Exercises2_sol.txt
Taken from http://www.loria.fr/~rougier/teaching/numpy.100/index.html

1. Import the numpy package under the name np
	import numpy as np

2. Create a null vector of size 10
	Z = np.zeros(10)

3. Create a null vector of size 10 but the fifth value which is 1
	Z = np.zeros(10)
	Z[4] = 1
	
4. Create a vector with values ranging from 10 to 99
	Z = 10 + np.arange(90)
	
5. Create a 3x3 matrix with values ranging from 0 to 8
	Z = np.arange(9).reshape(3,3)

6. Find indices of non-zero elements from [1,2,0,0,4,0]
	nz = np.nonzero([1,2,0,0,4,0])

7. Declare a 3x3 identity matrix
	Z = np.eye(3)
	
8.  a) Declare a 10x10x10 array with random values
	Z = np.random.random((10,10,10))

	b) Generate an array with 10 random numbers extracted from a poisson distribution with \lambda = 5:
	Z = np.random.poisson(5,10)
	
9. Declare a 8x8 matrix and fill it with a checkerboard pattern
	Z = np.zeros((8,8))
	Z[1::2,::2] = 1
	Z[::2,1::2] = 1

10. a) Declare a 10x10 array with random values and find the minimum and maximum values
	Z = np.random.random((10,10))
	Zmin, Zmax = Z.min(), Z.max()
	
	b) Generate an array with random 100 integer numbers, ranging from 5 to 97. Check the mean of the even numbers.
	Z = np.random.random_integers(5,97,100)
	Z = Z[np.where(Z % 2 == 0)]
	Z.mean()
	c) Generate an  array with 5 numbers for 1 to 50. Make sure there is no numbers repeated in the selected 5.
	Z = range(1,51)
	np.random.shuffle(Z)
	Z5 = Z[:5]

	Alternative solution with random module:
	import random
	Z = np.random.sample(range(1,51),5)
	
11. Normalize a 5x5 random matrix (between 0 and 1)
	Z = np.random.random((5,5))
	Zmax,Zmin = Z.max(), Z.min()
	Z = (Z - Zmin)/(Zmax - Zmin)
	
12. a) Multiply a 3x3 matrix by a 3x3 matrix 
		Z = np.ones((3,3)) * np.ones((3,3))
	b) Multiply a 5x3 matrix by a 3x2 matrix (real matrix product)
		Z = np.dot(np.ones((3,3)), np.ones((3,3)))
	c) Multiply a 5x3 matrix by a 3x2 matrix (real matrix product)
		Z = np.dot(np.ones((5,3)), np.ones((3,2)))
	d) try to multiply a 5x3 matrix by a 3x2 matrix
		Z = np.ones((5,3)) * np.ones((3,2))

13. Create a vector of size 1000 with values ranging from 0 to 1, both excluded
	Z = np.random.linspace(0,1,1002,endpoint=True)[1:-1]
	
14. Create a random vector of size 100 and sort it, and find its mean value
	Z = np.random.random(100)
	Z.sort()
	m = Z.mean()
	
15. Create random vector of size 100 and replace the maximum value by 0
	Z = np.random.random(100)
	Z[Z.argmax()] = 0

16. Generate a generic 2D Gaussian-like array
	X, Y = np.meshgrid(np.linspace(-1,1,100), np.linspace(-1,1,100))
	D = np.sqrt(X*X+Y*Y)
	sigma, mu = 1.0, 0.0
	G = np.exp(-( (D-mu)**2 / ( 2.0 * sigma**2 ) ) )

17. Find the nearest value from a given value in an array
	Z.flat[np.abs(Z - z).argmin()]	

18. Consider the following file:

1,2,3,4,5
6,,,7,8
,,9,10,11

How to read it ?
	Z = genfromtxt("missing.dat", delimiter=",")