Check isomorphism

datatree/__init__.py

@@ -1,4 +1,5 @@
 # flake8: noqa
 # Ignoring F401: imported but unused
-from .datatree import DataNode, DataTree, map_over_subtree
+from .datatree import DataNode, DataTree
 from .io import open_datatree
+from .mapping import map_over_subtree

datatree/datatree.py

@@ -1,6 +1,5 @@
 from __future__ import annotations
 
-import functools
 import textwrap
 from typing import Any, Callable, Dict, Hashable, Iterable, List, Mapping, Union
 
@@ -14,6 +13,7 @@
 from xarray.core.ops import NAN_CUM_METHODS, NAN_REDUCE_METHODS, REDUCE_METHODS
 from xarray.core.variable import Variable
 
+from .mapping import map_over_subtree
 from .treenode import PathType, TreeNode, _init_single_treenode
 
 """
@@ -50,62 +50,6 @@
 """
 
 
-def map_over_subtree(func):
-    """
-    Decorator which turns a function which acts on (and returns) single Datasets into one which acts on DataTrees.
-
-    Applies a function to every dataset in this subtree, returning a new tree which stores the results.
-
-    The function will be applied to any dataset stored in this node, as well as any dataset stored in any of the
-    descendant nodes. The returned tree will have the same structure as the original subtree.
-
-    func needs to return a Dataset, DataArray, or None in order to be able to rebuild the subtree after mapping, as each
-    result will be assigned to its respective node of new tree via `DataTree.__setitem__`.
-
-    Parameters
-    ----------
-    func : callable
-        Function to apply to datasets with signature:
-        `func(node.ds, *args, **kwargs) -> Dataset`.
-
-        Function will not be applied to any nodes without datasets.
-    *args : tuple, optional
-        Positional arguments passed on to `func`.
-    **kwargs : Any
-        Keyword arguments passed on to `func`.
-
-    Returns
-    -------
-    mapped : callable
-        Wrapped function which returns tree created from results of applying ``func`` to the dataset at each node.
-
-    See also
-    --------
-    DataTree.map_over_subtree
-    DataTree.map_over_subtree_inplace
-    """
-
-    @functools.wraps(func)
-    def _map_over_subtree(tree, *args, **kwargs):
-        """Internal function which maps func over every node in tree, returning a tree of the results."""
-
-        # Recreate and act on root node
-        out_tree = DataNode(name=tree.name, data=tree.ds)
-        if out_tree.has_data:
-            out_tree.ds = func(out_tree.ds, *args, **kwargs)
-
-        # Act on every other node in the tree, and rebuild from results
-        for node in tree.descendants:
-            # TODO make a proper relative_path method
-            relative_path = node.pathstr.replace(tree.pathstr, "")
-            result = func(node.ds, *args, **kwargs) if node.has_data else None
-            out_tree[relative_path] = result
-
-        return out_tree
-
-    return _map_over_subtree
-
-
 class DatasetPropertiesMixin:
     """Expose properties of wrapped Dataset"""
 

