issue_comments

@shoyer Thanks. It's not datetime64 arrays, this is the result of np.isnat(t): File "/public/software/anaconda/anaconda3/envs/python36/lib/python3.6/site-packages/xarray-0.12.3-py3.6.egg/xarray/core/arithmetic.py", line 69, in __array_ufunc__ dask='allowed') File "/public/software/anaconda/anaconda3/envs/python36/lib/python3.6/site-packages/xarray-0.12.3-py3.6.egg/xarray/core/computation.py", line 969, in apply_ufunc keep_attrs=keep_attrs) File "/public/software/anaconda/anaconda3/envs/python36/lib/python3.6/site-packages/xarray-0.12.3-py3.6.egg/xarray/core/computation.py", line 217, in apply_dataarray_vfunc result_var = func(*data_vars) File "/public/software/anaconda/anaconda3/envs/python36/lib/python3.6/site-packages/xarray-0.12.3-py3.6.egg/xarray/core/computation.py", line 564, in apply_variable_ufunc result_data = func(*input_data) TypeError: ufunc 'isnat' is only defined for datetime and timedelta.

I use pd.isnull(t).all() to check it, it works. Actually it's all nan. There's something wrong with the nc file, I will contact the data center. Thank you for all your help :)

For datetime64 arrays, use np.isnat() instead of isnan.

On Sat, Sep 7, 2019 at 7:39 PM Xin Zhang notifications@github.com wrote:

@keewis https://github.com/keewis I tried to using np.isnan(t.values).all() to check whether it's all nan. But, I got this error:

print (np.isnan(t.values).all())

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 ''safe''

This is the type of t.values: <class 'numpy.ndarray'>

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@keewis I tried to using np.isnan(t.values).all() to check whether it's all nan. But, I got this error: print (np.isnan(t.values).all()) 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 ''safe'' This is the type of t.values: <class 'numpy.ndarray'>

do you actually have any non-nan values in your array? From what I understand of how nanops work is that AlwaysGreaterThan should only be returned by min() if there are no non-nan values.

@shoyer Thank. It works now. But, I get another question. This is the result of t = ds['time_utc']: <xarray.DataArray 'time_utc' (time: 1, scanline: 357, ground_pixel: 450)> array([[[nan, nan, ..., nan, nan], [nan, nan, ..., nan, nan], ..., [nan, nan, ..., nan, nan], [nan, nan, ..., nan, nan]]], dtype=object) Coordinates: * scanline (scanline) float64 1.0 2.0 3.0 4.0 ... 354.0 355.0 356.0 357.0 * ground_pixel (ground_pixel) float64 1.0 2.0 3.0 4.0 ... 448.0 449.0 450.0 * time (time) datetime64[ns] 2019-08-25 Attributes: long_name: Time of observation as ISO 8601 date-time string If I want to get the minimum value by t.min(skipna=True), I get the strange type: <xarray.DataArray 'time_utc' ()> array(<xarray.core.dtypes.AlwaysGreaterThan object at 0x7f96ac188550>, dtype=object) Can't convert it to string by str(t.min(skipna=True)).

I think this may have been fixed by https://github.com/pydata/xarray/pull/2924 (which removed the line with dtypes.fill_value(value.dtype) if valid_count == 0 else data)

Can you try upgrading to xarray 0.12.3?

@max-sixty Actually, I'm using numpy = 1.13.1 and I need skipna= True. Don't understand the error it shows.

Thanks for the issue @zxdawn . Did you try doing this?

NotImplementedError: min is not available with skipna=False with the installed version of numpy; upgrade to numpy 1.12