autopep8

graphtools/base.py

@@ -245,7 +245,7 @@ def _reduce_data(self):
         If data is dense, uses randomized PCA. If data is sparse, uses
         randomized SVD.
         TODO: should we subtract and store the mean?
-        TODO: Fix the rank estimation so we do not compute the full SVD. 
+        TODO: Fix the rank estimation so we do not compute the full SVD.
 
         Returns
         -------

test/test_exact.py

@@ -96,7 +96,7 @@ def test_precomputed_invalid():
 def test_duplicate_data():
     with assert_warns_regex(
         RuntimeWarning,
-        "Detected zero distance between samples ([0-9and,\s]*). Consider removing duplicates to avoid errors in downstream processing.",
+        r"Detected zero distance between samples ([0-9and,\s]*). Consider removing duplicates to avoid errors in downstream processing.",
     ):
         build_graph(np.vstack([data, data[:10]]), n_pca=20, decay=10, thresh=0)
 
@@ -447,7 +447,10 @@ def test_exact_graph_fixed_bandwidth():
 def test_exact_graph_callable_bandwidth():
     decay = 2
     knn = 5
-    bandwidth = lambda x: 2
+
+    def bandwidth(x):
+        return 2
+
     n_pca = 20
     thresh = 1e-4
     pca = PCA(n_pca, svd_solver="randomized", random_state=42).fit(data)
@@ -474,7 +477,10 @@ def test_exact_graph_callable_bandwidth():
     np.testing.assert_equal(G.dw, G2.dw)
     assert (G2.W != G.W).sum() == 0
     assert (G.W != G2.W).nnz == 0
-    bandwidth = lambda x: np.percentile(x, 10, axis=1)
+
+    def bandwidth(x):
+        return np.percentile(x, 10, axis=1)
+
     K = np.exp(-1 * (pdx / bandwidth(pdx)) ** decay)
     K[K < thresh] = 0
     K = K + K.T

test/test_knn.py

@@ -49,7 +49,7 @@ def test_build_knn_with_sample_idx():
 def test_duplicate_data():
     with assert_warns_regex(
         RuntimeWarning,
-        "Detected zero distance between samples ([0-9and,\s]*). Consider removing duplicates to avoid errors in downstream processing.",
+        r"Detected zero distance between samples ([0-9and,\s]*). Consider removing duplicates to avoid errors in downstream processing.",
     ):
         build_graph(np.vstack([data, data[:10]]), n_pca=20, decay=10, thresh=1e-4)
 
@@ -332,7 +332,10 @@ def test_knn_graph_callable_bandwidth():
     ):
         k = 3
         decay = 5
-        bandwidth = lambda x: 2
+
+        def bandwidth(x):
+            return 2
+
         n_pca = 20
         thresh = 1e-4
         build_graph(