content
stringlengths 1
1.04M
| input_ids
sequencelengths 1
774k
| ratio_char_token
float64 0.38
22.9
| token_count
int64 1
774k
|
|---|---|---|---|
# 2020 Tommaso Ciussani and Giacomo Giuliari
"""
This file contains the type definitions and conversions between coordinate schemes.
All length values in m
"""
import numpy as np
from typing import Tuple
from typing_extensions import TypedDict
from icarus_simulator.sat_core.planetary_const import EARTH_RADIUS
CartCoords = Tuple[float, float, float]
def geo2cart(geo_coord: GeodeticPosition) -> CartCoords:
"""
Converts a {lat, long, elevation} point to cartesian (x, y, z).
Args:
geo_coord: SatPosition. Coordinates of the point in geodesic format.
Returns:
Tuple[float, float, float]: Tuple of cartesian coordinates.
"""
theta = np.deg2rad(geo_coord["lon"])
phi = np.deg2rad(90 - geo_coord["lat"])
r = geo_coord["elev"] + EARTH_RADIUS
x = r * np.sin(phi) * np.cos(theta)
y = r * np.sin(phi) * np.sin(theta)
z = r * np.cos(phi)
cart = (x, y, z)
rad = np.sqrt(np.sum(np.square(cart)))
assert rad >= EARTH_RADIUS - 1000 # Allow for approximation error
return cart
| [
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] | 2.568293 | 410 |
# help | [
2,
1037
] | 3 | 2 |
# -*- coding: utf-8 -*-
import entity.cards.LETLT_060.LETLT_060
import entity.cards.LETLT_060.LETLT_060
| [
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# Generated by Django 2.1.5 on 2019-02-16 03:24
from django.db import migrations, models
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] | 2.84375 | 32 |
# Copyright (c) 2018 Ansible, Inc.
# All Rights Reserved.
from ansiblelint import AnsibleLintRule
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] | 3.225806 | 31 |
import PyPDF2
import textract
#from nltk.tokenize import word_tokenize
#from nltk.corpus import stopwords | [
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] | 2.944444 | 36 |
import torch
from torch import nn
from .utils import conv3x3, DepthwiseSeparableConv
from .base import BackboneBaseModule
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from django.contrib import admin
from django.urls import path
from coreRelback import views
app_name = 'coreRelback'
urlpatterns = [
path('', views.index, name='index'),
path('admin/', admin.site.urls),
path('creators/', views.creators, name='creators'),
# Routes - Clients
path('client/', views.clientRead.as_view(), name='client'),
path('client/create/', views.clientCreate.as_view(), name='clientCreate'),
path('client/update/', views.clientUpdate.as_view(), name='clientUpdate'),
path('client/delete/', views.clientDelete.as_view(), name='clientDelete'),
# Routes - Hosts
path('host/', views.hostRead.as_view(), name='host'),
path('host/create/', views.hostCreate.as_view(), name='hostCreate'),
path('host/update/', views.hostUpdate.as_view(), name='hostUpdate'),
path('host/delete/', views.hostDelete.as_view(), name='hostDelete'),
# Routes - Databases
path('database/', views.databaseRead.as_view(), name='database'),
path('database/create/', views.databaseCreate.as_view(), name='databaseCreate'),
path('database/update/', views.databaseUpdate.as_view(), name='databaseUpdate'),
path('database/delete/', views.databaseDelete.as_view(), name='databaseDelete'),
path('database/hostsList/', views.hostsList, name='hostsList'),
# Routes - Policies
path('policy/', views.policyRead.as_view(), name='policy'),
path('policy/detail/', views.policyRead.policyDetail, name='policyDetail'),
path('policy/create/', views.policyCreate.as_view(), name='policyCreate'),
path('policy/update/', views.policyUpdate.as_view(), name='policyUpdate'),
path('policy/delete/', views.policyDelete.as_view(), name='policyDelete'),
path('policy/hostsList/', views.hostsList, name='hostsList'),
path('policy/databasesList/', views.databasesList, name='databasesList'),
# Routes - Reports
path('reports/', views.reportRead, name='reportRead'),
path('reports/readLogDetail/<int:idPolicy>/<int:dbKey>/<int:sessionKey>/', views.reportReadLogDetail, name='reportReadLogDetail'),
path('reports/refreshSchedule', views.reportRefreshSchedule, name='refreshSchedule'),
]
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11,
1438,
11639,
5420,
3447,
27054,
5950,
33809,
198,
198,
60,
198
] | 2.965894 | 733 |
from typing import Set, Optional, TYPE_CHECKING
from ...knowledge_plugins.key_definitions import LiveDefinitions
from .. import register_analysis
from .reaching_definitions import ReachingDefinitionsAnalysis
if TYPE_CHECKING:
from angr.knowledge_plugins.key_definitions.definition import Definition
from angr.storage.memory_object import SimMemoryObject
from angr.storage.memory_mixins import MultiValuedMemory
from angr.storage.memory_mixins.paged_memory.pages import MVListPage
register_analysis(ReachingDefinitionsAnalysis, 'ReachingDefinitions')
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11,
705,
3041,
8103,
7469,
50101,
11537,
198
] | 3.666667 | 156 |
import numpy as np
import matplotlib.pyplot as plt | [
11748,
299,
32152,
355,
45941,
198,
11748,
2603,
29487,
8019,
13,
9078,
29487,
355,
458,
83
] | 3.125 | 16 |
import logging
import time
from .profile import Profile
from .question import Questions
log = logging.getLogger(__name__)
class Copy(object):
"""Copy photos, essays and other attributes from one profile to another."""
copy_methods = ['photos', 'essays', 'looking_for', 'details', 'questions']
def __init__(self, source_profile_or_user, dest_user):
"""
:param source_profile_or_user: A :class:`~okcupyd.user.User` or
:class:`~okcupyd.profile.Profile` object
from which to copy attributes.
:meth:`~.Copy.questions` will not
will not preserve the importance of
copied questions if a
:class:`~okcupyd.profile.Profile`
instance is provided.
:param dest_user: A :class:`~okcupyd.user.User` to which data will be
copied
"""
if isinstance(source_profile_or_user, Profile):
self.source_profile = source_profile_or_user
self.source_user = None
else:
self.source_user = source_profile_or_user
self.source_profile = self.source_user.profile
self.dest_user = dest_user
def questions(self):
"""Copy questions to the destination user. When this class was
initialized with a :class:`~okcupyd.profile.Profile`, this will
delete any existing questions answers on the destination account.
"""
if self.source_user:
return self._copy_questions_from_user()
else:
return self._copy_questions_from_profile()
def photos(self):
"""Copy photos to the destination user."""
# Reverse because pictures appear in inverse chronological order.
for photo_info in self.dest_user.profile.photo_infos:
self.dest_user.photo.delete(photo_info)
return [self.dest_user.photo.upload_and_confirm(info)
for info in reversed(self.source_profile.photo_infos)]
def essays(self):
"""Copy essays from the source profile to the destination profile."""
for essay_name in self.dest_user.profile.essays.essay_names:
setattr(self.dest_user.profile.essays, essay_name,
getattr(self.source_profile.essays, essay_name))
def looking_for(self):
"""Copy looking for attributes from the source profile to the
destination profile.
"""
looking_for = self.source_profile.looking_for
return self.dest_user.profile.looking_for.update(
gentation=looking_for.gentation,
single=looking_for.single,
near_me=looking_for.near_me,
kinds=looking_for.kinds,
ages=looking_for.ages
)
def details(self):
"""Copy details from the source profile to the destination profile."""
return self.dest_user.profile.details.convert_and_update(
self.source_profile.details.as_dict
)
def all(self):
"""Invoke all of :meth:`~.Copy.questions`, :meth:`~.Copy.details`,
:meth:`~.Copy.essays`, :meth:`~.Copy.photos`, :meth:`~.Copy.looking_for`
"""
for method_name in self.copy_methods:
getattr(self, method_name)()
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651,
35226,
7,
944,
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2446,
62,
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198
] | 2.190446 | 1,570 |
print(convert(10,2))
print(convert_inv(10,2))
| [
198,
198,
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7,
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7,
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11,
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198
] | 2 | 25 |
"""Configuration for tests."""
import {{ cookiecutter.project_slug }}
def pytest_report_header():
"""Additional report header."""
return f"version: { {{- cookiecutter.project_slug -}}.__version__}"
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GUEST_URL_PREFIX = '/guest'
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#! /usr/bin/env python3
# -*- coding: utf-8 -*-
# vim:fenc=utf-8
#
# Copyright © 2021 Peter Lau <[email protected]>
#
# Distributed under terms of the MIT license.
import time
import asyncio
# method 1
# asyncio.run(main())
# print(main())
# asyncio.run(main2())
asyncio.run(main3())
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#!/usr/bin/python
# -*- coding: utf-8 -*-
# Author: Jānis Zuters
from __future__ import unicode_literals, division
import sys
import argparse
from io import open
argparse.open = open
from prpe_ne import collect_ne_pairs
if __name__ == '__main__':
parser = create_parser()
args = parser.parse_args()
collect_ne_pairs(args.input1.name,args.input2.name,args.output1.name,args.output2.name)
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def gn_place_uri(geonamesid,fcode,country,admin1,admin2,admin3,admin4):
"Return URI of place from a geonames id"
if fcode=="PCLI":
return "geonames/place/Country/"+country
elif fcode=="ADM1":
return "geonames/place/State1stDiv/"+country+"_"+admin1
elif fcode=="ADM2":
return "geonames/place/CountyProvince2ndDiv/"+country+"_"+admin1+"_"+admin2
elif fcode=="ADM3":
return "geonames/place/Community3rdDiv/"+country+"_"+admin1+"_"+admin2+"_"+admin3
elif fcode=="ADM4":
return "geonames/place/SubCommunity4thDiv/"+country+"_"+admin1+"_"+admin2+"_"+admin3+"_"+admin4
return "geonames/place/"+geonamesid
def gn_place_spacetimevolume_uri(class_uri):
"Return URI of SpaceTimeVolume for a class with Spacetimevolume"
return class_uri+"/SpaceTimeVolume"
def gn_place_identifier_uri(geonamesid):
"Return URI of place from a geonames id"
return "geonames/place/"+geonamesid+"/identifier"
def gn_name_uri(geonamesid,name):
"Return URI of name for a place with geonames id"
return "geonames/place/"+geonamesid+"/Name/"+name
def gn_nametype(type):
"Return Nametype of name"
return "http://dig.isi.edu/gazetteer/data/SKOS/NameTypes/"+type
def gn_select_not_populated_or_administrative(fclass):
"Return Nametype of name"
return fclass!="P" and fclass!="A"
def gn_nametype_conditional(type,condition):
"Return Nametype of name if condition is 1, used for alternamtenames which have flags for historic, colloquial,..."
if condition == 1:
return "http://dig.isi.edu/gazetteer/data/SKOS/NameTypes/"+type
return ''
def gn_countrycodeconcept_uri(country):
"Return country code concept_uri of country taken from SKOS vocabulary http://eulersharp.sourceforge.net/2003/03swap/countries"
return "http://eulersharp.sourceforge.net/2003/03swap/countries#"+country
def gn_languagecodeconcept_uri(language):
"Return language code concept_uri of language taken from SKOS vocabulary http://eulersharp.sourceforge.net/2003/03swap/languages"
if len(language) == 2:
return "http://eulersharp.sourceforge.net/2003/03swap/languages#"+language
return ''
def gn_pointgeometry_uri(place_uri):
"Return URI of PointGeometry for a place"
return place_uri+"/PointGeometry"
def gn_country_uri(country):
"Return URI for Place of class country"
return "geonames/place/Country/"+country
def gn_geojson(lat,long):
"Return geojson point representation"
return """{"type": "Point","coordinates": ["""+lat+","+long+"]}"
def gn_State1stDiv_uri(country,admin1):
"Return URI for Place of class State1stDiv"
if admin1 == None or admin1 =='00':
return ''
return "geonames/place/State1stDiv/"+country+"_"+admin1
def gn_CountyProvince2ndDiv_uri(country,admin1,admin2):
"Return URI for Place of class CountyProvince2ndDiv"
if admin2 == '' or admin2 =='00':
return ''
return "geonames/place/CountyProvince2ndDiv/"+country+"_"+admin1+"_"+admin2
def gn_Community3rdDiv_uri(country,admin1,admin2,admin3):
"Return URI for Place of class CountyProvince2ndDiv"
if admin3 == '' or admin3 =='00':
return ''
return "geonames/place/CountyProvince2ndDiv/"+country+"_"+admin1+"_"+admin2+"_"+admin3
def gn_SubCommunity4thDiv_uri(country,admin1,admin2,admin3,admin4):
"Return URI for Place of class CountyProvince2ndDiv"
if admin4 == '' or admin4 =='00':
return ''
return "geonames/place/SubCommunity4thDiv/"+country+"_"+admin1+"_"+admin2+"_"+admin3+"_"+admin4
def fcode_to_class(fclass,fcode):
"Compute the name of the class in the ontology from a geonames fcode"
c = fclass_dictionary[fclass]
if fclass=="P":
return dgeo+c
c = fcode_dictionary[fcode]
if c == None:
return ''
return dgeo+c
#
dgeo = "http://dig.isi.edu/ontology/dgeo/"
fcode_dictionary = {}
fcode_dictionary['PCLI'] = "Country"
fcode_dictionary['ADM1'] = "State1stDiv"
fcode_dictionary['ADM2'] = "CountyProvince2ndDiv"
fcode_dictionary['ADM3'] = "Community3rdDiv"
fcode_dictionary['ADM4'] = "SubCommunity4thDiv"
fclass_dictionary = {}
fclass_dictionary['P'] = "PopulatedPlace"
fclass_dictionary['A'] = "AdministrativeArea" | [
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1
] | 2.516827 | 1,664 |
import os
import pytest
from random import randint
import socket
import uuid
from volttrontesting.utils.platformwrapper import PlatformWrapper
from volttrontesting.utils.utils import get_hostname_and_random_port, get_rand_vip, get_rand_ip_and_port
from volttron.platform import is_rabbitmq_available
PRINT_LOG_ON_SHUTDOWN = False
HAS_RMQ = is_rabbitmq_available()
rmq_skipif = pytest.mark.skipif(not HAS_RMQ, reason='RabbitMQ is not setup')
@pytest.fixture(scope="module",
params=[dict(messagebus='zmq', ssl_auth=False),
rmq_skipif(dict(messagebus='rmq', ssl_auth=True))
])
# IPC testing is removed since it is not used from VOLTTRON 6.0
@pytest.fixture(scope="function")
@pytest.fixture(scope="module")
# Generic fixtures. Ideally we want to use the below instead of
# Use this fixture when you want a single instance of volttron platform for
# test
@pytest.fixture(scope="module",
params=(
dict(messagebus='zmq', ssl_auth=False),
rmq_skipif(dict(messagebus='rmq', ssl_auth=True)),
))
def volttron_instance(request, **kwargs):
"""Fixture that returns a single instance of volttron platform for testing
@param request: pytest request object
@return: volttron platform instance
"""
address = kwargs.pop("vip_address", get_rand_vip())
wrapper = build_wrapper(address,
messagebus=request.param['messagebus'],
ssl_auth=request.param['ssl_auth'],
**kwargs)
yield wrapper
cleanup_wrapper(wrapper)
# Use this fixture to get more than 1 volttron instance for test.
# Usage example:
# def test_function_that_uses_n_instances(request, get_volttron_instances):
# instances = get_volttron_instances(3)
#
# TODO allow rmq to be added to the multi platform request.
@pytest.fixture(scope="module",
params=[
dict(messagebus='zmq', ssl_auth=False)
])
def get_volttron_instances(request):
""" Fixture to get more than 1 volttron instance for test
Use this fixture to get more than 1 volttron instance for test. This
returns a function object that should be called with number of instances
as parameter to get a list of volttron instnaces. The fixture also
takes care of shutting down all the instances at the end
Example Usage:
def test_function_that_uses_n_instances(get_volttron_instances):
instance1, instance2, instance3 = get_volttron_instances(3)
@param request: pytest request object
@return: function that can used to get any number of
volttron instances for testing.
