""" Data export module for HVAC Load Calculator. This module provides functionality for exporting calculation results. """ import streamlit as st import pandas as pd import numpy as np from typing import Dict, List, Any, Optional, Tuple import json import os import base64 import io from datetime import datetime import xlsxwriter class DataExport: """Class for data export functionality.""" @staticmethod def export_to_csv(data: Dict[str, Any], file_path: str = None) -> Optional[str]: """ Export data to CSV format. Args: data: Dictionary with data to export file_path: Optional path to save the CSV file Returns: CSV string if file_path is None, otherwise None """ try: # Create DataFrame from data df = pd.DataFrame(data) # Convert to CSV csv_data = df.to_csv(index=False) # Save to file if path provided if file_path: df.to_csv(file_path, index=False) return None # Return CSV string if no path provided return csv_data except Exception as e: st.error(f"Error exporting to CSV: {e}") return None @staticmethod def export_to_excel(data_dict: Dict[str, pd.DataFrame], file_path: str = None) -> Optional[bytes]: """ Export data to Excel format. Args: data_dict: Dictionary with sheet names and DataFrames file_path: Optional path to save the Excel file Returns: Excel bytes if file_path is None, otherwise None """ try: # Create Excel file in memory or on disk if file_path: writer = pd.ExcelWriter(file_path, engine='xlsxwriter') else: output = io.BytesIO() writer = pd.ExcelWriter(output, engine='xlsxwriter') # Write each DataFrame to a different sheet for sheet_name, df in data_dict.items(): df.to_excel(writer, sheet_name=sheet_name, index=False) # Auto-adjust column widths worksheet = writer.sheets[sheet_name] for i, col in enumerate(df.columns): max_width = max( df[col].astype(str).map(len).max(), len(col) ) + 2 worksheet.set_column(i, i, max_width) # Save the Excel file writer.close() # Return Excel bytes if no path provided if not file_path: output.seek(0) return output.getvalue() return None except Exception as e: st.error(f"Error exporting to Excel: {e}") return None @staticmethod def export_scenario_to_json(scenario: Dict[str, Any], file_path: str = None) -> Optional[str]: """ Export scenario data to JSON format. Args: scenario: Dictionary with scenario data file_path: Optional path to save the JSON file Returns: JSON string if file_path is None, otherwise None """ try: # Convert to JSON json_data = json.dumps(scenario, indent=4) # Save to file if path provided if file_path: with open(file_path, "w") as f: f.write(json_data) return None # Return JSON string if no path provided return json_data except Exception as e: st.error(f"Error exporting scenario to JSON: {e}") return None @staticmethod def get_download_link(data: Any, filename: str, text: str, mime_type: str = "text/csv") -> str: """ Generate a download link for data. Args: data: Data to download filename: Name of the file to download text: Text to display for the download link mime_type: MIME type of the file Returns: HTML string with download link """ if isinstance(data, str): b64 = base64.b64encode(data.encode()).decode() else: b64 = base64.b64encode(data).decode() href = f'{text}' return href @staticmethod def create_cooling_load_dataframes(results: Dict[str, Any]) -> Dict[str, pd.DataFrame]: """ Create DataFrames for cooling load results. Args: results: Dictionary with calculation results Returns: Dictionary with DataFrames for Excel export """ dataframes = {} # Create summary DataFrame summary_data = { "Metric": [ "Total Cooling Load", "Sensible Cooling Load", "Latent Cooling Load", "Cooling Load per Area" ], "Value": [ results["cooling"]["total_load"], results["cooling"]["sensible_load"], results["cooling"]["latent_load"], results["cooling"]["load_per_area"] ], "Unit": [ "kW", "kW", "kW", "W/m²" ] } dataframes["Cooling Summary"] = pd.DataFrame(summary_data) # Create component breakdown DataFrame component_data = { "Component": [ "Walls", "Roof", "Windows", "Doors", "People", "Lighting", "Equipment", "Infiltration", "Ventilation" ], "Load (kW)": [ results["cooling"]["component_loads"]["walls"], results["cooling"]["component_loads"]["roof"], results["cooling"]["component_loads"]["windows"], results["cooling"]["component_loads"]["doors"], results["cooling"]["component_loads"]["people"], results["cooling"]["component_loads"]["lighting"], results["cooling"]["component_loads"]["equipment"], results["cooling"]["component_loads"]["infiltration"], results["cooling"]["component_loads"]["ventilation"] ], "Percentage (%)": [ results["cooling"]["component_loads"]["walls"] / results["cooling"]["total_load"] * 100, results["cooling"]["component_loads"]["roof"] / results["cooling"]["total_load"] * 100, results["cooling"]["component_loads"]["windows"] / results["cooling"]["total_load"] * 100, results["cooling"]["component_loads"]["doors"] / results["cooling"]["total_load"] * 100, results["cooling"]["component_loads"]["people"] / results["cooling"]["total_load"] * 100, results["cooling"]["component_loads"]["lighting"] / results["cooling"]["total_load"] * 100, results["cooling"]["component_loads"]["equipment"] / results["cooling"]["total_load"] * 100, results["cooling"]["component_loads"]["infiltration"] / results["cooling"]["total_load"] * 100, results["cooling"]["component_loads"]["ventilation"] / results["cooling"]["total_load"] * 100 ] } dataframes["Cooling Components"] = pd.DataFrame(component_data) # Create detailed loads DataFrames # Walls wall_data = [] for wall in results["cooling"]["detailed_loads"]["walls"]: wall_data.append({ "Name": wall["name"], "Orientation": wall["orientation"], "Area (m²)": wall["area"], "U-Value (W/m²·K)": wall["u_value"], "CLTD (°C)": wall["cltd"], "Load (kW)": wall["load"] }) if wall_data: dataframes["Cooling Walls"] = pd.DataFrame(wall_data) # Roofs roof_data = [] for roof in results["cooling"]["detailed_loads"]["roofs"]: roof_data.append({ "Name": roof["name"], "Orientation": roof["orientation"], "Area (m²)": roof["area"], "U-Value (W/m²·K)": roof["u_value"], "CLTD (°C)": roof["cltd"], "Load (kW)": roof["load"] }) if roof_data: dataframes["Cooling Roofs"] = pd.DataFrame(roof_data) # Windows window_data = [] for window in results["cooling"]["detailed_loads"]["windows"]: window_data.append({ "Name": window["name"], "Orientation": window["orientation"], "Area (m²)": window["area"], "U-Value (W/m²·K)": window["u_value"], "SHGC": window["shgc"], "SCL (W/m²)": window["scl"], "Load (kW)": window["load"] }) if window_data: dataframes["Cooling Windows"] = pd.DataFrame(window_data) # Doors door_data = [] for door in results["cooling"]["detailed_loads"]["doors"]: door_data.append({ "Name": door["name"], "Orientation": door["orientation"], "Area (m²)": door["area"], "U-Value (W/m²·K)": door["u_value"], "CLTD (°C)": door["cltd"], "Load (kW)": door["load"] }) if door_data: dataframes["Cooling Doors"] = pd.DataFrame(door_data) # Internal loads internal_data = [] for internal_load in results["cooling"]["detailed_loads"]["internal"]: internal_data.append({ "Type": internal_load["type"], "Name": internal_load["name"], "Quantity": internal_load["quantity"], "Heat Gain (W)": internal_load["heat_gain"], "CLF": internal_load["clf"], "Load (kW)": internal_load["load"] }) if internal_data: dataframes["Cooling Internal Loads"] = pd.DataFrame(internal_data) # Infiltration and ventilation air_data = [ { "Type": "Infiltration", "Air Flow (m³/s)": results["cooling"]["detailed_loads"]["infiltration"]["air_flow"], "Sensible Load (kW)": results["cooling"]["detailed_loads"]["infiltration"]["sensible_load"], "Latent Load (kW)": results["cooling"]["detailed_loads"]["infiltration"]["latent_load"], "Total Load (kW)": results["cooling"]["detailed_loads"]["infiltration"]["total_load"] }, { "Type": "Ventilation", "Air Flow (m³/s)": results["cooling"]["detailed_loads"]["ventilation"]["air_flow"], "Sensible Load (kW)": results["cooling"]["detailed_loads"]["ventilation"]["sensible_load"], "Latent Load (kW)": results["cooling"]["detailed_loads"]["ventilation"]["latent_load"], "Total Load (kW)": results["cooling"]["detailed_loads"]["ventilation"]["total_load"] } ] dataframes["Cooling