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|---|---|---|---|
from functools import reduce
from math import factorial
if __name__ == '__main__':
main() | [
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import pytest
from hotel import Hotel
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######################################################################################################################
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. #
# #
# Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance #
# with the License. A copy of the License is located at #
# #
# http://www.apache.org/licenses/LICENSE-2.0 #
# #
# or in the 'license' file accompanying this file. This file is distributed on an 'AS IS' BASIS, WITHOUT WARRANTIES #
# OR CONDITIONS OF ANY KIND, express or implied. See the License for the specific language governing permissions #
# and limitations under the License. #
######################################################################################################################
import os
from unittest import TestCase, mock
from mock import patch
import botocore
mock_env_variables = {
"botLanguage": "English",
"AWS_SDK_USER_AGENT": '{ "user_agent_extra": "AwsSolution/1234/1.6.0" }',
}
@patch.dict(os.environ, mock_env_variables)
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] | 1.902247 | 890 |
#####################################################################################################
# PARETO was produced under the DOE Produced Water Application for Beneficial Reuse Environmental
# Impact and Treatment Optimization (PARETO), and is copyright (c) 2021 by the software owners: The
# Regents of the University of California, through Lawrence Berkeley National Laboratory, et al. All
# rights reserved.
#
# NOTICE. This Software was developed under funding from the U.S. Department of Energy and the
# U.S. Government consequently retains certain rights. As such, the U.S. Government has been granted
# for itself and others acting on its behalf a paid-up, nonexclusive, irrevocable, worldwide license
# in the Software to reproduce, distribute copies to the public, prepare derivative works, and perform
# publicly and display publicly, and to permit other to do so.
#####################################################################################################
# Title: OPERATIONAL Produced Water Optimization Model
# Notes:
# - Introduced new completions-to-completions trucking arc (CCT) to account for possible flowback reuse
# - Implemented a generic OPERATIONAL case study example (updated model sets, additional input data)
# - Implemented an initial formulation for production tank modeling (see updated documentation)
# - Implemented a corrected version of the disposal capacity constraint considering more trucking-to-disposal arcs (PKT, SKT, SKT, RKT) [June 28]
# - Implemented an improved slack variable display loop [June 29]
# - Implemented fresh sourcing via trucking [July 2]
# - Implemented completions pad storage [July 6]
# - Implemeted an equalized production tank formulation [July 7]
# - Implemented changes to flowback processing [July 13]
# - Implemented production tank config option [August 4]
# Import
from pyomo.environ import (
Var,
Param,
Set,
ConcreteModel,
Constraint,
Objective,
minimize,
NonNegativeReals,
Reals,
Binary,
)
from pareto.utilities.get_data import get_data
from importlib import resources
import pyomo.environ
from pyomo.core.base.constraint import simple_constraint_rule
# import gurobipy
from pyomo.common.config import ConfigBlock, ConfigValue, In
from enum import Enum
from pareto.utilities.solvers import get_solver
# create config dictionary
CONFIG = ConfigBlock()
CONFIG.declare(
"has_pipeline_constraints",
ConfigValue(
default=True,
domain=In([True, False]),
description="build pipeline constraints",
doc="""Indicates whether holdup terms should be constructed or not.
**default** - True.
**Valid values:** {
**True** - construct pipeline constraints,
**False** - do not construct pipeline constraints}""",
),
)
CONFIG.declare(
"production_tanks",
ConfigValue(
default=ProdTank.individual,
domain=In(ProdTank),
description="production tank type selection",
doc="Type of production tank arrangement (i.e., Individual, Equalized)",
),
)
# Creation of a Concrete Model
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8,
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198,
2,
21582,
286,
257,
1482,
38669,
9104,
628,
628
] | 3.639151 | 848 |
import requests
from pprint import pprint
import redis
import json
from datetime import datetime
from config_snips import cluster_config
if __name__ == '__main__':
github = 'https://github.com/natemellendorf/tr_templates'
gitlab = 'http://gitlab/root/awesome'
test = get_ext_repo(gitlab)
pprint(test)
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] | 2.815789 | 114 |
import heterocl as hcl
import numpy as np
import time
import os
######################################### HELPER FUNCTIONS #########################################
# Update the value function at position (i,j,k,l)
# iVals: holds index values (i,j,k,l) that correspond to state values (si,sj,sk,sl)
# intermeds: holds the estimated value associated with taking each action
# interpV: holds the estimated value of a successor state (linear interpolation only)
# gamma: discount factor
# ptsEachDim: the number of grid points in each dimension of the state space
# useNN: a mode flag (0: use linear interpolation, 1: use nearest neighbour)
# Returns 0 if convergence has been reached
# Converts state values into indeces using nearest neighbour rounding
# Convert indices into state values
# Sets iVals equal to (i,j,k,l) and sVals equal to the corresponding state values
######################################### VALUE ITERATION ##########################################
# Main value iteration algorithm
# reSweep: a convergence flag (1: continue iterating, 0: convergence reached)
# epsilon: convergence criteria
# maxIters: maximum number of iterations that can occur without convergence being reached
# count: the number of iterations that have been performed
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587,
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628,
198
] | 3.966565 | 329 |
from django import template
from blog.models import Category
register = template.Library()
def get_categories(context, order, count):
"""Получаю список категорий"""
# categories = Category.objects.filter(published=True, parent__isnull=True).order_by(order)
categories = Category.objects.filter(published=True).order_by(order)
if count is not None:
categories = categories[:count]
return categories
@register.inclusion_tag('base/tags/base_tag.html', takes_context=True)
def category_list(context, order='-name', count=None, template='base/blog/categories.html'):
"""template tag вывода категорий"""
categories = get_categories(context, order, count)
return {'template': template, "category_list": categories}
@register.simple_tag(takes_context=True)
def for_category_list(context, count=None, order='-name'):
"""template tag вывода категорий без шаблона"""
return get_categories(context, order, count)
| [
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66,
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7,
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11,
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11,
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8,
198
] | 2.614754 | 366 |
#!/usr/bin/env python3
# http://adventofcode.com/2017/day/4
import sys
if __name__ == '__main__':
main(sys.argv)
| [
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] | 2.236364 | 55 |
from django.contrib import admin
from models import Bucketlist, Item
admin.site.register(Bucketlist)
admin.site.register(Item)
| [
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#
# Copyright (C) 2020 GrammaTech, Inc.
#
# This code is licensed under the MIT license. See the LICENSE file in
# the project root for license terms.
#
# This project is sponsored by the Office of Naval Research, One Liberty
# Center, 875 N. Randolph Street, Arlington, VA 22203 under contract #
# N68335-17-C-0700. The content of the information does not necessarily
# reflect the position or policy of the Government and no official
# endorsement should be inferred.
#
import imp
import setuptools
__version__ = imp.load_source(
"pkginfo.version", "gtirb_capstone/version.py"
).__version__
if __name__ == "__main__":
with open("README.md", "r") as fh:
long_description = fh.read()
setuptools.setup(
name="gtirb-capstone",
version=__version__,
author="Grammatech",
author_email="[email protected]",
description="Utilities for rewriting GTIRB with capstone and keystone",
packages=setuptools.find_packages(),
install_requires=[
"capstone-gt",
"dataclasses ; python_version<'3.7.0'",
"gtirb",
"keystone-engine",
],
classifiers=["Programming Language :: Python :: 3"],
extras_require={
"test": [
"flake8",
"isort",
"pytest",
"pytest-cov",
"tox",
"tox-wheel",
"pre-commit",
"mcasm",
]
},
long_description=long_description,
long_description_content_type="text/markdown",
url="https://github.com/grammatech/gtirb-functions",
license="MIT",
)
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from .login import LoginView, SocialLoginView
from .logout import LogoutView
from .register import RegisterView, VerifyView, ActivateView
from .profile import ProfileView
from .change_password import ChangePasswordView
from .reset_password import ResetPasswordView, ResetPasswordConfirmView, ResetPasswordCompleteView
from .set_password import SetPasswordView
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from django.contrib import admin
from .models import Post, Comments
# Register your models here.
admin.site.register(Post)
admin.site.register(Comments)
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# Generated from UnitX.g4 by ANTLR 4.5.1
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# Exit a parse tree produced by UnitXParser#program.
# Enter a parse tree produced by UnitXParser#typeDeclaration.
# Exit a parse tree produced by UnitXParser#typeDeclaration.
# Enter a parse tree produced by UnitXParser#functionDeclaration.
# Exit a parse tree produced by UnitXParser#functionDeclaration.
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# Exit a parse tree produced by UnitXParser#formalParameters.
# Enter a parse tree produced by UnitXParser#formalParameterList.
# Exit a parse tree produced by UnitXParser#formalParameterList.
# Enter a parse tree produced by UnitXParser#formalParameter.
# Exit a parse tree produced by UnitXParser#formalParameter.
# Enter a parse tree produced by UnitXParser#block.
# Exit a parse tree produced by UnitXParser#block.
# Enter a parse tree produced by UnitXParser#blockStatement.
# Exit a parse tree produced by UnitXParser#blockStatement.
# Enter a parse tree produced by UnitXParser#statement.
# Exit a parse tree produced by UnitXParser#statement.
# Enter a parse tree produced by UnitXParser#repStatement.
# Exit a parse tree produced by UnitXParser#repStatement.
# Enter a parse tree produced by UnitXParser#ifStatement.
# Exit a parse tree produced by UnitXParser#ifStatement.
# Enter a parse tree produced by UnitXParser#expressionStatement.
# Exit a parse tree produced by UnitXParser#expressionStatement.
# Enter a parse tree produced by UnitXParser#printStatement.
# Exit a parse tree produced by UnitXParser#printStatement.
# Enter a parse tree produced by UnitXParser#assertStatement.
# Exit a parse tree produced by UnitXParser#assertStatement.
# Enter a parse tree produced by UnitXParser#dumpStatement.
# Exit a parse tree produced by UnitXParser#dumpStatement.
# Enter a parse tree produced by UnitXParser#borderStatement.
# Exit a parse tree produced by UnitXParser#borderStatement.
# Enter a parse tree produced by UnitXParser#expressionList.
# Exit a parse tree produced by UnitXParser#expressionList.
# Enter a parse tree produced by UnitXParser#parExpression.
# Exit a parse tree produced by UnitXParser#parExpression.
# Enter a parse tree produced by UnitXParser#repControl.
# Exit a parse tree produced by UnitXParser#repControl.
# Enter a parse tree produced by UnitXParser#endRep.
# Exit a parse tree produced by UnitXParser#endRep.
# Enter a parse tree produced by UnitXParser#expression.
# Exit a parse tree produced by UnitXParser#expression.
# Enter a parse tree produced by UnitXParser#unit.
# Exit a parse tree produced by UnitXParser#unit.
# Enter a parse tree produced by UnitXParser#unitSingleOrPairOperator.
# Exit a parse tree produced by UnitXParser#unitSingleOrPairOperator.
# Enter a parse tree produced by UnitXParser#unitOperator.
# Exit a parse tree produced by UnitXParser#unitOperator.
# Enter a parse tree produced by UnitXParser#primary.
# Exit a parse tree produced by UnitXParser#primary.
# Enter a parse tree produced by UnitXParser#literal.
# Exit a parse tree produced by UnitXParser#literal.
# Enter a parse tree produced by UnitXParser#string.
# Exit a parse tree produced by UnitXParser#string.
# Enter a parse tree produced by UnitXParser#halfString.
# Exit a parse tree produced by UnitXParser#halfString.
# Enter a parse tree produced by UnitXParser#number.
# Exit a parse tree produced by UnitXParser#number.
# Enter a parse tree produced by UnitXParser#integer.
# Exit a parse tree produced by UnitXParser#integer.
# Enter a parse tree produced by UnitXParser#boolean.
# Exit a parse tree produced by UnitXParser#boolean.
# Enter a parse tree produced by UnitXParser#none.
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628,
198,
220,
220,
220,
1303,
6062,
257,
21136,
5509,
4635,
416,
11801,
27481,
28198,
2,
2127,
21052,
13,
628,
220,
220,
220,
1303,
29739,
257,
21136,
5509,
4635,
416,
11801,
27481,
28198,
2,
2127,
21052,
13,
628,
198,
220,
220,
220,
1303,
6062,
257,
21136,
5509,
4635,
416,
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27481,
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2,
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13,
628,
220,
220,
220,
1303,
29739,
257,
21136,
5509,
4635,
416,
11801,
27481,
28198,
2,
23108,
13,
628,
198
] | 3.404858 | 1,235 |
from copy import deepcopy
import cv2 as cv
import numpy as np
from sortedcontainers import SortedDict
import vision.utils.box_utils_numpy as box_utils
from wagon_tracking.transforms import ImageDownscaleTransform
| [
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] | 3.453125 | 64 |
from __future__ import annotations
from collections import defaultdict
from math import ceil
from typing import Dict, NamedTuple
from random import randint, sample
if __name__ == "__main__":
# g = Graph.random_generator(10, 0.2)
# print(g)
# print(len(g["0"].values()))
for i in (0.25, 0.5, 1):
g = Graph.random_generator(10, i)
print(g, end="\n\n")
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198
] | 2.519481 | 154 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Nov 30 14:04:57 2020
@author: mike_ubuntu
"""
from copy import deepcopy
import numpy as np
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#!/usr/bin/env python
import rospy
import math
from sensor_msgs.msg import Joy
from geometry_msgs.msg import Twist
if __name__ == '__main__':
rospy.init_node('joy_twist')
joy_twist = JoyTwist()
rospy.spin()
| [
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] | 2.47191 | 89 |
import numpy as np
if __name__ == "__main__":
# Part 1.
assert part1(get_data(7, 'test')) == 37, "Part 1 failed."
print(f"Part 1: {part1(get_data(7, 'data')):.0f}")
# Part 2.
assert part2(get_data(7, 'test')) == 168, "Part 2 failed."
print(f"Part 2: {part2(get_data(7, 'data')):.0f}")
| [
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] | 2.143836 | 146 |
"""ADS1220 example (polling ADC).
Uses single shot mode and wait for data ready."""
from time import sleep
from machine import Pin, SPI # type: ignore
from ads1220 import ADC
cs = 15 # Chip select pin
drdy = 27 # Data ready pin
spi = SPI(1,
baudrate=10000000, # 10 MHz (try lower speed to troubleshoot)
sck=Pin(14),
mosi=Pin(13),
miso=Pin(12),
phase=1) # ADS1220 uses SPI mode 1
adc = ADC(spi, cs, drdy)
def test():
"""Test code."""
vref = 2.048 # Internal voltage reference
res = 8388607 # ADC resolution 23 bit (2^23, assumes 1 bit polarity)
adc.conversion_single_shot() # Set single shot conversion mode
adc.select_channel(0) # Select channel 0 (0 to 3 ADC channels)
sleep(.1) # Ensure ADC ready
try:
while True:
adc.start_conversion() # Conversion must be started each shot
reading = adc.read_wait()
v = reading * vref / res
print("raw: {0}, volts: {1}".format(reading, v))
sleep(3)
except KeyboardInterrupt:
print("\nCtrl-C pressed to exit.")
finally:
adc.power_down()
spi.deinit()
test()
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599,
72,
13,
2934,
15003,
3419,
628,
198,
9288,
3419,
198
] | 2.334638 | 511 |
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""
Tests for tfx_addons.feast_examplegen.component.
"""
import pytest
try:
import feast
except ImportError:
pytest.skip("feast not available, skipping", allow_module_level=True)
from tfx.v1.proto import Input
from tfx_addons.feast_examplegen.component import FeastExampleGen
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62,
20688,
5235,
13,
42895,
1330,
42936,
16281,
13746,
628,
198
] | 3.84585 | 253 |
#!/usr/bin/env python3
# Utility functioins
import sys
from Crypto.Cipher import AES
from Crypto.PublicKey import RSA
from Crypto.Signature import PKCS1_v1_5
from Crypto.Signature import pss
from Crypto.Hash import SHA256
import array
import hashlib
import hmac
import os
import binascii
MAX_DOWNLOAD_SIZE = 0x48000 # 288K
AM_SECBOOT_DEFAULT_NONSECURE_MAIN = 0x18000
# Encryption Algorithm
# String constants
# Authentication Algorithm
FLASH_INVALID = 0xFFFFFFFF
# KeyWrap Mode
#******************************************************************************
#
# Magic Numbers
#
#******************************************************************************
AM_IMAGE_MAGIC_SBL = 0xA3
AM_IMAGE_MAGIC_ICV_CHAIN = 0xAC
AM_IMAGE_MAGIC_SECURE = 0xC0
AM_IMAGE_MAGIC_OEM_CHAIN = 0xCC
AM_IMAGE_MAGIC_NONSECURE = 0xCB
AM_IMAGE_MAGIC_INFO0 = 0xCF
AM_IMAGE_MAGIC_CONTAINER = 0xC1
AM_IMAGE_MAGIC_KEYREVOKE = 0xCE
AM_IMAGE_MAGIC_DOWNLOAD = 0xCD
#******************************************************************************
#
# Wired Message Types
#
#******************************************************************************
AM_SECBOOT_WIRED_MSGTYPE_HELLO = 0
AM_SECBOOT_WIRED_MSGTYPE_STATUS = 1
AM_SECBOOT_WIRED_MSGTYPE_OTADESC = 2
AM_SECBOOT_WIRED_MSGTYPE_UPDATE = 3
AM_SECBOOT_WIRED_MSGTYPE_ABORT = 4
AM_SECBOOT_WIRED_MSGTYPE_RECOVER = 5
AM_SECBOOT_WIRED_MSGTYPE_RESET = 6
AM_SECBOOT_WIRED_MSGTYPE_ACK = 7
AM_SECBOOT_WIRED_MSGTYPE_DATA = 8
#******************************************************************************
#
# Wired Message ACK Status
#
#******************************************************************************
AM_SECBOOT_WIRED_ACK_STATUS_SUCCESS = 0
AM_SECBOOT_WIRED_ACK_STATUS_FAILURE = 1
AM_SECBOOT_WIRED_ACK_STATUS_INVALID_INFO0 = 2
AM_SECBOOT_WIRED_ACK_STATUS_CRC = 3
AM_SECBOOT_WIRED_ACK_STATUS_SEC = 4
AM_SECBOOT_WIRED_ACK_STATUS_MSG_TOO_BIG = 5
AM_SECBOOT_WIRED_ACK_STATUS_UNKNOWN_MSGTYPE = 6
AM_SECBOOT_WIRED_ACK_STATUS_INVALID_ADDR = 7
AM_SECBOOT_WIRED_ACK_STATUS_INVALID_OPERATION = 8
AM_SECBOOT_WIRED_ACK_STATUS_INVALID_PARAM = 9
AM_SECBOOT_WIRED_ACK_STATUS_SEQ = 10
AM_SECBOOT_WIRED_ACK_STATUS_TOO_MUCH_DATA = 11
#******************************************************************************
#
# Definitions related to Image Headers
#
#******************************************************************************
AM_MAX_UART_MSG_SIZE = 8192 # 8K buffer in SBL
# Max Wired Update Image header size - this includes optional sign & encryption info
AM_WU_IMAGEHDR_SIZE = (16 + 384 + 48 + 16)
#******************************************************************************
#
# INFOSPACE related definitions
#
#******************************************************************************
AM_SECBOOT_INFO0_SIGN_PROGRAMMED0 = 0x48EAAD88
AM_SECBOOT_INFO0_SIGN_PROGRAMMED1 = 0xC9705737
AM_SECBOOT_INFO0_SIGN_PROGRAMMED2 = 0x0A6B8458
AM_SECBOOT_INFO0_SIGN_PROGRAMMED3 = 0xE41A9D74
AM_SECBOOT_INFO0_SIGN_UINIT0 = 0x5B75A5FA
AM_SECBOOT_INFO0_SIGN_UINIT1 = 0x7B9C8674
AM_SECBOOT_INFO0_SIGN_UINIT2 = 0x869A96FE
AM_SECBOOT_INFO0_SIGN_UINIT3 = 0xAEC90860
INFO0_SIZE_BYTES = (2 * 1024)
INFO1_SIZE_BYTES = (6 * 1024)
#******************************************************************************
#
# CRC using ethernet poly, as used by Corvette hardware for validation
#
#******************************************************************************
#******************************************************************************
#
# Pad the text to the block_size. bZeroPad determines how to handle text which
# is already multiple of block_size
#
#******************************************************************************
#******************************************************************************
#
# AES CBC encryption
#
#******************************************************************************
#******************************************************************************
#
# AES 128 CBC encryption
#
#******************************************************************************
#******************************************************************************
#
# SHA256 HMAC
#
#******************************************************************************
#******************************************************************************
#
# RSA PKCS1_v1_5 sign
#
#******************************************************************************
#******************************************************************************
#
# RSA PKCS1_v1_5 sign verification
#
#******************************************************************************
#******************************************************************************
#
# RSA PSS signing function.
#
#******************************************************************************
#******************************************************************************
#
# RSA PSS signature verification.
#
#******************************************************************************
#******************************************************************************
#
# Fill one word in bytearray
#
#******************************************************************************
#******************************************************************************
#
# Turn a 32-bit number into a series of bytes for transmission.
#
# This command will split a 32-bit integer into an array of bytes, ordered
# LSB-first for transmission over the UART.
#
#******************************************************************************
#******************************************************************************
#
# Extract a word from a byte array
#
#******************************************************************************
#******************************************************************************
#
# automatically figure out the integer format (base 10 or 16)
#
#******************************************************************************
#******************************************************************************
#
# User controllable Prints control
#
#******************************************************************************
# Defined print levels
AM_PRINT_LEVEL_MIN = 0
AM_PRINT_LEVEL_NONE = AM_PRINT_LEVEL_MIN
AM_PRINT_LEVEL_ERROR = 1
AM_PRINT_LEVEL_INFO = 2
AM_PRINT_LEVEL_VERBOSE = 4
AM_PRINT_LEVEL_DEBUG = 5
AM_PRINT_LEVEL_MAX = AM_PRINT_LEVEL_DEBUG
# Global variable to control the prints
AM_PRINT_VERBOSITY = AM_PRINT_LEVEL_INFO
helpPrintLevel = 'Set Log Level (0: None), (1: Error), (2: INFO), (4: Verbose), (5: Debug) [Default = Info]'
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] | 3.231904 | 2,169 |
import csv
import requests
import socket
from bs4 import BeautifulSoup
import re
import json
with open('artists.json', 'w') as outfile:
json.dump(parse_artists(), outfile)
'''artist_urls = get_artist_urls()
artist_array = compile_artist_profiles(artist_urls)
outfile = open("./toronto-artists.csv", "wb")
writer = csv.writer(outfile)
writer.writerows(recipe_array)'''
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] | 2.772059 | 136 |
from __future__ import print_function
import sys
import re
import sqlparse
from collections import namedtuple
from sqlparse.sql import Comparison, Identifier, Where
from .parseutils.utils import (
last_word, find_prev_keyword, parse_partial_identifier)
from .parseutils.tables import extract_tables
from .parseutils.ctes import isolate_query_ctes
from pgspecial.main import parse_special_command
PY2 = sys.version_info[0] == 2
PY3 = sys.version_info[0] == 3
if PY3:
string_types = str
else:
string_types = basestring
Special = namedtuple('Special', [])
Database = namedtuple('Database', [])
Schema = namedtuple('Schema', ['quoted'])
Schema.__new__.__defaults__ = (False,)
# FromClauseItem is a table/view/function used in the FROM clause
# `table_refs` contains the list of tables/... already in the statement,
# used to ensure that the alias we suggest is unique
FromClauseItem = namedtuple('FromClauseItem', 'schema table_refs local_tables')
Table = namedtuple('Table', ['schema', 'table_refs', 'local_tables'])
TableFormat = namedtuple('TableFormat', [])
View = namedtuple('View', ['schema', 'table_refs'])
# JoinConditions are suggested after ON, e.g. 'foo.barid = bar.barid'
JoinCondition = namedtuple('JoinCondition', ['table_refs', 'parent'])
# Joins are suggested after JOIN, e.g. 'foo ON foo.barid = bar.barid'
Join = namedtuple('Join', ['table_refs', 'schema'])
Function = namedtuple('Function', ['schema', 'table_refs', 'usage'])
# For convenience, don't require the `usage` argument in Function constructor
Function.__new__.__defaults__ = (None, tuple(), None)
Table.__new__.__defaults__ = (None, tuple(), tuple())
View.__new__.__defaults__ = (None, tuple())
FromClauseItem.__new__.__defaults__ = (None, tuple(), tuple())
Column = namedtuple(
'Column',
['table_refs', 'require_last_table', 'local_tables', 'qualifiable',
'context']
)
Column.__new__.__defaults__ = (None, None, tuple(), False, None)
Keyword = namedtuple('Keyword', ['last_token'])
Keyword.__new__.__defaults__ = (None,)
NamedQuery = namedtuple('NamedQuery', [])
Datatype = namedtuple('Datatype', ['schema'])
Alias = namedtuple('Alias', ['aliases'])
Path = namedtuple('Path', [])
def suggest_type(full_text, text_before_cursor):
"""Takes the full_text that is typed so far and also the text before the
cursor to suggest completion type and scope.
Returns a tuple with a type of entity ('table', 'column' etc) and a scope.
A scope for a column category will be a list of tables.
"""
if full_text.startswith('\\i '):
return (Path(),)
# This is a temporary hack; the exception handling
# here should be removed once sqlparse has been fixed
try:
stmt = SqlStatement(full_text, text_before_cursor)
except (TypeError, AttributeError):
return []
# Check for special commands and handle those separately
if stmt.parsed:
# Be careful here because trivial whitespace is parsed as a
# statement, but the statement won't have a first token
tok1 = stmt.parsed.token_first()
if tok1 and tok1.value == '\\':
text = stmt.text_before_cursor + stmt.word_before_cursor
return suggest_special(text)
return suggest_based_on_last_token(stmt.last_token, stmt)
named_query_regex = re.compile(r'^\s*\\ns\s+[A-z0-9\-_]+\s+')
def _strip_named_query(txt):
"""
This will strip "save named query" command in the beginning of the line:
'\ns zzz SELECT * FROM abc' -> 'SELECT * FROM abc'
' \ns zzz SELECT * FROM abc' -> 'SELECT * FROM abc'
"""
if named_query_regex.match(txt):
txt = named_query_regex.sub('', txt)
return txt
function_body_pattern = re.compile(r'(\$.*?\$)([\s\S]*?)\1', re.M)
SPECIALS_SUGGESTION = {
'dT': Datatype,
'df': Function,
'dt': Table,
'dv': View,
'sf': Function,
}
def identifies(id, ref):
"""Returns true if string `id` matches TableReference `ref`"""
return id == ref.alias or id == ref.name or (
ref.schema and (id == ref.schema + '.' + ref.name))
def _allow_join_condition(statement):
"""
Tests if a join condition should be suggested
We need this to avoid bad suggestions when entering e.g.
select * from tbl1 a join tbl2 b on a.id = <cursor>
So check that the preceding token is a ON, AND, or OR keyword, instead of
e.g. an equals sign.
:param statement: an sqlparse.sql.Statement
:return: boolean
"""
if not statement or not statement.tokens:
return False
last_tok = statement.token_prev(len(statement.tokens))[1]
return last_tok.value.lower() in ('on', 'and', 'or')
def _allow_join(statement):
"""
Tests if a join should be suggested
We need this to avoid bad suggestions when entering e.g.
select * from tbl1 a join tbl2 b <cursor>
So check that the preceding token is a JOIN keyword
:param statement: an sqlparse.sql.Statement
:return: boolean
"""
if not statement or not statement.tokens:
return False
last_tok = statement.token_prev(len(statement.tokens))[1]
return (last_tok.value.lower().endswith('join')
and last_tok.value.lower() not in('cross join', 'natural join'))
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] | 2.736733 | 1,922 |
import random
from .prepare import app, db, model_repr
class Answer(db.Model):
'''
使用的sql语句:
```sql
CREATE TABLE `answers` (
`accept_id` int(11) NOT NULL COMMENT '接受id',
`problem_id` int(11) NOT NULL COMMENT '问题id',
`answer` int(11) NOT NULL DEFAULT '-1' COMMENT '具体答案的选项',
PRIMARY KEY (`accept_id`,`problem_id`),
KEY `problem_index` (`problem_id`),
CONSTRAINT `answers_ibfk_1` FOREIGN KEY (`accept_id`) REFERENCES `accepts` (`id`) ON DELETE CASCADE,
CONSTRAINT `answers_ibfk_2` FOREIGN KEY (`problem_id`) REFERENCES `problems` (`id`) ON DELETE CASCADE
) ENGINE=InnoDB DEFAULT CHARSET=utf8
```
属性:
基本属性
problem: 关联的问题
task: 关联的任务
'''
__tablename__ = 'answers'
accept_id = db.Column('accept_id', db.Integer, db.ForeignKey(
'accepts.id', ondelete='cascade'), nullable=False, comment='接受id')
problem_id = db.Column('problem_id', db.Integer, db.ForeignKey(
'problems.id', ondelete='cascade'), nullable=False, comment='问题id')
# answer_id = db.Column('answer_id', db.Integer, db.ForeignKey(
# 'answers.openid', ondelete='cascade'), nullable=False, comment='回答者id')
# task_id = db.Column('task_id', db.Integer, db.ForeignKey(
# 'tasks.id', ondelete='cascade'), nullable=False, comment='任务id')
answer = db.Column('answer', db.Integer(
), nullable=False, server_default='-1', comment='具体答案的选项')
accept = db.relationship('Accept', back_populates='answers')
problem = db.relationship('Problem', back_populates='answers')
# task = db.relationship('Task', back_populates='answers')
# student = db.relationship('Student', back_populates='answers')
__table_args__ = (
db.PrimaryKeyConstraint('accept_id', 'problem_id'),
db.Index('problem_index', 'problem_id'),
)
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] | 2.114383 | 883 |
"""
Clean bioenergy data from AZEL
"""
import os
import json
import itertools
import geopandas as gpd
import pandas as pd
os.makedirs('data', exist_ok=True)
projection = 'epsg:4326'
name = ['pecuarios', 'forestales', 'industriales', 'urbanos']
scenario = ['E3', 'E1']
for scenario, name in itertools.product(scenario, name):
# Load bioenergy shape file
print ('Reading file: {}_R{}.shp'.format(scenario, name))
df = gpd.read_file('../data/interim/shapes/FBio_{0}_R{1}.shp'.format(scenario,
name))
df = df[df.geometry.notnull()].to_crs({'init': projection})
# Load transmission region dictionary
with open(os.path.join('../data/interim/', 'trans-regions.json'), 'r') as fp:
trans_regions = json.load(fp)
# Load transmission region shapefiles
lz = gpd.read_file('../data/interim/shapes/Mask_T.shp')
lz = lz.to_crs({'init': projection})
lz.loc[:, 'trans-region'] = (lz['ID'].astype(int)
.map('{0:02}'.format)
.map(trans_regions))
assert lz.crs == df.crs
if not 'forestal' in name:
join = gpd.sjoin(df, lz, op='within')
else:
join = gpd.overlay(lz, df, how='intersection')
# Get specific columns for output data
try:
columns = ['trans-region', 'X', 'Y', 'CLASIFICAC', 'TIPO', 'PROCESO',
'GENE_GWha', 'CAPINST_MW', 'FP']
bio = join[columns].copy();
except KeyError:
columns = ['trans-region', 'CLASIFICAC', 'TIPO', 'PROCESO',
'GENE_GWha', 'CAPINST_MW', 'FP']
bio = join[columns].copy();
bio['CLASIFICAC'] = bio.CLASIFICAC.map(str.lower).str.replace(' ', '_')
bio['TIPO'] = bio.TIPO.map(str.lower).str.replace(' ', '_')
bio['PROCESO'] = bio.PROCESO.map(str.lower).str.replace(' ', '_')
if 'E3' in scenario:
scenario = 'high'
else:
scenario = 'low'
bio.loc[:, 'scenario'] = scenario
bio.loc[:, 'id'] = name
bio = bio.rename(columns={'X': 'lng', 'Y': 'lat', 'CLASIFICAC': 'source',
'TIPO': 'category', 'FP': 'cf',
'GENE_GWha': 'gen_GWha', 'CAPINST_MW':'cap_MW',
'PROCESO': 'fuel_type'})
print ('Saving data: {0}_{1}'.format(scenario, name))
bio.to_csv('data/bioenergy_{0}_{1}.csv'.format(scenario, name), index=False)
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] | 2.003236 | 1,236 |
#the table should be read T[deg][a] where a is the multiplicity of the Q
def build1DCeilingTable(c):
'''entry for A is max k s.t. l(A) = l(A+kP) and l(A+kQ) '''
max_deg = [0 for _ in range(c.m)]
CLP = c.fill_degree_table_reverse(update, max_deg)
CLQ = c.fill_degree_table_reverse(update, max_deg)
return ['CLP','CLQ'], [CLP,CLQ]
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] | 2.229299 | 157 |
# 1. Observe os dois métodos de exclusão listados abaixo.
#
# 2. Identifique a quais estruturas pertencem os métodos, respectivamente.
# R: A primeira é Lista duplamente encadeada (double_linked), metodo de remoção. A segunda é Lista encadeada, metodo de remoção
#
# 3. Explique qual a diferença FUNDAMENTAL entre os dois métodos.
# R: na lista duplamente encadeada um nodo sempre aponta para o next e um prev, pois assim a lista pode ser percorrida de qualquer direção, tanto no inicio para o fim, quanto do fim para o inicio. Já a lista ordenada, possui apenas o next, ou seja, a lista só é acessada percorrendo uma unica direção.
# Método 1
# Método 2 | [
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] | 2.561538 | 260 |
# -*- coding: utf-8 -*-
#
# This file is part of Invenio.
# Copyright (C) 2020 CERN.
#
# Invenio is free software; you can redistribute it and/or modify it
# under the terms of the MIT License; see LICENSE file for more details.
"""CSRF Middleware.
The implementation is highly inspred from Django's initial implementation
about CSRF protection. For more information you can see here:
<https://github.com/django/django/blob/master/django/middleware/csrf.py>
"""
import re
import secrets
import string
from datetime import datetime, timedelta, timezone
from flask import Blueprint, abort, current_app, request
from itsdangerous import BadSignature, SignatureExpired, \
TimedJSONWebSignatureSerializer
from six import string_types
from six.moves.urllib.parse import urlparse
from .errors import RESTCSRFError
REASON_NO_REFERER = "Referer checking failed - no Referer."
REASON_BAD_REFERER = (
"Referer checking failed - %s does not match any trusted origins."
)
REASON_NO_CSRF_COOKIE = "CSRF cookie not set."
REASON_BAD_TOKEN = "CSRF token missing or incorrect."
REASON_MALFORMED_REFERER = "Referer checking failed - Referer is malformed."
REASON_INSECURE_REFERER = (
"Referer checking failed - Referer is insecure while host is secure."
)
REASON_TOKEN_EXPIRED = "CSRF token expired. Try again."
def _get_csrf_serializer(expires_in=None):
"""Note that this serializer is used to encode/decode the CSRF cookie.
In case you change this implementation bear in mind that the token
generated must be signed so as to avoid any client-side tampering.
"""
expires_in = expires_in or current_app.config['CSRF_TOKEN_EXPIRES_IN']
return TimedJSONWebSignatureSerializer(
current_app.config.get(
'CSRF_SECRET',
current_app.config.get('SECRET_KEY') or 'CHANGE_ME'),
salt=current_app.config['CSRF_SECRET_SALT'],
expires_in=expires_in,
)
def csrf_validate():
"""Check CSRF cookie against request headers."""
if request.is_secure:
referer = request.referrer
if referer is None:
return _abort400(REASON_NO_REFERER)
referer = urlparse(referer)
# Make sure we have a valid URL for Referer.
if '' in (referer.scheme, referer.netloc):
return _abort400(REASON_MALFORMED_REFERER)
# Ensure that our Referer is also secure.
if not _is_referer_secure(referer):
return _abort400(REASON_INSECURE_REFERER)
is_hostname_allowed = referer.hostname in \
current_app.config.get('APP_ALLOWED_HOSTS')
if not is_hostname_allowed:
reason = REASON_BAD_REFERER % referer.geturl()
return _abort400(reason)
csrf_token = _get_csrf_token()
if csrf_token is None:
return _abort400(REASON_NO_CSRF_COOKIE)
request_csrf_token = _get_submitted_csrf_token()
if not request_csrf_token:
_abort400(REASON_BAD_TOKEN)
decoded_request_csrf_token = _decode_csrf(request_csrf_token)
if csrf_token != decoded_request_csrf_token:
return _abort400(REASON_BAD_TOKEN)
def reset_token():
"""Change the CSRF token in use for a request."""
request.csrf_cookie_needs_reset = True
class CSRFTokenMiddleware():
"""CSRF Token Middleware."""
def __init__(self, app=None):
"""Middleware initialization.
:param app: An instance of :class:`flask.Flask`.
"""
if app:
self.init_app(app)
def init_app(self, app):
"""Initialize middleware extension.
:param app: An instance of :class:`flask.Flask`.
"""
app.config.setdefault('CSRF_COOKIE_NAME', 'csrftoken')
app.config.setdefault('CSRF_HEADER', 'X-CSRFToken')
app.config.setdefault(
'CSRF_METHODS', ['POST', 'PUT', 'PATCH', 'DELETE'])
app.config.setdefault('CSRF_TOKEN_LENGTH', 32)
app.config.setdefault(
'CSRF_ALLOWED_CHARS', string.ascii_letters + string.digits)
app.config.setdefault('CSRF_SECRET_SALT', 'invenio-csrf-token')
app.config.setdefault('CSRF_FORCE_SECURE_REFERER', True)
app.config.setdefault(
'CSRF_COOKIE_SAMESITE',
app.config.get('SESSION_COOKIE_SAMESITE') or 'Lax')
# The token last for 24 hours, but the cookie for 7 days. This allows
# us to implement transparent token rotation during those 7 days. Note,
# that the token is automatically rotated on login, thus you can also
# change PERMANENT_SESSION_LIFETIME
app.config.setdefault('CSRF_TOKEN_EXPIRES_IN', 60*60*24)
# We allow usage of an expired CSRF token during this period. This way
# we can rotate the CSRF token without the user getting an CSRF error.
# Align with CSRF_COOKIE_MAX_AGE
app.config.setdefault('CSRF_TOKEN_GRACE_PERIOD', 60*60*24*7)
@app.after_request
app.extensions['invenio-csrf'] = self
class CSRFProtectMiddleware(CSRFTokenMiddleware):
"""CSRF Middleware."""
def __init__(self, app=None):
"""Middleware initialization.
:param app: An instance of :class:`flask.Flask`.
"""
self._exempt_views = set()
self._exempt_blueprints = set()
self._before_protect_funcs = []
if app:
self.init_app(app)
def init_app(self, app):
"""Initialize middleware extension.
:param app: An instance of :class:`flask.Flask`.
"""
super(CSRFProtectMiddleware, self).init_app(app)
@app.before_request
def csrf_protect():
"""CSRF protect method."""
for func in self._before_protect_funcs:
func()
is_method_vulnerable = request.method in app.config['CSRF_METHODS']
if not is_method_vulnerable:
return
if request.blueprint in self._exempt_blueprints:
return
if hasattr(request, 'skip_csrf_check'):
return
view = app.view_functions.get(request.endpoint)
if view:
dest = '{0}.{1}'.format(view.__module__, view.__name__)
if dest in self._exempt_views:
return
return csrf_validate()
def before_csrf_protect(self, f):
"""Register functions to be invoked before checking csrf.
The function accepts nothing as parameters.
"""
self._before_protect_funcs.append(f)
return f
def exempt(self, view):
"""Mark a view or blueprint to be excluded from CSRF protection."""
if isinstance(view, Blueprint):
self._exempt_blueprints.add(view.name)
return view
if isinstance(view, string_types):
view_location = view
else:
view_location = '.'.join((view.__module__, view.__name__))
self._exempt_views.add(view_location)
return view
csrf = CSRFProtectMiddleware()
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6359,
41871,
796,
9429,
32754,
41426,
34621,
1574,
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198
] | 2.362901 | 2,965 |
import pytest
import unittest
from unittest.mock import MagicMock
from polymetis import GripperInterface
import polymetis_pb2
@pytest.fixture
@pytest.mark.parametrize("blocking", [True, False])
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] | 2.970149 | 67 |
# Generated by Django 2.2.10 on 2021-01-17 17:26
from django.db import migrations, models
| [
2,
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] | 2.875 | 32 |
from pathlib import Path
from shutil import copyfile
from unittest import mock
import click
from click.testing import CliRunner
from pixivapi import BadApiResponse, LoginError, Visibility
from pixi.commands import (
_confirm_table_wipe,
_get_starting_bookmark_offset,
artist,
auth,
bookmarks,
config,
failed,
illust,
migrate,
wipe,
)
from pixi.database import Migration, database
from pixi.errors import DownloadFailed, PixiError
@mock.patch("pixi.commands.Config")
@mock.patch("pixi.commands.Client")
@mock.patch("pixi.commands.Config")
@mock.patch("pixi.commands.Client")
@mock.patch("click.edit")
@mock.patch("click.edit")
@mock.patch("pixi.commands.calculate_migrations_needed")
@mock.patch("pixi.commands.calculate_migrations_needed")
@mock.patch("pixi.commands.download_image")
@mock.patch("pixi.commands.Client")
@mock.patch("pixi.commands.Config")
@mock.patch("pixi.commands.download_image")
@mock.patch("pixi.commands.Client")
@mock.patch("pixi.commands.Config")
@mock.patch("pixi.commands.download_pages")
@mock.patch("pixi.commands.Client")
@mock.patch("pixi.commands.Config")
@mock.patch("pixi.commands.download_pages")
@mock.patch("pixi.commands.Client")
@mock.patch("pixi.commands.Config")
@mock.patch("pixi.commands.download_pages")
@mock.patch("pixi.commands.Client")
@mock.patch("pixi.commands.Config")
@mock.patch("pixi.commands._confirm_table_wipe")
@mock.patch("pixi.commands._confirm_table_wipe")
@mock.patch("pixi.commands._confirm_table_wipe")
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31,
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1746,
13557,
10414,
2533,
62,
11487,
62,
86,
3757,
4943,
628,
198
] | 2.439873 | 632 |
import torch
from torch import nn
from torch import optim
from vae import *
from loader import *
from skempi_lib import *
from torch_utils import *
BATCH_SIZE = 32
LR = 1e-3
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
add_arguments(parser)
args = parser.parse_args()
net = VAE2(nc=24, ngf=64, ndf=64, latent_variable_size=256)
net.to(device)
# opt = optim.SGD(net.parameters(), lr=LR, momentum=0.9, nesterov=True)
opt = ScheduledOptimizer(optim.Adam(net.parameters(), lr=LR), LR, num_iterations=200)
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '%s'" % args.resume)
checkpoint = torch.load(args.resume, map_location=lambda storage, loc: storage)
init_epoch = checkpoint['epoch']
net.load_state_dict(checkpoint['net'])
opt.load_state_dict(checkpoint['opt'])
else:
print("=> no checkpoint found at '%s'" % args.resume)
num_epochs = args.num_epochs
init_epoch = 0
n_iter = 0
for epoch in range(init_epoch, num_epochs):
train_iter, eval_iter = 20000, 5000
loader = pdb_loader(PDB_ZIP, TRAINING_SET, train_iter, 19.9, 1.25, handle_error=handle_error)
n_iter = train(net, opt, batch_generator(loader, BATCH_SIZE), train_iter, n_iter)
if epoch < num_epochs - 1 and epoch % args.eval_every != 0:
continue
loader = pdb_loader(PDB_ZIP, VALIDATION_SET, eval_iter, 19.9, 1.25, handle_error=handle_error)
loss = evaluate(net, batch_generator(loader, BATCH_SIZE), eval_iter, n_iter)
print("[Epoch %d/%d] (Validation Loss: %.5f" % (epoch + 1, num_epochs, loss))
save_checkpoint({
'lr': opt.lr,
'epoch': epoch,
'net': net.state_dict(),
'opt': opt.state_dict()
}, loss, "beast", args.out_dir)
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8964,
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1350,
459,
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26498,
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62,
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8,
198
] | 2.215596 | 872 |
import argparse
from src.client.client import ClientHelper
import logging
from src.data.orders_handler import load_and_process
from src.data.preprocessing.orders import OrdersProcessor
from src.utils.logging import log_format, log_level
logging.basicConfig(format=log_format, level=log_level)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('api_key', type=str, help='Key from binance profile')
parser.add_argument('api_secret', type=str, help='Secret key from binance profile')
parser.add_argument('open_file', type=int, nargs='?', default=1, choices=[0,1], help='Open html report after creating')
args = parser.parse_args()
client_helper = ClientHelper(args.api_key, args.api_secret)
orders_processor = OrdersProcessor(client_helper=client_helper)
data = load_and_process(client_helper, orders_processor)
print(data.shape)
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] | 3.085911 | 291 |
from railroads import Track
track = Track("day13-input.txt")
track.run_partone()
track2 = Track("day13-input.txt")
track2.run_parttwo()
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] | 2.816327 | 49 |
"""
Extract information about chr16 for several patients for ben
"""
import pandas as pd
crc01_path = r"H:\Study\university\Computational-Biology\Year " \
r"3\Projects\proj_scwgbs\resource\cpg_format\filtered_by_bl_and_cpgi\CRC01\all_cpg_ratios_CRC01_chr16.dummy.pkl.zip"
crc11_path = r"H:\Study\university\Computational-Biology\Year " \
r"3\Projects\proj_scwgbs\resource\cpg_format\filtered_by_bl_and_cpgi\CRC11" \
r"\all_cpg_ratios_CRC11_chr16.dummy.pkl.zip"
crc13_path = r"H:\Study\university\Computational-Biology\Year " \
r"3\Projects\proj_scwgbs\resource\cpg_format\filtered_by_bl_and_cpgi\CRC13" \
r"\all_cpg_ratios_CRC13_chr16.dummy.pkl.zip"
crc02_path = r"H:\Study\university\Computational-Biology\Year " \
r"3\Projects\proj_scwgbs\resource\cpg_format\filtered_by_bl_and_cpgi\CRC02" \
r"\all_cpg_ratios_CRC02_chr16.dummy.pkl.zip"
crc04_path = r"H:\Study\university\Computational-Biology\Year " \
r"3\Projects\proj_scwgbs\resource\cpg_format\filtered_by_bl_and_cpgi\CRC04" \
r"\all_cpg_ratios_CRC04_chr16.dummy.pkl.zip"
crc09_path = r"H:\Study\university\Computational-Biology\Year " \
r"3\Projects\proj_scwgbs\resource\cpg_format\filtered_by_bl_and_cpgi\CRC09" \
r"\all_cpg_ratios_CRC09_chr16.dummy.pkl.zip"
crc10_path = r"H:\Study\university\Computational-Biology\Year " \
r"3\Projects\proj_scwgbs\resource\cpg_format\filtered_by_bl_and_cpgi\CRC10" \
r"\all_cpg_ratios_CRC10_chr16.dummy.pkl.zip"
crc12_path = r"H:\Study\university\Computational-Biology\Year " \
r"3\Projects\proj_scwgbs\resource\cpg_format\filtered_by_bl_and_cpgi\CRC12" \
r"\all_cpg_ratios_CRC12_chr16.dummy.pkl.zip"
crc14_path = r"H:\Study\university\Computational-Biology\Year " \
r"3\Projects\proj_scwgbs\resource\cpg_format\filtered_by_bl_and_cpgi\CRC14" \
r"\all_cpg_ratios_CRC14_chr16.dummy.pkl.zip"
crc15_path = r"H:\Study\university\Computational-Biology\Year " \
r"3\Projects\proj_scwgbs\resource\cpg_format\filtered_by_bl_and_cpgi\CRC15" \
r"\all_cpg_ratios_CRC15_chr16.dummy.pkl.zip"
valid_path = r"H:\Study\university\Computational-Biology\Year 3\Projects\proj_scwgbs\covariance\valid_cpg.pkl"
if __name__ == '__main__':
valid_data = pd.read_pickle(valid_path)
valid_data = valid_data[valid_data["chromosome"] == "16"]
valid_data["small_seq"] = valid_data["sequence"].str[73:77]
cpg1 = valid_data[valid_data["sequence"].str.count("CG") == 1]
cpg1["context"] = "other"
cpg1.loc[cpg1["small_seq"].str.contains("[AT]CG[AT]", regex=True), "context"] = "WCGW"
cpg1.loc[cpg1["small_seq"].str.contains("[CG]CG[CG]", regex=True), "context"] = "SCGS"
only_needed = cpg1[["small_seq", "sequence", "context"]]
only_needed = only_needed.transpose()
only_needed.to_csv("info.csv")
# crc01 = pd.read_pickle(crc01_path)
# good = crc01[cpg1["location"]]
# good.to_csv("crc01.csv")
#
# crc11 = pd.read_pickle(crc11_path)
# good = crc11[cpg1["location"]]
# good.to_csv("crc11.csv")
#
# crc13 = pd.read_pickle(crc13_path)
# good = crc13[cpg1["location"]]
# good.to_csv("crc13.csv")
# rows = good.index.values
# columns = list(good.columns.values)
# data = good.values
# data_added = np.vstack((data, cpg1["small_seq"]))
# data_added = np.vstack((data_added, cpg1["context"]))
# df = pd.DataFrame(data=data_added, index=columns + ["small_seq", "context"], columns=columns)
crc02 = pd.read_pickle(crc02_path)
good = crc02[cpg1["location"]]
good.to_csv("crc02.csv")
crc04 = pd.read_pickle(crc04_path)
good = crc04[cpg1["location"]]
good.to_csv("crc04.csv")
crc09 = pd.read_pickle(crc09_path)
good = crc09[cpg1["location"]]
good.to_csv("crc09.csv")
crc10 = pd.read_pickle(crc10_path)
good = crc10[cpg1["location"]]
good.to_csv("crc10.csv")
crc12 = pd.read_pickle(crc12_path)
good = crc12[cpg1["location"]]
good.to_csv("crc12.csv")
crc14 = pd.read_pickle(crc14_path)
good = crc14[cpg1["location"]]
good.to_csv("crc14.csv")
crc15 = pd.read_pickle(crc15_path)
good = crc15[cpg1["location"]]
good.to_csv("crc15.csv")
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] | 1.961573 | 2,212 |
from pathlib import PurePath
from typing import List
from setuptools import find_packages, setup
version = '0.0.4'
def load_requirements(path: PurePath) -> List[str]:
""" Load dependencies from a requirements.txt style file, ignoring comments etc. """
res = []
with open(path) as fd:
for line in fd.readlines():
while line.endswith('\n') or line.endswith('\\'):
line = line[:-1]
line = line.strip()
if not line or line.startswith('-') or line.startswith('#'):
continue
res += [line]
return res
here = PurePath(__file__)
README = open(here.with_name('README.md')).read()
install_requires = load_requirements(here.with_name('requirements.txt'))
test_requires = load_requirements(here.with_name('test_requirements.txt'))
setup(
name='eduid-queue',
version=version,
packages=find_packages('src'),
package_dir={'': 'src'},
url='https://github.com/sunet/eduid-queue',
license='BSD-2-Clause',
keywords='eduid',
author='Johan Lundberg',
author_email='[email protected]',
description='MongoDB based task queue',
install_requires=install_requires,
test_requires=test_requires,
extras_require={'testing': [],
'client': load_requirements(here.with_name('client_requirements.txt')),
},
include_package_data=True,
entry_points={'console_scripts': ['run-mail-worker=eduid_queue.workers.mail:start_worker',],},
)
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220,
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6,
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220,
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220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
220,
705,
16366,
10354,
3440,
62,
8897,
18883,
7,
1456,
13,
4480,
62,
3672,
10786,
16366,
62,
8897,
18883,
13,
14116,
11537,
828,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
8964,
198,
220,
220,
220,
2291,
62,
26495,
62,
7890,
28,
17821,
11,
198,
220,
220,
220,
5726,
62,
13033,
34758,
6,
41947,
62,
46521,
10354,
37250,
5143,
12,
4529,
12,
28816,
28,
15532,
312,
62,
36560,
13,
22896,
13,
4529,
25,
9688,
62,
28816,
3256,
4357,
5512,
198,
8,
198
] | 2.453659 | 615 |
from numpy.core.fromnumeric import shape
import taichi as ti
import numpy as np
lin_iters = 20
N = 64
dt = 0.1
diff = 0.0
visc = 0.0
force = 5e5
source = 100.0
dvel = False
v = ti.Vector.field(2, float, shape=(N + 2, N + 2), offset = (-1, -1))
v_prev = ti.Vector.field(2, float, shape=(N + 2, N + 2), offset = (-1, -1))
dens = ti.field(float, shape=(N + 2, N + 2), offset = (-1, -1))
dens_prev = ti.field(float, shape=(N + 2, N + 2), offset = (-1, -1))
div = ti.field(float, shape=(N + 2, N + 2), offset = (-1, -1))
p = ti.field(float, shape=(N + 2, N + 2), offset = (-1, -1))
pixels = ti.field(float, shape=(N, N))
@ti.kernel
@ti.kernel
@ti.func
@ti.kernel
@ti.kernel
| [
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31,
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201,
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31,
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198,
31,
20259,
13,
33885,
201,
198,
201,
198,
31,
20259,
13,
33885,
201
] | 2.154545 | 330 |
import sys
import os
os.chdir(os.path.dirname(os.path.realpath(__file__)))
sys.path.append(os.path.realpath(os.pardir))
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'cnap_v2.settings')
import django
from django.conf import settings
django.setup()
from base.models import AvailableZones, CurrentZone
if __name__ == '__main__':
if settings.CONFIG_PARAMS['cloud_environment'] == settings.GOOGLE:
default_zone = settings.CONFIG_PARAMS['default_google_zone']
avail_zones_csv = settings.CONFIG_PARAMS['available_google_zones']
avail_zones = [x.strip() for x in avail_zones_csv.split(',')]
for z in avail_zones:
a = AvailableZones.objects.create(cloud_environment=settings.GOOGLE, zone=z)
a.save()
dz = AvailableZones.objects.get(zone=default_zone)
c = CurrentZone.objects.create(zone=dz)
c.save()
else:
print('Only Google-related settings have been implemented so far. Exiting.')
sys.exit(1)
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220,
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] | 2.43309 | 411 |
import torch
import numpy as np, reward.utils as U
from pathlib import Path
from .space import Space
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# -*- coding: utf-8 -*-
# Generated by Django 1.11.23 on 2020-03-03 13:32
from __future__ import unicode_literals
from django.db import migrations
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] | 2.690909 | 55 |
import tensorflow as tf
from tensorflow.keras import backend as K
class OrdinalMeanAbsoluteError(tf.keras.metrics.Metric):
"""Computes mean absolute error for ordinal labels."""
def __init__(self, name="mean_absolute_error_labels",
**kwargs):
"""Creates a `OrdinalMeanAbsoluteError` instance."""
super().__init__(name=name, **kwargs)
self.maes = self.add_weight(name='maes', initializer='zeros')
self.count = self.add_weight(name='count', initializer='zeros')
def update_state(self, y_true, y_pred, sample_weight=None):
"""Computes mean absolute error for ordinal labels.
Args:
y_true: Cumulatiuve logits from CondorOrdinal layer.
y_pred: CondorOrdinal Encoded Labels.
sample_weight (optional): Not implemented.
"""
# Predict the label as in Cao et al. - using cumulative probabilities
cum_probs = tf.math.cumprod(
tf.math.sigmoid(y_pred),
axis=1) # tf.map_fn(tf.math.sigmoid, y_pred)
# Calculate the labels using the style of Cao et al.
above_thresh = tf.map_fn(
lambda x: tf.cast(
x > 0.5,
tf.float32),
cum_probs)
# Sum across columns to estimate how many cumulative thresholds are
# passed.
labels_v2 = tf.reduce_sum(above_thresh, axis=1)
y_true = tf.cast(tf.reduce_sum(y_true, axis=1), y_pred.dtype)
# remove all dimensions of size 1 (e.g., from [[1], [2]], to [1, 2])
y_true = tf.squeeze(y_true)
if sample_weight is not None:
values = tf.abs(y_true - labels_v2)
sample_weight = tf.cast(tf.squeeze(sample_weight), y_pred.dtype)
sample_weight = tf.broadcast_to(sample_weight, values.shape)
values = tf.multiply(values, sample_weight)
self.maes.assign_add(tf.reduce_sum(values))
self.count.assign_add(tf.reduce_sum(sample_weight))
else:
self.maes.assign_add(tf.reduce_sum(tf.abs(y_true - labels_v2)))
self.count.assign_add(tf.cast(tf.size(y_true), tf.float32))
def reset_state(self):
"""Resets all of the metric state variables at the start of each epoch."""
self.maes.assign(0.0)
self.count.assign(0.0)
def get_config(self):
"""Returns the serializable config of the metric."""
config = {}
base_config = super().get_config()
return {**base_config, **config}
class SparseOrdinalMeanAbsoluteError(OrdinalMeanAbsoluteError):
"""Computes mean absolute error for ordinal labels."""
def __init__(self, name="mean_absolute_error_labels",
**kwargs):
"""Creates a `OrdinalMeanAbsoluteError` instance."""
super().__init__(name=name, **kwargs)
def update_state(self, y_true, y_pred, sample_weight=None):
"""Computes mean absolute error for ordinal labels.
Args:
y_true: Cumulatiuve logits from CondorOrdinal layer.
y_pred: CondorOrdinal Encoded Labels.
sample_weight (optional): Not implemented.
"""
# Predict the label as in Cao et al. - using cumulative probabilities
cum_probs = tf.math.cumprod(
tf.math.sigmoid(y_pred),
axis=1) # tf.map_fn(tf.math.sigmoid, y_pred)
# Calculate the labels using the style of Cao et al.
above_thresh = tf.map_fn(
lambda x: tf.cast(
x > 0.5,
tf.float32),
cum_probs)
# Sum across columns to estimate how many cumulative thresholds are
# passed.
labels_v2 = tf.reduce_sum(above_thresh, axis=1)
y_true = tf.cast(y_true, y_pred.dtype)
# remove all dimensions of size 1 (e.g., from [[1], [2]], to [1, 2])
y_true = tf.squeeze(y_true)
if sample_weight is not None:
values = tf.abs(y_true - labels_v2)
sample_weight = tf.cast(tf.squeeze(sample_weight), y_pred.dtype)
sample_weight = tf.broadcast_to(sample_weight, values.shape)
values = tf.multiply(values, sample_weight)
self.maes.assign_add(tf.reduce_sum(values))
self.count.assign_add(tf.reduce_sum(sample_weight))
else:
self.maes.assign_add(tf.reduce_sum(tf.abs(y_true - labels_v2)))
self.count.assign_add(tf.cast(tf.size(y_true), tf.float32))
class OrdinalAccuracy(tf.keras.metrics.Metric):
"""Computes accuracy for ordinal labels (tolerance is allowed rank
distance to be considered 'correct' predictions)."""
def __init__(self, name=None,
tolerance=0,
**kwargs):
"""Creates a `OrdinalAccuracy` instance."""
if name is not None:
super().__init__(name=name, **kwargs)
else:
super().__init__(name="ordinal_accuracy_tol"+str(tolerance),
**kwargs)
self.accs = self.add_weight(name='accs', initializer='zeros')
self.count = self.add_weight(name='count', initializer='zeros')
self.tolerance = tolerance
def update_state(self, y_true, y_pred, sample_weight=None):
"""Computes accuracy for ordinal labels.
Args:
y_true: Cumulatiuve logits from CondorOrdinal layer.
y_pred: CondorOrdinal Encoded Labels.
sample_weight (optional): Not implemented.
"""
# Predict the label as in Cao et al. - using cumulative probabilities
cum_probs = tf.math.cumprod(
tf.math.sigmoid(y_pred),
axis=1) # tf.map_fn(tf.math.sigmoid, y_pred)
# Calculate the labels using the style of Cao et al.
above_thresh = tf.map_fn(
lambda x: tf.cast(
x > 0.5,
tf.float32),
cum_probs)
# Sum across columns to estimate how many cumulative thresholds are
# passed.
labels_v2 = tf.reduce_sum(above_thresh, axis=1)
y_true = tf.cast(tf.reduce_sum(y_true, axis=1), y_pred.dtype)
# remove all dimensions of size 1 (e.g., from [[1], [2]], to [1, 2])
y_true = tf.squeeze(y_true)
if sample_weight is not None:
values = tf.cast(tf.less_equal(
tf.abs(y_true-labels_v2),tf.cast(self.tolerance,y_pred.dtype)),
y_pred.dtype)
sample_weight = tf.cast(tf.squeeze(sample_weight), y_pred.dtype)
sample_weight = tf.broadcast_to(sample_weight, values.shape)
values = tf.multiply(values, sample_weight)
self.accs.assign_add(tf.reduce_sum(values))
self.count.assign_add(tf.reduce_sum(sample_weight))
else:
self.accs.assign_add(tf.reduce_sum(tf.cast(tf.less_equal(
tf.abs(y_true-labels_v2),tf.cast(self.tolerance,y_pred.dtype)),
y_pred.dtype)))
self.count.assign_add(tf.cast(tf.size(y_true), tf.float32))
def reset_state(self):
"""Resets all of the metric state variables at the start of each epoch."""
self.accs.assign(0.0)
self.count.assign(0.0)
def get_config(self):
"""Returns the serializable config of the metric."""
config = {'tolerance': self.tolerance}
base_config = super().get_config()
return {**base_config, **config}
class SparseOrdinalAccuracy(OrdinalAccuracy):
"""Computes accuracy for ordinal labels (tolerance is allowed rank
distance to be considered 'correct' predictions)."""
def update_state(self, y_true, y_pred, sample_weight=None):
"""Computes accuracy for ordinal labels.
Args:
y_true: Cumulatiuve logits from CondorOrdinal layer.
y_pred: CondorOrdinal Encoded Labels.
sample_weight (optional): Not implemented.
"""
# Predict the label as in Cao et al. - using cumulative probabilities
cum_probs = tf.math.cumprod(
tf.math.sigmoid(y_pred),
axis=1) # tf.map_fn(tf.math.sigmoid, y_pred)
# Calculate the labels using the style of Cao et al.
above_thresh = tf.map_fn(
lambda x: tf.cast(
x > 0.5,
tf.float32),
cum_probs)
# Sum across columns to estimate how many cumulative thresholds are
# passed.
labels_v2 = tf.reduce_sum(above_thresh, axis=1)
y_true = tf.cast(y_true, y_pred.dtype)
# remove all dimensions of size 1 (e.g., from [[1], [2]], to [1, 2])
y_true = tf.squeeze(y_true)
if sample_weight is not None:
values = tf.cast(tf.less_equal(
tf.abs(y_true-labels_v2),tf.cast(self.tolerance,y_pred.dtype)),
y_pred.dtype)
sample_weight = tf.cast(tf.squeeze(sample_weight), y_pred.dtype)
sample_weight = tf.broadcast_to(sample_weight, values.shape)
values = tf.multiply(values, sample_weight)
self.accs.assign_add(tf.reduce_sum(values))
self.count.assign_add(tf.reduce_sum(sample_weight))
else:
self.accs.assign_add(tf.reduce_sum(tf.cast(tf.less_equal(
tf.abs(y_true-labels_v2),tf.cast(self.tolerance,y_pred.dtype)),
y_pred.dtype)))
self.count.assign_add(tf.cast(tf.size(y_true), tf.float32))
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2116,
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220,
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331,
62,
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22305,
198,
220,
220,
220,
220,
220,
220,
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2116,
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13,
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62,
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13,
7857,
7,
88,
62,
7942,
828,
48700,
13,
22468,
2624,
4008,
628
] | 2.117117 | 4,440 |
#https://www.geeksforgeeks.org/puzzle-maximum-number-kings-chessboard-without-check/
if __name__ == '__main__':
print(main(9,3)) | [
198,
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] | 2.431034 | 58 |
# Generated by Django 3.1.4 on 2021-01-10 21:41
from django.db import migrations
| [
2,
2980,
515,
416,
37770,
513,
13,
16,
13,
19,
319,
33448,
12,
486,
12,
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25,
3901,
198,
198,
6738,
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14208,
13,
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15720,
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628
] | 2.766667 | 30 |
# coding=utf-8
import os
import threading
import logging
import utils
if utils.getSystem() == utils.System.WINDOWS:
from SysTrayIcon import SysTrayIcon
trayThread = None
tray = None
show = True
def initTray():
"初始化系统托盘线程"
logging.info("Start a new thread to manage system tray.")
global trayThread
trayThread = threading.Thread(target = runTray, daemon = True)
trayThread.start()
return
def runTray():
"添加系统托盘"
global tray
if utils.getSystem() == utils.System.WINDOWS:
logging.info("Init system tray for windows.")
menuOptions = (("显示/隐藏", None, onOptionClicked), ("退出", None, onOptionClicked))
tray = SysTrayIcon("./icon.ico", "我的热点新闻服务器", onTrayClicked, menuOptions)
tray.loop()
elif utils.getSystem() == utils.System.LINUX:
logging.info("System tray doesn't support linux.")
elif utils.getSystem() == utils.System.MACOS:
logging.info("System tray doesn't support macOS.")
else:
logging.info("System tray doesn't support this system.")
return
def removeTray():
"移除系统托盘"
global tray
if tray is not None:
if utils.getSystem() == utils.System.WINDOWS:
tray.close()
tray = None
return
def onTrayClicked():
"托盘被点击"
global show
if show:
utils.hideWindow()
else:
utils.showWindow()
show = not show
return
def onOptionClicked(id):
"托盘菜单选项被点击"
if id == 0:
onTrayClicked()
elif id == 1:
# _thread.interrupt_main() # 读取输入时好像无效
removeTray()
os._exit(0)
return
| [
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119,
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247,
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119,
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119,
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220,
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220,
220,
220,
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51,
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276,
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220,
220,
220,
366,
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246,
33566,
246,
164,
95,
104,
163,
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117,
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119,
1,
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220,
220,
220,
3298,
905,
198,
220,
220,
220,
611,
905,
25,
198,
220,
220,
220,
220,
220,
220,
220,
3384,
4487,
13,
24717,
27703,
3419,
198,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
3384,
4487,
13,
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198,
220,
220,
220,
905,
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407,
905,
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220,
220,
220,
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198,
198,
4299,
319,
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276,
7,
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220,
220,
220,
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95,
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163,
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117,
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1,
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220,
220,
220,
611,
4686,
6624,
657,
25,
198,
220,
220,
220,
220,
220,
220,
220,
319,
51,
2433,
8164,
276,
3419,
198,
220,
220,
220,
1288,
361,
4686,
6624,
352,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
4808,
16663,
13,
3849,
3622,
62,
12417,
3419,
1303,
5525,
107,
119,
20998,
244,
164,
122,
241,
17739,
98,
33768,
114,
25001,
121,
161,
225,
237,
33768,
254,
46763,
230,
198,
220,
220,
220,
220,
220,
220,
220,
4781,
51,
2433,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
28686,
13557,
37023,
7,
15,
8,
198,
220,
220,
220,
1441,
198
] | 2.03949 | 785 |
# class Tree:
# def __init__(self, val, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
| [
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] | 2.041096 | 73 |
import logging
from django.core.management.base import BaseCommand
| [
11748,
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42625,
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13,
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13,
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628
] | 4.058824 | 17 |
"""Command-line interfacing workflows"""
import argparse
import os
import sys
import imp
import inspect
import argcomplete
from . import core
from . common import WorkflowError, WORKFLOW_DEFAULT_FILENAME
from . import decorators
from . userargument import USER_ARGS_CONTEXT
from . _ui import ui
from . import _util as util
def open_graph(directory, graph_name, wf_filename=WORKFLOW_DEFAULT_FILENAME):
"""Opens an existing workflow and return the specified graph instance.
Args:
directory (str): A directory containg a `workflow.py` file, or
a file named by the `wf_filename` argument.
graph_name (str): The graph's name to open, see
:func:`rflow.decorators.graph`
wf_filename (str): The workflow python script. Default is
`"workflow.py"`.
Returns:
:obj:`rflow.core.Graph`: DAG object.
Raises:
:obj:`rflow.common.WorkflowError`: If the graph
isn't found.
`FileNotFoundError`: If the directory doesn't exists or if the
`workflow.py` or what passed to `wf_filename` does not
exists.
"""
if core.exists_graph(graph_name, directory):
return core.get_graph(graph_name, directory, existing=True)
graph_def_list = _get_all_graph_def(
os.path.abspath(directory), wf_filename)
defgraph_info_list = [graph_def for graph_def in graph_def_list
if graph_def.name == graph_name]
if not defgraph_info_list:
raise WorkflowError(
"Graph not {} found on directory {}. Available ones are: {}".format(
graph_name, directory, ', '.join(
[deco.name for _1, _2, deco in defgraph_info_list])))
else:
defgraph_info = defgraph_info_list[0]
defgraph_info.function()
return core.get_graph(graph_name, directory, existing=True)
ACTIONS = ['run', 'touch', 'print-run', 'viz-dag', 'help', 'clean']
def main(argv=None):
"""Command-line auto main generator.
Generates a command-line main for executing the graphs defined in
the current source file. See the decorator
:class:`rflow.decorators.graph` for how to define
graphs. The default behavior is quit the process when an error is
encountered.
For example::
@srwf.graph()
def workflow1(g):
g.add = Add()
g.add.args.a = 1
g.add.args.b = 2
g.sub = Sub(srwf.FSResource('sub.pkl'))
g.sub.args.a = 8
g.sub.args.b = g.add
if __name__ == '__main__':
srwf.command.main()
In a shell execute::
$ srwf workflow1 run sub
For passing custom arguments by command-line, use the class
:class:`rflow.userargument.UserArgument`.
Args:
args (str, optional): sys.args like command-line arguments.
Returns:
int: exit code.
"""
# pylint: disable=too-many-return-statements
try:
all_graphs = _get_all_graph_def(os.path.abspath(os.path.curdir),
WORKFLOW_DEFAULT_FILENAME)
except WorkflowError as err:
print(str(err))
return 1
arg_parser = argparse.ArgumentParser(
description="RFlow workflow runner",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
arg_parser.add_argument(
'graph', choices=[graph.name for graph in all_graphs])
arg_parser.add_argument('action', choices=ACTIONS)
argcomplete.autocomplete(arg_parser)
if not argv:
argv = sys.argv
args = arg_parser.parse_args(argv[1:3])
if int(os.environ.get("RFLOW_DEBUG", 0)) == 1:
ui.complete_traceback = True
abs_path = os.path.abspath('.')
graph = open_graph(abs_path, args.graph)
argv = argv[3:]
if args.action == 'print-run':
raise NotImplementedError()
elif args.action == 'run':
return _run_main(graph, argv)
elif args.action == 'touch':
return _touch_main(graph, argv)
elif args.action == 'clean':
return _clean_main(graph, argv)
elif args.action == 'help':
return _help_main(graph, argv)
elif args.action == 'viz-dag':
return _viz_main(graph, argv)
return 1
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8,
628,
220,
220,
220,
1441,
352,
198
] | 2.343819 | 1,812 |
#!/usr/bin/env python
import rospy
import tf
import serial
import numpy as np
from nav_msgs.msg import Odometry
from geometry_msgs.msg import Point
from std_msgs.msg import Int64
global x, y, theta, v_L, v_R, v_x, v_y, omega
x = 0
y = 0
theta = 0
v_L = 0
v_R = 0
v_x = 0
v_y = 0
omega = 0
pub_tf = False # Use estimate result as tf
if(pub_tf):
br = tf.TransformBroadcaster()
if __name__ == '__main__':
rospy.init_node('whel_odom_node', anonymous = False)
port = rospy.get_param("~port", "/dev/ttyACM0") # default port: /dev/ttyUSB0
ard = serial.Serial(port, 9600)
rospy.Timer(rospy.Duration.from_sec(0.1), read_data) # 10Hz
rospy.spin()
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198,
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88,
13,
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198
] | 2.371324 | 272 |
from nbgrader.auth import JupyterHubAuthPlugin
c = get_config()
c.Application.log_level = 30
c.Authenticator.plugin_class = JupyterHubAuthPlugin
c.Exchange.path_includes_course = True
c.Exchange.root = "/srv/nbgrader/exchange"
c.ExecutePreprocessor.iopub_timeout=1800
c.ExecutePreprocessor.timeout=3600
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28,
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198
] | 2.734513 | 113 |
from iota import Iota
from iota import Address, ProposedTransaction, Tag, Transaction
from iota import TryteString
from iota import ProposedBundle
from iota.commands.extended import utils
from datetime import datetime
from pprint import pprint
import hashlib
import time
import random
import string
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] | 3.710843 | 83 |
from __future__ import unicode_literals
import datetime
from django.contrib import admin
from django.contrib.admin.options import IncorrectLookupParameters
from django.contrib.admin.templatetags.admin_list import pagination
from django.contrib.admin.tests import AdminSeleniumWebDriverTestCase
from django.contrib.admin.views.main import ALL_VAR, SEARCH_VAR, ChangeList
from django.contrib.auth.models import User
from django.core.urlresolvers import reverse
from django.template import Context, Template
from django.test import TestCase, override_settings
from django.test.client import RequestFactory
from django.utils import formats, six
from .admin import (
BandAdmin, ChildAdmin, ChordsBandAdmin, CustomPaginationAdmin,
CustomPaginator, DynamicListDisplayChildAdmin,
DynamicListDisplayLinksChildAdmin, DynamicListFilterChildAdmin,
DynamicSearchFieldsChildAdmin, FilteredChildAdmin, GroupAdmin,
InvitationAdmin, NoListDisplayLinksParentAdmin, ParentAdmin, QuartetAdmin,
SwallowAdmin, site as custom_site,
)
from .models import (
Band, Child, ChordsBand, ChordsMusician, CustomIdUser, Event, Genre, Group,
Invitation, Membership, Musician, OrderedObject, Parent, Quartet, Swallow,
UnorderedObject,
)
@override_settings(ROOT_URLCONF="admin_changelist.urls")
@override_settings(PASSWORD_HASHERS=['django.contrib.auth.hashers.SHA1PasswordHasher'],
ROOT_URLCONF="admin_changelist.urls")
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] | 3.127155 | 464 |
"""
These are emukit model wrappers that contain the specific optimization procedures we found worked well for each model.
The constructor for each model takes X and Y as lists, with each entry of the list corresponding to data for a fidelity
"""
import logging
import GPy
import numpy as np
from ...core.interfaces import IModel
from ...model_wrappers import GPyMultiOutputWrapper
from ...multi_fidelity.convert_lists_to_array import convert_xy_lists_to_arrays
from ...multi_fidelity.kernels import LinearMultiFidelityKernel
from ...multi_fidelity.models import GPyLinearMultiFidelityModel
from ...multi_fidelity.models.non_linear_multi_fidelity_model import (
NonLinearMultiFidelityModel, make_non_linear_kernels)
_log = logging.getLogger(__name__)
class HighFidelityGp(IModel):
"""
GP at high fidelity only.
The optimization is restarted from random initial points 10 times.
The noise parameter is initialized at 1e-6 for the first optimization round.
"""
def predict(self, X):
"""
Predict from high fidelity
"""
return self.model.predict(X[:, :-1])
@property
@property
class LinearAutoRegressiveModel(IModel):
"""
Linear model, AR1 in paper. Optimized with noise fixed at 1e-6 until convergence then the noise parameter is freed
and the model is optimized again
"""
def __init__(self, X, Y, n_restarts=10):
"""
:param X: List of training data at each fidelity
:param Y: List of training targets at each fidelity
:param n_restarts: Number of restarts during optimization of hyper-parameters
"""
x_train, y_train = convert_xy_lists_to_arrays(X, Y)
n_dims = X[0].shape[1]
kernels = [GPy.kern.RBF(n_dims, ARD=True) for _ in range(len(X))]
lin_mf_kernel = LinearMultiFidelityKernel(kernels)
gpy_lin_mf_model = GPyLinearMultiFidelityModel(x_train, y_train, lin_mf_kernel, n_fidelities=len(X))
gpy_lin_mf_model.mixed_noise.Gaussian_noise.fix(1e-6)
gpy_lin_mf_model.mixed_noise.Gaussian_noise_1.fix(1e-6)
if len(Y) == 3:
gpy_lin_mf_model.mixed_noise.Gaussian_noise_2.fix(1e-6)
self.model = GPyMultiOutputWrapper(gpy_lin_mf_model, len(X), n_optimization_restarts=n_restarts)
self.name = 'ar1'
self.n_fidelities = len(X)
def predict(self, X):
"""
Predict from high fidelity
"""
return self.model.predict(X)
@property
@property
class NonLinearAutoRegressiveModel(IModel):
"""
Non-linear model, NARGP in paper
"""
def predict(self, X):
"""
Predict from high fidelity
"""
return self.model.predict(X)
@property
@property
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] | 2.531622 | 1,091 |
import argparse
import typing
import aztk.spark
from aztk_cli import log, config
| [
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] | 3.32 | 25 |
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
#
# Copyright (c) 2019, Eurecat / UPF
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of the <organization> 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 <COPYRIGHT HOLDER> 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.
#
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
#
# @file evaluate_sht_filters.py
# @author Andrés Pérez-López
# @date 01/10/2019
#
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
import numpy as np
import matplotlib.pyplot as plt
from masp.validate_data_types import _validate_int, _validate_ndarray_1D, \
_validate_ndarray_2D, _validate_ndarray_3D, _validate_boolean, _validate_number
def evaluate_sht_filters(M_mic2sh, H_array, fs, Y_grid, w_grid=None, plot=False):
"""
Evaluate frequency-dependent performance of SHT filters.
Parameters
----------
M_mic2sh : ndarray
SHT filtering matrix produced by one of the methods included in the library.
Dimension = ( (order+1)^2, nMics, nBins ).
H_array : ndarray, dtype = 'complex'
Modeled or measured spherical array responses in a dense grid of `nGrid` directions.
Dimension = ( nBins, nMics, nGrid ).
fs : int
Target sampling rate.
Y_grid : ndarray
Spherical harmonics matrix for the `nGrid` directions of the evaluation grid.
Dimension = ( nGrid, (order+1)^2 ).
w_grid : ndarray, optional
Vector of integration weights for the grid points.
Dimension = ( nGrid ).
plot : bool, optional
Plot responses. Default to false.
Returns
-------
cSH : ndarray, dtype = 'complex'
Spatial correlation coefficient, for each SHT order and frequency bin.
Dimension = ( nBins, order+1 ).
lSH : ndarray
Level difference, for each SHT order, for each SHT order and frequency bin.
Dimension = ( nBins, order+1 ).
WNG : ndarray
Maximum amplification of all output SH components.
Dimension = ( nBins ).
Raises
-----
TypeError, ValueError: if method arguments mismatch in type, dimension or value.
Notes
-----
The SHT filters can be evaluated in terms of how ideal are the SH
components that they generate. The evaluation here follows the metrics
introduced in
Moreau, S., Daniel, J., Bertet, S., 2006,
`3D sound field recording with higher order ambisonics-objectiv
measurements and validation of spherical microphone.`
In Audio Engineering Society Convention 120.
These are a) the spatial correlation coefficient between each ideal
spherical harmonic and the reconstructed pattern, evaluated at a dense
grid of directions, b) level difference between the mean spatial power
of the reconstructed pattern (diffuse power) over the one from an ideal
SH component. Ideally, correlaiton should be close to one, and the
level difference should be close to 0dB.
Additionally, the maximum amplification of all output SH components is
evaluated, through the maximum eigenvalue of the filtering matrix.
Due to the matrix nature of computations,
the minimum valid value for `nMics` and `nGrid` is 2.
"""
_validate_ndarray_3D('M_mic2sh', M_mic2sh)
n_sh = M_mic2sh.shape[0]
order_sht = int(np.sqrt(n_sh) - 1)
nMics = M_mic2sh.shape[1]
_validate_number('nMics', nMics, limit=[2, np.inf])
nBins = M_mic2sh.shape[2]
_validate_ndarray_3D('H_array', H_array, shape0=nBins, shape1=nMics)
nGrid = H_array.shape[2]
_validate_number('nGrid', nGrid, limit=[2, np.inf])
_validate_ndarray_2D('Y_grid', Y_grid, shape0=nGrid, shape1=n_sh)
if w_grid is None:
w_grid = 1/nGrid*np.ones(nGrid)
_validate_ndarray_1D('w_grid', w_grid, size=nGrid)
_validate_int('fs', fs, positive=True)
if plot is not None:
_validate_boolean('plot', plot)
nFFT = 2 * (nBins - 1)
f = np.arange(nFFT // 2 + 1) * fs / nFFT
# Compute spatial correlations and integrated level difference between
# ideal and reconstructed harmonics
cSH = np.empty((nBins, order_sht+1), dtype='complex')
lSH = np.empty((nBins, order_sht+1))
# rSH = np.empty((nBins, order_sht+1))
for kk in range(nBins):
H_kk = H_array[kk,:,:]
y_recon_kk = np.matmul(M_mic2sh[:,:, kk], H_kk)
for n in range(order_sht+1):
cSH_n = 0 # spatial correlation (mean per order)
lSH_n = 0 # diffuse level difference (mean per order)
# rSH_n = 0 # mean level difference (mean per order)
for m in range(-n, n+1):
q = np.power(n, 2) + n + m
y_recon_nm = y_recon_kk[q,:].T
y_ideal_nm = Y_grid[:, q]
cSH_nm = np.matmul((y_recon_nm * w_grid).conj(), y_ideal_nm) / np.sqrt( np.matmul((y_recon_nm*w_grid).conj(), y_recon_nm ))
cSH_n = cSH_n + cSH_nm
lSH_nm = np.real(np.matmul((y_recon_nm * w_grid).conj(), y_recon_nm ))
lSH_n = lSH_n + lSH_nm
# rSH_nm = np.sum(np.power(np.abs(y_recon_nm - y_ideal_nm), 2) * w_grid)
# rSH_n = rSH_n + rSH_nm;
cSH[kk, n] = cSH_n / (2 * n + 1)
lSH[kk, n] = lSH_n / (2 * n + 1)
# rSH[kk, n] = rSH_n / (2 * n + 1)
# Maximum noise amplification of all filters in matrix
WNG = np.empty(nBins)
for kk in range(nBins):
# TODO: Matlab implementation warns when M matrix is complex, e.g. TEST_SCRIPTS l. 191-199
# Avoid ComplexWarning: imaginary parts appearing due to numerical precission
eigM = np.real(np.linalg.eigvals(np.matmul(M_mic2sh[:,:,kk].T.conj(), M_mic2sh[:,:,kk])))
WNG[kk] = np.max(eigM)
# Plots
if plot:
str_legend = [None]*(order_sht+1)
for n in range(order_sht+1):
str_legend[n] = str(n)
plt.figure()
plt.subplot(311)
plt.semilogx(f, np.abs(cSH))
plt.grid()
plt.legend(str_legend)
plt.axis([50, 20000, 0, 1])
plt.title('Spatial correlation')
plt.subplot(312)
plt.semilogx(f, 10 * np.log10(lSH))
plt.grid()
plt.legend(str_legend)
plt.axis([50, 20000, -30, 10])
plt.title('Level correlation')
plt.subplot(313)
plt.semilogx(f, 10 * np.log10(WNG))
plt.grid()
plt.xlim([50, 20000])
plt.title('Maximum amplification')
plt.xlabel('Frequency (Hz)')
# plt.subplot(414)
# plt.semilogx(f, 10 * np.log10(rSH))
# plt.grid()
# plt.xlim([50, 20000])
# plt.title('MSE')
# plt.xlabel('Frequency (Hz)')
plt.show()
return cSH, lSH, WNG
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269,
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300,
9693,
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370,
10503,
198
] | 2.356299 | 3,469 |
import pandas as pd
import sys
import math
import requests | [
11748,
19798,
292,
355,
279,
67,
198,
11748,
25064,
198,
11748,
10688,
198,
11748,
7007
] | 3.866667 | 15 |
"""
Given a linked list, find the first node of a cycle in it.
1 -> 2 -> 3 -> 4 -> 5 -> 1 => 1
A -> B -> C -> D -> E -> C => C
Note: The solution is a direct implementation
Floyd's cycle-finding algorithm (Floyd's Tortoise and Hare).
"""
def firstCyclicNode(head):
"""
:type head: Node
:rtype: Node
"""
runner = walker = head
while runner and runner.next:
runner = runner.next.next
walker = walker.next
if runner is walker:
break
if runner is None or runner.next is None:
return None
walker = head
while runner is not walker:
runner, walker = runner.next, walker.next
return runner
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] | 2.489437 | 284 |
#! /usr/bin/python
import serial
import time
import requests
import datetime
import thread
import time
bluetoothSerial = serial.Serial( "/dev/tty.HC-06-DevB", baudrate=9600 )
serverIP = "localhost"
serverPort = "8080"
publisherEndpoint = "/ConnectedDevices/pushdata"
#"/pushdata/{ip}/{owner}/{type}/{mac}/{time}/{pin}/{value}")
deviceIP = "/192.168.1.999"
deviceOwner = "/SMEAN"
deviceType = "/ArduinoUNO"
deviceMAC = "/98:D3:31:80:38:D3"
publisherEndpoint = "http://" + serverIP + ":" + serverPort + publisherEndpoint + deviceIP + deviceOwner + deviceType + deviceMAC + "/"
import termios, fcntl, sys, os
if __name__=='__main__':
main()
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12417,
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10354,
198,
220,
220,
220,
220,
220,
220,
220,
1388,
3419,
198
] | 2.553846 | 260 |
####
# This sample is published as part of the blog article at www.toptal.com/blog
# Visit www.toptal.com/blog and subscribe to our newsletter to read great posts
####
import logging
import os
from concurrent.futures import ThreadPoolExecutor
from functools import partial
from time import time
from download import setup_download_dir, get_links, download_link
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
if __name__ == '__main__':
main()
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18931,
13,
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7,
834,
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834,
8,
628,
198,
198,
361,
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3672,
834,
6624,
705,
834,
12417,
834,
10354,
198,
220,
220,
220,
1388,
3419,
198
] | 3.174419 | 172 |
from django.utils import translation
from django.core.exceptions import ObjectDoesNotExist
from django.conf import settings
from userena import settings as userena_settings
from userena.compat import SiteProfileNotAvailable
from userena.utils import get_user_profile
class UserenaLocaleMiddleware(object):
"""
Set the language by looking at the language setting in the profile.
It doesn't override the cookie that is set by Django so a user can still
switch languages depending if the cookie is set.
"""
| [
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] | 3.673611 | 144 |
"""
First Three Words
Write a program which asks
the user to enter a sentence.
Print the first three words in the sentence.
(Assume the user enters at least 3 words.)
"""
sentence = input("Enter a sentence: ")
words = sentence.split()
for word in words[:3]:
print(word)
| [
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] | 3.309524 | 84 |
# Generated by Django 2.2.5 on 2022-03-02 12:32
from django.db import migrations, models
| [
2,
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11,
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] | 2.84375 | 32 |
#!/usr/bin/env python3
import datetime
import os
import gi
gi.require_version("Gtk", "3.0")
from gi.repository import Gtk, GObject
BASEDIR = os.path.dirname(os.path.abspath(__file__))
if __name__ == '__main__':
win = StopWatch()
icon_path = os.path.join(BASEDIR, 'stopwatch.png')
win.set_icon_from_file(icon_path)
win.connect('destroy', Gtk.main_quit)
win.show_all()
Gtk.main()
| [
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198,
220,
220,
220,
402,
30488,
13,
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3419,
198
] | 2.318182 | 176 |
# Generated by Django 3.0.6 on 2021-01-10 17:55
from django.db import migrations, models
| [
2,
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515,
416,
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513,
13,
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13,
21,
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12,
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] | 2.84375 | 32 |
"""
Student management module
"""
students = []
def get_student_title_case() -> str:
"""
Function to return student name in title case
:return: student name
"""
students_title_case = []
for student in students:
students_title_case.append(student["name"].title())
return students_title_case
def print_students_title_case() -> None:
"""
Function to print student name using title case
"""
students_title_case = get_student_title_case()
print(students_title_case)
def add_student(name, stud_id=999):
"""
Function to add a student in the list
:param name: student name
:param stud_id: student id
"""
student = {"name": name, "id": stud_id}
students.append(student)
print("Student count is {0}".format(len(students)))
def save_file(student):
"""
Function to save student information to the file
:param student: student info
"""
try:
student_file = open("students.txt", "a")
student_file.write(student + "\n")
student_file.close()
except IOError:
print("Could not save")
def read_file():
"""
Function to read student information file
"""
try:
student_file = open("students.txt", "r")
for student in student_file.readlines():
add_student(student)
student_file.close()
except IOError:
print("Could not read")
# ADD NEW STUDENT BLOCK
student_list = get_student_title_case()
add_student("Prasad", "101")
# ADD NEW STUDENT VIA USER INPUT AND DISPLAY THE LIST
student_name = input("Enter student name : ")
student_id = input("Enter student id : ")
add_student(student_name, student_id)
# PRINT STUDENT DETAILS
print_students_title_case()
# USE BELOW CODE BLOCK IF YOU WANT TO ADD NEW STUDENT IN A LOOP
ADD_NEW_STUDENT_FLAG: str = ""
MESSAGE = "Do you want to add new student record?? Press [Y] / [y] to continue."
ADD_NEW_STUDENT_FLAG = input(MESSAGE)
while ADD_NEW_STUDENT_FLAG in ("Y", "y"):
student_name = input("enter student name : ")
student_id = input("enter student id : ")
add_student(student_name, student_id)
ADD_NEW_STUDENT_FLAG = input(MESSAGE)
print_students_title_case()
# READ FROM File
read_file()
print_students_title_case()
# WRITE TO FILE
print("writing to file...")
student_name = input("enter student name : ")
student_id = input("enter student id : ")
add_student(student_name, student_id)
save_file(student_name)
| [
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312,
8,
201,
198,
21928,
62,
7753,
7,
50139,
62,
3672,
8,
201,
198
] | 2.532083 | 1,013 |
import gym
import numpy as np
from ray.rllib.utils.annotations import PublicAPI
@PublicAPI
class Repeated(gym.Space):
"""Represents a variable-length list of child spaces.
Example:
self.observation_space = spaces.Repeated(spaces.Box(4,), max_len=10)
--> from 0 to 10 boxes of shape (4,)
See also: documentation for rllib.models.RepeatedValues, which shows how
the lists are represented as batched input for ModelV2 classes.
"""
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] | 2.822485 | 169 |
# common_features.py
# Invoke on the command line like: python common_features.py pbtd
# Outputs all features common to all of the given segments, to help
# in rule writing.
from tabulate import tabulate
import csv
import sys
import os.path as path
base_directory = path.dirname(path.dirname(path.abspath(__file__)))
sys.path.append(base_directory)
def load_segments(filename):
'''Load a segment feature matrix from a CSV file, returning a list of
dictionaries with information about each segment.
'''
with open(filename, 'r') as f:
return [segment for segment in csv.DictReader(f)]
if __name__ == '__main__':
main(sys.argv[1])
| [
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] | 3.018182 | 220 |
#!/usr/bin/env python
import time
import sys
# import the MUD server class
from mudserver import MudServer, Event, EventType
#prints to stderr
VERBOSE_PRINT = False
# structure defining the rooms in the game. Try adding more rooms to the game!
rooms = {
"Tavern": {
"description": "You're in a cozy tavern warmed by an open fire.",
"exits": {"outside": "Outside"},
},
"Outside": {
"description": "You're standing outside a tavern. It's raining.",
"exits": {"inside": "Tavern"},
}
}
# stores the players in the game
players = {}
# start the server
mud = MudServer()
# main game loop. We loop forever (i.e. until the program is terminated)
while True:
# pause for 1/5 of a second on each loop, so that we don't constantly
# use 100% CPU time
time.sleep(0.2)
# 'update' must be called in the loop to keep the game running and give
# us up-to-date information
mud.update()
# handle events on the server_queue
while (len(mud.server_queue) > 0):
event = mud.server_queue.popleft()
err_print(event)
id = event.id
if event.type is EventType.PLAYER_JOIN:
# add the new player to the dictionary, noting that they've not been
# named yet.
# The dictionary key is the player's id number. We set their room to
# None initially until they have entered a name
err_print("Player %s joined." % event.id)
players[id] = {
"name": None,
"room": None,
}
#prompt the user for their name
mud.send_message(id, "What is your name?")
elif event.type is EventType.MESSAGE_RECEIVED:
# splitting into command + params to make porting the code easier
command, params = (event.message.split(" ", 1) + ["", ""])[:2]
err_print(event.message)
# all these elifs will be replaced with "character.parse([input])"
if players[id]["name"] is None:
players[id]["name"] = event.message.split(" ")[0]
players[id]["room"] = "Tavern"
for pid, pl in players.items():
# send each player a message to tell them about the new player
mud.send_message(pid, "%s entered the game" %
players[id]["name"])
mud.send_message(id, "Welcome to the game, %s. " %
players[id]["name"]
+ "Type 'help' for a list of commands. Have fun!")
# 'help' command
elif command == "help":
# send the player back the list of possible commands
mud.send_message(id, "Commands:")
mud.send_message(id, " say <message> - Says something out loud, "
+ "e.g. 'say Hello'")
mud.send_message(id, " look - Examines the "
+ "surroundings, e.g. 'look'")
mud.send_message(id, " go <exit> - Moves through the exit "
+ "specified, e.g. 'go outside'")
# 'say' command
elif command == "say":
# go through every player in the game
for pid, pl in players.items():
# if they're in the same room as the player
if players[pid]["room"] == players[id]["room"]:
# send them a message telling them what the player said
mud.send_message(pid, "{} says: {}".format(
players[id]["name"], params))
# 'look' command
elif command == "look":
# store the player's current room
rm = rooms[players[id]["room"]]
# send the player back the description of their current room
mud.send_message(id, rm["description"])
playershere = []
# go through every player in the game
for pid, pl in players.items():
# if they're in the same room as the player
if players[pid]["room"] == players[id]["room"]:
# ... and they have a name to be shown
if players[pid]["name"] is not None:
# add their name to the list
playershere.append(players[pid]["name"])
# send player a message containing the list of players in the room
mud.send_message(id, "Players here: {}".format(
", ".join(playershere)))
# send player a message containing the list of exits from this room
mud.send_message(id, "Exits are: {}".format(
", ".join(rm["exits"])))
# 'go' command
elif command == "go":
# store the exit name
ex = params.lower()
# store the player's current room
rm = rooms[players[id]["room"]]
# if the specified exit is found in the room's exits list
if ex in rm["exits"]:
# go through all the players in the game
for pid, pl in players.items():
# if player is in the same room and isn't the player
# sending the command
if players[pid]["room"] == players[id]["room"] \
and pid != id:
# send them a message telling them that the player
# left the room
mud.send_message(pid, "{} left via exit '{}'".format(
players[id]["name"], ex))
# update the player's current room to the one the exit leads to
players[id]["room"] = rm["exits"][ex]
rm = rooms[players[id]["room"]]
# go through all the players in the game
for pid, pl in players.items():
# if player is in the same (new) room and isn't the player
# sending the command
if players[pid]["room"] == players[id]["room"] \
and pid != id:
# send them a message telling them that the player
# entered the room
mud.send_message(pid,
"{} arrived via exit '{}'".format(
players[id]["name"], ex))
# send the player a message telling them where they are now
mud.send_message(id, "You arrive at '{}'".format(
players[id]["room"]))
# the specified exit wasn't found in the current room
else:
# send back an 'unknown exit' message
mud.send_message(id, "Unknown exit '{}'".format(ex))
# some other, unrecognised command
else:
# send back an 'unknown command' message
mud.send_message(id, "Unknown command '{}'".format(command))
elif event.type is EventType.PLAYER_DISCONNECT:
err_print("Player %s left" % event.id)
#if the player has been added to the list, they must be removed
if event.id in players:
for pid in players:
mud.send_message(pid, "%s quit the game" % players[event.id]["name"])
del(players[id]) | [
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1,
3672,
8973,
8,
198,
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13,
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7,
32399,
58,
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1,
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17492,
13,
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62,
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7,
35317,
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] | 1.928864 | 4,147 |
# -*- coding: utf-8 -*-
from __future__ import absolute_import, unicode_literals
from mptt.forms import MPTTAdminForm
from parler.forms import TranslatableModelForm
from shopit.models.flag import Flag
| [
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from point import Point
from rectangle import Rectangle
from utils import areOverlapping
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"""
Created by akiselev on 2019-06-13
re.findall()
The expression re.findall() returns all the non-overlapping matches of patterns in a string as a list of strings.
Code
>>> import re
>>> re.findall(r'\w','http://www.hackerrank.com/')
['h', 't', 't', 'p', 'w', 'w', 'w', 'h', 'a', 'c', 'k', 'e', 'r', 'r', 'a', 'n', 'k', 'c', 'o', 'm']
re.finditer()
The expression re.finditer() returns an iterator yielding MatchObject instances over all non-overlapping matches for the re pattern in the string.
Code
>>> import re
>>> re.finditer(r'\w','http://www.hackerrank.com/')
<callable-iterator object at 0x0266C790>
>>> map(lambda x: x.group(),re.finditer(r'\w','http://www.hackerrank.com/'))
['h', 't', 't', 'p', 'w', 'w', 'w', 'h', 'a', 'c', 'k', 'e', 'r', 'r', 'a', 'n', 'k', 'c', 'o', 'm']
Task
You are given a string
. It consists of alphanumeric characters, spaces and symbols(+,-).
Your task is to find all the substrings of that contains or more vowels.
Also, these substrings must lie in between
consonants and should contain vowels only.
Note :
Vowels are defined as: AEIOU and aeiou.
Consonants are defined as: QWRTYPSDFGHJKLZXCVBNM and qwrtypsdfghjklzxcvbnm.
Input Format
A single line of input containing string
.
Constraints
Output Format
Print the matched substrings in their order of occurrence on separate lines.
If no match is found, print -1.
Sample Input
rabcdeefgyYhFjkIoomnpOeorteeeeet
Sample Output
ee
Ioo
Oeo
eeeee
"""
import re
x = re.compile(r'[qwrtypsdfghjklzxcvbnm]([aeiou]{2,})(?=[qwrtypsdfghjklzxcvbnm])', re.I)
m = re.findall(x, input().strip())
print('\n'.join(m or ['-1'])) | [
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59,
77,
4458,
22179,
7,
76,
393,
685,
29001,
16,
20520,
4008
] | 2.5968 | 625 |
from antlr4 import FileStream, CommonTokenStream
from src.Python3Lexer import Python3Lexer
from src.Python3Parser import Python3Parser
from antlr4.tree.Tree import TerminalNodeImpl
from antlr4.error.ErrorListener import ErrorListener
import json
class FileErrorListener(ErrorListener):
"""Class for storing errors which occured during the syntax analysis"""
def walk(subtree, rule_names):
""" Function for converting tree to dictionary
Function takes subtree and array of names and recursively
goes through each node and returns dictionary
(possibly array of dictionaries back)
Args:
@subtree - root of subtree to be walked through
@rule_names - corresponding to states rule names
Returnes: dict representation of the tree
"""
if isinstance(subtree, TerminalNodeImpl):
token = subtree.getSymbol()
token_name = Python3Parser.symbolicNames[token.type]
return {'Type': token_name, 'Value': token.text}
else:
child_nodes = []
name = rule_names[subtree.getRuleIndex()]
for i in range(subtree.getChildCount()):
child_nodes.append(walk(subtree.getChild(i), rule_names))
if len(child_nodes) == 1:
return {name: child_nodes[0]}
else:
return {name: child_nodes}
def lex(i_stream):
"""Makes lexical analysis
Returns: stream of tokens
"""
lexer = Python3Lexer(i_stream)
t_stream = CommonTokenStream(lexer)
t_stream.fill()
return t_stream
def parse(t_stream):
"""Handles parsing
Params:
t_stream: stream of tokens to parse
Returns:
resulting tree
error handler (with possible errors stored inside)
"""
py_parser = Python3Parser(t_stream)
py_parser.removeErrorListeners()
error_listener = FileErrorListener()
py_parser.addErrorListener(error_listener)
built_tree = py_parser.file_input()
return built_tree, error_listener
def tree_to_json(built_tree, error_listener):
"""Converts tree to json
Params:
built_tree - tree to be converted
error_listener - error hadling object
Returns:
json, if tree was constructed without errors
array of errors, otherwise
"""
if len(error_listener.errors) > 0:
return '\n'.join(["Syntax errors were found"] + error_listener.errors)
else:
result = walk(built_tree, Python3Parser.ruleNames)
return json.dumps(result, indent=2, ensure_ascii=False)
if __name__ == '__main__':
from tests.run_tests import run_tests
run_tests()
launch()
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1057,
62,
41989,
3419,
198,
220,
220,
220,
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3419,
198
] | 2.643644 | 999 |
from unplugged import Schema
from ...plugins import DatabasePlugin
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] | 4.3125 | 16 |
import GUI
from GUI import root, tk, MyButton
import ButtonClickHandler as BH
from ButtonClickHandler import MainMenuButtons as M
| [
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] | 3.4 | 40 |
import random
from copy import deepcopy
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] | 4.555556 | 9 |
import os
from bottle import Bottle, response
from whitenoise import WhiteNoise
from ..settings import DATA_PATH
api = Bottle()
@api.hook('after_request')
application = WhiteNoise(api)
application.add_files(os.path.join(DATA_PATH, 'thumbnails'), prefix='thumbnails/')
application.add_files(os.path.join(DATA_PATH, 'medias'), prefix='medias/')
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] | 3.25 | 108 |
# for testing only, 'alpha' is included in the preloaded section on Codewars
alpha = {'ABCDE': 1, 'FGHIJ': 2, 'KLMNO': 3, 'PQRST': 4, 'UVWXY': 5}
| [
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# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
##
# Module for loading and encoding Broombridge data
##
import logging
from qsharp.chemistry import load_broombridge, load_input_state, encode
from typing import List, Tuple
NumQubits = int
HamiltonianTermList = Tuple[List[Tuple[List[int], List[float]]]]
InputStateTerms = Tuple[int, List[Tuple[Tuple[float, float], List[int]]]]
EnergyOffset = float
JWEncodedData = Tuple[
NumQubits,
HamiltonianTermList,
InputStateTerms,
EnergyOffset
]
_log = logging.getLogger(__name__)
def load_and_encode(
file_name: str,
problem_description_index: int = 0,
initial_state_label: str = None
) -> JWEncodedData:
"""Wrapper function for loading and encoding Broombridge file into
JWEncodedData-compatible format.
:param file_name: Broombridge file name
:type file_name: str
:param problem_description_index: Index of problem description to use,
defaults to 0
:type problem_description_index: int, optional
:param initial_state_label: Label of initial state to use, defaults to
first available label
:type initial_state_label: str, optional
"""
broombridge_data = load_broombridge(file_name)
problem = broombridge_data.problem_description[problem_description_index]
if initial_state_label is None:
# Pick first in list
initial_state_label = problem.initial_state_suggestions[0].get("Label")
_log.info(f"Using initial state label: {initial_state_label}")
input_state = load_input_state(file_name, initial_state_label)
ferm_hamiltonian = problem.load_fermion_hamiltonian()
(
num_qubits,
hamiltonian_term_list,
input_state_terms,
energy_offset
) = encode(ferm_hamiltonian, input_state)
return (
num_qubits,
hamiltonian_term_list,
input_state_terms,
energy_offset
)
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220,
220,
31835,
48,
549,
896,
11,
198,
220,
220,
220,
11582,
666,
40596,
8053,
11,
198,
220,
220,
220,
23412,
9012,
15156,
907,
11,
198,
220,
220,
220,
6682,
34519,
198,
60,
198,
198,
62,
6404,
796,
18931,
13,
1136,
11187,
1362,
7,
834,
3672,
834,
8,
628,
198,
4299,
3440,
62,
392,
62,
268,
8189,
7,
198,
220,
220,
220,
2393,
62,
3672,
25,
965,
11,
198,
220,
220,
220,
1917,
62,
11213,
62,
9630,
25,
493,
796,
657,
11,
198,
220,
220,
220,
4238,
62,
5219,
62,
18242,
25,
965,
796,
6045,
198,
8,
4613,
449,
54,
27195,
9043,
6601,
25,
198,
220,
220,
220,
37227,
36918,
2848,
2163,
329,
11046,
290,
21004,
2806,
296,
9458,
2393,
656,
198,
220,
220,
220,
449,
54,
27195,
9043,
6601,
12,
38532,
5794,
13,
628,
220,
220,
220,
1058,
17143,
2393,
62,
3672,
25,
2806,
296,
9458,
2393,
1438,
198,
220,
220,
220,
1058,
4906,
2393,
62,
3672,
25,
965,
198,
220,
220,
220,
1058,
17143,
1917,
62,
11213,
62,
9630,
25,
12901,
286,
1917,
6764,
284,
779,
11,
198,
220,
220,
220,
220,
220,
220,
220,
26235,
284,
657,
198,
220,
220,
220,
1058,
4906,
1917,
62,
11213,
62,
9630,
25,
493,
11,
11902,
198,
220,
220,
220,
1058,
17143,
4238,
62,
5219,
62,
18242,
25,
36052,
286,
4238,
1181,
284,
779,
11,
26235,
284,
198,
220,
220,
220,
220,
220,
220,
220,
717,
1695,
6167,
198,
220,
220,
220,
1058,
4906,
4238,
62,
5219,
62,
18242,
25,
965,
11,
11902,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
47085,
9458,
62,
7890,
796,
3440,
62,
65,
3823,
9458,
7,
7753,
62,
3672,
8,
198,
220,
220,
220,
1917,
796,
47085,
9458,
62,
7890,
13,
45573,
62,
11213,
58,
45573,
62,
11213,
62,
9630,
60,
628,
220,
220,
220,
611,
4238,
62,
5219,
62,
18242,
318,
6045,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
12346,
717,
287,
1351,
198,
220,
220,
220,
220,
220,
220,
220,
4238,
62,
5219,
62,
18242,
796,
1917,
13,
36733,
62,
5219,
62,
47811,
507,
58,
15,
4083,
1136,
7203,
33986,
4943,
198,
220,
220,
220,
220,
220,
220,
220,
4808,
6404,
13,
10951,
7,
69,
1,
12814,
4238,
1181,
6167,
25,
1391,
36733,
62,
5219,
62,
18242,
92,
4943,
628,
220,
220,
220,
5128,
62,
5219,
796,
3440,
62,
15414,
62,
5219,
7,
7753,
62,
3672,
11,
4238,
62,
5219,
62,
18242,
8,
198,
220,
220,
220,
277,
7780,
62,
2763,
9044,
666,
796,
1917,
13,
2220,
62,
2232,
76,
295,
62,
2763,
9044,
666,
3419,
198,
220,
220,
220,
357,
198,
220,
220,
220,
220,
220,
220,
220,
997,
62,
421,
9895,
11,
198,
220,
220,
220,
220,
220,
220,
220,
8891,
9044,
666,
62,
4354,
62,
4868,
11,
198,
220,
220,
220,
220,
220,
220,
220,
5128,
62,
5219,
62,
38707,
11,
198,
220,
220,
220,
220,
220,
220,
220,
2568,
62,
28968,
198,
220,
220,
220,
1267,
796,
37773,
7,
2232,
76,
62,
2763,
9044,
666,
11,
5128,
62,
5219,
8,
628,
220,
220,
220,
1441,
357,
198,
220,
220,
220,
220,
220,
220,
220,
997,
62,
421,
9895,
11,
198,
220,
220,
220,
220,
220,
220,
220,
8891,
9044,
666,
62,
4354,
62,
4868,
11,
198,
220,
220,
220,
220,
220,
220,
220,
5128,
62,
5219,
62,
38707,
11,
198,
220,
220,
220,
220,
220,
220,
220,
2568,
62,
28968,
198,
220,
220,
220,
1267,
198
] | 2.7173 | 711 |
from collections import Counter
print('Number of words:', word_count('test.txt'))
| [
6738,
17268,
1330,
15034,
201,
198,
201,
198,
4798,
10786,
15057,
286,
2456,
25,
3256,
1573,
62,
9127,
10786,
9288,
13,
14116,
6,
4008,
201,
198
] | 3.307692 | 26 |
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import time
import datetime
import torch
import torchreid
from torchreid.engine import engine
from torchreid.losses import CrossEntropyLoss, TripletLoss, HctLoss
from torchreid.utils import AverageMeter, open_specified_layers, open_all_layers
from torchreid import metrics
class ImageTripletEngine(engine.Engine):
r"""Triplet-loss engine for image-reid.
Args:
datamanager (DataManager): an instance of ``torchreid.data.ImageDataManager``
or ``torchreid.data.VideoDataManager``.
model (nn.Module): model instance.
optimizer (Optimizer): an Optimizer.
margin (float, optional): margin for triplet loss. Default is 0.3.
weight_t (float, optional): weight for triplet loss. Default is 1.
weight_x (float, optional): weight for softmax loss. Default is 1.
scheduler (LRScheduler, optional): if None, no learning rate decay will be performed.
use_gpu (bool, optional): use gpu. Default is True.
label_smooth (bool, optional): use label smoothing regularizer. Default is True.
Examples::
import torch
import torchreid
datamanager = torchreid.data.ImageDataManager(
root='path/to/reid-data',
sources='market1501',
height=256,
width=128,
combineall=False,
batch_size=32,
num_instances=4,
train_sampler='RandomIdentitySampler' # this is important
)
model = torchreid.models.build_model(
name='resnet50',
num_classes=datamanager.num_train_pids,
loss='triplet'
)
model = model.cuda()
optimizer = torchreid.optim.build_optimizer(
model, optim='adam', lr=0.0003
)
scheduler = torchreid.optim.build_lr_scheduler(
optimizer,
lr_scheduler='single_step',
stepsize=20
)
engine = torchreid.engine.ImageTripletEngine(
datamanager, model, optimizer, margin=0.3,
weight_t=0.7, weight_x=1, scheduler=scheduler
)
engine.run(
max_epoch=60,
save_dir='log/resnet50-triplet-market1501',
print_freq=10
)
"""
| [
6738,
11593,
37443,
834,
1330,
4112,
62,
11748,
198,
6738,
11593,
37443,
834,
1330,
3601,
62,
8818,
198,
6738,
11593,
37443,
834,
1330,
7297,
198,
198,
11748,
640,
198,
11748,
4818,
8079,
198,
198,
11748,
28034,
198,
198,
11748,
28034,
260,
312,
198,
6738,
28034,
260,
312,
13,
18392,
1330,
3113,
198,
6738,
28034,
260,
312,
13,
22462,
274,
1330,
6372,
14539,
28338,
43,
793,
11,
19817,
83,
43,
793,
11,
367,
310,
43,
793,
198,
6738,
28034,
260,
312,
13,
26791,
1330,
13475,
44,
2357,
11,
1280,
62,
23599,
62,
75,
6962,
11,
1280,
62,
439,
62,
75,
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198,
6738,
28034,
260,
312,
1330,
20731,
628,
198,
4871,
7412,
14824,
37069,
13798,
7,
18392,
13,
13798,
2599,
198,
220,
220,
220,
374,
37811,
14824,
37069,
12,
22462,
3113,
329,
2939,
12,
260,
312,
13,
628,
220,
220,
220,
943,
14542,
25,
198,
220,
220,
220,
220,
220,
220,
220,
4818,
10546,
3536,
357,
6601,
13511,
2599,
281,
4554,
286,
7559,
13165,
354,
260,
312,
13,
7890,
13,
5159,
6601,
13511,
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198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
393,
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13165,
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6601,
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2746,
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320,
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2599,
281,
30011,
7509,
13,
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220,
220,
220,
220,
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10330,
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318,
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13,
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318,
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13,
628,
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628,
220,
220,
220,
220,
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1330,
28034,
198,
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220,
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220,
4818,
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260,
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13,
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13,
5159,
6601,
13511,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6808,
11639,
6978,
14,
1462,
14,
260,
312,
12,
7890,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4237,
11639,
10728,
1314,
486,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6001,
28,
11645,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
9647,
28,
12762,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
12082,
439,
28,
25101,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
15458,
62,
7857,
28,
2624,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
997,
62,
8625,
1817,
28,
19,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4512,
62,
37687,
20053,
11639,
29531,
7390,
26858,
16305,
20053,
6,
1303,
428,
318,
1593,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
2746,
796,
28034,
260,
312,
13,
27530,
13,
11249,
62,
19849,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1438,
11639,
411,
3262,
1120,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
997,
62,
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28,
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10546,
3536,
13,
22510,
62,
27432,
62,
79,
2340,
11,
198,
220,
220,
220,
220,
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220,
220,
2994,
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6,
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220,
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220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
2746,
796,
2746,
13,
66,
15339,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
6436,
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796,
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260,
312,
13,
40085,
13,
11249,
62,
40085,
7509,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2746,
11,
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11639,
324,
321,
3256,
300,
81,
28,
15,
13,
830,
18,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
6038,
18173,
796,
28034,
260,
312,
13,
40085,
13,
11249,
62,
14050,
62,
1416,
704,
18173,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6436,
7509,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
300,
81,
62,
1416,
704,
18173,
11639,
29762,
62,
9662,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4831,
1096,
28,
1238,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
3113,
796,
28034,
260,
312,
13,
18392,
13,
5159,
14824,
37069,
13798,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4818,
10546,
3536,
11,
2746,
11,
6436,
7509,
11,
10330,
28,
15,
13,
18,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3463,
62,
83,
28,
15,
13,
22,
11,
3463,
62,
87,
28,
16,
11,
6038,
18173,
28,
1416,
704,
18173,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
3113,
13,
5143,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3509,
62,
538,
5374,
28,
1899,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3613,
62,
15908,
11639,
6404,
14,
411,
3262,
1120,
12,
28461,
37069,
12,
10728,
1314,
486,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3601,
62,
19503,
80,
28,
940,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
37227,
628
] | 2.265385 | 1,040 |
# crypto/opisthocomus-hoazin
e, n = (65537, 15888457769674642859708800597310299725338251830976423740469342107745469667544014118426981955901595652146093596535042454720088489883832573612094938281276141337632202496209218136026441342435018861975571842724577501821204305185018320446993699281538507826943542962060000957702417455609633977888711896513101590291125131953317446916178315755142103529251195112400643488422928729091341969985567240235775120515891920824933965514217511971572242643456664322913133669621953247121022723513660621629349743664178128863766441389213302642916070154272811871674136669061719947615578346412919910075334517952880722801011983182804339339643)
flag_enc = [65639, 65645, 65632, 65638, 65658, 65653, 65609, 65584, 65650, 65630, 65640, 65634, 65586, 65630, 65634, 65651, 65586, 65589, 65644, 65630, 65640, 65588, 65630, 65618, 65646, 65630, 65607, 65651, 65646, 65627, 65586, 65647, 65630, 65640, 65571, 65612, 65630, 65649, 65651, 65586, 65653, 65621, 65656, 65630, 65618, 65652, 65651, 65636, 65630, 65640, 65621, 65574, 65650, 65630, 65589, 65634, 65653, 65652, 65632, 65584, 65645, 65656, 65630, 65635, 65586, 65647, 65605, 65640, 65647, 65606, 65630, 65644, 65624, 65630, 65588, 65649, 65585, 65614, 65647, 65660]
enc_map = {x ^ e % n: chr(x) for x in range(30,255)}
print(''.join([enc_map[c] for c in flag_enc])) | [
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] | 2.139423 | 624 |
a,b,A,B,l=list(map(int, input().split())), list(map(int, input().split())),0,0,0
for i in range(10): (A,B,l) = (A+3,B,1) if a[i]>b[i] else (A,B+3,2) if a[i]<b[i] else (A+1,B+1,l)
print('{} {}\n{}'.format(A,B, 'A' if A>B or (A==B and l==1) else 'B' if A<B or (A==B and l==2) else 'D')) | [
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] | 1.763975 | 161 |
# -*- coding: utf-8 -*-
| [
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] | 1.434783 | 23 |
#!/usr/bin/env python
# MiTuner_socket_server.py -- Python 2.7 socket server to be used with MiTuner Bridge
# Copyright 2018 Microsemi Inc. All rights reserved.
#Licensed under the MIT License. See LICENSE.txt in the project root for license information.
from os.path import dirname, realpath, isfile
import argparse
import sys
import struct
import socket
sys.path.append(dirname(realpath(__file__)) + "/../../vproc_sdk/libs")
from hbi import *
from tw_firmware_converter import GetFirmwareBinFileB
from hbi_load_firmware import LoadFirmware, SaveFirmwareToFlash, InitFlash, EraseFlash, SaveConfigToFlash, IsFirmwareRunning, LoadFirmwareFromFlash
# Port for the socket (random)
PORT = 5678
BUFFER_SZ = 2048
HEADER_SZ = 6
# ****************************************************************************
# ****************************************************************************
# ****************************************************************************
# ****************************************************************************
# ****************************************************************************
# ****************************************************************************
# ****************************************************************************
# ****************************************************************************
# ****************************************************************************
# ****************************************************************************
# ****************************************************************************
if __name__ == "__main__":
parser = argparse.ArgumentParser(description = "Raspberry Pi socket server for MiTuner V1.0.0")
parser.add_argument("-d", "--debug", help = "debug level 0: none, 1: in, 2: out, 3: in/out", type = int, default = 0)
# Parse the input arguments
args = parser.parse_args()
# Init the HBI driver
cfg = hbi_dev_cfg_t();
handle = HBI_open(cfg)
try:
# Create a socket and listen on port 'PORT'
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.bind(('', PORT))
s.listen(1)
# Accept connections from outside
print("Socket created on port %d, waiting for a connection" % PORT)
while True:
clientsocket, address = s.accept()
print("Incoming connection from: %s" % address[0])
message = ""
waitType = "header"
while True:
buff = clientsocket.recv(BUFFER_SZ).decode()
if (buff == ""):
print("Connection closed by the client (%s)" % address[0])
break
else:
message += buff
if ((waitType == "header") and (len(message) >= HEADER_SZ)):
header = message[0: HEADER_SZ]
message = message[HEADER_SZ:]
cmdLen = int(header[2: 6], 16)
waitType = "cmd"
if ((waitType == "cmd") and (len(message) >= cmdLen)):
cmd = message[0: cmdLen]
message = message[cmdLen:]
if (args.debug & 1):
print("header = %s, cmd = %s" % (header, cmd))
answer = ParseCmd(handle, header, cmd)
if (args.debug & 2):
print("\t" + answer)
clientsocket.send(answer.encode())
waitType = "header"
clientsocket.close()
except:
print("Server shut down")
# Close the Socket
s.close()
# Close HBI driver
HBI_close(handle)
| [
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] | 2.61779 | 1,439 |
import tensorflow as tf
from .tf import TFDataset
class TFCIFAR10(TFDataset):
"""`TFCIFAR10` class.
Represents a CIFAR10 dataset class for TensorFlow.
"""
TF_MODULE = tf.keras.datasets.cifar10
DATASET_SIZE = {"train": 50000, "test": 10000}
INPUT_SHAPE = (32, 32, 3)
N_CLASSES = 10
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] | 2.258993 | 139 |
#!/usr/bin/env python
# File name: name.py
import sys
import json
import os
import rsa
import struct
import json
from block import *
from controlblock import *
base = [str(x) for x in range(10)] + [chr(x) for x in range(ord('A'), ord('A') + 6)]
if __name__ == '__main__':
print 'Signing Tool for the new Secure Boot validation. Version: 1.01'
print ' This tool is to generate valid Configuration/Regional Blocks base on given DISK raw image'
print ' Usage(windows platform): python27 signing.py config.json'
print ' See the details in .json files'
if len(sys.argv) != 2 :
sys.exit(-1)
SigningObjects = []
NewRegionBlock = [None,None,None,None]
ConfigData = {}
ConfigFile = sys.argv[1]
with open(ConfigFile) as inputFile:
ConfigData = json.load(inputFile)
inputFile.close()
pass
ConfigData['Jobs'].sort(object_compare)
print ConfigData['InputFile']
'''
Extract raw data from each section.
'''
with open( ConfigData['InputFile'] , 'rb') as diskFile:
TargetFileSize = os.path.getsize( ConfigData['InputFile'] )
fileContent = diskFile.read(TargetFileSize)
diskFile.close()
fileContent = bytearray(fileContent)
MBR = fileContent[0:512]
Partition1LBA, = struct.unpack("<I", fileContent[0x1C6:0x1C6+4] )
if Partition1LBA != 0:
if Partition1LBA > 0x800 :
print "Warning!!! The size of MBR+Booloader exceeds 2048 sectors."
MBR = fileContent[0:Partition1LBA*512]
MBR_obj = PartitionBlock(MBR, 0)
MBR_obj.SetRawData(MBR)
SigningObjects.append(MBR_obj)
for dataElement in ConfigData['Jobs']:
if dataElement['RegionID'] == 1: #MBR
NewRegionBlock[0] = RegionBlock(int(dataElement['RegionID']),
int(dataElement['HashingType']),
dataElement['PrivateKeyFile'])
NewRegionBlock[0].SigningRegionalData(MBR_obj.GetRawData())
with open(dataElement['OutputRawFile'], 'wb+') as OutputFile:
OutputFile.write(MBR_obj.GetRawData())
OutputFile.close()
for dataElement in ConfigData['Jobs']:
if dataElement['RegionID'] != 1:
PartIndex = dataElement['RegionID']-2
PartitionEntity = fileContent[0x1C6+0x10*PartIndex:0x1C6+0x10*PartIndex+8]
PartitionLBA, PartitionSize = struct.unpack("<II", PartitionEntity )
if PartitionLBA == 0 or PartitionSize == 0:
print "Error!!! The config does not match to the structure in MBR."
sys.exit(-1)
Part_Objs = PartitionBlock(PartitionEntity, 1)
RawData = fileContent[Part_Objs.GetLBAStarting()*512:
Part_Objs.GetLBAStarting()*512 + Part_Objs.GetSize()*512 ]
Part_Objs.SetRawData(RawData)
SigningObjects.append(Part_Objs)
i = dataElement['RegionID']-1
NewRegionBlock[i] = RegionBlock(int(dataElement['RegionID']),
int(dataElement['HashingType']),
dataElement['PrivateKeyFile'])
NewRegionBlock[i].SigningRegionalData(Part_Objs.GetRawData())
with open(dataElement['OutputRawFile'], 'wb+') as OutputFile:
OutputFile.write(Part_Objs.GetRawData())
OutputFile.close()
pass
with open(ConfigData['OutputConfigBlock'], 'wb+') as OutputFile:
OutputFile.write(fileContent)
CB = ControlBlock(int(ConfigData['Version']),
3,
ConfigData['PrivateKeyFile'],
int(ConfigData['HashingType']))
# version, NumberOfRegions, CtrlPrivateKey, HashType
for rb in NewRegionBlock:
CB.add_region_block(rb)
OutputFile.write(CB.GetRawData())
OutputFile.close()
with open(ConfigData['OutputRawPubkey'], 'wb+') as PubRawFile:
PubRawFile.write(CB.Get_Raw_Public_Key())
PubRawFile.close()
pass
else:
print 'I am being imported from another module.'
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] | 2.003538 | 2,261 |
#! /usr/bin/python
# Copyright 2015 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
import argparse
import cgi
import json
import logging
import os
import subprocess
import sys
import tempfile
import time
_SRC_DIR = os.path.abspath(os.path.join(
os.path.dirname(__file__), '..', '..', '..'))
sys.path.append(os.path.join(_SRC_DIR, 'third_party', 'catapult', 'devil'))
from devil.android import device_utils
from devil.android.sdk import intent
sys.path.append(os.path.join(_SRC_DIR, 'build', 'android'))
import devil_chromium
from pylib import constants
import activity_lens
import clovis_constants
import content_classification_lens
import controller
import device_setup
import frame_load_lens
import loading_graph_view
import loading_graph_view_visualization
import loading_trace
import options
import request_dependencies_lens
import request_track
import xvfb_helper
# TODO(mattcary): logging.info isn't that useful, as the whole (tools) world
# uses logging info; we need to introduce logging modules to get finer-grained
# output. For now we just do logging.warning.
OPTIONS = options.OPTIONS
def _LoadPage(device, url):
"""Load a page on chrome on our device.
Args:
device: an AdbWrapper for the device on which to load the page.
url: url as a string to load.
"""
load_intent = intent.Intent(
package=OPTIONS.ChromePackage().package,
activity=OPTIONS.ChromePackage().activity,
data=url)
logging.warning('Loading ' + url)
device.StartActivity(load_intent, blocking=True)
def _GetPrefetchHtml(graph_view, name=None):
"""Generate prefetch page for the resources in resource graph.
Args:
graph_view: (LoadingGraphView)
name: optional string used in the generated page.
Returns:
HTML as a string containing all the link rel=prefetch directives necessary
for prefetching the given ResourceGraph.
"""
if name:
title = 'Prefetch for ' + cgi.escape(name)
else:
title = 'Generated prefetch page'
output = []
output.append("""<!DOCTYPE html>
<html>
<head>
<title>%s</title>
""" % title)
for node in graph_view.deps_graph.graph.Nodes():
output.append('<link rel="prefetch" href="%s">\n' % node.request.url)
output.append("""</head>
<body>%s</body>
</html>
""" % title)
return '\n'.join(output)
def _LogRequests(url, clear_cache_override=None):
"""Logs requests for a web page.
Args:
url: url to log as string.
clear_cache_override: if not None, set clear_cache different from OPTIONS.
Returns:
JSON dict of logged information (ie, a dict that describes JSON).
"""
xvfb_process = None
if OPTIONS.local:
chrome_ctl = controller.LocalChromeController()
if OPTIONS.headless:
xvfb_process = xvfb_helper.LaunchXvfb()
chrome_ctl.SetChromeEnvOverride(xvfb_helper.GetChromeEnvironment())
else:
chrome_ctl = controller.RemoteChromeController(
device_setup.GetFirstDevice())
clear_cache = (clear_cache_override if clear_cache_override is not None
else OPTIONS.clear_cache)
if OPTIONS.emulate_device:
chrome_ctl.SetDeviceEmulation(OPTIONS.emulate_device)
if OPTIONS.emulate_network:
chrome_ctl.SetNetworkEmulation(OPTIONS.emulate_network)
try:
with chrome_ctl.Open() as connection:
if clear_cache:
connection.ClearCache()
trace = loading_trace.LoadingTrace.RecordUrlNavigation(
url, connection, chrome_ctl.ChromeMetadata(),
categories=clovis_constants.DEFAULT_CATEGORIES)
except controller.ChromeControllerError as e:
e.Dump(sys.stderr)
raise
if xvfb_process:
xvfb_process.terminate()
return trace.ToJsonDict()
def _FullFetch(url, json_output, prefetch):
"""Do a full fetch with optional prefetching."""
if not url.startswith('http') and not url.startswith('file'):
url = 'http://' + url
logging.warning('Cold fetch')
cold_data = _LogRequests(url)
assert cold_data, 'Cold fetch failed to produce data. Check your phone.'
if prefetch:
assert not OPTIONS.local
logging.warning('Generating prefetch')
prefetch_html = _GetPrefetchHtml(_ProcessJsonTrace(cold_data), name=url)
tmp = tempfile.NamedTemporaryFile()
tmp.write(prefetch_html)
tmp.flush()
# We hope that the tmpfile name is unique enough for the device.
target = os.path.join('/sdcard/Download', os.path.basename(tmp.name))
device = device_setup.GetFirstDevice()
device.adb.Push(tmp.name, target)
logging.warning('Pushed prefetch %s to device at %s' % (tmp.name, target))
_LoadPage(device, 'file://' + target)
time.sleep(OPTIONS.prefetch_delay_seconds)
logging.warning('Warm fetch')
warm_data = _LogRequests(url, clear_cache_override=False)
with open(json_output, 'w') as f:
json.dump(warm_data, f)
logging.warning('Wrote ' + json_output)
with open(json_output + '.cold', 'w') as f:
json.dump(cold_data, f)
logging.warning('Wrote ' + json_output + '.cold')
else:
with open(json_output, 'w') as f:
json.dump(cold_data, f)
logging.warning('Wrote ' + json_output)
COMMAND_MAP = {
'png': DoPng,
'prefetch_setup': DoPrefetchSetup,
'log_requests': DoLogRequests,
'longpole': DoLongPole,
'nodecost': DoNodeCost,
'cost': DoCost,
'fetch': DoFetch,
}
if __name__ == '__main__':
main()
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] | 2.744456 | 1,984 |
import random
class NaiveResourceManager(object):
r"""
Overview:
the naive resource manager
Interface:
__init__, assign_collector, assign_learner, update
"""
def __init__(self) -> None:
r"""
Overview:
init the resouce manager
"""
self._worker_type = ['collector', 'learner']
self._resource_info = {k: {} for k in self._worker_type}
def assign_collector(self, collector_task: dict) -> dict:
r"""
Overview:
assign the collector_task randomly and return the resouce info
Arguments:
- collector_task (:obj:`dict`): the collector task to assign
"""
available_collector_list = list(self._resource_info['collector'].keys())
if len(available_collector_list) > 0:
selected_collector = random.sample(available_collector_list, 1)[0]
info = self._resource_info['collector'].pop(selected_collector)
return {'collector_id': selected_collector, 'resource_info': info}
else:
return None
def assign_learner(self, learner_task: dict) -> dict:
r"""
Overview:
assign the learner_task randomly and return the resouce info
Arguments:
- learner_task (:obj:`dict`): the learner task to assign
"""
available_learner_list = list(self._resource_info['learner'].keys())
if len(available_learner_list) > 0:
selected_learner = random.sample(available_learner_list, 1)[0]
info = self._resource_info['learner'].pop(selected_learner)
return {'learner_id': selected_learner, 'resource_info': info}
else:
return None
def update(self, name: str, worker_id: str, resource_info: dict) -> None:
r"""
Overview:
update the reource info
"""
assert name in self._worker_type, "invalid worker_type: {}".format(name)
self._resource_info[name][worker_id] = resource_info
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] | 2.314415 | 881 |
from rest_framework import serializers
from .models import Ml_test
#from .models import Account, Teleconference_transcribe
from rest_framework import filters
# class Teleconference_transcribeSerializer(serializers.HyperlinkedModelSerializer):
# class Meta:
# model = Teleconference_transcribe
# fields = ['filename', 'transcription', 'transcription_baseline'] | [
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from tkinter import *
from tkinter import filedialog
from tkinter.ttk import *
import os
import xlrd
import xlsxwriter
root = Tk()
root.title("CivilCon")
root.iconbitmap("CC.ico")
root.geometry("500x500")
e = CivilCon(root)
root.mainloop()
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#!/usr/bin/env python3
# Copyright (C) 2016-2019 Semtech (International) AG. All rights reserved.
#
# This file is subject to the terms and conditions defined in file 'LICENSE',
# which is part of this source code package.
import os
import shlex
import sys
import re
import yaml
from typing import Callable,Dict,List,Optional,Set,Tuple
from typing import cast
from argparse import Namespace as NS # type alias
from cc import CommandCollection
if __name__ == '__main__':
ServiceTool().run()
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] | 3.448276 | 145 |
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers
import matplotlib.pyplot as plt
import math
from tensorflow.keras.models import load_model
from matplotlib import animation
from env_predict import *
from buffer import *
from model import *
from noise import *
dt = 0.4
v = 1.0
ve = 1.2
#Dimension of State Space for single agent
dim_agent_state = 5
num_agents = 3
#Dimension of State Space
dim_state = dim_agent_state*num_agents
#Number of Episodes
num_episodes = 3000
#Number of Steps
num_steps = 400
std_dev = 0.2
ou_noise = OUActionNoise(mean=np.zeros(1), std_deviation=float(std_dev) * np.ones(1))
ac_models = []
cr_models = []
target_ac = []
target_cr = []
path = 'C:/Users/HP/Desktop/desktop_folders/MS_Project_Codes/maddpg/maddpg_models/'
for i in range(num_agents):
ac_models.append(load_model(path + 'actor'+str(i)+'.h5'))
cr_models.append(load_model(path + 'critic'+str(i)+'.h5'))
target_ac.append(load_model(path + 'target_actor'+str(i)+'.h5'))
target_cr.append(load_model(path + 'target_critic'+str(i)+'.h5'))
ep_reward_list = []
# To store average reward history of last few episodes
avg_reward_list = []
ag1_reward_list = []
ag2_reward_list = []
ev_reward_list = []
# Takes about 20 min to train
for ep in range(1):
env = environment()
prev_state = env.initial_obs()
episodic_reward = 0
ag1_reward = 0
ag2_reward = 0
ev_reward = 0
xp1 = []
yp1 = []
xp2 = []
yp2 = []
xce = []
yce = []
#while True:
for i in range(400):
tf_prev_state = tf.expand_dims(tf.convert_to_tensor(prev_state), 0)
actions = []
for j, model in enumerate(ac_models):
action = policy(tf_prev_state[:,5*j:5*(j+1)], ou_noise, model)
actions.append(float(action[0]))
# Recieve state and reward from environment.
#new_state, sys_state, ev_state = transition(prev_state, sys_state, actions, ev_state)
new_state = env.step(actions)
rewards = reward(new_state)
#buffer.record((prev_state, actions, rewards, new_state))
episodic_reward += sum(rewards)
ag1_reward += rewards[0]
ag2_reward += rewards[1]
ev_reward += rewards[2]
'''buffer.learn(ac_models, cr_models, target_ac, target_cr)
update_target(tau, ac_models, cr_models, target_ac, target_cr)'''
prev_state = new_state
xp1.append(env.p1_rx)
yp1.append(env.p1_ry)
xp2.append(env.p2_rx)
yp2.append(env.p2_ry)
xce.append(env.e_rx)
yce.append(env.e_ry)
d_p1_e = L(env.p1_rx, env.p1_ry, env.e_rx, env.e_ry)
d_p2_e = L(env.p2_rx, env.p2_ry, env.e_rx, env.e_ry)
if d_p1_e < 0.4 or d_p2_e < 0.4:
env = environment()
prev_state = env.initial_obs()
print("Captured")
#break
xc1 = [env.e_rx]
yc1 = [env.e_ry]
ep_reward_list.append(episodic_reward)
ag1_reward_list.append(ag1_reward)
ag2_reward_list.append(ag2_reward)
ev_reward_list.append(ev_reward)
# Mean of last 40 episodes
avg_reward = np.mean(ep_reward_list[-40:])
print("Trajectory plot will be generated")
avg_reward_list.append(avg_reward)
plt.plot(xp1,yp1)
plt.plot(xp2,yp2)
plt.plot(xce,yce)
plt.plot(xc1,yc1,'.')
