Update app/climate_data.py

app/climate_data.py

@@ -237,6 +237,11 @@ class ClimateDataManager:
                 if col in df.columns:
                     df[col] = pd.to_numeric(df[col], errors='coerce')
             
+            # Calculate diffuse fraction
+            df['diffuse_fraction'] = df.apply(
+                lambda row: row['diffuse_horizontal_radiation'] / row['global_horizontal_radiation'] if row['global_horizontal_radiation'] > 0 else 0.0, axis=1
+            )
+            
             # Calculate design conditions
             design_conditions = self._calculate_design_conditions(df)
             
@@ -426,7 +431,7 @@ class ClimateDataManager:
     
     def _process_hourly_data(self, df: pd.DataFrame) -> List[Dict[str, Any]]:
         """
-        Process hourly data from EPW DataFrame, including dew point, Sky Clearness Index, and total sky cover.
+        Process hourly data from EPW DataFrame, including dew point, Sky Clearness Index, diffuse fraction, and total sky cover.
         
         Args:
             df: DataFrame containing EPW data
@@ -441,7 +446,8 @@ class ClimateDataManager:
             numeric_columns = [
                 "dry_bulb_temp", "dew_point_temp", "relative_humidity", "atmospheric_pressure",
                 "global_horizontal_radiation", "direct_normal_radiation",
-                "diffuse_horizontal_radiation", "wind_speed", "wind_direction", "total_sky_cover"
+                "diffuse_horizontal_radiation", "wind_speed", "wind_direction", "total_sky_cover",
+                "diffuse_fraction"
             ]
             
             for col in numeric_columns:
@@ -473,6 +479,9 @@ class ClimateDataManager:
                 # Extract total sky cover
                 total_sky_cover = float(row["total_sky_cover"]) if not pd.isna(row["total_sky_cover"]) else None
                 
+                # Extract diffuse fraction
+                diffuse_fraction = float(row["diffuse_fraction"]) if not pd.isna(row["diffuse_fraction"]) else 0.0
+                
                 record = {
                     "month": int(row["month"]),
                     "day": int(row["day"]),
@@ -487,7 +496,8 @@ class ClimateDataManager:
                     "wind_speed": float(row["wind_speed"]) if not pd.isna(row["wind_speed"]) else 0.0,
                     "wind_direction": float(row["wind_direction"]) if not pd.isna(row["wind_direction"]) else 0.0,
                     "sky_clearness_index": sky_clearness_index if sky_clearness_index is not None else None,
-                    "total_sky_cover": total_sky_cover
+                    "total_sky_cover": total_sky_cover,
+                    "diffuse_fraction": diffuse_fraction
                 }
                 hourly_data.append(record)
             
@@ -863,7 +873,8 @@ def display_climate_summary(climate_data: Dict[str, Any]):
                 "Wind Speed (m/s)": f"{record['wind_speed']:.1f}",
                 "Wind Direction (°)": f"{record['wind_direction']:.1f}",
                 "Sky Clearness Index": f"{record['sky_clearness_index']:.3f}" if record['sky_clearness_index'] is not None else "N/A",
-                "Total Sky Cover": f"{record['total_sky_cover']:.1f}" if record['total_sky_cover'] is not None else "N/A"
+                "Total Sky Cover": f"{record['total_sky_cover']:.1f}" if record['total_sky_cover'] is not None else "N/A",
+                "Diffuse Fraction": f"{record['diffuse_fraction']:.3f}" if record['diffuse_fraction'] is not None else "N/A"
             }
             for record in climate_data["hourly_data"]
         ]
@@ -906,6 +917,7 @@ def display_climate_help():
     * Wind speed and direction
     * Atmospheric pressure
     * Sky Clearness Index (calculated)
+    * Diffuse Fraction (calculated)
     
     **Where to Find EPW Files:**
     
@@ -928,7 +940,7 @@ def display_climate_help():
     * Typical/Extreme periods
     * Ground temperatures by depth
     * Monthly average temperatures and solar radiation
-    * Hourly data statistics with downloadable tables (including dew point and Sky Clearness Index)
+    * Hourly data statistics with downloadable tables (including dew point, Sky Clearness Index, and Diffuse Fraction)
     
     This information will be used throughout the calculation process.
     """)