"""Generate LaTeX tables from MacroLens benchmark results.

Panel-driven: the method list comes from `experiments/panel.py` (the canonical
18-method registry: 4 naive + 2 classical + 3 sequence + 3 TSFM + 3 LLM
+ 2 LLM-TS-Multi + 1 LLM-FT), where the LLM-FT entry is a deferred-selection
slot resolved post-hoc (winner of the Family-6 ZS sweep) and rendered in
tab:zs_vs_ft / tab:ablation. Adding / removing methods updates the tables
without touching this file.

Tables produced (in dependency order, all driven by `panel.ALL_METHODS`):
  1. tab:tsf        - T1 results: methods covering T1 x horizons {5, 21, 63}
  2. tab:valuation  - T2 (Val-PT) + T5 (Priv-Val) side by side
  3. tab:generation - T3 (Stmt-Gen) + T6 (Gen-Eval) side by side
  4. tab:scenario   - T4 (Scen-Ret)
  5. tab:re         - T7 (RE-Val)
  6. tab:zs_vs_ft   - ZS vs FT for the deferred-FT cell (LLM-FT)
  7. tab:ablation   - 5 settings x 4 tasks for the deferred-selection model

Usage:
    uv run python -m projects.agent_builder.scripts.whatif_bench.experiments.gen_tables
    uv run python -m projects.agent_builder.scripts.whatif_bench.experiments.gen_tables --full
"""

from __future__ import annotations

import argparse
import json
import sys
from pathlib import Path
from typing import Any

import pandas as pd

from .. import config
from . import panel


# ----------------------------------------------------------------------------
# Result loading
# ----------------------------------------------------------------------------
# Canonical results live in ``experiments/paper_artifacts/aggregate.parquet``
# (one long-form row per (task, method_id, metric_name) with value + CIs).
# The legacy ``experiments/results/legacy_per_family/all_results.json``
# path is still consulted as a fallback (the file used to live under
# ``data_small_caps/benchmark/<g>/`` but moved to experiments/ once
# experiment outputs were separated from the benchmark tree); for new
# submissions the parquet is the single source of truth.


def _aggregate_path() -> Path:
    return Path(__file__).resolve().parent / "paper_artifacts" / "aggregate.parquet"


def _load_aggregate() -> pd.DataFrame | None:
    p = _aggregate_path()
    if not p.exists():
        return None
    try:
        return pd.read_parquet(p)
    except Exception as exc:  # pragma: no cover -- IO-level failure
        print(f"warning: could not read {p}: {exc}", file=sys.stderr)
        return None


def _load_results(granularity: str = "daily", quick: bool = True) -> dict[str, Any]:
    """Legacy nested-dict results loader.

    Retained so the ``--legacy-json`` path that pre-dates the canon
    aggregate keeps working. The primary table-generation path now
    consumes :func:`_load_aggregate` and only falls back to the legacy
    JSON when the parquet is missing. The legacy aggregates used to live
    under ``data_small_caps/benchmark/<g>/all_results*.json`` but moved
    to ``experiments/results/legacy_per_family/`` once experiment
    outputs were separated from the benchmark tree; ``granularity`` is
    kept for API compatibility (legacy aggregates are not per-granularity
    on disk).
    """
    del granularity  # legacy aggregates are not per-granularity on disk
    suffix = "_quick" if quick else ""
    legacy_dir = Path(__file__).resolve().parent / "results" / "legacy_per_family"
    path = legacy_dir / f"all_results{suffix}.json"
    if not path.exists():
        return {}
    return json.loads(path.read_text())


# ----------------------------------------------------------------------------
# Family routing: which family JSON does each method's results live under?
# ----------------------------------------------------------------------------
# Panel families and the orchestrator's per-family JSON keys are aligned
# 1:1 on the canonical names ("tsfm", "llm", "llm_ts"). The legacy panel
# ("tsfm_zs", "llm_zs", "llm_ts_multitask") was reconciled with the
# canon RunRecord families in experiments/panel.py; this map is now a
# trivial pass-through and is retained only so that adding a new family
# remains a one-line change.

_PANEL_FAMILY_TO_JSON_KEY: dict[str, str] = {
    "naive": "naive",
    "classical": "classical",
    "sequence": "sequence",
    "tsfm": "tsfm",
    "llm_ts": "llm_ts_reason",
    "llm": "llm",
}


# Each family's key-naming convention for the per-task result key. Kept here
# so the table generator never has to hardcode method-by-method.

def _result_key(method: panel.Method, task: panel.Task, horizon: int | None = None) -> list[str]:
    """Candidate result keys to try for (method, task) under that family's JSON.