datatree/mapping.py

@@ -0,0 +1,139 @@
+import functools
+
+from anytree.iterators import LevelOrderIter
+
+from .treenode import TreeNode
+
+
+class TreeIsomorphismError(ValueError):
+    """Error raised if two tree objects are not isomorphic to one another when they need to be."""
+
+    pass
+
+
+def _check_isomorphic(subtree_a, subtree_b, require_names_equal=False):
+    """
+    Check that two trees have the same structure, raising an error if not.
+
+    Does not check the actual data in the nodes, but it does check that if one node does/doesn't have data then its
+    counterpart in the other tree also does/doesn't have data.
+
+    Also does not check that the root nodes of each tree have the same parent - so this function checks that subtrees
+    are isomorphic, not the entire tree above (if it exists).
+
+    Can optionally check if respective nodes should have the same name.
+
+    Parameters
+    ----------
+    subtree_a : DataTree
+    subtree_b : DataTree
+    require_names_equal : Bool, optional
+        Whether or not to also check that each node has the same name as its counterpart. Default is False.
+
+    Raises
+    ------
+    TypeError
+        If either subtree_a or subtree_b are not tree objects.
+    TreeIsomorphismError
+        If subtree_a and subtree_b are tree objects, but are not isomorphic to one another, or one contains data at a
+        location the other does not. Also optionally raised if their structure is isomorphic, but the names of any two
+        respective nodes are not equal.
+    """
+    # TODO turn this into a public function called assert_isomorphic
+
+    if not isinstance(subtree_a, TreeNode):
+        raise TypeError(
+            f"Argument `subtree_a is not a tree, it is of type {type(subtree_a)}"
+        )
+    if not isinstance(subtree_b, TreeNode):
+        raise TypeError(
+            f"Argument `subtree_b is not a tree, it is of type {type(subtree_b)}"
+        )
+
+    # Walking nodes in "level-order" fashion means walking down from the root breadth-first.
+    # Checking by walking in this way implicitly assumes that the tree is an ordered tree (which it is so long as
+    # children are stored in a tuple or list rather than in a set).
+    for node_a, node_b in zip(LevelOrderIter(subtree_a), LevelOrderIter(subtree_b)):
+        path_a, path_b = node_a.pathstr, node_b.pathstr
+
+        if require_names_equal:
+            if node_a.name != node_b.name:
+                raise TreeIsomorphismError(
+                    f"Trees are not isomorphic because node '{path_a}' in the first tree has "
+                    f"name '{node_a.name}', whereas its counterpart node '{path_b}' in the "
+                    f"second tree has name '{node_b.name}'."
+                )
+
+        if node_a.has_data != node_b.has_data:
+            dat_a = "no " if not node_a.has_data else ""
+            dat_b = "no " if not node_b.has_data else ""
+            raise TreeIsomorphismError(
+                f"Trees are not isomorphic because node '{path_a}' in the first tree has "
+                f"{dat_a}data, whereas its counterpart node '{path_b}' in the second tree "
+                f"has {dat_b}data."
+            )
+
+        if len(node_a.children) != len(node_b.children):
+            raise TreeIsomorphismError(
+                f"Trees are not isomorphic because node '{path_a}' in the first tree has "
+                f"{len(node_a.children)} children, whereas its counterpart node '{path_b}' in "
+                f"the second tree has {len(node_b.children)} children."
+            )
+
+
+def map_over_subtree(func):
+    """
+    Decorator which turns a function which acts on (and returns) single Datasets into one which acts on DataTrees.
+
+    Applies a function to every dataset in this subtree, returning a new tree which stores the results.
+
+    The function will be applied to any dataset stored in this node, as well as any dataset stored in any of the
+    descendant nodes. The returned tree will have the same structure as the original subtree.
+
+    func needs to return a Dataset, DataArray, or None in order to be able to rebuild the subtree after mapping, as each
+    result will be assigned to its respective node of new tree via `DataTree.__setitem__`.
+
+    Parameters
+    ----------
+    func : callable
+        Function to apply to datasets with signature:
+        `func(node.ds, *args, **kwargs) -> Dataset`.
+
+        Function will not be applied to any nodes without datasets.
+    *args : tuple, optional
+        Positional arguments passed on to `func`.
+    **kwargs : Any
+        Keyword arguments passed on to `func`.
+
+    Returns
+    -------
+    mapped : callable
+        Wrapped function which returns tree created from results of applying ``func`` to the dataset at each node.
+
+    See also
+    --------
+    DataTree.map_over_subtree
+    DataTree.map_over_subtree_inplace
+    """
+
+    @functools.wraps(func)
+    def _map_over_subtree(tree, *args, **kwargs):
+        """Internal function which maps func over every node in tree, returning a tree of the results."""
+
+        # Recreate and act on root node
+        from .datatree import DataNode
+
+        out_tree = DataNode(name=tree.name, data=tree.ds)
+        if out_tree.has_data:
+            out_tree.ds = func(out_tree.ds, *args, **kwargs)
+
+        # Act on every other node in the tree, and rebuild from results
+        for node in tree.descendants:
+            # TODO make a proper relative_path method
+            relative_path = node.pathstr.replace(tree.pathstr, "")
+            result = func(node.ds, *args, **kwargs) if node.has_data else None
+            out_tree[relative_path] = result
+
+        return out_tree
+
+    return _map_over_subtree

datatree/tests/test_dataset_api.py

@@ -1,76 +1,9 @@
 import numpy as np
 import pytest
 import xarray as xr
-from test_datatree import create_test_datatree
 from xarray.testing import assert_equal
 
-from datatree import DataNode, DataTree, map_over_subtree
-
-
-class TestMapOverSubTree:
-    def test_map_over_subtree(self):
-        dt = create_test_datatree()
-
-        @map_over_subtree
-        def times_ten(ds):
-            return 10.0 * ds
-
-        result_tree = times_ten(dt)
-
-        # TODO write an assert_tree_equal function
-        for (
-            result_node,
-            original_node,
-        ) in zip(result_tree.subtree, dt.subtree):
-            assert isinstance(result_node, DataTree)
-
-            if original_node.has_data:
-                assert_equal(result_node.ds, original_node.ds * 10.0)
-            else:
-                assert not result_node.has_data
-
-    def test_map_over_subtree_with_args_and_kwargs(self):
-        dt = create_test_datatree()
-
-        @map_over_subtree
-        def multiply_then_add(ds, times, add=0.0):
-            return times * ds + add
-
-        result_tree = multiply_then_add(dt, 10.0, add=2.0)
-
-        for (
-            result_node,
-            original_node,
-        ) in zip(result_tree.subtree, dt.subtree):
-            assert isinstance(result_node, DataTree)
-
-            if original_node.has_data:
-                assert_equal(result_node.ds, (original_node.ds * 10.0) + 2.0)
-            else:
-                assert not result_node.has_data
-
-    def test_map_over_subtree_method(self):
-        dt = create_test_datatree()
-
-        def multiply_then_add(ds, times, add=0.0):
-            return times * ds + add
-
-        result_tree = dt.map_over_subtree(multiply_then_add, 10.0, add=2.0)
-
-        for (
-            result_node,
-            original_node,
-        ) in zip(result_tree.subtree, dt.subtree):
-            assert isinstance(result_node, DataTree)
-
-            if original_node.has_data:
-                assert_equal(result_node.ds, (original_node.ds * 10.0) + 2.0)
-            else:
-                assert not result_node.has_data
-
-    @pytest.mark.xfail
-    def test_map_over_subtree_inplace(self):
-        raise NotImplementedError
+from datatree import DataNode
 
 
 class TestDSProperties:

datatree/tests/test_datatree.py

@@ -7,7 +7,21 @@
 from datatree.io import open_datatree
 
 
-def create_test_datatree():
+def assert_tree_equal(dt_a, dt_b):
+    assert dt_a.name == dt_b.name
+    assert dt_a.parent is dt_b.parent
+
+    assert dt_a.ds.equals(dt_b.ds)
+    for a, b in zip(dt_a.descendants, dt_b.descendants):
+        assert a.name == b.name
+        assert a.pathstr == b.pathstr
+        if a.has_data:
+            assert a.ds.equals(b.ds)
+        else:
+            assert a.ds is b.ds
+
+
+def create_test_datatree(modify=lambda ds: ds):
     """
     Create a test datatree with this structure:
 
@@ -37,12 +51,11 @@ def create_test_datatree():
     The structure has deliberately repeated names of tags, variables, and
     dimensions in order to better check for bugs caused by name conflicts.
     """
-    set1_data = xr.Dataset({"a": 0, "b": 1})
-    set2_data = xr.Dataset({"a": ("x", [2, 3]), "b": ("x", [0.1, 0.2])})
-    root_data = xr.Dataset({"a": ("y", [6, 7, 8]), "set0": ("x", [9, 10])})
+    set1_data = modify(xr.Dataset({"a": 0, "b": 1}))
+    set2_data = modify(xr.Dataset({"a": ("x", [2, 3]), "b": ("x", [0.1, 0.2])}))
+    root_data = modify(xr.Dataset({"a": ("y", [6, 7, 8]), "set0": ("x", [9, 10])}))
 
     # Avoid using __init__ so we can independently test it
-    # TODO change so it has a DataTree at the bottom
     root = DataNode(name="root", data=root_data)
     set1 = DataNode(name="set1", parent=root, data=set1_data)
     DataNode(name="set1", parent=set1)