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import warnings
from .exceptions import (
ValidationError,
TooManyConditions,
InvalidPriority,
InvalidTimeToLive,
InvalidDataKey,
)
INVALID_DATA_KEYS = frozenset((
'from',
'to',
'registration_ids',
'condition',
'notification_key',
'collapse_key',
'priority',
'conten... |
import re
import time
import requests
from pandas import DataFrame
from pandas import read_csv
import os.path as op
# for PyCharm
# from src.helpers.mylogger import get_handler
from ..mylogger import get_handler
import logging
handler = get_handler(log_type="module")
log = logging.getLogger(__name__)
log.handlers... |
import random
from collections import defaultdict
class InconsistencyError(Exception):
pass
class Collapser(object):
def __init__(self, nodes, states):
self.valid = {}
self.dirty = {}
self.states = frozenset(states)
self.nodes = nodes
for n in nodes:
r = s... |
from copy import copy, deepcopy
from random import choice, uniform
from numpy import array
from algorithms.common.neural_network.activation_function import _NON_LINEAR_ACTIVATION_FUNCTIONS
from algorithms.common.neural_network.connection import Connection
from algorithms.common.neural_network.node import Neuron, Sens... |
#!/usr/bin/env python
import tensorflow as tf
import numpy as np
import scipy.signal
from scipy import misc
import scipy.io
from PIL import Image
import json
import os
from offline_feature import *
from bbox_tool import *
import glob
from reward_function import *
from semantic_environment import *
from shortest_path im... |
"""Autoencoder PyTorch example.
Here we'll train an autoencoder based on the MNIST dataset.
"""
# Import
import pathlib
import time
import torch
import torch.utils.data
import torchvision.datasets
import torchvision.transforms
import matplotlib.pyplot as plt
def main():
# Determine if we're running on the CPU ... |
import os
from unittest import TestCase
import vcr
from erpbrasil.assinatura.certificado import Certificado
from erpbrasil.base import misc
from erpbrasil.transmissao import TransmissaoSOAP
from requests import Session
from erpbrasil.edoc.provedores.cidades import NFSeFactory
try:
from nfselib.ginfes.v3_01.servi... |
import os
import time
import torch
import numpy as np
from elegantrl_helloworld.env import build_env
from elegantrl_helloworld.agent import ReplayBuffer, ReplayBufferList
def train_agent(args):
args.init_before_training()
gpu_id = args.learner_gpus
'''init'''
env = build_env(args.env_func, args.env_... |
from Bio import Entrez
from Bio import SeqIO
import numpy as np
import sys
import os
import json
import csv
from collections import defaultdict, OrderedDict
import re
from markdown2 import Markdown
from scipy import stats
from utils import importData, reference_retreive, div0
def map_PTMs(options, data, refProt... |
from time import time
from typing import Union
from logging import getLogger
from networkx import DiGraph, NetworkXException, has_path
from numpy import ndarray
from numpy.random import RandomState
LOG = getLogger(__name__)
def check(G,
max_res=None,
min_res=None,
direction=None,
... |
"""
Usage: update.py [options]
Options:
--profile PROFILE profile to use for AWS access. Note that you can
update multiple accounts by using a wildcard '*' in
the profile name, like this:
python update.py --profi... |
#!/usr/bin/python3
# This file is part of becalm-station
# https://github.com/idatis-org/becalm-station
# Copyright: Copyright (C) 2020 <NAME> <<EMAIL>>
# License: Apache License Version 2.0, January 2004
# The full text of the Apache License is available here
# http://www.apache.org/licenses/
... |
import requests
from datetime import datetime
from celery.task import task
from django.conf import settings
from django.utils import timezone
from .models import (
Flow,
DailyFlowRuns,
Group,
DailyGroupCount,
DailyChannelCount,
Channel,
Label,
LabelMessage,
)
def get_all_results(next_... |
# -*- coding: utf-8 -*-
# Copyright © 2007-2008 Stockholm TreeAligner Project
# Author: <NAME> <<EMAIL>>
# Licensed under the GNU GPLv2
import cPickle
import cStringIO
import zlib
from nltk_contrib.tiger.graph import NodeType, TigerGraph, NonterminalNode, TerminalNode
__all__ = ("GraphSerializer", "GraphDeserializer"... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import print_function
import os
import re
from collections import defaultdict
def path_splitter(path):
"""
Split a path into its constituent parts.
