"""
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()
# 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']['traceframe']['exprng'] = [0, None]
par['calibrations']['arcframe']['exprng'] = [None, None]
par['calibrations']['standardframe']['exprng'] = [1, 200]
par['scienceframe']['exprng'] = [200, 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 ['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', 'standard']:
return good_exp & (fitstbl['idname'] == 'OBJECT') & (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 ['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']
# get the x and y binning factors...
xbin, ybin = head['CCDXBIN'], head['CCDYBIN']
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 output array...
array = hdu[0].data.T * 1.0 ## 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)
## 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
# 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.
#par['calibrations']['wavelengths']['lamps'] = ['XeI','ArII','ArI','NeI','KrI']]
par['calibrations']['wavelengths']['lamps'] = ['ArI','NeI','KrI','XeI']
#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'] = 100.
# 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.
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/MODS1R 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
par['calibrations']['wavelengths']['lamps'] = ['XeI','KrI','ArI','HgI']
# slit
par['calibrations']['slitedges']['sync_predict'] = 'nearest'
par['calibrations']['slitedges']['edge_thresh'] = 100.
# 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)
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/MODS1R 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.
#par['calibrations']['wavelengths']['lamps'] = ['XeI','ArII','ArI','NeI','KrI']]
par['calibrations']['wavelengths']['lamps'] = ['ArI','NeI','KrI','XeI']
#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'] = 300.
# 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.
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/MODS1R 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
par['calibrations']['wavelengths']['lamps'] = ['XeI','KrI','ArI','HgI']
# slit
par['calibrations']['slitedges']['sync_predict'] = 'nearest'
par['calibrations']['slitedges']['edge_thresh'] = 100.
# 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)
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