test4

app.py

@@ -19,6 +19,7 @@ LIGHTS_1BR = 5
 LIGHTS_2BR = 8
 LIGHT_POWER = 6  # Watts per light
 
+
 def initialize_session_state():
     """Initialize session state variables"""
     defaults = {
@@ -32,6 +33,7 @@ def initialize_session_state():
         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 (
@@ -39,6 +41,7 @@ def calculate_lighting_consumption(occupancy_1br: float, occupancy_2br: float) -
         + (occupancy_2br * TWO_BR_UNITS * LIGHTS_2BR * LIGHT_POWER / 1000)
     ) * 6  # 6 hours per day
 
+
 def calculate_appliance_consumption(
     occupancy_1br: float, occupancy_2br: float
 ) -> float:
@@ -49,6 +52,7 @@ def calculate_appliance_consumption(
         occupancy_2br * TWO_BR_UNITS * (400 - (LIGHTS_2BR * LIGHT_POWER * 6 / 1000))
     )  # Daily kWh
 
+
 def total_consumption(
     occupancy_1br: float, occupancy_2br: float, common_area: float
 ) -> float:
@@ -57,41 +61,50 @@ def total_consumption(
     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"""
-    daily_production = panels * SOLAR_PANEL_RATING * 5 * (1 - SYSTEM_LOSSES) / 1000  # 5 sun hours
+    daily_production = (
+        panels * SOLAR_PANEL_RATING * 5 * (1 - SYSTEM_LOSSES) / 1000
+    )  # 5 sun hours
     return daily_production * 30  # Monthly kWh
 
+
 def battery_storage(batteries: int) -> float:
     """Usable battery capacity"""
     return batteries * BATTERY_CAPACITY * BATTERY_VOLTAGE * 0.8 / 1000  # kWh
 
+
 def financial_analysis(consumption: float, production: float, storage: float) -> Dict:
     """Detailed financial calculations"""
     solar_used = min(production, consumption)
     surplus = max(0, production - consumption)
     feed_in_revenue = surplus * FEED_IN_TARIFF / 100  # Convert to Ksh from cents/kWh
-    
+
     # Account for battery storage
     grid_purchased = max(0, consumption - solar_used)
     if storage > 0:
         # Battery can offset some grid purchases
         grid_offset = min(grid_purchased, storage)
         grid_purchased -= grid_offset
-    
-    monthly_savings = (consumption * st.session_state.grid_price / 100) - (grid_purchased * st.session_state.grid_price / 100) + feed_in_revenue
-    
+
+    monthly_savings = (
+        (consumption * st.session_state.grid_price / 100)
+        - (grid_purchased * st.session_state.grid_price / 100)
+        + feed_in_revenue
+    )
+
     total_investment = (
         st.session_state.solar_panels * st.session_state.panel_price
         + st.session_state.batteries * st.session_state.battery_price
     )
-    
+
     # Avoid division by zero
     if monthly_savings > 0:
         payback_years = total_investment / (monthly_savings * 12)
     else:
-        payback_years = float('inf')
-        
+        payback_years = float("inf")
+
     return {
         "consumption": consumption,
         "production": production,
@@ -102,24 +115,28 @@ def financial_analysis(consumption: float, production: float, storage: float) ->
         "grid_purchased": grid_purchased,
     }
 
+
 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,
+        "Appliances": calculate_appliance_consumption(occupancy_1br, occupancy_2br)
+        * 30,
         "Common Areas": common_area * 30,
     }
     return pd.DataFrame.from_dict(breakdown, orient="index", columns=["kWh"])
 
+
 # Streamlit Interface
 def main():
     st.set_page_config(page_title="Solar Analysis Suite", page_icon="🌞", layout="wide")
     initialize_session_state()
-    
+
     # Custom CSS
-    st.markdown("""
+    st.markdown(
+        """
         <style>
         .main .block-container {padding-top: 2rem;}
         h1, h2, h3 {color: #1E88E5;}
@@ -139,61 +156,99 @@ def main():
             color: white;
         }
         </style>
-    """, unsafe_allow_html=True)
-    
+    """,
+        unsafe_allow_html=True,
+    )
+
     # Header with logo
     col1, col2 = st.columns([1, 4])
     with col1:
         st.image("https://img.icons8.com/fluency/96/000000/sun.png", width=100)
     with col2:
         st.title("🌞 Advanced Solar Performance Analyzer")
-        st.markdown("Optimize your apartment complex solar installation with data-driven insights")
-    
+        st.markdown(
+            "Optimize your apartment complex solar installation with data-driven insights"
+        )
+
     # Sidebar for system configuration
     with st.sidebar:
         st.header("System Configuration")
-        
+
         # Add a nice header image
         st.image("https://img.icons8.com/color/96/000000/solar-panel.png", width=80)
-        
+
         # Create tabs for different settings
         tab1, tab2 = st.tabs(["Hardware", "Pricing"])
-        
+
         with tab1:
-            st.slider("Number of Solar Panels", 1, 300, key="solar_panels", help="Each panel rated at 625W")
-            st.slider("Number of Batteries", 0, 150, key="batteries", help="Each battery has 200Ah capacity at 12V")
-        
+            st.number_input(
+                "Number of Solar Panels",
+                1,
+                300,
+                step=5,
+                key="solar_panels",
+                help="Each panel rated at 625W",
+            )
+            st.number_input(
+                "Number of Batteries",
+                0,
+                150,
+                step=5,
+                key="batteries",
+                help="Each battery has 200Ah capacity at 12V",
+            )
+
         with tab2:
-            st.number_input("Panel Price (Ksh)", 1000, 50000, step=500, key="panel_price", 
-                          help="Cost per solar panel")
-            st.number_input("Battery Price (Ksh)", 5000, 100000, step=1000, key="battery_price",
-                          help="Cost per battery unit")
-            st.number_input("Grid Price (Ksh/kWh)", 10.0, 50.0, step=0.1, key="grid_price",
-                          help="Current electricity price from the grid")
-        
+            st.number_input(
+                "Panel Price (Ksh)",
+                1000,
+                50000,
+                step=500,
+                key="panel_price",
+                help="Cost per solar panel",
+            )
+            st.number_input(
+                "Battery Price (Ksh)",
+                5000,
+                100000,
+                step=1000,
+                key="battery_price",
+                help="Cost per battery unit",
+            )
+            st.number_input(
+                "Grid Price (Ksh/kWh)",
+                10.0,
+                50.0,
+                step=0.1,
+                key="grid_price",
+                help="Current electricity price from the grid",
+            )
+
         st.markdown("---")
-        st.markdown("""
+        st.markdown(
+            """
         📊 **System Totals**
         - **Total Panel Capacity**: {0:.1f} kW
         - **Total Battery Storage**: {1:.1f} kWh
         - **Total Investment**: {2:,.0f} Ksh
         """.format(
-            st.session_state.solar_panels * SOLAR_PANEL_RATING / 1000,
-            battery_storage(st.session_state.batteries),
-            st.session_state.solar_panels * st.session_state.panel_price + 
-            st.session_state.batteries * st.session_state.battery_price
-        ))
+                st.session_state.solar_panels * SOLAR_PANEL_RATING / 1000,
+                battery_storage(st.session_state.batteries),
+                st.session_state.solar_panels * st.session_state.panel_price
+                + st.session_state.batteries * st.session_state.battery_price,
+            )
+        )
 
     # Main content
     # Create scenarios with varying occupancy levels
     scenarios = {}
-    
+
     # Common area consumption remains constant
     common_area_consumption = 5.904  # kWh per day
-    
+
     # Generate scenarios with different occupancy combinations
     occupancy_levels = [0.0, 0.25, 0.50, 0.75, 1.0]
-    
+
     # Create scenarios for 1BR fixed, varying 2BR
     for br1_level in occupancy_levels:
         for br2_level in occupancy_levels:
@@ -201,13 +256,15 @@ def main():
             scenarios[scenario_name] = {
                 "1br": br1_level,
                 "2br": br2_level,
-                "common": common_area_consumption
+                "common": common_area_consumption,
             }
-    
+
     # Analysis tabs
     st.markdown("---")
-    tab1, tab2, tab3 = st.tabs(["📊 Energy Analysis", "💰 Financial Metrics", "🔍 Detailed Breakdown"])
-    
+    tab1, tab2, tab3 = st.tabs(
+        ["📊 Energy Analysis", "💰 Financial Metrics", "🔍 Detailed Breakdown"]
+    )
+
     # Prepare analysis data for all scenarios
     analysis_data = []
     for name, params in scenarios.items():
@@ -215,61 +272,64 @@ def main():
         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,
-            **financials
-        })
-    
+        analysis_data.append({"Scenario": name, **financials})
+
     df = pd.DataFrame(analysis_data)
-    
+
     # Tab 1: Energy Analysis
     with tab1:
         st.header("Energy Flow Analysis")
-        
+
         # Allow filtering by 1BR occupancy
         one_br_filter = st.selectbox(
-            "Filter by 1BR Occupancy", 
+            "Filter by 1BR Occupancy",
             ["All"] + [f"{int(level*100)}%" for level in occupancy_levels],
-            help="Filter scenarios by 1BR occupancy level"
+            help="Filter scenarios by 1BR occupancy level",
         )
-        
+
         # Filter the dataframe based on selection
         filtered_df = df
         if one_br_filter != "All":
             occupancy_value = int(one_br_filter.replace("%", ""))
             filtered_df = df[df["Scenario"].str.contains(f"1BR: {occupancy_value}%")]
-        
+
         # Chart 1: Energy Balance
         st.subheader("Energy Balance by Scenario")
-        
+
         energy_fig = plt.figure(figsize=(12, 7))
         ax = energy_fig.add_subplot(111)
-        
+
         # Create data for stacked bar chart
         chart_data = filtered_df.copy()
         chart_data["grid_energy"] = chart_data["grid_purchased"]
-        chart_data["solar_energy"] = chart_data["consumption"] - chart_data["grid_purchased"]
-        
+        chart_data["solar_energy"] = (
+            chart_data["consumption"] - chart_data["grid_purchased"]
+        )
+
         # Create normalized stacked bar chart
         chart_data = chart_data.set_index("Scenario")
-        energy_proportions = chart_data[["solar_energy", "grid_energy"]].div(chart_data["consumption"], axis=0) * 100
+        energy_proportions = (
+            chart_data[["solar_energy", "grid_energy"]].div(
+                chart_data["consumption"], axis=0
+            )
+            * 100
+        )
         energy_proportions = energy_proportions.reset_index()
-        
+
         # Reshape for seaborn
         energy_melt = pd.melt(
-            energy_proportions, 
-            id_vars=["Scenario"], 
+            energy_proportions,
+            id_vars=["Scenario"],
             value_vars=["solar_energy", "grid_energy"],
             var_name="Energy Source",
-            value_name="Percentage"
+            value_name="Percentage",
         )
-        
+
         # Rename for better labels
-        energy_melt["Energy Source"] = energy_melt["Energy Source"].replace({
-            "solar_energy": "Solar Generated",
-            "grid_energy": "Grid Purchased"
-        })
-        
+        energy_melt["Energy Source"] = energy_melt["Energy Source"].replace(
+            {"solar_energy": "Solar Generated", "grid_energy": "Grid Purchased"}
+        )
+
         # Plot with seaborn
         sns.set_theme(style="whitegrid")
         sns.barplot(
@@ -278,34 +338,42 @@ def main():
             y="Percentage",
             hue="Energy Source",
             palette=["#4CAF50", "#F44336"],
-            ax=ax
+            ax=ax,
         )
         ax.set_ylabel("Energy Contribution (%)")
         ax.set_title("Energy Source Distribution by Occupancy Scenario")
