Add documentation on custom indexes

doc/api-hidden.rst

@@ -451,6 +451,21 @@
    CFTimeIndex.values
    CFTimeIndex.year
 
+   Index.from_variables
+   Index.concat
+   Index.stack
+   Index.unstack
+   Index.create_variables
+   Index.to_pandas_index
+   Index.isel
+   Index.sel
+   Index.join
+   Index.reindex_like
+   Index.equals
+   Index.roll
+   Index.rename
+   Index.copy
+
    backends.NetCDF4DataStore.close
    backends.NetCDF4DataStore.encode
    backends.NetCDF4DataStore.encode_attribute

doc/api.rst

@@ -1090,7 +1090,8 @@ Advanced API
    Variable
    IndexVariable
    as_variable
-   indexes.Index
+   Index
+   IndexSelResult
    Context
    register_dataset_accessor
    register_dataarray_accessor

doc/internals/how-to-create-custom-index.rst

@@ -0,0 +1,233 @@
+.. currentmodule:: xarray
+
+How to create a custom index
+============================
+
+.. warning::
+
+   This feature is highly experimental. Support for custom indexes has been
+   introduced in v2022.06.0 and is still incomplete. API is subject to change
+   without deprecation notice. However we encourage you to experiment and report issues that arise.
+
+Xarray's built-in support for label-based indexing (e.g. `ds.sel(latitude=40, method="nearest")`) and alignment operations
+relies on :py:class:`pandas.Index` objects. Pandas Indexes are powerful and suitable for many
+applications but also have some limitations:
+
+- it only works with 1-dimensional coordinates where explicit labels
+  are fully loaded in memory
+- it is hard to reuse it with irregular data for which there exist more
+  efficient, tree-based structures to perform data selection
+- it doesn't support extra metadata that may be required for indexing and
+  alignment (e.g., a coordinate reference system)
+
+Fortunately, Xarray now allows extending this functionality with custom indexes,
+which can be implemented in 3rd-party libraries.
+
+The Index base class
+--------------------
+
+Every Xarray index must inherit from the :py:class:`Index` base class. It is for
+example the case of Xarray built-in ``PandasIndex`` and ``PandasMultiIndex``
+subclasses, which wrap :py:class:`pandas.Index` and
+:py:class:`pandas.MultiIndex` respectively.
+
+The ``Index`` API closely follows the :py:class:`Dataset` and
+:py:class:`DataArray` API, e.g., for an index to support :py:meth:`DataArray.sel` it needs to
+implement :py:meth:`Index.sel`, to support :py:meth:`DataArray.stack` and :py:meth:`DataArray.unstack` it
+needs to implement :py:meth:`Index.stack` and :py:meth:`Index.unstack`, etc.
+
+Some guidelines and examples are given below. More details can be found in the
+documented :py:class:`Index` API.
+
+Minimal requirements
+--------------------
+
+Every index must at least implement the :py:meth:`Index.from_variables` class
+method, which is used by Xarray to build a new index instance from one or more
+existing coordinates in a Dataset or DataArray.
+
+Since any collection of coordinates can be passed to that method (i.e., the
+number, order and dimensions of the coordinates are all arbitrary), it is the
+responsibility of the index to check the consistency and validity of those input
+coordinates.
+
+For example, ``PandasIndex`` accepts only one coordinate and
+``PandasMultiIndex`` accepts one or more 1-dimensional coordinates that must all
+share the same dimension. Other, custom indexes need not have the same
+constraints, e.g.,
+
+- a georeferenced raster index which only accepts two 1-d coordinates with
+  distinct dimensions
+- a staggered grid index which takes coordinates with different dimension name
+  suffixes (e.g., "_c" and "_l" for center and left)
+
+Optional requirements
+---------------------
+
+Pretty much everything else is optional. Depending on the method, in the absence
+of a (re)implementation, an index will either raise a `NotImplementedError`
+or won't do anything specific (just drop, pass or copy itself
+from/to the resulting Dataset or DataArray).
+
+For example, you can just skip re-implementing :py:meth:`Index.rename` if there
+is no internal attribute or object to rename according to the new desired
+coordinate or dimension names. In the case of ``PandasIndex``, we rename the
+underlying ``pandas.Index`` object and/or update the ``PandasIndex.dim``
+attribute since the associated dimension name has been changed.
+
+Wrap index data as coordinate data
+----------------------------------
+
+In some cases it is possible to reuse the index's underlying object or structure
+as coordinate data and hence avoid data duplication.
+
+For ``PandasIndex`` and ``PandasMultiIndex``, we
+leverage the fact that ``pandas.Index`` objects expose some array-like API. In
+Xarray we use some wrappers around those underlying objects as a thin
+compatibility layer to preserve dtypes, handle explicit and n-dimensional
+indexing, etc.
+
+Other structures like tree-based indexes (e.g., kd-tree) may differ too much
+from arrays to reuse it as coordinate data.
+
+If the index data can be reused as coordinate data, the ``Index`` subclass
+should implement :py:meth:`Index.create_variables`. This method accepts a
+dictionary of variable names as keys and :py:class:`Variable` objects as values (used for propagating
