Feature/v3/feature/common resources

CHANGELOG.md

@@ -150,7 +150,7 @@ This replaces `specific_share_to_other_effects_*` parameters and inverts the dir
 - Rewrote README and landing page with clearer vision, roadmap, and universal applicability emphasis
 - Removed deprecated `docs/SUMMARY.md`, updated `mkdocs.yml` for new structure
 - Tightened docstrings in core modules with better cross-referencing
-- Added recipies section to docs
+- Added recipes section to docs
 
 ### 🚧 Known Issues
 

docs/index.md

@@ -19,7 +19,7 @@ We believe that optimization modeling should be **approachable for beginners** y
 - **Interactive tutorials**: Browser-based, reactive tutorials for learning FlixOpt without local installation (marimo)
 - **Standardized cost calculations**: Align with industry standards (VDI 2067) for CAPEX/OPEX calculations
 - **Advanced result analysis**: Time-series aggregation, automated reporting, and rich visualization options
-- **Recipe collection**: Community-driven library of common modeling patterns, data manipulation techniques, and optimization strategies (see [Recipes](user-guide/recipies/index.md) - help wanted!)
+- **Recipe collection**: Community-driven library of common modeling patterns, data manipulation techniques, and optimization strategies (see [Recipes](user-guide/recipes/index.md) - help wanted!)
 
 **Long-term vision:**
 

examples/02_Complex/complex_example.py

@@ -75,9 +75,8 @@
                 on_hours_total_min=0,  # Minimum operating hours
                 on_hours_total_max=1000,  # Maximum operating hours
                 consecutive_on_hours_max=10,  # Max consecutive operating hours
-                consecutive_on_hours_min=np.array(
-                    [1, 1, 1, 1, 1, 2, 2, 2, 2]
-                ),  # min consecutive operation hoursconsecutive_off_hours_max=10,  # Max consecutive off hours
+                consecutive_on_hours_min=np.array([1, 1, 1, 1, 1, 2, 2, 2, 2]),  # min consecutive operation hours
+                consecutive_off_hours_max=10,  # Max consecutive off hours
                 effects_per_switch_on=0.01,  # Cost per switch-on
                 switch_on_total_max=1000,  # Max number of starts
             ),
@@ -191,7 +190,7 @@
     flow_system.add_elements(bhkw_2) if use_chp_with_piecewise_conversion else flow_system.add_elements(bhkw)
 
     pprint(flow_system)  # Get a string representation of the FlowSystem
-    flow_system.start_network_app()  # Start the network app. DOes only work with extra dependencies installed
+    flow_system.start_network_app()  # Start the network app. Does only work with extra dependencies installed
 
     # --- Solve FlowSystem ---
     calculation = fx.FullCalculation('complex example', flow_system, time_indices)

examples/03_Calculation_types/example_calculation_types.py

@@ -33,7 +33,9 @@
     excess_penalty = 1e5  # or set to None if not needed
 
     # Data Import
-    data_import = pd.read_csv(pathlib.Path('Zeitreihen2020.csv'), index_col=0).sort_index()
+    data_import = pd.read_csv(
+        pathlib.Path(__file__).parent.parent / 'resources' / 'Zeitreihen2020.csv', index_col=0
+    ).sort_index()
     filtered_data = data_import['2020-01-01':'2020-01-02 23:45:00']
     # filtered_data = data_import[0:500]  # Alternatively filter by index
 

examples/04_Scenarios/scenario_example.py

@@ -132,8 +132,13 @@
     # Convert the results for the storage component to a dataframe and display
     df = calculation.results['Storage'].node_balance_with_charge_state()
     print(df)
-    calculation.results['Storage'].plot_charge_state(engine='matplotlib')
+
+    # 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')
 
     # Save results to file for later usage
     calculation.results.to_file()
-    fig, ax = calculation.results['Storage'].plot_charge_state(engine='matplotlib')