Update app/climate_data.py

app/climate_data.py

@@ -199,8 +199,8 @@ class ClimateDataManager:
                 if line.strip():  # Skip empty lines
                     data.append(line.split(','))
             
-            # Create DataFrame
-            columns = [
+            # Define core columns (common to both 32 and 35 column formats)
+            core_columns = [
                 "year", "month", "day", "hour", "minute", "data_source", "dry_bulb_temp",
                 "dew_point_temp", "relative_humidity", "atmospheric_pressure", "extraterrestrial_radiation",
                 "extraterrestrial_radiation_normal", "horizontal_infrared_radiation", "global_horizontal_radiation",
@@ -208,11 +208,22 @@ class ClimateDataManager:
                 "direct_normal_illuminance", "diffuse_horizontal_illuminance", "zenith_luminance",
                 "wind_direction", "wind_speed", "total_sky_cover", "opaque_sky_cover", "visibility",
                 "ceiling_height", "present_weather_observation", "present_weather_codes",
-                "precipitable_water", "aerosol_optical_depth", "snow_depth", "days_since_last_snowfall",
-                "albedo", "liquid_precipitation_depth", "liquid_precipitation_quantity"
+                "precipitable_water", "aerosol_optical_depth", "snow_depth", "days_since_last_snowfall"
             ]
             
-            df = pd.DataFrame(data, columns=columns)
+            # Additional columns for 35-column format
+            additional_columns = ["albedo", "liquid_precipitation_depth", "liquid_precipitation_quantity"]
+            
+            # Determine number of columns in data
+            num_columns = len(data[0]) if data else 0
+            if num_columns not in [32, 35]:
+                raise ValueError(f"Invalid number of columns in EPW file: {num_columns}. Expected 32 or 35 columns.")
+            
+            # Select appropriate columns based on file format
+            columns = core_columns if num_columns == 32 else core_columns + additional_columns
+            
+            # Create DataFrame
+            df = pd.DataFrame(data, columns=columns[:num_columns])
             
             # Convert numeric columns
             numeric_columns = [
@@ -222,7 +233,8 @@ class ClimateDataManager:
             ]
             
             for col in numeric_columns:
-                df[col] = pd.to_numeric(df[col], errors='coerce')
+                if col in df.columns:
+                    df[col] = pd.to_numeric(df[col], errors='coerce')
             
             # Calculate design conditions
             design_conditions = self._calculate_design_conditions(df)
@@ -359,14 +371,15 @@ class ClimateDataManager:
         
         try:
             # Ensure numeric columns
-            df["dry_bulb_temp"] = pd.to_numeric(df["dry_bulb_temp"], errors='coerce')
-            df["relative_humidity"] = pd.to_numeric(df["relative_humidity"], errors='coerce')
-            df["atmospheric_pressure"] = pd.to_numeric(df["atmospheric_pressure"], errors='coerce')
-            df["global_horizontal_radiation"] = pd.to_numeric(df["global_horizontal_radiation"], errors='coerce')
-            df["direct_normal_radiation"] = pd.to_numeric(df["direct_normal_radiation"], errors='coerce')
-            df["diffuse_horizontal_radiation"] = pd.to_numeric(df["diffuse_horizontal_radiation"], errors='coerce')
-            df["wind_speed"] = pd.to_numeric(df["wind_speed"], errors='coerce')
-            df["wind_direction"] = pd.to_numeric(df["wind_direction"], errors='coerce')
+            numeric_columns = [
+                "dry_bulb_temp", "relative_humidity", "atmospheric_pressure",
+                "global_horizontal_radiation", "direct_normal_radiation",
+                "diffuse_horizontal_radiation", "wind_speed", "wind_direction"
+            ]
+            
+            for col in numeric_columns:
+                if col in df.columns:
+                    df[col] = pd.to_numeric(df[col], errors='coerce')
             
             # Convert to integers for month, day, hour
             df["month"] = pd.to_numeric(df["month"], errors='coerce').astype('Int64')
@@ -642,17 +655,29 @@ def display_climate_summary(climate_data: Dict[str, Any]):
     design = climate_data["design_conditions"]
     location = climate_data["location"]
     
