content stringlengths 1 1.05M | input_ids listlengths 1 883k | ratio_char_token float64 1 22.9 | token_count int64 1 883k |
|---|---|---|---|
import unittest
import dace
import numpy as np
from dace.transformation.dataflow import MapTiling, OutLocalStorage
N = dace.symbol('N')
if __name__ == '__main__':
unittest.main()
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... | 2.764706 | 68 |
# Licensed under a 3-clause BSD style license - see PYFITS.rst
import gzip
import os
from .base import _BaseHDU, BITPIX2DTYPE
from .hdulist import HDUList
from .image import PrimaryHDU
from astropy.io.fits.file import _File
from astropy.io.fits.header import _pad_length
from astropy.io.fits.util import fileobj_name
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import django
from django.test import TestCase
from django.template import Template, Context
def render(template_string, context_dict=None):
"""
A shortcut for testing template output.
"""
if context_dict is None:
context_dict = {}
c = Context(context_dict)
t = Template(template_string)
return t.render(c).strip()
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116... | 2.622378 | 143 |
# Copyright (C) 2016 Google Inc.
# Licensed under http://www.apache.org/licenses/LICENSE-2.0 <see LICENSE file>
"""A module containing the workflow TaskGroup model."""
from sqlalchemy import or_
from ggrc import db
from ggrc.login import get_current_user
from ggrc.models.associationproxy import association_proxy
from ggrc.models.mixins import (
Titled, Slugged, Described, Timeboxed, WithContact
)
from ggrc.models.reflection import AttributeInfo
from ggrc.models.reflection import PublishOnly
from ggrc.models import all_models
from ggrc_workflows.models.task_group_object import TaskGroupObject
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import pytest
import rumps
from src.app_functions.menu.change_auto_login import change_auto_login
def test_setting_is_true(mocker, basic_app):
"""Check if setting is changed correctly if True"""
basic_app.settings["auto_login"] = True
mock_function = mocker.patch("src.app_functions.menu.change_auto_login.update_menu")
mocker.patch("src.app_functions.menu.change_auto_login.save_settings")
change_auto_login(basic_app)
assert basic_app.settings["auto_login"] is False
mock_function.assert_called_once_with(basic_app)
def test_setting_is_false(mocker, basic_app):
"""Check if setting is changed correctly if false"""
basic_app.settings["auto_login"] = False
mock_function = mocker.patch("src.app_functions.menu.change_auto_login.update_menu")
mocker.patch("src.app_functions.menu.change_auto_login.save_settings")
change_auto_login(basic_app)
assert basic_app.settings["auto_login"] is True
mock_function.assert_called_once_with(basic_app)
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# -*- coding: utf-8 -*-
"""VGG 19 architecture for CIFAR-100."""
import tensorflow as tf
from ._vgg import _vgg
from ..datasets.cifar100 import cifar100
from .testproblem import TestProblem
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year = int(input())
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"""Contains utility functions."""
BIN_MODE_ARGS = {'mode', 'buffering', }
TEXT_MODE_ARGS = {'mode', 'buffering', 'encoding', 'errors', 'newline'}
def split_args(args):
"""Splits args into two groups: open args and other args.
Open args are used by ``open`` function. Other args are used by
``load``/``dump`` functions.
Args:
args: Keyword args to split.
Returns:
open_args: Arguments for ``open``.
other_args: Arguments for ``load``/``dump``.
"""
mode_args = BIN_MODE_ARGS if 'b' in args['mode'] else TEXT_MODE_ARGS
open_args = {}
other_args = {}
for arg, value in args.items():
if arg in mode_args:
open_args[arg] = value
else:
other_args[arg] = value
return open_args, other_args
def read_wrapper(load, **base_kwargs):
"""Wraps ``load`` function to avoid context manager boilerplate.
Args:
load: Function that takes the return of ``open``.
**base_kwargs: Base arguments that ``open``/``load`` take.
Returns:
Wrapper for ``load``.
"""
return wrapped
def write_wrapper(dump, **base_kwargs):
"""Wraps ``dump`` function to avoid context manager boilerplate.
Args:
dump: Function that takes the return of ``open`` and data to dump.
**base_kwargs: Base arguments that ``open``/``dump`` take.
Returns:
Wrapper for ``dump``.
"""
return wrapped
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... | 2.55536 | 569 |
import asyncio
import functools
import time
import weakref
from collections import defaultdict
from typing import AsyncIterable
from typing import Awaitable
from typing import Callable
from typing import Dict
from typing import List
from typing import Optional
from typing import TypeVar
T = TypeVar("T")
# NOTE: this method is not thread-safe due to lack of locking while checking
# and updating the cache
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#!/usr/bin/python
''' generate dataset '''
import csv
import argparse
import numpy as np
import sklearn.metrics
import theanets
from sklearn.metrics import accuracy_score
import logging
from trendStrategy import OptTrendStrategy, TrendStrategy
from util import visu
def load_dataset(stock, ratio=0.8, name=OptTrendStrategy.__name__):
''' return train, valid (x,y) '''
orders = np.loadtxt("{0}_{1}_orders.csv".format(stock, name), usecols=[1], delimiter=',')
orders[orders==-1]=0
features = np.loadtxt("{0}_input.csv".format(stock), delimiter=',')
if len(orders)!=len(features):
logging.error("len(orders)!=len(features) -> %s!=%s" %(len(orders),len(features)))
features = features.astype('f')
orders = orders.astype('i')
pos = round(len(features)*ratio)
train = (features[:pos], orders[:pos])
valid = (features[pos:], orders[pos:])
return train, valid
if __name__ == "__main__":
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument('--stock', '-s', default="TSLA", help='stock')
parser.add_argument('--ratio', '-r', default=0.8, type=int, help='train/valid ratio')
parser.add_argument('--min', '-m', default=0.001, type=int, help='min improvement (stop learning)')
parser.add_argument('--field', default='orders', help='compare field')
args = parser.parse_args()
if args.field:
compare(args.stock, args.field)
train, valid = load_dataset(args.stock)
exp = train_strategy(args.stock, args.ratio, args.min)
exp = load_strategy(args.stock, True)
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# This file is part of the SPORCO package. Details of the copyright
# and user license can be found in the 'LICENSE.txt' file distributed
# with the package.
"""
Basis Pursuit DeNoising
=======================
This example demonstrates the use of class :class:`.admm.bpdn.BPDN` to solve the Basis Pursuit DeNoising (BPDN) problem :cite:`chen-1998-atomic`
$$\mathrm{argmin}_\mathbf{x} \; (1/2) \| D \mathbf{x} - \mathbf{s} \|_2^2 + \lambda \| \mathbf{x} \|_1 \;,$$
where $D$ is the dictionary, $\mathbf{x}$ is the sparse representation, and $\mathbf{s}$ is the signal to be represented. In this example the BPDN problem is used to estimate the reference sparse representation that generated a signal from a noisy version of the signal.
"""
from __future__ import print_function
from builtins import input
import numpy as np
from sporco.admm import bpdn
from sporco import util
from sporco import plot
"""
Configure problem size, sparsity, and noise level.
"""
N = 512 # Signal size
M = 4*N # Dictionary size
L = 32 # Number of non-zero coefficients in generator
sigma = 0.5 # Noise level
"""
Construct random dictionary, reference random sparse representation, and test signal consisting of the synthesis of the reference sparse representation with additive Gaussian noise.
"""
# Construct random dictionary and random sparse coefficients
np.random.seed(12345)
D = np.random.randn(N, M)
x0 = np.zeros((M, 1))
si = np.random.permutation(list(range(0, M-1)))
x0[si[0:L]] = np.random.randn(L, 1)
# Construct reference and noisy signal
s0 = D.dot(x0)
s = s0 + sigma*np.random.randn(N,1)
"""
Set BPDN solver class options.
"""
opt = bpdn.BPDN.Options({'Verbose': False, 'MaxMainIter': 500,
'RelStopTol': 1e-3, 'AutoRho': {'RsdlTarget': 1.0}})
"""
Select regularization parameter $\lambda$ by evaluating the error in recovering the sparse representation over a logarithmicaly spaced grid. (The reference representation is assumed to be known, which is not realistic in a real application.) A function is defined that evalues the BPDN recovery error for a specified $\lambda$, and this function is evaluated in parallel by :func:`sporco.util.grid_search`.
"""
# Function computing reconstruction error at lmbda
# Parallel evalution of error function on lmbda grid
lrng = np.logspace(1, 2, 20)
sprm, sfvl, fvmx, sidx = util.grid_search(evalerr, (lrng,))
lmbda = sprm[0]
print('Minimum 1 error: %5.2f at = %.2e' % (sfvl, lmbda))
"""
Once the best $\lambda$ has been determined, run BPDN with verbose display of ADMM iteration statistics.
"""
# Initialise and run BPDN object for best lmbda
opt['Verbose'] = True
b = bpdn.BPDN(D, s, lmbda, opt)
x = b.solve()
print("BPDN solve time: %.2fs" % b.timer.elapsed('solve'))
"""
Plot comparison of reference and recovered representations.
"""
plot.plot(np.hstack((x0, x)), title='Sparse representation',
lgnd=['Reference', 'Reconstructed'])
"""
Plot lmbda error curve, functional value, residuals, and rho
"""
its = b.getitstat()
fig = plot.figure(figsize=(15, 10))
plot.subplot(2, 2, 1)
plot.plot(fvmx, x=lrng, ptyp='semilogx', xlbl='$\lambda$',
ylbl='Error', fig=fig)
plot.subplot(2, 2, 2)
plot.plot(its.ObjFun, xlbl='Iterations', ylbl='Functional', fig=fig)
plot.subplot(2, 2, 3)
plot.plot(np.vstack((its.PrimalRsdl, its.DualRsdl)).T,
ptyp='semilogy', xlbl='Iterations', ylbl='Residual',
lgnd=['Primal', 'Dual'], fig=fig)
plot.subplot(2, 2, 4)
plot.plot(its.Rho, xlbl='Iterations', ylbl='Penalty Parameter', fig=fig)
fig.show()
# Wait for enter on keyboard
input()
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# This file was generated automatically by the Snowball to Python compiler
# http://snowballstem.org/
from .basestemmer import BaseStemmer
from .among import Among
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# -*- coding: utf-8 -*-
# =================================================================
# uspec
#
# Copyright (c) 2020 Takahide Nogayama
#
# This software is released under the MIT License.
# http://opensource.org/licenses/mit-license.php
# =================================================================
from __future__ import unicode_literals, print_function, division
import unittest
import uspec
from uspec import describe, context, it
###################################
with describe("Game", test_class=TestGame):
assert test_class is TestGame
assert TestGame is not None
##################################
TEST_CLASS_NAME_GAME2 = None
with describe("Game2"):
TEST_CLASS_NAME_GAME2 = test_class.__name__
assert TEST_CLASS_NAME_GAME2 in globals()
##################################
wrap()
assert TEST_CLASS_NAME_GAME3 in globals()
if __name__ == '__main__':
import unittest
unittest.main(verbosity=2)
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... | 3.494505 | 273 |
import pygame
from game.game_logic.game import Game
import matplotlib.pyplot as plt
if __name__ == "__main__":
main()
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... | 2.717391 | 46 |
import os
import sys
from glob import glob
if __name__=="__main__":
main() | [
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#!/usr/bin/env python
# coding: utf-8
# In[1]:
# src: http://datareview.info/article/prognozirovanie-ottoka-klientov-so-scikit-learn/
# In[ ]:
# -,
#
# .
# ,
# ,
# ( 5 20 ).
# :
# 1. ,
# ,
# 2. ,
# ,
# .
# 3. A ,
# , .
# ,
# , ,
# , , ,
# .
# In[ ]:
# datset src: https://raw.githubusercontent.com/michaelulin/churn/master/work/churn_model/data/churn.csv
# In[88]:
# Load libraries
import matplotlib.pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline')
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score, confusion_matrix, precision_recall_fscore_support
from sklearn.model_selection import KFold, train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
# In[3]:
# Load dataset
raw_churn_df = pd.read_csv('churn.csv')
# In[17]:
display(raw_churn_df.shape)
display(raw_churn_df.head(), raw_churn_df.tail())
display(raw_churn_df.columns.values)
display(raw_churn_df.dtypes)
display(raw_churn_df.isnull().sum())
# In[78]:
# Isolate target data
y = raw_churn_df['Churn?']
X = raw_churn_df.drop('Churn?', axis=1)
# In[79]:
# Drop irrelevant features
features_to_drop = ['State', 'Area Code', 'Phone']
X = X.drop(features_to_drop, axis=1)
# In[80]:
# Encode yes/no with 1/0 values
X["Int'l Plan"] = X["Int'l Plan"].map({'no': 0, 'yes': 1})
X["VMail Plan"] = X["VMail Plan"].map({'no': 0, 'yes': 1})
# In[81]:
# Scale everything
std_scaler = StandardScaler(with_mean=True)
X = std_scaler.fit_transform(X)
display(X.shape)
# In[90]:
# Perform CV for SVM, random forest and kNN
try_clf(X, y, SVC(gamma='scale'))
try_clf(X, y, RandomForestClassifier(n_estimators=100, n_jobs=-1))
try_clf(X, y, KNeighborsClassifier())
# std scaler with_mean=False accuracies:
# 0.9256594724220624
# 0.9484412470023981
# 0.8896882494004796
# std scaler with_mean=True accuracies:
# 0.9256594724220624
# 0.9496402877697842
# 0.8896882494004796
# In[86]:
# Recall
#
# ?
# Precision
#
# ?
# In[101]:
# # Predict probabilities
# def try_probab(X, y, clf_nofit):
# X_tr, X_val, y_tr, y_val = train_test_split(X, y, random_state=42)
# clf = clf_nofit.fit(X_tr, y_tr)
# y_prob = clf.predict_proba(X_val)
# # for i in range(len(X)):
# # display("y_true={0}, Predicted={1}".format(y[i], y_prob[i]))
# display(pd.value_counts(y_prob[:, 1]))
# try_probab(X, y, SVC(gamma='scale', probability=True))
# # try_probab(X, y, RandomForestClassifier(n_estimators=100, n_jobs=-1))
# # try_probab(X, y, KNeighborsClassifier())
# # for i in range(len(Xnew)):
# # print("X=%s, Predicted=%s" % (Xnew[i], ynew[i]))
# In[ ]:
# todo: calibration and discrimination
# https://github.com/ghuiber/churn/blob/master/churn_measurements.py
# from churn_measurements import calibration, discrimination
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4... | 2.185501 | 1,407 |
# MIT License
#
# Copyright (c) 2020 Airbyte
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
from __future__ import annotations
from typing import List, Optional
from pydantic import BaseModel, Extra, Field
| [
2,
17168,
13789,
198,
2,
198,
2,
15069,
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66,
8,
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2,
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357,
... | 3.846395 | 319 |
"""
A simple python api wrapper for https://opentdb.com/
"""
from aiohttp import ClientSession
from requests import get
from pytrivia.__helpers import decode_dict, get_token, make_request
from pytrivia.enums import *
| [
37811,
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32,
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3... | 3.384615 | 65 |
import requests
import tarfile
import os | [
11748,
7007,
198,
11748,
13422,
7753,
198,
11748,
28686
] | 4.444444 | 9 |
import shlex
from os import path
from itertools import imap, ifilter
from urlparse import urljoin
from .css import CSSParser, iter_events
def get_spritemap_url(self, fname):
"Get output image URL for spritemap *fname*."
return self.absurl(path.relpath(fname, self.root))
def get_css_out(self, fname):
"Get output image filename for spritemap directory *fname*."
(dirn, base) = path.split(fname)
if "output_css" in self._data:
(base, ext) = path.splitext(base)
names = dict(filename=fname, dirname=dirn,
basename=base, extension=ext)
return self.normpath(self._data["output_css"].format(**names))
else:
return path.join(dirn, "sm_" + base)
def print_config(fname):
from pprint import pprint
from .css import CSSParser
with open(fname, "rb") as fp:
print "%s\n%s\n" % (fname, "=" * len(fname))
pprint(dict(iter_css_config(CSSParser.read_file(fp))))
print
def main():
import sys
for fn in sys.argv[1:]:
print_config(fn)
if __name__ == "__main__":
main()
| [
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... | 2.196154 | 520 |
""" A bar graph.
(c) September 2017 by Daniel Seita
"""
import argparse
from collections import defaultdict
from keras.models import Sequential
from keras.layers import Dense, Activation
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import sys
np.set_printoptions(suppress=True, linewidth=200)
# Some matplotlib settings.
plt.style.use('seaborn-darkgrid')
titlesize = 21
labelsize = 17
legendsize = 15
ticksize = 15
bar_width = 0.80
opacity = 1.0
error_config = {'ecolor': '0.0', 'linewidth':3.0}
def deprecated():
"""
This is a deprecated method, only to show how to possibly combine these into
one plot. However, I find this unwieldly.
"""
fig, ax = plt.subplots()
bar_width = 0.80
opacity = 0.5
error_config = {'ecolor': '0.3'}
rects1 = plt.bar(np.array([0,1]), means_lin, bar_width,
alpha=opacity,
color='b',
yerr=std_lin,
error_kw=error_config,
label='Lin')
rects2 = plt.bar(np.array([3,4,5,6,7]), means_rfs, bar_width,
alpha=opacity,
color='r',
yerr=std_rfs,
error_kw=error_config,
label='RF')
rects3 = plt.bar(np.array([9,10]), means_dnn, bar_width,
alpha=opacity,
color='y',
yerr=std_dnn,
error_kw=error_config,
label='DNN')
plt.xticks(np.arange(11) + bar_width / 2,
('A','B','','D','E','F','G','','','J','K'))
plt.xlabel('Group')
plt.ylabel('Scores')
plt.title('Scores by group and gender')
plt.tight_layout()
plt.legend()
plt.savefig('figures/validation_set_results.png')
if __name__ == "__main__":
pp = argparse.ArgumentParser()
pp.add_argument('--version', type=int)
pp.add_argument('--kfolds', type=int, default=10)
args = pp.parse_args()
assert args.version is not None
VERSION = str(args.version).zfill(2)
file_name = 'results/results_kfolds10_v'+VERSION+'.npy'
results = np.load(file_name)[()]
print("results has keys: {}".format(results.keys()))
plot(results, VERSION)
| [
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2... | 2.042895 | 1,119 |
#!/usr/bin/env python
from setuptools import find_packages, setup
import os
import re
ROOT = os.path.dirname(__file__)
VERSION_RE = re.compile(r'''__version__ = \'([0-9.]+)\'''')
setup(
name='groceries-api',
version=get_version(),
license='MIT',
packages=find_packages(),
include_package_data=True,
install_requires=[
'alembic==0.7.5.post2',
'APScheduler==3.1.0',
'Flask==0.10.1',
'Flask-Cors==2.0.0',
'Flask-SQLAlchemy==2.0',
'gunicorn==19.3.0',
'psycopg2==2.6.1',
'PyJWT==1.1.0',
'requests==2.8.1',
'six==1.9.0',
],
extras_require={
'dev': {
'coverage==3.7.1',
'coveralls==0.5',
'flake8==2.4.0',
'mock==1.0.1',
'pytest==2.7.0',
'tox==2.1.1',
},
},
)
| [
2,
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13,
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13,
15908,
3672,
7,
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7753,
834,
8,
198,
... | 1.728916 | 498 |
from direct.showbase.ShowBase import *
from direct.interval.IntervalGlobal import *
from toontown.battle.BattleProps import *
from direct.distributed.ClockDelta import *
from direct.showbase.PythonUtil import Functor
from direct.showbase.PythonUtil import StackTrace
from direct.gui.DirectGui import *
from panda3d.core import *
from libotp import *
from direct.fsm import FSM
from direct.fsm import ClassicFSM
from direct.fsm import State
from direct.directnotify import DirectNotifyGlobal
from toontown.toonbase import ToontownGlobals
from toontown.toonbase import ToontownBattleGlobals
import DistributedBossCog
from toontown.toonbase import TTLocalizer
import SuitDNA
from toontown.toon import Toon
from toontown.battle import BattleBase
from direct.directutil import Mopath
from direct.showutil import Rope
from toontown.distributed import DelayDelete
from toontown.battle import MovieToonVictory
from toontown.building import ElevatorUtils
from toontown.battle import RewardPanel
from toontown.toon import NPCToons
from direct.task import Task
import random
import math
from toontown.coghq import CogDisguiseGlobals
from toontown.building import ElevatorConstants
from toontown.toonbase import ToontownTimer
OneBossCog = None
| [
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13... | 3.431755 | 359 |
# SPDX-License-Identifier: Apache-2.0
"""Unit Tests for custom rnns."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
from tensorflow.python.ops import init_ops
from backend_test_base import Tf2OnnxBackendTestBase
from common import * # pylint: disable=wildcard-import, unused-wildcard-import
from tf2onnx.tf_loader import is_tf2
# pylint: disable=missing-docstring,invalid-name,unused-argument,using-constant-test
# pylint: disable=abstract-method,arguments-differ
if is_tf2():
BasicLSTMCell = tf.compat.v1.nn.rnn_cell.BasicLSTMCell
LSTMCell = tf.compat.v1.nn.rnn_cell.LSTMCell
GRUCell = tf.compat.v1.nn.rnn_cell.GRUCell
RNNCell = tf.compat.v1.nn.rnn_cell.RNNCell
MultiRNNCell = tf.compat.v1.nn.rnn_cell.MultiRNNCell
dynamic_rnn = tf.compat.v1.nn.dynamic_rnn
bidirectional_dynamic_rnn = tf.compat.v1.nn.bidirectional_dynamic_rnn
else:
LSTMBlockCell = tf.contrib.rnn.LSTMBlockCell
LSTMCell = tf.nn.rnn_cell.LSTMCell
GRUCell = tf.nn.rnn_cell.LSTMCell
RNNCell = tf.nn.rnn_cell.RNNCell
MultiRNNCell = tf.contrib.rnn.MultiRNNCell
dynamic_rnn = tf.nn.dynamic_rnn
bidirectional_dynamic_rnn = tf.nn.bidirectional_dynamic_rnn
if __name__ == '__main__':
unittest_main()
| [
2,
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55,
12,
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25,
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834,
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62,
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198,
6738,
11593,
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834,
1330,
... | 2.359155 | 568 |
#!/user/bin/env python3
# -*- coding: utf-8 -*-
#!/user/bin/env python3
# -*- coding: utf-8 -*-
# @Author: Kevin Brgisser
# @Email:kevin.buergisser@edu.hefr.ch
# @Date: 04.2020
# Context: CHARMPROJECT- Harzard perception
"""
Module documentation.
