Add all files with LFS support

analysis.py

@@ -922,13 +922,16 @@ def plot_seasonal_salinity(
     basemap_provider,
     alpha=0.5,
     shapefile_path="data/SAB/SAB.shp",
+    reporting_end_month: int = 10,
 ):
     """
     Create seasonal plots of mean salinity values by WBID with basemap.
+    Uses configurable Reporting Year with meteorological seasons.
 
     Args:
         salinity_data: DataFrame containing salinity measurements
-        year: Year to filter data for (str)
+        year: Reporting Year to filter data for (str)
+        reporting_end_month: Last month of the reporting year (1-12, default=10 for October)
     """
     # Read and filter WBIDs
     wbids = gpd.read_file(shapefile_path)
@@ -937,20 +940,29 @@ def plot_seasonal_salinity(
     wbids = wbids.to_crs(epsg=3857)
 
     # Process data - create a copy to avoid SettingWithCopyWarning
-    year_data = salinity_data[
-        salinity_data["Activity_Start_Date_Time"].dt.year == int(year)
-    ].copy()
-
-    # Add season column using loc
-    year_data.loc[:, "season"] = pd.cut(
-        year_data["Activity_Start_Date_Time"].dt.month,
-        bins=[0, 3, 6, 9, 12],
-        labels=["Winter", "Spring", "Summer", "Fall"],
+    year_data = salinity_data[salinity_data["Reporting_Year"] == int(year)].copy()
+
+    # Function to determine quarter based on date and reporting year end
+    def get_quarter(date, reporting_end_month):
+        month = date.month
+
+        # Calculate month offset to align with reporting year
+        month_offset = (12 - reporting_end_month) % 12
+
+        # Adjust month to align with reporting year
+        adjusted_month = ((month + month_offset) % 12) or 12
+
+        # Determine quarter (1-4)
+        return f"Q{((adjusted_month - 1) // 3) + 1}"
+
+    # Add quarter column
+    year_data.loc[:, "quarter"] = year_data["Activity_Start_Date_Time"].apply(
+        lambda x: get_quarter(x, reporting_end_month)
     )
 
-    # Calculate seasonal means with observed=True
+    # Calculate quarterly means
     seasonal_means = (
-        year_data.groupby(["WBID", "season"], observed=True)["Salinity"]
+        year_data.groupby(["WBID", "quarter"], observed=True)["Salinity"]
         .mean()
         .reset_index()
     )
@@ -989,11 +1001,38 @@ def plot_seasonal_salinity(
         bottom=0.05,  # Slightly increased bottom margin to give more space
     )
 
-    for idx, season in enumerate(["Winter", "Spring", "Summer", "Fall"]):
+    # Function to get quarter date range
+    def get_quarter_dates(quarter: str, year: int, reporting_end_month: int) -> str:
+        # Calculate first month of reporting year
+        first_month = (reporting_end_month % 12) + 1
+
+        # Calculate start month for each quarter
+        quarter_num = int(quarter[1])
+        start_month = ((first_month - 1 + ((quarter_num - 1) * 3)) % 12) + 1
+        end_month = ((start_month + 2) % 12) or 12
+
+        # For Reporting Year X, the start date is actually in year X-1 if the month
+        # is after the reporting end month
+        start_year = int(year) - 1 if start_month > reporting_end_month else int(year)
+        end_year = start_year
+        if end_month < start_month:
+            end_year += 1
+
+        start_date = pd.Timestamp(f"{start_year}-{start_month:02d}-01")
+        end_date = pd.Timestamp(
+            f"{end_year}-{end_month:02d}-{pd.Timestamp(f'{end_year}-{end_month:02d}').days_in_month}"
+        )
+
+        return f"{start_date.strftime('%b %d, %Y')} - {end_date.strftime('%b %d, %Y')}"
+
+    # Use quarters instead of seasons
+    quarters = ["Q1", "Q2", "Q3", "Q4"]
+
+    for idx, quarter in enumerate(quarters):
         ax = fig.add_subplot(gs[idx // 2, idx % 2])
 
-        season_data = seasonal_means[seasonal_means["season"] == season]
-        merged = wbids.merge(season_data, on="WBID", how="left")
+        quarter_data = seasonal_means[seasonal_means["quarter"] == quarter]
+        merged = wbids.merge(quarter_data, on="WBID", how="left")
 
