import os
import argparse
import logging
import pickle
import threading
import time
import warnings
from datetime import datetime, timedelta
from collections import defaultdict
import csv
import json
# Suppress warnings for cleaner output
warnings.filterwarnings('ignore', category=FutureWarning)
warnings.filterwarnings('ignore', category=UserWarning, module='umap')
warnings.filterwarnings('ignore', category=UserWarning, module='sklearn')
import gradio as gr
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import plotly.graph_objects as go
import plotly.express as px
from plotly.subplots import make_subplots
from sklearn.manifold import TSNE
from sklearn.cluster import DBSCAN, KMeans
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_absolute_error, r2_score
from scipy.interpolate import interp1d, RBFInterpolator
import statsmodels.api as sm
import requests
import tempfile
import shutil
import xarray as xr
# NEW: Advanced ML imports
try:
import umap.umap_ as umap
UMAP_AVAILABLE = True
except ImportError:
UMAP_AVAILABLE = False
print("UMAP not available - clustering features limited")
# Optional CNN imports with robust error handling
CNN_AVAILABLE = False
try:
# Set environment variables before importing TensorFlow
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # Suppress TensorFlow warnings
import tensorflow as tf
from tensorflow.keras import layers, models
# Test if TensorFlow actually works
tf.config.set_visible_devices([], 'GPU') # Disable GPU to avoid conflicts
CNN_AVAILABLE = True
print("TensorFlow successfully loaded - CNN features enabled")
except Exception as e:
CNN_AVAILABLE = False
print(f"TensorFlow not available - CNN features disabled: {str(e)[:100]}...")
try:
import cdsapi
CDSAPI_AVAILABLE = True
except ImportError:
CDSAPI_AVAILABLE = False
import tropycal.tracks as tracks
# -----------------------------
# Configuration and Setup
# -----------------------------
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s'
)
# Remove argument parser to simplify startup
DATA_PATH = '/tmp/typhoon_data' if 'SPACE_ID' in os.environ else tempfile.gettempdir()
# Ensure directory exists and is writable
try:
os.makedirs(DATA_PATH, exist_ok=True)
# Test write permissions
test_file = os.path.join(DATA_PATH, 'test_write.txt')
with open(test_file, 'w') as f:
f.write('test')
os.remove(test_file)
logging.info(f"Data directory is writable: {DATA_PATH}")
except Exception as e:
logging.warning(f"Data directory not writable, using temp dir: {e}")
DATA_PATH = tempfile.mkdtemp()
logging.info(f"Using temporary directory: {DATA_PATH}")
# Update file paths
ONI_DATA_PATH = os.path.join(DATA_PATH, 'oni_data.csv')
TYPHOON_DATA_PATH = os.path.join(DATA_PATH, 'processed_typhoon_data.csv')
MERGED_DATA_CSV = os.path.join(DATA_PATH, 'merged_typhoon_era5_data.csv')
# IBTrACS settings
BASIN_FILES = {
'EP': 'ibtracs.EP.list.v04r01.csv',
'NA': 'ibtracs.NA.list.v04r01.csv',
'WP': 'ibtracs.WP.list.v04r01.csv'
}
IBTRACS_BASE_URL = 'https://www.ncei.noaa.gov/data/international-best-track-archive-for-climate-stewardship-ibtracs/v04r01/access/csv/'
LOCAL_IBTRACS_PATH = os.path.join(DATA_PATH, 'ibtracs.WP.list.v04r01.csv')
CACHE_FILE = os.path.join(DATA_PATH, 'ibtracs_cache.pkl')
CACHE_EXPIRY_DAYS = 1
# -----------------------------
# ENHANCED: Color Maps and Standards with TD Support (FIXED TAIWAN CLASSIFICATION)
# -----------------------------
# Enhanced color mapping with TD support (for Plotly)
enhanced_color_map = {
'Unknown': 'rgb(200, 200, 200)',
'Tropical Depression': 'rgb(128, 128, 128)', # Gray for TD
'Tropical Storm': 'rgb(0, 0, 255)',
'C1 Typhoon': 'rgb(0, 255, 255)',
'C2 Typhoon': 'rgb(0, 255, 0)',
'C3 Strong Typhoon': 'rgb(255, 255, 0)',
'C4 Very Strong Typhoon': 'rgb(255, 165, 0)',
'C5 Super Typhoon': 'rgb(255, 0, 0)'
}
# Matplotlib-compatible color mapping (hex colors)
matplotlib_color_map = {
'Unknown': '#C8C8C8',
'Tropical Depression': '#808080', # Gray for TD
'Tropical Storm': '#0000FF', # Blue
'C1 Typhoon': '#00FFFF', # Cyan
'C2 Typhoon': '#00FF00', # Green
'C3 Strong Typhoon': '#FFFF00', # Yellow
'C4 Very Strong Typhoon': '#FFA500', # Orange
'C5 Super Typhoon': '#FF0000' # Red
}
# FIXED Taiwan color mapping with correct categories
taiwan_color_map = {
'Tropical Depression': '#808080', # Gray
'Tropical Storm': '#0000FF', # Blue
'Moderate Typhoon': '#FFA500', # Orange
'Intense Typhoon': '#FF0000' # Red
}
def rgb_string_to_hex(rgb_string):
"""Convert 'rgb(r,g,b)' string to hex color for matplotlib"""
try:
# Extract numbers from 'rgb(r,g,b)' format
import re
numbers = re.findall(r'\d+', rgb_string)
if len(numbers) == 3:
r, g, b = map(int, numbers)
return f'#{r:02x}{g:02x}{b:02x}'
else:
return '#808080' # Default gray
except:
return '#808080' # Default gray
def get_matplotlib_color(category):
"""Get matplotlib-compatible color for a storm category"""
return matplotlib_color_map.get(category, '#808080')
def get_taiwan_color(category):
"""Get Taiwan standard color for a storm category"""
return taiwan_color_map.get(category, '#808080')
# Cluster colors for route visualization
CLUSTER_COLORS = [
'#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4', '#FFEAA7',
'#DDA0DD', '#98D8C8', '#F7DC6F', '#BB8FCE', '#85C1E9',
'#F8C471', '#82E0AA', '#F1948A', '#85C1E9', '#D2B4DE'
]
# Route prediction colors
ROUTE_COLORS = [
'#FF0066', '#00FF66', '#6600FF', '#FF6600', '#0066FF',
'#FF00CC', '#00FFCC', '#CC00FF', '#CCFF00', '#00CCFF'
]
# Original color map for backward compatibility
color_map = {
'C5 Super Typhoon': 'rgb(255, 0, 0)',
'C4 Very Strong Typhoon': 'rgb(255, 165, 0)',
'C3 Strong Typhoon': 'rgb(255, 255, 0)',
'C2 Typhoon': 'rgb(0, 255, 0)',
'C1 Typhoon': 'rgb(0, 255, 255)',
'Tropical Storm': 'rgb(0, 0, 255)',
'Tropical Depression': 'rgb(128, 128, 128)'
}
atlantic_standard = {
'C5 Super Typhoon': {'wind_speed': 137, 'color': 'Red', 'hex': '#FF0000'},
'C4 Very Strong Typhoon': {'wind_speed': 113, 'color': 'Orange', 'hex': '#FFA500'},
'C3 Strong Typhoon': {'wind_speed': 96, 'color': 'Yellow', 'hex': '#FFFF00'},
'C2 Typhoon': {'wind_speed': 83, 'color': 'Green', 'hex': '#00FF00'},
'C1 Typhoon': {'wind_speed': 64, 'color': 'Cyan', 'hex': '#00FFFF'},
'Tropical Storm': {'wind_speed': 34, 'color': 'Blue', 'hex': '#0000FF'},
'Tropical Depression': {'wind_speed': 0, 'color': 'Gray', 'hex': '#808080'}
}
# FIXED Taiwan standard with correct official CWA thresholds
taiwan_standard = {
'Intense Typhoon': {'wind_speed': 51.0, 'color': 'Red', 'hex': '#FF0000'}, # 100+ knots (51.0+ m/s)
'Moderate Typhoon': {'wind_speed': 32.7, 'color': 'Orange', 'hex': '#FFA500'}, # 64-99 knots (32.7-50.9 m/s)
'Tropical Storm': {'wind_speed': 17.2, 'color': 'Blue', 'hex': '#0000FF'}, # 34-63 knots (17.2-32.6 m/s)
'Tropical Depression': {'wind_speed': 0, 'color': 'Gray', 'hex': '#808080'} # <34 knots (<17.2 m/s)
}
# -----------------------------
# Utility Functions for HF Spaces
# -----------------------------
def safe_file_write(file_path, data_frame, backup_dir=None):
"""Safely write DataFrame to CSV with backup and error handling"""
try:
# Create directory if it doesn't exist
os.makedirs(os.path.dirname(file_path), exist_ok=True)
# Try to write to a temporary file first
temp_path = file_path + '.tmp'
data_frame.to_csv(temp_path, index=False)
# If successful, rename to final file
os.rename(temp_path, file_path)
logging.info(f"Successfully saved {len(data_frame)} records to {file_path}")
return True
except PermissionError as e:
logging.warning(f"Permission denied writing to {file_path}: {e}")
if backup_dir:
try:
backup_path = os.path.join(backup_dir, os.path.basename(file_path))
data_frame.to_csv(backup_path, index=False)
logging.info(f"Saved to backup location: {backup_path}")
return True
except Exception as backup_e:
logging.error(f"Failed to save to backup location: {backup_e}")
return False
except Exception as e:
logging.error(f"Error saving file {file_path}: {e}")
# Clean up temp file if it exists
temp_path = file_path + '.tmp'
if os.path.exists(temp_path):
try:
os.remove(temp_path)
except:
pass
return False
def get_fallback_data_dir():
"""Get a fallback data directory that's guaranteed to be writable"""
fallback_dirs = [
tempfile.gettempdir(),
'/tmp',
os.path.expanduser('~'),
os.getcwd()
]
for directory in fallback_dirs:
try:
test_dir = os.path.join(directory, 'typhoon_fallback')
os.makedirs(test_dir, exist_ok=True)
test_file = os.path.join(test_dir, 'test.txt')
with open(test_file, 'w') as f:
f.write('test')
os.remove(test_file)
return test_dir
except:
continue
# If all else fails, use current directory
return os.getcwd()
# -----------------------------
# ONI and Typhoon Data Functions
# -----------------------------
def download_oni_file(url, filename):
"""Download ONI file with retry logic"""
max_retries = 3
for attempt in range(max_retries):
try:
response = requests.get(url, timeout=30)
response.raise_for_status()
with open(filename, 'wb') as f:
f.write(response.content)
return True
except Exception as e:
logging.warning(f"Attempt {attempt + 1} failed to download ONI: {e}")
if attempt < max_retries - 1:
time.sleep(2 ** attempt) # Exponential backoff
else:
logging.error(f"Failed to download ONI after {max_retries} attempts")
return False
def convert_oni_ascii_to_csv(input_file, output_file):
"""Convert ONI ASCII format to CSV"""
data = defaultdict(lambda: [''] * 12)
season_to_month = {'DJF':12, 'JFM':1, 'FMA':2, 'MAM':3, 'AMJ':4, 'MJJ':5,
'JJA':6, 'JAS':7, 'ASO':8, 'SON':9, 'OND':10, 'NDJ':11}
try:
with open(input_file, 'r') as f:
lines = f.readlines()[1:] # Skip header
for line in lines:
parts = line.split()
if len(parts) >= 4:
season, year, anom = parts[0], parts[1], parts[-1]
if season in season_to_month:
month = season_to_month[season]
if season == 'DJF':
year = str(int(year)-1)
data[year][month-1] = anom
# Write to CSV with safe write
df = pd.DataFrame(data).T.reset_index()
df.columns = ['Year','Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
df = df.sort_values('Year').reset_index(drop=True)
return safe_file_write(output_file, df, get_fallback_data_dir())
except Exception as e:
logging.error(f"Error converting ONI file: {e}")
return False
def update_oni_data():
"""Update ONI data with error handling"""
url = "https://www.cpc.ncep.noaa.gov/data/indices/oni.ascii.txt"
temp_file = os.path.join(DATA_PATH, "temp_oni.ascii.txt")
input_file = os.path.join(DATA_PATH, "oni.ascii.txt")
output_file = ONI_DATA_PATH
try:
if download_oni_file(url, temp_file):
if not os.path.exists(input_file) or not os.path.exists(output_file):
os.rename(temp_file, input_file)
convert_oni_ascii_to_csv(input_file, output_file)
else:
os.remove(temp_file)
else:
# Create fallback ONI data if download fails
logging.warning("Creating fallback ONI data")
create_fallback_oni_data(output_file)
except Exception as e:
logging.error(f"Error updating ONI data: {e}")
create_fallback_oni_data(output_file)
def create_fallback_oni_data(output_file):
"""Create minimal ONI data for testing"""
years = range(2000, 2026) # Extended to include 2025
months = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
# Create synthetic ONI data
data = []
for year in years:
row = [year]
for month in months:
# Generate some realistic ONI values
value = np.random.normal(0, 1) * 0.5
row.append(f"{value:.2f}")
data.append(row)
df = pd.DataFrame(data, columns=['Year'] + months)
safe_file_write(output_file, df, get_fallback_data_dir())
# -----------------------------
# FIXED: IBTrACS Data Loading
# -----------------------------
def download_ibtracs_file(basin, force_download=False):
"""Download specific basin file from IBTrACS"""
filename = BASIN_FILES[basin]
local_path = os.path.join(DATA_PATH, filename)
url = IBTRACS_BASE_URL + filename
# Check if file exists and is recent (less than 7 days old)
if os.path.exists(local_path) and not force_download:
file_age = time.time() - os.path.getmtime(local_path)
if file_age < 7 * 24 * 3600: # 7 days
logging.info(f"Using cached {basin} basin file")
return local_path
try:
logging.info(f"Downloading {basin} basin file from {url}")
response = requests.get(url, timeout=60)
response.raise_for_status()
# Ensure directory exists
os.makedirs(os.path.dirname(local_path), exist_ok=True)
with open(local_path, 'wb') as f:
f.write(response.content)
logging.info(f"Successfully downloaded {basin} basin file")
return local_path
except Exception as e:
logging.error(f"Failed to download {basin} basin file: {e}")
return None
def examine_ibtracs_structure(file_path):
"""Examine the actual structure of an IBTrACS CSV file"""
try:
with open(file_path, 'r') as f:
lines = f.readlines()
# Show first 5 lines
logging.info("First 5 lines of IBTrACS file:")
for i, line in enumerate(lines[:5]):
logging.info(f"Line {i}: {line.strip()}")
# The first line contains the actual column headers
# No need to skip rows for IBTrACS v04r01
df = pd.read_csv(file_path, nrows=5)
logging.info(f"Columns from first row: {list(df.columns)}")
return list(df.columns)
except Exception as e:
logging.error(f"Error examining IBTrACS structure: {e}")
return None
def load_ibtracs_csv_directly(basin='WP'):
"""Load IBTrACS data directly from CSV - FIXED VERSION"""
filename = BASIN_FILES[basin]
local_path = os.path.join(DATA_PATH, filename)
# Download if not exists
if not os.path.exists(local_path):
downloaded_path = download_ibtracs_file(basin)
if not downloaded_path:
return None
try:
# First, examine the structure
actual_columns = examine_ibtracs_structure(local_path)
if not actual_columns:
logging.error("Could not examine IBTrACS file structure")
return None
# Read IBTrACS CSV - DON'T skip any rows for v04r01
# The first row contains proper column headers
logging.info(f"Reading IBTrACS CSV file: {local_path}")
df = pd.read_csv(local_path, low_memory=False) # Don't skip any rows
logging.info(f"Original columns: {list(df.columns)}")
logging.info(f"Data shape before cleaning: {df.shape}")
# Check which essential columns exist
required_cols = ['SID', 'ISO_TIME', 'LAT', 'LON']
available_required = [col for col in required_cols if col in df.columns]
if len(available_required) < 2:
logging.error(f"Missing critical columns. Available: {list(df.columns)}")
return None
# Clean and standardize the data with format specification
if 'ISO_TIME' in df.columns:
df['ISO_TIME'] = pd.to_datetime(df['ISO_TIME'], format='%Y-%m-%d %H:%M:%S', errors='coerce')
# Clean numeric columns
numeric_columns = ['LAT', 'LON', 'WMO_WIND', 'WMO_PRES', 'USA_WIND', 'USA_PRES']
for col in numeric_columns:
if col in df.columns:
df[col] = pd.to_numeric(df[col], errors='coerce')
# Filter out invalid/missing critical data
valid_rows = df['LAT'].notna() & df['LON'].notna()
df = df[valid_rows]
# Ensure LAT/LON are in reasonable ranges
df = df[(df['LAT'] >= -90) & (df['LAT'] <= 90)]
df = df[(df['LON'] >= -180) & (df['LON'] <= 180)]
# Add basin info if missing
if 'BASIN' not in df.columns:
df['BASIN'] = basin
# Add default columns if missing
if 'NAME' not in df.columns:
df['NAME'] = 'UNNAMED'
if 'SEASON' not in df.columns and 'ISO_TIME' in df.columns:
df['SEASON'] = df['ISO_TIME'].dt.year
logging.info(f"Successfully loaded {len(df)} records from {basin} basin")
return df
except Exception as e:
logging.error(f"Error reading IBTrACS CSV file: {e}")
return None
def load_ibtracs_data_fixed():
"""Fixed version of IBTrACS data loading"""
ibtracs_data = {}
# Try to load each basin, but prioritize WP for this application
load_order = ['WP', 'EP', 'NA']
for basin in load_order:
try:
logging.info(f"Loading {basin} basin data...")
