"""
Module for MDM/Modspec specific methods.
.. include:: ../include/links.rst
"""
import array as arr
import numpy as np
from astropy.time import Time
from pypeit import msgs
from pypeit import telescopes
from pypeit.core import framematch
from pypeit.spectrographs import spectrograph
from pypeit.core import parse
from pypeit.images import detector_container
[docs]class MDMModspecEchelleSpectrograph(spectrograph.Spectrograph):
"""
Child to handle MDM Modspec Echelle instrument+detector
"""
ndet = 1
name = 'mdm_modspec'
telescope = telescopes.HiltnerTelescopePar()
camera = 'Echelle'
header_name = 'Modspec'
pypeline = 'MultiSlit'
supported = True
comment = 'MDM Modspec spectrometer; Only 1200l/mm disperser (so far)'
[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.
"""
# Detector 1
# See Echelle at 2.4m f/7.5 scale : http://mdm.kpno.noirlab.edu/mdm-ccds.html
gain = np.atleast_1d([1.3]) # Hardcoded in the header
ronoise = np.atleast_1d([7.90]) # Hardcoded in the header
# Allowing hdu=None is only needed for the automated documentation.
# TODO: See if there's a better way to automatically create the detector
# table for the docs.
if hdu is None:
lenSpat = None
lenSpec = None
datasec = None
oscansec = None
binning = '1,1' # Most common use mode
else:
# length of spatial axis, including overscan. Horizontal axis of
# original .fits files
lenSpat = hdu[0].header['NAXIS1']
# length of spectral axis. Vertical axis of original .fits files
lenSpec = hdu[0].header['NAXIS2']
datasec = np.atleast_1d([f'[1:{lenSpec},1:3002]'])
oscansec = np.atleast_1d([f'[1:{lenSpec},308:{lenSpat}]'])
binning = self.compound_meta(self.get_headarr(hdu), 'binning')
if binning != '1,1':
msgs.error("Not ready for any binning except 1x1; contact the developers")
# Detector 1 continued
detector_dict = dict(
binning = binning,
det = 1,
dataext = 0,
specaxis = 0, # Native spectrum is along the x-axis
specflip = True, # Wavelength decreases as pixel number increases
spatflip = False, # Spatial position increases as pixel number increases
platescale = 0.28, # Arcsec / pixel
darkcurr = 0.0, # e-/pixel/hour
saturation = 65535., # 16-bit ADC
nonlinear = 0.97, # Linear to ~97% of saturation
mincounts = -1e10,
numamplifiers = 1,
gain = gain, # See above
ronoise = ronoise, # See above
# Data & Overscan Sections -- Edge tracing can handle slit edges
datasec = datasec, # See above
oscansec = oscansec # See above
)
# Return
return detector_container.DetectorContainer(**detector_dict)
[docs] @classmethod
def default_pypeit_par(cls):
"""
Return the default parameters to use for this instrument.
Returns:
:class:`~pypeit.par.pypeitpar.PypeItPar`: Parameters required by
all of ``PypeIt`` methods.
"""
par = super().default_pypeit_par()
# Slit edge method
par['calibrations']['slitedges']['sync_predict'] = 'nearest' # Ignore PCA
par['calibrations']['slitedges']['bound_detector'] = True # Edges of slit fall off the detector, so assign edges of detector as the edges of the slit
# Pixel flat method
par['calibrations']['pixelflatframe']['process']['combine'] = 'mean'
par['calibrations']['pixelflatframe']['process']['clip'] = True
par['calibrations']['pixelflatframe']['process']['comb_sigrej'] = 3.0
par['calibrations']['pixelflatframe']['process']['n_lohi'] = [1, 1]
par['calibrations']['pixelflatframe']['process']['use_overscan'] = True
# Wavelength calibration method
par['calibrations']['wavelengths']['method'] = 'full_template'
par['calibrations']['wavelengths']['lamps'] = ['ArI', 'XeI', 'NeI']
par['calibrations']['wavelengths']['reid_arxiv'] = 'mdm_modspec_1200_5100.fits'
par['calibrations']['wavelengths']['sigdetect'] = 5.0 # Sigma threshold above fluctuations for arc-line detection
#par['calibrations']['wavelengths']['ech_fix_format'] = False
par['calibrations']['wavelengths']['n_final'] = 9
# Flat fielding
par['calibrations']['flatfield']['slit_illum_finecorr'] = False
# Bias method
par['calibrations']['biasframe']['process']['overscan_method'] = 'median'
# Arc method
par['calibrations']['arcframe']['process']['subtract_continuum'] = True
par['calibrations']['arcframe']['process']['clip'] = False
par['calibrations']['arcframe']['process']['combine'] = 'mean'
# Tilt method
par['calibrations']['tiltframe']['process']['subtract_continuum'] = True
par['calibrations']['tiltframe']['process']['clip'] = False
par['calibrations']['tiltframe']['process']['combine'] = 'mean'
# 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']['arcframe']['exprng'] = [None, None] # Long arc exposures on this telescope
par['scienceframe']['exprng'] = [10, 600]
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 ['dispname', 'cenwave', 'filter1', 'binning']
[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() + ['slitwid']
[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.
This table uses the Pypeit-specific metadata keywords, as defined
under def init_meta(self).
exprng (:obj:`list`, optional):
Range in the allowed exposure time for a frame of type
``ftype``. See
:func:`pypeit.core.framematch.check_frame_exptime`.
Returns:
`numpy.ndarray`_: Boolean array with the flags selecting the
exposures in ``fitstbl`` that are ``ftype`` type frames.
"""
good_exp = framematch.check_frame_exptime(fitstbl['exptime'], exprng)
if ftype in ['science']: # Standards and Sciences lumped together under 'science'
return good_exp & (fitstbl['idname'] == 'Object') & (fitstbl['mirror'] == 'OUT')
if ftype == 'bias':
return good_exp & (fitstbl['idname'] == 'Bias')
if ftype in ['arc','tilt']: # Lamps and Arcs
return good_exp & np.array([target in ['Comp','Object'] for target in fitstbl['idname']]) & (fitstbl['mirror'] == 'IN')
if ftype in ['pixelflat']: # Internal Flats
return good_exp & (fitstbl['idname'] == 'Flat') & (fitstbl['mirror'] == 'IN')
if ftype in ['illumflat', 'trace']: # Twilight Flats
return good_exp & (fitstbl['idname'] == 'Flat') & (fitstbl['mirror'] == 'OUT')
msgs.warn('Cannot determine if frames are of type {0}.'.format(ftype))
return np.zeros(len(fitstbl), dtype=bool)