compilation of bugfixes

src/censo/ensembleopt/optimization.py

@@ -203,14 +203,6 @@ def _optimize(self, cut: bool = True) -> None:
         for conf in self._ensemble.conformers:
             self.data["results"][conf.name]["gtot"] = self._grrho(conf)
 
-        # sort conformers list with optimization key (gtot)
-        self._ensemble.conformers.sort(
-            key=lambda conf: self.data["results"][conf.name]["gtot"]
-        )
-
-        # sort conformers list with optimization key (gtot)
-        self.ensemble.conformers.sort(key=lambda conf: conf.results[self._name]["gtot"])
-
         # calculate boltzmann weights from gtot values calculated here
         self._update_results(self._calc_boltzmannweights())
 
@@ -348,9 +340,6 @@ def __macrocycle_opt(self, cut: bool):
             # TODO - crestcheck each iteration if ncyc >= 6
             # => replace this with molbar later
 
-            # sort conformers
-            self._ensemble.conformers.sort(key=self._grrho)
-
             # remove converged conformers from 'todo-list'
             for conf in list(
                 filter(

src/censo/ensembleopt/optimizer.py

@@ -89,14 +89,18 @@ class respectively.
         # print instructions
         self._print_info()
 
-        # Print information about ensemble before optimization
-        self._print_update()
+        # Store number of conformer put in
         self.data["nconf_in"] = len(self._ensemble.conformers)
 
         # Perform the actual optimization logic
         self._optimize(cut=cut)
         self.data["nconf_out"] = len(self._ensemble.conformers)
 
+        # Resort the ensemble
+        self._ensemble.conformers.sort(
+            key=lambda conf: self.data["results"][conf.name]["gtot"],
+        )
+
         # DONE
 
     def _optimize(self, cut: bool = True):
@@ -277,11 +281,6 @@ def _print_update(self) -> None:
             + f"{len(self._ensemble.conformers)}"
         )
 
-        # Make sure that the sorting is correct
-        self._ensemble.conformers.sort(
-            key=lambda conf: self.data["results"][conf.name]["gtot"]
-        )
-
         print(
             "Highest ranked conformer:".ljust(DIGILEN // 2, " ")
             + f"{self._ensemble.conformers[0].name}"

src/censo/ensembleopt/prescreening.py

@@ -116,11 +116,6 @@ def _optimize(self, cut: bool = True) -> None:
             # calculate free enthalpy
             self.data["results"][conf.name]["gtot"] = self._gsolv(conf)
 
-        # sort conformers list with prescreening key (gtot)
-        self._ensemble.conformers.sort(
-            key=lambda conf: self.data["results"][conf.name]["gtot"],
-        )
-
         # calculate boltzmann weights from gtot values calculated here
         self._update_results(self._calc_boltzmannweights())
 

src/censo/ensembleopt/refinement.py

@@ -79,11 +79,6 @@ def _optimize(self, cut: bool = True) -> None:
                     ][conf.name]["xtb_rrho"]
                     self.data["results"][conf.name]["gtot"] = self._grrho(conf)
 
-        # sort conformers list
-        self._ensemble.conformers.sort(
-            key=lambda conf: self.data["results"][conf.name]["gtot"]
-        )
-
         # calculate boltzmann weights from gtot values calculated here
         # trying to get temperature from instructions, set it to room temperature if that fails for some reason
         self._update_results(self._calc_boltzmannweights())

src/censo/ensembleopt/screening.py

@@ -87,11 +87,6 @@ def _optimize(self, cut: bool = True) -> None:
                 # calculate new gtot including RRHO contribution
                 self.data["results"][conf.name]["gtot"] = self._grrho(conf)
 
-            # sort conformers list
-            self._ensemble.conformers.sort(
-                key=lambda conf: self.data["results"][conf.name]["gtot"]
-            )
-
             if cut and len(self._ensemble.conformers) > 1:
                 # calculate fuzzyness of threshold (adds 1 kcal/mol at max to the threshold)
                 fuzzy = (1 / AU2KCAL) * (

src/censo/properties/uvvis.py

@@ -165,7 +165,7 @@ def __excitation_averaging(self):
         # eps is a list of tuples that contain each excitation wavelength with the respective epsilon_max
         eps = []
         for conf in self._ensemble.conformers:
-            for excitation in conf.results[self.name]["uvvis"]["excitations"]:
+            for excitation in self.data["results"][conf.name]["excitations"]:
                 epsilon_max = (
                     self.data["results"][conf.name]["bmw"] * excitation["osc_str"]
                 )