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import numpy as np
from maxframe import opcodes
from maxframe.core import ExecutableTuple
from maxframe.serialization.serializables import KeyField
from maxframe.tensor.core import TensorOrder
from maxframe.tensor.datasource import tensor as astensor
from maxframe.tensor.operators import TensorHasInput, TensorOperatorMixin
class TensorNonzero(TensorHasInput, TensorOperatorMixin):
_op_type_ = opcodes.NONZERO
_input = KeyField("input")
@property
def output_limit(self):
return float("inf")
def __call__(self, a):
kws = [
{"shape": (np.nan,), "order": TensorOrder.C_ORDER, "_idx_": i}
for i in range(a.ndim)
]
return ExecutableTuple(self.new_tensors([a], kws=kws, output_limit=len(kws)))
[docs]
def nonzero(a):
"""
Return the indices of the elements that are non-zero.
Returns a tuple of tensors, one for each dimension of `a`,
containing the indices of the non-zero elements in that
dimension. The values in `a` are always tested and returned.
The corresponding non-zero
values can be obtained with::
a[nonzero(a)]
To group the indices by element, rather than dimension, use::
transpose(nonzero(a))
The result of this is always a 2-D array, with a row for
each non-zero element.
Parameters
----------
a : array_like
Input tensor.
Returns
-------
tuple_of_arrays : tuple
Indices of elements that are non-zero.
See Also
--------
flatnonzero :
Return indices that are non-zero in the flattened version of the input
tensor.
Tensor.nonzero :
Equivalent tensor method.
count_nonzero :
Counts the number of non-zero elements in the input tensor.
Examples
--------
>>> import maxframe.tensor as mt
>>> x = mt.array([[1,0,0], [0,2,0], [1,1,0]])
>>> x.execute()
array([[1, 0, 0],
[0, 2, 0],
[1, 1, 0]])
>>> mt.nonzero(x).execute()
(array([0, 1, 2, 2]), array([0, 1, 0, 1]))
>>> x[mt.nonzero(x)].execute()
>>> mt.transpose(mt.nonzero(x)).execute()
A common use for ``nonzero`` is to find the indices of an array, where
a condition is True. Given an array `a`, the condition `a` > 3 is a
boolean array and since False is interpreted as 0, np.nonzero(a > 3)
yields the indices of the `a` where the condition is true.
>>> a = mt.array([[1,2,3],[4,5,6],[7,8,9]])
>>> (a > 3).execute()
array([[False, False, False],
[ True, True, True],
[ True, True, True]])
>>> mt.nonzero(a > 3).execute()
(array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))
The ``nonzero`` method of the boolean array can also be called.
>>> (a > 3).nonzero().execute()
(array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))
"""
a = astensor(a)
op = TensorNonzero(dtype=np.dtype(np.intp))
return op(a)