def __init__(self):
# Get it started
super(MMTBINOSPECSpectrograph, self).__init__()
self.spectrograph = 'mmt_binospec'
self.telescope = telescopes.MMTTelescopePar()
self.camera = 'BINOSPEC'
self.numhead = 11
def __init__(self):
# Get it started
super(MMTBINOSPECSpectrograph, self).__init__()
self.spectrograph = 'mmt_binospec'
self.telescope = telescopes.MMTTelescopePar()
self.camera = 'BINOSPEC'
self.numhead = 11
self.detector = [
# Detector 1
pypeitpar.DetectorPar(
dataext=1,
specaxis=0,
specflip=False,
xgap=0.,
ygap=0.,
ysize=1.,
platescale=0.24,
darkcurr=3.0, ##ToDO: To Be update
saturation=65535.,
nonlinear=0.95, #ToDO: To Be update
numamplifiers=4,
gain=[1.085, 1.046, 1.042, 0.975],
ronoise=[3.2] * 4,
suffix='_01'),
# Detector 2
pypeitpar.DetectorPar(
dataext=2,
specaxis=0,
specflip=False,
xgap=0.,
ygap=0.,
ysize=1.,
platescale=0.24,
darkcurr=3.0, ##ToDO: To Be update
saturation=65535.,
nonlinear=0.95, #ToDO: To Be update
numamplifiers=4,
gain=[1.028, 1.163, 1.047, 1.045],
ronoise=[3.2] * 4,
suffix='_02')
]
class MMTBlueChannelSpectrograph(spectrograph.Spectrograph):
"""
Child to handle MMT/Blue Channel specific code
"""
ndet = 1
name = 'mmt_bluechannel'
telescope = telescopes.MMTTelescopePar()
camera = 'Blue_Channel'
supported = True
def get_detector_par(self, hdu, det):
"""
Return metadata for the selected detector.
Args:
hdu (`astropy.io.fits.HDUList`_):
The open fits file with the raw image of interest.
det (:obj:`int`):
1-indexed detector number.
Returns:
:class:`~pypeit.images.detector_container.DetectorContainer`:
Object with the detector metadata.
"""
header = hdu[0].header
# Binning
binning = self.get_meta_value(self.get_headarr(hdu), 'binning')
# Detector 1
detector_dict = dict(
binning=binning,
det=1,
dataext=0,
specaxis=0,
specflip=False,
spatflip=False,
xgap=0.,
ygap=0.,
ysize=1.,
platescale=0.3,
darkcurr=header['DARKCUR'],
saturation=65535.,
nonlinear=0.95, # need to look up and update
mincounts=-1e10,
numamplifiers=1,
gain=np.atleast_1d(header['GAIN']),
ronoise=np.atleast_1d(header['RDNOISE']),
# note that the header entries use the binned sizes
datasec=np.atleast_1d(header['DATASEC']),
oscansec=np.atleast_1d(header['BIASSEC']))
return detector_container.DetectorContainer(**detector_dict)
def init_meta(self):
"""
Define how metadata are derived from the spectrograph files.
That is, this associates the ``PypeIt``-specific metadata keywords
with the instrument-specific header cards using :attr:`meta`.
"""
self.meta = {}
# Required (core)
self.meta['ra'] = dict(ext=0, card='RA')
self.meta['dec'] = dict(ext=0, card='DEC')
self.meta['target'] = dict(ext=0, card='OBJECT')
self.meta['decker'] = dict(ext=0, card='APERTURE')
self.meta['dichroic'] = dict(ext=0, card='INSFILTE')
self.meta['binning'] = dict(ext=0, card=None, compound=True)
self.meta['mjd'] = dict(ext=0, card=None, compound=True)
self.meta['exptime'] = dict(ext=0, card='EXPTIME')
self.meta['airmass'] = dict(ext=0, card='AIRMASS')
# Extras for config and frametyping
self.meta['dispname'] = dict(ext=0, card='DISPERSE')
self.meta['idname'] = dict(ext=0, card='IMAGETYP')
# used for arc and continuum lamps
self.meta['lampstat01'] = dict(ext=0, card=None, compound=True)
def compound_meta(self, headarr, meta_key):
"""
Methods to generate metadata requiring interpretation of the header
data, instead of simply reading the value of a header card.
