@mathause

thanks for your suggestions. Your first solution works fine for correcting the time data stored in the array. I also don't understand why ds_test.time.encoding is empty and yet, its the reason for an error while saving it. Maybe its a bug?

@Chan-Jer Another work around which I used was to set the correct time value using cdo's settime function.

Hi Ryan, thanks for your reply. Apologies for not creating a reproducible problem earlier as the files weren't created by xarray routine. Please find my attempt at reproducing the problem below:

Minimum steps to reproduce the error

```python import numpy as np import xarray as xr import pandas as pd

data1 = np.ones(shape=(1, 181, 360)) lats=np.arange(-90,91, 1) lons=np.arange(-180,180,1) time1 = np.array([0])

creating the first dataset

da_1 = xr.DataArray(data1, coords=[time1, lats, lons], dims=['time', 'lats', 'lons']) da_1.time.attrs['units'] = "hours since 1988-01-01 00:00:00" da_1.time.attrs['calendar'] = "proleptic_gregorian" da_1.time.attrs['standard_name'] = "time" ds_1 = xr.Dataset({'V':da_1}) ds_1.attrs['Conventions'] = 'CF' ds_1.to_netcdf('ds_1.nc', encoding=None)

creating second test dataset

time2=np.array([6]) # wrong time value da_2 = xr.DataArray(data1, coords=[time2, lats, lons], dims=['time', 'lats', 'lons']) da_2.time.attrs['units'] = "hours since 1988-01-01 06:00:00" da_2.time.attrs['calendar'] = "proleptic_gregorian" da_2.time.attrs['standard_name'] = "time"

ds_2 = xr.Dataset({'V':da_2}) ds_2.attrs['Conventions'] = 'CF'

saving it with wrong time value

ds_2.to_netcdf('ds_2.nc', encoding=None)

Reading the 2 files and concatenating them

files = ['/path/to/ds_1.nc', '/path/to/ds_2.nc']

ds_test = xr.open_mfdataset(files, combine='nested', concat_dim='time', decode_cf=False) yr = 1988 # year dates = pd.date_range(start=(yr), end=str(yr+1), freq='6H', closed='left') ds_test.time.values=dates[:2] # fixing the time values ds_test.time.attrs['units'] = "Seconds since 1970-01-01 00:00:00" #required encoding ds_test.to_netcdf('ds_1_2.nc') # gives the same error ```

ValueError: failed to prevent overwriting existing key units in attrs on variable 'time'. This is probably an encoding field used by xarray to describe how a variable is serialized. To proceed, remove this key from the variable's attributes manually.

I've also mentioned your suggestion earlier in the original post. It also gives the same error message Please find the following reproducible steps incorporating your suggestion.

Trying time encoding solution

```

Reading the files

files = ['/path/to/ds_1.nc', '/path/to/ds_2.nc']

ds_test = xr.open_mfdataset(files, combine='nested', concat_dim='time', decode_cf=False) yr = 1988 # year dates = pd.date_range(start=(yr), end=str(yr+1), freq='6H', closed='left') ds_test.time.values=dates[:2] # fixing the time values

encoding try

ds_test.time.encoding['units'] = "Seconds since 1970-01-01 00:00:00" ds_test.to_netcdf('ds_1_2.nc') # gives same error ```

ValueError: failed to prevent overwriting existing key calendar in attrs on variable 'time'. This is probably an encoding field used by xarray to describe how a variable is serialized. To proceed, remove this key from the variable's attributes manually.

@dcherian Thanks a lot for providing an example with another approach

@vnoel I'm trying to plot coastline using facet. I tried to give ax.coastline() as a subplot_kws argument but that didn't work: ``` # ds_v_test is my Dataset containing V as variable

p = ds_v_test.sel(time=slice('2012-12-01', '2012-12-02 18:00'),\ lat= slice(-90,0)).V.squeeze().plot.pcolormesh(figsize=(16, 12), col='time', col_wrap=2, levels=16 ,\ cbar_kwargs={'label':'meridional v (m/s)'}, subplot_kws={'projection':ccrs.PlateCarree(),\ 'ax':ax.coastlines()}) ``` It gives me

