midas-calibrate 0.2.4

Native Python/Torch detector calibration for MIDAS — replaces AutoCalibrateZarr → CalibrantIntegratorOMP. CPU & GPU; LM-based refinement.

midas-calibrate

Native Python/Torch detector geometry calibration for MIDAS. Replaces AutoCalibrateZarr → CalibrantIntegratorOMP → CalibrationCore. Same input parameter file format, byte-compatible output, runs on CPU or GPU.

Notebooks

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

Quick start

import tifffile
from midas_calibrate import CalibrationParams, autocalibrate

params = CalibrationParams.from_file("calib.txt")
image = tifffile.imread("ceo2_calibrant.tif")
result = autocalibrate(params, image)

result.params.write("calib_refined.txt")
print(f"final mean strain: {result.history[-1].mean_strain_uE:.1f} μϵ")

CLI:

midas-autocalibrate calib.txt --image ceo2.tif --output calib_refined.txt

How it works

• E-step — midas-integrate builds a CSR pixel→bin map from the current geometry and integrates the image into a 2D (R, η) cake. Per (ring × η-bin) the radial peak position is extracted via weighted centroid.
• M-step — fit detector geometry to the (Y_pix, Z_pix, ring) data using a custom batched Levenberg-Marquardt solver (midas_peakfit.lm_solve_generic) with sigmoid-bounded reparameterisation, Cholesky_ex, and optional Huber loss reshaping.
• Orchestrator — alternating E↔M iterations with optional σ-clip outlier rejection between iterations.

The geometry forward model in geometry_torch.py is a byte-for-byte port of midas_integrate.geometry.pixel_to_REta — verified to fp64 epsilon by parity tests.

Dependencies

• midas-hkls — pure-Python crystallography (sginfo replacement)
• midas-integrate — CSR pixel→bin mapper + integration
• midas-peakfit ≥ 0.2.0 — generic LM solver

Synthetic-data parity test

The end-to-end synthetic test forward-simulates a CeO₂ calibrant image at known geometry, perturbs the seed (Lsd ±300μm, BC ±1.5px, tilts ±0.06°), and verifies recovery:

[iter 0] n_fits= 176  rc=0  strain=  105.2μϵ  Lsd=1000219.4  BC=(512.20,511.91)  ty=0.343  tz=0.180
[iter 1] n_fits= 176  rc=0  strain=   25.7μϵ  Lsd= 999973.5  BC=(512.01,512.00)  ty=0.403  tz=0.250
[iter 2] n_fits= 176  rc=0  strain=   19.4μϵ  Lsd= 999946.1  BC=(511.99,512.00)  ty=0.400  tz=0.267
[iter 3] n_fits= 176  rc=0  strain=   21.6μϵ  Lsd= 999918.1  BC=(511.99,512.00)  ty=0.392  tz=0.285

Final recovery: Lsd within 82μm of truth, BC within 0.01 px, tilts within 0.04°. Mean strain 21.6μϵ, well under the 50μϵ MIDAS calibration target.

Engines

autocalibrate is the alternating E↔M engine (default).

autocalibrate_joint will be the fully differentiable engine — geometry + per-(ring × η-bin) peak-shape parameters jointly refined in one batched Schur-complement-reduced LM (see §13 of the design doc). v0.1 ships with a working stub that delegates to the alternating engine; the arrowhead-LM infrastructure (midas_peakfit.lm_solve_arrowhead) is in place and tested.

Status

v0.2.x — alternating engine production-ready, joint engine scaffolded. For the torch-native, fully differentiable next-generation calibration stack see midas-calibrate-v2.

See AutoCalibrate.md for the manual.