Add compute_pca_statistics method to DataSetFilters #7694
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This commit adds Principal Component Analysis (PCA) statistics computation capability to PyVista's DataSetFilters class. The implementation wraps VTK's vtkPCAStatistics filter to provide easy access to PCA analysis from PyVista. Key features: - Compute PCA statistics on point or cell data arrays - Support for analyzing all arrays or specific scalar arrays - Returns meaningful statistical measures including means and variances - Comprehensive test coverage with error handling - Follows PyVista's established patterns for filter implementation The method integrates seamlessly with PyVista's existing API and provides a Pythonic interface to VTK's statistical capabilities for data analysis and dimensionality reduction tasks. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]>
Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## main #7694 +/- ##
==========================================
- Coverage 96.38% 96.26% -0.12%
==========================================
Files 150 150
Lines 32527 32578 +51
Branches 4114 4129 +15
==========================================
+ Hits 31351 31362 +11
- Misses 569 593 +24
- Partials 607 623 +16 |
Add type ignore comment for unreachable code warning when checking preference parameter that is already constrained by Literal type. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]>
- Add missing return type annotation: -> pyvista.Table
- Fix docstring example to use correct output keys ('means' instead of 'eigenvalues')
- Update Returns section to accurately describe the actual output structure
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <[email protected]>
When no meaningful data is extracted in compute_pca_statistics, return an empty Table as declared in the return type annotation instead of trying to return a wrapped multiblock output. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]>
🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]>
🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]>
- Add test for KeyError when scalar not found in point data - Add test for KeyError when scalar not found in cell data - Improve test coverage for uncovered lines 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]>
Add tests for specific scalar arrays, result field validation, and empty mesh handling to improve test coverage of the compute_pca_statistics functionality. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]>
user27182
requested changes
Jul 20, 2025
There was a problem hiding this comment.
vtkPCAStatistics looks really interesting and powerful. But compute_pca_statistics seems to only implement very basic parts of that class to compute the mean and variance of arrays, which doesn't seem much more useful than using np.mean and np.var on the arrays directly.
If we could expand on this class and bit and show its use with an example like dimensionality reduction, I think it would make this more useful. There is vtk demo:
https://examples.vtk.org/site/PythonicAPI/Utilities/PCADemo/
Co-authored-by: user27182 <[email protected]>
Co-authored-by: user27182 <[email protected]>
for more information, see https://pre-commit.ci
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Summary
This PR adds Principal Component Analysis (PCA) statistics computation capability to PyVista's DataSetFilters class by implementing a
compute_pca_statisticsmethod that wraps VTK'svtkPCAStatisticsfilter.Changes
vtkPCAStatisticsimport to_vtk_core.pycompute_pca_statisticsmethod inDataSetFiltersclassFeatures
Test plan
🤖 Generated with Claude Code