issue_comments: 211138272
This data as json
| html_url | issue_url | id | node_id | user | created_at | updated_at | author_association | body | reactions | performed_via_github_app | issue |
|---|---|---|---|---|---|---|---|---|---|---|---|
| https://github.com/pydata/xarray/issues/827#issuecomment-211138272 | https://api.github.com/repos/pydata/xarray/issues/827 | 211138272 | MDEyOklzc3VlQ29tbWVudDIxMTEzODI3Mg== | 1217238 | 2016-04-18T00:25:00Z | 2016-04-18T00:25:00Z | MEMBER | Ah, I finally figured out what's going on. We use pandas to cleanup time units in an attempt to always write ISO-8601 compatible reference times. Unfortunately, pandas interprets dates like ``` In [21]: pd.Timestamp('1-1-1 00:00:0.0') Out[21]: Timestamp('2001-01-01 00:00:00') In [25]: pd.Timestamp('01-JAN-0001 00:00:00') Out[25]: Timestamp('2001-01-01 00:00:00') ``` One might argue this is a bug in pandas, but nonetheless that's what it does. xarray can currently handle datetimes outside the range dates hangled by pandas (roughly 1700-2300), but only if pandas raises an OutOfBoundDatetime error. Two fixes that we need for this: - use netCDF4's reference time decoding (if available) before trying to use pandas in decode_cf_datetime. Note that it is important to only decode only the one reference time if possible using netCDF4, because it's a lot faster to parse dates with vectorized operations with pandas/numpy. - stop using _cleanup_netcdf_time_units, since apparently it can go wrong. cc @jhamman who has some experience with these issues |
{
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
} |
148876551 |