def __init__(self):
# Get it started
super(VLTXShooterSpectrograph, self).__init__()
self.spectrograph = 'vlt_xshooter_base'
self.telescope = telescopes.VLTTelescopePar()
def __init__(self):
# Get it started
super(VLTFORSSpectrograph, self).__init__()
self.spectrograph = 'vlt_fors_base'
self.telescope = telescopes.VLTTelescopePar()
class VLTSINFONISpectrograph(spectrograph.Spectrograph):
"""
Child to handle VLT/SINFONI specific code
"""
ndet = 1
name = 'vlt_sinfoni'
telescope = telescopes.VLTTelescopePar()
camera = 'SINFONI'
header_name = 'SINFONI'
supported = True
comment = 'Gratings tested: K'
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=0,
specflip=True,
spatflip=False,
platescale=0.0125,
darkcurr=0.15,
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(2.42),
ronoise=np.atleast_1d(7.0),
datasec=np.atleast_1d('[:,:]'),
oscansec=None, #np.atleast_1d('[:,:]')
)
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()
# Wavelengths
# 1D wavelength solution
par['calibrations']['wavelengths'][
'rms_threshold'] = 0.30 #0.20 # Might be grating dependent..
par['calibrations']['wavelengths']['sigdetect'] = 5.0
par['calibrations']['wavelengths']['fwhm'] = 5.0
par['calibrations']['wavelengths']['n_final'] = 4
par['calibrations']['wavelengths']['lamps'] = ['OH_FIRE_Echelle']
#par['calibrations']['wavelengths']['nonlinear_counts'] = self.detector[0]['nonlinear'] * self.detector[0]['saturation']
#par['calibrations']['wavelengths']['method'] = 'holy-grail'
par['calibrations']['wavelengths']['method'] = 'full_template'
par['calibrations']['wavelengths']['reid_arxiv'] = 'vlt_sinfoni_K.fits'
# Reidentification parameters
par['calibrations']['slitedges']['edge_thresh'] = 50.
par['calibrations']['slitedges']['sync_predict'] = 'nearest'
par['calibrations']['slitedges'][
'rm_slits'] = '1:1024:983' # Remove the center slit that is not illuminated
# Tilts parameters
par['calibrations']['tilts']['tracethresh'] = 5.0
# Set the default exposure time ranges for the frame typing
par['calibrations']['standardframe']['exprng'] = [None, 20]
par['calibrations']['arcframe']['exprng'] = [20, None]
par['calibrations']['darkframe']['exprng'] = [20, None]
par['scienceframe']['exprng'] = [20, None]
# TODO: We need to implement dark subtraction for the arcframe and
# tiltframe. Currently the pypeit file won't let me do this.
par['calibrations']['arcframe']['process']['sigclip'] = 20.0
#par['calibrations']['arcframe']['process']['combine'] = 'median'
par['calibrations']['arcframe']['process']['mask_cr'] = True
par['calibrations']['tiltframe']['process']['sigclip'] = 20.0
#par['calibrations']['tiltframe']['process']['combine'] = 'median'
par['calibrations']['tiltframe']['process']['mask_cr'] = True
par['calibrations']['skyframe']['process']['sigclip'] = 20.0
#par['calibrations']['skyframe']['process']['combine'] = 'median'
par['calibrations']['skyframe']['process']['mask_cr'] = True
# Flats
turn_off = dict(use_biasimage=False,
use_overscan=False,
use_darkimage=False)
par.reset_all_processimages_par(**turn_off)
