Customizing Matplotlib Axis Direction

Introduction

Matplotlib is a popular data visualization library in Python. It provides a wide variety of options for customizing plots and charts. In this lab, we will explore how to set the axis direction in Matplotlib using the mpl_toolkits.axisartist module.

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

Before we begin, we need to import the necessary libraries. In this lab, we will be using matplotlib.pyplot and mpl_toolkits.axisartist.

import matplotlib.pyplot as plt
import mpl_toolkits.axisartist as axisartist

Create a Function to Set Up Axes

We will create a function called setup_axes to set up the axes for our plots. This function takes in two parameters, a fig object and a pos object. The fig object is the figure object that we will be plotting on, and the pos object is the position of the subplot within the figure.

def setup_axes(fig, pos):
    ax = fig.add_subplot(pos, axes_class=axisartist.Axes)

    ax.set_ylim(-0.1, 1.5)
    ax.set_yticks([0, 1])

    ax.axis[:].set_visible(False)

    ax.axis["x"] = ax.new_floating_axis(1, 0.5)
    ax.axis["x"].set_axisline_style("->", size=1.5)

    return ax

Set Up Axis Direction

We will now create a figure object and set up the axis direction for our plots. We will create five different subplots to demonstrate different axis directions.

plt.rcParams.update({
    "axes.titlesize": "medium",
    "axes.titley": 1.1,
})

fig = plt.figure(figsize=(10, 4))
fig.subplots_adjust(bottom=0.1, top=0.9, left=0.05, right=0.95)

ax1 = setup_axes(fig, 251)
ax1.axis["x"].set_axis_direction("left")

ax2 = setup_axes(fig, 252)
ax2.axis["x"].label.set_text("Label")
ax2.axis["x"].toggle(ticklabels=False)
ax2.axis["x"].set_axislabel_direction("+")
ax2.set_title("label direction=$+$")

ax3 = setup_axes(fig, 253)
ax3.axis["x"].label.set_text("Label")
ax3.axis["x"].toggle(ticklabels=False)
ax3.axis["x"].set_axislabel_direction("-")
ax3.set_title("label direction=$-$")

ax4 = setup_axes(fig, 254)
ax4.axis["x"].set_ticklabel_direction("+")
ax4.set_title("ticklabel direction=$+$")

ax5 = setup_axes(fig, 255)
ax5.axis["x"].set_ticklabel_direction("-")
ax5.set_title("ticklabel direction=$-$")

ax7 = setup_axes(fig, 257)
ax7.axis["x"].label.set_text("rotation=10")
ax7.axis["x"].label.set_rotation(10)
ax7.axis["x"].toggle(ticklabels=False)

ax8 = setup_axes(fig, 258)
ax8.axis["x"].set_axislabel_direction("-")
ax8.axis["x"].label.set_text("rotation=10")
ax8.axis["x"].label.set_rotation(10)
ax8.axis["x"].toggle(ticklabels=False)

plt.show()

Interpretation of Results

The code will produce a figure with five subplots that demonstrate different axis directions. The following is a summary of the subplots:

1. Subplot 1: The axis direction is set to left.
2. Subplot 2: The axis label direction is set to positive.
3. Subplot 3: The axis label direction is set to negative.
4. Subplot 4: The tick label direction is set to positive.
5. Subplot 5: The tick label direction is set to negative.

Summary

In this lab, we learned how to set the axis direction in Matplotlib using the mpl_toolkits.axisartist module. We created a function to set up the axes for our plots and demonstrated different axis directions using multiple subplots. This is a useful tool for customizing plots and charts in Matplotlib.