pypeit.specobj module

Module for the SpecObj classes

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

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: 1.1.11

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_FLAT`	numpy.ndarray	float	Boxcar extracted flatfield spectrum, normalized to the peak value.
`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_FLAT`	numpy.ndarray	float	Optimally extracted flatfield spectrum, normalised to the peak value.
`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__()[source]

Over-ride print representation

Returns:: Basics of the Data Container
Return type:: str

_bundle(**kwargs)[source]

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()[source]: 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)[source]

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. Note: This is deprecated and will be removed in a future version.
extinct_correct (bool, optional) – 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)[source]

Apply a heliocentric correction

Wavelength arrays are modified in place

Parameters:

vel_corr (float)
refframe (str)

apply_spectral_flexure(shift, sky_spec)[source]

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)[source]

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()[source]

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_FLAT': {'atype': <class 'float'>, 'descr': 'Boxcar extracted flatfield spectrum, normalized to the peak value.', '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_FLAT': {'atype': <class 'float'>, 'descr': 'Optimally extracted flatfield spectrum, normalised to the peak value.', '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, flat=None, mode='OPT', DET='DET01', SLITID=0, **kwargs)[source]

get_box_ext(fluxed=False)[source]

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)[source]

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)[source]

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)[source]

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()[source]

Return median spatial FWHM of the spectrum

Returns:: float

med_s2n()[source]

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()[source]

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()[source]

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)[source]

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)[source]: 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)[source]

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')[source]

Store the total spectral flexure shift in pixels

Parameters:: shift (float) – additive spectral flexure in pixels

version = '1.1.11': Current datamodel version number.