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plots/map.py

@@ -13,204 +13,203 @@ from osgeo import gdal
 
 from utils.data_loading import timer
 
-
-def plot_seasonal_salinity(
-    salinity_data: pd.DataFrame,
-    year: str,
-    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: 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)
-    relevant_wbids = salinity_data["WBID"].unique()
-    wbids = wbids[wbids["WBID"].isin(relevant_wbids)]
-    wbids = wbids.to_crs(epsg=3857)
-
-    # Process data - create a copy to avoid SettingWithCopyWarning
-    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 quarterly means
-    seasonal_means = (
-        year_data.groupby(["WBID", "quarter"], observed=True)["Salinity"]
-        .mean()
-        .reset_index()
-    )
-
-    fig = plt.figure(figsize=(20, 14))
-
-    # Create custom colormap with focused range
-    colors = ["#08519c", "#73a9cf", "#fee090", "#fc8d59", "#d73027"]
-    cmap = LinearSegmentedColormap.from_list("custom", colors, N=100)
-
-    # Get global min/max for consistent colormap
-    vmin = seasonal_means["Salinity"].min()
-    vmax = 40
-
-    # Calculate map extent
-    bounds = wbids.total_bounds
-    x_buffer = (bounds[2] - bounds[0]) * 0.05
-    y_buffer = (bounds[3] - bounds[1]) * 0.05
-    extent = [
-        bounds[0] - x_buffer,
-        bounds[2] + x_buffer,
-        bounds[1] - y_buffer,
-        bounds[3] + y_buffer,
-    ]
-
-    # Create subplots with tighter spacing
-    gs = fig.add_gridspec(
-        2,
-        2,
-        width_ratios=[1, 1],
-        wspace=0.05,  # Minimal horizontal space between plots
-        hspace=-0.15,  # More negative value to further reduce vertical space
-        left=0.02,  # Left margin
-        right=0.98,  # Right margin
-        top=0.95,  # Slightly reduced top margin to give more space
-        bottom=0.05,  # Slightly increased bottom margin to give more space
-    )
-
-    # 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])
-
-        quarter_data = seasonal_means[seasonal_means["quarter"] == quarter]
-        merged = wbids.merge(quarter_data, on="WBID", how="left")
-
-        # Plot WBIDs
-        merged.plot(
-            column="Salinity",
-            ax=ax,
-            cmap=cmap,
-            vmin=vmin,
-            vmax=vmax,
-            alpha=0.7,
-            missing_kwds={"color": "lightgrey", "alpha": 0.5},
-        )
-
-        ctx.add_basemap(ax, source=basemap_provider, zoom=11, alpha=alpha)  # type: ignore
-
-        ax.set_xlim(extent[0], extent[1])
-        ax.set_ylim(extent[2], extent[3])
-
-        # 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"Quarter {quarter[1]} Mean Salinity\n{date_range}",
-                pad=15,
-                fontsize=10,
-            )
-        else:  # Bottom row
-            ax.set_title(
-                f"Quarter {quarter[1]} Mean Salinity\n{date_range}",
-                pad=5,
-                fontsize=10,
-            )
-        ax.set_axis_off()
-
-    # Add colorbar
-    norm = plt.Normalize(vmin=vmin, vmax=vmax)  # type: ignore
-    sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
-    sm.set_array([])
-    fig.colorbar(
-        sm,
-        ax=fig.axes,
-        orientation="vertical",
-        label="Salinity (ppt)",
-        pad=0.01,
-        fraction=0.015,
-        ticks=np.arange(0, 45, 5),  # Add ticks every 5 units
-    )
-
-    return fig
-
-
-def plot_seasonal_salinity_for_bays(
-    salinity_data: pd.DataFrame,
-    year: str,
-    basemap_provider=ctx.providers.USGS.USTopo,  # type: ignore
-    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.
-    """
-    if wbids is None:
-        wbids = gpd.read_file(shapefile_path)
-        if wbids.crs is None:
-            wbids.set_crs(epsg=6439, inplace=True)
-        wbids = wbids.to_crs(epsg=3857)
-    fig = plot_seasonal_salinity(
-        salinity_data.query(
-            "WBID.isin(['1061A', '1061B', '1061C', '1061D', '1061E', '1061F', '1061G', '1061H', '1055A'])"
-        ),
-        year=year,
-        basemap_provider=basemap_provider,
-        alpha=alpha,
-        shapefile_path=shapefile_path,
-        reporting_end_month=reporting_end_month,
-    )
-    return fig
+# def plot_seasonal_salinity(
+#     salinity_data: pd.DataFrame,
+#     year: str,
+#     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: 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)
+#     relevant_wbids = salinity_data["WBID"].unique()
+#     wbids = wbids[wbids["WBID"].isin(relevant_wbids)]
+#     wbids = wbids.to_crs(epsg=3857)
+
+#     # Process data - create a copy to avoid SettingWithCopyWarning
+#     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 quarterly means
+#     seasonal_means = (
+#         year_data.groupby(["WBID", "quarter"], observed=True)["Salinity"]
+#         .mean()
+#         .reset_index()
+#     )
+
+#     fig = plt.figure(figsize=(20, 14))
+
+#     # Create custom colormap with focused range
+#     colors = ["#08519c", "#73a9cf", "#fee090", "#fc8d59", "#d73027"]
+#     cmap = LinearSegmentedColormap.from_list("custom", colors, N=100)
+
+#     # Get global min/max for consistent colormap
+#     vmin = seasonal_means["Salinity"].min()
+#     vmax = 40
+
+#     # Calculate map extent
+#     bounds = wbids.total_bounds
+#     x_buffer = (bounds[2] - bounds[0]) * 0.05
+#     y_buffer = (bounds[3] - bounds[1]) * 0.05
+#     extent = [
+#         bounds[0] - x_buffer,
+#         bounds[2] + x_buffer,
+#         bounds[1] - y_buffer,
+#         bounds[3] + y_buffer,
+#     ]
+
+#     # Create subplots with tighter spacing
+#     gs = fig.add_gridspec(
+#         2,
+#         2,
+#         width_ratios=[1, 1],
+#         wspace=0.05,  # Minimal horizontal space between plots
+#         hspace=-0.15,  # More negative value to further reduce vertical space
+#         left=0.02,  # Left margin
+#         right=0.98,  # Right margin
+#         top=0.95,  # Slightly reduced top margin to give more space
+#         bottom=0.05,  # Slightly increased bottom margin to give more space
+#     )
+
+#     # 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])
+
+#         quarter_data = seasonal_means[seasonal_means["quarter"] == quarter]
+#         merged = wbids.merge(quarter_data, on="WBID", how="left")
+
+#         # Plot WBIDs
+#         merged.plot(
+#             column="Salinity",
+#             ax=ax,
+#             cmap=cmap,
+#             vmin=vmin,
+#             vmax=vmax,
+#             alpha=0.7,
+#             missing_kwds={"color": "lightgrey", "alpha": 0.5},
+#         )
+
+#         ctx.add_basemap(ax, source=basemap_provider, zoom=11, alpha=alpha)  # type: ignore
+
+#         ax.set_xlim(extent[0], extent[1])
+#         ax.set_ylim(extent[2], extent[3])
+
+#         # 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"Quarter {quarter[1]} Mean Salinity\n{date_range}",
+#                 pad=15,
+#                 fontsize=10,
+#             )
+#         else:  # Bottom row
+#             ax.set_title(
+#                 f"Quarter {quarter[1]} Mean Salinity\n{date_range}",
+#                 pad=5,
+#                 fontsize=10,
+#             )
+#         ax.set_axis_off()
+
+#     # Add colorbar
+#     norm = plt.Normalize(vmin=vmin, vmax=vmax)  # type: ignore
+#     sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
+#     sm.set_array([])
+#     fig.colorbar(
+#         sm,
+#         ax=fig.axes,
+#         orientation="vertical",
+#         label="Salinity (ppt)",
+#         pad=0.01,
+#         fraction=0.015,
+#         ticks=np.arange(0, 45, 5),  # Add ticks every 5 units
+#     )
+
+#     return fig
+
+
+# def plot_seasonal_salinity_for_bays(
+#     salinity_data: pd.DataFrame,
+#     year: str,
+#     basemap_provider=ctx.providers.USGS.USTopo,  # type: ignore
+#     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.
+#     """
+#     if wbids is None:
+#         wbids = gpd.read_file(shapefile_path)
+#         if wbids.crs is None:
+#             wbids.set_crs(epsg=6439, inplace=True)
+#         wbids = wbids.to_crs(epsg=3857)
+#     fig = plot_seasonal_salinity(
+#         salinity_data.query(
+#             "WBID.isin(['1061A', '1061B', '1061C', '1061D', '1061E', '1061F', '1061G', '1061H', '1055A'])"
+#         ),
+#         year=year,
+#         basemap_provider=basemap_provider,
+#         alpha=alpha,
+#         shapefile_path=shapefile_path,
+#         reporting_end_month=reporting_end_month,
+#     )
+#     return fig
 
