User-level Parameters

PypeIt allows you to customize its execution without having to change the code directly.

Although not ubiquitous, most optional arguments of PypeIt’s algorithms are contained within the PypeItPar superset. PypeIt uses the configobj class to parse the user-supplied arguments in the PypeIt Reduction File into an instance of PypeItPar that is passed to all of PypeIt’s main modules. The syntax used to set parameters using the PypeIt Reduction File is important and the nesting of the parameter changes must match the Current PypeItPar Parameter Hierarchy.

Parameter Precedence

The parameter changes also follow a specific precedence. From lowest to highest, the precedence order is as follows:

Defaults: The parameter tables below, starting with PypeItPar Keywords, all provide the global defaults for each parameter.

Instrument-specific parameters: Each Spectrograph derived class (e.g., ShaneKastSpectrograph) provides its own default values for PypeItPar, as defined by its default_pypeit_par method. This allows the developers to define parameters as a general expectation for what works for each spectrograph. For example, see default_pypeit_par() for Shane/Kast. All of the default changes made for each spectrograph are listed here. Importantly, the parameters tabulated there are not required to be put in your PypeIt Reduction File if you’re trying to reduce data from that instrument. Only those parameters that the user wishes to be different from the default as set by their specified instrument need to be changed via the PypeIt Reduction File.

Configuration-specific parameters: Each Spectrograph derived class (e.g., ShaneKastSpectrograph) also defines default parameters to use for specific instrument configurations via its config_specific_par method. This allows the code to automatically define, e.g., the archived arc spectrum used for wavelength calibration given the grating used. For example, see config_specific_par() for Shane/Kast. These configuration-specific parameters are currently not documented here; however, they can be viewed by looking at the source code display in the API documentation.

User-specified parameters: Finally, parameters defined by the user in the PypeIt Reduction File take ultimate precedence.

Warning

Default values of parameters that actually point to data files provided by PypeIt (e.g. the spectrum parameter for FlexurePar) in its root directory will point to the relevant location on disk of whoever generated the documentation, which will be different for your installation.

How to change a parameter

To change a parameter, set its value in the Parameter Block of the PypeIt Reduction File. The syntax of the configuration block is important (particularly the number of square brackets used in the parameter hierarchy), but the indentation is not. The indentation will just make the block easier to read. The PypeIt Reduction File Parameter Block always includes the lines that sets the spectrograph:

[rdx]
    spectrograph = keck_deimos

The nesting of the PypeIt parameters is as illustrated in the Current PypeItPar Parameter Hierarchy section below. Here are a few examples of how to change various parameters; for additional examples see the Instrument-Specific Default Configuration section. Errors should be raised if you try to define a parameter that doesn’t exist.

To change the threshold used for detecting slit/order edges, add:
[calibrations]
    [[slitedges]]
        edge_thresh = 100
To change the exposure time range used to identify an arc and flat-field frames and to increase the LA Cosmic sigma-clipping threshold for arc frames, add:
[calibrations]
    [[arcframe]]
        exprng = None,10
        [[process]]
            sigclip = 6.
    [[pixelflatframe]]
        exprng = 11,30

How to change the image processing parameters for all frame types

To change the base-level image processing parameters that will be applied to all frame types, you can use the baseprocess parameter group. This allows you to set these parameters once instead of having to include lines in your PypeIt file for each frame type. Any frame-type-specific alterations can still be made and will overwrite the base-level processing parameters. For example, to change the sigma-clipping level used by the LA Cosmic routine to default to 3.0 but to use a value of 6.0 for arc frames, you can add the following to your PypeIt file:

[baseprocess]
    sigclip = 3.0
[calibrations]
    [[arcframe]]
        [[[process]]]
            sigclip = 6.0

Warning

Specifically for developers, note that baseprocess is only a “pseudo” parameter group and is not actually associated with any underlying PypeIt parameter class. It is instead a flag for the code that parses the PypeIt Reduction File to distribute the associated image processing parameters to all of the frame-specific parameter sets.

Current PypeItPar Parameter Hierarchy

PypeItPar Keywords

[rdx]: ReduxPar Keywords

[calibrations]: CalibrationsPar Keywords

[[biasframe]]: FrameGroupPar Keywords

[[[process]]]: ProcessImagesPar Keywords

[[[[scattlight]]]]: ScatteredLightPar Keywords

[[darkframe]]: FrameGroupPar Keywords

[[[process]]]: ProcessImagesPar Keywords

[[[[scattlight]]]]: ScatteredLightPar Keywords

[[arcframe]]: FrameGroupPar Keywords

[[[process]]]: ProcessImagesPar Keywords

[[[[scattlight]]]]: ScatteredLightPar Keywords

[[tiltframe]]: FrameGroupPar Keywords

[[[process]]]: ProcessImagesPar Keywords

[[[[scattlight]]]]: ScatteredLightPar Keywords

[[pixelflatframe]]: FrameGroupPar Keywords

[[[process]]]: ProcessImagesPar Keywords

[[[[scattlight]]]]: ScatteredLightPar Keywords

[[pinholeframe]]: FrameGroupPar Keywords

[[[process]]]: ProcessImagesPar Keywords

[[[[scattlight]]]]: ScatteredLightPar Keywords

[[alignframe]]: FrameGroupPar Keywords

[[[process]]]: ProcessImagesPar Keywords

[[[[scattlight]]]]: ScatteredLightPar Keywords

[[alignment]]: AlignPar Keywords

[[traceframe]]: FrameGroupPar Keywords

[[[process]]]: ProcessImagesPar Keywords

[[[[scattlight]]]]: ScatteredLightPar Keywords

[[illumflatframe]]: FrameGroupPar Keywords

[[[process]]]: ProcessImagesPar Keywords

[[[[scattlight]]]]: ScatteredLightPar Keywords

[[lampoffflatsframe]]: FrameGroupPar Keywords

[[[process]]]: ProcessImagesPar Keywords

[[[[scattlight]]]]: ScatteredLightPar Keywords

[[scattlightframe]]: FrameGroupPar Keywords

[[[process]]]: ProcessImagesPar Keywords

[[[[scattlight]]]]: ScatteredLightPar Keywords

[[skyframe]]: FrameGroupPar Keywords

[[[process]]]: ProcessImagesPar Keywords

[[[[scattlight]]]]: ScatteredLightPar Keywords

[[standardframe]]: FrameGroupPar Keywords

[[[process]]]: ProcessImagesPar Keywords

[[[[scattlight]]]]: ScatteredLightPar Keywords

[[flatfield]]: FlatFieldPar Keywords

[[wavelengths]]: WavelengthSolutionPar Keywords

[[slitedges]]: EdgeTracePar Keywords

[[tilts]]: WaveTiltsPar Keywords

[scienceframe]: FrameGroupPar Keywords

[[process]]: ProcessImagesPar Keywords

[[[scattlight]]]: ScatteredLightPar Keywords

[reduce]: ReducePar Keywords

[[findobj]]: FindObjPar Keywords
[[skysub]]: SkySubPar Keywords
[[extraction]]: ExtractionPar Keywords
[[cube]]: CubePar Keywords
[[slitmask]]: SlitMaskPar Keywords

[flexure]: FlexurePar Keywords

[fluxcalib]: FluxCalibratePar Keywords

[coadd1d]: Coadd1DPar Keywords

[coadd2d]: Coadd2DPar Keywords

[sensfunc]: SensFuncPar Keywords

[[UVIS]]: SensfuncUVISPar Keywords

[[IR]]: TelluricPar Keywords

[telluric]: TelluricPar Keywords

[collate1d]: Collate1DPar Keywords

PypeItPar Keywords

Class Instantiation: PypeItPar

Key	Type	Default	Description
`calibrations`	`CalibrationsPar`	CalibrationsPar Keywords	Parameters for the calibration algorithms
`coadd1d`	`Coadd1DPar`	Coadd1DPar Keywords	Par set to control 1D coadds. Only used in the after-burner script.
`coadd2d`	`Coadd2DPar`	Coadd2DPar Keywords	Par set to control 2D coadds. Only used in the after-burner script.
`collate1d`	`Collate1DPar`	Collate1DPar Keywords	Par set to control collating 1d spectra. Only used in the after-burner script.
`flexure`	`FlexurePar`	FlexurePar Keywords	Parameters used by the flexure-correction procedure. Flexure corrections are not performed by default. To turn on, either set the parameters in the ‘flexure’ parameter group or set ‘flexure = True’ in the ‘rdx’ parameter group to use the default flexure-correction parameters.
`fluxcalib`	`FluxCalibratePar`	FluxCalibratePar Keywords	Parameters used by the flux-calibration procedure. Flux calibration is not performed by default. To turn on, either set the parameters in the ‘fluxcalib’ parameter group or set ‘fluxcalib = True’ in the ‘rdx’ parameter group to use the default flux-calibration parameters.
`rdx`	`ReduxPar`	ReduxPar Keywords	PypeIt reduction rules.
`reduce`	`ReducePar`	ReducePar Keywords	Parameters determining sky-subtraction, object finding, and extraction
`scienceframe`	`FrameGroupPar`	FrameGroupPar Keywords	The frames and combination rules for the science observations
`sensfunc`	`SensFuncPar`	SensFuncPar Keywords	Par set to control sensitivity function computation. Only used in the after-burner script.
`telluric`	`TelluricPar`	TelluricPar Keywords	Par set to control telluric fitting. Only used in the pypeit_sensfunc and pypeit_telluric after-burner scripts.

CalibrationsPar Keywords

Class Instantiation: CalibrationsPar

Key	Type	Default	Description
`alignframe`	`FrameGroupPar`	FrameGroupPar Keywords	The frames and combination rules for the align frames
`alignment`	`AlignPar`	AlignPar Keywords	Define the procedure for the alignment of traces
`arcframe`	`FrameGroupPar`	FrameGroupPar Keywords	The frames and combination rules for the wavelength calibration
`biasframe`	`FrameGroupPar`	FrameGroupPar Keywords	The frames and combination rules for the bias correction
`bpm_usebias`	bool	False	Make a bad pixel mask from bias frames? Bias frames must be provided.
`calib_dir`	str	`Calibrations`	The name of the directory for the processed calibration frames. The host path for the directory is set by the redux_path (see `ReduxPar`). Beware that success when changing the default value is not well tested!
`darkframe`	`FrameGroupPar`	FrameGroupPar Keywords	The frames and combination rules for the dark-current correction
`flatfield`	`FlatFieldPar`	FlatFieldPar Keywords	Parameters used to set the flat-field procedure
`illumflatframe`	`FrameGroupPar`	FrameGroupPar Keywords	The frames and combination rules for the illumination flat
`lampoffflatsframe`	`FrameGroupPar`	FrameGroupPar Keywords	The frames and combination rules for the lamp off flats
`pinholeframe`	`FrameGroupPar`	FrameGroupPar Keywords	The frames and combination rules for the pinholes
`pixelflatframe`	`FrameGroupPar`	FrameGroupPar Keywords	The frames and combination rules for the pixel flat
`raise_chk_error`	bool	True	Raise an error if the calibration check fails
`scattlight_pad`	int	5	Number of unbinned pixels to extend the slit edges by when masking the slits.
`scattlightframe`	`FrameGroupPar`	FrameGroupPar Keywords	The frames and combination rules for the scattered light frames
`skyframe`	`FrameGroupPar`	FrameGroupPar Keywords	The frames and combination rules for the sky background observations
`slitedges`	`EdgeTracePar`	EdgeTracePar Keywords	Slit-edge tracing parameters
`standardframe`	`FrameGroupPar`	FrameGroupPar Keywords	The frames and combination rules for the spectrophotometric standard observations
`tiltframe`	`FrameGroupPar`	FrameGroupPar Keywords	The frames and combination rules for the wavelength tilts
`tilts`	`WaveTiltsPar`	WaveTiltsPar Keywords	Define how to trace the slit tilts using the trace frames
`traceframe`	`FrameGroupPar`	FrameGroupPar Keywords	The frames and combination rules for images used for slit tracing
`wavelengths`	`WavelengthSolutionPar`	WavelengthSolutionPar Keywords	Parameters used to derive the wavelength solution