Air Exchange"] = pd.DataFrame(air_data) return dataframes @staticmethod def create_heating_load_dataframes(results: Dict[str, Any]) -> Dict[str, pd.DataFrame]: """ Create DataFrames for heating load results. Args: results: Dictionary with calculation results Returns: Dictionary with DataFrames for Excel export """ dataframes = {} # Create summary DataFrame summary_data = { "Metric": [ "Total Heating Load", "Heating Load per Area", "Design Heat Loss", "Safety Factor" ], "Value": [ results["heating"]["total_load"], results["heating"]["load_per_area"], results["heating"]["design_heat_loss"], results["heating"]["safety_factor"] ], "Unit": [ "kW", "W/m²", "kW", "%" ] } dataframes["Heating Summary"] = pd.DataFrame(summary_data) # Create component breakdown DataFrame component_data = { "Component": [ "Walls", "Roof", "Floor", "Windows", "Doors", "Infiltration", "Ventilation" ], "Load (kW)": [ results["heating"]["component_loads"]["walls"], results["heating"]["component_loads"]["roof"], results["heating"]["component_loads"]["floor"], results["heating"]["component_loads"]["windows"], results["heating"]["component_loads"]["doors"], results["heating"]["component_loads"]["infiltration"], results["heating"]["component_loads"]["ventilation"] ], "Percentage (%)": [ results["heating"]["component_loads"]["walls"] / results["heating"]["total_load"] * 100, results["heating"]["component_loads"]["roof"] / results["heating"]["total_load"] * 100, results["heating"]["component_loads"]["floor"] / results["heating"]["total_load"] * 100, results["heating"]["component_loads"]["windows"] / results["heating"]["total_load"] * 100, results["heating"]["component_loads"]["doors"] / results["heating"]["total_load"] * 100, results["heating"]["component_loads"]["infiltration"] / results["heating"]["total_load"] * 100, results["heating"]["component_loads"]["ventilation"] / results["heating"]["total_load"] * 100 ] } dataframes["Heating Components"] = pd.DataFrame(component_data) # Create detailed loads DataFrames # Walls wall_data = [] for wall in results["heating"]["detailed_loads"]["walls"]: wall_data.append({ "Name": wall["name"], "Orientation": wall["orientation"], "Area (m²)": wall["area"], "U-Value (W/m²·K)": wall["u_value"], "Temperature Difference (°C)": wall["delta_t"], "Load (kW)": wall["load"] }) if wall_data: dataframes["Heating Walls"] = pd.DataFrame(wall_data) # Roofs roof_data = [] for roof in results["heating"]["detailed_loads"]["roofs"]: roof_data.append({ "Name": roof["name"], "Orientation": roof["orientation"], "Area (m²)": roof["area"], "U-Value (W/m²·K)": roof["u_value"], "Temperature Difference (°C)": roof["delta_t"], "Load (kW)": roof["load"] }) if roof_data: dataframes["Heating Roofs"] = pd.DataFrame(roof_data) # Floors floor_data = [] for floor in results["heating"]["detailed_loads"]["floors"]: floor_data.append({ "Name": floor["name"], "Area (m²)": floor["area"], "U-Value (W/m²·K)": floor["u_value"], "Temperature Difference (°C)": floor["delta_t"], "Load (kW)": floor["load"] }) if floor_data: dataframes["Heating Floors"] = pd.DataFrame(floor_data) # Windows window_data = [] for window in results["heating"]["detailed_loads"]["windows"]: window_data.append({ "Name": window["name"], "Orientation": window["orientation"], "Area (m²)": window["area"], "U-Value (W/m²·K)": window["u_value"], "Temperature Difference (°C)": window["delta_t"], "Load (kW)": window["load"] }) if window_data: dataframes["Heating Windows"] = pd.DataFrame(window_data) # Doors door_data = [] for door in results["heating"]["detailed_loads"]["doors"]: door_data.append({ "Name": door["name"], "Orientation": door["orientation"], "Area (m²)": door["area"], "U-Value (W/m²·K)": door["u_value"], "Temperature Difference (°C)": door["delta_t"], "Load (kW)": door["load"] }) if door_data: dataframes["Heating Doors"] = pd.DataFrame(door_data) # Infiltration and ventilation air_data = [ { "Type": "Infiltration", "Air Flow (m³/s)": results["heating"]["detailed_loads"]["infiltration"]["air_flow"], "Temperature Difference (°C)": results["heating"]["detailed_loads"]["infiltration"]["delta_t"], "Load (kW)": results["heating"]["detailed_loads"]["infiltration"]["load"] }, { "Type": "Ventilation", "Air Flow (m³/s)": results["heating"]["detailed_loads"]["ventilation"]["air_flow"], "Temperature Difference (°C)": results["heating"]["detailed_loads"]["ventilation"]["delta_t"], "Load (kW)": results["heating"]["detailed_loads"]["ventilation"]["load"] } ] dataframes["Heating Air Exchange"] = pd.DataFrame(air_data) return dataframes @staticmethod def display_export_interface(session_state: Dict[str, Any]) -> None: """ Display export interface in Streamlit. Args: session_state: Streamlit session state containing calculation results """ st.header("Export Results") # Check if calculations have been performed if "calculation_results" not in session_state or not session_state["calculation_results"]: st.warning("No calculation results available. Please run calculations first.") return # Create tabs for different export options tab1, tab2, tab3 = st.tabs(["CSV Export", "Excel Export", "Scenario Export"]) with tab1: DataExport._display_csv_export(session_state) with tab2: DataExport._display_excel_export(session_state) with tab3: DataExport._display_scenario_export(session_state) @staticmethod def _display_csv_export(session_state: Dict[str, Any]) -> None: """ Display CSV export interface. Args: session_state: Streamlit session state containing calculation results """ st.subheader("CSV Export") # Get results results = session_state["calculation_results"] # Create tabs for cooling and heating loads tab1, tab2 = st.tabs(["Cooling Load CSV", "Heating Load CSV"]) with tab1: # Create cooling load DataFrames cooling_dfs = DataExport.create_cooling_load_dataframes(results) # Display and export each DataFrame for sheet_name, df in cooling_dfs.items(): st.write(f"### {sheet_name}") st.dataframe(df) # Add download button csv_data = DataExport.export_to_csv(df) if csv_data: filename = f"{sheet_name.replace(' ', '_').lower()}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv" download_link = DataExport.get_download_link(csv_data, filename, f"Download {sheet_name} CSV") st.markdown(download_link, unsafe_allow_html=True) with tab2: # Create heating load DataFrames heating_dfs = DataExport.create_heating_load_dataframes(results) # Display and export each DataFrame for sheet_name, df in heating_dfs.items(): st.write(f"### {sheet_name}") st.dataframe(df) # Add download button csv_data = DataExport.export_to_csv(df) if csv_data: filename = f"{sheet_name.replace(' ', '_').lower()}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv" download_link = DataExport.get_download_link(csv_data, filename, f"Download {sheet_name} CSV") st.markdown(download_link, unsafe_allow_html=True) @staticmethod def _display_excel_export(session_state: Dict[str, Any]) -> None: """ Display Excel export interface. Args: session_state: Streamlit session state containing calculation results """ st.subheader("Excel Export") # Get results results = session_state["calculation_results"] # Create tabs for cooling, heating, and combined loads tab1, tab2, tab3 = st.tabs(["Cooling Load Excel", "Heating Load Excel", "Combined Excel"]) with tab1: # Create cooling load DataFrames cooling_dfs = DataExport.create_cooling_load_dataframes(results) # Add download button excel_data = DataExport.export_to_excel(cooling_dfs) if excel_data: filename = f"cooling_load_results_{datetime.now().strftime('%Y%m%d_%H%M%S')}.xlsx" download_link = DataExport.get_download_link( excel_data, filename, "Download Cooling Load Excel", "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet" ) st.markdown(download_link, unsafe_allow_html=True) # Display preview st.write("### Excel Preview") st.write("The Excel file will contain the following sheets:") for sheet_name in cooling_dfs.keys(): st.write(f"- {sheet_name}") with tab2: # Create heating load DataFrames heating_dfs = DataExport.create_heating_load_dataframes(results) # Add download button excel_data = DataExport.export_to_excel(heating_dfs) if excel_data: filename = f"heating_load_results_{datetime.now().strftime('%Y%m%d_%H%M%S')}.xlsx" download_link = DataExport.get_download_link( excel_data, filename, "Download Heating Load Excel", "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet" ) st.markdown(download_link, unsafe_allow_html=True) # Display preview st.write("### Excel Preview") st.write("The Excel file will contain the following sheets:") for sheet_name in heating_dfs.keys(): st.write(f"- {sheet_name}") with tab3: # Create combined DataFrames combined_dfs = {} # Add project information if "building_info" in session_state: project_info = [ {"Parameter": "Project Name", "Value": session_state["building_info"].get("project_name", "")}, {"Parameter": "Building Name", "Value": session_state["building_info"].get("building_name", "")}, {"Parameter": "Location", "Value": session_state["building_info"].get("location", "")}, {"Parameter": "Climate Zone", "Value": session_state["building_info"].get("climate_zone", "")}, {"Parameter": "Building Type", "Value": session_state["building_info"].get("building_type", "")}, {"Parameter": "Floor Area", "Value": session_state["building_info"].get("floor_area", "")}, {"Parameter": "Number of Floors", "Value": session_state["building_info"].get("num_floors", "")}, {"Parameter": "Floor Height", "Value": session_state["building_info"].get("floor_height", "")}, {"Parameter": "Orientation", "Value": session_state["building_info"].get("orientation", "")}, {"Parameter": "Occupancy", "Value": session_state["building_info"].get("occupancy", "")}, {"Parameter": "Operating Hours", "Value": session_state["building_info"].get("operating_hours", "")}, {"Parameter": "Date", "Value": datetime.now().strftime("%Y-%m-%d")}, {"Parameter": "Time", "Value": datetime.now().strftime("%H:%M:%S")} ] combined_dfs["Project Information"] = pd.DataFrame(project_info) # Add cooling load DataFrames cooling_dfs = DataExport.create_cooling_load_dataframes(results) for sheet_name, df in cooling_dfs.items(): combined_dfs[sheet_name] = df # Add heating load DataFrames heating_dfs = DataExport.create_heating_load_dataframes(results) for sheet_name, df in heating_dfs.items(): combined_dfs[sheet_name] = df # Add download button excel_data = DataExport.export_to_excel(combined_dfs) if excel_data: filename = f"hvac_load_results_{datetime.now().strftime('%Y%m%d_%H%M%S')}.xlsx" download_link = DataExport.get_download_link( excel_data, filename, "Download Combined Excel Report", "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet" ) st.markdown(download_link, unsafe_allow_html=True) # Display preview st.write("### Excel Preview") st.write("The Excel file will contain the following sheets:") for sheet_name in combined_dfs.keys(): st.write(f"- {sheet_name}") @staticmethod def _display_scenario_export(session_state: Dict[str, Any]) -> None: """ Display scenario export interface. Args: session_state: Streamlit session state containing calculation results """ st.subheader("Scenario Export") # Check if there are saved scenarios if "saved_scenarios" not in session_state or not session_state["saved_scenarios"]: st.info("No saved scenarios available for export. Save the current results as a scenario to enable export.") # Add button to save current results as a scenario scenario_name = st.text_input("Scenario Name", value="Baseline") if st.button("Save Current Results as Scenario"): if "saved_scenarios" not in session_state: session_state["saved_scenarios"] = {} # Save current results as a scenario session_state["saved_scenarios"][scenario_name] = { "results": session_state["calculation_results"], "building_info": session_state["building_info"], "components": session_state["components"], "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S") } st.success(f"Scenario '{scenario_name}' saved successfully!") st.experimental_rerun() else: # Display saved scenarios st.write("### Saved Scenarios") # Create selectbox for scenarios scenario_names = list(session_state["saved_scenarios"].keys()) selected_scenario = st.selectbox("Select Scenario to Export", scenario_names) if selected_scenario: # Get selected