Delete unreachable code in _make_rule

It looks like the unreachable code is just a slight variation of the code above it and was never intended to be included in the first place.

Author: sevyharris

rmgpy/data/kinetics/family.py

@@ -4670,69 +4670,6 @@ def _make_rule(rr):
     else:
         return None
 
-    if isinstance(rs[0].kinetics, Arrhenius):
-        arr = ArrheniusBM
-    else:
-        arr = ArrheniusChargeTransferBM
-    if n > 1:
-        kin = arr().fit_to_reactions(rs, recipe=recipe)
-    if n == 1 or kin.E0.value_si < 0.0:
-        # 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:
-            kin.uncertainty = RateUncertainty(mu=0.0, var=(np.log(fmax) / 2.0) ** 2, N=1, Tref=Tref, data_mean=data_mean, correlation=label)
-            kin.comment = f"Only one reaction rate: {rs[0]!s}"
-        else:
-            kin.comment = f"Blowers-Masel fit was bad (E0<0) so instead averaged from {n} reactions."
-            dlnks = np.array([
-                np.log(
-                        average_kinetics([r.kinetics for r in rs[list(set(range(len(rs))) - {i})]]).get_rate_coefficient(T=Tref) / rxn.get_rate_coefficient(T=Tref)
-                    ) for i, rxn in enumerate(rs)
-                ])  # 1) fit to set of reactions without the current reaction (k)  2) compute log(kfit/kactual) at Tref
-            varis = (np.array([rank_accuracy_map[rxn.rank].value_si for rxn in rs]) / (2.0 * 8.314 * Tref)) ** 2
-            # weighted average calculations
-            ws = 1.0 / varis
-            V1 = ws.sum()
-            V2 = (ws ** 2).sum()
-            mu = np.dot(ws, dlnks) / V1
-            s = np.sqrt(np.dot(ws, (dlnks - mu) ** 2) / (V1 - V2 / V1))
-            kin.uncertainty = RateUncertainty(mu=mu, var=s ** 2, N=n, Tref=Tref, data_mean=data_mean, correlation=label)
-    else: # Blowers-Masel fit was good
-        if isinstance(rs[0].kinetics, Arrhenius):
-            dlnks = np.array([
-                np.log(
-                    arr().fit_to_reactions(rs[list(set(range(len(rs))) - {i})], recipe=recipe)
-                    .to_arrhenius(rxn.get_enthalpy_of_reaction(298.))
-                    .get_rate_coefficient(T=Tref) / rxn.get_rate_coefficient(T=Tref)
-                ) for i, rxn in enumerate(rs)
-            ])  # 1) fit to set of reactions without the current reaction (k)  2) compute log(kfit/kactual) at Tref
-        else: # SurfaceChargeTransfer or ArrheniusChargeTransfer
-            dlnks = np.array([
-                np.log(
-                    arr().fit_to_reactions(rs[list(set(range(len(rs))) - {i})], recipe=recipe)
-                    .to_arrhenius_charge_transfer(rxn.get_enthalpy_of_reaction(298.))
-                    .get_rate_coefficient(T=Tref) / rxn.get_rate_coefficient(T=Tref)
-                ) for i, rxn in enumerate(rs)
-            ])  # 1) fit to set of reactions without the current reaction (k)  2) compute log(kfit/kactual) at Tref
-        varis = (np.array([rank_accuracy_map[rxn.rank].value_si for rxn in rs]) / (2.0 * 8.314 * Tref)) ** 2
-        # weighted average calculations
-        ws = 1.0 / varis
-        V1 = ws.sum()
-        V2 = (ws ** 2).sum()
-        mu = np.dot(ws, dlnks) / V1
-        s = np.sqrt(np.dot(ws, (dlnks - mu) ** 2) / (V1 - V2 / V1))
-        kin.uncertainty = RateUncertainty(mu=mu, var=s ** 2, N=n, Tref=Tref, data_mean=data_mean, correlation=label)
-
-    #site solute parameters
-    site_datas = [get_site_solute_data(rxn) for rxn in rxns]
-    site_datas = [sdata for sdata in site_datas if sdata is not None]
-    if len(site_datas) > 0:
-        site_data = SoluteTSData()
-        for sdata in site_datas:
-            site_data += sdata
-        site_data = site_data * (1.0/len(site_datas))
-        kin.solute = site_data
-    return kin
 
 def _spawn_tree_process(family, template_rxn_map, obj, T, nprocs, depth, min_splitable_entry_num, min_rxns_to_spawn, extension_iter_max, extension_iter_item_cap):
     parent_conn, child_conn = mp.Pipe()