Add to NumPy a function to compute cumulative sums from 0.
`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 fencepostlike problems. https://en.wikipedia.org/wiki/Offbyone_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:`ufuncsoutputtype` 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 nth 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 floatingpoint values since ``sum`` may use a pairwise summation routine, reducing the roundofferror. 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, 2e9, 3e9] * 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) ```
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 fencepostlike problems. https://en.wikipedia.org/wiki/Offbyone_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:`ufuncsoutputtype` 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 nth 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 floatingpoint values since ``sum`` may use a pairwise summation routine, reducing the roundofferror. 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, 2e9, 3e9] * 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) ``` _______________________________________________ NumPyDiscussion mailing list  numpydiscussion@python.org To unsubscribe send an email to numpydiscussionleave@python.org https://mail.python.org/mailman3/lists/numpydiscussion.python.org/ Member address: jni@fastmail.com
From my point of view, such function is a bit of a cornercase to be added to numpy. And it doesn’t justify it’s naming anymore. It is not one operation anymore. It is a cumsum and prepending 0. And it is very difficult to argue why prepending 0 to cumsum is a part of cumsum. What I would rather vouch for is adding an argument to `np.diff` so that it leaves first row unmodified. def diff0(a, axis=1): """Differencing which appends first item along the axis""" a0 = np.take(a, [0], axis=axis) return np.concatenate([a0, np.diff(a, n=1, axis=axis)], axis=axis) This would be more sensible from conceptual point of view. As difference can not be made, the result is the difference from absolute origin. With recognition that first nonorigin value in a sequence is the one after it. And if the first row is the origin in a specific case, then that origin is correctly defined in relation to absolute origin. Then, if origin row is needed, then it can be prepended in the beginning of a procedure. And np.diff and np.cumsum are inverses throughout the sequential code. np.diff0 was one the first functions I had added to my numpy utils and been using it instead of np.diff quite a lot. I think general flag to prevent fencepost errors could be added to all functions, where required, so that the flow is seamless retains initial dimension length. Taking some time to ensure consistency across numpy in this dimension could be of long term value. E.g. rolling functions in numbagg and bottleneck leave nans, because there is no other sensible value to go there instead. While in this case, sensible value exists. Just not in `cumsum` function.
On 11 Aug 2023, at 15:53, Juan NunezIglesias <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 fencepostlike problems. https://en.wikipedia.org/wiki/Offbyone_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:`ufuncsoutputtype` 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 nth 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 floatingpoint values since ``sum`` may use a pairwise summation routine, reducing the roundofferror. 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, 2e9, 3e9] * 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) ``` _______________________________________________ NumPyDiscussion mailing list  numpydiscussion@python.org To unsubscribe send an email to numpydiscussionleave@python.org https://mail.python.org/mailman3/lists/numpydiscussion.python.org/ Member address: jni@fastmail.com
NumPyDiscussion mailing list  numpydiscussion@python.org To unsubscribe send an email to numpydiscussionleave@python.org https://mail.python.org/mailman3/lists/numpydiscussion.python.org/ Member address: dom.grigonis@gmail.com
From my point of view, such function is a bit of a cornercase to be added to numpy. And it doesn’t justify it’s naming anymore. It is not one operation anymore. It is a cumsum and prepending 0. And it is very difficult to argue why prepending 0 to cumsum is a part of cumsum.
That is backwards. Consider the array [x0, x1, x2]. The sum of the first 0 elements is 0. The sum of the first 1 elements is x0. The sum of the first 2 elements is x0+x1. The sum of the first 3 elements is x0+x1+x2. Hence, the array of partial sums is [0, x0, x0+x1, x0+x1+x2]. Thus, the operation [x0, x1, x2] > [0, x0, x0+x1, x0+x1+x2] is a natural and primitive one. The current behaviour of numpy.cumsum is the composition of two basic operations, computing the partial sums and omitting the initial value: [x0, x1, x2] > [0, x0, x0+x1, x0+x1+x2] > [x0, x0+x1, x0+x1+x2].
What I would rather vouch for is adding an argument to `np.diff` so that it leaves first row unmodified. def diff0(a, axis=1): """Differencing which appends first item along the axis""" a0 = np.take(a, [0], axis=axis) return np.concatenate([a0, np.diff(a, n=1, axis=axis)], axis=axis) This would be more sensible from conceptual point of view. As difference can not be made, the result is the difference from absolute origin. With recognition that first nonorigin value in a sequence is the one after it. And if the first row is the origin in a specific case, then that origin is correctly defined in relation to absolute origin. Then, if origin row is needed, then it can be prepended in the beginning of a procedure. And np.diff and np.cumsum are inverses throughout the sequential code. np.diff0 was one the first functions I had added to my numpy utils and been using it instead of np.diff quite a lot.
