hold

src/spikeinterface/preprocessing/tests/test_whiten.py

@@ -475,20 +475,17 @@ def test_passed_W_and_M(self):
 
     def test_whiten_general(self, create_cache_folder):
         """
-<<<<<<< HEAD
-=======
         Perform some general tests on the whitening functionality.
 
         First, perform smoke test that `compute_whitening_matrix` is running,
         check recording output datatypes are as expected. Check that
         saving preseves datatype, `int_scale` is propagated, and
         regularisation reduces the norm.
->>>>>>> 19105e25a (Finalise tests, tidy up and add docs.)
         """
         cache_folder = create_cache_folder
         rec = generate_recording(num_channels=4, seed=2205)
 
-        random_chunk_kwargs = {}
+        random_chunk_kwargs = {"seed": 2205}
         W1, M = compute_whitening_matrix(rec, "global", random_chunk_kwargs, apply_mean=False, radius_um=None)
 
         with pytest.raises(AssertionError):
@@ -501,28 +498,23 @@ def test_whiten_general(self, create_cache_folder):
         rec2 = whiten(rec)
         rec2.save(verbose=False)
 
-    # test dtype
-    rec_int = scale(rec2, dtype="int16")
-    rec3 = whiten(rec_int, dtype="float16")
-    rec3 = rec3.save(folder=cache_folder / "rec1")
-    assert rec3.get_dtype() == "float16"
+        # test dtype
+        rec_int = scale(rec2, dtype="int16")
+        rec3 = whiten(rec_int, dtype="float16")
+        rec3 = rec3.save(folder=cache_folder / "rec1")
+        assert rec3.get_dtype() == "float16"
 
-    # test parallel
-    rec_par = rec3.save(folder=cache_folder / "rec_par", n_jobs=2)
-    np.testing.assert_array_equal(rec3.get_traces(segment_index=0), rec_par.get_traces(segment_index=0))
-
-    with pytest.raises(AssertionError):
-        rec4 = whiten(rec_int, dtype=None)  # int_scale should be applied
-    rec4 = whiten(rec_int, dtype=None, int_scale=256)
-    assert rec4.get_dtype() == "int16"
-    assert rec4._kwargs["M"] is None
-
-    # test regularization : norm should be smaller
-    W2, M = compute_whitening_matrix(rec, "global", random_chunk_kwargs, apply_mean=False, regularize=True)
-    assert np.linalg.norm(W1) > np.linalg.norm(W2)
-
-    # test regularization : norm should be smaller
-    if HAS_SKLEARN:
-        W2, M = compute_whitening_matrix(rec, "global", random_chunk_kwargs, apply_mean=False, regularize=True)
-        assert np.linalg.norm(W1) > np.linalg.norm(W2)
+        # test parallel
+        rec_par = rec3.save(folder=cache_folder / "rec_par", n_jobs=2)
+        np.testing.assert_array_equal(rec3.get_traces(segment_index=0), rec_par.get_traces(segment_index=0))
 
+        with pytest.raises(AssertionError):
+            rec4 = whiten(rec_int, dtype=None)  # int_scale should be applied
+        rec4 = whiten(rec_int, dtype=None, int_scale=256)
+        assert rec4.get_dtype() == "int16"
+        assert rec4._kwargs["M"] is None
+
+        # test regularization : norm should be smaller
+        if HAS_SKLEARN:
+            W2, M = compute_whitening_matrix(rec, "global", random_chunk_kwargs, apply_mean=False, regularize=True)
+            assert np.linalg.norm(W1) > np.linalg.norm(W2)

src/spikeinterface/preprocessing/whiten.py

@@ -76,9 +76,9 @@ def __init__(
         dtype_ = fix_dtype(recording, dtype)
 
         if dtype_.kind == "i":
-            assert int_scale is not None, (
-                "For recording with dtype=int you must set the output dtype to float OR set a int_scale"
-            )
+            assert (
+                int_scale is not None
+            ), "For recording with dtype=int you must set the output dtype to float OR set a int_scale"
 
         if not apply_mean and regularize:
             raise ValueError("`apply_mean` must be `True` if regularising. `assume_centered` is fixed to `True`.")
@@ -273,7 +273,7 @@ def compute_sklearn_covariance_matrix(data, regularize_kwargs):
     import sklearn.covariance
 
     if "assume_centered" in regularize_kwargs and not regularize_kwargs["assume_centered"]:
-        raise ValueError("Cannot use `assume_centered=False` for `regularize_kwargs`. " "Fixing to `True`.")
+        raise ValueError("Cannot use `assume_centered=False` for `regularize_kwargs`. Fixing to `True`.")
 
     method = regularize_kwargs.pop("method")
     regularize_kwargs["assume_centered"] = True