Python¶

pybind11 extension module. Wheels for Linux (x86_64, aarch64) and macOS (Intel, Apple Silicon) are attached to each GitHub release.

API coverage¶

C API (`Lib_Palioxis_c.h`)	Python wrapper
Module-level
—	`palioxis.set_root(path)`
—	`palioxis.root()`
`getPalioxisVersion`	`palioxis.version()`
`getMinMobileConc`	`palioxis.get_min_mobile_conc()`
Construction / destruction
`Palioxis_ctor`	`MultipleDefectModel(xml_filename)`
`Palioxis_delete`	destructor
Model metadata (read-only properties)
`getnGas`	`model.n_gas`
`getnTrap`	`model.n_trap_types`
`getnDofAfterConstraint`	`model.n_ne_traps`
`getGas_name` / `gas_name_len`	`model.gas_names`
`getTrap_name` / `trap_name_len`	`model.defect_names`
`getDof_name` / `dof_name_len`	`model.trap_names`
`getBoltzmannConst`	cached at construction
`self_consistent`	`model.self_consistent()`
`getMaxOccupancy`	`model.get_max_trap_occupancy(trap_index)`
Diffusion
`getDiffusionConstant`	`model.get_diffusion_constant(T)` → `list[float]`
`getDiffusionConstantStress`	`model.get_diffusion_constant_stress(T, stress)`
`effectiveDiffusionCoefficientPrefactor`	`model.effective_diffusion_prefactor(mobile, defects, T)` → `np.ndarray`
`effectiveDiffusionCoefficientPrefactorStress`	`model.effective_diffusion_prefactor_stress(mobile, defects, T, stress)`
`getRelaxationVolume`	`model.get_relaxation_volume()`
`getHeatOfTransport`	—
`getHeatOfTransportStress`	—
Time integration
`getTimeDerivative`	`model.get_time_derivative(mobile, trapped, T)`
`getMobileTimeDerivative`	`model.get_mobile_time_derivative(mobile, defects, trapped, T)`
—	`model.get_time_derivatives(mobile, defects, trapped, T)` (both)
`getTimeDerivativesJacobian`	`model.get_time_derivatives_jacobian(defects, mobile, trapped, T)` → `TimeDerivativesJacobian`
`getMobileTimeDerivativeJacobianFromTrapped`	`model.get_mobile_time_derivative_jacobian_from_trapped(defects, trapped, T, dyc, ddyc_dx, ddyc_dyc)` → `MobileTimeDerivativeJacobian`
`getTimeDerivativeskBTJacobian`	`model.get_time_derivatives_temperature_sensitivity(defects, mobile, trapped, T)` → `TimeDerivativesTemperatureSensitivity`
Initialisation
`defect_density`	`model.get_defect_concentrations(z1, z2)`
`gas_fluence`	`model.set_fluence_source_rate(z1, z2)`
`setInitial_condition`	`model.set_initial_conditions(mobile, T)`
`gas_flux`	`model.apply_gas_flux(z1, z2)`
—	`model.get_ic_from_xml(location, z1, z2, T)` (composite)
Bounds / validation
`ensureBounds`	`model.ensure_bounds(mobile, defects, trapped)` → `(mobile, defects, trapped)`
`inBounds`	`model.in_bounds(mobile, defects, trapped)`
Surface
`surfaceRate`	`model.get_surface_rate(location, mobile, T)`
`surfaceVoxSetDirichlet`	`model.set_surface_dirichlet(location, mobile, T)`
Retention
`retention`	`model.retention(mobile, defects, trapped, T, location, thickness)`
`retention_noneq`	`model.non_equilibrium_retention(defects, trapped, T)`
`retention_trapped_by_occupation`	`model.trapped_retention_by_occupation(trap_index, mobile, defects, trapped, T)`
`retention_trapped_by_trap`	`model.trapped_retention_by_trap(mobile, defects, trapped, T)`
`probability_of_occupation`	`model.probability_of_occupation(trap_index, mobile, trapped, T)`
`calculate_mobile_steady_state`	`model.calculate_mobile_steady_state(mobile, defects, T, trapped)`
`calculate_steady_state`	`model.calculate_steady_state(mobile, T, trapped, defects)`
Diagnostics
`report`	`model.report(verbose=False)`
`Lib_Palioxis_init_MPI`	—
`echo_message`	—

Usage¶

Install from a downloaded wheel:

pip install palioxis-<version>-<platform>.whl

Example:

import palioxis
import numpy as np

# Set the Palioxis data root once per process.
palioxis.set_root("/path/to/palioxis")

# Construct a model from an XML configuration file.
m = palioxis.MultipleDefectModel("input.xml")

# Cached metadata available as properties.
print(m.n_gas, m.gas_names, m.defect_names)

# State vectors — sizes follow the cached dimensions.
mobile  = np.zeros(m.n_gas)
defects = np.zeros(m.n_trap_types)
trapped = np.zeros(m.n_ne_traps)
T = 800.0  # Kelvin

# Effective diffusion coefficient prefactor matrix (n_gas × n_gas numpy array).
D_eff = m.effective_diffusion_prefactor(mobile, defects, T)

# Both mobile and trapped time derivatives in a single call.
dy = m.get_time_derivatives(mobile, defects, trapped, T)
# dy.mobile  — mobile rates  [n_gas]
# dy.trapped — trapped rates [n_ne_traps]