rugnux: add opt-in stills partiality (--still-partiality)

Serial stills currently treat every reflection as a full (partiality hardcoded to 1).
Add an opt-in Gaussian excitation-error partiality set at prediction time (CPU + CUDA):
p = exp(-dist_ewald^2 / (2*sigma^2)), sigma^2 = profile_radius^2 + (bandwidth_sigma*|recip_z|)^2,
with sigma = the per-image profile radius (ewald_dist_cutoff/2), so an edge-of-acceptance
reflection keeps p ~ exp(-2). Off by default; the merge weight (~p^2) then down-weights
far-from-Ewald partials instead of trusting them as fulls.

Validated: helps medium/strong stills (LOV R-free 0.336->0.329, lyso8 0.433->0.410, lowers
the systematic error-model b in both) but HURTS weak OCP (dividing by a small, uncertain p
amplifies orientation error -> high-res noise, resolution collapse), so it is left opt-in.
A static forward p explains only ~6-10% of the partiality scatter; the full win needs per-image
post-refinement (future work), for which this is the prediction-side groundwork.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

image_analysis/IndexAndRefine.cpp

@@ -324,7 +324,11 @@ void IndexAndRefine::QuickPredictAndIntegrate(DataMessage &msg,
         .wedge_deg = std::fabs(wedge_deg),
         .mosaicity_deg = std::fabs(mos_deg),
         // FWHM -> sigma; 0 when monochromatic, leaving the prediction unchanged.
-        .bandwidth_sigma = experiment.GetBandwidthFWHM().value_or(0.0f) / 2.3548f
+        .bandwidth_sigma = experiment.GetBandwidthFWHM().value_or(0.0f) / 2.3548f,
+        // Experimental stills partiality (off by default): sigma = ewald_dist_cutoff/2 = the per-image
+        // profile radius, so a reflection at the acceptance edge (dist_ewald ~ 2*sigma) keeps p ~ exp(-2).
+        .still_partiality = experiment.GetBraggIntegrationSettings().GetStillPartiality(),
+        .profile_radius_recipA = ewald_dist_cutoff * 0.5f
     };
 
     // Predict, then integrate with the selected integrator (box-sum or profile-fit).