def get_detector_par(self, hdu, det):
"""
Return metadata for the selected detector.
Args:
hdu (`astropy.io.fits.HDUList`_):
The open fits file with the raw image of interest.
det (:obj:`int`):
1-indexed detector number.
Returns:
:class:`~pypeit.images.detector_container.DetectorContainer`:
Object with the detector metadata.
"""
# Detector 1
detector_dict = dict(
binning='1,1',
det=1,
dataext=0,
specaxis=0,
specflip=False,
spatflip=False,
platescale=0.15,
darkcurr=0.01,
saturation=
320000., #32000 for low gain, I set to a higher value to keep data in K-band
nonlinear=0.875,
mincounts=-1e10,
numamplifiers=1,
gain=np.atleast_1d(3.8),
ronoise=np.atleast_1d(6.0), # SUTR readout mode with exposure~600s
datasec=np.atleast_1d('[5:2044, 900:1250]'),
oscansec=np.atleast_1d('[:5, 900:1250]'))
return detector_container.DetectorContainer(**detector_dict)
def get_detector_par(self, hdu, det):
"""
Return metadata for the selected detector.
Args:
hdu (`astropy.io.fits.HDUList`_):
The open fits file with the raw image of interest.
det (:obj:`int`):
1-indexed detector number.
Returns:
:class:`~pypeit.images.detector_container.DetectorContainer`:
Object with the detector metadata.
"""
detector_dict = dict(
det=1,
binning ='1,1', # No binning allowed
dataext = 0,
specaxis = 0,
specflip = False,
spatflip = False,
platescale = 0.193,
darkcurr = 0.8,
saturation = 100000.,
nonlinear = 1.00, # docs say linear to 90,000 but our flats are usually higher
numamplifiers = 1,
mincounts = -1e10,
gain = np.atleast_1d(5.8),
ronoise = np.atleast_1d(23.),
datasec = np.atleast_1d('[:,:]'),
oscansec = np.atleast_1d('[:,:]')
)
return detector_container.DetectorContainer(**detector_dict)
def get_detector_par(self, hdu, det):
"""
Return a DectectorContainer for the current image
Args:
hdu (`astropy.io.fits.HDUList`):
HDUList of the image of interest.
Ought to be the raw file, or else..
det (int):
Returns:
:class:`pypeit.images.detector_container.DetectorContainer`:
"""
# Detector 1
detector_dict = dict(
binning = '1,1',
det = 1,
dataext = 0,
specaxis = 1,
specflip = False,
spatflip = False,
platescale = 0.1798,
darkcurr = 0.8,
saturation = 1e9, # ADU, this is hacked for now
nonlinear = 1.00, # docs say linear to 90,000 but our flats are usually higher
numamplifiers = 1,
mincounts = -1e10,
gain = np.atleast_1d(2.15), # Taken from MOSFIRE detector webpage
ronoise = np.atleast_1d(5.8), # This is for 16 non-destructuve reads, the default readout mode
datasec = np.atleast_1d('[:,:]'),
oscansec = np.atleast_1d('[:,:]')
)
return detector_container.DetectorContainer(**detector_dict)
def get_detector_par(self, hdu, det):
"""
Return metadata for the selected detector.
Args:
hdu (`astropy.io.fits.HDUList`_):
The open fits file with the raw image of interest.
det (:obj:`int`):
1-indexed detector number.
Returns:
:class:`~pypeit.images.detector_container.DetectorContainer`:
Object with the detector metadata.
"""
# Detector 1
detector_dict = dict(
binning = '1,1',
det = 1,
dataext = 0,
specaxis = 1,
specflip = False,
spatflip = False,
platescale = 0.1798,
darkcurr = 0.8,
saturation = 1e9, # ADU, this is hacked for now
nonlinear = 1.00, # docs say linear to 90,000 but our flats are usually higher
numamplifiers = 1,
mincounts = -1e10,
gain = np.atleast_1d(2.15), # Taken from MOSFIRE detector webpage
ronoise = np.atleast_1d(5.8), # This is for 16 non-destructuve reads, the default readout mode
datasec = np.atleast_1d('[:,:]'),
oscansec = np.atleast_1d('[:,:]')
)
return detector_container.DetectorContainer(**detector_dict)
def test_io():
detector = detector_container.DetectorContainer(**def_det)
detector.to_file(data_path('tmp_detector.fits'), overwrite=True)
_new_detector = detector.from_file(data_path('tmp_detector.fits'))
os.remove(data_path('tmp_detector.fits'))
def get_detector_par(self, hdu, det):
"""
Return metadata for the selected detector.
