"""
Module for LBT/MODS specific methods.
.. include:: ../include/links.rst
"""
import numpy as np
from astropy.io import fits
from pypeit import msgs
from pypeit import telescopes
from pypeit import utils
from pypeit import io
from pypeit.core import framematch
from pypeit.par import pypeitpar
from pypeit.spectrographs import spectrograph
from pypeit.core import parse
from pypeit.images.detector_container import DetectorContainer
# TODO: FW: test MODS1B and MODS2B
[docs]
class LBTMODSSpectrograph(spectrograph.Spectrograph):
"""
Child to handle Shane/Kast specific code
"""
ndet = 1
telescope = telescopes.LBTTelescopePar()
url = 'https://scienceops.lbto.org/mods/'
# def __init__(self):
# super().__init__()
# self.timeunit = 'isot'
[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()
par.reset_all_processimages_par(use_biasimage=False, use_overscan=True, overscan_method='odd_even')
# Scienceimage default parameters
# Set the default exposure time ranges for the frame typing
par['calibrations']['biasframe']['exprng'] = [None, 0.001]
par['calibrations']['darkframe']['exprng'] = [999999, None] # No dark frames
par['calibrations']['pinholeframe']['exprng'] = [999999, None] # No pinhole frames
par['calibrations']['pixelflatframe']['exprng'] = [0, None]
par['calibrations']['slitless_pixflatframe']['exprng'] = [0, None]
par['calibrations']['traceframe']['exprng'] = [0, None]
par['calibrations']['arcframe']['exprng'] = [None, None]
par['calibrations']['standardframe']['exprng'] = [1, 200]
par['scienceframe']['exprng'] = [1, None]
# Do not sigmaclip the arc frames for better Arc and better wavecalib
par['calibrations']['arcframe']['process']['clip'] = False
# Do not sigmaclip the tilt frames
par['calibrations']['tiltframe']['process']['clip'] = False
return par
[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.
"""
# decker is not included because standards are usually taken with a 5" slit and arc using 0.8" slit
return ['instrument', 'dichroic', 'dispname', 'binning' ]
[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)
if ftype in ['science']:
return good_exp & (fitstbl['idname'] == 'OBJECT') & (fitstbl['ra'] != 'none') \
& (fitstbl['dispname'] != 'Flat')
if ftype in ['standard']:
return good_exp & (fitstbl['idname'] == 'STD') & (fitstbl['ra'] != 'none') \
& (fitstbl['dispname'] != 'Flat')
if ftype == 'bias':
return good_exp & (fitstbl['idname'] == 'BIAS')
if ftype in ['pixelflat', 'trace', 'illumflat']:
# Flats and trace frames are typed together
return good_exp & (fitstbl['idname'] == 'FLAT') & (fitstbl['decker'] != 'Imaging')
if ftype in ['slitless_pixflat']:
# Slitless Pixel Flats
return good_exp & (fitstbl['idname'] == 'FLAT') & (fitstbl['decker'] == 'Imaging') & (fitstbl['dispname'] != 'Flat')
if ftype in ['pinhole', 'dark']:
# Don't type pinhole or dark frames
return np.zeros(len(fitstbl), dtype=bool)
if ftype in ['arc', 'tilt']:
return good_exp & (fitstbl['idname'] == 'COMP') & (fitstbl['dispname'] != 'Flat')
msgs.warn('Cannot determine if frames are of type {0}.'.format(ftype))
return np.zeros(len(fitstbl), dtype=bool)
[docs]
def get_rawimage(self, raw_file, det):
"""
Read raw images and generate a few other bits and pieces
that are key for image processing.
Parameters
----------
raw_file : :obj:`str`
File to read
det : :obj:`int`
1-indexed detector to read
Returns
-------
detector_par : :class:`pypeit.images.detector_container.DetectorContainer`
Detector metadata parameters.
raw_img : `numpy.ndarray`_
Raw image for this detector.
hdu : `astropy.io.fits.HDUList`_
Opened fits file
exptime : :obj:`float`
Exposure time read from the file header
rawdatasec_img : `numpy.ndarray`_
Data (Science) section of the detector as provided by setting the
(1-indexed) number of the amplifier used to read each detector
pixel. Pixels unassociated with any amplifier are set to 0.
oscansec_img : `numpy.ndarray`_
Overscan section of the detector as provided by setting the
(1-indexed) number of the amplifier used to read each detector
pixel. Pixels unassociated with any amplifier are set to 0.
"""
fil = utils.find_single_file(f'{raw_file}*', required=True)
# Read
msgs.info(f'Reading LBT/MODS file: {fil}')
hdu = io.fits_open(fil)
head = hdu[0].header
# TODO These parameters should probably be stored in the detector par
# Number of amplifiers (could pull from DetectorPar but this avoids needing the spectrograph, e.g. view_fits)
detector_par = self.get_detector_par(det if det is not None else 1, hdu=hdu)
numamp = detector_par['numamplifiers']
# change the way the array size is determined, by replacing DETSIZE with a string that uses NAXIS1(X), NAXIS2(Y).
# [x1:x2,y1:y2] -> [1:NAXIS1,1:NAXIS2]
# This definition is the same for both raw and processed (overscan-subtracted and trimmed) images.
# NAXIS1, NAXIS2 values already incorporate the binning; there is no need to use CCDXBIN,CCDYBIN here.
# get the x and y dimensions of the image ...
naxis1, naxis2 = head['NAXIS1'], head['NAXIS2']
# get the x and y binning factors...
xbin, ybin = head['CCDXBIN'], head['CCDYBIN']
# Use the value of NAXIS1 to determine whether the image has been processed or not.
# Processed images will have NAXIS1 = 8192 (unbinned) or 4096 (xbin=2)
# Raw images will have NAXIS1 = 8288 (unbinned) or 4144 (xbin=2)
if (naxis1*xbin)==8288:
proc = False
elif (naxis1*xbin)==8192:
proc = True
# allocate output array...
# For the processed images, was getting an error in bspline/utilc.py l:180 solution arrays, datatype must be float64
# so changed typecast from *1.0 to astype(float). OPK 01/2025
#array = hdu[0].data.T * 1.0 ## Convert to float in order to get it processed with procimg.py
array = hdu[0].data.T.astype(float) ## Convert to float in order to get it processed with procimg.py
#
#
rawdatasec_img = np.zeros_like(array, dtype=int)
oscansec_img = np.zeros_like(array, dtype=int)
# oscansec_img is not needed for the proc subclasses, however get_rawimage returns oscansec_img so we'll keep it for now.
#### if not processed: ####
# Is naxis1*xbin equal 8288? If Y, then not processed.
# In this case, set datasize using the keyword value DETSIZE and keep xbin, ybin
#
#if (naxis1*xbin)==8288:
if not proc:
cbias = 48 # number of columns in the prescan at either end
datasize = head['DETSIZE'] # Unbinned size of detector full array
_, nx_full, _, ny_full = np.array(parse.load_sections(datasize, fmt_iraf=False)).flatten()
# Determine the size of the output array...
nx, ny = int(nx_full / xbin), int(ny_full / ybin)
nbias1 = 48
nbias2 = 8240
## allocate datasec and oscansec to the image
# apm 1
rawdatasec_img[int(nbias1/xbin):int(nx/2), :int(ny/2)] = 1
oscansec_img[1:int(nbias1/xbin), :int(ny/2)] = 1 # exclude the first pixel since it always has problem
# apm 2
rawdatasec_img[int(nx/2):int(nbias2/xbin), :int(ny/2)] = 2
oscansec_img[int(nbias2/xbin):nx-1, :int(ny/2)] = 2 # exclude the last pixel since it always has problem
# apm 3
rawdatasec_img[int(nbias1/xbin):int(nx/2), int(ny/2):] = 3
oscansec_img[1:int(nbias1/xbin), int(ny/2):] = 3 # exclude the first pixel since it always has problem
# apm 4
rawdatasec_img[int(nx/2):int(nbias2/xbin), int(ny/2):] = 4
oscansec_img[int(nbias2/xbin):nx-1, int(ny/2):] = 4 # exclude the last pixel since it always has problem
#### else if processed: ####
# For processed images, set datasize using naxis1, naxis2 and don't carry along xbin, ybin.
#elif naxis1==8192 or naxis1==4096:
#if (naxis1*xbin)==8192:
elif proc:
datasize = "[1:"+str(naxis1)+",1:"+str(naxis2)+"]" # Size of image
_, nx_full, _, ny_full = np.array(parse.load_sections(datasize, fmt_iraf=False)).flatten()
# Determine the size of the output array...
nx, ny = int(nx_full), int(ny_full)
## allocate datasec and oscansec to the image
# apm 1
rawdatasec_img[ :int(nx/2), :int(ny/2)] = 1
# apm 2
rawdatasec_img[int(nx/2): , :int(ny/2)] = 2
# apm 3
rawdatasec_img[ :int(nx/2),int(ny/2): ] = 3
# apm 4
rawdatasec_img[int(nx/2): ,int(ny/2): ] = 4
# Need the exposure time
exptime = hdu[self.meta['exptime']['ext']].header[self.meta['exptime']['card']]
# Return, transposing array back to orient the overscan properly
return detector_par,np.flipud(array), hdu, exptime, np.flipud(rawdatasec_img), np.flipud(oscansec_img)
[docs]
class LBTMODS1RSpectrograph(LBTMODSSpectrograph):
"""
Child to handle LBT/MODS1R specific code
"""
name = 'lbt_mods1r'
camera = 'MODS1R'
header_name = 'MODS1R'
supported = True
comment = 'MODS-I red spectrometer'
[docs]
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
binning = '1,1' if hdu is None \
else f"{hdu[0].header['CCDXBIN']},{hdu[0].header['CCDYBIN']}"
# Detector 1
detector_dict = dict(
binning= binning,
det=1,
dataext = 0,
specaxis = 0,
specflip = False,
spatflip = False,
platescale = 0.123,
darkcurr = 0.4, # e-/pixel/hour
saturation = 65535.,
nonlinear = 0.99,
mincounts = -1e10,
numamplifiers = 4,
gain = np.atleast_1d([2.38,2.50,2.46,2.81]),
ronoise = np.atleast_1d([3.78,4.04,4.74,4.14])
# TODO: The raw image reader sets these up by hand
# datasec = np.atleast_1d('[:,:]'),
# oscansec = np.atleast_1d('[:,:]')
)
return 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()
par['flexure']['spec_method'] = 'boxcar'
# 1D wavelength solution
par['calibrations']['wavelengths']['sigdetect'] = 5.
par['calibrations']['wavelengths']['rms_thresh_frac_fwhm'] = 0.09
par['calibrations']['wavelengths']['fwhm'] = 10.
# Red: Dual uses all lamps, Red-Only does not use Hg(Ar) lamp
par['calibrations']['wavelengths']['lamps'] = ['HgI_MODS','ArI_MODS','NeI_MODS','KrI_MODS','XeI_MODS']
par['calibrations']['wavelengths']['n_first'] = 3
par['calibrations']['wavelengths']['match_toler'] = 2.5
# slit
par['calibrations']['slitedges']['sync_predict'] = 'nearest'
par['calibrations']['slitedges']['edge_thresh'] = 50.
# Set wave tilts order
par['calibrations']['tilts']['spat_order'] = 5
par['calibrations']['tilts']['spec_order'] = 5
par['calibrations']['tilts']['maxdev_tracefit'] = 0.02
par['calibrations']['tilts']['maxdev2d'] = 0.02
# Sensitivity function defaults
par['sensfunc']['algorithm'] = 'IR'
par['sensfunc']['IR']['telgridfile'] = 'TellPCA_3000_26000_R10000.fits'
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.
"""
par = super().config_specific_par(scifile, inp_par=inp_par)
if self.get_meta_value(scifile, 'dispname') == 'G670L':
par['calibrations']['wavelengths']['method'] = 'full_template'
par['calibrations']['wavelengths']['reid_arxiv'] = 'lbt_mods1r_red.fits'
return par
[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 MODS1R")
# TODO: Fix this
# Get the binning
hdu = io.fits_open(filename)
header = hdu[0].header
xbin, ybin = header['CCDXBIN'], header['CCDYBIN']
hdu.close()
# Apply the mask
bpm_img[6278//xbin:6289//xbin, 1544//ybin:1634//ybin] = 1
bpm_img[4202//xbin:4204//xbin, 1474//ybin:1544//ybin] = 1
bpm_img[3551//xbin:3558//xbin, 2391//ybin:2903//ybin] = 1
bpm_img[3553//xbin:3558//xbin, 1454//ybin:1544//ybin] = 1
bpm_img[5650//xbin, 1280//ybin:1544//ybin] = 1
bpm_img[4780//xbin, 1406//ybin:1536//ybin] = 1
bpm_img[3554//xbin, 1544//ybin:2392//ybin] = 1
bpm_img[163//xbin, 1544//ybin:1963//ybin] = 1
return bpm_img
[docs]
class LBTMODS1BSpectrograph(LBTMODSSpectrograph):
"""
Child to handle LBT/MODS1B specific code
"""
name = 'lbt_mods1b'
camera = 'MODS1B'
header_name = 'MODS1B'
supported = True
comment = 'MODS-I blue spectrometer'
[docs]
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 = '1,1' if hdu is None \
else f"{hdu[0].header['CCDXBIN']},{hdu[0].header['CCDYBIN']}"
# Detector 1
detector_dict = dict(
binning= binning,
det=1,
dataext = 0,
specaxis = 0,
specflip = True,
spatflip = False,
platescale = 0.120,
darkcurr = 0.5, # e-/pixel/hour
saturation = 65535.,
nonlinear = 0.99,
mincounts = -1e10,
numamplifiers = 4,
gain = np.atleast_1d([2.55,1.91,2.09,2.02]),
ronoise = np.atleast_1d([3.41,2.93,2.92,2.76])
# TODO: The raw image reader sets these up by hand
# datasec = np.atleast_1d('[:,:]'),
# oscansec = np.atleast_1d('[:,:]')
)
return 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()
par['flexure']['spec_method'] = 'boxcar'
# 1D wavelength solution
par['calibrations']['wavelengths']['sigdetect'] = 10.
