Fix shallow copy of hc structure

src/lsdb/catalog/catalog.py

@@ -614,21 +614,17 @@ def nest_lists(
             recommend setting the following dask config setting to prevent this:
             `dask.config.set({"dataframe.convert-string":False})`
         """
-        new_ddf = super().nest_lists(
+        catalog = super().nest_lists(
             base_columns=base_columns,
             list_columns=list_columns,
             name=name,
         )
-
-        catalog = Catalog(new_ddf._ddf, self._ddf_pixel_map, self.hc_structure)
-
         if self.margin is not None:
             catalog.margin = self.margin.nest_lists(
                 base_columns=base_columns,
                 list_columns=list_columns,
                 name=name,
             )
-
         return catalog
 
     def dropna(
@@ -708,6 +704,53 @@ def dropna(
         return catalog
 
     def reduce(self, func, *args, meta=None, **kwargs) -> Catalog:
+        """
+        Takes a function and applies it to each top-level row of the Catalog.
+
+        docstring copied from nested-pandas
+
+        The user may specify which columns the function is applied to, with
+        columns from the 'base' layer being passsed to the function as
+        scalars and columns from the nested layers being passed as numpy arrays.
+
+        Parameters
+        ----------
+        func : callable
+            Function to apply to each nested dataframe. The first arguments to `func` should be which
+            columns to apply the function to. See the Notes for recommendations
+            on writing func outputs.
+        args : positional arguments
+            Positional arguments to pass to the function, the first *args should be the names of the
+            columns to apply the function to.
+        meta : dataframe or series-like, optional
+            The dask meta of the output. If append_columns is True, the meta should specify just the
+            additional columns output by func.
+        append_columns : bool
+            If the output columns should be appended to the orignal dataframe.
+        kwargs : keyword arguments, optional
+            Keyword arguments to pass to the function.
+
+        Returns
+        -------
+        `HealpixDataset`
+            `HealpixDataset` with the results of the function applied to the columns of the frame.
+
+        Notes
+        -----
+        By default, `reduce` will produce a `NestedFrame` with enumerated
+        column names for each returned value of the function. For more useful
+        naming, it's recommended to have `func` return a dictionary where each
+        key is an output column of the dataframe returned by `reduce`.
+
+        Example User Function:
+
+        >>> def my_sum(col1, col2):
+        >>>    '''reduce will return a NestedFrame with two columns'''
+        >>>    return {"sum_col1": sum(col1), "sum_col2": sum(col2)}
+        >>>
+        >>> catalog.reduce(my_sum, 'sources.col1', 'sources.col2')
+
+        """
         catalog = super().reduce(func, *args, meta=meta, **kwargs)
         if self.margin is not None:
             catalog.margin = self.margin.reduce(func, *args, meta=meta, **kwargs)

src/lsdb/catalog/dataset/healpix_dataset.py

@@ -81,14 +81,14 @@ def __len__(self):
         """
         return len(self.hc_structure)
 
-    def _create_modified_hc_structure(self) -> HCHealpixDataset:
-        """Copy the catalog structure and invalidate the number of rows.
+    def _create_modified_hc_structure(self, **kwargs) -> HCHealpixDataset:
+        """Copy the catalog structure and override the specified catalog info parameters.
 
         Returns:
-            A copy of the catalog's structure with the total number of rows set to None.
+            A copy of the catalog's structure with updated info parameters.
         """
         return self.hc_structure.__class__(
-            catalog_info=self.hc_structure.catalog_info.copy_and_update(total_rows=0),
+            catalog_info=self.hc_structure.catalog_info.copy_and_update(**kwargs),
             pixels=self.hc_structure.pixel_tree,
             catalog_path=self.hc_structure.catalog_path,
             schema=self.hc_structure.schema,
@@ -159,7 +159,7 @@ def query(self, expr: str) -> Self:
             with the query expression
         """
         ndf = self._ddf.query(expr)
-        hc_structure = self._create_modified_hc_structure()
+        hc_structure = self._create_modified_hc_structure(total_rows=0)
         return self.__class__(ndf, self._ddf_pixel_map, hc_structure)
 
     def _perform_search(
@@ -539,7 +539,7 @@ def drop_na_part(df: npd.NestedFrame):
             return df
 
         ndf = self._ddf.map_partitions(drop_na_part, meta=self._ddf._meta)
-        hc_structure = self._create_modified_hc_structure()
+        hc_structure = self._create_modified_hc_structure(total_rows=0)
         return self.__class__(ndf, self._ddf_pixel_map, hc_structure)
 
     def nest_lists(
@@ -585,7 +585,7 @@ def nest_lists(
             list_columns=list_columns,
             name=name,
         )
-        hc_structure = self._create_modified_hc_structure()
+        hc_structure = self._create_modified_hc_structure(total_rows=0)
         return self.__class__(new_ddf, self._ddf_pixel_map, hc_structure)
 
     def reduce(self, func, *args, meta=None, append_columns=False, **kwargs) -> Self:
@@ -652,13 +652,10 @@ def reduce_part(df):
 
         ndf = nd.NestedFrame.from_dask_dataframe(self._ddf.map_partitions(reduce_part, meta=meta))
 
-        hc_catalog = self.hc_structure
+        hc_updates: dict = {"total_rows": 0}
         if not append_columns:
-            new_catalog_info = self.hc_structure.catalog_info.copy_and_update(ra_column="", dec_column="")
-            hc_catalog = self.hc_structure.__class__(
-                new_catalog_info,
-                self.hc_structure.pixel_tree,
-                schema=get_arrow_schema(ndf),
-                moc=self.hc_structure.moc,
-            )
+            hc_updates = {**hc_updates, "ra_column": "", "dec_column": ""}
+
+        hc_catalog = self._create_modified_hc_structure(**hc_updates)
+        hc_catalog.schema = get_arrow_schema(ndf)
         return self.__class__(ndf, self._ddf_pixel_map, hc_catalog)

tests/lsdb/catalog/test_catalog.py

@@ -667,7 +667,7 @@ def test_modified_hc_structure_is_a_deep_copy(small_sky_order1_catalog):
     assert small_sky_order1_catalog.hc_structure.moc is not None
     assert small_sky_order1_catalog.hc_structure.catalog_info.total_rows == 131
 
-    modified_hc_structure = small_sky_order1_catalog._create_modified_hc_structure()
+    modified_hc_structure = small_sky_order1_catalog._create_modified_hc_structure(total_rows=0)
     modified_hc_structure.pixel_tree = None
     modified_hc_structure.catalog_path = None
     modified_hc_structure.schema = None

tests/lsdb/catalog/test_nested.py

@@ -72,6 +72,9 @@ def mean_mag(ra, dec, mag):
     assert isinstance(reduced_cat, Catalog)
     assert isinstance(reduced_cat._ddf, nd.NestedFrame)
 
+    assert reduced_cat.hc_structure.catalog_info.ra_column == ""
+    assert reduced_cat.hc_structure.catalog_info.dec_column == ""
+
     reduced_cat_compute = reduced_cat.compute()
     assert isinstance(reduced_cat_compute, npd.NestedFrame)