"""Measured patterns across recordings (no AI).

* A comparison table of headline metrics.
* Pairwise acoustic similarity from three components that work even with two
  files (no cross-file standardisation): timbre (MFCC distance scaled by the
  recordings' own variability), spectral balance (Hellinger affinity of band
  energies) and voice pitch (overlap of pitch histograms).
* Pairwise content similarity (TF-IDF cosine of transcripts), shared and
  distinctive vocabulary.
* With 3+ files: closest pair / most distinct file and monotonic trends over
  recording dates; with 4+: robust outliers and similarity groups.
"""

from __future__ import annotations

import itertools
import re

import numpy as np

METRICS = [
    # key, label, unit, decimals, higher-is (for wording only)
    ("duration_min", "Duration", "min", 1),
    ("lufs", "Loudness", "LUFS", 1),
    ("snr_db", "Signal-to-noise (est.)", "dB", 0),
    ("activity_pct", "Sound activity", "%", 0),
    ("pauses_per_min", "Pauses (0.5 s+) per minute", "/min", 1),
    ("longest_pause_s", "Longest pause", "s", 1),
    ("pitch_median_hz", "Median voice pitch", "Hz", 0),
    ("centroid_hz", "Spectral centroid", "Hz", 0),
    ("bandwidth_khz", "Effective bandwidth", "kHz", 1),
    ("words_per_minute", "Speaking rate", "words/min", 0),
    ("speakers", "Speakers", "", 0),
    ("dominant_share_pct", "Main speaker's talk share", "%", 0),
    ("turns_per_minute", "Speaker turns per minute", "/min", 1),
    ("questions", "Questions asked", "", 0),
    ("fillers_per_100", "Fillers per 100 words", "", 1),
    ("lexical_diversity", "Vocabulary variety (MATTR)", "", 2),
    ("sentiment_score", "Sentiment (AI, -1 to +1)", "", 2),
]
METRIC_INFO = {k: (label, unit, dec) for k, label, unit, dec in METRICS}

STOP_EXTRA = {"um", "uh", "erm", "er", "uhm", "umm", "hmm", "mm", "mhm", "ah", "eh", "yeah", "okay", "ok",
              "like", "just", "really", "know", "think", "going", "gonna", "kind", "sort", "thing", "things",
              "right", "lot", "actually", "mean", "maybe", "got", "get", "inaudible", "laughs", "laughter",
              "don", "didn", "doesn", "isn", "wasn", "aren", "weren", "couldn", "wouldn", "shouldn", "won", "ain",
              "say", "said", "says", "tell", "told", "come", "came", "did", "does", "having", "trying", "want",
              "wanted", "sorry", "thank", "thanks", "guess", "yes", "sure", "pretty", "stuff", "feel", "feels",
              "went", "goes", "make", "made", "way", "good", "great", "little", "bit", "able", "look", "need"}


def metric_values(rec: dict) -> dict:
    ac, sm, an = rec.get("acoustics"), rec.get("speech") or {}, rec.get("analysis") or {}
    v: dict = {}
    if ac is not None:
        v["duration_min"] = ac.duration_s / 60
        v["lufs"] = ac.integrated_lufs
        v["snr_db"] = None if ac.steady_signal else ac.snr_db
        v["activity_pct"] = ac.activity_ratio * 100
        v["pauses_per_min"] = ac.pauses_per_min
        v["longest_pause_s"] = ac.longest_pause_s
        v["pitch_median_hz"] = ac.pitch_median_hz
        v["centroid_hz"] = ac.spectral_centroid_hz
        v["bandwidth_khz"] = ac.bandwidth_hz / 1000
    if sm:
        v["words_per_minute"] = sm.get("words_per_minute")
        v["speakers"] = sm.get("speakers")
        v["dominant_share_pct"] = sm["dominant_speaker_share"] * 100 if sm.get("dominant_speaker_share") is not None else None
        v["turns_per_minute"] = sm.get("turns_per_minute")
        v["questions"] = sm.get("questions")
        v["fillers_per_100"] = sm.get("fillers_per_100_words")
        v["lexical_diversity"] = sm.get("lexical_diversity")
    sent = an.get("sentiment") if isinstance(an, dict) else None
    if sent and isinstance(sent.get("score"), (int, float)):
        v["sentiment_score"] = max(-1.0, min(1.0, float(sent["score"])))
    return {k: (float(x) if x is not None and np.isfinite(x) else None) for k, x in v.items()}


