Feature/format (#209)

ruff format

Author: FBumann

examples/00_Minmal/minimal_example.py

@@ -55,7 +55,6 @@
     calculation.do_modeling()
     calculation.solve(fx.solvers.HighsSolver(0.01, 60))
 
-
     # --- Analyze Results ---
     # Access the results of an element
     df1 = calculation.results['costs'].filter_solution('time').to_dataframe()

examples/01_Simple/simple_example.py

@@ -116,6 +116,5 @@
     df = calculation.results['Storage'].node_balance_with_charge_state()
     print(df)
 
-    #Save results to file for later usage
+    # Save results to file for later usage
     calculation.results.to_file()
-

examples/02_Complex/complex_example.py

@@ -50,7 +50,9 @@
     Gaskessel = fx.linear_converters.Boiler(
         'Kessel',
         eta=0.5,  # Efficiency ratio
-        on_off_parameters=fx.OnOffParameters(effects_per_running_hour={Costs.label: 0, CO2.label: 1000}),  # CO2 emissions per hour
+        on_off_parameters=fx.OnOffParameters(
+            effects_per_running_hour={Costs.label: 0, CO2.label: 1000}
+        ),  # CO2 emissions per hour
         Q_th=fx.Flow(
             label='Q_th',  # Thermal output
             bus='Fernwärme',  # Linked bus

examples/03_Calculation_types/example_calculation_types.py

@@ -121,12 +121,14 @@
 
     # Gas Tariff
     a_gas_tarif = fx.Source(
-        'Gastarif', source=fx.Flow('Q_Gas', bus='Gas', size=1000, effects_per_flow_hour={costs.label: gas_price, CO2.label: 0.3})
+        'Gastarif',
+        source=fx.Flow('Q_Gas', bus='Gas', size=1000, effects_per_flow_hour={costs.label: gas_price, CO2.label: 0.3}),
     )
 
     # Coal Tariff
     a_kohle_tarif = fx.Source(
-        'Kohletarif', source=fx.Flow('Q_Kohle', bus='Kohle', size=1000, effects_per_flow_hour={costs.label: 4.6, CO2.label: 0.3})
+        'Kohletarif',
+        source=fx.Flow('Q_Kohle', bus='Kohle', size=1000, effects_per_flow_hour={costs.label: 4.6, CO2.label: 0.3}),
     )
 
     # Electricity Tariff and Feed-in
@@ -136,7 +138,9 @@
 
     a_strom_tarif = fx.Source(
         'Stromtarif',
-        source=fx.Flow('P_el', bus='Strom', size=1000, effects_per_flow_hour={costs.label: TS_electricity_price_buy, CO2: 0.3}),
+        source=fx.Flow(
+            'P_el', bus='Strom', size=1000, effects_per_flow_hour={costs.label: TS_electricity_price_buy, CO2: 0.3}
+        ),
     )
 
     # Flow System Setup
@@ -190,26 +194,34 @@ def get_solutions(calcs: List, variable: str) -> xr.Dataset:
     # --- Plotting for comparison ---
     fx.plotting.with_plotly(
         get_solutions(calculations, 'Speicher|charge_state').to_dataframe(),
-        mode='line', title='Charge State Comparison', ylabel='Charge state',
+        mode='line',
+        title='Charge State Comparison',
+        ylabel='Charge state',
     ).write_html('results/Charge State.html')
 
     fx.plotting.with_plotly(
         get_solutions(calculations, 'BHKW2(Q_th)|flow_rate').to_dataframe(),
-        mode='line', title='BHKW2(Q_th) Flow Rate Comparison', ylabel='Flow rate',
+        mode='line',
+        title='BHKW2(Q_th) Flow Rate Comparison',
+        ylabel='Flow rate',
     ).write_html('results/BHKW2 Thermal Power.html')
 
     fx.plotting.with_plotly(
         get_solutions(calculations, 'costs(operation)|total_per_timestep').to_dataframe(),
-        mode='line', title='Operation Cost Comparison', ylabel='Costs [€]'
+        mode='line',
+        title='Operation Cost Comparison',
+        ylabel='Costs [€]',
     ).write_html('results/Operation Costs.html')
 
     fx.plotting.with_plotly(
         pd.DataFrame(get_solutions(calculations, 'costs(operation)|total_per_timestep').to_dataframe().sum()).T,
-        mode='bar', title='Total Cost Comparison', ylabel='Costs [€]'
+        mode='bar',
+        title='Total Cost Comparison',
+        ylabel='Costs [€]',
     ).update_layout(barmode='group').write_html('results/Total Costs.html')
 
     fx.plotting.with_plotly(
         pd.DataFrame([calc.durations for calc in calculations], index=[calc.name for calc in calculations]), 'bar'
-    ).update_layout(
-        title='Duration Comparison', xaxis_title='Calculation type', yaxis_title='Time (s)'
-    ).write_html('results/Speed Comparison.html')
+    ).update_layout(title='Duration Comparison', xaxis_title='Calculation type', yaxis_title='Time (s)').write_html(
+        'results/Speed Comparison.html'
+    )

flixopt/aggregation.py

@@ -16,6 +16,7 @@
 
 try:
     import tsam.timeseriesaggregation as tsam
+
     TSAM_AVAILABLE = True
 except ImportError:
     TSAM_AVAILABLE = False
@@ -52,7 +53,6 @@ def __init__(
         time_series_for_high_peaks: List[str] = None,
         time_series_for_low_peaks: List[str] = None,
     ):
-
         """
         Args:
             original_data: The original data to aggregate
@@ -64,8 +64,9 @@ def __init__(
             time_series_for_low_peaks: List of time series to use for explicitly selecting periods with low values.
         """
         if not TSAM_AVAILABLE:
-            raise ImportError("The 'tsam' package is required for clustering functionality. "
-                              "Install it with 'pip install tsam'.")
+            raise ImportError(
+                "The 'tsam' package is required for clustering functionality. Install it with 'pip install tsam'."
+            )
         self.original_data = copy.deepcopy(original_data)
         self.hours_per_time_step = hours_per_time_step
         self.hours_per_period = hours_per_period
@@ -140,12 +141,7 @@ def describe_clusters(self) -> str:
     def use_extreme_periods(self):
         return self.time_series_for_high_peaks or self.time_series_for_low_peaks
 
