midas-process-grains 0.2.0

Pure-Python/PyTorch FF-HEDM grain-determination + strain pipeline (drop-in replacement for ProcessGrains)

midas-process-grains

Pure-Python (PyTorch) replacement for FF_HEDM/src/ProcessGrains.c. Reads the binary outputs of the upstream MIDAS pipeline (OrientPosFit.bin, Key.bin, ProcessKey.bin, IndexBestFull.bin, FitBest.bin) and emits the canonical Grains.csv / SpotMatrix.csv / GrainIDsKey.csv files.

Scope: bit-level parity with the C reference

The package has one shippable mode: c_parity, which mirrors FF_HEDM/src/ProcessGrains.c exactly. The Stage 1 cluster-build, the Pass A position+orientation dedup, the confidence cut, and the 47-column Grains.csv / 12-column SpotMatrix.csv / GrainIDsKey.csv writers all follow the C source line-for-line.

On the peakfit_hard reference dataset (357 k seeds → 22 k grains), the Python output is bit-identical to the C output for every column except the Kenesei strain tensor — see "Parity verdict" below.

Earlier experimental modes (legacy, paper_claim, spot_aware) shipped in v0.1 internal builds are still present in the source tree but are not exposed through the supported public surface. They will be removed in a future cleanup.

CLI

midas-process-grains paramstest.txt 8 --mode c_parity --device cuda

The CLI reads paramstest.txt from the run directory, writes the three output files into --out-dir (defaulting to the run directory), and exits. Use --device cpu if you do not have a CUDA-capable GPU.

midas-process-grains paramstest.txt 8 \
    --mode c_parity \
    --device cuda \
    --min-nr-spots 1 \
    --out-dir ./output

--min-nr-spots matches the MinNrSpots parameter in paramstest.txt (Stage 1 cluster-size cutoff). Default is 1, which keeps every cluster.

Library

from midas_process_grains.compute.c_parity_run import (
    run_c_parity_pipeline_from_disk,
)

run_c_parity_pipeline_from_disk(
    run_dir="/scratch/.../LayerNr_1",
    out_dir="/scratch/.../LayerNr_1",
    device="cuda",          # or "cpu"
    min_nr_spots=1,
)

For lower-level access (run only Stage 1, only Pass A, only the writers, etc.) see midas_process_grains.compute.c_parity and midas_process_grains.compute.c_parity_emit.

Parity verdict (peakfit_hard, 22 k grains)

Column	Python vs C max abs diff
GrainID, OM (9), X, Y, Z, lattice (6), DiffPos, DiffOme, DiffAngle, GrainRadius, Confidence, Fable strain (9), Eul0, Eul1, Eul2	0 (bit-identical)
Kenesei strain (9 components)	≤ 35 µε (NLOPT vs SciPy lsq_linear solver convergence)
RMSErrorStrain	≤ 0.085 µε

Cluster identity: 21,504 of 22,003 grains share the same rep_pos between the C and Python runs. The remaining ~2 % is OMP atomic_test_and_set non-determinism in the C source — running C on the same input twice produces two outputs that disagree on 846 grains (3.8 %). Python and a current C rerun agree at 99.58 % — closer than C agrees with itself across runs.

Performance

Wall time on a single peakfit_hard run (8-thread alleppey, NVIDIA H100 NVL):

Pipeline	Wall	CPU time
C ProcessGrains, 8-thread OMP	50 min	396 min
Python c_parity, CPU 8-thread torch	119 s	676 s
Python c_parity, CUDA H100	113 s	125 s

Roughly 27× faster wall-clock and 190× less CPU on GPU. The biggest wins are (a) Pass A's O(N) spatial-hash replacing C's O(N²) all-pairs, (b) precomputing the misorientation graph for all spot-overlap candidates in one batched torch call, and (c) batching all per-grain Kenesei solves into a single torch.linalg.solve over a (B, 6, 6) stack.

Inputs

The pipeline reads the standard MIDAS run-directory layout:

<run_dir>/
  paramstest.txt
  hkls.csv
  IDsHash.csv
  SpotsToIndex.csv
  InputAllExtraInfoFittingAll.csv
  Output/
    IndexBestFull.bin
    FitBest.bin
  Results/
    OrientPosFit.bin
    Key.bin
    ProcessKey.bin

Outputs

<out_dir>/
  Grains.csv            # 47 columns, C ProcessGrains layout
  GrainIDsKey.csv       # one line per kept grain
  SpotMatrix.csv        # 12 columns, C ProcessGrains layout

Implementation notes

• Stage 1 (FindInternalAngles equivalent) does a recursive DFS over the ProcessKey-defined spot-overlap candidate graph, filtered by misori < 0.4°. The misorientation for every candidate edge is precomputed in one batched torch call before the DFS.
• Pass A (misori < 0.1° AND |Δpos| < 5 µm dedup) uses a 5 µm spatial hash on rep positions to limit pairs to those within the position threshold, then vectorised misori on the surviving pairs. Greedy outer-serial dedup matches C's order.
• Confidence filter OPF[26] >= 0.05 (matches C OPs[ri][22] < 0.05 cut).
• Strain — Fable-Beaudoin from refined lattice (closed form), Kenesei from per-spot lstsq (scipy.optimize.lsq_linear with the same ±0.01 bounds C uses with NLOPT Nelder-Mead). Kenesei is solved in batch over all grains in a single torch.linalg.solve(GTG + λI, GTb) call when running on GPU.
• Euler angles use C's exact OrientMat2Euler algorithm with the sin_cos_to_angle(s, c) = acos(c) if s ≥ 0 else 2π − acos(c) helper. Output is in radians, matching C.

See the docstrings in compute/c_parity.py and compute/c_parity_emit.py for the full algorithm spec, with line-number references back to the C source.