Args:
hdu (`astropy.io.fits.HDUList`_):
The open fits file with the raw image of interest.
det (:obj:`int`):
1-indexed detector number.
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=False,
spatflip=False,
platescale=0.2012,
darkcurr=0.01,
saturation=700000., #155400.,
nonlinear=1.0,
mincounts=-1e10,
numamplifiers=1,
gain=np.atleast_1d(0.95),
ronoise=np.atleast_1d(3.14),
datasec=np.atleast_1d('[:,:]'),
oscansec=np.atleast_1d('[:,:]'))
return detector_container.DetectorContainer(**detector_dict)
def get_detector_par(self, hdu, det):
"""
Return a DectectorContainer for the current image
Args:
hdu (`astropy.io.fits.HDUList`):
HDUList of the image of interest.
Ought to be the raw file, or else..
det (int):
Returns:
:class:`pypeit.images.detector_container.DetectorContainer`:
"""
detector_dict = dict(
det=1,
binning='1,1', # No binning allowed
dataext=0,
specaxis=0,
specflip=False,
spatflip=False,
platescale=0.193,
darkcurr=0.8,
saturation=100000.,
nonlinear=
1.00, # docs say linear to 90,000 but our flats are usually higher
numamplifiers=1,
mincounts=-1e10,
gain=np.atleast_1d(5.8),
ronoise=np.atleast_1d(23.),
datasec=np.atleast_1d('[:,:]'),
oscansec=np.atleast_1d('[:,:]'))
return detector_container.DetectorContainer(**detector_dict)
def get_detector_par(self, hdu, det):
# Binning
xbin = hdu[0].header['CCDXBIN']
ybin = hdu[0].header['CCDYBIN']
binning = '{:},{:}'.format(xbin, ybin)
# Detector 1
detector_dict = dict(binning=binning,
det=1,
dataext=0,
specaxis=0,
specflip=False,
spatflip=False,
platescale=0.120,
darkcurr=0.5,
saturation=65535.,
nonlinear=0.99,
mincounts=-1e10,
numamplifiers=4,
gain=np.atleast_1d([1.99, 2.06, 1.96, 2.01]),
ronoise=np.atleast_1d([3.66, 3.62, 3.72, 3.64]),
datasec=np.atleast_1d('[:,:]'),
oscansec=np.atleast_1d('[:,:]'))
detector = detector_container.DetectorContainer(**detector_dict)
return detector
def get_detector_par(self, hdu, det):
"""
Return a DectectorContainer for the current image
Args:
hdu (`astropy.io.fits.HDUList`):
HDUList of the image of interest.
Ought to be the raw file, or else..
det (int):
Returns:
:class:`pypeit.images.detector_container.DetectorContainer`:
"""
# Detector 1
detector_dict = dict(
binning='1,1',
det=1,
dataext=0,
specaxis=0,
specflip=False,
spatflip=False,
platescale=0.15,
darkcurr=0.01,
saturation=
320000., #32000 for low gain, I set to a higher value to keep data in K-band
nonlinear=0.875,
mincounts=-1e10,
numamplifiers=1,
gain=np.atleast_1d(3.8),
ronoise=np.atleast_1d(6.0), # SUTR readout mode with exposure~600s
datasec=np.atleast_1d('[5:2044, 900:1250]'),
oscansec=np.atleast_1d('[:5, 900:1250]'))
return detector_container.DetectorContainer(**detector_dict)
def get_detector_par(self, hdu, det):
"""
Return a DectectorContainer for the current image
Args:
hdu (`astropy.io.fits.HDUList`):
HDUList of the image of interest.
Ought to be the raw file, or else..
det (int):
Returns:
:class:`pypeit.images.detector_container.DetectorContainer`:
"""
# 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('[:,:]'),#'[1:1024,1:1022]',
oscansec = np.atleast_1d('[:,:]'),#'[1:1024,1:1022]'
)
return detector_container.DetectorContainer(**detector_dict)
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)
def get_detector_par(self, det, hdu=None):
"""
Return metadata for the selected detector.
