move cost comparison to dashboard (closes #131)

app/tab_dashboard.py

@@ -1,13 +1,17 @@
 # -*- coding: utf-8 -*-
 """Content of dashboard tab."""
+import pandas as pd
 import streamlit as st
 from plotly.subplots import make_subplots
 
+from app.layout_elements import display_costs
 from app.plot_functions import (
     create_bar_chart_costs,
     create_box_plot,
     plot_costs_on_map,
 )
+from app.ptxboa_functions import remove_subregions
+from ptxboa.api import PtxboaAPI
 
 
 def _create_infobox(context_data: dict):
@@ -30,7 +34,14 @@ def write_info(info):
     write_info(info4)
 
 
-def content_dashboard(api, res_costs: dict, context_data: dict):
+def content_dashboard(
+    api: PtxboaAPI,
+    costs_per_region: pd.DataFrame,
+    costs_per_scenario: pd.DataFrame,
+    costs_per_res_gen: pd.DataFrame,
+    costs_per_chain: pd.DataFrame,
+    context_data: dict,
+):
     with st.expander("What is this?"):
         st.markdown(
             """
@@ -39,6 +50,8 @@ def content_dashboard(api, res_costs: dict, context_data: dict):
 regional distribution of total costs across supply regions
 - a split-up of costs by category for your chosen supply region
 - key information on your chosen demand country.
+- total cost and cost components for different supply countries, scenarios,
+renewable electricity sources and process chains.
 
 Switch to other tabs to explore data and results in more detail!
             """
@@ -48,14 +61,16 @@ def content_dashboard(api, res_costs: dict, context_data: dict):
 
     with c_1:
         fig_map = plot_costs_on_map(
-            api, res_costs, scope="world", cost_component="Total"
+            api, costs_per_region, scope="world", cost_component="Total"
         )
         st.plotly_chart(fig_map, use_container_width=True)
 
     with c_2:
         # create box plot and bar plot:
-        fig1 = create_box_plot(res_costs)
-        filtered_data = res_costs[res_costs.index == st.session_state["region"]]
+        fig1 = create_box_plot(costs_per_region)
+        filtered_data = costs_per_region[
+            costs_per_region.index == st.session_state["region"]
+        ]
         fig2 = create_bar_chart_costs(filtered_data)
         doublefig = make_subplots(rows=1, cols=2, shared_yaxes=True)
 
@@ -70,3 +85,17 @@ def content_dashboard(api, res_costs: dict, context_data: dict):
         st.plotly_chart(doublefig, use_container_width=True)
 
         _create_infobox(context_data)
+
+    display_costs(
+        remove_subregions(api, costs_per_region, st.session_state["country"]),
+        "region",
+        "Costs by region:",
+    )
+
+    display_costs(costs_per_scenario, "scenario", "Costs by data scenario:")
+
+    display_costs(
+        costs_per_res_gen, "res_gen", "Costs by renewable electricity source:"
+    )
+
+    display_costs(costs_per_chain, "chain", "Costs by supply chain:")

ptxboa_streamlit.py

@@ -10,7 +10,6 @@
 from app.context_data import load_context_data
 from app.sidebar import make_sidebar
 from app.tab_certification_schemes import content_certification_schemes
-from app.tab_compare_costs import content_compare_costs
 from app.tab_country_fact_sheets import content_country_fact_sheets
 from app.tab_dashboard import content_dashboard
 from app.tab_deep_dive_countries import content_deep_dive_countries
@@ -62,7 +61,6 @@
 (
     t_dashboard,
     t_market_scanning,
-    t_compare_costs,
     t_input_data,
     t_deep_dive_countries,
     t_country_fact_sheets,
@@ -74,7 +72,6 @@
     [
         "Dashboard",
         "Market scanning",
-        "Compare costs",
         "Input data",
         "Deep-dive countries",
         "Country fact sheets",
@@ -130,20 +127,18 @@
 
 # dashboard:
 with t_dashboard:
-    content_dashboard(api, costs_per_region, cd)
-
-with t_market_scanning:
-    content_market_scanning(api, costs_per_region)
-
-with t_compare_costs:
-    content_compare_costs(
+    content_dashboard(
         api,
         costs_per_region=costs_per_region,
         costs_per_scenario=costs_per_scenario,
         costs_per_res_gen=costs_per_res_gen,
         costs_per_chain=costs_per_chain,
+        context_data=cd,
     )
 
+with t_market_scanning:
+    content_market_scanning(api, costs_per_region)
+
 with t_input_data:
     content_input_data(api)