Vectorized lazy indexing

asv_bench/benchmarks/dataset_io.py

@@ -5,7 +5,7 @@
 
 import xarray as xr
 
-from . import randn, requires_dask
+from . import randn, randint, requires_dask
 
 try:
     import dask
@@ -71,6 +71,15 @@ def make_ds(self):
 
         self.ds.attrs = {'history': 'created for xarray benchmarking'}
 
+        self.oinds = {'time': randint(0, self.nt, 120),
+                      'lon': randint(0, self.nx, 20),
+                      'lat': randint(0, self.ny, 10)}
+        self.vinds = {'time': xr.DataArray(randint(0, self.nt, 120),
+                                           dims='x'),
+                      'lon': xr.DataArray(randint(0, self.nx, 120),
+                                          dims='x'),
+                      'lat': slice(3, 20)}
+
 
 class IOWriteSingleNetCDF3(IOSingleNetCDF):
     def setup(self):
@@ -98,6 +107,14 @@ def setup(self):
     def time_load_dataset_netcdf4(self):
         xr.open_dataset(self.filepath, engine='netcdf4').load()
 
+    def time_orthogonal_indexing(self):
+        ds = xr.open_dataset(self.filepath, engine='netcdf4')
+        ds = ds.isel(**self.oinds).load()
+
+    def time_vectorized_indexing(self):
+        ds = xr.open_dataset(self.filepath, engine='netcdf4')
+        ds = ds.isel(**self.vinds).load()
+
 
 class IOReadSingleNetCDF3(IOReadSingleNetCDF4):
     def setup(self):
@@ -111,6 +128,14 @@ def setup(self):
     def time_load_dataset_scipy(self):
         xr.open_dataset(self.filepath, engine='scipy').load()
 
+    def time_orthogonal_indexing(self):
+        ds = xr.open_dataset(self.filepath, engine='scipy')
+        ds = ds.isel(**self.oinds).load()
+
+    def time_vectorized_indexing(self):
+        ds = xr.open_dataset(self.filepath, engine='scipy')
+        ds = ds.isel(**self.vinds).load()
+
 
 class IOReadSingleNetCDF4Dask(IOSingleNetCDF):
     def setup(self):
@@ -127,6 +152,16 @@ def time_load_dataset_netcdf4_with_block_chunks(self):
         xr.open_dataset(self.filepath, engine='netcdf4',
                         chunks=self.block_chunks).load()
 
+    def time_load_dataset_netcdf4_with_block_chunks_oindexing(self):
+        ds = xr.open_dataset(self.filepath, engine='netcdf4',
+                             chunks=self.block_chunks)
+        ds = ds.isel(**self.oinds).load()
+
+    def time_load_dataset_netcdf4_with_block_chunks_vindexing(self):
+        ds = xr.open_dataset(self.filepath, engine='netcdf4',
+                             chunks=self.block_chunks)
+        ds = ds.isel(**self.vinds).load()
+
     def time_load_dataset_netcdf4_with_block_chunks_multiprocessing(self):
         with dask.set_options(get=dask.multiprocessing.get):
             xr.open_dataset(self.filepath, engine='netcdf4',
@@ -158,6 +193,16 @@ def time_load_dataset_scipy_with_block_chunks(self):
             xr.open_dataset(self.filepath, engine='scipy',
                             chunks=self.block_chunks).load()
 
+    def time_load_dataset_scipy_with_block_chunks_oindexing(self):
+        ds = xr.open_dataset(self.filepath, engine='scipy',
+                             chunks=self.block_chunks)
+        ds = ds.isel(**self.oinds).load()
+
+    def time_load_dataset_scipy_with_block_chunks_vindexing(self):
+        ds = xr.open_dataset(self.filepath, engine='scipy',
+                             chunks=self.block_chunks)
+        ds = ds.isel(**self.vinds).load()
+
     def time_load_dataset_scipy_with_time_chunks(self):
         with dask.set_options(get=dask.multiprocessing.get):
             xr.open_dataset(self.filepath, engine='scipy',

doc/whats-new.rst

@@ -38,6 +38,12 @@ Documentation
 Enhancements
 ~~~~~~~~~~~~
 
+- Support lazy vectorized-indexing. After this change, flexible indexing such
+  as orthogonal/vectorized indexing, becomes possible for all the backend
+  arrays. (:issue:`1897`)
+  By `Keisuke Fujii <https://github.com/fujiisoup>`_.
+
+
 Bug fixes
 ~~~~~~~~~
 
