Source code for pypeit.spectrographs.gemini_gnirs
"""
Module for Gemini/GNIRS specific methods.
.. include:: ../include/links.rst
"""
import numpy as np
from astropy import wcs, units
from astropy.coordinates import SkyCoord, EarthLocation
from astropy.time import Time
from pypeit import msgs
from pypeit import telescopes
from pypeit.core import framematch, parse
from pypeit.images import detector_container
from pypeit.spectrographs import spectrograph
[docs]class GeminiGNIRSSpectrograph(spectrograph.Spectrograph):
"""
Child to handle Gemini/GNIRS specific code
"""
ndet = 1
camera = 'GNIRS'
url = 'https://www.gemini.edu/instrumentation/gnirs'
header_name = 'GNIRS'
telescope = telescopes.GeminiNTelescopePar()
def __init__(self):
super().__init__()
# TODO :: Might consider changing TelescopePar to use the astropy EarthLocation.
self.location = EarthLocation.of_site('Gemini North')
[docs] def get_detector_par(self, det, hdu=None):
"""
Return metadata for the selected detector.
Args:
det (:obj:`int`):
1-indexed detector number.
hdu (`astropy.io.fits.HDUList`_, optional):
The open fits file with the raw image of interest. If not
provided, frame-dependent parameters are set to a default.
Returns:
:class:`~pypeit.images.detector_container.DetectorContainer`:
Object with the detector metadata.
"""
# Detector 1
detector_dict = dict(
binning = '1,1',
det = 1,
dataext = 1,
specaxis = 0,
specflip=True,
spatflip=True,
platescale = 0.15,
darkcurr = 540.0, # e-/hour/pixel (=0.15 e-/pixel/s)
saturation = 150000.,
nonlinear = 0.71,
mincounts = -1e10,
numamplifiers = 1,
gain = np.atleast_1d(13.5),
ronoise = np.atleast_1d(7.0),
datasec = np.atleast_1d('[:,:]'),
oscansec = None,
)
return detector_container.DetectorContainer(**detector_dict)
[docs] def init_meta(self):
"""
Define how metadata are derived from the spectrograph files.
That is, this associates the PypeIt-specific metadata keywords
with the instrument-specific header cards using :attr:`meta`.
"""
self.meta = {}
# Required (core)
self.meta['ra'] = dict(ext=0, card='RA')
self.meta['dec'] = dict(ext=0, card='DEC')
self.meta['target'] = dict(ext=0, card='OBJECT')
self.meta['decker'] = dict(ext=0, card='SLIT')
self.meta['binning'] = dict(ext=0, card=None, default='1,1')
self.meta['mjd'] = dict(ext=0, card='MJD_OBS')
self.meta['exptime'] = dict(ext=0, card='EXPTIME')
self.meta['airmass'] = dict(ext=0, card='AIRMASS')
# Dithering
self.meta['dithpos'] = dict(ext=0, card='QOFFSET')
self.meta['dithoff'] = dict(card=None, compound=True)
# Extras for config and frametyping
self.meta['filter1'] = dict(ext=0, card='FILTER2')
self.meta['slitwid'] = dict(ext=0, compound=True, card=None)
self.meta['dispname'] = dict(ext=0, card='GRATING')
self.meta['hatch'] = dict(ext=0, card='COVER')
self.meta['dispangle'] = dict(ext=0, card='GRATTILT', rtol=1e-4)
self.meta['idname'] = dict(ext=0, card='OBSTYPE')
self.meta['instrument'] = dict(ext=0, card='INSTRUME')
[docs] def compound_meta(self, headarr, meta_key):
"""
Methods to generate metadata requiring interpretation of the header
data, instead of simply reading the value of a header card.
Args:
headarr (:obj:`list`):
List of `astropy.io.fits.Header`_ objects.
meta_key (:obj:`str`):
Metadata keyword to construct.
Returns:
object: Metadata value read from the header(s).
