"""
Module for LRIS specific methods.
.. include:: ../include/links.rst
"""
import os
from IPython import embed
import numpy as np
from astropy.io import fits
from astropy import time
from astropy.coordinates import SkyCoord
from astropy import units
import linetools.utils
from pypeit import msgs
from pypeit import telescopes
from pypeit import utils
from pypeit import io
from pypeit.core import parse
from pypeit.core import framematch
from pypeit.spectrographs import spectrograph
from pypeit.spectrographs import slitmask
from pypeit.images import detector_container
from pypeit import data
[docs]class KeckLRISSpectrograph(spectrograph.Spectrograph):
"""
Child to handle Keck/LRIS specific code
"""
ndet = 2
telescope = telescopes.KeckTelescopePar()
url = 'https://www2.keck.hawaii.edu/inst/lris/'
[docs] def check_spectrograph(self, filename):
"""
Check that the selected spectrograph is the correct one for the input data.
Args:
filename (:obj:`str`): File to use when determining if the input spectrograph is the correct one.
"""
instrume = self.get_meta_value(filename, 'instrument')
if 'keck_lris_red' in self.name and instrume != 'LRIS':
msgs.error('This is not the correct spectrograph. You may want to use keck_lris_blue instead.')
elif 'keck_lris_blue' in self.name and instrume == 'LRIS':
msgs.error('This is not the correct spectrograph. You may want to use keck_lris_red instead.')
[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()
# Set wave tilts order
par['calibrations']['slitedges']['edge_thresh'] = 15.
par['calibrations']['slitedges']['fit_order'] = 3
par['calibrations']['slitedges']['sync_center'] = 'gap'
# TODO: I had to increase this from 1. to 2. to deal with
# Keck_LRIS_red/multi_1200_9000_d680_1x2/ . May need a
# different solution given that this is binned data and most of
# the data in the dev suite is unbinned.
# lris alignment boxes are typically 4 arcsec
par['calibrations']['slitedges']['minimum_slit_length_sci'] = 5.
# Remove slits that are too short
par['calibrations']['slitedges']['minimum_slit_length'] = 3.
# 1D wavelengths
par['calibrations']['wavelengths']['sigdetect'] = 10.0
par['calibrations']['wavelengths']['rms_thresh_frac_fwhm'] = 0.05 # Might be grism dependent
par['calibrations']['wavelengths']['n_first'] = 3
par['calibrations']['wavelengths']['n_final'] = 5
# 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'] = [None, 60]
par['calibrations']['traceframe']['exprng'] = [None, 60]
par['calibrations']['illumflatframe']['exprng'] = [None, 60]
par['calibrations']['standardframe']['exprng'] = [1, 61]
# Flexure
# Always correct for spectral flexure, starting with default parameters
par['flexure']['spec_method'] = 'boxcar'
# Always correct for spatial flexure on science images
# TODO -- Decide whether to make the following defaults
# May not want to do them for LongSlit
par['scienceframe']['process']['spat_flexure_correct'] = True
par['calibrations']['standardframe']['process']['spat_flexure_correct'] = True
par['scienceframe']['exprng'] = [61, None]
# If telluric is triggered
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)
# Ignore PCA if longslit
# This is a little risky as a user could put long into their maskname
# But they would then need to over-ride in their PypeIt file
if scifile is None:
msgs.error("You have not included a standard or science file in your PypeIt file to determine the configuration")
if 'long' in self.get_meta_value(scifile, 'decker'):
par['calibrations']['slitedges']['sync_predict'] = 'nearest'
# This might only be required for det=2, but we'll see..
# TODO: Why is this here and not in KeckLRISRSpectrograph???
if self.name == 'keck_lris_red':
par['calibrations']['slitedges']['edge_thresh'] = 1000.
# Wave FWHM
binning = parse.parse_binning(self.get_meta_value(scifile, 'binning'))
par['calibrations']['wavelengths']['fwhm'] = 8.0 / binning[0]
# Arc lamps list from header
par['calibrations']['wavelengths']['lamps'] = ['use_header']
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.
"""
return super().configuration_keys() + ['amp', 'binning']
[docs] def config_independent_frames(self):
"""
Define frame types that are independent of the fully defined
instrument configuration.
Bias and dark frames are considered independent of a configuration,
but the DATE-OBS keyword is used to assign each to the most-relevant
configuration frame group. See
:func:`~pypeit.metadata.PypeItMetaData.set_configurations`.
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': ['amp', 'binning', 'dateobs'], 'dark': ['amp', 'binning', 'dateobs']}
[docs] def pypeit_file_keys(self):
"""
Define the list of keys to be output into a standard PypeIt file.
Returns:
:obj:`list`: The list of keywords in the relevant
:class:`~pypeit.metadata.PypeItMetaData` instance to print to the
:ref:`pypeit_file`.
"""
return super().pypeit_file_keys() + ['hatch', 'lampstat01', 'dateobs', 'utc', 'frameno']
[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 == 'science':
return good_exp & self.lamps(fitstbl, 'off') & (fitstbl['hatch'] == 'open')
if ftype == 'standard':
return good_exp & self.lamps(fitstbl, 'off') & (fitstbl['hatch'] == 'open')
if ftype == 'bias':
return good_exp & self.lamps(fitstbl, 'off') & (fitstbl['hatch'] == 'closed')
if ftype in ['pixelflat', 'trace', 'illumflat']:
# Allow for dome or internal
good_dome = self.lamps(fitstbl, 'dome') & (fitstbl['hatch'] == 'open')
good_internal = self.lamps(fitstbl, 'internal') & (fitstbl['hatch'] == 'closed')
# Flats and trace frames are typed together
return good_exp & (good_dome + good_internal)
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 & self.lamps(fitstbl, 'arcs') & (fitstbl['hatch'] == 'closed')
msgs.warn('Cannot determine if frames are of type {0}.'.format(ftype))
return np.zeros(len(fitstbl), dtype=bool)
[docs] def lamps(self, fitstbl, status):
"""
Check the lamp status.
Args:
fitstbl (`astropy.table.Table`_):
The table with the fits header meta data.
status (:obj:`str`):
The status to check. Can be ``'off'``, ``'arcs'``, or
``'dome'``.
