nums.numpy.trace

nums.numpy.trace(a, offset=0, axis1=0, axis2=1, dtype=None, out=None)[source]

Return the sum along diagonals of the array.

This docstring was copied from numpy.trace.

Some inconsistencies with the NumS version may exist.

If a is 2-D, the sum along its diagonal with the given offset is returned, i.e., the sum of elements a[i,i+offset] for all i.

If a has more than two dimensions, then the axes specified by axis1 and axis2 are used to determine the 2-D sub-arrays whose traces are returned. The shape of the resulting array is the same as that of a with axis1 and axis2 removed.

Parameters

  • a (BlockArray) – Input array, from which the diagonals are taken.

  • offset (int, optional) – Offset of the diagonal from the main diagonal. Can be both positive and negative. Defaults to 0.

  • axis1 (int, optional) – Axes to be used as the first and second axis of the 2-D sub-arrays from which the diagonals should be taken. Defaults are the first two axes of a.

  • axis2 (int, optional) – Axes to be used as the first and second axis of the 2-D sub-arrays from which the diagonals should be taken. Defaults are the first two axes of a.

  • dtype (dtype, optional) – Determines the data-type of the returned array and of the accumulator where the elements are summed. If dtype has the value None and a is of integer type of precision less than the default integer precision, then the default integer precision is used. Otherwise, the precision is the same as that of a.

  • out (BlockArray, optional) – Array into which the output is placed. Its type is preserved and it must be of the right shape to hold the output.

Returns

sum_along_diagonals – If a is 2-D, the sum along the diagonal is returned. If a has larger dimensions, then an array of sums along diagonals is returned.

Return type

BlockArray

See also

diag, diagonal

Notes

offset != 0 is currently not supported.

out is currently not supported.

axis1 != 0 or axis2 != 1 is currently not supported.

Examples

The doctests shown below are copied from NumPy. They won’t show the correct result until you operate get().

>>> nps.trace(nps.eye(3)).get()  
array(3.)
>>> a = nps.arange(8).reshape((2,2,2))