app.py

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
import pickle
import requests
import os
import argparse
from datetime import datetime
import statsmodels.api as sm
import shutil
import tempfile
import csv
from collections import defaultdict
from sklearn.manifold import TSNE
from sklearn.cluster import DBSCAN
from scipy.interpolate import interp1d

# Import tropycal for IBTrACS processing (for typhoon options)
import tropycal.tracks as tracks

# ------------------ Argument Parsing ------------------
parser = argparse.ArgumentParser(description='Typhoon Analysis Dashboard')
parser.add_argument('--data_path', type=str, default=os.getcwd(), help='Path to the data directory')
args = parser.parse_args()
DATA_PATH = args.data_path

# ------------------ 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')

# ------------------ IBTrACS Files (for typhoon options) ------------------
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/'

CACHE_FILE = 'ibtracs_cache.pkl'
CACHE_EXPIRY_DAYS = 0  # Force refresh for testing

# ------------------ Color Maps and Standards ------------------
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'}
}
taiwan_standard = {
    'Strong Typhoon': {'wind_speed': 51.0, 'color': 'Red', 'hex': '#FF0000'},
    'Medium Typhoon': {'wind_speed': 33.7, 'color': 'Orange', 'hex': '#FFA500'},
    'Mild Typhoon': {'wind_speed': 17.2, 'color': 'Yellow', 'hex': '#FFFF00'},
    'Tropical Depression': {'wind_speed': 0, 'color': 'Gray', 'hex': '#808080'}
}

# ------------------ Season and Regions ------------------
season_months = {
    'all': list(range(1, 13)),
    'summer': [6, 7, 8],
    'winter': [12, 1, 2]
}
regions = {
    "Taiwan Land": {"lat_min": 21.8, "lat_max": 25.3, "lon_min": 119.5, "lon_max": 122.1},
    "Taiwan Sea": {"lat_min": 19, "lat_max": 28, "lon_min": 117, "lon_max": 125},
    "Japan": {"lat_min": 20, "lat_max": 45, "lon_min": 120, "lon_max": 150},
    "China": {"lat_min": 18, "lat_max": 53, "lon_min": 73, "lon_max": 135},
    "Hong Kong": {"lat_min": 21.5, "lat_max": 23, "lon_min": 113, "lon_max": 115},
    "Philippines": {"lat_min": 5, "lat_max": 21, "lon_min": 115, "lon_max": 130}
}

# ------------------ ONI and Typhoon Data Functions ------------------
def download_oni_file(url, filename):
    response = requests.get(url)
    response.raise_for_status()
    with open(filename, 'wb') as f:
        f.write(response.content)
    return True

def convert_oni_ascii_to_csv(input_file, output_file):
    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}
    with open(input_file, 'r') as f:
        lines = f.readlines()[1:]
        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
    with open(output_file, 'w', newline='') as f:
        writer = csv.writer(f)
        writer.writerow(['Year', 'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])
        for year in sorted(data.keys()):
            writer.writerow([year] + data[year])

def update_oni_data():
    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
    if download_oni_file(url, temp_file):
        if not os.path.exists(input_file) or not os.path.exists(output_file):
            os.replace(temp_file, input_file)
            convert_oni_ascii_to_csv(input_file, output_file)
        else:
            os.remove(temp_file)

def load_data(oni_path, typhoon_path):
    oni_data = pd.read_csv(oni_path)
    typhoon_data = pd.read_csv(typhoon_path, low_memory=False)
    typhoon_data['ISO_TIME'] = pd.to_datetime(typhoon_data['ISO_TIME'], errors='coerce')
    typhoon_data = typhoon_data.dropna(subset=['ISO_TIME'])
    return oni_data, typhoon_data

def process_oni_data(oni_data):
    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):
    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')
    print(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()
    typhoon_max['Month'] = typhoon_max['ISO_TIME'].dt.strftime('%m')
    typhoon_max['Year'] = typhoon_max['ISO_TIME'].dt.year
    typhoon_max['Category'] = typhoon_max['USA_WIND'].apply(categorize_typhoon)
    return typhoon_max

def merge_data(oni_long, typhoon_max):
    return pd.merge(typhoon_max, oni_long, on=['Year', 'Month'])

def categorize_typhoon(wind_speed):
    if wind_speed >= 137:
        return 'C5 Super Typhoon'
    elif wind_speed >= 113:
        return 'C4 Very Strong Typhoon'
    elif wind_speed >= 96:
        return 'C3 Strong Typhoon'
    elif wind_speed >= 83:
        return 'C2 Typhoon'
    elif wind_speed >= 64:
        return 'C1 Typhoon'
    elif wind_speed >= 34:
        return 'Tropical Storm'
    else:
        return 'Tropical Depression'

def classify_enso_phases(oni_value):
    if isinstance(oni_value, pd.Series):
        oni_value = oni_value.iloc[0]
    if oni_value >= 0.5:
        return 'El Nino'
    elif oni_value <= -0.5:
        return 'La Nina'
    else:
        return 'Neutral'

# ------------------ Regression Functions ------------------
def perform_wind_regression(start_year, start_month, end_year, end_month):
    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']
    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}"

def perform_pressure_regression(start_year, start_month, end_year, end_month):
    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']
    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}"

def perform_longitude_regression(start_year, start_month, end_year, end_month):
    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']
    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"Longitude Regression: β1={beta_1:.4f}, Odds Ratio={exp_beta_1:.4f}, P-value={p_value:.4f}"

# ------------------ IBTrACS Data Loading (for typhoon options) ------------------
def load_ibtracs_data():
    ibtracs_data = {}
    for basin, filename in BASIN_FILES.items():
        local_path = os.path.join(DATA_PATH, filename)
        if not os.path.exists(local_path):
            print(f"Downloading {basin} basin file...")
            response = requests.get(IBTRACS_BASE_URL + filename)
            response.raise_for_status()
            with open(local_path, 'wb') as f:
                f.write(response.content)
            print(f"Downloaded {basin} basin file.")
        try:
            print(f"--> Starting to read in IBTrACS data for basin {basin}")
            ds = tracks.TrackDataset(source='ibtracs', ibtracs_url=local_path)
            print(f"--> Completed reading in IBTrACS data for basin {basin}")
            ibtracs_data[basin] = ds
        except ValueError as e:
            print(f"Warning: Skipping basin {basin} due to error: {e}")
            ibtracs_data[basin] = None
    return ibtracs_data

ibtracs = load_ibtracs_data()

# ------------------ Load and Process Data ------------------
update_oni_data()
oni_data, typhoon_data = load_data(ONI_DATA_PATH, TYPHOON_DATA_PATH)
oni_long = process_oni_data(oni_data)
typhoon_max = process_typhoon_data(typhoon_data)
merged_data = merge_data(oni_long, typhoon_max)

# ------------------ Visualization Functions ------------------
def generate_typhoon_tracks(filtered_data, typhoon_search):
    fig = go.Figure()
    for sid in filtered_data['SID'].unique():
        storm_data = filtered_data[filtered_data['SID'] == sid]
        phase = storm_data['ENSO_Phase'].iloc[0]
        color = {'El Nino': 'red', 'La Nina': 'blue', 'Neutral': 'green'}.get(phase, 'black')
        fig.add_trace(go.Scattergeo(
            lon=storm_data['LON'], lat=storm_data['LAT'], mode='lines',
            name=storm_data['NAME'].iloc[0], line=dict(width=2, color=color)
        ))
    if typhoon_search:
        mask = filtered_data['NAME'].str.contains(typhoon_search, case=False, na=False)
        if mask.any():
            storm_data = filtered_data[mask]
            fig.add_trace(go.Scattergeo(
                lon=storm_data['LON'], lat=storm_data['LAT'], mode='lines',
                name=f'Matched: {typhoon_search}', line=dict(width=5, color='yellow')
            ))
    fig.update_layout(
        title='Typhoon Tracks',
        geo=dict(projection_type='natural earth', showland=True),
        height=700
    )
    return fig

def generate_wind_oni_scatter(filtered_data, typhoon_search):
    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=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) + ')'
            ))
    return fig

def generate_pressure_oni_scatter(filtered_data, typhoon_search):
    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=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) + ')'
            ))
    return fig

def generate_regression_analysis(filtered_data):
    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']
        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}"
    else:
        slopes_text = "Insufficient data for regression"
    return fig, slopes_text

def generate_main_analysis(start_year, start_month, end_year, end_month, enso_phase, typhoon_search):
    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()]
    tracks_fig = generate_typhoon_tracks(filtered_data, typhoon_search)
    wind_scatter = generate_wind_oni_scatter(filtered_data, typhoon_search)
    pressure_scatter = generate_pressure_oni_scatter(filtered_data, typhoon_search)
    regression_fig, slopes_text = generate_regression_analysis(filtered_data)
    return tracks_fig, wind_scatter, pressure_scatter, regression_fig, slopes_text

def get_full_tracks(start_year, start_month, end_year, end_month, enso_phase, typhoon_search):
    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]
        name = storm_data['NAME'].iloc[0] if pd.notnull(storm_data['NAME'].iloc[0]) else "Unnamed"
        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} ({storm_data['SEASON'].iloc[0]})",
            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]} ({storm_data['SEASON'].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 Niña, 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):
    results = generate_main_analysis(start_year, start_month, end_year, end_month, enso_phase, typhoon_search)
    regression = perform_wind_regression(start_year, start_month, end_year, end_month)
    return results[1], regression

def get_pressure_analysis(start_year, start_month, end_year, end_month, enso_phase, typhoon_search):
    results = generate_main_analysis(start_year, start_month, end_year, end_month, enso_phase, typhoon_search)
    regression = perform_pressure_regression(start_year, start_month, end_year, end_month)
    return results[2], regression

def get_longitude_analysis(start_year, start_month, end_year, end_month, enso_phase, typhoon_search):
    results = generate_main_analysis(start_year, start_month, end_year, end_month, enso_phase, typhoon_search)
    regression = perform_longitude_regression(start_year, start_month, end_year, end_month)
    return results[3], results[4], regression

def categorize_typhoon_by_standard(wind_speed, standard):
    if standard == 'taiwan':
        wind_speed_ms = wind_speed * 0.514444
        if wind_speed_ms >= 51.0:
            return 'Strong Typhoon', taiwan_standard['Strong Typhoon']['hex']
        elif wind_speed_ms >= 33.7:
            return 'Medium Typhoon', taiwan_standard['Medium Typhoon']['hex']
        elif wind_speed_ms >= 17.2:
            return 'Mild Typhoon', taiwan_standard['Mild Typhoon']['hex']
        return 'Tropical Depression', taiwan_standard['Tropical Depression']['hex']
    else:
        if wind_speed >= 137:
            return 'C5 Super Typhoon', atlantic_standard['C5 Super Typhoon']['hex']
        elif wind_speed >= 113:
            return 'C4 Very Strong Typhoon', atlantic_standard['C4 Very Strong Typhoon']['hex']
        elif wind_speed >= 96:
            return 'C3 Strong Typhoon', atlantic_standard['C3 Strong Typhoon']['hex']
        elif wind_speed >= 83:
            return 'C2 Typhoon', atlantic_standard['C2 Typhoon']['hex']
        elif wind_speed >= 64:
            return 'C1 Typhoon', atlantic_standard['C1 Typhoon']['hex']
        elif wind_speed >= 34:
            return 'Tropical Storm', atlantic_standard['Tropical Storm']['hex']
        return 'Tropical Depression', atlantic_standard['Tropical Depression']['hex']

