Question

I have a numpy ndarray that looks something like:

[[0, 0.25, 1,  ...., 0.5, 0.23 ],
 [0.3, 0.75, 1, ..., 0.5, 0.37 ],
  ...,
  ...,
 [0, 0.25, 1,  ...., 0.5, 0.23 ],
 [0.3, 0.75, 1, ..., 0.5, 0.37 ]]

Basically every value is in the range 0 – 1.0

I would like to visualize this as a bitmap and currently I have a very slow loop which basically does this:

for i, row in enumerate(data):
    for j, val in enumerate(row):
        yield val_to_rgb(val)

It then will take the 3-tuple of rgb components and do a PIL putdata on it and create a PNG.

I need to do this many times and this ghetto method is slow, and the colorization is very ugly.

My question is this:

Is there a series of matrix operations I can apply which will yield a colorized matrix containing the raw RGB values?

Which really consists of two questions:

1. What is the most efficient transformation I can apply to get RGB tuples from the above matrix
2. Is there a “nice” way to convert (0, 1.0) values into a colorized representation?

Edit: Clarification- I’m looking to SAVE this as PNG, not just view it in real time. The reason being that a lot of this is getting executed on a headless machine which I then inspect after the fact.

The output of the current algo looks pretty nasty:

Answer 1

matplotlib has imshow function you can use out-of-the-box.

What you’re doing is usually done via “vectorization”. You define a function and let numpy do the iteration:

vec = np.vectorize(val_to_rgb)
rgb_data = vec(data)