pypeit.onespec module

Provides a simple datamodel for a single spectrum.

class pypeit.onespec.OneSpec(wave, wave_grid_mid, flux, PYP_SPEC=None, ivar=None, sigma=None, mask=None, telluric=None, obj_model=None, ext_mode=None, fluxed=None)[source]

Bases: DataContainer

DataContainer to hold single spectra, e.g., from CoAdd1D.

The datamodel attributes are:

Version: 1.0.2

Attribute	Type	Array Type	Description
`PYP_SPEC`	str		`PypeIt` spectrograph designation
`ext_mode`	str		Extraction mode (options: BOX, OPT)
`flux`	numpy.ndarray	numpy.floating	Flux array in units of counts/s or 10^-17 erg/s/cm^2/Ang; see `fluxed`
`fluxed`	bool		Boolean indicating if the spectrum is fluxed.
`ivar`	numpy.ndarray	numpy.floating	Inverse variance array (matches units of flux)
`mask`	numpy.ndarray	numpy.integer	Mask array (1=Good,0=Bad)
`obj_model`	numpy.ndarray	numpy.floating	Object model for tellurics
`sigma`	numpy.ndarray	numpy.floating	One sigma noise array, equivalent to 1/sqrt(ivar) (matches units of flux)
`spect_meta`	dict		header dict
`telluric`	numpy.ndarray	numpy.floating	Telluric model
`wave`	numpy.ndarray	numpy.floating	Wavelength array (angstroms in vacuum), weighted by pixel contributions
`wave_grid_mid`	numpy.ndarray	numpy.floating	Wavelength (angstroms in vacuum) evaluated at the bin centers of a grid that is uniformly spaced in either lambda or log10-lambda/velocity

Parameters:

wave
wave_grid_mid
flux
PYP_SPEC
ivar
mask
telluric
obj_model
ext_mode
fluxed

head0

Primary header

Type:: astropy.io.fits.Header

spect_meta

Parsed meta from the header

Type:: dict

spectrograph

Build from PYP_SPEC

Type:: pypeit.spectrographs.spectrograph.Spectrograph

_bundle()[source]: Override the base class method simply to set the HDU extension name.

datamodel = {'PYP_SPEC': {'descr': '``PypeIt`` spectrograph designation', 'otype': <class 'str'>}, 'ext_mode': {'descr': 'Extraction mode (options: BOX, OPT)', 'otype': <class 'str'>}, 'flux': {'atype': <class 'numpy.floating'>, 'descr': 'Flux array in units of counts/s or 10^-17 erg/s/cm^2/Ang; see ``fluxed``', 'otype': <class 'numpy.ndarray'>}, 'fluxed': {'descr': 'Boolean indicating if the spectrum is fluxed.', 'otype': <class 'bool'>}, 'ivar': {'atype': <class 'numpy.floating'>, 'descr': 'Inverse variance array (matches units of flux)', 'otype': <class 'numpy.ndarray'>}, 'mask': {'atype': <class 'numpy.integer'>, 'descr': 'Mask array (1=Good,0=Bad)', 'otype': <class 'numpy.ndarray'>}, 'obj_model': {'atype': <class 'numpy.floating'>, 'descr': 'Object model for tellurics', 'otype': <class 'numpy.ndarray'>}, 'sigma': {'atype': <class 'numpy.floating'>, 'descr': 'One sigma noise array, equivalent to 1/sqrt(ivar) (matches units of flux)', 'otype': <class 'numpy.ndarray'>}, 'spect_meta': {'descr': 'header dict', 'otype': <class 'dict'>}, 'telluric': {'atype': <class 'numpy.floating'>, 'descr': 'Telluric model', 'otype': <class 'numpy.ndarray'>}, 'wave': {'atype': <class 'numpy.floating'>, 'descr': 'Wavelength array (angstroms in vacuum), weighted by pixel contributions', 'otype': <class 'numpy.ndarray'>}, 'wave_grid_mid': {'atype': <class 'numpy.floating'>, 'descr': 'Wavelength (angstroms in vacuum) evaluated at the bin centers of a grid that is uniformly spaced in either lambda or log10-lambda/velocity', 'otype': <class 'numpy.ndarray'>}}

Provides the class data model. This is undefined in the abstract class and should be overwritten in the derived classes.