"""
all_instances = []
request.addfinalizer(cleanup)
return get_n_volttron_instances
# Use this fixture when you want a single instance of volttron platform for zmq message bus
# test
@pytest.fixture(scope="module")
def volttron_instance_zmq(request):
"""Fixture that returns a single instance of volttron platform for testing
@param request: pytest request object
@return: volttron platform instance
"""
address = get_rand_vip()
wrapper = build_wrapper(address)
yield wrapper
cleanup_wrapper(wrapper)
# Use this fixture when you want a single instance of volttron platform for rmq message bus
# test
@pytest.fixture(scope="module")
def volttron_instance_rmq(request):
"""Fixture that returns a single instance of volttron platform for testing
@param request: pytest request object
@return: volttron platform instance
"""
wrapper = None
address = get_rand_vip()
wrapper = build_wrapper(address,
messagebus='rmq',
ssl_auth=True)
yield wrapper
cleanup_wrapper(wrapper)
@pytest.fixture(scope="module",
params=[
dict(messagebus='zmq', ssl_auth=False),
rmq_skipif(dict(messagebus='rmq', ssl_auth=True))
])
@pytest.fixture(scope="module",
params=[
dict(sink='zmq_web', source='zmq'),
rmq_skipif(dict(sink='rmq_web', source='zmq')),
rmq_skipif(dict(sink='rmq_web', source='rmq')),
rmq_skipif(dict(sink='zmq_web', source='rmq'))
])
def volttron_multi_messagebus(request):
""" This fixture allows multiple two message bus types to be configured to work together
This case will create a source (where data comes from) and a sink (where data goes to) to
allow connections from source to sink to be tested for the different cases. In particular,
the case of VolttronCentralPlatform, Forwarder and DataMover agents should use this
case.
:param request:
:return:
"""
print("volttron_multi_messagebus source: {} sink: {}".format(request.param['source'],
request.param['sink']))
sink_address = get_rand_vip()
if request.param['sink'] == 'rmq_web':
hostname, port = get_hostname_and_random_port()
web_address = 'https://{hostname}:{port}'.format(hostname=hostname, port=port)
messagebus = 'rmq'
ssl_auth = True
else:
web_address = "http://{}".format(get_rand_ip_and_port())
messagebus = 'zmq'
ssl_auth = False
sink = build_wrapper(sink_address,
ssl_auth=ssl_auth,
messagebus=messagebus,
bind_web_address=web_address,
volttron_central_address=web_address)
source_address = get_rand_vip()
messagebus = 'zmq'
ssl_auth = False
if request.param['source'] == 'rmq':
messagebus = 'rmq'
ssl_auth = True
if sink.messagebus == 'rmq':
# sink_ca_file = sink.certsobj.cert_file(sink.certsobj.root_ca_name)
source = build_wrapper(source_address,
ssl_auth=ssl_auth,
messagebus=messagebus,
volttron_central_address=sink.bind_web_address,
remote_platform_ca=sink.certsobj.cert_file(sink.certsobj.root_ca_name))
if source.messagebus == 'rmq':
# The _ca is how the auth subsystem saves the remote cert from discovery. We
# are effectively doing that here instead of making the discovery call.
source.certsobj.save_remote_cert(sink.certsobj.root_ca_name + "_ca", sink.certsobj.ca_cert(
public_bytes=True))
else:
source = build_wrapper(source_address,
ssl_auth=ssl_auth,
messagebus=messagebus,
volttron_central_address=sink.bind_web_address)
yield source, sink
cleanup_wrapper(source)
cleanup_wrapper(sink)
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220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
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220,
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220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2322,
926,
1313,
62,
31463,
62,
21975,
28,
82,
676,
13,
21653,
62,
12384,
62,
21975,
8,
628,
220,
220,
220,
7800,
2723,
11,
14595,
628,
220,
220,
220,
27425,
62,
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7,
10459,
8,
198,
220,
220,
220,
27425,
62,
48553,
7,
82,
676,
8,
198
] | 2.292555 | 3,049 |
# -*- coding: utf-8 -*-
# Generated by Django 1.11.2 on 2017-07-12 21:01
from __future__ import unicode_literals
import django.contrib.postgres.fields
from django.db import migrations, models
| [
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] | 2.811594 | 69 |
import unittest
from lisa.core import genome_tools
import numpy as np
if __name__ == '__main__':
unittest.main()
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] | 2.767442 | 43 |
# MIT License
#
# Copyright (c) 2019 Tuomas Halvari, Juha Harviainen, Juha Mylläri, Antti Röyskö, Juuso Silvennoinen
#
# 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.
import sys
import matplotlib.pyplot as plt
from dpemu.nodes import Array, Series
from dpemu.filters.image import Rotation
from dpemu.dataset_utils import load_mnist
def main():
"""An example that rotates MNIST digits and displays one.
Usage: python run_rotate_MNIST_example <angle>
where <angle> is the angle of rotation
(e.g. 90 to rotate by pi / 2)
"""
x, _, _, _ = load_mnist()
xs = x[:20] # small subset of x
angle = float(sys.argv[1])
print(f"x subset shape: {xs.shape}")
img_node = Array(reshape=(28, 28))
root_node = Series(img_node)
img_node.addfilter(Rotation("angle"))
result = root_node.generate_error(xs, {'angle': angle})
plt.matshow(result[0].reshape((28, 28)))
plt.show()
if __name__ == "__main__":
main()
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7,
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6,
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220,
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220,
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83,
13,
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7,
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58,
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11,
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13,
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6624,
366,
834,
12417,
834,
1298,
198,
220,
220,
220,
1388,
3419,
198
] | 3.016743 | 657 |
import json
| [
11748,
33918,
628
] | 4.333333 | 3 |
import datetime
import json
import logging
import logging.config
import os
import sys
import traceback
from os.path import join as pjoin
from subprocess import Popen, PIPE
import unittest2 as unittest
| [
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import os
import unittest
from maggma.stores import MemoryStore
from maggma.runner import Runner
from maggma.builders import Builder
from emmet.workflows.property_workflows import PropertyWorkflowBuilder,\
get_elastic_wf_builder
from pymatgen.util.testing import PymatgenTest
from atomate.vasp.workflows.presets.core import wf_elastic_constant
from fireworks import LaunchPad, Workflow
from monty.tempfile import ScratchDir
from monty.serialization import dumpfn, loadfn
__author__ = "Joseph Montoya"
__email__ = "[email protected]"
module_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)))
if __name__ == "__main__":
unittest.main()
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834,
1298,
198,
220,
220,
220,
555,
715,
395,
13,
12417,
3419,
198
] | 2.955157 | 223 |
n = 28433 * pow(2,7830457,10**10) + 1
print str(n)[-10:]
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8,
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352,
198,
4798,
965,
7,
77,
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940,
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198
] | 1.965517 | 29 |
from fastapi import HTTPException
from sqlalchemy.orm import Session
from core.models.table import TagDB, TagInRepoDB, RepoDB
from core.models.schema import TagCreate
from core.models.schema import TagInRepoCreate
def _create_tag(db: Session, tag_name: str, user_id: int):
""" Creates a new tag in the database
Args:
db (Session): sqlAlchemy connection object
tag (TagCreate): Schema for creating a tag in database
Raises:
HTTPException: 422, Tag name cannot be empty
Returns:
sql_object : Tag data
"""
if tag_name.strip() == "":
raise HTTPException(
status_code=422, detail="Tag name cannot be empty")
db_tag = TagDB(name=tag_name, auth_id=user_id)
db.add(db_tag)
db.commit()
db.refresh(db_tag)
return db_tag
def _get_tag_by_name(tag_name: str, auth_id: int, db: Session):
""" Returns tag data by passing the tag name
Args:
tag_name (str): Tag Name
auth_id (int): User Id
db (Session): sqlAlchemy connection object
Returns:
sql_object : Tag data
"""
return db.query(TagDB).filter(TagDB.name == tag_name,
TagDB.auth_id == auth_id).first()
def _get_tag_by_id(tag_id: int, db: Session):
""" Returns tag data by passing the tag id
Args:
tag_id (int): Tag id
db (Session): sqlAlchemy connection object
Returns:
sql_object : Tag data
"""
return db.query(TagDB).filter(TagDB.id == tag_id).first()
def _get_all_tags(db: Session, auth_id: int):
""" Returns data for all tags
Args:
db (Session): sqlAlchemy connection object
auth_id (int): User id
Returns:
sql_object : All tags data
"""
return db.query(TagDB).filter(auth_id == auth_id).all()
def _add_tag_in_repo(tag_in_repo: TagInRepoCreate, db: Session):
""" Adds a tag's link to a repository
Args:
tag_in_repo (TagInRepoCreate): Schema for creating a relationship
between a tag and a repository
db (Session): sqlAlchemy connection object
Returns:
sql_object: Tag associated with the repository
"""
db_tag_in_repo = _get_tag_in_repo(repo_id=tag_in_repo.repo_id,
tag_id=tag_in_repo.tag_id,
db=db)
if db_tag_in_repo:
raise HTTPException(
status_code=400, detail="Tag is already associated")
db_tag_in_repo = TagInRepoDB(repo_id=tag_in_repo.repo_id,
tag_id=tag_in_repo.tag_id)
db.add(db_tag_in_repo)
db.commit()
db.refresh(db_tag_in_repo)
return db_tag_in_repo
def _get_tag_in_repo(repo_id: int, tag_id: int, db: Session):
""" Returns a tag's link data to a repository
Args:
repo_id (int): Repository ID
tag_id (int): Tag ID
db (Session): sqlAlchemy connection object
Returns:
sql_object: Tag associated with the repository
Raises:
HTTPException: 404, Tag not found
HTTPException: 404, Repository not found
Returns:
sql_object: Tag associated with the repository
"""
tag_in_repo_db = db.query(TagInRepoDB).filter(
TagInRepoDB.repo_id == repo_id,
TagInRepoDB.tag_id == tag_id).first()
user_has_the_tag = db.query(TagDB).filter(
TagDB.id == tag_id).first()
user_has_the_repo = db.query(RepoDB).filter(
RepoDB.id == repo_id).first()
if not user_has_the_tag:
raise HTTPException(
status_code=404, detail="Tag not found")
elif not user_has_the_repo:
raise HTTPException(
status_code=404, detail="Repository not found"
)
else:
return tag_in_repo_db
def _get_all_tags_in_repo(repo_id: int, db: Session):
""" Returns data for all tag's link data to a repository
Args:
repo_id (int): Repository ID
db (Session): sqlAlchemy connection object
Returns:
sql_object: All tag relationships associated with the repository
"""
tags_in_repo = db.query(TagInRepoDB).filter(
TagInRepoDB.repo_id == repo_id).all()
tags = []
for tag in tags_in_repo:
tag_data = _get_tag_by_id(
tag_id=tag.tag_id, db=db)
tag_info = {
"id": tag_data.id,
"name": tag_data.name
}
tags.append(tag_info)
return tags
def _remove_tag_in_repo(tag_in_repo_id: int, db: Session):
""" Removes a tag's link to a repository
Args:
tag_in_repo_id (int): Relationship id between a tag and a repository
db (Session): sqlAlchemy connection object
"""
db_tag_in_repo = db.query(TagInRepoDB).filter(
TagInRepoDB.id == tag_in_repo_id).first()
db.delete(db_tag_in_repo)
db.commit()
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] | 2.221815 | 2,182 |
from overtime.algorithms.centrality.betweenness import *
from overtime.algorithms.centrality.closeness import *
from overtime.algorithms.centrality.pagerank import *
from overtime.algorithms.centrality.degree import *
| [
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] | 3.57377 | 61 |
#!/usr/bin/python
# This sample code is for use with Dropbox desktop client
# versions 1.2 and below. It is likely to be deprecated in all
# other future releases. Use it at your own risk.
# Read more at http://www.dropbox.com/developers/desktop_apps
import base64
import os.path
import platform
if platform.system() == 'Windows':
HOST_DB_PATH = os.path.expandvars(r'%APPDATA%\Dropbox\host.db')
else:
HOST_DB_PATH = os.path.expanduser(r'~/.dropbox/host.db')
if __name__ == '__main__':
print read_dropbox_location() | [
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] | 2.791667 | 192 |
import io
import os
import posixpath
import re
from urllib import urlencode
from seafileapi.utils import querystr, utf8lize,raise_does_not_exist
ZERO_OBJ_ID = '0000000000000000000000000000000000000000'
class _SeafDirentBase(object):
"""Base class for :class:`SeafFile` and :class:`SeafDir`.
It provides implementation of their common operations.
"""
isdir = None
def __init__(self, repo_id, path, object_id, size=0, client=None):
"""
:param:`path` the full path of this entry within its repo, like
"/documents/example.md"
:param:`size` The size of a file. It should be zero for a dir.
"""
self.client = client
self.repo_id = repo_id
self.path = path
self.id = object_id
self.size = size
@property
# @property
# def path(self):
# return self.path
#
# @property
# def repo_id(self):
# return self.repo_id
def rename(self, newname):
"""Change file/folder name to newname
"""
suffix = 'dir' if self.isdir else 'file'
url = '/api2/repos/%s/%s/' % (self.repo.id, suffix) + querystr(p=self.path, reloaddir='true')
postdata = {'operation': 'rename', 'newname': newname}
resp = self.client.post(url, data=postdata)
succeeded = resp.status_code == 200
if succeeded:
if self.isdir:
new_dirent = self.repo.get_dir(os.path.join(os.path.dirname(self.path), newname))
else:
new_dirent = self.repo.get_file(os.path.join(os.path.dirname(self.path), newname))
for key in self.__dict__.keys():
self.__dict__[key] = new_dirent.__dict__[key]
return succeeded
def copyTo(self, dst_dir, dst_repo_id=None):
"""Copy file/folder to other directory (also to a different repo)
"""
if dst_repo_id is None:
dst_repo_id = self.repo.id
dirent_type = 'dir' if self.isdir else 'file'
resp = self._copy_move_task('copy', dirent_type, dst_dir, dst_repo_id)
return resp.status_code == 200
def moveTo(self, dst_dir, dst_repo_id=None):
"""Move file/folder to other directory (also to a different repo)
"""
if dst_repo_id is None:
dst_repo_id = self.repo.id
dirent_type = 'dir' if self.isdir else 'file'
resp = self._copy_move_task('move', dirent_type, dst_dir, dst_repo_id)
succeeded = resp.status_code == 200
if succeeded:
new_repo = self.client.repos.get_repo(dst_repo_id)
dst_path = os.path.join(dst_dir, os.path.basename(self.path))
if self.isdir:
new_dirent = new_repo.get_dir(dst_path)
else:
new_dirent = new_repo.get_file(dst_path)
for key in self.__dict__.keys():
self.__dict__[key] = new_dirent.__dict__[key]
return succeeded
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] | 2.113395 | 1,411 |
import pandas as p#导入目前所需要的库并给与简称
data_train = '../homework/train.csv' #查看基本数据
data_train = p.read_csv(data_train)#导入训练模型
print(data_train.info())#查看数据类型
print(data_train.describe())#粗略查看基本数据
###导入并且查看原始数据
import matplotlib.pyplot as pt
import numpy as n
pt.rcParams['font.sans-serif']=['Simhei'] #解决中文为方块的问题
pt.rcParams['axes.unicode_minus'] = False #解决图像是负号显示为方块的问题
fig = pt.figure()
fig.set(alpha=0.2) # 设定图表颜色alpha参数
pt.subplot2grid((2,3),(0,0)) # 在一张大图里分一些小图并设定位置
data_train.Survived.value_counts().plot(kind='bar') #以生存总数为标准 设置图标种类为柱状图
pt.title("生存 (1 Survived)")
pt.ylabel("生存人数")
pt.subplot2grid((2,3),(0,1))
data_train.Pclass.value_counts().plot(kind="bar")
pt.ylabel("总人数")
pt.title("仓位")
pt.subplot2grid((2,3),(0,2))
pt.scatter(data_train.Survived, data_train.Age)
pt.ylabel("年龄")
pt.grid(b=True, which='major', axis='y')
pt.title("年龄 (1 Survived)")
pt.subplot2grid((2,3),(1,0), colspan=2)
data_train.Age[data_train.Pclass == 1].plot(kind='kde')
data_train.Age[data_train.Pclass == 2].plot(kind='kde')
data_train.Age[data_train.Pclass == 3].plot(kind='kde')
pt.xlabel("年龄")
pt.ylabel("密度")
pt.title("各等级的乘客年龄分布")
pt.legend(('头等舱', '2等舱','3等舱'),loc='best') # 设置图例
pt.subplot2grid((2,3),(1,2))
data_train.Embarked.value_counts().plot(kind='bar')
pt.title("各登船口岸上船人数")
pt.ylabel("人数")
pt.show()
#粗略的以数据可视化的形式更直观的查看原始数据
fig = pt.figure()
fig.set(alpha=0.2) # 设定图表颜色alpha参数
Survived_0 = data_train.Pclass[data_train.Survived == 0].value_counts()#将未生存总数0存入value并与仓位对应
print(Survived_0)
Survived_1 = data_train.Pclass[data_train.Survived == 1].value_counts()
df=p.DataFrame({'生存':Survived_1, '未生存':Survived_0})
df.plot(kind='bar', stacked=False)
pt.title("仓位与生存率是否相关")
pt.xlabel("仓位")
pt.ylabel("总人数")
pt.show()
#设立假设 仓位 也就是阶级 与生存率有关与否
fig = pt.figure()
fig.set(alpha=0.2) # 设定图表颜色alpha参数
Survived_m = data_train.Survived[data_train.Sex == 'male'].value_counts()
Survived_f = data_train.Survived[data_train.Sex == 'female'].value_counts()
df=p.DataFrame({'男性':Survived_m, '女性':Survived_f})
df.plot(kind='bar', stacked=False)
pt.title("性别与生存率是否相关")
pt.xlabel("性别")
pt.ylabel("总人数")
pt.show()
#设立假设 性别与生存率是否相关
fig = pt.figure()
fig.set(alpha=0.2) # 设定图表颜色alpha参数
Survived_0 = data_train.Embarked[data_train.Survived == 0].value_counts()
Survived_1 = data_train.Embarked[data_train.Survived == 1].value_counts()
df=p.DataFrame({'生存':Survived_1, '未幸存':Survived_0})
df.plot(kind='bar', stacked=False)
pt.title("假设登船港口与生存率是否有关")
pt.xlabel("港口")
pt.ylabel("总人数")
pt.show()
#假设登船港口与生存率是否有关
g = data_train.groupby(['SibSp','Survived'])
df = p.DataFrame(g.count()['PassengerId'])
print(df)
g = data_train.groupby(['Parch','Survived'])
df = p.DataFrame(g.count()['PassengerId'])
print(df)
#判断是否有兄弟姐妹在船上以及是否有父母子女在船上与生存率是否有关
###设立假设 进行数据分析
### 处理空值年龄
from sklearn.ensemble import RandomForestRegressor #从sklearn库中导入随机森林
### 使用 RandomForest 填补缺失的年龄属性
data_train, rfr = set_missing_ages(data_train)#将预测值存入训练样本中以供使用
data_train = set_Cabin_type(data_train)#将Yes及No存入训练样本中以供使用
data_train.info()#再次查看整理过的数据
### 处理空值港口
data_train = set_Embarked_type(data_train)
data_train.Embarked = data_train.Embarked.fillna(0)
data_train.Embarked = list(map(int,data_train.Embarked))
print(data_train.Embarked.mean())
data_train = set_Embarked_type(data_train)
### 使用随机森林处理票价为0的值
data_train, rfr = set_missing_fare(data_train)
print(data_train.Fare.describe())
###数据处理
### 使用算法开始建模 这里使用逻辑回归
data_train.Pclass = data_train.Pclass.astype('object')
cate =p.get_dummies(data_train[['Cabin','Sex','Embarked','Pclass']])
data_new = data_train[['Survived','Age','SibSp','Parch','Fare']].join(cate) #数据的转储以及整理
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(data_new.iloc[:,1:], data_new.Survived, test_size = 0.2, random_state=34)
lr = LogisticRegression()
lr.fit(x_train,y_train)#用数据X，y来训练模型
pred = lr.predict(x_test)
from sklearn.metrics import classification_report, accuracy_score
print(classification_report(y_test,pred))#预测准确率
print(accuracy_score(y_test,pred))#分类准确率分数
#尝试使用不同算法 这里使用决策树
from sklearn.tree import *
dt = DecisionTreeClassifier(random_state=99,splitter='best', presort=True)
dt.fit(x_train,y_train)
pred = dt.predict(x_test)
from sklearn.metrics import classification_report, accuracy_score
print(classification_report(y_test,pred))
print(accuracy_score(y_test,pred))
####模型构建
data_test = p.read_csv('../homework/test.csv')#导入测试样本
data_test = set_missing_ages(data_test, rfr)
data_test = set_Cabin_type(data_test)
data_test.Pclass = data_test.Pclass.astype('object')
cate_test =p.get_dummies(data_test[['Cabin','Sex','Embarked','Pclass']])
data_test_new = data_test[['PassengerId','Age','SibSp','Parch','Fare']].join(cate_test)
final = dt.predict(data_test_new.fillna(0))
final_1=data_test[['PassengerId','Age']]
final_1['Survived'] = final
final = final_1[['PassengerId','Survived']]
final.to_csv('C:/Users/yang/Desktop/code/python/homework/6.csv')
print(final.describe())
print(data_test_new)