df = load_ibtracs_csv_directly(basin)
if df is not None and not df.empty:
ibtracs_data[basin] = df
logging.info(f"Successfully loaded {basin} basin with {len(df)} records")
else:
logging.warning(f"No data loaded for basin {basin}")
ibtracs_data[basin] = None
except Exception as e:
logging.error(f"Failed to load basin {basin}: {e}")
ibtracs_data[basin] = None
return ibtracs_data
def load_data_fixed(oni_path, typhoon_path):
"""Fixed version of load_data function"""
# Load ONI data
oni_data = pd.DataFrame({'Year': [], 'Jan': [], 'Feb': [], 'Mar': [], 'Apr': [],
'May': [], 'Jun': [], 'Jul': [], 'Aug': [], 'Sep': [],
'Oct': [], 'Nov': [], 'Dec': []})
if not os.path.exists(oni_path):
logging.warning(f"ONI data file not found: {oni_path}")
update_oni_data()
try:
oni_data = pd.read_csv(oni_path)
logging.info(f"Successfully loaded ONI data with {len(oni_data)} years")
except Exception as e:
logging.error(f"Error loading ONI data: {e}")
update_oni_data()
try:
oni_data = pd.read_csv(oni_path)
except Exception as e:
logging.error(f"Still can't load ONI data: {e}")
# Load typhoon data - NEW APPROACH
typhoon_data = None
# First, try to load from existing processed file
if os.path.exists(typhoon_path):
try:
typhoon_data = pd.read_csv(typhoon_path, low_memory=False)
# Ensure basic columns exist and are valid
required_cols = ['LAT', 'LON']
if all(col in typhoon_data.columns for col in required_cols):
if 'ISO_TIME' in typhoon_data.columns:
typhoon_data['ISO_TIME'] = pd.to_datetime(typhoon_data['ISO_TIME'], errors='coerce')
logging.info(f"Loaded processed typhoon data with {len(typhoon_data)} records")
else:
logging.warning("Processed typhoon data missing required columns, will reload from IBTrACS")
typhoon_data = None
except Exception as e:
logging.error(f"Error loading processed typhoon data: {e}")
typhoon_data = None
# If no valid processed data, load from IBTrACS
if typhoon_data is None or typhoon_data.empty:
logging.info("Loading typhoon data from IBTrACS...")
ibtracs_data = load_ibtracs_data_fixed()
# Combine all available basin data, prioritizing WP
combined_dfs = []
for basin in ['WP', 'EP', 'NA']:
if basin in ibtracs_data and ibtracs_data[basin] is not None:
df = ibtracs_data[basin].copy()
df['BASIN'] = basin
combined_dfs.append(df)
if combined_dfs:
typhoon_data = pd.concat(combined_dfs, ignore_index=True)
# Ensure SID has proper format
if 'SID' not in typhoon_data.columns and 'BASIN' in typhoon_data.columns:
# Create SID from basin and other identifiers if missing
if 'SEASON' in typhoon_data.columns:
typhoon_data['SID'] = (typhoon_data['BASIN'].astype(str) +
typhoon_data.index.astype(str).str.zfill(2) +
typhoon_data['SEASON'].astype(str))
else:
typhoon_data['SID'] = (typhoon_data['BASIN'].astype(str) +
typhoon_data.index.astype(str).str.zfill(2) +
'2000')
# Save the processed data for future use
safe_file_write(typhoon_path, typhoon_data, get_fallback_data_dir())
logging.info(f"Combined IBTrACS data: {len(typhoon_data)} total records")
else:
logging.error("Failed to load any IBTrACS basin data")
# Create minimal fallback data
typhoon_data = create_fallback_typhoon_data()
# Final validation of typhoon data
if typhoon_data is not None:
# Ensure required columns exist with fallback values
required_columns = {
'SID': 'UNKNOWN',
'ISO_TIME': pd.Timestamp('2000-01-01'),
'LAT': 0.0,
'LON': 0.0,
'USA_WIND': np.nan,
'USA_PRES': np.nan,
'NAME': 'UNNAMED',
'SEASON': 2000
}
for col, default_val in required_columns.items():
if col not in typhoon_data.columns:
typhoon_data[col] = default_val
logging.warning(f"Added missing column {col} with default value")
# Ensure data types
if 'ISO_TIME' in typhoon_data.columns:
typhoon_data['ISO_TIME'] = pd.to_datetime(typhoon_data['ISO_TIME'], errors='coerce')
typhoon_data['LAT'] = pd.to_numeric(typhoon_data['LAT'], errors='coerce')
typhoon_data['LON'] = pd.to_numeric(typhoon_data['LON'], errors='coerce')
typhoon_data['USA_WIND'] = pd.to_numeric(typhoon_data['USA_WIND'], errors='coerce')
typhoon_data['USA_PRES'] = pd.to_numeric(typhoon_data['USA_PRES'], errors='coerce')
# Remove rows with invalid coordinates
typhoon_data = typhoon_data.dropna(subset=['LAT', 'LON'])
logging.info(f"Final typhoon data: {len(typhoon_data)} records after validation")
return oni_data, typhoon_data
def create_fallback_typhoon_data():
"""Create minimal fallback typhoon data - FIXED VERSION"""
# Use proper pandas date_range instead of numpy
dates = pd.date_range(start='2000-01-01', end='2025-12-31', freq='D') # Extended to 2025
storm_dates = dates[np.random.choice(len(dates), size=100, replace=False)]
data = []
for i, date in enumerate(storm_dates):
# Create realistic WP storm tracks
base_lat = np.random.uniform(10, 30)
base_lon = np.random.uniform(130, 160)
# Generate 20-50 data points per storm
track_length = np.random.randint(20, 51)
sid = f"WP{i+1:02d}{date.year}"
for j in range(track_length):
lat = base_lat + j * 0.2 + np.random.normal(0, 0.1)
lon = base_lon + j * 0.3 + np.random.normal(0, 0.1)
wind = max(25, 70 + np.random.normal(0, 20))
pres = max(950, 1000 - wind + np.random.normal(0, 5))
data.append({
'SID': sid,
'ISO_TIME': date + pd.Timedelta(hours=j*6), # Use pd.Timedelta instead
'NAME': f'FALLBACK_{i+1}',
'SEASON': date.year,
'LAT': lat,
'LON': lon,
'USA_WIND': wind,
'USA_PRES': pres,
'BASIN': 'WP'
})
df = pd.DataFrame(data)
logging.info(f"Created fallback typhoon data with {len(df)} records")
return df
def process_oni_data(oni_data):
"""Process ONI data into long format"""
oni_long = oni_data.melt(id_vars=['Year'], var_name='Month', value_name='ONI')
month_map = {'Jan':'01','Feb':'02','Mar':'03','Apr':'04','May':'05','Jun':'06',
'Jul':'07','Aug':'08','Sep':'09','Oct':'10','Nov':'11','Dec':'12'}
oni_long['Month'] = oni_long['Month'].map(month_map)
oni_long['Date'] = pd.to_datetime(oni_long['Year'].astype(str)+'-'+oni_long['Month']+'-01')
oni_long['ONI'] = pd.to_numeric(oni_long['ONI'], errors='coerce')
return oni_long
def process_typhoon_data(typhoon_data):
"""Process typhoon data"""
if 'ISO_TIME' in typhoon_data.columns:
typhoon_data['ISO_TIME'] = pd.to_datetime(typhoon_data['ISO_TIME'], errors='coerce')
typhoon_data['USA_WIND'] = pd.to_numeric(typhoon_data['USA_WIND'], errors='coerce')
typhoon_data['USA_PRES'] = pd.to_numeric(typhoon_data['USA_PRES'], errors='coerce')
typhoon_data['LON'] = pd.to_numeric(typhoon_data['LON'], errors='coerce')
logging.info(f"Unique basins in typhoon_data: {typhoon_data['SID'].str[:2].unique()}")
typhoon_max = typhoon_data.groupby('SID').agg({
'USA_WIND':'max','USA_PRES':'min','ISO_TIME':'first','SEASON':'first','NAME':'first',
'LAT':'first','LON':'first'
}).reset_index()
if 'ISO_TIME' in typhoon_max.columns:
typhoon_max['Month'] = typhoon_max['ISO_TIME'].dt.strftime('%m')
typhoon_max['Year'] = typhoon_max['ISO_TIME'].dt.year
else:
# Fallback if no ISO_TIME
typhoon_max['Month'] = '01'
typhoon_max['Year'] = typhoon_max['SEASON']
typhoon_max['Category'] = typhoon_max['USA_WIND'].apply(categorize_typhoon_enhanced)
return typhoon_max
def merge_data(oni_long, typhoon_max):
"""Merge ONI and typhoon data"""
return pd.merge(typhoon_max, oni_long, on=['Year','Month'])
# -----------------------------
# ENHANCED: Categorization Functions (FIXED TAIWAN)
# -----------------------------
def categorize_typhoon_enhanced(wind_speed):
"""Enhanced categorization that properly includes Tropical Depressions"""
if pd.isna(wind_speed):
return 'Unknown'
# Convert to knots if in m/s (some datasets use m/s)
if wind_speed < 10: # Likely in m/s, convert to knots
wind_speed = wind_speed * 1.94384
# FIXED thresholds to include TD
if wind_speed < 34: # Below 34 knots = Tropical Depression
return 'Tropical Depression'
elif wind_speed < 64: # 34-63 knots = Tropical Storm
return 'Tropical Storm'
elif wind_speed < 83: # 64-82 knots = Category 1 Typhoon
return 'C1 Typhoon'
elif wind_speed < 96: # 83-95 knots = Category 2 Typhoon
return 'C2 Typhoon'
elif wind_speed < 113: # 96-112 knots = Category 3 Strong Typhoon
return 'C3 Strong Typhoon'
elif wind_speed < 137: # 113-136 knots = Category 4 Very Strong Typhoon
return 'C4 Very Strong Typhoon'
else: # 137+ knots = Category 5 Super Typhoon
return 'C5 Super Typhoon'
def categorize_typhoon_taiwan(wind_speed):
"""FIXED Taiwan categorization system according to official CWA standards"""
if pd.isna(wind_speed):
return 'Tropical Depression'
# Convert from knots to m/s (official CWA uses m/s thresholds)
if wind_speed > 200: # Likely already in m/s
wind_speed_ms = wind_speed
else: # Likely in knots, convert to m/s
wind_speed_ms = wind_speed * 0.514444
# Official CWA Taiwan classification thresholds
if wind_speed_ms >= 51.0: # 100+ knots
return 'Intense Typhoon'
elif wind_speed_ms >= 32.7: # 64-99 knots
return 'Moderate Typhoon'
elif wind_speed_ms >= 17.2: # 34-63 knots
return 'Tropical Storm'
else: # <34 knots
return 'Tropical Depression'
# Original function for backward compatibility
def categorize_typhoon(wind_speed):
"""Original categorize typhoon function for backward compatibility"""
return categorize_typhoon_enhanced(wind_speed)
def classify_enso_phases(oni_value):
"""Classify ENSO phases based on ONI value"""
if isinstance(oni_value, pd.Series):
oni_value = oni_value.iloc[0]
if pd.isna(oni_value):
return 'Neutral'
if oni_value >= 0.5:
return 'El Nino'
elif oni_value <= -0.5:
return 'La Nina'
else:
return 'Neutral'
def categorize_typhoon_by_standard(wind_speed, standard='atlantic'):
"""FIXED categorization function with correct Taiwan standards"""
if pd.isna(wind_speed):
return 'Tropical Depression', '#808080'
if standard == 'taiwan':
category = categorize_typhoon_taiwan(wind_speed)
color = taiwan_color_map.get(category, '#808080')
return category, color
else:
# Atlantic/International standard (existing logic is correct)
if wind_speed >= 137:
return 'C5 Super Typhoon', '#FF0000' # Red
elif wind_speed >= 113:
return 'C4 Very Strong Typhoon', '#FFA500' # Orange
elif wind_speed >= 96:
return 'C3 Strong Typhoon', '#FFFF00' # Yellow
elif wind_speed >= 83:
return 'C2 Typhoon', '#00FF00' # Green
elif wind_speed >= 64:
return 'C1 Typhoon', '#00FFFF' # Cyan
elif wind_speed >= 34:
return 'Tropical Storm', '#0000FF' # Blue
return 'Tropical Depression', '#808080' # Gray
# -----------------------------
# FIXED: Genesis Potential Index (GPI) Based Prediction System
# -----------------------------
def calculate_genesis_potential_index(sst, rh, vorticity, wind_shear, lat, lon, month, oni_value):
"""
Calculate Genesis Potential Index based on environmental parameters
Following Emanuel and Nolan (2004) formulation with modifications for monthly predictions
"""
try:
# Base environmental parameters
# SST factor - optimal range 26-30°C
sst_factor = max(0, (sst - 26.5) / 4.0) if sst > 26.5 else 0
# Humidity factor - mid-level relative humidity (600 hPa)
rh_factor = max(0, (rh - 40) / 50.0) # Normalized 40-90%
# Vorticity factor - low-level absolute vorticity (850 hPa)
vort_factor = max(0, min(vorticity / 5e-5, 3.0)) # Cap at reasonable max
# Wind shear factor - vertical wind shear (inverse relationship)
shear_factor = max(0, (20 - wind_shear) / 15.0) if wind_shear < 20 else 0
# Coriolis factor - latitude dependency
coriolis_factor = max(0, min(abs(lat) / 20.0, 1.0)) if abs(lat) > 5 else 0
# Seasonal factor
seasonal_weights = {
1: 0.3, 2: 0.2, 3: 0.4, 4: 0.6, 5: 0.8, 6: 1.0,
7: 1.2, 8: 1.4, 9: 1.5, 10: 1.3, 11: 0.9, 12: 0.5
}
seasonal_factor = seasonal_weights.get(month, 1.0)
# ENSO modulation
if oni_value > 0.5: # El Niño
enso_factor = 0.6 if lon > 140 else 0.8 # Suppress in WP
elif oni_value < -0.5: # La Niña
enso_factor = 1.4 if lon > 140 else 1.1 # Enhance in WP
else: # Neutral
enso_factor = 1.0
# Regional modulation (Western Pacific specifics)
if 10 <= lat <= 25 and 120 <= lon <= 160: # Main Development Region
regional_factor = 1.3
elif 5 <= lat <= 15 and 130 <= lon <= 150: # Prime genesis zone
regional_factor = 1.5
else:
regional_factor = 0.8
# Calculate GPI
gpi = (sst_factor * rh_factor * vort_factor * shear_factor *
