galenseilis

import numpy as np
import matplotlib.pyplot as plt

def laplacian(grid):
    return np.roll(grid, 1, 0) +\
           np.roll(grid, -1, 0) +\
           np.roll(grid, 1, 1) +\
           np.roll(grid, -1, 1) - 4 * grid

def evolve(grid, df, D=1):

galenseilis

'''
Easier method: regress y−2x against x and read the t-test results directly off the summary, making sure to halve any reported p-values to account for the one-sided alternative.

- whuber
'''

import numpy as np
import statsmodels.api as sm

x = np.array([1,2,3,3,4,5,5])

galenseilis

'''
Adding very small probabilities - How to compute?
'''

import mpmath as mp

mp.mp.dps = 130

print(mp.mpf(1.3573e-123) + mp.mpf(0.99))

galenseilis

from math import exp, log1p

l1 = -3006
l2 = -3012

def logsum(l1, l2):
    return max(l1, l2) + log1p(exp(-abs(l1-l2)))

print(logsum(l1, l2))

galenseilis

f = lambda n: sum([int(i) for i in str(n)])

print(f(2**1000))

galenseilis

cache = {1: 1}
def collatz_count(n):
    if n not in cache:
        if n % 2 == 0:
            cache[n] = 1 + collatz_count(n // 2)
        else:
            cache[n] = 1 + collatz_count(3 * n + 1)
    return cache[n]

galenseilis

from itertools import permutations

names = ['Amelia', 'Boris', 'Carlos', 'Dara']

for p in permutations(names):
    if p.index('Dara') >= p.index('Amelia'):
        continue
    elif p.index('Boris') == 2:
        continue
    elif abs(p.index('Amelia') - p.index('Carlos')) != 3:

galenseilis

import numpy as np
from scipy import stats

def wilson_cont(n1, n2, alpha=0.05):
    '''
    Wilson score interval with continuity correction.
    Two-tail interval is assumed.

    Parameters:
        n1 (int): Count of outcome 1.

galenseilis

from more_itertools import set_partitions

S = set(range(1,6))
for i in S:
    s_i = S - {i}
    for left, right in set_partitions(s_i, 2):
        if len(left) == len(right) and sum(left) + i == 9 and sum(right) + i == 9:
            print(i, left, right)
            break

galenseilis

import numpy as np
from scipy.stats import t
import statsmodels.api as sm

# Prepare data
x = np.array([1,2,3,3,4,5,5])
y = np.array([3,7,5,8,11,14,12])
n = y.size
X = sm.add_constant(x)