Update data/climate_data.py

data/climate_data.py

@@ -95,20 +95,10 @@ class ClimateData:
 
     @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
+        """Calculate Wet Bulb Temperature using Stull (2011) approximation."""
         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)
@@ -117,7 +107,6 @@ class ClimateData:
         
         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
         
@@ -151,7 +140,7 @@ class ClimateData:
                     hdd = st.number_input("Heating Degree Days (base 18°C)", min_value=0.0, value=0.0, step=100.0)
                     cdd = st.number_input("Cooling Degree Days (base 18°C)", min_value=0.0, value=0.0, step=100.0)
                     winter_design_temp = st.number_input("Winter Design Temp (99.6%) (°C)", min_value=-50.0, max_value=20.0, value=0.0, step=0.5)
-                    summer_design_temp_db = st.number_input("Summer Design Temp EXPORTDB (0.4%) (°C)", min_value=0.0, max_value=50.0, value=35.0, step=0.5)
+                    summer_design_temp_db = st.number_input("Summer Design Temp DB (0.4%) (°C)", min_value=0.0, max_value=50.0, value=35.0, step=0.5)
                     summer_design_temp_wb = st.number_input("Summer Design Temp WB (0.4%) (°C)", min_value=0.0, max_value=40.0, value=25.0, step=0.5)
                     summer_daily_range = st.number_input("Summer Daily Range (°C)", min_value=0.0, value=5.0, step=0.5)
                 
@@ -162,7 +151,7 @@ class ClimateData:
                 col1, col2 = st.columns(2)
                 with col1:
                     for month in month_names[:6]:
-                        monthly_temps[month] = st.number_input(f"{month} Temp (°C)", min_value=- diaspora50.0, max_value=50.0, value=20.0, step=0.5, key=f"temp_{month}")
+                        monthly_temps[month] = st.number_input(f"{month} Temp (°C)", min_value=-50.0, max_value=50.0, value=20.0, step=0.5, key=f"temp_{month}")
                 with col2:
                     for month in month_names[6:]:
                         monthly_temps[month] = st.number_input(f"{month} Temp (°C)", min_value=-50.0, max_value=50.0, value=20.0, step=0.5, key=f"temp_{month}")
@@ -211,43 +200,35 @@ class ClimateData:
                     longitude = float(header_parts[7])
                     elevation = float(header_parts[8])
                     
-                    # Find data start after "DATA PERIODS"
                     data_start_idx = next(i for i, line in enumerate(epw_lines) if line.startswith("DATA PERIODS")) + 1
                     epw_data = pd.read_csv(StringIO("\n".join(epw_lines[data_start_idx:])), header=None, dtype=str)
                     if len(epw_data) != 8760:
                         raise ValueError(f"EPW file has {len(epw_data)} records, expected 8760.")
                     
-                    # Convert to numeric, handling non-numeric values
                     for col in epw_data.columns:
                         epw_data[col] = pd.to_numeric(epw_data[col], errors='coerce')
                     
-                    # Extract key columns (adjusted for your file)
                     months = epw_data[1].values  # Month
-                    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
+                    dry_bulb = epw_data[6].values  # Dry-bulb temperature (°C)
+                    humidity = epw_data[8].values  # Relative humidity (%)
+                    pressure = epw_data[9].values  # Atmospheric pressure (Pa)
                     
-                    # 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)) 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)
                     hdd = round(np.nansum(np.maximum(18 - daily_temps, 0)))
                     cdd = round(np.nansum(np.maximum(daily_temps - 18, 0)))
                     
-                    # 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_design_temp_wb = round(np.nanpercentile(wet_bulb, 99.6), 1)  # 0.4% cooling design WB
+                    winter_design_temp = round(np.nanpercentile(dry_bulb, 0.4), 1)
+                    summer_design_temp_db = round(np.nanpercentile(dry_bulb, 99.6), 1)
+                    summer_design_temp_wb = round(np.nanpercentile(wet_bulb, 99.6), 1)
                     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)
                     
