)s')
cpc_formatter = logging.Formatter(CPC)
hdlr.setFormatter(base_formatter)
LOGGER.addHandler(hdlr)
LOGGER.setLevel(logging.DEBUG)
logger = logging.getLogger('oicServer')
# noinspection PyUnresolvedReferences
if __name__ == '__main__':
root = './'
lookup = TemplateLookup(directories=[root + 'Templates', root + 'htdocs'],
module_directory=root + 'modules',
input_encoding='utf-8', output_encoding='utf-8')
usernamePasswords = {
"user1": "1",
"user2": "2"
}
passwordEndPointIndex = 0 # what is this, and what does its value mean?
# JWKS: JSON Web Key
jwksFileName = "static/jwks.json"
# parse the parameters
parser = argparse.ArgumentParser()
parser.add_argument('-c', dest='config')
parser.add_argument('-d', dest='debug', action='store_true')
args = parser.parse_args()
# parse and setup configuration
config = importlib.import_module(args.config)
config.ISSUER = config.ISSUER + ':{}/'.format(config.PORT)
config.SERVICEURL = config.SERVICEURL.format(issuer=config.ISSUER)
endPoints = config.AUTHENTICATION["UserPassword"]["EndPoints"]
fullEndPointsPath = ["%s%s" % (config.ISSUER, ep) for ep in endPoints]
# TODO: why this instantiation happens so early? can I move it later?
# An OIDC Authorization/Authentication server is designed to
# allow more than one authentication method to be used by the server.
# And that is what the AuthBroker is for.
# Given information about the authorisation request, the AuthBroker
# chooses which method(s) to be used for authenticating the person/entity.
# According to the OIDC standard a Relaying Party can say
# 'I want this type of authentication', and the AuthnBroker tries to pick
# methods from the set it has been supplied, to map that request.
authnBroker = AuthnBroker()
# UsernamePasswordMako: authenticas a user using the username/password form in a
# WSGI environment using Mako as template system
usernamePasswordAuthn = UsernamePasswordMako(
None, # server instance
"login.mako", # a mako template
lookup, # lookup template
usernamePasswords, # username/password dictionary-like database
"%sauthorization" % config.ISSUER, # where to send the user after authentication
None, # templ_arg_func ??!!
fullEndPointsPath) # verification endpoints
# AuthnIndexedEndpointWrapper is a wrapper class for using an authentication module with multiple endpoints.
authnIndexedEndPointWrapper = AuthnIndexedEndpointWrapper(usernamePasswordAuthn, passwordEndPointIndex)
authnBroker.add(config.AUTHENTICATION["UserPassword"]["ACR"], # (?!)
authnIndexedEndPointWrapper, # (?!) method: an identifier of the authentication method.
config.AUTHENTICATION["UserPassword"]["WEIGHT"], # security level
"") # (?!) authentication authority
# ?!
authz = AuthzHandling()
clientDB = shelve_wrapper.open(config.CLIENTDB)
# In-Memory non-persistent SessionDB issuing DefaultTokens
sessionDB = create_session_db(config.ISSUER,
secret=rndstr(32),
password=rndstr(32))
provider = Provider(
name=config.ISSUER, # name
sdb=sessionDB, # session database.
cdb=clientDB, # client database
authn_broker=authnBroker, # authn broker
userinfo=None, # user information
authz=authz, # authz
client_authn=verify_client, # client authentication
symkey=config.SYM_KEY, # Used for Symmetric key authentication
# urlmap = None, # ?
# keyjar = None, # ?
# hostname = "", # ?
template_renderer=mako_renderer, # Rendering custom templates
# verify_ssl = True, # Enable SSL certs
# capabilities = None, # ?
# schema = OpenIDSchema, # ?
# jwks_uri = '', # ?
# jwks_name = '', # ?
baseurl=config.ISSUER,
# client_cert = None # ?
)
# SessionDB:
# This is database where the provider keeps information about
# the authenticated/authorised users. It includes information
# such as "what has been asked for (claims, scopes, and etc. )"
# and "the state of the session". There is one entry in the
# database per person
#
# __________ Note __________
# provider.keyjar is an interesting parameter,
# currently it uses default values, but
# if you have time, it worth investigating.
for authnIndexedEndPointWrapper in authnBroker:
authnIndexedEndPointWrapper.srv = provider
# TODO: this is a point to consider: what if user data in a database?
if config.USERINFO == "SIMPLE":
provider.userinfo = UserInfo(config.USERDB)
provider.cookie_ttl = config.COOKIETTL
provider.cookie_name = config.COOKIENAME
if args.debug:
provider.debug = True
try:
# JWK: JSON Web Key
# JWKS: is a dictionary of JWK
# __________ NOTE __________
# JWKS contains private key information.
#
# keyjar_init configures cryptographic key
# based on the provided configuration "keys".
jwks = keyjar_init(
provider, # server/client instance
config.keys, # key configuration
kid_template="op%d") # template by which to build the kids (key ID parameter)
except Exception as err:
# LOGGER.error("Key setup failed: %s" % err)
provider.key_setup("static", sig={"format": "jwk", "alg": "rsa"})
else:
for key in jwks["keys"]:
for k in key.keys():
key[k] = as_unicode(key[k])
f = open(jwksFileName, "w")
f.write(json.dumps(jwks))
f.close()
provider.jwks_uri = "%s%s" % (provider.baseurl, jwksFileName)
# for b in OAS.keyjar[""]:
# LOGGER.info("OC3 server keys: %s" % b)
# TODO: Questions:
# END_POINT is defined as a dictionary in the configuration file,
# why not defining it as string with "verify" value?
# after all, we have only one end point.
# can we have multiple end points for password? why?
endPoint = config.AUTHENTICATION["UserPassword"]["EndPoints"][passwordEndPointIndex]
_urls = []
_urls.append((r'^' + endPoint, make_auth_verify(authnIndexedEndPointWrapper.verify)))
_app = Application(provider, _urls)
# Setup the web server
server = wsgiserver.CherryPyWSGIServer(('0.0.0.0', config.PORT), _app.application) # nosec
server.ssl_adapter = BuiltinSSLAdapter(config.SERVER_CERT, config.SERVER_KEY)
print("OIDC Provider server started (issuer={}, port={})".format(config.ISSUER, config.PORT))
try:
server.start()
except KeyboardInterrupt:
server.stop()
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] | 2.322803 | 3,925 |
#!/usr/bin/env python
import argparse
import numpy as np
import os
import data_utils
import mle_sphere
import gen_sphere
import gen_sphere_grid
import gen_r_sig_3d
import gen_selection_in_g_3d
import metrics
import param_ss
import mle_priors_3d
DEFAULT_MLE_SPHERE_PARAM_DICT = dict(xc=0, yc=0, zc=0, r=1, rSig=0.3, xE=1.2, yE=1, zE=0.8)
GRID = np.array([-3, -2, -1, 1, 2, 3])
SCALEFACTOR = 1
parser = argparse.ArgumentParser()
parser.add_argument('--burnin', action='store', dest='burnin', type=int, required=False, default=250, help='Not sure')
parser.add_argument('--input_dir', action='store', dest='input_dir', help='Directory of input csv image point files')
parser.add_argument('--nGrid', action='store', dest='nGrid', type=int, required=False, default=6, help='Not sure')
parser.add_argument('--output_csv_dir', action='store', dest='output_csv_dir', help='Directory to output csv files')
parser.add_argument('--output_json_dir', action='store', dest='output_json_dir', help='Directory to output json files')
parser.add_argument('--output_log', action='store', dest='output_log', help='Output process log file')
parser.add_argument('--plotFigures', action='store', dest='plotFigures', type=bool, required=False, default=False, help='Plot figures')
parser.add_argument('--randomStart', action='store', dest='randomStart', type=bool, required=False, default=True, help='Do not use DEFAULT_MLE_SPHERE_PARAM_DICT')
parser.add_argument("--fixR", action='store', dest='fixR', type=bool, required=False, default=True, help="Not sure")
parser.add_argument('--sample', action='store', dest='sample', type=int, required=False, default=250, help='Not sure')
parser.add_argument('--thin', action='store', dest='thin', type=int, required=False, default=10, help='Not sure')
parser.add_argument('--xTau', action='store', dest='xTau', type=float, required=False, default=0.01, help='Not sure')
parser.add_argument('--yTau', action='store', dest='yTau', type=float, required=False, default=0.01, help='Not sure')
parser.add_argument('--zTau', action='store', dest='zTau', type=float, required=False, default=0.01, help='Not sure')
parser.add_argument('--rTau', action='store', dest='rTau', type=float, required=False, default=0.01, help='Not sure')
parser.add_argument('--xETau', action='store', dest='xETau', type=float, required=False, default=0.01, help='Not sure')
parser.add_argument('--yETau', action='store', dest='yETau', type=float, required=False, default=0.01, help='Not sure')
parser.add_argument('--zETau', action='store', dest='zETau', type=float, required=False, default=0.01, help='Not sure')
args = parser.parse_args()
param_dict = get_param_dict(args)
# TODO: nLevelSet = 150
log_fh = open(args.output_log, "w")
for file_name in sorted(os.listdir(args.input_dir)):
file_path = os.path.abspath(os.path.join(args.input_dir, file_name))
# Extract the cell_id from the file name.
cell_id = get_base_file_name(file_name)
# Load or generate synthetic level set data
# if params_dict['genData']:
if False:
# TODO: data = genSphereLevelSet(DEFAULT_MLE_SPHERE_PARAM_DICT, bounding_box, param_dict, nLevelSet)
meanR = [0, 0, 0]
# TODO: save(PrjCtrl.inputFileLevelSetDataM,'data')
else:
data = data_utils.load_raw3d_data(file_path)
log_fh.write("First 5 data points before normalization: {}\n".format(data[:5]))
data, meanR = data_utils.normalize_data(data, log_fh)
log_fh.write("\nFirst 5 data points after normalization: {}\n\n".format(data[:5]))
log_fh.write("mean radius {}".format(meanR))
# TODO: nLevelSet = data.shape[0]
# Set summary parameters
param_ss = param_ss.ParamSS(data.shape[0], meanR)
# Starting value for parameters
if param_dict['randomStart']:
mles_param_dict = mle_sphere.mle_sphere(data, cell_id, param_dict, log_fh)
else:
mles_param_dict = DEFAULT_MLE_SPHERE_PARAM_DICT
# Set Priors
prior = mle_priors_3d.MLEPriors3D(cMean=[mles_param_dict['xc'],
mles_param_dict['yc'],
mles_param_dict['zc']],
cStd=[1, 1, 1],
rMean=mles_param_dict['r'],
rStd=0.1,
eMean=[mles_param_dict['xE'],
mles_param_dict['yE'],
mles_param_dict['zE']],
eStd=[1, 1, 1])
# MCMC Analysis
for n in range(args.burnin + args.sample):
if (np.mod(n, args.thin) == 0 or n == 0):
log_fh.write("\nn {}\n".format(n))
mles_param_dict['cLogLike'] = metrics.calc_log_like_sphere_mix(mles_param_dict['xc'],
mles_param_dict['yc'],
mles_param_dict['zc'],
mles_param_dict['xE'],
mles_param_dict['yE'],
mles_param_dict['zE'],
mles_param_dict['r'],
data[mles_param_dict['sInGIndex'], :],
mles_param_dict['rSig'])
log_fh.write("\nmles_param_dict:\n")
log_fh.write("cLogLike {}\n".format(mles_param_dict['cLogLike']))
log_fh.write("xc {}\n".format(mles_param_dict['xc']))
log_fh.write("yc {}\n".format(mles_param_dict['yc']))
log_fh.write("zc {}\n".format(mles_param_dict['zc']))
log_fh.write("r {}\n".format(mles_param_dict['r']))
log_fh.write("xE {}\n".format(mles_param_dict['xE']))
log_fh.write("yE {}\n".format(mles_param_dict['yE']))
log_fh.write("zE {}\n".format(mles_param_dict['zE']))
log_fh.write("rSig {}\n".format(mles_param_dict['rSig']))
if args.plotFigures:
pass
tup = gen_sphere.gen_sphere(data, mles_param_dict, prior, param_dict)
mles_param_dict['xc'], mles_param_dict['yc'], mles_param_dict['zc'], mles_param_dict['r'], mles_param_dict['xE'], mles_param_dict['yE'], mles_param_dict['zE'] = tup
tup = gen_sphere_grid.gen_sphere_grid(data, mles_param_dict, prior, param_dict, SCALEFACTOR)
mles_param_dict['xc'], mles_param_dict['yc'], mles_param_dict['zc'], mles_param_dict['r'], mles_param_dict['xE'], mles_param_dict['yE'], mles_param_dict['zE'] = tup
mles_param_dict['rSig'] = gen_r_sig_3d.gen_r_sig_3d(data, mles_param_dict, prior)
mles_param_dict['sInG'], mles_param_dict['sInGIndex'], mles_param_dict['sOutGIndex'] = gen_selection_in_g_3d.gen_selection_in_g_3d(data, mles_param_dict, prior)
if n > args.burnin:
param_ss.set_params(mles_param_dict)
# param_ss = storeParam3D(ParamSS, param_dict)
log_fh.close()
# Summarize Parameter and print reports
param_ss.summarize_params(args.sample)
param_ss.output_csv(args.output_csv_dir, cell_id)
param_ss.output_json(args.output_json_dir, cell_id)
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] | 2.004286 | 3,733 |
#!/usr/bin/env python
#
# Copyright 2004,2007,2010,2012,2013 Free Software Foundation, Inc.
#
# This file is part of GNU Radio
#
# GNU Radio is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3, or (at your option)
# any later version.
#
# GNU Radio is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with GNU Radio; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street,
# Boston, MA 02110-1301, USA.
#
from gnuradio import gr, gr_unittest, filter, blocks
import math
if __name__ == '__main__':
gr_unittest.run(test_hilbert, "test_hilbert.xml")
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] | 3.422535 | 284 |
#!/usr/bin/env python3
import abc
from typing import List
import torch
from reagent.core.fb_checker import IS_FB_ENVIRONMENT
from reagent.core.registry_meta import RegistryMeta
from reagent.models.base import ModelBase
from reagent.parameters import NormalizationData
from reagent.prediction.predictor_wrapper import ActorWithPreprocessor
from reagent.preprocessing.preprocessor import Preprocessor
if IS_FB_ENVIRONMENT:
from reagent.fb.prediction.fb_predictor_wrapper import (
FbActorPredictorWrapper as ActorPredictorWrapper,
)
else:
from reagent.prediction.predictor_wrapper import ActorPredictorWrapper
class DiscreteActorNetBuilder(metaclass=RegistryMeta):
"""
Base class for discrete actor net builder.
"""
@abc.abstractmethod
def build_serving_module(
self,
actor: ModelBase,
state_normalization_data: NormalizationData,
action_feature_ids: List[int],
) -> torch.nn.Module:
"""
Returns a TorchScript predictor module
"""
state_preprocessor = Preprocessor(
state_normalization_data.dense_normalization_parameters, use_gpu=False
)
actor_with_preprocessor = ActorWithPreprocessor(
actor.cpu_model().eval(),
state_preprocessor,
)
return ActorPredictorWrapper(actor_with_preprocessor, action_feature_ids)
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62,
4480,
62,
3866,
41341,
11,
2223,
62,
30053,
62,
2340,
8,
198
] | 2.682081 | 519 |
show_process = False
iterations = 50
import turtle
if not show_process:
turtle.tracer(0)
turtle.colormode(255)
turtle.color((0, 150, 0))
turtle.penup()
turtle.goto(-330, 0)
turtle.pendown()
fern(iterations)
turtle.update()
turtle.exitonclick()
| [
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261,
12976,
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] | 2.184 | 125 |
# source: https://stackoverflow.com/a/30875830
from rest_framework.authentication import SessionAuthentication
| [
2,
2723,
25,
3740,
1378,
25558,
2502,
11125,
13,
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14,
64,
14,
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1334,
62,
30604,
13,
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3299,
1330,
23575,
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628
] | 3.862069 | 29 |
from .base_widget import *
from .mdt2json import *
| [
6738,
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] | 3 | 17 |
import time
from sys import exit, version_info
try:
from smbus import SMBus
except ImportError:
if version_info[0] < 3:
exit("This library requires python-smbus\nInstall with: sudo apt-get install python-smbus")
elif version_info[0] == 3:
exit("This library requires python3-smbus\nInstall with: sudo apt-get install python3-smbus")
adc_available = True
address = 0x48
i2c = SMBus(i2c_bus_id())
REG_CONV = 0x00
REG_CFG = 0x01
samples_per_second_map = {128: 0x0000, 250: 0x0020, 490: 0x0040, 920: 0x0060, 1600: 0x0080, 2400: 0x00A0, 3300: 0x00C0}
channel_map = {0: 0x4000, 1: 0x5000, 2: 0x6000, 3: 0x7000}
programmable_gain_map = {6144: 0x0000, 4096: 0x0200, 2048: 0x0400, 1024: 0x0600, 512: 0x0800, 256: 0x0A00}
PGA_6_144V = 6144
PGA_4_096V = 4096
PGA_2_048V = 2048
PGA_1_024V = 1024
PGA_0_512V = 512
PGA_0_256V = 256
try:
read_se_adc()
except IOError:
adc_available = False
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62,
15,
62,
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22243,
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6968,
32,
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53,
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24418,
12331,
25,
198,
220,
220,
220,
512,
66,
62,
15182,
796,
10352,
198
] | 2.197619 | 420 |
from typing import TYPE_CHECKING, TypedDict
if TYPE_CHECKING:
from .snowflake import Snowflake, SnowflakeList
__all__ = ("Category",)
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] | 3 | 47 |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import datasets
from myConvexHull import convex_hull, random_color
from myConvexHull.point_utils import X, Y
data = datasets.load_digits()
df = pd.DataFrame(data.data, columns=data.feature_names)
df['Target'] = pd.DataFrame(data.target)
plt.figure(figsize=(10, 6))
plt.title('Pixel [1, 3] vs Pixel [1, 4]')
plt.xlabel(data.feature_names[11])
plt.ylabel(data.feature_names[12])
for i in range(len(data.target_names)):
bucket = df[df['Target'] == i]
bucket = bucket.iloc[:, [11, 12]].values
hull = convex_hull(bucket)
color = random_color()
plt.scatter(bucket[:, 0], bucket[:, 1],
label=data.target_names[i], color=color)
hull = np.transpose(hull)
plt.plot(hull[X], hull[Y], color=color)
plt.legend()
plt.show()
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7,
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7,
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58,
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28,
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8,
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198,
489,
83,
13,
1455,
437,
3419,
198,
489,
83,
13,
12860,
3419,
198
] | 2.396011 | 351 |
from random import sample
from time import sleep
colors = {"clean": "\033[m",
"red": "\033[31m",
"green": "\033[32m",
"yellow": "\033[33m",
"blue": "\033[34m",
"purple": "\033[35m",
"cian": "\033[36m"}
order = ["Breno", "Edu", "Miguel", "Lucas"]
print("{}Hmm...let me see{}".format(colors["cian"], colors["clean"]))
sleep(2)
print("The presentation order is {}{}{} "
.format(colors["yellow"], sample(order, k=4), colors["clean"]))
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] | 2.170306 | 229 |
# AP Ruymgaart DMD, main script
import numpy as np, time, sys, copy, matplotlib.pyplot as plt
from videoFunctions import *
from tensorFiles import *
from plottingFunctions import *
from dmd import *
#==== input (command line, from run.sh) ====
print('===================================== start DMD =================================\nInput:')
cmds = processCmdArgs(sys.argv)
for c in cmds: print('\t', c, cmds[c])
dt,nv,fname,ftype,images,f0,f1,outname,frPlot,binv = None,None,None,None,None,None,None,None,[],None
try:
nv = int(cmds['modes'])
dt,thresh = float(cmds['dt']), float(cmds['thresh'])
fname,ftype,outname = cmds['movie'], cmds['type'], cmds['outname']
f0,f1 = int(cmds['framestart']), int(cmds['framestop'])
szplotfr = cmds['plotframes'].split(',')
binv = cmds['inv'].lower() == 'true'
for h in szplotfr: frPlot.append(int(h))
except: print('** input error **'), exit()
if ftype == 'npz': images = tnsrFile2numpy(fname)
else: images = video2numpy(fname)
print('Movie-shape=', images.shape, 'dt=',dt, 'Nr modes=', nv, 'file-type=', ftype, 'frame', f0, 'to', f1)
print('Plot frames', frPlot, 'output file name', outname)
#==== DMD & dmdDynamics ====
X, szX, szY = flattenVideo(images, f0, f1)
[Phi,A,S,L,X1,X2,b] = DMD(X, szY, szX, nv)
Xdmd, T, omega = dmdDynamics(X1,L,b,Phi,nv,dt=dt)
#==== foreground/background ====
BG = abs(copy.copy(Xdmd.T))
FG = X[0:len(X)-1] - BG + 0.3 #- subtract low rank BG and add a grey background
print(np.min(FG), np.max(FG))
if False:
R = copy.copy(FG)
R[R > 0] = 0.0
FG = FG - R
BG = BG + R
for n in range(len(FG)): FG[n] = FG[n]/np.max(FG[n])
FG[FG < thresh] = 0.0 # thresholding (see paper)
if False: #- alternative attempt to select modes, not used now
omegaCut = 0.0
Xlr,Xhr = np.zeros(Xdmd.shape), np.zeros(Xdmd.shape)
for k in range(T.shape[1]):
LRt, HRt = dmdDynamicsLrVec(X1, L, b, Phi, k, dt, omegaCut=omegaCut)
Xlr[:,k] = LRt
Xhr[:,k] = HRt
L2 = np.abs(Xlr.T)
H2 = np.abs(Xhr.T)
lrMv = reshape2video(L2/np.max(L2), szY, szX)
np2movieFile(lrMv, outname+'_xlr', invert=binv)
hrMv = reshape2video(H2/np.max(H2), szY, szX)
np2movieFile(hrMv, outname+'_xhr', invert=binv)
#==== output ====
plotSV(np.log(np.abs(S) + 1), fname=outname+'_logSV.png')
plotSV(np.abs(omega), fname=outname+'_omega.png')
bgMv = reshape2video(BG, szY, szX)
bgMv = bgMv/np.max(bgMv)
np2movieFile(bgMv, outname+'_LR', invert=binv)
fgMv = reshape2video(FG, szY, szX)
np2movieFile(fgMv, outname+'_diff', invert=binv)
origMv = reshape2video(X[0:len(X)-1], szY, szX)
for fr in frPlot:
plotFrame(bgMv, fr, outname+'_LR_%d' % (fr))
plotFrame(fgMv, fr, outname+'_diff_%d' % (fr))
plotFrame(origMv, fr, outname+'_orig_%d' % (fr)) | [
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] | 2.146049 | 1,253 |
from pathlib import Path
import environ
env = environ.Env()
READ_DOT_ENV_FILE = env.bool('READ_DOT_ENV_FILE', default=True)
if READ_DOT_ENV_FILE:
env.read_env()
BASE_DIR = Path(__file__).resolve().parent.parent
SECRET_KEY = env('SECRET_KEY')
DEBUG = env.bool('DEBUG')
ALLOWED_HOSTS = ['127.0.0.1', 'payment-rounding.adrian-kalinin.dev', 'payment-rounding-api-5x85t.ondigitalocean.app']
INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'rest_framework',
'api',
]
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
ROOT_URLCONF = 'payment_rounding.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'payment_rounding.wsgi.application'
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': BASE_DIR / 'db.sqlite3',
}
}
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
},
]
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
STATIC_URL = '/static/'
DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField'
REST_FRAMEWORK = {
'DEFAULT_RENDERER_CLASSES': (
'rest_framework.renderers.JSONRenderer',
)
}
| [
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] | 2.213393 | 1,120 |
# vim: set et sw=4 sts=4 fileencoding=utf-8:
#
# Python camera library for the Rasperry-Pi camera module
# Copyright (c) 2013-2015 Dave Jones <[email protected]>
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of the copyright holder nor the
# names of its contributors may be used to endorse or promote products
# derived from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
from __future__ import (
unicode_literals,
print_function,
division,
absolute_import,
)
# Make Py2's str equivalent to Py3's
str = type('')
import mimetypes
import ctypes as ct
from functools import reduce
from operator import or_
from . import bcm_host, mmalobj as mo, mmal
from .encoders import PiCookedOneImageEncoder, PiRawOneImageEncoder
from .exc import PiCameraRuntimeError, PiCameraValueError
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] | 3.285938 | 640 |
import os
from typing import Any, Dict
import pytest
from src.stairlight.config import Configurator
from src.stairlight.key import StairlightConfigKey
from src.stairlight.source.redash import (
RedashTemplate,
RedashTemplateSource,
TemplateSourceType,
)
@pytest.mark.parametrize(
"env_key, path",
[
("REDASH_DATABASE_URL", "src/stairlight/source/sql/redash_queries.sql"),
],
)
@pytest.mark.parametrize(
(
"query_id, query_name, query_str, "
"data_source_name, params, mapped_table_attributes"
),
[
(
5,
"Copy of (#4) New Query",
"SELECT * FROM {{ table }}",
"metadata",
{"table": "dashboards"},
{
"Labels": {"Category": "Redash test"},
"Parameters": {"table": "dashboards"},
"TableName": "Copy of (#4) New Query",
},
),
],
)
| [
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62,
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1,
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220,
220,
10612,
198,
220,
220,
220,
685,
198,
220,
220,
220,
220,
220,
220,
220,
357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
642,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
29881,
286,
17426,
19,
8,
968,
43301,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
46506,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1391,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
17822,
1424,
1298,
19779,
27313,
1298,
366,
7738,
1077,
1332,
25719,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
48944,
1298,
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1298,
366,
42460,
12821,
25719,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
10962,
5376,
1298,
366,
29881,
286,
17426,
19,
8,
968,
43301,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
8964,
198,
220,
220,
220,
220,
220,
220,
220,
10612,
198,
220,
220,
220,
16589,
198,
8,
198
] | 2.063457 | 457 |
import os
import pickle
import numpy as np
import pandas as pd
from torch.utils.data import Dataset
DATASET_DIR = "datasets"
| [
11748,
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62,
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366,
19608,
292,
1039,
1,
628,
628
] | 2.75 | 48 |
# Exercise 013 - That Classic Average
"""Create a program that reads two grades from a student and calculates their average, showing a message at the end, according to the average achieved:
- Average below 5.0: FAIL"""
grade_01 = float(input("Enter the first grade: "))
grade_02 = float(input("Enter the second grade: "))
average = (grade_01 + grade_02) / 2
if 5 <= average <= 10:
print(f"The student took {average} of note, was approved! ")
elif 0 <= average < 5:
print(f"The student took {average} of note, was disapproved! ")
else:
print("Wrong value")
| [
2,
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25,
198,
220,
220,
220,
3601,
7203,
39213,
506,
1988,
4943,
198
] | 3.255682 | 176 |
import datetime
import logging
import json
import hashlib
import hmac
import base64
import aiohttp
import asyncio
from collections import deque
| [
11748,
4818,
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30351,
952,
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6738,
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1330,
390,
4188,
628,
198
] | 3.842105 | 38 |
import configparser
BACKUP_SUFFIX = ".bak"
_parser = configparser.ConfigParser()
def parse_file(filename):
"""Return all infomation you needed to patch files"""
_parser.read(filename)
result = {'files': {}}
result['metadata'] = {
"name": _parser['metadata']['name'],
"description": _parser['metadata']['description'],
"congratulation": _parser['metadata']['congratulation'],
} if 'metadata' in _parser else {}
for patch_name in _parser.sections():
if not patch_name.startswith('patch:'):
continue
s = _parser[patch_name]
patch_name = patch_name[6:]
this = {
"unsign": s.getboolean('unsign', False),
"file": s['file'],
"relatives": [],
"absolutes": [],
}
if 'relatives' in s:
for line in s['relatives'].strip('\n, ').split('\n'):
line = line.split(',')
this['relatives'].append({
"src": bytes.fromhex(line[0]),
"dst": bytes.fromhex(line[1]),
"fg": dict(zip(
(_str2range(i) for i in line[2::2]),
(bytes.fromhex(i) for i in line[3::2]))),
})
if 'absolutes' in s:
for line in s['absolutes'].strip('\n, ').split('\n'):
this['absolutes'].append({
"pos": int(line[0]),
"src": bytes.fromhex(line[1]),
"dst": bytes.fromhex(line[2]),
})
result['files'][patch_name] = this
return result
| [
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220,
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220,
220,
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35312,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
428,
17816,
2411,
2929,
6,
4083,
33295,
15090,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
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13,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
366,
67,
301,
1298,
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13,
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33095,
7,
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58,
16,
46570,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
40616,
1298,
8633,
7,
13344,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
44104,
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17,
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7,
72,
8,
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1312,
287,
1627,
58,
17,
3712,
17,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
33661,
13,
6738,
33095,
7,
72,
8,
329,
1312,
287,
1627,
58,
18,
3712,
17,
60,
4008,
828,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
32092,
198,
220,
220,
220,
220,
220,
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220,
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6,
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25,
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220,
220,
220,
220,
220,
220,
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220,
220,
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77,
6,
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220,
220,
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220,
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220,
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220,
220,
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220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
1930,
1298,
493,
7,
1370,
58,
15,
46570,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
10677,
1298,
9881,
13,
6738,
33095,
7,
1370,
58,
16,
46570,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
67,
301,
1298,
9881,
13,
6738,
33095,
7,
1370,
58,
17,
46570,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
32092,
628,
220,
220,
220,
220,
220,
220,
220,
1255,
17816,
16624,
6,
7131,
17147,
62,
3672,
60,
796,
428,
628,
220,
220,
220,
1441,
1255,
628
] | 1.905484 | 857 |
#!/usr/bin/env python
# for i from 2 to 20
# compute prime factorization of i.
# use largest multiplicity in any
# prime factor seen thus far
facs = {}
for i in xrange(2,21):
f = factorize(i)
for j in f:
facs[j] = max(facs.get(j,0),f[j])
print reduce(lambda x,y: x*y, (i**facs[i] for i in facs))
| [
2,
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14629,
14,
8800,
14,
24330,
21015,
198,
198,
2,
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2,
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7,
17,
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] | 2.325926 | 135 |
# Generated by the protocol buffer compiler. DO NOT EDIT!
# sources: onos/config/diags/diags.proto
# plugin: python-betterproto
from dataclasses import dataclass
from typing import AsyncIterator, Dict
import betterproto
from betterproto.grpc.grpclib_server import ServiceBase
import grpclib
class Type(betterproto.Enum):
"""Change (Network or Device) event type"""
# NONE indicates this response does not represent a modification of the
# Change
NONE = 0
# ADDED is an event which occurs when a Change is added to the topology
ADDED = 1
# UPDATED is an event which occurs when a Change is updated
UPDATED = 2
# REMOVED is an event which occurs when a Change is removed from the
# configuration
REMOVED = 3
@dataclass(eq=False, repr=False)
class OpStateRequest(betterproto.Message):
"""
OpStateRequest is a message for specifying GetOpState query parameters.
"""
# The request is always in the context of a Device ID. If the device does not
# exist or is disconnected an error will be returned.
device_id: str = betterproto.string_field(1)
# subscribe indicates whether to subscribe to events (e.g. ADD, UPDATE, and
# REMOVE) that occur after all paths for the device have been streamed to the
# client
subscribe: bool = betterproto.bool_field(2)
@dataclass(eq=False, repr=False)
@dataclass(eq=False, repr=False)
class ListNetworkChangeRequest(betterproto.Message):
"""
ListNetworkChangeRequest requests a stream of changes and updates to them
By default, the request requests a stream of all changes that are present
in the topology when the request is received by the service. However, if
`subscribe` is `true`, the stream will remain open after all changes have
been sent and events that occur following the last changes will be streamed
to the client until the stream is closed. If "withoutReplay" is true then
only changes that happen after the call will be returned
"""
# subscribe indicates whether to subscribe to events (e.g. ADD, UPDATE, and
# REMOVE) that occur after all devices have been streamed to the client
subscribe: bool = betterproto.bool_field(1)
# option to specify a specific network change - if blank or '*' then select
# all Can support `*` (match many chars) or '?' (match one char) as wildcard
changeid: str = betterproto.string_field(2)
# option to request only changes that happen after the call
without_replay: bool = betterproto.bool_field(3)
@dataclass(eq=False, repr=False)
class ListNetworkChangeResponse(betterproto.Message):
"""ListNetworkChangeResponse carries a single network change event"""
# change is the network change on which the event occurred
change: "_change_network__.NetworkChange" = betterproto.message_field(1)
# type is a qualification of the type of change being made
type: "Type" = betterproto.enum_field(2)
@dataclass(eq=False, repr=False)
class ListDeviceChangeRequest(betterproto.Message):
"""
ListDeviceChangeRequest requests a stream of changes and updates to them By
default, the request requests a stream of all changes that are present in
the topology when the request is received by the service. However, if
`subscribe` is `true`, the stream will remain open after all changes have
been sent and events that occur following the last changes will be streamed
to the client until the stream is closed. If "withoutReplay" is true then
only changes that happen after the call will be returned
"""
# subscribe indicates whether to subscribe to events (e.g. ADD, UPDATE, and
# REMOVE) that occur after all devices have been streamed to the client
subscribe: bool = betterproto.bool_field(1)
# option to specify a specific device change - if blank or '*' then select
# all Can support `*` (match many chars) or '?' (match one char) as wildcard
device_id: str = betterproto.string_field(2)
# device_version is an optional device version
device_version: str = betterproto.string_field(3)
# option to request only changes that happen after the call
without_replay: bool = betterproto.bool_field(4)
@dataclass(eq=False, repr=False)
class ListDeviceChangeResponse(betterproto.Message):
"""ListDeviceChangeResponse carries a single network change event"""
# change is the device change on which the event occurred
change: "_change_device__.DeviceChange" = betterproto.message_field(1)
# type is a qualification of the type of change being made
type: "Type" = betterproto.enum_field(2)
from .. import admin as _admin__
from ..change import device as _change_device__
from ..change import network as _change_network__
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] | 3.394155 | 1,403 |
import sys
sys.path.insert(0, 'src/reversion')
from distutils.core import setup
from version import __version__
# Load in babel support, if available.
try:
from babel.messages import frontend as babel
cmdclass = {"compile_catalog": babel.compile_catalog,
"extract_messages": babel.extract_messages,
"init_catalog": babel.init_catalog,
"update_catalog": babel.update_catalog,}
except ImportError:
cmdclass = {}
setup(name="django-reversion",
version='.'.join(str(x) for x in __version__),
license="BSD",
description="An extension to the Django web framework that provides comprehensive version control facilities",
long_description=open("README.markdown").read(),
author="Dave Hall",
author_email="[email protected]",
url="http://github.com/etianen/django-reversion",
zip_safe=False,
packages=["reversion", "reversion.management", "reversion.management.commands", "reversion.migrations"],
package_dir={"": "src"},
package_data = {"reversion": ["locale/*/LC_MESSAGES/django.*", "templates/reversion/*.html"]},
cmdclass = cmdclass,
classifiers=["Development Status :: 5 - Production/Stable",
"Environment :: Web Environment",
"Intended Audience :: Developers",
"License :: OSI Approved :: BSD License",
"Operating System :: OS Independent",
"Programming Language :: Python",
"Framework :: Django",])
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] | 2.458003 | 631 |
import random
class Account_user:
"""
Class to create new user accounts and save information
"""
users_list = []
def __init__(self,first_name,password):
'''
Method that helps us define properties that each user account will have
Args:
first_name : main account user name
password : main account password
'''
self.first_name = first_name
self.password = password
def save_user(self):
'''
Method to save new user account objects into users_list
'''
Account_user.users_list.append(self)
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] | 2.438462 | 260 |
#!/usr/bin/env python
import argparse
import csv
import glob
import os
import itertools
from pylab import *
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
from asyncore import loop
# Values we care about
keys = []
keys.append('num_read')
keys.append('num_writes')
keys.append('num_ops')
keys.append('num_rw_time_edges')
keys.append('num_ww_time_edges')
keys.append('num_time_edges')
keys.append('num_harmful')
keys.append('num_commute')
keys.append('num_races')
keys.append('num_covered')
keys.append('num_per_pkt_races')
keys.append('num_per_pkt_inconsistent')
keys.append('num_per_pkt_inconsistent_covered')
keys.append('num_per_pkt_entry_version_race')
keys.append('num_per_pkt_inconsistent_no_repeat')
keys.append('total_time_sec')
keys.append('load_time_sec')
keys.append('detect_races_time_sec')
keys.append('extract_traces_time_sec')
keys.append('find_reactive_cmds_time_sec')
keys.append('find_proactive_cmds_time_sec')
keys.append('find_covered_races_time')
keys.append('per_packet_inconsistent_time_sec')
keys.append('find_inconsistent_update_time_sec')
per_pkt_consistency = ['num_per_pkt_races', 'num_per_pkt_inconsistent',
'num_per_pkt_inconsistent_covered',
'num_per_pkt_entry_version_race']
prefixes = ['True-','False-']
timing_values = {'0': 0,
'1': 1,
'2': 2,
'3': 3,
'4': 4,
'5': 5,
'6': 6,
'7': 7,
'8': 8,
'9': 9,
'10': 10,
'inf': 11, # hack for plots
}
sorted_timing_values = sorted(timing_values.items(), key=lambda x: x[1])
# http://matplotlib.org/api/markers_api.html
markers = ['x',
'+',
'.',
'o',
'*',
# ',',
'1',
'2',
'3',
'4',
'8',
'<',
'>',
'D',
'H',
'^',
'_',
'd',
'h',
'p',
's',
'v',
'|',
0,
1,
2,
3,
4,
5,
6,
7,]
def get_correct_alt_barr_prefix(name):
"""
Whether to use alt-barr results or not. Should be True for purely reactive controllers, and False for proactive controllers that use barriers.
"""
prefix_for_name = {}
prefix_for_name['trace_floodlight_learningswitch-StarTopology2-steps200'] = True
prefix_for_name['trace_floodlight_learningswitch-StarTopology4-steps200'] = True
prefix_for_name['trace_floodlight_learningswitch-StarTopology8-steps200'] = True
prefix_for_name['trace_floodlight_learningswitch-BinaryLeafTreeTopology1-steps200'] = True
prefix_for_name['trace_floodlight_learningswitch-BinaryLeafTreeTopology2-steps200'] = True
prefix_for_name['trace_floodlight_forwarding-StarTopology2-steps200'] = True
prefix_for_name['trace_floodlight_forwarding-StarTopology4-steps200'] = True
prefix_for_name['trace_floodlight_forwarding-StarTopology8-steps200'] = True
prefix_for_name['trace_floodlight_forwarding-BinaryLeafTreeTopology1-steps200'] = True
prefix_for_name['trace_floodlight_forwarding-BinaryLeafTreeTopology2-steps200'] = True
prefix_for_name['trace_floodlight_circuitpusher-BinaryLeafTreeTopology1-steps200'] = False
# prefix_for_name['trace_floodlight_circuitpusher-BinaryLeafTreeTopology1-steps400'] = False
prefix_for_name['trace_floodlight_circuitpusher-BinaryLeafTreeTopology2-steps200'] = False
# prefix_for_name['trace_floodlight_circuitpusher-BinaryLeafTreeTopology2-steps400'] = False
# consistent, barriers
prefix_for_name['trace_pox_ConsistencyTopology-False-False-steps200'] = True
prefix_for_name['trace_pox_ConsistencyTopology-False-True-steps200'] = True
prefix_for_name['trace_pox_ConsistencyTopology-True-False-steps200'] = True
prefix_for_name['trace_pox_ConsistencyTopology-True-True-steps200'] = True
# prefix_for_name['trace_pox_l2_multi-BinaryLeafTreeTopology1-steps200'] = True
# prefix_for_name['trace_pox_l2_multi-BinaryLeafTreeTopology2-steps200'] = True
# prefix_for_name['trace_pox_l2_multi-StarTopology2-steps200'] = True
# prefix_for_name['trace_pox_l2_multi-StarTopology4-steps200'] = True
prefix_for_name['trace_onos-ifwd-StarTopology2-steps200'] = False
prefix_for_name['trace_onos-ifwd-MeshTopology2-steps200'] = False
prefix_for_name['trace_onos-ifwd-BinaryLeafTreeTopology1-steps200'] = False
prefix_for_name['trace_onos-ifwd-BinaryLeafTreeTopology2-steps200'] = False
prefix_for_name['trace_onos-noinstr-ifwd-StarTopology2-steps200'] = False
prefix_for_name['trace_onos-noinstr-ifwd-MeshTopology2-steps200'] = False
prefix_for_name['trace_onos-noinstr-ifwd-BinaryLeafTreeTopology1-steps200'] = False
prefix_for_name['trace_onos-noinstr-ifwd-BinaryLeafTreeTopology2-steps200'] = False
if name in prefix_for_name:
if prefix_for_name[name]:
return 'True-'
else:
return 'False-'
else:
print "get_correct_alt_barr_prefix() unknown for " + name
return # nothing
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('result_dirs', nargs='+' )
parser.add_argument('--no-plots', dest='no_plots', action='store_true',
default=False, help="Do not write any plot PDFs to the disk.")
args = parser.parse_args()
main(args.result_dirs, args.no_plots)
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#!/usr/bin/env python
# This is a script for loading some test data into the video-asset-manager using
# REST calls. Each insert is being done as a separate call, which is the only type of
# insert that the API supports at this point.
#
# Note that for production loads, we would not do this in this manner. Instead, we might
# use a script that takes a few parameters (e.g. file URL, camera_id) then
# - auto-generates some parameters, such as video-sequence name and video name.
# - parses the other metadata from the video file as json, using something like ffprobe
# - maps the json from ffprobe to the asset managers metadata.
# - generates the video-sequence and video objects via API's calls as needed.
import requests
import json
base_url = "http://localhost:8080/v1/"
vs_url = base_url + "videosequence"
v_url = base_url + "video"
vr_url = base_url + "videoreference"
vs1 = read(vs_url,
data = {"name": "T0097",
"camera_id": "Tiburon"})
v1_1 = read(v_url,
data = {"name": "T0097-01",
"start": "2016-04-05T00:01:00Z",
"duration_millis": 15 * 60 * 1000,
"video_sequence_uuid": vs1["uuid"]})
vr1_1_1 = read(vr_url,
data = {"uri": "http://www.mbari.org/foo/bar/T0097_20160405T000100Z.mov",
"video_uuid": v1_1["uuid"],
"container": "video/quicktime",
"video_codec": "ProRes HQ",
"audio_codec": "AAC",
"width": 1920,
"height": 1080,
"frame_rate": 59.97})
vr1_1_2 = read(vr_url,
data = {"uri": "http://www.mbari.org/foo/bar/T0097_20160405T000100Z.mp4",
"video_uuid": v1_1["uuid"],
"container": "video/mp4",
"video_codec": "H.264",
"audio_codec": "AAC",
"width": 1920,
"height": 1080,
"frame_rate": 59.97})
v1_2 = read(v_url,
data = {"name": "T0097-02",
"start": "2016-04-05T00:01:15Z",
"duration_millis": 15 * 60 * 1000,
"video_sequence_uuid": vs1["uuid"],
"description": "This video is cool"})
vr1_2_1 = read(vr_url,
data = {"uri": "http://www.mbari.org/foo/bar/T0097_20160405T000115Z.mov",
"video_uuid": v1_2["uuid"],
"container": "video/quicktime",
"video_codec": "ProRes HQ",
"audio_codec": "AAC",
"width": 1920,
"height": 1080,
"frame_rate": 59.97})
vr1_2_2 = read(vr_url,
data = {"uri": "http://www.mbari.org/foo/bar/T0097_20160405T000115Z.mp4",
"video_uuid":v1_2["uuid"],
"container":"video/mp4",
"video_codec":"H.264",
"audio_codec":"AAC",
"width":1920,
"height":1080,
"frame_rate":19})
v1_3 = read(v_url,
data = {"name": "T0097-01HD",
"start": "2016-04-05T00:01:00Z",
"duration_millis": 45 * 60 * 1000,
"video_sequence_uuid": vs1["uuid"],
"description": "This is a reference to a tape that overlaps with video files"})
vr1_3_1 = read(vr_url,
data = {"uri": "urn:T0097-01HD",
"video_uuid": v1_3["uuid"],
"width": 1920,
"height": 1080,
"frame_rate": 29.97,
"description": "D5 Tape"})
vs2 = read(vs_url,
data = {"name": "V1234", "camera_id": "Ventana"})
v2_1 = read(v_url,
data = {"name": "V1234-01",
"start": "2016-06-12T00:18:31Z",
"duration_millis": 15 * 60 * 1000,
"video_sequence_uuid": vs2["uuid"]})
vr2_1_1 = read(vr_url,
data = {"uri": "http://www.mbari.org/foo/bar/V1234_20160612T001831.mov",
"video_uuid": v2_1["uuid"],
"container": "video/quicktime",
"video_codec": "ProRes HQ",
"audio_codec": "AAC",
"width": 1920,
"height": 1080,
"frame_rate": 59.97})
vr2_1_2 = read(vr_url,
data = {"uri": "http://www.mbari.org/foo/bar/V1234_20160612T001831.mp4",
"video_uuid": v2_1["uuid"],
"container": "video/mp4",
"video_codec": "H.264",
"audio_codec": "AAC",
"width": 1920,
"height": 1080,
"frame_rate": 19})
vs3 = read(vs_url,
data = {"name": "V9931", "camera_id": "Ventana"})
v3_1 = read(v_url,
data = {"name": "V9931-01",
"start": "2011-12-12T00:00:10Z",
"duration_millis": 45 * 60 * 1000,
"video_sequence_uuid": vs3["uuid"]})
vr3_1_1 = read(vr_url,
data = {"uri": "http://www.mbari.org/foo/bar/V9931_201101212T000010Z.mov",
"video_uuid": v3_1["uuid"],
"container": "video/quicktime",
"video_codec": "ProRes HQ",
"audio_codec": "AAC",
"width": 1920,
"height": 1080,
"frame_rate": 59.97})
vr3_1_2 = read(vr_url,
data = {"uri": "http://www.mbari.org/foo/bar/V9931_201101212T000010Z.mp4",
"video_uuid": v3_1["uuid"],
"container": "video/mp4",
"video_codec": "H.264",
"audio_codec": "AAC",
"width": 1920,
"height": 1080,
"frame_rate": 30})
vr3_1_3 = read(vr_url,
data = {"uri": "http://www.mbari.org/foo/bar/V9931_201101212T000010Z_midres.mp4",
"video_uuid": v3_1["uuid"],
"container": "video/mp4",
"video_codec": "H.264",
"audio_codec": "AAC",
"width": 720,
"height": 640,
"frame_rate": 19})
print("--- Database Dump -----------------------------------------------------")
db_dump = json.loads(requests.get(v_url).text)
print(json.dumps(db_dump, sort_keys=True, indent=2, separators=(',', ': '))) | [
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19779,
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19779,
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19779,
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32257,
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19779,
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34924,
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"""
Module for gathering info from Wolf Heating System via ISM8 adapter
"""
import logging
import asyncio
class Ism8(asyncio.Protocol):
"""
This protocol class is invoked to listen to message from ISM8 module and
feed data into internal data array
"""
ISM_HEADER = b'\x06\x20\xf0\x80'
ISM_CONN_HEADER = b'\x04\x00\x00\x00'
ISM_ACK = b'\xF0\x86\x00\x00\x00\x00\x00'
ISM_POLL = b'\x06\x20\xF0\x80\x00\x16\x04\x00\x00\x00\xF0\xD0'
# constant byte arrays for creating ISM8 network messages
DP_DEVICE = 0
# index of Wolf ISM main device name
DP_NAME = 1
# index of datapoint name
DP_TYPE = 2
# index of datapoint type (as described in Wolf API)
DP_RW = 3
# index of R/W-flag (writing not implemented so far)
DP_UNIT = 4
# index of unit description, if applicable
DATAPOINTS = {
1: ('HG1', 'Stoerung', 'DPT_Switch', False, ''),
2: ('HG1', 'Betriebsart', 'DPT_HVACContrMode', False, ''),
3: ('HG1', 'Brennerleistung', 'DPT_Scaling', False, '%'),
4: ('HG1', 'Kesseltemperatur', 'DPT_Value_Temp', False, 'C'),
5: ('HG1', 'Sammlertemperatur', 'DPT_Value_Temp', False, 'C'),
6: ('HG1', 'Ruecklauftemperatur', 'DPT_Value_Temp', False, 'C'),
7: ('HG1', 'Warmwassertemperatur', 'DPT_Value_Temp', False, 'C'),
8: ('HG1', 'Aussentemperatur', 'DPT_Value_Temp', False, 'C'),
9: ('HG1', 'Status Brenner', 'DPT_Switch', False, ''),
10: ('HG1', 'Status Heizkreispumpe', 'DPT_Switch', False, ''),
11: ('HG1', 'Status Speicherladepumpe', 'DPT_Switch', False, ''),
12: ('HG1', 'Status 3W-Umschaltventil', 'DPT_OpenClose', False, ''),
13: ('HG1', 'Anlagendruck', 'DPT_Value_Pres', False, 'Pa'),
14: ('HG2', 'Stoerung', 'DPT_Switch', False, ''),
15: ('HG2', 'Betriebsart', 'DPT_HVACContrMode', False, ''),
16: ('HG2', 'Brennerleistung', 'DPT_Scaling', False, '%'),
17: ('HG2', 'Kesseltemperatur', 'DPT_Value_Temp', False, 'C'),
18: ('HG2', 'Sammlertemperatur', 'DPT_Value_Temp', False, 'C'),
19: ('HG2', 'Ruecklauftemperatur', 'DPT_Value_Temp', False, 'C'),
20: ('HG2', 'Warmwassertemperatur', 'DPT_Value_Temp', False, 'C'),
21: ('HG2', 'Aussentemperatur', 'DPT_Value_Temp', False, 'C'),
22: ('HG2', 'Status Brenner', 'DPT_Switch', False, ''),
23: ('HG2', 'Status Heizkreispumpe', 'DPT_Switch', False, ''),
24: ('HG2', 'Status Speicherladepumpe', 'DPT_Switch', False, ''),
25: ('HG2', 'Status 3W-Umschaltventil', 'DPT_OpenClose', False, ''),
26: ('HG2', 'Anlagendruck', 'DPT_Value_Pres', False, 'Pa'),
27: ('HG3', 'Stoerung', 'DPT_Switch', False, ''),
28: ('HG3', 'Betriebsart', 'DPT_HVACContrMode', False, ''),
29: ('HG3', 'Brennerleistung', 'DPT_Scaling', False, '%'),
30: ('HG3', 'Kesseltemperatur', 'DPT_Value_Temp', False, 'C'),
31: ('HG3', 'Sammlertemperatur', 'DPT_Value_Temp', False, 'C'),
32: ('HG3', 'Ruecklauftemperatur', 'DPT_Value_Temp', False, 'C'),
33: ('HG3', 'Warmwassertemperatur', 'DPT_Value_Temp', False, 'C'),
34: ('HG3', 'Aussentemperatur', 'DPT_Value_Temp', False, 'C'),
35: ('HG3', 'Status Brenner', 'DPT_Switch', False, ''),
36: ('HG3', 'Status Heizkreispumpe', 'DPT_Switch', False, ''),
37: ('HG3', 'Status Speicherladepumpe', 'DPT_Switch', False, ''),
38: ('HG3', 'Status 3W-Umschaltventil', 'DPT_OpenClose', False, ''),
39: ('HG3', 'Anlagendruck', 'DPT_Value_Pres', False, 'Pa'),
40: ('HG4', 'Stoerung', 'DPT_Switch', False, ''),
41: ('HG4', 'Betriebsart', 'DPT_HVACContrMode', False, ''),
42: ('HG4', 'Brennerleistung', 'DPT_Scaling', False, '%'),
43: ('HG4', 'Kesseltemperatur', 'DPT_Value_Temp', False, 'C'),
44: ('HG4', 'Sammlertemperatur', 'DPT_Value_Temp', False, 'C'),
45: ('HG4', 'Ruecklauftemperatur', 'DPT_Value_Temp', False, 'C'),
46: ('HG4', 'Warmwassertemperatur', 'DPT_Value_Temp', False, 'C'),
47: ('HG4', 'Aussentemperatur', 'DPT_Value_Temp', False, 'C'),
48: ('HG4', 'Status Brenner', 'DPT_Switch', False, ''),
49: ('HG4', 'Status Heizkreispumpe', 'DPT_Switch', False, ''),
50: ('HG4', 'Status Speicherladepumpe', 'DPT_Switch', False, ''),
51: ('HG4', 'Status 3W-Umschaltventil', 'DPT_OpenClose', False, ''),
52: ('HG4', 'Anlagendruck', 'DPT_Value_Pres', False, 'a'),
53: ('BM1', 'Stoerung', 'DPT_Switch', False, ''),
54: ('BM1', 'Aussentemperatur', 'DPT_Value_Temp', False, 'C'),
55: ('BM1', 'Raumtemperatur', 'DPT_Value_Temp', False, 'C'),
56: ('BM1', 'Warmwassersolltemperatur', 'DPT_Value_Temp', True, 'C'),
57: ('BM1', 'Programmwahl Heizkreis', 'DPT_HVACMode', True, ''),
58: ('BM1', 'Programmwahl Warmwasser', 'DPT_DHWMode', True, ''),
59: ('BM1', 'Heizkreis Zeitprogramm 1', 'DPT_Switch', True, ''),
60: ('BM1', 'Heizkreis Zeitprogramm 2', 'DPT_Switch', True, ''),
61: ('BM1', 'Heizkreis Zeitprogramm 3', 'DPT_Switch', True, ''),
62: ('BM1', 'Warmwasser Zeitprogramm 1', 'DPT_Switch', True, ''),
63: ('BM1', 'Warmwasser Zeitprogramm 2', 'DPT_Switch', True, ''),
64: ('BM1', 'Warmwasser Zeitprogramm 3', 'DPT_Switch', True, ''),
65: ('BM1', 'Sollwertkorrektur', 'DPT_Tempd', True, 'K'),
66: ('BM1', 'Sparfaktor', 'DPT_Tempd', True, 'K'),
67: ('BM2', 'Stoerung', 'DPT_Switch', False, ''),
68: ('BM2', 'Raumtemperatur', 'DPT_Value_Temp', False, 'C'),
69: ('BM2', 'Warmwassersolltemperatur', 'DPT_Value_Temp', True, 'C'),
70: ('BM2', 'Programmwahl Mischer', 'DPT_HVACMode', True, ''),
71: ('BM2', 'Programmwahl Warmwasser', 'DPT_DHWMode', True, ''),
72: ('BM2', 'Mischer Zeitprogramm 1', 'DPT_Switch', True, ''),
73: ('BM2', 'Mischer Zeitprogramm 2', 'DPT_Switch', True, ''),
74: ('BM2', 'Mischer Zeitprogramm 3', 'DPT_Switch', True, ''),
75: ('BM2', 'Warmwasser Zeitprogramm 1', 'DPT_Switch', True, ''),
76: ('BM2', 'Warmwasser Zeitprogramm 2', 'DPT_Switch', True, ''),
77: ('BM2', 'Warmwasser Zeitprogramm 3', 'DPT_Switch', True, ''),
78: ('BM2', 'Sollwertkorrektur', 'DPT_Tempd', True, 'K'),
79: ('BM2', 'Sparfaktor', 'DPT_Tempd', True, 'K'),
80: ('BM3', 'Stoerung', 'DPT_Switch', False, ''),
81: ('BM3', 'Raumtemperatur', 'DPT_Value_Temp', False, 'C'),
82: ('BM3', 'Warmwassersolltemperatur', 'DPT_Value_Temp', True, 'C'),
83: ('BM3', 'Programmwahl Mischer', 'DPT_HVACMode', True, ''),
84: ('BM3', 'Programmwahl Warmwasser', 'DPT_DHWMode', True, ''),
85: ('BM3', 'Mischer Zeitprogramm 1', 'DPT_Switch', True, ''),
86: ('BM3', 'Mischer Zeitprogramm 2', 'DPT_Switch', True, ''),
87: ('BM3', 'Mischer Zeitprogramm 3', 'DPT_Switch', True, ''),
88: ('BM3', 'Warmwasser Zeitprogramm 1', 'DPT_Switch', True, ''),
89: ('BM3', 'Warmwasser Zeitprogramm 2', 'DPT_Switch', True, ''),
90: ('BM3', 'Warmwasser Zeitprogramm 3', 'DPT_Switch', True, ''),
91: ('BM3', 'Sollwertkorrektur', 'DPT_Tempd', True, 'K'),
92: ('BM3', 'Sparfaktor', 'DPT_Tempd', True, 'K'),
93: ('BM4', 'Stoerung', 'DPT_Switch', False, ''),
94: ('BM4', 'Raumtemperatur', 'DPT_Value_Temp', False, 'C'),
95: ('BM4', 'Warmwassersolltemperatur', 'DPT_Value_Temp', True, 'C'),
96: ('BM4', 'Programmwahl Mischer', 'DPT_HVACMode', True, ''),
97: ('BM4', 'Programmwahl Warmwasser', 'DPT_DHWMode', True, ''),
98: ('BM4', 'Mischer Zeitprogramm 1', 'DPT_Switch', True, ''),
99: ('BM4', 'Mischer Zeitprogramm 2', 'DPT_Switch', True, ''),
100: ('BM4', 'Mischer Zeitprogramm 3', 'DPT_Switch', True, ''),
101: ('BM4', 'Warmwasser Zeitprogramm 1', 'DPT_Switch', True, ''),
102: ('BM4', 'Warmwasser Zeitprogramm 2', 'DPT_Switch', True, ''),
103: ('BM4', 'Warmwasser Zeitprogramm 3', 'DPT_Switch', True, ''),
104: ('BM4', 'Sollwertkorrektur', 'DPT_Tempd', True, 'K'),
105: ('BM4', 'Sparfaktor', 'DPT_Tempd', True, 'K'),
106: ('KM', 'Stoerung', 'DPT_Switch', False, ''),
107: ('KM', 'Sammlertemperatur', 'DPT_Value_Temp', False, 'C'),
108: ('KM', 'Gesamtmodulationsgrad', 'DPT_Scaling', False, '%'),
109: ('KM', 'Vorlauftemperatur Mischer', 'DPT_Value_Temp', False, 'C'),
110: ('KM', 'Status Mischerkreispumpe', 'DPT_Switch', False, ''),
111: ('KM', 'Status Ausgang A1', 'DPT_Enable', False, ''),
112: ('KM', 'Eingang E1', 'DPT_Value_Temp', False, 'C'),
113: ('KM', 'Eingang E2', 'DPT_Value_Temp', False, 'C'),
114: ('MM1', 'Stoerung', 'DPT_Switch', False, ''),
115: ('MM1', 'Warmwassertemperatur', 'DPT_Value_Temp', False, 'C'),
116: (
'MM1', 'Vorlauftemperatur Mischer', 'DPT_Value_Temp', False, 'C'),
117: ('MM1', 'Status Mischerkreispumpe', 'DPT_Switch', False, ''),
118: ('MM1', 'Status Ausgang A1', 'DPT_Enable', False, ''),
119: ('MM1', 'Eingang E1', 'DPT_Value_Temp', False, 'C'),
120: ('MM1', 'Eingang E2', 'DPT_Value_Temp', False, 'C'),
121: ('MM2', 'Stoerung', 'DPT_Switch', False, ''),
122: ('MM2', 'Warmwassertemperatur', 'DPT_Value_Temp', False, 'C'),
123: (
'MM2', 'Vorlauftemperatur Mischer', 'DPT_Value_Temp', False, 'C'),
124: ('MM2', 'Status Mischerkreispumpe', 'DPT_Switch', False, ''),
125: ('MM2', 'Status Ausgang A1', 'DPT_Enable', False, ''),
126: ('MM2', 'Eingang E1', 'DPT_Value_Temp', False, 'C'),
127: ('MM2', 'Eingang E2', 'DPT_Value_Temp', False, 'C'),
128: ('MM3', 'Stoerung', 'DPT_Switch', False, ''),
129: ('MM3', 'Warmwassertemperatur', 'DPT_Value_Temp', False, 'C'),
130: (
'MM3', 'Vorlauftemperatur Mischer', 'DPT_Value_Temp', False, 'C'),
131: ('MM3', 'Status Mischerkreispumpe', 'DPT_Switch', False, ''),
132: ('MM3', 'Status Ausgang A1', 'DPT_Enable', False, ''),
133: ('MM3', 'Eingang E1', 'DPT_Value_Temp', False, 'C'),
134: ('MM3', 'Eingang E2', 'DPT_Value_Temp', False, 'C'),
135: ('SM', 'Stoerung', 'DPT_Switch', False, ''),
136: ('SM', 'Warmwassertemperatur Solar 1', 'DPT_Value_Temp', False,
'C'),
137: ('SM', 'Temperatur Kollektor 1', 'DPT_Value_Temp', False, 'C'),
138: ('SM', 'Eingang E1', 'DPT_Value_Temp', False, 'C'),
139: ('SM', 'Eingang E2 (Durchfluss)', 'DPT_Value_Volume_Flow', False,
'l/h'),
140: ('SM', 'Eingang E3', 'DPT_Value_Temp', False, 'C'),
141: ('SM', 'Status Solarkreispumpe SKP1', 'DPT_Switch', False, ''),
142: ('SM', 'Status Ausgang A1', 'DPT_Enable', False, ''),
143: ('SM', 'Status Ausgang A2', 'DPT_Enable', False, ''),
144: ('SM', 'Status Ausgang A3', 'DPT_Enable', False, ''),
145: ('SM', 'Status Ausgang A4', 'DPT_Enable', False, ''),
146: ('SM', 'Durchfluss', 'DPT_Value_Volume_Flow', False, 'l/h'),
147: ('SM', 'aktuelle Leistung', 'DPT_Power', False, 'kW'),
148: ('CWL', 'Stoerung', 'DPT_Switch', False, ''),
149: ('CWL', 'Programm', 'DPT_DHWMode', True, ''),
150: ('CWL', 'Zeitprogramm 1', 'DPT_Switch', True, ''),
151: ('CWL', 'Zeitprogramm 2', 'DPT_Switch', True, ''),
152: ('CWL', 'Zeitprogramm 3', 'DPT_Switch', True, ''),
153: ('CWL', 'Intensivlueftung AN_AUS', 'DPT_Switch', True, ''),
154: ('CWL', 'Intensivlueftung Startdatum', 'DPT_Date', True, ''),
155: ('CWL', 'Intensivlueftung Enddatum', 'DPT_Date', True, ''),
156: ('CWL', 'Intensivlueftung Startzeit', 'DPT_TimeOfDay', True, ''),
157: ('CWL', 'Intensivlueftung Endzeit', 'DPT_TimeOfDay', True, ''),
158: ('CWL', 'Zeitw. Feuchteschutz AN_AUS', 'DPT_Switch', True, ''),
159: ('CWL', 'Zeitw. Feuchteschutz Startdatum', 'DPT_Date', True, ''),
160: ('CWL', 'Zeitw. Feuchteschutz Enddatum', 'DPT_Date', True, ''),
161: (
'CWL', 'Zeitw. Feuchteschutz Startzeit', 'DPT_TimeOfDay', True,
''),
162: (
'CWL', 'Zeitw. Feuchteschutz Endzeit', 'DPT_TimeOfDay', True, ''),
163: ('CWL', 'Lueftungsstufe', 'DPT_Scaling', False, '%'),
164: ('CWL', 'Ablufttemperatur', 'DPT_Value_Temp', False, 'C'),
165: ('CWL', 'Frischlufttemperatur', 'DPT_Value_Temp', False, 'C'),
166: ('CWL', 'Durchsatz Zuluft', 'DPT_FlowRate_m3/h', False, 'ccm/h'),
167: ('CWL', 'Durchsatz Abluft', 'DPT_FlowRate_m3/h', False, 'ccm/h'),
168: ('CWL', 'Bypass Initialisierung', 'DPT_Bool', False, ''),
169: ('CWL', 'Bypass oeffnet_offen', 'DPT_Bool', False, ''),
170: ('CWL', 'Bypass schliesst_geschlossen', 'DPT_Bool', False, ''),
171: ('CWL', 'Bypass Fehler', 'DPT_Bool', False, ''),
172: ('CWL', 'Frost Status: Init_Warte', 'DPT_Bool', False, ''),
173: ('CWL', 'Frost Status: Kein Frost', 'DPT_Bool', False, ''),
174: ('CWL', 'Frost Status: Vorwaermer', 'DPT_Bool', False, ''),
175: ('CWL', 'Frost Status: Fehler', 'DPT_Bool', False, ''),
176: ('BWL', 'Stoerung', 'DPT_Switch', False, ''),
177: ('BWL', 'Betriebsart', 'DPT_HVACContrMode', False, ''),
178: ('BWL', 'Heizleistung', 'DPT_Power', False, 'W'),
179: ('BWL', 'Kuehlleistung', 'DPT_Power', False, 'W'),
180: ('BWL', 'Kesseltemperatur', 'DPT_Value_Temp', False, 'C'),
181: ('BWL', 'Sammlertemperatur', 'DPT_Value_Temp', False, 'C'),
182: ('BWL', 'Ruecklauftemperatur', 'DPT_Value_Temp', False, 'C'),
183: ('BWL', 'Warmwassertemperatur', 'DPT_Value_Temp', False, 'C'),
184: ('BWL', 'Aussentemperatur', 'DPT_Value_Temp', False, 'C'),
185: ('BWL', 'Status Heizkreispumpe', 'DPT_Switch', False, ''),
186: ('BWL', 'Status Aux-Pumpe', 'DPT_Switch', False, ''),
187: ('BWL', '3W-Umschaltventil HZ_WW', 'DPT_OpenClose', False, ''),
188: ('BWL', '3W-Umschaltventil HZ_K', 'DPT_OpenClose', False, ''),
189: ('BWL', 'Status E-Heizung', 'DPT_Switch', False, ''),
190: ('BWL', 'Anlagendruck', 'DPT_Value_Pres', False, 'Pa'),
191: ('BWL', 'Leistungsaufnahme', 'DPT_Power', False, 'W'),
192: ('CWL', 'Filterwarnung aktiv', 'DPT_Switch', False, '-'),
193: ('CWL', 'Filterwarnung zuruecksetzen', 'DPT_Switch', True, '-'),
194: ('BM1', '1x Warmwasserladung (gobal)', 'DPT_Switch', True, '-'),
195: ('SM', 'Tagesertrag', 'DPT_ActiveEnergy', False, 'Wh'),
196: ('SM', 'Gesamtertrag', 'DPT_ActiveEnergy_kWh', False, 'kWh'),
197: ('HG1', 'Abgastemperatur', 'DPT_Value_Temp', False, 'C'),
198: ('HG1', 'Leistungsvorgabe', 'DPT_Scaling', True, '%'),
199: ('HG1', 'Kesseltemperaturvorgabe', 'DPT_Value_Temp', True, 'C'),
200: ('HG2', 'Abgastemperatur', 'DPT_Value_Temp', False, 'C'),
201: ('HG2', 'Leistungsvorgabe', 'DPT_Scaling', True, '%'),
202: ('HG2', 'Kesseltemperaturvorgabe', 'DPT_Value_Temp', True, 'C'),
203: ('HG3', 'Abgastemperatur', 'DPT_Value_Temp', False, 'C'),
204: ('HG3', 'Leistungsvorgabe', 'DPT_Scaling', True, '%'),
205: ('HG3', 'Kesseltemperaturvorgabe', 'DPT_Value_Temp', True, 'C'),
206: ('HG4', 'Abgastemperatur', 'DPT_Value_Temp', False, 'C'),
207: ('HG4', 'Leistungsvorgabe', 'DPT_Scaling', True, '%'),
208: ('HG4', 'Kesseltemperaturvorgabe', 'DPT_Value_Temp', True, 'C'),
209: ('KM', 'Gesamtmodulationsgradvorgabe', 'DPT_Scaling', True, '%'),
210: ('KM', 'Sammlertemperaturvorgabe', 'DPT_Value_Temp', True, 'C')
}
@staticmethod
def get_device(dp_id):
""" returns sensor value from private array of sensor-readings """
if dp_id in Ism8.DATAPOINTS.keys():
return Ism8.DATAPOINTS[dp_id][Ism8.DP_DEVICE]
else:
return None
@staticmethod
def get_name(dp_id):
""" returns sensor value from private array of sensor-readings """
if dp_id in Ism8.DATAPOINTS.keys():
return Ism8.DATAPOINTS[dp_id][Ism8.DP_NAME]
else:
return None
@staticmethod
def get_type(dp_id):
""" returns sensor value from private array of sensor-readings """
if dp_id in Ism8.DATAPOINTS.keys():
return Ism8.DATAPOINTS[dp_id][Ism8.DP_TYPE]
else:
return None
@staticmethod
def get_unit(dp_id):
""" returns sensor value from private array of sensor-readings """
if dp_id in Ism8.DATAPOINTS.keys():
return Ism8.DATAPOINTS[dp_id][Ism8.DP_UNIT]
else:
return None
@staticmethod
def get_all_sensors():
""" returns pointer all possible values of ISM8 datapoints """
return Ism8.DATAPOINTS
def factory(self):
"""
returns reference to itself for using in protocol_factory with
create_server
"""
return self
def connection_made(self, transport):
""" is called as soon as an ISM8 connects to server """
_peername = transport.get_extra_info('peername')
self._LOGGER.info("Connection from ISM8: %s", _peername)
self._transport = transport
self._connected = True
def data_received(self, data):
""" is called whenever data is ready """
_header_ptr = 0
msg_length = 0
self._LOGGER.debug('Raw data received: %s', data)
while _header_ptr < len(data):
_header_ptr = data.find(Ism8.ISM_HEADER, _header_ptr)
if _header_ptr >= 0:
if len(data[_header_ptr:]) >= 9:
# smallest processable data:
# hdr plus 5 bytes=>at least 9 bytes
msg_length = 256 * data[_header_ptr + 4] + data[
_header_ptr + 5]
# msg_length comes in bytes 4 and 5
else:
msg_length = len(data) + 1
# 2 possible outcomes here: Buffer is to short for message=>abort
# buffer is larger => than msg: process 1 message,
# then continue loop
if len(data) < _header_ptr + msg_length:
self._LOGGER.debug(
"Buffer shorter than expected / broken Message.")
