ebsdsim 0.1.4

GPU-accelerated dynamical EBSD master-pattern simulation.

ebsdsim

Dynamical EBSD master-pattern simulation for Python.

ebsdsim computes Lambert-projected Kikuchi master patterns from a crystal structure using multi-beam dynamical electron diffraction (Bloch formulation). Beams are classified with Bethe perturbation theory (bethe_c_* cutoffs); the pattern is shaped equally by the beam and Monte Carlo model (energy, tilt, depth / energy distribution) and by those dynamical settings (Bethe cutoffs, rank, dmin).

The default Lambert raster uses halfw=250, i.e. 501×501 pixels (side = 1 + 2 × halfw, always odd).

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Features

• Dynamical diffraction — Bloch-theory multi-beam solve with fixed-rank Smith / Lyapunov iteration; Bethe theory selects which reflections enter the system.
• Experiment-aware weighting — Monte Carlo depth / energy distributions (fast surrogate by default, or full GPU Monte Carlo) keyed to beam voltage and specimen tilt.
• Automatic stopping — energy-bin integration and MC both stop when their outputs stop changing.
• Portable output — compressed .npz files with symmetry-reduced fundamental-sector data; expand to full Lambert hemispheres with NumPy only via mploader.

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What drives the pattern?

Input family	Examples	Typical use
Beam & specimen	voltage_kv, sigma_deg, omega_deg, energy_binwidth_keV	Match microscope settings and sample tilt — usually the first knobs to tune against experiment.
Monte Carlo	mc_backend, mc_auto_stop, mc_relative_tol, n_trajectories	Control how depth and energy loss are sampled (or approximated by the surrogate).
Dynamical diffraction	bethe_c_strong, bethe_c_weak, bethe_c_cutoff, dbdiff_sg_cutoff, rank, dmin	Bethe beam selection and multi-beam solve settings.
Raster	halfw	Lambert half-width; output size is (1 + 2×halfw)².
Structure	lattice, sites, b_iso	Crystal geometry and thermal motion (structure factors).

All of the above are keyword arguments to master_pattern and master_pattern_from_cif.

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Debye–Waller factor

Per-site isotropic Debye–Waller factors are often unknown when only a lattice and composition are available. When b_iso is omitted on an Atom, or when a CIF lacks _atom_site_B_iso_or_equiv / _atom_site_U_iso_or_equiv, ebsdsim defaults to 0.5 Ų (stored internally as 0.005 nm²) — a room-temperature isotropic fallback.

Override per site when you have better data:

es.Atom("Ga", x=1/3, y=2/3, z=0.0, b_iso=0.45)  # Ų

In the future, defaults should come from the CIF when present (e.g. values refined against neutron diffraction), from DFT, or from modern surrogates that predict site-resolved thermal parameters reliably. Until then, treat the default as a placeholder and set b_iso explicitly when it matters for your comparison.

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Requirements

Python	3.10 – 3.13
Runtime deps	NumPy, wgpu ≥ 0.29
GPU	WebGPU-capable adapter (see platforms below)

Supported platforms

OS	Backend	Notes
macOS	Metal
Windows	Vulkan or DirectX 12	Requires recent GPU drivers.
Linux	Vulkan	Install Mesa/Vulkan drivers for your GPU.

Simulation requires a working WebGPU adapter. Headless servers without GPU passthrough will not be able to run master_pattern*.

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Install

pip install ebsdsim

From source (editable), with dev and notebook extras:

git clone https://github.com/ZacharyVarley/ebsdsim.git
cd ebsdsim
pip install -e ".[dev,docs]"

---

Quick start

import ebsdsim as es

mp = es.master_pattern_from_cif(
    "GaN.cif",                # bundled preset, or any path to a .cif file
    halfw=250,                # Lambert half-width → 501×501 output
    voltage_kv=20.0,
    sigma_deg=70.0,           # specimen tilt (degrees)
    omega_deg=0.0,            # azimuthal sample rotation (degrees)
    energy_binwidth_keV=1.0,
    mc_backend="surrogate",   # or "gpu"
    bethe_c_strong=20.0,
    bethe_c_weak=40.0,
    bethe_c_cutoff=200.0,
    dbdiff_sg_cutoff=1.0,
    rank=20,
    dmin=0.05,                # nm
)

print(mp.pattern.shape)       # (501, 501)
mp.save("GaN-master-pattern.npz")

Bundled presets: "GaN.cif", "Ni.cif" (also accepts a full filesystem path).

Manual lattice + sites (lengths in Å). Omit b_iso to use the 0.5 Ų default:

ni = es.Material(
    cell=es.Cell(a=3.52, b=3.52, c=3.52, space_group="Fm-3m"),
    atoms=[es.Atom("Ni", x=0.0, y=0.0, z=0.0)],
    name="Ni",
)
mp = es.master_pattern(ni, voltage_kv=20.0, sigma_deg=70.0, halfw=250)

---

Beam & Monte Carlo parameters

These set the incident beam and how electrons lose energy and penetrate the sample before diffraction. They are passed directly to master_pattern*:

