some fixes w.r.t. part execution

src/censo/ensembledata.py

@@ -216,7 +216,7 @@ def __setup_conformers(self, input_path: str, maxconf: int = None) -> None:
             self.__conformers.append(
                 MoleculeData(
                     confname,
-                    lines[conf_index:conf_end_index],
+                    lines[conf_index + 2 : conf_end_index],
                 )
             )
 

src/censo/ensembleopt/optimization.py

@@ -143,8 +143,8 @@ def _optimize(self, cut: bool = True) -> None:
             self.ensemble.remove_conformers(failed)
 
             # update results for each conformer
-            self.results.update(results_opt)
             for conf in self.ensemble.conformers:
+                self.results[conf.name].update(results_opt[conf.name])
                 # update geometry of the conformer
                 conf.geom.xyz = results_opt[conf.name][jobtype[0]]["geom"]
 
@@ -193,19 +193,18 @@ def _optimize(self, cut: bool = True) -> None:
         # Remove failed conformers
         self.ensemble.remove_conformers(failed)
 
-        # Update results
-        self.results.update(results)
-
         # TODO - Add the possibility to explicitely calculate solvation contributions
 
+        # Update results
         for conf in self.ensemble.conformers:
+            self.results[conf.name].update(results[conf.name])
             self.results[conf.name]["gtot"] = self._grrho(conf)
 
         # sort conformers list with optimization key (gtot)
         self.ensemble.conformers.sort(key=lambda conf: self.results[conf.name]["gtot"])
 
         # calculate boltzmann weights from gtot values calculated here
-        self.results.update(self._calc_boltzmannweights())
+        self._calc_boltzmannweights()
 
         # write final results
         self._write_results()
@@ -337,10 +336,10 @@ def __macrocycle_opt(self, cut: bool):
                 self.set_general_setting("bhess", tmp)
 
                 # Update results
-                self.results.update(results)
                 for conf in filter(
                     lambda x: x.name in self.confs_nc, self.ensemble.conformers
                 ):
+                    self.results[conf.name].update(results[conf.name])
                     self.results[conf.name]["gtot"] = self._grrho(conf)
 
                 # flag to make sure that rrho is only calculated once

src/censo/ensembleopt/optimizer.py

@@ -75,7 +75,7 @@ def _validate(cls, tovalidate: dict[str, any]) -> None:
 
     @timeit
     @CensoPart._create_dir
-    def __call__(self, ensemble: EnsembleData, cut: bool = True) -> None:
+    def __call__(self, cut: bool = True) -> None:
         """
         Boilerplate run logic for any ensemble optimization step. The 'optimize' method should be implemented for every
         class respectively.

src/censo/ensembleopt/prescreening.py

@@ -81,7 +81,8 @@ def _optimize(self, cut: bool = True) -> None:
                 self.ensemble.remove_conformers(failed)
 
                 # Update results
-                self.results.update(results)
+                for conf in self.ensemble.conformers:
+                    self.results[conf.name].update(results[conf.name])
 
                 jobtype = ["sp"]
             else:
@@ -108,10 +109,9 @@ def _optimize(self, cut: bool = True) -> None:
         self.ensemble.remove_conformers(failed)
 
         # Update results
-        self.results.update(results)
-
-        # update results for each conformer
         for conf in self.ensemble.conformers:
+            self.results[conf.name].update(results[conf.name])
+
             # calculate free enthalpy
             self.results[conf.name]["gtot"] = self._gsolv(conf)
 
@@ -121,14 +121,14 @@ def _optimize(self, cut: bool = True) -> None:
         )
 
         # calculate boltzmann weights from gtot values calculated here
-        self.results.update(self._calc_boltzmannweights())
+        self._calc_boltzmannweights()
 
         self._write_results()
 
         if cut:
             print("\n")
             # update conformers with threshold
-            threshold = self.get_settings()["threshold"] / Params.Params.AU2KCAL
+            threshold = self.get_settings()["threshold"] / Params.AU2KCAL
             limit = min(
                 self.results[conf.name]["gtot"] for conf in self.ensemble.conformers
             )

src/censo/ensembleopt/refinement.py

@@ -66,9 +66,9 @@ def _optimize(self, cut: bool = True) -> None:
                 self.ensemble.remove_conformers(failed)
 
                 # Update results
-                self.results.update(results)
-
                 for conf in self.ensemble.conformers:
+                    self.results[conf.name].update(results[conf.name])
+
                     # calculate new gtot including RRHO contribution
                     self.results[conf.name]["gtot"] = self._grrho(conf)
             else:
@@ -88,8 +88,7 @@ def _optimize(self, cut: bool = True) -> None:
         self.ensemble.conformers.sort(key=lambda conf: self.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.results.update(self._calc_boltzmannweights())
+        self._calc_boltzmannweights()
 
         if cut:
             # Get Boltzmann population threshold from settings
@@ -111,7 +110,7 @@ def _optimize(self, cut: bool = True) -> None:
                 print(f"No longer considering {confname}.")
 
