Python API

faser is a regular Python package. The simulator takes a PSFConfig (a pydantic model with the same fields as the CLI flags and the GUI) and returns a numpy array of shape (Nz, Nxy, Nxy).

import tifffile
from faser.generators.base import PSFConfig, mode
from faser.generators.vectorial.stephane import generate_psf

config = PSFConfig(
    NA=1.2,
    Wavelength=0.488,   # µm
    n1=1.33, n2=1.52, n3=1.38,
    Thickness=170, Collar=170,
    Depth=20,           # µm below the coverslip
    Mode=mode.DONUT,    # STED-style vortex phase mask
    Nxy=64, Nz=32,
    Normalize="YES",
)

psf = generate_psf(config)   # (Nz, Nxy, Nxy), float64
tifffile.imwrite("psf.tif", psf.astype("float32"))

Backends

generate_psf uses the native multi-core simulator (written in Rust and shipped as a compiled extension) whenever it is available and falls back to the pure numpy reference implementation otherwise. Both give the same result:

from faser.generators.vectorial import stephane

stephane.BACKEND            # "rust" or "numpy"
stephane.generate_psf_numpy # the reference implementation, always available

Derived quantities

The config exposes the quantities the integrator actually uses as properties, e.g. the semi-aperture angle, the pupil radius and the axial shift of the aberrated focus:

config.alpha   # arcsin(NA / n1), in rad
config.r0      # pupil radius WD * sin(alpha), in µm
config.Dfoc    # focus shift caused by index mismatch, in µm

Saving and loading configs

The JSON produced by config.model_dump_json() is the same psf_config.json the GUI, the CLI and the Playground write, so configs can be exchanged freely between them:

config = PSFConfig.model_validate_json(open("psf_config.json").read())

Backends

Derived quantities

Saving and loading configs

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