# -*- coding: utf-8 -*-
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#         Denis Engemann <denis.engemann@gmail.com>
#         Stefan Appelhoff <stefan.appelhoff@mailbox.org>
#
# License: BSD (3-clause)

from copy import deepcopy
from distutils.version import LooseVersion
from functools import partial
from io import BytesIO
import os.path as op
import pickle

import pytest
from numpy.testing import (assert_array_equal, assert_array_almost_equal,
                           assert_allclose, assert_equal)
import numpy as np
import matplotlib.pyplot as plt

import mne
from mne import (Epochs, Annotations, read_events, pick_events, read_epochs,
                 equalize_channels, pick_types, pick_channels, read_evokeds,
                 write_evokeds, create_info, make_fixed_length_events,
                 combine_evoked)
from mne.baseline import rescale
from mne.fixes import rfft, rfftfreq
from mne.preprocessing import maxwell_filter
from mne.epochs import (
    bootstrap, equalize_epoch_counts, combine_event_ids, add_channels_epochs,
    EpochsArray, concatenate_epochs, BaseEpochs, average_movements,
    _handle_event_repeated)
from mne.utils import (requires_pandas, run_tests_if_main, object_diff,
                       catch_logging, _FakeNoPandas,
                       assert_meg_snr, check_version, _dt_to_stamp)
from mne.chpi import read_head_pos, head_pos_to_trans_rot_t

from mne.io import RawArray, read_raw_fif
from mne.io.proj import _has_eeg_average_ref_proj
from mne.io.write import _get_split_size
from mne.event import merge_events
from mne.io.constants import FIFF
from mne.datasets import testing

data_path = testing.data_path(download=False)
fname_raw_testing = op.join(data_path, 'MEG', 'sample',
                            'sample_audvis_trunc_raw.fif')
fname_raw_move = op.join(data_path, 'SSS', 'test_move_anon_raw.fif')
fname_raw_movecomp_sss = op.join(
    data_path, 'SSS', 'test_move_anon_movecomp_raw_sss.fif')
fname_raw_move_pos = op.join(data_path, 'SSS', 'test_move_anon_raw.pos')

base_dir = op.join(op.dirname(__file__), '..', 'io', 'tests', 'data')
raw_fname = op.join(base_dir, 'test_raw.fif')
event_name = op.join(base_dir, 'test-eve.fif')
evoked_nf_name = op.join(base_dir, 'test-nf-ave.fif')

event_id, tmin, tmax = 1, -0.2, 0.5
event_id_2 = np.int64(2)  # to test non Python int types
rng = np.random.RandomState(42)


def test_event_repeated():
    """Test epochs takes into account repeated events."""
    n_samples = 100
    n_channels = 2
    ch_names = ['chan%i' % i for i in range(n_channels)]
    info = mne.create_info(ch_names=ch_names, sfreq=1000.)
    data = np.zeros((n_channels, n_samples))
    raw = mne.io.RawArray(data, info)

    events = np.array([[10, 0, 1], [10, 0, 2]])
    epochs = mne.Epochs(raw, events, event_repeated='drop')
    assert epochs.drop_log == [[], ['DROP DUPLICATE']]
    assert_array_equal(epochs.selection, [0])
    epochs = mne.Epochs(raw, events, event_repeated='merge')
    assert epochs.drop_log == [[], ['MERGE DUPLICATE']]
    assert_array_equal(epochs.selection, [0])


def test_handle_event_repeated():
    """Test handling of repeated events."""
    # A general test case
    EVENT_ID = {'aud': 1, 'vis': 2, 'foo': 3}
    EVENTS = np.array([[0, 0, 1], [0, 0, 2],
                       [3, 0, 2], [3, 0, 1],
                       [5, 0, 2], [5, 0, 1], [5, 0, 3],
                       [7, 0, 1]])
    SELECTION = np.arange(len(EVENTS))
    DROP_LOG = [list() for _ in range(len(EVENTS))]
    with pytest.raises(RuntimeError, match='Event time samples were not uniq'):
        _handle_event_repeated(EVENTS, EVENT_ID, event_repeated='error',
                               selection=SELECTION,
                               drop_log=deepcopy(DROP_LOG))

    events, event_id, selection, drop_log = _handle_event_repeated(
        EVENTS, EVENT_ID, 'drop', SELECTION, deepcopy(DROP_LOG))
    assert_array_equal(events, [[0, 0, 1], [3, 0, 2], [5, 0, 2], [7, 0, 1]])
    assert_array_equal(events, EVENTS[selection])
    unselection = np.setdiff1d(SELECTION, selection)
    assert all(drop_log[k] == ['DROP DUPLICATE'] for k in unselection)
    assert event_id == {'aud': 1, 'vis': 2}

    events, event_id, selection, drop_log = _handle_event_repeated(
        EVENTS, EVENT_ID, 'merge', SELECTION, deepcopy(DROP_LOG))
    assert_array_equal(events[0][-1], events[1][-1])
    assert_array_equal(events, [[0, 0, 4], [3, 0, 4], [5, 0, 5], [7, 0, 1]])
    assert_array_equal(events[:, :2], EVENTS[selection][:, :2])
    unselection = np.setdiff1d(SELECTION, selection)
    assert all(drop_log[k] == ['MERGE DUPLICATE'] for k in unselection)
    assert set(event_id.keys()) == set(['aud', 'aud/vis', 'aud/foo/vis'])
    assert event_id['aud/vis'] == 4

    # Test early return with no changes: no error for wrong event_repeated arg
    fine_events = np.array([[0, 0, 1], [1, 0, 2]])
    events, event_id, selection, drop_log = _handle_event_repeated(
        fine_events, EVENT_ID, 'no', [0, 2], deepcopy(DROP_LOG))
    assert event_id == EVENT_ID
    assert_array_equal(selection, [0, 2])
    assert drop_log == DROP_LOG
    assert_array_equal(events, fine_events)
    del fine_events

    # Test falling back on 0 for heterogeneous "prior-to-event" codes
    # order of third column does not determine new event_id key, we always
    # take components, sort, and join on "/"
    # should make new event_id value: 5 (because 1,2,3,4 are taken)
    heterogeneous_events = np.array([[0, 3, 2], [0, 4, 1]])
    events, event_id, selection, drop_log = _handle_event_repeated(
        heterogeneous_events, EVENT_ID, 'merge', [0, 1], deepcopy(DROP_LOG))
    assert set(event_id.keys()) == set(['aud/vis'])
    assert event_id['aud/vis'] == 5
    assert_array_equal(selection, [0])
    assert drop_log[1] == ['MERGE DUPLICATE']
    assert_array_equal(events, np.array([[0, 0, 5], ]))
    del heterogeneous_events

    # Test keeping a homogeneous "prior-to-event" code (=events[:, 1])
    homogeneous_events = np.array([[0, 99, 1], [0, 99, 2],
                                   [1, 0, 1], [2, 0, 2]])
    events, event_id, selection, drop_log = _handle_event_repeated(
        homogeneous_events, EVENT_ID, 'merge', [1, 3, 4, 7],
        deepcopy(DROP_LOG))
    assert set(event_id.keys()) == set(['aud', 'vis', 'aud/vis'])
    assert_array_equal(events, np.array([[0, 99, 4], [1, 0, 1], [2, 0, 2]]))
    assert_array_equal(selection, [1, 4, 7])
    assert drop_log[3] == ['MERGE DUPLICATE']
    del homogeneous_events

    # Test dropping instead of merging, if event_codes to be merged are equal
    equal_events = np.array([[0, 0, 1], [0, 0, 1]])
    events, event_id, selection, drop_log = _handle_event_repeated(
        equal_events, EVENT_ID, 'merge', [3, 5], deepcopy(DROP_LOG))
    assert_array_equal(events, np.array([[0, 0, 1], ]))
    assert_array_equal(selection, [3])
    assert drop_log[5] == ['MERGE DUPLICATE']
    assert set(event_id.keys()) == set(['aud'])


def _get_data(preload=False):
    """Get data."""
    raw = read_raw_fif(raw_fname, preload=preload)
    events = read_events(event_name)
    picks = pick_types(raw.info, meg=True, eeg=True, stim=True,
                       ecg=True, eog=True, include=['STI 014'],
                       exclude='bads')
    return raw, events, picks


reject = dict(grad=1000e-12, mag=4e-12, eeg=80e-6, eog=150e-6)
flat = dict(grad=1e-15, mag=1e-15)


def test_hierarchical():
    """Test hierarchical access."""
    raw, events, picks = _get_data()
    event_id = {'a/1': 1, 'a/2': 2, 'b/1': 3, 'b/2': 4}
    epochs = Epochs(raw, events, event_id, preload=True)
    epochs_a1 = epochs['a/1']
    epochs_a2 = epochs['a/2']
    epochs_b1 = epochs['b/1']
    epochs_b2 = epochs['b/2']
    epochs_a = epochs['a']
    assert_equal(len(epochs_a), len(epochs_a1) + len(epochs_a2))
    epochs_b = epochs['b']
    assert_equal(len(epochs_b), len(epochs_b1) + len(epochs_b2))
    epochs_1 = epochs['1']
    assert_equal(len(epochs_1), len(epochs_a1) + len(epochs_b1))
    epochs_2 = epochs['2']
    assert_equal(len(epochs_2), len(epochs_a2) + len(epochs_b2))
    epochs_all = epochs[('1', '2')]
    assert_equal(len(epochs), len(epochs_all))
    assert_array_equal(epochs.get_data(), epochs_all.get_data())


@pytest.mark.slowtest
@testing.requires_testing_data
def test_average_movements():
    """Test movement averaging algorithm."""
    # usable data
    crop = 0., 10.
    origin = (0., 0., 0.04)
    raw = read_raw_fif(fname_raw_move, allow_maxshield='yes')
    raw.info['bads'] += ['MEG2443']  # mark some bad MEG channel
    raw.crop(*crop).load_data()
    raw.filter(None, 20, fir_design='firwin')
    events = make_fixed_length_events(raw, event_id)
    picks = pick_types(raw.info, meg=True, eeg=True, stim=True,
                       ecg=True, eog=True, exclude=())
    epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks, proj=False,
                    preload=True)
    epochs_proj = Epochs(raw, events[:1], event_id, tmin, tmax, picks=picks,
                         proj=True, preload=True)
    raw_sss_stat = maxwell_filter(raw, origin=origin, regularize=None,
                                  bad_condition='ignore')
    del raw
    epochs_sss_stat = Epochs(raw_sss_stat, events, event_id, tmin, tmax,
                             picks=picks, proj=False)
    evoked_sss_stat = epochs_sss_stat.average()
    del raw_sss_stat, epochs_sss_stat
    head_pos = read_head_pos(fname_raw_move_pos)
    trans = epochs.info['dev_head_t']['trans']
    head_pos_stat = (np.array([trans[:3, 3]]),
                     np.array([trans[:3, :3]]),
                     np.array([0.]))

    # SSS-based
    pytest.raises(TypeError, average_movements, epochs, None)
    evoked_move_non = average_movements(epochs, head_pos=head_pos,
                                        weight_all=False, origin=origin)
    evoked_move_all = average_movements(epochs, head_pos=head_pos,
                                        weight_all=True, origin=origin)
    evoked_stat_all = average_movements(epochs, head_pos=head_pos_stat,
                                        weight_all=True, origin=origin)
    evoked_std = epochs.average()
    for ev in (evoked_move_non, evoked_move_all, evoked_stat_all):
        assert_equal(ev.nave, evoked_std.nave)
        assert_equal(len(ev.info['bads']), 0)
    # substantial changes to MEG data
    for ev in (evoked_move_non, evoked_stat_all):
        assert_meg_snr(ev, evoked_std, 0., 0.1)
        pytest.raises(AssertionError, assert_meg_snr,
                      ev, evoked_std, 1., 1.)
    meg_picks = pick_types(evoked_std.info, meg=True, exclude=())
    assert_allclose(evoked_move_non.data[meg_picks],
                    evoked_move_all.data[meg_picks], atol=1e-20)
    # compare to averaged movecomp version (should be fairly similar)
    raw_sss = read_raw_fif(fname_raw_movecomp_sss)
    raw_sss.crop(*crop).load_data()
    raw_sss.filter(None, 20, fir_design='firwin')
    picks_sss = pick_types(raw_sss.info, meg=True, eeg=True, stim=True,
                           ecg=True, eog=True, exclude=())
    assert_array_equal(picks, picks_sss)
    epochs_sss = Epochs(raw_sss, events, event_id, tmin, tmax,
                        picks=picks_sss, proj=False)
    evoked_sss = epochs_sss.average()
    assert_equal(evoked_std.nave, evoked_sss.nave)
    # this should break the non-MEG channels
    pytest.raises(AssertionError, assert_meg_snr,
                  evoked_sss, evoked_move_all, 0., 0.)
    assert_meg_snr(evoked_sss, evoked_move_non, 0.02, 2.6)
    assert_meg_snr(evoked_sss, evoked_stat_all, 0.05, 3.2)
    # these should be close to numerical precision
    assert_allclose(evoked_sss_stat.data, evoked_stat_all.data, atol=1e-20)

    # pos[0] > epochs.events[0] uses dev_head_t, so make it equivalent
    destination = deepcopy(epochs.info['dev_head_t'])
    x = head_pos_to_trans_rot_t(head_pos[1])
    epochs.info['dev_head_t']['trans'][:3, :3] = x[1]
    epochs.info['dev_head_t']['trans'][:3, 3] = x[0]
    pytest.raises(AssertionError, assert_allclose,
                  epochs.info['dev_head_t']['trans'],
                  destination['trans'])
    evoked_miss = average_movements(epochs, head_pos=head_pos[2:],
                                    origin=origin, destination=destination)
    assert_allclose(evoked_miss.data, evoked_move_all.data,
                    atol=1e-20)
    assert_allclose(evoked_miss.info['dev_head_t']['trans'],
                    destination['trans'])

    # degenerate cases
    destination['to'] = destination['from']  # bad dest
    pytest.raises(RuntimeError, average_movements, epochs, head_pos,
                  origin=origin, destination=destination)
    pytest.raises(TypeError, average_movements, 'foo', head_pos=head_pos)
    pytest.raises(RuntimeError, average_movements, epochs_proj,
                  head_pos=head_pos)  # prj


def test_reject():
    """Test epochs rejection."""
    raw, events, picks = _get_data()
    # cull the list just to contain the relevant event
    events = events[events[:, 2] == event_id, :]
    selection = np.arange(3)
    drop_log = [[]] * 3 + [['MEG 2443']] * 4
    pytest.raises(TypeError, pick_types, raw)
    picks_meg = pick_types(raw.info, meg=True, eeg=False)
    pytest.raises(TypeError, Epochs, raw, events, event_id, tmin, tmax,
                  picks=picks, preload=False, reject='foo')
    pytest.raises(ValueError, Epochs, raw, events, event_id, tmin, tmax,
                  picks=picks_meg, preload=False, reject=dict(eeg=1.))
    # this one is okay because it's not actually requesting rejection
    Epochs(raw, events, event_id, tmin, tmax, picks=picks_meg,
           preload=False, reject=dict(eeg=np.inf))
    for val in (None, -1):  # protect against older MNE-C types
        for kwarg in ('reject', 'flat'):
            pytest.raises(ValueError, Epochs, raw, events, event_id,
                          tmin, tmax, picks=picks_meg, preload=False,
                          **{kwarg: dict(grad=val)})
    pytest.raises(KeyError, Epochs, raw, events, event_id, tmin, tmax,
                  picks=picks, preload=False, reject=dict(foo=1.))

    data_7 = dict()
    keep_idx = [0, 1, 2]
    for preload in (True, False):
        for proj in (True, False, 'delayed'):
            # no rejection
            epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                            preload=preload)
            pytest.raises(ValueError, epochs.drop_bad, reject='foo')
            epochs.drop_bad()
            assert_equal(len(epochs), len(events))
            assert_array_equal(epochs.selection, np.arange(len(events)))
            assert epochs.drop_log == [[]] * 7
            if proj not in data_7:
                data_7[proj] = epochs.get_data()
            assert_array_equal(epochs.get_data(), data_7[proj])

            # with rejection
            epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                            reject=reject, preload=preload)
            epochs.drop_bad()
            assert_equal(len(epochs), len(events) - 4)
            assert_array_equal(epochs.selection, selection)
            assert epochs.drop_log == drop_log
            assert_array_equal(epochs.get_data(), data_7[proj][keep_idx])

            # rejection post-hoc
            epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                            preload=preload)
            epochs.drop_bad()
            assert_equal(len(epochs), len(events))
            assert_array_equal(epochs.get_data(), data_7[proj])
            epochs.drop_bad(reject)
            assert_equal(len(epochs), len(events) - 4)
            assert_equal(len(epochs), len(epochs.get_data()))
            assert_array_equal(epochs.selection, selection)
            assert epochs.drop_log == drop_log
            assert_array_equal(epochs.get_data(), data_7[proj][keep_idx])

            # rejection twice
            reject_part = dict(grad=1100e-12, mag=4e-12, eeg=80e-6, eog=150e-6)
            epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                            reject=reject_part, preload=preload)
            epochs.drop_bad()
            assert_equal(len(epochs), len(events) - 1)
            epochs.drop_bad(reject)
            assert_equal(len(epochs), len(events) - 4)
            assert_array_equal(epochs.selection, selection)
            assert epochs.drop_log == drop_log
            assert_array_equal(epochs.get_data(), data_7[proj][keep_idx])

            # ensure that thresholds must become more stringent, not less
            pytest.raises(ValueError, epochs.drop_bad, reject_part)
            assert_equal(len(epochs), len(events) - 4)
            assert_array_equal(epochs.get_data(), data_7[proj][keep_idx])
            epochs.drop_bad(flat=dict(mag=1.))
            assert_equal(len(epochs), 0)
            pytest.raises(ValueError, epochs.drop_bad,
                          flat=dict(mag=0.))

