From c258938cb2fa7c0bfab51cf87f95ec7ebe04ace3 Mon Sep 17 00:00:00 2001
From: ren <ren@velend.eu>
Date: Tue, 19 Nov 2024 15:03:03 +0200
Subject: [PATCH] Still needs some work, functional.

---
 .gitignore    |  2 ++
 visualizer.py | 80 +++++++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 82 insertions(+)
 create mode 100644 .gitignore
 create mode 100644 visualizer.py

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..b2552b7
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,2 @@
+*.html
+gps/*.txt
\ No newline at end of file
diff --git a/visualizer.py b/visualizer.py
new file mode 100644
index 0000000..094e0c8
--- /dev/null
+++ b/visualizer.py
@@ -0,0 +1,80 @@
+import pandas as pd
+import glob
+import folium
+from folium.plugins import HeatMap
+import matplotlib.pyplot as plt
+import base64
+from io import BytesIO
+
+file_location = "gps/*.txt"
+
+files = glob.glob(file_location) # Combine all files
+data_list = []
+
+for file in files:
+    df = pd.read_csv(
+        file,
+        header=None,
+        names=["type", "line", "long", "lat", "ignore1", "ignore2", "tak", "ignore3", "ignore4", "destination"],
+    )
+    data_list.append(df)
+
+data = pd.concat(data_list)
+
+data['type'] = data['type'].astype(int)
+data['long'] = data['long'] / 1e6  # Convert to decimal degrees
+data['lat'] = data['lat'] / 1e6
+
+data = data.dropna(subset=['long', 'lat']) # Drop rows with invalid coordinates
+
+vehicle_types = {1: "Trolleybus", 2: "Bus", 3: "Tram", 7: "Nightbus"} # Vehicle types
+
+# Create heatmaps for each vehicle type
+for v_type, v_name in vehicle_types.items():
+    # Filter data for the current vehicle type
+    v_data = data[data['type'] == v_type]
+    
+    # Skip if no data for the vehicle type
+    if v_data.empty:
+        print(f"No data found for {v_name}. Skipping...")
+        continue
+
+    # Create the heatmap
+    print(f"Creating heatmap for {v_name}...")
+    map_center = [v_data['lat'].mean(), v_data['long'].mean()]
+    heatmap_map = folium.Map(location=map_center, zoom_start=12)
+    
+    # Don't change these values.
+    HeatMap(
+        data=v_data[['lat', 'long']].values,
+        radius=10,
+        blur=15,
+        min_opacity=1,
+        max_zoom=15,
+        gradient={0.2: 'blue', 0.4: 'green', 0.6: 'yellow', 0.8: 'orange', 1.0: 'red'},  # Gradient colors
+    ).add_to(heatmap_map)
+    
+    # Generate destination graph
+    dest_counts = v_data['destination'].value_counts()
+    plt.figure(figsize=(6, 4))
+    dest_counts.plot(kind='bar', color='red')
+    plt.title(f"Destinations for {v_name}")
+    plt.xlabel("Destination")
+    plt.ylabel("Count")
+    plt.tight_layout()
+    
+    # Save graph to a base64-encoded image
+    img = BytesIO()
+    plt.savefig(img, format='png', bbox_inches='tight')
+    img.seek(0)
+    encoded_img = base64.b64encode(img.getvalue()).decode('utf-8')
+    plt.close()
+
+    # Add the image to the map
+    html = f'<img src="data:image/png;base64,{encoded_img}" style="width:400px;height:300px;">'
+    popup = folium.Popup(html, max_width=500)
+    folium.Marker(location=map_center, popup=popup).add_to(heatmap_map)
+    
+    # Save the heatmap
+    heatmap_map.save(f"{v_name}_heatmap.html")
+    print(f"{v_name} heatmap saved as {v_name}_heatmap.html.")