Deploy from GitHub Actions

components.py

@@ -62,6 +62,42 @@ def render_sidebar_analyte_multiselect(
     )
 
 
+def render_sidebar_analyte_pills(
+    all_analytes: List[str],
+    default_analytes: Optional[List[str]] = None,
+    key_prefix: str = "",
+    help_text: str = "Choose one or more analytes to display.",
+) -> List[str | None]:
+    """Render analyte pills"""
+    if default_analytes is None:
+        default_analytes = []
+
+    # Filter out any default values that aren't in the options list
+    valid_defaults = [
+        analyte for analyte in default_analytes if analyte in all_analytes
+    ]
+
+    widget_key = f"{key_prefix}_analyte_select"
+
+    if f"{widget_key}_previous_value" not in st.session_state:
+        values = valid_defaults
+    else:
+        values = st.session_state[f"{widget_key}_previous_value"]
+
+    def on_analyte_pills_change():
+        st.session_state[f"{widget_key}_previous_value"] = st.session_state[widget_key]
+
+    return st.sidebar.pills(
+        "Select Analytes:",
+        selection_mode="multi",
+        default=values,
+        options=all_analytes,
+        key=widget_key,
+        help=help_text,
+        on_change=on_analyte_pills_change,
+    )
+
+
 def render_filtered_data_preview(
     df: pd.DataFrame,
     display_columns: List[str],
@@ -168,12 +204,17 @@ def render_records_by_year(raw_df: pd.DataFrame, reporting_month: int) -> None:
 
         yearly_data = yearly_data[["Reporting Year", "Reporting Period", "Records"]]
 
+        row_height = 36
+        max_height = 600
+        calculated_height = min(len(yearly_data) * row_height, max_height) + 38
+
         st.dataframe(
             yearly_data.style.format(
                 {"Records": "{:,}"}
             ),  # use this instead of column_config for Records column
             hide_index=True,
             use_container_width=True,
+            height=calculated_height,
         )
 
 

pages.py

@@ -22,7 +22,6 @@ from components import (
     render_correlation_plots,
     render_filtered_data_preview,
     render_sidebar_analyte_multiselect,
-    render_sidebar_position_filter_selectbox,
     render_stations_map,
 )
 from dashboard_analytics import log_visit
@@ -350,46 +349,60 @@ def calendar_heatmaps_section():
     st.title("Calendar Heatmaps")
     st.info(
         """
-        💡 You can customize the colormaps using the **Plot Settings** expander in the sidebar.  
-          
         📅 Calendar heatmaps are grouped by calendar year instead of reporting year.
+          
+        💡 You can customize the colormaps using the **Plot Settings** expander in the sidebar.  
         """
     )
     raw_df = st.session_state.data["raw_df"]
     raw_df["Date"] = pd.to_datetime(raw_df["Activity_Start_Date_Time"]).dt.date
-
+    sector_filter = st.sidebar.selectbox(
+        "Sector:",
+        ["All"] + sorted(raw_df["Sector"].unique().tolist()),
+        index=0,
+        key="calendar_sector_select",
+    )
+    # Add year filter slider
+    years = sorted(raw_df["Activity_Start_Date_Time"].dt.year.unique())
+    default_min_year = 2018 if 2018 in years else min(years)
+    if len(years) >= 2:
+        year_range = st.sidebar.slider(
+            "Year Range:",
+            min_value=min(years),
+            max_value=max(years),
+            value=(default_min_year, max(years)),
+            key="calendar_year_range",
+        )
+    else:
+        year_range = (min(years), max(years))
     default_analytes = [
         "Temperature, Water",
         "Dissolved Oxygen",
         "Salinity",
         "pH",
         "Turbidity",
-        "Depth, Secchi Disk Depth",
-        "Fecal Coliform (MPN)",
-        "Total Nitrogen",
-        "Total Phosphorus",
-        "Chlorophyll-uncorrected",
     ]
 
     selected_analytes = render_sidebar_analyte_multiselect(
         all_analytes=sorted(raw_df["Org_Analyte_Name"].unique()),
         default_analytes=default_analytes,
         key_prefix="calendar",
-        help_text="Choose one or more analytes to display in the heatmap.",
+        help_text="Select analytes to display.",
     )
     if not selected_analytes:
         st.warning("Please select at least one analyte to display.")
         return
 
