v0.8.2 — Fix: NaN in plot_state_feature_clustermap

Resolves the follow-up flagged in v0.8.1: plot_state_feature_clustermap
no longer crashes on per-Leiden-cluster mean matrices that contain NaN
entries. The fix is designed to preserve as much data as possible — drop
only what is unrecoverable, keep partial-signal columns, and render the
genuinely unknown cells as gray so the viewer can tell observation apart
from imputation.

Fixed

• plot_state_feature_clustermap accepts a new nan_policy kwarg.
  Defaults to "mask_impute": drop columns with no signal anywhere
  (all-NaN across every Leiden cluster), impute remaining NaN cells with
  the column median of finite values, and render them as gray via
  seaborn.clustermap's mask= argument. Alternatives: "drop" (drop
  any column with one or more NaN cells; no imputation), "raise" (loud
  error listing NaN-bearing statistic names for diagnostics).
• _collapse_repeated_measures now uses np.nanmean. Previously a
  single NaN in any window-repeat of a statistic propagated NaN through
  the entire post-collapse cell; now it reflects the windows where the
  statistic was observed.
• Single-column edge case handled. When nan_policy="drop" reduces
  the kept-statistic count to 1, the column dendrogram is skipped
  (col_cluster=False) so seaborn's internal scipy.pdist doesn't fail
  with "empty distance matrix".
• MCP tool surfaces the policy and diagnostics. The tool's summary
  dict gains nan_policy: str, dropped_statistics: list[str], and
  imputed_cell_count: int so the calling agent can see what the viz
  decision did.

Hardened (defensive; no observable change for current pipelines)

• cluster_ensemble switches .mean() -> np.nanmean() for the
  per-Leiden-cluster mean computation, and drops all-NaN columns of
  leiden_cluster_means before scipy.spatial.distance.pdist to keep
  the Ward linkage finite. In the current code path, sklearn's
  kneighbors_graph already rejects NaN-bearing embeddings before this
  code runs, so these changes are no-ops; they harden future paths
  (alternative clustering strategies, manually constructed cluster Zarrs
  carrying NaN) without altering existing behaviour. Existing
  tests/unit/test_cluster_leiden_ward.py tests pass unchanged.

NaN semantics (the rationale)

In every observed source, NaN in this pipeline means the statistic
could not be computed because the underlying cells / interactions were
not present — a structural absence, not a low-sample noise estimate.
mean_nearest_neighbor_distance_by_type returns NaN when mask.sum() < 2
(can't compute pairwise NN with fewer than 2 cells). cell_type_ratio
returns NaN when both numerator and denominator types are absent.
marker_statistics_by_type produces NaN when the type's population is 0.
The summarize/aggregate.py NaN-fill at line 107 marks the gap when a
timepoint's panel didn't emit a statistic because the underlying cells
weren't there.

That semantic rules out zero-fill (claiming "neighbour distance is 0 µm"
or "ratio is 0" when the cells don't exist is wrong) and any imputation
that hides the absence. The numerical value placed in a masked cell is a
computational sentinel needed only because seaborn cannot accept NaN;
the gray mask is the truth-telling layer.

Unchanged (by design)

• summarize_ensemble still emits NaN where a statistic can't be
  computed (schema stability per ADR 0009 + intentional aggregator
  NaN-fill).
• normalize_ensemble's fill_nan_with default of 0.0 still handles
  fully-NaN columns. Operators who want NaN preserved through normalize
  set it to NaN explicitly.
• tmelandscape.viz.* stays backend-agnostic (per v0.8.1).

Verification snapshot

• uv run pytest tests/unit tests/integration -q — 506 passed (was
  500; 5 new viz NaN-policy tests + 1 new integration test).
• uv run ruff check . / uv run ruff format --check . /
  uv run mypy src — clean.
• Manual end-to-end against M00_S01 canary cluster zarr:
  plot_state_feature_clustermap produces a PNG with imputed cells
  rendered gray; the MCP tool summary lists the dropped all-NaN
  statistics and the imputed cell count.