Make Indexer classes not inherit from tuple. (#1705)

* Make Indexer classes not inherit from tuple.

I'm not entirely sure this is a good idea. The advantage is that it ensures that
all our indexing code is entirely explicit: everything that reaches a backend
*must* be an ExplicitIndexer. The downside is that it removes a bit of
internal flexibility: we can't just use tuples in place of basic indexers
anymore. On the whole, I think this is probably worth it but I would appreciate
feedback.

* Add validation to ExplicitIndexer classes

* Fix pynio test failure

* Rename and add comments

* flake8

* Fix windows test failure

* typo

* leftover from debugging

xarray/backends/common.py

@@ -10,7 +10,8 @@
 from distutils.version import LooseVersion
 
 from ..conventions import cf_encoder
-from ..core.utils import FrozenOrderedDict
+from ..core import indexing
+from ..core.utils import FrozenOrderedDict, NdimSizeLenMixin
 from ..core.pycompat import iteritems, dask_array_type
 
 try:
@@ -76,6 +77,13 @@ def robust_getitem(array, key, catch=Exception, max_retries=6,
             time.sleep(1e-3 * next_delay)
 
 
+class BackendArray(NdimSizeLenMixin, indexing.ExplicitlyIndexed):
+
+    def __array__(self, dtype=None):
+        key = indexing.BasicIndexer((slice(None),) * self.ndim)
+        return np.asarray(self[key], dtype=dtype)
+
+
 class AbstractDataStore(Mapping):
     _autoclose = False
 

xarray/backends/h5netcdf_.py

@@ -15,11 +15,8 @@
 
 class H5NetCDFArrayWrapper(BaseNetCDF4Array):
     def __getitem__(self, key):
-        if isinstance(key, indexing.VectorizedIndexer):
-            raise NotImplementedError(
-                 'Vectorized indexing for {} is not implemented. Load your '
-                 'data first with .load() or .compute().'.format(type(self)))
-        key = indexing.to_tuple(key)
+        key = indexing.unwrap_explicit_indexer(
+            key, self, allow=(indexing.BasicIndexer, indexing.OuterIndexer))
         with self.datastore.ensure_open(autoclose=True):
             return self.get_array()[key]
 

xarray/backends/netCDF4_.py

@@ -10,12 +10,10 @@
 from .. import Variable
 from ..conventions import pop_to
 from ..core import indexing
-from ..core.utils import (FrozenOrderedDict, NdimSizeLenMixin,
-                          DunderArrayMixin, close_on_error,
-                          is_remote_uri)
+from ..core.utils import (FrozenOrderedDict, close_on_error, is_remote_uri)
 from ..core.pycompat import iteritems, basestring, OrderedDict, PY3, suppress
 
-from .common import (WritableCFDataStore, robust_getitem,
+from .common import (WritableCFDataStore, robust_getitem, BackendArray,
                      DataStorePickleMixin, find_root)
 from .netcdf3 import (encode_nc3_attr_value, encode_nc3_variable)
 
@@ -27,8 +25,7 @@
                   '|': 'native'}
 
 
-class BaseNetCDF4Array(NdimSizeLenMixin, DunderArrayMixin,
-                       indexing.NDArrayIndexable):
+class BaseNetCDF4Array(BackendArray):
     def __init__(self, variable_name, datastore):
         self.datastore = datastore
         self.variable_name = variable_name
@@ -51,12 +48,8 @@ def get_array(self):
 
 class NetCDF4ArrayWrapper(BaseNetCDF4Array):
     def __getitem__(self, key):
-        if isinstance(key, indexing.VectorizedIndexer):
-            raise NotImplementedError(
-             'Vectorized indexing for {} is not implemented. Load your '
-             'data first with .load() or .compute().'.format(type(self)))
-
-        key = indexing.to_tuple(key)
+        key = indexing.unwrap_explicit_indexer(
+            key, self, allow=(indexing.BasicIndexer, indexing.OuterIndexer))
 
         if self.datastore.is_remote:  # pragma: no cover
             getitem = functools.partial(robust_getitem, catch=RuntimeError)

xarray/backends/pydap_.py

@@ -4,14 +4,14 @@
 import numpy as np
 
 from .. import Variable
-from ..core.utils import FrozenOrderedDict, Frozen, NDArrayMixin
+from ..core.utils import FrozenOrderedDict, Frozen
 from ..core import indexing
 from ..core.pycompat import integer_types
 
-from .common import AbstractDataStore, robust_getitem
+from .common import AbstractDataStore, BackendArray, robust_getitem
 
 
-class PydapArrayWrapper(NDArrayMixin, indexing.NDArrayIndexable):
+class PydapArrayWrapper(BackendArray):
     def __init__(self, array):
         self.array = array
 
