fix(analysis): keep pre_E alive through prediction range subsampling

`_row_subsample` hash-filters on `pre_E`, but `build_context`'s
prediction branch called it on a frame already projected down to
`pred_<var>`/`true_<var>` columns, so any run whose prediction file
exceeds `sample_rows` (the default is 1M; real predict outputs can be
100M+ rows) failed with `ColumnNotFoundError: pre_E`.

Subsample the full paired frame first, then project — matching every
other _row_subsample call site — and restructure the loop to subsample
once per prediction side instead of once per paired variable, cutting
6 streaming passes over the prediction file down to 1.

Add a regression test with sample_rows below the fixture's row count
so the hash-filter branch is actually exercised (the existing test
never took it).

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Qb7xBAa6aAR94AzimgpPxq

tests/test_analysis_prediction.py

@@ -193,6 +193,28 @@ def test_build_context_resolves_prediction_ranges():
     assert ctx.pred_top_sec_pdgs  # secondaries present in the fixture
 
 
+def test_build_context_resolves_prediction_ranges_when_subsampled():
+    """Regression test: `sample_rows` smaller than the prediction row count
+    must still work — `_row_subsample`'s hash key (`pre_E`) has to survive
+    into the prediction branch's subsample call, not be projected away first
+    (see giant/analysis/context.py's predictions loop)."""
+    ctx = build_context(
+        [RolloutSpec("rollout", _rollout_frame())],
+        _reference_frame(),
+        predictions=[PredictionSpec("pred", _global_prediction_frame())],
+        n_energy_bins=2,
+        n_marginal_bins=10,
+        top_k_pdg=3,
+        sample_rows=2,  # < the 3-row prediction fixture: forces the hash-filter branch
+        seed=0,
+    )
+    for var in PAIRED_SCALARS + ("cos_scatter", "cos_travel"):
+        lo, hi = ctx.pred_var_ranges[var]
+        assert np.isfinite(lo) and np.isfinite(hi) and lo < hi
+        lo, hi = ctx.pred_residual_ranges[var]
+        assert np.isfinite(lo) and np.isfinite(hi) and lo < hi
+
+
 def test_prediction_specs_compute_valid_reduced():
     ctx = _ctx_with_predictions()
     bundle = Bundle.open(