Fix function structure and remove duplicates

- Moved process_daily_snow to module level so both calculate_total_new_snow and create_plots can use it
- Removed duplicate create_plots function
- Removed orphaned code blocks
- Fixed calculate_total_new_snow to properly return calculated total

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <[email protected]>

app.py

@@ -158,16 +158,21 @@ def calculate_total_new_snow(df):
         lambda x: x.date() if x.hour >= 9 else (x - pd.Timedelta(days=1)).date()
     )
 
-    def process_daily_snow(group):
-        """Sum up ONLY the 3-hour snowfall amounts for each day period"""
-        # Sort by time to ensure proper sequence
-        group = group.sort_values('datetime')
+    # Calculate daily snow totals
+    daily_totals = snow_df.groupby('day_group').apply(process_daily_snow)
+
+    return daily_totals.sum()
+
+def process_daily_snow(group):
+    """Sum up ONLY the 3-hour snowfall amounts for each day period"""
+    # Sort by time to ensure proper sequence
+    group = group.sort_values('datetime')
 
-        # Sum only the valid 3-hour amounts, treating NaN as 0
-        valid_amounts = group['snowfall_3hr'].fillna(0)
-        daily_total = valid_amounts.sum()
+    # Sum only the valid 3-hour amounts, treating NaN as 0
+    valid_amounts = group['snowfall_3hr'].fillna(0)
+    daily_total = valid_amounts.sum()
 
-        return daily_total
+    return daily_total
 
 def create_plots(df):
     """Create all weather plots including SWE estimates"""
@@ -251,121 +256,16 @@ def create_plots(df):
     fig_rose.subplots_adjust(top=0.95, bottom=0.05, left=0.1, right=0.95)
     
     return fig, fig_rose
-    
-    def process_daily_snow(group):
-        """Sum up ONLY the 3-hour snowfall amounts for each day period"""
-        # Sort by time to ensure proper sequence
-        group = group.sort_values('datetime')
-        
-        # Print debugging information
-        print(f"\nSnowfall amounts for {group['day_group'].iloc[0]}:")
-        for _, row in group.iterrows():
-            if pd.notna(row['snowfall_3hr']):
-                print(f"{row['datetime'].strftime('%Y-%m-%d %H:%M')}: {row['snowfall_3hr']} inches")
-        
-        # Sum only the valid 3-hour amounts, treating NaN as 0
-        valid_amounts = group['snowfall_3hr'].fillna(0)
-        daily_total = valid_amounts.sum()
-        
-        print(f"Daily total: {daily_total} inches")
-        return daily_total
-    
-    # Calculate daily snow totals
-    daily_totals = snow_df.groupby('day_group').apply(process_daily_snow)
-    
-    return daily_totals.sum()
 
 def create_wind_rose(df, ax):
     """Create a wind rose plot"""
     if not isinstance(ax, WindroseAxes):
         ax = WindroseAxes.from_ax(ax=ax)
-    ax.bar(df['wind_dir_deg'].dropna(), df['wind_speed'].dropna(), 
+    ax.bar(df['wind_dir_deg'].dropna(), df['wind_speed'].dropna(),
            bins=np.arange(0, 40, 5), normed=True, opening=0.8, edgecolor='white')
     ax.set_legend(title='Wind Speed (mph)')
     ax.set_title('Wind Rose')
 
-def create_plots(df):
-    """Create all weather plots including SWE estimates"""
-    # Create figure with adjusted height and spacing
-    fig = plt.figure(figsize=(20, 24))
-    
-    # Calculate height ratios for different plots
-    height_ratios = [1, 1, 1, 1, 1]  # Equal height for all plots
-    gs = GridSpec(5, 1, figure=fig, height_ratios=height_ratios)
-    gs.update(hspace=0.4)  # Increase vertical spacing between plots
-    
-    # Temperature plot
-    ax1 = fig.add_subplot(gs[0])
-    ax1.plot(df['datetime'], df['temp'], label='Temperature', color='red')
-    ax1.plot(df['datetime'], df['wind_chill'], label='Wind Chill', color='blue')
-    ax1.set_title('Temperature and Wind Chill Over Time', pad=20)
-    ax1.set_xlabel('Date')
-    ax1.set_ylabel('Temperature (°F)')
-    ax1.legend()
-    ax1.grid(True)
-    ax1.tick_params(axis='x', rotation=45)
-    
-    # Wind speed plot
-    ax2 = fig.add_subplot(gs[1])
-    ax2.plot(df['datetime'], df['wind_speed'], label='Wind Speed', color='blue')
-    ax2.plot(df['datetime'], df['wind_gust'], label='Wind Gust', color='orange')
-    ax2.set_title('Wind Speed and Gusts Over Time', pad=20)
-    ax2.set_xlabel('Date')
-    ax2.set_ylabel('Wind Speed (mph)')
-    ax2.legend()
-    ax2.grid(True)
-    ax2.tick_params(axis='x', rotation=45)
-    
-    # Snow depth plot
-    ax3 = fig.add_subplot(gs[2])
-    ax3.plot(df['datetime'], df['snow_depth'], color='blue', label='Snow Depth')
-    ax3.set_title('Snow Depth Over Time', pad=20)
-    ax3.set_xlabel('Date')
-    ax3.set_ylabel('Snow Depth (inches)')
-    ax3.grid(True)
-    ax3.tick_params(axis='x', rotation=45)
-    
-    # Daily new snow bar plot
-    ax4 = fig.add_subplot(gs[3])
-    snow_df = df[['datetime', 'snowfall_3hr']].copy()
-    snow_df['day_group'] = snow_df['datetime'].apply(
-        lambda x: x.date() if x.hour >= 9 else (x - pd.Timedelta(days=1)).date()
-    )
-    daily_snow = snow_df.groupby('day_group').apply(process_daily_snow).reset_index()
-    daily_snow.columns = ['date', 'new_snow']
-    
-    ax4.bar(daily_snow['date'], daily_snow['new_snow'], color='blue')
-    ax4.set_title('Daily New Snow (Sum of 3-hour amounts, 9 AM Reset)', pad=20)
-    ax4.set_xlabel('Date')
-    ax4.set_ylabel('New Snow (inches)')
-    ax4.tick_params(axis='x', rotation=45)
-    ax4.grid(True, axis='y', linestyle='--', alpha=0.7)
-    
-    # Add value labels on top of each bar
-    for i, v in enumerate(daily_snow['new_snow']):
-        if v > 0:  # Only label bars with snow
-            ax4.text(i, v, f'{v:.1f}"', ha='center', va='bottom')
-    
-    # SWE bar plot
-    ax5 = fig.add_subplot(gs[4])
-    daily_swe = df.groupby('date')['swe'].mean()
-    ax5.bar(daily_swe.index, daily_swe.values, color='lightblue')
-    ax5.set_title('Snow/Water Equivalent', pad=20)
-    ax5.set_xlabel('Date')
-    ax5.set_ylabel('SWE (inches)')
-    ax5.tick_params(axis='x', rotation=45)
-    
-    # Adjust layout
-    plt.subplots_adjust(top=0.95, bottom=0.05, left=0.1, right=0.95)
-    
-    # Create separate wind rose figure
-    fig_rose = plt.figure(figsize=(10, 10))
-    ax_rose = WindroseAxes.from_ax(fig=fig_rose)
-    create_wind_rose(df, ax_rose)
-    fig_rose.subplots_adjust(top=0.95, bottom=0.05, left=0.1, right=0.95)
-    
-    return fig, fig_rose
-
 def analyze_weather_data(site_id, hours):
     """Analyze weather data and create visualizations"""
     try: