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import pandas as pd
import os
from pathlib import Path
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.graph_objects as go
import numpy as np
import io
import base64
import webbrowser

# Define paths
current_dir = Path.cwd()
if (current_dir / "data" / "cop_modelling").exists():
    data_path = current_dir / "data" / "cop_modelling"
elif (current_dir.parent / "data" / "cop_modelling").exists():
    data_path = current_dir.parent / "data" / "cop_modelling"
else:
    data_path = Path("..") / "data" / "cop_modelling"

output_file = data_path / "joined_results.parquet"
print(f"Loading data from {output_file}...")
joined_df = pd.read_parquet(output_file)

html_parts = [
    "<!DOCTYPE html>",
    "<html><head><title>COP Analysis Report</title>",
    "<style>",
    "body { font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; margin: 40px auto; max-width: 1200px; color: #333; }",
    "h1 { text-align: center; color: #2c3e50; border-bottom: 2px solid #eee; padding-bottom: 20px; }",
    "h2 { color: #34495e; margin-top: 40px; }",
    ".plot { margin-bottom: 60px; background: #fff; padding: 20px; border-radius: 8px; box-shadow: 0 4px 6px rgba(0,0,0,0.1); text-align: center; }",
    ".plot img { max-width: 100%; height: auto; }",
    ".grid-container { display: grid; grid-template-columns: 1fr 1fr; gap: 20px; margin-bottom: 60px; }",
    ".grid-item { background: #fff; padding: 15px; border-radius: 8px; box-shadow: 0 4px 6px rgba(0,0,0,0.1); text-align: center; display: flex; flex-direction: column; align-items: center; justify-content: center; }",
    ".grid-item h2 { font-size: 1.2rem; margin-top: 0; }",
    ".grid-item img { max-width: 100%; height: auto; }",
    "</style>",
    "</head><body><h1>COP Analysis Comprehensive Report</h1>"
]

def add_matplotlib_fig(title):
    buf = io.BytesIO()
    plt.savefig(buf, format='png', bbox_inches='tight', dpi=120)
    plt.close()
    buf.seek(0)
    b64 = base64.b64encode(buf.read()).decode('utf-8')
    html_parts.append(f"<div class='grid-item'><h2>{title}</h2><img src='data:image/png;base64,{b64}'/></div>")

def add_plotly_fig(fig, title):
    # include_plotlyjs='cdn' ensures the HTML doesn't bundle the 3MB plotly.js library
    html_div = fig.to_html(full_html=False, include_plotlyjs='cdn')
    html_parts.append(f"<div class='plot'><h2>{title}</h2>{html_div}</div>")

print("Generating Correlation Matrix...")
html_parts.append("<div class='grid-container'>")
# 1. Correlation matrix
sns.set_theme(style="whitegrid")
numerical_cols = joined_df.select_dtypes(include=['number']).columns

if len(numerical_cols) > 0:
    plt.figure(figsize=(10, 8))
    corr_matrix = joined_df[numerical_cols].corr()
    sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt=".2f", vmin=-1, vmax=1)
    plt.title('Correlation Matrix of Numerical Variables')
    plt.tight_layout()
    add_matplotlib_fig('Correlation Matrix')

print("Generating Distribution Plots...")
# 2. Distributions
for col in numerical_cols:
    plt.figure(figsize=(8, 4))
    sns.histplot(joined_df[col].dropna(), kde=True, bins=30)
    plt.title(f'Distribution of {col}')
    plt.tight_layout()
    add_matplotlib_fig(f'Distribution of {col}')

categorical_cols = joined_df.select_dtypes(exclude=['number']).columns
for col in categorical_cols:
    plt.figure(figsize=(10, 5))
    top_categories = joined_df[col].value_counts().nlargest(20).index
    sns.countplot(data=joined_df[joined_df[col].isin(top_categories)], x=col, order=top_categories)
    plt.title(f'Distribution of {col} (Top 20 categories)')
    plt.xticks(rotation=45, ha='right')
    plt.tight_layout()
    add_matplotlib_fig(f'Distribution of {col}')

html_parts.append("</div>")

print("Generating Plotly 3D Visualizations...")
# Plotly
cols = joined_df.columns.tolist()
cols_lower = [c.lower() for c in cols]
def find_col(possible_names):
    for name in possible_names:
        for idx, c in enumerate(cols_lower):
            if name.lower() in c:
                return cols[idx]
    return None

