# Author: Mainak Jas <mainak.jas@telecom-paristech.fr>
#         Mikolaj Magnuski <mmagnuski@swps.edu.pl>
#
# License: BSD (3-clause)

import os.path as op
import shutil

import warnings
from nose.tools import assert_raises, assert_equal, assert_true
import numpy as np
from numpy.testing import assert_array_equal, assert_array_almost_equal

from mne import write_events, read_epochs_eeglab, Epochs, find_events
from mne.io import read_raw_eeglab
from mne.io.tests.test_raw import _test_raw_reader
from mne.io.eeglab.eeglab import read_events_eeglab
from mne.datasets import testing
from mne.utils import _TempDir, run_tests_if_main, requires_version

base_dir = op.join(testing.data_path(download=False), 'EEGLAB')
raw_fname = op.join(base_dir, 'test_raw.set')
raw_fname_onefile = op.join(base_dir, 'test_raw_onefile.set')
epochs_fname = op.join(base_dir, 'test_epochs.set')
epochs_fname_onefile = op.join(base_dir, 'test_epochs_onefile.set')
montage = op.join(base_dir, 'test_chans.locs')

warnings.simplefilter('always')  # enable b/c these tests throw warnings


@requires_version('scipy', '0.12')
@testing.requires_testing_data
def test_io_set():
    """Test importing EEGLAB .set files."""
    from scipy import io
    with warnings.catch_warnings(record=True) as w:
        warnings.simplefilter('always')
        # main tests, and test missing event_id
        _test_raw_reader(read_raw_eeglab, input_fname=raw_fname,
                         montage=montage)
        _test_raw_reader(read_raw_eeglab, input_fname=raw_fname_onefile,
                         montage=montage)
    for want in ('Events like', 'consist entirely', 'could not be mapped',
                 'string preload is not supported'):
        assert_true(any(want in str(ww.message) for ww in w))
    with warnings.catch_warnings(record=True) as w:
        warnings.simplefilter('always')
        # test finding events in continuous data
        event_id = {'rt': 1, 'square': 2}
        raw0 = read_raw_eeglab(input_fname=raw_fname, montage=montage,
                               event_id=event_id, preload=True)
        raw1 = read_raw_eeglab(input_fname=raw_fname, montage=montage,
                               event_id=event_id, preload=False)
        raw2 = read_raw_eeglab(input_fname=raw_fname_onefile, montage=montage,
                               event_id=event_id)
        raw3 = read_raw_eeglab(input_fname=raw_fname, montage=montage,
                               event_id=event_id)
        raw4 = read_raw_eeglab(input_fname=raw_fname, montage=montage)
        Epochs(raw0, find_events(raw0), event_id)
        epochs = Epochs(raw1, find_events(raw1), event_id)
        assert_equal(len(find_events(raw4)), 0)  # no events without event_id
        assert_equal(epochs["square"].average().nave, 80)  # 80 with
        assert_array_equal(raw0[:][0], raw1[:][0], raw2[:][0], raw3[:][0])
        assert_array_equal(raw0[:][-1], raw1[:][-1], raw2[:][-1], raw3[:][-1])
        assert_equal(len(w), 4)
        # 1 for preload=False / str with fname_onefile, 3 for dropped events
        raw0.filter(1, None, l_trans_bandwidth='auto', filter_length='auto',
                    phase='zero')  # test that preloading works

    # test that using uint16_codec does not break stuff
    raw0 = read_raw_eeglab(input_fname=raw_fname, montage=montage,
                           event_id=event_id, preload=False,
                           uint16_codec='ascii')

    # test old EEGLAB version event import
    eeg = io.loadmat(raw_fname, struct_as_record=False,
                     squeeze_me=True)['EEG']
    for event in eeg.event:  # old version allows integer events
        event.type = 1
    assert_equal(read_events_eeglab(eeg)[-1, -1], 1)
    eeg.event = eeg.event[0]  # single event
    assert_equal(read_events_eeglab(eeg)[-1, -1], 1)

    with warnings.catch_warnings(record=True) as w:
        warnings.simplefilter('always')
        epochs = read_epochs_eeglab(epochs_fname)
        epochs2 = read_epochs_eeglab(epochs_fname_onefile)
    # one warning for each read_epochs_eeglab because both files have epochs
    # associated with multiple events
    assert_equal(len(w), 2)
    assert_array_equal(epochs.get_data(), epochs2.get_data())