+variable metadata) and should return a dictionary of new :py:class:`Variable` or
+:py:class:`IndexVariable` objects.
+
+Data selection
+--------------
+
+For an index to support label-based selection, it needs to at least implement
+:py:meth:`Index.sel`. This method accepts a dictionary of labels where the keys
+are coordinate names (already filtered for the current index) and the values can
+be pretty much anything (e.g., a slice, a tuple, a list, a numpy array, a
+:py:class:`Variable` or a :py:class:`DataArray`). It is the responsibility of
+the index to properly handle those input labels.
+
+:py:meth:`Index.sel` must return an instance of :py:class:`IndexSelResult`. The
+latter is a small data class that holds positional indexers (indices) and that
+may also hold new variables, new indexes, names of variables or indexes to drop,
+names of dimensions to rename, etc. For example, this is useful in the case of
+``PandasMultiIndex`` as it allows Xarray to convert it into a single ``PandasIndex``
+when only one level remains after the selection.
+
+The :py:class:`IndexSelResult` class is also used to merge results from label-based
+selection performed by different indexes. Note that it is now possible to have
+two distinct indexes for two 1-d coordinates sharing the same dimension, but it
+is not currently possible to use those two indexes in the same call to
+:py:meth:`Dataset.sel`.
+
+Optionally, the index may also implement :py:meth:`Index.isel`. In the case of
+``PandasIndex`` we use it to create a new index object by just indexing the
+underlying ``pandas.Index`` object. In other cases this may not be possible,
+e.g., a kd-tree object may not be easily indexed. If ``Index.isel()`` is not
+implemented, the index in just dropped in the DataArray or Dataset resulting
+from the selection.
+
+Alignment
+---------
+
+For an index to support alignment, it needs to implement:
+
+- :py:meth:`Index.equals`, which compares the index with another index and
+  returns either ``True`` or ``False``
+- :py:meth:`Index.join`, which combines the index with another index and returns
+  a new Index object
+- :py:meth:`Index.reindex_like`, which queries the index with another index and
+  returns positional indexers that are used to re-index Dataset or DataArray
+  variables along one or more dimensions
+
+Xarray ensures that those three methods are called with an index of the same
+type as argument.
+
+Meta-indexes
+------------
+
+Nothing prevents writing a custom Xarray index that itself encapsulates other
+Xarray index(es). We call such index a "meta-index".
+
+Here is a small example of a meta-index for geospatial, raster datasets (i.e.,
+regularly spaced 2-dimensional data) that internally relies on two
+``PandasIndex`` instances for the x and y dimensions respectively:
+
+.. code-block:: python
+
+    from xarray import Index
+    from xarray.core.indexes import PandasIndex
+    from xarray.core.indexing import merge_sel_results
+
+
+    class RasterIndex(Index):
+        def __init__(self, xy_indexes):
+            assert len(xy_indexes) == 2
+
+            # must have two distinct dimensions
+            dim = [idx.dim for idx in xy_indexes.values()]
+            assert dim[0] != dim[1]
+
+            self._xy_indexes = xy_indexes
+
+        @classmethod
+        def from_variables(cls, variables):
+            assert len(variables) == 2
+
+            xy_indexes = {
+                k: PandasIndex.from_variables({k: v}) for k, v in variables.items()
+            }
+
+            return cls(xy_indexes)
+
+        def create_variables(self, variables):
+            idx_variables = {}
+
+            for index in self._xy_indexes.values():
+                idx_variables.update(index.create_variables(variables))
+
+            return idx_variables
+
+        def sel(self, labels):
+            results = []
+
+            for k, index in self._xy_indexes.items():
+                if k in labels:
+                    results.append(index.sel({k: labels[k]}))
+
+            return merge_sel_results(results)
+
+
+This basic index only supports label-based selection. Providing a full-featured
+index by implementing the other ``Index`` methods should be pretty
+straightforward for this example, though.
+
+This example is also not very useful unless we add some extra functionality on
+top of the two encapsulated ``PandasIndex`` objects, such as a coordinate
+reference system.
+
+How to use a custom index
+-------------------------
+
+You can use :py:meth:`Dataset.set_xindex` or :py:meth:`DataArray.set_xindex` to assign a
+custom index to a Dataset or DataArray, e.g., using the ``RasterIndex`` above:
+
+.. code-block:: python
+
+    import numpy as np
+    import xarray as xr
+
+    da = xr.DataArray(
+        np.random.uniform(size=(100, 50)),
+        coords={"x": ("x", np.arange(50)), "y": ("y", np.arange(100))},
+        dims=("y", "x"),
+    )
+
+    # Xarray create default indexes for the 'x' and 'y' coordinates
+    # we first need to explicitly drop it
+    da = da.drop_indexes(["x", "y"])
+
+    # Build a RasterIndex from the 'x' and 'y' coordinates
+    da_raster = da.set_xindex(["x", "y"], RasterIndex)
+
+    # RasterIndex now takes care of label-based selection
+    selected = da_raster.sel(x=10, y=slice(20, 50))