"""
if meta_key == 'dithoff':
if headarr[0].get('OBSTYPE') == 'OBJECT':
return headarr[0].get('QOFFSET')
else:
return 0.0
elif meta_key == 'slitwid':
deckname = headarr[0].get('DECKER')
if 'LR-IFU' in deckname:
return 0.15/3600.0 # divide by 3600 for degrees
elif 'HR-IFU' in deckname:
return 0.05/3600.0 # divide by 3600 for degrees
else:
# TODO :: Need to provide a more complete set of options here
return None
elif meta_key == 'obstime':
try:
return Time(headarr[0]['DATE-OBS'] + "T" + headarr[0]['TIME-OBS'])
except KeyError:
msgs.warn("Time of observation is not in header")
return 0.0
elif meta_key == 'pressure':
try:
return headarr[0]['PRESSUR2']/100.0 # Must be in astropy.units.mbar
except KeyError:
msgs.warn("Pressure is not in header - The default pressure (611 mbar) will be assumed")
return 611.0
elif meta_key == 'temperature':
try:
return headarr[0]['TAMBIENT'] # Must be in astropy.units.deg_C
except KeyError:
msgs.warn("Temperature is not in header - The default temperature (1.5 deg C) will be assumed")
return 1.5 # van Kooten & Izett, arXiv:2208.11794
elif meta_key == 'humidity':
try:
# Humidity expressed as a percentage, not a fraction
return headarr[0]['HUMIDITY']
except KeyError:
msgs.warn("Humidity is not in header - The default relative humidity (20 %) will be assumed")
return 20.0 # van Kooten & Izett, arXiv:2208.11794
elif meta_key == 'parangle':
try:
# Humidity expressed as a percentage, not a fraction
msgs.warn("Parallactic angle is not available for GNIRS - DAR correction may be incorrect")
return headarr[0]['PARANGLE'] # Must be expressed in radians
except KeyError:
msgs.warn("Parallactic angle is not in header - The default parallactic angle (0 degrees) will be assumed")
return 0.0
else:
msgs.error("Not ready for this compound meta")
[docs] def configuration_keys(self):
"""
Return the metadata keys that define a unique instrument
configuration.
This list is used by :class:`~pypeit.metadata.PypeItMetaData` to
identify the unique configurations among the list of frames read
for a given reduction.
Returns:
:obj:`list`: List of keywords of data pulled from file headers
and used to constuct the :class:`~pypeit.metadata.PypeItMetaData`
object.
"""
return ['decker', 'dispname', 'dispangle']
[docs] def raw_header_cards(self):
"""
Return additional raw header cards to be propagated in
downstream output files for configuration identification.
The list of raw data FITS keywords should be those used to populate
the :meth:`~pypeit.spectrographs.spectrograph.Spectrograph.configuration_keys`
or are used in :meth:`~pypeit.spectrographs.spectrograph.Spectrograph.config_specific_par`
for a particular spectrograph, if different from the name of the
PypeIt metadata keyword.
This list is used by :meth:`~pypeit.spectrographs.spectrograph.Spectrograph.subheader_for_spec`
to include additional FITS keywords in downstream output files.
Returns:
:obj:`list`: List of keywords from the raw data files that should
be propagated in output files.
"""
return ['SLIT', 'GRATING', 'GRATTILT']
[docs] def pypeit_file_keys(self):
"""
Define the list of keys to be output into a standard ``PypeIt`` file.
Returns:
:obj:`list`: The list of keywords in the relevant
:class:`~pypeit.metadata.PypeItMetaData` instance to print to the
:ref:`pypeit_file`.
"""
return super().pypeit_file_keys() + ['dithoff']
[docs] def check_frame_type(self, ftype, fitstbl, exprng=None):
"""
Check for frames of the provided type.
Args:
ftype (:obj:`str`):
Type of frame to check. Must be a valid frame type; see
frame-type :ref:`frame_type_defs`.
fitstbl (`astropy.table.Table`_):
The table with the metadata for one or more frames to check.
exprng (:obj:`list`, optional):
Range in the allowed exposure time for a frame of type
``ftype``. See
:func:`pypeit.core.framematch.check_frame_exptime`.
Returns:
`numpy.ndarray`_: Boolean array with the flags selecting the
exposures in ``fitstbl`` that are ``ftype`` type frames.
