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/pull/4155#issuecomment-674321185,https://api.github.com/repos/pydata/xarray/issues/4155,674321185,MDEyOklzc3VlQ29tbWVudDY3NDMyMTE4NQ==,6815844,2020-08-15T00:30:21Z,2020-08-15T00:30:21Z,MEMBER,"@cyhsu Yes, because it is not yet released.
(I'm not sure when the next release will be, but maybe a few months later)
If you do `pip install git+https://github.com/pydata/xarray`, the current master will be installed in your system and interpolation over the chunks can be used.
But note that this means you will install (a kind of) beta version.","{""total_count"": 1, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 1, ""rocket"": 0, ""eyes"": 0}",,638909879
https://github.com/pydata/xarray/pull/4155#issuecomment-674305570,https://api.github.com/repos/pydata/xarray/issues/4155,674305570,MDEyOklzc3VlQ29tbWVudDY3NDMwNTU3MA==,6815844,2020-08-14T23:07:03Z,2020-08-14T23:07:03Z,MEMBER,"@cyhsu 
Yes, in the current master.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,638909879
https://github.com/pydata/xarray/pull/4155#issuecomment-672348216,https://api.github.com/repos/pydata/xarray/issues/4155,672348216,MDEyOklzc3VlQ29tbWVudDY3MjM0ODIxNg==,6815844,2020-08-11T23:16:07Z,2020-08-11T23:16:07Z,MEMBER,Thanks @pums974 :),"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,638909879
https://github.com/pydata/xarray/pull/4155#issuecomment-667412134,https://api.github.com/repos/pydata/xarray/issues/4155,667412134,MDEyOklzc3VlQ29tbWVudDY2NzQxMjEzNA==,6815844,2020-07-31T22:28:07Z,2020-07-31T22:28:07Z,MEMBER,"This PR looks good for me.
Maybe we can wait for a few days in case anyone has some comments on it.
If no comments, I'll merge this then.","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,638909879
https://github.com/pydata/xarray/pull/4155#issuecomment-666720655,https://api.github.com/repos/pydata/xarray/issues/4155,666720655,MDEyOklzc3VlQ29tbWVudDY2NjcyMDY1NQ==,6815844,2020-07-30T21:38:55Z,2020-07-30T21:38:55Z,MEMBER,"OK.
If you have additional time, it would be nicer if you could add more comments on tests, like what is being tested there ;)","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,638909879
https://github.com/pydata/xarray/pull/4155#issuecomment-663788117,https://api.github.com/repos/pydata/xarray/issues/4155,663788117,MDEyOklzc3VlQ29tbWVudDY2Mzc4ODExNw==,6815844,2020-07-25T01:08:52Z,2020-07-25T01:08:52Z,MEMBER,"Thanks @pums974 for this update and sorry for my late response.
It looks good but I'll take a deeper look in the next week.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,638909879
https://github.com/pydata/xarray/pull/4155#issuecomment-651589183,https://api.github.com/repos/pydata/xarray/issues/4155,651589183,MDEyOklzc3VlQ29tbWVudDY1MTU4OTE4Mw==,6815844,2020-06-30T07:01:31Z,2020-06-30T07:01:31Z,MEMBER,"> Hum, ok, but I don't see how it would work if all points are between chunks (see my second example)

Maybe we can support sequential interpolation only at this moment.
In this case,
```python
res = data.interp(x=np.linspace(0, 1), y=0.5)
```
can be interpreted as
```python
res = data.interp(x=np.linspace(0, 1)).interp(y=0.5)
```
which might not be too difficult.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,638909879
https://github.com/pydata/xarray/pull/4155#issuecomment-650428037,https://api.github.com/repos/pydata/xarray/issues/4155,650428037,MDEyOklzc3VlQ29tbWVudDY1MDQyODAzNw==,6815844,2020-06-26T22:17:22Z,2020-06-26T22:17:22Z,MEMBER,"> As for implementing this in dask, you may be right, it probably belong there,
> But I am even less use to their code base, and have no clue where to put it.

OK. 
Even so, I would suggest restructuring the code base;
maybe we can add an `interp1d` equivalence into
core.dask_array_ops.interp1d
which works with dask-arrays (non-xarray object).
It'll be easier to test.
The API should be the as same with `scipy.interp.interp1d` as possible.

In missing.py, we can call this function.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,638909879
https://github.com/pydata/xarray/pull/4155#issuecomment-649836609,https://api.github.com/repos/pydata/xarray/issues/4155,649836609,MDEyOklzc3VlQ29tbWVudDY0OTgzNjYwOQ==,6815844,2020-06-25T21:53:36Z,2020-06-25T21:53:36Z,MEMBER,"Also in my local environment, it gives 
```AttributeError: 'memoryview' object has no attribute 'dtype'```

The full stack trace is
```
_______________________________________________________ test_interpolate_1d[1-y-cubic] ________________________________________________________

method = 'cubic', dim = 'y', case = 1

    @pytest.mark.parametrize(""method"", [""linear"", ""cubic""])
    @pytest.mark.parametrize(""dim"", [""x"", ""y""])
    @pytest.mark.parametrize(""case"", [0, 1])
    def test_interpolate_1d(method, dim, case):
        if not has_scipy:
            pytest.skip(""scipy is not installed."")
    
        if not has_dask and case in [1]:
            pytest.skip(""dask is not installed in the environment."")
    
        da = get_example_data(case)
        xdest = np.linspace(0.0, 0.9, 80)
    
        actual = da.interp(method=method, **{dim: xdest})
    
        # scipy interpolation for the reference
        def func(obj, new_x):
            return scipy.interpolate.interp1d(
                da[dim],
                obj.data,
                axis=obj.get_axis_num(dim),
                bounds_error=False,
                fill_value=np.nan,
                kind=method,
            )(new_x)
    
        if dim == ""x"":
            coords = {""x"": xdest, ""y"": da[""y""], ""x2"": (""x"", func(da[""x2""], xdest))}
        else:  # y
            coords = {""x"": da[""x""], ""y"": xdest, ""x2"": da[""x2""]}
    
        expected = xr.DataArray(func(da, xdest), dims=[""x"", ""y""], coords=coords)
>       assert_allclose(actual, expected)

xarray/tests/test_interp.py:86: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
xarray/testing.py:132: in compat_variable
    return a.dims == b.dims and (a._data is b._data or equiv(a.data, b.data))
xarray/testing.py:31: in _data_allclose_or_equiv
    return duck_array_ops.allclose_or_equiv(arr1, arr2, rtol=rtol, atol=atol)
xarray/core/duck_array_ops.py:221: in allclose_or_equiv
    arr1 = np.array(arr1)
../../../anaconda3/envs/xarray/lib/python3.7/site-packages/dask/array/core.py:1314: in __array__
    x = self.compute()
../../../anaconda3/envs/xarray/lib/python3.7/site-packages/dask/base.py:165: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../../../anaconda3/envs/xarray/lib/python3.7/site-packages/dask/base.py:436: in compute
    results = schedule(dsk, keys, **kwargs)
../../../anaconda3/envs/xarray/lib/python3.7/site-packages/dask/local.py:527: in get_sync
    return get_async(apply_sync, 1, dsk, keys, **kwargs)
../../../anaconda3/envs/xarray/lib/python3.7/site-packages/dask/local.py:494: in get_async
    fire_task()
../../../anaconda3/envs/xarray/lib/python3.7/site-packages/dask/local.py:466: in fire_task
    callback=queue.put,
../../../anaconda3/envs/xarray/lib/python3.7/site-packages/dask/local.py:516: in apply_sync
    res = func(*args, **kwds)
../../../anaconda3/envs/xarray/lib/python3.7/site-packages/dask/local.py:227: in execute_task
    result = pack_exception(e, dumps)
../../../anaconda3/envs/xarray/lib/python3.7/site-packages/dask/local.py:222: in execute_task
    result = _execute_task(task, data)
../../../anaconda3/envs/xarray/lib/python3.7/site-packages/dask/core.py:119: in _execute_task
    return func(*args2)
../../../anaconda3/envs/xarray/lib/python3.7/site-packages/dask/optimization.py:982: in __call__
    return core.get(self.dsk, self.outkey, dict(zip(self.inkeys, args)))
../../../anaconda3/envs/xarray/lib/python3.7/site-packages/dask/core.py:149: in get
    result = _execute_task(task, cache)
../../../anaconda3/envs/xarray/lib/python3.7/site-packages/dask/core.py:119: in _execute_task
    return func(*args2)
xarray/core/missing.py:830: in _dask_aware_interpnd
    return _interpnd(var, old_x, new_x, func, kwargs)
xarray/core/missing.py:793: in _interpnd
    x, new_x = _floatize_x(x, new_x)
xarray/core/missing.py:577: in _floatize_x
    if _contains_datetime_like_objects(x[i]):
xarray/core/common.py:1595: in _contains_datetime_like_objects
    return is_np_datetime_like(var.dtype) or contains_cftime_datetimes(var)
xarray/core/common.py:1588: in contains_cftime_datetimes
    return _contains_cftime_datetimes(var.data)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

array = <memory at 0x7f771d6daef0>

    def _contains_cftime_datetimes(array) -> bool:
        """"""Check if an array contains cftime.datetime objects
        """"""
        try:
            from cftime import datetime as cftime_datetime
        except ImportError:
            return False
        else:
>           if array.dtype == np.dtype(""O"") and array.size > 0:
E           AttributeError: 'memoryview' object has no attribute 'dtype'

xarray/core/common.py:1574: AttributeError
```","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,638909879
https://github.com/pydata/xarray/pull/4155#issuecomment-649827797,https://api.github.com/repos/pydata/xarray/issues/4155,649827797,MDEyOklzc3VlQ29tbWVudDY0OTgyNzc5Nw==,6815844,2020-06-25T21:30:17Z,2020-06-25T21:30:17Z,MEMBER,"Hi @pums974 

Thanks for sending the PR.
I'm working to review it, but it may take more time.

A few comments;
Does it work with an unsorted destination?
e.g.,
```python
da.interp(y=[0, -1, 2])
```

I'm feeling that the basic algorithm, such as `np.interp`-equivalence, should be interpreted in upstream.
I'm sure Dask community welcomes this addition.
Do you have an interest on it?
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,638909879