@@ -27,17 +27,9 @@ def dtype(self):
             return np.dtype(t.typecode + str(t.size))
 
     def __getitem__(self, key):
-        if isinstance(key, indexing.VectorizedIndexer):
-            raise NotImplementedError(
-             'Vectorized indexing for {} is not implemented. Load your '
-             'data first with .load() or .compute().'.format(type(self)))
-        key = indexing.to_tuple(key)
-        if not isinstance(key, tuple):
-            key = (key,)
-        for k in key:
-            if not (isinstance(k, integer_types + (slice,)) or k is Ellipsis):
-                raise IndexError('pydap only supports indexing with int, '
-                                 'slice and Ellipsis objects')
+        key = indexing.unwrap_explicit_indexer(
+            key, target=self, allow=indexing.BasicIndexer)
+
         # pull the data from the array attribute if possible, to avoid
         # downloading coordinate data twice
         array = getattr(self.array, 'array', self.array)

xarray/backends/pynio_.py

@@ -7,16 +7,13 @@
 import numpy as np
 
 from .. import Variable
-from ..core.utils import (FrozenOrderedDict, Frozen,
-                          NdimSizeLenMixin, DunderArrayMixin)
+from ..core.utils import (FrozenOrderedDict, Frozen)
 from ..core import indexing
-from ..core.pycompat import integer_types
 
-from .common import AbstractDataStore, DataStorePickleMixin
+from .common import AbstractDataStore, DataStorePickleMixin, BackendArray
 
 
-class NioArrayWrapper(NdimSizeLenMixin, DunderArrayMixin,
-                      indexing.NDArrayIndexable):
+class NioArrayWrapper(BackendArray):
 
     def __init__(self, variable_name, datastore):
         self.datastore = datastore
@@ -30,13 +27,9 @@ def get_array(self):
         return self.datastore.ds.variables[self.variable_name]
 
     def __getitem__(self, key):
-        if isinstance(key, (indexing.VectorizedIndexer,
-                            indexing.OuterIndexer)):
-            raise NotImplementedError(
-                'Nio backend does not support vectorized / outer indexing. '
-                'Load your data first with .load() or .compute(). '
-                'Given {}'.format(key))
-        key = indexing.to_tuple(key)
+        key = indexing.unwrap_explicit_indexer(
+            key, target=self, allow=indexing.BasicIndexer)
+
         with self.datastore.ensure_open(autoclose=True):
             array = self.get_array()
             if key == () and self.ndim == 0:

xarray/backends/rasterio_.py

@@ -3,8 +3,9 @@
 import numpy as np
 
 from .. import DataArray
-from ..core.utils import DunderArrayMixin, NdimSizeLenMixin, is_scalar
+from ..core.utils import is_scalar
 from ..core import indexing
+from .common import BackendArray
 try:
     from dask.utils import SerializableLock as Lock
 except ImportError:
@@ -17,8 +18,7 @@
               'first.')
 
 
-class RasterioArrayWrapper(NdimSizeLenMixin, DunderArrayMixin,
-                           indexing.NDArrayIndexable):
+class RasterioArrayWrapper(BackendArray):
     """A wrapper around rasterio dataset objects"""
     def __init__(self, rasterio_ds):
         self.rasterio_ds = rasterio_ds
@@ -38,11 +38,9 @@ def shape(self):
         return self._shape
 
     def __getitem__(self, key):
-        if isinstance(key, indexing.VectorizedIndexer):
-            raise NotImplementedError(
-             'Vectorized indexing for {} is not implemented. Load your '
-             'data first with .load() or .compute().'.format(type(self)))
-        key = indexing.to_tuple(key)
+        key = indexing.unwrap_explicit_indexer(
+            key, self, allow=(indexing.BasicIndexer, indexing.OuterIndexer))
+
         # bands cannot be windowed but they can be listed
         band_key = key[0]
         n_bands = self.shape[0]

xarray/backends/scipy_.py

@@ -9,11 +9,10 @@
 
 from .. import Variable
 from ..core.pycompat import iteritems, OrderedDict, basestring
-from ..core.utils import (Frozen, FrozenOrderedDict, NdimSizeLenMixin,
-                          DunderArrayMixin)
-from ..core.indexing import NumpyIndexingAdapter, NDArrayIndexable
+from ..core.utils import (Frozen, FrozenOrderedDict)
+from ..core.indexing import NumpyIndexingAdapter
 
-from .common import WritableCFDataStore, DataStorePickleMixin
+from .common import WritableCFDataStore, DataStorePickleMixin, BackendArray
 from .netcdf3 import (is_valid_nc3_name, encode_nc3_attr_value,
                       encode_nc3_variable)
 
@@ -31,7 +30,7 @@ def _decode_attrs(d):
                        for (k, v) in iteritems(d))
 
 
-class ScipyArrayWrapper(NdimSizeLenMixin, DunderArrayMixin, NDArrayIndexable):
+class ScipyArrayWrapper(BackendArray):
 
     def __init__(self, variable_name, datastore):
         self.datastore = datastore

xarray/conventions.py

@@ -337,7 +337,7 @@ def encode_cf_timedelta(timedeltas, units=None):
     return (num, units)
 
