Source code for jwst.extract_1d.extract_1d_step

from stdatamodels.jwst import datamodels

from jwst.datamodels import ModelContainer, SourceModelContainer

from ..stpipe import Step
from . import extract
from .soss_extract import soss_extract

__all__ = ["Extract1dStep"]


[docs] class Extract1dStep(Step): """Extract a 1-d spectrum from 2-d data Attributes ---------- smoothing_length : int or None If not None, the background regions (if any) will be smoothed with a boxcar function of this width along the dispersion direction. This should be an odd integer. bkg_fit : str A string indicating the type of fitting to be applied to background values in each column (or row, if the dispersion is vertical). Allowed values are `poly`, `mean`, and `median`. Default is `None`. bkg_order : int or None If not None, a polynomial with order `bkg_order` will be fit to each column (or row, if the dispersion direction is vertical) of the background region or regions. For a given column (row), one polynomial will be fit to all background regions. The polynomial will be evaluated at each pixel of the source extraction region(s) along the column (row), and the fitted value will be subtracted from the data value at that pixel. If both `smoothing_length` and `bkg_order` are not None, the boxcar smoothing will be done first. bkg_sigma_clip : float Background sigma clipping value to use on background to remove outliers and maximize the quality of the 1d spectrum log_increment : int if `log_increment` is greater than 0 (the default is 50) and the input data are multi-integration (which can be CubeModel or SlitModel), a message will be written to the log with log level INFO every `log_increment` integrations. This is intended to provide progress information when invoking the step interactively. subtract_background : bool or None A flag which indicates whether the background should be subtracted. If None, the value in the extract_1d reference file will be used. If not None, this parameter overrides the value in the extract_1d reference file. use_source_posn : bool or None If True, the source and background extraction positions specified in the extract1d reference file (or the default position, if there is no reference file) will be shifted to account for the computed position of the source in the data. If None (the default), the values in the reference file will be used. Aperture offset is determined by computing the pixel location of the source based on its RA and Dec. It does not make sense to apply aperture offsets for extended sources, so this parameter can be overridden (set to False) internally by the step. center_xy : int or None A list of 2 pixel coordinate values at which to place the center of the IFU extraction aperture, overriding any centering done by the step. Two values, in x,y order, are used for extraction from IFU cubes. Default is None. apply_apcorr : bool Switch to select whether or not to apply an APERTURE correction during the Extract1dStep. Default is True ifu_autocen : bool Switch to turn on auto-centering for point source spectral extraction in IFU mode. Default is False. ifu_rfcorr : bool Switch to select whether or not to apply a 1d residual fringe correction for MIRI MRS IFU spectra. Default is False. ifu_set_srctype : str For MIRI MRS IFU data override srctype and set it to either POINT or EXTENDED. ifu_rscale : float For MRS IFU data a value for changing the extraction radius. The value provided is the number of PSF FWHMs to use for the extraction radius. Values accepted are between 0.5 to 3.0. The default extraction size is set to 2 * FWHM. Values below 2 will result in a smaller radius, a value of 2 results in no change to the radius and a value above 2 results in a larger extraction radius. soss_atoca : bool, default=False Switch to toggle extraction of SOSS data with the ATOCA algorithm. WARNING: ATOCA results not fully validated, and require the photom step be turned off. Default is False, meaning SOSS data use box extraction. soss_threshold : float Threshold value above which a pixel will be included when modeling the SOSS trace in ATOCA. Default is 0.01. soss_n_os : int Oversampling factor of the underlying wavelength grid when modeling the SOSS trace in ATOCA. Default is 2. soss_transform : list[float] Rotation applied to the reference files to match the observation orientation. Default is None. soss_tikfac : float The regularization factor used for