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Add documentation on custom indexes (#6975)
* improve Index base class type annotations Use T_Index generic when possible. * import Index base class in Xarray root namespace * import IndexSelResult into Xarray root namespace * wip: Index API docstrings * wip: doc: add how to add custom index section * add Index method docstrings * add user guide on how to create a custom index * review comments + tweaks * update what's new * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Apply uncontroversial suggestions from Deepak's code review Co-authored-by: Deepak Cherian <[email protected]> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Apply more suggestions from code review Co-authored-by: Deepak Cherian <[email protected]> * Link to source code for PandasIndex and PandasMultiIndex --------- Co-authored-by: Thomas Nicholas <[email protected]> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Deepak Cherian <[email protected]>
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.. currentmodule:: xarray | ||
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How to create a custom index | ||
============================ | ||
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.. warning:: | ||
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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. | ||
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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: | ||
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- 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) | ||
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Fortunately, Xarray now allows extending this functionality with custom indexes, | ||
which can be implemented in 3rd-party libraries. | ||
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The Index base class | ||
-------------------- | ||
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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. | ||
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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. | ||
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Some guidelines and examples are given below. More details can be found in the | ||
documented :py:class:`Index` API. | ||
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Minimal requirements | ||
-------------------- | ||
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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. | ||
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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. | ||
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For example, :py:class:`~xarray.core.indexes.PandasIndex` accepts only one coordinate and | ||
:py:class:`~xarray.core.indexes.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., | ||
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- 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) | ||
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Optional requirements | ||
--------------------- | ||
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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). | ||
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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. | ||
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Wrap index data as coordinate data | ||
---------------------------------- | ||
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In some cases it is possible to reuse the index's underlying object or structure | ||
as coordinate data and hence avoid data duplication. | ||
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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. | ||
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Other structures like tree-based indexes (e.g., kd-tree) may differ too much | ||
from arrays to reuse it as coordinate data. | ||
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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. | ||
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Data selection | ||
-------------- | ||
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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. | ||
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: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. | ||
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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`. | ||
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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. | ||
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Alignment | ||
--------- | ||
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For an index to support alignment, it needs to implement: | ||
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- :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 | ||
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Xarray ensures that those three methods are called with an index of the same | ||
type as argument. | ||
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Meta-indexes | ||
------------ | ||
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Nothing prevents writing a custom Xarray index that itself encapsulates other | ||
Xarray index(es). We call such index a "meta-index". | ||
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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: | ||
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.. 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. | ||
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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. | ||
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How to use a custom index | ||
------------------------- | ||
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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: | ||
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.. 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)) |
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