code/figures/make_figures.py

#!/usr/bin/env python3
"""Regenerate every number, table and figure in paper.tex from the item-level evidence.

  python3 figures/make_figures.py --decl <batch1 decl dir> --decl <batch2 decl dir> \
      --items <dir holding every run's items.jsonl> [--p100-decl <dir>]

Writes figures/*.pdf + *.png, tables/*.tex and build/numbers.json (plus tables/numbers.tex,
a file of \\newcommand macros that paper.tex \\input's, so no number in the text is typed by hand).
Everything is rebuilt from the per-item runs and cross-checked against the signed-capsule
declarations by xhw.load(); a mismatch aborts.
"""
from __future__ import annotations

import argparse
import collections
import json
import math
import os
import sys

HERE = os.path.dirname(os.path.abspath(__file__))
ROOT = os.path.dirname(HERE)
sys.path.insert(0, HERE)
import xhw  # noqa: E402

import matplotlib  # noqa: E402
matplotlib.use("Agg")
import matplotlib.pyplot as plt  # noqa: E402

# palette: validated with the dataviz validator (light surface, adjacent pairs, all hard gates pass)
C_IDENT, C_RAWONLY, C_SWAP, C_FLIP = "#2a78d6", "#1baf7a", "#4a3aa7", "#eb6834"
INK, INK2, MUTED, GRID = "#0b0b0b", "#52514e", "#898781", "#e1e0d9"
SEQ = ["#fcfcfb", "#cde2fb", "#9ec5f4", "#6da7ec", "#3987e5", "#256abf", "#184f95", "#0d366b"]

plt.rcParams.update({
    "font.family": "DejaVu Sans", "font.size": 8, "axes.edgecolor": "#c3c2b7", "axes.labelcolor": INK2,
    "xtick.color": INK2, "ytick.color": INK2, "axes.spines.top": False, "axes.spines.right": False,
    "axes.grid": False, "pdf.fonttype": 42, "savefig.dpi": 200,
})

# Read from the T4 ollama serve logs (print_info: file type; load_tensors: CUDA0/CUDA1 model buffer).
# The same GGUF blobs (same manifest digest) ran on the 3090; the 3090-side layout is UNRECORDED.
QUANT = {"mistral-nemo:12b": "Q4_0", "gemma3:12b": "Q4_K_M", "gemma3:4b": "Q4_K_M", "llama3.1:8b": "Q4_K_M",
         "llama3.2:3b": "Q4_K_M", "mistral:7b": "Q4_K_M", "phi3.5:3.8b": "Q4_0", "qwen2.5:0.5b-instruct": "Q4_K_M",
         "qwen2.5:1.5b": "Q4_K_M", "qwen2.5:7b": "Q4_K_M", "qwen3:8b": "Q4_K_M"}
T4_SPLIT = {"mistral-nemo:12b": True, "gemma3:12b": True, "phi3.5:3.8b": True}
PARAMS_B = {"mistral-nemo:12b": 12.2, "gemma3:12b": 12.2, "gemma3:4b": 4.3, "llama3.1:8b": 8.0, "llama3.2:3b": 3.2,
            "mistral:7b": 7.2, "phi3.5:3.8b": 3.8, "qwen2.5:0.5b-instruct": 0.5, "qwen2.5:1.5b": 1.5,
            "qwen2.5:7b": 7.6, "qwen3:8b": 8.2}


def wilson(k: int, n: int, z: float = 1.96) -> tuple[float, float]:
    if n == 0:
        return (float("nan"), float("nan"))
    p = k / n
    d = 1 + z * z / n
    c = (p + z * z / (2 * n)) / d
    h = z * math.sqrt(p * (1 - p) / n + z * z / (4 * n * n)) / d
    return (c - h, c + h)


def binom_two_sided(k: int, n: int) -> float:
    """Exact two-sided sign test p-value, H0 p = 1/2."""
    tail = sum(math.comb(n, i) for i in range(0, min(k, n - k) + 1)) / 2 ** n
    return min(1.0, 2 * tail)


def pct(a: int, b: int, d: int = 1) -> str:
    return f"{100 * a / b:.{d}f}" if b else "--"


def common_prefix(a: str, b: str) -> int:
    n = min(len(a), len(b))
    i = 0
    while i < n and a[i] == b[i]:
        i += 1
    return i


def tex_escape(s: str) -> str:
    return s.replace("_", r"\_").replace("%", r"\%").replace("&", r"\&")


def model_order(cards):
    return sorted({c.model for c in cards}, key=lambda m: (PARAMS_B.get(m, 0), m))


def main() -> None:
    ap = argparse.ArgumentParser()
    ap.add_argument("--decl", action="append", required=True)
    ap.add_argument("--items", action="append", required=True)
    ap.add_argument("--p100-decl", default=None, help="third-runtime declarations, when the P100 run completes")
    a = ap.parse_args()

    cards = xhw.load(a.decl, a.items)
    os.makedirs(os.path.join(ROOT, "build"), exist_ok=True)
    os.makedirs(os.path.join(ROOT, "tables"), exist_ok=True)
    N = {}

    # ------------------------------------------------------------------ totals
    def tally(cs):
        t = collections.Counter()
        for c in cs:
            p = c.pair()
            for k in ("n", "raw_eq", "label_eq", "grade_eq", "status_eq", "flips", "flips_up", "flips_down", "swaps"):
                t[k] += p[k]
            t["cards"] += 1
            t["itemwise"] += p["itemwise_grade_equal"]
            t["admit_item"] += p["admitted_item_rule"]
            t["admit_count"] += p["admitted_count_rule"]
            t["agg_only"] += p["aggregate_equal"] and not p["itemwise_grade_equal"]
            t["itemwise_not_agg"] += p["itemwise_grade_equal"] and not p["aggregate_equal"]
            t["raw_identical_cards"] += p["raw_eq"] == p["n"]
            t["n30"] += c.decl["n"] >= 30
            t["admit_item_n30"] += p["admitted_item_rule"] and c.decl["n"] >= 30
        return t

    for label, cs in (("all", cards), ("b1", [c for c in cards if c.batch == "batch1"]),
                      ("b2", [c for c in cards if c.batch == "batch2"])):
        t = tally(cs)
        for k, v in t.items():
            N[f"{label}_{k}"] = v
        N[f"{label}_raw_eq_pct"] = pct(t["raw_eq"], t["n"])
        N[f"{label}_grade_eq_pct"] = pct(t["grade_eq"], t["n"])
        N[f"{label}_label_eq_pct"] = pct(t["label_eq"], t["n"])
        N[f"{label}_status_eq_pct"] = pct(t["status_eq"], t["n"])
        lo, hi = wilson(t["n"] - t["grade_eq"], t["n"])
        N[f"{label}_flip_rate_pct"] = pct(t["n"] - t["grade_eq"], t["n"], 2)
        N[f"{label}_flip_rate_ci"] = f"{100*lo:.2f}--{100*hi:.2f}"
        N[f"{label}_models"] = len({c.model for c in cs})
        N[f"{label}_axes"] = len({c.axis for c in cs})
    N["sign_test_p"] = f"{binom_two_sided(N['all_flips_up'], N['all_flips']):.2f}"

    # raw differs -> how often does the grade move?
    raw_diff = N["all_n"] - N["all_raw_eq"]
    N["all_raw_diff"] = raw_diff
    N["raw_diff_grade_moves_pct"] = pct(N["all_n"] - N["all_grade_eq"], raw_diff)

    # ------------------------------------------------------------------ repeat
    rep = collections.defaultdict(collections.Counter)
    rep_flip_overlap = 0
    for c in cards:
        if not c.has_repeat():
            continue
        q = c.pair("t4", "t4_repeat")
        r = rep[c.model]
        r["cards"] += 1
        r["items"] += q["n"]
        r["nonident_cards"] += q["raw_eq"] < q["n"]
        r["raw_diff"] += q["n"] - q["raw_eq"]
        r["grade_diff"] += q["n"] - q["grade_eq"]
        r["status_diff"] += q["n"] - q["status_eq"]
        r["count_diff_cards"] += not q["aggregate_equal"]
        r["itemwise_fail_cards"] += q["grade_eq"] < q["n"]
        unstable = {i for i in c.order() if c.items[i]["t4"]["raw_output_sha256"] != c.items[i]["t4_repeat"]["raw_output_sha256"]}
        rep_flip_overlap += len(unstable & set(c.pair()["flip_ids"]))
    R = collections.Counter()
    for m in rep.values():
        R.update(m)
    for k, v in R.items():
        N[f"rep_{k}"] = v
    N["rep_missing_cards"] = sum(1 for c in cards if not c.has_repeat())
    N["rep_flip_overlap"] = rep_flip_overlap
    N["rep_b1_nonident"] = sum(1 for c in cards if c.batch == "batch1" and c.has_repeat()
                               and c.pair("t4", "t4_repeat")["raw_eq"] < len(c.items))
    N["rep_models_nonident"] = sum(1 for m in rep.values() if m["nonident_cards"])

    # ------------------------------------------------------------------ swaps and first divergence
    swap_rows, prefixes, prefix_len_stop = [], [], collections.Counter()
    length_items = length_raw_diff = stop_items = stop_raw_diff = 0
    for c in cards:
        p = c.pair()
        for i in p["swap_ids"]:
            x, y = c.items[i]["rtx3090"], c.items[i]["t4"]
            swap_rows.append((c, i, xhw.item_status(x), xhw.item_status(y)))
        for i in c.order():
            x, y = c.items[i]["rtx3090"], c.items[i]["t4"]
            is_len = "length" in (x["done_reason"], y["done_reason"])
            if is_len:
                length_items += 1
                length_raw_diff += x["raw_output_sha256"] != y["raw_output_sha256"]
            else:
                stop_items += 1
                stop_raw_diff += x["raw_output_sha256"] != y["raw_output_sha256"]
            if x["raw_output_sha256"] != y["raw_output_sha256"]:
                k = common_prefix(x["raw_output"], y["raw_output"])
                prefixes.append((k, x["grade"] != y["grade"]))
    N["swaps_total"] = len(swap_rows)
    N["swaps_pe_on_3090"] = sum(1 for r in swap_rows if r[2] == "excluded")
    N["swaps_pe_on_t4"] = sum(1 for r in swap_rows if r[3] == "excluded")
    N["swap_cards"] = len({r[0].card_id for r in swap_rows})
    N["len_items"], N["len_raw_diff_pct"] = length_items, pct(length_raw_diff, length_items)
    N["stop_items"], N["stop_raw_diff_pct"] = stop_items, pct(stop_raw_diff, stop_items)
    ks = sorted(k for k, _ in prefixes)
    N["prefix_median"] = ks[len(ks) // 2]
    N["prefix_zero"] = sum(1 for k in ks if k == 0)
    N["prefix_zero_pct"] = pct(N["prefix_zero"], len(ks))

    # cards whose item-level disagreement is hidden by the total, and the size of the total's error
    hidden = [c for c in cards if c.pair()["aggregate_equal"] and not c.pair()["itemwise_grade_equal"]]
    N["hidden_cards"] = len(hidden)
    N["hidden_flips"] = sum(c.pair()["flips"] for c in hidden)
    N["hidden_b1_list"] = ", ".join(f"\\texttt{{{c.axis}}}" for c in hidden if c.batch == "batch1")
    within = beyond = 0
    dacc = []
    for c in cards:
        p = c.pair()
        if p["itemwise_grade_equal"] and p["aggregate_equal"]:
            continue
        a3, a4 = c.accuracy("rtx3090"), c.accuracy("t4")
        n3 = c.counts("rtx3090")["graded_n"]
        se = math.sqrt(max(a3 * (1 - a3), 1e-9) / n3) if n3 else float("inf")
        d = abs(a4 - a3)
        dacc.append(d)
        within += d <= 1.96 * se
        beyond += d > 1.96 * se
    N["dacc_cards"], N["dacc_within_ci"], N["dacc_beyond_ci"] = len(dacc), within, beyond
    N["dacc_max_pp"] = f"{100 * max(dacc):.1f}"
    N["dacc_median_pp"] = f"{100 * sorted(dacc)[len(dacc) // 2]:.1f}"

