pypeit.specobj module

Module for the SpecObj classes

class pypeit.specobj.SpecObj(PYPELINE, DET, OBJTYPE='unknown', SLITID=None, ECH_ORDER=None, ECH_ORDERINDX=None)

Bases: DataContainer

Class to handle single spectra from a single exposure.

One generates one of these objects for each spectrum in the exposure. They are instantiated by the object finding routine, and then all spectral extraction information for the object are assigned as attributes

The datamodel attributes are:

Version: 1.1.10

Attribute	Type	Array Type	Description
BOX_CHI2	numpy.ndarray	float	Reduced chi2 of the model fit for this spectral pixel
BOX_COUNTS	numpy.ndarray	float	Boxcar flux (counts)
BOX_COUNTS_IVAR	numpy.ndarray	float	Inverse variance of optimally extracted flux using modelivar image (counts^2)
BOX_COUNTS_NIVAR	numpy.ndarray	float	Boxcar extracted noise variance, sky+read noise only (counts^2)
BOX_COUNTS_SIG	numpy.ndarray	float	Boxcar extracted noise from IVAR (counts)
BOX_COUNTS_SIG_DET	numpy.ndarray	float	Boxcar extracted detector noise (counts)
BOX_COUNTS_SKY	numpy.ndarray	float	Boxcar extracted sky (counts)
BOX_FLAM	numpy.ndarray	float	Boxcar flux (erg/s/cm^2/Ang)
BOX_FLAM_IVAR	numpy.ndarray	float	Boxcar flux inverse variance (1e-17 erg/s/cm^2/Ang)^-2
BOX_FLAM_SIG	numpy.ndarray	float	Boxcar flux uncertainty (1e-17 erg/s/cm^2/Ang)
BOX_FRAC_USE	numpy.ndarray	float	Fraction of pixels in the object profile subimage used for this extraction
BOX_FWHM	numpy.ndarray	float	Spectral FWHM (in Angstroms) at every pixel of the boxcar extracted flux.
BOX_MASK	numpy.ndarray	numpy.bool	Mask for boxcar extracted flux. True=good
BOX_NPIX	numpy.ndarray	float	Number of pixels used for the boxcar extraction; can be fractional
BOX_RADIUS	float		Size of boxcar radius (pixels)
BOX_WAVE	numpy.ndarray	float	Boxcar Wavelengths in vacuum (Angstroms)
DEC	float		Declination (J2000) decimal degree
DET	str		A string identifier for the reduced detector or mosaic.
DETECTOR	DetectorContainer, Mosaic		Object with the detector or mosaic metadata
ECH_FRACPOS	float, numpy.floating		Synced echelle fractional location of the object on the slit
ECH_NAME	str		Name of the object for echelle data. Same as NAME above but order numbers are omitted giving a unique name per object.
ECH_OBJID	int, numpy.integer		Object ID for echelle data. Each object is given an index in the order it appears increasing from from left to right. These are one based.
ECH_ORDER	int, numpy.integer		Physical echelle order
ECH_ORDERINDX	int, numpy.integer		Order indx, analogous to SLITID for echelle. Zero based.
FLEX_SHIFT_GLOBAL	float		Global shift of the spectrum to correct for spectralflexure (pixels). This is based on the sky spectrum atthe center of the slit
FLEX_SHIFT_LOCAL	float		Local shift of the spectrum to correct for spectralflexure (pixels). This should be a small correction tothe global value, and is based on the sky spectrumextracted near the object
FLEX_SHIFT_TOTAL	float		Total shift of the spectrum to correct for spectralflexure (pixels). This is the sum of the global andlocal FLEX_SHIFT
FWHM	float		Spatial FWHM of the object (pixels)