@@ -64,7 +70,6 @@ Documentation
 
 Enhancements
 ~~~~~~~~~~~~
-
 **New functions and methods**:
 
 - Added :py:meth:`DataArray.to_iris` and

xarray/backends/h5netcdf_.py

@@ -16,15 +16,20 @@
 
 class H5NetCDFArrayWrapper(BaseNetCDF4Array):
     def __getitem__(self, key):
-        key = indexing.unwrap_explicit_indexer(
-            key, self, allow=(indexing.BasicIndexer, indexing.OuterIndexer))
+        key, np_inds = indexing.decompose_indexer(
+            key, self.shape, indexing.IndexingSupport.OUTER_1VECTOR)
+
         # h5py requires using lists for fancy indexing:
         # https://github.com/h5py/h5py/issues/992
-        # OuterIndexer only holds 1D integer ndarrays, so it's safe to convert
-        # them to lists.
-        key = tuple(list(k) if isinstance(k, np.ndarray) else k for k in key)
+        key = tuple(list(k) if isinstance(k, np.ndarray) else k for k in
+                    key.tuple)
         with self.datastore.ensure_open(autoclose=True):
-            return self.get_array()[key]
+            array = self.get_array()[key]
+
+        if len(np_inds.tuple) > 0:
+            array = indexing.NumpyIndexingAdapter(array)[np_inds]
+
+        return array
 
 
 def maybe_decode_bytes(txt):

xarray/backends/netCDF4_.py

@@ -48,17 +48,16 @@ def get_array(self):
 
 class NetCDF4ArrayWrapper(BaseNetCDF4Array):
     def __getitem__(self, key):
-        key = indexing.unwrap_explicit_indexer(
-            key, self, allow=(indexing.BasicIndexer, indexing.OuterIndexer))
-
+        key, np_inds = indexing.decompose_indexer(
+            key, self.shape, indexing.IndexingSupport.OUTER)
         if self.datastore.is_remote:  # pragma: no cover
             getitem = functools.partial(robust_getitem, catch=RuntimeError)
         else:
             getitem = operator.getitem
 
         with self.datastore.ensure_open(autoclose=True):
             try:
-                data = getitem(self.get_array(), key)
+                array = getitem(self.get_array(), key.tuple)
             except IndexError:
                 # Catch IndexError in netCDF4 and return a more informative
                 # error message.  This is most often called when an unsorted
@@ -71,7 +70,10 @@ def __getitem__(self, key):
                     msg += '\n\nOriginal traceback:\n' + traceback.format_exc()
                 raise IndexError(msg)
 
-        return data
+        if len(np_inds.tuple) > 0:
+            array = indexing.NumpyIndexingAdapter(array)[np_inds]
+
+        return array
 
 
 def _encode_nc4_variable(var):

xarray/backends/pydap_.py

@@ -22,18 +22,22 @@ def dtype(self):
         return self.array.dtype
 
     def __getitem__(self, key):
-        key = indexing.unwrap_explicit_indexer(
-            key, target=self, allow=indexing.BasicIndexer)
+        key, np_inds = indexing.decompose_indexer(
+            key, self.shape, indexing.IndexingSupport.BASIC)
 
         # pull the data from the array attribute if possible, to avoid
         # downloading coordinate data twice
         array = getattr(self.array, 'array', self.array)
-        result = robust_getitem(array, key, catch=ValueError)
+        result = robust_getitem(array, key.tuple, catch=ValueError)
         # pydap doesn't squeeze axes automatically like numpy
-        axis = tuple(n for n, k in enumerate(key)
+        axis = tuple(n for n, k in enumerate(key.tuple)
                      if isinstance(k, integer_types))
         if len(axis) > 0:
             result = np.squeeze(result, axis)
+
+        if len(np_inds.tuple) > 0:
+            result = indexing.NumpyIndexingAdapter(np.asarray(result))[np_inds]
+
         return result
 
 

xarray/backends/pynio_.py

@@ -24,14 +24,19 @@ def get_array(self):
         return self.datastore.ds.variables[self.variable_name]
 
     def __getitem__(self, key):
-        key = indexing.unwrap_explicit_indexer(
-            key, target=self, allow=indexing.BasicIndexer)
+        key, np_inds = indexing.decompose_indexer(
+            key, self.shape, indexing.IndexingSupport.BASIC)
 
         with self.datastore.ensure_open(autoclose=True):