coriolis_factor * seasonal_factor * enso_factor * regional_factor)
return max(0, min(gpi, 5.0)) # Cap at reasonable maximum
except Exception as e:
logging.error(f"Error calculating GPI: {e}")
return 0.0
def get_environmental_conditions(lat, lon, month, oni_value):
"""
Get realistic environmental conditions for a given location and time
Based on climatological patterns and ENSO modulation
"""
try:
# Base SST calculation (climatological)
base_sst = 28.5 - 0.15 * abs(lat - 15) # Peak at 15°N
seasonal_sst_adj = 2.0 * np.cos(2 * np.pi * (month - 9) / 12) # Peak in Sep
enso_sst_adj = oni_value * 0.8 if lon > 140 else oni_value * 0.4
sst = base_sst + seasonal_sst_adj + enso_sst_adj
# Relative humidity (600 hPa)
base_rh = 75 - 0.5 * abs(lat - 12) # Peak around 12°N
seasonal_rh_adj = 10 * np.cos(2 * np.pi * (month - 8) / 12) # Peak in Aug
monsoon_effect = 5 if 100 <= lon <= 120 and month in [6,7,8,9] else 0
rh = max(40, min(90, base_rh + seasonal_rh_adj + monsoon_effect))
# Low-level vorticity (850 hPa)
base_vort = 2e-5 * (1 + 0.1 * np.sin(2 * np.pi * lat / 30))
seasonal_vort_adj = 1e-5 * np.cos(2 * np.pi * (month - 8) / 12)
itcz_effect = 1.5e-5 if 5 <= lat <= 15 else 0
vorticity = max(0, base_vort + seasonal_vort_adj + itcz_effect)
# Vertical wind shear (200-850 hPa)
base_shear = 8 + 0.3 * abs(lat - 20) # Lower near 20°N
seasonal_shear_adj = 4 * np.cos(2 * np.pi * (month - 2) / 12) # Low in Aug-Sep
enso_shear_adj = oni_value * 3 if lon > 140 else 0 # El Niño increases shear
wind_shear = max(2, base_shear + seasonal_shear_adj + enso_shear_adj)
return {
'sst': sst,
'relative_humidity': rh,
'vorticity': vorticity,
'wind_shear': wind_shear
}
except Exception as e:
logging.error(f"Error getting environmental conditions: {e}")
return {
'sst': 28.0,
'relative_humidity': 70.0,
'vorticity': 2e-5,
'wind_shear': 10.0
}
def generate_genesis_prediction_monthly(month, oni_value, year=2025):
"""
Generate realistic typhoon genesis prediction for a given month using GPI
Returns day-by-day genesis potential and storm development scenarios
"""
try:
logging.info(f"Generating GPI-based prediction for month {month}, ONI {oni_value}")
# Define the Western Pacific domain
lat_range = np.arange(5, 35, 2.5) # 5°N to 35°N
lon_range = np.arange(110, 180, 2.5) # 110°E to 180°E
# Number of days in the month
days_in_month = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31][month - 1]
if month == 2 and year % 4 == 0: # Leap year
days_in_month = 29
# Daily GPI evolution
daily_gpi_maps = []
genesis_events = []
for day in range(1, days_in_month + 1):
# Calculate GPI for each grid point
gpi_field = np.zeros((len(lat_range), len(lon_range)))
for i, lat in enumerate(lat_range):
for j, lon in enumerate(lon_range):
# Get environmental conditions
env_conditions = get_environmental_conditions(lat, lon, month, oni_value)
# Add daily variability
daily_variation = 0.1 * np.sin(2 * np.pi * day / 30) + np.random.normal(0, 0.05)
# Calculate GPI
gpi = calculate_genesis_potential_index(
sst=env_conditions['sst'] + daily_variation,
rh=env_conditions['relative_humidity'],
vorticity=env_conditions['vorticity'],
wind_shear=env_conditions['wind_shear'],
lat=lat,
lon=lon,
month=month,
oni_value=oni_value
)
gpi_field[i, j] = gpi
daily_gpi_maps.append({
'day': day,
'gpi_field': gpi_field,
'lat_range': lat_range,
'lon_range': lon_range
})
# Check for genesis events (GPI > threshold)
genesis_threshold = 1.5 # Adjusted threshold
if np.max(gpi_field) > genesis_threshold:
# Find peak genesis locations
peak_indices = np.where(gpi_field > genesis_threshold)
if len(peak_indices[0]) > 0:
# Select strongest genesis point
max_idx = np.argmax(gpi_field)
max_i, max_j = np.unravel_index(max_idx, gpi_field.shape)
genesis_lat = lat_range[max_i]
genesis_lon = lon_range[max_j]
genesis_gpi = gpi_field[max_i, max_j]
# Determine probability of actual genesis
genesis_prob = min(0.8, genesis_gpi / 3.0)
if np.random.random() < genesis_prob:
genesis_events.append({
'day': day,
'lat': genesis_lat,
'lon': genesis_lon,
'gpi': genesis_gpi,
'probability': genesis_prob,
'date': f"{year}-{month:02d}-{day:02d}"
})
# Generate storm tracks for genesis events
storm_predictions = []
for i, genesis in enumerate(genesis_events):
storm_track = generate_storm_track_from_genesis(
genesis['lat'],
genesis['lon'],
genesis['day'],
month,
oni_value,
storm_id=i+1
)
if storm_track:
storm_predictions.append({
'storm_id': i + 1,
'genesis_event': genesis,
'track': storm_track,
'uncertainty': calculate_track_uncertainty(storm_track)
})
return {
'month': month,
'year': year,
'oni_value': oni_value,
'daily_gpi_maps': daily_gpi_maps,
'genesis_events': genesis_events,
'storm_predictions': storm_predictions,
'summary': {
'total_genesis_events': len(genesis_events),
'total_storm_predictions': len(storm_predictions),
'peak_gpi_day': max(daily_gpi_maps, key=lambda x: np.max(x['gpi_field']))['day'],
'peak_gpi_value': max(np.max(day_data['gpi_field']) for day_data in daily_gpi_maps)
}
}
except Exception as e:
logging.error(f"Error in genesis prediction: {e}")
import traceback
traceback.print_exc()
return {
'error': str(e),
'month': month,
'oni_value': oni_value,
'storm_predictions': []
}
def generate_storm_track_from_genesis(genesis_lat, genesis_lon, genesis_day, month, oni_value, storm_id=1):
"""
Generate a realistic storm track from a genesis location
"""
try:
track_points = []
current_lat = genesis_lat
current_lon = genesis_lon
current_intensity = 25 # Start as TD
# Track duration (3-10 days typically)
track_duration_hours = np.random.randint(72, 240)
for hour in range(0, track_duration_hours + 6, 6):
# Calculate storm motion
# Base motion patterns for Western Pacific
if current_lat < 20: # Low latitude - westward motion
lat_speed = 0.1 + np.random.normal(0, 0.05) # Slight poleward
lon_speed = -0.3 + np.random.normal(0, 0.1) # Westward
elif current_lat < 25: # Mid latitude - WNW motion
lat_speed = 0.15 + np.random.normal(0, 0.05)
lon_speed = -0.2 + np.random.normal(0, 0.1)
else: # High latitude - recurvature
lat_speed = 0.2 + np.random.normal(0, 0.05)
lon_speed = 0.1 + np.random.normal(0, 0.1) # Eastward
# ENSO effects on motion
if oni_value > 0.5: # El Niño - more eastward
lon_speed += 0.05
elif oni_value < -0.5: # La Niña - more westward
lon_speed -= 0.05
# Update position
current_lat += lat_speed
current_lon += lon_speed
# Intensity evolution
# Get environmental conditions for intensity change
env_conditions = get_environmental_conditions(current_lat, current_lon, month, oni_value)
# Intensity change factors
sst_factor = max(0, env_conditions['sst'] - 26.5)
shear_factor = max(0, (15 - env_conditions['wind_shear']) / 10)
# Basic intensity change
if hour < 48: # Development phase
intensity_change = 3 + sst_factor + shear_factor + np.random.normal(0, 2)
elif hour < 120: # Mature phase
intensity_change = 1 + sst_factor * 0.5 + np.random.normal(0, 1.5)
else: # Weakening phase
intensity_change = -2 + sst_factor * 0.3 + np.random.normal(0, 1)
# Environmental limits
if current_lat > 30: # Cool waters
intensity_change -= 5
if current_lon < 120: # Land interaction
intensity_change -= 8
current_intensity += intensity_change
current_intensity = max(15, min(180, current_intensity)) # Realistic bounds
# Calculate pressure
pressure = max(900, 1013 - (current_intensity - 25) * 0.9)
# Add uncertainty
position_uncertainty = 0.5 + (hour / 120) * 1.5 # Growing uncertainty
intensity_uncertainty = 5 + (hour / 120) * 15
track_points.append({
'hour': hour,
'day': genesis_day + hour / 24.0,
'lat': current_lat,
'lon': current_lon,
'intensity': current_intensity,
'pressure': pressure,
'category': categorize_typhoon_enhanced(current_intensity),
'position_uncertainty': position_uncertainty,
'intensity_uncertainty': intensity_uncertainty
})
# Stop if storm moves too far or weakens significantly
if current_lat > 40 or current_lat < 0 or current_lon < 100 or current_intensity < 20:
break
return track_points
except Exception as e:
logging.error(f"Error generating storm track: {e}")
return None
def calculate_track_uncertainty(track_points):
"""Calculate uncertainty metrics for a storm track"""
if not track_points:
return {'position': 0, 'intensity': 0}
# Position uncertainty grows with time
position_uncertainty = [point['position_uncertainty'] for point in track_points]
# Intensity uncertainty
intensity_uncertainty = [point['intensity_uncertainty'] for point in track_points]
return {
'position_mean': np.mean(position_uncertainty),
'position_max': np.max(position_uncertainty),
'intensity_mean': np.mean(intensity_uncertainty),
'intensity_max': np.max(intensity_uncertainty),
'track_length': len(track_points)
}
def create_predict_animation(prediction_data, enable_animation=True):
"""
Typhoon genesis PREDICT tab animation:
shows monthly genesis-potential + progressive storm positions
"""
try:
daily_maps = prediction_data.get('daily_gpi_maps', [])
if not daily_maps:
return create_error_plot("No GPI data for prediction")
storms = prediction_data.get('storm_predictions', [])
month = prediction_data['month']
oni = prediction_data['oni_value']
year = prediction_data.get('year', 2025)
# -- 1) static underlay: full storm routes (dashed gray lines)
static_routes = []
for s in storms:
track = s.get('track', [])
if not track: continue
lats = [pt['lat'] for pt in track]
lons = [pt['lon'] for pt in track]
static_routes.append(
go.Scattergeo(
lat=lats, lon=lons,
mode='lines',
line=dict(width=2, dash='dash', color='gray'),
showlegend=False, hoverinfo='skip'
)
)
# figure out map bounds
all_lats = [pt['lat'] for s in storms for pt in s.get('track',[])]
all_lons = [pt['lon'] for s in storms for pt in s.get('track',[])]
mb = {
'lat_min': min(5, min(all_lats)-5) if all_lats else 5,
'lat_max': max(35, max(all_lats)+5) if all_lats else 35,
'lon_min': min(110, min(all_lons)-10) if all_lons else 110,
'lon_max': max(180, max(all_lons)+10) if all_lons else 180
}
# -- 2) build frames
frames = []
for idx, day_data in enumerate(daily_maps):
day = day_data['day']
gpi = day_data['gpi_field']
lats = day_data['lat_range']
lons = day_data['lon_range']
traces = []
# genesis‐potential scatter
traces.append(go.Scattergeo(
lat=np.repeat(lats, len(lons)),
lon=np.tile(lons, len(lats)),
mode='markers',
marker=dict(
size=6, color=gpi.flatten(),
colorscale='Viridis', cmin=0, cmax=3, opacity=0.6,
showscale=(idx==0),
colorbar=(dict(
title=dict(text="Genesis
Potential
Index", side="right")
) if idx==0 else None)
),
name='GPI',
showlegend=(idx==0),
hovertemplate=(
'GPI: %{marker.color:.2f}
'
'Lat: %{lat:.1f}°N
'
'Lon: %{lon:.1f}°E
'
f'Day {day} of {month:02d}/{year}'
)
))
# storm positions up to this day
for s in storms:
past = [pt for pt in s.get('track',[]) if pt['day'] <= day]
if not past: continue
lats_p = [pt['lat'] for pt in past]
lons_p = [pt['lon'] for pt in past]
intens = [pt['intensity'] for pt in past]
cats = [pt['category'] for pt in past]
# line history
traces.append(go.Scattergeo(
lat=lats_p, lon=lons_p, mode='lines',
line=dict(width=2, color='gray'),
showlegend=(idx==0), hoverinfo='skip'
))
# current position
traces.append(go.Scattergeo(
lat=[lats_p[-1]], lon=[lons_p[-1]],
mode='markers',
marker=dict(size=10, symbol='circle', color='red'),
showlegend=(idx==0),
hovertemplate=(
f"{s['storm_id']}
"
f"Intensity: {intens[-1]} kt
"
f"Category: {cats[-1]}"
)
))
frames.append(go.Frame(
data=traces,
name=str(day), # ← name is REQUIRED as string :contentReference[oaicite:1]{index=1}
layout=go.Layout(
geo=dict(
projection_type="natural earth",
showland=True, landcolor="lightgray",
showocean=True, oceancolor="lightblue",
showcoastlines=True, coastlinecolor="darkgray",
center=dict(lat=(mb['lat_min']+mb['lat_max'])/2,
lon=(mb['lon_min']+mb['lon_max'])/2),
lonaxis_range=[mb['lon_min'], mb['lon_max']],
lataxis_range=[mb['lat_min'], mb['lat_max']],
resolution=50
),
title=f"Day {day} of {month:02d}/{year} | ONI: {oni:.2f}"
)
))
# -- 3) initial Figure (static + first frame)
init_data = static_routes + list(frames[0].data)
fig = go.Figure(data=init_data, frames=frames)
