refined

app.py

@@ -19,185 +19,215 @@ LIGHTS_1BR = 5
 LIGHTS_2BR = 8
 LIGHT_POWER = 6  # Watts per light
 
+
 def initialize_session_state():
     """Initialize session state variables"""
     defaults = {
-        'solar_panels': 100,
-        'batteries': 50,
-        'panel_price': 13000,
-        'battery_price': 39000,
-        'grid_price': 28.44
+        "solar_panels": 100,
+        "batteries": 50,
+        "panel_price": 13000,
+        "battery_price": 39000,
+        "grid_price": 28.44,
     }
     for key, value in defaults.items():
         if key not in st.session_state:
             st.session_state[key] = value
 
+
 def calculate_lighting_consumption(occupancy_1br: float, occupancy_2br: float) -> float:
     """Calculate daily lighting consumption"""
     return (
-        (occupancy_1br * ONE_BR_UNITS * LIGHTS_1BR * LIGHT_POWER / 1000) +
-        (occupancy_2br * TWO_BR_UNITS * LIGHTS_2BR * LIGHT_POWER / 1000)
+        (occupancy_1br * ONE_BR_UNITS * LIGHTS_1BR * LIGHT_POWER / 1000)
+        + (occupancy_2br * TWO_BR_UNITS * LIGHTS_2BR * LIGHT_POWER / 1000)
     ) * 24  # Daily kWh
 
-def calculate_appliance_consumption(occupancy_1br: float, occupancy_2br: float) -> float:
+
+def calculate_appliance_consumption(
+    occupancy_1br: float, occupancy_2br: float
+) -> float:
     """Calculate daily appliance consumption"""
     return (
-        (occupancy_1br * ONE_BR_UNITS * (250 - (LIGHTS_1BR * LIGHT_POWER * 24 / 1000))) +
-        (occupancy_2br * TWO_BR_UNITS * (400 - (LIGHTS_2BR * LIGHT_POWER * 24 / 1000)))
+        occupancy_1br * ONE_BR_UNITS * (250 - (LIGHTS_1BR * LIGHT_POWER * 24 / 1000))
+    ) + (
+        occupancy_2br * TWO_BR_UNITS * (400 - (LIGHTS_2BR * LIGHT_POWER * 24 / 1000))
     )  # Daily kWh
 
-def total_consumption(occupancy_1br: float, occupancy_2br: float, common_area: float) -> float:
+
+def total_consumption(
+    occupancy_1br: float, occupancy_2br: float, common_area: float
+) -> float:
     """Calculate total monthly consumption"""
     lighting = calculate_lighting_consumption(occupancy_1br, occupancy_2br)
     appliances = calculate_appliance_consumption(occupancy_1br, occupancy_2br)
     return (lighting + appliances + common_area) * 30  # Monthly kWh
 
+
 def solar_production(panels: int) -> float:
     """Monthly solar production with losses"""
     return panels * SOLAR_PANEL_RATING * 5 * 0.8 * 30 / 1000  # 5 sun hours
 
+
 def battery_storage(batteries: int) -> float:
     """Usable battery capacity"""
     return batteries * BATTERY_CAPACITY * BATTERY_VOLTAGE * 0.8 / 1000
 
+
 def financial_analysis(consumption: float, production: float, storage: float) -> Dict:
     """Detailed financial calculations"""
     solar_used = min(production, consumption)
     grid_purchased = max(consumption - solar_used - storage, 0)
-    
+
     return {
-        'solar_contribution': solar_used / consumption * 100,
-        'grid_dependency': grid_purchased / consumption * 100,
-        'monthly_savings': (consumption * st.session_state.grid_price) - 
-                          (grid_purchased * st.session_state.grid_price),
-        'payback_period': (st.session_state.solar_panels * st.session_state.panel_price +
-                          st.session_state.batteries * st.session_state.battery_price) /
-                          ((consumption - grid_purchased) * st.session_state.grid_price * 12)
+        "solar_contribution": solar_used / consumption * 100,
+        "grid_dependency": grid_purchased / consumption * 100,
+        "monthly_savings": (consumption * st.session_state.grid_price)
+        - (grid_purchased * st.session_state.grid_price),
+        "payback_period": (
+            st.session_state.solar_panels * st.session_state.panel_price
+            + st.session_state.batteries * st.session_state.battery_price
+        )
+        / ((consumption - grid_purchased) * st.session_state.grid_price * 12),
     }
 
-def create_consumption_breakdown(occupancy_1br: float, occupancy_2br: float, common_area: float):
+
+def create_consumption_breakdown(
+    occupancy_1br: float, occupancy_2br: float, common_area: float
+):
     """Create detailed consumption breakdown"""
     breakdown = {
-        'Lighting': calculate_lighting_consumption(occupancy_1br, occupancy_2br) * 30,
-        'Appliances': calculate_appliance_consumption(occupancy_1br, occupancy_2br) * 30,
-        'Common Areas': common_area * 30
+        "Lighting": calculate_lighting_consumption(occupancy_1br, occupancy_2br) * 30,
+        "Appliances": calculate_appliance_consumption(occupancy_1br, occupancy_2br)
+        * 30,
+        "Common Areas": common_area * 30,
     }
-    return pd.DataFrame.from_dict(breakdown, orient='index', columns=['kWh'])
+    return pd.DataFrame.from_dict(breakdown, orient="index", columns=["kWh"])
+
 
 # Streamlit Interface
 def main():
     st.set_page_config("Solar Analysis Suite", "🌞", "wide")
     initialize_session_state()
-    
+
     st.title("🌞 Advanced Solar Performance Analyzer")
-    
+
     with st.sidebar:
         st.header("System Configuration")
-        st.number_input("Solar Panels", 1, 1000, key='solar_panels')
-        st.number_input("Batteries", 0, 500, key='batteries')
-        st.number_input("Panel Price (Ksh)", 1000, 50000, key='panel_price')
-        st.number_input("Battery Price (Ksh)", 5000, 100000, key='battery_price')
-        st.number_input("Grid Price (Ksh/kWh)", 10.0, 50.0, key='grid_price')
-    
+        st.number_input("Solar Panels", 1, 1000, key="solar_panels")
+        st.number_input("Batteries", 0, 500, key="batteries")
+        st.number_input("Panel Price (Ksh)", 1000, 50000, key="panel_price")
+        st.number_input("Battery Price (Ksh)", 5000, 100000, key="battery_price")
+        st.number_input("Grid Price (Ksh/kWh)", 10.0, 50.0, key="grid_price")
+
     scenarios = {
         "Low Occupancy": {"1br": 0.0, "2br": 1.0, "common": 5.904},
         "Medium Occupancy": {"1br": 0.25, "2br": 1.0, "common": 5.904},
-        "High Occupancy": {"1br": 0.5, "2br": 1.0, "common": 5.904}
+        "High Occupancy": {"1br": 0.5, "2br": 1.0, "common": 5.904},
     }
-    
+
     analysis_data = []
     for name, params in scenarios.items():
-        consumption = total_consumption(
-            params["1br"], params["2br"], params["common"]
-        )
+        consumption = total_consumption(params["1br"], params["2br"], params["common"])
         production = solar_production(st.session_state.solar_panels)
         storage = battery_storage(st.session_state.batteries)
         financials = financial_analysis(consumption, production, storage)
-        
-        analysis_data.append({
-            "Scenario": name,
-            "Consumption": consumption,
-            "Production": production,
-            **financials
-        })
-    
+
+        analysis_data.append(
+            {
+                "Scenario": name,
+                "Consumption": consumption,
+                "Production": production,
+                **financials,
+            }
+        )
+
     df = pd.DataFrame(analysis_data)
-    
+
     # Energy Flow Analysis
     st.header("Energy Flow Composition")
     fig, ax = plt.subplots(figsize=(10, 6))
-    sns.barplot(df.melt(id_vars=['Scenario'], 
-                value_vars=['solar_contribution', 'grid_dependency'],
-                x='Scenario', y='value', hue='variable', ax=ax)
+    sns.barplot(
+        df.melt(
+            id_vars=["Scenario"],
+            value_vars=["solar_contribution", "grid_dependency"],
+            x="Scenario",
+            y="value",
+            hue="variable",
+            ax=ax,
+        )
+    )
     ax.set_ylabel("Percentage (%)")
     ax.set_title("Energy Contribution Breakdown")
     st.pyplot(fig)
-    
+
     with st.expander("🔍 Energy Flow Interpretation"):
-        st.markdown("""
+        st.markdown(
+            """
         **Understanding the Chart:**
         - **Solar Contribution**: Percentage of total energy needs met by solar production
         - **Grid Dependency**: Remaining energy required from grid
         - Ideal scenario shows high solar contribution with minimal grid dependency
-        """)
-    
+        """
+        )
+
     # Financial Analysis
     st.header("Financial Performance Metrics")
-    
+
     # Metric 1: Monthly Savings
     fig1, ax1 = plt.subplots(figsize=(10, 4))
-    sns.barplot(data=df, x='Scenario', y='monthly_savings', ax=ax1)
+    sns.barplot(data=df, x="Scenario", y="monthly_savings", ax=ax1)
     ax1.set_title("Monthly Cost Savings")
     ax1.set_ylabel("Ksh")
     st.pyplot(fig1)
-    
+
     with st.expander("💵 Savings Analysis"):
-        st.markdown(f"""
+        st.markdown(
+            f"""
         **Key Observations:**
         - Savings calculated as: `(Total Consumption × Grid Price) - (Grid Purchased × Grid Price)`
         - Current Grid Price: Ksh {st.session_state.grid_price}/kWh
         - Maximum potential savings limited by solar production capacity
-        """)
-        st.table(df[['Scenario', 'Consumption', 'Production', 'monthly_savings']])
-    
+        """
+        )
+        st.table(df[["Scenario", "Consumption", "Production", "monthly_savings"]])
+
     # Metric 2: Payback Period
     fig2, ax2 = plt.subplots(figsize=(10, 4))
-    sns.barplot(data=df, x='Scenario', y='payback_period', ax=ax2)
+    sns.barplot(data=df, x="Scenario", y="payback_period", ax=ax2)
     ax2.set_title("System Payback Period")
     ax2.set_ylabel("Years")
     st.pyplot(fig2)
-    
+
     with st.expander("⏳ Payback Explanation"):
-        st.markdown(f"""
+        st.markdown(
+            f"""
         **Calculation Methodology:**
         - Total Investment: (Panels × {st.session_state.panel_price:,}Ksh) + (Batteries × {st.session_state.battery_price:,}Ksh)
         - Annual Savings: Monthly Savings × 12
         - Payback Period = Total Investment / Annual Savings
-        """)
-        st.table(df[['Scenario', 'payback_period']])
-    
+        """
+        )
+        st.table(df[["Scenario", "payback_period"]])
+
     # Consumption Breakdown
     st.header("Detailed Consumption Analysis")
     scenario_select = st.selectbox("Select Scenario", list(scenarios.keys()))
-    
+
     selected_params = scenarios[scenario_select]
     breakdown_df = create_consumption_breakdown(
-        selected_params["1br"], 
-        selected_params["2br"], 
-        selected_params["common"]
+        selected_params["1br"], selected_params["2br"], selected_params["common"]
     )
-    
+
     col1, col2 = st.columns(2)
     with col1:
         st.subheader("Energy Composition")
         fig3, ax3 = plt.subplots()
-        breakdown_df.plot.pie(y='kWh', ax=ax3, autopct='%1.1f%%')
+        breakdown_df.plot.pie(y="kWh", ax=ax3, autopct="%1.1f%%")
         st.pyplot(fig3)
-    
+
     with col2:
         st.subheader("Component Breakdown")
         st.table(breakdown_df)
-        
+
         analysis_text = f"""
         **Key Insights for {scenario_select}:**
         - Lighting contributes {breakdown_df.loc['Lighting', 'kWh']/breakdown_df.sum().values[0]*100:.1f}% of total consumption
@@ -206,5 +236,6 @@ def main():
         """
         st.markdown(analysis_text)
 
+
 if __name__ == "__main__":
     main()