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199b9fe3c8a3ba98e7b1141db31d1f32f85abbd2 | 545 | py | Python | manage.py | kojwangbora/elly-blog | 95946ece071784fa5c15c21618011cbb97ebfc46 | [
"MIT"
]
| null | null | null | manage.py | kojwangbora/elly-blog | 95946ece071784fa5c15c21618011cbb97ebfc46 | [
"MIT"
]
| null | null | null | manage.py | kojwangbora/elly-blog | 95946ece071784fa5c15c21618011cbb97ebfc46 | [
"MIT"
]
| null | null | null | from app import create_app, db
from flask_migrate import Migrate
from app.models import User, Posts
from flask_script import Manager,Server
app = create_app()
# manager = Manager(app)
# manager.add_command('server', Server)
# #Creating migration instance
# migrate = Migrate(app, db)
# manager.add_command('db',MigrateCommand)
# # @manager.shell
# @manager.shell_context_processor
# def make_shell_context():
# return dict(db = db, User =User, Posts=Posts)
# if __name__ == '__main__':
# # manager.debug = True
# app.run()
| 23.695652 | 51 | 0.717431 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 364 | 0.66789 |
199c463aaa4e8ff9bc128e9fe6eb09901f8c3ffb | 2,354 | py | Python | API/AdminPanel/smoke_all_pages/credentials.py | Mikhail-QA/HS | 8ddbc09a0d1493128f3af6b8078c295609908dd7 | [
"Apache-2.0"
]
| null | null | null | API/AdminPanel/smoke_all_pages/credentials.py | Mikhail-QA/HS | 8ddbc09a0d1493128f3af6b8078c295609908dd7 | [
"Apache-2.0"
]
| null | null | null | API/AdminPanel/smoke_all_pages/credentials.py | Mikhail-QA/HS | 8ddbc09a0d1493128f3af6b8078c295609908dd7 | [
"Apache-2.0"
]
| null | null | null | # Админка раздел редактор курсов
# Энпоинты меню редактора курсов ДШ в тек. уг
path_admin_schedules_grade_1 = '/schedules?grade=1&school=true&'
path_admin_schedules_grade_2 = '/schedules?grade=2&school=true&'
path_admin_schedules_grade_3 = '/schedules?grade=3&school=true&'
path_admin_schedules_grade_4 = '/schedules?grade=4&school=true&'
path_admin_schedules_grade_5 = '/schedules?grade=5&school=true&'
path_admin_schedules_grade_6 = '/schedules?grade=6&school=true&'
path_admin_schedules_grade_7 = '/schedules?grade=7&school=true&'
path_admin_schedules_grade_8 = '/schedules?grade=8&school=true&'
path_admin_schedules_grade_9 = '/schedules?grade=9&school=true&'
path_admin_schedules_grade_10 = '/schedules?grade=10&school=true&'
path_admin_schedules_grade_11 = '/schedules?grade=11&school=true&'
# Прикрепление\удаление предмета
path_admin_add_subject = '/schedules?'
path_admin_delete_subject = '/schedules/5583026?'
# Раздел редактирования предмета
path_admin_item_editor = '/schedule_items.json?schedule_id=3908531&' # переход в редактор предмета
path_admin_add_topic = '/topics?' # добавить тему
path_admin_add_lesson = 'lessons.json?' # Создание нового урока
path_admin_lesson_for_day = '/schedule_items.json?' # привязка урока к дате
path_admin_remove_lesson = '/lessons/37865.json?' # удаление урока
path_admin_remove_topic = '/topics/24273?addLessonHide=true&addLessonNameEvent=click&calendarActive=false&editTopicNameHide=true&lessonsHide=false&name=тест&schedule_id=3908531&subject_id=201&'
path_admin_save_date_ege = '/schedules/3908531?' # сохранение даты ЕГЭ
# редактор МДЗ
path_admin_monthly_homework_editor = '/monthly_homeworks?schedule_id=3908531&' # открытие редактора МДЗ
path_admin_create_monthly_homework = '/monthly_homeworks?' # создание МДЗ
path_admin_delete_monthly_homework = '/monthly_homeworks/7229?' # удаление МДЗ
# Энпоинты редактора курсов ЕГЭ
path_admin_editor_ege = '/schedules?grade=11&school=false&' # переход в редактор егэ
path_admin_attach_subject_ege = '/schedules?' # прикрепление предмета егэ
path_admin_delete_subject_ege = '/schedules/5583707?' # удаление предмета егэ
path_admin_add_topic = '/topics?' # добавить тему
def __init__(self, token=None):
self.token = token
def get_token(self):
headers_user = {
"Authorization": self.access_token,
}
return headers_user
| 52.311111 | 193 | 0.800765 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1,714 | 0.627609 |
199c573a26d9f3609be1f4e385345250d059dabc | 248 | py | Python | swagger2/__init__.py | ppbug/swagger2 | a60585a85aad9c16757e67ff70d30e70657a78b8 | [
"MIT"
]
| 1 | 2021-07-12T07:06:03.000Z | 2021-07-12T07:06:03.000Z | swagger2/__init__.py | ppbug/swagger2 | a60585a85aad9c16757e67ff70d30e70657a78b8 | [
"MIT"
]
| null | null | null | swagger2/__init__.py | ppbug/swagger2 | a60585a85aad9c16757e67ff70d30e70657a78b8 | [
"MIT"
]
| null | null | null | from .loader import *
from .swagger import *
def parse(url,deep=5,**kwargs):
source = load_url(url,**kwargs)
return Swagger(source,deep=deep)
def parse_file(path,deep=5):
source = load_file(path)
return Swagger(source,deep=deep)
| 20.666667 | 36 | 0.697581 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
199d8a7e9157f48b6df5a88dec9570242bb4b50a | 963 | py | Python | 2021/aoc07.py | wolfgangmuender/AdventOfCode | 3956c245f6d3e789cb5424ac54832ebcfc03eb21 | [
"Apache-2.0"
]
| null | null | null | 2021/aoc07.py | wolfgangmuender/AdventOfCode | 3956c245f6d3e789cb5424ac54832ebcfc03eb21 | [
"Apache-2.0"
]
| null | null | null | 2021/aoc07.py | wolfgangmuender/AdventOfCode | 3956c245f6d3e789cb5424ac54832ebcfc03eb21 | [
"Apache-2.0"
]
| null | null | null | with open("input/input07.txt") as f:
content = f.read().splitlines()
positions = list(map(lambda x: int(x), content[0].split(",")))
max_pos = max(positions)
min_pos = min(positions)
possible_pos = range(min_pos, max_pos + 1)
fuel_costs1 = []
for align_pos in possible_pos:
fuel_costs1.append(sum([abs(pos - align_pos) for pos in positions]))
final_fuel_cost1 = min(fuel_costs1)
align_position1 = possible_pos[fuel_costs1.index(final_fuel_cost1)]
print("Solution 1: the crabs need to spend {} fuel to align at position {}".format(final_fuel_cost1, align_position1))
fuel_costs2 = []
for align_pos in possible_pos:
fuel_costs2.append(sum([int(abs(pos - align_pos) * (abs(pos - align_pos) + 1) / 2) for pos in positions]))
final_fuel_cost2 = min(fuel_costs2)
align_position2 = possible_pos[fuel_costs2.index(final_fuel_cost2)]
print("Solution 2: the crabs need to spend {} fuel to align at position {}".format(final_fuel_cost2, align_position2))
| 35.666667 | 118 | 0.74351 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 160 | 0.166147 |
199dc81e3c59d04bf93fe983d5b45a4485997a89 | 2,227 | py | Python | sources/install_deps.py | Radeon4650/PocketBudgetTracker | 61b792f648e73e8043e42e0e1c55b3acf1868f90 | [
"Apache-2.0"
]
| 1 | 2018-12-03T12:41:50.000Z | 2018-12-03T12:41:50.000Z | sources/install_deps.py | Radeon4650/PocketBudgetTracker | 61b792f648e73e8043e42e0e1c55b3acf1868f90 | [
"Apache-2.0"
]
| 16 | 2018-10-08T17:37:52.000Z | 2019-01-06T12:02:54.000Z | sources/install_deps.py | Radeon4650/PocketBudgetTracker | 61b792f648e73e8043e42e0e1c55b3acf1868f90 | [
"Apache-2.0"
]
| 1 | 2018-10-11T20:01:05.000Z | 2018-10-11T20:01:05.000Z | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Copyright © 2018 PocketBudgetTracker. All rights reserved.
Author: Andrey Shelest ([email protected])
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
import os.path
import subprocess
import sys
modules_dir = os.path.dirname(__file__)
req_file_name = 'requirements.txt'
class Installer:
python_path = os.path.dirname(sys.executable)
pip_path = os.path.join(python_path, 'pip')
def __init__(self, upgrade=False):
print('Python location prefix is {}'.format(self.python_path))
self.upgrade = upgrade
def _pip_install(self, install_args=None):
args = [self.pip_path, 'install']
if install_args is not None:
args += install_args
if self.upgrade:
args.append('--upgrade')
if subprocess.call(args):
print("ERROR: installing of [{}] failed".format(' '.join(args)))
def install(self, name):
print("Installing [%s]" % name)
self._pip_install([name])
def install_modules_deps(self):
print('Search for submodules into {} dir'.format(modules_dir))
modules = next(os.walk(modules_dir))[1]
print('Found modules {}'.format(modules))
for module in modules:
dep_file = os.path.join(modules_dir, module, req_file_name)
if os.path.isfile(dep_file):
self._pip_install(['--requirement', dep_file])
else:
print('Module does not have {} file'.format(req_file_name))
if __name__ == '__main__':
installer = Installer(True)
# Upgrade pip
installer.install('pip')
# Install all modules dependencies
installer.install_modules_deps()
sys.exit(0)
| 28.922078 | 76 | 0.671756 | 1,204 | 0.540395 | 0 | 0 | 0 | 0 | 0 | 0 | 970 | 0.435368 |
199fc92154f499c85aed3bce44e91934109ae7d3 | 5,305 | py | Python | tools/utils/building/layer.py | shinh/dldt | 693ab4e79a428e0801f17f4511b129a3fa8f4a62 | [
"Apache-2.0"
]
| 1 | 2021-02-20T21:48:36.000Z | 2021-02-20T21:48:36.000Z | tools/utils/building/layer.py | erinpark33/dldt | edd86d090592f7779f4dbb2681546e1f4e81284f | [
"Apache-2.0"
]
| null | null | null | tools/utils/building/layer.py | erinpark33/dldt | edd86d090592f7779f4dbb2681546e1f4e81284f | [
"Apache-2.0"
]
| 1 | 2021-02-19T01:06:12.000Z | 2021-02-19T01:06:12.000Z | """
Copyright (C) 2018-2019 Intel Corporation
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from ..biases import Biases
from ..weights import Weights
class Layer:
TEMPLATE = (
'<layer name="{name}" type="{type}" precision="FP32" id="{id}">'
'{data}'
'{input}'
'{output}'
'{weights}'
'{biases}'
'</layer>')
def __init__(
self, id: int,
type: str,
name: str,
params: dict,
input_dims: list,
output_dims: list,
weights: Weights = None,
biases: Biases = None):
self._id = id
self._type = type
self._name = name
self._params = params
self._input_dims = input_dims
self._output_dims = output_dims
self._weights = weights
self._biases = biases
@property
def id(self) -> str:
return self._id
@property
def type(self) -> str:
return self._type
@property
def name(self) -> str:
return self._name
@property
def params(self) -> dict:
return self._params
@property
def input_dims(self) -> list:
return self._input_dims
@property
def output_dims(self) -> list:
return self._output_dims
@property
def weights(self) -> Weights:
return self._weights
@property
def biases(self) -> Biases:
return self._biases
def _output_dims_to_xml(self) -> str:
if self._output_dims:
if len(self._output_dims) == 2:
output_xml = (
'<output>'
'<port id="1">'
'<dim>{}</dim>'
'<dim>{}</dim>'
'</port>'
'</output>').format(self._output_dims[0], self._output_dims[1])
elif len(self._output_dims) == 4:
output_xml = (
'<output>'
'<port id="1">'
'<dim>{}</dim>'
'<dim>{}</dim>'
'<dim>{}</dim>'
'<dim>{}</dim>'
'</port>'
'</output>').format(self._output_dims[0], self._output_dims[1], self._output_dims[2], self._output_dims[3])
else:
raise NotImplementedError("{} dimensions for outputs (layer name '{}', type '{}') are not supported".format(
len(self._output_dims),
self._name,
self._type))
else:
output_xml = None
return output_xml
def _input_dims_to_xml(self) -> str:
if self._input_dims:
if len(self._input_dims) == 2:
input_xml = (
'<input>'
'<port id="0">'
'<dim>{}</dim>'
'<dim>{}</dim>'
'</port>'
'</input>').format(self._input_dims[0], self._input_dims[1])
elif len(self._input_dims) == 4:
input_xml = (
'<input>'
'<port id="0">'
'<dim>{}</dim>'
'<dim>{}</dim>'
'<dim>{}</dim>'
'<dim>{}</dim>'
'</port>'
'</input>').format(self._input_dims[0], self._input_dims[1], self._input_dims[2], self._input_dims[3])
else:
raise NotImplementedError("{} dimensions for inputs (layer name '{}', type '{}') are not supported".format(
len(self._input_dims),
self._name,
self._type))
else:
input_xml = None
return input_xml
def __str__(self) -> str:
if self._params:
data_xml = "<data "
for param_key in self._params.keys():
data_xml += '{}="{}" '.format(param_key, self._params[param_key])
data_xml += " />"
else:
data_xml = None
return self.TEMPLATE.format(
name=self._name,
type=self._type,
id=self._id,
data=(data_xml if data_xml else ''),
input=(self._input_dims_to_xml() if self._input_dims else ''),
output=(self._output_dims_to_xml() if self._output_dims else ''),
weights=('<weights offset="{offset}" size="{size}"/>'.format(offset=self._weights.offset, size=self._weights.size) if self._weights else ''),
biases=('<biases offset="{offset}" size="{size}"/>'.format(offset=self._biases.offset, size=self._biases.size) if self._biases else '')
)
| 33.575949 | 153 | 0.489915 | 4,667 | 0.879736 | 0 | 0 | 542 | 0.102168 | 0 | 0 | 1,321 | 0.24901 |
19a02ddbcaad9d7bf3192c753f7e060611b72ae8 | 19,686 | py | Python | tests/test_lightningStub.py | engenegr/lnd_grpc | 89336bc83fcfa286dd7927e92fbc88293bd29547 | [
"MIT"
]
| null | null | null | tests/test_lightningStub.py | engenegr/lnd_grpc | 89336bc83fcfa286dd7927e92fbc88293bd29547 | [
"MIT"
]
| null | null | null | tests/test_lightningStub.py | engenegr/lnd_grpc | 89336bc83fcfa286dd7927e92fbc88293bd29547 | [
"MIT"
]
| null | null | null | import os
import time
import unittest
import bitcoin.rpc
import grpc
import lnd_grpc.lnd_grpc as py_rpc
import lnd_grpc.protos.rpc_pb2 as rpc_pb2
# from google.protobuf.internal.containers import RepeatedCompositeFieldContainer
#######################
# Configure variables #
#######################
CWD = os.getcwd()
ALICE_LND_DIR = '/Users/will/regtest/.lnd/'
ALICE_NETWORK = 'regtest'
ALICE_RPC_HOST = '127.0.0.1'
ALICE_RPC_PORT = '10009'
ALICE_MACAROON_PATH = '/Users/will/regtest/.lnd/data/chain/bitcoin/regtest/admin.macaroon'
ALICE_PEER_PORT = '9735'
ALICE_HOST_ADDR = ALICE_RPC_HOST + ':' + ALICE_PEER_PORT
BOB_LND_DIR = '/Users/will/regtest/.lnd2/'
BOB_NETWORK = 'regtest'
BOB_RPC_HOST = '127.0.0.1'
BOB_RPC_PORT = '11009'
BOB_MACAROON_PATH = '/Users/will/regtest/.lnd2/data/chain/bitcoin/regtest/admin.macaroon'
BOB_PEER_PORT = '9734'
BOB_HOST_ADDR = BOB_RPC_HOST + ':' + BOB_PEER_PORT
BITCOIN_SERVICE_PORT = 18443
BITCOIN_CONF_FILE = '/Users/will/regtest/.bitcoin/bitcoin.conf'
BITCOIN_ADDR = None
DEBUG_LEVEL = 'error'
SLEEP_TIME = 0.5
def initialise_clients():
global BITCOIN_ADDR
alice = py_rpc.Client(lnd_dir=ALICE_LND_DIR,
network=ALICE_NETWORK,
grpc_host=ALICE_RPC_HOST,
grpc_port=ALICE_RPC_PORT,
macaroon_path=ALICE_MACAROON_PATH)
alice.pub_key = alice.get_info().identity_pubkey
alice.lightning_addr = py_rpc.Client.lightning_address(
pubkey=alice.pub_key,
host=ALICE_HOST_ADDR)
bob = py_rpc.Client(lnd_dir=BOB_LND_DIR,
network=BOB_NETWORK,
grpc_host=BOB_RPC_HOST,
grpc_port=BOB_RPC_PORT,
macaroon_path=BOB_MACAROON_PATH)
bob.pub_key = bob.get_info().identity_pubkey
bob.lightning_addr = py_rpc.Client.lightning_address(
pubkey=bob.pub_key,
host=BOB_HOST_ADDR)
bitcoin_rpc = bitcoin.rpc.RawProxy(service_port=BITCOIN_SERVICE_PORT,
btc_conf_file=BITCOIN_CONF_FILE)
BITCOIN_ADDR = bitcoin_rpc.getnewaddress()
return alice, bob, bitcoin_rpc
def ensure_peer_connected(alice, bob):
if len(alice.list_peers()) == 0:
alice.connect_peer(addr=bob.lightning_addr)
assert (len(alice.list_peers()) > 0)
def ensure_channel_open(alice, bob, bitcoin_rpc, address):
if len(alice.list_channels()) == 0:
alice.open_channel_sync(local_funding_amount=1_000_000,
push_sat=500_000,
node_pubkey_string=bob.pub_key)
bitcoin_rpc.generatetoaddress(3, address)
time.sleep(SLEEP_TIME)
assert (len(alice.list_channels()) > 0)
def disconnect_all_peers(alice):
for peer in alice.list_peers():
alice.disconnect_peer(pub_key=peer.pub_key)
time.sleep(SLEEP_TIME)
assert (0 == len(alice.list_peers()))
def close_all_channels(peer):
if len(peer.list_channels()) > 0:
peer.close_all_channels()
time.sleep(SLEEP_TIME)
assert (0 == len(peer.list_channels()))
##################
# Test framework #
##################i
class TestLightningStubResponses(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.alice, cls.bob, cls.bitcoin_rpc = initialise_clients()
def test_aaa_assert_variables(self):
self.assertIsInstance(self.alice, py_rpc.Client)
self.assertIsInstance(self.alice.channel, grpc._channel.Channel)
self.assertIsInstance(self.bob, py_rpc.Client)
self.assertIsInstance(self.bitcoin_rpc, bitcoin.rpc.RawProxy)
def test_wallet_balance(self):
self.assertIsInstance(self.alice.wallet_balance(), rpc_pb2.WalletBalanceResponse)
# lambda function prevents TypeError being raised before assert is run.
self.assertRaises(TypeError, lambda: self.alice.wallet_balance('please'))
def test_channel_balance(self):
self.assertIsInstance(self.alice.channel_balance(), rpc_pb2.ChannelBalanceResponse)
self.assertRaises(TypeError, lambda: self.alice.channel_balance('please'))
def test_get_transactions(self):
self.assertIsInstance(self.alice.get_transactions(), rpc_pb2.TransactionDetails)
self.assertRaises(TypeError, lambda: self.alice.get_transactions('please'))
def test_send_coins(self):
response1 = self.alice.send_coins(self.alice.new_address(address_type='p2wkh').address,
amount=100000)
response2 = self.alice.send_coins(self.alice.new_address(address_type='np2wkh').address,
amount=100000)
self.assertIsInstance(response1, rpc_pb2.SendCoinsResponse)
self.assertIsInstance(response2, rpc_pb2.SendCoinsResponse)
# negative send
self.assertRaises(grpc.RpcError,
lambda: self.alice.send_coins(
self.alice.new_address(address_type='p2wkh').address,
amount=100000 * -1))
# impossibly large send
self.assertRaises(grpc.RpcError,
lambda: self.alice.send_coins(
self.alice.new_address(address_type='p2wkh').address,
amount=1000000000000000))
def test_list_unspent(self):
self.assertIsInstance(self.alice.list_unspent(0, 1000), rpc_pb2.ListUnspentResponse)
def subscribe_transactions(self):
self.assertIsInstance(self.alice.subscribe_transactions(), rpc_pb2.Transaction)
def send_many(self):
pass
def test_new_address(self):
self.address_p2wkh = self.alice.new_address(address_type='p2wkh')
self.address_np2wkh = self.alice.new_address(address_type='np2wkh')
self.assertIsInstance(self.address_p2wkh, rpc_pb2.NewAddressResponse)
self.assertIsInstance(self.address_np2wkh, rpc_pb2.NewAddressResponse)
self.assertRaises(TypeError, self.alice.new_address(address_type='segwit'))
def test_sign_message(self):
self.assertIsInstance(self.alice.sign_message(msg='test message content'),
rpc_pb2.SignMessageResponse)
self.assertRaises(AttributeError,
lambda: self.alice.sign_message(msg=b'bytes message'))
def test_verify_message(self):
message = 'test message content'
message_sig_true = self.alice.sign_message(msg=message).signature
message_sig_false = message_sig_true + '1'
self.assertTrue(self.alice.verify_message(msg=message, signature=message_sig_true).valid)
self.assertFalse(self.alice.verify_message(msg=message, signature=message_sig_false).valid)
def test_disconnect_peer(self):
bitcoin_address = self.bitcoin_rpc.getnewaddress()
# make sure we have a peer
ensure_peer_connected(self.alice, self.bob)
# make sure all channels closed otherwise disconnect will fail
close_all_channels(self.alice)
self.bitcoin_rpc.generatetoaddress(3, bitcoin_address)
time.sleep(SLEEP_TIME)
# now disconnect all peers
for peer in self.alice.list_peers():
self.alice.disconnect_peer(pub_key=peer.pub_key)
time.sleep(SLEEP_TIME)
self.bitcoin_rpc.generatetoaddress(3, bitcoin_address)
time.sleep(SLEEP_TIME)
self.assertEqual(0, len(self.alice.list_peers()))
def test_connect(self):
# close any open channels:
close_all_channels(self.alice)
self.bitcoin_rpc.generatetoaddress(3, BITCOIN_ADDR)
time.sleep(SLEEP_TIME)
# check we are fully disconnected from peer before proceeding
disconnect_all_peers(self.alice)
time.sleep(SLEEP_TIME)
# now test the connect
self.alice.connect_peer(addr=self.bob.lightning_addr)
self.bitcoin_rpc.generatetoaddress(3, BITCOIN_ADDR)
time.sleep(SLEEP_TIME)
self.assertEqual(1, len(self.alice.list_peers()))
self.assertEqual(self.alice.list_peers()[0].pub_key, self.bob.pub_key)
def test_list_peers(self):
# make sure we are connected to one peer
ensure_peer_connected(self.alice, self.bob)
# Test length with connected peer
self.assertGreater(len(self.alice.list_peers()), 0)
# close and active channels before disconnect
close_all_channels(self.alice)
self.bitcoin_rpc.generatetoaddress(3, BITCOIN_ADDR)
time.sleep(SLEEP_TIME)
# disconnect
disconnect_all_peers(self.alice)
time.sleep(SLEEP_TIME)
# test after disconnect
self.assertEqual(0, len(self.alice.list_peers()))
def test_get_info(self):
self.assertIsInstance(self.alice.get_info(), rpc_pb2.GetInfoResponse)
def test_pending_channels(self):
self.assertIsInstance(self.alice.pending_channels(), rpc_pb2.PendingChannelsResponse)
def test_list_channels(self):
# self.assertTrue(self.test_connect())
# TODO: open a channel
# self.assertGreater(len(self.alice.list_channels()), 0)
pass
def test_closed_channels(self):
# TODO: open a channel
# ... then close it ...
# self.assertGreater(len(self.alice.closed_channels()), 0)
pass
def test_open_channel_sync(self):
# make sure we are connected
ensure_peer_connected(self.alice, self.bob)
start_channels = len(self.alice.list_channels())
self.assertIsInstance(self.alice.open_channel_sync(local_funding_amount=500_000,
node_pubkey_string=self.bob.pub_key),
rpc_pb2.ChannelPoint)
self.bitcoin_rpc.generatetoaddress(3, BITCOIN_ADDR)
time.sleep(SLEEP_TIME)
end_channels = len(self.alice.list_channels())
self.assertGreater(end_channels, start_channels)
def test_open_channel(self):
pass
def test_close_channel(self):
# setup
ensure_peer_connected(self.alice, self.bob)
ensure_channel_open(self.alice, self.bob, self.bitcoin_rpc, BITCOIN_ADDR)
self.assertGreater(len(self.alice.list_channels()), 0)
# close all active channels
self.alice.close_all_channels()
self.bitcoin_rpc.generatetoaddress(3, BITCOIN_ADDR)
time.sleep(SLEEP_TIME)
self.assertEqual(0, len(self.alice.list_channels()))
def test_abandon_channel(self):
# this is a development function only and does not need test
pass
def test_send_payment(self):
pass
def test_send_payment_sync(self):
# setup
inv_amt = 10000
ensure_peer_connected(self.alice, self.bob)
ensure_channel_open(self.alice, self.bob, self.bitcoin_rpc, BITCOIN_ADDR)
# test payment_request
payment_request = self.bob.add_invoice(value=inv_amt).payment_request
start_len = len(self.alice.list_payments().payments)
self.assertIsInstance(self.alice.send_payment_sync(payment_request=payment_request),
rpc_pb2.SendResponse)
time.sleep(SLEEP_TIME)
end_len = len(self.alice.list_payments().payments)
self.assertGreater(end_len, start_len)
# test manual request
invoice = self.bob.add_invoice(value=inv_amt)
start_len = len(self.alice.list_payments().payments)
self.assertIsInstance(self.alice.send_payment_sync(dest_string=self.bob.pub_key,
amt=inv_amt,
payment_hash=invoice.r_hash,
final_cltv_delta=144),
rpc_pb2.SendResponse)
time.sleep(SLEEP_TIME)
end_len = len(self.alice.list_payments().payments)
self.assertGreater(end_len, start_len)
def test_send_to_route(self):
pass
def test_send_to_route_sync(self):
pass
def test_add_invoice(self):
# setup
ensure_peer_connected(self.alice, self.bob)
ensure_channel_open(self.alice, self.bob, self.bitcoin_rpc, BITCOIN_ADDR)
channel_0_balance = self.alice.list_channels()[0].local_balance
# test valid
start_index = self.alice.list_invoices().invoices[0].add_index
self.assertIsInstance(self.alice.add_invoice(memo='test',
value=channel_0_balance // 2,
expiry=3600),
rpc_pb2.AddInvoiceResponse)
end_index = self.alice.list_invoices().invoices[0].add_index
time.sleep(SLEEP_TIME)
self.assertGreater(end_index, start_index)
# test invalid
start_index = self.alice.list_invoices().invoices[0].add_index
self.assertRaises(grpc.RpcError,
lambda:
self.alice.add_invoice(memo='test',
value=100_000_000,
expiry=3600)
)
end_index = self.alice.list_invoices().invoices[0].add_index
self.assertEqual(end_index, start_index)
def test_list_invoices(self):
self.assertIsInstance(self.alice.list_invoices(),
rpc_pb2.ListInvoiceResponse)
def test_lookup_invoice(self):
test_invoice = self.alice.list_invoices().invoices[0]
self.assertIsInstance(self.alice.lookup_invoice(r_hash=test_invoice.r_hash),
rpc_pb2.Invoice)
def test_subscribe_invoices(self):
self.assertIsInstance(self.alice.subscribe_invoices(),
grpc._channel._Rendezvous)
def test_decode_pay_req(self):
inv_value = 15000
expiry = 3600
cltv_expiry = 144
invoice = self.bob.add_invoice(value=inv_value, expiry=expiry,
cltv_expiry=cltv_expiry)
decoded_inv = self.alice.decode_pay_req(pay_req=invoice.payment_request)
self.assertEqual(invoice.r_hash, bytes.fromhex(decoded_inv.payment_hash))
self.assertEqual(self.bob.pub_key, decoded_inv.destination)
self.assertEqual(inv_value, decoded_inv.num_satoshis)
self.assertEqual(expiry, decoded_inv.expiry)
self.assertEqual(cltv_expiry, decoded_inv.cltv_expiry)
def test_list_payments(self):
self.assertIsInstance(self.alice.list_payments(), rpc_pb2.ListPaymentsResponse)
def test_z_delete_all_payments(self):
# this will delete your payment history -- be careful
# pass
start_len = len(self.alice.list_payments().payments)
if start_len == 0:
ensure_peer_connected(self.alice, self.bob)
ensure_channel_open(self.alice, self.bob, self.bitcoin_rpc, BITCOIN_ADDR)
invoice = self.bob.add_invoice(value=5000)
self.alice.pay_invoice(payment_request=invoice.payment_request)
self.assertGreater(start_len, 0)
self.assertIsInstance(self.alice.delete_all_payments(),
rpc_pb2.DeleteAllPaymentsResponse)
time.sleep(SLEEP_TIME)
end_len = len(self.alice.list_payments().payments)
self.assertEqual(end_len, 0)
def test_describe_graph(self):
self.assertIsInstance(self.alice.describe_graph(), rpc_pb2.ChannelGraph)
def test_get_chan_info(self):
if len(self.alice.list_channels()) == 0:
ensure_peer_connected(self.alice, self.bob)
ensure_channel_open(self.alice, self.bob, self.bitcoin_rpc, BITCOIN_ADDR)
chan_id = self.alice.list_channels()[0].chan_id
self.assertIsInstance(self.alice.get_chan_info(chan_id=chan_id),
rpc_pb2.ChannelEdge)
def test_subscribe_channel_events(self):
ensure_peer_connected(self.alice, self.bob)
ensure_channel_open(self.alice, self.bob, self.bitcoin_rpc, BITCOIN_ADDR)
results = []
subscription = self.alice.subscribe_channel_events()
self.assertIsInstance(subscription, grpc._channel._Rendezvous)
self.alice.open_channel_sync(local_funding_amount=500_000,
node_pubkey_string=self.bob.pub_key)
self.bitcoin_rpc.generatetoaddress(3, BITCOIN_ADDR)
time.sleep(SLEEP_TIME)
results.append(subscription.next())
self.assertGreater(len(results), 0)
def test_get_node_info(self):
ensure_peer_connected(self.alice, self.bob)
ensure_channel_open(self.alice, self.bob, self.bitcoin_rpc, BITCOIN_ADDR)
self.assertIsInstance(self.alice.get_node_info(pub_key=self.bob.pub_key),
rpc_pb2.NodeInfo)
def test_query_routes(self):
ensure_peer_connected(self.alice, self.bob)
ensure_channel_open(self.alice, self.bob, self.bitcoin_rpc, BITCOIN_ADDR)
self.assertIsInstance(self.alice.query_routes(pub_key=self.bob.pub_key,
amt=10000,
num_routes=1),
rpc_pb2.QueryRoutesResponse)
def test_network_info(self):
ensure_peer_connected(self.alice, self.bob)
ensure_channel_open(self.alice, self.bob, self.bitcoin_rpc, BITCOIN_ADDR)
self.assertIsInstance(self.alice.get_network_info(), rpc_pb2.NetworkInfo)
def test_stop_daemon(self):
pass
def test_subscribe_channel_graph(self):
ensure_peer_connected(self.alice, self.bob)
ensure_channel_open(self.alice, self.bob, self.bitcoin_rpc, BITCOIN_ADDR)
results = []
subscription = self.alice.subscribe_channel_graph()
self.assertIsInstance(subscription, grpc._channel._Rendezvous)
def test_debug_level(self):
self.assertIsInstance(self.alice.debug_level(level_spec='off'),
rpc_pb2.DebugLevelResponse)
def test_fee_report(self):
self.assertIsInstance(self.alice.fee_report(), rpc_pb2.FeeReportResponse)
def test_update_channel_policy(self):
ensure_peer_connected(self.alice, self.bob)
ensure_channel_open(self.alice, self.bob, self.bitcoin_rpc, BITCOIN_ADDR)
# testing global policy setting
self.assertIsInstance(self.alice.update_channel_policy(base_fee_msat=1000,
fee_rate=1,
time_lock_delta=144),
rpc_pb2.PolicyUpdateResponse)
# test single channel
channel_point = self.alice.list_channels()[0].channel_point
self.assertIsInstance(self.alice.update_channel_policy(base_fee_msat=1000,
fee_rate=1,
time_lock_delta=144,
chan_point=channel_point),
rpc_pb2.PolicyUpdateResponse)
def test_forwarding_history(self):
ensure_peer_connected(self.alice, self.bob)
ensure_channel_open(self.alice, self.bob, self.bitcoin_rpc, BITCOIN_ADDR)
self.assertIsInstance(self.alice.forwarding_history(),
rpc_pb2.ForwardingHistoryResponse)
| 39.689516 | 99 | 0.642436 | 16,483 | 0.837296 | 0 | 0 | 104 | 0.005283 | 0 | 0 | 1,697 | 0.086203 |
19a16d103bbd6587ea4e474ed212a036bf6bb483 | 3,683 | py | Python | tvb_hpc/metric.py | DeLaVlag/tvb-hpc | 6559707dfee8ec712d9624aaf441f9901919fe63 | [
"Apache-2.0"
]
| 3 | 2017-02-22T10:17:19.000Z | 2019-06-20T13:13:53.000Z | tvb_hpc/metric.py | DeLaVlag/tvb-hpc | 6559707dfee8ec712d9624aaf441f9901919fe63 | [
"Apache-2.0"
]
| 27 | 2017-02-07T10:25:31.000Z | 2020-07-01T09:48:13.000Z | tvb_hpc/metric.py | DeLaVlag/tvb-hpc | 6559707dfee8ec712d9624aaf441f9901919fe63 | [
"Apache-2.0"
]
| 10 | 2017-03-06T10:52:02.000Z | 2020-11-20T02:17:17.000Z | # Copyright 2018 TVB-HPC contributors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from .base import BaseKernel
class Covar:
template = """
// stable one-pass co-moment algo, cf wikipedia
__kernel void update_cov(int i_sample,
int n_node,
__global float *cov,
__global float *means,
__global float *data)
{
int it = get_global_id(0), nt = get_global_size(0);
if (i_sample == 0)
{
for (int i_node = 0; i_node < n_node; i_node++)
means[i_node * nt + it] = data[i_node * nt + it];
return;
}
float recip_n = 1.0f / i_sample;
// double buffer to avoid copying memory
__global float *next_mean = means, *prev_mean = means;
if (i_sample % 2 == 0) {
prev_mean += n_node * nt;
} else {
next_mean += n_node * nt;
}
for (int i_node = 0; i_node < n_node; i_node++)
{
int i_idx = i_node * nt + it;
next_mean[i_idx] = prev_mean[i_idx] \
+ (data[i_idx] - prev_mean[i_idx]) * recip_n;
}
for (int i_node = 0; i_node < n_node; i_node++)
{
int i_idx = i_node * nt + it;
float data_mean_i = data[i_idx] - prev_mean[i_idx];
for (int j_node = 0; j_node < n_node; j_node++)
{
int j_idx = j_node * nt + it;
float data_mean_j = data[j_idx] - next_mean[j_idx];
int cij_idx = (j_node * n_node + i_node) * nt + it;
cov[cij_idx] += data_mean_j * data_mean_i;
}
}
}
"""
class BatchCov(BaseKernel):
domains = '{[i,j,t]: 0<=i<n and 0<=j<n and 0<t<m}'
dtypes = {'cov,x,u': 'f', 'm,n': 'i'}
instructions = """
for i
u[i] = sum(t, x[t, i])
end
for i
for j
cov[j, i] = sum(t, (x[t, i] - u[i]) * (x[t, j] - u[j]))
end
end
"""
class OnlineCov(BaseKernel):
domains = '{[i,j]: 0<=i<n and 0<=j<n}'
dtypes = {'cov,x,u0,u1': 'f', 't,n': 'i'}
instructions = """
if (t == 0)
for i
u0[i] = x[i]
end
end
for i
u1[i] = u0[i] + (x[i] - u0[i]) / t
end
for i
<> dui = x[i] - u0[i]
for j
<> duj = x[j] - u1[j]
cov[j, i] = cov[j, i] + duj * dui
end
end
"""
class CovToCorr:
template = """
__kernel void cov_to_corr(int n_sample, int n_node,
__global float *cov,
__global float *corr)
{
int it = get_global_id(0), nt = get_global_size(0);
float recip_n_samp = 1.0f / n_sample;
// normalize comoment to covariance
for (int ij = 0; ij < (n_node * n_node); ij++)
cov[ij*nt + it] *= recip_n_samp;
// compute correlation coefficient
#define COV(i, j) cov[(i*n_node + j)*nt + it]
#define CORR(i, j) corr[(i*n_node + j)*nt + it]
for (int i = 0; i < n_node; i++)
{
float var_i = COV(i, i);
for (int j = 0; j < n_node; j++)
{
float var_j = COV(j, j);
CORR(i, j) = COV(i, j) / sqrt(var_i * var_j);
}
}
}
"""
| 26.307143 | 78 | 0.519414 | 3,018 | 0.819441 | 0 | 0 | 0 | 0 | 0 | 0 | 3,399 | 0.922889 |
19a1cf2092a5e1441835b5519079835b5bb7b437 | 484 | py | Python | 20_valid_parens.py | claytonjwong/leetcode-py | 16bbf8ac0ba5c80fe3ef67ade0d61a12991270a7 | [
"MIT"
]
| 1 | 2020-07-15T14:16:23.000Z | 2020-07-15T14:16:23.000Z | 20_valid_parens.py | claytonjwong/leetcode-py | 16bbf8ac0ba5c80fe3ef67ade0d61a12991270a7 | [
"MIT"
]
| null | null | null | 20_valid_parens.py | claytonjwong/leetcode-py | 16bbf8ac0ba5c80fe3ef67ade0d61a12991270a7 | [
"MIT"
]
| null | null | null | #
# 20. Valid Parentheses
#
# Q: https://leetcode.com/problems/valid-parentheses/
# A: https://leetcode.com/problems/valid-parentheses/discuss/9214/Kt-Js-Py3-Cpp-Stack
#
class Solution:
def isValid(self, A: str) -> bool:
s = []
for c in A:
if c == '(': s.append(')')
elif c == '[': s.append(']')
elif c == '{': s.append('}')
elif not len(s) or c != s.pop():
return False
return not len(s)
| 26.888889 | 85 | 0.506198 | 312 | 0.644628 | 0 | 0 | 0 | 0 | 0 | 0 | 182 | 0.376033 |
19a1f5173da1a19ab9a723e6c007b756a7bf4d27 | 1,474 | py | Python | 2016/23/solution.py | Artemigos/advent-of-code | 0b2ae6af3788b9e6891219b2b64ce0d510b3e1aa | [
"MIT"
]
| null | null | null | 2016/23/solution.py | Artemigos/advent-of-code | 0b2ae6af3788b9e6891219b2b64ce0d510b3e1aa | [
"MIT"
]
| null | null | null | 2016/23/solution.py | Artemigos/advent-of-code | 0b2ae6af3788b9e6891219b2b64ce0d510b3e1aa | [
"MIT"
]
| null | null | null | import common
lines = common.read_file('2016/23/data.txt').splitlines()
# part 1
eggs = 7
# part 2
eggs = 12
registers = dict(a=eggs, b=0, c=0, d=0)
def get_val(val_spec):
if val_spec in registers.keys():
return registers[val_spec]
return int(val_spec)
def is_reg(val_spec):
return val_spec in registers.keys()
instructions = [x.split(' ') for x in lines]
i = 0
while i < len(instructions) and i >= 0:
print(registers)
instr = instructions[i]
code = instr[0]
if code == 'cpy':
if is_reg(instr[2]):
registers[instr[2]] = get_val(instr[1])
i += 1
elif code == 'inc':
if is_reg(instr[1]):
registers[instr[1]] += 1
i += 1
elif code == 'dec':
if is_reg(instr[1]):
registers[instr[1]] -= 1
i += 1
elif code == 'jnz':
if get_val(instr[1]) != 0:
i += get_val(instr[2])
else:
i += 1
else: # tgl
instr_offset = get_val(instr[1])
instr_idx = i + instr_offset
if 0 <= instr_idx < len(instructions):
to_tgl = instructions[instr_idx]
if to_tgl[0] == 'inc':
to_tgl[0] = 'dec'
elif to_tgl[0] == 'dec' or to_tgl[0] == 'tgl':
to_tgl[0] = 'inc'
elif to_tgl[0] == 'jnz':
to_tgl[0] = 'cpy'
else: # cpy
to_tgl[0] = 'jnz'
i += 1
print('a=', registers['a'])
| 24.566667 | 58 | 0.501357 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 114 | 0.077341 |
19a5490d295df8c74ba7a1b675a66941ae74fa96 | 798 | py | Python | game/gamesrc/conf/at_server_startstop.py | abbacode/avaloria | 02e1805ac6e74543c96408b7951429f94bc140ca | [
"ClArtistic"
]
| null | null | null | game/gamesrc/conf/at_server_startstop.py | abbacode/avaloria | 02e1805ac6e74543c96408b7951429f94bc140ca | [
"ClArtistic"
]
| null | null | null | game/gamesrc/conf/at_server_startstop.py | abbacode/avaloria | 02e1805ac6e74543c96408b7951429f94bc140ca | [
"ClArtistic"
]
| null | null | null | """
This module contains functions that are imported and called by the
server whenever it changes its running status. At the point these
functions are run, all applicable hooks on individual objects have
already been executed. The main purpose of this is module is to have a
safe place to initialize eventual custom modules that your game needs
to start up or load.
The module should define at least these global functions:
at_server_start()
at_server_stop()
The module used is defined by settings.AT_SERVER_STARTSTOP_MODULE.
"""
def at_server_start():
"""
This is called every time the server starts up (also after a
reload or reset).
"""
pass
def at_server_stop():
"""
This is called just before a server is shut down, reloaded or
reset.
"""
pass
| 25.741935 | 70 | 0.738095 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 722 | 0.904762 |
19a70dde6b545bdfbf0067d8087a6296f69db3a9 | 407 | py | Python | registration/urls.py | NJnisarg/IEEEProfMatch | 0a67108c910b40f878cc3e0ed9f37ec59089f02d | [
"MIT"
]
| null | null | null | registration/urls.py | NJnisarg/IEEEProfMatch | 0a67108c910b40f878cc3e0ed9f37ec59089f02d | [
"MIT"
]
| null | null | null | registration/urls.py | NJnisarg/IEEEProfMatch | 0a67108c910b40f878cc3e0ed9f37ec59089f02d | [
"MIT"
]
| null | null | null | from django.urls import path
from . import views
urlpatterns = [
path('register/student/', views.StudentCreate.as_view(), name='student-register'),
path('register/prof/', views.ProfCreate.as_view(), name='prof-register'),
path('remove/student/<pk>/', views.StudentRemove.as_view(), name='student-remove'),
path('remove/prof/<pk>/', views.ProfRemove.as_view(), name='prof-remove'),
] | 45.222222 | 88 | 0.687961 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 138 | 0.339066 |
19a87db683c9165ec3ec45d64b90db5e4bc925f8 | 3,272 | py | Python | src/experiment/experiment.py | ricoms/credit-fraud-dealing-with-imbalanced-datasets-mlops | aa483832493faa88affd00eff5489d03506abdeb | [
"MIT"
]
| 5 | 2021-04-29T22:20:08.000Z | 2021-05-21T03:29:52.000Z | src/experiment/experiment.py | ricoms/credit-fraud-dealing-with-imbalanced-datasets-mlops | aa483832493faa88affd00eff5489d03506abdeb | [
"MIT"
]
| null | null | null | src/experiment/experiment.py | ricoms/credit-fraud-dealing-with-imbalanced-datasets-mlops | aa483832493faa88affd00eff5489d03506abdeb | [
"MIT"
]
| 3 | 2021-04-29T22:41:59.000Z | 2021-10-03T20:06:05.000Z | import sys
from dataclasses import dataclass
from io import StringIO
from pathlib import Path
from typing import Any
import numpy as np
from sklearn.model_selection import StratifiedKFold
from utils.logger import logger
from .artifacts import ExperimentArtifacts
INPUT_COLUMNS = [
"Time", "V1", "V2", "V3", "V4", "V5", "V6", "V7", "V8", "V9",
"V10", "V11", "V12", "V13", "V14", "V15", "V16", "V17", "V18", "V19", "V20",
"V21", "V22", "V23", "V24", "V25", "V26", "V27", "V28", "Amount", "Class",
]
@dataclass
class Experiment:
run_tag: str
model: Any
input_dir: Path
artifacts_handler: ExperimentArtifacts
training_portion: float = .8
random_state: int = 42
def load_data(self):
with open(self.input_dir, 'r') as file:
data = file.read().replace('"', '')
data = np.genfromtxt(StringIO(data), delimiter=',', skip_header=1) # , dtype=None
self.X, self.y = data[:, :-1], data[:, -1]
shape0 = self.y.shape[0]
bin_counts = np.bincount(self.y.astype(np.int32))
number_no_frauds = round(bin_counts[0]/shape0 * 100, 2)
number_frauds = round(bin_counts[1]/shape0 * 100, 2)
logger.info(f'Number of instances of the dataset: {shape0}')
logger.info(f'No Frauds {number_no_frauds} of the dataset')
logger.info(f'Frauds {number_frauds} of the dataset')
def split_data(self):
self.sss = StratifiedKFold(n_splits=5, random_state=None, shuffle=False)
def train(self):
accuracy_lst = []
precision_lst = []
recall_lst = []
f1_lst = []
auc_lst = []
for train, validation in self.sss.split(self.X, self.y):
self.model.train(
self.X[train],
self.y[train],
)
acc, precision, recall, f1, auc = self.model.evaluate(
self.X[validation],
self.y[validation],
)
accuracy_lst.append(acc)
precision_lst.append(precision)
recall_lst.append(recall)
f1_lst.append(f1)
auc_lst.append(auc)
visual_results = self.model.gen_plots(
self.X[validation],
self.y[validation],
)
artifacts = {
"metrics": {
"avg_accuracy": np.mean(accuracy_lst),
"avg_precision": np.mean(precision_lst),
"avg_recall": np.mean(recall_lst),
"avg_f1": np.mean(f1_lst),
"avg_auc": np.mean(auc_lst),
},
"images": visual_results,
}
logger.info(artifacts)
return artifacts
def save(self):
self.artifacts_handler.save()
self.model.save(self.artifacts_handler.output_prefix)
def run(self):
logger.info(f"Begin Experiment {self.run_tag} for model {self.model.model_id}.")
self.load_data()
self.split_data()
try:
artifacts = self.train()
except Exception as e:
self.artifacts_handler.training_error(e)
sys.exit(255)
self.artifacts_handler.get_artifacts(
artifacts
)
self.save()
self.artifacts_handler.create_package_with_models()
| 31.161905 | 90 | 0.573655 | 2,746 | 0.839242 | 0 | 0 | 2,757 | 0.842604 | 0 | 0 | 452 | 0.138142 |
19aaba565d436c4904687e34d86883a59ba3510f | 92 | py | Python | 29.operacoes_com_lista/17.exercicio4.py | robinson-1985/python-zero-dnc | df510d67e453611fcd320df1397cdb9ca47fecb8 | [
"MIT"
]
| null | null | null | 29.operacoes_com_lista/17.exercicio4.py | robinson-1985/python-zero-dnc | df510d67e453611fcd320df1397cdb9ca47fecb8 | [
"MIT"
]
| null | null | null | 29.operacoes_com_lista/17.exercicio4.py | robinson-1985/python-zero-dnc | df510d67e453611fcd320df1397cdb9ca47fecb8 | [
"MIT"
]
| null | null | null | # 4.Crie uma lista que vá de 1 até 100.
list = [ cont for cont in range(1,101)]
print(list) | 23 | 39 | 0.673913 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 41 | 0.43617 |
19ab282eb7fda2cf0e66686848e93a742af27a72 | 1,385 | py | Python | src/responsibility/problems/drsc_fig3.py | pik-gane/pyresponsibility | e0f43be1a9712754832bb97b3797851cdb24842d | [
"BSD-2-Clause"
]
| null | null | null | src/responsibility/problems/drsc_fig3.py | pik-gane/pyresponsibility | e0f43be1a9712754832bb97b3797851cdb24842d | [
"BSD-2-Clause"
]
| null | null | null | src/responsibility/problems/drsc_fig3.py | pik-gane/pyresponsibility | e0f43be1a9712754832bb97b3797851cdb24842d | [
"BSD-2-Clause"
]
| null | null | null | """Fig. 3 from Heitzig & Hiller (2020) Degrees of individual and groupwise
backward and forward responsibility in extensive-form games with ambiguity,
and their application to social choice problems. ArXiv:2007.07352
drsc_fig3:
v1: i
├─╴dont_hesitate╶─╴w3: lives ✔
╰─╴hesitate╶─╴v2: i
├─╴try_after_all╶─╴delayed_try
│ ├─╴succeed╶─╴w4: lives ✔
│ ╰─╴fail╶─╴w6: dies ✖
╰─╴pass_╶─╴w5: dies ✖
Situation related to the Independence of Nested Decisions (IND) axiom. The agent
sees someone having a heart attack and may either try to rescue them without
hesitation, applying CPU until the ambulance arrives, or hesitate and then
reconsider and try rescuing them after all, in which case it is ambiguous
whether the attempt can still succeed.
"""
from ..__init__ import *
global_players("i")
global_actions("hesitate", "dont_hesitate", "try_after_all", "pass_")
lives = Ou("lives", ac=True)
dies = Ou("dies", ac=False)
T = Tree("drsc_fig3", ro=DeN("v1", pl=i, co={
dont_hesitate: OuN("w3", ou=lives),
hesitate: DeN("v2", pl=i, co={
try_after_all: PoN("delayed_try", su={
("succeed", OuN("w4", ou=lives)),
("fail", OuN("w6", ou=dies))
}),
pass_: OuN("w5", ou=dies)
})
})
)
T.make_globals()
| 30.108696 | 81 | 0.608664 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1,045 | 0.711368 |
19ad8e362fd7bad525b8a921fc18f92df7f7282b | 82 | py | Python | www/speed/benchmarks/fib.py | raspberrypieman/brython | 2cc23d1da6acda604d4a56b4c9d464eb7e374eda | [
"BSD-3-Clause"
]
| 5,926 | 2015-01-01T07:45:08.000Z | 2022-03-31T12:34:38.000Z | www/speed/benchmarks/fib.py | raspberrypieman/brython | 2cc23d1da6acda604d4a56b4c9d464eb7e374eda | [
"BSD-3-Clause"
]
| 1,728 | 2015-01-01T01:09:12.000Z | 2022-03-30T23:25:22.000Z | www/speed/benchmarks/fib.py | raspberrypieman/brython | 2cc23d1da6acda604d4a56b4c9d464eb7e374eda | [
"BSD-3-Clause"
]
| 574 | 2015-01-02T01:36:10.000Z | 2022-03-26T10:18:48.000Z | def fib(n):
if (n<2):
return n
return fib(n-2) + fib(n-1)
fib(30)
| 11.714286 | 30 | 0.47561 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
19b063208b212cda110697a8227b09dc8f98a0db | 414 | py | Python | catalog/bindings/csw/role.py | NIVANorge/s-enda-playground | 56ae0a8978f0ba8a5546330786c882c31e17757a | [
"Apache-2.0"
]
| null | null | null | catalog/bindings/csw/role.py | NIVANorge/s-enda-playground | 56ae0a8978f0ba8a5546330786c882c31e17757a | [
"Apache-2.0"
]
| null | null | null | catalog/bindings/csw/role.py | NIVANorge/s-enda-playground | 56ae0a8978f0ba8a5546330786c882c31e17757a | [
"Apache-2.0"
]
| null | null | null | from dataclasses import dataclass
from bindings.csw.code_type_1 import CodeType1
__NAMESPACE__ = "http://www.opengis.net/ows"
@dataclass
class Role(CodeType1):
"""Function performed by the responsible party.