"""
good_exp = framematch.check_frame_exptime(fitstbl['exptime'], exprng)
if ftype == 'science':
return good_exp & (fitstbl['idname'] == 'OBJECT')
if ftype == 'standard':
return good_exp & (fitstbl['idname'] == 'OBJECT')
if ftype in ['pixelflat', 'trace']:
# Flats and trace frames are typed together
return good_exp & (fitstbl['idname'] == 'FLAT')
if ftype in ['pinhole', 'dark', 'bias']:
# Don't type pinhole, dark, or bias frames
return np.zeros(len(fitstbl), dtype=bool)
if ftype in ['arc', 'tilt']:
## FW ToDo: self.dispname does not work yet. need to replace the following later.
if '32/mm' in fitstbl['dispname'][0]:
return good_exp & (fitstbl['idname'] == 'OBJECT')
elif '10/mmLBSX' in fitstbl['dispname'][0]:
return good_exp & (fitstbl['idname'] == 'ARC')
msgs.warn('Cannot determine if frames are of type {0}.'.format(ftype))
return np.zeros(len(fitstbl), dtype=bool)
[docs] @classmethod
def default_pypeit_par(cls):
"""
Return the default parameters to use for this instrument.
Returns:
:class:`~pypeit.par.pypeitpar.PypeItPar`: Parameters required by
all of PypeIt methods.
"""
par = super().default_pypeit_par()
# Image processing steps
turn_off = dict(use_illumflat=False, use_biasimage=False, use_overscan=False,
use_darkimage=False)
par.reset_all_processimages_par(**turn_off)
# Flats
par['calibrations']['flatfield']['tweak_slits_thresh'] = 0.90
par['calibrations']['flatfield']['tweak_slits_maxfrac'] = 0.10
# Relatively short slit, so keep the spatial tilt order low
par['calibrations']['tilts']['spat_order'] = 1
# Reduce parameters
# par['reduce']['findobj']['snr_thresh'] = 5.0 # Object finding threshold
par['reduce']['findobj']['find_trim_edge'] = [2, 2] # Slit is too short to trim 5,5 especially
par['reduce']['skysub']['bspline_spacing'] = 0.8
par['reduce']['skysub']['global_sky_std'] = False # Do not perform global sky subtraction for standard stars
par['reduce']['skysub']['no_poly'] = True # Do not use polynomial degree of freedom for global skysub
par['reduce']['extraction']['model_full_slit'] = True # local sky subtraction operates on entire slit
par['reduce']['findobj']['maxnumber_sci'] = 2 # Slit is narrow so allow two objects per order
par['reduce']['findobj']['maxnumber_std'] = 1 # Slit is narrow so allow one object per order
# Standards
par['calibrations']['standardframe']['process']['mask_cr'] = False # Do not mask_cr standards
# Do not correct for flexure
par['flexure']['spec_method'] = 'skip'
# Set the default exposure time ranges for the frame typing
par['calibrations']['pixelflatframe']['exprng'] = [None, 30]
par['calibrations']['traceframe']['exprng'] = [None, 30]
par['calibrations']['standardframe']['exprng'] = [None, 30]
par['scienceframe']['exprng'] = [30, None]
# Sensitivity function parameters
par['sensfunc']['algorithm'] = 'IR'
par['sensfunc']['polyorder'] = 6
par['sensfunc']['IR']['telgridfile'] = 'TellPCA_3000_26000_R10000.fits'
return par
[docs] def config_specific_par(self, scifile, inp_par=None):
"""
Modify the PypeIt parameters to hard-wired values used for
specific instrument configurations.
Args:
scifile (:obj:`str`):
File to use when determining the configuration and how
to adjust the input parameters.
inp_par (:class:`~pypeit.par.parset.ParSet`, optional):
Parameter set used for the full run of PypeIt. If None,
use :func:`default_pypeit_par`.
Returns:
:class:`~pypeit.par.parset.ParSet`: The PypeIt parameter set
adjusted for configuration specific parameter values.
"""
par = super().config_specific_par(scifile, inp_par=inp_par)
# TODO This is a hack for now until we figure out how to set dispname
# and other meta information in the spectrograph class itself
self.dispname = self.get_meta_value(scifile, 'dispname')
# 32/mmSB_G5533 setup, covering XYJHK with short blue camera
if '32/mm' in self.dispname:
# Edges
par['calibrations']['slitedges']['edge_thresh'] = 20.
par['calibrations']['slitedges']['trace_thresh'] = 10.
par['calibrations']['slitedges']['fit_order'] = 5
par['calibrations']['slitedges']['max_shift_adj'] = 0.5
par['calibrations']['slitedges']['fit_min_spec_length'] = 0.5
# Wavelengths
par['calibrations']['wavelengths']['rms_thresh_frac_fwhm'] = 0.4
par['calibrations']['wavelengths']['sigdetect'] = 10.0
par['calibrations']['wavelengths']['lamps'] = ['OH_GNIRS']
# par['calibrations']['wavelengths']['nonlinear_counts'] = self.detector[0]['nonlinear'] * self.detector[0]['saturation']
par['calibrations']['wavelengths']['n_first'] = 2
par['calibrations']['wavelengths']['n_final'] = 3
# Reidentification parameters
par['calibrations']['wavelengths']['method'] = 'reidentify'
par['calibrations']['wavelengths']['cc_thresh'] = 0.6
par['calibrations']['wavelengths']['reid_arxiv'] = 'gemini_gnirs.fits'
# Tilts
par['calibrations']['tilts']['tracethresh'] = 10
par['calibrations']['tilts']['sig_neigh'] = 5.0
par['calibrations']['tilts']['nfwhm_neigh'] = 2.0
# Coadding. Not for longslit data this might be problematic but that is not yet supported.
par['coadd1d']['wave_method'] = 'log10'
# 10/mmLBSX_G5532 setup, covering YJHK with the long blue camera and SXD prism
elif '10/mmLBSX' in self.dispname:
# Edges
par['calibrations']['slitedges']['edge_thresh'] = 20.
par['calibrations']['slitedges']['trace_thresh'] = 10.
par['calibrations']['slitedges']['fit_order'] = 2
par['calibrations']['slitedges']['max_shift_adj'] = 0.5
par['calibrations']['slitedges']['det_min_spec_length'] = 0.20
par['calibrations']['slitedges']['fit_min_spec_length'] = 0.20
par['calibrations']['slitedges']['sync_predict'] = 'nearest'
# Wavelengths
par['calibrations']['wavelengths']['rms_thresh_frac_fwhm'] = 0.05
par['calibrations']['wavelengths']['sigdetect'] = 5.0
par['calibrations']['wavelengths']['lamps'] = ['Ar_IR_GNIRS']
# par['calibrations']['wavelengths']['nonlinear_counts'] = self.detector[0]['nonlinear'] * self.detector[0]['saturation']
par['calibrations']['wavelengths']['n_first'] = 2
par['calibrations']['wavelengths']['n_final'] = 3
# Reidentification parameters
par['calibrations']['wavelengths']['method'] = 'reidentify'
par['calibrations']['wavelengths']['cc_thresh'] = 0.6
par['calibrations']['wavelengths']['reid_arxiv'] = 'gemini_gnirs_10mm_LBSX.fits'
# Tilts
par['calibrations']['tilts']['tracethresh'] = 10
par['calibrations']['tilts']['sig_neigh'] = 5.0
par['calibrations']['tilts']['nfwhm_neigh'] = 2.0
elif '10/mmLBHR_G5532' in self.dispname:
# TODO :: Need to fill this in
pass
else:
msgs.error(f'Unrecognized GNIRS dispname: {self.dispname}')
return par
[docs] def bpm(self, filename, det, shape=None, msbias=None):
"""
Generate a default bad-pixel mask.
Even though they are both optional, either the precise shape for
the image (``shape``) or an example file that can be read to get
the shape (``filename`` using :func:`get_image_shape`) *must* be
provided.
Args:
filename (:obj:`str` or None):
An example file to use to get the image shape.
det (:obj:`int`):
1-indexed detector number to use when getting the image
shape from the example file.
shape (tuple, optional):
Processed image shape
Required if filename is None
Ignored if filename is not None
msbias (`numpy.ndarray`_, optional):
Processed bias frame used to identify bad pixels
Returns:
`numpy.ndarray`_: An integer array with a masked value set
to 1 and an unmasked value set to 0. All values are set to
0.
"""
msgs.info("Custom bad pixel mask for GNIRS")
# Call the base-class method to generate the empty bpm
bpm_img = super().bpm(filename, det, shape=shape, msbias=msbias)
# JFH Changed. Dealing with detector scratch
if det == 1:
bpm_img[687:765,12:16] = 1.
bpm_img[671:687,8:13] = 1.
# bpm_img[:, 1000:] = 1.
return bpm_img
[docs]class GeminiGNIRSEchelleSpectrograph(GeminiGNIRSSpectrograph):
"""
Child to handle Gemini/GNIRS echelle specific code
"""
name = 'gemini_gnirs_echelle'
pypeline = 'Echelle'
ech_fixed_format = True
[docs] def config_specific_par(self, scifile, inp_par=None):
"""
Modify the ``PypeIt`` parameters to hard-wired values used for
specific instrument configurations.
