# Copyright 2022-2023 XProbe Inc.
# derived from copyright 1999-2021 Alibaba Group Holding Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from collections.abc import Iterable
import numpy as np
from ... import opcodes as OperandDef
from ...serialization.serializables import FieldTypes, ListField
from .arange import arange
from .core import TensorNoInput
from .empty import empty
from .meshgrid import meshgrid
class TensorIndices(TensorNoInput):
_op_type_ = OperandDef.TENSOR_INDICES
_dimensions = ListField("dimensions", FieldTypes.uint64)
def __init__(self, dimensions=None, **kw):
super().__init__(_dimensions=dimensions, **kw)
@property
def dimensions(self):
return self._dimensions
[docs]def indices(dimensions, dtype=int, chunk_size=None):
"""
Return a tensor representing the indices of a grid.
Compute a tensor where the subtensors contain index values 0,1,...
varying only along the corresponding axis.
Parameters
----------
dimensions : sequence of ints
The shape of the grid.
dtype : dtype, optional
Data type of the result.
chunk_size : int or tuple of int or tuple of ints, optional
Desired chunk size on each dimension
Returns
-------
grid : Tensor
The tensor of grid indices,
``grid.shape = (len(dimensions),) + tuple(dimensions)``.
See Also
--------
mgrid, meshgrid
Notes
-----
The output shape is obtained by prepending the number of dimensions
in front of the tuple of dimensions, i.e. if `dimensions` is a tuple
``(r0, ..., rN-1)`` of length ``N``, the output shape is
``(N,r0,...,rN-1)``.
The subtensors ``grid[k]`` contains the N-D array of indices along the
``k-th`` axis. Explicitly::
grid[k,i0,i1,...,iN-1] = ik
Examples
--------
>>> import mars.tensor as mt
>>> grid = mt.indices((2, 3))
>>> grid.shape
(2, 2, 3)
>>> grid[0].execute() # row indices
array([[0, 0, 0],
[1, 1, 1]])
>>> grid[1].execute() # column indices
array([[0, 1, 2],
[0, 1, 2]])
The indices can be used as an index into a tensor.
>>> x = mt.arange(20).reshape(5, 4)
>>> row, col = mt.indices((2, 3))
>>> # x[row, col] # TODO(jisheng): accomplish this if multiple fancy indexing is supported
Note that it would be more straightforward in the above example to
extract the required elements directly with ``x[:2, :3]``.
"""
from ..merge import stack
dimensions = tuple(dimensions)
dtype = np.dtype(dtype)
raw_chunk_size = chunk_size
if chunk_size is not None and isinstance(chunk_size, Iterable):
chunk_size = tuple(chunk_size)
else:
chunk_size = (chunk_size,) * len(dimensions)
xi = []
for ch, dim in zip(chunk_size, dimensions):
xi.append(arange(dim, dtype=dtype, chunk_size=ch))
grid = None
if np.prod(dimensions):
grid = meshgrid(*xi, indexing="ij")
if grid:
grid = stack(grid)
else:
if raw_chunk_size is None:
empty_chunk_size = None
else:
empty_chunk_size = (1,) + chunk_size
grid = empty(
(len(dimensions),) + dimensions, dtype=dtype, chunk_size=empty_chunk_size
)
return grid