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Fix lookup table enhancement removing transparency (NaNs) #3377

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Merged
mraspaud merged 5 commits into from
May 4, 2026
Merged

Fix lookup table enhancement removing transparency (NaNs) #3377

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b737d08
Preserve NaNs
Grukank Apr 13, 2026
98e3ed0
Update test_lookup to reflect preserved NaN values
Grukank Apr 13, 2026
39b485e
Add name to AUTHORS
Grukank Apr 13, 2026
9b97ef6
Remove white space
Grukank Apr 13, 2026
4c5ade6
Move implementation into _lookup_table function instead
Grukank Apr 20, 2026
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1 change: 1 addition & 0 deletions AUTHORS.md
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Original file line number Diff line number Diff line change
Expand Up @@ -115,3 +115,4 @@ The following people have made contributions to this project:
- [Albert Brotzer](https://github.com/albertbrotzer)
- [Alexandra Melzer](https://github.com/armelzer)
- [Francesc Lucas Carbó (cesclc)](https://github.com/cesclc)
- [Göte Kleringer (Grukank)](https://github.com/Grukank)
6 changes: 5 additions & 1 deletion satpy/enhancements/colormap.py
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Expand Up @@ -41,12 +41,16 @@ def lookup(img, **kwargs):
@on_separate_bands
@using_map_blocks
def _lookup_table(band_data, luts=None, index=-1):
# Save positions of NaNs
nans = np.isfinite(band_data)
# NaN/null values will become 0
lut = luts[:, index] if len(luts.shape) == 2 else luts
band_data = band_data.clip(0, lut.size - 1)
# Convert to uint8, with NaN/null values changed into 0
band_data = np.nan_to_num(band_data).astype(np.uint8)
return lut[band_data]
# Lookup data, but with replaced NaNs from saved positions
res = np.where(nans, lut[band_data], np.nan)
return res


def colorize(img, **kwargs): # noqa: D417
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8 changes: 4 additions & 4 deletions satpy/tests/enhancement_tests/test_colormap.py
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Expand Up @@ -42,16 +42,16 @@ def test_lookup(self):
"""Test the lookup enhancement function."""
from satpy.enhancements.colormap import lookup
expected = np.array([[
[0., 0., 0., 0.333333, 0.705882],
[np.nan, 0., 0., 0.333333, 0.705882],
[1., 1., 1., 1., 1.]]])
lut = np.arange(256.)
run_and_check_enhancement(lookup, self.ch1, expected, luts=lut)

expected = np.array([[[0., 0., 0., 0.333333, 0.705882],
expected = np.array([[[np.nan, 0., 0., 0.333333, 0.705882],
[1., 1., 1., 1., 1.]],
[[0., 0., 0., 0.333333, 0.705882],
[[np.nan, 0., 0., 0.333333, 0.705882],
[1., 1., 1., 1., 1.]],
[[0., 0., 0., 0.333333, 0.705882],
[[np.nan, 0., 0., 0.333333, 0.705882],
[1., 1., 1., 1., 1.]]])
lut = np.arange(256.)
lut = np.vstack((lut, lut, lut)).T
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