Update data/climate_data.py

data/climate_data.py

@@ -93,6 +93,36 @@ class ClimateData:
         self.countries = sorted(list(set(loc.country for loc in self.locations.values())))
         self.country_states = self._group_locations_by_country_state()
 
+    @staticmethod
+    def calculate_wet_bulb(dry_bulb: np.ndarray, relative_humidity: np.ndarray) -> np.ndarray:
+        """Calculate Wet Bulb Temperature using Stull (2011) approximation.
+        
+        Args:
+            dry_bulb (np.ndarray): Dry Bulb Temperature (°C)
+            relative_humidity (np.ndarray): Relative Humidity (%)
+        
+        Returns:
+            np.ndarray: Wet Bulb Temperature (°C)
+        """
+        # Ensure inputs are numpy arrays and handle NaN values
+        db = np.array(dry_bulb, dtype=float)
+        rh = np.array(relative_humidity, dtype=float)
+        
+        # Stull formula
+        term1 = db * np.arctan(0.151977 * (rh + 8.313659)**0.5)
+        term2 = np.arctan(db + rh)
+        term3 = np.arctan(rh - 1.676331)
+        term4 = 0.00391838 * rh**1.5 * np.arctan(0.023101 * rh)
+        term5 = -4.686035
+        
+        wet_bulb = term1 + term2 - term3 + term4 + term5
+        
+        # Mask invalid values (e.g., RH < 5% or > 99%, or extreme DBT)
+        invalid_mask = (rh < 5) | (rh > 99) | (db < -20) | (db > 50) | np.isnan(db) | np.isnan(rh)
+        wet_bulb[invalid_mask] = np.nan
+        
+        return wet_bulb
+
     def display_climate_input(self, session_state: Dict[str, Any]):
         """Display form for manual input or EPW upload in Streamlit."""
         st.title("Climate Data")
@@ -191,15 +221,18 @@ class ClimateData:
                     for col in epw_data.columns:
                         epw_data[col] = pd.to_numeric(epw_data[col], errors='coerce')
                     
-                    # Extract key columns (using specified indices: 7, 8, 9)
+                    # Extract key columns (adjusted for your file)
                     months = epw_data[1].values  # Month
-                    dry_bulb = epw_data[7].values  # Dry-bulb temperature (°C)
-                    wet_bulb = epw_data[8].values  # Wet-bulb temperature (°C)
-                    humidity = epw_data[9].values  # Relative humidity (%)
+                    dry_bulb = epw_data[6].values  # Dry-bulb temperature (°C) - 7th in Excel
+                    humidity = epw_data[8].values  # Relative humidity (%) - 9th in Excel
+                    pressure = epw_data[9].values  # Atmospheric pressure (Pa) - 10th in Excel
+                    
+                    # Calculate Wet Bulb Temperature
+                    wet_bulb = self.calculate_wet_bulb(dry_bulb, humidity)
                     
                     # Check for critical NaN issues
-                    if np.all(np.isnan(dry_bulb)) or np.all(np.isnan(humidity)):
-                        raise ValueError("Dry bulb temperature or humidity data is entirely NaN.")
+                    if np.all(np.isnan(dry_bulb)) or np.all(np.isnan(humidity)) or np.all(np.isnan(wet_bulb)):
+                        raise ValueError("Dry bulb, humidity, or calculated wet bulb data is entirely NaN.")
                     
                     # Calculate HDD and CDD (base 18°C)
                     daily_temps = np.nanmean(dry_bulb.reshape(-1, 24), axis=1)
@@ -209,8 +242,7 @@ class ClimateData:
                     # Design conditions
                     winter_design_temp = round(np.nanpercentile(dry_bulb, 0.4), 1)  # 99.6% heating design
                     summer_design_temp_db = round(np.nanpercentile(dry_bulb, 99.6), 1)  # 0.4% cooling design DB
-                    summer_idx = np.argmax(dry_bulb >= summer_design_temp_db)
-                    summer_design_temp_wb = round(wet_bulb[summer_idx], 1) if not np.isnan(wet_bulb[summer_idx]) else 25.0
+                    summer_design_temp_wb = round(np.nanpercentile(wet_bulb, 99.6), 1)  # 0.4% cooling design WB
                     summer_mask = (months >= 6) & (months <= 8)
                     summer_temps = dry_bulb[summer_mask].reshape(-1, 24)
                     summer_daily_range = round(np.nanmean(np.nanmax(summer_temps, axis=1) - np.nanmin(summer_temps, axis=1)), 1)
@@ -247,7 +279,7 @@ class ClimateData:
                         monthly_humidity=monthly_humidity
                     )
                     self.add_location(location)
-                    st.success("Climate data extracted from EPW file!")
+                    st.success("Climate data extracted from EPW file with calculated Wet Bulb Temperature!")
                     self.display_design_conditions(location)
                     self.visualize_data(location, epw_data=epw_data)
                 except Exception as e:
@@ -333,7 +365,7 @@ class ClimateData:
         
         # Add min/max for EPW data only
         if epw_data is not None:
-            dry_bulb = epw_data[7].values  # Dry-bulb temperature (°C)
+            dry_bulb = epw_data[6].values  # Dry-bulb temperature (°C)
             month_col = epw_data[1].values
             temps_min = []
             temps_max = []
@@ -382,7 +414,8 @@ class ClimateData:
         
         # Add min/max for EPW data only
         if epw_data is not None:
-            humidity = epw_data[9].values  # Relative humidity (%)
+            humidity = epw_data[8].values  # Relative humidity (%) - adjusted to 9th column
+            month_col = epw_data[1].values
             humidity_min = []
             humidity_max = []
             for i in range(1, 13):
@@ -425,23 +458,17 @@ class ClimateData:
 
     @classmethod
     def from_json(cls, file_path: str) -> 'ClimateData':
-        """Create a ClimateData instance from a JSON file."""
+        """Load climate data from a JSON file."""
         with open(file_path, 'r') as f:
             data = json.load(f)
         climate_data = cls()
-        climate_data.locations = {}
         for loc_id, loc_dict in data.items():
-            climate_data.locations[loc_id] = ClimateLocation(**loc_dict)
-        climate_data.countries = sorted(list(set(loc.country for loc in climate_data.locations.values())))
-        climate_data.country_states = climate_data._group_locations_by_country_state()
+            location = ClimateLocation(**loc_dict)
+            climate_data.add_location(location)
         return climate_data
 
-
+# Example usage
 if __name__ == "__main__":
-    if "building_info" not in st.session_state:
-        st.session_state.building_info = {"country": "Iceland", "city": "Reykjavik"}
-    if "page" not in st.session_state:
-        st.session_state.page = "Climate Data"
-    
     climate_data = ClimateData()
-    climate_data.display_climate_input(st.session_state)
+    session_state = {"building_info": {"country": "Iceland", "city": "Reykjavik"}, "page": "Climate Data"}
+    climate_data.display_climate_input(session_state)