Source code for pypeit.spectrographs.vlt_fors
"""
Module for VLT FORS (1 and 2)
.. include:: ../include/links.rst
"""
import numpy as np
from pypeit import msgs
from pypeit import telescopes
from pypeit.core import parse
from pypeit.core import framematch
from pypeit.core import meta
from pypeit.spectrographs import spectrograph
from pypeit.images import detector_container
from astropy.coordinates import SkyCoord
from astropy import units
from astropy.io import fits
from IPython import embed
[docs]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()
url = 'https://www.eso.org/sci/facilities/paranal/instruments/fors.html'
[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()
# 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_thresh_frac_fwhm'] = 0.07
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'] = 5
par['sensfunc']['IR']['telgridfile'] = 'TellPCA_3000_26000_R10000.fits'
return par
[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', 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')
[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 == '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':
mode = headarr[0]['HIERARCH ESO INS MODE']
if mode in ['LSS', 'MOS']:
try: # Science
return headarr[0]['HIERARCH ESO INS SLIT NAME']
except KeyError: # Standard!
try:
return headarr[0]['HIERARCH ESO SEQ SPEC TARG']
except KeyError:
return headarr[0]['HIERARCH ESO INS MOS CHECKSUM']
elif mode == 'IMG':
# This is for the bias frames
return None
else:
msgs.error(f"PypeIt does not currently support VLT/FORS2 '{mode}' data reduction.")
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 []
[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)
# 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)
[docs]class VLTFORS2Spectrograph(VLTFORSSpectrograph):
"""
Child to handle VLT/FORS2 specific code
"""
name = 'vlt_fors2'
camera = 'FORS2'
header_name = 'FORS2'
supported = True
comment = '300I, 300V gratings. Supports LSS and MOS mode only.'
[docs] def get_detector_par(self, det, hdu=None):
"""
Return metadata for the selected detector.
Args:
det (:obj:`int`):
1-indexed detector number. ESO writes each of the two detectors
to separate files. When ``hdu`` is provided, this is ignored
and instead the chip is determined by the header parameter
"EXTNAME". If ``hdu`` is None (for automatically generated
documentation only), this can be used to set the chip (1 or 2)
that is returned.
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'
chip = 'CHIP1' if det == 1 else 'CHIP2'
else:
# Binning
# TODO: Could this be detector dependent??
binning = self.get_meta_value(self.get_headarr(hdu), 'binning')
chip = self.get_meta_value(self.get_headarr(hdu), 'detector')
# These numbers are from the ESO FORS2 user manual at: 0
# http://www.eso.org/sci/facilities/paranal/instruments/fors/doc/VLT-MAN-ESO-13100-1543_P01.1.pdf
# They are for the MIT CCD (which is the default detector) for the high-gain, 100 khZ readout mode used for
# spectroscopy. The other readout modes are not yet implemented. The E2V detector is not yet supported!!
# CHIP1
detector_dict1 = dict(
binning = binning,
det = 1,
dataext = 0,
specaxis = 1,
specflip = False,
spatflip = False,
platescale = 0.126, # average between order 11 & 30, see manual
darkcurr = 2.1, # e-/pixel/hour
saturation = 2.0e5, # I think saturation may never be a problem here since there are many DITs
nonlinear = 0.80,
mincounts = -1e10,
numamplifiers = 1,
gain = np.atleast_1d(0.70),
ronoise = np.atleast_1d(2.9), # High gain
datasec = np.atleast_1d('[11:2059,:]'), # For 1x binning, I think
#oscansec=np.atleast_1d('[2062:,:]'),
oscansec=np.atleast_1d('[1:10,:]'), # Overscan has artifacts so use pre-scan
)
# CHIP2
detector_dict2 = dict(
binning = binning,
det = 1, # ESO writes these to separate FITS images!!
dataext = 0,
specaxis = 1,
specflip = False,
spatflip = False,
platescale = 0.126, # average between order 11 & 30, see manual
darkcurr = 1.4, # e-/pixel/hour
saturation = 2.0e5, # I think saturation may never be a problem here since there are many DITs
nonlinear = 0.80,
mincounts = -1e10,
numamplifiers = 1,
gain = np.atleast_1d(0.70),
ronoise = np.atleast_1d(3.15), # High gain
datasec=np.atleast_1d('[11:2059,:]'),
oscansec=np.atleast_1d('[2062:,:]'), # Pre-scan has artifacts, so use overscan
#datasec=np.atleast_1d('[20:,0:2048]'),
#oscansec=np.atleast_1d('[4:20,4:2044]'),
)
# Finish
if chip == 'CHIP1':
return detector_container.DetectorContainer(**detector_dict1)
elif chip == 'CHIP2':
return detector_container.DetectorContainer(**detector_dict2)
else:
msgs.error(f'Unknown chip: {chip}!')
[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.