-    # Location Details
-    st.markdown(f"""
-    ### Location Details
-    - **Country:** {location['country']}
-    - **City:** {location['city']}
-    - **State/Province:** {location['state_province']}
-    - **Latitude:** {location['latitude']}°
-    - **Longitude:** {location['longitude']}°
-    - **Elevation:** {location['elevation']} m
-    - **Time Zone:** {location['timezone']} hours (UTC)
-    """)
+    # Location Details and Typical/Extreme Periods side by side
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        st.markdown("### Location Details")
+        st.markdown(f"""
+        - **Country:** {location['country']}
+        - **City:** {location['city']}
+        - **State/Province:** {location['state_province']}
+        - **Latitude:** {location['latitude']}°
+        - **Longitude:** {location['longitude']}°
+        - **Elevation:** {location['elevation']} m
+        - **Time Zone:** {location['timezone']} hours (UTC)
+        """)
+    
+    with col2:
+        if climate_data.get("typical_extreme_periods"):
+            st.markdown("### Typical/Extreme Periods")
+            period_items = [
+                f"- **{key.replace('_', ' ').title()}:** {period['start']['month']}/{period['start']['day']} to {period['end']['month']}/{period['end']['day']}"
+                for key, period in climate_data["typical_extreme_periods"].items()
+            ]
+            st.markdown("\n".join(period_items))
     
     # Climate Zone
     st.markdown(f"### ASHRAE Climate Zone: {climate_data['climate_zone']}")
@@ -674,34 +699,6 @@ def display_climate_summary(climate_data: Dict[str, Any]):
         st.write(f"**Average Wind Speed:** {design['wind_speed']} m/s")
         st.write(f"**Average Atmospheric Pressure:** {design['pressure']} Pa")
     
-    # Typical/Extreme Periods
-    if climate_data.get("typical_extreme_periods"):
-        st.subheader("Typical/Extreme Periods")
-        period_items = [
-            f"- **{key.replace('_', ' ').title()}:** {period['start']['month']}/{period['start']['day']} to {period['end']['month']}/{period['end']['day']}"
-            for key, period in climate_data["typical_extreme_periods"].items()
-        ]
-        st.markdown("\n".join(period_items))
-    
-    # Ground Temperatures
-    if climate_data.get("ground_temperatures"):
-        st.subheader("Ground Temperatures")
-        table_data = []
-        for depth, temps in climate_data["ground_temperatures"].items():
-            row = {"Depth (m)": float(depth)}
-            row.update({month: f"{temp:.2f}" for month, temp in zip(MONTHS, temps)})
-            table_data.append(row)
-        df = pd.DataFrame(table_data)
-        st.dataframe(df, use_container_width=True)
-        csv = df.to_csv(index=False)
-        st.download_button(
-            label="Download Ground Temperatures as CSV",
-            data=csv,
-            file_name=f"ground_temperatures_{location['city']}_{location['country']}.csv",
-            mime="text/csv",
-            key=f"download_ground_temperatures_{climate_data['id']}"
-        )
-    
     # Monthly Temperature Chart
     st.subheader("Monthly Average Temperatures")
     
@@ -744,18 +741,28 @@ def display_climate_summary(climate_data: Dict[str, Any]):
     
     st.plotly_chart(fig_rad, use_container_width=True)
     
-    # Hourly Data Statistics
-    st.subheader("Hourly Data Statistics")
+    # Ground Temperatures
+    if climate_data.get("ground_temperatures"):
+        st.subheader("Ground Temperatures")
+        table_data = []
+        for depth, temps in climate_data["ground_temperatures"].items():
+            row = {"Depth (m)": float(depth)}
+            row.update({month: f"{temp:.2f}" for month, temp in zip(MONTHS, temps)})
+            table_data.append(row)
+        df = pd.DataFrame(table_data)
+        st.dataframe(df, use_container_width=True)
+        csv = df.to_csv(index=False)
+        st.download_button(
+            label="Download Ground Temperatures as CSV",
+            data=csv,
+            file_name=f"ground_temperatures_{location['city']}_{location['country']}.csv",
+            mime="text/csv",
+            key=f"download_ground_temperatures_{climate_data['id']}"
+        )
     
+    # Hourly Climate Data Table
+    st.subheader("Hourly Climate Data")
     if "hourly_data" in climate_data and climate_data["hourly_data"]:
-        hourly_count = len(climate_data["hourly_data"])
-        st.write(f"**Number of Hourly Records:** {hourly_count}")
-        
-        if hourly_count < 8760:
-            st.warning(f"Expected 8760 hourly records for a full year, but found {hourly_count}. Some data may be missing.")
-        
-        # Hourly Climate Data Table
-        st.subheader("Hourly Climate Data")
         hourly_table_data = [
             {
                 "Month": record["month"],
@@ -782,6 +789,14 @@ def display_climate_summary(climate_data: Dict[str, Any]):
             mime="text/csv",
             key=f"download_hourly_climate_{climate_data['id']}"
         )
+        
+        # Hourly Data Statistics
+        st.subheader("Hourly Data Statistics")
+        hourly_count = len(climate_data["hourly_data"])
+        st.write(f"**Number of Hourly Records:** {hourly_count}")
+        
+        if hourly_count < 8760:
+            st.warning(f"Expected 8760 hourly records for a full year, but found {hourly_count}. Some data may be missing.")
     else:
         st.warning("No hourly data available.")