def test_filters():
Fs = 500
sig_len_secs = 60
# Filtering of short signals (filter length = len(a))
a = np.random.randn(sig_len_secs * Fs)
bp = band_pass_filter(a, Fs, 4, 8)
lp = low_pass_filter(a, Fs, 8)
hp = high_pass_filter(lp, Fs, 4)
assert_array_almost_equal(hp, bp, 2)
# Overlap-add filtering with a fixed filter length
filter_length = 8192
bp_oa = band_pass_filter(a, Fs, 4, 8, filter_length)
lp_oa = low_pass_filter(a, Fs, 8, filter_length)
hp_oa = high_pass_filter(lp_oa, Fs, 4, filter_length)
assert_array_almost_equal(hp_oa, bp_oa, 2)
# The two methods should give the same result
# As filtering for short signals uses a circular convolution (FFT) and
# the overlap-add filter implements a linear convolution, the signal
# boundary will be slightly different and we ignore it
n_edge_ignore = 1000
assert_array_almost_equal(hp[n_edge_ignore:-n_edge_ignore],
hp_oa[n_edge_ignore:-n_edge_ignore], 2)
def test_cuda():
"""Test CUDA-based filtering"""
# NOTE: don't make test_cuda() the last test, or pycuda might spew
# some warnings about clean-up failing
# Also, using `n_jobs='cuda'` on a non-CUDA system should be fine,
# as it should fall back to using n_jobs=1.
sfreq = 500
sig_len_secs = 20
a = rng.randn(sig_len_secs * sfreq)
with catch_logging() as log_file:
for fl in ['auto', '10s', 2048]:
bp = band_pass_filter(a, sfreq, 4, 8, fl, 1.0, 1.0, n_jobs=1,
phase='zero')
bs = band_stop_filter(a, sfreq, 4 - 1.0, 8 + 1.0, fl, 1.0, 1.0,
n_jobs=1, phase='zero')
lp = low_pass_filter(a, sfreq, 8, fl, 1.0, n_jobs=1, phase='zero')
hp = high_pass_filter(lp, sfreq, 4, fl, 1.0, n_jobs=1,
phase='zero')
bp_c = band_pass_filter(a, sfreq, 4, 8, fl, 1.0, 1.0,
n_jobs='cuda', verbose='INFO',
phase='zero')
bs_c = band_stop_filter(a, sfreq, 4 - 1.0, 8 + 1.0, fl, 1.0, 1.0,
n_jobs='cuda', verbose='INFO',
phase='zero')
lp_c = low_pass_filter(a, sfreq, 8, fl, 1.0,
n_jobs='cuda', verbose='INFO',
phase='zero')
hp_c = high_pass_filter(lp, sfreq, 4, fl, 1.0,
n_jobs='cuda', verbose='INFO',
phase='zero')
assert_array_almost_equal(bp, bp_c, 12)
assert_array_almost_equal(bs, bs_c, 12)
assert_array_almost_equal(lp, lp_c, 12)
assert_array_almost_equal(hp, hp_c, 12)
# check to make sure we actually used CUDA
out = log_file.getvalue().split('\n')[:-1]
# triage based on whether or not we actually expected to use CUDA
from mne.cuda import _cuda_capable # allow above funs to set it
tot = 12 if _cuda_capable else 0
assert_true(sum(['Using CUDA for FFT FIR filtering' in o
for o in out]) == tot)
# check resampling
for window in ('boxcar', 'triang'):
for N in (997, 1000): # one prime, one even
a = rng.randn(2, N)
for fro, to in ((1, 2), (2, 1), (1, 3), (3, 1)):
a1 = resample(a, fro, to, n_jobs=1, npad='auto',
window=window)
a2 = resample(a, fro, to, n_jobs='cuda', npad='auto',
window=window)
assert_allclose(a1, a2, rtol=1e-7, atol=1e-14)
assert_array_almost_equal(a1, a2, 14)
assert_array_equal(resample([0, 0], 2, 1, n_jobs='cuda'), [0., 0., 0., 0.])
assert_array_equal(resample(np.zeros(2, np.float32), 2, 1, n_jobs='cuda'),
[0., 0., 0., 0.])
def test_cuda():
"""Test CUDA-based filtering
"""
Fs = 500
sig_len_secs = 20
a = np.random.randn(sig_len_secs * Fs)
set_log_file(log_file, overwrite=True)
for fl in [None, 2048]:
bp = band_pass_filter(a, Fs, 4, 8, n_jobs=1, filter_length=fl)
bs = band_stop_filter(a, Fs, 4 - 0.5, 8 + 0.5, n_jobs=1,
filter_length=fl)
lp = low_pass_filter(a, Fs, 8, n_jobs=1, filter_length=fl)
hp = high_pass_filter(lp, Fs, 4, n_jobs=1, filter_length=fl)
bp_c = band_pass_filter(a, Fs, 4, 8, n_jobs='cuda', filter_length=fl,
verbose='INFO')
bs_c = band_stop_filter(a, Fs, 4 - 0.5, 8 + 0.5, n_jobs='cuda',
filter_length=fl, verbose='INFO')
lp_c = low_pass_filter(a, Fs, 8, n_jobs='cuda', filter_length=fl,
verbose='INFO')
hp_c = high_pass_filter(lp, Fs, 4, n_jobs='cuda', filter_length=fl,
verbose='INFO')
assert_array_almost_equal(bp, bp_c, 12)
assert_array_almost_equal(bs, bs_c, 12)
assert_array_almost_equal(lp, lp_c, 12)
assert_array_almost_equal(hp, hp_c, 12)
# check to make sure we actually used CUDA
set_log_file()
out = open(log_file).readlines()
assert_true(sum(['Using CUDA for FFT FIR filtering' in o
for o in out]) == 8)
def test_filter_auto():
"""Test filter auto parameters"""
# test that our overlap-add filtering doesn't introduce strange
# artifacts (from mne_analyze mailing list 2015/06/25)
N = 300
sfreq = 100.
lp = 10.
sine_freq = 1.
x = np.ones(N)
t = np.arange(N) / sfreq
x += np.sin(2 * np.pi * sine_freq * t)
x_orig = x.copy()
x_filt = low_pass_filter(x, sfreq, lp)
assert_array_equal(x, x_orig)
# the firwin2 function gets us this close
assert_allclose(x, x_filt, rtol=1e-4, atol=1e-4)
assert_array_equal(x_filt, low_pass_filter(x, sfreq, lp))
assert_array_equal(x, x_orig)
assert_array_equal(x_filt, filter_data(x, sfreq, None, lp))
assert_array_equal(x, x_orig)
assert_array_equal(x_filt, filter_data(x, sfreq, None, lp, copy=False))
assert_array_equal(x, x_filt)
# degenerate conditions
assert_raises(ValueError, filter_data, x, -sfreq, 1, 10)
assert_raises(ValueError, filter_data, x, sfreq, 1, sfreq * 0.75)
assert_raises(TypeError, filter_data, x.astype(np.float32), sfreq, None,
10, filter_length='auto', h_trans_bandwidth='auto')
def test_cuda():
"""Test CUDA-based filtering
"""
# NOTE: don't make test_cuda() the last test, or pycuda might spew
# some warnings about clean-up failing
# Also, using `n_jobs='cuda'` on a non-CUDA system should be fine,
# as it should fall back to using n_jobs=1.
tempdir = _TempDir()
log_file = op.join(tempdir, 'temp_log.txt')
sfreq = 500
sig_len_secs = 20
a = np.random.randn(sig_len_secs * sfreq)
set_log_file(log_file, overwrite=True)
for fl in ['10s', None, 2048]:
bp = band_pass_filter(a, sfreq, 4, 8, n_jobs=1, filter_length=fl)
bs = band_stop_filter(a, sfreq, 4 - 0.5, 8 + 0.5, n_jobs=1,
filter_length=fl)
lp = low_pass_filter(a, sfreq, 8, n_jobs=1, filter_length=fl)
hp = high_pass_filter(lp, sfreq, 4, n_jobs=1, filter_length=fl)
bp_c = band_pass_filter(a, sfreq, 4, 8, n_jobs='cuda',
filter_length=fl, verbose='INFO')
bs_c = band_stop_filter(a, sfreq, 4 - 0.5, 8 + 0.5, n_jobs='cuda',
filter_length=fl, verbose='INFO')
lp_c = low_pass_filter(a, sfreq, 8, n_jobs='cuda', filter_length=fl,
verbose='INFO')
hp_c = high_pass_filter(lp, sfreq, 4, n_jobs='cuda', filter_length=fl,
verbose='INFO')
assert_array_almost_equal(bp, bp_c, 12)
assert_array_almost_equal(bs, bs_c, 12)
assert_array_almost_equal(lp, lp_c, 12)
assert_array_almost_equal(hp, hp_c, 12)
# check to make sure we actually used CUDA
set_log_file()
with open(log_file) as fid:
out = fid.readlines()
# triage based on whether or not we actually expected to use CUDA
from mne.cuda import _cuda_capable # allow above funs to set it
tot = 12 if _cuda_capable else 0
assert_true(sum(['Using CUDA for FFT FIR filtering' in o
for o in out]) == tot)
# check resampling
a = np.random.RandomState(0).randn(3, sig_len_secs * sfreq)
a1 = resample(a, 1, 2, n_jobs=2, npad=0)
a2 = resample(a, 1, 2, n_jobs='cuda', npad=0)
a3 = resample(a, 2, 1, n_jobs=2, npad=0)
a4 = resample(a, 2, 1, n_jobs='cuda', npad=0)
assert_array_almost_equal(a3, a4, 14)
assert_array_almost_equal(a1, a2, 14)
assert_array_equal(resample([0, 0], 2, 1, n_jobs='cuda'), [0., 0., 0., 0.])