            # rejection of subset of trials (ensure array ownership)
            reject_part = dict(grad=1100e-12, mag=4e-12, eeg=80e-6, eog=150e-6)
            epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                            reject=None, preload=preload)
            epochs = epochs[:-1]
            epochs.drop_bad(reject=reject)
            assert_equal(len(epochs), len(events) - 4)
            assert_array_equal(epochs.get_data(), data_7[proj][keep_idx])

        # rejection on annotations
        sfreq = raw.info['sfreq']
        onsets = [(event[0] - raw.first_samp) / sfreq for event in
                  events[::2][:3]]
        onsets[0] = onsets[0] + tmin - 0.499  # tmin < 0
        onsets[1] = onsets[1] + tmax - 0.001
        stamp = _dt_to_stamp(raw.info['meas_date'])
        first_time = (stamp[0] + stamp[1] * 1e-6 + raw.first_samp / sfreq)
        for orig_time in [None, first_time]:
            annot = Annotations(onsets, [0.5, 0.5, 0.5], 'BAD', orig_time)
            raw.set_annotations(annot)
            epochs = Epochs(raw, events, event_id, tmin, tmax, picks=[0],
                            reject=None, preload=preload)
            epochs.drop_bad()
            assert_equal(len(events) - 3, len(epochs.events))
            assert_equal(epochs.drop_log[0][0], 'BAD')
            assert_equal(epochs.drop_log[2][0], 'BAD')
            assert_equal(epochs.drop_log[4][0], 'BAD')
        raw.set_annotations(None)


def test_own_data():
    """Test for epochs data ownership (gh-5346)."""
    raw, events = _get_data()[:2]
    n_epochs = 10
    events = events[:n_epochs]
    epochs = mne.Epochs(raw, events, preload=True)
    assert epochs._data.flags['C_CONTIGUOUS']
    assert epochs._data.flags['OWNDATA']

    epochs.crop(tmin=-0.1, tmax=0.4)
    assert len(epochs) == epochs._data.shape[0] == len(epochs.events)
    assert len(epochs) == n_epochs
    assert not epochs._data.flags['OWNDATA']

    # data ownership value error
    epochs.drop_bad(flat=dict(eeg=8e-6))
    n_now = len(epochs)
    assert 5 < n_now < n_epochs
    assert len(epochs) == epochs._data.shape[0] == len(epochs.events)

    good_chan = epochs.copy().pick_channels([epochs.ch_names[0]])
    good_chan.rename_channels({good_chan.ch_names[0]: 'good'})
    epochs.add_channels([good_chan])
    # "ValueError: resize only works on single-segment arrays"
    epochs.drop_bad(flat=dict(eeg=10e-6))
    assert 1 < len(epochs) < n_now


def test_decim():
    """Test epochs decimation."""
    # First with EpochsArray
    dec_1, dec_2 = 2, 3
    decim = dec_1 * dec_2
    n_epochs, n_channels, n_times = 5, 10, 20
    sfreq = 1000.
    sfreq_new = sfreq / decim
    data = rng.randn(n_epochs, n_channels, n_times)
    events = np.array([np.arange(n_epochs), [0] * n_epochs, [1] * n_epochs]).T
    info = create_info(n_channels, sfreq, 'eeg')
    info['lowpass'] = sfreq_new / float(decim)
    epochs = EpochsArray(data, info, events)
    data_epochs = epochs.copy().decimate(decim).get_data()
    data_epochs_2 = epochs.copy().decimate(decim, offset=1).get_data()
    data_epochs_3 = epochs.decimate(dec_1).decimate(dec_2).get_data()
    assert_array_equal(data_epochs, data[:, :, ::decim])
    assert_array_equal(data_epochs_2, data[:, :, 1::decim])
    assert_array_equal(data_epochs, data_epochs_3)

    # Now let's do it with some real data
    raw, events, picks = _get_data()
    events = events[events[:, 2] == 1][:2]
    raw.load_data().pick_channels([raw.ch_names[pick] for pick in picks[::30]])
    raw.info.normalize_proj()
    del picks
    sfreq_new = raw.info['sfreq'] / decim
    raw.info['lowpass'] = sfreq_new / 12.  # suppress aliasing warnings
    pytest.raises(ValueError, epochs.decimate, -1)
    pytest.raises(ValueError, epochs.decimate, 2, offset=-1)
    pytest.raises(ValueError, epochs.decimate, 2, offset=2)
    for this_offset in range(decim):
        epochs = Epochs(raw, events, event_id,
                        tmin=-this_offset / raw.info['sfreq'], tmax=tmax,
                        baseline=None)
        idx_offsets = np.arange(decim) + this_offset
        for offset, idx_offset in zip(np.arange(decim), idx_offsets):
            expected_times = epochs.times[idx_offset::decim]
            expected_data = epochs.get_data()[:, :, idx_offset::decim]
            must_have = offset / float(epochs.info['sfreq'])
            assert (np.isclose(must_have, expected_times).any())
            ep_decim = epochs.copy().decimate(decim, offset)
            assert (np.isclose(must_have, ep_decim.times).any())
            assert_allclose(ep_decim.times, expected_times)
            assert_allclose(ep_decim.get_data(), expected_data)
            assert_equal(ep_decim.info['sfreq'], sfreq_new)

    # More complicated cases
    epochs = Epochs(raw, events, event_id, tmin, tmax)
    expected_data = epochs.get_data()[:, :, ::decim]
    expected_times = epochs.times[::decim]
    for preload in (True, False):
        # at init
        epochs = Epochs(raw, events, event_id, tmin, tmax, decim=decim,
                        preload=preload)
        assert_allclose(epochs.get_data(), expected_data)
        assert_allclose(epochs.get_data(), expected_data)
        assert_equal(epochs.info['sfreq'], sfreq_new)
        assert_array_equal(epochs.times, expected_times)

        # split between init and afterward
        epochs = Epochs(raw, events, event_id, tmin, tmax, decim=dec_1,
                        preload=preload).decimate(dec_2)
        assert_allclose(epochs.get_data(), expected_data)
        assert_allclose(epochs.get_data(), expected_data)
        assert_equal(epochs.info['sfreq'], sfreq_new)
        assert_array_equal(epochs.times, expected_times)
        epochs = Epochs(raw, events, event_id, tmin, tmax, decim=dec_2,
                        preload=preload).decimate(dec_1)
        assert_allclose(epochs.get_data(), expected_data)
        assert_allclose(epochs.get_data(), expected_data)
        assert_equal(epochs.info['sfreq'], sfreq_new)
        assert_array_equal(epochs.times, expected_times)

        # split between init and afterward, with preload in between
        epochs = Epochs(raw, events, event_id, tmin, tmax, decim=dec_1,
                        preload=preload)
        epochs.load_data()
        epochs = epochs.decimate(dec_2)
        assert_allclose(epochs.get_data(), expected_data)
        assert_allclose(epochs.get_data(), expected_data)
        assert_equal(epochs.info['sfreq'], sfreq_new)
        assert_array_equal(epochs.times, expected_times)
        epochs = Epochs(raw, events, event_id, tmin, tmax, decim=dec_2,
                        preload=preload)
        epochs.load_data()
        epochs = epochs.decimate(dec_1)
        assert_allclose(epochs.get_data(), expected_data)
        assert_allclose(epochs.get_data(), expected_data)
        assert_equal(epochs.info['sfreq'], sfreq_new)
        assert_array_equal(epochs.times, expected_times)

        # decimate afterward
        epochs = Epochs(raw, events, event_id, tmin, tmax,
                        preload=preload).decimate(decim)
        assert_allclose(epochs.get_data(), expected_data)
        assert_allclose(epochs.get_data(), expected_data)
        assert_equal(epochs.info['sfreq'], sfreq_new)
        assert_array_equal(epochs.times, expected_times)

        # decimate afterward, with preload in between
        epochs = Epochs(raw, events, event_id, tmin, tmax, preload=preload)
        epochs.load_data()
        epochs.decimate(decim)
        assert_allclose(epochs.get_data(), expected_data)
        assert_allclose(epochs.get_data(), expected_data)
        assert_equal(epochs.info['sfreq'], sfreq_new)
        assert_array_equal(epochs.times, expected_times)

        # test picks when getting data
        picks = [3, 4, 7]
        d1 = epochs.get_data(picks=picks)
        d2 = epochs.get_data()[:, picks]
        assert_array_equal(d1, d2)


def test_base_epochs():
    """Test base epochs class."""
    raw = _get_data()[0]
    epochs = BaseEpochs(raw.info, None, np.ones((1, 3), int),
                        event_id, tmin, tmax)
    pytest.raises(NotImplementedError, epochs.get_data)
    # events have wrong dtype (float)
    with pytest.raises(TypeError, match='events should be a NumPy array'):
        BaseEpochs(raw.info, None, np.ones((1, 3), float), event_id, tmin,
                   tmax)
    # events have wrong shape
    with pytest.raises(ValueError, match='events must be of shape'):
        BaseEpochs(raw.info, None, np.ones((1, 3, 2), int), event_id, tmin,
                   tmax)
    # events are tuple (like returned by mne.events_from_annotations)
    with pytest.raises(TypeError, match='events should be a NumPy array'):
        BaseEpochs(raw.info, None, (np.ones((1, 3), int), {'foo': 1}))


def test_savgol_filter():
    """Test savgol filtering."""
    h_freq = 20.
    raw, events = _get_data()[:2]
    epochs = Epochs(raw, events, event_id, tmin, tmax)
    pytest.raises(RuntimeError, epochs.savgol_filter, 10.)
    epochs = Epochs(raw, events, event_id, tmin, tmax, preload=True)
    epochs.pick_types(meg='grad')
    freqs = rfftfreq(len(epochs.times), 1. / epochs.info['sfreq'])
    data = np.abs(rfft(epochs.get_data()))
    pass_mask = (freqs <= h_freq / 2. - 5.)
    stop_mask = (freqs >= h_freq * 2 + 5.)
    epochs.savgol_filter(h_freq)
    data_filt = np.abs(rfft(epochs.get_data()))
    # decent in pass-band
    assert_allclose(np.mean(data[:, :, pass_mask], 0),
                    np.mean(data_filt[:, :, pass_mask], 0),
                    rtol=1e-2, atol=1e-18)
    # suppression in stop-band
    assert (np.mean(data[:, :, stop_mask]) >
            np.mean(data_filt[:, :, stop_mask]) * 5)


def test_filter(tmpdir):
    """Test filtering."""
    h_freq = 40.
    raw, events = _get_data()[:2]
    epochs = Epochs(raw, events, event_id, tmin, tmax)
    assert round(epochs.info['lowpass']) == 172
    pytest.raises(RuntimeError, epochs.savgol_filter, 10.)
    epochs = Epochs(raw, events, event_id, tmin, tmax, preload=True)
    epochs.pick_types(meg='grad')
    freqs = rfftfreq(len(epochs.times), 1. / epochs.info['sfreq'])
    data_fft = np.abs(rfft(epochs.get_data()))
    pass_mask = (freqs <= h_freq / 2. - 5.)
    stop_mask = (freqs >= h_freq * 2 + 5.)
    epochs_orig = epochs.copy()
    epochs.filter(None, h_freq)
    assert epochs.info['lowpass'] == h_freq
    data_filt = epochs.get_data()
    data_filt_fft = np.abs(rfft(data_filt))
    # decent in pass-band
    assert_allclose(np.mean(data_filt_fft[:, :, pass_mask], 0),
                    np.mean(data_fft[:, :, pass_mask], 0),
                    rtol=5e-2, atol=1e-16)
    # suppression in stop-band
    assert (np.mean(data_fft[:, :, stop_mask]) >
            np.mean(data_filt_fft[:, :, stop_mask]) * 10)

    # smoke test for filtering I/O data (gh-5614)
    temp_fname = op.join(str(tmpdir), 'test-epo.fif')
    epochs_orig.save(temp_fname, overwrite=True)
    epochs = mne.read_epochs(temp_fname)
    epochs.filter(None, h_freq)
    assert_allclose(epochs.get_data(), data_filt, atol=1e-17)


def test_epochs_hash():
    """Test epoch hashing."""
    raw, events = _get_data()[:2]
    epochs = Epochs(raw, events, event_id, tmin, tmax)
    pytest.raises(RuntimeError, epochs.__hash__)
    epochs = Epochs(raw, events, event_id, tmin, tmax, preload=True)
    assert_equal(hash(epochs), hash(epochs))
    epochs_2 = Epochs(raw, events, event_id, tmin, tmax, preload=True)
    assert_equal(hash(epochs), hash(epochs_2))
    # do NOT use assert_equal here, failing output is terrible
    assert (pickle.dumps(epochs) == pickle.dumps(epochs_2))

    epochs_2._data[0, 0, 0] -= 1
    assert hash(epochs) != hash(epochs_2)


def test_event_ordering():
    """Test event order."""
    raw, events = _get_data()[:2]
    events2 = events.copy()[::-1]
    Epochs(raw, events, event_id, tmin, tmax, reject=reject, flat=flat)
    with pytest.warns(RuntimeWarning, match='chronologically'):
        Epochs(raw, events2, event_id, tmin, tmax, reject=reject, flat=flat)
    # Duplicate events should be an error...
    events2 = events[[0, 0]]
    events2[:, 2] = [1, 2]
    pytest.raises(RuntimeError, Epochs, raw, events2, event_id=None)
    # But only if duplicates are actually used by event_id
    assert_equal(len(Epochs(raw, events2, event_id=dict(a=1), preload=True)),
                 1)


def test_events_type():
    """Test type of events."""
    raw, events = _get_data()[:2]
    events_id = {'A': 1, 'B': 2}
    events = (events, events_id)
    with pytest.raises(TypeError, match='events should be a NumPy array'):
        Epochs(raw, events, event_id, tmin, tmax)


def test_rescale():
    """Test rescale."""
    data = np.array([2, 3, 4, 5], float)
    times = np.array([0, 1, 2, 3], float)
    baseline = (0, 2)
    tester = partial(rescale, data=data, times=times, baseline=baseline)
    assert_allclose(tester(mode='mean'), [-1, 0, 1, 2])
    assert_allclose(tester(mode='ratio'), data / 3.)
    assert_allclose(tester(mode='logratio'), np.log10(data / 3.))
    assert_allclose(tester(mode='percent'), (data - 3) / 3.)
    assert_allclose(tester(mode='zscore'), (data - 3) / np.std([2, 3, 4]))
    x = data / 3.
    x = np.log10(x)
    s = np.std(x[:3])
    assert_allclose(tester(mode='zlogratio'), x / s)


