* perf(prediction): evaluate self-consumption tables without SciPy
The GENETIC inverter (and the GENETIC0 one) evaluates a pickled
scipy.interpolate.RegularGridInterpolator for the self-consumption
probabilities. SciPy is used nowhere else in EOS, yet loading it costs
about 35 MB RSS (21 MB anonymous) on Linux arm64, which every
optimization run pays.
Add utils/gridinterpolator.py:
- LinearGridInterpolator: 2-D linear interpolation with the same steps
as SciPy's linear 2-D path (interval search, normalised distances,
corner weights summed in the same order, fill value outside the grid,
NaN for NaN input).
- load_grid_interpolator(): reads the shipped pickles with a restricted
unpickler that maps the SciPy class to a stand-in and accepts only
the NumPy globals needed for the arrays. SciPy is not imported, and
the data files no longer depend on SciPy's private module paths.
Results are bit-identical to SciPy on Linux (aarch64, checked on 3.6M
points per table including the query pattern of the inverter). The
macOS arm64 SciPy wheel fuses multiply-add, so there SciPy itself
differs by <= 1.1e-16; GENETIC plans for fixed seeds are unchanged on
both platforms.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* feat(prediction): make the SciPy-free table evaluation opt-in
Add optimization.self_consumption_interpolator (scipy | numpy, default scipy).
The default keeps evaluating the self-consumption tables with SciPy's
RegularGridInterpolator, built from the same grid, values and fill value the
pickle holds, so results are unchanged. With numpy the tables are evaluated by
LinearGridInterpolator and SciPy is never imported. The backend is read on
every evaluation, so a config change applies to the next run.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---------
Co-authored-by: Christin <christin@BigBrother.bikini.bottom.zone>
Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
Co-authored-by: Andreas <drbacke@gmx.de>
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