Args:
headarr (:obj:`list`):
List of `astropy.io.fits.Header`_ objects.
meta_key (:obj:`str`):
Metadata keyword to construct.
Returns:
object: Metadata value read from the header(s).
"""
if meta_key == 'binning':
"""
Binning in blue channel headers is space-separated rather than comma-separated.
"""
binspec, binspatial = headarr[0]['CCDSUM'].split()
binning = parse.binning2string(binspec, binspatial)
return binning
elif meta_key == 'mjd':
"""
Need to combine 'DATE-OBS' and 'UT' headers and then use astropy to make an mjd.
"""
date = headarr[0]['DATE-OBS']
ut = headarr[0]['UT']
ttime = Time(f"{date}T{ut}", format='isot')
return ttime.mjd
elif meta_key == 'lampstat01':
"""
If the comparison mirror is in, there will be a 'COMPLAMP' header entry containing the lamps
that are turned on. However, if the comparison mirror is out, then this header entry doesn't exist.
So need to test for it and set to 'Off' if it's not there.
"""
if 'COMPLAMP' in headarr[0]:
return headarr[0]['COMPLAMP']
else:
return 'off'
msgs.error(
f"Not ready for compound meta, {meta_key}, for MMT Blue Channel.")
@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()
# Wavelengths
# 1D wavelength solution
par['calibrations']['wavelengths']['rms_threshold'] = 0.5
par['calibrations']['wavelengths']['sigdetect'] = 5.
par['calibrations']['wavelengths']['fwhm'] = 5.0
# HeNeAr is by far most commonly used, though ThAr is used for some situations.
par['calibrations']['wavelengths']['lamps'] = [
'ArI', 'ArII', 'HeI', 'NeI'
]
par['calibrations']['wavelengths']['method'] = 'holy-grail'
# Processing steps
turn_off = dict(use_biasimage=False, use_darkimage=False)
par.reset_all_processimages_par(**turn_off)
# Extraction
par['reduce']['skysub']['bspline_spacing'] = 0.8
par['reduce']['extraction']['sn_gauss'] = 4.0
## Do not perform global sky subtraction for standard stars
par['reduce']['skysub']['global_sky_std'] = False
# cosmic ray rejection parameters for science frames
par['scienceframe']['process']['sigclip'] = 5.0
par['scienceframe']['process']['objlim'] = 2.0
# Set the default exposure time ranges for the frame typing
# Appropriate exposure times for Blue Channel can vary a lot depending
# on grating and wavelength. E.g. 300 and 500 line gratings need very
# short exposures for flats to avoid saturation, but the 1200 and 832
# can use much longer exposures due to the higher resolution and the
# continuum lamp not being very bright in the blue/near-UV.
par['calibrations']['pixelflatframe']['exprng'] = [None, 100]
par['calibrations']['traceframe']['exprng'] = [None, 100]
par['calibrations']['standardframe']['exprng'] = [None, 600]
par['calibrations']['arcframe']['exprng'] = [10, None]
par['calibrations']['darkframe']['exprng'] = [300, None]
# less than 30 sec implies conditions are bright enough for scattered
# light to be significant which affects the illumination of the slit.
par['calibrations']['illumflatframe']['exprng'] = [30, None]
# Need to specify this for long-slit data
par['calibrations']['slitedges']['sync_predict'] = 'nearest'
# Sensitivity function parameters
par['sensfunc']['polyorder'] = 7
return par
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):
Master 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)
if det == 1:
msgs.info("Using hard-coded BPM for Blue Channel")
bpm_img[-1, :] = 1
else:
msgs.error(
f"Invalid detector number, {det}, for MMT Blue Channel (only one detector)."
)
return bpm_img
def configuration_keys(self):
"""
Return the metadata keys that define a unique instrument
configuration.
