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"""Data Equivalence Tests"""
from __future__ import print_function
# Authors: Teon Brooks <teon@nyu.edu>
# Martin Billinger <martin.billinger@tugraz.at>
#
# License: BSD (3-clause)
import os.path as op
import inspect
from nose.tools import assert_equal, assert_true
from numpy.testing import assert_array_almost_equal, assert_array_equal
from numpy.testing import assert_raises
from scipy import io
import numpy as np
from mne.externals.six import iterbytes
from mne.utils import _TempDir
from mne import pick_types
from mne.io import Raw
from mne.io import read_raw_edf
import mne.io.edf.edf as edfmodule
from mne.event import find_events
FILE = inspect.getfile(inspect.currentframe())
data_dir = op.join(op.dirname(op.abspath(FILE)), 'data')
hpts_path = op.join(data_dir, 'biosemi.hpts')
bdf_path = op.join(data_dir, 'test.bdf')
edf_path = op.join(data_dir, 'test.edf')
bdf_eeglab_path = op.join(data_dir, 'test_bdf_eeglab.mat')
edf_eeglab_path = op.join(data_dir, 'test_edf_eeglab.mat')
tempdir = _TempDir()
def test_bdf_data():
"""Test reading raw bdf files
"""
raw_py = read_raw_edf(bdf_path, hpts=hpts_path, preload=True)
picks = pick_types(raw_py.info, meg=False, eeg=True, exclude='bads')
data_py, _ = raw_py[picks]
print(raw_py) # to test repr
print(raw_py.info) # to test Info repr
# this .mat was generated using the EEG Lab Biosemi Reader
raw_eeglab = io.loadmat(bdf_eeglab_path)
raw_eeglab = raw_eeglab['data'] * 1e-6 # data are stored in microvolts
data_eeglab = raw_eeglab[picks]
assert_array_almost_equal(data_py, data_eeglab)
# Manually checking that float coordinates are imported
assert_true((raw_py.info['chs'][0]['eeg_loc']).any())
assert_true((raw_py.info['chs'][25]['eeg_loc']).any())
assert_true((raw_py.info['chs'][63]['eeg_loc']).any())
def test_edf_data():
"""Test reading raw edf files
"""
raw_py = read_raw_edf(edf_path, stim_channel=139, preload=True)
picks = pick_types(raw_py.info, meg=False, eeg=True,
exclude=['EDF Annotations'])
data_py, _ = raw_py[picks]
print(raw_py) # to test repr
print(raw_py.info) # to test Info repr
# this .mat was generated using the EEG Lab Biosemi Reader
raw_eeglab = io.loadmat(edf_eeglab_path)
raw_eeglab = raw_eeglab['data'] * 1e-6 # data are stored in microvolts
data_eeglab = raw_eeglab[picks]
assert_array_almost_equal(data_py, data_eeglab)
def test_read_segment():
"""Test writing raw edf files when preload is False
"""
raw1 = read_raw_edf(edf_path, stim_channel=139, preload=False)
raw1_file = op.join(tempdir, 'test1-raw.fif')
raw1.save(raw1_file, overwrite=True, buffer_size_sec=1)
raw11 = Raw(raw1_file, preload=True)
data1, times1 = raw1[:139, :]
data11, times11 = raw11[:139, :]
assert_array_almost_equal(data1, data11, 10)
assert_array_almost_equal(times1, times11)
assert_equal(sorted(raw1.info.keys()), sorted(raw11.info.keys()))
raw2 = read_raw_edf(edf_path, stim_channel=139, preload=True)
raw2_file = op.join(tempdir, 'test2-raw.fif')
raw2.save(raw2_file, overwrite=True)
data2, times2 = raw2[:139, :]
assert_array_equal(data1, data2)
assert_array_equal(times1, times2)
raw1 = Raw(raw1_file, preload=True)
raw2 = Raw(raw2_file, preload=True)
assert_array_equal(raw1._data, raw2._data)
# test the _read_segment function by only loading some of the data
raw1 = read_raw_edf(edf_path, preload=False)
raw2 = read_raw_edf(edf_path, preload=True)
# select some random range of data to compare
data1, times1 = raw1[:, 345:417]
data2, times2 = raw2[:, 345:417]
assert_array_equal(data1, data2)
assert_array_equal(times1, times2)
def test_append():
"""Test appending raw edf objects using Raw.append
"""
# Author: Alan Leggitt <alan.leggitt@ucsf.edu>
raw = read_raw_edf(bdf_path, hpts=hpts_path, preload=False)
raw0 = raw.copy()
raw1 = raw.copy()
raw0.append(raw1)
assert_true(2 * len(raw) == len(raw0))
def test_parse_annotation():
"""Test parsing the tal channel
"""
# test the parser
annot = (b'+180\x14Lights off\x14Close door\x14\x00\x00\x00\x00\x00'
b'+180\x14Lights off\x14\x00\x00\x00\x00\x00\x00\x00\x00'
b'+180\x14Close door\x14\x00\x00\x00\x00\x00\x00\x00\x00'
b'+3.14\x1504.20\x14nothing\x14\x00\x00\x00\x00'
b'+1800.2\x1525.5\x14Apnea\x14\x00\x00\x00\x00\x00\x00\x00'
b'+123\x14\x14\x00\x00\x00\x00\x00\x00\x00')
annot = [a for a in iterbytes(annot)]
annot[1::2] = [a * 256 for a in annot[1::2]]
tal_channel = map(sum, zip(annot[0::2], annot[1::2]))
events = edfmodule._parse_tal_channel(tal_channel)
assert_equal(events, [[180.0, 0, 'Lights off'],
[180.0, 0, 'Close door'],
[180.0, 0, 'Lights off'],
[180.0, 0, 'Close door'],
[3.14, 4.2, 'nothing'],
[1800.2, 25.5, 'Apnea']])
def test_edf_annotations():
"""Test if events are detected correctly in a typical MNE workflow.
"""
# test an actual file
raw = read_raw_edf(edf_path, tal_channel=-1,
hpts=hpts_path, preload=True)
edf_events = find_events(raw, output='step', shortest_event=0,
stim_channel='STI 014')
# onset, duration, id
events = [[0.1344, 0.2560, 2],
[0.3904, 1.0000, 2],
[2.0000, 0.0000, 3],
[2.5000, 2.5000, 2]]
events = np.array(events)
events[:, :2] *= 512 # convert time to samples
events = np.array(events, dtype=int)
events[:, 1] -= 1
events[events[:, 1] <= 0, 1] = 1
events[:, 1] += events[:, 0]
onsets = events[:, [0, 2]]
offsets = events[:, [1, 2]]
events = np.zeros((2 * events.shape[0], 3), dtype=int)
events[0::2, [0, 2]] = onsets
events[1::2, [0, 1]] = offsets
assert_array_equal(edf_events, events)
def test_write_annotations():
"""Test writing raw files when annotations were parsed.
"""
raw1 = read_raw_edf(edf_path, tal_channel=-1, preload=True)
raw1_file = op.join(tempdir, 'test1-raw.fif')
raw1.save(raw1_file, overwrite=True, buffer_size_sec=1)
raw11 = Raw(raw1_file, preload=True)
data1, times1 = raw1[:, :]
data11, times11 = raw11[:, :]
assert_array_almost_equal(data1, data11)
assert_array_almost_equal(times1, times11)
assert_equal(sorted(raw1.info.keys()), sorted(raw11.info.keys()))
assert_raises(RuntimeError, read_raw_edf,
edf_path, tal_channel=-1, preload=False)
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