"""
# Imports
import sys
#import os
# Global variables
# Class declarations
# Function declarations
# Main body
if __name__ == '__main__':
main() | [
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9,
... | 2.579618 | 157 |
import numpy
| [
11748,
299,
32152,
628
] | 3.5 | 4 |
import traceback
try:
raise Exception('This is the error message.')
except:
errorFile = open('./Chapter 10/errorInfo.txt', 'w')
errorFile.write(traceback.format_exc())
errorFile.close()
print('The traceback info was written to errorInfo.txt') | [
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... | 2.977273 | 88 |
# Databricks notebook source
from pyspark.sql.types import *
from pyspark.sql import functions as F
import base64
import array
# COMMAND ----------
# s is a base64 encoded float[] with first element being the magnitude
# Register udf functions so that it could be used in dataframe
#
# Perform same computation as cosineSimilarity()
#
# COMMAND ----------
# MAGIC %md **NetworkSimilarity** class to compute Network Similarity
# COMMAND ----------
# Parameters:
# resource: resource stream path
# container: container name in Azure Storage (AS) account
# account: Azure Storage (AS) account
# sas: complete 'Blob service SAS URL' of the shared access signature (sas) for the container
# key: access key for the container, if sas is specified, key is ignored
#
# Note:
# resource does not have header
# you need to provide value for either sas or key
#
class NetworkSimilarity(AzureStorageAccess):
# constructor
def __init__(self, resource, container, account, sas='', key=''):
AzureStorageAccess.__init__(self, container, account, sas, key)
schema = StructType()
schema.add(StructField('EntityId', LongType(), False))
schema.add(StructField('EntityType', StringType(), False))
schema.add(StructField('Data', StringType(), False))
self.df = spark.read.format('csv').options(header='false', delimiter='\t').schema(schema).load(self.getFullpath(resource))
def getDataframe(self):
return self.df
def raiseErrorIfNotFound(self, row, e):
if row is None:
raise KeyError('entity ' + str(e) + ' not found')
| [
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4... | 3.184739 | 498 |
print("Press q to quit")
quit = False
while quit is False:
in_val = input("Please enter a positive integer.\n > ")
if in_val is 'q':
quit = True
elif int(in_val) % 3 == 0 and int(in_val) % 5 == 0:
print("FizzBuzz")
elif int(in_val) % 5 == 0:
print("Buzz")
elif int(in_val) % 3 == 0:
print("Fizz")
else:
pass
| [
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22... | 2.083799 | 179 |
from lesson14_projects.pen.data.const import (
A,
E_A,
E_AN,
E_IS,
E_OVER,
E_PEN,
E_PIN,
E_THAT,
E_THIS,
E_WAS,
INIT,
IS,
PEN,
THIS,
)
pen_transition_doc_v19 = {
"title": "This is a pen",
"entry_state": INIT,
"data": {
INIT: {
E_OVER: [INIT],
E_THAT: [INIT],
E_THIS: [INIT, THIS],
THIS: {
E_OVER: [INIT],
E_WAS: [INIT],
E_IS: [INIT, THIS, IS],
IS: {
E_OVER: [INIT],
E_AN: [INIT],
E_A: [INIT, THIS, IS, A],
A: {
E_OVER: [INIT],
E_PIN: [INIT],
E_PEN: [PEN],
},
},
},
},
PEN: {
E_OVER: None,
},
},
}
| [
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11,
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220,
220,
220,
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62,
32,
11,
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220,
220,
220,
412,
62,
1565,
11,
198,
220,
220,
220,
412,
62,
1797,
11,
198,
220,
2... | 1.38003 | 671 |
import gd,os,time
from Html import Animation_Html
from Iteration import Animation_Iteration
from Write import Animation_Write
from Base import *
from Canvas2 import *
from Canvas2 import Canvas2
from Image import Image
from HTML import HTML
__Canvas__=None
| [
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1680... | 3.513514 | 74 |
#! /usr/bin/env python3
from .base_miner import BasePostGradientMiner
import torch
from ..utils import loss_and_miner_utils as lmu
# adapted from
# https://github.com/chaoyuaw/incubator-mxnet/blob/master/example/gluon/
# /embedding_learning/model.py
| [
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62,
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30... | 2.75 | 92 |
# -*- coding: utf-8 -*-
from .Alarm.alarm import Alarm
from .DeliveryView.bolus import Bolus
from .DeliveryView.info import Info
from .DeliveryView.infusion import Infusion
from .DeliveryView.infusion_parameter import InfusionParameter
from .DeliveryView.priming import Priming
from .HardwareControl.motor import Motor
from .MenuSettings.device_report import DeviceReport
from .MenuSettings.history_log import HistoryLog
from .MenuSettings.infusion_setting import InfusionSetting
from .MenuSettings.maintenance import Maintenance
from .MenuSettings.safety_setting import SafetySetting
from .MenuSettings.system_setting import SystemSetting
from .SensorControl.sensor import Sensor
__all__ = ["Alarm",
"Bolus",
"Info",
"Infusion",
"InfusionParameter",
"Priming",
"Motor",
"DeviceReport",
"HistoryLog",
"InfusionSetting",
"Maintenance",
"SafetySetting",
"SystemSetting",
"Sensor",
]
| [
2,
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9,
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25,
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12,
23,
532,
9,
12,
198,
198,
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2348,
1670,
13,
282,
1670,
1330,
978,
1670,
198,
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385,
1330,
10797,
385,
198,
6738,
764,
33129,
7680,
13,
10951,
1330,
... | 2.645408 | 392 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
| [
2,
20368,
22369,
12,
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2,
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66,
8,
5413,
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13,
1439,
2489,
10395,
13,
198,
2,
20368,
22369,
12,
628,
628
] | 7.625 | 24 |
from functools import partial
from pulsar import Connection, Pool, get_actor
from pulsar.utils.pep import to_string
from pulsar.apps.data import RemoteStore
from pulsar.apps.ds import redis_parser
from .client import RedisClient, Pipeline, Consumer, ResponseError
from .pubsub import RedisPubSub, RedisChannels
def client(self):
'''Get a :class:`.RedisClient` for the Store'''
return RedisClient(self)
def pipeline(self):
'''Get a :class:`.Pipeline` for the Store'''
return Pipeline(self)
def pubsub(self, protocol=None):
return RedisPubSub(self, protocol=protocol)
def channels(self, protocol=None, **kw):
return RedisChannels(self.pubsub(protocol=protocol), **kw)
def ping(self):
return self.client().ping()
def flush(self):
return self.execute('flushdb')
def close(self):
'''Close all open connections.'''
return self._pool.close()
def has_query(self, query_type):
return query_type in self.supported_queries
def basekey(self, meta, *args):
key = '%s%s' % (self.namespace, meta.table_name)
postfix = ':'.join((to_string(p) for p in args if p is not None))
return '%s:%s' % (key, postfix) if postfix else key
def meta(self, meta):
'''Extract model metadata for lua script stdnet/lib/lua/odm.lua'''
# indices = dict(((idx.attname, idx.unique) for idx in meta.indices))
data = meta.as_dict()
data['namespace'] = self.basekey(meta)
return data
class CompiledQuery:
def __init__(self, pipe, query):
self.pipe = pipe
| [
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1330,
21520,
... | 2.516923 | 650 |
# Generated by Django 3.0.7 on 2020-06-16 05:23
from django.db import migrations, models
import django.utils.timezone
| [
2,
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416,
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513,
13,
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13,
22,
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12131,
12,
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198,
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602,
11,
4981,
198,
11748,
42625,
14208,
13,
26791,
13,
2435,
11340,
628
] | 2.926829 | 41 |
# Copyright (c) 2019 - The Procedural Generation for Gazebo authors
# For information on the respective copyright owner see the NOTICE file
#
# 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 . import XMLBase
import collections
| [
2,
15069,
357,
66,
8,
13130,
532,
383,
23652,
1523,
16588,
329,
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198,
2,
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319,
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766,
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2393,
198,
2,
198,
2,
49962,
739,
262,
24843,
13789,
11,
10628,
362,
13,
15,
... | 4.005495 | 182 |
import mock
import pytest
from werkzeug.exceptions import NotFound
import app.main.helpers as helpers
from dmcontent.content_loader import ContentLoader
from dmtestutils.api_model_stubs import BriefStub, FrameworkStub, LotStub
content_loader = ContentLoader('tests/fixtures/content')
content_loader.load_manifest('dos', 'data', 'edit_brief')
questions_builder = content_loader.get_manifest('dos', 'edit_brief')
| [
11748,
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198,
6738,
288,
76,
11299,
13,
11299,
62,
29356,
1330,
14041,
1740... | 3.242188 | 128 |
import deephaven.TableTools as tt
import deephaven.Plot as plt
t = tt.emptyTable(50)\
.update("X = i + 5", "XLow = X -1", "XHigh = X + 1", "Y = Math.random() * 5", "YLow = Y - 1", "YHigh = Y + 1", "USym = i % 2 == 0 ? `AAPL` : `MSFT`")
p = plt.plot("S1", t, "X", "Y").lineColor("black").show()
p2 = plt.plot("S1", t, "X", "Y").plotStyle("bar").gradientVisible(True).show()
p3 = plt.plot("S1", t, "X", "Y").plotStyle("scatter").pointColor("black").pointSize(2).show()
p4 = plt.plot("S1", t, "X", "Y").plotStyle("area").seriesColor("red").show()
p4 = plt.plot3d("S1", t, "X", "X", "Y").show()
pBy = plt.plotBy("S1", t, "X", "Y", "USym").show()
pBy = plt.plot3dBy("S1", t, "X", "X", "Y", "USym").show()
cp = plt.catPlot("S1", t, "X", "Y").lineColor("black").show()
cp2 = plt.catPlot("S1", t, "X", "Y").plotStyle("bar").gradientVisible(True).show()
cp3 = plt.catPlot("S1", t, "X", "Y").plotStyle("scatter").pointColor("black").pointSize(2).show()
cp4 = plt.catPlot("S1", t, "X", "Y").plotStyle("area").seriesColor("red").show()
cp = plt.catPlot3d("S1", t, "X", "X", "Y").show()
cpBy = plt.catPlotBy("S1", t, "X", "Y", "USym").show()
cpBy = plt.catPlot3dBy("S1", t, "X", "X", "Y", "USym").show()
pp = plt.piePlot("S1", t, "X", "Y")
chp = plt.catHistPlot("S1", t, "X").show()
hp = plt.histPlot("S1", t, "X", 5).show()
hp = plt.histPlot("S1", t, "X", 0, 10, 5).show()
ep = plt.errorBarXY("S1", t, "X", "XLow", "XHigh", "Y", "YLow", "YHigh").show()
epBy = plt.errorBarXYBy("S1", t, "X", "XLow", "XHigh", "Y", "YLow", "YHigh", "USym").show()
ep2 = plt.errorBarX("S1", t, "X", "XLow", "XHigh", "Y").show()
epBy2 = plt.errorBarXBy("S1", t, "X", "XLow", "XHigh", "Y", "USym").show()
ep3 = plt.errorBarY("S1", t, "X", "Y", "YLow", "YHigh").show()
epBy3 = plt.errorBarYBy("S1", t, "X", "Y", "YLow", "YHigh", "USym").show()
doubles = [3, 4, 3, 5, 4, 5]
time = 1491946585000000000
t = tt.newTable(tt.col("USym", ["A", "B", "A", "B", "A", "B"]),
tt.doubleCol("Open", doubles), tt.doubleCol("High", doubles),
tt.doubleCol("Low", doubles), tt.doubleCol("Close", doubles))
t = t.updateView("Time = new DBDateTime(time + (MINUTE * i))")
ohlc = plt.ohlcPlot("Test1", t, "Time", "Open", "High", "Low", "Close")
ohlcPlotBy = plt.figure().newChart(0)\
.chartTitle("Chart Title")\
.newAxes()\
.xLabel("X")\
.yLabel("Y")\
.ohlcPlotBy("Test1", t, "Time", "Open", "High", "Low", "Close", "USym")
categories = ["Samsung", "Others", "Nokia", "Apple", "MSFT"]
valuesD = [27.8, 55.3, 16.8, 17.1, 23.1]
valuesI = [27, 55, 16, 17, 15]
ap = plt.plot("S1", valuesD, valuesI).show()
ap = plt.plot3d("S1", valuesI, valuesI, valuesI).show()
acp = plt.catPlot("S1", categories, valuesI).show()
acp2 = plt.catPlot3d("S1", categories, categories, valuesD).show()
achp = plt.catHistPlot("S1", categories).show()
app = plt.figure().xLabel("X").yLabel("Y").piePlot("S1", categories, valuesI).pointLabelFormat("{0}").show()
aep = plt.errorBarXY("S1", valuesD, valuesD, valuesD, valuesD, valuesD, valuesD).show()
aep2 = plt.errorBarX("S1", valuesD, valuesD, valuesD, valuesD).show()
aep3 = plt.errorBarY("S1", valuesD, valuesD, valuesD, valuesD).show()
hp = plt.histPlot("S1", valuesD, 5).show()
hp = plt.histPlot("S1", valuesD, 0, 10, 5).show()
hp = plt.histPlot("S1", valuesI, 5).show()
| [
11748,
2769,
39487,
13,
10962,
33637,
355,
256,
83,
198,
11748,
2769,
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458,
83,
628,
198,
83,
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83,
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19415,
198,
220,
220,
220,
764,
19119,
7203,
55,
796,
1312,
1343,
642,
1600... | 2.191573 | 1,519 |
# Copyright 2019 Arie Bregman
#
# 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 __future__ import absolute_import
from flask import current_app as app
from flask import render_template
from flask import url_for
import logging
LOG = logging.getLogger(__name__)
from rhoci.test import bp # noqa
| [
2,
15069,
13130,
317,
5034,
347,
2301,
805,
198,
2,
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2,
220,
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11,
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15341,
345,
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198,
2,
220,
220,
220,
407,
779,
428,
2393,
2845,
287,
11846... | 3.393443 | 244 |
from .read import (
read_request_head,
read_response_head,
connection_close,
expected_http_body_size,
validate_headers,
)
from .assemble import (
assemble_request, assemble_request_head,
assemble_response, assemble_response_head,
assemble_body,
)
__all__ = [
"read_request_head",
"read_response_head",
"connection_close",
"expected_http_body_size",
"validate_headers",
"assemble_request", "assemble_request_head",
"assemble_response", "assemble_response_head",
"assemble_body",
]
| [
6738,
764,
961,
1330,
357,
198,
220,
220,
220,
1100,
62,
25927,
62,
2256,
11,
198,
220,
220,
220,
1100,
62,
26209,
62,
2256,
11,
198,
220,
220,
220,
4637,
62,
19836,
11,
198,
220,
220,
220,
2938,
62,
4023,
62,
2618,
62,
7857,
11... | 2.6 | 210 |
# -*- coding: utf-8 -*-
# Generated by Django 1.10 on 2017-05-21 19:33
from __future__ import unicode_literals
from django.db import migrations, models
import django.db.models.deletion
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
2,
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515,
416,
37770,
352,
13,
940,
319,
2177,
12,
2713,
12,
2481,
678,
25,
2091,
198,
6738,
11593,
37443,
834,
1330,
28000,
1098,
62,
17201,
874,
198,
198,
6738,
... | 2.791045 | 67 |
#4 lines: Fibonacci, tuple assignment
parents, babies = (1, 1)
while babies < 100:
print ('This generation has {0} babies'.format(babies))
parents, babies = (babies, parents + babies) | [
2,
19,
3951,
25,
41566,
261,
44456,
11,
46545,
16237,
198,
23743,
11,
11903,
796,
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16,
11,
352,
8,
198,
4514,
11903,
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1802,
25,
198,
220,
220,
220,
3601,
19203,
1212,
5270,
468,
1391,
15,
92,
11903,
4458,
18982,
7,
65,
43... | 3.131148 | 61 |
import typing
from .core import Component
_Controller = typing.TypeVar('_Controller')
_ControllerType = typing.Type[_Controller]
ControllerFactory = typing.NewType('ControllerFactory', typing.Callable[[typing.Type], object])
_controller_factory: typing.Optional[ControllerFactory] = None
| [
11748,
19720,
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6738,
764,
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1330,
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198,
198,
62,
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198,
62,
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19720,
13,
6030,
29795,
22130,
60,
198,
22130,
22810,
796,
19720,
13,
3791,
6030... | 3.868421 | 76 |
# Copyright 2014 The Bazel Authors. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Public definitions for Go rules.
All public Go rules, providers, and other definitions are imported and
re-exported in this file. This allows the real location of definitions
to change for easier maintenance.
Definitions outside this file are private unless otherwise noted, and
may change without notice.
"""
load(
"//go/private:context.bzl",
_go_context = "go_context",
)
load(
"//go/private:providers.bzl",
_GoArchive = "GoArchive",
_GoArchiveData = "GoArchiveData",
_GoLibrary = "GoLibrary",
_GoPath = "GoPath",
_GoSDK = "GoSDK",
_GoSource = "GoSource",
)
load(
"//go/private/rules:sdk.bzl",
_go_sdk = "go_sdk",
)
load(
"//go/private:go_toolchain.bzl",
_declare_toolchains = "declare_toolchains",
_go_toolchain = "go_toolchain",
)
load(
"//go/private/rules:wrappers.bzl",
_go_binary_macro = "go_binary_macro",
_go_library_macro = "go_library_macro",
_go_test_macro = "go_test_macro",
)
load(
"//go/private/rules:source.bzl",
_go_source = "go_source",
)
load(
"//extras:embed_data.bzl",
_go_embed_data = "go_embed_data",
)
load(
"//go/private/tools:path.bzl",
_go_path = "go_path",
)
load(
"//go/private/rules:library.bzl",
_go_tool_library = "go_tool_library",
)
load(
"//go/private/rules:nogo.bzl",
_nogo = "nogo_wrapper",
)
# TOOLS_NOGO is a list of all analysis passes in
# golang.org/x/tools/go/analysis/passes.
# This is not backward compatible, so use caution when depending on this --
# new analyses may discover issues in existing builds.
TOOLS_NOGO = [
"@org_golang_x_tools//go/analysis/passes/asmdecl:go_default_library",
"@org_golang_x_tools//go/analysis/passes/assign:go_default_library",
"@org_golang_x_tools//go/analysis/passes/atomic:go_default_library",
"@org_golang_x_tools//go/analysis/passes/atomicalign:go_default_library",
"@org_golang_x_tools//go/analysis/passes/bools:go_default_library",
"@org_golang_x_tools//go/analysis/passes/buildssa:go_default_library",
"@org_golang_x_tools//go/analysis/passes/buildtag:go_default_library",
# TODO(#2396): pass raw cgo sources to cgocall and re-enable.
# "@org_golang_x_tools//go/analysis/passes/cgocall:go_default_library",
"@org_golang_x_tools//go/analysis/passes/composite:go_default_library",
"@org_golang_x_tools//go/analysis/passes/copylock:go_default_library",
"@org_golang_x_tools//go/analysis/passes/ctrlflow:go_default_library",
"@org_golang_x_tools//go/analysis/passes/deepequalerrors:go_default_library",
"@org_golang_x_tools//go/analysis/passes/errorsas:go_default_library",
"@org_golang_x_tools//go/analysis/passes/findcall:go_default_library",
"@org_golang_x_tools//go/analysis/passes/httpresponse:go_default_library",
"@org_golang_x_tools//go/analysis/passes/ifaceassert:go_default_library",
"@org_golang_x_tools//go/analysis/passes/inspect:go_default_library",
"@org_golang_x_tools//go/analysis/passes/loopclosure:go_default_library",
"@org_golang_x_tools//go/analysis/passes/lostcancel:go_default_library",
"@org_golang_x_tools//go/analysis/passes/nilfunc:go_default_library",
"@org_golang_x_tools//go/analysis/passes/nilness:go_default_library",
"@org_golang_x_tools//go/analysis/passes/pkgfact:go_default_library",
"@org_golang_x_tools//go/analysis/passes/printf:go_default_library",
"@org_golang_x_tools//go/analysis/passes/shadow:go_default_library",
"@org_golang_x_tools//go/analysis/passes/shift:go_default_library",
"@org_golang_x_tools//go/analysis/passes/sortslice:go_default_library",
"@org_golang_x_tools//go/analysis/passes/stdmethods:go_default_library",
"@org_golang_x_tools//go/analysis/passes/stringintconv:go_default_library",
"@org_golang_x_tools//go/analysis/passes/structtag:go_default_library",
"@org_golang_x_tools//go/analysis/passes/testinggoroutine:go_default_library",
"@org_golang_x_tools//go/analysis/passes/tests:go_default_library",
"@org_golang_x_tools//go/analysis/passes/unmarshal:go_default_library",
"@org_golang_x_tools//go/analysis/passes/unreachable:go_default_library",
"@org_golang_x_tools//go/analysis/passes/unsafeptr:go_default_library",
"@org_golang_x_tools//go/analysis/passes/unusedresult:go_default_library",
]
