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"""
Scenario comparison visualization module for HVAC Load Calculator.
This module provides visualization tools for comparing different scenarios.
"""
import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
import plotly.express as px
from typing import Dict, List, Any, Optional, Tuple
import math
# Import calculation modules
from utils.cooling_load import CoolingLoadCalculator
from utils.heating_load import HeatingLoadCalculator
class ScenarioComparisonVisualization:
"""Class for scenario comparison visualization."""
@staticmethod
def create_scenario_summary_table(scenarios: Dict[str, Dict[str, Any]]) -> pd.DataFrame:
"""
Create a summary table of different scenarios.
Args:
scenarios: Dictionary with scenario data
Returns:
DataFrame with scenario summary
"""
# Initialize data
data = []
# Process scenarios
for scenario_name, scenario_data in scenarios.items():
# Extract cooling and heating loads
cooling_loads = scenario_data.get("cooling_loads", {})
heating_loads = scenario_data.get("heating_loads", {})
# Create summary row
row = {
"Scenario": scenario_name,
"Cooling Load (W)": cooling_loads.get("total", 0),
"Sensible Heat Ratio": cooling_loads.get("sensible_heat_ratio", 0),
"Heating Load (W)": heating_loads.get("total", 0)
}
# Add to data
data.append(row)
# Create DataFrame
df = pd.DataFrame(data)
return df
@staticmethod
def create_load_comparison_chart(scenarios: Dict[str, Dict[str, Any]], load_type: str = "cooling") -> go.Figure:
"""
Create a bar chart comparing loads across scenarios.
Args:
scenarios: Dictionary with scenario data
load_type: Type of load to compare ("cooling" or "heating")
Returns:
Plotly figure with load comparison
"""
# Initialize data
scenario_names = []
total_loads = []
component_loads = {}
# Process scenarios
for scenario_name, scenario_data in scenarios.items():
# Extract loads based on load type
if load_type == "cooling":
loads = scenario_data.get("cooling_loads", {})
components = ["walls", "roofs", "floors", "windows_conduction", "windows_solar",
"doors", "infiltration_sensible", "infiltration_latent",
"people_sensible", "people_latent", "lights", "equipment_sensible", "equipment_latent"]
else: # heating
loads = scenario_data.get("heating_loads", {})
components = ["walls", "roofs", "floors", "windows", "doors",
"infiltration_sensible", "infiltration_latent",
"ventilation_sensible", "ventilation_latent"]
# Add scenario name
scenario_names.append(scenario_name)
# Add total load
total_loads.append(loads.get("total", 0))
# Add component loads
for component in components:
if component not in component_loads:
component_loads[component] = []
component_loads[component].append(loads.get(component, 0))
# Create figure
fig = go.Figure()
# Add total load bars
fig.add_trace(go.Bar(
x=scenario_names,
y=total_loads,
name="Total Load",
marker_color="rgba(55, 83, 109, 0.7)",
opacity=0.7
))
# Add component load bars
for component, loads in component_loads.items():
# Skip components with zero loads
if sum(loads) == 0:
continue
# Format component name for display
display_name = component.replace("_", " ").title()
fig.add_trace(go.Bar(
x=scenario_names,
y=loads,
name=display_name,
visible="legendonly"
))
# Update layout
title = f"{load_type.title()} Load Comparison"
y_title = f"{load_type.title()} Load (W)"
fig.update_layout(
title=title,
xaxis_title="Scenario",
yaxis_title=y_title,
barmode="group",
height=500,
legend=dict(
orientation="h",
yanchor="bottom",
y=1.02,
xanchor="right",
x=1
)
)
return fig
@staticmethod
def create_percentage_difference_chart(scenarios: Dict[str, Dict[str, Any]],
baseline_scenario: str,
load_type: str = "cooling") -> go.Figure:
"""
Create a bar chart showing percentage differences from a baseline scenario.