assert_array_equal(resample(np.zeros(2, np.float32), 2, 1, n_jobs='cuda'),
[0., 0., 0., 0.])
def test_cuda():
"""Test CUDA-based filtering
"""
# NOTE: don't make test_cuda() the last test, or pycuda might spew
# some warnings about clean-up failing
# Also, using `n_jobs='cuda'` on a non-CUDA system should be fine,
# as it should fall back to using n_jobs=1.
tempdir = _TempDir()
log_file = op.join(tempdir, 'temp_log.txt')
sfreq = 500
sig_len_secs = 20
a = np.random.randn(sig_len_secs * sfreq)
set_log_file(log_file, overwrite=True)
for fl in ['10s', None, 2048]:
bp = band_pass_filter(a, sfreq, 4, 8, n_jobs=1, filter_length=fl)
bs = band_stop_filter(a, sfreq, 4 - 0.5, 8 + 0.5, n_jobs=1,
filter_length=fl)
lp = low_pass_filter(a, sfreq, 8, n_jobs=1, filter_length=fl)
hp = high_pass_filter(lp, sfreq, 4, n_jobs=1, filter_length=fl)
bp_c = band_pass_filter(a, sfreq, 4, 8, n_jobs='cuda',
filter_length=fl, verbose='INFO')
bs_c = band_stop_filter(a, sfreq, 4 - 0.5, 8 + 0.5, n_jobs='cuda',
filter_length=fl, verbose='INFO')
lp_c = low_pass_filter(a, sfreq, 8, n_jobs='cuda', filter_length=fl,
verbose='INFO')
hp_c = high_pass_filter(lp, sfreq, 4, n_jobs='cuda', filter_length=fl,
verbose='INFO')
assert_array_almost_equal(bp, bp_c, 12)
assert_array_almost_equal(bs, bs_c, 12)
assert_array_almost_equal(lp, lp_c, 12)
assert_array_almost_equal(hp, hp_c, 12)
# check to make sure we actually used CUDA
set_log_file()
with open(log_file) as fid:
out = fid.readlines()
# triage based on whether or not we actually expected to use CUDA
tot = 12 if cuda_capable else 0
assert_true(sum(['Using CUDA for FFT FIR filtering' in o
for o in out]) == tot)
# check resampling
a = np.random.RandomState(0).randn(3, sig_len_secs * sfreq)
a1 = resample(a, 1, 2, n_jobs=2, npad=0)
a2 = resample(a, 1, 2, n_jobs='cuda', npad=0)
a3 = resample(a, 2, 1, n_jobs=2, npad=0)
a4 = resample(a, 2, 1, n_jobs='cuda', npad=0)
assert_array_almost_equal(a3, a4, 14)
assert_array_almost_equal(a1, a2, 14)
def test_cuda():
"""Test CUDA-based filtering
"""
# NOTE: don't make test_cuda() the last test, or pycuda might spew
# some warnings about clean-up failing
# Also, using `n_jobs='cuda'` on a non-CUDA system should be fine,
# as it should fall back to using n_jobs=1.
sfreq = 500
sig_len_secs = 20
a = rng.randn(sig_len_secs * sfreq)
with catch_logging() as log_file:
for fl in ['10s', None, 2048]:
bp = band_pass_filter(a, sfreq, 4, 8, n_jobs=1, filter_length=fl)
bs = band_stop_filter(a, sfreq, 4 - 0.5, 8 + 0.5, n_jobs=1,
filter_length=fl)
lp = low_pass_filter(a, sfreq, 8, n_jobs=1, filter_length=fl)
hp = high_pass_filter(lp, sfreq, 4, n_jobs=1, filter_length=fl)
bp_c = band_pass_filter(a, sfreq, 4, 8, n_jobs='cuda',
filter_length=fl, verbose='INFO')
bs_c = band_stop_filter(a, sfreq, 4 - 0.5, 8 + 0.5, n_jobs='cuda',
filter_length=fl, verbose='INFO')
lp_c = low_pass_filter(a, sfreq, 8, n_jobs='cuda',
filter_length=fl, verbose='INFO')
hp_c = high_pass_filter(lp, sfreq, 4, n_jobs='cuda',
filter_length=fl, verbose='INFO')
assert_array_almost_equal(bp, bp_c, 12)
assert_array_almost_equal(bs, bs_c, 12)
assert_array_almost_equal(lp, lp_c, 12)
assert_array_almost_equal(hp, hp_c, 12)
# check to make sure we actually used CUDA
out = log_file.getvalue().split('\n')[:-1]
# triage based on whether or not we actually expected to use CUDA
from mne.cuda import _cuda_capable # allow above funs to set it
tot = 12 if _cuda_capable else 0
assert_true(sum(['Using CUDA for FFT FIR filtering' in o
for o in out]) == tot)
# check resampling
a = rng.randn(3, sig_len_secs * sfreq)
a1 = resample(a, 1, 2, n_jobs=2, npad=0)
a2 = resample(a, 1, 2, n_jobs='cuda', npad=0)
a3 = resample(a, 2, 1, n_jobs=2, npad=0)
a4 = resample(a, 2, 1, n_jobs='cuda', npad=0)
assert_array_almost_equal(a3, a4, 14)
assert_array_almost_equal(a1, a2, 14)
assert_array_equal(resample([0, 0], 2, 1, n_jobs='cuda'), [0., 0., 0., 0.])
assert_array_equal(resample(np.zeros(2, np.float32), 2, 1, n_jobs='cuda'),
[0., 0., 0., 0.])
def decimate_nophase(arr, sfreq, q, **kwargs):
"""Decimate a signal without shifting the phase.
Parameters
----------
arr : numpy array, shape (..., n_times)
The data we'll decimate.
sfreq : float | int
The sampling frequency of the signal.
q : integer, factor of sfreq
How much to decimate the signal
kwargs : passed to mne low_pass_filter function.
Returns
-------
arr : np.array, shape (..., n_times / q).
The decimated data
"""
# Calculate downsampling parameters
arr = np.atleast_2d(arr)
if np.mod(sfreq, q) != 0:
raise ValueError('q must be a factor of sfreq')
new_sfreq = sfreq / q
# Perform the low-pass filter then decimate manually
arr = low_pass_filter(arr, sfreq, new_sfreq / 2., **kwargs)
arr = arr[..., ::q]
return arr
def decimate_nophase(arr, sfreq, q, **kwargs):
"""Decimate a signal without shifting the phase.
Parameters
----------
arr : numpy array, shape (..., n_times)
The data we'll decimate.
sfreq : float | int
The sampling frequency of the signal.
q : integer, factor of sfreq
How much to decimate the signal
kwargs : passed to mne low_pass_filter function.
Returns
-------
arr : np.array, shape (..., n_times / q).
