Here is code used to read/write a masked_array with the netCDF4 and xarray modules. As seen if you run the code, for 3 cases the masked_value is read as a np.nan. However, for the netcdf file written by netCDF4 and read by xarray, the masked_value is the default _FillValue of 9.96920997e+36.

I wonder if this is expected or if I am doing something wrong.

```python import xarray as xr import netCDF4 as nc import numpy as np import os data = np.ma.array([1.,2.], mask = [True, False])

create file with netcdf$

nc_file = 'ncfile.nc' if os.path.exists(nc_file): os.remove(nc_file) ds = nc.Dataset(nc_file, 'w') ds.createDimension('dim1', 2) var = ds.createVariable('data', 'f8', dimensions = ('dim1')) var[:] = data ds.close()

create file with xarray

da = xr.DataArray(data, name = 'data', dims = {'dim1':2}) nc_file = 'xrfile.nc' if os.path.exists(nc_file): os.remove(nc_file) da.to_netcdf(nc_file, 'w') da.close()

print('original data: {}'.format(data))

da = xr.open_dataset('ncfile.nc').data print('data from nc read by xr: {}'.format(da.values)) da = xr.open_dataset('xrfile.nc').data print('data from xr read by xr: {}'.format(da.values))

data = nc.Dataset('ncfile.nc').variables['data'][:] print('data from nc read by nc: {}'.format(da.values)) data = nc.Dataset('xrfile.nc').variables['data'][:] print('data from xr read by nc: {}'.format(da.values))

print('done')

``` Here is the output I get:

``` original data: [-- 2.0] data from nc read by xr: [9.96920997e+36 2.00000000e+00] data from xr read by xr: [nan 2.] data from nc read by nc: [nan 2.] data from xr read by nc: [nan 2.] done

```

Output of xr.show_versions()

I am trying to use xarray with different types of calendars. I made a few tests and wonder if somebody can help me make sense of the results. In my test, I generate a DataArray da time series with a 365_day calendar using cftime. I then write that DataArray in a netCDF file and read it in another DataArray da2

Code Sample, a copy-pastable example if possible

```python import xarray as xr import cftime import numpy as np

generate data for 365_days calendar

units = 'days since 2000-01-01 00:00' time_365 = cftime.num2date(np.arange(0, 10 * 365), units, '365_day') da = xr.DataArray(np.arange(time_365.size), coords = [time_365], dims = 'time', name = 'data')

write dataArray in netcdf and read it in new DataArray

da.to_netcdf('data_365.nc', 'w') da2 = xr.open_dataset('data_365.nc').data

try resample da

try: mean = da.resample(time='Y').mean() print(mean.values) except TypeError: print('got TypeError for da')

try resample da2

mean = da2.resample(time = 'Y').mean() print (mean.values) ```

Problem description

As seen in the code the resampledoes not work for da while it does for da2. The problem is related to the the type of da.time which is cftime.DatetimeNoLeap while da2.time is a datetime64. I thought that xarray is using cftime to make the conversion from time numerical values to dates but it looks to me as if it is not the case.

I wonder if this makes sense or if it is something that should eventually be corrected.

INSTALLED VERSIONS In [6]: print (cftime.version) 1.0.1

Code Sample

```python import xarray as xr import shutil import numpy as np

create netcdf file

data = np.random.rand(4, 3) foo = xr.DataArray(data) foo.to_netcdf('test.nc')

f_nc = 'a.nc' shutil.copy('test.nc', f_nc) while 1: print '{:05n} characteres'.format(len(f_nc)) ds1 = xr.open_dataset(f_nc) ds1.close() nf_nc = 'a' + f_nc shutil.move(f_nc, nf_nc) f_nc = nf_nc

if len(f_nc) == 100:
    break

```

Problem description

On my linux machine (CentOS) this code crashes (memory corrruption) when the filename length hits 32 characters.

On my OSX machine it is fine until 255 character and stops with an IOError

Output of xr.show_versions()