Args:
scifile (:obj:`str`):
File to use when determining the configuration and how
to adjust the input parameters.
inp_par (:class:`~pypeit.par.parset.ParSet`, optional):
Parameter set used for the full run of PypeIt. If None,
use :func:`default_pypeit_par`.
Returns:
:class:`~pypeit.par.parset.ParSet`: The PypeIt parameter set
adjusted for configuration specific parameter values.
"""
par = super().config_specific_par(scifile, inp_par=inp_par)
# TODO This is a hack for now until we figure out how to set dispname
# and other meta information in the spectrograph class itself
self.dispname = self.get_meta_value(scifile, 'dispname')
# 32/mmSB_G5533 setup, covering XYJHK with short blue camera
if '32/mm' in self.dispname:
# Edges
par['calibrations']['slitedges']['left_right_pca'] = True
par['calibrations']['slitedges']['pca_order'] = 3
# Wavelengths
par['calibrations']['wavelengths']['sigdetect'] = [4.0, 5.0, 5.0, 5.0, 5.0, 5.0] #5.0
par['calibrations']['wavelengths']['n_final'] = [1, 3, 3, 3, 3, 3]
# Echelle parameters
# JFH This is provisional these IDs should be checked.
par['calibrations']['wavelengths']['echelle'] = True
par['calibrations']['wavelengths']['ech_nspec_coeff'] = 3
par['calibrations']['wavelengths']['ech_norder_coeff'] = 5
par['calibrations']['wavelengths']['ech_sigrej'] = 3.0
# Tilts
par['calibrations']['tilts']['tracethresh'] = [5.0, 10, 10, 10, 10, 10]
# 10/mmLBSX_G5532 setup, covering YJHK with the long blue camera and SXD prism
elif '10/mmLBSX' in self.dispname:
# Edges
par['calibrations']['slitedges']['left_right_pca'] = True # Actually we need a parameter to disable PCA entirely
# Wavelengths
par['calibrations']['wavelengths']['n_final'] = [3, 3, 3, 3]
# Echelle parameters
par['calibrations']['wavelengths']['echelle'] = True
par['calibrations']['wavelengths']['ech_nspec_coeff'] = 3
par['calibrations']['wavelengths']['ech_norder_coeff'] = 3
par['calibrations']['wavelengths']['ech_sigrej'] = 3.0
# Tilts
par['calibrations']['tilts']['tracethresh'] = [10, 10, 10, 10]
else:
msgs.error('Unrecognized GNIRS dispname')
return par
[docs] def order_platescale(self, order_vec, binning=None):
"""
Return the platescale for each echelle order.
Args:
order_vec (`numpy.ndarray`_):
The vector providing the order numbers.
binning (:obj:`str`, optional):
The string defining the spectral and spatial binning.
Returns:
`numpy.ndarray`_: An array with the platescale for each order
provided by ``order``.
"""
# TODO: Binning is ignored. Should it be?
self.check_disperser()
if '10/mmLBSX' in self.dispname:
return np.full(order_vec.size, 0.05)
elif '32/mm' in self.dispname:
return np.full(order_vec.size, 0.15)
else:
msgs.error('Unrecognized disperser')
@property
def norders(self):
"""
Number of orders for this spectograph.
"""
self.check_disperser()
if '10/mmLBSX' in self.dispname:
return 4
elif '32/mm' in self.dispname:
return 6
else:
msgs.error('Unrecognized disperser')
@property
def order_spat_pos(self):
"""
Return the expected spatial position of each echelle order.
"""
self.check_disperser()
if '10/mmLBSX' in self.dispname:
return np.array([0.050, 0.215, 0.442, 0.759])
elif '32/mm' in self.dispname:
#ToDo: create self.date similar to self.dispname and use that to decide which numbers to use
## Old data, i.e. before 2011
#return np.array([0.241211 , 0.3173828, 0.387695, 0.456054, 0.530273, 0.640625])
##New data
return np.array([0.2955097 , 0.37635756, 0.44952223, 0.51935601, 0.59489503, 0.70210309])
else:
msgs.error('Unrecognized disperser')
@property
def orders(self):
"""
Return the order number for each echelle order.
