content: # Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
  #         Denis Engemann <denis.engemann@gmail.com>
  #
  # License: BSD (3-clause)
  
  import os.path as op
  from copy import deepcopy
  
  from nose.tools import (assert_true, assert_equal, assert_raises,
                          assert_not_equal)
  
  from numpy.testing import (assert_array_equal, assert_array_almost_equal,
                             assert_allclose)
  import numpy as np
  import copy as cp
  import warnings
  from scipy import fftpack
  import matplotlib
  
  from mne import (io, Epochs, read_events, pick_events, read_epochs,
                   equalize_channels, pick_types, pick_channels, read_evokeds,
                   write_evokeds)
  from mne.epochs import (
      bootstrap, equalize_epoch_counts, combine_event_ids, add_channels_epochs,
      EpochsArray, concatenate_epochs, _BaseEpochs)
  from mne.utils import (_TempDir, requires_pandas, slow_test,
                         clean_warning_registry, run_tests_if_main,
                         requires_scipy_version)
  
  from mne.io.meas_info import create_info
  from mne.io.proj import _has_eeg_average_ref_proj
  from mne.event import merge_events
  from mne.io.constants import FIFF
  from mne.externals.six import text_type
  from mne.externals.six.moves import zip, cPickle as pickle
  
  matplotlib.use('Agg')  # for testing don't use X server
  
  warnings.simplefilter('always')  # enable b/c these tests throw warnings
  
  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 = 2
  
  
  def _get_data():
      raw = io.Raw(raw_fname, add_eeg_ref=False)
      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)
  
  clean_warning_registry()  # really clean warning stack
  
  
  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
      assert_raises(TypeError, pick_types, raw)
      picks_meg = pick_types(raw.info, meg=True, eeg=False)
      assert_raises(TypeError, Epochs, raw, events, event_id, tmin, tmax,
                    picks=picks, preload=False, reject='foo')
      assert_raises(ValueError, Epochs, raw, events, event_id, tmin, tmax,
                    picks=picks_meg, preload=False, reject=dict(eeg=1.))
      assert_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)
              assert_raises(ValueError, epochs.drop_bad_epochs, reject='foo')
              epochs.drop_bad_epochs()
              assert_equal(len(epochs), len(events))
              assert_array_equal(epochs.selection, np.arange(len(events)))
              assert_array_equal(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_epochs()
              assert_equal(len(epochs), len(events) - 4)
              assert_array_equal(epochs.selection, selection)
              assert_array_equal(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_epochs()
              assert_equal(len(epochs), len(events))
              assert_array_equal(epochs.get_data(), data_7[proj])
              epochs.drop_bad_epochs(reject)
              assert_equal(len(epochs), len(events) - 4)
              assert_equal(len(epochs), len(epochs.get_data()))
              assert_array_equal(epochs.selection, selection)
              assert_array_equal(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_epochs()
              assert_equal(len(epochs), len(events) - 1)
              epochs.drop_bad_epochs(reject)
              assert_equal(len(epochs), len(events) - 4)
              assert_array_equal(epochs.selection, selection)
              assert_array_equal(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
              assert_raises(ValueError, epochs.drop_bad_epochs, reject_part)
              assert_equal(len(epochs), len(events) - 4)
              assert_array_equal(epochs.get_data(), data_7[proj][keep_idx])
              epochs.drop_bad_epochs(flat=dict(mag=1.))
              assert_equal(len(epochs), 0)
              assert_raises(ValueError, epochs.drop_bad_epochs,
                            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_epochs(reject=reject)
              assert_equal(len(epochs), len(events) - 4)
              assert_array_equal(epochs.get_data(), data_7[proj][keep_idx])
  
  
  def test_decim():
      """Test epochs decimation
      """
      # First with EpochsArray
      n_epochs, n_channels, n_times = 5, 10, 20
      dec_1, dec_2 = 2, 3
      decim = dec_1 * dec_2
      sfreq = 1000.
      sfreq_new = sfreq / decim
      data = np.random.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.decimate(decim, copy=True).get_data()
      data_epochs_2 = epochs.decimate(dec_1).decimate(dec_2).get_data()
      assert_array_equal(data_epochs, data[:, :, ::decim])
      assert_array_equal(data_epochs, data_epochs_2)
  
      # Now let's do it with some real data
      raw, events, picks = _get_data()
      sfreq_new = raw.info['sfreq'] / decim
      raw.info['lowpass'] = sfreq_new / 4.  # suppress aliasing warnings
      epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                      preload=False)
      assert_raises(ValueError, epochs.decimate, -1)
      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.preload_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.preload_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.preload_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)
  
  
  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)
      assert_raises(NotImplementedError, epochs.get_data)
      # events with non integers
      assert_raises(ValueError, _BaseEpochs, raw.info, None,
                    np.ones((1, 3), float), event_id, tmin, tmax)
      assert_raises(ValueError, _BaseEpochs, raw.info, None,
                    np.ones((1, 3, 2), int), event_id, tmin, tmax)
  
