Matplotlib Contour

Matplotlib Contour

Matplotlib is a popular data visualization library in Python that allows users to create a wide range of plots and charts. One of the key features of Matplotlib is the ability to create contour plots, which are often used to visualize 3D data on a 2D surface.

In this article, we will explore how to create contour plots using Matplotlib. We will cover the basics of contour plots, customization options, and advanced techniques for creating complex plots.

Basic Contour Plot

Let’s start by creating a basic contour plot using Matplotlib. In this example, we will generate a simple 2D grid of data and plot it as a contour plot.

import numpy as np
import matplotlib.pyplot as plt

# Create a 2D grid of data
x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)
Z = np.sin(X) * np.cos(Y)

# Plot the contour plot
plt.contour(X, Y, Z)
plt.show()

Output:

In this example, we first generate a grid of data using np.meshgrid and then calculate the values of the function z = sin(x) * cos(y). Finally, we plot the contour plot using plt.contour and display the plot using plt.show().

Basic Contourf Plot

Another common type of contour plot is the filled contour plot, which uses color to represent the value of the function at different points in the grid. We can create a filled contour plot using the plt.contourf function.

import numpy as np
import matplotlib.pyplot as plt

# Create a 2D grid of data
x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)
Z = np.sin(X) * np.cos(Y)

# Plot the filled contour plot
plt.contourf(X, Y, Z)
plt.colorbar()
plt.show()

Output:

In this example, we use plt.contourf to create a filled contour plot and add a colorbar to provide a visual representation of the function values.

Customizing Contour Plots

Matplotlib provides a wide range of customization options for contour plots. You can customize the colormap, contour levels, line styles, and other properties of the plot to make it more visually appealing.

Custom Colormap

You can use custom colormaps to change the colors used in the contour plot. Matplotlib provides a variety of built-in colormaps, or you can create your own colormap using the ListedColormap class.

from matplotlib.colors import ListedColormap
import numpy as np
import matplotlib.pyplot as plt

# Create a 2D grid of data
x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)
Z = np.sin(X) * np.cos(Y)

# Create a custom colormap
colors = ['red', 'green', 'blue']
cmap = ListedColormap(colors)

# Plot the contour plot with custom colormap
plt.contourf(X, Y, Z, cmap=cmap)
plt.colorbar()
plt.show()

In this example, we create a custom colormap using the ListedColormap class and use it to plot the filled contour plot with custom colors.

Contour Levels

You can customize the contour levels to adjust the number and spacing of the contour lines. You can specify the contour levels using the levels parameter in the plt.contour function.

import numpy as np
import matplotlib.pyplot as plt

# Create a 2D grid of data
x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)
Z = np.sin(X) * np.cos(Y)

# Plot the contour plot with custom levels
plt.contour(X, Y, Z, levels=[-1, 0, 1])
plt.show()

Output:

In this example, we specify the contour levels as [-1, 0, 1], which will create contour lines at -1, 0, and 1.

Line Styles

You can customize the line styles of the contour plot using the linestyles parameter in the plt.contour function. You can choose from a variety of line styles, such as solid, dashed, or dotted lines.

import numpy as np
import matplotlib.pyplot as plt

# Create a 2D grid of data
x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)
Z = np.sin(X) * np.cos(Y)

# Plot the contour plot with custom line styles
plt.contour(X, Y, Z, linestyles='dashed')
plt.show()

Output:

In this example, we use the linestyles parameter to plot the contour lines with dashed lines.

Advanced Techniques

In addition to the basic customization options, Matplotlib also provides advanced techniques for creating complex contour plots. Let’s explore some of these techniques.

Subplots

You can create subplots with multiple contour plots using the plt.subplot function. This allows you to compare different datasets or perspectives in a single figure.

import numpy as np
import matplotlib.pyplot as plt

# Create a 2D grid of data
x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)
Z = np.sin(X) * np.cos(Y)

# Create subplots with multiple contour plots
plt.subplot(2, 2, 1)
plt.contourf(X, Y, Z)
plt.colorbar()

plt.subplot(2, 2, 2)
plt.contourf(X, Y, Z, levels=[-1, 0, 1])
plt.colorbar()

plt.subplot(2, 2, 3)
plt.contour(X, Y, Z)
plt.colorbar()

plt.subplot(2, 2, 4)
plt.contour(X, Y, Z, linestyles='dotted')
plt.show()

Output:

In this example, we create a 2×2 grid of subplots with different types of contour plots.

Contour Labels

You can add labels to the contour plot to provide additional information about the data. You can customize the labels using the plt.clabel function.

import numpy as np
import matplotlib.pyplot as plt

# Create a 2D grid of data
x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)
Z = np.sin(X) * np.cos(Y)

# Plot the contour plot with custom labels
contour = plt.contour(X, Y, Z)
plt.clabel(contour)
plt.show()

Output:

In this example, we use the plt.clabel function to add labels to the contour plot.

Hidden Contour Lines

You can create a contour plot with hidden contour lines using the plt.contourf function with a low alpha value. This creates a filled contour plot with subtle contour lines in the background.

import numpy as np
import matplotlib.pyplot as plt

# Create a 2D grid of data
x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)
Z = np.sin(X) * np.cos(Y)

# Plot the filled contour plot with hidden contour lines
plt.contourf(X, Y, Z, alpha=0.3)
plt.colorbar()
plt.show()

Output:

In this example, we set the alpha parameter to 0.3 to create a filled contour plot with hidden contour lines.

Matplotlib Contour Conclusion

In this article, we explored how to create contour plots using Matplotlib. We covered the basics of contour plots, customization options, and advanced techniques for creating complex plots. With the variety of customization options and advanced techniques available in Matplotlib, you can create visually appealing contour plots to visualize your data effectively.