Unify use of spot_setup hymod example

docs/Hydrological_model.md

@@ -142,8 +142,9 @@ Keep in mind, that the \__init\__ function is only called once during the sampli
 The more you can separate from you model into the \__init\__ function, the faster you sampling will be.
 
 	class spotpy_setup(object):
-		def __init__(self, used_algorithm = 'default'):
-			self._used_algorithm = _used_algorithm
+		def __init__(self, obj_func=None):
+			#Just a way to keep this example flexible and applicable to various examples
+			self.obj_func = obj_func 
 			datastart     = datetime(1998,6,1)
 			dataend       = datetime(2000,1,1)
 			analysestart  = datetime(1999,1,1)
@@ -172,11 +173,18 @@ To define the VanGenuchten parameter boundaries we use a normal distribution.
 				return self.cmfmodel.observations
 
 		def objectivefunction(self,simulation,evaluation):
-			if self._used_algorithm == 'sceua': # The SCE-UA algorithm minimizes the objectivefunction
-				like = spotpy.objectivefunctions.rmse(evaluation,simulation)
-			else: # All other implemented algorithm maximize the objectivefunction
-				objectivefunction= -spotpy.objectivefunctions.rmse(evaluation,simulation)
-			return objectivefunction
+		        #SPOTPY expects to get one or multiple values back, 
+				#that define the performence of the model run
+				if not self.obj_func:
+					# This is used if not overwritten by user
+					# RMSE (root mean squared error) works good for the SCE-UA algorithm, 
+					# as it minimizes the objective function.
+					# All other implemented algorithm maximize the objectivefunction
+					model_performance = spotpy.objectivefunctions.rmse(evaluation,simulation)
+				else:
+					#Way to ensure flexible spot setup class
+					model_performance = self.obj_func(evaluation,simulation) 
+			return model_performance
 
 
 ## Sampling

spotpy/examples/gui_hymod.py

@@ -10,9 +10,10 @@
 import spotpy
 from spotpy.gui.mpl import GUI
 from spotpy.examples.spot_setup_hymod_python import spot_setup
+from  spotpy.objectivefunctions import rmse 
 
 if __name__ == '__main__':
-    setup_class=spot_setup(_used_algorithm='sceua')
+    setup_class=spot_setup(rmse)
 
     #Select number of maximum allowed repetitions
     rep=10000

spotpy/examples/spot_setup_hymod_python.py

@@ -73,6 +73,6 @@ def objectivefunction(self,simulation,evaluation, params=None):
             # This is used if not overwritten by user
             like = rmse(evaluation,simulation)
         else:
-            #Way to ensure on flexible spot setup class
+            #Way to ensure flexible spot setup class
             like = self.obj_func(evaluation,simulation)    
         return like

spotpy/examples/spot_setup_hymod_python_pareto.py

@@ -24,8 +24,7 @@ class spot_setup(object):
     Ks    = spotpy.parameter.Uniform(low=0.0 , high=0.10, optguess=0.0404)
     Kq    = spotpy.parameter.Uniform(low=0.1 , high=0.99, optguess=0.5592)
 
-    def __init__(self, _used_algorithm = 'default'):
-        self._used_algorithm = _used_algorithm  
+    def __init__(self):
         #Transform [mm/day] into [l s-1], where 1.783 is the catchment area
         self.Factor = 1.783 * 1000 * 1000 / (60 * 60 * 24) 
         #Load Observation data from file

spotpy/examples/tutorial_dream_hymod.py

@@ -13,20 +13,20 @@
 #from spotpy.examples.spot_setup_hymod_exe import spot_setup
 from spotpy.examples.spot_setup_hymod_python import spot_setup
 import matplotlib.pyplot as plt
-
+from  spotpy.likelihoods import gaussianLikelihoodMeasErrorOut as GausianLike
 
 if __name__ == "__main__":
     parallel ='seq'
     # Initialize the Hymod example (will only work on Windows systems)
     #spot_setup=spot_setup(parallel=parallel)
-    spot_setup=spot_setup(obj_func = spotpy.likelihoods.gaussianLikelihoodMeasErrorOut)
+    spot_setup=spot_setup(GausianLike)
 
     # Create the Dream sampler of spotpy, al_objfun is set to None to force SPOTPY
     # to jump into the def objectivefunction in the spot_setup class (default is
     # spotpy.objectivefunctions.log_p) 
 
     #Select number of maximum repetitions
-    rep=10000
+    rep=5000
 
     # Select five chains and set the Gelman-Rubin convergence limit
     nChains                = 4

tests/test_algorithms.py

@@ -17,6 +17,8 @@
 from spotpy.examples.tutorial_padds import padds_spot_setup
 from spotpy.describe import describe
 import os
+from  spotpy.likelihoods import gaussianLikelihoodMeasErrorOut as GausianLike
+
 
 #https://docs.python.org/3/library/unittest.html
 
@@ -50,37 +52,37 @@ def test_mle(self):
         self.assertEqual(len(results), self.rep)
 
     def test_mcmc(self):
-        sampler=spotpy.algorithms.mcmc(spot_setup(used_algorithm='mcmc'),parallel=self.parallel, dbname='Rosen', dbformat=self.dbformat, sim_timeout=self.timeout)
+        sampler=spotpy.algorithms.mcmc(spot_setup(GausianLike),parallel=self.parallel, dbname='Rosen', dbformat=self.dbformat, sim_timeout=self.timeout)
         sampler.sample(self.rep)
         results = sampler.getdata()
         self.assertEqual(len(results), self.rep)
 
     def test_demcz(self):
-        sampler=spotpy.algorithms.demcz(spot_setup(used_algorithm='demcz'),parallel=self.parallel, dbname='Rosen', dbformat=self.dbformat, sim_timeout=self.timeout)
+        sampler=spotpy.algorithms.demcz(spot_setup(GausianLike),parallel=self.parallel, dbname='Rosen', dbformat=self.dbformat, sim_timeout=self.timeout)
         sampler.sample(self.rep, convergenceCriteria=0)
         results = sampler.getdata()
         self.assertEqual(len(results), self.rep)
 
     def test_dream(self):
-        sampler=spotpy.algorithms.dream(spot_setup(used_algorithm='dream'),parallel=self.parallel, dbname='Rosen', dbformat=self.dbformat, sim_timeout=self.timeout)
+        sampler=spotpy.algorithms.dream(spot_setup(GausianLike),parallel=self.parallel, dbname='Rosen', dbformat=self.dbformat, sim_timeout=self.timeout)
         sampler.sample(self.rep, convergence_limit=0.9, runs_after_convergence=500)
         results = sampler.getdata()
         self.assertEqual(len(results), self.rep)
 
     def test_sceua(self):
-        sampler=spotpy.algorithms.sceua(spot_setup(used_algorithm='sceua'),parallel=self.parallel, dbname='Rosen', dbformat=self.dbformat, sim_timeout=self.timeout)
+        sampler=spotpy.algorithms.sceua(spot_setup(),parallel=self.parallel, dbname='Rosen', dbformat=self.dbformat, sim_timeout=self.timeout)
         sampler.sample(self.rep)
         results = sampler.getdata()
         self.assertLessEqual(len(results), self.rep) #Sceua save per definition not all sampled runs
 
     def test_abc(self):
-        sampler=spotpy.algorithms.abc(spot_setup(used_algorithm='abc'),parallel=self.parallel, dbname='Rosen', dbformat=self.dbformat, sim_timeout=self.timeout)
+        sampler=spotpy.algorithms.abc(spot_setup(),parallel=self.parallel, dbname='Rosen', dbformat=self.dbformat, sim_timeout=self.timeout)
         sampler.sample(self.rep)
         results = sampler.getdata()
         self.assertEqual(len(results), self.rep)
 
     def test_fscabc(self):
-        sampler=spotpy.algorithms.fscabc(spot_setup(used_algorithm='fscabc'),parallel=self.parallel, dbname='Rosen', dbformat=self.dbformat, sim_timeout=self.timeout)
+        sampler=spotpy.algorithms.fscabc(spot_setup(),parallel=self.parallel, dbname='Rosen', dbformat=self.dbformat, sim_timeout=self.timeout)
         sampler.sample(self.rep)
         results = sampler.getdata()
         self.assertEqual(len(results), self.rep)

tests/test_analyser.py

@@ -23,7 +23,7 @@
 from spotpy.examples.spot_setup_rosenbrock import spot_setup as rosenbrock_setup
 from spotpy.examples.spot_setup_griewank import spot_setup as griewank_setup
 from spotpy.examples.spot_setup_hymod_python import spot_setup as hymod_setup
-
+from  spotpy.likelihoods import gaussianLikelihoodMeasErrorOut as GausianLike
 
 class TestAnalyser(unittest.TestCase):
     @classmethod
@@ -51,7 +51,7 @@ def setUpClass(self):
             sampler.sample(self.rep)
             self.sens_results = sampler.getdata()
             #Hymod resuts are empty with Python <3.6
-            sampler = spotpy.algorithms.dream(hymod_setup(_used_algorithm='dream'), 
+            sampler = spotpy.algorithms.dream(hymod_setup(GausianLike), 
                                                sim_timeout=self.timeout)
             self.r_hat = sampler.sample(self.rep)
             self.hymod_results = sampler.getdata()