"""
self.check_disperser()
if '10/mmLBSX' in self.dispname:
return np.arange(6,2,-1, dtype=int)
elif '32/mm' in self.dispname:
return np.arange(8,2,-1,dtype=int)
else:
msgs.error('Unrecognized disperser')
@property
def spec_min_max(self):
"""
Return the minimum and maximum spectral pixel expected for the
spectral range of each order.
"""
# TODO: Why aren't these numbers in fraction of the detector
# size instead of the pixel number?
self.check_disperser()
if '10/mmLBSX' in self.dispname:
spec_max = np.asarray([1022, 1022, 1022, 1022])
spec_min = np.asarray([450, 0, 0, 0])
return np.vstack((spec_min, spec_max))
elif '32/mm' in self.dispname:
spec_max = np.asarray([1022, 1022, 1022, 1022, 1022, 1022])
spec_min = np.asarray([512, 280, 0, 0, 0, 0])
return np.vstack((spec_min, spec_max))
else:
msgs.error('Unrecognized disperser')
[docs]class GNIRSIFUSpectrograph(GeminiGNIRSSpectrograph):
# TODO :: A list of steps that could improve the reduction
# * Have a high threshold for detecting slit edges (par['calibrations']['slitedges']['edge_thresh'] = 100.), and have an option when inserting new traces to be the median of all other slit lengths (or a fit to the slit lengths).
# * Need to store a wavelength solution for different grating options (Note, the Holy Grail algorithm works pretty well, most of the time)
name = 'gemini_gnirs_ifu'
pypeline = 'SlicerIFU'
[docs] def init_meta(self):
super().init_meta()
self.meta['obstime'] = dict(card=None, compound=True, required=False)
self.meta['pressure'] = dict(card=None, compound=True, required=False)
self.meta['temperature'] = dict(card=None, compound=True, required=False)
self.meta['humidity'] = dict(card=None, compound=True, required=False)
self.meta['parangle'] = dict(card=None, compound=True, required=False)
[docs] @classmethod
def default_pypeit_par(cls):
par = super().default_pypeit_par()
# LACosmics parameters
par['scienceframe']['process']['sigclip'] = 4.0
par['scienceframe']['process']['objlim'] = 1.5
par['scienceframe']['process']['use_illumflat'] = False # illumflat is applied when building the relative scale image in reduce.py, so should be applied to scienceframe too.
par['scienceframe']['process']['use_specillum'] = False # apply relative spectral illumination
par['scienceframe']['process']['spat_flexure_correct'] = False # don't correct for spatial flexure - varying spatial illumination profile could throw this correction off. Also, there's no way to do astrometric correction if we can't correct for spatial flexure of the contbars frames
par['scienceframe']['process']['use_biasimage'] = False
par['scienceframe']['process']['use_darkimage'] = False
par['calibrations']['flatfield']['slit_illum_finecorr'] = False
# Don't do 1D extraction for 3D data - it's meaningless because the DAR correction must be performed on the 3D data.
par['reduce']['extraction']['skip_extraction'] = True # Because extraction occurs before the DAR correction, don't extract
#par['calibrations']['flatfield']['tweak_slits'] = False # Do not tweak the slit edges (we want to use the full slit)
par['calibrations']['flatfield']['tweak_slits_thresh'] = 0.0 # Make sure the full slit is used (i.e. when the illumination fraction is > 0.5)
par['calibrations']['flatfield']['tweak_slits_maxfrac'] = 0.0 # Make sure the full slit is used (i.e. no padding)
par['calibrations']['flatfield']['slit_trim'] = 2 # Trim the slit edges
par['calibrations']['slitedges']['pad'] = 2 # Need to pad out the tilts for the astrometric transform when creating a datacube.
