content
stringlengths 1
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| ratio_char_token
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int64 1
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|---|---|---|---|
# Prerequisite: directories for "in_strProtRefsDir" and "sparseData2", should not contain any ".txt" file
# Output: under sparseData2 directory: target.csv, metaInfo.csv, *.txt
import sys
import os
sys.path.append('../..')
import prepLib
in_strFastaFilename = '{!s}/data/protein/plos_HumanEKC/HumanEKC_uniprot-reviewed_up000005640_DECOY.fasta'.format(os.environ.get('HOME'))
in_strPeptideFilename = '{!s}/data/protein/plos_HumanEKC/HumanEKC_dataset_peptide_identification_plos.txt'.format(os.environ.get('HOME'))
in_strProtRefsDir = './protRefs'
out_strOutputBaseDir = './sparseData2'
protDic, pepDic = prepLib.loadProtPeptideDic(in_strPeptideFilename)
prepLib.breakFasta(in_strFastaFilename, in_strProtRefsDir, protDic)
listProtRefFileName = prepLib.getProtRefFileNames(in_strProtRefsDir)
# match peptides with proteins
prepLib.fuRunAllProt(listProtRefFileName, in_strProtRefsDir, out_strOutputBaseDir, protDic)
strMetaInfoFilename = '{!s}/metaInfo.csv'.format(out_strOutputBaseDir)
prepLib.fuSaveMetaInfo(out_strOutputBaseDir, strMetaInfoFilename, in_strProtRefsDir)
pepProbsList = sorted(list(pepDic.values()),key=lambda x: x[0])
pepProbsList = [pepProbsList[i][1:3] for i in range(0,len(pepProbsList))]
prepLib.fuSavePepProbsTargetFromList('{!s}/target.csv'.format(out_strOutputBaseDir), pepProbsList) | [
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] | 2.62 | 500 |
import numpy as np
import unittest
import discretize
from SimPEG.maps import Wires
from SimPEG.utils import (
mkvc,
WeightedGaussianMixture,
GaussianMixtureWithPrior,
)
from scipy.stats import norm, multivariate_normal
if __name__ == "__main__":
unittest.main()
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] | 2.728155 | 103 |
from llvmlite import ir, binding
from lang.scope import Scope
from collections import defaultdict
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from draftjs_exporter.dom import DOM
from wagtail.admin.rich_text.converters import editor_html
from wagtail.admin.rich_text.converters.contentstate_models import Entity
from wagtail.admin.rich_text.converters.html_to_contentstate import AtomicBlockEntityElementHandler
from wagtail.embeds import embeds, format
from wagtail.embeds.exceptions import EmbedException
# Front-end conversion
def media_embedtype_handler(attrs):
"""
Given a dict of attributes from the <embed> tag, return the real HTML
representation for use on the front-end.
"""
return format.embed_to_frontend_html(attrs['url'])
# hallo.js / editor-html conversion
class MediaEmbedHandler:
"""
MediaEmbedHandler will be invoked whenever we encounter an element in HTML content
with an attribute of data-embedtype="media". The resulting element in the database
representation will be:
<embed embedtype="media" url="http://vimeo.com/XXXXX">
"""
@staticmethod
def get_db_attributes(tag):
"""
Given a tag that we've identified as a media embed (because it has a
data-embedtype="media" attribute), return a dict of the attributes we should
have on the resulting <embed> element.
"""
return {
'url': tag['data-url'],
}
@staticmethod
def expand_db_attributes(attrs):
"""
Given a dict of attributes from the <embed> tag, return the real HTML
representation for use within the editor.
"""
try:
return format.embed_to_editor_html(attrs['url'])
except EmbedException:
# Could be replaced with a nice error message
return ''
EditorHTMLEmbedConversionRule = [
editor_html.EmbedTypeRule('media', MediaEmbedHandler)
]
# draft.js / contentstate conversion
def media_embed_entity(props):
"""
Helper to construct elements of the form
<embed embedtype="media" url="https://www.youtube.com/watch?v=y8Kyi0WNg40"/>
when converting from contentstate data
"""
return DOM.create_element('embed', {
'embedtype': 'media',
'url': props.get('url'),
})
class MediaEmbedElementHandler(AtomicBlockEntityElementHandler):
"""
Rule for building an embed entity when converting from database representation
to contentstate
"""
ContentstateMediaConversionRule = {
'from_database_format': {
'embed[embedtype="media"]': MediaEmbedElementHandler(),
},
'to_database_format': {
'entity_decorators': {'EMBED': media_embed_entity}
}
}
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220,
220,
220,
705,
26858,
62,
12501,
273,
2024,
10354,
1391,
6,
3620,
33,
1961,
10354,
2056,
62,
20521,
62,
26858,
92,
198,
220,
220,
220,
1782,
198,
92,
198
] | 2.79027 | 925 |
# Englishクラスをインポートし、nlpオブジェクトを作成
from ____ import ____
nlp = ____
# テキストを処理
doc = ____("I like tree kangaroos and narwhals.")
# 「tree kangaroors」のスライスを選択
tree_kangaroos = ____
print(tree_kangaroos.text)
# 「tree kangaroos and narwhals」のスライスを選択（「.」は含まない）
tree_kangaroos_and_narwhals = ____
print(tree_kangaroos_and_narwhals.text)
| [
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] | 1.775401 | 187 |
from causal_world.task_generators.base_task import BaseTask
import numpy as np
| [
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] | 3.111111 | 27 |
from telegram import InputMediaPhoto
from ..language import get_text
from ..database.query import count_occurrence_of_specified_rating
from .buttons import (
get_list_of_buttons,
tamplate_for_show_a_list_of_products,
tamplate_for_show_a_detailed_product)
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] | 2.878788 | 99 |
# -*- coding: utf-8 -*-
#
# This file is part of Sequana software
#
# Copyright (c) 2016-2017 - Sequana Development Team
#
# File author(s):
# Thomas Cokelaer <[email protected]>
#
# Distributed under the terms of the 3-clause BSD license.
# The full license is in the LICENSE file, distributed with this software.
#
# website: https://github.com/sequana/sequana
# documentation: http://sequana.readthedocs.io
#
##############################################################################
import re
import ruamel.yaml
import colorlog
logger = colorlog.getLogger(__name__)
__all__ = ["YamlDocParser"]
class YamlDocParser(object):
"""A simple parser to extract block content to be found in YAML files
So as to create tooltips automatically in :ref:`sequanix`, one can comment
YAML configuration file with block comments (see developers guide in
:ref:`developers` )
Once read and parsed, all block comments before top-level sections are to
be found in the dictionary :attr:`sections`.
.. doctest::
from sequana import snaketools
from sequana.iotools import YamlDocParser
module = snaketools.Module('quality_control')
r = YamlDocParser(module.config)
r.sections['fastqc']
Those lines are removed from the docstring but available as a dictionary
"""
def __init__(self, filename):
""".. rubric:: constructor
:param str filename: the YAML file to parse
::
# main documentation
# block comment
section1:
- item
# block comment
section2:
# a comment
section3:
Here, section1 and section2 have block comments but not section3
"""
self.filename = filename
self.regex_section = re.compile("^[a-z,A-Z,_,0-9]+:")
self._specials = ["choice__"]
self.sections = {}
self._read_data()
self._parse_data()
def _get_expected_sections(self):
"""Get the top level keys in the YAML file
:return: list of top level sections' names"""
with open(self.filename, "r") as fh:
data = ruamel.yaml.load(fh.read(), ruamel.yaml.RoundTripLoader)
keys = list(data.keys())
return keys
def _parse_data(self):
"""Parse the YAML file to get the block content (comments)
before each top-level sections. See doc in the constructor
Removes all # so that the block of comments can be interpreted as
a standard docstring in Sequanix
"""
current_block = []
current_section = "docstring"
# if we get a line that starts with #, this is a new comment or
# part of a block comment. Otherwise, it means the current block
# comment has ended.
for this in self.data:
# Beginning of a new section at top level
if self.regex_section.findall(this):
name = self.regex_section.findall(this)[0]
current_section = name.strip(":")
self.sections[current_section] = "".join(current_block)
current_block = []
current_section = None
elif this.startswith('#'): # a comment at top level
current_block.append(this)
elif this.strip() == "": # an empty line
#this was the main comment, or an isolated comment
current_block = []
else: # a non-empty line to skip
current_block = []
for key in self._get_expected_sections():
if key not in self.sections.keys():
logger.warning("section %s not dealt by the parsing function" % key)
def _get_specials(self, section):
"""This method extracts data from the docstring
Lines such as ::
field_choice__ = ["a", "b"]
are extracted. Where _choice is a special keyword to be
found.
"""
if section not in self.sections.keys():
logger.warning("%s not found in the yaml " % section)
return
comments = self.sections[section]
specials = {}
for line in comments.split("\n"):
if "#############" in line:
pass
elif sum([this in line for this in self._specials]):
for special in self._specials:
line = line[2:]
key, value = line.split("=", 1)
key = key.strip().rstrip("__")
value = value.strip()
specials[key] = list(eval(value))
return specials
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38102,
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4008,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
38102,
198
] | 2.356749 | 1,993 |
'''Tests the websocket middleware in pulsar.apps.ws.'''
import unittest
import asyncio
from pulsar.api import send
from pulsar.apps.ws import WebSocket, WS
from pulsar.apps.http import HttpClient
from pulsar.apps.test import run_test_server
from examples.websocket.manage import server
| [
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] | 3.222222 | 90 |
"""
"""
import app.utils6L.utils6L as utils
import logging
import os
import PySimpleGUI as sg
from app.main.views import view_create_link_address
from app.model import db_session
from app.model.Company import Address, Company
from PySimpleGUI.PySimpleGUI import popup_scrolled
logger_name = os.getenv("LOGGER_NAME")
logger = logging.getLogger(logger_name)
NO_COMPANY_ADDRESS = 'No company address'
@utils.log_wrap
@utils.log_wrap
@utils.log_wrap
@utils.log_wrap
| [
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31,
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] | 2.86747 | 166 |
# COUNT CONTAINED PERMUTATIONS
# O(M * U + N) time and O(U) space, where M -> length of big string,
# U -> number of unique characters in small string, N -> length
# of small string.
# U is actually a constant since it can't be greater than 26. and
# M > N, so M will dissolve N
# So, modified complexities:
# O(M) time and O(1) space, M -> length of big string | [
198,
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] | 3.165217 | 115 |
import urllib.request, json
print(Users.get_user(Users("INfoUpgradersYT")))
| [
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from torch.distributions import constraints
from torch.distributions.exponential import Exponential
from torch.distributions.transformed_distribution import TransformedDistribution
from torch.distributions.transforms import AffineTransform, ExpTransform
from torch.distributions.utils import broadcast_all
class Pareto(TransformedDistribution):
r"""
Samples from a Pareto Type 1 distribution.
Example::
>>> m = Pareto(torch.tensor([1.0]), torch.tensor([1.0]))
>>> m.sample() # sample from a Pareto distribution with scale=1 and alpha=1
tensor([ 1.5623])
Args:
scale (float or Tensor): Scale parameter of the distribution
alpha (float or Tensor): Shape parameter of the distribution
"""
arg_constraints = {'alpha': constraints.positive, 'scale': constraints.positive}
@property
@property
@constraints.dependent_property
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220,
220,
2488,
1102,
2536,
6003,
13,
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62,
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198
] | 3.106529 | 291 |
from bitmovin.bitmovin_object import BitmovinObject
from .s3_output_service import S3
from .gcs_output_service import GCS
from .akamai_netstorage_output_service import AkamaiNetStorage
from .azure_output_service import Azure
from .ftp_output_service import FTP
from .sftp_output_service import SFTP
from .generic_s3_output_service import GenericS3
from .local_output_service import Local
from .s3_role_based_output_service import S3RoleBased
| [
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] | 3.164286 | 140 |
#!/usr/bin/env python3
from btcmarkets_api import Market
BTC = Market("/market/BTC/AUD/tick", "BTC")
LTC = Market("/market/LTC/AUD/tick", "LTC")
ETH = Market("/market/ETH/AUD/tick", "ETH")
ETC = Market("/market/ETC/AUD/tick", "ETC")
XRP = Market("/market/XRP/AUD/tick", "XRP")
BCH = Market("/market/BCH/AUD/tick", "BCH")
BTC.update_data()
LTC.update_data()
ETH.update_data()
ETC.update_data()
XRP.update_data()
BCH.update_data()
| [
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from funcs.concordance import concordance
def test_concordance_string():
"""
concordance can be called with a string (e.g. a single cell containing a string)
"""
grams = concordance('Hello world. Hello, my great world! Hello Alice and Bob.', 'world')
assert grams == [
('Hello world'),
('Hello, my great world')
]
def test_concordance_array_string():
"""
concordance can be called with an array of strings (e.g. a column
of cells containing strings)
"""
grams = concordance(['Hello world.', 'Hello, my great world!', 'Hello Alice and Bob.'], 'world')
assert grams == [
('Hello world'),
('Hello, my great world')
]
| [
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] | 2.738281 | 256 |
# -*- coding: utf-8 -*-
import numpy as np
import cv2 as cv
from keras.preprocessing import image
from keras.models import model_from_json
import click
import pandas as pd
from keras.layers import Input
from keras import models
from keras.models import load_model
import pyautogui
import statistics
from PyQt5 import QtWidgets, QtGui
from configurar import configurarWindow
import sys
import configuracoes as cfg
import camera
import mouse
import teclado
import matplotlib.pyplot as plt
pyautogui.FAILSAFE = False
#Captura um posicao padrao da cabeca para que possa
#fazer o deslocamento do mouse
#ponto de referencia #melhorar
#trata imagem da face e faz a predicao
#Objetivo: Reconhecer expressões faciais e posição da cabeça
# em quadro extraído do vídeo recebido de uma chamada de rotina.
#-----------------------------
#Objetivo: Determinar a partir de informações fornecidas pelo UC 001 se
# ocorreu alguma intenção de ação por parte dos usuários a partir do quadro extraído do vídeo capturado pela webcam.
#Realiza emulacao de comando que está associada a expressao
#-----------------------------
#Objetivo: Identificar a ocorrência de expressões faciais e
# movimentos realizados com a cabeça utilizando imagens de vídeos capturadas pela webcam.
if __name__== '__main__':
mouse_expressions().executar() | [
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] | 2.123537 | 769 |
# -*- coding: utf-8 -*-
# Copyright: (c) 2018, Ansible Project
# Copyright: (c) 2018, Abhijeet Kasurde <[email protected]>
# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)
| [
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] | 2.417582 | 91 |
import json
from .models import URL
| [
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198,
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764,
27530,
1330,
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628
] | 3.8 | 10 |
"""
IN PROGRESS:Transliterating Carlo's routine from Fortran
Form the B-Matrix and C-Matrix used to convert the coordinates
Calcualtes all of the derivaties via finite-difference
define starting xyz geometry.
convention: atom 1 is is 0 0 0
atom 2 bd 0 0
atom 3 on xy plane
"""
# import numpy as np
#
# NATOMS = 10 # maybe need, don't know
# INT_COORDS = ''
# DELTAX = 0.01
# DELTAY = 0.01
# DELTAZ = 0.01
#
#
# def compute_bmat(natoms, coords, deltax, deltay, deltaz):
# """ compute the bmatrix by central difference
# where B_ik = dq_i / dx_k
# """
#
# b_mat = np.zeros(3*natoms, 3*natoms)
# for j in range(3):
# for k in range(3):
#
# # perturb x + dx and x - dx
# xpert_xp = 1
# xpert_xn = 1
# _perturb_coordinates(coords, jpert, delta)
#
# # perturb y + dy and y - dy
# xpert_yp = 1
# xpert_yn = 1
# _perturb_coordinates(coords, jpert, delta)
#
# # perturb z + dz and z - dz
# xpert_zp = 1
# xpert_zn = 1
# _perturb_coordinates(coords, jpert, delta)
#
# # Now calculate the jk component C-Matrix
# _calculate_bmat_k_component(b_mat, coords, j, j*k,
# x_pert_pp, x_pert_pn,
# x_pert_np, x_pert_nn)
#
# # now update iangsub1 bmat component (whatever this is)
# b_mat = _update_bmat(bmat, coords)
#
# return b_mat
#
#
# def compute_cmat(natoms, coords, deltax, deltay, deltaz):
# """ compute the bmatrix by central difference
# where C_ijk = d2q_i / (dx_j.dx_k)
# """
#
# c_mat = np.zeros(3*natoms, 3*natoms, 3*natoms)
# for j in range(3):
# for k in range(3):
# # perturb xj + dxj and xk + dxk
# x_pert_pp = _perturb_coordinates(coords, jpert, kpert, d1, d2)
#
# # perturb xj - dxj and yk + dyk
# x_pert_np = _perturb_coordinates(coords, jpert, kpert, d1, d2)
#
# # perturb xj + dxj and yk - dyk
# x_pert_pn = _perturb_coordinates(coords, jpert, kpert, d1, d2)
#
# # perturb xj - dxj and xk - dxk
# x_pert_nn = _perturb_coordinates(coords, jpert, kpert, d1, d2)
#
# # Now calculate the jk component C-Matrix
# _calculate_cmat_k_component(c_mat, coords, j, j*k,
# x_pert_pp, x_pert_pn,
# x_pert_np, x_pert_nn)
#
# return c_mat
#
#
# def _perturb_coordinates(coords, jpert, delta1, kpert=None, delta2=None):
# """ Generate coordinates that have been perturbed
# """
# coords[jpert] += delta1
# coords[kpert] += delta2
# # call update_zmat(natom,natomt,intcoor,bislab,ibconn,
# # $ iaconn,idconn,bname,anname,dname,atname,cooxpp,cooypp,
# # $ coozpp,xintpp,tauopt,ntau,idummy,ilin_fr,aconnt,bconnt,
# # $ dconnt,atomlabel,ifilu)
#
# return coords
#
#
# def _calculate_bmat_k_component(b_mat, j_idx, coords, delta,
# x_pert_p, x_pert_n):
# """ Calculate one nine components of B_ij for given __
# """
#
# for i, coord in enumerate(coords):
# if abs(xpert_p[i] - xpert_np[i]) > 300.0:
# if xpert_n[i] < 0.0:
# xpert_n[i] += 360.0
# elif xpert_n[i] > 0.0:
# xpert_n[i] -= 360.0
# if abs(xpert_p[i] - xpert_n[i]) > 300.0:
# raise ValueError(
# 'something did not work here: k, j coord', kind, jind, i)
# b_mat[i, j_idx] = (
# ((xpert_p[i] - xpert_n[i]) / 2.0) * (1.0 / delta)
# )
#
# return b_mat
#
#
# def _calculate_cmat_k_component(c_mat, k_idx, coords, delta1, delta2,
# x_pert_pp, x_pert_pn, x_pert_np, x_pert_nn):
# """ Calculate one nine components of C_ijk for given j
# """
#
# for i, coord in enumerate(coords):
#
# if abs(xpert_pp[i] - xpert_np[i]) > 300.0:
# if xpert_pp[i] < 0.0:
# xpert_pp[i] += 360.0
# elif xpert_pp[i] > 0.0:
# xpert_pp[i] -= 360.0
# if abs(xpert_pp[i] - xpert_np[i]) > 300.0:
# raise ValueError(
# 'something did not work here: k, j coord',
# kind, jind, i)
#
# if abs(xpert_np[i] - xpert_np[i]) > 300.0:
# if xpert_pn[i] < 0.0:
# xpert_pn[i] += 360.0
# elif xpert_pn[i] > 0.0:
# xpert_pn[i] -= 360.0
# if abs(xpert_pp[i] - xpert_pn[i]) > 300.0:
# raise ValueError(
# 'something did not work here: k, j coord',
# kind, jind, i)
#
# if abs(xpert_np[i] - xpert_nn[i]) > 300.0:
# if xpert_nn[i] < 0.0:
# xpert_nn[i] += 360.0
# elif xpert_nn[i] > 0.0:
# xpert_nn[i] -= 360.0
# if abs(xpert_np[i] - xpert_nn[i]) > 300.0:
# raise ValueError(
# 'something did not work here: k, j coord',
# kind, jind, i)
#
# c_mat[i, j_idx, k_idx] = (
# xpert_pp[i] - xpert_np[i] - xpert_pn[i] +
# (xpert_nn[i] / 4.0) * (1.0 / deltax) * (1.0 / deltaz)
# )
#
# return c_mat
#
#
# if __name__ == '__main__':
# b_mat = compute_bmat(NATOMS, COORDS, DELTAX, DELTAY, DELTAZ)
# c_mat = compute_cmat(NATOMS, COORDS, DELTAX, DELTAY, DELTAZ)
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] | 1.706776 | 3,291 |
# --------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# --------------------------------------------------------------------------------------------
import json
import requests
from knack.util import CLIError
from azure.cli.command_modules.botservice import adal_authenticator
| [
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] | 6.089744 | 78 |
n = int(input())
pieces = {}
for _ in range(n):
piece, composer, key = input().split("|")
pieces[piece] = {'composer': composer, 'key': key}
data = input()
while not data == "Stop":
command = data.split("|")
if command[0] == "Add":
piece, composer, key = command[1:]
if piece in pieces:
print(f"{piece} is already in the collection!")
