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Background correction
=====================
The `specreduce.background` module generates and subtracts a background image from
the input 2D spectral image. The `~specreduce.background.Background` object is
defined by one or more windows, where each window is a region parallel to a
`~specreduce.tracing.Trace`, offset from that `~specreduce.tracing.Trace` by a
specified separation in the cross-dispersion direction, and extending over a
specified width (also measured along the cross-dispersion axis) in pixels. The
object can be generated with:
* `~specreduce.background.Background`
* `Background.one_sided <specreduce.background.Background.one_sided>`
* `Background.two_sided <specreduce.background.Background.two_sided>`
The center of the window can either be passed as a float/integer or as a
`~specreduce.tracing.Trace`.
.. code-block:: python
bg = specreduce.background.Background.one_sided(image, trace, separation=5, width=2)
or, equivalently
.. code-block:: python
bg = specreduce.background.Background.one_sided(image, 15, separation=5, width=2)
The background image can be accessed via `~specreduce.background.Background.bkg_image`
and the background-subtracted image via `~specreduce.background.Background.sub_image`
(or ``image - bg``).
The background and trace steps can be done iteratively, to refine an automated
trace using the background-subtracted image as input.
Outlier rejection
-----------------
The background estimation supports sigma clipping for outlier rejection, which is useful
for removing cosmic rays and other artifacts from the background region. The ``sigma``
parameter controls the number of standard deviations used for clipping (default is 5.0).
Set ``sigma=None`` to disable sigma clipping.
.. code-block:: python
# Use tighter sigma clipping for aggressive outlier rejection
bg = Background.two_sided(image, trace, separation=5, width=4, sigma=3.0)
# Disable sigma clipping
bg = Background.two_sided(image, trace, separation=5, width=4, sigma=None)
Uncertainty propagation
-----------------------
The `~specreduce.background.Background` class propagates uncertainties through
background subtraction. When the input image is an `~astropy.nddata.NDData` object
with ``image.uncertainty`` provided, the uncertainties are propagated using variance
formulas appropriate for the chosen statistic (``average`` or ``median``). When no
uncertainty is provided, it is estimated from the variance of flux values in the
background region.
The background image, background spectrum, and background-subtracted outputs all
include proper uncertainties:
.. code-block:: python
bg = Background.two_sided(image, trace, separation=5, width=4)
# Access background with uncertainty
bkg_spec = bg.bkg_spectrum()
print(bkg_spec.uncertainty)
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