wf_psf.psf_models.psf_model_parametric

PSF Model Parametric.

A module which defines the classes and methods to manage the parameters of the psf parametric model.

Authors:

Tobias Liaudat <tobiasliaudat@gmail.com> and Jennifer Pollack <jennifer.pollack@cea.fr>

Classes

TFParametricPSFFieldModel(*args, **kwargs)

Parametric PSF field model.
class wf_psf.psf_models.psf_model_parametric.TFParametricPSFFieldModel(*args: Any, **kwargs: Any)[source]

Bases: Model

Parametric PSF field model.

Fully parametric model based on the Zernike polynomial basis.

Parameters:

  • ids (tuple) – A tuple storing the string attribute of the PSF model class

  • zernike_maps (Tensor(n_batch, opd_dim, opd_dim)) – Zernike polynomial maps.

  • obscurations (Tensor(opd_dim, opd_dim)) – Predefined obscurations of the phase.

  • batch_size (int) – Batch size.

  • output_Q (float) – Oversampling used. This should match the oversampling Q used to generate the diffraction zero padding that is found in the input packed_SEDs. We call this other Q the input_Q. In that case, we replicate the original sampling of the model used to calculate the input packed_SEDs. The final oversampling of the generated PSFs with respect to the original instrument sampling depend on the division input_Q/output_Q. It is not recommended to use output_Q < 1. Although it works with float values it is better to use integer values.

  • l2_param (float) – Parameter going with the l2 loss on the opd. If it is 0. the loss is not added. Default is 0..

  • output_dim (int) – Output dimension of the PSF stamps.

  • n_zernikes (int) – Order of the Zernike polynomial for the parametric model.

  • d_max (int) – Maximum degree of the polynomial for the Zernike coefficient variations.

  • x_lims ([float, float]) – Limits for the x coordinate of the PSF field.

  • y_lims ([float, float]) – Limits for the x coordinate of the PSF field.

  • coeff_mat (Tensor or None) – Initialization of the coefficient matrix defining the parametric psf field model.

Methods

__call__(*args, **kwargs)

Call self as a function.

assign_coeff_matrix(coeff_mat)

Assign coefficient matrix.

call(inputs)

Define the PSF field forward model.

get_coeff_matrix()

Get coefficient matrix.

predict_mono_psfs(input_positions, ...)

Predict a set of monochromatic PSF at desired positions.

predict_opd(input_positions)

Predict the OPD at some positions.

set_output_Q(output_Q[, output_dim])

Set the value of the output_Q parameter.
assign_coeff_matrix(coeff_mat)[source]

Assign coefficient matrix.

call(inputs)[source]

Define the PSF field forward model.

[1] From positions to Zernike coefficients [2] From Zernike coefficients to OPD maps [3] From OPD maps and SED info to polychromatic PSFs

OPD: Optical Path Differences

get_coeff_matrix()[source]

Get coefficient matrix.

ids = ('parametric',)
predict_mono_psfs(input_positions, lambda_obs, phase_N)[source]

Predict a set of monochromatic PSF at desired positions.

input_positions: Tensor(batch_dim x 2)

lambda_obs: float

Observed wavelength in um.

phase_N: int

Required wavefront dimension. Should be calculated with as: simPSF_np = wf_psf.sims.psf_simulator.PSFSimulator(...) phase_N = simPSF_np.feasible_N(lambda_obs)

predict_opd(input_positions)[source]

Predict the OPD at some positions.

Parameters:

input_positions (Tensor(batch_dim x 2)) – Positions to predict the OPD.

Returns:

opd_maps – OPD at requested positions.

Return type:

Tensor [batch x opd_dim x opd_dim]

set_output_Q(output_Q, output_dim=None)[source]

Set the value of the output_Q parameter.

Useful for generating/predicting PSFs at a different sampling wrt the observation sampling.

Parameters:

  • output_Q (float) – New value of the output_Q parameter.

  • output_dim (int or None) – New value of the output_dim parameter. If None, the output_dim parameter is not changed.