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  • Coordinates passed to interp have nan values · 9 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
681813956 https://github.com/pydata/xarray/pull/3924#issuecomment-681813956 https://api.github.com/repos/pydata/xarray/issues/3924 MDEyOklzc3VlQ29tbWVudDY4MTgxMzk1Ng== mathause 10194086 2020-08-27T08:51:32Z 2020-08-27T08:51:32Z MEMBER

Thank you for the PR - this now fixed in #4233

 
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   Coordinates passed to interp have nan values 591643901
612561659 https://github.com/pydata/xarray/pull/3924#issuecomment-612561659 https://api.github.com/repos/pydata/xarray/issues/3924 MDEyOklzc3VlQ29tbWVudDYxMjU2MTY1OQ== zxdawn 30388627 2020-04-12T04:14:45Z 2020-04-12T04:14:45Z NONE

@dcherian Oh, thanks! After scipy is installed, it works: ``` =============================================== test session starts =============================================== platform linux -- Python 3.7.6, pytest-5.4.1, py-1.8.1, pluggy-0.12.0 -- /yin_raid/xin/miniconda3/envs/xarray_dev/bin/python cachedir: .pytest_cache rootdir: /yin_raid/xin/github/xarray, inifile: setup.cfg collected 2 items

test_interp.py::test_nans[True] SKIPPED [ 50%] test_interp.py::test_nans[False] PASSED [100%]

================================================ warnings summary ================================================= xarray/tests/test_interp.py::test_nans[False] xarray/tests/test_interp.py::test_nans[False] /yin_raid/xin/miniconda3/envs/xarray_dev/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject return f(args, *kwds)

-- Docs: https://docs.pytest.org/en/latest/warnings.html ==================================== 1 passed, 1 skipped, 2 warnings in 0.88s ===================================== ```

I will update the test and pull it soon.

 
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   Coordinates passed to interp have nan values 591643901
612560952 https://github.com/pydata/xarray/pull/3924#issuecomment-612560952 https://api.github.com/repos/pydata/xarray/issues/3924 MDEyOklzc3VlQ29tbWVudDYxMjU2MDk1Mg== dcherian 2448579 2020-04-12T04:03:37Z 2020-04-12T04:03:37Z MEMBER

Do you have scipy installed?

 
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   Coordinates passed to interp have nan values 591643901
612548656 https://github.com/pydata/xarray/pull/3924#issuecomment-612548656 https://api.github.com/repos/pydata/xarray/issues/3924 MDEyOklzc3VlQ29tbWVudDYxMjU0ODY1Ng== zxdawn 30388627 2020-04-12T01:38:32Z 2020-04-12T01:38:32Z NONE

It's my first time to write test for xarray. I tried this method: pytest test_interp.py::test_nans -v, but all tests are skipped: ``` =================================================== test session starts ==================================================== platform linux -- Python 3.7.6, pytest-5.4.1, py-1.8.1, pluggy-0.12.0 -- /yin_raid/xin/miniconda3/envs/xarray_dev/bin/python cachedir: .pytest_cache rootdir: /yin_raid/xin/github/xarray, inifile: setup.cfg collected 2 items

test_interp.py::test_nans[True] SKIPPED [ 50%] test_interp.py::test_nans[False] SKIPPED [100%]

==================================================== 2 skipped in 0.50s ==================================================== ```

How to make the test actually run?

 
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   Coordinates passed to interp have nan values 591643901
609528106 https://github.com/pydata/xarray/pull/3924#issuecomment-609528106 https://api.github.com/repos/pydata/xarray/issues/3924 MDEyOklzc3VlQ29tbWVudDYwOTUyODEwNg== zxdawn 30388627 2020-04-06T01:57:54Z 2020-04-06T02:03:41Z NONE

@spencerkclark Maybe converting the datetime into number? BTW, Why not forcing numpy >= 1.18.1?

 
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   Coordinates passed to interp have nan values 591643901
609512874 https://github.com/pydata/xarray/pull/3924#issuecomment-609512874 https://api.github.com/repos/pydata/xarray/issues/3924 MDEyOklzc3VlQ29tbWVudDYwOTUxMjg3NA== spencerkclark 6628425 2020-04-06T00:37:29Z 2020-04-06T00:37:29Z MEMBER

Note that it looks like this causes problems for older NumPy versions when interpolating with a datetime coordinate, e.g. in the Linux py36-min-nep18 build:

``` 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 = <xarray.tests.test_dataarray.TestDataArray object at 0x7f360ed41080> 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 = <no value> 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.

 
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   Coordinates passed to interp have nan values 591643901
609510303 https://github.com/pydata/xarray/pull/3924#issuecomment-609510303 https://api.github.com/repos/pydata/xarray/issues/3924 MDEyOklzc3VlQ29tbWVudDYwOTUxMDMwMw== spencerkclark 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.

 
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   Coordinates passed to interp have nan values 591643901
608106094 https://github.com/pydata/xarray/pull/3924#issuecomment-608106094 https://api.github.com/repos/pydata/xarray/issues/3924 MDEyOklzc3VlQ29tbWVudDYwODEwNjA5NA== zxdawn 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.

 
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   Coordinates passed to interp have nan values 591643901
608102002 https://github.com/pydata/xarray/pull/3924#issuecomment-608102002 https://api.github.com/repos/pydata/xarray/issues/3924 MDEyOklzc3VlQ29tbWVudDYwODEwMjAwMg== max-sixty 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?

 
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   Coordinates passed to interp have nan values 591643901

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