Figures

High quality figures are important for scientific reporting, and it is recommended that you export your plots in PDF format for inclusion in your report. The code below will generate a PDF file and save it in the same folder as your code.

import matplotlib.pyplot as plt
import numpy as np

x = np.array([[0, 1, 0],
              [1, 2, 1],
              [2, 3, 2]])

fig = plt.figure()
plt.imshow(x)
fig.savefig("square_map.pdf")

Below shows one way to annotate your plot with a discrete colorbar which acts as a legend.

# use plt.cm.get_cmap(cmap, N) to get an N-bin version of cmap
fig = plt.figure()
plt.imshow(x, cmap=plt.cm.get_cmap('Greens', 3))

# We must be sure to specify the ticks matching our target names
plt.colorbar(ticks=[0, 1, 2])

# Set the clim so that labels are centred on each block
plt.clim(-0.5, 2.5)

fig.savefig("square_map_green.pdf")

Matplotlib is very powerful, and it can be complicated, so you’ll need to dedicate some time to finessing your plots! See the week 5 notes for one way to make subplots.

The online Matplotlib documentation has plenty of examples of a variety of plotting methods.