# Copyright (c) 2013,Vienna University of Technology, Department of Geodesy and Geoinformation
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'''
Created on Oct 16, 2013
@author: Christoph Paulik christoph.paulik@geo.tuwien.ac.at
'''
import pandas as pd
import numpy as np
from cadati.jd_date import julday
from pytesmo.time_series.filters import boxcar_filter
[docs]def moving_average(Ser,
window_size=1,
fillna=False,
min_obs=1):
'''
Applies a moving average (box) filter on an input time series
Parameters
----------
Ser : pandas.Series (index must be a DateTimeIndex or julian date)
window_size : float, optional
The size of the moving_average window [days] that will be applied on the
input Series
Default: 1
fillna: bool, optional
Fill nan values at the center window value
min_obs: int
The minimum amount of observations necessary for a valid moving average
Returns
-------
Ser : pandas.Series
moving-average filtered time series
'''
# if index is datetimeindex then convert it to julian date
if type(Ser.index) == pd.DatetimeIndex:
jd_index = julday(np.asarray(Ser.index.month),
np.asarray(Ser.index.day),
np.asarray(Ser.index.year),
np.asarray(Ser.index.hour),
np.asarray(Ser.index.minute),
np.asarray(Ser.index.second))
else:
jd_index = Ser.index.values
filtered = boxcar_filter(
np.atleast_1d(np.squeeze(Ser.values.astype(np.double))),
jd_index.astype(np.double),
window=window_size, fillna=fillna, min_obs=min_obs)
result = pd.Series(filtered, index=Ser.index)
return result