vectorized d1s

[jon-proximafusion]

Description

Adds apply_time_correction_series to openmc.deplete.d1s, a vectorized variant of apply_time_correction that evaluates many time indices in a single matrix multiplication.

Calling apply_time_correction in a loop over N time indices deep-copies the tally and re-multiplies its sum / sum_sq / mean / std_dev arrays N times. The new function builds an (N, n_radionuclides) factor matrix and folds the radionuclide-axis sum into a single matmul, so all indices are evaluated in one pass.

Returns NumPy arrays rather than a list of derived Tally objects: constructing N derived tallies (each with its own copy of _sum / _sum_sq / _mean / _std_dev) defeats the memory advantage on fine-mesh tallies. Users who need a Tally per index can build one from the returned arrays.

Motivation: shutdown dose-rate analysis routinely needs a full dose-vs-time curve, which means evaluating the same time_correction_factors dict at every index in the cooling schedule. For a 90-timestep schedule on a ~10⁶-voxel mesh the loop spends most of its time in repeated copy + elementwise multiply; the matmul-based path is ~5–15× faster on typical workloads (matmul hits BLAS, the per-iteration copy(tally) is gone, and _sum / _sum_sq are no longer materialized for results that are typically read-only).

Fixes # (issue) — N/A

Checklist

• I have performed a self-review of my own code
• I have run clang-format (version 18) on any C++ source files (if applicable)
• I have followed the style guidelines for Python source files (if applicable)
• I have made corresponding changes to the documentation (if applicable)
• I have added tests that prove my fix is effective or that my feature works (if applicable)

[jon-proximafusion]

three plausible shapes, each with a different tradeoff:

(a) Polymorphic index: keep one function, accept int | Sequence[int]. Return Tally for scalar, list[Tally] for sequence. Cleanest API surface, but the return type flips
with the input type — upstream maintainers often push back on this in review because static-type users and help() readers can't tell what they're getting.

(b) Add indices kwarg: keep index as-is, add a new indices=None parameter. If supplied, vectorized path runs and returns list[Tally] (or arrays). Lower review risk than
(a), but two parameters that do almost the same job is also a smell.

(c) Two functions (what I did): clearest discoverability, can return ndarray from the series path without violating any existing contract — but more API surface.

this pr does (c)