parameters: (self)
return_statement: return narray(self.filled(0), copy=False).nonzero()
docstring: Return the indices of unmasked elements that are not zero.
  
          Returns a tuple of arrays, one for each dimension, containing the
          indices of the non-zero elements in that dimension. The corresponding
          non-zero values can be obtained with::
  
              a[a.nonzero()]
  
          To group the indices by element, rather than dimension, use
          instead::
  
              np.transpose(a.nonzero())
  
          The result of this is always a 2d array, with a row for each non-zero
          element.
  
          Parameters
          ----------
          None
  
          Returns
          -------
          tuple_of_arrays : tuple
              Indices of elements that are non-zero.
  
          See Also
          --------
          numpy.nonzero :
              Function operating on ndarrays.
          flatnonzero :
              Return indices that are non-zero in the flattened version of the input
              array.
          ndarray.nonzero :
              Equivalent ndarray method.
          count_nonzero :
              Counts the number of non-zero elements in the input array.
  
          Examples
          --------
          >>> import numpy.ma as ma
          >>> x = ma.array(np.eye(3))
          >>> x
          masked_array(data =
           [[ 1.  0.  0.]
           [ 0.  1.  0.]
           [ 0.  0.  1.]],
                mask =
           False,
                fill_value=1e+20)
          >>> x.nonzero()
          (array([0, 1, 2]), array([0, 1, 2]))
  
          Masked elements are ignored.
  
          >>> x[1, 1] = ma.masked
          >>> x
          masked_array(data =
           [[1.0 0.0 0.0]
           [0.0 -- 0.0]
           [0.0 0.0 1.0]],
                mask =
           [[False False False]
           [False  True False]
           [False False False]],
                fill_value=1e+20)
          >>> x.nonzero()
          (array([0, 2]), array([0, 2]))
  
          Indices can also be grouped by element.
  
          >>> np.transpose(x.nonzero())
          array([[0, 0],
                 [2, 2]])
  
          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, ma.nonzero(a > 3)
          yields the indices of the `a` where the condition is true.
  
          >>> a = ma.array([[1,2,3],[4,5,6],[7,8,9]])
          >>> a > 3
          masked_array(data =
           [[False False False]
           [ True  True  True]
           [ True  True  True]],
                mask =
           False,
                fill_value=999999)
          >>> ma.nonzero(a > 3)
          (array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))
  
          The ``nonzero`` method of the condition array can also be called.
  
          >>> (a > 3).nonzero()
          (array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))
docstring_summary: Return the indices of unmasked elements that are not zero.
docstring_tokens: Return the indices of unmasked elements that are not zero .
function: def nonzero(self):
          """
          Return the indices of unmasked elements that are not zero.
  
          Returns a tuple of arrays, one for each dimension, containing the
          indices of the non-zero elements in that dimension. The corresponding
          non-zero values can be obtained with::
  
              a[a.nonzero()]
  
          To group the indices by element, rather than dimension, use
          instead::
  
              np.transpose(a.nonzero())
  
          The result of this is always a 2d array, with a row for each non-zero
          element.
  
          Parameters
          ----------
          None
  
          Returns
          -------
          tuple_of_arrays : tuple
              Indices of elements that are non-zero.
  
          See Also
          --------
          numpy.nonzero :
              Function operating on ndarrays.
          flatnonzero :
              Return indices that are non-zero in the flattened version of the input
              array.
          ndarray.nonzero :
              Equivalent ndarray method.
          count_nonzero :
              Counts the number of non-zero elements in the input array.
  
          Examples
          --------
          >>> import numpy.ma as ma
          >>> x = ma.array(np.eye(3))
          >>> x
          masked_array(data =
           [[ 1.  0.  0.]
           [ 0.  1.  0.]
           [ 0.  0.  1.]],
                mask =
           False,
                fill_value=1e+20)
          >>> x.nonzero()
          (array([0, 1, 2]), array([0, 1, 2]))
  
          Masked elements are ignored.
  
          >>> x[1, 1] = ma.masked
          >>> x
          masked_array(data =
           [[1.0 0.0 0.0]
           [0.0 -- 0.0]
           [0.0 0.0 1.0]],
                mask =
           [[False False False]
           [False  True False]
           [False False False]],
                fill_value=1e+20)
          >>> x.nonzero()
          (array([0, 2]), array([0, 2]))
  
          Indices can also be grouped by element.
  
          >>> np.transpose(x.nonzero())
          array([[0, 0],
                 [2, 2]])
  
          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, ma.nonzero(a > 3)
          yields the indices of the `a` where the condition is true.
  
          >>> a = ma.array([[1,2,3],[4,5,6],[7,8,9]])
          >>> a > 3
          masked_array(data =
           [[False False False]
           [ True  True  True]
           [ True  True  True]],
                mask =
           False,
                fill_value=999999)
          >>> ma.nonzero(a > 3)
          (array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))
  
          The ``nonzero`` method of the condition array can also be called.
  
          >>> (a > 3).nonzero()
          (array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))
  
          """
          return narray(self.filled(0), copy=False).nonzero()
function_tokens: def nonzero ( self ) : return narray ( self . filled ( 0 ) , copy = False ) . nonzero ( )