# -- 4) play/pause + slider (redraw=True!)
if enable_animation and len(frames)>1:
steps = [
dict(method="animate",
args=[[fr.name],
{"mode":"immediate",
"frame":{"duration":600,"redraw":True},
"transition":{"duration":0}}],
label=fr.name)
for fr in frames
]
fig.update_layout(
updatemenus=[dict(
type="buttons", showactive=False,
x=1.05, y=0.05, xanchor="right", yanchor="bottom",
buttons=[
dict(label="▶ Play",
method="animate",
args=[None, # None＝all frames
{"frame":{"duration":600,"redraw":True}, # ← redraw fixes dead ▶
"fromcurrent":True,"transition":{"duration":0}}]),
dict(label="⏸ Pause",
method="animate",
args=[[None],
{"frame":{"duration":0,"redraw":False},
"mode":"immediate"}])
]
)],
sliders=[dict(active=0, pad=dict(t=50), steps=steps)]
)
else:
# fallback: show only final day + static routes
final = static_routes + list(frames[-1].data)
fig = go.Figure(data=final)
# -- 5) shared layout styling
fig.update_layout(
title={
'text': f"🌊 Typhoon Prediction — {month:02d}/{year} | ONI: {oni:.2f}",
'x':0.5,'font':{'size':18}
},
geo=dict(
projection_type="natural earth",
showland=True, landcolor="lightgray",
showocean=True, oceancolor="lightblue",
showcoastlines=True, coastlinecolor="darkgray",
showlakes=True, lakecolor="lightblue",
showcountries=True, countrycolor="gray",
resolution=50,
center=dict(lat=20, lon=140),
lonaxis_range=[110,180], lataxis_range=[5,35]
),
width=1100, height=750,
showlegend=True,
legend=dict(
x=0.02,y=0.98,
bgcolor="rgba(255,255,255,0.7)",
bordercolor="gray",borderwidth=1
)
)
return fig
except Exception as e:
logging.error(f"Error in predict animation: {e}")
import traceback; traceback.print_exc()
return create_error_plot(f"Animation error: {e}")
def create_genesis_animation(prediction_data, enable_animation=True):
"""
Create professional typhoon track animation showing daily genesis potential and storm development
Following NHC/JTWC visualization standards with proper geographic map and time controls
"""
try:
daily_maps = prediction_data.get('daily_gpi_maps', [])
if not daily_maps:
return create_error_plot("No GPI data available for animation")
storm_predictions = prediction_data.get('storm_predictions', [])
month = prediction_data['month']
oni_value = prediction_data['oni_value']
year = prediction_data.get('year', 2025)
# ---- 1) Prepare static full-track routes ----
static_routes = []
for storm in storm_predictions:
track = storm.get('track', [])
if not track:
continue
lats = [pt['lat'] for pt in track]
lons = [pt['lon'] for pt in track]
static_routes.append(
go.Scattergeo(
lat=lats,
lon=lons,
mode='lines',
line=dict(width=2, dash='dash', color='gray'),
showlegend=False,
hoverinfo='skip'
)
)
# ---- 2) Build animation frames ----
frames = []
# determine map bounds from all storm tracks
all_lats = [pt['lat'] for storm in storm_predictions for pt in storm.get('track', [])]
all_lons = [pt['lon'] for storm in storm_predictions for pt in storm.get('track', [])]
map_bounds = {
'lat_min': min(5, min(all_lats) - 5) if all_lats else 5,
'lat_max': max(35, max(all_lats) + 5) if all_lats else 35,
'lon_min': min(110, min(all_lons) - 10) if all_lons else 110,
'lon_max': max(180, max(all_lons) + 10) if all_lons else 180
}
for day_idx, day_data in enumerate(daily_maps):
day = day_data['day']
gpi = day_data['gpi_field']
lats = day_data['lat_range']
lons = day_data['lon_range']
traces = []
# Genesis potential dots
traces.append(go.Scattergeo(
lat=np.repeat(lats, len(lons)),
lon=np.tile(lons, len(lats)),
mode='markers',
marker=dict(
size=6,
color=gpi.flatten(),
colorscale='Viridis',
cmin=0, cmax=3, opacity=0.6,
showscale=(day_idx == 0),
colorbar=(dict(
title=dict(text="Genesis
Potential
Index", side="right")
) if day_idx == 0 else None)
),
name='Genesis Potential',
showlegend=(day_idx == 0),
hovertemplate=(
'GPI: %{marker.color:.2f}
' +
'Lat: %{lat:.1f}°N
' +
'Lon: %{lon:.1f}°E
' +
f'Day {day} of {month:02d}/{year}'
)
))
# Storm positions up to this day
for storm in storm_predictions:
past = [pt for pt in storm.get('track', []) if pt['day'] <= day]
if not past:
continue
lats_p = [pt['lat'] for pt in past]
lons_p = [pt['lon'] for pt in past]
intens = [pt['intensity'] for pt in past]
cats = [pt['category'] for pt in past]
# historical line
traces.append(go.Scattergeo(
lat=lats_p, lon=lons_p, mode='lines',
line=dict(width=2, color='gray'),
name=f"{storm['storm_id']} Track",
showlegend=(day_idx == 0),
hoverinfo='skip'
))
# current position
traces.append(go.Scattergeo(
lat=[lats_p[-1]], lon=[lons_p[-1]], mode='markers',
marker=dict(size=10, symbol='circle', color='red'),
name=f"{storm['storm_id']} Position",
showlegend=(day_idx == 0),
hovertemplate=(
f"{storm['storm_id']}
"
f"Intensity: {intens[-1]} kt
"
f"Category: {cats[-1]}"
)
))
frames.append(go.Frame(
data=traces,
name=str(day),
layout=go.Layout(
geo=dict(
projection_type="natural earth",
showland=True, landcolor="lightgray",
showocean=True, oceancolor="lightblue",
showcoastlines=True, coastlinecolor="darkgray",
center=dict(
lat=(map_bounds['lat_min'] + map_bounds['lat_max'])/2,
lon=(map_bounds['lon_min'] + map_bounds['lon_max'])/2
),
lonaxis_range=[map_bounds['lon_min'], map_bounds['lon_max']],
lataxis_range=[map_bounds['lat_min'], map_bounds['lat_max']],
resolution=50
),
title=f"Day {day} of {month:02d}/{year} ONI: {oni_value:.2f}"
)
))
# ---- 3) Initialize figure with static routes + first frame ----
initial_data = static_routes + list(frames[0].data)
fig = go.Figure(data=initial_data, frames=frames)
# ---- 4) Add play/pause buttons with redraw=True ----
if enable_animation and len(frames) > 1:
# slider steps
steps = [
dict(method="animate",
args=[[fr.name],
{"mode": "immediate",
"frame": {"duration": 600, "redraw": True},
"transition": {"duration": 0}}],
label=fr.name)
for fr in frames
]
fig.update_layout(
updatemenus=[dict(
type="buttons", showactive=False,
x=1.05, y=0.05, xanchor="right", yanchor="bottom",
buttons=[
dict(label="▶ Play",
method="animate",
args=[None, # None means “all frames”
{"frame": {"duration": 600, "redraw": True},
"fromcurrent": True,
"transition": {"duration": 0}}
]), # redraw=True fixes the dead play button :contentReference[oaicite:1]{index=1}
dict(label="⏸ Pause",
method="animate",
args=[[None],
{"frame": {"duration": 0, "redraw": False},
"mode": "immediate"}])
]
)],
sliders=[dict(active=0, pad=dict(t=50), steps=steps)]
)
# No-animation fallback: just show final day + routes
else:
final = static_routes + list(frames[-1].data)
fig = go.Figure(data=final)
# ---- 5) Common layout styling ----
fig.update_layout(
title={
'text': f"🌊 Typhoon Genesis & Development Forecast
"
f"{month:02d}/{year} | ONI: {oni_value:.2f}",
'x': 0.5, 'font': {'size': 18}
},
geo=dict(
projection_type="natural earth",
showland=True, landcolor="lightgray",
showocean=True, oceancolor="lightblue",
showcoastlines=True, coastlinecolor="darkgray",
showlakes=True, lakecolor="lightblue",
showcountries=True, countrycolor="gray",
resolution=50,
center=dict(lat=20, lon=140),
lonaxis_range=[110, 180], lataxis_range=[5, 35]
),
width=1100, height=750,
showlegend=True,
legend=dict(x=0.02, y=0.98,
bgcolor="rgba(255,255,255,0.7)",
bordercolor="gray", borderwidth=1)
)
return fig
except Exception as e:
logging.error(f"Error creating professional genesis animation: {e}")
import traceback; traceback.print_exc()
return create_error_plot(f"Animation error: {e}")
def create_error_plot(error_message):
"""Create a simple error plot"""
fig = go.Figure()
fig.add_annotation(
text=error_message,
xref="paper", yref="paper",
x=0.5, y=0.5, xanchor='center', yanchor='middle',
showarrow=False, font_size=16
)
fig.update_layout(title="Error in Visualization")
return fig
def create_prediction_summary(prediction_data):
"""Create a comprehensive summary of the prediction"""
try:
if 'error' in prediction_data:
return f"Error in prediction: {prediction_data['error']}"
month = prediction_data['month']
oni_value = prediction_data['oni_value']
summary = prediction_data.get('summary', {})
genesis_events = prediction_data.get('genesis_events', [])
storm_predictions = prediction_data.get('storm_predictions', [])
month_names = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',
'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
month_name = month_names[month - 1]
summary_text = f"""
TYPHOON GENESIS PREDICTION SUMMARY - {month_name.upper()} 2025
{'='*70}
🌊 ENVIRONMENTAL CONDITIONS:
• Month: {month_name} (Month {month})
• ONI Value: {oni_value:.2f} {'(El Niño)' if oni_value > 0.5 else '(La Niña)' if oni_value < -0.5 else '(Neutral)'}
• Season Phase: {'Peak Season' if month in [7,8,9,10] else 'Off Season' if month in [1,2,3,4,11,12] else 'Transition Season'}
📊 GENESIS POTENTIAL ANALYSIS:
• Peak GPI Day: Day {summary.get('peak_gpi_day', 'Unknown')}
• Peak GPI Value: {summary.get('peak_gpi_value', 0):.2f}
• Total Genesis Events: {summary.get('total_genesis_events', 0)}
• Storm Development Success: {summary.get('total_storm_predictions', 0)}/{summary.get('total_genesis_events', 0)} events
🎯 GENESIS EVENTS BREAKDOWN:
"""
if genesis_events:
for i, event in enumerate(genesis_events, 1):
summary_text += f"""
Event {i}:
• Date: {event['date']}
• Location: {event['lat']:.1f}°N, {event['lon']:.1f}°E
• GPI Value: {event['gpi']:.2f}
• Genesis Probability: {event['probability']*100:.0f}%
"""
else:
summary_text += "\n• No significant genesis events predicted for this month\n"
summary_text += f"""
🌪️ STORM TRACK PREDICTIONS:
"""
if storm_predictions:
for storm in storm_predictions:
track = storm['track']
if track:
genesis = storm['genesis_event']
max_intensity = max(pt['intensity'] for pt in track)
max_category = categorize_typhoon_enhanced(max_intensity)
track_duration = len(track) * 6 # hours
final_pos = track[-1]
summary_text += f"""
Storm {storm['storm_id']}:
• Genesis: Day {genesis['day']}, {genesis['lat']:.1f}°N {genesis['lon']:.1f}°E
• Peak Intensity: {max_intensity:.0f} kt ({max_category})
• Track Duration: {track_duration} hours ({track_duration/24:.1f} days)
• Final Position: {final_pos['lat']:.1f}°N, {final_pos['lon']:.1f}°E
• Uncertainty: ±{storm['uncertainty']['position_mean']:.1f}° position, ±{storm['uncertainty']['intensity_mean']:.0f} kt intensity
"""
else:
summary_text += "\n• No storm development predicted from genesis events\n"
# Add climatological context
summary_text += f"""
📈 CLIMATOLOGICAL CONTEXT:
• {month_name} Typical Activity: {'Very High' if month in [8,9] else 'High' if month in [7,10] else 'Moderate' if month in [6,11] else 'Low'}
• ENSO Influence: {'Strong suppression expected' if oni_value > 1.0 else 'Moderate suppression' if oni_value > 0.5 else 'Strong enhancement likely' if oni_value < -1.0 else 'Moderate enhancement' if oni_value < -0.5 else 'Near-normal activity'}
• Regional Focus: Western Pacific Main Development Region (10-25°N, 120-160°E)
🔧 METHODOLOGY DETAILS:
• Genesis Potential Index: Emanuel & Nolan (2004) formulation
• Environmental Factors: SST, humidity, vorticity, wind shear, Coriolis effect
• Temporal Resolution: Daily evolution throughout month
• Spatial Resolution: 2.5° grid spacing
• ENSO Modulation: Integrated ONI effects on environmental parameters
• Track Prediction: Physics-based storm motion and intensity evolution
⚠️ UNCERTAINTY & LIMITATIONS:
• Genesis timing: ±2-3 days typical uncertainty
• Track position: Growing uncertainty with time (±0.5° to ±2°)
• Intensity prediction: ±5-15 kt uncertainty range
• Environmental assumptions: Based on climatological patterns
• Model limitations: Simplified compared to operational NWP systems
🎯 FORECAST CONFIDENCE:
• Genesis Location: {'High' if summary.get('peak_gpi_value', 0) > 2 else 'Moderate' if summary.get('peak_gpi_value', 0) > 1 else 'Low'}
• Genesis Timing: {'High' if month in [7,8,9] else 'Moderate' if month in [6,10] else 'Low'}
• Track Prediction: Moderate (physics-based motion patterns)
• Intensity Evolution: Moderate (environmental constraints applied)
📋 OPERATIONAL IMPLICATIONS:
• Monitor Days {', '.join([str(event['day']) for event in genesis_events[:3]])} for potential development
• Focus regions: {', '.join([f"{event['lat']:.0f}°N {event['lon']:.0f}°E" for event in genesis_events[:3]])}
• Preparedness level: {'High' if len(storm_predictions) > 2 else 'Moderate' if len(storm_predictions) > 0 else 'Routine'}
🔬 RESEARCH APPLICATIONS:
• Suitable for seasonal planning and climate studies
• Genesis mechanism investigation
• ENSO-typhoon relationship analysis
• Environmental parameter sensitivity studies
⚠️ IMPORTANT DISCLAIMERS:
• This is a research prediction system, not operational forecast
• Use official meteorological services for real-time warnings
• Actual conditions may differ from climatological assumptions
• Model simplified compared to operational prediction systems
• Uncertainty grows significantly beyond 5-7 day lead times
"""
return summary_text
except Exception as e:
logging.error(f"Error creating prediction summary: {e}")
return f"Error generating summary: {str(e)}"
# -----------------------------
# FIXED: ADVANCED ML FEATURES WITH ROBUST ERROR HANDLING
# -----------------------------
def extract_storm_features(typhoon_data):
"""Extract comprehensive features for clustering analysis - FIXED VERSION"""
try:
if typhoon_data is None or typhoon_data.empty:
logging.error("No typhoon data provided for feature extraction")
return None
# Basic features - ensure columns exist
basic_features = []
for sid in typhoon_data['SID'].unique():
storm_data = typhoon_data[typhoon_data['SID'] == sid].copy()
if len(storm_data) == 0:
continue
# Initialize feature dict with safe defaults
features = {'SID': sid}
# Wind statistics
if 'USA_WIND' in storm_data.columns:
wind_values = pd.to_numeric(storm_data['USA_WIND'], errors='coerce').dropna()
if len(wind_values) > 0:
features['USA_WIND_max'] = wind_values.max()
features['USA_WIND_mean'] = wind_values.mean()
features['USA_WIND_std'] = wind_values.std() if len(wind_values) > 1 else 0
else:
features['USA_WIND_max'] = 30
features['USA_WIND_mean'] = 30
features['USA_WIND_std'] = 0
else:
features['USA_WIND_max'] = 30
features['USA_WIND_mean'] = 30
features['USA_WIND_std'] = 0
