dpnp.cumulative_sum

dpnp.cumulative_sum(x, /, *, axis=None, dtype=None, out=None, include_initial=False)[source]

Return the cumulative sum of the elements along a given axis.

This function is an Array API compatible alternative to dpnp.cumsum.

For full documentation refer to numpy.cumulative_sum.

Parameters:

x ({dpnp.ndarray, usm_ndarray}) -- Input array.
axis ({None, int}, optional) -- Axis along which the cumulative sum is computed. The default value is only allowed for one-dimensional arrays. For arrays with more than one dimension axis is required. Default: None.
dtype ({None, dtype}, optional) -- Type of the returned array and of the accumulator in which the elements are summed. If dtype is not specified, it defaults to the dtype of x, unless x has an integer dtype with a precision less than that of the default platform integer. In that case, the default platform integer is used. Default: None.
out ({None, dpnp.ndarray, usm_ndarray}, optional) -- Alternative output array in which to place the result. It must have the same shape and buffer length as the expected output but the type will be cast if necessary. Default: None.
include_initial (bool, optional) -- Boolean indicating whether to include the initial value (ones) as the first value in the output. With include_initial=True the shape of the output is different than the shape of the input. Default: False.

Returns:

out -- A new array holding the result is returned unless out is specified, in which case a reference to out is returned. The result has the same shape as x if include_initial=False.

Return type:

dpnp.ndarray

See also

dpnp.sum: Sum array elements.
dpnp.trapezoid: Integration of array values using composite trapezoidal rule.
dpnp.diff: Calculate the n-th discrete difference along given axis.

Examples

>>> import dpnp as np
>>> a = np.array([1, 2, 3, 4, 5, 6])
>>> a
array([1, 2, 3, 4, 5, 6])
>>> np.cumulative_sum(a)
array([ 1,  3,  6, 10, 15, 21])
>>> np.cumulative_sum(a, dtype=np.float32)   # specifies output dtype
array([ 1.,  3.,  6., 10., 15., 21.], dtype=np.float32)

>>> b = np.array([[1, 2, 3], [4, 5, 6]])
>>> np.cumulative_sum(b, axis=0)  # sum over rows for each of the 3 columns
array([[1, 2, 3],
       [5, 7, 9]])
>>> np.cumulative_sum(b, axis=1)  # sum over columns for each of the 2 rows
array([[ 1,  3,  6],
       [ 4,  9, 15]])

cumulative_sum(c)[-1] may not be equal to sum(c)

>>> c = np.array([1, 2e-7, 3e-7] * 100000, dtype=np.float32)
>>> np.cumulative_sum(c).dtype == c.sum().dtype == np.float32
True
>>> np.cumulative_sum(c)[-1] == c.sum()
array(False)