### 使用训练好的模型进行预测
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] | 1.609608 | 3,289 |
from django.urls import include, path
from chemreg.jsonapi.routers import SimpleRouter
from chemreg.substance import views
# Create a router and register our viewsets with it.
router = SimpleRouter()
router.register(views.QCLevelsTypeViewSet, "qcLevels")
router.register(views.RelationshipTypeViewSet)
router.register(views.SynonymViewSet)
router.register(views.SynonymTypeViewSet)
router.register(views.SourceViewSet)
router.register(views.SubstanceViewSet)
router.register(views.SubstanceTypeViewSet)
router.register(views.SynonymQualityViewSet, prefix="synonymQualities")
router.register(views.SubstanceRelationshipViewSet)
urlpatterns = [
path("", include(router.urls)),
]
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] | 3.071749 | 223 |
import pytest_cases as pytest
@pytest.fixture
@pytest.parametrize_with_cases("x,y", cases=CaseY, debug=True)
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] | 2.521739 | 46 |
import os
import random
import datetime
import aiofiles
from enum import Enum
from dateutil.parser import parse
import aiohttp
try:
import ujson as json
except:
import json
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] | 3.098361 | 61 |
import unittest
from app.utils import KMP
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] | 3.307692 | 13 |
import cv2
import numpy as np
from matplotlib import pyplot as plt
import argparse
from itertools import permutations
from lxml import etree as ET
ap=argparse.ArgumentParser()
ap.add_argument('-i', '--image', type=str)
args=vars(ap.parse_args())
image = cv2.imread(args['image'])
source = image.copy()
target = image.copy()
phone=source[101:286,209:394]
music=source[101:286,479:664]
maps=source[101:286,749:934]
messages=source[101:286,1019:1204]
playing=source[419:604,209:394]
podcasts=source[419:604,479:664]
audiobook=source[419:604,749:934]
audiotest=source[419:604,1019:1204]
icons_coordinate = {
0:[101,286,209,394],
1:[101,286,479,664],
2:[101,286,749,934],
3:[101,286,1019,1204],
4:[419,604,209,394],
5:[419,604,479,664],
6:[419,604,749,934],
7:[419,604,1019,1204]
}
icons = [phone, music, maps, messages, playing, podcasts, audiobook, audiotest]
icon_name = ["phone", "music", "maps", "messages", "playing", "podcasts", "audiobook", "audiotest"]
index = [0, 1, 2, 3, 4, 5, 6, 7]
index_set = []
for p in permutations(index):
index_set.append(p)
#print(index_set)
#print(len(index_set))
#for i in range(len(index_set)):
# print(index_set[i][0])
for i in range(len(index_set)):
target[101:286,209:394]=icons[index_set[i][0]]
target[101:286,479:664]=icons[index_set[i][1]]
target[101:286,749:934]=icons[index_set[i][2]]
target[101:286,1019:1204]=icons[index_set[i][3]]
target[419:604,209:394]=icons[index_set[i][4]]
target[419:604,479:664]=icons[index_set[i][5]]
target[419:604,749:934]=icons[index_set[i][6]]
target[419:604,1019:1204]=icons[index_set[i][7]]
filename='./images/'+str(i)+'.jpg'
cv2.imwrite(filename, target)
write_xml_file(i)
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72,
7131,
21,
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198,
220,
220,
220,
2496,
58,
45068,
25,
31916,
11,
8784,
24,
25,
1065,
3023,
22241,
34280,
58,
9630,
62,
2617,
58,
72,
7131,
22,
11907,
628,
220,
220,
220,
29472,
28,
4458,
14,
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14,
6,
10,
2536,
7,
72,
47762,
4458,
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6,
198,
220,
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269,
85,
17,
13,
320,
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7,
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11,
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8,
198,
220,
220,
220,
3551,
62,
19875,
62,
7753,
7,
72,
8,
628,
198
] | 2.247706 | 763 |
import re
import glob
import os
import sys
import skimage
import numpy as np
import theano.tensor as T
from sklearn.cross_validation import StratifiedShuffleSplit
import string
import lasagne as nn
# TODO clean this mess up
# TODO: very ugly stuff here, can probably be done a lot better
| [
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201,
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] | 2.710744 | 121 |
# Dash app initialization
import dash
# User management initialization
import os
from flask_login import LoginManager, UserMixin
from users_mgt import db, User as base
from config import config
app = dash.Dash(
__name__,
meta_tags=[
{
'charset': 'utf-8',
},
{
'name': 'viewport',
'content': 'width=device-width, initial-scale=1, shrink-to-fit=no'
}
]
)
server = app.server
app.config.suppress_callback_exceptions = True
app.css.config.serve_locally = True
app.scripts.config.serve_locally = True
# config
server.config.update(
SECRET_KEY=os.urandom(12),
SQLALCHEMY_DATABASE_URI=config.get('database', 'con'),
SQLALCHEMY_TRACK_MODIFICATIONS=False
)
db.init_app(server)
# Setup the LoginManager for the server
login_manager = LoginManager()
login_manager.init_app(server)
login_manager.login_view = '/login'
# Create User class with UserMixin
# callback to reload the user object
@login_manager.user_loader
| [
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62,
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62,
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13,
15003,
62,
1324,
7,
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8,
198,
38235,
62,
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13,
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62,
1177,
796,
31051,
38235,
6,
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198,
2,
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1398,
351,
11787,
35608,
259,
628,
198,
2,
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18126,
262,
2836,
2134,
198,
31,
38235,
62,
37153,
13,
7220,
62,
29356,
198
] | 2.588689 | 389 |
from functools import wraps
from typing import Callable
from heuristic.classes import Solution
def remove_empty_routes(operator: Callable[..., Solution]):
"""
Wrapper function that removes empty routes from the returned solution
instance. These routes may come into existence because all customers have
been removed by e.g. a destroy operator.
"""
@wraps(operator)
return decorator
| [
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] | 3.568966 | 116 |
'''
Compare File Contents and remove duplicate files
get sha256 hash for each file found
use dictionary to check for duplicates
delete duplicates
Dave Cuthbert
(C) 2021-02-12
MIT License
'''
import os
from collections import defaultdict
import hashlib
import sys
if "__main__" == __name__:
find_duplicates()
#EOF
| [
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16856,
3419,
198,
220,
220,
220,
220,
198,
2,
4720,
37,
198
] | 2.625899 | 139 |
import sys
import os
import numpy as np
import torch
import subprocess
from pathlib import Path
import requests
import zipfile
import shutil
if __name__ == "__main__":
url = "https://public.ukp.informatik.tu-darmstadt.de/reimers/sentence-transformers/v0.2/distiluse-base-multilingual-cased.zip"
path = download_model(url, "distiluse-base-multilingual-cased")
convert_to_c_array(path + '/0_DistilBERT', prefix='distilbert.')
convert_to_c_array(path + '/2_Dense', suffix=True)
| [
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220,
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220,
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7,
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1343,
31051,
17,
62,
35,
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3256,
35488,
28,
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8,
198
] | 2.619792 | 192 |
# coding: utf-8
from __future__ import unicode_literals
import datetime
from django.contrib.auth.models import Group, User
from django.test import TestCase
from .models import Album, AlbumAccessPolicy, Photo, PhotoAccessPolicy
| [
2,
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] | 3.462687 | 67 |
#!/usr/bin/python
import socket
from termcolor import colored
print(colored("Select Option"),"blue")
print(colored("1. Single Port\n2. Multi Port"),"blue")
option = int(input())
if option == 1:
print(colored("Enter Host IP Address:","green"))
host = input()
print(colored("Enter port number to scan:","green"))
port = int(input())
if option == 2:
print(colored("[*] Enter Host IP Address:","green"))
host = input()
print(colored("[*] Enter number of ports to scan:","green"))
num = int(input())
if option == 1:
PScanner(host,port,option)
if option == 2:
PScanner(host,num,option) | [
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361,
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25,
198,
220,
220,
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350,
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7,
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11,
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11,
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8,
198,
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198,
220,
220,
220,
350,
33351,
1008,
7,
4774,
11,
22510,
11,
18076,
8
] | 2.845455 | 220 |
from django.apps import AppConfig
| [
6738,
42625,
14208,
13,
18211,
1330,
2034,
16934,
628
] | 3.888889 | 9 |
from .WBits import WBits
from .event import Event
| [
6738,
764,
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896,
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198,
6738,
764,
15596,
1330,
8558,
201,
198,
201,
198,
201,
198,
220,
220,
220,
220
] | 2.5 | 24 |
from __future__ import print_function
from .field_parser import parse_internal_field, parse_boundary_field, parse_field_all
from .mesh_parser import FoamMesh
from .utils import *
| [
6738,
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201,
198,
6738,
764,
26791,
1330,
1635,
201,
198,
201,
198
] | 3.133333 | 60 |
import threading
import time
import psutil
import rostopic
import rospy
from glog import logging
from node_evaluator.msg import Bandwidth as BandwidthMsg
@EvaluatorFactory.register('cpu')
@EvaluatorFactory.register('mem')
@EvaluatorFactory.register('net')
@EvaluatorFactory.register('topic_bw')
@EvaluatorFactory.register('bw_from_msg')
@EvaluatorFactory.register('sys_bw')
| [
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65,
86,
62,
6738,
62,
19662,
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36,
2100,
84,
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22810,
13,
30238,
10786,
17597,
62,
65,
86,
11537,
198
] | 2.888889 | 135 |
#/usr/bin/env python
import io
from setuptools import setup
with io.open('README.rst', encoding='utf8') as readme:
long_description = readme.read()
setup(
name='rubicon',
version='0.0.0',
description='A collection of tools to bridge between Python and other language environments.',
long_description=long_description,
author='Russell Keith-Magee',
author_email='[email protected]',
url='http://pybee.org/rubicon',
packages=[],
license='New BSD',
classifiers=[
'Development Status :: 3 - Alpha',
'Intended Audience :: Developers',
'License :: OSI Approved :: BSD License',
'Topic :: Software Development',
'Topic :: Software Development :: User Interfaces',
'Topic :: Software Development :: Widget Sets',
],
)
| [
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220,
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7904,
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7712,
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220,
220,
220,
220,
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705,
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7712,
7904,
370,
17484,
21394,
3256,
198,
220,
220,
220,
16589,
198,
8,
198
] | 2.791096 | 292 |
# -*- coding: utf-8 -*-
# Generated by Django 1.11.29 on 2021-05-24 18:30
from __future__ import unicode_literals
from django.db import migrations, models
import django.db.models.deletion
| [
2,
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] | 2.753623 | 69 |
"""
Capitalize!
:author: Dela Anthonio
:hackerrank: https://hackerrank.com/delaanthonio
:problem: https://www.hackerrank.com/contests/pythonist3/challenges/capitalize
"""
print(solve('hi jake hj ')) | [
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Initialization for Solstice Tools
"""
from __future__ import print_function, division, absolute_import
__author__ = "Tomas Poveda"
__license__ = "MIT"
__maintainer__ = "Tomas Poveda"
__email__ = "[email protected]"
print('=' * 100)
print('| Solstice Pipeline | > Loading Solstice Tools')
try:
import solstice.loader
solstice.loader.init(import_libs=True)
print('| Solstice Pipeline | Solstice Tools loaded successfully!')
print('=' * 100)
except Exception as e:
print('ERROR: Impossible to load Solstice Tools, contact TD!')
print(str(e))
| [
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7,
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] | 2.861751 | 217 |
from project_restaurant.beverage.beverage import Beverage
| [
6738,
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] | 3.277778 | 18 |
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
import re
import threading
import lldbagilityutils
from PyFDP.FDP import FDP
from VMSN import VMSN
logger = lldbagilityutils.create_indented_logger(__name__, "/tmp/stubvm.log")
NULL = 0x0
# https://github.com/apple/darwin-xnu/blob/xnu-4903.221.2/osfmk/i386/eflags.h
EFL_TF = 0x00000100
# https://github.com/apple/darwin-xnu/blob/xnu-4903.221.2/osfmk/mach/i386/vm_param.h
I386_PGBYTES = 4096
VM_MIN_KERNEL_ADDRESS = 0xFFFFFF8000000000
VM_MAX_KERNEL_ADDRESS = 0xFFFFFFFFFFFFEFFF
# https://github.com/apple/darwin-xnu/blob/xnu-4903.221.2/EXTERNAL_HEADERS/mach-o/loader.h
MH_MAGIC_64 = 0xFEEDFACF
# https://github.com/apple/darwin-xnu/blob/xnu-4903.221.2/osfmk/mach/exception_types.h
EXC_SOFTWARE = 0x5
EXC_BREAKPOINT = 0x6
EXC_SOFT_SIGNAL = 0x10003
# https://github.com/apple/darwin-xnu/blob/xnu-4903.221.2/osfmk/mach/i386/exception.h
EXC_I386_BPTFLT = 0x3
# https://github.com/apple/darwin-xnu/blob/xnu-4903.221.2/bsd/sys/signal.h
SIGINT = 0x2
# https://github.com/apple/darwin-xnu/blob/xnu-4903.221.2/osfmk/i386/proc_reg.h
MSR_IA32_GS_BASE = 0xC0000101
MSR_IA32_KERNEL_GS_BASE = 0xC0000102
# https://github.com/apple/darwin-xnu/blob/xnu-4903.221.2/osfmk/mach/machine.h
CPU_TYPE_X86 = 0x7
CPU_ARCH_ABI64 = 0x01000000
CPU_TYPE_X86_64 = CPU_TYPE_X86 | CPU_ARCH_ABI64
CPU_SUBTYPE_X86_ARCH1 = 0x4
@lldbagilityutils.indented(logger)
@lldbagilityutils.indented(logger)
| [
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55,
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657,
87,
22,
198,
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62,
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62,
32,
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2414,
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62,
32,
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62,
50,
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62,
55,
4521,
62,
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16,
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657,
87,
19,
628,
628,
198,
31,
297,
9945,
363,
879,
26791,
13,
521,
4714,
7,
6404,
1362,
8,
628,
198,
198,
31,
297,
9945,
363,
879,
26791,
13,
521,
4714,
7,
6404,
1362,
8,
628,
198
] | 2.026989 | 704 |
import spotipy
from spotipy.oauth2 import SpotifyClientCredentials
spotify = spotipy.Spotify(auth_manager=SpotifyClientCredentials())
url = "http://httpbin.org/post"
payload = dict(key1='value1', key2='value2')
res = requests.post(url, data=payload)
print(res.text) | [
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] | 2.810526 | 95 |
import json
import requests
from elasticsearch.connection import create_ssl_context
from elasticsearch import Elasticsearch
from getpass import getpass
class ElasticSearch:
"""Wrapper of Elasticsearch module.
