from oemof import solph
 
 solver = "cbc"  # 'glpk', 'gurobi',...
 solver_verbose = True  # show/hide solver output
-time_steps = 24*31  # 8760
+time_steps = 24 * 31  # 8760
 
 date_time_index = pd.date_range("1/1/2000", periods=time_steps, freq="H")
 
 rng = np.random.default_rng(seed=1337)
 random_costs = rng.exponential(size=time_steps)
 random_demands = rng.uniform(size=time_steps)
 random_losses = np.minimum(
-    np.ones(time_steps),
-    rng.exponential(scale=1e-1, size=time_steps)
+    np.ones(time_steps), rng.exponential(scale=1e-1, size=time_steps)
 )
 
 
 def run_energy_system(index, costs, demands, losses, expected=None):
     energy_system = solph.EnergySystem(timeindex=index)

         }
 
     bus = solph.buses.Bus(label="bus")
     source = solph.components.Source(
         label="source",
-        outputs={bus: solph.flows.Flow(
-            variable_costs=costs,
-            nonconvex=solph.NonConvex(activity_costs=0.01),
-            min=0.2,
-            nominal_value=100,
-        )},
+        outputs={
+            bus: solph.flows.Flow(
+                variable_costs=costs,
+                nonconvex=solph.NonConvex(activity_costs=0.01),
+                min=0.2,
+                nominal_value=100,
+            )
+        },
     )
     storage = solph.components.GenericStorage(
         label="storage",
-        inputs={bus: solph.flows.Flow(
-            expected=expected[(("bus", "storage"), "flow")])},
-        outputs={bus: solph.flows.Flow(
-            expected=expected[(("storage", "bus"), "flow")])},
+        inputs={
+            bus: solph.flows.Flow(
+                expected=expected[(("bus", "storage"), "flow")]
+            )
+        },
+        outputs={
+            bus: solph.flows.Flow(
+                expected=expected[(("storage", "bus"), "flow")]
+            )
+        },
         nominal_storage_capacity=1e4,
         loss_rate=losses,
     )
     sink = solph.components.Sink(
         label="sink",
-        inputs={
-            bus: solph.flows.Flow(nominal_value=1, fix=demands)
-        },
+        inputs={bus: solph.flows.Flow(nominal_value=1, fix=demands)},
     )
 
     energy_system.add(bus, source, sink, storage)
     model = solph.Model(energy_system)
-    model.solve(solver=solver, solve_kwargs={
-        "tee": solver_verbose,
-        "warmstart": True,
-    })
+    model.solve(
+        solver=solver,
+        solve_kwargs={
+            "tee": solver_verbose,
+            "warmstart": True,
+        },
+    )
 
     _results = solph.processing.results(model)
     _meta_results = solph.processing.meta_results(model)
     return _results, _meta_results
 
 
 meta_results = {}
 
 results, meta_results["no hints 1"] = run_energy_system(
-    date_time_index, random_costs, random_demands, random_losses)
+    date_time_index, random_costs, random_demands, random_losses
+)
 bus_data = solph.views.node(results, "bus")["sequences"]
 _, meta_results["no hints 2"] = run_energy_system(
-    date_time_index, random_costs, random_demands, random_losses)
+    date_time_index, random_costs, random_demands, random_losses
+)
 _, meta_results["with hints"] = run_energy_system(
-    date_time_index, random_costs, random_demands, random_losses,
+    date_time_index,
+    random_costs,
+    random_demands,
+    random_losses,
     expected=bus_data,
 )
 _, meta_results["no hints 3"] = run_energy_system(
-    date_time_index, random_costs, random_demands, random_losses)
+    date_time_index, random_costs, random_demands, random_losses
+)
 
 for meta_result in meta_results:
-    print("Time to solve run {run}: {time:.2f} s".format(
-        run=meta_result,
-        time=meta_results[meta_result]['solver']['Wallclock time'])
+    print(
+        "Time to solve run {run}: {time:.2f} s".format(
+            run=meta_result,
+            time=meta_results[meta_result]["solver"]["Wallclock time"],
+        )
     )