# Pressure statistics
if 'USA_PRES' in storm_data.columns:
pres_values = pd.to_numeric(storm_data['USA_PRES'], errors='coerce').dropna()
if len(pres_values) > 0:
features['USA_PRES_min'] = pres_values.min()
features['USA_PRES_mean'] = pres_values.mean()
features['USA_PRES_std'] = pres_values.std() if len(pres_values) > 1 else 0
else:
features['USA_PRES_min'] = 1000
features['USA_PRES_mean'] = 1000
features['USA_PRES_std'] = 0
else:
features['USA_PRES_min'] = 1000
features['USA_PRES_mean'] = 1000
features['USA_PRES_std'] = 0
# Location statistics
if 'LAT' in storm_data.columns and 'LON' in storm_data.columns:
lat_values = pd.to_numeric(storm_data['LAT'], errors='coerce').dropna()
lon_values = pd.to_numeric(storm_data['LON'], errors='coerce').dropna()
if len(lat_values) > 0 and len(lon_values) > 0:
features['LAT_mean'] = lat_values.mean()
features['LAT_std'] = lat_values.std() if len(lat_values) > 1 else 0
features['LAT_max'] = lat_values.max()
features['LAT_min'] = lat_values.min()
features['LON_mean'] = lon_values.mean()
features['LON_std'] = lon_values.std() if len(lon_values) > 1 else 0
features['LON_max'] = lon_values.max()
features['LON_min'] = lon_values.min()
# Genesis location (first valid position)
features['genesis_lat'] = lat_values.iloc[0]
features['genesis_lon'] = lon_values.iloc[0]
features['genesis_intensity'] = features['USA_WIND_mean'] # Use mean as fallback
# Track characteristics
features['lat_range'] = lat_values.max() - lat_values.min()
features['lon_range'] = lon_values.max() - lon_values.min()
# Calculate track distance
if len(lat_values) > 1:
distances = []
for i in range(1, len(lat_values)):
dlat = lat_values.iloc[i] - lat_values.iloc[i-1]
dlon = lon_values.iloc[i] - lon_values.iloc[i-1]
distances.append(np.sqrt(dlat**2 + dlon**2))
features['total_distance'] = sum(distances)
features['avg_speed'] = np.mean(distances) if distances else 0
else:
features['total_distance'] = 0
features['avg_speed'] = 0
# Track curvature
if len(lat_values) > 2:
bearing_changes = []
for i in range(1, len(lat_values)-1):
dlat1 = lat_values.iloc[i] - lat_values.iloc[i-1]
dlon1 = lon_values.iloc[i] - lon_values.iloc[i-1]
dlat2 = lat_values.iloc[i+1] - lat_values.iloc[i]
dlon2 = lon_values.iloc[i+1] - lon_values.iloc[i]
angle1 = np.arctan2(dlat1, dlon1)
angle2 = np.arctan2(dlat2, dlon2)
change = abs(angle2 - angle1)
bearing_changes.append(change)
features['avg_curvature'] = np.mean(bearing_changes) if bearing_changes else 0
else:
features['avg_curvature'] = 0
else:
# Default location values
features.update({
'LAT_mean': 20, 'LAT_std': 0, 'LAT_max': 20, 'LAT_min': 20,
'LON_mean': 140, 'LON_std': 0, 'LON_max': 140, 'LON_min': 140,
'genesis_lat': 20, 'genesis_lon': 140, 'genesis_intensity': 30,
'lat_range': 0, 'lon_range': 0, 'total_distance': 0,
'avg_speed': 0, 'avg_curvature': 0
})
else:
# Default location values if columns missing
features.update({
'LAT_mean': 20, 'LAT_std': 0, 'LAT_max': 20, 'LAT_min': 20,
'LON_mean': 140, 'LON_std': 0, 'LON_max': 140, 'LON_min': 140,
'genesis_lat': 20, 'genesis_lon': 140, 'genesis_intensity': 30,
'lat_range': 0, 'lon_range': 0, 'total_distance': 0,
'avg_speed': 0, 'avg_curvature': 0
})
# Track length
features['track_length'] = len(storm_data)
# Add seasonal information
if 'SEASON' in storm_data.columns:
features['season'] = storm_data['SEASON'].iloc[0]
else:
features['season'] = 2000
# Add basin information
if 'BASIN' in storm_data.columns:
features['basin'] = storm_data['BASIN'].iloc[0]
elif 'SID' in storm_data.columns:
features['basin'] = sid[:2] if len(sid) >= 2 else 'WP'
else:
features['basin'] = 'WP'
basic_features.append(features)
if not basic_features:
logging.error("No valid storm features could be extracted")
return None
# Convert to DataFrame
storm_features = pd.DataFrame(basic_features)
# Ensure all numeric columns are properly typed
numeric_columns = [col for col in storm_features.columns if col not in ['SID', 'basin']]
for col in numeric_columns:
storm_features[col] = pd.to_numeric(storm_features[col], errors='coerce').fillna(0)
logging.info(f"Successfully extracted features for {len(storm_features)} storms")
logging.info(f"Feature columns: {list(storm_features.columns)}")
return storm_features
except Exception as e:
logging.error(f"Error in extract_storm_features: {e}")
import traceback
traceback.print_exc()
return None
def perform_dimensionality_reduction(storm_features, method='umap', n_components=2):
"""Perform UMAP or t-SNE dimensionality reduction - FIXED VERSION"""
try:
if storm_features is None or storm_features.empty:
raise ValueError("No storm features provided")
# Select numeric features for clustering - FIXED
feature_cols = []
for col in storm_features.columns:
if col not in ['SID', 'basin'] and storm_features[col].dtype in ['float64', 'int64']:
# Check if column has valid data
valid_data = storm_features[col].dropna()
if len(valid_data) > 0 and valid_data.std() > 0: # Only include columns with variance
feature_cols.append(col)
if len(feature_cols) == 0:
raise ValueError("No valid numeric features found for clustering")
logging.info(f"Using {len(feature_cols)} features for clustering: {feature_cols}")
X = storm_features[feature_cols].fillna(0)
# Check if we have enough samples
if len(X) < 2:
raise ValueError("Need at least 2 storms for clustering")
# Standardize features
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Perform dimensionality reduction
if method.lower() == 'umap' and UMAP_AVAILABLE and len(X_scaled) >= 4:
# UMAP parameters optimized for typhoon data - fixed warnings
n_neighbors = min(15, len(X_scaled) - 1)
reducer = umap.UMAP(
n_components=n_components,
n_neighbors=n_neighbors,
min_dist=0.1,
metric='euclidean',
random_state=42,
n_jobs=1 # Explicitly set to avoid warning
)
elif method.lower() == 'tsne' and len(X_scaled) >= 4:
# t-SNE parameters
perplexity = min(30, len(X_scaled) // 4)
perplexity = max(1, perplexity) # Ensure perplexity is at least 1
reducer = TSNE(
n_components=n_components,
perplexity=perplexity,
learning_rate=200,
n_iter=1000,
random_state=42
)
else:
# Fallback to PCA
reducer = PCA(n_components=n_components, random_state=42)
# Fit and transform
embedding = reducer.fit_transform(X_scaled)
logging.info(f"Dimensionality reduction successful: {X_scaled.shape} -> {embedding.shape}")
return embedding, feature_cols, scaler
except Exception as e:
logging.error(f"Error in perform_dimensionality_reduction: {e}")
raise
def cluster_storms_data(embedding, method='dbscan', eps=0.5, min_samples=3):
"""Cluster storms based on their embedding - FIXED NAME VERSION"""
try:
if len(embedding) < 2:
return np.array([0] * len(embedding)) # Single cluster for insufficient data
if method.lower() == 'dbscan':
# Adjust min_samples based on data size
min_samples = min(min_samples, max(2, len(embedding) // 5))
clusterer = DBSCAN(eps=eps, min_samples=min_samples)
elif method.lower() == 'kmeans':
# Adjust n_clusters based on data size
n_clusters = min(5, max(2, len(embedding) // 3))
clusterer = KMeans(n_clusters=n_clusters, random_state=42)
else:
raise ValueError("Method must be 'dbscan' or 'kmeans'")
clusters = clusterer.fit_predict(embedding)
logging.info(f"Clustering complete: {len(np.unique(clusters))} clusters found")
return clusters
except Exception as e:
logging.error(f"Error in cluster_storms_data: {e}")
# Return single cluster as fallback
return np.array([0] * len(embedding))
def create_separate_clustering_plots(storm_features, typhoon_data, method='umap'):
"""Create separate plots for clustering analysis - ENHANCED CLARITY VERSION"""
try:
# Validate inputs
if storm_features is None or storm_features.empty:
raise ValueError("No storm features available for clustering")
if typhoon_data is None or typhoon_data.empty:
raise ValueError("No typhoon data available for route visualization")
logging.info(f"Starting clustering visualization with {len(storm_features)} storms")
# Perform dimensionality reduction
embedding, feature_cols, scaler = perform_dimensionality_reduction(storm_features, method)
# Perform clustering
cluster_labels = cluster_storms_data(embedding, 'dbscan')
# Add clustering results to storm features
storm_features_viz = storm_features.copy()
storm_features_viz['cluster'] = cluster_labels
storm_features_viz['dim1'] = embedding[:, 0]
storm_features_viz['dim2'] = embedding[:, 1]
# Merge with typhoon data for additional info - SAFE MERGE
try:
storm_info = typhoon_data.groupby('SID').first()[['NAME', 'SEASON']].reset_index()
storm_features_viz = storm_features_viz.merge(storm_info, on='SID', how='left')
# Fill missing values
storm_features_viz['NAME'] = storm_features_viz['NAME'].fillna('UNNAMED')
storm_features_viz['SEASON'] = storm_features_viz['SEASON'].fillna(2000)
except Exception as merge_error:
logging.warning(f"Could not merge storm info: {merge_error}")
storm_features_viz['NAME'] = 'UNNAMED'
storm_features_viz['SEASON'] = 2000
# Get unique clusters and assign distinct colors
unique_clusters = sorted([c for c in storm_features_viz['cluster'].unique() if c != -1])
noise_count = len(storm_features_viz[storm_features_viz['cluster'] == -1])
# 1. Enhanced clustering scatter plot with clear cluster identification
fig_cluster = go.Figure()
# Add noise points first
if noise_count > 0:
noise_data = storm_features_viz[storm_features_viz['cluster'] == -1]
fig_cluster.add_trace(
go.Scatter(
x=noise_data['dim1'],
y=noise_data['dim2'],
mode='markers',
marker=dict(color='lightgray', size=8, opacity=0.5, symbol='x'),
name=f'Noise ({noise_count} storms)',
hovertemplate=(
'%{customdata[0]}
'
'Season: %{customdata[1]}
'
'Cluster: Noise
'
f'{method.upper()} Dim 1: %{{x:.2f}}
'
f'{method.upper()} Dim 2: %{{y:.2f}}
'
''
),
customdata=np.column_stack((
noise_data['NAME'].fillna('UNNAMED'),
noise_data['SEASON'].fillna(2000)
))
)
)
# Add clusters with distinct colors and shapes
cluster_symbols = ['circle', 'square', 'diamond', 'triangle-up', 'triangle-down',
'pentagon', 'hexagon', 'star', 'cross', 'circle-open']
for i, cluster in enumerate(unique_clusters):
cluster_data = storm_features_viz[storm_features_viz['cluster'] == cluster]
color = CLUSTER_COLORS[i % len(CLUSTER_COLORS)]
symbol = cluster_symbols[i % len(cluster_symbols)]
fig_cluster.add_trace(
go.Scatter(
x=cluster_data['dim1'],
y=cluster_data['dim2'],
mode='markers',
marker=dict(color=color, size=10, symbol=symbol, line=dict(width=1, color='white')),
name=f'Cluster {cluster} ({len(cluster_data)} storms)',
hovertemplate=(
'%{customdata[0]}
'
'Season: %{customdata[1]}
'
f'Cluster: {cluster}
'
f'{method.upper()} Dim 1: %{{x:.2f}}
'
f'{method.upper()} Dim 2: %{{y:.2f}}
'
'Intensity: %{customdata[2]:.0f} kt
'
''
),
customdata=np.column_stack((
cluster_data['NAME'].fillna('UNNAMED'),
cluster_data['SEASON'].fillna(2000),
cluster_data['USA_WIND_max'].fillna(0)
))
)
)
fig_cluster.update_layout(
title=f'Storm Clustering Analysis using {method.upper()}
Each symbol/color represents a distinct storm pattern group',
xaxis_title=f'{method.upper()} Dimension 1',
yaxis_title=f'{method.upper()} Dimension 2',
height=600,
showlegend=True
)
# 2. ENHANCED route map with cluster legends and clearer representation
fig_routes = go.Figure()
# Create a comprehensive legend showing cluster characteristics
cluster_info_text = []
for i, cluster in enumerate(unique_clusters):
cluster_storm_ids = storm_features_viz[storm_features_viz['cluster'] == cluster]['SID'].tolist()
color = CLUSTER_COLORS[i % len(CLUSTER_COLORS)]
# Get cluster statistics for legend
cluster_data = storm_features_viz[storm_features_viz['cluster'] == cluster]
avg_intensity = cluster_data['USA_WIND_max'].mean() if 'USA_WIND_max' in cluster_data.columns else 0
avg_pressure = cluster_data['USA_PRES_min'].mean() if 'USA_PRES_min' in cluster_data.columns else 1000
cluster_info_text.append(
f"Cluster {cluster}: {len(cluster_storm_ids)} storms, "
f"Avg: {avg_intensity:.0f}kt/{avg_pressure:.0f}hPa"
)
# Add multiple storms per cluster with clear identification
storms_added = 0
for j, sid in enumerate(cluster_storm_ids[:8]): # Show up to 8 storms per cluster
try:
storm_track = typhoon_data[typhoon_data['SID'] == sid].sort_values('ISO_TIME')
if len(storm_track) > 1:
# Ensure valid coordinates
valid_coords = storm_track['LAT'].notna() & storm_track['LON'].notna()
storm_track = storm_track[valid_coords]
if len(storm_track) > 1:
storm_name = storm_track['NAME'].iloc[0] if pd.notna(storm_track['NAME'].iloc[0]) else 'UNNAMED'
storm_season = storm_track['SEASON'].iloc[0] if 'SEASON' in storm_track.columns else 'Unknown'
# Vary line style for different storms in same cluster
line_styles = ['solid', 'dash', 'dot', 'dashdot']
line_style = line_styles[j % len(line_styles)]
line_width = 3 if j == 0 else 2 # First storm thicker
fig_routes.add_trace(
go.Scattergeo(
lon=storm_track['LON'],
lat=storm_track['LAT'],
mode='lines+markers',
line=dict(color=color, width=line_width, dash=line_style),
marker=dict(color=color, size=3),
name=f'C{cluster}: {storm_name} ({storm_season})',
showlegend=True,
legendgroup=f'cluster_{cluster}',
hovertemplate=(
f'Cluster {cluster}: {storm_name}
'
'Lat: %{lat:.1f}°
'
'Lon: %{lon:.1f}°
'
f'Season: {storm_season}
'
f'Pattern Group: {cluster}
'
''
)
)
)
storms_added += 1
except Exception as track_error:
logging.warning(f"Error adding track for storm {sid}: {track_error}")
continue
# Add cluster centroid marker
if len(cluster_storm_ids) > 0:
# Calculate average genesis location for cluster
cluster_storm_data = storm_features_viz[storm_features_viz['cluster'] == cluster]
if 'genesis_lat' in cluster_storm_data.columns and 'genesis_lon' in cluster_storm_data.columns:
avg_lat = cluster_storm_data['genesis_lat'].mean()
avg_lon = cluster_storm_data['genesis_lon'].mean()
fig_routes.add_trace(
go.Scattergeo(
lon=[avg_lon],
lat=[avg_lat],
mode='markers',
marker=dict(
color=color,
size=20,
symbol='star',
line=dict(width=2, color='white')
),
name=f'C{cluster} Center',
showlegend=True,
legendgroup=f'cluster_{cluster}',
hovertemplate=(
f'Cluster {cluster} Genesis Center
'
f'Avg Position: {avg_lat:.1f}°N, {avg_lon:.1f}°E
'
f'Storms: {len(cluster_storm_ids)}
'
f'Avg Intensity: {avg_intensity:.0f} kt
'
''
)
)
)
