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test_raw_fiff.py
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test_raw_fiff.py
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# Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import os
import os.path as op
import glob
from copy import deepcopy
import warnings
import itertools as itt
import numpy as np
from numpy.testing import (assert_array_almost_equal, assert_array_equal,
assert_allclose, assert_equal)
from nose.tools import assert_true, assert_raises, assert_not_equal
from mne.datasets import testing
from mne.io.constants import FIFF
from mne.io import Raw, RawArray, concatenate_raws, read_raw_fif
from mne.io.tests.test_raw import _test_concat, _test_raw_reader
from mne import (concatenate_events, find_events, equalize_channels,
compute_proj_raw, pick_types, pick_channels, create_info)
from mne.utils import (_TempDir, requires_pandas, slow_test,
requires_mne, run_subprocess, run_tests_if_main)
from mne.externals.six.moves import zip, cPickle as pickle
from mne.io.proc_history import _get_sss_rank
from mne.io.pick import _picks_by_type
from mne.annotations import Annotations
warnings.simplefilter('always') # enable b/c these tests throw warnings
data_dir = op.join(testing.data_path(download=False), 'MEG', 'sample')
fif_fname = op.join(data_dir, 'sample_audvis_trunc_raw.fif')
base_dir = op.join(op.dirname(__file__), '..', '..', 'tests', 'data')
test_fif_fname = op.join(base_dir, 'test_raw.fif')
test_fif_gz_fname = op.join(base_dir, 'test_raw.fif.gz')
ctf_fname = op.join(base_dir, 'test_ctf_raw.fif')
ctf_comp_fname = op.join(base_dir, 'test_ctf_comp_raw.fif')
fif_bad_marked_fname = op.join(base_dir, 'test_withbads_raw.fif')
bad_file_works = op.join(base_dir, 'test_bads.txt')
bad_file_wrong = op.join(base_dir, 'test_wrong_bads.txt')
hp_fname = op.join(base_dir, 'test_chpi_raw_hp.txt')
hp_fif_fname = op.join(base_dir, 'test_chpi_raw_sss.fif')
rng = np.random.RandomState(0)
def test_fix_types():
"""Test fixing of channel types
"""
for fname, change in ((hp_fif_fname, True), (test_fif_fname, False),
(ctf_fname, False)):
raw = Raw(fname)
mag_picks = pick_types(raw.info, meg='mag')
other_picks = np.setdiff1d(np.arange(len(raw.ch_names)), mag_picks)
# we don't actually have any files suffering from this problem, so
# fake it
if change:
for ii in mag_picks:
raw.info['chs'][ii]['coil_type'] = FIFF.FIFFV_COIL_VV_MAG_T2
orig_types = np.array([ch['coil_type'] for ch in raw.info['chs']])
raw.fix_mag_coil_types()
new_types = np.array([ch['coil_type'] for ch in raw.info['chs']])
if not change:
assert_array_equal(orig_types, new_types)
else:
assert_array_equal(orig_types[other_picks], new_types[other_picks])
assert_true((orig_types[mag_picks] != new_types[mag_picks]).all())
assert_true((new_types[mag_picks] ==
FIFF.FIFFV_COIL_VV_MAG_T3).all())
def test_concat():
"""Test RawFIF concatenation
"""
# we trim the file to save lots of memory and some time
tempdir = _TempDir()
raw = read_raw_fif(test_fif_fname)
raw.crop(0, 2., copy=False)
test_name = op.join(tempdir, 'test_raw.fif')
raw.save(test_name)
# now run the standard test
_test_concat(read_raw_fif, test_name)
@testing.requires_testing_data
def test_hash_raw():
"""Test hashing raw objects
"""
raw = read_raw_fif(fif_fname)
assert_raises(RuntimeError, raw.__hash__)
raw = Raw(fif_fname).crop(0, 0.5, False)
raw.load_data()
raw_2 = Raw(fif_fname).crop(0, 0.5, False)
raw_2.load_data()
assert_equal(hash(raw), hash(raw_2))
# do NOT use assert_equal here, failing output is terrible
assert_equal(pickle.dumps(raw), pickle.dumps(raw_2))
raw_2._data[0, 0] -= 1
assert_not_equal(hash(raw), hash(raw_2))
@testing.requires_testing_data
def test_subject_info():
"""Test reading subject information
"""
tempdir = _TempDir()
raw = Raw(fif_fname).crop(0, 1, False)
assert_true(raw.info['subject_info'] is None)
# fake some subject data
keys = ['id', 'his_id', 'last_name', 'first_name', 'birthday', 'sex',
'hand']
vals = [1, 'foobar', 'bar', 'foo', (1901, 2, 3), 0, 1]
subject_info = dict()
for key, val in zip(keys, vals):
subject_info[key] = val
raw.info['subject_info'] = subject_info
out_fname = op.join(tempdir, 'test_subj_info_raw.fif')
raw.save(out_fname, overwrite=True)
raw_read = Raw(out_fname)