# --- acoustic similarity --------------------------------------------------------

def _timbre(a, b) -> float:
    ma, mb = np.array(a.mfcc_mean[1:13]), np.array(b.mfcc_mean[1:13])
    sa, sb = np.array(a.mfcc_std[1:13]), np.array(b.mfcc_std[1:13])
    pooled = np.sqrt((sa ** 2 + sb ** 2) / 2 + 1e-9)
    d = float(np.sqrt(np.mean(((ma - mb) / pooled) ** 2)))
    return float(np.exp(-d))


def _spectral(a, b) -> float:
    p = np.array(list(a.band_fractions.values()))
    q = np.array(list(b.band_fractions.values()))
    bc = float(np.sum(np.sqrt(np.clip(p, 0, None) * np.clip(q, 0, None))))
    return float(1 - np.sqrt(max(0.0, 1 - bc)))


def _pitch(a, b) -> float | None:
    if not a.pitch_hist or not b.pitch_hist:
        return None
    return float(np.minimum(np.array(a.pitch_hist), np.array(b.pitch_hist)).sum())


def acoustic_similarity(recs: list[dict]) -> dict:
    n = len(recs)
    comps = {"timbre": np.full((n, n), np.nan), "spectral balance": np.full((n, n), np.nan),
             "voice pitch": np.full((n, n), np.nan)}
    for i, j in itertools.product(range(n), range(n)):
        a, b = recs[i]["acoustics"], recs[j]["acoustics"]
        if i == j:
            for m in comps.values():
                m[i, j] = 1.0
            continue
        if j < i:
            continue
        vals = {"timbre": _timbre(a, b), "spectral balance": _spectral(a, b), "voice pitch": _pitch(a, b)}
        for k, v in vals.items():
            if v is not None:
                comps[k][i, j] = comps[k][j, i] = v
    stack = np.stack(list(comps.values()))
    with np.errstate(all="ignore"):
        overall = np.nanmean(stack, axis=0)
    return {"overall": overall, "components": comps}


# --- content similarity -----------------------------------------------------------

def content_similarity(texts: list[str]) -> dict | None:
    """TF-IDF cosine similarity plus shared / distinctive terms (2+ transcripts)."""
    if sum(1 for t in texts if len(t.split()) >= 30) < 2:
        return None
    from sklearn.feature_extraction.text import ENGLISH_STOP_WORDS, CountVectorizer, TfidfTransformer

    stop = sorted(set(ENGLISH_STOP_WORDS) | STOP_EXTRA)
    cleaned = [re.sub(r"\[[^\]]*\]", " ", t) for t in texts]
    cv = CountVectorizer(stop_words=stop, token_pattern=r"(?u)\b[^\W\d_]{3,}\b", ngram_range=(1, 2),
                         max_features=30000, lowercase=True)
    try:
        counts = cv.fit_transform(cleaned)
    except ValueError:            # empty vocabulary
        return None
    terms = np.array(cv.get_feature_names_out())
    tfidf = TfidfTransformer(sublinear_tf=True).fit_transform(counts)
    sim = (tfidf @ tfidf.T).toarray()
    dense = counts.toarray()
    df = (dense > 0).sum(axis=0)
    n = len(texts)
    shared = []
    if n >= 2:
        mask = df >= 2
        order = np.argsort(-(dense[:, mask].sum(axis=0)))
        idx = np.flatnonzero(mask)[order][:25]
        shared = [{"term": terms[i], "files": int(df[i]), "count": int(dense[:, i].sum())} for i in idx]
    distinctive = []
    tf_dense = tfidf.toarray()
    for d in range(n):
        own = np.flatnonzero((dense[d] >= 2) & (df == 1))
        pool = own if own.size >= 5 else np.flatnonzero(dense[d] >= 2)
        if pool.size == 0:
            pool = np.flatnonzero(dense[d] > 0)
        top = pool[np.argsort(-tf_dense[d, pool])][:12]
        distinctive.append([terms[i] for i in top])
    return {"matrix": sim, "shared_terms": shared, "distinctive_terms": distinctive}


# --- trends / outliers / groups ------------------------------------------------------

def trends(recs: list[dict], values: list[dict]) -> list[dict]:
    dated = [(r["info"].recorded_at, v, r["id"]) for r, v in zip(recs, values) if r["info"].recorded_at]
    if len(dated) < 3:
        return []
    dated.sort(key=lambda x: x[0])
    out = []
    for key, label, unit, dec in METRICS:
        series = [(d, v.get(key), fid) for d, v, fid in dated if v.get(key) is not None]
        if len(series) < 3:
            continue
        ys = np.array([s[1] for s in series])
        if np.ptp(ys) == 0:
            continue
        diffs = np.diff(ys)
        if np.all(diffs > 0) or np.all(diffs < 0):
            from scipy.stats import spearmanr
            rho = float(spearmanr(np.arange(len(ys)), ys).statistic)
            out.append({
                "metric": key, "label": label, "unit": unit,
                "direction": "increases" if diffs[0] > 0 else "decreases",
                "rho": round(rho, 2), "n": len(ys),
                "first": round(float(ys[0]), dec), "last": round(float(ys[-1]), dec),
                "file_ids": [s[2] for s in series],
            })
    return out


def outliers(recs: list[dict], values: list[dict]) -> list[dict]:
    if len(recs) < 4:
        return []
    out = []
    for key, label, unit, dec in METRICS:
        pairs = [(r["id"], v.get(key)) for r, v in zip(recs, values) if v.get(key) is not None]
        if len(pairs) < 4:
            continue
        xs = np.array([p[1] for p in pairs])
        med = float(np.median(xs))
        mad = float(np.median(np.abs(xs - med)))
        if mad <= 1e-9:
            continue
        for fid, x in pairs:
            z = 0.6745 * (x - med) / mad
            if abs(z) > 3.5:
                out.append({"file_id": fid, "metric": key, "label": label, "unit": unit,
                            "value": round(x, dec), "median": round(med, dec), "z": round(float(z), 1)})
    return out