par['calibrations']['wavelengths']['rms_thresh_frac_fwhm'] = 0.09
# Blue: Dual uses all five lamps; Blue-Only does not use Ne lamp
par['calibrations']['wavelengths']['lamps'] = ['HgI_MODS','ArI_MODS','NeI_MODS','KrI_MODS','XeI_MODS']
# slit
par['calibrations']['slitedges']['sync_predict'] = 'nearest'
par['calibrations']['slitedges']['edge_thresh'] = 50.
# Set wave tilts order
par['calibrations']['tilts']['spat_order'] = 5
par['calibrations']['tilts']['spec_order'] = 5
par['calibrations']['tilts']['maxdev_tracefit'] = 0.02
par['calibrations']['tilts']['maxdev2d'] = 0.02
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.
.. todo::
Document the changes made!
Args:
scifile (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 paramter set
adjusted for configuration specific parameter values.
"""
# Start with instrument wide
par = super().config_specific_par(scifile, inp_par=inp_par)
#par = super(LBTMODS1BSpectrograph, self).config_specific_par(scifile, inp_par=inp_par)
if self.get_meta_value(scifile, 'dispname') == 'G400L':
par['calibrations']['wavelengths']['method'] = 'full_template'
par['calibrations']['wavelengths']['reid_arxiv'] = 'lbt_mods1b_blue.fits'
return par
[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 MODS1B")
# Get the binning
hdu = io.fits_open(filename)
header = hdu[0].header
xbin, ybin = header['CCDXBIN'], header['CCDYBIN']
hdu.close()
# Apply the mask
bpm_img[6390//xbin:6392//xbin, 1262//ybin:1545//ybin] = 1
bpm_img[3064//xbin, 1437//ybin:1937//ybin] = 1
bpm_img[6490//xbin, 1161//ybin:1545//ybin] = 1
bpm_img[7306//xbin, 783//ybin:1531//ybin] = 1
return bpm_img
[docs]
class LBTMODS2RSpectrograph(LBTMODSSpectrograph):
"""
Child to handle LBT/MODS2R specific code
"""
name = 'lbt_mods2r'
camera = 'MODS2R'
header_name = 'MODS2R'
supported = True
comment = 'MODS-II red spectrometer'
[docs]
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
binning = '1,1' if hdu is None \
else f"{hdu[0].header['CCDXBIN']},{hdu[0].header['CCDYBIN']}"
# Detector 1
detector_dict = dict(
binning= binning,
det=1,
dataext = 0,
specaxis = 0,
specflip = False,
spatflip = False,
platescale = 0.123,
darkcurr = 0.4, # e-/pixel/hour
saturation = 65535.,
nonlinear = 0.99,
mincounts = -1e10,
numamplifiers = 4,
gain = np.atleast_1d([1.70,1.67,1.66,1.66]),
ronoise = np.atleast_1d([2.95,2.65,2.78,2.87])
# TODO: The raw image reader sets these up by hand
# datasec = np.atleast_1d('[:,:]'),
# oscansec = np.atleast_1d('[:,:]')
)
return 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()
par['flexure']['spec_method'] = 'boxcar'
# 1D wavelength solution
par['calibrations']['wavelengths']['sigdetect'] = 5.
par['calibrations']['wavelengths']['rms_thresh_frac_fwhm'] = 0.22
par['calibrations']['wavelengths']['fwhm'] = 10.
# Red: Dual uses all five lamps; Red-Only does not use Hg(Ar) lamp
par['calibrations']['wavelengths']['lamps'] = ['HgI_MODS','ArI_MODS','NeI_MODS','KrI_MODS','XeI_MODS']
#par['calibrations']['wavelengths']['lamps'] = ['OH_MODS']
par['calibrations']['wavelengths']['n_first'] = 3
par['calibrations']['wavelengths']['match_toler'] = 2.5
# slit
par['calibrations']['slitedges']['sync_predict'] = 'nearest'
par['calibrations']['slitedges']['edge_thresh'] = 50.
# Set wave tilts order
par['calibrations']['tilts']['spat_order'] = 5
par['calibrations']['tilts']['spec_order'] = 5
par['calibrations']['tilts']['maxdev_tracefit'] = 0.02
par['calibrations']['tilts']['maxdev2d'] = 0.02
# Sensitivity function defaults
par['sensfunc']['algorithm'] = 'IR'
par['sensfunc']['IR']['telgridfile'] = 'TellPCA_3000_26000_R10000.fits'
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.
"""
par = super().config_specific_par(scifile, inp_par=inp_par)
if self.get_meta_value(scifile, 'dispname') == 'G670L':
par['calibrations']['wavelengths']['method'] = 'full_template'
par['calibrations']['wavelengths']['reid_arxiv'] = 'lbt_mods2r_red.fits'
return par
[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 MODS2R")
# Get the binning
hdu = io.fits_open(filename)
header = hdu[0].header
xbin, ybin = header['CCDXBIN'], header['CCDYBIN']
hdu.close()
# Apply the mask
bpm_img[6148//xbin:6150//xbin, 1333//ybin:1544//ybin] = 1
bpm_img[6207//xbin:6209//xbin, 1396//ybin:1544//ybin] = 1
bpm_img[6101//xbin, 1342//ybin:1544//ybin] = 1
bpm_img[6159//xbin, 1399//ybin:1544//ybin] = 1
bpm_img[6189//xbin, 1316//ybin:1544//ybin] = 1
bpm_img[7552//xbin, 1544//ybin:2771//ybin] = 1
bpm_img[7504//xbin, 1544//ybin:2774//ybin] = 1
bpm_img[4203//xbin, 0:1544//ybin] = 1
bpm_img[4155//xbin, 0:1544//ybin] = 1
return bpm_img
[docs]
class LBTMODS2BSpectrograph(LBTMODSSpectrograph):
"""
Child to handle LBT/MODS2B specific code
"""
name = 'lbt_mods2b'
camera = 'MODS2B'
header_name = 'MODS2B'
supported = True
comment = 'MODS-II blue spectrometer'
[docs]
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
binning = '1,1' if hdu is None \
else f"{hdu[0].header['CCDXBIN']},{hdu[0].header['CCDYBIN']}"
# Detector 1
detector_dict = dict(
binning= binning,
det=1,
dataext = 0,
specaxis = 0,
specflip = True,
spatflip = False,
platescale = 0.120,
darkcurr = 0.5, # e-/pixel/hour
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]),
# TODO: The raw image reader sets these up by hand
# datasec = np.atleast_1d('[:,:]'),
# oscansec = np.atleast_1d('[:,:]')
)
return 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()
par['flexure']['spec_method'] = 'boxcar'
# 1D wavelength solution
par['calibrations']['wavelengths']['sigdetect'] = 10.
par['calibrations']['wavelengths']['rms_thresh_frac_fwhm'] = 0.09
# Blue: Dual uses all five lamps; Blue-Only does not use Ne lamp
par['calibrations']['wavelengths']['lamps'] = ['HgI_MODS','ArI_MODS','NeI_MODS','KrI_MODS','XeI_MODS']
# slit
par['calibrations']['slitedges']['sync_predict'] = 'nearest'
par['calibrations']['slitedges']['edge_thresh'] = 50.
# Set wave tilts order
par['calibrations']['tilts']['spat_order'] = 5
par['calibrations']['tilts']['spec_order'] = 5
par['calibrations']['tilts']['maxdev_tracefit'] = 0.02
par['calibrations']['tilts']['maxdev2d'] = 0.02
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.
.. todo::
Document the changes made!
Args:
scifile (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 paramter set
adjusted for configuration specific parameter values.
