Update the cross_validate method to match the _make_rule method, for fitting kinetics.

I tried using a helper function that could be re-used, but it started to get too messy 
and complicated. This unfortunately requires continued maintenance keeping three
different bits of code in sync doing the same thing, eg. rejecting BM fits when abs(n)>5
or E0<0.

Author: rwest

rmgpy/data/kinetics/family.py

@@ -3678,19 +3678,22 @@ def cross_validate(self, folds=5, template_rxn_map=None, test_rxn_inds=None, T=1
 
                 uncertainties[rxn] = self.rules.entries[entry.label][0].data.uncertainty
 
-
                 L = list(set(template_rxn_map[entry.label]) - set(rxns_test))
 
                 if L != []:
                     if isinstance(L[0].kinetics, Arrhenius):
                         kinetics = ArrheniusBM().fit_to_reactions(L, recipe=self.forward_recipe.actions)
-                        if kinetics.E0.value_si < 0.0 or len(L) == 1:
+                        # These conditions (eg. n>5) for rejecting the BM fit should correspond
+                        # to those in _marke_rule, and those just below:
+                        if len(L) == 1 or kinetics.E0.value_si < 0.0 or abs(kinetics.n.value_si) > 5.0:
                             kinetics = average_kinetics([r.kinetics for r in L])
                         else:
                             kinetics = kinetics.to_arrhenius(rxn.get_enthalpy_of_reaction(298.))
                     else:
                         kinetics = ArrheniusChargeTransferBM().fit_to_reactions(L, recipe=self.forward_recipe.actions)
-                        if kinetics.E0.value_si < 0.0 or len(L) == 1:
+                        # These conditions (eg. n>5) for rejecting the BM fit should correspond
+                        # to those in _marke_rule, and those just above:
+                        if len(L) == 1 or kinetics.E0.value_si < 0.0 or abs(kinetics.n.value_si) > 5.0:
                             kinetics = average_kinetics([r.kinetics for r in L])
                         else:
                             kinetics = kinetics.to_arrhenius_charge_transfer(rxn.get_enthalpy_of_reaction(298.))
@@ -4612,24 +4615,23 @@ def _make_rule(rr):
         arr = ArrheniusChargeTransferBM
     if n > 1:
         kin = arr().fit_to_reactions(rs, recipe=recipe)
+
+    # If you update the following conditions, also update the cross_validate function.
     if n == 1 or kin.E0.value_si < 0.0 or abs(kin.n.value_si) > 5.0:
-        
         if n > 1: #we have an ArrheniusBM
             if kin.E0.value_si < 0.0:
                 reason = "E0<0"
             elif abs(kin.n.value_si) > 5.0:
                 reason = "abs(n)>5"
             else:
                 reason = "?"
-        
+
         # still run it through the averaging function when n=1 to standardize the units and run checks
         kin = average_kinetics([r.kinetics for r in rs])
-        
-        if n == 1:
 
+        if n == 1:
             kin.uncertainty = RateUncertainty(mu=0.0, var=(np.log(fmax) / 2.0) ** 2, N=1, Tref=Tref, data_mean=data_mean, correlation=label)
         else:
-            
             kin.comment = f"Blowers-Masel fit was bad ({reason}) so instead averaged from {n} reactions."
             dlnks = np.array([
                 np.log(