"""
Module for P200/Triplespec specific methods.
.. include:: ../include/links.rst
"""
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.images import detector_container
[docs]class P200TSPECSpectrograph(spectrograph.Spectrograph):
"""
Child to handle P200/TripleSpec specific code
"""
ndet = 1
name = 'p200_tspec'
telescope = telescopes.P200TelescopePar()
camera = 'TSPEC'
url = 'https://sites.astro.caltech.edu/palomar/observer/200inchResources/tspeccookbook.html'
header_name = 'TSPEC_SPEC'
pypeline = 'Echelle'
ech_fixed_format = True
supported = True
comment = 'TripleSpec spectrograph'
[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']
[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
detector_dict = dict(
binning='1,1',
det=1,
dataext = 0,
specaxis = 1,
specflip = True,
spatflip=False,
platescale = 0.37,
darkcurr = 306.0, # e-/pixel/hour (=0.085 e-/pixel/s)
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)
[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()
# Wavelengths
# 1D wavelength solution
par['calibrations']['wavelengths']['rms_thresh_frac_fwhm'] = 0.103
par['calibrations']['wavelengths']['sigdetect']=5.0
par['calibrations']['wavelengths']['fwhm']= 2.9 # As measured in DevSuite
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'] = 'TellPCA_3000_26000_R10000.fits'
# Coadding
par['coadd1d']['wave_method'] = 'log10'
return par
[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`.
"""
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
[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 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)
[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
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))
[docs] 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)