content
stringlengths 1
1.04M
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sequencelengths 1
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| ratio_char_token
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int64 1
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|
|---|---|---|---|
from django import forms
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import os
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
SECRET_KEY = 'tdzf@9g8lofi@lo$=126jrka1ydzjix^!8j)vg$6cd+kz^ei5h'
INSTALLED_APPS = [
'django.contrib.contenttypes',
'tests'
]
test_db = os.environ.get('TEST_DB_CONFIG', 'postgres')
db_user = os.environ.get('TEST_DB_USER', os.environ.get('USER', ''))
db_name = 'timerseries_tests' + os.environ.get('TEST_DB_NAME', '')
DB_CONFIGS = {
# N.B. sqlite doesn't support DISTINCT ON for some reason... ???
'postgres': {
'ENGINE': 'django.db.backends.postgresql_psycopg2',
'NAME': db_name,
'USER': db_user,
'PASSWORD': ''
}
}
DATABASES = {
'default': DB_CONFIGS.get(test_db)
}
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import torch.nn as nn
import torch.nn.functional as F
from .layers import GraphConvolution
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from django.db import models
# class Person(models.Model):
# name = models.CharField(max_length=50)
# age = models.IntegerField()
# city = models.CharField(max_length=50)
# rating = models.IntegerField()
#
#
# class PersonalData(models.Model):
# name = models.CharField(max_length=50)
# mail = models.EmailField(max_length=254)
# telephone = models.CharField(max_length=20)
# social_id_1 = models.CharField(max_length=50)
# social_id_2 = models.CharField(max_length=50)
# social_id_3 = models.CharField(max_length=50)
# social_id_4 = models.CharField(max_length=50) | [
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import logging
import traceback
import sys
from .processor_work_manager import ProcessorWorkManager
class ProcessorWorker:
"""Process worker class"""
def run(self):
"""Run worker"""
while self._running:
try:
self.process()
except Exception as ex:
logging.error(traceback.format_exception(*sys.exc_info()))
continue
def process(self):
"""Call process on queue"""
self.processor_work_manager.run()
def join(self):
"""Join processes"""
self._running = False | [
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from email.charset import QP
from serial import Serial
from serial.tools.list_ports import comports
qPort = None
for p in list(comports()):
if p.product and 'FT232R' in p.product:
qPort = p.device
break
if not qPort:
raise Exception('No Quiet Board found')
quite = Serial(qPort, 57600, timeout=1)
loops = 0
while True:
loops += 1
quite.write('DIGI?\r\n'.encode())
read = quite.read_until()
if len(read) <= 0:
raise Exception(f'Failed after {loops} rounds')
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from setuptools import setup, find_packages
long_description = (
open("README.rst", encoding="utf-8").read()
+ "\n\n"
+ open("CHANGES.rst", encoding="utf-8").read()
)
setup(
name="more.body_model",
version="0.1dev0",
description="load_json infrastructure for Morepath",
long_description=long_description,
author="Henri Hulski",
author_email="[email protected]",
keywords="morepath validation",
license="BSD",
url="https://github.com/morepath/more.body_model",
namespace_packages=["more"],
packages=find_packages(),
include_package_data=True,
zip_safe=False,
classifiers=[
"Intended Audience :: Developers",
"License :: OSI Approved :: BSD License",
"Programming Language :: Python :: 3.6",
"Programming Language :: Python :: 3.7",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
],
install_requires=["morepath >= 0.17"],
extras_require=dict(
test=["pytest >= 2.9.1", "pytest-remove-stale-bytecode", "webtest"],
coverage=[
"pytest-cov",
],
pep8=[
"flake8",
"pep8-naming",
],
),
)
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198,
220,
220,
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220,
220,
220,
16589,
198,
220,
220,
220,
10612,
198,
8,
198
] | 2.321361 | 529 |
from flask import abort
from flask_restful_swagger_3 import Resource, swagger
from flask_jwt_extended import jwt_required
from models.message import Messages, MessagesComment, MessagesUser
from models.user import silence_user_fields
from database.manager import db
| [
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# Teary Expression (6508)
from net.swordie.ms.enums import Stat
camilla = 1012108
rubble = 4000022
ouch = 5160024
# Grab current fame for quest-induced defame later
fame = chr.getStat(Stat.pop)
sm.setSpeakerID(camilla)
sm.sendNext(''.join(["Hello. Is there something I can... Eeek! "
"Is that #t", repr(rubble), "#? Is there a Golem nearby?! I'm scared! WAAAAAAH! \r\n\r\n"
"#fUI/UIWindow2.img/QuestIcon/4/0# \r\n"
"#i", repr(ouch), "# #t", repr(ouch), "# x 1 \r\n"
"#fUI/UIWindow2.img/QuestIcon/6/0# -1"]))
sm.giveItem(ouch)
chr.setStatAndSendPacket(Stat.pop, fame-1)
sm.completeQuest(parentID)
sm.setPlayerAsSpeaker()
sm.sendNext("#b(You learned the Teary Expression from Camilla. "
"However, your Fame went down as a result...)") | [
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11,
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] | 2.479866 | 298 |
from import_export import resources
from import_export.admin import ImportExportModelAdmin
# from import_export.admin import ImportExportActionModelAdmin
from django.contrib import admin
from .models import TimePost, Client, Project
# admin.site.register(TimePost)
@admin.register(TimePost)
# class TimePostAdmin(ImportExportActionModelAdmin):
admin.site.register(Client)
admin.site.register(Project)
| [
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] | 3.669643 | 112 |
from __future__ import print_function
from builtins import range
from builtins import object
import scipy.interpolate as interp
import numpy as np
import pdb
"""
This module calculates the galaxy and intrinsic alignment bias
using the flexible grid parameterisation of Joachimi and Bridle
(2010) p 6-9.
Outputs both stochastic and systematic terms rI, bI, rg and bg.
"""
| [
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198,
37811,
628,
198
] | 3.441441 | 111 |
import psycopg2
import re
from backend.mg import MGBackend
'''
@author: anant bhardwaj
@date: Oct 3, 2013
DataHub DB wrapper for backends (only postgres implemented)
Any new backend must implement the DataHubConnection interface
'''
class DataHubConnection:
'''
The following methods works only in superuser mode
'''
| [
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] | 3.357143 | 98 |
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from debug_toolbar.panels import Panel
from django.utils.translation import ugettext_lazy as _, ungettext
import requests.sessions
| [
2,
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5239,
198,
198,
11748,
7007,
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6202,
628,
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] | 3.030303 | 66 |
from models.villain.villain import Villain
| [
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13,
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] | 3.066667 | 15 |
t_carros = ("HRV", "Golf", "Argo")#tupla, não pode alterar o valor
l_carros = list(t_carros)
l_carros[2] = "Focus"
t_carros = tuple(l_carros)
for x in t_carros:
print(x)
input()#lista fica aberta | [
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] | 2.030303 | 99 |
#!/usr/bin/env python3
# Copyright 2022 The IREE Authors
#
# Licensed under the Apache License v2.0 with LLVM Exceptions.
# See https://llvm.org/LICENSE.txt for license information.
# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
"""Utils for accessing Linux device information."""
import re
from typing import Optional, Sequence
from .benchmark_definition import (execute_cmd_and_get_output, DeviceInfo,
PlatformType)
def get_linux_cpu_arch(verbose: bool = False) -> str:
"""Returns CPU Architecture, e.g., 'x86_64'."""
return _get_lscpu_field("Architecture", verbose)
def get_linux_cpu_features(verbose: bool = False) -> Sequence[str]:
"""Returns CPU feature lists, e.g., ['mmx', 'fxsr', 'sse', 'sse2']."""
return _get_lscpu_field("Flags", verbose).split(" ")
def get_linux_device_info(device_model: str = "Unknown",
cpu_uarch: Optional[str] = None,
verbose: bool = False) -> DeviceInfo:
"""Returns device info for the Linux device.
Args:
- device_model: the device model name, e.g., 'ThinkStation P520'
- cpu_uarch: the CPU microarchitecture, e.g., 'CascadeLake'
"""
return DeviceInfo(
PlatformType.LINUX,
# Includes CPU model as it is the key factor of the device performance.
model=device_model,
# Currently we only have x86, so CPU ABI = CPU arch.
cpu_abi=get_linux_cpu_arch(verbose),
cpu_uarch=cpu_uarch,
cpu_features=get_linux_cpu_features(verbose),
# We don't yet support GPU benchmark on Linux devices.
gpu_name="Unknown")
| [
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62,
36166,
62,
998,
7,
19011,
577,
828,
198,
220,
220,
220,
220,
220,
42804,
62,
84,
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28,
36166,
62,
84,
998,
11,
198,
220,
220,
220,
220,
220,
42804,
62,
40890,
28,
1136,
62,
23289,
62,
36166,
62,
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7,
19011,
577,
828,
198,
220,
220,
220,
220,
220,
1303,
775,
836,
470,
1865,
1104,
11362,
18335,
319,
7020,
4410,
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220,
220,
220,
220,
220,
308,
19944,
62,
3672,
2625,
20035,
4943,
198
] | 2.604502 | 622 |
input = """nop +116
acc +12
acc -8
acc +34
jmp +485
acc +42
jmp +388
acc +36
nop +605
acc +17
jmp +411
acc +49
jmp +1
acc -9
jmp +289
jmp +288
jmp +74
acc +4
acc +42
jmp +258
acc +14
acc -13
nop +106
jmp +280
jmp +534
acc +41
acc +40
jmp +224
acc +43
acc +10
nop +240
jmp +211
acc +7
acc -3
acc +7
jmp +1
jmp +559
jmp +415
jmp +528
acc -16
jmp +568
jmp +442
nop +113
jmp +464
acc +42
jmp +336
acc -2
acc +39
jmp +251
acc -4
acc +42
jmp +528
acc +5
acc +30
nop +429
acc +49
jmp +86
acc +15
nop +145
acc -8
jmp +1
jmp +404
acc +26
acc +50
jmp +251
acc +47
jmp +1
acc +45
acc -5
jmp +357
acc +31
jmp +62
acc +25
nop +540
acc -13
acc +0
jmp +72
acc +28
acc +36
nop +475
acc -17
jmp +166
acc +4
acc +20
acc +30
acc +43
jmp +464
acc +4
jmp +94
jmp +44
nop +446
acc -16
nop +267
acc +30
jmp +519
acc +45
acc +47
jmp +62
acc +28
acc -13
acc +45
jmp +239
acc +12
jmp +1
nop +153
jmp +245
jmp +244
acc -12
jmp +308
jmp +190
jmp -86
acc +45
acc +1
acc +15
acc +30
jmp +350
acc +30
jmp +42
jmp +214
jmp +447
acc +24
jmp +453
acc +29
acc +42
jmp +302
acc -4
acc +33
jmp +447
acc -18
acc +15
acc -2
jmp -24
jmp -4
jmp +35
acc +0
jmp -83
acc -13
nop +437
acc -15
jmp +95
nop +289
jmp +348
acc +17
acc +23
acc +45
jmp +359
acc +18
jmp +352
acc +0
acc +13
acc +25
acc +11
jmp +331
acc -2
jmp +19
jmp -103
acc +34
acc +48
jmp +141
acc +44
jmp +1
acc +42
jmp +374
acc +45
acc +35
nop -37
acc -2
jmp +244
jmp +151
acc +36
acc +4
nop -64
jmp +231
nop +321
nop +291
acc +16
jmp -161
acc +17
nop +412
nop -89
nop +179
jmp -8
nop -167
acc +44
acc +4
jmp +42
acc +22
acc +28
acc +22
jmp +192
acc -18
acc -7
jmp -70
acc +27
acc +25
jmp +312
acc +50
acc -16
jmp -121
acc +14
acc +43
nop -111
jmp -54
nop +39
acc -4
acc +41
jmp +236
acc -11
jmp -118
jmp +150
acc -15
jmp -141
acc +14
jmp +1
acc -8
jmp -96
acc +11
nop -95
jmp +1
acc +47
jmp -113
nop +257
jmp +35
acc +45
acc +25
acc -6
jmp +31
jmp +1
nop +153
nop -39
jmp +25
acc +0
acc +50
jmp +362
acc -15
acc +0
acc +31
acc +22
jmp +69
acc -18
acc +24
jmp -38
acc +39
acc -10
acc +40
jmp +6
jmp +143
jmp -44
acc +32
acc -8
jmp +358
jmp +248
nop +343
nop -11
jmp +116
jmp +74
jmp +120
acc +37
acc -19
acc +36
jmp +341
acc +49
jmp -164
acc +14
acc +13
acc +0
acc +50
jmp +291
jmp +1
jmp -79
acc +19
jmp +243
acc +25
acc -13
acc -12
acc -7
jmp +228
jmp -81
acc +18
nop -163
acc +0
acc +8
jmp +212
acc +38
acc -12
jmp +6
acc +24
acc +42
acc +21
acc +12
jmp +136
acc -12
acc -2
acc +46
acc +35
jmp +290
acc +6
acc +36
jmp -182
acc +14
acc +7
jmp +228
jmp -19
acc +48
acc +25
jmp +106
jmp +70
acc +24
jmp +1
acc +24
acc +29
jmp -156
nop +296
acc +34
jmp +115
acc -12
acc +41
jmp +28
jmp +165
acc +0
acc +24
acc +42
acc +27
jmp +106
acc +24
acc -11
acc +4
acc -6
jmp -180
acc -2
jmp +2
jmp -314
acc -9
acc +1
jmp -327
acc -8
acc +7
acc -6
acc +32
jmp -157
acc +10
acc +10
acc -16
jmp +278
jmp +6
acc +0
nop +178
acc +26
jmp +231
jmp +175
acc +29
acc +36
acc +7
jmp -255
acc +46
acc +45
acc +7
nop -7
jmp -101
jmp +3
acc -13
jmp -140
nop -115
jmp +1
jmp -336
acc +9
acc +9
nop -68
acc -3
jmp -37
acc -13
nop +128
jmp +1
jmp -90
acc +49
jmp -124
acc +16
acc +9
jmp +212
acc -18
jmp -303
acc +33
acc +23
acc +26
jmp +140
acc +25
nop -123
acc +22
jmp +148
acc +1
acc +44
jmp -352
acc -11
jmp +33
acc +16
nop -199
acc +15
jmp -351
jmp +5
jmp -357
nop -284
acc +32
jmp -43
acc +5
acc +23
acc +3
jmp +59
acc -10
nop -266
nop +43
jmp +79
acc +21
jmp -42
acc +35
acc +5
jmp +68
acc +24
acc -4
jmp -155
acc +45
jmp +154
jmp -311
acc +10
acc +17
acc +39
jmp -297
jmp -175
acc +49
jmp -151
acc -4
acc -9
jmp -219
acc +48
acc -17
acc +30
jmp -9
acc +10
jmp -61
nop -396
acc +11
acc +37
jmp -331
acc +14
acc +22
acc +30
acc +2
jmp -43
nop -265
acc +5
acc +40
acc -15
jmp -35
acc -3
acc +24
jmp -415
acc +0
jmp +98
acc +17
acc +25
nop -48
acc -17
jmp -302
acc +11
acc +11
jmp -181
acc +46
acc +19
jmp -331
nop +90
acc +45
acc +8
jmp -237
acc -11
nop -421
jmp -145
acc -16
acc +47
jmp -387
acc +50
jmp -375
acc +38
jmp +1
jmp -225
acc +47
acc +39
jmp +69
acc +46
acc +41
jmp -89
acc +19
jmp -453
nop +63
acc +18
jmp -386
nop -243
acc +48
jmp +70
acc +25
jmp -191
acc +48
acc +31
jmp +40
acc -10
jmp -46
acc +45
jmp -48
jmp -12
acc +16
acc -16
jmp -120
acc -10
jmp +1
acc -10
jmp -124
acc +48
acc +15
acc +8
acc -15
jmp -66
nop -130
acc +16
acc +10
acc +31
jmp -375
acc +9
acc +20
jmp -37
acc +14
jmp -134
acc -9
acc -6
jmp -120
acc +24
acc +17
acc +49
jmp -332
acc +7
acc +35
nop -149
jmp -103
jmp -277
acc -1
acc +28
nop -211
jmp -371
nop -129
acc -15
acc +6
acc +19
jmp -120
acc -6
jmp -79
acc +0
jmp -64
acc +33
acc +33
jmp -440
jmp -85
acc +37
nop -183
acc +24
acc +42
jmp -545
acc +50
acc +6
jmp -7
nop +8
acc +1
jmp -359
acc -1
nop -388
acc -7
acc +28
jmp -211
jmp -384
acc +32
acc +16
acc +40
jmp +17
acc +0
acc +43
acc -14
jmp -512
nop -264
jmp -474
nop -543
acc +17
nop -288
jmp -38