Args:
scenarios: Dictionary with scenario data
baseline_scenario: Name of the baseline scenario
load_type: Type of load to compare ("cooling" or "heating")
Returns:
Plotly figure with percentage difference chart
"""
# Check if baseline scenario exists
if baseline_scenario not in scenarios:
raise ValueError(f"Baseline scenario '{baseline_scenario}' not found in scenarios")
# Get baseline loads
if load_type == "cooling":
baseline_loads = scenarios[baseline_scenario].get("cooling_loads", {})
components = ["walls", "roofs", "floors", "windows_conduction", "windows_solar",
"doors", "infiltration_sensible", "infiltration_latent",
"people_sensible", "people_latent", "lights", "equipment_sensible", "equipment_latent"]
else: # heating
baseline_loads = scenarios[baseline_scenario].get("heating_loads", {})
components = ["walls", "roofs", "floors", "windows", "doors",
"infiltration_sensible", "infiltration_latent",
"ventilation_sensible", "ventilation_latent"]
baseline_total = baseline_loads.get("total", 0)
# Initialize data
scenario_names = []
percentage_diffs = []
component_diffs = {}
# Process scenarios (excluding baseline)
for scenario_name, scenario_data in scenarios.items():
if scenario_name == baseline_scenario:
continue
# Extract loads based on load type
if load_type == "cooling":
loads = scenario_data.get("cooling_loads", {})
else: # heating
loads = scenario_data.get("heating_loads", {})
# Add scenario name
scenario_names.append(scenario_name)
# Calculate percentage difference for total load
scenario_total = loads.get("total", 0)
if baseline_total != 0:
percentage_diff = (scenario_total - baseline_total) / baseline_total * 100
else:
percentage_diff = 0
percentage_diffs.append(percentage_diff)
# Calculate percentage differences for components
for component in components:
if component not in component_diffs:
component_diffs[component] = []
baseline_component = baseline_loads.get(component, 0)
scenario_component = loads.get(component, 0)
if baseline_component != 0:
component_diff = (scenario_component - baseline_component) / baseline_component * 100
else:
component_diff = 0
component_diffs[component].append(component_diff)
# Create figure
fig = go.Figure()
# Add total percentage difference bars
fig.add_trace(go.Bar(
x=scenario_names,
y=percentage_diffs,
name="Total Load",
marker_color="rgba(55, 83, 109, 0.7)",
opacity=0.7
))
# Add component percentage difference bars
for component, diffs in component_diffs.items():
# Skip components with zero differences
if sum([abs(diff) for diff in diffs]) == 0:
continue
# Format component name for display
display_name = component.replace("_", " ").title()
fig.add_trace(go.Bar(
x=scenario_names,
y=diffs,
name=display_name,
visible="legendonly"
))
# Update layout
title = f"{load_type.title()} Load Percentage Difference from {baseline_scenario}"
y_title = "Percentage Difference (%)"
fig.update_layout(
title=title,
xaxis_title="Scenario",
yaxis_title=y_title,
barmode="group",
height=500,
legend=dict(
orientation="h",
yanchor="bottom",
y=1.02,
xanchor="right",
x=1
)
)
# Add zero line
fig.add_shape(
type="line",
x0=-0.5,
x1=len(scenario_names) - 0.5,
y0=0,
y1=0,
line=dict(
color="black",
width=1,
dash="dash"
)
)
return fig
@staticmethod
def create_radar_chart(scenarios: Dict[str, Dict[str, Any]], load_type: str = "cooling") -> go.Figure:
"""
Create a radar chart comparing key metrics across scenarios.
Args:
scenarios: Dictionary with scenario data
load_type: Type of load to compare ("cooling" or "heating")
Returns:
Plotly figure with radar chart
"""
# Define metrics based on load type
if load_type == "cooling":
metrics = [
"total",
"total_sensible",
"total_latent",
"walls",
"roofs",
"windows_conduction",
"windows_solar",
"infiltration_sensible",
"people_sensible",
"lights",
"equipment_sensible"
]
metric_names = [
"Total Load",
"Sensible Load",
"Latent Load",
"Walls",
"Roofs",
"Windows (Conduction)",
"Windows (Solar)",
"Infiltration",
"People",
"Lights",
"Equipment"
]
else: # heating
metrics = [
"total",
"walls",
"roofs",
"floors",
"windows",
"doors",
"infiltration_sensible",
"ventilation_sensible"
]
metric_names = [
"Total Load",
"Walls",
"Roofs",
"Floors",
"Windows",
"Doors",
"Infiltration",
"Ventilation"
]
# Initialize figure
fig = go.Figure()
# Process scenarios
for scenario_name, scenario_data in scenarios.items():
# Extract loads based on load type
if load_type == "cooling":
loads = scenario_data.get("cooling_loads", {})
else: # heating
loads = scenario_data.get("heating_loads", {})
# Extract metric values
values = [loads.get(metric, 0) for metric in metrics]
# Add trace
fig.add_trace(go.Scatterpolar(
r=values,
theta=metric_names,
fill="toself",
name=scenario_name
))
# Update layout
title = f"{load_type.title()} Load Comparison (Radar Chart)"
fig.update_layout(
title=title,
polar=dict(
radialaxis=dict(
visible=True,
range=[0, max([max([scenarios[s].get(f"{load_type}_loads", {}).get(m, 0) for m in metrics]) for s in scenarios]) * 1.1]
)
),
height=600,
showlegend=True
)
return fig
@staticmethod
def create_parallel_coordinates_chart(scenarios: Dict[str, Dict[str, Any]]) -> go.Figure:
"""
Create a parallel coordinates chart comparing scenarios.
