rok commented on code in PR #34883:
URL: https://github.com/apache/arrow/pull/34883#discussion_r1162142218
##########
python/pyarrow/array.pxi:
##########
@@ -3076,6 +3076,111 @@ cdef class ExtensionArray(Array):
return Array._to_pandas(self.storage, options, **kwargs)
+class FixedShapeTensorArray(ExtensionArray):
+ """
+ Concrete class for fixed shape tensor extension arrays.
+
+ Examples
+ --------
+ Define the extension type for tensor array
+
+ >>> import pyarrow as pa
+ >>> tensor_type = pa.fixed_shape_tensor(pa.int32(), [2, 2])
+
+ Create an extension array
+
+ >>> arr = [[1, 2, 3, 4], [10, 20, 30, 40], [100, 200, 300, 400]]
+ >>> storage = pa.array(arr, pa.list_(pa.int32(), 4))
+ >>> pa.ExtensionArray.from_storage(tensor_type, storage)
+ <pyarrow.lib.FixedShapeTensorArray object at ...>
+ [
+ [
+ 1,
+ 2,
+ 3,
+ 4
+ ],
+ [
+ 10,
+ 20,
+ 30,
+ 40
+ ],
+ [
+ 100,
+ 200,
+ 300,
+ 400
+ ]
+ ]
+ """
+
+ def to_numpy_ndarray(self):
+ """
+ Convert fixed shape tensor extension array to a numpy array (with
dim+1).
+ """
+ np_flat = np.asarray(self.storage.values)
+ numpy_tensor = np_flat.reshape((len(self),) + tuple(self.type.shape),
+ order='C')
Review Comment:
> 2. `to_numpy_ndarray` is reshaping 1-D array so there is no need to check
the layout style as 1-D array is always both C-contiguous and F-contiguous.
While that's true for the physical layout it's not necessarily true for the
logical layout. If permutation is non-trivial tenor will not be laid out in
memory in C-contiguous way. I propose
(https://github.com/apache/arrow/pull/34883#pullrequestreview-1378208716) we
block converting tensors with non-trivial permutations and add correct logic to
handle those later. (Another option is to go via `array.ToTensor().to_numpy()`
once [`FromTensor/ToTensor`](https://github.com/apache/arrow/pull/34797) is
merged.)
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