for key in keys:
assert_equal(subject_info[key], raw_read.info['subject_info'][key])
raw_read.anonymize()
assert_true(raw_read.info.get('subject_info') is None)
out_fname_anon = op.join(tempdir, 'test_subj_info_anon_raw.fif')
raw_read.save(out_fname_anon, overwrite=True)
raw_read = Raw(out_fname_anon)
assert_true(raw_read.info.get('subject_info') is None)
@testing.requires_testing_data
def test_copy_append():
"""Test raw copying and appending combinations
"""
raw = Raw(fif_fname, preload=True).copy()
raw_full = Raw(fif_fname)
raw_full.append(raw)
data = raw_full[:, :][0]
assert_equal(data.shape[1], 2 * raw._data.shape[1])
@slow_test
@testing.requires_testing_data
def test_rank_estimation():
"""Test raw rank estimation
"""
iter_tests = itt.product(
[fif_fname, hp_fif_fname], # sss
['norm', dict(mag=1e11, grad=1e9, eeg=1e5)]
)
for fname, scalings in iter_tests:
raw = Raw(fname)
(_, picks_meg), (_, picks_eeg) = _picks_by_type(raw.info,
meg_combined=True)
n_meg = len(picks_meg)
n_eeg = len(picks_eeg)
raw = Raw(fname, preload=True)
if 'proc_history' not in raw.info:
expected_rank = n_meg + n_eeg
else:
mf = raw.info['proc_history'][0]['max_info']
expected_rank = _get_sss_rank(mf) + n_eeg
assert_array_equal(raw.estimate_rank(scalings=scalings), expected_rank)
assert_array_equal(raw.estimate_rank(picks=picks_eeg,
scalings=scalings),
n_eeg)
raw = Raw(fname, preload=False)
if 'sss' in fname:
tstart, tstop = 0., 30.
raw.add_proj(compute_proj_raw(raw))
raw.apply_proj()
else:
tstart, tstop = 10., 20.
raw.apply_proj()
n_proj = len(raw.info['projs'])
assert_array_equal(raw.estimate_rank(tstart=tstart, tstop=tstop,
scalings=scalings),
expected_rank - (1 if 'sss' in fname else n_proj))
@testing.requires_testing_data
def test_output_formats():
"""Test saving and loading raw data using multiple formats
"""
tempdir = _TempDir()
formats = ['short', 'int', 'single', 'double']
tols = [1e-4, 1e-7, 1e-7, 1e-15]
# let's fake a raw file with different formats
raw = Raw(test_fif_fname).crop(0, 1, copy=False)
temp_file = op.join(tempdir, 'raw.fif')
for ii, (fmt, tol) in enumerate(zip(formats, tols)):
# Let's test the overwriting error throwing while we're at it
if ii > 0:
assert_raises(IOError, raw.save, temp_file, fmt=fmt)
raw.save(temp_file, fmt=fmt, overwrite=True)
raw2 = Raw(temp_file)
raw2_data = raw2[:, :][0]
assert_allclose(raw2_data, raw[:, :][0], rtol=tol, atol=1e-25)
assert_equal(raw2.orig_format, fmt)
def _compare_combo(raw, new, times, n_times):
for ti in times: # let's do a subset of points for speed
orig = raw[:, ti % n_times][0]
# these are almost_equals because of possible dtype differences
assert_allclose(orig, new[:, ti][0])
@slow_test
@testing.requires_testing_data
def test_multiple_files():
"""Test loading multiple files simultaneously
"""
# split file
tempdir = _TempDir()
raw = Raw(fif_fname).crop(0, 10, False)
raw.load_data()
raw.load_data() # test no operation
split_size = 3. # in seconds
sfreq = raw.info['sfreq']
nsamp = (raw.last_samp - raw.first_samp)
tmins = np.round(np.arange(0., nsamp, split_size * sfreq))
tmaxs = np.concatenate((tmins[1:] - 1, [nsamp]))
tmaxs /= sfreq
tmins /= sfreq
assert_equal(raw.n_times, len(raw.times))
# going in reverse order so the last fname is the first file (need later)
raws = [None] * len(tmins)
for ri in range(len(tmins) - 1, -1, -1):
fname = op.join(tempdir, 'test_raw_split-%d_raw.fif' % ri)
raw.save(fname, tmin=tmins[ri], tmax=tmaxs[ri])
raws[ri] = Raw(fname)
events = [find_events(r, stim_channel='STI 014') for r in raws]
last_samps = [r.last_samp for r in raws]
first_samps = [r.first_samp for r in raws]
# test concatenation of split file
assert_raises(ValueError, concatenate_raws, raws, True, events[1:])
all_raw_1, events1 = concatenate_raws(raws, preload=False,
events_list=events)
assert_equal(raw.first_samp, all_raw_1.first_samp)
assert_equal(raw.last_samp, all_raw_1.last_samp)
assert_allclose(raw[:, :][0], all_raw_1[:, :][0])
raws[0] = Raw(fname)
all_raw_2 = concatenate_raws(raws, preload=True)
assert_allclose(raw[:, :][0], all_raw_2[:, :][0])
# test proper event treatment for split files
events2 = concatenate_events(events, first_samps, last_samps)
events3 = find_events(all_raw_2, stim_channel='STI 014')
assert_array_equal(events1, events2)
assert_array_equal(events1, events3)
# test various methods of combining files
raw = Raw(fif_fname, preload=True)
n_times = raw.n_times
# make sure that all our data match
times = list(range(0, 2 * n_times, 999))
# add potentially problematic points
times.extend([n_times - 1, n_times, 2 * n_times - 1])
raw_combo0 = Raw([fif_fname, fif_fname], preload=True)
_compare_combo(raw, raw_combo0, times, n_times)
raw_combo = Raw([fif_fname, fif_fname], preload=False)
_compare_combo(raw, raw_combo, times, n_times)
raw_combo = Raw([fif_fname, fif_fname], preload='memmap8.dat')
_compare_combo(raw, raw_combo, times, n_times)
assert_raises(ValueError, Raw, [fif_fname, ctf_fname])
assert_raises(ValueError, Raw, [fif_fname, fif_bad_marked_fname])
assert_equal(raw[:, :][0].shape[1] * 2, raw_combo0[:, :][0].shape[1])
assert_equal(raw_combo0[:, :][0].shape[1], raw_combo0.n_times)
# with all data preloaded, result should be preloaded
raw_combo = Raw(fif_fname, preload=True)
raw_combo.append(Raw(fif_fname, preload=True))
assert_true(raw_combo.preload is True)
assert_equal(raw_combo.n_times, raw_combo._data.shape[1])
_compare_combo(raw, raw_combo, times, n_times)
# with any data not preloaded, don't set result as preloaded
raw_combo = concatenate_raws([Raw(fif_fname, preload=True),
Raw(fif_fname, preload=False)])
assert_true(raw_combo.preload is False)
assert_array_equal(find_events(raw_combo, stim_channel='STI 014'),
find_events(raw_combo0, stim_channel='STI 014'))
_compare_combo(raw, raw_combo, times, n_times)
# user should be able to force data to be preloaded upon concat
raw_combo = concatenate_raws([Raw(fif_fname, preload=False),
Raw(fif_fname, preload=True)],
preload=True)
assert_true(raw_combo.preload is True)
_compare_combo(raw, raw_combo, times, n_times)
raw_combo = concatenate_raws([Raw(fif_fname, preload=False),
Raw(fif_fname, preload=True)],
preload='memmap3.dat')
_compare_combo(raw, raw_combo, times, n_times)
raw_combo = concatenate_raws([Raw(fif_fname, preload=True),
Raw(fif_fname, preload=True)],
preload='memmap4.dat')
_compare_combo(raw, raw_combo, times, n_times)
raw_combo = concatenate_raws([Raw(fif_fname, preload=False),
Raw(fif_fname, preload=False)],
preload='memmap5.dat')
_compare_combo(raw, raw_combo, times, n_times)
# verify that combining raws with different projectors throws an exception
raw.add_proj([], remove_existing=True)
assert_raises(ValueError, raw.append, Raw(fif_fname, preload=True))
# now test event treatment for concatenated raw files
events = [find_events(raw, stim_channel='STI 014'),
find_events(raw, stim_channel='STI 014')]
last_samps = [raw.last_samp, raw.last_samp]
first_samps = [raw.first_samp, raw.first_samp]
events = concatenate_events(events, first_samps, last_samps)
events2 = find_events(raw_combo0, stim_channel='STI 014')
assert_array_equal(events, events2)
# check out the len method
assert_equal(len(raw), raw.n_times)
assert_equal(len(raw), raw.last_samp - raw.first_samp + 1)
@testing.requires_testing_data
def test_split_files():
"""Test writing and reading of split raw files
"""
tempdir = _TempDir()
raw_1 = Raw(fif_fname, preload=True)
assert_allclose(raw_1.info['buffer_size_sec'], 10., atol=1e-2) # samp rate
split_fname = op.join(tempdir, 'split_raw.fif')
raw_1.save(split_fname, buffer_size_sec=1.0, split_size='10MB')
raw_2 = Raw(split_fname)
assert_allclose(raw_2.info['buffer_size_sec'], 1., atol=1e-2) # samp rate
data_1, times_1 = raw_1[:, :]
data_2, times_2 = raw_2[:, :]
assert_array_equal(data_1, data_2)
assert_array_equal(times_1, times_2)
# test the case where the silly user specifies the split files
fnames = [split_fname]
fnames.extend(sorted(glob.glob(op.join(tempdir, 'split_raw-*.fif'))))
with warnings.catch_warnings(record=True):
warnings.simplefilter('always')
raw_2 = Raw(fnames)
data_2, times_2 = raw_2[:, :]
assert_array_equal(data_1, data_2)
assert_array_equal(times_1, times_2)
def test_load_bad_channels():
"""Test reading/writing of bad channels
"""
tempdir = _TempDir()
# Load correctly marked file (manually done in mne_process_raw)
raw_marked = Raw(fif_bad_marked_fname)
correct_bads = raw_marked.info['bads']
raw = Raw(test_fif_fname)
# Make sure it starts clean
assert_array_equal(raw.info['bads'], [])
# Test normal case
raw.load_bad_channels(bad_file_works)