FWHMFIT	numpy.ndarray	float	Spatial FWHM across the detector (pixels)
MASKDEF_EXTRACT	bool		Boolean indicating if this is a forced extraction at the expected location from slitmask design.
MASKDEF_ID	int, numpy.integer		Slitmask definition ID
MASKDEF_OBJMAG	float		Magnitude of the object from the slitmask definition
MASKDEF_OBJMAG_BAND	str		Magnitude band of the object from the slitmask definition
MASKDEF_OBJNAME	str		Name of the object from the slitmask definition
NAME	str		Name of the object following the naming model
OBJID	int, numpy.integer		Object ID for multislit data. Each object is given an index for the slit it appears increasing from from left to right. These are one based.
OBJTYPE	str		Object type (e.g., standard, science)
OPT_CHI2	numpy.ndarray	float	Reduced chi2 of the model fit for this spectral pixel
OPT_COUNTS	numpy.ndarray	float	Optimal flux (counts)
OPT_COUNTS_IVAR	numpy.ndarray	float	Inverse variance of optimally extracted flux using modelivar image (counts^2)
OPT_COUNTS_NIVAR	numpy.ndarray	float	Optimally extracted noise variance, sky+read noise only (counts^2)
OPT_COUNTS_SIG	numpy.ndarray	float	Optimally extracted noise from IVAR (counts)
OPT_COUNTS_SIG_DET	numpy.ndarray	float	Optimally extracted detector noise (counts)
OPT_COUNTS_SKY	numpy.ndarray	float	Optimally extracted sky (counts)
OPT_FLAM	numpy.ndarray	float	Optimal flux (1e-17 erg/s/cm^2/Ang)
OPT_FLAM_IVAR	numpy.ndarray	float	Optimal flux inverse variance (1e-17 erg/s/cm^2/Ang)^-2
OPT_FLAM_SIG	numpy.ndarray	float	Optimal flux uncertainty (1e-17 erg/s/cm^2/Ang)
OPT_FRAC_USE	numpy.ndarray	float	Fraction of pixels in the object profile subimage used for this extraction
OPT_FWHM	numpy.ndarray	float	Spectral FWHM (in Angstroms) at every pixel of the optimally extracted flux.
OPT_MASK	numpy.ndarray	numpy.bool	Mask for optimally extracted flux. True=good
OPT_WAVE	numpy.ndarray	float	Optimal Wavelengths in vacuum (Angstroms)
PYPELINE	str		Name of the PypeIt pipeline mode
RA	float		Right Ascension (J2000) decimal degree
S2N	float		Median signal to noise ratio of the extracted spectrum(OPT if available, otherwise BOX)
SLITID	int, numpy.integer		PypeIt slit ID (aka SPAT_ID).
SPAT_FRACPOS	float, numpy.floating		Fractional location of the object on the slit
SPAT_FWHM	float		Spatial FWHM of the object (arcsec)
SPAT_PIXPOS	float, numpy.floating		Spatial location of the trace on detector (pixel) at half-way
TRACE_SPAT	numpy.ndarray	float	Object trace along the spec (spatial pixel)
VEL_CORR	float		Relativistic velocity correction for wavelengths
VEL_TYPE	str		Type of heliocentric correction (if any)
WAVE_RMS	float, numpy.floating		RMS (pix) for the wavelength solution for this slit.
hand_extract_flag	bool		Boolean indicating if this is a forced extraction at the location provided by the user.
maskwidth	float, numpy.floating		Size (in units of spatial fwhm) of the region used for local sky subtraction
smash_peakflux	float		Peak value of the spectral direction collapsed spatial profile
smash_snr	float		Peak S/N ratio of the spectral direction collapsed patial profile
trace_spec	numpy.ndarray	int, numpy.integer	Array of pixels along the spectral direction

