optiland.wavefront.opd

This module defines the OPD class.

Kramer Harrison, 2024

Classes

`OPD`(optic, field, wavelength[, num_rays, ...])	Represents an Optical Path Difference (OPD) wavefront.
`OPDData`

class OPD(optic: Optic, field: tuple[float, float], wavelength: str | float, num_rays: int = 15, distribution: DistributionType = 'hexapolar', strategy: WavefrontStrategyType = 'chief_ray', remove_tilt: bool = False, **kwargs)[source]

Represents an Optical Path Difference (OPD) wavefront.

Parameters:

optic (Optic) – The optic object.
field (tuple) – The field at which to calculate the OPD.
wavelength (str | float) – The wavelength of the wavefront. Can be ‘primary’ or a float value.
num_rings (int, optional) – The number of rings for ray tracing. Defaults to 15.
strategy (str) – The calculation strategy to use. Supported options are “chief_ray”, “centroid”, and “best_fit”. Defaults to “chief_ray”.
afocal (bool) – If True, uses a planar reference geometry for afocal systems. If False, uses a spherical reference geometry. Defaults to False.
remove_tilt (bool) – If True, removes tilt and piston from the OPD data. Defaults to False.
**kwargs – Additional keyword arguments passed to the strategy.

optic

The optic object.

Type:: Optic

field

The field coordinates (Hx, Hy).

Type:: tuple[float, float]

wavelength

The wavelength of the wavefront in micrometers.

Type:: float

num_rays

The number of rays (or rings for hexapolar distribution) to use for pupil sampling.

Type:: int

distribution

The pupil sampling distribution instance.

Type:: BaseDistribution

data

A dictionary mapping (field, wavelength) tuples to WavefrontData objects. Inherited from Wavefront.

Type:: dict

view(projection='2d', num_points=256, figsize=(7, 5.5))[source]: Visualizes the OPD wavefront.

rms()[source]: Calculates the root mean square (RMS) of the OPD wavefront.

static fit_and_remove_tilt(data: WavefrontData, remove_piston: bool = False, ridge: float = 1e-12) → be.ndarray

Removes piston and tilt from OPD data using weighted least squares.

Parameters:

data (WavefrontData) – The wavefront data containing pupil coordinates and OPD.
remove_piston (bool, optional) – If True, removes piston term as well as tilt. Defaults to False.
ridge (float, optional) – Small diagonal regularization for stability. Defaults to 1e-12.

Returns:

OPD with piston and tilt removed, shape (N,).

Return type:

opd_detrended (be.ndarray)

generate_opd_map(num_points: int = 256) → OPDData[source]

Generates the OPD map data.

Parameters:: num_points (int, optional) – The number of points for interpolation along each axis of the grid. Defaults to 256.
Returns:: A dictionary containing the interpolated OPD map, with keys ‘x’, ‘y’, and ‘z’. The values are NumPy arrays.
Return type:: dict[str, np.ndarray]

get_data(field: tuple[float, float], wl: float) → WavefrontData

Retrieves precomputed wavefront data for a field and wavelength.

Parameters:

field (tuple[float, float]) – The field coordinates, or a FieldPoint.
wl (float) – The wavelength in µm, or a WavelengthPoint.

Returns:

A data container with the computed wavefront results.

Return type:

WavefrontData

rms() → be.ndarray[source]

Calculates the root mean square (RMS) of the OPD wavefront.

Returns:: The RMS value.
Return type:: float

view(fig_to_plot_on: Figure | None = None, projection: PlotProjection = '2d', num_points: int = 256, figsize: tuple[float, float] = (7, 5.5)) → tuple[Figure, Axes][source]

Visualizes the OPD wavefront.

Parameters:

fig_to_plot_on (plt.Figure, optional) – The figure to plot on. If None, a new figure is created.
projection (str, optional) – The projection type. Defaults to ‘2d’.
num_points (int, optional) – The number of points for interpolation. Defaults to 256.
figsize (tuple, optional) – The figure size. Defaults to (7, 5.5).

Returns:

A tuple containing the figure and axes objects.

Return type:

tuple

Raises:

ValueError – If the projection is not ‘2d’ or ‘3d’.

class OPDData[source]

clear() → None. Remove all items from D.

copy() → a shallow copy of D

fromkeys(value=None, /): Create a new dictionary with keys from iterable and values set to value.

get(key, default=None, /): Return the value for key if key is in the dictionary, else default.

items() → a set-like object providing a view on D's items

keys() → a set-like object providing a view on D's keys

pop(k[, d]) → v, remove specified key and return the corresponding value.: If the key is not found, return the default if given; otherwise, raise a KeyError.

popitem()

Remove and return a (key, value) pair as a 2-tuple.

Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty.

setdefault(key, default=None, /)

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

update([E, ]**F) → None. Update D from mapping/iterable E and F.: If E is present and has a .keys() method, then does: for k in E.keys(): D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values() → an object providing a view on D's values

x: NDArray

y: NDArray

z: NDArray