jmp +24
acc -4
jmp -321
acc +49
acc -16
jmp -532
acc +0
acc -11
acc -16
jmp -104
acc -12
jmp -301
acc +6
nop -498
acc +0
jmp -126
nop -127
acc +1
jmp -6
acc +40
jmp -547
acc +16
acc +18
jmp -123
acc -5
acc +27
acc +44
acc +15
jmp -22
acc +48
acc -18
jmp -350
acc -7
acc +30
acc +26
jmp +1
jmp +1"""
lines = input.split("\n")
commands = []
for line in lines:
c, i = line.split(' ')
if c == "nop":
com = nop()
com.parse_args(i)
commands.append(com)
elif c == "jmp":
com = jmp()
com.parse_args(i)
commands.append(com)
elif c == "acc":
com = acc()
com.parse_args(i)
commands.append(com)
#print(commands)
#s = stack(commands)
#while(s.next()):
# pass
#print(s.acc)
for it in range(len(commands)):
commands_2 = commands[:]
if isinstance(commands[it], nop):
com = jmp()
com.parse_args(commands[it].args)
commands_2[it] = com
if isinstance(commands[it], jmp):
com = nop()
commands_2[it] = com
if isinstance(commands[it], acc):
continue
s2 = stack_2(commands_2)
while(s2.next()):
pass
if s2.sp == len(commands_2):
print(s2.acc)
break
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] | 1.693945 | 3,947 |
# -*- encoding: utf-8 -*-
# Copyright (c) 2020 Dantali0n
#
# 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 copy import copy
from unittest import mock
from cliff.show import ShowOne
from sqlalchemy.orm.exc import MultipleResultsFound
from radloggerpy.cli.v1.device import device_show
from radloggerpy.device.device_manager import DeviceManager as dm
from radloggerpy.tests import base
from radloggerpy.types.device_interfaces import DeviceInterfaces
from radloggerpy.types.device_interfaces import INTERFACE_CHOICES
from radloggerpy.types.device_types import DeviceTypes
from radloggerpy.types.serial_bytesize import SerialBytesizeTypes
from radloggerpy.types.serial_parity import SerialParityTypes
from radloggerpy.types.serial_stopbit import SerialStopbitTypes
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] | 3.564607 | 356 |
from unittest.mock import patch
from django.core.management import call_command
from django.db.utils import OperationalError
from django.test import TestCase
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# RaiBlocks Telegram bot
# @RaiWalletBot https://t.me/RaiWalletBot
#
# Source code:
# https://github.com/SergiySW/RaiWalletBot
#
# Released under the BSD 3-Clause License
#
#
# Run by cron every hour, 1-2 minutes after distribution starts
# With new rules it can be inaccurate
#
from telegram.ext import Updater, CommandHandler, MessageHandler, Filters
from telegram import Bot, ParseMode
import logging
import urllib3, certifi, socket, json
import time, math
# Parse config
import ConfigParser
config = ConfigParser.ConfigParser()
config.read('bot.cfg')
api_key = config.get('main', 'api_key')
log_file_faucet = config.get('main', 'log_file_faucet')
# Enable logging
logging.basicConfig(format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
level=logging.INFO, filename=log_file_faucet)
logger = logging.getLogger(__name__)
# MySQL requests
from common_mysql import mysql_select_accounts_list, mysql_select_blacklist, mysql_select_language, mysql_select_accounts_list_extra
# Common functions
from common import push_simple
# Translation
with open('language.json') as lang_file:
language = json.load(lang_file)
# Faucet
faucet() | [
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198,
197,
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69,
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316,
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] | 2.875 | 424 |
# TODO: check https://en.wikipedia.org/wiki/Earley_parser
data = '''
0: 1 2 3
1: "a"
2: "b"
3: 1 1 4 2
4: 1 | 2
abaabb
baaabb
ababab
abaaab
abaabb
abaabc'''.strip()
def solve(rules, message):
"""check whether message matches r"""
queue = [(0, ['0'])]
while queue:
position, (rule, *following) = queue.pop()
rule = rules[rule]
# skip too short messages
if position >= len(message):
continue
if is_char(rule):
if message[position] == rule[0][1]:
if not following and position == len(message) - 1:
# match last char with terminal -> success
return 1
if following:
queue.append((position + 1, following))
else:
# check OR blocks
for subrule in rule:
queue.append((position, subrule + following))
return 0
rules, msg = parse(data)
assert count(rules, message) == 3
rules, msg = parse(open('input_noloop.txt').read())
assert count(rules, message) == 3
rules, msg = parse(open('input.txt').read())
assert count(rules, message) == 12
rules, msg = parse(open('input_big_mod.txt').read()) # --> 306
print(count(rules, message))
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] | 2.19554 | 583 |
#todo / schedule/ internet connection
#todo instabot.log at api.py / instabot started at bot.py
#todo set follow followers count limit
#generate random number at server send to apps
#both check
import os
import random
import shutil
import sys
import time
import threading
import errno
import requests
import API_Util
from PyQt5 import QtCore,QtGui, QtWidgets
from PyQt5 import uic
from tqdm import tqdm
#IMPORT INSTABOT
sys.path.append(os.path.join(sys.path[0], '../'))
from instabot import Bot
from UI import mainwindow
#IMPORT DIC
from dic import dicAcc
############ IMPORT QWIDGET / QDIALOG #################
from Limit_Setting import Limit_Setting_class
from Login import Login_class
from Like import Like_class
############ WRITE FILE .TXT ################
hashtag_file = "Private/hashtagsdb.txt"
users_file = "Private/usersdb.txt"
whitelist = "Private/whitelist.txt"
blacklist = "Private/blacklist.txt"
userlist = "Private/userlist.txt"
comment = "Private/comments.txt"
setting = "Private/setting.txt"
SECRET_FILE = "Private/secret.txt"
unfollow_job_file = "Private/unfollow_job.txt"
follow_job_file = "Private/follow_job.txt"
follow_random_file = "Private/follow_random.txt"
like_job_file = "Private/like_job.txt"
like_random_file = "Private/like_random.txt"
comment_job_file = "Private/comment_job.txt"
comment_file = "Private/comment.txt"
comment_usertag_file = "Private/comment_usertag.txt"
class OutputWrapper(QtCore.QObject):
""" to show all output in ui text edit"""
outputWritten = QtCore.pyqtSignal(object, object)
############ OPEN QWIDGET / QDIALOG #################
if __name__== "__main__":
try:
os.makedirs("Private")
except OSError as e:
if e.errno != errno.EEXIST:
raise
app = QtWidgets.QApplication(sys.argv)
MainWindow = MainWindow_class()
MainWindow.show()
sys.exit(app.exec_())
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220,
25064,
13,
37023,
7,
1324,
13,
18558,
62,
28955,
201,
198
] | 2.576471 | 765 |
#!/usr/bin/python3
import subprocess,sys
## Rules
#I1 = ['''-A OUTPUT -m state --state RELATED,ESTABLISHED -j ACCEPT''','''-A OUTPUT -j REJECT''']
I1 = ['''ufw-not-local -j DROP'''] #For testing purposes
B1 = ['''-A INPUT -p tcp --dport 53 -j DROP''']
B2 = ['''-A INPUT -p icmp -j REJECT --reject-with icmp-port-unreachable''']
## Check if Rules are Applied
# I'm lazy
main()
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] | 2.4 | 160 |
#!/usr/bin/python
# -*- coding: utf-8 -*-
root = TreeNode(1)
root.left = TreeNode(2)
root.right = TreeNode(3)
solution = Solution()
ans = solution.pathSum(root, 4)
for i in range(len(ans)):
print(ans[i])
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] | 2.322222 | 90 |
import countries
import e404
import index
import search
import static_files
import timeline
if __name__ == "__main__":
run()
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] | 2.895833 | 48 |
# Copyright (c) 2021 Delbert Yip
#
# This software is released under the MIT License.
# https://opensource.org/licenses/MIT
import os
import logging
import pandas as pd
from pathlib import Path
from typing import Dict, Any, List
import GeneralProcess.base as base
from GeneralProcess.ephys_info_filter import EphysInfoFiltering
import regex as re
from pydantic import BaseModel, validator
import pyabf
"""Find CSV and ABF files that meet selection criteria
This module contains classes and methods that specialize in
1. Applying, but not validating/reading, selection criteria
2. Finding files with given extensions and at given location(s)
The key class is `DataLoader`, which holds information such as
1. Raw data files (CSV, ABF)
2. Experimental parameters (e.g. series resistance, recording protocol name)
3. Paths to all files
An alternative use case would be to work with non-ABF file formats. To do so,
implement a subclass of `DataLoader` and override the `getDataFiles` method.
"""
# -------------------------------- Find files -------------------------------- #
class FileFinder(BaseModel):
"""Find files at `path`. Optionally ignore files in `to_ignore`
:param paths: path(s) to check for files
:type paths: List[str]
:param to_ignore: list of filenames to ignore, defaults to None
:type to_ignore: List[str], optional
"""
paths: List[str]
to_ignore: List[str] = None
fmt: str = '.csv'
@validator('fmt')
@validator('to_ignore')
@staticmethod
def _readFiles(self, files: List[Path]) -> None:
"""Load csv files"""
ignore_msg = "File ignored: {0}"
for file in files:
fname = file.stem
if not file.is_file():
continue
elif fname in self.to_ignore:
logging.info(ignore_msg.format(fname))
else:
df = self._readFile(file, fname)
self.data_files[fname] = df
return
# -------------------------------- Main parser ------------------------------- #
class DataLoader:
"""Select and find CSV and ABF files"""
def __init__(self,
main_dir: str, csv_path: str, abf_path: str, ephys_info_path: str,
filter_criteria: Dict[str, Any],
log_path: str = None, out_path: str = None
) -> None:
"""Load CSV and ABF files
:param main_dir: [description]
:type main_dir: str
:param csv_path: path to CSV files
:type csv_path: str
:param abf_path: path to ABF files
:type abf_path: str
:param filter_criteria: criteria used to select files. See `EphysInfoFiltering` for more information
:type filter_criteria: Dict[str, Any]
:param log_path: path to save logs, defaults to None
:type log_path: str, optional
:param out_path: path for output files, defaults to None
:type out_path: str, optional
:raises ValueError: if filter criteria are not provided
"""
if not filter_criteria:
raise ValueError(f"No filter criteria provided.")
self.paths = self.validatePaths(
dict(main=main_dir, csv=csv_path, abf=abf_path,
ephys_info_path=ephys_info_path, log=log_path, out=out_path))
self.criteria = filter_criteria
self.filenames: List[str] = None
self.ephys_info: pd.DataFrame = None
self.exp_params: pd.DataFrame = None
self.paired_files: Dict[str, Any] = None
def validatePaths(self, paths: Dict[str, str]) -> Dict[str, Path]:
"""Check that paths are valid and convert to `Path` objects"""
for key, path in paths.items():
if os.path.isdir(path):
paths[key] = Path(path)
elif os.path.isdir(paths['main'] + path):
paths[key] = Path(paths['main'] + path)
else:
raise ValueError(f'{path} is not a valid path.')
return paths
def getDataFiles(self, filenames: List[str], to_ignore: List[str]) -> List[str]:
"""Get Dataframes and pyABF objects for CSV and ABF files, respectively
:param filenames: file names
:type filenames: List[str]
:param to_ignore: list of file names to ignore
:type to_ignore: List[str]
:return: list of missing files
:rtype: List[str]
"""
CSVs = FileFinder(self.paths['csv'], to_ignore=to_ignore,
fmt='.csv').find(filenames, rglob=False)
ABFs = ABF_Finder(self.paths['abf'], to_ignore=to_ignore,
fmt='.abf').find(filenames, rglob=True)
missing: List[str] = []
missing_msg = "{0} in CSVs: {1}\n{0} in ABFs: {2}"
for f in filenames:
if f in CSVs and f in ABFs:
continue
logging.info(
missing_msg.format(f, (f in CSVs), (f in ABFs))
)
missing.append(f)
self.CSVs = CSVs
self.ABFs = ABFs
return missing
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198
] | 2.350137 | 2,182 |
# -*- coding: utf-8 -*-
from .cost_breakdown import CostBreakdown
from .manufacturing_expense import ManufacturingExpense
from .stock import Stock
from .factory import TableAdaptorFactory
from .me2 import ManufacturingExpense2
from .product_price import ProductPrice
from .product_cost import ProductCost
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] | 3.731707 | 82 |
from pathlib import Path
from fhir.resources.valueset import ValueSet as _ValueSet
from oops_fhir.utils import ValueSet
from oops_fhir.r4.code_system.v3_processing_mode import (
v3ProcessingMode as v3ProcessingMode_,
)
__all__ = ["v3ProcessingMode"]
_resource = _ValueSet.parse_file(Path(__file__).with_suffix(".json"))
class v3ProcessingMode(v3ProcessingMode_):
"""
v3 Code System ProcessingMode
**** MISSING DEFINITIONS ****
Status: active - Version: 2018-08-12
http://terminology.hl7.org/ValueSet/v3-ProcessingMode
"""
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85,
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12,
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198,
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220,
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] | 2.701923 | 208 |
from django.contrib import admin
from .models import Post,ReviewRating
# Register your models here.
admin.site.register(Post)
admin.site.register(ReviewRating) | [
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] | 3.577778 | 45 |
import joblib
from wickedhot import OneHotEncoder
from cd4ml.train import get_trained_model
from cd4ml.model_utils import get_target_id_features_lists
import logging
import mlflow.sklearn
import mlflow
import os
from cd4ml.utils.utils import mini_batch_eval
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# -*- coding: utf-8 -*-
"""Parses the ranking CSVs and writes them to the database."""
import csv
import logging
import os
import sys
from datetime import datetime, timezone
from functools import lru_cache
from itertools import groupby
from pathlib import Path
import pandas as pd
from django.core.management.base import BaseCommand
from pytility import arg_to_iter, batchify, parse_date
from snaptime import snap
from ...models import Game, Ranking
from ...utils import format_from_path
csv.field_size_limit(sys.maxsize)
LOGGER = logging.getLogger(__name__)
WEEK_DAYS = ("SUN", "MON", "TUE", "WED", "THU", "FRI", "SAT")
@lru_cache(maxsize=128)
@lru_cache(maxsize=128)
def parse_ranking_csv(path_file, date=None, tzinfo=timezone.utc):
"""Parses a ranking CSV file."""