Possible values of this Role shall include the values and the
meanings listed in Subclause B.5.5 of ISO 19115:2003.
"""
class Meta:
namespace = "http://www.opengis.net/ows"
| 24.352941 | 65 | 0.724638 | 273 | 0.65942 | 0 | 0 | 284 | 0.68599 | 0 | 0 | 236 | 0.570048 |
5fd5ed7c8159f71250918b127471e85178346d85 | 31 | py | Python | utils_folder/__init__.py | codevideo/Airsim_Test | 0b47665c67ba674d277fc8de58739a59648bb7eb | [
"MIT"
]
| null | null | null | utils_folder/__init__.py | codevideo/Airsim_Test | 0b47665c67ba674d277fc8de58739a59648bb7eb | [
"MIT"
]
| null | null | null | utils_folder/__init__.py | codevideo/Airsim_Test | 0b47665c67ba674d277fc8de58739a59648bb7eb | [
"MIT"
]
| null | null | null | from .image_generator import *
| 15.5 | 30 | 0.806452 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
5fd63dd37aacbace93081d1f24365379814ddc62 | 1,847 | py | Python | discogstagger/lyrics.py | makzyt4/discogs-tagger | b578257922230b634d43349f2efd4fda72fdd008 | [
"MIT"
]
| 2 | 2019-09-05T04:26:20.000Z | 2020-08-05T02:56:04.000Z | discogstagger/lyrics.py | makzyt4/discogs-tagger | b578257922230b634d43349f2efd4fda72fdd008 | [
"MIT"
]
| null | null | null | discogstagger/lyrics.py | makzyt4/discogs-tagger | b578257922230b634d43349f2efd4fda72fdd008 | [
"MIT"
]
| 1 | 2019-10-02T13:07:34.000Z | 2019-10-02T13:07:34.000Z | import warnings
import urllib
warnings.filterwarnings("ignore", category=UserWarning)
from fuzzywuzzy import fuzz
from bs4 import BeautifulSoup
from discogstagger.crawler import WebCrawler
class LyricsSearcher:
def __init__(self, artist_name):
self.artist_name = artist_name
self.url_base = "http://lyrics.wikia.com"
self.crawler = WebCrawler()
url_search_fragment = "/wiki/Special:Search?query="
artist_query = urllib.parse.quote_plus(artist_name)
url = self.url_base + url_search_fragment + artist_query
self.soup = self.crawler.get_soup(url)
def load(self):
li = self.soup.find("li", {"class": "result"})
if li is None:
return False
first_artist = li.find_all("a", {"class": "result-link"})[0].text
ratio = fuzz.ratio(first_artist, self.artist_name)
if ratio < 60:
return False
first_link = li.find_all("a", {"class": "result-link"})[0]['href']
self.soup = self.crawler.get_soup(first_link)
return True
def search_lyrics(self, track_title):
ols = self.soup.find_all("ol")
max_ratio = 0
found = None
for ol in ols:
links = ol.find_all("a")
for link in links:
ratio = fuzz.ratio(track_title, link.text)
if ratio > max_ratio:
max_ratio = ratio
found = {'link': link['href'], 'ratio': ratio}
if found == None or found['ratio'] < 80:
return ''
track_soup = self.crawler.get_soup(self.url_base + found['link'])
lyricbox = track_soup.find("div", {"class": "lyricbox"})
if lyricbox is None:
return ''
for br in lyricbox.find_all("br"):
br.replace_with("\n")
return lyricbox.text
| 35.519231 | 74 | 0.593936 | 1,654 | 0.895506 | 0 | 0 | 0 | 0 | 0 | 0 | 206 | 0.111532 |
5fd72d6c14585afca0b8d98d7d4aed63853fc97b | 1,478 | py | Python | policy_sentry/util/conditions.py | patricksanders/policy_sentry | 3559ce8d3a19728b4f64dfff4cbdf075e7629b39 | [
"MIT"
]
| null | null | null | policy_sentry/util/conditions.py | patricksanders/policy_sentry | 3559ce8d3a19728b4f64dfff4cbdf075e7629b39 | [
"MIT"
]
| 20 | 2020-03-20T06:13:09.000Z | 2022-02-10T18:15:35.000Z | policy_sentry/util/conditions.py | ssmbct-netops/policy_sentry | c9f4752c633fe229220b7f476aa766ea65330489 | [
"MIT"
]
| null | null | null | """
Just some text transformation functions related to our
conditions-related workarounds
"""
# Borrowed from parliament. Altered with normalization for lowercase.
def translate_condition_key_data_types(condition_str):
"""
The docs use different type names, so this standardizes them.
Example: The condition secretsmanager:RecoveryWindowInDays is listed as using a "Long"
So return "Number"
"""
if condition_str.lower() in ["arn", "arn"]:
return "Arn"
elif condition_str.lower() in ["bool", "boolean"]:
return "Bool"
elif condition_str.lower() in ["date"]:
return "Date"
elif condition_str.lower() in ["long", "numeric"]:
return "Number"
elif condition_str.lower() in ["string", "string", "arrayofstring"]:
return "String"
elif condition_str.lower() in ["ip"]:
return "Ip"
else:
raise Exception("Unknown data format: {}".format(str))
def get_service_from_condition_key(condition_key):
"""Given a condition key, return the service prefix"""
elements = condition_key.split(":", 2)
return elements[0]
def get_comma_separated_condition_keys(condition_keys):
"""
:param condition_keys: String containing multiple condition keys, separated by double spaces
:return: result: String containing multiple condition keys, comma-separated
"""
result = condition_keys.replace(" ", ",") # replace the double spaces with a comma
return result
| 32.844444 | 96 | 0.688769 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 788 | 0.533153 |
5fd7391d4fbb21e69c15f5d957bc435fc6d49a18 | 7,798 | py | Python | env/lib/python3.7/site-packages/cleo/inputs/argv_input.py | Kolawole39/masonite-guides-tutorial | 9a21cc635291a42f0722f69925be1809bb20e01c | [
"MIT"
]
| null | null | null | env/lib/python3.7/site-packages/cleo/inputs/argv_input.py | Kolawole39/masonite-guides-tutorial | 9a21cc635291a42f0722f69925be1809bb20e01c | [
"MIT"
]
| null | null | null | env/lib/python3.7/site-packages/cleo/inputs/argv_input.py | Kolawole39/masonite-guides-tutorial | 9a21cc635291a42f0722f69925be1809bb20e01c | [
"MIT"
]
| null | null | null | # -*- coding: utf-8 -*-
import sys
import re
from .input import Input
from ..exceptions import NoSuchOption, BadOptionUsage, TooManyArguments
class ArgvInput(Input):
def __init__(self, argv=None, definition=None):
super(ArgvInput, self).__init__(definition)
if argv is None:
argv = sys.argv[:]
argv.pop(0)
self._tokens = argv
self._parsed = None
def parse(self):
parse_options = True
self._parsed = self._tokens
while True:
try:
token = self._parsed.pop(0)
except IndexError:
break
if parse_options and token == '':
self.parse_argument(token)
elif parse_options and token == '--':
parse_options = False
elif parse_options and token.find('--') == 0:
self.parse_long_option(token)
elif parse_options and token[0] == '-' and token != '-':
self.parse_short_option(token)
else:
self.parse_argument(token)
def parse_short_option(self, token):
name = token[1:]
if len(name) > 1:
if self.definition.has_shortcut(name[0])\
and self.definition.get_option_for_shortcut(name[0]).accept_value():
# an option with a value (with no space)
self.add_short_option(name[0], name[1:])
else:
self.parse_short_option_set(name)
else:
if self.definition.has_shortcut(name) and self.definition.get_option_for_shortcut(name).accept_value():
try:
value = self._parsed.pop(0)
except IndexError:
value = None
if value and value.startswith('-'):
self._parsed.insert(0, value)
value = None
self.add_short_option(name, value)
else:
self.add_short_option(name, None)
def parse_short_option_set(self, name):
l = len(name)
for i in range(0, l):
if not self.definition.has_shortcut(name[i]):
raise NoSuchOption('The "-%s" option does not exist.' % name[i])
option = self.definition.get_option_for_shortcut(name[i])
if option.accept_value():
self.add_long_option(option.get_name(), None if l - 1 == i else name[i + 1:])
break
else:
self.add_long_option(option.get_name(), None)
def parse_long_option(self, token):
name = token[2:]
pos = name.find('=')
if pos != -1:
self.add_long_option(name[:pos], name[pos + 1:])
else:
if self.definition.has_option(name) and self.definition.get_option(name).accept_value():
try:
value = self._parsed.pop(0)
except IndexError:
value = None
if value and value.startswith('-'):
self._parsed.insert(0, value)
value = None
self.add_long_option(name, value)
else:
self.add_long_option(name, None)
def parse_argument(self, token):
c = len(self.arguments)
# if input is expecting another argument, add it
if self.definition.has_argument(c):
arg = self.definition.get_argument(c)
self.arguments[arg.get_name()] = [token] if arg.is_list() else token
elif self.definition.has_argument(c - 1) and self.definition.get_argument(c - 1).is_list():
arg = self.definition.get_argument(c - 1)
self.arguments[arg.get_name()].append(token)
# unexpected argument
else:
raise TooManyArguments('Too many arguments.')
def add_short_option(self, shortcut, value):
if not self.definition.has_shortcut(shortcut):
raise NoSuchOption('The "-%s" option does not exist.' % shortcut)
self.add_long_option(self.definition.get_option_for_shortcut(shortcut).get_name(), value)
def add_long_option(self, name, value):
if not self.definition.has_option(name):
raise NoSuchOption('The "--%s" option does not exist.' % name)
option = self.definition.get_option(name)
if value is False:
value = None
if value is not None and not option.accept_value():
raise BadOptionUsage('The "--%s" option does not accept a value.' % name)
if value is None and option.accept_value() and len(self._parsed):
# if option accepts an optional or mandatory argument
# let's see if there is one provided
try:
nxt = self._parsed.pop(0)
except IndexError:
nxt = None
if nxt and len(nxt) >= 1 and nxt[0] != '-':
value = nxt
elif not nxt:
value = ''
else:
self._parsed.insert(0, nxt)
# This test is here to handle cases like --foo=
# and foo option value is optional
if value == '':
value = None
if value is None:
if option.is_value_required():
raise BadOptionUsage('The "--%s" option requires a value.' % name)
if not option.is_list():
value = option.get_default() if option.is_value_optional() else True
if option.is_list():
if name not in self.options:
self.options[name] = [value]
else:
self.options[name].append(value)
else:
self.options[name] = value
def get_first_argument(self):
for token in self._tokens:
if token and token[0] == '-':
continue
return token
def has_parameter_option(self, values):
values = [values] if not isinstance(values, (list, tuple)) else values
for token in self._tokens:
for value in values:
if token == value:
return True
# Options with values:
# For long options, test for '--option=' at beginning
# For short options, test for '-o' at beginning
leading = value + "=" if value.find("--") == 0 else value
if leading and token.find(leading) == 0:
return True
return False
def get_parameter_option(self, values, default=False):
values = [values] if not isinstance(values, (list, tuple)) else values
tokens = self._tokens[:]
while True:
try:
token = tokens.pop(0)
except IndexError:
break
for value in values:
if token == value:
try:
return tokens.pop(0)
except IndexError:
return
# Options with values:
# For long options, test for '--option=' at beginning
# For short options, test for '-o' at beginning
leading = value + "=" if value.find("--") == 0 else value
if leading and token.find(leading) == 0:
return token[len(leading):]
return default
def __str__(self):
def stringify(token):
m = re.match('^(-[^=]+=)(.+)', token)
if m:
return m.group(1) + self.escape_token(m.group(2))
if token and token[0] != '-':
return self.escape_token(token)
return token
tokens = map(stringify, self._tokens)
return ' '.join(tokens)
| 33.467811 | 115 | 0.530649 | 7,651 | 0.981149 | 0 | 0 | 0 | 0 | 0 | 0 | 822 | 0.105412 |
5fd7b9ebd6a6a68297eb56bfd50e24457d0aa02b | 1,528 | py | Python | general/email-sender/email_sender.py | caesarcc/python-code-tutorials | aa48ebe695e86440b206b641501ad55d021309bf | [
"MIT"
]
| 1,059 | 2019-08-04T17:51:43.000Z | 2022-03-31T10:13:37.000Z | general/email-sender/email_sender.py | kylearrigoni/pythoncode-tutorials | 6fd30ed854d1ea7573351ddb10c1eaae55ca5717 | [
"MIT"
]
| 56 | 2019-12-10T16:46:40.000Z | 2022-03-29T01:36:50.000Z | general/email-sender/email_sender.py | kylearrigoni/pythoncode-tutorials | 6fd30ed854d1ea7573351ddb10c1eaae55ca5717 | [
"MIT"
]
| 1,330 | 2019-08-16T01:36:18.000Z | 2022-03-31T16:57:22.000Z | import smtplib
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart
from bs4 import BeautifulSoup as bs
def send_mail(email, password, FROM, TO, msg):
# initialize the SMTP server
server = smtplib.SMTP("smtp.gmail.com", 587)
# connect to the SMTP server as TLS mode (secure) and send EHLO
server.starttls()
# login to the account using the credentials
server.login(email, password)
# send the email
server.sendmail(FROM, TO, msg.as_string())
# terminate the SMTP session
server.quit()
# your credentials
email = "[email protected]"
password = "password"
# the sender's email
FROM = "[email protected]"
# the receiver's email
TO = "[email protected]"
# the subject of the email (subject)
subject = "Just a subject"
# initialize the message we wanna send
msg = MIMEMultipart("alternative")
# set the sender's email
msg["From"] = FROM
# set the receiver's email
msg["To"] = TO
# set the subject
msg["Subject"] = subject
# set the body of the email as HTML
html = """
This email is sent using <b>Python </b>!
"""
# uncomment below line if you want to use the HTML template
# located in mail.html
# html = open("mail.html").read()
# make the text version of the HTML
text = bs(html, "html.parser").text
text_part = MIMEText(text, "plain")
html_part = MIMEText(html, "html")
# attach the email body to the mail message
# attach the plain text version first
msg.attach(text_part)
msg.attach(html_part)
# send the mail
send_mail(email, password, FROM, TO, msg) | 27.781818 | 67 | 0.716623 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 861 | 0.563482 |
5fd809b863e1c38e1b5883293cb0d69f18e7c0cd | 140 | py | Python | src/app/models/__init__.py | starlite-api/backend-starlite-postgres | c2d539d88dd13cbe06fa49ab95c1fd6eb398b051 | [
"MIT"
]
| 3 | 2022-01-18T16:35:12.000Z | 2022-01-25T14:55:32.000Z | src/app/models/__init__.py | starlite-api/backend-starlite-postgres | c2d539d88dd13cbe06fa49ab95c1fd6eb398b051 | [
"MIT"
]
| 1 | 2022-01-24T22:37:35.000Z | 2022-01-25T08:09:19.000Z | src/app/models/__init__.py | starlite-api/backend-starlite-postgres | c2d539d88dd13cbe06fa49ab95c1fd6eb398b051 | [
"MIT"
]
| null | null | null | # flake8: noqa
from .base import Base
from .item import Item, ItemCreateModel, ItemModel
from .user import User, UserCreateModel, UserModel
| 28 | 50 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 14 | 0.1 |
5fda0bec3ea02ea3a6f8218190306677fcc6bbb6 | 3,371 | py | Python | digibanner.py | w4mhi/digipi | 1fb00450cf9ed14a30c293e744848a7cad09f489 | [
"MIT"
]
| null | null | null | digibanner.py | w4mhi/digipi | 1fb00450cf9ed14a30c293e744848a7cad09f489 | [
"MIT"
]
| null | null | null | digibanner.py | w4mhi/digipi | 1fb00450cf9ed14a30c293e744848a7cad09f489 | [
"MIT"
]
| null | null | null | #!/usr/bin/python3
# direwatch
"""
Craig Lamparter KM6LYW, 2021, MIT License
modified by W4MHI February 2022
- see the init_display.py module for display settings
- see https://www.delftstack.com/howto/python/get-ip-address-python/ for the ip address
"""
import sys
import argparse
import time
from netifaces import interfaces, ifaddresses, AF_INET
sys.path.insert(0, '/home/pi/common')
from display_util import *
from constants import *
def parse_arguments():
ap = argparse.ArgumentParser()
ap.add_argument("-f", "--fontsize", required=False, help="Font size for messages")
ap.add_argument("-b", "--big", required=False, help="large text to display")
ap.add_argument("-s", "--small", required=False, help="smaller text underneath")
args = vars(ap.parse_args())
return args
args = parse_arguments()
if args["fontsize"]:
fontsize = int(args["fontsize"])
if fontsize > 34:
print("The input: " + str(fontsize) + " is greater than: 34 that is maximum value supported.")
print("Setting to maximum value: 34.")
fontsize = 34
elif fontsize < 20:
print("The input: " + str(fontsize) + " is lower than: 20 that is minimum value supported.")
print("Setting to minimum value: 20.")
fontsize = 20
else:
print("Setting font size to default value: 24.")
fontsize = 24
if args["big"]:
message_big = args["big"]
else:
message_big = "DigiPi"
if args["small"]:
message_small = args["small"]
else:
message_small = "DigiPi Operational!"
# title
font_title = get_titlefont()
# define writing fonts
font_message = get_writingfont(fontsize)
spacing = get_spacing(fontsize)
last_line = get_lastline(fontsize)
# Draw a black filled box to clear the image.
draw.rectangle((0, 0, width, height), outline=0, fill="#000000")
# title bar
draw.rectangle((0, 0, width, TITLE_BAR_H), outline=0, fill="#333333")
draw.text((10, 0) , TITLE, font=font_title, fill="#888888")
# ip addresses message
count = 1
draw.text((PAD_LEFT, count*spacing), "Net's IP Addresses", font=font_message, fill="#00FF00")
first_pass = True
ip_present = False
while ip_present == False:
count = 1
# ip addresses
for ifaceName in interfaces():
for i in ifaddresses(ifaceName).setdefault(AF_INET, [{'addr':'No IP yet'}]):
if ifaceName.startswith("wlan") or ifaceName.startswith("eth"):
# increment for interface name
count = count + 1
if first_pass:
# show the interface name
draw.text((PAD_LEFT, count*spacing), "[" + ifaceName + "]", font=font_message, fill="#00FF00")
# increment for the ip address
count = count + 1
#delete the previous line if exists
draw.rectangle((0, count*spacing, width, (count+1)*spacing), outline=0, fill="#000000")
draw.text((4*PAD_LEFT, count*spacing), i['addr'], font=font_message, fill="#00FF00")
if i['addr'].startswith('No IP') == False:
ip_present = True
disp.image(image)
first_pass = False
# wait and re-iterate if no ip address
if ip_present == False:
time.sleep(3)
# message
count = count + 1
if last_line >= count:
draw.text((PAD_LEFT, last_line*spacing), message_small, font=font_message, fill="#FFFF00")
#with display_lock:
disp.image(image)
print("DigiPi operational!\n")
exit(0)
| 29.570175 | 110 | 0.664195 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1,168 | 0.346485 |
5fdb29602cc883d0c3c33fa3a91c2d4157fe4739 | 7,007 | py | Python | autotorrent/utils.py | jyggen/autotorrent | 5a8f2b40ccc8c66c73dc520f98b886d21e163afa | [
"MIT"
]
| 278 | 2015-02-12T19:19:53.000Z | 2022-03-22T21:17:28.000Z | autotorrent/utils.py | jyggen/autotorrent | 5a8f2b40ccc8c66c73dc520f98b886d21e163afa | [
"MIT"
]
| 56 | 2015-03-27T00:38:37.000Z | 2022-03-26T17:52:58.000Z | autotorrent/utils.py | jyggen/autotorrent | 5a8f2b40ccc8c66c73dc520f98b886d21e163afa | [
"MIT"
]
| 48 | 2015-03-10T16:50:19.000Z | 2022-03-20T12:11:50.000Z | from __future__ import division
import hashlib
import logging
import os
import re
__all__ = [
'is_unsplitable',
'get_root_of_unsplitable',
'Pieces',
]
UNSPLITABLE_FILE_EXTENSIONS = [
set(['.rar', '.sfv']),
set(['.mp3', '.sfv']),
set(['.vob', '.ifo']),
]
logger = logging.getLogger(__name__)
def is_unsplitable(files):
"""
Checks if a list of files can be considered unsplitable, e.g. VOB/IFO or scene release.
This means the files can only be used in this combination.
"""
extensions = set(os.path.splitext(f)[1].lower() for f in files)
found_unsplitable_extensions = False
for exts in UNSPLITABLE_FILE_EXTENSIONS:
if len(extensions & exts) == len(exts):
found_unsplitable_extensions = True
break
lowercased_files = set([f.lower() for f in files])
found_magic_file = False
if 'movieobject.bdmv' in lowercased_files:
found_magic_file = True
return found_unsplitable_extensions or found_magic_file
def get_root_of_unsplitable(path):
"""
Scans a path for the actual scene release name, e.g. skipping cd1 folders.
Returns None if no scene folder could be found
"""
path = path[::-1]
for p in path:
if not p:
continue
if re.match(r'^(cd[1-9])|(samples?)|(proofs?)|((vob)?sub(title)?s?)$', p, re.IGNORECASE): # scene paths
continue
if re.match(r'^(bdmv)|(disc\d*)|(video_ts)$', p, re.IGNORECASE): # bluray / dd
continue
return p
class Pieces(object):
"""
Can help check if files match the files found in a torrent.
"""
def __init__(self, torrent):
self.piece_size = torrent[b'info'][b'piece length']
self.pieces = []
for i in range(0, len(torrent[b'info'][b'pieces']), 20):
self.pieces.append(torrent[b'info'][b'pieces'][i:i+20])
def get_complete_pieces(self, start_size, end_size):
"""
Finds complete pieces and returns the alignment needed from
the beginning and the end (to match the file).
"""
logger.debug('Getting complete pieces for file starting at %i and ending at %i. Piece size is %i' % (start_size, end_size, self.piece_size))
start_piece, start_offset = divmod(start_size, self.piece_size)
if start_offset:
start_piece += 1
if start_offset:
start_offset = self.piece_size - start_offset
end_piece, end_offset = divmod(end_size, self.piece_size)
logger.debug('Start piece:%i end piece:%i' % (start_piece, end_piece-1))
return start_offset, end_offset, self.pieces[start_piece:end_piece]
def find_piece_breakpoint(self, file_path, start_size, end_size):
"""
Finds the point where a file with a different size is modified and tries to align it with pieces.
"""
start_offset, end_offset, pieces = self.get_complete_pieces(start_size, end_size)
failed_pieces = (len(pieces) // 20) or 1 # number of pieces that can fail in a row and then put an end to checking
success_count = failed_pieces
piece_status = []
with open(file_path, 'rb') as f:
f.seek(start_offset)
for i, piece in enumerate(pieces):
logger.debug('Checking piece %i for breakingpoint' % (i, ))
h = hashlib.sha1(f.read(self.piece_size)).digest()
if h == piece:
logger.debug('Piece %i matched' % i)
if success_count < failed_pieces:
success_count += 1
piece_status.append(True)
else:
logger.debug('Piece %i did not match' % i)
success_count -= 1
piece_status.append(False)
if success_count <= 0:
logger.debug('The breakingpoint has been found after piece %i - more than %i failed pieces' % (i, failed_pieces))
break
for p in piece_status[::-1]:
if p:
break
i -= 1
breakingpoint = start_offset + self.piece_size*i
logger.debug('A total of %i pieces were ok, so we set breakingpoint at %i' % (i, breakingpoint))
return breakingpoint
def match_file(self, file_path, start_size, end_size):
"""
Try to match file starting at start_size and ending at end_size.
"""
start_offset, end_offset, pieces = self.get_complete_pieces(start_size, end_size)
logger.debug('Stuff to check start_offset:%i end_offset:%i pieces:%s' % (start_offset, end_offset, len(pieces)))
if not pieces:
logger.debug('No whole pieces found for %r, taking this as a not-match' % file_path)
return False, False
check_pieces = (len(pieces) // 10) or 1
match_start, match_end = 0, 0
size = os.path.getsize(file_path)
with open(file_path, 'rb') as f:
for i in range(check_pieces): # check from beginning
seek_offset = start_offset+self.piece_size*i
logger.debug('Checking piece %i from beginning of file, reading from %i bytes. Filesize: %i' % (i, seek_offset, size))
f.seek(seek_offset)
h = hashlib.sha1(f.read(self.piece_size)).digest()
logger.debug('Matching hash %r against %r' % (h, pieces[i]))
if h == pieces[i]:
logger.debug('Piece %i matched' % i)
match_start += 1
else:
logger.debug('Piece %i did not match' % i)
for i in range(check_pieces): # check from end
seek_offset = size-end_offset-self.piece_size*(i+1)
logger.debug('Checking piece %i from end of file, reading from %i bytes. Filesize: %i' % (i, seek_offset, size))
f.seek(seek_offset)
h = hashlib.sha1(f.read(self.piece_size)).digest()
piece = pieces[(i+1)*-1]
logger.debug('Matching hash %r against %r' % (h, piece))
if h == piece:
logger.debug('Piece %i matched' % i)
match_end += 1
else:
logger.debug('Piece %i did not match' % i)
logger.debug('Checked %i pieces from both start and end. %i matched from start and %i matched from end.' % (check_pieces, match_start, match_end))
if check_pieces < 4:
must_match = 1
elif check_pieces < 10:
must_match = 2
else:
must_match = max(check_pieces // 10, 3)
return (match_start and check_pieces - match_start <= must_match,
match_end and check_pieces - match_end <= must_match)
| 38.927778 | 154 | 0.572285 | 5,425 | 0.774226 | 0 | 0 | 0 | 0 | 0 | 0 | 1,955 | 0.279007 |
5fdbaa30fa591b9ff594e38b62a435e122fd9e26 | 232 | py | Python | Documentation/matplotlib/users/tight_layout_guide-11.py | leesavide/pythonista-docs-deprecated | 9ec3363f07e328bde0a58738a16907f11dfd06e1 | [
"Apache-2.0"
]
| 16 | 2016-06-14T19:45:35.000Z | 2020-11-30T19:02:58.000Z | Documentation/matplotlib/users/tight_layout_guide-11.py | leesavide/pythonista-docs | 9ec3363f07e328bde0a58738a16907f11dfd06e1 | [
"Apache-2.0"
]
| 1 | 2016-06-15T07:10:27.000Z | 2016-06-15T07:10:27.000Z | Documentation/matplotlib/users/tight_layout_guide-11.py | leesavide/pythonista-docs | 9ec3363f07e328bde0a58738a16907f11dfd06e1 | [
"Apache-2.0"
]
| null | null | null | gs2 = gridspec.GridSpec(3, 1)
for ss in gs2:
ax = fig.add_subplot(ss)
example_plot(ax)
ax.set_title("")
ax.set_xlabel("")
ax.set_xlabel("x-label", fontsize=12)
gs2.tight_layout(fig, rect=[0.5, 0, 1, 1], h_pad=0.5) | 21.090909 | 53 | 0.637931 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 13 | 0.056034 |
5fdde3756ec64880272edcdfd80736c9d7a32118 | 5,679 | py | Python | FaceRec/dataset.py | JasonLee/FaceRecognitionInLowResolutionVideos | 719ef88ddfbed9d5d40df2480b6e9a197febc1b7 | [
"MIT"
]
| 2 | 2020-07-13T06:00:36.000Z | 2021-04-15T06:12:16.000Z | FaceRec/dataset.py | JasonLee/FaceRecognitionInLowResolutionVideos | 719ef88ddfbed9d5d40df2480b6e9a197febc1b7 | [
"MIT"
]
| 3 | 2020-05-15T04:49:28.000Z | 2022-01-13T02:47:26.000Z | FaceRec/dataset.py | JasonLee/FaceRecognitionInLowResolutionVideos | 719ef88ddfbed9d5d40df2480b6e9a197febc1b7 | [
"MIT"
]
| null | null | null | from __future__ import print_function, division
import os
import torch
import pandas as pd
from skimage import io, transform
import numpy as np
import matplotlib.pyplot as plt
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
import random
import model
from torch.autograd import Variable
class FaceDataset(Dataset):
def __init__(self, csv_file,root_dir, forTrain, transform = None):
self.image_name = pd.read_csv(csv_file)
self.root_dir = root_dir
self.transform = transform
self.forTrain = forTrain
if forTrain:
self.labelSet = {}
for idx in range(len(self.image_name)):
i = self.image_name.iloc[idx,0]
name, label = i.split(' ')
if(not label in self.labelSet):
self.labelSet[label] = [name]
else:
self.labelSet[label].append(name)
def __len__(self):
return len(self.image_name)
def __getitem__(self, idx): # TODO: Change item selecting.
if self.forTrain:
anchor_name = os.path.join(self.root_dir, self.image_name.iloc[idx,0].split(' ')[0])
anchor = io.imread(anchor_name)
label = self.image_name.iloc[idx,0].split(' ')[1]
positive_name = anchor_name
count = 0
while anchor_name == positive_name:
count+=1
positive_name = np.random.choice(self.labelSet[label])
if count == 10:
break
positive = io.imread(os.path.join(self.root_dir, positive_name))
index_neg = random.randint(0,len(self)-1)
while(self.image_name.iloc[index_neg,0].split(' ')[1]==label):
index_neg = random.randint(0,len(self)-1)
negative = io.imread(os.path.join(self.root_dir, self.image_name.iloc[index_neg,0].split(' ')[0]))
if self.transform:
anchor = self.transform(anchor)
positive = self.transform(positive)
negative = self.transform(negative)
return anchor, positive, negative
else:
sample = io.imread(os.path.join(self.root_dir, self.image_name.iloc[idx,0].split(' ')[0]))
label = self.image_name.iloc[idx,0].split(' ')[1]
if self.transform:
sample = self.transform(sample)
return sample,label
class ToTensor(object):
def __call__(self,sample):
image = sample
image = image.transpose((2,0,1))
return image
class Rescale(object):
"""Rescale the image in a sample to a given size.
Args:
output_size (tuple or int): Desired output size. If tuple, output is
matched to output_size. If int, smaller of image edges is matched
to output_size keeping aspect ratio the same.
"""
def __init__(self, output_size):
assert isinstance(output_size, list)
self.output_size = output_size
def __call__(self, image):
return transform.resize(image, (self.output_size[0], self.output_size[1]))
class FaceDatasetOrganised(Dataset):
def __init__(self,root_dir, forTrain, transform = None):
self.root_dir = root_dir
self.transform = transform
self.forTrain = forTrain
self.labelSet = {}
self.normalSet = []
labels = os.listdir(root_dir)
for label in labels:
faces = os.listdir(root_dir+'/'+label)
self.labelSet[label] = faces[:]
for i in range(len(faces)):
faces[i] = [faces[i], label]
self.normalSet.extend(faces)
def __len__(self):
return len(self.normalSet)
def __getitem__(self, idx): # TODO: Change item selecting.
if self.forTrain:
label = self.normalSet[idx][1]
anchor_name = self.normalSet[idx][0]
anchor = io.imread(self.root_dir + '/' + label + '/' + anchor_name)
positive_name = anchor_name
count = 0
while anchor_name == positive_name:
count+=1
positive_name = np.random.choice(self.labelSet[label])
if count == 10:
break
positive = io.imread(self.root_dir + '/' + label + '/' + positive_name)
index_neg = random.randint(0,len(self)-1)
while(self.normalSet[index_neg][1] == label):
index_neg = random.randint(0,len(self)-1)
negative = io.imread(self.root_dir + '/' + self.normalSet[index_neg][1] + '/' + self.normalSet[index_neg][0])
if self.transform:
anchor = self.transform(anchor)
positive = self.transform(positive)
negative = self.transform(negative)
return anchor, positive, negative
else:
sample = io.imread(self.root_dir + '/' + self.normalSet[idx][1] + '/' + self.normalSet[idx][0])
label = self.normalSet[idx][1]
if self.transform:
sample = self.transform(sample)
return sample,label
def buildBaseLine(self, net):
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
result = {}
for label in self.labelSet:
image = self.transform(io.imread(self.root_dir + '/' + label + '/' + np.random.choice(self.labelSet[label])))
image = np.expand_dims(image, axis=0)
image = Variable(torch.from_numpy(image).float(), requires_grad=False)
image.to(device)
result[label] = net(image).data
return result
| 39.713287 | 121 | 0.585138 | 5,339 | 0.94013 | 0 | 0 | 0 | 0 | 0 | 0 | 408 | 0.071844 |
5fde36f357f3d09233e7473cce22e0c33f10115a | 5,203 | py | Python | scripts/posts.py | flashreads/mediumish-theme-jekyll | 428e3338c00a3aee9fde119f0885496edd082f12 | [
"MIT"
]
| null | null | null | scripts/posts.py | flashreads/mediumish-theme-jekyll | 428e3338c00a3aee9fde119f0885496edd082f12 | [
"MIT"
]
| null | null | null | scripts/posts.py | flashreads/mediumish-theme-jekyll | 428e3338c00a3aee9fde119f0885496edd082f12 | [
"MIT"
]
| null | null | null | from argparse import ArgumentParser
from datetime import datetime
from os import path, listdir, getcwd, chdir
import subprocess
from dateutil.parser import parse as dt_parse
from ruamel.yaml import YAML
def transform_posts(src_dir, dest_dir, featured_path=None):
featured = load_featured(featured_path) if featured_path else []
for p in (src_dir, dest_dir):
if not path.isdir(p):
raise Exception('{} is not a directory.'.format(p))
for dir_name in listdir(src_dir):
if path.isdir(path.join(src_dir, dir_name)):
transform_category(dir_name, path.join(src_dir, dir_name), dest_dir, featured)
def transform_category(category, src_dir, dest_dir, featured):
for file_name in listdir(src_dir):
if not path.isfile(path.join(src_dir, file_name)):
continue
_, file_ext = path.splitext(file_name)
if file_ext.lower() not in ['.md', '.mdx']:
continue
if file_name.lower() in ['readme.md']:
continue
try:
transform_post(category, path.join(src_dir, file_name), dest_dir, featured)
except Exception as e:
print('Failed to transform post: ', file_name, 'Error: {}'.format(e))
def transform_post(category, post_file, dest_dir, featured):
frontmatter, fm_length = read_frontmatter(post_file)
post_date = frontmatter.get('date')
if not post_date:
dt = path.getmtime(post_file)
post_date = datetime.fromtimestamp(dt)
else:
if isinstance(post_date, str):
post_date = dt_parse(post_date)
frontmatter['date'] = post_date.isoformat()
if not frontmatter.get('layout'):
frontmatter['layout'] = 'post'
categories = frontmatter.get('categories')
if isinstance(categories, str):
categories = [categories]
if category not in categories:
categories.append(category)
frontmatter['categories'] = categories
tags = frontmatter.get('tags')
if isinstance(tags, str):
tags = [tags]
frontmatter['tags'] = tags
try:
frontmatter['author_email'] = get_email_from_git(post_file)
except Exception as e:
print('Failed to get author email: ', e)
file_name = path.basename(post_file)
file_name = '{}-{}'.format(
post_date.strftime('%Y-%m-%d'),
file_name,
)
post_id = frontmatter.get('id')
if post_id and post_id in featured:
frontmatter['featured'] = True
with open(post_file) as pf:
pf_content = pf.readlines()
pf_content = pf_content[fm_length:]
yaml = YAML(typ='safe')
with open(path.join(dest_dir, file_name), 'w') as tf:
tf.write('---\n')
yaml.dump(frontmatter, tf)
tf.write('---\n')
tf.write('\n')
tf.write('\n'.join(pf_content))
def read_frontmatter(post_file):
frontmatter = []
reading_fm = False
read_fm = False
count = 0
with open(post_file) as pf:
for line in pf:
count += 1
if line.strip() == '---':
if reading_fm:
read_fm = True
reading_fm = False
break
reading_fm = True
continue
elif reading_fm:
frontmatter.append(line)
if not read_fm:
if reading_fm:
raise Exception('Invalid front-matter in post: {}'.format(post_file))
raise Exception('No front-matter in post: {}'.format(post_file))
yaml = YAML(typ='safe')
yaml.allow_duplicate_keys = True
yaml.allow_unicode = True
return (yaml.load(''.join(frontmatter)), count)
def get_email_from_git(post_file):
cwd = getcwd()
try:
chdir(path.dirname(post_file))
file_name = path.basename(post_file)
#email = exec_cmd(['git', 'log', '-1', '--format=\'%ae\'', '--', file_name])
# --diff-filter=A
email = exec_cmd(['git', 'log', '--diff-filter=A', '--format=\'%ae\'', '--', file_name])
return email.strip().replace("'", '')
finally:
chdir(cwd)
def exec_cmd(cmd):
result = subprocess.run(cmd, capture_output=True, text=True)
return result.stdout
def load_featured(file_path):
if path.isfile(file_path):
featured = []
with open(file_path) as fp:
for line in fp:
featured += [feat.strip() for feat in line.strip().split(',')]
return featured
return []
if __name__ == '__main__':
parser = ArgumentParser(description='Transform the FlashReads posts to Jekyll-style MD posts.')
parser.add_argument('-s', '--source', type=str, dest='src_dir', help='The source directory where the flash-reads posts are contained.')
parser.add_argument('-d', '--dest', type=str, dest='dest_dir', help='The destination directory where the Jekyll-style posts will reside - usually <repo>/_posts.')
parser.add_argument('-f', '--featured', type=str, dest='featured_path', help='File containing the featured posts by id, separated by a comma.')
args = parser.parse_args()
transform_posts(args.src_dir, args.dest_dir, args.featured_path)
| 32.51875 | 166 | 0.617528 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 854 | 0.164136 |
5fe3ceaa4d994eb74303ce409f0c500eac7328b4 | 4,262 | py | Python | gelweb/gel2clin/views.py | moka-guys/GeL2MDT | 09bf25b8452e2e887dbf74b1cd4771d234c6166c | [
"MIT"
]
| null | null | null | gelweb/gel2clin/views.py | moka-guys/GeL2MDT | 09bf25b8452e2e887dbf74b1cd4771d234c6166c | [
"MIT"
]
| 1 | 2020-02-06T13:17:40.000Z | 2020-02-06T13:17:40.000Z | gelweb/gel2clin/views.py | byronmews/GeL2MDT | 1449831f0d7c570b71e7f46fb4dd1fcb805b0325 | [
"MIT"
]
| null | null | null | """Copyright (c) 2018 Great Ormond Street Hospital for Children NHS Foundation
Trust & Birmingham Women's and Children's NHS Foundation Trust
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 django.shortcuts import render
from django.shortcuts import render, redirect
from . import *
from gel2mdt.models import *
from gel2mdt.config import load_config
from django.contrib.auth.decorators import login_required
from .forms import ProbandCancerForm
from django.contrib import messages
@login_required
def index(request):
return render(request, 'gel2clin/index.html', {'sample_type': None})
@login_required
def cancer_main(request):
'''
Shows all the Cancer cases the user has access to and allows easy searching of cases
:param request:
:return:
'''
return render(request, 'gel2clin/cancer_main.html', {'sample_type': 'cancer'})
@login_required
def rare_disease_main(request):
'''
Shows all the RD cases the user has access to and allows easy searching of cases
:param request:
:return:
'''
return render(request, 'gel2clin/rare_disease_main.html', {'sample_type': 'raredisease'})
@login_required
def proband_view(request, report_id):
'''
Shows details about a particular proband, some fields are editable by clinical scientists
:param request:
:param report_id: GEL Report ID
:return:
'''
report = GELInterpretationReport.objects.get(id=report_id)
relatives = Relative.objects.filter(proband=report.ir_family.participant_family.proband)
proband_variants = ProbandVariant.objects.filter(interpretation_report=report)
proband_mdt = MDTReport.objects.filter(interpretation_report=report)
panels = InterpretationReportFamilyPanel.objects.filter(ir_family=report.ir_family)
if request.method=='POST':
form = ProbandCancerForm(request.POST, instance=report.ir_family.participant_family.proband)
form.save()
messages.add_message(request, 25, 'Clinical History Updated')
if report.sample_type == 'cancer':
form = ProbandCancerForm(instance=report.ir_family.participant_family.proband)
return render(request, 'gel2clin/cancer_proband.html', {'report': report,
'relatives': relatives,
'proband_variants': proband_variants,
'proband_mdt': proband_mdt,
'panels': panels,
'sample_type': report.sample_type,
'form': form})
else:
return render(request, 'gel2clin/raredisease_proband.html', {'report': report,
'relatives': relatives,
'proband_variants': proband_variants,
'proband_mdt': proband_mdt,
'panels': panels,
'sample_type': report.sample_type})
| 45.827957 | 106 | 0.644533 | 0 | 0 | 0 | 0 | 2,783 | 0.65298 | 0 | 0 | 2,000 | 0.469263 |
5fe6da3d5fdab2a2eb9fd6fc794811538cea5b7c | 639 | py | Python | lib/clock.py | greymistcube/racing_game_ai | 7e5e6ec781eb3c98729d370cbcc0ab6ed053962f | [
"MIT"
]
| null | null | null | lib/clock.py | greymistcube/racing_game_ai | 7e5e6ec781eb3c98729d370cbcc0ab6ed053962f | [
"MIT"
]
| null | null | null | lib/clock.py | greymistcube/racing_game_ai | 7e5e6ec781eb3c98729d370cbcc0ab6ed053962f | [
"MIT"
]
| null | null | null | import pygame
import lib.common as common
# initializing module
pygame.init()
class Clock:
__instance = None
# implementing this class as singleton
def __new__(cls, *args, **kwargs):
if cls.__instance is None:
cls.__instance = super().__new__(cls)
return cls.__instance
else:
raise Exception("only single instance is allowed")
def __init__(self):
self.clock = pygame.time.Clock()
return
def tick(self):
self.clock.tick(common.settings.tickrate)
return
def get_FPS(self):
return 1000 // max(1, self.clock.get_time())
| 22.034483 | 62 | 0.621283 | 557 | 0.871674 | 0 | 0 | 0 | 0 | 0 | 0 | 92 | 0.143975 |
5fe6e8bc747c5f980ad7fbb164a7c5c2ef4b9edf | 231 | py | Python | setup.py | powerd4/SNA_project | 458dae1df4d198f6ce82062cd2e59f8408cbb3cf | [
"FTL"
]
| null | null | null | setup.py | powerd4/SNA_project | 458dae1df4d198f6ce82062cd2e59f8408cbb3cf | [
"FTL"
]
| null | null | null | setup.py | powerd4/SNA_project | 458dae1df4d198f6ce82062cd2e59f8408cbb3cf | [
"FTL"
]
| null | null | null | from setuptools import find_packages, setup
setup(
name='src',
packages=find_packages(),
version='0.1.0',
description='Social Network Analysis module group project 2020',
author='Dean Power',
license='',
)
| 21 | 68 | 0.679654 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 77 | 0.333333 |
5fe74a248465ad5e98c9c5e8a0c0fa8593134941 | 994 | py | Python | jigu/core/bank.py | bongtrop/jigu | 448bce8ce693f3f7c530bea0f2f268e22100937a | [
"MIT"
]
| 14 | 2020-03-03T06:46:39.000Z | 2021-05-01T15:29:35.000Z | jigu/core/bank.py | bongtrop/jigu | 448bce8ce693f3f7c530bea0f2f268e22100937a | [
"MIT"
]
| 9 | 2020-03-09T06:36:30.000Z | 2021-02-15T14:40:48.000Z | jigu/core/bank.py | bongtrop/jigu | 448bce8ce693f3f7c530bea0f2f268e22100937a | [
"MIT"
]
| 5 | 2020-05-30T22:38:34.000Z | 2021-02-11T00:56:20.000Z | from __future__ import annotations
from jigu.core import AccAddress, Coins
from jigu.util.serdes import JsonDeserializable, JsonSerializable
from jigu.util.validation import Schemas as S
from jigu.util.validation import validate_acc_address
__all__ = ["Input", "Output"]
class MultiSendIO(JsonSerializable, JsonDeserializable):
__schema__ = S.OBJECT(address=S.ACC_ADDRESS, coins=Coins.__schema__)
def __init__(self, address: AccAddress, coins: Coins):
address = validate_acc_address(address)
self.address = address
self.coins = Coins(coins)
@classmethod
def from_data(cls, data: dict) -> MultiSendIO:
return cls(address=data["address"], coins=Coins.from_data(data["coins"]))
class Input(MultiSendIO):
def __repr__(self) -> str:
return f"Input(address='{self.address}', coins={self.coins!r})"
class Output(MultiSendIO):
def __repr__(self) -> str:
return f"Output(address='{self.address}', coins={self.coins!r})"
| 30.121212 | 81 | 0.719316 | 712 | 0.716298 | 0 | 0 | 145 | 0.145875 | 0 | 0 | 144 | 0.144869 |
5fea42b894dab2e2c30e2950d94a43b828f9c4c1 | 1,743 | py | Python | research/controllers/cartpole_regression_sysid.py | MinRegret/TigerControl | b1ca0617cbb2198f9d5cb37f725f3d7accbab08f | [
"Apache-2.0"
]
| 31 | 2019-11-08T06:01:54.000Z | 2021-11-20T04:50:43.000Z | research/controllers/cartpole_regression_sysid.py | johnhallman/ctsb | b1ca0617cbb2198f9d5cb37f725f3d7accbab08f | [
"Apache-2.0"
]
| 32 | 2019-06-27T15:05:04.000Z | 2019-08-07T04:23:47.000Z | research/controllers/cartpole_regression_sysid.py | MinRegret/tigercontrol | b1ca0617cbb2198f9d5cb37f725f3d7accbab08f | [
"Apache-2.0"
]
| 3 | 2020-09-30T17:06:50.000Z | 2021-04-12T22:39:34.000Z | class RegressionSystemID:
def __init__(self):
self.initialized = False
def initialize(self, n, m, K=None, learning_rate=0.001):
self.initialized = True
self.n, self.m = n, m
self.T = 0
self.K = K if K != None else np.zeros((m, n))
self.lr = learning_rate
self.stash = []
self.x_history = []
self.u_history = []
# initialize matrices
self.A = np.identity(n)
self.B = np.zeros((n, m))
def get_action(self, x_t, done):
""" return action """
self.T += 1
# regular numpy
eta_t = 1 - 2*random.randint(2, size=(self.m,))
u_t = - self.K @ x_t + np.expand_dims(eta_t, axis=1)
self.x_history.append(np.squeeze(x_t, axis=1))
self.u_history.append(np.squeeze(u_t, axis=1))
if done:
if len(self.x_history) > 1:
self.stash.append((self.x_history, self.u_history))
self.x_history = []
self.u_history = []
return u_t
def system_id(self):
""" returns current estimate of hidden system dynamics """
assert self.T > 1 # need at least 2 data points
if len(self.x_history) > 1:
self.stash.append((self.x_history, self.u_history))
# transform x and u into regular numpy arrays for least squares
x_t = onp.vstack([onp.array(x[:-1]) for x, u in self.stash])
u_t = onp.vstack([onp.array(u[:-1]) for x, u in self.stash])
x_t1 = onp.vstack([onp.array(x[1:]) for x, u in self.stash])
# regression on A and B jointly
A_B = scilinalg.lstsq(np.hstack((x_t, u_t)), x_t1)[0]
A, B = np.array(A_B[:self.n]).T, np.array(A_B[self.n:]).T
return (A, B) | 36.3125 | 71 | 0.560528 | 1,743 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 238 | 0.136546 |
5feabd6e1aae4b67ada18a6e640a09f3df166ffa | 85 | py | Python | hwtLib/mem/__init__.py | optical-o/hwtLib | edad621f5ad4cdbea20a5751ff4468979afe2f77 | [
"MIT"
]
| 24 | 2017-02-23T10:00:50.000Z | 2022-01-28T12:20:21.000Z | hwtLib/mem/__init__.py | optical-o/hwtLib | edad621f5ad4cdbea20a5751ff4468979afe2f77 | [
"MIT"
]
| 32 | 2017-04-28T10:29:34.000Z | 2021-04-27T09:16:43.000Z | hwtLib/mem/__init__.py | optical-o/hwtLib | edad621f5ad4cdbea20a5751ff4468979afe2f77 | [
"MIT"
]
| 8 | 2019-09-19T03:34:36.000Z | 2022-01-21T06:56:58.000Z | """
A package dedicated to a memory related components, interfaces and utilities.
""" | 28.333333 | 77 | 0.764706 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 85 | 1 |
5fead45ea3ba403eb9b2c9548ef8cb56dd3f5f96 | 1,121 | py | Python | binary_tree/vertical_order_traversal.py | x899/algorithms | 38a5de72db14ef2664489da9857b598d24c4e276 | [
"MIT"
]
| 472 | 2018-05-25T06:45:44.000Z | 2020-01-06T15:46:09.000Z | binary_tree/vertical_order_traversal.py | pratik-a/algorithms-2 | 241115c64a7518c34f672eb2b851b05f353247f1 | [
"MIT"
]
| 6 | 2020-01-25T22:22:44.000Z | 2021-06-01T04:53:25.000Z | binary_tree/vertical_order_traversal.py | pratik-a/algorithms-2 | 241115c64a7518c34f672eb2b851b05f353247f1 | [
"MIT"
]
| 44 | 2019-03-02T07:38:38.000Z | 2020-01-01T16:05:06.000Z | """ Vertical Order Traversal On Binary Tree
"""
from collections import defaultdict
class Node:
""" class representing node in binary tree """
def __init__(self, data):
self.data = data
self.left = None
self.right = None
def preorder(root, horizontal_dist, hd_map):
if root is None:
return
hd_map[horizontal_dist].append(root.data)
preorder(root.left, horizontal_dist-1, hd_map)
preorder(root.right, horizontal_dist+1, hd_map)
def vertical_order(root):
""" vertical order traversal of binary tree """
hd_map = defaultdict(list)
horizontal_dist = 0
preorder(root, horizontal_dist, hd_map)
for key, value in hd_map.items():
print(f"{key}: {value}")
def main():
""" operational function """
root = Node(2)
root.left = Node(7)
root.left.left = Node(2)
root.left.right = Node(6)
root.left.right.left = Node(5)
root.left.right.right = Node(11)
root.right = Node(5)
root.right.right = Node(9)
root.right.right.left = Node(4)
vertical_order(root)
if __name__ == "__main__":
main()
| 21.557692 | 52 | 0.6405 | 168 | 0.149866 | 0 | 0 | 0 | 0 | 0 | 0 | 196 | 0.174844 |
5feaf4c73afb53db46a1e5e5e4652d4a41e6e80e | 1,452 | py | Python | data_preprocessing/load_cifar_ten.py | gwenniger/multi-hare | fb3f655cbbf4af6ccbfc77d587b8ea2924b300cd | [
"Apache-2.0"
]
| 7 | 2019-12-04T05:58:40.000Z | 2021-08-04T07:19:55.000Z | data_preprocessing/load_cifar_ten.py | gwenniger/multi-hare | fb3f655cbbf4af6ccbfc77d587b8ea2924b300cd | [
"Apache-2.0"
]
| null | null | null | data_preprocessing/load_cifar_ten.py | gwenniger/multi-hare | fb3f655cbbf4af6ccbfc77d587b8ea2924b300cd | [
"Apache-2.0"
]
| 4 | 2019-12-03T23:42:00.000Z | 2020-12-19T19:48:04.000Z | import torchvision.transforms as transforms
import torch
import torchvision
__author__ = "Dublin City University"
__copyright__ = "Copyright 2019, Dublin City University"
__credits__ = ["Gideon Maillette de Buy Wenniger"]
__license__ = "Dublin City University Software License (enclosed)"
def get_train_set():
transform = transforms.Compose(
[transforms.Resize((32, 32)), transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform)
return trainset
def get_test_set():
transform = transforms.Compose(
[transforms.Resize((32, 32)), transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
testset = torchvision.datasets.CIFAR10(root='./data', train=False,
download=True, transform=transform)
return testset
def get_train_loader(batch_size: int):
train_loader = torch.utils.data.DataLoader(get_train_set(), batch_size=batch_size,
shuffle=True, num_workers=2)
return train_loader
def get_test_loader(batch_size: int):
test_loader = torch.utils.data.DataLoader(get_test_set(), batch_size=batch_size,
shuffle=True, num_workers=2)
return test_loader
| 37.230769 | 86 | 0.637741 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 166 | 0.114325 |
5feb07c430256fe7dba892fc904f0cd15150fcbf | 1,018 | py | Python | dprofiler/profile.py | disktnk/dprofiler | be820de440c21d3fef711db1220bfddb9bdee177 | [
"MIT"
]
| null | null | null | dprofiler/profile.py | disktnk/dprofiler | be820de440c21d3fef711db1220bfddb9bdee177 | [
"MIT"
]
| 3 | 2018-10-16T04:03:50.000Z | 2018-10-17T01:34:16.000Z | dprofiler/profile.py | disktnk/dprofiler | be820de440c21d3fef711db1220bfddb9bdee177 | [
"MIT"
]
| null | null | null | import cProfile
import functools
import logging
import pstats
import sys
import six
_logger = logging.getLogger(__name__)
_logger.setLevel(logging.DEBUG)
_stream_handler = logging.StreamHandler(sys.stdout)
_stream_handler.setLevel(logging.DEBUG)
_logger.addHandler(_stream_handler)
def profile(
fn=None, sort_key='cumtime', n=20, prefix='', suffix='', logger=None):
if fn is None:
return functools.partial(
profile, sort_key=sort_key, n=n, prefix=prefix, suffix=suffix,
logger=logger)
@functools.wraps(fn)
def hook(*args, **kwargs):
out = logger
if out is None:
out = _logger
stream = six.StringIO()
cp = cProfile.Profile()
cp.enable()
stream.write(prefix)
ret = fn(*args, **kwargs)
st = pstats.Stats(cp, stream=stream)
st.strip_dirs().sort_stats(sort_key).print_stats(n)
stream.write(suffix)
out.debug(stream.getvalue())
return ret
return hook
| 23.674419 | 78 | 0.642436 | 0 | 0 | 0 | 0 | 460 | 0.451866 | 0 | 0 | 13 | 0.01277 |
5feb9d3de56d225150681a0ca825ae08aaac3052 | 79 | py | Python | helper_funcs/__init__.py | mschoder/trajectory_lib | c75476c53fa32b3dd69e998996c6e2a6a72b24b4 | [
"MIT"
]
| 1 | 2021-11-04T14:05:45.000Z | 2021-11-04T14:05:45.000Z | helper_funcs/__init__.py | mschoder/trajectory_lib | c75476c53fa32b3dd69e998996c6e2a6a72b24b4 | [
"MIT"
]
| null | null | null | helper_funcs/__init__.py | mschoder/trajectory_lib | c75476c53fa32b3dd69e998996c6e2a6a72b24b4 | [
"MIT"
]
| null | null | null | import helper_funcs.spline
import helper_funcs.plotter
import helper_funcs.grid | 26.333333 | 27 | 0.898734 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
5fec5184bee3516b0d2cd771d0ad21b3d6f1997f | 3,948 | py | Python | server.py | HusseinKabbout/qwc-document-service | c8d856390006e0f5ecc0d28b0d53da55e5505381 | [
"MIT"
]
| null | null | null | server.py | HusseinKabbout/qwc-document-service | c8d856390006e0f5ecc0d28b0d53da55e5505381 | [
"MIT"
]
| null | null | null | server.py | HusseinKabbout/qwc-document-service | c8d856390006e0f5ecc0d28b0d53da55e5505381 | [
"MIT"
]
| 1 | 2020-04-24T11:36:26.000Z | 2020-04-24T11:36:26.000Z | import os
import sys
from urllib.parse import urlencode
from flask import Flask, Response, abort, request, stream_with_context, jsonify
from flask_restx import Api, Resource, fields, reqparse
import requests
from qwc_services_core.api import CaseInsensitiveArgument
from qwc_services_core.app import app_nocache
from qwc_services_core.auth import auth_manager, optional_auth, get_auth_user
from qwc_services_core.tenant_handler import TenantHandler
from qwc_services_core.runtime_config import RuntimeConfig
from qwc_services_core.permissions_reader import PermissionsReader
# Flask application
app = Flask(__name__)
app_nocache(app)
api = Api(app, version='1.0', title='Document service API',
description="""API for QWC Document service.
The document service delivers reports from the Jasper reporting service.
""",
default_label='Document operations', doc='/api/')
app.config.SWAGGER_UI_DOC_EXPANSION = 'list'
# disable verbose 404 error message
app.config['ERROR_404_HELP'] = False
auth = auth_manager(app, api)
tenant_handler = TenantHandler(app.logger)
config_handler = RuntimeConfig("document", app.logger)
def get_document(tenant, template, format):
"""Return report with specified template and format.
:param str template: Template ID
:param str format: Document format
"""
config = config_handler.tenant_config(tenant)
jasper_service_url = config.get(
'jasper_service_url', 'http://localhost:8002/reports')
jasper_timeout = config.get("jasper_timeout", 60)
resources = config.resources().get('document_templates', [])
permissions_handler = PermissionsReader(tenant, app.logger)
permitted_resources = permissions_handler.resource_permissions(
'document_templates', get_auth_user()
)
if template in permitted_resources:
resource = list(filter(
lambda entry: entry.get("template") == template, resources))
if len(resource) != 1:
app.logger.info("Template '%s' not found in config", template)
abort(404)
jasper_template = resource[0]['report_filename']
# http://localhost:8002/reports/BelasteteStandorte/?format=pdf&p1=v1&..
url = "%s/%s/" % (jasper_service_url, jasper_template)
params = {"format": format}
for k, v in request.args.lists():
params[k] = v
app.logger.info("Forward request to %s?%s" %
(url, urlencode(params)))
response = requests.get(url, params=params, timeout=jasper_timeout)
r = Response(
stream_with_context(response.iter_content(chunk_size=16*1024)),
content_type=response.headers['content-type'],
status=response.status_code)
return r
else:
app.logger.info("Missing permissions for template '%s'", template)
abort(404)
# routes
@api.route('/<template>')
@api.param('template', 'The report template')
class Document(Resource):
@api.doc('document')
@optional_auth
def get(self, template):
"""Request document
Return report with specified template.
The extension is inferred from the template name, and defaults to PDF.
Query parameters are passed to the reporting engine.
"""
tenant = tenant_handler.tenant()
pos = template.rfind('.')
if pos != -1:
format = template[pos + 1:]
template = template[:pos]
else:
format = 'pdf'
return get_document(tenant, template, format)
""" readyness probe endpoint """
@app.route("/ready", methods=['GET'])
def ready():
return jsonify({"status": "OK"})
""" liveness probe endpoint """
@app.route("/healthz", methods=['GET'])
def healthz():
return jsonify({"status": "OK"})
# local webserver
if __name__ == '__main__':
print("Starting GetDocument service...")
app.run(host='localhost', port=5018, debug=True)
| 32.360656 | 79 | 0.680091 | 598 | 0.151469 | 0 | 0 | 848 | 0.214792 | 0 | 0 | 1,210 | 0.306484 |
5fed3ac57957a704f707984e8e8650e01df266c1 | 153 | py | Python | modules/2.79/bpy/types/BoneGroups.py | cmbasnett/fake-bpy-module | acb8b0f102751a9563e5b5e5c7cd69a4e8aa2a55 | [
"MIT"
]
| null | null | null | modules/2.79/bpy/types/BoneGroups.py | cmbasnett/fake-bpy-module | acb8b0f102751a9563e5b5e5c7cd69a4e8aa2a55 | [
"MIT"
]
| null | null | null | modules/2.79/bpy/types/BoneGroups.py | cmbasnett/fake-bpy-module | acb8b0f102751a9563e5b5e5c7cd69a4e8aa2a55 | [
"MIT"
]
| null | null | null | class BoneGroups:
active = None
active_index = None
def new(self, name="Group"):
pass
def remove(self, group):
pass
| 11.769231 | 32 | 0.562092 | 149 | 0.973856 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 0.045752 |
5fedb1afd0a343dd3c21ad7f3aee3e96bedef35d | 403 | py | Python | scripts/Government Spending.py | MarcosDaNight/Dashboard-COVID19 | 26967d2fb7b3dd44c3c51b8ac79608d91bf26d71 | [
"MIT"
]
| null | null | null | scripts/Government Spending.py | MarcosDaNight/Dashboard-COVID19 | 26967d2fb7b3dd44c3c51b8ac79608d91bf26d71 | [
"MIT"
]
| null | null | null | scripts/Government Spending.py | MarcosDaNight/Dashboard-COVID19 | 26967d2fb7b3dd44c3c51b8ac79608d91bf26d71 | [
"MIT"
]
| null | null | null |
from matplotlib import pyplot as plt
# data speding from Federal Government Plan
planes = ['Transferência para saúde', 'FPE e FPM', 'Assistência Social',
'Suspensão de dívidas da União', 'Renegociação com bancos']
spend = [8, 16, 2, 12.6, 9.3]
plt.axis("equal")
plt.pie(spend, labels=planes, autopct='%1.2f%%')
plt.title("Gastos dos Planos Governamentais")
plt.show()
| 22.388889 | 73 | 0.665012 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 215 | 0.523114 |
5fee310b12b10f820f272b0dba6075da741be40b | 9,817 | py | Python | nexusmaker/tests/test_CognateParser.py | SimonGreenhill/NexusMaker | 12591bd4217bc1ba4477845e524083c2164df91a | [
"BSD-3-Clause"
]
| null | null | null | nexusmaker/tests/test_CognateParser.py | SimonGreenhill/NexusMaker | 12591bd4217bc1ba4477845e524083c2164df91a | [
"BSD-3-Clause"
]
| 2 | 2017-10-12T08:59:51.000Z | 2021-05-31T00:37:16.000Z | nexusmaker/tests/test_CognateParser.py | SimonGreenhill/NexusMaker | 12591bd4217bc1ba4477845e524083c2164df91a | [
"BSD-3-Clause"
]
| 1 | 2017-10-12T08:59:09.000Z | 2017-10-12T08:59:09.000Z | import unittest
from nexusmaker import CognateParser
class TestCognateParser(unittest.TestCase):
def test_simple(self):
self.assertEqual(CognateParser().parse_cognate('1'), ['1'])
self.assertEqual(CognateParser().parse_cognate('10'), ['10'])
self.assertEqual(CognateParser().parse_cognate('100'), ['100'])
self.assertEqual(CognateParser().parse_cognate('111'), ['111'])
def test_subset(self):
self.assertEqual(CognateParser().parse_cognate('1,2'), ['1', '2'])
self.assertEqual(CognateParser().parse_cognate('1 , 2'), ['1', '2'])
def test_dubious(self):
self.assertEqual(CognateParser().parse_cognate('1?'), ['u_1'])
self.assertEqual(CognateParser().parse_cognate('?'), ['u_1'])
def test_dubious_subset(self):
self.assertEqual(CognateParser().parse_cognate('1, 2?'), ['1'])
self.assertEqual(CognateParser().parse_cognate('1?, 2'), ['2'])
self.assertEqual(CognateParser().parse_cognate('91?, 42'), ['42'])
self.assertEqual(CognateParser().parse_cognate('?, 31'), ['31'])
# note that both of these are dubious, should become a unique
# state instead
self.assertEqual(CognateParser().parse_cognate('1?, 2?'), ['u_1'])
def test_bad_entries_uniques(self):
# coded as x
self.assertEqual(CognateParser(uniques=True).parse_cognate('X'), ['u_1'])
self.assertEqual(CognateParser(uniques=True).parse_cognate('x'), ['u_1'])
def test_bad_entries_in_forms_uniques(self):
# coded as x
self.assertEqual(CognateParser(uniques=True).parse_cognate('X20'), ['u_1'])
self.assertEqual(CognateParser(uniques=True).parse_cognate('x20'), ['u_1'])
def test_bad_entries_nouniques(self):
self.assertEqual(CognateParser(uniques=False).parse_cognate('X'), [])
self.assertEqual(CognateParser(uniques=False).parse_cognate('x'), [])
def test_s_entries_uniques(self):
# entries that are in the wrong word (e.g. you sg. not you pl.)
self.assertEqual(CognateParser(uniques=True).parse_cognate('s'), ['u_1'])
def test_s_entries_nouniques(self):
# entries that are in the wrong word (e.g. you sg. not you pl.)
self.assertEqual(CognateParser(uniques=False).parse_cognate('s'), [])
def test_empty_entries(self):
with self.assertRaises(ValueError):
CognateParser(uniques=False).parse_cognate(',,')
def test_is_unique_cognateset(self):
self.assertEqual(CognateParser().is_unique_cognateset('u_1'), True)
self.assertEqual(
CognateParser().is_unique_cognateset('u_1', labelled=False), True
)
self.assertEqual(
CognateParser().is_unique_cognateset('hand_u_1', labelled=False), False
)
self.assertEqual(
CognateParser().is_unique_cognateset('hand_u_1', labelled=True), True
)
def test_add_unique(self):
CP = CognateParser()
self.assertEqual(CP.parse_cognate(''), ['u_1'])
self.assertEqual(CP.parse_cognate(''), ['u_2'])
self.assertEqual(CP.parse_cognate(''), ['u_3'])
self.assertEqual(CP.parse_cognate(''), ['u_4'])
def test_no_uniques(self):
CP = CognateParser(uniques=False)
self.assertEqual(CP.parse_cognate(''), [])
self.assertEqual(CP.parse_cognate(''), [])
self.assertEqual(CP.parse_cognate(''), [])
def test_dubious_with_no_strict(self):
self.assertEqual(CognateParser(strict=False).parse_cognate('1?'), ['1'])
self.assertEqual(CognateParser(strict=False).parse_cognate('1, 2?'), ['1', '2'])
def test_null(self):
self.assertEqual(CognateParser().parse_cognate(None), ['u_1'])
def test_bad_cog_int(self):
with self.assertRaises(ValueError):
CognateParser().parse_cognate(1)
def test_complicated_strict_unique(self):
CP = CognateParser(strict=True, uniques=True)
# # 3. right
# Maori katau 5, 40
# Maori matau 5
# South Island Maori tika
self.assertEqual(CP.parse_cognate('5, 40'), ['5', '40'])
self.assertEqual(CP.parse_cognate('5'), ['5'])
self.assertEqual(CP.parse_cognate(''), ['u_1'])
# # 8. turn
# Maori huri 15
# South Island Maori tahuli 15
# South Island Maori tahuri to turn, to turn around 15
self.assertEqual(CP.parse_cognate('15'), ['15'])
self.assertEqual(CP.parse_cognate('15'), ['15'])
self.assertEqual(CP.parse_cognate('15'), ['15'])
# # 20. to know
# Maori moohio 52
# South Island Maori matau 1
# South Island Maori mohio to know 52
# South Island Maori ara to know, to awake
self.assertEqual(CP.parse_cognate('52'), ['52'])
self.assertEqual(CP.parse_cognate('1'), ['1'])
self.assertEqual(CP.parse_cognate('52'), ['52'])
self.assertEqual(CP.parse_cognate(''), ["u_2"])
# # 36: to spit
# Maori tuha 19, 34?
# South Island Maori huare 18
# South Island Maori tuha to expectorate, to spit 19, 34?
self.assertEqual(CP.parse_cognate('19, 34?'), ['19'])
self.assertEqual(CP.parse_cognate('18'), ['18'])
self.assertEqual(CP.parse_cognate('19, 34?'), ['19'])
def test_complicated_nostrict_unique(self):
CP = CognateParser(strict=False, uniques=True)
# # 3. right
# Maori katau 5, 40
# Maori matau 5
# South Island Maori tika
self.assertEqual(CP.parse_cognate('5, 40'), ['5', '40'])
self.assertEqual(CP.parse_cognate('5'), ['5'])
self.assertEqual(CP.parse_cognate(''), ['u_1'])
# # 8. turn
# Maori huri 15
# South Island Maori tahuli 15
# South Island Maori tahuri to turn, to turn around 15
self.assertEqual(CP.parse_cognate('15'), ['15'])
self.assertEqual(CP.parse_cognate('15'), ['15'])
self.assertEqual(CP.parse_cognate('15'), ['15'])
# # 20. to know
# Maori moohio 52
# South Island Maori matau 1
# South Island Maori mohio to know 52
# South Island Maori ara to know, to awake
self.assertEqual(CP.parse_cognate('52'), ['52'])
self.assertEqual(CP.parse_cognate('1'), ['1'])
self.assertEqual(CP.parse_cognate('52'), ['52'])
self.assertEqual(CP.parse_cognate(''), ["u_2"])
# # 36: to spit
# Maori tuha 19, 34?
# South Island Maori huare 18
# South Island Maori tuha to expectorate, to spit 19, 34?
self.assertEqual(CP.parse_cognate('19, 34?'), ['19', '34'])
self.assertEqual(CP.parse_cognate('18'), ['18'])
self.assertEqual(CP.parse_cognate('19, 34?'), ['19', '34'])
def test_complicated_nostrict_nounique(self):
CP = CognateParser(strict=False, uniques=False)
# # 3. right
# Maori katau 5, 40
# Maori matau 5
# South Island Maori tika
self.assertEqual(CP.parse_cognate('5, 40'), ['5', '40'])
self.assertEqual(CP.parse_cognate('5'), ['5'])
self.assertEqual(CP.parse_cognate(''), [])
# # 8. turn
# Maori huri 15
# South Island Maori tahuli 15
# South Island Maori tahuri to turn, to turn around 15
self.assertEqual(CP.parse_cognate('15'), ['15'])
self.assertEqual(CP.parse_cognate('15'), ['15'])
self.assertEqual(CP.parse_cognate('15'), ['15'])
# # 20. to know
# Maori moohio 52
# South Island Maori matau 1
# South Island Maori mohio to know 52
# South Island Maori ara to know, to awake
self.assertEqual(CP.parse_cognate('52'), ['52'])
self.assertEqual(CP.parse_cognate('1'), ['1'])
self.assertEqual(CP.parse_cognate('52'), ['52'])
self.assertEqual(CP.parse_cognate(''), [])
# # 36: to spit
# Maori tuha 19, 34?
# South Island Maori huare 18
# South Island Maori tuha to expectorate, to spit 19, 34?
self.assertEqual(CP.parse_cognate('19, 34?'), ['19', '34'])
self.assertEqual(CP.parse_cognate('18'), ['18'])
self.assertEqual(CP.parse_cognate('19, 34?'), ['19', '34'])
def test_complicated_with_slash(self):
CP = CognateParser(strict=True, uniques=True)
self.assertEqual(CP.parse_cognate('53/54'), ['53', '54'])
def test_combined_cognate(self):
self.assertEqual(CognateParser().parse_cognate('1a'), ['1', '1a'])
self.assertEqual(CognateParser().parse_cognate('2b'), ['2', '2b'])
self.assertEqual(CognateParser().parse_cognate('3az'), ['3', '3az'])
self.assertEqual(CognateParser().parse_cognate('45c'), ['45', '45c'])
self.assertEqual(CognateParser().parse_cognate('1a,2b'), ['1', '1a', '2', '2b'])
def test_trailing_dash(self):
with self.assertRaises(ValueError):
CognateParser().parse_cognate('1-')
def test_semicolon(self):
with self.assertRaises(ValueError):
CognateParser().parse_cognate('2, 63; 87')
def test_sorting(self):
self.assertEqual(CognateParser(sort=True).parse_cognate('2, 1'), ['1', '2'])
self.assertEqual(CognateParser(sort=False).parse_cognate('2, 1'), ['2', '1'])
| 44.220721 | 88 | 0.581237 | 9,761 | 0.994296 | 0 | 0 | 0 | 0 | 0 | 0 | 2,716 | 0.276663 |
5ff029ed47a23de0243a31bef9cc5542860e44d1 | 867 | py | Python | ngo_point1/ngo_requirements/urls.py | Sanayshah2/T046_Scrapshut | ecb269373e9b78a738ceb99675379ca21ee313a5 | [
"MIT"
]
| null | null | null | ngo_point1/ngo_requirements/urls.py | Sanayshah2/T046_Scrapshut | ecb269373e9b78a738ceb99675379ca21ee313a5 | [
"MIT"
]
| null | null | null | ngo_point1/ngo_requirements/urls.py | Sanayshah2/T046_Scrapshut | ecb269373e9b78a738ceb99675379ca21ee313a5 | [
"MIT"
]
| 1 | 2021-04-23T17:06:44.000Z | 2021-04-23T17:06:44.000Z | from django.contrib import admin
from django.urls import path
from . import views
from django.conf.urls import url
urlpatterns = [
path('',views.home,name='home'),
path('register/',views.register,name='register'),
path('login/',views.Login,name='login'),
path('logout/', views.logout_view, name='logout_view'),
path('add-requirement/', views.addRequirement, name='addRequirement'),
path('ngo-dashboard/', views.ngoDashboard, name='ngoDashboard'),
path('ngo-requirement-view/<int:rid>/', views.ngoRequirementView, name='ngoRequirementView'),
path('donor-requirement-view/<int:rid>/', views.donorRequirementView, name='donorRequirementView'),
path('donor-dashboard/', views.donorDashboard, name='donorDashboard'),
path('requirement-fulfillment/<int:rid>/', views.requirement_fulfillment, name='requirement_fulfillment'),
] | 37.695652 | 110 | 0.726644 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 335 | 0.38639 |
5ff34356a914a7907c97352a87bdb8eb2ee14f4e | 32,810 | py | Python | parakeet/models/wavenet.py | lfchener/Parakeet | b0ba6e7bf9b44b6309ca45927d4405bb85fcd103 | [
"Apache-2.0"
]
| 1 | 2021-02-03T12:11:21.000Z | 2021-02-03T12:11:21.000Z | parakeet/models/wavenet.py | gzfffff/Parakeet | a84b6d3383b2a8a5fb45d0c233bee1ed80d0b389 | [
"Apache-2.0"
]
| null | null | null | parakeet/models/wavenet.py | gzfffff/Parakeet | a84b6d3383b2a8a5fb45d0c233bee1ed80d0b389 | [
"Apache-2.0"
]
| null | null | null | # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
import time
from typing import Union, Sequence, List
from tqdm import trange
import numpy as np
import paddle
from paddle import nn
from paddle.nn import functional as F
import paddle.fluid.initializer as I
import paddle.fluid.layers.distributions as D
from parakeet.modules.conv import Conv1dCell
from parakeet.modules.audio import quantize, dequantize, STFT
from parakeet.utils import checkpoint, layer_tools
__all__ = ["WaveNet", "ConditionalWaveNet"]
def crop(x, audio_start, audio_length):
"""Crop the upsampled condition to match audio_length.
The upsampled condition has the same time steps as the whole audio does.
But since audios are sliced to 0.5 seconds randomly while conditions are
not, upsampled conditions should also be sliced to extactly match the time
steps of the audio slice.
Parameters
----------
x : Tensor [shape=(B, C, T)]
The upsampled condition.
audio_start : Tensor [shape=(B,), dtype:int]
The index of the starting point of the audio clips.
audio_length : int
The length of the audio clip(number of samples it contaions).
Returns
-------
Tensor [shape=(B, C, audio_length)]
Cropped condition.
"""
# crop audio
slices = [] # for each example
# paddle now supports Tensor of shape [1] in slice
# starts = audio_start.numpy()
for i in range(x.shape[0]):
start = audio_start[i]
end = start + audio_length
slice = paddle.slice(x[i], axes=[1], starts=[start], ends=[end])
slices.append(slice)
out = paddle.stack(slices)
return out
class UpsampleNet(nn.LayerList):
"""A network used to upsample mel spectrogram to match the time steps of
audio.
It consists of several layers of Conv2DTranspose. Each Conv2DTranspose
layer upsamples the time dimension by its `stride` times.
Also, each Conv2DTranspose's filter_size at frequency dimension is 3.
Parameters
----------
upscale_factors : List[int], optional
Time upsampling factors for each Conv2DTranspose Layer.
The ``UpsampleNet`` contains ``len(upscale_factor)`` Conv2DTranspose
Layers. Each upscale_factor is used as the ``stride`` for the
corresponding Conv2DTranspose. Defaults to [16, 16], this the default
upsampling factor is 256.
Notes
------
``np.prod(upscale_factors)`` should equals the ``hop_length`` of the stft
transformation used to extract spectrogram features from audio.
For example, ``16 * 16 = 256``, then the spectrogram extracted with a stft
transformation whose ``hop_length`` equals 256 is suitable.
See Also
---------
``librosa.core.stft``
"""
def __init__(self, upscale_factors=[16, 16]):
super(UpsampleNet, self).__init__()
self.upscale_factors = list(upscale_factors)
self.upscale_factor = 1
for item in upscale_factors:
self.upscale_factor *= item
for factor in self.upscale_factors:
self.append(
nn.utils.weight_norm(
nn.Conv2DTranspose(
1,
1,
kernel_size=(3, 2 * factor),
stride=(1, factor),
padding=(1, factor // 2))))
def forward(self, x):
r"""Compute the upsampled condition.
Parameters
-----------
x : Tensor [shape=(B, F, T)]
The condition (mel spectrogram here). ``F`` means the frequency
bands, which is the feature size of the input.
In the internal Conv2DTransposes, the frequency dimension
is treated as ``height`` dimension instead of ``in_channels``.
Returns:
Tensor [shape=(B, F, T \* upscale_factor)]
The upsampled condition.
"""
x = paddle.unsqueeze(x, 1)
for sublayer in self:
x = F.leaky_relu(sublayer(x), 0.4)
x = paddle.squeeze(x, 1)
return x
class ResidualBlock(nn.Layer):
"""A Residual block used in wavenet. Conv1D-gated-tanh Block.
It consists of a Conv1DCell and an Conv1D(kernel_size = 1) to integrate
information of the condition.
Notes
--------
It does not have parametric residual or skip connection.
Parameters
-----------
residual_channels : int
The feature size of the input. It is also the feature size of the
residual output and skip output.
condition_dim : int
The feature size of the condition.
filter_size : int
Kernel size of the internal convolution cells.
dilation :int
Dilation of the internal convolution cells.
"""
def __init__(self,
residual_channels: int,
condition_dim: int,
filter_size: Union[int, Sequence[int]],
dilation: int):
super(ResidualBlock, self).__init__()
dilated_channels = 2 * residual_channels
# following clarinet's implementation, we do not have parametric residual
# & skip connection.
_filter_size = filter_size[0] if isinstance(filter_size, (
list, tuple)) else filter_size
std = math.sqrt(1 / (_filter_size * residual_channels))
conv = Conv1dCell(
residual_channels,
dilated_channels,
filter_size,
dilation=dilation,
weight_attr=I.Normal(scale=std))
self.conv = nn.utils.weight_norm(conv)
std = math.sqrt(1 / condition_dim)
condition_proj = Conv1dCell(
condition_dim,
dilated_channels, (1, ),
weight_attr=I.Normal(scale=std))
self.condition_proj = nn.utils.weight_norm(condition_proj)
self.filter_size = filter_size
self.dilation = dilation
self.dilated_channels = dilated_channels
self.residual_channels = residual_channels
self.condition_dim = condition_dim
def forward(self, x, condition=None):
"""Forward pass of the ResidualBlock.
Parameters
-----------
x : Tensor [shape=(B, C, T)]
The input tensor.
condition : Tensor, optional [shape(B, C_cond, T)]
The condition.
It has been upsampled in time steps, so it has the same time steps
as the input does.(C_cond stands for the condition's channels).
Defaults to None.
Returns
-----------
residual : Tensor [shape=(B, C, T)]
The residual, which is used as the input to the next ResidualBlock.
skip_connection : Tensor [shape=(B, C, T)]
Tthe skip connection. This output is accumulated with that of
other ResidualBlocks.
"""
h = x
# dilated conv
h = self.conv(h)
# condition
if condition is not None:
h += self.condition_proj(condition)
# gated tanh
content, gate = paddle.split(h, 2, axis=1)
z = F.sigmoid(gate) * paddle.tanh(content)
# projection
residual = paddle.scale(z + x, math.sqrt(.5))
skip_connection = z
return residual, skip_connection
def start_sequence(self):
"""Prepare the ResidualBlock to generate a new sequence.
Warnings
---------
This method should be called before calling ``add_input`` multiple times.
"""
self.conv.start_sequence()
self.condition_proj.start_sequence()
def add_input(self, x, condition=None):
"""Take a step input and return a step output.
This method works similarily with ``forward`` but in a
``step-in-step-out`` fashion.
Parameters
----------
x : Tensor [shape=(B, C)]
Input for a step.
condition : Tensor, optional [shape=(B, C_cond)]
Condition for a step. Defaults to None.
Returns
----------
residual : Tensor [shape=(B, C)]
The residual for a step, which is used as the input to the next
layer of ResidualBlock.
skip_connection : Tensor [shape=(B, C)]
T he skip connection for a step. This output is accumulated with
that of other ResidualBlocks.
"""
h = x
# dilated conv
h = self.conv.add_input(h)
# condition
if condition is not None:
h += self.condition_proj.add_input(condition)
# gated tanh
content, gate = paddle.split(h, 2, axis=1)
z = F.sigmoid(gate) * paddle.tanh(content)
# projection
residual = paddle.scale(z + x, math.sqrt(0.5))
skip_connection = z
return residual, skip_connection
class ResidualNet(nn.LayerList):
"""The residual network in wavenet.
It consists of ``n_stack`` stacks, each of which consists of ``n_loop``
ResidualBlocks.
Parameters
----------
n_stack : int
Number of stacks in the ``ResidualNet``.
n_loop : int
Number of ResidualBlocks in a stack.
residual_channels : int
Input feature size of each ``ResidualBlock``'s input.
condition_dim : int
Feature size of the condition.
filter_size : int
Kernel size of the internal ``Conv1dCell`` of each ``ResidualBlock``.
"""
def __init__(self,
n_stack: int,
n_loop: int,
residual_channels: int,
condition_dim: int,
filter_size: int):
super(ResidualNet, self).__init__()
# double the dilation at each layer in a stack
dilations = [2**i for i in range(n_loop)] * n_stack
self.context_size = 1 + sum(dilations)
for dilation in dilations:
self.append(
ResidualBlock(residual_channels, condition_dim, filter_size,
dilation))
def forward(self, x, condition=None):
"""Forward pass of ``ResidualNet``.
Parameters
----------
x : Tensor [shape=(B, C, T)]
The input.
condition : Tensor, optional [shape=(B, C_cond, T)]
The condition, it has been upsampled in time steps, so it has the
same time steps as the input does. Defaults to None.
Returns
--------
Tensor [shape=(B, C, T)]
The output.
"""
for i, func in enumerate(self):
x, skip = func(x, condition)
if i == 0:
skip_connections = skip
else:
skip_connections = paddle.scale(skip_connections + skip,
math.sqrt(0.5))
return skip_connections
def start_sequence(self):
"""Prepare the ResidualNet to generate a new sequence. This method
should be called before starting calling ``add_input`` multiple times.
"""
for block in self:
block.start_sequence()
def add_input(self, x, condition=None):
"""Take a step input and return a step output.
This method works similarily with ``forward`` but in a
``step-in-step-out`` fashion.
Parameters
----------
x : Tensor [shape=(B, C)]
Input for a step.
condition : Tensor, optional [shape=(B, C_cond)]
Condition for a step. Defaults to None.
Returns
----------
Tensor [shape=(B, C)]
The skip connection for a step. This output is accumulated with
that of other ResidualBlocks.
"""
for i, func in enumerate(self):
x, skip = func.add_input(x, condition)
if i == 0:
skip_connections = skip
else:
skip_connections = paddle.scale(skip_connections + skip,
math.sqrt(0.5))
return skip_connections
class WaveNet(nn.Layer):
"""Wavenet that transform upsampled mel spectrogram into waveform.
Parameters
-----------
n_stack : int
``n_stack`` for the internal ``ResidualNet``.
n_loop : int
``n_loop`` for the internal ``ResidualNet``.
residual_channels : int
Feature size of the input.
output_dim : int
Feature size of the input.
condition_dim : int
Feature size of the condition (mel spectrogram bands).
filter_size : int
Kernel size of the internal ``ResidualNet``.
loss_type : str, optional ["mog" or "softmax"]
The output type and loss type of the model, by default "mog".
If "softmax", the model input is first quantized audio and the model
outputs a discret categorical distribution.
If "mog", the model input is audio in floating point format, and the
model outputs parameters for a mixture of gaussian distributions.
Namely, the weight, mean and log scale of each gaussian distribution.
Thus, the ``output_size`` should be a multiple of 3.
log_scale_min : float, optional
Minimum value of the log scale of gaussian distributions, by default
-9.0.
This is only used for computing loss when ``loss_type`` is "mog", If
the predicted log scale is less than -9.0, it is clipped at -9.0.
"""
def __init__(self, n_stack, n_loop, residual_channels, output_dim,
condition_dim, filter_size, loss_type, log_scale_min):
super(WaveNet, self).__init__()
if loss_type not in ["softmax", "mog"]:
raise ValueError("loss_type {} is not supported".format(loss_type))
if loss_type == "softmax":
self.embed = nn.Embedding(output_dim, residual_channels)
else:
if (output_dim % 3 != 0):
raise ValueError(
"with Mixture of Gaussians(mog) output, the output dim must be divisible by 3, but get {}".
format(output_dim))
self.embed = nn.utils.weight_norm(
nn.Linear(1, residual_channels), dim=1)
self.resnet = ResidualNet(n_stack, n_loop, residual_channels,
condition_dim, filter_size)
self.context_size = self.resnet.context_size
skip_channels = residual_channels # assume the same channel
self.proj1 = nn.utils.weight_norm(
nn.Linear(skip_channels, skip_channels), dim=1)
self.proj2 = nn.utils.weight_norm(
nn.Linear(skip_channels, skip_channels), dim=1)
# if loss_type is softmax, output_dim is n_vocab of waveform magnitude.
# if loss_type is mog, output_dim is 3 * gaussian, (weight, mean and stddev)
self.proj3 = nn.utils.weight_norm(
nn.Linear(skip_channels, output_dim), dim=1)
self.loss_type = loss_type
self.output_dim = output_dim
self.input_dim = 1
self.skip_channels = skip_channels
self.log_scale_min = log_scale_min
def forward(self, x, condition=None):
"""Forward pass of ``WaveNet``.
Parameters
-----------
x : Tensor [shape=(B, T)]
The input waveform.
condition : Tensor, optional [shape=(B, C_cond, T)]
the upsampled condition. Defaults to None.
Returns
-------
Tensor: [shape=(B, T, C_output)]
The parameters of the output distributions.
"""
# Causal Conv
if self.loss_type == "softmax":
x = paddle.clip(x, min=-1., max=0.99999)
x = quantize(x, self.output_dim)
x = self.embed(x) # (B, T, C)
else:
x = paddle.unsqueeze(x, -1) # (B, T, 1)
x = self.embed(x) # (B, T, C)
x = paddle.transpose(x, perm=[0, 2, 1]) # (B, C, T)
# Residual & Skip-conenection & linears
z = self.resnet(x, condition)
z = paddle.transpose(z, [0, 2, 1])
z = F.relu(self.proj2(F.relu(self.proj1(z))))
y = self.proj3(z)
return y
def start_sequence(self):
"""Prepare the WaveNet to generate a new sequence. This method should
be called before starting calling ``add_input`` multiple times.
"""
self.resnet.start_sequence()
def add_input(self, x, condition=None):
"""Compute the output distribution (represented by its parameters) for
a step. It works similarily with the ``forward`` method but in a
``step-in-step-out`` fashion.
Parameters
-----------
x : Tensor [shape=(B,)]
A step of the input waveform.
condition : Tensor, optional [shape=(B, C_cond)]
A step of the upsampled condition. Defaults to None.
Returns
--------
Tensor: [shape=(B, C_output)]
A step of the parameters of the output distributions.
"""
# Causal Conv
if self.loss_type == "softmax":
x = paddle.clip(x, min=-1., max=0.99999)
x = quantize(x, self.output_dim)
x = self.embed(x) # (B, C)
else:
x = paddle.unsqueeze(x, -1) # (B, 1)
x = self.embed(x) # (B, C)
# Residual & Skip-conenection & linears
z = self.resnet.add_input(x, condition)
z = F.relu(self.proj2(F.relu(self.proj1(z)))) # (B, C)
# Output
y = self.proj3(z)
return y
def compute_softmax_loss(self, y, t):
"""Compute the loss when output distributions are categorial
distributions.
Parameters
----------
y : Tensor [shape=(B, T, C_output)]
The logits of the output distributions.
t : Tensor [shape=(B, T)]
The target audio. The audio is first quantized then used as the
target.
Notes
-------
Output distributions whose input contains padding is neglected in
loss computation. So the first ``context_size`` steps does not
contribute to the loss.
Returns
--------
Tensor: [shape=(1,)]
The loss.
"""
# context size is not taken into account
y = y[:, self.context_size:, :]
t = t[:, self.context_size:]
t = paddle.clip(t, min=-1.0, max=0.99999)
quantized = quantize(t, n_bands=self.output_dim)
label = paddle.unsqueeze(quantized, -1)
loss = F.softmax_with_cross_entropy(y, label)
reduced_loss = paddle.mean(loss)
return reduced_loss
def sample_from_softmax(self, y):
"""Sample from the output distribution when the output distributions
are categorical distriobutions.
Parameters
----------
y : Tensor [shape=(B, T, C_output)]
The logits of the output distributions.
Returns
--------
Tensor [shape=(B, T)]
Waveform sampled from the output distribution.
"""
# dequantize
batch_size, time_steps, output_dim, = y.shape
y = paddle.reshape(y, (batch_size * time_steps, output_dim))
prob = F.softmax(y)
quantized = paddle.fluid.layers.sampling_id(prob)
samples = dequantize(quantized, n_bands=self.output_dim)
samples = paddle.reshape(samples, (batch_size, -1))
return samples
def compute_mog_loss(self, y, t):
"""Compute the loss where output distributions is a mixture of
Gaussians distributions.
Parameters
-----------
y : Tensor [shape=(B, T, C_output)]
The parameterd of the output distribution. It is the concatenation
of 3 parts, the logits of every distribution, the mean of each
distribution and the log standard deviation of each distribution.
Each part's shape is (B, T, n_mixture), where ``n_mixture`` means
the number of Gaussians in the mixture.
t : Tensor [shape=(B, T)]
The target audio.
Notes
-------
Output distributions whose input contains padding is neglected in
loss computation. So the first ``context_size`` steps does not
contribute to the loss.
Returns
--------
Tensor: [shape=(1,)]
The loss.
"""
n_mixture = self.output_dim // 3
# context size is not taken in to account
y = y[:, self.context_size:, :]
t = t[:, self.context_size:]
w, mu, log_std = paddle.split(y, 3, axis=2)
# 100.0 is just a large float
log_std = paddle.clip(log_std, min=self.log_scale_min, max=100.)
inv_std = paddle.exp(-log_std)
p_mixture = F.softmax(w, -1)
t = paddle.unsqueeze(t, -1)
if n_mixture > 1:
# t = F.expand_as(t, log_std)
t = paddle.expand(t, [-1, -1, n_mixture])
x_std = inv_std * (t - mu)
exponent = paddle.exp(-0.5 * x_std * x_std)
pdf_x = 1.0 / math.sqrt(2.0 * math.pi) * inv_std * exponent
pdf_x = p_mixture * pdf_x
# pdf_x: [bs, len]
pdf_x = paddle.sum(pdf_x, -1)
per_sample_loss = -paddle.log(pdf_x + 1e-9)
loss = paddle.mean(per_sample_loss)
return loss
def sample_from_mog(self, y):
"""Sample from the output distribution when the output distribution
is a mixture of Gaussian distributions.
Parameters
------------
y : Tensor [shape=(B, T, C_output)]
The parameterd of the output distribution. It is the concatenation
of 3 parts, the logits of every distribution, the mean of each
distribution and the log standard deviation of each distribution.
Each part's shape is (B, T, n_mixture), where ``n_mixture`` means
the number of Gaussians in the mixture.
Returns
--------
Tensor: [shape=(B, T)]
Waveform sampled from the output distribution.
"""
batch_size, time_steps, output_dim = y.shape
n_mixture = output_dim // 3
w, mu, log_std = paddle.split(y, 3, -1)
reshaped_w = paddle.reshape(w, (batch_size * time_steps, n_mixture))
prob_ids = paddle.fluid.layers.sampling_id(F.softmax(reshaped_w))
prob_ids = paddle.reshape(prob_ids, (batch_size, time_steps))
prob_ids = prob_ids.numpy()
# do it
index = np.array([[[b, t, prob_ids[b, t]] for t in range(time_steps)]
for b in range(batch_size)]).astype("int32")
index_var = paddle.to_tensor(index)
mu_ = paddle.gather_nd(mu, index_var)
log_std_ = paddle.gather_nd(log_std, index_var)
dist = D.Normal(mu_, paddle.exp(log_std_))
samples = dist.sample(shape=[])
samples = paddle.clip(samples, min=-1., max=1.)
return samples
def sample(self, y):
"""Sample from the output distribution.
Parameters
----------
y : Tensor [shape=(B, T, C_output)]
The parameterd of the output distribution.
Returns
--------
Tensor [shape=(B, T)]
Waveform sampled from the output distribution.
"""
if self.loss_type == "softmax":
return self.sample_from_softmax(y)
else:
return self.sample_from_mog(y)
def loss(self, y, t):
"""Compute the loss given the output distribution and the target.
Parameters
----------
y : Tensor [shape=(B, T, C_output)]
The parameters of the output distribution.
t : Tensor [shape=(B, T)]
The target audio.
Returns
---------
Tensor: [shape=(1,)]
The loss.
"""
if self.loss_type == "softmax":
return self.compute_softmax_loss(y, t)
else:
return self.compute_mog_loss(y, t)
class ConditionalWaveNet(nn.Layer):
r"""Conditional Wavenet. An implementation of
`WaveNet: A Generative Model for Raw Audio <http://arxiv.org/abs/1609.03499>`_.
It contains an UpsampleNet as the encoder and a WaveNet as the decoder.
It is an autoregressive model that generate raw audio.
Parameters
----------
upsample_factors : List[int]
The upsampling factors of the UpsampleNet.
n_stack : int
Number of convolution stacks in the WaveNet.
n_loop : int
Number of convolution layers in a convolution stack.
Convolution layers in a stack have exponentially growing dilations,
from 1 to .. math:: `k^{n_{loop} - 1}`, where k is the kernel size.
residual_channels : int
Feature size of each ResidualBlocks.
output_dim : int
Feature size of the output. See ``loss_type`` for details.
n_mels : int
The number of bands of mel spectrogram.
filter_size : int, optional
Convolution kernel size of each ResidualBlock, by default 2.
loss_type : str, optional ["mog" or "softmax"]
The output type and loss type of the model, by default "mog".
If "softmax", the model input should be quantized audio and the model
outputs a discret distribution.
If "mog", the model input is audio in floating point format, and the
model outputs parameters for a mixture of gaussian distributions.
Namely, the weight, mean and logscale of each gaussian distribution.
Thus, the ``output_size`` should be a multiple of 3.
log_scale_min : float, optional
Minimum value of the log scale of gaussian distributions, by default
-9.0.
This is only used for computing loss when ``loss_type`` is "mog", If
the predicted log scale is less than -9.0, it is clipped at -9.0.
"""
def __init__(self,
upsample_factors: List[int],
n_stack: int,
n_loop: int,
residual_channels: int,
output_dim: int,
n_mels: int,
filter_size: int=2,
loss_type: str="mog",
log_scale_min: float=-9.0):
super(ConditionalWaveNet, self).__init__()
self.encoder = UpsampleNet(upsample_factors)
self.decoder = WaveNet(
n_stack=n_stack,
n_loop=n_loop,
residual_channels=residual_channels,
output_dim=output_dim,
condition_dim=n_mels,
filter_size=filter_size,
loss_type=loss_type,
log_scale_min=log_scale_min)
def forward(self, audio, mel, audio_start):
"""Compute the output distribution given the mel spectrogram and the input(for teacher force training).
Parameters
-----------
audio : Tensor [shape=(B, T_audio)]
ground truth waveform, used for teacher force training.
mel : Tensor [shape(B, F, T_mel)]
Mel spectrogram. Note that it is the spectrogram for the whole
utterance.
audio_start : Tensor [shape=(B,), dtype: int]
Audio slices' start positions for each utterance.
Returns
----------
Tensor [shape(B, T_audio - 1, C_output)]
Parameters for the output distribution, where ``C_output`` is the
``output_dim`` of the decoder.)
"""
audio_length = audio.shape[1] # audio clip's length
condition = self.encoder(mel)
condition_slice = crop(condition, audio_start, audio_length)
# shifting 1 step
audio = audio[:, :-1]
condition_slice = condition_slice[:, :, 1:]
y = self.decoder(audio, condition_slice)
return y
def loss(self, y, t):
"""Compute loss with respect to the output distribution and the target
audio.
Parameters
-----------
y : Tensor [shape=(B, T - 1, C_output)]
Parameters of the output distribution.
t : Tensor [shape(B, T)]
target waveform.
Returns
--------
Tensor: [shape=(1,)]
the loss.
"""
t = t[:, 1:]
loss = self.decoder.loss(y, t)
return loss
def sample(self, y):
"""Sample from the output distribution.
Parameters
-----------
y : Tensor [shape=(B, T, C_output)]
Parameters of the output distribution.
Returns
--------
Tensor [shape=(B, T)]
Sampled waveform from the output distribution.
"""
samples = self.decoder.sample(y)
return samples
@paddle.no_grad()
def infer(self, mel):
r"""Synthesize waveform from mel spectrogram.
Parameters
-----------
mel : Tensor [shape=(B, F, T)]
The ondition (mel spectrogram here).
Returns
-----------
Tensor [shape=(B, T \* upsacle_factor)]
Synthesized waveform.
``upscale_factor`` is the ``upscale_factor`` of the encoder
``UpsampleNet``.
"""
condition = self.encoder(mel)
batch_size, _, time_steps = condition.shape
samples = []
self.decoder.start_sequence()
x_t = paddle.zeros((batch_size, ), dtype=mel.dtype)
for i in trange(time_steps):
c_t = condition[:, :, i] # (B, C)
y_t = self.decoder.add_input(x_t, c_t) #(B, C)
y_t = paddle.unsqueeze(y_t, 1)
x_t = self.sample(y_t) # (B, 1)
x_t = paddle.squeeze(x_t, 1) #(B,)
samples.append(x_t)
samples = paddle.stack(samples, -1)
return samples
@paddle.no_grad()
def predict(self, mel):
r"""Synthesize audio from mel spectrogram.
The output and input are numpy arrays without batch.
Parameters
----------
mel : np.ndarray [shape=(C, T)]
Mel spectrogram of an utterance.
Returns
-------
Tensor : np.ndarray [shape=(C, T \* upsample_factor)]
The synthesized waveform of an utterance.
"""
mel = paddle.to_tensor(mel)
mel = paddle.unsqueeze(mel, 0)
audio = self.infer(mel)
audio = audio[0].numpy()
return audio
@classmethod
def from_pretrained(cls, config, checkpoint_path):
"""Build a ConditionalWaveNet model from a pretrained model.
Parameters
----------
config: yacs.config.CfgNode
model configs
checkpoint_path: Path or str
the path of pretrained model checkpoint, without extension name
Returns
-------
ConditionalWaveNet
The model built from pretrained result.
"""
model = cls(upsample_factors=config.model.upsample_factors,
n_stack=config.model.n_stack,
n_loop=config.model.n_loop,
residual_channels=config.model.residual_channels,
output_dim=config.model.output_dim,
n_mels=config.data.n_mels,
filter_size=config.model.filter_size,
loss_type=config.model.loss_type,
log_scale_min=config.model.log_scale_min)
layer_tools.summary(model)
checkpoint.load_parameters(model, checkpoint_path=checkpoint_path)
return model
| 33.548057 | 111 | 0.569857 | 30,553 | 0.93121 | 0 | 0 | 2,817 | 0.085858 | 0 | 0 | 19,062 | 0.580981 |
5ff3bdba3407ef79de92445150f4e8cf96492395 | 661 | py | Python | xmas/util.py | drocco007/xmas_pi | 23d8f7b95ec4682d057b68d4a53e5b0ae3b193fa | [
"MIT"
]
| null | null | null | xmas/util.py | drocco007/xmas_pi | 23d8f7b95ec4682d057b68d4a53e5b0ae3b193fa | [
"MIT"
]
| null | null | null | xmas/util.py | drocco007/xmas_pi | 23d8f7b95ec4682d057b68d4a53e5b0ae3b193fa | [
"MIT"
]
| null | null | null | def normalize(values, gammas):
"""Adjust a list of brightness values according to a list of gamma values.
Given a list of light brightness values from 0.0 to 1.0, adjust the value
according to the corresponding maximum brightness in the gamma list. For
example::
>>> gammas = [0.3, 1.0]
>>> values = [1.0, 1.0]
>>> normalize(values, gammas)
[0.3, 1.0]
>>> values = [0.5, 0.5]
>>> normalize(values, gammas)
[0.15, 0.5]
>>> values = [0.1, 0.0]
>>> normalize(values, gammas)
[0.03, 0.0]
"""
return [value * gamma for value, gamma in zip(values, gammas)]
| 27.541667 | 78 | 0.559758 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 557 | 0.842663 |
5ff67231767a550306fa1eed298e972631db8363 | 1,407 | py | Python | 06/TemplateMaching.py | mastnk/GCIP | 1d1ec23388629f145a021efd69797aa18b3891e3 | [
"MIT"
]
| null | null | null | 06/TemplateMaching.py | mastnk/GCIP | 1d1ec23388629f145a021efd69797aa18b3891e3 | [
"MIT"
]
| null | null | null | 06/TemplateMaching.py | mastnk/GCIP | 1d1ec23388629f145a021efd69797aa18b3891e3 | [
"MIT"
]
| null | null | null | import sys
import cv2 # it is necessary to use cv2 library
import numpy as np
# https://docs.opencv.org/4.5.2/d4/dc6/tutorial_py_template_matching.html
def main( input_filename, template_filename ):
img = cv2.imread(input_filename,0)
template = cv2.imread(template_filename,0)
w, h = template.shape[::-1]
# chose a method
# https://docs.opencv.org/4.5.2/df/dfb/group__imgproc__object.html#ga3a7850640f1fe1f58fe91a2d7583695d
method = 'TM_SQDIFF' #SSD
# method = 'TM_SQDIFF_NORMED'#Normalized SSD
# method = 'TM_CCORR_NORMED' #NCC
# method = 'TM_CCOEFF_NORMED'#ZNCC
# Apply template Matching
res = cv2.matchTemplate(img,template,eval('cv2.'+method))
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
# If the method is TM_SQDIFF or TM_SQDIFF_NORMED, take minimum
if method in ['TM_SQDIFF', 'TM_SQDIFF_NORMED']:
top_left = min_loc
else:
top_left = max_loc
bottom_right = (top_left[0] + w, top_left[1] + h)
cv2.rectangle(img,top_left, bottom_right, 255, 2)
cv2.imwrite( method+'_img.png', img )
res = res / np.max(res) * 255
res = res.clip(0,255).astype('uint8')
cv2.imwrite( method+'_res.png', res )
if( __name__ == '__main__' ):
if( len(sys.argv) >= 3 ):
main( sys.argv[1], sys.argv[2] )
else:
print( 'usage: python '+sys.argv[0]+' input_filenname template_filename' )
| 31.977273 | 105 | 0.668088 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 569 | 0.404407 |
5ff692081eaaca772a3292cf14f5309c58c43958 | 516 | py | Python | contest/tenka1-pbc2019/C_Stones.py | mola1129/atcoder | 1d3b18cb92d0ba18c41172f49bfcd0dd8d29f9db | [
"MIT"
]
| null | null | null | contest/tenka1-pbc2019/C_Stones.py | mola1129/atcoder | 1d3b18cb92d0ba18c41172f49bfcd0dd8d29f9db | [
"MIT"
]
| null | null | null | contest/tenka1-pbc2019/C_Stones.py | mola1129/atcoder | 1d3b18cb92d0ba18c41172f49bfcd0dd8d29f9db | [
"MIT"
]
| null | null | null | n = int(input())
s = input()
bcnt = [0]*n
wcnt = [0]*n
result = 2 * 10 ** 5
# 累積和
if s[0] == "#":
bcnt[0] = 1
if s[n-1] == ".":
wcnt[n-1] = 1
for i in range(1,n):
bcnt[i] = bcnt[i-1]
if s[i] == "#":
bcnt[i] += 1
for i in range(n-2,-1,-1):
wcnt[i] = wcnt[i+1]
if s[i] == ".":
wcnt[i] += 1
# 最小コストを求める
for i in range(0,n-1):
result = min(bcnt[i] + wcnt[i+1],result)
result = min(result,bcnt[n-1])
result = min(result,wcnt[0])
print(result)
| 17.2 | 45 | 0.45155 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 54 | 0.1 |
5ff9658511513524516ba459b859ca485a53d2a3 | 9,413 | py | Python | scripts/myclient.py | Anant16/Networks_Project | a0f15bde4b2625a5ffe382da958f04d4538d23d2 | [
"MIT"
]
| null | null | null | scripts/myclient.py | Anant16/Networks_Project | a0f15bde4b2625a5ffe382da958f04d4538d23d2 | [
"MIT"
]
| null | null | null | scripts/myclient.py | Anant16/Networks_Project | a0f15bde4b2625a5ffe382da958f04d4538d23d2 | [
"MIT"
]
| null | null | null | #!/usr/bin/env python3
"""Script for Tkinter GUI chat client."""
from socket import AF_INET, socket, SOCK_STREAM
from threading import Thread
import tkinter
from tkinter import filedialog, Tk
import os
import time
def recv_file():
print("file request from ")
fname = client_socket.recv(BUFSIZ).decode("utf8")
print ("recieving file " + fname )
fsize = client_socket.recv(BUFSIZ)
fsize = int(fsize)
data_len = 0
print("fsize: {}".format(fsize))
local_file = "../received_files/" + fname
with open(local_file, 'wb') as f:
print ('opened file')
while data_len<fsize:
data = client_socket.recv(BUFSIZ)
if not data:
break
data_len += len(data)
f.write(data)
print("Done writing file at client")
return fname, fsize
def private_recv_file(pclient_socket):
print("file request from ")
fname = pclient_socket.recv(BUFSIZ).decode("utf8")
print ("recieving file " + fname )
fsize = pclient_socket.recv(BUFSIZ)
fsize = int(fsize)
data_len = 0
print("fsize: {}".format(fsize))
local_file = "../received_files/" + fname
with open(local_file, 'wb') as f:
print ('opened file')
while data_len<fsize:
data = pclient_socket.recv(BUFSIZ)
if not data:
break
data_len += len(data)
f.write(data)
print("Done writing file at client")
return fname, fsize
def send_file():
fpath = filedialog.askopenfilename(initialdir = "/",title = "Select file")
fname = fpath.split('/')[-1]
fsize = os.path.getsize(fpath)
client_socket.send(bytes('{file}', "utf8"))
time.sleep(0.5)
client_socket.send(bytes(fname, "utf8"))
time.sleep(0.5)
client_socket.send(bytes(str(fsize), "utf8"))
time.sleep(0.5)
with open(fpath, 'rb') as f:
while True:
data = f.read(BUFSIZ)
if not data:
break
client_socket.sendall(data)
print("File sent to server")
time.sleep(0.5)
def private_send_file(pclient_socket):
fpath = filedialog.askopenfilename(initialdir = "/",title = "Select file")
fname = fpath.split('/')[-1]
fsize = os.path.getsize(fpath)
pclient_socket.send(bytes('{file}', "utf8"))
time.sleep(0.5)
pclient_socket.send(bytes(fname, "utf8"))
time.sleep(0.5)
pclient_socket.send(bytes(str(fsize), "utf8"))
time.sleep(0.5)
with open(fpath, 'rb') as f:
while True:
data = f.read(BUFSIZ)
if not data:
break
pclient_socket.sendall(data)
print("File sent to server")
time.sleep(0.5)
def private_receive(pmsg_list, pclient_socket):
"""Handles receiving of messages."""
# pmsg_list = ptop.messages_frame.msg_list
while True:
try:
msg = pclient_socket.recv(BUFSIZ)
if msg == bytes("{file}", "utf8"):
pmsg_list.insert(tkinter.END, "Receiving File")
fname, fsize = private_recv_file(pclient_socket)
pmsg_list.insert(tkinter.END, "File Recieved")
elif msg == bytes("{quit}", "utf8"):
break
else:
msg = msg.decode('utf8')
pmsg_list.insert(tkinter.END, msg)
except OSError: # Possibly client has left the chat.
break
def receive():
"""Handles receiving of messages."""
buttons_frame = tkinter.Frame(top)
while True:
try:
msg = client_socket.recv(BUFSIZ).decode("utf8")
# print(msg)
if msg == '{quit}':
break
elif '{prequest}' in msg[0:12]:
name = msg[11:]
handle_connection_request(name)
elif '{name}' in msg[0:6]:
print(msg)
uname.insert(tkinter.END, msg[7:])
elif '{namelist}' in msg[0:12]:
nlist = msg.split('_')[1]
name_list = nlist.split(',')[1:]
print(name_list)
buttons_frame.destroy()
buttons_frame = tkinter.Frame(top)
for name in name_list:
private_button = tkinter.Button(buttons_frame, text=name, command=lambda user=name: create_private(user))
private_button.pack(side=tkinter.LEFT)
buttons_frame.pack(side=tkinter.LEFT)
else:
msg_list.insert(tkinter.END, msg)
except OSError: # Possibly client has left the chat.
break
def private_send(client_socket_no, pmy_msg, pmsg_list, event=None): # event is passed by binders.
"""Handles sending of messages."""
print("socket")
print(client_socket_no)
print(pmy_msg)
print(pmsg_list)
msg = pmy_msg.get()
pmy_msg.delete(0, 100) # Clears input field.
print("message sent is: " + msg)
try:
client_socket_no.send(bytes(msg, "utf8"))
except BrokenPipeError:
error_msg = "Unable to send"
pmsg_list.insert(tkinter.END, error_msg)
if msg == "{quit}":
client_socket_no.close()
top.quit()
def send(event=None): # event is passed by binders.
"""Handles sending of messages."""
print("socket")
print(client_socket)
msg = my_msg.get()
my_msg.set("") # Clears input field.
try:
client_socket.send(bytes(msg, "utf8"))
except BrokenPipeError:
error_msg = "Unable to send"
msg_list.insert(tkinter.END, error_msg)
if msg == "{quit}":
client_socket.close()
top.quit()
def create_private(name):
print("create_private")
print(name)
new_name = uname.get('1.0', tkinter.END) + '_' + name
new_name = new_name.replace('\n', '')
print(new_name)
Thread(target=private_client, args=(new_name,)).start()
def private_client(name):
pclient_socket = socket(AF_INET, SOCK_STREAM)
pclient_socket.connect(ADDR)
pclient_socket.send(bytes(name, "utf8"))
ptop = tkinter.Tk()
ptop.title("Private Chat - " + uname.get('1.0', tkinter.END))
messages_frame = tkinter.Frame(ptop)
my_msg = tkinter.StringVar() # For the messages to be sent.
# my_msg.set("Type your messages here.")
scrollbar = tkinter.Scrollbar(messages_frame) # To navigate through past messages.
# Following will contain the messages.
msg_list = tkinter.Listbox(messages_frame, height=15, width=50, yscrollcommand=scrollbar.set)
scrollbar.pack(side=tkinter.RIGHT, fill=tkinter.Y)
msg_list.pack(side=tkinter.LEFT, fill=tkinter.BOTH)
msg_list.pack()
messages_frame.pack()
print(my_msg)
entry_field = tkinter.Entry(ptop, textvariable=my_msg)
entry_field.bind("<Return>", lambda event, temp = pclient_socket: private_send(temp, entry_field, msg_list))
entry_field.pack()
send_button = tkinter.Button(ptop, text="Send", command=lambda: private_send(pclient_socket, entry_field, msg_list))
send_button.pack()
send_file_button = tkinter.Button(ptop, text="Send File", command= lambda: private_send_file(pclient_socket))
send_file_button.pack()
receive_thread = Thread(target=private_receive, args=(msg_list, pclient_socket,))
receive_thread.start()
ptop.mainloop() # Starts GUI execution.
def handle_connection_request(name):
new_name = uname.get('1.0', tkinter.END) + '_' + name + '_'
new_name = new_name.replace('\n', '')
Thread(target=private_client, args=(new_name,)).start()
# def on_closing(event=None):
# """This function is to be called when the window is closed."""
# my_msg.set("{quit}")
# try:
# send()
# except BrokenPipeError:
# print("BrokenPipeError")
# top.quit()
#----Now comes the sockets part----
HOST = input('Enter host: ')
PORT = input('Enter port: ')
if not PORT:
PORT = 35000
else:
PORT = int(PORT)
if not HOST:
HOST = '127.0.0.1'
BUFSIZ = 1024
ADDR = (HOST, PORT)
client_socket = socket(AF_INET, SOCK_STREAM)
client_socket.connect(ADDR)
top = tkinter.Tk()
top.title("Group Chat")
uname = tkinter.Text(top)
# uname.pack()
messages_frame = tkinter.Frame(top)
my_msg = tkinter.StringVar() # For the messages to be sent.
# my_msg.set("Type your messages here.")
scrollbar = tkinter.Scrollbar(messages_frame) # To navigate through past messages.
# Following will contain the messages.
msg_list = tkinter.Listbox(messages_frame, height=15, width=50, yscrollcommand=scrollbar.set)
scrollbar.pack(side=tkinter.RIGHT, fill=tkinter.Y)
msg_list.pack(side=tkinter.LEFT, fill=tkinter.BOTH)
msg_list.pack()
messages_frame.pack()
entry_field = tkinter.Entry(top, textvariable=my_msg)
entry_field.bind("<Return>", send)
entry_field.pack()
send_button = tkinter.Button(top, text="Send", command=send)
send_button.pack()
send_file_button = tkinter.Button(top, text="Send File", command=send_file)
send_file_button.pack()
# top.protocol("WM_DELETE_WINDOW", on_closing)
# #----Now comes the sockets part----
# HOST = input('Enter host: ')
# PORT = input('Enter port: ')
# if not PORT:
# PORT = 33000
# else:
# PORT = int(PORT)
# BUFSIZ = 1024
# ADDR = (HOST, PORT)
# client_socket = socket(AF_INET, SOCK_STREAM)
# client_socket.connect(ADDR)
receive_thread = Thread(target=receive)
receive_thread.start()
top.mainloop() # Starts GUI execution. | 31.272425 | 125 | 0.627961 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2,133 | 0.226602 |
5ffb47974af4fe5ac023573e2b360b56ebf1ffbc | 1,689 | py | Python | issues/i10_parent.py | Raph-xyz/wexpect | c1e81b3a69d4d3821cb01b61bd5297b51a24539f | [
"MIT"
]
| 52 | 2019-04-24T14:38:43.000Z | 2022-03-08T22:03:11.000Z | issues/i10_parent.py | Raph-xyz/wexpect | c1e81b3a69d4d3821cb01b61bd5297b51a24539f | [
"MIT"
]
| 51 | 2019-05-13T12:15:09.000Z | 2021-12-15T14:00:15.000Z | issues/i10_parent.py | Raph-xyz/wexpect | c1e81b3a69d4d3821cb01b61bd5297b51a24539f | [
"MIT"
]
| 20 | 2019-07-15T15:48:31.000Z | 2022-03-27T08:55:17.000Z | import wexpect
import time
import sys
import os
here = os.path.dirname(os.path.abspath(__file__))
sys.path.insert(0, here)
from long_printer import puskas_wiki
print(wexpect.__version__)
# With quotes (C:\Program Files\Python37\python.exe needs quotes)
python_executable = '"' + sys.executable + '" '
child_script = here + '\\long_printer.py'
def main():
longPrinter = python_executable + ' ' + child_script
prompt = 'puskas> '
# Start the child process
p = wexpect.spawn(longPrinter)
# Wait for prompt
p.expect(prompt)
try:
for i in range(10):
print('.', end='')
p.sendline('0')
p.expect(prompt)
if p.before.splitlines()[1] != puskas_wiki[0]:
print(p.before.splitlines()[1])
raise Exception()
p.sendline('all')
p.expect(prompt)
for a,b in zip(p.before.splitlines()[1:], puskas_wiki):
if a!=b:
print(a)
print(b)
raise Exception()
for j, paragraph in enumerate(puskas_wiki):
p.sendline(str(j))
p.expect(prompt)
if p.before.splitlines()[1] != paragraph:
print(p.before.splitlines()[1])
print(i)
print(j)
print(paragraph)
raise Exception()
except:
p.interact()
time.sleep(5)
else:
print('')
print('[PASS]')
main()
| 26.390625 | 68 | 0.474837 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 172 | 0.101835 |
5ffcd7c87c77f89439cef7d88357fcf225e1b8ea | 3,694 | py | Python | sir_sampler.py | bveitch/EpidPy | 455cd67afa2efbb774300115abb5fc7d4600b37d | [
"BSD-3-Clause"
]
| null | null | null | sir_sampler.py | bveitch/EpidPy | 455cd67afa2efbb774300115abb5fc7d4600b37d | [
"BSD-3-Clause"
]
| null | null | null | sir_sampler.py | bveitch/EpidPy | 455cd67afa2efbb774300115abb5fc7d4600b37d | [
"BSD-3-Clause"
]
| null | null | null | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jun 26 10:17:51 2020
description : Sampling methods from sir space to data space
author : bveitch
version : 1.0
project : EpidPy (epidemic modelling in python)
Usage:
Data fitting in least squares fit
Also acts on labels for data description and fitting
randSampler: adds random noise/sampling bias to synthetics
"""
import numpy as np;
def labels_from_type(sir_type,nstage=0):
if(sir_type == 'sir'):
return list(sir_type)
elif(sir_type == 'seir'):
return list(sir_type)
elif(sir_type == 'sirs'):
return list(sir_type)
elif(sir_type == 'si2r'):
return ['s','i1','i2','r']
elif(sir_type == 'sir_nstage'):
assert(nstage>0),'nstage must be >0'
labels=['s']
[labels.append('i'+str(istage)) for istage in range(nstage)]
labels.append('r')
return labels
else:
raise ValueError("Invalid modelling type")
class Sampler:
def __init__(self, nsize,intervals):
for ival in intervals:
assert(ival[1] > ival[0]),'interval must be increasing'
self.insize = nsize
self.intervals = intervals
self.outsize = len(intervals)
def F(self,data):
[nt,nsize]=data.shape
assert(nsize == self.insize),"data size doesnt match insize"
data_samp=np.zeros((nt,self.outsize))
icount=0
for ival in self.intervals:
isum = np.zeros(nt)
for i in range(ival[0],ival[1]):
isum += data[:,i]
# isum=np.sum(data[:,ival[0]:ival[1]],1)
data_samp[:,icount]=isum
icount+=1
return data_samp
def Ft(self,data_samp):
[nt,isize]=data_samp.shape
assert(isize == self.outsize),"data sampled size doesnt match outsize"
data=np.zeros((nt,self.insize))
icount=0
for ival in self.intervals:
isum = data_samp[:,icount]
# data[:,ival[0]:ival[1]] +=isum
for i in range(ival[0],ival[1]):
data[:,i]+=isum
icount+=1
return data
def Flabels(self,labels):
sampled_labels=[]
for ival in self.intervals:
if(ival[1] == ival[0]+1):
l0=labels[ival[0]]
sampled_labels.append(l0)
else:
l0=labels[ival[0] ]
l1=labels[ival[1]-1]
sampled_labels.append(l0 + '-' + l1)
return sampled_labels
def Flabels_from_type(self,sir_type,nstage=0):
labels=labels_from_type(sir_type,nstage)
return self.Flabels(labels)
#def draw_samples(pT,n,Ntests):
# np.random.seed(0)
# s = np.random.normal(0, 1,n)
# mu = Ntests*pT
# sigma = np.sqrt(Ntests*pT*(1-pT))
# data_samp = (sigma*s+mu)/Ntests
# return data_samp
def randSampler(data,pTT,pTF,Ntests):
def draw_samples(pT,n,Ntests):
np.random.seed(0)
s = np.random.normal(0, 1,n)
mu = Ntests*pT
sigma = np.sqrt(Ntests*pT*(1-pT))
data_samp = (sigma*s+mu)/Ntests
return data_samp
if(len(data.shape)==1):
n=data.shape[0]
pT=pTT*data+pTF*(1-data)
data_samp=draw_samples(pT,n,Ntests)
return data_samp
else:
[n,m]=data.shape
assert (m == len(pTT)),"data size doesnt match size pTT"
pT=np.zeros(n)
for i in range(m):
pT=pTT[i]*data[:,i]+pTF*(1-np.sum(data,1))
data_samp=draw_samples(pT,n,Ntests)
return data_samp
| 29.790323 | 78 | 0.554413 | 1,709 | 0.462642 | 0 | 0 | 0 | 0 | 0 | 0 | 958 | 0.259339 |
27004bc90d0396548daec8e9a418a7f70ae5efe9 | 4,597 | py | Python | database_sanitizer/session.py | sharescape/python-database-sanitizer | 560bf402e3896285980abb21a74d5be8d2da1698 | [
"MIT"
]
| 37 | 2018-05-07T13:07:25.000Z | 2022-02-07T18:58:10.000Z | database_sanitizer/session.py | sharescape/python-database-sanitizer | 560bf402e3896285980abb21a74d5be8d2da1698 | [
"MIT"
]
| 31 | 2018-04-27T13:16:28.000Z | 2021-12-10T10:08:00.000Z | database_sanitizer/session.py | sharescape/python-database-sanitizer | 560bf402e3896285980abb21a74d5be8d2da1698 | [
"MIT"
]
| 15 | 2018-05-04T12:28:12.000Z | 2022-02-17T09:27:58.000Z | """
API to sanitation session.
Sanitation session allows having a state within a single sanitation
process.
One important thing stored to the session is a secret key which is
generated to a new random value for each sanitation session, but it
stays constant during the whole sanitation process. Its value is never
revealed, so that it is possible to generate such one way hashes with
it, that should not be redoable afterwards. I.e. during the sanitation
session it's possible to do ``hash(C) -> H`` for any clear text C, but
it is not possible to check if H is the hashed value of C after the
sanitation session has ended.
"""
import hashlib
import hmac
import random
import sys
import threading
from six import int2byte
if sys.version_info >= (3, 6):
from typing import Callable, Optional, Sequence # noqa
SECRET_KEY_BITS = 128
_thread_local_storage = threading.local()
def hash_text_to_int(value, bit_length=32):
# type: (str, int) -> int
"""
Hash a text value to an integer.
Generates an integer number based on the hash derived with
`hash_text` from the given text value.
:param bit_length: Number of bits to use from the hash value.
:return: Integer value within ``0 <= result < 2**bit_length``
"""
hash_value = hash_text(value)
return int(hash_value[0:(bit_length // 4)], 16)
def hash_text_to_ints(value, bit_lengths=(16, 16, 16, 16)):
# type: (str, Sequence[int]) -> Sequence[int]
"""
Hash a text value to a sequence of integers.
Generates a sequence of integer values with given bit-lengths
similarly to `hash_text_to_int`, but allowing generating many
separate numbers with a single call.
:param bit_lengths:
Tuple of bit lengths for the resulting integers. Defines also the
length of the result tuple.
:return:
Tuple of ``n`` integers ``(R_1, ... R_n)`` with the requested
bit-lengths ``(L_1, ..., L_n)`` and values ranging within
``0 <= R_i < 2**L_i`` for each ``i``.
"""
hash_value = hash_text(value)
hex_lengths = [x // 4 for x in bit_lengths]
hex_ranges = (
(sum(hex_lengths[0:i]), sum(hex_lengths[0:(i + 1)]))
for i in range(len(hex_lengths)))
return tuple(int(hash_value[a:b], 16) for (a, b) in hex_ranges)
def hash_text(value, hasher=hashlib.sha256, encoding='utf-8'):
# type: (str, Callable, str) -> str
"""
Generate a hash for a text value.
The hash will be generated by encoding the text to bytes with given
encoding and then generating a hash with HMAC using the session
secret as the key and the given hash function.
:param value: Text value to hash
:param hasher: Hash function to use, SHA256 by default
:param encoding: Encoding to use, UTF-8 by default
:return: Hexadecimal presentation of the hash as a string
"""
return hash_bytes(value.encode(encoding), hasher)
def hash_bytes(value, hasher=hashlib.sha256):
# type: (bytes, Callable) -> str
"""
Generate a hash for a bytes value.
The hash will be generated by generating a hash with HMAC using the
session secret as the key and the given hash function.
:param value: Bytes value to hash
:param hasher: Hash function to use.
:return: Hexadecimal presentation of the hash as a string
"""
return hmac.new(get_secret(), value, hasher).hexdigest()
def get_secret():
# type: () -> bytes
"""
Get session specific secret key.
:return: Session key as bytes
"""
if not getattr(_thread_local_storage, 'secret_key', None):
_initialize_session()
return _thread_local_storage.secret_key # type: ignore
def reset(secret_key=None):
# type: (Optional[bytes]) -> None
"""
Reset the session.
By default, this resets the value of the secret to None so that, if
there was an earlier sanitation process ran on the same thread, then
a next call that needs the secret key of the session will generate a
new value for it.
This may also be used to set a predefined value for the secret key.
:param secret_key:
Value to set as the new session secret key or None if a new one
should be generated as soon as one is needed.
"""
_thread_local_storage.secret_key = secret_key
def _initialize_session():
# type: () -> None
"""
Generate a new session key and store it to thread local storage.
"""
sys_random = random.SystemRandom()
_thread_local_storage.secret_key = b''.join(
int2byte(sys_random.randint(0, 255))
for _ in range(SECRET_KEY_BITS // 8))
| 31.272109 | 72 | 0.688057 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3,157 | 0.686752 |
270052ade6542cf9600c8d97910142b209cecc23 | 573 | py | Python | ciaa-nxp/pyExamples/Main_Timers_2.py | braytac/micropython | 4fba270b6061395367e7f49eb3baa77365557ace | [
"MIT"
]
| 2 | 2019-07-28T05:27:42.000Z | 2020-05-26T04:39:43.000Z | ciaa-nxp/pyExamples/Main_Timers_2.py | braytac/micropython | 4fba270b6061395367e7f49eb3baa77365557ace | [
"MIT"
]
| null | null | null | ciaa-nxp/pyExamples/Main_Timers_2.py | braytac/micropython | 4fba270b6061395367e7f49eb3baa77365557ace | [
"MIT"
]
| 1 | 2018-06-23T22:07:06.000Z | 2018-06-23T22:07:06.000Z | import pyb
def callb(timer):
print("Interval interrupt")
print(timer)
def callbTimeout (timer):
print("Timeout interrupt")
print(timer)
print("Test Timers")
t1 = pyb.Timer(1)
t2 = pyb.Timer(2)
t1.interval(2000,callb)
t2.timeout(5000,callbTimeout)
t1.freq(1)
print("f t1:"+str(t1.freq()))
t1.period(204000000)
t1.prescaler(3)
print("period t1:"+str(t1.period()))
print("presc t1:"+str(t1.prescaler()))
t1.counter(0)
print("counter t1:"+str(t1.counter()))
print("src freq t1:"+str(t1.source_freq()))
while True:
pyb.delay(100)
#t1.counter(0)
#t1.deinit()
| 14.692308 | 43 | 0.685864 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 135 | 0.235602 |
2701a91ae74b0da4e98a4cd24af4fe84d580b309 | 17,219 | py | Python | tests/test_viewer.py | glenvorel/waterfalls | 900e9fb51a487b7ca1f5ed5274c0bdba95a4d435 | [
"MIT"
]
| 1 | 2020-08-06T03:58:54.000Z | 2020-08-06T03:58:54.000Z | tests/test_viewer.py | glenvorel/performancepy | 900e9fb51a487b7ca1f5ed5274c0bdba95a4d435 | [
"MIT"
]
| null | null | null | tests/test_viewer.py | glenvorel/performancepy | 900e9fb51a487b7ca1f5ed5274c0bdba95a4d435 | [
"MIT"
]
| null | null | null | import json
import os
import tempfile
import unittest
from unittest.mock import patch
from waterfalls import Viewer, viewer
class TestViewer(unittest.TestCase):
"""
Tests the `waterfalls.Viewer` module.
"""
def test_detect_overlap(self) -> None:
"""
Tests detecting overlap of timing blocks.
"""
# Blocks do not overlap
blocks = [{"start_time": 25, "stop_time": 31}, {"start_time": 35, "stop_time": 42}]
self.assertFalse(Viewer._detect_overlap(blocks))
# Blocks touch
blocks = [{"start_time": 25, "stop_time": 31}, {"start_time": 31, "stop_time": 42}]
self.assertFalse(Viewer._detect_overlap(blocks))
# Blocks overlap
blocks = [{"start_time": 25, "stop_time": 31}, {"start_time": 30, "stop_time": 42}]
self.assertTrue(Viewer._detect_overlap(blocks))
def test_load_blocks_from_reports(self) -> None:
"""
Tests loading timing blocks from report files.
"""
report_a = [
{"name": "Timer A", "text": "Block A", "start_time": 25, "stop_time": 35, "thread_id": 100},
{"name": "Timer B", "text": "Block B", "start_time": 45, "stop_time": 55, "thread_id": 101},
]
report_b = [
{"name": "Timer C", "text": "Block C", "start_time": 40, "stop_time": 50, "thread_id": 100},
{"name": "Timer A", "text": "Block D", "start_time": 33, "stop_time": 43, "thread_id": 101},
]
with tempfile.TemporaryDirectory() as temp_dir_name:
with open(os.path.join(temp_dir_name, "waterfalls.json"), "w") as f:
json.dump(report_a, f)
with open(os.path.join(temp_dir_name, "waterfalls.1.json"), "w") as f:
json.dump(report_b, f)
viewer_instance = Viewer(directory=temp_dir_name)
report_file_paths = viewer_instance._get_report_file_paths()
self.assertEqual(len(report_file_paths), 2)
blocks = viewer_instance._load_blocks_from_reports(report_file_paths)
expected_blocks = [
{
"name": "Timer A",
"text": "Block A",
"start_time": 25,
"stop_time": 35,
"thread_id": 100,
},
{
"name": "Timer B",
"text": "Block B",
"start_time": 45,
"stop_time": 55,
"thread_id": 101,
},
{
"name": "Timer C",
"text": "Block C",
"start_time": 40,
"stop_time": 50,
"thread_id": 100,
},
{
"name": "Timer A",
"text": "Block D",
"start_time": 33,
"stop_time": 43,
"thread_id": 101,
},
]
self.assertCountEqual(blocks, expected_blocks)
def test_group_blocks_to_timers(self) -> None:
"""
Tests grouping of timing blocks into named timers.
"""
viewer_instance = Viewer()
blocks = [
{
"name": "Timer A",
"start_time": 25,
"stop_time": 35,
},
{
"name": "Timer B",
"start_time": 45,
"stop_time": 55,
},
{
"name": "Timer C",
"start_time": 40,
"stop_time": 50,
},
{
"name": "Timer A",
"start_time": 33,
"stop_time": 43,
},
]
timers, time_total, time_min = viewer_instance._group_blocks_to_timers(blocks)
expected_timers = {
"Timer A": [
{
"name": "Timer A",
"start_time": 25,
"stop_time": 35,
},
{
"name": "Timer A",
"start_time": 33,
"stop_time": 43,
},
],
"Timer B": [
{
"name": "Timer B",
"start_time": 45,
"stop_time": 55,
}
],
"Timer C": [
{
"name": "Timer C",
"start_time": 40,
"stop_time": 50,
}
],
}
self.assertCountEqual(timers, expected_timers)
self.assertEqual(time_total, 30)
self.assertEqual(time_min, 25)
def test_format_timers_names(self) -> None:
"""
Tests formatting of timers' names depending on whether `show_thead_id` is enabled or disabled.
"""
viewer_instance = Viewer()
timers = {
"Timer A": [
{
"name": "Timer A",
"start_time": 25,
"stop_time": 35,
"thread_id": 100,
},
{
"name": "Timer A",
"start_time": 33,
"stop_time": 43,
"thread_id": 101,
},
],
"Timer B": [
{
"name": "Timer B",
"start_time": 45,
"stop_time": 55,
"thread_id": 101,
}
],
"Timer C": [
{
"name": "Timer C",
"start_time": 40,
"stop_time": 50,
"thread_id": 100,
}
],
}
formatted_timers = viewer_instance._format_timer_names(timers)
expected_formatted_timers = {
"Timer A\nthread: 100": [
{
"name": "Timer A",
"start_time": 25,
"stop_time": 35,
"thread_id": 100,
}
],
"Timer A\nthread: 101": [
{
"name": "Timer A",
"start_time": 33,
"stop_time": 43,
"thread_id": 101,
}
],
"Timer B": [
{
"name": "Timer B",
"start_time": 45,
"stop_time": 55,
"thread_id": 101,
}
],
"Timer C": [
{
"name": "Timer C",
"start_time": 40,
"stop_time": 50,
"thread_id": 100,
}
],
}
self.assertCountEqual(formatted_timers, expected_formatted_timers)
viewer_instance.show_thread_id = True
formatted_timers = viewer_instance._format_timer_names(timers)
expected_formatted_timers = {
"Timer A\nthread: 100": [
{
"name": "Timer A",
"start_time": 25,
"stop_time": 35,
"thread_id": 100,
}
],
"Timer A\nthread: 101": [
{
"name": "Timer A",
"start_time": 33,
"stop_time": 43,
"thread_id": 101,
}
],
"Timer B\nthread: 101": [
{
"name": "Timer B",
"start_time": 45,
"stop_time": 55,
"thread_id": 101,
}
],
"Timer C\nthread: 100": [
{
"name": "Timer C",
"start_time": 40,
"stop_time": 50,
"thread_id": 100,
}
],
}
self.assertCountEqual(formatted_timers, expected_formatted_timers)
def test_sort_timers(self) -> None:
"""
Tests sorting of timers by the `start_time` of the first block.
When thread ID showing is enabled, then timers should be primarily sorted by their `thread_id`
and then by `start_time`.
"""
viewer_instance = Viewer()
formatted_timers = {
"Timer B": [
{
"start_time": 45,
"thread_id": 100,
}
],
"Timer A": [
{
"start_time": 25,
"thread_id": 101,
}
],
"Timer C": [
{
"start_time": 40,
"thread_id": 101,
}
],
"Timer D": [
{
"start_time": 33,
"thread_id": 100,
}
],
}
sorted_timers = viewer_instance._sort_timers(formatted_timers)
timer_names = list(sorted_timers.keys())
self.assertEqual(timer_names[0], "Timer A")
self.assertEqual(timer_names[1], "Timer D")
self.assertEqual(timer_names[2], "Timer C")
self.assertEqual(timer_names[3], "Timer B")
viewer_instance.show_thread_id = True
sorted_timers = viewer_instance._sort_timers(formatted_timers)
timer_names = list(sorted_timers.keys())
self.assertEqual(timer_names[0], "Timer D")
self.assertEqual(timer_names[1], "Timer B")
self.assertEqual(timer_names[2], "Timer A")
self.assertEqual(timer_names[3], "Timer C")
def test_coplete_timers_formatting(self) -> None:
"""
Tests the complete pipeline from loading report files to generating formatted and sorted timers.
"""
report_a = [
{"name": "Timer A", "text": "Block A", "start_time": 25, "stop_time": 35, "thread_id": 100},
{"name": "Timer B", "text": "Block B", "start_time": 45, "stop_time": 55, "thread_id": 101},
]
report_b = [
{"name": "Timer C", "text": "Block C", "start_time": 40, "stop_time": 50, "thread_id": 100},
{"name": "Timer A", "text": "Block D", "start_time": 33, "stop_time": 43, "thread_id": 101},
]
with tempfile.TemporaryDirectory() as temp_dir_name:
with open(os.path.join(temp_dir_name, "waterfalls.json"), "w") as f:
json.dump(report_a, f)
with open(os.path.join(temp_dir_name, "waterfalls.1.json"), "w") as f:
json.dump(report_b, f)
viewer_instance = Viewer(directory=temp_dir_name)
report_file_paths = viewer_instance._get_report_file_paths()
blocks = viewer_instance._load_blocks_from_reports(report_file_paths)
timers, time_total, time_min = viewer_instance._group_blocks_to_timers(blocks)
timers = viewer_instance._format_timer_names(timers)
timers = viewer_instance._sort_timers(timers)
time_unit = viewer_instance._determine_time_unit(time_total)
expected_timers = {
"Timer A\nthread: 100": [
{
"name": "Timer A",
"text": "Block A",
"start_time": 25,
"stop_time": 35,
"thread_id": 100,
}
],
"Timer A\nthread: 101": [
{
"name": "Timer A",
"text": "Block D",
"start_time": 33,
"stop_time": 43,
"thread_id": 101,
}
],
"Timer B": [
{
"name": "Timer B",
"text": "Block B",
"start_time": 45,
"stop_time": 55,
"thread_id": 101,
}
],
"Timer C": [
{
"name": "Timer C",
"text": "Block C",
"start_time": 40,
"stop_time": 50,
"thread_id": 100,
}
],
}
self.assertCountEqual(timers, expected_timers)
self.assertEqual(time_unit.name, "nanoseconds")
self.assertEqual(time_total, 30)
self.assertEqual(time_min, 25)
def test_determine_time_unit(self):
"""
Tests automatic determination of time unit and a manual override.
"""
viewer_instance = Viewer()
time_unit = viewer_instance._determine_time_unit(time_total=-1)
self.assertEqual(time_unit.name, "nanoseconds")
time_unit = viewer_instance._determine_time_unit(time_total=0)
self.assertEqual(time_unit.name, "nanoseconds")
time_unit = viewer_instance._determine_time_unit(time_total=1)
self.assertEqual(time_unit.name, "nanoseconds")
time_unit = viewer_instance._determine_time_unit(time_total=1000)
self.assertEqual(time_unit.name, "microseconds")
time_unit = viewer_instance._determine_time_unit(time_total=999999)
self.assertEqual(time_unit.name, "microseconds")
time_unit = viewer_instance._determine_time_unit(time_total=1000000)
self.assertEqual(time_unit.name, "milliseconds")
time_unit = viewer_instance._determine_time_unit(time_total=1000000000)
self.assertEqual(time_unit.name, "seconds")
time_unit = viewer_instance._determine_time_unit(time_total=1000000000 * 60)
self.assertEqual(time_unit.name, "minutes")
time_unit = viewer_instance._determine_time_unit(time_total=1000000000 * 3600)
self.assertEqual(time_unit.name, "hours")
time_unit = viewer_instance._determine_time_unit(time_total=1000000000 * 3600 * 1234)
self.assertEqual(time_unit.name, "hours")
viewer_instance.user_time_unit = "msec"
time_unit = viewer_instance._determine_time_unit(time_total=1000000000 * 3600 * 1234)
self.assertEqual(time_unit.name, "milliseconds")
def test_non_existent_directory(self) -> None:
"""
Tests that trying to load report files from a non existent directory raises `SystemExit` exception.
"""
viewer_instance = Viewer(directory="/nonexistent")
with self.assertRaises(SystemExit):
viewer_instance._get_report_file_paths()
def test_empty_report_files(self) -> None:
"""
Tests that when there is not even one timing block in any of the loaded report files,
the `SystemExit` exception is raised.
"""
with tempfile.TemporaryDirectory() as temp_dir_name:
with open(os.path.join(temp_dir_name, "waterfalls.json"), "w") as file:
json.dump([], file)
with open(os.path.join(temp_dir_name, "waterfalls.1.json"), "w") as file:
json.dump([], file)
viewer_instance = Viewer(directory=temp_dir_name)
report_file_paths = viewer_instance._get_report_file_paths()
with self.assertRaises(SystemExit):
viewer_instance._load_blocks_from_reports(report_file_paths)
def test_save_as_image(self) -> None:
"""
Tests that an image file is generated.
"""
with tempfile.TemporaryDirectory() as temp_dir_name:
report = [
{
"name": "Timer A",
"text": None,
"start_time": 10,
"stop_time": 20,
"thread_duration": 5,
"thread_id": 100,
}
]
with open(os.path.join(temp_dir_name, "waterfalls.json"), "w") as file:
json.dump(report, file)
Viewer(directory=temp_dir_name, save_image=True).visualize_report()
files = [f for f in os.listdir(temp_dir_name) if os.path.isfile(os.path.join(temp_dir_name, f))]
self.assertIn("waterfalls.svg", files)
def test_argument_parser(self):
"""
Tests that command line arguments are parsed properly.
"""
with patch("sys.argv", ["viewer.py"]):
args = viewer._parse_arguments()
self.assertEqual(args.directory, os.getcwd())
self.assertIsNone(args.unit)
self.assertFalse(args.thread_id)
self.assertFalse(args.lines)
self.assertFalse(args.image)
with patch("sys.argv", ["viewer.py", "/path/to/records", "-u", "msec", "-tli"]):
args = viewer._parse_arguments()
self.assertEqual(args.directory, "/path/to/records")
self.assertEqual(args.unit, "msec")
self.assertTrue(args.thread_id)
self.assertTrue(args.lines)
self.assertTrue(args.image)
if __name__ == "__main__":
unittest.main()
| 34.856275 | 108 | 0.475812 | 17,042 | 0.989721 | 0 | 0 | 0 | 0 | 0 | 0 | 4,308 | 0.250189 |
2704c694bb778a6cd16b19f2de02eece854903d3 | 4,491 | py | Python | reflect/menus.py | stefanthaler/daily-reflection | 2aba4873742b205f75a71b2ce382d126153f14c5 | [
"MIT"
]
| null | null | null | reflect/menus.py | stefanthaler/daily-reflection | 2aba4873742b205f75a71b2ce382d126153f14c5 | [
"MIT"
]
| null | null | null | reflect/menus.py | stefanthaler/daily-reflection | 2aba4873742b205f75a71b2ce382d126153f14c5 | [
"MIT"
]
| null | null | null | from prompt_toolkit.key_binding import KeyBindings
from prompt_toolkit import PromptSession,prompt,print_formatted_text
from reflect.style import *
import os
from prompt_toolkit.shortcuts import clear
from prompt_toolkit.formatted_text import FormattedText
def get_menu(menu_items):
message = []
for k in menu_items:
if "title" == k:
message.append( ('class:title', menu_items[k]["text"]+"\n") )
message.append(('class:separator',"="*20+"\n\n") )
continue
if "==" in k:
message.append( ('class:separator', "="*20+"\n" ) )
continue
if "--" in k:
message.append( ('class:separator', "-"*20+"\n" ) )
continue
if not menu_items[k]: # key only
continue
m=menu_items[k]
message.append( ('class:key', "(%s) " %k) )
message.append( ('class:menu_item' , "%s \n"%m["text"]) )
return message
def key_press_menu(menu_items, loop_display=""):
bindings = KeyBindings()
message = get_menu(menu_items)
global key_pressed
key_pressed = ""
@bindings.add('c-m')
@bindings.add('up')
@bindings.add('left')
@bindings.add('right')
@bindings.add('escape', eager=True)
@bindings.add('down')
@bindings.add('<any>')
def _(event):
event.app.exit()
global key_pressed
key_pressed=str(event.key_sequence[0].key)
session = PromptSession()
loop = True
current_key = 0
key_positions = []
for i, m in enumerate(message):
if m[0]=="class:key":
key_positions.append(i)
while loop:
if len(loop_display)>0:
print_formatted_text(FormattedText(loop_display), style=style)
for k in key_positions:
if not message[k]: # ignore menu items that don't have a message binding
continue
message[k]=('class:key', message[k][1])
message[k+1]=('class:menu_item', message[k+1][1])
message[key_positions[current_key]]=('class:current_key',message[key_positions[current_key]][1])
message[key_positions[current_key]+1]=('class:current_key',message[key_positions[current_key]+1][1])
session.prompt(message, style=style, key_bindings=bindings)
if str(key_pressed)=="Keys.Up":
if current_key>0:
current_key=current_key-1
clear()
continue
if str(key_pressed)=="Keys.Down":
if current_key<len(key_positions)-1:
current_key=current_key+1
clear()
continue
if str(key_pressed)=="Keys.ControlM": # enter has been pressed
key_pressed=message[key_positions[current_key]][1].split(")")[0][1:]
if str(key_pressed)=="Keys.Escape": # enter has been pressed
key_pressed="escape"
loop=False
return key_pressed
if not (str(key_pressed) in list(menu_items.keys())):
clear()
continue
else:
loop=False
break
return key_pressed
def time_menu():
items = {
"title":{"text":"Which type of reflection do you want to do?"},
"m":{"text":"Morning"},
"e":{"text":"Evening"},
"b":{"text":"Back"}
}
return items[key_press_menu(items)]["text"]
def formatted_questions(questions, title):
items = {
"title":{"text":title}
}
key_codes = [c for c in "123456789abcdefghijklmnoprstuvwxyz"]
for i,q in enumerate(questions):
items[key_codes[i]]={"text":q["text"]}
items["--1"]={}
items["q"]={"text":"Abort"}
return get_menu(items)
def menu_from_questions(questions, title, loop_display="", menu_generator=key_press_menu):
items = {
"title":{"text":title}
}
key_codes = [c for c in "123456789abcdefghijklmnoprstuvwxyz"]
for i,q in enumerate(questions):
items[key_codes[i]]={"text":q["text"]}
items["--1"]={}
items["q"]={"text":"Abort"}
return menu_generator(items,loop_display)
def browse_menu(day_string):
items = {
"title":{"text":"What do you want to do?"},
"n":{"text":"Next day", "handler": quit},
"p":{"text":"Previous day", "handler": quit },
"g":{"text":"Goto day", "handler": quit },
"b":{"text":"Back to main menu", "handler": quit },
"Keys.Left":False,
"Keys.Right":False
}
return key_press_menu(items,day_string)
| 30.760274 | 108 | 0.583389 | 0 | 0 | 0 | 0 | 311 | 0.06925 | 0 | 0 | 921 | 0.205077 |
27059812bfb36013c718b5579e1caa2896f39e29 | 636 | py | Python | matrix/main.py | oleggator/python-advanced | 0e951fe6e7ae74129536ded5c02a9f1ea3337a7d | [
"MIT"
]
| null | null | null | matrix/main.py | oleggator/python-advanced | 0e951fe6e7ae74129536ded5c02a9f1ea3337a7d | [
"MIT"
]
| 2 | 2019-04-28T20:37:35.000Z | 2019-10-29T08:11:24.000Z | matrix/main.py | oleggator/python-advanced | 0e951fe6e7ae74129536ded5c02a9f1ea3337a7d | [
"MIT"
]
| null | null | null | from matrix import Matrix as CMatrix
from py_matrix import PyMatrix
def test(matrix_class, rows=3, columns=2):
m = matrix_class([
[column for column in range(row * columns, row * columns + columns)]
for row in range(rows)
])
# # or
# m = matrix_class([
# [1, 2],
# [3, 4],
# [5, 6],
# ])
n = 2 * m # multiply matrix by integer
n = n / 2 # divide matrix by integer
o = m + m # sum matrices
p = m.transpose() # transpose
q = m @ p # multiply matrices
r = 2 in m
def main():
test(CMatrix)
test(PyMatrix)
if __name__ == '__main__':
main()
| 21.2 | 76 | 0.550314 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 177 | 0.278302 |
2707004f28feaa5db63a1087307863b0d70fe4ec | 100 | py | Python | Algorithms/Bit Manipulation/Given a list of numbers where every numbers occurs twice except one number.py | cnm06/Competitive-Programming | 94242ae458570d503b8218f37624b88cc5020d23 | [
"MIT"
]
| 994 | 2017-02-28T06:13:47.000Z | 2022-03-31T10:49:00.000Z | Algorithms/Bit Manipulation/Given a list of numbers where every numbers occurs twice except one number.py | Quadrified/Competitive-Programming | bccb69952cc5260fb3647b3301ddac1023dacac8 | [
"MIT"
]
| 16 | 2018-01-01T02:59:55.000Z | 2021-11-22T12:49:16.000Z | Algorithms/Bit Manipulation/Given a list of numbers where every numbers occurs twice except one number.py | Quadrified/Competitive-Programming | bccb69952cc5260fb3647b3301ddac1023dacac8 | [
"MIT"
]
| 325 | 2017-06-15T03:32:43.000Z | 2022-03-28T22:43:42.000Z | def answer(numbers):
ans = numbers[0]
for i in numbers[1:]:
ans ^= i
return ans
| 16.666667 | 25 | 0.54 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
27085e60eb21632a2fb266c8e1fc048c12001579 | 3,947 | py | Python | horizon_telemetry/compute/views.py | simonpasquier/horizon-telemetry-dashboard | f284ec6ae8b1932079852fe3e9ab4b7a27ff58d7 | [
"Apache-2.0"
]
| null | null | null | horizon_telemetry/compute/views.py | simonpasquier/horizon-telemetry-dashboard | f284ec6ae8b1932079852fe3e9ab4b7a27ff58d7 | [
"Apache-2.0"
]
| 2 | 2017-10-10T07:30:41.000Z | 2017-10-19T18:34:13.000Z | horizon_telemetry/compute/views.py | simonpasquier/horizon-telemetry-dashboard | f284ec6ae8b1932079852fe3e9ab4b7a27ff58d7 | [
"Apache-2.0"
]
| null | null | null | import datetime
import json
from django.conf import settings
from django.http import HttpResponse
from django.utils.translation import ugettext_lazy as _
from django.views.generic import TemplateView
from horizon import exceptions, tables
from horizon_telemetry.forms import DateForm
from horizon_telemetry.utils.influxdb_client import (get_host_usage_metrics,
get_host_cpu_metric,
get_host_disk_metric,
get_host_memory_metric,
get_host_network_metric)
from openstack_dashboard import api
from . import tables as project_tables
class AdminIndexView(tables.DataTableView):
table_class = project_tables.AdminHypervisorsTable
template_name = 'telemetry/compute/index.html'
def get_data(self):
hypervisors = []
try:
hypervisors = api.nova.hypervisor_list(self.request)
except Exception:
exceptions.handle(self.request,
_('Unable to retrieve hypervisor information.'))
return hypervisors
def get_context_data(self, **kwargs):
context = super(AdminIndexView, self).get_context_data(**kwargs)
try:
context["stats"] = api.nova.hypervisor_stats(self.request)
except Exception:
exceptions.handle(self.request,
_('Unable to retrieve hypervisor statistics.'))