AttributeError: Unknown property ax

At the moment, I can get around it by using this approach:

for ax in p.axes.flat: ax.coastlines()

INSTALLED VERSIONS

commit: None python: 3.6.8 |Anaconda, Inc.| (default, Dec 30 2018, 01:22:34) [GCC 7.3.0] python-bits: 64 OS: Linux OS-release: 4.15.0-45-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: en_US.UTF-8 libhdf5: 1.10.4 libnetcdf: 4.6.2

xarray: 0.12.0 pandas: 0.24.2 numpy: 1.16.2 scipy: 1.2.1 netCDF4: 1.4.2 pydap: None h5netcdf: None h5py: None Nio: 1.5.5 zarr: None cftime: 1.0.3.4 nc_time_axis: None PseudonetCDF: None rasterio: None cfgrib: 0.9.6.1.post1 iris: None bottleneck: None dask: None distributed: None matplotlib: 3.0.3 cartopy: 0.17.0 seaborn: 0.9.0 setuptools: 40.8.0 pip: 19.0.3 conda: None pytest: None IPython: 7.3.0 sphinx: None

​

@shoyer Ah! thanks. Certainly, a better error message would have helped me in this case. I agree that the easiest way is to just let xarray handle the datetime conversion.

@shoyer Sure, I found a way around by using cdo but I can revisit it and provide more details

I'm facing the same problem of the unexpected encoding parameter

big_ds.to_netcdf(out_dir + 'V250hov_N2000_2018_v4' + '.nc', encoding={'V':{'_FillValue': -999.0}, 'time':{'units': "seconds since 1970-01-01 00:00:00"}}) The error is as follows: File "./R_metric.py", line 61, in <module> big_ds.to_netcdf(out_dir + 'V250hov_N2000_2018_v4' + '.nc',encoding={'V':{'_FillValue': -999.0}, 'time':{'units': "seconds since 1970-01-01 00:00:00"}}) File "/usr/local/anaconda3/envs/work_env/lib/python3.6/site-packages/xarray/core/dataset.py", line 1222, in to_netcdf compute=compute) File "/usr/local/anaconda3/envs/work_env/lib/python3.6/site-packages/xarray/backends/api.py", line 718, in to_netcdf unlimited_dims=unlimited_dims) File "/usr/local/anaconda3/envs/work_env/lib/python3.6/site-packages/xarray/backends/api.py", line 761, in dump_to_store unlimited_dims=unlimited_dims) File "/usr/local/anaconda3/envs/work_env/lib/python3.6/site-packages/xarray/backends/common.py", line 266, in store unlimited_dims=unlimited_dims) File "/usr/local/anaconda3/envs/work_env/lib/python3.6/site-packages/xarray/backends/common.py", line 304, in set_variables name, v, check, unlimited_dims=unlimited_dims) File "/usr/local/anaconda3/envs/work_env/lib/python3.6/site-packages/xarray/backends/netCDF4_.py", line 450, in prepare_variable unlimited_dims=unlimited_dims) File "/usr/local/anaconda3/envs/work_env/lib/python3.6/site-packages/xarray/backends/netCDF4_.py", line 225, in _extract_nc4_variable_encoding ' %r' % (backend, invalid)) ValueError: unexpected encoding parameters for 'netCDF4' backend: ['units']

However, the same syntax works when I tried writing out a different file. I'm using xarray version 11.0

For anyone stumbling upon this thread in the future, I would like to mention that I used the above grouping approach suggested by @spencerkclark for my dataset to calculate climatology with calendar day and it works smoothly. The only thing one should be careful is that you can't directly plot the data using

In[1]: da.groupby(month_day_str).mean('time').plot() Out[1]: TypeError: Plotting requires coordinates to be numeric or dates of type np.datetime64 or datetime.datetime.

To get around it, either use group by the

modified_ordinal _day

Or convert back the grouped coordinate month_day_str to numeric. However, after doing all this I found out that the CDO function also calculates climatology by the ordinal day of the year. So, to be consistent I would stick to that method but it's anyway good to know that there is a way around to group by day and month if required in Xarray.