MatLab vs Python - Vectorizing and reshaping arrays

Question

Consider three different MatLab arrays: a, b and c. All of them are equally sized arrays. Let's say, 64 x 64. Now, in order to re-organize the elements of one of these arrays in form of a one-dimension vector inside another array, X, I can do the following:

X = [X a(:)];

Now, I have a 4096 x 1 array. If I want to have an array with that contains in each column the elements of different arrays, I can just repeat the process above for b and c.

Is there an equivalent of this in Python?

numpy.ndarray.flatten() and numpy.ndarray.vstack()? And this answer has a comparison of ways to flatten a 2D array in python. — eugenhu
– eugenhu, Commented Apr 3, 2018 at 6:47
size(a(:))` is (4096,1). A column vector. Not quite the same as a 1d numpy array. [a(:), b(:), c(:)] will be (4096,3). In numpy do you want (3, 4096) or (4096,3) array? numpy is C ordered (by default), MATLAB F ordered. — hpaulj
– hpaulj, Commented Apr 3, 2018 at 7:12
I want a (4096,3) array. Thanks for the replies, they helped me solving my problem! I used np.vstack and that gave me an (3,4096) array. Then just got the tranpose of the resulting array and it worked! Thanks! X=np.vstack((X,Itmp.flatten(1))) X=np.transpose(X) — Gabriel Costa
– Gabriel Costa, Commented Apr 3, 2018 at 7:28

ibezito · Accepted Answer · 2018-04-03 07:01:11Z

1

You can use np.concatanate function. Example:

import numpy as np
a = np.array([1,2,3])
b = np.array([4,5,6])
c = np.array([7,8,9])
res = np.concatenate([a,b,c])

It is also possible to do it sequentially as follows:

res = a
res = np.concatenate([res,b])
res = np.concatenate([res,c])

result:

res = array([1, 2, 3, 4, 5, 6, 7, 8, 9])

answered Apr 3, 2018 at 7:01

ibezito

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Comments

Salman Ghauri · Accepted Answer · 2018-04-03 07:07:50Z

0

In order to achieve 4x1, you can use reshape() function is this way:

np.reshape((-1, 1))

a = np.zeros((2,2)) #creates 2,2 matrix
print(a.shape) #2,2
print(a.reshape((-1, 1)) #4,1

This will make sure that you achieve 1 column in resulting array irrespective of the row elements which is set to -1.

As mentioned in the comment, you can use numpy's flatten() function which make you matrix flat into a vector. E.g; if you have a 2x2 matrix, flatten() will make it to 1x4 vector.

a = np.zeros((2,2)) # creates 2,2 matrix

print(a.shape) # 2,2
print(a.flatten()) # 1,4

answered Apr 3, 2018 at 7:07

Salman Ghauri

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