GeospatialScenario.meta_KDtree

pvdeg/geospatial.py

@@ -546,7 +546,7 @@ def meta_KDtree(meta_df, leaf_size=40, fp=None):
     Create a sklearn.neighbors.KDTree for fast geospatial lookup operations.
     Requires Scikit Learn library. Not included in pvdeg depency list.
 
-    Parameters:
+    Parameters
     -----------
     meta_df: pd.DataFrame
         Dataframe of metadata as generated by pvdeg.weather.get for geospatial
@@ -557,7 +557,7 @@ def meta_KDtree(meta_df, leaf_size=40, fp=None):
         If none, no file saved. must be ``.pkl`` file extension. Open saved
         pkl file with joblib (sklearn dependency).
 
-    Returns:
+    Returns
     --------
     kdtree: sklearn.neighbors.KDTree
         kdtree containing latitude-longitude pairs for quick lookups

pvdeg/scenario.py

@@ -1397,6 +1397,27 @@ def classify_mountains_weights(
         self.meta_data["mountain"] = (self.meta_data.index).isin(gids)
         return
 
+    def meta_KDtree(self, leaf_size=40, fp=None):
+        """
+        Create a KDTree from this scenario's current metadata
+        
+        Parameters
+        ----------
+        leaf_size:
+            Number of points at which to switch to brute-force. See sci kit docs.
+        fp: str, optional
+            Location to save pickled kdtree so we don't have to rebuild the tree.
+            If none, no file saved. must be ``.pkl`` file extension. Open saved
+            pkl file with joblib (sklearn dependency).
+
+        Returns
+        --------
+        kdtree: sklearn.neighbors.KDTree
+            kdtree containing latitude-longitude pairs for quick lookups
+        """
+
+        return pvdeg.geospatial.meta_KDtree(meta_df=self.meta_data, leaf_size=leaf_size, fp=fp)
+
     def classify_feature(
         self,
         kdtree=None,