# On the computation of π

On the computation of π

## 1 Asking the math library

My computer tells me that π is approximatively

from math import *pi
3.141592653589793

## 2 Buffon's needle

By the method of Buffon's needle we get the approximation

import numpy as np

N = 10000
np.random.seed(seed=42)

x = np.random.uniform(size=N, low=0, high=1)
theta = np.random.uniform(size=N, low=0, high=pi/2)

P = sum(x + np.sin(theta) > 1) / N
2/P

3.128911138923655

## 3 Using Monte Carlo experiments

A method that is easier to understand and does not make use of the $$\sin$$ function is based on the fact that if $$X \sim U(0,1)$$ and $$Y \sim U(0,1)$$, then $$P[X^2+Y^2 \leq 1] = \pi/4$$ (see "Monte Carlo method" on Wikipedia). The following code uses this approach:

import matplotlib.pyplot as plt

np.random.seed(seed=42)
x = np.random.uniform(size=N, low=0, high=1)
y = np.random.uniform(size=N, low=0, high=1)

inside = (x**2 + y**2) <= 1
outside = np.logical_not(inside)

fig, ax = plt.subplots(1)
ax.scatter(x[inside], y[inside], c='b', alpha=0.2, edgecolor=None)
ax.scatter(x[outside], y[outside], c='r', alpha=0.2, edgecolor=None)
ax.set_aspect('equal')

plt.savefig(matplot_lib_filename)
print(matplot_lib_filename) It is then straightforward to obtain an approximation to π by counting how many times, on average, $$X^2 + Y^2 \le 1$$:

4 * np.mean(inside)
3.1556

## 4 Through Wallis' product

John Wallis' product for π states that the infinite product below converges to $$\pi / 2$$. Check out the geometrical proof.

prod = 1
for i in range(2, N):
prod *= i / (i-1) if i % 2 == 0 else (i-1) / i

prod * 2

3.141435562175509

## 5 As the root of a function

As we know, $$sin(\pi) = 0$$, thus value for π can be found by approximating the root (one of them, that is) of $$sin(x)$$. This can be done using Newton's method, for instace.

from scipy.optimize import fsolve
results = fsolve(sin, x0=3)
results

3.141592653589793

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