self._LOGGER.debug("Discarding: %s ", data[_header_ptr:])
# setting Ptr to end of data will end loop
_header_ptr = len(data)
else:
# send ACK to ISM8 according to API: ISM Header,
# then msg-length(17), then ACK w/ 2 bytes from original msg
ack_msg = bytearray(Ism8.ISM_HEADER)
ack_msg.append(0x00)
ack_msg.append(0x11)
ack_msg.extend(Ism8.ISM_CONN_HEADER)
ack_msg.extend(Ism8.ISM_ACK)
ack_msg[12] = data[_header_ptr + 12]
ack_msg[13] = data[_header_ptr + 13]
self._LOGGER.debug('Sending ACK: %s ', ack_msg)
self._transport.write(ack_msg)
self.process_msg(
data[_header_ptr + 10:_header_ptr + msg_length])
# process message without header (first 10 bytes)
_header_ptr += msg_length
# prepare to get next message; advance Ptr to next Msg
def process_msg(self, msg):
"""
Processes received datagram(s) according to ISM8 API specification
into message length, command, values delivered
"""
max_dp = msg[4] * 256 + msg[5]
# number of DATAPOINTS are coded into bytes 4 and 5 of message
i = 0
# byte counter
dp_nbr = 1
# datapoint counter
while dp_nbr <= max_dp:
self._LOGGER.debug('DP {0:d} / {1:d} in datagram:'.format(
dp_nbr, max_dp))
dp_id = msg[i + 6] * 256 + msg[i + 7]
# dp_command = msg[i + 8]
# to be implemented for writing values to ISM8
dp_length = msg[i + 9]
dp_raw_value = bytearray(msg[i + 10:i + 10 + dp_length])
self._LOGGER.debug('Processing DP-ID %s, %s bytes: message: %s',
dp_id, dp_length, dp_raw_value)
self.decode_datapoint(dp_id, dp_length, dp_raw_value)
# now advance byte counter and datapoint counter
dp_nbr += 1
i = i + 10 + dp_length
def decode_datapoint(self, dp_id, length, raw_bytes):
"""
decodes a single value according to API;
receives raw bytes from network and
decodes them according to API data type
"""
result = 0
for single_byte in raw_bytes:
result = result * 256 + int(single_byte)
if dp_id not in Ism8.DATAPOINTS:
self._LOGGER.error("unknown datapoint: %s, data:%s",
dp_id, result)
return
dp_type = Ism8.DATAPOINTS[dp_id][Ism8.DP_TYPE]
if (length == 1) and dp_type in ("DPT_Switch",
"DPT_Bool",
"DPT_Enable",
"DPT_OpenClose"):
# take 1st bit and cast to Bool
self._dp_values.update({dp_id: bool(result & 1)})
elif (length == 1) and (dp_type == "DPT_HVACMode"):
# translate values to clear status-text
if result == 0:
self._dp_values.update({dp_id: 'Auto'})
elif result == 1:
self._dp_values.update({dp_id: 'Comfort'})
elif result == 2:
self._dp_values.update({dp_id: 'Standby'})
elif result == 3:
self._dp_values.update({dp_id: 'Economy'})
elif result == 4:
self._dp_values.update({dp_id: 'Building Protection'})
elif (length == 1) and (dp_type == "DPT_Scaling"):
# take byte value and multiply by 100/255
self._dp_values.update({dp_id: 100 / 255 * result})
elif (length == 1) and (dp_type == "DPT_DHWMode"):
if result == 0:
self._dp_values.update({dp_id: 'Auto'})
elif result == 1:
self._dp_values.update({dp_id: 'LegioProtect'})
elif result == 2:
self._dp_values.update({dp_id: 'Normal'})
elif result == 3:
self._dp_values.update({dp_id: 'Reduced'})
elif result == 4:
self._dp_values.update({dp_id: 'Off'})
elif (length == 1) and (dp_type == "DPT_HVACContrMode"):
# translate values to clear status-text
if result == 0:
self._dp_values.update({dp_id: 'Auto'})
elif result == 1:
self._dp_values.update({dp_id: 'Heat'})
elif result == 2:
self._dp_values.update({dp_id: 'Morning Warmup'})
elif result == 3:
self._dp_values.update({dp_id: 'Cool'})
elif result == 4:
self._dp_values.update({dp_id: 'Night Purge'})
elif result == 5:
self._dp_values.update({dp_id: 'Precool'})
elif result == 6:
self._dp_values.update({dp_id: 'Off'})
elif result == 7:
self._dp_values.update({dp_id: 'Test'})
elif result == 8:
self._dp_values.update({dp_id: 'Emergency Heat'})
elif result == 9:
self._dp_values.update({dp_id: 'Fan Only'})
elif result == 10:
self._dp_values.update({dp_id: 'Free Cool'})
elif result == 11:
self._dp_values.update({dp_id: 'Ice'})
elif result == 12:
self._dp_values.update({dp_id: 'Maximum Heating Mode'})
elif result == 13:
self._dp_values.update({dp_id: 'Economic Heat/Cool Mode'})
elif result == 14:
self._dp_values.update({dp_id: 'Dehumidification'})
elif result == 15:
self._dp_values.update({dp_id: 'Calibration Mode'})
elif result == 16:
self._dp_values.update({dp_id: 'Emergency Cool Mode'})
elif result == 17:
self._dp_values.update({dp_id: 'Emergency Steam Mode'})
elif result == 20:
self._dp_values.update({dp_id: 'NoDem'})
elif (length == 2) and (dp_type in ("DPT_Value_Temp",
"DPT_Value_Tempd",
"DPT_Tempd",
"DPT_Value_Pres",
"DPT_Power",
"DPT_Value_Volume_Flow"
)):
_sign = (result & 0b1000000000000000) >> 15
_exponent = (result & 0b0111100000000000) >> 11
_mantisse = result & 0b0000011111111111
self._LOGGER.debug(
'binary format {0:b} -> s:{1:b} , m:{2:b} , e:{3:b}'
.format(result, _sign, _mantisse, _exponent))
if _sign == 1:
_mantisse = -(~(_mantisse - 1) & 0x07ff)
self._dp_values.update(
{dp_id: (0.01 * (2 ** _exponent) * _mantisse)})
elif (length == 4) and (dp_type in ("DPT_ActiveEnergy",
"DPT_ActiveEnergy_kWh"
)):
self._dp_values.update({dp_id: result})
else:
self._LOGGER.error('datatype not implemented: %s ', dp_type)
return
if dp_id in self._dp_values.keys():
self._LOGGER.debug('decoded DP %s : %s = %s\n',
dp_id, Ism8.DATAPOINTS[dp_id],
self._dp_values[dp_id])
else:
self._LOGGER.error('could not decode DP %s : %s\n',
dp_id, Ism8.DATAPOINTS[dp_id])
def connection_lost(self, exc):
"""
Is called when connection ends. closes socket.
"""
self._LOGGER.debug('ISM8 closed the connection.Stopping')
self._connected = False
self._transport.close()
def read(self, dp_id):
"""
Returns sensor value from private array of sensor-readings
"""
if dp_id in self._dp_values.keys():
return self._dp_values[dp_id]
else:
return None
if __name__ == "__main__":
_LOGGER = logging.getLogger(__name__)
logging.basicConfig()
_LOGGER.setLevel(logging.DEBUG)
# for testing purposes only, relies on debug output
myProtocol = Ism8()
for keys, values in myProtocol.get_all_sensors().items():
print("%s: %s\n" % (keys, values))
_eventloop = asyncio.get_event_loop()
coro = _eventloop.create_server(myProtocol.factory, '', 12004)
_server = _eventloop.run_until_complete(coro)
# Serve requests until Ctrl+C is pressed
_LOGGER.debug('Waiting for ISM8 connection on %s',
_server.sockets[0].getsockname())
_eventloop.run_forever()
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] | 1.921392 | 14,566 |
# -*- coding: utf-8 -*-
# pylint: disable=missing-docstring
# pylint: disable=wildcard-import
# pylint: disable=unused-wildcard-import
# pylint: disable=invalid-name
from datetime import datetime
import json
from clx.xms import api, exceptions, deserialize
from nose.tools import *
from iso8601 import UTC
@raises(exceptions.UnexpectedResponseException)
@raises(exceptions.UnexpectedResponseException)
@raises(exceptions.UnexpectedResponseException)
@raises(exceptions.UnexpectedResponseException)
@raises(exceptions.UnexpectedResponseException)
@raises(exceptions.UnexpectedResponseException)
| [
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] | 3.179894 | 189 |
# Copyright 2021 The TensorFlow Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import tensorflow as tf
from .retrieval_metric import RetrievalMetric
from tensorflow_similarity.types import FloatTensor, IntTensor, BoolTensor
class RecallAtK(RetrievalMetric):
"""The metric learning version of Recall@K.
A query is counted as a positive when ANY lookup in top K match the query
class, 0 otherwise.
Args:
name: Name associated with the metric object, e.g., recall@5
canonical_name: The canonical name associated with metric,
e.g., recall@K
k: The number of nearest neighbors over which the metric is computed.
distance_threshold: The max distance below which a nearest neighbor is
considered a valid match.
average: {'micro'} Determines the type of averaging performed over the
queries.
* 'micro': Calculates metrics globally over all queries.
* 'macro': Calculates metrics for each label and takes the unweighted
mean.
"""
def compute(
self,
*, # keyword only arguments see PEP-570
query_labels: IntTensor,
match_mask: BoolTensor,
**kwargs,
) -> FloatTensor:
"""Compute the metric
Args:
query_labels: A 1D tensor of the labels associated with the
embedding queries.
match_mask: A 2D mask where a 1 indicates a match between the
jth query and the kth neighbor and a 0 indicates a mismatch.
**kwargs: Additional compute args.
Returns:
A rank 0 tensor containing the metric.
"""
self._check_shape(query_labels, match_mask)
k_slice = match_mask[:, : self.k]
match_indicator = tf.math.reduce_any(k_slice, axis=1)
match_indicator = tf.cast(match_indicator, dtype="float")
if self.average == "micro":
recall_at_k = tf.math.reduce_mean(match_indicator)
elif self.average == "macro":
per_class_metrics = 0
class_labels = tf.unique(query_labels)[0]
# TODO(ovallis): potential slowness.
for label in class_labels:
idxs = tf.where(query_labels == label)
c_slice = tf.gather(match_indicator, indices=idxs)
per_class_metrics += tf.math.reduce_mean(c_slice)
recall_at_k = tf.math.divide(per_class_metrics, len(class_labels))
else:
raise ValueError(
f"{self.average} is not a supported average " "option"
)
result: FloatTensor = recall_at_k
return result
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from rdflib.namespace import DefinedNamespace, Namespace
from rdflib.term import URIRef
class ODRL2(DefinedNamespace):
"""
ODRL Version 2.2
The ODRL Vocabulary and Expression defines a set of concepts and terms (the vocabulary) and encoding mechanism
(the expression) for permissions and obligations statements describing digital content usage based on the ODRL
Information Model.
Generated from: https://www.w3.org/ns/odrl/2/ODRL22.ttl
Date: 2020-05-26 14:20:02.352356
"""
_fail = True
# http://www.w3.org/1999/02/22-rdf-syntax-ns#Property
action: URIRef # The operation relating to the Asset for which the Rule is being subjected.
andSequence: URIRef # The relation is satisfied when each of the Constraints are satisfied in the order specified.
assignee: URIRef # The Party is the recipient of the Rule.
assigneeOf: URIRef # Identifies an ODRL Policy for which the identified Party undertakes the assignee functional role.
assigner: URIRef # The Party is the issuer of the Rule.
assignerOf: URIRef # Identifies an ODRL Policy for which the identified Party undertakes the assigner functional role.
attributedParty: URIRef # The Party to be attributed.
attributingParty: URIRef # The Party who undertakes the attribution.
compensatedParty: URIRef # The Party is the recipient of the compensation.
compensatingParty: URIRef # The Party that is the provider of the compensation.
conflict: URIRef # The conflict-resolution strategy for a Policy.
consentedParty: URIRef # The Party who obtains the consent.
consentingParty: URIRef # The Party to obtain consent from.
consequence: URIRef # Relates a Duty to another Duty, the latter being a consequence of not fulfilling the former.
constraint: URIRef # Constraint applied to a Rule
contractedParty: URIRef # The Party who is being contracted.
contractingParty: URIRef # The Party who is offering the contract.
dataType: URIRef # The datatype of the value of the rightOperand or rightOperandReference of a Constraint.
duty: URIRef # Relates an individual Duty to a Permission.
failure: URIRef # Failure is an abstract property that defines the violation (or unmet) relationship between Rules.
function: URIRef # Function is an abstract property whose sub-properties define the functional roles which may be fulfilled by a party in relation to a Rule.
hasPolicy: URIRef # Identifies an ODRL Policy for which the identified Asset is the target Asset to all the Rules.
implies: URIRef # An Action asserts that another Action is not prohibited to enable its operational semantics.
includedIn: URIRef # An Action transitively asserts that another Action that encompasses its operational semantics.
informedParty: URIRef # The Party to be informed of all uses.
informingParty: URIRef # The Party who provides the inform use data.
inheritAllowed: URIRef # Indicates if the Policy entity can be inherited.
inheritFrom: URIRef # Relates a (child) policy to another (parent) policy from which terms are inherited.
inheritRelation: URIRef # Identifies the type of inheritance.
leftOperand: URIRef # The left operand in a constraint expression.
obligation: URIRef # Relates an individual Duty to a Policy.
operand: URIRef # Operand is an abstract property for a logical relationship.
operator: URIRef # The operator function applied to operands of a Constraint
output: URIRef # The output property specifies the Asset which is created from the output of the Action.
partOf: URIRef # Identifies an Asset/PartyCollection that the Asset/Party is a member of.
payeeParty: URIRef # The Party is the recipient of the payment.
permission: URIRef # Relates an individual Permission to a Policy.
profile: URIRef # The identifier(s) of an ODRL Profile that the Policy conforms to.
prohibition: URIRef # Relates an individual Prohibition to a Policy.
proximity: URIRef # An value indicating the closeness or nearness.
refinement: URIRef # Constraint used to refine the semantics of an Action, or Party/Asset Collection
relation: URIRef # Relation is an abstract property which creates an explicit link between an Action and an Asset.
remedy: URIRef # Relates an individual remedy Duty to a Prohibition.
rightOperand: URIRef # The value of the right operand in a constraint expression.
rightOperandReference: URIRef # A reference to a web resource providing the value for the right operand of a Constraint.
scope: URIRef # The identifier of a scope that provides context to the extent of the entity.
source: URIRef # Reference to a Asset/PartyCollection
status: URIRef # the value generated from the leftOperand action or a value related to the leftOperand set as the reference for the comparison.
target: URIRef # The target property indicates the Asset that is the primary subject to which the Rule action directly applies.
timedCount: URIRef # The number of seconds after which timed metering use of the asset begins.
trackedParty: URIRef # The Party whose usage is being tracked.
trackingParty: URIRef # The Party who is tracking usage.
uid: URIRef # An unambiguous identifier
undefined: URIRef # Relates the strategy used for handling undefined actions to a Policy.
unit: URIRef # The unit of measurement of the value of the rightOperand or rightOperandReference of a Constraint.
xone: URIRef # The relation is satisfied when only one, and not more, of the Constraints is satisfied
# http://www.w3.org/2002/07/owl#NamedIndividual
All: URIRef # Specifies that the scope of the relationship is all of the collective individuals within a context.
All2ndConnections: URIRef # Specifies that the scope of the relationship is all of the second-level connections to the Party.
AllConnections: URIRef # Specifies that the scope of the relationship is all of the first-level connections of the Party.
AllGroups: URIRef # Specifies that the scope of the relationship is all of the group connections of the Party.
Group: URIRef # Specifies that the scope of the relationship is the defined group with multiple individual members.
Individual: URIRef # Specifies that the scope of the relationship is the single Party individual.
absolutePosition: URIRef # A point in space or time defined with absolute coordinates for the positioning of the target Asset.
absoluteSize: URIRef # Measure(s) of one or two axes for 2D-objects or measure(s) of one to tree axes for 3D-objects of the target Asset.
absoluteSpatialPosition: URIRef # The absolute spatial positions of four corners of a rectangle on a 2D-canvas or the eight corners of a cuboid in a 3D-space for the target Asset to fit.
absoluteTemporalPosition: URIRef # The absolute temporal positions in a media stream the target Asset has to fit.
count: URIRef # Numeric count of executions of the action of the Rule.
dateTime: URIRef # The date (and optional time and timezone) of exercising the action of the Rule. Right operand value MUST be an xsd:date or xsd:dateTime as defined by [[xmlschema11-2]].
delayPeriod: URIRef # A time delay period prior to exercising the action of the Rule. The point in time triggering this period MAY be defined by another temporal Constraint combined by a Logical Constraint (utilising the odrl:andSequence operand). Right operand value MUST be an xsd:duration as defined by [[xmlschema11-2]].
deliveryChannel: URIRef # The delivery channel used for exercising the action of the Rule.
device: URIRef # An identified device used for exercising the action of the Rule.
elapsedTime: URIRef # A continuous elapsed time period which may be used for exercising of the action of the Rule. Right operand value MUST be an xsd:duration as defined by [[xmlschema11-2]].
eq: URIRef # Indicating that a given value equals the right operand of the Constraint.
event: URIRef # An identified event setting a context for exercising the action of the Rule.
fileFormat: URIRef # A transformed file format of the target Asset.
gt: URIRef # Indicating that a given value is greater than the right operand of the Constraint.
gteq: URIRef # Indicating that a given value is greater than or equal to the right operand of the Constraint.
hasPart: URIRef # A set-based operator indicating that a given value contains the right operand of the Constraint.
ignore: URIRef # The Action is to be ignored and is not part of the policy โ and the policy remains valid.
industry: URIRef # A defined industry sector setting a context for exercising the action of the Rule.
invalid: URIRef # The policy is void.
isA: URIRef # A set-based operator indicating that a given value is an instance of the right operand of the Constraint.
isAllOf: URIRef # A set-based operator indicating that a given value is all of the right operand of the Constraint.
isAnyOf: URIRef # A set-based operator indicating that a given value is any of the right operand of the Constraint.
isNoneOf: URIRef # A set-based operator indicating that a given value is none of the right operand of the Constraint.
isPartOf: URIRef # A set-based operator indicating that a given value is contained by the right operand of the Constraint.
language: URIRef # A natural language used by the target Asset.
lt: URIRef # Indicating that a given value is less than the right operand of the Constraint.
lteq: URIRef # Indicating that a given value is less than or equal to the right operand of the Constraint.
media: URIRef # Category of a media asset setting a context for exercising the action of the Rule.
meteredTime: URIRef # An accumulated amount of one to many metered time periods which were used for exercising the action of the Rule. Right operand value MUST be an xsd:duration as defined by [[xmlschema11-2]].
neq: URIRef # Indicating that a given value is not equal to the right operand of the Constraint.
payAmount: URIRef # The amount of a financial payment. Right operand value MUST be an xsd:decimal.
percentage: URIRef # A percentage amount of the target Asset relevant for exercising the action of the Rule. Right operand value MUST be an xsd:decimal from 0 to 100.
perm: URIRef # Permissions take preference over prohibitions.
policyUsage: URIRef # Indicates the actual datetime the action of the Rule was exercised.
product: URIRef # Category of product or service setting a context for exercising the action of the Rule.
prohibit: URIRef # Prohibitions take preference over permissions.
purpose: URIRef # A defined purpose for exercising the action of the Rule.
recipient: URIRef # The party receiving the result/outcome of exercising the action of the Rule.
relativePosition: URIRef # A point in space or time defined with coordinates relative to full measures the positioning of the target Asset.
relativeSize: URIRef # Measure(s) of one or two axes for 2D-objects or measure(s) of one to tree axes for 3D-objects - expressed as percentages of full values - of the target Asset.
relativeSpatialPosition: URIRef # The relative spatial positions - expressed as percentages of full values - of four corners of a rectangle on a 2D-canvas or the eight corners of a cuboid in a 3D-space of the target Asset.
relativeTemporalPosition: URIRef # A point in space or time defined with coordinates relative to full measures the positioning of the target Asset.
resolution: URIRef # Resolution of the rendition of the target Asset.
spatial: URIRef # A named and identified geospatial area with defined borders which is used for exercising the action of the Rule. An IRI MUST be used to represent this value.
spatialCoordinates: URIRef # A set of coordinates setting the borders of a geospatial area used for exercising the action of the Rule. The coordinates MUST include longitude and latitude, they MAY include altitude and the geodetic datum.
support: URIRef # The Action is to be supported as part of the policy โ and the policy remains valid.
system: URIRef # An identified computing system used for exercising the action of the Rule.
systemDevice: URIRef # An identified computing system or computing device used for exercising the action of the Rule.
timeInterval: URIRef # A recurring period of time before the next execution of the action of the Rule. Right operand value MUST be an xsd:duration as defined by [[xmlschema11-2]].
unitOfCount: URIRef # The unit of measure used for counting the executions of the action of the Rule.
version: URIRef # The version of the target Asset.
virtualLocation: URIRef # An identified location of the IT communication space which is relevant for exercising the action of the Rule.
# http://www.w3.org/2004/02/skos/core#Collection
# http://www.w3.org/2004/02/skos/core#Concept
Action: URIRef # An operation on an Asset.
Agreement: URIRef # A Policy that grants the assignee a Rule over an Asset from an assigner.
Assertion: URIRef # A Policy that asserts a Rule over an Asset from parties.
Asset: URIRef # A resource or a collection of resources that are the subject of a Rule.
AssetCollection: URIRef # An Asset that is collection of individual resources
AssetScope: URIRef # Scopes for Asset Scope expressions.
ConflictTerm: URIRef # Used to establish strategies to resolve conflicts that arise from the merging of Policies or conflicts between Permissions and Prohibitions in the same Policy.
Constraint: URIRef # A boolean expression that refines the semantics of an Action and Party/Asset Collection or declare the conditions applicable to a Rule.
Duty: URIRef # The obligation to perform an Action
LeftOperand: URIRef # Left operand for a constraint expression.
LogicalConstraint: URIRef # A logical expression that refines the semantics of an Action and Party/Asset Collection or declare the conditions applicable to a Rule.
Offer: URIRef # A Policy that proposes a Rule over an Asset from an assigner.
Operator: URIRef # Operator for constraint expression.
Party: URIRef # An entity or a collection of entities that undertake Roles in a Rule.
PartyCollection: URIRef # A Party that is a group of individual entities
PartyScope: URIRef # Scopes for Party Scope expressions.
Permission: URIRef # The ability to perform an Action over an Asset.
Policy: URIRef # A non-empty group of Permissions and/or Prohibitions.
Privacy: URIRef # A Policy that expresses a Rule over an Asset containing personal information.
Prohibition: URIRef # The inability to perform an Action over an Asset.
Request: URIRef # A Policy that proposes a Rule over an Asset from an assignee.
RightOperand: URIRef # Right operand for constraint expression.
Rule: URIRef # An abstract concept that represents the common characteristics of Permissions, Prohibitions, and Duties.
Set: URIRef # A Policy that expresses a Rule over an Asset.
Ticket: URIRef # A Policy that grants the holder a Rule over an Asset from an assigner.