else:
pieces[piece] = {'composer': composer, 'key': key}
print(f"{piece} by {composer} in {key} added to the collection!")
elif command[0] == "Remove":
piece = command[1]
if piece in pieces:
del pieces[piece]
print(f"Successfully removed {piece}!")
else:
print(f"Invalid operation! {piece} does not exist in the collection.")
elif command[0] == "ChangeKey":
piece, new_key = command[1:]
if piece in pieces:
pieces[piece]['key'] = new_key
print(f"Changed the key of {piece} to {new_key}!")
else:
print(f"Invalid operation! {piece} does not exist in the collection.")
data = input()
sorted_pieces = sorted(pieces.items(), key=lambda tkvp: (tkvp[0], tkvp[1]['composer']))
for piece, data in sorted_pieces:
print(f"{piece} -> Composer: {data['composer']}, Key: {data['key']}") | [
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220,
220,
220,
220,
220,
220,
220,
220,
1619,
5207,
58,
12239,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3601,
7,
69,
1,
33244,
2759,
4615,
1391,
12239,
92,
2474,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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1,
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0,
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92,
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2152,
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3704,
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62,
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220,
220,
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611,
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220,
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220,
220,
220,
220,
5207,
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6,
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649,
62,
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220,
220,
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220,
220,
220,
220,
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3601,
7,
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1,
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262,
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92,
284,
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62,
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220,
220,
220,
1366,
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5128,
3419,
198,
198,
82,
9741,
62,
34154,
796,
23243,
7,
34154,
13,
23814,
22784,
1994,
28,
50033,
256,
74,
36133,
25,
357,
30488,
36133,
58,
15,
4357,
256,
74,
36133,
58,
16,
7131,
6,
785,
1930,
263,
20520,
4008,
198,
1640,
3704,
11,
1366,
287,
23243,
62,
34154,
25,
198,
220,
220,
220,
3601,
7,
69,
1,
90,
12239,
92,
4613,
29936,
263,
25,
1391,
7890,
17816,
785,
1930,
263,
20520,
5512,
7383,
25,
1391,
7890,
17816,
2539,
20520,
92,
4943
] | 2.29636 | 577 |
import insightconnect_plugin_runtime
from .schema import LookupAlertInput, LookupAlertOutput, Input, Output, Component
# Custom imports below
from insightconnect_plugin_runtime.exceptions import PluginException
from komand_recorded_future.util.api import Endpoint
| [
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#!/usr/bin/env python3
# Copyright (c) 2020-2021 The Bitcoin Core developers
# Distributed under the MIT software license, see the accompanying
# file COPYING or http://www.opensource.org/licenses/mit-license.php.
"""Test indices in conjunction with prune."""
from test_framework.test_framework import BitcoinTestFramework
from test_framework.util import (
assert_equal,
assert_greater_than,
assert_raises_rpc_error,
)
if __name__ == '__main__':
FeatureIndexPruneTest().main()
| [
2,
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] | 3.2 | 155 |
from django.contrib.auth.models import User
from django.shortcuts import get_object_or_404
from django.views.generic import TemplateView
from ...models import Commit, UTopic
from ..utils import paginator
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] | 3.678571 | 56 |
from abaqusConstants import *
from .ContactProperty import ContactProperty
class FluidCavityProperty(ContactProperty):
"""The FluidCavityProperty object is an interaction property that defines the fluid
behavior for a surface-based fluid cavity.
The FluidCavityProperty object is derived from the InteractionProperty object.
Notes
-----
This object can be accessed by:
.. code-block:: python
import interaction
mdb.models[name].interactionProperties[name]
The corresponding analysis keywords are:
- FLUID BEHAVIOR
- CAPACITY
- FLUID BULK MODULUS
- FLUID DENSITY
- FLUID EXPANSION
- MOLECULAR WEIGHT
"""
def __init__(self, name: str, definition: SymbolicConstant = HYDRAULIC, fluidDensity: float = None,
molecularWeight: float = None, useExpansion: Boolean = OFF,
expansionTempDep: Boolean = OFF, expansionDependencies: int = 0,
referenceTemperature: float = 0, expansionTable: tuple = (),
useBulkModulus: Boolean = OFF, bulkModulusTempDep: Boolean = OFF,
bulkModulusDependencies: int = 0, bulkModulusTable: tuple = (),
useCapacity: Boolean = OFF, capacityType: SymbolicConstant = POLYNOMIAL,
capacityTempDep: Boolean = OFF, capacityDependencies: int = 0,
capacityTable: tuple = ()):
"""This method creates a FluidCavityProperty object.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].FluidCavityProperty
Parameters
----------
name
A String specifying the interaction property repository key.
definition
A SymbolicConstant specifying the type of fluid cavity property to be defined. Possible
values are HYDRAULIC and PNEUMATIC. The default value is HYDRAULIC.
fluidDensity
None or a Float specifying the reference fluid density. This argument is applicable only
when *definition*=HYDRAULIC, and is required in that case. The default value is None.
molecularWeight
None or a Float specifying the molecular weight of the ideal gas species. This argument
is applicable only when *definition*=PNEUMATIC, and is required in that case. The
default value is None.
useExpansion
A Boolean specifying whether thermal expansion coefficients will be defined. This
argument is applicable only when *definition*=HYDRAULIC. The default value is OFF.
expansionTempDep
A Boolean specifying whether the thermal fluid expansion data will have temperature
dependency. This argument is applicable only when *definition*=HYDRAULIC and when
*useExpansion*=True. The default value is OFF.
expansionDependencies
An Int specifying the number of field variable dependencies in the thermal fluid
expansion data. This argument is applicable only when *definition*=HYDRAULIC and when
*useExpansion*=True. The default value is 0.
referenceTemperature
A Float specifying the reference temperature for the coefficient of thermal expansion.
This argument is applicable only when *definition*=HYDRAULIC, when *useExpansion*=True,
and when either *expansionTempDep*=True or when *expansionDependencies* is greater than
0. The default value is 0.0.
expansionTable
A sequence of sequences of Floats specifying the thermal expansion coefficients. This
argument is applicable only when *definition*=HYDRAULIC and when *useExpansion*=True.
Each sequence contains the following data:
- The mean coefficient of thermal expansion.
- Temperature, if the data depend on temperature.
- Value of the first field variable, if the data depend on field variables.
- Value of the second field variable.
- Etc.
useBulkModulus
A Boolean specifying whether fluid bulk modulus values will be defined. This argument is
applicable only when *definition*=HYDRAULIC. The default value is OFF.
bulkModulusTempDep
A Boolean specifying whether the fluid bulk modulus data will have temperature
dependency. This argument is applicable only when *definition*=HYDRAULIC and when
*useBulkModulus*=True. The default value is OFF.
bulkModulusDependencies
An Int specifying the number of field variable dependencies in the fluid bulk modulus
data. This argument is applicable only when *definition*=HYDRAULIC and when
*useBulkModulus*=True. The default value is 0.
bulkModulusTable
A sequence of sequences of Floats specifying the fluid bulk modulus values. This
argument is applicable only when *definition*=HYDRAULIC and when *useBulkModulus*=True.
Each sequence contains the following data:
- The fluid bulk modulus.
- Temperature, if the data depend on temperature.
- Value of the first field variable, if the data depend on field variables.
- Value of the second field variable.
- Etc.
useCapacity
A Boolean specifying whether molar heat capacity values will be defined. This argument
is applicable only when *definition*=PNEUMATIC. The default value is OFF.
capacityType
A SymbolicConstant specifying the method to define the molar heat capacity. Possible
values are POLYNOMIAL and TABULAR. The default value is POLYNOMIAL.
capacityTempDep
A Boolean specifying whether the molar heat capacity data will have temperature
dependency. This argument is applicable only when *definition*=PNEUMATIC, when
*useCapacity*=True, and when *capacityType*=TABULAR. The default value is OFF.
capacityDependencies
An Int specifying the number of field variable dependencies in the molar heat capacity
data. This argument is applicable only when *definition*=PNEUMATIC, when
*useCapacity*=True, and when *capacityType*=TABULAR. The default value is 0.
capacityTable
A sequence of sequences of Floats specifying the molar heat capacity values in the form
of a polynomial expression. This argument is applicable only when
*definition*=PNEUMATIC, when *useCapacity*=True, and when *capacityType*=POLYNOMIAL. In
this form, only one sequence is specified and that sequence contains the following data:
- The first molar heat capacity coefficient.
- The second molar heat capacity coefficient.
- The third molar heat capacity coefficient.
- The fourth molar heat capacity coefficient.
- The fifth molar heat capacity coefficient.
Alternatively, the sequence data may specify the molar heat capacity values at constant
pressure for an ideal gas species. This argument is applicable only when
*definition*=PNEUMATIC, when *useCapacity*=True, and when *capacityType*=TABULAR. Each
sequence contains the following data:
- The molar heat capacity at constant pressure.
- Temperature, if the data depend on temperature.
- Value of the first field variable, if the data depend on field variables.
- Value of the second field variable.
- Etc.
Returns
-------
A FluidCavityProperty object.
"""
super().__init__(name)
pass
def setValues(self, definition: SymbolicConstant = HYDRAULIC, fluidDensity: float = None,
molecularWeight: float = None, useExpansion: Boolean = OFF,
expansionTempDep: Boolean = OFF, expansionDependencies: int = 0,
referenceTemperature: float = 0, expansionTable: tuple = (),
useBulkModulus: Boolean = OFF, bulkModulusTempDep: Boolean = OFF,
bulkModulusDependencies: int = 0, bulkModulusTable: tuple = (),
useCapacity: Boolean = OFF, capacityType: SymbolicConstant = POLYNOMIAL,
capacityTempDep: Boolean = OFF, capacityDependencies: int = 0,
capacityTable: tuple = ()):
"""This method modifies the FluidCavityProperty object.
Parameters
----------
definition
A SymbolicConstant specifying the type of fluid cavity property to be defined. Possible
values are HYDRAULIC and PNEUMATIC. The default value is HYDRAULIC.
fluidDensity
None or a Float specifying the reference fluid density. This argument is applicable only
when *definition*=HYDRAULIC, and is required in that case. The default value is None.
molecularWeight
None or a Float specifying the molecular weight of the ideal gas species. This argument
is applicable only when *definition*=PNEUMATIC, and is required in that case. The
default value is None.
useExpansion
A Boolean specifying whether thermal expansion coefficients will be defined. This
argument is applicable only when *definition*=HYDRAULIC. The default value is OFF.
expansionTempDep
A Boolean specifying whether the thermal fluid expansion data will have temperature
dependency. This argument is applicable only when *definition*=HYDRAULIC and when
*useExpansion*=True. The default value is OFF.
expansionDependencies
An Int specifying the number of field variable dependencies in the thermal fluid
expansion data. This argument is applicable only when *definition*=HYDRAULIC and when
*useExpansion*=True. The default value is 0.
referenceTemperature
A Float specifying the reference temperature for the coefficient of thermal expansion.
This argument is applicable only when *definition*=HYDRAULIC, when *useExpansion*=True,
and when either *expansionTempDep*=True or when *expansionDependencies* is greater than
0. The default value is 0.0.
expansionTable
A sequence of sequences of Floats specifying the thermal expansion coefficients. This
argument is applicable only when *definition*=HYDRAULIC and when *useExpansion*=True.
Each sequence contains the following data:
- The mean coefficient of thermal expansion.
- Temperature, if the data depend on temperature.
- Value of the first field variable, if the data depend on field variables.
- Value of the second field variable.
- Etc.
useBulkModulus
A Boolean specifying whether fluid bulk modulus values will be defined. This argument is
applicable only when *definition*=HYDRAULIC. The default value is OFF.
bulkModulusTempDep
A Boolean specifying whether the fluid bulk modulus data will have temperature
dependency. This argument is applicable only when *definition*=HYDRAULIC and when
*useBulkModulus*=True. The default value is OFF.
bulkModulusDependencies
An Int specifying the number of field variable dependencies in the fluid bulk modulus
data. This argument is applicable only when *definition*=HYDRAULIC and when
*useBulkModulus*=True. The default value is 0.
bulkModulusTable
A sequence of sequences of Floats specifying the fluid bulk modulus values. This
argument is applicable only when *definition*=HYDRAULIC and when *useBulkModulus*=True.
Each sequence contains the following data:
- The fluid bulk modulus.
- Temperature, if the data depend on temperature.
- Value of the first field variable, if the data depend on field variables.
- Value of the second field variable.
- Etc.
useCapacity
A Boolean specifying whether molar heat capacity values will be defined. This argument
is applicable only when *definition*=PNEUMATIC. The default value is OFF.
capacityType
A SymbolicConstant specifying the method to define the molar heat capacity. Possible
values are POLYNOMIAL and TABULAR. The default value is POLYNOMIAL.
capacityTempDep
A Boolean specifying whether the molar heat capacity data will have temperature
dependency. This argument is applicable only when *definition*=PNEUMATIC, when
*useCapacity*=True, and when *capacityType*=TABULAR. The default value is OFF.
capacityDependencies
An Int specifying the number of field variable dependencies in the molar heat capacity
data. This argument is applicable only when *definition*=PNEUMATIC, when
*useCapacity*=True, and when *capacityType*=TABULAR. The default value is 0.
capacityTable
A sequence of sequences of Floats specifying the molar heat capacity values in the form
of a polynomial expression. This argument is applicable only when
*definition*=PNEUMATIC, when *useCapacity*=True, and when *capacityType*=POLYNOMIAL. In
this form, only one sequence is specified and that sequence contains the following data:
- The first molar heat capacity coefficient.
- The second molar heat capacity coefficient.
- The third molar heat capacity coefficient.
- The fourth molar heat capacity coefficient.
- The fifth molar heat capacity coefficient.
Alternatively, the sequence data may specify the molar heat capacity values at constant
pressure for an ideal gas species. This argument is applicable only when
*definition*=PNEUMATIC, when *useCapacity*=True, and when *capacityType*=TABULAR. Each
sequence contains the following data:
- The molar heat capacity at constant pressure.
- Temperature, if the data depend on temperature.
- Value of the first field variable, if the data depend on field variables.
- Value of the second field variable.
- Etc.
"""
pass
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4160,
3103,
18797,
796,
20634,
40760,
2662,
12576,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5339,
30782,
12156,
25,
41146,
796,
18562,
11,
5339,
35,
2690,
3976,
25,
493,
796,
657,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5339,
10962,
25,
46545,
796,
7499,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
1212,
2446,
8075,
257,
1610,
27112,
34,
615,
414,
21746,
2134,
13,
628,
220,
220,
220,
220,
220,
220,
220,
11822,
198,
220,
220,
220,
220,
220,
220,
220,
37404,
198,
220,
220,
220,
220,
220,
220,
220,
770,
2163,
460,
307,
17535,
416,
25,
628,
220,
220,
220,
220,
220,
220,
220,
11485,
2438,
12,
9967,
3712,
21015,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
285,
9945,
13,
27530,
58,
3672,
4083,
37,
2290,
312,
34,
615,
414,
21746,
198,
220,
220,
220,
220,
220,
220,
220,
220,
198,
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,
1438,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
317,
10903,
31577,
262,
10375,
3119,
16099,
1994,
13,
220,
198,
220,
220,
220,
220,
220,
220,
220,
6770,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
317,
41327,
4160,
3103,
18797,
31577,
262,
2099,
286,
11711,
31643,
3119,
284,
307,
5447,
13,
33671,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3815,
389,
367,
35755,
3861,
6239,
2149,
290,
350,
12161,
5883,
1404,
2149,
13,
383,
4277,
1988,
318,
367,
35755,
3861,
6239,
2149,
13,
220,
198,
220,
220,
220,
220,
220,
220,
220,
11711,
35,
6377,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6045,
393,
257,
48436,
31577,
262,
4941,
11711,
12109,
13,
770,
4578,
318,
9723,
691,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
618,
1635,
46758,
9,
28,
39,
35755,
3861,
6239,
2149,
11,
290,
318,
2672,
287,
326,
1339,
13,
383,
4277,
1988,
318,
6045,
13,
220,
198,
220,
220,
220,
220,
220,
220,
220,
18955,
25844,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6045,
393,
257,
48436,
31577,
262,
18955,
3463,
286,
262,
7306,
3623,
4693,
13,
770,
4578,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
318,
9723,
691,
618,
1635,
46758,
9,
28,
47,
12161,
5883,
1404,
2149,
11,
290,
318,
2672,
287,
326,
1339,
13,
383,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4277,
1988,
318,
6045,
13,
220,
198,
220,
220,
220,
220,
220,
220,
220,
779,
16870,
5487,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
317,
41146,
31577,
1771,
18411,
7118,
44036,
481,
307,
5447,
13,
770,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4578,
318,
9723,
691,
618,
1635,
46758,
9,
28,
39,
35755,
3861,
6239,
2149,
13,
383,
4277,
1988,
318,
18562,
13,
220,
198,
220,
220,
220,
220,
220,
220,
220,
7118,
30782,
12156,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
317,
41146,
31577,
1771,
262,
18411,
11711,
7118,
1366,
481,
423,
5951,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
20203,
13,
770,
4578,
318,
9723,
691,
618,
1635,
46758,
9,
28,
39,
35755,
3861,
6239,
2149,
290,
618,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1635,
1904,
16870,
5487,
9,
28,
17821,
13,
383,
4277,
1988,
318,
18562,
13,
220,
198,
220,
220,
220,
220,
220,
220,
220,
7118,
35,
2690,
3976,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1052,
2558,
31577,
262,
1271,
286,
2214,
7885,
20086,
287,
262,
18411,
11711,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7118,
1366,
13,
770,
4578,
318,
9723,
691,
618,
1635,
46758,
9,
28,
39,
35755,
3861,
6239,
2149,
290,
618,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1635,
1904,
16870,
5487,
9,
28,
17821,
13,
383,
4277,
1988,
318,
657,
13,
220,
198,
220,
220,
220,
220,
220,
220,
220,
4941,
42492,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
317,
48436,
31577,
262,
4941,
5951,
329,
262,
35381,
286,
18411,
7118,
13,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
770,
4578,
318,
9723,
691,
618,
1635,
46758,
9,
28,
39,
35755,
3861,
6239,
2149,
11,
618,
1635,
1904,
16870,
5487,
9,
28,
17821,
11,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
290,
618,
2035,
1635,
11201,
5487,
30782,
12156,
9,
28,
17821,
393,
618,
1635,
11201,
5487,
35,
2690,
3976,
9,
318,
3744,
621,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
657,
13,
383,
4277,
1988,
318,
657,
13,
15,
13,
220,
198,
220,
220,
220,
220,
220,
220,
220,
7118,
10962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
317,
8379,
286,
16311,
286,
29075,
1381,
31577,
262,
18411,
7118,
44036,
13,
770,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4578,
318,
9723,
691,
618,
1635,
46758,
9,
28,
39,
35755,
3861,
6239,
2149,
290,
618,
1635,
1904,
16870,
5487,
9,
28,
17821,
13,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5501,
8379,
4909,
262,
1708,
1366,
25,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
383,
1612,
35381,
286,
18411,
7118,
13,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
34467,
11,
611,
262,
1366,
4745,
319,
5951,
13,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
11052,
286,
262,
717,
2214,
7885,
11,
611,
262,
1366,
4745,
319,
2214,
9633,
13,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
11052,
286,
262,
1218,
2214,
7885,
13,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
17906,
66,
13,
220,
198,
220,
220,
220,
220,
220,
220,
220,
779,
33,
12171,
5841,
23515,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
317,
41146,
31577,
1771,
11711,
11963,
953,
23515,
3815,
481,
307,
5447,
13,
770,
4578,
318,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
9723,
691,
618,
1635,
46758,
9,
28,
39,
35755,
3861,
6239,
2149,
13,
383,
4277,
1988,
318,
18562,
13,
220,
198,
220,
220,
220,
220,
220,
220,
220,
11963,
5841,
23515,
30782,
12156,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
317,
41146,
31577,
1771,
262,
11711,
11963,
953,
23515,
1366,
481,
423,
5951,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
20203,
13,
770,
4578,
318,
9723,
691,
618,
1635,
46758,
9,
28,
39,
35755,
3861,
6239,
2149,
290,
618,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1635,
1904,
33,
12171,
5841,
23515,
9,
28,
17821,
13,
383,
4277,
1988,
318,
18562,
13,
220,
198,
220,
220,
220,
220,
220,
220,
220,
11963,
5841,
23515,
35,
2690,
3976,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1052,
2558,
31577,
262,
1271,
286,
2214,
7885,
20086,
287,
262,
11711,
11963,
953,
23515,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1366,
13,
770,
4578,
318,
9723,
691,
618,
1635,
46758,
9,
28,
39,
35755,
3861,
6239,
2149,
290,
618,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1635,
1904,
33,
12171,
5841,
23515,
9,
28,
17821,
13,
383,
4277,
1988,
318,
657,
13,
220,
198,
220,
220,
220,
220,
220,
220,
220,
11963,
5841,
23515,
10962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
317,
8379,
286,
16311,
286,
29075,
1381,
31577,
262,
11711,
11963,
953,
23515,
3815,
13,
770,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4578,
318,
9723,
691,
618,
1635,
46758,
9,
28,
39,
35755,
3861,
6239,
2149,
290,
618,
1635,
1904,
33,
12171,
5841,
23515,
9,
28,
17821,
13,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5501,
8379,
4909,
262,
1708,
1366,
25,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
383,
11711,
11963,
953,
23515,
13,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
34467,
11,
611,
262,
1366,
4745,
319,
5951,
13,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
11052,
286,
262,
717,
2214,
7885,
11,
611,
262,
1366,
4745,
319,
2214,
9633,
13,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
11052,
286,
262,
1218,
2214,
7885,
13,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
17906,
66,
13,
220,
198,
220,
220,
220,
220,
220,
220,
220,
779,
15610,
4355,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
317,
41146,
31577,
1771,
285,
6192,
4894,
5339,
3815,
481,
307,
5447,
13,
770,
4578,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
318,
9723,
691,
618,
1635,
46758,
9,
28,
47,
12161,
5883,
1404,
2149,
13,
383,
4277,
1988,
318,
18562,
13,
220,
198,
220,
220,
220,
220,
220,
220,
220,
5339,
6030,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
317,
41327,
4160,
3103,
18797,
31577,
262,
2446,
284,
8160,
262,
285,
6192,
4894,
5339,
13,
33671,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3815,
389,
20634,
40760,
2662,
12576,
290,
309,
6242,
37232,
13,
383,
4277,
1988,
318,
20634,
40760,
2662,
12576,
13,
220,
198,
220,
220,
220,
220,
220,
220,
220,
5339,
30782,
12156,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
317,
41146,
31577,
1771,
262,
285,
6192,
4894,
5339,
1366,
481,
423,
5951,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
20203,
13,
770,
4578,
318,
9723,
691,
618,
1635,
46758,
9,
28,
47,
12161,
5883,
1404,
2149,
11,
618,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1635,
1904,
15610,
4355,
9,
28,
17821,
11,
290,
618,
1635,
42404,
6030,
9,
28,
5603,
33,
37232,
13,
383,
4277,
1988,
318,
18562,
13,
220,
198,
220,
220,
220,
220,
220,
220,
220,
5339,
35,
2690,
3976,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1052,
2558,
31577,
262,
1271,
286,
2214,
7885,
20086,
287,
262,
285,
6192,
4894,
5339,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1366,
13,
770,
4578,
318,
9723,
691,
618,
1635,
46758,
9,
28,
47,
12161,
5883,
1404,
2149,
11,
618,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1635,
1904,
15610,
4355,
9,
28,
17821,
11,
290,
618,
1635,
42404,
6030,
9,
28,
5603,
33,
37232,
13,
383,
4277,
1988,
318,
657,
13,
220,
198,
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37227,
198,
220,
220,
220,
220,
220,
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1208,
198
] | 2.686129 | 5,515 |
import re
import csv
line = []
list2 = []
with open('output2.txt') as f:
for i in f:
line.append(i)
outList = re.findall(r"[-+]?\d*\.\d+|\d+", line[0]) # extracting integers from string
list2.append(outList[0])
list2.append(outList[2])
#writing into csv file
with open('epoch_loss.csv', 'a') as csvFile:
writer = csv.writer(csvFile)
writer.writerow(list2)
line.clear()
list2.clear()