Might be better written as a recursive function.
:param path: The path to split.
:retu... |
import copy
import logging
import os.path
import pathlib
import re
import shutil
import tempfile
import mkhost.cfg
import mkhost.cfg_parser
import mkhost.common
re_key_value = re.compile(
'^(\w+)\s+(\S+)\s*$', re.ASCII)
OPENDKIM_CONFIG = {
"AllowSHA1Only" : False,
"KeyTable" : mkhost.cfg.OPEND... |
from google.appengine.ext import ndb
from ..models import SportModel, EventModel, MatchModel, BetModel
from random import randint
from XMLOddsParser import parseXMLOdds
import MatchValidator
def populate_odds():
sports = parseXMLOdds()
for sport in sports:
if sport["name"] in ["Football"... |
import os
import uuid
from glob import glob
from typing import TYPE_CHECKING, Union
import gin
import yaml
from tqdm import trange
from colosseum.utils.miscellanea import ensure_folder
if TYPE_CHECKING:
from colosseum.mdps import MDP
def find_hardness_report_file(
mdp: "MDP", hardness_reports_folder="hardn... |
#!/usr/bin/env python3
#
# DirDigger
# ---------
# A simple Python script that tries to do something like DIRB or DirBuster.
#
# Coded by: <NAME> (<EMAIL>)
#
import argparse
import getopt
import os
import re
import requests
import signal
import socket
import subprocess
import sys
import time
import urllib3
import val... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Licensed to Cloudera, Inc. under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. Cloudera, Inc. licenses this file
# to you under the Apache License, Version 2.0 (... |
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as f
from torch.autograd import Variable
import os
import numpy as np
from tqdm import tqdm
import sys
def reparameterize(mu, logvar):
eps = Variable(torch.randn(mu.size(0), mu.size(1))).cuda()
z = mu + eps * ... |
import pytest
from datahub.omis.order.test.factories import (
OrderAssigneeFactory,
OrderCancelledFactory,
OrderCompleteFactory,
OrderFactory,
OrderPaidFactory,
OrderSubscriberFactory,
OrderWithAcceptedQuoteFactory,
)
from datahub.search.omis import OrderSearchApp
from datahub.search.omis.m... |
import time
import pytest
import json
import re
import autoposter
from autoposter import select_threads, Poster
import api
class TestAutoposter:
@pytest.fixture(autouse=True)
def fake_opts(self, monkeypatch):
monkeypatch.setattr(autoposter, 'OPTS', OptsFake())
@pytest.fixture()