This suggestion is bad: diff0 is conceptually confused. numpy.diff changes an array of numpy.datetime64s to an array of numpy.timedelta64s, but numpy.diff0 changes an array of numpy.datetime64s to a heterogeneous array where one element is a numpy.datetime64 and the rest are numpy.timedelta64s. In general, whereas numpy.diff changes an array of positions to an array of displacements, diff0 changes an array of positions to a heterogeneous array where one element is a position and the rest are displacements.
On Tue, Aug 15, 2023 at 2:44 PM <john.dawson@camlingroup.com> wrote:
From my point of view, such function is a bit of a cornercase to be added to numpy. And it doesn’t justify it’s naming anymore. It is not one operation anymore. It is a cumsum and prepending 0. And it is very difficult to argue why prepending 0 to cumsum is a part of cumsum.
That is backwards. Consider the array [x0, x1, x2].
The sum of the first 0 elements is 0. The sum of the first 1 elements is x0. The sum of the first 2 elements is x0+x1. The sum of the first 3 elements is x0+x1+x2.
Hence, the array of partial sums is [0, x0, x0+x1, x0+x1+x2].
Thus, the operation [x0, x1, x2] > [0, x0, x0+x1, x0+x1+x2] is a natural and primitive one.
You are describing ndarray.sum() behavior here inside an array as intermediate results; sum is an aggregator that produces single item from a list of items. Then you can argue about missing items behavior and the values you have provided are exactly the values the accumulator would get. However, cumsum, cumprod, diff etc. are "array functions". In other words they provide fast vectorized access to otherwise laborious for loops. You have to consider the equivalent for loops working on the array *data*, not the ideal math framework over the number field. You don't start with the array element that is before the first element for an array function hence no elements > 0 is only applicable to sum but not to the array function. Or at least that would be my argument. If you have no element meaning 0 elements the cumulative sum is not 0, it is the empty array. Because there is no array to cumulatively "sum" (remember we are working on the array to generate another array, not aggregating). You can argue what empty set translates to under summation etc. but I don't think it applies here. But that's my opinion. I'm not sure why folks wanted to have this at all. It is the same as asking whether this code for k in range(0): ...some code ... should at least spin once (fortranish behavior). I don't know why it should. But then again, it becomes a bikeshedding with some conflicting idealistic mathy axioms thrown at each other. NumPy cumsum returns empty array for empty array (I think all software does this including matlab). ndarray.sum() however returns scalar 0 (and I think most software does this too), because that's pretty much a noop over the initialization value and aggregated, in the example above x=0 for k in range(0): x += 1 return x # returns 0 I think all these point to the missing convenient functionality that extends arrays. In matlab "[0 arr 10]" nicely extends the array to a new one but in NumPy you need to punch quite some code and some courage to remember whether it is hstack or vstack or concat or block as the correct naming which decreases the "code morale". So if people want to quickly extend arrays they either have to change the code for their needs or create larger arrays which is pretty much #6044. So I think this is a feature request of "prepend", "append" in a convenient fashion not to ufuncs but to ndarray. Because concatenation is just pain in NumPy and ubiquitous operation all around. Hence probably we should get a decision on that instead of discussing each case separately.
With this I agree, this sounds like a more radical (in a good way) solution.
So I think this is a feature request of "prepend", "append" in a convenient fashion not to ufuncs but to ndarray. Because concatenation is just pain in NumPy and ubiquitous operation all around. Hence probably we should get a decision on that instead of discussing each case separately.
Ilhan Polat wrote:
I think all these point to the missing convenient functionality that extends arrays. In matlab "[0 arr 10]" nicely extends the array to a new one but in NumPy you need to punch quite some code and some courage to remember whether it is hstack or vstack or concat or block as the correct naming which decreases the "code morale".
Not having a convenient workaround is not the only problem. The workaround is wastefull with memory and involves unnecessary copying of an array. Having a keyword implemented with these concerns in mind might avoid this.
I was trying to get a feel for how often the work around occurs. I found three clear examples in Scipy and one unclear case. One case in holoviews. Two in numpy. One from soundappraisal's code base. Next to prepending to the output, I also see prepending to the input as a workaround. Some examples of workarounds: scipy: (prepending to the output) scipy/scipy/sparse/construct.py: '''Python row_offsets = np.append(0, np.cumsum(brow_lengths)) col_offsets = np.append(0, np.cumsum(bcol_lengths)) ''' scipy/scipy/sparse/dia.py: '''Python indptr = np.zeros(num_cols + 1, dtype=idx_dtype) indptr[1:offset_len+1] = np.cumsum(mask.sum(axis=0)) ''' scipy/scipy/sparse/csgraph/_tools.pyx: '''Python indptr = np.zeros(N + 1, dtype=ITYPE) indptr[1:] = mask.sum(1).cumsum() ''' Not sure whether this is also an example: scipy/scipy/stats/_hypotests_pythran.py '''Python # Now fill in the values. We cannot use cumsum, unfortunately. val = 0.0 if minj == 0 else 1.0 for jj in range(maxj  minj): j = jj + minj val = (A[jj + minj  lastminj] * i + val * j) / (i + j) A[jj] = val ''' holoviews: (prepending to the input) '''Python # We add a zero in the begging for the cumulative sum points = np.zeros((areas_in_radians.shape[0] + 1)) points[1:] = areas_in_radians points = points.cumsum() ''' numpy (prepending to the input): numpy/numpy/lib/_iotools.py : '''Python idx = np.cumsum([0] + list(delimiter)) ''' numpy/numpy/lib/histograms.py '''Python cw = np.concatenate((zero, sw.cumsum())) ''' soundappraisal own code: (prepending to the output) '''Python def get_cumulativepixelareas(whiteboard): whiteboard['cumulativepixelareas'] = \ np.concatenate((np.array([0, ]), np.cumsum(whiteboard['pixelareas']))) return True '''
On Fri, Aug 18, 2023 at 10:59 AM Ronald van Elburg < r.a.j.van.elburg@hetnet.nl> wrote:
I was trying to get a feel for how often the work around occurs. I found three clear examples in Scipy and one unclear case. One case in holoviews. Two in numpy. One from soundappraisal's code base.
Thank you Ronald. I think we indeed have more than enough evidence that allowing prepending an initial zero is useful. I think the API currently proposed in https://github.com/dataapis/arrayapi/pull/653 should work for that: def cumulative_sum( x: array, /, *, axis: Optional[int] = None, dtype: Optional[dtype] = None, include_initial: bool = False, ) > array: Whether it's necessary to have other keywords to prepend anything other than zero, or append rather than prepend, is a lot less clear. Did you find a clear need for those things? Cheers, Ralf
Whether it's necessary to have other keywords to prepend anything other than zero, or append rather than prepend, is a lot less clear. Did you find a clear need for those things?
No, I haven't found them. For streaming data there might be usecases for starting with an initial offset, but I expect there might be no need for a returned offset there. What is notable is that all examples above are 1D. To get the behavior of the API right, the simplest solution is to make the workaround part of the implementation. What I was pondering on is whether it is desirable to allocate the memory once and avoid copying the data. What is the price to pay in terms of code complexity and developer time? Also if the accumulation would run in place on a copy of the input data then prepending the input might be a good option introducing very little new overhead.
On Fri, Aug 18, 2023 at 4:59 AM Ronald van Elburg < r.a.j.van.elburg@hetnet.nl> wrote:
I was trying to get a feel for how often the work around occurs. I found three clear examples in Scipy and one unclear case. One case in holoviews. Two in numpy. One from soundappraisal's code base.
See also my comment from back in 2020: https://github.com/numpy/numpy/pull/14542#issuecomment586494608 Anyone interested in this enhancement is encouraged to review the discussion in that pull request (https://github.com/numpy/numpy/pull/14542), and an earlier issue from 2015: https://github.com/numpy/numpy/issues/6044 Warren
Next to prepending to the output, I also see prepending to the input as a workaround.