"""
# Start with instrument wide
par = super().config_specific_par(scifile, inp_par=inp_par)
if self.get_meta_value(scifile, 'dispname') == 'G400L':
par['calibrations']['wavelengths']['method'] = 'full_template'
par['calibrations']['wavelengths']['reid_arxiv'] = 'lbt_mods1b_blue.fits'
return par
[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 MODS2B")
# Get the binning
hdu = io.fits_open(filename)
header = hdu[0].header
xbin, ybin = header['CCDXBIN'], header['CCDYBIN']
hdu.close()
# Apply the mask
bpm_img[5176//xbin:5179//xbin, 549//ybin:1544//ybin] = 1
bpm_img[5176//xbin:5179//xbin, 1544//ybin:1628//ybin] = 1
bpm_img[4408//xbin:4410//xbin, 1544//ybin:2661//ybin] = 1
bpm_img[4408//xbin:4411//xbin, 2660//ybin:2663//ybin] = 1
bpm_img[2495//xbin:2499//xbin, 1326//ybin:1544//ybin] = 1
bpm_img[2391//xbin:2394//xbin, 1048//ybin:1051//ybin] = 1
bpm_img[1974//xbin:1980//xbin, 806//ybin:1544//ybin] = 1
bpm_img[1975//xbin:1980//xbin, 1544//ybin:1607//ybin] = 1
bpm_img[1972//xbin:1974//xbin, 1587//ybin:1589//ybin] = 1
bpm_img[274//xbin:278//xbin, 1341//ybin:1544//ybin] = 1
bpm_img[275//xbin:278//xbin, 1251//ybin:1341//ybin] = 1
bpm_img[276//xbin:278//xbin, 1242//ybin:1251//ybin] = 1
bpm_img[274//xbin:277//xbin, 1544//ybin:3066//ybin] = 1
bpm_img[2392//xbin, 1051//ybin:1544//ybin] = 1
bpm_img[275//xbin, 1220//ybin:1242//ybin] = 1
return bpm_img
##### The classes below are for pre-processed grating spectra #####
[docs]
class LBTMODS1RSpectrographProc(LBTMODSSpectrograph):
"""
Child to handle LBT/MODS1R specific code for pre-processed images
"""
name = 'lbt_mods1r_proc'
camera = 'MODS1R'
header_name = 'MODS1R'
supported = True
comment = 'MODS-I red spectrometer pre-processed'
[docs]
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
binning = '1,1' if hdu is None \
else f"{hdu[0].header['CCDXBIN']},{hdu[0].header['CCDYBIN']}"
# Detector 1
detector_dict = dict(
binning= binning,
det=1,
dataext = 0,
specaxis = 0,
#specflip = False,
# While _raw_ MODS Red channel spectra require specflip=False, the spectral
# axis of the *otf spectra pre-processed by modsCCDRed has already been flipped.
specflip = True,
spatflip = False,
platescale = 0.123,
darkcurr = 0.4, # e-/pixel/hour
saturation = 65535.,
nonlinear = 0.99,
mincounts = -1e10,
numamplifiers = 4,
# Because we're reading in the flipped-about-vertical spectrum, the quadrants mapping needs to be changed [1,2,3,4] -> [2,1,4,3]
gain = np.atleast_1d([2.50,2.38,2.81,2.46]),
ronoise = np.atleast_1d([4.04,3.78,4.14,4.74])
#gain = np.atleast_1d([2.38,2.50,2.46,2.81]),
#ronoise = np.atleast_1d([3.78,4.04,4.74,4.14])
)
return 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()
# Processing steps
par.reset_all_processimages_par(use_illumflat=False, use_biasimage=False, use_overscan=False,
use_pixelflat=False, use_specillum=False, apply_gain=False, trim=False)
par['flexure']['spec_method'] = 'boxcar'
# 1D wavelength solution
par['calibrations']['wavelengths']['sigdetect'] = 5.
par['calibrations']['wavelengths']['rms_thresh_frac_fwhm'] = 0.09
par['calibrations']['wavelengths']['fwhm'] = 10.
# Red: Dual uses all five lamps; Red-Only does not use Hg(Ar)
par['calibrations']['wavelengths']['lamps'] = ['HgI_MODS','ArI_MODS','NeI_MODS','KrI_MODS','XeI_MODS']
par['calibrations']['wavelengths']['n_first'] = 3
par['calibrations']['wavelengths']['match_toler'] = 2.5
# slit
par['calibrations']['slitedges']['sync_predict'] = 'nearest'
par['calibrations']['slitedges']['edge_thresh'] = 50.
# Set wave tilts order
par['calibrations']['tilts']['spat_order'] = 5
par['calibrations']['tilts']['spec_order'] = 5
par['calibrations']['tilts']['maxdev_tracefit'] = 0.02
par['calibrations']['tilts']['maxdev2d'] = 0.02
# Sensitivity function defaults
par['sensfunc']['algorithm'] = 'IR'
par['sensfunc']['IR']['telgridfile'] = 'TellPCA_3000_26000_R10000.fits'
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.
