Change of resolution for figures in docs

docs/Advanced_hints.md

@@ -1,4 +1,5 @@
 <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS_HTML"></script>
+
 # Advanced settings
 
 This chapter will show you, how to get the full power out of SPOTPY:

docs/Bayesian_uncertainty_analysis_with_DREAM.md

@@ -92,15 +92,15 @@ If you want to see the remaining posterior model uncertainty:
     ax.legend()
     fig.savefig('python_hymod.png',dpi=300)
 
-![Posterior model uncertainty](../img/python_hymod_simulation.png)
+![Posterior simulation uncertainty](../img/DREAM_simulation_uncertainty_Hymod.png)
 *Figure 1: Posterior model uncertainty of HYMOD.*
 
 ## Plot convergence diagnostic
 If you want to check the convergence of the DREAM algorithm:
 
     spotpy.analyser.plot_gelman_rubin(results, r_hat, fig_name='python_hymod_convergence.png')
 
-![Convergence diagnostic](../img/python_hymod_convergence.png)
+![Convergence diagnostic](../img/DREAM_r_hat.png)
 
 *Figure 2: Gelman-Rubin onvergence diagnostic of DREAM results.*
 
@@ -121,8 +121,8 @@ Or if you want to check the posterior parameter distribution:
     plt.show()
     fig.savefig('hymod_parameters.png',dpi=300)
 
-![Parameter distribution](../img/python_hymod_parameters.png)
+![DREAM_parameter_uncertainty_Hymod](../img/DREAM_parameter_uncertainty_Hymod.png)
 
-*Figure 3: Posterior parameter distribution of HYMOD.*
+*Figure 3: Posterior parameter distribution of HYMOD. Plotting the last 100 repetitions of the algorithm as a histogram.*
 
 The corresponding code is available for download [here](https://github.com/thouska/spotpy/blob/master/spotpy/examples/tutorial_dream_hymod.py).

docs/Calibration_with_SCE-UA.md

@@ -57,14 +57,15 @@ Herein we show a example how the objective function was minimized during samplin
 *Figure 1: Root Mean Squared Error of Hymod model results during the optimization with SCE-UA algorithm.*
 
 
-## Plot parameter uncertainty
+## Plot best model run
 Or if you want to check the best model run, we first need to find the run_id with the minimal objective function value
+
     bestindex,bestobjf = spotpy.analyser.get_minlikeindex(results)
 
 Than we select the best model run:
 
     best_model_run = results[bestindex]
-    
+
 And filter results for simulation results only:
 
     fields=[word for word in best_model_run.dtype.names if word.startswith('sim')]

docs/Sensitivity_analysis_with_FAST.md

@@ -1,4 +1,5 @@
 <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS_HTML"></script>
+
 # Sensitivity analysis of HYMOD with FAST
 
 SPOTPY gives you the opportunity to start a sensitivity analysis of your model. In this case, we included a global sensitivity analysis called "FAST" based on 
@@ -8,7 +9,7 @@ The algorithm will tell you, how sensitive your parameters are based on whatever
 iterations you need to get an reliable information about your parameter. The number of iteration can be calculate after [Henkel et al. GLOBAL SENSITIVITY ANALYSIS OF NONLINEAR MATHEMATICAL MODELS - AN 
 IMPLEMENTATION OF TWO COMPLEMENTING VARIANCE-BASED ALGORITHMS, 2012] (https://www.informs-sim.org/wsc12papers/includes/files/con308.pdf): 
 
-$$N = (1+4M^2(1+(k-2)d))k$$ 
+	$$N = (1+4M^2(1+(k-2)d))k$$ 
 
 with N = needed parameter iterations, M= inference factor (SPOTPY default M=4) and d= frequency step (SPOTPY default d=2) and k as the number of parameters of your model.
 

spotpy/analyser.py

@@ -283,13 +283,13 @@ def plot_parameter_trace(ax, results, parameter):
     #THis function plots the parameter setting for each run
     for i in range(int(max(results['chain']))):
         index=np.where(results['chain']==i)
-        ax.plot(results['par'+parameter['name']][index],'.')
+        ax.plot(results['par'+parameter['name']][index],'.', markersize=2)
     ax.set_ylabel(parameter['name'])
     ax.set_ylim(parameter['minbound'], parameter['maxbound'])
 
 def plot_posterior_parameter_histogram(ax, results, parameter):
-    #This functing is plotting the last 10% of the results
-    ax.hist(results['par'+parameter['name']][int(len(results)*0.9):], 
+    #This functing is the last 100 runs
+    ax.hist(results['par'+parameter['name']][-100:], 
              bins =np.linspace(parameter['minbound'],parameter['maxbound'],20))
     ax.set_ylabel('Density')
     ax.set_xlim(parameter['minbound'], parameter['maxbound'])
@@ -468,7 +468,7 @@ def plot_fast_sensitivity(results,like_index=1,number_of_sensitiv_pars=10,fig_na
     import matplotlib.pyplot as plt
 
     parnames=get_parameternames(results)
-    fig=plt.figure(figsize=(16,6))
+    fig=plt.figure(figsize=(9,6))
 
     ax  = plt.subplot(1,1,1)
     Si  = get_sensitivity_of_fast(results, like_index=like_index)
@@ -530,7 +530,7 @@ def plot_fast_sensitivity(results,like_index=1,number_of_sensitiv_pars=10,fig_na
     ax.plot(np.arange(-1,len(parnames)+1,1),[threshold]*(len(parnames)+2),'r--')
     ax.set_xlim(-0.5,len(parnames)-0.5)
     plt.tight_layout()
-    fig.savefig(fig_name,dpi=300)
+    fig.savefig(fig_name,dpi=150)
 
 
 def plot_heatmap_griewank(results,algorithms, fig_name='heatmap_griewank.png'):
@@ -989,7 +989,7 @@ def plot_gelman_rubin(results, r_hat_values,fig_name='gelman_rub.png'):
        Output: Plot as seen for e.g. in (Sadegh and Vrugt 2014)'''
     import matplotlib.pyplot as plt
 
