pypeit.core.slitdesign_matching module

Module with slit - mask design matching routines.

Routines are primarily used for matching the traced slit edges to the predicted trace from the mask design/optical model.

TODO: These routines are specific for DEIMOS. Can they be generalized?

These routines are taken from the DEEP2 IDL-based pipeline.

pypeit.core.slitdesign_matching.best_offset(x_det, x_model, step=1, xlag_range=None)[source]

Script to determine the best offset between the slit edge predicted by the optical model and the one found in the image. This is used iteratively.

Taken from DEEP2/spec2d/pro/ discrete_correlate.pro x_det==x1, x_model==x2

Parameters:

  • x_det (numpy.ndarray) – 1D array of slit edge spatial positions found from image

  • x_model (numpy.ndarray) – 1D array of slit edge spatial positions predicted by the optical model

  • step (int) – step size in pixels used to generate a list of possible offsets within the offsets_range

  • xlag_range (list, optional) – range of offsets in pixels allowed between the slit positions predicted by the mask design and the traced slit positions.

Returns:

best offset between the slit edge predicted by the optical model and the one found in the image

Return type:

float

pypeit.core.slitdesign_matching.discrete_correlate_match(x_det, x_model, step=1, xlag_range=[-50, 50])[source]

Script to find the the x_model values that match the traced edges.

This method uses best_offset() to determine the best offset between slit edge predicted by the optical model and the one found in the image, given a range of offsets. This is used iteratively.

Taken from in DEEP2/spec2d/pro/discrete_correlate_match.pro x_det==x1, x_model==x2_in

Parameters:

  • x_det (numpy.ndarray) – 1D array of slit edge spatial positions found from image

  • x_model (numpy.ndarray) – 1D array of slit edge spatial positions predicted by the optical model

  • step (int) – step size in pixels used to generate a list of possible offsets within the offsets_range

  • xlag_range (list, optional) – range of offsets in pixels allowed between the slit positions predicted by the mask design and the traced slit positions.

Returns:

array of indices for x_model, which defines the matches to x_det, i.e., x_det matches x_model[ind]

Return type:

numpy.ndarray

pypeit.core.slitdesign_matching.plot_matches(edgetrace, ind, x_model, yref, slit_index, nspat=2048, duplicates=None, missing=None, edge=None)[source]

Plot the slit mask matching results.

Parameters:

  • edgetrace (numpy.ndarray) – 2D array with the location of the slit edges for each spectral pixel as measured from the trace image. Shape is \((N_{\rm spec},N_{\rm trace})\).

  • ind (numpy.ndarray) – 1D array of indices for x_model, which defines the matches to x_det.

  • x_model (numpy.ndarray) – 1D array of slit edge spatial positions predicted by the optical model.

  • yref (float) – Reference pixel in the spec direction.

  • slit_index (numpy.ndarray) – 1D array of slit-mask design indices.

  • nspat (int, optional) – Spatial dimension of the detector, for plotting purpose.

  • duplicates (numpy.ndarray, optional) – 1D array of bool that flags which ind are duplicates.

  • missing (numpy.ndarray, optional) – 1D array of indices for x_model, which defines the missing traces, if any.

  • edge (str, optional) – String that indicates which edges are being plotted, i.e., left of right.

pypeit.core.slitdesign_matching.slit_match(x_det, x_model, step=1, xlag_range=[-50, 50], sigrej=3, print_matches=False, edge=None)[source]

Script that perform the slit edges matching.

This method uses discrete_correlate_match() to find the indices of x_model that match x_det.

Taken from DEEP2/spec2d/pro/deimos_slit_match.pro

Parameters:

  • x_det (numpy.ndarray) – 1D array of slit edge spatial positions found from image.

  • x_model (numpy.ndarray) – 1D array of slit edge spatial positions predicted by the optical model.

  • step (int, optional) – Step size in pixels used to generate a list of possible offsets within the offsets_range.

  • xlag_range (list, optional) – Range of offsets in pixels allowed between the slit positions predicted by the mask design and the traced slit positions.

  • sigrej (float, optional) – Reject slit matches larger than this number of sigma in the match residuals.

  • print_matches (bool, optional) – Print the result of the matching.

  • edge (str, optional) – String that indicates which edges are being plotted, i.e., left of right. Ignored if print_matches is False.

Returns:

  • ind (numpy.ndarray) – 1D array of indices for x_model, which defines the matches to x_det, i.e., x_det matches x_model[ind]

  • dupl (numpy.ndarray) – 1D array of bool that flags which ind are duplicates.

  • coeff (numpy.ndarray) – pypeitFit coefficients of the fitted relation between x_det and x_model[ind]

  • sigres (float) – RMS residual for the fitted relation between x_det and x_model[ind]