worldmind

from array import array
from pympler.asizeof import asizeof

SIZE = 1000


the_array = array('i')
the_list = []

for_tuple = []

worldmind

from urllib.parse import quote, urlencode, ParseResult as Url


Url.__str__ = Url.geturl

bucket_name = 'something_with space'
file_name = 'data.txt'

url = Url(
    scheme='https',

worldmind

from decimal import Decimal
from fractions import Fraction


def muller_recurrence(y, z):
 return 108 - ((815-1500/z)/y)


N = 25

worldmind

from time import sleep
import signal
import multiprocessing as mp


_SIGTERM = None


def sigterm_handler(signum, frame):
    global _SIGTERM

worldmind

import numpy as np
import pandas as pd
from sklearn.model_selection import GridSearchCV, train_test_split
from sklearn.metrics import roc_auc_score, precision_score, recall_score, f1_score, accuracy_score
import torch
from torch import nn, tensor, optim
from skorch import NeuralNetClassifier


def get_data():

worldmind

from math import ceil, floor
import numpy as np


LEVELS = 10
CENTER = int(LEVELS/2)
FACTORS = [
    'Agreeableness',
    'Conscientiousness',
    'Extraversion',

worldmind

def main():
    y = 0
    for k in range(1,3):
        z = k
    def inside_main():
        print(y)
        print(z)
        print(k)
    inside_main()

worldmind

import sys
import unittest
from contextlib import contextmanager


@contextmanager
def assert_not_raises(self, exc_type):
    try:
        yield None
    except exc_type:

worldmind

from pympler import asizeof


def list_to_str(lst):
    str = ''
    for val in lst:
        str = str + val + ', '
    return str[0:len(str)-2]

worldmind

from pympler import asizeof

class Coordinates():
    def __init__(self, x, y):
        self.x = x
        self.y = y


class CoordinatesSlots():
    __slots__ = ('x', 'y')