Skip to main content

midas-dfxm

Differentiable forward + inverse for Dark-Field X-ray Microscopy (DFXM).

DFXM images a bulk grain through a magnifying objective (CRL / MLL) placed on one diffracted beam. The objective is a reciprocal-space bandpass: a sample voxel contributes intensity to its magnified detector pixel only if its local scattering vector falls inside the instrument's resolution function for the current goniometer setting. Rocking the sample (a mosaicity scan) maps local orientation; scanning 2θ / energy (a strain scan) maps local d-spacing; weak-beam settings image individual dislocations.

This package models that signal from a voxelised deformation-gradient field F(r), and (later phases) inverts a DFXM image stack back to F(r) and to per-dislocation defect types (edge/screw, Burgers vector, slip system).

Reuse-first, no re-porting. Built on the MIDAS differentiable stack: midas-stress (orientation/strain), midas-hkls (structure factors, form factors, DWF), midas-2d (continuous-q structure factor, resolution convolution), midas-defect (Stroh anisotropic-elasticity contrast solver, slip systems, GND, planar-defect rods), midas-invert (fit / UQ / experiment design), midas-distortion (detector model). Everything is torch-differentiable and device-portable (CPU / CUDA / MPS).

See implementation_plan.md for the full roadmap, physics reuse map, phase gates, and honest scope/novelty gates.

Status

Pre-alpha (v0.0.1a0). Phases 0,1,3,4,5 + a simulation-anchored roadmap implemented and tested — 64 tests pass (CPU + MPS; 3 CUDA-skipped), ~2400 LOC.

Highlights:

  • Phase 4 (defect typing): anisotropic-elasticity (Stroh) per-dislocation forward; g·b invisibility; edge/screw character; Burgers-vector recovery.
  • Phase 5 (inverse typing): identify_dislocation recovers slip system, character, core position, and the Burgers-vector sign (the gap Borgi 2025 leaves open) from multi-reflection weak-beam images.
  • Phase 3 (field inverse): full strain-tensor recovery + identifiability + UQ; honest finding — regularisation helps at low SNR, ~neutral at high SNR.
  • #1 physics coupling: recover a GND density end-to-end through the forward (Nye κ = ρb) — the DFXM↔DDD/CP interface, differentiably.
  • #2 credibility anchor: independent numpy oracle agrees bit-for-bit (~1e-16).
  • External-field ready: field_from_deformation_gradient consumes an external F(r) with zero rework.

See SIMULATION_CATALOG.md and the shipped tutorials in midas_dfxm/examples/, each runnable as a module:

python -m midas_dfxm.examples.tutorial_dislocation_typing

Figures land in dev/paper/figures/ inside a clone, ./figures otherwise; set MIDAS_FIGDIR to send them somewhere else.

Earlier milestone note

Phases 0–1 implemented and tested (30 passing, CPU + MPS):

  • Phase 0 — conventions + field. conventions.py (lab/sample/imaging frames, goniometer), field.py (DeformationField, the kinematic deform operator Q = F⁻ᵀ G0, polar decomposition F = R·U), io.py (synthetic-field generators: perfect crystal, orientation gradient, uniform strain, isotropic screw dislocation; plus a stub loader for an external collaborator field).
  • Phase 1 — geometrical-optics forward. resolution.py (anisotropic-Gaussian reciprocal-space acceptance), optics.py (magnifying inclined projection + bilinear detector splat), scan.py (mosaicity / rocking / strain scan builders, Bragg-angle helpers), forward.py (dfxm_image, dfxm_stack, mosaicity_curve).

Validated analytic limits: rocking-curve FWHM = 2√(2ln2)·σ⊥ / |axis×q|, FCC forbidden reflection is dark, uniaxial strain shifts |Q| correctly; plus gradcheck on the deform operator and end-to-end autograd to F and the instrument widths.

Next: Phase 2 (field forward on a collaborator's realistic F(r) when it lands), Phase 3 (field inverse + identifiability study), Phase 4 (per-dislocation forward via the Stroh solver + g·b defect typing), Phase 5 (inverse defect discovery).

Quickstart

import torch
from midas_dfxm import (
    make_uniform_field, with_orientation_gradient,
    GoniometerSetting, reference_q_nom, aligned_resolution,
    ObjectiveOptics, bragg_two_theta_deg, dfxm_image,
)

# A curved crystal grain (smooth lattice rotation across x).
field = make_uniform_field(shape=(64, 64, 1), spacing_um=0.5)
field = with_orientation_gradient(field, axis=(0, 0, 1), deg_per_um=0.01, along=0)

hkl, center = (1, 1, 1), GoniometerSetting()
q_nom = reference_q_nom(field, hkl, center)
res = aligned_resolution(q_nom, sigma_par=5e-3, sigma_perp=5e-3)
tt = bragg_two_theta_deg(float(torch.linalg.vector_norm(q_nom)), wavelength_A=0.172979)
optics = ObjectiveOptics(two_theta_deg=tt, magnification=10.0, detector_shape=(256, 256))

image = dfxm_image(field, hkl, center, res, optics)   # (256, 256), differentiable

Lab frame and scattering plane

Two beamline conventions are settable, and both default to the ESRF ID06-HXM case so existing code is unchanged.