LOGGER.info("Reading ranking from <%s>...", path_file)
date = _extract_date(path_file=path_file, tzinfo=tzinfo) if date is None else date
ranking = pd.read_csv(path_file)
ranking["date"] = date
return ranking
def parse_ranking_csvs(
path_dir,
week_day="SUN",
tzinfo=timezone.utc,
min_date=None,
max_date=None,
):
"""Parses all ranking CSV files in a directory."""
path_dir = Path(path_dir).resolve()
LOGGER.info("Iterating through all CSV files in <%s>...", path_dir)
files = (file for file in path_dir.iterdir() if format_from_path(file) == "csv")
files = (
(_extract_date(path_file=file, tzinfo=tzinfo), file) for file in sorted(files)
)
if min_date:
LOGGER.info("Filter out files before %s", min_date)
files = ((date, file) for date, file in files if date >= min_date)
if max_date:
LOGGER.info("Filter out files after %s", max_date)
files = ((date, file) for date, file in files if date <= max_date)
if not week_day:
for date, file in files:
LOGGER.info("Processing rankings from %s...", date)
yield date, parse_ranking_csv(path_file=file, date=date)
return
for group_date, group in groupby(
files,
key=lambda pair: _following(date=pair[0], week_day=week_day, tzinfo=tzinfo),
):
LOGGER.info("Processing rankings from the week ending in %s...", group_date)
dfs = (
parse_ranking_csv(path_file=path_file, date=date)
for date, path_file in group
)
yield group_date, pd.concat(dfs, ignore_index=True)
class Command(BaseCommand):
"""Parses the ranking CSVs and writes them to the database."""
help = "Parses the ranking CSVs and writes them to the database."
ranking_types = {
Ranking.BGG: ("bgg", "last", None, None),
Ranking.RECOMMEND_GAMES: ("r_g", "mean", None, 0),
Ranking.FACTOR: ("factor", "mean", None, None),
Ranking.SIMILARITY: ("similarity", "mean", None, None),
Ranking.CHARTS: (
"charts",
"all",
datetime(2016, 1, 1, tzinfo=timezone.utc),
None,
),
}
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] | 2.415064 | 1,248 |
import json
import logging
import os
import threading
import uuid
import grpc
from event_store_pb2 import PublishRequest, SubscribeRequest, UnsubscribeRequest, GetRequest
from event_store_pb2_grpc import EventStoreStub
EVENT_STORE_HOSTNAME = os.getenv('EVENT_STORE_HOSTNAME', 'localhost')
EVENT_STORE_PORTNR = os.getenv('EVENT_STORE_PORTNR', '50051')
def create_event(_action, _data):
"""
Create an event.
:param _action: The event action.
:param _data: A dict with the event data.
:return: A dict with the event information.
"""
return {
'event_id': str(uuid.uuid4()),
'event_action': _action,
'event_data': json.dumps(_data)
}
class EventStoreClient(object):
"""
Event Store Client class.
"""
def publish(self, _topic, _info):
"""
Publish an event.
:param _topic: The event topic.
:param _info: A dict with the event information.
:return: The entry ID.
"""
response = self.stub.publish(PublishRequest(
event_topic=_topic,
event_info=json.dumps(_info)
))
return response.entry_id
def subscribe(self, _topic, _handler, _group=None):
"""
Subscribe to an event topic.
:param _topic: The event topic.
:param _handler: The event handler.
:param _group: Optional group name.
:return: Success.
"""
if _topic in self.subscribers:
self.subscribers[_topic].add_handler(_handler)
else:
subscriber = Subscriber(_topic, _handler, self.stub, _group)
subscriber.start()
self.subscribers[_topic] = subscriber
return True
def unsubscribe(self, _topic, _handler):
"""
Unsubscribe from an event topic.
:param _topic: The event topic.
:param _handler: The event handler.
:return: Success.
"""
subscriber = self.subscribers.get(_topic)
if not subscriber:
return False
response = self.stub.unsubscribe(UnsubscribeRequest(event_topic=_topic))
subscriber.rem_handler(_handler)
if not subscriber:
del self.subscribers[_topic]
return response.success
def get(self, _topic):
"""
Get events for a topic.
:param _topic: The event topic, i.e name of event stream.
:return: A list with entities.
"""
response = self.stub.get(GetRequest(event_topic=_topic))
return json.loads(response.events) if response.events else None
class Subscriber(threading.Thread):
"""
Subscriber Thread class.
"""
def __init__(self, _topic, _handler, _stub, _group=None):
"""
:param _topic: The topic to subscirbe to.
:param _handler: A handler function.
:param _group: The name of the subscriber.
"""
super(Subscriber, self).__init__()
self._running = False
self.handlers = [_handler]
self.topic = _topic
self.stub = _stub
self.group = _group
def run(self):
"""
Poll the event stream and call each handler with each entry returned.
"""
if self._running:
return
self._running = True
for item in self.stub.subscribe(
SubscribeRequest(event_topic=self.topic, group_name=self.group)):
for handler in self.handlers:
try:
handler(item)
except Exception as e:
logging.error(
'error calling handler function ({}) for {}.{}: {}'.format(
e.__class__.__name__, self.topic, handler.__name__, str(e)
)
)
self._running = False
def add_handler(self, _handler):
"""
Add an event handler.
:param _handler: The event handler function.
"""
self.handlers.append(_handler)
def rem_handler(self, _handler):
"""
Remove an event handler.
:param _handler: The event handler function.
"""
self.handlers.remove(_handler)
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] | 2.240701 | 1,882 |
import re
from typing import Optional
def normalize_field_name(name: str, leading_undescores_prefix: Optional[str] = None) -> str:
"""
Normalize a string to take a Pythonic form.
Normalize a string to take a Pythonic form by:
- replacing leading underscores with a given (optional) prefix
- converting the name so snake_case
"""
return convert_to_snake_case(replace_leading_underscores(name, prefix=leading_undescores_prefix))
def convert_to_snake_case(name: str) -> str:
"""
Convert a given string to snake_case.
Converts a given string to snake_case from camel case or kebab case
>>> normalize_field_name('SomeCamelCase')
'some_camel_case'
>>> normalize_field_name('sample-kebab-case')
'sample_kebab_case'
"""
return re.sub(r"(?<!^)(?=[A-Z])", "_", name).replace("-", "_").lower()
def normalize_class_name(name: str) -> str:
"""
Normalize class name by converting it to PascalCase.
>>> normalize_class_name('some_hyphen_case')
'SomeHyphenCase'
"""
if re.match(r"(?:[A-Z][a-z]+)+", name):
return name
return "".join([fragment.capitalize() for fragment in normalize_field_name(name).split("_")])
def replace_leading_underscores(name: str, prefix: Optional[str] = None) -> str:
"""
Replace leading underscores with a given prefix.
Replaces leading underscores with a given prefix. If no prefix is specified, the leading underscores are removed.
>>> replace_leading_underscores('_private_field')
'private_field'
>>> replace_leading_underscores('__private_field', prefix='dunder')
'dunder_private_field'
"""
return re.sub(r"^_+", f"{prefix}_" if prefix else "", name)
def indent_statement(indent: int, statement: str) -> str:
"""
Indents the given string by a specified number of indents.
Indents the given string by a specified number of indents, e.g. indenting by 1 will preprend the string
with 4 space characters:
>>> indent_statement(0, 'x = 3')
'x = 3'
>>> indent_statement(1, 'x = 3')
' x = 3'
"""
return " " * 4 * indent + statement
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] | 2.813984 | 758 |
#!/usr/bin/env python3
from math import pi
from multiprocessing import Pool
from tempfile import NamedTemporaryFile
from subprocess import call, DEVNULL
import time
import os
import os.path as path
from shared import update_progress, plot_data
baseflags = ['--interpolate=linear', '--enable-weights']
config = {
"base": baseflags + ['-n=128'],
"smoothing": baseflags + ['-n=128', '--enable-aa'],
"128x128-multigrid-3-layers": baseflags + ['-n=128', '-l=3'],
"64x128-multigrid-3-layers": baseflags + ['-n=64x128', '-l=3'],
}
if __name__ == "__main__":
main()
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] | 2.607143 | 224 |
#!/usr/bin/env python3
import random
for i in range(100000):print(chr(9585+random.randint(0,1)), end="")
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] | 2.465116 | 43 |
# -*- coding: utf-8 -*-
# ---------------------------------------------------------------------
# Zyxel.ZyNOS.get_switchport
# ---------------------------------------------------------------------
# Copyright (C) 2007-2015 The NOC Project
# See LICENSE for details
# ---------------------------------------------------------------------
# Python modules
import re
# NOC modules
from noc.core.script.base import BaseScript
from noc.sa.interfaces.igetswitchport import IGetSwitchport
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] | 4.53271 | 107 |
#!/usr/bin/python
# -*- coding: UTF-8 -*-
if __name__ == '__main__':
a = 077
b = a | 3
print 'a | b = %d' % b
b |= 7
print 'a|b=%d' % b
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] | 1.784091 | 88 |
import sys, operator
sys.path.append('..')
from scipy.ndimage.morphology import binary_fill_holes
from configuration import Configuration
from dataset.reader import *
from dataset.folder import TrainFolder
from utility.draw import *
from net.lib.nms.cython_nms.cython_nms import cython_nms
from net.layer.mask import instance_to_binary
from multiprocessing import Pool
from numba import jit
def clustering_masks(instances, iou_threshold=0.5, overlap_threshold=0.8):
"""
:param instances: numpy array of instances
:return:
"""
clusters = []
num = instances.shape[0]
instance_sizes = []
for i in range(num):
instance = instances[i]
instance_sizes.append((i, instance.sum()))
sorted_sizes = sorted(instance_sizes, key=lambda tup: tup[1], reverse=True)
for i, instance_size in sorted_sizes:
instance = instances[i]
added_to_group = False
for c in clusters:
cluster_size = c.core_size
inter = np.logical_and(c.core, instance).sum()
union = np.logical_or(c.core, instance).sum()
iou = inter / (union + 1e-12)
if ((inter / cluster_size) > overlap_threshold) or \
((inter / instance_size) > overlap_threshold) or \
(iou > iou_threshold):
c.add(instance)
added_to_group = True
if added_to_group == False:
c = MaskCluster()
c.add(instance)
clusters.append(c)
return clusters
@jit
@jit
def filter_small(proposals, instances, area_threshold=36):
"""
:param instances: numpy array of 0/1 instance in one image
:param area_threshold: do filter if max mask / min mask > this
:param min_threshold: min area ratio
:return: filtered instances
"""
H, W = instances[0].shape[:2]
keep_instances = []
keep_proposals = []
max_size = 0
min_size = H*W
for i in range(instances.shape[0]):
size = instances[i].sum()
if size > max_size:
max_size = size
elif size < min_size:
min_size = size
size_threshold = max_size / area_threshold
if (max_size / min_size) > area_threshold:
for i in range(instances.shape[0]):
size = instances[i].sum()
if size > size_threshold:
#print('%d: %d'%(i, size), ' > ', size_threshold, 'append')
keep_instances.append(instances[i])
keep_proposals.append(proposals[i])
else:
pass
#print('%d: %d'%(i, size), ' < ', size_threshold, 'exclude')
else:
keep_instances = instances
keep_proposals = proposals
keep_proposals = np.array(keep_proposals)
keep_instances = np.array(keep_instances)
return keep_proposals, keep_instances
if __name__ == '__main__':
print('%s: calling main function ... ' % os.path.basename(__file__))
ensemble_masks(multiprocess=False)
print('\nsucess!') | [
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] | 2.232575 | 1,363 |
import cv2
import keras
from keras.applications.imagenet_utils import preprocess_input
from keras.backend.tensorflow_backend import set_session
from keras.models import Model
from keras.preprocessing import image
import matplotlib.pyplot as plt
import numpy as np
from scipy.misc import imread
import tensorflow as tf
import matplotlib.image as mpimg
from ssd_k2 import SSD300
#
# for keras version 1.2
# from ssd import SSD300
#
from ssd_utils import BBoxUtility
plt.rcParams['figure.figsize'] = (8, 8)
plt.rcParams['image.interpolation'] = 'nearest'
np.set_printoptions(suppress=True)
from PIL import ImageEnhance
from PIL import Image as pil_image
from timeit import default_timer as timer
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.45
set_session(tf.Session(config=config))
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] | 2.956989 | 279 |
# Copyright 2013-2021 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
from spack import *
class Libtree(CMakePackage):
"""ldd as a tree with an option to bundle dependencies into a
single folder"""
homepage = "https://github.com/haampie/libtree"
git = "https://github.com/haampie/libtree.git"
url = "https://github.com/haampie/libtree/archive/refs/tags/v2.0.0.tar.gz"
maintainers = ['haampie']
version('master', branch='master')
version('2.0.0', sha256='099e85d8ba3c3d849ce05b8ba2791dd25cd042a813be947fb321b0676ef71883')
version('1.2.3', sha256='4a912cf97109219fe931942a30579336b6ab9865395447bd157bbfa74bf4e8cf')
version('1.2.2', sha256='4ccf09227609869b85a170550b636defcf0b0674ecb0785063b81785b1c29bdd')
version('1.2.1', sha256='26791c0f418b93d502879db0e1fd2fd3081b885ad87326611d992a5f8977a9b0')
version('1.2.0', sha256='3e74655f22b1dcc19e8a1b9e7796b8ad44bc37f29e9a99134119e8521e28be97')
version('1.1.4', sha256='38648f67c8fa72c3a4a3af2bb254b5fd6989c0f1362387ab298176db5cbbcc4e')
version('1.1.3', sha256='4c681d7b67ef3d62f95450fb7eb84e33ff10a3b9db1f7e195b965b2c3c58226b')
version('1.1.2', sha256='31641c6bf6c2980ffa7b4c57392460434f97ba66fe51fe6346867430b33a0374')
version('1.1.1', sha256='3e8543145a40a94e9e2ce9fed003d2bf68294e1fce9607028a286bc132e17dc4')
version('1.1.0', sha256='6cf36fb9a4c8c3af01855527d4931110732bb2d1c19be9334c689f1fd1c78536')
version('1.0.4', sha256='b15a54b6f388b8bd8636e288fcb581029f1e65353660387b0096a554ad8e9e45')
version('1.0.3', sha256='67ce886c191d50959a5727246cdb04af38872cd811c9ed4e3822f77a8f40b20b')
variant('chrpath', default=False, description='Use chrpath for deployment')
variant('strip', default=False, description='Use binutils strip for deployment')
# header only dependencies
depends_on('cpp-termcolor', when='@2.0:', type='build')
depends_on('cxxopts', when='@2.0:', type='build')
depends_on('elfio', when='@2.0:', type='build')
# runtime deps
depends_on('chrpath', when='+chrpath', type='run')
depends_on('binutils', when='+strip', type='run')
# testing
depends_on('googletest', type='test')
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2188,
519,
1616,
395,
3256,
2099,
11639,
9288,
11537,
198
] | 2.127424 | 1,083 |
import psycopg2
# Redownloading the models is a pain, maybe a better solution is to keep the models on the ram and transfer back and forth from video card | [
11748,
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] | 4.076923 | 39 |
from django.urls import path, include # new
| [
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] | 3.384615 | 13 |
# TODO: Utils ? like load so we don't duplicate code ?
from unittest import TestCase
from unittest.mock import patch
import pytest
from ruamel.yaml import YAML
from yaml import SafeLoader
from conda_vendor.env_yaml_from_manifest import YamlFromManifest
@pytest.fixture
| [
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] | 3.01087 | 92 |
cont=0
while cont<5:
print(cont)
cont=cont+1
else:
print('o loop while foi encerrado com sucesso.')
| [
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"""
Spire library and API version.