# Update route map layout with enhanced information and LARGER SIZE
fig_routes.update_layout(
title=f"Storm Routes by {method.upper()} Clusters
Different line styles = different storms in same cluster | Stars = cluster centers",
geo=dict(
projection_type="natural earth",
showland=True,
landcolor="LightGray",
showocean=True,
oceancolor="LightBlue",
showcoastlines=True,
coastlinecolor="Gray",
center=dict(lat=20, lon=140),
projection_scale=2.5 # Larger map
),
height=800, # Much larger height
width=1200, # Wider map
showlegend=True
)
# Add cluster info annotation
cluster_summary = "
".join(cluster_info_text)
fig_routes.add_annotation(
text=f"Cluster Summary:
{cluster_summary}",
xref="paper", yref="paper",
x=0.02, y=0.98,
showarrow=False,
align="left",
bgcolor="rgba(255,255,255,0.8)",
bordercolor="gray",
borderwidth=1
)
# 3. Enhanced pressure evolution plot with cluster identification
fig_pressure = go.Figure()
for i, cluster in enumerate(unique_clusters):
cluster_storm_ids = storm_features_viz[storm_features_viz['cluster'] == cluster]['SID'].tolist()
color = CLUSTER_COLORS[i % len(CLUSTER_COLORS)]
cluster_pressures = []
for j, sid in enumerate(cluster_storm_ids[:5]): # Limit to 5 storms per cluster
try:
storm_track = typhoon_data[typhoon_data['SID'] == sid].sort_values('ISO_TIME')
if len(storm_track) > 1 and 'USA_PRES' in storm_track.columns:
pressure_values = pd.to_numeric(storm_track['USA_PRES'], errors='coerce').dropna()
if len(pressure_values) > 0:
storm_name = storm_track['NAME'].iloc[0] if pd.notna(storm_track['NAME'].iloc[0]) else 'UNNAMED'
time_hours = range(len(pressure_values))
# Normalize time to show relative progression
normalized_time = np.linspace(0, 100, len(pressure_values))
fig_pressure.add_trace(
go.Scatter(
x=normalized_time,
y=pressure_values,
mode='lines',
line=dict(color=color, width=2, dash='solid' if j == 0 else 'dash'),
name=f'C{cluster}: {storm_name}' if j == 0 else None,
showlegend=(j == 0),
legendgroup=f'pressure_cluster_{cluster}',
hovertemplate=(
f'Cluster {cluster}: {storm_name}
'
'Progress: %{x:.0f}%
'
'Pressure: %{y:.0f} hPa
'
''
),
opacity=0.8 if j == 0 else 0.5
)
)
cluster_pressures.extend(pressure_values)
except Exception as e:
continue
# Add cluster average line
if cluster_pressures:
avg_pressure = np.mean(cluster_pressures)
fig_pressure.add_hline(
y=avg_pressure,
line_dash="dot",
line_color=color,
annotation_text=f"C{cluster} Avg: {avg_pressure:.0f}",
annotation_position="right"
)
fig_pressure.update_layout(
title=f"Pressure Evolution by {method.upper()} Clusters
Normalized timeline (0-100%) | Dotted lines = cluster averages",
xaxis_title="Storm Progress (%)",
yaxis_title="Pressure (hPa)",
height=500
)
# 4. Enhanced wind evolution plot
fig_wind = go.Figure()
for i, cluster in enumerate(unique_clusters):
cluster_storm_ids = storm_features_viz[storm_features_viz['cluster'] == cluster]['SID'].tolist()
color = CLUSTER_COLORS[i % len(CLUSTER_COLORS)]
cluster_winds = []
for j, sid in enumerate(cluster_storm_ids[:5]): # Limit to 5 storms per cluster
try:
storm_track = typhoon_data[typhoon_data['SID'] == sid].sort_values('ISO_TIME')
if len(storm_track) > 1 and 'USA_WIND' in storm_track.columns:
wind_values = pd.to_numeric(storm_track['USA_WIND'], errors='coerce').dropna()
if len(wind_values) > 0:
storm_name = storm_track['NAME'].iloc[0] if pd.notna(storm_track['NAME'].iloc[0]) else 'UNNAMED'
# Normalize time to show relative progression
normalized_time = np.linspace(0, 100, len(wind_values))
fig_wind.add_trace(
go.Scatter(
x=normalized_time,
y=wind_values,
mode='lines',
line=dict(color=color, width=2, dash='solid' if j == 0 else 'dash'),
name=f'C{cluster}: {storm_name}' if j == 0 else None,
showlegend=(j == 0),
legendgroup=f'wind_cluster_{cluster}',
hovertemplate=(
f'Cluster {cluster}: {storm_name}
'
'Progress: %{x:.0f}%
'
'Wind: %{y:.0f} kt
'
''
),
opacity=0.8 if j == 0 else 0.5
)
)
cluster_winds.extend(wind_values)
except Exception as e:
continue
# Add cluster average line
if cluster_winds:
avg_wind = np.mean(cluster_winds)
fig_wind.add_hline(
y=avg_wind,
line_dash="dot",
line_color=color,
annotation_text=f"C{cluster} Avg: {avg_wind:.0f}",
annotation_position="right"
)
fig_wind.update_layout(
title=f"Wind Speed Evolution by {method.upper()} Clusters
Normalized timeline (0-100%) | Dotted lines = cluster averages",
xaxis_title="Storm Progress (%)",
yaxis_title="Wind Speed (kt)",
height=500
)
# Generate enhanced cluster statistics with clear explanations
try:
stats_text = f"ENHANCED {method.upper()} CLUSTER ANALYSIS RESULTS\n" + "="*60 + "\n\n"
stats_text += f"🔍 DIMENSIONALITY REDUCTION: {method.upper()}\n"
stats_text += f"🎯 CLUSTERING ALGORITHM: DBSCAN (automatic pattern discovery)\n"
stats_text += f"📊 TOTAL STORMS ANALYZED: {len(storm_features_viz)}\n"
stats_text += f"🎨 CLUSTERS DISCOVERED: {len(unique_clusters)}\n"
if noise_count > 0:
stats_text += f"❌ NOISE POINTS: {noise_count} storms (don't fit clear patterns)\n"
stats_text += "\n"
for cluster in sorted(storm_features_viz['cluster'].unique()):
cluster_data = storm_features_viz[storm_features_viz['cluster'] == cluster]
storm_count = len(cluster_data)
if cluster == -1:
stats_text += f"❌ NOISE GROUP: {storm_count} storms\n"
stats_text += " → These storms don't follow the main patterns\n"
stats_text += " → May represent unique or rare storm behaviors\n\n"
continue
stats_text += f"🎯 CLUSTER {cluster}: {storm_count} storms\n"
stats_text += f" 🎨 Color: {CLUSTER_COLORS[cluster % len(CLUSTER_COLORS)]}\n"
# Add detailed statistics if available
if 'USA_WIND_max' in cluster_data.columns:
wind_mean = cluster_data['USA_WIND_max'].mean()
wind_std = cluster_data['USA_WIND_max'].std()
stats_text += f" 💨 Intensity: {wind_mean:.1f} ± {wind_std:.1f} kt\n"
if 'USA_PRES_min' in cluster_data.columns:
pres_mean = cluster_data['USA_PRES_min'].mean()
pres_std = cluster_data['USA_PRES_min'].std()
stats_text += f" 🌡️ Pressure: {pres_mean:.1f} ± {pres_std:.1f} hPa\n"
if 'track_length' in cluster_data.columns:
track_mean = cluster_data['track_length'].mean()
stats_text += f" 📏 Avg Track Length: {track_mean:.1f} points\n"
if 'genesis_lat' in cluster_data.columns and 'genesis_lon' in cluster_data.columns:
lat_mean = cluster_data['genesis_lat'].mean()
lon_mean = cluster_data['genesis_lon'].mean()
stats_text += f" 🎯 Genesis Region: {lat_mean:.1f}°N, {lon_mean:.1f}°E\n"
# Add interpretation
if wind_mean < 50:
stats_text += " 💡 Pattern: Weaker storm group\n"
elif wind_mean > 100:
stats_text += " 💡 Pattern: Intense storm group\n"
else:
stats_text += " 💡 Pattern: Moderate intensity group\n"
stats_text += "\n"
# Add explanation of the analysis
stats_text += "📖 INTERPRETATION GUIDE:\n"
stats_text += f"• {method.upper()} reduces storm characteristics to 2D for visualization\n"
stats_text += "• DBSCAN finds natural groupings without preset number of clusters\n"
stats_text += "• Each cluster represents storms with similar behavior patterns\n"
stats_text += "• Route colors match cluster colors from the similarity plot\n"
stats_text += "• Stars on map show average genesis locations for each cluster\n"
stats_text += "• Temporal plots show how each cluster behaves over time\n\n"
stats_text += f"🔧 FEATURES USED FOR CLUSTERING:\n"
stats_text += f" Total: {len(feature_cols)} storm characteristics\n"
stats_text += f" Including: intensity, pressure, track shape, genesis location\n"
except Exception as stats_error:
stats_text = f"Error generating enhanced statistics: {str(stats_error)}"
return fig_cluster, fig_routes, fig_pressure, fig_wind, stats_text
except Exception as e:
logging.error(f"Error in enhanced clustering analysis: {e}")
import traceback
traceback.print_exc()
error_fig = go.Figure()
error_fig.add_annotation(
text=f"Error in clustering analysis: {str(e)}",
xref="paper", yref="paper",
x=0.5, y=0.5, xanchor='center', yanchor='middle',
showarrow=False, font_size=16
)
return error_fig, error_fig, error_fig, error_fig, f"Error in clustering: {str(e)}"
# -----------------------------
# Regression Functions (Original)
# -----------------------------
def perform_wind_regression(start_year, start_month, end_year, end_month):
"""Perform wind regression analysis"""
start_date = datetime(start_year, start_month, 1)
end_date = datetime(end_year, end_month, 28)
data = merged_data[(merged_data['ISO_TIME']>=start_date) & (merged_data['ISO_TIME']<=end_date)].dropna(subset=['USA_WIND','ONI'])
data['severe_typhoon'] = (data['USA_WIND']>=64).astype(int)
X = sm.add_constant(data['ONI'])
y = data['severe_typhoon']
try:
model = sm.Logit(y, X).fit(disp=0)
beta_1 = model.params['ONI']
exp_beta_1 = np.exp(beta_1)
p_value = model.pvalues['ONI']
return f"Wind Regression: β1={beta_1:.4f}, Odds Ratio={exp_beta_1:.4f}, P-value={p_value:.4f}"
except Exception as e:
return f"Wind Regression Error: {e}"
def perform_pressure_regression(start_year, start_month, end_year, end_month):
"""Perform pressure regression analysis"""
start_date = datetime(start_year, start_month, 1)
end_date = datetime(end_year, end_month, 28)
data = merged_data[(merged_data['ISO_TIME']>=start_date) & (merged_data['ISO_TIME']<=end_date)].dropna(subset=['USA_PRES','ONI'])
data['intense_typhoon'] = (data['USA_PRES']<=950).astype(int)
X = sm.add_constant(data['ONI'])
y = data['intense_typhoon']
try:
model = sm.Logit(y, X).fit(disp=0)
beta_1 = model.params['ONI']
exp_beta_1 = np.exp(beta_1)
p_value = model.pvalues['ONI']
return f"Pressure Regression: β1={beta_1:.4f}, Odds Ratio={exp_beta_1:.4f}, P-value={p_value:.4f}"
except Exception as e:
return f"Pressure Regression Error: {e}"
def perform_longitude_regression(start_year, start_month, end_year, end_month):
"""Perform longitude regression analysis"""
start_date = datetime(start_year, start_month, 1)
end_date = datetime(end_year, end_month, 28)
data = merged_data[(merged_data['ISO_TIME']>=start_date) & (merged_data['ISO_TIME']<=end_date)].dropna(subset=['LON','ONI'])
data['western_typhoon'] = (data['LON']<=140).astype(int)
X = sm.add_constant(data['ONI'])
y = data['western_typhoon']
try:
model = sm.OLS(y, sm.add_constant(X)).fit()
beta_1 = model.params['ONI']
exp_beta_1 = np.exp(beta_1)
p_value = model.pvalues['ONI']
return f"Longitude Regression: β1={beta_1:.4f}, Odds Ratio={exp_beta_1:.4f}, P-value={p_value:.4f}"
except Exception as e:
return f"Longitude Regression Error: {e}"
# -----------------------------
# Visualization Functions (Enhanced)
# -----------------------------
def get_full_tracks(start_year, start_month, end_year, end_month, enso_phase, typhoon_search):
"""Get full typhoon tracks"""
start_date = datetime(start_year, start_month, 1)
end_date = datetime(end_year, end_month, 28)
filtered_data = merged_data[(merged_data['ISO_TIME']>=start_date) & (merged_data['ISO_TIME']<=end_date)].copy()
filtered_data['ENSO_Phase'] = filtered_data['ONI'].apply(classify_enso_phases)
if enso_phase != 'all':
filtered_data = filtered_data[filtered_data['ENSO_Phase'] == enso_phase.capitalize()]
unique_storms = filtered_data['SID'].unique()
count = len(unique_storms)
fig = go.Figure()
for sid in unique_storms:
storm_data = typhoon_data[typhoon_data['SID']==sid]
if storm_data.empty:
continue
name = storm_data['NAME'].iloc[0] if pd.notnull(storm_data['NAME'].iloc[0]) else "Unnamed"
basin = storm_data['SID'].iloc[0][:2]
storm_oni = filtered_data[filtered_data['SID']==sid]['ONI'].iloc[0]
color = 'red' if storm_oni>=0.5 else ('blue' if storm_oni<=-0.5 else 'green')
fig.add_trace(go.Scattergeo(
lon=storm_data['LON'], lat=storm_data['LAT'], mode='lines',
name=f"{name} ({basin})",
line=dict(width=1.5, color=color), hoverinfo="name"
))
if typhoon_search:
search_mask = typhoon_data['NAME'].str.contains(typhoon_search, case=False, na=False)
if search_mask.any():
for sid in typhoon_data[search_mask]['SID'].unique():
storm_data = typhoon_data[typhoon_data['SID']==sid]
fig.add_trace(go.Scattergeo(
lon=storm_data['LON'], lat=storm_data['LAT'], mode='lines+markers',
name=f"MATCHED: {storm_data['NAME'].iloc[0]}",
line=dict(width=3, color='yellow'),
marker=dict(size=5), hoverinfo="name"
))
fig.update_layout(
title=f"Typhoon Tracks ({start_year}-{start_month} to {end_year}-{end_month})",
geo=dict(
projection_type='natural earth',
showland=True,
showcoastlines=True,
landcolor='rgb(243,243,243)',
countrycolor='rgb(204,204,204)',
coastlinecolor='rgb(204,204,204)',
center=dict(lon=140, lat=20),
projection_scale=3
),
legend_title="Typhoons by ENSO Phase",
showlegend=True,
height=700
)
fig.add_annotation(
x=0.02, y=0.98, xref="paper", yref="paper",
text="Red: El Niño, Blue: La Nina, Green: Neutral",
showarrow=False, align="left",
bgcolor="rgba(255,255,255,0.8)"
)
return fig, f"Total typhoons displayed: {count}"
def get_wind_analysis(start_year, start_month, end_year, end_month, enso_phase, typhoon_search):
"""Get wind analysis with enhanced categorization"""
start_date = datetime(start_year, start_month, 1)
end_date = datetime(end_year, end_month, 28)
filtered_data = merged_data[(merged_data['ISO_TIME']>=start_date) & (merged_data['ISO_TIME']<=end_date)].copy()
filtered_data['ENSO_Phase'] = filtered_data['ONI'].apply(classify_enso_phases)
if enso_phase != 'all':
filtered_data = filtered_data[filtered_data['ENSO_Phase'] == enso_phase.capitalize()]
fig = px.scatter(filtered_data, x='ONI', y='USA_WIND', color='Category',
hover_data=['NAME','Year','Category'],
title='Wind Speed vs ONI',
labels={'ONI':'ONI Value','USA_WIND':'Max Wind Speed (knots)'},
color_discrete_map=enhanced_color_map)
if typhoon_search:
mask = filtered_data['NAME'].str.contains(typhoon_search, case=False, na=False)
if mask.any():
fig.add_trace(go.Scatter(
x=filtered_data.loc[mask,'ONI'], y=filtered_data.loc[mask,'USA_WIND'],
mode='markers', marker=dict(size=10, color='red', symbol='star'),
name=f'Matched: {typhoon_search}',
text=filtered_data.loc[mask,'NAME']+' ('+filtered_data.loc[mask,'Year'].astype(str)+')'
))
regression = perform_wind_regression(start_year, start_month, end_year, end_month)
return fig, regression
def get_pressure_analysis(start_year, start_month, end_year, end_month, enso_phase, typhoon_search):
"""Get pressure analysis with enhanced categorization"""
start_date = datetime(start_year, start_month, 1)
end_date = datetime(end_year, end_month, 28)
filtered_data = merged_data[(merged_data['ISO_TIME']>=start_date) & (merged_data['ISO_TIME']<=end_date)].copy()
filtered_data['ENSO_Phase'] = filtered_data['ONI'].apply(classify_enso_phases)
if enso_phase != 'all':
filtered_data = filtered_data[filtered_data['ENSO_Phase'] == enso_phase.capitalize()]
fig = px.scatter(filtered_data, x='ONI', y='USA_PRES', color='Category',
hover_data=['NAME','Year','Category'],
title='Pressure vs ONI',
labels={'ONI':'ONI Value','USA_PRES':'Min Pressure (hPa)'},
color_discrete_map=enhanced_color_map)
if typhoon_search:
mask = filtered_data['NAME'].str.contains(typhoon_search, case=False, na=False)
if mask.any():
fig.add_trace(go.Scatter(
x=filtered_data.loc[mask,'ONI'], y=filtered_data.loc[mask,'USA_PRES'],
mode='markers', marker=dict(size=10, color='red', symbol='star'),
name=f'Matched: {typhoon_search}',
text=filtered_data.loc[mask,'NAME']+' ('+filtered_data.loc[mask,'Year'].astype(str)+')'
))
regression = perform_pressure_regression(start_year, start_month, end_year, end_month)
return fig, regression
def get_longitude_analysis(start_year, start_month, end_year, end_month, enso_phase, typhoon_search):
"""Get longitude analysis"""
start_date = datetime(start_year, start_month, 1)
end_date = datetime(end_year, end_month, 28)
filtered_data = merged_data[(merged_data['ISO_TIME']>=start_date) & (merged_data['ISO_TIME']<=end_date)].copy()
filtered_data['ENSO_Phase'] = filtered_data['ONI'].apply(classify_enso_phases)
if enso_phase != 'all':
filtered_data = filtered_data[filtered_data['ENSO_Phase'] == enso_phase.capitalize()]
fig = px.scatter(filtered_data, x='LON', y='ONI', hover_data=['NAME'],
title='Typhoon Generation Longitude vs ONI (All Years)')
if len(filtered_data) > 1:
X = np.array(filtered_data['LON']).reshape(-1,1)
y = filtered_data['ONI']
try:
model = sm.OLS(y, sm.add_constant(X)).fit()
y_pred = model.predict(sm.add_constant(X))
fig.add_trace(go.Scatter(x=filtered_data['LON'], y=y_pred, mode='lines', name='Regression Line'))
slope = model.params[1]
slopes_text = f"All Years Slope: {slope:.4f}"
except Exception as e:
slopes_text = f"Regression Error: {e}"
else:
slopes_text = "Insufficient data for regression"
regression = perform_longitude_regression(start_year, start_month, end_year, end_month)
return fig, slopes_text, regression
# -----------------------------
# ENHANCED: Animation Functions with Taiwan Standard Support
# -----------------------------
def get_available_years(typhoon_data):
"""Get all available years including 2025 - with error handling"""
try:
if typhoon_data is None or typhoon_data.empty:
return [str(year) for year in range(2000, 2026)]
if 'ISO_TIME' in typhoon_data.columns:
years = typhoon_data['ISO_TIME'].dt.year.dropna().unique()
elif 'SEASON' in typhoon_data.columns:
years = typhoon_data['SEASON'].dropna().unique()
else:
years = range(2000, 2026) # Default range including 2025
# Convert to strings and sort
year_strings = sorted([str(int(year)) for year in years if not pd.isna(year)])
# Ensure we have at least some years
if not year_strings:
return [str(year) for year in range(2000, 2026)]
return year_strings
except Exception as e:
print(f"Error in get_available_years: {e}")
return [str(year) for year in range(2000, 2026)]
def update_typhoon_options_enhanced(year, basin):
"""Enhanced typhoon options with TD support and 2025 data"""
try:
year = int(year)
# Filter by year - handle both ISO_TIME and SEASON columns
if 'ISO_TIME' in typhoon_data.columns:
year_mask = typhoon_data['ISO_TIME'].dt.year == year
elif 'SEASON' in typhoon_data.columns:
year_mask = typhoon_data['SEASON'] == year
else:
# Fallback - try to extract year from SID or other fields
year_mask = typhoon_data.index >= 0 # Include all data as fallback
year_data = typhoon_data[year_mask].copy()
# Filter by basin if specified
if basin != "All Basins":
basin_code = basin.split(' - ')[0] if ' - ' in basin else basin[:2]
if 'SID' in year_data.columns:
year_data = year_data[year_data['SID'].str.startswith(basin_code, na=False)]
elif 'BASIN' in year_data.columns:
year_data = year_data[year_data['BASIN'] == basin_code]
if year_data.empty:
return gr.update(choices=["No storms found"], value=None)
# Get unique storms - include ALL intensities (including TD)
storms = year_data.groupby('SID').agg({
'NAME': 'first',
'USA_WIND': 'max'
}).reset_index()
# Enhanced categorization including TD
storms['category'] = storms['USA_WIND'].apply(categorize_typhoon_enhanced)
# Create options with category information
options = []
for _, storm in storms.iterrows():
name = storm['NAME'] if pd.notna(storm['NAME']) and storm['NAME'] != '' else 'UNNAMED'
sid = storm['SID']
category = storm['category']
max_wind = storm['USA_WIND'] if pd.notna(storm['USA_WIND']) else 0
option = f"{name} ({sid}) - {category} ({max_wind:.0f}kt)"
options.append(option)
if not options:
return gr.update(choices=["No storms found"], value=None)
return gr.update(choices=sorted(options), value=options[0])
except Exception as e:
print(f"Error in update_typhoon_options_enhanced: {e}")
return gr.update(choices=["Error loading storms"], value=None)
def generate_enhanced_track_video(year, typhoon_selection, standard):
"""Enhanced track video generation with TD support, Taiwan standard, and 2025 compatibility"""
if not typhoon_selection or typhoon_selection == "No storms found":
return None
try:
# Extract SID from selection
sid = typhoon_selection.split('(')[1].split(')')[0]
# Get storm data
storm_df = typhoon_data[typhoon_data['SID'] == sid].copy()
if storm_df.empty:
print(f"No data found for storm {sid}")
return None
# Sort by time
if 'ISO_TIME' in storm_df.columns:
storm_df = storm_df.sort_values('ISO_TIME')
# Extract data for animation
lats = storm_df['LAT'].astype(float).values
lons = storm_df['LON'].astype(float).values
if 'USA_WIND' in storm_df.columns:
winds = pd.to_numeric(storm_df['USA_WIND'], errors='coerce').fillna(0).values
else:
winds = np.full(len(lats), 30) # Default TD strength
# Enhanced metadata
storm_name = storm_df['NAME'].iloc[0] if pd.notna(storm_df['NAME'].iloc[0]) else "UNNAMED"
season = storm_df['SEASON'].iloc[0] if 'SEASON' in storm_df.columns else year
print(f"Generating video for {storm_name} ({sid}) with {len(lats)} track points using {standard} standard")
# Create figure with enhanced map
fig, ax = plt.subplots(figsize=(16, 10), subplot_kw={'projection': ccrs.PlateCarree()})
# Enhanced map features
ax.stock_img()
ax.add_feature(cfeature.COASTLINE, linewidth=0.8)
ax.add_feature(cfeature.BORDERS, linewidth=0.5)
ax.add_feature(cfeature.OCEAN, color='lightblue', alpha=0.5)
ax.add_feature(cfeature.LAND, color='lightgray', alpha=0.5)
# Set extent based on track
padding = 5
ax.set_extent([
min(lons) - padding, max(lons) + padding,
min(lats) - padding, max(lats) + padding
])
# Add gridlines
gl = ax.gridlines(draw_labels=True, alpha=0.3)
gl.top_labels = gl.right_labels = False
# Title with enhanced info and standard
ax.set_title(f"{season} {storm_name} ({sid}) Track Animation - {standard.upper()} Standard",
fontsize=18, fontweight='bold')
# Animation elements
line, = ax.plot([], [], 'b-', linewidth=3, alpha=0.7, label='Track')
point, = ax.plot([], [], 'o', markersize=15)
# Enhanced info display
info_box = ax.text(0.02, 0.98, '', transform=ax.transAxes,
fontsize=12, verticalalignment='top',
bbox=dict(boxstyle="round,pad=0.5", facecolor='white', alpha=0.9))
# Color legend with both standards - ENHANCED
legend_elements = []
if standard == 'taiwan':
categories = ['Tropical Depression', 'Tropical Storm', 'Moderate Typhoon', 'Intense Typhoon']
for category in categories:
color = get_taiwan_color(category)
legend_elements.append(plt.Line2D([0], [0], marker='o', color='w',
markerfacecolor=color, markersize=10, label=category))
else:
categories = ['Tropical Depression', 'Tropical Storm', 'C1 Typhoon', 'C2 Typhoon',
'C3 Strong Typhoon', 'C4 Very Strong Typhoon', 'C5 Super Typhoon']
for category in categories:
color = get_matplotlib_color(category)
legend_elements.append(plt.Line2D([0], [0], marker='o', color='w',
markerfacecolor=color, markersize=10, label=category))
ax.legend(handles=legend_elements, loc='upper right', fontsize=10)
def animate(frame):
try:
if frame >= len(lats):
return line, point, info_box
# Update track line
line.set_data(lons[:frame+1], lats[:frame+1])
# Update current position with appropriate categorization
current_wind = winds[frame]
if standard == 'taiwan':
category, color = categorize_typhoon_by_standard(current_wind, 'taiwan')
else:
category, color = categorize_typhoon_by_standard(current_wind, 'atlantic')
# Debug print for first few frames
if frame < 3:
print(f"Frame {frame}: Wind={current_wind:.1f}kt, Category={category}, Color={color}, Standard={standard}")
point.set_data([lons[frame]], [lats[frame]])
point.set_color(color)
point.set_markersize(10 + current_wind/8) # Size based on intensity
# Enhanced info display with standard information
if 'ISO_TIME' in storm_df.columns and frame < len(storm_df):
current_time = storm_df.iloc[frame]['ISO_TIME']
time_str = current_time.strftime('%Y-%m-%d %H:%M UTC') if pd.notna(current_time) else 'Unknown'
else:
time_str = f"Step {frame+1}"
# Convert wind speed for Taiwan standard display
if standard == 'taiwan':
wind_ms = current_wind * 0.514444 # Convert to m/s for display
wind_display = f"{current_wind:.0f} kt ({wind_ms:.1f} m/s)"
else:
wind_display = f"{current_wind:.0f} kt"
info_text = (
f"Storm: {storm_name}\n"
f"Time: {time_str}\n"
f"Position: {lats[frame]:.1f}°N, {lons[frame]:.1f}°E\n"
f"Max Wind: {wind_display}\n"
f"Category: {category}\n"
f"Standard: {standard.upper()}\n"
f"Frame: {frame+1}/{len(lats)}"
)
info_box.set_text(info_text)
return line, point, info_box
except Exception as e:
print(f"Error in animate frame {frame}: {e}")
return line, point, info_box
# Create animation
anim = animation.FuncAnimation(
fig, animate, frames=len(lats),
interval=400, blit=False, repeat=True # Slightly slower for better viewing
)
# Save animation with graceful fallback if FFmpeg is unavailable
if shutil.which('ffmpeg'):
writer = animation.FFMpegWriter(
fps=3, bitrate=2000, codec='libx264',
extra_args=['-pix_fmt', 'yuv420p']
)
suffix = '.mp4'
else:
print("FFmpeg not found - generating GIF instead")
writer = animation.PillowWriter(fps=3)
suffix = '.gif'
temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=suffix,
dir=tempfile.gettempdir())
print(f"Saving animation to {temp_file.name}")
anim.save(temp_file.name, writer=writer, dpi=120)
plt.close(fig)
print(f"Video generated successfully: {temp_file.name}")
return temp_file.name
except Exception as e:
print(f"Error generating video: {e}")
import traceback
traceback.print_exc()
return None
# Simplified wrapper for backward compatibility - FIXED
def simplified_track_video(year, basin, typhoon, standard):
"""Simplified track video function with fixed color handling"""
if not typhoon:
return None
return generate_enhanced_track_video(year, typhoon, standard)
# -----------------------------
# Load & Process Data
# -----------------------------
# Global variables initialization
oni_data = None
typhoon_data = None
merged_data = None
def initialize_data():
"""Initialize all data safely"""
global oni_data, typhoon_data, merged_data
try:
logging.info("Starting data loading process...")
update_oni_data()
oni_data, typhoon_data = load_data_fixed(ONI_DATA_PATH, TYPHOON_DATA_PATH)
if oni_data is not None and typhoon_data is not None:
oni_long = process_oni_data(oni_data)
typhoon_max = process_typhoon_data(typhoon_data)
merged_data = merge_data(oni_long, typhoon_max)
logging.info("Data loading complete.")
else:
logging.error("Failed to load required data")
# Create minimal fallback data
oni_data = pd.DataFrame({'Year': [2000], 'Jan': [0], 'Feb': [0], 'Mar': [0], 'Apr': [0],
'May': [0], 'Jun': [0], 'Jul': [0], 'Aug': [0], 'Sep': [0],
'Oct': [0], 'Nov': [0], 'Dec': [0]})
typhoon_data = create_fallback_typhoon_data()
oni_long = process_oni_data(oni_data)
typhoon_max = process_typhoon_data(typhoon_data)
merged_data = merge_data(oni_long, typhoon_max)
except Exception as e:
logging.error(f"Error during data initialization: {e}")
# Create minimal fallback data
oni_data = pd.DataFrame({'Year': [2000], 'Jan': [0], 'Feb': [0], 'Mar': [0], 'Apr': [0],
'May': [0], 'Jun': [0], 'Jul': [0], 'Aug': [0], 'Sep': [0],
'Oct': [0], 'Nov': [0], 'Dec': [0]})
typhoon_data = create_fallback_typhoon_data()
oni_long = process_oni_data(oni_data)
typhoon_max = process_typhoon_data(typhoon_data)
merged_data = merge_data(oni_long, typhoon_max)
# Initialize data
initialize_data()
# -----------------------------
# ENHANCED: Gradio Interface with Fixed Route Visualization and Enhanced Features
# -----------------------------
def create_interface():
"""Create the enhanced Gradio interface with robust error handling"""
try:
# Ensure data is available
if oni_data is None or typhoon_data is None or merged_data is None:
logging.warning("Data not properly loaded, creating minimal interface")
return create_minimal_fallback_interface()
# Get safe data statistics
try:
total_storms = len(typhoon_data['SID'].unique()) if 'SID' in typhoon_data.columns else 0
total_records = len(typhoon_data)
available_years = get_available_years(typhoon_data)
year_range_display = f"{available_years[0]} - {available_years[-1]}" if available_years else "Unknown"
except Exception as e:
logging.error(f"Error getting data statistics: {e}")
total_storms = 0
total_records = 0
year_range_display = "Unknown"
available_years = [str(year) for year in range(2000, 2026)]
with gr.Blocks(title="Enhanced Typhoon Analysis Platform", theme=gr.themes.Soft()) as demo:
gr.Markdown("# 🌪️ Enhanced Typhoon Analysis Platform")
gr.Markdown("**Advanced ML clustering, route predictions, and comprehensive tropical cyclone analysis including Tropical Depressions**")
with gr.Tab("🏠 Overview"):
overview_text = f"""
## Welcome to the Enhanced Typhoon Analysis Dashboard
This dashboard provides comprehensive analysis of typhoon data in relation to ENSO phases with advanced machine learning capabilities.
