"""
Module for NTT EFOSC2
.. 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.spectrographs import spectrograph
from pypeit.images import detector_container
from IPython import embed
[docs]class NTTEFOSC2Spectrograph(spectrograph.Spectrograph):
"""
Child of Spectrograph to handle NTT/EFOSC2 specific code
"""
ndet = 1 # Because each detector is written to a separate FITS file
telescope = telescopes.NTTTelescopePar()
name = 'ntt_efosc2'
header_name = 'EFOSC'
camera = 'EFOSC2'
url = 'https://www.eso.org/sci/facilities/lasilla/instruments/efosc.html'
supported = True
comment = 'The ESO Faint Object Spectrograph and Camera version 2'
[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': ['binning', 'datasec'], 'dark': ['binning', 'datasec']}
[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', 'decker', 'binning', 'datasec']
[docs] 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
NOT/EFOSC2. 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:
binning = self.get_meta_value(self.get_headarr(hdu), 'binning')
datasec = np.atleast_1d(self.get_meta_value(self.get_headarr(hdu), 'datasec'))
oscansec = np.atleast_1d(self.get_meta_value(self.get_headarr(hdu), 'oscansec'))
# Manual: https://www.eso.org/sci/facilities/lasilla/instruments/efosc/doc/manual/EFOSC2manual_v4.2.pdf
# Instrument paper: http://articles.adsabs.harvard.edu/pdf/1984Msngr..38....9B
detector_dict = dict(
binning = binning,
det = 1, # only one detector
dataext = 0,
specaxis = 0,
specflip = False,
spatflip = False,
platescale = 0.12, # Manual 2.2
darkcurr = 0.0, # e-/pixel/hour
saturation = 65535, # Maual Table 8
nonlinear = 0.80,
mincounts = -1e10,
numamplifiers = 1,
gain = np.atleast_1d(0.91), # See fits header ['HIERARCH ESO DET OUT1 GAIN']
ronoise = np.atleast_1d(10.0), # manual page 108
datasec = datasec,
oscansec = oscansec,
#suffix = '_Thor',
)
return detector_container.DetectorContainer(**detector_dict)
[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'
# Adjustments to slit and tilts for NIR
par['calibrations']['traceframe']['process']['use_darkimage'] = False
par['calibrations']['pixelflatframe']['process']['use_darkimage'] = False
par['calibrations']['illumflatframe']['process']['use_darkimage'] = False
# Ignore PCA
par['calibrations']['slitedges']['sync_predict'] = 'nearest'
#par['calibrations']['slitedges']['rm_slits'] = '1:500:120' # remove the fake slit due to bad pixels
#edge parameters
par['calibrations']['slitedges']['edge_thresh'] = 75.
# Tilt parameters
par['calibrations']['tilts']['tracethresh'] = 25.0
par['calibrations']['tilts']['spat_order'] = 3
par['calibrations']['tilts']['spec_order'] = 4
# Image processing
# The overscan region might cause oversubtraction of the background, set it to False
#par['scienceframe']['process']['use_overscan'] = False
# 1D wavelength solution
par['calibrations']['wavelengths']['method'] = 'full_template'
par['calibrations']['wavelengths']['lamps'] = ['HeI', 'ArI']
par['calibrations']['wavelengths']['rms_thresh_frac_fwhm'] = 0.07
par['calibrations']['wavelengths']['sigdetect'] = 10.0
par['calibrations']['wavelengths']['fwhm'] = 4.0
par['calibrations']['wavelengths']['n_final'] = 4
# Flats
par['calibrations']['flatfield']['tweak_slits_thresh'] = 0.90
par['calibrations']['flatfield']['tweak_slits_maxfrac'] = 0.10
# Extraction
par['reduce']['skysub']['bspline_spacing'] = 0.8
par['reduce']['skysub']['no_poly'] = True
par['reduce']['skysub']['bspline_spacing'] = 0.6
par['reduce']['skysub']['joint_fit'] = False
par['reduce']['skysub']['global_sky_std'] = False
par['reduce']['extraction']['sn_gauss'] = 4.0
par['reduce']['skysub']['sky_sigrej'] = 5.0
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.
"""
# Start with instrument wide
par = super().config_specific_par(scifile, inp_par=inp_par)
# Wavelength calibrations
if self.get_meta_value(scifile, 'dispname') == 'Gr#6':
par['calibrations']['wavelengths']['reid_arxiv'] = 'ntt_efosc2_Gr6.fits'
elif self.get_meta_value(scifile, 'dispname') == 'Gr#5':
par['calibrations']['wavelengths']['reid_arxiv'] = 'ntt_efosc2_Gr5.fits'
# Fringes are affecting this Grism significantly, skip flat fielding
par['scienceframe']['process']['use_pixelflat'] = False
par['scienceframe']['process']['use_illumflat'] = False
par['scienceframe']['process']['use_specillum'] = False
elif self.get_meta_value(scifile, 'dispname') == 'Gr#4':
par['calibrations']['wavelengths']['reid_arxiv'] = 'ntt_efosc2_Gr4.fits'
return par
[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')
|(fitstbl['target'] == 'DARK'))
if ftype in ['pixelflat', 'trace', 'illumflat']:
# Flats and trace frames are typed together
return good_exp & ((fitstbl['target'] == 'FLAT')
| (fitstbl['target'] == 'SKY,FLAT')
| (fitstbl['target'] == 'DOME'))
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'))
msgs.warn('Cannot determine if frames are of type {0}.'.format(ftype))
return np.zeros(len(fitstbl), dtype=bool)
[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.
"""
# Call the base-class method to generate the empty bpm
bpm_img = super().bpm(filename, det, shape=shape, msbias=msbias)
msgs.info("Using hard-coded BPM for NTT EFOSC2")
binning = self.get_meta_value(filename, 'binning')
binspatial = int(binning[0])
binspec = int(binning[2])
bpm_img[int(232/binspec):, int(362/binspatial):int(366/binspatial)] = 1
bpm_img[int(340/binspec):, int(1292/binspatial)] = 1
#bpm_img[int(2050/binspec):, :] = 1
return bpm_img