"""Rule-based pattern detection / differential-diagnosis hints.

Looks at the pattern of regional z-scores (and asymmetry) and flags
clinically recognized signatures. This is NOT a diagnosis engine — it
surfaces pattern hints that a radiologist should consider.

Each rule returns:
    { "pattern": short name,
      "confidence": low | moderate | high,
      "ddx_consider": list of possible diagnoses to rule in/out,
      "supporting_findings": list of (region, z) that triggered the rule }

Patterns encoded (initial set — extensible):
  - Medial temporal atrophy      (AD, TLE)
  - Ventriculomegaly + cortex OK (normal-pressure hydrocephalus / hydro)
  - Global GM atrophy, WM OK     (primary GM degeneration)
  - Bilateral frontal reduction  (frontotemporal pattern)
  - Asymmetric focal pattern     (tumor / stroke / focal pathology)
  - Accelerated WM myelination   (pediatric developmental anomaly)
  - Posterior atrophy dominant   (posterior cortical atrophy)
"""
from __future__ import annotations
from typing import Iterable


def _find(regions: Iterable[dict], name_frag: str,
          min_abs_z: float = 0.0,
          direction: str | None = None) -> list[dict]:
    out = []
    for r in regions:
        if name_frag.lower() not in r.get("name", "").lower():
            continue
        z = r.get("z_score")
        if z is None:
            continue
        if abs(z) < min_abs_z:
            continue
        if direction == "low" and z >= 0:
            continue
        if direction == "high" and z <= 0:
            continue
        out.append(r)
    return out


def detect_patterns(regions: list[dict], asymmetry: list[dict],
                    tissue: dict) -> list[dict]:
    hits = []

    # ---- 1. Medial temporal atrophy (AD, TLE) ---------------------------
    hipp = _find(regions, "hippocamp", min_abs_z=1.5, direction="low")
    phipp = _find(regions, "parahippocamp", min_abs_z=1.0, direction="low")
    if len(hipp) >= 1 and len(phipp) >= 1:
        bilateral = any(r["hemi"] == "L" for r in hipp) and \
                    any(r["hemi"] == "R" for r in hipp)
        conf = "high" if bilateral else "moderate"
        hits.append({
            "pattern": "Medial temporal atrophy",
            "confidence": conf,
            "ddx_consider": ["Alzheimer's disease (early)",
                             "Temporal lobe epilepsy (mesial sclerosis)",
                             "Limbic encephalitis"],
            "supporting_findings": [f"{r['name']} z={r['z_score']:+.1f}"
                                    for r in hipp + phipp],
        })

    # ---- 2. Ventriculomegaly with preserved cortex ---------------------
    vent = _find(regions, "lateral ventricle", min_abs_z=1.5, direction="high")
    cort = _find(regions, "cerebral cortex", direction="low")
    if vent and (not cort or max(abs(r["z_score"]) for r in cort) < 1.0):
        hits.append({
            "pattern": "Ventriculomegaly with cortical preservation",
            "confidence": "moderate",
            "ddx_consider": ["Normal pressure hydrocephalus",
                             "Obstructive hydrocephalus",
                             "Ex-vacuo (if secondary to WM loss)"],
            "supporting_findings": [f"{r['name']} z={r['z_score']:+.1f}"
                                    for r in vent],
        })

    # ---- 3. Global GM atrophy with WM preserved ------------------------
    gm_lo = [r for r in regions
             if r.get("group") == "Cortical"
             and r.get("z_score") is not None
             and r["z_score"] < -1.0]
    wm = _find(regions, "cerebral white matter")
    wm_ok = all(abs(r.get("z_score", 0)) < 1.0 for r in wm) if wm else True
    if len(gm_lo) >= 10 and wm_ok:
        hits.append({
            "pattern": "Diffuse grey matter volume loss (white matter preserved)",
            "confidence": "moderate",
            "ddx_consider": ["Primary GM degeneration",
                             "Early neurodegenerative process"],
            "supporting_findings": [f"{len(gm_lo)} cortical regions with z<-1"],
        })

    # ---- 4. Bilateral frontal reduction --------------------------------
    front_lo = [r for r in regions
                if "frontal" in r.get("name", "").lower()
                and r.get("z_score") is not None
                and r["z_score"] < -1.5]
    if len(front_lo) >= 3:
        hits.append({
            "pattern": "Frontal-predominant volume reduction",
            "confidence": "moderate" if len(front_lo) >= 5 else "low",
            "ddx_consider": ["Frontotemporal dementia (behavioral variant)",
                             "Chronic traumatic encephalopathy",
                             "Prefrontal developmental delay (pediatric)"],
            "supporting_findings": [f"{r['name']} z={r['z_score']:+.1f}"
                                    for r in front_lo[:5]],
        })

    # ---- 5. Asymmetric focal pattern -----------------------------------
    sig_asym = [a for a in asymmetry if a.get("significant") and
                abs(a.get("asymmetry_pct", 0)) >= 10.0]
    if len(sig_asym) >= 2:
        hits.append({
            "pattern": "Asymmetric focal volume pattern",
            "confidence": "low" if len(sig_asym) < 4 else "moderate",
            "ddx_consider": ["Focal mass lesion (tumor)",
                             "Chronic infarct",
                             "Focal cortical dysplasia (if pediatric)",
                             "Mesial temporal sclerosis (if medial temporal)"],
            "supporting_findings": [
                f"{a['region']} AI {a['asymmetry_pct']:+.1f}%"
                for a in sig_asym[:5]],
        })

    # ---- 6. Posterior-dominant atrophy ---------------------------------
    occ = [r for r in regions if r.get("lobe") == "Occipital"
           and (r.get("z_score") or 0) < -1.0]
    par = [r for r in regions if r.get("lobe") == "Parietal"
           and (r.get("z_score") or 0) < -1.0]
    front_ok = [r for r in regions if r.get("lobe") == "Frontal"
                and abs(r.get("z_score") or 0) < 1.0]
    if (len(occ) + len(par)) >= 4 and len(front_ok) >= 5:
        hits.append({
            "pattern": "Posterior cortical volume loss",
            "confidence": "moderate",
            "ddx_consider": ["Posterior cortical atrophy (PCA)",
                             "Lewy body disease variant",
                             "Late-stage Alzheimer's"],
            "supporting_findings": [f"occipital regions: {len(occ)}, "
                                    f"parietal: {len(par)} with z<-1"],
        })

    return hits