Deploy from GitHub Actions

app.py

@@ -10,7 +10,6 @@ from pages import (
     home_page,
     nutrient_ratios_page,
     parameter_correlations_page,
-    parameter_summary_tables_page,
     raw_data_page,
     scatter_plots_page,
     seasonal_line_charts_page,
@@ -18,6 +17,7 @@ from pages import (
     sector_compare_page,
     sector_trends_page,
     settings_page,
+    tables_page,
     trends_by_station_page,
 )
 from utils.session import ensure_session_initialized
@@ -46,11 +46,11 @@ analytics = st.Page(
 page_dict = {}
 
 page_dict["Annual Report Draft Charts/Tables"] = [
-    seasonal_line_charts_page,
-    seasonal_maps_page,
     sector_compare_page,
+    seasonal_maps_page,
+    seasonal_line_charts_page,
     scatter_plots_page,
-    parameter_summary_tables_page,
+    tables_page,
 ]
 
 page_dict["Water Quality Portal"] = [

pages.py

@@ -659,9 +659,9 @@ dissolved_oxygen_page = st.Page(
     icon=":material/water_drop:",
 )
 
-parameter_summary_tables_page = st.Page(
-    "ui/pages/parameter_summary_tables.py",
-    title="Parameter Summary Tables",
+tables_page = st.Page(
+    "ui/pages/tables.py",
+    title="Tables",
     icon=":material/table_chart:",
 )
 scatter_plots_page = st.Page(
@@ -687,7 +687,7 @@ __all__ = [
     "do_temp_relationship_page",
     "raw_data_page",
     "settings_page",
-    "parameter_summary_tables_page",
+    "tables_page",
     "scatter_plots_page",
     "seasonal_line_charts_page",
     "sector_compare_page",

plots/map.py

@@ -13,6 +13,15 @@ from osgeo import gdal
 
 from utils.data_loading import timer
 
+BASEMAP_PROVIDERS = {
+    "USGS Topo": ctx.providers.USGS.USTopo,  # type: ignore
+    "OpenStreetMap": ctx.providers.OpenStreetMap.Mapnik,  # type: ignore
+    "CartoDB Light": ctx.providers.CartoDB.Positron,  # type: ignore
+    "CartoDB Voyager": ctx.providers.CartoDB.Voyager,  # type: ignore
+    "NASAGIBS.ASTER_GDEM_Greyscale_Shaded_Relief": ctx.providers.NASAGIBS.ASTER_GDEM_Greyscale_Shaded_Relief,  # type: ignore
+    "OpenTopoMap": ctx.providers.OpenTopoMap,  # type: ignore
+}
+
 
 @timer(include_params=True)
 def generate_seasonal_plot(

ui/pages/seasonal_maps.py

@@ -1,12 +1,11 @@
 import io
 
-import contextily as ctx
 import pandas as pd
 import streamlit as st
 
 from components import render_filtered_data_preview
 from dashboard_analytics import log_visit
-from plots.map import generate_seasonal_plot
+from plots.map import BASEMAP_PROVIDERS, generate_seasonal_plot
 from utils.data_loading import add_lat_long, get_stations_data
 from utils.date_utils import get_reporting_year
 
@@ -24,7 +23,7 @@ timeframe = st.sidebar.radio(
 # parameter selection
 parameters = sorted(raw_df["Org_Analyte_Name"].unique())
 selected_parameter = st.sidebar.selectbox(
-    "Select Parameter:",
+    "Parameter:",
     parameters,
     index=parameters.index("Salinity") if "Salinity" in parameters else 0,
     key="seasonal_parameter_select",
@@ -38,7 +37,7 @@ param_years = sorted(
 # Show either single year selector or year range based on timeframe
 if timeframe == "Single Year":
     selected_year = st.sidebar.selectbox(
-        "Select Year:",
+        "Year:",
         param_years,
         index=len(param_years) - 1,
         key="seasonal_year_select",
@@ -55,7 +54,7 @@ else:
 
