# -*- coding: utf-8 -*-
import biosppy
import matplotlib.pyplot as plt
import numpy as np
import pytest

import neurokit2 as nk


def test_ecg_simulate():
    ecg1 = nk.ecg_simulate(
        duration=20, length=5000, method="simple", noise=0, random_state=0
    )
    assert len(ecg1) == 5000

    ecg2 = nk.ecg_simulate(duration=20, length=5000, heart_rate=500, random_state=1)
    #    pd.DataFrame({"ECG1":ecg1, "ECG2": ecg2}).plot()
    #    pd.DataFrame({"ECG1":ecg1, "ECG2": ecg2}).hist()
    assert len(nk.signal_findpeaks(ecg1, height_min=0.6)["Peaks"]) < len(
        nk.signal_findpeaks(ecg2, height_min=0.6)["Peaks"]
    )

    ecg3 = nk.ecg_simulate(duration=10, length=5000, random_state=2)
    #    pd.DataFrame({"ECG1":ecg1, "ECG3": ecg3}).plot()
    assert len(nk.signal_findpeaks(ecg2, height_min=0.6)["Peaks"]) > len(
        nk.signal_findpeaks(ecg3, height_min=0.6)["Peaks"]
    )


def test_ecg_clean():
    sampling_rate = 1000
    noise = 0.05

    ecg = nk.ecg_simulate(sampling_rate=sampling_rate, noise=noise, random_state=3)
    ecg_cleaned_nk = nk.ecg_clean(ecg, sampling_rate=sampling_rate, method="neurokit")

    assert ecg.size == ecg_cleaned_nk.size

    # Assert that highpass filter with .5 Hz lowcut was applied.
    fft_raw = np.abs(np.fft.rfft(ecg))
    fft_nk = np.abs(np.fft.rfft(ecg_cleaned_nk))

    freqs = np.fft.rfftfreq(ecg.size, 1 / sampling_rate)

    assert np.sum(fft_raw[freqs < 0.5]) > np.sum(fft_nk[freqs < 0.5])

    # Comparison to biosppy
    ecg_biosppy = nk.ecg_clean(ecg, sampling_rate=sampling_rate, method="biosppy")
    assert np.allclose(ecg_biosppy.mean(), 0, atol=1e-6)


def test_ecg_peaks():
    sampling_rate = 200
    noise = 1

    ecg = nk.ecg_simulate(
        duration=120, sampling_rate=sampling_rate, noise=noise, random_state=42
    )
    ecg[3000:3600] = 0

    # Test without request to correct artifacts.
    signals, _ = nk.ecg_peaks(
        ecg, sampling_rate=sampling_rate, method="neurokit", correct_artifacts=False
    )

    assert signals.shape == (24000, 1)
    assert np.allclose(signals["ECG_R_Peaks"].values.sum(dtype=np.int64), 137, atol=1)

    # Test with request to correct artifacts.
    signals, info = nk.ecg_peaks(
        ecg,
        sampling_rate=sampling_rate,
        correct_artifacts=True,
        method="neurokit",
    )

    assert signals.shape == (24000, 1)
    assert np.allclose(signals["ECG_R_Peaks"].values.sum(dtype=np.int64), 136, atol=1)
    assert 17 in info["ECG_fixpeaks_longshort"]


def test_ecg_process():
    sampling_rate = 1000
    noise = 0.05

    ecg = nk.ecg_simulate(sampling_rate=sampling_rate, noise=noise, random_state=4)
    _ = nk.ecg_process(ecg, sampling_rate=sampling_rate, method="neurokit")


def test_ecg_plot():
    ecg = nk.ecg_simulate(duration=60, heart_rate=70, noise=0.05, random_state=5)

    ecg_summary, info = nk.ecg_process(ecg, sampling_rate=1000, method="neurokit")

    # Plot data over seconds.
    nk.ecg_plot(ecg_summary, info)
    fig = plt.gcf()
    assert len(fig.axes) == 3
    assert fig.get_axes()[1].get_xlabel() == "Time (seconds)"
    np.testing.assert_array_equal(fig.axes[0].get_xticks(), fig.axes[1].get_xticks())
    plt.close(fig)

    # Make sure it works with cropped data
    nk.ecg_plot(ecg_summary[0:5000], info)
    fig = plt.gcf()
    assert fig.get_axes()[2].get_xlabel() == "Time (seconds)"


def test_ecg_findpeaks():
    sampling_rate = 1000

    ecg = nk.ecg_simulate(
        duration=60,
        sampling_rate=sampling_rate,
        noise=0,
        method="simple",
        random_state=42,
    )

    ecg_cleaned = nk.ecg_clean(ecg, sampling_rate=sampling_rate, method="neurokit")

    # Test neurokit methodwith show=True
    info_nk = nk.ecg_findpeaks(ecg_cleaned, show=True)

    assert info_nk["ECG_R_Peaks"].size == 69
    # This will identify the latest figure.
    fig = plt.gcf()
    assert len(fig.axes) == 2