-                    # Monthly averages
                     monthly_temps = {}
                     monthly_humidity = {}
                     month_names = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
@@ -256,7 +237,6 @@ class ClimateData:
                         monthly_temps[month_names[i-1]] = round(np.nanmean(dry_bulb[month_mask]), 1)
                         monthly_humidity[month_names[i-1]] = round(np.nanmean(humidity[month_mask]), 1)
                     
-                    # Assign climate zone
                     avg_humidity = np.nanmean(humidity)
                     climate_zone = self.assign_climate_zone(hdd, cdd, avg_humidity)
                     
@@ -285,7 +265,6 @@ class ClimateData:
                 except Exception as e:
                     st.error(f"Error processing EPW file: {str(e)}. Ensure it has 8760 hourly records and correct format.")
 
-        # Navigation buttons
         col1, col2 = st.columns(2)
         with col1:
             st.button("Back to Building Information", on_click=lambda: setattr(session_state, "page", "Building Information"))
@@ -299,7 +278,6 @@ class ClimateData:
         """Display a table of design conditions including additional parameters for HVAC calculations."""
         st.subheader("Design Conditions for HVAC Calculations")
         
-        # Prepare the design data with additional parameters
         design_data = pd.DataFrame({
             "Parameter": [
                 "Latitude",
@@ -327,23 +305,18 @@ class ClimateData:
             ]
         })
         
-        # Add monthly temperatures
         month_names = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
         monthly_temp_data = pd.DataFrame({
             "Parameter": [f"{month} Avg Temp" for month in month_names],
             "Value": [f"{location.monthly_temps[month]} °C" for month in month_names]
         })
         
-        # Add monthly humidity
         monthly_humidity_data = pd.DataFrame({
             "Parameter": [f"{month} Avg Humidity" for month in month_names],
             "Value": [f"{location.monthly_humidity[month]} %" for month in month_names]
         })
         
-        # Combine all data into one table
         full_design_data = pd.concat([design_data, monthly_temp_data, monthly_humidity_data], ignore_index=True)
-        
-        # Display the table in Streamlit
         st.table(full_design_data)
 
     @staticmethod
@@ -378,7 +351,6 @@ class ClimateData:
         temps_avg = [location.monthly_temps[m] for m in month_names]
         humidity_avg = [location.monthly_humidity[m] for m in month_names]
         
-        # Temperature Plot
         fig_temp = go.Figure()
         fig_temp.add_trace(go.Scatter(
             x=months,
@@ -389,9 +361,8 @@ class ClimateData:
             marker=dict(size=8)
         ))
         
-        # Add min/max for EPW data only
         if epw_data is not None:
-            dry_bulb = epw_data[6].values  # Dry-bulb temperature (°C)
+            dry_bulb = epw_data[6].values
             month_col = epw_data[1].values
             temps_min = []
             temps_max = []
@@ -427,7 +398,6 @@ class ClimateData:
         )
         st.plotly_chart(fig_temp, use_container_width=True)
         
-        # Humidity Plot
         fig_hum = go.Figure()
         fig_hum.add_trace(go.Scatter(
             x=months,
@@ -438,9 +408,8 @@ class ClimateData:
             marker=dict(size=8)
         ))
         
-        # Add min/max for EPW data only
         if epw_data is not None:
-            humidity = epw_data[8].values  # Relative humidity (%) - adjusted to 9th column
+            humidity = epw_data[8].values
             month_col = epw_data[1].values
             humidity_min = []
             humidity_max = []
@@ -493,7 +462,6 @@ class ClimateData:
             climate_data.add_location(location)
         return climate_data
 
-# Example usage
 if __name__ == "__main__":
     climate_data = ClimateData()
     session_state = {"building_info": {"country": "Iceland", "city": "Reykjavik"}, "page": "Climate Data"}