# Current version or next version to be tagged. Gazelle and other tools may
# check this to determine compatibility.
RULES_GO_VERSION = "0.30.0"
declare_toolchains = _declare_toolchains
go_context = _go_context
go_embed_data = _go_embed_data
go_sdk = _go_sdk
go_tool_library = _go_tool_library
go_toolchain = _go_toolchain
nogo = _nogo
# See go/providers.rst#GoLibrary for full documentation.
GoLibrary = _GoLibrary
# See go/providers.rst#GoSource for full documentation.
GoSource = _GoSource
# See go/providers.rst#GoPath for full documentation.
GoPath = _GoPath
# See go/providers.rst#GoArchive for full documentation.
GoArchive = _GoArchive
# See go/providers.rst#GoArchiveData for full documentation.
GoArchiveData = _GoArchiveData
# See go/providers.rst#GoSDK for full documentation.
GoSDK = _GoSDK
# See docs/go/core/rules.md#go_library for full documentation.
go_library = _go_library_macro
# See docs/go/core/rules.md#go_binary for full documentation.
go_binary = _go_binary_macro
# See docs/go/core/rules.md#go_test for full documentation.
go_test = _go_test_macro
# See docs/go/core/rules.md#go_test for full documentation.
go_source = _go_source
# See docs/go/core/rules.md#go_path for full documentation.
go_path = _go_path
| [
2,
15069,
1946,
383,
347,
41319,
46665,
13,
1439,
2489,
10395,
13,
198,
2,
198,
2,
49962,
739,
262,
24843,
13789,
11,
10628,
362,
13,
15,
357,
1169,
366,
34156,
15341,
198,
2,
345,
743,
407,
779,
428,
2393,
2845,
287,
11846,
351,
... | 2.596121 | 2,372 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import calendar
import csv
from datetime import datetime
import os
from flask_sqlalchemy import SQLAlchemy
from sqlalchemy import and_
from ..constants import CONST
from ..models import AccidentMarker
from ..utilities import init_flask, decode_hebrew, open_utf8
from ..import importmail
from xml.dom import minidom
import math
import requests
import logging
############################################################################################
# United.py is responsible for the parsing and deployment of "united hatzala" data to the DB
############################################################################################
PROVIDER_CODE = CONST.UNITED_HATZALA_CODE
TIME_ZONE = 2
# convert IMS hours code to hours
RAIN_DURATION_CODE_TO_HOURS = {"1": 6, "2": 12, "3": 18, "4": 24, "/": 24, "5": 1, "6": 2, "7": 3, "8": 9, "9": 15}
WEATHER = {"0": 1, "1": 2, "3": 3, "4": 4, "5": 5, "7": 6, "8": 6, "9": 7, "10": 8, "11": 9,
"12": 10, "17": 11, "18": 12, "19": 13, "20": 14, "21": 15, "22": 16, "23": 17, "24": 18,
"25": 19, "26": 20, "27": 21, "28": 22, "29": 23, "30": 24, "31": 24, "32": 24, "33": 7,
"34": 7, "35": 7, "36": 25, "37": 25, "38": 25, "39": 25, "40": 26, "41": 27, "42": 28,
"43": 29, "44": 9, "45": 30, "46": 30, "47": 30, "48": 31, "49": 32, "50": 33, "51": 34,
"52": 33, "53": 35, "54": 36, "55": 37, "56": 38, "57": 39, "58": 37, "59": 37, "61": 37, "60": 36,
"62": 40, "63": 15, "64": 41, "65": 19, "66": 42, "67": 43, "68": 44, "69": 45, "70": 46, "71": 47,
"72": 48, "73": 16, "74": 50, "75": 51, "76": 52, "77": 53, "78": 54, "79": 55, "80": 56, "81": 57,
"82": 58, "83": 59, "84": 60, "85": 61, "86": 62, "87": 63, "88": 64, "89": 65, "90": 66, "91": 67,
"92": 68, "93": 69, "94": 70, "95": 71, "96": 72, "97": 73, "98": 74, "99": 75}
def parse_date(created):
"""
:param created: Date & Time string from csv
:return: Python datetime object
"""
global time
global hour
DATE_FORMATS = ['%m/%d/%Y %I:%M:%S', '%Y-%m-%d %H:%M:%S', '%Y/%m/%d %I:%M:%S', '%d/%m/%Y %I:%M', '%Y/%m/%d %I:%M', '%m/%d/%Y %I:%M']
for date_format in DATE_FORMATS:
try:
if date_format == '%Y-%m-%d %H:%M:%S':
time = datetime.strptime(str(created)[:-4], date_format)
hour = time.strftime('%H')
hour = int(hour)
else:
time = datetime.strptime(str(created)[:-3], date_format)
hour = time.strftime('%H')
hour = int(hour) if str(created).endswith('AM') else int(hour) + 12
break
except ValueError:
pass
return datetime(time.year, time.month, time.day, hour, time.minute, 0)
CSVMAP = [
{"id": 0, "time": 1, "lat": 2, "long": 3, "street": 4, "city": 6, "comment": 7, "type": 8, "casualties": 9},
{"id": 0, "time": 1, "type": 2, "long": 3, "lat": 4, "city": 5, "street": 6, "comment": 7, "casualties": 8},
]
def import_to_db(collection, path):
"""
:param path: Local files directory ('united_path' on main() below)
:return: length of DB entries after execution
"""
app = init_flask()
db = SQLAlchemy(app)
accidents = list(create_accidents(collection, path))
if not accidents:
return 0
new_ids = [m["id"] for m in accidents
if 0 == db.session.query(AccidentMarker).filter(and_(AccidentMarker.id == m["id"],
AccidentMarker.provider_code == m["provider_code"])).count()]
if not new_ids:
logging.info("\t\tNothing loaded, all accidents already in DB")
return 0
db.session.execute(AccidentMarker.__table__.insert(), [m for m in accidents if m["id"] in new_ids])
db.session.commit()
return len(new_ids)
def update_db(collection):
"""
:return: length of DB entries after execution
"""
app = init_flask()
db = SQLAlchemy(app)
united = db.session.query(AccidentMarker).filter(AccidentMarker.provider_code == 2)
for accident in united:
if not accident.weather:
accident.weather = process_weather_data(collection, accident.latitude, accident.longitude)
db.session.commit()
logging.info("\tFinished commiting the changes")
def main(light=True, username='', password='', lastmail=False):
"""
Calls importmail.py prior to importing to DB
"""
collection = retrieve_ims_xml()
if not light:
logging.info("Importing data from mail...")
importmail.main(username, password, lastmail)
united_path = "static/data/united/"
total = 0
logging.info("Loading United accidents...")
for united_file in os.listdir(united_path):
if united_file.endswith(".csv"):
total += import_to_db(collection, united_path + united_file)
logging.info("\tImported {0} items".format(total))
update_db(collection)
| [
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198,
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532,
9,
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25,
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69,
12,
23,
532,
9,
12,
198,
11748,
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198,
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198,
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4818,
8079,
1330,
4818,
8079,
198,
11748,
28686,
198,
198,
6738,
42903,
... | 2.271087 | 2,217 |
import unittest
from numpy.testing import assert_array_equal
import numpy as np
from libact.base.dataset import Dataset
from libact.models import LogisticRegression
from libact.query_strategies import VarianceReduction
from .utils import run_qs
if __name__ == '__main__':
unittest.main()
| [
11748,
555,
715,
395,
198,
198,
6738,
299,
32152,
13,
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1330,
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... | 3.09375 | 96 |
from __future__ import unicode_literals
from __future__ import print_function
from __future__ import division
from __future__ import absolute_import
from builtins import open
from builtins import str
from future import standard_library
standard_library.install_aliases()
import os
import re
import json
import copy
import socket
import msgpack
import traceback
import types
import backoff
from datetime import datetime
from uuid import uuid4
from redis import BlockingConnectionPool, StrictRedis, RedisError
from celery.utils.log import get_task_logger
import hysds
from hysds.celery import app
from prov_es.model import get_uuid, ProvEsDocument
# logger
logger = get_task_logger(__name__)
# redis connection pools
JOB_STATUS_POOL = None
JOB_INFO_POOL = None
WORKER_STATUS_POOL = None
EVENT_STATUS_POOL = None
# job status key template
JOB_STATUS_KEY_TMPL = "hysds-job-status-%s"
# worker status key template
WORKER_STATUS_KEY_TMPL = "hysds-worker-status-%s"
# task worker key template
TASK_WORKER_KEY_TMPL = "hysds-task-worker-%s"
def backoff_max_value():
"""Return max value for backoff."""
return app.conf.BACKOFF_MAX_VALUE
def backoff_max_tries():
"""Return max tries for backoff."""
return app.conf.BACKOFF_MAX_TRIES
def hard_time_limit_gap():
"""Return minimum gap time after soft time limit."""
return app.conf.HARD_TIME_LIMIT_GAP
def ensure_hard_time_limit_gap(soft_time_limit, time_limit):
"""Ensure hard time limit gap."""
gap = hard_time_limit_gap()
if soft_time_limit is not None and (time_limit is None or
time_limit <= soft_time_limit+gap):
time_limit = soft_time_limit + gap
return soft_time_limit, time_limit
def set_redis_job_status_pool():
"""Set redis connection pool for job status."""
global JOB_STATUS_POOL
if JOB_STATUS_POOL is None:
JOB_STATUS_POOL = BlockingConnectionPool.from_url(
app.conf.REDIS_JOB_STATUS_URL)
def set_redis_job_info_pool():
"""Set redis connection pool for job info metrics."""
global JOB_INFO_POOL
if JOB_INFO_POOL is None:
JOB_INFO_POOL = BlockingConnectionPool.from_url(
app.conf.REDIS_JOB_INFO_URL)
def set_redis_worker_status_pool():
"""Set redis connection pool for worker status."""
global WORKER_STATUS_POOL
if WORKER_STATUS_POOL is None:
WORKER_STATUS_POOL = BlockingConnectionPool.from_url(
app.conf.REDIS_JOB_STATUS_URL)
def set_redis_event_status_pool():
"""Set redis connection pool for event status."""
global EVENT_STATUS_POOL
if EVENT_STATUS_POOL is None:
EVENT_STATUS_POOL = BlockingConnectionPool.from_url(
app.conf.REDIS_JOB_STATUS_URL)
def log_prov_es(job, prov_es_info, prov_es_file):
"""Log PROV-ES document. Create temp PROV-ES document to populate
attributes that only the worker has access to (e.g. PID)."""
# create PROV-ES doc to generate attributes that only verdi know
ps_id = "hysds:%s" % get_uuid(job['job_id'])
bundle_id = "hysds:%s" % get_uuid('bundle-%s' % job['job_id'])
doc = ProvEsDocument()
# get bundle
#bndl = doc.bundle(bundle_id)
bndl = None
# create sofware agent
sa_label = "hysds:pge_wrapper/%s/%d/%s" % (job['job_info']['execute_node'],
job['job_info']['pid'],
datetime.utcnow().isoformat())
sa_id = "hysds:%s" % get_uuid(sa_label)
doc.softwareAgent(sa_id, str(job['job_info']['pid']),
job['job_info']['execute_node'],
role=job.get('username', None),
label=sa_label, bundle=bndl)
# create processStep
doc.processStep(ps_id, job['job_info']['cmd_start'],
job['job_info']['cmd_end'], [], sa_id,
None, [], [], bundle=bndl,
prov_type="hysds:%s" % job['type'])
# get json
pd = json.loads(doc.serialize())
# update software agent and process step
if 'bundle' in prov_es_info:
if len(prov_es_info['bundle']) == 1:
bundle_id_orig = list(prov_es_info['bundle'].keys())[0]
# update software agent
prov_es_info['bundle'][bundle_id_orig].setdefault(
'agent', {}).update(pd['bundle'][bundle_id]['agent'])
# update wasAssociatedWith
prov_es_info['bundle'][bundle_id_orig].setdefault(
'wasAssociatedWith', {}).update(pd['bundle'][bundle_id]['wasAssociatedWith'])
# update activity
if 'activity' in prov_es_info['bundle'][bundle_id_orig]:
if len(prov_es_info['bundle'][bundle_id_orig]['activity']) == 1:
ps_id_orig = list(
prov_es_info['bundle'][bundle_id_orig]['activity'].keys())[0]
prov_es_info['bundle'][bundle_id_orig]['activity'][ps_id_orig][
'prov:startTime'] = pd['bundle'][bundle_id]['activity'][ps_id]['prov:startTime']
prov_es_info['bundle'][bundle_id_orig]['activity'][ps_id_orig][
'prov:endTime'] = pd['bundle'][bundle_id]['activity'][ps_id]['prov:endTime']
prov_es_info['bundle'][bundle_id_orig]['activity'][ps_id_orig]['hysds:job_id'] = job['job_id']
prov_es_info['bundle'][bundle_id_orig]['activity'][ps_id_orig]['hysds:job_type'] = job['type']
prov_es_info['bundle'][bundle_id_orig]['activity'][ps_id_orig]['hysds:job_url'] = job['job_info']['job_url']
prov_es_info['bundle'][bundle_id_orig]['activity'][ps_id_orig]['hysds:mozart_url'] = app.conf.MOZART_URL
if 'prov:type' not in prov_es_info['bundle'][bundle_id_orig]['activity'][ps_id_orig]:
prov_es_info['bundle'][bundle_id_orig]['activity'][ps_id_orig][
'prov:type'] = pd['bundle'][bundle_id]['activity'][ps_id]['prov:type']
# update wasAssociatedWith activity ids
for waw_id in prov_es_info['bundle'][bundle_id_orig]['wasAssociatedWith']:
if prov_es_info['bundle'][bundle_id_orig]['wasAssociatedWith'][waw_id]['prov:activity'] == ps_id:
prov_es_info['bundle'][bundle_id_orig]['wasAssociatedWith'][waw_id]['prov:activity'] = ps_id_orig
else:
prov_es_info['bundle'][bundle_id_orig]['activity'].update(
pd['bundle'][bundle_id]['activity'])
else:
prov_es_info['bundle'][bundle_id_orig]['activity'] = pd['bundle'][bundle_id]['activity']
else:
# update software agent
prov_es_info.setdefault('agent', {}).update(pd['agent'])
# update wasAssociatedWith
prov_es_info.setdefault('wasAssociatedWith', {}).update(
pd['wasAssociatedWith'])
# update process step
if 'activity' in prov_es_info:
if len(prov_es_info['activity']) == 1:
ps_id_orig = list(prov_es_info['activity'].keys())[0]
prov_es_info['activity'][ps_id_orig]['prov:startTime'] = pd['activity'][ps_id]['prov:startTime']
prov_es_info['activity'][ps_id_orig]['prov:endTime'] = pd['activity'][ps_id]['prov:endTime']
prov_es_info['activity'][ps_id_orig]['hysds:job_id'] = job['job_id']
prov_es_info['activity'][ps_id_orig]['hysds:job_type'] = job['type']
prov_es_info['activity'][ps_id_orig]['hysds:job_url'] = job['job_info']['job_url']
prov_es_info['activity'][ps_id_orig]['hysds:mozart_url'] = app.conf.MOZART_URL
if 'prov:type' not in prov_es_info['activity'][ps_id_orig]:
prov_es_info['activity'][ps_id_orig]['prov:type'] = pd['activity'][ps_id]['prov:type']
# update wasAssociatedWith activity ids
for waw_id in prov_es_info['wasAssociatedWith']:
if prov_es_info['wasAssociatedWith'][waw_id]['prov:activity'] == ps_id:
prov_es_info['wasAssociatedWith'][waw_id]['prov:activity'] = ps_id_orig
else:
prov_es_info['activity'].update(pd['activity'])
else:
prov_es_info['activity'] = pd['activity']
# write prov
with open(prov_es_file, 'w') as f:
json.dump(prov_es_info, f, indent=2)
def log_publish_prov_es(prov_es_info, prov_es_file, prod_path, pub_urls,
prod_metrics, objectid):
"""Log publish step in PROV-ES document."""
# create PROV-ES doc
doc = ProvEsDocument(namespaces=prov_es_info['prefix'])
# get bundle
#bndl = doc.bundle(bundle_id)
bndl = None
# add input entity
execute_node = socket.getfqdn()
prod_url = "file://%s%s" % (execute_node, prod_path)
input_id = "hysds:%s" % get_uuid(prod_url)
input_ent = doc.granule(input_id, None, [prod_url], [], None, None, None,
label=os.path.basename(prod_url), bundle=bndl)
# add output entity
output_id = "hysds:%s" % get_uuid(pub_urls[0])
output_ent = doc.product(output_id, None, [pub_urls[0]], [], None, None,
None, label=objectid, bundle=bndl)
# software and algorithm
algorithm = "eos:product_publishing"
software_version = hysds.__version__
software_title = "%s v%s" % (hysds.__description__, software_version)
software = "eos:HySDS-%s" % software_version
software_location = hysds.__url__
doc.software(software, [algorithm], software_version, label=software_title,
location=software_location, bundle=bndl)
# create sofware agent
pid = os.getpid()
sa_label = "hysds:publish_dataset/%s/%d/%s" % (execute_node, pid,
prod_metrics['time_start'])
sa_id = "hysds:%s" % get_uuid(sa_label)
doc.softwareAgent(sa_id, str(pid), execute_node, role="invoked",
label=sa_label, bundle=bndl)
# create processStep
job_id = "publish_dataset-%s" % os.path.basename(prod_path)
doc.processStep("hysds:%s" % get_uuid(job_id), prod_metrics['time_start'],
prod_metrics['time_end'], [software], sa_id, None,
[input_id], [output_id], label=job_id, bundle=bndl,
prov_type="hysds:publish_dataset")
# get json
pd = json.loads(doc.serialize())
# update input entity
orig_ent = prov_es_info.get('entity', {}).get(input_id, {})
pd['entity'][input_id].update(orig_ent)
# update output entity
for attr in orig_ent:
if attr in ('prov:location', 'prov:label', 'prov:type'):
continue
pd['entity'][output_id][attr] = orig_ent[attr]
# write prov
with open(prov_es_file, 'w') as f:
json.dump(pd, f, indent=2)
| [
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834,
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62,
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874,
198,
6738,
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834,
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3601,
62,
8818,
198,
6738,
11593,
37443,
834,
1330,
7297,
198,
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834,
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4112,
62,
11748,
628,
198,
6738,
3170,
1040,
1... | 2.093696 | 5,251 |
# Copyright 2015, Rackspace, US, 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.
"""API for the swift service.
"""
import os
from django import forms
from django.http import StreamingHttpResponse
from django.utils.http import urlunquote
from django.views.decorators.csrf import csrf_exempt
from django.views import generic
import six
from horizon import exceptions
from openstack_dashboard import api
from openstack_dashboard.api.rest import urls
from openstack_dashboard.api.rest import utils as rest_utils
from openstack_dashboard.api import swift
class UploadObjectForm(forms.Form):
file = forms.FileField(required=False)
| [
2,
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11,
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11,
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11,
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2,
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739,
262,
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13789,
11,
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362,
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15,
357,
1169,
366,
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15341,
198,
2,
345,
743,
407,
779,
428,
2393,
2845,
287,
11846,
351,
262,
1378... | 3.660256 | 312 |
'''Load image/class/box from a annotation file.
The annotation file is organized as:
image_name #obj xmin ymin xmax ymax class_index ..
'''
from __future__ import print_function
import os
import sys
import os.path
import random
import numpy as np
import torch
import torch.utils.data as data
import torchvision.transforms as transforms
from encoder import DataEncoder
from PIL import Image, ImageOps
| [
7061,
6,
8912,
2939,
14,
4871,
14,
3524,
422,
257,
23025,
2393,
13,
198,
198,
464,
23025,
2393,
318,
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355,
25,
198,
220,
220,
220,
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62,
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26801,
2124,
1084,
331,
1084,
2124,
9806,
331,
9806,
1398,
62,
9630,
11485... | 3.474576 | 118 |
# Lint as: python3
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""A library to build composite layers.
WARNING:
The builder pattern is still experimental and we need to gain experience
on when to use and when not to use.
Please discuss w/ teammates before using it to build complicated
layers.
"""
import functools
from lingvo.core import activations
from lingvo.core import builder_layers
from lingvo.core import hyperparams
from lingvo.core import layers
from lingvo.core import py_utils
from lingvo.core import tshape
######################################################################
# Layers to compose multiple layers.
#
# Sub-classes are discouraged to override these composition method.
######################################################################
def _Rep(self, name, repeat, *subs):
r"""Connects sub-layers sequentially and repeat multiple times.
E.g., _Rep('foo', 2, sa, sb, sc) constructs a layer with 6 layers
sequentially connected: [sa1, sb1, sc1, sa2, sb2, sc2]. sa1 and sa2 have
the same structure as the given sa, but sa1 and sa2 do not share the same
weight.
Args:
name: The layer name.
repeat: Repeat \*subs this many times in the compose layer.
*subs: A list of sub-layers.
Returns:
The param for the composed layer.
"""
iterations = []
for i in range(repeat):
iterations.append(self._Seq('iter_%03d' % i, *[p.Copy() for p in subs]))
return self._Seq(name, *iterations)
def _Seq(self, name, *subs):
"""Connects sub-layers sequentially."""
return builder_layers.SequentialLayer.Params().Set(
name=name, sub=list(subs))
def _Graph(self, name, input_endpoints, output_endpoints,
*signature_sub_param_list):
"""Connects sub-layers into a data flow graph."""
return builder_layers.GraphLayer.Params().Set(
name=name,
input_endpoints=input_endpoints,
output_endpoints=output_endpoints,
sub=list(signature_sub_param_list))
def _Id(self, name):
"""Identity. (t_1, ..., t_n) -> (t1, ..., t_n)."""
return self._Seq(name)
def _Arg(self, name, index):
"""Picks index-th element. (t_1, ..., t_n) -> (t_{index},)."""
return builder_layers.ArgIndexLayer.Params().Set(name=name, idx=[index])
def _Par(self, name, *subs):
"""y = (f1, f2, ..., fn)(x).
