Refactor app functions and tabs

app/context_data.py

@@ -0,0 +1,30 @@
+# -*- coding: utf-8 -*-
+"""Module for loading context data."""
+import pandas as pd
+import streamlit as st
+
+
+@st.cache_data()
+def load_context_data():
+    """Import context data from excel file."""
+    filename = "data/context_data.xlsx"
+    cd = {}
+    cd["demand_countries"] = pd.read_excel(
+        filename, sheet_name="demand_countries", skiprows=1
+    )
+    cd["certification_schemes_countries"] = pd.read_excel(
+        filename, sheet_name="certification_schemes_countries"
+    )
+    cd["certification_schemes"] = pd.read_excel(
+        filename, sheet_name="certification_schemes", skiprows=1
+    )
+    cd["sustainability"] = pd.read_excel(filename, sheet_name="sustainability")
+    cd["supply"] = pd.read_excel(filename, sheet_name="supply", skiprows=1)
+    cd["literature"] = pd.read_excel(filename, sheet_name="literature")
+    cd["infobox"] = pd.read_excel(
+        filename,
+        sheet_name="infobox",
+        usecols="A:F",
+        skiprows=1,
+    ).set_index("country_name")
+    return cd

app/plot_functions.py

@@ -0,0 +1,216 @@
+# -*- coding: utf-8 -*-
+"""Functions for plotting input data and results (cost_data)."""
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+import streamlit as st
+
+from app.ptxboa_functions import remove_subregions
+from ptxboa.api import PtxboaAPI
+
+
+def create_world_map(api: PtxboaAPI, res_costs: pd.DataFrame):
+    """Create world map."""
+    parameter_to_show_on_map = "Total"
+
+    # define title:
+    title_string = (
+        f"{parameter_to_show_on_map} cost of exporting"
+        f"{st.session_state['chain']} to "
+        f"{st.session_state['country']}"
+    )
+    # define color scale:
+    color_scale = [
+        (0, st.session_state["colors"][0]),  # Starting color at the minimum data value
+        (0.5, st.session_state["colors"][6]),
+        (1, st.session_state["colors"][9]),  # Ending color at the maximum data value
+    ]
+
+    # remove subregions from deep dive countries (otherwise colorscale is not correct)
+    res_costs = remove_subregions(api, res_costs, st.session_state["country"])
+
+    # Create custom hover text:
+    custom_hover_data = res_costs.apply(
+        lambda x: f"<b>{x.name}</b><br><br>"
+        + "<br>".join(
+            [
+                f"<b>{col}</b>: {x[col]:.1f}" f"{st.session_state['output_unit']}"
+                for col in res_costs.columns[:-1]
+            ]
+            + [
+                f"──────────<br><b>{res_costs.columns[-1]}</b>: "
+                f"{x[res_costs.columns[-1]]:.1f}"
+                f"{st.session_state['output_unit']}"
+            ]
+        ),
+        axis=1,
+    )
+
+    # Create a choropleth world map:
+    fig = px.choropleth(
+        locations=res_costs.index,  # List of country codes or names
+        locationmode="country names",  # Use country names as locations
+        color=res_costs[parameter_to_show_on_map],  # Color values for the countries
+        custom_data=[custom_hover_data],  # Pass custom data for hover information
+        color_continuous_scale=color_scale,  # Choose a color scale
+        title=title_string,  # set title
+    )
+
+    # update layout:
+    fig.update_geos(
+        showcountries=True,  # Show country borders
+        showcoastlines=True,  # Show coastlines
+        countrycolor="black",  # Set default border color for other countries
+        countrywidth=0.2,  # Set border width
+        coastlinewidth=0.2,  # coastline width
+        coastlinecolor="black",  # coastline color
+        showland=True,  # show land areas
+        landcolor="#f3f4f5",  # Set land color to light gray
+        oceancolor="#e3e4ea",  # Optionally, set ocean color slightly darker gray
+        showocean=True,  # show ocean areas
+        framewidth=0.2,  # width of frame around map
+    )
+
+    fig.update_layout(
+        coloraxis_colorbar={"title": st.session_state["output_unit"]},  # colorbar
+        height=600,  # height of figure
+        margin={"t": 20, "b": 20, "l": 20, "r": 20},  # reduce margin around figure
+    )
+
