[Numpy-discussion] timezones and datetime64
Chris Barker - NOAA Federal
chris.barker at noaa.gov
Thu Apr 4 14:56:41 EDT 2013
On Thu, Apr 4, 2013 at 10:54 AM, Francesc Alted <francesc at continuum.io> wrote:
> That makes a difference. This can be specially important for creating
> user-defined time origins:
>
> In []: np.array(int(1.5e9), dtype='datetime64[s]') + np.array(1,
> dtype='timedelta64[ns]')
> Out[]: numpy.datetime64('2017-07-14T04:40:00.000000001+0200')
but that's worthless if you try it higher-resolution:
In [40]: np.array(int(1.5e9), dtype='datetime64[s]')
Out[40]: array(datetime.datetime(2017, 7, 14, 2, 40), dtype='datetime64[s]')
# Start at 2017
# add a picosecond:
In [41]: np.array(int(1.5e9), dtype='datetime64[s]') + np.array(1,
dtype='timedelta64[ps]')
Out[41]: numpy.datetime64('1970-03-08T22:55:30.029526319105-0800')
# get 1970???
And even with nanoseconds, given the leap-second issues, etc, you
really wouldn't want to do this anyway -- rather, keep your epoch
close by.
Now that I think about it -- being able to set your epoch could lessen
the impact of leap-seconds for second-resolution as well.
-Chris
--
Christopher Barker, Ph.D.
Oceanographer
Emergency Response Division
NOAA/NOS/OR&R (206) 526-6959 voice
7600 Sand Point Way NE (206) 526-6329 fax
Seattle, WA 98115 (206) 526-6317 main reception
Chris.Barker at noaa.gov
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