def deimos_spectrum1D_reader(file_name):
"""
Data loader for Keck/DEIMOS 1D spectra.
This loads the 'Bxspf-B' (extension 1)
and 'Bxspf-R' (extension 2) and appends them
together to proudce the combined Red/Blue Spectrum
along with their Wavelength and Inverse Variance
arrays.
"""
with fits.open(file_name) as hdulist:
data = Data(label='1D Spectrum')
hdulist[1].header['CTYPE1'] = 'WAVE'
hdulist[1].header['CUNIT1'] = 'Angstrom'
data.header = hdulist[1].header
wcs = WCS(hdulist[1].header)
data.coords = coordinates_from_wcs(wcs)
full_wl = np.append(hdulist[1].data['LAMBDA'][0],
hdulist[2].data['LAMBDA'][0])
full_spec = np.append(hdulist[1].data['SPEC'][0],
hdulist[2].data['SPEC'][0])
full_ivar = np.append(hdulist[1].data['IVAR'][0],
hdulist[2].data['IVAR'][0])
data.add_component(full_wl, 'Wavelength')
data.add_component(full_spec, 'Flux')
data.add_component(1 / np.sqrt(full_ivar), 'Uncertainty')
return data
def nirspec_spectrum1d_reader(file_name):
"""
Data loader for MOSViz 1D spectrum.
This function extracts the DATA, QUALITY, and VAR
extensions and returns them as a glue Data object.
It then uses the header keywords of the DATA extension
to detemine the wavelengths.
"""
hdulist = fits.open(file_name)
# make wavelength a seperate component in addition to coordinate
# so you can plot it on the x axis
wavelength = np.linspace(hdulist['DATA'].header['CRVAL1'],
hdulist['DATA'].header['CRVAL1']*hdulist['DATA'].header['CDELT1'],
hdulist['DATA'].header['NAXIS1'])[::-1]
data = Data(label='1D Spectrum')
data.header = hdulist['DATA'].header
data.add_component(wavelength, 'Wavelength')
data.add_component(hdulist['DATA'].data, 'Flux')
data.add_component(hdulist['VAR'].data, 'Uncertainty')
return data
def pre_nirspec_spectrum1d_reader(file_name):
"""
Data loader for MOSViz 1D spectrum.
This function extracts the DATA, QUALITY, and VAR
extensions and returns them as a glue Data object.
It then uses the header keywords of the DATA extension
to detemine the wavelengths.
"""
hdulist = fits.open(file_name)
# make wavelength a seperate component in addition to coordinate
# so you can plot it on the x axis
wavelength = np.linspace(
hdulist['DATA'].header['CRVAL1'],
hdulist['DATA'].header['CRVAL1'] * hdulist['DATA'].header['CDELT1'],
hdulist['DATA'].header['NAXIS1'])[::-1]
data = Data(label='1D Spectrum')
data.header = hdulist['DATA'].header
data.add_component(wavelength, 'Wavelength')
data.add_component(hdulist['DATA'].data, 'Flux')
data.add_component(np.sqrt(hdulist['VAR'].data), 'Uncertainty')
hdulist.close()
return data
def deimos_spectrum1D_reader(file_name):
"""
Data loader for Keck/DEIMOS 1D spectra.
This loads the 'Bxspf-B' (extension 1)
and 'Bxspf-R' (extension 2) and appends them
together to proudce the combined Red/Blue Spectrum
along with their Wavelength and Inverse Variance
arrays.
"""
with fits.open(file_name) as hdulist:
data = Data(label='1D Spectrum')
hdulist[1].header['CTYPE1'] = 'WAVE'
hdulist[1].header['CUNIT1'] = 'Angstrom'
data.header = hdulist[1].header
wcs = WCS(hdulist[1].header)
data.coords = coordinates_from_wcs(wcs)
full_wl = np.append(hdulist[1].data['LAMBDA'][0], hdulist[2].data['LAMBDA'][0])
full_spec = np.append(hdulist[1].data['SPEC'][0], hdulist[2].data['SPEC'][0])
full_ivar = np.append(hdulist[1].data['IVAR'][0], hdulist[2].data['IVAR'][0])
data.add_component(full_wl, 'Wavelength')
data.add_component(full_spec, 'Flux')
data.add_component(1 / np.sqrt(full_ivar), 'Uncertainty')
return data
def deimos_spectrum1D_reader(file_name):
"""
Data loader for Keck/DEIMOS 1D spectra.
This loads the 'Bxspf-B' (extension 1)
and 'Bxspf-R' (extension 2) and appends them
together to proudce the combined Red/Blue Spectrum
along with their Wavelength and Inverse Variance
arrays.
"""
hdulist = fits.open(file_name)
data = Data(label='1D Spectrum')
data.header = hdulist[1].header
full_wl = np.append(hdulist[1].data['LAMBDA'][0],
hdulist[2].data['LAMBDA'][0])
full_spec = np.append(hdulist[1].data['SPEC'][0],
hdulist[2].data['SPEC'][0])
full_ivar = np.append(hdulist[1].data['IVAR'][0],
hdulist[2].data['IVAR'][0])
data.add_component(full_wl, 'Wavelength')
data.add_component(full_spec, 'Flux')
data.add_component(1 / np.sqrt(full_ivar), 'Uncertainty')
return data
def nirspec_spectrum1d_reader(file_name):
with fits.open(file_name) as hdulist:
header = hdulist['PRIMARY'].header
tab = Table.read(file_name)
data = Data(label="1D Spectrum")
data.header = header
data.add_component(tab['WAVELENGTH'], "Wavelength")
data.add_component(tab['FLUX'], "Flux")
data.add_component(tab['ERROR'], "Uncertainty")
return data
def pre_nirspec_level2_reader(file_name):
"""
THIS IS A TEST!
"""
#TODO The level 2 file has multiple exposures.
#TODO the level 2 test file has SCI extensions with different shapes.
#TODO
hdulist = fits.open(file_name)
data = Data(label='2D Spectra')
hdulist[1].header['CTYPE2'] = 'Spatial Y'
data.header = hdulist[1].header
# This is a stop gap fix to let fake data be ingested as
# level 2 apectra. The level 2 file we have for testing
# right now has SCI extensions with different sized arrays
# among them. It remains to be seen if this is a expected
# feature of level 2 spectra, or just a temporary glitch.
# In case it's actually what lvel 2 spectral files look
# like, proper handling must be put in place to allow
# glue Data objects with different sized components. Or,
# if that is not feasible, to properly cut the arrays so
# as to make them all of the same size. The solution below
# is a naive interpretation of this concept.
x_min = 10000
y_min = 10000
for k in range(1, len(hdulist)):
if 'SCI' in hdulist[k].header['EXTNAME']:
x_min = min(x_min, hdulist[k].data.shape[0])
y_min = min(y_min, hdulist[k].data.shape[1])
# hdulist[k].header['CTYPE2'] = 'Spatial Y'
# wcs = WCS(hdulist[1].header)
# original WCS has both axes named "LAMBDA", glue requires unique component names
# data.coords = coordinates_from_wcs(wcs)
# data.header = hdulist[k].header
# data.add_component(hdulist[1].data['FLUX'][0], 'Flux')
count = 1
for k in range(1, len(hdulist)):
if 'SCI' in hdulist[k].header['EXTNAME']:
data.add_component(hdulist[k].data[0:x_min, 0:y_min],
'Flux_' + '{:03d}'.format(count))
count += 1
# data.add_component(1 / np.sqrt(hdulist[1].data['IVAR'][0]), 'Uncertainty')
return data
def acs_cutout_image_reader(file_name):
"""
Data loader for the ACS cut-outs for the DEIMOS spectra.
The cutouts contain only the image.
"""
hdulist = fits.open(file_name)
data = Data(label='ACS Cutout Image')
data.coords = coordinates_from_header(hdulist[0].header)
data.header = hdulist[0].header
data.add_component(hdulist[0].data, 'Flux')
return data
def pre_nirspec_level2_reader(file_name):
"""
THIS IS A TEST!
"""
#TODO The level 2 file has multiple exposures.
#TODO the level 2 test file has SCI extensions with different shapes.
#TODO
hdulist = fits.open(file_name)
data = Data(label='2D Spectra')
hdulist[1].header['CTYPE2'] = 'Spatial Y'
data.header = hdulist[1].header
# This is a stop gap fix to let fake data be ingested as
# level 2 apectra. The level 2 file we have for testing
# right now has SCI extensions with different sized arrays
# among them. It remains to be seen if this is a expected
# feature of level 2 spectra, or just a temporary glitch.
# In case it's actually what lvel 2 spectral files look
# like, proper handling must be put in place to allow
# glue Data objects with different sized components. Or,
# if that is not feasible, to properly cut the arrays so
# as to make them all of the same size. The solution below
# is a naive interpretation of this concept.
x_min = 10000
y_min = 10000
for k in range(1, len(hdulist)):
if 'SCI' in hdulist[k].header['EXTNAME']:
x_min = min(x_min, hdulist[k].data.shape[0])
y_min = min(y_min, hdulist[k].data.shape[1])
# hdulist[k].header['CTYPE2'] = 'Spatial Y'
# wcs = WCS(hdulist[1].header)
# original WCS has both axes named "LAMBDA", glue requires unique component names
# data.coords = coordinates_from_wcs(wcs)
# data.header = hdulist[k].header
# data.add_component(hdulist[1].data['FLUX'][0], 'Flux')
count = 1
for k in range(1, len(hdulist)):
if 'SCI' in hdulist[k].header['EXTNAME']:
data.add_component(hdulist[k].data[0:x_min, 0:y_min], 'Flux_' + '{:03d}'.format(count))
count += 1
# data.add_component(1 / np.sqrt(hdulist[1].data['IVAR'][0]), 'Uncertainty')
return data
def acs_cutout_image_reader(file_name):
"""
Data loader for the ACS cut-outs for the DEIMOS spectra.
The cutouts contain only the image.
"""
hdulist = fits.open(file_name)
data = Data(label='ACS Cutout Image')
data.coords = coordinates_from_header(hdulist[0].header)
data.header = hdulist[0].header
data.add_component(hdulist[0].data, 'Flux')
return data
def nirspec_spectrum1d_reader(file_name):
with fits.open(file_name) as hdulist:
header = hdulist['PRIMARY'].header
tab = Table.read(file_name, hdu=1)
data = Data(label="1D Spectrum")
data.header = header
# This assumes the wavelength is in microns
data.coords = SpectralCoordinates(tab['WAVELENGTH'] * u.micron)
data.add_component(tab['WAVELENGTH'], "Wavelength")
data.add_component(tab['FLUX'], "Flux")
data.add_component(tab['ERROR'], "Uncertainty")
return data
def nirspec_spectrum2d_reader(file_name):
"""
Data loader for simulated NIRSpec 2D spectrum.
This function extracts the DATA, QUALITY, and VAR
extensions and returns them as a glue Data object.
It then uses the header keywords of the DATA extension
to detemine the wavelengths.
"""
hdulist = fits.open(file_name)
data = Data(label='2D Spectrum')
data.header = hdulist['DATA'].header
data.coords = coordinates_from_header(hdulist[1].header)
data.add_component(hdulist['DATA'].data, 'Flux')
data.add_component(np.sqrt(hdulist['VAR'].data), 'Uncertainty')
return data
def deimos_spectrum2D_reader(file_name):
"""
Data loader for Keck/DEIMOS 2D spectra.
This loads only the Flux and Inverse variance.
Wavelength information comes from the WCS.
"""
hdulist = fits.open(file_name)
data = Data(label='2D Spectrum')
hdulist[1].header['CTYPE2'] = 'Spatial Y'
wcs = WCS(hdulist[1].header)
# original WCS has both axes named "LAMBDA", glue requires unique component names
data.coords = coordinates_from_wcs(wcs)
data.header = hdulist[1].header
data.add_component(hdulist[1].data['FLUX'][0], 'Flux')
data.add_component(hdulist[1].data['IVAR'][0], 'Uncertainty')
return data
def nirspec_spectrum1d_reader(file_name):
file_name, ext = split_file_name(file_name, default_ext=1)
with fits.open(file_name) as hdulist:
header = hdulist['PRIMARY'].header
tab = Table.read(file_name, hdu=ext)
data = Data(label="1D Spectrum")
data.header = header
# This assumes the wavelength is in microns
data.coords = SpectralCoordinates(np.array(tab['WAVELENGTH']) * u.micron)
data.add_component(tab['WAVELENGTH'], "Wavelength")
data.add_component(tab['FLUX'], "Flux")
data.add_component(tab['ERROR'], "Uncertainty")
return data
def nirspec_spectrum2d_reader(file_name):
"""
Data loader for simulated NIRSpec 2D spectrum.
This function extracts the DATA, QUALITY, and VAR
extensions and returns them as a glue Data object.
It then uses the header keywords of the DATA extension
to detemine the wavelengths.
"""
hdulist = fits.open(file_name)
data = Data(label='2D Spectrum')
data.header = hdulist['DATA'].header
data.coords = coordinates_from_header(hdulist[1].header)
data.add_component(hdulist['DATA'].data, 'Flux')
data.add_component(hdulist['VAR'].data, 'Uncertainty')
return data
def deimos_spectrum2D_reader(file_name):
"""
Data loader for Keck/DEIMOS 2D spectra.
This loads only the Flux and Inverse variance.
Wavelength information comes from the WCS.
"""
hdulist = fits.open(file_name)
data = Data(label='2D Spectrum')
hdulist[1].header['CTYPE2'] = 'Spatial Y'
wcs = WCS(hdulist[1].header)
# original WCS has both axes named "LAMBDA", glue requires unique component names
data.coords = coordinates_from_wcs(wcs)
data.header = hdulist[1].header
data.add_component(hdulist[1].data['FLUX'][0], 'Flux')
data.add_component(1 / np.sqrt(hdulist[1].data['IVAR'][0]), 'Uncertainty')
return data
def pre_nircam_image_reader(file_name):
"""
Data loader for simulated NIRCam image. This is for the
full image, where cut-outs will be created on the fly.
From the header:
If ISWFS is T, structure is:
- Plane 1: Signal [frame3 - frame1] in ADU
- Plane 2: Signal uncertainty [sqrt(2*RN/g + \|frame3\|)]
If ISWFS is F, structure is:
- Plane 1: Signal from linear fit to ramp [ADU/sec]
- Plane 2: Signal uncertainty [ADU/sec]
Note that in the later case, the uncertainty is simply the formal
uncertainty in the fit parameter (eg. uncorrelated, WRONG!). Noise
model to be implemented at a later date.
In the case of WFS, error is computed as SQRT(2*sigma_read + \|frame3\|)
which should be a bit more correct - ~Fowler sampling.
The FITS file has a single extension with a data cube.
The data is the first slice of the cube and the uncertainty
is the second slice.
"""
hdulist = fits.open(file_name)
data = Data(label='NIRCam Image')
data.header = hdulist[0].header
wcs = WCS(hdulist[0].header)
# drop the last axis since the cube will be split
data.coords = coordinates_from_wcs(wcs)
data.add_component(hdulist[0].data, 'Flux')
data.add_component(hdulist[0].data / 100, 'Uncertainty')
hdulist.close()
return data
def pre_nircam_image_reader(file_name):
"""
Data loader for simulated NIRCam image. This is for the
full image, where cut-outs will be created on the fly.
From the header:
If ISWFS is T, structure is:
- Plane 1: Signal [frame3 - frame1] in ADU
- Plane 2: Signal uncertainty [sqrt(2*RN/g + \|frame3\|)]
If ISWFS is F, structure is:
- Plane 1: Signal from linear fit to ramp [ADU/sec]
- Plane 2: Signal uncertainty [ADU/sec]
Note that in the later case, the uncertainty is simply the formal
uncertainty in the fit parameter (eg. uncorrelated, WRONG!). Noise
model to be implemented at a later date.
In the case of WFS, error is computed as SQRT(2*sigma_read + \|frame3\|)
which should be a bit more correct - ~Fowler sampling.
The FITS file has a single extension with a data cube.
The data is the first slice of the cube and the uncertainty
is the second slice.