The decimated data
"""
# Calculate downsampling parameters
arr = np.atleast_2d(arr)
if np.mod(sfreq, q) != 0:
raise ValueError('q must be a factor of sfreq')
new_sfreq = sfreq / q
# Perform the low-pass filter then decimate manually
arr = low_pass_filter(arr, sfreq, new_sfreq / 2., **kwargs)
arr = arr[..., ::q]
return arr
def test_cuda():
"""Test CUDA-based filtering
"""
# NOTE: don't make test_cuda() the last test, or pycuda might spew
# some warnings about clean-up failing
Fs = 500
sig_len_secs = 20
a = np.random.randn(sig_len_secs * Fs)
set_log_file(log_file, overwrite=True)
for fl in ['10s', None, 2048]:
bp = band_pass_filter(a, Fs, 4, 8, n_jobs=1, filter_length=fl)
bs = band_stop_filter(a, Fs, 4 - 0.5, 8 + 0.5, n_jobs=1,
filter_length=fl)
lp = low_pass_filter(a, Fs, 8, n_jobs=1, filter_length=fl)
hp = high_pass_filter(lp, Fs, 4, n_jobs=1, filter_length=fl)
bp_c = band_pass_filter(a, Fs, 4, 8, n_jobs='cuda', filter_length=fl,
verbose='INFO')
bs_c = band_stop_filter(a, Fs, 4 - 0.5, 8 + 0.5, n_jobs='cuda',
filter_length=fl, verbose='INFO')
lp_c = low_pass_filter(a, Fs, 8, n_jobs='cuda', filter_length=fl,
verbose='INFO')
hp_c = high_pass_filter(lp, Fs, 4, n_jobs='cuda', filter_length=fl,
verbose='INFO')
assert_array_almost_equal(bp, bp_c, 12)
assert_array_almost_equal(bs, bs_c, 12)
assert_array_almost_equal(lp, lp_c, 12)
assert_array_almost_equal(hp, hp_c, 12)
# check to make sure we actually used CUDA
set_log_file()
with open(log_file) as fid:
out = fid.readlines()
assert_true(sum(['Using CUDA for FFT FIR filtering' in o
for o in out]) == 12)
# check resampling
a = np.random.RandomState(0).randn(3, sig_len_secs * Fs)
a1 = resample(a, 1, 2, n_jobs=2, npad=0)
a2 = resample(a, 1, 2, n_jobs='cuda', npad=0)
a3 = resample(a, 2, 1, n_jobs=2, npad=0)
a4 = resample(a, 2, 1, n_jobs='cuda', npad=0)
assert_array_almost_equal(a3, a4, 14)
assert_array_almost_equal(a1, a2, 14)
def test_cuda():
"""Test CUDA-based filtering
"""
# NOTE: don't make test_cuda() the last test, or pycuda might spew
# some warnings about clean-up failing
Fs = 500
sig_len_secs = 20
a = np.random.randn(sig_len_secs * Fs)
set_log_file(log_file, overwrite=True)
for fl in ['10s', None, 2048]:
bp = band_pass_filter(a, Fs, 4, 8, n_jobs=1, filter_length=fl)
bs = band_stop_filter(a, Fs, 4 - 0.5, 8 + 0.5, n_jobs=1,
filter_length=fl)
lp = low_pass_filter(a, Fs, 8, n_jobs=1, filter_length=fl)
hp = high_pass_filter(lp, Fs, 4, n_jobs=1, filter_length=fl)
bp_c = band_pass_filter(a, Fs, 4, 8, n_jobs='cuda', filter_length=fl,
verbose='INFO')
bs_c = band_stop_filter(a, Fs, 4 - 0.5, 8 + 0.5, n_jobs='cuda',
filter_length=fl, verbose='INFO')
lp_c = low_pass_filter(a, Fs, 8, n_jobs='cuda', filter_length=fl,
verbose='INFO')
hp_c = high_pass_filter(lp, Fs, 4, n_jobs='cuda', filter_length=fl,
verbose='INFO')
assert_array_almost_equal(bp, bp_c, 12)
assert_array_almost_equal(bs, bs_c, 12)
assert_array_almost_equal(lp, lp_c, 12)
assert_array_almost_equal(hp, hp_c, 12)
# check to make sure we actually used CUDA
set_log_file()
out = open(log_file).readlines()
assert_true(sum(['Using CUDA for FFT FIR filtering' in o
for o in out]) == 12)
# check resampling
a = np.random.RandomState(0).randn(3, sig_len_secs * Fs)
a1 = resample(a, 1, 2, n_jobs=2, npad=0)
a2 = resample(a, 1, 2, n_jobs='cuda', npad=0)
a3 = resample(a, 2, 1, n_jobs=2, npad=0)
a4 = resample(a, 2, 1, n_jobs='cuda', npad=0)
assert_array_almost_equal(a3, a4, 14)
assert_array_almost_equal(a1, a2, 14)
def _decimate_epochs(subject, overwrite=True):
# check overwrite
epo_fname = paths('epochs_decim', subject=subject)
if op.exists(epo_fname) and not overwrite:
return
# load non decimated data
epochs = load('epochs', subject=subject, preload=True)
epochs._data = low_pass_filter(epochs._data, epochs.info['sfreq'], 30.,
n_jobs=-1)
epochs.crop(-.200, 1.600)
epochs.decimate(10)
save(epochs, 'epochs_decim', subject=subject, overwrite=True, upload=True)
def test_filters():
"""Test low-, band-, and high-pass filters"""
Fs = 500
sig_len_secs = 60
# Filtering of short signals (filter length = len(a))
a = np.random.randn(sig_len_secs * Fs)
bp = band_pass_filter(a, Fs, 4, 8)
lp = low_pass_filter(a, Fs, 8)
hp = high_pass_filter(lp, Fs, 4)
assert_array_almost_equal(hp, bp, 2)
# Overlap-add filtering with a fixed filter length
filter_length = 8192
bp_oa = band_pass_filter(a, Fs, 4, 8, filter_length)
lp_oa = low_pass_filter(a, Fs, 8, filter_length)
hp_oa = high_pass_filter(lp_oa, Fs, 4, filter_length)
assert_array_almost_equal(hp_oa, bp_oa, 2)
# The two methods should give the same result
# As filtering for short signals uses a circular convolution (FFT) and
# the overlap-add filter implements a linear convolution, the signal
# boundary will be slightly different and we ignore it
n_edge_ignore = 1000
assert_array_almost_equal(hp[n_edge_ignore:-n_edge_ignore],
hp_oa[n_edge_ignore:-n_edge_ignore], 2)
# and since these are low-passed, downsampling/upsampling should be close
n_resamp_ignore = 10
bp_up_dn = resample(resample(bp_oa, 2, 1), 1, 2)
assert_array_almost_equal(bp_oa[n_resamp_ignore:-n_resamp_ignore],
bp_up_dn[n_resamp_ignore:-n_resamp_ignore], 2)
# make sure we don't alias
t = np.array(range(Fs * sig_len_secs)) / float(Fs)
# make sinusoid close to the Nyquist frequency
sig = np.sin(2 * np.pi * Fs / 2.2 * t)
# signal should disappear with 2x downsampling
sig_gone = resample(sig, 1, 2)[n_resamp_ignore:-n_resamp_ignore]
assert_array_almost_equal(np.zeros_like(sig_gone), sig_gone, 2)
def test_filters():
"""Test low-, band-, and high-pass filters"""
Fs = 500
sig_len_secs = 60
# Filtering of short signals (filter length = len(a))
a = np.random.randn(sig_len_secs * Fs)
bp = band_pass_filter(a, Fs, 4, 8)
lp = low_pass_filter(a, Fs, 8)
hp = high_pass_filter(lp, Fs, 4)
assert_array_almost_equal(hp, bp, 2)
# Overlap-add filtering with a fixed filter length
filter_length = 8192
bp_oa = band_pass_filter(a, Fs, 4, 8, filter_length)
lp_oa = low_pass_filter(a, Fs, 8, filter_length)
hp_oa = high_pass_filter(lp_oa, Fs, 4, filter_length)
assert_array_almost_equal(hp_oa, bp_oa, 2)
# The two methods should give the same result
# As filtering for short signals uses a circular convolution (FFT) and
# the overlap-add filter implements a linear convolution, the signal
# boundary will be slightly different and we ignore it
n_edge_ignore = 1000
assert_array_almost_equal(hp[n_edge_ignore:-n_edge_ignore],
hp_oa[n_edge_ignore:-n_edge_ignore], 2)
# and since these are low-passed, downsampling/upsampling should be close
n_resamp_ignore = 10
bp_up_dn = resample(resample(bp_oa, 2, 1), 1, 2)
assert_array_almost_equal(bp_oa[n_resamp_ignore:-n_resamp_ignore],
bp_up_dn[n_resamp_ignore:-n_resamp_ignore], 2)
# make sure we don't alias
t = np.array(range(Fs*sig_len_secs))/float(Fs)
# make sinusoid close to the Nyquist frequency
sig = np.sin(2*np.pi*Fs/2.2*t)
# signal should disappear with 2x downsampling
sig_gone = resample(sig,1,2)[n_resamp_ignore:-n_resamp_ignore]
assert_array_almost_equal(np.zeros_like(sig_gone), sig_gone, 2)
def process(self, data):
if self.type == 'low-pass':
return low_pass_filter(data, **self.params)
elif self.type == 'high-pass':
return high_pass_filter(data, **self.params)
elif self.type == 'band-pass':
return band_pass_filter(data, **self.params)
elif self.type == 'band-stop':
return band_stop_filter(data, **self.params)
elif self.type == 'notch':
return notch_filter(data, **self.params)
else:
raise ValueError('Unsupported filter type: {}'.format(self.type))
def _decimate_epochs(subject, overwrite=True):
# check overwrite
epo_fname = paths('epochs_decim', subject=subject)
if op.exists(epo_fname) and not overwrite:
return
# load non decimated data
epochs = load('epochs', subject=subject, preload=True)
epochs._data = low_pass_filter(epochs._data,
epochs.info['sfreq'],
30.,
n_jobs=-1)
epochs.crop(-.200, 1.600)
epochs.decimate(10)
save(epochs, 'epochs_decim', subject=subject, overwrite=True, upload=True)
def process(self, data):
# fix for new MNE requirements
import numpy as np
data = np.asarray(data, dtype=np.float64)
if self.type == 'low-pass':
return low_pass_filter(data, **self.params)
elif self.type == 'high-pass':
return high_pass_filter(data, **self.params)
elif self.type == 'band-pass':
return band_pass_filter(data, **self.params)
elif self.type == 'band-stop':
return band_stop_filter(data, **self.params)
elif self.type == 'notch':
return notch_filter(data, **self.params)
else:
raise ValueError('Unsupported filter type: {}'.format(self.type))
def process(self, data):
# fix for new MNE requirements
import numpy as np
data = np.asarray(data, dtype=np.float64)
if self.type == 'low-pass':
return low_pass_filter(data, **self.params)
elif self.type == 'high-pass':
return high_pass_filter(data, **self.params)
elif self.type == 'band-pass':
return band_pass_filter(data, **self.params)
elif self.type == 'band-stop':
return band_stop_filter(data, **self.params)
elif self.type == 'notch':
return notch_filter(data, **self.params)
else:
raise ValueError('Unsupported filter type: {}'.format(self.type))
def test_filter_auto():
"""Test filter auto parameters"""
# test that our overlap-add filtering doesn't introduce strange
# artifacts (from mne_analyze mailing list 2015/06/25)
N = 300
sfreq = 100.
lp = 10.
sine_freq = 1.
x = np.ones(N)
t = np.arange(N) / sfreq
x += np.sin(2 * np.pi * sine_freq * t)
x_filt = low_pass_filter(x, sfreq, lp, 'auto', 'auto', phase='zero')
# the firwin2 function gets us this close
assert_allclose(x, x_filt, rtol=1e-4, atol=1e-4)
# degenerate conditions
assert_raises(ValueError, filter_data, x, sfreq, 1, 10) # not 2D
assert_raises(ValueError, filter_data, x[np.newaxis], -sfreq, 1, 10)
assert_raises(ValueError, filter_data, x[np.newaxis], sfreq, 1,
sfreq * 0.75)
def test_filter_auto():
"""Test filter auto parameters"""
# test that our overlap-add filtering doesn't introduce strange
# artifacts (from mne_analyze mailing list 2015/06/25)
N = 300
sfreq = 100.
