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# Authors: The MNE-Python contributors.

# License: BSD-3-Clause

# Copyright the MNE-Python contributors.





import numpy as np

import pytest

from numpy.testing import assert_allclose

from scipy.linalg import norm



from mne import SourceEstimate, read_source_spaces

from mne.datasets import testing

from mne.simulation import metrics

from mne.simulation.metrics import (

    cosine_score,

    f1_score,

    peak_position_error,

    precision_score,

    recall_score,

    region_localization_error,

    roc_auc_score,

    spatial_deviation_error,

)



data_path = testing.data_path(download=False)

src_fname = data_path / "subjects" / "sample" / "bem" / "sample-oct-6-src.fif"





@testing.requires_testing_data

def test_uniform_and_thresholding():

    """Test simulation metrics."""

    src = read_source_spaces(src_fname)

    vert = [src[0]["vertno"][0:1], []]

    data = np.array([[0.8, -1.0]])

    stc_true = SourceEstimate(data, vert, 0, 0.002, subject="sample")

    stc_bad = SourceEstimate(data, vert, 0, 0.002, subject="sample")

    stc_bad.vertices = [stc_bad.vertices[0]]

    with pytest.raises(ValueError, match="same number of vertices"):

        metrics._uniform_stc(stc_true, stc_bad)



    threshold = 0.9

    stc1, stc2 = metrics._thresholding(stc_true, stc_true, threshold)

    assert_allclose(stc1._data, np.array([[0, -1.0]]))

    assert_allclose(stc2._data, np.array([[0, -1.0]]))

    assert_allclose(threshold, metrics._check_threshold(threshold))



    threshold = "90"

    with pytest.raises(ValueError, match="Threshold if a str.*"):

        metrics._check_threshold(threshold)





@testing.requires_testing_data

def test_cosine_score():

    """Test simulation metrics."""

    src = read_source_spaces(src_fname)

    vert1 = [src[0]["vertno"][0:1], []]

    vert2 = [src[0]["vertno"][1:2], []]

    data1 = np.ones((1, 2))

    data2 = data1.copy()

    stc_true = SourceEstimate(data1, vert1, 0, 0.002, subject="sample")

    stc_est1 = SourceEstimate(data2, vert2, 0, 0.002, subject="sample")

    stc_est2 = SourceEstimate(data2, vert1, 0, 0.002, subject="sample")



    E_per_sample1 = cosine_score(stc_true, stc_est1)

    E_unique1 = cosine_score(stc_true, stc_est1, per_sample=False)



    E_per_sample2 = cosine_score(stc_true, stc_est2)

    E_unique2 = cosine_score(stc_true, stc_est2, per_sample=False)



    assert_allclose(E_per_sample1, np.zeros(2))

    assert_allclose(E_unique1, 0.0, atol=1e-08)

    assert_allclose(E_per_sample2, np.ones(2))

    assert_allclose(E_unique2, 1.0, atol=1e-08)





@testing.requires_testing_data

def test_region_localization_error():

    """Test simulation metrics."""

    pytest.importorskip("sklearn")

    src = read_source_spaces(src_fname)

    vert1 = [src[0]["vertno"][0:1], []]

    vert2 = [src[0]["vertno"][1:2], []]

    dist = norm(src[0]["rr"][vert1[0]] - src[0]["rr"][vert2[0]])

    data1 = np.ones((1, 2))

    data2 = np.array([[0.8, 1]])

    stc_true = SourceEstimate(data1, vert1, 0, 0.002, subject="sample")

    stc_est1 = SourceEstimate(data2, vert2, 0, 0.002, subject="sample")



    E_per_sample1 = region_localization_error(stc_true, stc_est1, src)

    E_per_sample2 = region_localization_error(stc_true, stc_est1, src, threshold="70%")

    E_unique = region_localization_error(stc_true, stc_est1, src, per_sample=False)



    assert_allclose(E_per_sample1, [np.inf, dist])

    assert_allclose(E_per_sample2, [dist, dist])

    assert_allclose(E_unique, dist)





@testing.requires_testing_data

def test_precision_score():

    """Test simulation metrics."""

    pytest.importorskip("sklearn")

    from sklearn.exceptions import UndefinedMetricWarning



    src = read_source_spaces(src_fname)

    vert1 = [src[0]["vertno"][0:2], []]

    vert2 = [src[0]["vertno"][1:3], []]

    vert3 = [src[0]["vertno"][0:1], []]

    data1 = np.ones((2, 2))

    data2 = np.ones((2, 2))

    data3 = np.array([[0.8, 1]])

    stc_true = SourceEstimate(data1, vert1, 0, 0.002, subject="sample")

    stc_est1 = SourceEstimate(data2, vert2, 0, 0.002, subject="sample")

    stc_est2 = SourceEstimate(data3, vert3, 0, 0.002, subject="sample")



    E_unique1 = precision_score(stc_true, stc_est1, per_sample=False)

    E_unique2 = precision_score(stc_true, stc_est2, per_sample=False)

    with pytest.warns(UndefinedMetricWarning, match="no predicted samples"):

        E_per_sample1 = precision_score(stc_true, stc_est2)

    E_per_sample2 = precision_score(stc_true, stc_est2, threshold="70%")

    with pytest.raises(ValueError, match="0 and 1"):

        precision_score(stc_true, stc_est2, threshold=2)



    # ### Tests to add

    assert_allclose(E_unique1, 0.5)

    assert_allclose(E_unique2, 1.0)

    assert_allclose(E_per_sample1, [0.0, 1.0])

    assert_allclose(E_per_sample2, [1.0, 1.0])





@testing.requires_testing_data

def test_recall_score():

    """Test simulation metrics."""

