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Fix for issue #240 #243

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merged 2 commits into from
Feb 5, 2025
Merged

Fix for issue #240 #243

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26619b3
Fixed issue #240 where Horne extraction would crash if the 2D spectru…
hpparvi Jan 17, 2025
a361185
Codestyle fix.
hpparvi Jan 17, 2025
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34 changes: 15 additions & 19 deletions specreduce/extract.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,7 @@
from astropy import units as u
from astropy.modeling import Model, models, fitting
from astropy.nddata import NDData, VarianceUncertainty
from numpy import ndarray
from scipy.integrate import trapezoid
from scipy.interpolate import RectBivariateSpline
from specutils import Spectrum1D
Expand Down Expand Up @@ -445,27 +446,21 @@ def _parse_image(self, image,
return Spectrum1D(img * unit, spectral_axis=spectral_axis,
uncertainty=variance, mask=mask)

def _fit_gaussian_spatial_profile(self, img, disp_axis, crossdisp_axis,
or_mask, bkgrd_prof):
def _fit_gaussian_spatial_profile(self, img: ndarray, disp_axis: int, crossdisp_axis: int,
or_mask: ndarray, bkgrd_prof: Model):
"""Fit a 1D Gaussian spatial profile to spectrum in `img`.

Fits an 1D Gaussian profile to spectrum in `img`. Takes the weighted mean
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Suggested change
Fits an 1D Gaussian profile to spectrum in `img`. Takes the weighted mean
Fits a 1D Gaussian profile to spectrum in `img`. Takes the weighted mean

of ``img`` along the cross-dispersion axis all ignoring masked pixels
(i.e, takes the mean of each row for a horizontal trace). A Background model
(optional) is fit simultaneously. Returns an `astropy.model.Gaussian1D` (or
compound model, if `bkgrd_prof` is supplied) fit to data.
"""
Fits an 1D Gaussian profile to spectrum in `img`. Takes the mean
of ``img`` along the cross-dispersion axis (i.e, takes the mean of
each row for a horizontal trace). Any columns with non-finite values
are omitted from the fit. Background model (optional) is fit
simultaneously. Returns an `astropy.model.Gaussian1D` (or compound
model, if `bkgrd_prof` is supplied) fit to data.
"""

# co-add signal in each image row
nrows = img.shape[crossdisp_axis]
ncols = img.shape[disp_axis]
xd_pixels = np.arange(nrows)

# for now, mask row with any non-finite value.
row_mask = np.logical_or.reduce(or_mask, axis=disp_axis)
coadd = np.ma.masked_array(np.sum(img, axis=disp_axis) / ncols,
mask=row_mask)
# co-add signal in each image row, ignore masked pixels
coadd = np.ma.masked_array(img, mask=or_mask).mean(disp_axis)

# use the sum of brightest row as an inital guess for Gaussian amplitude,
# the the location of the brightest row as an initial guess for the mean
Expand All @@ -481,9 +476,10 @@ def _fit_gaussian_spatial_profile(self, img, disp_axis, crossdisp_axis,
# add a trivial constant model so attribute names are the same
ext_prof = gauss_prof + models.Const1D(0, fixed={'amplitude': True})

fitter = fitting.LevMarLSQFitter()
fit_ext_kernel = fitter(ext_prof, xd_pixels[~row_mask], coadd[~row_mask])

with warnings.catch_warnings():
warnings.simplefilter('ignore')
fitter = fitting.LMLSQFitter()
fit_ext_kernel = fitter(ext_prof, xd_pixels[~coadd.mask], coadd.compressed())
return fit_ext_kernel

def _fit_self_spatial_profile(self, img, disp_axis, crossdisp_axis, or_mask,
Expand Down
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