Creating 2d histogram from 2d numpy array

Question

I have a numpy array like this:

[[[0,0,0], [1,0,0], ..., [1919,0,0]],
[[0,1,0], [1,1,0], ..., [1919,1,0]],
...,
[[0,1019,0], [1,1019,0], ..., [1919,1019,0]]]

To create I use function (thanks to @Divakar and @unutbu for helping in other question):

def indices_zero_grid(m,n):
     I,J = np.ogrid[:m,:n]
     out = np.zeros((m,n,3), dtype=int)
     out[...,0] = I
     out[...,1] = J
     return out

I can access this array by command:

>>> out = indices_zero_grid(3,2)
>>> out
array([[[0, 0, 0],
        [0, 1, 0]],

       [[1, 0, 0],
        [1, 1, 0]],

       [[2, 0, 0],
        [2, 1, 0]]])
>>> out[1,1]
array([1, 1,  0])

Now I wanted to plot 2d histogram where (x,y) (out[(x,y]) is the coordinates and the third value is number of occurrences. I've tried using normal matplotlib plot, but I have so many values for each coordinates (I need 1920x1080) that program needs too much memory.

That is practically a screen resolution. If your screen has that resolution each bar will be a pixel. Are you sure that this is what you want? Why not make an image (imshow) plot or a surface? — armatita
– armatita, Commented Oct 4, 2017 at 8:05
@armatita Yes, this is screen resolution. But I need histogram because I wanted to create some kind of map of activity. Third element of each coordinates is a repetition level of each pixel in my specific application. — wnukers
– wnukers, Commented Oct 4, 2017 at 8:15
Consider adding a minimal case and the expected output. To create the minimal one, you can use something like indices_zero_grid(3,4) from your previous question's answer. — Divakar
– Divakar, Commented Oct 4, 2017 at 8:22

unutbu · Accepted Answer · 2017-10-05 22:01:16Z

1

If I understand correctly, you want an image of size 1920x1080 which colors the pixel at coordinate (x, y) according to the value of out[x, y].

In that case, you could use

import numpy as np
import matplotlib.pyplot as plt

def indices_zero_grid(m,n):
     I,J = np.ogrid[:m,:n]
     out = np.zeros((m,n,3), dtype=int)
     out[...,0] = I
     out[...,1] = J
     return out

h, w = 1920, 1080
out = indices_zero_grid(h, w)
out[..., 2] = np.random.randint(256, size=(h, w))
plt.imshow(out[..., 2])
plt.show()

which yields

Notice that the other two "columns", out[..., 0] and out[..., 1] are not used. This suggests that indices_zero_grid is not really needed here.

plt.imshow can accept an array of shape (1920, 1080). This array has a scalar value at each location in the array. The structure of the array tells imshow where to color each cell. Unlike a scatter plot, you don't need to generate the coordinates yourself.

answered Oct 5, 2017 at 22:01

unutbu

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1 Comment

wnukers Over a year ago

You're right! This solution works perfectly. I don't really need the other two "columns" and it works faster than previous. I didn't think about it. Thanks You very much!

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