@pytest.mark.parametrize('preload', (True, False))
def test_epochs_baseline(preload):
    """Test baseline and rescaling modes with and without preloading."""
    data = np.array([[2, 3], [2, 3]], float)
    info = create_info(2, 1000., ('eeg', 'misc'))
    raw = RawArray(data, info)
    events = np.array([[0, 0, 1]])

    epochs = mne.Epochs(raw, events, None, 0, 1e-3, baseline=None,
                        preload=preload)
    epochs.drop_bad()
    epochs_data = epochs.get_data()
    assert epochs_data.shape == (1, 2, 2)
    expected = data.copy()
    assert_array_equal(epochs_data[0], expected)
    # the baseline period (1 sample here)
    epochs.apply_baseline((0, 0))
    expected[0] = [0, 1]
    if preload:
        assert_allclose(epochs_data[0][0], expected[0])
    else:
        assert_allclose(epochs_data[0][0], expected[1])
    assert_allclose(epochs.get_data()[0], expected, atol=1e-7)
    # entire interval
    epochs.apply_baseline((None, None))
    expected[0] = [-0.5, 0.5]
    assert_allclose(epochs.get_data()[0], expected)


def test_epochs_bad_baseline():
    """Test Epochs initialization with bad baseline parameters."""
    raw, events = _get_data()[:2]
    pytest.raises(ValueError, Epochs, raw, events, None, -0.1, 0.3, (-0.2, 0))
    pytest.raises(ValueError, Epochs, raw, events, None, -0.1, 0.3, (0, 0.4))
    pytest.raises(ValueError, Epochs, raw, events, None, -0.1, 0.3, (0.1, 0))
    pytest.raises(ValueError, Epochs, raw, events, None, 0.1, 0.3, (None, 0))
    pytest.raises(ValueError, Epochs, raw, events, None, -0.3, -0.1, (0, None))
    epochs = Epochs(raw, events, None, 0.1, 0.3, baseline=None)
    epochs.load_data()
    pytest.raises(ValueError, epochs.apply_baseline, (None, 0))
    pytest.raises(ValueError, epochs.apply_baseline, (0, None))
    # put some rescale options here, too
    data = np.arange(100, dtype=float)
    pytest.raises(ValueError, rescale, data, times=data, baseline=(-2, -1))
    rescale(data.copy(), times=data, baseline=(2, 2))  # ok
    pytest.raises(ValueError, rescale, data, times=data, baseline=(2, 1))
    pytest.raises(ValueError, rescale, data, times=data, baseline=(100, 101))


def test_epoch_combine_ids():
    """Test combining event ids in epochs compared to events."""
    raw, events, picks = _get_data()
    epochs = Epochs(raw, events, {'a': 1, 'b': 2, 'c': 3,
                                  'd': 4, 'e': 5, 'f': 32},
                    tmin, tmax, picks=picks, preload=False)
    events_new = merge_events(events, [1, 2], 12)
    epochs_new = combine_event_ids(epochs, ['a', 'b'], {'ab': 12})
    assert_equal(epochs_new['ab']._name, 'ab')
    assert_array_equal(events_new, epochs_new.events)
    # should probably add test + functionality for non-replacement XXX


def test_epoch_multi_ids():
    """Test epoch selection via multiple/partial keys."""
    raw, events, picks = _get_data()
    epochs = Epochs(raw, events, {'a/b/a': 1, 'a/b/b': 2, 'a/c': 3,
                                  'b/d': 4, 'a_b': 5},
                    tmin, tmax, picks=picks, preload=False)
    epochs_regular = epochs['a/b']
    epochs_reverse = epochs['b/a']
    epochs_multi = epochs[['a/b/a', 'a/b/b']]
    assert_array_equal(epochs_multi.events, epochs_regular.events)
    assert_array_equal(epochs_reverse.events, epochs_regular.events)
    assert_allclose(epochs_multi.get_data(), epochs_regular.get_data())
    assert_allclose(epochs_reverse.get_data(), epochs_regular.get_data())


def test_read_epochs_bad_events():
    """Test epochs when events are at the beginning or the end of the file."""
    raw, events, picks = _get_data()
    # Event at the beginning
    epochs = Epochs(raw, np.array([[raw.first_samp, 0, event_id]]),
                    event_id, tmin, tmax, picks=picks)
    with pytest.warns(RuntimeWarning, match='empty'):
        evoked = epochs.average()

    epochs = Epochs(raw, np.array([[raw.first_samp, 0, event_id]]),
                    event_id, tmin, tmax, picks=picks)
    assert (repr(epochs))  # test repr
    epochs.drop_bad()
    assert (repr(epochs))
    with pytest.warns(RuntimeWarning, match='empty'):
        evoked = epochs.average()

    # Event at the end
    epochs = Epochs(raw, np.array([[raw.last_samp, 0, event_id]]),
                    event_id, tmin, tmax, picks=picks)

    with pytest.warns(RuntimeWarning, match='empty'):
        evoked = epochs.average()
    assert evoked


def test_io_epochs_basic(tmpdir):
    """Test epochs from raw files with IO as fif file."""
    raw, events, picks = _get_data(preload=True)
    baseline = (None, 0)
    epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                    baseline=baseline, preload=True)
    evoked = epochs.average()
    data = epochs.get_data()

    # Bad tmin/tmax parameters
    pytest.raises(ValueError, Epochs, raw, events, event_id, tmax, tmin,
                  baseline=None)

    epochs_no_id = Epochs(raw, pick_events(events, include=event_id),
                          None, tmin, tmax, picks=picks)
    assert_array_equal(data, epochs_no_id.get_data())

    eog_picks = pick_types(raw.info, meg=False, eeg=False, stim=False,
                           eog=True, exclude='bads')
    eog_ch_names = [raw.ch_names[k] for k in eog_picks]
    epochs.drop_channels(eog_ch_names)
    assert (len(epochs.info['chs']) == len(epochs.ch_names) ==
            epochs.get_data().shape[1])
    data_no_eog = epochs.get_data()
    assert (data.shape[1] == (data_no_eog.shape[1] + len(eog_picks)))

    # test decim kwarg
    with pytest.warns(RuntimeWarning, match='aliasing'):
        epochs_dec = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                            decim=2)

    # decim without lowpass
    epochs_dec.info['lowpass'] = None
    with pytest.warns(RuntimeWarning, match='aliasing'):
        epochs_dec.decimate(2)

    data_dec = epochs_dec.get_data()
    assert_allclose(data[:, :, epochs_dec._decim_slice], data_dec, rtol=1e-7,
                    atol=1e-12)

    evoked_dec = epochs_dec.average()
    assert_allclose(evoked.data[:, epochs_dec._decim_slice],
                    evoked_dec.data, rtol=1e-12, atol=1e-17)

    n = evoked.data.shape[1]
    n_dec = evoked_dec.data.shape[1]
    n_dec_min = n // 4
    assert (n_dec_min <= n_dec <= n_dec_min + 1)
    assert (evoked_dec.info['sfreq'] == evoked.info['sfreq'] / 4)


@pytest.mark.parametrize('proj', (True, 'delayed', False))
def test_epochs_io_proj(tmpdir, proj):
    """Test epochs I/O with projection."""
    # Test event access on non-preloaded data (#2345)

    # due to reapplication of the proj matrix, this is our quality limit
    # for some tests
    tols = dict(atol=1e-3, rtol=1e-20)

    raw, events, picks = _get_data()
    events[::2, 1] = 1
    events[1::2, 2] = 2
    event_ids = dict(a=1, b=2)
    temp_fname = op.join(str(tmpdir), 'test-epo.fif')

    epochs = Epochs(raw, events, event_ids, tmin, tmax, picks=picks,
                    proj=proj, reject=reject, flat=dict(),
                    reject_tmin=tmin + 0.01, reject_tmax=tmax - 0.01)
    assert_equal(epochs.proj, proj if proj != 'delayed' else False)
    data1 = epochs.get_data()
    epochs2 = epochs.copy().apply_proj()
    assert_equal(epochs2.proj, True)
    data2 = epochs2.get_data()
    assert_allclose(data1, data2, **tols)
    epochs.save(temp_fname, overwrite=True)
    epochs_read = read_epochs(temp_fname, preload=False)
    assert_allclose(epochs.get_data(), epochs_read.get_data(), **tols)
    assert_allclose(epochs['a'].get_data(),
                    epochs_read['a'].get_data(), **tols)
    assert_allclose(epochs['b'].get_data(),
                    epochs_read['b'].get_data(), **tols)
    assert epochs.reject is not None
    assert object_diff(epochs.reject, reject) == ''
    assert epochs.flat is None  # empty dict is functionally the same
    assert epochs.reject_tmin == tmin + 0.01
    assert epochs.reject_tmax == tmax - 0.01

    # ensure we don't leak file descriptors
    epochs_read = read_epochs(temp_fname, preload=False)
    epochs_copy = epochs_read.copy()
    del epochs_read
    epochs_copy.get_data()
    del epochs_copy


@pytest.mark.parametrize('preload', (False, True))
def test_epochs_io_preload(tmpdir, preload):
    """Test epochs I/O with preloading."""
    # due to reapplication of the proj matrix, this is our quality limit
    # for some tests
    tols = dict(atol=1e-3, rtol=1e-20)

    raw, events, picks = _get_data(preload=True)
    tempdir = str(tmpdir)
    temp_fname = op.join(tempdir, 'test-epo.fif')
    temp_fname_no_bl = op.join(tempdir, 'test_no_bl-epo.fif')
    baseline = (None, 0)
    epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                    baseline=baseline, preload=True)
    evoked = epochs.average()
    epochs.save(temp_fname, overwrite=True)

    epochs_no_bl = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                          baseline=None, preload=True)
    assert (epochs_no_bl.baseline is None)
    epochs_no_bl.save(temp_fname_no_bl, overwrite=True)

    epochs_read = read_epochs(temp_fname, preload=preload)
    epochs_no_bl.save(temp_fname_no_bl, overwrite=True)
    epochs_read = read_epochs(temp_fname)
    epochs_no_bl_read = read_epochs(temp_fname_no_bl)
    pytest.raises(ValueError, epochs.apply_baseline, baseline=[1, 2, 3])
    epochs_with_bl = epochs_no_bl_read.copy().apply_baseline(baseline)
    assert (isinstance(epochs_with_bl, BaseEpochs))
    assert (epochs_with_bl.baseline == baseline)
    assert (epochs_no_bl_read.baseline != baseline)
    assert (str(epochs_read).startswith('<Epochs'))

    epochs_no_bl_read.apply_baseline(baseline)
    assert_array_equal(epochs_no_bl_read.times, epochs.times)
    assert_array_almost_equal(epochs_read.get_data(), epochs.get_data())
    assert_array_almost_equal(epochs.get_data(),
                              epochs_no_bl_read.get_data())
    assert_array_equal(epochs_read.times, epochs.times)
    assert_array_almost_equal(epochs_read.average().data, evoked.data)
    assert_equal(epochs_read.proj, epochs.proj)
    bmin, bmax = epochs.baseline
    if bmin is None:
        bmin = epochs.times[0]
    if bmax is None:
        bmax = epochs.times[-1]
    baseline = (bmin, bmax)
    assert_array_almost_equal(epochs_read.baseline, baseline)
    assert_array_almost_equal(epochs_read.tmin, epochs.tmin, 2)
    assert_array_almost_equal(epochs_read.tmax, epochs.tmax, 2)
    assert_equal(epochs_read.event_id, epochs.event_id)

    epochs.event_id.pop('1')
    epochs.event_id.update({'a:a': 1})  # test allow for ':' in key
    fname_temp = op.join(tempdir, 'foo-epo.fif')
    epochs.save(fname_temp, overwrite=True)
    epochs_read = read_epochs(fname_temp, preload=preload)
    assert_equal(epochs_read.event_id, epochs.event_id)
    assert_equal(epochs_read['a:a'].average().comment, 'a:a')

    # now use a baseline, crop it out, and I/O round trip afterward
    assert epochs.times[0] < 0
    assert epochs.times[-1] > 0
    epochs.apply_baseline((None, 0))
    with pytest.warns(RuntimeWarning,
                      match=r'setting epochs\.baseline = None'):
        epochs.crop(1. / epochs.info['sfreq'], None)
    assert epochs.baseline is None
    epochs.save(fname_temp, overwrite=True)
    epochs_read = read_epochs(fname_temp, preload=preload)
    assert epochs_read.baseline is None
    assert_allclose(epochs.get_data(), epochs_read.get_data(),
                    rtol=6e-4)  # XXX this rtol should be better...?
    del epochs, epochs_read

    # add reject here so some of the epochs get dropped
    epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                    reject=reject)
    epochs.save(temp_fname, overwrite=True)
    # ensure bad events are not saved
    epochs_read3 = read_epochs(temp_fname, preload=preload)
    assert_array_equal(epochs_read3.events, epochs.events)
    data = epochs.get_data()
    assert (epochs_read3.events.shape[0] == data.shape[0])

    # test copying loaded one (raw property)
    epochs_read4 = epochs_read3.copy()
    assert_array_almost_equal(epochs_read4.get_data(), data)
    # test equalizing loaded one (drop_log property)
    epochs_read4.equalize_event_counts(epochs.event_id)

    epochs.drop([1, 2], reason='can we recover orig ID?')
    epochs.save(temp_fname, overwrite=True)
    epochs_read5 = read_epochs(temp_fname, preload=preload)
    assert_array_equal(epochs_read5.selection, epochs.selection)
    assert_equal(len(epochs_read5.selection), len(epochs_read5.events))
    assert epochs_read5.drop_log == epochs.drop_log

    if preload:
        # Test that one can drop channels on read file
        epochs_read5.drop_channels(epochs_read5.ch_names[:1])

    # test warnings on bad filenames
    epochs_badname = op.join(tempdir, 'test-bad-name.fif.gz')
    with pytest.warns(RuntimeWarning, match='-epo.fif'):
        epochs.save(epochs_badname, overwrite=True)
    with pytest.warns(RuntimeWarning, match='-epo.fif'):
        read_epochs(epochs_badname, preload=preload)

    # test loading epochs with missing events
    epochs = Epochs(raw, events, dict(foo=1, bar=999), tmin, tmax,
                    picks=picks, on_missing='ignore')
    epochs.save(temp_fname, overwrite=True)
    split_fname_1 = temp_fname[:-4] + '-1.fif'
    split_fname_2 = temp_fname[:-4] + '-2.fif'
    assert op.isfile(temp_fname)
    assert not op.isfile(split_fname_1)
    epochs_read = read_epochs(temp_fname, preload=preload)
    assert_allclose(epochs.get_data(), epochs_read.get_data(), **tols)
    assert_array_equal(epochs.events, epochs_read.events)
    assert_equal(set(epochs.event_id.keys()),
                 {str(x) for x in epochs_read.event_id.keys()})

    # test saving split epoch files
    split_size = '7MB'
    # ensure that we're in a position where just the data itself could fit
    # if that were all that we saved ...
    split_size_bytes = _get_split_size(split_size)
    assert epochs.get_data().nbytes // 2 < split_size_bytes
    epochs.save(temp_fname, split_size=split_size, overwrite=True)
    # ... but we correctly account for the other stuff we need to write,
    # so end up with two files ...
    assert op.isfile(temp_fname)
    assert op.isfile(split_fname_1)
    assert not op.isfile(split_fname_2)
    epochs_read = read_epochs(temp_fname, preload=preload)
    # ... and none of the files exceed our limit.
    for fname in (temp_fname, split_fname_1):
        with open(fname, 'r') as fid:
            fid.seek(0, 2)
            fsize = fid.tell()
        assert fsize <= split_size_bytes
    assert_allclose(epochs.get_data(), epochs_read.get_data(), **tols)
    assert_array_equal(epochs.events, epochs_read.events)
    assert_array_equal(epochs.selection, epochs_read.selection)
    assert epochs.drop_log == epochs_read.drop_log

    # Test that having a single time point works
    assert epochs.baseline is not None
    with pytest.warns(RuntimeWarning, match=r'setting epochs\.baseline'):
        epochs.load_data().crop(0, 0)
    assert epochs.baseline is None
    assert_equal(len(epochs.times), 1)
    assert_equal(epochs.get_data().shape[-1], 1)
    epochs.save(temp_fname, overwrite=True)
    epochs_read = read_epochs(temp_fname, preload=preload)
    assert_equal(len(epochs_read.times), 1)
    assert_equal(epochs.get_data().shape[-1], 1)


def test_split_saving(tmpdir):
    """Test saving split epochs."""
    # See gh-5102
    tempdir = str(tmpdir)
    raw = mne.io.RawArray(np.random.RandomState(0).randn(100, 10000),
                          mne.create_info(100, 1000.))
    events = mne.make_fixed_length_events(raw, 1)
    epochs = mne.Epochs(raw, events)
    epochs_data = epochs.get_data()
    assert len(epochs) == 9
    fname = op.join(tempdir, 'test-epo.fif')
    epochs.save(fname, split_size='1MB', overwrite=True)
    size = _get_split_size('1MB')
    assert size == 1048576 == 1024 * 1024
    written_fnames = [fname] + [
        fname[:-4] + '-%d.fif' % ii for ii in range(1, 4)]
    for this_fname in written_fnames:
        assert op.isfile(this_fname)
        with open(this_fname, 'r') as fid:
            fid.seek(0, 2)
            file_size = fid.tell()
        assert size * 0.5 < file_size <= size
    assert not op.isfile(fname[:-4] + '-4.fif')
    for preload in (True, False):
        epochs2 = mne.read_epochs(fname, preload=preload)
        assert_allclose(epochs2.get_data(), epochs_data)
        assert_array_equal(epochs.events, epochs2.events)


def test_epochs_proj(tmpdir):
    """Test handling projection (apply proj in Raw or in Epochs)."""
    tempdir = str(tmpdir)
    raw, events, picks = _get_data()
    exclude = raw.info['bads'] + ['MEG 2443', 'EEG 053']  # bads + 2 more
    this_picks = pick_types(raw.info, meg=True, eeg=False, stim=True,
                            eog=True, exclude=exclude)
    epochs = Epochs(raw, events[:4], event_id, tmin, tmax, picks=this_picks,
                    proj=True)
    assert (all(p['active'] is True for p in epochs.info['projs']))
    evoked = epochs.average()
    assert (all(p['active'] is True for p in evoked.info['projs']))
    data = epochs.get_data()

    raw_proj = read_raw_fif(raw_fname).apply_proj()
    epochs_no_proj = Epochs(raw_proj, events[:4], event_id, tmin, tmax,
                            picks=this_picks, proj=False)

    data_no_proj = epochs_no_proj.get_data()
    assert (all(p['active'] is True for p in epochs_no_proj.info['projs']))
    evoked_no_proj = epochs_no_proj.average()
    assert (all(p['active'] is True for p in evoked_no_proj.info['projs']))
    assert (epochs_no_proj.proj is True)  # as projs are active from Raw

    assert_array_almost_equal(data, data_no_proj, decimal=8)

