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Exclude 'zero-fill' as a masking option for FitTrace #2

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Dec 9, 2024
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Exclude 'zero-fill' as a masking option for FitTrace #2

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39 changes: 7 additions & 32 deletions specreduce/tests/test_tracing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -417,21 +417,11 @@ def test_mask_treatment_filter(self, peak_method, expected):
np.testing.assert_allclose(trace.trace, all_bin_fit)

@pytest.mark.filterwarnings("ignore:All pixels in bins")
@pytest.mark.parametrize("peak_method,expected",
[("max", [5., 3., 5., 5., 7., 5.,
0., 5., 5., 5., 2., 5.]),
("gaussian", [5., 1.09382384, 5., 5., 7.81282206,
5., 0., 5., 5., 5., 1.28216332, 5.]),
("centroid", [4.27108332, 2.24060342, 4.27108332,
4.27108332, 6.66827608, 4.27108332,
9., 4.27108332, 4.27108332,
4.27108332, 1.19673467, 4.27108332])])
def test_mask_treatment_zero_fill(self, peak_method, expected):
@pytest.mark.parametrize("peak_method", ["max", "gaussian", "centroid"])
def test_mask_treatment_zero_fill(self, peak_method):
"""
Test for mask_treatment=`zero_fill` for FitTrace.
Masked and nonfinite data are replaced with zero in the data array,
and the input mask is then dropped. Parametrized over different
`peak_method` options.
Test to ensure mask_treatment=`zero_fill` for FitTrace raises a `ValueError`.
Parametrized over different `peak_method` options.
"""

# Make an image with some nonfinite values.
Expand All @@ -441,27 +431,12 @@ def test_mask_treatment_zero_fill(self, peak_method, expected):

# Also make an image that doesn't have nonf data values, but has masked
# values at the same locations, to make sure they give the same results.
mask = ~np.isfinite(image1)
dat = mk_img(nrows=10, ncols=12, add_noise=False)
image2 = NDData(dat, mask=mask)
image2 = NDData(dat, mask=~np.isfinite(image1))

for imgg in [image1, image2]:
# run FitTrace, with the testing-only flag _save_bin_peaks_testing set
# to True to return the bin peak values before fitting the trace
trace = FitTrace(imgg, peak_method=peak_method,
mask_treatment='zero-fill',
_save_bin_peaks_testing=True)
x_bins, y_bins = trace._bin_peaks_testing
np.testing.assert_allclose(y_bins, expected)

# check that final fit to all bins, accouting for fully-masked bins,
# matches the trace
fitter = fitting.LevMarLSQFitter()
mask = np.isfinite(y_bins)
all_bin_fit = fitter(trace.trace_model, x_bins[mask], y_bins[mask])
all_bin_fit = all_bin_fit((np.arange(12)))

np.testing.assert_allclose(trace.trace, all_bin_fit)
with pytest.raises(ValueError):
FitTrace(imgg, peak_method=peak_method, mask_treatment='zero-fill')

@pytest.mark.filterwarnings("ignore:All pixels in bins")
@pytest.mark.parametrize("peak_method,expected",
Expand Down
42 changes: 22 additions & 20 deletions specreduce/tracing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,7 @@
import warnings
from copy import deepcopy
from dataclasses import dataclass, field
from typing import Literal

import numpy as np
from astropy.modeling import Model, fitting, models
Expand Down Expand Up @@ -256,37 +257,38 @@ class FitTrace(Trace, _ImageParser):
``max``: Saves the position with the maximum flux in each bin.
[default: ``max``]
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.
The method for handling masked or non-finite data. Choice of `filter` or
`omit`. 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).
For both 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``]

"""
bins: int = None
guess: float = None
window: int = None
bins: int | None = None
guess: float | None = None
window: int | None = None
trace_model: Model = field(default=models.Polynomial1D(degree=1))
peak_method: str = 'max'
_crossdisp_axis = 0
_disp_axis = 1
mask_treatment: str = 'filter'
_valid_mask_treatment_methods = ('filter', 'omit', 'zero-fill')
peak_method: Literal['gaussian', 'centroid', 'max'] = 'max'
_crossdisp_axis: int = 0
_disp_axis: int = 1
mask_treatment: Literal['filter', 'omit'] = 'filter'
_valid_mask_treatment_methods = ('filter', 'omit')
# for testing purposes only, save bin peaks if requested
_save_bin_peaks_testing: bool = False

def __post_init__(self):

# Parse image, including masked/nonfinite data handling based on
# choice of `mask_treatment`. returns a Spectrum1D
if self.mask_treatment not in self._valid_mask_treatment_methods:
raise ValueError("`mask_treatment` must be one of "
f"{self._valid_mask_treatment_methods}")

self.image = self._parse_image(self.image, disp_axis=self._disp_axis,
mask_treatment=self.mask_treatment)

Expand Down
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