Converting irregularly time stamped measurements into equally spaced, time-weighted averages

Question

I have series of measurements which are time stamped and irregularly spaced. Values in these series always represent changes of the measurement -- i.e. without a change no n

Answer 1

You can do this with traces.

from datetime import datetime
import traces

ts = traces.TimeSeries(data=[
    (datetime(2016, 9, 27, 23, 0, 0, 100000), 10),
    (datetime(2016, 9, 27, 23, 0, 1, 200000), 8),
    (datetime(2016, 9, 27, 23, 0, 1, 600000), 0),
    (datetime(2016, 9, 27, 23, 0, 6, 300000), 4),
])

regularized = ts.moving_average(
    start=datetime(2016, 9, 27, 23, 0, 1),
    sampling_period=1,
    placement='left',
)

Which results in :

[(datetime(2016, 9, 27, 23, 0, 1), 5.2),
 (datetime(2016, 9, 27, 23, 0, 2), 0.0),
 (datetime(2016, 9, 27, 23, 0, 3), 0.0),
 (datetime(2016, 9, 27, 23, 0, 4), 0.0),
 (datetime(2016, 9, 27, 23, 0, 5), 0.0),
 (datetime(2016, 9, 27, 23, 0, 6), 2.8)]