This list is used by :class:`~pypeit.metadata.PypeItMetaData` to
identify the unique configurations among the list of frames read
for a given reduction.
Returns:
:obj:`list`: List of keywords of data pulled from file headers
and used to constuct the :class:`~pypeit.metadata.PypeItMetaData`
object.
"""
return ['dispname']
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 == 'bias':
return fitstbl['idname'] == 'zero'
if ftype in ['science', 'standard']:
return good_exp & (fitstbl['lampstat01']
== 'off') & (fitstbl['idname'] == 'object')
if ftype in ['arc', 'tilt']:
# should flesh this out to include all valid arc lamp combos
return good_exp & (fitstbl['lampstat01'] != 'off') & (
fitstbl['idname'] == 'comp') & (fitstbl['decker'] != '5.0x180')
if ftype in ['trace', 'pixelflat']:
# i think the bright lamp, BC, is the only one ever used for this. imagetyp should always be set to flat.
return good_exp & (fitstbl['lampstat01']
== 'BC') & (fitstbl['idname'] == 'flat')
if ftype in ['illumflat']:
# these can be set to flat or object depending if they're twilight or dark sky
return good_exp & (fitstbl['idname'] in ['flat', 'object']) & (
fitstbl['lampstat01'] == 'off')
msgs.warn('Cannot determine if frames are of type {0}.'.format(ftype))
return np.zeros(len(fitstbl), dtype=bool)
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.
"""
# Check for file; allow for extra .gz, etc. suffix
fil = glob.glob(raw_file + '*')
if len(fil) != 1:
msgs.error("Found {:d} files matching {:s}".format(len(fil)))
# Read FITS image
msgs.info("Reading MMT Blue Channel file: {:s}".format(fil[0]))
hdu = fits.open(fil[0])
hdr = hdu[0].header
# we're flipping FITS x/y to pypeit y/x here. pypeit wants blue on the
# bottom, slit bottom on the right...
rawdata = np.fliplr(hdu[0].data.astype(float).transpose())
exptime = hdr['EXPTIME']
# TODO Store these parameters in the DetectorPar.
# Number of amplifiers
detector_par = self.get_detector_par(hdu, det if det is None else 1)
numamp = detector_par['numamplifiers']
# First read over the header info to determine the size of the output array...
datasec = hdr['DATASEC']
xdata1, xdata2, ydata1, ydata2 = np.array(
parse.load_sections(datasec, fmt_iraf=False)).flatten()
# Get the overscan section
biassec = hdr['BIASSEC']
xbias1, xbias2, ybias1, ybias2 = np.array(
parse.load_sections(biassec, fmt_iraf=False)).flatten()
# allocate output arrays and fill in with mask values
rawdatasec_img = np.zeros_like(rawdata, dtype=int)
oscansec_img = np.zeros_like(rawdata, dtype=int)
# trim bad sections at beginning of data and bias sections
rawdatasec_img[xdata1 + 2:xdata2, ydata1:ydata2 - 1] = 1
oscansec_img[xbias1 + 2:xbias2, ybias1:ybias2 - 1] = 1
return detector_par, rawdata, hdu, exptime, rawdatasec_img, oscansec_img
class MMTBINOSPECSpectrograph(spectrograph.Spectrograph):
"""
Child to handle MMT/BINOSPEC specific code
"""
ndet = 2
name = 'mmt_binospec'
telescope = telescopes.MMTTelescopePar()
camera = 'BINOSPEC'
header_name = 'Binospec'
supported = True
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 = 1,
specaxis = 0,
specflip = False,
spatflip = False,
xgap = 0.,
ygap = 0.,
ysize = 1.,
platescale = 0.24,
darkcurr = 3.0, #ToDO: To Be update
saturation = 65535.,
nonlinear = 0.95, #ToDO: To Be update
mincounts = -1e10,
numamplifiers = 4,
gain = np.atleast_1d([1.085,1.046,1.042,0.975]),
ronoise = np.atleast_1d([3.2,3.2,3.2,3.2]),
)
# Detector 2
detector_dict2 = detector_dict1.copy()
detector_dict2.update(dict(
det=2,
dataext=2,
gain=np.atleast_1d([1.028,1.115,1.047,1.045]), #ToDo: FW measures 1.115 for amp2 but 1.163 in IDL pipeline
ronoise=np.atleast_1d([3.6,3.6,3.6,3.6])
))
# Instantiate
detector_dicts = [detector_dict1, detector_dict2]
return detector_container.DetectorContainer(**detector_dicts[det-1])
def init_meta(self):
"""
Define how metadata are derived from the spectrograph files.
