Added a pyproject.toml and tested black

mala/interfaces/ase_calculator.py

@@ -3,8 +3,7 @@
 from ase.calculators.calculator import Calculator, all_changes
 import numpy as np
 
-from mala import Parameters, Network, DataHandler, Predictor, LDOS, Density, \
-                 DOS
+from mala import Parameters, Network, DataHandler, Predictor, LDOS, Density, DOS
 from mala.common.parallelizer import get_rank, get_comm, barrier
 
 
@@ -38,34 +37,40 @@ class MALA(Calculator):
         from the atomic positions.
     """
 
-    implemented_properties = ['energy', 'forces']
+    implemented_properties = ["energy", "forces"]
 
-    def __init__(self, params: Parameters, network: Network,
-                 data: DataHandler, reference_data=None,
-                 predictor=None):
+    def __init__(
+        self,
+        params: Parameters,
+        network: Network,
+        data: DataHandler,
+        reference_data=None,
+        predictor=None,
+    ):
         super(MALA, self).__init__()
 
         # Copy the MALA relevant objects.
         self.mala_parameters: Parameters = params
         if self.mala_parameters.targets.target_type != "LDOS":
-            raise Exception("The MALA calculator currently only works with the"
-                            "LDOS.")
+            raise Exception("The MALA calculator currently only works with the" "LDOS.")
 
         self.network: Network = network
         self.data_handler: DataHandler = data
 
         # Prepare for prediction.
         if predictor is None:
-            self.predictor = Predictor(self.mala_parameters, self.network,
-                                       self.data_handler)
+            self.predictor = Predictor(
+                self.mala_parameters, self.network, self.data_handler
+            )
         else:
             self.predictor = predictor
 
         if reference_data is not None:
             # Get critical values from a reference file (cutoff,
             # temperature, etc.)
-            self.data_handler.target_calculator.\
-                read_additional_calculation_data(reference_data)
+            self.data_handler.target_calculator.read_additional_calculation_data(
+                reference_data
+            )
 
         # Needed for e.g. Monte Carlo.
         self.last_energy_contributions = {}
@@ -86,15 +91,15 @@ def load_model(cls, run_name, path="./"):
         path : str
             Path where the model is saved.
         """
-        loaded_params, loaded_network, \
-            new_datahandler, loaded_runner = Predictor.\
-            load_run(run_name, path=path)
-        calculator = cls(loaded_params, loaded_network, new_datahandler,
-                         predictor=loaded_runner)
+        loaded_params, loaded_network, new_datahandler, loaded_runner = (
+            Predictor.load_run(run_name, path=path)
+        )
+        calculator = cls(
+            loaded_params, loaded_network, new_datahandler, predictor=loaded_runner
+        )
         return calculator
 
-    def calculate(self, atoms=None, properties=['energy'],
-                  system_changes=all_changes):
+    def calculate(self, atoms=None, properties=["energy"], system_changes=all_changes):
         """
         Perform the calculations.
 
@@ -123,24 +128,20 @@ def calculate(self, atoms=None, properties=['energy'],
 
         # If an MPI environment is detected, ASE will use it for writing.
         # Therefore we have to do this before forking.
-        self.data_handler.\
-            target_calculator.\
-            write_tem_input_file(atoms,
-                                 self.data_handler.
-                                 target_calculator.qe_input_data,
-                                 self.data_handler.
-                                 target_calculator.qe_pseudopotentials,
-                                 self.data_handler.
-                                 target_calculator.grid_dimensions,
-                                 self.data_handler.
-                                 target_calculator.kpoints)
+        self.data_handler.target_calculator.write_tem_input_file(
+            atoms,
+            self.data_handler.target_calculator.qe_input_data,
+            self.data_handler.target_calculator.qe_pseudopotentials,
+            self.data_handler.target_calculator.grid_dimensions,
+            self.data_handler.target_calculator.kpoints,
+        )
 
         ldos_calculator: LDOS = self.data_handler.target_calculator
 
         ldos_calculator.read_from_array(ldos)
-        energy, self.last_energy_contributions \
-            = ldos_calculator.get_total_energy(return_energy_contributions=
-                                               True)
+        energy, self.last_energy_contributions = ldos_calculator.get_total_energy(
+            return_energy_contributions=True
+        )
         barrier()
 
         # Use the LDOS determined DOS and density to get energy and forces.
@@ -170,17 +171,23 @@ def calculate_properties(self, atoms, properties):
         # TODO: Check atoms.
 
         if "rdf" in properties:
-            self.results["rdf"] = self.data_handler.target_calculator.\
-                get_radial_distribution_function(atoms)
+            self.results["rdf"] = (
+                self.data_handler.target_calculator.get_radial_distribution_function(
+                    atoms
+                )
+            )
         if "tpcf" in properties:
-            self.results["tpcf"] = self.data_handler.target_calculator.\
-                get_three_particle_correlation_function(atoms)
+            self.results["tpcf"] = (
+                self.data_handler.target_calculator.get_three_particle_correlation_function(
+                    atoms
+                )
+            )
         if "static_structure_factor" in properties:
-            self.results["static_structure_factor"] = self.data_handler.\
-                target_calculator.get_static_structure_factor(atoms)
+            self.results["static_structure_factor"] = (
+                self.data_handler.target_calculator.get_static_structure_factor(atoms)
+            )
         if "ion_ion_energy" in properties:
-            self.results["ion_ion_energy"] = self.\
-                last_energy_contributions["e_ewald"]
+            self.results["ion_ion_energy"] = self.last_energy_contributions["e_ewald"]
 
     def save_calculator(self, filename, save_path="./"):
         """
@@ -197,6 +204,6 @@ def save_calculator(self, filename, save_path="./"):
             Path where the calculator should be saved.
 
         """
-        self.predictor.save_run(filename, save_path=save_path,
-                                additional_calculation_data=True)
-
+        self.predictor.save_run(
+            filename, save_path=save_path, additional_calculation_data=True
+        )

pyproject.toml

@@ -0,0 +1,2 @@
+[tool.black]
+line-length = 88