Returns:
`numpy.ndarray`_: A boolean array selecting fits files that meet
the selected lamp status.
Raises:
ValueError:
Raised if the status is not one of the valid options.
"""
if status == 'off':
# Check if all are off
return fitstbl['lampstat01'] == 'off'
elif status == 'arcs':
# Check if any arc lamps are on
return np.array([lamp not in ['Halogen', '2H', 'on', 'off'] for lamp in fitstbl['lampstat01']])
elif status == 'dome':
# Check if any dome lamps are on
return fitstbl['lampstat01'] == 'on'
elif status == 'internal':
return np.array([lamp in ['Halogen', '2H'] for lamp in fitstbl['lampstat01']])
else:
msgs.error(f"Bad status option! {status}")
raise ValueError('No implementation for status = {0}'.format(status))
[docs] def get_lamps(self, fitstbl):
"""
Extract the list of arc lamps used from header
Args:
fitstbl (`astropy.table.Table`_):
The table with the metadata for one or more arc frames.
Returns:
lamps (:obj:`list`) : List used arc lamps
"""
lamps = [f'{lamp}' for lamp in np.unique(np.concatenate([lname.split() for lname in fitstbl['lampstat01']]))]
# sometimes the flat lamps keyword are set ON for arc frames, remove them to avoid crashes in wavecalib
flat_lamps = ['Halogen', '2H']
for fl in flat_lamps:
if fl in lamps:
lamps.remove(fl)
return lamps
[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.
Based on readmhdufits.pro
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 LRIS file: {fil}')
hdu = io.fits_open(fil)
head0 = hdu[0].header
# Get post, pre-pix values
precol = head0['PRECOL']
postpix = head0['POSTPIX']
preline = head0['PRELINE']
postline = head0['POSTLINE']
# get the x and y binning factors...
binning = head0['BINNING']
xbin, ybin = [int(ibin) for ibin in binning.split(',')]
# First read over the header info to determine the size of the output array...
extensions = []
for kk, ihdu in enumerate(hdu):
if 'VidInp' in ihdu.name:
extensions.append(kk)
n_ext = len(extensions)
xcol = []
xmax = 0
ymax = 0
xmin = 10000
ymin = 10000
for i in extensions:
theader = hdu[i].header
detsec = theader['DETSEC']
if detsec != '0':
# parse the DETSEC keyword to determine the size of the array.
x1, x2, y1, y2 = np.array(parse.load_sections(detsec, fmt_iraf=False)).flatten()
# find the range of detector space occupied by the data
# [xmin:xmax,ymin:ymax]
xt = max(x2, x1)
xmax = max(xt, xmax)
yt = max(y2, y1)
ymax = max(yt, ymax)
# find the min size of the array
xt = min(x1, x2)
xmin = min(xmin, xt)
yt = min(y1, y2)
ymin = min(ymin, yt)
# Save
xcol.append(xt)
# determine the output array size...
nx = xmax - xmin + 1
ny = ymax - ymin + 1
# change size for binning...
nx = nx // xbin
ny = ny // ybin
# Update PRECOL and POSTPIX
precol = precol // xbin
postpix = postpix // xbin
# Deal with detectors
if det in [1, 2]:
nx = nx // 2
n_ext = n_ext // 2
det_idx = np.arange(n_ext, dtype=int) + (det - 1) * n_ext
elif det is None:
det_idx = np.arange(n_ext).astype(int)
else:
raise ValueError('Bad value for det')
# change size for pre/postscan...
nx += n_ext * (precol + postpix)
ny += preline + postline
# allocate output arrays...
array = np.zeros((nx, ny))
order = np.argsort(np.array(xcol))
rawdatasec_img = np.zeros_like(array, dtype=int)
oscansec_img = np.zeros_like(array, dtype=int)
# insert extensions into calibration image...
for amp, i in enumerate(order[det_idx]):
# grab complete extension...
data, predata, postdata, x1, y1 = lris_read_amp(hdu, i + 1)
# insert predata...
buf = predata.shape
nxpre = buf[0]
xs = amp * precol
xe = xs + nxpre
# predata (ignored)
array[xs:xe, :] = predata
# insert data...
buf = data.shape
nxdata = buf[0]
xs = n_ext * precol + amp * nxdata # (x1-xmin)/xbin
xe = xs + nxdata
array[xs:xe, :] = data
rawdatasec_img[xs:xe, preline:ny-postline] = amp+1
# ; insert postdata...
buf = postdata.shape
nxpost = buf[0]
xs = nx - n_ext * postpix + amp * postpix
xe = xs + nxpost
array[xs:xe, :] = postdata
oscansec_img[xs:xe, preline:ny-postline] = amp+1
# Need the exposure time
exptime = hdu[self.meta['exptime']['ext']].header[self.meta['exptime']['card']]
# Return
return self.get_detector_par(det if det is not None else 1, hdu=hdu), \
array.T, hdu, exptime, rawdatasec_img.T, oscansec_img.T
[docs] def get_slitmask(self, filename:str):
"""
Parse the slitmask data from a LRIS file into :attr:`slitmask`, a
:class:`~pypeit.spectrographs.slitmask.SlitMask` object.
Args:
filename (:obj:`str`):
Name of the file to read.
Returns:
:class:`~pypeit.spectrographs.slitmask.SlitMask`: The slitmask
data read from the file. The returned object is the same as
:attr:`slitmask`.
"""
self.slitmask = slitmask.load_keck_deimoslris(filename, self.name)
return self.slitmask
[docs] def get_maskdef_slitedges(self, ccdnum=None, filename=None, debug=None,
trc_path=None, binning=None):
"""
Provides the slit edges positions predicted by the slitmask design using
the mask coordinates already converted from mm to pixels by the method
`mask_to_pixel_coordinates`.
If not already instantiated, the :attr:`slitmask`, :attr:`amap`,
and :attr:`bmap` attributes are instantiated. If so, a file must be provided.
Args:
ccdnum (:obj:`int`):
Detector number
filename (:obj:`str`):
The filename to use to (re)instantiate the :attr:`slitmask` and :attr:`grating`.