AlignPar Keywords

Class Instantiation: AlignPar

Key	Type	Default	Description
`locations`	list, ndarray	0.0, 1.0	Locations of the bars, in a list, specified as a fraction of the slit width
`snr_thresh`	int, float	1.0	S/N ratio threshold for finding an alignment trace. This should be a low number to ensure that the algorithm finds all bars. The algorithm will then only use the N most significant detections, where N is the number of elements specified in the “locations” keyword argument
`trace_npoly`	int	4	Order of the polynomial to use when fitting the trace of a single bar
`trim_edge`	list	0, 0	Trim the slit by this number of pixels left/right before finding alignment bars

FlatFieldPar Keywords

Class Instantiation: FlatFieldPar

Key	Type	Options	Default	Description
`fit_2d_det_response`	bool		False	Set this variable to True if you want to compute and account for the detector response in the flatfield image. Note that `detector response` refers to pixel sensitivity variations that primarily depend on (x,y) detector coordinates. In most cases, the default 2D bspline is sufficient to account for detector response (i.e. set this parameter to False). Note that this correction will _only_ be performed for the spectrographs that have a dedicated response correction implemented. Currently,this correction is only implemented for Keck+KCWI.
`illum_iter`	int		0	The number of rejection iterations to perform when constructing the slit-illumination profile. No rejection iterations are performed if 0. WARNING: Functionality still being tested.
`illum_rej`	int, float		5.0	The sigma threshold used in the rejection iterations used to refine the slit-illumination profile. Rejection iterations are only performed if `illum_iter > 0`.
`method`	str	`bspline`, `skip`	`bspline`	Method used to flat field the data; use skip to skip flat-fielding. Options are: None, bspline, skip
`pixelflat_file`	str			Filename of the image to use for pixel-level field flattening
`pixelflat_max_wave`	int, float			All values of the normalized pixel flat are set to 1 for wavelengths above this value.
`pixelflat_min_wave`	int, float			All values of the normalized pixel flat are set to 1 for wavelengths below this value.
`rej_sticky`	bool		False	Propagate the rejected pixels through the stages of the flat-field fitting (i.e, from the spectral fit, to the spatial fit, and finally to the 2D residual fit). If False, pixels rejected in each stage are included in each subsequent stage.
`saturated_slits`	str	`crash`, `mask`, `continue`	`crash`	Behavior when a slit is encountered with a large fraction of saturated pixels in the flat-field. The options are: ‘crash’ - Raise an error and halt the data reduction; ‘mask’ - Mask the slit, meaning no science data will be extracted from the slit; ‘continue’ - ignore the flat-field correction, but continue with the reduction.
`slit_illum_finecorr`	bool		True	If True, a fine correction to the spatial illumination profile will be performed. The fine correction is a low order 2D polynomial fit to account for a gradual change to the spatial illumination profile as a function of wavelength.
`slit_illum_pad`	int, float		5.0	The number of pixels to pad the slit edges when constructing the slit-illumination profile. Single value applied to both edges.
`slit_illum_ref_idx`	int		0	The index of a reference slit (0-indexed) used for estimating the relative spectral sensitivity (or the relative blaze). This parameter is only used if `slit_illum_relative = True`.
`slit_illum_relative`	bool		False	Generate an image of the relative spectral illumination for a multi-slit setup. If you set `use_specillum = True` for any of the frames that use the flatfield model, this must be set to True. Currently, this is only used for SlicerIFU reductions.
`slit_illum_smooth_npix`	int		10	The number of pixels used to determine smoothly varying relative weights is given by `nspec/slit_illum_smooth_npix`, where nspec is the number of spectral pixels.
`slit_trim`	int, float, tuple		3.0	The number of pixels to trim each side of the slit when selecting pixels to use for fitting the spectral response function. Single values are used for both slit edges; a two-tuple can be used to trim the left and right sides differently.
`spat_samp`	int, float		5.0	Spatial sampling for slit illumination function. This is the width of the median filter in pixels used to determine the slit illumination function, and thus sets the minimum scale on which the illumination function will have features.
`spec_samp_coarse`	int, float		50.0	bspline break point spacing in units of pixels for 2-d bspline-polynomial fit to flat field image residuals. This should be a large number unless you are trying to fit a sky flat with lots of narrow spectral features.
`spec_samp_fine`	int, float		1.2	bspline break point spacing in units of pixels for spectral fit to flat field blaze function.
`tweak_slits`	bool		True	Use the illumination flat field to tweak the slit edges. This will work even if illumflatten is set to False
`tweak_slits_maxfrac`	float		0.1	If tweak_slit is True, this sets the maximum fractional amount (of a slits width) allowed for trimming each (i.e. left and right) slit boundary, i.e. the default is 10% which means slits would shrink or grow by at most 20% (10% on each side)
`tweak_slits_thresh`	float		0.93	If tweak_slits is True, this sets the illumination function threshold used to tweak the slit boundaries based on the illumination flat. It should be a number less than 1.0
`twod_fit_npoly`	int			Order of polynomial used in the 2D bspline-polynomial fit to flat-field image residuals. The code determines the order of these polynomials to each slit automatically depending on the slit width, which is why the default is None. Alter this paramter at your own risk!

EdgeTracePar Keywords

Class Instantiation: EdgeTracePar

Key	Type	Options	Default	Description
`add_missed_orders`	bool		False	For any Echelle spectrograph (fixed-format or otherwise), attempt to add orders that have been missed by the automated edge tracing algorithm. For fixed-format Echelles, this is based on the expected positions on on the detector. Otherwise, the detected orders are modeled and roughly used to predict the locations of missed orders; see additional parameters `order_width_poly`, `order_gap_poly`, and `order_spat_range`.
`add_predict`	str		`nearest`	Sets the method used to predict the shape of the left and right traces for a user-defined slit inserted. Options are (1) `straight` inserts traces with a constant spatial pixels position, (2) `nearest` inserts traces with a form identical to the automatically identified trace at the nearest spatial position to the inserted slit, or (3) `pca` uses the PCA decomposition to predict the shape of the traces.
`add_slits`	str, list			Add one or more user-defined slits. The syntax to define a slit to add is: ‘det:spec:spat_left:spat_right’ where det=detector, spec=spectral pixel, spat_left=spatial pixel of left slit boundary, and spat_righ=spatial pixel of right slit boundary. For example, ‘2:2000:2121:2322,3:2000:1201:1500’ will add a slit to detector 2 passing through spec=2000 extending spatially from 2121 to 2322 and another on detector 3 at spec=2000 extending from 1201 to 1500.
`auto_pca`	bool		True	During automated tracing, attempt to construct a PCA decomposition of the traces. When True, the edge traces resulting from the initial detection, centroid refinement, and polynomial fitting must meet a set of criteria for performing the pca; see `pypeit.edgetrace.EdgeTraceSet.can_pca()`. If False, the `sync_predict` parameter cannot be set to `pca`; if it is not, the value is set to `nearest` and a warning is issued when validating the parameter set.
`bound_detector`	bool		False	When the code is ready to synchronize the left/right trace edges, the traces should have been constructed, vetted, and cleaned. This can sometimes lead to no valid traces. This parameter dictates what to do next. If `bound_detector` is True, the code will artificially add left and right edges that bound the detector; if False, the code identifies the slit-edge tracing as being unsuccessful, warns the user, and ends gracefully. Note that setting `bound_detector` to True is needed for some long-slit data where the slit edges are, in fact, beyond the edges of the detector.
`clip`	bool		True	Remove traces flagged as bad, instead of only masking them. This is currently only used by `centroid_refine()`.
`det_buffer`	int		5	The minimum separation between the detector edges and a slit edge for any added edge traces. Must be positive.
`det_min_spec_length`	int, float		0.33	The minimum spectral length (as a fraction of the detector size) of a trace determined by direct measurements of the detector data (as opposed to what should be included in any modeling approach; see fit_min_spec_length).
`dlength_range`	int, float			Similar to `minimum_slit_dlength`, but constrains the fractional change in the slit length as a function of wavelength. For example, a value of 0.2 means that slit length should not vary more than 20%as a function of wavelength.
`edge_detect_clip`	int, float			Sigma clipping level for peaks detected in the collapsed, Sobel-filtered significance image.
`edge_thresh`	int, float		20.0	Threshold for finding edges in the Sobel-filtered significance image.
`exclude_regions`	list, str			User-defined regions to exclude from the slit tracing. To set this parameter, the text should be a comma separated list of pixel ranges (in the x direction) to be excluded and the detector number. For example, the following string 1:0:20,1:300:400 would select two regions in det=1 between pixels 0 and 20 and between 300 and 400.
`filt_iter`	int		0	Number of median-filtering iterations to perform on sqrt(trace) image before applying to Sobel filter to detect slit/order edges.
`fit_function`	str	`polynomial`, `legendre`, `chebyshev`	`legendre`	Function fit to edge measurements. Options are: polynomial, legendre, chebyshev
`fit_maxdev`	int, float		5.0	Maximum deviation between the fitted and measured edge position for rejection in spatial pixels.
`fit_maxiter`	int		25	Maximum number of rejection iterations during edge fitting.
`fit_min_spec_length`	float		0.6	Minimum unmasked spectral length of a traced slit edge to use in any modeling procedure (polynomial fitting or PCA decomposition).
`fit_niter`	int		1	Number of iterations of re-measuring and re-fitting the edge data; see `fit_trace()`.
`fit_order`	int		5	Order of the function fit to edge measurements.
`follow_span`	int		20	In the initial connection of spectrally adjacent edge detections, this sets the number of previous spectral rows to consider when following slits forward.
`fwhm_gaussian`	int, float		3.0	The fwhm parameter to use when using Gaussian weighting in `fit_trace()` when refining the PCA predictions of edges. See description `peak_trace()`.
`fwhm_uniform`	int, float		3.0	The fwhm parameter to use when using uniform weighting in `fit_trace()` when refining the PCA predictions of edges. See description of `peak_trace()`.
`gap_offset`	int, float		5.0	Offset (pixels) used for the slit edge gap width when inserting slit edges (see sync_center) or when nudging predicted slit edges to avoid slit overlaps. This should be larger than minimum_slit_gap when converted to arcseconds.
`left_right_pca`	bool		False	Construct a PCA decomposition for the left and right traces separately. This can be important for cross-dispersed echelle spectrographs (e.g., Keck-NIRES)
`length_range`	int, float			Allowed range in slit length compared to the median slit length. For example, a value of 0.3 means that slit lengths should not vary more than 30%. Relatively shorter or longer slits are masked or clipped. Most useful for echelle or multi-slit data where the slits should have similar or identical lengths.
`maskdesign_filename`	str, list			Mask design info contained in this file or files (comma separated)
`maskdesign_maxsep`	int, float		50	Maximum allowed offset in pixels between the slit edges defined by the slit-mask design and the traced edges.
`maskdesign_sigrej`	int, float		3	Number of sigma for sigma-clipping rejection during slit-mask design matching.
`maskdesign_step`	int, float		1	Step in pixels used to generate a list of possible offsets (within +/- maskdesign_maxsep) between the slit edges defined by the mask design and the traced edges.
`match_tol`	int, float		3.0	Same-side slit edges below this separation in pixels are considered part of the same edge.
`max_nudge`	int, float			If parts of any (predicted) trace fall off the detector edge, allow them to be nudged away from the detector edge up to and including this maximum number of pixels. If None, no limit is set; otherwise should be 0 or larger.
`max_shift_abs`	int, float		0.5	Maximum spatial shift in pixels between an input edge location and the recentroided value.
`max_shift_adj`	int, float		0.15	Maximum spatial shift in pixels between the edges in adjacent spectral positions.
`max_spat_error`	int, float			Maximum error in the spatial position of edges in pixels.
`minimum_slit_dlength`	int, float			Minimum change in the slit length (arcsec) as a function of wavelength in arcsec. This is mostly meant to catch cases when the polynomial fit to the detected edges becomes ill-conditioned (e.g., when the slits run off the edge of the detector) and leads to wild traces. If reducing the order of the polynomial (`fit_order`) does not help, try using this to remove poorly constrained slits.
`minimum_slit_gap`	int, float			Minimum slit gap in arcsec. Gaps between slits are determined by the median difference between the right and left edge locations of adjacent slits. Slits with small gaps are merged by removing the intervening traces.If None, no minimum slit gap is applied. This should be smaller than gap_offset when converted to pixels.
`minimum_slit_length`	int, float			Minimum slit length in arcsec. Slit lengths are determined by the median difference between the left and right edge locations for the unmasked trace locations. This is used to identify traces that are erroneously matched together to form slits. Short slits are expected to be ignored or removed (see `clip`). If None, no minimum slit length applied.
`minimum_slit_length_sci`	int, float			Minimum slit length in arcsec for a science slit. Slit lengths are determined by the median difference between the left and right edge locations for the unmasked trace locations. Used in combination with `minimum_slit_length`, this parameter is used to identify box or alignment slits; i.e., those slits that are shorter than `minimum_slit_length_sci` but larger than `minimum_slit_length` are box/alignment slits. Box slits are never removed (see `clip`), but no spectra are extracted from them. If None, no minimum science slit length is applied.
`niter_gaussian`	int		6	The number of iterations of `fit_trace()` to use when using Gaussian weighting.
`niter_uniform`	int		9	The number of iterations of `fit_trace()` to use when using uniform weighting.
`order_gap_poly`	int		3	Order of the Legendre polynomial used to model the spatial gap between orders as a function of the order spatial position. See `add_missed_orders`.
`order_match`	int, float			Orders for fixed-format echelle spectrographs are always matched to a predefined expectation for the number of orders found and their relative placement in the detector. This sets the tolerance allowed for matching identified “slits” to echelle orders. Must be relative to the fraction of the detector spatial scale (i.e., a value of 0.05 means that the order locations must be within 5% of the expected value). If None, no limit is used.
`order_offset`	int, float			Orders for fixed-format echelle spectrographs are always matched to a predefined expectation for the number of orders found and their relative placement in the detector. This sets the offset to introduce to the expected order positions to improve the match for this specific data. This is an additive offset to the measured slit positions; i.e., this should minimize the difference between the expected order positions and `self.slit_spatial_center() + offset`. Must be in the fraction of the detector spatial scale. If None, no offset is applied.
`order_spat_range`	list			The spatial range of the detector/mosaic over which to predict order locations. If None, the full detector/mosaic range is used. See `add_missed_orders`.
`order_width_poly`	int		2	Order of the Legendre polynomial used to model the spatial width of each order as a function of spatial pixel position. See `add_missed_orders`.
`overlap`	bool		False	Assume slits identified as abnormally short are actually due to overlaps between adjacent slits/orders. If set to True, you must have also used `length_range` to identify left-right edge pairs that have an abnormally short separation. For those short slits, the code attempts to convert the short slits into slit gaps. This is particularly useful for blue orders in Keck-HIRES data.
`pad`	int		0	Integer number of pixels to consider beyond the slit edges when selecting pixels that are ‘on’ the slit.
`pca_function`	str	`polynomial`, `legendre`, `chebyshev`	`polynomial`	Type of function fit to the PCA coefficients for each component. Options are: polynomial, legendre, chebyshev
`pca_maxiter`	int		25	Maximum number of rejection iterations when fitting the PCA coefficients.
`pca_maxrej`	int		1	Maximum number of PCA coefficients rejected during a given fit iteration.
`pca_min_edges`	int		4	Minimum number of edge traces required to perform a PCA decomposition of the trace form. If left_right_pca is True, this minimum applies to the number of left and right traces separately.
`pca_n`	int			The number of PCA components to keep, which must be less than the number of detected traces. If not provided, determined by calculating the minimum number of components required to explain a given percentage of variance in the edge data; see pca_var_percent.
`pca_order`	int		2	Order of the function fit to the PCA coefficients.
`pca_sigrej`	int, float, list		2.0, 2.0	Sigma rejection threshold for fitting PCA components. Individual numbers are used for both lower and upper rejection. A list of two numbers sets these explicitly (e.g., [2., 3.]).
`pca_var_percent`	int, float		99.8	The percentage (i.e., not the fraction) of the variance in the edge data accounted for by the PCA used to truncate the number of PCA coefficients to keep (see pca_n). Ignored if pca_n is provided directly.
`rm_slits`	str, list			Remove one or more user-specified slits. The syntax used to define a slit to remove is: ‘det:spec:spat’ where det=detector, spec=spectral pixel, spat=spatial pixel. For example, ‘2:2000:2121,3:2000:1500’ will remove the slit on detector 2 that contains pixel (spat,spec)=(2000,2121) and on detector 3 that contains pixel (2000,2121).
`smash_range`	list		0.0, 1.0	Range of the slit in the spectral direction (in fractional units) to smash when searching for slit edges. If the spectrum covers only a portion of the image, use that range.
`sobel_enhance`	int		0	Enhance the sobel filtering? A value of 0 will not enhance the sobel filtering. Any other value > 0 will sum the sobel values. For example, a value of 3 will combine the sobel values for the 3 nearest pixels. This is useful when a slit edge is poorly defined (e.g. vignetted).
`sobel_mode`	str	`nearest`, `constant`	`nearest`	Mode for Sobel filtering. Default is ‘nearest’; note we find’constant’ works best for DEIMOS.
`sync_center`	str	`median`, `nearest`, `gap`	`median`	Mode to use for determining the location of traces to insert. Use median to use the median of the matched left and right edge pairs, nearest to use the length of the nearest slit, or gap to offset by a fixed gap width from the next slit edge.
`sync_predict`	str	`pca`, `nearest`, `auto`	`pca`	Mode to use when predicting the form of the trace to insert. Use pca to use the PCA decomposition, nearest to reproduce the shape of the nearest trace, or auto to let PypeIt decide which mode to use between pca and nearest. In general, it will first try pca, and if that is not possible, it will use nearest.
`sync_to_edge`	bool		True	If adding a first left edge or a last right edge, ignore center_mode for these edges and place them at the edge of the detector (with the relevant shape).
`trace_median_frac`	int, float			After detection of peaks in the rectified Sobel-filtered image and before refitting the edge traces, the rectified image is median filtered with a kernel width of trace_median_frac*nspec along the spectral dimension.
`trace_rms_tol`	int, float			After retracing edges using peaks detected in the rectified and collapsed image, the RMS difference (in pixels) between the original and refit traces are calculated. This sets the upper limit of the RMS for traces that will be removed. If None, no limit is set and all new traces are kept.
`trace_thresh`	int, float			After rectification and median filtering of the Sobel-filtered image (see trace_median_frac), values in the median-filtered image below this threshold are masked in the refitting of the edge trace data. If None, no masking applied.
`use_maskdesign`	bool		False	Use slit-mask designs to identify slits.

WaveTiltsPar Keywords

Class Instantiation: WaveTiltsPar

Key	Type	Default	Description
`cont_rej`	int, float, list, ndarray	3, 1.5	The sigma threshold for rejection. Can be a single number or two numbers that give the low and high sigma rejection, respectively.
`func2d`	str	`legendre2d`	Type of function for 2D fit
`idsonly`	bool	False	Only use the arc lines that have an identified wavelength to trace tilts (CURRENTLY NOT USED!)
`maxdev2d`	int, float	0.25	Maximum absolute deviation (in units of fwhm) rejection threshold used to determines which pixels in global 2d fits to arc line tilts are rejected because they deviate from the model by more than this value
`maxdev_tracefit`	int, float	0.2	Maximum absolute deviation (in units of fwhm) for the legendre polynomial fits to individual arc line tilt fits during iterative trace fitting (flux weighted, then gaussian weighted)
`minmax_extrap`	list, ndarray	150.0, 1000.0	Sets how far below the last measured tilt line is extrapolated in tracewave.fit_tilts()
`nfwhm_neigh`	int, float	3.0	Required separation between neighboring arc lines for them to be considered for tilt tracing in units of the the spectral fwhm (see wavelength parset where fwhm is defined)
`rm_continuum`	bool	False	Before tracing the line center at each spatial position, remove any low-order continuum in the 2D spectra.
`sig_neigh`	int, float	10.0	Significance threshold for arcs to be used in line identification for the purpose of identifying neighboring lines. The tracethresh parameter above determines the significance threshold of lines that will be traced, but these lines must be at least nfwhm_neigh fwhm away from neighboring lines. This parameter determines the significance above which a line must be to be considered a possible colliding neighbor. A low value of sig_neigh will result in an overall larger number of lines, which will result in more lines above tracethresh getting rejected
`sigrej2d`	int, float	3.0	Outlier rejection significance determining which pixels on a fit to an arc line tilt are rejected by the global 2D fit
`sigrej_trace`	int, float	3.0	Outlier rejection significance to determine which traced arc lines should be included in the global fit
`spat_order`	int, float, list, ndarray	3	Order of the legendre polynomial to be fit to the tilt of an arc line. This parameter determines both the order of the individual arc line tilts, as well as the order of the spatial direction of the 2d legendre polynomial (spatial, spectral) that is fit to obtain a global solution for the tilts across the slit/order. This can be a single number or a list/array providing the value for each slit
`spec_order`	int, float, list, ndarray	4	Order of the spectral direction of the 2d legendre polynomial (spatial, spectral) that is fit to obtain a global solution for the tilts across the slit/order. This can be a single number or a list/array providing the value for each slit
`tracethresh`	int, float, list, ndarray	20.0	Significance threshold for arcs to be used in tracing wavelength tilts. This can be a single number or a list/array providing the value for each slit/order.

WavelengthSolutionPar Keywords

Class Instantiation: WavelengthSolutionPar

Key	Type	Options	Default	Description
`bad_orders_maxfrac`	float		0.25	For echelle spectrographs (i.e., `echelle=True`), this is the maximum fraction of orders (per detector) with failed 1D fit, for PypeIt to attempt a refit.
`cc_local_thresh`	float		0.7	Threshold for the local cross-correlation coefficient, evaluated at each reidentified line, between an input spectrum and the shifted and stretched archive spectrum above which a line must be to be considered a good line for reidentification. The local cross-correlation is evaluated at each candidate reidentified line (using a window of nlocal_cc), and is then used to score the the reidentified lines to arrive at the final set of good reidentifications.
`cc_shift_range`	tuple			Range of pixel shifts allowed when cross-correlating the input arc spectrum with the archive spectrum. If None, the range will be automatically determined.
`cc_thresh`	float, list, ndarray		0.7	Threshold for the global cross-correlation coefficient between an input spectrum and member of the archive required to attempt reidentification. Spectra from the archive with a lower cross-correlation are not used for reidentification. This can be a single number or a list/array providing the value for each slit.
`ech_norder_coeff`	int		4	For echelle spectrographs, this is the order of the final 2d fit to the order dimension.
`ech_nspec_coeff`	int		4	For echelle spectrographs, this is the order of the final 2d fit to the spectral dimension. You should choose this to be the n_final of the fits to the individual orders.
`ech_separate_2d`	bool		False	For echelle spectrographs, fit the 2D solutions on separate detectors separately
`ech_sigrej`	int, float		2.0	For echelle spectrographs, this is the sigma-clipping rejection threshold in the 2d fit to spectral and order dimensions
`echelle`	bool		False	Is this an echelle spectrograph? If yes an additional 2-d fit wavelength fit will be performed as a function of spectral pixel and order number to improve the wavelength solution
`frac_rms_thresh`	float		1.5	For echelle spectrographs (i.e., `echelle=True`), this is the fractional change in the RMS threshold used when a 1D fit is re-attempted for failed orders.
`func`	str		`legendre`	Function used for wavelength solution fits
`fwhm`	int, float		4.0	Spectral sampling of the arc lines. This is the FWHM of an arcline in binned pixels of the input arc image
`fwhm_fromlines`	bool		True	Estimate spectral resolution in each slit using the arc lines. If True, the estimated FWHM will override `fwhm` only in the determination of the wavelength solution (including the calculation of the threshold for the solution RMS, see `rms_thresh_frac_fwhm`), but not for the wave tilts calibration.
`fwhm_spat_order`	int		0	This parameter determines the spatial polynomial order to use in the 2D polynomial fit to the FWHM of the arc lines. See also, fwhm_spec_order.
`fwhm_spec_order`	int		1	This parameter determines the spectral polynomial order to use in the 2D polynomial fit to the FWHM of the arc lines. See also, fwhm_spat_order.
`lamps`	list			Name of one or more ions used for the wavelength calibration. Use `None` for no calibration. Choose `use_header` to use the list of lamps recorded in the header of the arc frames (this is currently available only for Keck DEIMOS, Keck LRIS, MMT Blue Channel, and LDT DeVeny).
`match_toler`	float		2.0	Matching tolerance in pixels when searching for new lines. This is the difference in pixels between the wavlength assigned to an arc line by an iteration of the wavelength solution to the wavelength in the line list. This parameter is also used as the matching tolerance in pixels for a line reidentification. A good line match must match within this tolerance to the shifted and stretched archive spectrum, and the archive wavelength solution at this match must be within match_toler dispersion elements from the line in line list.
`method`	str	`holy-grail`, `identify`, `reidentify`, `echelle`, `full_template`	`holy-grail`	Method to use to fit the individual arc lines. Note that some of the available methods should not be used; they are unstable and require significant parameter tweaking to succeed. You should use one of ‘holy-grail’, ‘reidentify’, or ‘full_template’. ‘holy-grail’ attempts to get a first guess at line IDs by looking for patterns in the line locations. It is fully automated. When it works, it works well; however, it can fail catastrophically. Instead, ‘reidentify’ and ‘full_template’ are the preferred methods. They require an archived wavelength solution for your specific instrument/grating combination as a reference. This is used to anchor the wavelength solution for the data being reduced. All options are: holy-grail, identify, reidentify, echelle, full_template.
`n_final`	int, float, list, ndarray		4	Order of final fit to the wavelength solution (there are n_final+1 parameters in the fit). This can be a single number or a list/array providing the value for each slit
`n_first`	int		2	Order of first guess fit to the wavelength solution.
`nfitpix`	int		5	Number of pixels to fit when deriving the centroid of the arc lines (an odd number is best)
`nlocal_cc`	int		11	Size of pixel window used for local cross-correlation computation for each arc line. If not an odd number one will be added to it to make it odd.
`nreid_min`	int		1	Minimum number of times that a given candidate reidentified line must be properly matched with a line in the arxiv to be considered a good reidentification. If there is a lot of duplication in the arxiv of the spectra in question (i.e. multislit) set this to a number like 1-4. For echelle this depends on the number of solutions in the arxiv. Set this to 1 for fixed format echelle spectrographs. For an echelle with a tiltable grating, this will depend on the number of solutions in the arxiv.
`nsnippet`	int		2	Number of spectra to chop the arc spectrum into when `method` is ‘full_template’
`numsearch`	int		20	Number of brightest arc lines to search for in preliminary identification
`qa_log`	bool		True	Governs whether the wavelength solution arc line QA plots will have log or linear scalingIf True, the scaling will be log, if False linear
`redo_slits`	int, list			Redo the input slit(s) [multislit] or order(s) [echelle]
`reference`	str	`arc`, `sky`, `pixel`	`arc`	Perform wavelength calibration with an arc, sky frame. Use ‘pixel’ for no wavelength solution.
`refframe`	str	`observed`, `heliocentric`, `barycentric`	`heliocentric`	Frame of reference for the wavelength calibration. Options are: observed, heliocentric, barycentric
`reid_arxiv`	str			Name of the archival wavelength solution file that will be used for the wavelength reidentification. Only used if `method` is ‘reidentify’ or ‘full_template’.
`reid_cont_sub`	bool		True	If True, continuum subtract the arc and arxiv spectrum before the wavelength reidentification.
`rms_thresh_frac_fwhm`	float		0.15	Maximum RMS (expressed as fraction of the FWHM) for keeping a slit/order solution. If `fwhm_fromlines` is True, FWHM will be computed from the arc lines in each slits, otherwise `fwhm` will be used. This parameter is used for the ‘holy-grail’, ‘reidentify’, and ‘echelle’ methods and when re-analyzing a slit using the `redo_slits` parameter.
`sigdetect`	int, float, list, ndarray		5.0	Sigma threshold above fluctuations for arc-line detection. Arcs are continuum subtracted and the fluctuations are computed after continuum subtraction. This can be a single number or a vector (list or numpy array) that provides the detection threshold for each slit.
`sigrej_final`	float		3.0	Number of sigma for rejection for the final guess to the wavelength solution.
`sigrej_first`	float		2.0	Number of sigma for rejection for the first guess to the wavelength solution.
`use_instr_flag`	bool		False	If True, restrict to lines matching the instrument. WARNING: This is only implemented for shane_kast_red + HolyGrail. Do not use it unless you really know what you are doing.
`wvrng_arxiv`	list			Cut the arxiv template down to this specified wavelength range [min,max]

Coadd1DPar Keywords

Class Instantiation: Coadd1DPar

Key	Type	Default	Description
`coaddfile`	str		Output filename
`dloglam`	int, float		Dispersion in units of log10(wave) in case you want to specify it in the get_wave_grid (for the ‘velocity’ or ‘log10’ options), otherwise a median value is computed from the data.
`dv`	int, float		Dispersion in units of km/s in case you want to specify it in the get_wave_grid (for the ‘velocity’ option), otherwise a median value is computed from the data.
`dwave`	int, float		Dispersion in Angstroms in case you want to specify it in the get_wave_grid (for the ‘linear’ option), otherwise a median value is computed from the data.
`ex_value`	str	`OPT`	The extraction to coadd, i.e. optimal or boxcar. Must be either ‘OPT’ or ‘BOX’
`filter`	str	`none`	Filter for scaling. See flux_calib.load_fitler_file() for naming. Ignore if none
`filter_mag`	float		Magnitude of the source in the given filter
`filter_mask`	str, list		List of wavelength regions to mask when doing the scaling (i.e., occasional junk pixels). Colon and comma separateed, e.g. 5552:5559,6010:6030
`flux_value`	bool	True	If True (default), the code will coadd the fluxed spectra (i.e. the FLAM) in the spec1d files. If False, it will coadd the counts.
`lower`	int, float	3.0	Lower rejection threshold used for rejecting pixels when combining spectra in units of sigma.
`mag_type`	str	`AB`	Magnitude type. AB is the only option currently allowed
`maxiter_reject`	int	5	Maximum number of iterations for stacking and rejection. The code stops iterating either when the output mask does not change betweeen successive iterations or when maxiter_reject is reached.
`maxiter_scale`	int	5	Maximum number of iterations performed for rescaling spectra.
`maxrej`	int		Coadding performs iterative rejection by comparing each exposure to a preliminary stack of all the exposures. If this parameter is set then it will not reject more than maxrej pixels per iteration of this rejection. The default is None, which means no maximum on rejected pixels.
`nbests`	list, int		Number of orders to use for estimating the per exposure weights. Default is None, which will just use one fourth of the total number of orders. This is only used for Echelle
`nmaskedge`	int	2	Number of edge pixels to mask. This should be removed/fixed.
`ref_percentile`	int, float	70.0	Percentile used for selecting the minimum SNR cut from a reference spectrum used to robustly determine the median ratio between spectra. This parameter is used by coadd1d.robust_median_ratio as part of the automatic rescaling procedure. Pixels above this percentile cut are deemed the “good” pixels and are used to compute the ratio of two spectra. This must be a number between 0 and 100.
`scale_method`	str	`auto`	Method used to rescale the spectra prior to coadding. The options are: ‘auto’ – Determine the scaling method automatically based on the S/N ratio which works well. ‘poly’ – Polynomial rescaling. ‘median’ – Median rescaling ‘none’ – Do not rescale. ‘hand’ – Pass in hand scaling factors. This option is not well tested.
`sigrej_exp`	int, float		Rejection threshold used for rejecting exposures with S/N more than sigrej_exp*sigma above the median S/N. If None (the default), no rejection is performed. Currently, only available for multi-slit observations.
`sigrej_scale`	int, float	3.0	Rejection threshold used for rejecting pixels when rescaling spectra with scale_spec.
`sn_clip`	int, float	30.0	Errors are capped during rejection so that the S/N is never greater than sn_clip. This prevents overly aggressive rejection in high S/N ratio spectrum which neverthless differ at a level greater than the formal S/N due to systematics.
`sn_min_medscale`	int, float	0.5	For scale method set to `auto`, this sets the minimum SNR for which median scaling is attempted.
`sn_min_polyscale`	int, float	2.0	For scale method set to `auto`, this sets the minimum SNR for which polynomial scaling is attempted.
`sn_smooth_npix`	int, float		Number of pixels to median filter by when computing S/N used to decide how to scale and weight spectra. If set to None (default), the code will determine the effective number of good pixels per spectrum in the stack that is being co-added and use 10% of this neff.
`spec_samp_fact`	float	1.0	Make the wavelength grid sampling finer (spec_samp_fact < 1.0) or coarser (spec_samp_fact > 1.0) by this sampling factor. This basically multiples the ‘native’ spectral pixels by spec_samp_fact, i.e. units spec_samp_fact are pixels.
`upper`	int, float	3.0	Upper rejection threshold used for rejecting pixels when combining spectra in units of sigma.
`wave_grid_max`	int, float		Used in case you want to specify the maximum wavelength in your wavelength grid, default=None computes from data
`wave_grid_min`	int, float		Used in case you want to specify the minimum wavelength in your wavelength grid, default=None computes from data
`wave_method`	str	`linear`	Method used to construct wavelength grid for coadding spectra. The routine that creates the wavelength is `get_wave_grid()`. The options are: ‘iref’ – Use the first wavelength array. ‘velocity’ – Grid is uniform in velocity. ‘log10’ – Grid is uniform in log10(wave). This is the same as velocity. ‘linear’ – Grid is uniform in lambda. ‘concatenate’ – Meld the input wavelength arrays
`weight_method`	str	`auto`	Method used to weight the spectra for coadding. The options are: ‘auto’ – Use constant weights if rms_sn < 3.0, otherwise use wavelength dependent.’constant’ – Constant weights based on rms_sn**2’uniform’ – Uniform weighting’wave_dependent’ – Wavelength dependent weights will be used irrespective of the rms_sn ratio. This option will not work well at low S/N ratio although it is useful for objects where only a small fraction of the spectral coverage has high S/N ratio (like high-z quasars).’relative’ – Apply relative weights implying one reference exposure will receive unit weight at all wavelengths and all others receive relatively wavelength dependent weights . Note, relative weighting will only work well when there is at least one spectrum with a reasonable S/N, and a continuum. This option may only be better when the object being used has a strong continuum + emission lines. This is particularly useful if you are dealing with highly variable spectra (e.g. emission lines) andrequire a precision better than ~1 per cent.’ivar’ – Use inverse variance weighting. This is not well tested and should probably be deprecated.

Coadd2DPar Keywords

Class Instantiation: Coadd2DPar

Key	Type	Default	Description
`exclude_slits`	str, list		Exclude one or more slits from the coaddition. Example syntax – DET01:175,DET02:205 or MSC02:2234. This and `only_slits` are mutually exclusive. If both are provided, `only_slits` takes precedence.
`manual`	str		Manual extraction parameters. det:spat:spec:fwhm:boxcar_radius. Multiple manual extractions are semi-colon separated, and spat,spec are in the pseudo-image generated by COADD2D.boxcar_radius is optional and in pixels (not arcsec!).
`offsets`	str, list	`auto`	Offsets for the images being combined (spat pixels). Options are: `maskdef_offsets`, `header`, `auto`, and a list of offsets. Use `maskdef_offsets` to use the offsets computed during the slitmask design matching (currently available for these Slit-mask design Spectrographs only). If equal to `header`, the dither offsets recorded in the header, when available, will be used. If `auto` is chosen, PypeIt will try to compute the offsets using a reference object with the highest S/N, or an object selected by the user (see `user_obj`). If a list of offsets is provided, PypeIt will use it.
`only_slits`	str, list		Restrict coaddition to one or more of slits. Example syntax – DET01:175,DET02:205 or MSC02:2234. This and `exclude_slits` are mutually exclusive. If both are provided, `only_slits` takes precedence.
`spat_toler`	int	5	This parameter provides the desired tolerance in spatial pixel used to identify slits in different exposures
`use_slits4wvgrid`	bool	False	If True, use the slits to set the trace down the center
`user_obj`	int, list		Object that the user wants to use to compute the weights and/or the offsets for coadding images. For longslit/multislit spectroscopy, provide the `SLITID` and the `OBJID`, separated by comma, of the selected object. For echelle spectroscopy, provide the `ECH_OBJID` of the selected object. See Spec1D Output for more info about `SLITID`, `OBJID` and `ECH_OBJID`. If this parameter is not `None`, it will be used to compute the offsets only if `offsets = auto`, and it will used to compute the weights only if `weights = auto`.
`wave_method`	str		Argument to `get_wave_grid()` method, which determines how the 2d coadd wavelength grid is constructed. The default is None, which will use a linear gridfor longslit/multislit coadds and a log10 grid for echelle coadds. Currently supported options with 2d coadding are:* ‘iref’ – Use one of the exposures (the first) as the reference for the wavelength grid * ‘velocity’ – Grid is uniform in velocity* ‘log10’ – Grid is uniform in log10(wave). This is the same as velocity.* ‘linear’ – Grid is uniform in wavelength
`weights`	str, list	`auto`	Mode for the weights used to coadd images. Options are: `auto`, `uniform`, or a list of weights. If `auto` is used, PypeIt will try to compute the weights using a reference object with the highest S/N, or an object selected by the user (see `user_obj`), if `uniform` is used, uniform weights will be applied. If a list of weights is provided, PypeIt will use it.

Collate1DPar Keywords

Class Instantiation: Collate1DPar

Key	Type	Default	Description
`dry_run`	bool	False	If set, the script will display the matching File and Object Ids but will not flux, coadd or archive.
`exclude_serendip`	bool	False	Whether to exclude SERENDIP objects from collating.
`exclude_slit_trace_bm`	list, str	[]	A list of slit trace bitmask bits that should be excluded.
`flux`	bool	False	If set, the script will flux calibrate using archived sensfuncs before coadding.
`ignore_flux`	bool	False	If set, the script will only coadd non-fluxed spectra even if flux data is present. Otherwise fluxed spectra are coadded if all spec1ds have been fluxed calibrated.
`match_using`	str	`ra/dec`	Determines how 1D spectra are matched as being the same object. Must be either ‘pixel’ or ‘ra/dec’.
`outdir`	str	`/Users/westfall/Work/packages/pypeit/doc`	The path where all coadded output files and report files will be placed.
`refframe`	str		Perform reference frame correction prior to coadding. Options are: observed, heliocentric, barycentric
`spec1d_outdir`	str		The path where all modified spec1d files are placed. These are only created if flux calibration or refframe correction are asked for.
`tolerance`	str, float	`1.0`	The tolerance used when comparing the coordinates of objects. If two objects are within this distance from each other, they are considered the same object. If match_using is ‘ra/dec’ (the default) this is an angular distance. The defaults units are arcseconds but other units supported by astropy.coordinates.Angle can be used (e.g., ‘0.003d’ or ‘0h1m30s’). If match_using is ‘pixel’ this is a float.
`wv_rms_thresh`	float		If set, any objects with a wavelength RMS > this value are skipped, else all wavelength RMS values are accepted.

FlexurePar Keywords

Class Instantiation: FlexurePar

Key	Type	Options	Default	Description
`excessive_shift`	str	`crash`, `set_to_zero`, `continue`, `use_median`	`use_median`	Behavior when the measured spectral flexure shift is larger than `spec_maxshift`. The options are: ‘crash’ - Raise an error and halt the data reduction; ‘set_to_zero’ - Set the flexure shift to zero and continue with the reduction; ‘continue’ - Use the large flexure value whilst issuing a warning; and ‘use_median’ - Use the median flexure shift among all the objects in the same slit (if more than one object is detected) or among all the other slits; if not available, the flexure correction will not be applied.
`maxwave`	int, float			Maximum wavelength to use for the correlation. If `None` or greater than the maximum wavelength of either the object or archive sky spectrum, this this parameter has no effect.
`minwave`	int, float			Minimum wavelength to use for the correlation. If `None` or less than the minimum wavelength of either the object or archive sky spectrum, this this parameter has no effect.
`multi_min_SN`	int, float		1	Minimum S/N for analyzing sky spectrum for flexure
`spec_maxshift`	int		20	Maximum allowed spectral flexure shift in pixels.
`spec_method`	str	`boxcar`, `slitcen`, `skip`	`skip`	Method used to correct for flexure. Use skip for no correction. If slitcen is used, the flexure correction is performed before the extraction of objects (not recommended). Options are: None, boxcar, slitcen, skip
`spectrum`	str		`paranal_sky.fits`	Archive sky spectrum to be used for the flexure correction.

FluxCalibratePar Keywords

Class Instantiation: FluxCalibratePar

Key	Type	Default	Description
`extinct_correct`	bool		The default behavior for atmospheric extinction corrections is that if UVIS algorithm is used (which does not correct for telluric absorption) than an atmospheric extinction model is used to correct for extinction below 10,000A, whereas if the IR algorithm is used, then no extinction correction is applied since the atmosphere is modeled directly. To follow these defaults based on the algorithm this parameter should be set to `extinct_correct=None`. If instead this parameter is set, this overide this default behavior. In other words, it will force an extinction correction if `extinct_correct=True`, and will not perform an extinction correction if `extinct_correct=False`.
`extinct_file`	str	`closest`	If `extinct_file='closest'` the code will select the PypeIt-included extinction file for the closest observatory (within 5 deg, geographic coordinates) to the telescope identified in `std_file` (see Extinction Correction for the list of currently included files). If constructing a sesitivity function for a telescope not within 5 deg of a listed observatory, this parameter may be set to the name of one of the listed extinction files. Alternatively, a custom extinction file may be installed in the PypeIt cache using the `pypeit_install_extinctfile` script; this parameter may then be set to the name of the custom extinction file.
`extrap_sens`	bool	False	If False (default), the code will crash if one tries to use sensfunc at wavelengths outside its defined domain. By changing the par[‘sensfunc’][‘extrap_blu’] and par[‘sensfunc’][‘extrap_red’] this domain can be extended. If True the code will blindly extrapolate.
`use_archived_sens`	bool	False	Use an archived sensfunc to flux calibration

ReduxPar Keywords

Class Instantiation: ReduxPar

Key	Type	Default	Description
`calwin`	int, float	0	The window of time in hours to search for calibration frames for a science frame
`chk_version`	bool	True	If True enforce strict PypeIt version checking to ensure that all files were created with the current version of PypeIt. If set to False, the code will attempt to read out-of-date files and keep going. Beware (!!) that this can lead to unforeseen bugs that either cause the code to crash or lead to erroneous results. I.e., you really need to know what you are doing if you set this to False!
`detnum`	int, list		Restrict reduction to a list of detector indices. In case of mosaic reduction (currently only available for Gemini/GMOS and Keck/DEIMOS) `detnum` should be a list of tuples of the detector indices that are mosaiced together. E.g., for Gemini/GMOS `detnum` would be `[(1,2,3)]` and for Keck/DEIMOS it would be `[(1, 5), (2, 6), (3, 7), (4, 8)]`
`ignore_bad_headers`	bool	False	Ignore bad headers (NOT recommended unless you know it is safe).
`maskIDs`	str, int, list		Restrict reduction to a set of slitmask IDs Example syntax – `maskIDs = 818006,818015` This must be used with detnum (for now).
`qadir`	str	`QA`	Directory relative to calling directory to write quality assessment files.
`quicklook`	bool	False	Run a quick look reduction? This is usually good if you want to quickly reduce the data (usually at the telescope in real time) to get an initial estimate of the data quality.
`redux_path`	str	`/Users/westfall/Work/packages/pypeit/doc`	Path to folder for performing reductions. Default is the current working directory.
`scidir`	str	`Science`	Directory relative to calling directory to write science files.
`slitspatnum`	str, list		Restrict reduction to a set of slit DET:SPAT values (closest slit is used). Example syntax – slitspatnum = DET01:175,DET01:205 or MSC02:2234 If you are re-running the code, (i.e. modifying one slit) you must have the precise SPAT_ID index.
`sortroot`	str		A filename given to output the details of the sorted files. If None, the default is the root name of the pypeit file. If off, no output is produced.
`spectrograph`	str		Spectrograph that provided the data to be reduced. See Spectrographs for valid options.

ReducePar Keywords

Class Instantiation: ReducePar

Key	Type	Default	Description
`cube`	`CubePar`	CubePar Keywords	Parameters for cube generation algorithms
`extraction`	`ExtractionPar`	ExtractionPar Keywords	Parameters for extraction algorithms
`findobj`	`FindObjPar`	FindObjPar Keywords	Parameters for the find object and tracing algorithms
`skysub`	`SkySubPar`	SkySubPar Keywords	Parameters for sky subtraction algorithms
`slitmask`	`SlitMaskPar`	SlitMaskPar Keywords	Parameters for slitmask
`trim_edge`	list	3, 3	Trim the slit by this number of pixels left/right when performing sky subtraction

