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center scale for spatial normalization callback #697

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center scale for spatial normalization callback #697

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ed00ac4
center scale
giovp May 22, 2024
fe37d7b
update
giovp Jun 17, 2024
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remove comment
giovp Jun 17, 2024
516eb9b
update
giovp Jun 17, 2024
08efd4e
Merge branch 'main' into giovp/spatial_callback
giovp Jun 17, 2024
778b534
Merge branch 'main' into giovp/spatial_callback
giovp Jul 9, 2024
7b566fc
Merge branch 'main' into giovp/spatial_callback
giovp Jul 23, 2024
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2 changes: 1 addition & 1 deletion docs/notebooks
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Submodule notebooks updated 10 files
+1 −90 CONTRIBUTING.rst
+1 −1 examples/plotting/300_sankey.ipynb
+35 −40 examples/problems/100_tagged_arrays.ipynb
+22 −68 examples/problems/200_custom_cost_matrices.ipynb
+4 −4 examples/problems/900_annotation_mapping.ipynb
+15 −34 examples/solvers/300_quad_problems_basic.ipynb
+17 −27 examples/solvers/400_quad_problems_advanced.ipynb
+5 −7 tutorials/100_lineage.ipynb
+2 −2 tutorials/600_tutorial_translation.ipynb
+0 −463 tutorials/700_tutorial_perturbation_with_Sinkhorn.ipynb
2 changes: 1 addition & 1 deletion src/moscot/base/problems/problem.py
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Original file line number Diff line number Diff line change
Expand Up @@ -630,7 +630,7 @@ def _spatial_norm_callback(
spatial = TaggedArray._extract_data(adata, attr=attr, key=key)

logger.info(f"Normalizing spatial coordinates of `{term}`.")
spatial = (spatial - spatial.mean()) / spatial.std()
spatial = (spatial - spatial.mean(axis=0)) / spatial.std()
return TaggedArray(spatial, tag=Tag.POINT_CLOUD)

@staticmethod
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3 changes: 1 addition & 2 deletions tests/problems/space/test_alignment_problem.py
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Expand Up @@ -51,8 +51,7 @@ def test_prepare_sequential(
ap = ap.prepare(batch_key="batch", joint_attr=joint_attr, normalize_spatial=normalize_spatial)
assert len(ap) == 2
if normalize_spatial:
np.testing.assert_allclose(ap[("1", "2")].x.data_src.std(), ap[("0", "1")].x.data_src.std(), atol=1e-15)
np.testing.assert_allclose(ap[("1", "2")].x.data_src.std(), 1.0, atol=1e-15)
np.testing.assert_allclose(ap[("1", "2")].x.data_src.std(), ap[("0", "1")].y.data_src.std(), atol=1e-15)
np.testing.assert_allclose(ap[("1", "2")].x.data_src.mean(), 0, atol=1e-15)
np.testing.assert_allclose(ap[("0", "1")].x.data_src.mean(), 0, atol=1e-15)

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