Support multiple dimensions in DataArray.argmin() and DataArray.argmax() methods #3936

johnomotani · 2020-04-05T18:52:52Z

These return dicts of the indices of the minimum or maximum of a DataArray over several dimensions. Inspired by @fujiisoup's work in #1469. With #3871, replaces #1469. Provides a simpler solution to #3160.

Implemented so that

da.isel(da.argmin(list_of_dim)) == da.min(list_of_dim)
da.isel(da.argmax(list_of_dim)) == da.max(list_of_dim)

Closes Argmin indexes #1469
Tests added
Passes isort -rc . && black . && mypy . && flake8
Fully documented, including whats-new.rst for all changes and api.rst for new API

These return dicts of the indices of the minimum or maximum of a DataArray over several dimensions.

shoyer · 2020-04-05T19:19:41Z

This looks really comprehensive, thank you!

Before doing a really careful review here, I'd like to try to work out the full API design we want. I'll write out some of my thoughts here, but your thoughts would also be very welcome!

Here's my summary of the current situation:

We have two pairs of methods, argmin/argmax and the new idxmin/idxmax for calculating integer positions or coordinate labels of the minimum/maximum along one dimension.
We would like to have a way to do a similar calculation in multiple dimensions, which should return a dictionary of DataArray objects suitable for feeding into isel/sel.

This PR implements the multidimensional equivalent of argmin/argmax. It's fine to save the multidimensional equivalent of idxmin/idxmax for later, but we'll want to copy whatever design we pick here.

My first concern is about the name: it isn't obvious to me whether indices_min refers to integer positions or coordinate labels. I think a name based on argmin/argmax could help, e.g., perhaps argmin_nd or argmin_dict.

Another option would be to overload argmin/argmax to also support return a dictionary, depending on the type of the arguments. If you pass a list of dimensions over which to calculate the min or max locations, you would get a dictionary back:

array.argmin('x') would return a single DataArray
array.argmin(['x', 'y']) would return a dict of DataArray objects with keys {'x', 'y'}
array.argmin(['x']) would also return a dict {'x': ...}

I think I like this last option best but I would be curious what others think! @pydata/xarray any thoughts on this?

johnomotani · 2020-04-05T20:26:12Z

@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?

johnomotani · 2020-04-05T20:33:04Z

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

TomNicholas · 2020-04-05T21:39:19Z

Another option would be to overload argmin

+1 for overloading argmin (and later idxmin). IMO we should never have one function for a 1D operation and one for an N-D operation if we can avoid it, everything should be N-dimensional.

I also really like how neat this resultant property is

da.isel(da.argmin(list_of_dim)) == da.min(list_of_dim)

we could even use a hypothesis test to check it...

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())).

Although it's breaking and would require a deprecation cycle, I think this is what we should aim for.

there's a not-very-helpful exception

Yes let's take the time to make that clearer for users - this will be a commonly-used function.

kmuehlbauer · 2020-04-06T06:31:12Z

+1 for making argmin/argmax (and idxmin/idxmax) work, when given multiple dimensions.

According to the current docstring it should already work that way for argmin/'argmax`:

Reduce this DataArray’s data by applying argmin along some dimension(s).

Returns: New DataArray/Dataset object with argmin applied to it's data and the indicated dimension(s) removed

But this behaviour is broken currently (works only for one given dim).

My main concern for changing the API as suggested above is, how should we discern (at least for argmin/argmax), if the user want's to:

reduce over all given dimensions? (current behaviour according to docstring)
reduce along all given dimensions? (suggested behaviour by @johnomotani [please correct if I'm wrong])

TomNicholas · 2020-04-06T06:49:18Z

how should we discern (at least for argmin/argmax), if the user wants to...

That's a good question @kmuehlbauer, and the distinction probably needs to be clearer in the docs in general.

reduce over all given dimensions?

By this do you mean find the minimum as if the array were first (partially or totally) flattened along the given dims somehow? I'm not sure we provide that kind of behaviour anywhere in the current API.

kmuehlbauer · 2020-04-06T07:09:45Z

reduce over all given dimensions?

By this do you mean find the minimum as if the array were first (partially or totally) flattened along the given dims somehow? I'm not sure we provide that kind of behaviour anywhere in the current API.