CubePar Keywords

Class Instantiation: CubePar

Key	Type	Options	Default	Description
`align`	bool		False	If set to True, the input frames will be spatially aligned by cross-correlating the whitelight images with either a reference image (see `reference_image`) or the whitelight image that is generated using the first spec2d listed in the coadd3d file. Alternatively, the user can specify the offsets (i.e. Delta RA x cos(dec) and Delta Dec, both in arcsec) in the spec2d block of the coadd3d file. See the documentation for examples of this usage.
`astrometric`	bool		True	If true, an astrometric correction will be applied using the alignment frames.
`combine`	bool		False	If set to True, the input frames will be combined. Otherwise, a separate datacube will be generated for each input spec2d file, and will be saved as a spec3d file.
`dec_max`	float			Maximum DEC to use when generating the WCS. If None, the default is maximum DEC based on the WCS of all spaxels. Units should be degrees.
`dec_min`	float			Minimum DEC to use when generating the WCS. If None, the default is minimum DEC based on the WCS of all spaxels. Units should be degrees.
`grating_corr`	bool		True	This option performs a small correction for the relative blaze function of all input frames that have (even slightly) different grating angles, or if you are flux calibrating your science data with a standard star that was observed with a slightly different setup.
`method`	str	`subpixel`, `ngp`	`subpixel`	What method should be used to generate the datacube. There are currently two options: (1) “subpixel” (default) - this algorithm divides each pixel in the spec2d frames into subpixels, and assigns each subpixel to a voxel of the datacube. Flux is conserved, but voxels are correlated, and the error spectrum does not account for covariance between adjacent voxels. See also, spec_subpixel and spat_subpixel. (2) “ngp” (nearest grid point) - this algorithm is effectively a 3D histogram. Flux is conserved, voxels are not correlated, however this option suffers the same downsides as any histogram; the choice of bin sizes can change how the datacube appears. This algorithm takes each pixel on the spec2d frame and puts the flux of this pixel into one voxel in the datacube. Depending on the binning used, some voxels may be empty (zero flux) while a neighboring voxel might contain the flux from two spec2d pixels. Note that all spec2d pixels that contribute to the same voxel are inverse variance weighted (e.g. if two pixels have the same variance, the voxel would be assigned the average flux of the two pixels).
`output_filename`	str			If combining multiple frames, this string sets the output filename of the combined datacube. If combine=False, the output filenames will be prefixed with `spec3d_*`
`ra_max`	float			Maximum RA to use when generating the WCS. If None, the default is maximum RA based on the WCS of all spaxels. Units should be degrees.
`ra_min`	float			Minimum RA to use when generating the WCS. If None, the default is minimum RA based on the WCS of all spaxels. Units should be degrees.
`reference_image`	str			White light image of a previously combined datacube. The white light image will be used as a reference when calculating the offsets of the input spec2d files. Ideally, the reference image should have the same shape as the data to be combined (i.e. set the ra_min, ra_max etc. params so they are identical to the reference image).
`save_whitelight`	bool		False	Save a white light image of the combined datacube. The output filename will be given by the “output_filename” variable with a suffix “_whitelight”. Note that the white light image collapses the flux along the wavelength axis, so some spaxels in the 2D white light image may have different wavelength ranges. To set the wavelength range, use the “whitelight_range” parameter. If combine=False, the individual spec3d files will have a suffix “_whitelight”.
`scale_corr`	str			This option performs a small correction for the relative spectral illumination scale of different spec2D files. Specify the relative path+file to the spec2D file that you would like to use for the relative scaling. If you want to perform this correction, it is best to use the spec2d file with the highest S/N sky spectrum. You should choose the same frame for both the standards and science frames.
`skysub_frame`	str		`image`	Set the sky subtraction to be implemented. The default behaviour is to subtract the sky using the model that is derived from each individual image (i.e. set this parameter to “image”). To turn off sky subtraction completely, set this parameter to “none” (all lowercase). Finally, if you want to use a different frame for the sky subtraction, specify the relative path+file to the spec2D file that you would like to use for the sky subtraction. The model fit to the sky of the specified frame will be used. Note, the sky and science frames do not need to have the same exposure time; the sky model will be scaled to the science frame based on the relative exposure time.
`slice_subpixel`	int		5	When method=subpixel, slice_subpixel sets the subpixellation scale of each IFU slice. The default option is to divide each slice into 5 sub-slices during datacube creation. See also, spec_subpixel and spat_subpixel.
`slit_spec`	bool		True	If the data use slits in one spatial direction, set this to True. If the data uses fibres for all spaxels, set this to False.
`spat_subpixel`	int		5	When method=subpixel, spat_subpixel sets the subpixellation scale of each detector pixel in the spatial direction. The total number of subpixels in each pixel is given by spec_subpixel x spat_subpixel. The default option is to divide each spec2d pixel into 25 subpixels during datacube creation. See also, spec_subpixel and slice_subpixel.
`spatial_delta`	float			The spatial size of each spaxel to use when generating the WCS (in arcsec). If None, the default is set by the spectrograph file.
`spec_subpixel`	int		5	When method=subpixel, spec_subpixel sets the subpixellation scale of each detector pixel in the spectral direction. The total number of subpixels in each pixel is given by spec_subpixel x spat_subpixel. The default option is to divide each spec2d pixel into 25 subpixels during datacube creation. See also, spat_subpixel and slice_subpixel.
`standard_cube`	str			Filename of a standard star datacube. This cube will be used to correct the relative scales of the slits, and to flux calibrate the science datacube.
`wave_delta`	float			The wavelength step to use when generating the WCS (in Angstroms). If None, the default is set by the wavelength solution.
`wave_max`	float			Maximum wavelength to use when generating the WCS. If None, the default is maximum wavelength based on the WCS of all spaxels. Units should be Angstroms.
`wave_min`	float			Minimum wavelength to use when generating the WCS. If None, the default is minimum wavelength based on the WCS of all spaxels. Units should be Angstroms.
`weight_method`	str	`auto`, `constant`, `uniform`, `wave_dependent`, `relative`, `ivar`	`auto`	Method used to weight the spectra for coadding. The options are: ‘auto’ – Use constant weights if rms_sn < 3.0, otherwise use wavelength dependent.’constant’ – Constant weights based on rms_sn**2’uniform’ – Uniform weighting’wave_dependent’ – Wavelength dependent weights will be used irrespective of the rms_sn ratio. This option will not work well at low S/N ratio although it is useful for objects where only a small fraction of the spectral coverage has high S/N ratio (like high-z quasars).’relative’ – Apply relative weights implying one reference exposure will receive unit weight at all wavelengths and all others receive relatively wavelength dependent weights . Note, relative weighting will only work well when there is at least one spectrum with a reasonable S/N, and a continuum. This option may only be better when the object being used has a strong continuum + emission lines. This is particularly useful if you are dealing with highly variable spectra (e.g. emission lines) andrequire a precision better than ~1 per cent.’ivar’ – Use inverse variance weighting. This is not well tested and should probably be deprecated.
`whitelight_range`	list		None, None	A two element list specifying the wavelength range over which to generate the white light image. The first (second) element is the minimum (maximum) wavelength to use. If either of these elements are None, PypeIt will automatically use a wavelength range that ensures all spaxels have the same wavelength coverage. Note, if you are using a reference_image to align all frames, it is preferable to use the same white light wavelength range for all white light images. For example, you may wish to use an emission line map to register two frames.

ExtractionPar Keywords

Class Instantiation: ExtractionPar

Key	Type	Default	Description
`boxcar_radius`	int, float	1.5	Boxcar radius in arcseconds used for boxcar extraction
`model_full_slit`	bool	False	If True local sky subtraction will be performed on the entire slit. If False, local sky subtraction will be applied to only a restricted region around each object. This should be set to True for either multislit observations using narrow slits or echelle observations with narrow slits
`return_negative`	bool	False	If `True` the negative traces will be extracted and saved to disk
`skip_extraction`	bool	False	Do not perform an object extraction
`skip_optimal`	bool	False	Perform boxcar extraction only (i.e. skip Optimal and local skysub)
`sn_gauss`	int, float	4.0	S/N threshold for performing the more sophisticated optimal extraction which performs a b-spline fit to the object profile. For S/N < sn_gauss the code will simply optimal extractwith a Gaussian with FWHM determined from the object finding.
`std_prof_nsigma`	float	30.0	prof_nsigma parameter for Standard star extraction. Prevents undesired rejection. NOTE: Not consumed by the code at present.
`use_2dmodel_mask`	bool	True	Mask pixels rejected during profile fitting when extracting.Turning this off may help with bright emission lines.
`use_user_fwhm`	bool	False	Boolean indicating if PypeIt should use the FWHM provided by the user (`find_fwhm` in FindObjPar) for the optimal extraction. If this parameter is `False` (default), PypeIt estimates the FWHM for each detected object, and uses `find_fwhm` as initial guess.

FindObjPar Keywords

Class Instantiation: FindObjPar

Key	Type	Default	Description
`ech_find_max_snr`	int, float	1.0	Criteria for keeping echelle objects. They must either have a maximum S/N across all the orders greater than this value or satisfy the min_snr criteria described by the min_snr parameters. If maxnumber is set (see above) then these criteria will be applied but only the maxnumber highest (median) S/N ratio objects will be kept.
`ech_find_min_snr`	int, float	0.3	Criteria for keeping echelle objects. They must either have a maximum S/N across all the orders greater than ech_find_max_snr, value or they must have S/N > ech_find_min_snr on >= ech_find_nabove_min_snr orders. If maxnumber is set (see above) then these criteria will be applied but only the maxnumber highest (median) S/N ratio objects will be kept.
`ech_find_nabove_min_snr`	int	2	Criteria for keeping echelle objects. They must either have a maximum S/N across all the orders greater than ech_find_max_snr, value or they must have S/N > ech_find_min_snr on >= ech_find_nabove_min_snr orders. If maxnumber is set (see above) then these criteria will be applied but only the maxnumber highest (median) S/N ratio objects will be kept.
`find_extrap_npoly`	int	3	Polynomial order used for trace extrapolation
`find_fwhm`	int, float	5.0	Indicates roughly the fwhm of objects in pixels for object finding
`find_maxdev`	int, float	2.0	Maximum deviation of pixels from polynomial fit to trace used to reject bad pixels in trace fitting.
`find_min_max`	list		It defines the minimum and maximum of your object in pixels in the spectral direction on the detector. It only used for object finding. This parameter is helpful if your object only has emission lines or at high redshift and the trace only shows in part of the detector.
`find_negative`	bool		Identify negative objects in object finding for spectra that are differenced. This is used to manually override the default behavior in PypeIt for object finding by setting this parameter to something other than None The default behavior is that PypeIt will search for negative object traces if background frames are present in the PypeIt file that are classified as “science” (i.e. via pypeit_setup -b, and setting bkg_id in the PypeIt file). If background frames are present that are classified as “sky”, then PypeIt will NOT search for negative object traces. If one wishes to explicitly override this default behavior, set this parameter to True to find negative objects or False to ignore them.
`find_trim_edge`	list	5, 5	Trim the slit by this number of pixels left/right before finding objects
`maxnumber_sci`	int	10	Maximum number of objects to extract in a science frame. Use None for no limit. This parameter can be useful in situations where systematics lead to spurious extra objects. Setting this parameter means they will be trimmed. For mulitslit maxnumber applies per slit, for echelle observations this applies per order. Note that objects on a slit/order impact the sky-modeling and so maxnumber should never be lower than the true number of detectable objects on your slit. For image differenced observations with positive and negative object traces, maxnumber applies to the number of positive (or negative) traces individually. In other words, if you had two positive objects and one negative object, then you would set maxnumber to be equal to two (not three). Note that if manually extracted apertures are explicitly requested, they do not count against this maxnumber. If more than maxnumber objects are detected, then highest S/N ratio objects will be the ones that are kept. For multislit observations the choice here depends on the slit length. For echelle observations with short slits we set the default to be 1
`maxnumber_std`	int	5	Maximum number of objects to extract in a standard star frame. Same functionality as maxnumber_sci documented above. For multislit observations the default here is 5, for echelle observations the default is 1
`skip_final_global`	bool	False	If True, do not update initial sky to get global sky using updated noise model. This should be True for quicklook to save time. This should also be True for near-IR reductions which perform difference imaging, since there we fit sky-residuals rather than the sky itself, so there is no noise model to update.
`skip_second_find`	bool	False	Only perform one round of object finding (mainly for quick_look)
`skip_skysub`	bool	False	If True, do not sky subtract when performing object finding. This should be set to True for example when running on data that is already sky-subtracted. Note that for near-IR difference imaging one still wants to remove sky-residuals via sky-subtraction, and so this is typically set to False
`snr_thresh`	int, float	10.0	S/N threshold for object finding in wavelength direction smashed image.
`std_spec1d`	str		A PypeIt spec1d file of a previously reduced standard star. The trace of the standard star spectrum is used as a crutch for tracing the object spectra, when a direct trace is not possible (i.e., faint sources). If provided, this overrides use of any standards included in your pypeit file; the standard exposures will still be reduced.
`trace_npoly`	int	5	Order of legendre polynomial fits to object traces.