lp = 10.
sine_freq = 1.
x = np.ones(N)
t = np.arange(N) / sfreq
x += np.sin(2 * np.pi * sine_freq * t)
x_filt = low_pass_filter(x, sfreq, lp, 'auto', 'auto', phase='zero')
# the firwin2 function gets us this close
assert_allclose(x, x_filt, rtol=1e-4, atol=1e-4)
# degenerate conditions
assert_raises(ValueError, filter_data, x, sfreq, 1, 10) # not 2D
assert_raises(ValueError, filter_data, x[np.newaxis], -sfreq, 1, 10)
assert_raises(ValueError, filter_data, x[np.newaxis], sfreq, 1,
sfreq * 0.75)
def test_filters():
"""Test low-, band-, high-pass, and band-stop filters plus resampling
"""
sfreq = 500
sig_len_secs = 30
a = np.random.randn(2, sig_len_secs * sfreq)
# let's test our catchers
for fl in ['blah', [0, 1], 1000.5, '10ss', '10']:
assert_raises(ValueError, band_pass_filter, a, sfreq, 4, 8,
filter_length=fl)
for nj in ['blah', 0.5]:
assert_raises(ValueError, band_pass_filter, a, sfreq, 4, 8, n_jobs=nj)
# > Nyq/2
assert_raises(ValueError, band_pass_filter, a, sfreq, 4, sfreq / 2.)
assert_raises(ValueError, low_pass_filter, a, sfreq, sfreq / 2.)
# check our short-filter warning:
with warnings.catch_warnings(record=True) as w:
# Warning for low attenuation
band_pass_filter(a, sfreq, 1, 8, filter_length=1024)
# Warning for too short a filter
band_pass_filter(a, sfreq, 1, 8, filter_length='0.5s')
assert_true(len(w) >= 2)
# try new default and old default
for fl in ['10s', '5000ms', None]:
bp = band_pass_filter(a, sfreq, 4, 8, filter_length=fl)
bs = band_stop_filter(a, sfreq, 4 - 0.5, 8 + 0.5, filter_length=fl)
lp = low_pass_filter(a, sfreq, 8, filter_length=fl, n_jobs=2)
hp = high_pass_filter(lp, sfreq, 4, filter_length=fl)
assert_array_almost_equal(hp, bp, 2)
assert_array_almost_equal(bp + bs, a, 1)
# Overlap-add filtering with a fixed filter length
filter_length = 8192
bp_oa = band_pass_filter(a, sfreq, 4, 8, filter_length)
bs_oa = band_stop_filter(a, sfreq, 4 - 0.5, 8 + 0.5, filter_length)
lp_oa = low_pass_filter(a, sfreq, 8, filter_length)
hp_oa = high_pass_filter(lp_oa, sfreq, 4, filter_length)
assert_array_almost_equal(hp_oa, bp_oa, 2)
# Our filters are no longer quite complementary with linear rolloffs :(
# this is the tradeoff for stability of the filtering
# obtained by directly using the result of firwin2 instead of
# modifying it...
assert_array_almost_equal(bp_oa + bs_oa, a, 1)
# The two methods should give the same result
# As filtering for short signals uses a circular convolution (FFT) and
# the overlap-add filter implements a linear convolution, the signal
# boundary will be slightly different and we ignore it
n_edge_ignore = 0
assert_array_almost_equal(hp[n_edge_ignore:-n_edge_ignore],
hp_oa[n_edge_ignore:-n_edge_ignore], 2)
# and since these are low-passed, downsampling/upsampling should be close
n_resamp_ignore = 10
bp_up_dn = resample(resample(bp_oa, 2, 1, n_jobs=2), 1, 2, n_jobs=2)
assert_array_almost_equal(bp_oa[n_resamp_ignore:-n_resamp_ignore],
bp_up_dn[n_resamp_ignore:-n_resamp_ignore], 2)
# note that on systems without CUDA, this line serves as a test for a
# graceful fallback to n_jobs=1
bp_up_dn = resample(resample(bp_oa, 2, 1, n_jobs='cuda'), 1, 2,
n_jobs='cuda')
assert_array_almost_equal(bp_oa[n_resamp_ignore:-n_resamp_ignore],
bp_up_dn[n_resamp_ignore:-n_resamp_ignore], 2)
# test to make sure our resamling matches scipy's
bp_up_dn = sp_resample(sp_resample(bp_oa, 2 * bp_oa.shape[-1], axis=-1,
window='boxcar'),
bp_oa.shape[-1], window='boxcar', axis=-1)
assert_array_almost_equal(bp_oa[n_resamp_ignore:-n_resamp_ignore],
bp_up_dn[n_resamp_ignore:-n_resamp_ignore], 2)
# make sure we don't alias
t = np.array(list(range(sfreq * sig_len_secs))) / float(sfreq)
# make sinusoid close to the Nyquist frequency
sig = np.sin(2 * np.pi * sfreq / 2.2 * t)
# signal should disappear with 2x downsampling
sig_gone = resample(sig, 1, 2)[n_resamp_ignore:-n_resamp_ignore]
assert_array_almost_equal(np.zeros_like(sig_gone), sig_gone, 2)
# let's construct some filters
iir_params = dict(ftype='cheby1', gpass=1, gstop=20)
iir_params = construct_iir_filter(iir_params, 40, 80, 1000, 'low')
# this should be a third order filter
assert_true(iir_params['a'].size - 1 == 3)
assert_true(iir_params['b'].size - 1 == 3)
iir_params = dict(ftype='butter', order=4)
iir_params = construct_iir_filter(iir_params, 40, None, 1000, 'low')
assert_true(iir_params['a'].size - 1 == 4)
assert_true(iir_params['b'].size - 1 == 4)
# check that picks work for 3d array with one channel and picks=[0]
a = np.random.randn(5 * sfreq, 5 * sfreq)
b = a[:, None, :]
with warnings.catch_warnings(record=True) as w:
a_filt = band_pass_filter(a, sfreq, 4, 8)
b_filt = band_pass_filter(b, sfreq, 4, 8, picks=[0])
assert_array_equal(a_filt[:, None, :], b_filt)
# check for n-dimensional case
a = np.random.randn(2, 2, 2, 2)
assert_raises(ValueError, band_pass_filter, a, sfreq, Fp1=4, Fp2=8,
picks=np.array([0, 1]))
# test that our overlap-add filtering doesn't introduce strange
# artifacts (from mne_analyze mailing list 2015/06/25)
N = 300
sfreq = 100.
lp = 10.
sine_freq = 1.
x = np.ones(N)
x += np.sin(2 * np.pi * sine_freq * np.arange(N) / sfreq)
with warnings.catch_warnings(record=True): # filter attenuation
x_filt = low_pass_filter(x, sfreq, lp, '1s')
# the firwin2 function gets us this close
assert_allclose(x, x_filt, rtol=1e-3, atol=1e-3)
# length = .01 """ Main loop across subjects """ for isub in range(len(subjects)): # isub=1 #------------------------- for single task ------------------------------------- subject = subjects[isub] fname = op.join(data_path, 'abse_' + subject + '_train.mat') # import data epochs = sm_fieldtrip2mne(fname) n_trial = len(epochs) # additional preproc steps epochs.apply_baseline((0,.05)) epochs._data = flt.low_pass_filter(epochs.get_data(), 1000, lowpass_filter) # import behavior and conditions mat = sio.loadmat(fname) behavior = mat['data']['behavior'] stim = np.squeeze(behavior[0][0][0][0][0][0]) response = np.squeeze(behavior[0][0][0][0][1][0]) face = stim == 'f' obj = stim == 'o' body = stim == 'b' house = stim == 'h' square = stim == 's' stim_list = np.zeros((n_trial,)) # note that shape should not be ntrial X 1 like in matlab but instead (ntrial,) for itrl in range(n_trial): if face[itrl]: stim_list[itrl] = 1
def test_cuda():
"""Test CUDA-based filtering
"""
# NOTE: don't make test_cuda() the last test, or pycuda might spew
# some warnings about clean-up failing
# Also, using `n_jobs='cuda'` on a non-CUDA system should be fine,
# as it should fall back to using n_jobs=1.
sfreq = 500
sig_len_secs = 20
a = rng.randn(sig_len_secs * sfreq)
with catch_logging() as log_file:
for fl in ['10s', None, 2048]:
bp = band_pass_filter(a, sfreq, 4, 8, n_jobs=1, filter_length=fl)
bs = band_stop_filter(a,
sfreq,
4 - 0.5,
8 + 0.5,
n_jobs=1,
filter_length=fl)
lp = low_pass_filter(a, sfreq, 8, n_jobs=1, filter_length=fl)
hp = high_pass_filter(lp, sfreq, 4, n_jobs=1, filter_length=fl)
bp_c = band_pass_filter(a,
sfreq,
4,
8,
n_jobs='cuda',
filter_length=fl,
verbose='INFO')
bs_c = band_stop_filter(a,
sfreq,
4 - 0.5,
8 + 0.5,
n_jobs='cuda',
filter_length=fl,
verbose='INFO')
lp_c = low_pass_filter(a,
sfreq,
8,
n_jobs='cuda',
filter_length=fl,
verbose='INFO')
hp_c = high_pass_filter(lp,
sfreq,
4,
n_jobs='cuda',
filter_length=fl,
verbose='INFO')
assert_array_almost_equal(bp, bp_c, 12)
assert_array_almost_equal(bs, bs_c, 12)
assert_array_almost_equal(lp, lp_c, 12)
assert_array_almost_equal(hp, hp_c, 12)
# check to make sure we actually used CUDA
out = log_file.getvalue().split('\n')[:-1]
# triage based on whether or not we actually expected to use CUDA
from mne.cuda import _cuda_capable # allow above funs to set it
tot = 12 if _cuda_capable else 0
assert_true(
sum(['Using CUDA for FFT FIR filtering' in o for o in out]) == tot)
# check resampling
for window in ('boxcar', 'triang'):
for N in (997, 1000): # one prime, one even
a = rng.randn(2, N)
for fro, to in ((1, 2), (2, 1), (1, 3), (3, 1)):
a1 = resample(a, fro, to, n_jobs=1, npad='auto', window=window)
a2 = resample(a,
fro,
to,
n_jobs='cuda',
npad='auto',
window=window)
assert_allclose(a1, a2, rtol=1e-7, atol=1e-14)
assert_array_almost_equal(a1, a2, 14)
assert_array_equal(resample([0, 0], 2, 1, n_jobs='cuda'), [0., 0., 0., 0.])
