0.1.0

• New: cx.pp.subsample – streaming, stratified/cluster subsampling.
  The mask is computed entirely from .obs metadata (no matrix pass
  needed to decide which cells survive), then streamed out via the same
  writer every other filtering function uses. groupby stratifies (one
  column, several columns, or None for a single global stratum);
  unit="cell" (default) draws individual cells, while passing an
  obs column name instead (e.g. unit="batch") switches to cluster
  sampling, where a chosen unit's cells are kept in full and an unchosen
  unit's are dropped in full. n (exact count) or frac (proportion)
  is drawn independently per stratum, matching
  pandas.DataFrameGroupBy.sample(n=, frac=) semantics.
  drop_insufficient controls what happens to a stratum smaller than the
  requested count (drop it entirely by default, or keep it in full), and
  every affected stratum is reported via a warning regardless of
  verbose. Sampling is deterministic for a fixed random_state and
  independent of chunk_size.
• New: cx.pp.downsample_counts – streaming, dependency-free
  equivalent of scanpy.pp.downsample_counts(..., replace=False): thins
  every cell's total count down to a target via exact sampling without
  replacement (a cell already at or below the target is left unchanged).
  Complements subsample on the orthogonal axis — subsample decides
  which cells survive, downsample_counts decides how many counts
  survive within a surviving cell. A single streaming pass with a
  resizable HDF5 output avoids a separate counting pass over the source.
• Fix: filtered/subsampled/normalized outputs now keep every AnnData slot.
  write_filtered_subset — the shared streaming writer behind
  cx.pp.filter_cells, cx.pp.filter_genes, cx.pp.filter_perturbations,
  cx.pp.qc_summary, and the new cx.pp.subsample — previously wrote
  only X, obs, and var, silently dropping layers, obsm,
  varm, obsp, varp, and uns from every filtered output. It now
  streams layers the same way as X and carries
  obsm/varm/obsp/varp/uns through (subset on whichever axis
  applies); a source .raw is not copied, and a warning says so instead of
  the data silently disappearing. cx.pp.downsample_counts and
  cx.pp.normalize_total_log1p carry the same slots through unchanged, with
  the same .raw warning — including for an all-empty X, which
  previously skipped the slot copy-through entirely.
• Fix: cx.pp.downsample_counts per-cell thinning seed collisions.
  The per-row RNG seed was truncated to 32 bits, which collides often enough
  at the "hundreds of thousands to millions of cells" scale this function
  targets that distinct cells could draw bit-identical thinning outcomes.
  Seeds now use the full 64-bit range, and the thinning kernel itself now
  draws via numpy.random.Generator.multivariate_hypergeometric in one
  call per row instead of a hand-rolled cumsum/choice/searchsorted/bincount
  sequence.
• Fix: cx.pp.downsample_counts on dense-stored input. A dense-stored
  X was previously always cast to float32 regardless of its actual
  on-disk dtype (e.g. int32); it's now read and preserved like the sparse
  path already did. X must hold non-negative integer counts — non-count
  (e.g. already-normalized) input now raises instead of being silently
  truncated and mostly no-op'd.
• write_filtered_subset is now exported at the top level
  (crispyx.write_filtered_subset), reflecting that it is already relied
  on directly by real pipelines, not just an internal implementation
  detail of the filtering functions above.
• Removed: compute_average_log_expression and
  compute_pseudobulk_expression (and the cx.pb.average_log_expression /
  cx.pb.pseudobulk namespace methods), deprecated in 0.0.9 with an explicit
  promise to remove them in 0.1.0. Use compute_normalized_effects /
  cx.pb.normalized_effects with method="mean_log1p" or
  method="log_mean" respectively instead.