This module is designed for using elastic search more friendly.
"""
def _set_condition_func(self):
"""Generate functions dynamically for setting conditions.
This function will generate 6 funcions, including:
must(conditions):
All of the conditions must be satisfied.
must_reg(conditions)
All of the conditions must be satisfied in regular
expression matching.
must_not(conditions)
All of the conditions must not be satisfied.
must_not_reg(conditions)
All of the conditions must not be satisfied in regular
expression matching.
should(conditions)
One of the conditions must be satisfied.
should_reg(conditions)
One of the conditions must be satisfied in regular
expression matching.
All these funcions have one parameter in `list of dict` type.
Each element in `list` is a `dict` which have only one
key-value pair.
"""
for operation in ['must', 'must_not', 'should']:
for method in ['match_phrase', 'regexp']:
func(operation, method)
if __name__ == '__main__':
es_host = 'https://140.113.194.82:9200'
es_username = input('username:')
es_password = getpass('password:')
es = ElasticSearch(es_host, (es_username, es_password))
es.index('logstash-router.zeek*')
# es.column()
# es.time('now-30s', 'now')
# es.time('2020-05-14T03:10:00+0800')
es.time('2021-04-17T15:19:19+0800', '2021-04-17T15:22:40+0800')
# es.should_reg([{'id_orig_h': '192.168.1.*'}])
data = es.search(size=10, clean=True)
print(len(data))
[print(datum) for datum in data]
data = es.search()
print(len(data))
# print(data) | [
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7,
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4008,
198,
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8
] | 2.370913 | 887 |
#!/usr/bin/env python
import os
import sys
if __name__ == "__main__":
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "refugeedata.app.settings")
try:
from refugeedata.app import local_settings
except ImportError:
pass
else:
os.environ.update(local_settings.SETTINGS_DICT)
from django.core.management import execute_from_command_line
execute_from_command_line(sys.argv)
| [
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85,
8,
198
] | 2.497041 | 169 |
"""
aiodec
======
Decorators for coroutines
"""
import time
import logging
from functools import wraps
from string import Template
import inspect
from inspect import Signature
from typing import Callable, Optional, Mapping, Any
__version__ = '2018.10.1'
logger = logging.getLogger(__name__)
Callback = Callable[[Signature, Mapping[str, Any]], None]
| [
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] | 3.296296 | 108 |
# Copyright 2020-2022 Robert Bosch Car Multimedia GmbH
#
# 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.
# -*- coding: utf-8 -*-
# **************************************************************************************************************
#
# CConfig.py
#
# CM-CI1/ECA3-Queckenstedt
#
# Purpose:
# - Compute and store all repository specific information, like the repository name,
# paths to repository subfolder, paths to interpreter and so on ...
#
# - All paths to subfolder depends on the repository root path that has to be provided to constructor of CConfig
#
# Additional hints:
# - Variable names like SPHINXBUILD, SOURCEDIR and BUILDDIR are taken over from original output of Sphinx
# (when documentation project files like make.bat are generated by Sphinx; for better understanding
# no new names here).
#
# - Output in PDF format requires LaTeX compiler and self.__bGenPDFSupported set to True (True is default)
#
# - Don't be confused: We have 'doc/_build' containing the documentation builder output
# and we have 'build' containing the build of the setup tools. These are different things.
#
# --------------------------------------------------------------------------------------------------------------
#
# 11.10.2021 / XC-CI1/ECA3-Queckenstedt
# Fixed path within site-packages (Linux)
#
# 06.10.2021 / XC-CI1/ECA3-Queckenstedt
# Added Linux support
#
# 01.10.2021 / XC-CI1/ECA3-Queckenstedt
# Added environment check
#
# 01.10.2021 / XC-CI1/ECA3-Queckenstedt
# Added wrapper for error messages
#
# Initial version 08/2021
#
# --------------------------------------------------------------------------------------------------------------
import os, sys, platform, shlex, subprocess
import colorama as col
import pypandoc
col.init(autoreset=True)
COLBR = col.Style.BRIGHT + col.Fore.RED
COLBG = col.Style.BRIGHT + col.Fore.GREEN
# --------------------------------------------------------------------------------------------------------------
# --------------------------------------------------------------------------------------------------------------
# eof def __InitConfig(self):
# eof def PrintConfig(self):
# eof def Get(self, sName=None):
# eof class CConfig():
# --------------------------------------------------------------------------------------------------------------
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] | 3.930939 | 724 |
"""Test the Request class"""
# Copyright (c) 2017
# Authors: Guillaume Lemaitre <[email protected]>
# License: BSD 3 clause
import shutil
import os
from os.path import dirname, join, sep, expanduser
import pytest
from pytest import raises
from specio import core
from specio.core import Request
DATA_PATH = module_path = dirname(__file__)
@pytest.mark.parametrize(
'type_error,msg,params',
[(IOError, "Cannot understand given URI", ['invalid', 'uri'] * 10),
(IOError, "Cannot understand given URI", 4),
(IOError, "No such file", '/does/not/exist'),
(IOError, "No such file", '/does/not/exist.zip/spam.png')])
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] | 2.778723 | 235 |
# -*- coding: utf-8 -*-
import os, shutil
from pelican.generators import ArticlesGenerator
from pelican.tests.support import get_settings, unittest
from pelican.writers import Writer
from ctags_generator import generate_ctags
CUR_DIR = os.path.dirname(__file__)
TEST_CONTENT_DIR = os.path.join(CUR_DIR, 'test_content')
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] | 2.892857 | 112 |
"""Module containing convenient functions for plotting"""
__author__ = 'wittawat'
import fsic.ex.exglobal as exglo
import fsic.glo as glo
import matplotlib.pyplot as plt
import numpy as np
def plot_2d_data(pdata):
"""
pdata: an instance of PairedData
Return a figure handle
"""
X, Y = pdata.xy()
n, d = X.shape
if d != 2:
raise ValueError('d must be 2 to plot.')
# plot
fig = plt.figure()
plt.plot(X, Y, 'ob')
plt.title(pdata.label)
return fig
def plot_prob_reject(ex, fname, h1_true, func_xvalues, xlabel,
func_title=None):
"""
plot the empirical probability that the statistic is above the threshold.
This can be interpreted as type-1 error (when H0 is true) or test power
(when H1 is true). The plot is against the specified x-axis.
- ex: experiment number
- fname: file name of the aggregated result
- h1_true: True if H1 is true
- func_xvalues: function taking aggregated results dictionary and return the values
to be used for the x-axis values.
- xlabel: label of the x-axis.
- func_title: a function: results dictionary -> title of the plot
Return loaded results
"""
#from IPython.core.debugger import Tracer
#Tracer()()
results = glo.ex_load_result(ex, fname)
#value_accessor = lambda job_results: job_results['test_result']['h0_rejected']
vf_pval = np.vectorize(rej_accessor)
# results['job_results'] is a dictionary:
# {'test_result': (dict from running perform_test(te) '...':..., }
rejs = vf_pval(results['job_results'])
repeats, _, n_methods = results['job_results'].shape
mean_rejs = np.mean(rejs, axis=0)
#print mean_rejs
#std_pvals = np.std(rejs, axis=0)
#std_pvals = np.sqrt(mean_rejs*(1.0-mean_rejs))
xvalues = func_xvalues(results)
#ns = np.array(results[xkey])
#te_proportion = 1.0 - results['tr_proportion']
#test_sizes = ns*te_proportion
line_styles = exglo.func_plot_fmt_map()
method_labels = exglo.get_func2label_map()
func_names = [f.__name__ for f in results['method_job_funcs'] ]
for i in range(n_methods):
te_proportion = 1.0 - results['tr_proportion']
fmt = line_styles[func_names[i]]
#plt.errorbar(ns*te_proportion, mean_rejs[:, i], std_pvals[:, i])
method_label = method_labels[func_names[i]]
plt.plot(xvalues, mean_rejs[:, i], fmt, label=method_label)
'''
else:
# h0 is true
z = stats.norm.isf( (1-confidence)/2.0)
for i in range(n_methods):
phat = mean_rejs[:, i]
conf_iv = z*(phat*(1-phat)/repeats)**0.5
#plt.errorbar(test_sizes, phat, conf_iv, fmt=line_styles[i], label=method_labels[i])
plt.plot(test_sizes, mean_rejs[:, i], line_styles[i], label=method_labels[i])
'''
ylabel = 'Test power' if h1_true else 'Type-I error'
plt.ylabel(ylabel)
plt.xlabel(xlabel)
plt.xticks( np.hstack((xvalues) ))
alpha = results['alpha']
plt.legend(loc='best')
title = '%s. %d trials. $\\alpha$ = %.2g.'%( results['prob_label'],
repeats, alpha) if func_title is None else func_title(results)
plt.title(title)
#plt.grid()
return results
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] | 2.26749 | 1,458 |
from PythonHackathon.PythonHackathon_Mos.GroupStatistics import *
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] | 4.125 | 16 |
import json
import logging
import boto
import boto.s3.connection
import datetime
import dateutil
from nose.tools import eq_ as eq
from six.moves import range
from .multisite import *
from .tests import *
from .zone_es import *
log = logging.getLogger(__name__)
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] | 3.178571 | 84 |
# SPDX-License-Identifier: Apache-2.0
# -*- coding: utf-8 -*-
""" Render related functions
"""
import bpy
import threading
import xrs.collection
import xrs.material
def add_render_camera():
""" Adds one render camera that can have base settings in one place """
bpy.ops.object.camera_add()
renderCam = bpy.data.cameras['Camera']
renderCam.clip_start = 0.0254
def disable_direct_indirect_for_bake():
""" Turn off direct and indirect lighting in bake settings """
bpy.context.scene.render.bake.use_pass_direct = False
bpy.context.scene.render.bake.use_pass_indirect = False
def render_and_save():
""" Render the image from the active camera and save it """
bpy.ops.render.render(write_still=True)
result = None
def render_and_save():
""" Render the image from the active camera and save it """
bpy.ops.render.render(write_still=True)
def set_cpu():
""" Use the CPU for rendering """
bpy.context.scene.cycles.device = 'CPU'
def set_cycles(samples = 128):
""" Set the render engine to use cycles """
bpy.context.scene.render.engine = "CYCLES"
bpy.context.scene.cycles.samples = samples
def set_eevee():
""" Set the render engine to use cycles """
bpy.context.scene.render.engine = "BLENDER_EEVEE"
def set_gpu():
""" Use the GPU for rendering """
bpy.context.scene.cycles.device = 'GPU'
def set_resolution(x=4096,y=4096):
""" Set the resolution of the image to render """
bpy.context.scene.render.resolution_percentage = 100
bpy.context.scene.render.resolution_x = x
bpy.context.scene.render.resolution_y = y
if x <= 512:
bpy.context.scene.render.tile_x = x
elif x <= 1024:
bpy.context.scene.render.tile_x = x / 2
elif x <= 2048:
bpy.context.scene.render.tile_x = x / 4
else:
bpy.context.scene.render.tile_x = x / 8
if y <= 512:
bpy.context.scene.render.tile_y = y
elif y <= 1024:
bpy.context.scene.render.tile_y = y / 2
elif y <= 2048:
bpy.context.scene.render.tile_y = y / 4
else:
bpy.context.scene.render.tile_y = y / 8
def set_filepath_with_format(path, format):
""" Set the path of the file to be saved on render in the given format """
bpy.context.scene.render.filepath = path
bpy.context.scene.render.image_settings.file_format = format
def set_bake_render(resolution = 4096):
""" Set for the optimal baking settings by default """
set_cycles()
bpy.context.scene.display_settings.display_device = 'sRGB'
set_resolution(resolution, resolution)
def shadow_render(planeName):
""" Sets up AO shadow for renders """
longest_dim = xrs.collection.get_largest_dimension("master")
bpy.ops.mesh.primitive_plane_add(size=longest_dim*2, location=(0,0,-0.0001))
bpy.data.objects['Plane'].name = planeName
bpy.data.meshes['Plane'].name = planeName
planeObj = bpy.data.objects[planeName]
xrs.material.make_material()
planeMat = bpy.data.materials[planeName]
planeMat.blend_method = "BLEND"
xrs.material.new_image_texture(planeMat.name, "ao_plane", size=1024)
bpy.data.worlds['World'].light_settings.use_ambient_occlusion = True
distAO = bpy.data.worlds['World'].light_settings.distance
shortDim = xrs.collection.get_shortest_dimension("master")
distAO = 0.23*shortDim
if distAO > 6:
distAO = 6
bpy.data.scenes['Scene'].cycles.samples = 1024
bpy.context.scene.cycles.bake_type = 'AO'
planeObj.select_set(True)
planeMat.node_tree.nodes['ao_plane'].select = True
bpy.ops.object.bake(type="AO", save_mode='INTERNAL')
aoPlane = planeMat.node_tree.nodes['ao_plane']
transparentBSDF = planeMat.node_tree.nodes.new("ShaderNodeBsdfTransparent")
matOutput = planeMat.node_tree.nodes['Material Output']
xrs.material.link_output_to_slot_named(planeMat, aoPlane.outputs[0], transparentBSDF, "Color")
xrs.material.link_output_to_slot_named(planeMat, transparentBSDF.outputs[0], matOutput, "Surface")
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] | 2.775937 | 1,388 |
#!/usr/bin/python2.7
import datetime
| [
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] | 2.352941 | 17 |
from datetime import datetime
import discord
from discord.ext import commands
import psutil
import pytz
from cogs.utils.message_manager import MessageManager
from cogs.utils import constants
| [
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] | 4.041667 | 48 |
#!/usr/bin/env python
from networks.cifar.cifar_model import CifarModel
class LeNet(CifarModel):
"""
TODO: Write Comment
"""
def __init__(self, args):
"""
TODO: Write Comment
"""
self.name = 'LeNet'
CifarModel.__init__(self, args)
def network(self, img_input):
"""
TODO: Write Comment
"""
from tensorflow.keras import initializers, layers, regularizers
weight_decay = 0.0001
x = layers.Conv2D(6, (5, 5), padding='valid', kernel_initializer=initializers.he_normal(), kernel_regularizer=regularizers.l2(weight_decay))(img_input)
x = layers.BatchNormalization()(x)
x = layers.Activation('relu')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2))(x)
x = layers.Conv2D(16, (5, 5), padding='valid', kernel_initializer=initializers.he_normal(), kernel_regularizer=regularizers.l2(weight_decay))(x)
x = layers.BatchNormalization()(x)
x = layers.Activation('relu')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2))(x)
x = layers.Flatten()(x)
x = layers.Dense(120, kernel_initializer=initializers.he_normal(), kernel_regularizer=regularizers.l2(weight_decay) )(x)
x = layers.BatchNormalization()(x)
x = layers.Activation('relu')(x)
x = layers.Dense(84, kernel_initializer=initializers.he_normal(), kernel_regularizer=regularizers.l2(weight_decay) )(x)
x = layers.BatchNormalization()(x)
x = layers.Activation('relu', name='Penultimate')(x)
x = layers.Dense(self.num_classes, name='Output', activation = 'softmax', kernel_initializer=initializers.he_normal(), kernel_regularizer=regularizers.l2(weight_decay) )(x)
return x
def scheduler(self, epoch):
"""
TODO: Write Comment
"""
if epoch < 100:
return 0.01
if epoch < 150:
return 0.005
return 0.001
| [
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] | 2.172043 | 930 |
#!/usr/bin/python
# -*- coding: utf-8 -*-
from optparse import OptionParser
import locale
from gsmmodem.modem import GsmModem
import logging
parser = OptionParser()
parser.add_option("-a", "--address", action="store", dest="ip", type="string", help="Cureent ip address")
PORT = '/dev/modem0'
BAUDRATE = 115200
PIN = "0000" # SIM card PIN (if any)
if __name__ == '__main__':
encode = locale.getdefaultlocale()
(options, args) = parser.parse_args()
if options.ip is None:
parser.print_help()
exit()
logging.basicConfig(format='%(levelname)s: %(message)s', level=logging.DEBUG)
modem = GsmModem(PORT, BAUDRATE)
modem.smsTextMode = True
modem.connect(PIN)
modem.sendSms(+79227814419, options.ip)
try:
modem.rxThread.join(10) # Specify a (huge) timeout so that it essentially blocks indefinitely, but still receives CTRL+C interrupt signal
finally:
modem.close(); | [
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220,
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220,
220,
220,
220,
220,
220,
30751,
13,
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62,
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220,
220,
220,
220,
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220,
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13,
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10,
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13,
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8,
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220,
220,
220,
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25,
198,
220,
220,
220,
220,
220,
220,
220,
38053,
13,
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16818,
13,
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7,
940,
8,
220,
1303,
18291,
1958,
257,
357,
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8,
26827,
523,
326,
340,
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7021,
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11,
475,
991,
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45249,
10,
34,
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220,
220,
220,
3443,
25,
198,
220,
220,
220,
220,
220,
220,
220,
38053,
13,
19836,
9783
] | 2.627451 | 357 |
# Some examples are given below.
import os
import testinfra.utils.ansible_runner
testinfra_hosts = testinfra.utils.ansible_runner.AnsibleRunner(
os.environ['MOLECULE_INVENTORY_FILE']).get_hosts('all')
| [
2,
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] | 2.609756 | 82 |
import psycopg2
from .config import Config
config = Config()
| [
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] | 3.368421 | 19 |
# -*- coding: utf-8 -*-
from pygments.style import Style
from pygments.token import Token
from pygments.styles.default import DefaultStyle
| [
2,
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] | 3.302326 | 43 |
# Copyright (C) 2018 Henrique Pereira Coutada Miranda
# All rights reserved.
#
# This file is part of yambopy
#
import unittest
import os
from qepy.pw import PwIn
from yambopy.data.structures import Si
if __name__ == '__main__':
unittest.main()
| [
2,
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220,
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555,
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3419,
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] | 2.840909 | 88 |
from itertools import groupby
import attr
import typing
@attr.s
@attr.s
@attr.s
class ExpositionClient:
"""
Very simple implementation of client that will get all available metrics from a
namespace and expose a string formatted accoring to Prometheus expositions format.