    # make sure we can exclude avg ref
    this_picks = pick_types(raw.info, meg=True, eeg=True, stim=True,
                            eog=True, exclude=exclude)
    epochs = Epochs(raw, events[:4], event_id, tmin, tmax, picks=this_picks,
                    proj=True)
    epochs.set_eeg_reference(projection=True).apply_proj()
    assert (_has_eeg_average_ref_proj(epochs.info['projs']))
    epochs = Epochs(raw, events[:4], event_id, tmin, tmax, picks=this_picks,
                    proj=True)
    assert (not _has_eeg_average_ref_proj(epochs.info['projs']))

    # make sure we don't add avg ref when a custom ref has been applied
    raw.info['custom_ref_applied'] = True
    epochs = Epochs(raw, events[:4], event_id, tmin, tmax, picks=this_picks,
                    proj=True)
    assert (not _has_eeg_average_ref_proj(epochs.info['projs']))

    # From GH#2200:
    # This has no problem
    proj = raw.info['projs']
    epochs = Epochs(raw, events[:4], event_id, tmin, tmax, picks=this_picks,
                    proj=False)
    epochs.info['projs'] = []
    data = epochs.copy().add_proj(proj).apply_proj().get_data()
    # save and reload data
    fname_epo = op.join(tempdir, 'temp-epo.fif')
    epochs.save(fname_epo, overwrite=True)  # Save without proj added
    epochs_read = read_epochs(fname_epo)
    epochs_read.add_proj(proj)
    epochs_read.apply_proj()  # This used to bomb
    data_2 = epochs_read.get_data()  # Let's check the result
    assert_allclose(data, data_2, atol=1e-15, rtol=1e-3)

    # adding EEG ref (GH #2727)
    raw = read_raw_fif(raw_fname)
    raw.add_proj([], remove_existing=True)
    raw.info['bads'] = ['MEG 2443', 'EEG 053']
    picks = pick_types(raw.info, meg=False, eeg=True, stim=True, eog=False,
                       exclude='bads')
    epochs = Epochs(raw, events, event_id, tmin, tmax, proj=True, picks=picks,
                    preload=True)
    epochs.pick_channels(['EEG 001', 'EEG 002'])
    assert_equal(len(epochs), 7)  # sufficient for testing
    temp_fname = op.join(tempdir, 'test-epo.fif')
    epochs.save(temp_fname, overwrite=True)
    for preload in (True, False):
        epochs = read_epochs(temp_fname, proj=False, preload=preload)
        epochs.set_eeg_reference(projection=True).apply_proj()
        assert_allclose(epochs.get_data().mean(axis=1), 0, atol=1e-15)
        epochs = read_epochs(temp_fname, proj=False, preload=preload)
        epochs.set_eeg_reference(projection=True)
        pytest.raises(AssertionError, assert_allclose,
                      epochs.get_data().mean(axis=1), 0., atol=1e-15)
        epochs.apply_proj()
        assert_allclose(epochs.get_data().mean(axis=1), 0, atol=1e-15)


def test_evoked_arithmetic():
    """Test arithmetic of evoked data."""
    raw, events, picks = _get_data()
    epochs1 = Epochs(raw, events[:4], event_id, tmin, tmax, picks=picks)
    evoked1 = epochs1.average()
    epochs2 = Epochs(raw, events[4:8], event_id, tmin, tmax, picks=picks)
    evoked2 = epochs2.average()
    epochs = Epochs(raw, events[:8], event_id, tmin, tmax, picks=picks)
    evoked = epochs.average()
    evoked_sum = combine_evoked([evoked1, evoked2], weights='nave')
    assert_array_equal(evoked.data, evoked_sum.data)
    assert_array_equal(evoked.times, evoked_sum.times)
    assert_equal(evoked_sum.nave, evoked1.nave + evoked2.nave)
    evoked_diff = combine_evoked([evoked1, evoked1], weights=[1, -1])
    assert_array_equal(np.zeros_like(evoked.data), evoked_diff.data)


def test_evoked_io_from_epochs(tmpdir):
    """Test IO of evoked data made from epochs."""
    tempdir = str(tmpdir)
    raw, events, picks = _get_data()
    raw.info['lowpass'] = 40  # avoid aliasing warnings
    # offset our tmin so we don't get exactly a zero value when decimating
    epochs = Epochs(raw, events[:4], event_id, tmin + 0.011, tmax,
                    picks=picks, decim=5)
    evoked = epochs.average()
    evoked.info['proj_name'] = ''  # Test that empty string shortcuts to None.
    evoked.save(op.join(tempdir, 'evoked-ave.fif'))
    evoked2 = read_evokeds(op.join(tempdir, 'evoked-ave.fif'))[0]
    assert_equal(evoked2.info['proj_name'], None)
    assert_allclose(evoked.data, evoked2.data, rtol=1e-4, atol=1e-20)
    assert_allclose(evoked.times, evoked2.times, rtol=1e-4,
                    atol=1 / evoked.info['sfreq'])

    # now let's do one with negative time
    epochs = Epochs(raw, events[:4], event_id, 0.1, tmax,
                    picks=picks, baseline=(0.1, 0.2), decim=5)
    evoked = epochs.average()
    evoked.save(op.join(tempdir, 'evoked-ave.fif'))
    evoked2 = read_evokeds(op.join(tempdir, 'evoked-ave.fif'))[0]
    assert_allclose(evoked.data, evoked2.data, rtol=1e-4, atol=1e-20)
    assert_allclose(evoked.times, evoked2.times, rtol=1e-4, atol=1e-20)

    # should be equivalent to a cropped original
    epochs = Epochs(raw, events[:4], event_id, -0.2, tmax,
                    picks=picks, baseline=(0.1, 0.2), decim=5)
    evoked = epochs.average()
    evoked.crop(0.099, None)
    assert_allclose(evoked.data, evoked2.data, rtol=1e-4, atol=1e-20)
    assert_allclose(evoked.times, evoked2.times, rtol=1e-4, atol=1e-20)


def test_evoked_standard_error(tmpdir):
    """Test calculation and read/write of standard error."""
    raw, events, picks = _get_data()
    tempdir = str(tmpdir)
    epochs = Epochs(raw, events[:4], event_id, tmin, tmax, picks=picks)
    evoked = [epochs.average(), epochs.standard_error()]
    write_evokeds(op.join(tempdir, 'evoked-ave.fif'), evoked)
    evoked2 = read_evokeds(op.join(tempdir, 'evoked-ave.fif'), [0, 1])
    evoked3 = [read_evokeds(op.join(tempdir, 'evoked-ave.fif'), '1'),
               read_evokeds(op.join(tempdir, 'evoked-ave.fif'), '1',
                            kind='standard_error')]
    for evoked_new in [evoked2, evoked3]:
        assert (evoked_new[0]._aspect_kind ==
                FIFF.FIFFV_ASPECT_AVERAGE)
        assert (evoked_new[0].kind == 'average')
        assert (evoked_new[1]._aspect_kind ==
                FIFF.FIFFV_ASPECT_STD_ERR)
        assert (evoked_new[1].kind == 'standard_error')
        for ave, ave2 in zip(evoked, evoked_new):
            assert_array_almost_equal(ave.data, ave2.data)
            assert_array_almost_equal(ave.times, ave2.times)
            assert ave.nave == ave2.nave
            assert ave._aspect_kind == ave2._aspect_kind
            assert ave.kind == ave2.kind
            assert ave.last == ave2.last
            assert ave.first == ave2.first


def test_reject_epochs():
    """Test of epochs rejection."""
    raw, events, picks = _get_data()
    events1 = events[events[:, 2] == event_id]
    epochs = Epochs(raw, events1, event_id, tmin, tmax,
                    reject=reject, flat=flat)
    pytest.raises(RuntimeError, len, epochs)
    n_events = len(epochs.events)
    data = epochs.get_data()
    n_clean_epochs = len(data)
    # Should match
    # mne_process_raw --raw test_raw.fif --projoff \
    #   --saveavetag -ave --ave test.ave --filteroff
    assert (n_events > n_clean_epochs)
    assert (n_clean_epochs == 3)
    assert (epochs.drop_log == [[], [], [], ['MEG 2443'], ['MEG 2443'],
                                ['MEG 2443'], ['MEG 2443']])

    # Ensure epochs are not dropped based on a bad channel
    raw_2 = raw.copy()
    raw_2.info['bads'] = ['MEG 2443']
    reject_crazy = dict(grad=1000e-15, mag=4e-15, eeg=80e-9, eog=150e-9)
    epochs = Epochs(raw_2, events1, event_id, tmin, tmax,
                    reject=reject_crazy, flat=flat)
    epochs.drop_bad()

    assert (all('MEG 2442' in e for e in epochs.drop_log))
    assert (all('MEG 2443' not in e for e in epochs.drop_log))

    # Invalid reject_tmin/reject_tmax/detrend
    pytest.raises(ValueError, Epochs, raw, events1, event_id, tmin, tmax,
                  reject_tmin=1., reject_tmax=0)
    pytest.raises(ValueError, Epochs, raw, events1, event_id, tmin, tmax,
                  reject_tmin=tmin - 1, reject_tmax=1.)
    pytest.raises(ValueError, Epochs, raw, events1, event_id, tmin, tmax,
                  reject_tmin=0., reject_tmax=tmax + 1)

    epochs = Epochs(raw, events1, event_id, tmin, tmax, picks=picks,
                    reject=reject, flat=flat, reject_tmin=0., reject_tmax=.1)
    data = epochs.get_data()
    n_clean_epochs = len(data)
    assert (n_clean_epochs == 7)
    assert (len(epochs) == 7)
    assert (epochs.times[epochs._reject_time][0] >= 0.)
    assert (epochs.times[epochs._reject_time][-1] <= 0.1)

    # Invalid data for _is_good_epoch function
    epochs = Epochs(raw, events1, event_id, tmin, tmax)
    assert_equal(epochs._is_good_epoch(None), (False, ['NO_DATA']))
    assert_equal(epochs._is_good_epoch(np.zeros((1, 1))),
                 (False, ['TOO_SHORT']))
    data = epochs[0].get_data()[0]
    assert_equal(epochs._is_good_epoch(data), (True, None))


def test_preload_epochs():
    """Test preload of epochs."""
    raw, events, picks = _get_data()
    epochs_preload = Epochs(raw, events[:16], event_id, tmin, tmax,
                            picks=picks, preload=True,
                            reject=reject, flat=flat)
    data_preload = epochs_preload.get_data()

    epochs = Epochs(raw, events[:16], event_id, tmin, tmax, picks=picks,
                    preload=False, reject=reject, flat=flat)
    data = epochs.get_data()
    assert_array_equal(data_preload, data)
    assert_array_almost_equal(epochs_preload.average().data,
                              epochs.average().data, 18)


def test_indexing_slicing():
    """Test of indexing and slicing operations."""
    raw, events, picks = _get_data()
    epochs = Epochs(raw, events[:20], event_id, tmin, tmax, picks=picks,
                    reject=reject, flat=flat)

    data_normal = epochs.get_data()

    n_good_events = data_normal.shape[0]

    # indices for slicing
    start_index = 1
    end_index = n_good_events - 1

    assert((end_index - start_index) > 0)

    for preload in [True, False]:
        epochs2 = Epochs(raw, events[:20], event_id, tmin, tmax, picks=picks,
                         preload=preload, reject=reject, flat=flat)

        if not preload:
            epochs2.drop_bad()

        # using slicing
        epochs2_sliced = epochs2[start_index:end_index]

        data_epochs2_sliced = epochs2_sliced.get_data()
        assert_array_equal(data_epochs2_sliced,
                           data_normal[start_index:end_index])

        # using indexing
        pos = 0
        for idx in range(start_index, end_index):
            data = epochs2_sliced[pos].get_data()
            assert_array_equal(data[0], data_normal[idx])
            pos += 1

        # using indexing with an int
        data = epochs2[data_epochs2_sliced.shape[0]].get_data()
        assert_array_equal(data, data_normal[[idx]])

        # using indexing with an array
        idx = rng.randint(0, data_epochs2_sliced.shape[0], 10)
        data = epochs2[idx].get_data()
        assert_array_equal(data, data_normal[idx])

        # using indexing with a list of indices
        idx = [0]
        data = epochs2[idx].get_data()
        assert_array_equal(data, data_normal[idx])
        idx = [0, 1]
        data = epochs2[idx].get_data()
        assert_array_equal(data, data_normal[idx])


def test_comparision_with_c():
    """Test of average obtained vs C code."""
    raw, events = _get_data()[:2]
    c_evoked = read_evokeds(evoked_nf_name, condition=0)
    epochs = Epochs(raw, events, event_id, tmin, tmax, baseline=None,
                    preload=True, proj=False)
    evoked = epochs.set_eeg_reference(projection=True).apply_proj().average()
    sel = pick_channels(c_evoked.ch_names, evoked.ch_names)
    evoked_data = evoked.data
    c_evoked_data = c_evoked.data[sel]

    assert (evoked.nave == c_evoked.nave)
    assert_array_almost_equal(evoked_data, c_evoked_data, 10)
    assert_array_almost_equal(evoked.times, c_evoked.times, 12)


def test_crop():
    """Test of crop of epochs."""
    raw, events, picks = _get_data()
    epochs = Epochs(raw, events[:5], event_id, tmin, tmax, picks=picks,
                    preload=False, reject=reject, flat=flat)
    pytest.raises(RuntimeError, epochs.crop, None, 0.2)  # not preloaded
    data_normal = epochs.get_data()

    epochs2 = Epochs(raw, events[:5], event_id, tmin, tmax,
                     picks=picks, preload=True, reject=reject, flat=flat)
    with pytest.warns(RuntimeWarning, match='tmax is set to'):
        epochs2.crop(-20, 200)

    # indices for slicing
    tmin_window = tmin + 0.1
    tmax_window = tmax - 0.1
    tmask = (epochs.times >= tmin_window) & (epochs.times <= tmax_window)
    assert (tmin_window > tmin)
    assert (tmax_window < tmax)
    epochs3 = epochs2.copy().crop(tmin_window, tmax_window)
    data3 = epochs3.get_data()
    epochs2.crop(tmin_window, tmax_window)
    data2 = epochs2.get_data()
    assert_array_equal(data2, data_normal[:, :, tmask])
    assert_array_equal(data3, data_normal[:, :, tmask])
    assert_array_equal(epochs.time_as_index([tmin, tmax], use_rounding=True),
                       [0, len(epochs.times) - 1])
    assert_array_equal(epochs3.time_as_index([tmin_window, tmax_window],
                                             use_rounding=True),
                       [0, len(epochs3.times) - 1])

    # test time info is correct
    epochs = EpochsArray(np.zeros((1, 1, 1000)), create_info(1, 1000., 'eeg'),
                         np.ones((1, 3), int), tmin=-0.2)
    epochs.crop(-.200, .700)
    last_time = epochs.times[-1]
    with pytest.warns(RuntimeWarning, match='aliasing'):
        epochs.decimate(10)
    assert_allclose(last_time, epochs.times[-1])
    want_time = epochs.times[-1] - 1. / epochs.info['sfreq']
    epochs.crop(None, epochs.times[-1], include_tmax=False)
    assert_allclose(epochs.times[-1], want_time)

    epochs = Epochs(raw, events[:5], event_id, -1, 1,
                    picks=picks, preload=True, reject=reject, flat=flat)
    # We include nearest sample, so actually a bit beyond our bounds here
    assert_allclose(epochs.tmin, -1.0006410259015925, rtol=1e-12)
    assert_allclose(epochs.tmax, 1.0006410259015925, rtol=1e-12)
    epochs_crop = epochs.copy().crop(-1, 1)
    assert_allclose(epochs.times, epochs_crop.times, rtol=1e-12)
    # Ensure we don't allow silly crops
    with pytest.warns(RuntimeWarning, match='is set to'):
        pytest.raises(ValueError, epochs.crop, 1000, 2000)
        pytest.raises(ValueError, epochs.crop, 0.1, 0)


def test_resample():
    """Test of resample of epochs."""
    raw, events, picks = _get_data()
    epochs = Epochs(raw, events[:10], event_id, tmin, tmax, picks=picks,
                    preload=False, reject=reject, flat=flat)
    pytest.raises(RuntimeError, epochs.resample, 100)

    epochs_o = Epochs(raw, events[:10], event_id, tmin, tmax, picks=picks,
                      preload=True, reject=reject, flat=flat)
    epochs = epochs_o.copy()

    data_normal = deepcopy(epochs.get_data())
    times_normal = deepcopy(epochs.times)
    sfreq_normal = epochs.info['sfreq']
    # upsample by 2
    epochs = epochs_o.copy()
    epochs.resample(sfreq_normal * 2, npad=0)
    data_up = deepcopy(epochs.get_data())
    times_up = deepcopy(epochs.times)
    sfreq_up = epochs.info['sfreq']
    # downsamply by 2, which should match
    epochs.resample(sfreq_normal, npad=0)
    data_new = deepcopy(epochs.get_data())
    times_new = deepcopy(epochs.times)
    sfreq_new = epochs.info['sfreq']
    assert (data_up.shape[2] == 2 * data_normal.shape[2])
    assert (sfreq_up == 2 * sfreq_normal)
    assert (sfreq_new == sfreq_normal)
    assert (len(times_up) == 2 * len(times_normal))
    assert_array_almost_equal(times_new, times_normal, 10)
    assert (data_up.shape[2] == 2 * data_normal.shape[2])
    assert_array_almost_equal(data_new, data_normal, 5)

    # use parallel
    epochs = epochs_o.copy()
    epochs.resample(sfreq_normal * 2, n_jobs=1, npad=0)
    assert (np.allclose(data_up, epochs._data, rtol=1e-8, atol=1e-16))

    # test copy flag
    epochs = epochs_o.copy()
    epochs_resampled = epochs.copy().resample(sfreq_normal * 2, npad=0)
    assert (epochs_resampled is not epochs)
    epochs_resampled = epochs.resample(sfreq_normal * 2, npad=0)
    assert (epochs_resampled is epochs)

    # test proper setting of times (#2645)
    n_trial, n_chan, n_time, sfreq = 1, 1, 10, 1000.
    data = np.zeros((n_trial, n_chan, n_time))
    events = np.zeros((n_trial, 3), int)
    info = create_info(n_chan, sfreq, 'eeg')
    epochs1 = EpochsArray(data, deepcopy(info), events)
    epochs2 = EpochsArray(data, deepcopy(info), events)
    epochs = concatenate_epochs([epochs1, epochs2])
    epochs1.resample(epochs1.info['sfreq'] // 2, npad='auto')
    epochs2.resample(epochs2.info['sfreq'] // 2, npad='auto')
    epochs = concatenate_epochs([epochs1, epochs2])
    for e in epochs1, epochs2, epochs:
        assert_equal(e.times[0], epochs.tmin)
        assert_equal(e.times[-1], epochs.tmax)
    # test that cropping after resampling works (#3296)
    this_tmin = -0.002
    epochs = EpochsArray(data, deepcopy(info), events, tmin=this_tmin)
    for times in (epochs.times, epochs._raw_times):
        assert_allclose(times, np.arange(n_time) / sfreq + this_tmin)
    epochs.resample(info['sfreq'] * 2.)
    for times in (epochs.times, epochs._raw_times):
        assert_allclose(times, np.arange(2 * n_time) / (sfreq * 2) + this_tmin)
    epochs.crop(0, None)
    for times in (epochs.times, epochs._raw_times):
        assert_allclose(times, np.arange((n_time - 2) * 2) / (sfreq * 2))
    epochs.resample(sfreq)
    for times in (epochs.times, epochs._raw_times):
        assert_allclose(times, np.arange(n_time - 2) / sfreq)


def test_detrend():
    """Test detrending of epochs."""
    raw, events, picks = _get_data()

    # test first-order
    epochs_1 = Epochs(raw, events[:4], event_id, tmin, tmax, picks=picks,
                      baseline=None, detrend=1)
    epochs_2 = Epochs(raw, events[:4], event_id, tmin, tmax, picks=picks,
                      baseline=None, detrend=None)
    data_picks = pick_types(epochs_1.info, meg=True, eeg=True,
                            exclude='bads')
    evoked_1 = epochs_1.average()
    evoked_2 = epochs_2.average()
    evoked_2.detrend(1)
    # Due to roundoff these won't be exactly equal, but they should be close
    assert_allclose(evoked_1.data, evoked_2.data, rtol=1e-8, atol=1e-20)

    # test zeroth-order case
    for preload in [True, False]:
        epochs_1 = Epochs(raw, events[:4], event_id, tmin, tmax, picks=picks,
                          baseline=(None, None), preload=preload)
        epochs_2 = Epochs(raw, events[:4], event_id, tmin, tmax, picks=picks,
                          baseline=None, preload=preload, detrend=0)
        a = epochs_1.get_data()
        b = epochs_2.get_data()
        # All data channels should be almost equal
        assert_allclose(a[:, data_picks, :], b[:, data_picks, :],
                        rtol=1e-16, atol=1e-20)
        # There are non-M/EEG channels that should not be equal:
        assert not np.allclose(a, b)

    for value in ['foo', 2, False, True]:
        pytest.raises(ValueError, Epochs, raw, events[:4], event_id,
                      tmin, tmax, detrend=value)


def test_bootstrap():
    """Test of bootstrapping of epochs."""
    raw, events, picks = _get_data()
    epochs = Epochs(raw, events[:5], event_id, tmin, tmax, picks=picks,
                    preload=True, reject=reject, flat=flat)
    random_states = [0]
    if check_version('numpy', '1.17'):
        random_states += [np.random.default_rng(0)]
    for random_state in random_states:
        epochs2 = bootstrap(epochs, random_state=random_state)
        assert (len(epochs2.events) == len(epochs.events))
        assert (epochs._data.shape == epochs2._data.shape)


def test_epochs_copy():
    """Test copy epochs."""
    raw, events, picks = _get_data()
    epochs = Epochs(raw, events[:5], event_id, tmin, tmax, picks=picks,
                    preload=True, reject=reject, flat=flat)
    copied = epochs.copy()
    assert_array_equal(epochs._data, copied._data)

    epochs = Epochs(raw, events[:5], event_id, tmin, tmax, picks=picks,
                    preload=False, reject=reject, flat=flat)
    copied = epochs.copy()
    data = epochs.get_data()
    copied_data = copied.get_data()
    assert_array_equal(data, copied_data)


def test_iter_evoked():
    """Test the iterator for epochs -> evoked."""
    raw, events, picks = _get_data()
    epochs = Epochs(raw, events[:5], event_id, tmin, tmax, picks=picks)

    for ii, ev in enumerate(epochs.iter_evoked()):
        x = ev.data
        y = epochs.get_data()[ii, :, :]
        assert_array_equal(x, y)


def test_subtract_evoked():
    """Test subtraction of Evoked from Epochs."""
    raw, events, picks = _get_data()
    epochs = Epochs(raw, events[:10], event_id, tmin, tmax, picks=picks)

    # make sure subtraction fails if data channels are missing
    pytest.raises(ValueError, epochs.subtract_evoked,
                  epochs.average(picks[:5]))

    # do the subtraction using the default argument
    epochs.subtract_evoked()

    # apply SSP now
    epochs.apply_proj()

    # use preloading and SSP from the start
    epochs2 = Epochs(raw, events[:10], event_id, tmin, tmax, picks=picks,
                     preload=True)

    evoked = epochs2.average()
    epochs2.subtract_evoked(evoked)

    # this gives the same result
    assert_allclose(epochs.get_data(), epochs2.get_data())

    # if we compute the evoked response after subtracting it we get zero
    zero_evoked = epochs.average()
    data = zero_evoked.data
    assert_allclose(data, np.zeros_like(data), atol=1e-15)


def test_epoch_eq():
    """Test epoch count equalization and condition combining."""
    raw, events, picks = _get_data()
    # equalizing epochs objects
    epochs_1 = Epochs(raw, events, event_id, tmin, tmax, picks=picks)
    epochs_2 = Epochs(raw, events, event_id_2, tmin, tmax, picks=picks)
    epochs_1.drop_bad()  # make sure drops are logged
    assert_equal(len([log for log in epochs_1.drop_log if not log]),
                 len(epochs_1.events))
    drop_log1 = epochs_1.drop_log = [[] for _ in range(len(epochs_1.events))]
    drop_log2 = [[] if log == ['EQUALIZED_COUNT'] else log for log in
                 epochs_1.drop_log]
    assert_equal(drop_log1, drop_log2)
    assert_equal(len([l for l in epochs_1.drop_log if not l]),
                 len(epochs_1.events))
    assert (epochs_1.events.shape[0] != epochs_2.events.shape[0])
    equalize_epoch_counts([epochs_1, epochs_2], method='mintime')
    assert_equal(epochs_1.events.shape[0], epochs_2.events.shape[0])
    epochs_3 = Epochs(raw, events, event_id, tmin, tmax, picks=picks)
    epochs_4 = Epochs(raw, events, event_id_2, tmin, tmax, picks=picks)
    equalize_epoch_counts([epochs_3, epochs_4], method='truncate')
    assert_equal(epochs_1.events.shape[0], epochs_3.events.shape[0])
    assert_equal(epochs_3.events.shape[0], epochs_4.events.shape[0])

    # equalizing conditions
    epochs = Epochs(raw, events, {'a': 1, 'b': 2, 'c': 3, 'd': 4},
                    tmin, tmax, picks=picks, reject=reject)
    epochs.drop_bad()  # make sure drops are logged
    assert_equal(len([log for log in epochs.drop_log if not log]),
                 len(epochs.events))
    drop_log1 = deepcopy(epochs.drop_log)
    old_shapes = [epochs[key].events.shape[0] for key in ['a', 'b', 'c', 'd']]
    epochs.equalize_event_counts(['a', 'b'])
    # undo the eq logging
    drop_log2 = [[] if log == ['EQUALIZED_COUNT'] else log for log in
                 epochs.drop_log]
    assert_equal(drop_log1, drop_log2)

    assert_equal(len([log for log in epochs.drop_log if not log]),
                 len(epochs.events))
    new_shapes = [epochs[key].events.shape[0] for key in ['a', 'b', 'c', 'd']]
    assert_equal(new_shapes[0], new_shapes[1])
    assert_equal(new_shapes[2], new_shapes[2])
    assert_equal(new_shapes[3], new_shapes[3])
    # now with two conditions collapsed
    old_shapes = new_shapes
    epochs.equalize_event_counts([['a', 'b'], 'c'])
    new_shapes = [epochs[key].events.shape[0] for key in ['a', 'b', 'c', 'd']]
    assert_equal(new_shapes[0] + new_shapes[1], new_shapes[2])
    assert_equal(new_shapes[3], old_shapes[3])
    pytest.raises(KeyError, epochs.equalize_event_counts, [1, 'a'])

    # now let's combine conditions
    old_shapes = new_shapes
    epochs.equalize_event_counts([['a', 'b'], ['c', 'd']])
    new_shapes = [epochs[key].events.shape[0] for key in ['a', 'b', 'c', 'd']]
    assert_equal(old_shapes[0] + old_shapes[1], new_shapes[0] + new_shapes[1])
    assert_equal(new_shapes[0] + new_shapes[1], new_shapes[2] + new_shapes[3])
    pytest.raises(ValueError, combine_event_ids, epochs, ['a', 'b'], {'ab': 1})

    combine_event_ids(epochs, ['a', 'b'], {'ab': np.int32(12)}, copy=False)
    caught = 0
    for key in ['a', 'b']:
        try:
            epochs[key]
        except KeyError:
            caught += 1
    assert_equal(caught, 2)
    assert (not np.any(epochs.events[:, 2] == 1))
    assert (not np.any(epochs.events[:, 2] == 2))
    epochs = combine_event_ids(epochs, ['c', 'd'], {'cd': 34})
    assert np.all(np.logical_or(epochs.events[:, 2] == 12,
                                epochs.events[:, 2] == 34))
    assert_equal(epochs['ab'].events.shape[0], old_shapes[0] + old_shapes[1])
    assert_equal(epochs['ab'].events.shape[0], epochs['cd'].events.shape[0])

    # equalizing with hierarchical tags
    epochs = Epochs(raw, events, {'a/x': 1, 'b/x': 2, 'a/y': 3, 'b/y': 4},
                    tmin, tmax, picks=picks, reject=reject)
    cond1, cond2 = ['a', ['b/x', 'b/y']], [['a/x', 'a/y'], 'b']
    es = [epochs.copy().equalize_event_counts(c)[0]
          for c in (cond1, cond2)]
    assert_array_equal(es[0].events[:, 0], es[1].events[:, 0])
    cond1, cond2 = ['a', ['b', 'b/y']], [['a/x', 'a/y'], 'x']
    for c in (cond1, cond2):  # error b/c tag and id mix/non-orthogonal tags
        pytest.raises(ValueError, epochs.equalize_event_counts, c)
    pytest.raises(KeyError, epochs.equalize_event_counts,
                  ["a/no_match", "b"])
    # test equalization with no events of one type
    epochs.drop(np.arange(10))
    assert_equal(len(epochs['a/x']), 0)
    assert (len(epochs['a/y']) > 0)
    epochs.equalize_event_counts(['a/x', 'a/y'])
    assert_equal(len(epochs['a/x']), 0)
    assert_equal(len(epochs['a/y']), 0)


def test_access_by_name(tmpdir):
    """Test accessing epochs by event name and on_missing for rare events."""
    tempdir = str(tmpdir)
    raw, events, picks = _get_data()

    # Test various invalid inputs
    pytest.raises(TypeError, Epochs, raw, events, {1: 42, 2: 42}, tmin,
                  tmax, picks=picks)
    pytest.raises(TypeError, Epochs, raw, events, {'a': 'spam', 2: 'eggs'},
                  tmin, tmax, picks=picks)
    pytest.raises(TypeError, Epochs, raw, events, {'a': 'spam', 2: 'eggs'},
                  tmin, tmax, picks=picks)
    pytest.raises(TypeError, Epochs, raw, events, 'foo', tmin, tmax,
                  picks=picks)
    pytest.raises(TypeError, Epochs, raw, events, ['foo'], tmin, tmax,
                  picks=picks)

    # Test accessing non-existent events (assumes 12345678 does not exist)
    event_id_illegal = dict(aud_l=1, does_not_exist=12345678)
    pytest.raises(ValueError, Epochs, raw, events, event_id_illegal,
                  tmin, tmax)
    # Test on_missing
    pytest.raises(ValueError, Epochs, raw, events, 1, tmin, tmax,
                  on_missing='foo')
    with pytest.warns(RuntimeWarning, match='No matching events'):
        Epochs(raw, events, event_id_illegal, tmin, tmax, on_missing='warning')
    Epochs(raw, events, event_id_illegal, tmin, tmax, on_missing='ignore')

    # Test constructing epochs with a list of ints as events
    epochs = Epochs(raw, events, [1, 2], tmin, tmax, picks=picks)
    for k, v in epochs.event_id.items():
        assert_equal(int(k), v)

    epochs = Epochs(raw, events, {'a': 1, 'b': 2}, tmin, tmax, picks=picks)
    pytest.raises(KeyError, epochs.__getitem__, 'bar')

    data = epochs['a'].get_data()
    event_a = events[events[:, 2] == 1]
    assert (len(data) == len(event_a))

    epochs = Epochs(raw, events, {'a': 1, 'b': 2}, tmin, tmax, picks=picks,
                    preload=True)
    pytest.raises(KeyError, epochs.__getitem__, 'bar')
    temp_fname = op.join(tempdir, 'test-epo.fif')
    epochs.save(temp_fname, overwrite=True)
    epochs2 = read_epochs(temp_fname)

    for ep in [epochs, epochs2]:
        data = ep['a'].get_data()
        event_a = events[events[:, 2] == 1]
        assert (len(data) == len(event_a))

    assert_array_equal(epochs2['a'].events, epochs['a'].events)

    epochs3 = Epochs(raw, events, {'a': 1, 'b': 2, 'c': 3, 'd': 4},
                     tmin, tmax, picks=picks, preload=True)
    assert_equal(list(sorted(epochs3[('a', 'b')].event_id.values())),
                 [1, 2])
    epochs4 = epochs['a']
    epochs5 = epochs3['a']
    assert_array_equal(epochs4.events, epochs5.events)
    # 20 is our tolerance because epochs are written out as floats
    assert_array_almost_equal(epochs4.get_data(), epochs5.get_data(), 20)
    epochs6 = epochs3[['a', 'b']]
    assert all(np.logical_or(epochs6.events[:, 2] == 1,
                             epochs6.events[:, 2] == 2))
    assert_array_equal(epochs.events, epochs6.events)
    assert_array_almost_equal(epochs.get_data(), epochs6.get_data(), 20)

