"""Lognormal 拟合派生灌注指标的纯算法单元测试（照搬旧项目 test_perfusion_metrics.py）。"""

from dataclasses import replace

import numpy as np
import pytest

from cadx.ceus import (
    CeusConfig,
    LognormalFitResult,
    LognormalParameters,
    compute_lognormal_perfusion_metrics,
    evaluate_lognormal,
    fit_lognormal_curve,
)
from conftest import load_ceus_section

# 配置不设默认值：旧测试靠默认值构造的配置，改为读 config/ceus.yaml 的取值。
_CONFIG = CeusConfig.model_validate(load_ceus_section())


@pytest.fixture(scope="module")
def complete_fit() -> LognormalFitResult:
    times = np.linspace(0.0, 60.0, 601)
    parameters = LognormalParameters(400.0, 2.2, 0.5, 8.0, 30.0)
    fit = fit_lognormal_curve(
        times,
        evaluate_lognormal(times, parameters),
        baseline=parameters.baseline,
        baseline_noise=0.2,
        baseline_end_index=20,
        duplicate_mask=np.zeros(times.size, dtype=bool),
        sample_mask=None,
        cfg=_CONFIG.tic_analysis.fitting,
        anchor_cfg=_CONFIG.tic_analysis.anchor_frames,
    )
    assert fit.success is True
    assert fit.d4_time_s is not None
    return fit


def test_complete_bolus_metrics_match_curve_integrals(
    complete_fit: LognormalFitResult,
) -> None:
    metrics = compute_lognormal_perfusion_metrics(complete_fit)

    assert metrics is not None
    assert complete_fit.parameters is not None
    assert complete_fit.d2_time_s is not None
    assert complete_fit.d3_time_s is not None
    assert complete_fit.d4_time_s is not None
    assert metrics.ti_s is not None
    assert metrics.to_s is not None
    assert metrics.mtt_s is not None
    assert metrics.wi_auc_au_s is not None
    assert metrics.wo_auc_au_s is not None
    assert metrics.wiwo_auc_au_s is not None
    assert metrics.rt_s is not None
    assert metrics.ft_s is not None
    assert metrics.mttl_s is not None
    assert metrics.wipi_au is not None
    assert metrics.wor_au_per_s is not None

    assert (
        complete_fit.fit_start_s
        <= metrics.ti_s
        < complete_fit.d2_time_s
        < metrics.ttp_s
        < complete_fit.d4_time_s
        < metrics.to_s
        <= complete_fit.fit_end_s
    )
    assert metrics.pe_au == pytest.approx(
        float(
            evaluate_lognormal(np.asarray([metrics.ttp_s]), complete_fit.parameters)[0]
            - complete_fit.parameters.baseline
        )
    )
    assert metrics.ttp_s == pytest.approx(complete_fit.d3_time_s)
    assert metrics.wir_au_per_s > 0.0
    assert metrics.wor_au_per_s < 0.0
    assert metrics.rt_s == pytest.approx(metrics.ttp_s - metrics.ti_s)
    assert metrics.ft_s == pytest.approx(metrics.to_s - metrics.ttp_s)
    assert metrics.wiwo_auc_au_s == pytest.approx(metrics.wi_auc_au_s + metrics.wo_auc_au_s)
    assert metrics.wipi_au == pytest.approx(metrics.wi_auc_au_s / metrics.rt_s)
    assert metrics.mttl_s == pytest.approx(metrics.mtt_s - metrics.ti_s)
    assert metrics.ti_s < metrics.mtt_s < metrics.to_s

    grid = np.linspace(metrics.ti_s, metrics.to_s, 100_001)
    pulse = evaluate_lognormal(grid, complete_fit.parameters) - (complete_fit.parameters.baseline)
    split = int(np.searchsorted(grid, metrics.ttp_s))
    wi_grid = np.concatenate((grid[:split], np.asarray([metrics.ttp_s])))
    wo_grid = np.concatenate((np.asarray([metrics.ttp_s]), grid[split:]))
    wi_pulse = evaluate_lognormal(wi_grid, complete_fit.parameters) - (
        complete_fit.parameters.baseline
    )
    wo_pulse = evaluate_lognormal(wo_grid, complete_fit.parameters) - (
        complete_fit.parameters.baseline
    )
    assert metrics.wi_auc_au_s == pytest.approx(np.trapezoid(wi_pulse, wi_grid), rel=1e-7)
    assert metrics.wo_auc_au_s == pytest.approx(np.trapezoid(wo_pulse, wo_grid), rel=1e-7)
    assert metrics.mtt_s == pytest.approx(
        np.trapezoid(grid * pulse, grid) / np.trapezoid(pulse, grid), rel=1e-7
    )


def test_missing_washout_keeps_only_wash_in_metrics(
    complete_fit: LognormalFitResult,
) -> None:
    metrics = compute_lognormal_perfusion_metrics(replace(complete_fit, d4_time_s=None))

    assert metrics is not None
    assert metrics.pe_au > 0.0
    assert metrics.ttp_s > 0.0
    assert metrics.wir_au_per_s > 0.0
    assert metrics.wi_auc_au_s is not None
    assert metrics.rt_s is not None
    assert metrics.wipi_au is not None
    assert metrics.wor_au_per_s is None
    assert metrics.wo_auc_au_s is None
    assert metrics.wiwo_auc_au_s is None
    assert metrics.ft_s is None
    assert metrics.mttl_s is None
    assert metrics.to_s is None
    assert metrics.mtt_s is None


def test_failed_fit_has_no_metrics(complete_fit: LognormalFitResult) -> None:
    assert compute_lognormal_perfusion_metrics(replace(complete_fit, success=False)) is None


def test_tangent_intersections_outside_observation_gate_dependents(
    complete_fit: LognormalFitResult,
) -> None:
    complete = compute_lognormal_perfusion_metrics(complete_fit)
    assert complete is not None
    assert complete.ti_s is not None
    assert complete.to_s is not None

    missing_wash_in = compute_lognormal_perfusion_metrics(
        replace(complete_fit, fit_start_s=complete.ti_s + 0.01)
    )
    assert missing_wash_in is not None
    assert missing_wash_in.ti_s is None
    assert missing_wash_in.rt_s is None
    assert missing_wash_in.wi_auc_au_s is None
    assert missing_wash_in.wipi_au is None
    assert missing_wash_in.wo_auc_au_s is not None
    assert missing_wash_in.wiwo_auc_au_s is None
    assert missing_wash_in.mttl_s is None

    missing_wash_out = compute_lognormal_perfusion_metrics(
        replace(complete_fit, fit_end_s=complete.to_s - 0.01)
    )
    assert missing_wash_out is not None
    assert missing_wash_out.wor_au_per_s is not None
    assert missing_wash_out.to_s is None
    assert missing_wash_out.ft_s is None
    assert missing_wash_out.wo_auc_au_s is None
    assert missing_wash_out.wiwo_auc_au_s is None
    assert missing_wash_out.mttl_s is None