# ------------------ Animation Functions Using Processed CSV ------------------
def generate_track_video_from_csv(year, storm_id, standard):
    storm_df = typhoon_data[typhoon_data['SID'] == storm_id].copy()
    if storm_df.empty:
        print("No data found for storm:", storm_id)
        return None
    storm_df = storm_df.sort_values('ISO_TIME')
    lats = storm_df['LAT'].astype(float).values
    lons = storm_df['LON'].astype(float).values
    times = pd.to_datetime(storm_df['ISO_TIME']).values
    if 'USA_WIND' in storm_df.columns:
        winds = pd.to_numeric(storm_df['USA_WIND'], errors='coerce').values
    else:
        winds = np.full(len(lats), np.nan)
    storm_name = storm_df['NAME'].iloc[0]
    season = storm_df['SEASON'].iloc[0]
    
    min_lat, max_lat = np.min(lats), np.max(lats)
    min_lon, max_lon = np.min(lons), np.max(lons)
    lat_padding = max((max_lat - min_lat) * 0.3, 5)
    lon_padding = max((max_lon - min_lon) * 0.3, 5)
    
    fig = plt.figure(figsize=(12, 9), dpi=100)
    ax = plt.axes([0.05, 0.05, 0.60, 0.90],
                  projection=ccrs.PlateCarree(central_longitude=-25))
    ax.set_extent([min_lon - lon_padding, max_lon + lon_padding, min_lat - lat_padding, max_lat + lat_padding],
                  crs=ccrs.PlateCarree())
    
    ax.add_feature(cfeature.LAND, facecolor='lightgray')
    ax.add_feature(cfeature.OCEAN, facecolor='lightblue')
    ax.add_feature(cfeature.COASTLINE, edgecolor='black')
    ax.add_feature(cfeature.BORDERS, linestyle=':', edgecolor='gray')
    ax.gridlines(draw_labels=True, linestyle='--', color='gray', alpha=0.5)
    ax.set_title(f"{year} {storm_name} - {season}", fontsize=16)
    
    line, = ax.plot([], [], 'b-', linewidth=2, transform=ccrs.PlateCarree())
    point, = ax.plot([], [], 'o', markersize=10, transform=ccrs.PlateCarree())
    date_text = ax.text(0.02, 0.02, '', transform=ax.transAxes, fontsize=12,
                         bbox=dict(facecolor='white', alpha=0.8))
    # Dynamic state display at right side
    state_text = fig.text(0.70, 0.60, '', fontsize=14, verticalalignment='top',
                          bbox=dict(facecolor='white', alpha=0.8, boxstyle='round,pad=0.5'))
    
    # Persistent legend for category colors
    legend_elements = [plt.Line2D([0], [0], marker='o', color='w', label=f"{cat}",
                          markerfacecolor=details['hex'], markersize=10)
                       for cat, details in (atlantic_standard if standard=='atlantic' else taiwan_standard).items()]
    ax.legend(handles=legend_elements, title="Storm Categories", loc='upper right', fontsize=10)
    
    def init():
        line.set_data([], [])
        point.set_data([], [])
        date_text.set_text('')
        state_text.set_text('')
        return line, point, date_text, state_text

    def update(frame):
        line.set_data(lons[:frame+1], lats[:frame+1])
        point.set_data([lons[frame]], [lats[frame]])
        wind_speed = winds[frame] if frame < len(winds) else np.nan
        category, color = categorize_typhoon_by_standard(wind_speed, standard)
        point.set_color(color)
        dt_str = pd.to_datetime(times[frame]).strftime('%Y-%m-%d %H:%M')
        date_text.set_text(dt_str)
        state_info = (f"Name: {storm_name}\n"
                      f"Date: {dt_str}\n"
                      f"Wind: {wind_speed:.1f} kt\n"
                      f"Category: {category}")
        state_text.set_text(state_info)
        return line, point, date_text, state_text

    ani = animation.FuncAnimation(fig, update, init_func=init, frames=len(times),
                                  interval=200, blit=True, repeat=True)
    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.mp4')
    writer = animation.FFMpegWriter(fps=5, bitrate=1800)
    ani.save(temp_file.name, writer=writer)
    plt.close(fig)
    return temp_file.name

def simplified_track_video(year, basin, typhoon, standard):
    if not typhoon:
        return None
    storm_id = typhoon.split('(')[-1].strip(')')
    return generate_track_video_from_csv(year, storm_id, standard)

