pypeit.extraction module

Main driver class for skysubtraction and extraction

class pypeit.extraction.EchelleExtract(sciImg, slits, sobjs_obj, spectrograph, par, objtype, **kwargs)[source]

Bases: Extract

Child of Extract for Echelle reductions

See parent doc string for Args and Attributes

local_skysub_extract(global_sky, sobjs, spat_pix=None, model_noise=True, min_snr=2.0, fit_fwhm=False, show_profile=False, show_resids=False, show_fwhm=False, show=False)[source]

Perform local sky subtraction, profile fitting, and optimal extraction slit by slit

Wrapper to local_skysub_extract().

Parameters:

  • global_sky (numpy.ndarray) – Global sky model

  • sobjs (SpecObjs) – Class containing the information about the objects found

  • spat_pix (numpy.ndarray, optional) – Image containing the spatial location of pixels. If not input, it will be computed from spat_img = np.outer(np.ones(nspec), np.arange(nspat)).

  • model_noise (bool, optional) – If True, construct and iteratively update a model inverse variance image using variance_model(). If False, a variance model will not be created and instead the input sciivar will always be taken to be the inverse variance. See ~pypeit.core.skysub.local_skysub_extract for more info.

  • show_resids (bool, optional) – Show the model fits and residuals.

  • show_profile (bool, optional) – Show QA for the object profile fitting to the screen. Note that this will show interactive matplotlib plots which will block the execution of the code until the window is closed.

  • show (bool, optional) – Show debugging plots

Returns:

Return the model sky flux, object flux, inverse variance, and mask as numpy.ndarray objects, and returns a SpecObjs: instance c containing the information about the objects found.

Return type:

tuple

class pypeit.extraction.Extract(sciImg, slits, sobjs_obj, spectrograph, par, objtype, global_sky=None, waveTilts=None, tilts=None, wv_calib=None, waveimg=None, bkg_redux=False, return_negative=False, std_redux=False, show=False, basename=None)[source]

Bases: object

This class will organize and run actions relatedt to sky subtraction, and extraction for a Science or Standard star exposure

ivarmodel

Model of inverse variance

Type:

numpy.ndarray

objimage

Model of object

Type:

numpy.ndarray

skyimage

Final model of sky

Type:

numpy.ndarray

global_sky

Fit to global sky

Type:

numpy.ndarray

outmask

Final output mask

Type:

ImageBitMaskArray

extractmask

Extraction mask

Type:

numpy.ndarray

slits
Type:

SlitTraceSet

sobjs_obj

Only object finding but no extraction

Type:

SpecObjs

sobjs

Final extracted object list with trace corrections applied

Type:

SpecObjs

spat_flexure_shift
Type:

float

tilts

WaveTilts images generated on-the-spot

Type:

numpy.ndarray

waveimg

WaveImage image generated on-the-spot

Type:

numpy.ndarray

slitshift

Global spectral flexure correction for each slit (in pixels)

Type:

numpy.ndarray

vel_corr

Relativistic reference frame velocity correction (e.g. heliocentyric/barycentric/topocentric)

Type:

float

extract_bpm

Bad pixel mask for extraction

Type:

numpy.ndarray

extract(global_sky, model_noise=None, spat_pix=None)[source]

Main method to extract spectra from the ScienceImage

Parameters:

  • global_sky (numpy.ndarray) – Sky estimate

  • sobjs_obj (SpecObjs) – List of SpecObj that have been found and traced

  • model_noise (bool) – If True, construct and iteratively update a model inverse variance image using variance_model(). If False, a variance model will not be created and instead the input sciivar will always be taken to be the inverse variance. See local_skysub_extract() for more info. Default is None, which is to say pypeit will use the bkg_redux attribute to decide whether or not to model the noise.

  • spat_pix (numpy.ndarray) – Image containing the spatial coordinates. This option is used for 2d coadds where the spat_pix image is generated as a coadd of images. For normal reductions spat_pix is not required as it is trivially created from the image itself. Default is None.

classmethod get_instance(sciImg, slits, sobjs_obj, spectrograph, par, objtype, global_sky=None, waveTilts=None, tilts=None, wv_calib=None, waveimg=None, bkg_redux=False, return_negative=False, std_redux=False, show=False, basename=None)[source]

Instantiate the Extract subclass appropriate for the provided spectrograph.

The class must be subclassed from Extract. See Extract for the description of the valid keyword arguments.

Parameters:

  • sciImg (PypeItImage) – Image to reduce.

  • slits (SlitTraceSet) – Slit trace set object

  • sobjs_obj (SpecObjs) – Objects found but not yet extracted

  • spectrograph (Spectrograph) –

  • par (PypeItPar) – Parameter set for Extract

  • objtype (str) – Specifies object being reduced ‘science’, ‘standard’, or ‘science_coadd2d’. This is used only to determine the spat_flexure_shift and ech_order for coadd2d.

  • global_sky (numpy.ndarray, optional) – Fit to global sky. If None, an array of zeroes is generated the same size as sciImg.

  • waveTilts (WaveTilts, optional) – This is waveTilts object which is optional, but either waveTilts or tilts must be provided.

  • tilts (numpy.ndarray, optional) – Tilts image. Either a tilts image or waveTilts object (above) must be provided.