-    fig= plt.figure(figsize=(12,16))
+    fig= plt.figure(figsize=(9,6))
     ax1 = plt.subplot(2,1,1)
     for i in range(int(max(results['chain']))+1):
         index=np.where(results['chain']==i)
@@ -1008,7 +1008,7 @@ def plot_gelman_rubin(results, r_hat_values,fig_name='gelman_rub.png'):
     ax2.set_ylabel('R$^d$ - convergence diagnostic')
     ax2.set_xlabel('Number of chainruns')
     ax2.legend()
-    fig.savefig(fig_name,dpi=300)
+    fig.savefig(fig_name,dpi=150)
 
 def gelman_rubin(x):
     '''NOT USED YET'''

spotpy/examples/tutorial_dream_hymod.py

@@ -40,8 +40,8 @@
     acceptance_test_option = 6
 
     sampler=spotpy.algorithms.dream(spot_setup, dbname='DREAM_hymod', dbformat='csv')
-    r_hat = sampler.sample(rep, nChains, nCr, eps, convergence_limit, 
-                           runs_after_convergence,acceptance_test_option)
+    #r_hat = sampler.sample(rep, nChains, nCr, eps, convergence_limit, 
+    #                       runs_after_convergence,acceptance_test_option)
 
 
 
@@ -53,7 +53,7 @@
 
 
     # Example plot to show remaining parameter uncertainty #
-    fig= plt.figure(figsize=(16,9))
+    fig= plt.figure(figsize=(9,6))
     ax = plt.subplot(1,1,1)
     q5,q25,q75,q95=[],[],[],[]
     for field in fields:
@@ -63,16 +63,16 @@
     ax.plot(q95,color='dimgrey',linestyle='solid')
     ax.fill_between(np.arange(0,len(q5),1),list(q5),list(q95),facecolor='dimgrey',zorder=0,
                     linewidth=0,label='parameter uncertainty')  
-    ax.plot(spot_setup.evaluation(),'r.',label='data')
+    ax.plot(spot_setup.evaluation(),color='red', markersize=2,label='data')
     ax.set_ylim(-50,450)
     ax.set_xlim(0,729)
     ax.legend()
-    fig.savefig('python_hymod.png',dpi=300)
+    fig.savefig('DREAM_simulation_uncertainty_Hymod.png',dpi=150)
     #########################################################
 
 
     # Example plot to show the convergence #################
-    spotpy.analyser.plot_gelman_rubin(results, r_hat)
+    spotpy.analyser.plot_gelman_rubin(results, r_hat, fig_name='DREAM_r_hat.png')
     ########################################################
 
 
@@ -82,6 +82,8 @@
     parameters = spotpy.parameter.get_parameters_array(spot_setup)
 
     fig, ax = plt.subplots(nrows=5, ncols=2)
+    fig.set_figheight(9)
+    fig.set_figwidth(9)
     for par_id in range(len(parameters)):
         plot_parameter_trace(ax[par_id][0], results, parameters[par_id])
         plot_posterior_parameter_histogram(ax[par_id][1], results, parameters[par_id])
@@ -90,5 +92,6 @@
     ax[-1][1].set_xlabel('Parameter range')
 
     plt.show()
-    fig.savefig('hymod_parameters.png',dpi=300)
+    fig.tight_layout()
+    fig.savefig('DREAM_parameter_uncertainty_Hymod.png',dpi=300)
     #######################################################

spotpy/examples/tutorial_sceua_hymod.py

@@ -35,12 +35,12 @@
 
 
     #Plot how the objective function was minimized during sampling
-    fig= plt.figure(1,figsize=(9,5))
+    fig= plt.figure(1,figsize=(9,6))
     plt.plot(results['like1'])
     plt.show()
     plt.ylabel('RMSE')
     plt.xlabel('Iteration')
-    fig.savefig('SCEUA_objectivefunctiontrace.png',dpi=300)
+    fig.savefig('SCEUA_objectivefunctiontrace.png',dpi=150)
 
     # Plot the best model run
     #Find the run_id with the minimal objective function value
@@ -53,12 +53,12 @@
     fields=[word for word in best_model_run.dtype.names if word.startswith('sim')]
     best_simulation = list(best_model_run[fields])
 
-    fig= plt.figure(figsize=(16,9))
+    fig= plt.figure(figsize=(9,6))
     ax = plt.subplot(1,1,1)
     ax.plot(best_simulation,color='black',linestyle='solid', label='Best objf.='+str(bestobjf))
     ax.plot(spot_setup.evaluation(),'r.',markersize=3, label='Observation data')
     plt.xlabel('Number of Observation Points')
     plt.ylabel ('Discharge [l s-1]')
     plt.legend(loc='upper right')
-    fig.savefig('SCEUA_best_modelrun.png',dpi=300)
+    fig.savefig('SCEUA_best_modelrun.png',dpi=150)