# NIRSpec specific rountines go here
import numpy as np
from astropy.io import fits
import astraeus.xarrayIO as xrio
from . import nircam, sigrej
from ..lib.util import read_time
[docs]def read(filename, data, meta, log):
'''Reads single FITS file from JWST's NIRCam instrument.
Parameters
----------
filename : str
Single filename to read.
data : Xarray Dataset
The Dataset object in which the fits data will stored.
meta : eureka.lib.readECF.MetaClass
The metadata object.
log : logedit.Logedit
The current log.
Returns
-------
data : Xarray Dataset
The updated Dataset object with the fits data stored inside.
meta : eureka.lib.readECF.MetaClass
The metadata object
log : logedit.Logedit
The current log.
Notes
-----
History:
- November 2012 Kevin Stevenson
Initial version
- June 2021 Aarynn Carter/Eva-Maria Ahrer
Updated for NIRSpec
- Apr 22, 2022 Kevin Stevenson
Convert to using Xarray Dataset
'''
hdulist = fits.open(filename)
# Load master and science headers
data.attrs['filename'] = filename
data.attrs['mhdr'] = hdulist[0].header
data.attrs['shdr'] = hdulist['SCI', 1].header
try:
data.attrs['intstart'] = data.attrs['mhdr']['INTSTART']-1
data.attrs['intend'] = data.attrs['mhdr']['INTEND']
except:
# FINDME: Need to only catch the particular exception we expect
print(' WARNING: Manually setting INTSTART to 1 and INTEND to NINTS')
data.attrs['intstart'] = 0
data.attrs['intend'] = data.attrs['mhdr']['NINTS']
sci = hdulist['SCI', 1].data
err = hdulist['ERR', 1].data
dq = hdulist['DQ', 1].data
v0 = hdulist['VAR_RNOISE', 1].data
wave_2d = hdulist['WAVELENGTH', 1].data
int_times = hdulist['INT_TIMES', 1].data
# Record integration mid-times in BJD_TDB
if (hasattr(meta, 'time_file') and meta.time_file is not None):
time = read_time(meta, data, log)
elif len(int_times['int_mid_BJD_TDB']) == 0:
# There is no time information in the simulated NIRSpec data
print(' WARNING: The timestamps for the simulated NIRSpec data are '
'currently\n'
' hardcoded because they are not in the .fits files '
'themselves')
time = np.linspace(data.mhdr['EXPSTART'], data.mhdr['EXPEND'],
data.intend)
else:
time = int_times['int_mid_BJD_TDB']
# Record units
flux_units = data.attrs['shdr']['BUNIT']
time_units = 'BJD_TDB'
wave_units = 'microns'
data['flux'] = xrio.makeFluxLikeDA(sci, time, flux_units, time_units,
name='flux')
data['err'] = xrio.makeFluxLikeDA(err, time, flux_units, time_units,
name='err')
data['dq'] = xrio.makeFluxLikeDA(dq, time, "None", time_units,
name='dq')
data['v0'] = xrio.makeFluxLikeDA(v0, time, flux_units, time_units,
name='v0')
data['wave_2d'] = (['y', 'x'], wave_2d)
data['wave_2d'].attrs['wave_units'] = wave_units
return data, meta, log
[docs]def flag_bg(data, meta, log):
'''Outlier rejection of sky background along time axis.
Parameters
----------
data : Xarray Dataset
The Dataset object.
meta : eureka.lib.readECF.MetaClass
The metadata object.
log : logedit.Logedit
The current log.
Returns
-------
data : Xarray Dataset
The updated Dataset object with outlier background pixels flagged.
'''
log.writelog(' Performing background outlier rejection...',
mute=(not meta.verbose))
meta.bg_y2 = meta.src_ypos + meta.bg_hw
meta.bg_y1 = meta.src_ypos - meta.bg_hw
bgdata1 = data.flux[:, :meta.bg_y1]
bgmask1 = data.mask[:, :meta.bg_y1]
bgdata2 = data.flux[:, meta.bg_y2:]
bgmask2 = data.mask[:, meta.bg_y2:]
# FINDME: KBS removed estsig from inputs to speed up outlier detection.
# Need to test performance with and without estsig on real data.
if hasattr(meta, 'use_estsig') and meta.use_estsig:
# This might not be necessary for real data
bgerr1 = np.ma.median(np.ma.masked_equal(data.err[:, :meta.bg_y1], 0))
bgerr2 = np.ma.median(np.ma.masked_equal(data.err[:, meta.bg_y2:], 0))
estsig1 = [bgerr1 for j in range(len(meta.bg_thresh))]
estsig2 = [bgerr2 for j in range(len(meta.bg_thresh))]
else:
estsig1 = None
estsig2 = None
data['mask'][:, :meta.bg_y1] = sigrej.sigrej(bgdata1, meta.bg_thresh,
bgmask1, estsig1)
data['mask'][:, meta.bg_y2:] = sigrej.sigrej(bgdata2, meta.bg_thresh,
bgmask2, estsig2)
return data
[docs]def fit_bg(dataim, datamask, n, meta, isplots=0):
"""Fit for a non-uniform background.
Uses the code written for NIRCam which works for NIRSpec.
Parameters
----------
dataim : ndarray (2D)
The 2D image array.
datamask : ndarray (2D)
An array of which data should be masked.
n : int
The current integration.
meta : eureka.lib.readECF.MetaClass
The metadata object.
isplots : int; optional
The plotting verbosity, by default 0.
Returns
-------
bg : ndarray (2D)
The fitted background level.
mask : ndarray (2D)
The updated mask after background subtraction.
n : int
The current integration number.
"""
return nircam.fit_bg(dataim, datamask, n, meta, isplots=isplots)
[docs]def cut_aperture(data, meta, log):
"""Select the aperture region out of each trimmed image.
Uses the code written for NIRCam which works for NIRSpec.
Parameters
----------
data : Xarray Dataset
The Dataset object.
meta : eureka.lib.readECF.MetaClass
The metadata object.
log : logedit.Logedit
The current log.
Returns
-------
apdata : ndarray
The flux values over the aperture region.
aperr : ndarray
The noise values over the aperture region.
apmask : ndarray
The mask values over the aperture region.
apbg : ndarray
The background flux values over the aperture region.
apv0 : ndarray
The v0 values over the aperture region.
Notes
-----
History:
- 2022-06-17, Taylor J Bell
Initial version based on the code in s3_reduce.py
"""
return nircam.cut_aperture(data, meta, log)