"""Tests for time-series synthesis (T1.3)."""
import numpy as np
import pytest

from tsmm.data.synthesis import (
    generate_series,
    inject_anomaly,
    couple_event,
    add_missing,
)


# ─── generate_series ───────────────────────────────────────────────────────
def test_generate_series_shape():
    s = generate_series(T=256, C=3, seed=0)
    assert s.shape == (256, 3)
    assert np.isfinite(s).all(), "baseline series must be finite (no NaN before inject)"


def test_generate_series_reproducible():
    a = generate_series(T=128, C=2, seed=7)
    b = generate_series(T=128, C=2, seed=7)
    assert np.array_equal(a, b)


def test_generate_series_different_seed_differs():
    a = generate_series(T=128, C=2, seed=1)
    b = generate_series(T=128, C=2, seed=2)
    assert not np.array_equal(a, b)


def test_generate_series_has_structure():
    # baseline + trend + seasonality + noise: variance should be > 0 and the
    # series should not be constant.
    s = generate_series(T=512, C=2, seed=3)
    assert np.std(s) > 0
    assert np.ptp(s) > 0  # peak-to-peak


# ─── inject_anomaly ────────────────────────────────────────────────────────
def test_inject_anomaly_returns_series_and_labels_and_segments():
    s = generate_series(T=256, C=2, seed=0)
    out, labels, segments = inject_anomaly(s, types=["spike"], seed=0)
    assert out.shape == s.shape
    assert labels.shape == (256,)
    assert set(np.unique(labels)).issubset({0, 1})
    assert isinstance(segments, list)


def test_inject_anomaly_labels_cover_segments():
    s = generate_series(T=256, C=2, seed=0)
    out, labels, segments = inject_anomaly(s, types=["level_shift", "variance_change"], seed=1)
    # Each segment must mark a contiguous run of label==1 exactly matching [start,end)
    for seg in segments:
        st, en = seg["start"], seg["end"]
        assert 0 <= st < en <= len(labels)
        assert labels[st:en].all(), f"segment {seg} not fully labeled"
        # immediately outside (if exists) must be 0
        if st > 0:
            assert labels[st - 1] == 0
        if en < len(labels):
            assert labels[en] == 0


def test_inject_anomaly_changes_the_series_at_segments():
    s = generate_series(T=256, C=2, seed=0)
    out, labels, segments = inject_anomaly(s, types=["spike"], seed=0)
    assert np.any(labels == 1), "expected at least one anomaly segment"
    # at least somewhere the injected series differs from baseline
    assert not np.allclose(out, s)


def test_inject_anomaly_each_supported_type():
    s = generate_series(T=256, C=2, seed=0)
    for t in ["spike", "level_shift", "variance_change", "missing_segment", "drift"]:
        out, labels, segments = inject_anomaly(s, types=[t], seed=0)
        # missing_segment may place NaN, so use labels to assert effect
        assert np.any(labels == 1), f"type {t} produced no anomaly segment"


def test_inject_anomaly_missing_segment_inserts_nan():
    s = generate_series(T=256, C=2, seed=0)
    out, labels, segments = inject_anomaly(s, types=["missing_segment"], seed=0)
    assert np.isnan(out).any(), "missing_segment must inject NaN values"


def test_inject_anomaly_reproducible():
    s = generate_series(T=256, C=2, seed=0)
    o1, l1, _ = inject_anomaly(s, types=["spike", "level_shift"], seed=5)
    o2, l2, _ = inject_anomaly(s, types=["spike", "level_shift"], seed=5)
    assert np.array_equal(o1, o2) and np.array_equal(l1, l2)


# ─── couple_event ──────────────────────────────────────────────────────────
def test_couple_event_introduces_step_and_returns_text():
    s = generate_series(T=256, C=2, seed=0)
    before = s.copy()
    out, text = couple_event(s, t=128, kind="deploy", seed=0)
    assert isinstance(text, str) and len(text) > 0
    # a step change near t should alter the series magnitude after t
    assert not np.allclose(out[130:], before[130:])
    # immediately before t the series is unchanged
    assert np.allclose(out[:128], before[:128])


def test_couple_event_step_has_persistent_offset():
    s = generate_series(T=256, C=1, seed=0)
    out, _ = couple_event(s, t=100, kind="incident", seed=1)
    # mean of post-event window differs from pre-event baseline
    pre = out[:100].mean()
    post = out[120:].mean()
    assert abs(post - pre) > 1e-6


# ─── add_missing ───────────────────────────────────────────────────────────
def test_add_missing_introduces_nan_within_rate():
    s = generate_series(T=512, C=2, seed=0)
    out = add_missing(s, rate=0.1, seed=0)
    assert out.shape == s.shape
    frac = np.isnan(out).mean()
    # within reasonable tolerance of requested rate
    assert 0.0 < frac <= 0.15


def test_add_missing_reproducible():
    s = generate_series(T=256, C=2, seed=0)
    a = add_missing(s, rate=0.1, seed=3)
    b = add_missing(s, rate=0.1, seed=3)
    assert np.array_equal(a, b, equal_nan=True)


# ─── regression: _random_window end >= start (found during 50k generation) ─
def test_inject_anomaly_end_ge_start_across_many_seeds():
    """Regression: _random_window fallback used to draw end independently,
    producing end < start (ValueError in linspace). Sweep many seeds + types."""
    import numpy as np
    s0 = generate_series(T=512, C=5, seed=0)
    for seed in range(300):
        for types in (
            ["drift"], ["level_shift"], ["variance_change"],
            ["drift", "level_shift", "variance_change", "spike"],
        ):
            s = s0.copy()
            out, labels, segments = inject_anomaly(s, types=types, seed=seed)
            for seg in segments:
                assert seg["end"] >= seg["start"], (seed, types, seg)
            assert out.shape == s.shape
            assert labels.shape == (512,)