Custom materials

PyOptik can construct optical materials directly from measured arrays, CSV files, or dispersion coefficients. Custom materials support the same unit-aware calculations, interpolation policies, provenance fields, and YAML format as catalog materials.

Tabulated data from arrays

Attach units to the wavelength axis and provide at least one of n or k. The arrays must be one-dimensional, finite, equal in length, and strictly increasing in wavelength.

from TypedUnit import ureg
from PyOptik import TabulatedMaterial

sample = TabulatedMaterial.from_arrays(
    "measured-sample",
    [400, 500, 600] * ureg.nanometer,
    n=[1.40, 1.45, 1.50],
    k=[0.01, 0.02, 0.04],
    reference="Laboratory measurement",
    conditions={"temperature": "293 K"},
    comments="Uncoated sample",
    interpolation="pchip",
)

index = sample.compute_refractive_index(550 * ureg.nanometer)
sample.to_yaml("measured-sample.yml")

When wavelengths do not carry units, they are interpreted as micrometres by from_arrays. Unit-bearing values are recommended.

CSV import

from_csv expects a header row and uses the columns wavelength, n, and k by default. Either optical-constant column may be omitted.

wavelength,n,k
400,1.40,0.01
500,1.45,0.02
600,1.50,0.04

sample = TabulatedMaterial.from_csv(
    "measurement.csv",
    wavelength_unit=ureg.nanometer,
    reference="Laboratory measurement",
)

Use wavelength_column, n_column, and k_column when a file uses different headers.

Formula materials

Formula types 1 through 9 follow the RefractiveIndex.INFO definitions. Coefficient order therefore follows the selected upstream formula type.

from PyOptik import SellmeierMaterial

glass = SellmeierMaterial.from_coefficients(
    "fitted-glass",
    [0.1, 0.2, 0.3],
    formula_type=1,
    wavelength_range=[400, 900] * ureg.nanometer,
    reference="Internal fit",
)
glass.to_yaml("fitted-glass.yml")

Typed documents and validation

The lower-level parser returns an immutable MaterialDocument containing typed formula or tabulated datasets and shared metadata. It is useful for validation, inspection, and tools that do not need to evaluate a material.

from PyOptik import parse_material

document = parse_material("measured-sample.yml")
print(document.metadata.reference)
print(document.tabulated_datasets)

document.to_yaml("validated-copy.yml")

Malformed coefficient sets, invalid table shapes, non-finite values, unsorted wavelengths, unsupported formula types, and malformed metadata raise ValueError with source context. YAML export writes through a temporary file and atomically replaces the destination after successful serialization.