assert_array_equal(resample(np.zeros(2, np.float32), 2, 1, n_jobs='cuda'),
[0., 0., 0., 0.])
def test_filters():
"""Test low-, band-, high-pass, and band-stop filters plus resampling
"""
sfreq = 500
sig_len_secs = 30
a = rng.randn(2, sig_len_secs * sfreq)
# let's test our catchers
for fl in ['blah', [0, 1], 1000.5, '10ss', '10']:
assert_raises(ValueError,
band_pass_filter,
a,
sfreq,
4,
8,
filter_length=fl)
for nj in ['blah', 0.5]:
assert_raises(ValueError, band_pass_filter, a, sfreq, 4, 8, n_jobs=nj)
# > Nyq/2
assert_raises(ValueError, band_pass_filter, a, sfreq, 4, sfreq / 2.)
assert_raises(ValueError, low_pass_filter, a, sfreq, sfreq / 2.)
# check our short-filter warning:
with warnings.catch_warnings(record=True) as w:
# Warning for low attenuation
band_pass_filter(a, sfreq, 1, 8, filter_length=1024)
# Warning for too short a filter
band_pass_filter(a, sfreq, 1, 8, filter_length='0.5s')
assert_true(len(w) >= 2)
# try new default and old default
for fl in ['10s', '5000ms', None]:
bp = band_pass_filter(a, sfreq, 4, 8, filter_length=fl)
bs = band_stop_filter(a, sfreq, 4 - 0.5, 8 + 0.5, filter_length=fl)
lp = low_pass_filter(a, sfreq, 8, filter_length=fl, n_jobs=2)
hp = high_pass_filter(lp, sfreq, 4, filter_length=fl)
assert_array_almost_equal(hp, bp, 2)
assert_array_almost_equal(bp + bs, a, 1)
# Overlap-add filtering with a fixed filter length
filter_length = 8192
bp_oa = band_pass_filter(a, sfreq, 4, 8, filter_length)
bs_oa = band_stop_filter(a, sfreq, 4 - 0.5, 8 + 0.5, filter_length)
lp_oa = low_pass_filter(a, sfreq, 8, filter_length)
hp_oa = high_pass_filter(lp_oa, sfreq, 4, filter_length)
assert_array_almost_equal(hp_oa, bp_oa, 2)
# Our filters are no longer quite complementary with linear rolloffs :(
# this is the tradeoff for stability of the filtering
# obtained by directly using the result of firwin2 instead of
# modifying it...
assert_array_almost_equal(bp_oa + bs_oa, a, 1)
# The two methods should give the same result
# As filtering for short signals uses a circular convolution (FFT) and
# the overlap-add filter implements a linear convolution, the signal
# boundary will be slightly different and we ignore it
n_edge_ignore = 0
assert_array_almost_equal(hp[n_edge_ignore:-n_edge_ignore],
hp_oa[n_edge_ignore:-n_edge_ignore], 2)
# and since these are low-passed, downsampling/upsampling should be close
n_resamp_ignore = 10
bp_up_dn = resample(resample(bp_oa, 2, 1, n_jobs=2), 1, 2, n_jobs=2)
assert_array_almost_equal(bp_oa[n_resamp_ignore:-n_resamp_ignore],
bp_up_dn[n_resamp_ignore:-n_resamp_ignore], 2)
# note that on systems without CUDA, this line serves as a test for a
# graceful fallback to n_jobs=1
bp_up_dn = resample(resample(bp_oa, 2, 1, n_jobs='cuda'),
1,
2,
n_jobs='cuda')
assert_array_almost_equal(bp_oa[n_resamp_ignore:-n_resamp_ignore],
bp_up_dn[n_resamp_ignore:-n_resamp_ignore], 2)
# test to make sure our resamling matches scipy's
bp_up_dn = sp_resample(sp_resample(bp_oa,
2 * bp_oa.shape[-1],
axis=-1,
window='boxcar'),
bp_oa.shape[-1],
window='boxcar',
axis=-1)
assert_array_almost_equal(bp_oa[n_resamp_ignore:-n_resamp_ignore],
bp_up_dn[n_resamp_ignore:-n_resamp_ignore], 2)
# make sure we don't alias
t = np.array(list(range(sfreq * sig_len_secs))) / float(sfreq)
# make sinusoid close to the Nyquist frequency
sig = np.sin(2 * np.pi * sfreq / 2.2 * t)
# signal should disappear with 2x downsampling
sig_gone = resample(sig, 1, 2)[n_resamp_ignore:-n_resamp_ignore]
assert_array_almost_equal(np.zeros_like(sig_gone), sig_gone, 2)
# let's construct some filters
iir_params = dict(ftype='cheby1', gpass=1, gstop=20)
iir_params = construct_iir_filter(iir_params, 40, 80, 1000, 'low')
# this should be a third order filter
assert_true(iir_params['a'].size - 1 == 3)
assert_true(iir_params['b'].size - 1 == 3)
iir_params = dict(ftype='butter', order=4)
iir_params = construct_iir_filter(iir_params, 40, None, 1000, 'low')
assert_true(iir_params['a'].size - 1 == 4)
assert_true(iir_params['b'].size - 1 == 4)
# check that picks work for 3d array with one channel and picks=[0]
a = rng.randn(5 * sfreq, 5 * sfreq)
b = a[:, None, :]
with warnings.catch_warnings(record=True) as w:
a_filt = band_pass_filter(a, sfreq, 4, 8)
b_filt = band_pass_filter(b, sfreq, 4, 8, picks=[0])
assert_array_equal(a_filt[:, None, :], b_filt)
# check for n-dimensional case
a = rng.randn(2, 2, 2, 2)
assert_raises(ValueError,
band_pass_filter,
a,
sfreq,
Fp1=4,
Fp2=8,
picks=np.array([0, 1]))
# test that our overlap-add filtering doesn't introduce strange
# artifacts (from mne_analyze mailing list 2015/06/25)
N = 300
sfreq = 100.
lp = 10.
sine_freq = 1.
