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fix: index shift correction for blob detection in 2d tiles #2076

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fix: index shift correction for blob detection in 2d tiles #2076

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6 changes: 3 additions & 3 deletions starfish/core/spots/FindSpots/blob.py
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Original file line number Diff line number Diff line change
Expand Up @@ -137,18 +137,18 @@ def image_to_spots(

# measure intensities
data_image = np.asarray(data_image)
if self.is_volume:
if fitted_blobs_array.shape[1] == 4:
z_inds = fitted_blobs_array[:, 0].astype(int)
y_inds = fitted_blobs_array[:, 1].astype(int)
x_inds = fitted_blobs_array[:, 2].astype(int)
radius = np.round(fitted_blobs_array[:, 3] * np.sqrt(3))
intensities = data_image[tuple([z_inds, y_inds, x_inds])]
else:
elif fitted_blobs_array.shape[1] == 3:
Comment on lines +140 to +146
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Copilot AI Oct 21, 2025

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The condition checks only for shapes with 3 or 4 columns, but doesn't handle other potential array shapes. If fitted_blobs_array.shape[1] is neither 3 nor 4, the code will silently skip both branches, leaving intensities, z_inds, y_inds, x_inds, and radius undefined, which will cause errors in subsequent code. Add an else clause to raise an informative error for unexpected array shapes.

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z_inds = np.asarray([0 for x in range(len(fitted_blobs_array))])
y_inds = fitted_blobs_array[:, 0].astype(int)
x_inds = fitted_blobs_array[:, 1].astype(int)
radius = np.round(fitted_blobs_array[:, 2] * np.sqrt(2))
intensities = data_image[tuple([z_inds, y_inds, x_inds])]
intensities = data_image[tuple([y_inds, x_inds])]

# construct dataframe
spot_data = pd.DataFrame(
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