fix(latent): global masking in compute_latent_samples to prevent KeyError

Latent finite-masking was computed per batch on the JAX path, so a latent
that went NaN for one sample in a batch had its column dropped for that batch
only. Samples then carried inconsistent kwargs key sets and Samples.summary()
raised KeyError. Accumulate all batches and mask once globally (col-then-row);
return None when nothing finite remains. Depends on autoconf.test_mode hook.

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

Author: 2 people

autofit/non_linear/analysis/analysis.py

@@ -210,10 +210,23 @@ def _safe_compute(xx):
                 def batched_compute_latent(x):
                     return np.array([_safe_compute(xx) for xx in x])
 
+            from autoconf.test_mode import inject_latent_nans
+
             parameter_array = np.array(samples.parameter_lists)
-            latent_samples = []
 
-            # process in batches
+            # Compute every batch first and accumulate the raw, UN-masked latent
+            # values into one (n_samples, n_latents) array. Masking is then done
+            # ONCE, globally, after the loop (see below).
+            #
+            # Doing the finite mask per batch (the previous behaviour) was a bug:
+            # a latent that went NaN for a single sample in one batch had its whole
+            # column dropped *for that batch only*, while other batches kept it.
+            # The resulting `Sample` objects then carried inconsistent kwargs key
+            # sets, and `Samples.summary()` raised `KeyError` building its model
+            # from the first sample's keys. Masking globally guarantees every
+            # retained sample shares one identical key set.
+            all_values = []
+            all_samples = []
             for i in range(0, len(parameter_array), batch_size):
 
                 batch = parameter_array[i:i + batch_size]
@@ -225,36 +238,59 @@ def batched_compute_latent(x):
                 if self._use_jax:
                     import jax.numpy as jnp
                     latent_values_batch = jnp.stack(latent_values_batch, axis=-1)  # (batch, n_latents)
-                    mask = jnp.all(jnp.isfinite(latent_values_batch), axis=0)
-                    latent_values_batch = latent_values_batch[:, mask]
-                else:
-                    # Drop samples whose latent computation failed (e.g. FitException from
-                    # model assertions surfaced as a NaN row in _safe_compute). This leaves
-                    # the per-latent column mask to continue handling degenerate latent
-                    # dimensions that produce NaN for all remaining samples.
-                    row_mask = np.all(np.isfinite(latent_values_batch), axis=1)
-                    latent_values_batch = latent_values_batch[row_mask]
-                    batch_samples = [s for s, keep in zip(batch_samples, row_mask) if keep]
-
-                    if len(latent_values_batch):
-                        col_mask = np.all(np.isfinite(latent_values_batch), axis=0)
-                        latent_values_batch = latent_values_batch[:, col_mask]
-
-                for sample, values in zip(batch_samples, latent_values_batch):
-
-                    kwargs = {k: float(v) for k, v in zip(self.LATENT_KEYS, values)}
-
-                    latent_samples.append(
-                        Sample(
-                            log_likelihood=sample.log_likelihood,
-                            log_prior=sample.log_prior,
-                            weight=sample.weight,
-                            kwargs=kwargs,
-                        )
-                    )
+
+                # Unify to NumPy so the global masking below is a single code path
+                # for both backends (latent values are scalars, host transfer is
+                # cheap and was already forced by the downstream `float(v)`).
+                latent_values_batch = np.asarray(latent_values_batch)
+
+                # Test-only NaN injection (no-op unless PYAUTO_LATENT_NAN_INJECT set).
+                latent_values_batch = inject_latent_nans(latent_values_batch, start_index=i)
+
+                all_values.append(latent_values_batch)
+                all_samples.extend(batch_samples)
+
+            if all_values:
+                all_values = np.concatenate(all_values, axis=0)
+            else:
+                all_values = np.empty((0, len(self.LATENT_KEYS)))
+
+            # Global masking, in two stages:
+            # 1. Drop a latent column only if it is non-finite for EVERY sample
+            #    (a genuinely degenerate latent, e.g. a µJy latent with no magzero).
+            col_mask = np.any(np.isfinite(all_values), axis=0)
+            kept_keys = [k for k, keep in zip(self.LATENT_KEYS, col_mask) if keep]
+            kept_values = all_values[:, col_mask]
+
+            # 2. Drop individual samples that still carry a NaN in a surviving
+            #    latent (e.g. a FitException NaN row, or a latent that went NaN
+            #    for just that sample). Every survivor now has all `kept_keys`.
+            if kept_values.size:
+                row_mask = np.all(np.isfinite(kept_values), axis=1)
+            else:
+                row_mask = np.zeros(len(all_samples), dtype=bool)
+            kept_values = kept_values[row_mask]
+            kept_samples = [s for s, keep in zip(all_samples, row_mask) if keep]
 
             print(f"Time to compute latent variables: {time.time() - start_latent} seconds for {len(samples)} samples.")
 
+            if not kept_keys or len(kept_samples) == 0:
+                logger.warning(
+                    "compute_latent_samples: no finite latent samples remained "
+                    "after masking; skipping latent output."
+                )
+                return None
+
+            latent_samples = [
+                Sample(
+                    log_likelihood=sample.log_likelihood,
+                    log_prior=sample.log_prior,
+                    weight=sample.weight,
+                    kwargs={k: float(v) for k, v in zip(kept_keys, values)},
+                )
+                for sample, values in zip(kept_samples, kept_values)
+            ]
+
             return type(samples)(
                 sample_list=latent_samples,
                 model=simple_model_for_kwargs(latent_samples[0].kwargs),