     # Filter Options
-    st.sidebar.markdown("### Filter Options")
-    sector_filter = st.sidebar.selectbox(
-        "Sector:",
-        ["All"] + sorted(raw_df["Sector"].unique().tolist()),
-        index=0,
-        key="calendar_sector_select",
+    # st.sidebar.markdown("### Filter Options")
+
+    selected_position = st.sidebar.segmented_control(
+        "Sample Position:",
+        ("Surface", "Bottom"),
+        default="Surface",
+        selection_mode="single",
     )
-    position_filter = render_sidebar_position_filter_selectbox(key_prefix="calendar")
+    selected_position = selected_position or "Surface"
 
     def format_colormap_option(option):
         append = ""
@@ -432,7 +445,7 @@ def calendar_heatmaps_section():
     `Depth, Secchi Disk Depth`     : `Blues_r`    _(reversed blues)_  
     `All other analytes`           : `Blues`      _(blue)_
     """
-    with st.sidebar.expander("Plot Settings", expanded=False):
+    with st.sidebar.expander("Color Settings", expanded=False):
         colormap = st.radio(
             "Color Scheme",
             options=[
@@ -467,14 +480,23 @@ def calendar_heatmaps_section():
 
     # Filter data
     plot_df = raw_df.copy()
+    # Add year range filter
+    plot_df = plot_df[
+        (plot_df["Activity_Start_Date_Time"].dt.year >= year_range[0])
+        & (plot_df["Activity_Start_Date_Time"].dt.year <= year_range[1])
+    ]
     if sector_filter != "All":
         plot_df = plot_df[plot_df["Sector"] == sector_filter]
-    if position_filter != "All":
-        plot_df = plot_df[plot_df["Sample_Position"] == position_filter]
+    if selected_position != "All":
+        plot_df = plot_df[plot_df["Sample_Position"] == selected_position]
     if not plot_df.empty:
         for analyte in selected_analytes:
+            if not analyte:
+                continue
             try:
-                fig = plot_calendar_heatmap(plot_df, analyte, colormap, position_filter)
+                fig = plot_calendar_heatmap(
+                    plot_df, analyte, colormap, selected_position
+                )
                 st.pyplot(fig)
             except ValueError as e:
                 st.warning(str(e))
@@ -752,6 +774,23 @@ def trends_by_station_section():
         )
     )
     raw_df = st.session_state.data["raw_df"]
+    # Create a list of station options combining number and name
+    station_options = sorted(
+        [
+            f"{num}. {raw_df[raw_df['Station_Number'] == num]['Name'].iloc[0]}"
+            for num in sorted(raw_df["Station_Number"].unique())
+        ]
+    )
+
+    selected_station_full = st.sidebar.selectbox(
+        "Station:",
+        station_options,
+        index=station_options.index(
+            next(opt for opt in station_options if opt.startswith("3.20"))
+        ),
+    )
+    # Extract just the station number from the selection
+    selected_station = selected_station_full.split(". ")[0]
     default_analytes = [
         "Dissolved Oxygen",
         "Salinity",
@@ -762,21 +801,6 @@ def trends_by_station_section():
         "Total Nitrogen",
         "Total Phosphorus",
     ]
-    st.sidebar.markdown("### Filter Options")
-
-    selected_station = st.sidebar.selectbox(
-        "Station:",
-        sorted(raw_df["Station_Number"].unique()),
-        index=sorted(raw_df["Station_Number"].unique()).index("3.20"),
-    )
-    selected_position = st.sidebar.segmented_control(
-        "Sample Position:",
-        ("All", "Surface", "Bottom"),
-        default="All",
-        selection_mode="single",
-    )
-    # if no position is selected, default to "All"
-    selected_position = selected_position or "All"
     selected_analytes = render_sidebar_analyte_multiselect(
         all_analytes=sorted(raw_df["Org_Analyte_Name"].unique()),
         default_analytes=default_analytes,
@@ -786,20 +810,22 @@ def trends_by_station_section():
         st.warning("Please select at least one analyte to display.")
         return
 
+    selected_position = st.sidebar.segmented_control(
+        "Sample Position:",
+        ("Surface", "Bottom"),
+        default="Surface",
+        selection_mode="single",
+    )
+    selected_position = selected_position or "Surface"
+
     filtered_df = raw_df.query("Station_Number == @selected_station")
     if selected_position != "All":
         filtered_df = filtered_df.query("Sample_Position == @selected_position")
 
     csv_buffer = io.StringIO()
     filtered_df.to_csv(csv_buffer, index=False)
-    st.sidebar.download_button(
-        label="Download Filtered Data (CSV)",
-        data=csv_buffer.getvalue(),
-        file_name=f"station_{selected_station}_{selected_position.lower()}_data.csv",
-        mime="text/csv",
-    )
 
-    with st.sidebar.expander("Preview Filtered Data"):
+    with st.sidebar.expander("View and Download Data"):
         display_columns = [
             "Activity_Start_Date_Time",
             "Sample_Position",
@@ -809,6 +835,12 @@ def trends_by_station_section():
             "Reporting_Year",
         ]
         render_filtered_data_preview(filtered_df, display_columns)
+        st.download_button(
+            label="Download Station Data",
+            data=csv_buffer.getvalue(),
+            file_name=f"station_{selected_station}_{selected_position.lower()}_data.csv",
+            mime="text/csv",
+        )
 
     if not filtered_df.empty:
         fig = plot_trends_by_station(filtered_df, selected_analytes, selected_position)