return context
class AdminDetailView(TemplateView):
template_name = 'telemetry/compute/detail.html'
def get_data(self):
pass
def get_context_data(self, **kwargs):
context = super(AdminDetailView, self).get_context_data(**kwargs)
today = datetime.date.today()
date_range = {
'start': self.request.GET.get("start", today - datetime.timedelta(1)),
'end': self.request.GET.get('end', today)
}
form = DateForm(date_range)
self.request.session.update(date_range)
# convert inputs to date objects
if not isinstance(date_range['start'], datetime.date):
date_range['start'] = datetime.datetime.strptime(date_range['start'], "%Y-%m-%d").date()
date_range['end'] = datetime.datetime.strptime(date_range['end'], "%Y-%m-%d").date()
hour_interval = (date_range['end'] - date_range['start']).total_seconds() / 3600
if hour_interval > 24:
context['tickFormat'] = "%x"
else:
context['tickFormat'] = "%H:%M"
context['dateform'] = form
node = context['node'] = context['hypervisor']
context['cpu_data'] = json.dumps(
get_host_cpu_metric(settings.ENVIRONMENT_LABEL, node,
date_range['start'], date_range['end'])
)
context['mem_data'] = json.dumps(
get_host_memory_metric(settings.ENVIRONMENT_LABEL, node,
date_range['start'], date_range['end'])
)
context['hdd_data'] = json.dumps(
get_host_disk_metric(settings.ENVIRONMENT_LABEL, node,
date_range['start'], date_range['end'])
)
context['net_data'] = json.dumps(
get_host_network_metric(settings.ENVIRONMENT_LABEL, node,
date_range['start'], date_range['end'],
getattr(settings, 'TELEMETRY_COMPUTE_INTERFACES', None))
)
return context
class DataView(TemplateView):
template_name = 'telemetry/dummy.html'
def get(self, *args, **kwargs):
data = get_host_usage_metrics(settings.ENVIRONMENT_LABEL,
self.kwargs.get('hypervisor'))
return HttpResponse(json.dumps(data), content_type='application/json')
| 36.88785 | 100 | 0.593615 | 3,194 | 0.809222 | 0 | 0 | 0 | 0 | 0 | 0 | 507 | 0.128452 |
270af1960a206b26f220b68d5328bdfbbbe68aa2 | 1,983 | py | Python | src/pySanbot/test/test_util/test_boyer_moore_search.py | BHFaction/Tribot | 38d8caaa73775654125db3091b8de2aadb3dba99 | [
"MIT"
]
| 3 | 2017-04-04T04:03:28.000Z | 2021-05-29T08:08:48.000Z | src/pySanbot/test/test_util/test_boyer_moore_search.py | BHFaction/Tribot | 38d8caaa73775654125db3091b8de2aadb3dba99 | [
"MIT"
]
| null | null | null | src/pySanbot/test/test_util/test_boyer_moore_search.py | BHFaction/Tribot | 38d8caaa73775654125db3091b8de2aadb3dba99 | [
"MIT"
]
| 2 | 2018-02-28T14:16:11.000Z | 2019-05-07T18:02:16.000Z | from unittest import TestCase
from util.boyer_moore_search import z_array
from util.boyer_moore_search import boyer_moore, BoyerMoore
__author__ = 'Yifei'
class TestBoyerMooreSearch(TestCase):
def test_z_array(self):
# Test Case 0
s = 'aabcaabxaaaz'
n = len(s)
z = z_array(s)
self.assertListEqual([n, 1, 0, 0, 3, 1, 0, 0, 2, 2, 1, 0], z, "Test Case 1: " + s)
s = 'aaaaaa'
n = len(s)
z = z_array(s)
self.assertListEqual([n, 5, 4, 3, 2, 1], z, "Test Case 2: " + s)
s = 'aabaacd'
n = len(s)
z = z_array(s)
self.assertListEqual([n, 1, 0, 2, 1, 0, 0], z, "Test Case 3: " + s)
s = 'abababab'
n = len(s)
z = z_array(s)
self.assertListEqual([n, 0, 6, 0, 4, 0, 2, 0], z, "Test Case 4: " + s)
def test_boyer_moore(self):
t = [1, 2, 3, 1, 2, 3, 1, 2, 3]
p = [1]
p_bm = BoyerMoore(p)
occurrences = boyer_moore(p, p_bm, t)
self.assertListEqual([0, 3, 6], occurrences, "Test Case 1: " + str(p) + " in " + str(t))
t = [1, 2, 3, 1, 2, 3, 1, 2, 3]
p = [1, 2]
p_bm = BoyerMoore(p)
occurrences = boyer_moore(p, p_bm, t)
self.assertListEqual([0, 3, 6], occurrences, "Test Case 2: " + str(p) + " in " + str(t))
t = [1, 2, 3, 1, 2, 3, 1, 2, 3]
p = [1, 2, 3]
p_bm = BoyerMoore(p)
occurrences = boyer_moore(p, p_bm, t)
self.assertListEqual([0, 3, 6], occurrences, "Test Case 3: " + str(p) + " in " + str(t))
t = [1, 2, 3, 4, 5]
p = [6, 6, 6]
p_bm = BoyerMoore(p)
occurrences = boyer_moore(p, p_bm, t)
self.assertListEqual([], occurrences, "test case 4: " + str(p) + " in " + str(t))
t = [1, 2, 3, 4, 5]
p = [6]
p_bm = BoyerMoore(p)
occurrences = boyer_moore(p, p_bm, t)
self.assertListEqual([], occurrences, "test case 5: " + str(p) + " in " + str(t))
| 32.508197 | 96 | 0.498739 | 1,824 | 0.919818 | 0 | 0 | 0 | 0 | 0 | 0 | 226 | 0.113969 |
270b35ddd89f3b512a06eb79c89fed5ad60c1aca | 1,668 | py | Python | authentication/views.py | ManasUniyal/RoadView | 593a4f86cb10efdbf560fc4aec3d58a9b5b41bd2 | [
"MIT"
]
| null | null | null | authentication/views.py | ManasUniyal/RoadView | 593a4f86cb10efdbf560fc4aec3d58a9b5b41bd2 | [
"MIT"
]
| null | null | null | authentication/views.py | ManasUniyal/RoadView | 593a4f86cb10efdbf560fc4aec3d58a9b5b41bd2 | [
"MIT"
]
| null | null | null | from django.contrib import messages
from django.contrib.auth import authenticate, login, logout
from django.contrib.auth.models import User
from django.shortcuts import render, redirect
# Create your views here.
def loginUser(request):
if request.method == 'POST':
email_address = request.POST['email']
password = request.POST['password']
user = authenticate(username=email_address, password=password)
if user:
login(request, user)
return redirect('main:home')
else:
messages.error(request, 'username or password not correct')
return redirect('authentication:login')
return render(request, 'authentication/login.html')
def registerUser(request):
if request.method == 'POST':
first_name = request.POST['firstname']
last_name = request.POST['lastname']
email_address = request.POST['email']
password = request.POST['password']
if not User.objects.filter(username=email_address).exists():
User.objects.create_user(username=email_address,
first_name=first_name,
last_name=last_name,
password=password)
messages.success(request, 'Account created successfully')
return redirect('authentication:login')
else:
messages.error(request, 'Email already registered')
return redirect('authentication:register')
return render(request, 'authentication/register.html')
def logoutUser(request):
logout(request)
return redirect('welcome:welcome')
| 35.489362 | 71 | 0.63789 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 336 | 0.201439 |
270baa9c084cf98e0065fdca116072edd2dacd09 | 1,335 | py | Python | dz3/task3_5.py | EsipenkoAnna/PycharmProjects-EsipenkoAnna_2242 | 46fe55de36b7b1cfdeafcf685ea01c6220e16546 | [
"MIT"
]
| null | null | null | dz3/task3_5.py | EsipenkoAnna/PycharmProjects-EsipenkoAnna_2242 | 46fe55de36b7b1cfdeafcf685ea01c6220e16546 | [
"MIT"
]
| null | null | null | dz3/task3_5.py | EsipenkoAnna/PycharmProjects-EsipenkoAnna_2242 | 46fe55de36b7b1cfdeafcf685ea01c6220e16546 | [
"MIT"
]
| null | null | null | import random
nouns = ["автомобиль", "лес", "огонь", "город", "дом"]
adverbs = ["сегодня", "вчера", "завтра", "позавчера", "ночью"]
adjectives = ["веселый", "яркий", "зеленый", "утопичный", "мягкий"]
def get_jokes(count: int) -> list:
#формируем список состящий из фраз. Каждая фраза содержит по три слова, которые случайным образом берутся из списоков nouns, adverbs и adjectives
list_out = []
for i in range(count):
list_out.extend([random.choice(nouns)+" "+random.choice(adverbs)+" "+random.choice(adjectives)])
return list_out
print(get_jokes(6))
def get_jokes_adv(count: int, Double: bool) -> list:
#Если выставлен флаг Double как False, то после того как слово было использовано, оно будет удалено из списка
list_out_1 = []
for a in range(count):
if Double==False:
nouns_w = random.choice(nouns)
adverbs_w = random.choice(adverbs)
adjectives_w = random.choice(adjectives)
list_out_1.extend([nouns_w+" "+adverbs_w+" "+adjectives_w])
nouns.remove(nouns_w)
adverbs.remove(adverbs_w)
adjectives.remove(adjectives_w)
else:
list_out_1.extend([random.choice(nouns) + " " + random.choice(adverbs) + " " + random.choice(adjectives)])
return list_out_1
print(get_jokes_adv(2,False))
print(nouns, adverbs, adjectives) #проверяем, что элементы удалены | 36.081081 | 145 | 0.698127 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 722 | 0.442673 |
270e32ac710b649bd41e91bee859d8ce32b8ac58 | 1,140 | py | Python | unit_03/06_Dates_and_Time/1-Dates_and_Time/4_timestrings.py | duliodenis/python_master_degree | 3ab76838ce2fc1606f28e988a3273dd27122a621 | [
"MIT"
]
| 19 | 2019-03-14T01:39:32.000Z | 2022-02-03T00:36:43.000Z | unit_03/06_Dates_and_Time/1-Dates_and_Time/4_timestrings.py | duliodenis/python_master_degree | 3ab76838ce2fc1606f28e988a3273dd27122a621 | [
"MIT"
]
| 1 | 2020-04-10T01:01:16.000Z | 2020-04-10T01:01:16.000Z | unit_03/06_Dates_and_Time/1-Dates_and_Time/4_timestrings.py | duliodenis/python_master_degree | 3ab76838ce2fc1606f28e988a3273dd27122a621 | [
"MIT"
]
| 5 | 2019-01-02T20:46:05.000Z | 2020-07-08T22:47:48.000Z | #
# Dates and Times in Python: Dates & Time
# Python Techdegree
#
# Created by Dulio Denis on 12/24/18.
# Copyright (c) 2018 ddApps. All rights reserved.
# ------------------------------------------------
# Challenge 4: strftime & strptime
# ------------------------------------------------
# Challenge Task 1 of 2
# Create a function named to_string that takes a
# datetime and gives back a string in the format
# "24 September 2012".
# ## Examples
# to_string(datetime_object) => "24 September 2012"
# from_string("09/24/12 18:30", "%m/%d/%y %H:%M") => datetime
import datetime
def to_string(dt):
return dt.strftime("%d %B %Y")
# TEST
dt = datetime.datetime.now()
print(to_string(dt))
# Challenge Task 2 of 2
# Create a new function named from_string that takes two arguments:
# a date as a string and an strftime-compatible format string, and
# returns a datetime created from them.
def from_string(date_string, format_string):
return datetime.datetime.strptime(date_string, format_string)
# TEST
date_string = "24 December 2018"
date_format = "%d %B %Y"
print(from_string(date_string, date_format))
| 30 | 69 | 0.650877 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 817 | 0.716667 |
27103b13e18c6af23c06877c02e17d64b07756a6 | 177 | py | Python | server/DresseurModele.py | crancerkill/pokeploud | d01c596591382fdec62958896e3ec6a6bb3d1ab9 | [
"Apache-2.0"
]
| null | null | null | server/DresseurModele.py | crancerkill/pokeploud | d01c596591382fdec62958896e3ec6a6bb3d1ab9 | [
"Apache-2.0"
]
| null | null | null | server/DresseurModele.py | crancerkill/pokeploud | d01c596591382fdec62958896e3ec6a6bb3d1ab9 | [
"Apache-2.0"
]
| null | null | null | from google.appengine.ext import ndb
class Dresseur(ndb.Model):
tirage = ndb.KeyProperty(kind='Tirage')
nomig = ndb.StringProperty()
codeami = ndb.StringProperty()
| 25.285714 | 43 | 0.723164 | 138 | 0.779661 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0.045198 |
2712d71cebd1c1c0c5ebe71fa2604a188a12e044 | 331 | py | Python | core/migrations/0003_remove_resetpassword_expires_at.py | GolamMullick/HR_PROJECT | fc4c76cfc835ad014a62a3da9d32b8fc8d474397 | [
"MIT"
]
| null | null | null | core/migrations/0003_remove_resetpassword_expires_at.py | GolamMullick/HR_PROJECT | fc4c76cfc835ad014a62a3da9d32b8fc8d474397 | [
"MIT"
]
| 3 | 2020-02-12T02:52:01.000Z | 2021-06-10T22:18:25.000Z | core/migrations/0003_remove_resetpassword_expires_at.py | GolamMullick/HR_PROJECT | fc4c76cfc835ad014a62a3da9d32b8fc8d474397 | [
"MIT"
]
| null | null | null | # Generated by Django 2.1.4 on 2019-11-18 11:00
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('core', '0002_resetpassword'),
]
operations = [
migrations.RemoveField(
model_name='resetpassword',
name='expires_at',
),
]
| 18.388889 | 47 | 0.595166 | 246 | 0.743202 | 0 | 0 | 0 | 0 | 0 | 0 | 100 | 0.302115 |
2713ca1136e8fbc548b8d5161cb677f262b46b5a | 22,410 | py | Python | toolbox_plotting.py | roughhawkbit/robs-python-scripts | 7d9a28cff106887553970b5c4c37cd53a836c2ff | [
"MIT"
]
| null | null | null | toolbox_plotting.py | roughhawkbit/robs-python-scripts | 7d9a28cff106887553970b5c4c37cd53a836c2ff | [
"MIT"
]
| 1 | 2015-02-23T16:31:21.000Z | 2015-02-25T16:34:14.000Z | toolbox_plotting.py | roughhawkbit/robs-python-scripts | 7d9a28cff106887553970b5c4c37cd53a836c2ff | [
"MIT"
]
| null | null | null | #!/usr/bin/python
from __future__ import division
from __future__ import with_statement
import matplotlib
from matplotlib import rcParams
from matplotlib import pyplot
from mpl_toolkits.axes_grid1 import make_axes_locatable
from mpl_toolkits.mplot3d import Axes3D
from PIL import Image
#import Image
from pylab import *
import matplotlib.cbook as cbook
import numpy
import os
import random
from scipy.spatial import KDTree
from scipy.stats import linregress
import toolbox_basic
def mm2inch(mm):
return mm*0.0393700787
class JournalFigure:
def __init__(self):
self.subplots = {}
def save(self, path, dpi=300, clear=True):
print('Saving as '+path)
matplotlib.pyplot.savefig(path, dpi=dpi)
if clear:
matplotlib.pyplot.clf()
def set_font_size(self, fontsize):
matplotlib.rc('font', **{'size':fontsize})
def bottom_axis_only(self, axis):
axis.axes.get_yaxis().set_visible(False)
axis.spines['right'].set_color('none')
axis.spines['left'].set_color('none')
axis.spines['top'].set_color('none')
def add_subplot(self, label, position,
frameon=True, aspect='auto', axisbg='w', projection=None):
axis = self.fig.add_subplot(position, aspect=aspect,
axisbg=axisbg, projection=projection)
# Moved frameon here as the keyword was causing problems in 3D plots
if not frameon:
axis.axis('off')
setp( axis.get_xticklabels(), visible=frameon)
setp( axis.get_yticklabels(), visible=frameon)
if not frameon:
axis.tick_params(top="off", bottom="off", right="off", left="off")
self.subplots[axis] = label
return axis
def find_axis_from_label(self, label):
for ax, lab in self.subplots.iteritems():
if lab == label:
return ax
toolbox_basic.error_message('Could not find subplot with',
'label '+str(label))
return None
def use_image(self, axis, image_path):
image_path = toolbox_basic.check_path(image_path)
datafile = cbook.get_sample_data(image_path)
image = Image.open(datafile)
axis.set_ylim(0,image.size[0])
axis.set_ylim(image.size[1],0)
return imshow(image)
def process_subplots(self):
pass
def subplots_adjust(self, left=None, right=None, top=None, bottom=None,
hspace=None, wspace=None):
self.fig.subplots_adjust(left=left, right=right, top=top,
bottom=bottom, hspace=hspace, wspace=wspace)
def inset_axes(self, padding=0.02):
for axis, label in self.subplots.iteritems():
xlim = axis.get_xlim()
xdiff = padding*(xlim[1] - xlim[0])
ylim = axis.get_ylim()
ydiff = padding*(ylim[1] - ylim[0])
axis.set_xlim([xlim[0]-xdiff, xlim[1]+xdiff])
axis.set_ylim([ylim[0]-ydiff, ylim[1]+ydiff])
def process_lines(self):
for axis, label in self.subplots.iteritems():
for line in axis.spines.itervalues():
line.set_linewidth(1)
axis.tick_params(top='off', right='off')
for line in axis.get_xticklines() + axis.get_yticklines():
line.set_markeredgewidth(1)
def set_yaxis_label_positions(self, x, y, labels=[]):
if labels == []:
labels = self.subplots.values()
for (axis, label) in self.subplots.iteritems():
if label in labels:
axis.yaxis.set_label_coords(x, y, transform=axis.transAxes)
class BmcFigure(JournalFigure):
def __init__(self, double_column=False, height=0.0):
JournalFigure.__init__(self)
print("Using figure dimensions for BMC:")
# width and height
single, double, maximum = mm2inch(85), mm2inch(170), mm2inch(225)
if double_column: self.width = double
else: self.width = single
if height == 0.0: self.height = self.width
elif height == 'single': self.height = single
elif height == 'double': self.height = double
elif height == 'max': self.height = maximum
else: self.height = min(height, maximum)
print("width x height = "+str(self.width)+" x "+str(self.height))
# default settings
matplotlib.rc('font',**{'family':'Arial','weight':'normal','size':10})
matplotlib.rc('mathtext', fontset='stixsans', default='regular')
matplotlib.rcParams['xtick.direction'] = 'out'
matplotlib.rcParams['ytick.direction'] = 'out'
# the figure!
self.fig = matplotlib.pyplot.figure(figsize=(self.width, self.height),
facecolor='white')
def process_subplots(self, label_pos=(-0.20, 1.01), padding=0.02):
for axis, label in self.subplots.iteritems():
xlim = axis.get_xlim()
xdiff = 0.02*(xlim[1] - xlim[0])
ylim = axis.get_ylim()
ydiff = 0.02*(ylim[1] - ylim[0])
axis.set_xlim([xlim[0]-xdiff, xlim[1]+xdiff])
axis.set_ylim([ylim[0]-ydiff, ylim[1]+ydiff])
axis.text(label_pos[0], label_pos[1], label,
transform=axis.transAxes,
va='top', fontsize=12, fontweight='normal')
for line in axis.spines.itervalues():
line.set_linewidth(1)
axis.tick_params(bottom='on', top='off', left='on', right='off')
for line in axis.get_xticklines() + axis.get_yticklines():
line.set_markeredgewidth(1)
class PlosFigure(JournalFigure):
def __init__(self, double_column=False, height=0.0):
JournalFigure.__init__(self)
print("Using figure dimensions for PLoS:")
# width and height
single, double, maximum = 3.27, 6.83, 9.19
if double_column: self.width = double
else: self.width = single
if height == 0.0: self.height = self.width
elif height == 'single': self.height = single
elif height == 'double': self.height = double
elif height == 'max': self.height = maximum
else: self.height = min(height, maximum)
print("width x height = "+str(self.width)+" x "+str(self.height))
# default settings
matplotlib.rc('font',**{'family':'Arial','weight':'normal','size':10})
matplotlib.rc('mathtext', fontset='stixsans', default='regular')
matplotlib.rcParams['xtick.direction'] = 'out'
matplotlib.rcParams['ytick.direction'] = 'out'
# the figure!
self.fig = matplotlib.pyplot.figure(figsize=(self.width, self.height),
facecolor='white')
def add_labels(self, label_pos=(-0.18, 1.01)):
for axis, label in self.subplots.iteritems():
axis.text(label_pos[0], label_pos[1], label, fontsize=12, va='top',
transform=axis.transAxes, fontweight='bold')
def process_subplots(self, label_pos=(-0.18, 1.01), padding=0.02):
self.inset_axes(padding=padding)
self.add_labels(label_pos=label_pos)
self.process_lines()
class NatCommsFigure(JournalFigure):
def __init__(self, double_column=False, height=0.0):
JournalFigure.__init__(self)
print("Using figure dimensions for Nature Communications:")
# width and height
single, double, maximum = mm2inch(89), mm2inch(183), mm2inch(247)
if double_column: self.width = double
else: self.width = single
if height == 0.0: self.height = self.width
elif height == 'single': self.height = single
elif height == 'double': self.height = double
elif height == 'max': self.height = maximum
else: self.height = min(height, maximum)
print("width x height = "+str(self.width)+" x "+str(self.height))
# default settings
matplotlib.rc('font',**{'family':'Arial','weight':'normal','size':10})
matplotlib.rc('mathtext', fontset='stixsans', default='regular')
matplotlib.rcParams['xtick.direction'] = 'out'
matplotlib.rcParams['ytick.direction'] = 'out'
# the figure!
self.fig = matplotlib.pyplot.figure(figsize=(self.width, self.height),
facecolor='white')
def add_labels(self, label_pos=(-0.18, 1.01)):
for axis, label in self.subplots.iteritems():
axis.text(label_pos[0], label_pos[1], label.lower(), va='top',
transform=axis.transAxes, fontsize=12, fontweight='bold')
def process_subplots(self, label_pos=(-0.18, 1.01), padding=0.02):
self.inset_axes(padding=padding)
self.add_labels(label_pos=label_pos)
self.process_lines()
# University of Birmingham (UK) thesis guidelines state:
# - inner margin = 30mm, outer margin = 20mm, so double column = 160mm
# - top margin = bottom margin = 30mm, so maximum height = 237mm
class ThesisFigure(JournalFigure):
def __init__(self, double_column=False, height=0.0):
JournalFigure.__init__(self)
print("Using figure dimensions for a thesis (University of Birmingham):")
# width and height
single, double, maximum = mm2inch(80), mm2inch(160), mm2inch(237)
if double_column: self.width = double
else: self.width = single
if height == 0.0: self.height = self.width
elif height == 'single': self.height = single
elif height == 'double': self.height = double
elif height == 'max': self.height = maximum
else: self.height = min(height, maximum)
print("width x height = "+str(self.width)+" x "+str(self.height))
# default settings
matplotlib.rc('font',**{'family':'Arial','weight':'normal','size':12})
matplotlib.rc('mathtext', fontset='stixsans', default='regular')
matplotlib.rcParams['xtick.direction'] = 'out'
matplotlib.rcParams['ytick.direction'] = 'out'
# the figure!
self.fig = matplotlib.pyplot.figure(figsize=(self.width, self.height),
facecolor='white')
def add_labels(self, label_pos=(-0.18, 1.01)):
for axis, label in self.subplots.iteritems():
axis.text(label_pos[0], label_pos[1], label, fontsize=12, va='top',
transform=axis.transAxes, fontweight='bold')
def process_subplots(self, label_pos=(-0.18, 1.01), padding=0.02):
self.inset_axes(padding=padding)
self.add_labels(label_pos=label_pos)
self.process_lines()
class SlideFigure(JournalFigure):
def __init__(self, height=7.5, width=10):
JournalFigure.__init__(self)
self.height = height
self.width = width
matplotlib.rc('font',**{'family':'sans serif',
'weight':'normal',
'size':10})
matplotlib.rc('mathtext', fontset='stixsans', default='regular')
matplotlib.rcParams['xtick.direction'] = 'out'
matplotlib.rcParams['ytick.direction'] = 'out'
self.fig = matplotlib.pyplot.figure(
figsize=(self.width, self.height))
def process_subplots(self, padding=0.02):
self.inset_axes(padding=padding)
for axis, label in self.subplots.iteritems():
axis.text(-0.25, 1.02, label, transform=axis.transAxes,
va='top', fontsize=12, fontweight='bold')
for line in axis.spines.itervalues():
line.set_linewidth(1)
axis.tick_params(bottom='on', top='off', left='on', right='off')
for line in axis.get_xticklines() + axis.get_yticklines():
line.set_markeredgewidth(1)
class PovrayFile:
def __init__(self, agent_output, save_path):
self.agent_output = agent_output
self.save_path = os.path.join( \
toolbox_basic.check_path(os.path.dirname(save_path)),
os.path.basename(save_path))
nI, nJ = agent_output.grid_nI, agent_output.grid_nJ
nK, res = agent_output.grid_nK, agent_output.grid_res
max_dimension = max(nI, nJ, nK)
x_scale = nI / max_dimension
y_scale = nJ / max_dimension
self.scaling = res * max_dimension
### Set the header
self.script = '#declare white = color rgb < 1.0, 1.0, 1.0 >;\n\n'
self.script += 'background { white }\n'
# The camera
self.script += 'camera {\n'
self.script += '\t location < 0.5 %f 1 >\n'%(y_scale/2)
self.script += '\t look_at < 0.5, %f -1 >\n'%(y_scale/2)
self.script += '\t up < 0.0, %f, 0.0 >\n'%(1.2*y_scale)
self.script += '\t right < %f, 0.0, 0.0 >\n}\n\n'%(1.2*x_scale)
# The lights
for i in [0, 1]:
self.script += 'light_source {\n\t < %d, 1, 1 >\n'%(i)
self.script += '\t white\n\t shadowless}\n\n'
# The substratum
self.script += 'box {\n\t < 0.0, 0.0, 0.0 >\n'
self.script += '\t< 1.0, -0.01, -0.001 >\n'
self.script += '\t pigment { color rgb < 0.25, 0.25, 0.25 > }\n}\n\n'
def append_cell(self, cell_output, total_radius=True):
# You MUST have already set cell_output.vars['color']
x = float(cell_output.vars['locationX']) / self.scaling
y = float(cell_output.vars['locationY']) / self.scaling
z = float(cell_output.vars['locationZ']) / self.scaling
r_name = 'totalRadius' if total_radius else 'radius'
r = float(cell_output.vars[r_name]) / self.scaling
self.script += 'sphere {\n'
self.script += '\t < %f, %f, %f >\n\t %f\n'%(x, y, z, r)
self.script += '\t pigment { %s }\n}\n\n'%(cell_output.vars['colorRGB'])
def save(self):
print('Saving at %s'%(self.save_path))
with open(self.save_path, 'wb') as f:
f.write(self.script)
def call(self, x_pixels=1600, y_pixels=1000):
POVcmd = '''povray '%s' +A -D -geometry %dx%d''' \
%(self.save_path, x_pixels, y_pixels)
os.system(POVcmd)
def delete(self):
os.remove(self.save_path)
class PlotPoint:
def __init__(self, x, y,
edgecolor='k', facecolor='none', size=5, style='o'):
self.x = float(x)
self.y = float(y)
self.edgecolor = edgecolor
self.facecolor = facecolor
self.markersize = float(size)
self.style = style
def plot(self, axis):
axis.plot([self.x], [self.y], self.style, color=self.facecolor,
markeredgecolor=self.edgecolor, markersize=self.markersize)
class ScatterColumn:
def __init__(self, x, name):
self.x = float(x)
self.name = name
self.max_width = 0.5
self.edgecolor = 'k'
self.facecolor = 'white'
self.markersize = 5
self.min_dist = 0.01
self.style = 'o'
self.points = []
def set_defaults(self, edgecolor=None, facecolor=None, max_width=None,
markersize=None, min_dist=None, style=None):
self.edgecolor = self.edgecolor if (edgecolor == None) else edgecolor
self.facecolor = self.facecolor if (facecolor == None) else facecolor
self.max_width = self.max_width if (max_width == None) else max_width
self.markersize = self.markersize if (markersize == None) else markersize
self.min_dist = self.min_dist if (min_dist == None) else min_dist
self.style = self.style if (style == None) else style
def add_point(self, y, edgecolor=None, facecolor=None, style=None):
edgecolor = self.edgecolor if (edgecolor == None) else edgecolor
facecolor = self.facecolor if (facecolor == None) else facecolor
style = self.style if (style == None) else style
self.points.append(PlotPoint(self.x, y, edgecolor=edgecolor,
facecolor=facecolor, style=style, size=self.markersize))
def jiggle_points(self):
max_x, min_x = self.x + self.max_width/2, self.x - self.max_width/2
more_to_do = (len(self.points) > 0)
while more_to_do:
for i in range(len(self.points)):
current = self.points.pop(0)
points_array = numpy.array([[p.x, p.y] for p in self.points])
kdtree = KDTree(points_array)
ids = kdtree.query_ball_point([current.x, current.y], self.min_dist)
# If there are any neighbours too near
counter = 0
while not ids == []:
rand = random.uniform(-self.min_dist, self.min_dist)
current.x = min(max_x, max(min_x, current.x + rand))
ids = kdtree.query_ball_point([current.x, current.y], self.min_dist)
counter += 1
if counter > 100: break
self.points.append(current)
for i in range(len(self.points)):
current = self.points.pop(0)
points_array = numpy.array([[p.x, p.y] for p in self.points])
kdtree = KDTree(points_array)
ids = kdtree.query_ball_point([current.x, current.y], self.min_dist)
self.points.append(current)
more_to_do = (not ids == [])
if more_to_do: break
def plot(self, axis):
for point in self.points:
point.plot(axis)
class ScatterRow:
def __init__(self, y, name):
self.y = float(y)
self.name = name
self.max_height = 0.5
self.edgecolor = 'k'
self.facecolor = 'white'
self.markersize = 5
self.min_dist = 0.01
self.style = 'o'
self.points = []
def set_defaults(self, edgecolor=None, facecolor=None, max_height=None,
markersize=None, min_dist=None, style=None):
self.edgecolor = self.edgecolor if (edgecolor == None) else edgecolor
self.facecolor = self.facecolor if (facecolor == None) else facecolor
self.max_height = self.max_height if (max_height == None) else max_height
self.markersize = self.markersize if (markersize == None) else markersize
self.min_dist = self.min_dist if (min_dist == None) else min_dist
self.style = self.style if (style == None) else style
def add_point(self, x, edgecolor=None, facecolor=None, style=None):
edgecolor = self.edgecolor if (edgecolor == None) else edgecolor
facecolor = self.facecolor if (facecolor == None) else facecolor
style = self.style if (style == None) else style
self.points.append(PlotPoint(x, self.y, edgecolor=edgecolor,
facecolor=facecolor, style=style, size=self.markersize))
def jiggle_points(self):
max_y, min_y = self.y + self.max_height/2, self.y - self.max_height/2
more_to_do = (len(self.points) > 0)
while more_to_do:
for i in range(len(self.points)):
current = self.points.pop(0)
points_array = numpy.array([[p.x, p.y] for p in self.points])
kdtree = KDTree(points_array)
ids = kdtree.query_ball_point([current.x, current.y], self.min_dist)
# If there are any neighbours too near
counter = 0
while not ids == []:
rand = random.uniform(-self.min_dist, self.min_dist)
current.y = min(max_y, max(min_y, current.y + rand))
ids = kdtree.query_ball_point([current.x, current.y], self.min_dist)
counter += 1
if counter > 100: break
self.points.append(current)
for i in range(len(self.points)):
current = self.points.pop(0)
points_array = numpy.array([[p.x, p.y] for p in self.points])
kdtree = KDTree(points_array)
ids = kdtree.query_ball_point([current.x, current.y], self.min_dist)
self.points.append(current)
more_to_do = (not ids == [])
if more_to_do: break
def plot(self, axis):
for point in self.points:
point.plot(axis)
def padding_axis(axis, side="right", pad=0.04):
divider = make_axes_locatable(axis)
cax = divider.append_axes(side, size="5%", pad=pad, axisbg='none')
return cax
def empty_padding_axis(axis, side="right"):
cax = padding_axis(axis, side=side)
cax.set_xticklabels(['']*10)
cax.set_yticklabels(['']*10)
for spine in ['right', 'left', 'top', 'bottom']:
cax.spines[spine].set_color('none')
cax.tick_params(top="off", bottom="off", right="off", left="off")
return cax
def make_colorbar(axis, colorscheme, side="right", fontsize=10, pad=0.04):
cax = padding_axis(axis, side=side, pad=pad)
orientation = 'vertical' if side in ["right", "left"] else 'horizontal'
cbar = pyplot.colorbar(colorscheme, cax=cax, orientation=orientation)
cbar.ax.tick_params(labelsize=fontsize)
return cbar
def colorbar_on_right(axis, colorscheme, fontsize=10):
#divider = make_axes_locatable(axis)
#cax = divider.append_axes("right", size="5%", pad=0.04)
#cax = space_on_right(axis)
#cbar = pyplot.colorbar(colorscheme, cax=cax)
#cbar.ax.tick_params(labelsize=fontsize)
print '\n\ntoolbox_plotting.colorbar_on_right() is deprecated'
print 'Please use toolbox_plotting.make_colorbar() instead\n\n'
cbar = make_colorbar(axis, colorscheme, side="right")
return cbar
def draw_linear_regression(axis, color, x_vals, y_vals):
slope, intercept, r_value, p_value, std_err = linregress(x_vals, y_vals)
x1, x2 = min(x_vals), max(x_vals)
y1, y2 = x1*slope + intercept, x2*slope + intercept
axis.plot([x1, x2], [y1, y2], color)
return r_value, p_value, std_err
| 46.784969 | 88 | 0.585988 | 20,013 | 0.893039 | 0 | 0 | 0 | 0 | 0 | 0 | 2,938 | 0.131102 |
27148ed5395addc00af60f81418ead72ae8e82d5 | 568 | py | Python | melon_modules/moderation.py | WeMayNeverKnow/MelonMintBots | 6feac66a25ad86ab0c9a8b41f4cdae819f4e9fbd | [
"MIT"
]
| null | null | null | melon_modules/moderation.py | WeMayNeverKnow/MelonMintBots | 6feac66a25ad86ab0c9a8b41f4cdae819f4e9fbd | [
"MIT"
]
| null | null | null | melon_modules/moderation.py | WeMayNeverKnow/MelonMintBots | 6feac66a25ad86ab0c9a8b41f4cdae819f4e9fbd | [
"MIT"
]
| null | null | null | from hata import Client
MELON : Client
#@MELON.commands.from_class
#class prefix:
# pass
#@MELON.commands.from_class
#class slowmode:
# pass
#@MELON.commands.from_class
#class logs:
# pass
#@MELON.commands.from_class
#class warn:
# pass
#@MELON.commands.from_class
#class mute:
# pass
#@MELON.commands.from_class
#class kick:
# pass
#@MELON.commands.from_class
#class ban:
# pass
#@MELON.commands.from_class
#class lock:
# pass
#@MELON.commands.from_class
#class unlock:
# pass
#@MELON.commands.from_class
#class clear:
# pass
| 12.909091 | 27 | 0.702465 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 488 | 0.859155 |
27155b9a924aec60a88449cb892d18dddf705986 | 5,837 | py | Python | bundled/linter/linter_server.py | luabud/vscode-pylint | 2297f1a6210bf93f7d9c083e32c59ed03664ca05 | [
"MIT"
]
| null | null | null | bundled/linter/linter_server.py | luabud/vscode-pylint | 2297f1a6210bf93f7d9c083e32c59ed03664ca05 | [
"MIT"
]
| null | null | null | bundled/linter/linter_server.py | luabud/vscode-pylint | 2297f1a6210bf93f7d9c083e32c59ed03664ca05 | [
"MIT"
]
| null | null | null | # Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
"""
Implementation of linting support over LSP.
"""
import json
import pathlib
import sys
from typing import Dict, Sequence, Union
# Ensure that will can import LSP libraries, and other bundled linter libraries
sys.path.append(str(pathlib.Path(__file__).parent.parent / "libs"))
# pylint: disable=wrong-import-position,import-error
import utils
from pygls import lsp, server
from pygls.lsp import types
all_configurations = {
"name": "Pylint",
"module": "pylint",
"patterns": {
"default": {
"regex": "",
"args": ["--reports=n", "--output-format=json"],
"lineStartsAt1": True,
"columnStartsAt1": False,
"useStdin": True,
}
},
}
SETTINGS = {}
LINTER = {}
MAX_WORKERS = 5
LSP_SERVER = server.LanguageServer(max_workers=MAX_WORKERS)
def _get_severity(
symbol: str, code: str, code_type: str, severity: Dict[str, str]
) -> types.DiagnosticSeverity:
"""Converts severity provided by linter to LSP specific value."""
value = (
severity.get(symbol, None)
or severity.get(code, None)
or severity.get(code_type, "Error")
)
try:
return types.DiagnosticSeverity[value]
except KeyError:
pass
return types.DiagnosticSeverity.Error
def _parse_output(
content: str,
line_at_1: bool,
column_at_1: bool,
severity: Dict[str, str],
additional_offset: int = 0,
) -> Sequence[types.Diagnostic]:
"""Parses linter messages and return LSP diagnostic object for each message."""
diagnostics = []
line_offset = (1 if line_at_1 else 0) + additional_offset
col_offset = 1 if column_at_1 else 0
messages = json.loads(content)
for data in messages:
start = types.Position(
line=int(data["line"]) - line_offset,
character=int(data["column"]) - col_offset,
)
if data["endLine"] is not None:
end = types.Position(
line=int(data["endLine"]) - line_offset,
character=int(data["endColumn"]) - col_offset,
)
else:
end = start
diagnostic = types.Diagnostic(
range=types.Range(
start=start,
end=end,
),
message=data["message"],
severity=_get_severity(
data["symbol"], data["message-id"], data["type"], severity
),
code=f"{data['message-id']}:{ data['symbol']}",
source=LINTER["name"],
)
diagnostics.append(diagnostic)
return diagnostics
def _lint_and_publish_diagnostics(
params: Union[types.DidOpenTextDocumentParams, types.DidSaveTextDocumentParams]
) -> None:
"""Runs linter, processes the output, and publishes the diagnostics over LSP."""
document = LSP_SERVER.workspace.get_document(params.text_document.uri)
if utils.is_stdlib_file(document.path):
# Don't lint standard library python files.
# Publishing empty diagnostics clears the entry.
LSP_SERVER.publish_diagnostics(document.uri, [])
return
module = LINTER["module"]
use_stdin = LINTER["useStdin"]
use_path = len(SETTINGS["path"]) > 0
argv = SETTINGS["path"] if use_path else [module]
argv += LINTER["args"] + SETTINGS["args"]
argv += ["--from-stdin", document.path] if use_stdin else [document.path]
if use_path:
result = utils.run_path(argv, use_stdin, document.source)
else:
# This is needed to preserve sys.path, pylint modifies
# sys.path and that might not work for this scenario
# next time around.
with utils.SubstituteAttr(sys, "path", sys.path[:]):
result = utils.run_module(module, argv, use_stdin, document.source)
if result.stderr:
LSP_SERVER.show_message_log(result.stderr)
LSP_SERVER.show_message_log(f"{document.uri} :\r\n{result.stdout}")
diagnostics = _parse_output(
result.stdout,
LINTER["lineStartsAt1"],
LINTER["columnStartsAt1"],
SETTINGS["severity"],
)
LSP_SERVER.publish_diagnostics(document.uri, diagnostics)
@LSP_SERVER.feature(lsp.INITIALIZE)
def initialize(params: types.InitializeParams):
"""LSP handler for initialize request."""
paths = "\r\n".join(sys.path)
LSP_SERVER.show_message_log(f"sys.path used to run Linter:\r\n{paths}")
# First get workspace settings to know if we are using linter
# module or binary.
global SETTINGS # pylint: disable=global-statement
SETTINGS = params.initialization_options["settings"]
global LINTER # pylint: disable=global-statement
LINTER = utils.get_linter_options_by_version(
all_configurations,
SETTINGS["path"] if len(SETTINGS["path"]) > 0 else None,
)
@LSP_SERVER.feature(lsp.TEXT_DOCUMENT_DID_OPEN)
def did_open(_server: server.LanguageServer, params: types.DidOpenTextDocumentParams):
"""LSP handler for textDocument/didOpen request."""
_lint_and_publish_diagnostics(params)
@LSP_SERVER.feature(lsp.TEXT_DOCUMENT_DID_SAVE)
def did_save(_server: server.LanguageServer, params: types.DidSaveTextDocumentParams):
"""LSP handler for textDocument/didOpen request."""
_lint_and_publish_diagnostics(params)
@LSP_SERVER.feature(lsp.TEXT_DOCUMENT_DID_CLOSE)
def did_close(_server: server.LanguageServer, params: types.DidCloseTextDocumentParams):
"""LSP handler for textDocument/didClose request."""
# Publishing empty diagnostics to clear the entries for this file.
text_document = LSP_SERVER.workspace.get_document(params.text_document.uri)
LSP_SERVER.publish_diagnostics(text_document.uri, [])
if __name__ == "__main__":
LSP_SERVER.start_io()
| 31.38172 | 88 | 0.664211 | 0 | 0 | 0 | 0 | 1,513 | 0.259208 | 0 | 0 | 1,624 | 0.278225 |
2717b6d6d414bb9ece4ff0a008bac03ea5586ef2 | 27 | py | Python | api/web/__init__.py | AutoCoinDCF/NEW_API | f4abc48fff907a0785372b941afcd67e62eec825 | [
"Apache-2.0"
]
| null | null | null | api/web/__init__.py | AutoCoinDCF/NEW_API | f4abc48fff907a0785372b941afcd67e62eec825 | [
"Apache-2.0"
]
| null | null | null | api/web/__init__.py | AutoCoinDCF/NEW_API | f4abc48fff907a0785372b941afcd67e62eec825 | [
"Apache-2.0"
]
| null | null | null | from .webapi import WebAPI
| 13.5 | 26 | 0.814815 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
27183201b985ec1686bc23947d232084caa6809f | 422 | py | Python | example/test.py | long-gong/libbloom-wrapper | 628f8ce11a6708d53bb56104182909ca40951f4f | [
"BSD-2-Clause"
]
| null | null | null | example/test.py | long-gong/libbloom-wrapper | 628f8ce11a6708d53bb56104182909ca40951f4f | [
"BSD-2-Clause"
]
| null | null | null | example/test.py | long-gong/libbloom-wrapper | 628f8ce11a6708d53bb56104182909ca40951f4f | [
"BSD-2-Clause"
]
| null | null | null | from pybloom import BloomFilter
if __name__ == '__main__':
total_items = 9585058
error = 0.01
bf = BloomFilter(capacity=total_items, error_rate=error)
for i in range(total_items):
bf.add(i)
cf = 0
ct = 0
for i in range(total_items, 2*total_items):
if i in bf:
cf += 1
ct += 1
fpr = cf * 1.0 / ct * 100
print("false positive rate: %.2f" % fpr)
| 22.210526 | 60 | 0.566351 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 37 | 0.087678 |
2718f33aa7d3ea7dfd6692935997031e9e47c7ff | 397 | py | Python | ifcbdb/dashboard/migrations/0033_dataset_doi.py | veot/ifcbdb | 427be36d92ca3c2dc6c8c10aaba94fcadc7cc93e | [
"MIT"
]
| 4 | 2019-05-23T13:38:36.000Z | 2021-02-25T23:08:24.000Z | ifcbdb/dashboard/migrations/0033_dataset_doi.py | veot/ifcbdb | 427be36d92ca3c2dc6c8c10aaba94fcadc7cc93e | [
"MIT"
]
| 313 | 2019-05-14T19:43:17.000Z | 2022-03-21T14:40:15.000Z | ifcbdb/dashboard/migrations/0033_dataset_doi.py | veot/ifcbdb | 427be36d92ca3c2dc6c8c10aaba94fcadc7cc93e | [
"MIT"
]
| 3 | 2020-10-26T05:23:04.000Z | 2021-07-22T09:54:56.000Z | # Generated by Django 2.1.7 on 2019-10-31 16:30
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('dashboard', '0032_auto_20191008_2022'),
]
operations = [
migrations.AddField(
model_name='dataset',
name='doi',
field=models.CharField(blank=True, max_length=256),
),
]
| 20.894737 | 63 | 0.602015 | 304 | 0.765743 | 0 | 0 | 0 | 0 | 0 | 0 | 97 | 0.244332 |
271c49cbaa02490060b18e56f8f7193420d20ee7 | 2,411 | py | Python | src/utils/roles.py | dciborow/SubscriptionPolicy | 100718bca552fb92edcb1867a94aba1f2d131edc | [
"MIT"
]
| null | null | null | src/utils/roles.py | dciborow/SubscriptionPolicy | 100718bca552fb92edcb1867a94aba1f2d131edc | [
"MIT"
]
| null | null | null | src/utils/roles.py | dciborow/SubscriptionPolicy | 100718bca552fb92edcb1867a94aba1f2d131edc | [
"MIT"
]
| null | null | null | from .cmdline import CmdUtils
class AzRole:
def __init__(self):
self.subscription = None
self.id = None
self.name = None
self.principalId = None
self.principalName = None
self.principalType = None
self.roleDefinitionName = None
self.roleDefinitionId = None
self.scope = None
def get_delete_command(self):
command = "az role assignment delete --assignee {} --role {} --scope {} --subscription {}".format(
self.principalId,
self.roleDefinitionId,
self.scope,
self.subscription
)
return command
def delete(self):
command = self.get_delete_command()
command = command.split(' ')
print("Deleting {} role for {}".format(
self.principalType,
self.principalName
))
CmdUtils.get_command_output(command,False)
def _load_raw(self, az_role_json:dict):
for key in az_role_json:
setattr(self, key, az_role_json[key])
class AzRolesUtils:
@staticmethod
def get_sub_roles(sub_id, raw:bool = True):
output = CmdUtils.get_command_output(
[
"az",
"role",
"assignment",
"list",
"--include-classic-administrators",
"false",
"--subscription",
sub_id
]
)
if raw is True:
return output
return AzRolesUtils._convert_raw_roles(output, sub_id)
@staticmethod
def get_all_roles(sub_id : str, raw:bool = True):
output = CmdUtils.get_command_output(
[
"az",
"role",
"assignment",
"list",
"--all",
"--include-classic-administrators",
"false",
"--subscription",
sub_id
]
)
if raw is True:
return output
return AzRolesUtils._convert_raw_roles(output, sub_id)
@staticmethod
def _convert_raw_roles(raw_roles, sub_id):
return_list = []
for r in raw_roles:
cur_role = AzRole()
cur_role._load_raw(r)
cur_role.subscription = sub_id
return_list.append(cur_role)
return return_list
| 26.788889 | 106 | 0.520116 | 2,375 | 0.985068 | 0 | 0 | 1,319 | 0.547076 | 0 | 0 | 285 | 0.118208 |
271ca124718f33d6bf24a7362d654f72b7d37716 | 950 | py | Python | babyonboard/api/migrations/0005_auto_20171124_1824.py | BabyOnBoard/BabyOnBoard-API | eef34bf4e9649409a3158d6826432acb040afa32 | [
"MIT"
]
| null | null | null | babyonboard/api/migrations/0005_auto_20171124_1824.py | BabyOnBoard/BabyOnBoard-API | eef34bf4e9649409a3158d6826432acb040afa32 | [
"MIT"
]
| 10 | 2017-11-23T18:28:11.000Z | 2021-06-10T19:53:06.000Z | babyonboard/api/migrations/0005_auto_20171124_1824.py | BabyOnBoard/BabyOnBoard-API | eef34bf4e9649409a3158d6826432acb040afa32 | [
"MIT"
]
| null | null | null | # -*- coding: utf-8 -*-
# Generated by Django 1.11.5 on 2017-11-24 18:24
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('api', '0004_babycrib'),
]
operations = [
migrations.CreateModel(
name='Noise',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('is_crying', models.BooleanField()),
('date', models.DateField(auto_now=True)),
('time', models.TimeField(auto_now=True)),
],
),
migrations.AlterField(
model_name='babycrib',
name='status',
field=models.CharField(choices=[('resting', 'Resting'), ('front', 'Front'), ('side', 'Side'), ('vibration', 'Vibration')], default='resting', max_length=10),
),
]
| 31.666667 | 169 | 0.566316 | 792 | 0.833684 | 0 | 0 | 0 | 0 | 0 | 0 | 222 | 0.233684 |
271d8ca306320152e2b945243d46ba85a77910e7 | 1,880 | py | Python | harmoni_actuators/harmoni_speaker/test/unittest_speaker.py | interaction-lab/HARMONI | 9c88019601a983a1739744919a95247a997d3bb1 | [
"MIT"
]
| 7 | 2020-09-02T06:31:21.000Z | 2022-02-18T21:16:44.000Z | harmoni_actuators/harmoni_speaker/test/unittest_speaker.py | micolspitale93/HARMONI | cf6a13fb85e3efb4e421dbfd4555359c0a04acaa | [
"MIT"
]
| 61 | 2020-05-15T16:46:32.000Z | 2021-07-28T17:44:49.000Z | harmoni_actuators/harmoni_speaker/test/unittest_speaker.py | micolspitale93/HARMONI | cf6a13fb85e3efb4e421dbfd4555359c0a04acaa | [
"MIT"
]
| 3 | 2020-10-05T23:01:29.000Z | 2022-03-02T11:53:34.000Z | #!/usr/bin/env python3
PKG = 'test_harmoni_speaker'
# Common Imports
import unittest, rospy, rospkg, roslib, sys
#from unittest.mock import Mock, patch
# Specific Imports
from actionlib_msgs.msg import GoalStatus
from harmoni_common_msgs.msg import harmoniAction, harmoniFeedback, harmoniResult
from harmoni_common_lib.constants import State
from std_msgs.msg import String
import os, io
import ast
from harmoni_speaker.speaker_service import SpeakerService
import json
class TestSpeaker(unittest.TestCase):
def __init__(self, *args):
super(TestSpeaker, self).__init__(*args)
rospy.loginfo("For running unittest you should also run roscore into another terminal")
rospy.init_node("test_speaker")
def setUp(self):
rospack = rospkg.RosPack()
self.path = rospack.get_path("harmoni_tts") + "/temp_data/tts.wav"
self.result = False
rospy.loginfo("TestSpeaker: Started up. waiting for speaker startup")
self.speaker_service = SpeakerService("test_speaker")
rospy.loginfo("TestSpeaker: Started")
def test_play(self):
# Send a request to the real API server and store the response.
response = self.speaker_service.do(self.path)
# Confirm that the request-response cycle completed successfully.
rospy.loginfo(response)
if response["response"]==State.SUCCESS:
rospy.loginfo("The response succeed")
self.result = True #set the response to true if the request succeeded
assert(self.result == True)
def main():
#TODO convert to a test suite so that setup doesn't have to run over and over.
import rosunit
rospy.loginfo("test_speaker started")
rospy.loginfo("TestSpeaker: sys.argv: %s" % str(sys.argv))
rosunit.unitrun(PKG, 'test_speaker', TestSpeaker, sys.argv)
if __name__ == "__main__":
main()
| 35.471698 | 95 | 0.710106 | 1,083 | 0.576064 | 0 | 0 | 0 | 0 | 0 | 0 | 686 | 0.364894 |
271fa52916c14e0243668b5ba4593d20238992fd | 3,996 | py | Python | ExCon/explainer.py | DarrenZhang01/ExCon | 2467c2fa8c0c52edaf54091d2bfecd132eeae594 | [
"Apache-2.0"
]
| 17 | 2021-11-30T03:50:24.000Z | 2022-01-16T10:58:07.000Z | ExCon/explainer.py | DarrenZhang01/ExCon | 2467c2fa8c0c52edaf54091d2bfecd132eeae594 | [
"Apache-2.0"
]
| 1 | 2021-12-04T02:35:59.000Z | 2021-12-05T00:53:35.000Z | ExCon/explainer.py | DarrenZhang01/ExCon | 2467c2fa8c0c52edaf54091d2bfecd132eeae594 | [
"Apache-2.0"
]
| null | null | null | """
An utility class for initializing different explainer objects.
"""
from torch import nn
import numpy as np
from captum.attr import DeepLift, IntegratedGradients, ShapleyValueSampling, LayerGradCam, Saliency
from captum.attr._utils.attribution import LayerAttribution
class Explainer(nn.Module):
def __init__(self, method, model_name, dataset, model, training):
super(Explainer, self).__init__()
self.model = model
self.explainer = None
if method == "DeepLift":
self.explainer = DeepLift(self.model)
elif method == "IntegratedGradients":
self.explainer = IntegratedGradients(self.model)
elif method == "ShapleyValueSampling":
self.explainer = ShapleyValueSampling(self.model)
elif method == "Saliency":
self.explainer = Saliency(self.model)
elif method == "GradCAM":
# Need to retrieve the `module` in the data parallel mode for multi-GPU processing.
if model_name == 'vgg16':
if dataset.startswith('cifar'):
self.explainer = LayerGradCam(self.model, self.model[0].features[21])
else:
self.explainer = LayerGradCam(self.model, self.model[0].features[30])
elif model_name == 'resnet56':
self.explainer = LayerGradCam(self.model, self.model[0].layer3[8].conv2)
elif model_name == 'resnet50':
if dataset.startswith('cifar'):
if "CE" in training:
self.explainer = LayerGradCam(self.model, self.model.module.encoder.layer2[3].conv3)
else:
self.explainer = LayerGradCam(self.model, self.model[0].layer2[3].conv3)
else:
if "CE" in training:
self.explainer = LayerGradCam(self.model, self.model.module.encoder.layer4[2].conv3)
else:
self.explainer = LayerGradCam(self.model, self.model[0].layer4[2].conv3)
elif model_name == 'resnet18':
if dataset.startswith('cifar'):
if "CE" in training:
self.explainer = LayerGradCam(self.model, self.model.module.encoder.layer2[1].conv2)
else:
self.explainer = LayerGradCam(self.model, self.model[0].layer2[1].conv2)
else:
if "CE" in training:
self.explainer = LayerGradCam(self.model, self.model.module.encoder.layer4[1].conv2)
else:
self.explainer = LayerGradCam(self.model, self.model[0].layer4[1].conv2)
self.method = method
if dataset in ['cifar10', 'cifar100', 'SVHN']:
self.img_size = (32, 32)
elif dataset == 'ImageNet':
self.img_size = (64, 64)
def attribute(self, input, target, omit_channel=True):
"""
omit_channel: bool, whether to omit the channel dimension in the explanations.
"""
importances = self.explainer.attribute(inputs=input, target=target)
if self.method == "GradCAM":
importances = LayerAttribution.interpolate(importances,
self.img_size,
interpolate_mode="bilinear")
if omit_channel is False:
importances = importances.repeat((1, 3, 1, 1))
else:
if omit_channel is False:
importances = importances
else:
importances = importances.sum(dim=1)
importances = importances.squeeze().detach().cpu().numpy()
return importances
def normalize(self, importances):
importances = importances - np.min(importances, axis=(0, 1))
importances = importances / (np.max(importances, axis=(0, 1)) + 1e-8)
return importances
| 47.571429 | 108 | 0.567067 | 3,721 | 0.931181 | 0 | 0 | 0 | 0 | 0 | 0 | 455 | 0.113864 |
271fe8cb4638bb263355e0c702a01f25cb73e941 | 2,188 | py | Python | django/www/MeteoGaliciaDB/registros/views.py | hugo-lorenzo-mato/Meteo-Galicia-DB | 3dd52534c16216de5f25cd40877d2facc7cffe24 | [
"MIT"
]
| null | null | null | django/www/MeteoGaliciaDB/registros/views.py | hugo-lorenzo-mato/Meteo-Galicia-DB | 3dd52534c16216de5f25cd40877d2facc7cffe24 | [
"MIT"
]
| null | null | null | django/www/MeteoGaliciaDB/registros/views.py | hugo-lorenzo-mato/Meteo-Galicia-DB | 3dd52534c16216de5f25cd40877d2facc7cffe24 | [
"MIT"
]
| 1 | 2021-04-27T18:37:41.000Z | 2021-04-27T18:37:41.000Z | from django.shortcuts import render
from . import forms
from django.core.urlresolvers import reverse
from django.contrib.auth.decorators import login_required
from django.http import HttpResponse, HttpResponseRedirect
from django.contrib.auth import authenticate,login,logout
# Create your views here.
@login_required
def special(request):
return HttpResponse("Estás logueado!")
