add metadata to geojson files, plot flooded area over time

floodpy/Visualization/flood_over_time.py

@@ -5,16 +5,6 @@
 
 def plot_flooded_area_over_time(Floodpy_app, Floodpy_app_objs):
 
-    colorTones = {
-    6: '#CC3A5D', # dark pink
-    5: '#555555',   # dark grey 
-    4: '#A17C44',  # dark brown
-    3: '#8751A1', # dark purple
-    2: '#C1403D', # dark red
-    1: '#2E5A87',  # dark blue
-    0: '#57A35D', # dark green
-    }
-
     Flooded_regions_areas_km2 = {}
     for flood_date in Floodpy_app_objs.keys():
         # calculate the area of flooded regions
@@ -38,9 +28,9 @@ def getcolor(val):
 
     # Adjust the plot
     plt.ylabel('Flooded area (km²)', fontsize=16)
-    plt.title('Flooded Area(km²) Over Time', fontsize=16)
     plt.xticks(df['Datetime'].astype(str), df['Datetime'].dt.strftime('%d-%b-%Y'), rotation=30, ha='right', fontsize=16)  # Set custom date format
     plt.yticks(fontsize=16)
+    plt.grid()
     plt.tight_layout()  # Adjust layout for better fit
 
     # Display the plot

floodpy/utils/add_metadata_to_geojson.py

@@ -0,0 +1,78 @@
+import geopandas as gpd
+import pandas as pd
+import numpy as np
+import json
+import matplotlib.pyplot as plt
+import os
+
+
+def add_metadata(Floodpy_app_objs, Floodpy_app, plot_flag = True):
+
+    distinctDarkTones = np.array([
+        '#264653',  # dark teal/gray
+        '#2a9d8f',  # deep green/teal
+        '#1d3557',  # dark blue
+        '#4b5320',  # army green
+        '#039BE5',  # vivid blue
+        '#006400',  # dark green
+        '#81D4FA',  # sky blue
+    ])
+
+    # choose the visualization colors
+    num_flood_events = len(Floodpy_app_objs)
+    color_indices = np.array([np.ceil(len(distinctDarkTones)*flood_ind/num_flood_events) for flood_ind in range(num_flood_events)], dtype=np.int32)
+    colors = distinctDarkTones[color_indices]
+
+    # calculate the pandas dataframe with flooded regions and add metadata (plot_color and max_entend)
+    Flooded_regions_df = pd.DataFrame()
+    for flood_date in Floodpy_app_objs.keys():
+        # calculate the area of flooded regions
+        Flood_map_vector_data = gpd.read_file(Floodpy_app_objs[flood_date].Flood_map_vector_dataset_filename)
+        Flood_map_vector_data_projected = Flood_map_vector_data.to_crs(Flood_map_vector_data.estimate_utm_crs())
+        area_km2 = round(Flood_map_vector_data_projected.area.sum()/1000000,2 )
+        flooded_region_temp = pd.DataFrame({'Flooded area (km2)':area_km2,
+                                            'geojson_filename':Floodpy_app_objs[flood_date].Flood_map_vector_dataset_filename}, index=[flood_date])
+        Flooded_regions_df = pd.concat([Flooded_regions_df,flooded_region_temp])
+
+    # Ascending sorting of flood events based on flooded area
+    Flooded_regions_df = Flooded_regions_df.sort_values(by=['Flooded area (km2)'])
+    Flooded_regions_df['plot_color'] = colors
+    Flooded_regions_df['max_extend'] = 'false'
+    max_extend_ind = Flooded_regions_df['Flooded area (km2)'].idxmax()
+    Flooded_regions_df.loc[max_extend_ind, ['max_extend']] = 'true'
+
+    # overwrite existing geojson files with metadata information
+
+    for index, row in Flooded_regions_df.iterrows():
+        with open(row['geojson_filename']) as f:
+            flooded_regions_json = json.load(f)
+
+        #Add top-level metadata (e.g., title, description, etc.)
+        flooded_regions_json['plot_color'] = row['plot_color']
+        flooded_regions_json['max_extend'] = row['max_extend']
+
+        #Save the modified GeoJSON with metadata to a file
+        with open(row['geojson_filename'], "w") as f:
+            json.dump(flooded_regions_json, f, indent=2)
+
+
+    if plot_flag:
+        Flooded_regions_df['Datetime'] = pd.to_datetime(Flooded_regions_df.index)
+
+        df = Flooded_regions_df.sort_index().copy()
+        # Plot the data
+        fig = plt.figure(figsize=(6, 5))
+        plt.bar(df['Datetime'].astype(str), df['Flooded area (km2)'], color='royalblue', width=0.7)
+
+        # Adjust the plot
+        plt.ylabel('Flooded area (km²)', fontsize=16)
+        plt.xticks(df['Datetime'].astype(str), df['Datetime'].dt.strftime('%d-%b-%Y'), rotation=30, ha='right', fontsize=16)  # Set custom date format
+        plt.yticks(fontsize=16)
+        plt.grid()
+        plt.tight_layout()  # Adjust layout for better fit
+
+        # Display the plot
+        fig_filename = os.path.join(Floodpy_app.Results_dir, '{}.svg'.format(Floodpy_app.flood_event))
+        plt.savefig(fig_filename,format="svg")
+        # plt.close()
+        print('The figure can be found at: {}'.format(fig_filename))

floodpy/utils/geo_utils.py

@@ -7,21 +7,9 @@
 import datetime
 import json
 
-colorTones = {
-  6: '#CC3A5D', # dark pink
-  5: '#555555',   # dark grey 
-  4: '#A17C44',  # dark brown
-  3: '#8751A1', # dark purple
-  2: '#C1403D', # dark red
-  1: '#2E5A87',  # dark blue
-  0: '#57A35D', # dark green
-}
-
-
 def create_polygon(coordinates):
     return Polygon(np.array(coordinates['coordinates']).squeeze())
 
-
 def convert_to_vector(Floodpy_app):
     with rasterio.open(Floodpy_app.Flood_map_dataset_filename) as src:
         data = src.read(1).astype(np.int16)
@@ -42,16 +30,12 @@ def convert_to_vector(Floodpy_app):
         geojson_str = gdf.to_json()  # This gives the GeoJSON as a string
         geojson_dict = json.loads(geojson_str)  # Convert the string to a dictionary
 
-        # find the color of plotting
-        color_ind = Floodpy_app.flood_datetimes.index(Floodpy_app.flood_datetime) 
-        plot_color = colorTones[color_ind]
         #Add top-level metadata (e.g., title, description, etc.)
         geojson_dict['flood_event'] = Floodpy_app.flood_event
         geojson_dict['description'] = "This GeoJSON contains polygons of flooded regions using Sentinel-1 data."
         geojson_dict['produced_by'] = "Floodpy"
         geojson_dict['creation_date_UTC'] = datetime.datetime.now(datetime.timezone.utc).strftime('%Y%m%dT%H%M%S')
         geojson_dict['flood_datetime_UTC'] = Floodpy_app.flood_datetime_str
-        geojson_dict['plot_color'] = plot_color
         geojson_dict['bbox'] = Floodpy_app.bbox
 
         #Save the modified GeoJSON with metadata to a file