# Write it out, read it in, and check
raw.save(op.join(tempdir, 'foo_raw.fif'))
raw_new = Raw(op.join(tempdir, 'foo_raw.fif'))
assert_equal(correct_bads, raw_new.info['bads'])
# Reset it
raw.info['bads'] = []
# Test bad case
assert_raises(ValueError, raw.load_bad_channels, bad_file_wrong)
# Test forcing the bad case
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
raw.load_bad_channels(bad_file_wrong, force=True)
n_found = sum(['1 bad channel' in str(ww.message) for ww in w])
assert_equal(n_found, 1) # there could be other irrelevant errors
# write it out, read it in, and check
raw.save(op.join(tempdir, 'foo_raw.fif'), overwrite=True)
raw_new = Raw(op.join(tempdir, 'foo_raw.fif'))
assert_equal(correct_bads, raw_new.info['bads'])
# Check that bad channels are cleared
raw.load_bad_channels(None)
raw.save(op.join(tempdir, 'foo_raw.fif'), overwrite=True)
raw_new = Raw(op.join(tempdir, 'foo_raw.fif'))
assert_equal([], raw_new.info['bads'])
@slow_test
@testing.requires_testing_data
def test_io_raw():
"""Test IO for raw data (Neuromag + CTF + gz)
"""
tempdir = _TempDir()
# test unicode io
for chars in [b'\xc3\xa4\xc3\xb6\xc3\xa9', b'a']:
with Raw(fif_fname) as r:
assert_true('Raw' in repr(r))
desc1 = r.info['description'] = chars.decode('utf-8')
temp_file = op.join(tempdir, 'raw.fif')
r.save(temp_file, overwrite=True)
with Raw(temp_file) as r2:
desc2 = r2.info['description']
assert_equal(desc1, desc2)
# Let's construct a simple test for IO first
raw = Raw(fif_fname).crop(0, 3.5, False)
raw.load_data()
# put in some data that we know the values of
data = rng.randn(raw._data.shape[0], raw._data.shape[1])
raw._data[:, :] = data
# save it somewhere
fname = op.join(tempdir, 'test_copy_raw.fif')
raw.save(fname, buffer_size_sec=1.0)
# read it in, make sure the whole thing matches
raw = Raw(fname)
assert_allclose(data, raw[:, :][0], rtol=1e-6, atol=1e-20)
# let's read portions across the 1-sec tag boundary, too
inds = raw.time_as_index([1.75, 2.25])
sl = slice(inds[0], inds[1])
assert_allclose(data[:, sl], raw[:, sl][0], rtol=1e-6, atol=1e-20)
# now let's do some real I/O
fnames_in = [fif_fname, test_fif_gz_fname, ctf_fname]
fnames_out = ['raw.fif', 'raw.fif.gz', 'raw.fif']
for fname_in, fname_out in zip(fnames_in, fnames_out):
fname_out = op.join(tempdir, fname_out)
raw = Raw(fname_in)
nchan = raw.info['nchan']
ch_names = raw.info['ch_names']
meg_channels_idx = [k for k in range(nchan)
if ch_names[k][0] == 'M']
n_channels = 100
meg_channels_idx = meg_channels_idx[:n_channels]
start, stop = raw.time_as_index([0, 5])
data, times = raw[meg_channels_idx, start:(stop + 1)]
meg_ch_names = [ch_names[k] for k in meg_channels_idx]
# Set up pick list: MEG + STI 014 - bad channels
include = ['STI 014']
include += meg_ch_names
picks = pick_types(raw.info, meg=True, eeg=False, stim=True,
misc=True, ref_meg=True, include=include,
exclude='bads')
# Writing with drop_small_buffer True
raw.save(fname_out, picks, tmin=0, tmax=4, buffer_size_sec=3,
drop_small_buffer=True, overwrite=True)
raw2 = Raw(fname_out)
sel = pick_channels(raw2.ch_names, meg_ch_names)
data2, times2 = raw2[sel, :]
assert_true(times2.max() <= 3)
# Writing
raw.save(fname_out, picks, tmin=0, tmax=5, overwrite=True)
if fname_in == fif_fname or fname_in == fif_fname + '.gz':
assert_equal(len(raw.info['dig']), 146)
raw2 = Raw(fname_out)
sel = pick_channels(raw2.ch_names, meg_ch_names)
data2, times2 = raw2[sel, :]
assert_allclose(data, data2, rtol=1e-6, atol=1e-20)
assert_allclose(times, times2)
assert_allclose(raw.info['sfreq'], raw2.info['sfreq'], rtol=1e-5)
# check transformations
for trans in ['dev_head_t', 'dev_ctf_t', 'ctf_head_t']:
if raw.info[trans] is None:
assert_true(raw2.info[trans] is None)
else:
assert_array_equal(raw.info[trans]['trans'],
raw2.info[trans]['trans'])
# check transformation 'from' and 'to'
if trans.startswith('dev'):
from_id = FIFF.FIFFV_COORD_DEVICE
else:
from_id = FIFF.FIFFV_MNE_COORD_CTF_HEAD
if trans[4:8] == 'head':
to_id = FIFF.FIFFV_COORD_HEAD
else:
to_id = FIFF.FIFFV_MNE_COORD_CTF_HEAD
for raw_ in [raw, raw2]:
assert_equal(raw_.info[trans]['from'], from_id)
assert_equal(raw_.info[trans]['to'], to_id)
if fname_in == fif_fname or fname_in == fif_fname + '.gz':
assert_allclose(raw.info['dig'][0]['r'], raw2.info['dig'][0]['r'])
# test warnings on bad filenames
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
raw_badname = op.join(tempdir, 'test-bad-name.fif.gz')
raw.save(raw_badname)
Raw(raw_badname)
assert_true(len(w) > 0) # len(w) should be 2 but Travis sometimes has more
@testing.requires_testing_data
def test_io_complex():
"""Test IO with complex data types
"""
rng = np.random.RandomState(0)
tempdir = _TempDir()
dtypes = [np.complex64, np.complex128]
raw = _test_raw_reader(Raw, fnames=fif_fname)
picks = np.arange(5)
start, stop = raw.time_as_index([0, 5])
data_orig, _ = raw[picks, start:stop]
for di, dtype in enumerate(dtypes):
imag_rand = np.array(1j * rng.randn(data_orig.shape[0],
data_orig.shape[1]), dtype)
raw_cp = raw.copy()
raw_cp._data = np.array(raw_cp._data, dtype)
raw_cp._data[picks, start:stop] += imag_rand
# this should throw an error because it's complex
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
raw_cp.save(op.join(tempdir, 'raw.fif'), picks, tmin=0, tmax=5,
overwrite=True)
# warning gets thrown on every instance b/c simplifilter('always')
assert_equal(len(w), 1)
raw2 = Raw(op.join(tempdir, 'raw.fif'))
raw2_data, _ = raw2[picks, :]
n_samp = raw2_data.shape[1]
assert_allclose(raw2_data[:, :n_samp], raw_cp._data[picks, :n_samp])
# with preloading
raw2 = Raw(op.join(tempdir, 'raw.fif'), preload=True)
raw2_data, _ = raw2[picks, :]
n_samp = raw2_data.shape[1]
assert_allclose(raw2_data[:, :n_samp], raw_cp._data[picks, :n_samp])
@testing.requires_testing_data
def test_getitem():
"""Test getitem/indexing of Raw
"""
for preload in [False, True, 'memmap.dat']:
raw = Raw(fif_fname, preload=preload)
data, times = raw[0, :]
data1, times1 = raw[0]
assert_array_equal(data, data1)
assert_array_equal(times, times1)
data, times = raw[0:2, :]
data1, times1 = raw[0:2]
assert_array_equal(data, data1)
assert_array_equal(times, times1)
data1, times1 = raw[[0, 1]]
assert_array_equal(data, data1)
assert_array_equal(times, times1)
assert_array_equal(raw[-10:, :][0],
raw[len(raw.ch_names) - 10:, :][0])
assert_raises(ValueError, raw.__getitem__,
(slice(-len(raw.ch_names) - 1), slice(None)))
@testing.requires_testing_data
def test_proj():
"""Test SSP proj operations
"""
tempdir = _TempDir()
for proj in [True, False]:
raw = Raw(fif_fname, preload=False, proj=proj)
assert_true(all(p['active'] == proj for p in raw.info['projs']))
data, times = raw[0:2, :]
data1, times1 = raw[0:2]
assert_array_equal(data, data1)
assert_array_equal(times, times1)
# test adding / deleting proj
if proj:
assert_raises(ValueError, raw.add_proj, [],
{'remove_existing': True})
assert_raises(ValueError, raw.del_proj, 0)
else:
projs = deepcopy(raw.info['projs'])
n_proj = len(raw.info['projs'])
raw.del_proj(0)
assert_equal(len(raw.info['projs']), n_proj - 1)
raw.add_proj(projs, remove_existing=False)
assert_equal(len(raw.info['projs']), 2 * n_proj - 1)
raw.add_proj(projs, remove_existing=True)
assert_equal(len(raw.info['projs']), n_proj)
# test apply_proj() with and without preload
for preload in [True, False]:
raw = Raw(fif_fname, preload=preload, proj=False)
data, times = raw[:, 0:2]
raw.apply_proj()
data_proj_1 = np.dot(raw._projector, data)
# load the file again without proj
raw = Raw(fif_fname, preload=preload, proj=False)
# write the file with proj. activated, make sure proj has been applied
raw.save(op.join(tempdir, 'raw.fif'), proj=True, overwrite=True)
raw2 = Raw(op.join(tempdir, 'raw.fif'), proj=False)
data_proj_2, _ = raw2[:, 0:2]
assert_allclose(data_proj_1, data_proj_2)
assert_true(all(p['active'] for p in raw2.info['projs']))
# read orig file with proj. active
raw2 = Raw(fif_fname, preload=preload, proj=True)
data_proj_2, _ = raw2[:, 0:2]
assert_allclose(data_proj_1, data_proj_2)
assert_true(all(p['active'] for p in raw2.info['projs']))
# test that apply_proj works
raw.apply_proj()
data_proj_2, _ = raw[:, 0:2]
assert_allclose(data_proj_1, data_proj_2)
assert_allclose(data_proj_2, np.dot(raw._projector, data_proj_2))
tempdir = _TempDir()
out_fname = op.join(tempdir, 'test_raw.fif')
raw = read_raw_fif(test_fif_fname, preload=True).crop(0, 0.002, copy=False)
raw.pick_types(meg=False, eeg=True)
raw.info['projs'] = [raw.info['projs'][-1]]
raw._data.fill(0)
raw._data[-1] = 1.
raw.save(out_fname)
raw = read_raw_fif(out_fname, proj=True, preload=False)
assert_allclose(raw[:, :][0][:1], raw[0, :][0])
@testing.requires_testing_data
def test_preload_modify():
"""Test preloading and modifying data
"""
tempdir = _TempDir()
for preload in [False, True, 'memmap.dat']:
raw = Raw(fif_fname, preload=preload)
nsamp = raw.last_samp - raw.first_samp + 1
picks = pick_types(raw.info, meg='grad', exclude='bads')
data = rng.randn(len(picks), nsamp // 2)
try:
raw[picks, :nsamp // 2] = data
except RuntimeError as err:
if not preload:
continue
else:
raise err
tmp_fname = op.join(tempdir, 'raw.fif')
raw.save(tmp_fname, overwrite=True)
raw_new = Raw(tmp_fname)
data_new, _ = raw_new[picks, :nsamp / 2]
assert_allclose(data, data_new)
@slow_test
@testing.requires_testing_data
def test_filter():
"""Test filtering (FIR and IIR) and Raw.apply_function interface
"""
raw = Raw(fif_fname).crop(0, 7, False)
raw.load_data()
sig_dec = 11
sig_dec_notch = 12
sig_dec_notch_fit = 12
picks_meg = pick_types(raw.info, meg=True, exclude='bads')
picks = picks_meg[:4]
raw_lp = raw.copy()
raw_lp.filter(0., 4.0 - 0.25, picks=picks, n_jobs=2)
raw_hp = raw.copy()
raw_hp.filter(8.0 + 0.25, None, picks=picks, n_jobs=2)
raw_bp = raw.copy()
raw_bp.filter(4.0 + 0.25, 8.0 - 0.25, picks=picks)
raw_bs = raw.copy()
raw_bs.filter(8.0 + 0.25, 4.0 - 0.25, picks=picks, n_jobs=2)
data, _ = raw[picks, :]
lp_data, _ = raw_lp[picks, :]
hp_data, _ = raw_hp[picks, :]
bp_data, _ = raw_bp[picks, :]
bs_data, _ = raw_bs[picks, :]
assert_array_almost_equal(data, lp_data + bp_data + hp_data, sig_dec)
assert_array_almost_equal(data, bp_data + bs_data, sig_dec)
raw_lp_iir = raw.copy()
raw_lp_iir.filter(0., 4.0, picks=picks, n_jobs=2, method='iir')
raw_hp_iir = raw.copy()
raw_hp_iir.filter(8.0, None, picks=picks, n_jobs=2, method='iir')
raw_bp_iir = raw.copy()
raw_bp_iir.filter(4.0, 8.0, picks=picks, method='iir')
lp_data_iir, _ = raw_lp_iir[picks, :]
hp_data_iir, _ = raw_hp_iir[picks, :]
bp_data_iir, _ = raw_bp_iir[picks, :]
summation = lp_data_iir + hp_data_iir + bp_data_iir
assert_array_almost_equal(data[:, 100:-100], summation[:, 100:-100],
sig_dec)
# make sure we didn't touch other channels
data, _ = raw[picks_meg[4:], :]
bp_data, _ = raw_bp[picks_meg[4:], :]
assert_array_equal(data, bp_data)
bp_data_iir, _ = raw_bp_iir[picks_meg[4:], :]
assert_array_equal(data, bp_data_iir)
# do a very simple check on line filtering
raw_bs = raw.copy()
with warnings.catch_warnings(record=True):
warnings.simplefilter('always')
raw_bs.filter(60.0 + 0.5, 60.0 - 0.5, picks=picks, n_jobs=2)
data_bs, _ = raw_bs[picks, :]
raw_notch = raw.copy()
raw_notch.notch_filter(60.0, picks=picks, n_jobs=2, method='fft')
data_notch, _ = raw_notch[picks, :]
assert_array_almost_equal(data_bs, data_notch, sig_dec_notch)
# now use the sinusoidal fitting
raw_notch = raw.copy()
raw_notch.notch_filter(None, picks=picks, n_jobs=2, method='spectrum_fit')
data_notch, _ = raw_notch[picks, :]
data, _ = raw[picks, :]
assert_array_almost_equal(data, data_notch, sig_dec_notch_fit)
@testing.requires_testing_data
def test_crop():
"""Test cropping raw files
"""
# split a concatenated file to test a difficult case
raw = Raw([fif_fname, fif_fname], preload=False)