Some examples of workarounds:
scipy: (prepending to the output)
scipy/scipy/sparse/construct.py:
'''Python row_offsets = np.append(0, np.cumsum(brow_lengths)) col_offsets = np.append(0, np.cumsum(bcol_lengths)) '''
scipy/scipy/sparse/dia.py:
'''Python indptr = np.zeros(num_cols + 1, dtype=idx_dtype) indptr[1:offset_len+1] = np.cumsum(mask.sum(axis=0)) '''
scipy/scipy/sparse/csgraph/_tools.pyx:
'''Python indptr = np.zeros(N + 1, dtype=ITYPE) indptr[1:] = mask.sum(1).cumsum() '''
Not sure whether this is also an example:
scipy/scipy/stats/_hypotests_pythran.py '''Python # Now fill in the values. We cannot use cumsum, unfortunately. val = 0.0 if minj == 0 else 1.0 for jj in range(maxj  minj): j = jj + minj val = (A[jj + minj  lastminj] * i + val * j) / (i + j) A[jj] = val '''
holoviews: (prepending to the input)
'''Python # We add a zero in the begging for the cumulative sum points = np.zeros((areas_in_radians.shape[0] + 1)) points[1:] = areas_in_radians points = points.cumsum() '''
numpy (prepending to the input):
numpy/numpy/lib/_iotools.py :
'''Python idx = np.cumsum([0] + list(delimiter)) '''
numpy/numpy/lib/histograms.py
'''Python cw = np.concatenate((zero, sw.cumsum())) '''
soundappraisal own code: (prepending to the output)
'''Python def get_cumulativepixelareas(whiteboard): whiteboard['cumulativepixelareas'] = \ np.concatenate((np.array([0, ]), np.cumsum(whiteboard['pixelareas']))) return True ''' _______________________________________________ NumPyDiscussion mailing list  numpydiscussion@python.org To unsubscribe send an email to numpydiscussionleave@python.org https://mail.python.org/mailman3/lists/numpydiscussion.python.org/ Member address: warren.weckesser@gmail.com
Yet another example is ``` d = np.zeros(n) d[1:] = np.linalg.norm(np.diff(points, axis=1), axis=0) r = d.cumsum() ``` https://github.com/WarrenWeckesser/ufunclab/blob/main/examples/linear_interp...
Note that this is independent from the memory waste. There are way worse memory ops in NumPy than this so I don't think that argument applies here even if it was. And like I mentioned, this is a very common operation hence internals are secondary. But it is not an unnecessary copy of the array anyways because that is the definition of concatenation which is a new array. And it is very laborious to do in NumPy relatively speaking. If it was really easy, people would probably just slap a 0 in the beginning and move on. But instead we are now entering into a keyword commitment. I'm not sure I agree with this strategy being better. I'm not against it, clearly there is a demand, but probably inconvenience should not be the reason for keyword arguments elsewhere. On Fri, Aug 18, 2023 at 9:13 AM Ronald van Elburg < r.a.j.van.elburg@hetnet.nl> wrote:
Ilhan Polat wrote:
I think all these point to the missing convenient functionality that extends arrays. In matlab "[0 arr 10]" nicely extends the array to a new one but in NumPy you need to punch quite some code and some courage to remember whether it is hstack or vstack or concat or block as the correct naming which decreases the "code morale".
Not having a convenient workaround is not the only problem. The workaround is wastefull with memory and involves unnecessary copying of an array. Having a keyword implemented with these concerns in mind might avoid this. _______________________________________________ NumPyDiscussion mailing list  numpydiscussion@python.org To unsubscribe send an email to numpydiscussionleave@python.org https://mail.python.org/mailman3/lists/numpydiscussion.python.org/ Member address: ilhanpolat@gmail.com
I think ultimately the copy is unnecessary. That being said introducing prepend and append functions concentrates the complexity of the mapping in one place. Trying to avoid the extra copy would probably lead to a more complex implementation of accumulate. How would in your view the prepend interface differ from concatenation or stacking?
Unfortunately, I don’t have a good answer. For now, I can only tell you what I think might benefit from improvement. 1. Verbosity. I appreciate that bracket syntax such as one in julia or matlab `[A B C ...]` is not possible, so functional is the only option. E.g. julia has functions named ‘cat’, ‘vcat’, ‘hcat’, ‘vhcat’. I myself have recently redefined np.concatenate to `np_c`. For simple operations, it would surely be nice to have methods. E.g. `arr.append(axis)/arr.prepend(axis)`. 2. Excessive number of functions. There seems to be very many functions for concatenating and stacking. Many operations can be done using different functions and approaches and usually one of them is several times faster than the rest. I will give an example. Stacking two 1d vectors as columns of 2d array: arr = np.arange(100) TIMER.repeat([ lambda: np.array([arr, arr]).T, lambda: np.vstack([arr, arr]).T, lambda: np.stack([arr, arr]).T, lambda: np.c_[arr, arr], lambda: np.column_stack((arr, arr)), lambda: np.concatenate([arr[:, None], arr[:, None]], axis=1) ]).print(3) # mean [[0.012 0.044 0.052 0.13 0.032 0.024]] Instead, having fewer, but more intuitive/flexible and well optimised functions would be a bit more convenient. 3. Flattening and reshaping API is not very intuitive. e.g. torch flatten is an example of a function which has a desired level of flexibility in contrast to `np.flatten`. https://pytorch.org/docs/stable/generated/torch.flatten.html <https://pytorch.org/docs/stable/generated/torch.flatten.html>. I had similar issues with multidimensional searching, sorting, multidimensional overlaps and custom unique functions. In other words, all functionality is there already, but in more custom (although requirement is often very simple from perspective of how it looks in my mind) multidimensional cases, there is no easy API and I end up writing my own numpy functions and benchmarking numerous ways to achieve the same thing. By now, I have my own multidimensional unique, sort, search, flatten, more flexible ix_, which are not well tested, but already more convenient, flexible and often several times faster than numpy ones (although all they do is reuse existing numpy functionality). I think these are more along the lines of numpy 2.0, rather than simple extension. It feels that API can generally be more flexible and intuitive and there is enough of existing numpy material and external examples from which to draw from to make next level API happen. Although I appreciate required effort and difficulties. Having all that said, implementing julia’s equivalents ‘cat’, ‘vcat’, ‘hcat’, ‘vhcat’ together with `arr.append(others, axis), arr.prepend(others, axis)` while ensuring that they use most optimised approaches could potentially make life easier for the time being. —Nothing ever dies, just enters the state of deferred evaluation— Dg
On 19 Aug 2023, at 17:39, Ronald van Elburg <r.a.j.van.elburg@hetnet.nl> wrote:
I think ultimately the copy is unnecessary.