SkySubPar Keywords

Class Instantiation: SkySubPar

Key	Type	Default	Description
`bspline_spacing`	int, float	0.6	Break-point spacing for the bspline sky subtraction fits.
`global_sky_std`	bool	True	Global sky subtraction will be performed on standard stars. This should be turned off for example for near-IR reductions with narrow slits, since bright standards can fill the slit causing global sky-subtraction to fail. In these situations we go straight to local sky-subtraction since it is designed to deal with such situations
`joint_fit`	bool	False	Perform a simultaneous joint fit to sky regions using all available slits. Currently, this parameter is only used for IFU data reduction. Note that the current implementation does not account for variations in the instrument FWHM in different slits. This will be addressed by Issue #1660.
`local_maskwidth`	float	4.0	Initial width of the region in units of FWHM that will be used for local sky subtraction
`mask_by_boxcar`	bool	False	In global sky evaluation, mask the sky region around the object by the boxcar radius (set in ExtractionPar).
`max_mask_frac`	float	0.8	Maximum fraction of total pixels on a slit that can be masked by the input masks. If more than this threshold is masked the code will return zeros and throw a warning.
`no_local_sky`	bool	False	If True, turn off local sky model evaluation, but do fit object profile and perform optimal extraction
`no_poly`	bool	False	Turn off polynomial basis (Legendre) in global sky subtraction
`sky_sigrej`	float	3.0	Rejection parameter for local sky subtraction
`user_regions`	str, list		Provides a user-defined mask defining sky regions. By default, the sky regions are identified automatically. To specify sky regions for all slits, provide a comma separated list of percentages. For example, setting user_regions = :10,35:65,80: selects the first 10%, the inner 30%, and the final 20% of all slits as containing sky. Setting user_regions = user will attempt to load any SkyRegions files generated by the user via the pypeit_skysub_regions tool.

SlitMaskPar Keywords

Class Instantiation: SlitMaskPar

Key	Type	Default	Description
`assign_obj`	bool	False	If SlitMask object was generated, assign RA,DEC,name to detected objects
`bright_maskdef_id`	int		maskdef_id (corresponding e.g., to dSlitId and Slit_Number in the DEIMOS/LRIS and MOSFIRE slitmask design, respectively) of a slit containing a bright object that will be used to compute the slitmask offset. This parameter is optional and is ignored if `slitmask_offset` is provided.
`extract_missing_objs`	bool	False	Force extraction of undetected objects at the location expected from the slitmask design.
`missing_objs_boxcar_rad`	int, float	1.0	Indicates the boxcar radius in arcsec for the force extraction of undetected objects.
`missing_objs_fwhm`	int, float		Indicates the FWHM in arcsec for the force extraction of undetected objects. PypeIt will try to determine the FWHM from the flux profile (by using `missing_objs_fwhm` as initial guess). If the FWHM cannot be determined, `missing_objs_fwhm` will be assumed. If you do not want PypeIt to try to determine the FWHM set the parameter `use_user_fwhm` in `ExtractionPar` to True. If `missing_objs_fwhm` is `None` (which is the default) PypeIt will use the median FWHM of all the detected objects.
`obj_toler`	int, float	1.0	If slitmask design information is provided, and slit matching is performed (`use_maskdesign = True` in `EdgeTracePar`), this parameter provides the desired tolerance (arcsec) to match sources to targeted objects
`slitmask_offset`	int, float		User-provided slitmask offset (pixels) from the position expected by the slitmask design. This is optional, and if set PypeIt will NOT compute the offset using `snr_thrshd` or `bright_maskdef_id`.
`snr_thrshd`	int, float	50.0	Objects detected above this S/N threshold will be used to compute the slitmask offset. This is the default behaviour for DEIMOS unless `slitmask_offset`, `bright_maskdef_id` or `use_alignbox` is set.
`use_alignbox`	bool	False	Use stars in alignment boxes to compute the slitmask offset. If this is set to `True` PypeIt will NOT compute the offset using `snr_thrshd` or `bright_maskdef_id`
`use_dither_offset`	bool	False	Use the dither offset recorded in the header of science frames as the value of the slitmask offset. This is currently only available for Keck MOSFIRE reduction and it is set as the default for this instrument. If set PypeIt will NOT compute the offset using `snr_thrshd` or `bright_maskdef_id`. However, it is ignored if `slitmask_offset` is provided.

FrameGroupPar Keywords

Class Instantiation: FrameGroupPar

Key	Type	Options	Default	Description
`exprng`	list		None, None	Used in identifying frames of this type. This sets the minimum and maximum allowed exposure times. There must be two items in the list. Use None to indicate no limit; i.e., to select exposures with any time greater than 30 sec, use exprng = [30, None].
`frametype`	str	`align`, `arc`, `bias`, `dark`, `pinhole`, `pixelflat`, `illumflat`, `lampoffflats`, `scattlight`, `science`, `standard`, `trace`, `tilt`, `sky`	`science`	Frame type. Options are: align, arc, bias, dark, pinhole, pixelflat, illumflat, lampoffflats, scattlight, science, standard, trace, tilt, sky
`process`	`ProcessImagesPar`		ProcessImagesPar Keywords	Low level parameters used for basic image processing
`useframe`	str			A calibrations file to use if it exists.

ProcessImagesPar Keywords

Class Instantiation: ProcessImagesPar

Key	Type	Options	Default	Description
`apply_gain`	bool		True	Convert the ADUs to electrons using the detector gain
`clip`	bool		True	Perform sigma clipping when combining. Only used with combine=mean
`comb_sigrej`	float			Sigma-clipping level for when clip=True; Use None for automatic limit (recommended).
`combine`	str	`median`, `mean`	`mean`	Method used to combine multiple frames. Options are: median, mean
`dark_expscale`	bool		False	If designated dark frames are used and have a different exposure time than the science frames, scale the counts by the by the ratio in the exposure times to adjust the dark counts for the difference in exposure time. WARNING: You should always take dark frames that have the same exposure time as your science frames, so use this option with care!
`empirical_rn`	bool		False	If True, use the standard deviation in the overscan region to measure an empirical readnoise to use in the noise model.
`grow`	int, float		1.5	Factor by which to expand regions with cosmic rays detected by the LA cosmics routine.
`lamaxiter`	int		1	Maximum number of iterations for LA cosmics routine.
`mask_cr`	bool		False	Identify CRs and mask them
`n_lohi`	list		0, 0	Number of pixels to reject at the lowest and highest ends of the distribution; i.e., n_lohi = low, high. Use None for no limit.
`noise_floor`	float		0.0	Impose a noise floor by adding the provided fraction of the bias- and dark-subtracted electron counts to the error budget. E.g., a value of 0.01 means that the S/N of the counts in the image will never be greater than 100.
`objlim`	int, float		3.0	Object detection limit in LA cosmics routine
`orient`	bool		True	Orient the raw image into the PypeIt frame
`overscan_method`	str	`chebyshev`, `polynomial`, `savgol`, `median`, `odd_even`	`savgol`	Method used to fit the overscan. Options are: chebyshev, polynomial, savgol, median, odd_even Note: Method “polynomial” is identical to “chebyshev”; the former is deprecated and will be removed.
`overscan_par`	int, list		5, 65	Parameters for the overscan subtraction. For ‘chebyshev’ or ‘polynomial’, set overcan_par = order; for ‘savgol’, set overscan_par = order, window size ; for ‘median’, set overscan_par = None or omit the keyword.
`rmcompact`	bool		True	Remove compact detections in LA cosmics routine
`satpix`	str	`reject`, `force`, `nothing`	`reject`	Handling of saturated pixels. Options are: reject, force, nothing
`scattlight`	`ScatteredLightPar`		ScatteredLightPar Keywords	Scattered light subtraction parameters.
`shot_noise`	bool		True	Use the bias- and dark-subtracted image to calculate and include electron count shot noise in the image processing error budget
`sigclip`	int, float		4.5	Sigma level for rejection in LA cosmics routine
`sigfrac`	int, float		0.3	Fraction for the lower clipping threshold in LA cosmics routine.
`spat_flexure_correct`	bool		False	Correct slits, illumination flat, etc. for flexure
`subtract_continuum`	bool		False	Subtract off the continuum level from an image. This parameter should only be set to True to combine arcs with multiple different lamps. For all other cases, this parameter should probably be False.
`subtract_scattlight`	bool		False	Subtract off the scattered light from an image. This parameter should only be set to True for spectrographs that have dedicated methods to subtract scattered light. For all other cases, this parameter should be False.
`trim`	bool		True	Trim the image to the detector supplied region
`use_biasimage`	bool		True	Use a bias image. If True, one or more must be supplied in the PypeIt file.
`use_darkimage`	bool		False	Subtract off a dark image. If True, one or more darks must be provided.
`use_illumflat`	bool		True	Use the illumination flat to correct for the illumination profile of each slit.
`use_overscan`	bool		True	Subtract off the overscan. Detector must have one or code will crash.
`use_pattern`	bool		False	Subtract off a detector pattern. This pattern is assumed to be sinusoidal along one direction, with a frequency that is constant across the detector.
`use_pixelflat`	bool		True	Use the pixel flat to make pixel-level corrections. A pixelflat image must be provied.
`use_specillum`	bool		False	Use the relative spectral illumination profiles to correct the spectral illumination profile of each slit. This is primarily used for slicer IFUs. To use this, you must set `slit_illum_relative=True` in the `flatfield` parameter set!

ScatteredLightPar Keywords

Class Instantiation: ScatteredLightPar

Key	Type	Options	Default	Description
`finecorr_mask`	int, list			A list containing the inter-slit regions that the user wishes to mask during the fine correction to the scattered light. Each integer corresponds to an inter-slit region. For example, “0” corresponds to all pixels left of the leftmost slit, while a value of “1” corresponds to all pixels between the first and second slit (counting from the left). It should be either a single integer value, or a list of integer values. The default (None) means that no inter-slit regions will be masked.
`finecorr_method`	str	`median`, `poly`		If None, a fine correction to the scattered light will not be performed. Otherwise, the allowed methods include: median, poly. ‘median’ will subtract a constant value from an entire CCD row, based on a median of the pixels that are not on slits (see also, ‘finecorr_pad’). ‘poly’ will fit a polynomial to the scattered light in each row, based on the pixels that are not on slits (see also, ‘finecorr_pad’).
`finecorr_order`	int		2	Polynomial order to use for the fine correction to the scattered light subtraction. It should be a low value.
`finecorr_pad`	int		4	Number of unbinned pixels to extend the slit edges by when masking the slits for the fine correction to the scattered light.
`method`	str	`model`, `frame`, `archive`	`model`	Method used to fit the overscan. Options are: model, frame, archive. ‘model’ will the scattered light model parameters derived from a user-specified frame during their reduction (note, you will need to make sure that you set appropriate scattlight frames in your .pypeit file for this option). ‘frame’ will use each individual frame to determine the scattered light that affects this frame. ‘archive’ will use an archival model parameter solution for the scattered light (note that this option is not currently available for all spectrographs).

SensFuncPar Keywords

Class Instantiation: SensFuncPar

Key	Type	Options	Default	Description
`IR`	`TelluricPar`		TelluricPar Keywords	Parameters for the IR sensfunc algorithm
`UVIS`	`SensfuncUVISPar`		SensfuncUVISPar Keywords	Parameters for the UVIS sensfunc algorithm
`algorithm`	str	`UVIS`, `IR`	`UVIS`	Specify the algorithm for computing the sensitivity function. The options are: (1) UVIS = Should be used for data with \(\lambda < 7000\) A. No detailed model of telluric absorption but corrects for atmospheric extinction. (2) IR = Should be used for data with \(\lambda > 7000\) A. Peforms joint fit for sensitivity function and telluric absorption using HITRAN models.
`extrap_blu`	float		0.1	Fraction of minimum wavelength coverage to grow the wavelength coverage of the sensitivitity function in the blue direction (i.e., if the standard star spectrum cuts off at `wave_min`) the sensfunc will be extrapolated to cover down to (1.0 - `extrap_blu`) * `wave_min`
`extrap_red`	float		0.1	Fraction of maximum wavelength coverage to grow the wavelength coverage of the sensitivitity function in the red direction (i.e., if the standard star spectrumcuts off at `wave_max`) the sensfunc will be extrapolated to cover up to (1.0 + `extrap_red`) * `wave_max`
`flatfile`	str			Flat field file to be used if the sensitivity function model will utilize the blaze function computed from a flat field file in the Calibrations directory, e.g.Calibrations/Flat_A_0_DET01.fits
`hydrogen_mask_wid`	float		10.0	Mask width from line center for hydrogen recombination lines in Angstroms (total mask width is 2x this value).
`mask_helium_lines`	bool		False	Mask certain `HeII` recombination lines prominent in O-type stars in the sensitivity function fit A region equal to 0.5 * `hydrogen_mask_wid` on either side of the line center is masked.
`mask_hydrogen_lines`	bool		True	Mask hydrogen Balmer, Paschen, Brackett, and Pfund recombination lines in the sensitivity function fit. A region equal to `hydrogen_mask_wid` on either side of the line center is masked.
`multi_spec_det`	list			List of detectors (identified by their string name, like DET01) to splice together for multi-detector instruments (e.g. DEIMOS). It is assumed that there is no overlap in wavelength across detectors (might be ok if there is). If entered as a list of integers, they should be converted to the detector name. Cannot be used with detector mosaics.
`polyorder`	int, list		5	Polynomial order for sensitivity function fitting
`samp_fact`	float		1.5	Sampling factor to make the wavelength grid for sensitivity function finer or coarser. samp_fact > 1.0 oversamples (finer), samp_fact < 1.0 undersamples (coarser).
`star_dec`	float			DEC of the standard star. This will override values in the header (i.e., if they are wrong or absent)
`star_mag`	float			Magnitude of the standard star (for near-IR mainly)
`star_ra`	float			RA of the standard star. This will override values in the header (i.e., if they are wrong or absent)
`star_type`	str			Spectral type of the standard star (for near-IR mainly)

SensfuncUVISPar Keywords

Class Instantiation: SensfuncUVISPar

Key	Type	Default	Description
`extinct_correct`	bool	True	If `extinct_correct=True` the code will use an atmospheric extinction model to extinction correct the data below 10000A. Note that this correction makes no sense if one is telluric correcting and this shold be set to False
`extinct_file`	str	`closest`	If `extinct_file='closest'` the code will select the PypeIt-included extinction file for the closest observatory (within 5 deg, geographic coordinates) to the telescope identified in `std_file` (see Extinction Correction for the list of currently included files). If constructing a sesitivity function for a telescope not within 5 deg of a listed observatory, this parameter may be set to the name of one of the listed extinction files. Alternatively, a custom extinction file may be installed in the PypeIt cache using the `pypeit_install_extinctfile` script; this parameter may then be set to the name of the custom extinction file.
`nresln`	int, float	20	Parameter governing the spacing of the bspline breakpoints in terms of number of resolution elements.
`polycorrect`	bool	True	Whether you want to correct the sensfunc with polynomial in the telluric and recombination line regions
`polyfunc`	bool	False	Whether you want to use the polynomial fit as your final SENSFUNC
`resolution`	int, float	3000.0	Expected resolution of the standard star spectrum. This should be measured from the data.
`sensfunc`	str		FITS file that contains or will contain the sensitivity function.
`std_file`	str		Standard star file to generate sensfunc
`std_obj_id`	str, int		Specifies object in spec1d file to use as standard. The brightest object found is used otherwise.
`telluric`	bool	False	If `telluric=True` the code creates a synthetic standard star spectrum using the Kurucz models, the sens func is created setting nresln=1.5 it contains the correction for telluric lines.
`telluric_correct`	bool	False	If `telluric_correct=True` the code will grab the sens_dict[‘telluric’] tag from the sensfunc dictionary and apply it to the data.
`trans_thresh`	float	0.9	Parameter for selecting telluric regions which are masked. Locations below this transmission value are masked. If you have significant telluric absorption you should be using telluric.sensnfunc_telluric

TelluricPar Keywords

Class Instantiation: TelluricPar

Key	Type	Default	Description
`bal_wv_min_max`	list, ndarray		Min/max wavelength of broad absorption features. If there are several BAL features, the format for this mask is [wave_min_bal1, wave_max_bal1,wave_min_bal2, wave_max_bal2,…]. These masked pixels will be ignored during the fitting.
`bounds_norm`	tuple	(0.1, 3.0)	Normalization bounds for scaling the initial object model.
`delta_coeff_bounds`	tuple	(-20.0, 20.0)	Parameters setting the polynomial coefficient bounds for sensfunc optimization.
`delta_redshift`	float	0.1	Range within the redshift can be varied for telluric fitting, i.e. the code performs a bounded optimization within the redshift +- delta_redshift
`disp`	bool	False	Argument for scipy.optimize.differential_evolution which will display status messages to the screen indicating the status of the optimization. See documentation for telluric.Telluric for a description of the output and how to know if things are working well.
`fit_wv_min_max`	list		Pixels within this mask will be used during the fitting. The format is the same with bal_wv_min_max, but this mask is good pixel masks.
`func`	str	`legendre`	Polynomial model function
`lower`	int, float	3.0	Lower rejection threshold in units of sigma_corr*sigma, where sigma is the formal noise of the spectrum, and sigma_corr is an empirically determined correction to the formal error. The distribution of input chi (defined by chi = (data - model)/sigma) values is analyzed, and a correction factor to the formal error sigma_corr is returned which is multiplied into the formal errors. In this way, a rejection threshold of i.e. 3-sigma, will always correspond to roughly the same percentile. This renormalization is performed with coadd1d.renormalize_errors function, and guarantees that rejection is not too agressive in cases where the empirical errors determined from the chi-distribution differ significantly from the formal noise which is used to determine chi.
`mask_lyman_a`	bool	True	Mask the blueward of Lyman-alpha line during the fitting?
`maxiter`	int	2	Maximum number of iterations for the telluric + object model fitting. The code performs multiple iterations rejecting outliers at each step. The fit is then performed anew to the remaining good pixels. For this reason if you run with the disp=True option, you will see that the f(x) loss function gets progressively better during the iterations.
`minmax_coeff_bounds`	tuple	(-5.0, 5.0)	Parameters setting the polynomial coefficient bounds for sensfunc optimization. Bounds are currently determined as follows. We compute an initial fit to the sensfunc in the `init_sensfunc_model()` function. That deterines a set of coefficients. The bounds are then determined according to: [(np.fmin(np.abs(this_coeff)obj_params[‘delta_coeff_bounds’][0], obj_params[‘minmax_coeff_bounds’][0]), np.fmax(np.abs(this_coeff)obj_params[‘delta_coeff_bounds’][1], obj_params[‘minmax_coeff_bounds’][1]))]
`model`	str	`exp`	Types of polynomial model. Options are poly, square, exp corresponding to normal polynomial, squared polynomial, or exponentiated polynomial
`npca`	int	8	Number of pca for the objmodel=qso qso PCA fit
`objmodel`	str		The object model to be used for telluric fitting. Currently the options are: qso, star, and poly
`only_orders`	int, list, ndarray		Order number, or list of order numbers if you only want to fit specific orders
`pca_file`	str	`qso_pca_1200_3100.fits`	Fits file containing quasar PCA model. Needed for objmodel=qso. NOTE: This parameter no longer includes the full pathname to the Telluric Model file, but is just the filename of the model itself.
`pca_lower`	int, float	1220.0	Minimum wavelength for the qso pca model
`pca_upper`	int, float	3100.0	Maximum wavelength for the qso pca model
`pix_shift_bounds`	tuple	(-5.0, 5.0)	Bounds for the pixel shift optimization in telluric model fit in units of pixels. The atmosphere will be allowed to shift within this range during the fit.
`polish`	bool	True	If True then differential evolution will perform an additional optimizatino at the end to polish the best fit at the end, which can improve the optimization slightly. See scipy.optimize.differential_evolution for details.
`polyorder`	int	3	Order of the polynomial model fit
`popsize`	int	30	A multiplier for setting the total population size for the differential evolution optimization. See scipy.optimize.differential_evolution for details.
`recombination`	int, float	0.7	The recombination constant for the differential evolution optimization. This should be in the range [0, 1]. See scipy.optimize.differential_evolution for details.
`redshift`	int, float	0.0	The redshift for the object model. This is currently only used by objmodel=qso
`resln_frac_bounds`	tuple	(0.6, 1.4)	Bounds for the resolution fit optimization which is part of the telluric model. This range is in units of the resln_guess, so the (0.6, 1.4) would bound the spectral resolution fit to be within the range bounds_resln = (0.6resln_guess, 1.4resln_guess)
`resln_guess`	int, float		A guess for the resolution of your spectrum expressed as lambda/dlambda. The resolution is fit explicitly as part of the telluric model fitting, but this guess helps determine the bounds for the optimization (see next). If not provided, the wavelength sampling of your spectrum will be used and the resolution calculated using a typical sampling of 3 spectral pixels per resolution element.
`seed`	int	777	An initial seed for the differential evolution optimization, which is a random process. The default is a seed = 777 which will be used to generate a unique seed for every order. A specific seed is used because otherwise the random number generator will use the time for the seed, and the results will not be reproducible.
`sn_clip`	int, float	30.0	This adds an error floor to the ivar, preventing too much rejection at high-S/N (i.e., standard stars, bright objects) using the function utils.clip_ivar. A small erorr is added to the input ivar so that the output ivar_out will never give S/N greater than sn_clip. This prevents overly aggressive rejection in high S/N ratio spectra which neverthless differ at a level greater than the formal S/N due to the fact that our telluric models are only good to about 3%.
`star_dec`	float		Object declination in decimal deg
`star_mag`	float, int		AB magnitude in V band
`star_ra`	float		Object right-ascension in decimal deg
`star_type`	str		stellar type
`sticky`	bool	True	Sticky parameter for the utils.djs_reject algorithm for iterative model fit rejection. If set to True then points rejected from a previous iteration are kept rejected, in other words the bad pixel mask is the OR of all previous iterations and rejected pixels accumulate. If set to False, the bad pixel mask is the mask from the previous iteration, and if the model fit changes between iterations, points can alternate from being rejected to not rejected. At present this code only performs optimizations with differential evolution and experience shows that sticky needs to be True in order for these to converge. This is because the outliers can be so large that they dominate the loss function, and one never iteratively converges to a good model fit. In other words, the deformations in the model between iterations with sticky=False are too small to approach a reasonable fit.
`telgridfile`	str		File containing the telluric grid for the observatory in question. These grids are generated from HITRAN models for each observatory using nominal site parameters. They must be downloaded from the GoogleDrive and installed in your PypeIt installation via the pypeit_install_telluric script. NOTE: This parameter no longer includes the full pathname to the Telluric Grid file, but is just the filename of the grid itself.
`tell_norm_thresh`	int, float	0.9	Threshold of telluric absorption region
`tell_npca`	int	5	Number of telluric PCA components used. Can be set to any number from 1 to 10.
`teltype`	str	`pca`	Method used to evaluate telluric models, either pca or grid. The grid option uses a fixed grid of pre-computed HITRAN+LBLRTM atmospheric transmission models for each observatory, whereas the pca option uses principal components of a larger model grid to compute an accurate pseudo-telluric model with a much lighter telgridfile.
`tol`	float	0.001	Relative tolerance for converage of the differential evolution optimization. See scipy.optimize.differential_evolution for details.
`upper`	int, float	3.0	Upper rejection threshold in units of sigma_corr*sigma, where sigma is the formal noise of the spectrum, and sigma_corr is an empirically determined correction to the formal error. See above for description.

Instrument-Specific Default Configuration

The following provides the changes to the global default parameters provided above for each instrument. That is, if one were to include these in the PypeIt file, you would be reproducing the effect of the default_pypeit_par method specific to each derived Spectrograph class.