"""
# Start with instrument wide
par = super().config_specific_par(scifile, inp_par=inp_par)
# TODO: Should we allow the user to override these?
#detector = self.get_meta_value(scifile, 'detector')
#self.set_detector(detector)
# Wavelengths
#par['calibrations']['wavelengths']['nonlinear_counts'] = self.detector[0]['nonlinear'] * self.detector[0]['saturation']
if self.get_meta_value(scifile, 'dispname') == 'GRIS_300I':
par['calibrations']['wavelengths']['reid_arxiv'] = 'vlt_fors2_300I.fits'
par['calibrations']['wavelengths']['method'] = 'full_template'
elif self.get_meta_value(scifile, 'dispname') == 'GRIS_300V':
par['calibrations']['wavelengths']['reid_arxiv'] = 'vlt_fors2_300V.fits'
par['calibrations']['wavelengths']['method'] = 'full_template'
elif self.get_meta_value(scifile, 'dispname') == 'GRIS_600z':
par['calibrations']['wavelengths']['lamps'] = ['OH_NIRES']
par['calibrations']['wavelengths']['method'] = 'holy-grail'
# Since we are using the sky to fit the wavelengths don't correct for flexure
par['flexure']['spec_method'] = 'skip'
#par['reduce']['skysub']['bspline_spacing'] = 0.6
if 'lSlit' in self.get_meta_value(scifile, 'decker') or 'LSS' in self.get_meta_value(scifile, 'decker'):
par['calibrations']['slitedges']['sync_predict'] = 'nearest'
return par
[docs] def config_independent_frames(self):
"""
Define frame types that are independent of the fully defined
instrument configuration.
This method returns a dictionary where the keys of the dictionary are
the list of configuration-independent frame types. The value of each
dictionary element can be set to one or more metadata keys that can
be used to assign each frame type to a given configuration group. See
:func:`~pypeit.metadata.PypeItMetaData.set_configurations` and how it
interprets the dictionary values, which can be None.
Returns:
:obj:`dict`: Dictionary where the keys are the frame types that
are configuration-independent and the values are the metadata
keywords that can be used to assign the frames to a configuration
group.
"""
return {'bias': 'detector', 'dark': 'detector'}
[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 ['dispname', 'dispangle', 'decker', 'detector']
[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 ['HIERARCH ESO INS GRIS1 NAME', 'HIERARCH ESO INS GRIS1 WLEN',
'HIERARCH ESO INS SLIT NAME', 'HIERARCH ESO SEQ SPEC TARG']
# TODO -- Convert this into get_comb_group()
[docs] def parse_dither_pattern(self, file_list, ext=None):
"""
Parse headers from a file list to determine the dither pattern.
Parameters
----------
file_list (list of strings):
List of files for which dither pattern is desired
ext (int, optional):
Extension containing the relevant header for these files. Default=None. If None, code uses
self.primary_hdrext
Returns
-------
dither_pattern, dither_id, offset_arcsec
dither_pattern (str `numpy.ndarray`_):
Array of dither pattern names
dither_id (str `numpy.ndarray`_):
Array of dither pattern IDs
offset_arc (float `numpy.ndarray`_):
Array of dither pattern offsets
"""
nfiles = len(file_list)
offset_arcsec = np.zeros(nfiles)
dither_pattern = None
dither_id = None
for ifile, file in enumerate(file_list):
hdr = fits.getheader(file, self.primary_hdrext if ext is None else ext)
try:
ra, dec = meta.convert_radec(self.get_meta_value(hdr, 'ra', no_fussing=True),
self.get_meta_value(hdr, 'dec', no_fussing=True))
except:
msgs.warn('Encounter invalid value of your coordinates. Give zeros for both RA and DEC. Check that this does not cause problems with the offsets')
ra, dec = 0.0, 0.0
if ifile == 0:
coord_ref = SkyCoord(ra*units.deg, dec*units.deg)
offset_arcsec[ifile] = 0.0
# ESOs position angle appears to be the negative of the canonical astronomical convention
posang_ref = -(hdr['HIERARCH ESO INS SLIT POSANG']*units.deg)
posang_ref_rad = posang_ref.to('radian').value
# Unit vector pointing in direction of slit PA
u_hat_slit = np.array([np.sin(posang_ref), np.cos(posang_ref)]) # [u_hat_ra, u_hat_dec]
else:
coord_this = SkyCoord(ra*units.deg, dec*units.deg)
posang_this = coord_ref.position_angle(coord_this).to('deg')
separation = coord_ref.separation(coord_this).to('arcsec').value
ra_off, dec_off = coord_ref.spherical_offsets_to(coord_this)
u_hat_this = np.array([ra_off.to('arcsec').value/separation, dec_off.to('arcsec').value/separation])
dot_product = np.dot(u_hat_slit, u_hat_this)
if not np.isclose(np.abs(dot_product),1.0, atol=1e-2):
msgs.error('The slit appears misaligned with the angle between the coordinates: dot_product={:7.5f}'.format(dot_product) + msgs.newline() +
'The position angle in the headers {:5.3f} differs from that computed from the coordinates {:5.3f}'.format(posang_this, posang_ref))
offset_arcsec[ifile] = separation*np.sign(dot_product)
# dither_id.append(hdr['FRAMEID'])
# offset_arcsec[ifile] = hdr['YOFFSET']
return dither_pattern, dither_id, offset_arcsec