| [
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] | 2.111111 | 207 |
import tornado.web
import tornado.gen
import json
import io
import logging
import motor
from bson.objectid import ObjectId
import mickey.userfetcher
from mickey.basehandler import BaseHandler
| [
11748,
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] | 3.730769 | 52 |
import os
import re
# Parses a given input file and returns a list of parameters for all structures.
| [
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] | 3.888889 | 27 |
from eth_account import Account
import sha3
import json
| [
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] | 3.368421 | 19 |
# This file was automatically generated by SWIG (http://www.swig.org).
# Version 2.0.12
#
# Do not make changes to this file unless you know what you are doing--modify
# the SWIG interface file instead.
from sys import version_info
if version_info >= (2,6,0):
_rpi_pcm_ws281x = swig_import_helper()
del swig_import_helper
else:
import _rpi_pcm_ws281x
del version_info
try:
_swig_property = property
except NameError:
pass # Python < 2.2 doesn't have 'property'.
try:
_object = object
_newclass = 1
except AttributeError:
_newclass = 0
WS2811_TARGET_FREQ = _rpi_pcm_ws281x.WS2811_TARGET_FREQ
WS2811_STRIP_RGB = _rpi_pcm_ws281x.WS2811_STRIP_RGB
WS2811_STRIP_RBG = _rpi_pcm_ws281x.WS2811_STRIP_RBG
WS2811_STRIP_GRB = _rpi_pcm_ws281x.WS2811_STRIP_GRB
WS2811_STRIP_GBR = _rpi_pcm_ws281x.WS2811_STRIP_GBR
WS2811_STRIP_BRG = _rpi_pcm_ws281x.WS2811_STRIP_BRG
WS2811_STRIP_BGR = _rpi_pcm_ws281x.WS2811_STRIP_BGR
ws2811_channel_t_swigregister = _rpi_pcm_ws281x.ws2811_channel_t_swigregister
ws2811_channel_t_swigregister(ws2811_channel_t)
ws2811_t_swigregister = _rpi_pcm_ws281x.ws2811_t_swigregister
ws2811_t_swigregister(ws2811_t)
ws2811_init = _rpi_pcm_ws281x.ws2811_init
ws2811_fini = _rpi_pcm_ws281x.ws2811_fini
ws2811_render = _rpi_pcm_ws281x.ws2811_render
ws2811_wait = _rpi_pcm_ws281x.ws2811_wait
ws2811_led_get = _rpi_pcm_ws281x.ws2811_led_get
ws2811_led_set = _rpi_pcm_ws281x.ws2811_led_set
# This file is compatible with both classic and new-style classes.
| [
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] | 2.15043 | 698 |
'''
Here we consider a controller trained on nearest neighbor for the pendulum
environment in OpenAI Gym. The controller is taken from baselines ppo.
'''
import gym
import numpy as np
from gym import spaces
from baselines import deepq
from baselines.common import set_global_seeds, tf_util as U
import gym, logging
from baselines import logger
import numpy as np
import tensorflow as tf
from baselines.ppo1 import mlp_policy, pposgd_simple
from baselines.ppo1.pposgd_simple import *
U.make_session(num_cpu=1).__enter__()
env= gym.make('Pendulum-v1')
seed = 9699278477418928551
env.seed(seed)
num_timesteps=5e6
gym.logger.setLevel(logging.WARN)
pi = learn_return(env, policy_fn,
max_timesteps=num_timesteps,
timesteps_per_batch=2048,
clip_param=0.2, entcoeff=0.0,
optim_epochs=10, optim_stepsize=3e-4, optim_batchsize=64,
gamma=0.99, lam=0.95,
)
from scipy.stats import norm
# ------------------------------------------------------------------------------
from active_testing import pred_node, max_node, min_node, test_module
from active_testing.utils import sample_from
rand_nums = [1161003323,
415998644,
4057120664,
1747557171,
2890879164,
2055758971,
2911473105,
618390143,
691777806,
4168149016,
1809706292,
2771371912,
1956477866,
2141514268,
4025209431]
# Requirement 1: Find the initial configuration that minimizes the reward
# We need only one node for the reward. The reward is a smooth function
# given that the closed loop system is deterministic
bounds = [(-np.pi, np.pi)] # Bounds on theta
bounds.append((-1., 1.)) # Bounds on theta dot
bounds.append((7., 9.)) # Bounds on the speed
bounds.append((1.5, 2.5)) # Bounds on the torque magnitude
smooth_details_r1 = []
random_details_r1 = []
# This set assumes random sampling and checking
for r in rand_nums:
np.random.seed(r)
node0 = pred_node(f=lambda traj: traj[1]['reward']/200 )
TM = test_module(bounds=bounds, sut=lambda x0: sut(500,x0, ead=True),
f_tree = node0,init_sample = 60,
optimize_restarts=5, exp_weight=10, normalizer=True)
TM.initialize()
TM.run_BO(140)
smooth_details_r1.append([np.sum(TM.f_acqu.GP.Y < -5.),
np.sum(TM.f_acqu.GP.Y < -7.5),
TM.smooth_min_x,TM.smooth_min_val])
# With cost function
np.random.seed(r)
node0 = pred_node(f=lambda traj: traj[1]['reward']/200)
TM = test_module(bounds=bounds, sut=lambda x0: sut(500,x0, ead=True),
f_tree = node0, with_random = True, init_sample = 60,
optimize_restarts=5, exp_weight=10,
normalizer=True)
TM.initialize()
TM.run_BO(30)
TM.k = 5
TM.run_BO(40)
TM.k = 2
TM.run_BO(70)
smooth_details_r1.append([np.sum(TM.f_acqu.GP.Y < -5.),
np.sum(TM.f_acqu.GP.Y < -7.5),
TM.smooth_min_x, TM.smooth_min_val])
random_details_r1.append([np.sum(np.array(TM.random_Y) < -5.),
np.sum(np.array(TM.random_Y) < -7.5),
TM.rand_min_x, TM.rand_min_val])
print(r, smooth_details_r1[-2], smooth_details_r1[-1], random_details_r1[-1])
rand_nums.append(r)
# Requirement 2: Find the initial condition such that the pendulum stabilizes to 0
smooth_details_r2 = []
random_details_r2 = []
# This set assumes random sampling and checking
for r in rand_nums:
np.random.seed(r)
node0 = pred_node(f=lambda traj: pred1(traj))
TM = test_module(bounds=bounds, sut=lambda x0: sut(500,x0, ead=True),
f_tree = node0,init_sample = 60,
optimize_restarts=5, exp_weight=2, normalizer=True)
TM.initialize()
TM.run_BO(140)
smooth_vals = np.array(TM.f_acqu.find_GP_func())
smooth_details_r2.append([np.sum(smooth_vals < -1.00),
np.sum(smooth_vals < -10.0),
TM.smooth_min_x,TM.smooth_min_val,
TM.smooth_min_loc])
np.random.seed(r)
node0_ns = pred_node(f=lambda traj: pred1(traj))
TM_ns = test_module(bounds=bounds, sut=lambda x0: sut(500, x0, ead=True),
f_tree=node0_ns, init_sample=60, with_smooth=False,
with_ns=True,
optimize_restarts=5, exp_weight=10, normalizer=True)
TM_ns.initialize()
TM_ns.run_BO(30)
TM_ns.k = 5
TM_ns.run_BO(40)
TM_ns.k = 2
TM_ns.run_BO(70)
smooth_details_r2.append([np.sum(TM_ns.ns_GP.Y < -1.00),
np.sum(TM_ns.ns_GP.Y < -10.0),
TM_ns.ns_min_x, TM_ns.ns_min_val,
TM_ns.ns_min_loc])
# With cost function
np.random.seed(r)
node0_rand = pred_node(f=lambda traj: pred1(traj))
TM_rand = test_module(bounds=bounds, sut=lambda x0: sut(500,x0, ead=True),
f_tree = node0_rand, with_random = True, with_smooth=False,
init_sample = 60, optimize_restarts=5, exp_weight=10,
cost_model = cost_func, normalizer=True)
TM_rand.initialize()
TM_rand.run_BO(140)
random_details_r2.append([np.sum(np.array(TM_rand.random_Y) < -1.0),
np.sum(np.array(TM_rand.random_Y) < -10.0),
TM_rand.rand_min_x, TM_rand.rand_min_val,
TM_rand.rand_min_loc])
print(r, smooth_details_r2[-2], smooth_details_r2[-1],random_details_r2[-1])
# Requirement 3: Find the initial configuration such that it stabilizies to either
# 0 or to np.pi
smooth_details_r3 = []
ns_details_r3 = []
random_details_r3 = []
# This set assumes random sampling and checking
for r in rand_nums:
np.random.seed(r)
node0 = pred_node(f = lambda traj:pred1(traj))
node1 = pred_node(f = lambda traj:pred2(traj))
node2 = max_node(children=[node0, node1])
TM = test_module(bounds=bounds, sut=lambda x0: sut(500,x0, ead=True),
f_tree = node2,init_sample = 60,
optimize_restarts=5, exp_weight=2, normalizer=True)
TM.initialize()
TM.run_BO(140)
smooth_vals = np.array(TM.f_acqu.find_GP_func())
smooth_details_r3.append([np.sum(smooth_vals < -1.00),
np.sum(smooth_vals < -10.0),
TM.smooth_min_x,TM.smooth_min_val,
TM.smooth_min_loc])
np.random.seed(r)
node0_ns = pred_node(f=lambda traj: pred1(traj))
node1_ns = pred_node(f=lambda traj: pred2(traj))
node2_ns = max_node(children=[node0_ns, node1_ns])
TM_ns = test_module(bounds=bounds, sut=lambda x0: sut(500, x0, ead=True),
f_tree=node2_ns, init_sample=60, with_smooth=False,
with_ns=True,
optimize_restarts=5, exp_weight=2, normalizer=True)
TM_ns.initialize()
TM_ns.run_BO(140)
ns_details_r3.append([np.sum(TM_ns.ns_GP.Y < -1.00),
np.sum(TM_ns.ns_GP.Y < -10.0),
TM_ns.ns_min_x, TM_ns.ns_min_val,
TM_ns.ns_min_loc])
# With cost function
np.random.seed(r)
node0_rand = pred_node(f=lambda traj: pred1(traj))
node1_rand = pred_node(f=lambda traj: pred2(traj))
node2_rand = max_node(children=[node0_rand, node1_rand])
TM_rand = test_module(bounds=bounds, sut=lambda x0: sut(500,x0, ead=True),
f_tree = node2_rand, with_random = True, with_smooth=False,
init_sample = 60, optimize_restarts=5, exp_weight=10,
cost_model = cost_func, normalizer=True)
TM_rand.initialize()
TM_rand.run_BO(140)
random_details_r3.append([np.sum(np.array(TM_rand.random_Y) < -1.0),
np.sum(np.array(TM_rand.random_Y) < -10.0),
TM_rand.rand_min_x, TM_rand.rand_min_val,
TM_rand.rand_min_loc])
print(r, smooth_details_r3[-1], ns_details_r3[-1],random_details_r3[-1])
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] | 1.959115 | 4,158 |
import re
from setuptools import setup, find_packages
with open('README.md', 'r', encoding='utf-8') as f:
readme = f.read()
with open('gforms/__init__.py', encoding='utf-8') as f:
version = re.search(r"__version__ = '(.+)'", f.read()).group(1)
setup(
name='gforms',
description='Google Forms wrapper for Python',
long_description=readme,
long_description_content_type='text/markdown',
author='vvd170501',
url='https://github.com/vvd170501/python-gforms',
classifiers=[
'Development Status :: 5 - Production/Stable',
'License :: OSI Approved :: MIT License',
'Programming Language :: Python :: 3 :: Only',
'Programming Language :: Python :: 3.6',
'Programming Language :: Python :: 3.7',
'Programming Language :: Python :: 3.8',
'Programming Language :: Python :: 3.9',
],
packages=['gforms'],
version=version,
license_files=('LICENSE',),
python_requires='>=3.6',
install_requires=[
'beautifulsoup4',
'requests',
"typing-extensions;python_version<'3.8'",
],
extras_require={
'dev': [
'pytest',
]
},
)
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] | 2.392354 | 497 |
import seqcluster.libs.logger as mylog
import os
from seqcluster.libs.classes import annotation, dbannotation
logger = mylog.getLogger("run")
def read_gtf_line(cols, field="name"):
"""parse gtf line to get class/name information"""
field = field.lower()
try:
group = cols[2]
attrs = cols[8].split(";")
name = [attr.strip().split(" ")[1] for attr in attrs if attr.strip().split(" ")[0].lower().endswith(field)]
if not name:
name = [attr.strip().split(" ")[1] for attr in attrs if attr.strip().split(" ")[0].lower().endswith("gene_id")]
if not name:
name = ["None"]
biotype = [attr.strip().split(" ")[1] for attr in attrs if attr.strip().split(" ")[0].lower().endswith("biotype")]
if biotype:
group = biotype[0]
c = cols[0]
s = int(cols[3])
e = int(cols[4])
st = cols[6]
return [c, s, e, st, group, name[0]]
except(Exception, e):
logger.error(cols)
logger.error("File is not in correct format")
logger.error("Expect chr source feature start end . strand attributes")
logger.error("Attributes are 'gene_name SNCA; gene_id ENSG; '")
logger.error("The 3rd column is used as type of small RNA (like miRNA)")
logger.error("at least should contains '; *name NAME; '")
logger.error(e)
raise
def _position_in_feature(pos_a, pos_b):
"""return distance to 3' and 5' end of the feature"""
strd = "-"
if pos_a[2] in pos_b[2]:
strd = "+"
if pos_a[2] in "+" and pos_b[2] in "+":
lento5 = pos_a[0] - pos_b[1] + 1
lento3 = pos_a[1] - pos_b[1] + 1
if pos_a[2] in "+" and pos_b[2] in "-":
lento5 = pos_a[1] - pos_b[0] + 1
lento3 = pos_a[0] - pos_b[1] + 1
if pos_a[2] in "-" and pos_b[2] in "+":
lento5 = pos_a[0] - pos_b[1] + 1
lento3 = pos_a[1] - pos_b[0] + 1
if pos_a[2] in "-" and pos_b[2] in "-":
lento3 = pos_a[0] - pos_b[0] + 1
lento5 = pos_a[1] - pos_b[1] + 1
else:
lento5 = pos_a[0] - pos_b[0] + 1
lento3 = pos_a[1] - pos_b[1] + 1
return lento5, lento3, strd
def anncluster(c, clus_obj, db, type_ann, feature_id="name"):
"""intersect transcription position with annotation files"""
id_sa, id_ea, id_id, id_idl, id_sta = 1, 2, 3, 4, 5
if type_ann == "bed":
id_sb = 7
id_eb = 8
id_stb = 11
id_tag = 9
ida = 0
clus_id = clus_obj.clus
loci_id = clus_obj.loci
db = os.path.splitext(db)[0]
logger.debug("Type:%s\n" % type_ann)
for cols in c.features():
if type_ann == "gtf":
cb, sb, eb, stb, db, tag = read_gtf_line(cols[6:], feature_id)
else:
sb = int(cols[id_sb])
eb = int(cols[id_eb])
stb = cols[id_stb]
tag = cols[id_tag]
id = int(cols[id_id])
idl = int(cols[id_idl])
if (id in clus_id):
clus = clus_id[id]
sa = int(cols[id_sa])
ea = int(cols[id_ea])
ida += 1
lento5, lento3, strd = _position_in_feature([sa, ea, cols[id_sta]], [sb, eb, stb])
if db in loci_id[idl].db_ann:
ann = annotation(db, tag, strd, lento5, lento3)
tdb = loci_id[idl].db_ann[db]
tdb.add_db_ann(ida, ann)
loci_id[idl].add_db(db, tdb)
else:
ann = annotation(db, tag, strd, lento5, lento3)
tdb = dbannotation(1)
tdb.add_db_ann(ida, ann)
loci_id[idl].add_db(db, tdb)
clus_id[id] = clus
clus_obj.clus = clus_id
clus_obj.loci = loci_id
return clus_obj
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] | 1.854743 | 2,024 |
pattern()
| [
33279,
3419,
198
] | 3.333333 | 3 |
from __future__ import absolute_import
from __future__ import unicode_literals
import collections
import inspect
import json
import os
import re
from functools import partial
import compose
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import numpy as np
from ase.calculators.lj import LennardJones
from ase.units import Bohr, Ha
from pytest import approx, raises
from pygsm.level_of_theories.ase import ASELoT, geom_to_ase, xyz_to_ase
from pygsm.level_of_theories.base_lot import LoTError
xyz_4x4 = [
["H", 1.0, 2.0, 3.0],
["He", 4.0, 5.0, 6.0],
["Li", 7.0, 8.0, 9.0],
["Be", 10.0, 11.0, 12.0],
]
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] | 2.086022 | 186 |
from survey.features.page_objects.base import PageObject
__author__ = 'mnandri'
| [
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"""Debug Toolbar Plugin."""
import asyncio
import importlib
import ipaddress as ip
import os.path as op
import re
import sys
import uuid
from muffin import (
Response, StaticRoute, HTTPException, HTTPBadRequest, to_coroutine, HTTPForbidden)
from muffin.plugins import BasePlugin, PluginException
from muffin.utils import json
from . import panels, utils
from .tbtools.tbtools import get_traceback
RE_BODY = re.compile(b'<\/body>', re.I)
U_SSE_PAYLOAD = "id: {0}\nevent: new_request\ndata: {1}\n\n"
REDIRECT_CODES = (300, 301, 302, 303, 305, 307, 308)
PLUGIN_ROOT = op.dirname(op.abspath(__file__))
@asyncio.coroutine
def debugtoolbar_middleware_factory(app, handler):
"""Setup Debug middleware."""
dbtb = app.ps.debugtoolbar
@asyncio.coroutine
def debugtoolbar_middleware(request):
"""Integrate to application."""