x = np.ones(N)
x += np.sin(2 * np.pi * sine_freq * np.arange(N) / sfreq)
with warnings.catch_warnings(record=True): # filter attenuation
x_filt = low_pass_filter(x, sfreq, lp, '1s')
# the firwin2 function gets us this close
assert_allclose(x, x_filt, rtol=1e-3, atol=1e-3)
def test_filters():
"""Test low-, band-, high-pass, and band-stop filters plus resampling"""
sfreq = 100
sig_len_secs = 15
a = rng.randn(2, sig_len_secs * sfreq)
# let's test our catchers
for fl in ['blah', [0, 1], 1000.5, '10ss', '10']:
assert_raises(ValueError,
band_pass_filter,
a,
sfreq,
4,
8,
fl,
1.0,
1.0,
phase='zero')
for nj in ['blah', 0.5]:
assert_raises(ValueError,
band_pass_filter,
a,
sfreq,
4,
8,
100,
1.0,
1.0,
n_jobs=nj,
phase='zero',
fir_window='hann')
assert_raises(ValueError,
band_pass_filter,
a,
sfreq,
4,
8,
100,
1.0,
1.0,
phase='zero',
fir_window='foo')
# > Nyq/2
assert_raises(ValueError,
band_pass_filter,
a,
sfreq,
4,
sfreq / 2.,
100,
1.0,
1.0,
phase='zero',
fir_window='hann')
assert_raises(ValueError,
low_pass_filter,
a,
sfreq,
sfreq / 2.,
100,
1.0,
phase='zero',
fir_window='hann')
# check our short-filter warning:
with warnings.catch_warnings(record=True) as w:
# Warning for low attenuation
band_pass_filter(a, sfreq, 1, 8, filter_length=256, phase='zero')
assert_true(any('attenuation' in str(ww.message) for ww in w))
with warnings.catch_warnings(record=True) as w:
# Warning for too short a filter
band_pass_filter(a, sfreq, 1, 8, filter_length='0.5s', phase='zero')
assert_true(any('Increase filter_length' in str(ww.message) for ww in w))
# try new default and old default
for fl in ['auto', '10s', '5000ms', 1024]:
bp = band_pass_filter(a,
sfreq,
4,
8,
fl,
1.0,
1.0,
phase='zero',
fir_window='hamming')
bs = band_stop_filter(a,
sfreq,
4 - 1.0,
8 + 1.0,
fl,
1.0,
1.0,
phase='zero',
fir_window='hamming')
lp = low_pass_filter(a,
sfreq,
8,
fl,
1.0,
n_jobs=2,
phase='zero',
fir_window='hamming')
hp = high_pass_filter(lp,
sfreq,
4,
fl,
1.0,
phase='zero',
fir_window='hamming')
assert_array_almost_equal(hp, bp, 4)
assert_array_almost_equal(bp + bs, a, 4)
# and since these are low-passed, downsampling/upsampling should be close
n_resamp_ignore = 10
bp_up_dn = resample(resample(bp, 2, 1, n_jobs=2), 1, 2, n_jobs=2)
assert_array_almost_equal(bp[n_resamp_ignore:-n_resamp_ignore],
bp_up_dn[n_resamp_ignore:-n_resamp_ignore], 2)
# note that on systems without CUDA, this line serves as a test for a
# graceful fallback to n_jobs=1
bp_up_dn = resample(resample(bp, 2, 1, n_jobs='cuda'), 1, 2, n_jobs='cuda')
assert_array_almost_equal(bp[n_resamp_ignore:-n_resamp_ignore],
bp_up_dn[n_resamp_ignore:-n_resamp_ignore], 2)
# test to make sure our resamling matches scipy's
bp_up_dn = sp_resample(sp_resample(bp,
2 * bp.shape[-1],
axis=-1,
window='boxcar'),
bp.shape[-1],
window='boxcar',
axis=-1)
assert_array_almost_equal(bp[n_resamp_ignore:-n_resamp_ignore],
bp_up_dn[n_resamp_ignore:-n_resamp_ignore], 2)
# make sure we don't alias
t = np.array(list(range(sfreq * sig_len_secs))) / float(sfreq)
# make sinusoid close to the Nyquist frequency
sig = np.sin(2 * np.pi * sfreq / 2.2 * t)
# signal should disappear with 2x downsampling
sig_gone = resample(sig, 1, 2)[n_resamp_ignore:-n_resamp_ignore]
assert_array_almost_equal(np.zeros_like(sig_gone), sig_gone, 2)
# let's construct some filters
iir_params = dict(ftype='cheby1', gpass=1, gstop=20, output='ba')
iir_params = construct_iir_filter(iir_params, 40, 80, 1000, 'low')
# this should be a third order filter
assert_equal(iir_params['a'].size - 1, 3)
assert_equal(iir_params['b'].size - 1, 3)
iir_params = dict(ftype='butter', order=4, output='ba')
iir_params = construct_iir_filter(iir_params, 40, None, 1000, 'low')
assert_equal(iir_params['a'].size - 1, 4)
assert_equal(iir_params['b'].size - 1, 4)
iir_params = dict(ftype='cheby1', gpass=1, gstop=20, output='sos')
iir_params = construct_iir_filter(iir_params, 40, 80, 1000, 'low')
# this should be a third order filter, which requires 2 SOS ((2, 6))
assert_equal(iir_params['sos'].shape, (2, 6))
iir_params = dict(ftype='butter', order=4, output='sos')
iir_params = construct_iir_filter(iir_params, 40, None, 1000, 'low')
assert_equal(iir_params['sos'].shape, (2, 6))
# check that picks work for 3d array with one channel and picks=[0]
a = rng.randn(5 * sfreq, 5 * sfreq)
b = a[:, None, :]
a_filt = band_pass_filter(a,
sfreq,
4,
8,
400,
2.0,
2.0,
phase='zero',
fir_window='hamming')
b_filt = band_pass_filter(b,
sfreq,
4,
8,
400,
2.0,
2.0,
picks=[0],
phase='zero',
fir_window='hamming')
assert_array_equal(a_filt[:, None, :], b_filt)
# check for n-dimensional case
a = rng.randn(2, 2, 2, 2)
with warnings.catch_warnings(record=True): # filter too long
assert_raises(ValueError,
band_pass_filter,
a,
sfreq,
4,
8,
100,
1.0,
1.0,
picks=np.array([0, 1]),
phase='zero')
def chunkwise_common_average_ref(array):
block_median = common_average_reference(array)
denoised = array - block_median
return denoised
#LOW-PASS
low_pass_freq = 5000
iir_params = {'order': 3, 'ftype': 'butter', 'padlen': 0}
temp_filtered_band = filters.low_pass_filter(temp_filtered,
sampling_freq,
low_pass_freq,
method='iir',
iir_params=iir_params)
# temp_filtered = filters.high_pass_filter(temp_unfiltered,sampling_freq, high_pass_freq, method='iir',iir_params=iir_params)
temp_filtered_uV = temp_filtered_band * scale_uV * voltage_step_size
plt.plot(time_axis * scale_ms,
temp_filtered_uV[ch, :].T - yoffset,
color='r',
label=str(ch) + '_Neuronexus5_Pair3.0')
plt.ylabel('Voltage (\u00B5V)', fontsize=20)
plt.xlabel('Time (ms)', fontsize=20)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
def test_filters():
"""Test low-, band-, high-pass, and band-stop filters plus resampling."""
sfreq = 100
sig_len_secs = 15
a = rng.randn(2, sig_len_secs * sfreq)
# let's test our catchers
for fl in ['blah', [0, 1], 1000.5, '10ss', '10']:
assert_raises(ValueError, band_pass_filter, a, sfreq, 4, 8, fl, 1., 1.)
for nj in ['blah', 0.5]:
assert_raises(ValueError, band_pass_filter, a, sfreq, 4, 8, 100,
1., 1., n_jobs=nj)
assert_raises(ValueError, band_pass_filter, a, sfreq, 4, 8, 100,
1., 1., fir_window='foo')
# > Nyq/2
assert_raises(ValueError, band_pass_filter, a, sfreq, 4, sfreq / 2.,
100, 1.0, 1.0)
assert_raises(ValueError, low_pass_filter, a, sfreq, sfreq / 2.,
100, 1.0)
# check our short-filter warning:
with warnings.catch_warnings(record=True) as w:
# Warning for low attenuation
band_pass_filter(a, sfreq, 1, 8, filter_length=256)
assert_true(any('attenuation' in str(ww.message) for ww in w))
with warnings.catch_warnings(record=True) as w:
# Warning for too short a filter
band_pass_filter(a, sfreq, 1, 8, filter_length='0.5s')
assert_true(any('Increase filter_length' in str(ww.message) for ww in w))
# try new default and old default
for fl in ['auto', '10s', '5000ms', 1024]:
bp = band_pass_filter(a, sfreq, 4, 8, fl, 1.0, 1.0)
bs = band_stop_filter(a, sfreq, 4 - 1.0, 8 + 1.0, fl, 1.0, 1.0)
lp = low_pass_filter(a, sfreq, 8, fl, 1.0, n_jobs=2)
hp = high_pass_filter(lp, sfreq, 4, fl, 1.0)
assert_array_almost_equal(hp, bp, 4)
assert_array_almost_equal(bp + bs, a, 4)
# and since these are low-passed, downsampling/upsampling should be close
n_resamp_ignore = 10
bp_up_dn = resample(resample(bp, 2, 1, n_jobs=2), 1, 2, n_jobs=2)
assert_array_almost_equal(bp[n_resamp_ignore:-n_resamp_ignore],
bp_up_dn[n_resamp_ignore:-n_resamp_ignore], 2)
# note that on systems without CUDA, this line serves as a test for a
# graceful fallback to n_jobs=1
bp_up_dn = resample(resample(bp, 2, 1, n_jobs='cuda'), 1, 2, n_jobs='cuda')
assert_array_almost_equal(bp[n_resamp_ignore:-n_resamp_ignore],
bp_up_dn[n_resamp_ignore:-n_resamp_ignore], 2)
# test to make sure our resamling matches scipy's
bp_up_dn = sp_resample(sp_resample(bp, 2 * bp.shape[-1], axis=-1,
window='boxcar'),
bp.shape[-1], window='boxcar', axis=-1)
assert_array_almost_equal(bp[n_resamp_ignore:-n_resamp_ignore],
bp_up_dn[n_resamp_ignore:-n_resamp_ignore], 2)
# make sure we don't alias
t = np.array(list(range(sfreq * sig_len_secs))) / float(sfreq)
# make sinusoid close to the Nyquist frequency
sig = np.sin(2 * np.pi * sfreq / 2.2 * t)
# signal should disappear with 2x downsampling
sig_gone = resample(sig, 1, 2)[n_resamp_ignore:-n_resamp_ignore]
assert_array_almost_equal(np.zeros_like(sig_gone), sig_gone, 2)
# let's construct some filters
iir_params = dict(ftype='cheby1', gpass=1, gstop=20, output='ba')