We feed the input tuple to all sub-layers and concatenates their output
tuples into one tuple.
Args:
name: The layer name.
*subs: A list of sub-layers.
Returns:
The param for the composed layer.
"""
return builder_layers.ParallelLayer.Params().Set(
name=name, sub=list(subs), merge=ConcatTuples, merge_meta=ConcatMeta)
def _Fn(self, name, fn, fn_out=None, fn_flops=None):
"""y = fn(x).
Applies a fn: tuple(Tensor) -> a single Tensor or tuple(Tensor) to the input
tuple. Typically, fn is a very simple python function. This layer can be
used for prototyping but we advice to implement the logic as a sub-class of
BaseLayer for all established layers as FnLayer can't be serialized.
Args:
name: The layer name.
fn: A lambda tuple(Tensor) -> tuple(Tensor).
fn_out: A lambda tuple(tshape.Shape) -> output tuple(tshape.Shape)
fn_flops: A lambda tuple(tshape.Shape) -> estimated flops of fn.
If None, we assume flops == sum of elements in the inputs.
Returns:
The param for the composed layer.
"""
def FnMeta(*shapes):
"""A lambda tuple(tshape.Shape) -> NestedMap{flops, out_shapes}."""
if fn_out:
out_shapes = fn_out(*shapes)
if isinstance(out_shapes, tshape.Shape):
out_shapes = (out_shapes,)
else:
out_shapes = shapes
if fn_flops:
flops = fn_flops(*shapes)
else:
flops = sum([s.size for s in shapes])
return py_utils.NestedMap(flops=flops, out_shapes=out_shapes)
return builder_layers.FnLayer.Params().Set(name=name, fn=fn, fn_meta=FnMeta)
def _Save(self, name):
"""Returns a layer from which the activation and gradient can be accessed."""
return layers.FetchLayer.Params().Set(name=name)
def _AddFetches(self, name, body, fetches):
"""Fetches saved activations in the body sub-layer.
E.g.:
_AddFetches('foo', _Seq( 'stack', _Layer('layer1', ...),
_Save('layer1_out', ...), _Layer('layer2', ...), _Save('layer2_out', ...),
_Output('output', ...)), ['layer1_out', 'layer2_out'])
The layer returns the stack's final output together with intermediate
activations from layer1_out and layer2_out.
Args:
name: This layer's name.
body: The sub-layer.
fetches: A list of fetch names inside the sub-layer body.
Returns:
A layer whose outputs correspond to the activations of fetch points
in the sub-layer body. [input1, input2, ..., inputN, fetch1, ..., fetchM].
"""
return builder_layers.BranchLayer.Params().Set(
name=name, body=body, fetches=fetches)
def _Rematerialize(self, name, body):
"""Forces rematerialization on FProp of the body layer."""
return builder_layers.RematerializationLayer.Params().Set(
name=name, body=body)
def _BatchParallel(self, name, sub):
"""Splits the batch and compute the forward pass on multiple devices.
Args:
name: This layer's name.
sub: The sub-layer.
Returns:
A BatchParallel layer which splits the batch and computes the forward pass
on multiple devices.
"""
return builder_layers.BatchParallelLayer.Params().Set(name=name, sub=sub)
def _PrintShape(self, name):
"""Print FProp input shape information."""
return builder_layers.PrintShapeLayer.Params().Set(name=name)
def _CreateNestedMap(self, name, keys):
"""Returns a NestedMap with keys from fprop args."""
return builder_layers.CreateNestedMapLayer.Params().Set(
name=name, keys=keys)
###########################################################################
# Basic nn layers.
#
# The following method returns a layer param, whose FProp takes a single
# Tensor and returns a single Tensor.
#
# These methods are designed to have minimal knobs. Sub-classes which needs to
# be flexible can override these methods with different options. E.g., a
# sub-class builder can override _BN() to tune the decay option.
###########################################################################
def _BN(self, name, dims):
"""Batch norm."""
return layers.BatchNormLayer.Params().Set(name=name, dim=dims, decay=0.99)
def _LN(self, name, dims, use_fused_layernorm=False):
"""Layer norm."""
return layers.LayerNorm.Params().Set(
name=name,
input_dim=dims,
use_fused_layernorm=use_fused_layernorm,
fprop_dtype=self.params.fprop_dtype)
def _Dropout(self, name, keep_prob, noise_shape_broadcast_dims=None):
"""Returns a DropoutLayer Params."""
if self.params.deterministic_dropout:
return layers.DeterministicDropoutLayer.Params().Set(
name=name,
keep_prob=keep_prob,
noise_shape_broadcast_dims=noise_shape_broadcast_dims)
return layers.DropoutLayer.Params().Set(
name=name,
keep_prob=keep_prob,
noise_shape_broadcast_dims=noise_shape_broadcast_dims,
fprop_dtype=self.params.fprop_dtype)
def _Linear(self,
name,
idims,
odims,
device_mesh=None,
weight_split_dims_mapping=None,
qdomain=None):
"""Linear layer. y = matmul([..., idims], [idims, odims])."""
p = builder_layers.LinearLayer.Params()
p.name = name
p.input_dims = idims
p.output_dims = odims
p.fprop_dtype = self.params.fprop_dtype
p.device_mesh = device_mesh
p.weight_split_dims_mapping = weight_split_dims_mapping
p.qdomain.default = qdomain
return p
def _Bias(self, name, dims, device_mesh=None, weight_split_dims_mapping=None):
"""Bias layer. The bias is added to the last dimension of the input."""
return builder_layers.BiasLayer.Params().Set(
name=name,
dims=dims,
fprop_dtype=self.params.fprop_dtype,
device_mesh=device_mesh,
weight_split_dims_mapping=weight_split_dims_mapping)
def _Activation(self, name, fn='RELU'):
"""Activation layer."""
return activations.ActivationLayer.Params().Set(activation=fn, name=name)
def _FC(self, name, idims, odims, act='RELU'):
"""Feed-forward fully connected. y = act(matmul(x, w) + b)."""
# pyformat: disable
return self._Seq(
name,
self._Linear('linear', idims, odims),
self._Bias('bias', odims),
self._Activation('act', fn=act))
def _MLP(self, name, dims, act='RELU'):
"""Multiple layers of feed-forward fully connected.
Args:
name: The layer name.
dims: A list of int. i-th layer has dims[i] as its input dimension, and
dims[i+1] as its output dimensions.
act: The activation function.
Returns:
The param for the composed layer.
"""
l = []
for n, (i, o) in enumerate(zip(dims[:-1], dims[1:])):
l += [self._FC('l%03d' % n, i, o, act)]
return self._Seq(name, *l)
def _Conv2D(self, name, filter_shape, filter_stride):
"""Conv2D layer."""
return layers.Conv2DLayerNoPadding.Params().Set(
name=name, filter_shape=filter_shape, filter_stride=filter_stride,
fprop_dtype=self.params.fprop_dtype)
def _Reshape(self, name, shape):
"""Reshape inputs to the shape provided."""
return builder_layers.ReshapeLayer.Params().Set(name=name,
shape=shape)
| [
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743... | 2.619623 | 3,975 |
# Copyright (c) 2010 by Cisco Systems, Inc.
"""
Manage the tool plugins and use them appropriately.
"""
import os
TOOLNAME_PLUGIN_PREFIX = "toolname"
| [
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8,
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1... | 3.081633 | 49 |
import time
from http import HTTPStatus
from itertools import count
from typing import Sequence
import gevent
import grequests
import pytest
import structlog
from eth_utils import to_canonical_address
from flask import url_for
from raiden.api.python import RaidenAPI
from raiden.api.rest import APIServer, RestAPI
from raiden.constants import RoutingMode
from raiden.message_handler import MessageHandler
from raiden.network.transport import MatrixTransport
from raiden.raiden_event_handler import RaidenEventHandler
from raiden.raiden_service import RaidenService
from raiden.settings import RestApiConfig
from raiden.tests.integration.api.utils import wait_for_listening_port
from raiden.tests.integration.fixtures.raiden_network import RestartNode
from raiden.tests.utils.detect_failure import raise_on_failure
from raiden.tests.utils.protocol import HoldRaidenEventHandler
from raiden.tests.utils.transfer import (
assert_synced_channel_state,
wait_assert,
watch_for_unlock_failures,
)
from raiden.transfer import views
from raiden.ui.startup import RaidenBundle
from raiden.utils.formatting import to_checksum_address
from raiden.utils.typing import (
Address,
BlockNumber,
Host,
Iterator,
List,
Port,
TokenAddress,
TokenAmount,
TokenNetworkAddress,
Tuple,
)
log = structlog.get_logger(__name__)
def iwait_and_get(items: Sequence[gevent.Greenlet]) -> None:
"""Iteratively wait and get on passed greenlets.
This ensures exceptions in the greenlets are re-raised as soon as possible.
"""
for item in gevent.iwait(items):
item.get()
def restart_network_and_apiservers(
raiden_network: List[RaidenService],
restart_node: RestartNode,
api_servers: List[APIServer],
port_generator: Iterator[Port],
) -> Tuple[List[RaidenService], List[APIServer]]:
"""Stop an app and start it back"""
for rest_api in api_servers:
rest_api.stop()
new_network = restart_network(raiden_network, restart_node)
new_servers = start_apiserver_for_network(new_network, port_generator)
return (new_network, new_servers)
def stress_send_serial_transfers(
rest_apis: List[APIServer],
token_address: TokenAddress,
identifier_generator: Iterator[int],
deposit: TokenAmount,
) -> None:
"""Send `deposit` transfers of value `1` one at a time, without changing
the initial capacity.
"""
pairs = list(zip(rest_apis, rest_apis[1:] + [rest_apis[0]]))
# deplete the channels in one direction
for server_from, server_to in pairs:
sequential_transfers(
server_from=server_from,
server_to=server_to,
number_of_transfers=deposit,
token_address=token_address,
identifier_generator=identifier_generator,
)
# deplete the channels in the backwards direction
for server_to, server_from in pairs:
sequential_transfers(
server_from=server_from,
server_to=server_to,
number_of_transfers=deposit * 2,
token_address=token_address,
identifier_generator=identifier_generator,
)
# reset the balances balances by sending the "extra" deposit forward
for server_from, server_to in pairs:
sequential_transfers(
server_from=server_from,
server_to=server_to,
number_of_transfers=deposit,
token_address=token_address,
identifier_generator=identifier_generator,
)
def stress_send_parallel_transfers(
rest_apis: List[APIServer],
token_address: TokenAddress,
identifier_generator: Iterator[int],
deposit: TokenAmount,
) -> None:
"""Send `deposit` transfers in parallel, without changing the initial capacity."""
pairs = list(zip(rest_apis, rest_apis[1:] + [rest_apis[0]]))
# deplete the channels in one direction
iwait_and_get(
[
gevent.spawn(
sequential_transfers,
server_from=server_from,
server_to=server_to,
number_of_transfers=deposit,
token_address=token_address,
identifier_generator=identifier_generator,
)
for server_from, server_to in pairs
]
)
# deplete the channels in the backwards direction
iwait_and_get(
[
gevent.spawn(
sequential_transfers,
server_from=server_from,
server_to=server_to,
number_of_transfers=deposit * 2,
token_address=token_address,
identifier_generator=identifier_generator,
)
for server_to, server_from in pairs
]
)
# reset the balances balances by sending the "extra" deposit forward
iwait_and_get(
[
gevent.spawn(
sequential_transfers,
server_from=server_from,
server_to=server_to,
number_of_transfers=deposit,
token_address=token_address,
identifier_generator=identifier_generator,
)
for server_from, server_to in pairs
]
)
def stress_send_and_receive_parallel_transfers(
rest_apis: List[APIServer],
token_address: TokenAddress,
identifier_generator: Iterator[int],
deposit: TokenAmount,
) -> None:
"""Send transfers of value one in parallel"""
pairs = list(zip(rest_apis, rest_apis[1:] + [rest_apis[0]]))
forward_transfers = [
gevent.spawn(
sequential_transfers,
server_from=server_from,
server_to=server_to,
number_of_transfers=deposit,
token_address=token_address,
identifier_generator=identifier_generator,
)
for server_from, server_to in pairs
]
backwards_transfers = [
gevent.spawn(
sequential_transfers,
server_from=server_from,
server_to=server_to,
number_of_transfers=deposit,
token_address=token_address,
identifier_generator=identifier_generator,
)
for server_to, server_from in pairs
]
iwait_and_get(forward_transfers + backwards_transfers)
| [
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198,
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198,
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198,
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... | 2.341255 | 2,693 |
# -*- coding: utf-8 -*-
# file: preprocess.py
# author: jackie
# Copyright (C) 2021. All Rights Reserved.
import os
import pandas as pd
import argparse
import emoji
import re
from sklearn.model_selection import train_test_split
parser = argparse.ArgumentParser()
parser.add_argument("--inpath", type=str, required=True, default='./raw_data/data1.csv')
parser.add_argument("--folder_name", type=str, required=False, default='./custom')
parser.add_argument("--task", type=str, required=False, default='aptepc')
args = parser.parse_args()
main(args.inpath, args.folder_name, args.task) | [
2,
532,
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12,
19617,
25,
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198,
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197... | 2.984848 | 198 |
import os
import shutil
import requests
| [
11748,
28686,
198,
11748,
4423,
346,
198,
198,
11748,
7007,
628,
628
] | 3.666667 | 12 |
from __future__ import absolute_import, division, print_function
from fnmatch import fnmatch
from glob import glob
import os
import uuid
from warnings import warn
import pandas as pd
from toolz import merge
from .io import _link
from ...base import get_scheduler
from ..core import DataFrame, new_dd_object
from ... import config, multiprocessing
from ...base import tokenize, compute_as_if_collection
from ...bytes.utils import build_name_function
from ...compatibility import PY3
from ...delayed import Delayed, delayed
from ...utils import get_scheduler_lock
def _pd_to_hdf(pd_to_hdf, lock, args, kwargs=None):
""" A wrapper function around pd_to_hdf that enables locking"""
if lock:
lock.acquire()
try:
pd_to_hdf(*args, **kwargs)
finally:
if lock:
lock.release()
return None
def to_hdf(df, path, key, mode='a', append=False, get=None, scheduler=None,
name_function=None, compute=True, lock=None, dask_kwargs={},
**kwargs):
""" Store Dask Dataframe to Hierarchical Data Format (HDF) files
This is a parallel version of the Pandas function of the same name. Please
see the Pandas docstring for more detailed information about shared keyword
arguments.
This function differs from the Pandas version by saving the many partitions
of a Dask DataFrame in parallel, either to many files, or to many datasets
within the same file. You may specify this parallelism with an asterix
``*`` within the filename or datapath, and an optional ``name_function``.
The asterix will be replaced with an increasing sequence of integers
starting from ``0`` or with the result of calling ``name_function`` on each
of those integers.
This function only supports the Pandas ``'table'`` format, not the more
specialized ``'fixed'`` format.
Parameters
----------
path: string
Path to a target filename. May contain a ``*`` to denote many filenames
key: string
Datapath within the files. May contain a ``*`` to denote many locations
name_function: function
A function to convert the ``*`` in the above options to a string.
Should take in a number from 0 to the number of partitions and return a
string. (see examples below)
compute: bool
Whether or not to execute immediately. If False then this returns a
``dask.Delayed`` value.
lock: Lock, optional
Lock to use to prevent concurrency issues. By default a
``threading.Lock``, ``multiprocessing.Lock`` or ``SerializableLock``
will be used depending on your scheduler if a lock is required. See
dask.utils.get_scheduler_lock for more information about lock
selection.
**other:
See pandas.to_hdf for more information
Examples
--------
Save Data to a single file
>>> df.to_hdf('output.hdf', '/data') # doctest: +SKIP
Save data to multiple datapaths within the same file:
>>> df.to_hdf('output.hdf', '/data-*') # doctest: +SKIP
Save data to multiple files:
>>> df.to_hdf('output-*.hdf', '/data') # doctest: +SKIP
Save data to multiple files, using the multiprocessing scheduler:
>>> df.to_hdf('output-*.hdf', '/data', scheduler='processes') # doctest: +SKIP
Specify custom naming scheme. This writes files as
'2000-01-01.hdf', '2000-01-02.hdf', '2000-01-03.hdf', etc..
>>> from datetime import date, timedelta
>>> base = date(year=2000, month=1, day=1)
>>> def name_function(i):
... ''' Convert integer 0 to n to a string '''
... return base + timedelta(days=i)
>>> df.to_hdf('*.hdf', '/data', name_function=name_function) # doctest: +SKIP
Returns
-------
None: if compute == True
delayed value: if compute == False
See Also
--------
read_hdf:
to_parquet:
"""
name = 'to-hdf-' + uuid.uuid1().hex
pd_to_hdf = getattr(df._partition_type, 'to_hdf')
single_file = True
single_node = True
# if path is string, format using i_name
if isinstance(path, str):
if path.count('*') + key.count('*') > 1:
raise ValueError("A maximum of one asterisk is accepted in file "
"path and dataset key")
fmt_obj = lambda path, i_name: path.replace('*', i_name)
if '*' in path:
single_file = False
else:
if key.count('*') > 1:
raise ValueError("A maximum of one asterisk is accepted in "
"dataset key")
fmt_obj = lambda path, _: path
if '*' in key:
single_node = False
if 'format' in kwargs and kwargs['format'] not in ['t', 'table']:
raise ValueError("Dask only support 'table' format in hdf files.")
if mode not in ('a', 'w', 'r+'):
raise ValueError("Mode must be one of 'a', 'w' or 'r+'")
if name_function is None:
name_function = build_name_function(df.npartitions - 1)
# we guarantee partition order is preserved when its saved and read
# so we enforce name_function to maintain the order of its input.
if not (single_file and single_node):
formatted_names = [name_function(i) for i in range(df.npartitions)]
if formatted_names != sorted(formatted_names):
warn("To preserve order between partitions name_function "
"must preserve the order of its input")
# If user did not specify scheduler and write is sequential default to the
# sequential scheduler. otherwise let the _get method choose the scheduler
if (get is None and
not config.get('get', None) and
scheduler is None and
not config.get('scheduler', None) and
single_node and single_file):
scheduler = 'single-threaded'
# handle lock default based on whether we're writing to a single entity
_actual_get = get_scheduler(get=get, collections=[df], scheduler=scheduler)
if lock is None:
if not single_node:
lock = True
elif not single_file and _actual_get is not multiprocessing.get:
# if we're writing to multiple files with the multiprocessing
# scheduler we don't need to lock
lock = True
else:
lock = False
if lock:
lock = get_scheduler_lock(get, df, scheduler=scheduler)
kwargs.update({'format': 'table', 'mode': mode, 'append': append})
dsk = dict()
i_name = name_function(0)
dsk[(name, 0)] = (_pd_to_hdf, pd_to_hdf, lock,
[(df._name, 0), fmt_obj(path, i_name),
key.replace('*', i_name)], kwargs)
kwargs2 = kwargs.copy()
if single_file:
kwargs2['mode'] = 'a'
if single_node:
kwargs2['append'] = True
filenames = []
for i in range(0,df.npartitions):
i_name = name_function(i)
filenames.append(fmt_obj(path, i_name))
for i in range(1, df.npartitions):
i_name = name_function(i)
task = (_pd_to_hdf, pd_to_hdf, lock,
[(df._name, i), fmt_obj(path, i_name),
key.replace('*', i_name)], kwargs2)
if single_file:
link_dep = i - 1 if single_node else 0
task = (_link, (name, link_dep), task)
dsk[(name, i)] = task
dsk = merge(df.dask, dsk)
if single_file and single_node:
keys = [(name, df.npartitions - 1)]
else:
keys = [(name, i) for i in range(df.npartitions)]
if compute:
compute_as_if_collection(DataFrame, dsk, keys, get=get,
scheduler=scheduler, **dask_kwargs)
return filenames
else:
return delayed([Delayed(k, dsk) for k in keys])
dont_use_fixed_error_message = """
This HDFStore is not partitionable and can only be use monolithically with
pandas. In the future when creating HDFStores use the ``format='table'``
option to ensure that your dataset can be parallelized"""
read_hdf_error_msg = """
The start and stop keywords are not supported when reading from more than
one file/dataset.
The combination is ambiguous because it could be interpreted as the starting
and stopping index per file, or starting and stopping index of the global
dataset."""
def _read_single_hdf(path, key, start=0, stop=None, columns=None,
chunksize=int(1e6), sorted_index=False, lock=None,
mode='a'):
"""
Read a single hdf file into a dask.dataframe. Used for each file in
read_hdf.
"""
def get_keys_stops_divisions(path, key, stop, sorted_index, chunksize):
"""
Get the "keys" or group identifiers which match the given key, which
can contain wildcards. This uses the hdf file identified by the
given path. Also get the index of the last row of data for each matched
key.
"""
with pd.HDFStore(path, mode=mode) as hdf:
keys = [k for k in hdf.keys() if fnmatch(k, key)]
stops = []
divisions = []
for k in keys:
storer = hdf.get_storer(k)
if storer.format_type != 'table':
raise TypeError(dont_use_fixed_error_message)
if stop is None:
stops.append(storer.nrows)
elif stop > storer.nrows:
raise ValueError("Stop keyword exceeds dataset number "
"of rows ({})".format(storer.nrows))
else:
stops.append(stop)
if sorted_index:
division = [storer.read_column('index', start=start, stop=start + 1)[0]
for start in range(0, storer.nrows, chunksize)]
division_end = storer.read_column('index',
start=storer.nrows - 1,
stop=storer.nrows)[0]
division.append(division_end)
divisions.append(division)
else:
divisions.append(None)
return keys, stops, divisions
def one_path_one_key(path, key, start, stop, columns, chunksize, division, lock):
"""
Get the data frame corresponding to one path and one key (which should
not contain any wildcards).