 # Add reporting year end month selector
 reporting_end_month = st.sidebar.selectbox(
-    "Reporting Year End Month",
+    "Reporting Year End-Month",
     options=range(1, 13),
     index=10,  # Default to November
     format_func=lambda x: pd.Timestamp(2000, x, 1).strftime("%B"),  # Show month names
@@ -68,18 +67,9 @@ raw_df["Reporting_Year"] = raw_df["Activity_Start_Date_Time"].apply(
     lambda x: get_reporting_year(x, reporting_end_month)
 )
 # Add basemap selector
-basemap_options = {
-    "OpenStreetMap": ctx.providers.OpenStreetMap.Mapnik,  # type: ignore
-    "USGS Topo": ctx.providers.USGS.USTopo,  # type: ignore
-    "CartoDB Light": ctx.providers.CartoDB.Positron,  # type: ignore
-    "CartoDB Voyager": ctx.providers.CartoDB.Voyager,  # type: ignore
-    "NASAGIBS.ASTER_GDEM_Greyscale_Shaded_Relief": ctx.providers.NASAGIBS.ASTER_GDEM_Greyscale_Shaded_Relief,  # type: ignore
-    "OpenTopoMap": ctx.providers.OpenTopoMap,  # type: ignore
-}
-
 selected_basemap = st.sidebar.selectbox(
     "Basemap Provider:",
-    options=list(basemap_options.keys()),
+    options=list(BASEMAP_PROVIDERS.keys()),
     index=0,
     key="seasonal_basemap_select",
 )
@@ -125,7 +115,7 @@ if not raw_df.empty:
         areas=sectors,
         area_type="sector",
         reporting_end_month=reporting_end_month,
-        basemap_provider=basemap_options[selected_basemap],
+        basemap_provider=BASEMAP_PROVIDERS[selected_basemap],
     )
     st.pyplot(fig)
 

ui/pages/tables.py

@@ -0,0 +1,99 @@
+import calendar
+
+import pandas as pd
+import streamlit as st
+
+from dashboard_analytics import log_visit
+from utils.data_loading import create_summaries
+from utils.summary import create_overall_summary_table
+
+st.title("Summary Tables")
+log_visit("Tables")
+
+# Create summaries if they don't exist
+if "overall_summary" not in st.session_state.data:
+    summary_by_station, overall_summary, multiindex_df = create_summaries(
+        st.session_state.data["raw_df"]
+    )
+    st.session_state.data.update(
+        {
+            "summary_by_station": summary_by_station,
+            "overall_summary": overall_summary,
+            "multiindex_df": multiindex_df,
+        }
+    )
+
+# Create tabs for different summary views
+tab1, tab2 = st.tabs(["Overall Summary", "Monthly Means"])
+
+with tab1:
+    st.html(
+        create_overall_summary_table(
+            st.session_state.data["overall_summary"]
+        ).as_raw_html()
+    )
+
+with tab2:
+    # Get data from session state
+    raw_df = st.session_state.data["raw_df"].copy()
+
+    # Create datetime column if not exists
+    raw_df["Date"] = pd.to_datetime(raw_df["Activity_Start_Date_Time"])
+
+    # Add month and year columns
+    raw_df["Month"] = raw_df["Date"].dt.strftime("%b")  # 3-letter month names
+    raw_df["Year"] = raw_df["Date"].dt.year
+
+    # Move parameter selection into tab2
+    selected_parameter = st.selectbox(
+        "Parameter:",
+        options=sorted(raw_df["Org_Analyte_Name"].unique()),
+        index=0,
+        help="Select a parameter to display monthly averages.",
+    )
+
+    # Filter data for selected parameter
+    param_df = raw_df[raw_df["Org_Analyte_Name"] == selected_parameter]
+
+    # Create pivot table
+    pivot_table = pd.pivot_table(
+        param_df,
+        values="Org_Result_Value",
+        index="Year",
+        columns="Month",
+        aggfunc="mean",
+    )
+
+    # Reorder columns to match calendar months
+    month_order = [calendar.month_abbr[i] for i in range(1, 13)]
+    pivot_table = pivot_table.reindex(columns=month_order)
+    pivot_table = pivot_table.round(2)
+
+    # Add yearly mean column
+    pivot_table_with_yearly_mean = pivot_table.copy()
+    pivot_table_with_yearly_mean["Annual Mean"] = pivot_table.mean(axis=1)
+
+    # Add grand mean row
+    grand_mean = pivot_table.mean()
+    pivot_table_with_yearly_mean.loc["Grand Mean"] = grand_mean
+
+    # Display the pivot table
+    st.markdown(f"### Monthly Means: {selected_parameter}")
+    st.dataframe(
+        pivot_table_with_yearly_mean.reset_index().style.format(
+            na_rep="", precision=2, thousands=""
+        ),
+        use_container_width=True,
+        hide_index=True,
+        height=(len(pivot_table_with_yearly_mean) + 1) * 35
+        + 2,  # 35 pixels per row, +1 for header
+    )
+
+    # Add download button
+    csv_buffer = pivot_table_with_yearly_mean.to_csv().encode()
+    st.download_button(
+        label="Download Table (CSV)",
+        data=csv_buffer,
+        file_name=f"{selected_parameter}_monthly_means.csv",
+        mime="text/csv",
+    )