# ------------------ Typhoon Options Update Functions ------------------
basin_to_prefix = {
    "All Basins": "all",
    "NA - North Atlantic": "NA",
    "EP - Eastern North Pacific": "EP",
    "WP - Western North Pacific": "WP"
}

def update_typhoon_options(year, basin):
    try:
        if basin == "All Basins":
            summaries = []
            for data in ibtracs.values():
                if data is not None:
                    season_data = data.get_season(int(year))
                    if season_data.summary().empty:
                        continue
                    summaries.append(season_data.summary())
            if len(summaries) == 0:
                print("Error updating typhoon options: No storms identified for the given year and basin.")
                return gr.update(choices=[], value=None)
            combined_summary = pd.concat(summaries, ignore_index=True)
        else:
            prefix = basin_to_prefix.get(basin)
            ds = ibtracs.get(prefix)
            if ds is None:
                print("Error updating typhoon options: Dataset not found for the given basin.")
                return gr.update(choices=[], value=None)
            season_data = ds.get_season(int(year))
            if season_data.summary().empty:
                print("Error updating typhoon options: No storms identified for the given year and basin.")
                return gr.update(choices=[], value=None)
            combined_summary = season_data.summary()
        options = []
        for i in range(len(combined_summary)):
            try:
                name = combined_summary['name'][i] if pd.notnull(combined_summary['name'][i]) else "Unnamed"
                storm_id = combined_summary['id'][i]
                options.append(f"{name} ({storm_id})")
            except Exception:
                continue
        return gr.update(choices=options, value=options[0] if options else None)
    except Exception as e:
        print(f"Error updating typhoon options: {e}")
        return gr.update(choices=[], value=None)

def update_typhoon_options_anim(year, basin):
    try:
        # For animation, use the processed CSV data (without filtering by a specific prefix)
        data = typhoon_data.copy()
        data['Year'] = pd.to_datetime(data['ISO_TIME']).dt.year
        season_data = data[data['Year'] == int(year)]
        if season_data.empty:
            print("Error updating typhoon options (animation): No storms identified for the given year.")
            return gr.update(choices=[], value=None)
        summary = season_data.groupby('SID').first().reset_index()
        options = []
        for idx, row in summary.iterrows():
            name = row['NAME'] if pd.notnull(row['NAME']) else "Unnamed"
            options.append(f"{name} ({row['SID']})")
        return gr.update(choices=options, value=options[0] if options else None)
    except Exception as e:
        print(f"Error updating typhoon options (animation): {e}")
        return gr.update(choices=[], value=None)

# ------------------ TSNE Cluster Function ------------------
def update_route_clusters(start_year, start_month, end_year, end_month, enso_value, season):
    # Use only WP storms from processed CSV for clustering.
    wp_data = typhoon_data[typhoon_data['SID'].str.startswith("WP")]
    if wp_data.empty:
        return go.Figure(), go.Figure(), make_subplots(rows=2, cols=1), "No West Pacific storms found."
    wp_data['Year'] = pd.to_datetime(wp_data['ISO_TIME']).dt.year
    wp_season = wp_data[wp_data['Year'] == int(start_year)]
    if wp_season.empty:
        return go.Figure(), go.Figure(), make_subplots(rows=2, cols=1), "No storms found for the given period in WP."
    
    all_storms_data = []
    for sid, group in wp_data.groupby('SID'):
        group = group.sort_values('ISO_TIME')
        times = pd.to_datetime(group['ISO_TIME']).values
        lats = group['LAT'].astype(float).values
        lons = group['LON'].astype(float).values
        if len(lons) < 2:
            continue
        if season != 'all':
            month = pd.to_datetime(group['ISO_TIME']).iloc[0].month
            if season == 'summer' and not (4 <= month <= 8):
                continue
            if season == 'winter' and not (9 <= month <= 12):
                continue
        all_storms_data.append((lons, lats, np.array(group['USA_WIND'].astype(float)), times, sid))
    if not all_storms_data:
        return go.Figure(), go.Figure(), make_subplots(rows=2, cols=1), "No valid WP storms for clustering."
    
    max_length = max(len(item[0]) for item in all_storms_data)
    route_vectors = []
    filtered_storms = []
    for lons, lats, winds, times, sid in all_storms_data:
        t = np.linspace(0, 1, len(lons))
        t_new = np.linspace(0, 1, max_length)
        try:
            lon_i = interp1d(t, lons, kind='linear', fill_value='extrapolate')(t_new)
            lat_i = interp1d(t, lats, kind='linear', fill_value='extrapolate')(t_new)
        except Exception:
            continue
        route_vector = np.column_stack((lon_i, lat_i)).flatten()
        if np.isnan(route_vector).any():
            continue
        route_vectors.append(route_vector)
        filtered_storms.append((lon_i, lat_i, winds, times, sid))
    route_vectors = np.array(route_vectors)
    if len(route_vectors) == 0:
        return go.Figure(), go.Figure(), make_subplots(rows=2, cols=1), "No valid storms after interpolation."
    
    tsne = TSNE(n_components=2, random_state=42, verbose=1)
    tsne_results = tsne.fit_transform(route_vectors)
    dbscan = DBSCAN(eps=5, min_samples=3)
    best_labels = dbscan.fit_predict(tsne_results)
    unique_labels = sorted(set(best_labels) - {-1})
    fig_tsne = go.Figure()
    colors = px.colors.qualitative.Safe
    for i, label in enumerate(unique_labels):
        indices = np.where(best_labels == label)[0]
        fig_tsne.add_trace(go.Scatter(
            x=tsne_results[indices, 0],
            y=tsne_results[indices, 1],
            mode='markers',
            marker=dict(color=colors[i % len(colors)]),
            name=f"Cluster {label}"
        ))
    fig_tsne.update_layout(title="t-SNE of WP Storm Routes")
    fig_routes = go.Figure()  # Placeholder
    fig_stats = make_subplots(rows=2, cols=1)  # Placeholder
    info = "TSNE clustering complete."
    return fig_tsne, fig_routes, fig_stats, info

# ------------------ Gradio Interface ------------------
with gr.Blocks(title="Typhoon Analysis Dashboard") as demo:
    gr.Markdown("# Typhoon Analysis Dashboard")
    
    with gr.Tab("Overview"):
        gr.Markdown("""
        ## Welcome to the Typhoon Analysis Dashboard
        
        This dashboard allows you to analyze typhoon data in relation to ENSO phases.
        
        ### Features:
        - **Track Visualization**: View typhoon tracks by time period and ENSO phase (all basins available)
        - **Wind Analysis**: Examine wind speed vs ONI relationships
        - **Pressure Analysis**: Analyze pressure vs ONI relationships
        - **Longitude Analysis**: Study typhoon generation longitude vs ONI
        - **Path Animation**: Watch animated tropical cyclone paths with a dynamic state display and persistent legend (using processed CSV data)
        - **TSNE Cluster**: Perform t-SNE clustering on WP storm routes with mean routes and region analysis
        
