"""
Module for P200/Triplespec specific methods.
.. include:: ../include/links.rst
"""
import numpy as np
from astropy.time import Time
from pypeit import log
from pypeit import PypeItError
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
log.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)