  
  @requires_scipy_version('0.14')
  def test_savgol_filter():
      """Test savgol filtering
      """
      h_freq = 10.
      raw, events = _get_data()[:2]
      epochs = Epochs(raw, events, event_id, tmin, tmax)
      assert_raises(RuntimeError, epochs.savgol_filter, 10.)
      epochs = Epochs(raw, events, event_id, tmin, tmax, preload=True)
      freqs = fftpack.fftfreq(len(epochs.times), 1. / epochs.info['sfreq'])
      data = np.abs(fftpack.fft(epochs.get_data()))
      match_mask = np.logical_and(freqs >= 0, freqs <= h_freq / 2.)
      mismatch_mask = np.logical_and(freqs >= h_freq * 2, freqs < 50.)
      epochs.savgol_filter(h_freq)
      data_filt = np.abs(fftpack.fft(epochs.get_data()))
      # decent in pass-band
      assert_allclose(np.mean(data[:, :, match_mask], 0),
                      np.mean(data_filt[:, :, match_mask], 0),
                      rtol=1e-4, atol=1e-2)
      # suppression in stop-band
      assert_true(np.mean(data[:, :, mismatch_mask]) >
                  np.mean(data_filt[:, :, mismatch_mask]) * 5)
  
  
  def test_epochs_hash():
      """Test epoch hashing
      """
      raw, events = _get_data()[:2]
      epochs = Epochs(raw, events, event_id, tmin, tmax)
      assert_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_true(pickle.dumps(epochs) == pickle.dumps(epochs_2))
  
      epochs_2._data[0, 0, 0] -= 1
      assert_not_equal(hash(epochs), hash(epochs_2))
  
  
  def test_event_ordering():
      """Test event order"""
      raw, events = _get_data()[:2]
      events2 = events.copy()
      np.random.shuffle(events2)
      for ii, eve in enumerate([events, events2]):
          with warnings.catch_warnings(record=True) as w:
              warnings.simplefilter('always')
              Epochs(raw, eve, event_id, tmin, tmax,
                     baseline=(None, 0), reject=reject, flat=flat)
              assert_equal(len(w), ii)
              if ii > 0:
                  assert_true('chronologically' in '%s' % w[-1].message)
  
  
  def test_epochs_bad_baseline():
      """Test Epochs initialization with bad baseline parameters
      """
      raw, events = _get_data()[:2]
      assert_raises(ValueError, Epochs, raw, events, None, -0.1, 0.3, (-0.2, 0))
      assert_raises(ValueError, Epochs, raw, events, None, -0.1, 0.3, (0, 0.4))
  
  
  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_multi = epochs[['a/b/a', 'a/b/b']]
      assert_array_equal(epochs_regular.events, epochs_multi.events)
  
  
  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, baseline=(None, 0))
      with warnings.catch_warnings(record=True):
          evoked = epochs.average()
  
      epochs = Epochs(raw, np.array([[raw.first_samp, 0, event_id]]),
                      event_id, tmin, tmax, picks=picks, baseline=(None, 0))
      assert_true(repr(epochs))  # test repr
      epochs.drop_bad_epochs()
      assert_true(repr(epochs))
      with warnings.catch_warnings(record=True):
          evoked = epochs.average()
  
      # Event at the end
      epochs = Epochs(raw, np.array([[raw.last_samp, 0, event_id]]),
                      event_id, tmin, tmax, picks=picks, baseline=(None, 0))
  
      with warnings.catch_warnings(record=True):
          evoked = epochs.average()
          assert evoked
      warnings.resetwarnings()
  
  
  @slow_test
  def test_read_write_epochs():
      """Test epochs from raw files with IO as fif file
      """
      raw, events, picks = _get_data()
      tempdir = _TempDir()
      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)
      epochs_no_bl = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                            baseline=None, preload=True)
      assert_true(epochs_no_bl.baseline is None)
      evoked = epochs.average()
      data = epochs.get_data()
  
      # Bad tmin/tmax parameters
      assert_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,
                            baseline=(None, 0))
      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)
      epochs_no_bl.drop_channels(eog_ch_names)
      assert_true(len(epochs.info['chs']) == len(epochs.ch_names) ==
                  epochs.get_data().shape[1])
      data_no_eog = epochs.get_data()
      assert_true(data.shape[1] == (data_no_eog.shape[1] + len(eog_picks)))
  
      # test decim kwarg
      with warnings.catch_warnings(record=True) as w:
          # decim with lowpass
          warnings.simplefilter('always')
          epochs_dec = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                              baseline=(None, 0), decim=4)
          assert_equal(len(w), 1)
  
          # decim without lowpass
          lowpass = raw.info['lowpass']
          raw.info['lowpass'] = None
          epochs_dec = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                              baseline=(None, 0), decim=4)
          assert_equal(len(w), 2)
          raw.info['lowpass'] = lowpass
  
      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)
  
      n = evoked.data.shape[1]
      n_dec = evoked_dec.data.shape[1]
      n_dec_min = n // 4
      assert_true(n_dec_min <= n_dec <= n_dec_min + 1)
      assert_true(evoked_dec.info['sfreq'] == evoked.info['sfreq'] / 4)
  
      # test IO
      epochs.save(temp_fname)
      epochs_no_bl.save(temp_fname_no_bl)
      epochs_read = read_epochs(temp_fname)
      epochs_no_bl_read = read_epochs(temp_fname_no_bl)
      assert_raises(ValueError, epochs.apply_baseline, baseline=[1, 2, 3])
      epochs_no_bl_read.apply_baseline(baseline)
      assert_true(epochs_no_bl_read.baseline == baseline)
      assert_true(str(epochs_read).startswith('<Epochs'))
  