def groups(ids: list[str], sim: np.ndarray) -> list[list[str]]:
    n = len(ids)
    if n < 4 or not np.isfinite(sim).all():
        return []
    from scipy.cluster.hierarchy import fcluster, linkage
    from scipy.spatial.distance import squareform

    dist = np.clip(1 - sim, 0, None)
    np.fill_diagonal(dist, 0)
    z = linkage(squareform(dist, checks=False), method="average")
    heights = z[:, 2]
    gaps = np.diff(heights)
    if gaps.size == 0 or gaps.max() < 0.05:
        return []
    cut = heights[int(np.argmax(gaps))] + gaps.max() / 2
    labels = fcluster(z, t=cut, criterion="distance")
    out = [[ids[i] for i in range(n) if labels[i] == g] for g in sorted(set(labels))]
    if len(out) < 2 or len(out) == n:
        return []
    return sorted(out, key=len, reverse=True)


# --- main ---------------------------------------------------------------------------------

def compare(recs: list[dict]) -> dict:
    """recs: successful files [{id, info, acoustics, speech, analysis, listening}]."""
    ids = [r["id"] for r in recs]
    values = [metric_values(r) for r in recs]
    table = []
    for key, label, unit, dec in METRICS:
        row = {fid: v.get(key) for fid, v in zip(ids, values)}
        if any(x is not None for x in row.values()):
            table.append({"key": key, "label": label, "unit": unit, "decimals": dec, "values": row})
    result: dict = {"ids": ids, "table": table, "values": values}
    if len(recs) < 2:
        return result

    ac = acoustic_similarity(recs)
    result["acoustic"] = ac
    texts = [r["listening"].transcript_text() if r.get("listening") else "" for r in recs]
    content = content_similarity([re.sub(r"^\[[^\]]*\] [^:]*: ", "", t, flags=re.M) for t in texts])
    result["content"] = content
    combined = ac["overall"].copy()
    if content is not None:
        with np.errstate(all="ignore"):
            combined = np.nanmean(np.stack([ac["overall"], content["matrix"]]), axis=0)
    result["combined"] = combined

    n = len(ids)
    pairs = [(combined[i, j], ids[i], ids[j]) for i in range(n) for j in range(i + 1, n) if np.isfinite(combined[i, j])]
    if n >= 3 and pairs:
        best = max(pairs)
        result["closest_pair"] = {"ids": [best[1], best[2]], "similarity": round(float(best[0]), 3)}
        with np.errstate(all="ignore"):
            mean_sim = [(np.nanmean([combined[i, j] for j in range(n) if j != i]), ids[i]) for i in range(n)]
        low = min(mean_sim)
        result["most_distinct"] = {"id": low[1], "mean_similarity": round(float(low[0]), 3)}
    result["trends"] = trends(recs, values)
    result["outliers"] = outliers(recs, values)
    result["groups"] = groups(ids, combined)
    return result


def brief(cmp: dict) -> str:
    """Plain-text version for the synthesis prompt."""
    ids = cmp["ids"]
    lines = ["metric | " + " | ".join(ids)]
    for row in cmp["table"]:
        vals = []
        for fid in ids:
            x = row["values"].get(fid)
            vals.append("n/a" if x is None else f"{x:.{row['decimals']}f}")
        unit = f" ({row['unit']})" if row["unit"] else ""
        lines.append(f"{row['label']}{unit} | " + " | ".join(vals))
    if "acoustic" in cmp:
        n = len(ids)
        sims = []
        for i in range(n):
            for j in range(i + 1, n):
                a = cmp["acoustic"]["overall"][i, j]
                c = cmp["content"]["matrix"][i, j] if cmp.get("content") else None
                s = f"{ids[i]}-{ids[j]}: acoustic similarity {a:.2f}"
                if c is not None:
                    s += f", transcript vocabulary similarity {c:.2f}"
                sims.append(s)
        lines.append("Pairwise similarity (0-1): " + "; ".join(sims[:60]))
    if cmp.get("content") and cmp["content"]["shared_terms"]:
        lines.append("Terms shared by several transcripts: " + ", ".join(t["term"] for t in cmp["content"]["shared_terms"][:20]))
    for t in cmp.get("trends", []):
        lines.append(f"Trend over recording dates: {t['label']} {t['direction']} ({t['first']} -> {t['last']}, n={t['n']})")
    for o in cmp.get("outliers", []):
        lines.append(f"Outlier: {o['file_id']} {o['label']} = {o['value']} vs median {o['median']}")
    if cmp.get("groups"):
        lines.append("Similarity groups: " + " | ".join(", ".join(g) for g in cmp["groups"]))
    return "\n".join(lines)