"""
SPIRE_VERSION = "0.4.0"
| [
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import tensorflow as tf
import utils
| [
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import pickle
import os
import shutil
class File_Operation:
"""This class shall be used to save the model after training and load the saved model for prediction."""
def save_model(self,model,filename):
"""
Method Name: save_model
Description: Save the model file to directory
Outcome: File gets saved
On Failure: Raise Exception
"""
self.logger_object.log(self.file_object, 'Entered the save_model method of the File_Operation class')
try:
path = os.path.join(self.model_directory,filename) #create seperate directory for each cluster
if os.path.isdir(path): #remove previously existing models for each clusters
shutil.rmtree(self.model_directory)
os.makedirs(path)
else:
os.makedirs(path) #
with open(path +'/' + filename+'.sav',
'wb') as f:
pickle.dump(model, f) # save the model to file
self.logger_object.log(self.file_object,
'Model File '+filename+' saved. Exited the save_model method of the Model_Finder class')
return 'success'
except Exception as e:
self.logger_object.log(self.file_object,'Exception occured in save_model method of the Model_Finder class. Exception message: ' + str(e))
self.logger_object.log(self.file_object,
'Model File '+filename+' could not be saved. Exited the save_model method of the Model_Finder class')
raise Exception()
def load_model(self,filename):
"""
Method Name: load_model
Description: load the model file to memory
Output: The Model file loaded in memory
On Failure: Raise Exception
"""
self.logger_object.log(self.file_object, 'Entered the load_model method of the File_Operation class')
try:
with open(self.model_directory + filename + '/' + filename + '.sav',
'rb') as f:
self.logger_object.log(self.file_object,
'Model File ' + filename + ' loaded. Exited the load_model method of the Model_Finder class')
return pickle.load(f)
except Exception as e:
self.logger_object.log(self.file_object,
'Exception occured in load_model method of the Model_Finder class. Exception message: ' + str(
e))
self.logger_object.log(self.file_object,
'Model File ' + filename + ' could not be saved. Exited the load_model method of the Model_Finder class')
raise Exception()
def find_correct_model_file(self,cluster_number):
"""
Method Name: find_correct_model_file
Description: Select the correct model based on cluster number
Output: The Model file
On Failure: Raise Exception
"""
self.logger_object.log(self.file_object, 'Entered the find_correct_model_file method of the File_Operation class')
try:
self.cluster_number= cluster_number
self.folder_name=self.model_directory
self.list_of_model_files = []
self.list_of_files = os.listdir(self.folder_name)
for self.file in self.list_of_files:
try:
if (self.file.index(str( self.cluster_number))!=-1):
self.model_name=self.file
except:
continue
self.model_name=self.model_name.split('.')[0]
self.logger_object.log(self.file_object,
'Exited the find_correct_model_file method of the Model_Finder class.')
return self.model_name
except Exception as e:
self.logger_object.log(self.file_object,
'Exception occured in find_correct_model_file method of the Model_Finder class. Exception message: ' + str(
e))
self.logger_object.log(self.file_object,
'Exited the find_correct_model_file method of the Model_Finder class with Failure')
raise Exception() | [
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5298,
35528,
3419
] | 2.009624 | 2,286 |
from rest_framework import serializers
from roda_purchase import models
'''Aqui é onde transformamos dados em um formato que pode ser armazenado ou transmitido''' | [
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] | 3.346939 | 49 |
import setuptools
long_description = "Check full documentation [here](https://github.com/pptx704/torpedo)"
setuptools.setup(
name="mailtorpedo",
version="1.1.0",
author="Rafeed M. Bhuiyan",
author_email="[email protected]",
description="A Python package for sending personalized emails using own SMTP server.",
long_description=long_description,
long_description_content_type="text/markdown",
url="https://github.com/pptx704/torpedo",
project_urls={
"Bug Tracker": "https://github.com/pptx704/torpedo/issues",
},
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
],
install_requires=[
'beautifulsoup4',
'openpyxl'
],
package_dir={"": "src"},
packages=setuptools.find_packages(where="src"),
python_requires=">=3.6",
) | [
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21015,
62,
47911,
2625,
29,
28,
18,
13,
21,
1600,
198,
8
] | 2.562327 | 361 |
import six
from oct.core.exceptions import OctConfigurationError
from oct.results.output import output
from oct.results.models import db, set_database
from oct.utilities.configuration import configure, get_db_uri
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"""
In a row of trees, the i-th tree produces fruit with type tree[i].
You start at any tree of your choice, then repeatedly perform the following steps:
Add one piece of fruit from this tree to your baskets. If you cannot, stop.
Move to the next tree to the right of the current tree. If there is no tree to the right, stop.
Note that you do not have any choice after the initial choice of starting tree: you must perform step 1, then step 2, then back to step 1, then step 2, and so on until you stop.
You have two baskets, and each basket can carry any quantity of fruit, but you want each basket to only carry one type of fruit each.
What is the total amount of fruit you can collect with this procedure?
Example 1:
Input: [1,2,1]
Output: 3
Explanation: We can collect [1,2,1].
Example 2:
Input: [0,1,2,2]
Output: 3
Explanation: We can collect [1,2,2].
If we started at the first tree, we would only collect [0, 1].
Example 3:
Input: [1,2,3,2,2]
Output: 4
Explanation: We can collect [2,3,2,2].
If we started at the first tree, we would only collect [1, 2].
Example 4:
Input: [3,3,3,1,2,1,1,2,3,3,4]
Output: 5
Explanation: We can collect [1,2,1,1,2].
If we started at the first tree or the eighth tree, we would only collect 4 fruits.
Note:
1 <= tree.length <= 40000
0 <= tree[i] < tree.length
If I start from a tree I can't stop and have to put the fruit in a basket, but I want basket to have only one type of fruit. It is not clear if one needs to stop after a 3rd type of fruit is encountered.
Since about 4 contests there is at least one question that is harder to understand than to solve. Is it too hard to have someone proofread it before posting?
"""
s = Solution()
print(s.totalFruit([1,2,1]))
print(s.totalFruit([0,1,2,2]))
print(s.totalFruit([1,2,3,2,2]))
print(s.totalFruit([3,3,3,1,2,1,1,2,3,3,4]))
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11,
16,
11,
17,
11,
18,
11,
18,
11,
19,
60,
4008,
198
] | 3.033003 | 606 |
import time
import numpy as np
import pytest
from neuraxle.base import BaseStep, ExecutionContext
from neuraxle.data_container import DataContainer, AbsentValuesNullObject
from neuraxle.distributed.streaming import SequentialQueuedPipeline, ParallelQueuedFeatureUnion, QueueJoiner
from neuraxle.hyperparams.space import HyperparameterSamples
from neuraxle.pipeline import Pipeline
from neuraxle.steps.loop import ForEachDataInput
from neuraxle.steps.misc import FitTransformCallbackStep, Sleep
from neuraxle.steps.numpy import MultiplyByN
EXPECTED_OUTPUTS = np.array(range(100)) * 2 * 2 * 2 * 2
EXPECTED_OUTPUTS_PARALLEL = np.array((np.array(range(100)) * 2).tolist() * 4)
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604,
8,
628,
628,
628,
628,
628,
628,
628,
628,
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628,
198
] | 3.135135 | 222 |
import string
cipheren = dict(zip(list(string.ascii_lowercase), string.ascii_lowercase[::-1]))
cipherde = dict(zip(string.ascii_lowercase[::-1], list(string.ascii_lowercase)))
| [
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] | 2.478873 | 71 |
from dolfin import *
import matplotlib.pyplot as plt
import numpy as np
import scipy.interpolate as s
eps = 0.3
g = 0.0
w0 = 0.8
dt = 1e-5
gamma = 1.0
file = File('./test.pvd')
eps = 0.3
g = 0.0
w0 = 0.8
dt = 0.01
gamma = 1.0
img = plt.imread("./img/newton.jpeg")/256
(Nx, Ny) = img.shape
print(img)
L = 100
x = np.linspace(0, L, Nx)
y = np.linspace(0, L, Ny)
f = s.interp2d(x, y, img)
mesh = RectangleMesh(Point((0, 0)), Point(L, L), Nx-1, Ny-1)
X = FunctionSpace(mesh, 'CG', 1)
rho = Function(X)
field = Field()
rho.interpolate(field)
Vh = FiniteElement('CG', mesh.ufl_cell(), 2)
ME = FunctionSpace(mesh, Vh*Vh)
X = VectorFunctionSpace(mesh, 'CG', 1)
Y = FunctionSpace(mesh, 'CG', 1)
R = FunctionSpace(refine(mesh), 'CG', 1)
theta = Function(X)
vectorField = VectorField()
theta.interpolate(vectorField)
k = sqrt((np.pi*rho/w0)**2 - rho**2*gamma**2)
Uh = Function(ME)
Uh_0 = Function(ME)
U = TrialFunction(ME)
phi, psi = TestFunctions(ME)
initial = GaussianRandomField()
Uh.interpolate(initial)
Uh_0.interpolate(initial)
uh, qh = split(Uh)
uh_0, qh_0 = split(Uh_0)
qh_mid = 0.5*qh + 0.5*qh_0
dPhi = G1(uh, uh_0)*uh + G2(uh_0)
L0 = (uh-uh_0)*phi*dx + dt*A(qh_mid, phi, k) - dt*B(uh, phi, theta, gamma) + dt*dPhi*phi*dx
L1 = qh*psi*dx - A(uh, psi, k)
L = L0 + L1
a = derivative(L, Uh, U)
SH_problem = Problem(a, L)
solver = CustomSolver()
t = 0
T = 5
file = File('./result/newton.pvd')
while (t < T):
print('time: {}'.format(t))
t += dt
Uh_0.vector()[:] = Uh.vector()
solver.solve(SH_problem, Uh.vector())
sol_c = project(Uh.split()[0], Y)
sol_r = Function(R)
LagrangeInterpolator.interpolate(sol_r, sol_c)
sol_r.rename('field', 'label')
file << (sol_r, t) | [
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21003,
9521,
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1352,
13,
3849,
16104,
378,
7,
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62,
81,
11,
1540,
62,
66,
8,
198,
220,
220,
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1540,
62,
81,
13,
918,
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705,
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11537,
198,
220,
220,
220,
2393,
9959,
357,
34453,
62,
81,
11,
256,
8
] | 2.054087 | 832 |
from kw6.reader import Reader
from pkg_resources import get_distribution, DistributionNotFound
try:
__version__ = get_distribution("kw6").version
except DistributionNotFound:
pass
| [
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from __future__ import division
from matplotlib import pyplot as plt
import numpy as np
na = np.newaxis
from scipy.interpolate import griddata
import simplex, dirichlet, sampling, tests, density, timing
allfigfuncs = []
SAVING = True
# plt.interactive(False)
#################################
# Figure-Generating Functions #
#################################
allfigfuncs.append(prior_posterior_2D)
allfigfuncs.append(aux_posterior_2D)
allfigfuncs.append(Rhatp)
allfigfuncs.append(autocorrelation)
allfigfuncs.append(statistic_convergence)
###############
# Utilities #
###############
import os
def scoreatpercentile(data,per,axis):
'''
like the function in scipy.stats but with an axis argument, and works on
arrays.
'''
a = np.sort(data,axis=axis)
idx = per/100. * (data.shape[axis]-1)
if (idx % 1 == 0):
return a[[slice(None) if ii != axis else idx for ii in range(a.ndim)]]
else:
lowerweight = 1-(idx % 1)
upperweight = (idx % 1)
idx = int(np.floor(idx))
return lowerweight * a[[slice(None) if ii != axis else idx for ii in range(a.ndim)]] \
+ upperweight * a[[slice(None) if ii != axis else idx+1 for ii in range(a.ndim)]]
##########################
# Generate All Figures #
##########################
if __name__ == '__main__':
main()
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12417,
834,
10354,
198,
220,
220,
220,
1388,
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198
] | 2.611969 | 518 |
"""
* Project Name: NAD-Logging-Service
* File Name: exception_test.py
* Programmer: Kai Prince
* Date: Sun, Nov 15, 2020
* Description: This file contains exception tests for the Logger app.
"""
import pytest
from .sample_data import exception_logs as sample_logs
@pytest.mark.parametrize("data", sample_logs)
def test_all_bad_tests_fail(client, data):
""" All these tests should fail """
# Arrange
# Act
response = client.post(
"/logger/log",
content_type="application/json",
json=data,
headers={"x-access-token": data["authToken"]},
)
# Assert
assert response.status_code != 200
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62,
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198
] | 2.752101 | 238 |
from typing import Optional, Dict, Any
from pyoembed import oEmbed, PyOembedException
import json
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] | 3.62963 | 27 |
# Copyright 2020 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.
import base64
import utility.hex_utils as hex_utils
from Cryptodome.PublicKey import RSA
from Cryptodome.Cipher import PKCS1_OAEP
from Cryptodome.Cipher import AES
from Cryptodome.Random import get_random_bytes
from Cryptodome.Hash import SHA256
from ecdsa import SigningKey, SECP256k1
import logging
# 96 bits of randomness is recommended to prevent birthday attacks
IV_SIZE = 12
# Key size for authenticated encryption is 256 bits and tag size is 128 bits
KEY_SIZE = 32
TAG_SIZE = 16
logger = logging.getLogger(__name__)
# -----------------------------------------------------------------------------
def generate_signing_keys():
"""
Function to generate private key object
"""
return SigningKey.generate(curve=SECP256k1)
# -----------------------------------------------------------------------------
def get_verifying_key(private_key):
"""
Function to return serialized verifying key from the private key
"""
return private_key.get_verifying_key().to_pem().decode('ascii')
# -----------------------------------------------------------------
def generate_iv():
"""
Function to generate random initialization vector
"""
return get_random_bytes(IV_SIZE)
# -----------------------------------------------------------------
def generate_encrypted_key(key, encryption_key):
"""
Function to generate session key for the client
Parameters:
- encryption_key is a one-time encryption used to encrypt the passed key
- key that needs to be encrypted
"""
pub_enc_key = RSA.importKey(encryption_key)
# RSA encryption protocol according to PKCS#1 OAEP
cipher = PKCS1_OAEP.new(pub_enc_key)
return cipher.encrypt(key)
# -----------------------------------------------------------------
def generate_key():
"""
Function to generate symmetric key
"""
return get_random_bytes(KEY_SIZE)
# -----------------------------------------------------------------
def compute_data_hash(data):
'''
Computes SHA-256 hash of data
'''
data_hash = compute_message_hash(data.encode("UTF-8"))
return data_hash
# -----------------------------------------------------------------
def encrypt_data(data, encryption_key, iv=None):
"""
Function to encrypt data based on encryption key and iv
Parameters:
- data is each item in inData or outData part of workorder request
as per Trusted Compute EEA API 6.1.7 Work Order Data Formats
- encryption_key is the key used to encrypt the data
- iv is an initialization vector if required by the data encryption
algorithm.