  • wv_calib (WaveCalib, optional) – This is the waveCalib object which is optional, but either wv_calib or waveimg must be provided.

  • waveimg (numpy.ndarray, optional) – Wave image. Either a wave image or wv_calib object (above) must be provided

  • bkg_redux (bool, optional) – If True, the sciImg has been subtracted by a background image (e.g. standard treatment in the IR)

  • return_negative (bool, optional) – If True, negative objects from difference imaging will also be extracted and returned. Default=False. This option only applies to the case where bkg_redux=True, i.e. typically a near-IR reduction where difference imaging has been employed to perform a first-pass at sky-subtraction. The default behavior is to not extract these objects, although they are masked in global sky-subtraction (performed in the find_objects class), and modeled in local sky-subtraction (performed by this class).

  • std_redux (bool, optional) – If True the object being extracted is a standards star so that the reduction parameters can be adjusted accordingly.

  • basename (str, optional) – Output filename used for spectral flexure QA

  • show (bool, optional) – Show plots along the way?

Returns:

Extraction object.

Return type:

Extract

initialize_slits(slits, initial=False)[source]

Gather all the SlitTraceSet attributes that we’ll use here in Extract

Args
slits (SlitTraceSet):

SlitTraceSet object containing the slit boundaries that will be initialized.

initial (bool, optional):

Use the initial definition of the slits. If False, tweaked slits are used.

local_skysub_extract(global_sky, sobjs, model_noise=True, spat_pix=None, show_profile=False, show_resids=False, show=False)[source]

Dummy method for local sky-subtraction and extraction.

Overloaded by class specific skysub and extraction.

property nsobj_to_extract

Number of sobj objects in sobjs_obj taking into account whether or not we are returning negative traces

Returns:

run(model_noise=None, spat_pix=None)[source]

Primary code flow for PypeIt reductions

Parameters:

  • model_noise (bool) – If True, construct and iteratively update a model inverse variance image using variance_model(). If False, a variance model will not be created and instead the input sciivar will always be taken to be the inverse variance. See local_skysub_extract() for more info. Default is None, which is to say pypeit will use the bkg_redux attribute to decide whether or not to model the noise.

  • spat_pix (numpy.ndarray) – Image containing the spatial coordinates. This option is used for 2d coadds where the spat_pix image is generated as a coadd of images. For normal reductions spat_pix is not required as it is trivially created from the image itself. Default is None.

Returns:

skymodel (ndarray), objmodel (ndarray), ivarmodel (ndarray),

outmask (ndarray), sobjs (SpecObjs), waveimg (numpy.ndarray), tilts (numpy.ndarray), slits (SlitTraceSet). See main doc string for description

Return type:

tuple

show(attr, image=None, showmask=False, sobjs=None, chname=None, slits=False, clear=False)[source]

Show one of the internal images

Todo

Should probably put some of these in ProcessImages

Parameters:

  • attr (str) – global – Sky model (global) sci – Processed science image rawvar – Raw variance image modelvar – Model variance image crmasked – Science image with CRs set to 0 skysub – Science image with global sky subtracted image – Input image

  • display (str, optional) –

  • image (ndarray, optional) – User supplied image to display

spec_flexure_correct(mode='local', sobjs=None)[source]

Correct for spectral flexure

Spectra are modified in place (wavelengths are shifted)

Parameters:

  • mode (str) – “local” - Use sobjs to determine flexure correction “global” - Use waveimg and global_sky to determine flexure correction at the centre of the slit

  • sobjs (SpecObjs, None) – Spectrally extracted objects

class pypeit.extraction.MultiSlitExtract(sciImg, slits, sobjs_obj, spectrograph, par, objtype, **kwargs)[source]

Bases: Extract

Child of Extract for Multislit and Longslit reductions

See parent doc string for Args and Attributes

local_skysub_extract(global_sky, sobjs, spat_pix=None, model_noise=True, show_resids=False, show_profile=False, show=False)[source]

Perform local sky subtraction, profile fitting, and optimal extraction slit by slit.

Wrapper to local_skysub_extract().

Parameters:

  • global_sky (numpy.ndarray) – Global sky model

  • sobjs (SpecObjs) – Class containing the information about the objects found

  • spat_pix (numpy.ndarray, optional) – Image containing the spatial location of pixels. If not input, it will be computed from spat_img = np.outer(np.ones(nspec), np.arange(nspat)).

  • model_noise (bool, optional) – If True, construct and iteratively update a model inverse variance image using variance_model(). If False, a variance model will not be created and instead the input sciivar will always be taken to be the inverse variance. See local_skysub_extract() for more info.

  • show_resids (bool, optional) – Show the model fits and residuals.

  • show_profile (bool, optional) – Show QA for the object profile fitting to the screen. Note that this will show interactive matplotlib plots which will block the execution of the code until the window is closed.

  • show (bool, optional) – Show debugging plots

Returns:

Return the model sky flux, object flux, inverse variance, and mask as numpy.ndarray objects, and returns a SpecObjs: instance c containing the information about the objects found.

Return type:

tuple

class pypeit.extraction.SlicerIFUExtract(sciImg, slits, sobjs_obj, spectrograph, par, objtype, **kwargs)[source]

Bases: MultiSlitExtract

Child of Extract for IFU reductions

See parent doc string for Args and Attributes