plt.plot(xp1[-1],yp1[-1],'*')
plt.plot(xp2[-1],yp2[-1],'*')
plt.show()
print("Trajectory Animation will be generated")
# Creating animation of the complete episode during execution
# First set up the figure, the axis, and the plot element we want to animate
fig = plt.figure()
ax = plt.axes(xlim=(-1, 11), ylim=(-1, 11))
line, = ax.plot([], [], 'go')
line1, = ax.plot([], [], 'go')
line2, = ax.plot([], [], 'ro')
# initialization function: plot the background of each frame
# animation function. This is called sequentially
# call the animator. blit=True means only re-draw the parts that have changed.
anim = animation.FuncAnimation(fig, animate, init_func=init,
frames=600, interval=1, blit=True)
# save the animation as an mp4. This requires ffmpeg or mencoder to be
# installed. The extra_args ensure that the x264 codec is used, so that
# the video can be embedded in html5. You may need to adjust this for
# your system: for more information, see
# http://matplotlib.sourceforge.net/api/animation_api.html
anim.save('basic_animation.mp4', fps=20, extra_args=['-vcodec', 'libx264'])
# Plotting graph
# Episodes versus Avg. Rewards
plt.show()
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] | 2.13092 | 2,238 |
from ostinato.pages.views import PageView
from django.views.generic.detail import DetailView
from django.views.generic.dates import DateDetailView
from ostinato.pages.models import Page
from blog.models import Entry
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] | 3.622951 | 61 |
"""Load article sample (1%) into spreadsheet for manual content analysis"""
import pandas as pd
import utils
from question_1.is_about_climate_change_sql_statement import is_about_climate_change_sql_statement
import os.path
if __name__ == "__main__":
main()
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] | 3.341772 | 79 |
import numpy
from srxraylib.plot.gol import plot
import scipy.constants as codata
import xraylib
if __name__ == "__main__":
do_calculate_spectrum = True
diamond_thickness_in_mm = 0.8
outfile = "spectrumE.dat"
rho = 1.848
if do_calculate_spectrum:
energy, flux = create_spectrum()
energy, flux = diamond_filter(energy, flux, diamond_thickness_in_mm=diamond_thickness_in_mm)
f = open(outfile, "w")
for i in range(energy.size):
f.write("%g %g\n" % (energy[i], flux[i]))
f.close()
print("File %s written to disk." % outfile)
energy_for_pescao, flux_for_pescao = remove_points_for_pescao(energy, flux)
f = open("spectrumEF.dat", "w")
for i in range(energy_for_pescao.size):
f.write("%g %g\n" % (energy_for_pescao[i], flux_for_pescao[i]))
f.close()
print("File %s written to disk." % "spectrumEF.dat")
else: # just read file with spectrum
a = numpy.loadtxt(outfile)
energy = a[:,0]
flux = a[:,1]
spectral_power = flux * 1e3 * codata.e
estep = (energy[1] - energy[0])
integrated_power = (spectral_power.sum() * estep)
print("integrated power", integrated_power)
print("volumetric power", integrated_power / (0.8**2))
#
# NIST data
#
nist = nist_be()
print(nist.shape)
nist_interpolated = 10 ** numpy.interp(numpy.log10(energy), numpy.log10(1e6 * nist[:,0]), numpy.log10(rho * nist[:,2]))
# plot(1e6 * nist[:, 0], nist[:, 1],
# 1e6 * nist[:, 0], nist[:, 2],
# energy, nist_interpolated/rho, xlog=1, ylog=1,
# xtitle="Photon energy [eV]", ytitle="[cm2/g]")
#
# xraylib data
#
XRL_MU = numpy.zeros_like(energy)
XRL_MU_E = numpy.zeros_like(energy)
for i in range(energy.size):
XRL_MU[i] = rho * xraylib.CS_Total(xraylib.SymbolToAtomicNumber("Be"), 1e-3*energy[i])
XRL_MU_E[i] = rho * xraylib.CS_Energy(xraylib.SymbolToAtomicNumber("Be"), 1e-3*energy[i])
plot(
1e-3 * energy, XRL_MU,
1e-3 * energy, XRL_MU_E,
1e-3 * energy, nist_interpolated,
xlog=0, ylog=1, legend=["mu","mu_e","nist_e"],
xtitle="Photon energy [keV]", ytitle="mu [cm^-1]")
#
# loop on thicknesses
#
THICKNESS_MM = numpy.concatenate( (numpy.linspace(0,1,100),numpy.linspace(1,10,50)))
VOLUMETRIC_ABSORBED_POWER = numpy.zeros_like(THICKNESS_MM)
VOLUMETRIC_ABSORBED_POWER_E = numpy.zeros_like(THICKNESS_MM)
VOLUMETRIC_ABSORBED_POWER_NIST = numpy.zeros_like(THICKNESS_MM)
for i, thickness_mm in enumerate(THICKNESS_MM):
thickness_mm = THICKNESS_MM[i]
absorbed_fraction = 1.0 - numpy.exp(-XRL_MU * thickness_mm * 1e-1)
absorbed_fraction_e = 1.0 - numpy.exp(-XRL_MU_E * thickness_mm * 1e-1)
absorbed_fraction_nist = 1.0 - numpy.exp(-nist_interpolated * thickness_mm * 1e-1)
# plot(energy, absorbed_fraction, energy, absorbed_fraction_e)
absorbed_power = (flux * absorbed_fraction * codata.e * 1e3).sum() * estep
volumetric_absorbed_power = absorbed_power / (0.8 * 0.8 * thickness_mm)
absorbed_power_e = (flux * absorbed_fraction_e * codata.e * 1e3).sum() * estep
volumetric_absorbed_power_e = absorbed_power_e / (0.8 * 0.8 * thickness_mm)
absorbed_power_nist = (flux * absorbed_fraction_nist * codata.e * 1e3).sum() * estep
volumetric_absorbed_power_nist = absorbed_power_nist / (0.8 * 0.8 * thickness_mm)
VOLUMETRIC_ABSORBED_POWER[i] = volumetric_absorbed_power
VOLUMETRIC_ABSORBED_POWER_E[i] = volumetric_absorbed_power_e
VOLUMETRIC_ABSORBED_POWER_NIST[i] = volumetric_absorbed_power_nist
print(integrated_power, absorbed_power, volumetric_absorbed_power)
print(integrated_power, absorbed_power_e, volumetric_absorbed_power_e)
#
# load pescao results and make final plot
#
pescao = numpy.loadtxt("pescao_0p8.dat", skiprows=2)
plot(THICKNESS_MM, VOLUMETRIC_ABSORBED_POWER,
THICKNESS_MM, VOLUMETRIC_ABSORBED_POWER_E,
THICKNESS_MM, VOLUMETRIC_ABSORBED_POWER_NIST,
pescao[:,0], pescao[:,1]/(pescao[:,0] * 0.8 * 0.8),
xtitle="Depth [mm]", ytitle="Volumetric absorption [W/mm3]",
title="diamond window thickness = %g mm" % diamond_thickness_in_mm,
legend=["mu","mu_e","nist_e","Monte Carlo"])
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] | 2.0726 | 2,135 |
import random
import html
from functools import cached_property
from wyr.console import Console
| [
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] | 4.125 | 24 |
import requests
import math
import pandas as pd
from datetime import datetime
from datetime import timedelta
import requests
interval = 1
symbol = 'XBTUSD'
# get data from
timestamp_from = 1514761200
# till
timestamp_now = 1536530400
max_back_time = 0
max_bars = 10080
max_bars_time = ((interval * 60) * max_bars)
time_to_iterate = timestamp_now - timestamp_from
baseURI = "https://www.bitmex.com/api/v1"
endpoint = "/trade/bucketed"
time_ago = datetime.now() - timedelta(minutes=150)
request = requests.get(baseURI + endpoint, params={'binSize': '1m', 'symbol': 'XBTUSD', 'count': 750, 'startTime': time_ago})
print("data: start:", datetime.fromtimestamp(timestamp_from), "end:", datetime.fromtimestamp(timestamp_now))
data_frames = []
for x in range(int(math.ceil(time_to_iterate / max_bars_time))):
if x > 0:
if (max_back_time - max_bars_time) > timestamp_from:
max_back_time, timestamp_now = (max_back_time - max_bars_time), max_back_time
else:
max_back_time, timestamp_now = timestamp_from, max_back_time
elif x == 0:
if time_to_iterate < max_bars_time:
max_back_time = timestamp_from
else:
max_back_time = timestamp_now - max_bars_time
print("SPLIT TIMING", "start:", datetime.fromtimestamp(max_back_time), "end:", datetime.fromtimestamp(timestamp_now))
r = requests.get('https://www.bitmex.com/api/udf/history?symbol={}&resolution={}&from={}&to={}'.format(symbol, interval, max_back_time, timestamp_now)).json()
data = {
'Date': r['t'],
'Open': r['o'],
'High': r['o'],
'Low': r['o'],
'Close': r['c'],
'Adj Close': r['o'],
'Volume': r['v']
}
columns = ['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume']
df = pd.DataFrame(data, columns=columns)
df['Date'] = pd.to_datetime(df['Date'], unit='s')
data_frames.append(df)
print(pd.concat(data_frames))
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# Generated by Django 3.2.6 on 2021-08-05 22:52
from django.db import migrations, models
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# -*- coding: utf-8 -*-
# A Variational Autoencoder trained on the MNIST dataset.
import tensorflow as tf
import keras
import numpy as np
import matplotlib.pyplot as plt
from keras.layers import Input, Dense, Lambda, InputLayer, concatenate
from keras.models import Model, Sequential
from keras import backend as K
from keras.datasets import mnist
from keras.utils import np_utils
# Variational Lower Bound
def vlb_binomial(x, x_decoded_mean, t_mean, t_log_var):
"""Returns the value of Variational Lower Bound
The inputs are tf.Tensor
x: (batch_size x number_of_pixels) matrix with one image per row with zeros and ones
x_decoded_mean: (batch_size x number_of_pixels) mean of the distribution p(x | t), real numbers from 0 to 1
t_mean: (batch_size x latent_dim) mean vector of the (normal) distribution q(t | x)
t_log_var: (batch_size x latent_dim) logarithm of the variance vector of the (normal) distribution q(t | x)
Returns:
A tf.Tensor with one element (averaged across the batch), VLB
"""
klterm=0.5*K.sum(-1-t_log_var+K.square(t_mean)+K.exp(t_log_var),axis=1)#batch_size
reconst=K.sum(K.binary_crossentropy(x,x_decoded_mean),axis=1)
return K.mean(klterm+reconst)
# Sampling from the distribution
# q(t | x) = N(t_mean, exp(t_log_var))
# with reparametrization trick.
def sampling(args):
"""Returns sample from a distribution N(args[0], diag(args[1]))
The sample should be computed with reparametrization trick.
The inputs are tf.Tensor
args[0]: (batch_size x latent_dim) mean of the desired distribution
args[1]: (batch_size x latent_dim) logarithm of the variance vector of the desired distribution
Returns:
A tf.Tensor of size (batch_size x latent_dim), the samples.
"""
t_mean, t_log_var = args
output = tf.random_normal(t_mean.get_shape())
output = output * tf.exp(0.5 * t_log_var) + t_mean
return output
if __name__ == '__main__':
# Start tf session so we can run code.
sess = tf.InteractiveSession()
# Connect keras to the created session.
K.set_session(sess)
batch_size = 100
original_dim = 784 # Number of pixels in MNIST images.
latent_dim = 100 # d, dimensionality of the latent code t.
intermediate_dim = 256 # Size of the hidden layer.
epochs = 20
x = Input(batch_shape=(batch_size, original_dim))
encoder = create_encoder(original_dim)
get_t_mean = Lambda(lambda h: h[:, :latent_dim])
get_t_log_var = Lambda(lambda h: h[:, latent_dim:])
h = encoder(x)
t_mean = get_t_mean(h)
t_log_var = get_t_log_var(h)
t = Lambda(sampling)([t_mean, t_log_var])
decoder = create_decoder(latent_dim)
x_decoded_mean = decoder(t)
loss = vlb_binomial(x, x_decoded_mean, t_mean, t_log_var)
vae = Model(x, x_decoded_mean)
# Keras will provide input (x) and output (x_decoded_mean) to the function that
# should construct loss, but since our function also depends on other
# things (e.g. t_means), it is easier to build the loss in advance and pass
# a function that always returns it.
vae.compile(optimizer=keras.optimizers.RMSprop(lr=0.001), loss=lambda x, y: loss)
# Load and prepare the data
# train the VAE on MNIST digits
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# One hot encoding.
y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))
# Training the model
hist = vae.fit(x=x_train, y=x_train,
shuffle=True,
epochs=epochs,
batch_size=batch_size,
validation_data=(x_test, x_test),
verbose=2)
# Visualize reconstructions for train and validation data
fig = plt.figure(figsize=(10, 10))
for fid_idx, (data, title) in enumerate(
zip([x_train, x_test], ['Train', 'Validation'])):
n = 10 # figure with 10 x 2 digits
digit_size = 28
figure = np.zeros((digit_size * n, digit_size * 2))
decoded = sess.run(x_decoded_mean, feed_dict={x: data[:batch_size, :]})
for i in range(10):
figure[i * digit_size: (i + 1) * digit_size,
:digit_size] = data[i, :].reshape(digit_size, digit_size)
figure[i * digit_size: (i + 1) * digit_size,
digit_size:] = decoded[i, :].reshape(digit_size, digit_size)
ax = fig.add_subplot(1, 2, fid_idx + 1)
ax.imshow(figure, cmap='Greys_r')
ax.set_title(title)
ax.axis('off')
plt.show()
# Hallucinating new data
# generate new samples of images from your trained VAE
n_samples = 10 # To pass automatic grading please use at least 2 samples here.
# sampled_im_mean is a tf.Tensor of size 10 x 784 with 10 random
# images sampled from the vae model.
sampled_im_mean = decoder(tf.random_normal((n_samples,latent_dim)))
sampled_im_mean_np = sess.run(sampled_im_mean)
# Show the sampled images.
plt.figure()
for i in range(n_samples):
ax = plt.subplot(n_samples // 5 + 1, 5, i + 1)
plt.imshow(sampled_im_mean_np[i, :].reshape(28, 28), cmap='gray')
ax.axis('off')
plt.show()
# Conditional VAE
# Implement CVAE model
# One-hot labels placeholder.
x = Input(batch_shape=(batch_size, original_dim))
label = Input(batch_shape=(batch_size, 10))
cond_encoder = create_encoder(original_dim+10)
cond_h = cond_encoder(concatenate([x, label]))
cond_t_mean = get_t_mean(cond_h) # Mean of the latent code (without label) for cvae model.
cond_t_log_var = get_t_log_var(cond_h) # Logarithm of the variance of the latent code (without label) for cvae model.
cond_t = Lambda(sampling)([cond_t_mean, cond_t_log_var])
cond_decoder = create_decoder(latent_dim+10)
cond_x_decoded_mean = cond_decoder(concatenate([cond_t, label])) # Final output of the cvae model.
# Define the loss and the model
conditional_loss = vlb_binomial(x, cond_x_decoded_mean, cond_t_mean, cond_t_log_var)
cvae = Model([x, label], cond_x_decoded_mean)
cvae.compile(optimizer=keras.optimizers.RMSprop(lr=0.001), loss=lambda x, y: conditional_loss)
# Train the model
hist = cvae.fit(x=[x_train, y_train],
y=x_train,
shuffle=True,
epochs=epochs,
batch_size=batch_size,
validation_data=([x_test, y_test], x_test),
verbose=2)
# Visualize reconstructions for train and validation data
fig = plt.figure(figsize=(10, 10))
for fid_idx, (x_data, y_data, title) in enumerate(
zip([x_train, x_test], [y_train, y_test], ['Train', 'Validation'])):
n = 10 # figure with 10 x 2 digits
digit_size = 28
figure = np.zeros((digit_size * n, digit_size * 2))
decoded = sess.run(cond_x_decoded_mean,
feed_dict={x: x_data[:batch_size, :],
label: y_data[:batch_size, :]})
for i in range(10):
figure[i * digit_size: (i + 1) * digit_size,
:digit_size] = x_data[i, :].reshape(digit_size, digit_size)
figure[i * digit_size: (i + 1) * digit_size,
digit_size:] = decoded[i, :].reshape(digit_size, digit_size)
ax = fig.add_subplot(1, 2, fid_idx + 1)
ax.imshow(figure, cmap='Greys_r')
ax.set_title(title)
ax.axis('off')
plt.show()
# Conditionally hallucinate data
# Prepare one hot labels of form
# 0 0 0 0 0 1 1 1 1 1 2 2 2 2 2 ...
# to sample five zeros, five ones, etc
curr_labels = np.eye(10)
curr_labels = np.repeat(curr_labels, 5, axis=0) # Its shape is 50 x 10.
# cond_sampled_im_mean is a tf.Tensor of size 50 x 784 with 5 random zeros,
# then 5 random ones, etc sampled from the cvae model.
cond_sampled_im_mean = cond_decoder(concatenate([tf.random_normal((50,latent_dim)), tf.convert_to_tensor(curr_labels, dtype=tf.float32)]))
cond_sampled_im_mean_np = sess.run(cond_sampled_im_mean)
# Show the sampled images.
plt.figure(figsize=(10, 10))
global_idx = 0
for digit in range(10):
for _ in range(5):
ax = plt.subplot(10, 5, global_idx + 1)
plt.imshow(cond_sampled_im_mean_np[global_idx, :].reshape(28, 28), cmap='gray')
ax.axis('off')
global_idx += 1
plt.show() | [
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] | 2.21573 | 4,005 |
from .beta_calibration import _BetaCal, _BetaAMCal, _BetaABCal
from sklearn.base import BaseEstimator, RegressorMixin
class BetaCalibration(BaseEstimator, RegressorMixin):
"""Wrapper class for the three Beta regression models introduced in
Kull, M., Silva Filho, T.M. and Flach, P. Beta calibration: a well-founded
and easily implemented improvement on logistic calibration for binary
classifiers. AISTATS 2017.
Parameters
----------
parameters : string
Determines which parameters will be calculated by the model. Possible
values are: "abm" (default), "am" and "ab"
Attributes
----------
calibrator_ :
Internal calibrator object. The type depends on the value of parameters.
"""
def fit(self, X, y, sample_weight=None):
"""Fit the model using X, y as training data.
Parameters
----------
X : array-like, shape (n_samples,)
Training data.
y : array-like, shape (n_samples,)
Training target.
sample_weight : array-like, shape = [n_samples] or None
Sample weights. If None, then samples are equally weighted.
Currently, no sample weighting is done by the models.
Returns
-------
self : object
Returns an instance of self.
"""
self.calibrator_.fit(X, y, sample_weight)
return self
def predict(self, S):
"""Predict new values.
Parameters
----------
S : array-like, shape (n_samples,)
Data to predict from.
Returns
-------
: array, shape (n_samples,)
The predicted values.
"""
return self.calibrator_.predict(S)
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220,
220,
220,
220,
220,
220,
24200,
438,
198,
220,
220,
220,
220,
220,
220,
220,
1395,
1058,
7177,
12,
2339,
11,
5485,
357,
77,
62,
82,
12629,
35751,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
13614,
1366,
13,
628,
220,
220,
220,
220,
220,
220,
220,
331,
1058,
7177,
12,
2339,
11,
5485,
357,
77,
62,
82,
12629,
35751,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
13614,
2496,
13,
628,
220,
220,
220,
220,
220,
220,
220,
6291,
62,
6551,
1058,
7177,
12,
2339,
11,
5485,
796,
685,
77,
62,
82,
12629,
60,
393,
6045,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
27565,
19590,
13,
1002,
6045,
11,
788,
8405,
389,
8603,
26356,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
16888,
11,
645,
6291,
3463,
278,
318,
1760,
416,
262,
4981,
13,
628,
220,
220,
220,
220,
220,
220,
220,
16409,
198,
220,
220,
220,
220,
220,
220,
220,
35656,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
1058,
2134,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
16409,
281,
4554,
286,
2116,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
9948,
2889,
1352,
44807,
11147,
7,
55,
11,
331,
11,
6291,
62,
6551,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
628,
220,
220,
220,
825,
4331,
7,
944,
11,
311,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
47,
17407,
649,
3815,
13,
628,
220,
220,
220,
220,
220,
220,
220,
40117,
198,
220,
220,
220,
220,
220,
220,
220,
24200,
438,
198,
220,
220,
220,
220,
220,
220,
220,
311,
1058,
7177,
12,
2339,
11,
5485,
357,
77,
62,
82,
12629,
35751,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6060,
284,
4331,
422,
13,
628,
220,
220,
220,
220,
220,
220,
220,
16409,
198,
220,
220,
220,
220,
220,
220,
220,
35656,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
7177,
11,
5485,
357,
77,
62,
82,
12629,
35751,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
383,
11001,
3815,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13,
9948,
2889,
1352,
44807,
79,
17407,
7,
50,
8,
198
] | 2.505007 | 699 |
import numpy as np
from feature_cost_model.action_cost import ActionCost
| [
11748,
299,
32152,
355,
45941,
198,
198,
6738,
3895,
62,
15805,
62,
19849,
13,
2673,
62,
15805,
1330,
7561,
13729,
628,
628,
198
] | 3.391304 | 23 |
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