Customizing Matplotlib Axes and Colorbars

Introduction

Matplotlib is a Python library that enables the creation of static, animated, and interactive visualizations in Python. It is widely used in scientific computing, data analysis, machine learning, and more. In this lab, you will learn how to plot images using Matplotlib and how to manipulate the location of axes and colorbars.

VM Tips

After the VM startup is done, click the top left corner to switch to the Notebook tab to access Jupyter Notebook for practice.

Sometimes, you may need to wait a few seconds for Jupyter Notebook to finish loading. The validation of operations cannot be automated because of limitations in Jupyter Notebook.

If you face issues during learning, feel free to ask Labby. Provide feedback after the session, and we will promptly resolve the problem for you.

Import Libraries

In this step, we will import the necessary libraries that will be used in this lab. We will be using matplotlib.pyplot and cbook from matplotlib to get a sample image.

import matplotlib.pyplot as plt
from matplotlib import cbook

Get Demo Image

In this step, we will define a function to get a demo image and its extent. We will be using get_sample_data() function from cbook to get a sample image.

def get_demo_image():
    z = cbook.get_sample_data("axes_grid/bivariate_normal.npy")  ## 15x15 array
    return z, (-3, 4, -4, 3)

Simple Image and Colorbar

In this step, we will create a simple image and its colorbar. We will be using imshow() function from pyplot to create the image, and colorbar() function to create the colorbar.

def demo_simple_image(ax):
    Z, extent = get_demo_image()

    im = ax.imshow(Z, extent=extent)
    cb = plt.colorbar(im)
    cb.ax.yaxis.set_tick_params(labelright=False)

Image and Colorbar with Draw-Time Positioning - A Hard Way

In this step, we will create an image and its colorbar with draw-time positioning in a hard way. We will be using SubplotDivider from mpl_toolkits.axes_grid1 to create a divider for the axes and colorbar.

def demo_locatable_axes_hard(fig):
    from mpl_toolkits.axes_grid1 import Size, SubplotDivider

    divider = SubplotDivider(fig, 2, 2, 2, aspect=True)

    ## axes for image
    ax = fig.add_subplot(axes_locator=divider.new_locator(nx=0, ny=0))
    ## axes for colorbar
    ax_cb = fig.add_subplot(axes_locator=divider.new_locator(nx=2, ny=0))

    divider.set_horizontal([
        Size.AxesX(ax),  ## main axes
        Size.Fixed(0.05),  ## padding, 0.1 inch
        Size.Fixed(0.2),  ## colorbar, 0.3 inch
    ])
    divider.set_vertical([Size.AxesY(ax)])

    Z, extent = get_demo_image()

    im = ax.imshow(Z, extent=extent)
    plt.colorbar(im, cax=ax_cb)
    ax_cb.yaxis.set_tick_params(labelright=False)

Image and Colorbar with Draw-Time Positioning - An Easy Way

In this step, we will create an image and its colorbar with draw-time positioning in an easy way. We will be using make_axes_locatable from mpl_toolkits.axes_grid1 to create a divider for the axes and colorbar.

def demo_locatable_axes_easy(ax):
    from mpl_toolkits.axes_grid1 import make_axes_locatable

    divider = make_axes_locatable(ax)

    ax_cb = divider.append_axes("right", size="5%", pad=0.05)
    fig = ax.get_figure()
    fig.add_axes(ax_cb)

    Z, extent = get_demo_image()
    im = ax.imshow(Z, extent=extent)

    plt.colorbar(im, cax=ax_cb)
    ax_cb.yaxis.tick_right()
    ax_cb.yaxis.set_tick_params(labelright=False)

Two Images Side by Side with Fixed Padding

In this step, we will create two images side by side with fixed padding. We will be using make_axes_locatable from mpl_toolkits.axes_grid1 to create a divider for the axes and colorbar.

def demo_images_side_by_side(ax):
    from mpl_toolkits.axes_grid1 import make_axes_locatable

    divider = make_axes_locatable(ax)

    Z, extent = get_demo_image()
    ax2 = divider.append_axes("right", size="100%", pad=0.05)
    fig1 = ax.get_figure()
    fig1.add_axes(ax2)

    ax.imshow(Z, extent=extent)
    ax2.imshow(Z, extent=extent)
    ax2.yaxis.set_tick_params(labelleft=False)

Plotting

In this step, we will create a figure and add subplots for each image that we want to create.

def demo():
    fig = plt.figure(figsize=(6, 6))

    ## PLOT 1
    ## simple image & colorbar
    ax = fig.add_subplot(2, 2, 1)
    demo_simple_image(ax)

    ## PLOT 2
    ## image and colorbar with draw-time positioning -- a hard way
    demo_locatable_axes_hard(fig)

    ## PLOT 3
    ## image and colorbar with draw-time positioning -- an easy way
    ax = fig.add_subplot(2, 2, 3)
    demo_locatable_axes_easy(ax)

    ## PLOT 4
    ## two images side by side with fixed padding.
    ax = fig.add_subplot(2, 2, 4)
    demo_images_side_by_side(ax)

    plt.show()

Summary

In this lab, we learned how to plot images using Matplotlib and how to manipulate the location of axes and colorbars. We covered different ways of creating images and colorbars and how to position them in the figure. With the knowledge gained from this lab, you will be able to create more complex visualizations and manipulate them to suit your needs.