Skip to main content

midas-peakfit

Differentiable PyTorch peak-fitting for FF-HEDM Zarr archives. A drop-in replacement for PeaksFittingOMPZarrRefactor (C/OpenMP/NLopt) with the following changes:

  • Optimizer: Nelder-Mead → batched Levenberg-Marquardt on CPU/CUDA
  • Backend: OpenMP → PyTorch (autograd, batched linear algebra)
  • Precision: fp64 default; --dtype float32 available for speed
  • Output: identical binary format (AllPeaks_PS.bin, AllPeaks_PX.bin)
  • CLI: drop-in compatible

The C tool is kept as the validation oracle. Output is scientifically equivalent to the C tool, not bit-exact (LM and Nelder-Mead converge to slightly different minima within the same basin).

Installation

pip install -e packages/midas_peakfit[dev]

or once published:

pip install midas-peakfit

PyTorch with CUDA support must be installed separately if GPU acceleration is desired. Follow the PyTorch install guide for the right wheel for your CUDA version.

Notebooks

Worked-example Jupyter notebooks live in notebooks/. They are not shipped with pip install — get them by cloning the MIDAS repository.

Usage

peakfit_torch DataFile.MIDAS.zip 0 1 8                    # CPU/CUDA auto
peakfit_torch DataFile.MIDAS.zip 0 1 8 --device cuda --dtype float32
peakfit_torch DataFile.MIDAS.zip 0 1 8 OutputFolder 1     # explicit ResultFolder + fitPeaks
peakfit_torch DataFile.MIDAS.zip 0 1 8 \
    --validate-against /path/to/c_AllPeaks_PS.bin         # parity check

Positional args mirror the C tool exactly: DataFile blockNr nBlocks numProcs [ResultFolder] [fitPeaks].

New flags:

Flag Default Meaning
--device {cpu,cuda} cuda if available else cpu Compute device
--dtype {float32,float64} float64 Numeric precision
--batch-size N 4096 Cross-frame region batch threshold
--validate-against PATH — Compare to C-produced AllPeaks_PS.bin and emit parity report
--deterministic off Force deterministic algorithms (fp64 only)

Parity tolerances

Field Tolerance
nPeaks per frame, pixel sets, maxY/maxZ, returnCode, maskTouched exact
YCen, ZCen, Radius, diffY, diffZ ≤ 0.05 px
Eta ≤ 0.02°
IMax, IntegratedIntensity, RawSumIntensity ≤ 1% relative
BG, SigmaR, SigmaEta, σGR, σLR, σGEta, σLEta, MU, FitRMSE ≤ 5% relative

Downstream gate: indexer (IndexerOMP) on both outputs must produce identical grain orientations within 0.05° misorientation.

Output

Two binary files in {ResultFolder}/Temp/:

  • AllPeaks_PS.bin — peak summary (29 columns × nPeaks per frame; see FF_HEDM/src/PeaksFittingConsolidatedIO.h for layout).
  • AllPeaks_PX.bin — pixel coordinates for each peak.

These files are byte-compatible with the C tool's output and readable by ConsolidatedPeakReader/ConsolidatedPixelReader in that header.

Tests

cd packages/midas_peakfit
pytest tests/ -v                      # unit tests (fast)
pytest tests/ -v -m slow              # full pipeline parity (~3 min)
pytest tests/ -v -m gpu               # CUDA-only tests (skip on CPU)

License

BSD-3-Clause.

Metadata

Release files for midas-peakfit 0.8.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for midas-peakfit 0.8.0
File Size Uploaded
midas_peakfit-0.8.0.tar.gz 174.9 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for midas-peakfit 0.8.0
File Interpreter ABI Platform
midas_peakfit-0.8.0-py3-none-any.whl Python 3 none any Details

Total release size: 323.0 kB

Release files / midas_peakfit-0.8.0.tar.gz

Download URL midas_peakfit-0.8.0.tar.gz
Size 174.9 kB
Tags Source
SHA-256 checksum
How to use checksums
3b17c2762c9726fff8c578676e8040cbdbd124ec6cf0592bc7f49ba70740f1be
BLAKE2b-256 checksum
How to use checksums
82aa001bb6a60c0a1a78b55a567584bd2a42afbbf3dec0265c6b925b2926a072
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Oct 14, 2026.

Transparency log

Release files / midas_peakfit-0.8.0-py3-none-any.whl

Download URL midas_peakfit-0.8.0-py3-none-any.whl
Size 148.1 kB
Tags Python 3
SHA-256 checksum
How to use checksums
081f9fae8efa19266cc3c4093d2c341c55f4e9ee85adde5c5826d1fa0b12f6cb
BLAKE2b-256 checksum
How to use checksums
4dde7177ef664f50690955e3ed110710be85dbe7fb091d41201328dd6a7d7c33
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Oct 14, 2026.

Transparency log

Release history Release notifications | RSS feed

This release

0.8.0 This release

2 release files

0.7.0

2 release files

0.6.0

2 release files

0.5.1

2 release files

0.5.0

2 release files

0.4.6

2 release files

0.4.5

2 release files

0.4.4

2 release files

0.4.3

2 release files

0.4.2

2 release files

0.4.1

2 release files

0.4.0

2 release files

0.3.0

2 release files

0.2.0

2 release files

0.1.0

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page