pydata/pandas#14068

As somewhat hinted at above there seem to be several issues here. I tried to look into a solution for checking the first and last element (which seems work for Problem (1) in my original post) but the OverflowError persisted so I looked into this and now my code is a mess but I figured this second problem out.

Pandas does not raise an overflow error when adding a TimedeltaIndex and a Timestamp.

``` import pandas as pd

overflow error

pd.to_timedelta(106580, 'D') + pd.Timestamp('2000')

no overflow error

pd.to_timedelta([106580], 'D') + pd.Timestamp('2000') ```

This screws up line 145 in https://github.com/pydata/xarray/blob/master/xarray/conventions.py.

I tried to look into the logic of decoding datetimes and I am not sure I got it. So the dtype of the dates should be:

if 1678 < year < 2262: `datetime64[ns]` else: if calendar in ['standard', 'gregorian', 'proleptic_gregorian']: `datetime.datetime` else: `netcdftime._datetime.datetime`

(Is it ever a timedelta64[ns]?)

The necessary conversion seems to be determined lazily (which may be the core of my problem above), Try this:

``` import xarray as xr import numpy as np

units = 'days since 1850-01-01 00:00:00' dates = np.arange(850) * 365

dta = xr.conventions.DecodedCFDatetimeArray(dates, units)

dta[0:1] # a datetime64[ns] object dta[-1] # a datetime.datetime object dta[:] # a datetime.datetime object ```

However, when I load these dates from a netCDF file (see the example in the first post) it results in an error. (Thus, the behavior is not exactly the same as when using DecodedCFDatetimeArray, I haven't figured out why.)

Another (or the same) problem is that in DecodedCFDatetimeArray in (in __init__) only the first element is tested when trying to generate the error message, maybe the first and the last element should be tested.

Here: (https://github.com/pydata/xarray/blob/master/xarray/conventions.py, line 375)

Yes, definitely. However, the documentation states that we should get back a netcdftime.datetime object when we are out of these bounds (http://xarray.pydata.org/en/stable/time-series.html#creating-datetime64-data). This only worked for (3) and not for (1). Thus only when the date was < 1678 and not for > 2262. I have not looked into why this occurs.

In my example (2) it returned a working datetime64[ns] object by wrapping around the year, which looks like an overflow problem. I also haven't looked into where this happens (xarray or netcdftime or ...), but this feels a bit dangerous to me.