issues: 802734042

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802734042 MDU6SXNzdWU4MDI3MzQwNDI= 4870 Time encoding error associated with cftime > 1.4.0 6628425 closed 0     0 2021-02-06T16:15:20Z 2021-02-07T23:12:30Z 2021-02-07T23:12:30Z MEMBER      

As of cftime > 1.4.0, the return type of cftime.date2num can either be an integer or float. An integer dtype is used if the times can all be encoded exactly with the provided units; otherwise a float dtype is used. This causes problems in our current encoding pipeline, because we call cftime.date2num on dates one at a time through np.vectorize, and np.vectorize infers the type of the full returned array based on the result of the first function evaluation. If the first result is an integer, then the full array will be assumed to have an integer dtype, and any values that should be floats are cast as integers.

What happened:

``` In [1]: import cftime; import numpy as np; import xarray as xr

In [2]: times = np.array([cftime.DatetimeGregorian(2000, 1, 1), cftime.DatetimeGregorian(2000, 1, 1, 1)])

In [3]: xr.coding.times._encode_datetime_with_cftime(times, "days since 2000-01-01", calendar="gregorian") Out[3]: array([0, 0]) ```

What you expected to happen:

In [3]: xr.coding.times._encode_datetime_with_cftime(times, "days since 2000-01-01", calendar="gregorian") Out[3]: array([0. , 0.04166667])

A solution here would be to encode the times with a list comprehension instead, and cast the final result to an array, in which case NumPy infers the dtype in a more sensible way.

Environment:

Output of <tt>xr.show_versions()</tt> ``` INSTALLED VERSIONS ------------------ commit: None python: 3.7.3 | packaged by conda-forge | (default, Jul 1 2019, 14:38:56) [Clang 4.0.1 (tags/RELEASE_401/final)] python-bits: 64 OS: Darwin OS-release: 20.2.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: en_US.UTF-8 libhdf5: 1.10.5 libnetcdf: 4.6.2 xarray: 0.16.2.dev175+g8cc34cb4.d20210201 pandas: 1.1.3 numpy: 1.19.1 scipy: 1.2.1 netCDF4: 1.5.1.2 pydap: installed h5netcdf: 0.7.4 h5py: 2.9.0 Nio: None zarr: 2.3.2 cftime: 1.4.1 nc_time_axis: 1.1.1.dev5+g531dd0d PseudoNetCDF: None rasterio: 1.0.25 cfgrib: 0.9.7.1 iris: None bottleneck: 1.2.1 dask: 2.11.0 distributed: 2.11.0 matplotlib: 3.3.2 cartopy: None seaborn: 0.9.0 numbagg: installed pint: None setuptools: 51.0.0.post20201207 pip: 19.2.2 conda: None pytest: 5.0.1 IPython: 7.10.1 sphinx: 3.0.4 ``` 
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