    # Test pantompkins1985 method
    info_pantom = nk.ecg_findpeaks(
        nk.ecg_clean(ecg, method="pantompkins1985"), method="pantompkins1985"
    )
    assert info_pantom["ECG_R_Peaks"].size == 70

    # Test hamilton2002 method
    info_hamilton = nk.ecg_findpeaks(
        nk.ecg_clean(ecg, method="hamilton2002"), method="hamilton2002"
    )
    assert info_hamilton["ECG_R_Peaks"].size == 69

    # Test christov2004 method
    info_christov = nk.ecg_findpeaks(ecg_cleaned, method="christov2004")
    assert info_christov["ECG_R_Peaks"].size == 273

    # Test gamboa2008 method
    info_gamboa = nk.ecg_findpeaks(ecg_cleaned, method="gamboa2008")
    assert info_gamboa["ECG_R_Peaks"].size == 69

    # Test elgendi2010 method
    info_elgendi = nk.ecg_findpeaks(
        nk.ecg_clean(ecg, method="elgendi2010"), method="elgendi2010"
    )
    assert info_elgendi["ECG_R_Peaks"].size == 70

    # Test engzeemod2012 method
    info_engzeemod = nk.ecg_findpeaks(
        nk.ecg_clean(ecg, method="engzeemod2012"), method="engzeemod2012"
    )
    assert info_engzeemod["ECG_R_Peaks"].size == 69

    # Test kalidas2017 method
    info_kalidas = nk.ecg_findpeaks(
        nk.ecg_clean(ecg, method="kalidas2017"), method="kalidas2017"
    )
    assert np.allclose(info_kalidas["ECG_R_Peaks"].size, 68, atol=1)

    # Test martinez2004 method
    ecg = nk.ecg_simulate(
        duration=60, sampling_rate=sampling_rate, noise=0, random_state=42
    )
    ecg_cleaned = nk.ecg_clean(ecg, sampling_rate=sampling_rate, method="neurokit")
    info_martinez = nk.ecg_findpeaks(ecg_cleaned, method="martinez2004")
    assert np.allclose(info_martinez["ECG_R_Peaks"].size, 69, atol=1)


def test_ecg_eventrelated():
    ecg, _ = nk.ecg_process(nk.ecg_simulate(duration=20, random_state=6))
    epochs = nk.epochs_create(
        ecg, events=[5000, 10000, 15000], epochs_start=-0.1, epochs_end=1.9
    )
    ecg_eventrelated = nk.ecg_eventrelated(epochs)

    # Test rate features
    assert np.alltrue(
        np.array(ecg_eventrelated["ECG_Rate_Min"])
        < np.array(ecg_eventrelated["ECG_Rate_Mean"])
    )

    assert np.alltrue(
        np.array(ecg_eventrelated["ECG_Rate_Mean"])
        < np.array(ecg_eventrelated["ECG_Rate_Max"])
    )

    assert len(ecg_eventrelated["Label"]) == 3

    # Test warning on missing columns
    with pytest.warns(
        nk.misc.NeuroKitWarning, match=r".*does not have an `ECG_Phase_Artrial`.*"
    ):
        first_epoch_key = list(epochs.keys())[0]
        first_epoch_copy = epochs[first_epoch_key].copy()
        del first_epoch_copy["ECG_Phase_Atrial"]
        nk.ecg_eventrelated({**epochs, first_epoch_key: first_epoch_copy})

    with pytest.warns(
        nk.misc.NeuroKitWarning, match=r".*does not have an.*`ECG_Phase_Ventricular`"
    ):
        first_epoch_key = list(epochs.keys())[0]
        first_epoch_copy = epochs[first_epoch_key].copy()
        del first_epoch_copy["ECG_Phase_Ventricular"]
        nk.ecg_eventrelated({**epochs, first_epoch_key: first_epoch_copy})

    with pytest.warns(
        nk.misc.NeuroKitWarning, match=r".*does not have an `ECG_Quality`.*"
    ):
        first_epoch_key = list(epochs.keys())[0]
        first_epoch_copy = epochs[first_epoch_key].copy()
        del first_epoch_copy["ECG_Quality"]
        nk.ecg_eventrelated({**epochs, first_epoch_key: first_epoch_copy})

    with pytest.warns(
        nk.misc.NeuroKitWarning, match=r".*does not have an `ECG_Rate`.*"
    ):
        first_epoch_key = list(epochs.keys())[0]
        first_epoch_copy = epochs[first_epoch_key].copy()
        del first_epoch_copy["ECG_Rate"]
        nk.ecg_eventrelated({**epochs, first_epoch_key: first_epoch_copy})

    # Test warning on long epochs (eventrelated_utils)
    with pytest.warns(
        nk.misc.NeuroKitWarning, match=r".*duration of your epochs seems.*"
    ):
        first_epoch_key = list(epochs.keys())[0]
        first_epoch_copy = epochs[first_epoch_key].copy()
        first_epoch_copy.index = range(len(first_epoch_copy))
        nk.ecg_eventrelated({**epochs, first_epoch_key: first_epoch_copy})


def test_ecg_delineate():
    sampling_rate = 1000

    # test with simulated signals
    ecg = nk.ecg_simulate(duration=20, sampling_rate=sampling_rate, random_state=42)
    _, rpeaks = nk.ecg_peaks(ecg, sampling_rate=sampling_rate)
    number_rpeaks = len(rpeaks["ECG_R_Peaks"])

