# NIRISS specific rountines go here
import numpy as np
from astropy.io import fits
import astraeus.xarrayIO as xrio
from . import nircam, sigrej, optspex, plots_s3
from ..lib.util import read_time, supersample
from pastasoss import get_soss_traces
from .straighten import roll_columns
from .background import fitbg
__all__ = ['read', 'get_wave', 'straighten_trace', 'flag_ff', 'flag_bg',
'clean_median_flux', 'fit_bg', 'cut_aperture', 'standard_spectrum',
'residualBackground', 'lc_nodriftcorr']
'''
TODO:
Implement niriss.calibrated_spectra()
0th-order masking using F277W filter
Get 2D MAD calculation working
'''
[docs]
def read(filename, data, meta, log):
'''Reads single FITS file from JWST's NIRISS 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.
'''
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
data.attrs['intstart'] = data.attrs['mhdr']['INTSTART']-1
data.attrs['intend'] = data.attrs['mhdr']['INTEND']
sci = hdulist['SCI', 1].data
err = hdulist['ERR', 1].data
dq = hdulist['DQ', 1].data
v0 = hdulist['VAR_RNOISE', 1].data
int_times = hdulist['INT_TIMES', 1].data
# Increase pixel resolution along cross-dispersion direction
if meta.expand > 1:
log.writelog(f' Super-sampling y axis from {sci.shape[1]} ' +
f'to {sci.shape[1]*meta.expand} pixels...',
mute=(not meta.verbose))
sci = supersample(sci, meta.expand, 'flux', axis=1)
err = supersample(err, meta.expand, 'err', axis=1)
dq = supersample(dq, meta.expand, 'cal', axis=1)
v0 = supersample(v0, meta.expand, 'flux', axis=1)
# Record integration mid-times in BMJD_TDB
if meta.time_file is not None:
time = read_time(meta, data, log)
else:
time = int_times['int_mid_BJD_TDB']
# Record units
flux_units = data.attrs['shdr']['BUNIT']
time_units = 'BMJD_TDB'
# Duplicate science arrays for each order to be analyzed
if isinstance(meta.orders, int):
meta.orders = [meta.orders]
norders = len(meta.all_orders)
sci = np.repeat(sci[:, :, :, np.newaxis], norders, axis=3)
err = np.repeat(err[:, :, :, np.newaxis], norders, axis=3)
dq = np.repeat(dq[:, :, :, np.newaxis], norders, axis=3)
v0 = np.repeat(v0[:, :, :, np.newaxis], norders, axis=3)
if (meta.firstFile and meta.spec_hw == meta.spec_hw_range[0] and
meta.bg_hw == meta.bg_hw_range[0]):
# Only apply super-sampling expansion once
meta.ywindow[0] *= meta.expand
meta.ywindow[1] *= meta.expand
data['flux'] = xrio.makeFluxLikeDA(sci, time, flux_units, time_units,
name='flux', order=meta.all_orders)
data['err'] = xrio.makeFluxLikeDA(err, time, flux_units, time_units,
name='err', order=meta.all_orders)
data['dq'] = xrio.makeFluxLikeDA(dq, time, "None", time_units,
name='dq', order=meta.all_orders)
data['v0'] = xrio.makeFluxLikeDA(v0, time, flux_units, time_units,
name='v0', order=meta.all_orders)
# Initialize bad pixel mask (False = good, True = bad)
data['mask'] = (['time', 'y', 'x', 'order'], np.zeros(data.flux.shape,
dtype=bool))
return data, meta, log
[docs]
def get_wave(data, meta, log):
'''Use NIRISS pupil position to determine location
of traces and corresponding wavelength solutions.
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...
'''
# Report pupil position
pwcpos = data.attrs['mhdr']['PWCPOS']
log.writelog(f" The NIRISS pupil position is {pwcpos:3f} degrees",
mute=(not meta.verbose))
norders = len(meta.all_orders)
data['trace'] = (['x', 'order'],
np.zeros((data.x.shape[0], norders)) +
np.array(meta.src_ypos)[np.newaxis])
data['wave_1d'] = (['x', 'order'],
np.zeros((data.x.shape[0], norders))*np.nan)
data['wave_1d'].attrs['wave_units'] = 'microns'
for order in meta.all_orders:
# Get trace for the given order and pupil position
trace = get_soss_traces(pwcpos=pwcpos, order=str(order), interp=True)
if data.attrs['mhdr']['SUBARRAY'] == 'SUBSTRIP96' and \
meta.trace_offset is None:
# PASTASOSS doesn't account for different substrip starting rows;
# therefore, set trace offset to best guess (-12 pixels).
meta.trace_offset = -12
if meta.trace_offset is not None:
trace.y += meta.trace_offset
subarray = data.attrs['mhdr']['SUBARRAY']
log.writelog(f" Shifting trace by {meta.trace_offset} pixels "
f"for {subarray} and Order {order}.",
mute=(not meta.verbose))
# Assign trace and wavelength for given order
ind1 = np.nonzero(np.in1d(trace.x, data.x.values))[0]
ind2 = np.nonzero(np.in1d(data.x.values, trace.x))[0]
data['trace'].sel(order=order)[ind2] = trace.y[ind1]
data['wave_1d'].sel(order=order)[ind2] = trace.wavelength[ind1]
return data
def mask_other_orders(data, meta):
'''Mask trace regions from other orders.
Parameters
----------
data : Xarray Dataset
The Dataset object.
meta : eureka.lib.readECF.MetaClass
The metadata object.
Returns
-------
data : Xarray Dataset
The updated Dataset object with regions masked.
'''
for order in meta.all_orders:
trace = np.round(data.trace.sel(order=order).values).astype(int)
wave = data.wave_1d.sel(order=order).values
other_orders = list.copy(meta.all_orders)
other_orders.remove(order)
for other_order in other_orders:
if other_order in meta.orders:
other_trace = np.round(
data.trace.sel(order=other_order).values).astype(int)
# Loop over valid wavelengths in current order
for j in np.where(~np.isnan(wave))[0]:
ymin = np.max((0,
trace[j] - meta.spec_hw,
other_trace[j] + meta.bg_hw + 1))
ymax = np.min((len(data.y) + 1,
trace[j] + meta.spec_hw + 1))
# Mask extraction region for 'order' in 'other_order'
data['mask'].sel(order=other_order)[:, ymin:ymax, j] = True
return data
[docs]
def straighten_trace(data, meta, log, m):
'''Takes a set of integrations with a curved trace and shifts the
columns to bring the center of mass to the middle of the detector
(and straighten the trace)
The correction is made by whole pixels (i.e. no fractional pixel shifts)
The shifts to be applied are computed once from the median frame and then
applied to each integration in the timeseries
Parameters
----------
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 open log in which notes from this step can be added.
m : int
The file number.
Returns
-------
data : Xarray Dataset
The updated Dataset object with the fits data stored inside.
meta : eureka.lib.readECF.MetaClass
The updated metadata object.
'''
# Mask trace regions from other orders
data = mask_other_orders(data, meta)
for k, order in enumerate(meta.orders):
log.writelog(f' Correcting curvature for order {order} and ' +
f'bringing the trace to row {meta.src_ypos[k]}... ',
mute=(not meta.verbose))
shifts = np.round(data.trace.sel(order=order).values).astype(int)
new_center = meta.src_ypos[k]
new_shifts = new_center - shifts
# Keep shifts from exceeding the height of the detector
# This only happens with SUBSTRIP96 and Order 2,
# which is not recommended.
ymax = data.flux.shape[1]
new_shifts[new_shifts > ymax] = ymax
new_shifts[new_shifts < -ymax] = -ymax
# broadcast the shifts to the number of integrations
new_shifts = np.reshape(np.repeat(new_shifts,
data.flux.shape[0]),
(data.flux.shape[0],
data.flux.shape[2]),
order='F')
# Apply the shifts to the data
data['flux'].sel(order=order)[:] = roll_columns(
data.flux.sel(order=order).values, new_shifts)
data['mask'].sel(order=order)[:] = roll_columns(
data.mask.sel(order=order).values, new_shifts)
data['err'].sel(order=order)[:] = roll_columns(
data.err.sel(order=order).values, new_shifts)
data['dq'].sel(order=order)[:] = roll_columns(
data.dq.sel(order=order).values, new_shifts)
data['v0'].sel(order=order)[:] = roll_columns(
data.v0.sel(order=order).values, new_shifts)
data['medflux'].sel(order=order)[:] = roll_columns(
np.expand_dims(data.medflux.sel(order=order).values, axis=0),
new_shifts).squeeze()
return data, meta
[docs]
def flag_ff(data, meta, log):
'''Outlier rejection of full frame along time axis.
For data with deep transits, there is a risk of masking good transit data.
Proceed with caution.
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 pixels flagged.
'''
return nircam.flag_ff(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))
# Look for outliers above and below the curvature-corrected trace
for k, order in enumerate(meta.orders):
bgdata1 = data.flux.sel(order=order)[:, :meta.bg_y1[k]]
bgmask1 = data.mask.sel(order=order)[:, :meta.bg_y1[k]]
bgdata2 = data.flux.sel(order=order)[:, meta.bg_y2[k]:]
bgmask2 = data.mask.sel(order=order)[:, meta.bg_y2[k]:]
data['mask'].sel(order=order)[:, :meta.bg_y1[k]] = sigrej.sigrej(
bgdata1, meta.bg_thresh, bgmask1, None)
data['mask'].sel(order=order)[:, meta.bg_y2[k]:] = sigrej.sigrej(
bgdata2, meta.bg_thresh, bgmask2, None)
return data
[docs]
def fit_bg(dataim, datamask, n, meta, isplots=0):
"""Instrument wrapper for fitting the background.
Parameters
----------
dataim : ndarray (3D)
The 3D image array (y, x, order).
datamask : ndarray (3D)
A boolean array of which data (set to True) 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 boolean mask after background subtraction, where True
values should be masked.
n : int
The current integration number.