-    def plot(
-        self,
-        colormap: str = 'viridis',
-        show: bool = True,
-        save: Optional[pathlib.Path] = None
-    ) -> 'go.Figure':
+    def plot(self, colormap: str = 'viridis', show: bool = True, save: Optional[pathlib.Path] = None) -> 'go.Figure':
         from . import plotting
 
         df_org = self.original_data.copy().rename(
@@ -339,33 +335,44 @@ def do_modeling(self):
         penalty = self.aggregation_parameters.penalty_of_period_freedom
         if (self.aggregation_parameters.percentage_of_period_freedom > 0) and penalty != 0:
             for variable in self.variables_direct.values():
-                self._model.effects.add_share_to_penalty(
-                    'Aggregation',
-                    variable * penalty
-                )
+                self._model.effects.add_share_to_penalty('Aggregation', variable * penalty)
 
     def _equate_indices(self, variable: linopy.Variable, indices: Tuple[np.ndarray, np.ndarray]) -> None:
         assert len(indices[0]) == len(indices[1]), 'The length of the indices must match!!'
         length = len(indices[0])
 
         # Gleichung:
         # eq1: x(p1,t) - x(p3,t) = 0 # wobei p1 und p3 im gleichen Cluster sind und t = 0..N_p
-        con = self.add(self._model.add_constraints(
-            variable.isel(time=indices[0]) - variable.isel(time=indices[1]) == 0,
-            name=f'{self.label_full}|equate_indices|{variable.name}'),
-            f'equate_indices|{variable.name}')
+        con = self.add(
+            self._model.add_constraints(
+                variable.isel(time=indices[0]) - variable.isel(time=indices[1]) == 0,
+                name=f'{self.label_full}|equate_indices|{variable.name}',
+            ),
+            f'equate_indices|{variable.name}',
+        )
 
         # Korrektur: (bisher nur für Binärvariablen:)
-        if variable.name in self._model.variables.binaries and self.aggregation_parameters.percentage_of_period_freedom > 0:
-            var_k1 = self.add(self._model.add_variables(
-                binary=True,
-                coords={'time': variable.isel(time=indices[0]).indexes['time']},
-                name=f'{self.label_full}|correction1|{variable.name}'), f'correction1|{variable.name}')
+        if (
+            variable.name in self._model.variables.binaries
+            and self.aggregation_parameters.percentage_of_period_freedom > 0
+        ):
+            var_k1 = self.add(
+                self._model.add_variables(
+                    binary=True,
+                    coords={'time': variable.isel(time=indices[0]).indexes['time']},
+                    name=f'{self.label_full}|correction1|{variable.name}',
+                ),
+                f'correction1|{variable.name}',
+            )
 
-            var_k0 = self.add(self._model.add_variables(
-                binary=True,
-                coords={'time': variable.isel(time=indices[0]).indexes['time']},
-                name=f'{self.label_full}|correction0|{variable.name}'), f'correction0|{variable.name}')
+            var_k0 = self.add(
+                self._model.add_variables(
+                    binary=True,
+                    coords={'time': variable.isel(time=indices[0]).indexes['time']},
+                    name=f'{self.label_full}|correction0|{variable.name}',
+                ),
+                f'correction0|{variable.name}',
+            )
 
             # equation extends ...
             # --> On(p3) can be 0/1 independent of On(p1,t)!
@@ -377,16 +384,20 @@ def _equate_indices(self, variable: linopy.Variable, indices: Tuple[np.ndarray,
 
             # interlock var_k1 and var_K2:
             # eq: var_k0(t)+var_k1(t) <= 1.1
-            self.add(self._model.add_constraints(
-                var_k0 + var_k1 <= 1.1,
-                name=f'{self.label_full}|lock_k0_and_k1|{variable.name}'),
-                f'lock_k0_and_k1|{variable.name}'
+            self.add(
+                self._model.add_constraints(
+                    var_k0 + var_k1 <= 1.1, name=f'{self.label_full}|lock_k0_and_k1|{variable.name}'
+                ),
+                f'lock_k0_and_k1|{variable.name}',
             )
 
             # Begrenzung der Korrektur-Anzahl:
             # eq: sum(K) <= n_Corr_max
-            self.add(self._model.add_constraints(
-                sum(var_k0) + sum(var_k1) <= round(self.aggregation_parameters.percentage_of_period_freedom / 100 * length),
-                name=f'{self.label_full}|limit_corrections|{variable.name}'),
-                f'limit_corrections|{variable.name}'
+            self.add(
+                self._model.add_constraints(
+                    sum(var_k0) + sum(var_k1)
+                    <= round(self.aggregation_parameters.percentage_of_period_freedom / 100 * length),
+                    name=f'{self.label_full}|limit_corrections|{variable.name}',
+                ),
+                f'limit_corrections|{variable.name}',
             )