CODEWITHSUNDEEP
numpy
Fomalhaut
Fomalhaut

Reputation: 9745

How to add dimensions to an array as the result of multiplication in numpy?

I have a following numpy array:

a = np.array([[0, 1], [1, 0]])

What should I do with it to make it like this:

np.array([[[0, 0, 0], [255, 255, 255]], [[255, 255, 255], [0, 0, 0]]])

Each 0 is transformed to [0, 0, 0] and each 1 to [255, 255, 255].

I have tried different ways of multiplication but it didn't help.

I need such transformation to be as fast as possible, because a is supposed to have million elements and I want to store them into an image, so I need to prepare raw data for PIL.Image.fromarray. I want exactly RGB format because after the transformation over a I add some colored extra pixels in certain coordinaes.

Upvotes: 0

Views: 28

Answers (2)

rudolfovic
rudolfovic

Reputation: 3276

You could either replace each of the values with a [[[256, 256, 256]]] or [[[0, 0, 0]]]:

np.where(a.reshape(*a.shape, 1), np.full([1, 1, 3], 255), np.full([1, 1, 3], 0))

Or repeat the entire array 3 times and then reshape accordingly:

np.repeat(a * 255, 3).reshape(*a.shape, 3)

Upvotes: 1

obchardon
obchardon

Reputation: 10792

With basic array indexing you can achieve what you want:

# Example input:
a = np.array([[0, 1], [1, 0]])             # Your index
x = np.array([[0, 0, 0], [255, 255, 255]]) # The mapped array 

# Get the result:
r = x[a]

Upvotes: 2

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