"""
empty = pd.read_hdf(path, key, mode=mode, stop=0)
if columns is not None:
empty = empty[columns]
token = tokenize((path, os.path.getmtime(path), key, start,
stop, empty, chunksize, division))
name = 'read-hdf-' + token
if empty.ndim == 1:
base = {'name': empty.name, 'mode': mode}
else:
base = {'columns': empty.columns, 'mode': mode}
if start >= stop:
raise ValueError("Start row number ({}) is above or equal to stop "
"row number ({})".format(start, stop))
dsk = dict(((name, i), (_pd_read_hdf, path, key, lock,
update(s)))
for i, s in enumerate(range(start, stop, chunksize)))
if division:
divisions = division
else:
divisions = [None] * (len(dsk) + 1)
return new_dd_object(dsk, name, empty, divisions)
keys, stops, divisions = get_keys_stops_divisions(path, key, stop, sorted_index, chunksize)
if (start != 0 or stop is not None) and len(keys) > 1:
raise NotImplementedError(read_hdf_error_msg)
from ..multi import concat
return concat([one_path_one_key(path, k, start, s, columns, chunksize, d, lock)
for k, s, d in zip(keys, stops, divisions)])
def _pd_read_hdf(path, key, lock, kwargs):
""" Read from hdf5 file with a lock """
if lock:
lock.acquire()
try:
result = pd.read_hdf(path, key, **kwargs)
finally:
if lock:
lock.release()
return result
def read_hdf(pattern, key, start=0, stop=None, columns=None,
chunksize=1000000, sorted_index=False, lock=True, mode='a'):
"""
Read HDF files into a Dask DataFrame
Read hdf files into a dask dataframe. This function is like
``pandas.read_hdf``, except it can read from a single large file, or from
multiple files, or from multiple keys from the same file.
Parameters
----------
pattern : string, list
File pattern (string), buffer to read from, or list of file
paths. Can contain wildcards.
key : group identifier in the store. Can contain wildcards
start : optional, integer (defaults to 0), row number to start at
stop : optional, integer (defaults to None, the last row), row number to
stop at
columns : list of columns, optional
A list of columns that if not None, will limit the return
columns (default is None)
chunksize : positive integer, optional
Maximal number of rows per partition (default is 1000000).
sorted_index : boolean, optional
Option to specify whether or not the input hdf files have a sorted
index (default is False).
lock : boolean, optional
Option to use a lock to prevent concurrency issues (default is True).
mode : {'a', 'r', 'r+'}, default 'a'. Mode to use when opening file(s).
'r'
Read-only; no data can be modified.
'a'
Append; an existing file is opened for reading and writing,
and if the file does not exist it is created.
'r+'
It is similar to 'a', but the file must already exist.
Returns
-------
dask.DataFrame
Examples
--------
Load single file
>>> dd.read_hdf('myfile.1.hdf5', '/x') # doctest: +SKIP
Load multiple files
>>> dd.read_hdf('myfile.*.hdf5', '/x') # doctest: +SKIP
>>> dd.read_hdf(['myfile.1.hdf5', 'myfile.2.hdf5'], '/x') # doctest: +SKIP
Load multiple datasets
>>> dd.read_hdf('myfile.1.hdf5', '/*') # doctest: +SKIP
"""
if lock is True:
lock = get_scheduler_lock()
key = key if key.startswith('/') else '/' + key
if isinstance(pattern, str):
paths = sorted(glob(pattern))
else:
paths = pattern
if (start != 0 or stop is not None) and len(paths) > 1:
raise NotImplementedError(read_hdf_error_msg)
if chunksize <= 0:
raise ValueError("Chunksize must be a positive integer")
if (start != 0 or stop is not None) and sorted_index:
raise ValueError("When assuming pre-partitioned data, data must be "
"read in its entirety using the same chunksizes")
from ..multi import concat
return concat([_read_single_hdf(path, key, start=start, stop=stop,
columns=columns, chunksize=chunksize,
sorted_index=sorted_index,
lock=lock, mode=mode)
for path in paths])
if PY3:
from ..core import _Frame
_Frame.to_hdf.__doc__ = to_hdf.__doc__
| [
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... | 2.356103 | 6,456 |
# -*- coding: utf-8 -*-
"""
media info manager module.
"""
from pyrin.core.mixin import HookMixin
from pyrin.core.structs import Manager
import pyrin.utils.path as path_utils
from charma.media_info import MediaInfoPackage
from charma.media_info.interface import AbstractMediaInfoProvider
from charma.media_info.exceptions import InvalidMediaInfoProviderTypeError
| [
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... | 3.306306 | 111 |
import unittest
from boxrec.parsers import FightParser
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628
] | 3.411765 | 17 |
from datetime import datetime, timedelta
from bson.objectid import ObjectId
WORK_TIMEOUT = 600
| [
6738,
4818,
8079,
1330,
4818,
8079,
11,
28805,
12514,
198,
198,
6738,
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13,
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312,
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9515,
7390,
198,
198,
33249,
62,
34694,
12425,
796,
10053,
628
] | 3.37931 | 29 |
# -*- coding: utf-8 -*-
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You 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 sys
import ssl
from mock import Mock, call
from libcloud.test import unittest
from libcloud.common.base import Connection
from libcloud.common.base import LoggingConnection
if __name__ == '__main__':
sys.exit(unittest.main())
| [
2,
532,
9,
12,
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25,
3384,
69,
12,
23,
532,
9,
12,
198,
2,
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284,
262,
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10442,
5693,
357,
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37,
8,
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2,
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11704,
13,
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4091,
262,
28536,
2393,
9387,
351,
198,
2,
428,
67... | 3.779783 | 277 |
import os
import sys
import bpy
script_dir = os.path.dirname(os.path.abspath(__file__))
utils_dir = os.path.join(script_dir, "../../blender_utils")
sys.path.append(utils_dir)
from utils import bake_model, clean_unused, export_ig_object, import_obj_folder
#############################################
# Parse command line arguments
#############################################
should_bake = "--bake" in sys.argv
axis = ["X", "Y", "Z", "-X", "-Y", "-Z"]
import_axis_up = get_arg(sys.argv, "--up", default="Z")
if import_axis_up not in axis:
raise ValueError("Axis up not supported: {} (should be among X,Y,Z,-X,-Y,-Z)".format(import_axis_up))
import_axis_forward = get_arg(sys.argv, "--forward", default="X")
if import_axis_forward not in axis:
raise ValueError("Axis forward not supported: {} (should be among X,Y,Z,-X,-Y,-Z)".format(import_axis_forward))
source_dir = get_arg(sys.argv, "--source_dir")
if source_dir is None:
raise ValueError("Source directory not specified.")
dest_dir = get_arg(sys.argv, "--dest_dir")
if dest_dir is None:
raise ValueError("Destination directory not specified.")
os.makedirs(dest_dir, exist_ok=True)
model_id = os.path.basename(source_dir)
#############################################
# Importing obj files from source dir
#############################################
for on in bpy.context.scene.objects.keys():
obj = bpy.context.scene.objects[on]
bpy.data.objects.remove(obj)
clean_unused()
import_obj_folder(model_id, source_dir, up=import_axis_up, forward=import_axis_forward)
#############################################
# Optional UV Unwrapping
# This only needed if baking will be performed
#############################################
if should_bake:
uv_unwrapped = True
for o in bpy.context.scene.objects:
if not o.data.uv_layers:
uv_unwrapped = False
if not uv_unwrapped:
bpy.ops.object.mode_set(mode="OBJECT")
vl = bpy.context.view_layer
bpy.ops.object.select_all(action="DESELECT")
for on in bpy.context.scene.objects.keys():
obj = bpy.context.scene.objects[on]
new_uv = bpy.context.scene.objects[on].data.uv_layers.new(name="obj_uv")
vl.objects.active = obj
obj.select_set(True)
bpy.ops.object.editmode_toggle()
bpy.ops.mesh.select_all(action="SELECT")
bpy.ops.uv.smart_project(angle_limit=66, island_margin=0.02)
bpy.context.tool_settings.mesh_select_mode = (False, False, True)
bpy.ops.object.mode_set(mode="OBJECT")
#############################################
# Export models
#############################################
export_ig_object(dest_dir, save_material=not should_bake)
#############################################
# Optional Texture Baking
#############################################
if should_bake:
mat_dir = os.path.join(dest_dir, "material")
os.makedirs(mat_dir, exist_ok=True)
# bpy.ops.wm.open_mainfile(filepath=blend_path)
# import_ig_object(model_root, import_mat=True)
for obj in bpy.context.scene.objects:
obj.select_set(True)
bpy.context.view_layer.objects.active = obj
bpy.ops.object.select_all(action="SELECT")
bpy.ops.object.join()
channels = {
"DIFFUSE": (2048, 32),
"ROUGHNESS": (1024, 16),
"METALLIC": (1024, 16),
"NORMAL": (1024, 16),
}
bake_model(mat_dir, channels, overwrite=True)
bpy.ops.wm.quit_blender()
| [
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7,
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198,
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13,
69... | 2.636639 | 1,321 |
# Copyright 2012 OpenStack Foundation
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
"""
Base utilities to build API operation managers and objects on top of.
"""
import copy
from ceilometerclient.apiclient import base
from ceilometerclient.apiclient import exceptions
from ceilometerclient import exc
def getid(obj):
"""Extracts object ID.
Abstracts the common pattern of allowing both an object or an
object's ID (UUID) as a parameter when dealing with relationships.
"""
try:
return obj.id
except AttributeError:
return obj
| [
2,
15069,
2321,
4946,
25896,
5693,
198,
2,
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13,
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2,
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366,
34156,
15341,
345,
743,
198,
2,
220,
220,
220,
407,
779,
428,
... | 3.328402 | 338 |
import json
import os
import re
import subprocess
from base64 import b64decode
from enum import Enum
from math import ceil, floor
from pathlib import Path
from urllib.error import HTTPError
from urllib.request import urlopen
import yaml
from charmhelpers.core import hookenv
from charmhelpers.core.unitdata import kv
from charms.layer import status
ENTITY_PREFIX = 'charm.azure'
MODEL_UUID = os.environ['JUJU_MODEL_UUID']
MAX_ROLE_NAME_LEN = 64
MAX_POLICY_NAME_LEN = 128
# When debugging hooks, for some reason HOME is set to /home/ubuntu, whereas
# during normal hook execution, it's /root. Set it here to be consistent.
os.environ['HOME'] = '/root'
def get_credentials():
"""
Get the credentials from either the config or the hook tool.
Prefers the config so that it can be overridden.
"""
no_creds_msg = 'missing credentials; set credentials config'
config = hookenv.config()
# try to use Juju's trust feature
try:
result = subprocess.run(['credential-get'],
check=True,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
creds = yaml.load(result.stdout.decode('utf8'))
creds_data = creds['credential']['attributes']
login_cli(creds_data)
return True
except FileNotFoundError:
pass # juju trust not available
except subprocess.CalledProcessError as e:
if 'permission denied' not in e.stderr.decode('utf8'):
raise
no_creds_msg = 'missing credentials access; grant with: juju trust'
# try credentials config
if config['credentials']:
try:
creds_data = b64decode(config['credentials']).decode('utf8')
login_cli(creds_data)
return True
except Exception:
status.blocked('invalid value for credentials config')
return False
# no creds provided
status.blocked(no_creds_msg)
return False
def login_cli(creds_data):
"""
Use the credentials to authenticate the Azure CLI.
"""
app_id = creds_data['application-id']
app_pass = creds_data['application-password']
sub_id = creds_data['subscription-id']
tenant_id = _get_tenant_id(sub_id)
try:
log('Forcing logout of Azure CLI')
_azure('logout')
except AzureError:
pass
try:
log('Logging in to Azure CLI')
_azure('login',
'--service-principal',
'-u', app_id,
'-p', app_pass,
'-t', tenant_id)
# cache the subscription ID for use in roles
kv().set('charm.azure.sub-id', sub_id)
except AzureError as e:
# redact the credential info from the exception message
stderr = re.sub(app_id, '<app-id>', e.args[0])
stderr = re.sub(app_pass, '<app-pass>', stderr)
stderr = re.sub(tenant_id, '<tenant-id>', stderr)
# from None suppresses the previous exception from the stack trace
raise AzureError(stderr) from None
def send_additional_metadata(request):
"""
Get additional info about the requesting instance via the API that isn't
available from the metadata server.
"""
res_grp = _azure('group', 'show', '--name', request.resource_group)
# hard-code most of these because with Juju, they're always the same
# and the queries required to look them up are a PITA
request.send_additional_metadata(
resource_group_location=res_grp['location'],
vnet_name='juju-internal-network',
vnet_resource_group=request.resource_group,
subnet_name='juju-internal-subnet',
security_group_name='juju-internal-nsg',
)
def tag_instance(request):
"""
Tag the given instance with the given tags.
"""
log('Tagging instance with: {}', request.instance_tags)
_azure('vm', 'update',
'--name', request.vm_name,
'--resource-group', request.resource_group,
'--set', *['tags.{}={}'.format(tag, value)
for tag, value in request.instance_tags.items()])
def enable_instance_inspection(request):
"""
Enable instance inspection access for the given application.
"""
log('Enabling instance inspection')
_assign_role(request, _get_role('vm-reader'))
def enable_network_management(request):
"""
Enable network management for the given application.
"""
log('Enabling network management')
_assign_role(request, StandardRole.NETWORK_MANAGER)
def enable_security_management(request):
"""
Enable security management for the given application.
"""
log('Enabling security management')
_assign_role(request, StandardRole.SECURITY_MANAGER)
def enable_block_storage_management(request):
"""
Enable block storage (disk) management for the given application.
"""
log('Enabling block storage management')
_assign_role(request, _get_role('disk-manager'))
def enable_dns_management(request):
"""
Enable DNS management for the given application.
"""
log('Enabling DNS management')
_assign_role(request, StandardRole.DNS_MANAGER)
def enable_object_storage_access(request):
"""
Enable object storage read-only access for the given application.
"""
log('Enabling object storage read')
_assign_role(request, StandardRole.OBJECT_STORE_READER)
def enable_object_storage_management(request):
"""
Enable object storage management for the given application.
"""
log('Enabling object store management')
_assign_role(request, StandardRole.OBJECT_STORE_MANAGER)
def cleanup():
"""
Perform cleanup.
"""
pass
# Internal helpers
def _elide(s, max_len, ellipsis='...'):
"""
Elide s in the middle to ensure it is under max_len.
That is, shorten the string, inserting an ellipsis where the removed
characters were to show that they've been removed.
"""
if len(s) > max_len:
hl = (max_len - len(ellipsis)) / 2
headl, taill = floor(hl), ceil(hl)
s = s[:headl] + ellipsis + s[-taill:]
return s
def _get_tenant_id(subscription_id):
"""
Translate the subscription ID into a tenant ID by making an unauthorized
request to the API and extracting the tenant ID from the WWW-Authenticate
header in the error response.
"""
url = ('https://management.azure.com/subscriptions/'
'{}?api-version=2018-03-01-01.6.1'.format(subscription_id))
try:
urlopen(url)
log_err('Error getting tenant ID: did not get "unauthorized" response')
return None
except HTTPError as e:
if 'WWW-Authenticate' not in e.headers:
log_err('Error getting tenant ID: missing WWW-Authenticate header')
return None
www_auth = e.headers['WWW-Authenticate']
match = re.search(r'authorization_uri="[^"]*/([^/"]*)"', www_auth)
if not match:
log_err('Error getting tenant ID: unable to find in {}', www_auth)
return None
return match.group(1)
def _azure(cmd, *args, return_stderr=False):
"""
Call the azure-cli tool.
"""
cmd = ['az', cmd]
cmd.extend(args)
result = subprocess.run(cmd,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
stdout = result.stdout.decode('utf8').strip()
stderr = result.stderr.decode('utf8').strip()
if result.returncode != 0:
raise AzureError.get(stderr)
if return_stderr:
return stderr
if stdout:
stdout = json.loads(stdout)
return stdout
def _get_msi(vm_id):
"""
Get the Managed System Identity for the VM.
"""
vm_identities = kv().get('charm.azure.vm-identities', {})
return vm_identities.get(vm_id)
def _get_role(role_name):
"""
Translate short role name into a full role name and ensure that the
custom role is loaded.
The custom roles have to be applied to a specific subscription ID, but
the subscription ID applies to the entire credential, so will almost
certainly be reused, so there's not much danger in hitting the 2k
custom role limit.
"""
known_roles = kv().get('charm.azure.roles', {})
if role_name in known_roles:
return known_roles[role_name]
sub_id = kv().get('charm.azure.sub-id')
role_file = Path('files/roles/{}.json'.format(role_name))
role_data = json.loads(role_file.read_text())
role_fullname = role_data['Name'].format(sub_id)
scope = role_data['AssignableScopes'][0].format(sub_id)
role_data['Name'] = role_fullname
role_data['AssignableScopes'][0] = scope
try:
log('Ensuring role {}', role_fullname)
_azure('role', 'definition', 'create',
'--role-definition', json.dumps(role_data))
except AzureError as e:
if 'already exists' not in e.args[0]:
raise
known_roles[role_name] = role_fullname
return role_fullname
| [
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198,
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3108,
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1330,
... | 2.465649 | 3,668 |
import socket
import csv
import traceback
import threading
s=socket.socket(socket.AF_INET,socket.SOCK_STREAM)
usrpass={}
ihost=socket.gethostname()
host=socket.gethostbyname(ihost)
ihost=socket.gethostname()
host=socket.gethostbyname(ihost)
iport=[]
hostfile="host.csv"
with open(hostfile,'r')as host_file:
csv_hfile = csv.reader(host_file, delimiter=",")
for row in csv_hfile:
iport.append(row[1])
port=int(iport[4])
# def checkusr(username):
# if username in usrpass:
# return 1
# else:
# print("Invalid Username")
# return -1
main() | [
11748,
17802,
198,
11748,
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... | 2.037037 | 324 |
from itertools import product
from sklearn.base import clone
from sklearn.preprocessing import FunctionTransformer
from sklearn.model_selection import ParameterGrid
from imblearn.pipeline import Pipeline
from rlearn.utils import check_random_states
def check_pipelines(objects_list, random_state, n_runs):
"""Extract estimators and parameters grids."""
# Create random states
random_states = check_random_states(random_state, n_runs)
pipelines = []
param_grid = []
for comb, rs in product(product(*objects_list), random_states):
name = "|".join([i[0] for i in comb])
# name, object, sub grid
comb = [
(nm, ob, ParameterGrid(sg))
if ob is not None
else (nm, FunctionTransformer(), ParameterGrid(sg))
for nm, ob, sg in comb
]
# Create estimator
if name not in [n[0] for n in pipelines]:
est = Pipeline([(nm, ob) for nm, ob, _ in comb])
pipelines.append((name, est))
# Create intermediate parameter grids
sub_grids = [
[{f"{nm}__{k}": v for k, v in param_def.items()} for param_def in sg]
for nm, obj, sg in comb
]
# Create parameter grids
for sub_grid in product(*sub_grids):
param_prefix = "" if len(comb) == 1 else f"{name}__"
grid = {"est_name": [name]}
grid.update(
{f"{param_prefix}{k}": [v] for d in sub_grid for k, v in d.items()}
)
random_states = {
f"{param_prefix}{param}": [rs]
for param in est.get_params()
if "random_state" in param
}
grid.update(random_states)
# Avoid multiple runs over pipelines without random state
if grid not in param_grid:
param_grid.append(grid)
return pipelines, param_grid
| [
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13,
19849,
62,
49283,
1330,
25139,
2357,
41339,
198,
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545,
... | 2.237485 | 859 |
from mushroom_rl.utils.plots import PlotItemBuffer, DataBuffer
from mushroom_rl.utils.plots.plot_item_buffer import PlotItemBufferLimited
| [
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28114,
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28632,
37214,
628,
628,
628
] | 3.575 | 40 |
# -*- coding: utf-8 -*-
#
# test/test_metadata.py
# Part of python-daemon, an implementation of PEP 3143.
#
# This is free software, and you are welcome to redistribute it under
# certain conditions; see the end of this file for copyright
# information, grant of license, and disclaimer of warranty.
""" Unit test for _metadata private module.
