11 Aug
2023
11 Aug
'23
12:56 p.m.
On Fri, 2023-08-11 at 13:43 -0400, Benjamin Root wrote:
I'm really confused. Summing from zero should be what cumsum() does now.
What they mean is *including* the "implicit" 0 in the result. There are some old NumPy issues on this, suggesting something like a new kwarg like `include_initial=True`. This was also discussed here more recently: https://github.com/data-apis/array-api/issues/597 I think everyone always agreed with such an addition being good. It terribly be super hard, although the code needs some restructuring to do it, so not sure it is easy either. - Sebastian
```
np.__version__ '1.22.4' np.cumsum([[1, 2, 3], [4, 5, 6]]) array([ 1, 3, 6, 10, 15, 21])
which matches your example in the cumsum0() documentation. Did something change in a recent release? Ben Root On Fri, Aug 11, 2023 at 8:55 AM Juan Nunez-Iglesias <jni@fastmail.com> wrote: > I'm very sensitive to the issues of adding to the already bloated > numpy > API, but I would definitely find use in this function. I literally > made > this error (thinking that the first element of cumsum should be 0) > just a > couple of days ago! What are the plans for the "extended" NumPy API > after > 2.0? Is there a good place for these variants? > > On Fri, 11 Aug 2023, at 2:07 AM, john.dawson@camlingroup.com wrote: > > `cumsum` computes the sum of the first k summands for every k > > from 1. > > Judging by my experience, it is more often useful to compute the > > sum of > > the first k summands for every k from 0, as `cumsum`'s behaviour > > leads > > to fencepost-like problems. > > https://en.wikipedia.org/wiki/Off-by-one_error#Fencepost_error > > For example, `cumsum` is not the inverse of `diff`. I propose > > adding a > > function to NumPy to compute cumulative sums beginning with 0, > > that is, > > an inverse of `diff`. It might be called `cumsum0`. The following > > code > > is probably not the best way to implement it, but it illustrates > > the > > desired behaviour. > > > > ``` > > def cumsum0(a, axis=None, dtype=None, out=None): > > """ > > Return the cumulative sum of the elements along a given axis, > > beginning with 0. > > > > cumsum0 does the same as cumsum except that cumsum computes > > the sum > > of the first k summands for every k from 1 and cumsum, from > > 0. > > > > Parameters > > ---------- > > a : array_like > > Input array. > > axis : int, optional > > Axis along which the cumulative sum is computed. The > > default > > (None) is to compute the cumulative sum over the > > flattened > > array. > > 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 `a`, unless `a` 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. > > > > Returns > > ------- > > cumsum0_along_axis : ndarray. > > A new array holding the result is returned unless `out` > > is > > specified, in which case a reference to `out` is > > returned. If > > `axis` is not None the result has the same shape as `a` > > except > > along `axis`, where the dimension is smaller by 1. > > > > See Also > > -------- > > cumsum : Cumulatively sum array elements, beginning with the > > first. > > sum : Sum array elements. > > trapz : Integration of array values using the 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. > > > > ``cumsum0(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.cumsum0(a) > > array([ 0, 1, 3, 6, 10, 15, 21]) > > >>> np.cumsum0(a, dtype=float) # specifies type of output > > value(s) > > array([ 0., 1., 3., 6., 10., 15., 21.]) > > > > >>> np.cumsum0(a, axis=0) # sum over rows for each of the 3 > > columns > > array([[0, 0, 0], > > [1, 2, 3], > > [5, 7, 9]]) > > >>> np.cumsum0(a, axis=1) # sum over columns for each of the > > 2 rows > > array([[ 0, 1, 3, 6], > > [ 0, 4, 9, 15]]) > > > > ``cumsum(b)[-1]`` may not be equal to ``sum(b)`` > > > > >>> b = np.array([1, 2e-9, 3e-9] * 1000000) > > >>> np.cumsum0(b)[-1] > > 1000000.0050045159 > > >>> b.sum() > > 1000000.0050000029 > > > > """ > > empty = a.take([], axis=axis) > > zero = empty.sum(axis, dtype=dtype, keepdims=True) > > later_cumsum = a.cumsum(axis, dtype=dtype) > > return concatenate([zero, later_cumsum], axis=axis, > > dtype=dtype, > out=out) > > ``` > > _______________________________________________ > > NumPy-Discussion mailing list -- numpy-discussion@python.org > > To unsubscribe send an email to numpy-discussion-leave@python.org > > https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ > > Member address: jni@fastmail.com > _______________________________________________ > NumPy-Discussion mailing list -- numpy-discussion@python.org > To unsubscribe send an email to numpy-discussion-leave@python.org > https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ > Member address: ben.v.root@gmail.com > _______________________________________________ NumPy-Discussion mailing list -- numpy-discussion@python.org To unsubscribe send an email to numpy-discussion-leave@python.org https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ Member address: sebastian@sipsolutions.net