Parameters:

• PYPELINE (str) – Name of the PypeIt pipeline method. Allowed options are MultiSlit, Echelle, or SlicerIFU.

• DET (str) – The name of the detector or mosaic from which the spectrum was extracted. For example, DET01.

• OBJTYPE (str, optional) – Object type. For example: ‘unknown’, ‘standard’, ‘science’.

• SLITID (int, optional) – For multislit and SlicerIFU reductions, this is an identifier for the slit (max=9999).

• ECH_ORDER (int, optional) – Physical order number.

• ECH_ORDERINDX (int, optional) – Running index for the order.

__repr__()

Over-ride print representation

Returns:

Basics of the Data Container

Return type:

str

_bundle(**kwargs)

Override base class to handle inclusion of DetectorContainer or Mosaic objects for each spectrum.

Parameters:

kwargs (dict) – Passed directly to the base class method.

Returns:

List of dictionaries used to construct fits extensions.

Return type:

list

_validate()

Validate the object.

apply_flux_calib(wave_zp, zeropoint, exptime, tellmodel=None, extinct_correct=False, airmass=None, longitude=None, latitude=None, extinctfilepar=None, extrap_sens=False)

Apply a sensitivity function to our spectrum

FLAM, FLAM_SIG, and FLAM_IVAR are generated

Parameters:

• wave_zp (numpy.ndarray) – Zeropoint wavelength array

• zeropoint (numpy.ndarray) – zeropoint array

• exptime (float) – Exposure time

• tellmodel – Telluric correction

• extinct_correct – If True, extinction correct

• airmass (float, optional) – Airmass

• longitude (float, optional) – longitude in degree for observatory

• latitude (float, optional) – latitude in degree for observatory Used for extinction correction

• extinctfilepar (str, optional) – [sensfunc][UVIS][extinct_file] parameter Used for extinction correction

• extrap_sens (bool, optional) – Extrapolate the sensitivity function (instead of crashing out)

apply_helio(vel_corr, refframe)

Apply a heliocentric correction

Wavelength arrays are modified in place

Parameters:

• vel_corr (float)

• refframe (str)

apply_spectral_flexure(shift, sky_spec)

Apply interpolation with the flexure dict

Parameters:

• shift (float) – additive spectral flexure in pixels

• sky_spec (linetools.spectra.xspectrum1d.XSpectrum1D) – Sky Spectrum

Returns:

New sky spectrum (mainly for QA)

Return type:

linetools.spectra.xspectrum1d.XSpectrum1D

best_ext_match(extract=None, fluxed=True)

Determine the extraction and calibration type that best matches a user request.

Precedence is given to the requested extraction and calibration types. Beyond that, optimal extraction takes precedence over boxcar extraction, and flux-calibrated data take precedence over uncalibrated counts.

Parameters:

• extract (str, optional) – The extraction used to produce the spectrum. Must be either None, 'BOX' (for a boxcar extraction), or 'OPT' for optimal extraction. If None, the optimal extraction will be returned, if it exists, otherwise the boxcar extraction will be returned.

• fluxed (bool, optional) – If True, return the flux calibrated spectrum, if it exists. If the flux calibration hasn’t been performed or fluxed=False, the spectrum is returned in counts.

Returns:

The adjusted extraction type ('BOX' or 'OPT') and the adjusted calibration type (True for flux-calibrated, False for uncalibrated counts).

Return type:

tuple

copy()

Generate a copy of this object

Return type:

SpecObj

datamodel = {'BOX_CHI2': {'atype': <class 'float'>, 'descr': 'Reduced chi2 of the model fit for this spectral pixel', 'otype': <class 'numpy.ndarray'>}, 'BOX_COUNTS': {'atype': <class 'float'>, 'descr': 'Boxcar flux (counts)', 'otype': <class 'numpy.ndarray'>}, 'BOX_COUNTS_IVAR': {'atype': <class 'float'>, 'descr': 'Inverse variance of optimally extracted flux using modelivar image (counts^2)', 'otype': <class 'numpy.ndarray'>}, 'BOX_COUNTS_NIVAR': {'atype': <class 'float'>, 'descr': 'Boxcar extracted noise variance, sky+read noise only (counts^2)', 'otype': <class 'numpy.ndarray'>}, 'BOX_COUNTS_SIG': {'atype': <class 'float'>, 'descr': 'Boxcar extracted noise from IVAR (counts)', 'otype': <class 'numpy.ndarray'>}, 'BOX_COUNTS_SIG_DET': {'atype': <class 'float'>, 'descr': 'Boxcar extracted detector noise (counts)', 'otype': <class 'numpy.ndarray'>}, 'BOX_COUNTS_SKY': {'atype': <class 'float'>, 'descr': 'Boxcar extracted sky (counts)', 'otype': <class 'numpy.ndarray'>}, 'BOX_FLAM': {'atype': <class 'float'>, 'descr': 'Boxcar flux (erg/s/cm^2/Ang)', 'otype': <class 'numpy.ndarray'>}, 'BOX_FLAM_IVAR': {'atype': <class 'float'>, 'descr': 'Boxcar flux inverse variance (1e-17 erg/s/cm^2/Ang)^-2', 'otype': <class 'numpy.ndarray'>}, 'BOX_FLAM_SIG': {'atype': <class 'float'>, 'descr': 'Boxcar flux uncertainty (1e-17 erg/s/cm^2/Ang)', 'otype': <class 'numpy.ndarray'>}, 'BOX_FRAC_USE': {'atype': <class 'float'>, 'descr': 'Fraction of pixels in the object profile subimage used for this extraction', 'otype': <class 'numpy.ndarray'>}, 'BOX_FWHM': {'atype': <class 'float'>, 'descr': 'Spectral FWHM (in Angstroms) at every pixel of the boxcar extracted flux.', 'otype': <class 'numpy.ndarray'>}, 'BOX_MASK': {'atype': <class 'numpy.bool_'>, 'descr': 'Mask for boxcar extracted flux. True=good', 'otype': <class 'numpy.ndarray'>}, 'BOX_NPIX': {'atype': <class 'float'>, 'descr': 'Number of pixels used for the boxcar extraction; can be fractional', 'otype': <class 'numpy.ndarray'>}, 'BOX_RADIUS': {'descr': 'Size of boxcar radius (pixels)', 'otype': <class 'float'>}, 'BOX_WAVE': {'atype': <class 'float'>, 'descr': 'Boxcar Wavelengths in vacuum (Angstroms)', 'otype': <class 'numpy.ndarray'>}, 'DEC': {'descr': 'Declination (J2000) decimal degree', 'otype': <class 'float'>}, 'DET': {'descr': 'A string identifier for the reduced detector or mosaic.', 'otype': <class 'str'>}, 'DETECTOR': {'descr': 'Object with the detector or mosaic metadata', 'otype': (<class 'pypeit.images.detector_container.DetectorContainer'>, <class 'pypeit.images.mosaic.Mosaic'>)}, 'ECH_FRACPOS': {'descr': 'Synced echelle fractional location of the object on the slit', 'otype': (<class 'float'>, <class 'numpy.floating'>)}, 'ECH_NAME': {'descr': 'Name of the object for echelle data. Same as NAME above but order numbers are omitted giving a unique name per object.', 'otype': <class 'str'>}, 'ECH_OBJID': {'descr': 'Object ID for echelle data. Each object is given an index in the order it appears increasing from from left to right. These are one based.', 'otype': (<class 'int'>, <class 'numpy.integer'>)}, 'ECH_ORDER': {'descr': 'Physical echelle order', 'otype': (<class 'int'>, <class 'numpy.integer'>)}, 'ECH_ORDERINDX': {'descr': 'Order indx, analogous to SLITID for echelle. Zero based.', 'otype': (<class 'int'>, <class 'numpy.integer'>)}, 'FLEX_SHIFT_GLOBAL': {'descr': 'Global shift of the spectrum to correct for spectralflexure (pixels). This is based on the sky spectrum atthe center of the slit', 'otype': <class 'float'>}, 'FLEX_SHIFT_LOCAL': {'descr': 'Local shift of the spectrum to correct for spectralflexure (pixels). This should be a small correction tothe global value, and is based on the sky spectrumextracted near the object', 'otype': <class 'float'>}, 'FLEX_SHIFT_TOTAL': {'descr': 'Total shift of the spectrum to correct for spectralflexure (pixels). This is the sum of the global andlocal FLEX_SHIFT', 'otype': <class 'float'>}, 'FWHM': {'descr': 'Spatial FWHM of the object (pixels)', 'otype': <class 'float'>}, 'FWHMFIT': {'atype': <class 'float'>, 'descr': 'Spatial FWHM across the detector (pixels)', 'otype': <class 'numpy.ndarray'>}, 'MASKDEF_EXTRACT': {'descr': 'Boolean indicating if this is a forced extraction at the expected location from slitmask design. ', 'otype': <class 'bool'>}, 'MASKDEF_ID': {'descr': 'Slitmask definition ID', 'otype': (<class 'int'>, <class 'numpy.integer'>)}, 'MASKDEF_OBJMAG': {'descr': 'Magnitude of the object from the slitmask definition', 'otype': <class 'float'>}, 