"""Figures for the reproduction logbook.

Palette: validated categorical slots (validate_palette.js, light mode, ALL CHECKS PASS).
Every figure ships its raw data as CSV alongside the HTML, which serves as the table
view the contrast WARN obligates.
"""
import json, os, sys
import numpy as np
import plotly.graph_objects as go

os.chdir(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
OUT = "figs"
P = ["#2a78d6", "#e87ba4", "#eda100", "#1baf7a", "#4a3aa7"]   # categorical slots
INK, INK2, GRID = "#0b0b0b", "#52514e", "rgba(120,118,110,0.22)"


def base(fig, title, xt, yt, logx=False, logy=False):
    fig.update_layout(
        title=dict(text=title, font=dict(size=15, color=INK)),
        paper_bgcolor="rgba(0,0,0,0)", plot_bgcolor="rgba(0,0,0,0)",
        font=dict(family="Inter, system-ui, sans-serif", size=12, color=INK2),
        margin=dict(l=64, r=132, t=54, b=52), hovermode="x unified",
        legend=dict(bgcolor="rgba(0,0,0,0)", borderwidth=0, font=dict(color=INK2)),
        width=780, height=420)
    for ax, t, lg in ((fig.update_xaxes, xt, logx), (fig.update_yaxes, yt, logy)):
        ax(title_text=t, type="log" if lg else "linear", gridcolor=GRID,
           zeroline=False, linecolor=GRID, ticks="outside", tickcolor=GRID,
           title_font=dict(color=INK2))
    return fig


def endlabel(fig, x, y, text, color):
    fig.add_annotation(x=np.log10(x) if fig.layout.xaxis.type == "log" else x,
                       y=np.log10(y) if fig.layout.yaxis.type == "log" else y,
                       text=text, showarrow=False, xanchor="left", xshift=8,
                       font=dict(color=color, size=11))


def save(fig, name, rows, header):
    os.makedirs(OUT, exist_ok=True)
    fig.write_html(f"{OUT}/{name}.html", include_plotlyjs="cdn", full_html=True)
    with open(f"{OUT}/{name}.csv", "w") as f:
        f.write(",".join(header) + "\n")
        for r in rows:
            f.write(",".join(str(v) for v in r) + "\n")
    print(f"wrote {OUT}/{name}.html + .csv")


# ------------------------------------------------------------------ Claim 1
def fig_rate(path="outputs/claim1_rate.json"):
    """Exploitation-stage regret vs T: the sqrt(T) half of Theorem 2, confirmed."""
    f = json.load(open(path))["fits"]
    fig = go.Figure(); rows = []
    keys = [k for k in f if k.startswith("T:")]
    for i, k in enumerate(keys):
        T, R, s = f[k]["x"], f[k]["exploit"], f[k]["slope_exploit"]
        lab = k[2:].replace("_", ", ").replace("K", "K=").replace("d", "d=")
        fig.add_trace(go.Scatter(x=T, y=R, mode="lines+markers", name=f"{lab} (slope {s:.3f})",
                                 line=dict(color=P[i], width=2), marker=dict(size=8)))
        endlabel(fig, T[-1], R[-1], f"{s:.3f}", P[i])
        rows += [[lab, t, r] for t, r in zip(T, R)]
    T = np.array(f[keys[0]]["x"], float)
    ref = f[keys[0]]["exploit"][0] * np.sqrt(T / T[0])
    fig.add_trace(go.Scatter(x=T, y=ref, mode="lines", name="√T reference (slope 0.5)",
                             line=dict(color=INK2, width=2, dash="dot")))
    base(fig, "Claim 1 — exploitation-stage regret vs horizon T (5 seeds, T up to 2¹⁷)",
         "T (rounds, log)", "regret accrued after the cold start (log)", True, True)
    save(fig, "claim1_rate_T", rows, ["config", "T", "exploitation_regret"])


def fig_rate_Kd(path="outputs/claim1_rate.json"):
    """Exploitation-stage regret vs K and vs d: sqrt(d) holds, sqrt(K) does not."""
    f = json.load(open(path))["fits"]
    fig = go.Figure(); rows = []
    for i, (k, lab) in enumerate([("K", "vs K (d=5)"), ("d", "vs d (K=5)")]):
        x, y, s = f[k]["x"], f[k]["exploit"], f[k]["slope_exploit"]
        fig.add_trace(go.Scatter(x=x, y=y, mode="lines+markers",
                                 name=f"{lab} — slope {s:.3f}",
                                 line=dict(color=P[i], width=2), marker=dict(size=8)))
        endlabel(fig, x[-1], y[-1], f"{s:.3f}", P[i])
        rows += [[lab, a, b] for a, b in zip(x, y)]
    xs = np.array([2, 20], float)
    y0 = f["K"]["exploit"][0]
    fig.add_trace(go.Scatter(x=xs, y=y0 * np.sqrt(xs / 2), mode="lines",
                             name="√· reference — Theorem 2's claimed slope 0.5",
                             line=dict(color=INK2, width=2, dash="dot")))
    base(fig, "Claim 1 — exploitation regret vs arms K and dimension d (T = 2¹⁷)",
         "K or d (log)", "exploitation-stage regret (log)", True, True)
    save(fig, "claim1_rate_Kd", rows, ["sweep", "value", "exploitation_regret"])


def fig_warfarin_ablation(path="outputs/warfarin_ablation.json"):
    """Which reading of the paper's under-specified §5.1 setup reproduces Table 2?
    Distance is the L1 distance between the 3x3 dosage-correction matrix and Table 2."""
    d = json.load(open(path))
    fig = go.Figure(); rows = []
    labs, vals, cols = [], [], []
    for c in d["ceilings"]:
        labs.append(f"offline ceiling<br>{c['reward']} reward")
        vals.append(c["conf_L1_vs_paper"]); cols.append(P[4])
        rows.append(["ceiling", c["reward"], "-", "-", c["conf_L1_vs_paper"],
                     c["error"], c["score"]])
    for e in d["spec_grid"]:
        labs.append(f"{e['reward']}<br>E_F={e['EF']}, φ₀={e['phi0']}")
        vals.append(e["conf_L1_vs_paper"])
        cols.append(P[0] if e["reward"] == "binary" else P[1])
        rows.append(["online RCB", e["reward"], e["EF"], e["phi0"],
                     e["conf_L1_vs_paper"], e["error"], e["score"]])
    order = np.argsort(vals)
    fig.add_trace(go.Bar(x=[labs[i] for i in order], y=[vals[i] for i in order],
                         marker_color=[cols[i] for i in order],
                         hovertemplate="%{x}<br>L1 distance to Table 2: %{y:.3f}<extra></extra>"))
    base(fig, "Warfarin §5.1 — distance to the paper's Table 2 under each reading "
              "of the under-specified setup",
         "specification", "L1 distance of the 3×3 correction matrix to Table 2")
    fig.update_layout(height=470, margin=dict(l=64, r=40, t=54, b=120))
    save(fig, "warfarin_ablation", rows,
         ["policy", "reward", "EF", "phi0", "L1_to_table2", "error", "weighted_risk_score"])


def fig_warfarin_N(path="outputs/warfarin_ablation.json"):
    """Cold-start length vs clinical quality: the Claim-2 tradeoff on real data."""
    d = json.load(open(path))
    s = [e for e in d["N_sweep"] if e["Tcold"] < 5528]
    fig = go.Figure()
    fig.add_trace(go.Scatter(x=[e["Tcold"] for e in s], y=[e["score"] for e in s],
                             mode="lines+markers", name="RCB weighted risk score",
                             line=dict(color=P[0], width=2), marker=dict(size=9),
                             text=[f"N={e['N']}" for e in s],
                             hovertemplate="T_cold=%{x:.0f} (%{text})<br>score=%{y:.3f}<extra></extra>"))
    for yv, lab, col in [(0.291, "paper's reported RCB score 0.291", "#c0392b"),
                         (d["ceilings"][0]["score"], "offline full-data oracle ceiling 0.352", P[4]),
                         (d["meta"]["physician_score"], "physician baseline 0.224", INK2)]:
        fig.add_hline(y=yv, line=dict(color=col, width=2, dash="dot"),
                      annotation_text=lab, annotation_font_color=col)
    base(fig, "Warfarin — the price of incentives, measured: longer cold start ⇒ worse clinical score",
         "cold-start length T_cold (patients)", "weighted risk score")
    save(fig, "warfarin_N", [[e["N"], e["Tcold"], e["error"], e["score"]] for e in d["N_sweep"]],
         ["N", "Tcold", "error", "weighted_risk_score"])