def fake_request_for_select(self... |
import urllib.request
import xmltodict, json
#import pygrib
import numpy as np
import pandas as pd
from datetime import datetime
import time
# Query to extract parameter forecasts for one particular place (point)
#
# http://data.fmi.fi/fmi-apikey/f96cb70b-64d1-4bbc-9044-283f62a8c734/wfs?
# request=getFeature&storedq... |
# Copyright 2021, <NAME>, mailto:<EMAIL>
#
# 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 w... |
# Copyright (c) 2017 Ansible by Red Hat
# All Rights Reserved.
from __future__ import unicode_literals
import kombu
import six
import uuid
import msgpack
import socket
import threading
import datetime
import jsonpickle
from asgiref.base_layer import BaseChannelLayer
from collections import deque
from kombu.pools im... |
import matplotlib
import math
import collections
import re
import sys
from Line import Line
from Bar import Bar
def getGoldenRatioDimensions(width):
goldenRatio = (math.sqrt(5) - 1.0) / 2.0
return (width, goldenRatio * width)
def getXYValsFromFile(filename, regex, postFunction=None,
auto... |
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import Lasso, LogisticRegression
from penlm.base_estimators import BaseClassifier, BaseRegressor
from typing import Dict
class Rel... |
# Importing some modules and setting up a few properties
import sys
import os
import csv
from zipfile import ZipFile
sys.path.append(os.getcwd())
os.environ["DJANGO_SETTINGS_MODULE"] = "tsdb_app.settings"
import django
django.setup()
from core.models import Tag, FundSource, Organization, Strategy, Initiative, SocialP... |
# import os
#
# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
# os.environ["CUDA_VISIBLE_DEVICES"] = "0"
import tensorflow as tf
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
from keras.backend.tensorflow_backend import set_session
config = tf.ConfigProto()
config.gpu_options.allow_grow... |
import logging
import time
from .shell_utils import run
logging.basicConfig(format='%(asctime)s %(levelname)-8s %(message)s')
logger = logging.getLogger()
logger.setLevel(logging.INFO)
def _get_resource_info(
resource_type="pod",
labels={},
json_path="{.items[].metadata.name}",
error... |
import random
from sqlalchemy.exc import IntegrityError
from app import app, db, photos
from flask import render_template, redirect, url_for, session, flash
from models import *
from forms import *
from app_func import *
from mail_client import *
#Index
@app.route('/')
def index():
products = Product.query.a... |
import os
from skimage import io, transform
import torch
import torchvision
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms#, utils
import cv2
# import torch.optim as optim
from data import test... |
import asyncio
from aiohttp import (
web,
ClientSession,
ClientRequest,
ClientResponse,
ClientError,
ClientTimeout,
)
import json
from time import sleep
######################################################################
# coroutine utilities
################################################... |
import os
import boto3
import zipfile
import logging
import json
import decimal
from botocore.exceptions import ClientError
import decimal
from app.utils import utility_dynamo
from app.utils import config, helpers
logger = logging.getLogger()
logger.setLevel(logging.INFO)
s3 = boto3.client("s3", use_ssl=False)
# tab... |
import numbers
import collections.abc
import warnings
import numpy
import numpy.random
import numpy.lib.recfunctions
from logging import getLogger
_log = getLogger(__name__)
__all__ = ["sample"]
def __generate_points(rng, N, lower, upper, ndim):
ret = numpy.zeros((sum(N), ndim + 2))
_log.debug('{} points ... |
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
*t4mon* - T4 monitoring base test functions for functional tests
"""
import sys
import pickle
import shutil
import logging
import tempfile
import unittest
from os import path
import numpy as np
import pandas as pd
from t4mon import logger
from t4mon.arguments import ... |
# import face_recognition
import os
import time
from milvus import Milvus, DataType
import psycopg2
import numpy as np
import random
from faker import Faker
fake = Faker()
MILVUS_collection = 'mixe_query'
PG_TABLE_NAME = 'mixe_query'
FILE_PATH = 'bigann_base.bvecs'
VEC_NUM = 1000000
BASE_LEN = 100000
VEC_DIM =... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# pe_patch.py
#
# Copyright 2017 <NAME> <<EMAIL>>
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above copyri... |
from django import forms
from django.conf.urls import *
from django.forms import BaseInlineFormSet
from django.contrib import admin, messages
from django.http import HttpResponseRedirect
from django.contrib.auth.admin import UserAdmin
from django.contrib.auth.forms import ReadOnlyPasswordHashField
from squad_pantry_app... |
import argparse
import datetime
import logging
import os
import sys
import dateutil.parser
import pytz
import requests
from influxdb import InfluxDBClient
# sys.path.append(os.path.dirname(os.path.realpath(__file__)))
from config.config import API_KEY, API_URL, KPLIST
UNITS = {"s": 1, "m": 60, "h": 3600, "d": 86400,... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Module for computing markov transition probability matrices from
nucleotide sequence stored as text files.
compute() is the main function that wraps around smaller modular functions.
@author: pranay
"""
# Import calls
from pathlib import Path
from random import choi... |
from datetime import timedelta
from django.db import transaction
from django.db.models import F, Sum
from django.utils import timezone
from rest_framework import serializers
from care.facility.models import (
FacilityInventoryBurnRate,
FacilityInventoryItem,
FacilityInventoryItemTag,
FacilityInventory... |
"""
Convert 3DPW to TFRecords.
3DPW is NOT used for training.
Therefore this script only saves the test tfrecords.
Rectifies the 3DPW data so that camera is identity.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import pickle
import os
from os.path... |
"""
BaseDataset class
"""
from typing import Sequence, Union, Dict
from pathlib import Path
import random
import re
import yaml
import torch
import pandas as pd
import numpy as np
SequenceOrTensor = Union[Sequence, torch.tensor]
class Attribute:
"""
class Attribute
"""
def __init__(self, d: Dict[s... |
#-----------------------#
i = 10 #
#-----------------------#
import subprocess #
import time #
import os #
import requests #-------------#
from terminaltables import SingleTable#
#-------------------------------------#
#log function
def log():
log = datetime.datetime.now().time()
handle = open(".... |
import copy
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
def clones(module, N):
# Produce N identical layers.
return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])
class LayerNorm(nn.Module):
"""Construct a L... |
from collections import namedtuple
import torch
import torch.nn as nn
import torch.nn.functional as F
__all__ = ['Inception3', 'inception_v3']
model_urls = {
# Inception v3 ported from TensorFlow
'inception_v3_google': 'https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth',
}
_InceptionOuput... |
from flask import request
from flask_jwt_extended import jwt_required, get_jwt_identity
from flask_restplus import Resource
from api.middlewares.base_validator import ValidationError
from api.models import Favorite, Category, Audit
from api.schemas.favorite_schema import FavoriteSchema
from api.utilities.helpers.respo... |
#!/usr/bin/env python
# coding: UTF-8
#
# @package SimulationPanel
# @author <NAME>
# @date 08/10/2020
#
# Class for presenting the simulation in a graphical form.
#
##
from random import randint
from Disease import Disease
from tkinter import Tk, Canvas
from MyWorld import MyWorld
from Timer import Timer
from mapper ... |
import sys, logging, os, random, math, open_color, arcade, time
#check to make sure we are running the right version of Python
version = (3,7)
assert sys.version_info >= version, "This script requires at least Python {0}.{1}".format(version[0],version[1])
#turn on logging, in case we have to leave ourselves debugging... |
'''
Analysis of COVID-19 data
This module is only used with tweeter.py and is no longer being updated.
All plots are generated using JHU github repository data that is imported
into a pandas DataFrame and plotted with matplotlib to create image files
that are tweeted daily by tweeter.py in the package.
'''
import date... |
from variables import *
import matplotlib.pyplot as plt
from Launching import final_velocity_calculation
def coef_calculation(c, p, A, mass):
coef = 0.5 * c * p * A / mass
return coef
coef = coef_calculation(c, p, A, fruit_weight)
def traj_fr(coef, angle, v0, inity): # function that computes trajectory f... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""Este script se encarga de generar el archivo de misión con los datos que ingrese el usuario"""
# Imports
import os
import cgi
# globales
CARPETA_MISION = ""
AUTOR = '"ZR Army - Dept. Tecnico y Diseño";'
NOMBRE_MISION = '"TEMPLATE ZR";'
QUIERE_FOTO = False
FOTO_MISION... |
# Copyright 2012-2015 MongoDB, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... |
from dataclasses import dataclass, field
import sys
import random
from typing import List, Sequence, Set, Tuple
from itertools import combinations, product
import timeit
import matplotlib.pyplot as plt
from models import Instance
@dataclass
class Solver:
@dataclass
class Solution:
_solver: 'Solver' ... |
from datetime import datetime as DateTime, timedelta as TimeDelta
from enum import Enum
from collections import namedtuple
from structdict import named_fixed_struct_dict
import numpy as np
from utils.matrix_utils import atleast_2d_col
_TARIFF_TYPES = ['low_off_peak',
'low_stnd',
'low_... |
import numpy as np
import matplotlib.pyplot as plt
import json
import operator
generos = {
'0': 'Masculino',
'1': 'Feminino'
}
candidatos = {
'0': '<NAME>',
'1': '<NAME>',
'2': '<NAME>',
'3': 'Eymael',
'4': '<NAME>',
'5': '<NAME>',
'6': '<NAME>',
'7': '<NAME>',
'8': '<NAME>... |
# Import required module/s
import csv
def readMarkSheet(file_name):
"""Reads the input CSV file of Mark Sheet and creates a mapping of student name with his/her marks for each subject.
Parameters
----------
file_name : str
CSV file name of Mark Sheet
Returns
-------
dict
Mapping of each student's name and... |
# !/bin/python
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
"""State Machine Router module"""
import os.path
import botocore
import boto3
from state_machine.utils.logger import Logger
from state_machine.state_machine_handler import (
TransitGateway,
... |
#***************************************************************************************************
# Imports
#***************************************************************************************************
# Global imports
import os
import threading
from typing import Union
import json
# Project imports
import m... |
import torch
from torch import nn
from torch.distributions import Normal, Bernoulli, MultivariateNormal
import numpy as np
class NormalProposal(nn.Module):
# this doesn't need to be a module, but other proposal
# distributions will have learned parameters so might as
# well start here
def __init__(sel... |
import os
import pytest
from botocore.stub import Stubber
from unittest.mock import patch
from AwAws.Organizations.accounts import Accounts
mock_acc = 'AwAws.Organizations.accounts.Accounts'
@pytest.fixture
def env():
os.environ['AW_MASTER'] = '987659876512'
yield
os.environ.pop('AW_MASTER')
def test... |
import json
import logging
import os
from cryptoadvance.specter.managers.genericdata_manager import GenericDataManager
from cryptoadvance.specter.managers.singleton import ConfigurableSingleton
from cryptoadvance.specter.specter_error import SpecterError
from cryptoadvance.specter.user import User
logger = logging.ge... |
# linear_transform.py
# <NAME> School of Design Engineering
from manim import *
"""
(1) FOR R, ENTER THE TRANSFORMATION SCALAR BELOW:
"""
scalar = -2
"""
Then, render with: manim -pqh linear_transform.py R
(2) FOR R2, ENTER THE TRANSFORMATION MATRIX BELOW:
"""
M2 = np.array([
[1, 2],
... |
from pathlib import Path
from typing import *
import torch
import torch.optim as optim
import numpy as np
import pandas as pd
from functools import partial
from overrides import overrides
from allennlp.data import Instance
from allennlp.data.token_indexers import TokenIndexer
from allennlp.data.tokenizers import Token... |
import importlib
import imp
from .s3_sync import *
from .custom_losses import *
from .metrics import *
def get_w1_w0_training(args,train_generator):
w1=args.w1
w0=args.w0
if (args.w1_w0_file is not None) and (args.w1_w0_file.startswith('s3://')):
w1_w0_file=download_s3_file(args.w1_w0_file)
el... |
import gzip
import csv
import math
import argparse
import datetime
import os
import subprocess
def differ(l1, l2):
for i in range(len(l1)):
if (i % 2 == 1 and abs(l2[i] - l1[i]) > 1e-8) or (i % 2 == 0 and abs(l2[i] - l1[i]) > 0.01):
if i % 2 == 1 and (l2[i] - l1[i]) > 1e-8:
if ... |
import collections
import filecmp
import os
import sys
import pytest
import munge
import munge.codec.all
this_dir = os.path.dirname(__file__)
data_dir = os.path.join(this_dir, "data")
test_codecs = []
for tags, cls in list(munge.get_codecs().items()):
if any(name in ("json", "toml", "yaml") for name in tags):
... |
# These function can return and Column Expression or a list of columns expression
# Must return None if the data type can not be handle
# from dask_cudf.core import DataFrame as DaskCUDFDataFrame
import cudf
from optimus.engines.base.functions import Functions
from dask_cudf import Series
import random
import stri... |
import pdb
import argparse
import csv
import os
from tqdm import tqdm
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.backends.cudnn as cudnn
from torchvision import datasets, transforms
from utils import Cutout
from models import *
from utils import progress_bar
dataset_option... |
import json
import numpy as np
import pickle
import argparse
import os
import neptune
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
from torchvision.models import resnet18
from module.load_module import load_model, load_loss, load_optimizer, load_scheduler
from utili... |
import sys, os
import re, string
import requests, urllib.request
import colorama
import socket, socks
import asyncio, aiohttp, time, concurrent, random
from termcolor import colored
from bs4 import BeautifulSoup
from fake_useragent import UserAgent
from proxybroker import Broker
CONFIG_FILE = '_config.xml'
colorama.i... |
"""Copy files to remote, using earlier collected host-information.
