issue_comments

Thanks @dcherian :smile: You're very welcome!

Thanks @keewis! I think we've addressed all the review comments now.

calling on a array with missing values raises ValueError: All-NaN slice encountered (try ds = xr.tutorial.open_dataset("rasm"); ds.argmin(dim="y"))

Some missing values should be OK. In your example though for example In [27]: ds.Tair.isel(time=0, x=0).values Out[27]: array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]) So there are no values in the array slice that argmin should be applied over, which is an error.

I guess we could add some special handling for this (not sure what though, because we can't set a variable with type int to nan or None), but I think that is a separate issue that would need a new PR.

ds.argmin(dim=...) returns a single index (the same result as ds.argmin()) but ds.Tair.argmin(dim=...) returns something different from ds.Tair.argmin(). Is that intentional?

I think this is a bug. dim=... to Dataset.argmin should be an error because dim=... returns a dict of results, and it's not clear how to do that consistently for a Dataset that might have several members with different combinations of dimensions.

Merge conflicts fixed, this PR should be ready to review/merge.

I guess this would happen if somebody calls np.argmin(xarray_object)? Does this happen from inside xarray somewhere, or in external code?

If it's the former, then we should just fix xarray not to do this. If it's only in external code, I would still consider breaking this behavior.

test_units.py has a couple of tests that apply numpy functions to xr.Variables, xr.DataArrays and xr.Datasets. Those tests break for argmin and argmax without the out argument. Should I just remove the argmin and argmax function calls? The tests would still call them as methods.

@pydata/xarray - I think this PR is ready to be merged, are there any changes I should make? Thanks!

I eventually found that the cause of the errors I was getting was that the argmin and argmax methods did not have an out argument. In order for the methods to be wrapped by numpy (and I guess dask is the same), the call signature of np.argmin() has to be supported, which means axis and out arguments are needed.

@shoyer I've kept the use of injected functions, because removing the injected argmin and argmax completely meant re-implementing handling of skipna and only within _unravel_argminmax, which seemed less nice to me. If/when method injection is refactored, it would be nice to include a mechanism to override the core operation with an explicitly implemented, extended version like argmin/argmax.

I think this PR is ready for review now :smile:

These test failures seem to have uncovered a larger issue: overriding a method injected by ops.inject_all_ops_and_reduce_methods(DataArray, priority=60) is tricky. I think there may be a way around it by mangling the name of the injected operation if that method is already defined on the class; I tried this and it nearly works, but I think I need to implement an override for argmin/argmax on Variable, because DataArray.__array_wrap__ uses the Variable version of the method... Work in progress!

The rename to _argmin_base is also causing test_aggregation (which is a test of units with pint) to fail, because the numpy function and the Variable method do not have the same name. Any suggestions how to fix this? It seems tricky because I can't pass either argmin (no Variable method) or _argmin_base (no numpy method) to the test.

I've updated so the new functionality is provided by argmin() and argmax(), when they are passed a multiple dimensions. * I've added Dataset.argmin() and Dataset.argmax(), which only work for a single dimension, but give informative exceptions for unsupported cases. If anyone has some behaviour that they would like for Dataset.argmin() with multiple dimensions, I'm happy to implement it, but returning something like a dict of Datasets didn't seem nice, and as far as I understand a Dataset cannot contain something like a dict of DataArrays. * I renamed argmin and argmax in ops.py and duck_array_ops.py to _argmin_base and _argmax_base so that I could still use them inside the argmin and argmax methods. Any alternatives, or better suggestions for naming them? * Variable no longer has argmin or argmax methods. Is that a problem? I just updated test_variable.py and test_dask.py to use _argmin_base and _argmax_base.

@shoyer I think your last option sounds good. Questions: * What should da.argmin() with no arguments do? * Currently returns the flattened index of the global minimum. * I think returning a dict of indices would be much more useful, but it does change existing behaviour (more useful because you can then do da.isel(da.argmin())). * Could anyway do da.argmin(da.dims) to get the dict result, but that's a bit ugly * Could have something like da.argmin(...) - maybe as a temporary workaround while we deprecate the current behaviour of da.argmin()? * If we overload argmin, what's the cleanest way to get the existing argmin() method within the new one? Would something like from .duck_array_ops import argmin as argmin_1d work?

Maybe worth noting, at the moment if you try to call argmin(dim=("x", "y")) with multiple dimensions, there's a not-very-helpful exception ```

array.argmin(dim=("x", "y")) Traceback (most recent call last): File "/home/john/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py", line 61, in _wrapfunc return bound(args, *kwds) TypeError: 'tuple' object cannot be interpreted as an integer

During handling of the above exception, another exception occurred:

Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/<...>/xarray/xarray/core/common.py", line 46, in wrapped_func return self.reduce(func, dim, axis, skipna=skipna, kwargs) File "/<...>/xarray/xarray/core/dataarray.py", line 2288, in reduce var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, kwargs) File "/<...>/xarray/xarray/core/variable.py", line 1579, in reduce data = func(input_data, axis=axis, kwargs) File "/<...>/xarray/xarray/core/duck_array_ops.py", line 304, in f return func(values, axis=axis, kwargs) File "/<...>/xarray/xarray/core/duck_array_ops.py", line 47, in f return wrapped(args, kwargs) File "<array_function internals>", line 6, in argmin File "/<...>/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py", line 1267, in argmin return _wrapfunc(a, 'argmin', axis=axis, out=out) File "/<...>/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py", line 70, in _wrapfunc return _wrapit(obj, method, args, kwds) File "/<...>/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py", line 47, in _wrapit result = getattr(asarray(obj), method)(*args, kwds) TypeError: 'tuple' object cannot be interpreted as an integer ```