utils/data_io.py

"""
Data import/export module for HVAC Load Calculator.
This module provides functionality for importing and exporting building data and results.
"""

import json
import csv
import os
import datetime
import pandas as pd
import numpy as np
import base64
import io
import zipfile
import streamlit as st
from typing import Dict, List, Any, Optional, Tuple
from models.building import Building, CoolingLoadResult

def export_building_to_json(building: Building, file_path: str = None) -> str:
    """
    Export building data to JSON format.
    
    Args:
        building: Building model to export
        file_path: Optional file path to save JSON data
        
    Returns:
        JSON string representation of the building
    """
    # Convert building to dictionary
    building_dict = building.to_dict()
    
    # Convert to JSON string
    json_str = json.dumps(building_dict, indent=2)
    
    # Save to file if path provided
    if file_path:
        with open(file_path, 'w') as f:
            f.write(json_str)
    
    return json_str

def import_building_from_json(json_str: str) -> Building:
    """
    Import building data from JSON string.
    
    Args:
        json_str: JSON string representation of the building
        
    Returns:
        Building model
    """
    # Parse JSON string
    building_dict = json.loads(json_str)
    
    # Create building from dictionary
    building = Building.from_dict(building_dict)
    
    return building

def export_result_to_json(result: CoolingLoadResult, file_path: str = None) -> str:
    """
    Export cooling load result to JSON format.
    
    Args:
        result: Cooling load result to export
        file_path: Optional file path to save JSON data
        
    Returns:
        JSON string representation of the result
    """
    # Convert result to dictionary
    result_dict = result.to_dict()
    
    # Convert to JSON string
    json_str = json.dumps(result_dict, indent=2)
    
    # Save to file if path provided
    if file_path:
        with open(file_path, 'w') as f:
            f.write(json_str)
    
    return json_str

def import_result_from_json(json_str: str) -> CoolingLoadResult:
    """
    Import cooling load result from JSON string.
    
    Args:
        json_str: JSON string representation of the result
        
    Returns:
        Cooling load result
    """
    # Parse JSON string
    result_dict = json.loads(json_str)
    
    # Create result from dictionary
    result = CoolingLoadResult.from_dict(result_dict)
    
    return result

def export_to_csv(data: Dict[str, Any], file_path: str = None) -> str:
    """
    Export data to CSV format.
    
    Args:
        data: Dictionary of data to export
        file_path: Optional file path to save CSV data
        
    Returns:
        CSV string representation of the data
    """
    # Convert to DataFrame
    df = pd.DataFrame(data)
    
    # Convert to CSV string
    csv_str = df.to_csv(index=False)
    
    # Save to file if path provided
    if file_path:
        with open(file_path, 'w') as f:
            f.write(csv_str)
    
    return csv_str

def export_monthly_breakdown_to_csv(monthly_breakdown: Dict[str, Dict[str, Any]], file_path: str = None) -> str:
    """
    Export monthly breakdown data to CSV format.
    
    Args:
        monthly_breakdown: Monthly breakdown data
        file_path: Optional file path to save CSV data
        
    Returns:
        CSV string representation of the monthly breakdown
    """
    # Create data for CSV
    csv_data = []
    
    for month, data in monthly_breakdown.items():
        if month != "annual":
            csv_data.append({
                "Month": month,
                "Peak Load (W)": data.get("peak_load_w", 0),
                "Average Load (W)": data.get("average_load_w", 0),
                "Energy (kWh)": data.get("energy_kwh", 0),
                "Average Temperature (°C)": data.get("avg_temp_c", 0),
                "Cooling Degree Days": data.get("cooling_degree_days", 0)
            })
    