      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
      epochs.save(op.join(tempdir, 'foo-epo.fif'))
      epochs_read2 = read_epochs(op.join(tempdir, 'foo-epo.fif'))
      assert_equal(epochs_read2.event_id, epochs.event_id)
  
      # add reject here so some of the epochs get dropped
      epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                      baseline=(None, 0), reject=reject)
      epochs.save(temp_fname)
      # ensure bad events are not saved
      epochs_read3 = read_epochs(temp_fname)
      assert_array_equal(epochs_read3.events, epochs.events)
      data = epochs.get_data()
      assert_true(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_epochs([1, 2], reason='can we recover orig ID?')
      epochs.save(temp_fname)
      epochs_read5 = read_epochs(temp_fname)
      assert_array_equal(epochs_read5.selection, epochs.selection)
      assert_equal(len(epochs_read5.selection), len(epochs_read5.events))
      assert_array_equal(epochs_read5.drop_log, epochs.drop_log)
  
      # Test that one can drop channels on read file
      epochs_read5.drop_channels(epochs_read5.ch_names[:1])
  
      # test warnings on bad filenames
      with warnings.catch_warnings(record=True) as w:
          warnings.simplefilter('always')
          epochs_badname = op.join(tempdir, 'test-bad-name.fif.gz')
          epochs.save(epochs_badname)
          read_epochs(epochs_badname)
      assert_true(len(w) == 2)
  
      # 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)
      epochs_read = read_epochs(temp_fname)
      assert_allclose(epochs.get_data(), epochs_read.get_data())
      assert_array_equal(epochs.events, epochs_read.events)
      assert_equal(set(epochs.event_id.keys()),
                   set(text_type(x) for x in epochs_read.event_id.keys()))
  
      # test saving split epoch files
      epochs.save(temp_fname, split_size='7MB')
      epochs_read = read_epochs(temp_fname)
      assert_allclose(epochs.get_data(), epochs_read.get_data())
      assert_array_equal(epochs.events, epochs_read.events)
      assert_array_equal(epochs.selection, epochs_read.selection)
      assert_equal(epochs.drop_log, epochs_read.drop_log)
  
      # Test that having a single time point works
      epochs.preload_data()
      epochs.crop(0, 0, copy=False)
      assert_equal(len(epochs.times), 1)
      assert_equal(epochs.get_data().shape[-1], 1)
      epochs.save(temp_fname)
      epochs_read = read_epochs(temp_fname)
      assert_equal(len(epochs_read.times), 1)
      assert_equal(epochs.get_data().shape[-1], 1)
  
  
  def test_epochs_proj():
      """Test handling projection (apply proj in Raw or in Epochs)
      """
      tempdir = _TempDir()
      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,
                      baseline=(None, 0), proj=True)
      assert_true(all(p['active'] is True for p in epochs.info['projs']))
      evoked = epochs.average()
      assert_true(all(p['active'] is True for p in evoked.info['projs']))
      data = epochs.get_data()
  
      raw_proj = io.Raw(raw_fname, proj=True)
      epochs_no_proj = Epochs(raw_proj, events[:4], event_id, tmin, tmax,
                              picks=this_picks, baseline=(None, 0), proj=False)
  
      data_no_proj = epochs_no_proj.get_data()
      assert_true(all(p['active'] is True for p in epochs_no_proj.info['projs']))
      evoked_no_proj = epochs_no_proj.average()
      assert_true(all(p['active'] is True for p in evoked_no_proj.info['projs']))
      assert_true(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,
                      baseline=(None, 0), proj=True, add_eeg_ref=True)
      assert_true(_has_eeg_average_ref_proj(epochs.info['projs']))
      epochs = Epochs(raw, events[:4], event_id, tmin, tmax, picks=this_picks,
                      baseline=(None, 0), proj=True, add_eeg_ref=False)
      assert_true(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,
                      baseline=(None, 0), proj=True)
      assert_true(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,
                      baseline=(None, 0), 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)  # 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)
  
  
  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,
                       baseline=(None, 0))
      evoked1 = epochs1.average()
      epochs2 = Epochs(raw, events[4:8], event_id, tmin, tmax, picks=picks,
                       baseline=(None, 0))
      evoked2 = epochs2.average()
      epochs = Epochs(raw, events[:8], event_id, tmin, tmax, picks=picks,
                      baseline=(None, 0))
      evoked = epochs.average()
      evoked_sum = evoked1 + evoked2
      assert_array_equal(evoked.data, evoked_sum.data)
      assert_array_equal(evoked.times, evoked_sum.times)
      assert_true(evoked_sum.nave == (evoked1.nave + evoked2.nave))
      evoked_diff = evoked1 - evoked1
      assert_array_equal(np.zeros_like(evoked.data), evoked_diff.data)
  
  
  def test_evoked_io_from_epochs():
      """Test IO of evoked data made from epochs
      """
      tempdir = _TempDir()
      raw, events, picks = _get_data()
      # offset our tmin so we don't get exactly a zero value when decimating
      with warnings.catch_warnings(record=True) as w:
          warnings.simplefilter('always')
          epochs = Epochs(raw, events[:4], event_id, tmin + 0.011, tmax,
                          picks=picks, baseline=(None, 0), decim=5)
      assert_true(len(w) == 1)
      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=1 / evoked.info['sfreq'])
  
      # now let's do one with negative time
      with warnings.catch_warnings(record=True) as w:
          warnings.simplefilter('always')
          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
      with warnings.catch_warnings(record=True) as w:
          warnings.simplefilter('always')
          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():
      """Test calculation and read/write of standard error
      """
      raw, events, picks = _get_data()
      tempdir = _TempDir()
      epochs = Epochs(raw, events[:4], event_id, tmin, tmax, picks=picks,
                      baseline=(None, 0))
      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'), 'Unknown'),
                 read_evokeds(op.join(tempdir, 'evoked-ave.fif'), 'Unknown',
                              kind='standard_error')]
      for evoked_new in [evoked2, evoked3]:
          assert_true(evoked_new[0]._aspect_kind ==
                      FIFF.FIFFV_ASPECT_AVERAGE)
          assert_true(evoked_new[0].kind == 'average')
          assert_true(evoked_new[1]._aspect_kind ==
                      FIFF.FIFFV_ASPECT_STD_ERR)
          assert_true(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_equal(ave.nave, ave2.nave)
              assert_equal(ave._aspect_kind, ave2._aspect_kind)
              assert_equal(ave.kind, ave2.kind)
              assert_equal(ave.last, ave2.last)
              assert_equal(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, baseline=(None, 0),
                      reject=reject, flat=flat)
      assert_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_true(n_events > n_clean_epochs)
      assert_true(n_clean_epochs == 3)
      assert_true(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, baseline=(None, 0),
                      reject=reject_crazy, flat=flat)
      epochs.drop_bad_epochs()
  
      assert_true(all('MEG 2442' in e for e in epochs.drop_log))
      assert_true(all('MEG 2443' not in e for e in epochs.drop_log))
  
      # Invalid reject_tmin/reject_tmax/detrend
      assert_raises(ValueError, Epochs, raw, events1, event_id, tmin, tmax,
                    reject_tmin=1., reject_tmax=0)
      assert_raises(ValueError, Epochs, raw, events1, event_id, tmin, tmax,
                    reject_tmin=tmin - 1, reject_tmax=1.)
      assert_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,
                      baseline=(None, 0), reject=reject, flat=flat,
                      reject_tmin=0., reject_tmax=.1)
      data = epochs.get_data()
      n_clean_epochs = len(data)
      assert_true(n_clean_epochs == 7)
      assert_true(len(epochs) == 7)
      assert_true(epochs.times[epochs._reject_time][0] >= 0.)
      assert_true(epochs.times[epochs._reject_time][-1] <= 0.1)
  
      # Invalid data for _is_good_epoch function
      epochs = Epochs(raw, events1, event_id, tmin, tmax, reject=None, flat=None)
      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, baseline=(None, 0), preload=True,
                              reject=reject, flat=flat)
      data_preload = epochs_preload.get_data()
  
      epochs = Epochs(raw, events[:16], event_id, tmin, tmax, picks=picks,
                      baseline=(None, 0), 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,
                      baseline=(None, 0), preload=False,
                      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, baseline=(None, 0), preload=preload,
                           reject=reject, flat=flat)
  
          if not preload:
              epochs2.drop_bad_epochs()
  
          # 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 = np.random.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,
                      reject=None, flat=None)
      evoked = epochs.average()
      sel = pick_channels(c_evoked.ch_names, evoked.ch_names)
      evoked_data = evoked.data
      c_evoked_data = c_evoked.data[sel]
  
      assert_true(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,
                      baseline=(None, 0), preload=False,
                      reject=reject, flat=flat)
      assert_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, baseline=(None, 0), preload=True,
                       reject=reject, flat=flat)
      with warnings.catch_warnings(record=True) as w:
          epochs2.crop(-20, 200)
      assert_true(len(w) == 2)
  
      # indices for slicing
      tmin_window = tmin + 0.1
      tmax_window = tmax - 0.1
      tmask = (epochs.times >= tmin_window) & (epochs.times <= tmax_window)
      assert_true(tmin_window > tmin)
      assert_true(tmax_window < tmax)
      epochs3 = epochs2.crop(tmin_window, tmax_window, copy=True)
      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])
  
      # 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 warnings.catch_warnings(record=True):  # not LP filtered
          epochs.decimate(10)
      assert_allclose(last_time, epochs.times[-1])
  
  
  def test_resample():
      """Test of resample of epochs
      """
      raw, events, picks = _get_data()
      epochs = Epochs(raw, events[:10], event_id, tmin, tmax, picks=picks,
                      baseline=(None, 0), preload=False,
                      reject=reject, flat=flat)
      assert_raises(RuntimeError, epochs.resample, 100)
  
      epochs_o = Epochs(raw, events[:10], event_id, tmin, tmax, picks=picks,
                        baseline=(None, 0), preload=True,
                        reject=reject, flat=flat)
      epochs = epochs_o.copy()
  
      data_normal = cp.deepcopy(epochs.get_data())
      times_normal = cp.deepcopy(epochs.times)
      sfreq_normal = epochs.info['sfreq']
      # upsample by 2
      epochs = epochs_o.copy()
      epochs.resample(sfreq_normal * 2, npad=0)
      data_up = cp.deepcopy(epochs.get_data())
      times_up = cp.deepcopy(epochs.times)
      sfreq_up = epochs.info['sfreq']
      # downsamply by 2, which should match
      epochs.resample(sfreq_normal, npad=0)
      data_new = cp.deepcopy(epochs.get_data())
      times_new = cp.deepcopy(epochs.times)
      sfreq_new = epochs.info['sfreq']
      assert_true(data_up.shape[2] == 2 * data_normal.shape[2])
      assert_true(sfreq_up == 2 * sfreq_normal)
      assert_true(sfreq_new == sfreq_normal)
      assert_true(len(times_up) == 2 * len(times_normal))
      assert_array_almost_equal(times_new, times_normal, 10)
      assert_true(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=2, npad=0)
      assert_true(np.allclose(data_up, epochs._data, rtol=1e-8, atol=1e-16))
  
      # test copy flag
      epochs = epochs_o.copy()
      epochs_resampled = epochs.resample(sfreq_normal * 2, npad=0, copy=True)
      assert_true(epochs_resampled is not epochs)
      epochs_resampled = epochs.resample(sfreq_normal * 2, npad=0, copy=False)
      assert_true(epochs_resampled is epochs)
  
  
  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_true(np.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_true(np.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_true(not np.allclose(a, b))
  
      assert_raises(ValueError, Epochs, raw, events[:4], event_id, tmin, tmax,
                    detrend=2)
  
  
  def test_bootstrap():
      """Test of bootstrapping of epochs
      """
      raw, events, picks = _get_data()
      epochs = Epochs(raw, events[:5], event_id, tmin, tmax, picks=picks,
                      baseline=(None, 0), preload=True,
                      reject=reject, flat=flat)
      epochs2 = bootstrap(epochs, random_state=0)
      assert_true(len(epochs2.events) == len(epochs.events))
      assert_true(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,
                      baseline=(None, 0), 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,
                      baseline=(None, 0), 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,
                      baseline=(None, 0))
  
      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,
                      baseline=(None, 0))
  
      # make sure subraction fails if data channels are missing
      assert_raises(ValueError, epochs.subtract_evoked,
                    epochs.average(picks[:5]))
  
      # do the subraction 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,
                       baseline=(None, 0), preload=True, proj=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_epochs()  # make sure drops are logged
      assert_true(len([l for l in epochs_1.drop_log if not l]) ==
                  len(epochs_1.events))
      drop_log1 = epochs_1.drop_log = [[] for _ in range(len(epochs_1.events))]
      drop_log2 = [[] if l == ['EQUALIZED_COUNT'] else l for l in
                   epochs_1.drop_log]
      assert_true(drop_log1 == drop_log2)
      assert_true(len([l for l in epochs_1.drop_log if not l]) ==
                  len(epochs_1.events))
      assert_true(epochs_1.events.shape[0] != epochs_2.events.shape[0])
      equalize_epoch_counts([epochs_1, epochs_2], method='mintime')
      assert_true(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_true(epochs_1.events.shape[0] == epochs_3.events.shape[0])
      assert_true(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_epochs()  # make sure drops are logged
      assert_true(len([l for l in epochs.drop_log if not l]) ==
                  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'], copy=False)
      # undo the eq logging
      drop_log2 = [[] if l == ['EQUALIZED_COUNT'] else l for l in
                   epochs.drop_log]
      assert_true(drop_log1 == drop_log2)
  
      assert_true(len([l for l in epochs.drop_log if not l]) ==
                  len(epochs.events))
      new_shapes = [epochs[key].events.shape[0] for key in ['a', 'b', 'c', 'd']]
      assert_true(new_shapes[0] == new_shapes[1])
      assert_true(new_shapes[2] == new_shapes[2])
      assert_true(new_shapes[3] == new_shapes[3])
      # now with two conditions collapsed
      old_shapes = new_shapes
      epochs.equalize_event_counts([['a', 'b'], 'c'], copy=False)
      new_shapes = [epochs[key].events.shape[0] for key in ['a', 'b', 'c', 'd']]
      assert_true(new_shapes[0] + new_shapes[1] == new_shapes[2])
      assert_true(new_shapes[3] == old_shapes[3])
      assert_raises(KeyError, epochs.equalize_event_counts, [1, 'a'])
  
      # now let's combine conditions
      old_shapes = new_shapes
      epochs = epochs.equalize_event_counts([['a', 'b'], ['c', 'd']])[0]
      new_shapes = [epochs[key].events.shape[0] for key in ['a', 'b', 'c', 'd']]
      assert_true(old_shapes[0] + old_shapes[1] == new_shapes[0] + new_shapes[1])
      assert_true(new_shapes[0] + new_shapes[1] == new_shapes[2] + new_shapes[3])
      assert_raises(ValueError, combine_event_ids, epochs, ['a', 'b'],
                    {'ab': 1})
  
      combine_event_ids(epochs, ['a', 'b'], {'ab': 12}, copy=False)
      caught = 0
      for key in ['a', 'b']:
          try:
              epochs[key]
          except KeyError:
              caught += 1
      assert_raises(Exception, caught == 2)
      assert_true(not np.any(epochs.events[:, 2] == 1))
      assert_true(not np.any(epochs.events[:, 2] == 2))
      epochs = combine_event_ids(epochs, ['c', 'd'], {'cd': 34})
      assert_true(np.all(np.logical_or(epochs.events[:, 2] == 12,
                                       epochs.events[:, 2] == 34)))
      assert_true(epochs['ab'].events.shape[0] == old_shapes[0] + old_shapes[1])
      assert_true(epochs['ab'].events.shape[0] == epochs['cd'].events.shape[0])
  
  
  def test_access_by_name():
      """Test accessing epochs by event name and on_missing for rare events
      """
      tempdir = _TempDir()
      raw, events, picks = _get_data()
  
