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wradlib Documentation, Release 0.1.1
3.16 Utility functions
Module util provides a set of useful helpers which are currently not attributable to the other modules
aggregate_in_time
from_to
Aggregate time series data to a coarser temporal resolution.
Return a list of timesteps from to of length
3.16.1 wradlib.util.aggregate_in_time
wradlib.util.aggregate_in_time(src, dt_src, dt_trg, taxis=0, func=’sum’)
Aggregate time series data to a coarser temporal resolution.
Parameters src : array of shape (..., original number of time steps,...)
This is the time series data which should be aggregated. The position of the time dimension
is indicated by the taxis argument. The number of time steps corresponds to
the length of the time dimension.
taxis : integer
This is the position of the time dimension in array src.
dt_src : array of datetime objects
Must be of length original number of time steps + 1 because dt_src defines the limits of
the intervals corresponding to the time steps. This means: dt_src[0] is the lower limit of
time step 1, dt_src[1] is the upper limit of time step 1 and the lower limit of time step 2
and so on.
dt_trg : array of datetime objects
Must be of length number of output time steps + 1 analogously to dt_src. This means:
dt_trg[0] is the lower limit of output time step 1, dt_trg[1] is the upper limit of output
time step 1 and the lower limit of output time step 2 and so on.
func : numpy function name, e.g. ‘sum’, ‘mean’
Defines the way the data should be aggregated. The string must correspond to a valid
numpy function, e.g. ‘sum’, ‘mean’, ‘min’, ‘max’.
Returns output : array of shape (..., len(dt_trg) - 1, ...)
The length of the time dimension of the output array depends on the array dt_trg which
defines the limits of the output time step intervals.
Examples
>>> src = np.arange(8*4).reshape( (8,4) )
>>> print ’source time series:’
>>> print src
>>> dt_src = [dt.datetime.strptime(’2008-06-02’, ’%Y-%m-%d’ ) + dt.timedelta(hours=i) for i in r
>>> print ’source time interval limits:’
>>> for tim in dt_src: print tim
>>> print ’target time interval limits:’
>>> dt_trg = [dt.datetime.strptime(’2008-06-02’, ’%Y-%m-%d’ ) + dt.timedelta(seconds=i*3600*4) f
>>> for tim in dt_trg: print tim
>>> print ’target time series’
>>> print aggregate_in_time(src, dt_src, dt_trg, axis=0, func=’sum’)
90 Chapter 3. Library Reference