.. warning::
Many of the necessary detector parameters are read from the file
header, meaning the ``hdu`` argument is effectively **required** for
LTD/DeVeny. The optional use of ``hdu`` is only viable for
automatically generated documentation.
Args:
det (:obj:`int`):
1-indexed detector number.
hdu (`astropy.io.fits.HDUList`_, optional):
The open fits file with the raw image of interest.
Returns:
:class:`~pypeit.images.detector_container.DetectorContainer`:
Object with the detector metadata.
"""
if hdu is None:
binning = '1,1' # Most common use mode
gain = np.atleast_1d(1.52) # Hardcoded in the header
ronoise = np.atleast_1d(4.9) # Hardcoded in the header
datasec = np.atleast_1d('[5:512,53:2095]') # For 1x1 binning
oscansec = np.atleast_1d('[5:512,5:48]') # For 1x1 binning
else:
binning = self.get_meta_value(self.get_headarr(hdu), 'binning')
gain = np.atleast_1d(hdu[0].header['GAIN'])
ronoise = np.atleast_1d(hdu[0].header['RDNOISE'])
datasec = self.rotate_trimsections(hdu[0].header['TRIMSEC'],
hdu[0].header['NAXIS1'])
oscansec = self.rotate_trimsections(hdu[0].header['BIASSEC'],
hdu[0].header['NAXIS1'])
# Detector
detector_dict = dict(
binning=binning,
det=1,
dataext=0,
specaxis=1, # Native spectrum is along the x-axis
specflip=True, # DeVeny CCD has blue at the right
spatflip=False,
platescale=0.34, # Arcsec / pixel
darkcurr=4.5, # Electrons per hour
saturation=65535., # 16-bit ADC
nonlinear=0.97, # Linear to ~97% of saturation
mincounts=-1e10,
numamplifiers=1,
gain=gain, # See above
ronoise=ronoise, # See above
# Data & Overscan Sections -- Edge tracing can handle slit edges
datasec=datasec, # See above
oscansec=oscansec # See above
)
return detector_container.DetectorContainer(**detector_dict)
def get_detector_par(self, hdu, det):
# Binning
binning = self.get_meta_value(
self.get_headarr(hdu),
'binning') # Could this be detector dependent??
# Detector 1
detector_dict1 = dict(
binning=binning,
det=1,
dataext=1,
specaxis=0,
specflip=False,
spatflip=False,
platescale=0.135,
darkcurr=0.0,
saturation=65535.,
nonlinear=0.76,
mincounts=-1e10,
numamplifiers=2,
gain=np.atleast_1d([1.255, 1.18]),
ronoise=np.atleast_1d([4.64, 4.76]),
)
# Detector 2
detector_dict2 = detector_dict1.copy()
detector_dict2.update(
dict(det=2,
dataext=2,
gain=np.atleast_1d([1.191, 1.162]),
ronoise=np.atleast_1d([4.54, 4.62])))
# Instantiate
detector_dicts = [detector_dict1, detector_dict2]
detector = detector_container.DetectorContainer(**detector_dicts[det])
# Deal with number of amps
namps = hdu[0].header['NUMAMPS']
# The website does not give values for single amp per detector so we take the mean
# of the values provided
if namps == 2:
detector.numamplifiers = 1
# Long silt mode
if hdu[0].header['AMPPSIZE'] == '[1:1024,1:4096]':
idx = 0 if det == 1 else 1 # Vid1 for det=1, Vid4 for det=2
detector.gain = np.atleast_1d(detector.gain[idx])
detector.ronoise = np.atleast_1d(detector.ronoise[idx])
else:
detector.gain = np.atleast_1d(np.mean(detector.gain))
detector.ronoise = np.atleast_1d(np.mean(detector.ronoise))
elif namps == 4:
pass
else:
msgs.error("Did not see this namps coming..")
