changing pm.sample to remove kwargs but add cores

MonoTools/fit.py

@@ -1235,7 +1235,7 @@ def init_model(self, overwrite=False, **kwargs):
         elif self.use_multinest:
             self.run_multinest(**kwargs)
 
-    def init_GP(self, n_draws=900, max_len_lc=25000, use_binned=False, overwrite=False, n_chains=4, **kwargs):
+    def init_GP(self, n_draws=900, max_len_lc=25000, use_binned=False, overwrite=False, n_chains=4, cores=4, **kwargs):
         """Function to train GPs on out-of-transit photometry
 
         Args:
@@ -1349,7 +1349,7 @@ def init_GP(self, n_draws=900, max_len_lc=25000, use_binned=False, overwrite=Fal
 
             self.gp_init_soln = pmx.optimize(start=None, vars=optvars)
             if self.debug: print("sampling init GP", int(n_draws*0.66),"times with",len(self.lc.flux[mask]),"-point lightcurve")
-            self.gp_init_trace = pm.sample(tune=int(n_draws*0.66), draws=n_draws, start=self.gp_init_soln, chains=n_chains, **kwargs)
+            self.gp_init_trace = pm.sample(tune=int(n_draws*0.66), draws=n_draws, start=self.gp_init_soln, chains=n_chains,cores=cores)# **kwargs)
 
     def init_interpolated_Mp_prior(self):
         """Initialise a 2D interpolated prior for the mass of a planet given the radius
@@ -2649,7 +2649,7 @@ def create_orbit(pl, Rs, rho_S, pers, t0s, bs, n_marg=1, eccs=None, omegas=None)
             self.model = model
             self.init_soln = map_soln
 
-    def sample_model(self, n_draws=500, n_burn_in=None, overwrite=False, continue_sampling=False, n_chains=4, **kwargs):
+    def sample_model(self, n_draws=500, n_burn_in=None, overwrite=False, continue_sampling=False, n_chains=4, cores=4, **kwargs):
         """Run PyMC3 sampler
 
         Args:
@@ -2683,9 +2683,9 @@ def sample_model(self, n_draws=500, n_burn_in=None, overwrite=False, continue_sa
                 if self.debug: print(self.init_soln.keys())
                 if hasattr(self,'trace') and continue_sampling:
                     print("Using already-generated MCMC trace as start point for new trace")
-                    self.trace = pm.sample(tune=n_burn_in, draws=n_draws, chains=n_chains, trace=self.trace, compute_convergence_checks=False)#, **kwargs)
+                    self.trace = pm.sample(tune=n_burn_in, draws=n_draws, chains=n_chains, trace=self.trace, compute_convergence_checks=False, cores=cores)#, **kwargs)
                 else:
-                    self.trace = pm.sample(tune=n_burn_in, draws=n_draws, start=self.init_soln, chains=n_chains, compute_convergence_checks=False)#, **kwargs)
+                    self.trace = pm.sample(tune=n_burn_in, draws=n_draws, start=self.init_soln, chains=n_chains, compute_convergence_checks=False, cores=cores)#, **kwargs)
             #Saving both the class and a pandas dataframe of output data.
             self.save_model_to_file()
             _=self.make_table(save=True)