Use ``shelephant_hostinfo`` to collect host-information.
By default the back-end is ``rysnc -a --from-file="temp" source_dir host:dest_dir``.
Alternatively ``scp -p source_file host:dest_file`` can be used.
Typically, *rsync* will be faster, especially... |
"""Provides ray analogue to `multiprocessing.Manager` class."""
import abc
from collections import deque
import time
from typing import Generic, TypeVar
import ray
from ray.experimental.queue import Empty, Full, Queue
V = TypeVar("V")
class Value(Generic[V]):
@abc.abstractproperty
def value(self) -> V:
... |
# -*- coding: utf-8 -*-
"""
@author: <NAME> <<EMAIL>>
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicConvResBlock(nn.Module):
def __init__(self, input_dim=128, n_filters=256, kernel_size=3, padding=1, stride=1, shortcut=False, downsample=None):
super(BasicConvResBlock,... |
# Run a whole brain searchlight
# Import libraries
import nibabel as nib
import numpy as np
from mpi4py import MPI
from brainiak.searchlight.searchlight import Searchlight
from sklearn.model_selection import StratifiedShuffleSplit, GridSearchCV
from sklearn.svm import SVC
from scipy.spatial.distance import euclidean
i... |
# -*- coding: utf-8 -*-
"""Align the OBO Foundry with the Bioregistry."""
import requests
import requests.exceptions
import requests_ftp
from ..external import get_obofoundry
from ..utils import norm, secho, updater
requests_ftp.monkeypatch_session()
session = requests.Session()
OBO_KEYS = {
'id',
'prefix'... |
import pygame
import sys
import numpy as np
pygame.init()
WIDTH = 600
HEIGHT = 600
LINE_WIDTH = 15
WIN_LINE_WIDTH = 15
BOARD_ROWS = 3
BOARD_COLS = 3
SQUARE_SIZE = 200
CIRCLE_RADIUS = 60
CIRCLE_WIDTH = 15
CROSS_WIDTH = 25
SPACE = 55
BG_COLOR = (255,0,0)
LINE_COLOR = (0,0,0)
CIRCLE_COLOR = (239, 231... |
# coding=utf-8
"""A collection of utility functions non-specific to mapi's domain logic."""
import random
import re
from os import path
from sys import version_info
import requests_cache
from appdirs import user_cache_dir
from requests.adapters import HTTPAdapter
from mapi import log
from mapi.compatibility import ... |
"""Commonly used calculations on electrical quantities."""
from __future__ import division
import numpy as np
import math
import cmath
import sys
import time
import six
import scipy.io as sio
#from numba import jit
def Uunbalance_calc(ua,ub,uc):
"""Calculate voltage/current unbalance."""
uavg = (ua... |
import requests, os, time, math, glob, argparse
from threading import Thread
from urllib.parse import urlparse
class Globals:
def __init__(self):
self.contained = {}
def put(self,name,value):
self.contained[name] = value
def get(self,name):
return self.contained[name]
def has... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 19 09:42:17 2021
@author: philippbst
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.optim import Adam
from torch.utils.data import DataLoader
from torch.optim.lr_scheduler import StepLR
import numpy as np
from neu... |
"""Functions to read and process data from a LittleChef repository"""
import os
import simplejson as json
from littlechef import runner, lib, chef
from logbook import Logger
from kitchen.settings import REPO, REPO_BASE_PATH
from kitchen.backends.plugins import plugins
log = Logger(__name__)
KITCHEN_DIR = os.path.jo... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# -----------------------------------------------------------------------------
# Copyright (c) 2017 - <NAME> - MIT License
# -----------------------------------------------------------------------------
import re
from docutils import nodes
from docutils.parsers.rst import ... |
import errno
import fnmatch
import os
import os.path
import shutil
import subprocess
import sys
import tarfile
import requests
import stat
import dbt.compat
import dbt.exceptions
from dbt.logger import GLOBAL_LOGGER as logger
def find_matching(root_path,
relative_paths_to_search,
... |
import os
import json
import datetime
import requests
from binance.enums import *
from loguru import logger
from crypto_bot.crypto import get_price_changes, get_top_crypto
from crypto_bot.aws import S3
from crypto_bot.binance import binance
INITIAL_BALANCE = 500
BUCKET_NAME = "cryptobotmetadata"
BALANCE_PATH = "ba... |
#!/usr/bin/env python
######################################################################################
