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.
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 ...