Args:
scenarios: Dictionary with scenario data
Returns:
Plotly figure with parallel coordinates chart
"""
# Initialize data
data = []
# Process scenarios
for scenario_name, scenario_data in scenarios.items():
# Extract cooling and heating loads
cooling_loads = scenario_data.get("cooling_loads", {})
heating_loads = scenario_data.get("heating_loads", {})
# Create data point
point = {
"Scenario": scenario_name,
"Cooling Load (W)": cooling_loads.get("total", 0),
"Heating Load (W)": heating_loads.get("total", 0),
"Sensible Heat Ratio": cooling_loads.get("sensible_heat_ratio", 0),
"Walls (Cooling)": cooling_loads.get("walls", 0),
"Windows (Cooling)": cooling_loads.get("windows_conduction", 0) + cooling_loads.get("windows_solar", 0),
"Internal Gains (Cooling)": cooling_loads.get("people_sensible", 0) + cooling_loads.get("lights", 0) + cooling_loads.get("equipment_sensible", 0),
"Walls (Heating)": heating_loads.get("walls", 0),
"Windows (Heating)": heating_loads.get("windows", 0),
"Infiltration (Heating)": heating_loads.get("infiltration_sensible", 0)
}
# Add to data
data.append(point)
# Create DataFrame
df = pd.DataFrame(data)
# Create figure
fig = px.parallel_coordinates(
df,
color="Cooling Load (W)",
labels={
"Scenario": "Scenario",
"Cooling Load (W)": "Cooling Load (W)",
"Heating Load (W)": "Heating Load (W)",
"Sensible Heat Ratio": "Sensible Heat Ratio",
"Walls (Cooling)": "Walls (Cooling)",
"Windows (Cooling)": "Windows (Cooling)",
"Internal Gains (Cooling)": "Internal Gains (Cooling)",
"Walls (Heating)": "Walls (Heating)",
"Windows (Heating)": "Windows (Heating)",
"Infiltration (Heating)": "Infiltration (Heating)"
},
color_continuous_scale=px.colors.sequential.Viridis
)
# Update layout
fig.update_layout(
title="Scenario Comparison (Parallel Coordinates)",
height=600
)
return fig
@staticmethod
def display_scenario_comparison(scenarios: Dict[str, Dict[str, Any]]) -> None:
"""
Display scenario comparison visualization in Streamlit.
Args:
scenarios: Dictionary with scenario data
"""
st.header("Scenario Comparison Visualization")
# Check if scenarios exist
if not scenarios:
st.warning("No scenarios available for comparison.")
