rhessyscalibrator.postprocess_behavioral Namespace Reference

Tool for analyzing model run results generated by rhessys_calibrator_behavioral.py and stored in a database format managed by rhessyscalibrator.model_runner_db.py. More...

Classes
class	BehavioralComparison
class	BehavioralTimeseriesOut
class	RHESSysCalibratorPostprocessBehavioral
	Main driver class for rhessys_calibrator_postprocess_behavioral tool. More...

Functions
def	exceedance_prob (y)
def	to_percent (y, position)
def	calculateUncertaintyBounds (ysim, likelihood, lowerBound, upperBound)
	Calculate uncertainty bounds for matrix of simulated values. More...
def	calculateWeightedEnsembleMean (ysim, likelihood)
	Calculate weighted ensemble mean (Seibert and Beven 2009). More...

Detailed Description

Tool for analyzing model run results generated by rhessys_calibrator_behavioral.py and stored in a database format managed by rhessyscalibrator.model_runner_db.py.

Currently plots 95% uncertainty bounds around observed streamflow using NSE as a likelihood function.

This software is provided free of charge under the New BSD License. Please see the following license information:

Copyright (c) 2013, University of North Carolina at Chapel Hill All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

• Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
• Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
• Neither the name of the University of North Carolina at Chapel Hill nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

Author

Brian Miles brian.nosp@m._mil.nosp@m.es@un.nosp@m.c.ed.nosp@m.u

Function Documentation

def rhessyscalibrator.postprocess_behavioral.calculateUncertaintyBounds	(		ysim,
			likelihood,
			lowerBound,
			upperBound
	)

Calculate uncertainty bounds for matrix of simulated values.

Parameters

ysim	Numpy array containing a vector of data for a number of simuations, dimensions [NUM_SIMULATIONS, NUM_DATA_PER_SIMULATION]
likelihood	Numpy array containing model fitness parameter for each simulation, dimensions [NUM_SIMULATIONS]
lowerBound	Double representing percentile of lower bound of confidence interval (e.g. 2.5)
upperBound	Double representing percentile of upper bound of confidence interval (e.g. 97.5)

Returns

Tuple of numpy arrays representing minYsim, maxYsim, and medianYsim, dimensions [NUM_DATA_PER_SIMULATION]

def rhessyscalibrator.postprocess_behavioral.calculateWeightedEnsembleMean	(		ysim,
			likelihood
	)

Calculate weighted ensemble mean (Seibert and Beven 2009).

For each time step, weights are 0.02 for the "best" model run, 0.0 for the "worst" (based on likelihood) for each.

Parameters

ysim	Numpy array containing a vector of data for a number of simuations, dimensions [NUM_SIMULATIONS, NUM_DATA_PER_SIMULATION]
likelihood	Numpy array containing model fitness parameter for each simulation, dimensions [NUM_SIMULATIONS]

Returns

Numpy array representing weighted ensemble mean.

def rhessyscalibrator.postprocess_behavioral.exceedance_prob

(

y

)

def rhessyscalibrator.postprocess_behavioral.to_percent	(		y,
			position
	)