Examples

Examples#

Here is a gallery of several examples demonstrating what PyVista can do.

All of these examples are live and available on MyBinder.

Mesh Creation#

These examples demo how to read various file types into PyVista mesh objects, create meshes from NumPy arrays, and how to create primitive geometric objects like spheres, arrows, cubes, ellipsoids and more.

Once a mesh is loaded, it is ready for plotting with just a few lines of code - explore these examples to get started with using PyVista for your data.

Create Circular Arcs

Drape 2D Surface From Line

Creating an Explicit Structured Grid

Geometric Objects

Create a Kochanek Spline

Create Connected Lines from Points

Parametric Geometric Objects

Pixel Art of ALIEN MONSTERS

Unstructured Grid with Polyhedra

Create Sphere Mesh Multiple Ways

Creating a Spline

Creating a Structured Surface

Create 3D Text

Create Triangulated Surface

Plot Truss-like FEA Solution with Cylinders

Creating a Uniform Grid

Creating an Unstructured Grid

Linear Cells

Working with glTF Files

Working with VRML Files

Read FEniCS/Dolfin Meshes

Load and Plot from a File

Read Image Files

Parallel Files

Load data using a Reader

Terrain Following Mesh

Wrapping Other Objects

Filtering#

These examples show case various mesh analysis and filtering routines present in the Filters module. Explore these demos to perform tasks such as:

Slicing and cutting meshes
Computing mesh properties like volume, area, and surface normals
Mesh decimation
Extract regions of one mesh using another mesh’s surface
Ray tracing through surface meshes
Resampling and interpolating scalar/vector values across meshes
Integrating a vector field to generate streamlines
Smoothing surfaces

Boolean Operations

Extract Cell Centers

Clip and Cap a Closed Surface

Clipping with Planes & Boxes

Clipping with a Surface

Collision

Compare threshold filters

Computing Surface Normals

Connectivity

Contouring

Wrap a Point Cloud in a Convex Hull

Crop Labeled ImageData

Decimation

Distance Between Two Surfaces

Extract Cells Inside Surface

Farthest Point Sampling

Repair a Surface with fill_holes

Gaussian Smoothing

Geodesic Paths

Plotting Glyphs (Vectors or PolyData)

Table of Glyphs

Compute Gradients of a Field

Register a Surface with ICP

Fast Fourier Transform

Fast Fourier Transform with Perlin Noise

Image Data Representations

Integrate Data

Detailed Interpolating Points

Compare interpolation/sampling methods

Marching Cubes

Computing Mesh Quality

Sample Function: Perlin Noise in 2D

Sample Function: Perlin Noise in 3D

Measure Distance Between Point Clouds

Analyze the Orientation of a Point Cloud

Surface Reconstruction

Surface Smoothing

Using Common Filters

Volumetric Analysis

Voxelize a Surface Mesh

Warping by Vectors

Plotting#

These examples show case many of the possibilities for altering how you display spatial data. Explore these examples to learn how to leverage our powerful 3D plotting routines to perform tasks like:

Color mapping scalar values with matplotlib colormaps
Creating animations as GIFs or movie files
Showing the edges and nodes of different mesh types
Use sophisticated lighting techniques like smooth shading or Eye Dome Lighting
Glyph a vector or scalar field on a mesh (place/orient a mesh on another mesh’s nodes and scale/orient based on data values)
Label points in 3D space along side your meshes
Creating side-by-side comparisons
Making a dataset transparent or using a scalar value to map opacity
Adding textures/images draped over a mesh (texture mapping)
Rendering a depth image

Anti-Aliasing

Axes Objects

Setting Backface Properties

Build a Ball-and-Stick Molecule

Clearing a Mesh or the Entire Plot

Depth of Field Plotting

Depth Peeling

Label based on Distance on Line

Measuring distance

Eye Dome Lighting

Picking elements of a mesh

Plot Curved Flight Paths on a Globe

Plot with Floors

Hide Cells with Ghosting

Create a GIF Movie

Plot a 3D Graph Network

Render a depth image

Interpolate Before Mapping

Legends and glyphs

Lighting Properties

Linked Views in Subplots

Lookup Tables

Plot a Lorenz Attractor

Maximum Intensity Projection

Picking Meshes

Create a MP4 Movie

Save a Movie Using Glyphs

Create a GIF Movie of a Static Object with a Moving Colormap

Physically Based Rendering

Plot Scalars Over a Circular Arc

Plot Over Line

Point Cell Scalars

Highlight Nearest Neighbors in a Point Cloud

Plotting Point Clouds

Label Points

Picking points on a mesh

Point Sprite Shapes

Scaled Gaussian Points

Customize Scalar Bars

Plot data in spherical coordinates

Surface Space Ambient Occlusion

Picking a Point on the Surface of a Mesh

Applying Textures

Control Global and Local Plotting Themes

Topographic Map

Plot Vector Component

Visible Vertices

Volume Rendering

Widgets#

PyVista has several widgets that can be added to the rendering scene to control filters like clipping, slicing, and thresholding - specifically there are widgets to control the positions of boxes, planes, and lines or slider bars which can all be highly customized through the use of custom callback functions.

Here we’ll take a look at the various widgets, some helper methods that leverage those widgets to do common tasks, and demonstrate how to leverage the widgets for user defined tasks and processing routines.

Multiple Slider Widgets

Lighting#

These examples demonstrate how to take fine-tuned control over lighting conditions in a scene. Explore them to learn how to go beyond the default lighting setup to truly bring out the best of your visualization:

Choose between preset lighting systems for plotters
Disable lighting on the mesh level
Learn about the different light types
Customize the shape of positional lights
Use actors to visualize the beams of spotlights

Disabling Mesh Lighting

Plotter Lighting Systems

Shadows

Common#

The following are a collection of examples addressing common tasks and frequently asked questions when working with PyVista. These examples, while possibly complex, focus on single-purpose solutions to help users understand specific functionality.

Feel free to contribute examples or explore the FAQ-like structure, and check out our GitHub Discussions for more context.