"""
hdulist = fits.open(file_name)
data = Data(label='NIRCam Image')
data.header = hdulist[0].header
wcs = WCS(hdulist[0].header)
# drop the last axis since the cube will be split
data.coords = coordinates_from_wcs(wcs)
data.add_component(hdulist[0].data, 'Flux')
data.add_component(hdulist[0].data / 100, 'Uncertainty')
hdulist.close()
return data
def nirspec_level2_reader(file_name):
"""
Data Loader for level2 products.
Uses extension information to index
fits hdu list. The ext info is included
in the file_name as follows: <file_path>[<ext>]
"""
file_name, ext = split_file_name(file_name, default_ext=1)
hdulist = fits.open(file_name)
data = Data(label="2D Spectra")
data.header = hdulist[ext].header
data.coords = coordinates_from_header(hdulist[ext].header)
data.add_component(hdulist[ext].data, 'Level2 Flux')
# TODO: update uncertainty once data model becomes clear
data.add_component(np.sqrt(hdulist[ext + 2].data), 'Level2 Uncertainty')
hdulist.close()
return data
def nirspec_level2_reader(file_name):
"""
Data Loader for level2 products.
Uses extension information to index
fits hdu list. The ext info is included
in the file_name as follows: <file_path>[<ext>]
"""
file_name, ext = split_file_name(file_name)
hdulist = fits.open(file_name)
data = Data(label="2D Spectra")
data.header = hdulist[ext].header
data.coords = coordinates_from_header(hdulist[ext].header)
data.add_component(hdulist[ext].data, 'Level2 Flux')
# TODO: update uncertainty once data model becomes clear
data.add_component(np.sqrt(hdulist[ext + 2].data), 'Level2 Uncertainty')
hdulist.close()
return data
def nirspec_spectrum2d_reader(file_name):
"""
Data loader for simulated NIRSpec 2D spectrum.
This function extracts the DATA, QUALITY, and VAR
extensions and returns them as a glue Data object.
It then uses the header keywords of the DATA extension
to detemine the wavelengths.
"""
file_name, ext = split_file_name(file_name, default_ext=1)
hdulist = fits.open(file_name)
data = Data(label="2D Spectrum")
data.header = hdulist['PRIMARY'].header
data.coords = coordinates_from_header(hdulist[ext].header)
data.add_component(hdulist[ext].data, 'Flux')
data.add_component(np.sqrt(hdulist[ext + 2].data), 'Uncertainty')
hdulist.close()
return data
def nirspec_spectrum2d_reader(file_name):
"""
Data loader for simulated NIRSpec 2D spectrum.
This function extracts the DATA, QUALITY, and VAR
extensions and returns them as a glue Data object.
It then uses the header keywords of the DATA extension
to detemine the wavelengths.
"""
file_name, ext = split_file_name(file_name, default_ext=1)
hdulist = fits.open(file_name)
data = Data(label="2D Spectrum")
data.header = hdulist['PRIMARY'].header
data.coords = coordinates_from_header(hdulist[ext].header)
data.add_component(hdulist[ext].data, 'Flux')
data.add_component(np.sqrt(hdulist[ext + 2].data), 'Uncertainty')
hdulist.close()
return data
def deimos_spectrum1D_reader(file_name):
"""
Data loader for Keck/DEIMOS 1D spectra.
This loads the 'Bxspf-B' (extension 1)
and 'Bxspf-R' (extension 2) and appends them
together to proudce the combined Red/Blue Spectrum
along with their Wavelength and Inverse Variance
arrays.
"""
hdulist = fits.open(file_name)
data = Data(label='1D Spectrum')
data.header = hdulist[1].header
full_wl = np.append(hdulist[1].data['LAMBDA'][0], hdulist[2].data['LAMBDA'][0])
full_spec = np.append(hdulist[1].data['SPEC'][0], hdulist[2].data['SPEC'][0])
full_ivar = np.append(hdulist[1].data['IVAR'][0], hdulist[2].data['IVAR'][0])
data.add_component(full_wl, 'Wavelength')
data.add_component(full_spec, 'Flux')
data.add_component(full_ivar, 'Uncertainty')
return data