UndefinedTerm: URIRef # Is used to indicate how to support Actions that are not part of any vocabulary or profile in the policy expression system.
acceptTracking: URIRef # To accept that the use of the Asset may be tracked.
adHocShare: URIRef # The act of sharing the asset to parties in close proximity to the owner.
aggregate: URIRef # To use the Asset or parts of it as part of a composite collection.
annotate: URIRef # To add explanatory notations/commentaries to the Asset without modifying the Asset in any other way.
anonymize: URIRef # To anonymize all or parts of the Asset.
append: URIRef # The act of adding to the end of an asset.
appendTo: URIRef # The act of appending data to the Asset without modifying the Asset in any other way.
archive: URIRef # To store the Asset (in a non-transient form).
attachPolicy: URIRef # The act of keeping the policy notice with the asset.
attachSource: URIRef # The act of attaching the source of the asset and its derivatives.
attribute: URIRef # To attribute the use of the Asset.
commercialize: URIRef # The act of using the asset in a business environment.
compensate: URIRef # To compensate by transfer of some amount of value, if defined, for using or selling the Asset.
concurrentUse: URIRef # To create multiple copies of the Asset that are being concurrently used.
copy: URIRef # The act of making an exact reproduction of the asset.
core: URIRef # Identifier for the ODRL Core Profile
delete: URIRef # To permanently remove all copies of the Asset after it has been used.
derive: URIRef # To create a new derivative Asset from this Asset and to edit or modify the derivative.
digitize: URIRef # To produce a digital copy of (or otherwise digitize) the Asset from its analogue form.
display: URIRef # To create a static and transient rendition of an Asset.
distribute: URIRef # To supply the Asset to third-parties.
ensureExclusivity: URIRef # To ensure that the Rule on the Asset is exclusive.
execute: URIRef # To run the computer program Asset.
export: URIRef # The act of transforming the asset into a new form.
extract: URIRef # To extract parts of the Asset and to use it as a new Asset.
extractChar: URIRef # The act of extracting (replicating) unchanged characters from the asset.
extractPage: URIRef # The act of extracting (replicating) unchanged pages from the asset.
extractWord: URIRef # The act of extracting (replicating) unchanged words from the asset.
give: URIRef # To transfer the ownership of the Asset to a third party without compensation and while deleting the original asset.
grantUse: URIRef # To grant the use of the Asset to third parties.
include: URIRef # To include other related assets in the Asset.
index: URIRef # To record the Asset in an index.
inform: URIRef # To inform that an action has been performed on or in relation to the Asset.
install: URIRef # To load the computer program Asset onto a storage device which allows operating or running the Asset.
lease: URIRef # The act of making available the asset to a third-party for a fixed period of time with exchange of value.
lend: URIRef # The act of making available the asset to a third-party for a fixed period of time without exchange of value.
license: URIRef # The act of granting the right to use the asset to a third-party.
modify: URIRef # To change existing content of the Asset. A new asset is not created by this action.
move: URIRef # To move the Asset from one digital location to another including deleting the original copy.
nextPolicy: URIRef # To grant the specified Policy to a third party for their use of the Asset.
obtainConsent: URIRef # To obtain verifiable consent to perform the requested action in relation to the Asset.
pay: URIRef # The act of paying a financial amount to a party for use of the asset.
play: URIRef # To create a sequential and transient rendition of an Asset.
present: URIRef # To publicly perform the Asset.
preview: URIRef # The act of providing a short preview of the asset.
print: URIRef # To create a tangible and permanent rendition of an Asset.
read: URIRef # To obtain data from the Asset.
reproduce: URIRef # To make duplicate copies the Asset in any material form.
reviewPolicy: URIRef # To review the Policy applicable to the Asset.
secondaryUse: URIRef # The act of using the asset for a purpose other than the purpose it was intended for.
sell: URIRef # To transfer the ownership of the Asset to a third party with compensation and while deleting the original asset.
share: URIRef # The act of the non-commercial reproduction and distribution of the asset to third-parties.
shareAlike: URIRef # The act of distributing any derivative asset under the same terms as the original asset.
stream: URIRef # To deliver the Asset in real-time.
synchronize: URIRef # To use the Asset in timed relations with media (audio/visual) elements of another Asset.
textToSpeech: URIRef # To have a text Asset read out loud.
transfer: URIRef # To transfer the ownership of the Asset in perpetuity.
transform: URIRef # To convert the Asset into a different format.
translate: URIRef # To translate the original natural language of an Asset into another natural language.
uninstall: URIRef # To unload and delete the computer program Asset from a storage device and disable its readiness for operation.
use: URIRef # To use the Asset
watermark: URIRef # To apply a watermark to the Asset.
write: URIRef # The act of writing to the Asset.
writeTo: URIRef # The act of adding data to the Asset.
# Valid non-python identifiers
_extras = [
"and",
"or",
"#actionConcepts",
"#actions",
"#actionsCommon",
"#assetConcepts",
"#assetParty",
"#assetRelations",
"#assetRelationsCommon",
"#conflictConcepts",
"#constraintLeftOperandCommon",
"#constraintLogicalOperands",
"#constraintRelationalOperators",
"#constraintRightOpCommon",
"#constraints",
"#deprecatedTerms",
"#duties",
"#logicalConstraints",
"#partyConcepts",
"#partyRoles",
"#partyRolesCommon",
"#permissions",
"#policyConcepts",
"#policySubClasses",
"#policySubClassesCommon",
"#prohibitions",
"#ruleConcepts",
]
_NS = Namespace("http://www.w3.org/ns/odrl/2/")
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37902,
4663,
891,
220,
1303,
383,
3615,
284,
307,
14183,
13,
198,
220,
220,
220,
24548,
278,
33553,
25,
37902,
4663,
891,
220,
1303,
383,
3615,
508,
10122,
1124,
262,
39629,
13,
198,
220,
220,
220,
34304,
33553,
25,
37902,
4663,
891,
220,
1303,
383,
3615,
318,
262,
17800,
286,
262,
9836,
13,
198,
220,
220,
220,
7144,
803,
33553,
25,
37902,
4663,
891,
220,
1303,
383,
3615,
326,
318,
262,
10131,
286,
262,
9836,
13,
198,
220,
220,
220,
5358,
25,
37902,
4663,
891,
220,
1303,
383,
5358,
12,
29268,
4811,
329,
257,
7820,
13,
198,
220,
220,
220,
762,
4714,
33553,
25,
37902,
4663,
891,
220,
1303,
383,
3615,
508,
909,
12143,
262,
8281,
13,
198,
220,
220,
220,
8281,
278,
33553,
25,
37902,
4663,
891,
220,
1303,
383,
3615,
284,
7330,
8281,
422,
13,
198,
220,
220,
220,
12921,
25,
37902,
4663,
891,
220,
1303,
4718,
689,
257,
18104,
284,
1194,
18104,
11,
262,
6846,
852,
257,
12921,
286,
407,
26187,
262,
1966,
13,
198,
220,
220,
220,
32315,
25,
37902,
4663,
891,
220,
1303,
1482,
2536,
2913,
5625,
284,
257,
14330,
198,
220,
220,
220,
23407,
33553,
25,
37902,
4663,
891,
220,
1303,
383,
3615,
508,
318,
852,
23407,
13,
198,
220,
220,
220,
29148,
33553,
25,
37902,
4663,
891,
220,
1303,
383,
3615,
508,
318,
6011,
262,
2775,
13,
198,
220,
220,
220,
1366,
6030,
25,
37902,
4663,
891,
220,
1303,
383,
4818,
265,
2981,
286,
262,
1988,
286,
262,
826,
18843,
392,
393,
826,
18843,
392,
26687,
286,
257,
1482,
2536,
2913,
13,
198,
220,
220,
220,
7077,
25,
37902,
4663,
891,
220,
1303,
4718,
689,
281,
1981,
18104,
284,
257,
2448,
3411,
13,
198,
220,
220,
220,
5287,
25,
37902,
4663,
891,
220,
1303,
25743,
318,
281,
12531,
3119,
326,
15738,
262,
8747,
357,
273,
555,
4164,
8,
2776,
1022,
14252,
13,
198,
220,
220,
220,
2163,
25,
37902,
4663,
891,
220,
1303,
15553,
318,
281,
12531,
3119,
3025,
850,
12,
48310,
8160,
262,
10345,
9176,
543,
743,
307,
23085,
416,
257,
2151,
287,
8695,
284,
257,
14330,
13,
198,
220,
220,
220,
468,
36727,
25,
37902,
4663,
891,
220,
1303,
11440,
6945,
281,
440,
7707,
43,
7820,
329,
543,
262,
5174,
31433,
318,
262,
2496,
31433,
284,
477,
262,
14252,
13,
198,
220,
220,
220,
15565,
25,
37902,
4663,
891,
220,
1303,
1052,
7561,
29348,
326,
1194,
7561,
318,
407,
12244,
284,
7139,
663,
13919,
33815,
13,
198,
220,
220,
220,
3017,
818,
25,
37902,
4663,
891,
220,
1303,
1052,
7561,
1007,
1800,
306,
29348,
326,
1194,
7561,
326,
38932,
663,
13919,
33815,
13,
198,
220,
220,
220,
7981,
33553,
25,
37902,
4663,
891,
220,
1303,
383,
3615,
284,
307,
7981,
286,
477,
3544,
13,
198,
220,
220,
220,
29140,
33553,
25,
37902,
4663,
891,
220,
1303,
383,
3615,
508,
3769,
262,
4175,
779,
1366,
13,
198,
220,
220,
220,
16955,
3237,
6972,
25,
37902,
4663,
891,
220,
1303,
1423,
16856,
611,
262,
7820,
9312,
460,
307,
19552,
13,
198,
220,
220,
220,
16955,
4863,
25,
37902,
4663,
891,
220,
1303,
4718,
689,
257,
357,
9410,
8,
2450,
284,
1194,
357,
8000,
8,
2450,
422,
543,
2846,
389,
19552,
13,
198,
220,
220,
220,
16955,
6892,
341,
25,
37902,
4663,
891,
220,
1303,
11440,
6945,
262,
2099,
286,
24155,
13,
198,
220,
220,
220,
1364,
18843,
392,
25,
37902,
4663,
891,
220,
1303,
383,
1364,
1515,
392,
287,
257,
32315,
5408,
13,
198,
220,
220,
220,
12990,
25,
37902,
4663,
891,
220,
1303,
4718,
689,
281,
1981,
18104,
284,
257,
7820,
13,
198,
220,
220,
220,
1515,
392,
25,
37902,
4663,
891,
220,
1303,
6564,
392,
318,
281,
12531,
3119,
329,
257,
12219,
2776,
13,
198,
220,
220,
220,
10088,
25,
37902,
4663,
891,
220,
1303,
383,
10088,
2163,
5625,
284,
1515,
1746,
286,
257,
1482,
2536,
2913,
198,
220,
220,
220,
5072,
25,
37902,
4663,
891,
220,
1303,
383,
5072,
3119,
26052,
262,
31433,
543,
318,
2727,
422,
262,
5072,
286,
262,
7561,
13,
198,
220,
220,
220,
636,
5189,
25,
37902,
4663,
891,
220,
1303,
11440,
6945,
281,
31433,
14,
33553,
36307,
326,
262,
31433,
14,
33553,
318,
257,
2888,
286,
13,
198,
220,
220,
220,
1414,
1453,
33553,
25,
37902,
4663,
891,
220,
1303,
383,
3615,
318,
262,
17800,
286,
262,
6074,
13,
198,
220,
220,
220,
7170,
25,
37902,
4663,
891,
220,
1303,
4718,
689,
281,
1981,
2448,
3411,
284,
257,
7820,
13,
198,
220,
220,
220,
7034,
25,
37902,
4663,
891,
220,
1303,
383,
27421,
7,
82,
8,
286,
281,
440,
7707,
43,
13118,
326,
262,
7820,
17216,
82,
284,
13,
198,
220,
220,
220,
19548,
25,
37902,
4663,
891,
220,
1303,
4718,
689,
281,
1981,
43091,
284,
257,
7820,
13,
198,
220,
220,
220,
20387,
25,
37902,
4663,
891,
220,
1303,
1052,
1988,
12739,
262,
3542,
9449,
393,
1474,
1108,
13,
198,
220,
220,
220,
47517,
25,
37902,
4663,
891,
220,
1303,
1482,
2536,
2913,
973,
284,
35139,
262,
33815,
286,
281,
7561,
11,
393,
3615,
14,
45869,
12251,
198,
220,
220,
220,
8695,
25,
37902,
4663,
891,
220,
1303,
4718,
341,
318,
281,
12531,
3119,
543,
8075,
281,
7952,
2792,
1022,
281,
7561,
290,
281,
31433,
13,
198,
220,
220,
220,
21210,
25,
37902,
4663,
891,
220,
1303,
4718,
689,
281,
1981,
21210,
18104,
284,
257,
43091,
13,
198,
220,
220,
220,
826,
18843,
392,
25,
37902,
4663,
891,
220,
1303,
383,
1988,
286,
262,
826,
1515,
392,
287,
257,
32315,
5408,
13,
198,
220,
220,
220,
826,
18843,
392,
26687,
25,
37902,
4663,
891,
220,
1303,
317,
4941,
284,
257,
3992,
8271,
4955,
262,
1988,
329,
262,
826,
1515,
392,
286,
257,
1482,
2536,
2913,
13,
198,
220,
220,
220,
8354,
25,
37902,
4663,
891,
220,
1303,
383,
27421,
286,
257,
8354,
326,
3769,
4732,
284,
262,
6287,
286,
262,
9312,
13,
198,
220,
220,
220,
2723,
25,
37902,
4663,
891,
220,
1303,
20984,
284,
257,
31433,
14,
33553,
36307,
198,
220,
220,
220,
3722,
25,
37902,
4663,
891,
220,
1303,
262,
1988,
7560,
422,
262,
1364,
18843,
392,
2223,
393,
257,
1988,
3519,
284,
262,
1364,
18843,
392,
900,
355,
262,
4941,
329,
262,
7208,
13,
198,
220,
220,
220,
2496,
25,
37902,
4663,
891,
220,
1303,
383,
2496,
3119,
9217,
262,
31433,
326,
318,
262,
4165,
2426,
284,
543,
262,
14330,
2223,
3264,
8991,
13,
198,
220,
220,
220,
28805,
12332,
25,
37902,
4663,
891,
220,
1303,
383,
1271,
286,
4201,
706,
543,
28805,
1138,
1586,
779,
286,
262,
11171,
6140,
13,
198,
220,
220,
220,
18283,
33553,
25,
37902,
4663,
891,
220,
1303,
383,
3615,
3025,
8748,
318,
852,
18283,
13,
198,
220,
220,
220,
9646,
33553,
25,
37902,
4663,
891,
220,
1303,
383,
3615,
508,
318,
9646,
8748,
13,
198,
220,
220,
220,
334,
312,
25,
37902,
4663,
891,
220,
1303,
1052,
42053,
29709,
27421,
198,
220,
220,
220,
28721,
25,
37902,
4663,
891,
220,
1303,
4718,
689,
262,
4811,
973,
329,
9041,
28721,
4028,
284,
257,
7820,
13,
198,
220,
220,
220,
4326,
25,
37902,
4663,
891,
220,
1303,
383,
4326,
286,
15558,
286,
262,
1988,
286,
262,
826,
18843,
392,
393,
826,
18843,
392,
26687,
286,
257,
1482,
2536,
2913,
13,
198,
220,
220,
220,
2124,
505,
25,
37902,
4663,
891,
220,
1303,
383,
8695,
318,
11378,
618,
691,
530,
11,
290,
407,
517,
11,
286,
262,
1482,
2536,
6003,
318,
11378,
628,
220,
220,
220,
1303,
2638,
1378,
2503,
13,
86,
18,
13,
2398,
14,
16942,
14,
2998,
14,
4883,
2,
45,
2434,
35392,
198,
220,
220,
220,
1439,
25,
37902,
4663,
891,
220,
1303,
18291,
6945,
326,
262,
8354,
286,
262,
2776,
318,
477,
286,
262,
10098,
3925,
1626,
257,
4732,
13,
198,
220,
220,
220,
1439,
17,
358,
13313,
507,
25,
37902,
4663,
891,
220,
1303,
18291,
6945,
326,
262,
8354,
286,
262,
2776,
318,
477,
286,
262,
1218,
12,
5715,
8787,
284,
262,
3615,
13,
198,
220,
220,
220,
1439,
13313,
507,
25,
37902,
4663,
891,
220,
1303,
18291,
6945,
326,
262,
8354,
286,
262,
2776,
318,
477,
286,
262,
717,
12,
5715,
8787,
286,
262,
3615,
13,
198,
220,
220,
220,
1439,
38,
14459,
25,
37902,
4663,
891,
220,
1303,
18291,
6945,
326,
262,
8354,
286,
262,
2776,
318,
477,
286,
262,
1448,
8787,
286,
262,
3615,
13,
198,
220,
220,
220,
4912,
25,
37902,
4663,
891,
220,
1303,
18291,
6945,
326,
262,
8354,
286,
262,
2776,
318,
262,
5447,
1448,
351,
3294,
1981,
1866,
13,
198,
220,
220,
220,
18629,
25,
37902,
4663,
891,
220,
1303,
18291,
6945,
326,
262,
8354,
286,
262,
2776,
318,
262,
2060,
3615,
1981,
13,
198,
220,
220,
220,
4112,
26545,
25,
37902,
4663,
891,
220,
1303,
317,
966,
287,
2272,
393,
640,
5447,
351,
4112,
22715,
329,
262,
22097,
286,
262,
2496,
31433,
13,
198,
220,
220,
220,
4112,
10699,
25,
37902,
4663,
891,
220,
1303,
24291,
7,
82,
8,
286,
530,
393,
734,
34197,
329,
362,
35,
12,
48205,
393,
3953,
7,
82,
8,
286,
530,
284,
5509,
34197,
329,
513,
35,
12,
48205,
286,
262,
2496,
31433,
13,
198,
220,
220,
220,
4112,
4561,
34961,
26545,
25,
37902,
4663,
891,
220,
1303,
383,
4112,
21739,
6116,
286,
1440,
14371,
286,
257,
35991,
319,
257,
362,
35,
12,
5171,
11017,
393,
262,
3624,
14371,
286,
257,
13617,
1868,
287,
257,
513,
35,
12,
13200,
329,
262,
2496,
31433,
284,
4197,
13,
198,
220,
220,
220,
4112,
12966,
35738,
26545,
25,
37902,
4663,
891,
220,
1303,
383,
4112,
21964,
6116,
287,
257,
2056,
4269,
262,
2496,
31433,
468,
284,
4197,
13,
198,
220,
220,
220,
954,
25,
37902,
4663,
891,
220,
1303,
399,
39223,
954,
286,
30632,
286,
262,
2223,
286,
262,
14330,
13,
198,
220,
220,
220,
3128,
7575,
25,
37902,
4663,
891,
220,
1303,
383,
3128,
357,
392,
11902,
640,
290,
640,
11340,
8,
286,
25352,
262,
2223,
286,
262,
14330,
13,
6498,
1515,
392,
1988,
17191,
307,
281,
2124,
21282,
25,
4475,
393,
2124,
21282,
25,
4475,
7575,
355,
5447,
416,
16410,
19875,
15952,
2611,
1157,
12,
17,
60,
4083,
198,
220,
220,
220,
5711,
5990,
2101,
25,
37902,
4663,
891,
220,
1303,
317,
640,
5711,
2278,
3161,
284,
25352,
262,
2223,
286,
262,
14330,
13,
383,
966,
287,
640,
26555,
428,
2278,
26720,
307,
5447,
416,
1194,
21964,
1482,
2536,
2913,
5929,
416,
257,
5972,
605,
1482,
2536,
2913,
357,
22602,
1710,
262,
267,
7109,
75,
25,
392,
44015,
594,
1515,
392,
737,
6498,
1515,
392,
1988,
17191,
307,
281,
2124,
21282,
25,
32257,
355,
5447,
416,
16410,
19875,
15952,
2611,
1157,
12,
17,
60,
4083,
198,
220,
220,
220,
7585,
29239,
25,
37902,
4663,
891,
220,
1303,
383,
7585,
6518,
973,
329,
25352,
262,
2223,
286,
262,
14330,
13,
198,
220,
220,
220,
3335,
25,
37902,
4663,
891,
220,
1303,
1052,
5174,
3335,
973,
329,
25352,
262,
2223,
286,
262,
14330,
13,
198,
220,
220,
220,
42118,
7575,
25,
37902,
4663,
891,
220,
1303,
317,
12948,
42118,
640,
2278,
543,
743,
307,
973,
329,
25352,
286,
262,
2223,
286,
262,
14330,
13,
6498,
1515,
392,
1988,
17191,
307,
281,
2124,
21282,
25,
32257,
355,
5447,
416,
16410,
19875,
15952,
2611,
1157,
12,
17,
60,
4083,
198,
220,
220,
220,
37430,
25,
37902,
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891,
220,
1303,
1423,
12364,
326,
257,
1813,
1988,
21767,
262,
826,
1515,
392,
286,
262,
1482,
2536,
2913,
13,
198,
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13,
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25,
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2496,
31433,
13,
198,
220,
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220,
308,
83,
25,
37902,
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891,
220,
1303,
1423,
12364,
326,
257,
1813,
1988,
318,
3744,
621,
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1515,
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1482,
2536,
2913,
13,
198,
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308,
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80,
25,
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12364,
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1813,
1988,
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621,
393,
4961,
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13,
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25,
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220,
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317,
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10088,
12739,
326,
257,
1813,
1988,
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262,
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392,
286,
262,
1482,
2536,
2913,
13,
198,
220,
220,
220,
8856,
25,
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220,
1303,
383,
7561,
318,
284,
307,
9514,
290,
318,
407,
636,
286,
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2450,
784,
290,
262,
2450,
3793,
4938,
13,
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220,
2831,
25,
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220,
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317,
5447,
2831,
6567,
4634,
257,
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329,
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262,
2223,
286,
262,
14330,
13,
198,
220,
220,
220,
12515,
25,
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4663,
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220,
1303,
383,
2450,
318,
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13,
198,
220,
220,
220,
318,
32,
25,
37902,
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891,
220,
1303,
317,
900,
12,
3106,
10088,
12739,
326,
257,
1813,
1988,
318,
281,
4554,
286,
262,
826,
1515,
392,
286,
262,
1482,
2536,
2913,
13,
198,
220,
220,
220,
318,
3237,
5189,
25,
37902,
4663,
891,
220,
1303,
317,
900,
12,
3106,
10088,
12739,
326,
257,
1813,
1988,
318,
477,
286,
262,
826,
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286,
262,
1482,
2536,
2913,
13,
198,
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318,
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25,
37902,
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220,
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317,
900,
12,
3106,
10088,
12739,
326,
257,
1813,
1988,
318,
597,
286,
262,
826,
1515,
392,
286,
262,
1482,
2536,
2913,
13,
198,
220,
220,
220,
318,
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5189,
25,
37902,
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891,
220,
1303,
317,
900,
12,
3106,
10088,
12739,
326,
257,
1813,
1988,
318,
4844,
286,
262,
826,
1515,
392,
286,
262,
1482,
2536,
2913,
13,
198,
220,
220,
220,
318,
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5189,
25,
37902,
4663,
891,
220,
1303,
317,
900,
12,
3106,
10088,
12739,
326,
257,
1813,
1988,
318,
7763,
416,
262,
826,
1515,
392,
286,
262,
1482,
2536,
2913,
13,
198,
220,
220,
220,
3303,
25,
37902,
4663,
891,
220,
1303,
317,
3288,
3303,
973,
416,
262,
2496,
31433,
13,
198,
220,
220,
220,
300,
83,
25,
37902,
4663,
891,
220,
1303,
1423,
12364,
326,
257,
1813,
1988,
318,
1342,
621,
262,
826,
1515,
392,
286,
262,
1482,
2536,
2913,
13,
198,
220,
220,
220,
300,
660,
80,
25,
37902,
4663,
891,
220,
1303,
1423,
12364,
326,
257,
1813,
1988,
318,
1342,
621,
393,
4961,
284,
262,
826,
1515,
392,
286,
262,
1482,
2536,
2913,
13,
198,
220,
220,
220,
2056,
25,
37902,
4663,
891,
220,
1303,
21743,
286,
257,
2056,
11171,
4634,
257,
4732,
329,
25352,
262,
2223,
286,
262,
14330,
13,
198,
220,
220,
220,
1138,
1068,
7575,
25,
37902,
4663,
891,
220,
1303,
1052,
22425,
2033,
286,
530,
284,
867,
1138,
1068,
640,
9574,
543,
547,
973,
329,
25352,
262,
2223,
286,
262,
14330,
13,
6498,
1515,
392,
1988,
17191,
307,
281,
2124,
21282,
25,
32257,
355,
5447,
416,
16410,
19875,
15952,
2611,
1157,
12,
17,
60,
4083,
198,
220,
220,
220,
497,
80,
25,
37902,
4663,
891,
220,
1303,
1423,
12364,
326,
257,
1813,
1988,
318,
407,
4961,
284,
262,
826,
1515,
392,
286,
262,
1482,
2536,
2913,
13,
198,
220,
220,
220,
1414,
31264,
25,
37902,
4663,
891,
220,
1303,
383,
2033,
286,
257,
3176,
6074,
13,
6498,
1515,
392,
1988,
17191,
307,
281,
2124,
21282,
25,
12501,
4402,
13,
198,
220,
220,
220,
5873,
25,
37902,
4663,
891,
220,
1303,
317,
5873,
2033,
286,
262,
2496,
31433,
5981,
329,
25352,
262,
2223,
286,
262,
14330,
13,
6498,
1515,
392,
1988,
17191,
307,
281,
2124,
21282,
25,
12501,
4402,
422,
657,
284,
1802,
13,
198,
220,
220,
220,
9943,
25,
37902,
4663,
891,
220,
1303,
2448,
8481,
1011,
12741,
625,
49724,
13,
198,
220,
220,
220,
2450,
28350,
25,
37902,
4663,
891,
220,
1303,
1423,
16856,
262,
4036,
4818,
8079,
262,
2223,
286,
262,
14330,
373,
25805,
13,
198,
220,
220,
220,
1720,
25,
37902,
4663,
891,
220,
1303,