The format of the datamodel needed for each implementation of a DataContainer derived class is as follows.

The datamodel itself is a class attribute (i.e., it is a member of the class, not just of an instance of the class). The datamodel is a dictionary of dictionaries: Each key of the datamodel dictionary provides the name of a given datamodel element, and the associated item (dictionary) for the datamodel element provides the type and description information for that datamodel element. For each datamodel element, the dictionary item must provide:

otype: This is the type of the object for this datamodel item. E.g., for a float or a numpy.ndarray, you would set otype=float and otype=np.ndarray, respectively.

descr: This provides a text description of the datamodel element. This is used to construct the datamodel tables in the pypeit documentation.

If the object type is a numpy.ndarray, you should also provide the atype keyword that sets the type of the data contained within the array. E.g., for a floating point array containing an image, your datamodel could be simply:

datamodel = {'image' : dict(otype=np.ndarray, atype=float, descr='My image')}

More advanced examples are given in the top-level module documentation.

Currently, datamodel components are restricted to have otype that are tuple, int, float, numpy.integer, numpy.floating, numpy.ndarray, or astropy.table.Table objects. E.g., datamodel values for otype cannot be dict.

classmethod from_file(ifile, verbose=True, chk_version=True, **kwargs)[source]

Instantiate the object from an extension in the specified fits file.

Over-load from_file() to deal with the header

Parameters:

ifile (str, Path) – Fits file with the data to read
verbose (bool, optional) – Print informational messages (not currently used)
chk_version (bool, optional) – Passed to from_hdu().
kwargs (dict, optional) – Arguments passed directly to from_hdu().

classmethod from_xspec_file(ifile)[source]

Parameters:: ifile (str)

gauss_smooth(fwhm)[source]

Smooth the spectrum with a Gaussian kernal.

Warning

This does not propagate the errors! The returned OneSpec instance will not have any errors. All other attributes are included in the returned OneSpec instance; array attributes are copied.

Parameters:: fwhm (float) – FWHM of the Gaussian kernal in pixels.
Returns:: The smoothed spectrum
Return type:: OneSpec

internals = ['head0', 'filename', 'spectrograph', 'spect_meta', 'history']: A list of strings identifying a set of internal attributes that are not part of the datamodel.

property npix

Number of pixels in the spectrum.

Returns:: Number of pixels in the spectrum.
Return type:: int

rebin(new_wv, fill_value=0.0, grow_bad_sig=False)[source]

Rebin the spectrum to a new OneSpec object with the input array

Uses simple linear interpolation. The default (and only) option conserves counts (and flambda).

WARNING: Do not trust either edge pixel of the new array. In fact the sig is set to 0 for each of these Also be aware that neighboring pixels are likely to be correlated in a manner that is not described by the error array.

Parameters:

new_wv (np.ndarray) – New wavelength array
fill_value (float, optional) – Fill value at the edges Default = 0., but ‘extrapolate’ may be considered
grow_bad_sig (bool, optional) – Allow sig<=0. values and grow them

Returns:

newspec

Return type:

OneSpec

to_file(ofile, primary_hdr=None, history=None, **kwargs)[source]

Over-load pypeit.datamodel.DataContainer.to_file() to deal with the header

Parameters:

ofile (str) – Filename
primary_hdr (astropy.io.fits.Header, optional)
**kwargs – Passed to super.to_file()

version = '1.0.2'

Provides the string representation of the class version.

This is currently put to minimal use so far, but will used for I/O verification in the future.

Each derived class should provide a version to guard against data model changes during development.