Adding back extract to the init_gp trace when using the posterior values

MonoTools/fit.py

@@ -1824,8 +1824,11 @@ def init_pymc(self,ld_mult=1.5):
                 if self.debug: print(np.isnan(self.model_time),np.isnan(self.model_flux),np.isnan(self.model_flux_err))
                 if self.train_GP:
                     #Using histograms from the output of the previous GP training as priors for the true model.
-                    minmaxs={var:np.percentile(self.gp_init_trace.posterior[var].values,[0.5,99.5]).astype(floattype) for var in self.gp_init_trace.posterior if '__' not in var and len(self.gp_init_trace.posterior[var].shape)==1}
-                    hists={var:np.histogram(self.gp_init_trace.posterior[var].values,np.linspace(minmaxs[var][0],minmaxs[var][1],101))[0] for var in self.gp_init_trace.posterior if '__' not in var and len(self.gp_init_trace.posterior[var].shape)==1}
+                    vars=[var for var in self.gp_init_trace.posterior if '__' not in var and np.product(self.gp_init_trace.posterior[var].shape)<5*len(self.gp_init_trace.posterior.chain)*len(self.gp_init_trace.posterior.draw)]
+                    ext_gp_init_trace=az.extract(self.gp_init_trace.posterior,var_names=vars)
+
+                    minmaxs={var:np.percentile(ext_gp_init_trace[var].values,[0.5,99.5]).astype(floattype) for var in vars}
+                    hists={var:np.histogram(ext_gp_init_trace[var].values,np.linspace(minmaxs[var][0],minmaxs[var][1],101))[0] for var in vars}
                 gpvars=[]
                 if hasattr(self, 'periodic_kernel') and self.periodic_kernel is not None:
                     if self.train_GP: