midas-pf-odf

Joint per-grain peak-shape inversion of pf-HEDM (point-focused HEDM, also known as scanning 3DXRD) data.

Phase 1 (current): for each grain, fit all of its voxels' (R_V, ε_V) jointly to image-MSE on measured 3D peak patches, given voxel→grain assignment as input. Differentiable PyTorch forward; voxel-summed splat per (spot, scan); closed-form per-spot intensity scale c_s*.

Phase 2 (later): per-voxel ODF (sub-grain mosaic / GND density) on the same forward.

Notebooks

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

Status

Pre-alpha; private. See dev/RESTART.md for the active worklog and resume point.

Where this sits relative to the literature

Henningsson et al. 2020 (J. Appl. Cryst.) introduced PCR / ASR — joint multi-voxel intragranular strain reconstruction from scanning 3DXRD, using peak centroids only. They explicitly call out peak-shape inversion as the next direction (untapped at time of writing). This package takes that step:

• Centroid → peak-shape (image-MSE on (F, P, P) patches).
• Hand-derived Jacobian → autograd / PyTorch.
• Single (R_V, ε_V) per voxel → per-voxel ODF (Phase 2).

The forward model is midas_diffract.HEDMForwardModel; reusable infrastructure (sparse splatter, ODF parameterizations, held-out CV selector, sparse-tile chunking) comes from midas_grain_odf.

Quickstart

cd packages/midas_pf_odf
pip install -e .[dev]
python -m pytest tests/ -xvs

Layout

midas_pf_odf/        — public library
    simulate.py       — synthetic plant (orientation+strain gradients, multi-grain, noise)
    forward.py        — joint per-grain forward (soft beam gate, voxel-summed splat)
    inversion.py      — joint inversion driver (Adam / L-BFGS, identifiability knobs)
    validation.py     — per-voxel RMSE vs plant, held-out R²
    io.py             — I/O for real data (deferred)
dev/                  — implementation plan, paper, notebooks, worklog
tests/                — synthetic round-trip tests

Decisions locked

1. Identifiability knob: project ε to mean-zero per grain (default, recommended) or per-voxel-lattice over-parameterized (toggle).
2. Smoothness regularizer: off by default (λ_smooth = 0).
3. Synthetic source: torch-native simulator inside this package.
4. Outlier voxels: soft-mask by held-out R² in the loss.
5. Multi-grain parallelism: one grain × one GPU each via parsl (deferred until workflow integration).
6. Output schema: parallel HDF5; centroid path untouched (deferred).