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2967c9aae1a2f4cee2d1ad632cabf260
mooc-rr
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46472757
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46472757
authored
Jun 03, 2022
by
2967c9aae1a2f4cee2d1ad632cabf260
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module2/exo1/toy_notebook_en.ipynb
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46472757
# 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')
```

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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