The default is all zeros.iv must be a unique random number for every
encryption operation.
"""
# Generate a random iv
if iv is None:
iv = get_random_bytes(IV_SIZE)
generate_iv = True
iv_length = IV_SIZE
else:
generate_iv = False
iv_length = len(iv)
cipher = AES.new(encryption_key, AES.MODE_GCM, iv)
ciphered_data, tag = cipher.encrypt_and_digest(bytes(data))
if generate_iv:
# if iv passed by user is None, random iv generated
# above is prepended in encrypted data
# iv + Cipher + Tag
result = iv + ciphered_data + tag
else:
# Cipher + Tag
result = ciphered_data + tag
return result
# -----------------------------------------------------------------
def decrypt_data(encryption_key, data, iv=None):
"""
Function to decrypt the outData in the result
Parameters:
- encryption_key is the key used to decrypt the encrypted data of the
response.
- iv is an initialization vector if required by the data encryption
algorithm.
The default is all zeros.
- data is the parameter data in outData part of workorder request as
per Trusted Compute EEA API 6.1.7 Work Order Data Formats.
Returns decrypted data as a string
"""
if not data:
logger.debug("Outdata is empty, nothing to decrypt")
return data
# if iv is None the it's assumed that 12 bytes iv is
# prepended in encrypted data
data_byte = base64_to_byte_array(data)
if iv is None:
iv_length = IV_SIZE
iv = data_byte[:iv_length]
data_contains_iv = True
else:
iv_length = len(iv)
data_contains_iv = False
cipher = AES.new(encryption_key, AES.MODE_GCM, iv)
# Split data into iv, tag and ciphered data
if data_contains_iv:
ciphertext_len = len(data_byte) - iv_length - TAG_SIZE
ciphered_data = data_byte[iv_length: iv_length + ciphertext_len]
tag = data_byte[-TAG_SIZE:]
else:
ciphertext_len = len(data_byte) - TAG_SIZE
ciphered_data = data_byte[: ciphertext_len]
tag = data_byte[-TAG_SIZE:]
result = cipher.decrypt_and_verify(ciphered_data, tag).decode("utf-8")
logger.info("Decryption result at client - %s", result)
return result
# -----------------------------------------------------------------------------
def decrypted_response(input_json, session_key, session_iv, data_key=None,
data_iv=None):
"""
Function iterate through the out data items and decrypt the data using
encryptedDataEncryptionKey and returns json object.
Parameters:
- input_json is a dictionary object containing the work order response
payload
as per Trusted Compute EEA API 6.1.2
- session_key is the key used to decrypt the encrypted data of the
response.
- session_iv is an initialization vector corresponding to session_key.
- data_key is a one time key generated by participant used to encrypt
work order indata
- data_iv is an initialization vector used along with data_key.
Default is all zeros.
returns out data json object in response after decrypting output data
"""
i = 0
do_decrypt = True
data_objects = input_json['outData']
for item in data_objects:
data = item['data'].encode('UTF-8')
iv = item['iv'].encode('UTF-8')
e_key = item['encryptedDataEncryptionKey'].encode('UTF-8')
if not e_key or (e_key == "null".encode('UTF-8')):
data_encryption_key_byte = session_key
iv = session_iv
elif e_key == "-".encode('UTF-8'):
do_decrypt = False
else:
data_encryption_key_byte = data_key
iv = data_iv
if not do_decrypt:
input_json['outData'][i]['data'] = data
logger.info(
"Work order response data not encrypted, data in plain - %s",
base64.b64decode(data).decode('UTF-8'))
else:
logger.debug("encrypted_key: %s", data_encryption_key_byte)
# Decrypt output data
data_in_plain = decrypt_data(
data_encryption_key_byte, item['data'], iv)
input_json['outData'][i]['data'] = data_in_plain
i = i + 1
return input_json['outData']
# -----------------------------------------------------------------------------
def verify_data_hash(msg, data_hash):
'''
Function to verify data hash
msg - Input text
data_hash - hash of the data in hex format
'''
verify_success = True
msg_hash = compute_data_hash(msg)
# Convert both hash hex string values to upper case
msg_hash_hex = hex_utils.byte_array_to_hex_str(msg_hash).upper()
data_hash = data_hash.upper()
if msg_hash_hex == data_hash:
logger.info("Computed hash of message matched with data hash")
else:
logger.error("Computed hash of message does not match with data hash")
verify_success = False
return verify_success
# -----------------------------------------------------------------------------
def strip_begin_end_public_key(key):
"""
Strips off newline chars, BEGIN PUBLIC KEY and END PUBLIC KEY.
"""
return key.replace("\n", "")\
.replace("-----BEGIN PUBLIC KEY-----", "").replace(
"-----END PUBLIC KEY-----", "")
# -----------------------------------------------------------------------------
# -----------------------------------------------------------------------------
# -----------------------------------------------------------------------------
# -----------------------------------------------------------------------------
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] | 2.927438 | 3,087 |
from django.db import models
from addons.models import Category
import amo
import mkt
from mkt.webapps.models import Webapp
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import pyqrcode
import png
from pyqrcode import QRCode
# Text which is to be converted to QR code
print("Enter text to convert")
s = input(": ")
# Name of QR code png file
print("Enter image name to save")
n = input(": ")
# Adding extension as .pnf
d = n + ".png"
# Creating QR code
url = pyqrcode.create(s)
# Saving QR code as a png file
url.show()
url.png(d, scale=6)
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] | 2.776119 | 134 |
def log(funct):
"""
Logs the function.
"""
return wrapper
@log # decorator
if __name__ == '__main__':
f = log(multiply) # process and return `multipy` function.
print(f(2,5))
print(add(10, 7))
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] | 2.230769 | 104 |
from _rawffi import alt as _ffi
import _rawffi
import weakref
import sys
SIMPLE_TYPE_CHARS = "cbBhHiIlLdfguzZqQPXOv?"
from _ctypes.basics import (
_CData, _CDataMeta, cdata_from_address, CArgObject, sizeof)
from _ctypes.builtin import ConvMode
from _ctypes.array import Array, byteorder
from _ctypes.pointer import _Pointer, as_ffi_pointer
NULL = NULL()
TP_TO_DEFAULT = {
'c': 0,
'u': 0,
'b': 0,
'B': 0,
'h': 0,
'H': 0,
'i': 0,
'I': 0,
'l': 0,
'L': 0,
'q': 0,
'Q': 0,
'f': 0.0,
'd': 0.0,
'g': 0.0,
'P': None,
# not part of struct
'O': NULL,
'z': None,
'Z': None,
'?': False,
'v': 0,
}
if sys.platform == 'win32':
TP_TO_DEFAULT['X'] = NULL
DEFAULT_VALUE = object()
pyobj_container = GlobalPyobjContainer()
def from_param_char_p(cls, value):
"used by c_char_p and c_wchar_p subclasses"
res = generic_xxx_p_from_param(cls, value)
if res is not None:
return res
if isinstance(value, (Array, _Pointer)):
from ctypes import c_char, c_byte, c_wchar
if type(value)._type_ in [c_char, c_byte, c_wchar]:
return value
def from_param_void_p(cls, value):
"used by c_void_p subclasses"
from _ctypes.function import CFuncPtr
res = generic_xxx_p_from_param(cls, value)
if res is not None:
return res
if isinstance(value, Array):
return value
if isinstance(value, (_Pointer, CFuncPtr)):
return cls.from_address(value._buffer.buffer)
if isinstance(value, int):
return cls(value)
FROM_PARAM_BY_TYPE = {
'z': from_param_char_p,
'Z': from_param_char_p,
'P': from_param_void_p,
}
CTYPES_TO_PEP3118_TABLE = {
'i': {2: 'h', 4: 'i', 8: 'q'},
'I': {2: 'H', 4: 'I', 8: 'Q'},
'l': {4: 'l', 8: 'q'},
'L': {4: 'L', 8: 'Q'},
'?': {1: '?', 2: 'h', 4: 'l', 8: 'q'},
}
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] | 1.930664 | 1,024 |
# -*- coding: utf-8 -*-
import os
import sys
import signal
import cassandra.cluster as cassandra_cluster
import tempfile
import testing.cassandra3
from mock import patch
from time import sleep
from shutil import rmtree
if sys.version_info < (2, 7):
import unittest2 as unittest
else:
import unittest
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] | 2.961905 | 105 |
from typing import Union, Tuple, List, Dict, Any
from easydict import EasyDict
import random
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from ding.utils import SequenceType, REWARD_MODEL_REGISTRY
from ding.model import FCEncoder, ConvEncoder
from .base_reward_model import BaseRewardModel
from ding.utils import RunningMeanStd
from ding.torch_utils.data_helper import to_tensor
import copy
@REWARD_MODEL_REGISTRY.register('rnd')
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] | 3.24 | 150 |
# ===============================================================================
# Copyright 2015 Jake Ross
#
# 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.
# ===============================================================================
# ============= enthought library imports =======================
from __future__ import absolute_import
from traits.api import Instance, Button, Str
from traitsui.api import View, UItem, HGroup, VGroup
from traitsui.handler import Handler
from pychron.classifier.isotope_classifier import IsotopeClassifier
# ============= standard library imports ========================
# ============= local library imports ==========================
from pychron.graph.stacked_regression_graph import StackedRegressionGraph
from pychron.loggable import Loggable
UUIDS = ()
if __name__ == "__main__":
t = IsotopeTrainer()
t.configure_traits(view=View("test"))
# ============= EOF =============================================
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198,
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2,
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] | 4.246377 | 345 |
from operator import attrgetter
from PyQt5.QtCore import Qt, QRectF, QAbstractAnimation, QPropertyAnimation, QEasingCurve
from PyQt5.QtGui import QPen, QBrush, QColor, QPainter, QFont, QFontMetricsF, QTransform
from PyQt5.QtWidgets import QGraphicsObject, QGraphicsView, QSizePolicy, QGraphicsScene
from marker_mixin import MarkerMixin
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] | 2.905983 | 117 |
#!/usr/bin/env python3
'''
dataedit.py
menu password edit for passworddongle.py
'''
import time
import random
import getpass
import json
# all printable ascii chars, plus space, except double quote, tab, backslash
mychars="`aZ0+nM<bY1!oL>cX2@pK;dW3#qJ:eV4$rI'fU5%sH[gT6^tG]hS7&uF{iR8*vE}jQ9(wD-kP,)xC=lO.~yB_mN/ zA"
mylen=len(mychars)
# direction values
ENCRYPT=+1
DECRYPT=-1
def dataedit(data, key, level=0):
'''update data based on key and input'''
if type(data) == type([]):
if len(data) == 0:
print("ERROR - empty list",data)
elif (len(data) % 2) == 1:
print("ERROR - not even number of list members",data)
else:
for i,v in enumerate(data):
if (i % 2) == 0:
'''even items are labels'''
if type(v) == type([]):
print("ERROR? list as odd element in list?",json.dumps(data))
elif type(v) == type('string'):
print(v)
label=v
else:
if type(v) == type([]):
print('level:',level,'menu: ',label)
dataedit(v,key, level + 1)
elif type(v) == type('string'):
if label == 'back' and v == '':
print('back')
else:
while True:
'''password'''
x=input('password - encoded/decoded/input/next:')
if x == 'e':
print(v)
elif x == 'd':
print(tinydecrypt(v,key))
elif x == 'i':
plain=getpass.getpass()
v=tinyencrypt(plain,key)
data[i]=v
elif x == 'n':
break # out of while True
else:
print("ERROR - not a list",data)
print(data)
if __name__ == '__main__':
datafile=input('data file name:')
print('file:',datafile)
with open(datafile) as f:
dataraw=f.read()
if dataraw[0:5] == 'data=':
mylist=dataraw[5:]
data = json.loads(mylist)
orig=list(data)
print(data)
key=getpass.getpass(prompt='Password key: ')
dataedit(data,key)
print(data)
if data != orig:
print("changed")
if True:
outfile=input('data file to write:')
if outfile:
with open(outfile,'w') as fw:
dataraw="data=" + json.dumps(data,indent=4) + '\n'
fw.write(dataraw)
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220,
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220,
220,
220,
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1280,
7,
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8,
355,
277,
25,
198,
220,
220,
220,
220,
220,
220,
220,
4818,
283,
707,
28,
69,
13,
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3419,
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220,
220,
220,
611,
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15,
25,
20,
60,
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220,
220,
220,
220,
220,
220,
220,
616,
4868,
28,
67,
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707,
58,
20,
47715,
198,
220,
220,
220,
1366,
796,
33918,
13,
46030,
7,
1820,
4868,
8,
198,
220,
220,
220,
1796,
28,
4868,
7,
7890,
8,
198,
220,
220,
220,
3601,
7,
7890,
8,
198,
220,
220,
220,
1994,
28,
1136,
6603,
13,
1136,
6603,
7,
16963,
457,
11639,
35215,
1994,
25,
705,
8,
198,
220,
220,
220,
1366,
19312,
7,
7890,
11,
2539,
8,
198,
220,
220,
220,
3601,
7,
7890,
8,
198,
220,
220,
220,
611,
1366,
14512,
1796,
25,
198,
220,
220,
220,
220,
220,
220,
220,
3601,
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220,
220,
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25,
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220,
220,
220,
220,
220,
220,
503,
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28,
15414,
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7890,
2393,
284,
3551,
25,
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198,
220,
220,
220,
220,
220,
220,
220,
611,
503,
7753,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
351,
1280,
7,
448,
7753,
4032,
86,
11537,
355,
277,
86,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4818,
283,
707,
2625,
7890,
2625,
1343,
33918,
13,
67,
8142,
7,
7890,
11,
521,
298,
28,
19,
8,
1343,
705,
59,
77,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
277,
86,
13,
13564,
7,
67,
9459,
707,
8,
198
] | 1.651479 | 1,690 |
import unittest
if __name__ == "__main__":
unittest.main() | [
198,
198,
11748,
555,
715,
395,
628,
198,
198,
361,
11593,
3672,
834,
6624,
366,
834,
12417,
834,
1298,
198,
220,
220,
220,
555,
715,
395,
13,
12417,
3419
] | 2.310345 | 29 |
end
print factorial(5) # should output 120
| [
437,
198,
198,
4798,
1109,
5132,
7,
20,
8,
220,
220,
1303,
815,
5072,
7982,
198
] | 2.875 | 16 |
from __future__ import annotations
import signal
import subprocess
from time import sleep
from typing import Optional, Union, Tuple
from PIL.Image import Image
from pyautogui import hotkey
from sleuthdeck.deck import Action
from sleuthdeck.deck import ClickType
from sleuthdeck.deck import Key
from sleuthdeck.deck import KeyScene
from sleuthdeck.deck import Scene
from sleuthdeck.keys import IconKey
from sleuthdeck.windows import get_window, By
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# Imports from 3rd party libraries
import dash
import dash_bootstrap_components as dbc
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
# Imports from this application
from app import app
# 1 column layout
# https://dash-bootstrap-components.opensource.faculty.ai/l/components/layout
column1 = dbc.Col(
[
dcc.Markdown(
"""
## Insights
Overall I'm happy with my tree-based model's performance. The precision and recall of my model are both equal at a score
of 0.78 respectively. It also has a validation accuracy score of 0.76.