doc/internals/index.rst

@@ -25,3 +25,4 @@ The pages in this section are intended for:
    extending-xarray
    zarr-encoding-spec
    how-to-add-new-backend
+   how-to-create-custom-index

doc/whats-new.rst

@@ -795,6 +795,10 @@ Bug fixes
 
 Documentation
 ~~~~~~~~~~~~~
+
+- Add docstrings for the :py:class:`Index` base class and add some documentation on how to
+  create custom, Xarray-compatible indexes (:pull:`6975`)
+  By `Benoît Bovy <https://github.com/benbovy>`_.
 - Update merge docstrings. (:issue:`6935`, :pull:`7033`)
   By `Zach Moon <https://github.com/zmoon>`_.
 - Raise a more informative error when trying to open a non-existent zarr store. (:issue:`6484`, :pull:`7060`)

xarray/__init__.py

@@ -32,6 +32,8 @@
     register_dataarray_accessor,
     register_dataset_accessor,
 )
+from xarray.core.indexes import Index
+from xarray.core.indexing import IndexSelResult
 from xarray.core.merge import Context, MergeError, merge
 from xarray.core.options import get_options, set_options
 from xarray.core.parallel import map_blocks
@@ -100,6 +102,8 @@
     "Coordinate",
     "DataArray",
     "Dataset",
+    "Index",
+    "IndexSelResult",
     "IndexVariable",
     "Variable",
     # Exceptions