Default is None, i.e., to use previously instantiated attributes.
debug (:obj:`bool`, optional):
Run in debug mode.
Returns:
:obj:`tuple`: Three `numpy.ndarray`_ and a :class:`~pypeit.spectrographs.slitmask.SlitMask`.
Two arrays are the predictions of the slit edges from the slitmask design and
one contains the indices to order the slits from left to right in the PypeIt orientation
"""
# Re-initiate slitmask
if filename is not None:
self.get_slitmask(filename)
else:
msgs.error('The name of a science file should be provided for the mask info')
if self.slitmask is None:
msgs.error('Unable to read slitmask design info. Provide a file.')
platescale = self.get_detector_par(det=1)['platescale']
hdu = fits.open(filename)
binning = self.get_meta_value(self.get_headarr(hdu), 'binning')
_, bin_spat = parse.parse_binning(binning)
# Slit center
slit_coords = SkyCoord(ra=self.slitmask.onsky[:,0],
dec=self.slitmask.onsky[:,1], unit='deg')
mask_coord = SkyCoord(ra=self.slitmask.mask_radec[0],
dec=self.slitmask.mask_radec[1], unit='deg')
# build an array of values containing the bottom (right) edge of the slits
# starting edge
left_edges = []
#for islit in x_order:
for islit in range(self.slitmask.nslits):
sep = mask_coord.separation(slit_coords[islit])
PA = mask_coord.position_angle(slit_coords[islit])
#
alpha = sep.to('arcsec') * np.cos(PA-self.slitmask.posx_pa*units.deg)
#delta = sep.to('arcsec') * np.sin(PA-self.slitmask.posx_pa*units.deg)
dx_pix = (alpha.value-self.slitmask.onsky[islit,2]/2.) / (platescale*bin_spat)
# target is the slit number
left_edges.append(np.round(dx_pix))
left_edges = np.array(left_edges, dtype=int)
# Build up the right edges
right_edges = left_edges + np.round(
self.slitmask.onsky[:,2]/(platescale*bin_spat)).astype(int)
# Center of slit
centers = (left_edges + right_edges)/2.
# Trim down by detector
# TODO -- Deal with Mark4
if self.name == 'keck_lris_red_mark4':
max_spat = 4112//bin_spat
else:
max_spat = 2048//bin_spat
if ccdnum == 1:
if self.name == 'keck_lris_red':
good = centers < 0.
xstart = max_spat + 160//bin_spat # The 160 is for the chip gap
elif self.name == 'keck_lris_blue':
good = centers < 0.
xstart = max_spat + 30//bin_spat
elif self.name == 'keck_lris_red_mark4':
xstart = 2073//bin_spat
good = centers < max_spat # No chip gap
else:
msgs.error(f'Not ready to use slitmasks for {self.name}. Develop it!')
else:
if self.name in ['keck_lris_red', 'keck_lris_blue']:
good = centers >= 0.
xstart = -48//bin_spat
else:
msgs.error(f'Not ready to use slitmasks for {self.name}. Develop it!')
left_edges += xstart
right_edges += xstart
left_edges[~good] = -1
# Toss any left edges off the right-side of the detector
keep = left_edges < max_spat
left_edges[~keep] = -1
right_edges[left_edges == -1] = -1
# Deal with right edge off the detector
for islit in range(self.slitmask.nslits):
if left_edges[islit] != -1 and right_edges[islit] > max_spat:
right_edges[islit] = max_spat
# Now the left
for islit in range(self.slitmask.nslits):
if right_edges[islit] != -1 and left_edges[islit] < 0:
left_edges[islit] = 0
# Order from left to right
# Highest x is leftmost on DET=1
x_order = np.argsort(self.slitmask.corners[:,1,0])
sortindx = x_order[::-1]
# Return
return left_edges.astype(float), right_edges.astype(float), sortindx, self.slitmask
[docs]class KeckLRISBSpectrograph(KeckLRISSpectrograph):
"""
Child to handle Keck/LRISb specific code
"""
name = 'keck_lris_blue'
camera = 'LRISb'
header_name = 'LRISBLUE'
supported = True
comment = 'Blue camera; Current FITS file format; used from May 2009, see :doc:`lris`'
[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
# TODO: Could this be detector dependent?
binning = '1,1' if hdu is None else self.get_meta_value(self.get_headarr(hdu), 'binning')
# Detector 1
detector_dict1 = dict(
binning = binning,
det = 1,
dataext = 1,
specaxis = 0,
specflip = False,
spatflip = False,
platescale = 0.135,
darkcurr = 0.0, # e-/pixel/hour
saturation = 65535.,
nonlinear = 0.86,
mincounts = -1e10,
numamplifiers = 2,
gain = np.atleast_1d([1.55, 1.56]),
ronoise = np.atleast_1d([3.9, 4.2]),
)
# Detector 2
detector_dict2 = detector_dict1.copy()
detector_dict2.update(dict(
det=2,
dataext=2,
gain=np.atleast_1d([1.63, 1.70]),
ronoise=np.atleast_1d([3.6, 3.6])
))
# Instantiate
detector_dicts = [detector_dict1, detector_dict2]
detector = detector_container.DetectorContainer(**detector_dicts[det-1])
if hdu is None:
return detector
# TODO: I don't know how to handle the hack below for the auto-generated
# detector table...
# Deal with number of amps
namps = hdu[0].header['NUMAMPS']
# The website does not give values for single amp per detector so we take the mean
# of the values provided
if namps == 2 or (namps==4 and len(hdu)==3): # Longslit readout mode is the latter. This is a hack..
detector.numamplifiers = 1
detector.gain = np.atleast_1d(np.mean(detector.gain))
detector.ronoise = np.atleast_1d(np.mean(detector.ronoise))
elif namps == 4:
pass
else:
msgs.error("Did not see this namps coming..")
# Return
return detector
[docs] def check_spectrograph(self, filename):
"""
Check that the selected spectrograph is the correct one for the input data.
Args:
filename (:obj:`str`): File to use when determining if the input spectrograph is the correct one.
"""
super().check_spectrograph(filename)
# check that we are using the right spectrograph (keck_lris_blue or keck_lris_blue_orig)
_dateobs = time.Time(self.get_meta_value(filename, 'dateobs'), format='iso')
# last day of keck_lris_blue_orig
date_orig = time.Time('2009-04-30', format='iso')
if _dateobs <= date_orig and self.name in ['keck_lris_blue']:
msgs.error('This is not the correct spectrograph. Use keck_lris_blue_orig instead.')
elif _dateobs > date_orig and self.name in ['keck_lris_blue_orig']:
msgs.error('This is not the correct spectrograph. Use keck_lris_blue instead.')
[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['calibrations']['slitedges']['det_min_spec_length'] = 0.1
par['calibrations']['slitedges']['fit_min_spec_length'] = 0.2
# 1D wavelength solution
par['calibrations']['wavelengths']['rms_thresh_frac_fwhm'] = 0.06
# Allow for longer exposure times on blue side (especially if using the Dome lamps)
par['calibrations']['pixelflatframe']['exprng'] = [None, 300]
par['calibrations']['traceframe']['exprng'] = [None, 300]
par['calibrations']['illumflatframe']['exprng'] = [None, 300]
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') == '300/5000':
par['calibrations']['wavelengths']['method'] = 'full_template'
par['calibrations']['wavelengths']['reid_arxiv'] = 'keck_lris_blue_B300_5000_d680_ArCdHgKrNeXeZnFeAr.fits'
par['calibrations']['wavelengths']['n_final'] = 2
par['flexure']['spectrum'] = 'sky_LRISb_400.fits'
elif self.get_meta_value(scifile, 'dispname') == '400/3400':
par['calibrations']['wavelengths']['method'] = 'full_template'
par['calibrations']['wavelengths']['reid_arxiv'] = 'keck_lris_blue_B400_3400_d560_ArCdHgNeZnFeAr.fits'
par['calibrations']['wavelengths']['n_final'] = 2
par['flexure']['spectrum'] = 'sky_LRISb_400.fits'
elif self.get_meta_value(scifile, 'dispname') == '600/4000':
par['calibrations']['wavelengths']['method'] = 'full_template'
par['calibrations']['wavelengths']['reid_arxiv'] = 'keck_lris_blue_B600_4000_d560_ArCdHgKrNeXeZn.fits'
par['calibrations']['wavelengths']['sigdetect'] = 20.
par['calibrations']['wavelengths']['n_final'] = 6
par['flexure']['spectrum'] = 'sky_LRISb_600.fits'
elif self.get_meta_value(scifile, 'dispname') == '1200/3400':
par['calibrations']['wavelengths']['method'] = 'full_template'
par['calibrations']['wavelengths']['reid_arxiv'] = 'keck_lris_blue_B1200_3400_d560_ArCdHgNeZn.fits'
par['flexure']['spectrum'] = 'sky_LRISb_600.fits'
# Slit tracing
# Reduce the slit parameters because the flux does not span the full detector
# It is primarily on the upper half of the detector (usually)
if self.get_meta_value(scifile, 'dispname') == '300/5000':
par['calibrations']['slitedges']['smash_range'] = [0.5, 1.]
# Return
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)
# Only defined for det=1
if det == 1:
msgs.info("Using hard-coded BPM for det=1 on LRISb")
bpm_img[:,:3] = 1
return bpm_img
[docs]class KeckLRISBOrigSpectrograph(KeckLRISBSpectrograph):
"""
Child to handle the LRISb detector packed prior to 01 JUL 2009
"""
ndet = 2
name = 'keck_lris_blue_orig'
camera = 'LRISb'
supported = True # TODO: Is this true?
comment = 'Blue camera; Original FITS file format; used until April 2009; see :doc:`lris`'
[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.
"""
detpar = super().get_detector_par(det, hdu=hdu)
detpar['specflip'] = True
return detpar
[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.
Over-ride standard get_rawimage() for LRISb to deal
with the original approach
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.
"""
# Image info
image, hdul, elaptime, rawdatasec_img, oscansec_img = get_orig_rawimage(raw_file)
# Cut down
if np.max(rawdatasec_img) != 4:
msgs.error("Deal with not 2 AMP mode!!")
if det == 1:
bad_amp = rawdatasec_img > 2
rawdatasec_img[bad_amp] = 0
bad_amp = oscansec_img > 2
oscansec_img[bad_amp] = 0
elif det == 2:
# Kludge this to be 1 and 2's
for timage in [rawdatasec_img, oscansec_img]:
# Zero out
bad_amp = timage <= 2
timage[bad_amp] = 0
# Offset
good_amp = timage > 2
timage[good_amp] -= 2
else:
msgs.error("Should not be here in keck_lris!")
# Detector
detector_par = self.get_detector_par(det-1, hdu=hdul)
# # Flip the spectral axis
# detector_par['specflip'] = True
# Return
return detector_par, image, hdul, elaptime, rawdatasec_img, oscansec_img
[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. **This is
always ignored.**
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
return super().bpm(filename, det, shape=shape, msbias=None)
[docs]class KeckLRISRSpectrograph(KeckLRISSpectrograph):
"""
Child to handle Keck/LRISr specific code
"""
name = 'keck_lris_red'
camera = 'LRISr'
header_name = 'LRIS'
supported = True
ql_supported = True
comment = 'Red camera; Current FITS file format; ' \
'LBNL detector, 2kx4k; used from May 2009, see :doc:`lris`'
[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
# TODO: Could this be detector dependent??
binning = '1,1' if hdu is None else self.get_meta_value(self.get_headarr(hdu), 'binning')
# Detector 1
detector_dict1 = dict(
binning=binning,
det=1,
dataext=1,
specaxis=0,
specflip=False,
spatflip=False,
platescale=0.135, # confirmed from keck website at: https://www2.keck.hawaii.edu/inst/lris/lris-red-upgrade-notes.html#dewar3
darkcurr=0.0, # e-/pixel/hour
saturation=65535.,
nonlinear=0.76,
mincounts=-1e10,
numamplifiers=2,
gain=np.atleast_1d([1.255, 1.18]),
ronoise=np.atleast_1d([4.64, 4.76]),
)
# Detector 2
detector_dict2 = detector_dict1.copy()
detector_dict2.update(dict(
det=2,
dataext=2,
gain=np.atleast_1d([1.191, 1.162]),
ronoise=np.atleast_1d([4.54, 4.62])
))
if hdu is not None:
# Allow for post COVID detector issues
t2020_1 = time.Time("2020-06-30", format='isot') # First run
t2020_2 = time.Time("2020-07-29", format='isot') # Second run
# Check for the new detector (Mark4) upgrade
t2021_upgrade = time.Time("2021-04-22", format='isot')
date = time.Time(self.get_meta_value(self.get_headarr(hdu), 'mjd'),
format='mjd')
if date < t2020_1:
pass
elif date < t2020_2: # This is for the June 30 2020 run
msgs.warn("We are using LRISr gain/RN values based on WMKO estimates.")
