Add metadata attributes to ashist() for improved plotting (#635)

* test: Add simple benchmark test

* test: Add pedantic benchmark test to understand workflow

* Attempting to change results from data.js

* Reverted benchmarks.yml back to normal

* Added pull_request to benchmark.yml workflow to understand what happens when a pull request is made

* Added two branches for each fix and added ashist function to add-metadata-attributes branch

* Added unittests to add-metadata-attributes

* Changed benhmark.yml and core.py back to normal

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/stats/test_core_stats_functions.py

@@ -575,6 +575,68 @@ 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
+
+
+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'
+
+    # Test with density=True
+    df_density = H.edges.size.ashist(density=True)
+    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
+
+
+
+def test_ashist_single_unique_value():
+    """Test ashist when there is only one unique value and multiple bins."""
+    H = xgi.Hypergraph()
+    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
+
+    # Check that attributes are present
+    assert 'xlabel' in df.attrs
+    assert 'ylabel' in df.attrs
+    assert 'title' in df.attrs
+
+
+
 ### Attribute statistics
 
 

xgi/stats/__init__.py

@@ -162,6 +162,7 @@ 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.
 
@@ -180,7 +181,6 @@ def ashist(self, bins=10, bin_edges=False, density=False, log_binning=False):
             Whether to bin the values with log-sized bins.
             By default, False.
 
-
         Returns
         -------
         Pandas DataFrame
@@ -189,6 +189,11 @@ def ashist(self, bins=10, bin_edges=False, density=False, log_binning=False):
             is True, outputs two additional columns, `bin_lo` and `bin_hi`,
             which outputs the left and right bin edges respectively.
 
+            The DataFrame includes the following attributes:
+                - attrs['xlabel']: Label for x-axis
+            - attrs['ylabel']: 'Count' or 'Probability' based on density parameter
+            - attrs['title']: Plot title
+
         Notes
         -----
         Originally from https://github.com/jkbren/networks-and-dataviz
@@ -199,7 +204,19 @@ def ashist(self, bins=10, bin_edges=False, density=False, log_binning=False):
         if isinstance(bins, int) and len(set(self.aslist())) == 1:
             bins = 1
 
-        return hist(self.asnumpy(), bins, bin_edges, density, log_binning)
+        # My modifications below
+
+        # Get the histogram Dataframe
+        df = hist(self.asnumpy(), bins, bin_edges, density, log_binning)
+
+        # Add metadata attributes
+        df.attrs["xlabel"] = "Value"
+        df.attrs["ylabel"] = "Probability" if density else "Count"
+        df.attrs["title"] = "Histogram"
+
+        return df
+
+
 
     def max(self):
         """The maximum value of this stat."""