44 projects
midas-dfxm
Differentiable forward + inverse for Dark-Field X-ray Microscopy (DFXM): deformation-gradient field imaging through a magnifying objective, and per-dislocation defect-type discrimination, on top of midas-stress / midas-hkls / midas-defect.
midas-saxs
Differentiable small-angle X-ray scattering for MIDAS: particle form factors and polydispersity, plus a 2-D detector-image forward model for dislocation loops and voids driven by a discrete-dislocation network.
midas-defect
Differentiable, phase-agnostic diffuse-scattering defect metrology on top of standard FF-HEDM: intensity-budget closure, per-grain dislocation density, fault-rod and selection-rule tests.
midas-pipeline
Unified MIDAS HEDM orchestrator. FF is the single-scan degeneracy of PF; one orchestrator, --scan-mode {ff,pf}.
midas-calibrate-v2
Fully differentiable detector calibration for MIDAS — multi-image, Bayesian, NN-residual, joint forward cake. Coexists with midas-calibrate v1.
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-grain-odf
Differentiable per-grain orientation distribution functions from FF-HEDM data
midas-pdf
Differentiable, error-propagating total-scattering / pair-distribution-function (PDF, G(r)) pipeline. Thin Faber-Ziman layer over midas-hkls (form factors) and midas-integrate-v2 (integration, Compton, sine FT).
midas-process-grains
Pure-Python/PyTorch FF-HEDM grain-determination + strain pipeline (drop-in replacement for ProcessGrains)
midas-pf-odf
Joint per-grain peak-shape inversion of pf-HEDM data; per-voxel orientation/strain (and Phase 2: per-voxel ODF)
midas-transforms
Pure-Python/PyTorch FF-HEDM transforms (drop-in for MergeOverlappingPeaksAllZarr / CalcRadiusAllZarr / FitSetupZarr / SaveBinData)
midas-diffract
End-to-end differentiable forward model for High-Energy Diffraction Microscopy (FF, NF, pf-HEDM)
midas-integrate-v2
Differentiable, autodiff-aware radial integration for area X-ray detectors. Joint refinement counterpart to midas-integrate.
midas-params
MIDAS parameter-file registry, validator, and wizard for FF/NF/PF/RI pipelines
midas-integrate
Pure-Python radial integration for area X-ray detectors (MIDAS) — DetectorMapper + CSR integration + streaming server, CPU/GPU selectable
midas-dt
Diffraction / X-ray computed tomography (XRD-CT): scan model, sinogram assembly with error propagation, reconstruction and per-voxel peak fitting.
midas-peakfit
Differentiable PyTorch peak-fitting for FF-HEDM Zarr archives (drop-in replacement for PeaksFittingOMPZarrRefactor)
midas-fit-grain
PyTorch single/multi-grain refiner (drop-in replacement for FitPosOrStrainsOMP / FitPosOrStrainsGPU) + bundled c-omp C refiner
midas-nf-fitorientation
Differentiable NF-HEDM orientation and calibration fitter (drop-in replacement for FitOrientationOMP, FitOrientationParameters, FitOrientationParametersMultiPoint)
midas-pink
Pink-beam HEDM with spectrum-aware differentiable inversion (extends midas-diffract from mono to arbitrary S(E))
midas-zipper
Standalone zarr-zip generation for MIDAS FF/PF workflows (no C-binary or source-tree dependency).
midas-tomo
Gridrec filtered back-projection CT reconstruction (CPU + CUDA) with a NumPy API
midas-snapshot
Still-frame (no rotation) diffraction time series: per-frame matrix scale and halo traces, calibrated sparse-spot detection, feature linking, measured detection limits and candidate-cell tests with nulls and negative controls.
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-index
Pure-Python/PyTorch FF-HEDM indexer (drop-in replacement for IndexerOMP/IndexerGPU)
midas-nf-preprocess
Differentiable PyTorch port of NF-HEDM preprocessing (CPU/CUDA/MPS): hex grid, tomo filter, diffraction-spot prediction, image processing
midas-hkls
Pure-Python crystallography, CIF I/O, and differentiable structure factors for X-ray diffraction.
midas-plotting
Standard plots for MIDAS reconstructions - near-field, far-field and Laue: IPF maps and legends, grain maps, pole figures, strain and size distributions, and Laue texture diagnostics against their chance levels.
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
laue-index
LaueMatching Python orchestration: typed pipeline stages for white-beam Laue indexing
laue-jax
Differentiable JAX forward projection for white-beam Laue microdiffraction: a single-framework port of laue-torch so the Laue forward composes with JAX-CPFEM in one autodiff graph.
laue-torch
Differentiable PyTorch forward model for white-beam Laue microdiffraction, with per-voxel orientation- and strain-distribution (ODF/SDF) recovery, coded-aperture depth resolution, and joint multi-grain fitting.
midas-ddd
Dislocation networks for MIDAS: discrete-dislocation-dynamics (ExaDiS / ParaDiS) ingest, anisotropic-elasticity (Stroh) primitives, and the differentiable real-space and Fourier displacement fields that drive the SAXS, near-Bragg diffuse and DFXM forward models.
midas-dct-tt
Differentiable forward + inverse for diffraction contrast tomography (DCT) and topotomography (TT): grain-shape topographs, laminographic sampling, and joint (shape, deformation) recovery, on top of midas-dfxm / midas-diffract.
midas-suite
Meta-package: installs the MIDAS Python pipeline (FF/NF/PF HEDM, calibration, peakfit, integration, transforms, ODF, stress).
midas-xaf
Cross-Axis Faceted HEDM (XAF-HEDM): design + simulation toolkit for merged-reciprocal-space far-field HEDM through a six-faceted diamond anvil cell
midas-uq
Cross-validation based uncertainty quantification for HEDM grain refinement (FF, NF, pf)
midas-ff-pipeline
[RETIRED] Argv shim that delegates to midas-pipeline. Use midas-pipeline run --scan-mode ff instead; removed at 1.0.0.
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-2d
Differentiable diffraction for 2D / few-layer & ultrafast experiments: finite-size interference (Laue oscillations), crystal-truncation rods, and rocking curves on top of midas-hkls.
midas-parsl-configs
Bundled + user-extensible Parsl configs for MIDAS pipelines.
midas-propagate
End-to-end uncertainty propagation and joint re-refinement for HEDM (calibration -> indexing -> per-grain refinement -> stress)
midas-distortion
Canonical MIDAS radial-distortion model: layout tables, v1<->v2 coefficient mapping, and a backend-agnostic (numpy/torch) distortion kernel.