"""
def _get_data(self) -> typing.List[MetricSet]:
"""
Get and parse data from Redis.
:return:
"""
results = self.redis.hgetall(self.namespace)
sorted_results = sorted(
[
RedisKeyValuePair(key.decode(), val.decode())
for key, val in results.items()
]
)
metrics = list()
for metric_name, metric_items in groupby(
sorted_results, key=lambda x: x.key.split(":")[0]
):
metric = MetricSet(name=metric_name)
for item in list(metric_items):
metric.add_item(item)
metrics.append(metric)
return metrics
def expose(self):
"""
Returns Prometheus formatted data.
:return:
"""
metrics = self._get_data()
out = ""
for metric in metrics:
out += f"# HELP {metric.name} {metric.description}\n"
out += f"# TYPE {metric.name} {metric.type}\n"
for value in metric.values:
if value.type and value.labels:
out += (
f"{metric.name}_{value.type}{{{value.labels}}} {value.value}\n"
)
elif value.type and not value.labels:
out += f"{metric.name}_{value.type} {value.value}\n"
elif not value.type and value.labels:
out += f"{metric.name}{{{value.labels}}} {value.value}\n"
else:
out += f"{metric.name} {value.value}\n"
out += f"\n"
return out
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220,
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25,
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220,
220,
220,
220,
503,
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277,
1,
90,
4164,
1173,
13,
3672,
92,
1391,
8367,
13,
8367,
32239,
77,
1,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
503,
15853,
277,
1,
59,
77,
1,
628,
220,
220,
220,
220,
220,
220,
220,
1441,
503,
198
] | 1.988706 | 974 |
#
# Author: Nikhil Taneja ([email protected])
# fb_post.py (c) 2021
# Desc: description
# Created: Fri Jan 08 2021 04:19:09 GMT+0530 (India Standard Time)
# Modified: Fri Jan 08 2021 18:19:22 GMT+0530 (India Standard Time)
#
import logging
import re
from urllib.parse import parse_qs, unquote, urlparse
import requests
from bs4 import BeautifulSoup
from .model import PostModel
from .utils import find_account_number, validate_hashtag
logger = logging.getLogger('faucet')
| [
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] | 2.951515 | 165 |
import glob
import json
import math
import multiprocessing
import os
import re
import time
import h5py
import imageio
import numpy as np
import pandas as pd
from numpyencoder import NumpyEncoder
from skimage.measure import label, regionprops
from skimage.transform import resize
from sklearn.cluster import KMeans
from tqdm import tqdm
from analyzer.data.utils.data_raw import readvol, folder2Vol
from analyzer.utils.eval_model import Evaluationmodel
class Dataloader():
'''
Dataloader class for handling the em dataset and the related labels.
:param cfg: configuration manager.
:param volume: the EM volume.
:param labels: labels that are defined by human or segmentation and will be clustered soon.
:param gt: groundtruth data (cluster)
:param feature: Defines the feature that the VAE should go for.
:param chunk_size: (tuple) defines the chunks in which the data is loaded. Can help to overcome Memory errors.
:param ff: (string) defines the file format that you want to work with. (default: png)
'''
def __len__(self):
'''
Required by torch to return the length of the dataset.
:returns: integer
'''
with h5py.File(self.mito_volume_file_name, 'r') as f:
return len(f["id"])
def __getitem__(self, idx):
'''
Required by torch to return one item of the dataset.
:param idx: index of the object
:returns: object from the volume
'''
with h5py.File(self.mito_volume_file_name, 'r') as f:
return f["chunk"][idx], idx
def get_fns(self):
'''returns the em, label and gt filenames of every image.'''
emfns = sorted(glob.glob(self.volpath + '*.' + self.ff))
labelfns = sorted(glob.glob(self.labelpath + '*.' + self.ff))
gtfns = sorted(glob.glob(self.gtpath + '*.' + self.ff))
return (emfns, labelfns, gtfns)
def load_chunk(self, vol='all', mode='3d'):
'''
Load chunk of em and groundtruth data for further processing.
:param vol: (string) choose between -> 'all', 'em', 'label' in order to specify
with volume you want to load.
'''
emfns = sorted(glob.glob(self.volpath + '*.' + self.ff))
labelfns = sorted(glob.glob(self.labelpath + '*.' + self.ff))
if mode == '2d':
if (vol == 'em') or (vol == 'all'):
emdata = readvol(emfns[0])
emdata = np.squeeze(emdata)
print('em data loaded: ', emdata.shape)
if (vol == 'label') or (vol == 'all'):
labels = readvol(labelfns[0])
labels = np.squeeze(labels)
print('label data loaded: ', labels.shape)
if mode == '3d':
if (vol == 'em') or (vol == 'all'):
if self.volume is None:
emdata = folder2Vol(self.volpath, self.chunk_size, file_format=self.ff)
print('em data loaded: ', emdata.shape)
if (vol == 'label') or (vol == 'all'):
if self.labels is None:
labels = folder2Vol(self.labelpath, self.chunk_size, file_format=self.ff)
print('label data loaded: ', labels.shape)
if (vol == 'gt') or (vol == 'all'):
if self.gt is None:
gt = folder2Vol(self.gtpath, self.chunk_size, file_format=self.ff)
print('gt data loaded: ', gt.shape)
return (emdata, labels, gt)
def list_segments(self, vol, labels, min_size=2000, os=0, mode='3d'):
'''
This function creats a list of arrays that contain the unique segments.
:param vol: (np.array) volume that contains the pure em data. (2d || 3d)
:param label: (np.array) volume that contains the groundtruth. (2d || 3d)
:param min_size: (int) this sets the minimum size of mitochondria region in order to be safed to the list. Used only in 2d.
:param os: (int) defines the offset that should be used for cutting the bounding box. Be careful with offset as it can lead to additional regions in the chunks.
:param mode: (string) 2d || 3d --> 2d gives you 2d arrays of each slice (same mitochondria are treated differently as they loose their touch after slicing)
--> 3d gives you the whole mitochondria in a 3d volume.
:returns: (dict) of (np.array) objects that contain the segments with labels as keys.
'''
bbox_dict = {}
mask = np.zeros(shape=vol.shape, dtype=np.uint16)
mask[labels > 0] = 1
vol[mask == 0] = 0
if mode == '2d':
bbox_list = []
for idx in range(vol.shape[0]):
image = vol[idx, :, :]
gt_img = labels[idx, :, :]
label2d, num_label = label(gt_img, return_num=True)
regions = regionprops(label2d, cache=False)
for props in regions:
boundbox = props.bbox
if props.bbox_area > min_size:
if ((boundbox[0] - os) < 0) or ((boundbox[2] + os) > image.shape[0]) or (
(boundbox[1] - os) < 0) or ((boundbox[3] + os) > image.shape[1]):
tmparr = image[boundbox[0]:boundbox[2], boundbox[1]:boundbox[3]]
else:
tmparr = image[(boundbox[0] - os):(boundbox[2] + os), (boundbox[1] - os):(boundbox[3] + os)]
bbox_list.append(tmparr)
bbox_dict = {i: bbox_list[i] for i in range(len(bbox_list))}
elif mode == '3d':
chunk_dict = {}
label3d, num_label = label(labels, return_num=True)
regions = regionprops(label3d, cache=False)
for props in regions:
boundbox = props.bbox
if ((boundbox[1] - os) < 0) or ((boundbox[4] + os) > vol.shape[1]) or ((boundbox[2] - os) < 0) or (
(boundbox[5] + os) > vol.shape[2]):
tmparr = vol[boundbox[0]:boundbox[3], boundbox[1]:boundbox[4], boundbox[2]:boundbox[5]]
else:
tmparr = vol[boundbox[0]:boundbox[3], (boundbox[1] - os):(boundbox[4] + os),
(boundbox[2] - os):(boundbox[5] + os)]
bbox_dict[props.label] = tmparr
else:
raise ValueError('No valid dimensionality mode in function list_segments.')
return (bbox_dict)
def prep_data_info(self, volopt='label', save=False):
'''
This function aims as an inbetween function iterating over the whole dataset in efficient
and memory proof fashion in order to preserve information that is needed for further steps.
:param volopt: (string) this sets the volume you want to use for the operation. default: gt
:param kernel_n: (int) number of CPU kernels you want to use for multiprocessing.
:returns added: (dict) that contains the labels with respective information as (list): [pixelsize, [slice_index(s)]]
'''
if volopt == 'label':
fns = sorted(glob.glob(self.labelpath + '*.' + self.ff))
elif volopt == 'em':
fns = sorted(glob.glob(self.volpath + '*.' + self.ff))
else:
raise ValueError('Please enter the volume on which \'prep_data_info\' should run on.')
if self.exclude_borders:
fns = fns[1:-1]
with multiprocessing.Pool(processes=self.cpus) as pool:
result = pool.starmap(self.calc_props, enumerate(fns))
added = {}
for dicts in result:
for key, value in dicts.items():
if key in added:
added[key][0] += value[0]
added[key][1].append(value[1])
if self.exclude_borders:
if not added[key][2]:
added[key].append(value[2])
else:
added.setdefault(key, [])
added[key].append(value[0])
added[key].append([value[1]])
if self.exclude_borders:
added[key].append(value[2])
result_array = []
for result in added.keys():
if self.exclude_borders and added[result][2]:
continue
result_array.append({
'id': result,
'size': added[result][0],
'slices': added[result][1]
})
if save:
with open(os.path.join(self.cfg.SYSTEM.ROOT_DIR, self.cfg.DATASET.DATAINFO), 'w') as f:
json.dump(result_array, f, cls=NumpyEncoder)
f.close()
return (result_array)
def calc_props(self, idx, fns):
'''
Helper function for 'prep_data_info'
:param idx: (int) this is the slice index that correspondes to the image slice. E.g. idx 100 belongs to image 100.
:param fns: (string) list of filenames.
:returns result: (dict) with each segment. key: idx of segment -- value: [number of pixels in segment, idx of slice].
'''
result = {}
if os.path.exists(fns):
tmp = imageio.imread(fns)
for region in regionprops(tmp):
result.setdefault(region.label, [])
result[region.label].append(region.area)
result[region.label].append(idx)
result[region.label].append(False)
if self.exclude_borders:
minr, minc, maxr, maxc = region.bbox
if minr == 0 or minc == 0:
result[region.label][-1] = True
if maxr == tmp.shape[0] or maxc == tmp.shape[0]:
result[region.label][-1] = True
return result
def precluster(self, mchn='simple', n_groups=5):
'''
Function preclusters the mitochondria into buckets of similar size in order to avoid
sparsity and loss of information while extracting latent representation of the mitochondria.
'''
if os.path.exists(os.path.join(self.cfg.SYSTEM.ROOT_DIR, self.cfg.DATASET.DATAINFO)) \
and os.stat(os.path.join(self.cfg.SYSTEM.ROOT_DIR, self.cfg.DATASET.DATAINFO)).st_size != 0:
with open(os.path.join(self.cfg.SYSTEM.ROOT_DIR, self.cfg.DATASET.DATAINFO), 'r') as f:
data_info = json.loads(f.read())
else:
data_info = self.prep_data_info(save=True)
tmp = np.stack(([mito['id'] for mito in data_info], [mito['size'] for mito in data_info]), axis=-1)
if mchn == 'simple':
sorted = tmp[tmp[:, 1].argsort()[::-1]]
splitted = np.array_split(sorted, n_groups, axis=0)
id_lists = [tmp[:, 0].tolist() for tmp in splitted]
elif mchn == 'cluster':
model = KMeans(n_clusters=n_groups)
res_grps = model.fit_predict(np.array(tmp[:, 1]).reshape(-1, 1))
id_lists = [[]] * n_groups
for idx in range(len(res_grps)):
id_lists[res_grps[idx]].append(tmp[:, 0][idx])
else:
raise ValueError(
'Please enter the a valid mechanismn you want to group that mitochondria. \'simple\' or \'cluster\'.')
return id_lists
def extract_scale_mitos(self):
'''
Function to extract the objects as volumes and scale them. Then its saves the scaled volumes to an h5 file.
