Fix - Some minor fixes, see changelog

CHANGELOG.md

@@ -1,5 +1,11 @@
 # Changelog
 
+## [0.4.10] - 2023-05-21
+### Fix
+- Fixed wrong name of new cost sensor.
+- Fixed units of measurements of costs to €/kWh.
+- Added color sequence to plot figures, now avery line should be plotted with a different color.
+
 ## [0.4.9] - 2023-05-20
 ### Fix
 - Updated default value for total number of days for ML model training.

src/emhass/utils.py

@@ -136,8 +136,8 @@ def treat_runtimeparams(runtimeparams: str, params: str, retrieve_hass_conf: dic
                            'custom_batt_soc_forecast_id': {"entity_id": "sensor.soc_batt_forecast", "unit_of_measurement": "%", "friendly_name": "Battery SOC Forecast"},
                            'custom_grid_forecast_id': {"entity_id": "sensor.p_grid_forecast", "unit_of_measurement": "W", "friendly_name": "Grid Power Forecast"},
                            'custom_cost_fun_id': {"entity_id": "sensor.total_cost_fun_value", "unit_of_measurement": "", "friendly_name": "Total cost function value"},
-                           'custom_unit_load_cost_id': {"entity_id": "sensor.unit_load_cost", "unit_of_measurement": "€", "friendly_name": "Unit Load Cost"},
-                           'custom_unit_prod_price_id': {"entity_id": "sensor.unit_prod_price", "unit_of_measurement": "€", "friendly_name": "Unit Prod Price"},
+                           'custom_unit_load_cost_id': {"entity_id": "sensor.unit_load_cost", "unit_of_measurement": "€/kWh", "friendly_name": "Unit Load Cost"},
+                           'custom_unit_prod_price_id': {"entity_id": "sensor.unit_prod_price", "unit_of_measurement": "€/kWh", "friendly_name": "Unit Prod Price"},
                            'custom_deferrable_forecast_id': custom_deferrable_forecast_id}
     if 'passed_data' in params.keys():
         for key, value in default_passed_dict.items():

src/emhass/web_server.py

@@ -30,16 +30,23 @@
 def get_injection_dict(df, plot_size = 1366):
     # Create plots
     cols_p = [i for i in df.columns.to_list() if 'P_' in i]
+    n_colors = len(cols_p)
+    colors = px.colors.sample_colorscale("jet", [n/(n_colors -1) for n in range(n_colors)])
     fig_0 = px.line(df[cols_p], title='Systems powers schedule after optimization results', 
-                    template='presentation', width=plot_size, height=0.5*plot_size, line_shape="hv")
+                    template='presentation', width=plot_size, height=0.5*plot_size, line_shape="hv",
+                    color_discrete_sequence=colors)
     fig_0.update_layout(xaxis_title='Timestamp', yaxis_title='System powers (W)')
     if 'SOC_opt' in df.columns.to_list():
         fig_1 = px.line(df['SOC_opt'], title='Battery state of charge schedule after optimization results', 
-                        template='presentation', width=plot_size, height=0.5*plot_size, line_shape="hv")
+                        template='presentation', width=plot_size, height=0.5*plot_size, line_shape="hv",
+                        color_discrete_sequence=colors)
         fig_1.update_layout(xaxis_title='Timestamp', yaxis_title='Battery SOC (%)')
     cols_cost = [i for i in df.columns.to_list() if 'cost_' in i or 'unit_' in i]
+    n_colors = len(cols_cost)
+    colors = px.colors.sample_colorscale("jet", [n/(n_colors -1) for n in range(n_colors)])
     fig_2 = px.line(df[cols_cost], title='Systems costs obtained from optimization results', 
-                      template='presentation', width=plot_size, height=0.5*plot_size, line_shape="hv")
+                    template='presentation', width=plot_size, height=0.5*plot_size, line_shape="hv",
+                    color_discrete_sequence=colors)
     fig_2.update_layout(xaxis_title='Timestamp', yaxis_title='System costs (currency)')
     # Get full path to image
     image_path_0 = fig_0.to_html(full_html=False, default_width='75%')