"""Analyze Claim 1 results: MSE/MAE improvement of Informer+DropoutTS vs baseline
on the synthetic noise sweep, plus Claim 4c training-time comparison.

Reads claim1_results.json (from `modal run modal_repro.py::claim1`), writes:
  - claim1_table.csv     (per noise x horizon)
  - claim1_plot.html     (plotly: MSE improvement % by noise level)
  - prints a summary vs the paper's claimed 46.0% MSE / 24.5% MAE, peak 48.2% @sigma=0.3
"""
import json, sys, statistics as st

results = json.load(open("claim1_results.json"))

# index by (noise, horizon, dropout)
by = {}
for r in results:
    if not r or not r.get("metrics"):
        continue
    key = (r["noise"], r["output_len"], r["dropout"])
    by[key] = r

rows = []
noises = sorted({r["noise"] for r in results if r})
horizons = sorted({r["output_len"] for r in results if r})
for nl in noises:
    for h in horizons:
        b = by.get((nl, h, False))
        d = by.get((nl, h, True))
        if not b or not d:
            continue
        bm, dm = b["metrics"]["overall"], d["metrics"]["overall"]
        mse_imp = (bm["MSE"] - dm["MSE"]) / bm["MSE"] * 100
        mae_imp = (bm["MAE"] - dm["MAE"]) / bm["MAE"] * 100
        # per-epoch train time (Claim 4c)
        bpe = b["train_seconds"] / max(b.get("epochs_run") or 1, 1)
        dpe = d["train_seconds"] / max(d.get("epochs_run") or 1, 1)
        rows.append({
            "noise": nl, "horizon": h,
            "mse_base": bm["MSE"], "mse_drop": dm["MSE"], "mse_imp_pct": mse_imp,
            "mae_base": bm["MAE"], "mae_drop": dm["MAE"], "mae_imp_pct": mae_imp,
            "base_s_per_ep": bpe, "drop_s_per_ep": dpe,
            "base_epochs": b.get("epochs_run"), "drop_epochs": d.get("epochs_run"),
            "base_total_s": b["train_seconds"], "drop_total_s": d["train_seconds"],
        })

# CSV
import csv
with open("claim1_table.csv", "w", newline="") as f:
    w = csv.DictWriter(f, fieldnames=list(rows[0].keys()))
    w.writeheader(); w.writerows(rows)

# Summary
mse_imps = [r["mse_imp_pct"] for r in rows]
mae_imps = [r["mae_imp_pct"] for r in rows]
avg_mse = st.mean(mse_imps); avg_mae = st.mean(mae_imps)
peak = max(rows, key=lambda r: r["mse_imp_pct"])

print("=" * 72)
print("CLAIM 1 — Informer +DropoutTS on synthetic noise sweep")
print("=" * 72)
print(f"{'noise':>6} {'H':>5} {'MSE base':>10} {'MSE drop':>10} {'dMSE%':>8} {'dMAE%':>8}")
for r in rows:
    print(f"{r['noise']:>6} {r['horizon']:>5} {r['mse_base']:>10.4f} {r['mse_drop']:>10.4f} "
          f"{r['mse_imp_pct']:>8.1f} {r['mae_imp_pct']:>8.1f}")
print("-" * 72)
print(f"AVERAGE across all noise x horizon:  MSE {avg_mse:+.1f}%   MAE {avg_mae:+.1f}%")
print(f"  Paper Claim 1 (Informer):          MSE +46.0%   MAE +24.5%")
print(f"PEAK MSE improvement: {peak['mse_imp_pct']:+.1f}% at sigma={peak['noise']}, H={peak['horizon']}")
print(f"  Paper peak:          +48.2% at sigma=0.3")

# per-sigma averages (over horizons) for the plot
per_sigma = {}
for nl in noises:
    sub = [r for r in rows if r["noise"] == nl]
    if sub:
        per_sigma[nl] = st.mean([r["mse_imp_pct"] for r in sub])

# Claim 4c summary
print("\n" + "=" * 72)
print("CLAIM 4c — training time (baseline vs +DropoutTS)")
print("=" * 72)
spe = st.mean([r["drop_s_per_ep"] / r["base_s_per_ep"] for r in rows if r["base_s_per_ep"]])
tot = st.mean([r["drop_total_s"] / r["base_total_s"] for r in rows if r["base_total_s"]])
print(f"mean per-epoch time ratio  (drop/base): {spe:.2f}x   (>1 => dropout SLOWER per epoch)")
print(f"mean total wall-clock ratio(drop/base): {tot:.2f}x")
print(f"  Paper Claim 4c: 1.12-1.45x training SPEEDUP")

# Plotly figure
try:
    import plotly.graph_objects as go
    xs = [str(n) for n in per_sigma]
    ys = [per_sigma[n] for n in per_sigma]
    fig = go.Figure()
    fig.add_bar(x=xs, y=ys, name="Measured MSE improvement %",
                marker_color="#4C78A8", text=[f"{v:.1f}%" for v in ys], textposition="outside")
    fig.add_hline(y=46.0, line_dash="dash", line_color="#E45756",
                  annotation_text="Paper avg 46.0%")
    fig.update_layout(
        title="Claim 1: Informer + DropoutTS — MSE improvement vs noise level (avg over horizons)",
        xaxis_title="Noise level sigma", yaxis_title="MSE improvement %",
        template="plotly_white", height=460)
    fig.write_html("claim1_plot.html", include_plotlyjs="inline")
    print("\nWrote claim1_plot.html, claim1_table.csv")
except ImportError:
    print("\n(plotly not installed; wrote claim1_table.csv only)")