    # ------------------------------------------------------------------ tables
    models = model_order(cards)
    rows = []
    for m in models:
        cs = [c for c in cards if c.model == m]
        t = tally(cs)
        r = rep.get(m, collections.Counter())
        rows.append((m, cs[0].digest[:12], QUANT[m], "2" if T4_SPLIT.get(m) else "1", t, r))
    with open(os.path.join(ROOT, "tables", "models.tex"), "w") as fh:
        fh.write("\\begin{tabular}{lllrrr}\n\\toprule\n")
        fh.write("Model (Ollama tag) & Manifest digest & Quant. & T4s & Cards & Items \\\\\n\\midrule\n")
        for m, dg, q, sp, t, _ in rows:
            fh.write(f"\\texttt{{{tex_escape(m)}}} & \\texttt{{{dg}}}\\ldots & {tex_escape(q)} & {sp} & {t['cards']} & {t['n']:,} \\\\\n".replace(",", "{,}"))
        fh.write(f"\\midrule\nTotal & & & & {N['all_cards']} & {N['all_n']:,} \\\\\n".replace(",", "{,}"))
        fh.write("\\bottomrule\n\\end{tabular}\n")
    with open(os.path.join(ROOT, "tables", "per_model.tex"), "w") as fh:
        fh.write("\\begin{tabular}{lrrrrrrrr}\n\\toprule\n")
        fh.write(" & & \\multicolumn{3}{c}{Items equal (\\%)} & \\multicolumn{2}{c}{Flips} & \\multicolumn{2}{c}{Cards (of 14)} \\\\\n")
        fh.write("\\cmidrule(lr){3-5}\\cmidrule(lr){6-7}\\cmidrule(lr){8-9}\n")
        fh.write("Model & Items & Raw & Label & Grade & $\\uparrow$ & $\\downarrow$ & Item-eq. & Hidden \\\\\n\\midrule\n")
        for m, _, _, _, t, _ in rows:
            fh.write(f"\\texttt{{{tex_escape(m)}}} & {t['n']:,} & {pct(t['raw_eq'], t['n'])} & {pct(t['label_eq'], t['n'])} & "
                     f"{pct(t['grade_eq'], t['n'])} & {t['flips_up']} & {t['flips_down']} & {t['itemwise']} & {t['agg_only']} \\\\\n".replace(",", "{,}"))
        T = tally(cards)
        fh.write(f"\\midrule\nAll & {T['n']:,} & {pct(T['raw_eq'], T['n'])} & {pct(T['label_eq'], T['n'])} & {pct(T['grade_eq'], T['n'])} & "
                 f"{T['flips_up']} & {T['flips_down']} & {T['itemwise']} & {T['agg_only']} \\\\\n".replace(",", "{,}"))
        fh.write("\\bottomrule\n\\end{tabular}\n")
    axes = sorted({c.axis for c in cards})
    with open(os.path.join(ROOT, "tables", "per_axis.tex"), "w") as fh:
        fh.write("\\begin{tabular}{lrrrrrrr}\n\\toprule\n")
        fh.write("Axis & Items/card & Raw\\,\\% & Grade\\,\\% & Flips & Swaps & Item-eq. & Hidden \\\\\n\\midrule\n")
        for ax in axes:
            cs = [c for c in cards if c.axis == ax]
            t = tally(cs)
            ipc = sorted({len(c.items) for c in cs})
            ipc_s = str(ipc[0]) if len(ipc) == 1 else f"{ipc[0]}--{ipc[-1]}"
            fh.write(f"\\texttt{{{ax}}} & {ipc_s} & {pct(t['raw_eq'], t['n'])} & {pct(t['grade_eq'], t['n'])} & {t['flips']} & "
                     f"{t['swaps']} & {t['itemwise']}/{t['cards']} & {t['agg_only']} \\\\\n")
        fh.write("\\bottomrule\n\\end{tabular}\n")
    with open(os.path.join(ROOT, "tables", "repeat.tex"), "w") as fh:
        fh.write("\\begin{tabular}{lrrrrrr}\n\\toprule\n")
        fh.write("Model & Cards & Items & Cards not & Items raw & Items grade & Cards count \\\\\n")
        fh.write(" & repeated & & byte-identical & differs & differs & differs \\\\\n\\midrule\n")
        for m in models:
            r = rep[m]
            fh.write(f"\\texttt{{{tex_escape(m)}}} & {r['cards']} & {r['items']:,} & {r['nonident_cards']} & {r['raw_diff']} & {r['grade_diff']} & {r['count_diff_cards']} \\\\\n".replace(",", "{,}"))
        fh.write(f"\\midrule\nAll & {R['cards']} & {R['items']:,} & {R['nonident_cards']} & {R['raw_diff']} & {R['grade_diff']} & {R['count_diff_cards']} \\\\\n".replace(",", "{,}"))
        fh.write("\\bottomrule\n\\end{tabular}\n")
    # admission: counts-only rule vs item-level rule, per batch
    with open(os.path.join(ROOT, "tables", "admission.tex"), "w") as fh:
        fh.write("\\begin{tabular}{lrrr}\n\\toprule\n")
        fh.write("Outcome & Batch 1 & Batch 2 & All \\\\\n\\midrule\n")

