Layering a contourf plot and surface_plot in matplotlib

Layering a contourf plot and surface_plot in matplotlib

Layering a contourf plot and a surface_plot in Matplotlib can be achieved using the Axes3D module for creating 3D plots. Here's an example of how you can create such a visualization:

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# Generate sample data
x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)
Z1 = np.sin(np.sqrt(X**2 + Y**2))
Z2 = np.cos(X) + np.cos(Y)

# Create a figure and a 3D subplot
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

# Create surface plot
surf = ax.plot_surface(X, Y, Z1, cmap='viridis', antialiased=False)

# Create contourf plot on the same subplot
contour = ax.contourf(X, Y, Z2, zdir='z', offset=-2, cmap='inferno')

# Customize the plot
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
ax.set_title('Surface and Contourf Plot')
ax.dist = 10  # Adjust the viewpoint distance

# Add colorbars for both plots
fig.colorbar(surf, ax=ax, shrink=0.6, aspect=10)
fig.colorbar(contour, ax=ax, shrink=0.6, aspect=10)

plt.show()

In this example, the plot_surface function is used to create the surface plot, and the contourf function is used to create the filled contour plot. The zdir parameter of contourf determines the direction of the contour levels (along the z-axis in this case), and the offset parameter sets the base height of the contour plot.

You can customize the colormap, labels, title, and other aspects of the plot as needed.

Remember that layering different types of plots in 3D can sometimes lead to complex visualizations that might not always be easy to interpret. Adjusting the viewpoint, colors, and other parameters can help improve the readability of the plot.

Examples

  1. How to overlay contourf plot on a surface_plot in matplotlib?

    Description: This query seeks to understand how to combine a contourf plot with a surface_plot in Matplotlib, allowing for a layered visualization of data.

    import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# Generate data
x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)
Z = np.sin(np.sqrt(X**2 + Y**2))

# Create surface plot
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(X, Y, Z, cmap='viridis')

# Overlay contourf plot
ax.contourf(X, Y, Z, zdir='z', offset=-2, cmap='coolwarm', alpha=0.5)

plt.show()

    This code generates a surface plot of a 3D function and overlays a contourf plot on it, showcasing the data in both 3D and with contour lines.

  2. Matplotlib contourf plot over surface_plot with colorbar

    Description: This query delves into adding a colorbar to the contourf plot overlaid on a surface_plot for better visualization and understanding of data distribution.

    import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# Generate data
x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)
Z = np.sin(np.sqrt(X**2 + Y**2))

# Create surface plot
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
surf = ax.plot_surface(X, Y, Z, cmap='viridis')

# Overlay contourf plot
cset = ax.contourf(X, Y, Z, zdir='z', offset=-2, cmap='coolwarm', alpha=0.5)
fig.colorbar(cset)

plt.show()

    This code adds a colorbar to the contourf plot overlaid on the surface plot, aiding in interpreting the data distribution.

  3. How to adjust transparency of contourf plot in matplotlib?

    Description: This query focuses on adjusting the transparency or opacity of the contourf plot overlaid on a surface_plot to control its visibility and blending with the underlying plot.

    import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# Generate data
x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)
Z = np.sin(np.sqrt(X**2 + Y**2))

# Create surface plot
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(X, Y, Z, cmap='viridis')

# Overlay contourf plot with adjusted transparency
ax.contourf(X, Y, Z, zdir='z', offset=-2, cmap='coolwarm', alpha=0.3)

plt.show()

    This code adjusts the transparency of the contourf plot overlaid on the surface plot to 0.3, allowing some visibility of the underlying surface.

  4. How to change colormap of contourf plot in matplotlib?

    Description: This query aims to understand how to change the colormap of the contourf plot overlaid on a surface_plot to customize the visualization according to specific preferences.

    import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# Generate data
x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)
Z = np.sin(np.sqrt(X**2 + Y**2))

# Create surface plot
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(X, Y, Z, cmap='viridis')

# Overlay contourf plot with custom colormap
ax.contourf(X, Y, Z, zdir='z', offset=-2, cmap='inferno')

plt.show()

    This code changes the colormap of the contourf plot overlaid on the surface plot to 'inferno', providing a different color scheme for visualizing the data.