    # Make sure we preserve names
    assert_equal(epochs['a']._name, 'a')
    assert_equal(epochs[['a', 'b']]['a']._name, 'a')


@requires_pandas
def test_to_data_frame():
    """Test epochs Pandas exporter."""
    raw, events, picks = _get_data()
    epochs = Epochs(raw, events, {'a': 1, 'b': 2}, tmin, tmax, picks=picks)
    # test deprecation
    with pytest.deprecated_call(match='scaling_time is deprecated'):
        epochs.to_data_frame(scaling_time=1e2)
    # test index checking
    with pytest.raises(ValueError, match='options. Valid index options are'):
        epochs.to_data_frame(index=['foo', 'bar'])
    with pytest.raises(ValueError, match='"qux" is not a valid option'):
        epochs.to_data_frame(index='qux')
    with pytest.raises(TypeError, match='index must be `None` or a string or'):
        epochs.to_data_frame(index=np.arange(400))
    # test wide format
    df_wide = epochs.to_data_frame()
    assert all(np.in1d(epochs.ch_names, df_wide.columns))
    assert all(np.in1d(['time', 'epoch', 'condition'], df_wide.columns))
    # test long format
    df_long = epochs.to_data_frame(long_format=True)
    expected = ('condition', 'epoch', 'time', 'channel', 'ch_type', 'value')
    assert set(expected) == set(df_long.columns)
    assert set(epochs.ch_names) == set(df_long['channel'])
    assert(len(df_long) == epochs.get_data().size)
    # test long format w/ index
    df_long = epochs.to_data_frame(long_format=True, index=['epoch'])
    del df_wide, df_long
    # test scalings
    df = epochs.to_data_frame(index=['condition', 'epoch', 'time'])
    data = np.hstack(epochs.get_data())
    assert_array_equal(df.values[:, 0], data[0] * 1e13)
    assert_array_equal(df.values[:, 2], data[2] * 1e15)


@requires_pandas
@pytest.mark.parametrize('index', ('time', ['condition', 'time', 'epoch'],
                                   ['epoch', 'time'], ['time', 'epoch'], None))
def test_to_data_frame_index(index):
    """Test index creation in epochs Pandas exporter."""
    raw, events, picks = _get_data()
    epochs = Epochs(raw, events, {'a': 1, 'b': 2}, tmin, tmax, picks=picks)
    df = epochs.to_data_frame(picks=[11, 12, 14], index=index)
    # test index order/hierarchy preservation
    if not isinstance(index, list):
        index = [index]
    assert (df.index.names == index)
    # test that non-indexed data were present as columns
    non_index = list(set(['condition', 'time', 'epoch']) - set(index))
    if len(non_index):
        assert all(np.in1d(non_index, df.columns))


@requires_pandas
@pytest.mark.parametrize('time_format', (None, 'ms', 'timedelta'))
def test_to_data_frame_time_format(time_format):
    """Test time conversion in epochs Pandas exporter."""
    from pandas import Timedelta
    raw, events, picks = _get_data()
    epochs = Epochs(raw, events, {'a': 1, 'b': 2}, tmin, tmax, picks=picks)
    # test time_format
    df = epochs.to_data_frame(time_format=time_format)
    dtypes = {None: np.float64, 'ms': np.int64, 'timedelta': Timedelta}
    assert isinstance(df['time'].iloc[0], dtypes[time_format])


def test_epochs_proj_mixin():
    """Test SSP proj methods from ProjMixin class."""
    raw, events, picks = _get_data()
    for proj in [True, False]:
        epochs = Epochs(raw, events[:4], event_id, tmin, tmax, picks=picks,
                        proj=proj)

        assert (all(p['active'] == proj for p in epochs.info['projs']))

        # test adding / deleting proj
        if proj:
            epochs.get_data()
            assert (all(p['active'] == proj for p in epochs.info['projs']))
            pytest.raises(ValueError, epochs.add_proj, epochs.info['projs'][0],
                          {'remove_existing': True})
            pytest.raises(ValueError, epochs.add_proj, 'spam')
            pytest.raises(ValueError, epochs.del_proj, 0)
        else:
            projs = deepcopy(epochs.info['projs'])
            n_proj = len(epochs.info['projs'])
            epochs.del_proj(0)
            assert (len(epochs.info['projs']) == n_proj - 1)
            # Test that already existing projections are not added.
            epochs.add_proj(projs, remove_existing=False)
            assert (len(epochs.info['projs']) == n_proj)
            epochs.add_proj(projs[:-1], remove_existing=True)
            assert (len(epochs.info['projs']) == n_proj - 1)

    # catch no-gos.
    # wrong proj argument
    pytest.raises(ValueError, Epochs, raw, events[:4], event_id, tmin, tmax,
                  picks=picks, proj='crazy')

    for preload in [True, False]:
        epochs = Epochs(raw, events[:4], event_id, tmin, tmax, picks=picks,
                        proj='delayed', preload=preload,
                        reject=reject).set_eeg_reference(projection=True)
        epochs_proj = Epochs(
            raw, events[:4], event_id, tmin, tmax, picks=picks,
            proj=True, preload=preload,
            reject=reject).set_eeg_reference(projection=True).apply_proj()

        epochs_noproj = Epochs(raw, events[:4], event_id, tmin, tmax,
                               picks=picks, proj=False, preload=preload,
                               reject=reject)
        epochs_noproj.set_eeg_reference(projection=True)

        assert_allclose(epochs.copy().apply_proj().get_data(),
                        epochs_proj.get_data(), rtol=1e-10, atol=1e-25)
        assert_allclose(epochs.get_data(),
                        epochs_noproj.get_data(), rtol=1e-10, atol=1e-25)

        # make sure data output is constant across repeated calls
        # e.g. drop bads
        assert_array_equal(epochs.get_data(), epochs.get_data())
        assert_array_equal(epochs_proj.get_data(), epochs_proj.get_data())
        assert_array_equal(epochs_noproj.get_data(), epochs_noproj.get_data())

    # test epochs.next calls
    data = epochs.get_data().copy()
    data2 = np.array([e for e in epochs])
    assert_array_equal(data, data2)

    # cross application from processing stream 1 to 2
    epochs.apply_proj()
    assert_array_equal(epochs._projector, epochs_proj._projector)
    assert_allclose(epochs._data, epochs_proj.get_data())

    # test mixin against manual application
    epochs = Epochs(raw, events[:4], event_id, tmin, tmax, picks=picks,
                    baseline=None,
                    proj=False).set_eeg_reference(projection=True)
    data = epochs.get_data().copy()
    epochs.apply_proj()
    assert_allclose(np.dot(epochs._projector, data[0]), epochs._data[0])


def test_delayed_epochs():
    """Test delayed projection on Epochs."""
    raw, events, picks = _get_data()
    events = events[:10]
    picks = np.concatenate([pick_types(raw.info, meg=True, eeg=True)[::22],
                            pick_types(raw.info, meg=False, eeg=False,
                                       ecg=True, eog=True)])
    picks = np.sort(picks)
    raw.load_data().pick_channels([raw.ch_names[pick] for pick in picks])
    raw.info.normalize_proj()
    del picks
    n_epochs = 2  # number we expect after rejection
    raw.info['lowpass'] = 40.  # fake the LP info so no warnings
    for decim in (1, 3):
        proj_data = Epochs(raw, events, event_id, tmin, tmax, proj=True,
                           reject=reject, decim=decim)
        use_tmin = proj_data.tmin
        proj_data = proj_data.get_data()
        noproj_data = Epochs(raw, events, event_id, tmin, tmax, proj=False,
                             reject=reject, decim=decim).get_data()
        assert_equal(proj_data.shape, noproj_data.shape)
        assert_equal(proj_data.shape[0], n_epochs)
        for preload in (True, False):
            for proj in (True, False, 'delayed'):
                for ii in range(3):
                    print(decim, preload, proj, ii)
                    comp = proj_data if proj is True else noproj_data
                    if ii in (0, 1):
                        epochs = Epochs(raw, events, event_id, tmin, tmax,
                                        proj=proj, reject=reject,
                                        preload=preload, decim=decim)
                    else:
                        fake_events = np.zeros((len(comp), 3), int)
                        fake_events[:, 0] = np.arange(len(comp))
                        fake_events[:, 2] = 1
                        epochs = EpochsArray(comp, raw.info, tmin=use_tmin,
                                             event_id=1, events=fake_events,
                                             proj=proj)
                        epochs.info['sfreq'] /= decim
                        assert_equal(len(epochs), n_epochs)
                    assert (raw.proj is False)
                    assert (epochs.proj is
                            (True if proj is True else False))
                    if ii == 1:
                        epochs.load_data()
                    picks_data = pick_types(epochs.info, meg=True, eeg=True)
                    evoked = epochs.average(picks=picks_data)
                    assert_equal(evoked.nave, n_epochs, str(epochs.drop_log))
                    if proj is True:
                        evoked.apply_proj()
                    else:
                        assert (evoked.proj is False)
                    assert_array_equal(evoked.ch_names,
                                       np.array(epochs.ch_names)[picks_data])
                    assert_allclose(evoked.times, epochs.times)
                    epochs_data = epochs.get_data()
                    assert_allclose(evoked.data,
                                    epochs_data.mean(axis=0)[picks_data],
                                    rtol=1e-5, atol=1e-20)
                    assert_allclose(epochs_data, comp, rtol=1e-5, atol=1e-20)


def test_drop_epochs():
    """Test dropping of epochs."""
    raw, events, picks = _get_data()
    epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks)
    events1 = events[events[:, 2] == event_id]

    # Bound checks
    pytest.raises(IndexError, epochs.drop, [len(epochs.events)])
    pytest.raises(IndexError, epochs.drop, [-len(epochs.events) - 1])
    pytest.raises(ValueError, epochs.drop, [[1, 2], [3, 4]])

    # Test selection attribute
    assert_array_equal(epochs.selection,
                       np.where(events[:, 2] == event_id)[0])
    assert_equal(len(epochs.drop_log), len(events))
    assert (all(epochs.drop_log[k] == ['IGNORED']
                for k in set(range(len(events))) - set(epochs.selection)))

    selection = epochs.selection.copy()
    n_events = len(epochs.events)
    epochs.drop([2, 4], reason='d')
    assert_equal(epochs.drop_log_stats(), 2. / n_events * 100)
    assert_equal(len(epochs.drop_log), len(events))
    assert_equal([epochs.drop_log[k]
                  for k in selection[[2, 4]]], [['d'], ['d']])
    assert_array_equal(events[epochs.selection], events1[[0, 1, 3, 5, 6]])
    assert_array_equal(events[epochs[3:].selection], events1[[5, 6]])
    assert_array_equal(events[epochs['1'].selection], events1[[0, 1, 3, 5, 6]])


def test_drop_epochs_mult():
    """Test that subselecting epochs or making less epochs is equivalent."""
    raw, events, picks = _get_data()
    for preload in [True, False]:
        epochs1 = Epochs(raw, events, {'a': 1, 'b': 2},
                         tmin, tmax, picks=picks, reject=reject,
                         preload=preload)['a']
        epochs2 = Epochs(raw, events, {'a': 1},
                         tmin, tmax, picks=picks, reject=reject,
                         preload=preload)

        if preload:
            # In the preload case you cannot know the bads if already ignored
            assert_equal(len(epochs1.drop_log), len(epochs2.drop_log))
            for d1, d2 in zip(epochs1.drop_log, epochs2.drop_log):
                if d1 == ['IGNORED']:
                    assert (d2 == ['IGNORED'])
                if d1 != ['IGNORED'] and d1 != []:
                    assert ((d2 == d1) or (d2 == ['IGNORED']))
                if d1 == []:
                    assert (d2 == [])
            assert_array_equal(epochs1.events, epochs2.events)
            assert_array_equal(epochs1.selection, epochs2.selection)
        else:
            # In the non preload is should be exactly the same
            assert_equal(epochs1.drop_log, epochs2.drop_log)
            assert_array_equal(epochs1.events, epochs2.events)
            assert_array_equal(epochs1.selection, epochs2.selection)


def test_contains():
    """Test membership API."""
    raw, events = _get_data(True)[:2]
    # Add seeg channel
    seeg = RawArray(np.zeros((1, len(raw.times))),
                    create_info(['SEEG 001'], raw.info['sfreq'], 'seeg'))
    for key in ('dev_head_t', 'highpass', 'lowpass',
                'dig', 'description', 'acq_pars', 'experimenter',
                'proj_name'):
        seeg.info[key] = raw.info[key]
    raw.add_channels([seeg])
    tests = [(('mag', False, False), ('grad', 'eeg', 'seeg')),
             (('grad', False, False), ('mag', 'eeg', 'seeg')),
             ((False, True, False), ('grad', 'mag', 'seeg')),
             ((False, False, True), ('grad', 'mag', 'eeg'))]

    for (meg, eeg, seeg), others in tests:
        picks_contains = pick_types(raw.info, meg=meg, eeg=eeg, seeg=seeg)
        epochs = Epochs(raw, events, {'a': 1, 'b': 2}, tmin, tmax,
                        picks=picks_contains)
        if eeg:
            test = 'eeg'
        elif seeg:
            test = 'seeg'
        else:
            test = meg
        assert (test in epochs)
        assert (not any(o in epochs for o in others))

    pytest.raises(ValueError, epochs.__contains__, 'foo')
    pytest.raises(TypeError, epochs.__contains__, 1)


def test_drop_channels_mixin():
    """Test channels-dropping functionality."""
    raw, events = _get_data()[:2]
    # here without picks to get additional coverage
    epochs = Epochs(raw, events, event_id, tmin, tmax, preload=True)
    drop_ch = epochs.ch_names[:3]
    ch_names = epochs.ch_names[3:]

    ch_names_orig = epochs.ch_names
    dummy = epochs.copy().drop_channels(drop_ch)
    assert_equal(ch_names, dummy.ch_names)
    assert_equal(ch_names_orig, epochs.ch_names)
    assert_equal(len(ch_names_orig), epochs.get_data().shape[1])

    epochs.drop_channels(drop_ch)
    assert_equal(ch_names, epochs.ch_names)
    assert_equal(len(ch_names), epochs.get_data().shape[1])


def test_pick_channels_mixin():
    """Test channel-picking functionality."""
    raw, events, picks = _get_data()
    epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                    preload=True)
    ch_names = epochs.ch_names[:3]
    epochs.preload = False
    pytest.raises(RuntimeError, epochs.drop_channels, [ch_names[0]])
    epochs.preload = True
    ch_names_orig = epochs.ch_names
    dummy = epochs.copy().pick_channels(ch_names)
    assert_equal(ch_names, dummy.ch_names)
    assert_equal(ch_names_orig, epochs.ch_names)
    assert_equal(len(ch_names_orig), epochs.get_data().shape[1])

    epochs.pick_channels(ch_names)
    assert_equal(ch_names, epochs.ch_names)
    assert_equal(len(ch_names), epochs.get_data().shape[1])