return
# Create tabs for different visualizations
tab1, tab2, tab3, tab4, tab5 = st.tabs([
"Scenario Summary",
"Load Comparison",
"Percentage Difference",
"Radar Chart",
"Parallel Coordinates"
])
with tab1:
st.subheader("Scenario Summary")
df = ScenarioComparisonVisualization.create_scenario_summary_table(scenarios)
st.dataframe(df, use_container_width=True)
# Add download button for CSV
csv = df.to_csv(index=False).encode('utf-8')
st.download_button(
label="Download Scenario Summary as CSV",
data=csv,
file_name="scenario_summary.csv",
mime="text/csv"
)
with tab2:
st.subheader("Load Comparison")
# Add load type selector
load_type = st.radio(
"Select Load Type",
["cooling", "heating"],
horizontal=True,
key="load_comparison_type"
)
# Create and display chart
fig = ScenarioComparisonVisualization.create_load_comparison_chart(scenarios, load_type)
st.plotly_chart(fig, use_container_width=True)
with tab3:
st.subheader("Percentage Difference")
# Add baseline scenario selector
baseline_scenario = st.selectbox(
"Select Baseline Scenario",
list(scenarios.keys()),
key="baseline_scenario"
)
# Add load type selector
load_type = st.radio(
"Select Load Type",
["cooling", "heating"],
horizontal=True,
key="percentage_diff_type"
)
# Create and display chart
try:
fig = ScenarioComparisonVisualization.create_percentage_difference_chart(
scenarios, baseline_scenario, load_type
)
st.plotly_chart(fig, use_container_width=True)
except ValueError as e:
st.error(str(e))
with tab4:
st.subheader("Radar Chart")
# Add load type selector
load_type = st.radio(
"Select Load Type",
["cooling", "heating"],
horizontal=True,
key="radar_chart_type"
)
# Create and display chart
fig = ScenarioComparisonVisualization.create_radar_chart(scenarios, load_type)
st.plotly_chart(fig, use_container_width=True)
with tab5:
st.subheader("Parallel Coordinates")
# Create and display chart
fig = ScenarioComparisonVisualization.create_parallel_coordinates_chart(scenarios)
st.plotly_chart(fig, use_container_width=True)
# Create a singleton instance
scenario_comparison = ScenarioComparisonVisualization()
# Example usage
if __name__ == "__main__":
import streamlit as st
# Create sample scenarios
scenarios = {
"Base Case": {
"cooling_loads": {
"total": 5000,
"total_sensible": 4000,
"total_latent": 1000,
"sensible_heat_ratio": 0.8,
"walls": 1000,
"roofs": 800,
"floors": 200,
"windows_conduction": 500,
"windows_solar": 800,
"doors": 100,
"infiltration_sensible": 300,
"infiltration_latent": 200,
"people_sensible": 300,
"people_latent": 200,
"lights": 400,
"equipment_sensible": 400,
"equipment_latent": 600
},
"heating_loads": {
"total": 6000,
"walls": 1500,
"roofs": 1000,
"floors": 500,
"windows": 1200,
"doors": 200,
"infiltration_sensible": 800,
"infiltration_latent": 0,
"ventilation_sensible": 800,
"ventilation_latent": 0,
"internal_gains_offset": 1000
}
},
"Improved Insulation": {
"cooling_loads": {
"total": 4200,
"total_sensible": 3500,
"total_latent": 700,
"sensible_heat_ratio": 0.83,
"walls": 600,
"roofs": 500,
"floors": 150,
"windows_conduction": 500,
"windows_solar": 800,
"doors": 100,
"infiltration_sensible": 300,
"infiltration_latent": 200,
"people_sensible": 300,
"people_latent": 200,
"lights": 400,
"equipment_sensible": 400,
"equipment_latent": 300
},
"heating_loads": {
"total": 4500,
"walls": 900,
"roofs": 600,
"floors": 300,
"windows": 1200,
"doors": 200,
"infiltration_sensible": 800,
"infiltration_latent": 0,
"ventilation_sensible": 800,
"ventilation_latent": 0,
"internal_gains_offset": 1000
}
},
"Better Windows": {
"cooling_loads": {
"total": 4000,
"total_sensible": 3300,
"total_latent": 700,
"sensible_heat_ratio": 0.83,
"walls": 1000,
"roofs": 800,
"floors": 200,
"windows_conduction": 250,
"windows_solar": 400,
"doors": 100,
"infiltration_sensible": 300,
"infiltration_latent": 200,
"people_sensible": 300,
"people_latent": 200,
"lights": 400,
"equipment_sensible": 400,
"equipment_latent": 300
},
"heating_loads": {
"total": 5000,
"walls": 1500,
"roofs": 1000,
"floors": 500,
"windows": 600,
"doors": 200,
"infiltration_sensible": 800,
"infiltration_latent": 0,
"ventilation_sensible": 800,
"ventilation_latent": 0,
"internal_gains_offset": 1000
}
}
}
# Display scenario comparison
scenario_comparison.display_scenario_comparison(scenarios)
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