 
-class MaskedAndScaledArray(utils.NDArrayMixin, indexing.NDArrayIndexable):
+class MaskedAndScaledArray(indexing.ExplicitlyIndexedNDArrayMixin):
     """Wrapper around array-like objects to create a new indexable object where
     values, when accessed, are automatically scaled and masked according to
     CF conventions for packed and missing data values.
@@ -395,7 +395,7 @@ def __repr__(self):
                  self.scale_factor, self.add_offset, self._dtype))
 
 
-class DecodedCFDatetimeArray(utils.NDArrayMixin, indexing.NDArrayIndexable):
+class DecodedCFDatetimeArray(indexing.ExplicitlyIndexedNDArrayMixin):
     """Wrapper around array-like objects to create a new indexable object where
     values, when accessed, are automatically converted into datetime objects
     using decode_cf_datetime.
@@ -408,8 +408,9 @@ def __init__(self, array, units, calendar=None):
         # Verify that at least the first and last date can be decoded
         # successfully. Otherwise, tracebacks end up swallowed by
         # Dataset.__repr__ when users try to view their lazily decoded array.
-        example_value = np.concatenate([first_n_items(array, 1) or [0],
-                                        last_item(array) or [0]])
+        values = indexing.ImplicitToExplicitIndexingAdapter(self.array)
+        example_value = np.concatenate([first_n_items(values, 1) or [0],
+                                        last_item(values) or [0]])
 
         try:
             result = decode_cf_datetime(example_value, units, calendar)
@@ -434,7 +435,7 @@ def __getitem__(self, key):
                                   calendar=self.calendar)
 
 
-class DecodedCFTimedeltaArray(utils.NDArrayMixin, indexing.NDArrayIndexable):
+class DecodedCFTimedeltaArray(indexing.ExplicitlyIndexedNDArrayMixin):
     """Wrapper around array-like objects to create a new indexable object where
     values, when accessed, are automatically converted into timedelta objects
     using decode_cf_timedelta.
@@ -451,7 +452,7 @@ def __getitem__(self, key):
         return decode_cf_timedelta(self.array[key], units=self.units)
 
 
-class StackedBytesArray(utils.NDArrayMixin, indexing.NDArrayIndexable):
+class StackedBytesArray(indexing.ExplicitlyIndexedNDArrayMixin):
     """Wrapper around array-like objects to create a new indexable object where
     values, when accessed, are automatically stacked along the last dimension.
 
@@ -482,7 +483,7 @@ def shape(self):
     def __str__(self):
         # TODO(shoyer): figure out why we need this special case?
         if self.ndim == 0:
-            return str(self[...].item())
+            return str(np.array(self).item())
         else:
             return repr(self)
 
@@ -491,13 +492,13 @@ def __repr__(self):
 
     def __getitem__(self, key):
         # require slicing the last dimension completely
-        key = indexing.expanded_indexer(key, self.array.ndim)
-        if key[-1] != slice(None):
+        key = type(key)(indexing.expanded_indexer(key.tuple, self.array.ndim))
+        if key.tuple[-1] != slice(None):
             raise IndexError('too many indices')
         return char_to_bytes(self.array[key])
 
 
-class BytesToStringArray(utils.NDArrayMixin, indexing.NDArrayIndexable):
+class BytesToStringArray(indexing.ExplicitlyIndexedNDArrayMixin):
     """Wrapper that decodes bytes to unicode when values are read.
 
     >>> BytesToStringArray(np.array([b'abc']))[:]
@@ -524,7 +525,7 @@ def dtype(self):
     def __str__(self):
         # TODO(shoyer): figure out why we need this special case?
         if self.ndim == 0:
-            return str(self[...].item())
+            return str(np.array(self).item())
         else:
             return repr(self)
 
@@ -536,7 +537,7 @@ def __getitem__(self, key):
         return decode_bytes_array(self.array[key], self.encoding)
 
 
-class NativeEndiannessArray(utils.NDArrayMixin, indexing.NDArrayIndexable):
+class NativeEndiannessArray(indexing.ExplicitlyIndexedNDArrayMixin):
     """Decode arrays on the fly from non-native to native endianness
 
     This is useful for decoding arrays from netCDF3 files (which are all
@@ -565,7 +566,7 @@ def __getitem__(self, key):
         return np.asarray(self.array[key], dtype=self.dtype)
 
 
-class BoolTypeArray(utils.NDArrayMixin, indexing.NDArrayIndexable):
+class BoolTypeArray(indexing.ExplicitlyIndexedNDArrayMixin):
     """Decode arrays on the fly from integer to boolean datatype
 
     This is useful for decoding boolean arrays from integer typed netCDF
@@ -593,7 +594,7 @@ def __getitem__(self, key):
         return np.asarray(self.array[key], dtype=self.dtype)
 
 
-class UnsignedIntTypeArray(utils.NDArrayMixin, indexing.NDArrayIndexable):
+class UnsignedIntTypeArray(indexing.ExplicitlyIndexedNDArrayMixin):
     """Decode arrays on the fly from signed integer to unsigned
     integer. Typically used when _Unsigned is set at as a netCDF
     attribute on a signed integer variable.