def __init__(self):
# Get it started
super(P200TSPECSpectrograph, self).__init__()
self.spectrograph = 'p200_tspec'
self.telescope = telescopes.P200TelescopePar()
self.camera = 'TSPEC'
self.numhead = 1
class P200DBSPSpectrograph(spectrograph.Spectrograph):
"""
Child to handle P200/DBSP specific code
"""
ndet = 1
telescope = telescopes.P200TelescopePar()
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', 'binning', 'dispangle', 'dichroic']
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',
required_ftypes=['science', 'standard'])
self.meta['dec'] = dict(ext=0,
card='DEC',
required_ftypes=['science', 'standard'])
self.meta['target'] = dict(ext=0, card='OBJECT')
self.meta['dispname'] = dict(ext=0, card='GRATING')
self.meta['decker'] = dict(ext=0, card='APERTURE')
self.meta['binning'] = dict(card=None, compound=True)
self.meta['mjd'] = dict(card=None, compound=True)
self.meta['exptime'] = dict(ext=0, card='EXPTIME')
self.meta['airmass'] = dict(ext=0,
card='AIRMASS',
required_ftypes=['science', 'standard'])
# Extras for config and frametyping
self.meta['dichroic'] = dict(ext=0, card='DICHROIC')
self.meta['dispangle'] = dict(card=None, rtol=1e-2, compound=True)
self.meta['slitwid'] = dict(ext=0, card='APERTURE')
self.meta['idname'] = dict(ext=0, card='IMGTYPE')
# Lamps
self.meta['lampstat01'] = dict(ext=0, card='LAMPS')
def compound_meta(self, headarr: List[fits.Header], meta_key: str):
"""
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':
return Time(headarr[0]['UTSHUT']).mjd
elif meta_key == 'dispangle':
try:
return Angle(headarr[0]['ANGLE'].lower()).deg
except Exception as e:
msgs.warn("Could not read dispangle from header:" +
msgs.newline() + str(headarr[0]['ANGLE']))
raise e
else:
return None
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']
def check_frame_type(self, ftype, fitstbl, exprng=None):
"""
Check for frames of the provided type.
Args:
ftype (:obj:`str`):
Type of frame to check. Must be a valid frame type; see
frame-type :ref:`frame_type_defs`.
fitstbl (`astropy.table.Table`_):
The table with the metadata for one or more frames to check.
exprng (:obj:`list`, optional):
Range in the allowed exposure time for a frame of type
``ftype``. See
:func:`pypeit.core.framematch.check_frame_exptime`.
Returns:
`numpy.ndarray`_: Boolean array with the flags selecting the
exposures in ``fitstbl`` that are ``ftype`` type frames.
"""
good_exp = framematch.check_frame_exptime(fitstbl['exptime'], exprng)
if ftype in ['science', 'standard']:
return good_exp & (fitstbl['lampstat01']
== '0000000') & (fitstbl['idname'] == 'object')
if ftype == 'bias':
return good_exp & (fitstbl['idname'] == 'bias')
if ftype in ['pixelflat', 'trace', 'illumflat']:
return good_exp & (fitstbl['idname'] == '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 & (fitstbl['lampstat01'] !=
'0000000') & (fitstbl['idname'] == 'cal')
msgs.warn('Cannot determine if frames are of type {0}.'.format(ftype))
return np.zeros(len(fitstbl), dtype=bool)
def __init__(self):
# Get it started
super(P200DBSPSpectrograph, self).__init__()
self.spectrograph = 'p200_dbsp'
self.telescope = telescopes.P200TelescopePar()
class P200TSPECSpectrograph(spectrograph.Spectrograph):
"""
Child to handle P200/TripleSpec specific code
"""
ndet = 1
name = 'p200_tspec'
telescope = telescopes.P200TelescopePar()
camera = 'TSPEC'
header_name = 'TSPEC_SPEC'
pypeline = 'Echelle'
supported = True
comment = 'TripleSpec spectrograph'
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=None, default='default')
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')
# Extras for config and frametyping
self.meta['dispname'] = dict(ext=0, card='FPA')
self.meta['idname'] = dict(ext=0, card='OBSTYPE')
self.meta['instrument'] = dict(ext=0, card='FPA')
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]['UTSHUT']
ttime = Time(time, format='isot')
return ttime.mjd
else:
msgs.error("Not ready for this compound meta")
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
detector_dict = dict(
binning='1,1',
det=1,
dataext=0,
specaxis=1,
specflip=True,
spatflip=False,
platescale=0.37,
darkcurr=0.085,
saturation=28000,
nonlinear=0.9,
mincounts=-1e10,
numamplifiers=1,
gain=np.atleast_1d(3.8),
ronoise=np.atleast_1d(3.5),
datasec=np.atleast_1d('[:,:]'),
oscansec=None #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()
# Wavelengths
# 1D wavelength solution
par['calibrations']['wavelengths']['rms_threshold'] = 0.3
par['calibrations']['wavelengths']['sigdetect'] = 5.0
par['calibrations']['wavelengths']['fwhm'] = 5.0
par['calibrations']['wavelengths']['n_final'] = [3, 4, 4, 4, 4]
par['calibrations']['wavelengths']['lamps'] = ['OH_NIRES']
par['calibrations']['wavelengths']['method'] = 'reidentify'
# Reidentification parameters
par['calibrations']['wavelengths'][
'reid_arxiv'] = 'p200_triplespec.fits'
par['calibrations']['wavelengths']['ech_fix_format'] = True
# Echelle parameters
par['calibrations']['wavelengths']['echelle'] = True
par['calibrations']['wavelengths']['ech_nspec_coeff'] = 4
par['calibrations']['wavelengths']['ech_norder_coeff'] = 6
par['calibrations']['wavelengths']['ech_sigrej'] = 3.0
#par['calibrations']['slitedges']['edge_thresh'] = 15.
par['calibrations']['slitedges']['trace_thresh'] = 5.
par['calibrations']['slitedges']['fit_min_spec_length'] = 0.3
par['calibrations']['slitedges']['left_right_pca'] = True
par['calibrations']['slitedges']['fwhm_gaussian'] = 4.0
# Tilt parameters
par['calibrations']['tilts']['tracethresh'] = 10.0
# Processing steps
turn_off = dict(use_illumflat=False,
use_biasimage=False,
use_overscan=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
# Model entire slit
par['reduce']['extraction'][
'model_full_slit'] = True # local sky subtraction operates on entire slit
par['reduce']['findobj'][
'maxnumber_sci'] = 2 # Slit is narrow so allow one object per order
par['reduce']['findobj'][
'maxnumber_std'] = 1 # Slit is narrow so allow one object per order
# Flexure
par['flexure']['spec_method'] = 'skip'
par['scienceframe']['process']['sigclip'] = 20.0
par['scienceframe']['process']['satpix'] = 'nothing'
par['reduce']['extraction']['boxcar_radius'] = 0.75 # arcsec
# Set the default exposure time ranges for the frame typing
par['calibrations']['standardframe']['exprng'] = [None, 60]
par['calibrations']['arcframe']['exprng'] = [100, None]
par['calibrations']['tiltframe']['exprng'] = [100, None]
par['calibrations']['darkframe']['exprng'] = [0, None]
par['scienceframe']['exprng'] = [60, None]
# Sensitivity function parameters
par['sensfunc']['algorithm'] = 'IR'
par['sensfunc']['polyorder'] = 8
par['sensfunc']['IR'][
'telgridfile'] = 'TelFit_MaunaKea_3100_26100_R20000.fits'
return par
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 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`.
"""
pypeit_keys = super().pypeit_file_keys()
# TODO: Why are these added here? See
# pypeit.metadata.PypeItMetaData.set_pypeit_cols
pypeit_keys += ['calib', 'comb_id', 'bkg_id']
return pypeit_keys
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 frames
return np.zeros(len(fitstbl), dtype=bool)
if ftype == 'dark':
return good_exp & (fitstbl['target'] == 'lamp_off')
if ftype == 'standard':
return good_exp & ((fitstbl['idname'] == 'object') |
(fitstbl['idname'] == 'Object'))
if ftype in ['pixelflat', 'trace']:
return good_exp & (fitstbl['target'] == 'lamp_on')
if ftype in 'science':
return good_exp & ((fitstbl['idname'] == 'object') |
(fitstbl['idname'] == 'Object'))
if ftype in ['arc', 'tilt']:
return good_exp & ((fitstbl['idname'] == 'object') |
(fitstbl['idname'] == 'Object'))
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. **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
msgs.info("Custom bad pixel mask for TSPEC")
return super().bpm(filename, det, shape=shape, msbias=None)
@property
def norders(self):
"""
Number of orders for this spectograph. Should only defined for
echelle spectrographs, and it is undefined for the base class.
"""
return 5
@property
def order_spat_pos(self):
"""
Return the expected spatial position of each echelle order.
"""
return np.array([0.3096, 0.4863, 0.6406, 0.7813, 0.9424])
@property
def orders(self):
"""
Return the order number for each echelle order.
"""
return np.arange(7, 2, -1, dtype=int)
@property
def spec_min_max(self):
"""
Return the minimum and maximum spectral pixel expected for the
spectral range of each order.
"""
spec_max = np.asarray([np.inf] * self.norders)
spec_min = np.asarray([1024, -np.inf, -np.inf, -np.inf, -np.inf])
return np.vstack((spec_min, spec_max))
def order_platescale(self, order_vec, binning=None):
"""
Return the platescale for each echelle order.
This routine is only defined for echelle spectrographs, and it is
undefined in the base class.
Args:
order_vec (`numpy.ndarray`_):
The vector providing the order numbers.
binning (:obj:`str`, optional):
The string defining the spectral and spatial binning.
Returns:
`numpy.ndarray`_: An array with the platescale for each order
provided by ``order``.
"""
return np.full(order_vec.size, 0.37)