import os, json, numpy as np
from datetime import datetime, timezone

OUTPUT_ROOT = "/root/autodl-tmp/SplatAtlas/outputs/error_responsibility"
PROF_DIR = os.path.join(OUTPUT_ROOT, "_profiles")

METHODS = ['vanilla_3dgs', 'analyticsplatting', 'erankgs', 'ges', 'lightgaussian', 'minisplatting', 'opti3dgs', 'pgsr', 'steepgs']
SCENES = ['bicycle','bonsai','counter','flowers','garden','kitchen','room','stump','treehill',
          'auditorium','ballroom','barn','caterpillar','courtroom','lighthouse','museum','palace','playground','temple','train','truck',
          'DrJohnson','Playroom',
          'Chair','Drums','Ficus','Hotdog','Lego','Materials','Mic','Ship']

METRICS = ["psnr_full", "sh_net_effect", "sh_corruption_rate", "opacity_net_effect", "coverage", "coverage_error_fraction", "residual_error"]

# 1. 抓取所有 9 个方法在 31 个场景上的中位数 (Cross-Scene Median)
method_medians = {m: {} for m in METHODS}
for m in METHODS:
    for metric in METRICS:
        vals = []
        for s in SCENES:
            p = os.path.join(OUTPUT_ROOT, f"{m}_{s}", "counterfactual_summary.json")
            if os.path.exists(p):
                with open(p) as f:
                    d = json.load(f)
                    if d.get(metric) is not None:
                        vals.append(d[metric])
        method_medians[m][metric] = float(np.median(vals)) if vals else 0.0

# 2. 计算相对 IQR 统计量 (跨 9 种方法)
def calc_iqr_stats(vals_list):
    if not vals_list: return None, None, None
    med = float(np.median(vals_list))
    abs_iqr = float(np.percentile(vals_list, 75) - np.percentile(vals_list, 25))
    rel_iqr = (abs_iqr / abs(med) * 100) if med != 0 else None
    return med, abs_iqr, rel_iqr

cross_method_analysis = {"n_methods": len(METHODS), "methods_included": METHODS, "metrics": {}}

# 提取 PSNR 基础倍率
psnr_vals = [method_medians[m]["psnr_full"] for m in METHODS]
_, _, psnr_rel_iqr = calc_iqr_stats(psnr_vals)

for metric in METRICS:
    vals = [method_medians[m][metric] for m in METHODS]
    med, abs_iqr, rel_iqr = calc_iqr_stats(vals)
    ratio_rel = (rel_iqr / psnr_rel_iqr) if (rel_iqr is not None and psnr_rel_iqr) else None
    
    cross_method_analysis["metrics"][metric] = {
        "median_of_9_methods": med,
        "relative_iqr_pct": rel_iqr,
        "ratio_vs_psnr_relative": ratio_rel
    }

# 3. 输出报告
md_lines = [
    "# Phase 2 Part 3b — Final Heterogeneous Array Findings",
    f"\n**Scope**: {len(METHODS)} methods (Vanilla + 8 heterogeneous), {len(SCENES)} scenes.",
    "\n## Ultimate Discriminative Power (Relative IQR)",
    f"> **Baseline**: PSNR Relative IQR across 9 methods = **{psnr_rel_iqr:.2f}%**",
    "\n| Metric | Relative IQR (%) | Ratio vs PSNR (Discriminative Multiplier) |",
    "|---|---|---|"
]

for metric in METRICS:
    if metric == "psnr_full": continue
    d = cross_method_analysis["metrics"][metric]
    rel_str = f"{d['relative_iqr_pct']:.2f}%" if d['relative_iqr_pct'] is not None else "N/A"
    ratio_str = f"**{d['ratio_vs_psnr_relative']:.1f}x**" if d['ratio_vs_psnr_relative'] is not None else "N/A"
    md_lines.append(f"| {metric} | {rel_str} | {ratio_str} |")

md_content = "\n".join(md_lines)
with open(os.path.join(PROF_DIR, "part3b_findings.md"), "w") as f:
    f.write(md_content)

print(md_content)