fix: use max() for penalty scaling floor (closes #75)#77
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The penalty scaling base was computed with np.min([max_import_price, 0.1e-3]) despite the comment intending a positive floor. min() inverts penalties on negative market prices (turning them into rewards in the maximizing objective) and clamps them to the tiny floor whenever real prices are higher, making penalties too weak to dominate. Switch to np.max() for a proper positive floor that scales with the highest import price, and deduplicate the expression—repeated five times—into a single penalty_base. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
andig
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May 31, 2026
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Summary
Fixes #75. The penalty-scaling base was computed with
np.min([self.max_import_price, 0.1e-3]), despite the comment stating the intent was to keep the penalty positive.min()does the opposite:1e-4floor get clamped down to that tiny floor, making penalties too weak to reliably dominate real costs.The intended behavior is a positive floor that scales with the highest import price, which is
np.max().Changes
np.min→np.maxfor the penalty base.penalty_basecomputed once (the "clean up the calculation of incentives" part of the issue).Behavior is unchanged for typical positive prices above the floor, and corrected for the zero/negative-price edge cases the issue flagged.
Testing
uv run pytest→ 13 passeduv run ruff check→ clean🤖 Generated with Claude Code