         plt.xticks(rotation=45, ha="right")
         plt.tight_layout()
         st.pyplot(energy_fig)
-        
+
         # Detailed metrics
         col1, col2, col3 = st.columns(3)
         with col1:
             st.metric(
-                "Avg. Solar Contribution", 
+                "Avg. Solar Contribution",
                 f"{filtered_df['solar_contribution'].mean():.1f}%",
-                f"{filtered_df['solar_contribution'].mean() - 50:.1f}%" if filtered_df['solar_contribution'].mean() > 50 else f"{filtered_df['solar_contribution'].mean() - 50:.1f}%"
+                (
+                    f"{filtered_df['solar_contribution'].mean() - 50:.1f}%"
+                    if filtered_df["solar_contribution"].mean() > 50
+                    else f"{filtered_df['solar_contribution'].mean() - 50:.1f}%"
+                ),
             )
         with col2:
             st.metric(
-                "Avg. Grid Dependency", 
+                "Avg. Grid Dependency",
                 f"{filtered_df['grid_dependency'].mean():.1f}%",
-                f"{50 - filtered_df['grid_dependency'].mean():.1f}%" if filtered_df['grid_dependency'].mean() < 50 else f"{50 - filtered_df['grid_dependency'].mean():.1f}%"
+                (
+                    f"{50 - filtered_df['grid_dependency'].mean():.1f}%"
+                    if filtered_df["grid_dependency"].mean() < 50
+                    else f"{50 - filtered_df['grid_dependency'].mean():.1f}%"
+                ),
             )
         with col3:
             st.metric(
-                "Production/Consumption Ratio", 
-                f"{(filtered_df['production'].mean() / filtered_df['consumption'].mean() * 100):.1f}%"
+                "Production/Consumption Ratio",
+                f"{(filtered_df['production'].mean() / filtered_df['consumption'].mean() * 100):.1f}%",
             )
-        
+
         with st.expander("🔍 Energy Flow Interpretation"):
             st.markdown(
                 """
@@ -320,90 +388,104 @@ def main():
                 3. **Battery Storage**: Helps utilize excess daytime production for nighttime use
                 """
             )
-    
+
     # Tab 2: Financial Metrics
     with tab2:
         st.header("Financial Performance Analysis")
-        
+
         # Allow filtering by 2BR occupancy
         two_br_filter = st.selectbox(
-            "Filter by 2BR Occupancy", 
+            "Filter by 2BR Occupancy",
             ["All"] + [f"{int(level*100)}%" for level in occupancy_levels],
-            help="Filter scenarios by 2BR occupancy level"
+            help="Filter scenarios by 2BR occupancy level",
         )
-        
+
         # Filter the dataframe based on selection
         filtered_fin_df = df
         if two_br_filter != "All":
             occupancy_value = int(two_br_filter.replace("%", ""))
-            filtered_fin_df = df[df["Scenario"].str.contains(f"2BR: {occupancy_value}%")]
-        
+            filtered_fin_df = df[
+                df["Scenario"].str.contains(f"2BR: {occupancy_value}%")
+            ]
+
         # Monthly Savings Chart
         st.subheader("Monthly Cost Savings")
-        
+
         # Fix large values
-        filtered_fin_df['monthly_savings_fixed'] = filtered_fin_df['monthly_savings'].clip(0, 100000)
-        
+        filtered_fin_df["monthly_savings_fixed"] = filtered_fin_df[
+            "monthly_savings"
+        ].clip(0, 100000)
+
         fig1, ax1 = plt.subplots(figsize=(12, 6))
         sns.barplot(
             data=filtered_fin_df,
             x="Scenario",
             y="monthly_savings_fixed",
             palette="viridis",
-            ax=ax1
+            ax=ax1,
         )
         ax1.set_title("Monthly Cost Savings by Scenario")
         ax1.set_ylabel("Ksh")
         plt.xticks(rotation=45, ha="right")
         plt.tight_layout()
         st.pyplot(fig1)
-        
+
         # Payback Period Chart
         st.subheader("System Payback Period")
-        
+
         # Fix large values
-        filtered_fin_df['payback_period_fixed'] = filtered_fin_df['payback_period'].clip(0, 30)
-        
+        filtered_fin_df["payback_period_fixed"] = filtered_fin_df[
+            "payback_period"
+        ].clip(0, 30)
+
         fig2, ax2 = plt.subplots(figsize=(12, 6))
         sns.barplot(
             data=filtered_fin_df,
             x="Scenario",
             y="payback_period_fixed",
             palette="rocket_r",
-            ax=ax2
+            ax=ax2,
         )
         ax2.set_title("Investment Payback Period by Scenario")
         ax2.set_ylabel("Years")
         plt.xticks(rotation=45, ha="right")
         plt.tight_layout()
         st.pyplot(fig2)
-        
+
         # Financial summary metrics
         col1, col2, col3 = st.columns(3)
         with col1:
-            avg_savings = filtered_fin_df['monthly_savings'].mean()
+            avg_savings = filtered_fin_df["monthly_savings"].mean()
             st.metric(
-                "Avg. Monthly Savings", 
+                "Avg. Monthly Savings",
                 f"{avg_savings:,.0f} Ksh",
-                f"{avg_savings - df['monthly_savings'].mean():,.0f} Ksh" if avg_savings > df['monthly_savings'].mean() else f"{avg_savings - df['monthly_savings'].mean():,.0f} Ksh"
+                (
+                    f"{avg_savings - df['monthly_savings'].mean():,.0f} Ksh"
+                    if avg_savings > df["monthly_savings"].mean()
+                    else f"{avg_savings - df['monthly_savings'].mean():,.0f} Ksh"
+                ),
             )
         with col2:
-            min_payback = filtered_fin_df['payback_period'].min()
+            min_payback = filtered_fin_df["payback_period"].min()
             st.metric(
-                "Best Payback Period", 
+                "Best Payback Period",
                 f"{min_payback:.1f} years",
-                help="Shortest time to recover investment"
+                help="Shortest time to recover investment",
             )
         with col3:
-            total_investment = (st.session_state.solar_panels * st.session_state.panel_price + 
-                               st.session_state.batteries * st.session_state.battery_price)
-            annual_roi = (avg_savings * 12 / total_investment) * 100 if total_investment > 0 else 0
+            total_investment = (
+                st.session_state.solar_panels * st.session_state.panel_price
+                + st.session_state.batteries * st.session_state.battery_price
+            )
+            annual_roi = (
+                (avg_savings * 12 / total_investment) * 100
+                if total_investment > 0
+                else 0
+            )
             st.metric(
-                "Annual ROI", 
-                f"{annual_roi:.1f}%",
-                help="Annual Return on Investment"
+                "Annual ROI", f"{annual_roi:.1f}%", help="Annual Return on Investment"
             )
-        
+
         with st.expander("💵 Financial Analysis Details"):
             st.markdown(
                 f"""
@@ -421,109 +503,124 @@ def main():
                 """
             )
             st.dataframe(
-                filtered_fin_df[["Scenario", "consumption", "production", "monthly_savings", "payback_period"]].sort_values("monthly_savings", ascending=False),
-                hide_index=True
+                filtered_fin_df[
+                    [
+                        "Scenario",
+                        "consumption",
+                        "production",
+                        "monthly_savings",
+                        "payback_period",
+                    ]
+                ].sort_values("monthly_savings", ascending=False),
+                hide_index=True,
             )
-    
+
     # Tab 3: Detailed Breakdown
     with tab3:
         st.header("Consumption Breakdown Analysis")
-        
+
         # Select specific scenario for detailed analysis
-        scenario_select = st.selectbox("Select Specific Scenario", list(scenarios.keys()))
+        scenario_select = st.selectbox(
+            "Select Specific Scenario", list(scenarios.keys())
+        )
         selected_params = scenarios[scenario_select]
-        
+
         # Create consumption breakdown
         breakdown_df = create_consumption_breakdown(
             selected_params["1br"], selected_params["2br"], selected_params["common"]
         )
-        
+
         total_kwh = breakdown_df["kWh"].sum()
-        
+
         # Add percentage column
         breakdown_df["Percentage"] = (breakdown_df["kWh"] / total_kwh * 100).round(1)
-        
+
         col1, col2 = st.columns([2, 3])
-        
+
         with col1:
             st.subheader("Energy Composition")
-            
+
             # Create a more attractive pie chart