    # Invalid picks
    pytest.raises(ValueError, Epochs, raw, events, event_id, tmin, tmax,
                  picks=[])


def test_equalize_channels():
    """Test equalization of channels."""
    raw, events, picks = _get_data()
    epochs1 = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                     proj=False, preload=True)
    epochs2 = epochs1.copy()
    ch_names = epochs1.ch_names[2:]
    epochs1.drop_channels(epochs1.ch_names[:1])
    epochs2.drop_channels(epochs2.ch_names[1:2])
    my_comparison = [epochs1, epochs2]
    my_comparison = equalize_channels(my_comparison)
    for e in my_comparison:
        assert_equal(ch_names, e.ch_names)


def test_illegal_event_id():
    """Test handling of invalid events ids."""
    raw, events, picks = _get_data()
    event_id_illegal = dict(aud_l=1, does_not_exist=12345678)

    pytest.raises(ValueError, Epochs, raw, events, event_id_illegal, tmin,
                  tmax, picks=picks, proj=False)


def test_add_channels_epochs():
    """Test adding channels."""
    raw, events, picks = _get_data()

    def make_epochs(picks, proj):
        return Epochs(raw, events, event_id, tmin, tmax, preload=True,
                      proj=proj, picks=picks)

    picks = pick_types(raw.info, meg=True, eeg=True, exclude='bads')
    picks_meg = pick_types(raw.info, meg=True, eeg=False, exclude='bads')
    picks_eeg = pick_types(raw.info, meg=False, eeg=True, exclude='bads')

    for proj in (False, True):
        epochs = make_epochs(picks=picks, proj=proj)
        epochs_meg = make_epochs(picks=picks_meg, proj=proj)
        assert not epochs_meg.times.flags['WRITEABLE']
        epochs_eeg = make_epochs(picks=picks_eeg, proj=proj)
        epochs.info._check_consistency()
        epochs_meg.info._check_consistency()
        epochs_eeg.info._check_consistency()

        epochs2 = add_channels_epochs([epochs_meg, epochs_eeg])

        assert_equal(len(epochs.info['projs']), len(epochs2.info['projs']))
        assert_equal(len(epochs.info.keys()), len(epochs_meg.info.keys()))
        assert_equal(len(epochs.info.keys()), len(epochs_eeg.info.keys()))
        assert_equal(len(epochs.info.keys()), len(epochs2.info.keys()))

        data1 = epochs.get_data()
        data2 = epochs2.get_data()
        data3 = np.concatenate([e.get_data() for e in
                                [epochs_meg, epochs_eeg]], axis=1)
        assert_array_equal(data1.shape, data2.shape)
        assert_allclose(data1, data3, atol=1e-25)
        assert_allclose(data1, data2, atol=1e-25)

    assert not epochs_meg.times.flags['WRITEABLE']
    epochs_meg2 = epochs_meg.copy()
    assert not epochs_meg.times.flags['WRITEABLE']
    assert not epochs_meg2.times.flags['WRITEABLE']
    epochs_meg2.set_meas_date(0)
    add_channels_epochs([epochs_meg2, epochs_eeg])

    epochs_meg2 = epochs_meg.copy()
    epochs2 = add_channels_epochs([epochs_meg, epochs_eeg])

    epochs_meg2 = epochs_meg.copy()
    epochs_meg2.events[3, 2] -= 1
    pytest.raises(ValueError, add_channels_epochs, [epochs_meg2, epochs_eeg])

    pytest.raises(ValueError, add_channels_epochs,
                  [epochs_meg, epochs_eeg[:2]])

    epochs_meg.info['chs'].pop(0)
    epochs_meg.info._update_redundant()
    pytest.raises(RuntimeError, add_channels_epochs, [epochs_meg, epochs_eeg])

    epochs_meg2 = epochs_meg.copy()
    epochs_meg2.info['sfreq'] = None
    pytest.raises(RuntimeError, add_channels_epochs, [epochs_meg2, epochs_eeg])

    epochs_meg2 = epochs_meg.copy()
    epochs_meg2.info['sfreq'] += 10
    pytest.raises(RuntimeError, add_channels_epochs, [epochs_meg2, epochs_eeg])

    epochs_meg2 = epochs_meg.copy()
    epochs_meg2.info['chs'][1]['ch_name'] = epochs_meg2.info['ch_names'][0]
    epochs_meg2.info._update_redundant()
    with pytest.warns(RuntimeWarning, match='not unique'):
        pytest.raises(RuntimeError, add_channels_epochs,
                      [epochs_meg2, epochs_eeg])

    epochs_meg2 = epochs_meg.copy()
    epochs_meg2.info['dev_head_t']['to'] += 1
    pytest.raises(ValueError, add_channels_epochs, [epochs_meg2, epochs_eeg])

    epochs_meg2 = epochs_meg.copy()
    epochs_meg2.info['dev_head_t']['to'] += 1
    pytest.raises(ValueError, add_channels_epochs, [epochs_meg2, epochs_eeg])

    epochs_meg2 = epochs_meg.copy()
    epochs_meg2.info['expimenter'] = 'foo'
    pytest.raises(RuntimeError, add_channels_epochs, [epochs_meg2, epochs_eeg])

    epochs_meg2 = epochs_meg.copy()
    epochs_meg2.preload = False
    pytest.raises(ValueError, add_channels_epochs, [epochs_meg2, epochs_eeg])

    epochs_meg2 = epochs_meg.copy()
    epochs_meg2._set_times(epochs_meg.times + 0.4)
    pytest.raises(NotImplementedError, add_channels_epochs,
                  [epochs_meg2, epochs_eeg])

    epochs_meg2 = epochs_meg.copy()
    epochs_meg2._set_times(epochs_meg2.times + 0.5)
    assert not epochs_meg2.times.flags['WRITEABLE']
    pytest.raises(NotImplementedError, add_channels_epochs,
                  [epochs_meg2, epochs_eeg])

    epochs_meg2 = epochs_meg.copy()
    epochs_meg2.baseline = None
    pytest.raises(NotImplementedError, add_channels_epochs,
                  [epochs_meg2, epochs_eeg])

    epochs_meg2 = epochs_meg.copy()
    epochs_meg2.event_id['b'] = 2
    pytest.raises(NotImplementedError, add_channels_epochs,
                  [epochs_meg2, epochs_eeg])


def test_array_epochs(tmpdir):
    """Test creating epochs from array."""
    tempdir = str(tmpdir)

    # creating
    data = rng.random_sample((10, 20, 300))
    sfreq = 1e3
    ch_names = ['EEG %03d' % (i + 1) for i in range(20)]
    types = ['eeg'] * 20
    info = create_info(ch_names, sfreq, types)
    events = np.c_[np.arange(1, 600, 60),
                   np.zeros(10, int),
                   [1, 2] * 5]
    event_id = {'a': 1, 'b': 2}
    epochs = EpochsArray(data, info, events, tmin, event_id)
    assert (str(epochs).startswith('<EpochsArray'))
    # From GH#1963
    pytest.raises(ValueError, EpochsArray, data[:-1], info, events, tmin,
                  event_id)
    pytest.raises(ValueError, EpochsArray, data, info, events, tmin,
                  dict(a=1))
    pytest.raises(ValueError, EpochsArray, data, info, events, tmin,
                  selection=[1])
    # should be fine
    EpochsArray(data, info, events, tmin, selection=np.arange(len(events)) + 5)

    # saving
    temp_fname = op.join(tempdir, 'test-epo.fif')
    epochs.save(temp_fname, overwrite=True)
    epochs2 = read_epochs(temp_fname)
    data2 = epochs2.get_data()
    assert_allclose(data, data2)
    assert_allclose(epochs.times, epochs2.times)
    assert_equal(epochs.event_id, epochs2.event_id)
    assert_array_equal(epochs.events, epochs2.events)

    # plotting
    epochs[0].plot()
    plt.close('all')

    # indexing
    assert_array_equal(np.unique(epochs['a'].events[:, 2]), np.array([1]))
    assert_equal(len(epochs[:2]), 2)
    data[0, 5, 150] = 3000
    data[1, :, :] = 0
    data[2, 5, 210] = 3000
    data[3, 5, 260] = 0
    epochs = EpochsArray(data, info, events=events, event_id=event_id,
                         tmin=0, reject=dict(eeg=1000), flat=dict(eeg=1e-1),
                         reject_tmin=0.1, reject_tmax=0.2)
    assert_equal(len(epochs), len(events) - 2)
    assert_equal(epochs.drop_log[0], ['EEG 006'])
    assert_equal(len(epochs.drop_log), 10)
    assert_equal(len(epochs.events), len(epochs.selection))

    # baseline
    data = np.ones((10, 20, 300))
    epochs = EpochsArray(data, info, events, event_id=event_id, tmin=-.2,
                         baseline=(None, 0))
    ep_data = epochs.get_data()
    assert_array_equal(ep_data, np.zeros_like(ep_data))

    # one time point
    epochs = EpochsArray(data[:, :, :1], info, events=events,
                         event_id=event_id, tmin=0.)
    assert_allclose(epochs.times, [0.])
    assert_allclose(epochs.get_data(), data[:, :, :1])
    epochs.save(temp_fname, overwrite=True)
    epochs_read = read_epochs(temp_fname)
    assert_allclose(epochs_read.times, [0.])
    assert_allclose(epochs_read.get_data(), data[:, :, :1])

    # event as integer (#2435)
    mask = (events[:, 2] == 1)
    data_1 = data[mask]
    events_1 = events[mask]
    epochs = EpochsArray(data_1, info, events=events_1, event_id=1, tmin=-0.2)

    # default events
    epochs = EpochsArray(data_1, info)
    assert_array_equal(epochs.events[:, 0], np.arange(len(data_1)))
    assert_array_equal(epochs.events[:, 1], np.zeros(len(data_1), int))
    assert_array_equal(epochs.events[:, 2], np.ones(len(data_1), int))


def test_concatenate_epochs():
    """Test concatenate epochs."""
    raw, events, picks = _get_data()
    epochs = Epochs(raw=raw, events=events, event_id=event_id, tmin=tmin,
                    tmax=tmax, picks=picks)
    epochs2 = epochs.copy()
    epochs_list = [epochs, epochs2]
    epochs_conc = concatenate_epochs(epochs_list)
    assert_array_equal(
        epochs_conc.events[:, 0], np.unique(epochs_conc.events[:, 0]))

    expected_shape = list(epochs.get_data().shape)
    expected_shape[0] *= 2
    expected_shape = tuple(expected_shape)

    assert_equal(epochs_conc.get_data().shape, expected_shape)
    assert_equal(epochs_conc.drop_log, epochs.drop_log * 2)

    epochs2 = epochs.copy().load_data()
    with pytest.raises(ValueError, match='nchan.*must match'):
        concatenate_epochs(
            [epochs, epochs2.copy().drop_channels(epochs2.ch_names[:1])])

    epochs2._set_times(np.delete(epochs2.times, 1))
    with pytest.raises(ValueError, match='could not be broadcast'):
        concatenate_epochs([epochs, epochs2])

    assert_equal(epochs_conc._raw, None)

    # check if baseline is same for all epochs
    epochs2 = epochs.copy()
    epochs2.apply_baseline((-0.1, None))
    with pytest.raises(ValueError, match='Baseline must be same'):
        concatenate_epochs([epochs, epochs2])

    # check if dev_head_t is same
    epochs2 = epochs.copy()
    concatenate_epochs([epochs, epochs2])  # should work
    epochs2.info['dev_head_t']['trans'][:3, 3] += 0.0001
    with pytest.raises(ValueError, match='dev_head_t.*must match'):
        concatenate_epochs([epochs, epochs2])
    with pytest.raises(TypeError, match='must be a list or tuple'):
        concatenate_epochs('foo')
    with pytest.raises(TypeError, match='must be an instance of Epochs'):
        concatenate_epochs([epochs, 'foo'])
    epochs2.info['dev_head_t'] = None
    with pytest.raises(ValueError, match='dev_head_t.*must match'):
        concatenate_epochs([epochs, epochs2])
    epochs.info['dev_head_t'] = None
    concatenate_epochs([epochs, epochs2])  # should work

    # check that different event_id does not work:
    epochs1 = epochs.copy()
    epochs2 = epochs.copy()
    epochs1.event_id = dict(a=1)
    epochs2.event_id = dict(a=2)
    with pytest.raises(ValueError, match='identical keys'):
        concatenate_epochs([epochs1, epochs2])


def test_concatenate_epochs_large():
    """Test concatenating epochs on large data."""
    raw, events, picks = _get_data()
    epochs = Epochs(raw=raw, events=events, event_id=event_id, tmin=tmin,
                    tmax=tmax, picks=picks, preload=True)

    # check events are shifted, but relative position are equal
    epochs_list = [epochs.copy() for ii in range(3)]
    epochs_cat = concatenate_epochs(epochs_list)
    for ii in range(3):
        evs = epochs_cat.events[ii * len(epochs):(ii + 1) * len(epochs)]
        rel_pos = epochs_list[ii].events[:, 0] - evs[:, 0]
        assert (sum(rel_pos - rel_pos[0]) == 0)

    # test large number of epochs
    long_epochs_list = [epochs.copy() for ii in range(60)]
    many_epochs_cat = concatenate_epochs(long_epochs_list)
    max_expected_sample_index = 60 * 1.2 * np.max(epochs.events[:, 0])
    assert np.max(many_epochs_cat.events[:, 0]) < max_expected_sample_index


def test_add_channels():
    """Test epoch splitting / re-appending channel types."""
    raw, events, picks = _get_data()
    epoch_nopre = Epochs(
        raw=raw, events=events, event_id=event_id, tmin=tmin, tmax=tmax,
        picks=picks)
    epoch = Epochs(
        raw=raw, events=events, event_id=event_id, tmin=tmin, tmax=tmax,
        picks=picks, preload=True)
    epoch_eeg = epoch.copy().pick_types(meg=False, eeg=True)
    epoch_meg = epoch.copy().pick_types(meg=True)
    epoch_stim = epoch.copy().pick_types(meg=False, stim=True)
    epoch_eeg_meg = epoch.copy().pick_types(meg=True, eeg=True)
    epoch_new = epoch_meg.copy().add_channels([epoch_eeg, epoch_stim])
    assert all(ch in epoch_new.ch_names
               for ch in epoch_stim.ch_names + epoch_meg.ch_names)
    epoch_new = epoch_meg.copy().add_channels([epoch_eeg])

    assert (ch in epoch_new.ch_names for ch in epoch.ch_names)
    assert_array_equal(epoch_new._data, epoch_eeg_meg._data)
    assert all(ch not in epoch_new.ch_names
               for ch in epoch_stim.ch_names)

    # Now test errors
    epoch_badsf = epoch_eeg.copy()
    epoch_badsf.info['sfreq'] = 3.1415927
    epoch_eeg = epoch_eeg.crop(-.1, .1)

    epoch_meg.load_data()
    pytest.raises(RuntimeError, epoch_meg.add_channels, [epoch_nopre])
    pytest.raises(RuntimeError, epoch_meg.add_channels, [epoch_badsf])
    pytest.raises(AssertionError, epoch_meg.add_channels, [epoch_eeg])
    pytest.raises(ValueError, epoch_meg.add_channels, [epoch_meg])
    pytest.raises(TypeError, epoch_meg.add_channels, epoch_badsf)


def test_seeg_ecog():
    """Test the compatibility of the Epoch object with SEEG and ECoG data."""
    n_epochs, n_channels, n_times, sfreq = 5, 10, 20, 1000.
    data = np.ones((n_epochs, n_channels, n_times))
    events = np.array([np.arange(n_epochs), [0] * n_epochs, [1] * n_epochs]).T
    pick_dict = dict(meg=False, exclude=[])
    for key in ('seeg', 'ecog'):
        info = create_info(n_channels, sfreq, key)
        epochs = EpochsArray(data, info, events)
        pick_dict.update({key: True})
        picks = pick_types(epochs.info, **pick_dict)
        del pick_dict[key]
        assert_equal(len(picks), n_channels)


def test_default_values():
    """Test default event_id, tmax tmin values are working correctly."""
    raw, events = _get_data()[:2]
    epoch_1 = Epochs(raw, events[:1], preload=True)
    epoch_2 = Epochs(raw, events[:1], tmin=-0.2, tmax=0.5, preload=True)
    assert_equal(hash(epoch_1), hash(epoch_2))


@requires_pandas
def test_metadata(tmpdir):
    """Test metadata support with pandas."""
    from pandas import DataFrame

    data = np.random.randn(10, 2, 2000)
    chs = ['a', 'b']
    info = create_info(chs, 1000)
    meta = np.array([[1.] * 5 + [3.] * 5,
                     ['a'] * 2 + ['b'] * 3 + ['c'] * 3 + ['µ'] * 2]).T
    meta = DataFrame(meta, columns=['num', 'letter'])
    meta['num'] = np.array(meta['num'], float)
    events = np.arange(meta.shape[0])
    events = np.column_stack([events, np.zeros([len(events), 2])]).astype(int)
    events[5:, -1] = 1
    event_id = {'zero': 0, 'one': 1}
    epochs = EpochsArray(data, info, metadata=meta,
                         events=events, event_id=event_id)
    indices = np.arange(len(epochs))  # expected indices
    assert_array_equal(epochs.metadata.index, indices)

    assert len(epochs[[1, 2]].events) == len(epochs[[1, 2]].metadata)
    assert_array_equal(epochs[[1, 2]].metadata.index, indices[[1, 2]])
    assert len(epochs['one']) == 5

    # Construction
    with pytest.raises(ValueError):
        # Events and metadata must have same len
        epochs_arr = EpochsArray(epochs._data, epochs.info, epochs.events[:-1],
                                 tmin=0, event_id=epochs.event_id,
                                 metadata=epochs.metadata)

    with pytest.raises(ValueError):
        # Events and data must have same len
        epochs = EpochsArray(data, info, metadata=meta.iloc[:-1])

    for data in [meta.values, meta['num']]:
        # Metadata must be a DataFrame
        with pytest.raises(ValueError):
            epochs = EpochsArray(data, info, metadata=data)

