content
stringlengths 1
1.04M
| input_ids
sequencelengths 1
774k
| ratio_char_token
float64 0.38
22.9
| token_count
int64 1
774k
|
|---|---|---|---|
from project.server.config import RAIDER_CONFIG
from project.server.shop.actions import track_payment, perform_post_complete_actions
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#$Header: /nfs/slac/g/glast/ground/cvs/GlastRelease-scons/EbfWriter/EbfWriterLib.py,v 1.3 2009/11/13 23:20:55 jrb Exp $
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import os
os.makedirs('./img/', exist_ok=True)
IMAGE_URL = "https://mofanpy.com/static/img/description/learning_step_flowchart.png"
urllib_download()
print('download image1')
request_download()
print('download image2')
chunk_download()
print('download image3')
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from rdflib import Graph, Namespace
from rdflib.namespace import RDF
import sys
from pathlib import Path
sys.path.append(str(Path(__file__).parent.parent))
import owlrl
RELS = Namespace("http://example.org/relatives#")
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# -*- coding: utf-8 -*-
from model.contact import Contact
import random
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from argparse import ArgumentDefaultsHelpFormatter
from collections import OrderedDict
from os.path import expanduser, isfile
from sys import exit as system_exit, argv
from virtual_box_tools.custom_argument_parser import CustomArgumentParser
from virtual_box_tools.yaml_config import YamlConfig
from yaml import load, dump
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# vim: ai ts=4 sts=4 et sw=4 encoding=utf-8
from survey.features.page_objects.base import PageObject
from survey.features.page_objects.root import AboutPage, HomePage
from lettuce.django import django_url
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# Credits
# "faces" icon created by Vector Valley, PK from the Noun Project
# https://thenounproject.com/term/faces/4127357/
import sys, mood_db, json
from datetime import datetime
from PyQt5.QtWidgets import QApplication, QMainWindow, QMessageBox, QWidget
from PyQt5.QtGui import QIcon, QPalette, QColor
from interface import Ui_MainWindow
from pathlib import Path
cur_dir = Path.cwd()
with open(cur_dir / "src/config.json", "r", encoding="utf-8") as cfg:
config = json.load(cfg)
theme = config["theme"]
icon = config["icon"][theme]
if __name__ == "__main__":
app = QApplication(sys.argv)
# set stylesheet
if theme == "default":
app.setStyle("Fusion")
elif theme == "dark":
from PyQt5.QtCore import QFile, QTextStream
import src.breeze_resources
file = QFile(":/dark/stylesheet.qss")
file.open(QFile.ReadOnly | QFile.Text)
stream = QTextStream(file)
app.setStyleSheet(stream.readAll())
else:
print("No proper theme selected.")
win = Application()
win.show()
sys.exit(app.exec_())
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# -*- coding: utf-8 -*-
"""
HeteroCL Tutorial : K-Nearest-Neighbor Digit Recognition
========================================================
**Author**: Yi-Hsiang Lai (seanlatias@github)
HeteroCL is a domain-specific language (DSL) based on TVM that supports
heterogeneous backend devices. Moreover, HeteroCL also supports imperative
programming and bit-accurate data types. This tutorials demonstrates how to
implement KNN-based digit recognition in HeteroCL.
Digit recognition is wildly used in many fields, such as automotives.
Despite the fact that most digit recognition algorithms rely on deep learning,
here we simply use the KNN-based algorithm. The input is a 7x7 black and white
image, which are encoded to 1 and 0, respectively. After we have the input test
image, we perform bitwise XOR with all training images. We then calculate the
number of ones for each XOR result. To make the above process easier, we
flatten each 7x7 image to a 49-bit integer, which makes the bitwise XOR faster.
We called the results of number of ones "distance". Our goal is to find the
digit with the smallest distance. In this tutorial, we set K to 3. Namely, for
each digit we will have three candidates. After that, we perform voting
according to the candidates. The winner will be the final label we predict. To
sum up, we can use the following data flow graph to illustrate the whole
process.
.. code-block:: none
+----------------------+ +--------------------------------------------+
| 49-bit testing image | xor | 49-bit training images (10 classes x 1800) |
+----------------------+ +--------------------------------------------+
|
V
+--------------------------------+
| 49-bit diff shape = (10, 1800) |
+--------------------------------+
| popcount
V
+-----------------------------+ +-------------------------+
| distance shape = (10, 1800) | | knn_mat shape = (10, 3) |
+-----------------------------+ +-------------------------+
| |
+---------------------------------+
| update knn_mat
V
+--------------------------------+
| updated knn_mat shape = (10,3) |
+--------------------------------+
| vote
V
+--------------+
| label (0~10) |
+--------------+
In this tutorial, we assume that we want to offload every operation before
voting to FPGA. We create a top function for that accordingly.
"""
# Import necessary modules.
import heterocl as hcl
import time
import numpy as np
import math
from digitrec_data import read_digitrec_data
# Declare some constants and data types. For images, we need unsigned 49-bit
# integers, while for knn matrices, we need unsigned 6-bit integers.
N = 7 * 7
max_bit = int(math.ceil(math.log(N, 2)))
data_size = (10, 1800)
# HeteroCL provides users with a set of bit-accurate data types, which include
# unsigned/signed arbitrary-bit integers and unsigned/signed fixed-points.
# Here we use `UInt(N)` for an N-bit unsigned integer.
dtype_image = hcl.UInt(N)
dtype_knnmat = hcl.UInt(max_bit)
# We can initialize a HeteroCL environment with default data type by using
# `hcl.init(dtype)`. Here we set the default data type of each variable to
# the unsigned integer with the maximum bitwidth.
hcl.init(dtype_image)
##############################################################################
# Top Function Offloaded to FPGA
# ==============================
# Following we show the code first. For each code block, you can find a
# corresponding explanation at the end of the top function.
##############################################################################
# 1. Algorithm Definition
# -----------------------
# In HeteroCL, we define the algorithm in a separate function call. The
# arguments are the inputs/outputs. We can also return computed outputs at
# the end of the function call. Following we explain the code part by part.
#
# 2. Imperative Programming and Bit Operations
# --------------------------------------------
# This function calculate the number of ones of a 49-bit unsigned integer.
# Here we demonstrate that HeteroCL supports imperative code. All variables
# declared within the block will live in corresponding scope. In this function,
# `out` is an intermediate variable with initial value 0. Since we already set
# the default data type, the data type for `out` is `UInt(N)`. This function
# also shows the capability of bit operations.
#
# 3. Main Algorithm
# -----------------
#
# 3.1 First Step: XOR
# ~~~~~~~~~~~~~~~~~~~
# This is the first step of our algorithm. Namely, compute the XOR of a test
# image with a set of training images. In other words,
#
# `diff[x][y] = train_images[x][y] ^ test_image`,
#
# for all x and y in shape `(10, N)`.
#
# We can use "hcl.compute" to achieve the above computation. This API is
# declarative. Namely, we only specify the results we want, without explicitly
# writing how the results should be computed.
#
# `A = hcl.compute(shape, fcompute, name, dtype)`
#
# The first field is the shape; the second field is a lambda function that
# computes the results for each element of the output tensor. Without applying
# any scheduling function, the code is equivalent to
#
# .. code-block:: python
#
# for x in range(0, 10):
# for y in range(0, 1800):
# diff[x][y] = train_images[x][y] ^ test_image
#
# It is optional for users to specify the name and output data type. Here we
# do not specify the data type, since by default it is UInt(49).
#
# 3.2 Second Step: Popcount
# ~~~~~~~~~~~~~~~~~~~~~~~~~
# Our next step is to calculate the number of ones for each value in diff.
# This is where we call the function `popcount`. Since what we want to do here
# is similar to the XOR operation above, we can again use `hcl.compute`. Since
# the maximum difference is 49, we only need 6-bit unsigned integers. Here we
# do not specify the data type. We will use "downsize" later.
#
# 3.3 Third Step: Initialize the Candidates
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# The next step is to compute the candidates. In our algorithm, we find the
# maximum candidate and replace it if the new incoming value is smaller. Thus,
# we initialize the value of the candidate tensor with 50, which is larger
# than the maximum possible distance: 49. To initialize a tensor we can use
# still use "hcl.compute" API.
#
# 3.4 Fourth step: Update the Candidates
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Finally, we update our candidate. Here we can no longer use `hcl.compute`
# because we do not update the candidates one by one sequentially. Thus, we
# use another API called `mutate`, which compute the lambda function
# for a given mutation domain. The code is equivalent to the following
# Python code.
#
# `hcl.mutate(domain, fcompute, name)`
#
# .. code-block:: python
#
# for x in range(0, 10):
# for y in range(0, 1800):
# update_knn(dist, knn_mat, x, y)
#
# The interface is almost the same as `hcl.compute`. The only differences are:
# 1. the shape is the mutation domain instead of the output shape.
# 2. since we do not return any new output function, there is no field for the
# data type.
# 3. There is no output for this API.
#
# 3.5 Final step: Return the Candidates
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# We need to return the updated candidates as our final output tensor.
#
# 4. Inputs/Outputs Definition
# ----------------------------
#
# 4.1 Scalars
# ~~~~~~~~~~~
# To specify an input scalar, we use `hcl.var`. We can specify the name and
# data type of the it.
#
# `a = hcl.var(name, dtype)`
#
# Here the variable is the test image we want to classify. The data type is by
# default `UInt(49)`.
#
# 4.2 Tensors
# ~~~~~~~~~~~
# To specify an input tensor, we use `hcl.placeholder`.
#
# `A = hcl.placeholder(shape, name, dtype)`
#
# The first field is the shape of the tensor. It is optional for users to set
# the name and data type. Here the data type is again UInt(49).
#
# 5. Customization Primitives
# ---------------------------
#
# 5.1 Data Type Customization (Quantization)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# This is another feature of HeteroCL, which allows users to quantize/downsize
# the data type of variables independent of the algorithm specification. We can
# downsize a set of inputs, which can be a tensor or a scalar. Here, we apply
# the corresponding data type as we mentioned in the previous steps. Note that
# `downsize` is used for integers only.
#
# 5.2 Compute Customization
# ~~~~~~~~~~~~~~~~~~~~~~~~~
# Here we can describe how we want the loops to be scheduled or transformed for
# hardware optimization. The first thing we can do is merge the loops together.
# This first provides better data locality and thus exposes more parallelism.
# We can merge the loops by using `compute_at`. Here we show how it works.
#
# .. code-block:: none
#
# produce A {
# loop_1 {
# body_A
# }
# }
# produce B {
# loop_1 {
# body_B
# }
# }
#
# Since we have a common loop in both stage A and B, we can use `compute_at` to
# merge them.
#
# `s[A].compute_at(s[B], loop_1)`
#
# This is the equivalent result.
#
# .. code-block:: none
#
# produce B {
# loop_1 {
# produce A {
# body_A
# }
# body_B
# }
# }
#
# We can then apply loop parallelisms primitives such as pipelining and
# parallel.
###############################################################################
# Main function
# =============
# This is the main function. Namely, the complete algorithm we want to run. We
# get the offloaded function with the provided data types
offload = top()
###############################################################################
# Voting algorithm
# ----------------
# This function implements the voting algorithm. We first sort for each digit.
# After that, we compare the values of the first place in each digit. The digit
# with the shortest value get one point. Similarly, we give the point to digits
# according to their ranking for the second place and third place. Finally, we
# take the digit with the highest point as our prediction label.
###############################################################################
# Get the Results
# ---------------
if __name__ == "__main__":
# Data preparation
train_images, _, test_images, test_labels = read_digitrec_data()
# Classification and testing
correct = 0.0
# We have 180 test images
total_time = 0
for i in range(0, 180):
# Prepare input data to offload function
# To load the tensors into the offloaded function, we must first cast it to
# the correct data type.
hcl_train_images = hcl.asarray(train_images, dtype_image)
hcl_knn_mat = hcl.asarray(np.zeros((10, 3)), dtype_knnmat)
# Execute the offload function and collect the candidates
start = time.time()
offload(test_images[i], hcl_train_images, hcl_knn_mat)
total_time = total_time + (time.time() - start)
# Convert back to a numpy array
knn_mat = hcl_knn_mat.asnumpy()
# Feed the candidates to the voting algorithm and compare the labels
if knn_vote(knn_mat) == test_labels[i]:
correct += 1
print("Average kernel time (s): {:.2f}".format(total_time/180))
print("Accuracy (%): {:.2f}".format(100*correct/180))
# For testing
assert (correct >= 150.0)
# Generate HLS kernel code and OpenCL host code
hcl.init(dtype_image)
target = hcl.Platform.aws_f1
target.config(compiler="vitis", mode="debug")
code = top(target)
print(code)
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8,
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220,
220,
220,
3601,
7,
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8,
198
] | 2.981607 | 4,132 |
from utils.mnist_reader import *
import pca
import lda
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
Xtrain_m, Ytrain = load_mnist('data/fashion', kind='train')
Xtest_m, Ytest = load_mnist('data/fashion', kind='t10k')
classifier = KNeighborsClassifier(n_neighbors=1)
classifier.fit(Xtrain_m, Ytrain)
print("Test accuracy Knn Inbuilt:", score(classifier, Xtest_m, Ytest, 'Test')*100,'%')
Xtrain, Xtest = pca.pca_inbuilt(Xtrain_m, Ytrain, Xtest_m, 85)
classifier1 = KNeighborsClassifier(n_neighbors=3)
classifier1.fit(Xtrain, Ytrain)
print("Test accuracy Knn Inbuilt with PCA Inbuilt:", score(classifier1, Xtest, Ytest, 'Test')*100,'%')
Xtrain, Xtest = lda.lda_inbuilt(Xtrain_m, Ytrain, Xtest_m, 9)
classifier2 = KNeighborsClassifier(n_neighbors=1)
classifier2.fit(Xtrain, Ytrain)
print("Test accuracy Knn Inbuilt with LDA Inbuilt:", score(classifier2, Xtest, Ytest, 'Test')*100,'%')
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] | 2.437659 | 393 |
from random import choice
from time import sleep
from sys import exit
print('''Suas opções:
\033[30m[ 1 ] PEDRA
[ 2 ] PAPEL
[ 3 ] TESOURA\33[m''')
jogada = int(input('Qual a sua jogada? '))
if jogada == 1:
jogada = 'PEDRA'
elif jogada == 2:
jogada = 'PAPEL'
elif jogada == 3:
jogada = 'TESOURA'
else:
exit('PÊÊÊÊÊÊÊÊ!!! Digite uma jogada válida!')
print('\033[33mJO...')
sleep(0.8)
print('KEN...')
sleep(0.8)
print('\033[32;1mPO!!!\033[m')
sleep(0.5)
cpu = choice(['PEDRA', 'PAPEL', 'TESOURA'])
print('\033[35;1m*\033[m' * 26)
print('CPU escolheu \033[30;1m{}\033[m'.format(cpu))
print('Jogador escolheu \033[30;1m{}\033[m'.format(jogada))
print('\033[35;1m*\033[m' * 26)
if cpu == jogada:
print('\033[33;1mEMPATE!!!')
elif cpu == 'PEDRA' and jogada == 'TESOURA'\
or cpu == 'PAPEL' and jogada == 'PEDRA'\
or cpu == 'TESOURA' and jogada == 'PAPEL':
print('\033[31;1mVOCÊ PERDEU!\033[31m O Computador sai vitorioso!')
else:
print('\033[32;1mVOCÊ GANHOU!\033[32m PARABÉNS!')
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] | 1.907441 | 551 |
chars_to_remove = [',', '.', '!', ':', ';', '-', ' ', '?']
if __name__ == '__main__':
tests = [
"", # false
"lotion", # false
"racecar", # true
"Racecar", # true
"Step on no p ets", # true
"No lemon no melons", # false
"Eva - can I see bees in a cave?", # true
"A man, a plan, a canal: Panama!", # true
"Aaaaa", # ["Aaaaa"]
"Babcaaba", # ["aa"] --------> correction, should be ["bab", "aba"]
"A racecar", # ["racecar"]
"No lemon no melons", # ["No lemon no melon"]
]
for test in tests:
print(f'\nCase: {test}')
print(f'Cleaned: {_clean(test)}')
print(f'Is Palendrome: {is_palendrome(test)}')
print(f'Longest Palendromes: {get_longest_palendromes(test)}')
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import json
from django.contrib.auth import get_user_model
from rest_framework.test import APITestCase, APIClient
from oauth2_provider.models import AccessToken, Application
from django.utils import timezone
from .models import Currency, Debtor, Transaction, CurrencyOwner
from rest_framework.reverse import reverse
from rest_framework import status
import shutil
from io import BytesIO
import mimetypes
import os
from django.conf import settings
import xlrd
from django.core import mail
import re
from .views import RecaptchaAPIView
from django.core import management
from django.db import connection
User = get_user_model()
# Create your tests here.
class ApiUserTestClient(APITestCase):
"""
Helper base class for API test
"""
client = APIClient()
@classmethod
@classmethod
@classmethod
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2488,
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628,
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628
] | 3.373984 | 246 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Localization_1D.py: Localizing robot in Two dimensional space"""
__author__ ="Gunasekar Jabbala"
__credits__ =["Daniel Ingram", "Udacity", " AtsushiSakai"]
import random
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
# Rows and Cols
M = 50
N = 50
GOAL_NODE = (M-1, N-1)
OBSTACLE_PROBABILITY = 0.15
GRID = np.int8(np.random.random((M,N)) > OBSTACLE_PROBABILITY)
IMAGE = 255 * np.dstack((GRID, GRID, GRID))
IMAGE[GOAL_NODE] = [0, 255, 0]
DELTAS = [[-1, 0], [0, -1], [1, 0], [0, 1]]
cost =1
fig, ax = plt.subplots()
fig.set_size_inches(19.2, 9.43, True)
ax.imshow(IMAGE)
kwargs = {"width":0.01,
"head_width":0.4,
"length_includes_head":True}
value = 1000*np.ones((M,N))
change = True
anim = animation.FuncAnimation(fig, animate, 100, interval=50)
plt.show()
anim.save("DStarGrid.gif") | [
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# tut_mission_B737.py
#
# Created: Aug 2014, SUAVE Team
# Modified: Aug 2017, SUAVE Team
# Mar 2020, E. Botero
# ----------------------------------------------------------------------
# Imports
# ----------------------------------------------------------------------
# General Python Imports
import numpy as np
# Numpy is a commonly used mathematically computing package. It contains many frequently used
# mathematical functions and is faster than native Python, especially when using vectorized
# quantities.
import matplotlib.pyplot as plt
# Matplotlib's pyplot can be used to generate a large variety of plots. Here it is used to create
# visualizations of the aircraft's performance throughout the mission.
# SUAVE Imports
import SUAVE
if not SUAVE.__version__=='2.5.0':
assert('These tutorials only work with the SUAVE 2.5.0 release')
from SUAVE.Core import Data, Units
# The Data import here is a native SUAVE data structure that functions similarly to a dictionary.
# However, iteration directly returns values, and values can be retrieved either with the
# typical dictionary syntax of "entry['key']" or the more class-like "entry.key". For this to work
# properly, all keys must be strings.
# The Units import is used to allow units to be specified in the vehicle setup (or elsewhere).
# This is because SUAVE functions generally operate using metric units, so inputs must be
# converted. To use a length of 20 feet, set l = 20 * Units.ft . Additionally, to convert to SUAVE
# output back to a desired units, use l_ft = l_m / Units.ft
from SUAVE.Plots.Performance.Mission_Plots import *
# These are a variety of plotting routines that simplify the plotting process for commonly
# requested metrics. Plots of specifically desired metrics can also be manually created.
from SUAVE.Methods.Propulsion.turbofan_sizing import turbofan_sizing
# Rather than conventional sizing, this script builds the turbofan energy network. This process is
# covered in more detail in a separate tutorial. It does not size the turbofan geometry.
from copy import deepcopy
# ----------------------------------------------------------------------
# Main
# ----------------------------------------------------------------------
def main():
"""This function gets the vehicle configuration, analysis settings, and then runs the mission.
Once the mission is complete, the results are plotted."""
# Extract vehicle configurations and the analysis settings that go with them
configs, analyses = full_setup()
# Size each of the configurations according to a given set of geometry relations
simple_sizing(configs)
# Perform operations needed to make the configurations and analyses usable in the mission
configs.finalize()
analyses.finalize()
# Determine the vehicle weight breakdown (independent of mission fuel usage)
weights = analyses.configs.base.weights
breakdown = weights.evaluate()
# Perform a mission analysis
mission = analyses.missions.base
results = mission.evaluate()
# Plot all mission results, including items such as altitude profile and L/D
plot_mission(results)
return
# ----------------------------------------------------------------------
# Analysis Setup
# ----------------------------------------------------------------------
def full_setup():
"""This function gets the baseline vehicle and creates modifications for different
configurations, as well as the mission and analyses to go with those configurations."""
# Collect baseline vehicle data and changes when using different configuration settings
vehicle = vehicle_setup()
configs = configs_setup(vehicle)
# Get the analyses to be used when different configurations are evaluated
configs_analyses = analyses_setup(configs)
# Create the mission that will be flown
mission = mission_setup(configs_analyses)
missions_analyses = missions_setup(mission)
# Add the analyses to the proper containers
analyses = SUAVE.Analyses.Analysis.Container()
analyses.configs = configs_analyses
analyses.missions = missions_analyses
return configs, analyses
# ----------------------------------------------------------------------
# Define the Vehicle Analyses
# ----------------------------------------------------------------------
def analyses_setup(configs):
"""Set up analyses for each of the different configurations."""
analyses = SUAVE.Analyses.Analysis.Container()
# Build a base analysis for each configuration. Here the base analysis is always used, but
# this can be modified if desired for other cases.
for tag,config in configs.items():
analysis = base_analysis(config)
analyses[tag] = analysis
return analyses
def base_analysis(vehicle):
"""This is the baseline set of analyses to be used with this vehicle. Of these, the most
commonly changed are the weights and aerodynamics methods."""
# ------------------------------------------------------------------
# Initialize the Analyses
# ------------------------------------------------------------------
analyses = SUAVE.Analyses.Vehicle()
# ------------------------------------------------------------------
# Weights
weights = SUAVE.Analyses.Weights.Weights_Transport()
weights.vehicle = vehicle
analyses.append(weights)
# ------------------------------------------------------------------
# Aerodynamics Analysis
aerodynamics = SUAVE.Analyses.Aerodynamics.Fidelity_Zero()
aerodynamics.geometry = vehicle
analyses.append(aerodynamics)
# ------------------------------------------------------------------
# Stability Analysis
stability = SUAVE.Analyses.Stability.Fidelity_Zero()
stability.geometry = vehicle
analyses.append(stability)
# ------------------------------------------------------------------
# Energy
energy = SUAVE.Analyses.Energy.Energy()
energy.network = vehicle.networks
analyses.append(energy)
# ------------------------------------------------------------------
# Planet Analysis
planet = SUAVE.Analyses.Planets.Planet()
analyses.append(planet)
# ------------------------------------------------------------------
# Atmosphere Analysis
atmosphere = SUAVE.Analyses.Atmospheric.US_Standard_1976()
atmosphere.features.planet = planet.features
analyses.append(atmosphere)
return analyses
# ----------------------------------------------------------------------
# Define the Vehicle
# ----------------------------------------------------------------------
def vehicle_setup():
"""This is the full physical definition of the vehicle, and is designed to be independent of the
analyses that are selected."""
# ------------------------------------------------------------------
# Initialize the Vehicle
# ------------------------------------------------------------------
vehicle = SUAVE.Vehicle()
vehicle.tag = 'Boeing_737-800'
# ------------------------------------------------------------------
# Vehicle-level Properties
# ------------------------------------------------------------------
# Vehicle level mass properties
# The maximum takeoff gross weight is used by a number of methods, most notably the weight
# method. However, it does not directly inform mission analysis.
vehicle.mass_properties.max_takeoff = 79015.8 * Units.kilogram
# The takeoff weight is used to determine the weight of the vehicle at the start of the mission
vehicle.mass_properties.takeoff = 79015.8 * Units.kilogram
# Operating empty may be used by various weight methods or other methods. Importantly, it does
# not constrain the mission analysis directly, meaning that the vehicle weight in a mission
# can drop below this value if more fuel is needed than is available.
vehicle.mass_properties.operating_empty = 62746.4 * Units.kilogram
# The maximum zero fuel weight is also used by methods such as weights
vehicle.mass_properties.max_zero_fuel = 62732.0 * Units.kilogram
# Cargo weight typically feeds directly into weights output and does not affect the mission
vehicle.mass_properties.cargo = 10000. * Units.kilogram
# Envelope properties
# These values are typical FAR values for a transport of this type
vehicle.envelope.ultimate_load = 3.75
vehicle.envelope.limit_load = 2.5
# Vehicle level parameters
# The vehicle reference area typically matches the main wing reference area
vehicle.reference_area = 124.862 * Units['meters**2']
# Number of passengers, control settings, and accessories settings are used by the weights
# methods
vehicle.passengers = 170
vehicle.systems.control = "fully powered"
vehicle.systems.accessories = "medium range"
# ------------------------------------------------------------------
# Landing Gear
# ------------------------------------------------------------------
# The settings here can be used for noise analysis, but are not used in this tutorial
landing_gear = SUAVE.Components.Landing_Gear.Landing_Gear()
landing_gear.tag = "main_landing_gear"
landing_gear.main_tire_diameter = 1.12000 * Units.m
landing_gear.nose_tire_diameter = 0.6858 * Units.m
landing_gear.main_strut_length = 1.8 * Units.m
landing_gear.nose_strut_length = 1.3 * Units.m
landing_gear.main_units = 2 # Number of main landing gear
landing_gear.nose_units = 1 # Number of nose landing gear
landing_gear.main_wheels = 2 # Number of wheels on the main landing gear
landing_gear.nose_wheels = 2 # Number of wheels on the nose landing gear
vehicle.landing_gear = landing_gear
# ------------------------------------------------------------------
# Main Wing
# ------------------------------------------------------------------
# This main wing is approximated as a simple trapezoid. A segmented wing can also be created if
# desired. Segmented wings appear in later tutorials, and a version of the 737 with segmented
# wings can be found in the SUAVE testing scripts.
# SUAVE allows conflicting geometric values to be set in terms of items such as aspect ratio
# when compared with span and reference area. Sizing scripts may be used to enforce
# consistency if desired.
wing = SUAVE.Components.Wings.Main_Wing()
wing.tag = 'main_wing'
wing.aspect_ratio = 10.18
# Quarter chord sweep is used as the driving sweep in most of the low fidelity analysis methods.
# If a different known value (such as leading edge sweep) is given, it should be converted to
# quarter chord sweep and added here. In some cases leading edge sweep will be used directly as
# well, and can be entered here too.
wing.sweeps.quarter_chord = 25 * Units.deg
wing.thickness_to_chord = 0.1
wing.taper = 0.1
wing.spans.projected = 34.32 * Units.meter
wing.chords.root = 7.760 * Units.meter
wing.chords.tip = 0.782 * Units.meter
wing.chords.mean_aerodynamic = 4.235 * Units.meter
wing.areas.reference = 124.862 * Units['meters**2']
wing.twists.root = 4.0 * Units.degrees
wing.twists.tip = 0.0 * Units.degrees
wing.origin = [[13.61, 0, -1.27]] * Units.meter
wing.vertical = False
wing.symmetric = True
# The high lift flag controls aspects of maximum lift coefficient calculations
wing.high_lift = True
# The dynamic pressure ratio is used in stability calculations
wing.dynamic_pressure_ratio = 1.0
# ------------------------------------------------------------------
# Main Wing Control Surfaces
# ------------------------------------------------------------------
# Information in this section is used for high lift calculations and when conversion to AVL
# is desired.
# Deflections will typically be specified separately in individual vehicle configurations.
flap = SUAVE.Components.Wings.Control_Surfaces.Flap()
flap.tag = 'flap'
flap.span_fraction_start = 0.20
flap.span_fraction_end = 0.70
flap.deflection = 0.0 * Units.degrees
# Flap configuration types are used in computing maximum CL and noise
flap.configuration_type = 'double_slotted'
flap.chord_fraction = 0.30
wing.append_control_surface(flap)
slat = SUAVE.Components.Wings.Control_Surfaces.Slat()
slat.tag = 'slat'
slat.span_fraction_start = 0.324
slat.span_fraction_end = 0.963
slat.deflection = 0.0 * Units.degrees
slat.chord_fraction = 0.1
wing.append_control_surface(slat)
aileron = SUAVE.Components.Wings.Control_Surfaces.Aileron()
aileron.tag = 'aileron'
aileron.span_fraction_start = 0.7
aileron.span_fraction_end = 0.963
aileron.deflection = 0.0 * Units.degrees
aileron.chord_fraction = 0.16
wing.append_control_surface(aileron)
# Add to vehicle
vehicle.append_component(wing)
# ------------------------------------------------------------------
# Horizontal Stabilizer
# ------------------------------------------------------------------
wing = SUAVE.Components.Wings.Horizontal_Tail()
wing.tag = 'horizontal_stabilizer'
wing.aspect_ratio = 6.16
wing.sweeps.quarter_chord = 40.0 * Units.deg
wing.thickness_to_chord = 0.08
wing.taper = 0.2
wing.spans.projected = 14.2 * Units.meter
wing.chords.root = 4.7 * Units.meter
wing.chords.tip = 0.955 * Units.meter
wing.chords.mean_aerodynamic = 3.0 * Units.meter
wing.areas.reference = 32.488 * Units['meters**2']
wing.twists.root = 3.0 * Units.degrees
wing.twists.tip = 3.0 * Units.degrees
wing.origin = [[32.83 * Units.meter, 0 , 1.14 * Units.meter]]
wing.vertical = False
wing.symmetric = True
wing.dynamic_pressure_ratio = 0.9
# Add to vehicle
vehicle.append_component(wing)
# ------------------------------------------------------------------
# Vertical Stabilizer
# ------------------------------------------------------------------
wing = SUAVE.Components.Wings.Vertical_Tail()
wing.tag = 'vertical_stabilizer'
wing.aspect_ratio = 1.91
wing.sweeps.quarter_chord = 25. * Units.deg
wing.thickness_to_chord = 0.08
wing.taper = 0.25
wing.spans.projected = 7.777 * Units.meter
wing.chords.root = 8.19 * Units.meter
wing.chords.tip = 0.95 * Units.meter
wing.chords.mean_aerodynamic = 4.0 * Units.meter
wing.areas.reference = 27.316 * Units['meters**2']
wing.twists.root = 0.0 * Units.degrees
wing.twists.tip = 0.0 * Units.degrees
wing.origin = [[28.79 * Units.meter, 0, 1.54 * Units.meter]] # meters
wing.vertical = True
wing.symmetric = False
# The t tail flag is used in weights calculations
wing.t_tail = False
wing.dynamic_pressure_ratio = 1.0
# Add to vehicle
vehicle.append_component(wing)
# ------------------------------------------------------------------
# Fuselage
# ------------------------------------------------------------------
fuselage = SUAVE.Components.Fuselages.Fuselage()
fuselage.tag = 'fuselage'
# Number of coach seats is used in some weights methods
fuselage.number_coach_seats = vehicle.passengers
# The seats abreast can be used along with seat pitch and the number of coach seats to
# determine the length of the cabin if desired.
fuselage.seats_abreast = 6
fuselage.seat_pitch = 1 * Units.meter
# Fineness ratios are used to determine VLM fuselage shape and sections to use in OpenVSP
# output
fuselage.fineness.nose = 1.6
fuselage.fineness.tail = 2.
# Nose and tail lengths are used in the VLM setup
fuselage.lengths.nose = 6.4 * Units.meter
fuselage.lengths.tail = 8.0 * Units.meter
fuselage.lengths.total = 38.02 * Units.meter
# Fore and aft space are added to the cabin length if the fuselage is sized based on
# number of seats
fuselage.lengths.fore_space = 6. * Units.meter
fuselage.lengths.aft_space = 5. * Units.meter
fuselage.width = 3.74 * Units.meter
fuselage.heights.maximum = 3.74 * Units.meter
fuselage.effective_diameter = 3.74 * Units.meter
fuselage.areas.side_projected = 142.1948 * Units['meters**2']
fuselage.areas.wetted = 446.718 * Units['meters**2']
fuselage.areas.front_projected = 12.57 * Units['meters**2']
# Maximum differential pressure between the cabin and the atmosphere
fuselage.differential_pressure = 5.0e4 * Units.pascal
# Heights at different longitudinal locations are used in stability calculations and
# in output to OpenVSP
fuselage.heights.at_quarter_length = 3.74 * Units.meter
fuselage.heights.at_three_quarters_length = 3.65 * Units.meter
fuselage.heights.at_wing_root_quarter_chord = 3.74 * Units.meter
# add to vehicle
vehicle.append_component(fuselage)
# ------------------------------------------------------------------
# Nacelles
# ------------------------------------------------------------------
nacelle = SUAVE.Components.Nacelles.Nacelle()
nacelle.tag = 'nacelle_1'
nacelle.length = 2.71
nacelle.inlet_diameter = 1.90
nacelle.diameter = 2.05
nacelle.areas.wetted = 1.1*np.pi*nacelle.diameter*nacelle.length
nacelle.origin = [[13.72, -4.86,-1.9]]
nacelle.flow_through = True
nacelle_airfoil = SUAVE.Components.Airfoils.Airfoil()
nacelle_airfoil.naca_4_series_airfoil = '2410'
nacelle.append_airfoil(nacelle_airfoil)
nacelle_2 = deepcopy(nacelle)
nacelle_2.tag = 'nacelle_2'
nacelle_2.origin = [[13.72, 4.86,-1.9]]
vehicle.append_component(nacelle)
vehicle.append_component(nacelle_2)
# ------------------------------------------------------------------
# Turbofan Network
# ------------------------------------------------------------------
turbofan = SUAVE.Components.Energy.Networks.Turbofan()
# For some methods, the 'turbofan' tag is still necessary. This will be changed in the
# future to allow arbitrary tags.
turbofan.tag = 'turbofan'
# High-level setup
turbofan.number_of_engines = 2
turbofan.bypass_ratio = 5.4
turbofan.origin = [[13.72, 4.86,-1.9],[13.72, -4.86,-1.9]] * Units.meter
# Establish the correct working fluid
turbofan.working_fluid = SUAVE.Attributes.Gases.Air()
# Components use estimated efficiencies. Estimates by technology level can be
# found in textbooks such as those by J.D. Mattingly
# ------------------------------------------------------------------
# Component 1 - Ram
# Converts freestream static to stagnation quantities
ram = SUAVE.Components.Energy.Converters.Ram()
ram.tag = 'ram'
# add to the network
turbofan.append(ram)
# ------------------------------------------------------------------
# Component 2 - Inlet Nozzle
# Create component
inlet_nozzle = SUAVE.Components.Energy.Converters.Compression_Nozzle()
inlet_nozzle.tag = 'inlet_nozzle'
# Specify performance
inlet_nozzle.polytropic_efficiency = 0.98
inlet_nozzle.pressure_ratio = 0.98
# Add to network
turbofan.append(inlet_nozzle)
# ------------------------------------------------------------------
# Component 3 - Low Pressure Compressor
# Create component
compressor = SUAVE.Components.Energy.Converters.Compressor()
compressor.tag = 'low_pressure_compressor'
# Specify performance
compressor.polytropic_efficiency = 0.91
compressor.pressure_ratio = 1.14
# Add to network
turbofan.append(compressor)
# ------------------------------------------------------------------
# Component 4 - High Pressure Compressor
# Create component
compressor = SUAVE.Components.Energy.Converters.Compressor()
compressor.tag = 'high_pressure_compressor'
# Specify performance
compressor.polytropic_efficiency = 0.91
compressor.pressure_ratio = 13.415
# Add to network
turbofan.append(compressor)
# ------------------------------------------------------------------
# Component 5 - Low Pressure Turbine
# Create component
turbine = SUAVE.Components.Energy.Converters.Turbine()
turbine.tag='low_pressure_turbine'
# Specify performance
turbine.mechanical_efficiency = 0.99
turbine.polytropic_efficiency = 0.93
# Add to network
turbofan.append(turbine)
# ------------------------------------------------------------------
# Component 6 - High Pressure Turbine
# Create component
turbine = SUAVE.Components.Energy.Converters.Turbine()
turbine.tag='high_pressure_turbine'
# Specify performance
turbine.mechanical_efficiency = 0.99
turbine.polytropic_efficiency = 0.93
# Add to network
turbofan.append(turbine)
# ------------------------------------------------------------------
# Component 7 - Combustor
# Create component
combustor = SUAVE.Components.Energy.Converters.Combustor()
combustor.tag = 'combustor'
# Specify performance
combustor.efficiency = 0.99
combustor.alphac = 1.0
combustor.turbine_inlet_temperature = 1450 # K
combustor.pressure_ratio = 0.95
combustor.fuel_data = SUAVE.Attributes.Propellants.Jet_A()
# Add to network
turbofan.append(combustor)
# ------------------------------------------------------------------
# Component 8 - Core Nozzle
# Create component
nozzle = SUAVE.Components.Energy.Converters.Expansion_Nozzle()
nozzle.tag = 'core_nozzle'
# Specify performance
nozzle.polytropic_efficiency = 0.95
nozzle.pressure_ratio = 0.99
# Add to network
turbofan.append(nozzle)
# ------------------------------------------------------------------
# Component 9 - Fan Nozzle
# Create component
nozzle = SUAVE.Components.Energy.Converters.Expansion_Nozzle()
nozzle.tag = 'fan_nozzle'
# Specify performance
nozzle.polytropic_efficiency = 0.95
nozzle.pressure_ratio = 0.99
# Add to network
turbofan.append(nozzle)
# ------------------------------------------------------------------
# Component 10 - Fan
# Create component
fan = SUAVE.Components.Energy.Converters.Fan()
fan.tag = 'fan'
# Specify performance
fan.polytropic_efficiency = 0.93
fan.pressure_ratio = 1.7
# Add to network
turbofan.append(fan)
# ------------------------------------------------------------------
# Component 11 - thrust (to compute the thrust)
thrust = SUAVE.Components.Energy.Processes.Thrust()
thrust.tag ='compute_thrust'
# Design thrust is used to determine mass flow at full throttle
thrust.total_design = 2*24000. * Units.N #Newtons
# Add to network
turbofan.thrust = thrust
# Design sizing conditions are also used to determine mass flow
altitude = 35000.0*Units.ft
mach_number = 0.78
# Determine turbofan behavior at the design condition
turbofan_sizing(turbofan,mach_number,altitude)
# Add turbofan network to the vehicle
vehicle.append_component(turbofan)
# ------------------------------------------------------------------
# Vehicle Definition Complete
# ------------------------------------------------------------------
return vehicle
# ----------------------------------------------------------------------
# Define the Configurations
# ---------------------------------------------------------------------
def configs_setup(vehicle):
"""This function sets up vehicle configurations for use in different parts of the mission.
Here, this is mostly in terms of high lift settings."""
# ------------------------------------------------------------------
# Initialize Configurations
# ------------------------------------------------------------------
configs = SUAVE.Components.Configs.Config.Container()
base_config = SUAVE.Components.Configs.Config(vehicle)
base_config.tag = 'base'
configs.append(base_config)
# ------------------------------------------------------------------
# Cruise Configuration
# ------------------------------------------------------------------
config = SUAVE.Components.Configs.Config(base_config)
config.tag = 'cruise'
configs.append(config)
# ------------------------------------------------------------------
# Takeoff Configuration
# ------------------------------------------------------------------
config = SUAVE.Components.Configs.Config(base_config)
config.tag = 'takeoff'
config.wings['main_wing'].control_surfaces.flap.deflection = 20. * Units.deg
config.wings['main_wing'].control_surfaces.slat.deflection = 25. * Units.deg
# A max lift coefficient factor of 1 is the default, but it is highlighted here as an option
config.max_lift_coefficient_factor = 1.
configs.append(config)
# ------------------------------------------------------------------
# Cutback Configuration
# ------------------------------------------------------------------
config = SUAVE.Components.Configs.Config(base_config)
config.tag = 'cutback'
config.wings['main_wing'].control_surfaces.flap.deflection = 20. * Units.deg
config.wings['main_wing'].control_surfaces.slat.deflection = 20. * Units.deg
config.max_lift_coefficient_factor = 1.
configs.append(config)
# ------------------------------------------------------------------
# Landing Configuration
# ------------------------------------------------------------------
config = SUAVE.Components.Configs.Config(base_config)
config.tag = 'landing'
config.wings['main_wing'].control_surfaces.flap.deflection = 30. * Units.deg
config.wings['main_wing'].control_surfaces.slat.deflection = 25. * Units.deg
config.max_lift_coefficient_factor = 1.
configs.append(config)
# ------------------------------------------------------------------
# Short Field Takeoff Configuration
# ------------------------------------------------------------------
config = SUAVE.Components.Configs.Config(base_config)
config.tag = 'short_field_takeoff'
config.wings['main_wing'].control_surfaces.flap.deflection = 20. * Units.deg
config.wings['main_wing'].control_surfaces.slat.deflection = 20. * Units.deg
config.max_lift_coefficient_factor = 1.
configs.append(config)
return configs
def simple_sizing(configs):
"""This function applies a few basic geometric sizing relations and modifies the landing
configuration."""
base = configs.base
# Update the baseline data structure to prepare for changes
base.pull_base()
# Revise the zero fuel weight. This will only affect the base configuration. To do all
# configurations, this should be specified in the top level vehicle definition.
base.mass_properties.max_zero_fuel = 0.9 * base.mass_properties.max_takeoff
# Estimate wing areas
for wing in base.wings:
wing.areas.wetted = 2.0 * wing.areas.reference
wing.areas.exposed = 0.8 * wing.areas.wetted
wing.areas.affected = 0.6 * wing.areas.wetted
# Store how the changes compare to the baseline configuration
base.store_diff()
# ------------------------------------------------------------------
# Landing Configuration
# ------------------------------------------------------------------
landing = configs.landing
# Make sure base data is current
landing.pull_base()
# Add a landing weight parameter. This is used in field length estimation and in
# initially the landing mission segment type.
landing.mass_properties.landing = 0.85 * base.mass_properties.takeoff
# Store how the changes compare to the baseline configuration
landing.store_diff()
return
# ----------------------------------------------------------------------
# Define the Mission
# ----------------------------------------------------------------------
def mission_setup(analyses):
"""This function defines the baseline mission that will be flown by the aircraft in order
to compute performance."""
# ------------------------------------------------------------------
# Initialize the Mission
# ------------------------------------------------------------------
mission = SUAVE.Analyses.Mission.Sequential_Segments()
mission.tag = 'the_mission'
# Airport
# The airport parameters are used in calculating field length and noise. They are not
# directly used in mission performance estimation
airport = SUAVE.Attributes.Airports.Airport()
airport.altitude = 0.0 * Units.ft
airport.delta_isa = 0.0
airport.atmosphere = SUAVE.Attributes.Atmospheres.Earth.US_Standard_1976()
mission.airport = airport
# Unpack Segments module
Segments = SUAVE.Analyses.Mission.Segments
# Base segment
base_segment = Segments.Segment()
# ------------------------------------------------------------------
# First Climb Segment: Constant Speed, Constant Rate
# ------------------------------------------------------------------
# A constant speed, constant rate climb segment is used first. This means that the aircraft
# will maintain a constant airspeed and constant climb rate until it hits the end altitude.
# For this type of segment, the throttle is allowed to vary as needed to match required
# performance.
segment = Segments.Climb.Constant_Speed_Constant_Rate(base_segment)
# It is important that all segment tags must be unique for proper evaluation. At the moment
# this is not automatically enforced.
segment.tag = "climb_1"
# The analysis settings for mission segment are chosen here. These analyses include information
# on the vehicle configuration.
segment.analyses.extend( analyses.takeoff )
segment.altitude_start = 0.0 * Units.km
segment.altitude_end = 3.0 * Units.km
segment.air_speed = 125.0 * Units['m/s']
segment.climb_rate = 6.0 * Units['m/s']
# Add to misison
mission.append_segment(segment)
# ------------------------------------------------------------------
# Second Climb Segment: Constant Speed, Constant Rate
# ------------------------------------------------------------------
segment = Segments.Climb.Constant_Speed_Constant_Rate(base_segment)
segment.tag = "climb_2"
segment.analyses.extend( analyses.cruise )
# A starting altitude is no longer needed as it will automatically carry over from the
# previous segment. However, it could be specified if desired. This would potentially cause
# a jump in altitude but would otherwise not cause any problems.
segment.altitude_end = 8.0 * Units.km
segment.air_speed = 190.0 * Units['m/s']
segment.climb_rate = 6.0 * Units['m/s']
# Add to mission
mission.append_segment(segment)
# ------------------------------------------------------------------
# Third Climb Segment: constant Speed, Constant Rate
# ------------------------------------------------------------------
segment = Segments.Climb.Constant_Speed_Constant_Rate(base_segment)
segment.tag = "climb_3"
segment.analyses.extend( analyses.cruise )
segment.altitude_end = 10.668 * Units.km
segment.air_speed = 226.0 * Units['m/s']
segment.climb_rate = 3.0 * Units['m/s']
# Add to mission
mission.append_segment(segment)
# ------------------------------------------------------------------
# Cruise Segment: Constant Speed, Constant Altitude
# ------------------------------------------------------------------
segment = Segments.Cruise.Constant_Speed_Constant_Altitude(base_segment)
segment.tag = "cruise"
segment.analyses.extend( analyses.cruise )
segment.air_speed = 230.412 * Units['m/s']
segment.distance = 2490. * Units.nautical_miles
# Add to mission
mission.append_segment(segment)
# ------------------------------------------------------------------
# First Descent Segment: Constant Speed, Constant Rate
# ------------------------------------------------------------------
segment = Segments.Descent.Constant_Speed_Constant_Rate(base_segment)
segment.tag = "descent_1"
segment.analyses.extend( analyses.cruise )
segment.altitude_end = 8.0 * Units.km
segment.air_speed = 220.0 * Units['m/s']
segment.descent_rate = 4.5 * Units['m/s']
# Add to mission
mission.append_segment(segment)
# ------------------------------------------------------------------
# Second Descent Segment: Constant Speed, Constant Rate
# ------------------------------------------------------------------
segment = Segments.Descent.Constant_Speed_Constant_Rate(base_segment)
segment.tag = "descent_2"
segment.analyses.extend( analyses.landing )
segment.altitude_end = 6.0 * Units.km
segment.air_speed = 195.0 * Units['m/s']
segment.descent_rate = 5.0 * Units['m/s']
# Add to mission
mission.append_segment(segment)
# ------------------------------------------------------------------
# Third Descent Segment: Constant Speed, Constant Rate
# ------------------------------------------------------------------
segment = Segments.Descent.Constant_Speed_Constant_Rate(base_segment)
segment.tag = "descent_3"
segment.analyses.extend( analyses.landing )
# While it is set to zero here and therefore unchanged, a drag increment can be used if
# desired. This can avoid negative throttle values if drag generated by the base airframe
# is insufficient for the desired descent speed and rate.
analyses.landing.aerodynamics.settings.spoiler_drag_increment = 0.00
segment.altitude_end = 4.0 * Units.km
segment.air_speed = 170.0 * Units['m/s']
segment.descent_rate = 5.0 * Units['m/s']
# Add to mission
mission.append_segment(segment)
# ------------------------------------------------------------------
# Fourth Descent Segment: Constant Speed, Constant Rate
# ------------------------------------------------------------------
segment = Segments.Descent.Constant_Speed_Constant_Rate(base_segment)
segment.tag = "descent_4"
segment.analyses.extend( analyses.landing )
analyses.landing.aerodynamics.settings.spoiler_drag_increment = 0.00
segment.altitude_end = 2.0 * Units.km
segment.air_speed = 150.0 * Units['m/s']
segment.descent_rate = 5.0 * Units['m/s']
# Add to mission
mission.append_segment(segment)
# ------------------------------------------------------------------
# Fifth Descent Segment: Constant Speed, Constant Rate
# ------------------------------------------------------------------
segment = Segments.Descent.Constant_Speed_Constant_Rate(base_segment)
segment.tag = "descent_5"
segment.analyses.extend( analyses.landing )
analyses.landing.aerodynamics.settings.spoiler_drag_increment = 0.00
segment.altitude_end = 0.0 * Units.km
segment.air_speed = 145.0 * Units['m/s']
segment.descent_rate = 3.0 * Units['m/s']
# Append to mission
mission.append_segment(segment)
# ------------------------------------------------------------------
# Mission definition complete
# ------------------------------------------------------------------
return mission
def missions_setup(base_mission):
"""This allows multiple missions to be incorporated if desired, but only one is used here."""
# Setup the mission container
missions = SUAVE.Analyses.Mission.Mission.Container()
# ------------------------------------------------------------------
# Base Mission
# ------------------------------------------------------------------
# Only one mission (the base mission) is defined in this case
missions.base = base_mission
return missions
# ----------------------------------------------------------------------
# Plot Mission
# ----------------------------------------------------------------------
def plot_mission(results,line_style='bo-'):
"""This function plots the results of the mission analysis and saves those results to
png files."""
# Plot Flight Conditions
plot_flight_conditions(results, line_style)
# Plot Aerodynamic Forces
plot_aerodynamic_forces(results, line_style)
# Plot Aerodynamic Coefficients
plot_aerodynamic_coefficients(results, line_style)
# Drag Components
plot_drag_components(results, line_style)
# Plot Altitude, sfc, vehicle weight
plot_altitude_sfc_weight(results, line_style)
# Plot Velocities
plot_aircraft_velocities(results, line_style)
return
# This section is needed to actually run the various functions in the file
if __name__ == '__main__':
main()
# The show commands makes the plots actually appear
plt.show() | [
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33,
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62,
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13,
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62,
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13,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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9225,
25150,
13,
23,
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27719,
13,
34553,
21857,
220,
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220,
198,
220,
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1303,
24850,
6565,
743,
307,
973,
416,
2972,
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5050,
393,
584,
5050,
13,
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11,
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4038,
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287,
257,
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220,
220,
1303,
460,
4268,
2174,
428,
1988,
611,
517,
5252,
318,
2622,
621,
318,
1695,
13,
198,
220,
220,
220,
4038,
13,
22208,
62,
48310,
13,
3575,
803,
62,
28920,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
796,
718,
1983,
3510,
13,
19,
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27719,
13,
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21857,
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1303,
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62,
22570,
62,
25802,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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1983,
2624,
13,
15,
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27719,
13,
34553,
21857,
198,
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220,
220,
1303,
41061,
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62,
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13,
66,
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220,
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220,
220,
220,
220,
220,
220,
220,
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13,
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220,
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13,
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13,
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62,
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362,
13,
20,
628,
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220,
220,
1303,
21501,
1241,
10007,
198,
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220,
220,
1303,
383,
4038,
4941,
1989,
6032,
7466,
262,
1388,
8539,
4941,
1989,
220,
198,
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220,
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13,
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62,
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220,
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220,
220,
220,
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13,
4521,
17,
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17816,
4164,
364,
1174,
17,
20520,
220,
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220,
1303,
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286,
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11,
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11,
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13,
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13232,
1,
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13,
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62,
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1,
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62,
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315,
62,
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13,
23,
1635,
27719,
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] | 2.99885 | 13,040 |
from __future__ import division
import binascii
import base64
from six import int2byte, b, integer_types, text_type
# SEQUENCE([1, STRING(secexp), cont[0], OBJECT(curvename), cont[1], BINTSTRING)
# signatures: (from RFC3279)
# ansi-X9-62 OBJECT IDENTIFIER ::= {
# iso(1) member-body(2) us(840) 10045 }
#
# id-ecSigType OBJECT IDENTIFIER ::= {
# ansi-X9-62 signatures(4) }
# ecdsa-with-SHA1 OBJECT IDENTIFIER ::= {
# id-ecSigType 1 }
## so 1,2,840,10045,4,1
## so 0x42, .. ..
# Ecdsa-Sig-Value ::= SEQUENCE {
# r INTEGER,
# s INTEGER }
# id-public-key-type OBJECT IDENTIFIER ::= { ansi-X9.62 2 }
#
# id-ecPublicKey OBJECT IDENTIFIER ::= { id-publicKeyType 1 }
# I think the secp224r1 identifier is (t=06,l=05,v=2b81040021)
# secp224r1 OBJECT IDENTIFIER ::= {
# iso(1) identified-organization(3) certicom(132) curve(0) 33 }
# and the secp384r1 is (t=06,l=05,v=2b81040022)
# secp384r1 OBJECT IDENTIFIER ::= {
# iso(1) identified-organization(3) certicom(132) curve(0) 34 }
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# add your enums here
from enum import Enum
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2,
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] | 3.214286 | 14 |
# -*- coding: utf-8 -*-
import phone_transducer as pt
import itertools
def phone_substitution_transducer(add_meta_arc=True):
"""Substitute similar phones (optionally)."""