    Returns a list because some families historically used multiple naming
    schemes; we try them in order and use the first that matches.
    """
    mid = method.id
    family = method.family
    keys: list[str] = []

    if task == "T1":
        if family in ("naive", "classical", "sequence", "tsfm"):
            keys.append(f"tsf_{mid}_h{horizon}")
        elif family in ("llm_ts", "llm"):
            keys.append(f"tsf_llm_{mid}_h{horizon}")
            keys.append(f"tsf_{mid}_h{horizon}")
            # llm_ts uses chattime_task_1 style:
            keys.append(f"{mid}_task_1_h{horizon}")
            keys.append(f"{mid}_task_1")
    elif task == "T2":
        keys.append(f"task_2_{mid}")
        if family == "llm":
            keys.append(f"task_2_llm_{mid}")
        if family == "llm_ts":
            keys.append(f"{mid}_task_2")
    elif task == "T3":
        keys.append(f"task_3_{mid}")
        if family == "llm":
            keys.append(f"task_3_llm_{mid}")
        if family == "llm_ts":
            keys.append(f"{mid}_task_3")
    elif task == "T4":
        keys.append(f"task_4_{mid}")
        if family == "naive":
            # historical_analogue lives under the alias "task_4_analogue"
            keys.append("task_4_analogue")
        if family == "llm":
            keys.append(f"task_4_llm_{mid}")
        if family == "llm_ts":
            keys.append(f"{mid}_task_4")
    elif task == "T5":
        keys.append(f"task_5_{mid}")
        if family == "llm":
            keys.append(f"task_5_llm_{mid}")
        if family == "llm_ts":
            keys.append(f"{mid}_task_5")
    elif task == "T6":
        keys.append(f"task_6_{mid}")
        if family == "llm":
            keys.append(f"task_6_llm_{mid}")
        if family == "llm_ts":
            keys.append(f"{mid}_task_6")
    elif task == "T7":
        keys.append(f"task_7_{mid}")
        if family == "llm":
            keys.append(f"task_7_llm_{mid}")
        if family == "llm_ts":
            keys.append(f"{mid}_task_7")

    return keys


def _get_family_data(data: dict, method: panel.Method) -> dict:
    """Navigate `data` to the family dict for `method`."""
    json_key = _PANEL_FAMILY_TO_JSON_KEY.get(method.family, method.family)
    fam = data.get(json_key, {})
    if not isinstance(fam, dict):
        return {}
    return fam


def _lookup(data: dict, method: panel.Method, task: panel.Task,
            horizon: int | None = None) -> dict:
    """Find the result dict for (method, task[, horizon]); empty dict if missing.

    When ``data`` is the long-form parquet DataFrame (preferred path), the
    return dict is a flat mapping ``metric_name -> value`` augmented with
    paired ``<metric>_ci_lo`` / ``<metric>_ci_hi`` keys so existing
    per-table functions keep their ``r.get("mse")`` shape but can opt
    into CI rendering with ``r.get("mse_ci_lo")`` / ``_ci_hi``.

    When ``data`` is the legacy nested dict, the lookup returns the cell
    as-is (no CIs available).
    """
    if isinstance(data, pd.DataFrame):
        df = data[(data["method_id"] == method.id) & (data["task"] == task)]
        # Main-table lookups exclude ablation cells (the A--E settings
        # live in tab:ablation, not the per-task headline tables).
        df = df[df["ablation_setting"].isna()]
        if df.empty:
            return {}
        # Most cells have a single granularity/seed; pick the latest
        # timestamp deterministically.
        df = df.sort_values("timestamp").groupby("metric_name").tail(1)
        out: dict[str, Any] = {}
        for _, row in df.iterrows():
            name = row["metric_name"]
            out[name] = row["value"]
            if pd.notna(row.get("ci_lo")):
                out[f"{name}_ci_lo"] = row["ci_lo"]
            if pd.notna(row.get("ci_hi")):
                out[f"{name}_ci_hi"] = row["ci_hi"]
            if pd.notna(row.get("n_boot")):
                out[f"{name}_n_boot"] = int(row["n_boot"])
        return out
    fam = _get_family_data(data, method)
    if not fam:
        return {}
    for key in _result_key(method, task, horizon):
        result = fam.get(key)
        if isinstance(result, dict) and "error" not in result:
            return result
    return {}