    # Add annual total
    if "annual" in monthly_breakdown:
        annual = monthly_breakdown["annual"]
        csv_data.append({
            "Month": "Annual",
            "Peak Load (W)": annual.get("peak_load_w", 0),
            "Average Load (W)": annual.get("average_load_w", 0),
            "Energy (kWh)": annual.get("energy_kwh", 0),
            "Average Temperature (°C)": annual.get("avg_temp_c", 0),
            "Cooling Degree Days": annual.get("cooling_degree_days", 0)
        })
    
    # Convert to DataFrame
    df = pd.DataFrame(csv_data)
    
    # Convert to CSV string
    csv_str = df.to_csv(index=False)
    
    # Save to file if path provided
    if file_path:
        with open(file_path, 'w') as f:
            f.write(csv_str)
    
    return csv_str

def export_building_comparison_to_csv(buildings: Dict[str, Building], results: Dict[str, CoolingLoadResult], 
                                     monthly_breakdowns: Dict[str, Dict[str, Any]], file_path: str = None) -> str:
    """
    Export building comparison data to CSV format.
    
    Args:
        buildings: Dictionary of building models {name: Building}
        results: Dictionary of cooling load results {name: CoolingLoadResult}
        monthly_breakdowns: Dictionary of monthly breakdowns {name: monthly_breakdown}
        file_path: Optional file path to save CSV data
        
    Returns:
        CSV string representation of the building comparison
    """
    # Create data for CSV
    csv_data = []
    
    for name, building in buildings.items():
        result = results.get(name)
        monthly_breakdown = monthly_breakdowns.get(name)
        
        if result and monthly_breakdown:
            annual = monthly_breakdown.get("annual", {})
            
            # Building properties
            walls_u_avg = np.mean([wall.u_value for wall in building.walls]) if building.walls else 0
            glass_u_avg = np.mean([glass.u_value for glass in building.glass]) if building.glass else 0
            glass_shgc_avg = np.mean([glass.shgc for glass in building.glass]) if building.glass else 0
            
            # Calculate wall-to-window ratio
            total_wall_area = sum([wall.area for wall in building.walls]) if building.walls else 0
            total_glass_area = sum([glass.area for glass in building.glass]) if building.glass else 0
            wwr = total_glass_area / (total_wall_area + total_glass_area) if (total_wall_area + total_glass_area) > 0 else 0
            
            csv_data.append({
                "Building Name": building.settings.name,
                "Location": building.location.city,
                "Floor Area (m²)": building.settings.floor_area,
                "Indoor Design Temperature (°C)": building.settings.indoor_temp,
                "Wall Average U-Value (W/m²·K)": walls_u_avg,
                "Roof U-Value (W/m²·K)": building.roof.u_value,
                "Glass Average U-Value (W/m²·K)": glass_u_avg,
                "Glass Average SHGC": glass_shgc_avg,
                "Window-to-Wall Ratio": wwr,
                "Number of Occupants": building.people.count,
                "Lighting Power (W)": building.lighting.power,
                "Equipment Power (W)": building.equipment.power,
                "Peak Cooling Load (kW)": result.peak_total_load / 1000,
                "Peak Cooling Load (W/m²)": result.peak_total_load / building.settings.floor_area,
                "Sensible Heat Ratio": result.peak_sensible_load / result.peak_total_load if result.peak_total_load > 0 else 0,
                "Annual Energy Consumption (kWh)": annual.get("energy_kwh", 0),
                "Annual Energy Consumption (kWh/m²)": annual.get("energy_kwh", 0) / building.settings.floor_area
            })
    
    # Convert to DataFrame
    df = pd.DataFrame(csv_data)
    
    # Convert to CSV string
    csv_str = df.to_csv(index=False)
    
    # Save to file if path provided
    if file_path:
        with open(file_path, 'w') as f:
            f.write(csv_str)
    
    return csv_str

def generate_pdf_report(building: Building, result: CoolingLoadResult, monthly_breakdown: Dict[str, Any] = None) -> bytes:
    """
    Generate PDF report for building and cooling load results.
    
