python modularize attempt 2

geospatial_operations.py

@@ -0,0 +1,86 @@
+import pandas as pd
+import geopandas as gpd
+import matplotlib.pyplot as plt
+from shapely.ops import unary_union
+
+def process_buildings(input_gdf, sensitive_sites_gdf, default_building_height_m, multiplier_factor):
+    # List to store all intersected sensitive sites
+    intersected_sites = []
+    
+    # List to store all buffers
+    buffers = []
+
+    intersection_desc = ""
+    
+    # Iterate over each building in the input file
+    for idx, building in input_gdf.iterrows():
+        building_name = building.get('building_name', 'Unnamed building')
+        
+        # If the 'building_height' field exists and its value is not null or zero for this building,
+        # use it as the building height. Otherwise, use the default building height provided by the user.
+        if 'building_height' in building and pd.notnull(building['building_height']) and building['building_height'] != 0:
+            building_height_m = building['building_height'] * 0.3048
+        else:
+            building_height_m = default_building_height_m
+
+        buffer_distance_m = building_height_m * multiplier_factor
+
+        # Convert building's geometry to EPSG:3857 for accurate meter-based distance measurement
+        building_geometry = gpd.GeoSeries([building['geometry']], crs="EPSG:4326")
+        building_geometry_m = building_geometry.to_crs("EPSG:3857")
+
+        # Create a buffer around the building and convert it to a GeoDataFrame
+        building_buffer = building_geometry_m.buffer(buffer_distance_m)
+        building_buffer_gdf = gpd.GeoDataFrame(geometry=building_buffer, crs="EPSG:3857")
+        building_buffer_gdf = building_buffer_gdf.to_crs("EPSG:4326")
+
+        # Convert back to feet for storing and printing, rounding to the nearest foot
+        building_height_ft = round(building_height_m / 0.3048)
+        buffer_distance_ft = round(buffer_distance_m / 0.3048)
+
+        # Assign additional attributes
+        building_buffer_gdf['building_name'] = building_name
+        building_buffer_gdf['building_height'] = building_height_ft
+        building_buffer_gdf['buffer_distance'] = buffer_distance_ft
+
+        buffers.append(building_buffer_gdf)
+
+        # Check if the buffer intersects with any sensitive sites
+        intersects = gpd.overlay(building_buffer_gdf, sensitive_sites_gdf, how='intersection')
+        
+        if not intersects.empty:
+            building_intersect_desc = f"Building {idx} ({building_name}), height: {building_height_ft}, buffer distance: {buffer_distance_ft} is in the vicinity of a sensitive site."
+            intersected_sites.append(intersects)
+        else:
+            building_intersect_desc = f"Building {idx} ({building_name}), height: {building_height_ft}, buffer distance: {buffer_distance_ft} is not in the vicinity of any sensitive sites."
+
+        if intersection_desc == "":
+            intersection_desc = building_intersect_desc
+        else:
+            intersection_desc += "\n" + building_intersect_desc
+    
+    return buffers, intersected_sites, intersection_desc
+
+def get_max_extent(*gdfs): # takes in unlimited number of gdfs and calculates max/min xy extents
+    minx = min(gdf.total_bounds[0] for gdf in gdfs)
+    miny = min(gdf.total_bounds[1] for gdf in gdfs)
+    maxx = max(gdf.total_bounds[2] for gdf in gdfs)
+    maxy = max(gdf.total_bounds[3] for gdf in gdfs)
+    
+    return minx, miny, maxx, maxy
+
+def create_plot(filename, extent, *gdfs): # takes in unlimited number of gdfs
+    fig, ax = plt.subplots(figsize=(10, 8)) #Sets image size by width & height (in inches)
+    
+    colors = ['tan', 'mediumseagreen', 'thistle', 'lightcoral', 'sienna', 'yellow'] # Extend/improve this list as needed
+
+    for idx, gdf in enumerate(gdfs):
+        gdf.plot(ax=ax, color=colors[idx % len(colors)]) # Cycle through colors
+
+    ax.set_xlim(extent[0], extent[2])
+    ax.set_ylim(extent[1], extent[3])
+
+    # Hide axes
+    ax.axis('off')
+
+    plt.savefig(filename, bbox_inches='tight', pad_inches=0) # remove padding

plot.py

@@ -1,19 +0,0 @@
-import matplotlib.pyplot as plt
-import geopandas as gpd
-
-# all plotting functions 
-def create_plot(filename, extent, *gdfs): # takes in unlimited number of gdfs
-    fig, ax = plt.subplots(figsize=(10, 8)) #Sets image size by width & height (in inches)
-    
-    colors = ['tan', 'mediumseagreen', 'thistle', 'lightcoral', 'sienna', 'yellow'] # Extend/improve this list as needed
-
-    for idx, gdf in enumerate(gdfs):
-        gdf.plot(ax=ax, color=colors[idx % len(colors)]) # Cycle through colors
-
-    ax.set_xlim(extent[0], extent[2])
-    ax.set_ylim(extent[1], extent[3])
-
-    # Hide axes
-    ax.axis('off')
-
-    plt.savefig(filename, bbox_inches='tight', pad_inches=0) # remove padding