      # Test various invalid inputs
      assert_raises(ValueError, Epochs, raw, events, {1: 42, 2: 42}, tmin,
                    tmax, picks=picks)
      assert_raises(ValueError, Epochs, raw, events, {'a': 'spam', 2: 'eggs'},
                    tmin, tmax, picks=picks)
      assert_raises(ValueError, Epochs, raw, events, {'a': 'spam', 2: 'eggs'},
                    tmin, tmax, picks=picks)
      assert_raises(ValueError, Epochs, raw, events, 'foo', tmin, tmax,
                    picks=picks)
      assert_raises(ValueError, 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)
      assert_raises(ValueError, Epochs, raw, events, event_id_illegal,
                    tmin, tmax)
      # Test on_missing
      assert_raises(ValueError, Epochs, raw, events, 1, tmin, tmax,
                    on_missing='foo')
      with warnings.catch_warnings(record=True) as w:
          warnings.simplefilter('always')
          Epochs(raw, events, event_id_illegal, tmin, tmax, on_missing='warning')
          nw = len(w)
          assert_true(1 <= nw <= 2)
          Epochs(raw, events, event_id_illegal, tmin, tmax, on_missing='ignore')
          assert_equal(len(w), nw)
  
      # 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)
      assert_raises(KeyError, epochs.__getitem__, 'bar')
  
      data = epochs['a'].get_data()
      event_a = events[events[:, 2] == 1]
      assert_true(len(data) == len(event_a))
  
      epochs = Epochs(raw, events, {'a': 1, 'b': 2}, tmin, tmax, picks=picks,
                      preload=True)
      assert_raises(KeyError, epochs.__getitem__, 'bar')
      temp_fname = op.join(tempdir, 'test-epo.fif')
      epochs.save(temp_fname)
      epochs2 = read_epochs(temp_fname)
  
      for ep in [epochs, epochs2]:
          data = ep['a'].get_data()
          event_a = events[events[:, 2] == 1]
          assert_true(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_true(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)
      assert_raises(ValueError, epochs.to_data_frame, index=['foo', 'bar'])
      assert_raises(ValueError, epochs.to_data_frame, index='qux')
      assert_raises(ValueError, epochs.to_data_frame, np.arange(400))
  
      df = epochs.to_data_frame(index=['condition', 'epoch', 'time'],
                                picks=list(range(epochs.info['nchan'])))
  
      # Default index and picks
      df2 = epochs.to_data_frame()
      assert_equal(df.index.names, df2.index.names)
      assert_array_equal(df.columns.values, epochs.ch_names)
  
      data = np.hstack(epochs.get_data())
      assert_true((df.columns == epochs.ch_names).all())
      assert_array_equal(df.values[:, 0], data[0] * 1e13)
      assert_array_equal(df.values[:, 2], data[2] * 1e15)
      for ind in ['time', ['condition', 'time'], ['condition', 'time', 'epoch']]:
          df = epochs.to_data_frame(index=ind)
          assert_true(df.index.names == ind if isinstance(ind, list) else [ind])
          # test that non-indexed data were present as categorial variables
          assert_array_equal(sorted(df.reset_index().columns[:3]),
                             sorted(['time', 'condition', 'epoch']))
  
  
  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,
                          baseline=(None, 0), proj=proj)
  
          assert_true(all(p['active'] == proj for p in epochs.info['projs']))
  
          # test adding / deleting proj
          if proj:
              epochs.get_data()
              assert_true(all(p['active'] == proj for p in epochs.info['projs']))
              assert_raises(ValueError, epochs.add_proj, epochs.info['projs'][0],
                            {'remove_existing': True})
              assert_raises(ValueError, epochs.add_proj, 'spam')
              assert_raises(ValueError, epochs.del_proj, 0)
          else:
              projs = deepcopy(epochs.info['projs'])
              n_proj = len(epochs.info['projs'])
              epochs.del_proj(0)
              assert_true(len(epochs.info['projs']) == n_proj - 1)
              epochs.add_proj(projs, remove_existing=False)
              assert_true(len(epochs.info['projs']) == 2 * n_proj - 1)
              epochs.add_proj(projs, remove_existing=True)
              assert_true(len(epochs.info['projs']) == n_proj)
  
      # catch no-gos.
      # wrong proj argument
      assert_raises(ValueError, Epochs, raw, events[:4], event_id, tmin, tmax,
                    picks=picks, baseline=(None, 0), proj='crazy')
      # delayed without reject params
      assert_raises(RuntimeError, Epochs, raw, events[:4], event_id, tmin, tmax,
                    picks=picks, baseline=(None, 0), proj='delayed', reject=None)
  
      for preload in [True, False]:
          epochs = Epochs(raw, events[:4], event_id, tmin, tmax, picks=picks,
                          baseline=(None, 0), proj='delayed', preload=preload,
                          add_eeg_ref=True, reject=reject)
          epochs2 = Epochs(raw, events[:4], event_id, tmin, tmax, picks=picks,
                           baseline=(None, 0), proj=True, preload=preload,
                           add_eeg_ref=True, reject=reject)
  
          assert_allclose(epochs.copy().apply_proj().get_data()[0],
                          epochs2.get_data()[0], 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(epochs2.get_data(), epochs2.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, epochs2._projector)
      assert_allclose(epochs._data, epochs2.get_data())
  