"""
norders = len(meta.orders)
bg = np.zeros_like(dataim)
mask = np.zeros_like(dataim, dtype=bool)
for k in range(norders):
bg[:, :, k], mask[:, :, k] = fitbg(
dataim[:, :, k], meta, datamask[:, :, k],
meta.bg_y1[k], meta.bg_y2[k], deg=meta.bg_deg,
threshold=meta.p3thresh, isrotate=2, isplots=isplots)
return bg, mask, n
[docs]
def cut_aperture(data, meta, log):
"""Select the aperture region out of each trimmed image.
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. True values should be masked.
apbg : ndarray
The background flux values over the aperture region.
apv0 : ndarray
The v0 values over the aperture region.
apmedflux : ndarray
The median flux over the aperture region.
"""
log.writelog(' Extracting aperture region...',
mute=(not meta.verbose))
apdata = np.zeros((len(data.time), 2*meta.spec_hw+1,
len(data.x), len(meta.orders)))
aperr = np.zeros_like(apdata)
apmask = np.zeros_like(apdata, dtype=bool)
apbg = np.zeros_like(apdata)
apv0 = np.zeros_like(apdata)
apmedflux = np.zeros_like(apdata[0])
for k in range(len(meta.orders)):
ap_y1 = int(meta.src_ypos[k] - meta.spec_hw)
ap_y2 = int(meta.src_ypos[k] + meta.spec_hw + 1)
apdata[:, :, :, k] = data.flux.values[:, ap_y1:ap_y2, :, k]
aperr[:, :, :, k] = data.err.values[:, ap_y1:ap_y2, :, k]
apmask[:, :, :, k] = data.mask.values[:, ap_y1:ap_y2, :, k]
apbg[:, :, :, k] = data.bg.values[:, ap_y1:ap_y2, :, k]
apv0[:, :, :, k] = data.v0.values[:, ap_y1:ap_y2, :, k]
apmedflux[:, :, k] = data.medflux.values[ap_y1:ap_y2, :, k]
# Mask invalid regions
inan = np.isnan(data.wave_1d[:, k])
apmask[:, :, inan, k] = True
# Mask Order 2 regions that overlap with Order 1
if meta.orders[k] == 2:
ioverlap = data.wave_1d[:, k] > 1.05
apmask[:, :, ioverlap, k] = True
ioverlap = data.wave_1d[:, k] < 0.60
apmask[:, :, ioverlap, k] = True
return apdata, aperr, apmask, apbg, apv0, apmedflux
[docs]
def standard_spectrum(data, meta, apdata, apmask, aperr):
"""Instrument wrapper for computing the standard box spectrum.
Parameters
----------
data : Xarray Dataset
The Dataset object.
meta : eureka.lib.readECF.MetaClass
The metadata object.
apdata : ndarray
The pixel values in the aperture region.
apmask : ndarray
The outlier mask in the aperture region. True where pixels should be
masked.
aperr : ndarray
The noise values in the aperture region.
Returns
-------
data : Xarray Dataset
The updated Dataset object in which the spectrum data will stored.
"""
return nircam.standard_spectrum(data, meta, apdata, apmask, aperr)
[docs]
def residualBackground(data, meta, m, vmin=None, vmax=None):
"""Plot the median, BG-subtracted frame to study the residual BG region and
aperture/BG sizes. (Fig 3304)
Parameters
----------
data : Xarray Dataset
The Dataset object.
meta : eureka.lib.readECF.MetaClass
The metadata object.
m : int
The file number.
vmin : int; optional
Minimum value of colormap. Default is None.
vmax : int; optional
Maximum value of colormap. Default is None.
"""
for k, order in enumerate(meta.orders):
# Specify aperture region for given order
ap_y = [meta.src_ypos[k] - meta.spec_hw,
meta.src_ypos[k] + meta.spec_hw + 1]
# Specify bg region for given order
bg_y = [meta.bg_y1[k], meta.bg_y2[k]]
# Median flux of segment
flux = data.medflux.sel(order=order).values
plots_s3.residualBackground(data, meta, m, flux=flux, order=order,
ap_y=ap_y, bg_y=bg_y,
vmin=None, vmax=None)
[docs]
def lc_nodriftcorr(spec, meta):
'''Plot a 2D light curve without drift correction. (Fig 3101+3102)
Fig 3101 uses a linear wavelength x-axis, while Fig 3102 uses a linear
detector pixel x-axis.
Parameters
----------
spec : Xarray Dataset
The Dataset object.
meta : eureka.lib.readECF.MetaClass
The metadata object.
'''
for k, order in enumerate(meta.orders):
wave_1d = spec.wave_1d.sel(order=order)
optspec = spec.optspec.sel(order=order)
optmask = spec.optmask.sel(order=order)
mad = meta.mad_s3[k]
plots_s3.lc_nodriftcorr(meta, wave_1d, optspec, optmask=optmask,
mad=mad, order=order)