issue_comments

I would also be very interested in seeing your codes @lamorton. Overall, I think the xarray community could really benefit from some kind of centralized contrib package which has a low barrier to entry for these kinds of functions. So far, I suspect there has been a large amount of code duplication for routine tasks like the fft, since I have also written a function for that.

I usually agree that using too many (or any) switches within functions is not ideal. However, I think this is more important for low level or internal routines. For user facing interfaces, I think it is okay. After all, many numpy and scipy functions have convenient switches that control the return values.

By the way, the cumtrapz implementation I pasted above matches the scipy version when initial=0, which I also think would be a more sane default for integration.

As far as implementation is concerned. Is there any performance downside to using xarrays shift operators versus delving deeper into dask with map_blocks, etc? I looked into using dasks cumreduction function, but am not sure it is possible to implement the trapezoid method in that way without changing dask. On Mon, Mar 20, 2017 at 8:48 AM Fabien Maussion notifications@github.com wrote:

An argument against a single function is that the shape of the returned array is different in each case. Also, cumtrapz https://docs.scipy.org/doc/scipy-0.10.1/reference/generated/scipy.integrate.trapz.html has an inital keyword which changes the shape of the returned array. It is currently set to None per default, but should be set to 0 per default IMO.

I this is not a problem, I also like to have one single function for integration (simpler from a user perspective).

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I would also like to see an integrate function. I have had one monkey patched in my own xarray routines for a while now. Also wanted: cumtrapz and friends. Maybe this could be implemented by adding an optional cumulative flag. This shouldn't be too hard to do. For example, in the following cumtrapz implementation all that would need to be changed is the final cumsum call.

```python def cumtrapz(A, dim): """Cumulative Simpson's rule (aka Tai's method)

Notes
-----
Simpson rule is given by
    int f (x) = sum (f_i+f_i+1) dx / 2
"""
x = A[dim]
dx = x - x.shift(**{dim:1})
dx = dx.fillna(0.0)
return ((A.shift(**{dim:1}) + A)*dx/2.0)\
      .fillna(0.0)\
      .cumsum(dim)

```