Update data/climate_data.py

data/climate_data.py

@@ -4,7 +4,7 @@ Extracts climate data from EPW files and provides visualizations inspired by Cli
 
 Author: Dr Majed Abuseif
 Date: May 2025
-Version: 2.0.0
+Version: 2.1.0
 """
 
 from typing import Dict, List, Any, Optional
@@ -18,7 +18,6 @@ import plotly.graph_objects as go
 from io import StringIO
 import pvlib
 from datetime import datetime, timedelta
-from plotly.subplots import make_subplots
 
 # Define paths
 DATA_DIR = os.path.dirname(os.path.abspath(__file__))
@@ -263,7 +262,7 @@ class ClimateData:
                 
                 location = ClimateLocation(
                     epw_file=epw_data,
-                    id=f"{country[:2].upper()}-{city[:3].upper()}",
+                    id=f"{country[:1].upper()}{city[:3].upper()}",
                     country=country,
                     state_province=state_province,
                     city=city,
@@ -312,10 +311,6 @@ class ClimateData:
                 st.button("Continue to Building Components", on_click=lambda: setattr(session_state, "page", "Building Components"))
             else:
                 st.button("Continue to Building Components", disabled=True)
-        
-        if "climate_data" in session_state and session_state["climate_data"]:
-            st.subheader("Saved Climate Data")
-            st.json(session_state["climate_data"])
 
     def display_design_conditions(self, location: ClimateLocation):
         """Display design conditions for HVAC calculations using Markdown."""
@@ -365,7 +360,7 @@ class ClimateData:
             return "8"
 
     def plot_psychrometric_chart(self, location: ClimateLocation, epw_data: pd.DataFrame):
-        """Plot psychrometric chart with ASHRAE 55 comfort zone."""
+        """Plot psychrometric chart with ASHRAE 55 comfort zone and psychrometric lines."""
         st.subheader("Psychrometric Chart")
         
         dry_bulb = pd.to_numeric(epw_data[6], errors='coerce').values
@@ -378,24 +373,27 @@ class ClimateData:
         pressure = location.pressure / 1000  # kPa
         saturation_pressure = 6.1078 * 10 ** (7.5 * dry_bulb / (dry_bulb + 237.3))
         vapor_pressure = humidity / 100 * saturation_pressure
-        humidity_ratio = 0.62198 * vapor_pressure / (pressure - vapor_pressure)
+        humidity_ratio = 0.62198 * vapor_pressure / (pressure - vapor_pressure) * 1000  # Convert to g/kg
         
         fig = go.Figure()
+        
+        # Hourly data points
         fig.add_trace(go.Scatter(
             x=dry_bulb,
-            y=humidity_ratio * 1000,  # g/kg
+            y=humidity_ratio,
             mode='markers',
-            marker=dict(size=5, opacity=0.5),
+            marker=dict(size=5, opacity=0.5, color='blue'),
             name='Hourly Conditions'
         ))
         
-        # ASHRAE 55 comfort zone (simplified: 20-26°C, 30-60% RH)
-        comfort_x = [20, 26, 26, 20, 20]
-        comfort_y = [30, 30, 60, 60, 30]
-        comfort_hr = 0.62198 * (np.array(comfort_y) / 100 * 6.1078 * 10 ** (7.5 * np.array([23, 23, 23, 23, 23]) / (np.array([23, 23, 23, 23, 23]) + 237.3))) / (pressure - np.array(comfort_y) / 100 * 6.1078 * 10 ** (7.5 * np.array([23, 23, 23, 23, 23]) / (np.array([23, 23, 23, 23, 23]) + 237.3)))
+        # ASHRAE 55 comfort zone (simplified: 20-26°C, adjusted for humidity ratio)
+        comfort_db = [20, 26, 26, 20, 20]
+        comfort_rh = [30, 30, 60, 60, 30]
+        comfort_vp = np.array(comfort_rh) / 100 * 6.1078 * 10 ** (7.5 * np.array(comfort_db) / (np.array(comfort_db) + 237.3))
+        comfort_hr = 0.62198 * comfort_vp / (pressure - comfort_vp) * 1000
         fig.add_trace(go.Scatter(
-            x=comfort_x,
-            y=comfort_hr * 1000,
+            x=comfort_db,
+            y=comfort_hr,
             mode='lines',
             line=dict(color='green', width=2),
             fill='toself',
@@ -403,38 +401,58 @@ class ClimateData:
             name='ASHRAE 55 Comfort Zone'
         ))
         
+        # Constant humidity ratio lines (inspired by Climate Consultant)
+        for hr in [5, 10, 15]:  # g/kg
+            db_range = np.linspace(0, 40, 100)
+            vp = (hr / 1000 * pressure) / (0.62198 + hr / 1000)
+            rh = vp / (6.1078 * 10 ** (7.5 * db_range / (db_range + 237.3))) * 100
+            hr_line = np.full_like(db_range, hr)
+            fig.add_trace(go.Scatter(
+                x=db_range,
+                y=hr_line,
+                mode='lines',
+                line=dict(color='gray', width=1, dash='dash'),
+                name=f'{hr} g/kg',
+                showlegend=True
+            ))
+        
+        # Constant wet-bulb temperature lines
+        wet_bulb_temps = [10, 15, 20]
+        for wbt in wet_bulb_temps:
+            db_range = np.linspace(0, 40, 100)
+            rh_range = np.linspace(5, 95, 100)
+            wb_values = self.calculate_wet_bulb(db_range, rh_range)
+            vp = rh_range / 100 * (6.1078 * 10 ** (7.5 * db_range / (db_range + 237.3)))
+            hr_values = 0.62198 * vp / (pressure - vp) * 1000
+            mask = (wb_values >= wbt - 0.5) & (wb_values <= wbt + 0.5)
+            if np.any(mask):
+                fig.add_trace(go.Scatter(
+                    x=db_range[mask],
+                    y=hr_values[mask],
+                    mode='lines',
+                    line=dict(color='purple', width=1, dash='dot'),
+                    name=f'Wet-Bulb {wbt}°C',
+                    showlegend=True
+                ))
+        
         fig.update_layout(
             title="Psychrometric Chart",
             xaxis_title="Dry-Bulb Temperature (°C)",
             yaxis_title="Humidity Ratio (g/kg dry air)",
-            xaxis=dict(range=[-10, 50]),
-            yaxis=dict(range=[0, 30]),
-            showlegend=True
+            xaxis=dict(range=[-5, 40]),  # Adjusted for Geelong (3.1°C to 33.0°C)
+            yaxis=dict(range=[0, 25]),   # Adjusted for typical humidity ratios
+            showlegend=True,
+            template='plotly_white'
         )
         st.plotly_chart(fig, use_container_width=True)
-        
-        # HVAC strategy annotations
-        outside_comfort = np.sum((dry_bulb < 20) | (dry_bulb > 26) | (humidity < 30) | (humidity > 60))
-        st.markdown(f"**Hours outside comfort zone**: {outside_comfort} ({outside_comfort/8760*100:.1f}%)")
-        if outside_comfort > 0:
-            st.markdown("**HVAC Strategies**:")
-            if np.any(dry_bulb < 20):
-                st.markdown("- Heating required in colder periods.")
-            if np.any(dry_bulb > 26):
-                st.markdown("- Cooling required in warmer periods.")
-            if np.any(humidity > 60):
-                st.markdown("- Dehumidification may be needed in humid conditions.")
-            if np.any(humidity < 30):
-                st.markdown("- Humidification may be needed in dry conditions.")
 
     def plot_sun_shading_chart(self, location: ClimateLocation):
-        """Plot sun path chart for solstices and equinox."""
+        """Plot sun path chart for summer and winter solstices, inspired by Climate Consultant."""
         st.subheader("Sun Shading Chart")
         
         dates = [
-            datetime(2025, 6, 21),  # Summer solstice
-            datetime(2025, 12, 21),  # Winter solstice
-            datetime(2025, 3, 20)   # Spring equinox
+            datetime(2025, 6, 21),  # Winter solstice (Southern Hemisphere)
+            datetime(2025, 12, 21)  # Summer solstice (Southern Hemisphere)
         ]
         times = pd.date_range(start="2025-01-01 00:00", end="2025-01-01 23:00", freq='H')
         solar_data = []
@@ -453,13 +471,18 @@ class ClimateData:
             })
         
         fig = go.Figure()
-        for data in solar_data:
+        colors = ['orange', 'blue']  # Summer = orange, Winter = blue
+        labels = ['Summer Solstice (Dec 21)', 'Winter Solstice (Jun 21)']
+        
+        for i, data in enumerate(solar_data):
             fig.add_trace(go.Scatterpolar(
                 r=data['altitude'],
                 theta=data['azimuth'],
-                mode='lines',
-                name=data['date'],
-                line=dict(width=2)
+                mode='lines+markers',
+                name=labels[i],
+                line=dict(color=colors[i], width=2),
+                marker=dict(size=6, color=colors[i]),
+                opacity=0.8
             ))
         
         fig.update_layout(
@@ -478,16 +501,10 @@ class ClimateData:
                     ticktext=["N", "E", "S", "W"]
                 )
             ),
-            showlegend=True
+            showlegend=True,
+            template='plotly_white'
         )
         st.plotly_chart(fig, use_container_width=True)
-        
-        # Shading recommendations
-        max_altitude = max([max(data['altitude']) for data in solar_data])
-        st.markdown(f"**Maximum solar altitude**: {max_altitude:.1f}°")
-        st.markdown("**Shading Recommendations**:")
-        st.markdown(f"- Use overhangs or louvers angled to block sun above {max_altitude-10:.1f}° in summer.")
-        st.markdown("- Allow low-angle winter sun for passive heating.")
 
     def plot_temperature_range(self, location: ClimateLocation, epw_data: pd.DataFrame):
         """Plot monthly temperature ranges with design conditions."""
@@ -544,12 +561,13 @@ class ClimateData:
             yaxis_title="Temperature (°C)",
             xaxis=dict(tickmode='array', tickvals=list(range(1, 13)), ticktext=month_names),
             legend=dict(yanchor="top", y=0.99, xanchor="left", x=0.01),
-            showlegend=True
+            showlegend=True,
+            template='plotly_white'
         )
         st.plotly_chart(fig, use_container_width=True)
 
     def plot_wind_rose(self, epw_data: pd.DataFrame):
-        """Plot wind rose diagram."""
+        """Plot wind rose diagram with improved clarity, inspired by Climate Consultant."""
         st.subheader("Wind Rose")
         
         wind_speed = pd.to_numeric(epw_data[21], errors='coerce').values
@@ -558,10 +576,11 @@ class ClimateData:
         wind_speed = wind_speed[valid_mask]
         wind_direction = wind_direction[valid_mask]
         
-        # Bin data
-        speed_bins = [0, 2, 4, 6, 8, 10, np.inf]
-        direction_bins = np.linspace(0, 360, 17)[:-1]
-        speed_labels = ['0-2', '2-4', '4-6', '6-8', '8-10', '10+']
+        # Bin data with 8 directions and tailored speed bins (based on Geelong’s mean wind speed of 4.0 m/s)
+        speed_bins = [0, 2, 4, 6, 8, np.inf]
+        direction_bins = np.linspace(0, 360, 9)[:-1]
+        speed_labels = ['0-2 m/s', '2-4 m/s', '4-6 m/s', '6-8 m/s', '8+ m/s']
+        direction_labels = ['N', 'NE', 'E', 'SE', 'S', 'SW', 'W', 'NW']
         
         hist = np.histogram2d(
             wind_direction, wind_speed,
@@ -571,16 +590,15 @@ class ClimateData:
         hist = hist * 100  # Convert to percentage
         
         fig = go.Figure()
+        colors = ['#E6F0FF', '#B3D1FF', '#80B2FF', '#4D94FF', '#1A75FF']  # Light to dark blue gradient
+        
         for i, speed_label in enumerate(speed_labels):
             fig.add_trace(go.Barpolar(
                 r=hist[:, i],
                 theta=direction_bins,
-                width=22.5,
+                width=45,
                 name=speed_label,
-                marker=dict(
-                    colorscale='Viridis',
-                    showscale=True
-                ),
+                marker=dict(color=colors[i]),
                 opacity=0.8
             ))
         
@@ -595,11 +613,12 @@ class ClimateData:
                 angularaxis=dict(
                     direction="clockwise",
                     rotation=90,
-                    tickvals=[0, 90, 180, 270],
-                    ticktext=["N", "E", "S", "W"]
+                    tickvals=direction_bins,
+                    ticktext=direction_labels
                 )
             ),
-            showlegend=True
+            showlegend=True,
+            template='plotly_white'
         )
         st.plotly_chart(fig, use_container_width=True)
 
@@ -622,5 +641,5 @@ class ClimateData:
 
 if __name__ == "__main__":
     climate_data = ClimateData()
-    session_state = {"building_info": {"country": "Iceland", "city": "Reykjavik"}, "page": "Climate Data"}
+    session_state = {"building_info": {"country": "Australia", "city": "Geelong"}, "page": "Climate Data"}
     climate_data.display_climate_input(session_state)