      # test mixin against manual application
      epochs = Epochs(raw, events[:4], event_id, tmin, tmax, picks=picks,
                      baseline=None, proj=False, add_eeg_ref=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
      """
      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.info['lowpass'] = 40.  # fake the LP info so no warnings
      for preload in (True, False):
          for proj in (True, False, 'delayed'):
              for decim in (1, 3):
                  for ii in range(2):
                      epochs = Epochs(raw, events, event_id, tmin, tmax,
                                      picks=picks, proj=proj, reject=reject,
                                      preload=preload, decim=decim)
                      if ii == 1:
                          epochs.preload_data()
                      picks_data = pick_types(epochs.info, meg=True, eeg=True)
                      evoked = epochs.average(picks=picks_data)
                      if proj is True:
                          evoked.apply_proj()
                      epochs_data = epochs.get_data().mean(axis=0)[picks_data]
                      assert_array_equal(evoked.ch_names,
                                         np.array(epochs.ch_names)[picks_data])
                      assert_allclose(evoked.times, epochs.times)
                      assert_allclose(evoked.data, epochs_data,
                                      rtol=1e-5, atol=1e-15)
  
  
  def test_drop_epochs():
      """Test dropping of epochs.
      """
      raw, events, picks = _get_data()
      epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                      baseline=(None, 0))
      events1 = events[events[:, 2] == event_id]
  
      # Bound checks
      assert_raises(IndexError, epochs.drop_epochs, [len(epochs.events)])
      assert_raises(IndexError, epochs.drop_epochs, [-1])
      assert_raises(ValueError, epochs.drop_epochs, [[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_true(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_epochs([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_true(d2 == ['IGNORED'])
                  if d1 != ['IGNORED'] and d1 != []:
                      assert_true((d2 == d1) or (d2 == ['IGNORED']))
                  if d1 == []:
                      assert_true(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()[:2]
  
      tests = [(('mag', False), ('grad', 'eeg')),
               (('grad', False), ('mag', 'eeg')),
               ((False, True), ('grad', 'mag'))]
  
      for (meg, eeg), others in tests:
          picks_contains = pick_types(raw.info, meg=meg, eeg=eeg)
          epochs = Epochs(raw, events, {'a': 1, 'b': 2}, tmin, tmax,
                          picks=picks_contains, reject=None,
                          preload=False)
          test = 'eeg' if eeg is True else meg
          assert_true(test in epochs)
          assert_true(not any(o in epochs for o in others))
  
      assert_raises(ValueError, epochs.__contains__, 'foo')
      assert_raises(ValueError, 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, picks=None,
                      baseline=(None, 0), preload=True)
      drop_ch = epochs.ch_names[:3]
      ch_names = epochs.ch_names[3:]
  
      ch_names_orig = epochs.ch_names
      dummy = epochs.drop_channels(drop_ch, copy=True)
      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,
                      baseline=(None, 0), preload=True)
      ch_names = epochs.ch_names[:3]
      epochs.preload = False
      assert_raises(RuntimeError, epochs.drop_channels, ['foo'])
      epochs.preload = True
      ch_names_orig = epochs.ch_names
      dummy = epochs.pick_channels(ch_names, copy=True)
      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
      assert_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,
                       baseline=(None, 0), 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]
      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)
  
      assert_raises(ValueError, Epochs, raw, events, event_id_illegal, tmin,
                    tmax, picks=picks, baseline=(None, 0), 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, baseline=(None, 0),
                        reject=None, 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)
          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(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)
  
      epochs_meg2 = epochs_meg.copy()
      epochs_meg2.info['meas_date'] += 10
      add_channels_epochs([epochs_meg2, epochs_eeg])
  
      epochs_meg2 = epochs_meg.copy()
      epochs2.info['filename'] = epochs2.info['filename'].upper()
      epochs2 = add_channels_epochs([epochs_meg, epochs_eeg])
  
      epochs_meg2 = epochs_meg.copy()
      epochs_meg2.events[3, 2] -= 1
      assert_raises(ValueError, add_channels_epochs,
                    [epochs_meg2, epochs_eeg])
  
      assert_raises(ValueError, add_channels_epochs,
                    [epochs_meg, epochs_eeg[:2]])
  
      epochs_meg.info['chs'].pop(0)
      epochs_meg.info['ch_names'].pop(0)
      epochs_meg.info['nchan'] -= 1
      assert_raises(RuntimeError, add_channels_epochs,
                    [epochs_meg, epochs_eeg])
  
      epochs_meg2 = epochs_meg.copy()
      epochs_meg2.info['sfreq'] = None
      assert_raises(RuntimeError, add_channels_epochs,
                    [epochs_meg2, epochs_eeg])
  