# Check for debugtoolbar is enabled for the request
if not dbtb.cfg.enabled or any(map(request.path.startswith, dbtb.cfg.exclude)):
return (yield from handler(request))
remote_host, remote_port = request.transport.get_extra_info('peername')
for host in dbtb.cfg.hosts:
if ip.ip_address(remote_host) in ip.ip_network(host):
break
else:
return (yield from handler(request))
# Initialize a debugstate for the request
state = DebugState(app, request)
dbtb.history[state.id] = state
context_switcher = state.wrap_handler(handler)
# Make response
try:
response = yield from context_switcher(handler(request))
state.status = response.status
except HTTPException as exc:
response = exc
state.status = response.status
except Exception as exc:
# Store traceback for unhandled exception
state.status = 500
if not dbtb.cfg.intercept_exc:
raise
tb = get_traceback(
info=sys.exc_info(), skip=1, show_hidden_frames=False,
ignore_system_exceptions=True, exc=exc)
dbtb.exceptions[tb.id] = request['pdbt_tb'] = tb
for frame in tb.frames:
dbtb.frames[id(frame)] = frame
response = Response(text=tb.render_full(request), content_type='text/html')
# Intercept http redirect codes and display an html page with a link to the target.
if dbtb.cfg.intercept_redirects and response.status in REDIRECT_CODES \
and 'Location' in response.headers:
response = yield from app.ps.jinja2.render(
'debugtoolbar/redirect.html', response=response)
response = Response(text=response, content_type='text/html')
yield from state.process_response(response)
if isinstance(response, Response) and response.content_type == 'text/html' and \
RE_BODY.search(response.body):
return (yield from dbtb.inject(state, response))
return response
return debugtoolbar_middleware
class Plugin(BasePlugin):
"""The plugin implementation."""
name = 'debugtoolbar'
defaults = {
'enabled': True,
'hosts': ['127.0.0.1'],
'prefix': '/_debug',
'intercept_exc': 'debug', # debug/display/False,
'intercept_redirects': True,
'exclude': [],
'panels': [
panels.HeaderDebugPanel,
panels.RequestVarsDebugPanel,
panels.LoggingDebugPanel,
panels.TracebackDebugPanel,
],
'additional_panels': [],
'global_panels': [
panels.RoutesDebugPanel,
panels.ConfigurationDebugPanel,
panels.MiddlewaresDebugPanel,
panels.VersionsDebugPanel,
]
}
def setup(self, app):
"""Setup the plugin and prepare application."""
super(Plugin, self).setup(app)
if 'jinja2' not in app.plugins:
raise PluginException('The plugin requires Muffin-Jinja2 plugin installed.')
self.cfg.prefix = self.cfg.prefix.rstrip('/') + '/'
self.cfg.exclude.append(self.cfg.prefix)
# Setup debugtoolbar templates
app.ps.jinja2.cfg.template_folders.append(op.join(PLUGIN_ROOT, 'templates'))
self.cfg.panels += list(self.cfg.additional_panels)
panels_ = []
for panel in self.cfg.panels:
if isinstance(panel, str):
mod, _, panel = panel.partition(':')
mod = importlib.import_module(mod)
panel = eval(panel or 'DebugPanel', mod.__dict__)
panels_.append(panel)
self.cfg.panels = panels_
# Setup debugtoolbar static files
app.router.register_route(StaticRoute(
'debugtoolbar.static',
self.cfg.prefix + 'static/',
op.join(PLUGIN_ROOT, 'static')))
app.register(self.cfg.prefix + 'sse', name='debugtoolbar.sse')(self.sse)
app.register(
self.cfg.prefix + 'exception', name='debugtoolbar.exception')(self.exception)
app.register(
self.cfg.prefix + 'execute', name='debugtoolbar.execute')(self.execute)
app.register(
self.cfg.prefix + 'source', name='debugtoolbar.source')(self.source)
app.register(
self.cfg.prefix.rstrip('/'),
self.cfg.prefix,
self.cfg.prefix + '{request_id}', name='debugtoolbar.request')(self.view)
app['debugtoolbar'] = {}
app['debugtoolbar']['pdbt_token'] = uuid.uuid4().hex
self.history = app['debugtoolbar']['history'] = utils.History(50)
self.exceptions = app['debugtoolbar']['exceptions'] = utils.History(50)
self.frames = app['debugtoolbar']['frames'] = utils.History(100)
@asyncio.coroutine
def start(self, app):
""" Start application. """
app.middlewares.insert(0, debugtoolbar_middleware_factory)
self.global_panels = [Panel(self.app) for Panel in self.cfg.global_panels]
@asyncio.coroutine
def inject(self, state, response):
""" Inject Debug Toolbar code to response body. """
html = yield from self.app.ps.jinja2.render(
'debugtoolbar/inject.html',
static_path=self.cfg.prefix + 'static',
toolbar_url=self.cfg.prefix + state.id,
)
html = html.encode(state.request.charset or 'utf-8')
response.body = RE_BODY.sub(html + b'</body>', response.body)
return response
@asyncio.coroutine
def view(self, request):
""" Debug Toolbar. """
auth = yield from self.authorize(request)
if not auth:
raise HTTPForbidden()
request_id = request.match_info.get('request_id')
state = self.history.get(request_id, None)
response = yield from self.app.ps.jinja2.render(
'debugtoolbar/toolbar.html',
debugtoolbar=self,
state=state,
static_path=self.cfg.prefix + 'static',
panels=state and state.panels or [],
global_panels=self.global_panels,
request=state and state.request or None,
)
return Response(text=response, content_type='text/html')
@asyncio.coroutine
def authorize(self, request): # noqa
"""Default authorization."""
return True
def authorization(self, func):
"""Define a authorization handler.
::
debugtoolbar = muffin_debugtoolbar.Plugin()
debugtoolbar.setup(app)
@debugtoolbar.authorization
def current_user_is_logged(request):
user = yield from load_session(request)
return user
"""
self.authorize = to_coroutine(func)
return func
@asyncio.coroutine
def sse(self, request):
"""SSE."""
response = Response(status=200)
response.content_type = 'text/event-stream'
response.text = ''
active_request_id = request.GET.get('request_id')
client_last_request_id = str(request.headers.get('Last-Event-Id', 0))
if self.history:
last_request_id = next(reversed(self.history))
if not last_request_id == client_last_request_id:
data = []
for _id in reversed(self.history):
data.append([
_id, self.history[_id].json, 'active' if active_request_id == _id else ''])
if data:
response.text = U_SSE_PAYLOAD.format(last_request_id, json.dumps(data))
return response
@asyncio.coroutine
@asyncio.coroutine
@asyncio.coroutine
class DebugState:
""" Store debug state. """
def __init__(self, app, request):
"""Store the params."""
self.request = request
self.status = 200
self.panels = [Panel(app, request) for Panel in app.ps.debugtoolbar.cfg.panels]
@property
def id(self):
"""Return state ID."""
return str(id(self))
@property
def json(self):
"""Return JSON."""
return {'method': self.request.method,
'path': self.request.path,
'scheme': 'http',
'status_code': self.status}
@asyncio.coroutine
def process_response(self, response):
"""Process response."""
for panel in self.panels:
yield from panel.process_response(response)
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] | 2.222821 | 4,268 |
import numpy as np
from scipy import stats
import statsmodels.sandbox.stats.runs as runs
# 18/21 output statistics fully implemented from MATLAB, the other three are either from complex helper functions or MATLAB functions that don't transfer well
def PH_Walker(y, walkerRule='prop', walkerParams=np.array([])):
"""
PH_Walker simulates a hypothetical walker moving through the time domain
the hypothetical particle (or 'walker') moves in response to values of the time series at each point
Outputs from this operation are summaries of the walkers motion, and comparisons of it to the original time series
:param y: the input time series
:param walkerRule: the kinematic rule by which the walker moves in response to the time series over time
(i) 'prop': the walker narrows the gap between its value and that of the time series by a given proportion p
(ii) 'biasprop': the walker is biased to move more in one direction; when it is being pushed up by the time
series, it narrows the gap by a proportion p_{up}, and when it is being pushed down by the
time series it narrows the gap by a (potentially different) proportion p_{down}. walkerParams = [pup,pdown]
(iii) 'momentum': the walker moves as if it has mass m and inertia
from the previous time step and the time series acts
as a force altering its motion in a classical
Newtonian dynamics framework. [walkerParams = m], the mass.
(iv) 'runningvar': the walker moves with inertia as above, but
its values are also adjusted so as to match the local
variance of time series by a multiplicative factor.
walkerParams = [m,wl], where m is the inertial mass and wl
is the window length.
:param walkerParams: the parameters for the specified walker, explained above
:return: include the mean, spread, maximum, minimum, and autocorrelation of
the walker's trajectory, the number of crossings between the walker and the
original time series, the ratio or difference of some basic summary statistics
between the original time series and the walker, an Ansari-Bradley test
comparing the distributions of the walker and original time series, and
various statistics summarizing properties of the residuals between the
walker's trajectory and the original time series.
"""
# ----------------------------------------------------------------------------------------------------------------------------------
# PRELIMINARIES
#----------------------------------------------------------------------------------------------------------------------------------
N = len(y)
#----------------------------------------------------------------------------------------------------------------------------------
# CHECK INPUTS
#----------------------------------------------------------------------------------------------------------------------------------
if walkerRule == 'runningvar':
walkerParams = [1.5, 50]
if (len(walkerParams) == 0):
if walkerRule == 'prop':
walkerParams = np.array([0.5])
if walkerRule == 'biasprop':
walkerParams = np.array([0.1, 0.2])
if walkerRule == 'momentum':
walkerParams = np.array([2])
if walkerRule == 'runningvar':
walkerParams = [1.5, 50]
#----------------------------------------------------------------------------------------------------------------------------------
# (1) WALK
#----------------------------------------------------------------------------------------------------------------------------------
w = np.zeros(N)
if walkerRule == 'prop':
# walker starts at zero and narrows the gap between its position
# and the time series value at that point by the proportion given
# in walkerParams, to give the value at the subsequent time step
if isinstance(walkerParams,list):
walkerParams = walkerParams[0]
p = walkerParams
w[0] = 0
for i in range(1, N):
w[i] = w[i-1] + p*(y[i-1]-w[i-1])
elif walkerRule == 'biasprop':
# walker is biased in one or the other direction (i.e., prefers to
# go up, or down). Requires a vector of inputs: [p_up, p_down]
pup = walkerParams[0]
pdown = walkerParams[0]
w[0] = 0
for i in range (1, N):
if y[i] > y[i-1]:
w[i] = w[i-1] + pup*(y[i-1]-w[i-1])
else :
w[i] = w[i-1] + pdown*(y[i-1]-w[i-1])
elif walkerRule == 'momentum':
# walker moves as if it had inertia from the previous time step,
# i.e., it 'wants' to move the same amount; the time series acts as
# a force changing its motion
m = walkerParams[0] # inertial mass
w[0] = y[0]
w[1] = y[1]
for i in range(2, N):
w_inert = w[i-1] + (w[i-1]-w[i-2])
w[i] = w_inert + (y[i] - w_inert)/m # dissipative term
#equation of motion (s-s_0 = ut + F/m*t^2)
#where the 'force' is F is the change in the original time series at the point
elif walkerRule == 'runningvar':
m = walkerParams[0]
wl = walkerParams[1]
w[0] = y[0]
w[1] = y[1]
for i in range(2, N):
w_inert = w[i-1] + (w[i-1]-w[i-2])
w_mom = w_inert + (y[i] - w_inert)/m #dissipative term from time series
if i > wl:
w[i] = w_mom * (np.std(y[(i-wl):i]))/np.std(w[(i-wl):i])
else:
w[i] = w_mom
else :
print("Error: Unknown method: " + walkerRule + " for simulating walker on the time series")
#----------------------------------------------------------------------------------------------------------------------------------
# (2) STATISITICS ON THE WALK
#----------------------------------------------------------------------------------------------------------------------------------
out = {} # dictionary for storing variables
# (i) The walk itself -------------------------------------------------------------------------------------------
out['w_mean'] = np.mean(w)
out['w_median'] = np.median(w)
out['w_std'] = np.std(w)
out['w_ac1'] = CO_AutoCorr(w, 1, method='timedomainstat') # this function call in MATLAB uses method='Fourier', but we don't have that case implemented yet in autoCorr, however this seems to output the same thing
out['w_ac2'] = CO_AutoCorr(w, 2, method='timedomainstat')
out['w_tau'] = CO_FirstZero(w, 'ac')
out['w_min'] = np.min(w)
out['w_max'] = np.max(w)
out['propzcross'] = sum( np.multiply( w[0:(len(w)-2)], w[1:(len(w)-1)] ) < 0) / (N-1) # np.multiply performs elementwise multiplication like matlab .*
# differences between the walk at signal
# (ii) Differences between the walk at signal -------------------------------------------------------------------
out['sw_meanabsdiff'] = np.mean(np.abs(y-w))
out['sw_taudiff'] = CO_FirstZero(y, 'ac') - CO_FirstZero(w, 'ac')
out['sw_stdrat'] = np.std(w)/np.std(y) # will be thse same as w_std for z-scored signal
out['sw_ac1rat'] = out['w_ac1']/CO_AutoCorr(y, 1)
out['sw_minrat'] = min(w)/min(y)
out['sw_maxrat'] = max(w)/max(y)
out['sw_propcross'] = sum(np.multiply( w[0:(len(w)-1)] - y[0:(len(y)-1)] , w[1:(len(w))]-y[1:(len(y))]) < 0 )/(N-1) #np.multiply performs elementwise multiplication like matlab .*
ansari = stats.ansari(w, y)
out['sw_ansarib_pval'] = ansari[1]
# r = np.linspace( np.min(np.min(y), np.min(w)), np.max(np.max(y), np.max(w)), 200 )
# dy = stats.gaussian_kde(y, r)
# (iii) looking at residuals between time series and walker
res = w-y
# CLOSEST FUNCTION TO MATLAB RUNSTEST, found in statsmodels.sandbox.stats.runs
# runstest = runs.runstest_2samp(res, groups=2)
# out['res_runstest'] = runstest
out['res_acl'] = CO_AutoCorr(res, lag=1)
return out
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] | 2.694709 | 3,043 |
from typing import Dict
SKIP = "SKIP"
UNKNOWN = "UNKNOWN!"
def detect_change(first: Dict[str, str], second: Dict[str, str],
compareKeys: [str]) -> bool:
"""Detects change between two dictonaries
Args:
first (Dict[str, str]): First dictionary
second (Dict[str, str]): Second dictionary
compareKeys ([type]): Keys to handle comparison
Returns:
bool: Is there a change ?
"""
for key in compareKeys:
if key not in second or key not in first:
return True
if first[key] != second[key]:
return True
return False
| [
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220,
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220,
220,
220,
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220,
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220,
220,
1441,
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220,
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220,
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220,
611,
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58,
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58,
2539,
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198,
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220,
220,
220,
220,
220,
220,
1441,
6407,
198,
220,
220,
220,
1441,
10352,
198
] | 2.394636 | 261 |
from . helpers import get_timestamp
| [
6738,
764,
49385,
1330,
651,
62,
16514,
27823,
198
] | 4 | 9 |
from discord.ext import commands
| [
6738,
36446,
13,
2302,
1330,
9729,
628,
198
] | 4.375 | 8 |
import gym
import numpy as np
from PIL import Image
import sys
env = gym.make('Pong-v0')
env.reset()
done = False
i = 0
start = 0
if len(sys.argv) < 3:
print("Usage: collect_pong <games> <start_point>")
exit()
try:
games = int(sys.argv[1])
start = int(sys.argv[2])
i = start
except:
print("Please provide a valid number for games and start point.")
exit()
for _ in range(games):
count = 0
while not done:
o, r, done, info = env.step(env.action_space.sample())
count += 1
# Ignore first 25 frames of the game, since the games starts after this amount.
if count < 25:
continue
img = Image.fromarray(o)
img.save("images/pong_" + str(i) + ".png")
i += 1
done = False
env.reset()
print("Saved {} images.".format(i-start))
print("Total images: {}".format(i))
env.close()
| [
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220,
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198,
198,
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50,
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526,
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7,
72,
12,
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198,
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14957,
4263,
25,
23884,
1911,
18982,
7,
72,
4008,
198,
24330,
13,
19836,
3419,
198
] | 2.360963 | 374 |
import random
mylist = []
for somethin in range(1,10):
x = random.randrange(0,9)
mylist.append(x)
print(mylist)
last_index=len(mylist)
print ("length of mylist is:",len(mylist))
print ("first element is:",mylist[0])
print ("last element is:",mylist[len(mylist)-1])
#is mylist sorted?
is_mylist_sorted = False
x=0
y=1
intermediate=None
#how many switches?
number_of_switches = 0
#bubble sort
while not is_mylist_sorted:
if mylist[x] > mylist[y]:
intermediate=mylist[x]
mylist[x]=mylist[y]
mylist[y]=intermediate
number_of_switches+=1
x+=1
y+=1
if y==last_index:
x=0
y=1
if number_of_switches==0:
is_mylist_sorted = True
else:
number_of_switches = 0
print("finished")
print("is my list sorted?",is_mylist_sorted)
print("my list",mylist)
| [
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62,
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220,
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220,
220,
220,
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25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1271,
62,
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62,
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198,
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271,
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62,
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62,
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8,
198,
4798,
7203,
1820,
1351,
1600,
1820,
4868,
8,
628,
220,
220,
220,
220,
198
] | 2.062791 | 430 |
import os
import setuptools
try: # for pip >= 10
from pip._internal.req import parse_requirements
except ImportError: # for pip <= 9.0.3
from pip.req import parse_requirements
requirements_path = os.path.join(os.path.dirname(__file__), 'requirements.txt')
install_requires = parse_requirements(requirements_path, session='hack')
install_requires = [str(ir.req) for ir in install_requires]
with open(os.path.join(os.path.dirname(__file__), 'VERSION'), 'r') as f:
version = f.read()
with open(os.path.join(os.path.dirname(__file__), 'README.md'), 'r') as f:
long_description = f.read()
setuptools.setup(
name='afs2-datasource',
version=version,
description='For AFS developer to access Datasource',
long_description=long_description,
long_description_content_type='text/markdown',
author='WISE-PaaS/AFS',
author_email='[email protected]',
packages=setuptools.find_packages(),
install_requires=install_requires,
keywords=['AFS'],
license='Apache License 2.0',
url='https://github.com/stacy0416/afs2-datasource'
)
# python setup.py bdist_wheel | [
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31,
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13,
785,
13,
4246,
3256,
201,
198,
220,
10392,
28,
2617,
37623,
10141,
13,
19796,
62,
43789,
22784,
201,
198,
220,
2721,
62,
47911,
28,
17350,
62,
47911,
11,
201,
198,
220,
26286,
28,
17816,
8579,
50,
6,
4357,
201,
198,
220,
5964,
11639,
25189,
4891,
13789,
362,
13,
15,
3256,
201,
198,
220,
19016,
11639,
5450,
1378,
12567,
13,
785,
14,
301,
1590,
3023,
1433,
14,
1878,
82,
17,
12,
19608,
292,
1668,
6,
201,
198,
8,
201,
198,
201,
198,
2,
21015,
9058,
13,
9078,
275,
17080,
62,
22001
] | 2.711165 | 412 |
import pandas as pd
#============== First Round ===================#
#===============================================#
#============== Other Rounds ===================#
#===============================================#
| [
11748,
19798,
292,
355,
279,
67,
198,
198,
2,
25609,
855,
3274,
10485,
36658,
855,
2,
198,
2,
10052,
25609,
18604,
2,
198,
220,
220,
220,
220,
198,
198,
2,
25609,
855,
3819,
49049,
36658,
855,
2,
198,
2,
10052,
25609,
18604,
2,
198
] | 5.136364 | 44 |
"""
Exercício 03
Peça ao usuário para digitar 3 valores inteiros e imprima a soma deles.
