Visualization I

Objectives

  1. Recognize the basics of matplotlib figure elements.
  2. Create a basic line plot, add labels, and grid lines to the plot.
  3. Plot multiple series of data.
  4. Plot imshow, contour, and filled contour (contourf) plots.
  5. Introduction to cartopy and making maps.

Getting started with matplotlib

Use a magic function to set the backend of matplotlib to the ‘inline’ backend. This makes the matplotlib plots appear inline as images.

Import matplotlib’s pyplot interface as well as numpy.

%matplotlib inline

Import matplotlib’s pyplot interface as well as numpy.

import matplotlib.pyplot as plt
import numpy as np

Generate some data to use while experimenting with plotting:

times = np.array([ 93.,  96.,  99., 102., 105., 108., 111., 114., 117.,
                  120., 123., 126., 129., 132., 135., 138., 141., 144.,
                  147., 150., 153., 156., 159., 162.])
temps = np.array([310.7, 308.0, 296.4, 289.5, 288.5, 287.1, 301.1, 308.3,
                  311.5, 305.1, 295.6, 292.4, 290.4, 289.1, 299.4, 307.9,
                  316.6, 293.9, 291.2, 289.8, 287.1, 285.8, 303.3, 310.])

Create a figure

fig = plt.figure(figsize=(10, 6))

# get the first axis in a 1x1 grid of axes
ax = fig.add_subplot(1, 1, 1) #nrows=1, ncols=1, index=1)

# Plot a dotted line with x=time and y=temps
ax.plot(times, temps, '.-')

[<matplotlib.lines.Line2D at 0x1128f4898>]

png

Add some labels to the plot

ax.set_xlabel('Time')
ax.set_ylabel('Temperature')

# add a title
ax.set_title('GFS Temperature Forecast', fontdict={'size': 16})

# Prompt the notebook to re-display the figure after we modify it
fig

png

imshow/contour

  • imshow displays the values in an array as colored pixels, similar to a heat map.
  • contour creates contours around data.
  • contourf creates filled contours around data.

First let’s create some fake data to work with - let’s use a bivariate normal distribution.

x = y = np.arange(-3.0, 3.0, 0.025)
X, Y = np.meshgrid(x, y)
Z1 = np.exp(-X**2 - Y**2)
Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
Z = (Z1 - Z2) * 2

Create a filled contour plot

The cmap argument specifies the colormap to use. We can also specify the contour levels.

fig = plt.figure(figsize=(10, 4))

dz = 0.5
ax = fig.add_subplot(1, 2, 1)
c1 = ax.contourf(X, Y, Z, cmap='PiYG', levels=np.arange(-2., 2+dz, dz))
plt.colorbar(c1)

dz = 0.1
ax = fig.add_subplot(1, 2, 2)
c2 = ax.contourf(X, Y, Z, cmap='PiYG', levels=np.arange(-2., 2+dz, dz))
plt.colorbar(c2)

<matplotlib.colorbar.Colorbar at 0x11518e0f0>

png