@login_required
def user_logout(request):
logout(request)
logged = False
return render(request, 'registros/login.html',{'logged':logged})
def registro(request):
registered = False
if request.method == 'POST':
user_form = forms.UserForm(data=request.POST)
profile_form = forms.UserProfileInfo(data=request.POST)
if user_form.is_valid() and profile_form.is_valid():
user = user_form.save()
user.set_password(user.password)
user.save()
profile = profile_form.save(commit=False)
profile.user = user
if 'profile_pic' in request.FILES:
profile.profile_pic = request.FILES['profile_pic']
profile.save()
registered = True
else:
print(user_form.errors,profile_form.errors)
else:
user_form = forms.UserForm()
profile_form = forms.UserProfileInfo()
return render(request,'registros/registration.html', {'registered': registered, 'user_form': user_form,'profile_form':profile_form})
def user_login(request):
logged = False
if request.method == 'POST':
username = request.POST.get('username')
password = request.POST.get('password')
user = authenticate(username=username, password=password)
if user:
if user.is_active:
login(request, user)
logged = True
return render(request, 'registros/login.html', {'logged': logged})
else:
return HttpResponse("Cuenta inactiva")
else:
print("Alguien intento loguearse y falló")
return HttpResponse("Datos de acceso inválidos")
else:
return render(request, 'registros/login.html',{'logged':logged}) | 33.151515 | 136 | 0.652194 | 0 | 0 | 0 | 0 | 230 | 0.104975 | 0 | 0 | 338 | 0.154267 |
2721ab7a1f61ce9281bd09517b3b6a6328c17752 | 708 | py | Python | geotrek/outdoor/admin.py | numahell/Geotrek-admin | e279875b0b06ef60928c049d51533f76716c902a | [
"BSD-2-Clause"
]
| 1 | 2019-12-11T11:04:05.000Z | 2019-12-11T11:04:05.000Z | geotrek/outdoor/admin.py | numahell/Geotrek-admin | e279875b0b06ef60928c049d51533f76716c902a | [
"BSD-2-Clause"
]
| null | null | null | geotrek/outdoor/admin.py | numahell/Geotrek-admin | e279875b0b06ef60928c049d51533f76716c902a | [
"BSD-2-Clause"
]
| null | null | null | from django.conf import settings
from django.contrib import admin
from geotrek.common.admin import MergeActionMixin
from geotrek.outdoor.models import Practice, SiteType
if 'modeltranslation' in settings.INSTALLED_APPS:
from modeltranslation.admin import TranslationAdmin
else:
TranslationAdmin = admin.ModelAdmin
@admin.register(Practice)
class PracticeAdmin(MergeActionMixin, TranslationAdmin):
list_display = ('name', )
search_fields = ('name', )
merge_field = 'name'
@admin.register(SiteType)
class SiteTypeAdmin(MergeActionMixin, TranslationAdmin):
list_display = ('name', 'practice')
list_filter = ('practice', )
search_fields = ('name', )
merge_field = 'name'
| 28.32 | 56 | 0.75565 | 327 | 0.461864 | 0 | 0 | 379 | 0.535311 | 0 | 0 | 74 | 0.10452 |
2723629eca545ab7058f06d730db5b5baa33b30a | 3,516 | py | Python | gdrive.py | abwilf/Factorized | 64e7d2a54bbfbc8b1c5a2130f2b941c376402fe6 | [
"MIT"
]
| null | null | null | gdrive.py | abwilf/Factorized | 64e7d2a54bbfbc8b1c5a2130f2b941c376402fe6 | [
"MIT"
]
| null | null | null | gdrive.py | abwilf/Factorized | 64e7d2a54bbfbc8b1c5a2130f2b941c376402fe6 | [
"MIT"
]
| null | null | null | from __future__ import print_function
import os.path
from googleapiclient.discovery import build
from google_auth_oauthlib.flow import InstalledAppFlow
from google.auth.transport.requests import Request
from google.oauth2.credentials import Credentials
from apiclient.http import MediaFileUpload
import gdown
def gdrive_up(credentials_path, file_list, folder_id, token_path='/work/awilf/utils/gdrive_token.json'):
'''
credentials_path: json containing gdrive credentials of form {"installed":{"client_id":"<something>.apps.googleusercontent.com","project_id":"decisive-engine-<something>","auth_uri":"https://accounts.google.com/o/oauth2/auth","token_uri":"https://oauth2.googleapis.com/token","auth_provider_x509_cert_url":"https://www.googleapis.com/oauth2/v1/certs","client_secret":"<client_secret>","redirect_uris":["urn:ietf:wg:oauth:2.0:oob","http://localhost"]}}
file_list: full path of files to upload, e.g. ['/work/awilf/tonicnet.tar']
folder_id: id of folder you've already created in google drive (awilf@andrew account, for these credentials)
e.g.
gdrive_up('gdrive_credentials.json', ['hi.txt', 'yo.txt'], '1E1ub35TDJP59rlIqDBI9SLEncCEaI4aT')
note: if token_path does not exist, you will need to authenticate. here are the instructions
ON MAC: ssh -N -f -L localhost:8080:localhost:8080 awilf@taro
ON MAC (CHROME): go to link provided
'''
# If modifying these scopes, delete the file token.json.
SCOPES = ['https://www.googleapis.com/auth/drive.metadata.readonly', 'https://www.googleapis.com/auth/drive.file']
creds = None
if os.path.exists(token_path): # UNCOMMENT THIS IF DON'T WANT TO LOG IN EACH TIME
creds = Credentials.from_authorized_user_file(token_path, SCOPES)
if not creds or not creds.valid:
if creds and creds.expired and creds.refresh_token:
creds.refresh(Request())
else:
flow = InstalledAppFlow.from_client_secrets_file(credentials_path, SCOPES)
creds = flow.run_local_server(port=8080)
with open(token_path, 'w') as token:
token.write(creds.to_json())
service = build('drive', 'v3', credentials=creds)
for name in file_list:
file_metadata = {
'name': name,
'parents': [folder_id]
}
media = MediaFileUpload(file_metadata['name'], resumable=True)
file = service.files().create(body=file_metadata, media_body=media, fields='id').execute()
def gdrive_down(url, out_path=None):
'''
first, make sure file in url is available for anyone to view
url should be of one of these forms:
https://drive.google.com/file/d/195C6CoqMBYzteJIx-FFOsNFATvu5cr_z/view?usp=sharing
https://drive.google.com/uc?id=1eGj8DSau66NiklH30UIGab55cUWR_qw9
out_path can be None, in which case the result will be the file name from google drive saved in ./. else, save to out_path
'''
if 'uc?' not in url:
id = url.split('/')[-2]
url = f'https://drive.google.com/uc?id={id}'
gdown.download(url, out_path)
# gdrive_down('https://drive.google.com/file/d/1dAvxdsHWbtA1ZIh3Ex9DPn9Nemx9M1-L/view', out_path='/work/awilf/mfa/')
# gdrive_down('https://drive.google.com/file/d/1XEsc6rLXtjfo2rtms2GR0hDqfTiat5Zo/view?usp=sharing')
# gdrive_up('/work/awilf/utils/gdrive_credentials.json', ['pyg.sif'], '1zBldu3ipR6LtrJBxxNlaKBPW_kio6nli')
gdrive_down('https://drive.google.com/file/d/1eEdRQVgBCcq8DyasduZpMzTlCIjrekLM/view?usp=sharing')
| 45.662338 | 455 | 0.71587 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2,125 | 0.60438 |
2725c4c1b926b1b3e672d43af2ed2535f3dcf9a6 | 705 | py | Python | web_ext/sseq_gui/tests/test_load.py | hoodmane/sseq | 0f19a29c95486a629b0d054c703ca0a58999ae97 | [
"Apache-2.0",
"MIT"
]
| 7 | 2021-04-22T04:06:09.000Z | 2022-01-25T04:05:49.000Z | web_ext/sseq_gui/tests/test_load.py | hoodmane/sseq | 0f19a29c95486a629b0d054c703ca0a58999ae97 | [
"Apache-2.0",
"MIT"
]
| 68 | 2020-03-21T22:37:24.000Z | 2022-03-31T02:51:35.000Z | web_ext/sseq_gui/tests/test_load.py | hoodmane/sseq | 0f19a29c95486a629b0d054c703ca0a58999ae97 | [
"Apache-2.0",
"MIT"
]
| 5 | 2021-02-17T06:37:43.000Z | 2022-02-01T03:53:22.000Z | import pytest
from pathlib import Path
@pytest.mark.parametrize("module", ["S_2", "S_3", "C2v14"])
def test_load(driver, module: str):
driver.go("/")
driver.driver.find_element_by_css_selector(f'a[data="{module}"]').click()
driver.wait_complete()
driver.check_svg(f"{module}_load.svg")
@pytest.mark.parametrize("module", ["S_2", "S_3", "C2v14"])
def test_load_json(driver, module: str):
path = Path(__file__).parent / "../../../ext/steenrod_modules" / f"{module}.json"
path = path.resolve()
driver.go("/")
driver.driver.find_element_by_id("json-upload").send_keys(str(path))
driver.reply("40")
driver.wait_complete()
driver.check_svg(f"{module}_load.svg")
| 30.652174 | 85 | 0.673759 | 0 | 0 | 0 | 0 | 660 | 0.93617 | 0 | 0 | 181 | 0.256738 |
2726aff9726f405364127c08f5f1c2bf93aa5997 | 91 | py | Python | src/rocket/stage2/src/errors/src/SensorOverload.py | proballstar/atlas | 6e4eb36b7e43e750dbb281c2051439198c82f296 | [
"MIT"
]
| null | null | null | src/rocket/stage2/src/errors/src/SensorOverload.py | proballstar/atlas | 6e4eb36b7e43e750dbb281c2051439198c82f296 | [
"MIT"
]
| null | null | null | src/rocket/stage2/src/errors/src/SensorOverload.py | proballstar/atlas | 6e4eb36b7e43e750dbb281c2051439198c82f296 | [
"MIT"
]
| null | null | null | def init():
# @TODO(aaronhma): UPDATE
def catchErrors():
# @TODO(aaronhma): UPDATE | 18.2 | 29 | 0.626374 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 50 | 0.549451 |
2727cc439b34121ae42c9bebd5ffc173f65fc3c6 | 5,905 | py | Python | foresight/environment/environment_info_support.py | thundra-io/thundra-agent-python | 448e18c17d8730c381b2e2a773782cf80c5a7cfb | [
"Apache-2.0"
]
| 15 | 2021-07-28T08:03:50.000Z | 2021-11-08T08:36:06.000Z | foresight/environment/environment_info_support.py | thundra-io/thundra-agent-python | 448e18c17d8730c381b2e2a773782cf80c5a7cfb | [
"Apache-2.0"
]
| 1 | 2021-08-08T07:45:45.000Z | 2021-08-08T12:41:36.000Z | foresight/environment/environment_info_support.py | thundra-io/thundra-agent-python | 448e18c17d8730c381b2e2a773782cf80c5a7cfb | [
"Apache-2.0"
]
| 3 | 2021-08-07T14:19:23.000Z | 2021-12-08T15:35:40.000Z | import os, logging
from foresight.environment.git.git_helper import GitHelper
from foresight.environment.git.git_env_info_provider import GitEnvironmentInfoProvider
from foresight.environment.github.github_environment_info_provider import GithubEnvironmentInfoProvider
from foresight.environment.gitlab.gitlab_environment_info_provider import GitlabEnvironmentInfoProvider
from foresight.environment.jenkins.jenkins_environment_info_provider import JenkinsEnvironmentInfoProvider
from foresight.environment.travisci.travisci_environment_info_provider import TravisCIEnvironmentInfoProvider
from foresight.environment.circleci.circleci_environment_info_provider import CircleCIEnvironmentInfoProvider
from foresight.environment.bitbucket.bitbucket_environment_info_provider import BitbucketEnvironmentInfoProvider
from foresight.environment.azure.azure_environment_info_provider import AzureEnvironmentInfoProvider
from foresight.test_runner_tags import TestRunnerTags
from foresight.utils.generic_utils import print_debug_message_to_console
LOGGER = logging.getLogger(__name__)
class EnvironmentSupport:
ENVIRONMENTS_VARS = {
"GITHUB_ENV": GithubEnvironmentInfoProvider, # https://docs.github.com/en/actions/learn-github-actions/environment-variables
"CI_PROJECT_ID": GitlabEnvironmentInfoProvider, # https://docs.gitlab.com/ee/ci/variables/predefined_variables.html
"JENKINS_HOME": JenkinsEnvironmentInfoProvider, # https://e.printstacktrace.blog/jenkins-pipeline-environment-variables-the-definitive-guide/
"JENKINS_URL": JenkinsEnvironmentInfoProvider, # https://e.printstacktrace.blog/jenkins-pipeline-environment-variables-the-definitive-guide/
"TRAVIS": TravisCIEnvironmentInfoProvider, # https://docs.travis-ci.com/user/environment-variables/#default-environment-variables
"CIRCLECI": CircleCIEnvironmentInfoProvider, # https://circleci.com/docs/2.0/env-vars/#built-in-environment-variables
"BITBUCKET_GIT_HTTP_ORIGIN": BitbucketEnvironmentInfoProvider, # https://support.atlassian.com/bitbucket-cloud/docs/variables-and-secrets/
"BITBUCKET_GIT_SSH_ORIGIN": BitbucketEnvironmentInfoProvider, # https://support.atlassian.com/bitbucket-cloud/docs/variables-and-secrets/
"TF_BUILD": AzureEnvironmentInfoProvider # https://docs.microsoft.com/en-us/azure/devops/pipelines/build/variables?view=azure-devops&tabs=yaml
}
environment_info = None
@classmethod
def init(cls):
"""First check git provider, then iterate over ENVIRONMENTS_VARS dict.
"""
try:
LOGGER.debug("Checking ci environments...")
for key, clz in cls.ENVIRONMENTS_VARS.items():
LOGGER.debug("Current key, clz: {}, {}".format(key,clz))
if os.getenv(key):
ei = clz.build_env_info()
if ei:
cls.environment_info = ei
print_debug_message_to_console("Environment info: {}".format(cls.environment_info.to_json()))
print_debug_message_to_console("Founded key and class: {}, {}".format(key, clz))
break
if cls.environment_info == None:
if GitHelper.get_repo_url():
print_debug_message_to_console("Couldn't find any ci envrionment! Trying .git file...")
cls.environment_info = GitEnvironmentInfoProvider.build_env_info()
print_debug_message_to_console("Environment info: {}".format(cls.environment_info.to_json()))
else:
print_debug_message_to_console("Couldn't find .git file!")
except Exception as err:
LOGGER.error("Environment Support environment_info could not set: {}".format(err))
cls.environment_info = None
print_debug_message_to_console("environment_info is None!")
pass
@classmethod
def set_span_tags(cls, span):
"""Set span data tags corresponds to TestRunner
Args:
obj (ThundraSpan): Span or invocation data
"""
try:
if cls.environment_info:
span.set_tag(TestRunnerTags.TEST_ENVIRONMENT, cls.environment_info.environment)
span.set_tag(TestRunnerTags.SOURCE_CODE_REPO_URL, cls.environment_info.repo_url)
span.set_tag(TestRunnerTags.SOURCE_CODE_REPO_NAME, cls.environment_info.repo_name)
span.set_tag(TestRunnerTags.SOURCE_CODE_BRANCH, cls.environment_info.branch)
span.set_tag(TestRunnerTags.SOURCE_CODE_COMMIT_HASH, cls.environment_info.commit_hash)
span.set_tag(TestRunnerTags.SOURCE_CODE_COMMIT_MESSAGE, cls.environment_info.commit_message)
except Exception as e:
LOGGER.error("Couldn't set environment info for span tags.")
pass
@classmethod
def set_invocation_tags(cls, invocation_data):
"""Set invocation data tags corresponds to TestRunner
Args:
obj (invocation): Span or invocation data
"""
try:
if cls.environment_info:
invocation_data[TestRunnerTags.TEST_ENVIRONMENT] = cls.environment_info.environment
invocation_data[TestRunnerTags.SOURCE_CODE_REPO_URL] = cls.environment_info.repo_url
invocation_data[TestRunnerTags.SOURCE_CODE_REPO_NAME] = cls.environment_info.repo_name
invocation_data[TestRunnerTags.SOURCE_CODE_BRANCH] = cls.environment_info.branch
invocation_data[TestRunnerTags.SOURCE_CODE_COMMIT_HASH] = cls.environment_info.commit_hash
invocation_data[TestRunnerTags.SOURCE_CODE_COMMIT_MESSAGE] = cls.environment_info.commit_message
except Exception as e:
LOGGER.error("Couldn't set environment info for invocation tags.")
pass | 60.255102 | 150 | 0.720745 | 4,825 | 0.817104 | 0 | 0 | 3,454 | 0.584928 | 0 | 0 | 1,613 | 0.273158 |
272ad5e735d859c51d641dfdb613ff9296e5cbee | 660 | py | Python | Problem_Solving/Algorithms/Warmup/6_Plus_Minus/Solution.py | CFLSousa/HackerRank | 29ed039634e88d72981b2ecd619e5c65d37111e4 | [
"MIT"
]
| null | null | null | Problem_Solving/Algorithms/Warmup/6_Plus_Minus/Solution.py | CFLSousa/HackerRank | 29ed039634e88d72981b2ecd619e5c65d37111e4 | [
"MIT"
]
| null | null | null | Problem_Solving/Algorithms/Warmup/6_Plus_Minus/Solution.py | CFLSousa/HackerRank | 29ed039634e88d72981b2ecd619e5c65d37111e4 | [
"MIT"
]
| null | null | null | import math
import os
import random
import re
import sys
def plusMinus(arr):
posNums=0.0
negNums=0.0
zeroNums=0.0
posFraction=0.0
negFraction=0.0
zeroFraction=0.0
arrLen=len(arr)
for i in range(arrLen):
if arr[i]==0:
zeroNums+=1
elif arr[i]>0:
posNums+=1
else:
negNums+=1
posFraction=posNums/arrLen
negFraction=negNums/arrLen
zeroFraction=zeroNums/arrLen
print(f'{posFraction:.6f}\n{negFraction:.6f}\n{zeroFraction:.6f}')
if __name__ == '__main__':
n = int(input())
arr = list(map(int, input().rstrip().split()))
plusMinus(arr)
| 18.333333 | 71 | 0.59697 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 69 | 0.104545 |
272af550504e85a1489b88397788f6d4d8f037c0 | 1,403 | py | Python | golang/godoc.py | nlfiedler/devscripts | 122c7b1424b457d7d5499552065da83d76f6b922 | [
"BSD-3-Clause"
]
| null | null | null | golang/godoc.py | nlfiedler/devscripts | 122c7b1424b457d7d5499552065da83d76f6b922 | [
"BSD-3-Clause"
]
| 1 | 2015-03-04T15:01:08.000Z | 2015-03-04T15:01:08.000Z | golang/godoc.py | nlfiedler/devscripts | 122c7b1424b457d7d5499552065da83d76f6b922 | [
"BSD-3-Clause"
]
| null | null | null | #!/usr/bin/env python3
"""Start the godoc server and open the docs in a browser window.
This uses the 'open' command to open a web browser.
"""
import argparse
import http
import http.client
import subprocess
import time
def is_ready(host, port):
"""Check if the web server returns an OK status."""
conn = http.client.HTTPConnection(host, port)
try:
conn.request('HEAD', '/')
resp = conn.getresponse()
return resp.status == 200
except ConnectionRefusedError:
return False
def main():
"""Do the thing."""
parser = argparse.ArgumentParser(description='Spawn godoc and open browser window.')
parser.add_argument('--port', help='port on which to run godoc', default=6060)
args = parser.parse_args()
host = "localhost"
port = args.port
# If not already running, start godoc in the background and wait for it
# to be ready by making an HTTP request and checking the status.
if not is_ready(host, port):
subprocess.Popen(["godoc", "-http=:{port}".format(port=port)])
while True:
if is_ready(host, port):
break
print('Waiting for server to start...')
time.sleep(1)
# Open the docs in a browser window.
url = "http://{host}:{port}".format(host=host, port=port)
subprocess.check_call(["open", url])
if __name__ == "__main__":
main()
| 26.980769 | 88 | 0.639344 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 571 | 0.406985 |
272b0f6329b631e0b33f8623045bec69644ef0db | 19,371 | py | Python | mgsa/evaluate_repeats.py | supernifty/mgsa | 5f950f8c9c2bf0439a100a2348f1aef478e32934 | [
"MIT"
]
| 2 | 2016-11-02T20:27:00.000Z | 2019-10-23T08:14:44.000Z | mgsa/evaluate_repeats.py | supernifty/mgsa | 5f950f8c9c2bf0439a100a2348f1aef478e32934 | [
"MIT"
]
| null | null | null | mgsa/evaluate_repeats.py | supernifty/mgsa | 5f950f8c9c2bf0439a100a2348f1aef478e32934 | [
"MIT"
]
| null | null | null |
import glob
import os
import random
import re
import sys
import numpy as np
import bio
import config
def align( read, siteA, siteB, mapper_name, rm=True, alt_pos=1000, correct_pos=3000, single_insertion=False ):
prefix = 'tmp/tmp%i' % random.randint(1, 1e9)
# make fastq
fastq = '%s.fastq' % prefix
with open( fastq, 'w' ) as fq:
data = [ '@A', read, '+', '~' * len( read ) ]
fq.write( '\n'.join( data ) )
# fasta - both variations
#commands = 'i.1000.%s,i.3000.%s' % ( siteA, siteB )
commands = 'i.%i.%s,i.%i.%s' % ( alt_pos, siteB, correct_pos, siteA )
fasta = '%s.fa' % prefix
bio.fasta_edit( open( sys.argv[1], 'r' ), open( fasta, 'w' ), commands )
mapper = bio.get_mapper( config, mapper_name, fasta )
# align
sam = '%s.sam' % prefix
mapper( fastq, sam )
result_both = bio.SamFeatures( open( sam, 'r' ), None, classes="A", exclude_unmapped=False ).result[0]
if single_insertion:
# fasta - main variation only
commands = 'i.1000.%s' % ( siteA )
fasta = '%s.fa' % prefix
bio.fasta_edit( open( sys.argv[1], 'r' ), open( fasta, 'w' ), commands )
mapper = bio.get_mapper( config, mapper_name, fasta )
# align
sam = '%s.sam' % prefix
mapper( fastq, sam )
result_single = bio.SamFeatures( open( sam, 'r' ), None, classes="A", exclude_unmapped=False ).result[0]
else:
result_single = None
#result = bio.SamFeatures( open( sam, 'r' ), None, classes="A" ).result[0]
if rm:
[ os.remove( x ) for x in glob.glob( '%s*' % prefix ) ]
return result_both, result_single
#
# experiment to see what position does with one mutation
# what is the effect of position?
#
def experiment_pos( result_fh ):
#result_fh.write( "%s,%s,%s,%s,%s,%s,%s\n" % ( "read", "location", "alt", "mapper", "mapq_post", "mut_pos", "align_pos" ) )
result_fh.write( "%s,%s,%s,%s\n" % ( "mapper", "mapq_post", "mut_pos", "align_pos" ) )
read_len = 100
max_cand_mutations = 10
max_pos = 30 #100
matrix = { 'bwa': {}, 'bowtie2': {} }
sample = 100
gc = 0.5
for _ in xrange(sample):
s1 = bio.SequenceGenerator( read_len, (0.5-gc/2, gc/2, gc/2, 0.5-gc/2) )
s2 = bio.SequenceGenerator( 0 )
s2.sequence = s1.sequence
# 1 mutation
for mapper_name in matrix.keys():
for p in xrange(max_pos):
s2.sequence = s1.sequence
# just the 1 mutation to the read
read = bio.SequenceGenerator( 0 )
read.sequence = s1.sequence
read.mutate( p )
# apply mutations to candidate
for cand_mutations in xrange(max_cand_mutations):
cand_mutation_list = []
s2.sequence = s1.sequence
for m in xrange(cand_mutations):
#m_pos = 10 + ( read_len - 10 ) / cand_mutations * m + 5
while True:
#m_pos = random.randint(10, read_len - 1)
#m_pos = random.randint(0, 20)
m_pos = random.randint(10, read_len - 10)
if m_pos not in cand_mutation_list and m_pos != p:
break
#if m_pos == p:
# m_pos -= 2
cand_mutation_list.append( m_pos )
s2.mutate( cand_mutation_list[-1] )
r = align( read.sequence, s1.sequence, s2.sequence, mapper_name )
result_fh.write( "%s,%s,%i,%s,%f,%s,%s\n" % ( mapper_name, r[0]['mapq'], p, r[0]['pos'], gc, r[0]['cigar'], '-'.join([ str(x) for x in cand_mutation_list]) ) )
key = '%i,%i' % ( cand_mutations, p )
if key not in matrix[mapper_name]:
matrix[mapper_name][key] = []
matrix[mapper_name][key].append( int(r[0]['mapq']) )
#matrix[mapper_name]['%i,%i' % ( cand_mutations, p ) ] = int(r[0]['mapq'])
result_fh.write( 'x axis is candidate mutations, y axis is position of mutation on read\n' )
show_dist_matrix( result_fh, matrix, max_cand_mutations, max_pos )
#
# experiment to see what position does with one mutation
# what is the effect of position?
#
def experiment_genome_pos( result_fh ):
#result_fh.write( "%s,%s,%s,%s,%s,%s,%s\n" % ( "read", "location", "alt", "mapper", "mapq_post", "mut_pos", "align_pos" ) )
result_fh.write( "%s,%s,%s,%s\n" % ( "mapper", "mapq_post", "mut_pos", "align_pos" ) )
read_len = 100
offset_step = 25
matrix = { 'bwa': {}, 'bowtie2': {} }
max_cand_mutations = 6
gc = 0.5
samples = 100
for _ in xrange(samples):
s1 = bio.SequenceGenerator( read_len, (0.5-gc/2, gc/2, gc/2, 0.5-gc/2) )
s2 = bio.SequenceGenerator( 0 )
s2.sequence = s1.sequence
p = 20
# 1 mutation
for mapper_name in matrix.keys():
offset_count = 0
for offset in xrange(0, 1000, offset_step):
s2.sequence = s1.sequence
# just the 1 mutation to the read
read = bio.SequenceGenerator( 0 )
read.sequence = s1.sequence
read.mutate( p )
# apply mutations to candidate
for cand_mutations in xrange(max_cand_mutations):
cand_mutation_list = []
s2.sequence = s1.sequence
for m in xrange(cand_mutations):
#m_pos = 10 + ( read_len - 10 ) / cand_mutations * m + 5
while True:
#m_pos = random.randint(10, read_len - 1)
#m_pos = random.randint(0, 20)
m_pos = random.randint(10, read_len - 10)
if m_pos not in cand_mutation_list and m_pos != p:
break
#if m_pos == p:
# m_pos -= 2
cand_mutation_list.append( m_pos )
s2.mutate( cand_mutation_list[-1] )
r = align( read.sequence, s1.sequence, s2.sequence, mapper_name, alt_pos=1000+offset, correct_pos=3000-offset )
result_fh.write( "%s,%s,%i,%s,%f,%s,%s\n" % ( mapper_name, r[0]['mapq'], p, r[0]['pos'], gc, r[0]['cigar'], '-'.join([ str(x) for x in cand_mutation_list]) ) )
key = '%i,%i' % ( cand_mutations, offset_count )
if key not in matrix[mapper_name]:
matrix[mapper_name][key] = []
matrix[mapper_name][key].append( int(r[0]['mapq']) )
offset_count += 1
#matrix[mapper_name]['%i,%i' % ( cand_mutations, p ) ] = int(r[0]['mapq'])
result_fh.write( 'x axis is candidate mutations, y axis is offset\n' )
show_dist_matrix( result_fh, matrix, max_cand_mutations, offset_count )
#
# what is the mapping quality as the distance between repeats and the read change?
#
def experiment_candidate_distance( result_fh ):
result_fh.write( "%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s\n" % ( "read", "location", "alt", "mapper", "mapq_post", "mapq_pre", "pos_post", "pos_pre", "cand_mutations", "read_mutations", "post_cigar" ) )
read_len = 100
max_read_mutations = 12
max_cand_mutations = 12
samples = 100
gc = 0.5
#for gc in np.arange( 0., 1., 0.2 ):
matrix = { 'bwa': {}, 'bowtie2': {} }
for sample in xrange(samples):
s1 = bio.SequenceGenerator( read_len, (0.5-gc/2, gc/2, gc/2, 0.5-gc/2) )
s2 = bio.SequenceGenerator( 0 )
s2.sequence = s1.sequence
# 1 mutation
for mapper_name in ('bwa', 'bowtie2'):
#for p in xrange(0, read_len, 10):
#for p in xrange(10):
# s2.sequence = s1.sequence
# s2.mutate( p )
# r = align( s1.sequence, s1.sequence, s2.sequence, mapper_name )
# #result_fh.write( "%s,%s,%s,%s,%s\n" % ( s1.sequence, s1.sequence, s2.sequence, mapper_name, r ) )
# result_fh.write( "%s,%s,%s,%s,%i,%i,1\n" % ( s1.sequence, s1.sequence, s2.sequence, mapper_name, r[0], r[1] ) )
for read_mutations in xrange(max_read_mutations):
read = bio.SequenceGenerator( 0 )
read.sequence = s1.sequence
# add mutations to read
read_mutation_list = []
for m in xrange(read_mutations):
while True:
m_pos = random.randint( 10, read_len - 10 )
if m_pos not in read_mutation_list:
break
read_mutation_list.append( m_pos ) # don't mutate in the same place
read.mutate( read_mutation_list[-1] )
for cand_mutations in xrange(max_cand_mutations):
# add mutations to alternative
cand_mutation_list = []
s2.sequence = s1.sequence
for m in xrange(cand_mutations):
while True:
m_pos = random.randint( 10, read_len - 10 )
if m_pos not in read_mutation_list and m_pos not in cand_mutation_list:
break
cand_mutation_list.append( m_pos )
s2.mutate( cand_mutation_list[-1] )
s2.mutate( cand_mutation_list[-1] ) # mutate twice so it is still different from cand
# align and add result
r = align( read.sequence, s1.sequence, s2.sequence, mapper_name ) # only populates r[0]
#result_fh.write( "%s,%s,%s,%s,%s\n" % ( s1.sequence, s1.sequence, s2.sequence, mapper_name, r ) )
result_fh.write( "%i,%s,%i,%i,%s,%s,%s\n" % ( sample, mapper_name, int(r[0]['mapq']), int(r[0]['pos']), '-'.join( [ str(x) for x in cand_mutation_list ] ), '-'.join( [ str(x) for x in read_mutation_list ] ), r[0]['cigar'] ) )
#result_fh.write( "%s,%s,%s,%s,%i,%i,%i,%i,%s,%s,%s\n" % ( read.sequence, s1.sequence, s2.sequence, mapper_name, int(r[0]['mapq']), int(r[1]['mapq']), int(r[0]['pos']), int(r[1]['pos']), cand_mutations, read_mutations, r[0]['cigar'] ) )
key = '%i,%i' % ( cand_mutations, read_mutations )
if key not in matrix[mapper_name]:
matrix[mapper_name][key] = []
matrix[mapper_name][key].append( int(r[0]['mapq']) )
result_fh.write( 'x axis is candidate mutations, y axis is read mutations\n' )
show_dist_matrix( result_fh, matrix, max_cand_mutations, max_read_mutations )
#
# what is the mapping quality with ultra low entropy
#
def experiment_entropy( result_fh ):
result_fh.write( "%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s\n" % ( "read", "location", "alt", "mapper", "mapq_post", "mapq_pre", "pos_post", "pos_pre", "cand_mutations", "read_mutations", "post_cigar" ) )
read_len = 100
#max_read_mutations = 3
max_cand_mutations = 8
samples = 3 # 100
entropy_max = 0.1
#gc = 0.2
#for gc in np.arange( 0., 1., 0.2 ):
matrix = { 'bwa': {}, 'bowtie2': {} }
for sample in xrange(samples):
for entropy in np.arange( 0., entropy_max, 0.01 ):
s1 = bio.SequenceGenerator( read_len, (1 - entropy, entropy / 3, entropy / 3, entropy / 3 ) )
s2 = bio.SequenceGenerator( 0 )
s2.sequence = s1.sequence
# 1 mutation
for mapper_name in matrix.keys():
#for p in xrange(0, read_len, 10):
#for p in xrange(10):
# s2.sequence = s1.sequence
# s2.mutate( p )
# r = align( s1.sequence, s1.sequence, s2.sequence, mapper_name )
# #result_fh.write( "%s,%s,%s,%s,%s\n" % ( s1.sequence, s1.sequence, s2.sequence, mapper_name, r ) )
# result_fh.write( "%s,%s,%s,%s,%i,%i,1\n" % ( s1.sequence, s1.sequence, s2.sequence, mapper_name, r[0], r[1] ) )
# 1 read mutation
read = bio.SequenceGenerator( 0 )
read.sequence = s1.sequence
# add mutations to read
read_mutation_list = []
while True:
m_pos = random.randint( 10, read_len - 10 )
if m_pos not in read_mutation_list:
break
read_mutation_list.append( m_pos ) # don't mutate in the same place
read.mutate( read_mutation_list[-1] )
for cand_mutations in xrange(max_cand_mutations):
# add mutations to alternative
cand_mutation_list = []
s2.sequence = s1.sequence
for m in xrange(cand_mutations):
while True:
m_pos = random.randint( 10, read_len - 10 )
if m_pos not in read_mutation_list and m_pos not in cand_mutation_list:
break
cand_mutation_list.append( m_pos )
s2.mutate( cand_mutation_list[-1] )
s2.mutate( cand_mutation_list[-1] ) # mutate twice so it is still different from cand
# align and add result
r = align( read.sequence, s1.sequence, s2.sequence, mapper_name ) # only populates r[0]
#result_fh.write( "%s,%s,%s,%s,%s\n" % ( s1.sequence, s1.sequence, s2.sequence, mapper_name, r ) )
result_fh.write( "%i,%s,%i,%i,%s,%s,%s,%s,%s,%s\n" % ( sample, mapper_name, int(r[0]['mapq']), int(r[0]['pos']), '-'.join( [ str(x) for x in cand_mutation_list ] ), '-'.join( [ str(x) for x in read_mutation_list ] ), r[0]['cigar'], read.sequence, s1.sequence, s2.sequence ) )
#result_fh.write( "%s,%s,%s,%s,%i,%i,%i,%i,%s,%s,%s\n" % ( read.sequence, s1.sequence, s2.sequence, mapper_name, int(r[0]['mapq']), int(r[1]['mapq']), int(r[0]['pos']), int(r[1]['pos']), cand_mutations, read_mutations, r[0]['cigar'] ) )
key = '%i,%i' % ( cand_mutations, int(entropy*100) )
if key not in matrix[mapper_name]:
matrix[mapper_name][key] = []
matrix[mapper_name][key].append( int(r[0]['mapq']) )
result_fh.write( 'x axis is candidate mutations, y axis is entropy\n' )
show_dist_matrix( result_fh, matrix, max_cand_mutations, int(entropy_max*100) )
#
# what is the mapping quality with tandem repeats
#
def experiment_tandem_repeats( result_fh ):
result_fh.write( "%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s\n" % ( "read", "location", "alt", "mapper", "mapq_post", "mapq_pre", "pos_post", "pos_pre", "cand_mutations", "read_mutations", "post_cigar" ) )
read_len = 100
#max_read_mutations = 3
max_cand_mutations = 8
samples = 100
repeat_max = 40
gc = 0.5
#for gc in np.arange( 0., 1., 0.2 ):
matrix = { 'bwa': {}, 'bowtie2': {} }
for sample in xrange(samples):
for repeat in np.arange( 1, repeat_max ):
s1 = bio.SequenceGenerator( read_len, (0.5-gc/2, gc/2, gc/2, 0.5-gc/2), repeat_length=repeat )
s2 = bio.SequenceGenerator( 0 )
s2.sequence = s1.sequence
# 1 mutation
for mapper_name in matrix.keys():
#for p in xrange(0, read_len, 10):
#for p in xrange(10):
# s2.sequence = s1.sequence
# s2.mutate( p )
# r = align( s1.sequence, s1.sequence, s2.sequence, mapper_name )
# #result_fh.write( "%s,%s,%s,%s,%s\n" % ( s1.sequence, s1.sequence, s2.sequence, mapper_name, r ) )
# result_fh.write( "%s,%s,%s,%s,%i,%i,1\n" % ( s1.sequence, s1.sequence, s2.sequence, mapper_name, r[0], r[1] ) )
# 1 read mutation
read = bio.SequenceGenerator( 0 )
read.sequence = s1.sequence
# add mutations to read
read_mutation_list = []
while True:
m_pos = random.randint( 10, read_len - 10 )
if m_pos not in read_mutation_list:
break
read_mutation_list.append( m_pos ) # don't mutate in the same place
read.mutate( read_mutation_list[-1] )
for cand_mutations in xrange(max_cand_mutations):
# add mutations to alternative
cand_mutation_list = []
s2.sequence = s1.sequence
for m in xrange(cand_mutations):
while True:
m_pos = random.randint( 10, read_len - 10 )
if m_pos not in read_mutation_list and m_pos not in cand_mutation_list:
break
cand_mutation_list.append( m_pos )
s2.mutate( cand_mutation_list[-1] )
s2.mutate( cand_mutation_list[-1] ) # mutate twice so it is still different from cand
# align and add result
r = align( read.sequence, s1.sequence, s2.sequence, mapper_name ) # only populates r[0]
#result_fh.write( "%s,%s,%s,%s,%s\n" % ( s1.sequence, s1.sequence, s2.sequence, mapper_name, r ) )
result_fh.write( "%i,%s,%i,%i,%s,%s,%s,%s\n" % ( sample, mapper_name, int(r[0]['mapq']), int(r[0]['pos']), '-'.join( [ str(x) for x in cand_mutation_list ] ), '-'.join( [ str(x) for x in read_mutation_list ] ), r[0]['cigar'], read.sequence ) )
#result_fh.write( "%s,%s,%s,%s,%i,%i,%i,%i,%s,%s,%s\n" % ( read.sequence, s1.sequence, s2.sequence, mapper_name, int(r[0]['mapq']), int(r[1]['mapq']), int(r[0]['pos']), int(r[1]['pos']), cand_mutations, read_mutations, r[0]['cigar'] ) )
key = '%i,%i' % ( cand_mutations, repeat )
if key not in matrix[mapper_name]:
matrix[mapper_name][key] = []
matrix[mapper_name][key].append( int(r[0]['mapq']) )
result_fh.write( 'x axis is candidate mutations, y axis is repeat size\n' )
show_dist_matrix( result_fh, matrix, max_cand_mutations, repeat_max, ymin=1 )
def count_unmapped( fh_in, fh_out ):
matrix = { 'bwa': {}, 'bowtie2': {} }
xmax = 0
ymax = 0
first = True
for line in fh_in:
if first:
first = False
continue
fields = line.strip().split(',')
x = len(filter( None, re.split('-', fields[4]) ) )
y = len(filter( None, re.split('-', fields[5]) ) )
xmax = max(xmax, x)
ymax = max(ymax, y)
is_unmapped = fields[6] == '*'
key = '%i,%i' % (x, y)
if key not in matrix[fields[1]]:
matrix[fields[1]][key] = []
matrix[fields[1]][key].append( 0 if is_unmapped else 100 )
fh_out.write( 'x axis is candidate mutations, y axis is read mutations\n' )
show_dist_matrix( fh_out, matrix, xmax+1, ymax+1)
def count_clipped( fh_in, fh_out ):
matrix = { 'bwa': {}, 'bowtie2': {} }
xmax = 0
ymax = 0
first = True
for idx, line in enumerate(fh_in):
if first:
first = False
continue
fields = line.strip().split(',')
if len(fields) > 5:
x = len(filter( None, re.split('-', fields[6]) ) )
y = int(fields[2])
xmax = max(xmax, x)
ymax = max(ymax, y)
is_clipped = fields[5] != '100M'
key = '%i,%i' % (x, y)
if key not in matrix[fields[0]]:
matrix[fields[0]][key] = []
matrix[fields[0]][key].append( 100 if is_clipped else 0 )
print "line", idx
fh_out.write( 'x axis is candidate mutations, y axis is read mutations\n' )
show_dist_matrix( fh_out, matrix, xmax+1, ymax+1)
def show_dist_matrix( fh, matrix, xmax, ymax, ymin=0 ):
fh.write( 'Mean Quality' )
for mapper_name in matrix:
fh.write( '\n' + mapper_name )
fh.write( '\t' + '\t'.join( [ str(x) for x in xrange( xmax ) ] ) + '\n' )
for y in xrange(ymin, ymax):
fh.write( str(y) + '\t' )
xs = []
for x in xrange(xmax):
xs.append( np.mean( matrix[mapper_name]['%i,%i' % (x,y) ] ) )
fh.write( '\t'.join( [ '%.1f' % x for x in xs ] ) + '\n' )
fh.write( '\nSD' )
for mapper_name in matrix:
fh.write( '\n' + mapper_name )
fh.write( '\t' + '\t'.join( [ str(x) for x in xrange( xmax ) ] ) + '\n' )
for y in xrange(ymin, ymax):
fh.write( str(y) + '\t' )
xs = []
for x in xrange(xmax):
xs.append( np.std( matrix[mapper_name]['%i,%i' % (x,y) ] ) )
fh.write( '\t'.join( [ '%.1f' % x for x in xs ] ) + '\n' )
# make fasta
with open(sys.argv[2], 'w') as result_fh:
if sys.argv[3] == 'position':
experiment_pos( result_fh )
elif sys.argv[3] == 'genome':
experiment_genome_pos( result_fh )
elif sys.argv[3] == 'distance':
experiment_candidate_distance( result_fh )
elif sys.argv[3] == 'entropy':
experiment_entropy( result_fh )
elif sys.argv[3] == 'tandem':
experiment_tandem_repeats( result_fh )
elif sys.argv[3] == 'unmapped':
count_unmapped( sys.stdin, result_fh )
elif sys.argv[3] == 'clip':
count_clipped( sys.stdin, result_fh )
| 45.153846 | 285 | 0.58918 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6,089 | 0.314336 |
272ddcd0024554a1e6ae6a4493df8c6406ed30b3 | 195 | py | Python | scrape_etsy/exceptions.py | philjnicholls/etsy-scrape | d7acb69dc71428325e02859bc7c9d860bf409db4 | [
"MIT"
]
| 3 | 2020-12-17T10:00:54.000Z | 2021-10-30T09:23:55.000Z | scrape_etsy/exceptions.py | philjnicholls/etsy-scrape | d7acb69dc71428325e02859bc7c9d860bf409db4 | [
"MIT"
]
| null | null | null | scrape_etsy/exceptions.py | philjnicholls/etsy-scrape | d7acb69dc71428325e02859bc7c9d860bf409db4 | [
"MIT"
]
| null | null | null | class MissingValueException(Exception):
pass
class ProductScrapeException(Exception):
pass
class GetPageException(Exception):
pass
class NoResultsException(Exception):
pass
| 13 | 40 | 0.764103 | 185 | 0.948718 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
272e2592cfb00d569450c8c8739564bdd4f5c46a | 393 | py | Python | alembic/versions/e3dda7be6a95_extend_user_table.py | paul-wolf/bolo | 936e73d80217a0fdbbd983cb1f775686809e1d25 | [
"BSD-3-Clause"
]
| null | null | null | alembic/versions/e3dda7be6a95_extend_user_table.py | paul-wolf/bolo | 936e73d80217a0fdbbd983cb1f775686809e1d25 | [
"BSD-3-Clause"
]
| null | null | null | alembic/versions/e3dda7be6a95_extend_user_table.py | paul-wolf/bolo | 936e73d80217a0fdbbd983cb1f775686809e1d25 | [
"BSD-3-Clause"
]
| null | null | null | """Extend user table
Revision ID: e3dda7be6a95
Revises:
Create Date: 2021-06-07 11:43:45.144088
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = "e3dda7be6a95"
down_revision = None
branch_labels = None
depends_on = None
def upgrade():
op.add_column("auth_user", sa.Column("comment", sa.String(255)))
def downgrade():
pass
| 15.72 | 68 | 0.727735 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 176 | 0.447837 |
272f0cf0754b945f03c4fe2b4a5977930d84644b | 1,752 | py | Python | 7_kyu/grow_of_population.py | dimishpatriot/way_on_the_highway | 4865db946632b7bd3d74509a20a307841c02169d | [
"MIT"
]
| null | null | null | 7_kyu/grow_of_population.py | dimishpatriot/way_on_the_highway | 4865db946632b7bd3d74509a20a307841c02169d | [
"MIT"
]
| null | null | null | 7_kyu/grow_of_population.py | dimishpatriot/way_on_the_highway | 4865db946632b7bd3d74509a20a307841c02169d | [
"MIT"
]
| null | null | null | """In a small town the population is p0 = 1000 at the beginning of a year.
The population regularly increases by 2 percent per year and moreover 50 new
inhabitants per year come to live in the town.
How many years does the town need to see its population greater or equal to p = 1200 inhabitants?
1) At the end of the first year there will be:
1000 + 1000 * 0.02 + 50 => 1070 inhabitants
2) At the end of the 2nd year there will be:
1070 + 1070 * 0.02 + 50 => 1141 inhabitants (number of inhabitants is an integer)
3) At the end of the 3rd year there will be:
1141 + 1141 * 0.02 + 50 => 1213
It will need 3 entire years.
=== More generally given parameters: ===
p0, percent,
aug (inhabitants coming or leaving each year),
p (population to surpass)
the function nb_year should return n number of entire years needed to get
a population greater or equal to p.
aug is an integer,
percent a positive or null number,
p0 and p are positive integers (> 0).
=== Note: ===
Don't forget to convert the percent parameter as a percentage in the body of
your function: if the parameter percent is 2 you have to convert it to 0.02.
"""
import pytest
@pytest.mark.parametrize("test, result", [
((1500, 5, 100, 5000), 15),
((1500000, 2.5, 10000, 2000000), 10),
((1500000, 0.25, 1000, 2000000), 94),
((1000, 10, 0, 1000), 0),
((999, 10, 0, 1000), 1),
((999, 0, 1, 1000), 1),
((1000, 100, 0, 2000), 1),
])
def test_population(test, result):
assert nb_year(*test) == result
def nb_year(p0: int, percent: float, aug: int, p: int) -> int:
percent = percent / 100 # convert to percentage
current = p0
year = 0
while current < p:
current = current + current*percent + aug
year += 1
return year
| 30.736842 | 97 | 0.673516 | 0 | 0 | 0 | 0 | 350 | 0.199772 | 0 | 0 | 1,165 | 0.664954 |
273038458cb6e72fc3b79b5c6ab692951f85b0c3 | 4,144 | py | Python | pysimplegui/DemoPrograms/Demo_Multithreaded_Long_Task_Simple.py | konsan1101/py-etc | bcca13119b0d2453866988404fd1c4976f55d4d5 | [
"MIT"
]
| null | null | null | pysimplegui/DemoPrograms/Demo_Multithreaded_Long_Task_Simple.py | konsan1101/py-etc | bcca13119b0d2453866988404fd1c4976f55d4d5 | [
"MIT"
]
| 2 | 2020-06-06T00:30:56.000Z | 2021-06-10T22:30:37.000Z | pysimplegui/DemoPrograms/Demo_Multithreaded_Long_Task_Simple.py | konsan1101/py-etc | bcca13119b0d2453866988404fd1c4976f55d4d5 | [
"MIT"
]
| null | null | null | #!/usr/bin/python3
import threading
import time
import PySimpleGUI as sg
"""
DESIGN PATTERN - Multithreaded Long Tasks GUI using shared global variables
Presents one method for running long-running operations in a PySimpleGUI environment.
The PySimpleGUI code, and thus the underlying GUI framework, runs as the primary, main thread
The "long work" is contained in the thread that is being started. Communicating is done (carefully) using global variables
There are 2 ways "progress" is being reported to the user. If a the amount of time is known ahead of time and
the work can be broek down into countable units, then a progress bar is used. If a task is one long chunk
of time that cannot be broken down into smaller units, then an animated GIF is shown that spins as
long as the task is running.
"""
thread_done = 1
message = ''
thread_counter = 0
total = 100
progress = 0
def long_operation_thread(seconds):
"""
A worker thread that communicates with the GUI through a global variable
This thread can block for as long as it wants and the GUI will not be affected
:param seconds: (int) How long to sleep, the ultimate blocking call
:return:
"""
global thread_done, message, thread_counter, progress
print('Thread started - will sleep for {} seconds'.format(seconds))
thread_counter += 1
for i in range(int(seconds*10)):
time.sleep(.1) # sleep for a while
progress += total/(seconds*10)
message = f'***This is a message from the thread {thread_counter} ***'
thread_done = True
def the_gui():
"""
Starts and executes the GUI
Reads data from a global variable and displays
Returns when the user exits / closes the window
"""
global thread_done, message, progress
sg.theme('Light Brown 3')
layout = [[sg.Text('Long task to perform example')],
[sg.Output(size=(80, 12))],
[sg.Text('Number of seconds your task will take'),
sg.Input(key='-SECONDS-', size=(5, 1)),
sg.Button('Do Long Task', bind_return_key=True),
sg.CBox('ONE chunk, cannot break apart', key='-ONE CHUNK-')],
[sg.Text('Work progress'), sg.ProgressBar(total, size=(20,20), orientation='h', key='-PROG-')],
[sg.Button('Click Me'), sg.Button('Exit')], ]
window = sg.Window('Multithreaded Window', layout)
one_chunk = False
sg.popup_animated(None)
# --------------------- EVENT LOOP ---------------------
while True:
event, values = window.read(timeout=100)
if event in (None, 'Exit'):
break
elif event.startswith('Do'):
seconds = float(values['-SECONDS-'])
one_chunk = values['-ONE CHUNK-']
print('Thread Starting! Long work....sending value of {} seconds'.format(seconds))
threading.Thread(target=long_operation_thread, args=(seconds, ), daemon=True).start()
elif event == 'Click Me':
print('Your GUI is alive and well')
# --------------- Check for incoming messages from threads ---------------
if thread_done is True:
print('The thread has finished!')
print(f'message = {message}')
# reset everything for the next run
sg.popup_animated(None) # just in case one's running, stop it
thread_done = one_chunk = False
message, progress = '', 0
window['-PROG-'].update_bar(total, total) # show the bar as maxed out
if one_chunk: # flag signifies the thread is taking to take one big chunk of time
sg.popup_animated(sg.DEFAULT_BASE64_LOADING_GIF, background_color='white', transparent_color='white', time_between_frames=100)
elif progress != 0:
window['-PROG-'].update_bar(progress, total) # update the progress bar if non-zero
# if user exits the window, then close the window and exit the GUI func
window.close()
if __name__ == '__main__':
the_gui()
print('Exiting Program')
| 41.029703 | 138 | 0.625483 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2,175 | 0.524855 |
27315ae22ee7a238a46f0e67142e8517f30c8852 | 1,838 | py | Python | sidekick/tests/test_import_members.py | cybera/netbox-sidekick | ec5e2080513d088e2604d8755f34b1d2592b95dd | [
"Apache-2.0"
]
| 3 | 2020-09-07T12:14:31.000Z | 2021-11-11T11:46:43.000Z | sidekick/tests/test_import_members.py | cybera/netbox-sidekick | ec5e2080513d088e2604d8755f34b1d2592b95dd | [
"Apache-2.0"
]
| null | null | null | sidekick/tests/test_import_members.py | cybera/netbox-sidekick | ec5e2080513d088e2604d8755f34b1d2592b95dd | [
"Apache-2.0"
]
| null | null | null | from io import StringIO
from django.core.management import call_command
import os
from . import utils
class ImportMemberTest(utils.BaseTest):
def test_dry_run(self):
dir_path = os.path.dirname(os.path.realpath(__file__))
csv = f"{dir_path}/../fixtures/members.csv"
out = StringIO()
call_command('import_members', stdout=out, file=csv, dry_run=True)
self.assertIn('Would have created Tenant: West School District', out.getvalue())
self.assertIn('Changing description of East University to A description', out.getvalue())
self.assertIn('Changing comments of East University to Some Comment', out.getvalue())
self.assertIn('Changing member_type of East University to Post-Secondary Institution', out.getvalue())
self.assertIn('Changing latitude of East University to 3.5', out.getvalue())
self.assertIn('Changing latitude of East University to -13.0', out.getvalue())
self.assertIn('Changing description of South Not For Profit to A description', out.getvalue())
self.assertIn('Changing comments of South Not For Profit to A Comment', out.getvalue())
self.assertIn('Changing member_type of South Not For Profit to Government/Not For Profit', out.getvalue())
self.assertIn('Changing latitude of South Not For Profit to 2.5', out.getvalue())
self.assertIn('Changing latitude of South Not For Profit to -15.0', out.getvalue())
def test_import(self):
dir_path = os.path.dirname(os.path.realpath(__file__))
csv = f"{dir_path}/../fixtures/members.csv"
out = StringIO()
call_command('import_members', stdout=out, file=csv)
self.assertIn('Created Tenant: West School District', out.getvalue())
self.assertIn('Updated Tenant: South Not For Profit', out.getvalue())
| 48.368421 | 114 | 0.699129 | 1,731 | 0.941785 | 0 | 0 | 0 | 0 | 0 | 0 | 802 | 0.436344 |
27315e1db1e7700a9d36cef574e1deed8416f995 | 1,385 | py | Python | uselessAI.py | Arihant25/beginner-python-projects | 43c6489b6973522246073f2187a682487f1684c1 | [
"Unlicense"
]
| 1 | 2020-06-11T06:10:08.000Z | 2020-06-11T06:10:08.000Z | uselessAI.py | Arihant25/beginner-python-projects | 43c6489b6973522246073f2187a682487f1684c1 | [
"Unlicense"
]
| 1 | 2021-07-16T03:50:09.000Z | 2021-07-16T04:00:35.000Z | uselessAI.py | Arihant25/beginner-python-projects | 43c6489b6973522246073f2187a682487f1684c1 | [
"Unlicense"
]
| 1 | 2021-07-16T03:28:39.000Z | 2021-07-16T03:28:39.000Z | def greet(bot_name, birth_year):
print('Hello! My name is ' + bot_name + '.')
print('I was created in ' + birth_year + '.')
def remind_name():
print('Please, remind me your name.')
name = input()
print('What a great name you have, ' + name + '!')
def guess_age():
print('Let me guess your age.')
print('Enter remainders of dividing your age by 3, 5 and 7.')
rem3 = int(input())
rem5 = int(input())
rem7 = int(input())
age = (rem3 * 70 + rem5 * 21 + rem7 * 15) % 105
print("Your age is " + str(age) + "; that's a good time to start programming!")
def count():
print('Now I will prove to you that I can count to any number you want.')
num = int(input())
curr = 0
while curr <= num:
print(curr, '!')
curr = curr + 1
def test():
print("Let's test your programming knowledge.")