             array = self.get_array()
-            if key == () and self.ndim == 0:
+            if key.tuple == () and self.ndim == 0:
                 return array.get_value()
-            return array[key]
+
+            array = array[key.tuple]
+            if len(np_inds.tuple) > 0:
+                array = indexing.NumpyIndexingAdapter(array)[np_inds]
+
+            return array
 
 
 class NioDataStore(AbstractDataStore, DataStorePickleMixin):

xarray/backends/rasterio_.py

@@ -42,48 +42,73 @@ def dtype(self):
     def shape(self):
         return self._shape
 
-    def __getitem__(self, key):
-        key = indexing.unwrap_explicit_indexer(
-            key, self, allow=(indexing.BasicIndexer, indexing.OuterIndexer))
+    def _get_indexer(self, key):
+        """ Get indexer for rasterio array.
+
+        Parameter
+        ---------
+        key: ExplicitIndexer
+
+        Returns
+        -------
+        band_key: an indexer for the 1st dimension
+        window: two tuples. Each consists of (start, stop).
+        squeeze_axis: axes to be squeezed
+        np_ind: indexer for loaded numpy array
+
+        See also
+        --------
+        indexing.decompose_indexer
+        """
+        key, np_inds = indexing.decompose_indexer(
+            key, self.shape, indexing.IndexingSupport.OUTER)
 
         # bands cannot be windowed but they can be listed
-        band_key = key[0]
-        n_bands = self.shape[0]
+        band_key = key.tuple[0]
+        new_shape = []
+        np_inds2 = []
+        # bands (axis=0) cannot be windowed but they can be listed
         if isinstance(band_key, slice):
-            start, stop, step = band_key.indices(n_bands)
-            if step is not None and step != 1:
-                raise IndexError(_ERROR_MSG)
-            band_key = np.arange(start, stop)
+            start, stop, step = band_key.indices(self.shape[0])
+            band_key = np.arange(start, stop, step)
         # be sure we give out a list
         band_key = (np.asarray(band_key) + 1).tolist()
+        if isinstance(band_key, list):  # if band_key is not a scalar
+            new_shape.append(len(band_key))
+            np_inds2.append(slice(None))
 
         # but other dims can only be windowed
         window = []
         squeeze_axis = []
-        for i, (k, n) in enumerate(zip(key[1:], self.shape[1:])):
+        for i, (k, n) in enumerate(zip(key.tuple[1:], self.shape[1:])):
             if isinstance(k, slice):
+                # step is always positive. see indexing.decompose_indexer
                 start, stop, step = k.indices(n)
-                if step is not None and step != 1:
-                    raise IndexError(_ERROR_MSG)
+                np_inds2.append(slice(None, None, step))
+                new_shape.append(stop - start)
             elif is_scalar(k):
                 # windowed operations will always return an array
                 # we will have to squeeze it later
-                squeeze_axis.append(i + 1)
+                squeeze_axis.append(- (2 - i))
                 start = k
                 stop = k + 1
             else:
-                k = np.asarray(k)
-                start = k[0]
-                stop = k[-1] + 1
-                ids = np.arange(start, stop)
-                if not ((k.shape == ids.shape) and np.all(k == ids)):
-                    raise IndexError(_ERROR_MSG)
+                start, stop = np.min(k), np.max(k) + 1
+                np_inds2.append(k - start)
+                new_shape.append(stop - start)
             window.append((start, stop))
 
+        np_inds = indexing._combine_indexers(
+            indexing.OuterIndexer(tuple(np_inds2)), new_shape, np_inds)
+        return band_key, window, tuple(squeeze_axis), np_inds
+
+    def __getitem__(self, key):
+        band_key, window, squeeze_axis, np_inds = self._get_indexer(key)
+
         out = self.rasterio_ds.read(band_key, window=tuple(window))
         if squeeze_axis:
             out = np.squeeze(out, axis=squeeze_axis)
-        return out
+        return indexing.NumpyIndexingAdapter(out)[np_inds]
 
 
 def _parse_envi(meta):