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Use nanmedian3D for TSO data in outlier detection #8859

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merged 4 commits into from
Oct 16, 2024
Merged

Use nanmedian3D for TSO data in outlier detection #8859

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e91c002
changed to nanmedian3D
emolter Oct 7, 2024
42b7036
Merge branch 'main' of https://github.com/spacetelescope/jwst into ts…
emolter Oct 7, 2024
e4229d3
Merge branch 'main' of https://github.com/spacetelescope/jwst into ts…
emolter Oct 16, 2024
7c3639b
added simple median case for TSO data to unit tests
emolter Oct 16, 2024
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15 changes: 10 additions & 5 deletions jwst/outlier_detection/tests/test_outlier_detection.py
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Original file line number Diff line number Diff line change
Expand Up @@ -553,13 +553,13 @@ def test_outlier_step_image_weak_cr_coron(exptype, tsovisit, tmp_cwd):


@pytest.mark.parametrize("exptype, tsovisit", exptypes_tso)
def test_outlier_step_weak_cr_tso(exptype, tsovisit):
@pytest.mark.parametrize("rolling_window_width", [7, 0])
def test_outlier_step_weak_cr_tso(exptype, tsovisit, rolling_window_width):
'''Test outlier detection with rolling median on time-varying source
This test fails if rolling_window_width is set to 100, i.e., take simple median
This test fails if rolling_window_width is set to 0, i.e., take simple median
'''
bkg = 1.5
sig = 0.02
rolling_window_width = 7
numsci = 50
signal = 7.0
im = we_many_sci(
Expand Down Expand Up @@ -588,8 +588,13 @@ def test_outlier_step_weak_cr_tso(exptype, tsovisit):
assert np.all(np.isnan(result.data[i][dnu]))
assert np.allclose(model.data[~dnu], result.data[i][~dnu])

# Verify source is not flagged
assert np.all(result.dq[:, 7, 7] == datamodels.dqflags.pixel["GOOD"])
# Verify source is not flagged for rolling median
if rolling_window_width == 7:
assert np.all(result.dq[:, 7, 7] == datamodels.dqflags.pixel["GOOD"])
# But this fails for simple median
elif rolling_window_width == 0:
with pytest.raises(AssertionError):
assert np.all(result.dq[:, 7, 7] == datamodels.dqflags.pixel["GOOD"])

# Verify CR is flagged
assert result.dq[cr_timestep, 12, 12] == OUTLIER_DO_NOT_USE
Expand Down
4 changes: 2 additions & 2 deletions jwst/outlier_detection/tso.py
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Expand Up @@ -4,7 +4,7 @@
from jwst import datamodels as dm

from stcal.outlier_detection.utils import compute_weight_threshold
from .utils import flag_model_crs
from .utils import flag_model_crs, nanmedian3D
from ._fileio import save_median

import logging
Expand Down Expand Up @@ -41,7 +41,7 @@ def detect_outliers(
medians = compute_rolling_median(weighted_cube, weight_threshold, w=rolling_window_width)

else:
medians = np.nanmedian(weighted_cube.data, axis=0)
medians = nanmedian3D(weighted_cube.data, overwrite_input=False)
# this is a 2-D array, need to repeat it into the time axis
# for consistent shape with rolling median case
medians = np.broadcast_to(medians, weighted_cube.shape)
Expand Down
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