def __init__(self):
# Get it started
super(GeminiGNIRSSpectrograph, self).__init__()
self.spectrograph = 'gemini_gnirs'
self.telescope = telescopes.GeminiNTelescopePar()
self.camera = 'GNIRS'
self.numhead = 2
self.detector = [
# Detector 1
pypeitpar.DetectorPar(
dataext=1,
specaxis=0,
specflip=True,
spatflip=True,
xgap=0.,
ygap=0.,
ysize=1.,
platescale=0.15,
darkcurr=0.15,
saturation=150000.,
nonlinear=0.71,
numamplifiers=1,
gain=13.5,
ronoise=7.0,
datasec='[:,:]', #'[1:1024,1:1022]',
oscansec='[:,:]', #'[1:1024,1:1022]'
)
]
class GeminiGMOSNSpectrograph(GeminiGMOSSpectrograph):
"""
Child to handle Gemini/GMOS-N instrument
"""
telescope = telescopes.GeminiNTelescopePar()
camera = 'GMOS-N'
header_name = 'GMOS-N'
def __init__(self):
# Get it started
super(GeminiGNIRSSpectrograph, self).__init__()
self.spectrograph = 'gemini_gnirs'
self.telescope = telescopes.GeminiNTelescopePar()
self.camera = 'GNIRS'
self.dispname = None # TODO We need a model for setting setup specific parameters in spectrograph
self.numhead = 2
class GeminiGNIRSSpectrograph(spectrograph.Spectrograph):
"""
Child to handle Gemini/GNIRS specific code
"""
ndet = 1
name = 'gemini_gnirs'
camera = 'GNIRS'
header_name = 'GNIRS'
telescope = telescopes.GeminiNTelescopePar()
pypeline = 'Echelle'
supported = True
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=0.15,
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)
@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
# 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 one object 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'] = 'TelFit_MaunaKea_3100_26100_R20000.fits'
return par
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
par['calibrations']['slitedges']['left_right_pca'] = True
par['calibrations']['slitedges']['pca_order'] = 3
# Wavelengths
par['calibrations']['wavelengths'][
'rms_threshold'] = 1.0 # Might be grating dependent..
par['calibrations']['wavelengths']['sigdetect'] = [
4.0, 5.0, 5.0, 5.0, 5.0, 5.0
] #5.0
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'] = [1, 3, 3, 3, 3, 3]
# Reidentification parameters
par['calibrations']['wavelengths']['method'] = 'reidentify'
par['calibrations']['wavelengths']['cc_thresh'] = 0.6
par['calibrations']['wavelengths'][
'reid_arxiv'] = 'gemini_gnirs.fits'
par['calibrations']['wavelengths']['ech_fix_format'] = True
# 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
]
par['calibrations']['tilts']['sig_neigh'] = 5.0
par['calibrations']['tilts']['nfwhm_neigh'] = 2.0
# 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'][
'left_right_pca'] = True # Actually we need a parameter to disable PCA entirely
par['calibrations']['slitedges']['sync_predict'] = 'nearest'
# Wavelengths
par['calibrations']['wavelengths'][
'rms_threshold'] = 1.0 # Might be grating dependent..
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, 3, 3, 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'
par['calibrations']['wavelengths']['ech_fix_format'] = True
# 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]
par['calibrations']['tilts']['sig_neigh'] = 5.0
par['calibrations']['tilts']['nfwhm_neigh'] = 2.0
else:
msgs.error('Unrecognized GNIRS dispname')
return par
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['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')
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
else:
msgs.error("Not ready for this compound meta")
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']
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']
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)
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')
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):
Master 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