# write your code here
print("What's your feedback on me? (Enter the option number")
print("1. You're great!")
print("2. You're awesome!")
print("3. You're the best!")
print("4. Will you be my friend?")
ans = int(input())
if ans == 4:
print("Of course I will!")
else:
print("I knew it!")
print('Completed, have a nice day!')
def end():
print('Congratulations, have a nice day!')
greet('UselessAI', '2020')
remind_name()
guess_age()
count()
test()
end()
| 23.083333 | 83 | 0.581949 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 627 | 0.452708 |
2732112e3d71d0c3a2a906460b62d43c48b4fb95 | 241 | py | Python | pineboolib/fllegacy/aqsobjects/aqsmtpclient.py | juanjosepablos/pineboo | f6ce515aec6e0139821bb9c1d62536d9fb50dae4 | [
"MIT"
]
| 2 | 2015-09-19T16:54:49.000Z | 2016-09-12T08:06:29.000Z | pineboolib/fllegacy/aqsobjects/aqsmtpclient.py | Aulla/pineboo | 3ad6412d365a6ad65c3bb2bdc03f5798d7c37004 | [
"MIT"
]
| 36 | 2017-11-05T21:13:47.000Z | 2020-08-26T15:56:15.000Z | pineboolib/fllegacy/aqsobjects/aqsmtpclient.py | deavid/pineboo | acc96ab6d5b8bb182990af6dea4bf0986af15549 | [
"MIT"
]
| 9 | 2015-01-15T18:15:42.000Z | 2019-05-05T18:53:00.000Z | # -*- coding: utf-8 -*-
"""
AQSmtpClient Module.
This class provides utilities to use a Smtp client.
"""
from pineboolib.fllegacy.flsmtpclient import FLSmtpClient
class AQSmtpClient(FLSmtpClient):
"""AQSmtpClient Class."""
pass
| 16.066667 | 57 | 0.713693 | 73 | 0.302905 | 0 | 0 | 0 | 0 | 0 | 0 | 129 | 0.53527 |
2732966b89290e6982d4e90149bce48ffa294e63 | 871 | py | Python | assistant/core/views.py | kapiak/ware_prod | ae61256890834c434d2e38cc2ccacf00b638665a | [
"MIT"
]
| null | null | null | assistant/core/views.py | kapiak/ware_prod | ae61256890834c434d2e38cc2ccacf00b638665a | [
"MIT"
]
| null | null | null | assistant/core/views.py | kapiak/ware_prod | ae61256890834c434d2e38cc2ccacf00b638665a | [
"MIT"
]
| null | null | null | from typing import List
from django.contrib.auth.mixins import LoginRequiredMixin
from django.views.generic import TemplateView
from assistant.orders.models import Order
from assistant.products.models import Product
class DashboardViewMixin(LoginRequiredMixin):
title: str = None
breadcrumbs: List = []
def get_title(self):
return self.title
def get_context_data(self):
context = super().get_context_data()
context.update({'title': self.get_title()})
return context
class DashboardTemplateView(LoginRequiredMixin, TemplateView):
template_name = "core/dashboard.html"
def get_context_data(self, **kwargs):
context = super().get_context_data(**kwargs)
context.update({
'orders': Order.objects.all(),
'products': Product.objects.all()
})
return context
| 26.393939 | 62 | 0.69116 | 647 | 0.742824 | 0 | 0 | 0 | 0 | 0 | 0 | 46 | 0.052813 |
27346ed4d6dc889abd1e3fa083354a32e52ae98a | 2,024 | py | Python | chmap/examples/defunct/EUV_map_query_options.py | predsci/CHD | 35f29d1b62861f4ffed57b38d18689b282664bcf | [
"Apache-2.0"
]
| 3 | 2021-06-29T00:23:47.000Z | 2021-09-17T18:29:05.000Z | chmap/examples/defunct/EUV_map_query_options.py | predsci/CHD | 35f29d1b62861f4ffed57b38d18689b282664bcf | [
"Apache-2.0"
]
| null | null | null | chmap/examples/defunct/EUV_map_query_options.py | predsci/CHD | 35f29d1b62861f4ffed57b38d18689b282664bcf | [
"Apache-2.0"
]
| 1 | 2021-12-08T06:26:18.000Z | 2021-12-08T06:26:18.000Z |
import os
import datetime
import pandas as pd
from chmap.settings.app import App
from chmap.database.db_classes import *
from chmap.database.deprecated.db_funs import init_db_conn
from sqlalchemy.orm import joinedload
# Assume that we are using images from the 'reference_data' setup supplied with repo
# manually set the data dir
raw_data_dir = os.path.join(App.APP_HOME, 'reference_data', 'raw')
hdf_data_dir = os.path.join(App.APP_HOME, 'reference_data', 'processed')
# manually set the database location using the installed app settings.
database_dir = os.path.join(App.APP_HOME, 'reference_data')
# setup database path
use_db = "sqlite"
sqlite_filename = "dbtest.db"
sqlite_path = os.path.join(database_dir, sqlite_filename)
# re-initialize database file and establish a connection/session
db_session = init_db_conn(db_name=use_db, chd_base=Base, sqlite_path=sqlite_path)
# define query time range
query_time_min = datetime.datetime(2000, 1, 1, 1, 0, 0)
query_time_max = datetime.datetime(2019, 1, 1, 1, 0, 0)
mean_time_range = [query_time_min, query_time_max]
combo_query = db_session.query(Data_Combos.combo_id).filter(Data_Combos.date_mean.between(mean_time_range[0],
mean_time_range[1]))
euv_map_query = db_session.query(EUV_Maps).filter(EUV_Maps.combo_id.in_(combo_query))
euv_map_query_join = db_session.query(EUV_Maps, Data_Combos).filter(EUV_Maps.combo_id.in_(combo_query))
euv_map_rel_join = db_session.query(EUV_Maps).options(joinedload(EUV_Maps.combos)).filter(EUV_Maps.combo_id.in_(combo_query))
test = euv_map_query.all()
len(test)
# lazyload combo info
test[0].combos.combo_id
# lazyload which images the combo is made up of
for row in test[0].combos.images:
print(row.data_id)
test2 = pd.read_sql(euv_map_query.statement, db_session.bind)
test2_2 = pd.read_sql(euv_map_query_join.statement, db_session.bind)
test2_3 = pd.read_sql(euv_map_rel_join.statement, db_session.bind)
db_session.close()
| 37.481481 | 125 | 0.76334 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 442 | 0.218379 |
27376a84a1f1ba1faffcc4132873b55e2e8ca9cd | 308 | py | Python | hasensor/sensors/announcer.py | eblanton/hasensor | 4421bf939f0904fc85ecbdfbd404dabe33c1c443 | [
"BSD-2-Clause"
]
| 1 | 2020-01-02T21:53:22.000Z | 2020-01-02T21:53:22.000Z | hasensor/sensors/announcer.py | eblanton/hasensor | 4421bf939f0904fc85ecbdfbd404dabe33c1c443 | [
"BSD-2-Clause"
]
| null | null | null | hasensor/sensors/announcer.py | eblanton/hasensor | 4421bf939f0904fc85ecbdfbd404dabe33c1c443 | [
"BSD-2-Clause"
]
| null | null | null | from ..sensor import ArgDict, Sensor
class Announcer(Sensor):
_argtypes: ArgDict = {
"value": str
}
def __init__(self, value: str = "ON", **kwargs):
super().__init__(**kwargs)
self._value = value
def fire(self):
self._loop.publish(self.name, self._value)
| 19.25 | 52 | 0.594156 | 268 | 0.87013 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 0.035714 |
2738f4224e35bab74695218b88d121cd720f88b1 | 85,669 | py | Python | post_optimization_studies/mad_analyses/ma100MeV_L1pt8-2pt4TeV_deta2pt6/Output/Histos/MadAnalysis5job_0/selection_11.py | sheride/axion_pheno | 7d3fc08f5ae5b17a3500eba19a2e43f87f076ce5 | [
"MIT"
]
| null | null | null | post_optimization_studies/mad_analyses/ma100MeV_L1pt8-2pt4TeV_deta2pt6/Output/Histos/MadAnalysis5job_0/selection_11.py | sheride/axion_pheno | 7d3fc08f5ae5b17a3500eba19a2e43f87f076ce5 | [
"MIT"
]
| null | null | null | post_optimization_studies/mad_analyses/ma100MeV_L1pt8-2pt4TeV_deta2pt6/Output/Histos/MadAnalysis5job_0/selection_11.py | sheride/axion_pheno | 7d3fc08f5ae5b17a3500eba19a2e43f87f076ce5 | [
"MIT"
]
| null | null | null | def selection_11():
# Library import
import numpy
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
# Library version
matplotlib_version = matplotlib.__version__
numpy_version = numpy.__version__
# Histo binning
xBinning = numpy.linspace(0.0,2000.0,401,endpoint=True)
# Creating data sequence: middle of each bin
xData = numpy.array([2.5,7.5,12.5,17.5,22.5,27.5,32.5,37.5,42.5,47.5,52.5,57.5,62.5,67.5,72.5,77.5,82.5,87.5,92.5,97.5,102.5,107.5,112.5,117.5,122.5,127.5,132.5,137.5,142.5,147.5,152.5,157.5,162.5,167.5,172.5,177.5,182.5,187.5,192.5,197.5,202.5,207.5,212.5,217.5,222.5,227.5,232.5,237.5,242.5,247.5,252.5,257.5,262.5,267.5,272.5,277.5,282.5,287.5,292.5,297.5,302.5,307.5,312.5,317.5,322.5,327.5,332.5,337.5,342.5,347.5,352.5,357.5,362.5,367.5,372.5,377.5,382.5,387.5,392.5,397.5,402.5,407.5,412.5,417.5,422.5,427.5,432.5,437.5,442.5,447.5,452.5,457.5,462.5,467.5,472.5,477.5,482.5,487.5,492.5,497.5,502.5,507.5,512.5,517.5,522.5,527.5,532.5,537.5,542.5,547.5,552.5,557.5,562.5,567.5,572.5,577.5,582.5,587.5,592.5,597.5,602.5,607.5,612.5,617.5,622.5,627.5,632.5,637.5,642.5,647.5,652.5,657.5,662.5,667.5,672.5,677.5,682.5,687.5,692.5,697.5,702.5,707.5,712.5,717.5,722.5,727.5,732.5,737.5,742.5,747.5,752.5,757.5,762.5,767.5,772.5,777.5,782.5,787.5,792.5,797.5,802.5,807.5,812.5,817.5,822.5,827.5,832.5,837.5,842.5,847.5,852.5,857.5,862.5,867.5,872.5,877.5,882.5,887.5,892.5,897.5,902.5,907.5,912.5,917.5,922.5,927.5,932.5,937.5,942.5,947.5,952.5,957.5,962.5,967.5,972.5,977.5,982.5,987.5,992.5,997.5,1002.5,1007.5,1012.5,1017.5,1022.5,1027.5,1032.5,1037.5,1042.5,1047.5,1052.5,1057.5,1062.5,1067.5,1072.5,1077.5,1082.5,1087.5,1092.5,1097.5,1102.5,1107.5,1112.5,1117.5,1122.5,1127.5,1132.5,1137.5,1142.5,1147.5,1152.5,1157.5,1162.5,1167.5,1172.5,1177.5,1182.5,1187.5,1192.5,1197.5,1202.5,1207.5,1212.5,1217.5,1222.5,1227.5,1232.5,1237.5,1242.5,1247.5,1252.5,1257.5,1262.5,1267.5,1272.5,1277.5,1282.5,1287.5,1292.5,1297.5,1302.5,1307.5,1312.5,1317.5,1322.5,1327.5,1332.5,1337.5,1342.5,1347.5,1352.5,1357.5,1362.5,1367.5,1372.5,1377.5,1382.5,1387.5,1392.5,1397.5,1402.5,1407.5,1412.5,1417.5,1422.5,1427.5,1432.5,1437.5,1442.5,1447.5,1452.5,1457.5,1462.5,1467.5,1472.5,1477.5,1482.5,1487.5,1492.5,1497.5,1502.5,1507.5,1512.5,1517.5,1522.5,1527.5,1532.5,1537.5,1542.5,1547.5,1552.5,1557.5,1562.5,1567.5,1572.5,1577.5,1582.5,1587.5,1592.5,1597.5,1602.5,1607.5,1612.5,1617.5,1622.5,1627.5,1632.5,1637.5,1642.5,1647.5,1652.5,1657.5,1662.5,1667.5,1672.5,1677.5,1682.5,1687.5,1692.5,1697.5,1702.5,1707.5,1712.5,1717.5,1722.5,1727.5,1732.5,1737.5,1742.5,1747.5,1752.5,1757.5,1762.5,1767.5,1772.5,1777.5,1782.5,1787.5,1792.5,1797.5,1802.5,1807.5,1812.5,1817.5,1822.5,1827.5,1832.5,1837.5,1842.5,1847.5,1852.5,1857.5,1862.5,1867.5,1872.5,1877.5,1882.5,1887.5,1892.5,1897.5,1902.5,1907.5,1912.5,1917.5,1922.5,1927.5,1932.5,1937.5,1942.5,1947.5,1952.5,1957.5,1962.5,1967.5,1972.5,1977.5,1982.5,1987.5,1992.5,1997.5])
# Creating weights for histo: y12_PT_0
y12_PT_0_weights = numpy.array([0.0053070876447,0.0053070876447,0.0229973784604,0.0229973784604,0.0389186413945,0.0459947569208,0.0406876772761,0.0725301951443,0.0389186413945,0.0813753545521,0.0796063146705,0.109679792657,0.141522350525,0.127370111473,0.150367469933,0.148598470052,0.168057788749,0.168057788749,0.183979027683,0.206976426143,0.21405254567,0.222897665078,0.240587983893,0.258278262709,0.279506621288,0.261816342472,0.32727041809,0.302503979748,0.307811099393,0.369727095248,0.353805856314,0.387417414063,0.367958095366,0.435181170866,0.412183972405,0.422797971695,0.479406767905,0.500635166484,0.438719170629,0.4511023698,0.484713967549,0.576703561391,0.528939564589,0.498866366602,0.537784763997,0.594393960207,0.603239159615,0.605007959496,0.527170764707,0.589086760562,0.525401564826,0.642157557009,0.696997553338,0.661616755706,0.643926756891,0.62092915843,0.696997553338,0.709380752509,0.642157557009,0.695228353456,0.658078755943,0.735916350732,0.665155155469,0.661616755706,0.696997553338,0.673999954877,0.645695556772,0.661616755706,0.629774357838,0.626236358075,0.628005557956,0.583779560917,0.582010761036,0.594393960207,0.679307154522,0.590855960444,0.594393960207,0.576703561391,0.555475162812,0.58731756068,0.521863565062,0.544861163523,0.553705962931,0.498866366602,0.546629963404,0.550167963168,0.546629963404,0.493559166957,0.463485568971,0.458178769326,0.467023568734,0.435181170866,0.444026370273,0.488251967313,0.426335971458,0.428105171339,0.431643171102,0.442257170392,0.41749077205,0.452871569681,0.41749077205,0.403338772997,0.346729736787,0.419259971931,0.371496135129,0.401569573116,0.332577497735,0.405107572879,0.39449353359,0.376803214774,0.357343896077,0.352036816432,0.35026777655,0.336115537498,0.35026777655,0.378572254655,0.332577497735,0.313118179037,0.314887218919,0.313118179037,0.309580099274,0.313118179037,0.297196900103,0.277737581406,0.300734979866,0.281275661169,0.316656218801,0.29365886034,0.252971183064,0.286582740814,0.26004730259,0.288351780696,0.274199541643,0.231742824485,0.240587983893,0.252971183064,0.274199541643,0.279506621288,0.217590585433,0.229973784604,0.206976426143,0.194593226972,0.245895063538,0.210514465907,0.217590585433,0.175133908275,0.215821585551,0.20343834638,0.171595828512,0.219359625314,0.222897665078,0.18044098792,0.187517107446,0.215821585551,0.173364868394,0.189286147328,0.201669346499,0.166288748867,0.176902908157,0.141522350525,0.18044098792,0.16982678863,0.189286147328,0.168057788749,0.152136509815,0.164519708986,0.16982678863,0.145060390289,0.168057788749,0.164519708986,0.141522350525,0.148598470052,0.125601071591,0.116755912183,0.120293991947,0.136215230881,0.132677191118,0.109679792657,0.11321787242,0.148598470052,0.129139151354,0.120293991947,0.134446230999,0.125601071591,0.111448832539,0.12383203171,0.107910792776,0.118524952065,0.116755912183,0.0972965934862,0.12383203171,0.104372713012,0.130908151236,0.116755912183,0.100834673249,0.0972965934862,0.0937585537231,0.0902204739599,0.106141752894,0.114986912302,0.0884514740784,0.0849133943152,0.0902204739599,0.0813753545521,0.0919895138415,0.0884514740784,0.0866824341968,0.0849133943152,0.0566089162102,0.0707611552627,0.0636850357364,0.0919895138415,0.0919895138415,0.077837274789,0.077837274789,0.0831443544337,0.0636850357364,0.065454075618,0.0689921553811,0.0884514740784,0.0919895138415,0.065454075618,0.065454075618,0.0548399163286,0.0689921553811,0.0548399163286,0.0707611552627,0.0672231154996,0.0513018365655,0.0796063146705,0.0495328366839,0.0583779560917,0.065454075618,0.0813753545521,0.0725301951443,0.0566089162102,0.0583779560917,0.0513018365655,0.0566089162102,0.053070876447,0.0477637968023,0.0513018365655,0.0566089162102,0.0601469959733,0.0513018365655,0.0371496135129,0.0459947569208,0.0406876772761,0.0442257170392,0.0636850357364,0.0689921553811,0.0389186413945,0.0389186413945,0.0371496135129,0.0318425258682,0.0442257170392,0.0336115537498,0.0442257170392,0.0265354382235,0.0424567171576,0.0406876772761,0.0265354382235,0.0406876772761,0.0513018365655,0.053070876447,0.0318425258682,0.0318425258682,0.0318425258682,0.0353805856314,0.0283044661051,0.0459947569208,0.0406876772761,0.0229973784604,0.0336115537498,0.0229973784604,0.0300734979866,0.0353805856314,0.0353805856314,0.0336115537498,0.0229973784604,0.0212283505788,0.0406876772761,0.0371496135129,0.0300734979866,0.0353805856314,0.0459947569208,0.0353805856314,0.0336115537498,0.0300734979866,0.0141522350525,0.0194593226972,0.0212283505788,0.0194593226972,0.0194593226972,0.0229973784604,0.0247664103419,0.0247664103419,0.0265354382235,0.0212283505788,0.0300734979866,0.0212283505788,0.0159212629341,0.0247664103419,0.0229973784604,0.0212283505788,0.0141522350525,0.0159212629341,0.0300734979866,0.0212283505788,0.0176902908157,0.012383203171,0.0159212629341,0.0194593226972,0.0212283505788,0.0229973784604,0.0176902908157,0.012383203171,0.0141522350525,0.0194593226972,0.0159212629341,0.0159212629341,0.0176902908157,0.0159212629341,0.0229973784604,0.0141522350525,0.0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# Creating weights for histo: y12_PT_1
y12_PT_1_weights = numpy.array([0.00106958729206,0.00961806539118,0.013894551247,0.0192308709815,0.0213771694783,0.0288493720249,0.0256379798486,0.0416784059306,0.0427561854823,0.0587817114598,0.0438108634773,0.0577080486215,0.0587720391817,0.104732186772,0.094028452128,0.0737312564497,0.0951134659042,0.118604192019,0.118605830711,0.131437786284,0.123967110518,0.133583313396,0.129300572534,0.1667092672,0.162420770933,0.183804059526,0.190208426582,0.195546285088,0.213727410137,0.239386964762,0.225491977862,0.243644405822,0.272472151113,0.256470366181,0.272491575605,0.261787041599,0.245771267836,0.253236348069,0.321621232809,0.321630545374,0.315233092798,0.315266626026,0.353747224935,0.330202581865,0.358999711598,0.360102151462,0.339837529778,0.359025451049,0.366528021351,0.384670537224,0.363298199888,0.371853689589,0.370800970031,0.400754056902,0.384687843408,0.396432067374,0.455248151393,0.437065867269,0.450929998802,0.429541874069,0.414615390668,0.391126982703,0.392202364169,0.441350846344,0.408184924449,0.410334808083,0.419964320336,0.38684468149,0.41029843712,0.370798452041,0.385739923477,0.369684181622,0.383594396364,0.3761349916,0.334481605695,0.34942831295,0.357955944892,0.352624441312,0.347247254204,0.323751532078,0.310952869925,0.344104603149,0.318439972577,0.307741233944,0.333374409628,0.320590016083,0.341963112813,0.302404054895,0.302410090077,0.336604430932,0.305599343928,0.308808981505,0.27781668429,0.288525175137,0.259653544882,0.28315969868,0.276752613794,0.291731215586,0.255393505896,0.240383689024,0.235094391749,0.22974841972,0.261817617189,0.252194060182,0.254313847844,0.243632455363,0.198756921868,0.226544057931,0.23401173609,0.261790758632,0.196601282828,0.237213380049,0.196636454748,0.210521249784,0.210505062707,0.208378040821,0.194477778134,0.184863134011,0.180573238861,0.20515865103,0.178457088295,0.187007222272,0.195575421827,0.178444058697,0.184865971746,0.163478806248,0.183787512736,0.184889632856,0.174194851064,0.175231143737,0.157099019568,0.207305377185,0.147447285057,0.149585378104,0.152806406587,0.145314467797,0.142125893404,0.155999497374,0.13785870013,0.146398642243,0.132504414738,0.138920612349,0.145323460618,0.137847948713,0.142137484151,0.146382095454,0.121833973514,0.139990638091,0.122893487648,0.145331773981,0.14105298996,0.123953401463,0.129281867467,0.120743124396,0.127176748095,0.131437146794,0.132504854387,0.135719527944,0.114329005126,0.115414578455,0.116497673763,0.11112352423,0.107908850674,0.0908256091264,0.0972375301517,0.0812315086028,0.102591935448,0.102584781159,0.0812104853868,0.0876191290286,0.105773635329,0.0641124556456,0.0865723247477,0.0972410073757,0.0908312845954,0.117542919289,0.0983083152875,0.0769240274922,0.0790754698822,0.0951007560512,0.0961508377156,0.0662543456619,0.0886927918669,0.0683806880906,0.0683840853783,0.0833434225506,0.0918970337517,0.0683886017726,0.0534292646004,0.0737171077453,0.0651801632383,0.066243474341,0.0577006145565,0.0758761440728,0.0609115710806,0.0694622246429,0.0470270957894,0.0651902351974,0.058771279788,0.0705296124909,0.0651933127404,0.0641207690086,0.0480937242437,0.0587757961823,0.0459526735573,0.0705210593193,0.0641110167943,0.0609096925803,0.0587675627555,0.0566328669957,0.0598318730604,0.0630451477338,0.0555542081459,0.0523518047934,0.0448770922506,0.059843064126,0.0555583648274,0.040600706315,0.0619684473205,0.0512946088087,0.0598304741772,0.0438116628391,0.035258239488,0.0523649542955,0.0470204211181,0.0491464438021,0.0352537470746,0.0331288195163,0.0502328964297,0.0352523162169,0.026719516401,0.0438217347981,0.0341845926369,0.032051343722,0.0288557269514,0.0459373258102,0.0331198306925,0.0416634978325,0.0395336302181,0.0309848191848,0.0363323219913,0.0320700807633,0.032054737013,0.0288557269514,0.0320539736225,0.0427539872373,0.0373948856907,0.0341943448512,0.0267123940871,0.0192350316598,0.0288509028028,0.0213697154292,0.0224464238361,0.029926412167,0.0213753069652,0.0267123940871,0.0171070425458,0.0192368981697,0.0245755160695,0.0256417048748,0.0224321792083,0.0213678489193,0.0309922732339,0.0288418140588,0.0288605550969,0.026715787378,0.0224411640353,0.0331276204735,0.0277824118355,0.0245755160695,0.0235032960793,0.0203019918493,0.0170977219869,0.0224411640353,0.0224467555713,0.0245822027312,0.0160310975294,0.0170977219869,0.0181639147889,0.0160244068709,0.0170916987955,0.0213667537936,0.0192372299049,0.0203012244619,0.0267221463014,0.0170977219869,0.0149551525131,0.0160273685065,0.0235152465386,0.0149630422143,0.0224400689096,0.0235216014651,0.012819373618,0.0106839264581,0.0170969545995,0.0170950920865,0.0288546278289,0.0170977219869,0.0213764060877,0.00961806539118,0.0138919213466,0.0149593131914,0.0138836999102,0.017096191209,0.0149604123139,0.0138971811474,0.0160239752155,0.0138908222241,0.014952954268,0.0181635830538,0.0106895179941,0.0128286901801,0.0170977219869,0.0117535125511,0.0096217944141,0.0181680754672,0.0106876514842,0.0074807557182,0.0128215678662,0.00961696626866,0.00961806539118,0.00747965659569,0.01281860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# Creating weights for histo: y12_PT_2
y12_PT_2_weights = numpy.array([0.00138892472153,0.00486123712535,0.00763908576841,0.0104169344115,0.0194449485014,0.0201394086622,0.0215283329837,0.0305563470736,0.026389570109,0.0326397315559,0.0458345306104,0.0437511301281,0.0458345306104,0.0541680525396,0.0534736123788,0.0569459331827,0.0625016144688,0.0750019373625,0.0694462560764,0.0715296165587,0.0798631784879,0.0812520988094,0.0902801008993,0.104169344115,0.115975226848,0.108336145079,0.125697709098,0.109725065401,0.132642310706,0.125003228938,0.139586952314,0.155559596011,0.160420797137,0.171532199709,0.178476841316,0.171532199709,0.17222667987,0.179171281477,0.204866407425,0.196532845496,0.206949807908,0.227783652731,0.21806121048,0.229172573052,0.234033814178,0.235422734499,0.248617537554,0.243061856267,0.236811694821,0.255562179161,0.252784298518,0.249312017714,0.250006457875,0.259728940126,0.253478778679,0.286118506235,0.261117860447,0.284035105753,0.265979101573,0.293063147842,0.304174550415,0.29097974736,0.297229908807,0.278479424466,0.273618183341,0.261812340608,0.28195174527,0.277784944306,0.264590181251,0.271534782859,0.274312663502,0.270145862537,0.243756296428,0.260423380287,0.21806121048,0.229172573052,0.259034459965,0.24514521675,0.219450130801,0.234033814178,0.231950453695,0.197921765818,0.220144570962,0.22708921257,0.197921765818,0.19097716421,0.188893763728,0.193755004853,0.2000051663,0.205560887586,0.218755650641,0.202088566782,0.189588243889,0.194449485014,0.171532199709,0.186810403246,0.161115277297,0.169448839226,0.194449485014,0.170837759548,0.170143279387,0.160420797137,0.164587598101,0.168754359066,0.176393440834,0.152087275207,0.140975872635,0.148614954404,0.144448193439,0.155559596011,0.138198031992,0.173615600191,0.140281392474,0.149309434564,0.125003228938,0.137503551831,0.140281392474,0.120141987812,0.147226034082,0.134031231027,0.131253390384,0.125697709098,0.104863824275,0.118753067491,0.120836467973,0.114586306526,0.112502906044,0.115975226848,0.106252744597,0.104863824275,0.122225388294,0.103474903954,0.0993081429893,0.112502906044,0.0944469018639,0.120836467973,0.0888911805778,0.0965302623462,0.0944469018639,0.102085983632,0.0965302623462,0.115280746687,0.0875022602563,0.0743074572018,0.106252744597,0.0944469018639,0.0826410191309,0.0868077800955,0.0756964175233,0.0791686983271,0.0888911805778,0.0875022602563,0.085418859774,0.0715296165587,0.0680572957549,0.0777797780056,0.0840299394525,0.0743074572018,0.0763908576841,0.0694462560764,0.0756964175233,0.0708351763979,0.0743074572018,0.0604182139865,0.0708351763979,0.0659739352726,0.0673628555941,0.0625016144688,0.0659739352726,0.0583348535042,0.0708351763979,0.061807134308,0.0583348535042,0.0666683754334,0.0590292936649,0.0631960946295,0.0555569728611,0.0534736123788,0.0493068114142,0.0458345306104,0.0652794551118,0.0534736123788,0.0395843571636,0.050001291575,0.0486123712535,0.050001291575,0.0458345306104,0.0583348535042,0.047223450932,0.0486123712535,0.0465289707712,0.0527791322181,0.0451400504497,0.0375009686813,0.0437511301281,0.0465289707712,0.038195432842,0.0423622098066,0.0416677296458,0.0368065085205,0.0423622098066,0.0368065085205,0.0444456102889,0.0409732894851,0.047223450932,0.047223450932,0.0395843571636,0.0361120443597,0.0423622098066,0.0284729585913,0.035417584199,0.0347231200382,0.0423622098066,0.0319452713951,0.0319452713951,0.0465289707712,0.0291674187521,0.0340286558774,0.0340286558774,0.0298618829129,0.026389570109,0.0340286558774,0.0312508072344,0.0250006457875,0.0305563470736,0.0250006457875,0.0256951099483,0.0333341957167,0.0319452713951,0.026389570109,0.0222227971445,0.0319452713951,0.0305563470736,0.0284729585913,0.0284729585913,0.0312508072344,0.0229172573052,0.026389570109,0.0180560241799,0.0243061856267,0.0229172573052,0.0256951099483,0.0194449485014,0.0243061856267,0.0229172573052,0.0229172573052,0.0187504843406,0.0243061856267,0.0180560241799,0.0215283329837,0.0229172573052,0.0201394086622,0.0187504843406,0.0194449485014,0.0187504843406,0.0222227971445,0.0166670958583,0.023611721466,0.0180560241799,0.0229172573052,0.0138892472153,0.0138892472153,0.0187504843406,0.0173615600191,0.0180560241799,0.0194449485014,0.011805862733,0.0159726356976,0.0173615600191,0.014583711376,0.014583711376,0.0125003228938,0.0187504843406,0.0152781715368,0.0166670958583,0.0138892472153,0.0159726356976,0.0125003228938,0.011805862733,0.0131947870545,0.011805862733,0.00833354992917,0.011805862733,0.00694462560764,0.0159726356976,0.0138892472153,0.00763908576841,0.0131947870545,0.011805862733,0.014583711376,0.00694462560764,0.0125003228938,0.00902801008993,0.011805862733,0.00694462560764,0.0131947870545,0.0125003228938,0.011805862733,0.0138892472153,0.00625016144688,0.0138892472153,0.00555569728611,0.00833354992917,0.00763908576841,0.00625016144688,0.00625016144688,0.00833354992917,0.00555569728611,0.0131947870545,0.0097224742507,0.0111113985722,0.0097224742507,0.00555569728611,0.00416677296458,0.00347231200382,0.00763908576841,0.00902801008993,0.00833354992917,0.00694462560764,0.00694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# Creating weights for histo: y12_PT_3
y12_PT_3_weights = numpy.array([0.0,0.00426740125428,0.00711233409047,0.00663817795111,0.00995726692666,0.0128021997628,0.0142246681809,0.0142246681809,0.0289234925013,0.0227594706895,0.0284493363619,0.02987180478,0.0303459569193,0.0360358265917,0.0398290717066,0.0426740125428,0.0388807594279,0.0464672536578,0.0583211371419,0.050734654912,0.0587952972812,0.0734941016015,0.0659076193717,0.0711233409047,0.0640110188143,0.0763390624377,0.0886671060612,0.0924603471761,0.0929345073155,0.0919861870368,0.103365910382,0.100995149685,0.0986243489879,0.105262550939,0.133711879301,0.128970317907,0.120909675538,0.119961355259,0.129918638186,0.119961355259,0.136082639998,0.151729804597,0.15647136599,0.137979280555,0.161212887384,0.162161207663,0.155997205851,0.165954448778,0.172592650729,0.178756652541,0.171170170311,0.183972374074,0.170696010171,0.182075733516,0.184446534213,0.182549893655,0.198671178394,0.170696010171,0.217163263829,0.201041979091,0.189188095607,0.183024053795,0.211473382157,0.200093658812,0.189662255746,0.218111584108,0.193455496861,0.176385891844,0.176385891844,0.188239775328,0.165954448778,0.197248737976,0.165480288638,0.175911731704,0.176385891844,0.151255644457,0.157893806408,0.159316286827,0.160738767245,0.152678084875,0.16405784822,0.146514083064,0.1493590039,0.155523045712,0.14177252167,0.155523045712,0.131815238743,0.14177252167,0.148410683621,0.130392798325,0.126125397071,0.128496157768,0.147936563482,0.128496157768,0.124228756514,0.120435515399,0.118538914841,0.128021997628,0.133237719162,0.126125397071,0.117590594562,0.116642274284,0.113797353448,0.112374873029,0.108581631915,0.106210871218,0.111426552751,0.0986243489879,0.102417590103,0.101469309824,0.0953052680123,0.0976760687092,0.10431423066,0.105736711078,0.0905637066187,0.0990985091272,0.0915120268974,0.0862963053644,0.0962535882911,0.0862963053644,0.0848738649463,0.0943569477336,0.085822145225,0.0834513845282,0.0810806238314,0.0881929459219,0.0715975010441,0.0715975010441,0.0749165820196,0.0787098231346,0.0768132225771,0.070175020626,0.0711233409047,0.0711233409047,0.0659076193717,0.0697008604866,0.0715975010441,0.0673300997898,0.0687525802079,0.0692267003473,0.0730199814622,0.0687525802079,0.0706491807654,0.0587952972812,0.0678042599292,0.0625885383962,0.0720716611835,0.0625885383962,0.05974361756,0.0659076193717,0.0559503764451,0.0592694574206,0.0592694574206,0.0488380143546,0.0564244965844,0.0531054156089,0.0554762163057,0.0502604947727,0.0531054156089,0.0478897340759,0.0436223328216,0.0497863346333,0.0497863346333,0.0431481726822,0.0445706131003,0.054527896027,0.0469414137971,0.0384066032885,0.0535795757482,0.0440964529609,0.0388807594279,0.0483638542152,0.0436223328216,0.0346133581736,0.0341392020343,0.0426740125428,0.0360358265917,0.0393549155673,0.0303459569193,0.0440964529609,0.0431481726822,0.0412515321247,0.0407773719854,0.0331908937555,0.0331908937555,0.0360358265917,0.0322425814768,0.0327167376162,0.035087514313,0.0341392020343,0.02987180478,0.0265527118044,0.0279751802225,0.0275010240832,0.0293976486406,0.0346133581736,0.0279751802225,0.0308201130587,0.0293976486406,0.0265527118044,0.0322425814768,0.0293976486406,0.0336650458949,0.02987180478,0.0275010240832,0.0260785596651,0.0256044035257,0.0232336228289,0.0251302473863,0.0213370022714,0.0251302473863,0.0203886899927,0.0256044035257,0.019440377714,0.0222853145501,0.0222853145501,0.0232336228289,0.0218111584108,0.0256044035257,0.0161212887384,0.0241819351076,0.0199145338533,0.0199145338533,0.0199145338533,0.019440377714,0.0175437571565,0.0218111584108,0.0232336228289,0.0284493363619,0.0175437571565,0.0123280436235,0.0203886899927,0.0151729804597,0.0232336228289,0.0170696010171,0.0113797353448,0.0184920694352,0.0189662255746,0.0151729804597,0.0189662255746,0.0118538914841,0.019440377714,0.0151729804597,0.019440377714,0.0170696010171,0.0123280436235,0.015647136599,0.0132763559022,0.0128021997628,0.0175437571565,0.00853480250857,0.0184920694352,0.0132763559022,0.0113797353448,0.0151729804597,0.0146988243203,0.0161212887384,0.015647136599,0.00995726692666,0.00995726692666,0.0146988243203,0.00995726692666,0.0132763559022,0.00711233409047,0.0113797353448,0.0132763559022,0.00995726692666,0.0128021997628,0.015647136599,0.00900895464793,0.0113797353448,0.00900895464793,0.00663817795111,0.0151729804597,0.00426740125428,0.00853480250857,0.00900895464793,0.00758649022984,0.0123280436235,0.0080606463692,0.00616402181174,0.0094831107873,0.00853480250857,0.0132763559022,0.0080606463692,0.0113797353448,0.00900895464793,0.00853480250857,0.00568986567238,0.0094831107873,0.00758649022984,0.00853480250857,0.00616402181174,0.00853480250857,0.0142246681809,0.00663817795111,0.00426740125428,0.0080606463692,0.00568986567238,0.00616402181174,0.00853480250857,0.00521571353301,0.00331908937555,0.00711233409047,0.00900895464793,0.00616402181174,0.00663817795111,0.00853480250857,0.00474155739365,0.0123280436235,0.00663817795111,0.00426740125428,0.00521571353301,0.00426740125428,0.00568986567238,0.00568986567238,0.007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# Creating weights for histo: y12_PT_4
y12_PT_4_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y12_PT_5
y12_PT_5_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.05462838872,0.0,0.0,0.0,1.0521138287,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0529581672,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y12_PT_6
y12_PT_6_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.691821959624,0.230541945396,0.0,0.229694011257,0.921958055545,0.0,0.461330812657,0.460911975383,0.0,0.0,0.461015724066,0.0,0.461055302267,0.689861878035,0.229922719464,1.3821349523,0.229694011257,0.460149998949,0.690326441579,0.461198244896,0.690241905616,0.0,0.0,0.0,0.460772491044,0.0,0.0,0.690858633895,1.15158309314,1.15090949521,0.230119227154,0.230010867419,0.230176942161,0.691994489841,0.230418945569,0.922139807867,0.230541945396,0.691168727179,0.460080064652,0.92083103745,0.230610342676,0.230754707046,0.230635895592,0.461006117706,0.460752125561,0.691074200602,0.460187655878,0.23082921397,0.921374757397,0.229973210489,0.690960077051,0.230488995143,0.0,0.92123988411,0.0,0.0,0.0,0.0,0.0,0.230350855693,0.0,0.230578411137,0.229973210489,0.0,0.690543161049,0.229943161797,0.0,0.0,0.0,0.230663830885,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.230635895592,0.0,0.230742910437,0.0,0.0,0.0,0.0,0.230119227154,0.0,0.0,0.0,0.0,0.230663830885,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y12_PT_7
y12_PT_7_weights = numpy.array([0.0,0.0,0.498465790064,1.08020258588,0.830831584828,0.719937836214,0.636682603016,0.803421579095,0.71996630375,0.802806834191,0.581457121507,0.691928088738,0.609355691713,0.498359998544,0.553865539057,0.747749080329,0.442987562997,0.553622795606,0.553751668912,0.553869770718,0.359624499388,0.637003824539,0.443148750804,0.608930986848,0.664631568454,0.526137389373,0.60921027646,0.886262109536,0.41540867553,0.691940399024,0.637253877222,0.55376051693,0.359965725127,0.498681604764,0.609267211533,0.526248181946,0.35993298746,0.4155198528,0.276729034296,0.498259592774,0.304674960633,0.331949360872,0.221542561007,0.221689322697,0.304352123385,0.221565719732,0.332269428307,0.360295794668,0.276966238119,0.221351828514,0.387896340545,0.249397891333,0.138447784692,0.166159007733,0.249093788801,0.249264132382,0.166160161823,0.166078683117,0.13845551709,0.166095609761,0.0830753491451,0.110913752828,0.0276885798094,0.166079606389,0.0830635774342,0.0829495918803,0.0830054882724,0.055383283986,0.138491024571,0.0553308498619,0.22158326189,0.0554332945227,0.0277234410004,0.0276923267527,0.0,0.0276911880512,0.110631808811,0.0276846212831,0.0552960348344,0.0,0.0277220560932,0.0830744643433,0.0277322159261,0.0,0.0277388327048,0.0,0.0277194209226,0.0,0.0553699350197,0.0553896314772,0.027727903479,0.0553990180702,0.0,0.0,0.0,0.0,0.0,0.0277276765081,0.0,0.0277647035401,0.0,0.02769444643,0.0277647035401,0.0,0.0,0.0277234410004,0.0,0.0,0.0554033266703,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0276396541167,0.0,0.0,0.0,0.0276885798094,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y12_PT_8
y12_PT_8_weights = numpy.array([0.0,0.0,0.261904101177,0.322564160121,0.372973580475,0.363101592802,0.372927159329,0.393335776261,0.312496536141,0.312663591586,0.332672137124,0.362920519992,0.352912363624,0.201730827114,0.181428159622,0.241996529217,0.302499909208,0.211782613292,0.221884400808,0.262151559261,0.161329805853,0.32270591149,0.251934538076,0.262061690349,0.261968544651,0.262089967805,0.231952449557,0.181597885041,0.322522168706,0.161264816249,0.161327621329,0.191510104374,0.191637838377,0.0807151469907,0.100842870478,0.0907007906201,0.110987074179,0.191551428296,0.151324804909,0.0908049196222,0.100817869808,0.0503730176801,0.0705697296651,0.0705374472471,0.0806433004064,0.120817859292,0.080599609916,0.0706137842429,0.11092730316,0.0907076475998,0.11091013037,0.120972475083,0.0705784070819,0.0605353165257,0.0706209446288,0.060422473698,0.0705876913111,0.0504275215669,0.050361142362,0.0604792531311,0.0403161524832,0.0907816180273,0.100701422515,0.0504004880759,0.0302735655813,0.0706356294881,0.0201958078346,0.0706343551821,0.0302006934841,0.0605086895991,0.0,0.0302385039627,0.0302021316294,0.0302081997531,0.0302000320586,0.0302477335788,0.0504517819253,0.03028233402,0.0403462018316,0.0201756313234,0.0201727064878,0.0100721447323,0.0100941295444,0.0201609828728,0.0100962897964,0.0201623785413,0.0100909619838,0.0201662742767,0.0,0.0201894302366,0.0,0.0,0.0100817445039,0.0,0.0,0.0100840867997,0.0100936805032,0.0,0.010079341527,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0100784737853,0.0100784737853,0.0,0.0,0.0,0.0,0.0,0.010079341527,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0100700148209,0.0100417373646,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0100659249055,0.0,0.0,0.0,0.0,0.0,0.0,0.0100962897964,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0100695111666,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0100840867997,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y12_PT_9
y12_PT_9_weights = numpy.array([0.0,0.0,0.130135725793,0.121669398328,0.212166041627,0.223515254127,0.203626344505,0.203667165274,0.161212680902,0.16697525764,0.161257849217,0.152736388706,0.175387606275,0.155568449767,0.186707424743,0.192401556479,0.164060824038,0.164140041722,0.16695086521,0.132972942352,0.121690789795,0.147140865482,0.104721815538,0.115957645563,0.130152038711,0.107521289237,0.0848738036326,0.104693998935,0.0707592052767,0.124478836757,0.08489250193,0.0877586893076,0.0933518271523,0.0707365826452,0.0622422077336,0.0452929705679,0.0905475826943,0.0424346703322,0.0480677440468,0.0452736951625,0.0566027473578,0.0480874426647,0.0480941371169,0.0367937056283,0.0395932601225,0.0282815288603,0.0254676890208,0.025466923391,0.0452759266465,0.0452815438306,0.0226366513645,0.0367697171747,0.0565606954254,0.0367815440402,0.0282777622691,0.0395882200464,0.0367776658748,0.0282741187943,0.0396117275767,0.0339556888133,0.0254576627321,0.0198076220439,0.0254587130686,0.0141346432385,0.0141614018103,0.0169785888759,0.0254677736633,0.0169799200716,0.00848731495769,0.0141369516703,0.00849148937183,0.0197960452585,0.0141481167854,0.00848369071979,0.0197992385891,0.00848793438689,0.0169879688037,0.00848193246425,0.00564720131711,0.00848625692646,0.0141337660345,0.014143292163,0.0028258171378,0.0113195299142,0.0113134241121,0.014136251446,0.0028303705195,0.0113249778132,0.00847751566477,0.0056599323184,0.00283437026231,0.00848148232005,0.0112989502448,0.00283350729356,0.00565363029963,0.00283093493107,0.00282138456404,0.0,0.0113124199443,0.00283350729356,0.00283297596951,0.00283246080448,0.0056641452064,0.00282796205567,0.00565860112274,0.00283093493107,0.00283007811814,0.0,0.0,0.0,0.0,0.00283193755994,0.00566916219815,0.00283618468969,0.0,0.00282874692248,0.0,0.0,0.0,0.0,0.0,0.00282539700322,0.0,0.0,0.0,0.0,0.0,0.0028303705195,0.0,0.00282704060665,0.0,0.00282743380953,0.00283007811814,0.0,0.0,0.0,0.0,0.00283093493107,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00282343637516,0.0,0.0,0.0,0.00283618468969,0.0,0.0,0.0,0.0,0.0,0.00283618468969,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00283297596951,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00283493582809,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00282926631963,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y12_PT_10
y12_PT_10_weights = numpy.array([0.0,0.0,0.0212882389033,0.0258804492496,0.0411398939229,0.0380819371119,0.0228503744247,0.0334775343039,0.0304359404382,0.0273877659512,0.0518055333056,0.0274493071651,0.0197927956746,0.0304520552809,0.0152716016917,0.0289557259722,0.015199132157,0.0137091708103,0.0258966940508,0.0137661634799,0.0106461349884,0.0182722114009,0.0182324913853,0.00912756507011,0.00760884983466,0.0122066034979,0.0091393074031,0.0121642370565,0.00611460104189,0.00914477747068,0.00762346897425,0.00916897690791,0.00611575767173,0.01065319173,0.00456619267282,0.0121694826504,0.0075975091913,0.00152036570725,0.00304238779349,0.00611353892726,0.00460431538143,0.00304484873319,0.0060954132735,0.00910542488295,0.00915575068618,0.00612267027766,0.00607879986305,0.0,0.0,0.00612408800576,0.00304073259594,0.0045945082473,0.00304332349404,0.0060815986946,0.00607187898704,0.00758950729761,0.0,0.00608632091062,0.0,0.00153570552529,0.00152391120894,0.00457831542952,0.00306845272461,0.0,0.00153570552529,0.00305803242308,0.00153762418399,0.0030331182146,0.00304141192398,0.00305476928557,0.00456689562967,0.0,0.0,0.00609951641491,0.0015120731793,0.00304301631962,0.00305141281429,0.0,0.00304782359932,0.00152134393964,0.0,0.0,0.00150791687309,0.0,0.0,0.00153274719932,0.00152247103348,0.00152437433345,0.00150791687309,0.0015210450353,0.00152202208625,0.0015210450353,0.00454985690079,0.0030594418811,0.0,0.00151823557078,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0015258582222,0.0,0.0,0.0,0.0015120731793,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00154481679121,0.00152134393964,0.00152391120894,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00153274719932,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y12_PT_11
y12_PT_11_weights = numpy.array([0.0,0.0,0.00288999849517,0.00343250965607,0.00559594272456,0.00541745176339,0.00686198541252,0.00505721565831,0.00559714812006,0.00595780784657,0.00361210010738,0.00361391705818,0.00397070758065,0.00541591132185,0.00397120437305,0.00252606224951,0.00198698896902,0.00252657252077,0.00288994034351,0.00252752566897,0.00108223720349,0.0025286059036,0.00234674484146,0.00289071017916,0.00180512174511,0.000902872041746,0.00144504661616,0.00144531349766,0.000720855780116,0.00198592336859,0.00144389860211,0.00180688131446,0.00216585733836,0.00036066084333,0.00126364691626,0.000902458818303,0.00162300689522,0.00162583784166,0.000541114750639,0.00144397408374,0.00198572696229,0.000722584540633,0.000180699915824,0.000541399347214,0.00036031967404,0.00108382347316,0.000180612149167,0.0010834541523,0.00126532330177,0.000542508465122,0.000361438304175,0.000901943540608,0.000540770461955,0.000722046541426,0.000903718129261,0.00072142035194,0.000722090444009,0.000722024205023,0.000902530448834,0.000180262006805,0.000180685281629,0.000180366949385,0.0,0.000543683436906,0.000542287796872,0.000721845128694,0.000361360319322,0.0,0.000722769393618,0.00108432950821,0.0,0.000360644476138,0.000180366949385,0.000180262006805,0.000721624075333,0.0,0.000360523667011,0.000180366949385,0.0,0.000180592932159,0.000180057628724,0.0,0.000541544148718,0.000180461108874,0.000180062558137,0.000361092051428,0.0,0.0,0.0,0.000361774698096,0.000180745204805,0.0,0.0,0.000180562469927,0.0,0.0,0.000361262385751,0.000180699915824,0.0,0.000360929842933,0.0,0.000180262006805,0.00018040087761,0.000180685281629,0.0,0.000180745204805,0.000180612149167,0.000180793497647,0.0,0.0,0.000180793497647,0.000180562469927,0.0,0.0,0.0,0.0,0.0,0.000180793497647,0.0,0.0,0.0,0.0,0.000180461108874,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.000180592932159,0.0,0.0,0.000180671918299,0.0,0.0,0.0,0.0,0.0,0.0,0.000180826155008,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.000180699915824,0.0,0.000180908876718,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.000180366949385,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.000180908876718,0.000180612149167,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y12_PT_12
y12_PT_12_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0243458291178,0.0,0.0,0.0121240822392,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0121313846429,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y12_PT_13
y12_PT_13_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0100262834878,0.0100262834878,0.0200525091271,0.0100271594796,0.0100407290877,0.0,0.0,0.0100329195386,0.0100584803169,0.0100299568683,0.010069669872,0.0301274018933,0.0200763468462,0.0100602901489,0.050256308245,0.0602515807981,0.0200523355815,0.0301294679116,0.0300697517188,0.0200694174208,0.0401916050809,0.0501888320874,0.0100187053327,0.0301061177729,0.0602174914963,0.0301159065675,0.0100532491586,0.0401922000942,0.0301372030841,0.0200696198906,0.0301141297918,0.0602747202031,0.0100299568683,0.0301103779026,0.010035564042,0.0100153625151,0.0100262834878,0.0100609843311,0.0100153625151,0.0200594344204,0.0100187053327,0.020073950265,0.03012092286,0.0300812966291,0.0100702896775,0.010045732984,0.010069669872,0.0100609843311,0.0,0.0,0.010036700352,0.0,0.0100532491586,0.0,0.0,0.0,0.0,0.0,0.0100324443544,0.0,0.0,0.0,0.0,0.0200624012227,0.010045732984,0.0,0.0,0.0,0.0,0.010034093037,0.0100568564265,0.0100262834878,0.0,0.0,0.0,0.0,0.010054654051,0.0,0.0,0.0,0.0,0.0100568564265,0.0,0.0100568564265,0.0,0.0,0.0,0.0,0.0,0.0100324443544,0.0,0.0,0.010069669872,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y12_PT_14
y12_PT_14_weights = numpy.array([0.0,0.0,0.00549807765066,0.0384458680927,0.0604854925616,0.043981907341,0.0384939335531,0.0329657758865,0.0605161251009,0.0440202183285,0.0825228419348,0.0329611444415,0.0824654363938,0.0440247278932,0.115528874828,0.055037003873,0.0990400371032,0.0990143610226,0.115529606109,0.0715340075666,0.0990226894981,0.121036272246,0.115432142632,0.131959006957,0.120907891843,0.0770056128531,0.109987391601,0.0714536073078,0.082487943591,0.126524168843,0.109991779285,0.154095160489,0.131984601784,0.214593897358,0.13201811882,0.0935006664646,0.121051182249,0.148391901703,0.12651149331,0.181532205872,0.115532937499,0.115531353057,0.115485891769,0.0935011539852,0.0989567117213,0.0715314074572,0.120996945591,0.104563928965,0.104473168895,0.0494896297726,0.0550004804609,0.115592699389,0.0935586407796,0.109970003369,0.0659756642545,0.0770270231292,0.0604668449018,0.0880393803624,0.0494766698522,0.0660910034839,0.0495250156369,0.0990707508959,0.0605111686423,0.0769325254021,0.0550102308712,0.0715177975094,0.0439828823821,0.0329953480686,0.0440263123349,0.0219819061022,0.0880275579899,0.0220200342694,0.038480043281,0.0440327719818,0.0220025688469,0.0274906726403,0.0385451394583,0.0165371388203,0.0329902087898,0.0330241239672,0.0220070418476,0.0219984492985,0.0220039989071,0.0220231137741,0.0219912949349,0.0330194275195,0.0164920512977,0.00550505731941,0.0109966671979,0.00549249960339,0.0220139077616,0.0110050931775,0.00549807765066,0.010978921451,0.0110062185374,0.00547933654939,0.0,0.0110014042722,0.0165104876986,0.00549249960339,0.00549992210329,0.0109774507641,0.00549914613313,0.0,0.00551434458529,0.00547500574212,0.00549610725523,0.0,0.0109883265343,0.00548818910947,0.0,0.0,0.0,0.0,0.00550985533385,0.0,0.0,0.0,0.0,0.00550427322391,0.0,0.0109999417107,0.00549405154371,0.0,0.00549610725523,0.0,0.0,0.00548486990728,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00548818910947,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00549455531491,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00548486990728,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00549610725523,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00551434458529,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y12_PT_15
y12_PT_15_weights = numpy.array([0.0,0.0,0.0404556690405,0.0937437850239,0.10262474354,0.124358350759,0.132236155578,0.139152973868,0.151004392852,0.161867208218,0.129282329496,0.141138558423,0.142090887055,0.126309423973,0.143079450592,0.163785092825,0.135201485441,0.128304508166,0.111508788436,0.128268914588,0.12435233833,0.154963897851,0.113489803545,0.111509990921,0.153993692264,0.123338402345,0.131228151856,0.144055869022,0.133242315489,0.135197396989,0.116437296536,0.112467730739,0.10362052199,0.0976984399969,0.105598410637,0.0927603921765,0.0977027288627,0.0957216736704,0.103619760416,0.105591356054,0.079919888579,0.0848683579426,0.079946503597,0.0908126859224,0.0927545801621,0.0789106823709,0.0720513429001,0.0562453891915,0.0700737749164,0.0789544929351,0.0621687137535,0.0532905209549,0.0562378135312,0.0503481184874,0.0513349985434,0.0434091744598,0.0542815696282,0.0552631186641,0.0404557091233,0.0414579409141,0.0444035099275,0.0345568150635,0.0384992648061,0.0315813923372,0.0276185604611,0.0256486643365,0.0315818292403,0.0276289018386,0.0286150002789,0.0217116057384,0.018751803302,0.024665564094,0.0226939604397,0.0157858160806,0.0207196632174,0.0217077497674,0.0197404389873,0.0128318096255,0.0128261779839,0.00789091592223,0.0138283817167,0.0128273644365,0.0138207539488,0.0108607551064,0.00690865741975,0.00592437473293,0.00887459736617,0.00987658865982,0.00493547450083,0.00296282989469,0.00888368415017,0.00591667080755,0.00789248717027,0.00296265232762,0.00592452303951,0.00493262060131,0.00493400746821,0.00789299221429,0.00493504561424,0.00493550656711,0.00295757262698,0.00394801043753,0.00591889540619,0.00394193748366,0.00197257887025,0.00394930110557,0.00296270884445,0.00395037692949,0.0,0.00296461759017,0.00591844647818,0.00296474224786,0.00394994523711,0.000988893818849,0.00295865205836,0.00098613691985,0.0019723944891,0.000983907912096,0.00296243147107,0.0,0.0,0.0019738066082,0.0,0.0,0.000983907912096,0.00197208144198,0.0,0.0,0.0,0.00197226421981,0.000987658465154,0.0,0.0,0.0,0.000986365792971,0.00197611578168,0.000985634681634,0.0,0.00197058434722,0.00197634265065,0.000987943855105,0.0,0.0,0.000984218554248,0.00295976636182,0.000985634681634,0.000987861284417,0.0,0.0,0.0,0.000988437675921,0.0,0.0,0.0,0.0019755369852,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00098613691985,0.0,0.00197462630266,0.0,0.0,0.000983907912096,0.0,0.000983907912096,0.000987943855105,0.0,0.0,0.00098817192657,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00197239048082,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00098817192657,0.0,0.0,0.0,0.0,0.0,0.000985427854085,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.000988893818849,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.000988893818849,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00098679347707,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00098628963554,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y12_PT_16
y12_PT_16_weights = numpy.array([0.0,0.0,0.018152653632,0.0368011551876,0.0458826473779,0.0564683437715,0.0589818525069,0.0557132628773,0.0650361833011,0.0612489756898,0.0746237741423,0.063519899884,0.0640183525006,0.0685700034971,0.0710775506463,0.0688111076444,0.0652876502055,0.0693181625497,0.0660353291304,0.0541940186724,0.061748828679,0.0680654692624,0.0642843032585,0.0609957083064,0.0637656452662,0.0617619521706,0.0662964386003,0.0584862806568,0.0567151694411,0.0594905478486,0.0589902547424,0.056724531932,0.0466334871437,0.0433558951189,0.0448660569073,0.0468911556981,0.0428575225236,0.0390697467612,0.0400833444354,0.0418467335966,0.0342771676642,0.0347896880236,0.0347886077362,0.028739614362,0.0335190219546,0.0279829690516,0.0327704868019,0.0302410218213,0.0254604099083,0.0292429482773,0.0262114017145,0.0320142095894,0.0277388000888,0.0211775024128,0.0242007907784,0.023192642554,0.0216826248039,0.0224407345039,0.0214302056461,0.0171405283495,0.018654919263,0.018149428774,0.0163873799694,0.0166364942479,0.0141112863999,0.0143644537567,0.0115939766532,0.0121026599917,0.0133551972374,0.0100824384986,0.0103346295957,0.0121024599385,0.0105895334145,0.00781669568291,0.00781492321134,0.00680694303163,0.00806314125137,0.00730837644126,0.00806706629565,0.00630226075762,0.00478756576312,0.00403484549698,0.00277467542051,0.00579812662944,0.005545479411,0.00478847400477,0.00378157971397,0.00453883158577,0.00327529501362,0.00302550375502,0.00453517861388,0.00302606470427,0.00201591074621,0.00226848994656,0.002017007438,0.00277342588806,0.00201836739982,0.00277137974367,0.00151247600411,0.00226876281916,0.000756238002057,0.00252224505976,0.00277350350871,0.000756478466034,0.00176431620784,0.00126159525565,0.000503998492089,0.00176636275234,0.000757165848917,0.000755677052813,0.0,0.00100806700281,0.00126125876613,0.000756456060073,0.00151307576368,0.000253127346183,0.00050449102313,0.00176465109694,0.000755857900929,0.0,0.00100758727517,0.000755867503484,0.000755677052813,0.000756505673273,0.000504164936372,0.000504460214933,0.0,0.000504013696134,0.00050499835811,0.000503828046741,0.000251546685638,0.000252216823932,0.00100760287932,0.0,0.00100738082024,0.000252051619979,0.00100863835482,0.0,0.000503638396283,0.00025164107075,0.0,0.0,0.000504368990662,0.000252302006596,0.0,0.000504437408866,0.000504056907631,0.0,0.0,0.0,0.0,0.000252182934916,0.00025214412459,0.000253127346183,0.000252082548208,0.0,0.0,0.0,0.000251611022755,0.0,0.000252182934916,0.000252182934916,0.0,0.0,0.0,0.0,0.0,0.00025164107075,0.0,0.000504197344995,0.000252014210026,0.0,0.0,0.00025164107075,0.0,0.0,0.0,0.0,0.0,0.0,0.000251954514143,0.000503723618957,0.0,0.00025187117197,0.0,0.000252082548208,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.000253127346183,0.0,0.0,0.000252320971642,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y12_PT_17
y12_PT_17_weights = numpy.array([0.0,0.0,0.00974483703173,0.0160276848565,0.0260486358485,0.032348845898,0.0277662223676,0.0314826142559,0.0300694015523,0.0306256705942,0.0340684119865,0.0352264501767,0.0329318391289,0.036644391852,0.0400906824726,0.032946945844,0.0340865780364,0.0372195767808,0.0383877527665,0.0403577144078,0.0332086789314,0.038660913369,0.0400924820813,0.033200140788,0.0311959965795,0.0229080487542,0.0312058844295,0.0272092834713,0.0240575388031,0.0231800296133,0.0251748058587,0.0274928718063,0.0180261003106,0.0223391024689,0.0251891327434,0.0234815940395,0.0208933968307,0.0162962564569,0.0177509201472,0.0174669318991,0.0209131025457,0.0163188715393,0.0177472409472,0.0174667819317,0.0171864828814,0.0146046442899,0.0140299992437,0.0148819139989,0.0163188615415,0.01745952351,0.0134586834736,0.0143092285266,0.0143019501093,0.0143107481962,0.0145964360747,0.0103068088336,0.0068630226685,0.0131696763168,0.0114426918413,0.00859341540286,0.0108786244951,0.00830304254306,0.0108774147581,0.00944529716524,0.00630214662832,0.00773743653138,0.00544645269487,0.00688083179599,0.00715566703438,0.00658685671947,0.00657766571801,0.00486423229592,0.00687265357431,0.00715413036852,0.00429725058339,0.0054352801243,0.00457826147891,0.00286579284703,0.00315139774361,0.00343979903213,0.00343275956283,0.004296387771,0.00343401428999,0.00372818932304,0.00343579990172,0.00228826542761,0.00314850537254,0.00200308943777,0.00172091779465,0.00315068089949,0.00200100489104,0.00143467403698,0.00315310037338,0.0022934782941,0.00171710362402,0.00143150472613,0.00114489504804,0.000859059301658,0.000855112559858,0.00143420813829,0.00114172373763,0.000572930918908,0.000861728221314,0.00143319135938,0.000286843727202,0.00114653769086,0.000286460010639,0.000571275078963,0.00114353634348,0.000287793420695,0.000857225400431,0.000571266280876,0.000573620269012,0.000571131210246,0.000287273233807,0.000571929136741,0.00143436410438,0.0,0.000570313887969,0.0014307828831,0.000859816936914,0.00028633583764,0.000572675274497,0.000284655902933,0.0,0.000285189286952,0.000862216115224,0.000573303837819,0.000287149360743,0.00114642871455,0.00114197168371,0.0,0.00143326434351,0.000287149360743,0.00114607279195,0.00114405523065,0.00028633583764,0.000570937852291,0.0,0.0,0.0,0.00028633583764,0.0,0.000286470908269,0.0,0.000286460010639,0.0,0.0,0.0,0.0,0.000573485198383,0.0,0.0,0.000574942881416,0.000286460010639,0.000287793420695,0.0,0.0,0.000859692663936,0.0,0.0,0.0,0.0,0.00028633583764,0.000286460010639,0.000284655902933,0.0,0.0,0.0,0.0,0.0,0.000287793420695,0.0,0.0,0.0,0.0,0.0,0.000284930043323,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00028633583764,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.000286460010639,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.000284094025111,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y12_PT_18
y12_PT_18_weights = numpy.array([0.0,0.0,0.00209525919527,0.00287328487879,0.00364812365871,0.00446975384484,0.00513960292712,0.00490048876186,0.00507386151481,0.00531169320703,0.00578758710133,0.00671609758506,0.00591593918292,0.00563649417419,0.00604210770747,0.00475140336605,0.00511554188874,0.00470871293983,0.00445077659666,0.00520706687675,0.00446792233922,0.00466274089141,0.00414650064662,0.00479393453092,0.00416631401693,0.00459932972424,0.00392980628953,0.00369109614519,0.00339052637459,0.00399486874552,0.00336750849811,0.00358632145038,0.00334744701537,0.00302064983281,0.00360666415515,0.00291450129923,0.00298110649485,0.00248400862712,0.00233250706887,0.00302277052353,0.00287049277571,0.00272160813304,0.00276341843071,0.00224478214069,0.0021819200061,0.00252754188122,0.00213620696694,0.00179266051879,0.00211660608481,0.00172499623514,0.00209535600941,0.00168461132663,0.00170647246272,0.00174889130642,0.0022016570986,0.00176665523451,0.00159895092859,0.00146909382716,0.00155491516299,0.00159837130106,0.00125290407711,0.00149027768337,0.00107983308252,0.00136082041177,0.00118811362275,0.00103680527368,0.00101511806775,0.00116648425385,0.000864251040282,0.000885299943446,0.000885319222452,0.000842283450546,0.000734270720854,0.0012531739832,0.000777777990203,0.00084224237788,0.000646415868936,0.000626295291849,0.000475085014239,0.000560035022801,0.00066983776634,0.000626300321155,0.000518545343398,0.000756087850517,0.000453528570273,0.000518430926685,0.000518256996517,0.000367237036231,0.000410677038611,0.000496743301653,0.000410177293233,0.000237674987423,0.000280827564962,0.000323930771481,0.000324098373105,0.00036682249568,0.00032412850703,0.000323776958537,0.000345568229185,0.000410284543185,0.000302306892903,0.000237689446678,0.000259495386135,0.000237650804843,0.000259025858504,0.000323934711104,0.000151176371705,0.000151201560146,0.000216107604716,0.00023741069739,0.00030239071467,0.000280901956781,0.000194331338164,0.000172710644813,0.000194371153504,0.000151252439959,0.000129631159855,0.000194401874182,0.000172859679916,0.000194486617989,8.63644589794e-05,0.000151075366475,6.48040334792e-05,0.000194497053799,0.000107984062647,0.00010804060043,0.000108058286823,0.0001727109801,0.000172785246187,8.64802168404e-05,0.00015129942206,6.48464472936e-05,0.000107964699819,4.31063079237e-05,0.000107861389491,0.000129672442076,8.63596392277e-05,8.64632429325e-05,0.00010807999666,4.32510681163e-05,2.1656020035e-05,8.63223385412e-05,4.32970443558e-05,8.63972332905e-05,4.32045051244e-05,0.0,4.32329207036e-05,2.16264560976e-05,2.15990547618e-05,4.15849176958e-05,4.31969611653e-05,0.0,0.0,2.1609306164e-05,2.15563475712e-05,4.30829635614e-05,0.0,2.15563475712e-05,0.0,0.0,2.16100647509e-05,0.0,0.0,2.16148928848e-05,0.0,4.31686713188e-05,4.32709338752e-05,0.0,0.0,0.0,4.31436086103e-05,0.0,0.0,2.16148928848e-05,0.0,0.0,0.0,0.0,2.15990547618e-05,0.0,2.15990547618e-05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.16758774118e-05,2.16298927901e-05,0.0,0.0,0.0,0.0,0.0,0.0,2.1594084131e-05,0.0,0.0,0.0,2.15780909377e-05,0.0,4.3202116204e-05,0.0,0.0,0.0,4.31100799033e-05,0.0,0.0,0.0,6.47041598431e-05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.1579670978e-05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.15570307186e-05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y12_PT_19
y12_PT_19_weights = numpy.array([0.0,0.0,0.000227347609876,0.000593474829654,0.000766996054721,0.000993908323135,0.000934078949402,0.00102260877716,0.00105069832725,0.000905406270487,0.00104921302714,0.0010772920316,0.000962382085649,0.00119132624461,0.000906831564473,0.000793854142492,0.000877421731114,0.000624060575111,0.00107523994097,0.000737900362336,0.000739111773105,0.000851334365571,0.000880527196584,0.000796035008643,0.000681056738884,0.00104701077267,0.000507481745952,0.000797093879092,0.000795577536209,0.000907669719325,0.000508140773611,0.000595780461015,0.00070975585614,0.000622067747953,0.00053835964994,0.000624962449337,0.00048194082232,0.000482394135914,0.000595089796464,0.000479626427688,0.000509517943873,0.000479989880625,0.000593997803823,0.000312449662041,0.00036763702734,0.000455725275378,0.000565926077333,0.000312274990748,0.000510702173651,0.000369040933005,0.000508865005945,0.000397633108653,0.000197202998552,0.000255918842793,0.000255354577281,0.000423564517685,0.000255505335242,0.000340395435082,0.000227158828232,0.000197968225169,0.000198941542331,0.000252388878551,0.000340327853927,0.000198584773244,0.000142083244114,0.000141803859163,0.00011343675104,0.000255138614645,0.000283881102665,0.000141661730796,0.00014203690275,0.00016964206677,5.6764442103e-05,0.000197004562457,0.000198868911155,0.000170485420172,0.000113605748487,0.000170126126076,0.000170304659149,0.000142166316949,8.51335850871e-05,0.000113273308616,8.52219604436e-05,5.69150366812e-05,0.00011277539146,0.000113690677947,2.84059932798e-05,0.000113486820507,0.000113690677947,0.000113437954133,0.000113510644721,0.00014184927964,0.000140596354733,0.000113597831837,2.82249500494e-05,0.000113667967708,0.000113698431214,2.82080621871e-05,5.68333897341e-05,2.83584488233e-05,5.63978997419e-05,2.84575183406e-05,8.5321029961e-05,2.83584488233e-05,0.0,5.67830677664e-05,5.69150366812e-05,2.84575183406e-05,5.68149423068e-05,2.83584488233e-05,5.68159671638e-05,0.0,5.67963166434e-05,0.0,5.66655805277e-05,8.53013942935e-05,2.84575183406e-05,5.68159671638e-05,2.82080621871e-05,5.68159671638e-05,2.84059932798e-05,5.66784432267e-05,2.84059932798e-05,5.65095348981e-05,0.0,5.653433941e-05,2.83584488233e-05,0.0,5.65665110104e-05,2.83584488233e-05,0.0,2.84059932798e-05,2.83770744866e-05,0.0,2.84166874406e-05,0.0,0.0,2.84059932798e-05,2.84378678201e-05,0.0,5.68438759529e-05,5.66655805277e-05,0.0,2.84166874406e-05,0.0,2.84575183406e-05,0.0,5.62886707718e-05,2.83584488233e-05,0.0,0.0,0.0,0.0,2.84378678201e-05,0.0,2.84575183406e-05,0.0,0.0,0.0,0.0,0.0,2.83770744866e-05,0.0,0.0,5.67168827935e-05,0.0,2.70094547043e-05,2.83584488233e-05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.84059932798e-05,2.83770744866e-05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.83770744866e-05,0.0,2.84575183406e-05,0.0,2.83770744866e-05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.83770744866e-05,0.0,0.0,0.0,2.83770744866e-05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.84166874406e-05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating a new Canvas
fig = plt.figure(figsize=(12,6),dpi=80)
frame = gridspec.GridSpec(1,1,right=0.7)
pad = fig.add_subplot(frame[0])
# Creating a new Stack
pad.hist(x=xData, bins=xBinning, weights=y12_PT_0_weights+y12_PT_1_weights+y12_PT_2_weights+y12_PT_3_weights+y12_PT_4_weights+y12_PT_5_weights+y12_PT_6_weights+y12_PT_7_weights+y12_PT_8_weights+y12_PT_9_weights+y12_PT_10_weights+y12_PT_11_weights+y12_PT_12_weights+y12_PT_13_weights+y12_PT_14_weights+y12_PT_15_weights+y12_PT_16_weights+y12_PT_17_weights+y12_PT_18_weights+y12_PT_19_weights,\
label="$bg\_vbf\_1600\_inf$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#ccc6aa", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y12_PT_0_weights+y12_PT_1_weights+y12_PT_2_weights+y12_PT_3_weights+y12_PT_4_weights+y12_PT_5_weights+y12_PT_6_weights+y12_PT_7_weights+y12_PT_8_weights+y12_PT_9_weights+y12_PT_10_weights+y12_PT_11_weights+y12_PT_12_weights+y12_PT_13_weights+y12_PT_14_weights+y12_PT_15_weights+y12_PT_16_weights+y12_PT_17_weights+y12_PT_18_weights,\
label="$bg\_vbf\_1200\_1600$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#c1bfa8", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y12_PT_0_weights+y12_PT_1_weights+y12_PT_2_weights+y12_PT_3_weights+y12_PT_4_weights+y12_PT_5_weights+y12_PT_6_weights+y12_PT_7_weights+y12_PT_8_weights+y12_PT_9_weights+y12_PT_10_weights+y12_PT_11_weights+y12_PT_12_weights+y12_PT_13_weights+y12_PT_14_weights+y12_PT_15_weights+y12_PT_16_weights+y12_PT_17_weights,\
label="$bg\_vbf\_800\_1200$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#bab5a3", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y12_PT_0_weights+y12_PT_1_weights+y12_PT_2_weights+y12_PT_3_weights+y12_PT_4_weights+y12_PT_5_weights+y12_PT_6_weights+y12_PT_7_weights+y12_PT_8_weights+y12_PT_9_weights+y12_PT_10_weights+y12_PT_11_weights+y12_PT_12_weights+y12_PT_13_weights+y12_PT_14_weights+y12_PT_15_weights+y12_PT_16_weights,\
label="$bg\_vbf\_600\_800$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#b2a596", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y12_PT_0_weights+y12_PT_1_weights+y12_PT_2_weights+y12_PT_3_weights+y12_PT_4_weights+y12_PT_5_weights+y12_PT_6_weights+y12_PT_7_weights+y12_PT_8_weights+y12_PT_9_weights+y12_PT_10_weights+y12_PT_11_weights+y12_PT_12_weights+y12_PT_13_weights+y12_PT_14_weights+y12_PT_15_weights,\
label="$bg\_vbf\_400\_600$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#b7a39b", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y12_PT_0_weights+y12_PT_1_weights+y12_PT_2_weights+y12_PT_3_weights+y12_PT_4_weights+y12_PT_5_weights+y12_PT_6_weights+y12_PT_7_weights+y12_PT_8_weights+y12_PT_9_weights+y12_PT_10_weights+y12_PT_11_weights+y12_PT_12_weights+y12_PT_13_weights+y12_PT_14_weights,\
label="$bg\_vbf\_200\_400$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#ad998c", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y12_PT_0_weights+y12_PT_1_weights+y12_PT_2_weights+y12_PT_3_weights+y12_PT_4_weights+y12_PT_5_weights+y12_PT_6_weights+y12_PT_7_weights+y12_PT_8_weights+y12_PT_9_weights+y12_PT_10_weights+y12_PT_11_weights+y12_PT_12_weights+y12_PT_13_weights,\
label="$bg\_vbf\_100\_200$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#9b8e82", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y12_PT_0_weights+y12_PT_1_weights+y12_PT_2_weights+y12_PT_3_weights+y12_PT_4_weights+y12_PT_5_weights+y12_PT_6_weights+y12_PT_7_weights+y12_PT_8_weights+y12_PT_9_weights+y12_PT_10_weights+y12_PT_11_weights+y12_PT_12_weights,\
label="$bg\_vbf\_0\_100$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#876656", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y12_PT_0_weights+y12_PT_1_weights+y12_PT_2_weights+y12_PT_3_weights+y12_PT_4_weights+y12_PT_5_weights+y12_PT_6_weights+y12_PT_7_weights+y12_PT_8_weights+y12_PT_9_weights+y12_PT_10_weights+y12_PT_11_weights,\
label="$bg\_dip\_1600\_inf$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#afcec6", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y12_PT_0_weights+y12_PT_1_weights+y12_PT_2_weights+y12_PT_3_weights+y12_PT_4_weights+y12_PT_5_weights+y12_PT_6_weights+y12_PT_7_weights+y12_PT_8_weights+y12_PT_9_weights+y12_PT_10_weights,\
label="$bg\_dip\_1200\_1600$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#84c1a3", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y12_PT_0_weights+y12_PT_1_weights+y12_PT_2_weights+y12_PT_3_weights+y12_PT_4_weights+y12_PT_5_weights+y12_PT_6_weights+y12_PT_7_weights+y12_PT_8_weights+y12_PT_9_weights,\
label="$bg\_dip\_800\_1200$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#89a8a0", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y12_PT_0_weights+y12_PT_1_weights+y12_PT_2_weights+y12_PT_3_weights+y12_PT_4_weights+y12_PT_5_weights+y12_PT_6_weights+y12_PT_7_weights+y12_PT_8_weights,\
label="$bg\_dip\_600\_800$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#829e8c", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y12_PT_0_weights+y12_PT_1_weights+y12_PT_2_weights+y12_PT_3_weights+y12_PT_4_weights+y12_PT_5_weights+y12_PT_6_weights+y12_PT_7_weights,\
label="$bg\_dip\_400\_600$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#adbcc6", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y12_PT_0_weights+y12_PT_1_weights+y12_PT_2_weights+y12_PT_3_weights+y12_PT_4_weights+y12_PT_5_weights+y12_PT_6_weights,\
label="$bg\_dip\_200\_400$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#7a8e99", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y12_PT_0_weights+y12_PT_1_weights+y12_PT_2_weights+y12_PT_3_weights+y12_PT_4_weights+y12_PT_5_weights,\
label="$bg\_dip\_100\_200$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#758991", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y12_PT_0_weights+y12_PT_1_weights+y12_PT_2_weights+y12_PT_3_weights+y12_PT_4_weights,\
label="$bg\_dip\_0\_100$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#688296", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y12_PT_0_weights+y12_PT_1_weights+y12_PT_2_weights+y12_PT_3_weights,\
label="$signal\_2pt4TeVL$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#6d7a84", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y12_PT_0_weights+y12_PT_1_weights+y12_PT_2_weights,\
label="$signal\_2pt2TeVL$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#7c99d1", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y12_PT_0_weights+y12_PT_1_weights,\
label="$signal\_2TeVL$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#7f7f9b", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y12_PT_0_weights,\
label="$signal\_1pt8TeVL$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#aaa5bf", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
# Axis
plt.rc('text',usetex=False)
plt.xlabel(r"p_{T} [ a_{2} ] ( GeV ) ",\
fontsize=16,color="black")
plt.ylabel(r"$\mathrm{Events}$ $(\mathcal{L}_{\mathrm{int}} = 40.0\ \mathrm{fb}^{-1})$ ",\
fontsize=16,color="black")
# Boundary of y-axis
ymax=(y12_PT_0_weights+y12_PT_1_weights+y12_PT_2_weights+y12_PT_3_weights+y12_PT_4_weights+y12_PT_5_weights+y12_PT_6_weights+y12_PT_7_weights+y12_PT_8_weights+y12_PT_9_weights+y12_PT_10_weights+y12_PT_11_weights+y12_PT_12_weights+y12_PT_13_weights+y12_PT_14_weights+y12_PT_15_weights+y12_PT_16_weights+y12_PT_17_weights+y12_PT_18_weights+y12_PT_19_weights).max()*1.1
ymin=0 # linear scale
#ymin=min([x for x in (y12_PT_0_weights+y12_PT_1_weights+y12_PT_2_weights+y12_PT_3_weights+y12_PT_4_weights+y12_PT_5_weights+y12_PT_6_weights+y12_PT_7_weights+y12_PT_8_weights+y12_PT_9_weights+y12_PT_10_weights+y12_PT_11_weights+y12_PT_12_weights+y12_PT_13_weights+y12_PT_14_weights+y12_PT_15_weights+y12_PT_16_weights+y12_PT_17_weights+y12_PT_18_weights+y12_PT_19_weights) if x])/100. # log scale
plt.gca().set_ylim(ymin,ymax)
# Log/Linear scale for X-axis
plt.gca().set_xscale("linear")
#plt.gca().set_xscale("log",nonposx="clip")
# Log/Linear scale for Y-axis
plt.gca().set_yscale("linear")
#plt.gca().set_yscale("log",nonposy="clip")
# Legend
plt.legend(bbox_to_anchor=(1.05,1), loc=2, borderaxespad=0.)
# Saving the image
plt.savefig('../../HTML/MadAnalysis5job_0/selection_11.png')
plt.savefig('../../PDF/MadAnalysis5job_0/selection_11.png')
plt.savefig('../../DVI/MadAnalysis5job_0/selection_11.eps')
# Running!
if __name__ == '__main__':
selection_11()
| 392.977064 | 6,435 | 0.760824 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2,972 | 0.034692 |
27397a994d354b44efa7e09c80fd506b9289236b | 1,286 | py | Python | app/routers/users.py | Austinstevesk/fast-api-crud-and-user-authentication | 17e01e5fcaea2a0c3fa3dd65edfaee8414ff831e | [
"Apache-2.0"
]
| null | null | null | app/routers/users.py | Austinstevesk/fast-api-crud-and-user-authentication | 17e01e5fcaea2a0c3fa3dd65edfaee8414ff831e | [
"Apache-2.0"
]
| null | null | null | app/routers/users.py | Austinstevesk/fast-api-crud-and-user-authentication | 17e01e5fcaea2a0c3fa3dd65edfaee8414ff831e | [
"Apache-2.0"
]
| null | null | null | from fastapi import FastAPI, status, HTTPException, Response, APIRouter, Depends
from sqlalchemy.orm import Session
from typing import List
from .. import schemas, models, utils #You can move up 2 steps using ..
from ..database import get_db
router = APIRouter(
prefix="/users",
tags=['users']
)
@router.post('/new', status_code=status.HTTP_201_CREATED, response_model=schemas.CreateUserResponse)
def create_new_user(user: schemas.UserCreate, db: Session = Depends(get_db)):
#hash the password - user.password
hashed_password = utils.hash(user.password)
user.password = hashed_password
new_user = models.User(**user.dict())
db.add(new_user)
db.commit()
db.refresh(new_user)
return new_user
@router.get('/all', response_model=List[schemas.CreateUserResponse])
def get_all_users(db: Session = Depends(get_db)):
users = db.query(models.User).all()
return users
@router.get('/{id}', response_model=schemas.CreateUserResponse)
def get_user_by_id(id: int, db: Session = Depends(get_db)):
user = db.query(models.User).filter(models.User.id == id).first()
if not user:
raise(HTTPException(status_code=status.HTTP_404_NOT_FOUND,
detail=f'User with id: {id} does not exist'))
return user
| 29.906977 | 100 | 0.709953 | 0 | 0 | 0 | 0 | 972 | 0.755832 | 0 | 0 | 137 | 0.106532 |
273b0739f92587e2927e090ad5eb2c6345702f86 | 7,295 | py | Python | src/news/collector.py | aibazhang/multitrue-bot | 189c5163010269eec57ae48f8e9d08d22ed5fcb8 | [
"MIT"
]
| null | null | null | src/news/collector.py | aibazhang/multitrue-bot | 189c5163010269eec57ae48f8e9d08d22ed5fcb8 | [
"MIT"
]
| null | null | null | src/news/collector.py | aibazhang/multitrue-bot | 189c5163010269eec57ae48f8e9d08d22ed5fcb8 | [
"MIT"
]
| null | null | null | import os
import requests
import json
import pathlib
from abc import ABCMeta, abstractmethod
from .news import News, print_format_markdown, print_format_telebot
KEY_PATH = pathlib.Path(os.path.dirname(__file__), "../..")
class NewsCollector(metaclass=ABCMeta):
@abstractmethod
def format_news(self):
pass
@abstractmethod
def filter_news(self):
pass
@abstractmethod
def print_news(self):
pass
@abstractmethod
def collcet_news(self):
pass
@abstractmethod
def save_news(self):
pass
class WebNewsCollector(NewsCollector):
def __init__(
self,
params=None,
block_list=None,
print_format=None,
base_url=None,
mode=None,
news_list=None,
time_format=None,
headers=None,
):
self.params = params
self.print_format = print_format
self.base_url = base_url
self._mode = mode
if news_list is None:
news_list = list()
if block_list is None:
block_list = json.load(open(KEY_PATH / "block_list.json", "r"))["block_list"]
self.news_list = news_list
self.time_format = time_format
self.block_list = block_list
self.headers = headers
def _get(self):
self.response = requests.get(self.base_url + self.mode, headers=self.headers, params=self.params).text
data_json = json.loads(self.response)
if data_json["status"] == "error":
print("{}: {}".format(data_json["code"], data_json["message"]))
raise requests.exceptions.ConnectionError
def filter_news(self):
filtered_news_list = list()
for news in self.news_list:
if any(bl in news.title for bl in self.block_list):
continue
filtered_news_list.append(news)
self.news_list = filtered_news_list
def print_news(self, news):
if self.print_format not in ["markdown", "telebot"]:
raise NotImplementedError
if self.print_format == "telebot":
return print_format_telebot(news.source, news.author, news.published_time, news.title, news.url)
if self.print_format == "markdown":
print(print_format_markdown(news.published_time, news.title, news.url))
def collcet_news(self):
self._get()
self.format_news()
self.filter_news()
# transfer to local time
[news.trans_utc_to_local(news.published_time, self.time_format) for news in self.news_list]
self.news_list = [self.print_news(news) for news in self.news_list if news.is_latest()]
def format_news(self):
raise NotImplementedError
def save_news(self):
raise NotImplementedError
class NewsAPICollector(WebNewsCollector):
def __init__(
self,
print_format=None,
mode=None,
country=None,
category=None,
sources=None,
query=None,
page_size=None,
):
super().__init__()
self._mode = mode
self.print_format = print_format
self.base_url = "https://newsapi.org/v2/"
self.headers = {"X-Api-Key": json.load(open(KEY_PATH / "keys.json", "r"))["news_api_key"]}
self.time_format = "%Y-%m-%dT%H:%M:%S"
self.params = {
"country": country,
"category": category,
"sources": sources,
"q": query,
"pageSize": page_size,
}
@property
def mode(self):
if self._mode is None:
self._mode = "top-headlines"
if self._mode not in ["top-headlines", "everything", "sources"]:
raise NotImplementedError
return self._mode
def format_news(self):
for text in json.loads(self.response)["articles"]:
news = News()
news.title = text["title"]
news.source = text["source"]["name"]
news.author = text["author"]
news.url = text["url"]
news.published_time = text["publishedAt"]
self.news_list.append(news)
class NewsCatcherAPICollector(WebNewsCollector):
"""
{"message":"You are not subscribed to this API."}⏎
"""
def __init__(
self,
print_format=None,
mode=None,
language=None,
country=None,
category=None,
sources=None,
query=None,
page_size=None,
):
super().__init__()
self._mode = mode
self.print_format = print_format
self.time_format = "%Y-%m-%d %H:%M:%S"
self.base_url = "https://newscatcher.p.rapidapi.com/v1/"
self.headers = {
"x-rapidapi-key": json.load(open(KEY_PATH / "keys.json", "r"))["news_catcher_key"],
"x-rapidapi-host": "newscatcher.p.rapidapi.com",
}
self.params = {"lang": language, "country": country, "topic": category}
@property
def mode(self):
if self._mode is None:
self._mode = "latest_headlines"
if self._mode not in ["latest_headlines"]:
raise NotImplementedError
return self._mode
def format_news(self):
for text in json.loads(self.response)["articles"]:
news = News()
news.title = text["title"]
news.source = text["clean_url"]
news.author = text["author"]
news.url = text["link"]
news.published_time = text["published_date"]
news.country = text["country"]
news.language = text["language"]
news.copyright = text["rights"]
news.description = text["summary"]
self.news_list.append(news)
class MediastackCollector(WebNewsCollector):
"""
https_access_restricted","message":"Access Restricted -
Your current Subscription Plan does not support HTTPS Encryption.
"""
def __init__(
self,
print_format=None,
mode=None,
country=None,
category=None,
sources=None,
query=None,
page_size=None,
):
super().__init__()
self._mode = mode
self.print_format = print_format
self.base_url = "https://api.mediastack.com/v1/"
self.key = json.load(open(KEY_PATH / "keys.json", "r"))["mediastack_key"]
self.time_format = "%Y-%m-%dT%H:%M:%S"
self.params = {
"countries": country,
"categories": category,
"sources": sources,
"keywords": query,
"limit": page_size,
}
@property
def mode(self):
self._mode = "news"
return self._mode
def _get(self):
self.response = requests.get(
"{}{}?access_key={}".format(self.base_url, self._mode, self.key), params=self.params
).text
def format_news(self):
for text in json.loads(self.response)["data"]:
news = News()
news.author = text["author"]
news.title = text["title"]
news.description = text["description"]
news.url = text["url"]
news.source = text["source"]
news.image = text["image"]
news.published_time = text["published_at"]
self.news_list.append(news)
| 29.534413 | 110 | 0.580123 | 7,059 | 0.967384 | 0 | 0 | 813 | 0.111416 | 0 | 0 | 1,108 | 0.151843 |
273c7d9230855a61bacc38d30734d8a53bab556c | 982,074 | pyt | Python | models/mnist_FNN_1.pyt | CAS-LRJ/DeepPAC | 75059572c23474d32a762aca5640f4d799fd992a | [
"Apache-2.0"
]
| null | null | null | models/mnist_FNN_1.pyt | CAS-LRJ/DeepPAC | 75059572c23474d32a762aca5640f4d799fd992a | [
"Apache-2.0"
]
| null | null | null | models/mnist_FNN_1.pyt | CAS-LRJ/DeepPAC | 75059572c23474d32a762aca5640f4d799fd992a | [
"Apache-2.0"
]
| null | null | null | Normalize mean=[0.1307] std=[0.3081]
ReLU
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ReLU
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ReLU
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| 70,148.142857 | 860,281 | 0.8397 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
273c940a6cfe38710caeba8144fda95ba8e580c5 | 9,282 | py | Python | scheduler/generation.py | richplane/PyREmatcher | 93df987478849e5905f0ee5c9ee79801479acb1e | [
"Apache-2.0"
]
| 1 | 2020-03-10T14:35:32.000Z | 2020-03-10T14:35:32.000Z | scheduler/generation.py | richplane/PyREmatcher | 93df987478849e5905f0ee5c9ee79801479acb1e | [
"Apache-2.0"
]
| null | null | null | scheduler/generation.py | richplane/PyREmatcher | 93df987478849e5905f0ee5c9ee79801479acb1e | [
"Apache-2.0"
]
| null | null | null | # Renewable generation at Findhorn
from windpowerlib import WindFarm
from windpowerlib import WindTurbine
from windpowerlib import WindTurbineCluster
from windpowerlib.turbine_cluster_modelchain import TurbineClusterModelChain
import pvlib
from pvlib.pvsystem import PVSystem
from pvlib.location import Location
from pvlib.modelchain import ModelChain
from pvlib.forecast import GFS
from pvlib.irradiance import disc
import pandas as pd
import numpy as np
import datetime
import scipy
import sys
# University computers can't install tables (bosc needs C++ compiler)
try:
import tables
except ImportError:
pass
class RenewablesException(Exception):
pass
class LocalRE(object):
forecast_height = 10 # for DarkSky API
def __init__(
self,
wind_turbines: list = [],
pv_arrays: list = [],
latitude: float = 57.6568,
longitude: float = -3.5818,
altitude: float = 10,
roughness_length: float = 0.15, # roughness length (bit of a guess)
hellman_exp: float = 0.2
):
""" Set up the renewable energy generation
"""
# This needs to be repeated in every forecast
self.roughness_length = roughness_length
# Initialise empty forecast dataframe, just so nothing complains
self.wind_forecast = pd.DataFrame()
self.pv_forecast = pd.DataFrame()
# Wind turbine(s)
turbines = []
for turbine in wind_turbines:
turbines.append(
{
'wind_turbine' : WindTurbine(
turbine['name'],
turbine['hub_height'],
nominal_power = turbine['nominal_power'],
rotor_diameter = turbine['rotor_diameter'],
power_curve = turbine['power_curve']
),
'number_of_turbines' : turbine['qty']
}
)
local_wind_farm = WindFarm(
'Local windfarm',
turbines,
[latitude, longitude]
)
# TODO - check for learned local data & overwrite power_curve
self.wind_modelchain = TurbineClusterModelChain(
local_wind_farm,
smoothing = False,
hellman_exp = hellman_exp,
)
# Initialise PV models
self.pv_location = Location(
latitude=latitude,
longitude=longitude,
altitude=altitude
)
# Now set up the PV array & system.
cec_pv_model_params = pvlib.pvsystem.retrieve_sam('CECMod')
sandia_pv_model_params = pvlib.pvsystem.retrieve_sam('SandiaMod')
cec_inverter_model_params = pvlib.pvsystem.retrieve_sam('CECInverter')
adr_inverter_model_params = pvlib.pvsystem.retrieve_sam('ADRInverter')
self.pv_modelchains = {}
for pv_array in pv_arrays:
# Try to find the module names in the libraries
if pv_array['module_name'] in cec_pv_model_params:
pv_array['module_parameters'] = cec_pv_model_params[
pv_array['module_name']
]
elif pv_array['module_name'] in sandia_pv_model_params:
pv_array['module_parameters'] = sandia_pv_model_params[
pv_array['module_name']
]
else:
raise RenewablesException('Could not retrieve PV module data')
# Do the same with the inverter(s)
if pv_array['inverter_name'] in cec_inverter_model_params:
pv_array['inverter_parameters'] = cec_inverter_model_params[
pv_array['inverter_name']
]
elif pv_array['inverter_name'] in adr_inverter_model_params:
pv_array['inverter_parameters'] = adr_inverter_model_params[
pv_array['inverter_name']
]
else:
raise RenewablesException('Could not retrieve PV module data')
self.pv_modelchains[pv_array['name']] = ModelChain(
PVSystem(**pv_array),
self.pv_location,
aoi_model='physical',
spectral_model='no_loss'
)
def make_generation_forecasts(self, forecast):
""" Makes generation forecast data from the supplied Dark Sky forecast
Arguments:
forecast {pandas.DataFrame} -- DarkSky originated forecast
"""
self.pv_forecast = self._make_pv_forecast(forecast)
self.wind_forecast = self._make_wind_forecast(forecast)
def _make_pv_forecast(self, forecast) -> pd.DataFrame:
"""Compile the forecast required for PV generation prediction
Uses pvlib to generate solar irradiance predictions.
Arguments:
forecast {pandas.DataFrame} -- DarkSky originated forecast
"""
# Annoyingly, the PV & wind libraries want temperature named differently
pv_forecast = forecast.rename(
columns={
'temperature' : 'air_temp',
'windSpeed' : 'wind_speed',
}
)
# Use PV lib to get insolation based on the cloud cover reported here
model = GFS()
# Next up, we get hourly solar irradiance using interpolated cloud cover
# We can get this from the clearsky GHI...
if tables in sys.modules:
# We can use Ineichen clear sky model (uses pytables for turbidity)
clearsky = self.pv_location.get_clearsky(pv_forecast.index)
else:
# We can't, so use 'Simplified Solis'
clearsky = self.pv_location.get_clearsky(
pv_forecast.index, model='simplified_solis'
)
# ... and by knowledge of where the sun is
solpos = self.pv_location.get_solarposition(pv_forecast.index)
ghi = model.cloud_cover_to_ghi_linear(
pv_forecast['cloudCover'] * 100, clearsky['ghi']
)
dni = disc(ghi, solpos['zenith'], pv_forecast.index)['dni']
dhi = ghi - dni * np.cos(np.radians(solpos['zenith']))
# Whump it all together and we have our forecast!
pv_forecast['dni'] = dni
pv_forecast['dhi'] = dhi
pv_forecast['ghi'] = ghi
return pv_forecast
def _make_wind_forecast(self, forecast) -> pd.DataFrame:
"""Creates forecast needed for wind generation prediction
Creates renamed multidimensional columns needed for the windpowerlib
system.
Arguments:
forecast {pandas.DataFrame} -- DarkSky originated forecast
"""
# Easiest to build multiindexes up one by one.
columns_index = pd.MultiIndex.from_tuples(
[
('wind_speed', 10), ('temperature', 10),
('pressure', 10), ('roughness_length', 0),
('wind_bearing', 10)
]
)
wind_forecast = pd.DataFrame(
index=forecast.index.copy(),
columns = columns_index
)
wind_forecast.loc[:,('wind_speed',10)] = forecast['windSpeed'].loc[:]
wind_forecast.loc[:,('temperature',10)] = forecast['temperature'].loc[:]
wind_forecast.loc[:,('pressure',10)] = forecast['pressure'].loc[:]
wind_forecast.loc[:,('wind_bearing',10)] = forecast['windBearing'].loc[:]
wind_forecast.loc[:,('roughness_length', 0)] = self.roughness_length
return wind_forecast
def predict_generation(self, reserved_wind_consumption = 0) -> pd.DataFrame:
""" Predict electricity generated from forecast
Will use the timestamp index of the forecast property to estimate
instantaneous electricity generation. Returns table giving amounts in
kWh.
Arguments:
reserved_wind_consumption {float} - constant amount that is assumed
to be required from wind generation to meet other local need
"""
prediction = pd.DataFrame(index = self.pv_forecast.index.copy())
# First up - PV
# Create a total gen column of zeros
prediction['PV_AC_TOTAL'] = 0
for pv_array, pv_model in self.pv_modelchains.items():
pv_model.run_model(prediction.index, self.pv_forecast)
output_column_name = 'PV_AC_' + pv_array
prediction[output_column_name] = pv_model.ac
# Add to the total column
prediction['PV_AC_TOTAL'] = prediction['PV_AC_TOTAL'] + pv_model.ac
# Next - wind power.
self.wind_modelchain.run_model(
self.wind_forecast
)
prediction['WIND_AC'] = self.wind_modelchain.power_output
# Convert everything into kWh
prediction = prediction * 0.001
prediction['available_wind'] = prediction['WIND_AC'] - reserved_wind_consumption
prediction['available_wind'][prediction['available_wind']<0] = 0
prediction['total'] = prediction['WIND_AC'] + prediction['PV_AC_TOTAL']
prediction['surplus'] = prediction['available_wind'] + prediction['PV_AC_TOTAL']
prediction['surplus'][prediction['surplus']<0] = 0
return prediction
| 34 | 88 | 0.607735 | 8,657 | 0.932665 | 0 | 0 | 0 | 0 | 0 | 0 | 3,155 | 0.339905 |
273de09788e8d36ac29db8f66c63f35b9eb059df | 37 | py | Python | tests/components/minio/__init__.py | domwillcode/home-assistant | f170c80bea70c939c098b5c88320a1c789858958 | [
"Apache-2.0"
]
| 30,023 | 2016-04-13T10:17:53.000Z | 2020-03-02T12:56:31.000Z | tests/components/minio/__init__.py | jagadeeshvenkatesh/core | 1bd982668449815fee2105478569f8e4b5670add | [
"Apache-2.0"
]
| 31,101 | 2020-03-02T13:00:16.000Z | 2022-03-31T23:57:36.000Z | tests/components/minio/__init__.py | jagadeeshvenkatesh/core | 1bd982668449815fee2105478569f8e4b5670add | [
"Apache-2.0"
]
| 11,956 | 2016-04-13T18:42:31.000Z | 2020-03-02T09:32:12.000Z | """Tests for the minio component."""
| 18.5 | 36 | 0.675676 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 36 | 0.972973 |
273de3624cb2656a5b09bd44464400c778b4cc44 | 213 | py | Python | setup.py | ravi-bade/hive-ML | bd724580bf09d992a838da57f9fd5142bab4e1e5 | [
"MIT"
]
| null | null | null | setup.py | ravi-bade/hive-ML | bd724580bf09d992a838da57f9fd5142bab4e1e5 | [
"MIT"
]
| null | null | null | setup.py | ravi-bade/hive-ML | bd724580bf09d992a838da57f9fd5142bab4e1e5 | [
"MIT"
]
| null | null | null | from setuptools import find_packages, setup
setup(
name='src',
packages=find_packages(),
version='0.1.0',
description='Hive-ml project of OneApp',
author='Ravikant Bade',
license='MIT',
)
| 19.363636 | 44 | 0.661972 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 59 | 0.276995 |
273e3b061e019a5cb896032af82de6f73be47078 | 1,985 | py | Python | app.py | twitterdev/getting-started-with-dash | f0c81a03d0f31ed787a90492d2412c9fe48eeb6d | [
"Apache-2.0"
]
| 2 | 2022-03-13T22:54:16.000Z | 2022-03-28T02:37:48.000Z | app.py | twitterdev/getting-started-with-dash | f0c81a03d0f31ed787a90492d2412c9fe48eeb6d | [
"Apache-2.0"
]
| null | null | null | app.py | twitterdev/getting-started-with-dash | f0c81a03d0f31ed787a90492d2412c9fe48eeb6d | [
"Apache-2.0"
]
| null | null | null | import dash
import dash_bootstrap_components as dbc
import dash_core_components as dcc
import dash_html_components as html
import plotly.express as px
import pandas as pd
import os
import requests
app = dash.Dash(external_stylesheets=[dbc.themes.JOURNAL])
server = app.server
search_url = "https://api.twitter.com/2/tweets/counts/recent"
query_params = {"query": "from:jessicagarson", "granularity": "day"}
bearer_token = os.environ.get("BEARER_TOKEN")
def bearer_oauth(r):
r.headers["Authorization"] = f"Bearer {bearer_token}"
r.headers["User-Agent"] = "GettingStartedDash"
return r
def connect_to_endpoint(url, tweet_fields):
response = requests.request("GET", url, auth=bearer_oauth, params=tweet_fields)
print(response.status_code)
if response.status_code != 200:
raise Exception(
"Request returned an error: {} {}".format(
response.status_code, response.text
)
)
return response.json()
json_response = connect_to_endpoint(search_url, query_params)
df = pd.DataFrame(json_response["data"])
df["start"] = pd.to_datetime(df["start"])
final = df[["start", "tweet_count"]]
fig = px.line(final, x="start", y="tweet_count")
colors = {"background": "#FFFFFF", "text": "#1DA1F2"}
fig.update_layout(
plot_bgcolor=colors["background"],
paper_bgcolor=colors["background"],
font_color=colors["text"],
)
app.layout = html.Div(
style={"backgroundColor": colors["background"]},
children=[
html.H1(
children="Tweets by Date",
style={"textAlign": "center", "color": colors["text"]},
),
html.Div(
children="An example using Dash and the Twitter API recent search counts to see how much I've been Tweeting this week",
style={"textAlign": "center", "color": colors["text"]},
),
dcc.Graph(id="example-twitter", figure=fig),
],
)
if __name__ == "__main__":
app.run_server(debug=True)
| 26.824324 | 131 | 0.665995 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 588 | 0.296222 |
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