    # test different combinations of events and event_ids
    temp_dir = _TempDir()
    out_fname = op.join(temp_dir, 'test-eve.fif')
    write_events(out_fname, epochs.events)
    event_id = {'S255/S8': 1, 'S8': 2, 'S255/S9': 3}

    epochs = read_epochs_eeglab(epochs_fname, epochs.events, event_id)
    assert_equal(len(epochs.events), 4)
    assert_true(epochs.preload)
    assert_true(epochs._bad_dropped)
    epochs = read_epochs_eeglab(epochs_fname, out_fname, event_id)
    assert_raises(ValueError, read_epochs_eeglab, epochs_fname,
                  None, event_id)
    assert_raises(ValueError, read_epochs_eeglab, epochs_fname,
                  epochs.events, None)

    # test reading file with one event
    eeg = io.loadmat(raw_fname, struct_as_record=False,
                     squeeze_me=True)['EEG']
    one_event_fname = op.join(temp_dir, 'test_one_event.set')
    io.savemat(one_event_fname, {'EEG':
               {'trials': eeg.trials, 'srate': eeg.srate,
                'nbchan': eeg.nbchan, 'data': 'test_one_event.fdt',
                'epoch': eeg.epoch, 'event': eeg.event[0],
                'chanlocs': eeg.chanlocs, 'pnts': eeg.pnts}})
    shutil.copyfile(op.join(base_dir, 'test_raw.fdt'),
                    op.join(temp_dir, 'test_one_event.fdt'))
    event_id = {eeg.event[0].type: 1}
    read_raw_eeglab(input_fname=one_event_fname, montage=montage,
                    event_id=event_id, preload=True)

    # test overlapping events
    overlap_fname = op.join(temp_dir, 'test_overlap_event.set')
    io.savemat(overlap_fname, {'EEG':
               {'trials': eeg.trials, 'srate': eeg.srate,
                'nbchan': eeg.nbchan, 'data': 'test_overlap_event.fdt',
                'epoch': eeg.epoch, 'event': [eeg.event[0], eeg.event[0]],
                'chanlocs': eeg.chanlocs, 'pnts': eeg.pnts}})
    shutil.copyfile(op.join(base_dir, 'test_raw.fdt'),
                    op.join(temp_dir, 'test_overlap_event.fdt'))
    event_id = {'rt': 1, 'square': 2}
    with warnings.catch_warnings(record=True) as w:
        warnings.simplefilter('always')
        raw = read_raw_eeglab(input_fname=overlap_fname,
                              montage=montage, event_id=event_id,
                              preload=True)
    assert_equal(len(w), 1)  # one warning for the dropped event
    events_stimchan = find_events(raw)
    events_read_events_eeglab = read_events_eeglab(overlap_fname, event_id)
    assert_true(len(events_stimchan) == 1)
    assert_true(len(events_read_events_eeglab) == 2)

    # test reading file with one channel
    one_chan_fname = op.join(temp_dir, 'test_one_channel.set')
    io.savemat(one_chan_fname, {'EEG':
               {'trials': eeg.trials, 'srate': eeg.srate,
                'nbchan': 1, 'data': np.random.random((1, 3)),
                'epoch': eeg.epoch, 'event': eeg.epoch,
                'chanlocs': {'labels': 'E1', 'Y': -6.6069,
                             'X': 6.3023, 'Z': -2.9423},
                'times': eeg.times[:3], 'pnts': 3}})
    with warnings.catch_warnings(record=True) as w:
        warnings.simplefilter('always')
        read_raw_eeglab(input_fname=one_chan_fname, preload=True)
    # no warning for 'no events found'
    assert_equal(len(w), 0)

    # test reading file with 3 channels - one without position information
    # first, create chanlocs structured array
    ch_names = ['F3', 'unknown', 'FPz']
    x, y, z = [1., 2., np.nan], [4., 5., np.nan], [7., 8., np.nan]
    dt = [('labels', 'S10'), ('X', 'f8'), ('Y', 'f8'), ('Z', 'f8')]
    chanlocs = np.zeros((3,), dtype=dt)
    for ind, vals in enumerate(zip(ch_names, x, y, z)):
        for fld in range(4):
            chanlocs[ind][dt[fld][0]] = vals[fld]

    # save set file
    one_chanpos_fname = op.join(temp_dir, 'test_chanpos.set')
    io.savemat(one_chanpos_fname, {'EEG':
               {'trials': eeg.trials, 'srate': eeg.srate,
                'nbchan': 3, 'data': np.random.random((3, 3)),
                'epoch': eeg.epoch, 'event': eeg.epoch,
                'chanlocs': chanlocs, 'times': eeg.times[:3], 'pnts': 3}})
    # load it
    with warnings.catch_warnings(record=True) as w:
        warnings.simplefilter('always')
        raw = read_raw_eeglab(input_fname=one_chanpos_fname, preload=True)
    # one warning because some channels are not found in Montage
    assert_equal(len(w), 1)
    # position should be present for first two channels
    for i in range(2):
        assert_array_equal(raw.info['chs'][i]['loc'][:3],
                           np.array([-chanlocs[i]['Y'],
                                     chanlocs[i]['X'],
                                     chanlocs[i]['Z']]))
    # position of the last channel should be zero
    assert_array_equal(raw.info['chs'][-1]['loc'][:3], np.array([0., 0., 0.]))

    # test reading channel names from set and positions from montage
    with warnings.catch_warnings(record=True) as w:
        warnings.simplefilter('always')
        raw = read_raw_eeglab(input_fname=one_chanpos_fname, preload=True,
                              montage=montage)
    # one warning because some channels are not found in Montage
    assert_equal(len(w), 1)

    # when montage was passed - channel positions should be taken from there
    correct_pos = [[-0.56705965, 0.67706631, 0.46906776], [0., 0., 0.],
                   [0., 0.99977915, -0.02101571]]
    for ch_ind in range(3):
        assert_array_almost_equal(raw.info['chs'][ch_ind]['loc'][:3],
                                  np.array(correct_pos[ch_ind]))

    # test reading channel names but not positions when there is no X (only Z)
    # field in the EEG.chanlocs structure
    nopos_chanlocs = chanlocs[['labels', 'Z']]
    nopos_fname = op.join(temp_dir, 'test_no_chanpos.set')
    io.savemat(nopos_fname, {'EEG':
               {'trials': eeg.trials, 'srate': eeg.srate, 'nbchan': 3,
                'data': np.random.random((3, 2)), 'epoch': eeg.epoch,
                'event': eeg.epoch, 'chanlocs': nopos_chanlocs,
                'times': eeg.times[:2], 'pnts': 2}})
    # load the file
    with warnings.catch_warnings(record=True) as w:
        warnings.simplefilter('always')
        raw = read_raw_eeglab(input_fname=nopos_fname, preload=True)
    # test that channel names have been loaded but not channel positions
    for i in range(3):
        assert_equal(raw.info['chs'][i]['ch_name'], ch_names[i])
        assert_array_equal(raw.info['chs'][i]['loc'][:3],
                           np.array([0., 0., 0.]))

    # test if .dat file raises an error
    eeg = io.loadmat(epochs_fname, struct_as_record=False,
                     squeeze_me=True)['EEG']
    eeg.data = 'epochs_fname.dat'
    bad_epochs_fname = op.join(temp_dir, 'test_epochs.set')
    io.savemat(bad_epochs_fname, {'EEG':
               {'trials': eeg.trials, 'srate': eeg.srate,
                'nbchan': eeg.nbchan, 'data': eeg.data,
                'epoch': eeg.epoch, 'event': eeg.event,
                'chanlocs': eeg.chanlocs}})
    shutil.copyfile(op.join(base_dir, 'test_epochs.fdt'),
                    op.join(temp_dir, 'test_epochs.dat'))
    with warnings.catch_warnings(record=True) as w:
        warnings.simplefilter('always')
        assert_raises(NotImplementedError, read_epochs_eeglab,
                      bad_epochs_fname)
    assert_equal(len(w), 1)

run_tests_if_main()