+    # Set the hover template to use the custom data
+    fig.update_traces(hovertemplate="%{customdata}<extra></extra>")  # Custom data
+
+    # Display the map:
+    st.plotly_chart(fig, use_container_width=True)
+    return
+
+
+def create_bar_chart_costs(res_costs: pd.DataFrame, current_selection: str = None):
+    """Create bar plot for costs by components, and dots for total costs.
+
+    Parameters
+    ----------
+    res_costs : pd.DataFrame
+        data for plotting
+    settings : dict
+        settings dictionary, like output from create_sidebar()
+    current_selection : str
+        bar to highlight with an arrow. must be an element of res_costs.index
+
+    Output
+    ------
+    fig : plotly.graph_objects.Figure
+        Figure object
+    """
+    if res_costs.empty:  # nodata to plot (FIXME: migth not be required later)
+        return go.Figure()
+
+    fig = px.bar(
+        res_costs,
+        x=res_costs.index,
+        y=res_costs.columns[:-1],
+        height=500,
+        color_discrete_sequence=st.session_state["colors"],
+    )
+
+    # Add the dot markers for the "total" column using plotly.graph_objects
+    scatter_trace = go.Scatter(
+        x=res_costs.index,
+        y=res_costs["Total"],
+        mode="markers+text",  # Display markers and text
+        marker={"size": 10, "color": "black"},
+        name="Total",
+        text=res_costs["Total"].apply(
+            lambda x: f"{x:.2f}"
+        ),  # Use 'total' column values as text labels
+        textposition="top center",  # Position of the text label above the marker
+    )
+
+    fig.add_trace(scatter_trace)
+
+    # add highlight for current selection:
+    if current_selection is not None and current_selection in res_costs.index:
+        fig.add_annotation(
+            x=current_selection,
+            y=1.2 * res_costs.at[current_selection, "Total"],
+            text="current selection",
+            showarrow=True,
+            arrowhead=2,
+            arrowsize=1,
+            arrowwidth=2,
+            ax=0,
+            ay=-50,
+        )
+    fig.update_layout(
+        yaxis_title=st.session_state["output_unit"],
+    )
+    return fig
+
+
+def create_box_plot(res_costs: pd.DataFrame):
+    """Create a subplot with one row and one column.
+
+    Parameters
+    ----------
+    res_costs : pd.DataFrame
+        data for plotting
+    settings : dict
+        settings dictionary, like output from create_sidebar()
+
+    Output
+    ------
+    fig : plotly.graph_objects.Figure
+        Figure object
+    """
+    fig = go.Figure()
+
+    # Specify the row index of the data point you want to highlight
+    highlighted_row_index = st.session_state["region"]
+    # Extract the value from the specified row and column
+
+    if highlighted_row_index:
+        highlighted_value = res_costs.at[highlighted_row_index, "Total"]
+    else:
+        highlighted_value = 0
+
+    # Add the box plot to the subplot
+    fig.add_trace(go.Box(y=res_costs["Total"], name="Cost distribution"))
+
+    # Add a scatter marker for the highlighted data point
+    fig.add_trace(
+        go.Scatter(
+            x=["Cost distribution"],
+            y=[highlighted_value],
+            mode="markers",
+            marker={"size": 10, "color": "black"},
+            name=highlighted_row_index,
+            text=f"Value: {highlighted_value}",  # Add a text label
+        )
+    )
+
+    # Customize the layout as needed
+    fig.update_layout(
+        title="Cost distribution for all supply countries",
+        xaxis={"title": ""},
+        yaxis={"title": st.session_state["output_unit"]},
+        height=500,
+    )
+
+    return fig
+
+
+def create_scatter_plot(df_res, settings: dict):
+    df_res["Country"] = "Other countries"
+    df_res.at[st.session_state["region"], "Country"] = st.session_state["region"]
+
+    fig = px.scatter(
+        df_res,
+        y="Total",
+        x="tr_dst_sd",
+        color="Country",
+        text=df_res.index,
+        color_discrete_sequence=["blue", "red"],
+    )
+    fig.update_traces(texttemplate="%{text}", textposition="top center")
+    st.plotly_chart(fig)
+    st.write(df_res)