That is, this associates the ``PypeIt``-specific metadata keywords
with the instrument-specific header cards using :attr:`meta`.
"""
self.meta = {}
# Required (core)
self.meta['ra'] = dict(ext=1, card='RA')
self.meta['dec'] = dict(ext=1, card='DEC')
self.meta['target'] = dict(ext=1, card='OBJECT')
self.meta['decker'] = dict(ext=1, card=None, default='default')
self.meta['dichroic'] = dict(ext=1, card=None, default='default')
self.meta['binning'] = dict(ext=1, card='CCDSUM', compound=True)
self.meta['mjd'] = dict(ext=1, card='MJD')
self.meta['exptime'] = dict(ext=1, card='EXPTIME')
self.meta['airmass'] = dict(ext=1, card='AIRMASS')
# Extras for config and frametyping
self.meta['dispname'] = dict(ext=1, card='DISPERS1')
self.meta['idname'] = dict(ext=1, card='IMAGETYP')
# used for arclamp
self.meta['lampstat01'] = dict(ext=1, card='HENEAR')
# used for flatlamp, SCRN is actually telescope status
self.meta['lampstat02'] = dict(ext=1, card='SCRN')
self.meta['instrument'] = dict(ext=1, card='INSTRUME')
def compound_meta(self, headarr, meta_key):
"""
Methods to generate metadata requiring interpretation of the header
data, instead of simply reading the value of a header card.
Args:
headarr (:obj:`list`):
List of `astropy.io.fits.Header`_ objects.
meta_key (:obj:`str`):
Metadata keyword to construct.
Returns:
object: Metadata value read from the header(s).
"""
if meta_key == 'binning':
binspatial, binspec = parse.parse_binning(headarr[1]['CCDSUM'])
binning = parse.binning2string(binspec, binspatial)
return binning
@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()
# Wavelengths
# 1D wavelength solution
par['calibrations']['wavelengths']['rms_threshold'] = 0.5
par['calibrations']['wavelengths']['sigdetect'] = 5.
par['calibrations']['wavelengths']['fwhm']= 5.0
par['calibrations']['wavelengths']['lamps'] = ['ArI', 'ArII']
#par['calibrations']['wavelengths']['nonlinear_counts'] = self.detector[0]['nonlinear'] * self.detector[0]['saturation']
par['calibrations']['wavelengths']['method'] = 'holy-grail'
# Tilt and slit parameters
par['calibrations']['tilts']['tracethresh'] = 10.0
par['calibrations']['tilts']['spat_order'] = 6
par['calibrations']['tilts']['spec_order'] = 6
par['calibrations']['slitedges']['sync_predict'] = 'nearest'
# Processing steps
turn_off = dict(use_biasimage=False, use_darkimage=False)
par.reset_all_processimages_par(**turn_off)
# Extraction
par['reduce']['skysub']['bspline_spacing'] = 0.8
par['reduce']['extraction']['sn_gauss'] = 4.0
## Do not perform global sky subtraction for standard stars
par['reduce']['skysub']['global_sky_std'] = False
# Flexure
par['flexure']['spec_method'] = 'boxcar'
# cosmic ray rejection parameters for science frames
par['scienceframe']['process']['sigclip'] = 5.0
par['scienceframe']['process']['objlim'] = 2.0
# Set the default exposure time ranges for the frame typing
par['calibrations']['standardframe']['exprng'] = [None, 100]
par['calibrations']['arcframe']['exprng'] = [20, None]
par['calibrations']['darkframe']['exprng'] = [20, None]
par['scienceframe']['exprng'] = [20, None]
# Sensitivity function parameters
par['sensfunc']['polyorder'] = 7
par['sensfunc']['IR']['telgridfile'] = 'TelFit_MaunaKea_3100_26100_R20000.fits'
return par
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):
Master 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)
if det == 1:
msgs.info("Using hard-coded BPM for det=1 on BINOSPEC")
# TODO: Fix this
# Get the binning
hdu = io.fits_open(filename)
binning = hdu[1].header['CCDSUM']
hdu.close()
# Apply the mask
xbin, ybin = int(binning.split(' ')[0]), int(binning.split(' ')[1])
bpm_img[2447 // xbin, 2056 // ybin:4112 // ybin] = 1
bpm_img[2111 // xbin, 2056 // ybin:4112 // ybin] = 1
elif det == 2:
msgs.info("Using hard-coded BPM for det=2 on BINOSPEC")
# Get the binning
hdu = io.fits_open(filename)
binning = hdu[5].header['CCDSUM']
hdu.close()
# Apply the mask
xbin, ybin = int(binning.split(' ')[0]), int(binning.split(' ')[1])
#ToDo: Need to double check the BPM for detector 2
## Identified by FW from flat observations
bpm_img[3336 // xbin, 0:2056 // ybin] = 1
bpm_img[3337 // xbin, 0:2056 // ybin] = 1
bpm_img[4056 // xbin, 0:2056 // ybin] = 1
bpm_img[3011 // xbin, 2057 // ybin:4112 // ybin] = 1
## Got from IDL pipeline
#bpm_img[2378 // xbin, 0:2056 // ybin] = 1
#bpm_img[2096 // xbin, 2057 // ybin:4112 // ybin] = 1
#bpm_img[1084 // xbin, 0:2056 // ybin] = 1
return bpm_img
def configuration_keys(self):
"""
Return the metadata keys that define a unique instrument
configuration.
This list is used by :class:`~pypeit.metadata.PypeItMetaData` to
identify the unique configurations among the list of frames read
for a given reduction.
Returns:
:obj:`list`: List of keywords of data pulled from file headers
and used to constuct the :class:`~pypeit.metadata.PypeItMetaData`
object.
"""
return ['dispname']
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 & (fitstbl['lampstat01'] == 'off') & (fitstbl['lampstat02'] == 'stowed') & (fitstbl['exptime'] > 100.0)
if ftype == 'standard':
return good_exp & (fitstbl['lampstat01'] == 'off') & (fitstbl['lampstat02'] == 'stowed') & (fitstbl['exptime'] <= 100.0)
if ftype in ['arc', 'tilt']:
return good_exp & (fitstbl['lampstat01'] == 'on')
if ftype in ['pixelflat', 'trace', 'illumflat']:
return good_exp & (fitstbl['lampstat01'] == 'off') & (fitstbl['lampstat02'] == 'deployed')
msgs.warn('Cannot determine if frames are of type {0}.'.format(ftype))
return np.zeros(len(fitstbl), dtype=bool)
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.
"""
# Check for file; allow for extra .gz, etc. suffix
fil = glob.glob(raw_file + '*')
if len(fil) != 1:
msgs.error("Found {:d} files matching {:s}".format(len(fil)))
# Read
msgs.info("Reading BINOSPEC file: {:s}".format(fil[0]))
hdu = io.fits_open(fil[0])
head1 = hdu[1].header
# TOdO Store these parameters in the DetectorPar.
# Number of amplifiers
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...
binning = head1['CCDSUM']
xbin, ybin = [int(ibin) for ibin in binning.split(' ')]
# First read over the header info to determine the size of the output array...
datasec = head1['DATASEC']
x1, x2, y1, y2 = np.array(parse.load_sections(datasec, fmt_iraf=False)).flatten()
nxb = x1 - 1
# determine the output array size...
nx = (x2 - x1 + 1) * int(numamp/2) + nxb * int(numamp/2)
ny = (y2 - y1 + 1) * int(numamp/2)
#datasize = head1['DETSIZE']
#_, nx, _, ny = np.array(parse.load_sections(datasize, fmt_iraf=False)).flatten()
# allocate output array...
array = np.zeros((nx, ny))
rawdatasec_img = np.zeros_like(array, dtype=int)
oscansec_img = np.zeros_like(array, dtype=int)
if det == 1: # A DETECTOR
order = range(1, 5, 1)
elif det == 2: # B DETECTOR
order = range(5, 9, 1)
# insert extensions into master image...
for kk, jj in enumerate(order):
# grab complete extension...
data, overscan, datasec, biassec = binospec_read_amp(hdu, jj)
# insert components into output array...
inx = data.shape[0]
xs = inx * kk
xe = xs + inx
iny = data.shape[1]
ys = iny * kk
yn = ys + iny
b1, b2, b3, b4 = np.array(parse.load_sections(biassec, fmt_iraf=False)).flatten()
if kk == 0:
array[b2:inx+b2,:iny] = data #*1.028
rawdatasec_img[b2:inx+b2,:iny] = kk + 1
array[:b2,:iny] = overscan
oscansec_img[2:b2,:iny] = kk + 1
elif kk == 1:
array[b2+inx:2*inx+b2,:iny] = np.flipud(data) #* 1.115
rawdatasec_img[b2+inx:2*inx+b2:,:iny] = kk + 1
array[2*inx+b2:,:iny] = overscan
oscansec_img[2*inx+b2:,:iny] = kk + 1
elif kk == 2:
array[b2+inx:2*inx+b2,iny:] = np.fliplr(np.flipud(data)) #* 1.047
rawdatasec_img[b2+inx:2*inx+b2,iny:] = kk + 1
array[2*inx+b2:, iny:] = overscan
oscansec_img[2*inx+b2:, iny:] = kk + 1
elif kk == 3:
array[b2:inx+b2,iny:] = np.fliplr(data) #* 1.045
rawdatasec_img[b2:inx+b2,iny:] = kk + 1
array[:b2,iny:] = overscan
oscansec_img[2:b2,iny:] = kk + 1
# 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.fliplr(np.flipud(array)), hdu, exptime, np.fliplr(np.flipud(rawdatasec_img)), \
np.fliplr(np.flipud(oscansec_img))
class MMTMMIRSSpectrograph(spectrograph.Spectrograph):
"""
Child to handle MMT/MMIRS specific code
"""
ndet = 1
name = 'mmt_mmirs'
telescope = telescopes.MMTTelescopePar()
camera = 'MMIRS'
supported = True
def init_meta(self):
"""
Define how metadata are derived from the spectrograph files.
That is, this associates the ``PypeIt``-specific metadata keywords
with the instrument-specific header cards using :attr:`meta`.
"""
self.meta = {}
# Required (core)
self.meta['ra'] = dict(ext=1, card='RA')
self.meta['dec'] = dict(ext=1, card='DEC')
self.meta['target'] = dict(ext=1, card='OBJECT')
self.meta['decker'] = dict(ext=1, card='APERTURE')
self.meta['dichroic'] = dict(ext=1, card='FILTER')
self.meta['binning'] = dict(ext=1, card=None, default='1,1')
self.meta['mjd'] = dict(ext=0, card=None, compound=True)
self.meta['exptime'] = dict(ext=1, card='EXPTIME')
self.meta['airmass'] = dict(ext=1, card='AIRMASS')
# Extras for config and frametyping
self.meta['dispname'] = dict(ext=1, card='DISPERSE')
self.meta['idname'] = dict(ext=1, card='IMAGETYP')
def compound_meta(self, headarr, meta_key):
"""
Methods to generate metadata requiring interpretation of the header
data, instead of simply reading the value of a header card.
Args:
headarr (:obj:`list`):
List of `astropy.io.fits.Header`_ objects.
meta_key (:obj:`str`):
Metadata keyword to construct.
Returns:
object: Metadata value read from the header(s).
"""
# TODO: This should be how we always deal with timeunit = 'isot'. Are
# we doing that for all the relevant spectrographs?
if meta_key == 'mjd':
time = headarr[1]['DATE-OBS']
ttime = Time(time, format='isot')
return ttime.mjd
msgs.error("Not ready for this compound meta")
def get_detector_par(self, hdu, det):
"""
Return metadata for the selected detector.
Args:
hdu (`astropy.io.fits.HDUList`_):
The open fits file with the raw image of interest.
det (:obj:`int`):
1-indexed detector number.
Returns:
:class:`~pypeit.images.detector_container.DetectorContainer`:
Object with the detector metadata.
"""
# Detector 1
detector_dict = dict(
binning='1,1',
det=1,
dataext=1,
specaxis=0,
specflip=False,
spatflip=False,
platescale=0.2012,
darkcurr=0.01,
saturation=700000., #155400.,
nonlinear=1.0,
mincounts=-1e10,
numamplifiers=1,
gain=np.atleast_1d(0.95),
ronoise=np.atleast_1d(3.14),
datasec=np.atleast_1d('[:,:]'),
oscansec=np.atleast_1d('[:,:]'))
return detector_container.DetectorContainer(**detector_dict)
@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()
# Image processing steps
turn_off = dict(use_illumflat=False,
use_biasimage=False,
use_overscan=False,
use_darkimage=False)
par.reset_all_processimages_par(**turn_off)
#par['calibrations']['traceframe']['process']['use_darkimage'] = True
#par['calibrations']['pixelflatframe']['process']['use_darkimage'] = True
#par['calibrations']['illumflatframe']['process']['use_darkimage'] = True
#par['scienceframe']['process']['use_darkimage'] = True
par['scienceframe']['process']['use_illumflat'] = True
# Wavelengths
# 1D wavelength solution with arc lines
par['calibrations']['wavelengths']['rms_threshold'] = 0.5
par['calibrations']['wavelengths']['sigdetect'] = 5
par['calibrations']['wavelengths']['fwhm'] = 5
par['calibrations']['wavelengths']['n_first'] = 2
par['calibrations']['wavelengths']['n_final'] = 4
par['calibrations']['wavelengths']['lamps'] = ['OH_NIRES']
par['calibrations']['wavelengths']['match_toler'] = 5.0
# Set slits and tilts parameters
par['calibrations']['tilts']['tracethresh'] = 5
par['calibrations']['tilts']['spat_order'] = 7
par['calibrations']['tilts']['spec_order'] = 5
par['calibrations']['slitedges']['trace_thresh'] = 10.
par['calibrations']['slitedges']['edge_thresh'] = 100.
par['calibrations']['slitedges']['fit_min_spec_length'] = 0.4
par['calibrations']['slitedges']['sync_predict'] = 'nearest'
# Set the default exposure time ranges for the frame typing
par['calibrations']['standardframe']['exprng'] = [None, 60]
par['calibrations']['tiltframe']['exprng'] = [60, None]
par['calibrations']['arcframe']['exprng'] = [60, None]
par['calibrations']['darkframe']['exprng'] = [30, None]
par['scienceframe']['exprng'] = [30, None]
# dark
par['calibrations']['darkframe']['process']['apply_gain'] = True
# cosmic ray rejection
par['scienceframe']['process']['sigclip'] = 5.0
par['scienceframe']['process']['objlim'] = 2.0
par['scienceframe']['process']['grow'] = 0.5
# Science reduction
par['reduce']['findobj']['sig_thresh'] = 5.0
par['reduce']['skysub']['sky_sigrej'] = 5.0
par['reduce']['findobj']['find_trim_edge'] = [5, 5]
# Do not correct for flexure
par['flexure']['spec_method'] = 'skip'
# Sensitivity function parameters
par['sensfunc']['algorithm'] = 'IR'
par['sensfunc']['polyorder'] = 8
# ToDo: replace the telluric grid file for MMT site.
par['sensfunc']['IR']['telgridfile'] \
= resource_filename('pypeit',
'/data/telluric/TelFit_MaunaKea_3100_26100_R20000.fits')
return par
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)
if (self.get_meta_value(scifile,
'dispname') == 'HK') and (self.get_meta_value(
scifile, 'dichroic') == 'zJ'):
par['calibrations']['wavelengths']['method'] = 'full_template'
par['calibrations']['wavelengths'][
'reid_arxiv'] = 'mmt_mmirs_HK_zJ.fits'
elif (self.get_meta_value(
scifile, 'dispname') == 'K3000') and (self.get_meta_value(
scifile, 'dichroic') == 'Kspec'):
par['calibrations']['wavelengths']['method'] = 'full_template'
par['calibrations']['wavelengths'][
'reid_arxiv'] = 'mmt_mmirs_K3000_Kspec.fits'
elif (self.get_meta_value(scifile, 'dispname')
== 'J') and (self.get_meta_value(scifile, 'dichroic') == 'zJ'):
par['calibrations']['wavelengths']['method'] = 'full_template'
par['calibrations']['wavelengths'][
'reid_arxiv'] = 'mmt_mmirs_J_zJ.fits'
return par
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 ['pinhole', 'bias']:
# No pinhole or bias frames
return np.zeros(len(fitstbl), dtype=bool)
if ftype in ['pixelflat', 'trace', 'illumflat']:
return good_exp & (fitstbl['idname'] == 'flat')
if ftype == 'standard':
return good_exp & (fitstbl['idname'] == 'object')
if ftype == 'science':
return good_exp & (fitstbl['idname'] == 'object')
if ftype in ['arc', 'tilt']:
return good_exp & (fitstbl['idname'] == 'object')
if ftype == 'dark':
return good_exp & (fitstbl['idname'] == 'dark')
msgs.warn('Cannot determine if frames are of type {0}.'.format(ftype))
return np.zeros(len(fitstbl), dtype=bool)
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):
Master 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 det=1 on MMIRS")
# Get the binning
hdu = io.fits_open(filename)
binning = hdu[1].header['CCDSUM']
hdu.close()
# Apply the mask
xbin, ybin = int(binning.split(' ')[0]), int(binning.split(' ')[1])
bpm_img[:, 187 // ybin] = 1
return bpm_img
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.
"""
# Check for file; allow for extra .gz, etc. suffix
fil = glob.glob(raw_file + '*')
if len(fil) != 1:
msgs.error("Found {:d} files matching {:s}".format(len(fil)))
# Read
msgs.info("Reading MMIRS file: {:s}".format(fil[0]))
hdu = io.fits_open(fil[0])
head1 = fits.getheader(fil[0], 1)
detector_par = self.get_detector_par(hdu, det if det is None else 1)
# get the x and y binning factors...
binning = head1['CCDSUM']
xbin, ybin = [int(ibin) for ibin in binning.split(' ')]
# First read over the header info to determine the size of the output array...
datasec = head1['DATASEC']
x1, x2, y1, y2 = np.array(parse.load_sections(
datasec, fmt_iraf=False)).flatten()
# ToDo: I am currently using the standard double correlated frame, that is a difference between
# the first and final read-outs. In the future need to explore up-the-ramp fitting.
if len(hdu) > 2:
data = mmirs_read_amp(
hdu[1].data.astype('float64')) - mmirs_read_amp(
hdu[2].data.astype('float64'))
else:
data = mmirs_read_amp(hdu[1].data.astype('float64'))
array = data[x1 - 1:x2, y1 - 1:y2]
## ToDo: This is a hack. Need to solve this issue. I cut at 998 due to the HK zero order contaminating
## the blue part of the zJ+HK spectrum. For other setup, you do not need to cut the detector.
if (head1['FILTER'] == 'zJ') and (head1['DISPERSE'] == 'HK'):
array = array[:int(998 / ybin), :]
rawdatasec_img = np.ones_like(array, dtype='int')
oscansec_img = np.ones_like(array, dtype='int')
# 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(np.flipud(oscansec_img))
def __init__(self):
# Get it started
super(MMTMMIRSSpectrograph, self).__init__()
self.spectrograph = 'mmt_mmirs'
self.telescope = telescopes.MMTTelescopePar()
self.camera = 'MMIRS'
