Feature/398 feature facet plots in results (#419)

.github/CONTRIBUTING.md

@@ -12,7 +12,7 @@ Thanks for your interest in contributing to FlixOpt! 🚀
 
 2. **Install for Development**
    ```bash
-   pip install -e ".[full]"
+   pip install -e ".[full, dev, docs]"
    ```
 
 3. **Make Changes & Submit PR**

CHANGELOG.md

@@ -54,10 +54,12 @@ If upgrading from v2.x, see the [Migration Guide](https://flixopt.github.io/flix
 
 
 ### ✨ Added
+- Added faceting and animation options to plotting methods
 
 ### 💥 Breaking Changes
 
 ### ♻️ Changed
+- Changed indexer behaviour. Defaults to not indexing instead of the first value except for time. Also changed naming when indexing.
 
 ### 🗑️ Deprecated
 

examples/04_Scenarios/scenario_example.py

@@ -9,14 +9,17 @@
 
 if __name__ == '__main__':
     # Create datetime array starting from '2020-01-01' for the given time period
-    timesteps = pd.date_range('2020-01-01', periods=9, freq='h')
+    timesteps = pd.date_range('2020-01-01', periods=9 * 20, freq='h')
     scenarios = pd.Index(['Base Case', 'High Demand'])
     periods = pd.Index([2020, 2021, 2022])
 
     # --- Create Time Series Data ---
     # Heat demand profile (e.g., kW) over time and corresponding power prices
     heat_demand_per_h = pd.DataFrame(
-        {'Base Case': [30, 0, 90, 110, 110, 20, 20, 20, 20], 'High Demand': [30, 0, 100, 118, 125, 20, 20, 20, 20]},
+        {
+            'Base Case': [30, 0, 90, 110, 110, 20, 20, 20, 20] * 20,
+            'High Demand': [30, 0, 100, 118, 125, 20, 20, 20, 20] * 20,
+        },
         index=timesteps,
     )
     power_prices = np.array([0.08, 0.09, 0.10])
@@ -79,11 +82,11 @@
         discharging=fx.Flow('Q_th_unload', bus='Fernwärme', size=1000),
         capacity_in_flow_hours=fx.InvestParameters(effects_of_investment=20, fixed_size=30, mandatory=True),
         initial_charge_state=0,  # Initial storage state: empty
-        relative_maximum_charge_state=np.array([80, 70, 80, 80, 80, 80, 80, 80, 80]) * 0.01,
-        relative_maximum_final_charge_state=0.8,
+        relative_maximum_charge_state=np.array([80, 70, 80, 80, 80, 80, 80, 80, 80] * 20) * 0.01,
+        relative_maximum_final_charge_state=np.array([0.8, 0.5, 0.1]),
         eta_charge=0.9,
         eta_discharge=1,  # Efficiency factors for charging/discharging
-        relative_loss_per_hour=0.08,  # 8% loss per hour. Absolute loss depends on current charge state
+        relative_loss_per_hour=np.array([0.1, 0.2]),  # Assume 10% or 20% losses per hour in the scenarios
         prevent_simultaneous_charge_and_discharge=True,  # Prevent charging and discharging at the same time
     )
 
@@ -134,11 +137,7 @@
     print(df)
 
     # Plot charge state using matplotlib
-    fig, ax = calculation.results['Storage'].plot_charge_state(engine='matplotlib')
-    # Customize the plot further if needed
-    ax.set_title('Storage Charge State Over Time')
-    # Or save the figure
-    # fig.savefig('storage_charge_state.png')
+    calculation.results['Storage'].plot_charge_state()
 
     # Save results to file for later usage
     calculation.results.to_file()