Fix NaN propagation in rate averages and cost totals (v2.0.1)

[damonrand]

Summary

When simulating periods with sparse missing data (e.g., 30 half-hour slots out of 17,520 in a full year), the entire summary would show NaN values for rates and costs. This made annual simulations unusable when even 0.17% of imbalance pricing data was missing.

Root cause

Both aggregation paths used skipna=False, causing any NaN to propagate:

1. output.py: safe_average() used np.average() which returns NaN if any input value is NaN
2. breakdown.py: cost totals used .sum(skipna=False).sum(skipna=False) which returns NaN if any cell is NaN

Solution

Add a 5% NaN threshold to both aggregation functions:

• safe_average(): Filter out NaN values and their weights before calculating weighted average. Only return NaN if >5% of data is missing.
• safe_sum(): New helper function that uses np.nansum() to sum valid values. Only return NaN if >5% of data is missing.

This allows simulations to complete with valid results when small amounts of data are missing, while still flagging unreliable results when too much data (>5%) is absent.

Files changed

• output.py: Enhanced safe_average() with NaN threshold
• breakdown.py: Added safe_sum() helper, replaced double sum calls
• pyproject.toml: Bump version to 2.0.1

Test plan

• Full year 2025 Findhorn simulation now completes with valid BESS gains
• All 4 scenarios (Baseline, Bess500_500, Bess1000_1000, Bess500_1000) produce valid results
• Lint passes