Visualizing nonuniform 2D data

So far, we have assumed that we have uniformly sampled 2D data; our data is sampled with a grid pattern. However, nonuniformly sampled data is very common. For instance, we might want to visualize measurements from weather stations. Weather stations are built wherever it is possible; they are laid out into a perfect grid. When sampling functions, we might use a sophisticated sampling process (adaptive sampling, quasi-random sampling, and so on) which does not produce grid layouts. Here, we show a simple way to deal with such 2D data.

How to do it...

The script draws the Mandelbrot set sampled from the same square as in the previous recipes. However, instead of using a regular grid sampling, we randomly sample the Mandelbrot set, as shown in the following example:

import numpy as np
from numpy.random import uniform, seed 

from matplotlib import pyplot as plt 
from matplotlib.mlab import griddata 
import matplotlib.cm as cm 

def iter_count(C, max_iter): 
  X ...