Release 4.4.2

Changes from 4.4.1 to 4.4.2

This is a feature and maintenance release that promotes DSL kernels to
first-class CTable computed columns, adds a new CTable.__setitem__
assignment idiom, optimises bulk NDArray writes, and fixes several
correctness issues.

DSL kernels as first-class CTable columns

• add_computed_column() accepts DSL kernels: @blosc2.dsl_kernel-decorated
  functions can now back virtual computed columns directly, in addition to
  the existing string-expression form. The column survives save/open
  round-trips via persisted dsl_source.
• add_generated_column() accepts DSL kernels: stored generated columns
  (written during append/extend and on refresh_generated_column())
  now support DSL kernels as their transformer.
• CTable.where() accepts UDF/DSL kernels: filter predicates are no
  longer limited to expression strings — any DSL kernel can be passed directly.
• dtype inference for DSL kernels: when dtype is omitted,
  lazyudf() infers the output dtype via NumPy type promotion of the input
  column dtypes. Pass dtype explicitly for type-changing kernels
  (comparisons, casts).
• kernel_from_source() utility: new dsl_kernel.kernel_from_source()
  reconstructs a DSLKernel from its stored source text, shared by the
  CTable DSL-column loaders and the persisted LazyUDF decoder.
• Security note: .b2d files from untrusted sources that contain DSL
  computed columns execute stored Python source on open. A warning is now
  included in the documentation.

New CTable.__setitem__ column-assignment API

• t["col"] = arr: new shorthand equivalent to t["col"][:] = arr.
  Accepts any array-like including blosc2.NDArray. Raises KeyError for
  unknown columns and ValueError for views or read-only tables.

Chunked NDArray writes in extend() and Column.__setitem__

• extend({"col": ndarray}) decompresses chunk-by-chunk: when a
  blosc2.NDArray is passed as a column value to extend(), it is now
  written in chunks instead of being fully decompressed upfront. Pass
  validate=False to avoid a transient full decompression during constraint
  checking.
• col[:] = blosc2_ndarray fast path: a new no-holes fast path in
  Column.__setitem__ skips the O(n) validity-mask gather and writes the
  NDArray one chunk at a time using contiguous slice writes. Works for both
  scalar and fixed-shape ndarray columns. Falls back to a chunked fancy-index
  path when deleted rows are present.

BLOSC_ME_JIT environment variable override

• Full CLI override: BLOSC_ME_JIT now takes unconditional priority over
  both the jit= and jit_backend= keyword arguments, making it easy to
  switch JIT backends from the command line without modifying code.

Correctness fixes

• View corruption in Column.__setitem__: a None == None guard
  evaluation on view-backed columns could fire the NDArray fast path,
  bypassing physical-position remapping and silently corrupting rows. Fixed
  by explicitly checking base is None before activating the fast path.
• CTable.__setitem__ view guard: the new t["col"] = arr API now
  raises ValueError on views, matching the contract of all other mutating
  CTable methods.
• Fast path enabled for disk-opened tables: the fast path previously
  remained dormant for tables opened from disk because _last_pos starts as
  None. The guard now calls _resolve_last_pos() to lazily initialise it.
• DSL column jit_backend preserved in _empty_copy: the jit_backend
  setting was silently dropped during internal table copies; it is now
  retained.
• lazyexpr Column unwrapping: convert_inputs() now automatically
  unwraps CTable.Column objects to their backing NDArray so that shape and
  identity checks work correctly.

Documentation and examples

• Parquet-to-blosc2 walkthrough: new step-by-step tutorial added to the
  getting-started section. Thanks to @SyedIshmumAhnaf.
• CTable performance tips: new section in the overview covering when to
  prefer computed vs. generated columns, chunk sizing, and query optimisation.
• Simplified docstring examples: examples throughout ndarray.py and
  ctable.py now use blosc2.array(), blosc2.arange(), and
  blosc2.linspace() directly instead of two-step numpy-then-asarray
  patterns.
• udf-computed-col.py example: new end-to-end example demonstrating DSL
  kernel computed and generated columns.