#!/usr/bin/env python3
"""
saggital_ICC.py
Recompute ICC analysis for sagittal measurements only using the 2 CSV files.
Each column represents data from one rater, comparing all 5 columns for ICC.
"""

import pandas as pd  # type: ignore
import numpy as np  # type: ignore
import matplotlib.pyplot as plt  # type: ignore
from scipy import stats  # type: ignore
from scipy.stats import f  # type: ignore
import argparse
import sys
from pathlib import Path

ID_LIKE = {"case", "case_id", "id", "subject", "subject_id", "uid", "study", "study_id"}

def detect_rater_columns(df: pd.DataFrame, min_unique: int = 3):
    """Detect rater columns."""
    rater_like = [c for c in df.columns if str(c).strip().lower().startswith("rater")]
    if len(rater_like) >= 2:
        return rater_like
    num_cols = df.select_dtypes(include=[np.number]).columns.tolist()
    keep = []
    for c in num_cols:
        cl = str(c).strip().lower()
        if cl in ID_LIKE:
            continue
        if df[c].nunique(dropna=True) >= min_unique:
            keep.append(c)
    return keep

def icc2k_absolute(y: np.ndarray):
    """
    Compute ICC(2,k): two-way random-effects, absolute-agreement, average of k raters.
    Returns ICC(2,k) and mean-square terms.
    """
    y = np.asarray(y, float)
    n, k = y.shape
    mean_targets = y.mean(axis=1, keepdims=True)
    mean_raters = y.mean(axis=0, keepdims=True)
    grand_mean = y.mean()

    SSR = k * np.sum((mean_targets - grand_mean)**2)
    SSC = n * np.sum((mean_raters - grand_mean)**2)
    SSE = np.sum((y - mean_targets - mean_raters + grand_mean)**2)

    dfR, dfC = n - 1, k - 1
    dfE = (n - 1) * (k - 1)

    MSR = SSR / dfR if dfR > 0 else np.nan
    MSC = SSC / dfC if dfC > 0 else np.nan
    MSE = SSE / dfE if dfE > 0 else np.nan

    numerator = MSR - MSE
    denominator = MSR + (MSC - MSE) / n
    icc2k = numerator / denominator if denominator != 0 else np.nan
    return icc2k, MSR, MSC, MSE

def bootstrap_icc2k(y, n_boot=5000, seed=42):
    """Bootstrap ICC(2,k) confidence intervals."""
    rng = np.random.default_rng(seed)
    n, _ = y.shape
    boots = []
    for _ in range(n_boot):
        idx = rng.integers(0, n, size=n)
        icc, _, _, _ = icc2k_absolute(y[idx, :])
        boots.append(icc)
    boots = np.asarray(boots)
    lo, hi = np.nanpercentile(boots, [2.5, 97.5])
    return float(np.nanmean(boots)), float(lo), float(hi), boots

def format_pm(mean, sd, decimals=1):
    """Format mean ± SD."""
    if np.isnan(mean) or np.isnan(sd):
        return "NA"
    f = f"{{:.{decimals}f}} ± {{:.{decimals}f}}"
    return f.format(mean, sd)

def detect_cobb_series(df: pd.DataFrame) -> pd.Series:
    """Detect Cobb angle column in test data."""

    cobb_cols = [c for c in df.columns if "cobb" in str(c).lower()]
    if cobb_cols:
        s = pd.to_numeric(df[cobb_cols[0]], errors="coerce")  # type: ignore
        return s

    num_cols = df.select_dtypes(include=[np.number]).columns.tolist()
    if not num_cols:
        raise ValueError("No numeric columns found for Cobb angles.")
    return df[num_cols[0]]

def fmt(x, dec=1):
    return f"{x:.{dec}f}"

def create_test_cobb_summary(csv_path, outdir=".", decimals=1):
    """Create summary statistics for test dataset with single-observer Cobb angles."""
    csv_path = Path(csv_path)
    outdir = Path(outdir)
    outdir.mkdir(parents=True, exist_ok=True)

    if not csv_path.exists():
        print(f"[ERROR] CSV not found: {csv_path}")
        return

    df = pd.read_csv(csv_path)
    try:
        s = detect_cobb_series(df)
    except Exception as e:
        print(f"[ERROR] {e}")
        print(f"Columns seen: {list(df.columns)}")
        return

    x = pd.to_numeric(s, errors="coerce").dropna().to_numpy()  # type: ignore
    n = x.size
    if n == 0:
        print("[ERROR] No valid numeric Cobb values found.")
        return

    mean = float(np.mean(x))  # type: ignore
    sd = float(np.std(x, ddof=1)) if n > 1 else float("nan")  # type: ignore
    median = float(np.median(x))
    q1, q3 = [float(np.percentile(x, p)) for p in (25, 75)]
    iqr = q3 - q1
    xmin = float(np.min(x))
    xmax = float(np.max(x))

    print("\n=== Single-Observer Thoracic Cobb Summary (Test Set) ===")
    print(f"n = {n}")
    print(f"Mean ± SD: {fmt(mean, decimals)} ± {fmt(sd, decimals)} deg")
    print(f"Median [IQR]: {fmt(median, decimals)} [{fmt(q1, decimals)}–{fmt(q3, decimals)}] deg")
    print(f"Range: {fmt(xmin, decimals)}–{fmt(xmax, decimals)} deg")
    print("=========================================================")

    out_csv = outdir / "test_cobb_summary.csv"
    pd.DataFrame([{
        "n": n,
        "mean": mean,
        "sd": sd,
        "median": median,
        "q1": q1,
        "q3": q3,
        "iqr": iqr,
        "min": xmin,
        "max": xmax
    }]).to_csv(out_csv, index=False)

    print(f"[OK] Saved: {out_csv}")
    return mean, sd, median, q1, q3, iqr, xmin, xmax, n

def calculate_icc_2_1(data):
    """Calculate ICC(2,1)."""
    n_subjects, n_raters = data.shape

    subject_means = np.mean(data, axis=1)
    rater_means = np.mean(data, axis=0)
    grand_mean = np.mean(data)

    SS_between_subjects = n_raters * np.sum((subject_means - grand_mean) ** 2)
    SS_between_raters = n_subjects * np.sum((rater_means - grand_mean) ** 2)
    SS_error = 0
    for i in range(n_subjects):
        for j in range(n_raters):
            SS_error += (data[i, j] - subject_means[i] - rater_means[j] + grand_mean) ** 2

    MS_between_subjects = SS_between_subjects / (n_subjects - 1)
    MS_between_raters = SS_between_raters / (n_raters - 1)
    MS_error = SS_error / ((n_subjects - 1) * (n_raters - 1))

    icc_numerator = MS_between_subjects - MS_error
    icc_denominator = MS_between_subjects + (n_raters - 1) * MS_error
    icc_value = icc_numerator / icc_denominator

    f_stat = MS_between_subjects / MS_error
    df1 = n_subjects - 1
    df2 = (n_subjects - 1) * (n_raters - 1)
    p_value = 1 - f.cdf(f_stat, df1, df2)

    alpha = 0.05
    f_lower = f_stat / f.ppf(1 - alpha/2, df1, df2)
    f_upper = f_stat * f.ppf(1 - alpha/2, df1, df2)
    
    ci_lower = max(0, (f_lower - 1) / (f_lower + n_raters - 1))
    ci_upper = min(1, (f_upper - 1) / (f_upper + n_raters - 1))
    
    return icc_value, f_stat, p_value, (ci_lower, ci_upper)

def create_comprehensive_summary(csv_path, outdir=".", decimals=1, n_boot=5000):
    """Create summary statistics including bootstrap ICC."""
    csv_path = Path(csv_path)
    outdir = Path(outdir)
    outdir.mkdir(parents=True, exist_ok=True)

    if not csv_path.exists():
        print(f"[ERROR] CSV not found: {csv_path}")
        return

    df = pd.read_csv(csv_path, sep='\t', header=None)  # type: ignore

    raters = list(range(df.shape[1]))
    y = df.to_numpy(float)
    n, k = y.shape

    rater_means = np.nanmean(y, axis=0)
    rater_sds = np.nanstd(y, axis=0, ddof=1)

    per_case_sd = np.nanstd(y, axis=1, ddof=1)
    across_mean = float(np.nanmean(per_case_sd))
    across_sd = float(np.nanstd(per_case_sd, ddof=1))

    grand_mean = float(np.nanmean(y))

    icc2k, MSR, MSC, MSE = icc2k_absolute(y)
    _, lo, hi, boots = bootstrap_icc2k(y, n_boot=n_boot)

    print("\n=== Five-Observer Thoracic Cobb Summary (Development Set) ===")
    print(f"Detected raters (k={k}): {raters}")
    for i, (m, s) in enumerate(zip(rater_means, rater_sds)):
        print(f"Rater {i+1:>8d}: {m:.{decimals}f} ± {s:.{decimals}f} deg")
    print(f"Across-rater SD (per-case): mean ± SD = {across_mean:.{decimals}f} ± {across_sd:.{decimals}f} deg")
    print(f"Grand mean across all ratings: {grand_mean:.{decimals}f} deg")
    print(f"ICC(2,k) absolute agreement (bootstrap 95% CI): {icc2k:.3f} [{lo:.3f}, {hi:.3f}]")
    print("==============================================================")

    rows = []
    for i, (m, s) in enumerate(zip(rater_means, rater_sds)):
        rows.append({"measure": "rater_mean_sd", "rater": f"Rater_{i+1}", "mean": m, "sd": s})
    rows.append({"measure": "across_rater_sd_mean", "rater": "NA", "mean": across_mean, "sd": across_sd})
    rows.append({"measure": "grand_mean", "rater": "NA", "mean": grand_mean, "sd": np.nan})
    rows.append({"measure": "icc2k", "rater": "NA", "mean": icc2k, "sd": np.nan})
    rows.append({"measure": "icc2k_ci_lo", "rater": "NA", "mean": lo, "sd": np.nan})
    rows.append({"measure": "icc2k_ci_hi", "rater": "NA", "mean": hi, "sd": np.nan})
    pd.DataFrame(rows).to_csv(outdir / "dev_cobb_summary.csv", index=False)

    print(f"[OK] Saved summaries in {outdir.resolve()}")
    return icc2k, lo, hi

def create_sagittal_icc_plot():
    """Create ICC plot for sagittal measurements only"""

    csv_files = {
        '../cobb_angles/dev_cobb.csv': 'Sagittal Thoracic'
    }

    results = {}
    for filename, display_name in csv_files.items():
        try:
            df = pd.read_csv(filename, sep='\t', header=None)  # type: ignore
            data = df.values
            print(f"\n{display_name} Data Shape: {data.shape}")
            print(f"Data preview:\n{data[:5]}")

            icc_value, f_stat, p_value, ci = calculate_icc_2_1(data)
            
            results[display_name] = {
                'icc': icc_value,
                'f_stat': f_stat,
                'p_value': p_value,
                'ci_lower': ci[0],
                'ci_upper': ci[1],
                'n_subjects': data.shape[0],
                'n_raters': data.shape[1]
            }
            
            print(f"{display_name}: ICC = {icc_value:.4f}, CI = [{ci[0]:.3f}, {ci[1]:.3f}]")
            print(f"F-statistic = {f_stat:.4f}, p-value = {p_value:.4f}")
            
        except Exception as e:
            print(f"Error processing {filename}: {e}")
            continue
    
    if not results:
        print("No data processed successfully.")
        return

    fig1, ax1 = plt.subplots(1, 1, figsize=(4, 8))  # type: ignore

    names = list(results.keys())
    icc_values = [results[name]['icc'] for name in names]
    ci_lowers = [results[name]['ci_lower'] for name in names]
    ci_uppers = [results[name]['ci_upper'] for name in names]

    colors = ['#2E86AB', '#A23B72']

    bars = ax1.bar(names, icc_values, color=colors, alpha=0.8, width=0.3,
                   edgecolor='black', linewidth=1)

