[Numpy-discussion] Next Documentation team meeting
Hi all! Our next Documentation Team meeting will happen on *Monday, June 5* at ***4PM UTC***. We now alternate the meeting times to be a bit more inclusive. This means that we'll have a meeting at 12pm UTC every 28 days, and a meeting at 4pm UTC every 28 days. All are welcome - you don't need to already be a contributor to join. If you have questions or are curious about what we're doing, we'll be happy to meet you! If you wish to join on Zoom, use this (updated) link: https://numfocus-org.zoom.us/j/85016474448?pwd=TWEvaWJ1SklyVEpwNXUrcHV1YmFJQ... Here's the permanent hackmd document with the meeting notes (still being updated): https://hackmd.io/oB_boakvRqKR-_2jRV-Qjg Hope to see you around! * You can also visit https://scientific-python.org/calendars to add the NumPy community calendar as an .ics file to your preferred calendar manager. * Best wishes, Mukulika ___ NumPy-Discussion mailing list -- numpy-discussion@python.org To unsubscribe send an email to numpy-discussion-le...@python.org https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ Member address: arch...@mail-archive.com
[Numpy-discussion] Re: mean_std function returning both mean and std
I had a look at what it would take to improve the C-solution. However I find that it is beyond my C-programming skils. The gufunc defintion seems to be at odds with current working of the axis keyword for mean, std and var. The latter support computation over multiple axes, whereas the gufunc only seems to support calculation over a single axis. As the behaviour of std, mean and var is largely inherited from ufuncs those might offer a better starting point. If the operator used in the ufunc could take a parameter from the outer_loop accessing in this case the mean, then it would be possible to calculate the required intermediate quantities. This should be a possibility as somewhere the out array is also accessed in the correct manner and we should step through both arrays in the same way. Instead of: '''Pseudocode result = np.full(result_shape, op.identity) # op = ufunc loop_outer_axes_result_array: loop_over_inner_axes_input_array: result[outer_axes] = op(result[outer_axes], InArray[outer_axes + inner_axes]) ''' we would then get: '''Pseudocode result = np.full(result_shape, op.identity) # op = ufunc loop_outer_axes_result_array: loop_over_inner_axes_input_array: result[outer_axes] = op(result[outer_axes], InArray[outer_axes + inner_axes], ParameterArray[outer_axes]) ''' Using for op: '''Pseudocode op(a,b,c) = a+b-c ''' and for b the original data and for c the mean (M_1) you would obtain the Neely correction for the mean. Similarly using: '''Pseudocode op(a,b,c) = a+(b-c)^2 ''' you would obtain the sum of squared errors. ___ NumPy-Discussion mailing list -- numpy-discussion@python.org To unsubscribe send an email to numpy-discussion-le...@python.org https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ Member address: arch...@mail-archive.com
[Numpy-discussion] Re: mean_std function returning both mean and std
Note: the suggested solution requires no allocation of memory beyond that needed for storing the result. ___ NumPy-Discussion mailing list -- numpy-discussion@python.org To unsubscribe send an email to numpy-discussion-le...@python.org https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ Member address: arch...@mail-archive.com
[Numpy-discussion] Re: mean_std function returning both mean and std
2nd note: I implicit based this on the reduce function. ___ NumPy-Discussion mailing list -- numpy-discussion@python.org To unsubscribe send an email to numpy-discussion-le...@python.org https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ Member address: arch...@mail-archive.com