# # Require dark images to be subtracted from the flat images used for tracing, pixelflats, and illumflats
# par['calibrations']['pixelflatframe']['process']['use_darkimage'] = True
# par['calibrations']['illumflatframe']['process']['use_darkimage'] = True
# par['calibrations']['traceframe']['process']['use_darkimage'] = True
# TODO: `mask_cr` now defaults to True for darks. Should this be turned off?
# Extraction
par['reduce']['skysub']['bspline_spacing'] = 0.9
par['reduce']['extraction']['sn_gauss'] = 5.0
par['reduce']['extraction'][
'model_full_slit'] = True # local sky subtraction operates on entire slit
# Object finding
par['reduce']['findobj']['find_fwhm'] = 10
par['reduce']['findobj']['skip_second_find'] = True
# Sky subtraction
par['reduce']['skysub'][
'global_sky_std'] = False # Do not perform global sky subtraction for standard stars
# Flexure
par['flexure']['spec_method'] = 'skip'
par['scienceframe']['process']['sigclip'] = 20.0
par['scienceframe']['process']['satpix'] = 'nothing'
# Set the default exposure time ranges for the frame typing
par['calibrations']['standardframe']['exprng'] = [None, 20]
par['calibrations']['arcframe']['exprng'] = [20, None]
par['calibrations']['darkframe']['exprng'] = [20, None]
par['scienceframe']['exprng'] = [20, None]
# Sensitivity function parameters
par['sensfunc']['algorithm'] = 'IR'
par['sensfunc']['polyorder'] = 7
par['sensfunc']['IR'][
'telgridfile'] = 'TelFit_Paranal_NIR_9800_25000_R25000.fits'
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',
required_ftypes=[
'science', 'standard'
]) # Need to convert to : separated
self.meta['dec'] = dict(ext=0,
card='DEC',
required_ftypes=['science', 'standard'])
self.meta['target'] = dict(ext=0, card='OBJECT')
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='HIERARCH ESO TEL AIRM START',
required_ftypes=['science', 'standard'])
# Extras for config and frametyping
self.meta['decker'] = dict(ext=0, card='HIERARCH ESO INS OPTI1 NAME')
self.meta['filter1'] = dict(ext=0, card='HIERARCH ESO INS FILT1 NAME')
self.meta['dispname'] = dict(ext=0, card='HIERARCH ESO INS GRAT1 NAME')
self.meta['idname'] = dict(ext=0, card='HIERARCH ESO OCS DET IMGNAME')
self.meta['instrument'] = dict(ext=0, card='INSTRUME')
# self.meta['idname'] = dict(ext=0, card='HIERARCH ESO DPR CATG')
# Dithering
self.meta['dither'] = dict(ext=0,
card='HIERARCH ESO SEQ CUMOFFSETY',
required_ftypes=['science', 'standard'])
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 == 'decker':
try: # Science
decker = headarr[0]['HIERARCH ESO INS SLIT NAME']
except KeyError: # Standard!
try:
decker = headarr[0]['HIERARCH ESO SEQ SPEC TARG']
except KeyError:
return None
return decker
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', 'filter1']
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`.
"""
pypeit_keys = super().pypeit_file_keys()
# TODO: Why are these added here? See
# pypeit.metadata.PypeItMetaData.set_pypeit_cols
pypeit_keys += ['calib', 'comb_id', 'bkg_id']
return pypeit_keys
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)
# TODO: Allow for 'sky' frame type, for now include sky in
# 'science' category
if ftype == 'science':
return good_exp & ((fitstbl['idname'] == 'SINFONI_IFS_OBS')
| (fitstbl['target'] == 'STD,TELLURIC')
| (fitstbl['target'] == 'SKY,STD'))
if ftype == 'standard':
return good_exp & ((fitstbl['target'] == 'STD') |
(fitstbl['target'] == 'SKY,STD'))
#if ftype == 'bias':
# return good_exp & (fitstbl['target'] == 'BIAS')
if ftype == 'dark':
return good_exp & (fitstbl['target'] == 'DARK')
if ftype in ['pixelflat', 'trace']:
# Flats and trace frames are typed together
return good_exp & (fitstbl['target'] == 'FLAT,LAMP')
#if ftype == 'pinhole':
# # Don't type pinhole
# return np.zeros(len(fitstbl), dtype=bool)
if ftype in ['arc', 'tilt']:
return good_exp & ((fitstbl['target'] == 'WAVE,LAMP') |
(fitstbl['idname'] == 'SINFONI_IFS_OBS') |
(fitstbl['idname'] == 'SINFONI_IFS_SKY'))
# Putting this in now in anticipation of the sky class
if ftype in ['sky']:
return good_exp & (fitstbl['idname'] == 'SINFONI_IFS_SKY')
msgs.warn('Cannot determine if frames are of type {0}.'.format(ftype))
return np.zeros(len(fitstbl), dtype=bool)
def lamps(self, fitstbl, status):
"""
Check the lamp status.
Args:
fitstbl (`astropy.table.Table`_):
The table with the fits header meta data.
status (:obj:`str`):
The status to check. Can be ``'off'``, ``'arcs'``, or
``'dome'``.
Returns:
`numpy.ndarray`_: A boolean array selecting fits files that meet
the selected lamp status.
Raises:
ValueError:
Raised if the status is not one of the valid options.