def fig_gain(path="outputs/syn_s1.json"):
    d = json.load(open(path))["setting1"]
    fig = go.Figure(); rows = []
    sel = [e for e in d if e["d"] == 5][:4]
    for i, e in enumerate(sel):
        fig.add_trace(go.Bar(x=[f"K={e['K']}"], y=[e["gain_frac_ok"]],
                             marker_color=P[i], name=f"K={e['K']}", showlegend=False,
                             text=[f"{e['gain_frac_ok']:.3f}"], textposition="outside"))
        rows.append([e["K"], e["d"], e["gain_frac_ok"], e["gain_min"]])
    fig.add_hline(y=1.0, line=dict(color=INK2, width=2, dash="dot"),
                  annotation_text="required by Definition 1", annotation_font_color=INK2)
    base(fig, "Claim 1 — fraction of rounds satisfying the ε-DBIC constraint (d=5, T=10⁵)",
         "arms", "fraction of rounds with expected gain ≥ −ε")
    fig.update_yaxes(range=[0, 1.15])
    save(fig, "claim1_dbic", rows, ["K", "d", "frac_gain_ge_-eps", "min_gain"])


# ------------------------------------------------------------------ Claim 2
def fig_feasibility(path="outputs/feas.json"):
    d = json.load(open(path))["thm1_feasibility"]
    fig = go.Figure()
    tags = [e["tag"] for e in d]; ratio = [e["ratio"] for e in d]
    fig.add_trace(go.Bar(y=tags, x=ratio, orientation="h", marker_color=P[0],
                         text=[f"{r:,.0f}×" for r in ratio], textposition="outside",
                         showlegend=False))
    fig.add_vline(x=1.0, line=dict(color="#c0392b", width=2, dash="dot"),
                  annotation_text="cold start = the paper's own horizon T",
                  annotation_font_color="#c0392b")
    base(fig, "Claim 2 — Theorem 1's prescribed cold start K·L·N(ε), as a multiple of T",
         "K·L·N(ε) / T   (log scale)", "", True)
    fig.update_layout(height=460, margin=dict(l=190, r=110, t=54, b=52))
    save(fig, "claim2_feasibility", [[e["tag"].replace(",", ";"), e["T"], e["N"], e["L"], e["cold"], e["ratio"]]
                                     for e in d],
         ["config", "T", "N_eps", "L", "K_L_N", "ratio_to_T"])


def fig_tradeoff(path="outputs/syn_s3.json"):
    d = json.load(open(path))["setting3"]
    fig = go.Figure(); rows = []
    for i, lam in enumerate([3, 5, 10]):
        sub = sorted([e for e in d if e["lam"] == lam], key=lambda e: e["eps"])
        fig.add_trace(go.Scatter(x=[e["eps"] for e in sub], y=[e["regret"] for e in sub],
                                 mode="lines+markers", name=f"Σ₀ = (1/{lam})·I",
                                 line=dict(color=P[i], width=2), marker=dict(size=9)))
        endlabel(fig, sub[-1]["eps"], sub[-1]["regret"], f"1/{lam}", P[i])
        rows += [[lam, e["eps"], e["N"], e["Tcold"], e["regret"], e["frac_ok"]] for e in sub]
    base(fig, "Claim 2 — incentive budget ε vs cumulative regret (Setting 3, T=5×10⁴, K=d=5)",
         "incentive budget ε", "cumulative regret R(T) (log)", False, True)
    save(fig, "claim2_tradeoff", rows, ["inv_lambda", "eps", "N", "Tcold", "regret", "frac_dbic_ok"])


def fig_Neps():
    from rcb.core import N_eps
    fig = go.Figure(); rows = []
    specs = [("K (cubic)", [2, 3, 5, 10, 20, 40], lambda v: N_eps(v, 5, .05, .05, .01, 1.), 3.0),
             ("d at σ=0.05 (paper's own noise)", [2, 5, 10, 20, 50, 100, 200],
              lambda v: N_eps(5, v, .05, .05, .01, 1.), None),
             ("τ+ε (inverse quadratic)", [.02, .03, .04, .06, .11],
              lambda v: N_eps(5, 5, .05, v - .01, .01, 1.), -2.0)]
    for i, (lab, xs, f, th) in enumerate(specs):
        ys = [f(v) for v in xs]
        s = float(np.polyfit(np.log(xs), np.log(ys), 1)[0])
        fig.add_trace(go.Scatter(x=xs, y=ys, mode="lines+markers",
                                 name=f"{lab} — fitted {s:+.3f}"
                                      + (f" (paper {th:+.0f})" if th else " (paper +1)"),
                                 line=dict(color=P[i], width=2), marker=dict(size=8)))
        endlabel(fig, xs[-1], ys[-1], f"{s:+.2f}", P[i])
        rows += [[lab, v, y] for v, y in zip(xs, ys)]
    base(fig, "Claim 2 — measured exponents of Theorem 1's N(ε)",
         "parameter value (log)", "N(ε) (log)", True, True)
    save(fig, "claim2_Neps", rows, ["sweep", "value", "N_eps"])


# ------------------------------------------------------------------ warfarin
def fig_warfarin(path="outputs/warfarin.json"):
    d = json.load(open(path))
    fig = go.Figure(); rows = []
    for i, eps in enumerate([0.025, 0.035, 0.045]):
        e = [x for x in d["runs"] if x["eps"] == eps and x["prior_var"] == 0.4][0]
        y = e["error_curve"]; x = list(range(0, 20 * len(y), 20))
        fig.add_trace(go.Scatter(x=x[5:], y=y[5:], mode="lines", name=f"RCB, ε={eps}",
                                 line=dict(color=P[i], width=2)))
        endlabel(fig, x[-1], y[-1], f"{y[-1]:.2f}", P[i])
        rows += [[eps, a, b] for a, b in zip(x, y)]
    for yv, lab, col in [(0.35, "paper's reported RCB error ≈ 0.35", "#c0392b"),
                         (d["meta"]["physician_error"], "physician (always Medium)", INK2),
                         (0.3238, "offline full-data oracle ceiling 0.324", "#4a3aa7")]:
        fig.add_hline(y=yv, line=dict(color=col, width=2, dash="dot"),
                      annotation_text=lab, annotation_font_color=col,
                      annotation_position="top right")
    base(fig, "Warfarin (§5.1) — fraction of incorrect dosing decisions, Σ₀=0.4·I, 10 permutations",
         "patients seen", "cumulative error rate")
    fig.update_yaxes(range=[0.28, 0.62])
    save(fig, "warfarin_error", rows, ["eps", "t", "error_rate"])


def fig_gamma(path="outputs/warfarin_gamma_sweep.json"):
    d = json.load(open(path))
    fig = go.Figure()
    g = [e["gamma_mult"] for e in d["sweep"]]
    fig.add_trace(go.Scatter(x=g, y=[e["error"] for e in d["sweep"]], mode="lines+markers",
                             name="RCB error rate", line=dict(color=P[0], width=2),
                             marker=dict(size=9)))
    fig.add_trace(go.Scatter(x=g, y=[e["explore"] for e in d["sweep"]], mode="lines+markers",
                             name="forced exploration fraction",
                             line=dict(color=P[3], width=2), marker=dict(size=9)))
    for yv, lab, col in [(0.35, "paper's reported 0.35", "#c0392b"),
                         (d["ceiling_error"], "offline oracle ceiling", "#4a3aa7"),
                         (d["physician_error"], "physician baseline", INK2)]:
        fig.add_hline(y=yv, line=dict(color=col, width=2, dash="dot"),
                      annotation_text=lab, annotation_font_color=col)
    base(fig, "Warfarin — RCB error rate vs exploration aggressiveness (γ multiplier)",
         "multiplier on the spread parameter γ_m (log)", "rate", True)
    fig.update_yaxes(range=[0, 0.62])
    save(fig, "warfarin_gamma", [[e["gamma_mult"], e["error"], e["explore"], e["b_acc"],
                                  e["score"]] for e in d["sweep"]],
         ["gamma_mult", "error", "explore_frac", "oracle_b_acc", "weighted_risk_score"])


if __name__ == "__main__":
    sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
    for name in sys.argv[1:] or ["warfarin", "gamma", "feasibility", "Neps"]:
        globals()[f"fig_{name}"]()