col_quelle = find_col(['t_vorlauf_quelle', 'quelle'])
col_senke = find_col(['t_vorlauf_senke', 'senke'])
col_cop = find_col(['cop'])
col_komp = find_col(['kompressor', 'stufe'])
col_kalt = find_col(['kältemittel', 'kaltemittel', 'kaeltemittel', 'refrigerant'])
required_cols = {'Quelle (X)': col_quelle, 'Senke (Y)': col_senke, 'COP (Z)': col_cop, 'Kompressor': col_komp, 'Kältemittel': col_kalt}
missing = {k: v for k, v in required_cols.items() if v is None}

if not missing:
    # Fig 1
    fig = go.Figure()
    plot_df = joined_df.dropna(subset=list(required_cols.values())).copy()
    combinations = plot_df.groupby([col_komp, col_kalt]).size().reset_index()
    traces = []
    buttons = []
    
    for i, row in combinations.iterrows():
        komp_val = str(row[col_komp])
        kalt_val = str(row[col_kalt])
        subset = plot_df[(plot_df[col_komp] == row[col_komp]) & (plot_df[col_kalt] == row[col_kalt])]
        if len(subset) < 3: continue
        pivot = subset.pivot_table(values=col_cop, index=col_senke, columns=col_quelle, aggfunc='mean')
        trace_name = f"{komp_val} | {kalt_val}"
        trace = go.Surface(
            x=pivot.columns.values, y=pivot.index.values, z=pivot.values,
            name=trace_name, visible=(len(traces) == 0),
            hovertemplate=f"Quelle (X): %{{x}}<br>Senke (Y): %{{y}}<br>COP (Z): %{{z}}<extra>{trace_name}</extra>"
        )
        traces.append(trace)
        fig.add_trace(trace)
        
    for i, trace in enumerate(traces):
        visibility = [False] * len(traces)
        visibility[i] = True
        button = dict(label=trace.name, method="update", args=[{"visible": visibility}, {"title": f"COP Surface - {trace.name}"}])
        buttons.append(button)

    if traces:
        fig.update_layout(
            updatemenus=[dict(active=0, buttons=buttons, direction="down", pad={"r": 10, "t": 10}, showactive=True, x=0.1, xanchor="left", y=1.15, yanchor="top")],
            title=f"COP Surface - {traces[0].name}", scene=dict(xaxis_title=col_quelle, yaxis_title=col_senke, zaxis_title=col_cop),
            autosize=True, height=700, margin=dict(l=65, r=50, b=65, t=90)
        )
        add_plotly_fig(fig, 'Interactive COP Surfaces by Kompressor & Kältemittel')

    # Fig 2
    fig2 = go.Figure()
    colorscales = ['Viridis', 'Plasma', 'Inferno', 'Magma', 'Cividis', 'Blues', 'Greens', 'Reds']
    unique_kalt = plot_df[col_kalt].dropna().unique()
    for idx, kalt_val in enumerate(unique_kalt):
        kalt_val = str(kalt_val)
        subset = plot_df[plot_df[col_kalt] == kalt_val]
        if len(subset) < 3: continue
        pivot = subset.pivot_table(values=col_cop, index=col_senke, columns=col_quelle, aggfunc='mean')
        cscale = colorscales[idx % len(colorscales)]
        trace = go.Surface(
            x=pivot.columns.values, y=pivot.index.values, z=pivot.values,
            name=kalt_val, showscale=False, colorscale=cscale, showlegend=True,
            hovertemplate=f"Kältemittel: {kalt_val}<br>Quelle (X): %{{x}}<br>Senke (Y): %{{y}}<br>COP (Z): %{{z}}<extra></extra>"
        )
        fig2.add_trace(trace)
        
    fig2.update_layout(
        title="Stacked COP Surfaces by Kältemittel", scene=dict(xaxis_title=col_quelle, yaxis_title=col_senke, zaxis_title=col_cop),
        legend=dict(title="Kältemittel<br>(Click to toggle)", x=1.05, y=0.9),
        autosize=True, height=800, margin=dict(l=65, r=50, b=65, t=90)
    )
    add_plotly_fig(fig2, 'Stacked COP Surfaces Overview')

html_parts.append("</body></html>")

report_path = Path("cop_analysis_report.html").resolve()
with open(report_path, "w", encoding="utf-8") as f:
    f.write("\n".join(html_parts))

print(f"Report generated and saved to {report_path}")
webbrowser.open('file://' + str(report_path))