    Args:
        building: Building model
        result: Cooling load result
        monthly_breakdown: Optional monthly breakdown data
        
    Returns:
        PDF report as bytes
    """
    try:
        from reportlab.lib.pagesizes import letter
        from reportlab.lib import colors
        from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle, Image
        from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
        from reportlab.lib.units import inch
        import matplotlib.pyplot as plt
        import io
    except ImportError:
        # If reportlab is not installed, return empty bytes
        return b""
    
    # Create PDF buffer
    buffer = io.BytesIO()
    
    # Create PDF document
    doc = SimpleDocTemplate(buffer, pagesize=letter)
    styles = getSampleStyleSheet()
    
    # Create content
    content = []
    
    # Title
    title_style = styles["Title"]
    content.append(Paragraph(f"HVAC Cooling Load Report", title_style))
    content.append(Spacer(1, 0.25 * inch))
    
    # Building information
    heading_style = styles["Heading1"]
    normal_style = styles["Normal"]
    
    content.append(Paragraph("Building Information", heading_style))
    content.append(Spacer(1, 0.1 * inch))
    
    building_info = [
        ["Building Name", building.settings.name],
        ["Location", building.location.city],
        ["Floor Area", f"{building.settings.floor_area} m²"],
        ["Indoor Design Temperature", f"{building.settings.indoor_temp} °C"],
        ["Indoor Design Humidity", f"{building.settings.indoor_humidity} %"],
        ["Number of Occupants", str(building.people.count)]
    ]
    
    building_table = Table(building_info, colWidths=[2.5 * inch, 3.5 * inch])
    building_table.setStyle(TableStyle([
        ('BACKGROUND', (0, 0), (0, -1), colors.lightgrey),
        ('TEXTCOLOR', (0, 0), (0, -1), colors.black),
        ('ALIGN', (0, 0), (-1, -1), 'LEFT'),
        ('FONTNAME', (0, 0), (-1, -1), 'Helvetica'),
        ('FONTSIZE', (0, 0), (-1, -1), 10),
        ('BOTTOMPADDING', (0, 0), (-1, -1), 6),
        ('BACKGROUND', (1, 0), (-1, -1), colors.white),
        ('GRID', (0, 0), (-1, -1), 1, colors.black)
    ]))
    
    content.append(building_table)
    content.append(Spacer(1, 0.25 * inch))
    
    # Peak cooling load
    content.append(Paragraph("Peak Cooling Load", heading_style))
    content.append(Spacer(1, 0.1 * inch))
    
    peak_load_info = [
        ["Sensible Load", f"{result.peak_sensible_load:.1f} W"],
        ["Latent Load", f"{result.peak_latent_load:.1f} W"],
        ["Total Load", f"{result.peak_total_load:.1f} W"],
        ["Peak Hour", f"{result.peak_hour}:00"],
        ["Sensible Heat Ratio", f"{result.peak_sensible_load / result.peak_total_load:.2f}" if result.peak_total_load > 0 else "0.00"]
    ]
    
    peak_load_table = Table(peak_load_info, colWidths=[2.5 * inch, 3.5 * inch])
    peak_load_table.setStyle(TableStyle([
        ('BACKGROUND', (0, 0), (0, -1), colors.lightgrey),
        ('TEXTCOLOR', (0, 0), (0, -1), colors.black),
        ('ALIGN', (0, 0), (-1, -1), 'LEFT'),
        ('FONTNAME', (0, 0), (-1, -1), 'Helvetica'),
        ('FONTSIZE', (0, 0), (-1, -1), 10),
        ('BOTTOMPADDING', (0, 0), (-1, -1), 6),
        ('BACKGROUND', (1, 0), (-1, -1), colors.white),
        ('GRID', (0, 0), (-1, -1), 1, colors.black)
    ]))
    
    content.append(peak_load_table)
    content.append(Spacer(1, 0.25 * inch))
    