    # Need strings, ints, and floats
    with pytest.raises(ValueError):
        tmp_meta = meta.copy()
        tmp_meta['foo'] = np.array  # This should be of type object
        epochs = EpochsArray(data, info, metadata=tmp_meta)

    # Getitem
    assert len(epochs['num < 2']) == 5
    assert len(epochs['num < 5']) == 10
    assert len(epochs['letter == "b"']) == 3
    assert len(epochs['num < 5']) == len(epochs['num < 5'].metadata)

    with pytest.raises(KeyError):
        epochs['blah == "yo"']

    assert_array_equal(epochs.selection, indices)
    epochs.drop(0)
    assert_array_equal(epochs.selection, indices[1:])
    assert_array_equal(epochs.metadata.index, indices[1:])
    epochs.drop([0, -1])
    assert_array_equal(epochs.selection, indices[2:-1])
    assert_array_equal(epochs.metadata.index, indices[2:-1])
    assert_array_equal(len(epochs), 7)  # originally 10

    # I/O
    # Make sure values don't change with I/O
    tempdir = str(tmpdir)
    temp_fname = op.join(tempdir, 'tmp-epo.fif')
    temp_one_fname = op.join(tempdir, 'tmp-one-epo.fif')
    with catch_logging() as log:
        epochs.save(temp_fname, verbose=True, overwrite=True)
    assert log.getvalue() == ''  # assert no junk from metadata setting
    epochs_read = read_epochs(temp_fname, preload=True)
    assert_metadata_equal(epochs.metadata, epochs_read.metadata)
    epochs_arr = EpochsArray(epochs._data, epochs.info, epochs.events,
                             tmin=0, event_id=epochs.event_id,
                             metadata=epochs.metadata,
                             selection=epochs.selection)
    assert_metadata_equal(epochs.metadata, epochs_arr.metadata)

    with pytest.raises(TypeError):  # Needs to be a dataframe
        epochs.metadata = np.array([0])

    ###########################################################################
    # Now let's fake having no Pandas and make sure everything works

    epochs_one = epochs['one']
    epochs_one.save(temp_one_fname, overwrite=True)
    epochs_one_read = read_epochs(temp_one_fname)
    assert_metadata_equal(epochs_one.metadata, epochs_one_read.metadata)

    with _FakeNoPandas():
        epochs_read = read_epochs(temp_fname)
        assert isinstance(epochs_read.metadata, list)
        assert isinstance(epochs_read.metadata[0], dict)
        assert epochs_read.metadata[5]['num'] == 3.

        epochs_one_read = read_epochs(temp_one_fname)
        assert isinstance(epochs_one_read.metadata, list)
        assert isinstance(epochs_one_read.metadata[0], dict)
        assert epochs_one_read.metadata[0]['num'] == 3.

        epochs_one_nopandas = epochs_read['one']
        assert epochs_read.metadata[5]['num'] == 3.
        assert epochs_one_nopandas.metadata[0]['num'] == 3.
        # sel (no Pandas) == sel (w/ Pandas) -> save -> load (no Pandas)
        assert_metadata_equal(epochs_one_nopandas.metadata,
                              epochs_one_read.metadata)
        epochs_one_nopandas.save(temp_one_fname, overwrite=True)
        # can't make this query
        with pytest.raises(KeyError) as excinfo:
            epochs_read['num < 2']
            excinfo.match('.*Pandas query could not be performed.*')
        # still can't, but with no metadata the message should be different
        epochs_read.metadata = None
        with pytest.raises(KeyError) as excinfo:
            epochs_read['num < 2']
            excinfo.match(r'^((?!Pandas).)*$')
        del epochs_read
        # sel (no Pandas) == sel (no Pandas) -> save -> load (no Pandas)
        epochs_one_nopandas_read = read_epochs(temp_one_fname)
        assert_metadata_equal(epochs_one_nopandas_read.metadata,
                              epochs_one_nopandas.metadata)
    # sel (w/ Pandas) == sel (no Pandas) -> save -> load (w/ Pandas)
    epochs_one_nopandas_read = read_epochs(temp_one_fname)
    assert_metadata_equal(epochs_one_nopandas_read.metadata,
                          epochs_one.metadata)

    # gh-4820
    raw_data = np.random.randn(10, 1000)
    info = mne.create_info(10, 1000.)
    raw = mne.io.RawArray(raw_data, info)
    events = [[0, 0, 1], [100, 0, 1], [200, 0, 1], [300, 0, 1]]
    metadata = DataFrame([dict(idx=idx) for idx in range(len(events))])
    epochs = mne.Epochs(raw, events=events, tmin=-.050, tmax=.100,
                        metadata=metadata)
    epochs.drop_bad()
    assert len(epochs) == len(epochs.metadata)

    # gh-4821
    epochs.metadata['new_key'] = 1
    assert_array_equal(epochs['new_key == 1'].get_data(),
                       epochs.get_data())
    # ensure bad user changes break things
    epochs.metadata.drop(epochs.metadata.index[2], inplace=True)
    assert len(epochs.metadata) == len(epochs) - 1
    with pytest.raises(ValueError,
                       match='metadata must have the same number of rows .*'):
        epochs['new_key == 1']


def assert_metadata_equal(got, exp):
    """Assert metadata are equal."""
    if exp is None:
        assert got is None
    elif isinstance(exp, list):
        assert isinstance(got, list)
        assert len(got) == len(exp)
        for ii, (g, e) in enumerate(zip(got, exp)):
            assert list(g.keys()) == list(e.keys())
        for key in g.keys():
            assert g[key] == e[key], (ii, key)
    else:  # DataFrame
        import pandas
        assert isinstance(exp, pandas.DataFrame)
        assert isinstance(got, pandas.DataFrame)
        assert set(got.columns) == set(exp.columns)
        if LooseVersion(pandas.__version__) < LooseVersion('0.19'):
            # Old Pandas does not necessarily order them properly
            got = got[exp.columns]
        check = (got == exp)
        assert check.all().all()


def test_events_list():
    """Test that events can be a list."""
    events = [[100, 0, 1], [200, 0, 1], [300, 0, 1]]
    epochs = mne.Epochs(mne.io.RawArray(np.random.randn(10, 1000),
                                        mne.create_info(10, 1000.)),
                        events=events)
    assert_array_equal(epochs.events, np.array(events))


def test_save_overwrite(tmpdir):
    """Test saving with overwrite functionality."""
    tempdir = str(tmpdir)
    raw = mne.io.RawArray(np.random.RandomState(0).randn(100, 10000),
                          mne.create_info(100, 1000.))

    events = mne.make_fixed_length_events(raw, 1)
    epochs = mne.Epochs(raw, events)

    # scenario 1: overwrite=False and there isn't a file to overwrite
    # make a filename that has not already been saved to
    fname1 = op.join(tempdir, 'test_v1-epo.fif')
    # run function to be sure it doesn't throw an error
    epochs.save(fname1, overwrite=False)
    # check that the file got written
    assert op.isfile(fname1)

    # scenario 2: overwrite=False and there is a file to overwrite
    # fname1 exists because of scenario 1 above
    with pytest.raises(IOError, match='Destination file exists.'):
        epochs.save(fname1, overwrite=False)

    # scenario 3: overwrite=True and there isn't a file to overwrite
    # make up a filename that has not already been saved to
    fname2 = op.join(tempdir, 'test_v2-epo.fif')
    # run function to be sure it doesn't throw an error
    epochs.save(fname2, overwrite=True)
    # check that the file got written
    assert op.isfile(fname2)
    with pytest.raises(IOError, match='exists'):
        epochs.save(fname2)

    # scenario 4: overwrite=True and there is a file to overwrite
    # run function to be sure it doesn't throw an error
    # fname2 exists because of scenario 1 above
    epochs.save(fname2, overwrite=True)


@pytest.mark.parametrize('preload', (True, False))
@pytest.mark.parametrize('is_complex', (True, False))
@pytest.mark.parametrize('fmt, rtol', [('single', 2e-6), ('double', 1e-10)])
def test_save_complex_data(tmpdir, preload, is_complex, fmt, rtol):
    """Test whether epochs of hilbert-transformed data can be saved."""
    raw, events = _get_data()[:2]
    raw.load_data()
    if is_complex:
        raw.apply_hilbert(envelope=False, n_fft=None)
    epochs = Epochs(raw, events[:1], preload=True)[0]
    temp_fname = op.join(str(tmpdir), 'test-epo.fif')
    epochs.save(temp_fname, fmt=fmt)
    data = epochs.get_data().copy()
    epochs_read = read_epochs(temp_fname, proj=False, preload=preload)
    data_read = epochs_read.get_data()
    want_dtype = np.complex128 if is_complex else np.float64
    assert data.dtype == want_dtype
    assert data_read.dtype == want_dtype
    # XXX for some reason some random samples in here are off by a larger
    # factor...
    if fmt == 'single' and not preload and not is_complex:
        rtol = 2e-4
    assert_allclose(data_read, data, rtol=rtol)


def test_no_epochs(tmpdir):
    """Test that having the first epoch bad does not break writing."""
    # a regression noticed in #5564
    raw, events = _get_data()[:2]
    reject = dict(grad=4000e-13, mag=4e-12, eog=150e-6)
    raw.info['bads'] = ['MEG 2443', 'EEG 053']
    epochs = mne.Epochs(raw, events, reject=reject)
    epochs.save(op.join(str(tmpdir), 'sample-epo.fif'), overwrite=True)
    assert 0 not in epochs.selection
    assert len(epochs) > 0
    # and with no epochs remaining
    raw.info['bads'] = []
    epochs = mne.Epochs(raw, events, reject=reject)
    with pytest.warns(RuntimeWarning, match='no data'):
        epochs.save(op.join(str(tmpdir), 'sample-epo.fif'), overwrite=True)
    assert len(epochs) == 0  # all dropped


def test_readonly_times():
    """Test that the times property is read only."""
    raw, events = _get_data()[:2]
    epochs = Epochs(raw, events[:1], preload=True)
    with pytest.raises(ValueError, match='read-only'):
        epochs._times_readonly += 1
    with pytest.raises(ValueError, match='read-only'):
        epochs.times += 1
    with pytest.raises(ValueError, match='read-only'):
        epochs.times[:] = 0.


def test_channel_types_mixin():
    """Test channel types mixin."""
    raw, events = _get_data()[:2]
    epochs = Epochs(raw, events[:1], preload=True)
    ch_types = epochs.get_channel_types()
    assert len(ch_types) == len(epochs.ch_names)
    assert all(np.in1d(ch_types, ['mag', 'grad', 'eeg', 'eog', 'stim']))


def test_average_methods():
    """Test average methods."""
    n_epochs, n_channels, n_times = 5, 10, 20
    sfreq = 1000.
    data = rng.randn(n_epochs, n_channels, n_times)
    events = np.array([np.arange(n_epochs), [0] * n_epochs, [1] * n_epochs]).T
    info = create_info(n_channels, sfreq, 'eeg')
    epochs = EpochsArray(data, info, events)

    for method in ('mean', 'median'):
        if method == "mean":
            def fun(data):
                return np.mean(data, axis=0)
        elif method == "median":
            def fun(data):
                return np.median(data, axis=0)

        evoked_data = epochs.average(method=method).data
        assert_array_equal(evoked_data, fun(data))


@pytest.mark.parametrize('relative', (True, False))
def test_shift_time(relative):
    """Test the timeshift method."""
    timeshift = 13.5e-3  # Using sub-ms timeshift to test for sample accuracy.
    raw, events = _get_data()[:2]
    epochs = Epochs(raw, events[:1], preload=True, baseline=None)
    avg = epochs.average().shift_time(timeshift, relative=relative)
    avg2 = epochs.shift_time(timeshift, relative=relative).average()
    assert_array_equal(avg.times, avg2.times)
    assert_equal(avg.first, avg2.first)
    assert_equal(avg.last, avg2.last)
    assert_array_equal(avg.data, avg2.data)


@pytest.mark.parametrize('preload', (True, False))
def test_shift_time_raises_when_not_loaded(preload):
    """Test whether shift_time throws an exception when data is not loaded."""
    timeshift = 13.5e-3  # Using sub-ms timeshift to test for sample accuracy.
    raw, events = _get_data()[:2]
    epochs = Epochs(raw, events[:1], preload=preload, baseline=None)
    if not preload:
        pytest.raises(RuntimeError, epochs.shift_time, timeshift)
    else:
        epochs.shift_time(timeshift)


@testing.requires_testing_data
@pytest.mark.parametrize('preload', (True, False))
@pytest.mark.parametrize('fname', (fname_raw_testing, raw_fname))
def test_epochs_drop_selection(fname, preload):
    """Test epochs drop and selection."""
    raw = read_raw_fif(fname, preload=True)
    raw.info['bads'] = ['MEG 2443']
    events = mne.make_fixed_length_events(raw, id=1, start=0.5, duration=1.0)
    assert len(events) > 10
    kwargs = dict(tmin=-0.2, tmax=0.5, proj=False, baseline=(None, 0))
    reject = dict(mag=4e-12, grad=4000e-13)

    # Hack the first channel data to store the desired selection in epoch data
    raw._data[0] = 0.
    scale = 1e-13
    vals = scale * np.arange(1, len(events) + 1)
    raw._data[0, events[:, 0] - raw.first_samp + 1] = vals

    def _get_selection(epochs):
        """Get the desired selection from our modified epochs."""
        selection = np.round(epochs.get_data()[:, 0].max(axis=-1) / scale)
        return selection.astype(int) - 1

    # No rejection
    epochs = mne.Epochs(raw, events, preload=preload, **kwargs)
    if not preload:
        epochs.drop_bad()
    assert len(epochs) == len(events)  # none dropped
    selection = _get_selection(epochs)
    assert_array_equal(np.arange(len(events)), selection)  # kept all
    assert_array_equal(epochs.selection, selection)

    # Dropping during construction
    epochs = mne.Epochs(raw, events, preload=preload, reject=reject, **kwargs)
    if not preload:
        epochs.drop_bad()
    assert 4 < len(epochs) < len(events)  # some dropped
    selection = _get_selection(epochs)
    assert_array_equal(selection, epochs.selection)
    good_selection = selection

    # Dropping after construction
    epochs = mne.Epochs(raw, events, preload=preload, **kwargs)
    if not preload:
        epochs.drop_bad()
    assert len(epochs) == len(events)
    epochs.drop_bad(reject=reject, verbose=True)
    assert_array_equal(epochs.selection, good_selection)  # same as before
    selection = _get_selection(epochs)
    assert_array_equal(selection, epochs.selection)

    # Dropping after construction manually
    epochs = mne.Epochs(raw, events, preload=preload, **kwargs)
    if not preload:
        epochs.drop_bad()
    assert_array_equal(epochs.selection, np.arange(len(events)))  # no drops
    drop_idx = [1, 3]
    want_selection = np.setdiff1d(np.arange(len(events)), drop_idx)
    epochs.drop(drop_idx)
    assert_array_equal(epochs.selection, want_selection)
    selection = np.round(epochs.get_data()[:, 0].max(axis=-1) / scale)
    selection = selection.astype(int) - 1
    assert_array_equal(selection, epochs.selection)


@pytest.mark.parametrize('kind', ('file', 'bytes'))
@pytest.mark.parametrize('preload', (True, False))
def test_file_like(kind, preload, tmpdir):
    """Test handling with file-like objects."""
    tempdir = str(tmpdir)
    raw = mne.io.RawArray(np.random.RandomState(0).randn(100, 10000),
                          mne.create_info(100, 1000.))
    events = mne.make_fixed_length_events(raw, 1)
    epochs = mne.Epochs(raw, events, preload=preload)
    fname = op.join(tempdir, 'test-epo.fif')
    epochs.save(fname, overwrite=True)

    with open(fname, 'rb') as file_fid:
        fid = BytesIO(file_fid.read()) if kind == 'bytes' else file_fid
        assert not fid.closed
        assert not file_fid.closed
        with pytest.raises(ValueError, match='preload must be used with file'):
            read_epochs(fid, preload=False)
        assert not fid.closed
        assert not file_fid.closed
    assert file_fid.closed


@pytest.mark.parametrize('preload', (True, False))
def test_epochs_get_data_item(preload):
    """Test epochs.get_data(item=...)."""
    raw, events, _ = _get_data()
    epochs = Epochs(raw, events[:10], event_id, tmin, tmax, preload=preload)
    if not preload:
        with pytest.raises(ValueError, match='item must be None'):
            epochs.get_data(item=0)
        epochs.drop_bad()
    one_data = epochs.get_data(item=0)
    one_epo = epochs[0]
    assert_array_equal(one_data, one_epo.get_data())


run_tests_if_main()
