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Unify numerical kernels and mapped-array execution - #56
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…ased ChaCha RNG streams, lazy array views and repeated-key assignment
…tion total dtype; add reduced_power_slope and real_power/power_slope Ops
…types, ranges, partials and folds; skip native products for kind policies
Formula is the one home for element-wise arithmetic; key rules, Bitwise and slow events live here for BPL and Python alike. All tests pass.
Restore released complex and power/root algorithms through NativeSlices, retain constant shortcuts, and validate their public kernels against the accuracy corpus. Share structured write and fold geometry and Boolean scan packing. Calibrate x86 timing targets against released builds and document the accuracy exceptions in nbs/accuracy.qmd.
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Replace per-form dispatch tables with shared Formula and Kernel interfaces for numerical formulas, kind/domain rules, validity and repair, and derivatives. Use these interfaces across scalar and array operations, folds, scans, contractions and indexed updates.
Keep indexed and padded selections as shared views and route Python operations through the array core. Boolean selectors denote positions, missing-key reads use fill, keyed writes can extend axes, integer casts floor and saturate, and integer matrix products wrap. Execution reports expose structured records.
Use ChaCha8 counter streams for sampling and improve negative-power accuracy. Separate timing suites from correctness tests, remove performance checks from CI, and update optimized build profiles.