Iterative array creation in matlab and python

Question

I am trying to convert a snippet of MATLAB code into python, the MATLAB code is as follows:

M = 0;
for k=1:i
    M = [M,        M,      M;
    M, ones(3^(k-1)), M;
    M,        M,      M];
end

which creates a 2d array that mimics a sierpinski carpet
my python implementation is as such:

M = 0   
for x in range(1,count):
        square = np.array([[M, M, M], [M, np.ones([3**(x-1),3**(x-1)]), M], [M, M, M]])

I know I am missing something with the nature of how the arrays are concatenated, since my python output is coming up with more than two dimensions. How would I maintain a 2d array that creates the same output?

See this

Abhijeetk431
– Abhijeetk431

2018-03-01 08:48:50 +00:00
Commented Mar 1, 2018 at 8:48 — Abhijeetk431
– Abhijeetk431, Commented Mar 1, 2018 at 8:48

kuzand · Accepted Answer · 2018-03-01 11:21:34Z

2

You can use block()

import numpy as np
M = 0
for k in range(count):
    I = np.ones((3**k, 3**k))
    M = np.block([[M, M, M],
                  [M, I, M],
                  [M, M, M]])

For example, for count = 4, you get the following output (plotted with matplotlib -- if you are interested in making such plots let me know):

edited Mar 1, 2018 at 11:21

answered Mar 1, 2018 at 9:20

kuzand

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1 Comment

hpaulj Over a year ago

This block is relatively new, 1.13.0. Previously we had to use bmat or a set of nested concatenate calls. It works by recursively calling concatenate on the nested list.

Kardi Teknomo · Accepted Answer · 2018-03-01 09:41:56Z

0

You need to set the M as two dimensional array first, then you can use concatenate, based on its axis. Next, in python, range is up to count exclusive. Thus, you need to add 1.

Your code is modified into the following:

import numpy as np
import matplotlib.pylab as plt

def sierpinski(count=3):
    M = np.array([[0]])   
    for x in range(1,count+1):
        M = np.concatenate((np.concatenate((M, M, M), axis=1),
            np.concatenate((M, np.ones([3**(x-1),3**(x-1)]), M), axis=1),
            np.concatenate((M, M, M), axis=1)),axis=0)
    return M

# run the code
M=sierpinski()
plt.spy(M)
plt.show()

When you run it produces beautiful Sierpinski Gasket:

answered Mar 1, 2018 at 9:41

Kardi Teknomo

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