galenseilis

import networkx as nx
import matplotlib.pyplot as plt
from sympy import factorint


def f(n):
    result = factorint(n)
    result = result.items()
    result = [n*e//p for p, e in result]
    result = sum(result)

galenseilis

import numpy as np
from scipy.spatial.distance import pdist, squareform
from itertools import combinations

sph = 2000

def monte_plot(sph):
    X = 100 * np.random.random(sph * 2).reshape(sph, 2)
    radial_dists = np.sqrt(np.sum((X - np.array([50,50]))**2, axis=1))
    plot_X = X[radial_dists <= 3.999]

galenseilis

import numpy as np

def multiminor(arr, I):
    modes = len(arr.shape)
    assert modes == len(I)

    result = arr.copy()
    for mode in range(modes):
        result = np.delete(result, I[mode], axis=mode)

galenseilis

from math import factorial
import time

n = 241779
prod_n = factorial(n)

while 1:
    prod_n = prod_n * (n + 1)
    str_n = str(prod_n)
    n += 1

galenseilis

def exp_seq(iterable):
    '''Calculates the exponent tower of a sequence of numbers.'''
    a = iterable[0]
    for i in iterable[1:]:
        a = a**i
    return a

galenseilis

def prime_factors(n):
    '''Brute-force method of searching for primes.'''
    i = 2
    factors = []
    while i * i <= n:
        if n % i:
            i += 1
        else:
            n //= i
            factors.append(i)

galenseilis

import numpy as np

def mult_pers(n):

    if len(str(n)) == 1:
        return n
    else:
        prod = np.prod([int(i) for i in str(n)])
        return mult_pers(prod)

galenseilis

import collections

def prime_factors(n):
    '''Brute-force method of searching for primes.'''
    i = 2
    factors = []
    while i * i <= n:
        if n % i:
            i += 1
        else:

galenseilis

from itertools import chain, combinations
from random import sample
import matplotlib.pyplot as plt

def powerset(iterable):
    s = list(iterable)
    return chain.from_iterable(combinations(s, r) for r in range(len(s)+1))


pX = [set(i) for i in powerset(list(range(10)))]

galenseilis

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

import seaborn as sns

x = np.random.normal(size=1000)

y1 = np.cos(x)
y2 = np.sin(x)