Make sure number of clusters in misfit preprocessor is at least 1

src/ert/analysis/misfit_preprocessor.py

@@ -34,6 +34,9 @@ def get_nr_primary_components(
     """
     Calculate the number of principal components needed to achieve a cumulative
     variance less than a specified threshold using Singular Value Decomposition (SVD).
+
+    responses is expected to be on format n x p, where n is number of realizations and
+    p is number of observations.
     """
     data_matrix = responses - responses.mean(axis=0)
     _, singulars, _ = np.linalg.svd(data_matrix.astype(float), full_matrices=False)
@@ -42,7 +45,7 @@ def get_nr_primary_components(
     # We compute the cumulative sum of these, then divide by their total sum to get the
     # cumulative proportion of variance explained by each successive component.
     variance_ratio = np.cumsum(singulars**2) / np.sum(singulars**2)
-    return len([1 for i in variance_ratio[:-1] if i < threshold])
+    return int(np.argmax(variance_ratio >= threshold)) + 1
 
 
 def cluster_responses(
@@ -139,7 +142,7 @@ def main(
         # each other
         return scale_factors, np.ones(len(obs_errors), dtype=int), nr_components
 
-    prim_components = get_nr_primary_components(scaled_responses, threshold=0.95)
+    prim_components = get_nr_primary_components(scaled_responses.T, threshold=0.95)
 
     clusters = cluster_responses(scaled_responses.T, nr_clusters=prim_components)
 
@@ -150,9 +153,8 @@ def main(
             components = 1
         else:
             components = get_nr_primary_components(
-                scaled_responses[index], threshold=0.95
+                scaled_responses[index].T, threshold=0.95
             )
-            components = 1 if components == 0 else components
         scale_factor = get_scaling_factor(len(index), components)
         nr_components[index] *= components
         scale_factors[index] *= scale_factor

tests/ert/unit_tests/analysis/test_misfit_preprocessor.py

@@ -52,6 +52,9 @@ def test_that_get_nr_primary_components_is_according_to_theory():
     assert get_nr_primary_components(Y, threshold_2 + 0.01) == 2
     assert get_nr_primary_components(Y, threshold_3 + 0.01) == 3
 
+    # check that we always return at least 1
+    assert get_nr_primary_components(Y, 0) == 1
+
 
 @pytest.mark.parametrize("nr_observations", [4, 10, 100])
 def test_misfit_preprocessor(nr_observations):