21743,
286,
1720,
393,
2139,
4634,
257,
4732,
329,
25352,
262,
2223,
286,
262,
14330,
13,
198,
220,
220,
220,
20767,
25,
37902,
4663,
891,
220,
1303,
1041,
3145,
1756,
1011,
12741,
625,
21627,
13,
198,
220,
220,
220,
4007,
25,
37902,
4663,
891,
220,
1303,
317,
5447,
4007,
329,
25352,
262,
2223,
286,
262,
14330,
13,
198,
220,
220,
220,
17800,
25,
37902,
4663,
891,
220,
1303,
383,
2151,
6464,
262,
1255,
14,
448,
2958,
286,
25352,
262,
2223,
286,
262,
14330,
13,
198,
220,
220,
220,
3585,
26545,
25,
37902,
4663,
891,
220,
1303,
317,
966,
287,
2272,
393,
640,
5447,
351,
22715,
3585,
284,
1336,
5260,
262,
22097,
286,
262,
2496,
31433,
13,
198,
220,
220,
220,
3585,
10699,
25,
37902,
4663,
891,
220,
1303,
24291,
7,
82,
8,
286,
530,
393,
734,
34197,
329,
362,
35,
12,
48205,
393,
3953,
7,
82,
8,
286,
530,
284,
5509,
34197,
329,
513,
35,
12,
48205,
532,
6241,
355,
28071,
286,
1336,
3815,
532,
286,
262,
2496,
31433,
13,
198,
220,
220,
220,
3585,
4561,
34961,
26545,
25,
37902,
4663,
891,
220,
1303,
383,
3585,
21739,
6116,
532,
6241,
355,
28071,
286,
1336,
3815,
532,
286,
1440,
14371,
286,
257,
35991,
319,
257,
362,
35,
12,
5171,
11017,
393,
262,
3624,
14371,
286,
257,
13617,
1868,
287,
257,
513,
35,
12,
13200,
286,
262,
2496,
31433,
13,
198,
220,
220,
220,
3585,
12966,
35738,
26545,
25,
37902,
4663,
891,
220,
1303,
317,
966,
287,
2272,
393,
640,
5447,
351,
22715,
3585,
284,
1336,
5260,
262,
22097,
286,
262,
2496,
31433,
13,
198,
220,
220,
220,
6323,
25,
37902,
4663,
891,
220,
1303,
22406,
286,
262,
40570,
286,
262,
2496,
31433,
13,
198,
220,
220,
220,
21739,
25,
37902,
4663,
891,
220,
1303,
317,
3706,
290,
5174,
4903,
2117,
34961,
1989,
351,
5447,
11637,
543,
318,
973,
329,
25352,
262,
2223,
286,
262,
14330,
13,
1052,
314,
7112,
17191,
307,
973,
284,
2380,
428,
1988,
13,
198,
220,
220,
220,
21739,
7222,
585,
17540,
25,
37902,
4663,
891,
220,
1303,
317,
900,
286,
22715,
4634,
262,
11637,
286,
257,
4903,
2117,
34961,
1989,
973,
329,
25352,
262,
2223,
286,
262,
14330,
13,
383,
22715,
17191,
2291,
890,
3984,
290,
32477,
11,
484,
26720,
2291,
20334,
290,
262,
4903,
375,
5139,
4818,
388,
13,
198,
220,
220,
220,
1104,
25,
37902,
4663,
891,
220,
1303,
383,
7561,
318,
284,
307,
4855,
355,
636,
286,
262,
2450,
784,
290,
262,
2450,
3793,
4938,
13,
198,
220,
220,
220,
1080,
25,
37902,
4663,
891,
220,
1303,
1052,
5174,
14492,
1080,
973,
329,
25352,
262,
2223,
286,
262,
14330,
13,
198,
220,
220,
220,
1080,
24728,
25,
37902,
4663,
891,
220,
1303,
1052,
5174,
14492,
1080,
393,
14492,
3335,
973,
329,
25352,
262,
2223,
286,
262,
14330,
13,
198,
220,
220,
220,
640,
9492,
2100,
25,
37902,
4663,
891,
220,
1303,
317,
24824,
2278,
286,
640,
878,
262,
1306,
9706,
286,
262,
2223,
286,
262,
14330,
13,
6498,
1515,
392,
1988,
17191,
307,
281,
2124,
21282,
25,
32257,
355,
5447,
416,
16410,
19875,
15952,
2611,
1157,
12,
17,
60,
4083,
198,
220,
220,
220,
4326,
5189,
12332,
25,
37902,
4663,
891,
220,
1303,
383,
4326,
286,
3953,
973,
329,
14143,
262,
30632,
286,
262,
2223,
286,
262,
14330,
13,
198,
220,
220,
220,
2196,
25,
37902,
4663,
891,
220,
1303,
383,
2196,
286,
262,
2496,
31433,
13,
198,
220,
220,
220,
7166,
14749,
25,
37902,
4663,
891,
220,
1303,
1052,
5174,
4067,
286,
262,
7283,
6946,
2272,
543,
318,
5981,
329,
25352,
262,
2223,
286,
262,
14330,
13,
628,
220,
220,
220,
1303,
2638,
1378,
2503,
13,
86,
18,
13,
2398,
14,
15724,
14,
2999,
14,
8135,
418,
14,
7295,
2,
36307,
628,
220,
220,
220,
1303,
2638,
1378,
2503,
13,
86,
18,
13,
2398,
14,
15724,
14,
2999,
14,
8135,
418,
14,
7295,
2,
3103,
984,
198,
220,
220,
220,
7561,
25,
37902,
4663,
891,
220,
1303,
1052,
4905,
319,
281,
31433,
13,
198,
220,
220,
220,
12729,
25,
37902,
4663,
891,
220,
1303,
317,
7820,
326,
11455,
262,
8333,
1453,
257,
14330,
625,
281,
31433,
422,
281,
8333,
263,
13,
198,
220,
220,
220,
2195,
861,
295,
25,
37902,
4663,
891,
220,
1303,
317,
7820,
326,
29348,
257,
14330,
625,
281,
31433,
422,
4671,
13,
198,
220,
220,
220,
31433,
25,
37902,
4663,
891,
220,
1303,
317,
8271,
393,
257,
4947,
286,
4133,
326,
389,
262,
2426,
286,
257,
14330,
13,
198,
220,
220,
220,
31433,
36307,
25,
37902,
4663,
891,
220,
1303,
1052,
31433,
326,
318,
4947,
286,
1981,
4133,
198,
220,
220,
220,
31433,
43642,
25,
37902,
4663,
891,
220,
1303,
1446,
13920,
329,
31433,
41063,
14700,
13,
198,
220,
220,
220,
27863,
40596,
25,
37902,
4663,
891,
220,
1303,
16718,
284,
4474,
10064,
284,
10568,
12333,
326,
15058,
422,
262,
35981,
286,
42283,
393,
12333,
1022,
2448,
8481,
290,
1041,
3145,
1756,
287,
262,
976,
7820,
13,
198,
220,
220,
220,
1482,
2536,
2913,
25,
37902,
4663,
891,
220,
1303,
317,
25131,
5408,
326,
1006,
1127,
262,
33815,
286,
281,
7561,
290,
3615,
14,
45869,
12251,
393,
13627,
262,
3403,
9723,
284,
257,
14330,
13,
198,
220,
220,
220,
18104,
25,
37902,
4663,
891,
220,
1303,
383,
12990,
284,
1620,
281,
7561,
198,
220,
220,
220,
9578,
18843,
392,
25,
37902,
4663,
891,
220,
1303,
9578,
1515,
392,
329,
257,
32315,
5408,
13,
198,
220,
220,
220,
5972,
605,
3103,
2536,
2913,
25,
37902,
4663,
891,
220,
1303,
317,
12219,
5408,
326,
1006,
1127,
262,
33815,
286,
281,
7561,
290,
3615,
14,
45869,
12251,
393,
13627,
262,
3403,
9723,
284,
257,
14330,
13,
198,
220,
220,
220,
33085,
25,
37902,
4663,
891,
220,
1303,
317,
7820,
326,
26017,
257,
14330,
625,
281,
31433,
422,
281,
8333,
263,
13,
198,
220,
220,
220,
35946,
25,
37902,
4663,
891,
220,
1303,
35946,
329,
32315,
5408,
13,
198,
220,
220,
220,
3615,
25,
37902,
4663,
891,
220,
1303,
1052,
9312,
393,
257,
4947,
286,
12066,
326,
25340,
371,
4316,
287,
257,
14330,
13,
198,
220,
220,
220,
3615,
36307,
25,
37902,
4663,
891,
220,
1303,
317,
3615,
326,
318,
257,
1448,
286,
1981,
12066,
198,
220,
220,
220,
3615,
43642,
25,
37902,
4663,
891,
220,
1303,
1446,
13920,
329,
3615,
41063,
14700,
13,
198,
220,
220,
220,
2448,
3411,
25,
37902,
4663,
891,
220,
1303,
383,
2694,
284,
1620,
281,
7561,
625,
281,
31433,
13,
198,
220,
220,
220,
7820,
25,
37902,
4663,
891,
220,
1303,
317,
1729,
12,
28920,
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45895,
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198
] | 3.491665 | 6,299 |
from flask import Flask
from app.apis.v1 import api_v1_bp
def register_blueprints(app: "Flask") -> "Flask":
"""A function to register flask blueprint.
To register blueprints add them like the example
Example usage:
from app.blueprints import blueprint
app.register_blueprint(blueprint)
Args:
app (Flask): Flask Application instance
Returns:
Flask: Flask Application instance
"""
app.register_blueprint(api_v1_bp)
return app
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] | 2.785311 | 177 |
import networkx as nx
from cStringIO import StringIO
from Bio import Phylo
import matplotlib.pyplot as plt
import random
import logging
from tqdm import tqdm
logger = logging.getLogger()
logger.setLevel(logging.INFO)
import numpy as np
import trees
from trees.ddt import DirichletDiffusionTree, Inverse, GaussianLikelihoodModel
from trees.mcmc import MetropolisHastingsSampler
from trees.util import plot_tree_2d
from sklearn.decomposition import PCA
import seaborn as sns
sns.set_style('white')
import cPickle as pickle
dataset = trees.data.load('zoo')
X, y = dataset.X, dataset.y
X += np.random.normal(scale=0.01, size=X.shape)
pca = PCA(2)
pca.fit(X)
# X = pca.transform(X)
N, D = X.shape
df = Inverse(c=0.9)
lm = GaussianLikelihoodModel(sigma=np.eye(D) / 4.0, sigma0=np.eye(D) / 2.0, mu0=X.mean(axis=0)).compile()
model = DirichletDiffusionTree(df=df, likelihood_model=lm, constraints=[])
sampler = MetropolisHastingsSampler(model, X)
sampler.initialize_assignments()
likelihoods = []
fontsize = 18
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] | 2.635417 | 384 |
#!/usr/bin/python
import getopt, logging, sys, SneeqlLib, UtilLib, AvroraLib, os, checkTupleCount
queryMap = {'Q2' : 'input/pipes/Q2.txt', 'Q4' : 'input/pipes/QNest4.txt', 'Q5' : 'input/pipes/QNest5.txt'}
networkMap = {'10' : 'input/networks/10-node-topology.xml', '30' : 'scripts/qos-exp/scenarios/30-dense-net.xml', '100' : 'scripts/qos-exp/scenarios/ix-100-dense-net.xml'}
numSourcesMap = {'10_3' : 'input/pipes/10Sites-3Sources-schemas.xml', '10_10' : 'input/pipes/10Sites-10Sources-schemas.xml', '30_min' : 'scripts/qos-exp/scenarios/30-node-min-schema.xml', '30_maj' : 'scripts/qos-exp/scenarios/30-node-maj-schema.xml', '100_min' : 'scripts/qos-exp/scenarios/100-node-min-schema.xml', '100_maj' : 'scripts/qos-exp/scenarios/100-node-maj-schema.xml'}
optGoalMap = {'min_delivery' : 'input/QoS/qos-spec-min-delivery.xml', 'min_energy' : 'input/QoS/qos-spec-min-energy.xml', 'max_lifetime' : 'input/QoS/qos-spec-max-lifetime.xml'}
optMinNN = 0
optMaxNN = 30
optSolverOutput = None
#Ouput info message to screen and logger if applicable
#Ouput warning message to screen and logger if applicable
#Ouput error message to screen and logger if applicable
if __name__ == "__main__":
main()
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] | 2.251366 | 549 |
#!/usr/bin/python
import sys
from Foundation import *
from ScriptingBridge import *
ab = SBApplication.applicationWithBundleIdentifier_("com.apple.AddressBook")
for person in ab.people():
fname = person.firstName()
pfname = person.phoneticFirstName()
lname = person.lastName()
plname = person.phoneticLastName()
note = person.note()
print "%s %s %s %s %s" % (fname, pfname, lname, plname, note)
if pfname and plname:
cname = lname + fname
if note:
note = cname + "\n" + note
else:
note = cname
person.setPhoneticLastName_("")
person.setPhoneticFirstName_("")
person.setFirstName_(pfname)
person.setLastName_(plname)
person.setNote_(note)
print "Done."
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] | 2.569343 | 274 |
from django.db import models
from django.utils.text import ugettext_lazy as _
from model_utils.fields import AutoCreatedField, AutoLastModifiedField
class BaseModel(models.Model):
"""
An abstract base class model that providers self-updating `created` and
`modified` fields.
"""
date_added = AutoCreatedField(_('date added'))
date_updated = AutoLastModifiedField(_('date updated'))
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"""Contains integration measures."""
import abc
from typing import Optional, Tuple, Union
import numpy as np
import scipy.stats
from probnum.randvars import Normal
from probnum.typing import FloatArgType, IntArgType
class IntegrationMeasure(abc.ABC):
"""An abstract class for a measure against which a target function is integrated.
Child classes implement specific integration measures and, if available, make use
of random variables for sampling and evaluation of the density function.
Parameters
----------
dim :
Dimension of the integration domain.
domain :
Tuple which contains two arrays which define the start and end points,
respectively, of the rectangular integration domain.
"""
def __call__(self, points: Union[float, np.floating, np.ndarray]) -> np.ndarray:
"""Evaluate the density function of the integration measure.
Parameters
----------
points :
*shape=(n_points,) or (n_points, dim)* -- Input locations.
Returns
-------
density_evals :
*shape=(n_points,)* -- Density evaluated at given locations.
"""
# pylint: disable=no-member
return self.random_variable.pdf(points).squeeze()
def sample(
self,
rng: np.random.Generator,
n_sample: IntArgType,
) -> np.ndarray:
"""Sample ``n_sample`` points from the integration measure.
Parameters
----------
rng :
Random number generator
n_sample :
Number of points to be sampled
Returns
-------
points :
*shape=(n_sample,) or (n_sample,dim)* -- Sampled points
"""
# pylint: disable=no-member
return np.reshape(
self.random_variable.sample(rng=rng, size=n_sample),
newshape=(n_sample, self.dim),
)
def _set_dimension_domain(
self,
dim: IntArgType,
domain: Tuple[Union[np.ndarray, FloatArgType], Union[np.ndarray, FloatArgType]],
) -> None:
"""Sets the integration domain and dimension.
The following logic is used to set the domain and dimension:
1. If ``dim`` is not given (``dim == None``):
1a. If either ``domain[0]`` or ``domain[1]`` is a scalar, the dimension
is set as the maximum of their lengths and the scalar is expanded to
a constant vector.
1b. Otherwise, if the ``domain[0]`` and ``domain[1]`` are not of equal
length, an error is raised.
2. If ``dim`` is given:
2a. If both ``domain[0]`` and ``domain[1]`` are scalars, they are
expanded to constant vectors of length ``dim``.
2b. If only one of `domain[0]`` and ``domain[1]`` is a scalar and the
length of the other equals ``dim``, the scalar one is expanded to a
constant vector of length ``dim``.
2c. Otherwise, if neither of ``domain[0]`` and ``domain[1]`` is a
scalar, error is raised if either of them has length which does not
equal ``dim``.
"""
domain_a_dim = np.size(domain[0])
domain_b_dim = np.size(domain[1])
# Check that given dimensions match and are positive
dim_mismatch = False
if dim is None:
if domain_a_dim == domain_b_dim:
dim = domain_a_dim
elif domain_a_dim == 1 or domain_b_dim == 1:
dim = np.max([domain_a_dim, domain_b_dim])
else:
dim_mismatch = True
else:
if (domain_a_dim > 1 or domain_b_dim > 1) and dim != np.max(
[domain_a_dim, domain_b_dim]
):
dim_mismatch = True
if dim_mismatch:
raise ValueError(
"Domain limits must have the same length or at least "
"one of them has to be one-dimensional."
)
if dim < 1:
raise ValueError(f"Domain dimension dim = {dim} must be positive.")
# Use same domain limit in all dimensions if only one limit is given
if domain_a_dim == 1:
domain_a = np.full((dim,), domain[0])
else:
domain_a = domain[0]
if domain_b_dim == 1:
domain_b = np.full((dim,), domain[1])
else:
domain_b = domain[1]
# Check that the domain is non-empty
if not np.all(domain_a < domain_b):
raise ValueError("Domain must be non-empty.")
self.dim = dim
self.domain = (domain_a, domain_b)
class LebesgueMeasure(IntegrationMeasure):
"""Lebesgue measure on a hyper-rectangle.
Parameters
----------
dim :
Dimension of the integration domain
domain :
Tuple which contains two arrays which define the start and end points,
respectively, of the rectangular integration domain.
normalized :
Boolean which controls whether or not the measure is normalized (i.e.,
integral over the domain is one).
"""
# pylint: disable=too-few-public-methods
class GaussianMeasure(IntegrationMeasure):
"""Gaussian measure on Euclidean space with given mean and covariance.
If ``mean`` and ``cov`` are scalars but ``dim`` is larger than one, ``mean`` and
``cov`` are extended to a constant vector and diagonal matrix, respectively,
of appropriate dimensions.
Parameters
----------
mean :
*shape=(dim,)* -- Mean of the Gaussian measure.
cov :
*shape=(dim, dim)* -- Covariance matrix of the Gaussian measure.
dim :
Dimension of the integration domain.
"""
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] | 2.346247 | 2,478 |
# -*- coding: utf-8 -*-
"""
Copyright (C) 2021 Stefano Gottardo (script.appcast)
Exceptions
SPDX-License-Identifier: MIT
See LICENSES/MIT.md for more information.
"""
# Exceptions for DATABASE
class DBSQLiteConnectionError(Exception):
"""An error occurred in the database connection"""
class DBSQLiteError(Exception):
"""An error occurred in the database operations"""
class DBMySQLConnectionError(Exception):
"""An error occurred in the database connection"""
class DBMySQLError(Exception):
"""An error occurred in the database operations"""
class DBProfilesMissing(Exception):
"""There are no stored profiles in database"""
class DBRecordNotExistError(Exception):
"""The record do not exist in database"""
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] | 3.337719 | 228 |
# Copyright 2017 Red Hat, 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 logging
import math
import re
from mistral_lib import actions
from tripleo_common.actions import base
from tripleo_common import exception
LOG = logging.getLogger(__name__)
class GetDpdkNicsNumaInfoAction(base.TripleOAction):
"""Gets the DPDK NICs with MTU for NUMA nodes.
Find the DPDK interface names from the network config and
translate it to phsical interface names using the introspection
data. And then find the NUMA node associated with the DPDK
interface and the MTU value.
:param network_configs: network config list
:param inspect_data: introspection data
:param mtu_default: mtu default value for NICs
:return: DPDK NICs NUMA nodes info
"""
# TODO(jpalanis): Expose this utility from os-net-config to sort
# active nics
# TODO(jpalanis): Expose this utility from os-net-config to sort
# active nics
# TODO(jpalanis): Expose this utility from os-net-config to sort
# active nics
# Gets numa node id for physical NIC name
# Get physical interface name for NIC name
# Gets dpdk interfaces and mtu info for dpdk config
# Default mtu(recommended 1500) is used if no MTU is set for DPDK NIC
class GetDpdkCoreListAction(base.TripleOAction):
"""Gets the DPDK PMD Core List.
With input as the number of physical cores for each NUMA node,
find the right logical CPUs to be allocated along with its
siblings for the PMD core list.
:param inspect_data: introspection data
:param numa_nodes_cores_count: physical cores count for each NUMA
:return: DPDK Core List
"""
class GetHostCpusListAction(base.TripleOAction):
"""Gets the Host CPUs List.
CPU threads from first physical core is allocated for host processes
on each NUMA nodes.
:param inspect_data: introspection data
:return: Host CPUs List
"""
class GetDpdkSocketMemoryAction(base.TripleOAction):
"""Gets the DPDK Socket Memory List.
For NUMA node with DPDK nic, socket memory is calculated
based on MTU, Overhead and Packet size in buffer.
For NUMA node without DPDK nic, minimum socket memory is
assigned (recommended 1GB)
:param dpdk_nics_numa_info: DPDK nics numa info
:param numa_nodes: list of numa nodes
:param overhead: overhead value
:param packet_size_in_buffer: packet size in buffer
:param minimum_socket_memory: minimum socket memory
:return: DPDK Socket Memory List
"""
# Computes round off MTU value in bytes
# example: MTU value 9000 into 9216 bytes
# Calculates socket memory for a NUMA node
class ConvertNumberToRangeListAction(base.TripleOAction):
"""Converts number list into range list
:param num_list: comma delimited number list as string
:return: comma delimited range list as string
"""
# converts number list into range list.
# here input parameter and return value as list
# example: [12, 13, 14, 17] into ["12-14", "17"]
class ConvertRangeToNumberListAction(base.TripleOAction):
"""Converts range list to integer list
:param range_list: comma delimited range list as string / list
:return: comma delimited number list as string
"""
# converts range list into number list
# here input parameter and return value as list
# example: ["12-14", "^13", "17"] into [12, 14, 17]
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366,
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685,
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11,
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11,
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60,
198
] | 3.160543 | 1,252 |
#!/bin/python
#python
import sys
import os
import time
import re
import shutil
import MySQLdb
#appion
from appionlib import appionScript
from appionlib import apStack
from appionlib import apDisplay
from appionlib import apEMAN
from appionlib import apFile
from appionlib.apSpider import operations
from appionlib.apTilt import apTiltPair
from appionlib import apImagicFile
import sinedon
#=====================
if __name__ == "__main__":
tiltstacks = tiltStackSync()
tiltstacks.start()
tiltstacks.close()
| [
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198,
197,
83,
2326,
301,
4595,
13,
19836,
3419,
628,
198
] | 3.147239 | 163 |
# bug 3
# http://asu-compmethodsphysics-phy494.github.io/ASU-PHY494/2017/01/24/04_Debugging_1/#activity-fix-as-many-bugs-as-possible
# Print "error" for input 0:
x = float(input("Enter non-zero number --> "))
if x == 0:
print("ERROR: number cannot be 0")
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4943,
628
] | 2.574257 | 101 |
import sys
from openml.exceptions import OpenMLServerException
from requests.exceptions import ChunkedEncodingError
if __name__ == "__main__":
test_automl(600)
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] | 3.12963 | 54 |
from django.contrib import admin
from .models import Business, NeighborHood, Post, Profile
# Register your models here.
admin.site.register(Profile)
admin.site.register(Business)
admin.site.register(Post)
admin.site.register(NeighborHood)
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] | 3.442857 | 70 |
import json
import os
T = Translation()
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] | 3.307692 | 13 |
from django.conf.urls import include, url
from django.contrib import admin
from inventory import views as inventory_index
urlpatterns = [
# Examples:
# url(r'^$', 'rudra.views.home', name='home'),
# url(r'^blog/', include('blog.urls')),
url(r'^$', inventory_index.index, name='index'),
url(r'^item/(?P<id>\d+)/', inventory_index.item_detail, name='item_detail'),
url(r'^admin/', include(admin.site.urls)),
]
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#!/usr/bin/python
# -*- coding: utf-8 -*-
import time
import datetime
import string
from os import listdir
from os.path import join as pathjoin
from math import log, ceil
import subprocess
import pandas as pd
import nltk
from nltk.corpus import stopwords
import matplotlib.pyplot as plt
import tailer
from ttp import ttp
# use this if you want to include modules from a subfolder
#cmd_subfolder = os.path.realpath(os.path.abspath(os.path.join(os.path.split(inspect.getfile( inspect.currentframe() ))[0],"GetOldTweets-python")))
#if cmd_subfolder not in sys.path:
# sys.path.insert(0, cmd_subfolder)
pd.set_option('display.max_colwidth', -1)
class Twords(object):
""" Object that takes in tweets from Java twitter search engine and allows
manipulation, analysis and visualization.