        def row(name, key):
            fh.write(f"{name} & {N['b1_' + key]} & {N['b2_' + key]} & {N['all_' + key]} \\\\\n")
        row("Cards compared", "cards")
        row("Raw output byte-identical on every item", "raw_identical_cards")
        row("Grade equal on every item", "itemwise")
        row("Totals equal (counts-only rule admits)", "admit_count")
        row("\\quad of which an item's grade differs (hidden flips)", "agg_only")
        row("Grade equal on every item but totals differ (swaps)", "itemwise_not_agg")
        row("Admitted under the item-level rule", "admit_item")
        row("\\quad of which $n \\ge 30$", "admit_item_n30")
        fh.write("\\bottomrule\n\\end{tabular}\n")
    # the swaps, one row each (small)
    with open(os.path.join(ROOT, "tables", "swaps_by_model.tex"), "w") as fh:
        by = collections.Counter((r[0].model, r[2], r[3]) for r in swap_rows)
        fh.write("\\begin{tabular}{lrr}\n\\toprule\n")
        fh.write("Model & PE on 3090, wrong on T4 & wrong on 3090, PE on T4 \\\\\n\\midrule\n")
        for m in models:
            a1 = by[(m, "excluded", "wrong")]
            a2 = by[(m, "wrong", "excluded")]
            if a1 or a2:
                fh.write(f"\\texttt{{{tex_escape(m)}}} & {a1} & {a2} \\\\\n")
        fh.write(f"\\midrule\nAll & {N['swaps_pe_on_3090']} & {N['swaps_pe_on_t4']} \\\\\n\\bottomrule\n\\end{{tabular}}\n")