Which axis is the beam — LabFrame carries the choice; outboard is derived as up × beam, so a frame cannot be built inconsistently.

preset beam up outboard
MIDAS_FRAME (default, = ESRF) x z y
APS_FRAME (Park convention) Z Y X
BEAM_X_UP_Y x y −z

Any other orthogonal (beam, up) pair works too. frame_rotation, convert_vector, convert_tensor (strain, F, Nye) and convert_orientation move between them, and agree exactly with midas_stress.frames for the MIDAS↔APS pair — that module stays the repo-wide authority.

from midas_dfxm import GoniometerSetting, APS_FRAME, MIDAS_FRAME

g = GoniometerSetting.from_aps(mu=2.0, omega=4.0)   # geometry supplied in APS coords
g_midas = g.in_frame(MIDAS_FRAME)                   # what the rest of the package uses

The motor angles are the same numbers in both — only the axes they are taken about are relabelled (G_dst = R G_src Rᵀ).

Which plane it scatters in — ScatteringGeometry(plane="vertical"|"horizontal"), or an explicit deflection direction for an oblique plane. This is not a relabelling; it changes physics in two places:

  • Which motor is the base tilt. Vertical plane → mu. Horizontal plane → omega, and mu is inert for a reflection whose Q lies along outboard, since it rotates about the axis Q sits on.
  • Which divergence limits which width. Poulsen 2017 Eqs. 58–63 put div_v on sigma_rock/sigma_par and div_h on sigma_roll. That holds for a vertical plane; a horizontal one swaps them. poulsen_resolution_widths(..., geometry=...) resolves it. The stock defaults have div_v == div_h, which hides the swap entirely — on an anisotropic source it is a ~1.6× error in each width, with sigma_roll further amplified by 1/sin θ.
from midas_dfxm import diffracted_beam_direction, poulsen_resolution_widths

diffracted_beam_direction(12.0, geometry="horizontal")   # APS 6-ID-C transmission
poulsen_resolution_widths(3.0, two_theta_deg=20.0, geometry="horizontal",
                          div_v=0.2e-3, div_h=1.0e-3)

Note that two_theta_from_k_out needs the frame, and gets it wrong quietly without one: a horizontal-plane 12° reflection in APS components, read as MIDAS components, returns 78° (90 − 2θ); the vertical-plane case returns a flat 90° for any angle, since the beam component is identically zero. Pass geometry= whenever the vector did not come from this package.

Tests

# macOS: work around the duplicate-OpenMP-runtime abort (torch + MIDAS siblings).
export KMP_DUPLICATE_LIB_OK=TRUE
python -m pytest tests/ -q

Markers: unit (analytic correctness), autograd (gradcheck / autograd), device (CPU/CUDA/MPS parity), slow (heavy integration).

Metadata

Release files for midas-dfxm 0.6.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for midas-dfxm 0.6.1
File Size Uploaded
midas_dfxm-0.6.1.tar.gz 195.2 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for midas-dfxm 0.6.1
File Interpreter ABI Platform
midas_dfxm-0.6.1-py3-none-any.whl Python 3 none any Details

Total release size: 352.6 kB

Release files / midas_dfxm-0.6.1.tar.gz

Download URL midas_dfxm-0.6.1.tar.gz
Size 195.2 kB
Tags Source
SHA-256 checksum
How to use checksums
56956d0e77910bf11d47b95fa04cd7b2f6f8aada6e7bb773bfad723fdc6bfaf4
BLAKE2b-256 checksum
How to use checksums
967aac8101e71e6b4a94000444c8ca165fced4a47794c7a31e77c368230a1ee1
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Aug 18, 2026.

Transparency log

Release files / midas_dfxm-0.6.1-py3-none-any.whl

Download URL midas_dfxm-0.6.1-py3-none-any.whl
Size 157.4 kB
Tags Python 3
SHA-256 checksum
How to use checksums
864721dd13c54c8bf8e28eb67342c6827e9d79c443e92c15a32a109ed3f7c572
BLAKE2b-256 checksum
How to use checksums
f70d77ff6cd79eda60267f2716fe9647eefc129fd7a3496cbe8e98bbfafcc8c0
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Aug 18, 2026.

Transparency log

Release history Release notifications | RSS feed

0.14.0

2 release files

0.13.0

2 release files

0.12.0

2 release files

0.11.0

2 release files

0.10.0

2 release files

0.9.0

2 release files

0.8.0

2 release files

0.7.0

2 release files

0.6.3

2 release files

0.6.2

2 release files

This release

0.6.1 This release

2 release files

0.6.0

2 release files

0.5.0

2 release files

0.4.0

2 release files

0.3.3

2 release files

0.3.2

2 release files

0.3.1

2 release files

0.3.0

2 release files

0.2.0

2 release files

0.1.0

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page