"""
print('Digite três números inteiros para somá-los:\n')
num1 = int(float(input('Primeiro número: ').replace(',', '.')))
num2 = int(float(input('Segundo número: ').replace(',', '.')))
num3 = int(float(input('Terceiro número: ').replace(',', '.')))
sum = num1 + num2 + num3
print(f'_____\nA soma dos valores é: {sum}')
| [
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] | 2.369942 | 173 |
from __future__ import (absolute_import, division, print_function)
__metaclass__ = type
import json
module_definition = json.loads(
"""{
"family": "discovery",
"name": "discovery_network_device",
"operations": {
"get": [
"get_discovered_network_devices_by_discovery_id",
"get_discovered_devices_by_range",
"get_devices_discovered_by_id",
"get_network_devices_from_discovery"
]
},
"parameters": {
"get_devices_discovered_by_id": [
{
"name": "id",
"required": true,
"type": "string"
},
{
"name": "task_id",
"required": false,
"type": "string"
},
{
"artificial": true,
"name": "count",
"required": true,
"type": "boolean"
}
],
"get_discovered_devices_by_range": [
{
"name": "id",
"required": true,
"type": "string"
},
{
"name": "records_to_return",
"required": true,
"type": "integer"
},
{
"name": "start_index",
"required": true,
"type": "integer"
},
{
"name": "task_id",
"required": false,
"type": "string"
}
],
"get_discovered_network_devices_by_discovery_id": [
{
"name": "id",
"required": true,
"type": "string"
},
{
"name": "task_id",
"required": false,
"type": "string"
}
],
"get_network_devices_from_discovery": [
{
"name": "id",
"required": true,
"type": "string"
},
{
"name": "cli_status",
"required": false,
"type": "string"
},
{
"name": "http_status",
"required": false,
"type": "string"
},
{
"name": "ip_address",
"required": false,
"type": "string"
},
{
"name": "netconf_status",
"required": false,
"type": "string"
},
{
"name": "ping_status",
"required": false,
"type": "string"
},
{
"name": "snmp_status",
"required": false,
"type": "string"
},
{
"name": "sort_by",
"required": false,
"type": "string"
},
{
"name": "sort_order",
"required": false,
"type": "string"
},
{
"name": "task_id",
"required": false,
"type": "string"
},
{
"artificial": true,
"name": "summary",
"required": true,
"type": "boolean"
}
]
},
"responses": {
"get_devices_discovered_by_id": {
"properties": [
"response",
"version"
],
"type": "object"
},
"get_discovered_devices_by_range": {
"properties": [
"response",
"version"
],
"type": "object"
},
"get_discovered_network_devices_by_discovery_id": {
"properties": [
"response",
"version"
],
"type": "object"
},
"get_network_devices_from_discovery": {
"properties": [
"response",
"version"
],
"type": "object"
}
}
}"""
)
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8,
198
] | 1.549853 | 2,728 |
# Copyright 2013 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 sys
from lib.bucket import BUCKET_ID
from lib.subcommand import SubCommand
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] | 3.686567 | 67 |
burst_time=[]
print("Enter the number of process: ")
n=int(input())
print("Enter the burst time of the processes: \n")
burst_time=list(map(int, input().split()))
waiting_time=[]
avg_waiting_time=0
turnaround_time=[]
avg_turnaround_time=0
waiting_time.insert(0,0)
turnaround_time.insert(0,burst_time[0])
for i in range(1,len(burst_time)):
waiting_time.insert(i,waiting_time[i-1]+burst_time[i-1])
turnaround_time.insert(i,waiting_time[i]+burst_time[i])
avg_waiting_time+=waiting_time[i]
avg_turnaround_time+=turnaround_time[i]
avg_waiting_time=float(avg_waiting_time)/n
avg_turnaround_time=float(avg_turnaround_time)/n
print("\n")
print("Process\t Burst Time\t Waiting Time\t Turn Around Time")
for i in range(0,n):
print(str(i)+"\t\t"+str(burst_time[i])+"\t\t"+str(waiting_time[i])+"\t\t"+str(turnaround_time[i]))
print("\n")
print("Average Waiting time is: "+str(avg_waiting_time))
print("Average Turn Arount Time is: "+str(avg_turnaround_time)) | [
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] | 2.368039 | 413 |
import os
from airflow.hooks.base_hook import BaseHook
from airflow.operators.bash_operator import BashOperator
from airflow.utils.decorators import apply_defaults
| [
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] | 3.608696 | 46 |
"""
Author: Haoyin Xu
"""
import time
import psutil
import argparse
import numpy as np
import torchvision.datasets as datasets
from numpy.random import permutation
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from river import tree
from skgarden import MondrianForestClassifier
from sdtf import StreamDecisionForest
def write_result(filename, acc_ls):
"""Writes results to specified text file"""
output = open(filename, "w")
for acc in acc_ls:
output.write(str(acc) + "\n")
def prediction(classifier):
"""Generates predictions from model"""
predictions = classifier.predict(X_test)
p_t = 0
for i in range(X_test.shape[0]):
if predictions[i] == y_test[i]:
p_t += 1
return p_t / X_test.shape[0]
def experiment_dt():
"""Runs experiments for Batch Decision Tree"""
dt_l = []
train_time_l = []
test_time_l = []
v_m_l = []
s_m_l = []
dt = DecisionTreeClassifier()
for i in range(500):
X_t = X_r[: (i + 1) * 100]
y_t = y_r[: (i + 1) * 100]
# Train the model
start_time = time.perf_counter()
dt.fit(X_t, y_t)
end_time = time.perf_counter()
train_time_l.append(end_time - start_time)
# Test the model
start_time = time.perf_counter()
dt_l.append(prediction(dt))
end_time = time.perf_counter()
test_time_l.append(end_time - start_time)
# Check memory
v_m = psutil.virtual_memory()[2]
v_m_l.append(v_m)
s_m = psutil.swap_memory()[3]
s_m_l.append(s_m)
return dt_l, train_time_l, test_time_l, v_m_l, s_m_l
def experiment_rf():
"""Runs experiments for Random Forest"""
rf_l = []
train_time_l = []
test_time_l = []
v_m_l = []
s_m_l = []
rf = RandomForestClassifier()
for i in range(500):
X_t = X_r[: (i + 1) * 100]
y_t = y_r[: (i + 1) * 100]
# Train the model
start_time = time.perf_counter()
rf.fit(X_t, y_t)
end_time = time.perf_counter()
train_time_l.append(end_time - start_time)
# Test the model
start_time = time.perf_counter()
rf_l.append(prediction(rf))
end_time = time.perf_counter()
test_time_l.append(end_time - start_time)
# Check memory
v_m = psutil.virtual_memory()[2]
v_m_l.append(v_m)
s_m = psutil.swap_memory()[3]
s_m_l.append(s_m)
return rf_l, train_time_l, test_time_l, v_m_l, s_m_l
def experiment_ht():
"""Runs experiments for Hoeffding Tree"""
ht_l = []
train_time_l = []
test_time_l = []
v_m_l = []
s_m_l = []
ht = tree.HoeffdingTreeClassifier(max_size=1000, grace_period=2)
for i in range(X_train.shape[0]):
X_t = X_r[i]
y_t = y_r[i]
idx = range(1024)
X_t = dict(zip(idx, X_t))
start_time = time.perf_counter()
ht.learn_one(X_t, y_t)
end_time = time.perf_counter()
train_time_l.append(end_time - start_time)
if i > 0 and (i + 1) % 100 == 0:
p_t = 0.0
start_time = time.perf_counter()
for j in range(X_test.shape[0]):
y_pred = ht.predict_one(X_test[j])
if y_pred == y_test[j]:
p_t += 1
ht_l.append(p_t / X_test.shape[0])
end_time = time.perf_counter()
test_time_l.append(end_time - start_time)
# Check memory
v_m = psutil.virtual_memory()[2]
v_m_l.append(v_m)
s_m = psutil.swap_memory()[3]
s_m_l.append(s_m)
# Reformat the train times
new_train_time_l = []
for i in range(1, X_train.shape[0]):
train_time_l[i] += train_time_l[i - 1]
if i > 0 and (i + 1) % 100 == 0:
new_train_time_l.append(train_time_l[i])
train_time_l = new_train_time_l
return ht_l, train_time_l, test_time_l, v_m_l, s_m_l
def experiment_mf():
"""Runs experiments for Mondrian Forest"""
mf_l = []
train_time_l = []
test_time_l = []
v_m_l = []
s_m_l = []
mf = MondrianForestClassifier(n_estimators=10)
for i in range(500):
X_t = X_r[i * 100 : (i + 1) * 100]
y_t = y_r[i * 100 : (i + 1) * 100]
# Train the model
start_time = time.perf_counter()
mf.partial_fit(X_t, y_t)
end_time = time.perf_counter()
train_time_l.append(end_time - start_time)
# Test the model
start_time = time.perf_counter()
mf_l.append(prediction(mf))
end_time = time.perf_counter()
test_time_l.append(end_time - start_time)
# Check memory
v_m = psutil.virtual_memory()[2]
v_m_l.append(v_m)
s_m = psutil.swap_memory()[3]
s_m_l.append(s_m)
# Reformat the train times
for i in range(1, 500):
train_time_l[i] += train_time_l[i - 1]
return mf_l, train_time_l, test_time_l, v_m_l, s_m_l
def experiment_sdt():
"""Runs experiments for Stream Decision Tree"""
sdt_l = []
train_time_l = []
test_time_l = []
v_m_l = []
s_m_l = []
sdt = DecisionTreeClassifier()
for i in range(500):
X_t = X_r[i * 100 : (i + 1) * 100]
y_t = y_r[i * 100 : (i + 1) * 100]
# Train the model
start_time = time.perf_counter()
sdt.partial_fit(X_t, y_t, classes=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
end_time = time.perf_counter()
train_time_l.append(end_time - start_time)
# Test the model
start_time = time.perf_counter()
sdt_l.append(prediction(sdt))
end_time = time.perf_counter()
test_time_l.append(end_time - start_time)
# Check memory
v_m = psutil.virtual_memory()[2]
v_m_l.append(v_m)
s_m = psutil.swap_memory()[3]
s_m_l.append(s_m)
# Reformat the train times
for i in range(1, 500):
train_time_l[i] += train_time_l[i - 1]
return sdt_l, train_time_l, test_time_l, v_m_l, s_m_l
def experiment_sdf():
"""Runs experiments for Stream Decision Forest"""
sdf_l = []
train_time_l = []
test_time_l = []
v_m_l = []
s_m_l = []
sdf = StreamDecisionForest()
for i in range(500):
X_t = X_r[i * 100 : (i + 1) * 100]
y_t = y_r[i * 100 : (i + 1) * 100]
# Train the model
start_time = time.perf_counter()
sdf.partial_fit(X_t, y_t, classes=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
end_time = time.perf_counter()
train_time_l.append(end_time - start_time)
# Test the model
start_time = time.perf_counter()
sdf_l.append(prediction(sdf))
end_time = time.perf_counter()
test_time_l.append(end_time - start_time)
# Check memory
v_m = psutil.virtual_memory()[2]
v_m_l.append(v_m)
s_m = psutil.swap_memory()[3]
s_m_l.append(s_m)
# Reformat the train times
for i in range(1, 500):
train_time_l[i] += train_time_l[i - 1]
return sdf_l, train_time_l, test_time_l, v_m_l, s_m_l
# Prepare CIFAR data
# Normalize
scale = np.mean(np.arange(0, 256))
normalize = lambda x: (x - scale) / scale
# Train data
cifar_trainset = datasets.CIFAR10(root="../", train=True, download=True, transform=None)
X_train = normalize(cifar_trainset.data)
y_train = np.array(cifar_trainset.targets)
# Test data
cifar_testset = datasets.CIFAR10(root="../", train=False, download=True, transform=None)
X_test = normalize(cifar_testset.data)
y_test = np.array(cifar_testset.targets)
X_train = X_train.reshape(-1, 32 * 32 * 3)
X_test = X_test.reshape(-1, 32 * 32 * 3)
# Parse classifier choices
parser = argparse.ArgumentParser()
parser.add_argument("-all", help="all classifiers", required=False, action="store_true")
parser.add_argument("-dt", help="decision forests", required=False, action="store_true")
parser.add_argument("-rf", help="random forests", required=False, action="store_true")
parser.add_argument("-ht", help="hoeffding trees", required=False, action="store_true")
parser.add_argument("-mf", help="mondrian forests", required=False, action="store_true")
parser.add_argument(
"-sdt", help="stream decision trees", required=False, action="store_true"
)
parser.add_argument(
"-sdf", help="stream decision forests", required=False, action="store_true"
)
args = parser.parse_args()
# Perform experiments
if args.all or args.dt:
dt_acc_l = []
dt_train_t_l = []
dt_test_t_l = []
dt_v_m_l = []
dt_s_m_l = []
for i in range(10):
p = permutation(X_train.shape[0])
X_r = X_train[p]
y_r = y_train[p]
dt_acc, dt_train_t, dt_test_t, dt_v_m, dt_s_m = experiment_dt()
dt_acc_l.append(dt_acc)
dt_train_t_l.append(dt_train_t)
dt_test_t_l.append(dt_test_t)
dt_v_m_l.append(dt_v_m)
dt_s_m_l.append(dt_s_m)
write_result("../results/dt/cifar10_acc.txt", dt_acc_l)
write_result("../results/dt/cifar10_train_t.txt", dt_train_t_l)
write_result("../results/dt/cifar10_test_t.txt", dt_test_t_l)
write_result("../results/dt/cifar10_v_m.txt", dt_v_m_l)
write_result("../results/dt/cifar10_s_m.txt", dt_s_m_l)
if args.all or args.rf:
rf_acc_l = []
rf_train_t_l = []
rf_test_t_l = []
rf_v_m_l = []
rf_s_m_l = []
for i in range(10):
p = permutation(X_train.shape[0])
X_r = X_train[p]
y_r = y_train[p]
rf_acc, rf_train_t, rf_test_t, rf_v_m, rf_s_m = experiment_rf()
rf_acc_l.append(rf_acc)
rf_train_t_l.append(rf_train_t)
rf_test_t_l.append(rf_test_t)
rf_v_m_l.append(rf_v_m)
rf_s_m_l.append(rf_s_m)
write_result("../results/rf/cifar10_acc.txt", rf_acc_l)
write_result("../results/rf/cifar10_train_t.txt", rf_train_t_l)
write_result("../results/rf/cifar10_test_t.txt", rf_test_t_l)
write_result("../results/rf/cifar10_v_m.txt", rf_v_m_l)
write_result("../results/rf/cifar10_s_m.txt", rf_s_m_l)
if args.all or args.ht:
ht_acc_l = []
ht_train_t_l = []
ht_test_t_l = []
ht_v_m_l = []
ht_s_m_l = []
for i in range(10):
p = permutation(X_train.shape[0])
X_r = X_train[p]
y_r = y_train[p]
ht_acc, ht_train_t, ht_test_t, ht_v_m, ht_s_m = experiment_ht()
ht_acc_l.append(ht_acc)
ht_train_t_l.append(ht_train_t)
ht_test_t_l.append(ht_test_t)
ht_v_m_l.append(ht_v_m)
ht_s_m_l.append(ht_s_m)
write_result("../results/ht/cifar10_acc.txt", ht_acc_l)
write_result("../results/ht/cifar10_train_t.txt", ht_train_t_l)
write_result("../results/ht/cifar10_test_t.txt", ht_test_t_l)
write_result("../results/ht/cifar10_v_m.txt", ht_v_m_l)
write_result("../results/ht/cifar10_s_m.txt", ht_s_m_l)
if args.all or args.mf:
mf_acc_l = []
mf_train_t_l = []
mf_test_t_l = []
mf_v_m_l = []
mf_s_m_l = []
for i in range(10):
p = permutation(X_train.shape[0])
X_r = X_train[p]
y_r = y_train[p]
mf_acc, mf_train_t, mf_test_t, mf_v_m, mf_s_m = experiment_mf()
mf_acc_l.append(mf_acc)
mf_train_t_l.append(mf_train_t)
mf_test_t_l.append(mf_test_t)
mf_v_m_l.append(mf_v_m)
mf_s_m_l.append(mf_s_m)
write_result("../results/mf/cifar10_acc.txt", mf_acc_l)
write_result("../results/mf/cifar10_train_t.txt", mf_train_t_l)
write_result("../results/mf/cifar10_test_t.txt", mf_test_t_l)
write_result("../results/mf/cifar10_v_m.txt", mf_v_m_l)
write_result("../results/mf/cifar10_s_m.txt", mf_s_m_l)
if args.all or args.sdt:
sdt_acc_l = []
sdt_train_t_l = []
sdt_test_t_l = []
sdt_v_m_l = []
sdt_s_m_l = []
for i in range(10):
p = permutation(X_train.shape[0])
X_r = X_train[p]
y_r = y_train[p]
sdt_acc, sdt_train_t, sdt_test_t, sdt_v_m, sdt_s_m = experiment_sdt()
sdt_acc_l.append(sdt_acc)
sdt_train_t_l.append(sdt_train_t)
sdt_test_t_l.append(sdt_test_t)
sdt_v_m_l.append(sdt_v_m)
sdt_s_m_l.append(sdt_s_m)
write_result("../results/sdt/cifar10_acc.txt", sdt_acc_l)
write_result("../results/sdt/cifar10_train_t.txt", sdt_train_t_l)
write_result("../results/sdt/cifar10_test_t.txt", sdt_test_t_l)
write_result("../results/sdt/cifar10_v_m.txt", sdt_v_m_l)
write_result("../results/sdt/cifar10_s_m.txt", sdt_s_m_l)
if args.all or args.sdf:
sdf_acc_l = []
sdf_train_t_l = []
sdf_test_t_l = []
sdf_v_m_l = []
sdf_s_m_l = []
for i in range(10):
p = permutation(X_train.shape[0])
X_r = X_train[p]
y_r = y_train[p]
sdf_acc, sdf_train_t, sdf_test_t, sdf_v_m, sdf_s_m = experiment_sdf()
sdf_acc_l.append(sdf_acc)
sdf_train_t_l.append(sdf_train_t)
sdf_test_t_l.append(sdf_test_t)
sdf_v_m_l.append(sdf_v_m)
sdf_s_m_l.append(sdf_s_m)
write_result("../results/sdf/cifar10_acc.txt", sdf_acc_l)
write_result("../results/sdf/cifar10_train_t.txt", sdf_train_t_l)
write_result("../results/sdf/cifar10_test_t.txt", sdf_test_t_l)
write_result("../results/sdf/cifar10_v_m.txt", sdf_v_m_l)
write_result("../results/sdf/cifar10_s_m.txt", sdf_s_m_l)
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] | 1.891149 | 7,129 |
"""Text wrapping and filling.
