Refinement: a Refit's solvent grid starts around where the context's last one ended

Logic change (deterministic), kept on evidence. Every Refit searched the whole k_sol/b_sol box - an 88-point
coarse pass of plain least squares, then the robust fine pass around its winner. ModelScaleBox (and
ModelScaleGPUPool::Fit / ModelScaleGPU::FitSolvent) take an optional start; with one, the coarse pass is
skipped and the fine pass is laid around it. Refinement passes the grid winner of the context's previous
Refit (before MoveOverallB moves the overall B into b_sol); it travels with a trial's fit, and a trial's first
Refit without one (Context::ForgetStarts, which also clears the likelihood's D start) searches the whole box,
so a trial still depends on nothing its slot ran before (side by side = one at a time, md5).

Evidence (GPU, -N 8): full refinement of the 14 parity sets from their placed models - 12 refined CIFs
identical (md5), 9ig7 and 6iu9 differ, final R-work/R-free equal to 4 digits but 6iu9 R-work 0.2911 -> 0.2910;
of the 5 timing cases 4 identical, 7d98 (cif start) R-free 0.3096 -> 0.3095; 5f6m MD starts 01-04 median R-free
0.1692 -> 0.1692; 6o2h on the CPU engine identical (md5). The score-only parity table is not touched (one Refit,
no start). refit.scale (TIMING sum): 5f6m 1.52 -> 1.10 s. Wall: 5reo 1.68 -> 1.53, 5f6m 2.43 -> 2.24, 9yl4 7.19 ->
7.02, 7d98 3.70 -> 3.65, 5f6m MD start 4.57 -> 4.22 s.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_015CeEGgpKdfkCTJb3YNMJcu
(cherry picked from commit ff285fb525ecf4b480698d4aafe212a34301a8fe)

image_analysis/structure_refinement/ModelScaling.cpp

@@ -427,12 +427,16 @@ ModelScaleReport FitModelScale(gemmi::Scaling<float> &scaling, ModelScaleBox box
     // 10 A^2, or in eight values where the box is wider than the default, so a raised ceiling costs
     // no more grid points.
     const double b_step = std::max(10.0, (box.b_hi - box.b_lo) / 7.0);
-    std::vector<PointFit> coarse;
-    for (double ks = box.k_lo; ks <= box.k_hi + 1e-9; ks += 0.05)
-        for (double bs = box.b_lo; bs <= box.b_hi + 1e-9; bs += b_step)
-            coarse.push_back(PointFit{ks, bs});
-    try_points(coarse);
-    const double k0 = best_k_sol, b0 = best_b_sol;
+    double k0 = box.k_start, b0 = box.b_start;
+    if (k0 < 0) {
+        std::vector<PointFit> coarse;
+        for (double ks = box.k_lo; ks <= box.k_hi + 1e-9; ks += 0.05)
+            for (double bs = box.b_lo; bs <= box.b_hi + 1e-9; bs += b_step)
+                coarse.push_back(PointFit{ks, bs});
+        try_points(coarse);
+        k0 = best_k_sol;
+        b0 = best_b_sol;
+    }
     const double k_hi2 = std::min(box.k_hi, k0 + 0.05);
     const double b_hi2 = std::min(box.b_hi, b0 + b_step);
     std::vector<PointFit> fine;
@@ -511,7 +515,8 @@ ModelScaleGPUPool::~ModelScaleGPUPool() {
     set_gpu(current);
 }
 
-ModelScaleReport ModelScaleGPUPool::Fit(gemmi::Scaling<float> &scaling, double b_hi, size_t nthreads, bool robust) {
+ModelScaleReport ModelScaleGPUPool::Fit(gemmi::Scaling<float> &scaling, double b_hi, size_t nthreads, bool robust,
+                                        double k_start, double b_start) {
     const size_t n = scaling.points.size();
     std::vector<std::array<int, 3>> hkl(n);
     std::vector<double> stol2(n);
@@ -554,11 +559,11 @@ ModelScaleReport ModelScaleGPUPool::Fit(gemmi::Scaling<float> &scaling, double b
         if (cudaMemcpyAsync(e->fcalc, fc.data(), n * sizeof(float2), cudaMemcpyHostToDevice, e->stream) != cudaSuccess
             || cudaMemcpyAsync(e->fmask, fm.data(), n * sizeof(float2), cudaMemcpyHostToDevice, e->stream) != cudaSuccess)
             throw JFJochException(JFJochExceptionCategory::GPUCUDAError, "ModelScaleGPUPool: the upload failed");
-        fit = e->scale.FitSolvent(e->fcalc, e->fmask, b_hi, robust);
+        fit = e->scale.FitSolvent(e->fcalc, e->fmask, b_hi, robust, k_start, b_start);
     } catch (const ModelScaleGPUTooFewReflections &) {
         set_gpu(current);
         release();
-        return FitModelScale(scaling, {.b_hi = b_hi, .robust = robust}, nthreads);
+        return FitModelScale(scaling, {.b_hi = b_hi, .robust = robust, .k_start = k_start, .b_start = b_start}, nthreads);
     } catch (...) {
         set_gpu(current);
         release();