The linear model did not perform as well, with precision at 0.67 and recall at 0.42. It also has a validation accuracy
of 0.53. This is not an acceptable model as just guessing 'FALSE POSITIVE', the majority class, you will have an accuracy
score of 0.51.
As we continue to make new observations about the planets in our galaxy and continue to collect
data from deep space, it's important to have models like this one to make sense of the data and label that
observation correctly.
"""
),
],
)
column2 = dbc.Col(
[
html.Img(src='../assets/kpe_class_report.png', style={'height': '45%', 'width': '65%'}),
html.Br(),
html.Img(src='../assets/confusion_matrix.png', style={'height': '45%', 'width': '65%'})
],
)
layout = dbc.Row([column1, column2]) | [
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] | 2.551613 | 620 |
# --------------
#Importing header files
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
#Path of the file
path
data=pd.read_csv(path).rename(columns={'Total':'Total_Medals'})
data.head(10)
#Code starts here
# --------------
#Code starts here
data['Better_Event']=np.where(data['Total_Summer'] == data['Total_Winter'] , 'Both' , (np.where(data['Total_Summer'] > data['Total_Winter'] , 'Summer','Winter')))
better_event= data['Better_Event'].value_counts().idxmax()
# --------------
#Code starts here
top_countries = data[['Country_Name','Total_Summer', 'Total_Winter','Total_Medals']]
top_countries=top_countries[:-1]
top_10_summer= top_ten(top_countries,'Total_Summer')
print(top_10_summer)
top_10_winter= top_ten(top_countries,'Total_Winter')
print(top_10_winter)
top_10= top_ten(top_countries,'Total_Medals')
print(top_10)
common=list(set(top_10_summer) & set(top_10_winter) & set(top_10))
print(common)
# --------------
#Code starts here
summer_df=data[data['Country_Name'].isin(top_10_summer)]
winter_df =data[data['Country_Name'].isin(top_10_winter)]
top_df=data[data['Country_Name'].isin(top_10)]
# --------------
#Code starts here
summer_df['Golden_Ratio']= summer_df['Gold_Summer']/summer_df['Total_Summer']
summer_max_ratio = (summer_df['Golden_Ratio']).max()
summer_country_gold=summer_df.loc[summer_df['Golden_Ratio'].idxmax(),'Country_Name']
winter_df['Golden_Ratio']= winter_df['Gold_Winter']/winter_df['Total_Winter']
winter_max_ratio = (winter_df['Golden_Ratio']).max()
winter_country_gold=winter_df.loc[winter_df['Golden_Ratio'].idxmax(),'Country_Name']
top_df['Golden_Ratio']= top_df['Gold_Total']/top_df['Total_Medals']
top_max_ratio = (top_df['Golden_Ratio']).max()
top_country_gold=top_df.loc[top_df['Golden_Ratio'].idxmax(),'Country_Name']
# --------------
#Code starts here
data_1= data[:-1]
data_1['Total_Points']= data_1['Gold_Total']*3 +data_1['Silver_Total']*2 +data_1['Bronze_Total']
most_points=max(data_1['Total_Points'])
best_country=data_1.loc[data_1['Total_Points'].idxmax(),'Country_Name']
# --------------
#Code starts here
best=data[data['Country_Name']==best_country]
best=best[['Gold_Total','Silver_Total','Bronze_Total']]
best.plot.bar()
plt.xlabel('United States')
plt.ylabel('Medals Tally')
plt.xticks(rotation=45)
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994,
201,
198,
7890,
62,
16,
28,
1366,
58,
21912,
16,
60,
201,
198,
201,
198,
201,
198,
201,
198,
201,
198,
7890,
62,
16,
17816,
14957,
62,
40710,
20520,
28,
1366,
62,
16,
17816,
13306,
62,
14957,
20520,
9,
18,
1343,
7890,
62,
16,
17816,
26766,
62,
14957,
20520,
9,
17,
1343,
7890,
62,
16,
17816,
18760,
2736,
62,
14957,
20520,
201,
198,
201,
198,
1712,
62,
13033,
28,
9806,
7,
7890,
62,
16,
17816,
14957,
62,
40710,
6,
12962,
220,
201,
198,
13466,
62,
19315,
28,
7890,
62,
16,
13,
17946,
58,
7890,
62,
16,
17816,
14957,
62,
40710,
6,
4083,
312,
87,
9806,
22784,
6,
33921,
62,
5376,
20520,
628,
198,
2,
220,
26171,
198,
2,
10669,
4940,
994,
201,
198,
201,
198,
13466,
28,
7890,
58,
7890,
17816,
33921,
62,
5376,
20520,
855,
13466,
62,
19315,
60,
201,
198,
13466,
28,
13466,
58,
17816,
13306,
62,
14957,
41707,
26766,
62,
14957,
41707,
18760,
2736,
62,
14957,
6,
11907,
201,
198,
201,
198,
13466,
13,
29487,
13,
5657,
3419,
201,
198,
489,
83,
13,
87,
18242,
10786,
17013,
1829,
11537,
201,
198,
489,
83,
13,
2645,
9608,
10786,
9921,
874,
309,
453,
11537,
201,
198,
489,
83,
13,
742,
3378,
7,
10599,
341,
28,
2231,
8,
628,
198
] | 2.432024 | 993 |
import random
print(random.randint(1, 100))
| [
11748,
4738,
201,
198,
201,
198,
4798,
7,
25120,
13,
25192,
600,
7,
16,
11,
1802,
4008,
201,
198
] | 2.526316 | 19 |
# -*- coding: utf-8 -*-
"""Create an application instance."""
from flask.helpers import get_debug_flag
from web.app import create_app
from web.settings import DevConfig, ProdConfig
#CONFIG = DevConfig if get_debug_flag() else ProdConfig
CONFIG = DevConfig
app = create_app(CONFIG)
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
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9,
12,
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37811,
16447,
281,
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6738,
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1330,
2251,
62,
1324,
198,
6738,
3992,
13,
33692,
1330,
6245,
16934,
11,
1041,
67,
16934,
198,
198,
2,
10943,
16254,
796,
6245,
16934,
611,
651,
62,
24442,
62,
32109,
3419,
2073,
1041,
67,
16934,
198,
10943,
16254,
796,
6245,
16934,
198,
1324,
796,
2251,
62,
1324,
7,
10943,
16254,
8,
198
] | 3.10989 | 91 |
from tracking.abstract import AbstractTracker
| [
6738,
9646,
13,
397,
8709,
1330,
27741,
35694,
628
] | 5.222222 | 9 |
# https://www.hackerrank.com/challenges/30-dictionaries-and-maps/problem
# Enter your code here. Read input from STDIN. Print output to STDOUT
n = int(input())
book = {}
for x in range(0,n):
line = input().split()
book[line[0]] = line[1]
while True:
try:
line = input().split()
name = line[0]
if name in book:
entry = book[name]
print("{0}={1}".format(name, entry))
else:
print("Not found")
except EOFError:
break
| [
2,
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2503,
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13,
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12,
67,
2867,
3166,
12,
392,
12,
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16,
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7,
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11,
5726,
4008,
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220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3601,
7203,
3673,
1043,
4943,
198,
220,
220,
220,
2845,
412,
19238,
12331,
25,
198,
220,
220,
220,
220,
220,
220,
220,
2270,
198
] | 2.170213 | 235 |
import os
import sys
import torch
import random
import numpy as np
from torch import nn
from torch import optim
from tqdm import trange, tqdm
from collections import Counter
from datetime import datetime
from tensorboardX import SummaryWriter
from torch.utils.data import Dataset, DataLoader
import matplotlib
matplotlib.use("Agg") # this needs to come before other matplotlib imports
import matplotlib.pyplot as plt
plt.style.use("ggplot")
# ========================= models
class Lorentz(nn.Module):
"""
This will embed `n_items` in a `dim` dimensional lorentz space.
"""
def forward(self, I, Ks):
"""
Using the pairwise similarity matrix, generate the following inputs and
provide to this function.
Inputs:
- I : - long tensor
- size (B,)
- This denotes the `i` used in all equations.
- Ks : - long tensor
- size (B, N)
- This denotes at max `N` documents which come from the
nearest neighbor sample.
- The `j` document must be the first of the N indices.
This is used to calculate the losses
Return:
- size (B,)
- Ranking loss calculated using
document to the given `i` document.
"""
n_ks = Ks.size()[1]
ui = torch.stack([self.table(I)] * n_ks, dim=1)
uks = self.table(Ks)
# ---------- reshape for calculation
B, N, D = ui.size()
ui = ui.reshape(B * N, D)
uks = uks.reshape(B * N, D)
dists = -lorentz_scalar_product(ui, uks)
dists = torch.where(dists <= 1, torch.ones_like(dists) + 1e-6, dists)
# sometimes 2 embedding can come very close in R^D.
# when calculating the lorenrz inner product,
# -1 can become -0.99(no idea!), then arcosh will become nan
dists = -arcosh(dists)
# print(dists)
# ---------- turn back to per-sample shape
dists = dists.reshape(B, N)
loss = -(dists[:, 0] - torch.log(torch.exp(dists).sum(dim=1) + 1e-6))
return loss
def recon(table, pair_mat):
"Reconstruction accuracy"
count = 0
table = torch.tensor(table[1:])
for i in range(1, len(pair_mat)): # 0 padding, 1 root, we leave those two
x = table[i].repeat(len(table)).reshape([len(table), len(table[i])]) # N, D
mask = torch.tensor([0.0] * len(table))
mask[i] = 1
mask = mask * -10000.0
dists = lorentz_scalar_product(x, table) + mask
dists = (
dists.numpy()
) # arccosh is monotonically increasing, so no need of that here
# and no -dist also, as acosh in m i, -acosh(-l(x,y)) is nothing but l(x,y)
# print(dists)
predicted_parent = np.argmax(dists)
actual_parent = np.argmax(pair_mat[:, i])
# print(predicted_parent, actual_parent, i, end="\n\n")
count += actual_parent == predicted_parent
count = count / (len(pair_mat) - 1) * 100
return count
_moon_count = 0
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("dataset", help="File:pairwise_matrix")
parser.add_argument(
"-sample_size", help="How many samples in the N matrix", default=5, type=int
)
parser.add_argument(
"-batch_size", help="How many samples in the batch", default=32, type=int
)
parser.add_argument(
"-burn_c",
help="Divide learning rate by this for the burn epochs",
default=10,
type=int,
)
parser.add_argument(
"-burn_epochs",
help="How many epochs to run the burn phase for?",
default=100,
type=int,
)
parser.add_argument(
"-plot", help="Plot the embeddings", default=False, action="store_true"
)
parser.add_argument("-plot_size", help="Size of the plot", default=3, type=int)
parser.add_argument(
"-plot_graph",
help="Plot the Graph associated with the embeddings",
default=False,
action="store_true",
)
parser.add_argument(
"-overwrite_plots",
help="Overwrite the plots?",
default=False,
action="store_true",
)
parser.add_argument(
"-ckpt", help="Which checkpoint to use?", default=None, type=str
)
parser.add_argument(
"-shuffle", help="Shuffle within batch while learning?", default=True, type=bool
)
parser.add_argument(
"-epochs", help="How many epochs to optimize for?", default=1_000_000, type=int
)
parser.add_argument(
"-poincare_dim",
help="Poincare projection time. Lorentz will be + 1",
default=2,
type=int,
)
parser.add_argument(
"-n_items", help="How many items to embed?", default=None, type=int
)
parser.add_argument(
"-learning_rate", help="RSGD learning rate", default=0.1, type=float
)
parser.add_argument(
"-log_step", help="Log at what multiple of epochs?", default=1, type=int
)
parser.add_argument(
"-logdir", help="What folder to put logs in", default="runs", type=str
)
parser.add_argument(
"-save_step", help="Save at what multiple of epochs?", default=100, type=int
)
parser.add_argument(
"-savedir", help="What folder to put checkpoints in", default="ckpt", type=str
)
parser.add_argument(
"-loader_workers",
help="How many workers to generate tensors",
default=4,
type=int,
)
args = parser.parse_args()
# ----------------------------------- get the correct matrix
if not os.path.exists(args.logdir):
os.mkdir(args.logdir)
if not os.path.exists(args.savedir):
os.mkdir(args.savedir)
exec(f"from datasets import {args.dataset} as pairwise")
pairwise = pairwise[: args.n_items, : args.n_items]
args.n_items = len(pairwise) if args.n_items is None else args.n_items
print(f"{args.n_items} being embedded")
# ---------------------------------- Generate the proper objects
net = Lorentz(
args.n_items, args.poincare_dim + 1
) # as the paper follows R^(n+1) for this space
if args.plot:
if args.poincare_dim != 2:
print("Only embeddings with `-poincare_dim` = 2 are supported for now.")
sys.exit(1)
if args.ckpt is None:
print("Please provide `-ckpt` when using `-plot`")
sys.exit(1)
if os.path.isdir(args.ckpt):
paths = [
os.path.join(args.ckpt, c)
for c in os.listdir(args.ckpt)
if c.endswith("ckpt")
]
else:
paths = [args.ckpt]
paths = list(sorted(paths))
edges = [
tuple(edge)
for edge in set(
[
frozenset((a + 1, b + 1))
for a, row in enumerate(pairwise > 0)
for b, is_non_zero in enumerate(row)
if is_non_zero
]
)
]
print(len(edges), "nodes")
internal_nodes = set(
node
for node, count in Counter(
[node for edge in edges for node in edge]
).items()
if count > 1
)
edges = np.array([edge for edge in edges if edge[1] in internal_nodes])
print(len(edges), "internal nodes")
for path in tqdm(paths, desc="Plotting"):
save_path = f"{path}.svg"
if os.path.exists(save_path) and not args.overwrite_plots:
continue
net.load_state_dict(torch.load(path))
table = net.lorentz_to_poincare()
# skip padding. plot x y
plt.figure(figsize=(7, 7))
if args.plot_graph:
for edge in edges:
plt.plot(
table[edge, 0],
table[edge, 1],
color="black",
marker="o",
alpha=0.5,
)
else:
plt.scatter(table[1:, 0], table[1:, 1])
plt.title(path)
plt.gca().set_xlim(-1, 1)
plt.gca().set_ylim(-1, 1)
plt.gca().add_artist(plt.Circle((0, 0), 1, fill=False, edgecolor="black"))
plt.savefig(save_path)
plt.close()
sys.exit(0)
dataloader = DataLoader(
Graph(pairwise, args.sample_size),
shuffle=args.shuffle,
batch_size=args.batch_size,
num_workers=args.loader_workers,
)
rsgd = RSGD(net.parameters(), learning_rate=args.learning_rate)
name = f"{args.dataset} {datetime.utcnow()}"
writer = SummaryWriter(f"{args.logdir}/{name}")
with tqdm(ncols=80, mininterval=0.2) as epoch_bar:
for epoch in range(args.epochs):
rsgd.learning_rate = (
args.learning_rate / args.burn_c
if epoch < args.burn_epochs
else args.learning_rate
)
for I, Ks in dataloader:
rsgd.zero_grad()
loss = net(I, Ks).mean()
loss.backward()
rsgd.step()
writer.add_scalar("loss", loss, epoch)
writer.add_scalar(
"recon_preform", recon(net.get_lorentz_table(), pairwise), epoch
)
writer.add_scalar("table_test", net._test_table(), epoch)
if epoch % args.save_step == 0:
torch.save(net.state_dict(), f"{args.savedir}/{epoch} {name}.ckpt")
epoch_bar.set_description(
f"🔥 Burn phase loss: {float(loss)}"
if epoch < args.burn_epochs
else _moon(loss)
)
epoch_bar.update(1)
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] | 2.043123 | 4,893 |
# coding: utf-8
#
# Pythonでの正規表現サンプル
#
# Update: 2018/3/21
#
import re
# 検索対象のテキスト
textdata = '''
Welcome to Infra workshop!
