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Module « numpy »

Fonction cumulative_sum - module numpy

Signature de la fonction cumulative_sum

def cumulative_sum(x, /, *, axis=None, dtype=None, out=None, include_initial=False) 

Description

help(numpy.cumulative_sum)

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

This function is an Array API compatible alternative to `numpy.cumsum`.

Parameters
----------
x : array_like
    Input array.
axis : int, optional
    Axis along which the cumulative sum is computed. The default
    (None) is only allowed for one-dimensional arrays. For arrays
    with more than one dimension ``axis`` is required.
dtype : 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.
out : 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. See :ref:`ufuncs-output-type`
    for more details.
include_initial : bool, optional
    Boolean indicating whether to include the initial value (zeros) 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
-------
cumulative_sum_along_axis : ndarray
    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``.

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

Notes
-----
Arithmetic is modular when using integer types, and no error is
raised on overflow.

``cumulative_sum(a)[-1]`` may not be equal to ``sum(a)`` for
floating-point values since ``sum`` may use a pairwise summation routine,
reducing the roundoff-error. See `sum` for more information.

Examples
--------
>>> 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=float)  # specifies type of output value(s)
array([  1.,   3.,   6.,  10.,  15.,  21.])

>>> 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-9, 3e-9] * 1000000)
>>> np.cumulative_sum(c)[-1]
1000000.0050045159
>>> c.sum()
1000000.0050000029

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