# AUTHOR: <NAME> <<EMAIL>>
# CONTRIBUTORS: <NAME> <<EMAIL>>, <NAME> <<EMAIL>>
# DESCRIPTION: Perform a correlation analysis of two datasets.
######################################################################... |
"""
The MIT License (MIT)
Copyright (c) 2015-present Rapptz
Permission is hereby granted, free of charge, to any person obtaining a
copy of this software and associated documentation files (the "Software"),
to deal in the Software without restriction, including without limitation
the rights to use, copy, modify, merge,... |
import pytest
import serverly.utils
from serverly.utils import *
def test_parse_role_hierarchy():
e1 = {
"normal": "normal",
"admin": "normal"
}
e2 = {
"normal": "normal",
"admin": "normal",
"staff": "admin",
"root": "staff",
"god": "staff",
}
... |
# emacs: -*- mode: python-mode; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
#
# See COPYING file distributed along with the NiBabel package for the
# copyright and license terms.
#
### ### ### #... |
import os
import re
import json
import pandas as pd
import tqdm.notebook as tq
from collections import namedtuple
def parse_squad(raw_squad):
""" function used to parse the squad dataset """
def fix_index(context, idx, step=1):
""" function used to fix an index of an answer such that it covers the w... |
#!/usr/bin/env python3
# coding: utf8
# Author: <NAME>, 2019
"""
Retrieve similar strings based on cosine distance of s-grams.
"""
from __future__ import unicode_literals
import itertools as it
from collections import Counter
import numpy as np
from scipy.sparse import csr_matrix
__version__ = '1.0.0'
# Py2... |
import argparse
import ast
import importlib
from typing import *
import albumentations as A
import numpy as np
import torch
import yaml
from albumentations.pytorch import ToTensorV2
from omegaconf import DictConfig
from PIL import Image
from pytorch_retinanet.retinanet.models import Retinanet
from torch import nn
from... |
r"""Evaluate language model on dataset.
Tool for evaluating language model on dataset.
Pre-trained model are used to calculate perplexity on dataset.
This script serve as model validation, and it is usually run after training
model.
All evaluation results will be shown on both CLI and tensorboard.
Use ``pipenv run ten... |
import copy
import datetime
import logging
from sys import stderr
import numpy as np
import torch
from sklearn.metrics import f1_score, precision_score, recall_score, accuracy_score
# logger = logging.getLogger(__name__)
def debug(*msg, sep=' '):
print(sep.join((str(m) for m in msg)))
def evaluate_loss(model,... |
import numpy as np
import pandas as pd
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
from sklearn.metrics import confusion_matrix
import itertools
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras import Sequential
from tensorflow.keras.models import loa... |
import csv
from .postgre_model import Postgres_DB
import datetime
PROPERTIES_OF_TABLE = [
{
"table_name": "Inventory",
"column_1": "Brand",
"column_2": "Batch ID"
},
{
"table_name": "Sales",
"column_1": "<NAME>",
"column_2": "Current Loyalty Points"
},
... |
# ActivitySim
# See full license in LICENSE.txt.
import logging
import os
import numpy as np
import pandas as pd
logger = logging.getLogger(__name__)
def undupe_column_names(df, template="{} ({})"):
"""
rename df column names so there are no duplicates (in place)
e.g. if there are two columns named "... |
from microgridRLsimulator.agent.agent import Agent
from collections import namedtuple
from collections import defaultdict
import math
import random
from microgridRLsimulator.simulate.simulatorMCTS import SimulatorMCTS
from microgridRLsimulator.utils import time_string_for_storing_results
microgrid_node = namedtuple("m... |
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