Edges

Overview

This file describes the Edges object. It contains the core information required to describe the edges of each slit on a given detector (or orders for echelle).

See below for the Current EdgeTrace Data Model. This is written to disk as a multi-extension FITS file prefixed by Edges in the Calibrations/ folder. See the Calibration Frame Naming docs for more.

We also describe how to view the slit edges using pypeit_chk_edges.

Viewing

The preferred way to view the slit edges identified by PypeIt is with the pypeit_chk_edges script.

The script usage can be displayed by calling the script with the -h option:

$ pypeit_chk_edges -h
usage: pypeit_chk_edges [-h] [--slits_file SLITS_FILE] [--mpl] [--try_old]
                        trace_file

Display trace image and edge traces

positional arguments:
  trace_file            PypeIt Edges file [e.g. Edges_A_0_DET01.fits.gz]

options:
  -h, --help            show this help message and exit
  --slits_file SLITS_FILE
                        PypeIt Slits file [e.g. Slits_A_1_01.fits]. If this file
                        does not exist or is not provided, PypeIt will attempt
                        to read the default file name (in the Calibrations
                        directory). Ignored if plotting using a matplotlib
                        window instead of ginga. (default: None)
  --mpl                 Use a matplotlib window instead of ginga to show the
                        trace (default: False)
  --try_old             Attempt to load old datamodel versions. A crash may
                        ensue.. (default: False)

pypeit_chk_edges

There are currently 2 options for viewing the slit edges on the image used to construct them. Each uses the pypeit_chk_edges script.

ginga

This is the default mode when executing, e.g.:

pypeit_chk_edges Calibrations/Edges_A_1_DET01.fits.gz

Warning

These files can take an annoyingly long time to load because they tend to be large and require decompression.

Two images are shown, the trace image (typically a combination of dome flats) and the Sobel filtered image used to detect the slit edges.

Here is a zoom-in screen shot for the LRIS RED spectrograph.

A few things to note from this good-performing example:

The slits run nearly vertically across the image

The left/right edge of each slit identified is a green/red (purple in more recent versions) line

There were 13 slits identified (0 indexing)

The brightest slit is an alignment box and was discarded by the code

matplotlib

To avoid ginga, use the –mpl flag:

pypeit_chk_edges Calibrations/Edges_A_1_DET01.fits.gz --mpl

The color scheme is distinct and the labeling now includes -1 or +1 for left/right edges.

SlitTrace

The Edges file allows one to fully reconstruct its underlying object (EdgeTraceSet) when instantiated from the output file. Unfortunately, this means the PypeIt object and output file are a bit too unwieldy to pass through the remainder of the code just to access its primary product, the slit-edge pixel coordinates. For that reason, we also create a Slits object; follow the link for more description of that object.

Edges Troubleshooting

See Slit Tracing for a discussion of how to customize, debug, and finesse your slit tracing results.

Current EdgeTrace Data Model

Internally, the processed image is held in EdgeTraceSet, which subclasses from DataContainer.

The datamodel written to disk is:

Version 1.0.1

HDU Name	HDU Type	Data Type	Description
`PRIMARY`	astropy.io.fits.PrimaryHDU	…	Empty data HDU. Contains basic header information.
`TRACE_*`	…	…	Image used to construct the edge traces; see `TraceImage` and `PypeItImage`.
…	…	…	…
`TRACEBPM`	astropy.io.fits.ImageHDU	uint8	Bad-pixel mask for trace image
`SOBELSIG`	astropy.io.fits.ImageHDU	float32	Sobel-filtered image used to detect edges
`TRACEID`	astropy.io.fits.ImageHDU	int64	ID number for the edge traces. Negative and positive IDs are for, respectively, left and right edges.
`MASKDEF_ID`	astropy.io.fits.ImageHDU	int64	slitmask ID number for the edge traces. IDs are for, respectively, left and right edges. Only defined if mask-design metadata is available.
`EDGE_CEN`	astropy.io.fits.ImageHDU	float32	(Floating-point) Measured spatial coordinate of the edge traces for each spectral pixel. Shape is (Nspec,Ntrace).
`EDGE_ERR`	astropy.io.fits.ImageHDU	float32	Error in the measured spatial coordinate edge traces.
`EDGE_MSK`	astropy.io.fits.ImageHDU	int32	Bitmask for the edge trace positions.
`EDGE_FIT`	astropy.io.fits.ImageHDU	float32	The best-fit model result for the trace edge.
`PCA`	astropy.io.fits.BinTableHDU	…	The PCA decomposition of all edge traces. Not defined if PCA separated between left and right traces (i.e., the left_right_pca parameter is True). See `TracePCA`.
`PCA_MODEL_?`	astropy.io.fits.BinTableHDU	…	Model parameters for the ?th component of the PCA; see `pca_coeffs_model` in `TracePCA` and `PypeItFit`.
…	…	…	…
`LEFT_PCA`	astropy.io.fits.BinTableHDU	…	The PCA decomposition of the left-edge traces. Not defined if PCA performed on all traces, regardless of edge side (i.e., the left_right_pca parameter is False). See `TracePCA`.
`LEFT_PCA_MODEL_?`	astropy.io.fits.BinTableHDU	…	Model parameters for the ?th component of the left-edge PCA; see `pca_coeffs_model` in `TracePCA` and `PypeItFit`.
…	…	…	…
`RIGHT_PCA`	astropy.io.fits.BinTableHDU	…	The PCA decomposition of the right-edge traces. Not defined if PCA performed on all traces, regardless of edge side (i.e., the left_right_pca parameter is False). See `TracePCA`.
`RIGHT_PCA_MODEL_?`	astropy.io.fits.BinTableHDU	…	Model parameters for the ?th component of the right-edge PCA; see `RIGHT_pca_coeffs_model` in `TracePCA` and `PypeItFit`.
…	…	…	…