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#
# 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
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from collections.abc import Iterable
from typing import List
import numpy as np
from ... import opcodes
from ...core import EntityData, ExecutableTuple
from ...serialization.serializables import FieldTypes, KeyField, StringField, TupleField
from ..core import TensorOrder
from ..datasource import tensor as astensor
from ..operators import TensorHasInput, TensorOperatorMixin
class TensorUnravelIndex(TensorHasInput, TensorOperatorMixin):
_op_type_ = opcodes.UNRAVEL_INDEX
_input = KeyField("input")
dims = TupleField("dims", FieldTypes.int32, default=None)
order = StringField("order", default=None)
def __init__(self, order=None, **kw):
super().__init__(order=order, **kw)
if self.order is None:
self.order = "C"
@property
def output_limit(self):
return float("inf")
@classmethod
def _set_inputs(cls, op: "TensorUnravelIndex", inputs: List[EntityData]):
super()._set_inputs(op, inputs)
op._input = op._inputs[0]
def __call__(self, indices):
order = TensorOrder.C_ORDER if self.order == "C" else TensorOrder.F_ORDER
kws = [{"pos": i, "order": order} for i in range(len(self.dims))]
return ExecutableTuple(
self.new_tensors([indices], indices.shape, kws=kws, output_limit=len(kws))
)
[docs]
def unravel_index(indices, dims, order="C"):
"""
Converts a flat index or tensor of flat indices into a tuple
of coordinate tensors.
Parameters
----------
indices : array_like
An integer tensor whose elements are indices into the flattened
version of a tensor of dimensions ``dims``.
dims : tuple of ints
The shape of the tensor to use for unraveling ``indices``.
order : {'C', 'F'}, optional
Determines whether the indices should be viewed as indexing in
row-major (C-style) or column-major (Fortran-style) order.
Returns
-------
unraveled_coords : tuple of Tensor
Each tensor in the tuple has the same shape as the ``indices``
tensor.
See Also
--------
ravel_multi_index
Examples
--------
>>> import maxframe.tensor as mt
>>> mt.unravel_index([22, 41, 37], (7,6)).execute()
(array([3, 6, 6]), array([4, 5, 1]))
>>> mt.unravel_index(1621, (6,7,8,9)).execute()
(3, 1, 4, 1)
"""
indices = astensor(indices)
if isinstance(dims, Iterable):
dims = tuple(dims)
else:
dims = (dims,)
if order not in "CF":
raise TypeError("only 'C' or 'F' order is permitted")
op = TensorUnravelIndex(dims=dims, dtype=np.dtype(np.intp), order=order)
return op(indices)