    # Method 1: derivative
    _, waves_derivative = nk.ecg_delineate(
        ecg, rpeaks, sampling_rate=sampling_rate, method="peaks"
    )
    assert len(waves_derivative["ECG_P_Peaks"]) == number_rpeaks
    assert len(waves_derivative["ECG_Q_Peaks"]) == number_rpeaks
    assert len(waves_derivative["ECG_S_Peaks"]) == number_rpeaks
    assert len(waves_derivative["ECG_T_Peaks"]) == number_rpeaks
    assert len(waves_derivative["ECG_P_Onsets"]) == number_rpeaks
    assert len(waves_derivative["ECG_T_Offsets"]) == number_rpeaks

    # Method 2: CWT
    _, waves_cwt = nk.ecg_delineate(
        ecg, rpeaks, sampling_rate=sampling_rate, method="cwt"
    )
    assert np.allclose(len(waves_cwt["ECG_P_Peaks"]), 22, atol=1)
    assert np.allclose(len(waves_cwt["ECG_T_Peaks"]), 22, atol=1)
    assert np.allclose(len(waves_cwt["ECG_R_Onsets"]), 23, atol=1)
    assert np.allclose(len(waves_cwt["ECG_R_Offsets"]), 23, atol=1)
    assert np.allclose(len(waves_cwt["ECG_P_Onsets"]), 22, atol=1)
    assert np.allclose(len(waves_cwt["ECG_P_Offsets"]), 22, atol=1)
    assert np.allclose(len(waves_cwt["ECG_T_Onsets"]), 22, atol=1)
    assert np.allclose(len(waves_cwt["ECG_T_Offsets"]), 22, atol=1)


def test_ecg_invert():
    sampling_rate = 500
    noise = 0.05

    ecg = nk.ecg_simulate(sampling_rate=sampling_rate, noise=noise, random_state=3)
    ecg_inverted = ecg * -1 + 2 * np.nanmean(ecg)
    ecg_fixed, _ = nk.ecg_invert(ecg_inverted)
    assert np.allclose((ecg - ecg_fixed).mean(), 0, atol=1e-6)


def test_ecg_intervalrelated():
    data = nk.data("bio_resting_5min_100hz")
    df, _ = nk.ecg_process(data["ECG"], sampling_rate=100)

    columns = [
        "ECG_Rate_Mean",
        "HRV_RMSSD",
        "HRV_MeanNN",
        "HRV_SDNN",
        "HRV_SDSD",
        "HRV_CVNN",
        "HRV_CVSD",
        "HRV_MedianNN",
        "HRV_MadNN",
        "HRV_MCVNN",
        "HRV_IQRNN",
        "HRV_pNN50",
        "HRV_pNN20",
        "HRV_TINN",
        "HRV_HTI",
        "HRV_ULF",
        "HRV_VLF",
        "HRV_LF",
        "HRV_HF",
        "HRV_VHF",
        "HRV_LFHF",
        "HRV_LFn",
        "HRV_HFn",
        "HRV_LnHF",
        "HRV_SD1",
        "HRV_SD2",
        "HRV_SD1SD2",
        "HRV_S",
        "HRV_CSI",
        "HRV_CVI",
        "HRV_CSI_Modified",
        "HRV_PIP",
        "HRV_IALS",
        "HRV_PSS",
        "HRV_PAS",
        "HRV_GI",
        "HRV_SI",
        "HRV_AI",
        "HRV_PI",
        "HRV_C1d",
        "HRV_C1a",
        "HRV_SD1d",
        "HRV_SD1a",
        "HRV_C2d",
        "HRV_C2a",
        "HRV_SD2d",
        "HRV_SD2a",
        "HRV_Cd",
        "HRV_Ca",
        "HRV_SDNNd",
        "HRV_SDNNa",
        "HRV_DFA_alpha1",
        "HRV_DFA_alpha2",
        "HRV_ApEn",
        "HRV_SampEn",
        "HRV_MSE",
        "HRV_CMSE",
        "HRV_RCMSE",
        "HRV_CD",
    ]

    # Test with signal dataframe
    features_df = nk.ecg_intervalrelated(df, sampling_rate=100)

    # https://github.com/neuropsychology/NeuroKit/issues/304
    assert all(
        features_df == nk.ecg_analyze(df, sampling_rate=100, method="interval-related")
    )

    assert (elem in columns for elem in np.array(features_df.columns.values, dtype=str))
    assert features_df.shape[0] == 1  # Number of rows

    # Test with dict
    columns.append("Label")
    epochs = nk.epochs_create(df, events=[0, 15000], sampling_rate=100, epochs_end=150)
    features_dict = nk.ecg_intervalrelated(epochs, sampling_rate=100)

    assert (
        elem in columns for elem in np.array(features_dict.columns.values, dtype=str)
    )
    assert features_dict.shape[0] == 2  # Number of rows