Attributes:
jar_folder_path (string): path to where java jar twitter search files
are stored
data_path (string): path to data set from java twitter search.
It can be either path to single file, or path to
directory containing several csv files. Files are
assumed to be in format give by output of
create_java_tweets function below
background_path (string): path to background data. Form of background
data file is csv with columns 'word', 'occurrences',
and 'frequency' for words as they occur in some
background corpus.
background_dict (dictionary): dictionary of background rates of English
words, used in comparing word frequencies.
Can be set using create_Background_dict
function.
search_terms (list of strings): list of search terms used when collecting
tweets using create_java_tweets
tweets_df (pandas dataframe): pandas dataframe that holds all tweet data.
This is central object in an instance of
Twords.
word_bag (list of strings): list of all word tokens in tweets contained
in tweets_df, not including stop words (stop
words are contained in self.stop_words)
stop_words (list of string): list of words that shouldn't be included when
computing word bag for tweets. This includes
standard English words like "the" as well as
Twitter-data-specific things like "https://"
freq_dist (nltk object): nltk.FreqDist(self.word_bag); nltk object that
contains statistical properties of words in
word_bag
word_freq_df (pandas dataframe): pandas dataframe containing top n words
in tweets data along with data like
word frequency, word frequency divided
by background frequency for that word, etc.
More info under function
create_word_freq_df(self, n),
which creates word_freq_df.
"""
#############################################################
# Methods to set attributes
#############################################################
def set_Search_terms(self, search_terms):
""" search_terms is a list of strings that were used in twitter search
to obtain data in tweets_df.
The strings will be converted to unicode inside Twords, even though the
user may enter them as ordinary strings.
"""
assert type(search_terms) == list
for term in search_terms:
assert type(term) in (str, unicode)
unicode_list = [x.decode("utf-8") if type(x) == str
else x for x in search_terms]
self.search_terms = unicode_list
def create_Background_dict(self):
""" Create the dictionary of background word rates from file in the
background data path.
key: word (string)
value: tuple of form (frequency, occurrences), where
frequency is frequency of word in background data set, and
occurrences is total number of occurrences in background data
set
"""
sample_rates = pd.read_csv(self.background_path, sep=",", encoding='utf-8')
background_dict = dict(zip(sample_rates["word"], zip(sample_rates["frequency"],sample_rates["occurrences"])))
self.background_dict = background_dict
def create_Stop_words(self):
""" Create list of stop words used in create_word_bag function.
Stop words created here are defaults - the user may add new stop words
later with the add_stop_words function.
This default list combines English stopwords from nltk corpus
(stopwords), punctuation marks from python standard string library,
and a custom-list the author found useful when parsing tweets.
"""
punctuation = [item.decode('utf-8') for item in list(string.punctuation)]
stop = stopwords.words('english') + punctuation + \
[u'rt', u'RT', u'via', u'http', u"n't", u"'s", u"...", u"''",
u"'m", u"--", u"'ll", u"'ve", u"'re", u"//www"]
self.stop_words = stop
##############################################################
# Methods to gather tweets via keyword search with
# Java GetOldTweets
##############################################################
def create_java_tweets(self, total_num_tweets, tweets_per_run, querysearch,
final_until=None, output_folder="output",
decay_factor=4, all_tweets=True):
""" Function that calls java program iteratively further and further
back in time until the desired number of tweets are collected. The
"until" parameter gives the most recent date tweets can be found from,
and the search function works backward in time progressively from that
date until the max number of tweets are found. Thus each new call to
_get_one_java_run_and_return_last_line_date will start the search one
day further in the past.
total_num_tweets: (int) total number of tweets to collect
tweets_per_run: (int) number of tweets in call to java program - should
not be over 50,000, better to keep around 10,000
querysearch: (string) string defining query for twitter search - see
Henrique code
(e.g, "europe refugees" for search for tweets containing
BOTH "europe" and "refugees" - currently putting in OR by
hand does not yield desired result, so two separate
searches will have to be done for "OR" between words)
final_until: (string) date string of the form '2015-07-31' that gives
ending date that tweets are searched before (this is
distinguished from the changing "until" that is used in
the calls to _get_one_java_run_and_return_last_line_date).
If left as "None" it defaults to the current date.
output_folder: (string) name of folder to put output in
decay_factor: (int) how quickly to wind down tweet search if errors
occur and no tweets are found in a run - a failed run
will count as tweets_per_run/decay_factor tweets found,
so the higher the factor the longer the program will try
to search for tweets even if it gathers none in a run
all_tweets: (bool) flag for which jar to use - True means use
all_tweets jar, False means use top_tweets jar
"""
if final_until is None:
final_until = str(datetime.datetime.now())[:10]
print "Collecting", str(total_num_tweets), "tweets with", \
str(tweets_per_run), "tweets per run."
print "Expecting", \
str(int(ceil(total_num_tweets/float(tweets_per_run)))), \
"total runs"
start_time = time.time()
tweets_searched = 0
run_counter = 1
# create folder that tweets will be saved into
subprocess.call(['mkdir', output_folder])
until = final_until
while tweets_searched < total_num_tweets:
print "Collecting run", run_counter
run_counter += 1
# call java program and get date of last tweet found
last_date = self._get_one_java_run_and_return_last_line_date(
querysearch, until, tweets_per_run, all_tweets)
# rename each output file and put into new folder - output file
# is named by until date
new_file_location = output_folder + '/' + querysearch + '_' + \
until + '.csv'
subprocess.call(['mv', 'output_got.csv', new_file_location])
# if last_date is usual date proceed as normal - if not raise error
# and stop search
if self._validate_date(last_date):
until = last_date
tweets_searched += tweets_per_run
else:
# set search date one day further in past
new_until_date_object = datetime.datetime.strptime(until, '%Y-%m-%d') \
- datetime.timedelta(days=1)
until = str(new_until_date_object)[:10]
# consider this a few tweets searched so program doesn't run
# forever if it gathers no tweets
tweets_searched += (tweets_per_run)/float(decay_factor)
self.data_path = output_folder
self.search_terms = querysearch.split()
print "Total time to collect", str(total_num_tweets), "tweets:", \
round((time.time() - start_time)/60.,1), "minutes"
def get_tweets_from_single_java_csv(self):
""" Takes path to twitter data obtained with java tweet search library
and builds a dataframe of the tweets and their accompanying
information. Dataframe has columns for username, date, retweets,
favorites, text, mentions, and hashtag. The dataframe is stored under
the attribute tweets_pd.
"""
# Read in csv file with many columns to account for people who put many
# semicolons in tweets, then keep only the rows that don't have
# semicolons in a tweet by dropping rows with too many columns.
# (Semicolons are the delimeter in the java twitter search library.)
tweets = pd.read_csv(self.data_path, sep=";",
names=list('abcdefghijklmno'), encoding='utf-8')
tweets = tweets[tweets.k.isnull()]
# Rename the columns with correct labels and drop row that is just
# column names (this will index dataframe starting at 1).
tweets.columns = tweets.iloc[0]
tweets.drop(0, inplace=True)
# Drop the extra columns on the end
tweets = tweets[["username", "date", "retweets", "favorites", "text",
"mentions", "hashtags", "id", "permalink"]]
# Reindex dataframe
tweets.index = range(len(tweets))
self.tweets_df = tweets
def get_java_tweets_from_csv_list(self, list_of_csv_files=None):
""" Create tweets_df from list of tweet csv files
list_of_csv_files: python list of paths (the paths are strings) to csv
files containing tweets - if list_of_csv_files is
None then the files contained inside self.data_path
are used
"""
if list_of_csv_files is None:
list_of_csv_files = self._get_list_of_csv_files(self.data_path)
path_dict = {}
# create dictionary with paths for keys and corresponding tweets
# dataframe for values
for path in list_of_csv_files:
tweets = pd.read_csv(path, sep=";", names=list('abcdefghijklmno'),
encoding='utf-8')
tweets = tweets[tweets.k.isnull()]
tweets.columns = tweets.iloc[0]
tweets.drop(0, inplace=True)
tweets = tweets[["username", "date", "retweets", "favorites",
"text", "mentions", "hashtags", "id", "permalink"]]
tweets.index = range(len(tweets))
path_dict[path] = tweets
# join all created dataframes together into final tweets_df dataframe
self.tweets_df = pd.concat(path_dict.values(), ignore_index=True)
def _get_one_java_run_and_return_last_line_date(self, querysearch, until,
maxtweets, all_tweets=True,
since=None,
return_line=True):
""" Create one java csv using java jar (either Top Tweets or All tweets
as specified in all_tweets tag) and return date string from last tweet
collected.
querysearch: (string) query string, usually one word - multiple words
imply an "AND" between them
maxtweets: (int) number of tweets to return
since: (string of form '2015-09-30') string of date to search since;
this is optional and won't be used when using the
create_java_tweets function
until: (string of form '2015-09-30') string of date to search until,
since search is conducted backwards in time
return_line (bool): whether to return date from last line or not; if
true the date from the last line in the csv is
returned
"""
start_time = time.time()
# choose which jar file to use
jar_string = self.jar_folder_path + '/got_top_tweets.jar'
if all_tweets:
jar_string = self.jar_folder_path + '/got_all_tweets.jar'
# create search string
quotation_mark = '"'
query_string = 'querysearch=' + quotation_mark + querysearch + quotation_mark
until_string = 'until=' + until
maxtweets_string = 'maxtweets=' + str(maxtweets)
# create output_got.csv file of tweets with these search parameters
if since is None:
subprocess.call(['java', '-jar', jar_string, query_string,
until_string, maxtweets_string])
else:
since_string = 'since=' + since
subprocess.call(['java', '-jar', jar_string, query_string,
since_string, until_string, maxtweets_string])
# find date on last tweet in this file (in last line of file)
last_line = tailer.tail(open('output_got.csv'), 1)[0]
date_position = last_line.find(';')
date_string = last_line[date_position+1:date_position+11]
date_string = self._convert_date_to_standard(date_string)
print "Time to collect", str(maxtweets), "tweets:", \
round((time.time() - start_time)/60., 1), "minutes"
if return_line:
return date_string
def _get_list_of_csv_files(self, directory_path):
""" Return list of csv files inside a directory
directory_path: (string) path to directory holding csv files of
interest
"""
return [pathjoin(directory_path, f) for f in listdir(directory_path)
if f[-4:] == '.csv']
def _validate_date(self, date_text):
""" Return true if date_text is string of form '2015-06-29',
false otherwise.
date_text (string): date
"""
try:
datetime.datetime.strptime(date_text, '%Y-%m-%d')
return True
except ValueError:
return False
##############################################################
# Methods to gather user timeline tweets with
# Java GetOldTweets
##############################################################
def get_user_tweets(self, user, max_tweets, start_date=None,
end_date=None, all_tweets=True, return_line=True):
""" Returns max_tweets from Twitter timeline of user. Appears to work
better when start and end dates are included. The returned tweets
include tweets on the start_date, and up to (but not including) tweets
on the end_date.
If only an end_date is provided, then the tweets are searched backward
in time starting at the end date and continuing until max_tweets
have been found.
Creates folder named by twitter username searched that contains tweets
in series of csv files.
user (string): Twitter handle of user, e.g. barackobama
max_tweets (int): number of tweets to return for that user; set
max_tweets to -1 to return all tweets in timeline
start_date (string): starting date for search of form "2015-09-30"
end_date (string): ending date for search of form "2015-09-30"
all_tweets (bool): whether to use "top_tweets" or "all_tweets" java
jar file
return_line (bool): whether to return date on last tweet returned;
needed for function that makes repeated calls to
this function, e.g. get_all_user_tweets
"""
start_time = time.time()
# choose which jar file to use
jar_string = self.jar_folder_path + '/got_top_tweets.jar'
if all_tweets:
jar_string = self.jar_folder_path + '/got_all_tweets.jar'
# create search string
user_string = 'username=' + user
maxtweets_string = 'maxtweets=' + str(max_tweets)
if start_date is not None:
since_string = 'since=' + start_date
if end_date is not None:
until_string = 'until=' + end_date
# create output_got.csv file of tweets with these search parameters
if start_date is None and end_date is None:
subprocess.call(['java', '-jar', jar_string, user_string,
maxtweets_string])
elif start_date is None and end_date is not None:
subprocess.call(['java', '-jar', jar_string, user_string,
until_string, maxtweets_string])
else:
subprocess.call(['java', '-jar', jar_string, user_string,
since_string, until_string, maxtweets_string])
# find date on last tweet in this file (in last line of file)
last_line = tailer.tail(open('output_got.csv'), 1)[0]
date_position = last_line.find(';')
date_string = last_line[date_position+1:date_position+11]
date_string = self._convert_date_to_standard(date_string)
print "Time to collect", str(max_tweets), "tweets:", \
round((time.time() - start_time)/60.,1), "minutes"
if return_line:
return date_string
def get_all_user_tweets(self, user, tweets_per_run):
""" Return all tweets in a user's timeline. This is necessary
to do in batches since one call to get_user_tweets does not return
all of the tweets (too many in one run breaks the web-scrolling
functionality of GetOldTweets). The tweets are saved as series of
csv files into output folder named by username of twitter user.
The final date is one day after the current date, since tweets are
returned up to (but not including) the end_date in get_user_tweets
function.
This function will return duplicates of some tweets to be sure all
tweets are obtained - these can be eliminated by simply dropping
duplicates in the text column of resulting pandas dataframe.
Function typically fails to return every single tweet, but captures
most (~87 percent for barackobama) - best performance when
tweets_per_run is around 500.
Creates: folder (named by username searched) of csv files
user (string): twitter handle of user, e.g. "barackobama"
tweets_per_run (int): how many tweets to pull in each run
"""
# increment the date one day forward from returned day when calling
# get_user_tweets to be sure all tweets in overlapping
# range are returned - experimentation showed that tweets on the edge
# between date runs can be lost otherwise
start_time = time.time()
print "Collecting tweets with", str(tweets_per_run), "tweets per run."
# create folder that tweets will be saved into
subprocess.call(['mkdir', user])
# set one day in future so that all tweets up to today are returned;
# necessary because tweets are returned on dates up to but not
# including end date
final_until = str(datetime.datetime.now() +
datetime.timedelta(days=1))[:10]
until = final_until
continue_search = True
run_counter = 1
while continue_search:
print "Collecting run", run_counter
run_counter += 1
# call user function and get date of last tweet found
last_date = self.get_user_tweets(user, tweets_per_run,
end_date=until)
# rename each output file and put into new folder - output file
# is named by until date
new_file_location = user + '/' + until + '.csv'
subprocess.call(['mv', 'output_got.csv', new_file_location])
# if last_date is a date proceed as normal - if the last_date
# hasn't changed, raise comment below
if self._validate_date(last_date):
until_minus_day_object = datetime.datetime.strptime(until, '%Y-%m-%d') \
- datetime.timedelta(days=1)
until_minus_day = str(until_minus_day_object)[:10]
if last_date == until_minus_day:
# from experimentation sometimes a query of many tweets
# will get "stuck" on a day long before 500 tweets have
# been reached - solution is just increment day as usual
print "Tweets timeline incremented by only one day - may " \
"need larger tweets_per_run, or could just be " \
"regular stutter in querying timeline."
until = last_date
else:
# this increment is to avoid losing tweets at the edge
# between date queries - experimentation showed they can
# be lost without this redundancy - this means when tweets
# are read there may be duplicates that require deletion
new_until_date_object = datetime.datetime.strptime(last_date, '%Y-%m-%d') \
+ datetime.timedelta(days=1)
until = str(new_until_date_object)[:10]
else:
continue_search = False
# set data path to new output folder to read in new tweets easily
self.data_path = user
print "Total time to collect tweets:", \
round((time.time() - start_time)/60.,1), "minutes"
#############################################################
# Methods to clean and prune tweets (probably used
# before visual inspection)
#############################################################
def keep_column_of_original_tweets(self):
""" Devote a column of self.tweets_df to the original, unaltered tweets.
Can be useful for comparison after cleaning.
This should be done before any cleaning functions are applied to the
"text" column of self.tweets_df.
"""
self.tweets_df["original_tweets"] = self.tweets_df["text"]
def lower_tweets(self):
""" Lowers case of text in all the tweets, usernames, mentions and
hashtags in the tweets_df dataframe, if the dataframe has those
columns.
"""
column_names = list(self.tweets_df.columns.values)
if "username" in column_names:
self.tweets_df["username"] = self.tweets_df.username.str.lower()
if "text" in column_names:
self.tweets_df["text"] = self.tweets_df.text.str.lower()
if "mentions" in column_names:
self.tweets_df["mentions"] = self.tweets_df.mentions.str.lower()
if "hashtags" in column_names:
self.tweets_df["hashtags"] = self.tweets_df.hashtags.str.lower()
def keep_only_unicode_tweet_text(self):
""" Keeps only tweets where tweet text is unicode. This drops the
occasional tweet that has a NaN value in dataset, which becomes a float
when read into tweets_df.
"""
self.tweets_df["text_type"] = self.tweets_df["text"].map(lambda text: type(text))
self.tweets_df = self.tweets_df[self.tweets_df.text_type == unicode]
del self.tweets_df["text_type"]
# Reindex dataframe
self.tweets_df.index = range(len(self.tweets_df))
def _remove_urls_from_single_tweet(self, tweet):
""" Remove urls from text of a single tweet.
This uses python tweet parsing library that misses some tweets but
doesn't get hung up with evil regex taking too long.
"""
p = ttp.Parser()
result = p.parse(tweet)
for x in result.urls:
tweet = tweet.replace(x, "")
tweet = tweet.strip()
return tweet
def remove_urls_from_tweets(self):
""" Remove urls from all tweets in self.tweets_df
"""
start_time = time.time()
print "Removing urls from tweets..."
print "This may take a minute - cleaning rate is about 400,000" \
" tweets per minute"
self.tweets_df["text"] = self.tweets_df["text"].map(self._remove_urls_from_single_tweet)
minutes_to_complete = (time.time() - start_time)/60.
print "Time to complete:", round(minutes_to_complete,3), \
"minutes"
print "Tweets cleaned per minute:", round(len(self.tweets_df)/minutes_to_complete, 1)
def remove_punctuation_from_tweets(self):
""" Strip common punctuation from tweets in self.tweets_df
"""
self.tweets_df["text"] = self.tweets_df["text"].apply(lambda x:
''.join([i for i in x if i not in
string.punctuation]))
def drop_non_ascii_characters_from_tweets(self):
""" Remove all characters that are not standard ascii.
"""
self.tweets_df['text'] = self.tweets_df["text"].apply(lambda x:
''.join([i if 32 <= ord(i) < 126 else
"" for i in x]))
def _convert_date_to_standard(self, date_text):
""" Convert a date string of form u"yyyy/mm/dd" into form u"yyyy-mm-dd"
for use with the python date module.
"""
assert type(date_text) in (str, unicode)
date_text = date_text.replace('/', '-')
return date_text
def convert_tweet_dates_to_standard(self):
""" Convert tweet dates from form "yyyy/mm/dd" to "yyyy-mm-dd" in
tweets_df dataframe.
"""
self.tweets_df["date"] = self.tweets_df["date"].map(self._convert_date_to_standard)
def sort_tweets_by_date(self):
""" Sort tweets by their date - useful for any sort of time series
analysis, e.g. analyzing sentiment changes over time.
"""
self.tweets_df.sort_values("date", inplace=True)
# Reindex dataframe
self.tweets_df.index = range(len(self.tweets_df))
def drop_duplicate_tweets(self):
""" Drop duplicate tweets in tweets_df (except for the first instance
of each tweet)
"""
self.tweets_df.drop_duplicates("text", inplace=True)
# Reindex dataframe
self.tweets_df.index = range(len(self.tweets_df))
def drop_by_search_in_name(self):
""" Drop tweets that contain element from search_terms in either
username or mention (i.e., tweets where the search term in contained in
twitter handle of someone writing or mentioned in tweet). Default
values of terms list is search_terms attribute, but user can add
to self.search_terms attribute to drop by additional terms.
"""
if not self.search_terms:
print "search_terms is empty - add at least one term to " + \
"search_terms attribute"
return self
for term in self.search_terms:
assert type(term) in (str, unicode)
assert term # to make sure string isn't empty
# Drop the tweets that contain any of search terms in either a username
# or a mention
column_names = list(self.tweets_df.columns.values)
for term in self.search_terms:
if "mentions" in column_names:
mentions_index = self.tweets_df[self.tweets_df.mentions.str.contains(term) == True].index
self.tweets_df.drop(mentions_index, inplace=True)
if "username" in column_names:
username_index = self.tweets_df[self.tweets_df.username.str.contains(term) == True].index
self.tweets_df.drop(username_index, inplace=True)
# Reindex dataframe
self.tweets_df.index = range(len(self.tweets_df))
def keep_tweets_with_terms(self, term_list):
""" Drops all the tweets in tweets_df that do NOT contain at least one
term from term_list. This is useful for handling data from Twitter API
search stream, where it is often easiest to collect a single big stream
using several search terms and then parse the stream later.
Sometimes even tweets collected with java collector don't contain
desired terms, so this can be useful there as well.
term_list (string or list of strings): collection of terms to drop on
"""
if type(term_list) == str:
assert len(term_list) > 0
keep_index = self.tweets_df[self.tweets_df.text.str.contains(term_list) == True].index
self.tweets_df = self.tweets_df.iloc[keep_index]
if type(term_list) == list:
keep_index = pd.core.index.Int64Index([], dtype='int64')
for term in term_list:
assert len(term) > 0
term_keep_index = self.tweets_df[self.tweets_df.text.str.contains(term) == True].index
keep_index = keep_index.append(term_keep_index)
keep_index = keep_index.drop_duplicates()
self.tweets_df = self.tweets_df.iloc[keep_index]
# Reindex dataframe
self.tweets_df.index = range(len(self.tweets_df))
#############################################################
# Methods to prune tweets (probably used after visual
# inspection)
#############################################################
def drop_by_term_in_name(self, terms):
""" Drop tweets that contain element from terms in either username or
mention. The terms parameter must be a list of strings.
This method is the same as drop_by_search_in_name method, except it
takes arbitrary input from user. This can be used to help get rid of
spam.
terms (list): python list of strings
"""
if not terms:
print "terms is empty - enter at least one search terms string"
return self
for term in terms:
assert type(term) in (str, unicode)
assert term
# Drop the tweets that contain any of terms in either a username
# or a mention
# don't need to set " == True", that is redundant
column_names = list(self.tweets_df.columns.values)
for term in terms:
if "mentions" in column_names:
mentions_index = self.tweets_df[self.tweets_df.mentions.str.contains(term) == True].index
self.tweets_df.drop(mentions_index, inplace=True)
if "username" in column_names:
username_index = self.tweets_df[self.tweets_df.username.str.contains(term) == True].index
self.tweets_df.drop(username_index, inplace=True)
# Reindex dataframe
self.tweets_df.index = range(len(self.tweets_df))
def drop_by_term_in_tweet(self, terms):
""" Drop tweets that contain element from terms in the tweet text.
Terms can be either a string (which is treated as one term) or a list
of strings (which area each treated as a separate drop case).
This is most useful for getting rid of repetitive or spammy tweets that
appear to be distorting data.
This is also useful for dropping retweets, which can be accomplished
by dropping tweets containing the string "rt @"
terms (string or python list of strings): terms that appear in tweets
we want to drop
"""
if type(terms) in (str, unicode):
text_index = self.tweets_df[self.tweets_df.text.str.contains(terms) == True].index
self.tweets_df.drop(text_index, inplace=True)
elif type(terms) == list:
for term in terms:
assert type(term) in (str, unicode)
assert len(term) > 0
text_index = self.tweets_df[self.tweets_df.text.str.contains(term) == True].index
self.tweets_df.drop(text_index, inplace=True)
else:
raise Exception("Input must be string or list of string.")
# Reindex dataframe
self.tweets_df.index = range(len(self.tweets_df))
def drop_by_username_with_n_tweets(self, max_num_occurrences=1):
""" Drops all tweets by usernames that appear more than
max_num_occurrences times in tweets_df.
This function can be time consuming.
Dropping all users with more than 1 tweet should be a safe way to
filter out a lot of the spam.
"""
start_time = time.time()
print "Dropping tweets by repeated users..."
# get list of usernames that occur too much
repeat_user_counts = self.tweets_df["username"].value_counts()
for i in range(len(repeat_user_counts)):
if repeat_user_counts[i] <= max_num_occurrences:
break_index = i
break
repeated_usernames = list(repeat_user_counts[0:break_index].index)
print "Found", len(repeated_usernames), "users with more than", \
max_num_occurrences, "tweets in tweets_df"
# drop these usernames from tweets_df
percentile_num = len(repeated_usernames)//20
for i, twitter_username in enumerate(repeated_usernames):
if len(repeated_usernames) <= 100:
print "Dropping tweets from user", i
elif i%percentile_num == 0:
print "Finished", 5*i/percentile_num, "percent of user drops"
drop_index = self.tweets_df[self.tweets_df.username == twitter_username].index
self.tweets_df.drop(drop_index, inplace=True)
# Reindex dataframe
self.tweets_df.index = range(len(self.tweets_df))
print "Took", round((time.time() - start_time)/60.,3), \
"minutes to complete"
def add_stop_words(self, stopwords_item):
""" Add word or list of words to stop words used in create_word_bag.
The word might be a url or spam tag. A common case is parts of urls
that are parsed into words (e.g. from youtube) that appear repeatedly.
The new stopwords will appear at end of self.stop_words list, so user
can easily check to see which stopwords have been recently added by the
user.
stopwords: (string or list of strings):
"""
if type(stopwords_item) in (str, unicode):
if type(stopwords_item) == str:
# convert string to unicode if not unicode already
stopwords_item = stopwords_item.decode('utf-8')
self.stop_words = self.stop_words + [stopwords_item]
elif type(stopwords_item) == list:
for term in stopwords_item:
assert type(term) in (str, unicode)
assert len(term) > 0
unicode_terms_list = [term if type(term) == unicode
else term.decode('utf-8')
for term in stopwords_item]
self.stop_words = self.stop_words + unicode_terms_list
else:
raise Exception("Input must be string or list of strings.")
#############################################################
# Methods for investigating word frequencies
#############################################################
""" The create_word_freq_df method is used to create a dataframe
that gives the word occurrences and word frequencies of the top n words in
the corpus. This is created using the existing nltk object, and it is
changed depending on how many words we wish to inspect graphically.
This word frequency dataframe is fundamental object of interest, and is
stored in the word_freq_df attribute, which is a pandas dataframe.
For now the background corpus is derived from ~2.6 GB of twitter data,
composing about 72 million words. The word frequency rates from this
sample are stored in a frequency sample file that is then converted into
a python dictionary for fast lookup.
"""
def create_word_bag(self):
""" Takes tweet dataframe and outputs word_bag, which is a list of all
words in all tweets, with punctuation and stop words removed. word_bag
is contained inside the attribute self.word_bag.
This method will often be called repeatedly during data inspection, as
it needs to be redone every time some tweets are dropped from
tweets_df.
"""
start_time = time.time()
# Convert dataframe tweets column to python list of tweets, then join
# this list together into one long list of words
tweets_list = self.tweets_df["text"].tolist()
words_string = " ".join(tweets_list)
print "Time to make words_string: ", round((time.time() - start_time)/60., 3), "minutes"
start_time = time.time()
# Use nltk word tokenization to break list into words and remove
# stop words
tokens = nltk.word_tokenize(words_string)
print "Time to tokenize: ", round((time.time() - start_time)/60., 3), "minutes"
start_time = time.time()
self.word_bag = [word for word in tokens if word not in self.stop_words]
print "Time to compute word bag: ", round((time.time() - start_time)/60., 3), "minutes"
def make_nltk_object_from_word_bag(self, word_bag=None):
""" Creates nltk word statistical object from the current word_bag
attribute. word_bag is left as an input in case the user wants to
create an nltk object with an external word bag.
The most common method we'll use from this object is the
frequency method, i.e. freq_dist.freq(term), where term is word in
word bag.
Use print(freq_dist) to get the number of unique words in corpus, as
well as total number of words in corpus.
Can use freq_dist.most_common(50) to get list of 50 most common words
and the number of times each of them appears in text.
"""
if word_bag is None:
word_bag = self.word_bag
self.freq_dist = nltk.FreqDist(self.word_bag)
def create_word_freq_df(self, top_n_words):
""" Creates pandas dataframe called word_freq_df of the most common n
words in corpus, with columns:
occurrences: how often each of them occurred
frequency: word frequency in the corpus
frequency ratio: word relative frequency to background
log frequency ratio: log of the relative frequency to background rates
background_occur: the number of times word appears in background corpus
(The log is useful because, for example, a rate two times as high as
background has log ratio of +x, and a rate two times lower than
background rates has a log ratio of -x.)
n is the number of words we want to see. These words are draw in order
of how frequently they are found in the corpus, so a large number of
words should be chosen to make sure we find the interesting ones that
appear much more often than in background corpus. (If a word appears
often in our search corpus it may be because it also appear often in
the background corpus, which is not of interest.)
The actual words that were searched to collect the corpus are omitted
from this dataframe (as long as self.search_terms has been set).
n (int): number of most frequent words we want to appear in dataframe
"""
print "Creating word_freq_df..."
print "Takes about 1 minute per 1000 words"
start_time = time.time()
# make dataframe we'll use in plotting
num_words = top_n_words
word_frequencies_list = []
for word, occurrences in self.freq_dist.most_common(num_words):
# determine whether word appears in background dict; if it does
# not, the frequency ratio is set to zero
if word in self.search_terms:
continue
if word in self.background_dict.keys():
freq_ratio = self.freq_dist.freq(word)/self.background_dict[word][0]
background_freq = self.background_dict[word][0]
log_freq_ratio = log(freq_ratio)
background_occur = self.background_dict[word][1]
else:
freq_ratio = 0
background_freq = 0
log_freq_ratio = 0
background_occur = 0
# faster to make list and then make dataframe in one line
# than to repeatedly append to an existing dataframe
word_frequencies_list.append((word, occurrences,
self.freq_dist.freq(word),
freq_ratio, log_freq_ratio,
background_occur))
word_freq_df = pd.DataFrame(word_frequencies_list,
columns=['word', 'occurrences', 'frequency',
'relative frequency', 'log relative frequency',
'background occurrences'])
print "Time to create word_freq_df: ", \
round((time.time() - start_time)/60., 4), "minutes"
self.word_freq_df = word_freq_df
def custom_word_frequency_dataframe(self, words):
""" Same function as create_word_freq_df except instead of
using top n words from corpus, a custom list of words is used. This
function returns the dataframe it creates instead of setting it to
word_freq_df. (The user can append what this function creates to
word_freq_df by hand with pd.concat(df1, df1).)
An example use case is to use a list of known words of interest to
construct a type of "word vector" for a particular word (presumably
the word searched on using Java tweet collector). For example, for
politics one might choose words like "conservative", "liberal",
"regulation" and "liberty" as a set of word axes, and then see how
twitter-searched words like "taxes", "Obamacare", etc. appear as word
vectors along these axes.
words: list of words to put in dataframe - each word is a string
"""
word_frequencies_list = []
words = [x.decode("utf-8") if type(x) == str else x for x in words]
for word in words:
# determine whether word appears in both background dict and corpus
# if it does not, the frequency ratio is set to zero
if word in self.search_terms:
continue
occurrences = self.freq_dist[word]
if word in self.background_dict.keys() and occurrences != 0:
freq_ratio = self.freq_dist.freq(word)/self.background_dict[word][0]
background_freq = self.background_dict[word][0]
log_freq_ratio = log(freq_ratio)
background_occur = self.background_dict[word][1]
else:
freq_ratio = 0
background_freq = 0
log_freq_ratio = 0
background_occur = 0
# faster to make list and then make dataframe in one line
# than to repeatedly append to an existing dataframe
word_frequencies_list.append((word, occurrences,
self.freq_dist.freq(word),
freq_ratio, log_freq_ratio,
background_occur))
word_freq_df = pd.DataFrame(word_frequencies_list,
columns=['word', 'occurrences', 'frequency',
'relative frequency', 'log relative frequency',
'background_occur'])
return word_freq_df
def plot_word_frequencies(self, plot_string, dataframe=None):
""" Plots of given value about word, where plot_string is a string
that gives quantity to be plotted. This is just an example function,
user will want to use word_freq_df and matplotlib directly for more
detailed and better-looking plots.
Note that the plot can't display unicode characters correctly, so if a
word looks like a little box you'll have to pull up word_freq_df to see
what the character actually is.
plot_string (string): column of word_freq_df dataframe, e.g.
"occurrences", "frequency", "relative frequency",
"log relative frequency", etc.
dataframe (pandas dataframe): dataframe of the same form as
word_freq_df; if left empty then
self.word_freq_df is plotted
"""
if dataframe is None:
dataframe = self.word_freq_df
num_words = len(dataframe)
try:
dataframe.set_index("word")[plot_string].plot.barh(figsize=(20,
num_words/2.), fontsize=30, color="c");
plt.title(plot_string, fontsize=30);
ax = plt.axes();
ax.xaxis.grid(linewidth=4);
except:
raise Exception("Input string must be column name of word_freq_df")
""" This was more customized code that can be used later if needed - for
now the pandas default plotting code is good enough for most purposes
sns.set(style="darkgrid")
num_words = len(self.word_freq_df)
# Initialize the matplotlib figure - the second number in figure gives
# height, this will need to depend on how many words are included in
# figure
f, ax = plt.subplots(figsize=(16, num_words/2.))
plt.yticks(fontsize=20)
plt.xticks(fontsize=20)
# Plot the frequencies
sns.set_color_codes("pastel")
sns.barplot(x=plot_string, y="word", data=self.word_freq_df,
label="frequency", color="b")
# Add informative axis label
max_value = self.word_freq_df.iloc[0].frequency # find maximum frequency
# adjust axis to be slightly larger than this max frequency
ax.set(xlim=(0, max_value*1.1), ylabel="", xlabel="Word frequency")
ax.set_xlabel(plot_string, fontsize=30)
ax.xaxis.set_label_position('top')
ax.xaxis.tick_top()
ax.tick_params(axis='x', labelsize=20) # size of numerical labels
"""
#############################################################
# Methods to inspect tweets in tweets_df dataframe
#############################################################
""" These methods are used to inspect tweets of interest in the main
dataframe tweets_df. A typical workflow is to visualize tweet word
frequencies using visualization functions, then inspect a sample of tweets
that contain a word of interest. If these tweets appear to be unwanted they
can then be dropped using the dropping functions above.
Note about displaying tweets in pandas in readable form: need to set
pd.set_option('display.max_colwidth', -1) and/or
pd.set_option('display.width',800)
This makes it so entire tweet is displayed without cutoff when only tweets
are presented in dataframe.
Can enter pd.describe_option('display') to get comprehensive list of
settings for ipython displays.
"""
def tweets_containing(self, term):
""" Returns all tweets that contain term from tweets_df.
Term is a string.
The returned object is a dataframe that contains the rows of tweets_df
dataframe that have tweets containing term.
term (string): term of interest
"""
assert type(term) in (str, unicode)
assert term
tweets_containing = self.tweets_df[self.tweets_df.text.str.contains(term) == True]
print len(tweets_containing), "tweets contain this term"
return tweets_containing[["username", "text"]]
def tweets_by(self, username):
""" Returns all tweets by username from tweets_df.
Similar to above function except searches by username rather than
tweet text.
username (string): username of interest
"""
assert type(username) in (str, unicode)
assert username
tweets_by = self.tweets_df[self.tweets_df.username == username]
return tweets_by[["username", "text"]]
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] | 2.352486 | 21,564 |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""Reexport"""
from __future__ import print_function, division, unicode_literals
from .model import Model, Argument, BaseAdapter
from .exception import ArgumentError, ArgumentMissError, ArgumentInvalidError
__version__ = "0.0.2"
__all__ = ["ArgumentError", "ArgumentMissError", "ArgumentInvalidError",
"Model", "Argument", "BaseAdapter"]
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] | 3.069231 | 130 |
# Author: btjanaka (Bryon Tjanaka)
# Problem: (Kattis) missingnumbers
# Title: Missing Numbers
# Link: https://open.kattis.com/problems/missingnumbers
# Idea: Keep counting.
# Difficulty: easy
# Tags: implementation
n = int(input())
cur = 1
num_printed = 0
for _ in range(n):
k = int(input())
while cur < k:
num_printed += 1
print(cur)
cur += 1
cur = k + 1
if num_printed == 0: print("good job")
| [
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361,
997,
62,
49695,
6624,
657,
25,
3601,
7203,
11274,
1693,
4943,
198
] | 2.488506 | 174 |
#! /usr/bin/env python3
"""Unga programmerare kodkalender 2020, lucka 3."""
# https://ungaprogrammerare.se/kodkalender/lucka-3/
import functools
import math
import operator
a = math.factorial(100)
b = functools.reduce(operator.mul, range(2, 165, 2))
how_many = round(a / b)
print(f"Antal delar: {how_many}")
# Antal delar: 40599194442
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198
] | 2.421429 | 140 |
import datetime
import math
import os
import pickle
import random
import shutil
import time
import zipfile
import cv2 as cv
import numpy as np
import torch
from PIL import Image
from flask import request
from scipy.stats import norm
from torch import nn
from torch.utils.data import Dataset
from torchvision import transforms
from tqdm import tqdm
from werkzeug.utils import secure_filename
from align_faces import get_reference_facial_points, warp_and_crop_face
from config import STATIC_DIR, UPLOAD_DIR
from config import image_h, image_w, device, logger
from models import resnet101
from mtcnn.detector import detect_faces
from utils.common import ensure_folder, resize, AverageMeter
data_transforms = {
'train': transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
'val': transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
}
transformer = data_transforms['val']
times = AverageMeter()
config = HParams()
checkpoint = 'repo/face/insight-face-v3.pt'
logger.info('loading model: {}...'.format(checkpoint))
model = resnet101(config)
model.load_state_dict(torch.load(checkpoint))
model = nn.DataParallel(model)
model = model.to(device)
model.eval()
# model params
threshold = 76.75066649278368
mu_0 = 89.76046947988898
sigma_0 = 4.498024182861556
mu_1 = 42.66766813673472
sigma_1 = 8.62761102672923
class FaceNotFoundError(Exception):
"""Base class for other exceptions"""
pass
if __name__ == "__main__":
compare('id_card.jpg', 'photo_1.jpg')
compare('id_card.jpg', 'photo_2.jpg')
compare('id_card.jpg', 'photo_3.jpg')
compare('id_card.jpg', 'photo_4.jpg')
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628,
628,
628,
628,
628,
628,
628,
628,
628,
198,
361,
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3672,
834,
6624,
366,
834,
12417,
834,
1298,
198,
220,
220,
220,
8996,
10786,
312,
62,
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13,
9479,
3256,
705,
23074,
62,
16,
13,
9479,
11537,
198,
220,
220,
220,
8996,
10786,
312,
62,
9517,
13,
9479,
3256,
705,
23074,
62,
17,
13,
9479,
11537,
198,
220,
220,
220,
8996,
10786,
312,
62,
9517,
13,
9479,
3256,
705,
23074,
62,
18,
13,
9479,
11537,
198,
220,
220,
220,
8996,
10786,
312,
62,
9517,
13,
9479,
3256,
705,
23074,
62,
19,
13,
9479,
11537,
198
] | 2.708148 | 675 |
import numpy as np
import cv2
import reip
class OpticalFlow(reip.Block):
'''
https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_video/py_lucas_kanade/py_lucas_kanade.html#dense-optical-flow-in-opencv
'''
hsv = None
_prev = None
# pyr_scale=0.5, levels=3, winsize=15, iterations=3,
# poly_n=5, poly_sigma=1.2, flags=0 | [
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220,
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62,
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28,
15,
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11,
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11,
198,
220,
220,
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28,
20,
11,
7514,
62,
82,
13495,
28,
16,
13,
17,
11,
9701,
28,
15
] | 2.12069 | 174 |
import re
import subprocess
import os
from tqdm import tqdm
| [
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6738,
256,
80,
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1330,
256,
80,
36020,
628,
198
] | 3.1 | 20 |
# Find the genre in which there has been the greatest number of movie releases
import pandas as pd
import numpy as np
dataset=pd.read_csv('c:\\temp\\HollywoodMovies.csv')
selected_data=dataset.loc[:,['WorldGross','Genre']]
df=pd.DataFrame(selected_data)
df_notnull_genre=df[df.Genre.notnull()]
df_notnull_worldgross=df_notnull_genre[df_notnull_genre.WorldGross.notnull()]
df_final= df_notnull_worldgross
Series_genre = df_final['WorldGross'].groupby(df_final['Genre']).sum()
df_new=pd.DataFrame(Series_genre)
genre=df_new.sort_values(by='WorldGross', ascending=False).head(10) #finding top 10 genre movies released
print genre
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38,
1214,
3256,
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28,
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737,
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7,
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8,
220,
1303,
41070,
1353,
838,
12121,
6918,
2716,
198,
4798,
12121,
198
] | 2.77533 | 227 |
import esphome.codegen as cg
from esphome.components import binary_sensor
from .. import (
HOME_ASSISTANT_IMPORT_SCHEMA,
homeassistant_ns,
setup_home_assistant_entity,
)
DEPENDENCIES = ["api"]
HomeassistantBinarySensor = homeassistant_ns.class_(
"HomeassistantBinarySensor", binary_sensor.BinarySensor, cg.Component
)
CONFIG_SCHEMA = binary_sensor.binary_sensor_schema(HomeassistantBinarySensor).extend(
HOME_ASSISTANT_IMPORT_SCHEMA
)
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13,
33,
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11,
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70,
13,
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198,
8,
198,
198,
10943,
16254,
62,
50,
3398,
27630,
796,
13934,
62,
82,
22854,
13,
39491,
62,
82,
22854,
62,
15952,
2611,
7,
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562,
10167,
33,
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47864,
737,
2302,
437,
7,
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62,
10705,
8808,
8643,
62,
3955,
15490,
62,
50,
3398,
27630,
198,
8,
628
] | 2.613636 | 176 |
# -*-coding:utf-8-*-
"""
์ง๋ฒ ๋ณํ recursive ์๊ณ ๋ฆฌ์ฆ
2 <= n <= 16๊น์ง ๊ฐ๋ฅ
"""
"""
def test(n,t):
answer = ''
while t//n >= 1:
re = t%n
t = t//n
answer = str(re) + answer
print(answer)
if t < n:
answer = str(t) + answer
return int(answer)
"""
"""
# ์ง๋ฒ ๋ณํ ํจ์ ์ฌ๋์
def convert_2(t, n):
s = 'ABCDEF'
a = ''
while t:
if t%n > 9:
a = s[t%n -10] + a
else:
a = str(t%n) + a
t = t//n
return a
""" | [
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256,
1003,
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220,
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1441,
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] | 1.454286 | 350 |
from ..entity import Entity
from ..map import Map
from ..reference import Reference
from ..string import String
from ..void import Void
| [
6738,
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] | 4.566667 | 30 |
import sys
import pkg_resources
from setuptools import setup, find_packages
"""
setup.py
websocket - WebSocket client library for Python
Copyright 2022 engn33r
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.
"""
VERSION = "1.3.1"
install_requires = []
tests_require = []
setup(
name="websocket-client",
version=VERSION,
description="WebSocket client for Python with low level API options",
long_description=open("README.md").read(),
long_description_content_type='text/markdown',
author="liris",
author_email="[email protected]",
license="Apache-2.0",
url="https://github.com/websocket-client/websocket-client.git",
download_url='https://github.com/websocket-client/websocket-client/releases',
python_requires='>=3.7',
extras_require={
"test": ["websockets"],
"optional": ["python-socks", "wsaccel"],
"docs": ["Sphinx >= 3.4", "sphinx_rtd_theme >= 0.5"],
},
classifiers=[
"Development Status :: 4 - Beta",
"License :: OSI Approved :: Apache Software License",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.7",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Operating System :: MacOS :: MacOS X",
"Operating System :: POSIX",
"Operating System :: Microsoft :: Windows",
"Topic :: Internet",
"Topic :: Software Development :: Libraries :: Python Modules",
"Intended Audience :: Developers",
],
project_urls={
'Documentation': 'https://websocket-client.readthedocs.io/',
'Source': 'https://github.com/websocket-client/websocket-client/',
},
keywords='websockets client',
entry_points={
'console_scripts': [
'wsdump=websocket._wsdump:main',
],
},
install_requires=install_requires,
packages=find_packages(),
package_data={
'websocket.tests': ['data/*.txt']
},
tests_require=tests_require,
test_suite="websocket.tests"
)
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2385,
578,
2625,
732,
1443,
5459,
13,
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198,
8,
198
] | 2.733122 | 948 |
import numpy as np
import argparse, gym, time, os
import os.path as osp
import torch
import torch.nn as nn
import torch.optim as optim
from on_policy.utils import core
from on_policy.utils.model import ActorCritic
from on_policy.utils.replay_buffer import ReplayBuffer
from utils.logx import EpochLogger
from utils.run_utils import setup_logger_kwargs
from utils.mpi_tools import mpi_fork, num_procs, mpi_avg, proc_id
if __name__ == '__main__':
parser = argparse.ArgumentParser()
""" env """
parser.add_argument('--env', type=str, default='HalfCheetah-v2')
parser.add_argument('--epochs', type=int, default=50)
parser.add_argument('--steps_per_epoch', type=int, default=4000)
parser.add_argument('--max_ep_len', type=int, default=1000)
parser.add_argument('--train_pi_iters', type=int,default=80)
parser.add_argument('--train_v_iters', type=int, default=80)
parser.add_argument('--save_freq', type=int, default=5)
""" algorithm """
parser.add_argument('--gamma', type=float, default=0.99)
parser.add_argument('--lam', type=float, default=0.95)
parser.add_argument('--clip_ratio', type=float, default=0.2)
parser.add_argument('--target_kl', type=float, default=0.01)
parser.add_argument('--actor_lr', type=float, default=1e-4)
parser.add_argument('--critic_lr', type=float, default=1e-3)
""" others """
parser.add_argument('--seed', '-s', type=int, default=0)
parser.add_argument('--exp_name', type=str, default='ppo')
parser.add_argument('--cpu', type=int, default=2)
args = parser.parse_args()
# run parallel code with mpi
mpi_fork(args.cpu)
logger_kwargs = setup_logger_kwargs(args.exp_name, args.seed)
# Save model path
fpath = osp.join(logger_kwargs['output_dir'], 'models')
ppo = PPO()
ppo.run()
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] | 2.474308 | 759 |
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