"""Render the published charts from evaluation/2026-09-24/performance.json.

Usage: python evaluation/render_charts.py
"""
import json
from pathlib import Path

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

RESULTS = Path(__file__).resolve().parent / "2026-09-24"
data = json.loads((RESULTS / "performance.json").read_text(encoding="utf-8"))
bench, speed = data["benchmark"], data["speed"]
cohorts = bench["cohorts"]
names = list(cohorts)
accuracies = [100 * cohorts[name]["accuracy"] for name in names]
ece = [cohorts[name]["ece_10"] for name in names]
palette = ["#2a58d7", "#129184", "#dc8240"]

plt.rcParams.update({
    "font.family": "DejaVu Sans", "font.size": 11, "axes.spines.top": False,
    "axes.spines.right": False, "axes.edgecolor": "#c8d3e4",
    "axes.facecolor": "#f5f7fb", "text.color": "#182640",
})

def public_chart(ax):
    bars = ax.barh(names[::-1], accuracies[::-1], color=palette[::-1], height=.58)
    ax.set_xlim(0, 105)
    ax.set_xlabel("Accuracy (%)")
    ax.set_title("JevBench v1.4.1 public tasks", loc="left", fontweight="bold")
    for bar, name, value in zip(bars, names[::-1], accuracies[::-1]):
        ax.text(value + 2, bar.get_y() + bar.get_height() / 2,
                f"{value:.2f}%  (n={cohorts[name]['n']})", va="center", fontsize=10)
    ax.text(.02, -.18, f"Total  {bench['public_correct']} / {bench['public_n']} = {100*bench['public_accuracy']:.2f}%",
            transform=ax.transAxes, fontweight="bold")

def latency_chart(ax):
    x = np.arange(2)
    p50 = [speed[key]["single_decision"]["p50_ms"] for key in ("h200", "cpu")]
    p95 = [speed[key]["single_decision"]["p95_ms"] for key in ("h200", "cpu")]
    ax.bar(x - .18, p50, .35, color=palette[0], label="p50")
    ax.bar(x + .18, p95, .35, color=palette[2], label="p95")
    ax.set_yscale("log")
    ax.set_ylim(5, 2100)
    ax.set_xticks(x, ["H200 GPU", "Xeon CPU"])
    ax.set_ylabel("Milliseconds (log scale)")
    ax.set_title("Warm single-decision latency", loc="left", fontweight="bold")
    ax.legend(frameon=False)
    for pos, values, offset in ((0, p50, -.18), (1, p95, .18)):
        for index, value in enumerate(values):
            ax.text(index + offset, value * 1.15, f"{value:.1f} ms", ha="center", fontsize=9)

def throughput_chart(ax):
    values = [speed[key]["throughput"]["decisions_per_second"] for key in ("h200", "cpu")]
    batches = [speed[key]["throughput"]["batch_size"] for key in ("h200", "cpu")]
    ax.bar([f"H200\nbatch {batches[0]}", f"Xeon CPU\nbatch {batches[1]}"],
           values, color=palette[:2], width=.58)
    ax.set_yscale("log")
    ax.set_ylim(1, 2000)
    ax.set_ylabel("Decisions/s (log scale)")
    ax.set_title("Warm batched throughput", loc="left", fontweight="bold")
    for index, value in enumerate(values):
        ax.text(index, value * 1.15, f"{value:.2f}/s", ha="center", fontsize=10)

fig, ax = plt.subplots(figsize=(8.2, 4.8), layout="constrained")
public_chart(ax)
fig.savefig(RESULTS / "accuracy.png", dpi=180)
plt.close(fig)

fig, axes = plt.subplots(1, 2, figsize=(11, 4.7), layout="constrained")
latency_chart(axes[0])
throughput_chart(axes[1])
fig.suptitle("Pivot | local FP32 inference", fontsize=16, fontweight="bold")
fig.savefig(RESULTS / "latency_throughput.png", dpi=180)
plt.close(fig)

fig, axes = plt.subplots(2, 2, figsize=(12.6, 8.8), layout="constrained")
public_chart(axes[0, 0])
axes[0, 1].barh(names[::-1], ece[::-1], color=palette[::-1], height=.58)
axes[0, 1].set_xlim(0, .68)
axes[0, 1].set_xlabel("ECE, 10 bins (lower is better)")
axes[0, 1].set_title("Public calibration", loc="left", fontweight="bold")
for index, value in enumerate(ece[::-1]):
    axes[0, 1].text(value + .01, index, f"{value:.3f}", va="center")
latency_chart(axes[1, 0])
throughput_chart(axes[1, 1])
fig.suptitle("Pivot | measured public performance", fontsize=19, fontweight="bold")
fig.text(.02, -.015, "Pinned checkpoint | warm local tokenization + inference + scoring | throughput batch sizes differ",
         fontsize=9, color="#53637b")
fig.savefig(RESULTS / "performance_overview.png", dpi=180, bbox_inches="tight")
plt.close(fig)
print(f"Saved charts in {RESULTS}")