'''
if os.path.exists(os.path.join(self.cfg.SYSTEM.ROOT_DIR, self.cfg.DATASET.DATAINFO)) \
and os.stat(os.path.join(self.cfg.SYSTEM.ROOT_DIR, self.cfg.DATASET.DATAINFO)).st_size != 0:
with open(os.path.join(self.cfg.SYSTEM.ROOT_DIR, self.cfg.DATASET.DATAINFO), 'r') as f:
regions = json.loads(f.read())
else:
regions = self.prep_data_info(save=False)
print("{} objects found in the ground truth".format(len(regions)))
regions = pd.DataFrame(regions)
regions = regions[(self.upper_limit > regions['size']) & (self.lower_limit < regions['size']) & (
len(regions['slices']) > 1)].values.tolist()
filtered_length = len(regions)
print("{} within limits {} and {}".format(filtered_length, self.lower_limit, self.upper_limit))
if self.region_limit is not None:
regions = regions[:self.region_limit]
print("{} will be extracted due to set region_limit".format(self.region_limit))
with h5py.File(self.mito_volume_file_name, "w") as f:
f.create_dataset("shape_volume", (len(regions), 1, *self.target_size))
f.create_dataset("texture_volume", (len(regions), 1, *self.target_size))
f.create_dataset("id", (len(regions),))
if self.cpus < 2 and self.chunks_per_cpu < 2:
print("single cpu mode")
for i in tqdm(range(0, len(regions))):
if i < 11000:
continue
print(i)
result = self.get_mito_volume(regions[i])
f["id"][i] = result[0]
f["shape_volume"][i] = result[1]
f["texture_volume"][i] = result[2]
with multiprocessing.Pool(processes=self.cpus) as pool:
for i in tqdm(range(0, len(regions), int(self.cpus * self.chunks_per_cpu))):
try:
results = pool.map(self.get_mito_volume, regions[i:i + int(self.cpus * self.chunks_per_cpu)])
for j, result in enumerate(results):
f["id"][i + j] = result[0]
f["shape_volume"][i + j] = result[1]
f["texture_volume"][i + j] = result[2]
except:
print("error in extraction, i: {}".format(i))
exit()
def get_mito_volume(self, region):
'''
Preprocessing function to extract and scale the mitochondria as volume
:param region: (dict) one region object provided by Dataloader.prep_data_info
:returns result: (numpy.array) a numpy array with the target dimensions and the mitochondria in it
'''
gt_volume, em_volume = self.get_volumes_from_slices(region)
mito_regions = regionprops(gt_volume, cache=False)
if len(mito_regions) != 1:
print("something went wrong during volume building. region count: {}".format(len(mito_regions)))
mito_region = mito_regions[0]
if len(mito_region.bbox) < 6:
return [-1, np.zeros(shape=(1, *self.target_size)), np.zeros(shape=(1, *self.target_size))]
shape = gt_volume[mito_region.bbox[0]:mito_region.bbox[3] + 1,
mito_region.bbox[1]:mito_region.bbox[4] + 1,
mito_region.bbox[2]:mito_region.bbox[5] + 1].astype(np.float32)
texture = em_volume[mito_region.bbox[0]:mito_region.bbox[3] + 1,
mito_region.bbox[1]:mito_region.bbox[4] + 1,
mito_region.bbox[2]:mito_region.bbox[5] + 1].astype(np.float32)
scaled_shape = resize(shape, self.target_size, order=1, anti_aliasing=True)
scaled_shape = scaled_shape / scaled_shape.max()
scaled_shape = np.expand_dims(scaled_shape, 0)
scaled_texture = resize(texture, self.target_size, order=1, anti_aliasing=True)
scaled_texture = scaled_texture / scaled_texture.max()
scaled_texture = np.expand_dims(scaled_texture, 0)
if scaled_shape.sum() < self.lower_limit * 0.1:
print("region {} was too small".format(region[0]))
return [-1, np.zeros(shape=(1, *self.target_size)), np.zeros(shape=(1, *self.target_size))]
return [region[0], scaled_shape, scaled_texture]
def get_volumes_from_slices(self, region):
'''
#TODO
:param region:
:returns gt_volume, em_volume:
'''
gt_all_fn = sorted(glob.glob(self.labelpath + '*.' + self.ff))
em_all_fn = sorted(glob.glob(self.volpath + '*.' + self.ff))
gt_fns = [gt_all_fn[id] for id in region["slices"]]
em_fns = [em_all_fn[id] for id in region["slices"]]
gt_volume = []
em_volume = []
for i in range(len(gt_fns)):
gt_slice = imageio.imread(gt_fns[i])
em_slice = imageio.imread(em_fns[i])
gt_slice[gt_slice != region["id"]] = 0
em_slice[gt_slice != region["id"]] = 0
gt_volume.append(gt_slice)
em_volume.append(em_slice)
return np.array(gt_volume), np.array(em_volume)
def extract_scale_mitos_samples(self):
'''
Function to extract the objects as volumes and scale them. Then its saves the scaled volumes to an h5 file.
'''
regions = self.prep_data_info(save=True)
print("{} objects found in the ground truth".format(len(regions)))
regex = re.compile('([0-9]+)_mito_samples.h5')
for root, dirs, files in os.walk(self.cfg.DATASET.ROOTD):
for file in files:
if regex.match(file):
os.remove(self.cfg.DATASET.ROOTD + file)
in_q = multiprocessing.Queue()
processes = []
for region in regions:
in_q.put(region)
pbar = tqdm(total=len(regions))
for cpu in range(self.cpus):
p = multiprocessing.Process(target=self.get_mito_chunk, args=(in_q, cpu))
p.start()
processes.append(p)
progress = 0
while not in_q.empty():
progress_step = len(regions)-in_q.qsize()
if progress != progress_step:
pbar.update(progress_step-progress)
progress = progress_step
time.sleep(30)
for p in processes:
p.join()
self.cleanup_h5()
return
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20,
3419,
198,
220,
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220,
220,
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198
] | 2.04398 | 8,845 |
import argparse
import yaml
import datetime
import os
import torch
import torch.nn.functional as F
import wandb
import random
import numpy as np
import utils
import graph
import dgl
import models
from dgl import DGLError
from utils import pbar
import torchmetrics
import pickle as pkl
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("-c", "--config",
type = str,
required = True,
help = 'path to config file')
parser.add_argument("-o", "--output",
type = str,
default = f"output/{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M')}",
help = "path to output file"
)
parser.add_argument("-w","--wandb",
action = "store_true",
help = 'wandb logging')
parser.add_argument("-n", "--name",
type = str,
default = f"{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M')}",
help = 'name of wandb run')
args = parser.parse_args()
try:
config = yaml.safe_load(open(args.config, "r"))
except:
raise ValueError(f"Incorrect path to config file : {args.config}")
if not config.get('output', False):
config['output'] = args.output
if args.wandb:
if not config.get('name', False):
config['name'] = args.name
config['wandb'] = args.wandb
trainer = Trainer(config, pretrain = True)
trainer.run()
config['encoder_save'] = f"{config['output']}/best_encoder.ckpt"
config['lr'] = 1.0e-4
config['weight_decay'] = 1.0e-8
trainer = Trainer(config, pretrain = False, reinit = True)
trainer.run()
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21997,
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3419,
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] | 2.017335 | 923 |
# tests.test_text.test_base
# Tests for the text visualization base classes
#
# Author: Benjamin Bengfort
# Created: Mon Feb 20 06:34:50 2017 -0500
#
# Copyright (C) 2016 The scikit-yb developers
# For license information, see LICENSE.txt
#
# ID: test_base.py [6aa9198] [email protected] $
"""
Tests for the text visualization base classes
"""
##########################################################################
## Imports
##########################################################################
from yellowbrick.base import *
from yellowbrick.text.base import *
from sklearn.base import BaseEstimator, TransformerMixin
##########################################################################
## TextVisualizer Base Tests
##########################################################################
| [
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] | 4.413978 | 186 |
# -*- coding: utf-8 -*-
"""
Created on Thu Oct 7 09:42:38 2021
@author: VISHAKHA V
"""
arr=list(map(int,input().split()))
result=max_subarray_product(arr)
print("The maximum sub_array product =",result)
#Take the input as [6,-3,-10,0,2] if you want, then the output will be 180
| [
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] | 2.544643 | 112 |
# Copyright (c) 2022 Dai HBG
"""
该脚本用于将1分钟数据中不在市的部分删除
"""
import os
import pandas as pd
if __name__ == '__main__':
main()
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import math
import torch.nn as nn
import torch.nn.functional as F
# class InvertedResidual(nn.Module):
# def __init__(self, inp, oup, kernel_size, stride, expand_ratio):
# super(InvertedResidual, self).__init__()
# assert stride in [1, 2]
# self.stride = stride
# padding = kernel_size // 2
# hidden_dim = round(inp * expand_ratio)
# self.use_res_connect = self.stride == 1 and inp == oup
# self.conv1 = nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False)
# self.bn1 = nn.BatchNorm2d(hidden_dim)
# self.conv2 = nn.Conv2d(hidden_dim, hidden_dim, kernel_size, stride, padding, groups=hidden_dim, bias=False)
# self.bn2 = nn.BatchNorm2d(hidden_dim)
# self.conv3 = nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False)
# self.bn3 = nn.BatchNorm2d(oup)
#
# def forward(self, x):
# inputs = x
# x = self.conv1(x)
# x = self.bn1(x)
# x = F.relu6(x, inplace=True)
# x = self.conv2(x)
# x = self.bn2(x)
# x = F.relu6(x, inplace=True)
# x = self.conv3(x)
# x = self.bn3(x)
# if self.use_res_connect:
# return inputs + x
# else:
# return x
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] | 1.871988 | 664 |
# Copyright 2016 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.
"""Example of DNNClassifier for Iris plant dataset."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import pandas as pd
import tensorflow as tf
parser = argparse.ArgumentParser()
parser.add_argument('--batch_size', default=100, type=int, help='batch size')
parser.add_argument('--train_steps', default=200, type=int,
help='number of training steps')
TRAIN_URL = "http://download.tensorflow.org/data/iris_training.csv"
TEST_URL = "http://download.tensorflow.org/data/iris_test.csv"
COLUMNS = ['SepalLength', 'SepalWidth', 'PetalLength', 'PetalWidth', 'Species']
SPECIES = ['Sentosa', 'Versicolor', 'Virginica']
def load_data(train_fraction=0.8, seed=0, y_name='Species'):
"""Returns the iris dataset as (train_x, train_y), (test_x, test_y)."""
train_path = tf.keras.utils.get_file(TRAIN_URL.split('/')[-1], TRAIN_URL)
train = pd.read_csv(train_path, names=COLUMNS, header=0)
train_x, train_y = train, train.pop(y_name)
test_path = tf.keras.utils.get_file(TEST_URL.split('/')[-1], TEST_URL)
test = pd.read_csv(test_path, names=COLUMNS, header=0)
test_x, test_y = test, test.pop(y_name)
return (train_x, train_y), (test_x, test_y)
if __name__ == '__main__':
tf.logging.set_verbosity(tf.logging.INFO)
tf.app.run(main)
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10354,
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220,
220,
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62,
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7,
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8,
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220,
220,
220,
48700,
13,
1324,
13,
5143,
7,
12417,
8,
198
] | 2.819346 | 703 |
import unittest
import time
import asyncio
import random
import math
import collections
from .siridb import SiriDB
from .server import Server
from .client import Client
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] | 3.886364 | 44 |
from .types import StaticShape, RBF, Adjacency
from .math import tf_hgrid_coords, hgrid_normalize, bezier_multi, tri_ones_lower, tf_repeat_1d
from .math import piecewise_linear_curve_closest_pts
from .math import random_uniform_hypersphere, mutual_sq_distances, gauss_rbf, rbf_factory, crbf_wendland_d3c2
from .config import TF_FLOAT, EPSILON, BEZIER_DEFAULT_DEGREE
from .model import create_entity_name, get_active_model, Entity, SingleVarContrib, TensorRef, expand_tensor_ref
from .model import stop_gradient_tape
from .utils import join_ident, export, apply_colormap
from typing import Sequence, List, Optional, Union, Dict, Callable, Tuple
import abc
import tensorflow as tf
import pandas as pd
import numpy as np
import h5py
from copy import copy
from contextlib import nullcontext
RBF_DEFAULT = 'gauss'
class Lambda(Entity):
"""
Entity wrapping an arbitrary expression involving tensors.
"""
def __init__(self, fun: Callable, tensor_ref: Union[TensorRef, Dict[str, TensorRef], List[TensorRef]], *,
name=None, enable_grad: bool = True):
"""
Init lambda entity.
:param fun: The callable to be wrapped.
:param tensor_ref: Tensor ref or a list or dict of tensor refs.
:param name: The name of the entity.
:param enable_grad: Boolean (default true) specifying whether the gradient tape should be enabled when in
training mode.
"""
super().__init__(name=name)
self.fun = fun
self.tensor_ref = copy(tensor_ref)
self.enable_grad = enable_grad
self.add_to_active_model()
@export
def lambda_(fun: Callable, tensor_ref: Union[TensorRef, Dict[str, TensorRef], List[TensorRef]], *,
name=None, enable_grad: bool = True):
"""
Create an entity that wraps an arbitrary expression involving tensors.
:param fun: A lambda function invoked with the expanded tensor refs declared in the parameter tensor_ref.
:param tensor_ref: A tensor ref, a list of tensor refs or a dictionary. In the case of a dictionary the function
will be invoked with named parameters whereas in the other cases positional parameters will be used.
:param name: Name of the entity wrapping the expression.
:param enable_grad: if true (default) the tensor tape is enabled while in training mode.
:return: An entity wrapping the expression implemented in fun.
"""
return Lambda(fun=fun, tensor_ref=tensor_ref, name=name, enable_grad=enable_grad).get_value_ref()
TensorRefs = Union[Sequence[TensorRef], TensorRef]
def tensor_refs_clone(refs: TensorRefs) -> TensorRefs:
"""
Clone a tensor ref or a sequence of tensor refs.
:param refs:
:return:
"""
if isinstance(refs, (tf.Tensor, tf.Variable, Callable)):
return refs
return list(refs)
@export
@export
@export
def aggregate_rasters(rasters: Union[TensorRef, List[TensorRef]], *, bias: float = 0., name=None):
"""
Aggregate a list of images. The activation function ReLU is applied after the input tensors are stacked and added.
:param rasters: A list of tensor refs representing the input images.
:param bias: Bias value.
:param name: Name of the entity.
:return: An entity implementing the aggregation.
"""
return lambda_(adder, tensor_ref=rasters, name=name)
class RBFNet:
"""
Radial basis function network.
"""
def __init__(self, rbf: RBF):
"""
Init RBF network.
:param rbf: The radial basis function, eg the value returned by degraph.math.rbf_factory(...).
"""
self.rbf = rbf
@staticmethod
@tf.function
@tf.function
def grid_lines_on_grid(self, shape: StaticShape, centres: tf.Tensor):
"""
Apply radial basis function on a grid using distances from lines aligned with the axes.
"""
grid_acc = tf.zeros(shape=tf.reduce_prod(shape), dtype=TF_FLOAT)
grid = tf.cast(tf.expand_dims(tf_hgrid_coords(shape), axis=1), dtype=TF_FLOAT)
for k in range(centres.shape[1]):
level = tf.squeeze(tf.square(grid[:, :, k:k+1] - centres[:, k:k+1]), axis=-1)
level = self.rbf(level)
level = tf.reduce_sum(level, axis=-1) # form a mixture
grid_acc += level
grid_acc = tf.reshape(grid_acc, shape=shape) # reshape values to an image
return grid_acc
@export
class GraphRepr: # TODO extend tf.Module?
"""
Symmetric directed graph representation.
"""
# TODO Another ideas would be of getting rid of this class and have individual entities for var and other op,
# this is in line with the idea of using other type of coordinates (eg. polar).
HDF5_SER_TYPE = 'degraph.GraphRepr'
DEF_NAME_PREFIX = 'graph_def'
def __init__(self, adjacency: Adjacency, dim: int = 2, name=None, **kwargs):
"""
Init the graph representation object.
:param adjacency: Adjacency matrix, this can be either a Numpy array, a Pandas DataFrame or a tensor.
:param dim: Number of spatial dimensions of this representation, currently many internal components are limited
to the 2D case it is the only option.
:param name:
:param kwargs:
"""
# TODO optional param positions_generator (space positions)
self._name = create_entity_name(name, prefix=self.DEF_NAME_PREFIX)
self._static = False
if '_internal_skip_init' in kwargs:
return
if len(kwargs) != 0:
raise ValueError(f'Unsupported extra parameters: {kwargs.keys()}')
dim = int(dim)
assert dim >= 1
if dim not in (2, 3):
raise ValueError(f'Space dimensions not supported: {dim}')
if isinstance(adjacency, pd.DataFrame):
assert adjacency.index == adjacency.columns
adjacency = np.array(adjacency)
if isinstance(adjacency, np.ndarray):
adjacency = tf.convert_to_tensor(adjacency)
if not isinstance(adjacency, (tf.Tensor, tf.Variable)):
raise ValueError(f'Unsupported type for adjacency matrix: {type(adjacency)}')
assert len(adjacency.shape) == 2 and adjacency.shape[0] == adjacency.shape[1]
# Mask upper triangular values of the adjacency matrix
adjacency = tf.cast(adjacency, dtype=TF_FLOAT) * tf.cast(tri_ones_lower(adjacency.shape[0]), dtype=TF_FLOAT)
adjacency /= tf.reduce_max(tf.abs(adjacency)) + EPSILON # Normalize edge abs weights
edge_count = tf.math.count_nonzero(adjacency)
edge_extreme_indexes = tf.where(adjacency)
assert edge_count == edge_extreme_indexes.shape[0] and edge_extreme_indexes.shape[1] == 2
self.adjacency = adjacency
# The indexes of the extreme points of each edge. The indexes point at the first axis of the variable positions.
self.edge_extreme_indexes = edge_extreme_indexes
with tf.name_scope(self.name):
self.positions = tf.Variable(random_uniform_hypersphere(size=adjacency.shape[0], dim=dim), trainable=True,
name='positions')
total_ctrl_point_count = edge_extreme_indexes.shape[0] * ((BEZIER_DEFAULT_DEGREE+1)-2)
ctrl_points_init = tf.reshape(random_uniform_hypersphere(size=total_ctrl_point_count, dim=dim),
shape=(-1, (BEZIER_DEFAULT_DEGREE+1)-2, dim))
self.edge_internal_ctrl_points = tf.Variable(ctrl_points_init, trainable=True,
name='edge_internal_ctrl_points')
@property
@property
def static(self) -> bool:
"""
Boolean property, true if the current graph representation is static (i.e. the positions are not of the type
tf.Variable).
:return:
"""
return self._static
def copy(self, *, static=False, name: Optional[str] = None):
"""
Clone the current instance of GraphRepr with the excpection of the static flag which is passed through the
arguments.
:param static: When static is set the variables are transformed to static tensors in the destination object.
This is useful to take snapshots of the status.
:param name: Optional name for the graph, otherwise an automatic one is generated.