  5. How to add contours to surface_plot in matplotlib?

    Description: This query explores adding contour lines to a surface_plot in Matplotlib, enhancing the visualization by showing data variations in the third dimension.

    import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# Generate data
x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)
Z = np.sin(np.sqrt(X**2 + Y**2))

# Create surface plot with contours
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(X, Y, Z, cmap='viridis')
ax.contour(X, Y, Z, zdir='z', offset=-2, cmap='coolwarm')

plt.show()

    This code adds contour lines to the surface plot, providing additional insights into the data distribution in the third dimension.

  6. Matplotlib surface_plot with contour lines and filled contours

    Description: This query looks for a combined visualization of surface_plot with both contour lines and filled contour areas for a comprehensive understanding of the data distribution.

    import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# Generate data
x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)
Z = np.sin(np.sqrt(X**2 + Y**2))

# Create surface plot with contours
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(X, Y, Z, cmap='viridis')

# Overlay contour lines and filled contours
ax.contour(X, Y, Z, zdir='z', offset=-2, cmap='coolwarm')
ax.contourf(X, Y, Z, zdir='z', offset=-2, cmap='coolwarm', alpha=0.5)

plt.show()

    This code generates a surface plot with both contour lines and filled contour areas, offering a comprehensive view of the data distribution.

  7. How to control contour levels in matplotlib?

    Description: This query seeks to understand how to control the number and spacing of contour levels in a contourf plot overlaid on a surface_plot for better visualization customization.

    import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# Generate data
x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)
Z = np.sin(np.sqrt(X**2 + Y**2))

# Create surface plot
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(X, Y, Z, cmap='viridis')

# Overlay contourf plot with specified contour levels
levels = np.linspace(-1, 1, 10)  # Define contour levels
ax.contourf(X, Y, Z, zdir='z', offset=-2, cmap='coolwarm', levels=levels)

plt.show()

    This code controls the contour levels of the contourf plot overlaid on the surface plot by specifying custom levels, allowing for fine-tuning of the visualization.

  8. Matplotlib surface_plot with contourf plot and data interpolation

    Description: This query explores creating a surface plot overlaid with a contourf plot and data interpolation for a smoother visualization of the underlying data.

    import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from scipy.interpolate import interp2d

# Generate data
x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)
Z = np.sin(np.sqrt(X**2 + Y**2))

# Interpolate data for smoother visualization
f = interp2d(x, y, Z, kind='cubic')
x_new = np.linspace(-5, 5, 200)
y_new = np.linspace(-5, 5, 200)
Z_new = f(x_new, y_new)

# Create surface plot with interpolated data
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(X, Y, Z_new, cmap='viridis')

# Overlay contourf plot
ax.contourf(X, Y, Z_new, zdir='z', offset=-2, cmap='coolwarm', alpha=0.5)

plt.show()

    This code generates a surface plot with interpolated data and overlays a contourf plot, resulting in a smoother and visually appealing visualization.

  9. Matplotlib surface_plot with contourf plot and logarithmic scale

    Description: This query explores creating a surface plot overlaid with a contourf plot using a logarithmic scale for better visualization of data with large dynamic range.

    import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# Generate data
x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)
Z = np.exp(-(X**2 + Y**2))

# Create surface plot with logarithmic scale
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(X, Y, Z, cmap='viridis')

# Overlay contourf plot with logarithmic scale
ax.contourf(X, Y, Z, zdir='z', offset=-0.2, cmap='coolwarm', norm=matplotlib.colors.LogNorm())

plt.show()

    This code generates a surface plot with data following a logarithmic scale and overlays a contourf plot, allowing for better visualization of data with a large dynamic range.

  10. How to add labels and title to a combined plot in matplotlib?

    Description: This query focuses on adding axis labels and a title to a combined plot consisting of a surface_plot overlaid with a contourf plot for better context and understanding.

    import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# Generate data
x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)
Z = np.sin(np.sqrt(X**2 + Y**2))

# Create surface plot with labels and title
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(X, Y, Z, cmap='viridis')
ax.contourf(X, Y, Z, zdir='z', offset=-2, cmap='coolwarm', alpha=0.5)

# Add labels and title
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')
plt.title('Surface Plot with Contourf Overlay')

plt.show()

    This code adds axis labels and a title to the combined plot of a surface_plot overlaid with a contourf plot, providing better context and understanding of the visualization.

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