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# Author: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
import warnings
import matplotlib
import numpy as np
from numpy.testing import assert_array_almost_equal, assert_allclose
from nose.tools import assert_equal, assert_raises, assert_true
from mne import find_events, Epochs, pick_types
from mne.io import read_raw_fif
from mne.io.array import RawArray
from mne.io.tests.test_raw import _test_raw_reader
from mne.io.meas_info import create_info, _kind_dict
from mne.utils import slow_test, requires_version, run_tests_if_main
matplotlib.use('Agg') # for testing don't use X server
warnings.simplefilter('always') # enable b/c these tests might throw warnings
base_dir = op.join(op.dirname(__file__), '..', '..', 'tests', 'data')
fif_fname = op.join(base_dir, 'test_raw.fif')
@slow_test
@requires_version('scipy', '0.12')
def test_array_raw():
"""Test creating raw from array
"""
import matplotlib.pyplot as plt
# creating
raw = read_raw_fif(fif_fname, add_eeg_ref=False).crop(2, 5)
data, times = raw[:, :]
sfreq = raw.info['sfreq']
ch_names = [(ch[4:] if 'STI' not in ch else ch)
for ch in raw.info['ch_names']] # change them, why not
# del raw
types = list()
for ci in range(101):
types.extend(('grad', 'grad', 'mag'))
types.extend(['ecog', 'seeg', 'hbo']) # really 3 meg channels
types.extend(['stim'] * 9)
types.extend(['eeg'] * 60)
# wrong length
assert_raises(ValueError, create_info, ch_names, sfreq, types)
# bad entry
types.append('foo')
assert_raises(KeyError, create_info, ch_names, sfreq, types)
types[-1] = 'eog'
# default type
info = create_info(ch_names, sfreq)
assert_equal(info['chs'][0]['kind'], _kind_dict['misc'][0])
# use real types
info = create_info(ch_names, sfreq, types)
raw2 = _test_raw_reader(RawArray, test_preloading=False,
data=data, info=info, first_samp=2 * data.shape[1])
data2, times2 = raw2[:, :]
assert_allclose(data, data2)
assert_allclose(times, times2)
assert_true('RawArray' in repr(raw2))
assert_raises(TypeError, RawArray, info, data)
# filtering
picks = pick_types(raw2.info, misc=True, exclude='bads')[:4]
assert_equal(len(picks), 4)
raw_lp = raw2.copy()
raw_lp.filter(None, 4.0, h_trans_bandwidth=4.,
filter_length='auto', picks=picks, n_jobs=2, phase='zero',
fir_window='hamming')
raw_hp = raw2.copy()
raw_hp.filter(16.0, None, l_trans_bandwidth=4.,
filter_length='auto', picks=picks, n_jobs=2, phase='zero',
fir_window='hamming')
raw_bp = raw2.copy()
raw_bp.filter(8.0, 12.0, l_trans_bandwidth=4.,
h_trans_bandwidth=4., filter_length='auto', picks=picks,
phase='zero', fir_window='hamming')
raw_bs = raw2.copy()
raw_bs.filter(16.0, 4.0, l_trans_bandwidth=4., h_trans_bandwidth=4.,
filter_length='auto', picks=picks, n_jobs=2, phase='zero',
fir_window='hamming')
data, _ = raw2[picks, :]
lp_data, _ = raw_lp[picks, :]
hp_data, _ = raw_hp[picks, :]
bp_data, _ = raw_bp[picks, :]
bs_data, _ = raw_bs[picks, :]
sig_dec = 15
assert_array_almost_equal(data, lp_data + bp_data + hp_data, sig_dec)
assert_array_almost_equal(data, bp_data + bs_data, sig_dec)
# plotting
raw2.plot()
raw2.plot_psd()
plt.close('all')
# epoching
events = find_events(raw2, stim_channel='STI 014')
events[:, 2] = 1
assert_true(len(events) > 2)
epochs = Epochs(raw2, events, 1, -0.2, 0.4, preload=True,
add_eeg_ref=False)
epochs.plot_drop_log()
epochs.plot()
evoked = epochs.average()
evoked.plot()
assert_equal(evoked.nave, len(events) - 1)
plt.close('all')
# complex data
rng = np.random.RandomState(0)
data = rng.randn(1, 100) + 1j * rng.randn(1, 100)
raw = RawArray(data, create_info(1, 1000., 'eeg'))
assert_allclose(raw._data, data)
run_tests_if_main()
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