format with isort and black

benchmarks/core.py

@@ -157,4 +157,4 @@ def setup():
     def dual(H):
         H.dual()
 
-    benchmark.pedantic(dual, setup=setup, rounds=rounds)
+    benchmark.pedantic(dual, setup=setup, rounds=rounds)

tests/readwrite/test_xgi_data.py

@@ -66,7 +66,6 @@ def test_load_xgi_data(capfd):
     assert H.num_edges == 77733
 
 
-
 @pytest.mark.skipif(
     sys.version_info != (3, 12) and not platform.system() == "Linux",
     reason="only need one test",

tests/stats/test_core_stats_functions.py

@@ -575,44 +575,41 @@ def test_issue_468():
     assert H.edges.size.ashist().equals(df)
 
 
-
-
 def test_ashist_attrs_exist():
     """Test that ashist returns DataFrame with expected attributes."""
     H = xgi.sunflower(3, 1, 20)
     df = H.edges.size.ashist()
-    
+
     # Check that all expected attributes exist
-    assert 'xlabel' in df.attrs
-    assert 'ylabel' in df.attrs
-    assert 'title' in df.attrs
+    assert "xlabel" in df.attrs
+    assert "ylabel" in df.attrs
+    assert "title" in df.attrs
 
 
 def test_ashist_density_labels():
     """Test that ylabel changes based on density parameter."""
     H = xgi.sunflower(3, 1, 20)
-    
+
     # Test default (density=False)
     df_count = H.edges.size.ashist(density=False)
-    assert df_count.attrs['ylabel'] == 'Count'
-    
+    assert df_count.attrs["ylabel"] == "Count"
+
     # Test with density=True
     df_density = H.edges.size.ashist(density=True)
-    assert df_density.attrs['ylabel'] == 'Probability'
+    assert df_density.attrs["ylabel"] == "Probability"
 
 
 def test_ashist_original_functionality():
     """Test that adding attributes doesn't break original functionality."""
     H = xgi.sunflower(3, 1, 20)
     df = H.edges.size.ashist()
-    
+
     # Original test case should still pass
     expected_df = pd.DataFrame([[20.0, 3]], columns=["bin_center", "value"])
     assert df.equals(expected_df)  # Original functionality
-
-    # And should have attributes
-    assert 'xlabel' in df.attrs
 
+    # And should have attributes
+    assert "xlabel" in df.attrs
 
 
 def test_ashist_single_unique_value():
@@ -621,20 +618,19 @@ def test_ashist_single_unique_value():
     H.add_nodes_from(range(5))
     # All edges have the same size
     H.add_edges_from([[0, 1], [2, 3], [4, 0]])
-    
+
     # The edge sizes will all be 2
     df = H.edges.size.ashist(bins=10)
-    
+
     # Since there's only one unique value, bins should be set to 1
     assert len(df) == 1  # Only one bin should be present
-    assert df['bin_center'].iloc[0] == 2  # The bin center should be the unique value
-    assert df['value'].iloc[0] == 3  # There are three edges of size 2
+    assert df["bin_center"].iloc[0] == 2  # The bin center should be the unique value
+    assert df["value"].iloc[0] == 3  # There are three edges of size 2
 
     # Check that attributes are present
-    assert 'xlabel' in df.attrs
-    assert 'ylabel' in df.attrs
-    assert 'title' in df.attrs
-
+    assert "xlabel" in df.attrs
+    assert "ylabel" in df.attrs
+    assert "title" in df.attrs
 
 
 ### Attribute statistics

xgi/stats/__init__.py

@@ -162,7 +162,6 @@ def aspandas(self):
         """
         return pd.Series(self._val, name=self.name)
 
-
     def ashist(self, bins=10, bin_edges=False, density=False, log_binning=False):
         """Return the distribution of a numpy array.
 
@@ -216,8 +215,6 @@ def ashist(self, bins=10, bin_edges=False, density=False, log_binning=False):
 
         return df
 
-
-
     def max(self):
         """The maximum value of this stat."""
         return self.asnumpy().max(axis=0).item()