detector_dict1['gain'] = np.atleast_1d([37.6])
detector_dict2['gain'] = np.atleast_1d([1.26])
detector_dict1['ronoise'] = np.atleast_1d([99.])
detector_dict2['ronoise'] = np.atleast_1d([5.2])
elif date >= t2021_upgrade:
# Note: We are unlikely to trip this. Other things probably failed first
msgs.error("This is the new detector. Use keck_lris_red_mark4")
else: # This is the 2020 July 29 run
msgs.warn("We are using LRISr gain/RN values based on WMKO estimates.")
detector_dict1['gain'] = np.atleast_1d([1.45])
detector_dict2['gain'] = np.atleast_1d([1.25])
detector_dict1['ronoise'] = np.atleast_1d([4.47])
detector_dict2['ronoise'] = np.atleast_1d([4.75])
# Instantiate
detector_dicts = [detector_dict1, detector_dict2]
detector = detector_container.DetectorContainer(**detector_dicts[det-1])
if hdu is None:
return detector
# Deal with number of amps
namps = hdu[0].header['NUMAMPS']
# The website does not give values for single amp per detector so we take the mean
# of the values provided
if namps == 2 or (namps==4 and len(hdu)==3): # Longslit readout mode is the latter. This is a hack..
detector.numamplifiers = 1
# Long silt mode
if hdu[0].header['AMPPSIZE'] == '[1:1024,1:4096]':
idx = 0 if det==1 else 1 # Vid1 for det=1, Vid4 for det=2
detector.gain = np.atleast_1d(detector.gain[idx])
detector.ronoise = np.atleast_1d(detector.ronoise[idx])
else:
detector.gain = np.atleast_1d(np.mean(detector.gain))
detector.ronoise = np.atleast_1d(np.mean(detector.ronoise))
elif namps == 4:
pass
else:
msgs.error("Did not see this namps coming..")
# Return
return detector
[docs] def check_spectrograph(self, filename):
"""
Check that the selected spectrograph is the correct one for the input data.
Args:
filename (:obj:`str`): File to use when determining if the input spectrograph is the correct one.
"""
super().check_spectrograph(filename)
# check that we are using the right spectrograph (keck_lris_red, keck_lris_red_orig, or keck_lris_red_mark4)
_dateobs = time.Time(self.get_meta_value(filename, 'dateobs'), format='iso')
# starting date for keck_lris_red_mark4
date_mark4 = time.Time('2021-04-22', format='iso')
# last day of keck_lris_red_orig
date_orig = time.Time('2009-05-02', format='iso')
if _dateobs <= date_orig and self.name in ['keck_lris_red_mark4', 'keck_lris_red']:
msgs.error('This is not the correct spectrograph. Use keck_lris_red_orig instead.')
elif _dateobs >= date_mark4 and self.name in ['keck_lris_red_orig', 'keck_lris_red']:
msgs.error('This is not the correct spectrograph. Use keck_lris_red_mark4 instead.')
elif date_orig < _dateobs < date_mark4 and self.name in ['keck_lris_red_orig', 'keck_lris_red_mark4']:
msgs.error('This is not the correct spectrograph. Use keck_lris_red instead.')
[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['calibrations']['slitedges']['edge_thresh'] = 20.
# Tilts
# These are the defaults
par['calibrations']['tilts']['tracethresh'] = 25
par['calibrations']['tilts']['spat_order'] = 4
par['calibrations']['tilts']['spec_order'] = 7
par['calibrations']['tilts']['maxdev2d'] = 1.0
par['calibrations']['tilts']['maxdev_tracefit'] = 1.0
par['calibrations']['tilts']['sigrej2d'] = 5.0
# Sky Subtraction
par['reduce']['skysub']['bspline_spacing'] = 0.8
# Defaults for anything other than 1,1 binning
# Rest config_specific_par below if binning is (1,1)
par['scienceframe']['process']['sigclip'] = 5.
par['scienceframe']['process']['objlim'] = 5.
# Sensitivity function defaults
par['sensfunc']['algorithm'] = 'IR'
par['sensfunc']['polyorder'] = 9
return par
# NOTE: This function is used by the dev-suite
[docs] def get_ql_calib_dir(self, file):
"""
Returns calibration file directory for quicklook reductions.