:return:
"""
obj = GraphRepr(np.asarray(0), _internal_skip_init=True)
obj.adjacency = self.adjacency
obj.edge_extreme_indexes = self.edge_extreme_indexes
obj._static = static
obj._name = create_entity_name(name, prefix=self.DEF_NAME_PREFIX)
# Convert variables to tensors, note that when GraphRepr.static is set these are already tensors.
positions = tf.convert_to_tensor(self.positions)
edge_internal_ctrl_points = tf.convert_to_tensor(self.edge_internal_ctrl_points)
if static:
obj.positions = positions
obj.edge_internal_ctrl_points = edge_internal_ctrl_points
else:
obj.positions = tf.Variable(positions, trainable=True)
obj.edge_internal_ctrl_points = tf.Variable(edge_internal_ctrl_points, trainable=True)
return obj
def get_ctrl_points_vars(self) -> List[tf.Variable]:
"""
Get the control point variables relative to the vertexes and the edges.
:return: A list of elements of type tf.Variable.
"""
if self.static:
return []
return [self.positions, self.edge_internal_ctrl_points]
def serialize(self, fobj):
"""
Serialize the current object in HDF5 format using the file-like object provided.
:param fobj: A file-like object
:return:
"""
with h5py.File(fobj, mode='w') as f:
f['type'] = self.HDF5_SER_TYPE
f['adjacency'] = self.adjacency.numpy()
f['edge_extreme_indexes'] = self.edge_extreme_indexes.numpy()
f['positions'] = self.positions.numpy()
f['edge_internal_ctrl_points'] = self.edge_internal_ctrl_points.numpy()
@property
def dim(self) -> int:
"""
The number of spatial dimentions of this representation.
:return:
"""
return self.positions.shape[1]
def get_positions(self) -> tf.Tensor:
"""
Get a tensor containing the positions of the vertexes. The expected shape is [pt_count, dim].
:return:
"""
# TODO optionally we may include a calculation here, eg polar coordinates to cartesian
return tf.convert_to_tensor(self.positions)
def get_positions_ref(self) -> TensorRef:
"""
Get a tensor ref relative to the positions of the vertexes. See get_positions().
:return:
"""
return fun
def get_edges_ctrl_points(self) -> tf.Tensor:
"""
Get the control points of the edges, the shape of the tensor is [edge_count, ctrl_point_count, dim]
:return:
"""
# edge_extreme_points, shape: [edge_count, 2, dim]
edge_extreme_points = tf.gather(self.positions, indices=self.edge_extreme_indexes)
# Compose a tensor with edge's end point positions and internal control points
return tf.concat([edge_extreme_points[:, 0:1, :], # start points
self.edge_internal_ctrl_points, # internal points
edge_extreme_points[:, 1:2, :]], axis=1) # end points
class Vertexes(SingleVarContrib):
"""
Entity representing the vertexes of a graph.
"""
@export
def vertexes(graph: GraphRepr, *, trainable: bool = True, name=None) -> TensorRef:
"""
Create an entity that represents the vertexes of a graph.
:param graph: The graph object.
:param trainable: If true the variable relative to the positions of the vertexes are marked as trainable.
:param name: The name of the entity.
:return: The entity object.
"""
return Vertexes(graph=graph, trainable=trainable, name=name).get_value_ref()
@export
@export
def unit_sphere_bounds_loss(points_tensor_ref: TensorRef, *, factor: float = 1.0):
"""
Get a loss that penalises points laying outside the unit hyper-sphere centred in the origin.
:param points_tensor_ref: A tensor ref containing the coordinates of the points.
:param factor: A multiplicative factor for the loss.
:return: An entity implementing the loss.
"""
@tf.function
return lambda_(fun, points_tensor_ref)
@export
def mse_loss(value_ref: TensorRef, *, factor: float = 1.0) -> TensorRef:
"""
Get a MSE Loss.
:param value_ref: The input tensor.
:param factor: A multiplicative factor for the loss.
:return: An entity implementing the loss.
"""
@tf.function
return lambda_(fun, value_ref)
@export
def sse_loss(value_ref: TensorRef, *, factor: float = 1.0) -> TensorRef:
"""
Get a sum of squares loss.
:param value_ref: The input tensor.
:param factor: A multiplicative factor for the loss.
:return: An entity implementing the loss.
"""
@tf.function
return lambda_(fun, value_ref)
class RBFNetRaster(SingleVarContrib):
"""
An entity that creates a raster using a radial basis function network.
"""
def __init__(self, points_tensor_ref: TensorRef, *, shape: StaticShape,
rbf: str = RBF_DEFAULT, peak: float = 1.0, spread: float = 1.0, name=None):
"""
Init the entity.
:param points_tensor_ref: A tensor ref referencing a tensor with expected shape: [point_count, dim].
The points are used as centres of the radial basis functions.
:param shape: The shape of the raster.
:param rbf: The rbf to be used, see degraph.math.rbf_factory.
:param peak: The peak of the RBF.
:param spread: The spread of the RBF.
:param name: The name of the entity.
"""
super().__init__(name=name)
self.points_tensor_ref = points_tensor_ref
self.shape = shape
self.rbf = rbf
self.peak = peak
self.spread = spread
self._rbf_net = None
self.add_to_active_model()
@export
@export
def _scope_prepare(scope: str) -> Tuple[object, str]:
"""
Parse a scope string a return a tuple consisting of context manager for the assignation of the tf's scope
and a string representing the summary name. The scope is of the form "<ident1>.<ident2>. ... .<ident3>", the
righmost identifier is used as summary name whereas the prefix is used as scope name.
:param scope: A string containing a qualified name.
:return:
"""
splits = scope.rsplit('.', 1)
if any(map(lambda v: len(v) == 0, splits)):
raise ValueError(f'Invalid scope name: {scope}')
if len(splits) == 1:
return nullcontext(), splits[0]
return tf.name_scope(splits[0]), splits[1]
SummaryFunction = Callable[[tf.Tensor, str], None]
class SummaryBase(Entity):
"""
A base template for summary entities.
"""
def __init__(self, var: TensorRef, fun: SummaryFunction, *, scope: str, name=None):
"""
Init summary entity.
:param var: The variable to the summarised.
:param fun: A callable of the form fun(tensor, name) that invokes the low level Tensorflow functions.
:param scope:
:param name: The name of this entity, note that the name of the summary is taken from parameter scope.
"""
super().__init__(name=name)
self.var = var
self.fun = fun
self.scope = scope
self.add_to_active_model()
@export
@export
@export
def summary_image(var: TensorRef, *, scope: str, name=None, **kwargs):
"""
Create an image summary entity. This function wraps tf.summary.image.
:param var: The tensor to be interpreted as image.
:param scope:
:param name: The name of the entity representing this operation, note that the identifier of the summary in
Tensorboard is determined by the parameter scope.
:param kwargs: Additional parameters to be passed to tf.summary.image.
:return:
"""
return SummaryBase(var, fun=fun, scope=scope, name=name)
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] | 2.53379 | 6,422 |
# import unittest
# from unittest.mock import patch
# from selenium.common.exceptions import WebDriverException
# from draytekwebadmin.driver import load_driver, unload_driver
# class TestDriver(unittest.TestCase):
# def setUp(self):
# pass
# def tearDown(self):
# pass
# @patch("selenium.webdriver.FirefoxOptions", autospec=True)
# @patch("selenium.webdriver.Firefox", autospec=True)
# def test_Load_Driver_Named_Firefox(self, mock_firefox, mock_FirefoxOptions):
# load_driver(browser_name="firefox")
# self.assertTrue(mock_firefox.called)
# self.assertTrue(mock_FirefoxOptions.called)
# self.assertTrue(mock_firefox().maximize_window.called)
# self.assertFalse(mock_FirefoxOptions().headless)
# @patch("selenium.webdriver.FirefoxOptions", autospec=True)
# @patch("selenium.webdriver.Firefox", autospec=True)
# def test_Load_Driver_Named_Firefox_Headless(
# self, mock_firefox, mock_FirefoxOptions
# ):
# load_driver(browser_name="firefox", headless=True)
# self.assertTrue(mock_firefox.called)
# self.assertTrue(mock_FirefoxOptions.called)
# self.assertTrue(mock_FirefoxOptions().headless)
# @patch("selenium.webdriver.ChromeOptions", autospec=True)
# @patch("selenium.webdriver.Chrome", autospec=True)
# def test_Load_Driver_Named_Chrome(self, mock_chrome, mock_ChromeOptions):
# load_driver(browser_name="chrome")
# self.assertTrue(mock_chrome.called)
# self.assertTrue(mock_ChromeOptions.called)
# self.assertFalse(mock_ChromeOptions().headless)
# @patch("selenium.webdriver.ChromeOptions", autospec=True)
# @patch("selenium.webdriver.Chrome", autospec=True)
# def test_Load_Driver_Named_Chrome_Headless(self, mock_chrome, mock_ChromeOptions):
# load_driver(browser_name="chrome", headless=True)
# self.assertTrue(mock_chrome.called)
# self.assertTrue(mock_ChromeOptions.called)
# self.assertTrue(mock_ChromeOptions().headless)
# @patch("selenium.webdriver.Edge", autospec=True)
# def test_Load_Driver_Named_Edge(self, mock_edge):
# load_driver(browser_name="edge")
# self.assertTrue(mock_edge.called)
# @patch("draytekwebadmin.driver.load_firefox", autospec=True)
# def test_Load_Driver_Unspecified_Browser(self, mock_load_firefox):
# load_driver()
# self.assertTrue(mock_load_firefox.called)
# def test_Load_Driver_Unsupported_Browser(self):
# with self.assertRaises(Exception):
# load_driver(browser_name="NonExistent Browser")
# @patch("draytekwebadmin.driver.load_firefox", side_effect=WebDriverException())
# @patch("draytekwebadmin.driver.load_chrome", side_effect=WebDriverException())
# @patch("draytekwebadmin.driver.load_edge", side_effect=WebDriverException())
# def test_Load_Driver_FallThrough(self, mock_edge, mock_chrome, mock_firefox):
# with self.assertRaises(Exception) as cm:
# load_driver()
# self.assertEqual(
# "Unable to find suitable browser. Error Message: None",
# str(cm.exception).rstrip(),
# )
# @patch("selenium.webdriver.firefox")
# def test_Unload_Driver(self, mock_firefox):
# unload_driver(mock_firefox())
# self.assertTrue(mock_firefox().quit.called)
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] | 2.370551 | 1,433 |
import sys
import toml
import pytest
from teststack import cli
from teststack import import_commands
from teststack.errors import IncompatibleVersionError
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] | 3.809524 | 42 |
#! /usr/bin/python
# -*- coding: utf8 -*-
from __future__ import print_function
# Description:
"""
plot or save MRI images / masks in a screenshot
-------
Types of sequences in the data = ['Sag_T2' 'Sag_T1' 'Sag_Stir']
"""
# Imports
import os, time
import glob
import random
import pandas as pd
import numpy as np
import numpy.ma as ma
import matplotlib
import math
matplotlib.use('Agg')
import matplotlib.pyplot as plt
def plot_screenshot(images, mask = True, cols=4, smooth=True, ch=1, path_name = './', label_name='screenshot', save = True):
"""
plot or save images / masks in a screenshot
Parameters
----------
images = images or mask, np.ndarray image data [slice][width][height][channel]
mask = if True, returns mask's screenshot
smooth = if True, interpolation = 'spline16', if False = interpolation = 'nearest'
path_name = save path, str
label_name = filename, str
save = if True, save images in path_ + label_name + .jpg
Returns
-------
plot or save images / masks in a screenshot
"""
n = np.shape(images)[0]
rows = np.rint(n/np.float(cols)).astype(int)
if (rows*cols)>n:
cover = np.zeros((np.shape(images)[1],np.shape(images)[2]))
for i in range(rows*cols-n):
images=np.dstack((np.array(images), np.array(cover)))
# Create figure with sub-plots.
fig, axes = plt.subplots(rows, cols, figsize=(12,10))
# Adjust vertical spacing if we need to print ensemble and best-net.
hspace = 0.03
fig.subplots_adjust(hspace=hspace, wspace=0.03)
for i, ax in enumerate(axes.flat):
# Interpolation type.
if smooth:
interpolation = 'spline16'
else:
interpolation = 'nearest'
if mask :
# Plot mask.
ax.imshow(rotate((images[i,...,ch]/np.max(images[i,...,ch])),90).squeeze(), cmap='jet',
interpolation=interpolation)
xlabel = "Mask: {0}".format(i+1)
else:
# Plot image.
ax.imshow(rotate(images[i,...,ch],90).squeeze(), cmap='bone',
interpolation=interpolation)
xlabel = "MRI_Scan: {0}".format(i+1)
ax.set_xlabel(xlabel)
ax.set_xticks([])
ax.set_yticks([])
if save:
fig.savefig(path_name+label_name+'.jpg', format='jpg', bbox_inches='tight')#, dpi=600
plt.close(fig)
else: plt.show()
| [
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25,
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83,
13,
12860,
3419,
198,
220,
220,
220,
220,
198
] | 2.20793 | 1,135 |
#!/usr/bin/env python
# Copyright (c) 2014 CNRS
# Author: Florent Lamiraux
#
from .robot import Robot
| [
2,
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] | 2.783784 | 37 |
"""
Gary Simmons
March 2018
Kata Prompt Description: A Narcissistic Number is a number which is the sum of its own digits, each raised to the power of the number of digits in a given base. In this Kata, we will restrict ourselves to decimal (base 10).
For example, take 153 (3 digits):
1^3 + 5^3 + 3^3 = 1 + 125 + 27 = 153
and 1634 (4 digits):
1^4 + 6^4 + 3^4 + 4^4 = 1 + 1296 + 81 + 256 = 1634
The Challenge:
Your code must return true or false depending upon whether the given number is a Narcissistic number in base 10.
Error checking for text strings or other invalid inputs is not required, only valid integers will be passed into the function.
"""
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] | 3.484375 | 192 |
#!/usr/bin/env python
import logging
from daemonize import Daemonize
from rq import Connection, Worker
from core.utils.Executor import check_output
__workers__ = [
'alpha',
'beta',
'gamma',
'delta',
'epsilon',
'zeta',
'eta',
'theta',
'iota',
'kappa',
'lambda',
'mu',
'nu',
'xi',
'omicron',
'pi',
'rho',
'sigma',
'tau',
'upsilon',
'phi',
'chi',
'psi',
'omega'
]
def get_available_rq_worker_name() -> str:
"""
Assign a worker name which is not already used
:return: Name of the worker
"""
out = check_output(
cmd='rq info',
cwd='/'
)
for each in __workers__:
if each not in out:
return each
# TODO: Raise exception for worker limit
def launch_rq_worker() -> None:
"""
Blocking function to launch a worker using Python RQ's internal API
"""
with Connection():
w = Worker(
get_available_rq_worker_name()
)
w.work()
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] | 2.126016 | 492 |
import re
import numpy as np
from solution.utils.constant import (
QUESTION_COLUMN_NAME,
CONTEXT_COLUMN_NAME,
ANSWER_COLUMN_NAME,
)
def get_extractive_features(tokenizer, mode, data_args):
""" Get extractive features for train, eval and test.
Args:
tokenizer (BERT Tokenizer): tokenizer for preprocessing
mode (str): [description] : one of train, eval, test
data_args (DataArguments): data arguments
"""
def tokenize_fn(examples):
"""Tokenize questions and contexts
Args:
examples (Dict): DatasetDict
Returns:
Dict: Tokenized examples
"""
pad_on_right = tokenizer.padding_side == "right"
max_seq_length = min(data_args.max_seq_length,
tokenizer.model_max_length)
# truncation과 padding을 통해 tokenization을 진행
# stride를 이용하여 overflow를 유지
# 각 example들은 이전의 context와 조금씩 겹침
# overflow 발생 시 지정한 batch size보다 더 많은 sample이 들어올 수 있음 -> data augmentation
tokenized_examples = tokenizer(
examples[QUESTION_COLUMN_NAME if pad_on_right else CONTEXT_COLUMN_NAME],
examples[CONTEXT_COLUMN_NAME if pad_on_right else QUESTION_COLUMN_NAME],
# 길이가 긴 context가 등장할 경우 truncation을 진행
truncation="only_second" if pad_on_right else "only_first",
max_length=max_seq_length,
stride=data_args.doc_stride,
# overflow 발생 시 원래 인덱스를 찾을 수 있게 mapping 가능한 값이 필요
return_overflowing_tokens=True,
# token의 캐릭터 단위 position을 찾을 수 있는 offset을 반환
# start position과 end position을 찾는데 도움을 줌
return_offsets_mapping=True,
# sentence pair가 입력으로 들어올 때 0과 1로 구분지음
return_token_type_ids=data_args.return_token_type_ids,
padding="max_length" if data_args.pad_to_max_length else False,
# return_tensors='pt'
)
return tokenized_examples
def prepare_train_features(examples):
"""
Reset for train dataset that do not have the correct answer
or where the correct answer position has changed.
