"""
tests/unit/test_signal_processor.py
─────────────────────────────────────
Unit tests for ScipySignalProcessor.

No external services required — SciPy + NumPy only.
"""
from __future__ import annotations

import numpy as np
import pytest

from src.infrastructure.processing.scipy_signal_processor import ScipySignalProcessor
from src.shared.constants import PPG_SEGMENT_DURATION_SEC


@pytest.fixture
def processor() -> ScipySignalProcessor:
    return ScipySignalProcessor()


@pytest.fixture
def clean_signal() -> tuple[np.ndarray, float]:
    """A clean 30-second sine wave at 125 Hz (2 Hz fundamental)."""
    fs = 125.0
    duration = 30.0
    t = np.linspace(0, duration, int(fs * duration), endpoint=False)
    signal = np.sin(2 * np.pi * 2.0 * t)  # 2 Hz sine
    return signal, fs


# ── filter_signal ─────────────────────────────────────────────────────────────

class TestFilterSignal:
    def test_output_same_length_as_input(
        self, processor: ScipySignalProcessor, clean_signal: tuple
    ) -> None:
        sig, fs = clean_signal
        filtered = processor.filter_signal(sig, fs)
        assert filtered.shape == sig.shape

    def test_output_is_numpy_array(
        self, processor: ScipySignalProcessor, clean_signal: tuple
    ) -> None:
        sig, fs = clean_signal
        filtered = processor.filter_signal(sig, fs)
        assert isinstance(filtered, np.ndarray)

    def test_dc_component_removed(self, processor: ScipySignalProcessor) -> None:
        """DC offset (0 Hz) should be removed by the bandpass filter."""
        fs = 125.0
        t = np.linspace(0, 10, int(fs * 10), endpoint=False)
        signal = np.ones_like(t) * 5.0  # constant DC
        filtered = processor.filter_signal(signal, fs)
        # After bandpass filtering, mean should be near zero
        assert abs(np.mean(filtered)) < 0.1


# ── normalize ─────────────────────────────────────────────────────────────────

class TestNormalize:
    def test_mean_near_zero(
        self, processor: ScipySignalProcessor, clean_signal: tuple
    ) -> None:
        sig, _ = clean_signal
        normed = processor.normalize(sig)
        assert abs(np.mean(normed)) < 1e-6

    def test_std_near_one(
        self, processor: ScipySignalProcessor, clean_signal: tuple
    ) -> None:
        sig, _ = clean_signal
        normed = processor.normalize(sig)
        assert abs(np.std(normed) - 1.0) < 1e-6

    def test_constant_signal_returns_zeros(self, processor: ScipySignalProcessor) -> None:
        constant = np.ones(100) * 3.0
        result = processor.normalize(constant)
        assert np.allclose(result, 0.0)


# ── segment ───────────────────────────────────────────────────────────────────

class TestSegment:
    def test_segment_shape(
        self, processor: ScipySignalProcessor, clean_signal: tuple
    ) -> None:
        sig, fs = clean_signal
        segments = processor.segment(sig, fs)
        window_size = int(PPG_SEGMENT_DURATION_SEC * fs)
        expected_n = len(sig) // window_size
        assert segments.shape == (expected_n, window_size)

    def test_too_short_signal_returns_empty(self, processor: ScipySignalProcessor) -> None:
        fs = 125.0
        short_signal = np.zeros(10)  # much less than 8s × 125 = 1000 samples
        segments = processor.segment(short_signal, fs)
        assert segments.shape[0] == 0

    def test_segments_are_contiguous(
        self, processor: ScipySignalProcessor, clean_signal: tuple
    ) -> None:
        sig, fs = clean_signal
        segments = processor.segment(sig, fs)
        window_size = int(PPG_SEGMENT_DURATION_SEC * fs)
        # Check that first segment matches first window_size elements
        np.testing.assert_array_equal(segments[0], sig[:window_size])


# ── process (full pipeline) ───────────────────────────────────────────────────

class TestProcessPipeline:
    def test_process_returns_2d_array(
        self, processor: ScipySignalProcessor, clean_signal: tuple
    ) -> None:
        sig, fs = clean_signal
        result = processor.process(sig.tolist(), fs)
        assert result.ndim == 2

    def test_process_list_input(self, processor: ScipySignalProcessor) -> None:
        fs = 125.0
        raw = [0.1] * int(30 * fs)
        result = processor.process(raw, fs)
        assert isinstance(result, np.ndarray)