Matplotlib Marker Customization Tutorial

Introduction

This tutorial will guide you through the process of using the markevery property of Line2D to draw markers at a subset of data points in Matplotlib. We will cover various ways to specify the markers, including using integers, tuples, lists, slices, and floats. We will also explore how markevery behaves with linear and logarithmic scales, as well as with zoomed and polar plots.

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Define the Data Points

First, we define the data points that we will use for our plots. In this example, we use numpy to generate a set of x and y values for a sine wave.

import matplotlib.pyplot as plt
import numpy as np

## define a list of markevery cases to plot
cases = [
    None,
    8,
    (30, 8),
    [16, 24, 32],
    [0, -1],
    slice(100, 200, 3),
    0.1,
    0.4,
    (0.2, 0.4)
]

## data points
delta = 0.11
x = np.linspace(0, 10 - 2 * delta, 200) + delta
y = np.sin(x) + 1.0 + delta

Create Plots with Linear Scales

Next, we create a set of subplots to show how markevery behaves with linear scales. We iterate through the cases list and plot each case on a separate subplot. We use the markevery parameter to specify which data points to mark.

## create plots with linear scales
fig, axs = plt.subplots(3, 3, figsize=(10, 6), layout='constrained')
for ax, markevery in zip(axs.flat, cases):
    ax.set_title(f'markevery={markevery}')
    ax.plot(x, y, 'o', ls='-', ms=4, markevery=markevery)

Create Plots with Logarithmic Scales

We repeat the previous step, but this time with logarithmic scales. We note that the logarithmic scale causes a visual asymmetry in the marker distance for integer-based subsampling, while fraction-based subsampling creates even distributions.

## create plots with logarithmic scales
fig, axs = plt.subplots(3, 3, figsize=(10, 6), layout='constrained')
for ax, markevery in zip(axs.flat, cases):
    ax.set_title(f'markevery={markevery}')
    ax.set_xscale('log')
    ax.set_yscale('log')
    ax.plot(x, y, 'o', ls='-', ms=4, markevery=markevery)

Create Zoomed Plots

We create another set of subplots, this time to show how markevery behaves on zoomed plots. We note that integer-based subsampling selects points from the underlying data and is independent of the view, while float-based subsampling is related to the Axes diagonal and changes the displayed data range.

## create zoomed plots
fig, axs = plt.subplots(3, 3, figsize=(10, 6), layout='constrained')
for ax, markevery in zip(axs.flat, cases):
    ax.set_title(f'markevery={markevery}')
    ax.plot(x, y, 'o', ls='-', ms=4, markevery=markevery)
    ax.set_xlim((6, 6.7))
    ax.set_ylim((1.1, 1.7))

Create Polar Plots

Finally, we create a set of subplots to show how markevery behaves on polar plots. We note that the behavior is similar to that on linear scales.

## create polar plots
r = np.linspace(0, 3.0, 200)
theta = 2 * np.pi * r

fig, axs = plt.subplots(3, 3, figsize=(10, 6), layout='constrained',
                        subplot_kw={'projection': 'polar'})
for ax, markevery in zip(axs.flat, cases):
    ax.set_title(f'markevery={markevery}')
    ax.plot(theta, r, 'o', ls='-', ms=4, markevery=markevery)

Summary

In this tutorial, we learned how to use the markevery property of Line2D to draw markers at a subset of data points in Matplotlib. We explored various ways to specify the markers, including using integers, tuples, lists, slices, and floats. We also saw how markevery behaves with linear and logarithmic scales, as well as with zoomed and polar plots.