"""Attribute aggregation — pure maths, no models."""
from behavision.attributes import aggregate


def test_median_age_ignores_one_wild_frame():
    samples = [{"age": 47}, {"age": 51}, {"age": 12}]  # 12 = a bad frame
    out = aggregate(samples)
    assert out["age"] == 47          # median, not mean (mean would be 36)
    assert out["age_spread"] == 39   # and the disagreement is reported


def test_majority_gender_wins_over_a_single_flip():
    out = aggregate([
        {"gender": "Male", "gender_confidence": 0.9},
        {"gender": "Male", "gender_confidence": 0.8},
        {"gender": "Female", "gender_confidence": 0.99},
    ])
    assert out["gender"] == "Male"
    assert out["gender_confidence"] == 0.85  # mean over the winning frames only


def test_empty_and_partial_inputs():
    assert aggregate([]) == {}
    assert aggregate([{}, None]) == {}
    assert aggregate([{"gender": "Female", "gender_confidence": 0.7}]) == {
        "gender": "Female", "gender_confidence": 0.7}


def test_caffe_age_range_is_aggregated_too():
    out = aggregate([{"age_range": "38-43"}, {"age_range": "38-43"},
                     {"age_range": "25-32"}])
    assert out["age_range"] == "38-43"


def test_person_event_reports_the_quality_the_gate_actually_used():
    """The enrollment gate runs on track.best_quality. Reporting this frame's
    track.quality instead made events look like they had passed a threshold
    they were below (observed: quality=0.586 logged against a 0.65 gate)."""
    import threading

    import numpy as np

    from behavision.config import Config
    from behavision.engine import CameraWorker
    from behavision.gallery.service import Resolution
    from behavision.faces import FaceOutbox
    from behavision.tracking import Track

    cfg = Config()
    published = []

    class Enc:
        size = 112
        def encode_chip(self, chip):
            v = np.ones(512, np.float32)
            return v / np.linalg.norm(v)

    class Gal:
        def resolve(self, mean, quality, cam, ts, attributes=None, rcfg=None):
            self.seen_quality = quality
            self.seen_rcfg = rcfg
            return Resolution(kind="new", identity_id=1, label="Visitor 1",
                              similarity=0.1, new_sighting=True)

    w = object.__new__(CameraWorker)
    w.cfg, w.encoder, w.attrs = cfg, Enc(), None
    w.rcfg = cfg.recognition.merged(None)   # no per-camera overrides here
    w.commission = None   # no placement check running
    w.gallery = Gal()
    w.cam_cfg = type("C", (), {"id": "cam1"})()
    w.bus = type("B", (), {"publish": staticmethod(published.append)})()
    # Images are off, the product default. A real FaceOutbox rather than a
    # mock, so a worker built this way runs the same disabled path
    # production does when store_faces is unset.
    w.faces = FaceOutbox(cfg.app.data_dir, enabled=False)

    kps = np.array([[130, 100], [190, 100], [160, 130], [135, 165], [185, 165]],
                   np.float32)
    track = Track(id=1, box=(100, 60, 220, 200), kps=kps, score=0.9,
                  quality=0.40, best_quality=0.80, hits=10)
    frame = np.zeros((240, 320, 3), np.uint8)
    ts = 1000.0
    for _ in range(cfg.tracking.min_embeddings_for_id):
        ts += 0.04
        w._identify(track, frame, ts)

    assert published, "no event published"
    data = published[0].data
    assert w.gallery.seen_quality == 0.80        # the gate saw best_quality
    assert data["quality"] == 0.80               # and so does the event
    assert data["frame_quality"] == 0.40         # this frame, kept for context