Add zarr read/write xarray-contrib/datatree#30

* add test for roundtrip and support empty nodes

* update roundtrip test, improves empty node handling in IO

* add zarr read/write support

* support netcdf4 or h5netcdf

* netcdf is optional, zarr too!

* Apply suggestions from code review

Co-authored-by: Tom Nicholas <35968931+TomNicholas@users.noreply.github.com>

Co-authored-by: Tom Nicholas <35968931+TomNicholas@users.noreply.github.com>

ci/environment.yml

@@ -11,3 +11,4 @@ dependencies:
   - black
   - codecov
   - pytest-cov
+  - zarr

datatree/datatree.py

@@ -854,6 +854,37 @@ def to_netcdf(
             **kwargs,
         )
 
+    def to_zarr(self, store, mode: str = "w", encoding=None, **kwargs):
+        """
+        Write datatree contents to a netCDF file.
+
+        Parameters
+        ---------
+        store : MutableMapping, str or Path, optional
+            Store or path to directory in file system
+        mode : {{"w", "w-", "a", "r+", None}, default: "w"
+            Persistence mode: “w” means create (overwrite if exists); “w-” means create (fail if exists);
+            “a” means override existing variables (create if does not exist); “r+” means modify existing
+            array values only (raise an error if any metadata or shapes would change). The default mode
+            is “a” if append_dim is set. Otherwise, it is “r+” if region is set and w- otherwise.
+        encoding : dict, optional
+            Nested dictionary with variable names as keys and dictionaries of
+            variable specific encodings as values, e.g.,
+            ``{"root/set1": {"my_variable": {"dtype": "int16", "scale_factor": 0.1}, ...}, ...}``.
+            See ``xarray.Dataset.to_zarr`` for available options.
+        kwargs :
+            Addional keyword arguments to be passed to ``xarray.Dataset.to_zarr``
+        """
+        from .io import _datatree_to_zarr
+
+        _datatree_to_zarr(
+            self,
+            store,
+            mode=mode,
+            encoding=encoding,
+            **kwargs,
+        )
+
     def plot(self):
         raise NotImplementedError
 

datatree/io.py

@@ -1,10 +1,9 @@
-import os
-from typing import Dict, Sequence
+import pathlib
+from typing import Sequence
 
-import netCDF4
 from xarray import open_dataset
 
-from .datatree import DataNode, DataTree, PathType
+from .datatree import DataTree, PathType
 
 
 def _ds_or_none(ds):
@@ -14,37 +13,87 @@ def _ds_or_none(ds):
     return None
 
 
-def _open_group_children_recursively(filename, node, ncgroup, chunks, **kwargs):
-    for g in ncgroup.groups.values():
+def _iter_zarr_groups(root, parrent=""):
+    parrent = pathlib.Path(parrent)
+    for path, group in root.groups():
+        gpath = parrent / path
+        yield str(gpath)
+        yield from _iter_zarr_groups(group, parrent=gpath)
 
-        # Open and add this node's dataset to the tree
-        name = os.path.basename(g.path)
-        ds = open_dataset(filename, group=g.path, chunks=chunks, **kwargs)
-        ds = _ds_or_none(ds)
-        child_node = DataNode(name, ds)
-        node.add_child(child_node)
 
-        _open_group_children_recursively(filename, node[name], g, chunks, **kwargs)
+def _iter_nc_groups(root, parrent=""):
+    parrent = pathlib.Path(parrent)
+    for path, group in root.groups.items():
+        gpath = parrent / path
+        yield str(gpath)
+        yield from _iter_nc_groups(group, parrent=gpath)
 
 
-def open_datatree(filename: str, chunks: Dict = None, **kwargs) -> DataTree:
+def _get_nc_dataset_class(engine):
+    if engine == "netcdf4":
+        from netCDF4 import Dataset
+    elif engine == "h5netcdf":
+        from h5netcdf import Dataset
+    elif engine is None:
+        try:
+            from netCDF4 import Dataset
+        except ImportError:
+            from h5netcdf import Dataset
+    else:
+        raise ValueError(f"unsupported engine: {engine}")
+    return Dataset
+
+
+def open_datatree(filename_or_obj, engine=None, **kwargs) -> DataTree:
     """
     Open and decode a dataset from a file or file-like object, creating one Tree node for each group in the file.
 
     Parameters
     ----------
-    filename
-    chunks
+    filename_or_obj : str, Path, file-like, or DataStore
+        Strings and Path objects are interpreted as a path to a netCDF file or Zarr store.
+    engine : str, optional
+        Xarray backend engine to us. Valid options include `{"netcdf4", "h5netcdf", "zarr"}`.
+    kwargs :
+        Additional keyword arguments passed to ``xarray.open_dataset`` for each group.
 
     Returns
     -------
     DataTree
     """
 
-    with netCDF4.Dataset(filename, mode="r") as ncfile:
-        ds = open_dataset(filename, chunks=chunks, **kwargs)
+    if engine == "zarr":
+        return _open_datatree_zarr(filename_or_obj, **kwargs)
+    elif engine in [None, "netcdf4", "h5netcdf"]:
+        return _open_datatree_netcdf(filename_or_obj, engine=engine, **kwargs)
+
+
+def _open_datatree_netcdf(filename: str, **kwargs) -> DataTree:
+    ncDataset = _get_nc_dataset_class(kwargs.get("engine", None))
+
+    with ncDataset(filename, mode="r") as ncds:
+        ds = open_dataset(filename, **kwargs).pipe(_ds_or_none)
+        tree_root = DataTree(data_objects={"root": ds})
+        for key in _iter_nc_groups(ncds):
+            tree_root[key] = open_dataset(filename, group=key, **kwargs).pipe(
+                _ds_or_none
+            )
+    return tree_root
+
+
+def _open_datatree_zarr(store, **kwargs) -> DataTree:
+    import zarr
+
+    with zarr.open_group(store, mode="r") as zds:
+        ds = open_dataset(store, engine="zarr", **kwargs).pipe(_ds_or_none)
         tree_root = DataTree(data_objects={"root": ds})
-        _open_group_children_recursively(filename, tree_root, ncfile, chunks, **kwargs)
+        for key in _iter_zarr_groups(zds):
+            try:
+                tree_root[key] = open_dataset(
+                    store, engine="zarr", group=key, **kwargs
+                ).pipe(_ds_or_none)
+            except zarr.errors.PathNotFoundError:
+                tree_root[key] = None
     return tree_root
 
 
@@ -80,8 +129,10 @@ def _maybe_extract_group_kwargs(enc, group):
         return None
 
 
-def _create_empty_group(filename, group, mode):
-    with netCDF4.Dataset(filename, mode=mode) as rootgrp:
+def _create_empty_netcdf_group(filename, group, mode, engine):
+    ncDataset = _get_nc_dataset_class(engine)
+
+    with ncDataset(filename, mode=mode) as rootgrp:
         rootgrp.createGroup(group)
 
 
@@ -91,13 +142,14 @@ def _datatree_to_netcdf(
     mode: str = "w",
     encoding=None,
     unlimited_dims=None,
-    **kwargs
+    **kwargs,
 ):
 
     if kwargs.get("format", None) not in [None, "NETCDF4"]:
         raise ValueError("to_netcdf only supports the NETCDF4 format")
 
-    if kwargs.get("engine", None) not in [None, "netcdf4", "h5netcdf"]:
+    engine = kwargs.get("engine", None)
+    if engine not in [None, "netcdf4", "h5netcdf"]:
         raise ValueError("to_netcdf only supports the netcdf4 and h5netcdf engines")
 
     if kwargs.get("group", None) is not None:
@@ -118,14 +170,52 @@ def _datatree_to_netcdf(
         ds = node.ds
         group_path = node.pathstr.replace(dt.root.pathstr, "")
         if ds is None:
-            _create_empty_group(filepath, group_path, mode)
+            _create_empty_netcdf_group(filepath, group_path, mode, engine)
         else:
+
             ds.to_netcdf(
                 filepath,
                 group=group_path,
                 mode=mode,
                 encoding=_maybe_extract_group_kwargs(encoding, dt.pathstr),
                 unlimited_dims=_maybe_extract_group_kwargs(unlimited_dims, dt.pathstr),
-                **kwargs
+                **kwargs,
             )
         mode = "a"
+
+
+def _create_empty_zarr_group(store, group, mode):
+    import zarr
+
+    root = zarr.open_group(store, mode=mode)
+    root.create_group(group, overwrite=True)
+
+
+def _datatree_to_zarr(dt: DataTree, store, mode: str = "w", encoding=None, **kwargs):
+
+    if kwargs.get("group", None) is not None:
+        raise NotImplementedError(
+            "specifying a root group for the tree has not been implemented"
+        )
+
+    if not kwargs.get("compute", True):
+        raise NotImplementedError("compute=False has not been implemented yet")
+
+    if encoding is None:
+        encoding = {}
+
+    for node in dt.subtree:
+        ds = node.ds
+        group_path = node.pathstr.replace(dt.root.pathstr, "")
+        if ds is None:
+            _create_empty_zarr_group(store, group_path, mode)
+        else:
+            ds.to_zarr(
+                store,
+                group=group_path,
+                mode=mode,
+                encoding=_maybe_extract_group_kwargs(encoding, dt.pathstr),
+                **kwargs,
+            )
+        if "w" in mode:
+            mode = "a"

datatree/tests/test_datatree.py

@@ -322,12 +322,15 @@ def test_to_netcdf(self, tmpdir):
 
         roundtrip_dt = open_datatree(filepath)
 
-        original_dt.name == roundtrip_dt.name
-        assert original_dt.ds.identical(roundtrip_dt.ds)
-        for a, b in zip(original_dt.descendants, roundtrip_dt.descendants):
-            assert a.name == b.name
-            assert a.pathstr == b.pathstr
-            if a.has_data:
-                assert a.ds.identical(b.ds)
-            else:
-                assert a.ds is b.ds
+        assert_tree_equal(original_dt, roundtrip_dt)
+
+    def test_to_zarr(self, tmpdir):
+        filepath = str(
+            tmpdir / "test.zarr"
+        )  # casting to str avoids a pathlib bug in xarray
+        original_dt = create_test_datatree()
+        original_dt.to_zarr(filepath)
+
+        roundtrip_dt = open_datatree(filepath, engine="zarr")
+
+        assert_tree_equal(original_dt, roundtrip_dt)

requirements.txt

@@ -1,4 +1,3 @@
 xarray>=0.19.0
-netcdf4
 anytree
 future