Args:
file (str):
Image file
Returns:
:obj:`str`: Quicklook calibrations directory
"""
lris_grating = self.get_meta_value(file, 'dispname')
lris_dichroic = self.get_meta_value(file, 'dichroic')
setup_path = lris_grating.replace('/','_') + '_d' + lris_dichroic
return os.path.join(self.name, setup_path)
[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)
# Lacosmic CR settings
# Grab the defaults for LRISr
binning = self.get_meta_value(scifile, 'binning')
# Unbinned LRISr needs very aggressive LACosmics parameters for 1x1 binning
if binning == '1,1':
sigclip = 3.0
objlim = 0.5
par['scienceframe']['process']['sigclip'] = sigclip
par['scienceframe']['process']['objlim'] = objlim
# Wavelength calibrations
if self.get_meta_value(scifile, 'dispname') == '150/7500':
par['calibrations']['wavelengths']['reid_arxiv'] = 'keck_lris_red_R150_7500_ArCdHgNeZn.fits'
par['calibrations']['wavelengths']['method'] = 'full_template'
par['calibrations']['wavelengths']['n_first'] = 2
par['calibrations']['wavelengths']['n_final'] = 2
elif self.get_meta_value(scifile, 'dispname') == '300/5000':
par['calibrations']['wavelengths']['reid_arxiv'] = 'keck_lris_red_R300_5000_ArCdHgKrNeXeZn.fits'
par['calibrations']['wavelengths']['method'] = 'full_template'
elif self.get_meta_value(scifile, 'dispname') == '400/8500':
par['calibrations']['wavelengths']['reid_arxiv'] = 'keck_lris_red_R400_8500_ArCdHgKrNeXeZn.fits'
par['calibrations']['wavelengths']['method'] = 'full_template'
# par['calibrations']['wavelengths']['sigdetect'] = 20.0
elif self.get_meta_value(scifile, 'dispname') == '600/5000':
par['calibrations']['wavelengths']['reid_arxiv'] = 'keck_lris_red_R600_5000_ArCdHgKrNeXeZn.fits'
par['calibrations']['wavelengths']['method'] = 'full_template'
elif self.get_meta_value(scifile, 'dispname') == '600/7500':
par['calibrations']['wavelengths']['reid_arxiv'] = 'keck_lris_red_R600_7500_ArCdHgKrNeXeZn.fits'
par['calibrations']['wavelengths']['method'] = 'full_template'
elif self.get_meta_value(scifile, 'dispname') == '600/10000':
par['calibrations']['wavelengths']['reid_arxiv'] = 'keck_lris_red_R600_10000_ArCdHgKrNeXeZn.fits'
par['calibrations']['wavelengths']['method'] = 'full_template'
elif self.get_meta_value(scifile, 'dispname') == '831/8200':
par['calibrations']['wavelengths']['reid_arxiv'] = 'keck_lris_red_R831_8200_ArCdHgKrNeXeZn.fits'
par['calibrations']['wavelengths']['method'] = 'full_template'
par['calibrations']['wavelengths']['sigdetect'] = 30.0 # lots of ghost lines
elif self.get_meta_value(scifile, 'dispname') == '900/5500':
par['calibrations']['wavelengths']['reid_arxiv'] = 'keck_lris_red_R900_5500_ArCdHgNeZn.fits'
par['calibrations']['wavelengths']['method'] = 'full_template'
par['calibrations']['wavelengths']['sigdetect'] = 30.0 # lots of ghost lines
elif self.get_meta_value(scifile, 'dispname') == '1200/7500':
par['calibrations']['wavelengths']['reid_arxiv'] = 'keck_lris_red_R1200_7500_ArCdHgKrNeXeZn.fits'
par['calibrations']['wavelengths']['method'] = 'full_template'
elif self.get_meta_value(scifile, 'dispname') == '1200/9000':
par['calibrations']['wavelengths']['reid_arxiv'] = 'keck_lris_red_R1200_9000.fits'
par['calibrations']['wavelengths']['method'] = 'full_template'
# Return
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.
"""
return super().configuration_keys() + ['dispangle', 'cenwave']
[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)
# Only defined for det=2
if det == 2:
msgs.info("Using hard-coded BPM for det=2 on LRISr")
# Get the binning
hdu = io.fits_open(filename)
binning = hdu[0].header['BINNING']
hdu.close()
# Apply the mask
xbin = int(binning.split(',')[0])
badc = 16//xbin
bpm_img[:,:badc] = 1
# Mask the end too (this is risky as an edge may appear)
# But there is often weird behavior at the ends of these detectors
bpm_img[:,-10:] = 1
return bpm_img
[docs]class KeckLRISRMark4Spectrograph(KeckLRISRSpectrograph):
"""
Child to handle the new Mark4 detector
"""
ndet = 1
name = 'keck_lris_red_mark4'
supported = True
comment = 'Red camera; New Mark4 detector, in operation since May 2021; see :doc:`lris`'
[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 self.get_meta_value(self.get_headarr(hdu), 'binning')
# Detector 1
detector_dict1 = dict(
binning=binning,
det=1,
dataext=0,
specaxis=0,
specflip=True,
spatflip=True,
platescale=0.135, # From the web page
darkcurr=0.0, # e-/pixel/hour
saturation=65535.,
nonlinear=0.76,
mincounts=-1e10,
numamplifiers=2, # These are defaults but can modify below
gain=np.atleast_1d([1.61, 1.67*0.959]), # Assumes AMPMODE=HSPLIT,VUP; Corrected by JXP using 2x1 binned flats
ronoise=np.atleast_1d([3.65, 3.52]),
)
if hdu is None:
return detector_container.DetectorContainer(**detector_dict1)
# Date of Mark4 installation
t2021_upgrade = time.Time("2021-04-22", format='isot')
# TODO -- Update with the date we transitioned to the correct ones
t_gdhead = time.Time("2029-01-01", format='isot')
date = time.Time(hdu[0].header['MJD'], format='mjd')
if date < t2021_upgrade:
msgs.error("This is not the Mark4 detector. Use a different keck_lris_red spectrograph")
# Deal with the intermediate headers
if date < t_gdhead:
amp_mode = hdu[0].header['AMPMODE']
msgs.info("AMPMODE = {:s}".format(amp_mode))
# Load up translation dict
ampmode_translate_file = (
data.Paths.data / 'spectrographs' / 'keck_lris_red_mark4' / 'dict_for_ampmode.json'
)
# Force any possible pathlib.Path object to string before `loadjson`
ampmode_translate_dict = linetools.utils.loadjson(str(ampmode_translate_file))
# Load up the corrected header
swap_binning = f"{binning[-1]},{binning[0]}" # LRIS convention is oppopsite ours
header_file = (
data.Paths.data /
'spectrographs' /
'keck_lris_red_mark4' /
f'header{ampmode_translate_dict[amp_mode]}_{swap_binning.replace(",","_")}.fits'
)
correct_header = fits.getheader(header_file)
else:
correct_header = hdu[0].header
# Deal with number of amps
detector_dict1['numamplifiers'] = correct_header['TAPLINES']
# The website does not give values for single amp per detector so we take the mean
# of the values provided
if detector_dict1['numamplifiers'] == 2:
pass
elif detector_dict1['numamplifiers'] == 4:
# From the web page on 2021-10-04 (L1, L2, U1, U2)
# Corrected by JXP and SS using chk_lris_mark4_gain.py in the DevSuite
detector_dict1['gain'] = np.atleast_1d([1.710,
1.64*1.0245, # L2
1.61*1.0185, # U1
1.67*1.0052]) # U2
detector_dict1['ronoise'] = np.atleast_1d([3.64, 3.45, 3.65, 3.52])
else:
msgs.error("Did not see this namps coming..")
