Skip to content

Instantly share code, notes, and snippets.

import scipy.special as special
from math import comb
K = 18 # number of sections
def survival_prob(r , K):
probability = 2 ** (-K-1) * comb(K, r-1) * special.hyp2f1(1, 1+K, 2 + K - r, 1/2)
return probability
def main():
for r in range(16):
if r <= K:
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
Notatio Representation
$r$ right-aligned
col 2 is centered
zebra stripes are neat
def game_run():
fell = []
panes = [random.choice([[0,1], [1,0]]) for i in range(num_steps)] # 0 for breakable, 1 for solid
for i in range(num_players):
steps = [random.randint(0,1) for j in range(num_steps)] # 0 for left, 1 for right
for j in range(num_steps):
if panes[j][steps[j]] == 1:
panes[j] = [1, 1]
else:
panes[j] = [1, 1]
import scipy.stats, math
mu = 8000
xbar = 8100
s = 580
n = 40
t_dist = scipy.stats.t(n-1)
t = (xbar - mu)/(s/math.sqrt(n))
pval = 1 - t_dist.cdf(t)
import numpy as np
import seaborn as sns
samples = np.zeros(10000)
mu, sigma = 20, 3
for s in range(10000):
sample = np.random.normal(mu, sigma, 250) #sample of size 250
x_bar = sample.mean() #calculating sample mean
samples[s] = x_bar
sns.distplot(samples, hist = True, color = 'darkblue',
hist_kws={'edgecolor':'black'}).set(xlabel = 'Sample Mean',
import numpy as np
mu, sigma = 20, 3
sample = np.random.normal(mu, sigma, 250)
x_bar = sample.mean()

Problem

A lot of GitHub projects need to have pretty math formulas in READMEs, wikis or other markdown pages. The desired approach would be to just write inline LaTeX-style formulas like this:

$e^{i \pi} = -1$

Unfortunately, GitHub does not support inline formulas. The issue is tracked here.

Investigation

@behrouz-bakhtiari
behrouz-bakhtiari / README-Template.md
Created June 9, 2018 06:35 — forked from PurpleBooth/README-Template.md
A template to make good README.md

Project Title

One Paragraph of project description goes here

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites