restructure optimization tab

agoenergy · May 15, 2024 · 5587a06 · 5587a06
1 parent eeb9927
commit 5587a06
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Showing 2 changed files with 30 additions and 24 deletions.
diff --git a/app/plot_functions.py b/app/plot_functions.py
@@ -594,9 +594,8 @@ def transform_time_series(
     return df_sel
 
 
-def create_profile_figure_generation(df_sel: pd.DataFrame) -> None:
-    # generation:
-    st.subheader("Output")
+def create_profile_figure_generation(df_sel: pd.DataFrame) -> go.Figure:
+    """Create generation profile figure."""
     fig = go.Figure()
 
     add_trace_to_figure(
@@ -632,11 +631,11 @@ def create_profile_figure_generation(df_sel: pd.DataFrame) -> None:
         yaxis={"title": "output (MW)"},
     )
 
-    st.plotly_chart(fig, use_container_width=True)
+    return fig
 
 
-def create_profile_figure_soc(df_sel: pd.DataFrame) -> None:
-    st.subheader("Storage state of charge")
+def create_profile_figure_soc(df_sel: pd.DataFrame) -> go.Figure:
+    """Create storage state of charge figure."""
     include_final_product_storage = st.toggle("Show final product storage", value=False)
     # storage figure:
     fig = go.Figure()
@@ -672,12 +671,11 @@ def create_profile_figure_soc(df_sel: pd.DataFrame) -> None:
         yaxis={"title": "state of charge (MWh)"},
     )
 
-    st.plotly_chart(fig, use_container_width=True)
-
+    return fig
 
-def create_profile_figure_capacity_factors(df_sel: pd.DataFrame) -> None:
-    st.subheader("Capacity factors")
 
+def create_profile_figure_capacity_factors(df_sel: pd.DataFrame) -> go.Figure:
+    """Create capacity factors profile figure."""
     fig = go.Figure()
     add_trace_to_figure(
         df_sel, fig, component="PV tilted", parameter="cap. factor", color="yellow"
@@ -697,7 +695,5 @@ def create_profile_figure_capacity_factors(df_sel: pd.DataFrame) -> None:
         color="blue",
     )
     add_vertical_lines(fig)
-    st.plotly_chart(fig, use_container_width=True)
 
-    st.subheader("Profile data")
-    st.dataframe(df_sel, use_container_width=True)
+    return fig
diff --git a/app/tab_optimization.py b/app/tab_optimization.py
@@ -16,8 +16,6 @@
 
 
 def content_optimization(api: PtxboaAPI) -> None:
-    st.subheader("Optimization results")
-    st.warning("Warning: Preliminary debugging results. ")
 
     with st.expander("What is this?"):
         st.markdown(read_markdown_file("md/whatisthis_optimization.md"))
@@ -46,32 +44,45 @@ def content_optimization(api: PtxboaAPI) -> None:
         res_debug = calc_aggregate_statistics(n, include_debugging_output=True)
         df_sel = prepare_data_for_profile_figures(n)
 
-        create_profile_figure_generation(df_sel)
+        with st.container(border=True):
+            st.subheader("Generation profiles")
+            fig = create_profile_figure_generation(df_sel)
+            st.plotly_chart(fig, use_container_width=True)
 
-        with st.expander("Aggregate statistics"):
+        with st.container(border=True):
+            st.subheader("Capacities, full load hours and costs")
             st.dataframe(res.round(2), use_container_width=True)
 
+        with st.container(border=True):
+            st.subheader("Download model")
+            download_network_as_netcdf(st.session_state["network"], "network.nc")
+
         with st.expander("Debugging output"):
             st.warning(
                 "This output is for debugging only. It will be hidden from end users by default."  # noqa
             )
-            st.markdown("### Aggregate statistics:")
+            st.markdown("#### Aggregate statistics:")
             st.dataframe(res_debug)
 
-            create_profile_figure_soc(df_sel)
+            st.markdown("#### Storage state of charge")
+            fig = create_profile_figure_soc(df_sel)
+            st.plotly_chart(fig, use_container_width=True)
+
+            st.markdown("#### Capacity factors")
+            fig = create_profile_figure_capacity_factors(df_sel)
+            st.plotly_chart(fig, use_container_width=True)
 
-            create_profile_figure_capacity_factors(df_sel)
+            st.markdown("#### Profile data")
+            st.dataframe(df_sel, use_container_width=True)
 
-            st.markdown("### Input data:")
+            st.markdown("#### Input data")
             show_input_data(n)
 
     else:
         st.error(
             f"No optimal solution! -> model status is {st.session_state['model_status']}"  # noqa
         )
 
-    download_network_as_netcdf(st.session_state["network"], "network.nc")
-
 
 # calculate aggregate statistics:
 def calc_aggregate_statistics(
@@ -173,7 +184,6 @@ def calc_aggregate_statistics(
                 "Capacity (kW)",
                 "Output (kWh/a)",
                 "Curtailment (%)",
-                "Full load hours before curtailment (h)",
                 "Full load hours (h)",
                 "Cost (USD/MWh)",
             ]