Update app.py

app.py

@@ -9,6 +9,9 @@ import os
 import subprocess
 import sys
 import matplotlib.pyplot as plt
+from matplotlib.gridspec import GridSpec
+import matplotlib.dates as mdates
+from windrose import WindroseAxes
 
 # Install Playwright browsers on startup
 def install_playwright_browsers():
@@ -25,53 +28,49 @@ def install_playwright_browsers():
     except Exception as e:
         print(f"Error installing browsers: {e}")
 
-# Install browsers when the module loads
 install_playwright_browsers()
 
+def parse_direction(direction):
+    """Convert wind direction string to degrees"""
+    direction_map = {
+        'N': 0, 'NNE': 22.5, 'NE': 45, 'ENE': 67.5,
+        'E': 90, 'ESE': 112.5, 'SE': 135, 'SSE': 157.5,
+        'S': 180, 'SSW': 202.5, 'SW': 225, 'WSW': 247.5,
+        'W': 270, 'WNW': 292.5, 'NW': 315, 'NNW': 337.5
+    }
+    return direction_map.get(direction, np.nan)
+
 def scrape_weather_data(site_id, hours=720):
     """Scrape weather data from weather.gov timeseries"""
     url = f"https://www.weather.gov/wrh/timeseries?site={site_id}&hours={hours}&units=english&chart=on&headers=on&obs=tabular&hourly=false&pview=full&font=12&plot="
     
     with sync_playwright() as p:
-        # Launch browser with minimal settings
         browser = p.chromium.launch(
             headless=True,
             args=['--no-sandbox', '--disable-dev-shm-usage']
         )
-        
-        # Create context with desktop user agent
         context = browser.new_context(
-            user_agent='Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36'
+            user_agent='Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
         )
-        
-        # Create new page and navigate
         page = context.new_page()
         page.goto(url)
-        
-        # Wait for content to load
         time.sleep(5)
         
-        # Get all text content
         content = page.evaluate('''() => {
-            // Function to get all text content
             const getTextContent = () => {
-                const rows = [];
                 const tables = document.getElementsByTagName('table');
                 for (const table of tables) {
                     if (table.textContent.includes('Date/Time')) {
                         const headerRow = Array.from(table.querySelectorAll('th'))
                             .map(th => th.textContent.trim());
-                        
                         const dataRows = Array.from(table.querySelectorAll('tbody tr'))
                             .map(row => Array.from(row.querySelectorAll('td'))
                                 .map(td => td.textContent.trim()));
-                        
                         return {headers: headerRow, rows: dataRows};
                     }
                 }
                 return null;
             };
-            
             return getTextContent();
         }''')
         
@@ -83,22 +82,23 @@ def parse_weather_data(data):
     if not data or 'rows' not in data:
         raise ValueError("No valid weather data found")
         
-    # Convert to DataFrame
     df = pd.DataFrame(data['rows'])
     
-    # Assign column names (first 8 columns we care about)
-    columns = ['datetime', 'temp', 'dew_point', 'humidity', 'wind_chill', 'wind_dir', 'wind_speed', 'snow_depth']
-    df = df.iloc[:, :8]
+    # Get all relevant columns
+    columns = ['datetime', 'temp', 'dew_point', 'humidity', 'wind_chill', 
+              'wind_dir', 'wind_speed', 'snow_depth', 'snowfall_3hr', 
+              'snowfall_6hr', 'snowfall_24hr']
+    
+    df = df.iloc[:, :11]  # Take first 11 columns
     df.columns = columns
     
     # Convert numeric columns
-    df['temp'] = pd.to_numeric(df['temp'], errors='coerce')
-    df['dew_point'] = pd.to_numeric(df['dew_point'], errors='coerce')
-    df['humidity'] = pd.to_numeric(df['humidity'], errors='coerce')
-    df['wind_chill'] = pd.to_numeric(df['wind_chill'], errors='coerce')
-    df['snow_depth'] = pd.to_numeric(df['snow_depth'], errors='coerce')
+    numeric_cols = ['temp', 'dew_point', 'humidity', 'wind_chill', 'snow_depth',
+                   'snowfall_3hr', 'snowfall_6hr', 'snowfall_24hr']
+    for col in numeric_cols:
+        df[col] = pd.to_numeric(df[col], errors='coerce')
     
-    # Parse wind speed and gusts
+    # Parse wind data
     def parse_wind(x):
         if pd.isna(x): return np.nan, np.nan
         match = re.search(r'(\d+)G(\d+)', str(x))
@@ -110,47 +110,88 @@ def parse_weather_data(data):
     df['wind_speed'] = wind_data.apply(lambda x: x[0])
     df['wind_gust'] = wind_data.apply(lambda x: x[1])
     
+    # Convert wind directions to degrees
+    df['wind_dir_deg'] = df['wind_dir'].apply(parse_direction)
+    
+    # Convert datetime
+    df['datetime'] = pd.to_datetime(df['datetime'])
+    df['date'] = df['datetime'].dt.date
+    
     return df
 
-def create_temperature_plot(df):
-    """Create temperature plot"""
-    plt.figure(figsize=(12, 6))
-    plt.plot(range(len(df)), df['temp'], label='Temperature', color='red')
-    plt.plot(range(len(df)), df['wind_chill'], label='Wind Chill', color='blue')
-    plt.title('Temperature and Wind Chill Over Time')
-    plt.xlabel('Time')
-    plt.ylabel('Temperature (°F)')
-    plt.legend()
-    # Set x-axis ticks
-    x_ticks = np.linspace(0, len(df)-1, 10, dtype=int)
-    plt.xticks(x_ticks, df['datetime'].iloc[x_ticks], rotation=45)
-    plt.tight_layout()
-    return plt
+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'], df['wind_speed'], 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_wind_plot(df):
-    """Create wind plot"""
-    plt.figure(figsize=(12, 6))
-    plt.plot(range(len(df)), df['wind_speed'], label='Wind Speed', color='blue')
-    plt.plot(range(len(df)), df['wind_gust'], label='Wind Gust', color='orange')
-    plt.title('Wind Speed and Gusts Over Time')
-    plt.xlabel('Time')
-    plt.ylabel('Wind Speed (mph)')
-    plt.legend()
-    # Set x-axis ticks
-    x_ticks = np.linspace(0, len(df)-1, 10, dtype=int)
-    plt.xticks(x_ticks, df['datetime'].iloc[x_ticks], rotation=45)
+def create_plots(df):
+    """Create all weather plots"""
+    # Create figure with subplots
+    fig = plt.figure(figsize=(20, 15))
+    gs = GridSpec(3, 2, figure=fig)
+    
+    # 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')
+    ax1.set_xlabel('Date')
+    ax1.set_ylabel('Temperature (°F)')
+    ax1.legend()
+    ax1.grid(True)
+    plt.setp(ax1.xaxis.get_majorticklabels(), 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')
+    ax2.set_xlabel('Date')
+    ax2.set_ylabel('Wind Speed (mph)')
+    ax2.legend()
+    ax2.grid(True)
+    plt.setp(ax2.xaxis.get_majorticklabels(), rotation=45)
+    
+    # Snow depth plot
+    ax3 = fig.add_subplot(gs[2, 0])
+    ax3.plot(df['datetime'], df['snow_depth'], color='blue', label='Snow Depth')
+    ax3.set_title('Snow Depth Over Time')
+    ax3.set_xlabel('Date')
+    ax3.set_ylabel('Snow Depth (inches)')
+    ax3.grid(True)
+    plt.setp(ax3.xaxis.get_majorticklabels(), rotation=45)
+    
+    # Daily new snow bar plot
+    ax4 = fig.add_subplot(gs[2, 1])
+    daily_snow = df.groupby('date')['snowfall_24hr'].max().reset_index()
+    ax4.bar(daily_snow['date'], daily_snow['snowfall_24hr'], color='blue')
+    ax4.set_title('Daily New Snow')
+    ax4.set_xlabel('Date')
+    ax4.set_ylabel('New Snow (inches)')
+    plt.setp(ax4.xaxis.get_majorticklabels(), rotation=45)
+    
+    plt.tight_layout()
+    
+    # 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)
     plt.tight_layout()
-    return plt
+    
+    return fig, fig_rose
 
 def analyze_weather_data(site_id, hours):
     """Analyze weather data and create visualizations"""
     try:
-        # Scrape data
+        # Scrape and parse data
         raw_data = scrape_weather_data(site_id, hours)
         if not raw_data:
             return "Error: Could not retrieve weather data.", None, None
             
-        # Parse data
         df = parse_weather_data(raw_data)
         
         # Calculate statistics
@@ -160,7 +201,8 @@ def analyze_weather_data(site_id, hours):
             'Max Wind Speed': f"{df['wind_speed'].max():.1f} mph",
             'Max Wind Gust': f"{df['wind_gust'].max():.1f} mph",
             'Average Humidity': f"{df['humidity'].mean():.1f}%",
-            'Max Snow Depth': f"{df['snow_depth'].max():.1f} inches"
+            'Current Snow Depth': f"{df['snow_depth'].iloc[0]:.1f} inches",
+            'Total New Snow (24hr)': f"{df['snowfall_24hr'].sum():.1f} inches"
         }
         
         # Create HTML output
@@ -171,10 +213,9 @@ def analyze_weather_data(site_id, hours):
         html_output += "</div>"
         
         # Create plots
-        temp_fig = create_temperature_plot(df)
-        wind_fig = create_wind_plot(df)
+        main_plots, wind_rose = create_plots(df)
         
-        return html_output, temp_fig, wind_fig
+        return html_output, main_plots, wind_rose
         
     except Exception as e:
         return f"Error analyzing data: {str(e)}", None, None
@@ -209,13 +250,13 @@ with gr.Blocks(title="Weather Station Data Analyzer") as demo:
         stats_output = gr.HTML(label="Statistics")
     
     with gr.Row():
-        temp_plot = gr.Plot(label="Temperature Plot")
-        wind_plot = gr.Plot(label="Wind Plot")
+        weather_plots = gr.Plot(label="Weather Plots")
+        wind_rose = gr.Plot(label="Wind Rose")
     
     analyze_btn.click(
         fn=analyze_weather_data,
         inputs=[site_id, hours],
-        outputs=[stats_output, temp_plot, wind_plot]
+        outputs=[stats_output, weather_plots, wind_rose]
     )
 
 if __name__ == "__main__":