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Add auto_mask parameter to Model class - #555
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Performance improvements: - Use np.where() instead of xarray where() for mask application (~38x faster) - Use max() == -1 instead of all() == -1 for null expression check (~30% faster) These optimizations make auto_mask have minimal overhead compared to manual masking.
The switch from xarray's where() to numpy's where() broke dimension-aware broadcasting. A 1D mask with shape (10,) was being broadcast to (1, 10) instead of (10, 1), applying to the wrong dimension. Fix: Explicitly broadcast mask to match data.labels shape before using np.where.
FabianHofmann
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I very much like this, it is non-intrusive and sensible to try out in the future
| # Auto-mask based on null expressions or NaN RHS (use numpy for speed) | ||
| if self.auto_mask: | ||
| # Check if expression is null: all vars == -1 | ||
| # Use max() instead of all() - if max == -1, all are -1 (since valid vars >= 0) |
FBumann
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February 6, 2026 14:56
FBumann
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Feb 6, 2026
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Great! I think it greatly simplifies things for the user |
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Disclaimer
Im not sure how much value this adds, as i didnt use masking that much before. Im happy to discuss the ups and downsides of such a feature. I would have raised an issue, but i thought adding a benchmark and proof of concept would be more valuable.
Summary
Adds
auto_maskparameter toModelthat automatically masks variables and constraints where input data contains NaN values.Benefits
np.whereinstead of xarraywhere= 38x faster)Auto-mask conditions
Variables masked out where:
loweris NaN, ORupperis NaNConstraints masked out where:
rhsis NaNPerformance (30% NaN, includes external mask creation)
Variables
Constraints
Auto-mask is equal or faster than manual masking due to optimized numpy operations.
Checklist