             fig3 = plt.figure(figsize=(8, 8))
             ax3 = fig3.add_subplot(111)
-            
-            colors = ['#FF9800', '#2196F3', '#4CAF50']
+
+            colors = ["#FF9800", "#2196F3", "#4CAF50"]
             explode = (0.1, 0, 0)
-            
+
             wedges, texts, autotexts = ax3.pie(
-                breakdown_df["kWh"], 
-                labels=breakdown_df.index, 
-                autopct='%1.1f%%',
+                breakdown_df["kWh"],
+                labels=breakdown_df.index,
+                autopct="%1.1f%%",
                 explode=explode,
                 colors=colors,
                 shadow=True,
                 startangle=90,
-                textprops={'fontsize': 12}
+                textprops={"fontsize": 12},
             )
-            
+
             # Equal aspect ratio ensures that pie is drawn as a circle
-            ax3.axis('equal')
+            ax3.axis("equal")
             plt.tight_layout()
             st.pyplot(fig3)
-            
+
             # Show total consumption
             st.metric(
-                "Total Monthly Consumption", 
+                "Total Monthly Consumption",
                 f"{total_kwh:.1f} kWh",
-                help="Sum of all consumption components"
+                help="Sum of all consumption components",
             )
-            
+
         with col2:
             st.subheader("Detailed Component Analysis")
-            
+
             # Show breakdown as a horizontal bar chart
             fig4 = plt.figure(figsize=(10, 5))
             ax4 = fig4.add_subplot(111)
-            
+
             # Sort by consumption
             sorted_df = breakdown_df.sort_values("kWh", ascending=True)
-            
+
             # Create horizontal bar chart
             bars = sns.barplot(
-                y=sorted_df.index,
-                x="kWh",
-                data=sorted_df,
-                palette=colors[::-1],
-                ax=ax4
+                y=sorted_df.index, x="kWh", data=sorted_df, palette=colors[::-1], ax=ax4
             )
-            
+
             # Add data labels
             for i, v in enumerate(sorted_df["kWh"]):
-                ax4.text(v + 5, i, f"{v:.1f} kWh ({sorted_df['Percentage'].iloc[i]}%)", va='center')
-            
+                ax4.text(
+                    v + 5,
+                    i,
+                    f"{v:.1f} kWh ({sorted_df['Percentage'].iloc[i]}%)",
+                    va="center",
+                )
+
             ax4.set_title(f"Energy Consumption Breakdown - {scenario_select}")
             ax4.set_xlabel("Monthly Consumption (kWh)")
             ax4.set_ylabel("")
             plt.tight_layout()
             st.pyplot(fig4)
-            
+
             # Add scenario details
-            st.markdown(f"""
+            st.markdown(
+                f"""
             **Scenario Details:**
             - 1BR Units Occupancy: {selected_params['1br']*100:.0f}% ({selected_params['1br']*ONE_BR_UNITS:.0f} units)
             - 2BR Units Occupancy: {selected_params['2br']*100:.0f}% ({selected_params['2br']*TWO_BR_UNITS:.0f} units)
             - Common Areas Consumption: {selected_params['common']*30:.1f} kWh/month
-            """)
-            
+            """
+            )
+
             # Insight box
-            st.info(f"""
+            st.info(
+                f"""
             **Key Insights for {scenario_select}:**
             - Lighting contributes {breakdown_df.loc['Lighting', 'Percentage']:.1f}% of total consumption
             - Common areas account for {breakdown_df.loc['Common Areas', 'Percentage']:.1f}% of the total
             - {'2BR units dominate consumption at ' + str(selected_params['2br']*100) + '% occupancy' if selected_params['2br'] > selected_params['1br'] else '1BR units are the primary consumers at ' + str(selected_params['1br']*100) + '% occupancy'}
             - Total potential solar offset: {min(solar_production(st.session_state.solar_panels)/total_kwh*100, 100):.1f}%
-            """)
-    
+            """
+            )
+
     # Footer
     st.markdown("---")
     st.markdown(
@@ -532,8 +629,9 @@ def main():
         <p>Solar Analysis Suite v1.0 | Developed with ❤️ for sustainable energy solutions</p>
         </div>
         """,
-        unsafe_allow_html=True
+        unsafe_allow_html=True,
     )
 
+
 if __name__ == "__main__":
     main()