Course Overview

Class Description:

This recorded class is a technical session on working with various different scientific data types in Houdini. Topics covered include data wrangling, Houdini-friendly formats like Geo/Bgeo and OpenVDB, Houdini-unfriendly but common scientific data representations like Adaptive Mesh Refinement, and spatial and temporal data interpolation. This will be an introduction to the data and programming side of Houdini, with less focus on design or rendering.

Learning Outcomes:

Students will leave with a fundamental understanding of how to incorporate real scientific data into their Houdini scenes, and with Python starter code for accomplishing a few common data transformation tasks.

Course curriculum

    1. Files

    1. 1. Introduction

    2. 2. AVL Demo Reel

    3. 3. Types of Data

    4. 4. Course Overview

    1. 1. Understanding the Data

    2. 2. Project Overview

    3. 3. Setting up the Grid

    4. 4. Setting up the VOP

    5. 5. Using the Data to Create Displacement

    6. 6. Geo Tiffs

    1. 1. Overview of GEO Format

    2. 2. Understanding What Houdini is Reading in

    3. 3. Transfering Data Using Python

    4. 4. Improving the Visualization

    5. 5. Q&A: What does the 0 Value in the Diffuse Field Mean?

    1. 1. Project Overview

    2. 2. Frames vs Timesteps

    3. 3. Calculating the Frames

    4. 4. Q&A: How do you Open a Python File in Houdini?

    1. 1. Project Overview

    2. 2. Q&A: How to Run the Code

    3. 3. Script to Convert Data to Voxels

    4. 4. Importing the Data into Houdini

    5. 5. Q&A: Clarify the What Iso Value is Doing

    6. 6. Creating a Parameter to Control the Iso Value

    7. 7. Q&A: How do we run the previous example in an IDE like pycharm?

    8. 8. Removing the Wall Behind the Person

    9. 9. Q&A: Using Volume Data to Influence Particles or Fluids

About this course

  • $45.00
  • 42 lessons
  • 2 hours of video content

Course Teaser

Working with Scientific Datasets in Houdini

Python coding in and around Houdini, data formats, volumes, particles, and image data. Python starter code for accomplishing a few common data transformation tasks.
  1. Python start code and Python Coding
  2. Volumes, particles, image data 
  3. Data wrangling
  4. Geo/Bgeo and OpenVDB 
  5. Adaptive Mesh Refinement
  6. Spatial and temporal data interpolation


Kalina Borkiewicz

Director of Visualization @ NCSA

Kalina leads the Advanced Visualization Lab (AVL) at the National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign. Kalina’s background is in computer science, and she works in a “Renaissance Team” that consists of artists, scientists, and programmers. Since 2014, Kalina and the AVL have been using Houdini to create cinematic scientific visualizations of real data, captured by scientific instruments like satellites or simulated on massive supercomputers like Blue Waters. These visualizations are seen around the world in the form of planetarium fulldome shows, IMAX films, and streaming documentaries.



  1. Computer (Please see SideFx system requirements)
    2. A second monitor is recommended, but not necessary
    3. Three-button mouse is recommended, but not necessary
  2. Houdini (Apprentice License is free)
  3. Code Editor
    1. Simple, free options include (Windows only) or (all operating systems, free trial)
  4. Python 3
    1.     Recommended: Pydicom
    2.     Recommended: PyOpenVDB and its dependencies (OpenVDB, Boost, TBB, Blosc, Numpy)
      1. Install OpenVDB from . Modify CMakeLists.txt to set “OPENVDB_BUILD_PYTHON_MODULE” to “ON”. Modify openvdb/openvdb/python/CMakeLists.txt to set "USE_NUMPY" to "ON".
    3.     Optional: OpenCV


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See you in Class!