             # Recalculate boltzmann weights after cutting down the ensemble
-            self.results.update(self._calc_boltzmannweights())
+            self._calc_boltzmannweights()
 
         # second 'write_results' for the updated sorting with RRHO contributions
         self._write_results2()

src/censo/ensembleopt/screening.py

@@ -78,9 +78,8 @@ def _optimize(self, cut: bool = True) -> None:
             self.ensemble.remove_conformers(failed)
 
             # Update results
-            self.results.update(results)
-
             for conf in self.ensemble.conformers:
+                self.results[conf.name].update(results[conf.name])
                 # calculate new gtot including RRHO contribution
                 self.results[conf.name]["gtot"] = self._grrho(conf)
 
@@ -125,8 +124,7 @@ def _optimize(self, cut: bool = True) -> None:
                     print(f"No longer considering {confname}.")
 
             # 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.results.update(self._calc_boltzmannweights())
+            self._calc_boltzmannweights()
 
             # if no conformers are filtered basically nothing happens
 

src/censo/parallel.py

@@ -228,7 +228,7 @@ def dqp(jobs: list[ParallelJob], processor: QmProc) -> list[ParallelJob]:
 
                 try:
                     # submit the job
-                    tasks.append(executor.submit(processor.run, jobs[i]))
+                    tasks.append(executor.submit(processor, jobs[i]))
                     # NOTE: explanation of the lambda: the first argument passed to the done_callback is always the future
                     # itself, it is not assigned (_), the second parameter is the number of openmp threads of the job (i.e.
                     # job.omp) if this is not specified like this (omp=jobs[i].omp) the done_callback will instead use the

src/censo/part.py

@@ -287,13 +287,14 @@ def wrapper(self, *args, **kwargs):
 
         return wrapper
 
-    def __init__(self, ensemble: EnsembleData):
+    def __init__(self, ensemble: EnsembleData, dry: bool = False):
         """
         Initializes a part instance. Also calls the new instance in a single step.
         Yields the instance after execution of itself.
 
         Args:
             ensemble: The ensemble instance that manages the conformers.
+            dry: If True, the part will not be executed.
 
         Returns:
             None
@@ -326,7 +327,8 @@ def __init__(self, ensemble: EnsembleData):
         #    to get the single-point energy: self.results["CONF3"]["sp"]["energy"]
         #    (refer to the results for each jobtype)
 
-        self.runtime = self()
+        if not dry:
+            self.runtime = self()
 
         # Attach a reference to this part to the ensemble
         self.ensemble.results.append(self)
@@ -338,7 +340,7 @@ def __call__(self) -> None:
         """
         pass
 
-    def _calc_boltzmannweights(self) -> dict:
+    def _calc_boltzmannweights(self) -> None:
         """
         Calculate populations for boltzmann distribution of ensemble at given
         temperature given values for free enthalpy.
@@ -402,9 +404,8 @@ def _calc_boltzmannweights(self) -> dict:
         bsum: float = sum(bmfactors.values())
 
         # Return Boltzmann populations
-        return {
-            conf.name: bmfactors[conf.name] / bsum for conf in self.ensemble.conformers
-        }
+        for conf in self.ensemble.conformers:
+            self.results[conf.name]["bmw"] = bmfactors[conf.name] / bsum
 
     def _print_info(self) -> None:
         """

src/censo/properties/nmr.py

@@ -158,7 +158,8 @@ def _property(self) -> None:
         self.ensemble.remove_conformers(failed)
 
         # Update results
-        self.results.update(results)
+        for conf in self.ensemble.conformers:
+            self.results[conf.name].update(results[conf.name])
 
         # Generate files for ANMR
         self.__generate_anmr()

src/censo/properties/property_calculator.py

@@ -41,7 +41,7 @@ class respectively.
             self.results[conf.name]["gtot"] = self._gtot(conf)
 
         # Calculate Boltzmann populations
-        self.results.update(self._calc_boltzmannweights())
+        self._calc_boltzmannweights()
 
         # Perform the property calculations
         self._property()

src/censo/properties/uvvis.py

@@ -123,7 +123,8 @@ def _property(self) -> None:
         self.ensemble.remove_conformers(failed)
 
         # Update results
-        self.results.update(results)
+        for conf in self.ensemble.conformers:
+            self.results[conf.name].update(results[conf.name])
 
         # Ensemble averaging of excitations
         self.__excitation_averaging()

src/censo/qm_processor.py

@@ -95,7 +95,7 @@ def __init__(self, workdir: str):
 
         self.workdir = workdir
 
-    def run(self, job: ParallelJob) -> ParallelJob:
+    def __call__(self, job: ParallelJob) -> ParallelJob:
         """
         Run methods depending on jobtype.
         DO NOT OVERRIDE OR OVERLOAD! this will break e.g. censo.parallel.execute