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Update app.py
Browse files
app.py
CHANGED
@@ -118,7 +118,7 @@ def load_ibtracs_data():
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def convert_typhoondata(input_file, output_file):
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with open(input_file, 'r') as infile:
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next(infile); next(infile)
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reader = csv.reader(infile)
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sid_data = defaultdict(list)
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for row in reader:
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@@ -174,7 +174,7 @@ def merge_data(oni_long, typhoon_max):
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return pd.merge(typhoon_max, oni_long, on=['Year', 'Month'])
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def categorize_typhoon(wind_speed):
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wind_speed_kt = wind_speed
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if wind_speed_kt >= 137:
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return 'C5 Super Typhoon'
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elif wind_speed_kt >= 113:
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@@ -293,7 +293,7 @@ def generate_main_analysis(start_year, start_month, end_year, end_month, enso_ph
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return tracks_fig, wind_scatter, pressure_scatter, regression_fig, slopes_text
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# Path animation function
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def categorize_typhoon_by_standard(wind_speed, standard):
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if standard == 'taiwan':
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wind_speed_ms = wind_speed * 0.514444
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@@ -319,143 +319,76 @@ def categorize_typhoon_by_standard(wind_speed, standard):
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return 'Tropical Storm', atlantic_standard['Tropical Storm']['color']
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return 'Tropical Depression', atlantic_standard['Tropical Depression']['color']
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def
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if not typhoon:
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return
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typhoon_id = typhoon.split('(')[-1].strip(')')
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storm = ibtracs.get_storm(typhoon_id)
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# Create animation frames with growing track
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frames = []
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for i in range(len(storm.time)):
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category, color = categorize_typhoon_by_standard(storm.vmax[i], standard)
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frame_data = [
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go.Scattergeo(
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lon=storm.lon[:i+1], # Only include points up to current time
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lat=storm.lat[:i+1],
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mode='lines+markers',
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line=dict(width=2, color='blue'),
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marker=dict(size=8, color=color),
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name=f"{storm.name} at {storm.time[i].strftime('%Y-%m-%d %H:%M')}",
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text=[f"Time: {storm.time[i].strftime('%Y-%m-%d %H:%M')}<br>Wind: {storm.vmax[i]:.1f} kt<br>Category: {category}"],
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hoverinfo="text"
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)
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]
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frames.append(go.Frame(data=frame_data, name=str(i)))
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# Initial figure with just the starting point
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category, color = categorize_typhoon_by_standard(storm.vmax[0], standard)
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fig = go.Figure(
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data=[
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go.Scattergeo(
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lon=[storm.lon[0]],
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lat=[storm.lat[0]],
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mode='markers',
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marker=dict(size=12, color=color),
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name="Start",
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text=[f"Start: {storm.time[0].strftime('%Y-%m-%d %H:%M')}<br>Wind: {storm.vmax[0]:.1f} kt<br>Category: {category}"],
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hoverinfo="text"
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)
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],
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frames=frames
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)
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# Add category legend
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standard_dict = atlantic_standard if standard == 'atlantic' else taiwan_standard
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for cat, details in standard_dict.items():
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fig.add_trace(go.Scattergeo(
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lon=[None], lat=[None], mode='markers',
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marker=dict(size=10, color=details['color']),
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name=cat,
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showlegend=True
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))
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# Map focus
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min_lat, max_lat = min(storm.lat), max(storm.lat)
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min_lon, max_lon = min(storm.lon), max(storm.lon)
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lat_padding = max((max_lat - min_lat) * 0.3, 5)
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lon_padding = max((max_lon - min_lon) * 0.3, 5)
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#
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{
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"args": [None, {"frame": {"duration": 200, "redraw": True},
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"fromcurrent": True,
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"transition": {"duration": 0, "easing": "linear"},
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"mode": "immediate"}],
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"label": "Play",
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"method": "animate"
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},
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{
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"args": [[None], {"frame": {"duration": 0, "redraw": True},
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"mode": "immediate",
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"transition": {"duration": 0}}],
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"label": "Pause",
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"method": "animate"
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}
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],
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"direction": "left",
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"pad": {"r": 10, "t": 10},
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"showactive": True,
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"type": "buttons",
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"x": 0.1,
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"xanchor": "left",
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"y": 0,
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"yanchor": "bottom"
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}],
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sliders=[{
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"active": 0,
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"yanchor": "top",
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"xanchor": "left",
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"currentvalue": {
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"font": {"size": 12},
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"prefix": "Time: ",
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"visible": True,
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"xanchor": "right"
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},
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"transition": {"duration": 0, "easing": "linear"},
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"pad": {"b": 10, "t": 50},
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"len": 0.9,
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"x": 0.1,
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"y": 0,
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"steps": [
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{
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"args": [[str(i)], {"frame": {"duration": 200, "redraw": True},
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"mode": "immediate",
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"transition": {"duration": 0}}],
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"label": storm.time[i].strftime('%m/%d %H:%M'),
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"method": "animate"
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} for i in range(len(storm.time))
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]
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}],
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height=700,
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showlegend=True,
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legend=dict(
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yanchor="top",
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y=0.99,
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xanchor="left",
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x=0.01,
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bgcolor="rgba(255, 255, 255, 0.8)"
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),
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)
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# Logistic regression functions
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def perform_wind_regression(start_year, start_month, end_year, end_month):
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@@ -504,7 +437,7 @@ with gr.Blocks(title="Typhoon Analysis Dashboard") as demo:
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### Features:
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- **Track Visualization**: View typhoon tracks by time period and ENSO phase
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- **Statistical Analysis**: Examine relationships between ONI values and typhoon characteristics
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- **Path Animation**:
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- **Regression Analysis**: Perform statistical regression on typhoon data
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Select a tab above to begin your analysis.
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@@ -664,16 +597,15 @@ with gr.Blocks(title="Typhoon Analysis Dashboard") as demo:
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typhoon_dropdown = gr.Dropdown(label="Typhoon")
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standard_dropdown = gr.Dropdown(label="Classification Standard", choices=['atlantic', 'taiwan'], value='atlantic')
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animate_btn = gr.Button("Generate Animation")
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animation_info = gr.Markdown("""
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### Animation Instructions
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1. Select a year and typhoon from the dropdowns
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2. Choose a classification standard (Atlantic or Taiwan)
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3. Click "Generate Animation"
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4.
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5.
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6. The blue line represents the growing track, with markers colored by intensity
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""")
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def update_typhoon_options(year):
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year_dropdown.change(fn=update_typhoon_options, inputs=year_dropdown, outputs=typhoon_dropdown)
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animate_btn.click(
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fn=
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inputs=[year_dropdown, typhoon_dropdown, standard_dropdown],
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outputs=
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)
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# Custom CSS for better spacing and visibility
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gr.HTML("""
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<style>
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#tracks_plot
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height: 700px !important;
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width: 100%;
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}
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.plot-container {
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min-height: 600px;
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}
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def convert_typhoondata(input_file, output_file):
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with open(input_file, 'r') as infile:
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next(infile); next(infile)
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reader = csv.reader(infile)
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sid_data = defaultdict(list)
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for row in reader:
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return pd.merge(typhoon_max, oni_long, on=['Year', 'Month'])
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def categorize_typhoon(wind_speed):
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wind_speed_kt = wind_speed
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if wind_speed_kt >= 137:
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return 'C5 Super Typhoon'
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elif wind_speed_kt >= 113:
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return tracks_fig, wind_scatter, pressure_scatter, regression_fig, slopes_text
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# Path animation function using Gallery
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def categorize_typhoon_by_standard(wind_speed, standard):
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if standard == 'taiwan':
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wind_speed_ms = wind_speed * 0.514444
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return 'Tropical Storm', atlantic_standard['Tropical Storm']['color']
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return 'Tropical Depression', atlantic_standard['Tropical Depression']['color']
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def generate_track_gallery(year, typhoon, standard):
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if not typhoon:
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return []
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typhoon_id = typhoon.split('(')[-1].strip(')')
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storm = ibtracs.get_storm(typhoon_id)
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# Map focus
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min_lat, max_lat = min(storm.lat), max(storm.lat)
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min_lon, max_lon = min(storm.lon), max(storm.lon)
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lat_padding = max((max_lat - min_lat) * 0.3, 5)
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lon_padding = max((max_lon - min_lon) * 0.3, 5)
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# Generate a sequence of figures
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gallery = []
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for i in range(len(storm.time)):
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fig = go.Figure()
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# Add the growing track up to the current point
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category, color = categorize_typhoon_by_standard(storm.vmax[i], standard)
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fig.add_trace(go.Scattergeo(
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lon=storm.lon[:i+1],
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lat=storm.lat[:i+1],
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mode='lines+markers',
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line=dict(width=2, color='blue'),
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marker=dict(size=8, color=color),
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name=f"{storm.name}",
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text=[f"Time: {storm.time[j].strftime('%Y-%m-%d %H:%M')}<br>Wind: {storm.vmax[j]:.1f} kt<br>Category: {categorize_typhoon_by_standard(storm.vmax[j], standard)[0]}" for j in range(i+1)],
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hoverinfo="text"
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))
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# Add category legend
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standard_dict = atlantic_standard if standard == 'atlantic' else taiwan_standard
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for cat, details in standard_dict.items():
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fig.add_trace(go.Scattergeo(
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lon=[None], lat=[None], mode='markers',
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marker=dict(size=10, color=details['color']),
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name=cat,
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showlegend=True
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))
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# Update layout
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fig.update_layout(
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title=f"{year} {storm.name} at {storm.time[i].strftime('%Y-%m-%d %H:%M')}",
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geo=dict(
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projection_type='natural earth',
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showland=True,
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showcoastlines=True,
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landcolor='rgb(243, 243, 243)',
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countrycolor='rgb(204, 204, 204)',
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coastlinecolor='rgb(204, 204, 204)',
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showocean=True,
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oceancolor='rgb(230, 230, 255)',
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lataxis={'range': [min_lat - lat_padding, max_lat + lat_padding]},
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lonaxis={'range': [min_lon - lon_padding, max_lon + lon_padding]}
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),
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height=700,
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showlegend=True,
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legend=dict(
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yanchor="top",
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y=0.99,
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xanchor="left",
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x=0.01,
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bgcolor="rgba(255, 255, 255, 0.8)"
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)
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)
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gallery.append(fig)
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return gallery
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# Logistic regression functions
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def perform_wind_regression(start_year, start_month, end_year, end_month):
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### Features:
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- **Track Visualization**: View typhoon tracks by time period and ENSO phase
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- **Statistical Analysis**: Examine relationships between ONI values and typhoon characteristics
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- **Path Animation**: View a gallery of typhoon path progression over time
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- **Regression Analysis**: Perform statistical regression on typhoon data
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Select a tab above to begin your analysis.
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typhoon_dropdown = gr.Dropdown(label="Typhoon")
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standard_dropdown = gr.Dropdown(label="Classification Standard", choices=['atlantic', 'taiwan'], value='atlantic')
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animate_btn = gr.Button("Generate Animation Gallery")
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path_gallery = gr.Gallery(label="Typhoon Path Progression", elem_id="path_gallery", columns=1, height="auto")
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animation_info = gr.Markdown("""
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### Animation Instructions
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1. Select a year and typhoon from the dropdowns
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2. Choose a classification standard (Atlantic or Taiwan)
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3. Click "Generate Animation Gallery"
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4. Scroll through the gallery to see the typhoon track growing over time
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5. Each image represents a step in the typhoon's path, with the blue line extending and markers showing intensity
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""")
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def update_typhoon_options(year):
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year_dropdown.change(fn=update_typhoon_options, inputs=year_dropdown, outputs=typhoon_dropdown)
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animate_btn.click(
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fn=generate_track_gallery,
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inputs=[year_dropdown, typhoon_dropdown, standard_dropdown],
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outputs=path_gallery
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)
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# Custom CSS for better spacing and visibility
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gr.HTML("""
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<style>
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#tracks_plot {
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height: 700px !important;
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width: 100%;
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}
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#path_gallery .gallery-item {
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height: 700px !important;
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width: 100% !important;
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}
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.plot-container {
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min-height: 600px;
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}
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