Parameter	Default	Role
voltage_kv	20.0	Nominal beam energy (kV)
sigma_deg	70.0	Specimen tilt angle (degrees) fed to the MC / surrogate model
omega_deg	0.0	Azimuthal sample rotation (degrees); used by GPU MC
energy_binwidth_keV	1.0	Width of energy-loss bins integrated into the master pattern
relative_image_stop	0.01	Stop adding bins when Δimage/image falls below this
marginal_coverage	1.0	Fraction of MC energy bins to include (1.0 = all)
mc_backend	"surrogate"	"gpu" for full boundary Monte Carlo
mc_auto_stop	True	GPU MC: stop when depth/energy fits converge
mc_relative_tol	0.01	GPU MC convergence tolerance
n_trajectories	1048576	GPU MC trajectory budget when mc_auto_stop=False

GPU Monte Carlo example:

mp = es.master_pattern_from_cif(
    "GaN.cif",
    voltage_kv=20.0,
    sigma_deg=70.0,
    omega_deg=0.0,
    mc_backend="gpu",
    mc_auto_stop=True,
    mc_relative_tol=0.01,
)
print(mp.metadata["mc_n_trajectories"], mp.metadata["mc_converged"])

---

Dynamical diffraction parameters

The solve always uses the Bloch formulation. Bethe perturbation theory ranks reflections and decides which beams are strong, weak, or excluded (bethe_c_*). Those cutoffs usually matter more than fine-tuning dmin or rank when matching reference patterns:

Parameter	Default	Role
bethe_c_strong	20.0	Excitation-score threshold for strong beams
bethe_c_weak	40.0	Threshold band for weak beams
bethe_c_cutoff	200.0	Upper cutoff — beams above this are excluded
dbdiff_sg_cutoff	1.0	Structure-factor difference cutoff for beam coupling
rank	20	Truncation rank for the fixed-rank Lyapunov (Smith) solve
dmin	0.05 nm	Minimum d-spacing — sets the reflection sphere (smaller → more beams, slower)
halfw	250	Lambert half-width; pattern size is (1 + 2×halfw) × (1 + 2×halfw)

mp.pattern, mp.data, and mp.integrated are always raw dynamical intensities. For display scaling, call mp.lambert_data(normalize="minmax") or mp.lambert_data(normalize="robust", robust_p_low=0.01, robust_p_high=0.99). Scaling is never baked into the simulation result.

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Loading a saved pattern (NumPy only)

ebsdsim/mploader.py is self-contained (NumPy only). Copy it into any project that only needs to read .npz output.

from ebsdsim.mploader import load_master_pattern, to_uint8, save_png_gray

mp = load_master_pattern("GaN-master-pattern.npz")
disp, _ = mp.lambert_data(normalize="minmax")

nh = disp[0, 0, 0]   # energy-integrated, site-integrated, north hemisphere
save_png_gray(to_uint8(nh), "GaN_integrated_nh.png")

.npz layout (summary)

Array	Meaning
fundamental_sector	Raw symmetry-reduced intensities (E, S, n_k)
fundamental_kij, fundamental_khat	Lambert indices and unit directions per FS pixel
pg_operators, fs_normals	Point-group matrices and sector bounding normals
bin_voltages_kv, bin_weights	Per-bin beam voltage and energy weight
site_weights	Normalized occupancy × multiplicity weights for the site marginal
meta_json	UTF-8 JSON simulation metadata (includes beam, MC, and dynamical settings)

See examples/02_save_and_load.ipynb for a full walkthrough.

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Examples

Resource	Description
examples/01_quick_start.ipynb	GaN and Ni patterns
examples/02_save_and_load.ipynb	Save, reload, export PNGs
scripts/run_gan_example.py	Full-resolution GaN script

Preset CIFs ship under ebsdsim/data/preset_cifs/ (Ni, GaN).

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Development

pip install -e ".[dev,docs]"
pytest -m "not slow"          # CPU tests; GPU tests skip if no adapter
pytest -m slow                # end-to-end GPU runs (needs WebGPU)
python -m build               # sdist + wheel (requires `build` extra)

CI runs CPU tests on Ubuntu (Python 3.11–3.12) and full GPU tests on macOS (Metal). See .github/workflows/ci.yml.

Releasing (automated PyPI)

Releases are published by pushing a version tag. The .github/workflows/release.yml workflow builds the sdist/wheel, uploads them to the GitHub release, and publishes to PyPI via trusted publishing (no API token in the repo).

One-time setup

1. On PyPI: add a pending publisher
   • PyPI project name: ebsdsim
   • Owner: ZacharyVarley, repository: ebsdsim
   • Workflow: release.yml, environment: pypi
2. On GitHub: repo Settings → Environments → create environment pypi (no secrets required for trusted publishing).

Each release

1. Bump __version__ in ebsdsim/_version.py and add notes to CHANGELOG.md.

2. Commit and push to main.

3. Tag and push (tag must match ebsdsim._version, without the v prefix):

   git tag v0.1.0
   git push origin v0.1.0
   

4. Watch the Release workflow; the package appears on pypi.org/project/ebsdsim when it finishes.

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Contributing

1. Fork the repo and create a branch from main.
2. pip install -e ".[dev]" and run pytest -m "not slow" before opening a PR.
3. If your change touches GPU paths or save/load, also run pytest -m slow on a machine with WebGPU (macOS Metal works).
4. Keep PRs focused; include a short note in CHANGELOG.md under Unreleased when user-visible behavior changes.

Bug reports and feature requests: GitHub Issues.

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License

MIT — see LICENSE. Copyright (c) Zachary Varley.