"""
# Copyright (C) 1999-2001 Gregory P. Ward.
# Copyright (C) 2002, 2003 Python Software Foundation.
# Written by Greg Ward <[email protected]>
# Modified by Sophie Kirschner
# https://github.com/python/cpython/blob/master/Lib/textwrap.py
# https://github.com/python/cpython/blob/master/LICENSE
__revision__ = "$Id$"
import string, re
# Do the right thing with boolean values for all known Python versions
# (so this module can be copied to projects that don't depend on Python
# 2.3, e.g. Optik and Docutils) by uncommenting the block of code below.
#try:
# True, False
#except NameError:
# (True, False) = (1, 0)
__all__ = ['TextWrapper']
# Hardcode the recognized whitespace characters to the US-ASCII
# whitespace characters. The main reason for doing this is that in
# ISO-8859-1, 0xa0 is non-breaking whitespace, so in certain locales
# that character winds up in string.whitespace. Respecting
# string.whitespace in those cases would 1) make textwrap treat 0xa0 the
# same as any other whitespace char, which is clearly wrong (it's a
# *non-breaking* space), 2) possibly cause problems with Unicode,
# since 0xa0 is not in range(128).
_whitespace = '\t\n\x0b\x0c\r '
class TextWrapper:
"""
Object for wrapping/filling text. The public interface consists of
the wrap() and fill() methods; the other methods are just there for
subclasses to override in order to tweak the default behaviour.
If you want to completely replace the main wrapping algorithm,
you'll probably have to override _wrap_chunks().
Several instance attributes control various aspects of wrapping:
width (default: 70)
the maximum width of wrapped lines (unless break_long_words
is false)
initial_indent (default: "")
string that will be prepended to the first line of wrapped
output. Counts towards the line's width.
subsequent_indent (default: "")
string that will be prepended to all lines save the first
of wrapped output; also counts towards each line's width.
expand_tabs (default: true)
Expand tabs in input text to spaces before further processing.
Each tab will become 1 .. 8 spaces, depending on its position in
its line. If false, each tab is treated as a single character.
replace_whitespace (default: true)
Replace all whitespace characters in the input text by spaces
after tab expansion. Note that if expand_tabs is false and
replace_whitespace is true, every tab will be converted to a
single space!
break_long_words (default: true)
Break words longer than 'width'. If false, those words will not
be broken, and some lines might be longer than 'width'.
break_on_hyphens (default: true)
Allow breaking hyphenated words. If true, wrapping will occur
preferably on whitespaces and right after hyphens part of
compound words.
drop_whitespace (default: true)
Drop leading and trailing whitespace from lines.
"""
# This funky little regex is just the trick for splitting
# text up into word-wrappable chunks. E.g.
# "Hello there -- you goof-ball, use the -b option!"
# splits into
# Hello/ /there/ /--/ /you/ /goof-/ball,/ /use/ /the/ /-b/ /option!
# (after stripping out empty strings).
wordsep_re = re.compile(
r'(\s+|' # any whitespace
r'[^\s\w]*\w+[^0-9\W]-(?=\w+[^0-9\W])|' # hyphenated words
r'(?<=[\w\!\"\'\&\.\,\?])-{2,}(?=\w))') # em-dash
# This less funky little regex just split on recognized spaces. E.g.
# "Hello there -- you goof-ball, use the -b option!"
# splits into
# Hello/ /there/ /--/ /you/ /goof-ball,/ /use/ /the/ /-b/ /option!/
wordsep_simple_re = re.compile(r'(\s+)')
# -- Private methods -----------------------------------------------
# (possibly useful for subclasses to override)
def _split(self, text):
"""_split(text : string) -> [string]
Split the text to wrap into indivisible chunks. Chunks are
not quite the same as words; see _wrap_chunks() for full
details. As an example, the text
Look, goof-ball -- use the -b option!
breaks into the following chunks:
'Look,', ' ', 'goof-', 'ball', ' ', '--', ' ',
'use', ' ', 'the', ' ', '-b', ' ', 'option!'
if break_on_hyphens is True, or in:
'Look,', ' ', 'goof-ball', ' ', '--', ' ',
'use', ' ', 'the', ' ', '-b', ' ', option!'
otherwise.
"""
if self.break_on_hyphens:
pat = self.wordsep_re
else:
pat = self.wordsep_simple_re
chunks = pat.split(text.decode("latin-1"))
chunks = list(filter(None, chunks)) # remove empty chunks
return chunks
def _handle_long_word(self, reversed_chunks, cur_line, cur_len, width):
"""_handle_long_word(chunks : [string],
cur_line : [string],
cur_len : int, width : int)
Handle a chunk of text (most likely a word, not whitespace) that
is too long to fit in any line.
"""
# Figure out when indent is larger than the specified width, and make
# sure at least one character is stripped off on every pass
if width < 1:
space_left = 1
else:
space_left = width - cur_len
# If we're allowed to break long words, then do so: put as much
# of the next chunk onto the current line as will fit.
if self.break_long_words:
cur_line.append(reversed_chunks[-1][:space_left])
reversed_chunks[-1] = reversed_chunks[-1][space_left:]
# Otherwise, we have to preserve the long word intact. Only add
# it to the current line if there's nothing already there --
# that minimizes how much we violate the width constraint.
elif not cur_line:
cur_line.append(reversed_chunks.pop())
# If we're not allowed to break long words, and there's already
# text on the current line, do nothing. Next time through the
# main loop of _wrap_chunks(), we'll wind up here again, but
# cur_len will be zero, so the next line will be entirely
# devoted to the long word that we can't handle right now.
# Added to consider basic ANSI escape sequences as zero-width
def _wrap_chunks(self, chunks):
"""_wrap_chunks(chunks : [string]) -> [string]
Wrap a sequence of text chunks and return a list of lines of
length 'self.width' or less. (If 'break_long_words' is false,
some lines may be longer than this.) Chunks correspond roughly
to words and the whitespace between them: each chunk is
indivisible (modulo 'break_long_words'), but a line break can
come between any two chunks. Chunks should not have internal
whitespace; ie. a chunk is either all whitespace or a "word".
Whitespace chunks will be removed from the beginning and end of
lines, but apart from that whitespace is preserved.
"""
lines = []
if self.width <= 0:
raise ValueError("invalid width %r (must be > 0)" % self.width)
# Arrange in reverse order so items can be efficiently popped
# from a stack of chucks.
chunks.reverse()
while chunks:
# Start the list of chunks that will make up the current line.
# cur_len is just the length of all the chunks in cur_line.
cur_line = []
cur_len = 0
# Figure out which static string will prefix this line.
if lines:
indent = self.subsequent_indent
else:
indent = self.initial_indent
# Maximum width for this line.
width = self.width - len(indent)
# First chunk on line is whitespace -- drop it, unless this
# is the very beginning of the text (ie. no lines started yet).
if self.drop_whitespace and chunks[-1].strip() == '' and lines:
del chunks[-1]
while chunks:
l = self._get_chunk_length(chunks[-1])
# Can at least squeeze this chunk onto the current line.
if cur_len + l <= width:
cur_line.append(chunks.pop())
cur_len += l
# Nope, this line is full.
else:
break
# The current line is full, and the next chunk is too big to
# fit on *any* line (not just this one).
if chunks and self._get_chunk_length(chunks[-1]) > width:
self._handle_long_word(chunks, cur_line, cur_len, width)
# If the last chunk on this line is all whitespace, drop it.
if self.drop_whitespace and cur_line and cur_line[-1].strip() == '':
del cur_line[-1]
# Convert current line back to a string and store it in list
# of all lines (return value).
if cur_line:
lines.append(indent + ''.join(cur_line))
return lines
# -- Public interface ----------------------------------------------
def wrap(self, text):
"""wrap(text : string) -> [string]
Reformat the single paragraph in 'text' so it fits in lines of
no more than 'self.width' columns, and return a list of wrapped
lines. Tabs in 'text' are expanded with string.expandtabs(),
and all other whitespace characters (including newline) are
converted to space.
"""
chunks = self._split(text)
return self._wrap_chunks(chunks)
def fill(self, text):
"""fill(text : string) -> string
Reformat the single paragraph in 'text' to fit in lines of no
more than 'self.width' columns, and return a new string
containing the entire wrapped paragraph.
"""
return "\n".join(self.wrap(text))
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] | 2.520575 | 4,034 |
# Copyright 2014 Red Hat, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
import netaddr
from oslo_utils import versionutils
import nova.conf
from nova import db
from nova import exception
from nova.i18n import _
from nova import objects
from nova.objects import base as obj_base
from nova.objects import fields
CONF = nova.conf.CONF
# TODO(berrange): Remove NovaObjectDictCompat
@obj_base.NovaObjectRegistry.register
@obj_base.NovaObjectRegistry.register
| [
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] | 3.268852 | 305 |
from PIL import Image
from PIL.ExifTags import TAGS
import exifread
import re
import json
def get_exif_data(fname):
"""Get embedded EXIF data from image file."""
ret = {}
try:
img = Image.open(fname)
if hasattr( img, '_getexif' ):
exifinfo = img._getexif()
if exifinfo != None:
for tag, value in exifinfo.items():
decoded = TAGS.get(tag, tag)
ret[decoded] = value
except IOError:
print('IOERROR ' + fname)
return ret
if __name__ == '__main__':
fileName = "1 (36).jpg"
# exif = get_exif_data(fileName)
# print(exif)
read() | [
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3601,
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1441,
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6624,
705,
834,
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834,
10354,
198,
220,
220,
220,
2393,
5376,
796,
366,
16,
357,
2623,
737,
9479,
1,
198,
220,
220,
220,
1303,
409,
361,
796,
651,
62,
1069,
361,
62,
7890,
7,
7753,
5376,
8,
198,
220,
220,
220,
1303,
3601,
7,
1069,
361,
8,
628,
220,
220,
220,
1100,
3419
] | 2.049383 | 324 |
from flask import Flask
from flask_sqlalchemy import SQLAlchemy
from flask_migrate import Migrate
from flask_bootstrap import Bootstrap
from flask_login import LoginManager
from flask_moment import Moment
from flask_mail import Mail
# from flask_mail_sendgrid import MailSendGrid
from config import Config
from logging.handlers import RotatingFileHandler
import logging
import os
db = SQLAlchemy()
migrate = Migrate()
bootstrap = Bootstrap()
login = LoginManager()
moment = Moment()
mail = Mail()
from app import models
| [
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] | 3.824818 | 137 |
'''
File: test_conversions.py
Author: Adam Pah
Description:
py.test test ensemble
'''
import pytest
import conversions as conv
class TestConvertTimeseries:
'''
Covers the convert_timeseries_to_intervalseries function
'''
timeseries = [[0, 2], [2, 3], [5, 3]]
def test_basic(self):
'''
Timeseries conversion test.
'''
#Set up the answer
intervalseries = [[0, 2], [1, 3]]
#Get the intervalseries
test_intervals = conv.convert_timeseries_to_intervalseries(self.timeseries)
#Just make sure that these things aren't the same
assert intervalseries == test_intervals
def test_yaxis_only(self):
'''
Timeseries conversion test with the yaxis only
'''
#Set up the answer
intervalseries = [2, 3]
#Get the intervalseries
test_intervals = conv.convert_timeseries_to_intervalseries(self.timeseries, yaxis_only=True)
#Just make sure that these things aren't the same
assert intervalseries == test_intervals
def test_negative_bounds(self):
'''
Test to make sure that system exit happens
'''
#Load up the data
timeseries = [[0, 2], [-2, 3], [4, 3]]
#Check for the system exit
with pytest.raises(SystemExit):
conv.convert_timeseries_to_intervalseries(timeseries, yaxis_only=True)
| [
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7,
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220,
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220,
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220,
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62,
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62,
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12786,
10640,
7,
22355,
10640,
11,
331,
22704,
62,
8807,
28,
17821,
8,
198
] | 2.413379 | 583 |
from django.urls import reverse
from extforms.deprecated_forms import SWCEventRequestForm, DCEventRequestForm
from extrequests.models import (
EventRequest,
)
from workshops.models import Event, Organization
from workshops.tests.base import TestBase
| [
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] | 3.73913 | 69 |
"""
Our HADS database gets loaded up with duplicates, this cleans it up.
called from RUN_MIDNIGHT.sh
"""
from __future__ import print_function
import datetime
import sys
import pytz
from pyiem.util import get_dbconn, utc
def query(sql, args=None):
"""
Do a query and make it atomic
"""
pgconn = get_dbconn('hads')
hcursor = pgconn.cursor()
sts = datetime.datetime.now()
hcursor.execute("set work_mem='16GB'")
hcursor.execute(sql, args if args is not None else [])
ets = datetime.datetime.now()
print("%7s [%8.4fs] %s" % (hcursor.rowcount, (ets - sts).total_seconds(),
sql))
hcursor.close()
pgconn.commit()
def workflow(valid):
''' Do the work for this date, which is set to 00 UTC '''
# Delete schoolnet data, since we created it in the first place!
tbl = "raw%s" % (valid.strftime("%Y_%m"),)
sql = """DELETE from """ + tbl + """ WHERE station IN
(SELECT id from stations WHERE network in ('KCCI','KELO','KIMT')
)"""
query(sql)
# make sure our tmp table does not exist
query("DROP TABLE IF EXISTS tmp")
# Extract unique obs to special table
sql = """CREATE table tmp as select distinct * from """+tbl+"""
WHERE valid BETWEEN %s and %s"""
args = (valid, valid + datetime.timedelta(hours=24))
query(sql, args)
# Delete them all!
sql = """delete from """+tbl+""" WHERE valid BETWEEN %s and %s"""
query(sql, args)
sql = "DROP index IF EXISTS "+tbl+"_idx"
query(sql)
sql = "DROP index IF EXISTS "+tbl+"_valid_idx"
query(sql)
# Insert from special table
sql = "INSERT into "+tbl+" SELECT * from tmp"
query(sql)
sql = "CREATE index %s_idx on %s(station,valid)" % (tbl, tbl)
query(sql)
sql = "CREATE index %s_valid_idx on %s(valid)" % (tbl, tbl)
query(sql)
sql = "DROP TABLE IF EXISTS tmp"
query(sql)
def main(argv):
"""Go Main Go"""
if len(argv) == 4:
utcnow = utc(int(argv[1]), int(argv[2]), int(argv[3]))
workflow(utcnow)
return
utcnow = datetime.datetime.utcnow()
utcnow = utcnow.replace(hour=0, minute=0, second=0, microsecond=0,
tzinfo=pytz.utc)
# Run for 'yesterday' and 35 days ago
for day in [1, 35]:
workflow(utcnow - datetime.timedelta(days=day))
if __name__ == '__main__':
# See how we are called
main(sys.argv)
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7,
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28,
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220,
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1303,
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356,
389,
1444,
198,
220,
220,
220,
1388,
7,
17597,
13,
853,
85,
8,
198
] | 2.310377 | 1,060 |
from django.db import models
| [
6738,
42625,
14208,
13,
9945,
1330,
4981,
198,
220,
198
] | 3.1 | 10 |
import numpy as np
import os
import pickle
#128x128
####################################################
'''
Inputs 128x128 pixel array
Returns label where:
label 0 = 1
label 1 = 2
etc
'''
'''
returns an array of arrays, each one is the data from one image
'''
###########################################
# training Code for class (comment it before running flask app)
#train()
# for filename in os.listdir('[more here]/images'):
# data = readTrainingData(path + filename)
# character = data[6]
# character = np.array(character, dtype='int')
# for i in range(128):
# print()
# for j in range(128):
# if (character[i][j] == 255):
# print('*', end ="")
# else:
# print('7', end ="")
# print()
# print('------------------------------------------------------------')
# print()
# print()
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220,
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220,
220,
220,
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220,
220,
220
] | 2.482667 | 375 |
# -*- coding: utf-8 -*-
"""Python implementation of the StalinSort algorithm.
References
----------
- :cite:`mathew` : @[email protected] (2018/10/26 04:20:16)
''I came up with a single pass O(n) sort algorithm I call StalinSort. You
iterate down the list of elements checking if they're in order. Any element
which is out of order is eliminated. At the end you have a sorted list.''
"""
def stalinsort(iterable, key=None, ascending=False):
"""Sorts iterable according to the single pass O(n) StalinSort algorithm.
Parameters
----------
iterable: iterable object
key: function
A function of one argument that is used to extract a comparison key
from each element. Default is None.
Returns
-------
survivors: list
List of surviving elements of iterable.
Example
-------
>>>from stalinsort import stalinsort
>>>a = [3, 2, 5, 7, 1, 3]
>>>stalinsort(a)
[3, 2, 1]
"""
ascending = False # There is only descent under communism.
if key is not None:
keys = iterable.apply(key)
else:
keys = list(iterable)
survivors = iterable[:1] # I prefer to think in terms of survivors.
for index, victim in enumerate(iterable[1:]):
if survivors[-1] >= keys[index + 1]:
survivors.append(victim)
return survivors
| [
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220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
1441,
13644,
198
] | 2.729249 | 506 |
import matplotlib.pyplot as plt
import numpy as np
from pyfmi import load_fmu
model = load_fmu('./PadeSlave.fmu')
inputs = ('inputVariable', lambda t: 5. * np.cos(t))
simulation = model.simulate(final_time=30, input=inputs)
plt.plot(simulation['time'], simulation['inputVariable'])
plt.plot(simulation['time'], simulation['outputVariable'])
plt.legend(['inputVariable', 'outputVariable'])
plt.xlabel('time')
plt.show()
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3419,
198
] | 2.711538 | 156 |
# Requirements:
# - fmtc
# - nnedi3
# From:
# - https://github.com/mawen1250/VapourSynth-script
# - https://github.com/HomeOfVapourSynthEvolution/mvsfunc
import vapoursynth as vs
import math
## Gamma conversion functions from HAvsFunc-r18
# Convert the luma channel to linear light
# Convert back a clip to gamma-corrected luma
# Apply the inverse sigmoid curve to a clip in linear luminance
# Convert back a clip to linear luminance
## Gamma conversion functions from HAvsFunc-r18 | [
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# -*- coding: utf-8 -*-
# Generated by Django 1.9.7 on 2017-03-17 17:29
from __future__ import unicode_literals
from django.db import migrations, models
| [
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import numpy as np
import EZ.stderr as stderr
| [
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from PyQt5 import QtGui, QtCore, QtWidgets
from collections import namedtuple
import time
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
from utils import utils
HumanFeedback = namedtuple('HumanFeedback', ['feedback_value'])
SavedAction = namedtuple('SavedAction', ['state', 'action', 'logprob'])
SavedActionsWithFeedback = namedtuple('SavedActionsWithFeedback', ['saved_actions', 'final_feedback'])
| [
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import sys, random, string, time
rawBoard = ''
moves = 0
# size -> int
# generate board of size size x size filled with random chars
# @returns none
# textFile -> string
# loads a board from a text file
# @returns board in 2D list form
# board -> 2D array
# prints out the bogal board
# coordinate -> list, board -> 2D list
# @returns list of all possible next positions
# possibleMoves -> 2D list, usedPath -> 2D list
# @returns the list of all legal moves
# Function used for setting up all prefix dictionaries.
# This is not run with my program but was created because I'm lazy and
# didn't want to create the prefix dictionaries by hand.
# board -> 2D list, currPos -> list, path -> 2D list
# boggle board, xy pair current position, path that got to that position
# @returns tuple of the word created and whether it is a real word.
if __name__ == "__main__":
main() | [
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myFunc("That's neat")
| [
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import logging
import os
from netmiko import ConnectHandler
from paramiko import AutoAddPolicy, SSHClient
from routeros_diff.parser import RouterOSConfig
from scp import SCPClient
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] | 3.895833 | 48 |
import pytest_pydocstyle
# https://docs.pytest.org/en/5.2.2/writing_plugins.html#testing-plugins
pytest_plugins = ["pytester"]
| [
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from peewee import *
import peeweedbevolve
from models_data import Tweet, Branch, calldb
db = calldb()
create_tables()
