Creating a Contour Map Using Python PyVista

Contour maps are essential for visualizing three-dimensional data on a two-dimensional plane, often used in fields like geography, meteorology, and various scientific disciplines. PyVista, a powerful Python library built on top of the Visualization Toolkit (VTK), offers an intuitive interface for creating and visualizing such maps. In this article, we'll explore how to create a contour map using PyVista, specifically within a Google Colab environment.

Key Features of PyVista

• 3D Visualization: Simple tools for creating complex 3D plots.
• Mesh Analysis: Functions for detailed analysis and manipulation of mesh data.
• Interactivity: Interactive plotting for enhanced data exploration.
• Integration: Seamless integration with NumPy, Pandas, and other scientific libraries.

Setting Up PyVista

To begin using PyVista, we can ensure that installed Pyvist. If not use the below command to install the PyVista.

pip install pyvista

It requires VTK (Visualization Toolkit) to be installed, which is typically handled automatically during the PyVista installation.

Preparing Data for Contour Maps

# Create a simple grid with scalar data
x = np.linspace(0, 10, 100)
y = np.linspace(0, 10, 100)
X, Y = np.meshgrid(x, y)
Z = np.sin(X) * np.cos(Y)

In this example, we prepare the synthetic data using NumPy:

• numpy can be used to create the 1D arrays ('x', 'y') which are then meshgrid-ed to form the 2D arrays.
• z is computed as the sin(x) * cos(Y), it can provide the scalar values for the elevation.

Creating the StructuredGrid with PyVista

We can work with the data and create the structured grid:

# Create a PyVista structured grid
grid = pv.StructuredGrid(X, Y, Z)

• StructuredGrid: pv.StructuredGrid(X, Y, Z): It can be creates the structured grid directly from the meshgrid arrays X, Y and scalar Z.

Adding the Scalar Data to Grid

In PyVista, scalar data can be associated with the points or cells of grid. Here, we can associate the scalar data('Elevation') with the points of the grid.

# Add scalar data to the grid
grid.point_data["Elevation"] = Z.ravel()

• Z.ravel(): It can flattens the 2D z array into the 1D array to match the number of the points in grid.

Generating Contour Maps

We can generate the contours based on the scalar data('Elevation') added to the grid.

# Define the number of contour levels
contour_levels = 10

# Generate contours using the scalar data
contours = grid.contour(contour_levels, scalars="Elevation")

• grid.contour(contour_levels, scalars="Elevation"): It can be creates the contour lines('contours') based on the scalar data('Elevation') with specified number of the contour_levels.

Customizing Contours and Plotting

PyVista allows customization of the plot settings such as the colors, opacity and line widths:

# Create a plotter object
plotter = pv.Plotter()

# Add the structured grid and contours to the plotter
plotter.add_mesh(grid, opacity=0.5, color='lightgrey')
plotter.add_mesh(contours, color='blue', line_width=2)

# Show the plot
plotter.show()

• plotter.add_mesh(grid, opacity=0.5, color='lightgrey'): It can be adds the structured grid to the plotter with specified opacity and color.
• plotter.add_mesh(contours, color='blue', line_width=2): It can be adds the generated contours to the plotter with specified color and line width.

Complete Code

Example 1: Creating a Contour Map with Synthetic Data

# Import Libraries
import pyvista as pv
import numpy as np
import matplotlib.pyplot as plt

# Define grid size
n = 100
x = np.linspace(-10, 10, n)
y = np.linspace(-10, 10, n)
x, y = np.meshgrid(x, y)

# Define the surface using a mathematical function
z = np.sin(np.sqrt(x**2 + y**2))

# Create a PyVista grid object
grid = pv.StructuredGrid(x, y, np.zeros_like(z))

# Add the z-values as scalars to the grid
grid['scalars'] = z.ravel(order='F')

# Generate contour lines
contours = grid.contour(isosurfaces=10)

# Create a plotter object
plotter = pv.Plotter()

# Add the surface and contours to the plotter
plotter.add_mesh(grid, scalars='scalars', cmap='viridis')
plotter.add_mesh(contours, color='black', line_width=1)

# Add a color bar
plotter.add_scalar_bar(title='Scalar Value')

# Show the plot
plotter.show()

Output:

Example 2: Creating a Contour Map with Real-World Elevation Data

# Import Libraries
import pyvista as pv
import numpy as np
import matplotlib.pyplot as plt

# Load a DEM dataset from PyVista's examples
dem = pv.examples.download_st_helens()

# Generate contour lines
contours = dem.contour(isosurfaces=20)

# Create a plotter object
plotter = pv.Plotter()

# Add the DEM surface and contours to the plotter
plotter.add_mesh(dem, cmap='terrain')
plotter.add_mesh(contours, color='black', line_width=1)

# Add labels and show the plot
plotter.add_scalar_bar(title='Elevation (m)')
plotter.show()

Output:

1. How can i adjust the number of contour levels?

We can modify the contour_levels variable to increase or decrease the number of the contour lines displayed.

2. How can i adjust the contour levels to better the represent my data?

We can adjust the contour_levels parameter in the grid.contour(contour_levels, scalars='Elevation') to increase the number of the contour lines generated. Higher levels can provide more detail, while fewer levels simplify the visualization.

3. Can i customize the apperance of the contour lines such as the color and line thickness?

Yes, we can customize the apperance of the contour lines using the PyVista's plotting capabilities. For example, use the plotter.add_mesh(contours, color='blue', line_width=2) to change the lines color to blue with a line width of the 2 units.

4. What if my dataset has irregular dimensions or missing the data points?

PyVista's StructuredGrid can be expects the regular grid dimensions(X, Y, Z arrays) where all the dimensions match exactly. We can ensure that X,Y and Z arrays have the same shape and are correctly aligned to avoid the errors like ValueError: Input point array shapes must match exactly.

5. How can i add the labels or annotations to contour maps for the better interpretation?

PyVista can be allows adding the annotations and labels to plots using its plotting the capabilities. We can annotate specific contour lines or add the color legends to indicate the scalar values represented by the each contour.

Conclusion

Contour maps are valuable tools for visualizing the spatial data and it can be allowing the insights into patterns and gradients. PyVista simplifies the creation and customization of the contour plots, making it accessible for the scientific visualization and data analysis tasks.