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Correct handling of linear model fitting failure #187

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Apr 7, 2023
Merged

Correct handling of linear model fitting failure #187

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15 changes: 12 additions & 3 deletions ashlar/reg.py
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Original file line number Diff line number Diff line change
Expand Up @@ -532,7 +532,7 @@ def check_overlaps(self):
warn_data("Some neighboring tiles have zero overlap.")

def compute_threshold(self):
if self.max_error:
if self.max_error is not None:
if self.verbose:
print(" using explicit error threshold")
return
Expand Down Expand Up @@ -674,9 +674,18 @@ def fit_model(self):
cc0 = list(components[0])
self.lr = sklearn.linear_model.LinearRegression()
self.lr.fit(self.metadata.positions[cc0], self.positions[cc0])
# Fix up degenerate transform matrix (e.g. when we have only one tile).
if (self.lr.coef_ == 0).all():
# Fix up degenerate transform matrix. This happens when the spanning
# tree is completely edgeless or cc0's metadata positions fall in a
# straight line. In this case we fall back to the identity transform.
if np.linalg.det(self.lr.coef_) < 1e-3:
# FIXME We should probably exit here, not just warn. We may provide
# an option to force it anyway.
warn_data(
"Could not align enough edges, proceeding anyway with original"
" stage positions."
)
self.lr.coef_ = np.diag(np.ones(2))
self.lr.intercept_ = np.zeros(2)
# Adjust position of remaining components so their centroids match
# the predictions of the model.
for cc in components[1:]:
Expand Down