         # Plot WBIDs
         merged.plot(
@@ -1010,15 +1049,23 @@ def plot_seasonal_salinity(
 
         ax.set_xlim(extent[0], extent[1])
         ax.set_ylim(extent[2], extent[3])
-        # Adjust title position
+
+        # Get date range for this quarter
+        date_range = get_quarter_dates(quarter, int(year), reporting_end_month)
+
+        # Create title with two lines
         if idx < 2:  # Top row
             ax.set_title(
-                f"{season} {year} Mean Salinity", pad=15
-            )  # More padding for top row
+                f"Quarter {quarter[1]} Mean Salinity\n{date_range}",
+                pad=15,
+                fontsize=10,
+            )
         else:  # Bottom row
             ax.set_title(
-                f"{season} {year} Mean Salinity", pad=5
-            )  # Less padding for bottom row
+                f"Quarter {quarter[1]} Mean Salinity\n{date_range}",
+                pad=5,
+                fontsize=10,
+            )
         ax.set_axis_off()
 
     # Add colorbar
@@ -1045,6 +1092,7 @@ def plot_seasonal_salinity_for_bays(
     alpha=0.5,
     shapefile_path="data/SAB/SAB.shp",
     wbids=None,
+    reporting_end_month: int = 10,
 ):
     """
     Create seasonal plots of mean salinity values by WBID for N, E, W, SAB, GL and Lake Powell.
@@ -1062,6 +1110,7 @@ def plot_seasonal_salinity_for_bays(
         basemap_provider=basemap_provider,
         alpha=alpha,
         shapefile_path=shapefile_path,
+        reporting_end_month=reporting_end_month,
     )
     return fig
 

app.py

@@ -480,7 +480,11 @@ def generate_seasonal_plot(data, year, shapefile_path):
     )
 
     return plot_seasonal_salinity_for_bays(
-        data, year, shapefile_path=shapefile_path, wbids=wbids
+        data,
+        year,
+        shapefile_path=shapefile_path,
+        wbids=wbids,
+        reporting_end_month=st.session_state.dataset_reporting_month,
     )
 
 
@@ -1158,19 +1162,18 @@ elif section == "Calendar Heatmaps":
 elif section == "Seasonal Trends":
     st.title("Seasonal Trends")
     raw_df = data["raw_df"]
-    years = sorted(pd.to_datetime(raw_df["Activity_Start_Date_Time"]).dt.year.unique())
-    col1, col2 = st.columns(2)
-    with col1:
-        analyte = st.selectbox(
-            "Select Analyte:", ["Salinity"], index=0, key="seasonal_analyte_select"
-        )
-    with col2:
-        selected_year = st.selectbox(
-            "Select Year:",
-            sorted(years, reverse=True),
-            index=0,
-            key="seasonal_year_select",
-        )
+    # Use Reporting_Year instead of calendar year
+    years = sorted(raw_df["Reporting_Year"].unique())
+
+    # Move filters to sidebar
+    st.sidebar.markdown("### Filter Options")
+    analyte = st.sidebar.selectbox(
+        "Select Analyte:", ["Salinity"], index=0, key="seasonal_analyte_select"
+    )
+    selected_year = st.sidebar.selectbox(
+        "Select Year:", sorted(years, reverse=True), index=0, key="seasonal_year_select"
+    )
+
     if not raw_df.empty:
         seasonal_data = load_seasonal_data(raw_df, analyte)
         fig = generate_seasonal_plot(

main.py

@@ -73,6 +73,7 @@ def get_analyte_data_with_lat_long(df: pd.DataFrame, analyte: str) -> pd.DataFra
             - Station_Number
             - Org_Analyte_Name
             - Org_Result_Value
+            - Reporting_Year
         analyte (str): Name of the analyte to filter for (e.g., "Temperature, Water")
 
     Returns:
@@ -85,6 +86,7 @@ def get_analyte_data_with_lat_long(df: pd.DataFrame, analyte: str) -> pd.DataFra
             - Activity_Depth: Depth of measurement
             - Latitude: Station latitude
             - Longitude: Station longitude
+            - Reporting_Year: Reporting year
             - {analyte}: Measured value for the specified analyte
 
     Note:
@@ -104,6 +106,7 @@ def get_analyte_data_with_lat_long(df: pd.DataFrame, analyte: str) -> pd.DataFra
                 "Activity_Depth",
                 "Latitude",
                 "Longitude",
+                "Reporting_Year",
             ],
             values="Org_Result_Value",
             aggfunc="mean",