# Decrease the wave tilts order, given the shorter slits of the IFU
par['calibrations']['tilts']['spat_order'] = 1
par['calibrations']['tilts']['spec_order'] = 1
# Make sure that this is reduced as a slit (as opposed to fiber) spectrograph
par['reduce']['cube']['slit_spec'] = True
par['reduce']['cube']['grating_corr'] = False
par['reduce']['cube']['combine'] = False # Make separate spec3d files from the input spec2d files
# Sky subtraction parameters
par['reduce']['skysub']['no_poly'] = True
par['reduce']['skysub']['bspline_spacing'] = 0.6
par['reduce']['skysub']['joint_fit'] = False
# Don't correct flexure by default since the OH lines are used for wavelength calibration
# If someone does want to do a spectral flexure correction, you should use slitcen,
# because this is a slit-based IFU where no objects are extracted.
par['flexure']['spec_method'] = 'skip'
par['flexure']['spec_maxshift'] = 0 # The sky lines are used for calibration - don't allow flexure
# Flux calibration parameters
par['sensfunc']['UVIS']['extinct_correct'] = False # This must be False - the extinction correction is performed when making the datacube
return par
[docs] def config_specific_par(self, scifile, inp_par=None):
"""
Modify the ``PypeIt`` parameters to hard-wired values used for
specific instrument configurations.
Args:
scifile (:obj:`str`):
File to use when determining the configuration and how
to adjust the input parameters.
inp_par (:class:`~pypeit.par.parset.ParSet`, optional):
Parameter set used for the full run of PypeIt. If None,
use :func:`default_pypeit_par`.
Returns:
:class:`~pypeit.par.parset.ParSet`: The PypeIt parameter set
adjusted for configuration specific parameter values.
"""
par = super().config_specific_par(scifile, inp_par=inp_par)
# Obtain a header keyword to determine which range is being used
filter = self.get_meta_value(scifile, 'filter1')
par['calibrations']['slitedges']['edge_thresh'] = 30.
# TODO :: The following wavelength solutions are not general enough - need to implement a solution for each setup+grating
# TODO BEFORE PR MERGE :: The full_template solutions below were generated (quickly!) from holy-grail... might want to redo this...
if filter == 'X_G0518': # H band
par['calibrations']['wavelengths']['method'] = 'holy-grail'
elif filter == 'J_G0517': # K band
par['calibrations']['wavelengths']['method'] = 'holy-grail'
elif filter == 'H_G0516': # H band
par['calibrations']['wavelengths']['method'] = 'full_template'
par['calibrations']['wavelengths']['reid_arxiv'] = 'gemini_gnirs_lrifu_H.fits'
elif filter == 'K_G0515': # K band
par['calibrations']['wavelengths']['method'] = 'full_template'
par['calibrations']['wavelengths']['reid_arxiv'] = 'gemini_gnirs_lrifu_K.fits'
else:
par['calibrations']['wavelengths']['method'] = 'holy-grail'
return par
[docs] def get_wcs(self, hdr, slits, platescale, wave0, dwv, spatial_scale=None):
"""
Construct/Read a World-Coordinate System for a frame.
Args:
hdr (`astropy.io.fits.Header`_):
The header of the raw frame. The information in this
header will be extracted and returned as a WCS.
slits (:class:`~pypeit.slittrace.SlitTraceSet`):
Slit traces.
platescale (:obj:`float`):
The platescale of an unbinned pixel in arcsec/pixel (e.g.
detector.platescale).
wave0 (:obj:`float`):
The wavelength zeropoint.
dwv (:obj:`float`):
Change in wavelength per spectral pixel.
Returns:
`astropy.wcs.WCS`_: The world-coordinate system.
"""
msgs.info("Calculating the WCS")
# Get the x and y binning factors, and the typical slit length
binspec, binspat = parse.parse_binning(self.get_meta_value([hdr], 'binning'))
# Get the pixel and slice scales
pxscl = platescale * binspat / 3600.0 # Need to convert arcsec to degrees
msgs.work("NEED TO WORK OUT SLICER SCALE AND PIXEL SCALE")
slscl = self.get_meta_value([hdr], 'slitwid')
if spatial_scale is not None:
if pxscl > spatial_scale / 3600.0:
msgs.warn("Spatial scale requested ({0:f}'') is less than the pixel scale ({1:f}'')".format(spatial_scale, pxscl*3600.0))
# Update the pixel scale
pxscl = spatial_scale / 3600.0 # 3600 is to convert arcsec to degrees
# Get the typical slit length (this changes by ~0.3% over all slits, so a constant is fine for now)
slitlength = int(np.round(np.median(slits.get_slitlengths(initial=True, median=True))))
# Get RA/DEC
raval = self.get_meta_value([hdr], 'ra')
decval = self.get_meta_value([hdr], 'dec')
# Create a coordinate
coord = SkyCoord(raval, decval, unit=(units.deg, units.deg))
# Get rotator position
msgs.warn("CURRENTLY A HACK --- NEED TO FIGURE OUT RPOS and RREF FOR HRIFU FROM HEADER INFO")
if 'ROTPOSN' in hdr:
rpos = hdr['ROTPOSN']
else:
rpos = 0.
if 'ROTREFAN' in hdr:
rref = hdr['ROTREFAN']
else:
rref = 0.
