"""
Module for Magellan/MAGE specific methods.
.. include:: ../include/links.rst
"""
from IPython import embed
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.core import parse
from pypeit.spectrographs import spectrograph
from pypeit.images import detector_container
[docs]class MagellanMAGESpectrograph(spectrograph.Spectrograph):
"""
Child to handle Magellan/MAGE specific code
"""
ndet = 1
name = 'magellan_mage'
camera = 'MagE'
url = 'https://www.lco.cl/?epkb_post_type_1=mage'
header_name = 'MagE'
telescope = telescopes.MagellanTelescopePar()
pypeline = 'Echelle'
ech_fixed_format = True
supported = True
comment = 'See :doc:`mage`'
[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 = True,
spatflip = False,
# plate scale in arcsec/pixel
platescale = 0.3,
# electrons/pixel/hour. From: http://www.lco.cl/telescopes-information/magellan/instruments/mage/the-mage-spectrograph-user-manual
darkcurr = 1.0, # e-/pixel/hour
saturation = 65535.,
# CCD is linear to better than 0.5 per cent up to digital saturation (65,536 DN including bias) in the Fast readout mode.
nonlinear = 0.99,
mincounts = -1e10,
numamplifiers = 1,
gain = np.atleast_1d(1.02), # depends on the readout
ronoise = np.atleast_1d(2.9), # depends on the readout
datasec = np.atleast_1d('[1:1024, 1:2048]'),
oscansec = np.atleast_1d('[1:1024, 2049:2176]'),
)
# Taken from the MASE paper: https://arxiv.org/pdf/0910.1834.pdf
#self.norders = 15
# 20-6
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()
# Bias
#par['calibrations']['biasframe']['useframe'] = 'overscan'
# Wavelengths
# 1D wavelength solution
# The following is for 1x1 binning
par['calibrations']['wavelengths']['rms_thresh_frac_fwhm'] = 0.133
par['calibrations']['wavelengths']['fwhm'] = 3.0
#
par['calibrations']['wavelengths']['sigdetect'] = 5.0
par['calibrations']['wavelengths']['lamps'] = ['ThAr_MagE']
par['calibrations']['wavelengths']['method'] = 'reidentify'
par['calibrations']['wavelengths']['cc_thresh'] = 0.50
par['calibrations']['wavelengths']['cc_local_thresh'] = 0.50
# Reidentification parameters
par['calibrations']['wavelengths']['reid_arxiv'] = 'magellan_mage.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'] = 4
par['calibrations']['wavelengths']['ech_sigrej'] = 3.0
par['scienceframe']['process']['sigclip'] = 20.0
par['scienceframe']['process']['satpix'] = 'nothing'
# Set slits and tilts parameters
par['calibrations']['tilts']['tracethresh'] = 10. #[10]*self.norders
par['calibrations']['slitedges']['fit_order'] = 5
par['calibrations']['slitedges']['max_shift_adj'] = 3.
par['calibrations']['slitedges']['edge_thresh'] = 10. # Tough to get the bluest orders
par['calibrations']['slitedges']['left_right_pca'] = True
par['calibrations']['slitedges']['fit_min_spec_length'] = 0.3 # Allow for a short detected blue order
# Find object parameters
par['reduce']['findobj']['find_trim_edge'] = [4,4] # Slit is too short to trim 5,5 especially with 2x binning
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
par['reduce']['extraction']['model_full_slit'] = True # local sky subtraction operates on entire slit
# Always flux calibrate, starting with default parameters
# Do not correct for flexure
par['flexure']['spec_method'] = 'skip'
# Set the default exposure time ranges for the frame typing
par['calibrations']['standardframe']['exprng'] = [None, 20]
par['calibrations']['arcframe']['exprng'] = [20, None]
par['calibrations']['darkframe']['exprng'] = [20, None]
par['scienceframe']['exprng'] = [20, None]
# Coadding
par['coadd1d']['wave_method'] = 'log10'
return par
[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 []
[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.
"""
if ftype in ['pinhole', 'dark']:
# No pinhole or bias or dark frames
return np.zeros(len(fitstbl), dtype=bool)
elif ftype in ['bias']:
return fitstbl['idname'] == 'Bias'
elif ftype in ['pixelflat', 'trace']:
return fitstbl['idname'] == 'Flat'
elif ftype in ['arc']:
return fitstbl['idname'] == 'ThAr-Lamp'
else:
return (fitstbl['idname'] == 'Object') \
& framematch.check_frame_exptime(fitstbl['exptime'], exprng)
[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
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)
# Get the binning
msgs.info("Custom bad pixel mask for MAGE")
hdu = io.fits_open(filename)
binspatial, binspec = parse.parse_binning(hdu[0].header['BINNING'])
hdu.close()
# Do it
bpm_img[:, :10//binspatial] = 1. # Setting BPM on the edge of the detector often leads to false edges
bpm_img[:, 1020//binspatial:] = 1.
# Return
return bpm_img
@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 12 # 20-6
@property
def order_spat_pos(self):
"""
Return the expected spatial position of each echelle order.
"""
ord_spat_pos = np.array([0.316, 0.399, 0.475, 0.545, 0.609, 0.669, 0.723, 0.774, 0.823,
0.869, 0.915, 0.965])
return ord_spat_pos
@property
def orders(self):
"""
Return the order number for each echelle order.
"""
return np.arange(17, 5, -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.full(self.norders, np.inf)
spec_min = np.full(self.norders, -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``.
"""
norders = len(order_vec)
binspatial, binspec = parse.parse_binning(binning)
return np.full(norders, 0.30*binspatial)