split_size = 10. # in seconds
sfreq = raw.info['sfreq']
nsamp = (raw.last_samp - raw.first_samp + 1)
# do an annoying case (off-by-one splitting)
tmins = np.r_[1., np.round(np.arange(0., nsamp - 1, split_size * sfreq))]
tmins = np.sort(tmins)
tmaxs = np.concatenate((tmins[1:] - 1, [nsamp - 1]))
tmaxs /= sfreq
tmins /= sfreq
raws = [None] * len(tmins)
for ri, (tmin, tmax) in enumerate(zip(tmins, tmaxs)):
raws[ri] = raw.crop(tmin, tmax, True)
all_raw_2 = concatenate_raws(raws, preload=False)
assert_equal(raw.first_samp, all_raw_2.first_samp)
assert_equal(raw.last_samp, all_raw_2.last_samp)
assert_array_equal(raw[:, :][0], all_raw_2[:, :][0])
tmins = np.round(np.arange(0., nsamp - 1, split_size * sfreq))
tmaxs = np.concatenate((tmins[1:] - 1, [nsamp - 1]))
tmaxs /= sfreq
tmins /= sfreq
# going in revere order so the last fname is the first file (need it later)
raws = [None] * len(tmins)
for ri, (tmin, tmax) in enumerate(zip(tmins, tmaxs)):
raws[ri] = raw.copy()
raws[ri].crop(tmin, tmax, False)
# test concatenation of split file
all_raw_1 = concatenate_raws(raws, preload=False)
all_raw_2 = raw.crop(0, None, True)
for ar in [all_raw_1, all_raw_2]:
assert_equal(raw.first_samp, ar.first_samp)
assert_equal(raw.last_samp, ar.last_samp)
assert_array_equal(raw[:, :][0], ar[:, :][0])
# test shape consistency of cropped raw
data = np.zeros((1, 1002001))
info = create_info(1, 1000)
raw = RawArray(data, info)
for tmin in range(0, 1001, 100):
raw1 = raw.crop(tmin=tmin, tmax=tmin + 2, copy=True)
assert_equal(raw1[:][0].shape, (1, 2001))
@testing.requires_testing_data
def test_resample():
"""Test resample (with I/O and multiple files)
"""
tempdir = _TempDir()
raw = Raw(fif_fname).crop(0, 3, False)
raw.load_data()
raw_resamp = raw.copy()
sfreq = raw.info['sfreq']
# test parallel on upsample
raw_resamp.resample(sfreq * 2, n_jobs=2)
assert_equal(raw_resamp.n_times, len(raw_resamp.times))
raw_resamp.save(op.join(tempdir, 'raw_resamp-raw.fif'))
raw_resamp = Raw(op.join(tempdir, 'raw_resamp-raw.fif'), preload=True)
assert_equal(sfreq, raw_resamp.info['sfreq'] / 2)
assert_equal(raw.n_times, raw_resamp.n_times / 2)
assert_equal(raw_resamp._data.shape[1], raw_resamp.n_times)
assert_equal(raw._data.shape[0], raw_resamp._data.shape[0])
# test non-parallel on downsample
raw_resamp.resample(sfreq, n_jobs=1)
assert_equal(raw_resamp.info['sfreq'], sfreq)
assert_equal(raw._data.shape, raw_resamp._data.shape)
assert_equal(raw.first_samp, raw_resamp.first_samp)
assert_equal(raw.last_samp, raw.last_samp)
# upsampling then downsampling doubles resampling error, but this still
# works (hooray). Note that the stim channels had to be sub-sampled
# without filtering to be accurately preserved
# note we have to treat MEG and EEG+STIM channels differently (tols)
assert_allclose(raw._data[:306, 200:-200],
raw_resamp._data[:306, 200:-200],
rtol=1e-2, atol=1e-12)
assert_allclose(raw._data[306:, 200:-200],
raw_resamp._data[306:, 200:-200],
rtol=1e-2, atol=1e-7)
# now check multiple file support w/resampling, as order of operations
# (concat, resample) should not affect our data
raw1 = raw.copy()
raw2 = raw.copy()
raw3 = raw.copy()
raw4 = raw.copy()
raw1 = concatenate_raws([raw1, raw2])
raw1.resample(10.)
raw3.resample(10.)
raw4.resample(10.)
