Update app.py

app.py

@@ -115,7 +115,155 @@ regions = {
     "Hong Kong": {"lat_min": 21.5, "lat_max": 23, "lon_min": 113, "lon_max": 115},
     "Philippines": {"lat_min": 5, "lat_max": 21, "lon_min": 115, "lon_max": 130}
 }
+# Add these functions near the top of the file after imports
+def generate_sample_oni_data():
+    """Generate sample ONI data when the real data can't be loaded"""
+    years = range(1950, 2024)
+    months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
+    data = {'Year': list(years)}
+    
+    # Generate random ONI values between -2.5 and 2.5
+    np.random.seed(42)  # For reproducibility
+    for month in months:
+        data[month] = np.round(np.random.uniform(-2.5, 2.5, len(years)), 1)
+    
+    df = pd.DataFrame(data)
+    df.to_csv(ONI_DATA_PATH, index=False)
+    return df
+
+def generate_sample_typhoon_data():
+    """Generate sample typhoon data when the real data can't be loaded"""
+    # Create sample data with realistic values
+    np.random.seed(42)
+    
+    # Generate 100 sample typhoons
+    n_typhoons = 100
+    n_points_per_typhoon = 20
+    total_points = n_typhoons * n_points_per_typhoon
+    
+    data = {
+        'SID': [],
+        'ISO_TIME': [],
+        'NAME': [],
+        'SEASON': [],
+        'LAT': [],
+        'LON': [],
+        'USA_WIND': [],
+        'USA_PRES': []
+    }
+    
+    # Basin prefixes and sample names
+    basin_prefixes = ['WP', 'EP', 'NA']
+    typhoon_names = ['HAIYAN', 'YOLANDA', 'MANGKHUT', 'YUTU', 'HAGIBIS', 'MERANTI', 
+                     'MEGI', 'HAGUPIT', 'MAYSAK', 'HATO', 'NEPARTAK', 'SOUDELOR']
+    
+    # Generate data
+    for i in range(n_typhoons):
+        basin = np.random.choice(basin_prefixes)
+        year = np.random.randint(1980, 2024)
+        name = np.random.choice(typhoon_names)
+        sid = f"{basin}{year}{i:02d}"
+        
+        # Starting position
+        start_lon = np.random.uniform(120, 170)
+        start_lat = np.random.uniform(5, 30)
+        
+        # Generate track points
+        for j in range(n_points_per_typhoon):
+            # Time progression
+            date = datetime(year, np.random.randint(6, 11), np.random.randint(1, 28), 
+                           np.random.randint(0, 24))
+            date += timedelta(hours=j*6)  # 6-hour intervals
+            
+            # Position progression (typically moves northwest in WP)
+            lon = start_lon - j * np.random.uniform(0.3, 0.8)
+            lat = start_lat + j * np.random.uniform(0.2, 0.5)
+            
+            # Intensity progression (typically intensifies then weakens)
+            intensity_factor = min(1.0, j/(n_points_per_typhoon/2)) if j < n_points_per_typhoon/2 else \
+                             1.0 - min(1.0, (j-n_points_per_typhoon/2)/(n_points_per_typhoon/2))
+            wind = np.random.randint(30, 150) * intensity_factor
+            pressure = 1010 - (wind * 0.75)  # Approximate relationship
+            
+            # Add to data dict
+            data['SID'].append(sid)
+            data['ISO_TIME'].append(date)
+            data['NAME'].append(name)
+            data['SEASON'].append(year)
+            data['LAT'].append(lat)
+            data['LON'].append(lon)
+            data['USA_WIND'].append(wind)
+            data['USA_PRES'].append(pressure)
+    
+    df = pd.DataFrame(data)
+    df.to_csv(TYPHOON_DATA_PATH, index=False)
+    return df
+
+# Modify load_data function to handle missing data
+def load_data(oni_path, typhoon_path):
+    oni_data = pd.DataFrame()
+    typhoon_data = pd.DataFrame()
+    
+    # Try to load ONI data, generate sample if not found
+    if not os.path.exists(oni_path):
+        logging.warning(f"ONI data file not found: {oni_path}")
+        logging.info("Generating sample ONI data")
+        oni_data = generate_sample_oni_data()
+    else:
+        try:
+            oni_data = pd.read_csv(oni_path)
+        except Exception as e:
+            logging.error(f"Error loading ONI data: {e}")
+            logging.info("Generating sample ONI data")
+            oni_data = generate_sample_oni_data()
+    
+    # Try to load Typhoon data, generate sample if not found
+    if not os.path.exists(typhoon_path):
+        logging.warning(f"Typhoon data file not found: {typhoon_path}")
+        logging.info("Generating sample typhoon data")
+        typhoon_data = generate_sample_typhoon_data()
+    else:
+        try:
+            typhoon_data = pd.read_csv(typhoon_path, low_memory=False)
+            typhoon_data['ISO_TIME'] = pd.to_datetime(typhoon_data['ISO_TIME'], errors='coerce')
+            typhoon_data = typhoon_data.dropna(subset=['ISO_TIME'])
+        except Exception as e:
+            logging.error(f"Error loading typhoon data: {e}")
+            logging.info("Generating sample typhoon data")
+            typhoon_data = generate_sample_typhoon_data()
+    
+    return oni_data, typhoon_data
 
+# Also update the load_ibtracs_data function to be more robust
+def load_ibtracs_data():
+    ibtracs_data = {}
+    for basin, filename in BASIN_FILES.items():
+        local_path = os.path.join(DATA_PATH, filename)
+        try:
+            if not os.path.exists(local_path):
+                logging.info(f"Downloading {basin} basin file...")
+                try:
+                    response = requests.get(IBTRACS_BASE_URL+filename)
+                    response.raise_for_status()
+                    with open(local_path, 'wb') as f:
+                        f.write(response.content)
+                    logging.info(f"Downloaded {basin} basin file.")
+                except Exception as e:
+                    logging.error(f"Failed to download {basin} basin file: {e}")
+                    continue
+                    
+            logging.info(f"--> Starting to read in IBTrACS data for basin {basin}")
+            try:
+                ds = tracks.TrackDataset(source='ibtracs', ibtracs_url=local_path)
+                logging.info(f"--> Completed reading in IBTrACS data for basin {basin}")
+                ibtracs_data[basin] = ds
+            except Exception as e:
+                logging.warning(f"Skipping basin {basin} due to error: {e}")
+                ibtracs_data[basin] = None
+        except Exception as e:
+            logging.error(f"Error processing basin {basin}: {e}")
+            ibtracs_data[basin] = None
+    return ibtracs_data
 # -----------------------------
 # ONI and Typhoon Data Functions
 # -----------------------------