scenario scenario = session_state["saved_scenarios"][selected_scenario] # Display scenario information st.write(f"**Scenario:** {selected_scenario}") st.write(f"**Timestamp:** {scenario['timestamp']}") # Add download button json_data = DataExport.export_scenario_to_json(scenario) if json_data: filename = f"{selected_scenario.replace(' ', '_').lower()}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" download_link = DataExport.get_download_link( json_data, filename, "Download Scenario JSON", "application/json" ) st.markdown(download_link, unsafe_allow_html=True) # Add button to export all scenarios if st.button("Export All Scenarios"): # Create a zip file in memory import zipfile from io import BytesIO zip_buffer = BytesIO() with zipfile.ZipFile(zip_buffer, "w", zipfile.ZIP_DEFLATED) as zip_file: for scenario_name, scenario in session_state["saved_scenarios"].items(): # Export scenario to JSON json_data = DataExport.export_scenario_to_json(scenario) if json_data: filename = f"{scenario_name.replace(' ', '_').lower()}.json" zip_file.writestr(filename, json_data) # Add download button for zip file zip_buffer.seek(0) zip_data = zip_buffer.getvalue() filename = f"all_scenarios_{datetime.now().strftime('%Y%m%d_%H%M%S')}.zip" download_link = DataExport.get_download_link( zip_data, filename, "Download All Scenarios (ZIP)", "application/zip" ) st.markdown(download_link, unsafe_allow_html=True) # Create a singleton instance data_export = DataExport() # Example usage if __name__ == "__main__": import streamlit as st # Initialize session state with dummy data for testing if "calculation_results" not in st.session_state: st.session_state["calculation_results"] = { "cooling": { "total_load": 25.5, "sensible_load": 20.0, "latent_load": 5.5, "load_per_area": 85.0, "component_loads": { "walls": 5.0, "roof": 3.0, "windows": 8.0, "doors": 1.0, "people": 2.5, "lighting": 2.0, "equipment": 1.5, "infiltration": 1.0, "ventilation": 1.5 }, "detailed_loads": { "walls": [ {"name": "North Wall", "orientation": "NORTH", "area": 20.0, "u_value": 0.5, "cltd": 10.0, "load": 1.0} ], "roofs": [ {"name": "Main Roof", "orientation": "HORIZONTAL", "area": 100.0, "u_value": 0.3, "cltd": 15.0, "load": 3.0} ], "windows": [ {"name": "South Window", "orientation": "SOUTH", "area": 10.0, "u_value": 2.8, "shgc": 0.7, "scl": 800.0, "load": 8.0} ], "doors": [ {"name": "Main Door", "orientation": "NORTH", "area": 2.0, "u_value": 2.0, "cltd": 10.0, "load": 1.0} ], "internal": [ {"type": "People", "name": "Occupants", "quantity": 10, "heat_gain": 250, "clf": 1.0, "load": 2.5}, {"type": "Lighting", "name": "General Lighting", "quantity": 1000, "heat_gain": 2000, "clf": 1.0, "load": 2.0}, {"type": "Equipment", "name": "Office Equipment", "quantity": 5, "heat_gain": 300, "clf": 1.0, "load": 1.5} ], "infiltration": { "air_flow": 0.05, "sensible_load": 0.8, "latent_load": 0.2, "total_load": 1.0 }, "ventilation": { "air_flow": 0.1, "sensible_load": 1.0, "latent_load": 0.5, "total_load": 1.5 } } }, "heating": { "total_load": 30.0, "load_per_area": 100.0, "design_heat_loss": 27.0, "safety_factor": 10.0, "component_loads": { "walls": 8.0, "roof": 5.0, "floor": 4.0, "windows": 7.0, "doors": 1.0, "infiltration": 2.0, "ventilation": 3.0 }, "detailed_loads": { "walls": [ {"name": "North Wall", "orientation": "NORTH", "area": 20.0, "u_value": 0.5, "delta_t": 25.0, "load": 8.0} ], "roofs": [ {"name": "Main Roof", "orientation": "HORIZONTAL", "area": 100.0, "u_value": 0.3, "delta_t": 25.0, "load": 5.0} ], "floors": [ {"name": "Ground Floor", "area": 100.0, "u_value": 0.4, "delta_t": 10.0, "load": 4.0} ], "windows": [ {"name": "South Window", "orientation": "SOUTH", "area": 10.0, "u_value": 2.8, "delta_t": 25.0, "load": 7.0} ], "doors": [ {"name": "Main Door", "orientation": "NORTH", "area": 2.0, "u_value": 2.0, "delta_t": 25.0, "load": 1.0} ], "infiltration": { "air_flow": 0.05, "delta_t": 25.0, "load": 2.0 }, "ventilation": { "air_flow": 0.1, "delta_t": 25.0, "load": 3.0 } } } } # Display export interface data_export.display_export_interface(st.session_state)