Extract1dStep

class jwst.extract_1d.Extract1dStep(name=None, parent=None, config_file=None, _validate_kwds=True, **kws)[source]

Bases: JwstStep

Extract a 1D spectrum from 2D data.

Create a Step instance.

Parameters:
namestr

The name of the Step instance. Used in logging messages and in cache filenames. If not provided, one will be generated based on the class name.

parentStep

The parent step of this step. Used to determine a fully-qualified name for this step, and to determine the mode in which to run this step.

config_filestr or pathlib.Path

The path to the config file that this step was initialized with. Use to determine relative path names of other config files.

_validate_kwdsbool

Validate given kws against specs/config.

**kwsdict

Additional parameters to set. These will be set as member variables on the new Step instance.

Attributes Summary

class_alias

reference_file_types

spec

Methods Summary

process(input_data)

Execute the step.

Attributes Documentation

class_alias = 'extract_1d'
reference_file_types: ClassVar = ['extract1d', 'apcorr', 'pastasoss', 'specprofile', 'speckernel', 'psf']
spec
subtract_background = boolean(default=None)  # subtract background?
apply_apcorr = boolean(default=True)  # apply aperture corrections?

extraction_type = option("box", "optimal", None, default="box") # Perform box or optimal extraction
use_source_posn = boolean(default=None)  # use source coords to center extractions?
position_offset = float(default=0)  # number of pixels to shift source trace in the cross-dispersion direction
model_nod_pair = boolean(default=True)  # For optimal extraction, model a negative nod if possible
optimize_psf_location = boolean(default=True)  # For optimal extraction, optimize source location
smoothing_length = integer(default=None)  # background smoothing size
bkg_fit = option("poly", "mean", "median", None, default=None)  # background fitting type
bkg_order = integer(default=None, min=0)  # order of background polynomial fit
log_increment = integer(default=50)  # increment for multi-integration log messages
save_profile = boolean(default=False)  # save spatial profile to disk
save_scene_model = boolean(default=False)  # save flux model to disk
save_residual_image = boolean(default=False)  # save residual image to disk

center_xy = float_list(min=2, max=2, default=None)  # IFU extraction x/y center
ifu_autocen = boolean(default=False) # Auto source centering for IFU point source data.
bkg_sigma_clip = float(default=3.0)  # background sigma clipping threshold for IFU
ifu_rfcorr = boolean(default=True) # Apply 1d residual fringe correction (MIRI MRS only)
ifu_set_srctype = option("POINT", "EXTENDED", None, default=None) # user-supplied source type
ifu_rscale = float(default=None, min=0.5, max=3) # Radius in terms of PSF FWHM to scale extraction radii
ifu_covar_scale = float(default=1.0) # Scaling factor to apply to errors to account for IFU cube covariance

soss_atoca = boolean(default=True)  # use ATOCA algorithm
soss_threshold = float(default=1e-2)  # TODO: threshold could be removed from inputs. Its use is too specific now.
soss_n_os = integer(default=2)  # minimum oversampling factor of the underlying wavelength grid used when modeling trace.
soss_wave_grid_in = input_file(default = None)  # Input wavelength grid used to model the detector
soss_wave_grid_out = string(default = None)  # Output wavelength grid solution filename
soss_estimate = input_file(default = None)  # Estimate used to generate the wavelength grid
soss_rtol = float(default=1.0e-4)  # Relative tolerance needed on a pixel model
soss_max_grid_size = integer(default=20000)  # Maximum grid size, if wave_grid not specified
soss_tikfac = float(default=None)  # regularization factor for NIRISS SOSS extraction
soss_width = float(default=40.)  # aperture width used to extract the 1D spectrum from the de-contaminated trace.
soss_bad_pix = option("model", "masking", default="masking")  # method used to handle bad pixels
soss_modelname = output_file(default = None)  # Filename for optional model output of traces and pixel weights

Methods Documentation

process(input_data)[source]

Execute the step.

Parameters:
input_dataDataModel

The input model.

Returns:
DataModel

This will be input_model if the step was skipped; otherwise, it will be a model containing 1-D extracted spectra.