iir_params = construct_iir_filter(iir_params, 40, 80, 1000, 'low')
# this should be a third order filter
assert_equal(iir_params['a'].size - 1, 3)
assert_equal(iir_params['b'].size - 1, 3)
iir_params = dict(ftype='butter', order=4, output='ba')
iir_params = construct_iir_filter(iir_params, 40, None, 1000, 'low')
assert_equal(iir_params['a'].size - 1, 4)
assert_equal(iir_params['b'].size - 1, 4)
iir_params = dict(ftype='cheby1', gpass=1, gstop=20, output='sos')
iir_params = construct_iir_filter(iir_params, 40, 80, 1000, 'low')
# this should be a third order filter, which requires 2 SOS ((2, 6))
assert_equal(iir_params['sos'].shape, (2, 6))
iir_params = dict(ftype='butter', order=4, output='sos')
iir_params = construct_iir_filter(iir_params, 40, None, 1000, 'low')
assert_equal(iir_params['sos'].shape, (2, 6))
# check that picks work for 3d array with one channel and picks=[0]
a = rng.randn(5 * sfreq, 5 * sfreq)
b = a[:, None, :]
a_filt = band_pass_filter(a, sfreq, 4, 8, 400, 2.0, 2.0)
b_filt = band_pass_filter(b, sfreq, 4, 8, 400, 2.0, 2.0, picks=[0])
assert_array_equal(a_filt[:, None, :], b_filt)
# check for n-dimensional case
a = rng.randn(2, 2, 2, 2)
with warnings.catch_warnings(record=True): # filter too long
assert_raises(ValueError, band_pass_filter, a, sfreq, 4, 8, 100,
1.0, 1.0, picks=np.array([0, 1]))
def test_filters():
"""Test low-, band-, high-pass, and band-stop filters plus resampling
"""
Fs = 500
sig_len_secs = 30
a = np.random.randn(2, sig_len_secs * Fs)
# let's test our catchers
for fl in ['blah', [0, 1], 1000.5, '10ss', '10']:
assert_raises(ValueError, band_pass_filter, a, Fs, 4, 8,
filter_length=fl)
for nj in ['blah', 0.5, 0]:
assert_raises(ValueError, band_pass_filter, a, Fs, 4, 8, n_jobs=nj)
assert_raises(ValueError, band_pass_filter, a, Fs, 4, Fs / 2.) # > Nyq/2
assert_raises(ValueError, low_pass_filter, a, Fs, Fs / 2.) # > Nyq/2
# check our short-filter warning:
with warnings.catch_warnings(record=True) as w:
# Warning for low attenuation
band_pass_filter(a, Fs, 1, 8, filter_length=1024)
# Warning for too short a filter
band_pass_filter(a, Fs, 1, 8, filter_length='0.5s')
assert_true(len(w) >= 2)
# try new default and old default
for fl in ['10s', '5000ms', None]:
bp = band_pass_filter(a, Fs, 4, 8, filter_length=fl)
bs = band_stop_filter(a, Fs, 4 - 0.5, 8 + 0.5, filter_length=fl)
lp = low_pass_filter(a, Fs, 8, filter_length=fl, n_jobs=2)
hp = high_pass_filter(lp, Fs, 4, filter_length=fl)
assert_array_almost_equal(hp, bp, 2)
assert_array_almost_equal(bp + bs, a, 1)
# Overlap-add filtering with a fixed filter length
filter_length = 8192
bp_oa = band_pass_filter(a, Fs, 4, 8, filter_length)
bs_oa = band_stop_filter(a, Fs, 4 - 0.5, 8 + 0.5, filter_length)
lp_oa = low_pass_filter(a, Fs, 8, filter_length)
hp_oa = high_pass_filter(lp_oa, Fs, 4, filter_length)
assert_array_almost_equal(hp_oa, bp_oa, 2)
assert_array_almost_equal(bp_oa + bs_oa, a, 2)
# The two methods should give the same result
# As filtering for short signals uses a circular convolution (FFT) and
# the overlap-add filter implements a linear convolution, the signal
# boundary will be slightly different and we ignore it
n_edge_ignore = 0
assert_array_almost_equal(hp[n_edge_ignore:-n_edge_ignore],
hp_oa[n_edge_ignore:-n_edge_ignore], 2)
# and since these are low-passed, downsampling/upsampling should be close
n_resamp_ignore = 10
bp_up_dn = resample(resample(bp_oa, 2, 1, n_jobs=2), 1, 2, n_jobs=2)
assert_array_almost_equal(bp_oa[n_resamp_ignore:-n_resamp_ignore],
bp_up_dn[n_resamp_ignore:-n_resamp_ignore], 2)
# note that on systems without CUDA, this line serves as a test for a
# graceful fallback to n_jobs=1
bp_up_dn = resample(resample(bp_oa, 2, 1, n_jobs='cuda'), 1, 2,
n_jobs='cuda')
assert_array_almost_equal(bp_oa[n_resamp_ignore:-n_resamp_ignore],
bp_up_dn[n_resamp_ignore:-n_resamp_ignore], 2)
# test to make sure our resamling matches scipy's
bp_up_dn = sp_resample(sp_resample(bp_oa, 2 * bp_oa.shape[-1], axis=-1,
window='boxcar'),
bp_oa.shape[-1], window='boxcar', axis=-1)
assert_array_almost_equal(bp_oa[n_resamp_ignore:-n_resamp_ignore],
bp_up_dn[n_resamp_ignore:-n_resamp_ignore], 2)
# make sure we don't alias
t = np.array(list(range(Fs * sig_len_secs))) / float(Fs)
# make sinusoid close to the Nyquist frequency
sig = np.sin(2 * np.pi * Fs / 2.2 * t)
# signal should disappear with 2x downsampling
sig_gone = resample(sig, 1, 2)[n_resamp_ignore:-n_resamp_ignore]
assert_array_almost_equal(np.zeros_like(sig_gone), sig_gone, 2)
# let's construct some filters
iir_params = dict(ftype='cheby1', gpass=1, gstop=20)
iir_params = construct_iir_filter(iir_params, 40, 80, 1000, 'low')
# this should be a third order filter
assert_true(iir_params['a'].size - 1 == 3)
assert_true(iir_params['b'].size - 1 == 3)
iir_params = dict(ftype='butter', order=4)
iir_params = construct_iir_filter(iir_params, 40, None, 1000, 'low')
assert_true(iir_params['a'].size - 1 == 4)
assert_true(iir_params['b'].size - 1 == 4)
def test_filters():
"""Test low-, band-, high-pass, and band-stop filters plus resampling
"""
Fs = 500
sig_len_secs = 30
a = np.random.randn(2, sig_len_secs * Fs)
# let's test our catchers
for fl in ['blah', [0, 1], 1000.5, '10ss', '10']:
assert_raises(ValueError, band_pass_filter, a, Fs, 4, 8,
filter_length=fl)
for nj in ['blah', 0.5, 0]:
assert_raises(ValueError, band_pass_filter, a, Fs, 4, 8, n_jobs=nj)
# check our short-filter warning:
with warnings.catch_warnings(record=True) as w:
# Warning for low attenuation
band_pass_filter(a, Fs, 1, 8, filter_length=1024)
# Warning for too short a filter
band_pass_filter(a, Fs, 1, 8, filter_length='0.5s')
assert_true(len(w) >= 2)
# try new default and old default
for fl in ['10s', '5000ms', None]:
bp = band_pass_filter(a, Fs, 4, 8, filter_length=fl)
bs = band_stop_filter(a, Fs, 4 - 0.5, 8 + 0.5, filter_length=fl)
lp = low_pass_filter(a, Fs, 8, filter_length=fl, n_jobs=2)
hp = high_pass_filter(lp, Fs, 4, filter_length=fl)
assert_array_almost_equal(hp, bp, 2)
assert_array_almost_equal(bp + bs, a, 1)
# Overlap-add filtering with a fixed filter length
filter_length = 8192
bp_oa = band_pass_filter(a, Fs, 4, 8, filter_length)
bs_oa = band_stop_filter(a, Fs, 4 - 0.5, 8 + 0.5, filter_length)
lp_oa = low_pass_filter(a, Fs, 8, filter_length)
hp_oa = high_pass_filter(lp_oa, Fs, 4, filter_length)
assert_array_almost_equal(hp_oa, bp_oa, 2)
assert_array_almost_equal(bp_oa + bs_oa, a, 2)
# The two methods should give the same result
# As filtering for short signals uses a circular convolution (FFT) and
# the overlap-add filter implements a linear convolution, the signal
# boundary will be slightly different and we ignore it
n_edge_ignore = 0
assert_array_almost_equal(hp[n_edge_ignore:-n_edge_ignore],
hp_oa[n_edge_ignore:-n_edge_ignore], 2)
# and since these are low-passed, downsampling/upsampling should be close
n_resamp_ignore = 10
bp_up_dn = resample(resample(bp_oa, 2, 1, n_jobs=2), 1, 2, n_jobs=2)
assert_array_almost_equal(bp_oa[n_resamp_ignore:-n_resamp_ignore],
bp_up_dn[n_resamp_ignore:-n_resamp_ignore], 2)
# note that on systems without CUDA, this line serves as a test for a
# graceful fallback to n_jobs=1
bp_up_dn = resample(resample(bp_oa, 2, 1, n_jobs='cuda'), 1, 2,
n_jobs='cuda')
assert_array_almost_equal(bp_oa[n_resamp_ignore:-n_resamp_ignore],
bp_up_dn[n_resamp_ignore:-n_resamp_ignore], 2)
# test to make sure our resamling matches scipy's
bp_up_dn = sp_resample(sp_resample(bp_oa, 2 * len(bp_oa), window='boxcar'),
len(bp_oa), window='boxcar')
assert_array_almost_equal(bp_oa[n_resamp_ignore:-n_resamp_ignore],
bp_up_dn[n_resamp_ignore:-n_resamp_ignore], 2)
# make sure we don't alias
t = np.array(range(Fs * sig_len_secs)) / float(Fs)
# make sinusoid close to the Nyquist frequency
sig = np.sin(2 * np.pi * Fs / 2.2 * t)
# signal should disappear with 2x downsampling
sig_gone = resample(sig, 1, 2)[n_resamp_ignore:-n_resamp_ignore]
assert_array_almost_equal(np.zeros_like(sig_gone), sig_gone, 2)
# let's construct some filters
iir_params = dict(ftype='cheby1', gpass=1, gstop=20)
iir_params = construct_iir_filter(iir_params, 40, 80, 1000, 'low')
# this should be a third order filter
assert_true(iir_params['a'].size - 1 == 3)
assert_true(iir_params['b'].size - 1 == 3)
iir_params = dict(ftype='butter', order=4)
iir_params = construct_iir_filter(iir_params, 40, None, 1000, 'low')
assert_true(iir_params['a'].size - 1 == 4)
assert_true(iir_params['b'].size - 1 == 4)