Kusotsui
Hikariare
Misogi
Misogi999
Misogi9999
'''.strip()
# print m.groups(0)
# メインルーチン
if __name__ == "__main__":
main()
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198,
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] | 1.791667 | 144 |
from django.apps import AppConfig
| [
6738,
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13,
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628
] | 3.888889 | 9 |
"""
Caching
---------------------------
"""
import functools
from df_engine.core import Actor, Context
from df_engine.core.types import ActorStage
class OneTurnCache:
"""
Class that caches the information from the last turn.
"""
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] | 3.450704 | 71 |
import numpy as np
import elephant.spade as spade
import argparse
import yaml
from yaml import Loader
# Function to filter patterns when the output format of spade function
# is 'patterns'
def _pattern_spectrum_filter(patterns, ns_signature, spectrum, winlen):
"""
Filter to select concept which signature is significant
"""
if spectrum == '3d#':
keep_concept = patterns['signature'] + tuple([max(
np.abs(np.diff(np.array(patterns['lags']) % winlen)))]) \
not in ns_signature
else:
keep_concept = patterns['signature'] not in ns_signature
return keep_concept
if __name__ == '__main__':
# Load parameters dictionary
param_dict = np.load('../data/art_data.npy',
encoding='latin1').item()['params']
lengths = param_dict['lengths']
binsize = param_dict['binsize']
winlens = [int(l/binsize)+1 for l in lengths]
print(winlens)
# Filtering parameters
# Load general parameters
with open("configfile.yaml", 'r') as stream:
config = yaml.load(stream, Loader=Loader)
alpha = config['alpha']
psr_param = config['psr_param']
correction = config['correction']
min_occ = config['min_occ']
# Passing spectrum parameter
parser = argparse.ArgumentParser(description='Compute spade on artificial data'
' for the given winlen and '
'spectrum parameters')
parser.add_argument('spectrum', metavar='spectrum', type=str,
help='spectrum parameter of the spade function')
parser.add_argument('winlen', metavar='winlen', type=int,
help='winlen parameter of the spade function')
args = parser.parse_args()
spectrum = args.spectrum
winlen = args.winlen
# Filtering parameters for the different window length
# Loading result
res_spade, params = \
np.load('../results/{}/winlen{}/art_data_results.npy'.format(spectrum,
winlen),
encoding='latin1')
concepts = res_spade['patterns']
pval_spectrum = res_spade['pvalue_spectrum']
# SPADE parameters
spectrum = params['spectrum']
min_spikes = params['min_spikes']
n_surr = params['n_surr']
# PSF filtering
if len(pval_spectrum) == 0:
ns_sgnt = []
else:
# Computing non-significant entries of the spectrum applying
# the statistical correction
ns_sgnt = spade.test_signature_significance(
pval_spectrum, alpha, corr=correction, report='e',
spectrum=spectrum)
concepts_psf = list(filter(
lambda c: spade._pattern_spectrum_filter(
c, ns_sgnt, spectrum, winlen), concepts))
print('Winlen:', winlen)
print('Non significant signatures:', sorted(ns_sgnt))
print('Number of significant patterns before psr:', len(concepts_psf))
# PSR filtering
# Decide whether filter the concepts using psr
if psr_param is not None:
# Filter using conditional tests (psr)
if 0 < alpha < 1 and n_surr > 0:
concepts_psr = spade.pattern_set_reduction(concepts_psf, ns_sgnt,
winlen=winlen,
h=psr_param[0],
k=psr_param[1],
l=psr_param[2],
min_spikes=min_spikes,
min_occ=min_occ)
else:
concepts_psr = spade.pattern_set_reduction(concepts_psf, [],
winlen=winlen,
h=psr_param[0],
k=psr_param[1],
l=psr_param[2],
min_spikes=min_spikes,
min_occ=min_occ)
patterns = spade.concept_output_to_patterns(
concepts_psr, winlen, binsize, pval_spectrum)
else:
patterns = spade.concept_output_to_patterns(
concepts_psf, winlen, binsize, pval_spectrum)
print('Number of significant patterns after psr:', len(concepts_psf))
# Storing filtered results
params['alpha'] = alpha
params['psr_param'] = psr_param
params['correction'] = correction
params['min_occ'] = min_occ
np.save(
'../results/{}/winlen{}/filtered_patterns.npy'.format(
spectrum, winlen), [patterns, pval_spectrum, ns_sgnt, params])
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] | 1.965978 | 2,469 |
"""
Parser handles reading and interpreting the OA logfile
"""
from player import Player | [
37811,
198,
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17105,
3555,
290,
35391,
262,
440,
32,
2604,
7753,
198,
37811,
198,
198,
6738,
2137,
1330,
7853
] | 4.45 | 20 |
# coding=utf-8
from __future__ import unicode_literals
from django.db import models, migrations
| [
2,
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] | 3.266667 | 30 |
# -*-coding:utf-8 -*-
'''
@File : internet_search_demo.py
@Author : HW Shen
@Date : 2020/5/26
@Desc :
'''
from ServiceOrientedChatbot.search_dialog import SearchEngine
from ServiceOrientedChatbot.utils import logger
if __name__ == '__main__':
engine = SearchEngine()
logger.debug(engine.search("北京今天天气如何？"))
logger.debug(engine.search("上海呢？"))
# logger.debug(engine.search("武汉呢？"))
# logger.debug(engine.search("武汉明天呢？"))
#
# ans = engine.search("貂蝉是谁")
# logger.debug(ans)
# ans = engine.search("西施是谁")
# logger.debug(ans)
# ans = engine.search("你知道我是谁")
# logger.debug(ans)
context = engine.contents
print(context)
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] | 2.017241 | 348 |
from __future__ import print_function
import os
import sys
import ast
from inspect import getmembers
from fnmatch import fnmatchcase
from collections import defaultdict
from openmdao.core.system import System
from openmdao.core.problem import Problem
from openmdao.core.driver import Driver
from openmdao.solvers.solver import Solver
from openmdao.jacobians.jacobian import Jacobian
from openmdao.matrices.matrix import Matrix
from openmdao.vectors.vector import Vector, Transfer
class FunctionFinder(ast.NodeVisitor):
"""
This class locates all of the functions and methods in a file and associates any
method with its corresponding class.
"""
def find_qualified_name(filename, line, cache, full=True):
"""
Determine full function name (class.method) or function for unbound functions.
Parameters
----------
filename : str
Name of file containing source code.
line : int
Line number within the give file.
cache : dict
A dictionary containing infomation by filename.
full : bool
If True, assemble the full name else return the parts
Returns
-------
str or None
Fully qualified function/method name or None.
"""
if filename not in cache:
fcache = {}
with open(filename, 'Ur') as f:
contents = f.read()
if len(contents) > 0 and contents[-1] != '\n':
contents += '\n'
FunctionFinder(filename, fcache).visit(ast.parse(contents, filename))
cache[filename] = fcache
if full:
parts = cache[filename][line]
if parts[0]:
return '.'.join((parts[0], parts[2]))
else:
return '.'.join((parts[1], parts[2]))
return cache[filename][line]
# This maps a simple identifier to a group of classes and corresponding
# glob patterns for each class.
func_group = {
'openmdao': [
("*", (System, Jacobian, Matrix, Solver, Driver, Problem)),
],
'openmdao_all': [
("*", (System, Vector, Transfer, Jacobian, Matrix, Solver, Driver, Problem)),
],
'setup': [
("*setup*", (System, Solver, Driver, Problem)),
],
'dataflow': [
('*compute*', (System,)),
('*linear*', (System,)),
('*', (Transfer,)),
],
'linear': [
('*linear*', (System,)),
('*solve*', (Solver,)),
]
}
try:
from mpi4py import MPI
from petsc4py import PETSc
from openmdao.vectors.petsc_vector import PETScVector, PETScTransfer
#TODO: this needs work. Still lots of MPI calls not covered here...
func_group['mpi'] = [
('*', (PETScTransfer,)),
('get_norm', (PETScVector,)),
('_initialize_data', (PETScVector,))
]
except ImportError:
pass
def _collect_methods(method_patterns):
"""
Iterate over a dict of method name patterns mapped to classes. Search
through the classes for anything that matches and return a dict of
exact name matches and their corresponding classes.
Parameters
----------
method_patterns : [(pattern1, (class1, class2, ... class_n)), ... (pattern_n, (class_n1, class_n2, ...)]
List of tuples of glob patterns and lists of classes used for isinstance checks
Returns
-------
defaultdict
Dict of method names and tuples of all classes that matched for that method. Default value
of the dict is a class that matches nothing
"""
matches = defaultdict(list)
# TODO: update this to also work with stand-alone functions
for pattern, classes in method_patterns:
for class_ in classes:
for name, obj in getmembers(class_):
if callable(obj) and (pattern == '*' or fnmatchcase(name, pattern)):
matches[name].append(class_)
# convert values to tuples so we can use in isinstance call
for name in matches:
lst = matches[name]
if len(lst) == 1:
matches[name] = lst[0]
else:
matches[name] = tuple(matches[name])
return matches
def _create_profile_callback(stack, matches, do_call=None, do_ret=None, context=None):
"""
The wrapped function returned from here handles identification of matching calls when called
as a setprofile callback.
"""
return _wrapped
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] | 2.660319 | 1,628 |
# Copyright 2021 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://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 unittest
from absl.testing import absltest
import jax
import jax._src.lib.xla_bridge
from jax.config import config
import jax.test_util as jtu
config.parse_flags_with_absl()
# These tests simply test that the heap profiler API does not crash; they do
# not check functional correctness.
if __name__ == "__main__":
absltest.main(testLoader=jtu.JaxTestLoader())
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] | 3.507353 | 272 |
'''
Author: MJ.XU
Date: 2021-11-29 17:16:33
LastEditTime: 2021-12-18 23:28:25
LastEditors: MJ.XU
Description: Tech4better
FilePath: \Tutorial-HandWriting-Cls-master\model.py
Personal URL: https://www.squirrelled.cn/
'''
# pytorch related packages
import torch
import torch.nn as nn
import torch.nn.functional as F
# Model Definition
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] | 2.745902 | 122 |
import pandas as pd
import torch
from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
from data.utils import get_Data, calculate_max_len, get_tokenized
from transformers import AutoTokenizer
from config import cfg, BertConfig
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import os
import time
import unittest
from solr_instance import SolrInstance
from solrcloudpy import SolrCollection, SolrConnection
solrprocess = None
if __name__ == "__main__":
unittest.main()
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# -*- coding: utf-8 -*-
#
# pyvinecopulib documentation build configuration file
# Sphinx extension modules
from pkg_resources import get_distribution
# -- General configuration ------------------------------------------------
extensions = [
'sphinx.ext.autodoc',
'sphinx.ext.githubpages',
'sphinx.ext.mathjax',
'sphinx_rtd_theme',
'sphinx.ext.autosummary',
'sphinx.ext.napoleon',
]
napoleon_include_init_with_doc = True
autosummary_generate = True
# The suffix(es) of source filenames.
source_suffix = '.rst'
# For the templates.
templates_path = ['_templates']
# The master toctree document.
master_doc = 'index'
# General information about the project.
project = u'pyvinecopulib'
copyright = u'2019, Thomas Nagler and Thibault Vatter'
author = u'Thomas Nagler and Thibault Vatter'
# The version info.
release = get_distribution('pyvinecopulib').version
version = '.'.join(release.split('.')[:2])
# -- Options for HTML output -------------------------------------------------
html_theme = 'sphinx_rtd_theme'
html_static_path = ['_static']
html_copy_source = False
html_show_copyright = False
html_show_sphinx = False
add_module_names = False
pygments_style = 'sphinx'
html_logo = '_static/pyvinecopulib.png'
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] | 2.884793 | 434 |
"""
Copyright (c) 2018, salesforce.com, inc.
All rights reserved.
SPDX-License-Identifier: BSD-3-Clause
For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
Experiment Hyperparameters.
