ufig.psf_estimation package¶

Submodules¶

ufig.psf_estimation.correct_brighter_fatter module¶

Created on 05 May, 2018 @author: Tomasz Kacprzak

ufig.psf_estimation.correct_brighter_fatter.brighter_fatter_add(col_mag, col_fwhm, col_e1, col_e2, dict_corr)[source]¶

ufig.psf_estimation.correct_brighter_fatter.brighter_fatter_remove(col_mag, col_fwhm, col_e1, col_e2, dict_corr)[source]¶

ufig.psf_estimation.psf_estimation_coadd_cnn module¶

ufig.psf_estimation.psf_estimation_coadd_cnn.predict_psf(position_xy, position_weights, regressor, settings, n_per_chunk=1000)[source]¶

ufig.psf_estimation.psf_estimation_coadd_cnn.predict_psf_with_file(position_xy, filepath_psfmodel, id_pointing='all')[source]¶

ufig.psf_estimation.psf_utils module¶

ufig.psf_estimation.psf_utils.get_position_weights(x, y, pointings_maps)[source]¶

ufig.psf_estimation.psf_utils.position_weights_to_nexp(position_weights)[source]¶

ufig.psf_estimation.psf_utils.postprocess_catalog(cat)[source]¶

ufig.psf_estimation.psf_utils.transform_forward(vec, scale)[source]¶

ufig.psf_estimation.psf_utils.transform_inverse(vec_transformed, scale)[source]¶

ufig.psf_estimation.tiled_regressor module¶

class ufig.psf_estimation.tiled_regressor.TiledRobustPolynomialRegressor(poly_order=3, ridge_alpha=0, n_input_dim=2, polynomial_type='standard', poly_coefficients=None, set_unseen_to_mean=False, unseen_pointings=None, raise_underdetermined=False)[source]¶

Bases: BaseEstimator, RegressorMixin

fit(X, y, var_y=None, method='ridge')[source]¶

n_free_params(n_pointings)[source]¶

predict(X, batch_size=1000)[source]¶

set_fit_request(*, method: bool | None | str = '$UNCHANGED$', var_y: bool | None | str = '$UNCHANGED$') → TiledRobustPolynomialRegressor¶

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to fit.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters¶

methodstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for method parameter in fit.

var_ystr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for var_y parameter in fit.

Returns¶

selfobject

The updated object.

set_predict_request(*, batch_size: bool | None | str = '$UNCHANGED$') → TiledRobustPolynomialRegressor¶

Request metadata passed to the predict method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to predict if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to predict.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters¶

batch_sizestr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for batch_size parameter in predict.

Returns¶

selfobject

The updated object.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → TiledRobustPolynomialRegressor¶

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to score.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters¶

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in score.

Returns¶

selfobject

The updated object.

exception ufig.psf_estimation.tiled_regressor.UnderdeterminedError[source]¶

Bases: ValueError

Raised when trying to fit an underdetermined model.

ufig.psf_estimation.tiled_regressor.get_poly_weight_basis(position_xy_transformed, position_weights, poly_order, polynomial_type)[source]¶

ufig.psf_estimation.tiled_regressor.var_to_weight(v)[source]¶

Module contents¶