def __init__(self):
# Get it started
super(ShaneKastSpectrograph, self).__init__()
self.spectrograph = 'shane_kast'
self.telescope = telescopes.ShaneTelescopePar()
class ShaneKastSpectrograph(spectrograph.Spectrograph):
"""
Child to handle Shane/Kast specific code
"""
ndet = 1
telescope = telescopes.ShaneTelescopePar()
@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()
# Ignore PCA
par['calibrations']['slitedges']['sync_predict'] = 'nearest'
# Always correct for flexure, starting with default parameters
par['flexure']['spec_method'] = 'boxcar'
# Set the default exposure time ranges for the frame typing
par['calibrations']['biasframe']['exprng'] = [None, 1]
par['calibrations']['darkframe']['exprng'] = [999999,
None] # No dark frames
par['calibrations']['pinholeframe']['exprng'] = [999999, None
] # No pinhole frames
par['calibrations']['pixelflatframe']['exprng'] = [0, None]
par['calibrations']['traceframe']['exprng'] = [0, None]
par['calibrations']['arcframe']['exprng'] = [None, 61]
par['calibrations']['standardframe']['exprng'] = [1, 61]
#
par['scienceframe']['exprng'] = [61, None]
return par
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 == 'mjd':
time = headarr[0]['DATE']
ttime = Time(time, format='isot')
return ttime.mjd
msgs.error("Not ready for this compound meta")
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')
# dispname is arm specific (blue/red)
self.meta['decker'] = dict(ext=0, card='SLIT_N')
self.meta['binning'] = dict(ext=0, card=None, default='1,1')
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')
# Additional ones, generally for configuration determination or time
self.meta['dichroic'] = dict(ext=0, card='BSPLIT_N')
lamp_names = [
'1', '2', '3', '4', '5', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H',
'I', 'J', 'K'
]
for kk, lamp_name in enumerate(lamp_names):
self.meta['lampstat{:02d}'.format(kk + 1)] = dict(
ext=0, card='LAMPSTA{0}'.format(lamp_name))
def configuration_keys(self):
"""
Return the metadata keys that define a unique instrument
configuration.
This list is used by :class:`~pypeit.metadata.PypeItMetaData` to
identify the unique configurations among the list of frames read
for a given reduction.
Returns:
:obj:`list`: List of keywords of data pulled from file headers
and used to constuct the :class:`~pypeit.metadata.PypeItMetaData`
object.
"""
# decker is not included because arcs are often taken with a 0.5" slit
return ['dispname', 'dichroic']
def check_frame_type(self, ftype, fitstbl, exprng=None):
"""
Check for frames of the provided type.
Args:
ftype (:obj:`str`):
Type of frame to check. Must be a valid frame type; see
frame-type :ref:`frame_type_defs`.
fitstbl (`astropy.table.Table`_):
The table with the metadata for one or more frames to check.
exprng (:obj:`list`, optional):
Range in the allowed exposure time for a frame of type
``ftype``. See
:func:`pypeit.core.framematch.check_frame_exptime`.
Returns:
`numpy.ndarray`_: Boolean array with the flags selecting the
exposures in ``fitstbl`` that are ``ftype`` type frames.
"""
good_exp = framematch.check_frame_exptime(fitstbl['exptime'], exprng)
if ftype in ['science', 'standard']:
return good_exp & self.lamps(fitstbl, 'off')
if ftype == 'bias':
return good_exp # & (fitstbl['target'] == 'Bias')
if ftype in ['pixelflat', 'trace', 'illumflat']:
# Flats and trace frames are typed together
return good_exp & self.lamps(
fitstbl, 'dome') # & (fitstbl['target'] == 'Dome Flat')
if ftype in ['pinhole', 'dark']:
# Don't type pinhole or dark frames
return np.zeros(len(fitstbl), dtype=bool)
if ftype in ['arc', 'tilt']:
return good_exp & self.lamps(
fitstbl, 'arcs') # & (fitstbl['target'] == 'Arcs')
msgs.warn('Cannot determine if frames are of type {0}.'.format(ftype))
return np.zeros(len(fitstbl), dtype=bool)
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 np.all(np.array([
(fitstbl[k] == 'off') | (fitstbl[k] == 'None')
for k in fitstbl.keys() if 'lampstat' in k
]),
axis=0)
if status == 'arcs':
# Check if any arc lamps are on
arc_lamp_stat = ['lampstat{0:02d}'.format(i) for i in range(6, 17)]
return np.any(np.array([
fitstbl[k] == 'on' for k in fitstbl.keys()
if k in arc_lamp_stat
]),
axis=0)
if status == 'dome':
# Check if any dome lamps are on
dome_lamp_stat = ['lampstat{0:02d}'.format(i) for i in range(1, 6)]
return np.any(np.array([
fitstbl[k] == 'on' for k in fitstbl.keys()
if k in dome_lamp_stat
]),
axis=0)
raise ValueError('No implementation for status = {0}'.format(status))