Created on June 20, 2013
@author: Alexander Gruber Alexander.Gruber@geo.tuwien.ac.at
Calculates the anomaly of a time series (Pandas series). Both, climatology based, or moving-average based anomalies can be calculated
Parameters: | Ser : pandas.Series (index must be a DateTimeIndex) window_size : float, optional
climatology : pandas.Series (index: 1-366), optional
timespann : [timespan_from, timespan_to], datetime.datetime(y,m,d), optional
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Returns: | anomaly : pandas.Series
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Calculates the climatology of a data set
Parameters: | Ser : pandas.Series (index must be a DateTimeIndex or julian date) moving_avg_orig : float, optional
moving_avg_clim : float, optional
median : boolean, optional
timespan : [timespan_from, timespan_to], datetime.datetime(y,m,d), optional
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Returns: | climatology : pandas.Series
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Created on Oct 16, 2013
@author: Christoph Paulik christoph.paulik@geo.tuwien.ac.at
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
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Returns: | Ser : pandas.Series
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Created on Oct 16, 2013
Fast cython functions for calculating various filters
@author: Christoph Paulik christoph.paulik@geo.tuwien.ac.at
Calculates exponentially filtered time series using a boxcar filter - basically a moving average calculation
Parameters: | in_data : double numpy.array
in_jd : double numpy.array
window : int
nan : double
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Calculates exponentially smoothed time series using an iterative algorithm
Parameters: | in_data : double numpy.array
in_jd : double numpy.array
ctime : int
nan : double
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