new file: LICENSE

	new file:   README.md
	new file:   Run_exphydro_distributed_pso.py
	new file:   Run_exphydro_lumped_pso.py
	new file:   SampleData/PET_test.txt
	new file:   SampleData/P_test.txt
	new file:   SampleData/Q_test.txt
	new file:   SampleData/T_test.txt
	new file:   exphydro/__init__.py
	new file:   exphydro/distributed/ExphydroDistrModel.py
	new file:   exphydro/distributed/ExphydroDistrParameters.py
	new file:   exphydro/distributed/__init__.py
	new file:   exphydro/lumped/ExphydroModel.py
	new file:   exphydro/lumped/ExphydroParameters.py
	new file:   exphydro/lumped/__init__.py
	new file:   exphydro/utils/Calibration.py
	new file:   exphydro/utils/ObjectiveFunction.py
	new file:   exphydro/utils/OdeSolver.py
	new file:   exphydro/utils/Parameter.py
	new file:   exphydro/utils/__init__.py
	new file:   setup.py

LICENSE

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+The MIT License (MIT)
+
+Copyright (c) 2010-2016 Sopan Patil and Marc Stieglitz
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+# EXP-HYDRO Hydrological Model
+
+EXP-HYDRO is a catchment scale hydrological model that operates at a daily time-step. It takes as inputs the daily values of precipitation, air temperature, and potential evapotranspiration, and simulates daily streamflow at the catchment outlet. This model was originally developed by Dr Sopan Patil in 2010 as part of his PhD research. Our research group (http://sopanpatil.weebly.com) continues its active development in both spatially lumped and spatially distributed configurations.
+
+The source code provided is written in Python programming language and has been tested using Python 2.7.
+
+The following data from a sample catchment in Wales are provided (in SampleData folder) to test the model code: P_test.txt (Precipitation data), T_test.txt (Air temperature data), PET_test.txt (Potential evapotranspiration data), Q_test.txt (catchment streamflow data).
+
+Following are the execution files for running EXP-HYDRO:
+(1) Run_exphydro_lumped_pso.py: Performs a calibration and validation run of the lumped EXP-HYDRO model using Particle Swarm Optimisation (PSO) algorithm.
+(2) Run_exphydro_distributed_pso.py: Performs a calibration and validation run of the distributed EXP-HYDRO model using PSO algorithm.
+
+System Requirements: Please make sure that the following Python packages are installed on your computer before running any of the above execution files:
+(1) NumPy (http://www.numpy.org/)
+(2) SciPy (http://www.scipy.org/)
+(3) matplotlib (http://matplotlib.org/)
+
+Please cite as: Patil, S. and M. Stieglitz (2014) Modelling daily streamflow at ungauged catchments: What information is necessary?, Hydrological Processes, 28(3), 1159-1169, doi:10.1002/hyp.9660.

Run_exphydro_distributed_pso.py

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+#!/usr/bin/env python
+
+# Programmer(s): Sopan Patil.
+
+""" MAIN PROGRAM FILE
+Run this file to optimise the EXP-HYDRO model parameters
+using Particle Swarm Optimisation (PSO) algorithm.
+"""
+
+import numpy
+import os
+import matplotlib.pyplot as plt
+from exphydro.distributed import ExphydroDistrModel, ExphydroDistrParameters
+from exphydro.utils import Calibration, ObjectiveFunction
+
+######################################################################
+# SET WORKING DIRECTORY
+
+# Getting current directory, i.e., directory containing this file
+dir1 = os.path.dirname(os.path.abspath('__file__'))
+
+# Setting to current directory
+os.chdir(dir1)
+
+######################################################################
+# MAIN PROGRAM
+
+# Load meteorological and observed flow data
+P = numpy.genfromtxt('SampleData/P_test.txt')  # Observed rainfall (mm/day)
+T = numpy.genfromtxt('SampleData/T_test.txt')  # Observed air temperature (deg C)
+PET = numpy.genfromtxt('SampleData/PET_test.txt')  # Potential evapotranspiration (mm/day)
+Qobs = numpy.genfromtxt('SampleData/Q_test.txt')  # Observed streamflow (mm/day)
+
+# Specify the no. of parameter sets (particles) in a PSO swarm
+npart = 10
+
+# Specify the number of pixels in the catchment
+npixels = 5
+
+# Generate 'npart' initial EXP-HYDRO model parameters
+params = [ExphydroDistrParameters(npixels) for j in range(npart)]
+
+# Initialise the model by loading its climate inputs
+model = ExphydroDistrModel(P, PET, T, npixels)
+
+# Specify the start and end day numbers of the calibration period.
+# This is done separately for the observed and simulated data
+# because they might not be of the same length in some cases.
+calperiods_obs = [365, 2557]
+calperiods_sim = [365, 2557]
+
+# Calibrate the model to identify optimal parameter set
+paramsmax = Calibration.pso_maximise(model, params, Qobs, ObjectiveFunction.klinggupta, calperiods_obs, calperiods_sim)
+print 'Calibration run KGE value = ', paramsmax.objval
+
+# Run the optimised model for validation period
+Qsim = model.simulate(paramsmax)
+kge = ObjectiveFunction.klinggupta(Qobs[calperiods_obs[1]:], Qsim[calperiods_sim[1]:])
+print 'Independent run KGE value = ', kge
+
+# Plot the observed and simulated hydrographs
+plt.plot(Qobs[calperiods_obs[0]:],'b-')
+plt.hold(True)
+plt.plot(Qsim[calperiods_sim[0]:],'r-')
+plt.show()
+
+######################################################################

Run_exphydro_lumped_pso.py

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+#!/usr/bin/env python
+
+# Programmer(s): Sopan Patil.
+
+""" MAIN PROGRAM FILE
+Run this file to optimise the EXP-HYDRO model parameters
+using Particle Swarm Optimisation (PSO) algorithm.
+"""
+
+import numpy
+import os
+import matplotlib.pyplot as plt
+from exphydro.lumped import ExphydroModel, ExphydroParameters
+from exphydro.utils import Calibration, ObjectiveFunction
+
+######################################################################
+# SET WORKING DIRECTORY
+
+# Getting current directory, i.e., directory containing this file
+dir1 = os.path.dirname(os.path.abspath('__file__'))
+
+# Setting to current directory
+os.chdir(dir1)
+
+######################################################################
+# MAIN PROGRAM
+
+# Load meteorological and observed flow data
+P = numpy.genfromtxt('SampleData/P_test.txt')  # Observed rainfall (mm/day)
+T = numpy.genfromtxt('SampleData/T_test.txt')  # Observed air temperature (deg C)
+PET = numpy.genfromtxt('SampleData/PET_test.txt')  # Potential evapotranspiration (mm/day)
+Qobs = numpy.genfromtxt('SampleData/Q_test.txt')  # Observed streamflow (mm/day)
+
+# Specify the no. of parameter sets (particles) in a PSO swarm
+npart = 10
+
+# Generate 'npart' initial EXP-HYDRO model parameters
+params = [ExphydroParameters() for j in range(npart)]
+
+# Initialise the model by loading its climate inputs
+model = ExphydroModel(P, PET, T)
+
+# Specify the start and end day numbers of the calibration period.
+# This is done separately for the observed and simulated data
+# because they might not be of the same length in some cases.
+calperiods_obs = [365, 2557]
+calperiods_sim = [365, 2557]
+
+# Calibrate the model to identify optimal parameter set
+paramsmax = Calibration.pso_maximise(model, params, Qobs, ObjectiveFunction.klinggupta, calperiods_obs, calperiods_sim)
+print 'Calibration run KGE value = ', paramsmax.objval
+
+# Run the optimised model for validation period
+Qsim = model.simulate(paramsmax)
+kge = ObjectiveFunction.klinggupta(Qobs[calperiods_obs[1]:], Qsim[calperiods_sim[1]:])
+print 'Independent run KGE value = ', kge
+
+# Plot the observed and simulated hydrographs
+plt.plot(Qobs[calperiods_obs[0]:],'b-')
+plt.hold(True)
+plt.plot(Qsim[calperiods_sim[0]:],'r-')
+plt.show()
+
+######################################################################