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) 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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Applies a moving average (box) filter on an input time series
Parameters : | Ser : pandas.Series (index must be a DateTimeIndex) window_size : float, optional
no_date : boolean, optional
sample_to_days : boolean, optional
fast: boolean, optional :
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Returns : | Ser : pandas.Series
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Created on Apr 17, 2013
@author: Christoph Paulik christoph.paulik@geo.tuwien.ac.at @author: Sebastian Hahn sebastian.hahn@geo.tuwien.ac.at @author: Alexander Gruber alexander.gruber@geo.tuwien.ac.at
Redidual sum of squares
Parameters : | x : numpy.array
y : numpy.array
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Returns : | Residual sum of squares : |
Wrapper for scipy.stats.kendalltau
Parameters : | x : numpy.array
y : numpy.array
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Returns : | Kendall’s tau : float
p-value : float
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See also
scipy.stats.kendalltau
Mean square error (MSE) as a decomposition of the RMSD into individual error components
Nash Sutcliffe model efficiency coefficient
Parameters : | x : numpy.array
y : numpy.array
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Returns : | Nash Sutcliffe coefficient : float
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Wrapper for scipy.stats.pearsonr
Parameters : | x : numpy.array
y : numpy.array
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Returns : | Pearson’s r : float
p-value : float
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See also
scipy.stats.pearsonr
Wrapper for scipy.stats.spearmanr
Parameters : | x : numpy.array
y : numpy.array
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Returns : | rho : float
p-value : float
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See also
scipy.stats.spearmenr
Triple collocation error estimate
Parameters : | x : numpy.array
y : numpy.array
z : numpy.array
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Returns : | triple collocation error for x : float triple collocation error for y : float triple collocation error for z : float |
Created on Apr 17, 2013
@author: Christoph Paulik christoph.paulik@geo.tuwien.ac.at
takes a dataframe and appends a scaled time series to it. If no labels are given the first column will be scaled to the second column of the DataFrame
Parameters : | df : pandas.DataFrame
method : string
label_in: string, optional :
label_scale : string, optional
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Returns : | df : pandas.DataFrame
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Parameters : | in_data: numpy.array :
scale_to: numpy.array :
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Returns : | CDF matched values: numpy.array :
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computes cumulative density functions of in_data and scale_to at their respective bin-edges by linear interpolation; then matches CDF of in_data to CDF of scale_to
Parameters : | in_data: numpy.array :
scale_to: numpy.array :
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Returns : | CDF matched values: numpy.array :
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scales the input datasets using linear regression
Parameters : | in_data : numpy.array
scale_to : numpy.array
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Returns : | scaled dataset : numpy.array
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scales the input datasets so that they have the same mean and standard deviation afterwards
Parameters : | in_data : numpy.array
scale_to : numpy.array
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Returns : | scaled dataset : numpy.array
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scales the input datasets so that they have the same minimum and maximum afterwards
Parameters : | in_data : numpy.array
scale_to : numpy.array
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Returns : | scaled dataset : numpy.array
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Created on Apr 12, 2013
Provides a temporal matching function
@author: Sebastian Hahn Sebastian.Hahn@geo.tuwien.ac.at
Finds temporal match between the reference pandas.DataFrame (index has to be datetime) and n other pandas.DataFrame (index has to be datetime).
Parameters : | reference : pandas.DataFrame
*args : pandas.DataFrame
window : float
dropna : boolean
dropduplicates : boolean
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Returns : | temporal_matched_args : pandas.DataFrame or tuple of pandas.DataFrame
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