🐛 fix gaps description error.

tests/test_plumbing.py

@@ -292,13 +292,19 @@ def gaps_data(self, time_index):
 
         return data
 
-    def test_basic_gaps_description(self, gaps_data):
+    def test_basic_gaps_description(self):
         """Test basic gap analysis functionality."""
-        plumber = Plumber(gaps_data)
+        my_df = pd.DataFrame({
+
+            "temp__°C__Building": [np.nan, 1, np.nan, 3],
+            "power__W__Building": [np.nan, 1, 2, np.nan],
+        }, index=pd.date_range("2009", freq="h", periods=4, tz='UTC'))
+
+        plumber = Plumber(my_df)
         result = plumber.get_gaps_description()
 
         # Check structure
-        assert all(col in result.columns for col in gaps_data.columns)
+        assert all(col in result.columns for col in my_df.columns)
         expected_stats = [
             "data_presence_%",
             "count",
@@ -315,7 +321,8 @@ def test_basic_gaps_description(self, gaps_data):
         # Check specific values
         temp_col = "temp__°C__Building"
         assert result[temp_col]["count"] == 2
-        assert result[temp_col]["data_presence_%"] == pytest.approx(83.33, rel=1e-2)
+        assert result[temp_col]["data_presence_%"] == pytest.approx(50., rel=1e-2)
+        assert result["combination"]["max"] == pd.to_timedelta("02:00:00")
 
     def test_gap_thresholds(self, gaps_data):
         """Test gap analysis with duration thresholds."""

tide/plumbing.py

@@ -154,7 +154,7 @@ class Plumber:
 
     Examples
     --------
-    >>> from tide import Plumber
+    >>> from tide.plumbing import Plumber
     >>> import pandas as pd
     >>> # Create sample data with hierarchical column names
     >>> data = pd.DataFrame(
@@ -249,7 +249,7 @@ def get_gaps_description(
         return_combination: bool = True,
     ) -> pd.DataFrame:
         """
-        Get statistical description of gaps durations in the data.
+        Get a statistical description of gaps durations in the data.
 
         Parameters
         ----------
@@ -264,7 +264,8 @@ def get_gaps_description(
         gaps_gte : str or pd.Timedelta or dt.timedelta, optional
             Lower threshold for gap duration
         return_combination : bool, default True
-            Whether to include statistics for gaps present in any column
+            Whether to include statistics for gaps aggregation.
+            Useful to get statistics when all data are available.
 
         Returns
         -------
@@ -301,7 +302,7 @@ def get_gaps_description(
             durations = []
             for gap in gaps_list:
                 if len(gap) > 1:
-                    durations.append(gap[-1] - gap[0])
+                    durations.append(gap[-1] - gap[0] + pd.to_timedelta(gap.freq))
                 else:
                     durations.append(pd.to_timedelta(gap.freq))
 
@@ -405,7 +406,7 @@ def get_pipeline(
 
         Examples
         --------
-        >>> from tide import Plumber
+        >>> from tide.plumbing import Plumber
         >>> import pandas as pd
         >>> # Create sample data
         >>> data = pd.DataFrame(
@@ -507,7 +508,7 @@ def get_corrected_data(
 
         Examples
         --------
-        >>> from tide import Plumber
+        >>> from tide.plumbing import Plumber
         >>> import pandas as pd
         >>> # Create sample data
         >>> data = pd.DataFrame(
@@ -618,7 +619,7 @@ def plot_gaps_heatmap(
 
         Examples
         --------
-        >>> from tide import Plumber
+        >>> from tide.plumbing import Plumber
         >>> import pandas as pd
         >>> # Create sample data with gaps
         >>> data = pd.DataFrame(
@@ -793,7 +794,7 @@ def plot(
 
         Examples
         --------
-        >>> from tide import Plumber
+        >>> from tide.plumbing import Plumber
         >>> import pandas as pd
         >>> # Create sample data
         >>> data = pd.DataFrame(