'MASKDEF_OBJMAG_BAND': {'descr': 'Magnitude band of the object from the slitmask definition', 'otype': <class 'str'>}, 'MASKDEF_OBJNAME': {'descr': 'Name of the object from the slitmask definition', 'otype': <class 'str'>}, 'NAME': {'descr': 'Name of the object following the naming model', 'otype': <class 'str'>}, 'OBJID': {'descr': 'Object ID for multislit data. Each object is given an index for the slit it appears increasing from from left to right. These are one based.', 'otype': (<class 'int'>, <class 'numpy.integer'>)}, 'OBJTYPE': {'descr': 'Object type (e.g., standard, science)', 'otype': <class 'str'>}, 'OPT_CHI2': {'atype': <class 'float'>, 'descr': 'Reduced chi2 of the model fit for this spectral pixel', 'otype': <class 'numpy.ndarray'>}, 'OPT_COUNTS': {'atype': <class 'float'>, 'descr': 'Optimal flux (counts)', 'otype': <class 'numpy.ndarray'>}, 'OPT_COUNTS_IVAR': {'atype': <class 'float'>, 'descr': 'Inverse variance of optimally extracted flux using modelivar image (counts^2)', 'otype': <class 'numpy.ndarray'>}, 'OPT_COUNTS_NIVAR': {'atype': <class 'float'>, 'descr': 'Optimally extracted noise variance, sky+read noise only (counts^2)', 'otype': <class 'numpy.ndarray'>}, 'OPT_COUNTS_SIG': {'atype': <class 'float'>, 'descr': 'Optimally extracted noise from IVAR (counts)', 'otype': <class 'numpy.ndarray'>}, 'OPT_COUNTS_SIG_DET': {'atype': <class 'float'>, 'descr': 'Optimally extracted detector noise (counts)', 'otype': <class 'numpy.ndarray'>}, 'OPT_COUNTS_SKY': {'atype': <class 'float'>, 'descr': 'Optimally extracted sky (counts)', 'otype': <class 'numpy.ndarray'>}, 'OPT_FLAM': {'atype': <class 'float'>, 'descr': 'Optimal flux (1e-17 erg/s/cm^2/Ang)', 'otype': <class 'numpy.ndarray'>}, 'OPT_FLAM_IVAR': {'atype': <class 'float'>, 'descr': 'Optimal flux inverse variance (1e-17 erg/s/cm^2/Ang)^-2', 'otype': <class 'numpy.ndarray'>}, 'OPT_FLAM_SIG': {'atype': <class 'float'>, 'descr': 'Optimal flux uncertainty (1e-17 erg/s/cm^2/Ang)', 'otype': <class 'numpy.ndarray'>}, 'OPT_FRAC_USE': {'atype': <class 'float'>, 'descr': 'Fraction of pixels in the object profile subimage used for this extraction', 'otype': <class 'numpy.ndarray'>}, 'OPT_FWHM': {'atype': <class 'float'>, 'descr': 'Spectral FWHM (in Angstroms) at every pixel of the optimally extracted flux.', 'otype': <class 'numpy.ndarray'>}, 'OPT_MASK': {'atype': <class 'numpy.bool_'>, 'descr': 'Mask for optimally extracted flux. True=good', 'otype': <class 'numpy.ndarray'>}, 'OPT_WAVE': {'atype': <class 'float'>, 'descr': 'Optimal Wavelengths in vacuum (Angstroms)', 'otype': <class 'numpy.ndarray'>}, 'PYPELINE': {'descr': 'Name of the PypeIt pipeline mode', 'otype': <class 'str'>}, 'RA': {'descr': 'Right Ascension (J2000) decimal degree', 'otype': <class 'float'>}, 'S2N': {'descr': 'Median signal to noise ratio of the extracted spectrum(OPT if available, otherwise BOX)', 'otype': <class 'float'>}, 'SLITID': {'descr': 'PypeIt slit ID (aka SPAT_ID).', 'otype': (<class 'int'>, <class 'numpy.integer'>)}, 'SPAT_FRACPOS': {'descr': 'Fractional location of the object on the slit', 'otype': (<class 'float'>, <class 'numpy.floating'>)}, 'SPAT_FWHM': {'descr': 'Spatial FWHM of the object (arcsec)', 'otype': <class 'float'>}, 'SPAT_PIXPOS': {'descr': 'Spatial location of the trace on detector (pixel) at half-way', 'otype': (<class 'float'>, <class 'numpy.floating'>)}, 'TRACE_SPAT': {'atype': <class 'float'>, 'descr': 'Object trace along the spec (spatial pixel)', 'otype': <class 'numpy.ndarray'>}, 'VEL_CORR': {'descr': 'Relativistic velocity correction for wavelengths', 'otype': <class 'float'>}, 'VEL_TYPE': {'descr': 'Type of heliocentric correction (if any)', 'otype': <class 'str'>}, 'WAVE_RMS': {'descr': 'RMS (pix) for the wavelength solution for this slit.', 'otype': (<class 'float'>, <class 'numpy.floating'>)}, 'hand_extract_flag': {'descr': 'Boolean indicating if this is a forced extraction at the location provided by the user. ', 'otype': <class 'bool'>}, 'maskwidth': {'descr': 'Size (in units of spatial fwhm) of the region used for local sky subtraction', 'otype': (<class 'float'>, <class 'numpy.floating'>)}, 'smash_peakflux': {'descr': 'Peak value of the spectral direction collapsed spatial profile', 'otype': <class 'float'>}, 'smash_snr': {'descr': 'Peak S/N ratio of the spectral direction collapsed patial profile', 'otype': <class 'float'>}, 'trace_spec': {'atype': (<class 'int'>, <class 'numpy.integer'>), 'descr': 'Array of pixels along the spectral direction', 'otype': <class 'numpy.ndarray'>}}