"""
if status == 'off':
# Check if all are off
return np.all(np.array(
[fitstbl[k] == 0 for k in fitstbl.keys() if 'lampstat' in k]),
axis=0)
if status == 'arcs':
# Check if any arc lamps are on
arc_lamp_stat = ['lampstat{0:02d}'.format(i) for i in range(1, 6)]
return np.any(np.array([
fitstbl[k] == 1 for k in fitstbl.keys() if k in arc_lamp_stat
]),
axis=0)
if status == 'dome':
return fitstbl['lampstat01'] == '1'
raise ValueError('No implementation for status = {0}'.format(status))
class VLTXShooterSpectrograph(spectrograph.Spectrograph):
"""
Child to handle VLT/XSHOOTER specific code
"""
ndet = 1
telescope = telescopes.VLTTelescopePar()
pypeline = 'Echelle'
header_name = 'XSHOOTER'
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',
required_ftypes=[
'science', 'standard'
]) # Need to convert to : separated
self.meta['dec'] = dict(ext=0,
card='DEC',
required_ftypes=['science', 'standard'])
self.meta['target'] = dict(ext=0, card='OBJECT')
self.meta['binning'] = dict(card=None, compound=True)
self.meta['mjd'] = dict(ext=0, card='MJD-OBS')
self.meta['exptime'] = dict(ext=0, card='EXPTIME')
self.meta['airmass'] = dict(ext=0,
card='HIERARCH ESO TEL AIRM START',
required_ftypes=['science', 'standard'])
# Extras for config and frametyping
self.meta['dispname'] = dict(ext=0, card=None, default='default')
self.meta['idname'] = dict(ext=0, card='HIERARCH ESO DPR CATG')
self.meta['arm'] = dict(ext=0, card='HIERARCH ESO SEQ ARM')
self.meta['instrument'] = dict(ext=0, card='INSTRUME')
# Dithering -- Not required for redux
self.meta['dither'] = dict(
ext=0, card='HIERARCH ESO SEQ CUMOFF Y', required=False
) # This header card is *not* always present in science/standard frames
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 == 'binning':
if 'HIERARCH ESO DET WIN1 BINX' in headarr[0]:
binspatial = headarr[0]['HIERARCH ESO DET WIN1 BINX']
else:
binspatial = 1
if 'HIERARCH ESO DET WIN1 BINY' in headarr[0]:
binspec = headarr[0]['HIERARCH ESO DET WIN1 BINY']
else:
binspec = 1
return parse.binning2string(binspec, binspatial)
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 ['arm']
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() + ['dither']
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)
# TODO: Allow for 'sky' frame type, for now include sky in
# 'science' category
if ftype == 'science':
return good_exp & ((fitstbl['idname'] == 'SCIENCE')
| (fitstbl['target'] == 'STD,TELLURIC')
| (fitstbl['target'] == 'STD,SKY'))
if ftype == 'standard':
return good_exp & (fitstbl['target'] == 'STD,FLUX')
if ftype == 'bias':
return good_exp & (fitstbl['target'] == 'BIAS')
if ftype == 'dark':
return good_exp & (fitstbl['target'] == 'DARK')
if ftype in ['pixelflat', 'trace', 'illumflat']:
# Flats and trace frames are typed together
return good_exp & ((fitstbl['target'] == 'LAMP,DFLAT')
| (fitstbl['target'] == 'LAMP,QFLAT')
| (fitstbl['target'] == 'LAMP,FLAT'))
if ftype == 'pinhole':
# Don't type pinhole
return np.zeros(len(fitstbl), dtype=bool)
if ftype in ['arc', 'tilt']:
return good_exp & (fitstbl['target'] == 'LAMP,WAVE')
msgs.warn('Cannot determine if frames are of type {0}.'.format(ftype))
return np.zeros(len(fitstbl), dtype=bool)
class VLTFORSSpectrograph(spectrograph.Spectrograph):
"""
Child of Spectrograph to handle VLT/FORS specific code
Parent for FORS1 and FORS2
"""
ndet = 1 # Because each detector is written to a separate FITS file
telescope = telescopes.VLTTelescopePar()
@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()
# Always correct for flexure, starting with default parameters
par['flexure']['spec_method'] = 'boxcar'
# Median overscan
# IF YOU CHANGE THIS, YOU WILL NEED TO DEAL WITH THE OVERSCAN GOING ALONG ROWS
for key in par['calibrations'].keys():
if 'frame' in key:
par['calibrations'][key]['process']['overscan_method'] = 'median'
# Adjustments to slit and tilts for NIR
par['calibrations']['slitedges']['edge_thresh'] = 50.