detector_dict1['datasec'] = []
detector_dict1['oscansec'] = []
# Parse which AMPS were used
used_amps = []
for amp in range(4):
if f'AMPNM{amp}' in correct_header.keys():
used_amps.append(amp)
# Check
assert detector_dict1['numamplifiers'] == len(used_amps)
# Reverse engenieering to translate LRIS DSEC, BSEC
# into ones friendly for PypeIt...
binspec = int(binning[0])
binspatial = int(binning[-1])
for iamp in used_amps:
# These are column, row
dsecs = correct_header[f'DSEC{iamp}'].split(',')
d_rows = [int(item) for item in dsecs[1][:-1].split(':')]
d_cols = [int(item) for item in dsecs[0][1:].split(':')]
bsecs = correct_header[f'BSEC{iamp}'].split(',')
o_rows = [int(item) for item in bsecs[1][:-1].split(':')]
o_cols = [int(item) for item in bsecs[0][1:].split(':')]
# Deal with binning (heaven help me!!)
d_rows = [str(item*binspec) if item != 1 else str(item) for item in d_rows]
o_rows = [str(item*binspec) if item != 1 else str(item) for item in o_rows]
d_cols = [str(item*binspatial) if item != 1 else str(item) for item in d_cols]
o_cols = [str(item*binspatial) if item != 1 else str(item) for item in o_cols]
# These are now row, column
# And they need to be native!! i.e. no binning accounted for..
detector_dict1['datasec'] += [f"[{':'.join(d_rows)},{':'.join(d_cols)}]"]
detector_dict1['oscansec'] += [f"[{':'.join(o_rows)},{':'.join(o_cols)}]"]
detector_dict1['datasec'] = np.array(detector_dict1['datasec'])
detector_dict1['oscansec'] = np.array(detector_dict1['oscansec'])
# Instantiate
detector = detector_container.DetectorContainer(**detector_dict1)
#print('Dict1:', detector_dict1)
#print('Binning:', binning)
# Return
return detector
[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.
Over-ride standard get_rawimage() for LRIS
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.
"""
# Note: There is no way we know to super super super
return spectrograph.Spectrograph.get_rawimage(self, raw_file, det)
[docs]class KeckLRISROrigSpectrograph(KeckLRISRSpectrograph):
"""
Child to handle the original LRISr detector (up to 2009-05-02)
"""
ndet = 1
name = 'keck_lris_red_orig'
camera = 'LRISr'
supported = True
comment = 'Red camera; Original FITS file format; used until April 2009; see :doc:`lris`'
[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()
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') == '150/7500':
par['calibrations']['wavelengths']['reid_arxiv'] = 'keck_lris_red_orig_R150_7500_ArHgNe.fits'
par['calibrations']['wavelengths']['method'] = 'full_template'
par['calibrations']['wavelengths']['n_first'] = 2
par['calibrations']['wavelengths']['n_final'] = 2
elif self.get_meta_value(scifile, 'dispname') == '300/5000':
par['calibrations']['wavelengths']['reid_arxiv'] = 'keck_lris_red_orig_R300_5000_ArCdHgNeZn.fits'
par['calibrations']['wavelengths']['method'] = 'full_template'
elif self.get_meta_value(scifile, 'dispname') == '400/8500':
par['calibrations']['wavelengths']['reid_arxiv'] = 'keck_lris_red_orig_R400_8500_ArCdHgNeZn.fits'
par['calibrations']['wavelengths']['method'] = 'full_template'
elif self.get_meta_value(scifile, 'dispname') == '600/5000':
par['calibrations']['wavelengths']['reid_arxiv'] = 'keck_lris_red_orig_R600_5000_ArCdHgNeZn.fits'
par['calibrations']['wavelengths']['method'] = 'full_template'
elif self.get_meta_value(scifile, 'dispname') == '600/7500':
par['calibrations']['wavelengths']['reid_arxiv'] = 'keck_lris_red_orig_R600_7500_ArCdHgNeZn.fits'
par['calibrations']['wavelengths']['method'] = 'full_template'
elif self.get_meta_value(scifile, 'dispname') == '600/10000':
par['calibrations']['wavelengths']['reid_arxiv'] = 'keck_lris_red_orig_R600_10000_ArCdHgNeZn.fits'
par['calibrations']['wavelengths']['method'] = 'full_template'
elif self.get_meta_value(scifile, 'dispname') == '831/8200':
par['calibrations']['wavelengths']['reid_arxiv'] = 'keck_lris_red_orig_R831_8200_ArCdHgNeZn.fits'
par['calibrations']['wavelengths']['method'] = 'full_template'
elif self.get_meta_value(scifile, 'dispname') == '900/5500':
par['calibrations']['wavelengths']['reid_arxiv'] = 'keck_lris_red_orig_R900_5500_ArCdHgNeZn.fits'
par['calibrations']['wavelengths']['method'] = 'full_template'
elif self.get_meta_value(scifile, 'dispname') == '1200/7500':
par['calibrations']['wavelengths']['reid_arxiv'] = 'keck_lris_red_orig_R1200_7500_ArCdHgNeZn.fits'
par['calibrations']['wavelengths']['method'] = 'full_template'
# Return
return par
[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
# TODO: Could this be detector dependent??
binning = '1,1' if hdu is None else self.get_meta_value(self.get_headarr(hdu), 'binning')
# Detector 1
detector_dict1 = dict(
binning=binning,
det=1,
dataext=1,
specaxis=1,
specflip=False,
spatflip=False,
platescale=0.211, # confirmed from keck website at: https://www2.keck.hawaii.edu/inst/lris/lris-red-upgrade-notes.html#dewar3
darkcurr=0.0, # e-/pixel/hour
saturation=65535.,
nonlinear=0.76,
mincounts=-1e10,
numamplifiers=2,
gain=np.atleast_1d([1.98, 2.17]), # TAKEN FROM LOWREDUX
ronoise=np.atleast_1d([6.1, 6.3]), # TAKEN FROM LOWREDUX
)
# Instantiate
detector = detector_container.DetectorContainer(**detector_dict1)
# Deal with number of amps
if hdu is not None and hdu[0].header['NUMAMPS'] != 2:
msgs.error("Did not see this namps coming..")