Args:
examples (Dict): DatasetDict
Returns:
Dict: Tokenized examples where the answer has been reset
"""
pad_on_right = tokenizer.padding_side == "right"
tokenized_examples = tokenize_fn(examples)
sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
offset_mapping = tokenized_examples.pop("offset_mapping")
# 데이터셋에 "start position", "enc position" label을 부여합니다.
tokenized_examples["start_positions"] = []
tokenized_examples["end_positions"] = []
for i, offsets in enumerate(offset_mapping):
input_ids = tokenized_examples["input_ids"][i]
cls_index = input_ids.index(tokenizer.cls_token_id) # cls index
# sequence id를 설정합니다 (context와 question을 구분).
sequence_ids = tokenized_examples.sequence_ids(i)
context_index = 1 if pad_on_right else 0
# 길이가 긴 context에 대해 truncation을 진행하기 때문에
# 하나의 example이 여러 개의 span을 가질 수 있음
sample_index = sample_mapping[i]
answers = examples[ANSWER_COLUMN_NAME][sample_index]
# answer가 없을 경우 cls_index를 answer로 설정
# example에서 정답이 없는 경우가 있을 수 있음
if len(answers["answer_start"]) == 0:
tokenized_examples["start_positions"].append(cls_index)
tokenized_examples["end_positions"].append(cls_index)
else:
# text에서 정답의 start/end character index를 가져옴
start_char = answers["answer_start"][0]
end_char = start_char + len(answers["text"][0])
# sequence_ids는 0, 1, None의 세 값만 가짐
# None 0 0 ... 0 None 1 1 ... 1 None
# text에서 context가 시작하는 위치로 이동
token_start_index = 0
while sequence_ids[token_start_index] != context_index:
token_start_index += 1
# text에서 context가 끝나는 위치로 이동
token_end_index = len(input_ids) - 1
while sequence_ids[token_end_index] != context_index:
token_end_index -= 1
# 정답이 span을 벗어나는지 체크.
# 정답이 없는 경우 CLS index로 labeling (Retro일 경우 다르게 처리)
if not (
offsets[token_start_index][0] <= start_char and
offsets[token_end_index][1] >= end_char
):
tokenized_examples["start_positions"].append(cls_index)
tokenized_examples["end_positions"].append(cls_index)
else:
# token_start_index 및 token_end_index를 answer의 끝으로 이동
# Note: answer가 마지막 단어인 경우 last offset을 따라갈 수 있음
# token_start_index를 실제 위치로 맞춰주는 과정
while (
token_start_index < len(offsets) and
offsets[token_start_index][0] <= start_char
):
token_start_index += 1
tokenized_examples["start_positions"].append(
token_start_index - 1)
# token_end_index를 실제 위치로 맞춰주는 과정
while offsets[token_end_index][1] >= end_char:
token_end_index -= 1
tokenized_examples["end_positions"].append(
token_end_index + 1)
return tokenized_examples
def prepare_validation_features(examples, retriever=None):
"""Preprocessing validation dataset for extractive model
Args:
examples (Dict): DatasetDict
retriever (Dict): DatasetDict from wiki. Defaults to None.
Returns:
Dict: Tokenized examples
"""
pad_on_right = tokenizer.padding_side == "right"
tokenized_examples = tokenize_fn(examples)
sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
# evaluation을 위해 prediction을 context의 substring으로 변환
# corresponding example_id를 유지하고 offset mappings을 저장
tokenized_examples["example_id"] = []
for i in range(len(tokenized_examples["input_ids"])):
# sequence id를 설정합니다 (context와 question을 구분).
sequence_ids = tokenized_examples.sequence_ids(i)
context_index = 1 if pad_on_right else 0
# 하나의 example이 여러 개의 span을 가질 수 있음
sample_index = sample_mapping[i]
tokenized_examples["example_id"].append(
examples["id"][sample_index])
tokenized_examples["offset_mapping"][i] = [
(o if sequence_ids[k] == context_index else None)
for k, o in enumerate(tokenized_examples["offset_mapping"][i])
]
return tokenized_examples
if mode == "train":
get_features_fn = prepare_train_features
elif mode == "eval":
get_features_fn = prepare_validation_features
elif mode == "test":
get_features_fn = prepare_validation_features
return get_features_fn, True
PREP_PIPELINE = {
"extractive": get_extractive_features,
}
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] | 1.711012 | 4,277 |
#!/usr/bin/python3
import sys
import gi
gi.require_version('Gst', '1.0')
from gi.repository import Gst
from gi.repository import GLib
from gi.repository import Gtk
if __name__ == "__main__":
main(sys.argv)
#self.pipe = Gst.parse_launch ("videotestsrc is-live=true ! capsfilter name=caps ! x264enc speed-preset=superfast ! h264parse ! decodebin ! autovideosink")
| [
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] | 2.590278 | 144 |
from os import listdir
from random import choice
from django.conf import settings
from django.contrib.auth import login, authenticate
from django.contrib.auth.views import LoginView
from django.shortcuts import get_object_or_404, redirect
from django.views.generic import TemplateView, DetailView
from django.views.generic.edit import FormView
from ..forms import ImageForm, ChessUserCreationForm
from ..models import ChessUser
| [
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] | 3.798246 | 114 |
"""A module with the class `AbstractAlgorithm` defined"""
# from abc import ABC, abstractmethod
from abc import abstractmethod
from steinerpy.library.misc.abc_utils import abstract_attribute, ABC as newABC
from steinerpy.library.graphs.graph import IGraph
from typing import List
class AbstractAlgorithm(newABC):
"""An abstract barebones superclass for each algorithm implementation.
All algorithm implementations should inhereit :py:class:: AbstractAlgorithm.
Do not instantiate this directly!
Attributes:
terminals (list): A list of tuples representing terminals on a graph.
Exact format depends on the type of graph used (see below).
graph (SquareGrid, MyGraph): Graph classes from superclass IGraph.
Created using 'GraphFactory' class from the 'graph' module
S (dict): A dictionary containing information to output Steiner Tree
'sol': is a list of tree edges, e.g. ((x1,y1),(x2,y2)) if using SquareGrid graph
'dist': is a list of each tree edge's distance cost
'path': is a list of vertices of G, that make up each tree edge
'stats': {'run_time': x, closed_nodes: y, open_nodes: z}
"""
def return_solutions(self):
"""Return solution set of final tree
Returns:
S (dict): A dictionary containing information to output Steiner Tree
"""
return self.S
@abstractmethod
def run_algorithm(self):
"""Queries the algorithm and populates solution set 'S'
This is an abstract method, which must be explicitly defined
in subclasses
"""
pass | [
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220,
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220,
220,
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220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1208
] | 2.851282 | 585 |
from socket import *
from time import ctime
HOST = ''
PORT = 8888
BUFSIZ = 1024
ADDRESS = (HOST, PORT)
udpServerSocket = socket(AF_INET, SOCK_DGRAM)
udpServerSocket.bind(ADDRESS) # 绑定客户端口和地址
while True:
print("udp waiting for message...")
data, addr = udpServerSocket.recvfrom(BUFSIZ)
print("接收到数据：", data.decode('utf-8'))
content = '[%s] %s' % (bytes(ctime(), 'utf-8'), data.decode('utf-8'))
udpServerSocket.sendto(content.encode('utf-8'), addr)
print('...received from and returned to:', addr)
udpServerSocket.close()
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13,
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] | 2.191235 | 251 |
#!/usr/bin/python
# -*- coding: utf-8 -*-
from abc import abstractproperty, ABCMeta
from cloudshell.devices.networking_utils import command_logging
from cloudshell.devices.runners.interfaces.autoload_runner_interface import AutoloadOperationsInterface
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] | 3.355263 | 76 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created Nov 2020
@author: hassi
"""
from qiskit import QuantumCircuit, Aer, execute
from IPython.core.display import display
from qiskit.tools.visualization import plot_histogram
print("Ch 9: Grover with ancilla qubits")
print("--------------------------------")
# Create 3 qubit circuit with two classical bits
qc=QuantumCircuit(3,2)
qc.h([0,1])
qc.x(2)
# Code for the oracle
qc.barrier([0,1,2])
qc.x(0)
qc.barrier([0,1,2])
# Phase kickback using the ancilla qubit
qc.h(2)
qc.ccx(0,1,2)
qc.h(2)
# End code for the oracle
qc.barrier([0,1,2])
qc.x(0)
qc.barrier([0,1,2])
# Amplifier
qc.h([0,1])
qc.x([0,1])
qc.h(1)
qc.cx(0,1)
qc.h(1)
qc.barrier([0,1,2])
qc.x([0,1])
qc.h([0,1])
# Measure two qubits
qc.measure([0,1],[0,1])
# Display circuit and execute on simulator
display(qc.draw('mpl'))
backend = Aer.get_backend('qasm_simulator')
job = execute(qc, backend, shots=1)
result = job.result()
counts = result.get_counts(qc)
display(plot_histogram(counts))
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] | 2.200864 | 463 |
__author__ = 'saeedamen'
from pythalesians.util.loggermanager import LoggerManager
from pythalesians.market.requests.timeseriesrequest import TimeSeriesRequest
from pythalesians.timeseries.techind.techparams import TechParams
| [
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] | 3.507692 | 65 |
#!/usr/bin/env python
# coding: utf-8
# In[43]:
import cv2
import csv
from scipy import ndimage
import numpy as np
# In[44]:
lines = []
with open("./data/driving_log.csv") as dl:
reader = csv.reader(dl)
for line in reader:
lines.append(line)
# In[45]:
lines = lines[1:]
corr = 0.2
source_path = "./data/"
images = []
measurements = []
for line in lines:
m = float(line[3])
m_list = [m, m+corr, m-corr]
for i in range(3):
# try:
image = cv2.imread(source_path + line[i].strip())
# print(source_path + line[i])
image = cv2.cvtColor(image, cv2.COLOR_BGR2HLS)[:,:,1:]
# image = np.reshape(image, newshape = (image.shape[0], image.shape[1], 1))
images.append(image)
measurements.append(m_list[i])
# except Exception as e:
# continue
# In[5]:
print(image.shape)
aug_images = []
aug_measure = []
# In[6]:
for image, measurement in zip(images, measurements):
aug_images.append(image)
aug_measure.append(measurement)
aug_images.append(np.fliplr(image))
aug_measure.append(-1.0*measurement)
# In[7]:
X_train = np.array(aug_images)
y_train = np.array(aug_measure)
# In[19]:
import tensorflow as tf
# In[40]:
from keras.models import Sequential
from keras.layers import Dense, Flatten, Lambda, Cropping2D, Conv2D
# In[42]:
model = Sequential()
model.add(Cropping2D(cropping=((60,20),(0,0)), input_shape = (160,320,3)))
model.add(Lambda(lambda x:x/255.0 - 0.5))
model.add(Conv2D(24, (5,5),strides=(2,2),activation='relu'))
model.add(Conv2D(36, (5,5),strides=(2,2),activation='relu'))
model.add(Conv2D(48, (5,5),strides=(2,2),activation='relu'))
model.add(Conv2D(64, (3,3),activation='relu'))
model.add(Conv2D(64, (3,3),activation='relu'))
model.add(Flatten())
model.add(Dense(100, activation='relu'))
model.add(Dense(50, activation='relu'))
model.add(Dense(10))
model.add(Dense(1))
# In[13]:
model.compile(optimizer='adam', loss = 'mse')
model.fit(X_train, y_train, validation_split=0.2,shuffle=True, epochs=5)
# In[40]:
model.save('model_hls.h5')
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] | 2.210305 | 951 |
# Copyright (c) Jupyter Development Team.
# Distributed under the terms of the Modified BSD License.
"""
API interface.
"""
import os
import shutil
from .constants import EXTENSIONS_FOLDER
from .constants import JUPYTERLAB
from .constants import LANG_PACKS_FOLDER
from .constants import LC_MESSAGES
from .constants import LOCALE_FOLDER
from .converters import convert_catalog_to_json
from .utils import check_locale
from .utils import compile_to_mo
from .utils import compile_translations
from .utils import create_new_language_pack
from .utils import extract_translations
from .utils import update_translations
def check_locales(locales):
"""
Check if a given list of locale values is valid.
Raises an exception if an invalid locale value is found.
Parameters
----------
locales: list
List of locales
"""
for locale in locales:
if not check_locale(locale):
raise Exception("Invalid locale '{locale}'".format(locale=locale))
def normalize_project(project):
"""
FIXME:
Parameters
----------
project: str
FIXME:
"""
return project.lower().replace("-", "_")
def extract_package(package_repo_dir, project):
"""
FIXME:
"""
def update_package(package_repo_dir, project, locales):
"""
FIXME:
"""
if locales:
check_locales(locales)
project = normalize_project(project)
output_dir = os.path.join(package_repo_dir, project)
if not os.path.isdir(output_dir):
raise Exception(
"Output dir `{output_dir}` not found!".format(output_dir=output_dir)
)
update_translations(package_repo_dir, output_dir, project, locales)
def compile_package(package_repo_dir, project, locales):
"""
FIXME
"""
if locales:
check_locales(locales)
project = normalize_project(project)
output_dir = os.path.join(package_repo_dir, project)
po_paths = compile_translations(output_dir, project, locales)
for __, po_path in po_paths.items():
output_path = os.path.dirname(po_path)
convert_catalog_to_json(po_path, output_path, project)
def extract_language_pack(package_repo_dir, language_packs_repo_dir, project):
"""
FIXME:
"""
project = normalize_project(project)
if project == JUPYTERLAB:
output_dir = os.path.join(language_packs_repo_dir, project)
else:
output_dir = os.path.join(language_packs_repo_dir, EXTENSIONS_FOLDER, project)
os.makedirs(output_dir, exist_ok=True)
extract_translations(package_repo_dir, output_dir, project)
def update_language_pack(package_repo_dir, language_packs_repo_dir, project, locales):
"""
FIXME
"""
if locales:
check_locales(locales)
project = normalize_project(project)
if project == JUPYTERLAB:
output_dir = os.path.join(language_packs_repo_dir, project)
else:
output_dir = os.path.join(
language_packs_repo_dir, "jupyterlab_extensions", project
)
os.makedirs(output_dir, exist_ok=True)
update_translations(package_repo_dir, output_dir, project, locales)
def compile_language_pack(language_packs_repo_dir, project, locales):
"""
FIXME:
"""
if locales:
check_locales(locales)
project = normalize_project(project)
if project == JUPYTERLAB:
output_dir = os.path.join(language_packs_repo_dir, project)
else:
output_dir = os.path.join(language_packs_repo_dir, EXTENSIONS_FOLDER, project)
po_paths = compile_translations(output_dir, project, locales)
for locale, po_path in po_paths.items():
output_path = os.path.dirname(po_path)
json_path = convert_catalog_to_json(po_path, output_path, project)
mo_path = compile_to_mo(po_path)
# Move to language pack folder
language_packs_dir = os.path.join(language_packs_repo_dir, LANG_PACKS_FOLDER)
pkg_name = "jupyterlab-language-pack-{locale}".format(locale=locale).replace(
"_", "-"
)
locale_language_pack_dir = os.path.join(
language_packs_dir, pkg_name, pkg_name.replace("-", "_")
)
# Check if it exists, otherwise create it
if not os.path.isdir(locale_language_pack_dir):
create_new_language_pack(language_packs_dir, locale)
if project == JUPYTERLAB:
output_dir = os.path.join(locale_language_pack_dir)
else:
output_dir = os.path.join(locale_language_pack_dir, EXTENSIONS_FOLDER)
shutil.rmtree(
os.path.join(output_dir, os.path.basename(mo_path)), ignore_errors=True
)
shutil.rmtree(
os.path.join(output_dir, os.path.basename(json_path)), ignore_errors=True
)
shutil.move(mo_path, os.path.join(output_dir, os.path.basename(mo_path)))
shutil.move(json_path, os.path.join(output_dir, os.path.basename(json_path)))
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] | 2.399709 | 2,064 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# advent3_triangles.py
#
import itertools
import sys
_KNOWN_TRUE = ((5, 12, 13), (3, 3, 3), (200, 300, 450), (2, 1, 2))
_KNOWN_FALSE = ((1, 2, 3), (5, 10, 25))
if __name__ == '__main__':
assert sum(evaluate(_KNOWN_TRUE)) == len(_KNOWN_TRUE)
assert sum(evaluate(_KNOWN_FALSE)) == 0
from argparse import ArgumentParser
p = ArgumentParser()
p.add_argument("parse_mode", choices=('rows', 'cols'), help="how triples are oriented in the input")
p.add_argument("sidelengths", nargs="*", help="side lengths, every 3 of which are interpreted as a possible triangle; leave empty to read from stdin", default=[])
p.add_argument("--verbose", default=False, action="store_true")
args = p.parse_args()
if len(args.sidelengths) == 0:
args.sidelengths = sys.stdin.read().split()
sys.exit(main(args))
| [
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7,
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7,
22046,
4008,
220,
198,
220,
220,
220,
220,
198
] | 2.548571 | 350 |
from __future__ import annotations
from datetime import datetime
from pathlib import Path
from typing import List, Optional, Tuple
| [
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] | 4.290323 | 31 |
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