"""
Shared helpers for the synthetic-rater sampling analyses (scripts 19-21).

These live in their own module because the analysis scripts carry numeric
filename prefixes (`19_...py`), which are not importable as Python modules.
Anything more than one script needs goes here.

    human_panel    per prompt x rater signed preference, plus the panel gold
    synth_gaps     per prompt mean synthetic margin and its absolute gap
    prompt_matrix  per-prompt rater-preference vectors, aligned to a prompt list
"""

from __future__ import annotations

import numpy as np


def human_panel(long_df):
    """
    Per prompt x rater signed preference (clip0 - clip1), plus panel gold.

    Returns (wide, gold). `wide` has one row per (prompt, rater) with the
    signed preference in `pref`; `gold` has one row per prompt with the
    full-panel mean margin, the within-prompt rater SD, the panel size, and
    the gold winner (sign of the mean margin).
    """
    df = long_df[long_df["IsAttentionCheck"] == False].copy()
    df["ClipIndex"] = df["AudioClip"].str.extract(r"_(\d+)\.mp3$")[0].astype(int)

    wide = df.pivot_table(
        index=["PromptID", "ParticipantID"],
        columns="ClipIndex", values="Rating", aggfunc="first",
    ).reset_index()
    wide.columns.name = None
    wide = wide.rename(columns={0: "r0", 1: "r1"}).dropna(subset=["r0", "r1"])
    wide["pref"] = wide["r0"] - wide["r1"]

    gold = wide.groupby("PromptID")["pref"].agg(
        gold_margin="mean", sd_within="std", n_raters="size"
    ).reset_index()
    gold["gold_winner"] = np.sign(gold["gold_margin"])
    return wide, gold


def synth_gaps(synth_df):
    """Per prompt mean synthetic margin over repeated samples, and its gap."""
    s = synth_df.copy()
    s["ClipIndex"] = s["AudioClip"].str.extract(r"_(\d+)\.(?:mp3|wav)$")[0].astype(int)

    wide = s.pivot_table(
        index=["PromptID", "synthNo"],
        columns="ClipIndex", values="Rating", aggfunc="first",
    ).reset_index()
    wide.columns.name = None
    wide = wide.rename(columns={0: "s0", 1: "s1"}).dropna(subset=["s0", "s1"])
    wide["margin"] = wide["s0"] - wide["s1"]

    out = wide.groupby("PromptID")["margin"].agg(
        synth_margin="mean", n_synth="size"
    ).reset_index()
    out["synth_gap"] = out["synth_margin"].abs()
    out["synth_winner"] = np.sign(out["synth_margin"])
    return out


def prompt_matrix(panel, prompt_ids):
    """List of per-prompt rater-preference vectors, aligned to prompt_ids."""
    by_prompt = {p: g["pref"].to_numpy() for p, g in panel.groupby("PromptID")}
    return [by_prompt[p] for p in prompt_ids]