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Fix for the mask_treatment parameter documentation in the BoxcarExtract docstring. #236

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merged 2 commits into from
Dec 19, 2024
Merged

Fix for the mask_treatment parameter documentation in the BoxcarExtract docstring. #236

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de5601b
Fixed the 'mask_treatment' parameter documentation in the BoxCarExtra…
hpparvi Dec 18, 2024
cc1cec8
Fixed the code style errors.
hpparvi Dec 19, 2024
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58 changes: 29 additions & 29 deletions specreduce/extract.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,7 @@

import warnings
from dataclasses import dataclass, field
from typing import Literal

import numpy as np
from astropy import units as u
Expand Down Expand Up @@ -125,7 +126,7 @@ def _ap_weight_image(trace, width, disp_axis, crossdisp_axis, image_shape):
@dataclass
class BoxcarExtract(SpecreduceOperation):
"""
Does a standard boxcar extraction.
Standard boxcar extraction along a trace.

Example: ::

Expand All @@ -136,67 +137,66 @@ class BoxcarExtract(SpecreduceOperation):

Parameters
----------
image : `~astropy.nddata.NDData`-like or array-like, required
image
image with 2-D spectral image data
trace_object : Trace, required
trace_object
trace object
width : float, optional
width
width of extraction aperture in pixels
disp_axis : int, optional
disp_axis
dispersion axis
crossdisp_axis : int, optional
crossdisp_axis
cross-dispersion axis
mask_treatment
The method for handling masked or non-finite data. Choice of `filter`,
`omit`, or `zero-fill`. If `filter` is chosen, the mask is ignored
and the non-finite data will passed to the extraction as is. If `omit`
is chosen, columns along disp_axis with any masked or non-finite data
values will be fully masked (i.e, 2D mask is collapsed to 1D and applied).
If `zero-fill` is chosen, masked and non-finite data will be replaced
with 0.0 in the input image, and the mask will then be dropped.
For all three options, the input mask (optional on input NDData object)
will be combined with a mask generated from any non-finite values in the
image data.

Returns
-------
spec : `~specutils.Spectrum1D`
The extracted 1d spectrum expressed in DN and pixel units
"""
image: NDData
image: NDData | np.ndarray
trace_object: Trace
width: float = 5
disp_axis: int = 1
crossdisp_axis: int = 0
# TODO: should disp_axis and crossdisp_axis be defined in the Trace object?
mask_treatment: str = 'filter'
mask_treatment: Literal['filter', 'omit', 'zero-fill'] = 'filter'
_valid_mask_treatment_methods = ('filter', 'omit', 'zero-fill')

@property
def spectrum(self):
return self.__call__()

def __call__(self, image=None, trace_object=None, width=None,
disp_axis=None, crossdisp_axis=None):
def __call__(self, image: NDData | None = None,
trace: Trace | None = None,
width: float | None = None,
disp_axis: int | None = None,
crossdisp_axis: int | None = None):
"""
Extract the 1D spectrum using the boxcar method.

Parameters
----------
image : `~astropy.nddata.NDData`-like or array-like, required
image with 2-D spectral image data
trace_object : Trace, required
trace : Trace, required
trace object
width : float, optional
width of extraction aperture in pixels [default: 5]
disp_axis : int, optional
dispersion axis [default: 1]
crossdisp_axis : int, optional
cross-dispersion axis [default: 0]
mask_treatment : string, optional
The method for handling masked or non-finite data. Choice of `filter`,
`omit`, or `zero-fill`. If `filter` is chosen, masked/non-finite data
will be filtered during the fit to each bin/column (along disp. axis) to
find the peak. If `omit` is chosen, columns along disp_axis with any
masked/non-finite data values will be fully masked (i.e, 2D mask is
collapsed to 1D and applied). If `zero-fill` is chosen, masked/non-finite
data will be replaced with 0.0 in the input image, and the mask will then
be dropped. For all three options, the input mask (optional on input
NDData object) will be combined with a mask generated from any non-finite
values in the image data. Also note that because binning is an option in
FitTrace, that masked data will contribute zero to the sum when binning
adjacent columns.
[default: ``filter``]


Returns
-------
Expand All @@ -205,7 +205,7 @@ def __call__(self, image=None, trace_object=None, width=None,
units as the input image, or u.DN, and pixel units
"""
image = image if image is not None else self.image
trace_object = trace_object if trace_object is not None else self.trace_object
trace = trace if trace is not None else self.trace_object
width = width if width is not None else self.width
disp_axis = disp_axis if disp_axis is not None else self.disp_axis
crossdisp_axis = crossdisp_axis if crossdisp_axis is not None else self.crossdisp_axis
Expand All @@ -224,7 +224,7 @@ def __call__(self, image=None, trace_object=None, width=None,
raise ValueError("width must be positive")

# weight image to use for extraction
wimg = _ap_weight_image(trace_object,
wimg = _ap_weight_image(trace,
width,
disp_axis,
crossdisp_axis,
Expand Down Expand Up @@ -317,7 +317,7 @@ class HorneExtract(SpecreduceOperation):
image: NDData
trace_object: Trace
bkgrd_prof: Model = field(default=models.Polynomial1D(2))
spatial_profile: str = 'gaussian' # can actually be str, dict
spatial_profile: str | dict = 'gaussian'
variance: np.ndarray = field(default=None)
mask: np.ndarray = field(default=None)
unit: np.ndarray = field(default=None)
Expand Down
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