support for python 3.5 and 3.6

.gitignore

@@ -12,6 +12,7 @@ __pycache__/
 *.yml
 *.csv
 *.out
+*.in
 docs/*
 
 # Distribution / packaging
@@ -101,4 +102,4 @@ ENV/
 # LiClipse project settings
 .pydevproject
 .project
-.settings/
+.settings/

spotpy/algorithms/fast.py

@@ -215,7 +215,7 @@ def sample(self, repetitions):
         #param_generator = iter(Matrix)
         firstcall = True
         param_generator = (
-            (rep, Matrix[rep]) for rep in xrange(len(Matrix)))
+            (rep, Matrix[rep]) for rep in range(len(Matrix)))
         for rep, randompar, simulations in self.repeat(param_generator):
             # Calculate the objective function
             like = self.objectivefunction(

spotpy/algorithms/lhs.py

@@ -97,7 +97,7 @@ def sample(self, repetitions):
         # param_generator = iter(matrx)
         firstcall = True
         param_generator = ((rep, matrx[rep])
-                           for rep in xrange(int(repetitions) - 1))
+                           for rep in range(int(repetitions) - 1))
         for rep, randompar, simulations in self.repeat(param_generator):
             # Calculate the objective function
             like = self.objectivefunction(

spotpy/algorithms/mcmc.py

@@ -107,7 +107,7 @@ def sample(self, repetitions,nChains=1):
 
         print('Inititalize ',self.nChains, ' chain(s)...')
         self.iter=0
-        param_generator = ((curChain,self.parameter()['random']) for curChain in xrange(int(self.nChains)))                
+        param_generator = ((curChain,self.parameter()['random']) for curChain in range(int(self.nChains)))                
         for curChain,par,sim in self.repeat(param_generator):
 
             like = self.objectivefunction(
@@ -132,7 +132,7 @@ def sample(self, repetitions,nChains=1):
         print('Beginn of Random Walk')
         #Walf through chains
         while self.iter <= self.repetitions - self.burnIn:
-            param_generator = ((curChain,self.get_new_proposal_vector(self.bestpar[curChain])) for curChain in xrange(int(self.nChains)))                
+            param_generator = ((curChain,self.get_new_proposal_vector(self.bestpar[curChain])) for curChain in range(int(self.nChains)))                
             for cChain,par,sim in self.repeat(param_generator):
 
                 like = self.objectivefunction(

spotpy/algorithms/sceua.py

@@ -109,7 +109,7 @@ def simulate(self, id_params_tuple):
             likes = []
             sims = []
             pars = []
-            for loop in xrange(self.nspl):
+            for loop in range(self.nspl):
                 # Select simplex by sampling the complex according to a linear
                 # probability distribution
                 lcs = np.array([0] * self.nps)
@@ -202,7 +202,7 @@ def sample(self, repetitions, ngs=20, kstop=100, pcento=0.0000001, peps=0.000000
 
         # Burn in
         firstcall = True
-        param_generator = ((rep, x[rep]) for rep in xrange(int(npt)))
+        param_generator = ((rep, x[rep]) for rep in range(int(npt)))
         for rep, randompar, simulations in self.repeat(param_generator):
             # Calculate the objective function
             like = self.objectivefunction(
@@ -279,7 +279,7 @@ def sample(self, repetitions, ngs=20, kstop=100, pcento=0.0000001, peps=0.000000
             sce_vars = [self.npg, self.nopt, self.ngs, self.nspl,
                         self.nps, self.bl, self.bu, self.status]
             param_generator = ((rep, x, xf, icall, cx, cf, sce_vars)
-                               for rep in xrange(int(self.ngs)))
+                               for rep in range(int(self.ngs)))
             for igs, likes, pars, sims, cx, cf, k1, k2 in self.repeat(param_generator):
                 icall += len(likes)
                 x[k2, :] = cx[k1, :]

spotpy/database.py

@@ -89,7 +89,7 @@ def _make_header(self, parnames):
         self.header.append('chain')
 
     def _tuple_2_xrange(self, t):
-        return (xrange(1, x + 1) for x in t)
+        return (range(1, x + 1) for x in t)
 
 
 class ram(database):

spotpy/examples/3dplot.py

@@ -29,8 +29,8 @@
 from spotpy.examples.spot_setup_ackley import spot_setup
 
 Z = np.zeros(X.shape)
-for i in xrange(X.shape[0]):
-    for j in xrange(X.shape[1]):
+for i in range(X.shape[0]):
+    for j in range(X.shape[1]):
         sim=spot_setup().simulation([X[i,j],Y[i,j]])
         like=spotpy.objectivefunctions.rmse(sim,[0])
         Z[i,j] = like

spotpy/examples/tutorial_Parameterlist_iterator.py

@@ -25,7 +25,7 @@ def parameters(self):
 
     def simulation(self,vector):
         x=np.array(vector)
-        for i in xrange(self.slow):
+        for i in range(self.slow):
             s = np.sin(i)
         simulations= [sum(100.0*(x[1:] - x[:-1]**2.0)**2.0 + (1 - x[:-1])**2.0)]
         return simulations

spotpy/examples/tutorial_ownDatabase.py

@@ -27,7 +27,7 @@ def parameters(self):
 
     def simulation(self,vector):
         x=np.array(vector)
-        for i in xrange(self.slow):
+        for i in range(self.slow):
             s = np.sin(i)
         simulations= [sum(100.0*(x[1:] - x[:-1]**2.0)**2.0 + (1 - x[:-1])**2.0)]
         return simulations