reword docs a bit
@@ -2195,7 +2195,7 @@ cdef class DenseArray(ArraySchema):
return self.domain.shape[0]
def __getitem__(self, object selection):
- """Retrieve data for an item or region of the array.
+ """Retrieve data cells for an item or region of the array.
:param tuple selection: An int index, slice or tuple of integer/slice objects,
specifiying the selected subarray region for each dimension of the DenseArray.
@@ -2206,6 +2206,7 @@ cdef class DenseArray(ArraySchema):
:raises IndexError: invalid or unsupported index selection
:raises: :py:exc:`tiledb.TileDBError`
+ >>> # many aspects of Numpy's fancy indexing is supported
>>> A[1:10, ...]
>>> A[1:10, 100:999]
>>> A[1, 2]
@@ -2328,7 +2329,7 @@ cdef class DenseArray(ArraySchema):
return out
def __setitem__(self, object selection, object val):
- """Set/Modify dense subarray regions
+ """Set / update dense data cells
:param tuple selection: An int index, slice or tuple of integer/slice objects,
specifiying the selected subarray region for each dimension of the DenseArray.
@@ -2660,7 +2661,7 @@ cdef class SparseArray(ArraySchema):
raise TypeError("SparseArray length is ambiguous; use shape[0]")
def __setitem__(self, object selection, object val):
- """Modify sparse data cells
+ """Set / update sparse data cells
:param tuple selection: N coordinate value arrays (dim0, dim1, ...) where N in the rank of the SparseArray,
The format follows numpy sparse (point) indexing semantics.
