"""exp (d): 5-policy routing under tracets vs thinktime dispatch.

Shows the ranking FLIP: under the faithful `thinktime` load the parameter-free
LPWL (leastwork) is the clear winner, but under `tracets` (think-collapse bursts)
its advantage disappears (it ties unified_ab on TTFT p90 and *loses* on E2E mean).

Reads the two bench_report summaries; writes v2/figs/exp_d_policy_dispatch.png.
Usage: python v2/exp_d_policy_dispatch/plot.py
"""
import json
import os

import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt

HERE = os.path.dirname(__file__)
TC = json.load(open(os.path.join(HERE, "results/tracets.json")))
TT = json.load(open(os.path.join(HERE, "results/thinktime.json")))

# canonical order: LPWL first; pretty labels
ARMS = ["leastwork", "unified_ab", "unified_def", "lmetric", "sticky"]
LABEL = {"leastwork": "LPWL\n(leastwork)", "unified_ab": "unified\n+A+B",
         "unified_def": "unified\ndefault", "lmetric": "LMetric", "sticky": "sticky"}
C_TC, C_TT = "#d62728", "#2ca02c"  # tracets red / thinktime green (match exp_c)


def panel(ax, key, sub, title, ylab):
    tc = [TC[a][key][sub] / 1000.0 for a in ARMS]   # ms -> s
    tt = [TT[a][key][sub] / 1000.0 for a in ARMS]
    x = range(len(ARMS))
    w = 0.38
    b1 = ax.bar([i - w / 2 for i in x], tc, w, label="tracets (burst)", color=C_TC)
    b2 = ax.bar([i + w / 2 for i in x], tt, w, label="thinktime (faithful)", color=C_TT)
    for bars in (b1, b2):
        for r in bars:
            ax.text(r.get_x() + r.get_width() / 2, r.get_height(),
                    f"{r.get_height():.1f}", ha="center", va="bottom", fontsize=8)
    ax.set_xticks(list(x)); ax.set_xticklabels([LABEL[a] for a in ARMS], fontsize=9)
    ax.set_ylabel(ylab); ax.set_title(title, fontsize=11)
    ax.grid(axis="y", alpha=.3)
    ax.set_ylim(0, max(tc + tt) * 1.18)
    # mark LPWL-thinktime as the winner (lowest green) in each panel
    ax.annotate("LPWL wins\nunder thinktime", xy=(0 + w / 2, tt[0]),
                xytext=(0.9, max(tc + tt) * 0.86), fontsize=8.5, color=C_TT,
                ha="left", arrowprops=dict(arrowstyle="->", color=C_TT, lw=1.3))
    return b1, b2


fig, (axL, axR) = plt.subplots(1, 2, figsize=(11.2, 4.6))
panel(axL, "ttft_ms", "p90", "TTFT p90 (lower = better)", "TTFT p90 (s)")
panel(axR, "e2e_ms", "mean", "E2E mean (lower = better)", "E2E mean (s)")
axL.legend(loc="upper left", fontsize=9)
fig.suptitle("5-policy routing: dispatch mode flips the ranking — "
             "LPWL is best under faithful thinktime, only ties/loses under tracets bursts",
             fontsize=11.5)
fig.tight_layout(rect=(0, 0, 1, 0.95))
out = os.path.join(HERE, "..", "figs", "exp_d_policy_dispatch.png")
fig.savefig(out, dpi=140)
print("wrote", os.path.normpath(out))

# also print the deltas the README cites
print("\npolicy        TTFTp90 tc->tt    E2Emean tc->tt")
for a in ARMS:
    t1, t2 = TC[a]["ttft_ms"]["p90"], TT[a]["ttft_ms"]["p90"]
    e1, e2 = TC[a]["e2e_ms"]["mean"], TT[a]["e2e_ms"]["mean"]
    print(f"{a:<13} {t1/1000:5.1f}->{t2/1000:4.1f}s ({(t2-t1)/t1:+.0%})   "
          f"{e1/1000:5.1f}->{e2/1000:4.1f}s ({(e2-e1)/e1:+.0%})")