midas-diffract 0.2.0

End-to-end differentiable forward model for High-Energy Diffraction Microscopy (FF, NF, pf-HEDM)

midas-diffract

End-to-end differentiable forward model for High-Energy Diffraction Microscopy (HEDM), covering far-field (FF), near-field (NF), and point-focused (pf-HEDM) geometries. Pixel-exact agreement with the canonical C reference simulators in MIDAS.

Companion paper: Sharma, Zhang, Andrejevic & Cherukara, An End-to-End Differentiable Forward Model for High-Energy Diffraction Microscopy, IUCrJ (in preparation, 2026).

Installation

pip install midas-diffract           # core forward model + losses + optimizer
pip install midas-diffract[hkls]     # also installs midas-hkls for the
                                     # pure-Python reflection-list helper

Optional PyTorch CUDA or MPS back-ends are used automatically if available.

Quick start

import torch
import midas_diffract as md
from midas_hkls import Lattice, SpaceGroup           # optional, see [hkls]

# Detector + scan geometry
geom = md.HEDMGeometry(
    Lsd=1_000_000.0,              # um
    y_BC=1024.0, z_BC=1024.0,
    px=200.0,
    omega_start=0.0, omega_step=0.25, n_frames=1440,
    n_pixels_y=2048, n_pixels_z=2048,
    min_eta=6.0,
    wavelength=0.172979,           # Angstroms
)

# Reflection list: either compute from a SpaceGroup + Lattice via the
# midas-hkls helper, or supply (hkls_cart, thetas, hkls_int) yourself
# (e.g. parsed from MIDAS GetHKLList output).
sg = SpaceGroup.from_number(225)                        # FCC
lat = Lattice.for_system("cubic", a=4.08)               # Au
hkls_cart, thetas, hkls_int = md.hkls_for_forward_model(
    sg, lat, wavelength_A=geom.wavelength, two_theta_max_deg=15.0,
)

model = md.HEDMForwardModel(
    hkls=hkls_cart, thetas=thetas, geometry=geom, hkls_int=hkls_int,
)

# Forward pass: grain state -> predicted spots. All inputs are leaves
# of the autograd graph.
euler = torch.tensor([[45.0, 30.0, 60.0]], requires_grad=True) * (3.14159 / 180)
pos   = torch.tensor([[0.0, 0.0, 0.0]],  requires_grad=True)
latc  = torch.tensor([4.08, 4.08, 4.08, 90.0, 90.0, 90.0], requires_grad=True)
spots = model(euler, pos, lattice_params=latc)

# Scalar loss -> gradients w.r.t. orientation, position, lattice
loss = ((spots.omega * spots.valid) ** 2).sum()
loss.backward()

Output modes

• md.HEDMForwardModel.predict_spot_coords(spots, space="angular") — returns (2θ, η, ω) in radians for each valid reflection (FF and pf-HEDM).
• md.HEDMForwardModel.predict_spot_coords(spots, space="detector") — returns (y_pixel, z_pixel, frame_nr) in fractional units (FF and pf-HEDM).
• md.HEDMForwardModel.predict_images(spots, ...) — renders a differentiable 3D detector volume via Gaussian splatting (NF-HEDM output mode).

Validation

The forward model has been validated to pixel-exact agreement against the canonical C simulators ForwardSimulationCompressed and simulateNF in the MIDAS distribution. See the companion paper and the MIDAS repository fwd_sim/paper/ directory for reproducibility scripts.

Scope

midas-diffract v0.1.x is deliberately focused on the forward model and its gradient chain. The following capabilities build on this substrate and are released separately as they mature:

• Sub-voxel grain mixtures
• Physics-informed regularisation
• Bayesian uncertainty quantification via HMC / variational inference
• Temporal 4D-HEDM tracking
• Coupling to differentiable crystal plasticity (JAX-FEM)
• EM spot ownership for ambiguous FF patterns

Citation

If you use midas-diffract in published work, please cite the companion paper.

Licence

BSD-3-Clause.