# ----------------------------------------------------------------------------
# Number formatting
# ----------------------------------------------------------------------------

def _f(v, fmt: str = ".2f", default: str = "--") -> str:
    if v is None:
        return default
    try:
        if isinstance(v, (int, float)) and v != v:  # NaN check
            return default
        return format(float(v), fmt)
    except (TypeError, ValueError):
        return default


def _f_ci(value, ci_lo, ci_hi, fmt: str = ".2f", default: str = "--") -> str:
    """Format ``value [lo, hi]`` if CI is present; fall back to ``value``."""
    point = _f(value, fmt, default)
    if point == default:
        return default
    if ci_lo is None or ci_hi is None:
        return point
    try:
        if (isinstance(ci_lo, float) and ci_lo != ci_lo) or (
            isinstance(ci_hi, float) and ci_hi != ci_hi
        ):
            return point
    except TypeError:
        return point
    return (
        rf"{point}\,{{\scriptsize [{_f(ci_lo, fmt, default)},"
        rf"\,{_f(ci_hi, fmt, default)}]}}"
    )


def _pct(v) -> str:
    """Format a fraction (0..1) as `xx.x` percent."""
    if v is None:
        return "--"
    try:
        return f"{float(v) * 100:.1f}"
    except (TypeError, ValueError):
        return "--"


# ----------------------------------------------------------------------------
# Tables
# ----------------------------------------------------------------------------

def gen_tab_tsf(data: dict, granularity: str = "daily") -> str:
    """T1 (TSF) results across panel methods x horizons."""
    horizons = config.get_horizons(granularity)
    methods_t1 = [m for m in panel.ALL_METHODS if "T1" in m.tasks]

    n_h = len(horizons)
    col_spec = "ll " + " ".join(["rr"] * n_h)

    lines: list[str] = []
    lines.append(r"\begin{table}[t]")
    lines.append(r"\centering")
    lines.append(
        r"\caption{Task 1 (TSF) results, "
        f"{granularity}, lookback={config.get_lookback_windows(granularity)[0]}. "
        r"Best per-column \textbf{bold}.}")
    lines.append(r"\label{tab:tsf}")
    lines.append(r"\resizebox{\textwidth}{!}{%")
    lines.append(r"\begin{tabular}{" + col_spec + "}")
    lines.append(r"\toprule")
    headers = " & ".join(
        rf"\multicolumn{{2}}{{c}}{{\textbf{{H={h}}}}}" for h in horizons
    )
    lines.append(rf"& & {headers} \\")
    cmidrules = " ".join(
        rf"\cmidrule(lr){{{3 + 2*i}-{4 + 2*i}}}" for i in range(n_h)
    )
    lines.append(cmidrules)
    metric_hdr = " & ".join(["MSE", r"DA\%"] * n_h)
    lines.append(rf"\textbf{{Family}} & \textbf{{Method}} & {metric_hdr} \\")
    lines.append(r"\midrule")

    last_family: str | None = None
    for m in methods_t1:
        # Group by family with a midrule between groups.
        if last_family is not None and m.family != last_family:
            lines.append(r"\midrule")
        fam_label = m.family.replace("_", " ") if m.family != last_family else ""
        last_family = m.family
        row = [fam_label, m.name]
        for h in horizons:
            r = _lookup(data, m, "T1", horizon=h)
            if "overall" in r and isinstance(r["overall"], dict):
                mse = r["overall"].get("mse")
                mse_lo = mse_hi = None
                da = r["overall"].get("directional_accuracy")
            else:
                mse = r.get("mse")
                mse_lo = r.get("mse_ci_lo")
                mse_hi = r.get("mse_ci_hi")
                da = r.get("directional_accuracy")
            row += [_f_ci(mse, mse_lo, mse_hi, ".1f"), _pct(da)]
        lines.append(" & ".join(row) + r" \\")

    lines.append(r"\bottomrule")
    lines.append(r"\end{tabular}}")
    lines.append(r"\end{table}")
    return "\n".join(lines)