# Return
return detector
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 = 0,
specaxis = 1,
specflip = False,
spatflip = False,
platescale = 0.25,
# Dark current nominally is < 360 electrons per hours
# but the dark subtraction will effectively bring this to 0
darkcurr = 0.0,
# Saturation is 55000, but will be set to dummy value for
# now
saturation = 1e+8,
# NIR detectors are non-linear even in lower percentages
# of the full well, thus for precision measurements one
# should take into account a general non-linearity
# correction.
nonlinear = 0.80,
mincounts = -1e10,
# In fact there are 32 amplifiers, which gain and ronoise
# are extremely similar to each other, thus it will be
# mimicked as 1
numamplifiers = 1,
# The readout noise for LUCI are different for
# different readout modes. The LIR mode values will be
# commented in and the MER values will be uncommented:
gain= np.atleast_1d(2.0),
# ronoise= 10.3,
ronoise = np.atleast_1d(4.61),
datasec=np.atleast_1d('[5:2044,5:2044]'),
# For Luci the first 4 pixels on each side can
# technically be used for as a biassec. This is not
# included here.
oscansec= np.atleast_1d('[5:2044,1:4]'),
)
return detector_container.DetectorContainer(**detector_dict)
def get_detector_par(self, det: int, hdu: Optional[fits.HDUList] = None):
"""
Return metadata for the selected detector.
.. warning::
Many of the necessary detector parameters are read from the file
header, meaning the ``hdu`` argument is effectively **required** for
P200/DBSPr. The optional use of ``hdu`` is only viable for
automatically generated documentation.
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.
"""
if hdu is None:
binning = '1,1'
datasec = None
oscansec = None
else:
# TODO: Could this be detector dependent??
binning = self.get_meta_value(self.get_headarr(hdu), 'binning')
datasec = np.atleast_1d(flip_fits_slice(hdu[0].header['TSEC1']))
oscansec = np.atleast_1d(flip_fits_slice(hdu[0].header['BSEC1']))
# Detector 1
detector_dict = dict(
binning = binning,
det = 1,
dataext = 0,
specaxis = 1,
specflip = False,
spatflip = False, # check
platescale = 0.293,
darkcurr = 0.0,
saturation = 45000.,
nonlinear = 40./45.,
mincounts = -1e10, # check
numamplifiers = 1,
gain = np.atleast_1d(2.8),
ronoise = np.atleast_1d(8.5),
datasec = datasec,
oscansec = oscansec
)
return detector_container.DetectorContainer(**detector_dict)
def get_detector_par(self, hdu, det):
"""
Return metadata for the selected detector.
Args:
hdu (`astropy.io.fits.HDUList`_):
The open fits file with the raw image of interest.
det (:obj:`int`):
1-indexed detector number.
Returns:
:class:`~pypeit.images.detector_container.DetectorContainer`:
Object with the detector metadata.
"""
# Binning
binning = self.get_meta_value(self.get_headarr(hdu), 'binning')
# Detector 1
detector_dict1 = dict(
binning=binning,
det=1,
dataext=1,
specaxis=0,
specflip=False,
spatflip=False,
xgap=0.,
ygap=0.,
ysize=1.,
platescale=0.24,
darkcurr=3.0, #ToDO: To Be update
saturation=65535.,
nonlinear=0.95, #ToDO: To Be update
mincounts=-1e10,
numamplifiers=4,
gain=np.atleast_1d([1.085, 1.046, 1.042, 0.975]),
ronoise=np.atleast_1d([3.2, 3.2, 3.2, 3.2]),
)
# Detector 2
detector_dict2 = detector_dict1.copy()
detector_dict2.update(
dict(
det=2,
dataext=2,
gain=np.atleast_1d(
[1.028, 1.115, 1.047, 1.045]
), #ToDo: FW measures 1.115 for amp2 but 1.163 in IDL pipeline
ronoise=np.atleast_1d([3.6, 3.6, 3.6, 3.6])))
# Instantiate
detector_dicts = [detector_dict1, detector_dict2]
return detector_container.DetectorContainer(**detector_dicts[det])
def get_detector_par(self, hdu, det):
"""
Return a DectectorContainer for the current image
Args:
hdu (`astropy.io.fits.HDUList`):
HDUList of the image of interest.