    # ------------------------------------------------------------------ figures
    fig, ax = plt.subplots(figsize=(6.3, 3.3))
    ys = list(range(len(models)))[::-1]
    for y, (m, _, _, _, t, _) in zip(ys, rows):
        n = t["n"]
        seg = [(t["raw_eq"], C_IDENT), (t["grade_eq"] - t["raw_eq"] - 0, C_RAWONLY), (0, C_SWAP), (t["flips"], C_FLIP)]
        # grade-equal but raw-differs splits into status-equal and status-swapped
        seg = [(t["raw_eq"], C_IDENT), (t["grade_eq"] - t["raw_eq"] - t["swaps"], C_RAWONLY), (t["swaps"], C_SWAP), (t["flips"], C_FLIP)]
        left = 0.0
        for v, col in seg:
            w = 100 * v / n
            if w > 0:
                ax.barh(y, w, left=left, height=0.62, color=col, edgecolor="#fcfcfb", linewidth=1.0)
            left += w
        ax.text(101, y, f"{100 * (n - t['grade_eq']) / n:.1f}% flip", va="center", fontsize=7, color=INK2)
    ax.set_yticks(ys)
    ax.set_yticklabels(models, fontsize=7.5)
    ax.set_xlim(0, 112)
    ax.set_xticks([0, 20, 40, 60, 80, 100])
    ax.set_xlabel("share of items (%)  -  RTX 3090 vs. Tesla T4, same digest, prompts, decode settings")
    from matplotlib.patches import Patch
    ax.legend(handles=[Patch(color=C_IDENT, label="raw output byte-identical"),
                       Patch(color=C_RAWONLY, label="raw differs, same grade and status"),
                       Patch(color=C_SWAP, label="same grade, parse-error <-> wrong swap"),
                       Patch(color=C_FLIP, label="grade flips")],
              loc="upper center", bbox_to_anchor=(0.45, 1.2), ncol=2, frameon=False, fontsize=7)
    fig.tight_layout()
    for ext in ("pdf", "png"):
        fig.savefig(os.path.join(HERE, f"fig_per_model.{ext}"))
    plt.close(fig)

    # fig 2: flips vs net change, one point per card
    fig, ax = plt.subplots(figsize=(3.3, 2.8))
    pts = collections.Counter()
    for c in cards:
        p = c.pair()
        net = abs(c.counts("t4")["correct"] - c.counts("rtx3090")["correct"])
        pts[(p["flips"], net, p["aggregate_equal"])] += 1
    def _kind(f, agg):
        return "hidden" if (agg and f > 0) else ("equal" if (f == 0 and agg) else "other")
    for want in ("other", "equal", "hidden"):
        for (f, net, agg), k in sorted(pts.items()):
            if _kind(f, agg) != want:
                continue
            col = {"hidden": C_FLIP, "equal": C_IDENT, "other": MUTED}[want]
            ax.scatter(f, net, s=12 + 40 * math.sqrt(k), color=col, edgecolor="#fcfcfb", linewidth=0.8, zorder=3)
    mx = max(f for f, _, _ in pts)
    ax.plot([0, mx], [0, mx], color=GRID, lw=1, zorder=1)
    ax.set_xlabel("items whose grade flipped")
    ax.set_ylabel("|change in correct count|")
    ax.text(mx * 0.98, mx * 0.9, "flips all in\none direction", ha="right", va="top", fontsize=6.5, color=MUTED)
    ax.annotate(f"orange: {N['hidden_cards']} cards whose totals\nare equal but items are not", xy=(4.3, 0.3), xytext=(6.5, 6.5),
                fontsize=6.5, color=INK2, arrowprops=dict(arrowstyle="-", color=MUTED, lw=0.6))
    fig.tight_layout()
    for ext in ("pdf", "png"):
        fig.savefig(os.path.join(HERE, f"fig_flips_vs_net.{ext}"))
    plt.close(fig)

