Plotting random graphs in Python

Question

I am interested in plotting a random graph in Python.

More precisely, I consider all the points (x, y) with integer coordinates, such that |x| < 30, |y| < 30 (n = 30 is just an example here).

Then, for each couple of points, I decide with probability p (we can say p = 0.5) whether these two points are connected or not.

Then, I want to plot all the segments I got. For instance, we could get something like that:

The problem I encounter is that plotting all these segments takes quite some time. And of course, it increases with both n and p.

So my question is: how can I plot quickly all these segments using pyplot ?

Thank you for your help.

EDIT: here is an example of a code that works, but that is slow.

import numpy as np
import matplotlib.pyplot as plt

for i in range(101):
    for j in range(101):
        if i < 100:
            if np.random.uniform() < 1/2:
                plt.plot([i - 50, i - 50 + 1], [j - 50, j - 50], "r")
        if j < 100:
            if np.random.uniform() < 1/2:
                plt.plot([i - 50, i - 50], [j - 50, j - 50 + 1], "r")
                
plt.show()

Answer 1

You have not provided a source code, but I guess that you are generating your segments, and then plot them separately using plt.plot(point_A, point_B)

You should consider one of the following solutions:

1. Plot your graph as a 2D image

Instead of considering your graph as a list of segments, we consider it as a 2d matrix of connections, and simply plot that matrix once.

E.g.

segments = ... # you generate this list of pairs (POINT_A, POINT_B) that define your segments using your algorithm

matrix = np.zeros((30, 30))
for ((xA, yA), (xB, yB)) in segments:
   # we re-order coordinates in order to always have A inferior to B
   if yA > yB:
      yA, yB = yB, yA
   if xA > xB:
      xA, xB = xB, xA

   # we mark all points belonging to the segment [A, B] as True in the matrix
   matrix[yA:yB+1, xA:xB+1] = 1
plt.imshow(matrix)

I have not tested the code and provide it just to illustrate the idea.

2. Use lineCollections from matplotlib

Best described here: https://stackoverflow.com/a/21357666/5728381