Merge branch 'master' into PADDS_v03

* master: (29 commits)
  Updated the statistics output
  Updated fscabc
  Updated fscabc
  Fix for thouska#217
  Update version number
  Update new style of Rosenbrock example on docs thouska#213
  Add missing keyword, fix for thouska#213
  Update version number
  Add basic test for parallel computing
  Remove altobjfunc from dream example
  Enlarge runs for dream in unittest
  Update Test script for rosenbrock setup thouska#213
  Update new style of rosenbrock_setup
  Fix for thouska#213
  Making status print more informativ
  Remov Gelman Rubin plot from test suite for Python < 3.6
  Remove Hymod support for Python <3.6
  Remove copy functions for py2 support
  Add classmethod statement
  WIP fix Python 2 errors
  ...

# Conflicts:
#	spotpy/__init__.py
#	spotpy/algorithms/_algorithm.py

docs/Tutorial/2-Rosenbrock.md

@@ -84,45 +84,68 @@ We start directly with all algorithms. First we have to create a new file:
 Now we create samplers for every algorithm and sample 5,000 parameter combinations for every algorithm:
 
 	results=[]
-	spotpy_setup=spotpy_setup()
-	rep=5000
+	spot_setup=spot_setup()
+	rep=1000
+	timeout=10 #Given in Seconds
 
-	sampler=spotpy.algorithms.mc(spotpy_setup,    dbname='RosenMC',    dbformat='csv')
-	results.append(sampler.sample(rep))
+	parallel = "seq"
+	dbformat = "csv"
 
-	sampler=spotpy.algorithms.lhs(spotpy_setup,   dbname='RosenLHS',   dbformat='csv')
-	results.append(sampler.sample(rep))
+	sampler=spotpy.algorithms.mc(spot_setup,parallel=parallel, dbname='RosenMC', dbformat=dbformat, sim_timeout=timeout)
+	print(describe(sampler))
+	sampler.sample(rep)
+	results.append(sampler.getdata())
+
+	sampler=spotpy.algorithms.lhs(spot_setup,parallel=parallel, dbname='RosenLHS', dbformat=dbformat, sim_timeout=timeout)
+	sampler.sample(rep)
+	results.append(sampler.getdata())
 
-	sampler=spotpy.algorithms.mle(spotpy_setup,   dbname='RosenMLE',   dbformat='csv')
-	results.append(sampler.sample(rep))
+	sampler=spotpy.algorithms.mle(spot_setup, parallel=parallel, dbname='RosenMLE', dbformat=dbformat, sim_timeout=timeout)
+	sampler.sample(rep)
+	results.append(sampler.getdata())
 
-	sampler=spotpy.algorithms.mcmc(spotpy_setup,  dbname='RosenMCMC',  dbformat='csv')
-	results.append(sampler.sample(rep))
+	sampler=spotpy.algorithms.mcmc(spot_setup, parallel=parallel, dbname='RosenMCMC', dbformat=dbformat, sim_timeout=timeout)
+	sampler.sample(rep)
+	results.append(sampler.getdata())
 
-	sampler=spotpy.algorithms.sceua(spotpy_setup, dbname='RosenSCEUA', dbformat='csv')
-	results.append(sampler.sample(rep,ngs=4))
+	sampler=spotpy.algorithms.sceua(spot_setup, parallel=parallel, dbname='RosenSCEUA', dbformat=dbformat, sim_timeout=timeout)
+	sampler.sample(rep,ngs=4)
+	results.append(sampler.getdata())
 
-	sampler=spotpy.algorithms.sa(spotpy_setup,    dbname='RosenSA',    dbformat='csv')
-	results.append(sampler.sample(rep))
+	sampler=spotpy.algorithms.sa(spot_setup, parallel=parallel, dbname='RosenSA', dbformat=dbformat, sim_timeout=timeout)
+	sampler.sample(rep)
+	results.append(sampler.getdata())
 
-	sampler=spotpy.algorithms.demcz(spotpy_setup, dbname='RosenDEMCz', dbformat='csv')
-	results.append(sampler.sample(rep,nChains=4))
+	sampler=spotpy.algorithms.demcz(spot_setup,parallel=parallel, dbname='RosenDEMCz', dbformat=dbformat, sim_timeout=timeout)
+	sampler.sample(rep,nChains=4)
+	results.append(sampler.getdata())
 
-	sampler=spotpy.algorithms.rope(spotpy_setup,  dbname='RosenROPE',  dbformat='csv')
-	results.append(sampler.sample(rep))
-
-	sampler=spotpy.algorithms.abc(spotpy_setup, dbname='RosenDEMCz', dbformat='csv')
-	results.append(sampler.sample(rep))
+	sampler=spotpy.algorithms.rope(spot_setup, parallel=parallel, dbname='RosenROPE',  dbformat=dbformat,sim_timeout=timeout)
+	sampler.sample(rep)
+	results.append(sampler.getdata())
 
-	sampler=spotpy.algorithms.fscabd(spotpy_setup,  dbname='RosenROPE',  dbformat='csv')
-	results.append(sampler.sample(rep))
+	sampler=spotpy.algorithms.abc(spot_setup, parallel=parallel,   dbname='RosenABC',     dbformat=dbformat,sim_timeout=timeout)
+	sampler.sample(rep)
+	results.append(sampler.getdata())
+
+	sampler=spotpy.algorithms.fscabc(spot_setup, parallel=parallel, dbname='RosenFSABC', dbformat=dbformat, sim_timeout=timeout)
+	sampler.sample(rep)
+	results.append(sampler.getdata())
+
+	sampler=spotpy.algorithms.demcz(spot_setup, parallel=parallel, dbname='RosenDEMCZ', dbformat=dbformat, sim_timeout=timeout)
+	sampler.sample(rep)
+	results.append(sampler.getdata())
+
+	sampler=spotpy.algorithms.dream(spot_setup, parallel=parallel, dbname='RosenDREAM', dbformat=dbformat, sim_timeout=timeout)
+	sampler.sample(rep)
+	results.append(sampler.getdata())
 
 ## Plotting
 
 To see the all results of your sampling, just type something like this:
 
-	algorithms=['MC','LHS','MLE','MCMC','SCEUA','SA','DEMCz','ROPE','ABC','FSCABC']
-	spotpy.analyser.plot_parametertrace_algorithms(results,algorithmnames=algorithms,parameternames=['x','y']) 
+	algorithms = ['mc','lhs','mle','mcmc','sceua','sa','demcz','rope','abc','fscabc', 'demcz', 'dream']
+	spotpy.analyser.plot_parametertrace_algorithms(results, algorithms, spot_setup)
 
 This should give you an nice overview about, how the parameter settings evolve during the sampling of each algorithm: 
 

docs/getting_started.md

@@ -14,7 +14,7 @@ The example comes along with parameter boundaries, the Rosenbrock function, the
 So we can directly start to analyse the Rosenbrock function with one of the algorithms. We start with a simple Monte Carlo sampling:
 
 	# Give Monte Carlo algorithm the example setup and saves results in a RosenMC.csv file
-	sampler = spotpy.algorithms.mc(spotpy_setup(), dbname='RosenMC', dbformat='csv')
+	sampler = spotpy.algorithms.mc(spot_setup(), dbname='RosenMC', dbformat='csv')
 
 Now we can sample with the implemented Monte Carlo algorithm:
 
@@ -35,7 +35,7 @@ We can see that the parameters *x* and *y*, which drive the the Rosenbrock funct
 
 If you want to see the best 10% of your samples, which is called posterior parameter distribution, you have to do something like this:
 
-	posterior=spotpy.analyser.get_posterior(results,percentage=10)
+	posterior=spotpy.analyser.get_posterior(results, percentage=10)
 	spotpy.analyser.plot_parameterInteraction(posterior) 
 
 This should give you a parameter interaction plot of your best 10% samples, which should look like Fig. 2:

setup.py

@@ -5,7 +5,7 @@
 
 setup(
   name = 'spotpy',
-  version = '1.4.6',
+  version = '1.5.1',
   description = 'A Statistical Parameter Optimization Tool',
   long_description=open(os.path.join(os.path.dirname(__file__),
                                        "README.rst")).read(),
@@ -22,7 +22,7 @@
    },
   #include_package_data = True,
   use_2to3 = True,
-  keywords = 'Monte Carlo, MCMC, MLE, SCE-UA, Simulated Annealing, DE-MCz, DREAM, ROPE, Artifical Bee Colony, Uncertainty, Calibration, Model, Signatures',
+  keywords = 'Monte Carlo, MCMC, MLE, SCE-UA, Simulated Annealing, DE-MCz, DREAM, ROPE, Artifical Bee Colony, DDS, Uncertainty, Calibration, Model, Signatures',
   classifiers = [
         'Development Status :: 4 - Beta',
         'Intended Audience :: Developers',

spotpy/__init__.py

@@ -39,4 +39,4 @@
 from . import describe            # Contains some helper functions to describe smaplers and setups
 from .hydrology import signatures # Quantifies goodness of fit between simulation and evaluation data with hydrological signatures
 from . import unittests
-__version__ = '1.4.6'
+__version__ = '1.5.1'