"""
from __future__ import (absolute_import, unicode_literals)
import collections
import errno
import functools
import json
import re
try:
# Python 3 standard library.
import urllib.parse as urlparse
except ImportError:
# Python 2 standard library.
import urlparse
import mock
import pkg_resources
import testtools.helpers
import testtools.matchers
from . import scaffold
from .scaffold import unicode
import daemon._metadata as metadata
FakeYearRange = collections.namedtuple('FakeYearRange', ['begin', 'end'])
try:
FileNotFoundError
except NameError:
# Python 2 uses IOError.
FileNotFoundError = functools.partial(IOError, errno.ENOENT)
version_info_filename = "version_info.json"
def fake_func_has_metadata(testcase, resource_name):
""" Fake the behaviour of pkg_resources.Distribution.has_metadata. """
if (
resource_name != testcase.version_info_filename
or not hasattr(testcase, 'test_version_info')):
return False
return True
def fake_func_get_metadata(testcase, resource_name):
""" Fake the behaviour of pkg_resources.Distribution.get_metadata. """
if not fake_func_has_metadata(testcase, resource_name):
error = FileNotFoundError(resource_name)
raise error
content = testcase.test_version_info
return content
def fake_func_get_distribution(testcase, distribution_name):
""" Fake the behaviour of pkg_resources.get_distribution. """
if distribution_name != metadata.distribution_name:
raise pkg_resources.DistributionNotFound
if hasattr(testcase, 'get_distribution_error'):
raise testcase.get_distribution_error
mock_distribution = testcase.mock_distribution
mock_distribution.has_metadata.side_effect = functools.partial(
fake_func_has_metadata, testcase)
mock_distribution.get_metadata.side_effect = functools.partial(
fake_func_get_metadata, testcase)
return mock_distribution
# Copyright 20082018 Ben Finney <ben+python@benfinney.id.au>
#
# This is free software: you may copy, modify, and/or distribute this work
# under the terms of the GNU General Public License as published by the
# Free Software Foundation; version 3 of that license or any later version.
# No warranty expressed or implied. See the file LICENSE.GPL-3 for details.
# Local variables:
# coding: utf-8
# mode: python
# End:
# vim: fileencoding=utf-8 filetype=python :
| [
2,
532,
9,
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25,
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69,
12,
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9,
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2,
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2,
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286,
350,
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2,
198,
2,
770,
318,
... | 3.039046 | 922 |
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
from typing import Union, List
from .purpose import *
from .trait_reference import TraitReference
from cdm.utilities import JObject
| [
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13,
198,
198,
6738,
19720,
1330,
4479,
11,
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198,
198,
6738,... | 4.202899 | 69 |
import re
from typing import Union, List
import nltk
from bs4 import BeautifulSoup
| [
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82,
19,
1330,
23762,
50,
10486,
198
] | 3.36 | 25 |
"""
For backwards-compatibility. keep this file.
(Many people are going to have key bindings that rely on this file.)
"""
from __future__ import unicode_literals
from .app import *
__all__ = [
# Old names.
'HasArg',
'HasCompletions',
'HasFocus',
'HasSelection',
'HasValidationError',
'IsDone',
'IsReadOnly',
'IsMultiline',
'RendererHeightIsKnown',
'InEditingMode',
'InPasteMode',
'ViMode',
'ViNavigationMode',
'ViInsertMode',
'ViInsertMultipleMode',
'ViReplaceMode',
'ViSelectionMode',
'ViWaitingForTextObjectMode',
'ViDigraphMode',
'EmacsMode',
'EmacsInsertMode',
'EmacsSelectionMode',
'IsSearching',
'HasSearch',
'ControlIsSearchable',
]
# Keep the original classnames for backwards compatibility.
HasValidationError = lambda: has_validation_error
HasArg = lambda: has_arg
IsDone = lambda: is_done
RendererHeightIsKnown = lambda: renderer_height_is_known
ViNavigationMode = lambda: vi_navigation_mode
InPasteMode = lambda: in_paste_mode
EmacsMode = lambda: emacs_mode
EmacsInsertMode = lambda: emacs_insert_mode
ViMode = lambda: vi_mode
IsSearching = lambda: is_searching
HasSearch = lambda: is_searching
ControlIsSearchable = lambda: control_is_searchable
EmacsSelectionMode = lambda: emacs_selection_mode
ViDigraphMode = lambda: vi_digraph_mode
ViWaitingForTextObjectMode = lambda: vi_waiting_for_text_object_mode
ViSelectionMode = lambda: vi_selection_mode
ViReplaceMode = lambda: vi_replace_mode
ViInsertMultipleMode = lambda: vi_insert_multiple_mode
ViInsertMode = lambda: vi_insert_mode
HasSelection = lambda: has_selection
HasCompletions = lambda: has_completions
IsReadOnly = lambda: is_read_only
IsMultiline = lambda: is_multiline
HasFocus = has_focus # No lambda here! (Has_focus is callable that returns a callable.)
InEditingMode = in_editing_mode
| [
37811,
198,
1890,
16196,
12,
5589,
25901,
13,
1394,
428,
2393,
13,
198,
7,
7085,
661,
389,
1016,
284,
423,
1994,
34111,
326,
8814,
319,
428,
2393,
2014,
198,
37811,
198,
6738,
11593,
37443,
834,
1330,
28000,
1098,
62,
17201,
874,
198,... | 2.852359 | 657 |
#spaces.py
'''
AlgoHack Genetic Algorithm for University Semaster Planning
Version 0.03 2018
Niranjan Meegammana Shilpasayura.org
'''
import xdb
if __name__ == "__main__":
delay=0.05
conn=xdb.opendb('genetic56.db')
cursor =conn.cursor() # create a cursor object
success=crt_spaces_table(cursor, True) # create spaces table
#dedicated lecture hall, lab for group and semaster
success, count =insert_spaces(cursor,1,1,1,1,delay) # generate records
xdb.commit(conn)
xdb.closedb(conn)
| [
2,
2777,
2114,
13,
9078,
201,
198,
7061,
6,
201,
198,
2348,
2188,
32833,
42295,
978,
42289,
329,
2059,
12449,
1603,
21913,
201,
198,
14815,
657,
13,
3070,
2864,
201,
198,
45,
343,
272,
13881,
337,
1453,
28483,
805,
64,
911,
346,
444... | 2.429864 | 221 |
import urllib.request
import cv2
import numpy as np
import time
import threading
| [
11748,
2956,
297,
571,
13,
25927,
198,
11748,
269,
85,
17,
198,
11748,
299,
32152,
355,
45941,
198,
11748,
640,
198,
11748,
4704,
278,
198
] | 3.24 | 25 |
#! /usr/bin/python3
import time
import random
import json
import os
from pprint import pprint
from kubernetes.client.rest import ApiException
from pint import UnitRegistry
from collections import defaultdict
from kubernetes import client, config, watch
from timeloop import Timeloop
from datetime import timedelta
config.load_kube_config()
#config.load_incluster_config()
# doing this computation within a k8s cluster
#k8s.config.load_incluster_config()
core_api = client.CoreV1Api()
apis_api = client.AppsV1Api()
#sdclient = SdcClient(<Your Sysdig API token>)
sysdig_metric = "net.http.request.time"
metrics = [{ "id": sysdig_metric, "aggregations": { "time": "timeAvg", "group": "avg" } }]
#scheduler_name = "Ec2SpotK8sScheduler"
CustomSchedulerName ='K8SCustomScheduler'
ureg = UnitRegistry()
ureg.load_definitions('kubernetes_units.txt')
pendingPodsList = []
failedPodsList = []
runningPodsList =[]
nodesListPerNodeLabel = {}
Q_ = ureg.Quantity
#tl = Timeloop()
#@tl.job(interval=timedelta(seconds=10))
__all__ = ["get_node_available_nodes_list"]
if __name__ == '__main__':
#ready_nodes = nodes_available()
#pprint(ready_nodes)
#name='review-v1-787d8fbfbb-ltdzt'
node='ip-10-0-3-253.ec2.internal'
#namespace='ecommerce'
#ret=scheduler(name, node, namespace)
#pprint(ret)
#main()
#test()
#testpod()
#check_node_resources(node)
#RunEc2SpotCustomScheduler()
#getPodsListForDeployment(' ')
#lifecycle = 'OnDemand'
#lifecycle = 'Ec2Spot'
#get_node_available_nodes_list(lifecycle)
#RunEc2SpotCustomScheduler()
#NumOfPodsToDeleted = 1
#podsAlreadyRunningOnNodeLabelList = []
#d ={'name':'nginx-66cb875766-vx6bp'}
#podsAlreadyRunningOnNodeLabelList.append(d)
#deletePods(NumOfPodsToDeleted, podsAlreadyRunningOnNodeLabelList)
#deploymentName='nginx'
#deploymentName = 'kube-ops-view'
#getPodsListForDeployment(deploymentName)
#testlist()
#tl.start(block=True)
while True:
RunEc2SpotCustomScheduler()
time.sleep(10)
| [
2,
0,
1220,
14629,
14,
8800,
14,
29412,
18,
198,
198,
11748,
640,
198,
11748,
4738,
198,
11748,
33918,
198,
11748,
28686,
198,
6738,
279,
4798,
1330,
279,
4798,
198,
6738,
479,
18478,
3262,
274,
13,
16366,
13,
2118,
1330,
5949,
72,
... | 2.272727 | 957 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from clean_transcript import clean_transcript
ALPHABET_FILE_PATH = "/DeepSpeech/bin/bangor_welsh/alphabet.txt"
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
18,
198,
2,
532,
9,
12,
19617,
25,
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69,
12,
23,
532,
9,
12,
198,
198,
6738,
3424,
62,
7645,
6519,
1330,
3424,
62,
7645,
6519,
198,
198,
1847,
11909,
6242,
2767,
62,
25664,
62,
34... | 2.461538 | 65 |
# Copyright (c) 2019-2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List, Optional
import functools
import torch
import torch.nn as nn
import torch.nn.functional as F
def _init_weight(weight, d : int, init_scale : Optional[float], default=None):
assert init_scale or default
if init_scale is None:
std = default
else:
std = init_scale * (d ** -0.5)
nn.init.normal_(weight, mean=0, std=std)
_init_embed = functools.partial(_init_weight, default=0.02)
_init_proj = functools.partial(_init_weight, default=0.01)
### Just for this codebase, we need to squeeze the last dimension because inputs are always given as (B, L, D) instead of (B, L)
import src.models.nn.utils as U
# AdaptiveEmbedding = U.Squeeze(AdaptiveEmbedding)
| [
2,
15069,
357,
66,
8,
13130,
12,
42334,
11,
15127,
23929,
44680,
6234,
13,
1439,
2489,
10395,
13,
198,
2,
198,
2,
49962,
739,
262,
24843,
13789,
11,
10628,
362,
13,
15,
357,
1169,
366,
34156,
15341,
198,
2,
345,
743,
407,
779,
428... | 3.171838 | 419 |
import json
import csv
import sys
import os
import re
import codecs
import logging
from logging.config import dictConfig
import click
import yaml
from sqlalchemy import create_engine
from jsontableschema_sql import Storage
from smart_open import smart_open
from . import postgres
from . import carto
csv.field_size_limit(sys.maxsize)
| [
11748,
33918,
198,
11748,
269,
21370,
198,
11748,
25064,
198,
11748,
28686,
198,
11748,
302,
198,
11748,
40481,
82,
198,
11748,
18931,
198,
6738,
18931,
13,
11250,
1330,
8633,
16934,
198,
198,
11748,
3904,
198,
11748,
331,
43695,
198,
673... | 3.510417 | 96 |
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This script is based on speech_to_text_infer.py and allows you to score the hypotheses
with sclite. A local installation from https://github.com/usnistgov/SCTK is required.
Hypotheses and references are first saved in trn format and are scored after applying a glm
file (if provided).
"""
import errno
import json
import os
import subprocess
from argparse import ArgumentParser
import torch
from nemo.collections.asr.metrics.wer import WER
from nemo.collections.asr.models import EncDecCTCModel
from nemo.utils import logging
try:
from torch.cuda.amp import autocast
except ImportError:
from contextlib import contextmanager
can_gpu = torch.cuda.is_available()
if __name__ == '__main__':
main() # noqa pylint: disable=no-value-for-parameter
| [
2,
15069,
357,
66,
8,
12131,
11,
15127,
23929,
44680,
6234,
13,
220,
1439,
2489,
10395,
13,
198,
2,
198,
2,
49962,
739,
262,
24843,
13789,
11,
10628,
362,
13,
15,
357,
1169,
366,
34156,
15341,
198,
2,
345,
743,
407,
779,
428,
2393... | 3.45 | 400 |
from manimlib.imports import *
from manimlib.utils import bezier
import numpy as np | [
6738,
582,
320,
8019,
13,
320,
3742,
1330,
1635,
198,
6738,
582,
320,
8019,
13,
26791,
1330,
307,
89,
959,
198,
11748,
299,
32152,
355,
45941
] | 3.192308 | 26 |
from setuptools import setup
setup(name='rapid_plotly',
version='0.1',
description='Convenience functions to rapidly create beautiful Plotly graphs',
author='Joseph Dasenbrock',
author_email='dasenbrockjw@gmail.com',
packages=['rapid_plotly'],
zip_safe=False)
| [
6738,
900,
37623,
10141,
1330,
9058,
198,
198,
40406,
7,
3672,
11639,
2416,
312,
62,
29487,
306,
3256,
198,
220,
220,
220,
220,
220,
2196,
11639,
15,
13,
16,
3256,
198,
220,
220,
220,
220,
220,
6764,
11639,
3103,
574,
1240,
5499,
28... | 2.582609 | 115 |
#! /usr/bin/doit -f
# https://pydoit.org
# `pip install [--user] doit` adds `doit.exe` to the PATH
# - Note `doit auto`, the file watcher only works on Linux/Mac
# - All commands are relative to dodo.py (doit runs in the working dir of dodo.py
# even if ran from a different directory `doit -f path/to/dodo.py`)
from glob import glob
import json
from os import environ
from os.path import abspath, basename, dirname, exists, expanduser, join, splitext
from shutil import copyfile
from typing import Iterator, List, NewType, Optional
from doit.tools import title_with_actions
Path = NewType("Path", str)
home = Path(expanduser("~"))
bml_tools_dir = Path(environ.get("BML_TOOLS_DIRECTORY", join(home, "dev/bml")))
bml_includes_cache_file = ".include-deps.json"
def task_bmlcss():
"""Copy the bml CSS style sheet to this directory."""
css_basename = "bml.css"
src_css_file = Path(join(bml_tools_dir, css_basename))
return {
'actions': [copy_file],
'file_dep': [src_css_file],
'targets': [css_basename],
'title': title_with_actions
}
| [
2,
0,
1220,
14629,
14,
8800,
14,
4598,
270,
532,
69,
198,
2,
3740,
1378,
9078,
4598,
270,
13,
2398,
198,
2,
4600,
79,
541,
2721,
685,
438,
7220,
60,
466,
270,
63,
6673,
4600,
4598,
270,
13,
13499,
63,
284,
262,
46490,
198,
2,
... | 2.62201 | 418 |
filenames = ["myfile1", "nonExistent", "emptyFile", "myfile2"]
for file in filenames:
try:
f = open(file, 'r')
line = f.readline()
if line == "":
f.close()
raise EmptyFileError("%s: is empty" % file)
# except IOError as error:
# print("%s: could not be opened: %s" % (file, error.strerror)
## except EmptyFileError as error:
# print(error)
# else:
# print("%s: %s" % (file, f.readline()))
# finally:
# print("Done processing", file)
| [
10379,
268,
1047,
796,
14631,
1820,
7753,
16,
1600,
366,
13159,
3109,
7609,
1600,
366,
28920,
8979,
1600,
366,
1820,
7753,
17,
8973,
198,
1640,
2393,
287,
1226,
268,
1047,
25,
198,
220,
220,
220,
1949,
25,
198,
220,
220,
220,
220,
2... | 1.926667 | 300 |
# Crumbling In
# Like randomised coloured dots and then they
# increase on both sides getting closer and closer into the middle.
import sys, traceback, random
from numpy import array,full
| [
2,
3864,
14739,
554,
198,
2,
4525,
4738,
1417,
34746,
22969,
290,
788,
484,
198,
2,
2620,
319,
1111,
5389,
1972,
5699,
290,
5699,
656,
262,
3504,
13,
198,
198,
11748,
25064,
11,
12854,
1891,
11,
4738,
198,
6738,
299,
32152,
1330,
71... | 4.108696 | 46 |
from __future__ import division
from unittest import skipIf, TestCase
import os
from pandas import DataFrame
import numpy as np
from numpy.testing import assert_array_equal
BACKEND_AVAILABLE = os.environ.get("ETS_TOOLKIT", "qt4") != "null"
if BACKEND_AVAILABLE:
from app_common.apptools.testing_utils import assert_obj_gui_works
from pybleau.app.plotting.plot_config import HeatmapPlotConfigurator, \
HEATMAP_PLOT_TYPE, HistogramPlotConfigurator, HIST_PLOT_TYPE, \
LinePlotConfigurator, BarPlotConfigurator, ScatterPlotConfigurator, \
SCATTER_PLOT_TYPE, CMAP_SCATTER_PLOT_TYPE, LINE_PLOT_TYPE, \
BAR_PLOT_TYPE
LEN = 16
TEST_DF = DataFrame({"a": [1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4],
"b": [1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4],
"c": [1, 2, 3, 4, 2, 3, 1, 1, 4, 4, 5, 6, 4, 4, 5, 6],
"d": list("ababcabcdabcdeab"),
"e": np.random.randn(LEN),
"f": range(LEN),
# Highly repetitive column to split the entire data into 2
"g": np.array(["0", "1"] * (LEN // 2)),
"h": np.array([0, 1] * (LEN // 2), dtype=bool),
})
| [
6738,
11593,
37443,
834,
1330,
7297,
198,
198,
6738,
555,
715,
395,
1330,
14267,
1532,
11,
6208,
20448,
198,
11748,
28686,
198,
6738,
19798,
292,
1330,
6060,
19778,
198,
11748,
299,
32152,
355,
45941,
198,
6738,
299,
32152,
13,
33407,
1... | 1.9375 | 656 |
import os.path
from .. import *
| [
11748,
28686,
13,
6978,
198,
198,
6738,
11485,
1330,
1635,
628,
628
] | 3 | 12 |
from collections import namedtuple
# Basic example
Point = namedtuple('Point', ['x', 'y'])
p = Point(11, y=22)
print(p[0] + p[1])
x, y = p
print(x, y)
print(p.x + p.y)
print(Point(x=11, y=22))
from collections import namedtuple
import csv
f = open("users.csv", "r")
next(f)
reader = csv.reader(f)
student_list = []
for row in reader:
student_list.append(row)
print(row)
print(student_list)
columns = ["user_id", "integration_id", "login_id", "password", "first_name",
"last_name", "full_name", "sortable_name", "short_name",
"email", "status"]
Student = namedtuple('Student', columns)
student_namedtupe_list = []
for row in student_list:
student = Student(*row)
student_namedtupe_list.append(student)
print(student_namedtupe_list[0])
print(student_namedtupe_list[0].full_name)
| [
6738,
17268,
1330,
3706,
83,
29291,
198,
198,
2,
14392,
1672,
198,
12727,
796,
3706,
83,
29291,
10786,
12727,
3256,
37250,
87,
3256,
705,
88,
6,
12962,
198,
79,
796,
6252,
7,
1157,
11,
331,
28,
1828,
8,
198,
4798,
7,
79,
58,
15,
... | 2.472892 | 332 |
# Profile mitmdump with apachebench and
# yappi (https://code.google.com/p/yappi/)
#
# Requirements:
# - Apache Bench "ab" binary
# - pip install click yappi
from mitmproxy.main import mitmdump
from os import system
from threading import Thread
import time
import yappi
import click
if __name__ == '__main__':
main()
| [
2,
13118,
10255,
9132,
931,
351,
2471,
4891,
26968,
290,
198,
2,
331,
1324,
72,
357,
5450,
1378,
8189,
13,
13297,
13,
785,
14,
79,
14,
88,
1324,
72,
34729,
198,
2,
198,
2,
24422,
25,
198,
2,
532,
24843,
25187,
366,
397,
1,
13934... | 3 | 109 |
# -*- coding: utf-8 -*-
"""
Various plots
"""
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation, FFMpegWriter
import xarray as xr
import os
def quiver(data, arrScale = 25.0, threshold = None, nthArr = 1,
contourLevels = None, colbar = True, logscale = False,
aspectratio='equal', colbar_orient = 'vertical', units = None):
"""
Generates a quiver plot of a 'data' xarray DataArray object (single frame from a dataset)
Inputs:
data - xarray DataArray of the type defined in pivpy, one of the frames in the Dataset
selected by default using .isel(t=0)
threshold - values above the threshold will be set equal to threshold
arrScale - use to change arrow scales
nthArr - use to plot only every nth arrow from the array
contourLevels - use to specify the maximum value (abs) of contour plots
colbar - True/False wether to generate a colorbar or not
logscale - if true then colorbar is on log scale
aspectratio - set auto or equal for the plot's apearence
colbar_orient - 'horizontal' or 'vertical' orientation of the colorbar (if colbar is True)
Outputs:
none
Usage:
graphics.quiver(data, arrScale = 0.2, threshold = Inf, n)
"""
data = dataset_to_array(data)
x = data.x
y = data.y
u = data.u
v = data.v
if units is not None:
lUnits = units[0] # ['m' 'm' 'mm/s' 'mm/s']
velUnits = units[2]
tUnits = velUnits.split('/')[1] # make it 's' or 'dt'
else:
lUnits, velUnits, tUnits = '', '', ''
if threshold is not None:
data['u'] = xr.where(data['u']>threshold, threshold, data['u'])
data['v'] = xr.where(data['v']>threshold, threshold, data['v'])
S = np.array(np.sqrt(u**2 + v**2))
fig = plt.get_fignums()
if len(fig) == 0: # if no figure is open
fig, ax = plt.subplots() # open a new figure
else:
ax = plt.gca()
if contourLevels is None:
levels = np.linspace(0, np.max(S.flatten()), 30) # default contour levels up to max of S
else:
levels = np.linspace(0, contourLevels, 30)
if logscale:
c = ax.contourf(x,y,S,alpha=0.8,
cmap = plt.get_cmap("Blues"),
levels = levels, norm = plt.colors.LogNorm())
else:
c = ax.contourf(x,y,S,alpha=0.8,
cmap = plt.get_cmap("Blues"),
levels=levels)
if colbar:
cbar = plt.colorbar(c, orientation=colbar_orient)
cbar.set_label(r'$\left| \, V \, \right|$ ['+ lUnits +' $\cdot$ '+ tUnits +'$^{-1}$]')
ax.quiver(x[::nthArr],y[::nthArr],
u[::nthArr,::nthArr],v[::nthArr,::nthArr],units='width',
scale = np.max(S*arrScale),headwidth=2)
ax.set_xlabel('x (' + lUnits + ')')
ax.set_ylabel('y (' + lUnits + ')')
ax.set_aspect(aspectratio)
return fig,ax
def histogram(data, normed = False):
"""
this function will plot a normalized histogram of
the velocity data.
