averageDailyFlights_Matrix added

api_aerodatabox/averageDailyFlights_matrix.py

@@ -0,0 +1,59 @@
+import os
+import json
+import pandas as pd
+from scipy.sparse import csr_matrix, save_npz
+from collections import defaultdict
+
+# Base directory containing all the monthly folders
+base_directory = '.'
+
+# List of month folders
+month_folders = [f'{i:02d}-{month}' for i, month in enumerate(
+    ["January", "February", "March", "April", "May", "June",
+     "July", "August", "September", "October", "November", "December"], 1)]
+
+# Function to update the matrix
+def update_matrix(data, origin, destination, avg_daily_flights):
+    data[origin][destination] += float(avg_daily_flights)
+
+# Loop through each monthly folder
+for month_folder in month_folders:
+    directory = os.path.join(base_directory, month_folder)
+
+    # Initialize an empty dictionary for the matrix data
+    data = defaultdict(lambda: defaultdict(float))
+
+    if os.path.exists(directory) and os.path.isdir(directory):
+        # Loop through all JSON files in the directory
+        file_list = [f for f in os.listdir(directory) if f.endswith('.json')]
+
+        for i, filename in enumerate(file_list):
+            origin = filename.split('.')[0]
+            filepath = os.path.join(directory, filename)
+
+            with open(filepath, 'r') as file:
+                try:
+                    data_json = json.load(file)
+                    for route in data_json.get('routes', []):
+                        destination_info = route.get('destination', {})
+                        destination = destination_info.get('icao')
+                        avg_daily_flights = route.get('averageDailyFlights')
+                        if destination and avg_daily_flights is not None:
+                            update_matrix(data, origin, destination, avg_daily_flights)
+                except json.JSONDecodeError:
+                    print(f"Error decoding JSON from file {filename}")
+
+            # Save the matrix periodically
+            if (i + 1) % 100 == 0 or (i + 1) == len(file_list):
+                print(f'Processed {i + 1}/{len(file_list)} files in {month_folder}...')
+
+        # Create DataFrame from the data dictionary
+        matrix_df = pd.DataFrame.from_dict(data, orient='index').fillna(0).astype(pd.SparseDtype("float", 0))
+
+        # Convert DataFrame to sparse matrix and save
+        sparse_matrix = csr_matrix(matrix_df.sparse.to_coo())
+        save_npz(f'{month_folder}.npz', sparse_matrix)
+
+        print(f'Saved matrix for {month_folder}.')
+
+print('All files processed successfully.')

api_aerodatabox/case_study/aircraft_seat_list.py

@@ -263,4 +263,19 @@
     "Gulfstream GIII": 19,
     "Bombardier Challenger 300": 9,
     "Gulfstream Aerospace V": 19,
+    "Cessna 560 Citation Excel": 8,
+    "Airbus A321-200 (Sharklets)": 185,
+    "Bombardier Dash 8 / DHC-8": 37,
+    "77I": 0,
+    "78I": 0,
+    "Airbus A330-900 NEO": 287,
+    "Boeing 737-700 (winglets)": 128,
+    "Sukhoi Superjet 100": 98,
+    "Boeing 767-300 (winglets)": 218,
+    "ATR 72": 70,
+    "78E": 0,
+    "77N": 0,
+    "Boeing 787-10": 318,
+    "32E": 0,
+    "Airbus A321-200 (Sharklets)": 185,
 }

api_aerodatabox/case_study/data_collection.py

@@ -12,7 +12,7 @@ def generate_time_intervals(start_date, end_date, delta):
         current += delta
 
 # Airports list
-airports = ["DNMM", "DNAA", "KLAX", "KSFO", "YMML", "YSSY"]
+airports = ["OEJN", "OERK"]
 
 # API details
 base_url = "https://aerodatabox.p.rapidapi.com/flights/airports/icao/{airport}/{start}/{end}"
@@ -31,8 +31,8 @@ def generate_time_intervals(start_date, end_date, delta):
 }
 
 # Time interval settings
-start_date = datetime(2023, 5, 25)
-end_date = datetime(2024, 5, 23)
+start_date = datetime(2023, 6, 13)
+end_date = datetime(2023, 7, 13)
 time_interval = timedelta(hours=12)
 
 # Create output directory if it doesn't exist