Apply black

hdmf_ml/__init__.py

@@ -13,9 +13,7 @@ def __get_resources():
     __schema_dir = "schema"
 
     ret = dict()
-    ret["namespace_path"] = str(
-        __location_of_this_file / __schema_dir / __core_ns_file_name
-    )
+    ret["namespace_path"] = str(__location_of_this_file / __schema_dir / __core_ns_file_name)
     return ret
 
 

hdmf_ml/results_table.py

@@ -105,17 +105,13 @@ def __add_col(self, **kwargs):
                     # dim2 is a 1D array, so shape is N-D
                     shape = (self.n_samples, *dim2)
                 else:
-                    ValueError(
-                        f"Unrecognized type for dim2: {type(dim2)} - expected integer or 1-D array-like"
-                    )
+                    ValueError(f"Unrecognized type for dim2: {type(dim2)} - expected integer or 1-D array-like")
 
             # create empty DataIO object
             data = H5DataIO(shape=shape, dtype=dtype)
 
         if name in self:
-            raise ValueError(
-                f"Column '{name}' already exists in ResultsTable '{self.name}'"
-            )
+            raise ValueError(f"Column '{name}' already exists in ResultsTable '{self.name}'")
         if len(self.id) == 0:
             self.id.extend(np.arange(len(data)))
         elif len(self.id) != len(data):
@@ -124,9 +120,7 @@ def __add_col(self, **kwargs):
                 f"existings columns of length {len(self.id)}"
             )
 
-        self.add_column(
-            data=data, name=name, description=description, col_cls=col_cls, **kwargs
-        )
+        self.add_column(data=data, name=name, description=description, col_cls=col_cls, **kwargs)
 
         if self.__n_samples is None:
             self.__n_samples = len(data)

pyproject.toml

@@ -79,10 +79,10 @@ exclude_lines = [
 
 [tool.setuptools_scm]
 
-# [tool.black]
-# line-length = 120
-# preview = true
-# exclude = ".git|.mypy_cache|.tox|.venv|venv|.ipynb_checkpoints|_build/|dist/|__pypackages__|.ipynb"
+[tool.black]
+line-length = 120
+preview = true
+exclude = ".git|.mypy_cache|.tox|.venv|venv|.ipynb_checkpoints|_build/|dist/|__pypackages__|.ipynb"
 # force-exclude = "docs/gallery"
 
 [tool.ruff]

tests/test_results_table.py

@@ -41,10 +41,7 @@ def get_hdf5io(self):
     def test_add_col_diff_len(self):
         rt = ResultsTable(name="foo", description="a test results table")
         rt.add_tvt_split([0, 1, 2, 0, 1])
-        msg = (
-            "New column true_label of length 4 is not the same length as "
-            "existings columns of length 5"
-        )
+        msg = "New column true_label of length 4 is not the same length as " "existings columns of length 5"
         with self.assertRaisesRegex(ValueError, msg):
             rt.add_true_label([0, 0, 0, 1])
 
@@ -78,9 +75,7 @@ def test_add_cv_split(self):
 
     def test_add_cv_split_bad_splits(self):
         rt = ResultsTable(name="foo", description="a test results table")
-        with self.assertRaisesRegex(
-            ValueError, "Got non-integer data for cross-validation split"
-        ):
+        with self.assertRaisesRegex(ValueError, "Got non-integer data for cross-validation split"):
             rt.add_cv_split([0.0, 0.1, 0.2, 0.3, 0.4])
 
     def test_add_true_label(self):
@@ -99,9 +94,7 @@ def test_add_true_label_str(self):
 
     def test_add_predicted_probability(self):
         rt = ResultsTable(name="foo", description="a test results table")
-        rt.add_predicted_probability(
-            [[0.1, 0.9], [0.2, 0.8], [0.3, 0.7], [0.4, 0.6], [0.5, 0.5]]
-        )
+        rt.add_predicted_probability([[0.1, 0.9], [0.2, 0.8], [0.3, 0.7], [0.4, 0.6], [0.5, 0.5]])
         with self.get_hdf5io() as io:
             io.write(rt)
 
@@ -137,8 +130,6 @@ def test_add_topk_classes(self):
 
     def test_add_topk_probabilities(self):
         rt = ResultsTable(name="foo", description="a test results table")
-        rt.add_topk_probabilities(
-            [[0.9, 0.1], [0.8, 0.2], [0.7, 0.3], [0.6, 0.4], [0.5, 0.5]]
-        )
+        rt.add_topk_probabilities([[0.9, 0.1], [0.8, 0.2], [0.7, 0.3], [0.6, 0.4], [0.5, 0.5]])
         with self.get_hdf5io() as io:
             io.write(rt)