Ought to be the raw file, or else..
det (int):
Returns:
:class:`pypeit.images.detector_container.DetectorContainer`:
"""
# Some properties of the image
head0 = hdu[0].header
binning = self.compound_meta(self.get_headarr(hdu), "binning")
numamps = head0['NVIDINP']
specflip = True if head0['AMPID1'] == 2 else False
gainmul, gainarr = head0['GAINMUL'], np.zeros(numamps)
ronarr = np.ones(numamps) * 2.7
dsecarr = np.array([''] * numamps)
for ii in range(numamps):
# Assign the gain for this amplifier
gainarr[ii] = head0["GAIN{0:1d}".format(ii + 1)] * gainmul
detector = dict(
det=det,
binning=binning,
dataext=0,
specaxis=0,
specflip=specflip,
spatflip=False,
xgap=0.,
ygap=0.,
ysize=1.,
platescale=0.147, # arcsec/pixel
darkcurr=None, # <-- TODO : Need to set this
mincounts=-1e10,
saturation=65535.,
nonlinear=0.95, # For lack of a better number!
numamplifiers=numamps,
gain=gainarr,
ronoise=ronarr, # <-- TODO : Need to set this for other setups
datasec=dsecarr.copy(), # <-- This is provided in the header
oscansec=dsecarr.copy(), # <-- This is provided in the header
)
# Return
return detector_container.DetectorContainer(**detector)
def get_detector_par(self, hdu, det):
# Binning
binning = self.get_meta_value(
self.get_headarr(hdu),
'binning') # Could this be detector dependent??
# Detector 1
detector_dict1 = dict(
binning=binning,
det=1,
dataext=1,
specaxis=0,
specflip=False,
spatflip=False,
platescale=0.135,
darkcurr=0.0,
saturation=65535.,
nonlinear=0.86,
mincounts=-1e10,
numamplifiers=2,
gain=np.atleast_1d([1.55, 1.56]),
ronoise=np.atleast_1d([3.9, 4.2]),
)
# Detector 2
detector_dict2 = detector_dict1.copy()
detector_dict2.update(
dict(det=2,
dataext=2,
gain=np.atleast_1d([1.63, 1.70]),
ronoise=np.atleast_1d([3.6, 3.6])))
# Instantiate
detector_dicts = [detector_dict1, detector_dict2]
detector = detector_container.DetectorContainer(**detector_dicts[det])
# Deal with number of amps
namps = hdu[0].header['NUMAMPS']
# The website does not give values for single amp per detector so we take the mean
# of the values provided
if namps == 2:
detector.numamplifiers = 1
detector.gain = np.atleast_1d(np.mean(detector.gain))
detector.ronoise = np.atleast_1d(np.mean(detector.ronoise))
elif namps == 4:
pass
else:
msgs.error("Did not see this namps coming..")
# Return
return detector
def get_detector_par(self, hdu, det):
"""
Return metadata for the selected detector.
Args:
hdu (`astropy.io.fits.HDUList`_):
The open fits file with the raw image of interest.
det (:obj:`int`):
1-indexed detector number.
Returns:
:class:`~pypeit.images.detector_container.DetectorContainer`:
Object with the detector metadata.
"""
# Some properties of the image
head0 = hdu[0].header
binning = self.compound_meta(self.get_headarr(hdu), "binning")
numamps = head0['NVIDINP']
specflip = True if head0['AMPID1'] == 2 else False
gainmul, gainarr = head0['GAINMUL'], np.zeros(numamps)
ronarr = np.zeros(
numamps
) # Set this to zero (determine the readout noise from the overscan regions)
dsecarr = np.array([''] * numamps)
for ii in range(numamps):
# Assign the gain for this amplifier
gainarr[ii] = head0["GAIN{0:1d}".format(ii + 1)] # * gainmul
detector = dict(
det=det,
binning=binning,
dataext=0,
specaxis=0,
specflip=specflip,
spatflip=False,
platescale=0.145728, # arcsec/pixel
darkcurr=None, # <-- TODO : Need to set this
mincounts=-1e10,
saturation=65535.,
nonlinear=0.95, # For lack of a better number!
