Python and NumPy

The openbnct package exposes the authoritative Rust contracts and calculations. It supports verified cases, dose analysis, model evaluation, interchange and a bounded deterministic-solver interface.

Use python -m pip install openbnct for the published package. Build the current source wheel to use later additions; see installation.

Read and analyze a bundle

import openbnct

bundle = openbnct.load_physical_dose_bundle("dose.json")
dose = bundle.physical_total.as_array()
print(dose.shape)
print(dose.mean())

Inspect the bundle’s units and normalization before interpreting the mean. A whole-array mean is not a target statistic; use the appropriate structure mask for regional analysis.

Solve with current source

From a source checkout with current bindings installed:

import openbnct

# Substitute your declared transport-case and compatible multigroup data.
solution = openbnct.sn_solve("case.json", "multigroup-data.json", order=4)
flux = solution.flux.as_array()
physical = solution.dose.physical_total.as_array()

The call releases the GIL and uses the Rust solver. Unconverged solutions raise by default; explicitly allowing them returns provisional output with a warning. The Python interface exposes a subset of CLI solver controls, so use the CLI/project path for the full current photon and source-weighting workflow.

Array conventions

Voxel arrays have C-order shape (nz, ny, nx): array[k, j, i] maps to x-index i, y-index j, z-index k. Flattening preserves x-fastest voxel order. Geometry shape, origin and spacing retain x/y/z order.

Flux adds a leading group axis: (groups, nz, ny, nx), in descending energy-boundary order. A flux JSON carries no grid by itself; bind geometry with with_geometry where needed.

Run the worked example for actual fixture paths, case generation, dose analysis and a tiny solver demonstration. Binding reference.