Input:
data : xarray DataSet with ['u','v'] attrs['units']
normed : (optional) default is False to present normalized
histogram
"""
u = np.asarray(data.u).flatten()
v = np.asarray(data.v).flatten()
units = data.attrs['units']
f,ax = plt.subplots(2)
ax[0].hist(u,bins=np.int(np.sqrt(len(u))*0.5),density=normed)
ax[0].set_xlabel('u ['+units[2]+']')
ax[1] = plt.subplot2grid((2,1),(1,0))
ax[1].hist(v,bins=np.int(np.sqrt(len(v)*0.5)),density=normed)
ax[1].set_xlabel('v ['+units[2]+']')
plt.tight_layout()
return f, ax
def contour_plot(data, threshold = None, contourLevels = None,
colbar = True, logscale = False, aspectration='equal', units=None):
""" contourf ajusted for the xarray PIV dataset, creates a
contour map for the data['w'] property.
Input:
data : xarray PIV DataArray, converted automatically using .isel(t=0)
threshold : a threshold value, default is None (no data clipping)
contourLevels : number of contour levels, default is None
colbar : boolean (default is True) show/hide colorbar
logscale : boolean (True is default) create in linear/log scale
aspectration : string, 'equal' is the default
"""
data = dataset_to_array(data)
if units is not None:
lUnits = units[0] # ['m' 'm' 'mm/s' 'mm/s']
# velUnits = units[2]
# tUnits = velUnits.split('/')[1] # make it 's' or 'dt'
else:
# lUnits, velUnits = '', ''
lUnits = ''
f,ax = plt.subplots()
if threshold is not None:
data['w'] = xr.where(data['w']>threshold, threshold, data['w'])
m = np.amax(abs(data['w']))
if contourLevels == None:
levels = np.linspace(-m, m, 30)
else:
levels = np.linspace(-contourLevels, contourLevels, 30)
if logscale:
c = ax.contourf(data.x,data.y,np.abs(data['w']), levels=levels,
cmap = plt.get_cmap('RdYlBu'), norm=plt.colors.LogNorm())
else:
c = ax.contourf(data.x,data.y,data['w'], levels=levels,
cmap = plt.get_cmap('RdYlBu'))
plt.xlabel('x [' + lUnits + ']')
plt.ylabel('y [' + lUnits + ']')
if colbar:
cbar = plt.colorbar(c)
cbar.set_label(r'$\omega$ [s$^{-1}$]')
ax.set_aspect(aspectration)
return f,ax
def showf(data, variables=None, units=None, fig=None):
"""
showf(data, var, units)
Arguments:
data : xarray.DataSet that contains dimensions of t,x,y
and variables u,v and maybe w (scalar)
"""
if variables is None:
xlabel = ' '
ylabel = ' '
else:
xlabel = variables[0]
ylabel = variables[1]
if units is not None:
xlabel += ' ' + units[0]
ylabel += ' ' + units[1]
fig = plt.figure(None if fig is None else fig.number)
for t in data['t']:
d = data.isel(t=t)
plt.quiver(d['x'],d['y'],d['u'],d['v'],d['u']**2 + d['v']**2)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.draw()
plt.pause(0.1)
plt.show()
def showscal(data, property='ken'):
"""
showf(data, var, units)
Arguments:
data : xarray.DataSet that contains dimensions of t,x,y
and a variable w (scalar)
"""
# fig = plt.figure(None if fig is None else fig.number)
# import pdb; pdb.set_trace()
# xlabel = (None if var is None else var[0]) + ' [' + (None if units is None else units[0])+']'
# ylabel = (None if var is None else var[1]) + ' [' + (None if units is None else units[1])+']'
data = data.piv.vec2scal(property=property)
contour_plot(data)
def animate(data, arrowscale=1, savepath=None):
""" animates the quiver plot for the dataset (multiple frames)
Input:
data : xarray PIV type of DataSet
arrowscale : [optional] integer, default is 1
savepath : [optional] path to save the MP4 animation, default is None
Output:
if savepath is None, then only an image display of the animation
if savepath is an existing path, a file named im.mp4 is saved
"""
X, Y = data.x, data.y
U, V = data.u[:,:,0], data.v[:,:,0] # first frame
fig, ax = plt.subplots(1,1)
M = np.sqrt(U**2 + V**2)
Q = ax.quiver(X[::3,::3], Y[::3,::3],
U[::3,::3], V[::3,::3], M[::3,::3],
units='inches', scale=arrowscale)
cb = plt.colorbar(Q)
units = data.attrs['units']
cb.ax.set_ylabel('velocity (' + units[2] + ')')
text = ax.text(0.2,1.05, '1/'+str(len(data.t)), ha='center', va='center',
transform=ax.transAxes)
anim = FuncAnimation(fig, update_quiver, fargs=(Q,data,text),
frames = len(data.t), blit=False)
mywriter = FFMpegWriter()
if savepath:
p = os.getcwd()
os.chdir(savepath)
anim.save('im.mp4', writer=mywriter)
os.chdir(p)
else: anim.save('im.mp4', writer=mywriter)
def dataset_to_array(data,N=0):
""" converts xarray Dataset to array """
if 't' in data.dims:
print('Warning: function for a single frame, using first frame, supply data.isel(t=N)')
data = data.isel(t=N)
return data | [
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... | 2.058153 | 4,213 |
# model settings
norm_cfg = dict(type='BN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='pretrain/vit_base_patch16_224.pth',
backbone=dict(
type='VisionTransformer',
img_size=(224, 224),
patch_size=16,
in_channels=3,
embed_dim=768,
depth=12,
num_heads=12,
mlp_ratio=4,
# out_indices=(2, 5, 8, 11),
qkv_bias=True,
drop_rate=0.0,
attn_drop_rate=0.0,
drop_path_rate=0.0,
with_cls_token=True,
norm_cfg=dict(type='LN', eps=1e-6),
act_cfg=dict(type='GELU'),
norm_eval=False,
interpolate_mode='bicubic'),
neck=None,
decode_head=dict(
type='ASPPHead',
in_channels=768,
# in_index=3,
channels=512,
dilations=(1, 6, 12, 18),
dropout_ratio=0.1,
num_classes=21,
contrast=True,
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
auxiliary_head=None,
# model training and testing settings
train_cfg=dict(),
test_cfg=dict(mode='whole')) # yapf: disable | [
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220,
220,
220,
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11639,
27195,
12342,
10707,
12342,
3256,
198,
220,
220,
220,
2181,
13363,
... | 1.88785 | 642 |
#!/usr/bin/python
# Copyright (c) 2019 OpenStack Foundation
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
# flake8: noqa: E501
from __future__ import absolute_import, division, print_function
__metaclass__ = type
import json
from distutils.version import LooseVersion
import yaml
from ansible.module_utils.basic import AnsibleModule
from ansible.module_utils._text import to_bytes, to_native
ANSIBLE_METADATA = {
'metadata_version': '1.0',
'status': ['preview'],
'supported_by': 'community'
}
DOCUMENTATION = """
module: podman_container
author:
- "Sagi Shnaidman (@sshnaidm)"
version_added: '2.9'
short_description: Manage podman containers
notes: []
description:
- Start, stop, restart and manage Podman containers
requirements:
- "Podman installed on host"
options:
name:
description:
- Name of the container
required: True
type: str
executable:
description:
- Path to C(podman) executable if it is not in the C($PATH) on the
machine running C(podman)
default: 'podman'
type: str
state:
description:
- I(absent) - A container matching the specified name will be stopped and
removed.
- I(present) - Asserts the existence of a container matching the name and
any provided configuration parameters. If no container matches the
name, a container will be created. If a container matches the name but
the provided configuration does not match, the container will be
updated, if it can be. If it cannot be updated, it will be removed and
re-created with the requested config. Image version will be taken into
account when comparing configuration. Use the recreate option to force
the re-creation of the matching container.
- I(started) - Asserts there is a running container matching the name and
any provided configuration. If no container matches the name, a
container will be created and started. Use recreate to always re-create
a matching container, even if it is running. Use force_restart to force
a matching container to be stopped and restarted.
- I(stopped) - Asserts that the container is first I(present), and then
if the container is running moves it to a stopped state.
type: str
default: started
choices:
- absent
- present
- stopped
- started
image:
description:
- Repository path (or image name) and tag used to create the container.
If an image is not found, the image will be pulled from the registry.
If no tag is included, C(latest) will be used.
- Can also be an image ID. If this is the case, the image is assumed to
be available locally.
type: str
annotation:
description:
- Add an annotation to the container. The format is key value, multiple
times.
type: dict
authfile:
description:
- Path of the authentication file. Default is
``${XDG_RUNTIME_DIR}/containers/auth.json``
(Not available for remote commands) You can also override the default
path of the authentication file by setting the ``REGISTRY_AUTH_FILE``
environment variable. ``export REGISTRY_AUTH_FILE=path``
type: path
blkio_weight:
description:
- Block IO weight (relative weight) accepts a weight value between 10 and
1000
type: int
blkio_weight_device:
description:
- Block IO weight (relative device weight, format DEVICE_NAME[:]WEIGHT).
type: dict
cap_add:
description:
- List of capabilities to add to the container.
type: list
elements: str
cap_drop:
description:
- List of capabilities to drop from the container.
type: list
elements: str
cgroup_parent:
description:
- Path to cgroups under which the cgroup for the container will be
created.
If the path is not absolute, the path is considered to be relative to
the cgroups path of the init process. Cgroups will be created if they
do not already exist.
type: path
cgroupns:
description:
- Path to cgroups under which the cgroup for the container will be
created.
type: str
cgroups:
description:
- Determines whether the container will create CGroups.
Valid values are enabled and disabled, which the default being enabled.
The disabled option will force the container to not create CGroups,
and thus conflicts with CGroup options cgroupns and cgroup-parent.
type: str
choices:
- default
- disabled
cidfile:
description:
- Write the container ID to the file
type: path
cmd_args:
description:
- Any additionl command options you want to pass to podman command,
cmd_args - ['--other-param', 'value']
Be aware module doesn't support idempotency if this is set.
type: list
elements: str
conmon_pidfile:
description:
- Write the pid of the conmon process to a file.
conmon runs in a separate process than Podman,
so this is necessary when using systemd to restart Podman containers.
type: path
command:
description:
- Override command of container. Can be a string or a list.
type: raw
cpu_period:
description:
- Limit the CPU real-time period in microseconds
type: int
cpu_rt_period:
description:
- Limit the CPU real-time period in microseconds.
Limit the container's Real Time CPU usage. This flag tell the kernel to
restrict the container's Real Time CPU usage to the period you specify.
type: int
cpu_rt_runtime:
description:
- Limit the CPU real-time runtime in microseconds.
This flag tells the kernel to limit the amount of time in a given CPU
period Real Time tasks may consume.
type: int
cpu_shares:
description:
- CPU shares (relative weight)
type: int
cpus:
description:
- Number of CPUs. The default is 0.0 which means no limit.
type: str
cpuset_cpus:
description:
- CPUs in which to allow execution (0-3, 0,1)
type: str
cpuset_mems:
description:
- Memory nodes (MEMs) in which to allow execution (0-3, 0,1). Only
effective on NUMA systems.
type: str
detach:
description:
- Run container in detach mode
type: bool
default: True
debug:
description:
- Return additional information which can be helpful for investigations.
type: bool
default: False
detach_keys:
description:
- Override the key sequence for detaching a container. Format is a single
character or ctrl-value
type: str
device:
description:
- Add a host device to the container.
The format is <device-on-host>[:<device-on-container>][:<permissions>]
(e.g. device /dev/sdc:/dev/xvdc:rwm)
type: list
elements: str
device_read_bps:
description:
- Limit read rate (bytes per second) from a device
(e.g. device-read-bps /dev/sda:1mb)
type: list
device_read_iops:
description:
- Limit read rate (IO per second) from a device
(e.g. device-read-iops /dev/sda:1000)
type: list
device_write_bps:
description:
- Limit write rate (bytes per second) to a device
(e.g. device-write-bps /dev/sda:1mb)
type: list
device_write_iops:
description:
- Limit write rate (IO per second) to a device
(e.g. device-write-iops /dev/sda:1000)
type: list
dns:
description:
- Set custom DNS servers
type: list
elements: str
dns_option:
description:
- Set custom DNS options
type: str
dns_search:
description:
- Set custom DNS search domains (Use dns_search with '' if you don't wish
to set the search domain)
type: str
entrypoint:
description:
- Overwrite the default ENTRYPOINT of the image
type: str
env:
description:
- Set environment variables.
This option allows you to specify arbitrary environment variables that
are available for the process that will be launched inside of the
container.
type: dict
env_file:
description:
- Read in a line delimited file of environment variables
type: path
env_host:
description:
- Use all current host environment variables in container.
Defaults to false.
type: bool
etc_hosts:
description:
- Dict of host-to-IP mappings, where each host name is a key in the
dictionary. Each host name will be added to the container's
``/etc/hosts`` file.
type: dict
aliases:
- add_hosts
expose:
description:
- Expose a port, or a range of ports (e.g. expose "3300-3310") to set up
port redirection on the host system.
type: list
elements: str
aliases:
- exposed
- exposed_ports
force_restart:
description:
- Force restart of container.
type: bool
default: False
aliases:
- restart
gidmap:
description:
- Run the container in a new user namespace using the supplied mapping.
type: str
group_add:
description:
- Add additional groups to run as
type: list
healthcheck:
description:
- Set or alter a healthcheck command for a container.
type: str
healthcheck_interval:
description:
- Set an interval for the healthchecks
(a value of disable results in no automatic timer setup)
(default "30s")
type: str
healthcheck_retries:
description:
- The number of retries allowed before a healthcheck is considered to be
unhealthy. The default value is 3.
type: int
healthcheck_start_period:
description:
- The initialization time needed for a container to bootstrap.
The value can be expressed in time format like 2m3s. The default value
is 0s
type: str
healthcheck_timeout:
description:
- The maximum time allowed to complete the healthcheck before an interval
is considered failed. Like start-period, the value can be expressed in
a time format such as 1m22s. The default value is 30s
type: str
hostname:
description:
- Container host name. Sets the container host name that is available
inside the container.
type: str
http_proxy:
description:
- By default proxy environment variables are passed into the container if
set for the podman process. This can be disabled by setting the
http_proxy option to false. The environment variables passed in
include http_proxy, https_proxy, ftp_proxy, no_proxy, and also the
upper case versions of those.
Defaults to true
type: bool
image_volume:
description:
- Tells podman how to handle the builtin image volumes.
The options are bind, tmpfs, or ignore (default bind)
type: str
choices:
- 'bind'
- 'tmpfs'
- 'ignore'
image_strict:
description:
- Whether to compare images in idempotency by taking into account a full
name with registry and namespaces.
type: bool
default: False
init:
description:
- Run an init inside the container that forwards signals and reaps
processes.
type: str
init_path:
description:
- Path to the container-init binary.
type: str
interactive:
description:
- Keep STDIN open even if not attached. The default is false.
When set to true, keep stdin open even if not attached.
The default is false.
type: bool
ip:
description:
- Specify a static IP address for the container, for example
'10.88.64.128'.
Can only be used if no additional CNI networks to join were specified
via 'network:', and if the container is not joining another container's
network namespace via 'network container:<name|id>'.
The address must be within the default CNI network's pool
(default 10.88.0.0/16).
type: str
ipc:
description:
- Default is to create a private IPC namespace (POSIX SysV IPC) for the
container
type: str
kernel_memory:
description:
- Kernel memory limit
(format <number>[<unit>], where unit = b, k, m or g)
Note - idempotency is supported for integers only.
type: str
label:
description:
- Add metadata to a container, pass dictionary of label names and values
type: dict
label_file:
description:
- Read in a line delimited file of labels
type: str
log_driver:
description:
- Logging driver. Used to set the log driver for the container.
For example log_driver "k8s-file".
type: str
choices:
- k8s-file
- journald
- json-file
log_opt:
description:
- Logging driver specific options. Used to set the path to the container
log file. For example log_opt
"path=/var/log/container/mycontainer.json"
type: str
aliases:
- log_options
memory:
description:
- Memory limit (format 10k, where unit = b, k, m or g)
Note - idempotency is supported for integers only.
type: str
memory_reservation:
description:
- Memory soft limit (format 100m, where unit = b, k, m or g)
Note - idempotency is supported for integers only.
type: str
memory_swap:
description:
- A limit value equal to memory plus swap. Must be used with the -m
(--memory) flag.
The swap LIMIT should always be larger than -m (--memory) value.
By default, the swap LIMIT will be set to double the value of --memory
Note - idempotency is supported for integers only.
type: str
memory_swappiness:
description:
- Tune a container's memory swappiness behavior. Accepts an integer
between 0 and 100.
type: int
mount:
description:
- Attach a filesystem mount to the container. bind or tmpfs
For example mount
"type=bind,source=/path/on/host,destination=/path/in/container"
type: str
network:
description:
- Set the Network mode for the container
* bridge create a network stack on the default bridge
* none no networking
* container:<name|id> reuse another container's network stack
* host use the podman host network stack.
* <network-name>|<network-id> connect to a user-defined network
* ns:<path> path to a network namespace to join
* slirp4netns use slirp4netns to create a user network stack.
This is the default for rootless containers
type: list
elements: str
aliases:
- net
no_hosts:
description:
- Do not create /etc/hosts for the container
Default is false.
type: bool
oom_kill_disable:
description:
- Whether to disable OOM Killer for the container or not.
Default is false.
type: bool
oom_score_adj:
description:
- Tune the host's OOM preferences for containers (accepts -1000 to 1000)
type: int
pid:
description:
- Set the PID mode for the container
type: str
pids_limit:
description:
- Tune the container's pids limit. Set -1 to have unlimited pids for the
container.
type: str
pod:
description:
- Run container in an existing pod.
If you want podman to make the pod for you, preference the pod name
with "new:"
type: str
privileged:
description:
- Give extended privileges to this container. The default is false.
type: bool
publish:
description:
- Publish a container's port, or range of ports, to the host.
Format - ip:hostPort:containerPort | ip::containerPort |
hostPort:containerPort | containerPort
type: list
elements: str
aliases:
- ports
- published
- published_ports
publish_all:
description:
- Publish all exposed ports to random ports on the host interfaces. The
default is false.
type: bool
read_only:
description:
- Mount the container's root filesystem as read only. Default is false
type: bool
read_only_tmpfs:
description:
- If container is running in --read-only mode, then mount a read-write
tmpfs on /run, /tmp, and /var/tmp. The default is true
type: bool
recreate:
description:
- Use with present and started states to force the re-creation of an
existing container.
type: bool
default: False
restart_policy:
description:
- Restart policy to follow when containers exit.
Restart policy will not take effect if a container is stopped via the
podman kill or podman stop commands. Valid values are
* no - Do not restart containers on exit
* on-failure[:max_retries] - Restart containers when they exit with a
non-0 exit code, retrying indefinitely
or until the optional max_retries count is hit
* always - Restart containers when they exit, regardless of status,
retrying indefinitely
type: str
rm:
description:
- Automatically remove the container when it exits. The default is false.
type: bool
aliases:
- remove
rootfs:
description:
- If true, the first argument refers to an exploded container on the file
system. The dafault is false.
type: bool
security_opt:
description:
- Security Options. For example security_opt "seccomp=unconfined"
type: list
elements: str
shm_size:
description:
- Size of /dev/shm. The format is <number><unit>. number must be greater
than 0.
Unit is optional and can be b (bytes), k (kilobytes), m(megabytes), or
g (gigabytes).
If you omit the unit, the system uses bytes. If you omit the size
entirely, the system uses 64m
type: str
sig_proxy:
description:
- Proxy signals sent to the podman run command to the container process.
SIGCHLD, SIGSTOP, and SIGKILL are not proxied. The default is true.
type: bool
stop_signal:
description:
- Signal to stop a container. Default is SIGTERM.
type: int
stop_timeout:
description:
- Timeout (in seconds) to stop a container. Default is 10.
type: int
subgidname:
description:
- Run the container in a new user namespace using the map with 'name' in
the /etc/subgid file.
type: str
subuidname:
description:
- Run the container in a new user namespace using the map with 'name' in
the /etc/subuid file.
type: str
sysctl:
description:
- Configure namespaced kernel parameters at runtime
type: dict
systemd:
description:
- Run container in systemd mode. The default is true.
type: bool
tmpfs:
description:
- Create a tmpfs mount. For example tmpfs
"/tmp" "rw,size=787448k,mode=1777"
type: dict
tty:
description:
- Allocate a pseudo-TTY. The default is false.
type: bool
uidmap:
description:
- Run the container in a new user namespace using the supplied mapping.
type: list
ulimit:
description:
- Ulimit options
type: list
user:
description:
- Sets the username or UID used and optionally the groupname or GID for
the specified command.
type: str
userns:
description:
- Set the user namespace mode for the container.
It defaults to the PODMAN_USERNS environment variable.
An empty value means user namespaces are disabled.
type: str
uts:
description:
- Set the UTS mode for the container
type: str
volume:
description:
- Create a bind mount. If you specify, volume /HOST-DIR:/CONTAINER-DIR,
podman bind mounts /HOST-DIR in the host to /CONTAINER-DIR in the
podman container.
type: list
elements: str
aliases:
- volumes
volumes_from:
description:
- Mount volumes from the specified container(s).
type: list
elements: str
workdir:
description:
- Working directory inside the container.
