"""
Module for Bok/B&C 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 import io
from pypeit.core import framematch
from pypeit.spectrographs import spectrograph
from pypeit.core import parse
from pypeit.images import detector_container
[docs]
class BokBCSpectrograph(spectrograph.Spectrograph):
"""
Child to handle BOK specific code
"""
ndet = 1
telescope = telescopes.BokTelescopePar()
name = 'bok_bc'
camera = 'BC'
url = 'http://james.as.arizona.edu/~psmith/90inch/90inch.html'
comment = 'Bok B&C spectrometer'
header_name = 'Bok B&C spectrometer'
supported = True
[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', 'decker', 'binning', 'dispangle']
[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 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.
"""
# Binning
# TODO: Could this be detector dependent??
binning = '1,1' if hdu is None \
else self.get_meta_value(self.get_headarr(hdu), 'binning')
# Detector 1
detector_dict = dict(
binning = binning,
det = 1,
dataext = 0,
specaxis = 1,
specflip = False,
spatflip = False,
#platescale = 15.0/18.0,
platescale = 0.2,
darkcurr = 5.4, # e-/hour/unbinned pixel
saturation = 65535.,
nonlinear = 1.0,
mincounts = -1e10,
numamplifiers = 1,
gain = np.atleast_1d(1.5),
ronoise = np.atleast_1d(3.0),
datasec = np.atleast_1d('[:,1:1200]')
#datasec = np.atleast_1d('[250:650,1:1200]'),
)
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()
# Turn off illumflat
turn_off = dict(use_illumflat=False, use_biasimage=False, use_overscan=False,
use_darkimage=False)
par.reset_all_processimages_par(**turn_off)
# TODO: Note this comment doesn't match up with what's actually done...
# Require dark images to be subtracted from the flat images used for
# tracing, pixelflats, and illumflats
par['calibrations']['traceframe']['process']['use_darkimage'] = False
par['calibrations']['pixelflatframe']['process']['use_darkimage'] = False
par['calibrations']['illumflatframe']['process']['use_darkimage'] = False
# Ignore PCA
par['calibrations']['slitedges']['sync_predict'] = 'nearest'
par['calibrations']['slitedges']['edge_thresh'] = 50.0
# JFH Is this correct?
# Processing steps
#turn_off = dict(use_overscan=False)
#par.reset_all_processimages_par(**turn_off)
# Turn off the overscan
#for ftype in par['calibrations'].keys():
# try:
# par['calibrations'][ftype]['process']['overscan'] = 'none'
# except (TypeError, KeyError):
# pass
par['scienceframe']['process']['use_overscan'] = False
# Make a bad pixel mask
par['calibrations']['bpm_usebias'] = False
# Set pixel flat combination method
par['calibrations']['pixelflatframe']['process']['combine'] = 'median'
# Change the wavelength calibration method
par['calibrations']['wavelengths']['method'] = 'holy-grail'
#par['calibrations']['wavelengths']['method'] = 'reidentify'
par['calibrations']['wavelengths']['lamps'] = ['NeI', 'ArI', 'ArII', 'HeI']
# Wavelengths
# 1D wavelength solution
par['calibrations']['wavelengths']['rms_thresh_frac_fwhm'] = 0.19
par['calibrations']['wavelengths']['sigdetect'] = 5.
par['calibrations']['wavelengths']['fwhm']= 2.6
#par['calibrations']['wavelengths']['n_first'] = 3
#par['calibrations']['wavelengths']['n_final'] = 5
#par['calibrations']['wavelengths']['sigdetect'] = 10.0
#par['calibrations']['wavelengths']['wv_cen'] = 4859.0
#par['calibrations']['wavelengths']['disp'] = 0.2
# Do not flux calibrate
par['fluxcalib'] = None
# 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, 120]
par['calibrations']['standardframe']['exprng'] = [None, 120]
par['scienceframe']['exprng'] = [90, None]
# Extraction
par['reduce']['skysub']['bspline_spacing'] = 0.8
par['reduce']['skysub']['no_poly'] = True
par['reduce']['skysub']['bspline_spacing'] = 0.6
par['reduce']['skysub']['joint_fit'] = False
par['reduce']['skysub']['global_sky_std'] = False
par['reduce']['extraction']['sn_gauss'] = 4.0
par['reduce']['findobj']['snr_thresh'] = 5.0
par['reduce']['skysub']['sky_sigrej'] = 5.0
par['reduce']['findobj']['find_trim_edge'] = [5,5]
# cosmic ray rejection parameters for science frames
par['scienceframe']['process']['sigclip'] = 5.0
par['scienceframe']['process']['objlim'] = 2.0
# Sensitivity function parameters
par['sensfunc']['polyorder'] = 7
# Do not correct for flexure
par['flexure']['spec_method'] = 'skip'
return par
[docs]
def bpm(self, filename, det, shape=None, msbias=None):
"""
Generate a default bad-pixel mask.
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
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
bpm_img = super().bpm(filename, det, shape=shape, msbias=msbias)
if det == 1:
msgs.info("Using hard-coded BPM for Bok B&C")
bpm_img[:, -1] = 1
else:
msgs.error(f"Invalid detector number, {det}, for Bok B&C (only one detector).")
return bpm_img
[docs]
def config_specific_par(self, scifile, inp_par=None):
"""
Modify the PypeIt parameters to hard-wired values used for
specific instrument configurations.
Args:
scifile (:obj:`str`):
File to use when determining the configuration and how
to adjust the input parameters.
inp_par (:class:`~pypeit.par.parset.ParSet`, optional):
Parameter set used for the full run of PypeIt. If None,
use :func:`default_pypeit_par`.
Returns:
:class:`~pypeit.par.parset.ParSet`: The PypeIt parameter set
adjusted for configuration specific parameter values.
"""
# Start with instrument wide
par = super().config_specific_par(scifile, inp_par=inp_par)
# Wavelength calibrations
if self.get_meta_value(scifile, 'dispname') == '300':
par['calibrations']['wavelengths']['reid_arxiv'] = 'bok_bc_300.fits'
par['calibrations']['wavelengths']['method'] = 'full_template'
return par
[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 ['science']:
science = []
for obj in fitstbl['idname'].tolist():
science.append(not (("Dome Flat" in obj) or ("STANDARD" in obj) or ("HIP05" in obj) or ("HZ" in obj) or ("G191" in obj) or ("PG0220" in obj)))
return good_exp & (fitstbl['lampstat01'] == 'off') & science
if ftype in ['standard']:
standard = []
for obj in fitstbl['idname'].tolist():
standard.append(("STANDARD" in obj) or ("HIP05" in obj) or ("HZ" in obj) or ("G191" in obj) or ("PG0220" in obj) )
return good_exp & (fitstbl['lampstat01'] == 'off') & (fitstbl['idname'] != 'Dome Flat') & standard
if ftype == 'bias':
bias = []
for obj in fitstbl['idname'].tolist():
bias.append(("BIAS" in obj) or ("Bias" in obj))
return good_exp & (fitstbl['lampstat01'] == 'off') & bias
if ftype in ['pixelflat', 'trace']:
flat = []
for obj in fitstbl['idname'].tolist():
flat.append(("Dome Flat" in obj))
return good_exp & (fitstbl['lampstat01'] == 'off') & 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'] != 'off')
msgs.warn('Cannot determine if frames are of type {0}.'.format(ftype))
return np.zeros(len(fitstbl), dtype=bool)