"""
par = super().config_specific_par(scifile, inp_par=inp_par)
if self.get_meta_value(scifile, 'dispname') == 'G670L':
par['calibrations']['wavelengths']['method'] = 'full_template'
par['calibrations']['wavelengths']['reid_arxiv'] = 'lbt_mods1r_red.fits'
return par
# The processed images have been bad-pixel corrected already, so it is not necessary to
# generate a bad pixel mask, bpm_img, with function bpm.
[docs]
class LBTMODS1BSpectrographProc(LBTMODSSpectrograph):
"""
Child to handle LBT/MODS1B specific code for pre-processed images
"""
name = 'lbt_mods1b_proc'
camera = 'MODS1B'
header_name = 'MODS1B'
supported = True
comment = 'MODS-I blue spectrometer pre-processed'
[docs]
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 = '1,1' if hdu is None \
else f"{hdu[0].header['CCDXBIN']},{hdu[0].header['CCDYBIN']}"
# Detector 1
detector_dict = dict(
binning= binning,
det=1,
dataext = 0,
specaxis = 0,
specflip = True,
spatflip = False,
platescale = 0.120,
darkcurr = 0.5, # e-/pixel/hour
saturation = 65535.,
nonlinear = 0.99,
mincounts = -1e10,
numamplifiers = 4,
gain = np.atleast_1d([2.55,1.91,2.09,2.02]),
ronoise = np.atleast_1d([3.41,2.93,2.92,2.76])
)
return 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()
par.reset_all_processimages_par(use_illumflat=False, use_biasimage=False, use_overscan=False,
use_pixelflat=False, use_specillum=False, apply_gain=False, trim=False)
par['flexure']['spec_method'] = 'boxcar'
# 1D wavelength solution
par['calibrations']['wavelengths']['sigdetect'] = 10.
par['calibrations']['wavelengths']['rms_thresh_frac_fwhm'] = 0.09
# Blue: Dual uses all five lamps; Blue-Only does not use Ne lamp
par['calibrations']['wavelengths']['lamps'] = ['HgI_MODS','ArI_MODS','NeI_MODS','KrI_MODS','XeI_MODS']
# slit
par['calibrations']['slitedges']['sync_predict'] = 'nearest'
par['calibrations']['slitedges']['edge_thresh'] = 50.
# Set wave tilts order
par['calibrations']['tilts']['spat_order'] = 5
par['calibrations']['tilts']['spec_order'] = 5
par['calibrations']['tilts']['maxdev_tracefit'] = 0.02
par['calibrations']['tilts']['maxdev2d'] = 0.02
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.
.. todo::
Document the changes made!
Args:
scifile (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 paramter set
adjusted for configuration specific parameter values.
"""
# Start with instrument wide
#par = super(LBTMODS1BSpectrograph, self).config_specific_par(scifile, inp_par=inp_par)
par = super().config_specific_par(scifile, inp_par=inp_par)
if self.get_meta_value(scifile, 'dispname') == 'G400L':
par['calibrations']['wavelengths']['method'] = 'full_template'
par['calibrations']['wavelengths']['reid_arxiv'] = 'lbt_mods1b_blue.fits'
return par
# The processed images have been bad-pixel corrected already, so it is not necessary to
# generate a bad pixel mask, bpm_img, with function bpm.
[docs]
class LBTMODS2RSpectrographProc(LBTMODSSpectrograph):
"""
Child to handle LBT/MODS2R specific code for pre-processed images
"""
name = 'lbt_mods2r_proc'
camera = 'MODS2R'
header_name = 'MODS2R'
supported = True
comment = 'MODS-II red spectrometer pre-processed'
[docs]
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
binning = '1,1' if hdu is None \
else f"{hdu[0].header['CCDXBIN']},{hdu[0].header['CCDYBIN']}"
# Detector 1
detector_dict = dict(
binning= binning,
det=1,
dataext = 0,
specaxis = 0,
#specflip = False,
# While _raw_ MODS Red channel spectra require specflip=False, the spectral
# axis of the *otf spectra pre-processed by modsCCDRed has already been flipped.
specflip = True,
spatflip = False,
platescale = 0.123,
darkcurr = 0.4, # e-/pixel/hour
saturation = 65535.,
nonlinear = 0.99,
mincounts = -1e10,
numamplifiers = 4,
# Because we're reading in the flipped-about-vertical spectrum, the quadrants mapping needs to be changed [1,2,3,4] -> [2,1,4,3]
gain = np.atleast_1d([1.67,1.70,1.66,1.66]),
ronoise = np.atleast_1d([2.65,2.95,2.87,2.78])
#gain = np.atleast_1d([1.70,1.67,1.66,1.66]),
#ronoise = np.atleast_1d([2.95,2.65,2.78,2.87])
)
return 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()
par.reset_all_processimages_par(use_illumflat=False, use_biasimage=False, use_overscan=False,
use_pixelflat=False, use_specillum=False, apply_gain=False, trim=False)
par['flexure']['spec_method'] = 'boxcar'
# 1D wavelength solution
par['calibrations']['wavelengths']['sigdetect'] = 5.
par['calibrations']['wavelengths']['rms_thresh_frac_fwhm'] = 0.22
par['calibrations']['wavelengths']['fwhm'] = 10.
# Red: Dual uses all lamps, Red-Only does not use Hg(Ar) lamp
par['calibrations']['wavelengths']['lamps'] = ['HgI_MODS','ArI_MODS','NeI_MODS','KrI_MODS','XeI_MODS']
par['calibrations']['wavelengths']['n_first'] = 3
par['calibrations']['wavelengths']['match_toler'] = 2.5
# slit
par['calibrations']['slitedges']['sync_predict'] = 'nearest'
par['calibrations']['slitedges']['edge_thresh'] = 50.
# Set wave tilts order
par['calibrations']['tilts']['spat_order'] = 5
par['calibrations']['tilts']['spec_order'] = 5
par['calibrations']['tilts']['maxdev_tracefit'] = 0.02
par['calibrations']['tilts']['maxdev2d'] = 0.02
# Sensitivity function defaults
par['sensfunc']['algorithm'] = 'IR'
par['sensfunc']['IR']['telgridfile'] = 'TellPCA_3000_26000_R10000.fits'
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.
"""
par = super().config_specific_par(scifile, inp_par=inp_par)
if self.get_meta_value(scifile, 'dispname') == 'G670L':
par['calibrations']['wavelengths']['method'] = 'full_template'
par['calibrations']['wavelengths']['reid_arxiv'] = 'lbt_mods2r_red.fits'
return par
# The processed images have been bad-pixel corrected already, so it is not necessary to
# generate a bad pixel mask, bpm_img, with function bpm.
[docs]
class LBTMODS2BSpectrographProc(LBTMODSSpectrograph):
"""
Child to handle LBT/MODS2B specific code for pre-processed images
"""
name = 'lbt_mods2b_proc'
camera = 'MODS2B'
header_name = 'MODS2B'
supported = True
comment = 'MODS-II blue spectrometer pre-processed'
[docs]
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
binning = '1,1' if hdu is None \
else f"{hdu[0].header['CCDXBIN']},{hdu[0].header['CCDYBIN']}"
# Detector 1
detector_dict = dict(
binning= binning,
det=1,
dataext = 0,
specaxis = 0,
specflip = True,
spatflip = False,
platescale = 0.120,
darkcurr = 0.5, # e-/pixel/hour
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])
)
return 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()
par.reset_all_processimages_par(use_illumflat=False, use_biasimage=False, use_overscan=False,
use_pixelflat=False, use_specillum=False, apply_gain=False, trim=False)
par['flexure']['spec_method'] = 'boxcar'
# 1D wavelength solution
par['calibrations']['wavelengths']['sigdetect'] = 10.
par['calibrations']['wavelengths']['rms_thresh_frac_fwhm'] = 0.09
# Blue: Dual uses all lamps; Blue-Only does not use Ne lamp
par['calibrations']['wavelengths']['lamps'] = ['HgI_MODS','ArI_MODS','NeI_MODS','KrI_MODS','XeI_MODS']
# slit
par['calibrations']['slitedges']['sync_predict'] = 'nearest'
par['calibrations']['slitedges']['edge_thresh'] = 50.
# Set wave tilts order
par['calibrations']['tilts']['spat_order'] = 5
par['calibrations']['tilts']['spec_order'] = 5
par['calibrations']['tilts']['maxdev_tracefit'] = 0.02
par['calibrations']['tilts']['maxdev2d'] = 0.02
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.
.. todo::
Document the changes made!
Args:
scifile (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 paramter set
adjusted for configuration specific parameter values.
"""
# Start with instrument wide
#par = super(LBTMODS2BSpectrograph, self).config_specific_par(scifile, inp_par=inp_par)
par = super().config_specific_par(scifile, inp_par=inp_par)
if self.get_meta_value(scifile, 'dispname') == 'G400L':
par['calibrations']['wavelengths']['method'] = 'full_template'
par['calibrations']['wavelengths']['reid_arxiv'] = 'lbt_mods1b_blue.fits'
return par
# The processed images have been bad-pixel corrected already, so it is not necessary to
# generate a bad pixel mask, bpm_img, with function bpm.