That being said introducing prepend and append functions concentrates the complexity of the mapping in one place. Trying to avoid the extra copy would probably lead to a more complex implementation of accumulate.
How would in your view the prepend interface differ from concatenation or stacking? _______________________________________________ NumPyDiscussion mailing list  numpydiscussion@python.org To unsubscribe send an email to numpydiscussionleave@python.org https://mail.python.org/mailman3/lists/numpydiscussion.python.org/ Member address: dom.grigonis@gmail.com
On 14 Aug 2023, at 15:22, john.dawson@camlingroup.com wrote:
From my point of view, such function is a bit of a cornercase to be added to numpy. And it doesn’t justify it’s naming anymore. It is not one operation anymore. It is a cumsum and prepending 0. And it is very difficult to argue why prepending 0 to cumsum is a part of cumsum.
That is backwards. Consider the array [x0, x1, x2].
The sum of the first 0 elements is 0. The sum of the first 1 elements is x0. The sum of the first 2 elements is x0+x1. The sum of the first 3 elements is x0+x1+x2.
Hence, the array of partial sums is [0, x0, x0+x1, x0+x1+x2].
Thus, the operation [x0, x1, x2] > [0, x0, x0+x1, x0+x1+x2] is a natural and primitive one.
The current behaviour of numpy.cumsum is the composition of two basic operations, computing the partial sums and omitting the initial value:
[x0, x1, x2] > [0, x0, x0+x1, x0+x1+x2] > [x0, x0+x1, x0+x1+x2]. In reality both of these functions do exactly what they need to do. But the issue, as I understand it, is to have one of these in such way, so that they are inverses of each other. The only question is which one is better suitable for it and provides most benefits.
Arguments for np.diff0: 1. Dimension length stays constant, while cumusm0 extends length to n+1, then np.diff, truncates it back. This adds extra complexity, while things are very convenient to work with when dimension length stays constant throughout the code. 2. Although I see your argument about element 0, but the fact is that it doesn’t exist at all. in np.diff0 case at least half of it exists and the other half has a half decent rationale. In cumsum0 case it just appeared out of nowhere and in your example above you are providing very different logic to what np.cumsum is intrinsically. Ilhan has accurately pointed it out in his email. For now, I only see my point of view and I can list a number of cases from data analysis and modelling, where I found np.diff0 to be a fairly optimal choice to use and it made things smoother. While I haven’t seen any reallife examples where np.cumsum0 would be useful so I am naturally biased. I would appreciate If anyone provided some examples that justify np.cumsum0  for now I just can’t think of any case where this could actually be useful or why it would be more convenient/sensible than np.diff0.
What I would rather vouch for is adding an argument to `np.diff` so that it leaves first row unmodified. def diff0(a, axis=1): """Differencing which appends first item along the axis""" a0 = np.take(a, [0], axis=axis) return np.concatenate([a0, np.diff(a, n=1, axis=axis)], axis=axis) This would be more sensible from conceptual point of view. As difference can not be made, the result is the difference from absolute origin. With recognition that first nonorigin value in a sequence is the one after it. And if the first row is the origin in a specific case, then that origin is correctly defined in relation to absolute origin. Then, if origin row is needed, then it can be prepended in the beginning of a procedure. And np.diff and np.cumsum are inverses throughout the sequential code. np.diff0 was one the first functions I had added to my numpy utils and been using it instead of np.diff quite a lot.
This suggestion is bad: diff0 is conceptually confused. numpy.diff changes an array of numpy.datetime64s to an array of numpy.timedelta64s, but numpy.diff0 changes an array of numpy.datetime64s to a heterogeneous array where one element is a numpy.datetime64 and the rest are numpy.timedelta64s. In general, whereas numpy.diff changes an array of positions to an array of displacements, diff0 changes an array of positions to a heterogeneous array where one element is a position and the rest are displacements.
This isn’t really argument against np.diff0, just one aspect of it which would have to be dealt with. If instead of just prepending, the difference from 0 was made, it would result in numpy.timedelta64s. So not a big issue.
Dom Grigonis wrote:
1. Dimension length stays constant, while cumusm0 extends length to n+1, then np.diff, truncates it back. This adds extra complexity, while things are very convenient to work with when dimension length stays constant throughout the code.
For n values there are n1 differences. Equivalently, for k differences there are k+1 values. Herefor, `diff` ought to reduce length by 1 and `cumsum` ought to increase it by 1. Returning arrays of the same length is a fencepost error. This is a problem in the current behaviour of `cumsum` and the proposed behaviour of `diff0`. Dom Grigonis wrote:
For now, I only see my point of view and I can list a number of cases from data analysis and modelling, where I found np.diff0 to be a fairly optimal choice to use and it made things smoother. While I haven’t seen any reallife examples where np.cumsum0 would be useful so I am naturally biased. I would appreciate If anyone provided some examples that justify np.cumsum0  for now I just can’t think of any case where this could actually be useful or why it would be more convenient/sensible than np.diff0.
 EXAMPLE Consider a path given by a list of points, say (101, 203), (102, 205), (107, 204) and (109, 202). What are the positions at fractions, say 1/3 and 2/3, along the path (linearly interpolating)? The problem is naturally solved with `diff` and `cumsum0`: ``` import numpy as np from scipy import interpolate positions = np.array([[101, 203], [102, 205], [107, 204], [109, 202]], dtype=float) steps_2d = np.diff(positions, axis=0) steps_1d = np.linalg.norm(steps_2d, axis=1) distances = np.cumsum0(steps_1d) fractions = distances / distances[1] interpolate_at = interpolate.make_interp_spline(fractions, positions, 1) interpolate_at(1/3) interpolate_at(2/3) ``` Please show how to solve the problem with `diff0` and `cumsum`.  Both `diff0` and `cumsum` have a fencepost problem, but `diff0` has a second defect: it maps an array of positions to a heterogeneous array where one element is a position and the rest are displacements. The operations that make sense for displacements, like scaling, differ from those that make sense for positions.  EXAMPLE Money is invested on 20230101. The annualized rate is 4% until 20230204 and 5% thence until 20230402. By how much does the money multiply in this time? The problem is naturally solved with `diff`: ``` import numpy as np percents = np.array([4, 5], dtype=float) times = np.array(["20230101", "20230204", "20230402"], dtype=np.datetime64) durations = np.diff(times) YEAR = np.timedelta64(365, "D") multipliers = (1 + percents / 100) ** (durations / YEAR) multipliers.prod() ``` Please show how to solve the problem with `diff0`. It makes sense to divide `np.diff(times)` by `YEAR`, but it would not make sense to divide the output of `np.diff0(times)` by `YEAR` because of its incongruous initial value. 
`cumsum` provides a sequence of partial sums, exactly as expected. https://reference.wolfram.com/language/ref/Accumulate.html https://www.mathworks.com/help/matlab/ref/cumsum.html https://docs.julialang.org/en/v1/base/arrays/#Base.cumsum https://hackage.haskell.org/package/base4.12.0.0/docs/DataList.html#v:scan... `diff` also behaves as expected, and as you expect. But I do not think that is the question. The question is, how useful would it be for numpy to have a less commonly needed and closely related function. (I have no need of it, and I don't really see a pressing need.) On 8/22/2023 10:36 AM, john.dawson@camlingroup.com wrote:
For n values there are n1 differences. Equivalently, for k differences there are k+1 values. Herefor, `diff` ought to reduce length by 1 and `cumsum` ought to increase it by 1. Returning arrays of the same length is a fencepost error. This is a problem in the current behaviour of `cumsum` and the proposed behaviour of `diff0`.
I don’t have an issue with cumsum0 if it is approached as a request for a useful utility function. But arguing that this is what a cumulative sum function should be doing is a very big stretch. Cumulative sum has its foundational meaning and purpose which is clearly reflected in its name, which is not to solve fencepost error, but to accumulate the summation sequence. Prepending 0 as part of it feels very unnatural. It is simply extra operation. diff0, in my opinion, has a bit more intuitive sense to it, but obviously there is no need to add it if no one else needs/uses it.
On 22 Aug 2023, at 17:36, john.dawson@camlingroup.com wrote:
Dom Grigonis wrote:
1. Dimension length stays constant, while cumusm0 extends length to n+1, then np.diff, truncates it back. This adds extra complexity, while things are very convenient to work with when dimension length stays constant throughout the code.
For n values there are n1 differences. Equivalently, for k differences there are k+1 values. Herefor, `diff` ought to reduce length by 1 and `cumsum` ought to increase it by 1. Returning arrays of the same length is a fencepost error. This is a problem in the current behaviour of `cumsum` and the proposed behaviour of `diff0`.
diff0 doesn’t solve the error in a strict sense. However, the first value of diff0 result becomes the starting point from which to count remaining differences, so with the right approach it does solve the issue  if starting values are subtracted then it is doing the same thing, just in different order. See below:
 EXAMPLE
Consider a path given by a list of points, say (101, 203), (102, 205), (107, 204) and (109, 202). What are the positions at fractions, say 1/3 and 2/3, along the path (linearly interpolating)?
The problem is naturally solved with `diff` and `cumsum0`:
``` import numpy as np from scipy import interpolate
positions = np.array([[101, 203], [102, 205], [107, 204], [109, 202]], dtype=float) steps_2d = np.diff(positions, axis=0) steps_1d = np.linalg.norm(steps_2d, axis=1) distances = np.cumsum0(steps_1d) fractions = distances / distances[1] interpolate_at = interpolate.make_interp_spline(fractions, positions, 1) interpolate_at(1/3) interpolate_at(2/3) ```
Please show how to solve the problem with `diff0` and `cumsum`. 
 EXAMPLE
Money is invested on 20230101. The annualized rate is 4% until 20230204 and 5% thence until 20230402. By how much does the money multiply in this time?
The problem is naturally solved with `diff`:
``` import numpy as np
percents = np.array([4, 5], dtype=float) times = np.array(["20230101", "20230204", "20230402"], dtype=np.datetime64) durations = np.diff(times) YEAR = np.timedelta64(365, "D") multipliers = (1 + percents / 100) ** (durations / YEAR) multipliers.prod() ```
Please show how to solve the problem with `diff0`. It makes sense to divide `np.diff(times)` by `YEAR`, but it would not make sense to divide the output of `np.diff0(times)` by `YEAR` because of its incongruous initial value.  In my experience it is more sensible to use time series approach, where the whole path of investment is calculated. For modelling purposes, analysis and presentation to clients single code can then be used. I would do it like: r = np.log(1 + np.array([0, 0.04, 0.05])) start_date = np.array("20230101", dtype=np.datetime64) times = np.array(["20230101", "20230204", "20230402"], dtype=np.datetime64) t = (times  start_date).astype(float) / 365
positions = np.array([[101, 203], [102, 205], [107, 204], [109, 202]], dtype=float) positions_rel = positions  positions[0, None] steps_2d = diff0(positions_rel, axis=0) steps_1d = np.linalg.norm(steps_2d, axis=1) distances = np.cumsum(steps_1d) fractions = distances / distances[1] interpolate_at = interpolate.make_interp_spline(fractions, positions, 1) print(interpolate_at(1/3)) print(interpolate_at(2/3)) dt = diff0(t) normalised = np.exp(np.cumsum(r * dt)) # PLOT s0 = 1000 plt.plot(s0 * normalised) Apart from responses above, diff0 is useful in data analysis. Indices and observations usually have the same length. It is always convenient to keep it that way and it makes a nice, clean and simple code. t = dates s = observations # Plot changes: ds = diff0(s) plt.plot(dates, ds) # 2nd order changes plt.plot(dates, diff0(ds)) # Moving average of changes plt.plot(dates, bottleneck.move_mean(ds, 3))
_______________________________________________ NumPyDiscussion mailing list  numpydiscussion@python.org To unsubscribe send an email to numpydiscussionleave@python.org https://mail.python.org/mailman3/lists/numpydiscussion.python.org/ Member address: dom.grigonis@gmail.com
I'm really confused. Summing from zero should be what cumsum() does now. ```
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 NunezIglesias <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 fencepostlike problems.
> > https://en.wikipedia.org/wiki/Offbyone_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:`ufuncsoutputtype` 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 nth 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 floatingpoint
> > values since ``sum`` may use a pairwise summation routine, reducing
> > the roundofferror. 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, 2e9, 3e9] * 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)
> > ```
> > _______________________________________________
> > NumPyDiscussion mailing list  numpydiscussion@python.org
> > To unsubscribe send an email to numpydiscussionleave@python.org
> > https://mail.python.org/mailman3/lists/numpydiscussion.python.org/
> > Member address: jni@fastmail.com
> _______________________________________________
> NumPyDiscussion mailing list  numpydiscussion@python.org
> To unsubscribe send an email to numpydiscussionleave@python.org
> https://mail.python.org/mailman3/lists/numpydiscussion.python.org/
> Member address: ben.v.root@gmail.com
>
On Fri, Aug 11, 2023 at 1:47 PM Benjamin Root <ben.v.root@gmail.com> wrote:
I'm really confused. Summing from zero should be what cumsum() does now.
```
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?
That's not what's in his example.  Robert Kern
On 11 Aug 2023, at 7:52 pm, Robert Kern <robert.kern@gmail.com<mailto:robert.kern@gmail.com>> wrote:
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?
That's not what's in his example.
The example is creating a cumsumlike array of n+1 elements starting with the number 0,
not array[0] – i.e. essentially just inserting 0 along every axis, so that
np.diff(np.cumsum0(a)) = a
Not sure if this would be too complicated to effect with the existing ufuncs either…
Almost all of the documentation sounds very repetitive, so maybe implementing this
via a new kwarg to cumsum would be a better option?
Cheers,
Derek
After blinking and rubbing my eyes, I finally see what is meant by all of this. I see now that the difference is that `cumsum0()` would return a result that essentially have 0 be prepended to what would normally be the result from `cumsum()`. From the description, I thought the "problem" was that the summation starts from 1. Personally, I never really thought of cumsum() as starting from index 1, so I didn't understand the problem as stated. So, I think some workshopping of the description is in order. On Fri, Aug 11, 2023 at 1:53 PM Robert Kern <robert.kern@gmail.com> wrote:
On Fri, Aug 11, 2023 at 1:47 PM Benjamin Root <ben.v.root@gmail.com> wrote:
I'm really confused. Summing from zero should be what cumsum() does now.
```
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?
That's not what's in his example.
 Robert Kern _______________________________________________ NumPyDiscussion mailing list  numpydiscussion@python.org To unsubscribe send an email to numpydiscussionleave@python.org https://mail.python.org/mailman3/lists/numpydiscussion.python.org/ Member address: ben.v.root@gmail.com
On Fri, 20230811 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/dataapis/arrayapi/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 NunezIglesias <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 fencepostlike problems. > > https://en.wikipedia.org/wiki/Offbyone_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:`ufuncsoutputtype` 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 nth 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 > > floatingpoint > > values since ``sum`` may use a pairwise summation routine, > > reducing > > the roundofferror. 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, 2e9, 3e9] * 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) > > ``` > > _______________________________________________ > > NumPyDiscussion mailing list  numpydiscussion@python.org > > To unsubscribe send an email to numpydiscussionleave@python.org > > https://mail.python.org/mailman3/lists/numpydiscussion.python.org/ > > Member address: jni@fastmail.com > _______________________________________________ > NumPyDiscussion mailing list  numpydiscussion@python.org > To unsubscribe send an email to numpydiscussionleave@python.org > https://mail.python.org/mailman3/lists/numpydiscussion.python.org/ > Member address: ben.v.root@gmail.com > _______________________________________________ NumPyDiscussion mailing list  numpydiscussion@python.org To unsubscribe send an email to numpydiscussionleave@python.org https://mail.python.org/mailman3/lists/numpydiscussion.python.org/ Member address: sebastian@sipsolutions.net
This has come up before, see https://github.com/numpy/numpy/issues/6044 for the first time this came up; there were several subsequent discussions linked there. In the meantime, the data APIs consortium has been actively working on adding a `cumulative_sum` function to the array API standard, see https://github.com/dataapis/arrayapi/issues/597 and https://github.com/dataapis/arrayapi/pull/653. The proposed `cumulative_sum` function includes an `include_initial` keyword argument that gets the OP's desired behavior. I think we should probably eventually deprecate `cumsum` and `cumprod` in favor of the array API standard's `cumulative_sum` and `cumulative_product` if only because of the embarrassing naming issue. Once the array API standard has finalized the name for the keyword argument, I think it makes sense to add the keyword argument to np.cumsum, even if we don't deprecate it yet. I don't think it makes sense to add a new function just for this. On Fri, Aug 11, 2023 at 6:34 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 fencepostlike problems. https://en.wikipedia.org/wiki/Offbyone_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:`ufuncsoutputtype` 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 nth 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 floatingpoint values since ``sum`` may use a pairwise summation routine, reducing the roundofferror. 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, 2e9, 3e9] * 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) ``` _______________________________________________ NumPyDiscussion mailing list  numpydiscussion@python.org To unsubscribe send an email to numpydiscussionleave@python.org https://mail.python.org/mailman3/lists/numpydiscussion.python.org/ Member address: nathan12343@gmail.com
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