midas-joint-ff-calibrate 0.1.5

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-joint-ff-calibrate

Joint differentiable powder + FF-HEDM detector calibration.

The single-image powder calibration problem (Wright, Giacobbe & Lawrence Bright 2022 § 3; midas_calibrate_v2 paper § 9) is rank-deficient on per-panel (δy, δz) shifts: each panel's η coverage is too narrow to break the radial-vs-azimuthal degeneracy without translating the detector to multiple distances. For a ~100 kg detector on a translation stage that's impractical.

This package solves the same problem at a single distance by combining the calibrant powder image with a co-located HEDM grain-fit dataset. HEDM spots are determinate in (R, η) and distribute across all panels at varied azimuth, so the joint Fisher block on per-panel shifts becomes full rank. The same machinery generalises to refining any subset of the unified spec — geometry, distortion, panels, wavelength, per-grain orientation/position/ strain, or arbitrary user-defined blocks.

Notebooks

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

Architecture

                   midas_peakfit  (shared substrate)
                   ParameterSpec / pack / lm / laplace / TPSpline / Σ=0
                          ▲                       ▲
                          │                       │
              midas_calibrate_v2          midas_fit_grain
              (powder forward + loss)     (HEDM forward + loss)
                          ▲                       ▲
                          └──────────┬────────────┘
                                     │
                       midas_joint_ff_calibrate
                       (joint spec / loss / drivers)

Pure-Python, fully autograd-traced. No legacy C code (FitMultipleGrains.c is not used).

Drivers

• pipelines.alternating.AlternatingDriver — the recommended default. Outer loop alternates between (geometry + grain orientation/position) and (grain strain) passes. Cheap, robust.
• pipelines.full_joint.FullJointDriver — refine every refined parameter at once with a single LM call; report MAP plus Laplace covariance.
• pipelines.identifiability.fisher_block_rank — diagnostic that reports rank, condition number, and σ per parameter on a user-chosen Fisher block under powder-only, HEDM-only, or joint evidence.

Status

Pre-alpha. Companion paper to the J. Appl. Cryst. submission of midas_calibrate_v2 (paper 3); see dev/paper/.