Replace elements in Numpy array by value and location

Question

I am working on a program which will create contour data out of numpy arrays, and trying to avoid calls to matplotlib.

I have an array of length L which contains NxN arrays of booleans. I want to convert this into an LxNxN array where, for example, the "True"s in the first inner array get replaced by "red", in the second, by "blue" and so forth.

The following code works as expected:

import numpy as np
import pdb

def new_layer(N,p):
    return np.random.choice(a=[False,True],size=(N,N),p=[p,1-p])

a = np.array([new_layer(3,0.5),new_layer(3,0.5),new_layer(3,0.5)]).astype('object')

colors = np.array(["red","green","blue"])

for i in range(np.shape(a)[0]):
    b = a[i]
    b[np.where(b==True)] = colors[i]
    a[i] = b
print(a)

But I am wondering if there is a way to accomplish the same using Numpy's built-in tools, e.g., indexing. I am a newcomer to Numpy and I suspect there is a better way to do this but I can't think what it would be. Thank you.

Paul Panzer · Accepted Answer · 2018-10-04 18:11:11Z

2

You could use np.copyto:

np.copyto(a, colors[:, None, None], where=a.astype(bool))

answered Oct 4, 2018 at 18:11

Paul Panzer

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Divakar · Accepted Answer · 2018-10-04 18:05:59Z

0

Here's one way -

a_bool = a.astype(bool)
a[a_bool] = np.repeat(colors,a_bool.sum((1,2)))

Another with extending colors to 3D -

a_bool = a.astype(bool)
colors3D = np.broadcast_to(colors[:,None,None],a.shape)
a[a_bool] = colors3D[a_bool]

answered Oct 4, 2018 at 18:05

Divakar

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Comments

fvizeus · Accepted Answer · 2018-10-04 18:17:30Z

0

You can use a combination of boolean indexes and np.indices. Also you can use a as index to itself. Then you could do what you did in the for loop with this line (although I don't think it necessarily is a good idea):

a[a.astype(bool)] = colors[np.indices(a.shape)[0][a.astype(bool)]]

Also, for the new_layer function you could just use np.random.rand(N,N) > p (not sure if the actual distribution will be exactly the same as what you had).

answered Oct 4, 2018 at 18:17

fvizeus

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