# Return
return detector
[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.
Over-ride standard get_rawimage() for LRIS
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.
"""
# Image info
image, hdul, elaptime, rawdatasec_img, oscansec_img = get_orig_rawimage(raw_file)
# Detector
detector_par = self.get_detector_par(det, hdu=hdul)
# Return
return detector_par, image, hdul, elaptime, rawdatasec_img, oscansec_img
[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. **This is
always ignored.**
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
return super().bpm(filename, det, shape=shape, msbias=None)
[docs]def lris_read_amp(inp, ext):
"""
Read one amplifier of an LRIS multi-extension FITS image
Args:
inp (str, astropy.io.fits.HDUList):
filename or HDUList
ext (int):
Extension index
Returns:
tuple:
data
predata
postdata
x1
y1
"""
# Parse input
if isinstance(inp, str):
hdu = io.fits_open(inp)
else:
hdu = inp
# Count the number of extensions
n_ext = np.sum(['VidInp' in h.name for h in hdu])
# Get the pre and post pix values
# for LRIS red POSTLINE = 20, POSTPIX = 80, PRELINE = 0, PRECOL = 12
head0 = hdu[0].header
precol = head0['precol']
postpix = head0['postpix']
# Deal with binning
binning = head0['BINNING']
xbin, ybin = [int(ibin) for ibin in binning.split(',')]
precol = precol//xbin
postpix = postpix//xbin
# get entire extension...
temp = hdu[ext].data.transpose() # Silly Python nrow,ncol formatting
tsize = temp.shape
nxt = tsize[0]
# parse the DETSEC keyword to determine the size of the array.
header = hdu[ext].header
detsec = header['DETSEC']
x1, x2, y1, y2 = np.array(parse.load_sections(detsec, fmt_iraf=False)).flatten()
# parse the DATASEC keyword to determine the size of the science region (unbinned)
datasec = header['DATASEC']
xdata1, xdata2, ydata1, ydata2 = np.array(parse.load_sections(datasec, fmt_iraf=False)).flatten()
# grab the components...
predata = temp[0:precol, :]
# datasec appears to have the x value for the keywords that are zero
# based. This is only true in the image header extensions
# not true in the main header. They also appear inconsistent between
# LRISr and LRISb!
#data = temp[xdata1-1:xdata2-1,*]
#data = temp[xdata1:xdata2+1, :]
if (xdata1-1) != precol:
msgs.error("Something wrong in LRIS datasec or precol")
xshape = 1024 // xbin * (4//n_ext) # Allow for single amp
if (xshape+precol+postpix) != temp.shape[0]:
msgs.warn("Unexpected size for LRIS detector. We expect you did some windowing...")
xshape = temp.shape[0] - precol - postpix
data = temp[precol:precol+xshape,:]
postdata = temp[nxt-postpix:nxt, :]
# flip in X as needed...
if x1 > x2:
xt = x2
x2 = x1
x1 = xt
data = np.flipud(data)
# flip in Y as needed...
if y1 > y2:
yt = y2
y2 = y1
y1 = yt
data = np.fliplr(data)
predata = np.fliplr(predata)
postdata = np.fliplr(postdata)
return data, predata, postdata, x1, y1
[docs]def convert_lowredux_pixelflat(infil, outfil):
""" Convert LowRedux pixelflat to PYPIT format
Returns
-------
"""
# Read
hdu = io.fits_open(infil)
data = hdu[0].data
#
prihdu = fits.PrimaryHDU()
hdus = [prihdu]
prihdu.header['FRAMETYP'] = 'pixelflat'
# Detector 1
img1 = data[:,:data.shape[1]//2]
hdu = fits.ImageHDU(img1)
hdu.name = 'DET1'
prihdu.header['EXT0001'] = 'DET1-pixelflat'
hdus.append(hdu)
# Detector 2
img2 = data[:,data.shape[1]//2:]
hdu = fits.ImageHDU(img2)
hdu.name = 'DET2'
prihdu.header['EXT0002'] = 'DET2-pixelflat'
hdus.append(hdu)
# Finish
hdulist = fits.HDUList(hdus)
hdulist.writeto(outfil, clobber=True)
print('Wrote {:s}'.format(outfil))
[docs]def get_orig_rawimage(raw_file, debug=False):
"""
Read a raw, original LRIS data frame.
Ported from LOWREDUX long_oscan.pro lris_oscan()
Parameters
----------
raw_file : :obj:`str`
Filename
debug : :obj:`bool`, optional
Run in debug mode (doesn't do anything)
Returns
-------
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.
"""
# Open
hdul = io.fits_open(raw_file)
head0 = hdul[0].header
# TODO -- Check date here and error/warn if not after the upgrade
image = hdul[0].data.astype(float)
# Get post, pre-pix values
postpix = head0['POSTPIX']
prepix = head0['PREPIX']
post_buffer1 = 4
post_buffer2 = 8
namps = head0['NUMAMPS']
# get the x and y binning factors...
binning = head0['BINNING']
xbin, ybin = [int(ibin) for ibin in binning.split(',')]
rawdatasec_img = np.zeros_like(image, dtype=int)
oscansec_img = np.zeros_like(image, dtype=int)
datacol = namps * (prepix // xbin) + np.arange(namps) * 1024 // xbin
postcol = datacol[namps - 1] + (1024 + post_buffer1) // xbin
for iamp in range(namps): #= 0, namps - 1L
biascols = np.arange((postpix - post_buffer2) // xbin) + (
iamp * postpix) // xbin + postcol
oscansec_img[:, biascols] = iamp+1
imagecols = np.arange(1024 // xbin) + iamp * 1024 // xbin
rawdatasec_img[:,imagecols + namps*(prepix // xbin)] = iamp+1
return image, hdul, float(head0['ELAPTIME']), \
rawdatasec_img, oscansec_img