raw3 = concatenate_raws([raw3, raw4])
assert_array_equal(raw1._data, raw3._data)
assert_array_equal(raw1._first_samps, raw3._first_samps)
assert_array_equal(raw1._last_samps, raw3._last_samps)
assert_array_equal(raw1._raw_lengths, raw3._raw_lengths)
assert_equal(raw1.first_samp, raw3.first_samp)
assert_equal(raw1.last_samp, raw3.last_samp)
assert_equal(raw1.info['sfreq'], raw3.info['sfreq'])
# test resampling of stim channel
# basic decimation
stim = [1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0]
raw = RawArray([stim], create_info(1, len(stim), ['stim']))
assert_allclose(raw.resample(8.)._data,
[[1, 1, 0, 0, 1, 1, 0, 0]])
# decimation of multiple stim channels
raw = RawArray(2 * [stim], create_info(2, len(stim), 2 * ['stim']))
assert_allclose(raw.resample(8.)._data,
[[1, 1, 0, 0, 1, 1, 0, 0],
[1, 1, 0, 0, 1, 1, 0, 0]])
# decimation that could potentially drop events if the decimation is
# done naively
stim = [0, 0, 0, 1, 1, 0, 0, 0]
raw = RawArray([stim], create_info(1, len(stim), ['stim']))
assert_allclose(raw.resample(4.)._data,
[[0, 1, 1, 0]])
# two events are merged in this case (warning)
stim = [0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0]
raw = RawArray([stim], create_info(1, len(stim), ['stim']))
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
raw.resample(8.)
assert_true(len(w) == 1)
# events are dropped in this case (warning)
stim = [0, 1, 1, 0, 0, 1, 1, 0]
raw = RawArray([stim], create_info(1, len(stim), ['stim']))
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
raw.resample(4.)
assert_true(len(w) == 1)
# test resampling events: this should no longer give a warning
stim = [0, 1, 1, 0, 0, 1, 1, 0]
raw = RawArray([stim], create_info(1, len(stim), ['stim']))
events = find_events(raw)
raw, events = raw.resample(4., events=events)
assert_equal(events, np.array([[0, 0, 1], [2, 0, 1]]))
# test copy flag
stim = [1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0]
raw = RawArray([stim], create_info(1, len(stim), ['stim']))
raw_resampled = raw.resample(4., copy=True)
assert_true(raw_resampled is not raw)
raw_resampled = raw.resample(4., copy=False)
assert_true(raw_resampled is raw)
# resample should still work even when no stim channel is present
raw = RawArray(np.random.randn(1, 100), create_info(1, 100, ['eeg']))
raw.resample(10)
assert_true(len(raw) == 10)
@testing.requires_testing_data
def test_hilbert():
"""Test computation of analytic signal using hilbert
"""
raw = Raw(fif_fname, preload=True)
picks_meg = pick_types(raw.info, meg=True, exclude='bads')
picks = picks_meg[:4]
raw_filt = raw.copy()
raw_filt.filter(10, 20)
raw_filt_2 = raw_filt.copy()
raw2 = raw.copy()
raw3 = raw.copy()
raw.apply_hilbert(picks)
raw2.apply_hilbert(picks, envelope=True, n_jobs=2)
# Test custom n_fft
raw_filt.apply_hilbert(picks)
raw_filt_2.apply_hilbert(picks, n_fft=raw_filt_2.n_times + 1000)
assert_equal(raw_filt._data.shape, raw_filt_2._data.shape)
assert_allclose(raw_filt._data[:, 50:-50], raw_filt_2._data[:, 50:-50],
atol=1e-13, rtol=1e-2)
assert_raises(ValueError, raw3.apply_hilbert, picks,
n_fft=raw3.n_times - 100)
env = np.abs(raw._data[picks, :])
assert_allclose(env, raw2._data[picks, :], rtol=1e-2, atol=1e-13)
@testing.requires_testing_data
def test_raw_copy():
"""Test Raw copy
"""
raw = Raw(fif_fname, preload=True)
data, _ = raw[:, :]
copied = raw.copy()
copied_data, _ = copied[:, :]
assert_array_equal(data, copied_data)
assert_equal(sorted(raw.__dict__.keys()),
sorted(copied.__dict__.keys()))
raw = Raw(fif_fname, preload=False)
data, _ = raw[:, :]
copied = raw.copy()
copied_data, _ = copied[:, :]
assert_array_equal(data, copied_data)
assert_equal(sorted(raw.__dict__.keys()),
sorted(copied.__dict__.keys()))
@requires_pandas
def test_to_data_frame():
"""Test raw Pandas exporter"""
raw = Raw(test_fif_fname, preload=True)
_, times = raw[0, :10]
df = raw.to_data_frame()
assert_true((df.columns == raw.ch_names).all())
assert_array_equal(np.round(times * 1e3), df.index.values[:10])
df = raw.to_data_frame(index=None)
assert_true('time' in df.index.names)
assert_array_equal(df.values[:, 0], raw._data[0] * 1e13)
assert_array_equal(df.values[:, 2], raw._data[2] * 1e15)
@testing.requires_testing_data
def test_raw_index_as_time():
""" Test index as time conversion"""
raw = Raw(fif_fname, preload=True)
t0 = raw.index_as_time([0], True)[0]
t1 = raw.index_as_time([100], False)[0]
t2 = raw.index_as_time([100], True)[0]
assert_equal(t2 - t1, t0)
# ensure we can go back and forth
t3 = raw.index_as_time(raw.time_as_index([0], True), True)
assert_array_almost_equal(t3, [0.0], 2)
t3 = raw.index_as_time(raw.time_as_index(raw.info['sfreq'], True), True)
assert_array_almost_equal(t3, [raw.info['sfreq']], 2)