extraction in ATOCA. If left to default value of None, ATOCA will find an optimized value. soss_width : float Aperture width used to extract the SOSS spectrum from the decontaminated trace in ATOCA. Default is 40. soss_bad_pix : str Method used to handle bad pixels, accepts either "model" or "masking". Default method is "model". soss_modelname : str Filename for optional model output of ATOCA traces and pixel weights. soss_estimate : str or SpecModel or None Filename or SpecModel of the estimate of the target flux. The estimate must be a SpecModel with wavelength and flux values. soss_wave_grid_in : str or SossWaveGrid or None Filename or SossWaveGrid containing the wavelength grid used by ATOCA to model each pixel valid pixel of the detector. If not given, the grid is determined based on an estimate of the flux (soss_estimate), the relative tolerance (soss_rtol) required on each pixel model and the maximum grid size (soss_max_grid_size). soss_wave_grid_out : str or None Filename to hold the wavelength grid calculated by ATOCA. soss_rtol : float The relative tolerance needed on a pixel model. It is used to determine the sampling of the soss_wave_grid when not directly given. soss_max_grid_size: int Maximum grid size allowed. It is used when soss_wave_grid is not provided to make sure the computation time or the memory used stays reasonable. """ class_alias = "extract_1d" spec = """ 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 bkg_sigma_clip = float(default=3.0) # background sigma clipping threshold log_increment = integer(default=50) # increment for multi-integration log messages subtract_background = boolean(default=None) # subtract background? use_source_posn = boolean(default=None) # use source coords to center extractions? center_xy = float_list(min=2, max=2, default=None) # IFU extraction x/y center apply_apcorr = boolean(default=True) # apply aperture corrections? ifu_autocen = boolean(default=False) # Auto source centering for IFU point source data. ifu_rfcorr = boolean(default=False) # Apply 1d residual fringe correction 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 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_transform = list(default=None, min=3, max=3) # rotation applied to the ref files to match observation. 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 """ reference_file_types = ['extract1d', 'apcorr', 'wavemap', 'spectrace', 'specprofile', 'speckernel']
[docs] def process(self, input): """Execute the step. Parameters ---------- input: JWST data model Returns ------- JWST data model This will be `input_model` if the step was skipped; otherwise, it will be a model containing 1-D extracted spectra. """ # Open the input and figure out what type of model it is input_model = datamodels.open(input) was_source_model = False # default value if isinstance(input_model, datamodels.CubeModel): # It's a 3-D multi-integration model self.log.debug('Input is a CubeModel for a multiple integ. file') elif isinstance(input_model, datamodels.ImageModel): # It's a single 2-D image. This could be a resampled 2-D image self.log.debug('Input is an ImageModel') elif isinstance(input_model, SourceModelContainer): self.log.debug('Input is a SourceModelContainer') was_source_model = True elif isinstance(input_model, ModelContainer): self.log.debug('Input is a ModelContainer') elif isinstance(input_model, datamodels.MultiSlitModel): # If input is a 3D rateints (which is unsupported) skip the step if len((input_model[0]).shape) == 3: self.log.warning('3D input is unsupported; step will be skipped') input_model.meta.cal_step.extract_1d = 'SKIPPED' return input_model self.log.debug('Input is a MultiSlitModel') elif isinstance(input_model, datamodels.MultiExposureModel): self.log.warning('Input is a MultiExposureModel, ' 'which is not currently supported') elif isinstance(input_model, datamodels.IFUCubeModel): self.log.debug('Input is an IFUCubeModel') elif isinstance(input_model, datamodels.SlitModel): # NRS_BRIGHTOBJ and MIRI LRS fixed-slit (resampled) modes self.log.debug('Input is a SlitModel') else: self.log.error(f'Input is a {str(type(input_model))}, ') self.log.error('which was not expected for extract_1d') self.log.error('extract_1d will be skipped.') input_model.meta.cal_step.extract_1d = 'SKIPPED' return input_model if isinstance(input_model, datamodels.IFUCubeModel): exp_type = input_model.meta.exposure.type elif isinstance(input_model, ModelContainer): exp_type = input_model[0].meta.exposure.type else: exp_type = None if self.ifu_rfcorr: if exp_type != "MIR_MRS": self.log.warning("The option to apply a residual refringe correction is" f" not supported for {input_model.meta.exposure.type} data.") if self.ifu_rscale is not None: if exp_type != "MIR_MRS": self.log.warning("The option to change the extraction radius is" f" not supported for {input_model.meta.exposure.type} data.") if self.ifu_set_srctype is not None: if exp_type != "MIR_MRS": self.log.warning("The option to change the source type is" f" not supported for {input_model.meta.exposure.type} data.") # ______________________________________________________________________ # Do the extraction for ModelContainer - this might only be WFSS data if isinstance(input_model, ModelContainer): # This is the branch WFSS data take if len(input_model) > 1: self.log.debug(f"Input contains {len(input_model)} items") # -------------------------------------------------------------- # Data is WFSS if input_model[0].meta.exposure.type in extract.WFSS_EXPTYPES: # For WFSS level-3, the input is a single entry of a # SourceContainer, which contains a list of multiple # SlitModels for a single source. Send the whole list # into extract1d and put all results in a single product. apcorr_ref = ( self.get_reference_file(input_model[0], 'apcorr') if self.apply_apcorr is True else 'N/A' ) if apcorr_ref == 'N/A': self.log.info('APCORR reference file name is "N/A"') self.log.info('APCORR will NOT be applied') else: self.log.info(f'Using APCORR file {apcorr_ref}') extract_ref = 'N/A' self.log.info('No EXTRACT1D reference file will be used') result = extract.run_extract1d( input_model, extract_ref, apcorr_ref, self.smoothing_length, self.bkg_fit, self.bkg_order, self.bkg_sigma_clip, self.log_increment, self.subtract_background, self.use_source_posn, self.center_xy, self.ifu_autocen, self.ifu_rfcorr, self.ifu_set_srctype, self.ifu_rscale, was_source_model=was_source_model ) # Set the step flag to complete result.meta.cal_step.extract_1d = 'COMPLETE' # -------------------------------------------------------------- # Data is a ModelContainer but is not WFSS else: result = ModelContainer() for model in input_model: # Get the reference file names extract_ref = self.get_reference_file(model, 'extract1d') self.log.info(f'Using EXTRACT1D reference file {extract_ref}') apcorr_ref = self.get_reference_file(model, 'apcorr') if self.apply_apcorr is True else 'N/A' if apcorr_ref == 'N/A': self.log.info('APCORR reference file name is "N/A"') self.log.info('APCORR will NOT be applied') else: self.log.info(f'Using APCORR file {apcorr_ref}') temp = extract.run_extract1d( model, extract_ref, apcorr_ref, self.smoothing_length, self.bkg_fit, self.bkg_order, self.bkg_sigma_clip, self.log_increment, self.subtract_background, self.use_source_posn, self.center_xy, self.ifu_autocen, self.ifu_rfcorr, self.ifu_set_srctype, self.ifu_rscale, was_source_model=was_source_model, ) # Set the step flag to complete in each MultiSpecModel temp.meta.cal_step.extract_1d = 'COMPLETE' result.append(temp) del temp # ------------------------------------------------------------------------ # Still in ModelContainer type, but only 1 model elif len(input_model) == 1: if input_model[0].meta.exposure.type in extract.WFSS_EXPTYPES: extract_ref = 'N/A' self.log.info('No EXTRACT1D reference file will be used') else: # Get the extract1d reference file name for the one model in input extract_ref = self.get_reference_file(input_model[0], 'extract1d') self.log.info(f'Using EXTRACT1D reference file {extract_ref}') apcorr_ref = self.get_reference_file(input_model[0], 'apcorr') if self.apply_apcorr is True else 'N/A' if apcorr_ref == 'N/A': self.log.info('APCORR reference file name is "N/A"') self.log.info('APCORR will NOT be applied') else: self.log.info(f'Using APCORR file {apcorr_ref}') result = extract.run_extract1d( input_model[0], extract_ref, apcorr_ref, self.smoothing_length, self.bkg_fit, self.bkg_order, self.bkg_sigma_clip, self.log_increment, self.subtract_background, self.use_source_posn, self.center_xy, self.ifu_autocen, self.ifu_rfcorr, self.ifu_set_srctype, self.ifu_rscale, was_source_model=was_source_model, ) # Set the step flag to complete result.meta.cal_step.extract_1d = 'COMPLETE' else: self.log.error('Input model is empty;') self.log.error('extract_1d will be skipped.') return input_model # ______________________________________________________________________ # Data that is not a ModelContainer (IFUCube and other single models) else: # Data is NRISS SOSS observation. if input_model.meta.exposure.type == 'NIS_SOSS': self.log.info( 'Input is a NIRISS SOSS observation, the specialized SOSS extraction (ATOCA) will be used.') # Set the filter configuration if input_model.meta.instrument.filter == 'CLEAR': self.log.info('Exposure is through the GR700XD + CLEAR (science).') soss_filter = 'CLEAR' elif input_model.meta.instrument.filter == 'F277W': self.log.info('Exposure is through the GR700XD + F277W (calibration).') soss_filter = 'F277W' else: self.log.error('The SOSS extraction is implemented for the CLEAR or F277W filters only.') self.log.error('extract_1d will be skipped.') input_model.meta.cal_step.extract_1d = 'SKIPPED' return input_model # Set the subarray mode being processed if input_model.meta.subarray.name == 'SUBSTRIP256': self.log.info('Exposure is in the SUBSTRIP256 subarray.') self.log.info('Traces 1 and 2 will be modelled and decontaminated before extraction.') subarray = 'SUBSTRIP256' elif input_model.meta.subarray.name == 'SUBSTRIP96': self.log.info('Exposure is in the SUBSTRIP96 subarray.') self.log.info('Traces of orders 1 and 2 will be modelled but only order 1' ' will be decontaminated before extraction.') subarray = 'SUBSTRIP96' else: self.log.error('The SOSS extraction is implemented for the SUBSTRIP256' 'and SUBSTRIP96 subarrays only. Subarray is currently ' f'{input_model.meta.subarray.name}.') self.log.error('Extract1dStep will be skipped.') input_model.meta.cal_step.extract_1d = 'SKIPPED' return input_model # Load reference files. spectrace_ref_name = self.get_reference_file(input_model, 'spectrace') wavemap_ref_name = self.get_reference_file(input_model, 'wavemap') specprofile_ref_name = self.get_reference_file(input_model, 'specprofile') speckernel_ref_name = self.get_reference_file(input_model, 'speckernel') # Build SOSS kwargs dictionary. soss_kwargs = dict() soss_kwargs['threshold'] = self.soss_threshold soss_kwargs['n_os'] = self.soss_n_os soss_kwargs['tikfac'] = self.soss_tikfac soss_kwargs['width'] = self.soss_width soss_kwargs['bad_pix'] = self.soss_bad_pix soss_kwargs['transform'] = self.soss_transform soss_kwargs['subtract_background'] = self.subtract_background soss_kwargs['rtol'] = self.soss_rtol soss_kwargs['max_grid_size'] = self.soss_max_grid_size soss_kwargs['wave_grid_in'] = self.soss_wave_grid_in soss_kwargs['wave_grid_out'] = self.soss_wave_grid_out soss_kwargs['estimate'] = self.soss_estimate soss_kwargs['atoca'] = self.soss_atoca # Set flag to output the model and the tikhonov tests soss_kwargs['model'] = True if self.soss_modelname else False # Run the extraction. result, ref_outputs, atoca_outputs = soss_extract.run_extract1d( input_model, spectrace_ref_name, wavemap_ref_name, specprofile_ref_name, speckernel_ref_name, subarray, soss_filter, soss_kwargs) # Set the step flag to complete if result is None: return None else: result.meta.cal_step.extract_1d = 'COMPLETE' result.meta.target.source_type = None input_model.close() if self.soss_modelname: soss_modelname = self.make_output_path( basepath=self.soss_modelname, suffix='SossExtractModel' ) ref_outputs.save(soss_modelname) if self.soss_modelname: soss_modelname = self.make_output_path( basepath=self.soss_modelname, suffix='AtocaSpectra' ) atoca_outputs.save(soss_modelname) else: # Get the reference file names if input_model.meta.exposure.type in extract.WFSS_EXPTYPES: extract_ref = 'N/A' self.log.info('No EXTRACT1D reference file will be used') else: extract_ref = self.get_reference_file(input_model, 'extract1d') self.log.info(f'Using EXTRACT1D reference file {extract_ref}') apcorr_ref = self.get_reference_file(input_model, 'apcorr') if self.apply_apcorr is True else 'N/A' if apcorr_ref == 'N/A': self.log.info('APCORR reference file name is "N/A"') self.log.info('APCORR will NOT be applied') else: self.log.info(f'Using APCORR file {apcorr_ref}') result = extract.run_extract1d( input_model, extract_ref, apcorr_ref, self.smoothing_length, self.bkg_fit, self.bkg_order, self.bkg_sigma_clip, self.log_increment, self.subtract_background, self.use_source_posn, self.center_xy, self.ifu_autocen, self.ifu_rfcorr, self.ifu_set_srctype, self.ifu_rscale, was_source_model=False, ) # Set the step flag to complete result.meta.cal_step.extract_1d = 'COMPLETE' input_model.close() return result