 
 @timer(include_params=True)
@@ -490,9 +489,10 @@ def add_colorbar(
     # Get value range
     vmin = seasonal_means[parameter].min()
     vmax = get_parameter_max_value(parameter, seasonal_means[parameter].max())
+    data_max = seasonal_means[parameter].max()
 
     # Create colorbar
-    norm = plt.Normalize(vmin=vmin, vmax=vmax)  # type: ignore
+    norm = plt.Normalize(vmin=vmin, vmax=vmax if vmax is not None else data_max)  # type: ignore
     sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
     sm.set_array([])
 
@@ -500,6 +500,29 @@ def add_colorbar(
     unit = get_parameter_unit(parameter)
     label = f"{parameter} ({unit})" if unit else parameter
 
+    # Calculate appropriate tick spacing based on data range
+    if vmax is not None:
+        if vmax <= 1:
+            tick_spacing = 0.1
+        elif vmax <= 10:
+            tick_spacing = 1
+        elif vmax <= 50:
+            tick_spacing = 5
+        else:
+            tick_spacing = 10
+        ticks = np.arange(0, vmax + tick_spacing, tick_spacing)
+    else:
+        # Use data_max with automatic tick spacing
+        if data_max <= 1:
+            tick_spacing = 0.1
+        elif data_max <= 10:
+            tick_spacing = 1
+        elif data_max <= 50:
+            tick_spacing = 5
+        else:
+            tick_spacing = 10
+        ticks = np.arange(0, data_max + tick_spacing, tick_spacing)
+
     # Add colorbar to figure
     fig.colorbar(
         sm,
@@ -508,7 +531,7 @@ def add_colorbar(
         label=label,
         pad=0.01,
         fraction=0.015,
-        ticks=np.arange(0, vmax + 5, 5),  # Add ticks every 5 units
+        ticks=ticks,
     )
 
 

ui/pages/seasonal_maps.py

@@ -70,6 +70,7 @@ if not raw_df.empty:
                 "Activity_Start_Date_Time",
                 "Reporting_Year",
                 "WBID",
+                "Sector",
                 "Station_Number",
                 "Sample_Position",
                 "Org_Analyte_Name",