    ax1.errorbar(names, icc_values, 
                yerr=[np.array(icc_values) - np.array(ci_lowers),
                      np.array(ci_uppers) - np.array(icc_values)],
                fmt='none', color='red', capsize=5, capthick=2)

    for i, (bar, value) in enumerate(zip(bars, icc_values)):
        ax1.text(bar.get_x() + bar.get_width()/2, value + 0.02, 
                f'{value:.3f}', ha='center', va='bottom', 
                fontweight='bold', fontsize=12)
    
    ax1.set_ylabel('ICC Value', fontsize=12, fontweight='bold')
    ax1.set_title('Intraclass Correlation Coefficients\nSagittal Thoracic Measurements', 
                 fontsize=14, fontweight='bold')
    ax1.set_ylim(0, 1.1)
    ax1.grid(True, alpha=0.3, linestyle='--')
    ax1.set_axisbelow(True)

    ax1.tick_params(axis='x', rotation=0)

    plt.tight_layout()
    plt.show()

    fig2, ax2 = plt.subplots(1, 1, figsize=(4, 8))  # type: ignore
    ax2.axis('off')

    table_data = []
    for name in names:
        result = results[name]
        table_data.append([
            name,
            f"{result['icc']:.4f}",
            f"[{result['ci_lower']:.3f}, {result['ci_upper']:.3f}]",
            f"{result['p_value']:.4f}",
            f"{result['n_subjects']}x{result['n_raters']}"
        ])

    table = ax2.table(cellText=table_data,
                      colLabels=['Measurement Type', 'ICC(2,1)', '95% CI', 'p-value', 'Dimensions'],
                      cellLoc='center',
                      loc='center',
                      bbox=[0, 0, 1, 1])

    table.auto_set_font_size(False)
    table.set_fontsize(10)
    table.scale(1, 2)

    for i in range(len(table_data[0])):
        table[(0, i)].set_facecolor('#4CAF50')
        table[(0, i)].set_text_props(weight='bold', color='white')

    for i in range(1, len(table_data) + 1):
        for j in range(len(table_data[0])):
            table[(i, j)].set_facecolor('#F5F5F5' if i % 2 == 0 else 'white')
    
    ax2.set_title('ICC Analysis Results - Sagittal Thoracic', fontsize=14, fontweight='bold', pad=20)

    plt.tight_layout()
    plt.show()

    if results:
        fig3, ax3 = plt.subplots(1, 1, figsize=(6, 6))  # type: ignore

        first_name = list(results.keys())[0]

        for filename, display_name in csv_files.items():
            if display_name == first_name:
                df = pd.read_csv(filename, sep='\t', header=None)  # type: ignore
                data = df.values
                break

        if data.shape[1] >= 2:

            means = np.mean(data, axis=1)  # type: ignore

            differences = []
            for i in range(data.shape[0]):
                subject_ratings = data[i, :]
                subject_mean = np.mean(subject_ratings)  # type: ignore

                mean_abs_diff = np.mean(np.abs(subject_ratings - subject_mean))  # type: ignore
                differences.append(mean_abs_diff)
            differences = np.array(differences)

            mean_diff = np.mean(differences)  # type: ignore
            std_diff = np.std(differences)  # type: ignore
            upper_limit = mean_diff + 1.96 * std_diff
            lower_limit = mean_diff - 1.96 * std_diff

            ax3.scatter(means, differences, alpha=0.7, s=50, color='#2E86AB')
            ax3.axhline(y=mean_diff, color='red', linestyle='-', linewidth=2)
            ax3.axhline(y=upper_limit, color='red', linestyle='--', linewidth=1)
            ax3.axhline(y=lower_limit, color='red', linestyle='--', linewidth=1)
            
            ax3.set_xlabel('Mean Thoracic Cobb Angle of All Five Raters (deg)', fontsize=12, fontweight='bold')
            ax3.set_ylabel('Mean Absolute Difference from Average', fontsize=12, fontweight='bold')
            ax3.set_title('Inter-Rater Variability Plot\nSagittal Thoracic', fontsize=14, fontweight='bold')
            ax3.grid(True, alpha=0.3, linestyle='--')

            ax3.text(0.05, 0.95, f'Limits of Agreement:\n{lower_limit:.2f} to {upper_limit:.2f}', 
                    transform=ax3.transAxes, fontsize=10, verticalalignment='top',
                    bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.8))

        plt.tight_layout()
        plt.savefig('../ICC_results/sagittal_inter_rater_variability.png', dpi=300, bbox_inches='tight',   # type: ignore
                    facecolor='white', edgecolor='none')
        plt.show()

    bland_altman_values = {}
    if results:
        first_name = list(results.keys())[0]
        for filename, display_name in csv_files.items():
            if display_name == first_name:
                df = pd.read_csv(filename, sep='\t', header=None)  # type: ignore
                data = df.values
                break
        
        if data.shape[1] >= 2:

            means = np.mean(data, axis=1)  # type: ignore

            differences = []
            for i in range(data.shape[0]):
                subject_ratings = data[i, :]
                subject_mean = np.mean(subject_ratings)  # type: ignore

                mean_abs_diff = np.mean(np.abs(subject_ratings - subject_mean))  # type: ignore
                differences.append(mean_abs_diff)
            differences = np.array(differences)
            
            mean_diff = np.mean(differences)  # type: ignore
            std_diff = np.std(differences)  # type: ignore
            upper_limit = mean_diff + 1.96 * std_diff
            lower_limit = mean_diff - 1.96 * std_diff
            
            bland_altman_values = {
                'Mean_Difference': round(mean_diff, 2),
                'Upper_Limit': round(upper_limit, 2),
                'Lower_Limit': round(lower_limit, 2),
                'Std_Difference': round(std_diff, 2)
            }

    results_df = pd.DataFrame([
        {
            'Measurement_Type': name,
            'ICC_2_1': round(results[name]['icc'], 2),
            'CI_Lower': round(results[name]['ci_lower'], 2),
            'CI_Upper': round(results[name]['ci_upper'], 2),
            'F_Statistic': round(results[name]['f_stat'], 2),
            'P_Value': round(results[name]['p_value'], 2),
            'N_Subjects': results[name]['n_subjects'],
            'N_Raters': results[name]['n_raters'],
            'Bland_Altman_Mean_Diff': bland_altman_values.get('Mean_Difference', ''),
            'Bland_Altman_Upper_Limit': bland_altman_values.get('Upper_Limit', ''),
            'Bland_Altman_Lower_Limit': bland_altman_values.get('Lower_Limit', ''),
            'Bland_Altman_Std_Diff': bland_altman_values.get('Std_Difference', '')
        }
        for name in names
    ])
    
    results_df.to_csv('../ICC_results/sagittal_icc_results.csv', index=False)
    print(f"\nResults saved to '../ICC_results/sagittal_icc_results.csv'")
    print(f"Inter-Rater Variability Plot saved as '../ICC_results/sagittal_inter_rater_variability.png'")

    print(f"\n=== SAGITTAL THORACIC ICC ANALYSIS SUMMARY ===")
    for name in names:
        result = results[name]
        icc = result['icc']
        ci_lower = result['ci_lower']
        ci_upper = result['ci_upper']
            
        print(f"{name}: ICC = {icc:.4f}")
        print(f"  95% CI: [{ci_lower:.3f}, {ci_upper:.3f}]")

    print("\n" + "="*60)
    print("SUMMARY WITH BOOTSTRAP ICC(2,k)")
    print("="*60)
    create_comprehensive_summary(
        csv_path='../cobb_angles/dev_cobb.csv',
        outdir='../ICC_results',
        decimals=1,
        n_boot=5000
    )

    print("\n" + "="*60)
    print("TEST DATASET ANALYSIS (SINGLE-OBSERVER COBB ANGLES)")
    print("="*60)
    create_test_cobb_summary(
        csv_path='../cobb_angles/test_cobb.csv',
        outdir='../ICC_results',
        decimals=1
    )

if __name__ == "__main__":
    create_sagittal_icc_plot()