    # fig 3: model x axis heatmap of grade-flip rate
    fig, ax = plt.subplots(figsize=(6.3, 3.1))
    from matplotlib.colors import ListedColormap, BoundaryNorm
    grid = []
    for m in models:
        row_ = []
        for axn in axes:
            cs = [c for c in cards if c.model == m and c.axis == axn]
            if not cs:
                row_.append(float("nan"))
                continue
            p = cs[0].pair()
            row_.append(100 * (p["n"] - p["grade_eq"] + p["swaps"]) / p["n"])
        grid.append(row_)
    bounds = [0, 0.001, 2, 4, 6, 8, 12, 20, 100]
    cmap = ListedColormap(SEQ)
    im = ax.imshow(grid, cmap=cmap, norm=BoundaryNorm(bounds, cmap.N), aspect="auto")
    for yi, m in enumerate(models):
        for xi, axn in enumerate(axes):
            cs = [c for c in cards if c.model == m and c.axis == axn]
            if not cs:
                continue
            p = cs[0].pair()
            k = p["n"] - p["grade_eq"]
            s = p["swaps"]
            lab = f"{k}" + (f"+{s}" if s else "")
            ax.text(xi, yi, lab if (k or s) else "", ha="center", va="center", fontsize=5.5,
                    color="#ffffff" if grid[yi][xi] >= 6 else INK)
    ax.set_xticks(range(len(axes)))
    ax.set_xticklabels(axes, rotation=40, ha="right", fontsize=6.5)
    ax.set_yticks(range(len(models)))
    ax.set_yticklabels(models, fontsize=6.5)
    ax.tick_params(length=0)
    for s in ax.spines.values():
        s.set_visible(False)
    cb = fig.colorbar(im, ax=ax, fraction=0.03, pad=0.02, ticks=bounds[1:-1])
    cb.ax.set_yticklabels([">0", "2", "4", "6", "8", "12", "20"])
    cb.ax.tick_params(labelsize=6)
    cb.set_label("% of items with a grade flip or a status swap", fontsize=6.5)
    fig.tight_layout()
    for ext in ("pdf", "png"):
        fig.savefig(os.path.join(HERE, f"fig_heatmap.{ext}"))
    plt.close(fig)

    # fig 4: where raw outputs first diverge (characters of common prefix), ECDF
    fig, ax = plt.subplots(figsize=(3.3, 2.5))
    for flag, col, lab in ((False, C_RAWONLY, "grade unchanged"), (True, C_FLIP, "grade flipped")):
        v = sorted(k for k, f in prefixes if f == flag)
        if not v:
            continue
        ax.step(v, [(i + 1) / len(v) for i in range(len(v))], where="post", color=col, lw=1.6,
                label=f"{lab} (n={len(v):,})")
    ax.set_xscale("symlog", linthresh=1)
    ax.set_xlim(0, 3000)
    ax.set_xticks([0, 1, 10, 100, 1000])
    ax.set_xticklabels(["0", "1", "10", "100", "1000"])
    ax.set_xlabel("characters before the two raw outputs diverge")
    ax.set_ylabel("cumulative share of items")
    ax.legend(frameon=False, fontsize=6.5, loc="lower right")
    fig.tight_layout()
    for ext in ("pdf", "png"):
        fig.savefig(os.path.join(HERE, f"fig_divergence.{ext}"))
    plt.close(fig)

    N["prefix_flip_median"] = sorted(k for k, f in prefixes if f)[len([1 for _, f in prefixes if f]) // 2]
    N["prefix_n"] = len(prefixes)

    # ------------------------------------------------------------------ P100 (third runtime), if present
    N["p100_status"] = "PENDING"
    if a.p100_decl and os.path.isdir(a.p100_decl):
        N["p100_status"] = "PRESENT"  # the three-way analysis is filled in when the run lands

    with open(os.path.join(ROOT, "build", "numbers.json"), "w") as fh:
        json.dump(N, fh, indent=1, sort_keys=True, default=str)
    # LaTeX macros: \N<key> with non-letters removed and digits spelled out
    def macro(k: str) -> str:
        spell = {"0": "Zero", "1": "One", "2": "Two", "3": "Three", "4": "Four", "5": "Five", "6": "Six",
                 "7": "Seven", "8": "Eight", "9": "Nine"}
        return "\\N" + "".join(spell.get(ch, ch.upper() if i == 0 or k[i - 1] == "_" else ch)
                               for i, ch in enumerate(k) if ch.isalnum())
    with open(os.path.join(ROOT, "tables", "numbers.tex"), "w") as fh:
        fh.write("% generated by figures/make_figures.py -- do not edit\n")
        for k, v in sorted(N.items()):
            if isinstance(v, int):
                v = f"{v:,}".replace(",", "{,}")
            fh.write(f"\\newcommand{{{macro(k)}}}{{{v}}}\n")
    print(json.dumps(N, indent=1, sort_keys=True, default=str))


if __name__ == "__main__":
    main()