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import json
import base64
from rest_framework import status
from rest_framework.test import APITestCase
from rest_framework.authtoken.models import Token
from .models import User
# Create your tests here.
ACCEPT_STATUS = "A"
REJECT_STATUS = "R"
UNFRIEND_STATUS = "R"
| [
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# -*- coding: utf-8 -*-
################################################################################
# Copyright (C) 2009 Travis Shirk <[email protected]>
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
#
################################################################################
from __future__ import print_function
import os
from eyed3 import LOCAL_ENCODING as ENCODING
from eyed3.utils import formatSize, formatTime
from eyed3.utils.console import (printMsg, printError, printWarning, boldText,
Fore, HEADER_COLOR)
from eyed3.plugins import LoaderPlugin
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11,
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11,
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11,
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8206,
11,
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220,
220,
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] | 3.66474 | 346 |
# -*- coding: utf-8 -*-
"""Clothing_Recommender Project .ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1nw0ewNdkx8o3WULAp2ynhHpbq1kVq7YZ
Clean the data and use input
"""
## Import and Organize Data ##
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
#read clean file (downloaded from Task 1)
df=pd.read_csv('CleanedData.csv', sep=',')
#Pivot table (clothingID, age, rating) - Nan is replaced with 0
train = df.pivot_table(index='Age', columns='ClothingID', values='Rating')
#sort train data
train = train.sort_values('Age', ascending=True)
###Create a greeting
print("Welcome, let us recommend a product for you")
#Take user input
Name =input('Please enter your name: ')
Age = int(input('Please enter your age: '))
CID_user = int(input("Enter Clothing ID: ")) #90
while CID_user not in train.columns:
print('Invalid: No data for ID')
CID_user = int(input("Enter valid Clothing ID: "))
rating_user = float(input("Enter Rating for Clothing ID: ")) #4
##use this later (if user has more than one rating to enter)
#entries = int(input("How many ratings will you enter? "))
#for x in range(entries):
#create array with user data
userArray = pd.DataFrame().reindex_like(train)
userArray.dropna(thresh=1,inplace=True)
userArray.loc[Age,CID_user] = rating_user #enter user data
from sklearn.metrics.pairwise import nan_euclidean_distances
#find euclidean distance between all rows of train and first row of test *ignores nan
distance = np.zeros((0,2)) #create empty array
for index, row in train.iterrows(): #iterate through each row of train
result = float(nan_euclidean_distances([userArray.loc[Age]], [train.loc[index]])) #compute the euclidean distance between two rows, *confirmed it works thru excel
result_array = [index, result] #place age and distance into an array
distance = np.append(distance,[result_array],axis= 0)
#convert array to a dataframe
dfDistance = pd.DataFrame({'Age': distance[:, 0], 'E-Distance': distance[:, 1]})
dfDistance.head()
k= 5
#sort by distance, reset the index
dfDistance = dfDistance.sort_values('E-Distance', ascending=True).head(20)
dfDistance = dfDistance.reset_index(drop=True)
dfDistance.drop(dfDistance[dfDistance.index > k-1].index, inplace=True)
dfDistance.head()
#NOTE: for calculating the predicted rating, could use an IDW Interpolation function shown here https://stackoverflow.com/questions/3104781/inverse-distance-weighted-idw-interpolation-with-python
#just using mean of each to test a solution, will come back and try more complex/accurate functions later
#assume k of 5####
k_array = pd.DataFrame().reindex_like(train)
meanArray = pd.DataFrame()
for x in dfDistance['Age']:
k_array = k_array.append([train.loc[x]]) #make array of the k closest ages
meanArray = meanArray.append(k_array.mean(),ignore_index = True).transpose()
meanArray.dropna(axis=0,inplace=True)
meanArray.columns = ["Mean"]
meanArray = meanArray[meanArray.Mean == 5]
recommend = list(meanArray.index.values)
print("recommended ClothingID's are: ")
print(recommend)
#feedback, clothingID (choose top 5), department
#reverse lookup clothingID for department
# feedback (choose first 3)
| [
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2,
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329,
5011,
220,
198,
2,
7538,
357,
6679,
577,
717,
513,
8,
628
] | 3.056377 | 1,082 |
from rtree.index import Rtree
from src.features.helper import *
import sys
import logging
import time
if __name__ == '__main__':
train_data = sys.argv[1]
q_size = int(sys.argv[2])
main(train_data, q_size)
| [
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] | 2.528736 | 87 |
from app import app
import logging
logging.basicConfig(level=logging.WARNING)
if __name__ == "__main__":
app.debug = True
app.run() | [
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] | 2.764706 | 51 |
# Copyright 2013-2018 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
from spack import *
class RAffypdnn(RPackage):
"""The package contains functions to perform the PDNN method
described by Li Zhang et al."""
homepage = "https://www.bioconductor.org/packages/affypdnn/"
git = "https://git.bioconductor.org/packages/affypdnn.git"
version('1.50.0', commit='97ff68e9f51f31333c0330435ea23b212b3ed18a')
depends_on('[email protected]:3.4.9', when='@1.50.0')
depends_on('r-affy', type=('build', 'run'))
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28,
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6,
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198
] | 2.633065 | 248 |
#######################################################################
"""
@author: Emanuele Musumeci (https://github.com/EmanueleMusumeci)
PopulationInitializer abstract class and basic initializer that generates
a population of random binary strings of a given length
"""
#######################################################################
import abc
import numpy as np
from numpy import random
#Base abstract class for population initialization methods, that generate a population for the genetic optimization process
#Generate population of random binary strings of a given length
#Generates a single binary individual
#Generates a population of random binary individuals
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] | 4.417178 | 163 |
import os
import subprocess
from .utils import checkdir, get_condor_version, requires_command
from .basenode import BaseNode
from .job import Job
def _iter_job_args(job):
"""
Iterates over Job args list. Yields the name (and JobArg) for each node
to be used when adding job to a Dagman (i.e. the name in the
'JOB name job_submit_file' line).
Parameters
----------
job : Job
Job to iterate over. Note that the submit file for job must be built
prior to using _iter_job_args.
Yields
------
node_name : str
Node name to use in Dagman object.
job_arg : JobArg namedtuple
Job argument object (``arg``, ``name``, ``retry`` attributes).
"""
if not isinstance(job, Job):
raise TypeError('Expecting a Job object, got {}'.format(type(job)))
if not getattr(job, '_built', False):
raise ValueError('Job {} must be built before adding it '
'to a Dagman'.format(job.name))
if len(job.args) == 0:
raise StopIteration
else:
for idx, job_arg in enumerate(job):
arg, name, retry = job_arg
if name is not None:
node_name = '{}_{}'.format(job.submit_name, name)
else:
node_name = '{}_arg_{}'.format(job.submit_name, idx)
yield node_name, job_arg
def _get_parent_child_string(node):
"""Constructs the parent/child line for node to be added to a Dagman
"""
if not isinstance(node, BaseNode):
raise ValueError('Expecting a Job or Dagman object, '
'got {}'.format(type(node)))
parent_string = 'Parent'
for parent_node in node.parents:
if isinstance(parent_node, Job) and len(parent_node) > 0:
for node_name, job_arg in _iter_job_args(parent_node):
parent_string += ' {}'.format(node_name)
else:
parent_string += ' {}'.format(parent_node.submit_name)
child_string = 'Child'
if isinstance(node, Job) and len(node) > 0:
for node_name, job_arg in _iter_job_args(node):
child_string += ' {}'.format(node_name)
else:
child_string += ' {}'.format(node.submit_name)
parent_child_string = parent_string + ' ' + child_string
return parent_child_string
class Dagman(BaseNode):
"""
Dagman object consisting of a series of Jobs and sub-Dagmans to manage.
Note that the ``submit`` parameter can be explicitly given or configured
by setting the ``PYCONDOR_SUBMIT_DIR`` environment variable. An explicitly
given value for ``submit`` will be used over the environment variable,
while the environment variable will be used over a default value.
Parameters
----------
name : str
Name of the Dagman instance. This will also be the name of the
corresponding error, log, output, and submit files associated with
this Dagman.
submit : str
Path to directory where condor dagman submit files will be written
(defaults to the directory was the Dagman was submitted from).
extra_lines : list or None, optional
List of additional lines to be added to submit file.
.. versionadded:: 0.1.1
dag : Dagman, optional
If specified, Dagman will be added to dag as a subdag
(default is None).
verbose : int, optional
Level of logging verbosity option are 0-warning, 1-info,
2-debugging (default is 0).
Attributes
----------
jobs : list
The list of jobs for this Dagman instance to manage.
parents : list
List of parent Jobs and Dagmans. Ensures that Jobs and Dagmans in the
parents list will complete before this Dagman is submitted to HTCondor.
children : list
List of child Jobs and Dagmans. Ensures that Jobs and Dagmans in the
children list will be submitted only after this Dagman has completed.
"""
def add_job(self, job):
"""Add job to Dagman
Parameters
----------
job : Job
Job to append to Dagman jobs list.
Returns
-------
self : object
Returns self.
"""
self._add_node(job)
return self
def add_subdag(self, dag):
"""Add dag to Dagman
Parameters
----------
dag : Dagman
Subdag to append to Dagman jobs list.
Returns
-------
self : object
Returns self.
"""
self._add_node(dag)
return self
def _get_job_arg_lines(self, job, fancyname):
"""Constructs the lines to be added to a Dagman related to job
"""
if not isinstance(job, Job):
raise TypeError('Expecting a Job object, got {}'.format(type(job)))
if not getattr(job, '_built', False):
raise ValueError('Job {} must be built before adding it '
'to a Dagman'.format(job.name))
job_arg_lines = []
if len(job.args) == 0:
job_line = 'JOB {} {}'.format(job.submit_name, job.submit_file)
job_arg_lines.append(job_line)
else:
for node_name, job_arg in _iter_job_args(job):
# Check that '.' or '+' are not in node_name
if '.' in node_name or '+' in node_name:
self._has_bad_node_names = True
arg, name, retry = job_arg
# Add JOB line with Job submit file
job_line = 'JOB {} {}'.format(node_name, job.submit_file)
job_arg_lines.append(job_line)
# Add job ARGS line for command line arguments
arg_line = 'VARS {} ARGS="{}"'.format(node_name, arg)
job_arg_lines.append(arg_line)
# Define job_name variable if there are arg_names for job
if job._has_arg_names:
if name is not None:
job_name = node_name
else:
job_name = job.submit_name
job_name_line = 'VARS {} job_name="{}"'.format(node_name,
job_name)
job_arg_lines.append(job_name_line)
# Add retry line for Job
if retry is not None:
retry_line = 'Retry {} {}'.format(node_name, retry)
job_arg_lines.append(retry_line)
return job_arg_lines
def build(self, makedirs=True, fancyname=True):
"""Build and saves the submit file for Dagman
Parameters
----------
makedirs : bool, optional
If Job directories (e.g. error, output, log, submit) don't exist,
create them (default is ``True``).
fancyname : bool, optional
Appends the date and unique id number to error, log, output, and
submit files. For example, instead of ``dagname.submit`` the submit
file becomes ``dagname_YYYYMMD_id``. This is useful when running
several Dags/Jobs of the same name (default is ``True``).
Returns
-------
self : object
Returns self.
"""
if getattr(self, '_built', False):
self.logger.warning(
'{} submit file has already been built. '
'Skipping the build process...'.format(self.name))
return self
name = self._get_fancyname() if fancyname else self.name
submit_file = os.path.join(self.submit, '{}.submit'.format(name))
self.submit_file = submit_file
self.submit_name = name
checkdir(self.submit_file, makedirs)
# Build submit files for all nodes in self.nodes
# Note: nodes must be built before the submit file for self is built
for node_index, node in enumerate(self.nodes, start=1):
if isinstance(node, Job):
node._build_from_dag(makedirs, fancyname)
elif isinstance(node, Dagman):
node.build(makedirs, fancyname)
else:
raise TypeError('Nodes must be either a Job or Dagman object')
# Write dag submit file
self.logger.info('Building DAG submission file {}...'.format(
self.submit_file))
lines = []
parent_child_lines = []
for node_index, node in enumerate(self.nodes, start=1):
self.logger.info('Working on {} [{} of {}]'.format(node.name,
node_index, len(self.nodes)))
# Build the BaseNode submit file
if isinstance(node, Job):
# Add Job variables to Dagman submit file
job_arg_lines = self._get_job_arg_lines(node, fancyname)
lines.extend(job_arg_lines)
elif isinstance(node, Dagman):
subdag_string = _get_subdag_string(node)
lines.append(subdag_string)
else:
raise TypeError('Nodes must be either a Job or Dagman object')
# Add parent/child information, if necessary
if node.hasparents():
parent_child_string = _get_parent_child_string(node)
parent_child_lines.append(parent_child_string)
# Add any extra lines to submit file, if specified
if self.extra_lines:
lines.extend(self.extra_lines)
# Write lines to dag submit file
with open(submit_file, 'w') as dag:
dag.writelines('\n'.join(lines + ['\n#Inter-job dependencies'] +
parent_child_lines))
self._built = True
self.logger.info('Dagman submission file for {} successfully '
'built!'.format(self.name))
return self
@requires_command('condor_submit_dag')
def submit_dag(self, submit_options=None):
"""Submits Dagman to condor
Parameters
----------
submit_options : str, optional
Options to be passed to ``condor_submit_dag`` for this Dagman
(see the `condor_submit_dag documentation
<http://research.cs.wisc.edu/htcondor/manual/current/condor_submit_dag.html>`_
for possible options).
Returns
-------
self : object
Returns self.
"""
# Construct condor_submit_dag command
command = 'condor_submit_dag'
if submit_options is not None:
command += ' {}'.format(submit_options)
command += ' {}'.format(self.submit_file)
submit_dag_proc = subprocess.Popen([command],
stdout=subprocess.PIPE,
shell=True)
# Check that there are no illegal node names for newer condor versions
condor_version = get_condor_version()
if condor_version >= (8, 7, 2) and self._has_bad_node_names:
err = ("Found an illegal character (either '+' or '.') in the "
"name for a node in Dagman {}. As of HTCondor version "
"8.7.2, '+' and '.' are prohibited in Dagman node names. "
"This means a '+' or '.' character is in a Job name, "
"Dagman name, or the name for a Job argument.".format(
self.name))
raise RuntimeError(err)
# Execute condor_submit_dag command
out, err = submit_dag_proc.communicate()
print(out)
return self
@requires_command('condor_submit_dag')
def build_submit(self, makedirs=True, fancyname=True, submit_options=None):
"""Calls build and submit sequentially
Parameters
----------
makedirs : bool, optional
If Job directories (e.g. error, output, log, submit) don't exist,
create them (default is ``True``).
fancyname : bool, optional
Appends the date and unique id number to error, log, output, and
submit files. For example, instead of ``dagname.submit`` the submit
file becomes ``dagname_YYYYMMD_id``. This is useful when running
several Dags/Jobs of the same name (default is ``True``).
submit_options : str, optional
Options to be passed to ``condor_submit_dag`` for this Dagman
(see the `condor_submit_dag documentation
<http://research.cs.wisc.edu/htcondor/manual/current/condor_submit_dag.html>`_
for possible options).
Returns
-------
self : object
Returns self.
"""
self.build(makedirs, fancyname)
self.submit_dag(submit_options=submit_options)
return self
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] | 2.207223 | 5,815 |
"""
Module containing a numpy-like array which supports lazy reading of tiled 2D-image data.
"""
import abc
import dask.array as da
import numpy as np
class LazyArray:
"""
An abstract class of a numpy-like array which supports lazy reading of tiled 2D-image data.
The class represents a custom array container which is compatible with the numpy API.
For more details please refer to
https://numpy.org/doc/stable/user/basics.dispatch.html#writing-custom-array-containers.
The class is compatible with napari's image layer which expects a "numpy-like array" as
input which supports indexing and can be converted to a numpy array via np.asarray.
(ref: https://napari.org/tutorials/fundamentals/image.html#image-data-and-numpy-like-arrays)
"""
__metaclass__ = abc.ABCMeta
def __init__(self, shape, dtype, tile_size):
"""
Initialization method.
:param shape: The shape of the underlying array.
:param dtype: The type of the underlying array.
:param tile_size: The size of a single tile by which the image is divided.
"""
assert len(shape) == 2
self.shape = shape
self.dtype = dtype
self.tile_size = tile_size
self.ndim = 2
@property
def size(self):
"""
The number of elements in the array.
"""
return self.shape[0] * self.shape[1]
def __array__(self, dtype=None, **kwargs):
# pylint: disable=W0613
"""
Method used e.g. by numpy to obtain a standard numpy.ndarray.
"""
return np.asarray(self[0:self.shape[0], 0:self.shape[1]])
def __getitem__(self, idx):
"""
Method which implements the support for basic slicing.
It does not support field access nor advanced indexing.
Moreover, the start and stop of a slice must be positive integers.
This method is optimized for the napari viewer.
napari calls self[:] for obtaining the shape, dtype and ndim attributes - not the data.
To delay reading the underlying data this method does not return a numpy array
but self when calling self[:].
To access the underlying data napari calls np.asarray(self).
"""
if not (
isinstance(idx, slice) or
(isinstance(idx, tuple) and all(isinstance(i, slice) for i in idx))
):
raise ValueError("LazyArray only supports indexing by slices!")
if (
idx == slice(None, None, None) or
idx == (slice(None, None, None), slice(None, None, None))
):
return self
if len(idx) != 2:
raise Exception("Unsupported index!")
(y_min, y_max), (x_min, x_max) = [(i.start, i.stop) for i in idx]
y_off = y_min - (y_min % self.tile_size)
x_off = x_min - (x_min % self.tile_size)
assert (y_min >= 0) and (y_max >= 0) and (x_min >= 0) & (x_max >= 0)
if y_max % self.tile_size == 0:
max_y_tiles = (y_max // self.tile_size)
else:
max_y_tiles = (y_max // self.tile_size) + 1
if x_max % self.tile_size == 0:
max_x_tiles = (x_max // self.tile_size)
else:
max_x_tiles = (x_max // self.tile_size) + 1
dask_arrays = []
for y_tile in range(y_min // self.tile_size, max_y_tiles):
row_tiles = []
for x_tile in range(x_min // self.tile_size, max_x_tiles):
row_tiles.append(
da.from_delayed(
self.read_tile(y_tile, x_tile),
shape=(self.tile_size, self.tile_size), dtype=np.uint8
)
)
dask_arrays.append(row_tiles)
y_max = min(y_max, self.shape[0])
x_max = min(x_max, self.shape[1])
return da.block(dask_arrays)[y_min-y_off:y_max-y_off, x_min-x_off:x_max-x_off]
@abc.abstractmethod
def read_tile(self, y_tile, x_tile):
"""
Abstract method which reads a tile at the position (y_tile, x_tile).
