如何 plot 一个看起来像散点的零一二维矩阵？

Question

Might be a strange question, but I am wondering if it's possible to replace a 2d matrix made up of ones and zeros with a scatter plot of say, black dots where all the ones are but nothing for zeros:

Unfortunately I don't have the best reproducible answer, but I have a 2D array made up for zeros and ones (size 275 and 357):

I am hoping to basically cover the areas that are made up of ones with small black dots (assuming in the form of a scatter plot which will later be overlayed on another contour plot):

The original contour plot is on the left and the idea I'm going for is on the right (picture more black dots just on the areas made up of ones):

I tried making a reproducible array here:

#array of ones and zeros 
array = np.array(([0,0,0,0,0,0,1,1,0,0,0,1,1,1], [0,1,0,0,0,1,0,0,0,0,0,1,0,1]))

plt.pcolormesh(array)

I tried using this as an example and apply it to the 2D array, but getting some errors?

# as an example, borrowed from: https://stackoverflow.com/questions/41133419/how-to-do-the-scatter-plot-for-the-lists-or-2d-array-or-matrix-python
X=[[0,3,4,0,1,1],
    [0,0,0,5,1,1],
    [6,7,0,8,1,1],
    [3,6,1,5,6,1]]

Y=[12,15,11,10]

x_arr = np.array(X)
y = np.array(Y)

fig, ax = plt.subplots()

#colors=list('bgrcmykw')

for i, x in enumerate(x_arr.T):
    ax.scatter(x,y, c='k',s=5)

plt.show()

My goal is to basically convert this 2d matrix made up of ones and zeros to a scatter plot or some sort of graph where the ones are made up of black dots and the zeros have nothing. This will later be overlaid on another contour plot. how might I go about setting the ones to a scatter plot made up of black dots?

Answer 1

Here's what I would do. I didn't plot all the points to reduce the computational demand of creating the figure. you might want to do that if you have a lot of points to plot. either way, you can change that according to your need.

import numpy as np
from matplotlib import pyplot as plt
np.random.seed(0)
mask = np.random.randint(0, 2, (20, 20))
ys, xs = np.where(mask.astype(bool))

plt.imshow(mask)
plt.scatter(xs[::2], ys[::2])

output: