Create Multicolored Matplotlib Lines

Introduction

In this lab, we will learn how to create multicolored lines in Matplotlib. We will use the LineCollection function to create a set of line segments and color them individually based on their derivative. We will also learn how to use a boundary norm to color the line segments.

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Skills Graph

Import Libraries

We will start by importing the necessary libraries for this lab. We will import matplotlib.pyplot and numpy.

import matplotlib.pyplot as plt
import numpy as np

Create Data

We will create a numpy array x containing 500 evenly spaced values between 0 and 3π. We will also create another numpy array y containing the sine of the values in x. Finally, we will create a numpy array dydx containing the first derivative of y.

x = np.linspace(0, 3 * np.pi, 500)
y = np.sin(x)
dydx = np.cos(0.5 * (x[:-1] + x[1:]))

Create Line Segments

We will create a set of line segments so that we can color them individually. We will use the numpy concatenate function to concatenate two arrays points[:-1] and points[1:] along the second axis. We will then reshape the resulting array to an N x 1 x 2 array so that we can stack points together easily to get the segments. The segments array for line collection needs to be (numlines) x (points per line) x 2 (for x and y).

points = np.array([x, y]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)

Create a Continuous Norm

We will create a continuous norm to map from data points to colors. We will use the Normalize function from matplotlib.pyplot to normalize the dydx values between its minimum and maximum. We will then use the LineCollection function to create a set of line segments and color them individually based on their derivative. We will use the set_array function to set the values used for colormapping.

norm = plt.Normalize(dydx.min(), dydx.max())
lc = LineCollection(segments, cmap='viridis', norm=norm)
lc.set_array(dydx)
lc.set_linewidth(2)

Create a Colorbar

We will create a colorbar to show the mapping between colors and dydx values. We will use the colorbar function from matplotlib.pyplot to create a colorbar.

line = plt.gca().add_collection(lc)
plt.colorbar(line)

Use a Boundary Norm

We will use a boundary norm instead to color the line segments. We will create a ListedColormap containing three colors - red, green, and blue. We will then create a BoundaryNorm with boundaries -1, -0.5, 0.5, and 1, and the ListedColormap. We will use the set_array function to set the values used for colormapping.

cmap = ListedColormap(['r', 'g', 'b'])
norm = BoundaryNorm([-1, -0.5, 0.5, 1], cmap.N)
lc = LineCollection(segments, cmap=cmap, norm=norm)
lc.set_array(dydx)
lc.set_linewidth(2)

Create a Subplot

We will create a subplot to show the colored line segments. We will use the subplots function from matplotlib.pyplot to create a 2x1 grid of subplots, and the sharex and sharey parameters to share the x and y axes between the subplots.

fig, axs = plt.subplots(2, 1, sharex=True, sharey=True)
line = axs[0].add_collection(lc)
fig.colorbar(line, ax=axs[0])

Set Axis Limits

We will set the x and y axis limits for the subplots.

axs[0].set_xlim(x.min(), x.max())
axs[0].set_ylim(-1.1, 1.1)

Show the Plot

We will use the show function from matplotlib.pyplot to show the plot.

plt.show()

Summary

In this lab, we learned how to create multicolored lines in Matplotlib. We used the LineCollection function to create a set of line segments and color them individually based on their derivative. We also learned how to use a boundary norm to color the line segments. We used the Normalize function to normalize the dydx values between its minimum and maximum, and the ListedColormap function to create a colormap with three colors - red, green, and blue. We used the BoundaryNorm function to create a boundary norm with boundaries -1, -0.5, 0.5, and 1, and the ListedColormap. Finally, we used the subplots function to create a 2x1 grid of subplots, and the sharex and sharey parameters to share the x and y axes between the subplots. We then showed the plot using the show function.