max_node = 0
t = pt.Transducer()
for l in pt.abc.ALL_SYMS:
t.add_arc(0, 0, l, l)
for s_ar, s_sw, subst_cost in pt.abc.AR_SW_SIMILAR_PHONES:
prev_node = 0
manner_violated = False
place_violated = False
#sonority_violated = False
#voiced_violated = False
frontness_violated = False
openness_violated = False
roundness_violated = False
rule_violated = False
for l_ar, l_sw in itertools.zip_longest(s_ar, s_sw, fillvalue=pt.abc.EPSILON):
max_node += 1
t.add_arc(prev_node, max_node, l_ar, l_sw, subst_cost)
subst_cost = None
prev_node = max_node
if not manner_violated:
manner_violated = DetectViolation(l_ar, l_sw, pt.abc.MANNER_OF_ARTICULATION)
if not place_violated:
place_violated = DetectViolation(l_ar, l_sw, pt.abc.PLACE_OF_ARTICULATION)
#sonority_violated = DetectViolation(l_ar, l_sw, pt.abc.SONORITY)
#if not (manner_violated or place_violated):
# voiced_violated = DetectViolation(l_ar, l_sw, pt.abc.STATE_OF_GLOTTIS)
if not frontness_violated:
frontness_violated = DetectViolation(l_ar, l_sw, pt.abc.VOWEL_FRONTNESS)
if not openness_violated:
openness_violated = DetectViolation(l_ar, l_sw, pt.abc.VOWEL_OPENNESS)
if not roundness_violated:
roundness_violated = DetectViolation(l_ar, l_sw, pt.abc.VOWEL_ROUNDNESS)
if manner_violated:
rule_violated = True
rule_name = "<<IDENT-IO-manner>>"
if add_meta_arc:
t.add_arc(max_node, max_node + 1, pt.abc.EPSILON, rule_name)
else:
t.add_arc(max_node, max_node + 1, pt.abc.EPSILON, pt.abc.EPSILON, pt.abc.OT_CONSTRAINTS[rule_name])
max_node += 1
if place_violated:
rule_violated = True
rule_name = "<<IDENT-IO-place>>"
if add_meta_arc:
t.add_arc(max_node, max_node + 1, pt.abc.EPSILON, rule_name)
else:
t.add_arc(max_node, max_node + 1, pt.abc.EPSILON, pt.abc.EPSILON, pt.abc.OT_CONSTRAINTS[rule_name])
max_node += 1
"""
if sonority_violated:
rule_violated = True
rule_name = "<<IDENT-IO-sonority>>"
if add_meta_arc:
t.add_arc(max_node, max_node + 1, pt.abc.EPSILON, rule_name)
else:
t.add_arc(max_node, max_node + 1, pt.abc.EPSILON, pt.abc.EPSILON, pt.abc.OT_CONSTRAINTS[rule_name])
max_node += 1
if voiced_violated:
rule_violated = True
rule_name = "<<IDENT-IO-voiced>>"
if add_meta_arc:
t.add_arc(max_node, max_node + 1, pt.abc.EPSILON, rule_name)
else:
t.add_arc(max_node, max_node + 1, pt.abc.EPSILON, pt.abc.EPSILON, pt.abc.OT_CONSTRAINTS[rule_name])
max_node += 1
rule_name = None
if s_ar in pt.abc.PHARYNGEAL:
rule_name = "<<IDENT-IO-PHARYNGEAL>>"
elif s_ar in pt.abc.PHARYNGEALIZED:
rule_name = "<<IDENT-IO-PHARYNGEALIZED>>"
elif s_ar in pt.abc.GLOTTAL:
rule_name = "<<IDENT-IO-GLOTTAL>>"
el"""
if frontness_violated:
rule_violated = True
rule_name = "<<IDENT-IO-frontness>>"
if add_meta_arc:
t.add_arc(max_node, max_node + 1, pt.abc.EPSILON, rule_name)
else:
t.add_arc(max_node, max_node + 1, pt.abc.EPSILON, pt.abc.EPSILON, pt.abc.OT_CONSTRAINTS[rule_name])
max_node += 1
if openness_violated:
rule_violated = True
rule_name = "<<IDENT-IO-openness>>"
if add_meta_arc:
t.add_arc(max_node, max_node + 1, pt.abc.EPSILON, rule_name)
else:
t.add_arc(max_node, max_node + 1, pt.abc.EPSILON, pt.abc.EPSILON, pt.abc.OT_CONSTRAINTS[rule_name])
max_node += 1
if roundness_violated:
rule_violated = True
rule_name = "<<IDENT-IO-roundness>>"
if add_meta_arc:
t.add_arc(max_node, max_node + 1, pt.abc.EPSILON, rule_name)
else:
t.add_arc(max_node, max_node + 1, pt.abc.EPSILON, pt.abc.EPSILON, pt.abc.OT_CONSTRAINTS[rule_name])
max_node += 1
rule_name = None
if s_ar in pt.abc.VOWELS or s_sw in pt.abc.VOWELS and not rule_violated:
rule_name = "<<IDENT-IO-v>>"
elif rule_violated:
rule_name = "<<IDENT-IO-c>>"
if rule_name:
if add_meta_arc:
t.add_arc(max_node, max_node + 1, pt.abc.EPSILON, rule_name)
else:
t.add_arc(max_node, max_node + 1, pt.abc.EPSILON, pt.abc.EPSILON, pt.abc.OT_CONSTRAINTS[rule_name])
max_node += 1
t.add_arc(max_node, 0, pt.abc.EPSILON, pt.abc.EPSILON)
t[0].final = True
return t
def epenthesis_transducer(add_meta_arc=True):
"""Inserts a vowel between two consonants (states 1 and 2) or at the end of
the word after a consonant. Or just outputs the letters as-is."""
t = pt.Transducer()
for l in pt.abc.ALL_SYMS:
t.add_arc(0, 0, l, l)
for l in pt.abc.CONSONANTS:
t.add_arc(0, 1, l, l)
t.add_arc(2, 0, l, l)
next_node = 3
for l, ins_cost, del_cost in pt.abc.VOWEL_OPERATION_COSTS:
t.add_arc(1, next_node, pt.abc.EPSILON, l, ins_cost)
rule_name = "<<DEP-IO>>"
if add_meta_arc:
t.add_arc(next_node, 2, pt.abc.EPSILON, rule_name)
else:
t.add_arc(next_node, 2, pt.abc.EPSILON, pt.abc.EPSILON, pt.abc.OT_CONSTRAINTS[rule_name])
next_node += 1
t[0].final = True
t[2].final = True
return t
def degemination_transducer(add_meta_arc=True):
"""Remove repeated consonants (optionally)."""
t = pt.Transducer()
for l in pt.abc.ALL_SYMS:
t.add_arc(0, 0, l, l)
next_node = 1
for l in pt.abc.CONSONANTS:
t.add_arc(0, next_node, l, l)
rule_name = "<<MAX-IO>>"
if add_meta_arc:
t.add_arc(next_node, 0, pt.abc.EPSILON, rule_name)
else:
t.add_arc(next_node, 0, pt.abc.EPSILON, pt.abc.EPSILON, pt.abc.OT_CONSTRAINTS[rule_name])
next_node += 1
t[0].final = True
return t
def final_vowel_substitution_transducer(add_meta_arc=True):
"""Substitute final vowels (optionally)."""
t = pt.Transducer()
for l in pt.abc.ALL_SYMS:
t.add_arc(0, 0, l, l)
t.add_arc(0, 1, l, l)
max_node = 1
for s_ar, s_sw, subst_cost in pt.abc.AR_SW_FINAL_VOWELS:
prev_node = 0
for l_ar, l_sw in itertools.zip_longest(s_ar, s_sw, fillvalue=pt.abc.EPSILON):
max_node += 1
t.add_arc(prev_node, max_node, l_ar, l_sw, subst_cost)
subst_cost = None
prev_node = max_node
"""
if len(s_ar) < len(s_sw):
rule_name = "<<DEP-IO>>"
else:
rule_name = "<<IDENT-IO-final>>"
"""
rule_name = "<<RO_MORPH>>"
if add_meta_arc:
t.add_arc(max_node, 1, pt.abc.EPSILON, rule_name)
else:
t.add_arc(max_node, 1, pt.abc.EPSILON, pt.abc.EPSILON, pt.abc.OT_CONSTRAINTS[rule_name])
max_node += 1
t.add_arc(1, 1, pt.abc.CONSONANT_DOT, pt.abc.CONSONANT_DOT)
t.add_arc(1, 1, pt.abc.VOWEL_DOT, pt.abc.VOWEL_DOT)
t[1].final = True
return t
def vowel_deletion_transducer(add_meta_arc=True):
"""Deletion of vowels."""
t = pt.Transducer()
for l in pt.abc.ALL_SYMS:
t.add_arc(0, 0, l, l)
next_node = 1
for l, ins_cost, del_cost in pt.abc.VOWEL_OPERATION_COSTS:
t.add_arc(0, next_node, l, pt.abc.EPSILON, del_cost)
rule_name = "<<MAX-V>>"
if add_meta_arc:
t.add_arc(next_node, 0, pt.abc.EPSILON, rule_name)
else:
t.add_arc(next_node, 0, pt.abc.EPSILON, pt.abc.EPSILON, pt.abc.OT_CONSTRAINTS[rule_name])
next_node += 1
t[0].final = True
if add_meta_arc:
pt.AddPassThroughArcs(t)
return t
def min_consonant_count_transducer(min_consonant_count=3, add_meta_arc=True):
"""Allows only strings with at least |min_consonant_count| consonants."""
t = pt.Transducer()
for i in range(min_consonant_count+1):
for l in pt.abc.ALL_SYMS:
t.add_arc(i, i, l, l)
if i > 0:
for l in pt.abc.CONSONANTS:
t.add_arc(i-1, i, l, l)
t[min_consonant_count].final = True
if add_meta_arc:
pt.AddPassThroughArcs(t)
return t
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] | 2.017013 | 3,997 |
import pika, sys
# Inicia a conexão e cria o canal de conexao
connection = pika.BlockingConnection(pika.ConnectionParameters('localhost'))
channel = connection.channel()
# Cria a fila se ela não existir
channel.queue_declare(queue='task_queue', durable=True)
message = ' '.join(sys.argv[1:]) or 'Hello World...'
channel.basic_publish(exchange='',
routing_key='task_queue',
body=message,
properties=pika.BasicProperties(
delivery_mode=2 # Torna a mensagem persistente
))
print('(producer): Message sent!')
connection.close()
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] | 2.318519 | 270 |
"""The test for the version sensor platform."""
from __future__ import annotations
from typing import Any
from unittest.mock import patch
from pyhaversion import HaVersionChannel, HaVersionSource
from pyhaversion.exceptions import HaVersionException
import pytest
from homeassistant.components.version.const import (
CONF_BETA,
CONF_CHANNEL,
CONF_IMAGE,
CONF_VERSION_SOURCE,
DEFAULT_NAME_LATEST,
DOMAIN,
VERSION_SOURCE_DOCKER_HUB,
VERSION_SOURCE_VERSIONS,
)
from homeassistant.config_entries import ConfigEntry
from homeassistant.const import CONF_NAME, CONF_SOURCE
from homeassistant.core import HomeAssistant
from homeassistant.setup import async_setup_component
from .common import (
MOCK_VERSION,
MOCK_VERSION_DATA,
TEST_DEFAULT_IMPORT_CONFIG,
mock_get_version_update,
setup_version_integration,
)
async def async_setup_sensor_wrapper(
hass: HomeAssistant, config: dict[str, Any]
) -> ConfigEntry:
"""Set up the Version sensor platform."""
await async_setup_component(hass, "persistent_notification", {})
with patch(
"pyhaversion.HaVersion.get_version",
return_value=(MOCK_VERSION, MOCK_VERSION_DATA),
):
assert await async_setup_component(
hass, "sensor", {"sensor": {"platform": DOMAIN, **config}}
)
await hass.async_block_till_done()
config_entries = hass.config_entries.async_entries(DOMAIN)
print(config_entries)
config_entry = config_entries[-1]
assert config_entry.source == "import"
return config_entry
async def test_version_sensor(hass: HomeAssistant):
"""Test the Version sensor with different sources."""
await setup_version_integration(hass)
state = hass.states.get("sensor.local_installation")
assert state.state == MOCK_VERSION
assert state.attributes["source"] == "local"
assert state.attributes["channel"] == "stable"
async def test_update(hass: HomeAssistant, caplog: pytest.LogCaptureFixture):
"""Test updates."""
await setup_version_integration(hass)
assert hass.states.get("sensor.local_installation").state == MOCK_VERSION
await mock_get_version_update(hass, version="1970.1.1")
assert hass.states.get("sensor.local_installation").state == "1970.1.1"
assert "Error fetching version data" not in caplog.text
await mock_get_version_update(hass, side_effect=HaVersionException)
assert hass.states.get("sensor.local_installation").state == "unavailable"
assert "Error fetching version data" in caplog.text
@pytest.mark.parametrize(
"yaml,converted",
(
(
{},
TEST_DEFAULT_IMPORT_CONFIG,
),
(
{CONF_NAME: "test"},
{**TEST_DEFAULT_IMPORT_CONFIG, CONF_NAME: "test"},
),
(
{CONF_SOURCE: "hassio", CONF_IMAGE: "odroid-n2"},
{
**TEST_DEFAULT_IMPORT_CONFIG,
CONF_NAME: DEFAULT_NAME_LATEST,
CONF_SOURCE: HaVersionSource.SUPERVISOR,
CONF_VERSION_SOURCE: VERSION_SOURCE_VERSIONS,
CONF_IMAGE: "odroid-n2",
},
),
(
{CONF_SOURCE: "docker"},
{
**TEST_DEFAULT_IMPORT_CONFIG,
CONF_NAME: DEFAULT_NAME_LATEST,
CONF_SOURCE: HaVersionSource.CONTAINER,
CONF_VERSION_SOURCE: VERSION_SOURCE_DOCKER_HUB,
},
),
(
{CONF_BETA: True},
{
**TEST_DEFAULT_IMPORT_CONFIG,
CONF_CHANNEL: HaVersionChannel.BETA,
},
),
(
{CONF_SOURCE: "container", CONF_IMAGE: "odroid-n2"},
{
**TEST_DEFAULT_IMPORT_CONFIG,
CONF_NAME: DEFAULT_NAME_LATEST,
CONF_SOURCE: HaVersionSource.CONTAINER,
CONF_VERSION_SOURCE: VERSION_SOURCE_DOCKER_HUB,
CONF_IMAGE: "odroid-n2-homeassistant",
},
),
),
)
async def test_config_import(
hass: HomeAssistant, yaml: dict[str, Any], converted: dict[str, Any]
) -> None:
"""Test importing YAML configuration."""
config_entry = await async_setup_sensor_wrapper(hass, yaml)
assert config_entry.data == converted
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] | 2.222739 | 1,935 |
#!/bin/python3
import os
import itertools as it
# Complete the substrCount function below.
if __name__ == '__main__':
fptr = open(os.environ['OUTPUT_PATH'], 'w')
n = int(input())
s = input()
result = substrCount(n, s)
fptr.write(str(result) + '\n')
fptr.close()
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] | 2.143836 | 146 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Tests for KISS Classes."""
import logging
import random
import unittest
import aprs
from .context import kiss
from . import constants
__author__ = 'Greg Albrecht W2GMD <[email protected]>' # NOQA pylint: disable=R0801
__copyright__ = 'Copyright 2017 Greg Albrecht and Contributors' # NOQA pylint: disable=R0801
__license__ = 'Apache License, Version 2.0' # NOQA pylint: disable=R0801
class KISSTestClass(unittest.TestCase): # pylint: disable=R0904
"""Test class for KISS Python Module."""
_logger = logging.getLogger(__name__) # pylint: disable=R0801
if not _logger.handlers: # pylint: disable=R0801
_logger.setLevel(kiss.LOG_LEVEL) # pylint: disable=R0801
_console_handler = logging.StreamHandler() # pylint: disable=R0801
_console_handler.setLevel(kiss.LOG_LEVEL) # pylint: disable=R0801
_console_handler.setFormatter(kiss.LOG_FORMAT) # pylint: disable=R0801
_logger.addHandler(_console_handler) # pylint: disable=R0801
_logger.propagate = False # pylint: disable=R0801
@classmethod
def random(cls, length=8, alphabet=constants.ALPHANUM):
"""
Generates a random string for test cases.
:param length: Length of string to generate.
:param alphabet: Alphabet to use to create string.
:type length: int
:type alphabet: str
"""
return ''.join(random.choice(alphabet) for _ in range(length))
@classmethod
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] | 2.482702 | 607 |
"""The Base module of the `cpp_linter` package. This holds the objects shared by
multiple modules."""
import io
import os
import logging
FOUND_RICH_LIB = False
try:
from rich.logging import RichHandler
FOUND_RICH_LIB = True
logging.basicConfig(
format="%(name)s: %(message)s",
handlers=[RichHandler(show_time=False)],
)
except ImportError:
logging.basicConfig()
#: The logging.Logger object used for outputing data.
logger = logging.getLogger("CPP Linter")
if not FOUND_RICH_LIB:
logger.debug("rich module not found")
# global constant variables
GITHUB_SHA = os.getenv("GITHUB_SHA", "")
GITHUB_TOKEN = os.getenv("GITHUB_TOKEN", os.getenv("GIT_REST_API", ""))
API_HEADERS = {
"Authorization": f"token {GITHUB_TOKEN}",
"Accept": "application/vnd.github.v3.text+json",
}
class Globals:
"""Global variables for re-use (non-constant)."""
PAYLOAD_TIDY = ""
"""The accumulated output of clang-tidy (gets appended to OUTPUT)"""
OUTPUT = ""
"""The accumulated body of the resulting comment that gets posted."""
FILES = []
"""The reponding payload containing info about changed files."""
EVENT_PAYLOAD = {}
"""The parsed JSON of the event payload."""
response_buffer = None
"""A shared response object for `requests` module."""
class GlobalParser:
"""Global variables specific to output parsers. Each element in each of the
following attributes represents a clang-tool's output for 1 source file.
"""
tidy_notes = []
"""This can only be a `list` of type
[`TidyNotification`][cpp_linter.clang_tidy.TidyNotification]"""
tidy_advice = []
"""This can only be a `list` of type
[`YMLFixit`][cpp_linter.clang_tidy_yml.YMLFixit]"""
format_advice = []
"""This can only be a `list` of type
[`XMLFixit`][cpp_linter.clang_format_xml.XMLFixit]"""
def get_line_cnt_from_cols(file_path: str, offset: int) -> tuple:
"""Gets a line count and columns offset from a file's absolute offset.
Args:
file_path: Path to file.
offset: The byte offset to translate
Returns:
A `tuple` of 2 `int` numbers:
- Index 0 is the line number for the given offset.
- Index 1 is the column number for the given offset on the line.
"""
line_cnt = 1
last_lf_pos = 0
cols = 1
file_path = file_path.replace("/", os.sep)
# logger.debug("Getting line count from %s at offset %d", file_path, offset)
with io.open(file_path, "rb") as src_file:
max_len = src_file.seek(0, io.SEEK_END)
src_file.seek(0, io.SEEK_SET)
while src_file.tell() != offset and src_file.tell() < max_len:
char = src_file.read(1)
if char == b"\n":
line_cnt += 1
last_lf_pos = src_file.tell() - 1 # -1 because LF is part of offset
cols = src_file.tell() - last_lf_pos
return (line_cnt, cols)
def log_response_msg():
"""Output the response buffer's message on a failed request."""
if Globals.response_buffer.status_code >= 400:
logger.error("response returned message: %s", Globals.response_buffer.text)
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] | 2.56564 | 1,234 |
# -*- coding: Utf-8 -*
from .text import Text
from .window import Window
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26580,
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] | 2.846154 | 26 |
import datetime
from aioftx.http import PaginatedRequest, PaginatedResponse
from pydantic import BaseModel, Field
""" Option Volume """
""" Option Interest """
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## jgi_metagenome_scraping
"""
Scrape individual metagenome EC numbers from JGI.
`scrape_metagenomes_from_jgi` is the only function meant to be called directly.
Usage:
scrape_metagenomes_from_jgi.py SAVE_DIR
scrape_metagenomes_from_jgi.py SAVE_DIR [--database=<db>]
scrape_metagenomes_from_jgi.py SAVE_DIR [--homepage=<hp>]
scrape_metagenomes_from_jgi.py SAVE_DIR [--write_concatenated_json=<wj>]
scrape_metagenomes_from_jgi.py SAVE_DIR [--ecosystem_classes=<ec>]
scrape_metagenomes_from_jgi.py SAVE_DIR [--datatypes=<dt>]
Arguments:
SAVE_DIR directory to write jsons to (no \ required after name)
Options:
--database=<db> Database to use, either 'jgi' or 'all' [default: jgi]
--homepage=<hp> url of jgi homepage [default: https://img.jgi.doe.gov/cgi-bin/m/main.cgi]
--ecosystem_classes=<ec> list; can be 'Engineered', 'Environmental', or 'Host-associated' (these are 3 different links on the homepage) [default: ['Engineered', 'Environmental', 'Host-associated']]
--datatypes=<dt> list; can be 'assembled', 'unassembled', or 'both' (species which type of genomic data to pull ECs from) [default: ['assembled','unassembled','both']]
--write_concatenated_json=<wj> write single concatenated json after all individual jsons are written [default: True]
"""
from selenium import webdriver
import time
import os
import re
import json
from docopt import docopt
from ast import literal_eval
from bs4 import BeautifulSoup
def activate_driver():
"""
Activate chrome driver used to automate webpage navigation (see: https://sites.google.com/a/chromium.org/chromedriver/)
The chrome driver .exec file must be in the home directory
:returns: driver [object]
"""
homedir = os.path.expanduser('~')
return webdriver.Chrome(homedir+'/chromedriver')
def get_ecosystemclass_url_from_jgi_img_homepage(driver,homepage_url,ecosystemClass,database='jgi'):
"""
load homepage_url -> retrieve ecosytemClass_url
:param driver: the chrome driver object
:param homepage_url: url of the jgi homepage. should be 'https://img.jgi.doe.gov/cgi-bin/m/main.cgi' as of 6/15/2017
:param ecosystemClass: can be 'Engineered', 'Environmental', or 'Host-associated' (these are 3 different links on the homepage)
:param database: choose to use only the jgi database, or all database [default=jgi]
:returns: url of the eukarya database page
"""
driver.get(homepage_url)
time.sleep(5)
htmlSource = driver.page_source
## All ampersands (&) must be followed by 'amp;'
regex = r'href=\"main\.cgi(\?section=TaxonList&domain=Metagenome&seq_center=%s&page=metaCatList&phylum=%s)\"'%(database,ecosystemClass)
match = re.search(regex, htmlSource)
ecosystemClass_suffix = match.group(1)
ecosystemClass_url = homepage_url+ecosystemClass_suffix
# ecosystemClass_urls.append(ecosystemClass_url)
return ecosystemClass_url
def get_ecosystemclass_json_from_ecosystem_class_url(driver,ecosystemClass_url):
"""
load ecosystemClass_url- > retrieve ecosystemClass_json_url -> load ecosystemClass_json_url -> retrieve ecosystemClass_json
:param driver: the chrome driver object
:param ecosystemClass_url: url of either the 'Engineered', 'Environmental', or 'Host-associated' ecosystemClasses (these are 3 different links on the homepage)
:returns: json containing urls of each individual metagenome in specified ecosystemClass
"""
driver.get(ecosystemClass_url)
time.sleep(5)
htmlSource = driver.page_source
# driver.quit()
regex = r'var myDataSource = new YAHOO\.util\.DataSource\(\"(.*)\"\);'
match = re.search(regex, htmlSource)
ecosystemClass_json_suffix = match.group(1)
ecosystemClass_url_prefix = ecosystemClass_url.split('main.cgi')[0]
ecosystemClass_json_url = ecosystemClass_url_prefix+ecosystemClass_json_suffix
driver.get(ecosystemClass_json_url)
time.sleep(5)
# jsonSource = driver.page_source
## convert the jsonSource into a dict of dicts here
ecosystemClass_json = json.loads(driver.find_element_by_tag_name('body').text)
return ecosystemClass_json
def get_metagenome_urls_from_ecosystemclass_json(driver,homepage_url,ecosystemClass_json):
"""
parse ecosystemClass_json -> retrieve metagenome_urls
:param driver: the chrome driver object
:param homepage_url: url of the jgi homepage. should be 'https://img.jgi.doe.gov/cgi-bin/m/main.cgi' as of 6/15/2017
:param ecosystemClass_json: json text of either the 'Engineered', 'Environmental', or 'Host-associated' ecosystemClasses
:returns: list of all metagenome urls
"""
all_GenomeNameSampleNameDisp = [d['GenomeNameSampleNameDisp'] for d in ecosystemClass_json['records']]
metagenome_urls = list()
for htmlandjunk in all_GenomeNameSampleNameDisp:
regex = r"<a href='main\.cgi(.*)'>"
match = re.search(regex, htmlandjunk)
html_suffix = match.group(1)
full_url = homepage_url+html_suffix
metagenome_urls.append(full_url)
return metagenome_urls
def get_metagenome_htmlSource_and_metadata(driver, metagenome_url):
"""
load metagenome_url -> retrieve metagenome_htmlSource & metadata
:param driver: the chrome driver object
:param metagenome_url: url for an single metagenome
:returns: html source of single metagenome, all metadata for that metagenome
"""
driver.get(metagenome_url)
time.sleep(5)
metagenome_htmlSource = driver.page_source
metadata_table_dict = get_metagenome_metadata_while_on_metagenome_page(metagenome_htmlSource)
return metagenome_htmlSource, metadata_table_dict
def get_enzyme_url_from_metagenome_url(metagenome_url, metagenome_htmlSource, datatype):
"""
metagenome_htmlSource -> parse out enzyme_url
:param driver: the chrome driver object
:param metagenome_url: url for an single metagenome
:param data_type: can be assembled, unassembled, or both (refers to whether the metagenomes are assembled or not)
:returns: url of a single metagenome's enzyme page
"""
regex = r'<a href=\"(main\.cgi\?section=MetaDetail&page=enzymes.*data_type=%s.*)\" onclick'%datatype
match = re.search(regex, metagenome_htmlSource)
if match:
print "Metagenome url: %s ...\n...has datatype: %s"%(metagenome_url,datatype)
enzyme_url_suffix = match.group(1)
enzyme_url_prefix = metagenome_url.split('main.cgi')[0]
enzyme_url = enzyme_url_prefix+enzyme_url_suffix
else:
print "Metagenome url: %s ...\n...does not have datatype: %s"%(metagenome_url,datatype)
enzyme_url = None
return enzyme_url
def get_metagenome_metadata_while_on_metagenome_page(htmlSource):
"""
htmlSource -> dictionary of metagenome metadata
:param htmlSource: the metagenome_url driver's .page_source
:returns: all metadata from a metagenome's html
"""
# return dict of metagenome table data
bs = BeautifulSoup(htmlSource,"html.parser")
metadata_table = bs.findAll('table')[0]
metadata_table_dict = dict()
for row in metadata_table.findAll('tr'):
if (len(row.findAll('th')) == 1) and (len(row.findAll('td')) == 1):
row_key = row.findAll('th')[0].text.rstrip()
row_value = row.findAll('td')[0].text.rstrip() if row.findAll('td')[0] else None
metadata_table_dict[row_key] = row_value
metadata_table_dict.pop('Geographical Map', None)
## metadata_table_dict['Taxon Object ID'] should be the way we identify a metagenome
return metadata_table_dict
def get_enzyme_json_from_enzyme_url(driver,enzyme_url):
"""
load enzyme_url -> retrieve enzyme_json_url -> load enzyme_json_url -> retrieve enzyme_json
:param driver: the chrome driver object
:param enzyme_url: url for an single enzyme type from an single metagenome
:returns: json of single metagenome's enzyme data
"""
driver.get(enzyme_url)
time.sleep(5)
htmlSource = driver.page_source
# driver.quit()
regex = r'var myDataSource = new YAHOO\.util\.DataSource\(\"(.*)\"\);'
match = re.search(regex, htmlSource)
enzyme_json_suffix = match.group(1)
enzyme_url_prefix = enzyme_url.split('main.cgi')[0]
enzyme_json_url = enzyme_url_prefix+enzyme_json_suffix
driver.get(enzyme_json_url)
time.sleep(5)
# jsonSource = driver.page_source
## convert the jsonSource into a dict of dicts here
enzyme_json = json.loads(driver.find_element_by_tag_name('body').text)
return enzyme_json
def parse_enzyme_info_from_enzyme_json(enzyme_json):
"""
load enzyme_json -> return ec dict
:param enzyme_json: json of a single eukaryote's enzyme data
:returns: dict of a single metagenome (key=ec,value=[enzymeName,genecount])
"""
enzyme_dict = dict() # Dictionary of ec:[enzymeName,genecount] for all ecs in a single metagenome
for i, singleEnzymeDict in enumerate(enzyme_json['records']):
ec = singleEnzymeDict['EnzymeID']
enzymeName = singleEnzymeDict['EnzymeName']
genecount = singleEnzymeDict['GeneCount']
enzyme_dict[ec] = [enzymeName,genecount]
return enzyme_dict
def write_concatenated_json(save_dir,jgi_metagenomes):
"""
write single json of all metagenome data
:param save_dir: dir where each single_metagenome_dict.json is saved to
:param jgi_metagenomes: dict of single_metagenome_dicts. Used to write single json.
"""
print "Writing concatenated json to file..."
concatenated_fname = save_dir+'_concatenated.json'
with open(concatenated_fname, 'w') as outfile:
json.dump(jgi_metagenomes,outfile)
print "Done."
## Can i write it so that it scrapes many at a time?
if __name__ == '__main__':
arguments = docopt(__doc__, version='scrape_metagenomes_from_jgi 1.0')
if not os.path.exists(arguments['SAVE_DIR']):
os.makedirs(arguments['SAVE_DIR'])
scrape_metagenomes_from_jgi(arguments['SAVE_DIR'],
homepage_url=arguments['--homepage'],
database=arguments['--database'],
ecosystemClasses=literal_eval(arguments['--ecosystem_classes']),
datatypes=literal_eval(arguments['--datatypes']),
write_concatenated_json=literal_eval((arguments['--write_concatenated_json'])))
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] | 2.707247 | 3,836 |
#!/usr/bin/env python3
from collections import defaultdict, deque
import sys
import re
#from dataclasses import dataclass
if __name__ == '__main__':
sys.exit(main(sys.argv))
| [
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] | 2.857143 | 63 |
from django.shortcuts import render
from django.http import HttpResponse
from fullcalendar.models import CalendarEvent
from fullcalendar.util import events_to_json, calendar_options
# This is just an example for this demo. You may get this value
# from a separate file or anywhere you want
OPTIONS = """{ timeFormat: "H:mm",
header: {
left: 'prev,next today',
center: 'title',
right: 'month,agendaWeek,agendaDay',
},
allDaySlot: false,
firstDay: 0,
weekMode: 'liquid',
slotMinutes: 15,
defaultEventMinutes: 30,
minTime: 8,
maxTime: 20,
editable: false,
dayClick: function(date, allDay, jsEvent, view) {
if (allDay) {
$('#calendar').fullCalendar('gotoDate', date)
$('#calendar').fullCalendar('changeView', 'agendaDay')
}
},
eventClick: function(event, jsEvent, view) {
if (view.name == 'month') {
$('#calendar').fullCalendar('gotoDate', event.start)
$('#calendar').fullCalendar('changeView', 'agendaDay')
}
},
}""" | [
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198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1782,
37811
] | 1.809219 | 781 |
import urllib.request
import urllib.parse
import re
import asyncio
import aiohttp
from lib.base import *
if __name__ == '__main__':
main('hubu.edu.cn')
| [
11748,
2956,
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571,
13,
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198,
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2956,
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571,
13,
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30351,
952,
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952,
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] | 2.711864 | 59 |
""" JAYA Algorithm """
import random
import numpy
from solution import solution
| [
37811,
449,
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1330,
4610,
198
] | 3.857143 | 21 |
"""
News provider for the site rtve.es
Checks specifically the Alzheimer tag. Only checks news, not videos or audios
"""
from datetime import datetime
from textwrap import dedent
from lxml import html
from lxml.cssselect import CSSSelector
import requests
URL = 'http://www.rtve.es/temas/alzheimer/1060/'
SITE = 'rtve.es'
def retrieve(last_updated=datetime.now()):
""" Crawls news and returns a list of tweets to publish. """
print('Retrieving {} alzheimer news since {}.'.format(SITE, last_updated))
to_ret = list()
# Get all the content from the last page of the site's news
tree = html.fromstring(requests.get(URL).content)
# Get list of articles
articles = CSSSelector('article')(tree)
for article in articles:
# For each article parse the date on the metadata and compare to the last update of the bot.
# If the article is newer it should go on until it finds one that's not
link = CSSSelector('article h2 a')(article)[0].get('href')
if "/noticias/" not in link.lower():
continue
news_date = CSSSelector('article time')(article)[0].get('datetime')
news_datetime = datetime.strptime(news_date, '%Y-%m-%d')
if news_datetime < last_updated:
break
# Get the useful parts of each article to compose a tweet.
title_raw = CSSSelector('article .maintitle')(article)[0].text
title = ' '.join(title_raw.split())
# Compose a tweet with the article's information
tweet = """
{title}
Autor/a: RTVE.es
Enlace: {link} ({site})
""".format(title=title, link=link, site=SITE)
to_ret.append(dedent(tweet))
# Returns a list of tweets ready for the bot to tweet.
return to_ret
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284,
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13,
198,
220,
220,
220,
1441,
284,
62,
1186,
198
] | 2.57384 | 711 |
#calculate the extinction between two bands
#from Rieke & Lebofsky 1985 Table 3
import argparse
parser=argparse.ArgumentParser(
prog = 'CalcMIRExtinction',
formatter_class=argparse.RawDescriptionHelpFormatter,
description='''Calculate the MIR extinction and flux ratios between two wavelengths or bands based on Rieke & Lebofsky (1985).
Values are linearly interpolated between those in Table 3 (0.4-13 microns). The code will extrapolate out to 25 microns, but caution should be used!!!
Inputs will be rounded to 0.1 micron.
Band names must match Table 3 of Rieke & Lebofsky (1985).
Examples:
>>python CalcExtinction.py J 30 8.0
>>python CalcExtinction.py J 30 M
>>python CalcExtinction.py 8.0 2.0 J
>>python CalcExtinction.py 3.3 2.0 8.0
Required packages: numpy, scipy''',
epilog='''Author: R. C. Levy ([email protected]) - Last updated: 2020-11-20''')
parser.add_argument('ReferenceWavelength',type=str,help='Wavelength or band to use as the reference with known extinction (microns or band letter)')
parser.add_argument('ReferenceExtinction',type=float,help='Extinction (mag) in the reference band (A_ReferenceBand).')
parser.add_argument('TargetWavelength',type=str,help='Wavelength at which to calculate the extinction and flux ratio (microns or band letter).')
args=parser.parse_args()
import numpy as np
from scipy.interpolate import interp1d
#Rieke & Lebofsky 1985 Table 3
band = ['U','B','V','R','I','J','H','K','L','M','N','8.0','8.5','9.0','9.5','10.0','10.5','11.0','11.5','12.0','12.5','13.0'] #microns or band name
wl = np.array([0.365, 0.445, 0.551, 0.658, 0.806, 1.220, 1.630, 2.190, 3.450, 4.750, 10.50, 8., 8.5, 9., 9.5, 10., 10.5, 11., 11.5, 12., 12.5, 13.]) #microns
ElamV_EBV = np.array([1.64, 1.0, 0.0, -0.78, -1.60, -2.22, -2.55, -2.744, -2.91, -3.02, -2.93, -3.03, -2.96, -2.87, -2.83, -2.86, -2.87, -2.91, -2.95, -2.98, -3.00, -3.01])
Alam_Av = np.array([1.531, 1.324, 1.000, 0.748, 0.482, 0.282, 0.175, 0.112, 0.058, 0.023, 0.052, 0.020, 0.043, 0.074, 0.087, 0.083, 0.074, 0.060, 0.047, 0.037, 0.030, 0.027])
#sort the wavelengths and interpolate
idx_sort = np.argsort(wl)
wl_sort = wl[idx_sort]
Alam_Av_sort = Alam_Av[idx_sort]
interp = interp1d(wl_sort,Alam_Av_sort,fill_value='extrapolate')
#set up grid for interpolated wavelengths
l_step = 0.1
l_start = np.round(wl_sort[0],1)
l_end = 25.
ll = np.round(np.arange(l_start,l_end+l_step,l_step),1) #microns
Alam_Av_interp = interp(ll)
#input reference extinction and band
ref_wl = args.ReferenceWavelength
ref_A = args.ReferenceExtinction #mag
try: #input is a number
ref_wl = np.round(float(args.ReferenceWavelength),1)
ref_wl_str = str(ref_wl)
ref_idx = np.where(ll==ref_wl)[0][0]
ref_AlamAv = Alam_Av_interp[ref_idx]
except (TypeError,ValueError): #input is a string
ref_wl_str = ref_wl
ref_idx = band.index(ref_wl)
ref_AlamAv = Alam_Av[ref_idx]
Av = ref_A/ref_AlamAv #mag
#band in which to compute the extinction
target_wl = args.TargetWavelength
try: #input is a number
target_wl = np.round(float(args.TargetWavelength),1)
target_wl_str = str(target_wl)
target_idx = np.where(ll==target_wl)[0][0]
target_AlamAv = Alam_Av_interp[target_idx]
except (TypeError,ValueError): #input is a string
target_wl_str = target_wl
target_idx = band.index(target_wl)
target_AlamAv = Alam_Av[target_idx]
target_A = target_AlamAv*Av #mag
#convert this extinction in magnitudes to difference in flux
flux_ratio = 10**(-target_A/2.5)
#print the results
if isinstance(target_wl,np.float) and target_wl > 13:
print('Warning: Target wavelength is outside nominal range. Use caution when interpreting these values.\n')
print('Reference extinction: A_'+ref_wl_str+' = %.1f mag' %(ref_A))
print('Target extinction: A_'+target_wl_str+' = %.1f mag' %(target_A))
print('Flux ratio: F_extincted/F_intrinsic = %.3f at %s um' %(flux_ratio,target_wl_str))
| [
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] | 2.485549 | 1,557 |
import json
import os
from io import StringIO
from unittest import TestCase
from unittest.mock import patch
from freezegun import freeze_time
import ruterstop
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from analytic_diffuse import integrators, models, get_model, get_solution
import pytest
import numpy as np
@pytest.mark.parametrize(
'model,kwargs',
[('cosza', {}),
('polydome', {}),
('projgauss', {'a': 0.01}),
('projgauss', {'a': 2}),
('gauss_zenith', {'a': 0.01}),
('gauss_zenith', {'a': 2})
]
)
@pytest.mark.parametrize('a', (0.1, 0.2, 0.25, 0.5)) | [
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] | 2.041667 | 192 |
"""
Copyright 2022 Objectiv B.V.
"""
import pytest
from sql_models.model import escape_format_string
pytestmark = [pytest.mark.db_independent] # mark all tests here as database independent.
def assert_escape_compare_value(value):
""" helper for test__escape_value. Assert that the escaped value, after formatting equals the original"""
escaped = escape_format_string(value)
double_escaped = escape_format_string(value, times=2)
assert escaped.format() == value
assert double_escaped.format().format() == value
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] | 3.36478 | 159 |
number_of_workers = 4
config_SAC = {
# "gamma": 0.5,
# === Model ===
# Use two Q-networks (instead of one) for action-value estimation.
# Note: Each Q-network will have its own target network.
"twin_q": True,
# Use a e.g. conv2D state preprocessing network before concatenating the
# resulting (feature) vector with the action input for the input to
# the Q-networks.
"use_state_preprocessor": False,
# Model options for the Q network(s).
"Q_model": {
"fcnet_activation": "relu",
"fcnet_hiddens": [512, 512, 256, 128],
},
# Model options for the policy function.
"policy_model": {
"fcnet_activation": "tanh",
"fcnet_hiddens": [512, 512, 256, 128],
},
# Unsquash actions to the upper and lower bounds of env's action space.
# Ignored for discrete action spaces.
"normalize_actions": True,
# === Learning ===
# Disable setting done=True at end of episode. This should be set to True
# for infinite-horizon MDPs (e.g., many continuous control problems).
"no_done_at_end": True,
# Update the target by \tau * policy + (1-\tau) * target_policy.
"tau": 5e-2, # found with grid_search([5e-2, 5e-3, 5e-4]), # 5e-3
# Initial value to use for the entropy weight alpha. the higher alpha, the more exploration
"initial_alpha": 1.0,
# Target entropy lower bound. If "auto", will be set to -|A| (e.g. -2.0 for
# Discrete(2), -3.0 for Box(shape=(3,))).
# This is the inverse of reward scale, and will be optimized automatically.
"target_entropy": "auto",
# N-step target updates. If >1, sars' tuples in trajectories will be
# postprocessed to become sa[discounted sum of R][s t+n] tuples.
"n_step": 1,
# Number of env steps to optimize for before returning.
"timesteps_per_iteration": 360,
# === Replay buffer ===
# Size of the replay buffer. Note that if async_updates is set, then
# each worker will have a replay buffer of this size.
"buffer_size": int(100000),
# If True prioritized replay buffer will be used.
# replays first the situations where performance was poor
"prioritized_replay": True,
"prioritized_replay_alpha": 0.6,
"prioritized_replay_beta": 0.4,
"prioritized_replay_eps": 1e-6,
"prioritized_replay_beta_annealing_timesteps": 20000,
"final_prioritized_replay_beta": 1, # 0.4,
# Whether to LZ4 compress observations
"compress_observations": False,
# If set, this will fix the ratio of replayed from a buffer and learned on
# timesteps to sampled from an environment and stored in the replay buffer
# timesteps. Otherwise, the replay will proceed at the native ratio
# determined by (train_batch_size / rollout_fragment_length).
"training_intensity": None,
# === Optimization ===
# "optimization": {
# "actor_learning_rate": 3e-4,# 1e-6, # grid_search([0.0003, 0.0001]), # 3e-4,
# "critic_learning_rate": 3e-4,# 2e-5, # grid_search([0.003, 0.0003]), # 3e-4,
# "entropy_learning_rate": 3e-4,# 1e-3, # grid_search([0.003, 0.0003]), # 3e-4,
# },
# If not None, clip gradients during optimization at this value.
"grad_clip": 0.8,
# How many steps of the model to sample before learning starts.
"learning_starts": 100,
# Update the replay buffer with this many samples at once. Note that this
# setting applies per-worker if num_workers > 1.
"rollout_fragment_length": 1,
# Size of a batched sampled from replay buffer for training. Note that
# if async_updates is set, then each worker returns gradients for a
# batch of this size.
"train_batch_size": 512,
# Update the target network every `target_network_update_freq` steps.
"target_network_update_freq": 360,
# === Parallelism ===
# Whether to use a GPU for local optimization.
"num_gpus": 0,
# Number of workers for collecting samples with. This only makes sense
# to increase if your environment is particularly slow to sample, or if
# you"re using the Async or Ape-X optimizers.
"num_workers": number_of_workers,
# Whether to allocate GPUs for workers (if > 0).
"num_gpus_per_worker": 0,
# Whether to allocate CPUs for workers (if > 0).
"num_cpus_per_worker": 0,
# Whether to compute priorities on workers.
"worker_side_prioritization": False,
# Prevent iterations from going lower than this time span.
"min_iter_time_s": 1,
# Whether the loss should be calculated deterministically (w/o the
# stochastic action sampling step). True only useful for cont. actions and
# for debugging!
"_deterministic_loss": False, # not good, even with continuous actions
# Use a Beta-distribution instead of a SquashedGaussian for bounded,
# continuous action spaces (not recommended, for debugging only).
"_use_beta_distribution": False,
}
config_PPO = {
# Use GPUs if `RLLIB_NUM_GPUS` env var set to > 0.
"num_gpus": 0, # int(os.environ.get("RLLIB_NUM_GPUS", "0")),
# "num_gpus_per_worker": 1,
"num_workers": number_of_workers, # parallelism
"model": {
"custom_model": "my_model",
},
"lr": 1e-2, # grid_search([1e-2, 1e-4, 1e-6]), # try different lrs
# Should use a critic as a baseline (otherwise don't use value baseline;
# required for using GAE).
"use_critic": True,
# If true, use the Generalized Advantage Estimator (GAE)
# with a value function, see https://arxiv.org/pdf/1506.02438.pdf.
"use_gae": True,
# The GAE(lambda) parameter.
"lambda": 1.0,
# Initial coefficient for KL divergence.
"kl_coeff": 0.2,
# Size of batches collected from each worker.
"rollout_fragment_length": 200,
# Number of timesteps collected for each SGD round. This defines the size
# of each SGD epoch.
"train_batch_size": 4000,
# Total SGD batch size across all devices for SGD. This defines the
# minibatch size within each epoch.
"sgd_minibatch_size": 128,
# Whether to shuffle sequences in the batch when training (recommended).
"shuffle_sequences": True,
# Number of SGD iterations in each outer loop (i.e., number of epochs to
# execute per train batch).
"num_sgd_iter": 30,
# Stepsize of SGD.
# Learning rate schedule.
"lr_schedule": None,
# Share layers for value function. If you set this to True, it's important
# to tune vf_loss_coeff.
"vf_share_layers": True,
# Coefficient of the value function loss. IMPORTANT: you must tune this if
# you set vf_share_layers: True.
"vf_loss_coeff": 0.5,
# Coefficient of the entropy regularizer.
"entropy_coeff": 1,
# Decay schedule for the entropy regularizer.
"entropy_coeff_schedule": None,
# PPO clip parameter.
"clip_param": 0.3,
# Clip param for the value function. Note that this is sensitive to the
# scale of the rewards. If your expected V is large, increase this.
"vf_clip_param": 10.0,
# If specified, clip the global norm of gradients by this amount.
"grad_clip": 0.5,
# Target value for KL divergence.
"kl_target": 0.01,
# Whether to rollout "complete_episodes" or "truncate_episodes".
"batch_mode": "truncate_episodes",
# Which observation filter to apply to the observation.
"observation_filter": "NoFilter",
# Uses the sync samples optimizer instead of the multi-gpu one. This is
# usually slower, but you might want to try it if you run into issues with
# the default optimizer.
"simple_optimizer": False,
# Whether to fake GPUs (using CPUs).
# Set this to True for debugging on non-GPU machines (set `num_gpus` > 0).
"_fake_gpus": False,
# Switch on Trajectory View API for PPO by default.
# NOTE: Only supported for PyTorch so far.
"_use_trajectory_view_api": True,
}
config_DDPG = {
# === Twin Delayed DDPG (TD3) and Soft Actor-Critic (SAC) tricks ===
# TD3: https://spinningup.openai.com/en/latest/algorithms/td3.html
# In addition to settings below, you can use "exploration_noise_type" and
# "exploration_gauss_act_noise" to get IID Gaussian exploration noise
# instead of OU exploration noise.
# twin Q-net
"twin_q": True,
# delayed policy update
"policy_delay": 1,
# target policy smoothing
# (this also replaces OU exploration noise with IID Gaussian exploration
# noise, for now)
"smooth_target_policy": True,
# gaussian stddev of target action noise for smoothing
"target_noise": 0.2,
# target noise limit (bound)
"target_noise_clip": 0.5,
# === Evaluation ===
# Evaluate with epsilon=0 every `evaluation_interval` training iterations.
# The evaluation stats will be reported under the "evaluation" metric key.
# Note that evaluation is currently not parallelized, and that for Ape-X
# metrics are already only reported for the lowest epsilon workers.
"evaluation_interval": None,
# Number of episodes to run per evaluation period.
"evaluation_num_episodes": 10,
# === Model ===
# Apply a state preprocessor with spec given by the "model" config option
# (like other RL algorithms). This is mostly useful if you have a weird
# observation shape, like an image. Disabled by default.
"use_state_preprocessor": False,
# Postprocess the policy network model output with these hidden layers. If
# use_state_preprocessor is False, then these will be the *only* hidden
# layers in the network.
"actor_hiddens": [400, 300],
# Hidden layers activation of the postprocessing stage of the policy
# network
"actor_hidden_activation": "relu",
# Postprocess the critic network model output with these hidden layers;
# again, if use_state_preprocessor is True, then the state will be
# preprocessed by the model specified with the "model" config option first.
"critic_hiddens": [400, 300],
# Hidden layers activation of the postprocessing state of the critic.
"critic_hidden_activation": "relu",
# N-step Q learning
"n_step": 1,
# === Exploration ===
"exploration_config": {
# DDPG uses OrnsteinUhlenbeck (stateful) noise to be added to NN-output
# actions (after a possible pure random phase of n timesteps).
"type": "OrnsteinUhlenbeckNoise",
# For how many timesteps should we return completely random actions,
# before we start adding (scaled) noise?
"random_timesteps": 1000,
# The OU-base scaling factor to always apply to action-added noise.
"ou_base_scale": 0.1,
# The OU theta param.
"ou_theta": 0.15,
# The OU sigma param.
"ou_sigma": 0.2,
# The initial noise scaling factor.
"initial_scale": 1.0,
# The final noise scaling factor.
"final_scale": 1.0,
# Timesteps over which to anneal scale (from initial to final values).
"scale_timesteps": 10000,
},
# Number of env steps to optimize for before returning
"timesteps_per_iteration": 1000,
# Extra configuration that disables exploration.
"evaluation_config": {
"explore": False
},
# === Replay buffer ===
# Size of the replay buffer. Note that if async_updates is set, then
# each worker will have a replay buffer of this size.
"buffer_size": 150000,
# If True prioritized replay buffer will be used.
"prioritized_replay": True,
# Alpha parameter for prioritized replay buffer.
"prioritized_replay_alpha": 0.6,
# Beta parameter for sampling from prioritized replay buffer.
"prioritized_replay_beta": 0.4,
# Time steps over which the beta parameter is annealed.
"prioritized_replay_beta_annealing_timesteps": 20000,
# Final value of beta
"final_prioritized_replay_beta": 0.4,
# Epsilon to add to the TD errors when updating priorities.
"prioritized_replay_eps": 1e-6,
# Whether to LZ4 compress observations
"compress_observations": False,
# If set, this will fix the ratio of replayed from a buffer and learned on
# timesteps to sampled from an environment and stored in the replay buffer
# timesteps. Otherwise, the replay will proceed at the native ratio
# determined by (train_batch_size / rollout_fragment_length).
"training_intensity": None,
# === Optimization ===
# Learning rate for the critic (Q-function) optimizer.
"critic_lr": 1e-3,
# Learning rate for the actor (policy) optimizer.
"actor_lr": 1e-3,
# Update the target network every `target_network_update_freq` steps.
"target_network_update_freq": 360,
# Update the target by \tau * policy + (1-\tau) * target_policy
"tau": 0.002,
# If True, use huber loss instead of squared loss for critic network
# Conventionally, no need to clip gradients if using a huber loss
"use_huber": True,
# Threshold of a huber loss
"huber_threshold": 1.0,
# Weights for L2 regularization
"l2_reg": 1e-6,
# If not None, clip gradients during optimization at this value
"grad_clip": None,
# How many steps of the model to sample before learning starts.
"learning_starts": 1500,
# Update the replay buffer with this many samples at once. Note that this
# setting applies per-worker if num_workers > 1.
"rollout_fragment_length": 1,
# Size of a batched sampled from replay buffer for training. Note that
# if async_updates is set, then each worker returns gradients for a
# batch of this size.
"train_batch_size": 512,
# === Parallelism ===
# Number of workers for collecting samples with. This only makes sense
# to increase if your environment is particularly slow to sample, or if
# you're using the Async or Ape-X optimizers.
"num_workers": number_of_workers,
# Whether to compute priorities on workers.
"worker_side_prioritization": False,
# Prevent iterations from going lower than this time span
"min_iter_time_s": 1,
}
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] | 2.830813 | 4,959 |
import requests
import json
import argparse
import sys
import os.path
import urlparse
import urllib
base_url = "http://localhost:8080/catgenome/restapi/{0}/register"
file_types = {
'vcf': "vcf",
'vcf.gz': "vcf",
'gff': "gene",
'gtf': "gene",
'gff.gz': "gene",
'gtf.gz': "gene",
'gff3': "gene",
'gff3.gz': "gene",
'bam':"bam",
'seg':'seg',
'seg.gz':'seg',
'bw':'wig',
'bed':'bed',
'bed.gz':"bed",
'vg': "vg",
'maf': "maf",
'maf.gz': "maf"
}
if __name__ == "__main__":
main() | [
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] | 1.952756 | 254 |
#keithley2400.py
#Controls Keithley 2400 SourceMeter through RS232
#Assumes COM3, 9600 Baud, line feed as termination character
#Uses pyserial
#Dillon Wong 11/03/2018
import serial
import time
#Make decorator?
#Run voltage to 0 V in the event of an emergency.
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] | 3.044944 | 89 |
from KENN2.layers.residual.KnowledgeEnhancer import KnowledgeEnhancer
from KENN2.layers.Kenn import Kenn
from KENN2.layers.RelationalKENN import RelationalKENN
def unary_parser(knowledge_file, activation=lambda x: x, initial_clause_weight=0.5, save_training_data=False, **kwargs):
"""
Takes in input the knowledge file containing only unary clauses and returns a Kenn Layer,
with input the predicates and clauses found in the knowledge file.
:param knowledge_file: path of the prior knowledge file
"""
with open(knowledge_file, 'r') as kb_file:
predicates_string = kb_file.readline()
kb_file.readline()
clauses = kb_file.readlines()
predicates = predicates_string[:-1].split(',')
return Kenn(predicates, clauses, activation, initial_clause_weight, save_training_data, **kwargs)
def unary_parser_ke(knowledge_file, initial_clause_weight=0.5, **kwargs):
"""
Takes in input the knowledge file containing only unary clauses and returns a Knowledge Enhancer layer,
with input the predicates and clauses found in the knowledge file.
:param knowledge_file: path of the prior knowledge file;
"""
with open(knowledge_file, 'r') as kb_file:
predicates_string = kb_file.readline()
kb_file.readline()
clauses = kb_file.readlines()
predicates = predicates_string[:-1].split(',')
return KnowledgeEnhancer(predicates, clauses, initial_clause_weight, **kwargs)
def relational_parser(knowledge_file, activation=lambda x: x, initial_clause_weight=0.5, **kwargs):
"""
Takes in input the knowledge file containing both unary and binary clauses and returns a RelationalKENN
Layer, with input the predicates and clauses found in the knowledge file.
:param knowledge_file: path of the prior knowledge file;
"""
with open(knowledge_file, 'r') as kb_file:
unary_literals_string = kb_file.readline()
binary_literals_string = kb_file.readline()
kb_file.readline()
clauses = kb_file.readlines()
u_groundings = [u + '(x)' for u in unary_literals_string[:-1].split(',')]
b_groundings = [u + '(x)' for u in unary_literals_string[:-1].split(',')] + \
[u + '(y)' for u in unary_literals_string[:-1].split(',')] + \
[b + '(x.y)' for b in binary_literals_string[:-1].split(',')] + \
[b + '(y.x)' for b in binary_literals_string[:-1].split(',')]
unary_clauses = []
binary_clauses = []
reading_unary = True
for clause in clauses:
if clause[0] == '>':
reading_unary = False
continue
if reading_unary:
unary_clauses.append(clause)
else:
binary_clauses.append(clause)
return RelationalKENN(
u_groundings,
b_groundings,
unary_clauses,
binary_clauses,
activation,
initial_clause_weight)
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] | 2.556522 | 1,150 |
#!/usr/bin/env python
# -*- coding: UTF-8 -*-
"""__init__.py: Basic requirements for the module."""
__license__ = "MIT"
__author__ = "José Fonseca"
__copyright__ = "Copyright (c) 2020 José F. R. Fonseca"
# ======================================================================================================================
# IMPORTS
import os
import json
from hashlib import sha1
from copy import deepcopy
from random import shuffle, choice
from pandas import DataFrame
from scipy.stats import kurtosis, skew as skewness
from numpy import array, float64, nan, subtract, power, sum, random, max, min, mean, std, percentile
from symbolic_regression import Individual
# ======================================================================================================================
# PAYLOAD
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38093,
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2,
38444,
35613,
628
] | 3.923445 | 209 |
from typing import Union
import numpy as np
import pandas as pd
genres_cols = [f'feature{i + 1}' for i in range(18)]
def get_control_items(ratings, user_profiles=None, user_ids=None):
"""
Get control items of test users for evaluation purposes.
:param ratings: pd.DataFrame with columns <userId, movieId, rating, timestamp>.
:param user_profiles: Sparse user profiles.
:return: Tuple of pd.DataFrame with test users ratings and dictionary with control items.
"""
if user_profiles is not None:
user_ids = user_profiles.index
control_items = {}
for user_id in user_ids:
user_ratings = ratings[ratings['userId'] == user_id]
recent_index = user_ratings['timestamp'].idxmax()
recent_rating = user_ratings.loc[recent_index]
control_item = recent_rating['movieId'].astype(int)
control_items[user_id] = control_item
if user_profiles is not None:
user_profiles.loc[user_id][str(control_item)] = 0.0
else:
ratings = ratings.drop(recent_index)
return (ratings, control_items) if user_profiles is None else (user_profiles, control_items)
def get_user_profiles(data):
"""
Get sparse matrix with user profiles.
:param data: pd.DataFrame with columns <userId, movieId, rating, timestamp>.
:return: Sparse pd.DataFrame where each row represents a user.
"""
user_profiles = data.pivot_table(index=['userId'], columns=['movieId'], values='rating')
user_profiles.fillna(0, inplace=True)
return user_profiles
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8,
198,
220,
220,
220,
1441,
2836,
62,
5577,
2915,
628
] | 2.656463 | 588 |
"""Autocomplete fields for django-queryset-sequence and django-generic-m2m."""
from dal_genericm2m.fields import GenericM2MFieldMixin
from dal_queryset_sequence.fields import QuerySetSequenceModelMultipleField
class GenericM2MQuerySetSequenceField(GenericM2MFieldMixin,
QuerySetSequenceModelMultipleField):
"""Autocomplete field for GM2MField() for QuerySetSequence choices."""
| [
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] | 2.700637 | 157 |
import numpy as np
def direction_to_tuple(direction):
"""Returns the tuple coordinate offset from the start
hexagon given a certain direction"""
return {
'n' : (0, -1),
'ne' : (1, -1),
'se' : (1, 0),
's' : (0, 1),
'sw' : (-1, 1),
'nw' : (-1, 0)
}[direction]
def hex_distance(a, b):
"""Returns the distance between two hexagons in a grid"""
return (abs(a[0] - b[0])
+ abs(a[0] + a[1] - b[0] - b[1])
+ abs(a[1] - b[1])) / 2
with open('day11_input.txt') as file:
input = file.read()
start = (0, 0)
current = (0, 0)
furthest_steps = 0
for direction in input.split(','):
current = tuple(np.add(current, direction_to_tuple(direction)))
furthest_steps = max(furthest_steps, hex_distance(current, start))
print(hex_distance(current, start))
print(furthest_steps) | [
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11,
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4008,
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1169,
301,
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8
] | 2.209184 | 392 |
import sys
sys.path.insert(0, '../../')
import numpy as np
import time
from sde_gp import SDEGP
import approximate_inference as approx_inf
import priors
import likelihoods
import pickle
from sklearn.preprocessing import StandardScaler
plot_intermediate = False
print('loading data ...')
D = np.loadtxt('../heteroscedastic/mcycle.csv', delimiter=',')
X = D[:, 1:2]
Y = D[:, 2:]
N = X.shape[0]
# Standardize
X_scaler = StandardScaler().fit(X)
y_scaler = StandardScaler().fit(Y)
Xall = X_scaler.transform(X)
Yall = y_scaler.transform(Y)
np.random.seed(123)
if len(sys.argv) > 1:
method = int(sys.argv[1])
fold = 0
else:
method = 0
fold = 0
print('method number', method)
print('batch number', fold)
# Set training and test data
X = Xall
Y = Yall
XT = Xall
YT = Yall
var_f1 = 3. # GP variance
len_f1 = 1. # GP lengthscale
var_f2 = 3. # GP variance
len_f2 = 1. # GP lengthscale
prior1 = priors.Matern32(variance=var_f1, lengthscale=len_f1)
prior2 = priors.Matern32(variance=var_f2, lengthscale=len_f2)
prior = priors.Independent([prior1, prior2])
lik = likelihoods.HeteroscedasticNoise()
step_size = 5e-2
if method == 0:
inf_method = approx_inf.EEP(power=1, damping=0.5)
elif method == 1:
inf_method = approx_inf.EKS(damping=0.5)
elif method == 2:
inf_method = approx_inf.UEP(power=1, damping=0.5)
elif method == 3:
inf_method = approx_inf.UKS(damping=0.5)
elif method == 4:
inf_method = approx_inf.GHEP(power=1, damping=0.5)
elif method == 5:
inf_method = approx_inf.GHKS(damping=0.5)
elif method == 6:
inf_method = approx_inf.EP(power=0.01, intmethod='UT', damping=0.5)
elif method == 7:
inf_method = approx_inf.EP(power=0.01, intmethod='GH', damping=0.5)
elif method == 8:
inf_method = approx_inf.VI(intmethod='UT', damping=0.5)
elif method == 9:
inf_method = approx_inf.VI(intmethod='GH', damping=0.5)
model = SDEGP(prior=prior, likelihood=lik, t=X, y=Y, t_test=XT, y_test=YT, approx_inf=inf_method)
neg_log_marg_lik, gradients = model.run()
print(gradients)
neg_log_marg_lik, gradients = model.run()
print(gradients)
neg_log_marg_lik, gradients = model.run()
print(gradients)
print('optimising the hyperparameters ...')
time_taken = np.zeros([10, 1])
for j in range(10):
t0 = time.time()
neg_log_marg_lik, gradients = model.run()
print(gradients)
t1 = time.time()
time_taken[j] = t1-t0
print('optimisation time: %2.2f secs' % (t1-t0))
time_taken = np.mean(time_taken)
with open("output/heteroscedastic_" + str(method) + ".txt", "wb") as fp:
pickle.dump(time_taken, fp)
# with open("output/heteroscedastic_" + str(method) + ".txt", "rb") as fp:
# time_taken = pickle.load(fp)
# print(time_taken)
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2435,
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] | 2.367133 | 1,144 |
from absl import flags
FLAGS = flags.FLAGS
FLAGS(['train_smac.py'])
| [
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# Copyright (c) 2015, Oliver Jowett <[email protected]>
# Permission to use, copy, modify, and/or distribute this software for any
# purpose with or without fee is hereby granted, provided that the above
# copyright notice and this permission notice appear in all copies.
# THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES
# WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF
# MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR
# ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
# WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
# ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF
# OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
import collectd
import json, math
from contextlib import closing
from urllib2 import urlopen, URLError
import urlparse
import time
V=collectd.Values(host='', plugin='dump1090', time=0)
collectd.register_config(callback=handle_config, name='dump1090')
| [
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] | 3.290625 | 320 |
A, B = input().split(' ')
A = float(A)
B = float(B)
rate = B * 100
result_rate = rate / A
print('{:.2f}%'.format(result_rate-100)) | [
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# -*- coding: utf-8 -*-
"""
-------------------------------------------------
File Name： seven
Description :
Author : joe
date： 2019-07-23
-------------------------------------------------
Change Activity:
2019-07-23:
-------------------------------------------------
"""
# Python 循环语句
# 循环语句允许我们执行一个语句或语句组多次
# Python提供了for循环和while循环（在Python中没有do..while循环）
'''
循环类型 描述
while 循环 在给定的判断条件为 true 时执行循环体，否则退出循环体。
for 循环 重复执行语句
嵌套循环 你可以在while循环体中嵌套for循环
'''
# 循环控制语句
# 循环控制语句可以更改语句执行的顺序。Python支持以下循环控制语句
'''
控制语句 描述
break 语句 在语句块执行过程中终止循环，并且跳出整个循环
continue 语句 在语句块执行过程中终止当前循环，跳出该次循环，执行下一次循环。
pass 语句 pass是空语句，是为了保持程序结构的完整性。
'''
# xrange 0 - 7
for i in xrange(8):
print i
smaller_solutions = [[[1, 4]], [[2, 5]]]
for solution in smaller_solutions:
print solution
for r, c in reversed(solution):
print r, c
print [(1, 1)] + [(2, 2)]
print "========================================"
# * queen problem with recurison
BOARD_SIZE = 8
for answer in solve(BOARD_SIZE):
print answer
print "----------------------------------------"
print "========================================"
# 找出排序数组的索引
print deduplication(5, [1,3,5,6])
# Python While 循环语句
# Python 编程中 while 语句用于循环执行程序，
# 即在某条件下，循环执行某段程序，以处理需要重复处理的相同任务
count = 0
while count < 9:
print 'The count is:', count
count = count + 1
numbers = [12, 37, 5, 42, 8, 3]
even = []
odd = []
while len(numbers) > 0:
number = numbers.pop()
if number % 2 == 0:
even.append(number)
else:
odd.append(number)
print even, odd, numbers
# while 语句时还有另外两个重要的命令 continue，break 来跳过循环，
# continue 用于跳过该次循环，
# break 则是用于退出循环，
# 此外"判断条件"还可以是个常值，表示循环必定成立
i = 1
while i < 10:
i += 1
if i % 2 > 0: # 非双数时跳过输出
continue
print i # 输出双数2、4、6、8、10
i = 1
while 1: # 循环条件为1必定成立
print i # 输出1~10
i += 1
if i > 10: # 当i大于10时跳出循环
break
# 无限循环
# 如果条件判断语句永远为 true，循环将会无限的执行下去
var = 1
while var == 1: # 该条件永远为true，循环将无限执行下去
num = raw_input("Enter a number :")
print "You entered: ", num
# 为了执行下面的代码 强行中端循环
break
print "Good bye!"
# 循环使用 else 语句
# 在 python 中，while … else 在循环条件为 false 时执行 else 语句块：
count = 0
while count < 5:
print count, " is less than 5"
count = count + 1
else:
print count, " is not less than 5"
# 简单语句组
# 类似 if 语句的语法，
# 如果你的 while 循环体中只有一条语句，
# 你可以将该语句与while写在同一行中
flag = 1
# 无线循环 导致下面的代码不能执行，因此注释
# while (flag): print 'Given flag is really true!'
print "Good bye!"
# Python for 循环语句
# Python for循环可以遍历任何序列的项目，如一个列表或者一个字符串
for letter in 'Python': # 第一个实例
print '当前字母 :', letter
fruits = ['banana', 'apple', 'mango']
for fruit in fruits: # 第二个实例
print '当前水果 :', fruit
# 通过序列索引迭代
# 另外一种执行循环的遍历方式是通过索引
fruits = ['banana', 'apple', 'mango']
for index in range(len(fruits)):
print '当前水果 :', fruits[index]
print "Good bye!"
# 以上实例我们使用了内置函数 len() 和 range(),
# 函数 len() 返回列表的长度，即元素的个数。
# range返回一个序列的数。
# 循环使用 else 语句
# 在 python 中，for … else 表示这样的意思，
# for 中的语句和普通的没有区别，
# else 中的语句会在循环正常执行完（即 for 不是通过 break 跳出而中断的）的情况下执行，
# while … else 也是一样
for num in range(10, 20): # 迭代 10 到 20 之间的数字
for i in range(2, num): # 根据因子迭代
if num % i == 0: # 确定第一个因子
j = num / i # 计算第二个因子
print '%d 等于 %d * %d' % (num, i, j)
break # 跳出当前循环
else: # 循环的 else 部分
print num, '是一个质数'
# Python 循环嵌套
# Python 语言允许在一个循环体里面嵌入另一个循环
# 你可以在循环体内嵌入其他的循环体，如在while循环中可以嵌入for循环
# 反之，你可以在for循环中嵌入while循环
i = 2
while(i < 100):
j = 2
while(j <= (i/j)):
if not(i%j): break
j = j + 1
if (j > i/j) : print i, " 是素数"
i = i + 1
# Python break 语句
# Python break语句，就像在C语言中，打破了最小封闭for或while循环。
# break语句用来终止循环语句，即循环条件没有False条件或者序列还没被完全递归完，也会停止执行循环语句。
# break语句用在while和for循环中。
# 如果您使用嵌套循环，break语句将停止执行最深层的循环，并开始执行下一行代码
for letter in 'Python': # 第一个实例
if letter == 'h':
break
print '当前字母 :', letter
var = 10 # 第二个实例
while var > 0:
print '当前变量值 :', var
var = var - 1
if var == 5: # 当变量 var 等于 5 时退出循环
break
# Python continue 语句
# Python continue 语句跳出本次循环，而break跳出整个循环。
# continue 语句用来告诉Python跳过当前循环的剩余语句，然后继续进行下一轮循环。
# continue语句用在while和for循环中
for letter in 'Python': # 第一个实例
if letter == 'h':
continue
print '当前字母 :', letter
var = 10 # 第二个实例
while var > 0:
var = var - 1
if var == 5:
continue
print '当前变量值 :', var
# 只打印0-10之间的奇数，可以用continue语句跳过某些循环
n = 0
while n < 10:
n += 1
if n % 2 == 0:
continue
print n
# Python pass 语句
# Python pass 是空语句，是为了保持程序结构的完整性。
# pass 不做任何事情，一般用做占位语句
# 输出 Python 的每个字母
for letter in 'Python':
if letter == 'h':
pass
print '这是 pass 块'
print '当前字母 :', letter
# 在 Python 中有时候会看到一个 def 函数:
# 该处的 pass 便是占据一个位置，因为如果定义一个空函数程序会报错，
# 当你没有想好函数的内容是可以用 pass 填充，使程序可以正常运行。
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] | 1.183502 | 4,158 |
from rest_framework.generics import ListCreateAPIView
from utils.rest_framework.generics import RetrieveUpdateDestroy
from apps.shop import models as shop_models
from apps.shop.rest import serializers as shop_serializers
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from django.shortcuts import get_object_or_404
from gbe.forms import ProfileAdminForm
from gbe.functions import validate_perms
from gbe.models import (
Profile,
UserMessage,
)
from gbetext import admin_note
from gbe.views import EditProfileView
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# Copyright 2013-2020 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
from spack import *
class PyPyrsistent(PythonPackage):
"""Pyrsistent is a number of persistent collections
(by some referred to as functional data structures).
Persistent in the sense that they are immutable."""
homepage = "http://github.com/tobgu/pyrsistent/"
url = "https://pypi.io/packages/source/p/pyrsistent/pyrsistent-0.15.7.tar.gz"
version('0.15.7', sha256='cdc7b5e3ed77bed61270a47d35434a30617b9becdf2478af76ad2c6ade307280')
depends_on('[email protected]:2.8,3.5:', type=('build', 'run'))
depends_on('py-setuptools', type='build')
depends_on('py-six', type=('build', 'run'))
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] | 2.644231 | 312 |
"""
Write a program to prompt the user for hours and rate per hour using input to compute gross pay.
Use 35 hours and a rate of 2.75 per hour to test the program (the pay should be 96.25).
You should use input to read a string and float() to convert the string to a number.
Do not worry about error checking or bad user data.
"""
# This first line is provided for you
hrs = float(input("Enter Hours: "))
rate = float(input("Enter Rate Per Hour: "))
print("Pay: {}".format(hrs*rate))
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] | 3.554745 | 137 |
# provides *examples* of add adding node attributes using the python API
# hence does *not* represent a real suite definition
from ecflow import *
if __name__ == "__main__":
# adding variables
suite = Suite("s1");
suite.add_variable(Variable("ECF_HOME", "/tmp/"))
suite.add_variable("ECF_URL_CMD", "${BROWSER:=firefox} -remote 'openURL(%ECF_URL_BASE%/%ECF_URL%)'")
suite.add_variable("NAME", 10)
a_dict = { "name":"value", "name2":"value2", "name3":"value3", "name4":"value4" }
suite.add_variable(a_dict)
# adding limits
suite.add_limit( Limit("limitName1", 10) )
suite.add_limit( "limitName3", 10 )
# adding inlimits
suite.add_inlimit( InLimit("limitName1", "/s1/f1", 2) )
suite.add_inlimit( "limitName3", "/s1/f1", 2)
# add short triggers and complete
task = Task("task")
task.add_trigger( "t2 == active" )
task.add_complete( "t2 == complete" )
# add long triggers and complete, in example below 'True' mean AND and 'False' means OR
task = Task("trigger")
task.add_part_trigger( "t1 == complete" )
task.add_part_trigger( "t2 == active", True ) # for long and/or expressions, subsequent expr must be and/or
task.add_part_complete( "t3 == complete" )
task.add_part_complete( "t4 == active", False) # for long and/or expressions, subsequent expr must be and/or
# add events
task.add_event( Event(1) )
task.add_event( 2 )
task.add_event( Event(10, "Eventname") )
task.add_event( 10, "Eventname2" )
task.add_event( "fred" )
# add meter
task.add_meter( Meter("metername1", 0, 100, 50) )
task.add_meter( "metername3", 0, 100 )
# add label
task.add_label( Label("label_name1", "value") )
task.add_label( "label_name3", "value" )
# add Repeat. A node can only have one repeat, hence we delete the repeat before, adding another
task.add_repeat( RepeatInteger("integer", 0, 100, 2) )
task.delete_repeat()
task.add_repeat( RepeatEnumerated("enum", ["red", "green", "blue" ] ) )
task.delete_repeat()
task.add_repeat( RepeatDate("date", 20100111, 20100115, 2) )
task.delete_repeat()
task.add_repeat( RepeatString("string", ["a", "b", "c" ] ) )
task.delete_repeat()
# create a time series, used for adding time and today and cron
start = TimeSlot(0, 0)
finish = TimeSlot(23, 0)
incr = TimeSlot(0, 30)
time_series = TimeSeries( start, finish, incr, True) # True means relative to suite start
# add a today
task.add_today( "00:30" )
task.add_today( "+00:30" )
task.add_today( "+00:30 20:00 01:00" )
task.add_today( Today( time_series) )
task.add_today( Today( 0, 10 ))
task.add_today( 0, 59, True )
task.add_today( Today(TimeSlot(20, 10)) )
task.add_today( Today(TimeSlot(20, 20), False))
# add time
task.add_time( "00:30" )
task.add_time( "+00:30" )
task.add_time( "+00:30 20:00 01:00" )
task.add_time( Time(time_series ))
task.add_time( Time( 0, 10 ))
task.add_time( 0, 59, True)
task.add_time( Time(TimeSlot(20, 10)) )
task.add_time( Time(TimeSlot(20, 20), False))
# add date
for i in [ 1, 2, 4, 8, 16 ] :
task.add_date( i, 0, 0) # day,month,year, where corresponding 0 means any possible day,month, year
task.add_date( Date(1, 1, 2010))
# add day
task.add_day( Day(Days.sunday))
task.add_day( Days.monday)
task.add_day( "tuesday")
# create cron, showing different ways adding the time, to a cron attribute
cron = Cron()
cron.set_week_days( [0, 1, 2, 3, 4, 5, 6] )
cron.set_days_of_month( [1, 2, 3, 4, 5, 6] )
cron.set_months( [1, 2, 3, 4, 5, 6] )
start = TimeSlot(0, 0)
finish = TimeSlot(23, 0)
incr = TimeSlot(0, 30)
ts = TimeSeries( start, finish, incr, True) # True means relative to suite start
cron.set_time_series( ts )
cron1 = Cron()
cron1.set_week_days( [0, 1, 2, 3, 4, 5, 6] )
cron1.set_time_series( 1, 30, True )
cron2 = Cron()
cron2.set_week_days( [0, 1, 2, 3, 4, 5, 6] )
cron2.set_time_series( "00:30 01:30 00:01" )
cron3 = Cron()
cron3.set_week_days( [0, 1, 2, 3, 4, 5, 6] )
cron3.set_time_series( "+00:30" )
# add auto cancel
t1 = Task("t1");
t3 = Task("t3")
t4 = Task("t4")
t5 = Task("t5")
t1.add_autocancel( 3 ) # 3 days
t3.add_autocancel( 20, 10, True ) # hour,minutes,relative
t4.add_autocancel( TimeSlot(10, 10), True ) # hour,minutes,relative
t5.add_autocancel( Autocancel(1, 10, True) ) # hour,minutes,relative
# add late
late = Late()
late.submitted( TimeSlot(20, 10))
late.active( TimeSlot(20, 10))
late.complete( TimeSlot(20, 10), True)
task.add_late( late )
late = Late()
late.submitted( 20, 10 )
late.active( 20, 10 )
late.complete( 20, 10, True)
t1.add_late( late )
# add defstatus, last one set takes effect
task.add_defstatus( DState.complete )
task.add_defstatus( DState.queued )
task.add_defstatus( DState.aborted )
task.add_defstatus( DState.submitted )
task.add_defstatus( DState.suspended )
task.add_defstatus( DState.active )
# add clock
clock = Clock(1, 1, 2010, False) # day,month, year, hybrid(true), real(False)
clock.set_gain(1, 10, True) # True means positive gain
suite = Suite("suite")
suite.add_clock(clock)
clock = Clock(False) # Use the current time and date with real time clock
clock.set_gain_in_seconds(12, True)
s1 = Suite("s1")
s1.add_clock(clock) | [
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] | 2.259623 | 2,546 |
'''
OnionPerf
Authored by Rob Jansen, 2015
See LICENSE for licensing information
'''
import sys, os, socket, logging, random, re, shutil, datetime, urllib.request, urllib.parse, urllib.error, gzip, lzma
from threading import Lock
from io import StringIO
from abc import ABCMeta, abstractmethod
LINEFORMATS = "k-,r-,b-,g-,c-,m-,y-,k--,r--,b--,g--,c--,m--,y--,k:,r:,b:,g:,c:,m:,y:,k-.,r-.,b-.,g-.,c-.,m-.,y-."
def find_ip_address_url(data):
"""
Parses a string using a regular expression for identifying IPv4 addressses.
If more than one IP address is found, only the first one is returned.
If no IP address is found, the function returns None .
:param data: string
:returns: string
"""
ip_address = None
if data is not None and len(data) > 0:
ip_list = re.findall(r'[\d]{1,3}\.[\d]{1,3}\.[\d]{1,3}\.[\d]{1,3}', data)
if ip_list is not None and len(ip_list) > 0:
ip_address = ip_list[0]
return ip_address
def find_ip_address_local():
"""
Determines the local IP address of the host by opening a socket
connection to an external address. In doing so, the address used by the
host for initiating connections can be retrieved and then returned.
:returns: string
"""
s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
s.connect(("8.8.8.8", 53))
ip_address = s.getsockname()[0]
s.close()
return ip_address
def get_ip_address():
"""
Determines the public IPv4 address of the vantage point using the
check.torproject.org service. If it is not possible to reach the service,
or to parse the result recieved, it will fall back to determining the local
IP address used for outbound connections.
:returns: string
"""
ip_address = None
try:
data = urllib.request.urlopen('https://check.torproject.org/').read().decode('utf-8')
ip_address = find_ip_address_url(data)
if not ip_address:
logging.error(
"Unable to determine IP address from check.torproject.org. "
"The site was successfully contacted but the result could "
"not be parsed. Maybe the service is down? Falling back to "
"finding your IP locally...")
ip_address = find_ip_address_local()
except IOError:
logging.warning(
"An IOError occured attempting to contact check.torproject.org. "
"This will affect measurements unless your machine has a public "
"IP address. Falling back to finding your IP locally...")
ip_address = find_ip_address_local()
return ip_address
def get_random_free_port():
"""
Picks a random high port and checks its availability by opening a
socket connection to localhost on this port. If this raises an exception
the process is repeated until a free port is found.
:returns: int
"""
while True:
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
port = random.randint(10000, 60000)
rc = s.connect_ex(('127.0.0.1', port))
s.close()
if rc != 0: # error connecting, port is available
return port
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] | 2.562249 | 1,245 |
# Copyright 2019-2021 Huawei Technologies Co., Ltd
#
# 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.
"""reduce_max_run"""
import numpy as np
from tests.common.tensorio import compare_tensor
from akg.utils import kernel_exec as utils
from akg.ops.math import ReduceMax
from akg.utils.dsl_create import get_reduce_out_shape
from tests.common.gen_random import random_gaussian
from tests.common.base import get_rtol_atol
from akg.utils.result_analysis import target_profiling
from akg.utils.format_transform import to_tvm_nd_array
def reduce_max_run(shape, dtype, axis, keepdims, kernel_name="reduce_max", attrs=None):
"""run function for dsl function reduce_max"""
if attrs is None:
attrs = {}
op_attrs = [axis, keepdims]
if 'tuning' in attrs.keys():
t = attrs.get("tuning", False)
kernel_name = attrs.get("kernel_name", False)
mod = utils.op_build_test(ReduceMax, [shape], [dtype],
op_attrs=op_attrs, kernel_name=kernel_name, attrs=attrs, tuning=t)
if t:
expect, inputs, output = gen_data(axis, dtype, keepdims, shape)
return mod, expect, (inputs, output)
return mod
mod = utils.op_build_test(ReduceMax, [shape], [dtype],
op_attrs=op_attrs, kernel_name=kernel_name, attrs=attrs)
expect, inputs, output = gen_data(axis, dtype, keepdims, shape)
output = utils.mod_launch(mod, (inputs, output), expect=expect)
rtol, atol = get_rtol_atol("reduce_max", dtype)
if attrs.get("profiling", False):
import akg
target_name = attrs["target"].split()[0]
args_list = to_tvm_nd_array([inputs, output], akg.tvm.context(target_name, 0))
target_profiling(mod, *args_list, target=target_name, repeat_time=attrs["repeat_times"])
return inputs, output, expect, compare_tensor(output, expect, rtol=rtol, atol=atol, equal_nan=True)
def gen_data(axis, dtype, keepdims, shape):
"""Generates input, output and expect data."""
inputs = random_gaussian(shape, miu=0, sigma=100.0).astype("float16").astype(dtype.lower())
expect = np.amax(inputs, axis=axis, keepdims=keepdims)
if axis == None and keepdims == False:
expect = np.broadcast_to(expect, (1,))
out_shape = get_reduce_out_shape(shape, axis=axis, keepdims=keepdims)
output = np.full(out_shape, np.nan, dtype)
return expect, inputs, output
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] | 2.56343 | 1,143 |
import datetime
from pathlib import Path
import pytest
from blackbox.exceptions import MissingFields
from blackbox.handlers.databases import Redis
@pytest.fixture
def mock_valid_redis_config():
"""Mock valid Redis config."""
return {"password": "citrus", "host": "localhost", "port": "5432",
"id": "main_redis", }
@pytest.fixture
def mock_invalid_redis_config():
"""Mock invalid Redis config."""
return {"password": "limoncello"}
def test_can_be_instantiated_with_required_fields(mock_valid_redis_config):
"""Test if the redis database handler can be instantiated."""
Redis(**mock_valid_redis_config)
def test_fails_without_required_fields(mock_invalid_redis_config):
"""Test if the redis database handler cannot be instantiated with missing fields."""
with pytest.raises(MissingFields):
Redis(**mock_invalid_redis_config)
def test_redis_backup(mock_valid_redis_config, fake_process):
"""Test if the redis database handler executes a backup."""
redis = Redis(**mock_valid_redis_config)
date = datetime.date.today().strftime("%d_%m_%Y")
backup_path = Path.home() / f"main_redis_blackbox_{date}.rdb"
command_to_run = [
f"redis-cli -h {redis.config.get('host')} -p {redis.config.get('port')} --rdb {backup_path}"
]
fake_process.register_subprocess(
command_to_run, stdout=["redis backup"]
)
res = redis.backup()
assert res == backup_path
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] | 2.616487 | 558 |
# -*- coding: utf-8 -*-
#
# Copyright (c) 2019 SUNET
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or
# without modification, are permitted provided that the following
# conditions are met:
#
# 1. Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# 2. 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.
# 3. Neither the name of the SUNET 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 typing import Any, Mapping, Optional, cast
from flask import current_app
from eduid_common.api import am, mail_relay, msg, translation
from eduid_common.api.am import AmRelay
from eduid_common.api.app import EduIDBaseApp
from eduid_common.api.mail_relay import MailRelay
from eduid_common.api.msg import MsgRelay
from eduid_common.config.base import FlaskConfig
from eduid_common.config.parsers import load_config
from eduid_userdb.authninfo import AuthnInfoDB
from eduid_userdb.logs import ProofingLog
from eduid_userdb.reset_password import ResetPasswordStateDB, ResetPasswordUserDB
from eduid_webapp.reset_password.settings.common import ResetPasswordConfig
__author__ = 'eperez'
current_reset_password_app: ResetPasswordApp = cast(ResetPasswordApp, current_app)
def init_reset_password_app(
name: str = 'reset_password', test_config: Optional[Mapping[str, Any]] = None
) -> ResetPasswordApp:
"""
:param name: The name of the instance, it will affect the configuration loaded.
:param test_config: Override config. Used in tests.
"""
config = load_config(typ=ResetPasswordConfig, app_name=name, ns='webapp', test_config=test_config)
app = ResetPasswordApp(config)
app.logger.info(f'Init {app}...')
# Register views
from eduid_webapp.reset_password.views.reset_password import reset_password_views
app.register_blueprint(reset_password_views)
translation.init_babel(app)
return app
| [
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65,
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628,
220,
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598,
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] | 3.16546 | 967 |
from django_ical.views import ICalFeed
from events.models import Event
from events import utils
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from ..remote import RemoteModel
from infoblox_netmri.utils.utils import check_api_availability
class SettingsAuthAuditLogGridRemote(RemoteModel):
"""
| ``id:`` none
| ``attribute type:`` string
| ``created_at:`` none
| ``attribute type:`` string
| ``user_name:`` none
| ``attribute type:`` string
| ``event_type:`` none
| ``attribute type:`` string
| ``message:`` none
| ``attribute type:`` string
| ``field_changes:`` none
| ``attribute type:`` string
| ``field_changes_exportable:`` none
| ``attribute type:`` string
| ``client_ip:`` none
| ``attribute type:`` string
| ``DeviceID:`` none
| ``attribute type:`` string
"""
properties = ("id",
"created_at",
"user_name",
"event_type",
"message",
"field_changes",
"field_changes_exportable",
"client_ip",
"DeviceID",
)
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] | 1.938879 | 589 |
# Generated by Django 3.1.3 on 2020-12-13 13:48
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
import uuid
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] | 3.017857 | 56 |
from cyder.base.tests import ModelTestMixin, TestCase
from cyder.cydhcp.vlan.models import Vlan
| [
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] | 3.03125 | 32 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
update_mullvad.py
Description of update_mullvad.py.
"""
import requests
import platform
from bs4 import BeautifulSoup
from pprint import pprint
from urllib.parse import urlparse
from urllib.parse import urljoin
def main():
"""docstring for main"""
url = "https://mullvad.net/en/download/"
base_url = "https://mullvad.net"
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')
os_string = platform.system()
downloads = soup.findAll("a", {"class": "download-button"})
download_links = {}
for link in downloads:
if link["href"].endswith(".exe"):
download_links["windows"] = urljoin(base_url, link["href"])
elif link["href"].endswith(".pkg"):
download_links["osx"] = urljoin(base_url, link["href"])
elif link["href"].endswith(".deb"):
download_links["debian"] = urljoin(base_url, link["href"])
elif link["href"].endswith(".rpm"):
download_links["fedora"] = urljoin(base_url, link["href"])
elif link["href"].endswith(".apk"):
download_links["android"] = urljoin(base_url, link["href"])
pprint(download_links)
if __name__ == '__main__':
main()
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198
] | 2.394687 | 527 |
# coding: utf-8
"""
Hydrogen Integration API
The Hydrogen Integration API # noqa: E501
OpenAPI spec version: 1.2.1
Contact: [email protected]
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
import pprint
import re # noqa: F401
import six
class Identification(object):
"""NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
"""
"""
Attributes:
swagger_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
swagger_types = {
'country_of_issue': 'str',
'doc_number': 'str',
'doc_type': 'str',
'expiry_date': 'datetime',
'issue_date': 'datetime',
'issuing_authority': 'str',
'state_of_issue': 'str'
}
attribute_map = {
'country_of_issue': 'country_of_issue',
'doc_number': 'doc_number',
'doc_type': 'doc_type',
'expiry_date': 'expiry_date',
'issue_date': 'issue_date',
'issuing_authority': 'issuing_authority',
'state_of_issue': 'state_of_issue'
}
def __init__(self, country_of_issue=None, doc_number=None, doc_type=None, expiry_date=None, issue_date=None, issuing_authority=None, state_of_issue=None): # noqa: E501
"""Identification - a model defined in Swagger""" # noqa: E501
self._country_of_issue = None
self._doc_number = None
self._doc_type = None
self._expiry_date = None
self._issue_date = None
self._issuing_authority = None
self._state_of_issue = None
self.discriminator = None
if country_of_issue is not None:
self.country_of_issue = country_of_issue
if doc_number is not None:
self.doc_number = doc_number
if doc_type is not None:
self.doc_type = doc_type
if expiry_date is not None:
self.expiry_date = expiry_date
if issue_date is not None:
self.issue_date = issue_date
if issuing_authority is not None:
self.issuing_authority = issuing_authority
if state_of_issue is not None:
self.state_of_issue = state_of_issue
@property
def country_of_issue(self):
"""Gets the country_of_issue of this Identification. # noqa: E501
:return: The country_of_issue of this Identification. # noqa: E501
:rtype: str
"""
return self._country_of_issue
@country_of_issue.setter
def country_of_issue(self, country_of_issue):
"""Sets the country_of_issue of this Identification.
:param country_of_issue: The country_of_issue of this Identification. # noqa: E501
:type: str
"""
self._country_of_issue = country_of_issue
@property
def doc_number(self):
"""Gets the doc_number of this Identification. # noqa: E501
:return: The doc_number of this Identification. # noqa: E501
:rtype: str
"""
return self._doc_number
@doc_number.setter
def doc_number(self, doc_number):
"""Sets the doc_number of this Identification.
:param doc_number: The doc_number of this Identification. # noqa: E501
:type: str
"""
self._doc_number = doc_number
@property
def doc_type(self):
"""Gets the doc_type of this Identification. # noqa: E501
:return: The doc_type of this Identification. # noqa: E501
:rtype: str
"""
return self._doc_type
@doc_type.setter
def doc_type(self, doc_type):
"""Sets the doc_type of this Identification.
:param doc_type: The doc_type of this Identification. # noqa: E501
:type: str
"""
self._doc_type = doc_type
@property
def expiry_date(self):
"""Gets the expiry_date of this Identification. # noqa: E501
:return: The expiry_date of this Identification. # noqa: E501
:rtype: datetime
"""
return self._expiry_date
@expiry_date.setter
def expiry_date(self, expiry_date):
"""Sets the expiry_date of this Identification.
:param expiry_date: The expiry_date of this Identification. # noqa: E501
:type: datetime
"""
self._expiry_date = expiry_date
@property
def issue_date(self):
"""Gets the issue_date of this Identification. # noqa: E501
:return: The issue_date of this Identification. # noqa: E501
:rtype: datetime
"""
return self._issue_date
@issue_date.setter
def issue_date(self, issue_date):
"""Sets the issue_date of this Identification.
:param issue_date: The issue_date of this Identification. # noqa: E501
:type: datetime
"""
self._issue_date = issue_date
@property
def issuing_authority(self):
"""Gets the issuing_authority of this Identification. # noqa: E501
:return: The issuing_authority of this Identification. # noqa: E501
:rtype: str
"""
return self._issuing_authority
@issuing_authority.setter
def issuing_authority(self, issuing_authority):
"""Sets the issuing_authority of this Identification.
:param issuing_authority: The issuing_authority of this Identification. # noqa: E501
:type: str
"""
self._issuing_authority = issuing_authority
@property
def state_of_issue(self):
"""Gets the state_of_issue of this Identification. # noqa: E501
:return: The state_of_issue of this Identification. # noqa: E501
:rtype: str
"""
return self._state_of_issue
@state_of_issue.setter
def state_of_issue(self, state_of_issue):
"""Sets the state_of_issue of this Identification.
:param state_of_issue: The state_of_issue of this Identification. # noqa: E501
:type: str
"""
self._state_of_issue = state_of_issue
def to_dict(self):
"""Returns the model properties as a dict"""
result = {}
for attr, _ in six.iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
if issubclass(Identification, dict):
for key, value in self.items():
result[key] = value
return result
def to_str(self):
"""Returns the string representation of the model"""
return pprint.pformat(self.to_dict())
def __repr__(self):
"""For `print` and `pprint`"""
return self.to_str()
def __eq__(self, other):
"""Returns true if both objects are equal"""
if not isinstance(other, Identification):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Returns true if both objects are not equal"""
return not self == other
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] | 2.24614 | 3,433 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Copyright (c) 2014-2016 pocsuite developers (https://seebug.org)
See the file 'docs/COPYING' for copying permission
"""
import os
import time
import shutil
import tempfile
from textwrap import dedent
from pocsuite.lib.core.settings import REPORT_HTMLBASE
from pocsuite.lib.core.settings import REPORT_TABLEBASE
from pocsuite.lib.core.data import paths
from pocsuite.lib.core.exception import PocsuiteSystemException
from pocsuite.lib.core.exception import PocsuiteMissingPrivileges
from pocsuite.lib.core.common import getUnicode
from pocsuite.lib.core.common import reIndent
from pocsuite.lib.core.common import normalizeUnicode
from pocsuite.lib.core.data import logger
from pocsuite.lib.core.data import conf
from pocsuite.lib.core.data import kb
from pocsuite.lib.core.enums import CUSTOM_LOGGING
from pocsuite.lib.core.handlejson import execReq
from pocsuite.lib.core.threads import runThreads
from pocsuite.thirdparty.prettytable.prettytable import PrettyTable
def pocThreads():
"""
@function multiThread executing
"""
kb.pCollect = set()
while not kb.targets.empty() and kb.threadContinue:
target, poc, pocname = kb.targets.get()
infoMsg = "poc:'%s' target:'%s'" % (pocname, target)
logger.log(CUSTOM_LOGGING.SYSINFO, infoMsg)
# TODO json
if isinstance(poc, dict):
pocInfo = poc['pocInfo']
result = execReq(poc, conf.mode, target)
output = (target, pocname, pocInfo["vulID"], pocInfo["appName"], pocInfo["appVersion"], "success" if result else "failed", time.strftime("%Y-%m-%d %X", time.localtime()), str(result.result))
else:
kb.pCollect.add(poc.__module__)
result = poc.execute(target, headers=conf.httpHeaders, mode=conf.mode, params=conf.params, verbose=True)
if not result:
continue
result_error = "Error: {}".format(result.error[1]) if result.error[1] else "failed"
output = (target, pocname, result.vulID, result.appName, result.appVersion, "success" if result.is_success() else result_error, time.strftime("%Y-%m-%d %X", time.localtime()), str(result.result))
result.show_result()
kb.results.add(output)
if isinstance(conf.delay, (int, float)) and conf.delay > 0:
time.sleep(conf.delay / 1000)
def _createTargetDirs():
"""
Create the output directory.
"""
if not os.path.isdir(paths.POCSUITE_OUTPUT_PATH):
try:
if not os.path.isdir(paths.POCSUITE_OUTPUT_PATH):
os.makedirs(paths.POCSUITE_OUTPUT_PATH, 0755)
warnMsg = "using '%s' as the output directory" % paths.POCSUITE_OUTPUT_PATH
logger.log(CUSTOM_LOGGING.WARNING, warnMsg)
except (OSError, IOError), ex:
try:
tempDir = tempfile.mkdtemp(prefix="pocsuiteoutput")
except Exception, _:
errMsg = "unable to write to the temporary directory ('%s'). " % _
errMsg += "Please make sure that your disk is not full and "
errMsg += "that you have sufficient write permissions to "
errMsg += "create temporary files and/or directories"
raise PocsuiteSystemException(errMsg)
warnMsg = "unable to create regular output directory "
warnMsg += "'%s' (%s). " % (paths.POCSUITE_OUTPUT_PATH, getUnicode(ex))
warnMsg += "Using temporary directory '%s' instead" % getUnicode(tempDir)
logger.log(CUSTOM_LOGGING.WARNING, warnMsg)
paths.POCUSITE_OUTPUT_PATH = tempDir
| [
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628,
198
] | 2.345291 | 1,561 |
import os, django
import sys
path = os.path.dirname(os.path.abspath(os.path.dirname(os.path.abspath(__file__))))
sys.path.append(path)
# print(sys.path)
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "project.settings")
django.setup()
import requests
import datetime
from state import models
import requests
import json
from urllib.request import Request, urlopen
import pandas as pd
if __name__ == '__main__':
main()
state()
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220,
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3419,
201,
198,
220,
220,
220,
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] | 2.384236 | 203 |
from .direction_mapper import DirectionMapper
from .key_mapper import KeyMapper
from .mouse_handler import MouseHandler
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] | 3.870968 | 31 |
# tree
| [
2,
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] | 2.666667 | 3 |
import qrcode
from django import template
register = template.Library()
# qr = generateQR()
import cv2
import time
@register.simple_tag(name='scan')
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] | 2.962963 | 54 |
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
# --------------------------------------------------------------------------
import os
import random
import string
from azure.identity import DefaultAzureCredential
from azure.mgmt.compute import ComputeManagementClient
from azure.mgmt.network import NetworkManagementClient
from azure.mgmt.resource import ResourceManagementClient
if __name__ == "__main__":
main()
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] | 4.787402 | 127 |
import os
app_key = os.getenv('HL3_APP_KEY')
app_secret = os.getenv('HL3_APP_SECRET')
access_token = os.getenv('HL3_ACCESS_TOKEN')
refresh_token = os.getenv('HL3_REFRESH_TOKEN')
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] | 2.265823 | 79 |
import requests
if __name__ == "__main__":
movielist = input("Enter movies seprated by comma(,) - ").split(",")
recommendation_limit = input("Enter the limit of of recommended movies - ")
recommended_Movies = get_sorted_recommendations(movielist, recommendation_limit)
for movies in recommended_Movies:
print(f"{recommended_Movies.index(movies)+1}. {movies}")
| [
11748,
7007,
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198
] | 2.925373 | 134 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 27 11:00:02 2019
@author: marzio
"""
from tensorflow import keras
from tensorflow.keras import utils as tfku
import numpy as np
import os
import glob
import pandas as pd
import talos
from CryptoNet import mnist_model
from CryptoNet import util
verbosity = True
lr = []
for i in range(10):
lr.append(0.01)
p = {'last_activation': ['softmax'],
'optimizer': ['SGD'],
'loss': ['categorical_crossentropy'],
'batch_size': [200],
'epochs': [50],
'dropout': [0.1, 0.2, 0.3, 0.4, 0.5],
'learning_rate': lr}
exp = Experiment(verbose = True, params = p, name = 'DenseDropout01')
#exp.plotResults(exp.run())
#exp.plotResults(None, 'BestEvaluation03/113019213503.csv')
#exp.computeResult()
exp.plotResults(None, "all_results/final.csv")
| [
2,
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] | 2.264103 | 390 |
from __future__ import unicode_literals
import logging
from xml.dom.minidom import parseString
from django import forms
from django.utils import six
from django.utils.six.moves.urllib.error import HTTPError, URLError
from django.utils.translation import ugettext_lazy as _, ugettext
from reviewboard.hostingsvcs.errors import (AuthorizationError,
HostingServiceAPIError,
RepositoryError)
from reviewboard.hostingsvcs.forms import (HostingServiceAuthForm,
HostingServiceForm)
from reviewboard.hostingsvcs.service import (HostingService,
HostingServiceClient)
from reviewboard.scmtools.crypto_utils import (decrypt_password,
encrypt_password)
from reviewboard.scmtools.errors import FileNotFoundError
class CodebaseHQClient(HostingServiceClient):
"""Client for talking to the Codebase API.
This implements the API methods that the hosting service needs, converting
requests into API calls and those back into structured results.
"""
#: Mimetype used for API requests and responses.
API_MIMETYPE = 'application/xml'
def __init__(self, hosting_service):
"""Initialize the client.
Args:
hosting_service (CodebaseHQ):
The hosting service that owns this client.
"""
self.hosting_service = hosting_service
def api_get_file(self, repository, project_name, repo_name, path,
revision):
"""Return the content of a file in a repository.
Args:
repository (reviewboard.scmtools.models.Repository):
The repository entry in Review Board.
project_name (unicode):
The name of the Codebase project.
repo_name (unicode):
The name of the repository.
path (unicode):
The path to the file in the repository.
revision (unicode):
The revision of the file or commit.
Returns:
bytes:
The contents of the file.
"""
url = '%s/%s/blob/' % (project_name, repo_name)
if repository.tool.name == 'Git':
url += revision
else:
if path.startswith('/'):
path = path[1:]
url += '%s/%s' % (revision, path)
return self.api_get(self.build_api_url(url), raw_content=True)
def api_get_public_keys(self, username):
"""Return information on all public keys for a user.
Args:
username (unicode):
The user to fetch public keys for.
Returns:
dict:
Information on each of the user's public keys.
"""
return self.api_get(self.build_api_url('users/%s/public_keys'
% username))
def api_get_repository(self, project_name, repo_name):
"""Return information on a repository.
Args:
project_name (unicode):
The name of the Codebase project.
repo_name (unicode):
The name of the repository.
Returns:
dict:
Information on the repository.
See https://support.codebasehq.com/kb/repositories for the
data returned.
"""
return self.api_get(
self.build_api_url('%s/%s' % (project_name, repo_name)))
def build_api_url(self, url):
"""Return the URL for an API call.
Args:
url (unicode):
The relative URL for the API call.
Returns:
unicode:
The absolute URL for the API call.
"""
return 'https://api3.codebasehq.com/%s' % url
def api_get(self, url, raw_content=False):
"""Perform an HTTP GET request to the API.
Args:
url (unicode):
The full URL to the API resource.
raw_content (bool, optional):
If set to ``True``, the raw content of the result will be
returned, instead of a parsed XML result.
Returns:
object:
The parsed content of the result, as a dictionary, or the raw
bytes content if ``raw_content`` is ``True``.
"""
hosting_service = self.hosting_service
try:
account_data = hosting_service.account.data
api_username = '%s/%s' % (account_data['domain'],
hosting_service.account.username)
api_key = decrypt_password(account_data['api_key'])
data, headers = self.http_get(
url,
username=api_username,
password=api_key,
headers={
'Accept': self.API_MIMETYPE,
})
if raw_content:
return data
else:
return self.parse_xml(data)
except HTTPError as e:
data = e.read()
msg = six.text_type(e)
rsp = self.parse_xml(data)
if rsp and 'errors' in rsp:
errors = rsp['errors']
if 'error' in errors:
msg = errors['error']
if e.code == 401:
raise AuthorizationError(msg)
else:
raise HostingServiceAPIError(msg, http_code=e.code, rsp=rsp)
except URLError as e:
raise HostingServiceAPIError(e.reason)
def get_xml_text(self, nodes):
"""Return the text contents of a set of XML nodes.
Args:
nodes (list of xml.dom.minidom.Element):
The list of nodes.
Returns:
unicode:
The text content of the nodes.
"""
return ''.join(
node.data
for node in nodes
if node.nodeType == node.TEXT_NODE
)
def parse_xml(self, s):
"""Return the parsed content for an XML document.
Args:
s (unicode):
The XML document as a string.
Returns:
dict:
The parsed content of the XML document, with each key
being a dictionary of other parsed content.
If the document cannot be parsed, this will return ``None``.
"""
try:
doc = parseString(s)
except:
return None
root = doc.documentElement
return {
root.tagName: self._parse_xml_node(root),
}
def _parse_xml_node(self, node):
"""Return the parsed content for a node in an XML document.
This parses the content of a Codebase XML document, turning it into
arrays, strings, and dictionaries of data.
Args:
node (xml.dom.minidom.Element):
The node being parsed.
Returns:
object:
The parsed content of the node, based on the type of node being
processed.
"""
node_type = node.getAttribute('type')
is_nil = node.getAttribute('nil')
if node_type == 'array':
result = [
self._parse_xml_node(child)
for child in node.childNodes
if child.nodeType == child.ELEMENT_NODE
]
elif is_nil == 'true':
result = None
else:
child_nodes = [
child
for child in node.childNodes
if child.nodeType == child.ELEMENT_NODE
]
if child_nodes:
result = dict([
(child.tagName, self._parse_xml_node(child))
for child in child_nodes
])
else:
result = self.get_xml_text(node.childNodes)
return result
class CodebaseHQ(HostingService):
"""Repository hosting support for Codebase.
Codebase is a repository hosting service that supports Subversion, Git,
and Mercurial. It's available at https://codebasehq.com.
This integration provides repository validation and file fetching. Due to
API limitations, it does not support post-commit review at this time.
"""
name = 'Codebase HQ'
form = CodebaseHQForm
auth_form = CodebaseHQAuthForm
needs_authorization = True
supports_bug_trackers = True
supports_repositories = True
supported_scmtools = ['Git', 'Subversion', 'Mercurial']
repository_fields = {
'Git': {
'path': '[email protected]:%(domain)s/'
'%(codebasehq_project_name)s/'
'%(codebasehq_repo_name)s.git',
},
'Subversion': {
'path': 'https://%(domain)s.codebasehq.com/'
'%(codebasehq_project_name)s/'
'%(codebasehq_repo_name)s.svn',
},
'Mercurial': {
'path': 'https://%(domain)s.codebasehq.com/'
'projects/%(codebasehq_project_name)s/repositories/'
'%(codebasehq_repo_name)s/',
},
}
bug_tracker_field = (
'https://%(domain)s.codebasehq.com/projects/'
'%(codebasehq_project_name)s/tickets/%%s'
)
#: A mapping of Codebase SCM types to SCMTool names.
REPO_SCM_TOOL_MAP = {
'git': 'Git',
'svn': 'Subversion',
'hg': 'Mercurial',
}
def __init__(self, *args, **kwargs):
"""Initialize the hosting service.
Args:
*args (tuple):
Positional arguments for the parent constructor.
**kwargs (dict):
Keyword arguments for the parent constructor.
"""
super(CodebaseHQ, self).__init__(*args, **kwargs)
self.client = CodebaseHQClient(self)
def authorize(self, username, password, credentials, *args, **kwargs):
"""Authorize an account for Codebase.
Codebase usees HTTP Basic Auth with an API username (consisting of the
Codebase team's domain and the account username) and an API key (for
the password) for API calls, and a standard username/password for
Subversion repository access. We need to store all of this.
Args:
username (unicode):
The username to authorize.
password (unicode):
The API token used as a password.
credentials (dict):
Additional credentials from the authentication form.
*args (tuple):
Extra unused positional arguments.
**kwargs (dict):
Extra unused keyword arguments.
Raises:
reviewboard.hostingsvcs.errors.AuthorizationError:
The credentials provided were not valid.
"""
self.account.data.update({
'domain': credentials['domain'],
'api_key': encrypt_password(credentials['api_key']),
'password': encrypt_password(password),
})
# Test the account to make sure the credentials are fine. Note that
# we can only really sanity-check the API token, domain, and username
# from here. There's no way good way to check the actual password,
# which we only use for Subversion repositories.
#
# This will raise a suitable error message if authorization fails.
try:
self.client.api_get_public_keys(username)
except AuthorizationError:
raise AuthorizationError(
ugettext('One or more of the credentials provided were not '
'accepted by Codebase.'))
self.account.save()
def is_authorized(self):
"""Return if the account has been authorized.
This checks if all the modern authentication details are stored along
with the account.
Returns:
bool:
``True`` if all required credentials are set for the account.
"""
return (self.account.data.get('api_key') is not None and
self.account.data.get('password') is not None and
self.account.data.get('domain') is not None)
def get_password(self):
"""Return the password for this account.
This is used primarily for Subversion repositories, so that direct
access can be performed in order to fetch properties and other
information.
This does not return the API key.
Returns:
unicode:
The account password for repository access.
"""
return decrypt_password(self.account.data['password'])
def check_repository(self, codebasehq_project_name=None,
codebasehq_repo_name=None, tool_name=None,
*args, **kwargs):
"""Check the validity of a repository.
This will perform an API request against Codebase to get information on
the repository. This will throw an exception if the repository was not
found, and return cleanly if it was found.
Args:
codebase_project_name (unicode):
The name of the project on Codebase.
codebasehq_repo_name (unicode):
The name of the repository on Codebase.
tool_name (unicode):
The name of the SCMTool for the repository.
*args (tuple):
Extra unused positional arguments passed to this function.
**kwargs (dict):
Extra unused keyword arguments passed to this function.
Raises:
reviewboard.hostingsvcs.errors.RepositoryError:
The repository was not found.
"""
# The form should enforce these values.
assert codebasehq_project_name
assert codebasehq_repo_name
assert tool_name
try:
info = self.client.api_get_repository(codebasehq_project_name,
codebasehq_repo_name)
except HostingServiceAPIError as e:
logging.error('Error finding Codebase repository "%s" for '
'project "%s": %s',
codebasehq_repo_name, codebasehq_project_name,
e)
raise RepositoryError(
ugettext('A repository with this name and project was '
'not found.'))
try:
scm_type = info['repository']['scm']
except KeyError:
logging.error('Missing "scm" field for Codebase HQ repository '
'payload: %r',
info)
raise RepositoryError(
ugettext('Unable to determine the type of repository '
'from the Codebase API. Please report this.'))
try:
expected_tool_name = self.REPO_SCM_TOOL_MAP[scm_type]
except KeyError:
logging.error('Unexpected "scm" value "%s" for Codebase HQ '
'repository, using payload: %r',
scm_type, info)
raise RepositoryError(
ugettext('Unable to determine the type of repository '
'from the Codebase API. Please report this.'))
if expected_tool_name != tool_name:
raise RepositoryError(
ugettext("The repository type doesn't match what you "
"selected. Did you mean %s?")
% expected_tool_name)
def get_file(self, repository, path, revision, *args, **kwargs):
"""Returns the content of a file in a repository.
This will perform an API request to fetch the contents of a file.
Args:
repository (reviewboard.scmtools.models.Repository):
The repository containing the file.
path (unicode):
The path to the file in the repository.
revision (unicode):
The revision of the file in the repository.
*args (tuple):
Extra unused positional arguments passed to this function.
**kwargs (dict):
Extra unused keyword arguments passed to this function.
Returns:
byets:
The content of the file in the repository.
"""
try:
return self.client.api_get_file(
repository,
repository.extra_data['codebasehq_project_name'],
repository.extra_data['codebasehq_repo_name'],
path, revision)
except HostingServiceAPIError as e:
if e.http_code == 404:
raise FileNotFoundError(path, revision)
else:
logging.warning('Failed to fetch file from Codebase HQ '
'repository %s: %s',
repository, e)
raise
def get_file_exists(self, repository, path, revision, *args, **kwargs):
"""Returns whether a given file exists.
This will perform an API request to fetch the contents of a file,
returning ``True`` if the content could be fetched.
Args:
repository (reviewboard.scmtools.models.Repository):
The repository containing the file.
path (unicode):
The path to the file in the repository.
revision (unicode):
The revision of the file in the repository.
*args (tuple):
Extra unused positional arguments passed to this function.
**kwargs (dict):
Extra unused keyword arguments passed to this function.
Returns:
bool:
``True`` if the file exists in the repository.
"""
try:
self.client.api_get_file(
repository,
repository.extra_data['codebasehq_project_name'],
repository.extra_data['codebasehq_repo_name'],
path, revision)
return True
except HostingServiceAPIError:
return False
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18931,
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1,
329,
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220,
220,
220,
220,
220,
220,
304,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5298,
1432,
13264,
12331,
7,
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220,
220,
220,
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220,
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220,
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334,
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220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
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1662,
1043,
2637,
4008,
628,
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220,
220,
220,
220,
1949,
25,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
629,
76,
62,
4906,
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7508,
17816,
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1930,
37765,
6,
7131,
6,
1416,
76,
20520,
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220,
220,
220,
220,
220,
220,
220,
2845,
7383,
12331,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
18931,
13,
18224,
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43730,
366,
1416,
76,
1,
2214,
329,
6127,
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25112,
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705,
198,
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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,
220,
705,
15577,
2220,
25,
4064,
81,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7508,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5298,
1432,
13264,
12331,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
334,
1136,
5239,
10786,
3118,
540,
284,
5004,
262,
2099,
286,
16099,
705,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
6738,
262,
6127,
8692,
7824,
13,
4222,
989,
428,
2637,
4008,
628,
220,
220,
220,
220,
220,
220,
220,
1949,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2938,
62,
25981,
62,
3672,
796,
2116,
13,
2200,
16402,
62,
6173,
44,
62,
10468,
3535,
62,
33767,
58,
1416,
76,
62,
4906,
60,
198,
220,
220,
220,
220,
220,
220,
220,
2845,
7383,
12331,
25,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
18931,
13,
18224,
10786,
52,
42072,
366,
1416,
76,
1,
1988,
36521,
82,
1,
329,
6127,
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25112,
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198,
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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,
220,
705,
260,
1930,
37765,
11,
1262,
21437,
25,
4064,
81,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
629,
76,
62,
4906,
11,
7508,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5298,
1432,
13264,
12331,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
334,
1136,
5239,
10786,
3118,
540,
284,
5004,
262,
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286,
16099,
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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,
220,
705,
6738,
262,
6127,
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7824,
13,
4222,
989,
428,
2637,
4008,
628,
220,
220,
220,
220,
220,
220,
220,
611,
2938,
62,
25981,
62,
3672,
14512,
2891,
62,
3672,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5298,
1432,
13264,
12331,
7,
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220,
220,
220,
220,
220,
220,
220,
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220,
220,
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334,
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470,
2872,
644,
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366,
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220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
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220,
220,
366,
34213,
13,
7731,
345,
1612,
4064,
82,
1701,
8,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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4064,
2938,
62,
25981,
62,
3672,
8,
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825,
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62,
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7,
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11,
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11,
3108,
11,
18440,
11,
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11,
12429,
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22046,
2599,
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220,
220,
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37227,
35561,
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257,
2393,
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13,
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770,
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1620,
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7824,
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284,
21207,
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10154,
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13,
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943,
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25,
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220,
220,
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3526,
13,
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10141,
13,
27530,
13,
6207,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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13,
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3108,
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17221,
21958,
45203,
7159,
3804,
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2163,
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12429,
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17221,
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21179,
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2163,
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16409,
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220,
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37227,
198,
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1949,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13,
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13,
15042,
62,
1136,
62,
7753,
7,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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16099,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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16099,
13,
26086,
62,
7890,
17816,
8189,
8692,
71,
80,
62,
16302,
62,
3672,
6,
4357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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13,
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62,
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17816,
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62,
260,
7501,
62,
3672,
6,
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198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3108,
11,
18440,
8,
198,
220,
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] | 2.10159 | 8,741 |
"""
Used to create loggers for modules within the namespace.
These loggers are the standard python.logging loggers which can be adapted/changed via the standard methods.
"""
import logging
import logging.handlers
import os
from directories import LOG_DIR
# Default logging format
FUNSPACE_LOG_FORMAT = '%(asctime)s %(name)s %(levelname)s: %(message)s'
DEFAULT_LOG_FORMATTER = logging.Formatter(FUNSPACE_LOG_FORMAT)
DEFAULT_MAX_BYTES = 1000000
DEFAULT_MAX_BACKUPS = 5
# Create the log directory if it does not exist yet -raise error if unable
try:
os.makedirs(LOG_DIR, exist_ok=True)
except OSError:
raise
def get_file_logger(name: str,
file_path: str = None,
log_level=logging.DEBUG,
log_format: str = FUNSPACE_LOG_FORMAT):
"""
Return a logger for the specific file name and path.
:param name: Name of the logger, depending on where the method was called from
:param file_path: filepath to log into
:param log_level: log level (INFO, DEBUG, etc.), defaults at DEBUG
:param log_format: format to log - defaults at time, name, level, message
:return: Logger setup
"""
if file_path is None:
file_path = os.path.join(LOG_DIR, f'{name}.log')
# Get the logger from the logging module
logger = logging.getLogger(name=name)
handler = _get_file_handler(file_path=file_path, log_format=log_format)
# Clear existing handler and create a new one
logger.handlers.clear()
# Add the handler and set its default level
logger.addHandler(handler)
logger.setLevel(log_level)
return logger
def get_stream_and_file_logger(
name: str,
log_dir: str = LOG_DIR,
log_format: str = FUNSPACE_LOG_FORMAT,
overall_log_level: int = logging.DEBUG,
stream_log_level: int = logging.DEBUG,
file_log_level: int = logging.DEBUG,
file_max_bytes: int = DEFAULT_MAX_BYTES,
file_backup_count: int = DEFAULT_MAX_BACKUPS) -> logging.Logger:
"""
Wrapper to use for Loggers creation.
The Loggers will handle logs to both stream and Rotating File handlers. By default, only the name is required,
multiple options are available in order to allow better usage of the logs.
:param name: Log Name (usually the module using it)
:param log_dir: the directory to log to (defaults to C:\\\\Python_logs)
:param log_format: The Log Format
:param overall_log_level: The overall log level
:param stream_log_level: The stream log level
:param file_log_level: The file log level
:param file_max_bytes: the maximum bytes that the file can run to before rotating (default 100 KB)
:param file_backup_count: the maximum number of backups allowed before clearing down the files, defaults to 5
:return: logging.Logger configured to output to both a StreamHandler and a RotatingFileHandler with the
chosen config
"""
# Setup Logging
_logger = logging.getLogger(name)
_logger.handlers.clear() # clear all handlers
formatter = logging.Formatter(log_format)
# Stream handling (print to console)
stream_handler = logging.StreamHandler()
stream_handler.setFormatter(formatter)
stream_handler.setLevel(stream_log_level)
# File handling (log to log file)
file_handler = logging.handlers.RotatingFileHandler(
filename=os.path.join(log_dir, f'{name}.log'),
maxBytes=file_max_bytes,
backupCount=file_backup_count
)
file_handler.setFormatter(formatter)
file_handler.setLevel(file_log_level)
# Add both handlers to the logger
_logger.setLevel(overall_log_level)
_logger.addHandler(file_handler)
_logger.addHandler(stream_handler)
return _logger
def set_level_to_all_stream_handlers(lvl: int = logging.ERROR) -> None:
"""
This function is used to set all StreamHandlers to specific level.
:param lvl: target stream logging level
:return: None
"""
# Find all existing loggers
all_loggers = (logging.getLogger(name) for name in
logging.root.manager.loggerDict)
for logger in all_loggers:
if len(logger.handlers) != 0:
try:
stream_handlers = [x for x in logger.handlers if
isinstance(x, logging.StreamHandler)]
for stream_handler in stream_handlers:
stream_handler.setLevel(lvl)
except IndexError:
pass
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] | 2.651176 | 1,700 |
import argparse
import json
import os
import re
import socket
import sqlite3
import sys
from datetime import datetime
from typing import List
import psutil
from psutil._common import sconn
def get_processes_by_name(name: str) -> List[psutil.Process]:
"""
根据进程名，获取进程列表
"""
res = []
for proc in psutil.process_iter(["pid", "name", "create_time"]):
if proc.info["name"] == name:
res.append(proc)
return res
if __name__ == "__main__":
# 参数解析
parser = get_argparser()
args = parser.parse_args()
# 配置文件解析
configs = json.load(open(args.config_file, "r"))
# ss-manager 查活，否则清理，先清理ss-server，然后清理ss-manager，最后重启ss-manager
managers = get_processes_by_name("ss-manager")
retry = 0
while len(managers) != 1 and retry < 3:
os.system("systemctl restart shadowsocks-libev.service")
retry += 1
managers = get_processes_by_name("ss-manager")
if len(managers) != 1:
print("fatal error, 3 times retried and no ss-manager started")
sys.exit(1)
pid = managers[0].info["pid"]
create_time = datetime.fromtimestamp(
round(managers[0].info["create_time"], 0)
).strftime("%Y-%m-%d %H:%M:%S")
conns = get_conns_by_pids([x.info["pid"] for x in managers])
ip = conns[0].laddr[0]
port = conns[0].laddr[1]
# 获取统计，插入数据库
udp_socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
udp_socket.sendto("ping".encode(), ("127.0.0.1", 55092))
data = udp_socket.recvfrom(1024)
match = re.match(rb"^\w+:\s*(.*)", data[0])
udp_socket.close()
# 打开数据库
conn = sqlite3.connect("/root/softwares/ss-stats.db")
cur = conn.cursor()
cur.execute("""SELECT id, port FROM SERVER""")
servers = {k: v for v, k in cur.fetchall()}
json_data = json.loads(match[1])
print(pid, create_time)
records = []
for (k, v) in json_data.items():
server_id = servers[int(k)]
record = (server_id, pid, create_time, v)
print(record)
records.append(record)
for r in records:
cur.execute(
"""SELECT COUNT(1) FROM waterlog
WHERE server_id = ? AND pid = ? AND pid_created_at = ? AND total_traffic = ?""",
r,
)
cnt = cur.fetchone()[0]
if cnt == 0:
print(r)
res = cur.execute(
"""INSERT INTO waterlog (server_id, pid, pid_created_at, total_traffic)
VALUES (?, ?, ?, ?)""",
r,
)
print("insert result: ", res)
conn.commit()
conn.close()
if datetime.fromisoformat(create_time).date() < datetime.today().date():
os.system("systemctl restart shadowsocks-libev.service")
# ss-server 查活，否则，按照配置添加 ss-server
servers = get_processes_by_name("ss-server")
print(servers, json_data)
retry = 0
if len(servers) < len(json_data) and retry < 3:
os.system("systemctl restart shadowsocks-libev.service")
retry += 1
servers = get_processes_by_name("ss-server")
if len(servers) < len(json_data):
print("fatal error: ss-server's number is less than config")
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5796,
14304,
46,
18,
897,
80,
18,
89,
52,
56,
42,
37,
53,
17896,
24,
3118,
19,
67,
11674,
64,
363,
855,
198,
30934,
10619,
327,
17395,
30643,
6158,
30934,
198,
7061,
6,
628
] | 1.347635 | 909 |
# this is an embedded Python script it's really on GitHub
# and this is only a reference - so when it changes people
# will see the change on the webpage .. GOODTIMES !
pid = Runtime.start("pid","PID") | [
2,
428,
318,
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] | 3.740741 | 54 |
# possible exceptions caused by the API calls
from datetime import datetime
from typing import Optional
from xrpc.util import time_now
# a callable raised an exception
# the call was made to a point that does not support given arguments
| [
2,
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] | 3.907692 | 65 |
"""
This util package provides utility functions
that are used across the different gglsbl3 modules
e.g. string formatting
"""
from .format_utils import prettify_seconds, format_max_len
from .network_utils import int_to_ip, ip_to_int
| [
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] | 3.545455 | 66 |
from .main import Spys
__all__ = [
'Spys'
]
| [
6738,
764,
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] | 2.041667 | 24 |
# -*- coding: utf-8 -*-
"""
Tests for volt.utils
~~~~~~~~~~~~~~~~~~~~
"""
# (c) 2012-2020 Wibowo Arindrarto <[email protected]>
from pathlib import Path
import pytest
from pendulum.tz import local_timezone
from pendulum.tz.timezone import Timezone
from volt import exceptions as exc
from volt.utils import calc_relpath, get_tz, import_mod_attr
@pytest.fixture
@pytest.mark.parametrize("istr", ["os.mkdir", "os:mkdir"])
@pytest.mark.parametrize("istr", ["foo.bar.baz.custom.Test", "foo.bar.baz.custom:Test"])
@pytest.mark.parametrize("suffix", [":Test", ".Test"])
@pytest.mark.parametrize(
"tzname, exp_tz",
[
(None, local_timezone()),
("Asia/Jakarta", Timezone("Asia/Jakarta")),
],
)
@pytest.mark.parametrize(
"target, ref, exp",
[
# target is the same as ref
(Path("/a"), Path("/a"), Path(".")),
(Path("/a/b"), Path("/a/b"), Path(".")),
# target is a child of ref
(Path("/a/b"), Path("/a"), Path("b")),
(Path("/a/b/c"), Path("/a/b"), Path("c")),
(Path("/a/b/c"), Path("/a"), Path("b/c")),
# target is a sibling of ref
(Path("/b"), Path("/a"), Path("../b")),
(Path("/a/c"), Path("/a/b"), Path("../c")),
# target and ref shares a common parent
(Path("/a/b/c"), Path("/a/d/f"), Path("../../b/c/")),
(Path("/a/b/c/d"), Path("/a/b/d/x/z/q"), Path("../../../../c/d")),
(Path("/a/b/c/d/e/f"), Path("/a/x/y/z"), Path("../../../b/c/d/e/f")),
],
)
@pytest.mark.parametrize(
"target, ref",
[
(Path("a"), Path("a/b")),
(Path("a/b"), Path("a")),
(Path("/a"), Path("a/b")),
(Path("a"), Path("/a/b")),
],
)
| [
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14,
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357,
15235,
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7203,
64,
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828,
198,
220,
220,
220,
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357,
15235,
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7203,
64,
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828,
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220,
220,
220,
220,
357,
15235,
7203,
64,
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10644,
7203,
14,
64,
14,
65,
4943,
828,
198,
220,
220,
220,
16589,
198,
8,
198
] | 2.036643 | 846 |
import torch.nn as nn
from .util import init, get_clones
"""MLP modules."""
| [
11748,
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62,
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1952,
198,
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5805,
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526,
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198
] | 2.851852 | 27 |
import numpy as np
import matplotlib.pyplot as plt
from prettytable import PrettyTable
from sklearn.datasets import make_blobs, make_swiss_roll
from src.ndforest import NDForest
from sklearn import datasets
ct = 0.1
# DATASETS ------------------------------------------------------------------------------------------------------------
big_small_blob = make_blobs(centers=[[0, 0, 0], [4, 4, 4]], n_samples=[3600, 400],
n_features=3, cluster_std=[.8, 1.6])[0]
swiss_roll = make_swiss_roll(n_samples=500, noise=0.1, random_state=None)[0]
iris = datasets.load_iris()
iris_data = iris.data[:, :3]
# END OF DATASETS -----------------------------------------------------------------------------------------------------
data = big_small_blob
clf = NDForest(contamination=ct, k=20, points=data)
result = clf.fit_predict()
filtered_results = [p for p in result if p.is_outlier == 0]
filtered_results = sorted(filtered_results[:int(len(result) * ct)], key=lambda x: x.anomaly_score)
table = PrettyTable((['Coordinates', 'Anomaly Score']))
for res in filtered_results:
table.add_row([res.coordinates, res.anomaly_score])
print(table)
plot(result)
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7,
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198,
29487,
7,
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] | 2.962312 | 398 |
## https://scikit-optimize.github.io/notebooks/bayesian-optimization.html
import numpy as np
import matplotlib.pyplot as plt
from skopt import gp_minimize
## Implemented in /home/sbn/anaconda2/lib/python2.7/site-packages/skopt/optimizer/base.py
## Implemented in /home/sbn/anaconda2/lib/python2.7/site-packages/skopt/optimizer/optimizer.py
import matplotlib as mpl
from mpl_toolkits.mplot3d import Axes3D
from skopt.space import Integer
from helper_function_2 import *
import codecs, json
import subprocess
#%matplotlib inline
from skopt.plots import plot_convergence
import time
probed_points_x=[]
probed_points_y=[]
#################################################################
#################################################################
#################################################################
#################################################################
params={"no_of_fog_nodes":no_of_fog_nodes,"no_of_resource_type":no_of_resource_type,"no_of_time_slots":no_of_time_slots,"total_quantity_of_resource_needed":total_quantity_of_resource_needed,"no_of_services":no_of_services,"max_no_of_micro_services":max_no_of_micro_services,"index_of_fog_node":index_of_fog_node}
space = [Integer(0,no_of_fog_nodes) for i in range(max_no_of_micro_services)]
actualSpace=ConstrainedSpace(space,constraint=IsFeasible,**params)
X=[]
X1=getDictFrom('ini_alloc_mat_1.txt')
X2=getDictFrom('ini_alloc_mat_2.txt')
X.append(X1)
X.append(X2)
Y=[]
start_time = time.time()
Y1=allocation(X[0])
Y.append(Y1)
print "Y1 is:",Y1
itr_cnt=1
time_taken0 = time.time() - start_time
start_time = time.time()
Y2=allocation(X[1])
Y.append(Y2)
print "Y2 is:",Y2
time_taken1 = time.time() - start_time
print "#####################"
print "Iteration number:",itr_cnt
print "#####################"
if(Y1<Y2):
print "Min value of function till this iteration:", Y1
print "Corresponding allocation matrix is:",X[0]
print "Time taken to perform this iteration is ", time_taken0, "seconds."
else:
print "Min value of function till this iteration:", Y2
print "Corresponding allocation matrix is:",X[1]
print "Time taken to perform this iteration is ", time_taken1, "seconds."
print "End of iteration number:",itr_cnt
print "#####################"
for i in xrange(1,400):
start_time = time.time()
res = gp_minimize(allocation, # the function to minimize
actualSpace, # space i.e. the bounds on each dimension of x
base_estimator=None, # kernel function
acq_func="EI", # the acquisition function
n_random_starts=0, # reduce number of evaluation of target function
x0=X, # initial training data
y0=Y,
verbose=False,
n_calls=1) # the number of evaluations of function allocation
plot_convergence(res)
print "Loop counter is:",i
if(len(probed_points_x) > 0):
x_i=probed_points_x[-1]
y_i=probed_points_y[-1]
#y_i=allocation(x_i)
X.append(x_i)
Y.append(y_i)
if(res.func_vals[i+1]!=999):
itr_cnt+=1
print "#####################"
print "Iteration number:",itr_cnt
print "#####################"
print "Value of function returned in this step:",res.func_vals[i+1]
print "Corresponding allocation matrix returned in this step is:",res.x_iters[i+1]
#print "All value of function returned:",res.func_vals
#print "All allocation matrix returned is:",res.x_iters
time_taken = time.time() - start_time
print "Time taken to perform this iteration is ", time_taken, "seconds."
print "Min value of function till this iteration:", np.min(res.func_vals)
print "End of iteration number:",itr_cnt
print "#####################"
#print "FINAL OUTPUT\n X=",X,"\n\nY=",Y
index_x_min=np.argmin(Y)
x_min=X[index_x_min]
print "x_min is:",x_min
print "y_min is:",Y[index_x_min]
writeAllocMatToFile(x_min)
#################################################################
#################################################################
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] | 2.520696 | 1,667 |
import sys
import time
#Predefined ====================================================================
#caption------------------------------------------------------------------------
caption = '''
____ ____ ____________ ____________________ ___
| \ / | | ______ \ |_______ ________| | |
| \ / | | | \ \ | | | |
| \ / | | | / / | | | |
| \ / | | |_____/ / | | | |
| \/ | | _______ \ | | | |
| |\ /| | | | \ \ | | | |
| | \____/ | | | | \ \ | | | |
| | | | | |______/ / | | | |
|___| |___| |____________/ |___| |___|
MYERS-BRIGGS TYPE INDICATOR
'''
#Explanations-------------------------------------------------------------------
explanations = '''
Myers-Briggs Type Indicator je osobnostní test navržený pro určení osobnostních typů.
Zaměřuje se na to, jak různí lidé vnímají svět a činí svá rozhodnutí.
Tento test spoléhá na čtyři klíče k určení typu (a proto jich je dohromady 16):
1. JAK VNÍMÁTE OKOLNÍ PROSTŘEDÍ (INTROVERT | EXTROVERT)
2. ZÍSKÁVÁNÍ INFORMACÍ (SENSING | INTUITION)
3. ZPRACOVÁNÍ INFORMACÍ (FEELING | THINKING)
4. ŽIVOTNÍ STYL (PROSPECTING | JUDGING)
Vzhledem k možné předpojatosti, nebudeme vám říkati, co každý faktor znamená.
Pro přesné výsledky, prosím vyplňte test poctivě.
Otázky prosím odpovídejte od 1 do 5, kde 1 je nejméně souhlasím, 5 nejvíce a 3 neutrální.
'''
questionsFor1 = (("Komunikace s lidmi vám dělá značné potíže.",
"Obvykle sami od sebe nezačínáte rozhovor.",
"Zajímavá kniha nebo videohra je mnohdy lepší než společenská událost.",
"Jste poměrně rezervovaný a tichý člověk.",
"V plné místnosti se držíte blíže u zdi a vyhýbáte se jejímu středu."),
("Netrvá dlouho, než se na novém pracovišti zapojíte do společenského dění.",
"Rádi chodíte na společenská setkání s převleky a hraním rolí.",
"Cítíte, že po setkání se skupinou lidí máte více energie.",
"Ve společnosti často přebíráte iniciativu.",
"Radši byste strávili čas mezi lidi než doma."))
questionsFor2 = (("Obecně se dá říct, že se spoléháte spíše na své zkušenosti než představivost.",
"Nepovažujete se za snílka.",
"Máte radši prakci než teorii.",
"Vaše sny se týkají spíše reálného světa a jeho dění.",
"Považujete se více za praktického než kreativního."),
("Často se zamyslíte tak hluboce, že ignorujete své okolí či na něj zapomenete.",
"Na procházce přírodou se často ztratíte v myšlenkách.",
"Často trávíte čas přemýšlením nad nereálnými, neproveditelnými, ale přesto zajímavými nápady.",
"Často přemýšlíte nad smyslem lidské existence.",
"Vždy vás zajímaly neotřelé a nejednoznačné věci (například v knihách, výtvarném umění nebo filmech)." ))
questionsFor3 = (("Myslíte si, že je třeba respektovat názory všech bez ohledu na to, zda jsou podloženy fakty či nikoliv.",
"Zvítězit v debatě je pro vás méně důležité, než aby nebyl nikdo vyveden z míry.",
"Jako rodič byste byli raději, kdyby z vašeho dítěte vyrostl laskavý spíše než chytrý člověk.",
"Kamarádovi, který je z něčeho smutný, nabídnete spíše emoční podporu, než abyste mu navrhli praktické způsoby, jak problém vyřešit.",
"Ve vlastní firmě by pro vás bylo hodně těžké propustit věrné, ale málo výkonné zaměstnance."),
("V diskuzi by pravda měla být důležitější než ohleduplnost vůči druhým.",
"Nedovolíte druhým, aby ovlivnili vaše jednání.",
"Pravda by měla vždy vítězit.",
"V hádce je důležitější pravda než jsou emoce. ",
"Logika je při důležitém rozhodování obvykle důležitější než srdce." ))
questionsFor4 = (("Mít věci dobře uspořádané je pro vás důležitější než se umět přizpůsobit.",
"Své cestovní plány obvykle dobře promýšlíte.",
"Doma i v práci máte vcelku pořádek.",
"Na e-mailové zprávy se snažíte odpovědět co nejdříve a nesnášíte, když máte v příchozích zprávách nepořádek.",
"Málokdy něco uděláte z čisté zvědavosti."),
("Raději improvizujete, než abyste si připravili podrobný plán.",
"Dokážete zachovat klid a soustředit se i pod určitým tlakem.",
"Váš pracovní styl se podobá spíše nahodilým výbojům energie než metodickému a organizovanému přístupu.",
"Připravit plán a pak podle něj postupovat není nejdůležitější částí každého projektu.",
"Jste spíše rodilý improvizátor než pečlivý plánovač." ))
questions = [questionsFor1, questionsFor2, questionsFor3, questionsFor4]
#KeyFactors=====================================================================
possibleAnswers = ("1","2","3", "4", "5")
initialI ='''
IIIIIIIIIIII
IIIIIIIIIIII
IIIIII
IIIIII
IIIIII
IIIIII
IIIIII
IIIIII
IIIIII
IIIIII
IIIIII
IIIIII
IIIIII
IIIIIIIIIIII
IIIIIIIIIIII
'''
initialE ='''
EEEEEEEEEEEE
EEEEEEEEEEEE
EEE
EEE
EEE
EEE
EEE
EEEEEEEEEEEE
EEEEEEEEEEEE
EEE
EEE
EEE
EEE
EEEEEEEEEEEE
EEEEEEEEEEEE
'''
initialS ='''
SSSSSSSS
SSSSSSSSSSSSSS
SSSS SSS
SSSS SS
SSS
SSS
SSS
SSS
SSS
SSSS
SSSS
SSSS
SSS SSSS
SSS SSSSSS
SSSSSSSS
'''
initialN ='''
NNNN NNN
NNNNN NNN
NNNNNN NNN
NNN NNN NNN
NNN NNN NNN
NNN NNN NNN
NNN NNN NNN
NNN NNN NNN
NNN NNNNNNN
NNN NNNNNN
NNN NNNNN
NNN NNNN
NNN NNNN
NNN NNNN
'''
initialF ='''
FFFFFFFFFFFFFFFFF
FFFFFFFFFFFFFFFF
FFFF
FFFF
FFFF
FFFFFFFFFFF
FFFFFFFFFFF
FFFF
FFFF
FFFF
FFFF
FFFF
FFFF
FFFF
FFFF
'''
initialT ='''
TTTTTTTTTTTTTTTTT
TTTTTTTTTTTTTTTTT
TTTTT
TTTTT
TTTTT
TTTTT
TTTTT
TTTTT
TTTTT
TTTTT
TTTTT
TTTTT
TTTTT
TTTTT
TTTTT
'''
initialJ ='''
JJJJJ
JJJJJ
JJJJJ
JJJJJ
JJJJJ
JJJJJ
JJJJJ
JJJJJ
JJJJJ
JJJJJ
JJJJJ
JJ JJJJJ
JJJ JJJJ
JJJJJ JJJ
JJJJJ
'''
initialP = '''
PPPPPPPPPPPPP
PPPPPPPPPPPPPPP
PPPP PPPP
PPPP PPPP
PPPP PPPP
PPPPPPPPPPPPPPP
PPPPPPPPPPPPP
PPPP
PPPP
PPPP
PPPP
PPPP
PPPP
PPPP
PPPP
'''
initials = ((initialI, initialE), (initialS, initialN), (initialF, initialT), (initialP, initialJ))
factors = (("Introverze","Extraverze"), ("Smysly","Intuice"), ("Cítění","Myšlení"), ("Vnímání","Usuzování"))
shortcuts = (('i', 'e'), ('s', 'n'), ('f', 't'), ('p', 'j'))
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] | 1.72744 | 3,801 |
"""Top-level package for gym-gridverse."""
__author__ = """Andrea Baisero"""
__email__ = '[email protected]'
__version__ = '0.0.1'
import gym_gridverse.gym # noqa: F401
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#!/usr/bin/env python
# encoding: utf-8
import numpy as np
CHAR_TYPE = {
u'กขฃคฆงจชซญฎฏฐฑฒณดตถทธนบปพฟภมยรลวศษสฬอ': 'c',
u'ฅฉผฟฌหฮ': 'n',
u'ะาำิีืึุู': 'v', # า ะ ำ ิ ี ึ ื ั ู ุ
u'เแโใไ': 'w',
u'่้๊๋': 't', # วรรณยุกต์ ่ ้ ๊ ๋
u'์ๆฯ.': 's', # ์ ๆ ฯ .
u'0123456789๑๒๓๔๕๖๗๘๙': 'd',
u'"': 'q',
u"‘": 'q',
u"’": 'q',
u"'": 'q',
u' ': 'p',
u'abcdefghijklmnopqrstuvwxyz': 's_e',
u'ABCDEFGHIJKLMNOPQRSTUVWXYZ': 'b_e'
}
CHAR_TYPE_FLATTEN = {}
for ks, v in CHAR_TYPE.items():
for k in ks:
CHAR_TYPE_FLATTEN[k] = v
# create map of dictionary to character
CHARS = [
u'\n', u' ', u'!', u'"', u'#', u'$', u'%', u'&', "'", u'(', u')', u'*', u'+',
u',', u'-', u'.', u'/', u'0', u'1', u'2', u'3', u'4', u'5', u'6', u'7', u'8',
u'9', u':', u';', u'<', u'=', u'>', u'?', u'@', u'A', u'B', u'C', u'D', u'E',
u'F', u'G', u'H', u'I', u'J', u'K', u'L', u'M', u'N', u'O', u'P', u'Q', u'R',
u'S', u'T', u'U', u'V', u'W', u'X', u'Y', u'Z', u'[', u'\\', u']', u'^', u'_',
u'a', u'b', u'c', u'd', u'e', u'f', u'g', u'h', u'i', u'j', u'k', u'l', u'm',
u'n', u'o', u'other', u'p', u'q', u'r', u's', u't', u'u', u'v', u'w', u'x', u'y',
u'z', u'}', u'~', u'ก', u'ข', u'ฃ', u'ค', u'ฅ', u'ฆ', u'ง', u'จ', u'ฉ', u'ช',
u'ซ', u'ฌ', u'ญ', u'ฎ', u'ฏ', u'ฐ', u'ฑ', u'ฒ', u'ณ', u'ด', u'ต', u'ถ', u'ท',
u'ธ', u'น', u'บ', u'ป', u'ผ', u'ฝ', u'พ', u'ฟ', u'ภ', u'ม', u'ย', u'ร', u'ฤ',
u'ล', u'ว', u'ศ', u'ษ', u'ส', u'ห', u'ฬ', u'อ', u'ฮ', u'ฯ', u'ะ', u'ั', u'า',
u'ำ', u'ิ', u'ี', u'ึ', u'ื', u'ุ', u'ู', u'ฺ', u'เ', u'แ', u'โ', u'ใ', u'ไ',
u'ๅ', u'ๆ', u'็', u'่', u'้', u'๊', u'๋', u'์', u'ํ', u'๐', u'๑', u'๒', u'๓',
u'๔', u'๕', u'๖', u'๗', u'๘', u'๙', u'‘', u'’', u'\ufeff'
]
CHARS_MAP = {v: k for k, v in enumerate(CHARS)}
CHAR_TYPES = [
'b_e', 'c', 'd', 'n', 'o',
'p', 'q', 's', 's_e', 't',
'v', 'w'
]
CHAR_TYPES_MAP = {v: k for k, v in enumerate(CHAR_TYPES)}
def create_feature_array(text, n_pad=21):
"""
Create feature array of character and surrounding characters
"""
n = len(text)
n_pad_2 = int((n_pad - 1)/2)
text_pad = [' '] * n_pad_2 + [t for t in text] + [' '] * n_pad_2
x_char, x_type = [], []
for i in range(n_pad_2, n_pad_2 + n):
char_list = text_pad[i + 1: i + n_pad_2 + 1] + \
list(reversed(text_pad[i - n_pad_2: i])) + \
[text_pad[i]]
char_map = [CHARS_MAP.get(c, 80) for c in char_list]
char_type = [CHAR_TYPES_MAP.get(CHAR_TYPE_FLATTEN.get(c, 'o'), 4)
for c in char_list]
x_char.append(char_map)
x_type.append(char_type)
x_char = np.array(x_char).astype(float)
x_type = np.array(x_type).astype(float)
return x_char, x_type
def create_n_gram_df(df, n_pad):
"""
Given input dataframe, create feature dataframe of shifted characters
"""
n_pad_2 = int((n_pad - 1)/2)
for i in range(n_pad_2):
df['char-{}'.format(i+1)] = df['char'].shift(i + 1)
df['type-{}'.format(i+1)] = df['type'].shift(i + 1)
df['char{}'.format(i+1)] = df['char'].shift(-i - 1)
df['type{}'.format(i+1)] = df['type'].shift(-i - 1)
return df[n_pad_2: -n_pad_2]
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] | 1.533019 | 2,120 |
import collections
import os.path as osp
import sys
import PIL.Image
import numpy as np
import torch
from torch.utils import data
from .transforms import ImageTransformType, FlipType, apply_transform
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] | 3.759259 | 54 |
import os
import sys
import numpy as np
import macro
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] | 2.652174 | 23 |
from django.apps import AppConfig
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from django.conf import settings
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] | 4.125 | 8 |
import subprocess
import pytest
import os
import shutil
import time
from rl_baselines.student_eval import OnPolicyDatasetGenerator, mergeData, trainStudent
ENV_NAME = 'OmnirobotEnv-v0'
PATH_SRL = "srl_zoo/data/"
DEFAULT_SRL_TEACHERS = "ground_truth"
DEFAULT_SRL_STUDENT = "raw_pixels"
NUM_TIMESTEP = 25000
NUM_CPU = 4
@pytest.mark.fast
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] | 2.514706 | 136 |
from app import app, db
from flask import jsonify, request
from flask_cors import cross_origin
from app.models.score import Score, ScoreSchema
from app.models.event import Event
from flask_jwt_extended import jwt_required
@app.route('/api/score', methods=['POST'])
@jwt_required()
@cross_origin()
@app.route('/api/score/<id>', methods=['PUT','DELETE'])
@cross_origin()
@jwt_required()
@app.route('/api/competition/<id>/scores', methods=['GET'])
@cross_origin() | [
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28,
17816,
18851,
6,
12962,
198,
31,
19692,
62,
47103,
3419
] | 2.858025 | 162 |
import unittest
import falcon
from falcon.testing import helpers
import mock
from jumpgate.network.drivers.sl import subnets
SUBNET_DICT = {'id': 10,
'networkIdentifier': '9.0.3.192',
'tenant_id': '6',
'cidr': 28,
'networkVlanId': 5,
'gateway': '9.0.3.193',
'version': 4,
'name': 'name'}
| [
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10354,
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6,
92,
628,
628
] | 1.821918 | 219 |
#!/usr/bin/env python3
#
# Copyright 2020 IBM
# 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.IBM Confidential
#
import pathlib
import typing as typ
import pickle
import fastapi.testclient as tstc
import sqlalchemy.orm as saorm
import app.core.configuration as app_conf
import app.core.configuration as conf
import app.core.uri as app_uri
import app.tests.predictors.pmml_sample.model as app_test_pmml
import app.tests.predictors.scikit_learn.model as app_test_sklearn
| [
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] | 3.451957 | 281 |
import time
import allure
from selenium.webdriver.support import expected_conditions as ec
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
| [
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] | 3.555556 | 54 |
from msp import msp, simtools
import time
import math
import numpy as np
DATAFILE = "runs/Aerobraking.csv"
ATMOSPHEREDATA = "densityModels/MarsDensity.csv"
SPEED = 2000 # __ times speed
RUNTIME = 300000
print("Total runtime will be:", RUNTIME, "s or:", RUNTIME/3600, "hours or:", RUNTIME/86400, "days")
# USE km AS STANDARD DISTANCE UNIT
# USE s AS STANDARD TIME UNIT
AU = 149.6e6 # km
muSun = 1.327178e11
currentTime = time.time()
limitAltitude = 200 # 260 #[km]. At this altitude density is just below 1*10^-10
MarsAtmosphere=msp.Atmosphere(limitAltitude, densityFile=ATMOSPHEREDATA)
Mars = msp.Planet(4.282837e4, 3396.2, 1.52367934 * AU, muSun, MarsAtmosphere)
r = np.array([21508.114845629447, 0.0, 982.3450283462487])
v = np.array([-2.968111925169866, 0.0, -1.4808260236254678])
# CD is increased times 100 here to see the effect.
CD = 1.23 * 100
surfaceArea = 3.6**2 * math.pi
spacecraft = msp.Body(Mars, 3000, CD, surfaceArea )
spacecraft.initPositionOrbit(r,v)
# PROPAGATE Here
dt = 1
# These are some precalculated manoeuvres to see the effects
spacecraft.addManoeuvreByDirection(spacecraft.start + 100, -1.35, "t")
spacecraft.addManoeuvreByDirection(spacecraft.start + 8900, -0.2, "t")
rlist = spacecraft.propagate(RUNTIME, DATAFILE, True, dtAtmospheric = dt, dtNormal = dt)
print(f"Final eccentricity {spacecraft.e}")
print(f"Final velocity {np.sqrt(spacecraft.v.dot(spacecraft.v))}")
print(f"Periapsis alt {spacecraft.periapsis-Mars.r}")
print(f"Apoapsis alt {spacecraft.apoapsis-Mars.r}")
simtools.quickAnimate(SPEED,DATAFILE,Mars) | [
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12,
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7,
4303,
41841,
11,
35,
1404,
8579,
41119,
11,
43725,
8
] | 2.534959 | 615 |
# Copyright (c) 2013 Red Hat, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
# implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Tests for gluster.swift """
import os
import unittest
import shutil
import tempfile
import file_connector.swift as gs
class TestPkgInfo(unittest.TestCase):
"""
Tests for file_connector.swift PkgInfo class.
"""
| [
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] | 3.401674 | 239 |
mark = float(input('Mark?'))
outof = float(input('Out of?'))
print('Your mark is:',mark/outof*100,'%')
| [
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] | 2.142857 | 56 |
'''Returns a fact to indicate if this machine can be upgraded to macOS 12 Monterey'''
# Based on
# https://github.com/hjuutilainen/adminscripts/blob/master/
# check-10.12-sierra-compatibility.py
# sysctl function by Michael Lynn
# https://gist.github.com/pudquick/581a71425439f2cf8f09
# IOKit bindings by Michael Lynn
# https://gist.github.com/pudquick/
# c7dd1262bd81a32663f0#file-get_platform-py-L22-L23
# Information on what boardIDs and Models that are supported is buried in the installer found here:
# Install macOS Monterey/Contents/SharedSupport/SharedSupport.dmg - mount this
# /Volumes/Shared Support/com_apple_MobileAsset_MacSoftwareUpdate/bc70a04218e8e8bd40d2472aecbb2a06773ba42b.zip - decompress this, the name of the zip will most likely change with every OS update.
# bc70a04218e8e8bd40d2472aecbb2a06773ba42b/AssetData/boot/PlatformSupport.plist
#
#
# device_support_values are harvested from the full installer Distribution file.
# The macOS 12.0.1 Distribution from ProductID 002-23774 was found at http://swcdn.apple.com/content/downloads/39/60/002-23774-A_KNETE2LDIN/4ll6ahj3st7jhqfzzjt1bjp1nhwl4p4zx7/002-23774.English.dist
from __future__ import absolute_import, print_function
from ctypes import CDLL, c_uint, byref, create_string_buffer
from ctypes import cast, POINTER
from ctypes.util import find_library
import os
import objc
from Foundation import NSBundle, NSString, NSUTF8StringEncoding
# glue to call C and Cocoa stuff
libc = CDLL(find_library('c'))
IOKit_bundle = NSBundle.bundleWithIdentifier_('com.apple.framework.IOKit')
functions = [("IOServiceGetMatchingService", b"II@"),
("IOServiceMatching", b"@*"),
("IORegistryEntryCreateCFProperty", b"@I@@I"),
]
objc.loadBundleFunctions(IOKit_bundle, globals(), functions)
def io_key(keyname):
"""Gets a raw value from the IORegistry"""
return IORegistryEntryCreateCFProperty(
IOServiceGetMatchingService(
0, IOServiceMatching(b"IOPlatformExpertDevice")), keyname, None, 0)
def io_key_string_value(keyname):
"""Converts NSData/CFData return value to an NSString"""
raw_value = io_key(keyname)
return NSString.alloc().initWithData_encoding_(
raw_value, NSUTF8StringEncoding
).rstrip('\0')
def sysctl(name, output_type=str):
'''Wrapper for sysctl so we don't have to use subprocess'''
size = c_uint(0)
# Find out how big our buffer will be
libc.sysctlbyname(name, None, byref(size), None, 0)
# Make the buffer
buf = create_string_buffer(size.value)
# Re-run, but provide the buffer
libc.sysctlbyname(name, buf, byref(size), None, 0)
if output_type in (str, 'str'):
return buf.value.decode('UTF-8')
if output_type in (int, 'int'):
# complex stuff to cast the buffer contents to a Python int
if size.value == 4:
return cast(buf, POINTER(c_int32)).contents.value
if size.value == 8:
return cast(buf, POINTER(c_int64)).contents.value
if output_type == 'raw':
# sysctl can also return a 'struct' type; just return the raw buffer
return buf.raw
def is_virtual_machine():
'''Returns True if this is a VM, False otherwise'''
cpu_features = sysctl('machdep.cpu.features').split()
return 'VMM' in cpu_features
def is_supported_model():
'''Returns True if model is in list of supported models,
False otherwise'''
supported_models = [
u'MacBook10,1',
u'MacBook9,1',
u'MacBookAir7,1',
u'MacBookAir7,2',
u'MacBookAir8,1',
u'MacBookAir8,2',
u'MacBookAir9,1',
u'MacBookPro11,4',
u'MacBookPro11,5',
u'MacBookPro12,1',
u'MacBookPro13,1',
u'MacBookPro13,2',
u'MacBookPro13,3',
u'MacBookPro14,1',
u'MacBookPro14,2',
u'MacBookPro14,3',
u'MacBookPro15,1',
u'MacBookPro15,2',
u'MacBookPro15,3',
u'MacBookPro15,4',
u'MacBookPro16,1',
u'MacBookPro16,2',
u'MacBookPro16,3',
u'MacBookPro16,4',
u'MacPro6,1',
u'MacPro7,1',
u'Macmini7,1',
u'Macmini8,1',
u'iMac16,1',
u'iMac16,2',
u'iMac17,1',
u'iMac18,1',
u'iMac18,2',
u'iMac18,3',
u'iMac19,1',
u'iMac19,2',
u'iMac20,1',
u'iMac20,2',
u'iMacPro1,1'
]
current_model = get_current_model()
if not current_model:
return False
elif current_model in supported_models:
return True
else:
return False
def is_supported_board_id():
'''Returns True if current board_id is in list of supported board_ids,
False otherwise'''
platform_support_values = (
u'Mac-06F11F11946D27C5',
u'Mac-06F11FD93F0323C5',
u'Mac-0CFF9C7C2B63DF8D',
u'Mac-112818653D3AABFC',
u'Mac-1E7E29AD0135F9BC',
u'Mac-226CB3C6A851A671',
u'Mac-27AD2F918AE68F61',
u'Mac-35C5E08120C7EEAF',
u'Mac-473D31EABEB93F9B',
u'Mac-4B682C642B45593E',
u'Mac-53FDB3D8DB8CA971',
u'Mac-551B86E5744E2388',
u'Mac-5F9802EFE386AA28',
u'Mac-63001698E7A34814',
u'Mac-65CE76090165799A',
u'Mac-66E35819EE2D0D05',
u'Mac-77F17D7DA9285301',
u'Mac-7BA5B2D9E42DDD94',
u'Mac-7BA5B2DFE22DDD8C',
u'Mac-827FAC58A8FDFA22',
u'Mac-827FB448E656EC26',
u'Mac-937A206F2EE63C01',
u'Mac-937CB26E2E02BB01',
u'Mac-9AE82516C7C6B903',
u'Mac-9F18E312C5C2BF0B',
u'Mac-A369DDC4E67F1C45',
u'Mac-A5C67F76ED83108C',
u'Mac-A61BADE1FDAD7B05',
u'Mac-AA95B1DDAB278B95',
u'Mac-AF89B6D9451A490B',
u'Mac-B4831CEBD52A0C4C',
u'Mac-B809C3757DA9BB8D',
u'Mac-BE088AF8C5EB4FA2',
u'Mac-CAD6701F7CEA0921',
u'Mac-CFF7D910A743CAAF',
u'Mac-DB15BD556843C820',
u'Mac-E1008331FDC96864',
u'Mac-E43C1C25D4880AD6',
u'Mac-E7203C0F68AA0004',
u'Mac-EE2EBD4B90B839A8',
u'Mac-F60DEB81FF30ACF6',
u'Mac-FFE5EF870D7BA81A',
u'VMM-x86_64'
)
board_id = get_board_id()
return board_id in platform_support_values
def is_supported_device_id():
'''Returns True if current device_id is in list of supported device_ids,
False otherwise'''
device_support_values = (
u'J132AP',
u'J137AP',
u'J140AAP',
u'J140KAP',
u'J152FAP',
u'J160AP',
u'J174AP',
u'J185AP',
u'J185FAP',
u'J213AP',
u'J214AP',
u'J214KAP',
u'J215AP',
u'J223AP',
u'J230AP',
u'J230KAP',
u'J274AP',
u'J293AP',
u'J313AP',
u'J314cAP',
u'J314sAP',
u'J316cAP',
u'J316sAP'
u'J456AP',
u'J457AP',
u'J680AP',
u'J780AP',
u'VMA2MACOSAP',
u'VMM-x86_64',
u'X589AMLUAP',
u'X86LEGACYAP'
)
device_support_values = [deviceid.lower() for deviceid in device_support_values]
device_id = get_device_id()
return device_id in device_support_values
def get_minor_system_version():
'''Returns 20 for Big Sur, 21 for Monterey, etc'''
darwin_version = int(os.uname()[2].split('.')[0])
return darwin_version - 4
def is_supported_system_version():
'''Returns True if current macOS version is 10.9 through 11.x,
False otherwise'''
macos_minor_version = get_minor_system_version()
if macos_minor_version >= 17:
return False
elif macos_minor_version >= 9:
return True
else:
return False
def get_board_id():
'''Returns our board-id'''
return io_key_string_value("board-id")
def get_device_id():
'''Returns our device-id'''
deviceid = sysctl("hw.target")
return deviceid.lower()
def get_current_model():
'''Returns model info'''
return io_key_string_value("model")
def fact():
'''Return our monterey_upgrade_supported fact'''
if is_virtual_machine():
return {'monterey_upgrade_supported': True}
if ((is_supported_model() or is_supported_board_id() or is_supported_device_id()) and
is_supported_system_version()):
return {'monterey_upgrade_supported': True}
return {'monterey_upgrade_supported': False}
if __name__ == '__main__':
# Debug/testing output when run directly
print('is_virtual_machine: %s' % is_virtual_machine())
print('get_current_model: %s' % get_current_model())
print('is_supported_model: %s' % is_supported_model())
print('get_minor_system_version: %s' % get_minor_system_version())
print('is_supported_system_version: %s' % is_supported_system_version())
print('get_board_id: %s' % get_board_id())
print('is_supported_board_id: %s' % is_supported_board_id())
print('get_device_id: %s' % get_device_id())
print('is_supported_device_id: %s' % is_supported_device_id())
print(fact())
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11,
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25,
198,
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7,
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13,
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428,
318,
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11,
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4306,
7061,
6,
198,
220,
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220,
42804,
62,
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796,
25064,
34168,
10786,
76,
620,
10378,
13,
36166,
13,
40890,
27691,
35312,
3419,
198,
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1441,
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53,
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6,
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6,
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198,
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6,
14155,
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11,
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3256,
198,
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6,
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6,
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6,
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2964,
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11,
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3256,
198,
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220,
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334,
6,
14155,
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1485,
11,
17,
3256,
198,
220,
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220,
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334,
6,
14155,
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11,
18,
3256,
198,
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334,
6,
14155,
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1415,
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3256,
198,
220,
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220,
220,
220,
220,
334,
6,
14155,
10482,
2964,
1415,
11,
17,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
10482,
2964,
1415,
11,
18,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
10482,
2964,
1314,
11,
16,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
10482,
2964,
1314,
11,
17,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
10482,
2964,
1314,
11,
18,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
10482,
2964,
1314,
11,
19,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
10482,
2964,
1433,
11,
16,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
10482,
2964,
1433,
11,
17,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
10482,
2964,
1433,
11,
18,
3256,
198,
220,
220,
220,
220,
220,
220,
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334,
6,
14155,
10482,
2964,
1433,
11,
19,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
2964,
21,
11,
16,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
2964,
22,
11,
16,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
45313,
22,
11,
16,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
45313,
23,
11,
16,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
72,
14155,
1433,
11,
16,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
72,
14155,
1433,
11,
17,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
72,
14155,
1558,
11,
16,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
72,
14155,
1507,
11,
16,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
72,
14155,
1507,
11,
17,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
72,
14155,
1507,
11,
18,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
72,
14155,
1129,
11,
16,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
72,
14155,
1129,
11,
17,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
72,
14155,
1238,
11,
16,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
72,
14155,
1238,
11,
17,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
72,
14155,
2964,
16,
11,
16,
6,
198,
220,
220,
220,
2361,
198,
220,
220,
220,
1459,
62,
19849,
796,
651,
62,
14421,
62,
19849,
3419,
198,
220,
220,
220,
611,
407,
1459,
62,
19849,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
10352,
198,
220,
220,
220,
1288,
361,
1459,
62,
19849,
287,
4855,
62,
27530,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
6407,
198,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
10352,
198,
198,
4299,
318,
62,
15999,
62,
3526,
62,
312,
33529,
198,
220,
220,
220,
705,
7061,
35561,
6407,
611,
1459,
3096,
62,
312,
318,
287,
1351,
286,
4855,
3096,
62,
2340,
11,
198,
220,
220,
220,
10352,
4306,
7061,
6,
198,
220,
220,
220,
3859,
62,
11284,
62,
27160,
796,
357,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
12,
3312,
37,
1157,
37,
16315,
3510,
35,
1983,
34,
20,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
12,
3312,
37,
1157,
26009,
6052,
37,
3070,
1954,
34,
20,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
12,
15,
34,
5777,
24,
34,
22,
34,
17,
33,
5066,
8068,
23,
35,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
12,
16,
12762,
1507,
46435,
35,
18,
3838,
33,
4851,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
12,
16,
36,
22,
36,
1959,
2885,
486,
2327,
37,
24,
2749,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
12,
24909,
23199,
18,
34,
21,
32,
23,
4349,
32,
46250,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
12,
1983,
2885,
17,
37,
24,
1507,
14242,
3104,
37,
5333,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
12,
2327,
34,
20,
36,
2919,
10232,
34,
22,
6500,
8579,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
12,
37804,
35,
3132,
36,
6242,
30195,
6052,
37,
24,
33,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
12,
19,
33,
43950,
34,
41290,
33,
2231,
49051,
36,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
12,
4310,
37,
11012,
18,
35,
23,
11012,
23,
8141,
24,
4869,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
12,
43697,
33,
4521,
36,
3553,
2598,
36,
1954,
3459,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
12,
20,
37,
24,
30863,
36,
15112,
21734,
3838,
2078,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
12,
5066,
405,
1433,
4089,
36,
22,
32,
28978,
1415,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
12,
2996,
5222,
22,
31751,
486,
37680,
2079,
32,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
12,
2791,
36,
31128,
1129,
6500,
17,
35,
15,
35,
2713,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
12,
3324,
37,
1558,
35,
22,
5631,
24,
2078,
4310,
486,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
12,
22,
4339,
20,
33,
17,
35,
24,
36,
3682,
16458,
35,
5824,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
12,
22,
4339,
20,
33,
17,
8068,
36,
1828,
16458,
35,
23,
34,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
12,
23,
1983,
37,
2246,
3365,
32,
23,
26009,
7708,
1828,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
12,
23,
1983,
26001,
31115,
36,
37466,
2943,
2075,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
12,
24,
2718,
32,
22136,
37,
17,
6500,
5066,
34,
486,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
12,
24,
2718,
23199,
2075,
36,
17,
36,
2999,
15199,
486,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
12,
24,
14242,
47338,
1433,
34,
22,
34,
21,
33,
24,
3070,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
12,
24,
37,
1507,
36,
27970,
34,
20,
34,
17,
29499,
15,
33,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
12,
32,
30803,
35,
9697,
19,
36,
3134,
37,
16,
34,
2231,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
12,
32,
20,
34,
3134,
37,
4304,
1961,
5999,
15711,
34,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
12,
32,
5333,
33,
19266,
16,
26009,
2885,
22,
33,
2713,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
12,
3838,
3865,
33,
16,
35,
5631,
33,
25870,
33,
3865,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
12,
8579,
4531,
33,
21,
35,
24,
36330,
32,
31503,
33,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
12,
33,
2780,
3132,
5222,
14529,
4309,
32,
15,
34,
19,
34,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
12,
33,
34583,
34,
22318,
22,
5631,
24,
15199,
23,
35,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
12,
12473,
46556,
8579,
23,
34,
20,
30195,
19,
7708,
17,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
12,
34,
2885,
3134,
486,
37,
22,
5222,
32,
2931,
2481,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
12,
34,
5777,
22,
35,
43234,
32,
22,
3559,
34,
38540,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
12,
11012,
1314,
14529,
2816,
3104,
3559,
34,
41739,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
334,
6,
14155,
12,
36,
3064,
23,
31697,
37,
9697,
38956,
2414,
3256,
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] | 2.020719 | 4,537 |
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