numamplifiers=numamps,
gain=gainarr,
ronoise=ronarr,
datasec=dsecarr.copy(), # <-- This is provided in the header
oscansec=dsecarr.copy(), # <-- This is provided in the header
)
# Return
return detector_container.DetectorContainer(**detector)
def test_io():
detector = detector_container.DetectorContainer(**def_det)
detector.to_file(data_path('tmp_detector.fits'), overwrite=True)
_new_detector = detector.from_file(data_path('tmp_detector.fits'))
# Check a few attributes are equal
assert detector['dataext'] == _new_detector['dataext'], 'Bad read dataext'
assert np.array_equal(detector['gain'],
_new_detector['gain']), 'Bad read gain'
assert detector['binning'] == _new_detector['binning'], 'Bad read binning'
assert np.array_equal(detector['datasec'],
_new_detector['datasec']), 'Bad read datasec'
os.remove(data_path('tmp_detector.fits'))
def get_detector_par(self, hdu, det):
"""
Return metadata for the selected detector.
Detector data from `here
<http://www.not.iac.es/instruments/detectors/CCD14/>`__.
Args:
hdu (`astropy.io.fits.HDUList`_):
The open fits file with the raw image of interest.
det (:obj:`int`):
1-indexed detector number.
Returns:
:class:`~pypeit.images.detector_container.DetectorContainer`:
Object with the detector metadata.
"""
# http://www.not.iac.es/instruments/detectors/CCD14/
# Detector 1
detector_dict = dict(
binning=self.get_meta_value(self.get_headarr(hdu), 'binning'),
det=1,
dataext=1,
specaxis=0,
specflip=True,
spatflip=False,
xgap=0.,
ygap=0.,
ysize=1.,
platescale=0.2138,
mincounts=-1e10,
darkcurr=1.3, # e-/pix/hr
saturation=700000., # ADU
nonlinear=0.86,
datasec=np.atleast_1d('[:,{}:{}]'.format(1, 2062)), # Unbinned
oscansec=None,
numamplifiers=1,
)
# Parse datasec, oscancsec from the header
head1 = hdu[1].header
detector_dict['gain'] = np.atleast_1d(head1['GAIN']) # e-/ADU
detector_dict['ronoise'] = np.atleast_1d(head1['RDNOISE']) # e-
# Return
return detector_container.DetectorContainer(**detector_dict)
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.
"""
# Binning
# TODO: Could this be detector dependent??
binning = '1,1' if hdu is None else self.get_meta_value(
self.get_headarr(hdu), 'binning')
# Detector 1
detector_dict = dict(
binning=binning,
det=1,
dataext=0,
specaxis=1,
specflip=True,
spatflip=False,
# plate scale in arcsec/pixel
platescale=0.3,
# electrons/pixel/hour. From: http://www.lco.cl/telescopes-information/magellan/instruments/mage/the-mage-spectrograph-user-manual
darkcurr=1.00,
saturation=65535.,
# CCD is linear to better than 0.5 per cent up to digital saturation (65,536 DN including bias) in the Fast readout mode.
nonlinear=0.99,
mincounts=-1e10,
numamplifiers=1,
gain=np.atleast_1d(1.02), # depends on the readout
ronoise=np.atleast_1d(2.9), # depends on the readout
datasec=np.atleast_1d('[1:1024, 1:2048]'),
oscansec=np.atleast_1d('[1:1024, 2049:2176]'),
)
# Taken from the MASE paper: https://arxiv.org/pdf/0910.1834.pdf
#self.norders = 15
# 20-6
return detector_container.DetectorContainer(**detector_dict)
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' if hdu is None else self.get_meta_value(
self.get_headarr(hdu), 'binning'),
det=1,
dataext=0,
specaxis=1,
specflip=True,
spatflip=False,
xgap=0.,
ygap=0.,
ysize=1.,
platescale=0.273,
mincounts=-1e10,
darkcurr=0.0,
saturation=65535.,
nonlinear=0.86,
numamplifiers=4,
gain=np.atleast_1d([2.2, 2.2, 2.2, 2.2]),
ronoise=np.atleast_1d([5.0, 5.0, 5.0, 5.0]),
datasec=np.atleast_1d([
'[9:509,33:2064]', '[509:,33:2064]', '[9:509, 2065:4092',
'[509:, 2065:4092'
]),
oscansec=np.atleast_1d([
'[9:509, 1:32]', '[509:, 1:32]', '[9:509, 4098:]',
'[509:, 4098:]'
]),
)
# Return
return detector_container.DetectorContainer(**detector_dict)
def get_detector_par(self, hdu: fits.HDUList, det: int):
"""
Return metadata for the selected detector.
