Fix on the MCMC chains outputs)

Author: ebachelet

examples/pyLIMA_example_1.py

@@ -145,10 +145,11 @@
 # the parameters.
 ### Let's fit again using MCMC. This might take some time ...
 
-my_fit5 = MCMC_fit.MCMCfit(fspl)
-my_fit5.model_parameters_guess = guess_parameters
-my_fit5.fit()
+my_fit5 = MCMC_fit.MCMCfit(fspl,rescale_photometry=True)
 
+my_fit5.model_parameters_guess = guess_parameters[:4]
+my_fit5.fit()
+my_fit5.fit_outputs()
 ### Now your MCMC run is complete. Congratulations! 
 ### You can now plot the chains and explore how they evolve for each parameter.
 ### For example, to see how the chains for u0 evolve, do:

pyLIMA/fits/MCMC_fit.py

@@ -268,13 +268,12 @@ def reconstruct_chains(self, mcmc_samples, mcmc_prob):
             columns_to_swap += self.rescale_photometry_parameters_index
 
         if self.rescale_astrometry:
-            columns_to_swap += self.rescale_photometry_parameters_index
+            columns_to_swap += self.rescale_astrometry_parameters_index
 
         if (columns_to_swap != []):
 
             old_column = columns_to_swap
-            new_column = np.arange(old_column[-1]+1,Rangej-1,1).tolist()
-
+            new_column = np.arange(old_column[-1]+1,Rangej,1).tolist()
             MCMC_chains_with_fluxes[:, :, old_column + new_column] = MCMC_chains_with_fluxes[:, :,new_column +old_column]
 
         #MCMC_chains[:,:,-1] = np.copy(MCMC_chains_with_fluxes[:,:,-1])

pyLIMA/outputs/file_outputs.py

@@ -39,15 +39,12 @@ def json_output(fit_object,json_name='./pyLIMA_fit.json'):
 
                 elif (key == 'MCMC_chains_with_fluxes'):
 
-                    header =  [key for key in fit_object.priors_parameters.keys()] +[
-                        'priors',
-                                                                 fit_object.loss_function ]
+                    header =  [key for key in fit_object.priors_parameters.keys()]
                     distributions_to_output[key] = [tosave,header]
 
                 elif (key == 'DE_population'):
 
                     header =  [key for key in fit_object.priors_parameters.keys()]
-
                     distributions_to_output[key] = [tosave,header]
 
                 else: