Source code for eradiate.scenes.bsdfs._mqdiffuse

from __future__ import annotations

import attrs
import mitsuba as mi
import numpy as np
import xarray as xr

from ._core import BSDF
from ... import converters
from ...attrs import documented, parse_docs
from ...kernel import InitParameter, UpdateParameter
from ...units import to_quantity
from ...units import unit_registry as ureg
from ...util.misc import summary_repr

[docs] @parse_docs @attrs.define(eq=False, slots=False) class MQDiffuseBSDF(BSDF): """ Measured quasi-diffuse BSDF [``mqdiffuse``]. This BSDF models the reflection of light by opaque materials with a behaviour close to diffuse, *i.e* with no strong scattering lobe. Assumptions are as follows: * The material is isotropic. Consequently, only the azimuth difference matters. * The material is gray. Consequently, no spectral dimension is used. Notes ----- The input is specified using an xarray dataset. It must contain a ``brdf`` data variable with the following dimensions (the corresponding coordinate range is specified within brackets): * ``cos_theta_o`` [0, 1]: cosine of the outgoing zenith angle; * ``phi_d`` [0, 2π[: difference between the incoming and outgoing azimuth angles; * ``cos_theta_i`` [0, 1]: cosine of the incoming zenith angle. Coordinates must be evenly spaced and have a `"units"` metadata field. Warnings -------- * Table values are not checked internally: ensuring that the data is consistent (*e.g* that the corresponding reflectance is not greater than 1) is the user's responsibility. * While this BSDF may technically represent any isotropic material, its sampling routine's performance degrades as the material departs from a diffuse behaviour. """ data: xr.Dataset = documented( attrs.field( converter=converters.to_dataset(), kw_only=True, repr=summary_repr, ), type="Dataset", init_type="Dataset", doc="Measured quasi-diffuse BRDF data formatted as an xarray dataset.", ) @data.validator def _data_validator(self, attribute, value): # Check type attrs.validators.instance_of(xr.Dataset)(self, attribute, value) # Check data variable if "brdf" not in value.data_vars: raise ValueError( f"while validating '{}': missing required data " "variable 'brdf'" ) # Check dimensions if set(value.data_vars["brdf"].dims) != {"cos_theta_o", "phi_d", "cos_theta_i"}: raise ValueError( f"while validating '{}': incorrect dimension " f"list (got '{set(value.dims)}', " f"expected '{ {'cos_theta_o', 'phi_d', 'cos_theta_i'} }')" ) # Check coordinates for coord_name in ["cos_theta_o", "cos_theta_i", "phi_d"]: try: coord = to_quantity(value.coords[coord_name]) except ValueError as e: if e.args[0] == "this DataArray has no 'units' metadata field": raise ValueError( f"while validating '{}': input dataset " f"coordinate variable '{coord_name}' is missing " "'units' metadata field" ) from e else: raise e expected = ( np.linspace(0.0, 1.0, len(coord)) if coord_name.startswith("cos") else ( np.linspace(0.0, 2.0 * np.pi, len(coord), endpoint=False) * ureg.rad ) ) if not np.allclose(coord, expected): raise ValueError( f"while validating '{}': incorrect " f"coordinate values for field '{coord_name}'; got {coord}, " f"expected {expected}" ) def _eval_grid_impl(self, ctx): values = (["brdf"] .transpose("cos_theta_o", "phi_d", "cos_theta_i") .values ) # Add an extra row with φ_d = 0° data to ensure azimuthal periodicity values = np.concatenate((values, values[:, [0], :]), axis=1) return mi.VolumeGrid(values.astype(np.float32)) @property def template(self) -> dict: # Inherit docstring result = { "type": "mqdiffuse", "grid": InitParameter(lambda ctx: self._eval_grid_impl(ctx)), } if is not None: result["id"] = return result @property def params(self) -> dict[str, UpdateParameter]: # Inherit docstring return {}