Args:
hdu (`astropy.io.fits.HDUList`_):
The open fits file with the raw image of interest.
det (:obj:`int`):
1-indexed detector number.
Returns:
:class:`~pypeit.images.detector_container.DetectorContainer`:
Object with the detector metadata.
"""
# Binning
binning = self.get_meta_value(
self.get_headarr(hdu),
'binning') # Could this be detector dependent??
# Detector 1
detector_dict = dict(
binning=binning,
det=1,
dataext=0,
specaxis=1,
specflip=False,
spatflip=False, # check
platescale=0.293,
darkcurr=0.0,
saturation=45000.,
nonlinear=40. / 45.,
mincounts=-1e10, # check
numamplifiers=1,
gain=np.atleast_1d(2.8),
ronoise=np.atleast_1d(8.5))
header = hdu[0].header
datasec = header['TSEC1']
oscansec = header['BSEC1']
detector_dict['datasec'] = np.atleast_1d(flip_fits_slice(datasec))
detector_dict['oscansec'] = np.atleast_1d(flip_fits_slice(oscansec))
return detector_container.DetectorContainer(**detector_dict)
def get_detector_par(self, hdu, det):
"""
Return a DectectorContainer for the current image
Args:
hdu (`astropy.io.fits.HDUList`):
HDUList of the image of interest.
Ought to be the raw file, or else..
det (int):
Returns:
:class:`pypeit.images.detector_container.DetectorContainer`:
"""
# Binning
binning = self.get_meta_value(
self.get_headarr(hdu),
'binning') # Could this be detector dependent??
# Detector 1
detector_dict = dict(
binning=binning,
det=1,
dataext=0,
specaxis=1,
specflip=True,
spatflip=False,
# plate scale in arcsec/pixel
platescale=0.3,
# electrons/pixel/hour. From: http://www.lco.cl/telescopes-information/magellan/instruments/mage/the-mage-spectrograph-user-manual
darkcurr=1.00,
saturation=65535.,
# CCD is linear to better than 0.5 per cent up to digital saturation (65,536 DN including bias) in the Fast readout mode.
nonlinear=0.99,
mincounts=-1e10,
numamplifiers=1,
gain=np.atleast_1d(1.02), # depends on the readout
ronoise=np.atleast_1d(2.9), # depends on the readout
datasec=np.atleast_1d('[1:1024, 1:2048]'),
oscansec=np.atleast_1d('[1:1024, 2049:2176]'),
)
# Taken from the MASE paper: https://arxiv.org/pdf/0910.1834.pdf
#self.norders = 15
# 20-6
return detector_container.DetectorContainer(**detector_dict)
def get_detector_par(self, hdu, det):
"""
Args:
hdu (`astropy.io.fits.HDUList`):
HDUList of the image of interest.
Ought to be the raw file, or else..
det (int):
Ignored. Must be 1
Returns:
:class:`pypeit.images.detector_container.DetectorContainer`:
"""
# Binning
binning = self.get_meta_value(
self.get_headarr(hdu),
'binning') # Could this be detector dependent??
# Detector 1
detector_dict1 = dict(
binning=binning,
det=1,
dataext=1,
specaxis=1,
specflip=False,
spatflip=False,
platescale=0.135, # TO BE UPDATED!!
darkcurr=0.0,
saturation=65535.,
nonlinear=0.76,
mincounts=-1e10,
numamplifiers=2,
gain=np.atleast_1d([1.98, 2.17]), # TAKEN FROM LOWREDUX
ronoise=np.atleast_1d([6.1, 6.3]), # TAKEN FROM LOWREDUX
)
# Instantiate
detector = detector_container.DetectorContainer(**detector_dict1)
# Deal with number of amps
if hdu[0].header['NUMAMPS'] != 2:
msgs.error("Did not see this namps coming..")
# Return
return detector
def get_detector_par(self, hdu, det):
"""
Return a DectectorContainer for the current image
Args:
hdu (`astropy.io.fits.HDUList`):
HDUList of the image of interest.