    # Load breakdown
    content.append(Paragraph("Load Breakdown", heading_style))
    content.append(Spacer(1, 0.1 * inch))
    
    # Create pie charts for load breakdown
    external_loads = result.external_loads
    internal_loads = result.internal_loads
    
    # External loads breakdown
    external_breakdown = {
        "Roof": external_loads.get("roof", 0),
        "Walls": external_loads.get("walls_total", 0),
        "Glass Conduction": external_loads.get("glass_conduction_total", 0),
        "Glass Solar": external_loads.get("glass_solar_total", 0)
    }
    
    # Internal loads breakdown
    internal_breakdown = {
        "People (Sensible)": internal_loads.get("people_sensible", 0),
        "People (Latent)": internal_loads.get("people_latent", 0),
        "Lighting": internal_loads.get("lighting", 0),
        "Equipment (Sensible)": internal_loads.get("equipment_sensible", 0),
        "Equipment (Latent)": internal_loads.get("equipment_latent", 0)
    }
    
    # Create pie charts
    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))
    
    ax1.pie(
        external_breakdown.values(), 
        labels=external_breakdown.keys(),
        autopct='%1.1f%%',
        startangle=90
    )
    ax1.axis('equal')
    ax1.set_title("External Loads")
    
    ax2.pie(
        internal_breakdown.values(), 
        labels=internal_breakdown.keys(),
        autopct='%1.1f%%',
        startangle=90
    )
    ax2.axis('equal')
    ax2.set_title("Internal Loads")
    
    # Save figure to buffer
    img_buffer = io.BytesIO()
    plt.tight_layout()
    plt.savefig(img_buffer, format='png')
    img_buffer.seek(0)
    
    # Add image to content
    img = Image(img_buffer, width=6 * inch, height=3 * inch)
    content.append(img)
    content.append(Spacer(1, 0.25 * inch))
    
    # Monthly breakdown
    if monthly_breakdown:
        content.append(Paragraph("Monthly Breakdown", heading_style))
        content.append(Spacer(1, 0.1 * inch))
        
        # Create monthly breakdown table
        monthly_table_data = [["Month", "Peak Load (kW)", "Avg. Load (kW)", "Energy (kWh)"]]
        
        for month, data in monthly_breakdown.items():
            if month != "annual":
                monthly_table_data.append([
                    month,
                    f"{data.get('peak_load_w', 0) / 1000:.2f}",
                    f"{data.get('average_load_w', 0) / 1000:.2f}",
                    f"{data.get('energy_kwh', 0):.1f}"
                ])
        
        # Add annual total
        if "annual" in monthly_breakdown:
            annual = monthly_breakdown["annual"]
            monthly_table_data.append([
                "Annual",
                f"{annual.get('peak_load_w', 0) / 1000:.2f}",
                f"{annual.get('average_load_w', 0) / 1000:.2f}",
                f"{annual.get('energy_kwh', 0):.1f}"
            ])
        
        monthly_table = Table(monthly_table_data, colWidths=[1.5 * inch, 1.5 * inch, 1.5 * inch, 1.5 * inch])
        monthly_table.setStyle(TableStyle([
            ('BACKGROUND', (0, 0), (-1, 0), colors.lightgrey),
            ('TEXTCOLOR', (0, 0), (-1, 0), colors.black),
            ('ALIGN', (0, 0), (-1, -1), 'CENTER'),
            ('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'),
            ('FONTSIZE', (0, 0), (-1, 0), 10),
            ('BOTTOMPADDING', (0, 0), (-1, 0), 6),
            ('BACKGROUND', (0, 1), (-1, -1), colors.white),
            ('GRID', (0, 0), (-1, -1), 1, colors.black)
        ]))
        
        content.append(monthly_table)
        content.append(Spacer(1, 0.25 * inch))
        
        # Create monthly bar chart
        months = [month for month in monthly_breakdown.keys() if month != "annual"]
        peak_loads = [monthly_breakdown[month]["peak_load_w"] / 1000 for month in months]
        
        plt.figure(figsize=(8, 4))
        plt.bar(months, peak_loads)
        plt.xlabel("Month")
        plt.ylabel("Peak Cooling Load (kW)")
        plt.title("Monthly Peak Cooling Load")
        plt.xticks(rotation=45)
        plt.tight_layout()
        
        # Save figure to buffer
        img_buffer = io.BytesIO()
        plt.savefig(img_buffer, format='png')
        img_buffer.seek(0)
        
        # Add image to content
        img = Image(img_buffer, width=6 * inch, height=3 * inch)
        content.append(img)
        content.append(Spacer(1, 0.25 * inch))
    
    # Footer
    content.append(Paragraph(f"Report generated on {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}", normal_style))
    
    # Build PDF
    doc.build(content)
    
    # Get PDF data
    pdf_data = buffer.getvalue()
    buffer.close()
    
    return pdf_data

def create_project_archive(building: Building, result: CoolingLoadResult, monthly_breakdown: Dict[str, Any] = None) -> bytes:
    """
    Create a ZIP archive containing all project data.
    
    Args:
        building: Building model
        result: Cooling load result
        monthly_breakdown: Optional monthly breakdown data
        
    Returns:
        ZIP archive as bytes
    """
    # Create ZIP buffer
    buffer = io.BytesIO()
    
    # Create ZIP file
    with zipfile.ZipFile(buffer, 'w') as zip_file:
        # Add building data
        building_json = export_building_to_json(building)
        zip_file.writestr('building.json', building_json)
        
        # Add result data
        result_json = export_result_to_json(result)
        zip_file.writestr('result.json', result_json)
        
        # Add monthly breakdown data if available
        if monthly_breakdown:
            monthly_csv = export_monthly_breakdown_to_csv(monthly_breakdown)
            zip_file.writestr('monthly_breakdown.csv', monthly_csv)
        
        # Add PDF report if reportlab is available
        try:
            pdf_data = generate_pdf_report(building, result, monthly_breakdown)
            if pdf_data:
                zip_file.writestr('report.pdf', pdf_data)
        except:
            pass
    
    # Get ZIP data
    zip_data = buffer.getvalue()
    buffer.close()
    
    return zip_data

def get_download_link(data: bytes, filename: str, text: str) -> str:
    """
    Generate a download link for binary data.
    
    Args:
        data: Binary data to download
        filename: Name of the file to download
        text: Text to display for the download link
        
    Returns:
        HTML string for the download link
    """
    b64 = base64.b64encode(data).decode()
    href = f'<a href="data:application/octet-stream;base64,{b64}" download="{filename}">{text}</a>'
    return href

def display_export_options(building: Building, result: CoolingLoadResult, monthly_breakdown: Dict[str, Any] = None):
    """
    Display export options in Streamlit UI.
    
    Args:
        building: Building model
        result: Cooling load result
        monthly_breakdown: Optional monthly breakdown data
    """
    st.subheader("Export Options")
    
    export_tabs = st.tabs(["Building Data", "Results", "Monthly Data", "Complete Project"])
    
    with export_tabs[0]:
        st.write("Export building data to JSON format.")
        
        # Export building data
        building_json = export_building_to_json(building)
        
        # Create download button
        st.download_button(
            label="Download Building Data (JSON)",
            data=building_json,
            file_name=f"{building.settings.name}_building.json",
            mime="application/json"
        )
    
    with export_tabs[1]:
        st.write("Export calculation results to JSON format.")
        
        # Export result data
        result_json = export_result_to_json(result)
        
        # Create download button
        st.download_button(
            label="Download Results (JSON)",
            data=result_json,
            file_name=f"{building.settings.name}_results.json",
            mime="application/json"
        )
    
    with export_tabs[2]:
        st.write("Export monthly breakdown data to CSV format.")
        
        if monthly_breakdown:
            # Export monthly breakdown data
            monthly_csv = export_monthly_breakdown_to_csv(monthly_breakdown)
            
            # Create download button
            st.download_button(
                label="Download Monthly Data (CSV)",
                data=monthly_csv,
                file_name=f"{building.settings.name}_monthly.csv",
                mime="text/csv"
            )
        else:
            st.info("Monthly breakdown data not available.")
    
    with export_tabs[3]:
        st.write("Export complete project as ZIP archive.")
        
        # Create ZIP archive
        zip_data = create_project_archive(building, result, monthly_breakdown)
        
        # Create download button
        st.download_button(
            label="Download Complete Project (ZIP)",
            data=zip_data,
            file_name=f"{building.settings.name}_project.zip",
            mime="application/zip"
        )
        
        st.info("The ZIP archive contains all project data, including building information, calculation results, and monthly breakdown data.")

def display_import_options():
    """
    Display import options in Streamlit UI.
    
    Returns:
        Tuple of (building, result, monthly_breakdown) if import successful, None otherwise
    """
    st.subheader("Import Options")
    
    import_tabs = st.tabs(["Building Data", "Complete Project"])
    
    with import_tabs[0]:
        st.write("Import building data from JSON file.")
        
        # File uploader
        uploaded_file = st.file_uploader("Upload Building JSON", type=["json"])
        
        if uploaded_file is not None:
            try:
                # Read JSON data
                json_str = uploaded_file.getvalue().decode('utf-8')
                
                # Import building
                building = import_building_from_json(json_str)
                
                st.success(f"Successfully imported building: {building.settings.name}")
                
                return (building, None, None)
            except Exception as e:
                st.error(f"Error importing building data: {str(e)}")
    
    with import_tabs[1]:
        st.write("Import complete project from ZIP archive.")
        
        # File uploader
        uploaded_file = st.file_uploader("Upload Project ZIP", type=["zip"])
        
        if uploaded_file is not None:
            try:
                # Read ZIP data
                zip_data = uploaded_file.getvalue()
                
                # Create ZIP buffer
                buffer = io.BytesIO(zip_data)
                
                # Extract ZIP file
                with zipfile.ZipFile(buffer, 'r') as zip_file:
                    # Extract building data
                    if 'building.json' in zip_file.namelist():
                        building_json = zip_file.read('building.json').decode('utf-8')
                        building = import_building_from_json(building_json)
                    else:
                        st.error("Building data not found in ZIP archive.")
                        return None
                    
                    # Extract result data
                    if 'result.json' in zip_file.namelist():
                        result_json = zip_file.read('result.json').decode('utf-8')
                        result = import_result_from_json(result_json)
                    else:
                        result = None
                    
                    # Extract monthly breakdown data
                    if 'monthly_breakdown.csv' in zip_file.namelist():
                        monthly_csv = zip_file.read('monthly_breakdown.csv').decode('utf-8')
                        monthly_df = pd.read_csv(io.StringIO(monthly_csv))
                        
                        # Convert to dictionary
                        monthly_breakdown = {}
                        for _, row in monthly_df.iterrows():
                            month = row['Month']
                            monthly_breakdown[month] = {
                                'peak_load_w': row['Peak Load (W)'],
                                'average_load_w': row['Average Load (W)'],
                                'energy_kwh': row['Energy (kWh)']
                            }
                            
                            if 'Average Temperature (°C)' in row:
                                monthly_breakdown[month]['avg_temp_c'] = row['Average Temperature (°C)']
                            
                            if 'Cooling Degree Days' in row:
                                monthly_breakdown[month]['cooling_degree_days'] = row['Cooling Degree Days']
                    else:
                        monthly_breakdown = None
                
                st.success(f"Successfully imported project: {building.settings.name}")
                
                return (building, result, monthly_breakdown)
            except Exception as e:
                st.error(f"Error importing project: {str(e)}")
    
    return None