### 🚀 Enhanced Features:
- **Advanced ML Clustering**: UMAP/t-SNE storm pattern analysis with separate visualizations
- **Predictive Routing**: Advanced storm track and intensity forecasting with uncertainty quantification
- **Complete TD Support**: Now includes Tropical Depressions (< 34 kt)
- **Taiwan Standard**: Full support for Taiwan meteorological classification system
- **2025 Data Ready**: Real-time compatibility with current year data
- **Enhanced Animations**: High-quality storm track visualizations with both standards
### 📊 Data Status:
- **ONI Data**: {len(oni_data)} years loaded
- **Typhoon Data**: {total_records:,} records loaded
- **Merged Data**: {len(merged_data):,} typhoons with ONI values
- **Available Years**: {year_range_display}
### 🔧 Technical Capabilities:
- **UMAP Clustering**: {"✅ Available" if UMAP_AVAILABLE else "⚠️ Limited to t-SNE/PCA"}
- **AI Predictions**: {"🧠 Deep Learning" if CNN_AVAILABLE else "🔬 Physics-based"}
- **Enhanced Categorization**: Tropical Depression to Super Typhoon
- **Platform**: Optimized for Hugging Face Spaces
### 📈 Research Applications:
- Climate change impact studies
- Seasonal forecasting research
- Storm pattern classification
- ENSO-typhoon relationship analysis
- Intensity prediction model development
"""
gr.Markdown(overview_text)
with gr.Tab("🌊 Monthly Typhoon Genesis Prediction"):
gr.Markdown("## 🌊 Monthly Typhoon Genesis Prediction")
gr.Markdown("**Enter month (1-12) and ONI value to see realistic typhoon development throughout the month using Genesis Potential Index**")
with gr.Row():
with gr.Column(scale=1):
genesis_month = gr.Slider(
1, 12,
label="Month",
value=9,
step=1,
info="1=Jan, 2=Feb, ..., 12=Dec"
)
genesis_oni = gr.Number(
label="ONI Value",
value=0.0,
info="El Niño (+) / La Niña (-) / Neutral (0)"
)
enable_genesis_animation = gr.Checkbox(
label="Enable Animation",
value=True,
info="Watch daily genesis potential evolution"
)
generate_genesis_btn = gr.Button("🌊 Generate Monthly Genesis Prediction", variant="primary", size="lg")
with gr.Column(scale=2):
gr.Markdown("### 🌊 What You'll Get:")
gr.Markdown("""
- **Daily GPI Evolution**: See genesis potential change day-by-day throughout the month
- **Genesis Event Detection**: Automatic identification of likely cyclogenesis times/locations
- **Storm Track Development**: Physics-based tracks from each genesis point
- **Real-time Animation**: Watch storms develop and move with uncertainty visualization
- **Environmental Analysis**: SST, humidity, wind shear, and vorticity effects
- **ENSO Modulation**: How El Niño/La Niña affects monthly patterns
""")
with gr.Row():
genesis_animation = gr.HTML(label="🗺️ Daily Genesis Potential & Storm Development")
with gr.Row():
genesis_summary = gr.Textbox(label="📋 Monthly Genesis Analysis Summary", lines=25)
def run_genesis_prediction(month, oni, animation):
try:
# Generate monthly prediction using GPI
prediction_data = generate_genesis_prediction_monthly(month, oni, year=2025)
# Create animation
genesis_fig = create_genesis_animation(prediction_data, animation)
# Generate summary
summary_text = create_prediction_summary(prediction_data)
html = genesis_fig.to_html(include_plotlyjs='cdn', full_html=False)
return html, summary_text
except Exception as e:
import traceback
error_msg = f"Genesis prediction failed: {str(e)}\n\nDetails:\n{traceback.format_exc()}"
logging.error(error_msg)
err_fig = create_error_plot(error_msg)
return err_fig.to_html(include_plotlyjs='cdn', full_html=False), error_msg
generate_genesis_btn.click(
fn=run_genesis_prediction,
inputs=[genesis_month, genesis_oni, enable_genesis_animation],
outputs=[genesis_animation, genesis_summary]
)
with gr.Tab("🔬 Advanced ML Clustering"):
gr.Markdown("## 🎯 Storm Pattern Analysis with Separate Visualizations")
gr.Markdown("**Four separate plots: Clustering, Routes, Pressure Evolution, and Wind Evolution**")
with gr.Row():
with gr.Column(scale=2):
reduction_method = gr.Dropdown(
choices=['UMAP', 't-SNE', 'PCA'],
value='UMAP' if UMAP_AVAILABLE else 't-SNE',
label="🔍 Dimensionality Reduction Method",
info="UMAP provides better global structure preservation"
)
with gr.Column(scale=1):
analyze_clusters_btn = gr.Button("🚀 Generate All Cluster Analyses", variant="primary", size="lg")
with gr.Row():
with gr.Column():
cluster_plot = gr.Plot(label="📊 Storm Clustering Analysis")
with gr.Column():
routes_plot = gr.Plot(label="🗺️ Clustered Storm Routes")
with gr.Row():
with gr.Column():
pressure_plot = gr.Plot(label="🌡️ Pressure Evolution by Cluster")
with gr.Column():
wind_plot = gr.Plot(label="💨 Wind Speed Evolution by Cluster")
with gr.Row():
cluster_stats = gr.Textbox(label="📈 Detailed Cluster Statistics", lines=15, max_lines=20)
def run_separate_clustering_analysis(method):
try:
# Extract features for clustering
storm_features = extract_storm_features(typhoon_data)
if storm_features is None:
return None, None, None, None, "Error: Could not extract storm features"
fig_cluster, fig_routes, fig_pressure, fig_wind, stats = create_separate_clustering_plots(
storm_features, typhoon_data, method.lower()
)
return fig_cluster, fig_routes, fig_pressure, fig_wind, stats
except Exception as e:
import traceback
error_details = traceback.format_exc()
error_msg = f"Error: {str(e)}\n\nDetails:\n{error_details}"
return None, None, None, None, error_msg
analyze_clusters_btn.click(
fn=run_separate_clustering_analysis,
inputs=[reduction_method],
outputs=[cluster_plot, routes_plot, pressure_plot, wind_plot, cluster_stats]
)
with gr.Tab("🗺️ Track Visualization"):
with gr.Row():
start_year = gr.Number(label="Start Year", value=2020)
start_month = gr.Dropdown(label="Start Month", choices=list(range(1, 13)), value=1)
end_year = gr.Number(label="End Year", value=2025)
end_month = gr.Dropdown(label="End Month", choices=list(range(1, 13)), value=6)
enso_phase = gr.Dropdown(label="ENSO Phase", choices=['all', 'El Nino', 'La Nina', 'Neutral'], value='all')
typhoon_search = gr.Textbox(label="Typhoon Search")
analyze_btn = gr.Button("Generate Tracks")
tracks_plot = gr.Plot()
typhoon_count = gr.Textbox(label="Number of Typhoons Displayed")
analyze_btn.click(
fn=get_full_tracks,
inputs=[start_year, start_month, end_year, end_month, enso_phase, typhoon_search],
outputs=[tracks_plot, typhoon_count]
)
with gr.Tab("💨 Wind Analysis"):
with gr.Row():
wind_start_year = gr.Number(label="Start Year", value=2020)
wind_start_month = gr.Dropdown(label="Start Month", choices=list(range(1, 13)), value=1)
wind_end_year = gr.Number(label="End Year", value=2024)
wind_end_month = gr.Dropdown(label="End Month", choices=list(range(1, 13)), value=6)
wind_enso_phase = gr.Dropdown(label="ENSO Phase", choices=['all', 'El Nino', 'La Nina', 'Neutral'], value='all')
wind_typhoon_search = gr.Textbox(label="Typhoon Search")
wind_analyze_btn = gr.Button("Generate Wind Analysis")
wind_scatter = gr.Plot()
wind_regression_results = gr.Textbox(label="Wind Regression Results")
wind_analyze_btn.click(
fn=get_wind_analysis,
inputs=[wind_start_year, wind_start_month, wind_end_year, wind_end_month, wind_enso_phase, wind_typhoon_search],
outputs=[wind_scatter, wind_regression_results]
)
with gr.Tab("🌡️ Pressure Analysis"):
with gr.Row():
pressure_start_year = gr.Number(label="Start Year", value=2020)
pressure_start_month = gr.Dropdown(label="Start Month", choices=list(range(1, 13)), value=1)
pressure_end_year = gr.Number(label="End Year", value=2024)
pressure_end_month = gr.Dropdown(label="End Month", choices=list(range(1, 13)), value=6)
pressure_enso_phase = gr.Dropdown(label="ENSO Phase", choices=['all', 'El Nino', 'La Nina', 'Neutral'], value='all')
pressure_typhoon_search = gr.Textbox(label="Typhoon Search")
pressure_analyze_btn = gr.Button("Generate Pressure Analysis")
pressure_scatter = gr.Plot()
pressure_regression_results = gr.Textbox(label="Pressure Regression Results")
pressure_analyze_btn.click(
fn=get_pressure_analysis,
inputs=[pressure_start_year, pressure_start_month, pressure_end_year, pressure_end_month, pressure_enso_phase, pressure_typhoon_search],
outputs=[pressure_scatter, pressure_regression_results]
)
with gr.Tab("🌏 Longitude Analysis"):
with gr.Row():
lon_start_year = gr.Number(label="Start Year", value=2020)
lon_start_month = gr.Dropdown(label="Start Month", choices=list(range(1, 13)), value=1)
lon_end_year = gr.Number(label="End Year", value=2020)
lon_end_month = gr.Dropdown(label="End Month", choices=list(range(1, 13)), value=6)
lon_enso_phase = gr.Dropdown(label="ENSO Phase", choices=['all', 'El Nino', 'La Nina', 'Neutral'], value='all')
lon_typhoon_search = gr.Textbox(label="Typhoon Search (Optional)")
lon_analyze_btn = gr.Button("Generate Longitude Analysis")
regression_plot = gr.Plot()
slopes_text = gr.Textbox(label="Regression Slopes")
lon_regression_results = gr.Textbox(label="Longitude Regression Results")
lon_analyze_btn.click(
fn=get_longitude_analysis,
inputs=[lon_start_year, lon_start_month, lon_end_year, lon_end_month, lon_enso_phase, lon_typhoon_search],
outputs=[regression_plot, slopes_text, lon_regression_results]
)
with gr.Tab("🎬 Enhanced Track Animation"):
gr.Markdown("## 🎥 High-Quality Storm Track Visualization (Atlantic & Taiwan Standards)")
with gr.Row():
year_dropdown = gr.Dropdown(
label="Year",
choices=available_years,
value=available_years[-1] if available_years else "2024"
)
basin_dropdown = gr.Dropdown(
label="Basin",
choices=["All Basins", "WP - Western Pacific", "EP - Eastern Pacific", "NA - North Atlantic"],
value="All Basins"
)
with gr.Row():
typhoon_dropdown = gr.Dropdown(label="Storm Selection (All Categories Including TD)")
standard_dropdown = gr.Dropdown(
label="🎌 Classification Standard",
choices=['atlantic', 'taiwan'],
value='atlantic',
info="Atlantic: International standard | Taiwan: Local meteorological standard"
)
generate_video_btn = gr.Button("🎬 Generate Enhanced Animation", variant="primary")
video_output = gr.Video(label="Storm Track Animation")
# Update storm options when year or basin changes
for input_comp in [year_dropdown, basin_dropdown]:
input_comp.change(
fn=update_typhoon_options_enhanced,
inputs=[year_dropdown, basin_dropdown],
outputs=[typhoon_dropdown]
)
# Generate video
generate_video_btn.click(
fn=generate_enhanced_track_video,
inputs=[year_dropdown, typhoon_dropdown, standard_dropdown],
outputs=[video_output]
)
with gr.Tab("📊 Data Statistics & Insights"):
gr.Markdown("## 📈 Comprehensive Dataset Analysis")
# Create enhanced data summary
try:
if len(typhoon_data) > 0:
# Storm category distribution
storm_cats = typhoon_data.groupby('SID')['USA_WIND'].max().apply(categorize_typhoon_enhanced)
cat_counts = storm_cats.value_counts()
# Create distribution chart with enhanced colors
fig_dist = px.bar(
x=cat_counts.index,
y=cat_counts.values,
title="Storm Intensity Distribution (Including Tropical Depressions)",
labels={'x': 'Category', 'y': 'Number of Storms'},
color=cat_counts.index,
color_discrete_map=enhanced_color_map
)
# Seasonal distribution
if 'ISO_TIME' in typhoon_data.columns:
seasonal_data = typhoon_data.copy()
seasonal_data['Month'] = seasonal_data['ISO_TIME'].dt.month
monthly_counts = seasonal_data.groupby(['Month', 'SID']).size().groupby('Month').size()
fig_seasonal = px.bar(
x=monthly_counts.index,
y=monthly_counts.values,
title="Seasonal Storm Distribution",
labels={'x': 'Month', 'y': 'Number of Storms'},
color=monthly_counts.values,
color_continuous_scale='Viridis'
)
else:
fig_seasonal = None
# Basin distribution
if 'SID' in typhoon_data.columns:
basin_data = typhoon_data['SID'].str[:2].value_counts()
fig_basin = px.pie(
values=basin_data.values,
names=basin_data.index,
title="Distribution by Basin"
)
else:
fig_basin = None
with gr.Row():
gr.Plot(value=fig_dist)
if fig_seasonal:
with gr.Row():
gr.Plot(value=fig_seasonal)
if fig_basin:
with gr.Row():
gr.Plot(value=fig_basin)
except Exception as e:
gr.Markdown(f"Visualization error: {str(e)}")
# Enhanced statistics - FIXED formatting
total_storms = len(typhoon_data['SID'].unique()) if 'SID' in typhoon_data.columns else 0
total_records = len(typhoon_data)
if 'SEASON' in typhoon_data.columns:
try:
min_year = int(typhoon_data['SEASON'].min())
max_year = int(typhoon_data['SEASON'].max())
year_range = f"{min_year}-{max_year}"
years_covered = typhoon_data['SEASON'].nunique()
except (ValueError, TypeError):
year_range = "Unknown"
years_covered = 0
else:
year_range = "Unknown"
years_covered = 0
if 'SID' in typhoon_data.columns:
try:
basins_available = ', '.join(sorted(typhoon_data['SID'].str[:2].unique()))
avg_storms_per_year = total_storms / max(years_covered, 1)
except Exception:
basins_available = "Unknown"
avg_storms_per_year = 0
else:
basins_available = "Unknown"
avg_storms_per_year = 0
# TD specific statistics
try:
if 'USA_WIND' in typhoon_data.columns:
td_storms = len(typhoon_data[typhoon_data['USA_WIND'] < 34]['SID'].unique())
ts_storms = len(typhoon_data[(typhoon_data['USA_WIND'] >= 34) & (typhoon_data['USA_WIND'] < 64)]['SID'].unique())
typhoon_storms = len(typhoon_data[typhoon_data['USA_WIND'] >= 64]['SID'].unique())
td_percentage = (td_storms / max(total_storms, 1)) * 100
else:
td_storms = ts_storms = typhoon_storms = 0
td_percentage = 0
except Exception as e:
print(f"Error calculating TD statistics: {e}")
td_storms = ts_storms = typhoon_storms = 0
td_percentage = 0
# Create statistics text safely
stats_text = f"""
### 📊 Enhanced Dataset Summary:
- **Total Unique Storms**: {total_storms:,}
- **Total Track Records**: {total_records:,}
- **Year Range**: {year_range} ({years_covered} years)
- **Basins Available**: {basins_available}
- **Average Storms/Year**: {avg_storms_per_year:.1f}
### 🌪️ Storm Category Breakdown:
- **Tropical Depressions**: {td_storms:,} storms ({td_percentage:.1f}%)
- **Tropical Storms**: {ts_storms:,} storms
- **Typhoons (C1-C5)**: {typhoon_storms:,} storms
### 🚀 Platform Capabilities:
- **Complete TD Analysis** - First platform to include comprehensive TD tracking
- **Dual Classification Systems** - Both Atlantic and Taiwan standards supported
- **Advanced ML Clustering** - DBSCAN pattern recognition with separate visualizations
- **Real-time Predictions** - Physics-based and optional CNN intensity forecasting
- **2025 Data Ready** - Full compatibility with current season data
- **Enhanced Animations** - Professional-quality storm track videos
- **Multi-basin Analysis** - Comprehensive Pacific and Atlantic coverage
### 🔬 Research Applications:
- Climate change impact studies
- Seasonal forecasting research
- Storm pattern classification
- ENSO-typhoon relationship analysis
- Intensity prediction model development
- Cross-regional classification comparisons
"""
gr.Markdown(stats_text)
return demo
except Exception as e:
logging.error(f"Error creating Gradio interface: {e}")
import traceback
traceback.print_exc()
# Create a minimal fallback interface
return create_minimal_fallback_interface()
def create_minimal_fallback_interface():
"""Create a minimal fallback interface when main interface fails"""
with gr.Blocks() as demo:
gr.Markdown("# Enhanced Typhoon Analysis Platform")
gr.Markdown("**Notice**: Loading with minimal interface due to data issues.")
with gr.Tab("Status"):
gr.Markdown("""
## Platform Status
The application is running but encountered issues loading the full interface.
This could be due to:
- Data loading problems
- Missing dependencies
- Configuration issues
### Available Features:
- Basic interface is functional
- Error logs are being generated
- System is ready for debugging
### Next Steps:
1. Check the console logs for detailed error information
2. Verify all required data files are accessible
3. Ensure all dependencies are properly installed
4. Try restarting the application
""")
with gr.Tab("Debug"):
gr.Markdown("## Debug Information")
def get_debug_info():
debug_text = f"""
Python Environment:
- Working Directory: {os.getcwd()}
- Data Path: {DATA_PATH}
- UMAP Available: {UMAP_AVAILABLE}
- CNN Available: {CNN_AVAILABLE}
Data Status:
- ONI Data: {'Loaded' if oni_data is not None else 'Failed'}
- Typhoon Data: {'Loaded' if typhoon_data is not None else 'Failed'}
- Merged Data: {'Loaded' if merged_data is not None else 'Failed'}
File Checks:
- ONI Path Exists: {os.path.exists(ONI_DATA_PATH)}
- Typhoon Path Exists: {os.path.exists(TYPHOON_DATA_PATH)}
"""
return debug_text
debug_btn = gr.Button("Get Debug Info")
debug_output = gr.Textbox(label="Debug Information", lines=15)
debug_btn.click(fn=get_debug_info, outputs=debug_output)
return demo
# Create and launch the interface
demo = create_interface()
if __name__ == "__main__":
demo.launch(share=True) # Enable sharing with public link