Ought to be the raw file, or else..
det (int):
Returns:
:class:`pypeit.images.detector_container.DetectorContainer`:
"""
# http://www.not.iac.es/instruments/detectors/CCD14/
# Detector 1
detector_dict = dict(
binning=self.get_meta_value(self.get_headarr(hdu), 'binning'),
det=1,
dataext=1,
specaxis=0,
specflip=True,
spatflip=False,
xgap=0.,
ygap=0.,
ysize=1.,
platescale=0.2138,
mincounts=-1e10,
darkcurr=1.3, # e-/pix/hr
saturation=700000., # ADU
nonlinear=0.86,
datasec=np.atleast_1d('[:,{}:{}]'.format(1, 2062)), # Unbinned
oscansec=None,
numamplifiers=1,
)
# Parse datasec, oscancsec from the header
head1 = hdu[1].header
detector_dict['gain'] = np.atleast_1d(head1['GAIN']) # e-/ADU
detector_dict['ronoise'] = np.atleast_1d(head1['RDNOISE']) # e-
# Return
return detector_container.DetectorContainer(**detector_dict)
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', # No binning in near-IR
det=1,
dataext=0,
specaxis=1,
specflip=False,
spatflip=False,
platescale=0.197, # average between order 11 & 30, see manual
darkcurr=0.0,
saturation=
2.0e5, # I think saturation may never be a problem here since there are many DITs
nonlinear=0.86,
mincounts=-1e10,
numamplifiers=1,
gain=np.atleast_1d(2.12), #
ronoise=np.atleast_1d(
8.0
), # ?? more precise value? #TODO the read noise is exposure time dependent and should be grabbed from header
datasec=np.atleast_1d(
'[4:2044,4:]'), # These are all unbinned pixels
# EMA: No real overscan for XSHOOTER-NIR:
# See Table 6 in http://www.eso.org/sci/facilities/paranal/instruments/xshooter/doc/VLT-MAN-ESO-14650-4942_P103v1.pdf
# The overscan region below contains only zeros
# ToDo should we just set it as empty?
# JXP says yes
#oscansec = np.atleast_1d('[4:2044,1:3]'), # These are all unbinned pixels.
)
return detector_container.DetectorContainer(**detector_dict)
def get_detector_par(self, hdu, det):
"""
Return a DectectorContainer for the current image
Args:
hdu (`astropy.io.fits.HDUList`):
HDUList of the image of interest.
Ought to be the raw file, or else..
det (int):
Returns:
:class:`pypeit.images.detector_container.DetectorContainer`:
"""
# Detector 1
detector_dict = dict(
binning=self.get_meta_value(self.get_headarr(hdu), 'binning'),
det=1,
dataext=0,
specaxis=1,
specflip=False,
spatflip=False,
platescale=0.43,
saturation=65535.,
mincounts=-1e10,
nonlinear=0.76,
numamplifiers=2,
gain=np.asarray([1.2, 1.2]),
ronoise=np.asarray([3.7, 3.7]),
xgap=0.,
ygap=0.,
ysize=1.,
darkcurr=0.0,
datasec=np.asarray([
'[:, 1:1024]', '[:, 1025:2048]'
]), # These are rows, columns on the raw frame, 1-indexed
oscansec=np.asarray(['[:, 2050:2080]', '[:, 2081:2111]']),
)
# suffix='_blue'
detector = detector_container.DetectorContainer(**detector_dict)
# Return
return detector
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.
"""
# Binning
# TODO: Could this be detector dependent??
binning = '1,1' if hdu is None else self.get_meta_value(
self.get_headarr(hdu), 'binning')
# Detector 1
detector_dict = dict(
binning=binning,
det=1,
dataext=0,
specaxis=0,
specflip=False,
spatflip=False,
platescale=0.16, # average from order 17 and order 30, see manual
darkcurr=0.0,
saturation=65535.,
nonlinear=0.86,
mincounts=-1e10,
numamplifiers=1,
gain=np.atleast_1d(
0.595
), # FITS format is flipped: PrimaryHDU (2106, 4000) w/respect to Python
ronoise=np.atleast_1d(
3.1), # raw unbinned images are (4000,2106) (spec, spat)
datasec=np.atleast_1d(
'[:,11:2058]'), # pre and oscan are in the spatial direction
oscansec=np.atleast_1d('[:,2059:2106]'),
)
return detector_container.DetectorContainer(**detector_dict)
