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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Module provides grouping functions that can be used together with pandas to create a few strange timegroupings like e.g. decadal products were there are three products per month with timestamps on the 10th 20th and last of the month
Calculates timegroups for a given daterange. Groups are from day 1-10, 11-20, 21-last day of each month.
Parameters: | df : pandas.DataFrame
bins : list, optional
start : boolean, optional
dtindex : pandas.DatetimeIndex, optional
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Returns: | grouped : pandas.core.groupby.DataFrameGroupBy
dtindex : pandas.DatetimeIndex
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Between a start and end date give all dates that represent a bin See test for example.
Parameters: | start_date: date :
end_date: date :
bins: list, optional :
start: boolean, optional :
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Returns: | tstamps : list of datetimes
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Makes a datetimeindex that has for each entry the timestamp of the bin beginning or end this entry belongs to.
Parameters: | grps : numpy.array
bins : list
dtindex : pandas.DatetimeIndex
start : boolean, optional
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Returns: | grpdt : pd.DatetimeIndex
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