# Get the offset and PA
rotoff = 0.0 # IFU-SKYPA offset (degrees)
skypa = rpos + rref # IFU position angle (degrees)
crota = np.radians(-(skypa + rotoff))
# Calculate the fits coordinates
cdelt1 = -slscl
cdelt2 = pxscl
if coord is None:
ra = 0.
dec = 0.
crota = 1
else:
ra = coord.ra.degree
dec = coord.dec.degree
# Calculate the CD Matrix
cd11 = cdelt1 * np.cos(crota) # RA degrees per column
cd12 = abs(cdelt2) * np.sign(cdelt1) * np.sin(crota) # RA degrees per row
cd21 = -abs(cdelt1) * np.sign(cdelt2) * np.sin(crota) # DEC degress per column
cd22 = cdelt2 * np.cos(crota) # DEC degrees per row
# Get reference pixels (set these to the middle of the FOV)
crpix1 = 11 # i.e. see get_datacube_bins (11 is used as the reference point - somewhere in the middle of the FOV)
crpix2 = slitlength / 2.
crpix3 = 1.
# Get the offset
msgs.warn("HACK FOR HRIFU --- Need to obtain offset from header?")
off1 = 0.
off2 = 0.
off1 /= binspec
off2 /= binspat
crpix1 += off1
crpix2 += off2
# Create a new WCS object.
msgs.info("Generating GNIRS IFU WCS")
w = wcs.WCS(naxis=3)
w.wcs.equinox = hdr['EQUINOX']
w.wcs.name = 'GNIRS IFU'
w.wcs.radesys = 'FK5'
# Insert the coordinate frame
w.wcs.cname = ['RA', 'DEC', 'Wavelength']
w.wcs.cunit = [units.degree, units.degree, units.Angstrom]
w.wcs.ctype = ["RA---TAN", "DEC--TAN", "WAVE"] # Note, WAVE is in vacuum
w.wcs.crval = [ra, dec, wave0] # RA, DEC, and wavelength zeropoints
w.wcs.crpix = [crpix1, crpix2, crpix3] # RA, DEC, and wavelength reference pixels
w.wcs.cd = np.array([[cd11, cd12, 0.0], [cd21, cd22, 0.0], [0.0, 0.0, dwv]])
w.wcs.lonpole = 180.0 # Native longitude of the Celestial pole
w.wcs.latpole = 0.0 # Native latitude of the Celestial pole
return w
[docs] def get_datacube_bins(self, slitlength, minmax, num_wave):
r"""
Calculate the bin edges to be used when making a datacube.
Args:
slitlength (:obj:`int`):
Length of the slit in pixels
minmax (`numpy.ndarray`_):
An array with the minimum and maximum pixel locations on each
slit relative to the reference location (usually the centre
of the slit). Shape must be :math:`(N_{\rm slits},2)`, and is
typically the array returned by
:func:`~pypeit.slittrace.SlitTraceSet.get_radec_image`.
num_wave (:obj:`int`):
Number of wavelength steps. Given by::
int(round((wavemax-wavemin)/delta_wave))
Args:
:obj:`tuple`: Three 1D `numpy.ndarray`_ providing the bins to use
when constructing a histogram of the spec2d files. The elements
are :math:`(x,y,\lambda)`.
"""
# TODO :: The HRIFU might have 25 slits with 13 being the reference
xbins = np.arange(1 + 21) - 11.0 - 0.5 # 21 is for 21 slices, and 11 is the reference slit
ybins = np.linspace(np.min(minmax[:, 0]), np.max(minmax[:, 1]), 1+slitlength) - 0.5
spec_bins = np.arange(1+num_wave) - 0.5
return xbins, ybins, spec_bins
[docs] def pypeit_file_keys(self):
"""
Define the list of keys to be output into a standard ``PypeIt`` file.
Returns:
:obj:`list`: The list of keywords in the relevant
:class:`~pypeit.metadata.PypeItMetaData` instance to print to the
:ref:`pypeit_file`.
"""
return super().pypeit_file_keys() + ['filter']