      epochs_meg2 = epochs_meg.copy()
      epochs_meg2.info['sfreq'] += 10
      assert_raises(RuntimeError, add_channels_epochs,
                    [epochs_meg2, epochs_eeg])
  
      epochs_meg2 = epochs_meg.copy()
      epochs_meg2.info['ch_names'][1] = epochs_meg2.info['ch_names'][0]
      epochs_meg2.info['chs'][1]['ch_name'] = epochs_meg2.info['ch_names'][1]
      assert_raises(ValueError, add_channels_epochs,
                    [epochs_meg2, epochs_eeg])
  
      epochs_meg2 = epochs_meg.copy()
      epochs_meg2.info['dev_head_t']['to'] += 1
      assert_raises(ValueError, add_channels_epochs,
                    [epochs_meg2, epochs_eeg])
  
      epochs_meg2 = epochs_meg.copy()
      epochs_meg2.info['dev_head_t']['to'] += 1
      assert_raises(ValueError, add_channels_epochs,
                    [epochs_meg2, epochs_eeg])
  
      epochs_meg2 = epochs_meg.copy()
      epochs_meg2.info['expimenter'] = 'foo'
      assert_raises(RuntimeError, add_channels_epochs,
                    [epochs_meg2, epochs_eeg])
  
      epochs_meg2 = epochs_meg.copy()
      epochs_meg2.preload = False
      assert_raises(ValueError, add_channels_epochs,
                    [epochs_meg2, epochs_eeg])
  
      epochs_meg2 = epochs_meg.copy()
      epochs_meg2.tmin += 0.4
      assert_raises(NotImplementedError, add_channels_epochs,
                    [epochs_meg2, epochs_eeg])
  
      epochs_meg2 = epochs_meg.copy()
      epochs_meg2.tmin += 0.5
      assert_raises(NotImplementedError, add_channels_epochs,
                    [epochs_meg2, epochs_eeg])
  
      epochs_meg2 = epochs_meg.copy()
      epochs_meg2.baseline = None
      assert_raises(NotImplementedError, add_channels_epochs,
                    [epochs_meg2, epochs_eeg])
  
      epochs_meg2 = epochs_meg.copy()
      epochs_meg2.event_id['b'] = 2
      assert_raises(NotImplementedError, add_channels_epochs,
                    [epochs_meg2, epochs_eeg])
  
  
  def test_array_epochs():
      """Test creating epochs from array
      """
      import matplotlib.pyplot as plt
      tempdir = _TempDir()
  
      # creating
      rng = np.random.RandomState(42)
      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_true(str(epochs).startswith('<EpochsArray'))
      # From GH#1963
      assert_raises(ValueError, EpochsArray, data[:-1], info, events, tmin,
                    event_id)
      assert_raises(ValueError, EpochsArray, data, info, events, tmin,
                    dict(a=1))
  
      # saving
      temp_fname = op.join(tempdir, 'test-epo.fif')
      epochs.save(temp_fname)
      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=events, event_id=event_id,
                           tmin=-.2, baseline=(None, 0))
      ep_data = epochs.get_data()
      assert_array_equal(np.zeros_like(ep_data), ep_data)
  
      # one time point
      epochs = EpochsArray(data[:, :, :1], info, events=events,
                           event_id=event_id, tmin=0., baseline=None)
      assert_allclose(epochs.times, [0.])
      assert_allclose(epochs.get_data(), data[:, :, :1])
      epochs.save(temp_fname)
      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, baseline=(None, 0))
  
  
  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()
      epochs2._data = epochs2.get_data()
      epochs2.preload = True
      assert_raises(
          ValueError, concatenate_epochs,
          [epochs, epochs2.drop_channels(epochs2.ch_names[:1], copy=True)])
  
      epochs2.times = np.delete(epochs2.times, 1)
      assert_raises(
          ValueError,
          concatenate_epochs, [epochs, epochs2])
  
      assert_equal(epochs_conc._raw, None)
  
      # check if baseline is same for all epochs
      epochs2.baseline = (-0.1, None)
      assert_raises(ValueError, concatenate_epochs, [epochs, epochs2])
  
  
  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.pick_types(meg=False, eeg=True, copy=True)
      epoch_meg = epoch.pick_types(meg=True, copy=True)
      epoch_stim = epoch.pick_types(meg=False, stim=True, copy=True)
      epoch_eeg_meg = epoch.pick_types(meg=True, eeg=True, copy=True)
      epoch_new = epoch_meg.add_channels([epoch_eeg, epoch_stim], copy=True)
      assert_true(all(ch in epoch_new.ch_names
                      for ch in epoch_stim.ch_names + epoch_meg.ch_names))
      epoch_new = epoch_meg.add_channels([epoch_eeg], copy=True)
  
      assert_true(ch in epoch_new.ch_names for ch in epoch.ch_names)
      assert_array_equal(epoch_new._data, epoch_eeg_meg._data)
      assert_true(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)
  
      assert_raises(AssertionError, epoch_meg.add_channels, [epoch_nopre])
      assert_raises(RuntimeError, epoch_meg.add_channels, [epoch_badsf])
      assert_raises(AssertionError, epoch_meg.add_channels, [epoch_eeg])
      assert_raises(ValueError, epoch_meg.add_channels, [epoch_meg])
      assert_raises(AssertionError, epoch_meg.add_channels, epoch_badsf)
  
  
  run_tests_if_main()
  