"""
import argparse
import os
parser = argparse.ArgumentParser(
description="Multi-Hop Knowledge Graph Reasoning with Reward Shaping"
)
# Experiment control
parser.add_argument(
"--process_data",
action="store_true",
help="process knowledge graph (default: False)",
)
parser.add_argument(
"--train",
action="store_true",
help="run path selection set_policy training (default: False)",
)
parser.add_argument(
"--inference",
action="store_true",
help="run knowledge graph inference (default: False)",
)
parser.add_argument(
"--search_random_seed",
action="store_true",
help="run experiments with multiple random initializations and compute the result statistics "
"(default: False)",
)
parser.add_argument(
"--eval", action="store_true", help="compute evaluation metrics (default: False)"
)
parser.add_argument(
"--eval_by_relation_type",
action="store_true",
help="compute evaluation metrics for to-M and to-1 relations separately (default: False)",
)
parser.add_argument(
"--eval_by_seen_queries",
action="store_true",
help="compute evaluation metrics for seen queries and unseen queries separately (default: False)",
)
parser.add_argument(
"--run_ablation_studies", action="store_true", help="run ablation studies"
)
parser.add_argument(
"--run_analysis",
action="store_true",
help="run algorithm analysis and print intermediate results (default: False)",
)
parser.add_argument(
"--data_dir",
type=str,
default=os.path.join(os.path.dirname(os.path.dirname(__file__)), "data"),
help="directory where the knowledge graph data is stored (default: None)",
)
parser.add_argument(
"--model_root_dir",
type=str,
default=os.path.join(os.path.dirname(os.path.dirname(__file__)), "model"),
help="root directory where the model parameters are stored (default: None)",
)
parser.add_argument(
"--model_dir",
type=str,
default=os.path.join(os.path.dirname(os.path.dirname(__file__)), "model"),
help="directory where the model parameters are stored (default: None)",
)
parser.add_argument("--gpu", type=int, default=0, help="gpu device (default: 0)")
parser.add_argument(
"--checkpoint_path", type=str, default=None, help="path to a pretrained checkpoint"
)
# Data
parser.add_argument(
"--test",
action="store_true",
help="perform inference on the test set (default: False)",
)
parser.add_argument(
"--group_examples_by_query",
action="store_true",
help="group examples by topic entity + query relation (default: False)",
)
# Network Architecture
parser.add_argument(
"--model",
type=str,
default="point",
help="knowledge graph QA model (default: point)",
)
parser.add_argument(
"--entity_dim",
type=int,
default=200,
metavar="E",
help="entity embedding dimension (default: 200)",
)
parser.add_argument(
"--relation_dim",
type=int,
default=200,
metavar="R",
help="relation embedding dimension (default: 200)",
)
parser.add_argument(
"--history_dim",
type=int,
default=400,
metavar="H",
help="action history encoding LSTM hidden states dimension (default: 400)",
)
parser.add_argument(
"--history_num_layers",
type=int,
default=3,
metavar="L",
help="action history encoding LSTM number of layers (default: 1)",
)
parser.add_argument(
"--use_action_space_bucketing",
action="store_true",
help="bucket adjacency list by outgoing degree to avoid memory blow-up (default: False)",
)
parser.add_argument(
"--bucket_interval",
type=int,
default=10,
help="adjacency list bucket size (default: 32)",
)
parser.add_argument(
"--type_only",
action="store_true",
help="use denote knowledge graph node by entity types only (default: False)",
)
parser.add_argument(
"--relation_only",
action="store_true",
help="search with relation information only, ignoring entity representation (default: False)",
)
parser.add_argument(
"--relation_only_in_path",
action="store_true",
help="include intermediate entities in path (default: False)",
)
# Knowledge Graph
parser.add_argument(
"--num_graph_convolution_layers",
type=int,
default=0,
help="number of graph convolution layers to use (default: 0, no GC is used)",
)
parser.add_argument(
"--graph_convolution_rank", type=int, default=10, help="number of ranks "
)
parser.add_argument(
"--add_reverse_relations",
type=bool,
default=True,
help="add reverse relations to KB (default: True)",
)
parser.add_argument(
"--add_reversed_training_edges",
action="store_true",
help="add reversed edges to extend training set (default: False)",
)
parser.add_argument(
"--train_entire_graph",
type=bool,
default=False,
help="add all edges in the graph to extend training set (default: False)",
)
parser.add_argument(
"--emb_dropout_rate",
type=float,
default=0.3,
help="Knowledge graph embedding dropout rate (default: 0.3)",
)
parser.add_argument(
"--zero_entity_initialization",
type=bool,
default=False,
help="Initialize all entities to zero (default: False)",
)
parser.add_argument(
"--uniform_entity_initialization",
type=bool,
default=False,
help="Initialize all entities with the same random embedding (default: False)",
)
# Optimization
parser.add_argument(
"--num_epochs",
type=int,
default=200,
help="maximum number of pass over the entire training set (default: 20)",
)
parser.add_argument(
"--num_wait_epochs",
type=int,
default=5,
help="number of epochs to wait before stopping training if dev set performance drops",
)
parser.add_argument(
"--num_peek_epochs",
type=int,
default=2,
help="number of epochs to wait for next dev set result check (default: 2)",
)
parser.add_argument(
"--start_epoch",
type=int,
default=0,
help="epoch from which the training should start (default: 0)",
)
parser.add_argument(
"--batch_size", type=int, default=256, help="mini-batch size (default: 256)"
)
parser.add_argument(
"--train_batch_size",
type=int,
default=256,
help="mini-batch size during training (default: 256)",
)
parser.add_argument(
"--dev_batch_size",
type=int,
default=64,
help="mini-batch size during inferece (default: 64)",
)
parser.add_argument(
"--margin",
type=float,
default=0,
help="margin used for base MAMES training (default: 0)",
)
parser.add_argument(
"--learning_rate",
type=float,
default=0.0001,
help="learning rate (default: 0.0001)",
)
parser.add_argument(
"--learning_rate_decay",
type=float,
default=1.0,
help="learning rate decay factor for the Adam optimizer (default: 1)",
)
parser.add_argument(
"--adam_beta1",
type=float,
default=0.9,
help="Adam: decay rates for the first movement estimate (default: 0.9)",
)
parser.add_argument(
"--adam_beta2",
type=float,
default=0.999,
help="Adam: decay rates for the second raw movement estimate (default: 0.999)",
)
parser.add_argument(
"--grad_norm",
type=float,
default=10000,
help="norm threshold for gradient clipping (default 10000)",
)
parser.add_argument(
"--xavier_initialization",
type=bool,
default=True,
help="Initialize all model parameters using xavier initialization (default: True)",
)
parser.add_argument(
"--random_parameters",
type=bool,
default=False,
help="Inference with random parameters (default: False)",
)
# Fact Network
parser.add_argument(
"--label_smoothing_epsilon",
type=float,
default=0.1,
help="epsilon used for label smoothing",
)
parser.add_argument(
"--hidden_dropout_rate",
type=float,
default=0.3,
help="ConvE hidden layer dropout rate (default: 0.3)",
)
parser.add_argument(
"--feat_dropout_rate",
type=float,
default=0.2,
help="ConvE feature dropout rate (default: 0.2)",
)
parser.add_argument(
"--emb_2D_d1",
type=int,
default=10,
help="ConvE embedding 2D shape dimension 1 (default: 10)",
)
parser.add_argument(
"--emb_2D_d2",
type=int,
default=20,
help="ConvE embedding 2D shape dimension 2 (default: 20)",
)
parser.add_argument(
"--num_out_channels",
type=int,
default=32,
help="ConvE number of output channels of the convolution layer (default: 32)",
)
parser.add_argument(
"--kernel_size", type=int, default=3, help="ConvE kernel size (default: 3)"
)
parser.add_argument(
"--distmult_state_dict_path",
type=str,
default="",
help="Path to the DistMult network state_dict (default: " ")",
)
parser.add_argument(
"--complex_state_dict_path",
type=str,
default="",
help="Path to the ComplEx network state dict (default: " ")",
)
parser.add_argument(
"--conve_state_dict_path",
type=str,
default="",
help="Path to the ConvE network state dict (default: " ")",
)
# Policy Network
parser.add_argument(
"--ff_dropout_rate",
type=float,
default=0.1,
help="Feed-forward layer dropout rate (default: 0.1)",
)
parser.add_argument(
"--rnn_dropout_rate",
type=float,
default=0.0,
help="RNN Variational Dropout Rate (default: 0.0)",
)
parser.add_argument(
"--action_dropout_rate",
type=float,
default=0.1,
help="Dropout rate for randomly masking out knowledge graph edges (default: 0.1)",
)
parser.add_argument(
"--action_dropout_anneal_factor",
type=float,
default=0.95,
help="Decrease the action dropout rate once the dev set results stopped increase (default: 0.95)",
)
parser.add_argument(
"--action_dropout_anneal_interval",
type=int,
default=1000,
help="Number of epochs to wait before decreasing the action dropout rate (default: 1000. Action "
"dropout annealing is not used when the value is >= 1000.)",
)
parser.add_argument(
"--num_negative_samples",
type=int,
default=10,
help="Number of negative samples to use for embedding-based methods",
)
# Reward Shaping
parser.add_argument(
"--fn_state_dict_path",
type=str,
default="",
help="(Aborted) Path to the saved fact network model",
)
parser.add_argument(
"--fn_kg_state_dict_path",
type=str,
default="",
help="(Aborted) Path to the saved knowledge graph embeddings used by a fact network",
)
parser.add_argument(
"--reward_shaping_threshold",
type=float,
default=0,
help="Threshold cut off of reward shaping scores (default: 0)",
)
parser.add_argument(
"--mu",
type=float,
default=1.0,
help="Weight over the estimated reward (default: 1.0)",
)
# Graph Completion
parser.add_argument(
"--theta",
type=float,
default=0.2,
help="Threshold for sifting high-confidence facts (default: 0.2)",
)
# Reinforcement Learning
parser.add_argument(
"--num_rollouts", type=int, default=20, help="number of rollouts (default: 20)"
)
parser.add_argument(
"--num_rollout_steps", type=int, default=3, help="maximum path length (default: 3)"
)
parser.add_argument(
"--bandwidth",
type=int,
default=300,
help="maximum number of outgoing edges to explore at each step (default: 300)",
)
parser.add_argument(
"--r_bandwidth",
type=int,
default=10,
help="maximum number of unique relation types connecting a pair of entities (default: 10)",
)
parser.add_argument(
"--num_paths_per_entity",
type=int,
default=3,
help="number of paths used to calculate entity potential (default: 3)",
)
parser.add_argument(
"--beta",
type=float,
default=0.0,
help="entropy regularization weight (default: 0.0)",
)
parser.add_argument(
"--gamma", type=float, default=1, help="moving average weight (default: 1)"
)
# Policy Gradient
parser.add_argument(
"--baseline",
type=str,
default="n/a",
help="baseline used by the policy gradient algorithm (default: n/a)",
)
parser.add_argument(
"--seed", type=int, default=543, metavar="S", help="random seed (default: 543)"
)
# Search Decoding
parser.add_argument(
"--beam_size",
type=int,
default=100,
help="size of beam used in beam search inference (default: 100)",
)
parser.add_argument(
"--mask_test_false_negatives",
type=bool,
default=False,
help="mask false negative examples in the dev/test set during decoding (default: False. This flag "
"was implemented for sanity checking and was not used in any experiment.)",
)
parser.add_argument(
"--visualize_paths",
action="store_true",
help="generate path visualizations during inference (default: False)",
)
parser.add_argument(
"--save_beam_search_paths",
action="store_true",
help="save the decoded path into a CSV file (default: False)",
)
# Separate Experiments
parser.add_argument(
"--export_to_embedding_projector",
action="store_true",
help="export model embeddings to the Tensorflow Embedding Projector format (default: False)",
)
parser.add_argument(
"--export_reward_shaping_parameters",
action="store_true",
help="export KG embeddings and fact network parameters for reward shaping models (default: False)",
)
parser.add_argument(
"--compute_fact_scores",
action="store_true",
help="[Debugging Option] compute embedding based model scores (default: False)",
)
parser.add_argument(
"--export_fuzzy_facts",
action="store_true",
help="export the facts recovered by embedding based method (default: False)",
)
parser.add_argument(
"--export_error_cases",
action="store_true",
help="export the error cases of a model",
)
parser.add_argument(
"--compute_map",
action="store_true",
help="compute the Mean Average Precision evaluation metrics (default: False)",
)
# Hyperparameter Search
parser.add_argument(
"--tune",
type=str,
default="",
help="Specify the hyperparameters to tune during the search, separated by commas (default: None)",
)
parser.add_argument(
"--grid_search", action="store_true", help="Conduct grid search of hyperparameters"
)
default_args,_ = parser.parse_known_args()
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2446,
357,
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25,
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42501,
198,
8,
198,
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13,
2860,
62,
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7,
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1037,
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1,
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8,
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62,
22046,
11,
62,
796,
30751,
13,
29572,
62,
4002,
62,
22046,
3419,
198
] | 2.75606 | 5,239 |
from pymongo import MongoClient | [
6738,
279,
4948,
25162,
1330,
42591,
11792
] | 4.428571 | 7 |
import pytest
from django.core.management import call_command
from ....models import (
Plan,
PlanSlug,
Product,
ProductCategory,
ProductSlug,
Step,
Version,
)
@pytest.mark.django_db
| [
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13,
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13,
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62,
9945,
198
] | 2.535714 | 84 |
s = "11000010001"
n = 11
print(findLength(s, n))
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198,
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82,
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77,
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4798,
7,
19796,
24539,
7,
82,
11,
299,
4008,
198
] | 2.04 | 25 |
# (doesn't seem to be any official Enum support in Circuitpython)
Schedule = 1
ScoreBoard = 2 | [
2,
357,
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470,
1283,
284,
307,
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] | 3.481481 | 27 |
import m3u8
from typing import Tuple
# Rate variant stream by resolution, average bandwidth, and bandwidth.
# Select the best variant stream (best effort).
#
# Assumption: m3u8 object has one or more variants.
| [
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] | 3.706897 | 58 |
from big_ol_pile_of_manim_imports import *
| [
6738,
1263,
62,
349,
62,
79,
576,
62,
1659,
62,
805,
320,
62,
320,
3742,
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1635,
628,
198
] | 2.368421 | 19 |
import os
import logging
basedir = os.path.abspath(os.path.dirname(__file__))
| [
11748,
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198,
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13,
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13,
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3672,
7,
834,
7753,
834,
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] | 2.645161 | 31 |
# -*- coding: utf-8 -*-
"""
validate.py - Trivial data validater
Created on December 24, 2019
@author: c0redumb
"""
# To make print working for Python2/3
from __future__ import print_function
def validate(ticker, data, begindate='1920-01-01', verbose=0):
'''
This function perform a query and extract the matching cookie and crumb.
'''
new_data = []
last_date = None
for line in data:
# Filename lines, usually the first line
# Zero length lines, usually the last line
if len(line) == 0 or line.startswith('Date'):
new_data.append(line)
continue
# Extract all the fields
try:
field = line.split(',')
d = field[0]
o = float(field[1])
h = float(field[2])
l = float(field[3])
c = float(field[4])
adj_c = float(field[5])
except:
#print("Failed to parse:", line)
continue
# This is a wierd quirk we need to check
invalid_date = False
if last_date is None:
if d < begindate:
invalid_date = True
last_date = d
else:
if d <= last_date:
invalid_date = True
else:
last_date = d
if invalid_date:
if verbose > 0:
print("!!! {}: Invalid date {} in data".format(
ticker, field[0]))
continue
# Verify that the open/close is within the high/low range
mid = (h + l) / 2
corrected = False
if o > h * 1.0001 or o < l * 0.9999:
o = mid
corrected = True
if verbose > 0:
print("!!! {}: Open is out of range on {}".format(
ticker, field[0]))
if c > h * 1.0001 or c < l * 0.9999:
if c != 0.0:
adj_c *= mid / c
else:
adj_c = mid
c = mid
corrected = True
if verbose > 0:
print("!!! {}: Close is out of range on {}".format(
ticker, field[0]))
if corrected:
if verbose > 5:
print(line)
line = "{},{},{},{},{},{},{}".format(
field[0], o, h, l, c, adj_c, field[6])
if verbose > 5:
print(line)
new_data.append(line)
return new_data
| [
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220,
220,
220,
220,
220,
220,
220,
220,
1627,
796,
45144,
5512,
90,
5512,
90,
5512,
90,
5512,
90,
5512,
90,
5512,
90,
92,
1911,
18982,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2214,
58,
15,
4357,
267,
11,
289,
11,
300,
11,
269,
11,
9224,
62,
66,
11,
2214,
58,
21,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
15942,
577,
1875,
642,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3601,
7,
1370,
8,
198,
220,
220,
220,
220,
220,
220,
220,
649,
62,
7890,
13,
33295,
7,
1370,
8,
628,
220,
220,
220,
1441,
649,
62,
7890,
198
] | 1.869202 | 1,315 |
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