26 projects
midas-index
Pure-Python/PyTorch FF-HEDM indexer (drop-in replacement for IndexerOMP/IndexerGPU)
midas-diffract
End-to-end differentiable forward model for High-Energy Diffraction Microscopy (FF, NF, pf-HEDM)
midas-pipeline
Unified MIDAS HEDM orchestrator. FF is the single-scan degeneracy of PF; one orchestrator, --scan-mode {ff,pf}.
midas-process-grains
Pure-Python/PyTorch FF-HEDM grain-determination + strain pipeline (drop-in replacement for ProcessGrains)
midas-calibrate-v2
Fully differentiable detector calibration for MIDAS — multi-image, Bayesian, NN-residual, joint forward cake. Coexists with midas-calibrate v1.
midas-suite
Meta-package: installs the MIDAS Python pipeline (FF/NF/PF HEDM, calibration, peakfit, integration, transforms, ODF, stress).
midas-ff-pipeline
[DEPRECATED] End-to-end pure-Python FF-HEDM workflow orchestrator (1-N detectors). Use midas-pipeline run --scan-mode ff instead.
midas-params
MIDAS parameter-file registry, validator, and wizard for FF/NF/PF/RI pipelines
midas-fit-grain
PyTorch single/multi-grain refiner (drop-in replacement for FitPosOrStrainsOMP / FitPosOrStrainsGPU) + bundled c-omp C refiner
midas-joint-ff-calibrate
Joint differentiable powder + FF-HEDM detector calibration for MIDAS. Breaks single-distance per-panel rank-1 degeneracy by combining a powder calibrant pseudo-strain residual with HEDM grain spot residuals in one Bayesian MAP + Laplace inference. Pure-Python, fully autograd-traced; consumes the shared midas_peakfit substrate (ParameterSpec, lm_minimise, laplace_at_map, TPSpline, zero_sum_residual) plus midas_calibrate_v2 (powder forward + pseudo_strain) and midas_fit_grain (HEDM spot forward).
midas-calibrate
Native Python/Torch detector calibration for MIDAS — replaces AutoCalibrateZarr → CalibrantIntegratorOMP. CPU & GPU; LM-based refinement.
midas-uq
Cross-validation based uncertainty quantification for HEDM grain refinement (FF, NF, pf)
midas-invert
Domain-agnostic differentiable-inversion primitives for MIDAS: gradient fitting, scale-invariant losses, Laplace uncertainty, Fisher-information experiment design, mixture deconvolution, and amortised-inference surrogates.
midas-propagate
End-to-end uncertainty propagation and joint re-refinement for HEDM (calibration -> indexing -> per-grain refinement -> stress)
midas-integrate-v2
Differentiable, autodiff-aware radial integration for area X-ray detectors. Joint refinement counterpart to midas-integrate.
midas-integrate
Pure-Python radial integration for area X-ray detectors (MIDAS) — DetectorMapper + CSR integration + streaming server, CPU/GPU selectable
midas-peakfit
Differentiable PyTorch peak-fitting for FF-HEDM Zarr archives (drop-in replacement for PeaksFittingOMPZarrRefactor)
midas-transforms
Pure-Python/PyTorch FF-HEDM transforms (drop-in for MergeOverlappingPeaksAllZarr / CalcRadiusAllZarr / FitSetupZarr / SaveBinData)
midas-zipper
Standalone zarr-zip generation for MIDAS FF/PF workflows (no C-binary or source-tree dependency).
midas-distortion
Canonical MIDAS radial-distortion model: layout tables, v1<->v2 coefficient mapping, and a backend-agnostic (numpy/torch) distortion kernel.
midas-nf-preprocess
Differentiable PyTorch port of NF-HEDM preprocessing (CPU/CUDA/MPS): hex grid, tomo filter, diffraction-spot prediction, image processing
midas-nf-pipeline
Pure-Python NF-HEDM pipeline orchestrator (single + multi-resolution, multi-layer) — drop-in replacement for nf_MIDAS.py / nf_MIDAS_Multiple_Resolutions.py
midas-nf-fitorientation
Differentiable NF-HEDM orientation and calibration fitter (drop-in replacement for FitOrientationOMP, FitOrientationParameters, FitOrientationParametersMultiPoint)
midas-stress
Crystallographic stress-strain analysis with Voigt-Mandel notation and mechanical equilibrium constraints; torch-native API across orientation/frames/tensor/equilibrium/materials/plasticity/elastic_inverse
midas-hkls
Pure-Python crystallography, CIF I/O, and differentiable structure factors for X-ray diffraction.
midas-parsl-configs
Bundled + user-extensible Parsl configs for MIDAS pipelines.