par['calibrations']['slitedges']['fit_order'] = 3
par['calibrations']['slitedges']['max_shift_adj'] = 0.5
# Tilt parameters
par['calibrations']['tilts']['tracethresh'] = 25.0
par['calibrations']['tilts']['spat_order'] = 3
par['calibrations']['tilts']['spec_order'] = 4
# 1D wavelength solution
par['calibrations']['wavelengths']['lamps'] = ['HeI', 'ArI'] # Grating dependent
par['calibrations']['wavelengths']['rms_threshold'] = 0.25
par['calibrations']['wavelengths']['sigdetect'] = 10.0
par['calibrations']['wavelengths']['fwhm'] = 4.0 # Good for 2x binning
par['calibrations']['wavelengths']['n_final'] = 4
# Flats
par['calibrations']['flatfield']['tweak_slits_thresh'] = 0.90
par['calibrations']['flatfield']['tweak_slits_maxfrac'] = 0.10
# Sensitivity function parameters
par['sensfunc']['algorithm'] = 'IR'
par['sensfunc']['polyorder'] = 8
par['sensfunc']['IR']['telgridfile'] = 'TelFit_Paranal_NIR_9800_25000_R25000.fits'
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', required_ftypes=['science', 'standard']) # Need to convert to : separated
self.meta['dec'] = dict(ext=0, card='DEC', required_ftypes=['science', 'standard'])
self.meta['target'] = dict(ext=0, card='OBJECT')
self.meta['binning'] = dict(card=None, compound=True)
self.meta['mjd'] = dict(ext=0, card='MJD-OBS')
self.meta['exptime'] = dict(ext=0, card='EXPTIME')
self.meta['airmass'] = dict(ext=0, card='HIERARCH ESO TEL AIRM START', required_ftypes=['science', 'standard'])
#
self.meta['decker'] = dict(card=None, compound=True, required_ftypes=['science', 'standard'])
# Extras for config and frametyping
self.meta['dispname'] = dict(ext=0, card='HIERARCH ESO INS GRIS1 NAME', required_ftypes=['science', 'standard'])
self.meta['dispangle'] = dict(ext=0, card='HIERARCH ESO INS GRIS1 WLEN', rtol=2.0, required_ftypes=['science', 'standard'])
self.meta['idname'] = dict(ext=0, card='HIERARCH ESO DPR CATG')
self.meta['detector'] = dict(ext=0, card='EXTNAME')
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 == 'binning':
binspatial = headarr[0]['HIERARCH ESO DET WIN1 BINX']
binspec = headarr[0]['HIERARCH ESO DET WIN1 BINY']
binning = parse.binning2string(binspec, binspatial)
return binning
elif meta_key == 'decker':
try: # Science
decker = headarr[0]['HIERARCH ESO INS SLIT NAME']
except KeyError: # Standard!
try:
decker = headarr[0]['HIERARCH ESO SEQ SPEC TARG']
except KeyError:
return None
return decker
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 []
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)
# TODO: Allow for 'sky' frame type, for now include sky in
# 'science' category
if ftype == 'science':
return good_exp & ((fitstbl['idname'] == 'SCIENCE')
| (fitstbl['target'] == 'STD,TELLURIC')
| (fitstbl['target'] == 'STD,SKY'))
if ftype == 'standard':
return good_exp & ((fitstbl['target'] == 'STD,FLUX')
| (fitstbl['target'] == 'STD'))
if ftype == 'bias':
return good_exp & (fitstbl['target'] == 'BIAS')
if ftype == 'dark':
return good_exp & (fitstbl['target'] == 'DARK')
if ftype in ['pixelflat', 'trace', 'illumflat']:
# Flats and trace frames are typed together
return good_exp & ((fitstbl['target'] == 'LAMP,DFLAT')
| (fitstbl['target'] == 'LAMP,QFLAT')
| (fitstbl['target'] == 'FLAT,LAMP')
| (fitstbl['target'] == 'LAMP,FLAT'))
if ftype == 'pinhole':
# Don't type pinhole
return np.zeros(len(fitstbl), dtype=bool)
if ftype in ['arc', 'tilt']:
return good_exp & ((fitstbl['target'] == 'LAMP,WAVE')
| (fitstbl['target'] == 'WAVE,LAMP'))
msgs.warn('Cannot determine if frames are of type {0}.'.format(ftype))
return np.zeros(len(fitstbl), dtype=bool)