Update app.py

app.py

@@ -12,92 +12,9 @@ from matplotlib.gridspec import GridSpec
 from windrose import WindroseAxes
 from datetime import datetime
 
-# Install Playwright browsers on startup
-def install_playwright_browsers():
-    try:
-        if not os.path.exists('/home/user/.cache/ms-playwright'):
-            print("Installing Playwright browsers...")
-            subprocess.run(
-                [sys.executable, "-m", "playwright", "install", "chromium"],
-                check=True,
-                capture_output=True,
-                text=True
-            )
-            print("Playwright browsers installed successfully")
-    except Exception as e:
-        print(f"Error installing browsers: {e}")
-
-# Install browsers when the module loads
+# Previous installation and scraping functions remain the same
 install_playwright_browsers()
 
-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="
-    
-    try:
-        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'
-            )
-            
-            # Create new page and navigate
-            page = context.new_page()
-            response = page.goto(url)
-            print(f"Response status: {response.status}")
-            
-            # Wait for content to load
-            page.wait_for_selector('table', timeout=30000)
-            time.sleep(5)
-            
-            # Get all text content
-            print("Extracting data...")
-            content = page.evaluate('''() => {
-                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();
-            }''')
-            
-            print(f"Found {len(content['rows'] if content else [])} rows of data")
-            browser.close()
-            return content
-            
-    except Exception as e:
-        print(f"Error scraping data: {str(e)}")
-        raise e
-
-def parse_date(date_str):
-    """Parse date string to datetime"""
-    try:
-        # Handle format like "Feb 10, 10:00 am"
-        # Add current year to the date string
-        current_year = datetime.now().year
-        return pd.to_datetime(f"{date_str}, {current_year}", format="%b %d, %I:%M %p, %Y")
-    except:
-        return pd.NaT
-
 def parse_weather_data(data):
     """Parse the weather data into a pandas DataFrame"""
     if not data or 'rows' not in data:
@@ -105,21 +22,21 @@ def parse_weather_data(data):
         
     df = pd.DataFrame(data['rows'])
     
-    # Get all relevant columns
+    # Updated columns list to include SWE
     columns = ['datetime', 'temp', 'dew_point', 'humidity', 'wind_chill', 
               'wind_dir', 'wind_speed', 'snow_depth', 'snowfall_3hr', 
-              'snowfall_6hr', 'snowfall_24hr']
+              'snowfall_6hr', 'snowfall_24hr', 'swe']
     
-    df = df.iloc[:, :11]  # Take first 11 columns
+    df = df.iloc[:, :12]  # Take first 12 columns including SWE
     df.columns = columns
     
-    # Convert numeric columns
+    # Convert numeric columns including SWE
     numeric_cols = ['temp', 'dew_point', 'humidity', 'wind_chill', 'snow_depth',
-                   'snowfall_3hr', 'snowfall_6hr', 'snowfall_24hr']
+                   'snowfall_3hr', 'snowfall_6hr', 'snowfall_24hr', 'swe']
     for col in numeric_cols:
         df[col] = pd.to_numeric(df[col], errors='coerce')
     
-    # Parse wind data
+    # Previous wind data parsing remains the same
     def parse_wind(x):
         if pd.isna(x): return np.nan, np.nan
         match = re.search(r'(\d+)G(\d+)', str(x))
@@ -131,7 +48,7 @@ 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
+    # Previous wind direction parsing remains the same
     def parse_direction(direction):
         direction_map = {
             'N': 0, 'NNE': 22.5, 'NE': 45, 'ENE': 67.5,
@@ -149,20 +66,11 @@ def parse_weather_data(data):
     
     return df
 
-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(), 
-           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"""
-    # Create figure with subplots
-    fig = plt.figure(figsize=(20, 15))
-    gs = GridSpec(3, 2, figure=fig)
+    """Create all weather plots including SWE"""
+    # Create figure with subplots - updated layout to include SWE
+    fig = plt.figure(figsize=(20, 20))  # Increased height for additional plot
+    gs = GridSpec(4, 2, figure=fig)  # Added one more row for SWE
     
     # Temperature plot
     ax1 = fig.add_subplot(gs[0, :])
@@ -186,24 +94,40 @@ def create_plots(df):
     ax2.grid(True)
     plt.setp(ax2.xaxis.get_majorticklabels(), rotation=45)
     
-    # Snow depth plot
-    ax3 = fig.add_subplot(gs[2, 0])
+    # Snow depth and SWE comparison 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')
+    ax3_twin = ax3.twinx()
+    ax3_twin.plot(df['datetime'], df['swe'], color='red', label='SWE')
+    ax3.set_title('Snow Depth and Snow Water Equivalent Over Time')
     ax3.set_xlabel('Date')
-    ax3.set_ylabel('Snow Depth (inches)')
+    ax3.set_ylabel('Snow Depth (inches)', color='blue')
+    ax3_twin.set_ylabel('Snow Water Equivalent (inches)', color='red')
+    lines1, labels1 = ax3.get_legend_handles_labels()
+    lines2, labels2 = ax3_twin.get_legend_handles_labels()
+    ax3.legend(lines1 + lines2, labels1 + labels2, loc='upper right')
     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()
-    ax4.bar(daily_snow.index, daily_snow.values, color='blue')
-    ax4.set_title('Daily New Snow')
+    # Snow density plot (SWE/Snow Depth ratio)
+    ax4 = fig.add_subplot(gs[3, 0])
+    snow_density = (df['swe'] / df['snow_depth']) * 100  # Convert to percentage
+    ax4.plot(df['datetime'], snow_density, color='purple')
+    ax4.set_title('Snow Density (SWE/Snow Depth Ratio)')
     ax4.set_xlabel('Date')
-    ax4.set_ylabel('New Snow (inches)')
+    ax4.set_ylabel('Snow Density (%)')
+    ax4.grid(True)
     plt.setp(ax4.xaxis.get_majorticklabels(), rotation=45)
     
+    # Daily new snow bar plot
+    ax5 = fig.add_subplot(gs[3, 1])
+    daily_snow = df.groupby('date')['snowfall_24hr'].max()
+    ax5.bar(daily_snow.index, daily_snow.values, color='blue')
+    ax5.set_title('Daily New Snow')
+    ax5.set_xlabel('Date')
+    ax5.set_ylabel('New Snow (inches)')
+    plt.setp(ax5.xaxis.get_majorticklabels(), rotation=45)
+    
     plt.tight_layout()
     
     # Create separate wind rose figure
@@ -226,7 +150,7 @@ def analyze_weather_data(site_id, hours):
         print("Parsing data...")    
         df = parse_weather_data(raw_data)
         
-        # Calculate statistics
+        # Calculate statistics - updated to include SWE
         print("Calculating statistics...")
         stats = {
             'Temperature Range': f"{df['temp'].min():.1f}°F to {df['temp'].max():.1f}°F",
@@ -235,6 +159,8 @@ def analyze_weather_data(site_id, hours):
             'Max Wind Gust': f"{df['wind_gust'].max():.1f} mph",
             'Average Humidity': f"{df['humidity'].mean():.1f}%",
             'Current Snow Depth': f"{df['snow_depth'].iloc[0]:.1f} inches",
+            'Current SWE': f"{df['swe'].iloc[0]:.2f} inches",
+            'Snow Density': f"{(df['swe'].iloc[0] / df['snow_depth'].iloc[0] * 100):.1f}%",
             'Total New Snow (24hr)': f"{df['snowfall_24hr'].sum():.1f} inches"
         }
         
@@ -256,7 +182,7 @@ def analyze_weather_data(site_id, hours):
         print(f"Error in analysis: {str(e)}")
         return f"Error analyzing data: {str(e)}", None, None
 
-# Create Gradio interface
+# Gradio interface remains the same
 with gr.Blocks(title="Weather Station Data Analyzer") as demo:
     gr.Markdown("# Weather Station Data Analyzer")
     gr.Markdown("""