html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/2511#issuecomment-944328081,https://api.github.com/repos/pydata/xarray/issues/2511,944328081,IC_kwDOAMm_X844SU2R,16700639,2021-10-15T14:03:21Z,2021-10-15T14:03:21Z,CONTRIBUTOR,"I'll drop a PR, it might be easier to try and play with this than a piece of code lost in an issue.","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,374025325
https://github.com/pydata/xarray/issues/2511#issuecomment-931430066,https://api.github.com/repos/pydata/xarray/issues/2511,931430066,IC_kwDOAMm_X843hH6y,16700639,2021-09-30T15:30:02Z,2021-10-06T09:48:19Z,CONTRIBUTOR,"Okay I could re do my test.
If I manually call `compute()` before doing `isel(......)` my whole computation takes about **5.65 seconds**.
However if I try with my naive patch it takes **32.34 seconds**.

I'm sorry I cannot share as is my code, the relevant portion is really in the middle of many things.
I'll try to get a minimalist version of it to share with you.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,374025325
https://github.com/pydata/xarray/issues/2511#issuecomment-930153816,https://api.github.com/repos/pydata/xarray/issues/2511,930153816,IC_kwDOAMm_X843cQVY,16700639,2021-09-29T13:02:15Z,2021-10-06T09:46:10Z,CONTRIBUTOR,"@pl-marasco Ok that's strange.
I should have saved my use case :/ 
I will try to reproduce it and will provide a gist of it soon.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,374025325
https://github.com/pydata/xarray/issues/2511#issuecomment-932229595,https://api.github.com/repos/pydata/xarray/issues/2511,932229595,IC_kwDOAMm_X843kLHb,16700639,2021-10-01T13:29:32Z,2021-10-01T13:29:32Z,CONTRIBUTOR,"@pl-marasco Thanks for the example !
With it I have the same result as you, it takes the same time with patch or with compute.

However, I could construct an example giving very different results. It is quite close to my original code:

```
    time_start = time.perf_counter()
    COORDS = dict(
        time=pd.date_range(""2042-01-01"", periods=200,
                           freq=pd.DateOffset(days=1)),
    )
    da = xr.DataArray(
        np.random.rand(200 * 3500 * 350).reshape((200, 3500, 350)),
        dims=('time', 'x', 'y'),
        coords=COORDS
    ).chunk(dict(time=-1, x=100, y=100))

    resampled = da.resample(time=""MS"")

    for label, sample in resampled:
        # sample = sample.compute()
        idx = sample.argmax('time')
        sample.isel(time=idx)

    time_elapsed = time.perf_counter() - time_start
    print(time_elapsed, "" secs"")
```
(Basically I want for each month the first event occurring in it).

Without the patch and uncommenting `sample = sample.compute()`, it takes 5.7 secs.
With the patch it takes 53.9 seconds.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,374025325
https://github.com/pydata/xarray/issues/2511#issuecomment-922942743,https://api.github.com/repos/pydata/xarray/issues/2511,922942743,IC_kwDOAMm_X843Av0X,16700639,2021-09-20T13:45:56Z,2021-09-20T13:45:56Z,CONTRIBUTOR,"I wrote a very naive fix, it works but seems to perform **really** slowly, I would appreciate some feedback (I'm a beginner with Dask).
Basically, I added `k = dask.array.asarray(k, dtype=np.int64)` to do the exact same thing as with numpy.
_I can create a PR if it's better to review this_

The patch:
```
class VectorizedIndexer(ExplicitIndexer):
    """"""Tuple for vectorized indexing.

    All elements should be slice or N-dimensional np.ndarray objects with an
    integer dtype and the same number of dimensions. Indexing follows proposed
    rules for np.ndarray.vindex, which matches NumPy's advanced indexing rules
    (including broadcasting) except sliced axes are always moved to the end:
    https://github.com/numpy/numpy/pull/6256
    """"""

    __slots__ = ()

    def __init__(self, key):
        if not isinstance(key, tuple):
            raise TypeError(f""key must be a tuple: {key!r}"")

        new_key = []
        ndim = None
        for k in key:
            if isinstance(k, slice):
                k = as_integer_slice(k)
            elif isinstance(k, np.ndarray) or isinstance(k, dask.array.Array):
                if not np.issubdtype(k.dtype, np.integer):
                    raise TypeError(
                        f""invalid indexer array, does not have integer dtype: {k!r}""
                    )
                if ndim is None:
                    ndim = k.ndim
                elif ndim != k.ndim:
                    ndims = [k.ndim for k in key if isinstance(k, np.ndarray)]
                    raise ValueError(
                        ""invalid indexer key: ndarray arguments ""
                        f""have different numbers of dimensions: {ndims}""
                    )
                if isinstance(k, dask.array.Array):
                    k = dask.array.asarray(k, dtype=np.int64)
                else:
                    k = np.asarray(k, dtype=np.int64)
            else:
                raise TypeError(
                    f""unexpected indexer type for {type(self).__name__}: {k!r}""
                )
            new_key.append(k)

        super().__init__(new_key)

```
","{""total_count"": 2, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 2, ""eyes"": 0}",,374025325