```
2020-04-01T05:53:31.3406894Z ________________ TestDataArray.test_resample_drop_nondim_coords ________________
2020-04-01T05:53:31.3408485Z
2020-04-01T05:53:31.3409839Z self =
2020-04-01T05:53:31.3411145Z
2020-04-01T05:53:31.3413416Z @requires_scipy
2020-04-01T05:53:31.3414202Z def test_resample_drop_nondim_coords(self):
2020-04-01T05:53:31.3415025Z xs = np.arange(6)
2020-04-01T05:53:31.3415870Z ys = np.arange(3)
2020-04-01T05:53:31.3417272Z times = pd.date_range(""2000-01-01"", freq=""6H"", periods=5)
2020-04-01T05:53:31.3418156Z data = np.tile(np.arange(5), (6, 3, 1))
2020-04-01T05:53:31.3419186Z xx, yy = np.meshgrid(xs * 5, ys * 2.5)
2020-04-01T05:53:31.3420539Z tt = np.arange(len(times), dtype=int)
2020-04-01T05:53:31.3421488Z array = DataArray(data, {""time"": times, ""x"": xs, ""y"": ys}, (""x"", ""y"", ""time""))
2020-04-01T05:53:31.3422568Z xcoord = DataArray(xx.T, {""x"": xs, ""y"": ys}, (""x"", ""y""))
2020-04-01T05:53:31.3423540Z ycoord = DataArray(yy.T, {""x"": xs, ""y"": ys}, (""x"", ""y""))
2020-04-01T05:53:31.3424848Z tcoord = DataArray(tt, {""time"": times}, (""time"",))
2020-04-01T05:53:31.3426753Z ds = Dataset({""data"": array, ""xc"": xcoord, ""yc"": ycoord, ""tc"": tcoord})
2020-04-01T05:53:31.3427254Z ds = ds.set_coords([""xc"", ""yc"", ""tc""])
2020-04-01T05:53:31.3428134Z
2020-04-01T05:53:31.3428545Z # Select the data now, with the auxiliary coordinates in place
2020-04-01T05:53:31.3429410Z array = ds[""data""]
2020-04-01T05:53:31.3430268Z
2020-04-01T05:53:31.3431425Z # Re-sample
2020-04-01T05:53:31.3432662Z actual = array.resample(time=""12H"", restore_coord_dims=True).mean(""time"")
2020-04-01T05:53:31.3434512Z assert ""tc"" not in actual.coords
2020-04-01T05:53:31.3434892Z
2020-04-01T05:53:31.3435441Z # Up-sample - filling
2020-04-01T05:53:31.3435901Z actual = array.resample(time=""1H"", restore_coord_dims=True).ffill()
2020-04-01T05:53:31.3437046Z assert ""tc"" not in actual.coords
2020-04-01T05:53:31.3437939Z
2020-04-01T05:53:31.3438514Z # Up-sample - interpolation
2020-04-01T05:53:31.3439498Z actual = array.resample(time=""1H"", restore_coord_dims=True).interpolate(
2020-04-01T05:53:31.3439962Z > ""linear""
2020-04-01T05:53:31.3440256Z )
2020-04-01T05:53:31.3440495Z
2020-04-01T05:53:31.3441338Z xarray/tests/test_dataarray.py:2941:
2020-04-01T05:53:31.3441811Z _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
2020-04-01T05:53:31.3442181Z xarray/core/resample.py:142: in interpolate
2020-04-01T05:53:31.3442484Z return self._interpolate(kind=kind)
2020-04-01T05:53:31.3442807Z xarray/core/resample.py:156: in _interpolate
2020-04-01T05:53:31.3443109Z **{self._dim: self._full_index},
2020-04-01T05:53:31.3443425Z xarray/core/dataarray.py:1381: in interp
2020-04-01T05:53:31.3443714Z **coords_kwargs,
2020-04-01T05:53:31.3444238Z xarray/core/dataset.py:2637: in interp
2020-04-01T05:53:31.3444591Z variables[name] = missing.interp(var, var_indexers, method, **kwargs)
2020-04-01T05:53:31.3444968Z xarray/core/missing.py:611: in interp
2020-04-01T05:53:31.3445281Z var, indexes_coords = _localize(var, indexes_coords)
2020-04-01T05:53:31.3445618Z xarray/core/missing.py:552: in _localize
2020-04-01T05:53:31.3445952Z imin = index.get_loc(np.nanmin(new_x.values), method=""nearest"")
2020-04-01T05:53:31.3446293Z <__array_function__ internals>:6: in nanmin
2020-04-01T05:53:31.3446575Z ???
