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Autoformat python to fix lint errors (#1470)

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* Autoformat python to fix lint errors

* Fix lint errors not caught by black
This commit is contained in:
Matt Weiden
2020-05-12 13:19:38 -07:00
committed by GitHub
parent e55595cc55
commit 730410c5e1
10 changed files with 50 additions and 55 deletions
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server/test/test_writable_annotation.py
+15 -15
View File
@@ -27,7 +27,7 @@ class WritableAnnotationTest(unittest.TestCase):
def test_error_checks(self):
# verify that the expected errors are generated
n_rows = self.data.get_shape()[0]
fbs_bad = make_fbs({"louvain": pd.Series(["undefined" for l in range(0, n_rows)], dtype="category")})
fbs_bad = make_fbs({"louvain": pd.Series(["undefined"] * n_rows, dtype="category")})
# ensure we catch attempt to overwrite non-writable data
with self.assertRaises(KeyError):
@@ -38,8 +38,8 @@ class WritableAnnotationTest(unittest.TestCase):
n_rows = self.data.get_shape()[0]
fbs = make_fbs(
{
"cat_A": pd.Series(["label_A" for l in range(0, n_rows)], dtype="category"),
"cat_B": pd.Series(["label_B" for l in range(0, n_rows)], dtype="category"),
"cat_A": pd.Series(["label_A"] * n_rows, dtype="category"),
"cat_B": pd.Series(["label_B"] * n_rows, dtype="category"),
}
)
res = self.annotation_put_fbs(fbs)
@@ -49,14 +49,14 @@ class WritableAnnotationTest(unittest.TestCase):
self.assertEqual(df.shape, (n_rows, 2))
self.assertEqual(set(df.columns), {"cat_A", "cat_B"})
self.assertTrue(self.data.original_obs_index.equals(df.index))
self.assertTrue(np.all(df["cat_A"] == ["label_A" for l in range(0, n_rows)]))
self.assertTrue(np.all(df["cat_B"] == ["label_B" for l in range(0, n_rows)]))
self.assertTrue(np.all(df["cat_A"] == ["label_A"] * n_rows))
self.assertTrue(np.all(df["cat_B"] == ["label_B"] * n_rows))
# verify complete overwrite on second attempt, AND rotation occurs
fbs = make_fbs(
{
"cat_A": pd.Series(["label_A1" for l in range(0, n_rows)], dtype="category"),
"cat_C": pd.Series(["label_C" for l in range(0, n_rows)], dtype="category"),
"cat_A": pd.Series(["label_A1"] * n_rows, dtype="category"),
"cat_C": pd.Series(["label_C"] * n_rows, dtype="category"),
}
)
res = self.annotation_put_fbs(fbs)
@@ -64,8 +64,8 @@ class WritableAnnotationTest(unittest.TestCase):
self.assertTrue(path.exists(self.annotations.output_file))
df = pd.read_csv(self.annotations.output_file, index_col=0, header=0, comment="#")
self.assertEqual(set(df.columns), {"cat_A", "cat_C"})
self.assertTrue(np.all(df["cat_A"] == ["label_A1" for l in range(0, n_rows)]))
self.assertTrue(np.all(df["cat_C"] == ["label_C" for l in range(0, n_rows)]))
self.assertTrue(np.all(df["cat_A"] == ["label_A1"] * n_rows))
self.assertTrue(np.all(df["cat_C"] == ["label_C"] * n_rows))
# rotation
name, ext = path.splitext(self.annotations.output_file)
@@ -79,8 +79,8 @@ class WritableAnnotationTest(unittest.TestCase):
n_rows = self.data.get_shape()[0]
fbs = make_fbs(
{
"cat_A": pd.Series(["label_A" for l in range(0, n_rows)], dtype="category"),
"cat_B": pd.Series(["label_B" for l in range(0, n_rows)], dtype="category"),
"cat_A": pd.Series(["label_A"] * n_rows, dtype="category"),
"cat_B": pd.Series(["label_B"] * n_rows, dtype="category"),
}
)
for i in range(0, 11):
@@ -100,8 +100,8 @@ class WritableAnnotationTest(unittest.TestCase):
n_rows = self.data.get_shape()[0]
fbs = make_fbs(
{
"cat_A": pd.Series(["label_A" for l in range(0, n_rows)], dtype="category"),
"cat_B": pd.Series(["label_B" for l in range(0, n_rows)], dtype="category"),
"cat_A": pd.Series(["label_A"] * n_rows, dtype="category"),
"cat_B": pd.Series(["label_B"] * n_rows, dtype="category"),
}
)
@@ -123,8 +123,8 @@ class WritableAnnotationTest(unittest.TestCase):
[obs_index_col_name, "n_genes", "percent_mito", "n_counts", "louvain", "cat_A", "cat_B"],
)
col_idx = annotations["col_idx"]
self.assertEqual(annotations["columns"][col_idx.index("cat_A")], ["label_A" for l in range(0, n_rows)])
self.assertEqual(annotations["columns"][col_idx.index("cat_B")], ["label_B" for l in range(0, n_rows)])
self.assertEqual(annotations["columns"][col_idx.index("cat_A")], ["label_A"] * n_rows)
self.assertEqual(annotations["columns"][col_idx.index("cat_B")], ["label_B"] * n_rows)
# verify the schema was updated
all_col_schema = {c["name"]: c for c in schema["annotations"]["obs"]["columns"]}