Module contains wrappers for methods in pytesmo.metrics which can be given pandas.DataFrames instead of single numpy.arrays . If the DataFrame has more columns than the function has input parameters the function will be applied pairwise
Created on Aug 14, 2013
@author: Christoph Paulik Christoph.Paulik@geo.tuwien.ac.at
Redidual sum of squares
Returns: | result : namedtuple
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See also
Bias
Returns: | bias : pandas.Dataframe
See Also : ——– : pytesmo.metrics.bias : |
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Wrapper for scipy.stats.kendalltau
Returns: | result : namedtuple
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See also
pytesmo.metrics.kendalltau, scipy.stats.kendalltau
Mean square error (MSE) as a decomposition of the RMSD into individual error components
Returns: | result : namedtuple
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See also
Nash Sutcliffe model efficiency coefficient
Returns: | result : namedtuple
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See also
Normalized root-mean-square deviation
Returns: | result : namedtuple
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See also
Compute given method pairwise for all columns, excluding NA/null values
Parameters: | df : pandas.DataFrame
method : function
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Returns: | results : pandas.DataFrame |
Wrapper for scipy.stats.pearsonr
Returns: | result : namedtuple
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See also
pytesmo.metrics.pearsonr, scipy.stats.pearsonr
Root-mean-square deviation
Returns: | result : namedtuple
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See also
Wrapper for scipy.stats.spearmanr
Returns: | result : namedtuple
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See also
pytesmo.metrics.spearmenr, scipy.stats.spearmenr
Triple collocation error estimate In this case df has to have exactly 3 columns, since triple wise application of a function is not yet implemented and would probably return a complicated structure
Returns: | result : namedtuple
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See also
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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takes pandas.DataFrame and scales all columns to the column specified by reference_index with the chosen method
Parameters: | df : pandas.DataFrame
method : string, optional
reference_index : int, optional
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Returns: | scaled data : pandas.DataFrame
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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 or pandas.TimeSeries
*args : pandas.DataFrame or pandas.TimeSeries
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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Finds temporal match between the reference pandas.TimeSeries (index has to be datetime) and n other pandas.TimeSeries (index has to be datetime).
Parameters: | reference : pandas.TimeSeries
*args : pandas.TimeSeries
window : float
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Returns: | temporal_match : pandas.DataFrame
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