The default working directory for running binaries within a container
is the root directory (/).
type: str
"""
EXAMPLES = """
- name: Run container
podman_container:
name: container
image: quay.io/bitnami/wildfly
state: started
- name: Create a data container
podman_container:
name: mydata
image: busybox
volume:
- /tmp/data
- name: Re-create a redis container
podman_container:
name: myredis
image: redis
command: redis-server --appendonly yes
state: present
recreate: yes
expose:
- 6379
volumes_from:
- mydata
- name: Restart a container
podman_container:
name: myapplication
image: redis
state: started
restart: yes
etc_hosts:
other: "127.0.0.1"
restart_policy: "no"
device: "/dev/sda:/dev/xvda:rwm"
ports:
- "8080:9000"
- "127.0.0.1:8081:9001/udp"
env:
SECRET_KEY: "ssssh"
BOOLEAN_KEY: "yes"
- name: Container present
podman_container:
name: mycontainer
state: present
image: ubuntu:14.04
command: "sleep 1d"
- name: Stop a container
podman_container:
name: mycontainer
state: stopped
- name: Start 4 load-balanced containers
podman_container:
name: "container{{ item }}"
recreate: yes
image: someuser/anotherappimage
command: sleep 1d
with_sequence: count=4
- name: remove container
podman_container:
name: ohno
state: absent
- name: Writing output
podman_container:
name: myservice
image: busybox
log_options: path=/var/log/container/mycontainer.json
log_driver: k8s-file
"""
RETURN = """
container:
description:
- Facts representing the current state of the container. Matches the
podman inspection output.
- Note that facts are part of the registered vars since Ansible 2.8. For
compatibility reasons, the facts
are also accessible directly as C(podman_container). Note that the
returned fact will be removed in Ansible 2.12.
- Empty if C(state) is I(absent).
returned: always
type: dict
sample: '{
"AppArmorProfile": "",
"Args": [
"sh"
],
"BoundingCaps": [
"CAP_CHOWN",
...
],
"Config": {
"Annotations": {
"io.kubernetes.cri-o.ContainerType": "sandbox",
"io.kubernetes.cri-o.TTY": "false"
},
"AttachStderr": false,
"AttachStdin": false,
"AttachStdout": false,
"Cmd": [
"sh"
],
"Domainname": "",
"Entrypoint": "",
"Env": [
"PATH=/usr/sbin:/usr/bin:/sbin:/bin",
"TERM=xterm",
"HOSTNAME=",
"container=podman"
],
"Hostname": "",
"Image": "docker.io/library/busybox:latest",
"Labels": null,
"OpenStdin": false,
"StdinOnce": false,
"StopSignal": 15,
"Tty": false,
"User": {
"gid": 0,
"uid": 0
},
"Volumes": null,
"WorkingDir": "/"
},
"ConmonPidFile": "...",
"Created": "2019-06-17T19:13:09.873858307+03:00",
"Dependencies": [],
"Driver": "overlay",
"EffectiveCaps": [
"CAP_CHOWN",
...
],
"ExecIDs": [],
"ExitCommand": [
"/usr/bin/podman",
"--root",
...
],
"GraphDriver": {
...
},
"HostConfig": {
...
},
"HostnamePath": "...",
"HostsPath": "...",
"ID": "...",
"Image": "...",
"ImageName": "docker.io/library/busybox:latest",
"IsInfra": false,
"LogPath": "/tmp/container/mycontainer.json",
"MountLabel": "system_u:object_r:container_file_t:s0:c282,c782",
"Mounts": [
...
],
"Name": "myservice",
"Namespace": "",
"NetworkSettings": {
"Bridge": "",
...
},
"Path": "sh",
"ProcessLabel": "system_u:system_r:container_t:s0:c282,c782",
"ResolvConfPath": "...",
"RestartCount": 0,
"Rootfs": "",
"State": {
"Dead": false,
"Error": "",
"ExitCode": 0,
"FinishedAt": "2019-06-17T19:13:10.157518963+03:00",
"Healthcheck": {
"FailingStreak": 0,
"Log": null,
"Status": ""
},
"OOMKilled": false,
"OciVersion": "1.0.1-dev",
"Paused": false,
"Pid": 4083,
"Restarting": false,
"Running": false,
"StartedAt": "2019-06-17T19:13:10.152479729+03:00",
"Status": "exited"
},
"StaticDir": "..."
...
}'
"""
def ensure_image_exists(module, image):
"""If image is passed, ensure it exists, if not - pull it or fail.
Arguments:
module {obj} -- ansible module object
image {str} -- name of image
Returns:
list -- list of image actions - if it pulled or nothing was done
"""
image_actions = []
module_exec = module.params['executable']
if not image:
return image_actions
rc, out, err = module.run_command([module_exec, 'image', 'exists', image])
if rc == 0:
return image_actions
rc, out, err = module.run_command([module_exec, 'image', 'pull', image])
if rc != 0:
module.fail_json(msg="Can't pull image %s" % image, stdout=out,
stderr=err)
image_actions.append("pulled image %s" % image)
return image_actions
def _perform_action(self, action):
"""Perform action with container.
Arguments:
action {str} -- action to perform - start, create, stop, run,
delete
"""
b_command = PodmanModuleParams(action,
self.module.params,
self.version,
self.module,
).construct_command_from_params()
full_cmd = " ".join([self.module.params['executable']]
+ [to_native(i) for i in b_command])
self.module.log("PODMAN-CONTAINER-DEBUG: %s" % full_cmd)
self.actions.append(full_cmd)
if not self.module.check_mode:
rc, out, err = self.module.run_command(
[self.module.params['executable'], b'container'] + b_command,
expand_user_and_vars=False)
self.stdout = out
self.stderr = err
if rc != 0:
self.module.fail_json(
msg="Can't %s container %s" % (action, self.name),
stdout=out, stderr=err)
def run(self):
"""Run the container."""
self._perform_action('run')
def delete(self):
"""Delete the container."""
self._perform_action('delete')
def stop(self):
"""Stop the container."""
self._perform_action('stop')
def start(self):
"""Start the container."""
self._perform_action('start')
def create(self):
"""Create the container."""
self._perform_action('create')
def recreate(self):
"""Recreate the container."""
self.delete()
self.run()
def restart(self):
"""Restart the container."""
self.stop()
self.run()
class PodmanManager:
"""Module manager class.
Defines according to parameters what actions should be applied to container
"""
def __init__(self, module):
"""Initialize PodmanManager class.
Arguments:
module {obj} -- ansible module object
"""
super(PodmanManager, self).__init__()
self.module = module
self.results = {
'changed': False,
'actions': [],
'container': {},
}
self.name = self.module.params['name']
self.executable = \
self.module.get_bin_path(self.module.params['executable'],
required=True)
self.image = self.module.params['image']
image_actions = ensure_image_exists(self.module, self.image)
self.results['actions'] += image_actions
self.state = self.module.params['state']
self.restart = self.module.params['force_restart']
self.recreate = self.module.params['recreate']
self.container = PodmanContainer(self.module, self.name)
def update_container_result(self, changed=True):
"""Inspect the current container, update results with last info, exit.
Keyword Arguments:
changed {bool} -- whether any action was performed
(default: {True})
"""
facts = self.container.get_info() if changed else self.container.info
out, err = self.container.stdout, self.container.stderr
self.results.update({'changed': changed, 'container': facts,
'podman_actions': self.container.actions},
stdout=out, stderr=err)
if self.container.diff:
self.results.update({'diff': self.container.diff})
if self.module.params['debug']:
self.results.update({'podman_version': self.container.version})
self.module.exit_json(**self.results)
def make_started(self):
"""Run actions if desired state is 'started'."""
if self.container.running and \
(self.container.different or self.recreate):
self.container.recreate()
self.results['actions'].append('recreated %s' %
self.container.name)
self.update_container_result()
elif self.container.running and not self.container.different:
if self.restart:
self.container.restart()
self.results['actions'].append('restarted %s' %
self.container.name)
self.update_container_result()
self.update_container_result(changed=False)
elif not self.container.exists:
self.container.run()
self.results['actions'].append('started %s' % self.container.name)
self.update_container_result()
elif self.container.stopped and self.container.different:
self.container.recreate()
self.results['actions'].append('recreated %s' %
self.container.name)
self.update_container_result()
elif self.container.stopped and not self.container.different:
self.container.start()
self.results['actions'].append('started %s' % self.container.name)
self.update_container_result()
def make_stopped(self):
"""Run actions if desired state is 'stopped'."""
if not self.container.exists and not self.image:
self.module.fail_json(msg='Cannot create container when image'
' is not specified!')
if not self.container.exists:
self.container.create()
self.results['actions'].append('created %s' % self.container.name)
self.update_container_result()
if self.container.stopped:
self.update_container_result(changed=False)
elif self.container.running:
self.container.stop()
self.results['actions'].append('stopped %s' % self.container.name)
self.update_container_result()
def make_absent(self):
"""Run actions if desired state is 'absent'."""
if not self.container.exists:
self.results.update({'changed': False})
elif self.container.exists:
self.container.delete()
self.results['actions'].append('deleted %s' % self.container.name)
self.results.update({'changed': True})
self.results.update({'container': {},
'podman_actions': self.container.actions})
self.module.exit_json(**self.results)
def execute(self):
"""Execute the desired action according to map of actions & states."""
states_map = {
'present': self.make_started,
'started': self.make_started,
'absent': self.make_absent,
'stopped': self.make_stopped
}
process_action = states_map[self.state]
process_action()
self.module.fail_json(msg="Unexpected logic error happened, "
"please contact maintainers ASAP!")
if __name__ == '__main__':
main()
| [
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362,
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1169,
366,
34156,... | 2.438889 | 13,860 |
#!/usr/bin/env python
from setuptools import setup
# Modified from http://stackoverflow.com/questions/2058802/
# how-can-i-get-the-version-defined-in-setup-py-setuptools-in-my-package
# Explicitly list bin scripts to be installed, seeing as I have a few local
# bin files that are not (yet) part of the distribution.
scripts = [
'bin/aa-info.py',
'bin/aa-to-dna.py',
'bin/aa-to-properties.py',
'bin/adaptor-distances.py',
'bin/alignment-panel-civ.py',
'bin/alignments-per-read.py',
'bin/bit-score-to-e-value.py',
'bin/cat-json-blast-records.py',
'bin/check-fasta-json-blast-consistency.py',
'bin/codon-distance.py',
'bin/compare-consensuses.py',
'bin/compare-sequences.py',
'bin/convert-blast-xml-to-json.py',
'bin/convert-diamond-to-json.py',
'bin/convert-diamond-to-sam.py',
'bin/convert-sam-to-fastq.sh',
'bin/create-newick-relabeling-output.py',
'bin/dark-matter-version.py',
'bin/describe-protein-database.py',
'bin/dna-to-aa.py',
'bin/download-genbank.sh',
'bin/e-value-to-bit-score.py',
'bin/extract-ORFs.py',
'bin/fasta-base-indices.py',
'bin/fasta-count.py',
'bin/fasta-diff.sh',
'bin/fasta-identity-table.py',
'bin/fasta-ids.py',
'bin/fasta-join.py',
'bin/fasta-lengths.py',
'bin/fasta-sequences.py',
'bin/fasta-sort.py',
'bin/fasta-split-by-id.py',
'bin/fasta-subset.py',
'bin/fasta-subtraction.py',
'bin/fasta-to-phylip.py',
'bin/fasta-variable-sites.py',
'bin/filter-fasta-by-complexity.py',
'bin/filter-fasta-by-taxonomy.py',
'bin/filter-fasta.py',
'bin/filter-hits-to-fasta.py',
'bin/filter-reads-alignments.py',
'bin/filter-sam.py',
'bin/find-hits.py',
'bin/format-fasta.py',
'bin/genome-protein-summary.py',
'bin/get-features.py',
'bin/get-hosts.py',
'bin/get-reads.py',
'bin/get-taxonomy.py',
'bin/graph-evalues.py',
'bin/local-align.py',
'bin/make-consensus.py',
'bin/make-fasta-database.py',
'bin/make-protein-database.py',
'bin/ncbi-fetch-id.py',
'bin/newick-to-ascii.py',
'bin/noninteractive-alignment-panel.py',
'bin/parse-genbank-flat-file.py',
'bin/position-summary.py',
'bin/pre-commit.sh',
'bin/print-blast-xml-for-derek.py',
'bin/print-blast-xml.py',
'bin/print-read-lengths.py',
'bin/proteins-to-pathogens.py',
'bin/proteins-to-pathogens-civ.py',
'bin/randomize-fasta.py',
'bin/read-blast-json.py',
'bin/read-blast-xml.py',
'bin/relabel-newick-tree.py',
'bin/run-bwa.py',
'bin/run-bowtie2.py',
'bin/sam-coverage.py',
'bin/sam-coverage-depth.py',
'bin/sam-to-fasta-alignment.py',
'bin/sam-reference-read-counts.py',
'bin/sam-references.py',
'bin/sff-to-fastq.py',
'bin/split-fasta-by-adaptors.py',
'bin/subset-protein-database.py',
'bin/summarize-fasta-bases.py',
'bin/summarize-reads.py',
'bin/trim-primers.py',
'bin/trim-reads.py',
'bin/write-htcondor-job-spec.py',
]
setup(name='dark-matter',
version=version(),
packages=['dark', 'dark.blast', 'dark.diamond', 'dark.civ'],
url='https://github.com/acorg/dark-matter',
download_url='https://github.com/acorg/dark-matter',
author='Terry Jones, Barbara Muehlemann, Tali Veith, Sophie Mathias',
author_email='tcj25@cam.ac.uk',
keywords=['virus discovery'],
classifiers=[
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 3',
'Development Status :: 4 - Beta',
'Intended Audience :: Developers',
'License :: OSI Approved :: MIT License',
'Operating System :: OS Independent',
'Topic :: Software Development :: Libraries :: Python Modules',
],
license='MIT',
description='Python classes for working with genetic sequence data',
scripts=scripts,
install_requires=[
'biopython>=1.71',
'bz2file>=0.98',
'Cython>=0.29.16',
'ipython>=3.1.0',
'matplotlib>=1.4.3',
'mysql-connector-python==8.0.11',
'numpy>=1.14.2',
'pysam>=0.15.2',
'pyfaidx>=0.4.8.4',
'pyzmq>=14.3.1',
'requests>=2.18.4',
'cachetools>=3.1.0',
'simplejson>=3.5.3',
'six>=1.11.0',
])
| [
2,
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14,
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2,
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12,
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... | 2.044797 | 2,143 |
# ======================================================================================================================
# Fakulta informacnich technologii VUT v Brne
# Bachelor thesis
# Author: Filip Bali (xbalif00)
# License: MIT
# ======================================================================================================================
from django import template
import datetime
import time
from portfolioApp.models import NotificationEvent
register = template.Library()
import pandas as pd
register.filter(print_timestamp)
register.filter(print_timestamp_analysis)
register.filter(print_timestamp_notifications)
register.filter(print_notification_text)
register.filter(print_symbol_notifications)
register.filter(print_type_notifications) | [
2,
38093,
10052,
4770,
1421,
28,
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2,
376,
461,
586,
64,
4175,
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7344,
357,
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6893,
361,
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8,
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2,
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25,... | 4.612121 | 165 |
from pycfmodel.model.resources.properties.policy_document import PolicyDocument
from pycfmodel.model.resources.properties.property import Property
from pycfmodel.model.types import Resolvable, ResolvableStr
| [
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... | 4.078431 | 51 |
from .stacking import StackingClassifier, stack_features
from .multitask import MultiTaskEstimator
| [
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] | 3.807692 | 26 |
# Copyright (C) 2019 Intel Corporation. All rights reserved.
# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
load("@bazel_tools//tools/build_defs/cc:action_names.bzl", "ACTION_NAMES")
load(
"@bazel_tools//tools/cpp:cc_toolchain_config_lib.bzl",
"feature",
"flag_group",
"flag_set",
"tool_path",
)
all_compile_actions = [
ACTION_NAMES.c_compile,
ACTION_NAMES.cpp_compile,
]
all_link_actions = [
ACTION_NAMES.cpp_link_executable,
ACTION_NAMES.cpp_link_dynamic_library,
ACTION_NAMES.cpp_link_nodeps_dynamic_library,
]
emsdk_toolchain_config = rule(
implementation = _impl,
attrs = {},
provides = [CcToolchainConfigInfo],
)
| [
2,
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34,
8,
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8180,
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10... | 2.437276 | 279 |
from __future__ import print_function
import json
from typing import List
from functools import lru_cache
from cloud_storages.http_shortcuts import *
from database.database import Database
from models.models import StorageMetaInfo, Resource, Size
from cloud_storages.storage import Storage
from cloud_storages.gdrive.client_config import GOOGLE_DRIVE_CONFIG, SCOPES
from google_auth_oauthlib.flow import InstalledAppFlow
from google.auth.transport.requests import Request
from google.oauth2.credentials import Credentials
GOOGLE_DRIVE_DB_KEY = 'google'
def create_path(self, remote_path: List[str]) -> None:
"""
Creates the remote path on yandex disk
"""
print(f'[{__name__}] Trying to create directory {"/".join(remote_path)} on remote...')
dir_to_create = []
for dir in remote_path:
dir_to_create.append(dir)
path_to_create = '/'.join(dir_to_create)
response = put_with_OAuth(f'https://cloud-api.yandex.net/v1/disk/resources?path={path_to_create}',
token=self.token)
if 199 < response.status_code < 401:
print(f'[{__name__}] Created directory {path_to_create}')
continue
elif response.status_code == 409 and ' ' in response.json().get('message', ''):
continue
return
def save_resource_to_path(self, resource: Resource, remote_path: str, overwrite: bool, _rec_call:bool = False) -> Resource or None:
"""
Put an Item to the directory
:param resource: resource on the local fs
:param remote_path: string, path to resource on remote fs
:param _rec_call: bool, a system parameter, whether or not this function was called as a recursive call
:return: saved resource or raises exception
"""
upload_successful_flag = False
response = get_with_OAuth(
f'https://cloud-api.yandex.net/v1/disk/resources/upload?path={remote_path}&overwrite=${overwrite}',
token=self.token
)
if response.status_code == 200:
response_read = response.json()
upload_link = response_read['href']
with open(resource.path, 'rb') as f:
files = f
response = put_with_OAuth(upload_link, data=files)
if 199 < response.status_code < 401:
upload_successful_flag = True
response = get_with_OAuth(f'https://cloud-api.yandex.net/v1/disk/resources?path={remote_path}',
token=self.token)
resource_metainfo = self._deserialize_resource(response.json())
if 199 < response.status_code < 401:
return resource_metainfo
elif upload_successful_flag:
return resource
# This dir is not present in the storage
# We use _rec_call to tell that the next call was made as recursive call, so we don't cause SO
elif response.status_code == 409 and not _rec_call:
# We don't need to create a folder with the name equal to the filename, so we do [:-1]
self.create_path(remote_path.split('/')[:-1])
return self.save_resource_to_path(resource, remote_path, overwrite, _rec_call=True)
raise ValueError(f"Something went wrong with YD: Response: "
f"{str(response.status_code)} {response.json().get('message', '')}")
def main():
storage = GDriveStorage(None)
db = Database('../storage.db')
storage.auth(db)
authed_storage = GDriveStorage(json.loads(db.get(GOOGLE_DRIVE_DB_KEY))['token'])
result = authed_storage.list_resources_on_path('savezone')
print(result)
if __name__ == '__main__':
main() | [
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1... | 2.361214 | 1,614 |
from django.urls import re_path, include
from . import views
app_name='logged'
# url mappings for the webapp.
urlpatterns = [
re_path(r'^$', views.logged_count, name="logged_count"),
re_path(r'^loggedusers/', views.logged, name="logged_users"),
re_path(r'^settings/', views.user_settings, name="update_info"),
re_path(r'^administrators/', views.post_alert, name="post_alert"),
re_path(r'^alerts/$', views.list_alert, name="list_alert"),
re_path(r'^alerts/(?P<slug>[\w-]+)/$', views.view_alert, name="view_alert"),
re_path(r'^display/', views.display, name="display"),
re_path(r'^doorselection/', views.doors_election, name="door_selecttion")
] | [
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... | 2.516729 | 269 |
# Copyright 2021 UW-IT, University of Washington
# SPDX-License-Identifier: Apache-2.0
from scout.dao.space import get_spots_by_filter, _get_spot_filters, \
_get_extended_info_by_key
import copy
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... | 2.77027 | 74 |
#Scraper for Minnesota Court of Appeals Published Opinions
#CourtID: minnctapp
#Court Short Name: MN
#Author: mlr
#Date: 2016-06-03
from juriscraper.opinions.united_states.state import minn
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62... | 3.063492 | 63 |
from __future__ import absolute_import
import os
import errno
from contextlib import contextmanager
__author__ = 'Shyue Ping Ong'
__copyright__ = 'Copyright 2013, The Materials Project'
__version__ = '0.1'
__maintainer__ = 'Shyue Ping Ong'
__email__ = 'ongsp@ucsd.edu'
__date__ = '1/24/14'
def makedirs_p(path, **kwargs):
"""
Wrapper for os.makedirs that does not raise an exception if the directory already exists, in the fashion of
"mkdir -p" command. The check is performed in a thread-safe way
Args:
path: path of the directory to create
kwargs: standard kwargs for os.makedirs
"""
try:
os.makedirs(path, **kwargs)
except OSError as exc:
if exc.errno == errno.EEXIST and os.path.isdir(path):
pass
else:
raise | [
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... | 2.490798 | 326 |
import sys
REQUIRED_PYTHON = "python3"
required_major = 3
if __name__ == '__main__':
main()
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19... | 2.348837 | 43 |