Defines the current datmodel.

classmethod from_arrays(PYPELINE: str, wave: ndarray, counts: ndarray, ivar: ndarray, mode='OPT', DET='DET01', SLITID=0, **kwargs)

get_box_ext(fluxed=False)

Return the boxcar extraction values

Parameters:

fluxed (bool, optional) – Return the flux-calibrated data.

Returns:

BOX_WAVE, BOX_COUNTS, BOX_COUNTS_IVAR, BOX_MASK attributes of SpecObj

Return type:

tuple

get_opt_ext(fluxed=False)

Return the optimal extraction values

Parameters:

fluxed (bool, optional) – Return the flux-calibrated data.

Returns:

OPT_WAVE, OPT_COUNTS, OPT_COUNTS_IVAR, OPT_MASK attributes of SpecObj

Return type:

tuple

has_box_ext(fluxed=False)

Check that all the values of the boxcar extraction exist

Parameters:

fluxed (bool, optional) – Check that the flux-calibrated data exist.

Returns:

True if all BOX values are available

Return type:

bool

has_opt_ext(fluxed=False)

Check that all the values of the optimal extraction exist

Parameters:

fluxed (bool, optional) – Check that the flux-calibrated data exist.

Returns:

True if all OPT values are available

Return type:

bool

internals = ['smash_peakflux', 'smash_snr', 'hand_extract_flag', 'hand_extract_spec', 'hand_extract_spat', 'hand_extract_det', 'hand_extract_fwhm', 'prof_nsigma', 'sign', 'min_spat', 'max_spat', 'ech_frac_was_fit', 'ech_snr']

A list of strings identifying a set of internal attributes that are not part of the datamodel.

med_fwhm()

Return median spatial FWHM of the spectrum

Returns:

float

med_s2n()

Return median S/N of the spectrum Uses OPT_COUNTS if present and then BOX_COUNTS

Returns:

float

property mnx_wave

Return min, max wavelength of the spectrum Uses OPT_WAVE if present and then BOX_WAVE

Returns:

min, max (float)

Return type:

tuple

ready_for_extraction()

Simple method to check all the items are filled and ready for skysub and extraction.

Returns:

True if all checks have passed

Return type:

bool

set_name()

Construct the PypeIt name for this object.

The PypeIt name depends on the type of data being processed:

• For multislit and SlicerIFU data, the name is SPATnnnn-SLITmmmm-{DET}, where nnnn is the nearest integer pixel in the spatial direction (at the spectral midpoint) where the object was extracted, mmmm is the slit identification number, and {DET} is the string identifier for the detector or mosaic.

• For echelle data, the name is OBJnnnn-{DET}-ORDERoooo, where nnnn is 1000 times the fractional position along the spatial direction rounded to the nearest integer, {DET} is the string identifier for the detector or mosaic, and oooo is the echelle order.

For echelle data, this modifies both ECH_NAME and NAME; otherwise, only the latter is set.

property slit_order

property slit_orderindx

to_arrays(extraction='OPT', fluxed=True)

Convert spectrum into np.ndarray arrays

Parameters:

• extraction (str) – Extraction method to convert

• fluxed – Use the fluxed tags

Returns:

wave, flux, ivar, mask arrays

Return type:

tuple

to_hdu(**kwargs)

Override the base class function to force the main datamodel attributes to be written to an astropy.io.fits.BinTableHDU object. This is identical to the base class method except force_to_bintbl is always set to True.

to_xspec1d(masked=True, extraction='OPT', fluxed=True)

Create an XSpectrum1D using this spectrum.

Parameters:

• masked (bool, optional) – If True, only unmasked data are included.

• extraction (str) – Extraction method to convert

• fluxed – Use the fluxed tags

Returns:

Spectrum object

Return type:

linetools.spectra.xspectrum1d.XSpectrum1D

update_flex_shift(shift, flex_type='local')

Store the total spectral flexure shift in pixels

Parameters:

shift (float) – additive spectral flexure in pixels

version = '1.1.10'

Current datamodel version number.