        Select a tab above to begin your analysis.
        """)
    
    with gr.Tab("Track Visualization"):
        with gr.Row():
            start_year = gr.Number(label="Start Year", value=2000, minimum=1900, maximum=2024, step=1)
            start_month = gr.Dropdown(label="Start Month", choices=list(range(1, 13)), value=1)
            end_year = gr.Number(label="End Year", value=2024, minimum=1900, maximum=2024, step=1)
            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(label="Typhoon Tracks", elem_id="tracks_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=2000, minimum=1900, maximum=2024, step=1)
            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, minimum=1900, maximum=2024, step=1)
            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(label="Wind Speed vs ONI")
        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=2000, minimum=1900, maximum=2024, step=1)
            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, minimum=1900, maximum=2024, step=1)
            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(label="Pressure vs ONI")
        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=2000, minimum=1900, maximum=2024, step=1)
            lon_start_month = gr.Dropdown(label="Start Month", choices=list(range(1, 13)), value=1)
            lon_end_year = gr.Number(label="End Year", value=2024, minimum=1900, maximum=2024, step=1)
            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(label="Longitude vs ONI")
        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("Tropical Cyclone Path Animation"):
        with gr.Row():
            year_dropdown = gr.Dropdown(label="Year", choices=[str(y) for y in range(1950, 2025)], value="2000")
            basin_dropdown = gr.Dropdown(label="Basin", choices=["NA - North Atlantic", "EP - Eastern North Pacific", "WP - Western North Pacific", "All Basins"], value="NA - North Atlantic")
        with gr.Row():
            typhoon_dropdown = gr.Dropdown(label="Tropical Cyclone")
            standard_dropdown = gr.Dropdown(label="Classification Standard", choices=['atlantic', 'taiwan'], value='atlantic')
        animate_btn = gr.Button("Generate Animation")
        path_video = gr.Video(label="Tropical Cyclone Path Animation", format="mp4", interactive=False, elem_id="path_video")
        animation_info = gr.Markdown("""
        ### Animation Instructions
        1. Select a year and basin from the dropdowns. (This animation uses processed CSV data.)
        2. Choose a tropical cyclone from the populated list.
        3. Select a classification standard (Atlantic or Taiwan).
        4. Click "Generate Animation".
        5. The animation displays the storm track along with a dynamic sidebar that shows the current state (name, date, wind, category) and a persistent legend for colors.
        """)
        year_dropdown.change(fn=update_typhoon_options_anim, inputs=[year_dropdown, basin_dropdown], outputs=typhoon_dropdown)
        basin_dropdown.change(fn=update_typhoon_options_anim, inputs=[year_dropdown, basin_dropdown], outputs=typhoon_dropdown)
        animate_btn.click(fn=simplified_track_video, inputs=[year_dropdown, basin_dropdown, typhoon_dropdown, standard_dropdown], outputs=path_video)
    
    with gr.Tab("TSNE Cluster"):
        with gr.Row():
            tsne_start_year = gr.Number(label="Start Year", value=2000, minimum=1900, maximum=2024, step=1)
            tsne_start_month = gr.Dropdown(label="Start Month", choices=list(range(1, 13)), value=1)
            tsne_end_year = gr.Number(label="End Year", value=2024, minimum=1900, maximum=2024, step=1)
            tsne_end_month = gr.Dropdown(label="End Month", choices=list(range(1, 13)), value=12)
            tsne_enso_phase = gr.Dropdown(label="ENSO Phase", choices=['all', 'El Nino', 'La Nina', 'Neutral'], value='all')
            tsne_season = gr.Dropdown(label="Season", choices=['all', 'summer', 'winter'], value='all')
        tsne_analyze_btn = gr.Button("Analyze")
        tsne_plot = gr.Plot(label="t-SNE Clusters")
        routes_plot = gr.Plot(label="Typhoon Routes with Mean Routes")
        stats_plot = gr.Plot(label="Cluster Statistics")
        cluster_info = gr.Textbox(label="Cluster Information", lines=10)
        tsne_analyze_btn.click(fn=update_route_clusters, inputs=[tsne_start_year, tsne_start_month, tsne_end_year, tsne_end_month, tsne_enso_phase, tsne_season], outputs=[tsne_plot, routes_plot, stats_plot, cluster_info])

demo.launch(share=True)