Task 2 solution

app.py

@@ -1,16 +1,14 @@
-from flask import Flask, render_template
+from flask import Flask, render_template, request
 from mapping.folium_map import *
 
 app = Flask(__name__)
 
 @app.route("/")
-def home():
+def index():
+    return render_template("home.html")
 
-    gdf = create_gdf()
-    m = generate_map_from_gdf(gdf, w=800, h=600)
 
-    m.get_root().width = "800px"
-    m.get_root().height = "600px"
-    iframe = m.get_root()._repr_html_()
-
-    return render_template("home.html", iframe=iframe)
+@app.route("/slider_update", methods=["POST"])
+def slider():
+    received_data = request.data
+    return received_data

config.yaml

@@ -1,3 +1,5 @@
 # App configs
-window_width: 800
-window_height: 600
+width: 800
+width_px: "800px"
+height: 600
+height_px: "600px"

main.py

@@ -0,0 +1,32 @@
+from mapping.folium_map import *
+import yaml
+
+def main():
+    """
+    Produce all required map iframes
+    """
+    # Load config
+    with open("config.yaml") as f:
+        config = yaml.safe_load(f)
+
+    # Loop through required years
+    for i in range(2014, 2025):
+        file_extension = str(i)
+
+        # Create map
+        geo_df = create_gdf(i)
+        m = generate_map_from_gdf(geo_df, w=config["width"], h=config["height"])
+
+        # Serve map to iframe
+        m.get_root().width = config["width_px"]
+        m.get_root().height = config["height_px"]
+        iframe = m.get_root()._repr_html_()
+
+        # Write out iframe to HTML
+        filename = f"map_{file_extension}.html"
+        html_file = open(f"static/{filename}", "w")
+        html_file.write(iframe)
+        html_file.close
+
+if __name__ == "__main__":
+    main()

mapping/folium_map.py

@@ -3,29 +3,30 @@
 import json
 from mapping.prep_data import *
 
-def create_gdf():
+def create_gdf(period: int):
     """
     Function to create a GeoPandas DataFrame from the spatial data file 
     and then merge feature data into it
+    # Todo: Control for inflation on wage data
     """
 
     # Get feature data
     lad_df = get_df_from_db()
 
     # Data cleaning
-    lad_df_2024 = lad_df[lad_df["year"]==2024]
-    lad_df_2024 = lad_df_2024.dropna()
-    lad_df_2024["median_ann_pay"] = lad_df_2024["median_ann_pay"].astype("int64")
+    lad_df_filtered = lad_df[lad_df["year"]==period]
+    lad_df_filtered = lad_df_filtered.dropna()
+    lad_df_filtered["median_ann_pay"] = lad_df_filtered["median_ann_pay"].astype("int64")
 
     # Get spatial data
     gdf = geopandas.read_file("data/local_authority_district_boundaries.geojson")
 
     # Join feature data on spatial data
     gdf = gdf.merge(
-    lad_df_2024,
-    how="left",
-    left_on="LAD24NM",
-    right_on="lad"
+        lad_df_filtered,
+        how="left",
+        left_on="LAD24NM",
+        right_on="lad"
     )
 
     return gdf