update the plot detailes

Author: supathdhitalGEO

dist/.gitignore

@@ -0,0 +1 @@
+*

dist/fimeval-0.1.56-py3-none-any.whl

@@ -1,6 +1,6 @@
 [project]
 name = "fimeval"
-version = "0.1.55"
+version = "0.1.56"
 description = "A Framework for Automatic Evaluation of Flood Inundation Mapping Predictions Evaluation"
 readme = "README.md"
 requires-python = ">=3.10"

dist/fimeval-0.1.56.tar.gz

@@ -8,6 +8,7 @@
 import plotly.graph_objects as go
 import seaborn as sns
 import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
 
 
 def Changeintogpkg(input_path, output_dir, layer_name):
@@ -209,7 +210,7 @@ def count_centroids_in_contingency(raster_path):
     )
     fig.show()
 
-    # Seaborn for static PNG saving only
+    # Seaborn for static PNG
     df_left = pd.DataFrame(
         {
             "Category": ["Candidate", "Benchmark"],
@@ -224,56 +225,65 @@ def count_centroids_in_contingency(raster_path):
     )
 
     sns.set_theme(style="whitegrid")
-    fig_sb, axes = plt.subplots(1, 2, figsize=(8, 3), constrained_layout=True)
+
+    fig_sb = plt.figure(figsize=(10, 3), constrained_layout=True)
+    gs = gridspec.GridSpec(1, 3, figure=fig_sb, width_ratios=[1, 1, 0.4])
+
+    ax0 = fig_sb.add_subplot(gs[0, 0])
+    ax1 = fig_sb.add_subplot(gs[0, 1])
+    ax_leg = fig_sb.add_subplot(gs[0, 2])
+    ax_leg.axis("off")
 
     def style_axes(ax, title_text, xlab, show_ylabel: bool):
-        # Adding a bit of padding so bar labels don’t overlap with the title
-        ax.set_title(title_text, fontsize=16, pad=20)
-        ax.set_xlabel(xlab, fontsize=14, color="black")
+        ax.set_title(title_text, fontsize=14, pad=15)
+        ax.set_xlabel(xlab, fontsize=13, color="black")
         if show_ylabel:
-            ax.set_ylabel("Flooded Building Counts", fontsize=14, color="black")
+            ax.set_ylabel("Flooded Building Counts", fontsize=13, color="black")
         else:
             ax.set_ylabel("")
 
-        # Thicker black left/bottom spines
         for spine in ("left", "bottom"):
             ax.spines[spine].set_linewidth(1.5)
             ax.spines[spine].set_color("black")
 
         sns.despine(ax=ax, right=True, top=True)
-        ax.tick_params(axis="x", labelsize=12, colors="black")
-        ax.tick_params(axis="y", labelsize=12, colors="black")
+        ax.tick_params(axis="x", labelsize=11, colors="black")
+        ax.tick_params(axis="y", labelsize=11, colors="black")
 
     # Left panel
-    ax0 = axes[0]
-    sns.barplot(
-        data=df_left, x="Category", y="Count", ax=ax0, palette=["#1c83eb", "#a4490e"]
-    )
-    style_axes(
-        ax0, "Building Counts on Different FIMs", "Inundation Surface", show_ylabel=True
-    )
+    colors_left = ["#1c83eb", "#a4490e"]
+    sns.barplot(data=df_left, x="Category", y="Count", ax=ax0, palette=colors_left)
+    style_axes(ax0, "Building Counts on Different FIMs", "Inundation Surface", True)
     for c in ax0.containers:
         ax0.bar_label(
-            c, fmt="%.0f", label_type="edge", padding=3, fontsize=14, color="black"
+            c, fmt="%.0f", label_type="edge", padding=3, fontsize=12, color="black"
         )
 
     # Right panel
-    ax1 = axes[1]
-    sns.barplot(
-        data=df_right,
-        x="Category",
-        y="Count",
-        ax=ax1,
-        palette=["#ff5733", "#ffc300", "#28a745"],
-    )
-    style_axes(
-        ax1, "Contingency Flooded Building Counts", "Category", show_ylabel=False
-    )
+    colors_right = ["#ff5733", "#ffc300", "#28a745"]
+    sns.barplot(data=df_right, x="Category", y="Count", ax=ax1, palette=colors_right)
+    style_axes(ax1, "Contingency Flooded Building Counts", "Category", False)
     for c in ax1.containers:
         ax1.bar_label(
-            c, fmt="%.0f", label_type="edge", padding=3, fontsize=14, color="black"
+            c, fmt="%.0f", label_type="edge", padding=3, fontsize=12, color="black"
         )
 
+    # Combined legend
+    all_labels = ["Candidate", "Benchmark"] + third_raster_labels
+    all_colors = colors_left + colors_right
+    legend_handles = [
+        plt.Line2D(
+            [0],
+            [0],
+            marker="s",
+            color="w",
+            markerfacecolor=all_colors[i],
+            markersize=12,
+            label=f"{all_labels[i]} ({percentages[all_labels[i]]:.2f}%)",
+        )
+        for i in range(len(all_labels))
+    ]
+    ax_leg.legend(handles=legend_handles, fontsize=12, loc="center left", frameon=True)
     plot_dir = os.path.join(save_dir, "FinalPlots")
     os.makedirs(plot_dir, exist_ok=True)
     output_path = os.path.join(plot_dir, f"BuildingCounts_{basename}.png")

pyproject.toml

@@ -3,12 +3,10 @@
 
 Main_dir = (
     # "../docs/sampledata"
-    "/Users/supath/Downloads/MSResearch/FIMpef/fimpef/docs/sampledata"
 )
 PWD_dir = "./path/to/PWB"
 output_dir = (
     # "./path/to/output"  # This is the output directory where the results will be saved
-    "/Users/supath/Downloads/MSResearch/FIMpef/fimpef/docs/output"
 )
 target_crs = "EPSG:5070"  # Target CRS for reprojecting the FIMs, need to be in EPSG code of Projected CRS
 target_resolution = 10  # This will be in meters, if it passes the FIMS will be resampled to this resolution else, it will find the coarser resolution among all FIMS for this case and use that to resample!
@@ -31,7 +29,7 @@
 def test_evaluation_framework():
     # Run the evaluation
     # It has the Permanent Water Bodies (PWB) dataset as default for United States
-    fe.EvaluateFIM(Main_dir, method_name, output_dir)
+    # fe.EvaluateFIM(Main_dir, method_name, output_dir)
 
     # OR, If the Evaluation Study Area is outside the US or, user has their own PWB dataset
     # fe.EvaluateFIM(Main_dir, method_name, output_dir)
@@ -40,10 +38,10 @@ def test_evaluation_framework():
     # fe.EvaluateFIM(Main_dir, method_name, output_dir, target_crs=target_crs, shapefile_dir = AOI, target_resolution=target_resolution)
 
     # # Once the FIM evaluation is done, print the contingency map
-    fe.PrintContingencyMap(Main_dir, method_name, output_dir)
+    # fe.PrintContingencyMap(Main_dir, method_name, output_dir)
 
     # # Plot the evaluation metrics after the FIM evaluation
-    fe.PlotEvaluationMetrics(Main_dir, method_name, output_dir)
+    # fe.PlotEvaluationMetrics(Main_dir, method_name, output_dir)
 
     # FIM Evaluation with Building Footprint (by default, it uses the Microsoft Building Footprint dataset)
     fe.EvaluationWithBuildingFootprint(