ruff lint and format tests

Author: siliataider

bindings/pyroot/pythonizations/test/rdataframe_makenumpy.py

@@ -1,8 +1,9 @@
+import gc
+import sys
 import unittest
-import ROOT
+
 import numpy as np
-import sys
-import gc
+import ROOT
 
 
 class DataFrameFromNumpy(unittest.TestCase):
@@ -11,9 +12,7 @@ class DataFrameFromNumpy(unittest.TestCase):
     with RDataFrame.
     """
 
-    dtypes = [
-        "int32", "int64", "uint32", "uint64", "float32", "float64"
-    ]
+    dtypes = ["int32", "int64", "uint32", "uint64", "float32", "float64"]
 
     def test_dtypes(self):
         """
@@ -159,14 +158,14 @@ def test_sliced_array(self):
         """
         Test correct reading of a sliced numpy array (#13690)
         """
-        table = np.array([[1,2], [3,4]], dtype="int64")
-        columns = {'x': table[:,0], 'y': table[:,1]}
+        table = np.array([[1, 2], [3, 4]], dtype="int64")
+        columns = {"x": table[:, 0], "y": table[:, 1]}
         df = ROOT.RDF.FromNumpy(columns)
-        x_col = df.Take['Long64_t']("x")
-        y_col = df.Take['Long64_t']("y")
-        self.assertEqual(list(x_col.GetValue()), [1,3])
-        self.assertEqual(list(y_col.GetValue()), [2,4])
+        x_col = df.Take["Long64_t"]("x")
+        y_col = df.Take["Long64_t"]("y")
+        self.assertEqual(list(x_col.GetValue()), [1, 3])
+        self.assertEqual(list(y_col.GetValue()), [2, 4])
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     unittest.main()

bindings/pyroot/pythonizations/test/rvec_asrvec.py

@@ -1,8 +1,9 @@
+import gc
+import sys
 import unittest
-import ROOT
+
 import numpy as np
-import sys
-import gc
+import ROOT
 
 
 def get_maximum_for_dtype(dtype):
@@ -11,6 +12,7 @@ def get_maximum_for_dtype(dtype):
     if np.issubdtype(dtype, np.floating):
         return np.finfo(dtype).max
 
+
 def get_minimum_for_dtype(dtype):
     if np.issubdtype(dtype, np.integer):
         return np.iinfo(dtype).min
@@ -25,9 +27,7 @@ class AsRVec(unittest.TestCase):
     """
 
     # Helpers
-    dtypes = [
-        "int16", "int32", "int64", "uint16", "uint32", "uint64", "float32", "float64", "bool"
-    ]
+    dtypes = ["int16", "int32", "int64", "uint16", "uint32", "uint64", "float32", "float64", "bool"]
 
     def check_memory_adoption(self, root_obj, np_obj):
         np_obj[0] = get_maximum_for_dtype(np_obj.dtype)
@@ -49,12 +49,11 @@ def test_dtypes(self):
             self.check_memory_adoption(root_obj, np_obj)
             self.check_size(2, root_obj)
 
-
     def test_object_dtype_strings(self):
         """
         Test adoption of numpy arrays with dtype=object and dtype=str containing strings
         """
-        for dtype in [object, np.str_, '<U13']:
+        for dtype in [object, np.str_, "<U13"]:
             with self.subTest(dtype=dtype):
                 np_obj = np.array(["test_string_1", "test_string_2"], dtype=dtype)
                 root_obj = ROOT.VecOps.AsRVec(np_obj)
@@ -63,10 +62,9 @@ def test_object_dtype_strings(self):
                 self.assertEqual(root_obj[0], np_obj[0])
                 self.assertEqual(root_obj[1], np_obj[1])
 
-
     def test_object_dtype_mixed_types(self):
         """
-        Test that a TypeError is raised for numpy arrays with dtype=object 
+        Test that a TypeError is raised for numpy arrays with dtype=object
         containing elements of different types
         """
         np_obj = np.array(["string", {}], dtype=object)
@@ -76,7 +74,6 @@ def test_object_dtype_mixed_types(self):
 
         self.assertIn("All elements in the numpy array must be of the same type", str(context.exception))
 
-
     def test_multidim(self):
         """
         Test adoption of multi-dimensional numpy arrays
@@ -142,5 +139,5 @@ def test_refcount(self):
         self.assertEqual(sys.getrefcount(np_obj), 2)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     unittest.main()