"""Headline plot for exp0: the within-posts karma effect.

Comparing AF posts only to AF posts, denser rationalist dialect still tracks
modestly higher karma. This isolates the real effect from the posts-vs-comments
confound that inflates the pooled correlation.

Output: figures/headline_within_posts_karma.png
"""
import json
import os
import sys
from pathlib import Path
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy import stats

PROJECT_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.insert(0, PROJECT_DIR)
from config import COMPILED, count_all_terms, word_count

DATA = Path(PROJECT_DIR) / "data"
FIG = Path(PROJECT_DIR) / "figures"
FIG.mkdir(parents=True, exist_ok=True)
START_YEAR = 2022


def load_posts():
    rows = []
    with open(DATA / "alignmentforum_posts.jsonl") as f:
        for line in f:
            p = json.loads(line)
            text = p.get("body") or ""
            wc = max(1, word_count(text))
            counts = count_all_terms(text)
            rate = sum((c / wc) * 10000 for c in counts.values())
            rows.append({"id": p["id"], "posted_at": p.get("posted_at"),
                         "base_score": p.get("base_score"), "dialect_rate": rate})
    df = pd.DataFrame(rows)
    df["posted_at"] = pd.to_datetime(df["posted_at"], errors="coerce", utc=True)
    df["year"] = df["posted_at"].dt.year
    df["base_score"] = pd.to_numeric(df["base_score"], errors="coerce")
    # posts only, 2022-2025 (drop the partial-2026 crawl artifact), valid karma
    df = df[(df["year"] >= START_YEAR) & (df["year"] <= 2025)]
    df = df.dropna(subset=["base_score"]).reset_index(drop=True)
    return df


def main():
    df = load_posts()
    n = len(df)
    rho, p = stats.spearmanr(df["dialect_rate"], df["base_score"])
    pear_r, pear_p = stats.pearsonr(df["dialect_rate"], df["base_score"])
    print(f"AF posts only, 2022-2025: n={n}")
    print(f"  Spearman rho={rho:+.3f} (p={p:.2g})")
    print(f"  Pearson  r  ={pear_r:+.3f} (p={pear_p:.2g})")

    bins = [-0.001, 0, 5, 15, 30, 1e6]
    labels = ["0", "(0, 5]", "(5, 15]", "(15, 30]", "> 30"]
    df["bin"] = pd.cut(df["dialect_rate"], bins=bins, labels=labels)
    g = df.groupby("bin", observed=True).agg(
        mean_karma=("base_score", "mean"),
        median_karma=("base_score", "median"),
        std=("base_score", "std"),
        n=("base_score", "count"),
    ).reindex(labels)
    ci = 1.96 * g["std"] / np.sqrt(g["n"].clip(lower=1))

    fig, ax = plt.subplots(figsize=(9, 5.5))
    x = np.arange(len(labels))
    ax.bar(x, g["mean_karma"], 0.62, yerr=ci, capsize=5,
           color="tab:blue", alpha=0.88, label="mean karma (±95% CI)",
           error_kw={"elinewidth": 1.4, "ecolor": "black"})
    ax.plot(x, g["median_karma"], "D--", color="tab:orange", markersize=8,
            linewidth=1.8, label="median karma")
    for i in x:
        top = g["mean_karma"].iloc[i] + (ci.iloc[i] if not np.isnan(ci.iloc[i]) else 0)
        ax.text(i, top + 2, f"n={int(g['n'].iloc[i])}", ha="center",
                va="bottom", fontsize=9, color="dimgray")

    ax.set_xticks(x)
    ax.set_xticklabels(labels)
    ax.set_xlabel("rationalist-dialect density  (matches / 10k words, binned)", fontsize=11)
    ax.set_ylabel("post karma (base_score)", fontsize=11)
    ax.set_title("Within Alignment Forum posts, denser rationalist dialect\n"
                 "tracks modestly higher karma", fontsize=13, fontweight="bold")
    ax.legend(fontsize=10, loc="upper left")
    ax.grid(alpha=0.3, axis="y")
    ax.margins(y=0.34)

    ax.text(0.975, 0.965,
            f"posts only, 2022–2025   n = {n:,}\n"
            f"Spearman ρ = {rho:+.2f}   (p = {p:.1g})",
            transform=ax.transAxes, ha="right", va="top", fontsize=10,
            bbox=dict(boxstyle="round,pad=0.45", facecolor="whitesmoke",
                      edgecolor="gray"))

    fig.tight_layout()
    out = FIG / "headline_within_posts_karma.png"
    fig.savefig(out, dpi=140)
    plt.close(fig)
    print(f"wrote {out}")


if __name__ == "__main__":
    main()