Restored previous version

parent fd7b540d
# On the computation of $\pi$ {
"cells": [],
## Asking the maths library "metadata": {
My computer tells me that $\pi$ is _approximatively_ "kernelspec": {
"display_name": "Python 3",
"language": "python",
```python "name": "python3"
from math import * },
print(pi) "language_info": {
``` "codemirror_mode": {
"name": "ipython",
3.141592653589793 "version": 3
},
"file_extension": ".py",
## Buffon’s needle "mimetype": "text/x-python",
Applying the method of [Buffon’s needle](https://en.wikipedia.org/wiki/Buffon%27s_needle_problem), we get the __approximation__ "name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
```python "version": "3.6.3"
import numpy as np }
np.random.seed(seed=42) },
N = 10000 "nbformat": 4,
x = np.random.uniform(size=N, low=0, high=1) "nbformat_minor": 2
theta = np.random.uniform(size=N, low=0, high=pi/2) }
2/(sum((x+np.sin(theta))>1)/N) \ No newline at end of file
```
3.128911138923655
## Using a surface fraction argument
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 $P[X^2 + Y^2\le 1] = \pi/4$ (see ["Monte Carlo method"
on Wikipedia](https://en.wikipedia.org/wiki/Monte_Carlo_method)). The following code uses this approach:
```python
%matplotlib inline
import matplotlib.pyplot as plt
np.random.seed(seed=42)
N = 1000
x = np.random.uniform(size=N, low=0, high=1)
y = np.random.uniform(size=N, low=0, high=1)
accept = (x*x+y*y) <= 1
reject = np.logical_not(accept)
fig, ax = plt.subplots(1)
ax.scatter(x[accept], y[accept], c='b', alpha=0.2, edgecolor=None)
ax.scatter(x[reject], y[reject], c='r', alpha=0.2, edgecolor=None)
ax.set_aspect('equal')
```
![png](output_6_0.png)
It is then straightforward to obtain a (not really good) approximation to $\pi$ by counting how
many times, on average, $X^2 + Y^2$ is smaller than 1:
```python
4*np.mean(accept)
```
3.112
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