@TomNicholas Probably I was bit confused by the current docstring. I think I understand now and there should be no problem at all.

kmuehlbauer · 2020-04-06T07:13:00Z

@johnomotani FYI: For #3871 (merged) there is #3922 (yet unmerged) to fix dask-handling.

When argmin or argmax are called with a sequence for 'dim', they now return a dict with the indices for each dimension in dim.

If single dim is passed to Dataset.argmin() or Dataset.argmax(), then pass through to _argmin_base or _argmax_base. If a sequence is passed for dim, raise an exception, because the result for each DataArray would be a dict, which cannot be stored in a Dataset.

The basic numpy-style argmin() and argmax() methods were renamed when adding support for handling multiple dimensions in DataArray.argmin() and DataArray.argmax(). Variable.argmin() and Variable.argmax() are therefore renamed as Variable._argmin_base() and Variable._argmax_base().

johnomotani · 2020-04-06T22:57:50Z

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.

johnomotani · 2020-04-06T23:06:11Z

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.

johnomotani · 2020-04-07T09:45:24Z

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!

shoyer · 2020-04-07T17:23:24Z

Also, feel free to rewrite to avoid using inject_all_ops_and_reduce_methods() for argmin/argmax at all. Method injection is pretty hacky, and generally not worthwhile, e.g., see this note about removing it.

If a method (such as 'argmin') has been explicitly defined on a class (so that hasattr(cls, "argmin")==True), then do not inject that method, as it would override the explicitly defined one. Instead inject a private method, prefixed by "_injected_" (such as '_injected_argmin'), so that the injected method is available to the explicitly defined one. Do not perform the hasattr check on binary ops, because this breaks some operations (e.g. addition between DataArray and int in test_dask.py).

Now not needed because of change to injection in ops.py.

johnomotani · 2020-06-24T20:39:31Z

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

shoyer

This is beautiful work!

I am so excited about getting this into xarray. We are now one short step away from getting a canonical answer to the most frequently asked question about xarray on StackOverflow! https://stackoverflow.com/questions/40179593/how-to-get-the-coordinates-of-the-maximum-in-xarray

shoyer · 2020-06-25T04:08:34Z

xarray/core/dataarray.py

+    def argmin(
+        self,
+        dim: Union[Hashable, Sequence[Hashable]] = None,
+        axis: Union[int, None] = None,
+        keep_attrs: bool = None,
+        skipna: bool = None,
+    ) -> Union["DataArray", Dict[Hashable, "DataArray"]]:


In theory, it would be nice to use either TypeVar or @overload so tools like mypy can find out the type of the the return value from argmin() based on the type of dim.

But definitely don't worry about that now, we can save that for a follow-up :)

shoyer · 2020-06-25T04:10:50Z

I'm going to wait a little while before merging in case anyone else has comment, but otherwise will merge in a day or two (definitely before the next release)

dcherian

Thanks @johnomotani This is great!

I have some minor comments below that should be easy to fix.

xarray/core/dataarray.py

xarray/core/dataset.py

xarray/tests/test_units.py

xarray/core/dataset.py

xarray/tests/test_units.py

xarray/core/dataarray.py

xarray/core/variable.py

Co-authored-by: Deepak Cherian <dcherian@users.noreply.github.com>

Pass an explicit axis or dim argument instead to avoid the warning.

Prefer to pass reduce_dims=None when possible, including for variables with only one dimension. Avoids an error if an 'axis' keyword was passed.

xarray/core/dataset.py

keewis · 2020-06-26T14:51:51Z

I noticed a few issues while debugging:

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

johnomotani · 2020-06-26T16:29:35Z

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.

keewis · 2020-06-26T17:19:34Z

well, we'd need to somehow be able to use sel to index every variable in a Dataset differently. While that would probably be possible it would make sel way too complicated, so I agree that an error would be good here.

I think no further changes to the pint tests should be required (except refactoring, but I can do that in a new PR after this one has been merged).

johnomotani · 2020-06-26T17:24:55Z

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.

johnomotani · 2020-06-26T17:29:15Z

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

doc/whats-new.rst

Co-authored-by: keewis <keewis@users.noreply.github.com>

dcherian · 2020-06-29T19:36:28Z

Thanks @johnomotani . This is a significant contribution.

johnomotani · 2020-06-29T20:22:49Z

Thanks @dcherian 😄 You're very welcome!

* upstream/master: (21 commits) fix typo in error message in plot.py (pydata#4188) Support multiple dimensions in DataArray.argmin() and DataArray.argmax() methods (pydata#3936) Show data by default in HTML repr for DataArray (pydata#4182) Blackdoc (pydata#4177) Add CONTRIBUTING.md for the benefit of GitHub Correct dask handling for 1D idxmax/min on ND data (pydata#4135) use assert_allclose in the aggregation-with-units tests (pydata#4174) Remove old auto combine (pydata#3926) Fix 4009 (pydata#4173) Limit length of dataarray reprs (pydata#3905) Remove <pre> from nested HTML repr (pydata#4171) Proposal for better error message about in-place operation (pydata#3976) use builtin python types instead of the numpy alias (pydata#4170) Revise pull request template (pydata#4039) pint support for Dataset (pydata#3975) drop eccodes in docs (pydata#4162) Update issue templates inspired/based on dask (pydata#4154) Fix failing upstream-dev build & remove docs build (pydata#4160) Improve typehints of xr.Dataset.__getitem__ (pydata#4144) provide a error summary for assert_allclose (pydata#3847) ...

johnomotani added 2 commits April 5, 2020 19:47

DataArray.indices_min() and DataArray.indices_max() methods

8e7fb53

These return dicts of the indices of the minimum or maximum of a DataArray over several dimensions.

Update whats-new.rst and api.rst with indices_min(), indices_max()

2b06811

johnomotani added 2 commits April 5, 2020 20:43

Fix type checking in DataArray._unravel_argminmax()

f6a966c

Fix expected results for TestReduce3D.test_indices_max()

4395e7a

TomNicholas added API design enhancement labels Apr 5, 2020

kmuehlbauer mentioned this pull request Apr 6, 2020

FIX: correct dask array handling in _calc_idxminmax #3922

Merged

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johnomotani added 4 commits April 6, 2020 22:16

Respect global default for keep_attrs

deee3f8

Merge behaviour of indices_min/indices_max into argmin/argmax

be8b26c

When argmin or argmax are called with a sequence for 'dim', they now return a dict with the indices for each dimension in dim.

johnomotani force-pushed the indices_minmax branch from ef7b181 to 70aaa9d Compare April 6, 2020 22:13

johnomotani changed the title ~~DataArray.indices_min() and DataArray.indices_max() methods~~ Support multiple dimensions in DataArray.argmin() and DataArray.argmax() methods Apr 6, 2020

Update api-hidden.rst with _argmin_base and _argmax_base

f8952a8

jthielen mentioned this pull request Apr 7, 2020

Consistent Handling of Type Casting Hierarchy #3950

Open

johnomotani added 3 commits April 10, 2020 17:17

Move StringAccessor back to bottom of DataArray class definition

4778cfd

Revert use of _argmin_base and _argmax_base

66cf085

Now not needed because of change to injection in ops.py.

flake8 fixes

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johnomotani and others added 3 commits June 26, 2020 11:43

Tidy up argmin/argmax following code review

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Co-authored-by: Deepak Cherian <dcherian@users.noreply.github.com>

Remove filters for warnings from argmin/argmax from tests

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Pass an explicit axis or dim argument instead to avoid the warning.

Swap order of reduce_dims checks in Dataset.reduce()

a07ce29

Prefer to pass reduce_dims=None when possible, including for variables with only one dimension. Avoids an error if an 'axis' keyword was passed.

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keewis added 3 commits June 26, 2020 16:04

Merge branch 'master' into indices_minmax

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revert the changes to Dataset.reduce

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use dim instead of axis

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use dimension instead of Ellipsis

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johnomotani added 2 commits June 26, 2020 18:25

Make passing 'dim=...' to Dataset.argmin() or Dataset.argmax() an error

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Better docstrings for Dataset.argmin() and Dataset.argmax()

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Support multiple dimensions in DataArray.argmin() and DataArray.argmax() methods #3936

Support multiple dimensions in DataArray.argmin() and DataArray.argmax() methods #3936

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Support multiple dimensions in DataArray.argmin() and DataArray.argmax() methods #3936

Support multiple dimensions in DataArray.argmin() and DataArray.argmax() methods #3936

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