"""
return
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] | 2.209539 | 1,866 |
from django.core.management.base import BaseCommand
from core.datatools.fail_repeat import FailRepeater
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] | 3.655172 | 29 |
from typing import Dict
import requests
from config.env import starhubtvplus_app_key, starhubtvplus_client_uuid
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#
# Import section
#
import numpy
from syned.beamline.beamline_element import BeamlineElement
from syned.beamline.element_coordinates import ElementCoordinates
from wofry.propagator.propagator import PropagationManager, PropagationElements, PropagationParameters
from wofry.propagator.wavefront1D.generic_wavefront import GenericWavefront1D
from wofryimpl.propagator.propagators1D.fresnel import Fresnel1D
from wofryimpl.propagator.propagators1D.fresnel_convolution import FresnelConvolution1D
from wofryimpl.propagator.propagators1D.fraunhofer import Fraunhofer1D
from wofryimpl.propagator.propagators1D.integral import Integral1D
from wofryimpl.propagator.propagators1D.fresnel_zoom import FresnelZoom1D
from wofryimpl.propagator.propagators1D.fresnel_zoom_scaling_theorem import FresnelZoomScaling1D
#
# SOURCE========================
#
#
# BEAMLINE========================
#
#
# MAIN FUNCTION========================
#
#
# MAIN========================
#
# main()
if __name__ == "__main__":
from orangecontrib.esrf.wofry.util.tally import TallyCoherentModes, Tally
from oasys.util.oasys_util import get_fwhm
from srxraylib.plot.gol import plot
#
#
#
# size_at_aperture = 565e-6
APERTURE = [40.3e-6, 85.1e-6, 145e-6, 1000e-6, -40.3e-6, -85.1e-6, -145e-6, -1000e-6] # [ 5000e-6] # [-40.3e-6, -85.1e-6, -145e-6, -1000e-6] #
DISTANCE = numpy.linspace(10, 50, 50) # numpy.array([18.4]) # # # 31.19 28.4
number_of_points = 800 # 800
for aperture in APERTURE:
# src1, wf = main(aperture=aperture, distance=18.4168, number_of_points=number_of_points)
filename = "aperture_h_%g.dat" % (1e6 * aperture) #<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
f = open(filename, 'w')
f.write("# S 1 scored data\n")
f.write("# N 5\n")
f.write("# L distance fwhm total_intensity on_axis_intensity peak_intensity")
if aperture < 0:
aperture *= -1
nmodes = 1
else:
nmodes = 10
for i,distance in enumerate(DISTANCE):
tally = main(aperture=aperture, distance=distance, nmodes=nmodes)
spectral_density = tally.get_spectral_density() # numpy.zeros_like(abscissas)
abscissas = tally.get_abscissas()
fwhm, quote, coordinates = get_fwhm(spectral_density, 1e6 * abscissas)
I = spectral_density
x = abscissas
fwhm, quote, coordinates = get_fwhm(I, x)
intensity_at_center = I[I.size // 2]
intensity_total = I.sum() * (x[1] - x[0])
intensity_peak = I.max()
#<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
# plot(1e6 * abscissas, spectral_density,
# legend=["From Cross Spectral Density"],
# xtitle="x [um]", ytitle="Spectral Density", title="D=%g m,FWHM = %g um, a=%g um" % (distance, fwhm, aperture*1e6))
f.write("\n %g %g %g %g %g " % (distance, fwhm, intensity_total, intensity_at_center, intensity_peak))
f.close()
print("File %s written to disk" % filename)
# tally.save("aperture_h_%g.dat" % (aperture))
# main()
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] | 2.184335 | 1,481 |
import unittest
from models import Marker # for Marker.bounding_box_query
import datetime
# This tests year 2014 accidents as this is the current example git data for testing
# Once this changes to another year or to the current year's accidents (as should be) un-comment lines 11,13,15
# and change both 2014 and 2015 to: %s
class TestQueryFilters(unittest.TestCase):
"""
# cyear = str(datetime.datetime.now().strftime("%Y"))
global start_date
start_date = "01/01/2014" # % cyear
global end_date
end_date = "01/01/2015" # % str(int(cyear)-1)
"""
if __name__ == '__main__':
unittest.main()
suite = unittest.TestLoader().loadTestsFromTestCase(TestQueryFilters)
unittest.TextTestRunner(verbosity=2).run(suite)
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] | 2.778182 | 275 |
# Copyright 2021 Google LLC
#
# 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.
from absl.testing import absltest
from learner.brains import tensor_nest
import tensorflow as tf
class TensorNestTest(absltest.TestCase):
"""Tests for the tensor_nest module."""
def test_batch_size_valid_nest(self):
"""Get the batch size of a nest of tensors with the same batch size."""
nest = {
'a': {
'b': tf.constant([[1, 2, 3], [4, 5, 6]]),
'c': tf.constant([[7, 8, 9, 10], [11, 12, 13, 14]])
},
}
self.assertEqual(2, tensor_nest.batch_size(nest))
def test_batch_size_invalid_nest(self):
"""Get the batch size of a nest of tensors with different batch sizes."""
nest = {
'a': {
'b': tf.constant([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),
'c': tf.constant([[7, 8, 9, 10], [11, 12, 13, 14]])
},
}
self.assertRaisesRegex(
tensor_nest.MismatchedBatchSizeError,
'Tensors found in nest with mismatched batch sizes: {\'a\'.*}',
tensor_nest.batch_size, nest)
def test_batch_size_empty_nest(self):
"""Get the batch size of an empty tensor nest."""
self.assertIsNone(tensor_nest.batch_size({}))
def test_concatenate_batched(self):
"""Test the concatenation of a set of batched tensor nests."""
nests = [
{
'a': {
'b': tf.constant([[1, 2], [3, 4]]),
'c': tf.constant([[9, 8, 7], [6, 5, 4]]),
},
},
{
'a': {
'b': tf.constant([[5, 6]]),
'c': tf.constant([[3, 2, 1]]),
},
},
]
expected = {
'a': {
'b': tf.constant([[1, 2], [3, 4], [5, 6]]),
'c': tf.constant([[9, 8, 7], [6, 5, 4], [3, 2, 1]]),
},
}
tf.nest.assert_same_structure(tensor_nest.concatenate_batched(nests),
expected, expand_composites=True)
if __name__ == '__main__':
absltest.main()
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220,
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395,
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3419,
198
] | 2.170121 | 1,158 |
""" Longest Palindromic Subsequence
Given a string s, find the longest palindromic subsequence's length in s.
A subsequence is a sequence that can be derived from another sequence by
deleting some or no elements without changing the order of the remaining elements.
- Example 1:
- Input: s = "bbbab"
- Output: 4
- Explanation: One possible longest palindromic subsequence is "bbbb".
- Example 2:
- Input: s = "cbbd"
- Output: 2
- Explanation: One possible longest palindromic subsequence is "bb".
- Constraints:
- 1 <= s.length <= 1000
- s consists only of lowercase English letters.
"""
# A Dynamic Programming based Python
# program for LPS problem Returns the length
# of the longest palindromic subsequence in seq
# Driver program to test above functions
seq = "GEEKS FOR GEEKS"
n = len(seq)
print("The length of the LPS is " + str(lps(seq)))
# This code is contributed by Bhavya Jain | [
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318,
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449,
391
] | 3.244755 | 286 |
import statistics
data = [2.75, 1.75, 1.25, 0.25, 0.5, 1.25, 3.5]
print(statistics.mean(data)) # 平均
print(statistics.median(data)) # 中央値
print(statistics.variance(data)) # 標本標準分散
| [
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] | 1.826531 | 98 |
# Copyright 2021 Joshua Watt <[email protected]>
#
# SPDX-License-Identifier: MIT
| [
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from django.db import models
from django.contrib.auth.models import User
from ckeditor_uploader.fields import RichTextUploadingField
# Create your models here.
| [
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import os
from django import forms
from django.utils.translation import gettext_lazy as _
from wagtail.admin.widgets import AdminPageChooser
from wagtail.contrib.redirects.models import Redirect
from wagtail.models import Site
| [
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import numpy as np
import cv2
import copy
from Tkinter import *
from PIL import Image
from PIL import ImageTk
import tkFileDialog
root = Tk()
panelA = None
panelB = None
img = None
img2 = None
img3 = None
ConvolutionLabel = Label(root, text="Convolute").grid(row=0,column=0)
Conv00Entry = Entry(root, bd =5)
Conv01Entry = Entry(root, bd =5)
Conv02Entry = Entry(root, bd =5)
Conv10Entry = Entry(root, bd =5)
Conv11Entry = Entry(root, bd =5)
Conv12Entry = Entry(root, bd =5)
Conv20Entry = Entry(root, bd =5)
Conv21Entry = Entry(root, bd =5)
Conv22Entry = Entry(root, bd =5)
Conv00Entry.grid(row=1,column=0)
Conv01Entry.grid(row=1,column=1)
Conv02Entry.grid(row=1,column=2)
Conv10Entry.grid(row=2,column=0)
Conv11Entry.grid(row=2,column=1)
Conv12Entry.grid(row=2,column=2)
Conv20Entry.grid(row=3,column=0)
Conv21Entry.grid(row=3,column=1)
Conv22Entry.grid(row=3,column=2)
brightnessLabel = Label(root, text="Brightness").grid(row=4,column=0)
brightnessEntry = Entry(root, bd =5)
brightnessEntry.grid(row=4,column=1)
contrastLabel = Label(root, text="Contrast").grid(row=5,column=0)
contrastEntry = Entry(root, bd =5)
contrastEntry.grid(row=5,column=1)
zoomOutLabel = Label(root, text="ZoomOut").grid(row=6,column=0)
zoomOutXEntry = Entry(root, bd =5)
zoomOutXEntry.grid(row=6,column=1)
zoomOutYEntry = Entry(root, bd =5)
zoomOutYEntry.grid(row=6,column=2)
selectImageBtn = Button(root, text="Select an image", command=selectImage).grid(row=0,column=3)
horizontalBtn = Button(root, text ="Flip Horizontally", command = flipHorizontal).grid(row=1,column=3)
grayscaleBtn = Button(root, text ="Grayscale", command = grayscale).grid(row=2,column=3)
histogramBtn = Button(root, text ="Generate Histogram", command = generateHistogram).grid(row=3,column=3)
brightnessBtn = Button(root, text ="Change Brightness", command = changeBrightness).grid(row=4,column=3)
contrastBtn = Button(root, text ="Change Contrast", command = changeContrast).grid(row=5,column=3)
negativeBtn = Button(root, text ="Negative", command = negative).grid(row=6,column=3)
equalizeBtn = Button(root, text ="Equalize", command = equalize).grid(row=7,column=3)
zoomOutBtn = Button(root, text ="ZoomOut", command = zoomOut).grid(row=8,column=3)
zoomInBtn = Button(root, text ="ZoomIn", command = zoomIn).grid(row=9,column=3)
rotateClockWiseBtn = Button(root, text ="rotateClockWise", command = rotateClockWise).grid(row=10,column=3)
rotateAntiClockWiseBtn = Button(root, text ="rotateAntiClockWise", command = rotateAntiClockWise).grid(row=11,column=3)
convoluteBtn = Button(root, text ="Convolute", command = convolute).grid(row=12,column=3)
root.mainloop() | [
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] | 2.564453 | 1,024 |
#-------------------------------------------------------------------------------
# Post processing (color management) related Mari scripts
# coding: utf-8
# Copyright (c) 2011 The Foundry Visionmongers Ltd. All Rights Reserved.
#-------------------------------------------------------------------------------
import mari, time, PythonQt, os, math
QtGui = PythonQt.QtGui
QtCore = PythonQt.QtCore
ocio = mari.utils.ocio
##############################################################################################
GAIN_GROUP_MAX_WIDTH = 312
FSTOP_MAX_WIDTH = 50
EXPOSURE_MAX_WIDTH = 102
GAIN_MAX_WIDTH = 80
GAMMA_MAX_WIDTH = 200
TOOLBAR_SPACING = 3
toolbar = None
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
# Widgets:
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
# Metadata:
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
# External Connections:
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
# Filter:
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------
# Debugging:
#-----------------------------------------------------------------------------------------
##############################################################################################
# The following functions CAN'T be part of the toolbar class as a potential bug in PythonQt
# causes the disconnect function to fail
#-----------------------------------------------------------------------------------------
##############################################################################################
if mari.app.isRunning():
if not hasattr(mari.gl_render, 'createPostFilterCollection'):
ocio.printMessage(ocio.MessageType.ERROR, 'This version of Mari does not support the mari.gl_render.createPostFilterCollection API')
else:
if ocio.config_default is not None:
toolbar = OcioToolBar()
else:
# Destroy the OCIO post filter collection if present to prevent the user trying to use it.
filter_collection = mari.gl_render.findPostFilterCollection('Color Space')
if filter_collection is not None:
mari.gl_render.deletePostFilterCollection(filter_collection)
# Destroy the toolbar to prevent the user trying to use it.
mari.app.deleteToolBar('Color Space')
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13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1667,
72,
13,
1324,
13,
33678,
25391,
10374,
10786,
10258,
4687,
11537,
198
] | 6.726295 | 1,023 |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import shutil
from pathlib import Path
from libcst.testing.utils import UnitTest
from fixit.common.config import (
CACHE as CONFIG_CACHE,
get_lint_config,
get_rules_for_path,
)
from fixit.common.utils import (
dedent_with_lstrip,
DuplicateLintRuleNameError,
find_and_import_rule,
import_rule_from_package,
LintRuleNotFoundError,
)
DUMMY_PACKAGE: str = "fixit.common.tests.test_imports_dummy_package"
DUMMY_PACKAGE_PATH: Path = Path(__file__).parent / "test_imports_dummy_package"
DUPLICATE_DUMMY_PATH: Path = (
Path(__file__).parent / "test_imports_dummy_package_with_duplicate_rule"
)
# Using dummy config file, test whether the rule import helpers work as expected.
| [
2,
15069,
357,
66,
8,
3203,
11,
3457,
13,
290,
663,
29116,
13,
198,
2,
198,
2,
770,
2723,
2438,
318,
11971,
739,
262,
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1043,
287,
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198,
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24290,
2393,
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262,
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428,
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346,
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62,
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13,
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357,
198,
220,
220,
220,
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298,
62,
4480,
62,
75,
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11,
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220,
220,
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43,
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11,
198,
220,
220,
220,
1064,
62,
392,
62,
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62,
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11,
198,
220,
220,
220,
1330,
62,
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62,
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62,
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11,
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220,
220,
220,
406,
600,
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11,
198,
8,
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198,
35,
5883,
26708,
62,
47,
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25,
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796,
366,
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13,
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13,
9288,
62,
320,
3742,
62,
67,
13513,
62,
26495,
1,
198,
35,
5883,
26708,
62,
47,
8120,
11879,
62,
34219,
25,
10644,
796,
10644,
7,
834,
7753,
834,
737,
8000,
1220,
366,
9288,
62,
320,
3742,
62,
67,
13513,
62,
26495,
1,
198,
198,
35,
52,
31484,
6158,
62,
35,
5883,
26708,
62,
34219,
25,
10644,
796,
357,
198,
220,
220,
220,
10644,
7,
834,
7753,
834,
737,
8000,
1220,
366,
9288,
62,
320,
3742,
62,
67,
13513,
62,
26495,
62,
4480,
62,
646,
489,
5344,
62,
25135,
1,
198,
8,
198,
198,
2,
8554,
31548,
4566,
2393,
11,
1332,
1771,
262,
3896,
1330,
49385,
670,
355,
2938,
13,
628
] | 2.800623 | 321 |
"""
Announce addresses as they are received from other hosts
"""
import Queue
import state
from helper_random import randomshuffle
from network.assemble import assemble_addr
from network.connectionpool import BMConnectionPool
from queues import addrQueue
from threads import StoppableThread
class AddrThread(StoppableThread):
"""(Node) address broadcasting thread"""
name = "AddrBroadcaster"
| [
37811,
198,
18858,
8652,
9405,
355,
484,
389,
2722,
422,
584,
11453,
198,
37811,
198,
11748,
4670,
518,
198,
198,
11748,
1181,
198,
6738,
31904,
62,
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81,
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7,
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16818,
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220,
220,
220,
13538,
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8,
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37811,
198,
220,
220,
220,
1438,
796,
366,
4550,
81,
30507,
17970,
1,
198
] | 4.03 | 100 |
print("branches are fun") | [
4798,
7203,
1671,
12140,
389,
1257,
4943
] | 3.571429 | 7 |
from django import template
register = template.Library()
@register.filter(name='addcss')
| [
6738,
42625,
14208,
1330,
11055,
198,
198,
30238,
796,
11055,
13,
23377,
3419,
198,
198,
31,
30238,
13,
24455,
7,
3672,
11639,
2860,
25471,
11537,
628,
628
] | 3.518519 | 27 |
# -*- coding: utf-8 -*-
vars2d = [
'2m_temperature',
'10m_u_component_of_wind', '10m_v_component_of_wind',
'total_cloud_cover', 'total_precipitation',
'toa_incident_solar_radiation',
'temperature_850hPa',
]
vars3d = [
'geopotential', 'temperature',
'specific_humidity', 'relative_humidity',
'u_component_of_wind', 'v_component_of_wind',
'vorticity', 'potential_vorticity',
]
codes = {
'geopotential': 'z',
'temperature': 't',
'temperature_850hPa': 't',
'specific_humidity': 'q',
'relative_humidity': 'r',
'u_component_of_wind': 'u',
'v_component_of_wind': 'v',
'vorticity': 'vo',
'potential_vorticity': 'pv',
'2m_temperature': 't2m',
'10m_u_component_of_wind': 'u10',
'10m_v_component_of_wind': 'v10',
'total_cloud_cover': 'tcc',
'total_precipitation': 'tp',
'toa_incident_solar_radiation': 'tisr',
}
code2var = {
'z': 'geopotential',
't': 'temperature',
'q': 'specific_humidity',
'r': 'relative_humidity',
'u': 'u_component_of_wind',
'v': 'v_component_of_wind',
'vo': 'vorticity',
'pv': 'potential_vorticity',
't2m': '2m_temperature',
'u10': '10m_u_component_of_wind',
'v10': '10m_v_component_of_wind',
'tcc': 'total_cloud_cover',
'tp': 'total_precipitation',
'tisr': 'toa_incident_solar_radiation',
}
| [
2,
532,
9,
12,
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25,
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23,
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220,
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419,
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62,
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60,
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89,
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220,
220,
220,
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220,
220,
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62,
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71,
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83,
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198,
220,
220,
220,
705,
11423,
62,
17047,
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10354,
705,
80,
3256,
198,
220,
220,
220,
705,
43762,
62,
17047,
17995,
10354,
705,
81,
3256,
198,
220,
220,
220,
705,
84,
62,
42895,
62,
1659,
62,
7972,
10354,
705,
84,
3256,
198,
220,
220,
220,
705,
85,
62,
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62,
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62,
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85,
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198,
220,
220,
220,
705,
85,
419,
8467,
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705,
13038,
3256,
198,
220,
220,
220,
705,
13059,
1843,
62,
85,
419,
8467,
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705,
79,
85,
3256,
198,
220,
220,
220,
705,
17,
76,
62,
11498,
21069,
10354,
705,
83,
17,
76,
3256,
198,
220,
220,
220,
705,
940,
76,
62,
84,
62,
42895,
62,
1659,
62,
7972,
10354,
705,
84,
940,
3256,
198,
220,
220,
220,
705,
940,
76,
62,
85,
62,
42895,
62,
1659,
62,
7972,
10354,
705,
85,
940,
3256,
198,
220,
220,
220,
705,
23350,
62,
17721,
62,
9631,
10354,
705,
83,
535,
3256,
198,
220,
220,
220,
705,
23350,
62,
3866,
66,
541,
3780,
10354,
705,
34788,
3256,
198,
220,
220,
220,
705,
1462,
64,
62,
1939,
738,
62,
82,
6192,
62,
6335,
3920,
10354,
705,
48010,
81,
3256,
198,
92,
198,
198,
8189,
17,
7785,
796,
1391,
198,
220,
220,
220,
705,
89,
10354,
705,
469,
43372,
1843,
3256,
198,
220,
220,
220,
705,
83,
10354,
705,
11498,
21069,
3256,
198,
220,
220,
220,
705,
80,
10354,
705,
11423,
62,
17047,
17995,
3256,
198,
220,
220,
220,
705,
81,
10354,
705,
43762,
62,
17047,
17995,
3256,
198,
220,
220,
220,
705,
84,
10354,
705,
84,
62,
42895,
62,
1659,
62,
7972,
3256,
198,
220,
220,
220,
705,
85,
10354,
705,
85,
62,
42895,
62,
1659,
62,
7972,
3256,
198,
220,
220,
220,
705,
13038,
10354,
705,
85,
419,
8467,
3256,
198,
220,
220,
220,
705,
79,
85,
10354,
705,
13059,
1843,
62,
85,
419,
8467,
3256,
198,
220,
220,
220,
705,
83,
17,
76,
10354,
705,
17,
76,
62,
11498,
21069,
3256,
198,
220,
220,
220,
705,
84,
940,
10354,
705,
940,
76,
62,
84,
62,
42895,
62,
1659,
62,
7972,
3256,
198,
220,
220,
220,
705,
85,
940,
10354,
705,
940,
76,
62,
85,
62,
42895,
62,
1659,
62,
7972,
3256,
198,
220,
220,
220,
705,
83,
535,
10354,
705,
23350,
62,
17721,
62,
9631,
3256,
198,
220,
220,
220,
705,
34788,
10354,
705,
23350,
62,
3866,
66,
541,
3780,
3256,
198,
220,
220,
220,
705,
48010,
81,
10354,
705,
1462,
64,
62,
1939,
738,
62,
82,
6192,
62,
6335,
3920,
3256,
198,
92,
628
] | 2.08589 | 652 |
# -*- coding: utf-8 -*-
"""
StepPy
:copyright: (c) 2016-2017 by Yann Gravrand.
:license: BSD, see LICENSE for more details.
"""
from collections import OrderedDict
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
37811,
198,
220,
220,
220,
5012,
20519,
198,
220,
220,
220,
1058,
22163,
4766,
25,
357,
66,
8,
1584,
12,
5539,
416,
575,
1236,
32599,
25192,
13,
198,
220,
220,
220,
1058,
43085,
25,
347,
10305,
11,
766,
38559,
24290,
329,
517,
3307,
13,
198,
37811,
198,
198,
6738,
17268,
1330,
14230,
1068,
35,
713,
628
] | 2.542857 | 70 |
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