[Numpy-discussion] RFC: A proposal for implementing some date/time types in NumPy

[Matt Knox]

Christopher Barker <Chris.Barker <at> noaa.gov> writes:

>> I'm also imaging some extra utility functions/method that would be nice:
>> 
>> aDateTimeArray.hours(dtype=float)
>> 
>> to convert to hours (and days, and seconds, etc). And maybe some that 
>> would create a DateTimeArray from various time units.

The DateArray class in the timeseries scikits can do part of what you want.
Observe...

>>> import scikits.timeseries as ts
>>> a = ts.date_array(start_date=ts.now('hourly'), length=15)
>>> a
DateArray([12-Jul-2008 11:00, 12-Jul-2008 12:00, 12-Jul-2008 13:00,
       12-Jul-2008 14:00, 12-Jul-2008 15:00, 12-Jul-2008 16:00,
       12-Jul-2008 17:00, 12-Jul-2008 18:00, 12-Jul-2008 19:00,
       12-Jul-2008 20:00, 12-Jul-2008 21:00, 12-Jul-2008 22:00,
       12-Jul-2008 23:00, 13-Jul-2008 00:00, 13-Jul-2008 01:00],
          freq='H')
>>> a.year
array([2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008,
       2008, 2008, 2008, 2008])
>>> a.hour
array([11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,  0,  1])
>>> a.day
array([12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 13, 13])
>>>

Note that the DateArray (or TimeSeries) need not be continuous, I just
constructed a continuous DateArray in this example for simplicity.

I would encourage you to take a look at the wiki
(http://scipy.org/scipy/scikits/wiki/TimeSeries) as you may find some surprises
in there that prove useful.

>> 
>> I often have to read/write data files that have time in various units 
>> like that -- it would be nice to use array operations to work with them.

If peak performance is not a concern, parsing of most date formats can be done
automatically using the built in parser in the timeseries module (borrowed from
mx.DateTime). Observe...

>>> dlist = ['14-jan-2001 14:34:33', '16-jan-2001 10:09:11']
>>> a = ts.date_array(dlist, freq='secondly')
>>> a
DateArray([14-Jan-2001 14:34:33, 16-Jan-2001 10:09:11],
          freq='S')
>>> a.second
array([33, 11])


- Matt