2020-04-01T05:53:31.3446883Z _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
2020-04-01T05:53:31.3447158Z
2020-04-01T05:53:31.3448180Z a = array(['2000-01-01T00:00:00.000000000', '2000-01-01T01:00:00.000000000',
2020-04-01T05:53:31.3450286Z '2000-01-01T02:00:00.000000000', '2000...T22:00:00.000000000', '2000-01-01T23:00:00.000000000',
2020-04-01T05:53:31.3451512Z '2000-01-02T00:00:00.000000000'], dtype='datetime64[ns]')
2020-04-01T05:53:31.3452062Z axis = None, out = None, keepdims =
2020-04-01T05:53:31.3452523Z
2020-04-01T05:53:31.3452893Z @array_function_dispatch(_nanmin_dispatcher)
2020-04-01T05:53:31.3453666Z def nanmin(a, axis=None, out=None, keepdims=np._NoValue):
2020-04-01T05:53:31.3453973Z """"""
2020-04-01T05:53:31.3454324Z Return minimum of an array or minimum along an axis, ignoring any NaNs.
2020-04-01T05:53:31.3455156Z When all-NaN slices are encountered a ``RuntimeWarning`` is raised and
2020-04-01T05:53:31.3455561Z Nan is returned for that slice.
2020-04-01T05:53:31.3455844Z
2020-04-01T05:53:31.3456074Z Parameters
2020-04-01T05:53:31.3456499Z ----------
2020-04-01T05:53:31.3456799Z a : array_like
2020-04-01T05:53:31.3457148Z Array containing numbers whose minimum is desired. If `a` is not an
2020-04-01T05:53:31.3457706Z array, a conversion is attempted.
2020-04-01T05:53:31.3458025Z axis : {int, tuple of int, None}, optional
2020-04-01T05:53:31.3458400Z Axis or axes along which the minimum is computed. The default is to compute
2020-04-01T05:53:31.3458791Z the minimum of the flattened array.
2020-04-01T05:53:31.3459085Z out : ndarray, optional
2020-04-01T05:53:31.3459438Z Alternate output array in which to place the result. The default
2020-04-01T05:53:31.3459821Z is ``None``; if provided, it must have the same shape as the
2020-04-01T05:53:31.3460197Z expected output, but the type will be cast if necessary. See
2020-04-01T05:53:31.3460547Z `doc.ufuncs` for details.
2020-04-01T05:53:31.3460784Z
2020-04-01T05:53:31.3461033Z .. versionadded:: 1.8.0
2020-04-01T05:53:31.3461366Z keepdims : bool, optional
2020-04-01T05:53:31.3461697Z If this is set to True, the axes which are reduced are left
2020-04-01T05:53:31.3462096Z in the result as dimensions with size one. With this option,
2020-04-01T05:53:31.3462475Z the result will broadcast correctly against the original `a`.
2020-04-01T05:53:31.3462764Z
2020-04-01T05:53:31.3463111Z If the value is anything but the default, then
2020-04-01T05:53:31.3463473Z `keepdims` will be passed through to the `min` method
2020-04-01T05:53:31.3464040Z of sub-classes of `ndarray`. If the sub-classes methods
2020-04-01T05:53:31.3464479Z does not implement `keepdims` any exceptions will be raised.
2020-04-01T05:53:31.3464772Z
2020-04-01T05:53:31.3465021Z .. versionadded:: 1.8.0
2020-04-01T05:53:31.3465267Z
2020-04-01T05:53:31.3465501Z Returns
2020-04-01T05:53:31.3465911Z -------
2020-04-01T05:53:31.3466198Z nanmin : ndarray
2020-04-01T05:53:31.3466530Z An array with the same shape as `a`, with the specified axis
2020-04-01T05:53:31.3467115Z removed. If `a` is a 0-d array, or if axis is None, an ndarray
2020-04-01T05:53:31.3467548Z scalar is returned. The same dtype as `a` is returned.
2020-04-01T05:53:31.3467869Z
2020-04-01T05:53:31.3468088Z See Also
2020-04-01T05:53:31.3468465Z --------
2020-04-01T05:53:31.3468732Z nanmax :
2020-04-01T05:53:31.3469068Z The maximum value of an array along a given axis, ignoring any NaNs.
2020-04-01T05:53:31.3469380Z amin :
2020-04-01T05:53:31.3469713Z The minimum value of an array along a given axis, propagating any NaNs.
2020-04-01T05:53:31.3470028Z fmin :
2020-04-01T05:53:31.3470507Z Element-wise minimum of two arrays, ignoring any NaNs.
2020-04-01T05:53:31.3470866Z minimum :
2020-04-01T05:53:31.3471352Z Element-wise minimum of two arrays, propagating any NaNs.
2020-04-01T05:53:31.3471708Z isnan :
2020-04-01T05:53:31.3472115Z Shows which elements are Not a Number (NaN).
2020-04-01T05:53:31.3472405Z isfinite:
2020-04-01T05:53:31.3472702Z Shows which elements are neither NaN nor infinity.
2020-04-01T05:53:31.3473089Z
2020-04-01T05:53:31.3473323Z amax, fmax, maximum
2020-04-01T05:53:31.3473572Z
2020-04-01T05:53:31.3473786Z Notes
2020-04-01T05:53:31.3474196Z -----
2020-04-01T05:53:31.3474732Z NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
2020-04-01T05:53:31.3476305Z (IEEE 754). This means that Not a Number is not equivalent to infinity.
2020-04-01T05:53:31.3476608Z Positive infinity is treated as a very large number and negative
2020-04-01T05:53:31.3478858Z infinity is treated as a very small (i.e. negative) number.
2020-04-01T05:53:31.3479255Z
2020-04-01T05:53:31.3479619Z If the input has a integer type the function is equivalent to np.min.
2020-04-01T05:53:31.3479971Z
2020-04-01T05:53:31.3480308Z Examples
2020-04-01T05:53:31.3481117Z --------
2020-04-01T05:53:31.3481526Z >>> a = np.array([[1, 2], [3, np.nan]])
2020-04-01T05:53:31.3481875Z >>> np.nanmin(a)
2020-04-01T05:53:31.3482212Z 1.0
2020-04-01T05:53:31.3482518Z >>> np.nanmin(a, axis=0)
2020-04-01T05:53:31.3482838Z array([1., 2.])
2020-04-01T05:53:31.3483183Z >>> np.nanmin(a, axis=1)
2020-04-01T05:53:31.3483506Z array([1., 3.])
2020-04-01T05:53:31.3483789Z
2020-04-01T05:53:31.3484518Z When positive infinity and negative infinity are present:
2020-04-01T05:53:31.3484869Z
2020-04-01T05:53:31.3485207Z >>> np.nanmin([1, 2, np.nan, np.inf])
2020-04-01T05:53:31.3485532Z 1.0
2020-04-01T05:53:31.3485855Z >>> np.nanmin([1, 2, np.nan, np.NINF])
2020-04-01T05:53:31.3486387Z -inf
2020-04-01T05:53:31.3486734Z
2020-04-01T05:53:31.3487009Z """"""
2020-04-01T05:53:31.3487318Z kwargs = {}
2020-04-01T05:53:31.3487661Z if keepdims is not np._NoValue:
2020-04-01T05:53:31.3488198Z kwargs['keepdims'] = keepdims
2020-04-01T05:53:31.3488662Z if type(a) is np.ndarray and a.dtype != np.object_:
2020-04-01T05:53:31.3489117Z # Fast, but not safe for subclasses of ndarray, or object arrays,
2020-04-01T05:53:31.3489807Z # which do not implement isnan (gh-9009), or fmin correctly (gh-8975)
2020-04-01T05:53:31.3490313Z res = np.fmin.reduce(a, axis=axis, out=out, **kwargs)
2020-04-01T05:53:31.3490701Z > if np.isnan(res).any():
2020-04-01T05:53:31.3491523Z E TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule
```
It looks like support for `nanmin`/`nanmax` on datetime arrays is relatively new, https://github.com/numpy/numpy/issues/14831, so we may need to find a way around that.","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,591643901
https://github.com/pydata/xarray/pull/3924#issuecomment-609510303,https://api.github.com/repos/pydata/xarray/issues/3924,609510303,MDEyOklzc3VlQ29tbWVudDYwOTUxMDMwMw==,6628425,2020-04-06T00:22:27Z,2020-04-06T00:22:27Z,MEMBER,"Indeed, thanks @zxdawn, this seems like a reasonable change to me too. I say go ahead with writing the tests.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,591643901
https://github.com/pydata/xarray/pull/3924#issuecomment-608106094,https://api.github.com/repos/pydata/xarray/issues/3924,608106094,MDEyOklzc3VlQ29tbWVudDYwODEwNjA5NA==,30388627,2020-04-02T21:43:41Z,2020-04-02T21:43:41Z,NONE,"Hi @max-sixty, thanks.
If this looks well, I'm glad to add the test for it.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,591643901
https://github.com/pydata/xarray/pull/3924#issuecomment-608102002,https://api.github.com/repos/pydata/xarray/issues/3924,608102002,MDEyOklzc3VlQ29tbWVudDYwODEwMjAwMg==,5635139,2020-04-02T21:33:29Z,2020-04-02T21:33:29Z,MEMBER,"Hi @zxdawn thanks for the PR, appreciate you making a first contribution.
I don't know this code that well, but your examples look reasonable. Any thoughts from those that know this better, @spencerkclark @huard @dcherian ?
If we do go ahead and merge this solution, we'd need tests @zxdawn , would you be up for writing those?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,591643901