def gen_tab_valuation(data: dict) -> str:
    """T2 (Val-PT) + T5 (Priv-Val) side by side. MedAPE% / Spearman."""
    methods = [m for m in panel.ALL_METHODS if "T2" in m.tasks or "T5" in m.tasks]
    lines: list[str] = []
    lines.append(r"\begin{table}[t]")
    lines.append(r"\centering")
    lines.append(
        r"\caption{Valuation: Task~2 (Val-PT) vs Task~5 (Priv-Val). "
        r"MedAPE\%$\downarrow$, Spearman~$\rho\uparrow$.}")
    lines.append(r"\label{tab:valuation}")
    lines.append(r"\resizebox{\textwidth}{!}{%")
    lines.append(r"\begin{tabular}{ll cc cc}")
    lines.append(r"\toprule")
    lines.append(
        r"& & \multicolumn{2}{c}{\textbf{T2 Val-PT}} & "
        r"\multicolumn{2}{c}{\textbf{T5 Priv-Val}} \\")
    lines.append(r"\cmidrule(lr){3-4} \cmidrule(lr){5-6}")
    lines.append(
        r"\textbf{Family} & \textbf{Method} & "
        r"MedAPE\%$\downarrow$ & $\rho\uparrow$ & "
        r"MedAPE\%$\downarrow$ & $\rho\uparrow$ \\")
    lines.append(r"\midrule")

    last_family: str | None = None
    for m in methods:
        if last_family is not None and m.family != last_family:
            lines.append(r"\midrule")
        fam_label = m.family.replace("_", " ") if m.family != last_family else ""
        last_family = m.family
        row = [fam_label, m.name]
        for task in ("T2", "T5"):
            if task in m.tasks:
                r = _lookup(data, m, task)
                row += [
                    _f_ci(r.get("median_ape"),
                          r.get("median_ape_ci_lo"),
                          r.get("median_ape_ci_hi"), ".1f"),
                    _f(r.get("rank_correlation"), ".3f"),
                ]
            else:
                row += ["--", "--"]
        lines.append(" & ".join(row) + r" \\")

    lines.append(r"\bottomrule")
    lines.append(r"\end{tabular}}")
    lines.append(r"\end{table}")
    return "\n".join(lines)


def gen_tab_generation(data: dict) -> str:
    """T3 (Stmt-Gen) + T6 (Gen-Eval) side by side. Per-field MAPE%, Bal-Eq%."""
    methods = [m for m in panel.ALL_METHODS if "T3" in m.tasks or "T6" in m.tasks]
    lines: list[str] = []
    lines.append(r"\begin{table}[t]")
    lines.append(r"\centering")
    lines.append(
        r"\caption{Generation: Task~3 (Stmt-Gen) vs Task~6 (Gen-Eval). "
        r"per-field MAPE\%$\downarrow$, balance-equation accuracy\%$\uparrow$.}")
    lines.append(r"\label{tab:generation}")
    lines.append(r"\resizebox{\textwidth}{!}{%")
    lines.append(r"\begin{tabular}{ll cc cc}")
    lines.append(r"\toprule")
    lines.append(
        r"& & \multicolumn{2}{c}{\textbf{T3 Stmt-Gen}} & "
        r"\multicolumn{2}{c}{\textbf{T6 Gen-Eval}} \\")
    lines.append(r"\cmidrule(lr){3-4} \cmidrule(lr){5-6}")
    lines.append(
        r"\textbf{Family} & \textbf{Method} & "
        r"MAPE\%$\downarrow$ & Bal-Eq\%$\uparrow$ & "
        r"MAPE\%$\downarrow$ & Bal-Eq\%$\uparrow$ \\")
    lines.append(r"\midrule")

    last_family: str | None = None
    for m in methods:
        if last_family is not None and m.family != last_family:
            lines.append(r"\midrule")
        fam_label = m.family.replace("_", " ") if m.family != last_family else ""
        last_family = m.family
        row = [fam_label, m.name]
        for task in ("T3", "T6"):
            if task in m.tasks:
                r = _lookup(data, m, task)
                row += [
                    _f_ci(r.get("overall_mape"),
                          r.get("overall_mape_ci_lo"),
                          r.get("overall_mape_ci_hi"), ".1f"),
                    _pct(r.get("balance_equation_accuracy")),
                ]
            else:
                row += ["--", "--"]
        lines.append(" & ".join(row) + r" \\")

    lines.append(r"\bottomrule")
    lines.append(r"\end{tabular}}")
    lines.append(r"\end{table}")
    return "\n".join(lines)


def gen_tab_scenario(data: dict) -> str:
    """T4 (Scen-Ret): MAE%, DA%, CI calibration."""
    methods = [m for m in panel.ALL_METHODS if "T4" in m.tasks]
    lines: list[str] = []
    lines.append(r"\begin{table}[t]")
    lines.append(r"\centering")
    lines.append(
        r"\caption{Task~4 (Scen-Ret). Predict post-event return. "
        r"Best per-column \textbf{bold}.}")
    lines.append(r"\label{tab:scenario}")
    lines.append(r"\resizebox{0.85\textwidth}{!}{%")
    lines.append(r"\begin{tabular}{ll ccc}")
    lines.append(r"\toprule")
    lines.append(
        r"\textbf{Family} & \textbf{Method} & "
        r"MAE\%$\downarrow$ & DA\%$\uparrow$ & CI Cal.\%$\uparrow$ \\")
    lines.append(r"\midrule")

    last_family: str | None = None
    for m in methods:
        if last_family is not None and m.family != last_family:
            lines.append(r"\midrule")
        fam_label = m.family.replace("_", " ") if m.family != last_family else ""
        last_family = m.family
        r = _lookup(data, m, "T4")
        row = [
            fam_label, m.name,
            _f_ci(r.get("return_mae_pct"),
                  r.get("return_mae_pct_ci_lo"),
                  r.get("return_mae_pct_ci_hi"), ".2f"),
            _pct(r.get("directional_accuracy")),
            _pct(r.get("ci_calibration_95")),
        ]
        lines.append(" & ".join(row) + r" \\")

    lines.append(r"\bottomrule")
    lines.append(r"\end{tabular}}")
    lines.append(r"\end{table}")
    return "\n".join(lines)


def gen_tab_re(data: dict) -> str:
    """T7 (RE-Val): Rent MAPE / Price MAPE."""
    methods = [m for m in panel.ALL_METHODS if "T7" in m.tasks]
    lines: list[str] = []
    lines.append(r"\begin{table}[t]")
    lines.append(r"\centering")
    lines.append(
        r"\caption{Task~7 (RE-Val). Rent and price prediction across 100 metros.}")
    lines.append(r"\label{tab:re}")
    lines.append(r"\resizebox{0.7\textwidth}{!}{%")
    lines.append(r"\begin{tabular}{ll cc}")
    lines.append(r"\toprule")
    lines.append(
        r"\textbf{Family} & \textbf{Method} & "
        r"Rent MAPE\%$\downarrow$ & Price MAPE\%$\downarrow$ \\")
    lines.append(r"\midrule")

    last_family: str | None = None
    for m in methods:
        if last_family is not None and m.family != last_family:
            lines.append(r"\midrule")
        fam_label = m.family.replace("_", " ") if m.family != last_family else ""
        last_family = m.family
        r = _lookup(data, m, "T7")
        row = [
            fam_label, m.name,
            _f_ci(r.get("rent_MAPE"),
                  r.get("rent_MAPE_ci_lo"),
                  r.get("rent_MAPE_ci_hi"), ".1f"),
            _f_ci(r.get("price_MAPE"),
                  r.get("price_MAPE_ci_lo"),
                  r.get("price_MAPE_ci_hi"), ".1f"),
        ]
        lines.append(" & ".join(row) + r" \\")

    lines.append(r"\bottomrule")
    lines.append(r"\end{tabular}}")
    lines.append(r"\end{table}")
    return "\n".join(lines)


def gen_tab_zs_vs_ft(data: dict, granularity: str = "daily") -> str:
    """ZS vs FT comparison for the deferred-FT cell.

    Empty stub when `panel.LLM_FT_PANEL_HF_IDS` is empty. Once the
    deferred-selection rule populates that tuple, the table will resolve
    to the chosen FT cell automatically.
    """
    horizons = config.get_horizons(granularity)
    lines: list[str] = []
    lines.append(r"\begin{table}[t]")
    lines.append(r"\centering")
    lines.append(
        r"\caption{Zero-shot vs fine-tuned comparison. "
        r"Deferred-selection: a single FT cell for the panel-best "
        r"Family-6 ZS LLM (see paper \S6 / panel.py).}")
    lines.append(r"\label{tab:zs_vs_ft}")

    if not panel.LLM_FT_PANEL_HF_IDS:
        lines.append(
            r"\textit{Deferred -- target not yet selected from the full ZS sweep. "
            r"Selection rule pre-registered in \texttt{experiments/panel.py}.}")
        lines.append(r"\end{table}")
        return "\n".join(lines)

    # Both deferred slots resolved -> full table. Currently unreached.
    lines.append(
        r"\resizebox{\textwidth}{!}{%"
        r"\begin{tabular}{l " + " ".join(["rrr"] * len(horizons)) + "}")
    lines.append(r"\toprule")
    h_hdr = " & ".join(
        rf"\multicolumn{{3}}{{c}}{{\textbf{{H={h}}}}}" for h in horizons
    )
    lines.append(rf"& {h_hdr} \\")
    cmid = " ".join(
        rf"\cmidrule(lr){{{2 + 3*i}-{4 + 3*i}}}" for i in range(len(horizons))
    )
    lines.append(cmid)
    metric_hdr = " & ".join([r"ZS & FT & $\Delta$\%"] * len(horizons))
    lines.append(rf"\textbf{{Model}} & {metric_hdr} \\")
    lines.append(r"\midrule")
    # Rows resolved post-hoc once the deferred panels populate; left empty.
    lines.append(r"\bottomrule")
    lines.append(r"\end{tabular}}")
    lines.append(r"\end{table}")
    return "\n".join(lines)


def gen_tab_ablation(data: dict) -> str:
    """Family-9 ablation: 5 settings x 4 tasks for the deferred-selection model."""
    lines: list[str] = []
    lines.append(r"\begin{table}[t]")
    lines.append(r"\centering")
    lines.append(
        r"\caption{Context ablation. 5 feature settings (A-E) "
        r"$\times$ 4 tasks for the deferred-FT target.}")
    lines.append(r"\label{tab:ablation}")

    if not panel.ABLATION_MODEL_IDS:
        lines.append(
            r"\textit{Deferred -- ablation model resolves to the same target as "
            r"\texttt{LLM\_FT\_PANEL\_HF\_IDS} (post-hoc Family-7 ZS winner). "
            r"Selection rule pre-registered in \texttt{experiments/panel.py}.}")
        lines.append(r"\end{table}")
        return "\n".join(lines)

    # Once ABLATION_MODEL_IDS populates, render the 2 modes x 5 settings x 4 tasks.
    abl = data.get("ablation", {}) if isinstance(data.get("ablation"), dict) else {}
    settings = ["A", "B", "C", "D", "E"]

    # 4 tasks x 2 modes (ZS, FT) = 8 columns.
    col_spec = "ll " + " ".join(["rr"] * len(panel.ABLATION_TASKS))
    lines.append(r"\resizebox{\textwidth}{!}{%")
    lines.append(r"\begin{tabular}{" + col_spec + "}")
    lines.append(r"\toprule")
    task_hdr = " & ".join(
        rf"\multicolumn{{2}}{{c}}{{\textbf{{{t}}}}}" for t in panel.ABLATION_TASKS
    )
    lines.append(rf"& & {task_hdr} \\")
    cmid = " ".join(
        rf"\cmidrule(lr){{{3 + 2*i}-{4 + 2*i}}}" for i in range(len(panel.ABLATION_TASKS))
    )
    lines.append(cmid)
    mode_hdr = " & ".join(["ZS & FT"] * len(panel.ABLATION_TASKS))
    lines.append(rf"\textbf{{Setting}} & \textbf{{\#Feat}} & {mode_hdr} \\")
    lines.append(r"\midrule")

    for s in settings:
        s_meta = panel.ABLATION_SETTINGS[s]
        row = [s, str(s_meta["n_features"])]
        for t in panel.ABLATION_TASKS:
            for mode in panel.ABLATION_MODES:
                cell = abl.get(f"setting_{s}_{mode}_{t}", {})
                # Use the task's primary metric defined in panel.TASK_METADATA
                primary = panel.TASK_METADATA[t]["primary_metric"]
                key_map = {
                    "MSE": "mse",
                    "MedAPE": "median_ape",
                    "Return MAE": "return_mae_pct",
                    "per-field MAPE": "overall_mape",
                    "Rent + Price MAPE": "rent_MAPE",
                }
                k = key_map.get(primary, "mse")
                v = cell.get(k) if isinstance(cell, dict) else None
                row.append(_f(v, ".1f"))
        lines.append(" & ".join(row) + r" \\")

    lines.append(r"\bottomrule")
    lines.append(r"\end{tabular}}")
    lines.append(r"\end{table}")
    return "\n".join(lines)


def gen_tab_panel_summary() -> str:
    """Static appendix table: the 18-method panel from panel.py (incl. 1 deferred FT cell)."""
    lines: list[str] = []
    lines.append(r"\begin{table}[t]")
    lines.append(r"\centering")
    lines.append(
        r"\caption{MacroLens baseline panel. 17 fixed methods + 1 deferred-selection LLM-FT cell (post-hoc Family-6 ZS winner) = 18 entries.}")
    lines.append(r"\label{tab:panel}")
    lines.append(r"\begin{tabular}{lll l l}")
    lines.append(r"\toprule")
    lines.append(
        r"\textbf{Family} & \textbf{Method} & \textbf{HF id / source} & "
        r"\textbf{Tasks} & \textbf{Notes} \\")
    lines.append(r"\midrule")

    last_family: str | None = None
    for m in panel.ALL_METHODS:
        if last_family is not None and m.family != last_family:
            lines.append(r"\midrule")
        fam_label = m.family.replace("_", " ") if m.family != last_family else ""
        last_family = m.family
        tasks_str = ",".join(sorted(m.tasks))
        hf = m.hf_id or "--"
        # Truncate notes for table layout.
        note = m.notes.replace("\n", " ").strip()
        if len(note) > 60:
            note = note[:57] + "..."
        # Escape underscores for LaTeX in HF ids.
        hf_tex = hf.replace("_", r"\_")
        lines.append(
            f"{fam_label} & {m.name} & \\texttt{{{hf_tex}}} & {tasks_str} & {note} \\\\"
        )

    lines.append(r"\midrule")
    lines.append(
        r"\multicolumn{5}{l}{"
        r"\textit{Deferred: 1 LLM-FT cell (post-hoc Family-6 ZS winner).}} \\")

    lines.append(r"\bottomrule")
    lines.append(r"\end{tabular}")
    lines.append(r"\end{table}")
    return "\n".join(lines)


# ----------------------------------------------------------------------------
# Main
# ----------------------------------------------------------------------------

def _emit_all(data, granularity: str, output_dir: Path | None) -> None:
    print("% === MacroLens Paper Tables (panel-driven) ===")
    print(f"% panel summary: {panel.summary()}\n")

    tables = [
        ("tsf",        gen_tab_tsf(data, granularity)),
        ("valuation",  gen_tab_valuation(data)),
        ("generation", gen_tab_generation(data)),
        ("scenario",   gen_tab_scenario(data)),
        ("re",         gen_tab_re(data)),
        ("zs_vs_ft",   gen_tab_zs_vs_ft(data, granularity)),
        ("ablation",   gen_tab_ablation(data)),
        ("panel",      gen_tab_panel_summary()),
    ]
    for name, body in tables:
        print(f"\n% --- tab:{name} ---")
        print(body)
        if output_dir is not None:
            (output_dir / f"tab_{name}.tex").write_text(body)


def main():
    parser = argparse.ArgumentParser(
        description="Emit LaTeX tables for the MacroLens paper from aggregated results.",
    )
    parser.add_argument("--granularity", default="daily",
                        choices=["daily", "weekly", "monthly"])
    parser.add_argument("--legacy-json", action="store_true",
                        help=("Read the legacy nested-dict all_results.json "
                              "instead of the canon-aggregate parquet."))
    parser.add_argument("--full", action="store_true",
                        help=("Read full-run all_results.json instead of the "
                              "_quick variant (only meaningful with "
                              "--legacy-json)."))
    parser.add_argument("--out-dir", type=Path, default=None,
                        help="If provided, write each table to <out>/tab_<name>.tex.")
    args = parser.parse_args()

    if args.legacy_json:
        data: Any = _load_results(args.granularity, quick=not args.full)
    else:
        df = _load_aggregate()
        if df is None:
            print(
                f"error: aggregate.parquet not found at {_aggregate_path()}; "
                "run experiments/build_paper_artifacts.py or pass --legacy-json.",
                file=sys.stderr,
            )
            sys.exit(2)
        data = df

    if args.out_dir is not None:
        args.out_dir.mkdir(parents=True, exist_ok=True)
    _emit_all(data, args.granularity, args.out_dir)


if __name__ == "__main__":
    main()