    pytest.importorskip("sklearn")

    src = read_source_spaces(src_fname)

    vert1 = [src[0]["vertno"][0:2], []]

    vert2 = [src[0]["vertno"][1:3], []]

    vert3 = [src[0]["vertno"][0:1], []]

    data1 = np.ones((2, 2))

    data2 = np.ones((2, 2))

    data3 = np.array([[0.8, 1]])

    stc_true = SourceEstimate(data1, vert1, 0, 0.002, subject="sample")

    stc_est1 = SourceEstimate(data2, vert2, 0, 0.002, subject="sample")

    stc_est2 = SourceEstimate(data3, vert3, 0, 0.002, subject="sample")



    E_unique1 = recall_score(stc_true, stc_est1, per_sample=False)

    E_unique2 = recall_score(stc_true, stc_est2, per_sample=False)

    E_per_sample1 = recall_score(stc_true, stc_est2)

    E_per_sample2 = recall_score(stc_true, stc_est2, threshold="70%")

    with pytest.raises(TypeError, match="numeric"):

        precision_score(stc_true, stc_est2, threshold=None)



    # ### Tests to add

    assert_allclose(E_unique1, 0.5)

    assert_allclose(E_unique2, 0.5)

    assert_allclose(E_per_sample1, [0.0, 0.5])

    assert_allclose(E_per_sample2, [0.5, 0.5])





@testing.requires_testing_data

def test_f1_score():

    """Test simulation metrics."""

    pytest.importorskip("sklearn")

    src = read_source_spaces(src_fname)

    vert1 = [src[0]["vertno"][0:2], []]

    vert2 = [src[0]["vertno"][1:3], []]

    vert3 = [src[0]["vertno"][0:1], []]

    data1 = np.ones((2, 2))

    data2 = np.ones((2, 2))

    data3 = np.array([[0.8, 1]])

    stc_true = SourceEstimate(data1, vert1, 0, 0.002, subject="sample")

    stc_est1 = SourceEstimate(data2, vert2, 0, 0.002, subject="sample")

    stc_est2 = SourceEstimate(data3, vert3, 0, 0.002, subject="sample")



    E_unique1 = f1_score(stc_true, stc_est1, per_sample=False)

    E_unique2 = f1_score(stc_true, stc_est2, per_sample=False)

    E_per_sample1 = f1_score(stc_true, stc_est2)

    E_per_sample2 = f1_score(stc_true, stc_est2, threshold="70%")

    assert_allclose(E_unique1, 0.5)

    assert_allclose(E_unique2, 1.0 / 1.5)

    assert_allclose(E_per_sample1, [0.0, 1.0 / 1.5])

    assert_allclose(E_per_sample2, [1.0 / 1.5, 1.0 / 1.5])





@testing.requires_testing_data

def test_roc_auc_score():

    """Test simulation metrics."""

    pytest.importorskip("sklearn")

    src = read_source_spaces(src_fname)

    vert1 = [src[0]["vertno"][0:4], []]

    vert2 = [src[0]["vertno"][0:4], []]

    data1 = np.array([[0.0, 0.0, 1, 1]]).T

    data2 = np.array([[0.1, -0.4, 0.35, 0.8]]).T

    stc_true = SourceEstimate(data1, vert1, 0, 0.002, subject="sample")

    stc_est = SourceEstimate(data2, vert2, 0, 0.002, subject="sample")



    score = roc_auc_score(stc_true, stc_est, per_sample=False)

    assert_allclose(score, 0.75)





@testing.requires_testing_data

def test_peak_position_error():

    """Test simulation metrics."""

    src = read_source_spaces(src_fname)

    vert1 = [src[0]["vertno"][0:1], []]

    vert2 = [src[0]["vertno"][0:2], []]

    data1 = np.array([[1]])

    data2 = np.array([[1, 1.0]]).T

    stc_true = SourceEstimate(data1, vert1, 0, 0.002, subject="sample")

    stc_est = SourceEstimate(data2, vert2, 0, 0.002, subject="sample")

    r_mean = 0.5 * (src[0]["rr"][vert2[0][0]] + src[0]["rr"][vert2[0][1]])

    r_true = src[0]["rr"][vert2[0][0]]

    score = peak_position_error(stc_true, stc_est, src, per_sample=False)



    assert_allclose(score, norm(r_true - r_mean))

    with pytest.raises(ValueError, match="must contain only one dipole"):

        peak_position_error(stc_est, stc_est, src)



    data2 = np.array([[0, 0.0]]).T

    stc_est = SourceEstimate(data2, vert2, 0, 0.002, subject="sample")

    score = peak_position_error(stc_true, stc_est, src, per_sample=False)

    assert_allclose(score, np.inf)





@testing.requires_testing_data

def test_spatial_deviation():

    """Test simulation metrics."""

    src = read_source_spaces(src_fname)

    vert1 = [src[0]["vertno"][0:1], []]

    vert2 = [src[0]["vertno"][0:2], []]

    data1 = np.array([[1]])

    data2 = np.array([[1, 1.0]]).T

    stc_true = SourceEstimate(data1, vert1, 0, 0.002, subject="sample")

    stc_est = SourceEstimate(data2, vert2, 0, 0.002, subject="sample")

    std = np.sqrt(

        0.5 * (0 + norm(src[0]["rr"][vert2[0][1]] - src[0]["rr"][vert2[0][0]]) ** 2)

    )

    score = spatial_deviation_error(stc_true, stc_est, src, per_sample=False)

    assert_allclose(score, std)



    data2 = np.array([[0, 0.0]]).T

    stc_est = SourceEstimate(data2, vert2, 0, 0.002, subject="sample")

    score = spatial_deviation_error(stc_true, stc_est, src, per_sample=False)

    assert_allclose(score, np.inf)