###############################################################################
# Typical preprocessing step is the removal of power line artifact (50 Hz or
# 60 Hz). Here we notch filter the data at 60, 120 and 180 to remove the
# original 60 Hz artifact and the harmonics. Normally this would be done to
# raw data (with :func:`mne.io.Raw.filter`), but to reduce memory consumption
# of this tutorial, we do it at evoked stage.
if use_precomputed:
sfreq = evoked_std.info['sfreq']
nchan = evoked_std.info['nchan']
notches = [60, 120, 180]
for ch_idx in range(nchan):
evoked_std.data[ch_idx] = notch_filter(evoked_std.data[ch_idx], sfreq,
notches, verbose='ERROR')
evoked_dev.data[ch_idx] = notch_filter(evoked_dev.data[ch_idx], sfreq,
notches, verbose='ERROR')
evoked_std.data[ch_idx] = low_pass_filter(evoked_std.data[ch_idx],
sfreq, 100, verbose='ERROR')
evoked_dev.data[ch_idx] = low_pass_filter(evoked_dev.data[ch_idx],
sfreq, 100, verbose='ERROR')
###############################################################################
# Here we plot the ERF of standard and deviant conditions. In both conditions
# we can see the P50 and N100 responses. The mismatch negativity is visible
# only in the deviant condition around 100-200 ms. P200 is also visible around
# 170 ms in both conditions but much stronger in the standard condition. P300
# is visible in deviant condition only (decision making in preparation of the
# button press). You can view the topographies from a certain time span by
# painting an area with clicking and holding the left mouse button.
evoked_std.plot(window_title='Standard', gfp=True)
evoked_dev.plot(window_title='Deviant', gfp=True)
###############################################################################
def test_filters():
"""Test low-, band-, high-pass, and band-stop filters plus resampling
"""
Fs = 500
sig_len_secs = 30
# Filtering of short signals (filter length = len(a))
a = np.random.randn(2, sig_len_secs * Fs)
bp = band_pass_filter(a, Fs, 4, 8)
bs = band_stop_filter(a, Fs, 4 - 0.5, 8 + 0.5)
lp = low_pass_filter(a, Fs, 8)
hp = high_pass_filter(lp, Fs, 4)
assert_array_almost_equal(hp, bp, 2)
assert_array_almost_equal(bp + bs, a, 1)
# Overlap-add filtering with a fixed filter length
filter_length = 8192
bp_oa = band_pass_filter(a, Fs, 4, 8, filter_length)
bs_oa = band_stop_filter(a, Fs, 4 - 0.5, 8 + 0.5, filter_length)
lp_oa = low_pass_filter(a, Fs, 8, filter_length)
hp_oa = high_pass_filter(lp_oa, Fs, 4, filter_length)
assert_array_almost_equal(hp_oa, bp_oa, 2)
assert_array_almost_equal(bp_oa + bs_oa, a, 2)
# The two methods should give the same result
# As filtering for short signals uses a circular convolution (FFT) and
# the overlap-add filter implements a linear convolution, the signal
# boundary will be slightly different and we ignore it
n_edge_ignore = 0
assert_array_almost_equal(hp[n_edge_ignore:-n_edge_ignore],
hp_oa[n_edge_ignore:-n_edge_ignore], 2)
# and since these are low-passed, downsampling/upsampling should be close
n_resamp_ignore = 10
bp_up_dn = resample(resample(bp_oa, 2, 1, n_jobs=2), 1, 2, n_jobs=2)
assert_array_almost_equal(bp_oa[n_resamp_ignore:-n_resamp_ignore],
bp_up_dn[n_resamp_ignore:-n_resamp_ignore], 2)
# note that on systems without CUDA, this line serves as a test for a
# graceful fallback to n_jobs=1
bp_up_dn = resample(resample(bp_oa, 2, 1, n_jobs='cuda'), 1, 2,
n_jobs='cuda')
assert_array_almost_equal(bp_oa[n_resamp_ignore:-n_resamp_ignore],
bp_up_dn[n_resamp_ignore:-n_resamp_ignore], 2)
# test to make sure our resamling matches scipy's
bp_up_dn = sp_resample(sp_resample(bp_oa, 2 * len(bp_oa), window='boxcar'),
len(bp_oa), window='boxcar')
assert_array_almost_equal(bp_oa[n_resamp_ignore:-n_resamp_ignore],
bp_up_dn[n_resamp_ignore:-n_resamp_ignore], 2)
# make sure we don't alias
t = np.array(range(Fs * sig_len_secs)) / float(Fs)
# make sinusoid close to the Nyquist frequency
sig = np.sin(2 * np.pi * Fs / 2.2 * t)
# signal should disappear with 2x downsampling
sig_gone = resample(sig, 1, 2)[n_resamp_ignore:-n_resamp_ignore]
assert_array_almost_equal(np.zeros_like(sig_gone), sig_gone, 2)
# let's construct some filters
iir_params = dict(ftype='cheby1', gpass=1, gstop=20)
iir_params = construct_iir_filter(iir_params, 40, 80, 1000, 'low')
# this should be a third order filter
assert_true(iir_params['a'].size - 1 == 3)
assert_true(iir_params['b'].size - 1 == 3)
iir_params = dict(ftype='butter', order=4)
iir_params = construct_iir_filter(iir_params, 40, None, 1000, 'low')
assert_true(iir_params['a'].size - 1 == 4)
assert_true(iir_params['b'].size - 1 == 4)
def test_filter_auto():
"""Test filter auto parameters"""
# test that our overlap-add filtering doesn't introduce strange
# artifacts (from mne_analyze mailing list 2015/06/25)
N = 300
sfreq = 100.
lp = 10.
sine_freq = 1.
x = np.ones(N)
t = np.arange(N) / sfreq
x += np.sin(2 * np.pi * sine_freq * t)
x_orig = x.copy()
x_filt = low_pass_filter(x,
sfreq,
lp,
'auto',
'auto',
phase='zero',
fir_window='hamming')
assert_array_equal(x, x_orig)
# the firwin2 function gets us this close
assert_allclose(x, x_filt, rtol=1e-4, atol=1e-4)
assert_array_equal(
x_filt,
low_pass_filter(x,
sfreq,
lp,
'auto',
'auto',
phase='zero',
fir_window='hamming'))
assert_array_equal(x, x_orig)
assert_array_equal(
x_filt,
filter_data(x,
sfreq,
None,
lp,
h_trans_bandwidth='auto',
phase='zero',
fir_window='hamming',
filter_length='auto'))
assert_array_equal(x, x_orig)
assert_array_equal(
x_filt,
filter_data(x,
sfreq,
None,
lp,
h_trans_bandwidth='auto',
phase='zero',
fir_window='hamming',
filter_length='auto',
copy=False))
assert_array_equal(x, x_filt)
# degenerate conditions
assert_raises(ValueError, filter_data, x, -sfreq, 1, 10)
assert_raises(ValueError, filter_data, x, sfreq, 1, sfreq * 0.75)
assert_raises(TypeError,
filter_data,
x.astype(np.float32),
sfreq,
None,
10,
filter_length='auto',
h_trans_bandwidth='auto')
###############################################################################
# Typical preprocessing step is the removal of power line artifact (50 Hz or
# 60 Hz). Here we notch filter the data at 60, 120 and 180 to remove the
# original 60 Hz artifact and the harmonics. Normally this would be done to
# raw data (with :func:`mne.io.Raw.filter`), but to reduce memory consumption
# of this tutorial, we do it at evoked stage.
if use_precomputed:
sfreq = evoked_std.info['sfreq']
nchan = evoked_std.info['nchan']
notches = [60, 120, 180]
for ch_idx in range(nchan):
evoked_std.data[ch_idx] = notch_filter(evoked_std.data[ch_idx], sfreq,
notches, verbose='ERROR')
evoked_dev.data[ch_idx] = notch_filter(evoked_dev.data[ch_idx], sfreq,
notches, verbose='ERROR')
evoked_std.data[ch_idx] = low_pass_filter(evoked_std.data[ch_idx],
sfreq, 100, verbose='ERROR')
evoked_dev.data[ch_idx] = low_pass_filter(evoked_dev.data[ch_idx],
sfreq, 100, verbose='ERROR')
###############################################################################
# Here we plot the ERF of standard and deviant conditions. In both conditions
# we can see the P50 and N100 responses. The mismatch negativity is visible
# only in the deviant condition around 100-200 ms. P200 is also visible around
# 170 ms in both conditions but much stronger in the standard condition. P300
# is visible in deviant condition only (decision making in preparation of the
# button press). You can view the topographies from a certain time span by
# painting an area with clicking and holding the left mouse button.
evoked_std.plot(window_title='Standard', gfp=True)
evoked_dev.plot(window_title='Deviant', gfp=True)
###############################################################################
def filter(l_freq, h_freq, picks=None, filter_length='10s',
l_trans_bandwidth=0.5, h_trans_bandwidth=0.5, n_jobs=1,
method='fft', iir_params=None, verbose=None):
"""Filter a subset of channels.
Applies a zero-phase low-pass, high-pass, band-pass, or band-stop
filter to the channels selected by "picks". The data of the Raw
object is modified inplace.
The Raw object has to be constructed using preload=True (or string).
l_freq and h_freq are the frequencies below which and above which,
respectively, to filter out of the data. Thus the uses are:
* ``l_freq < h_freq``: band-pass filter
* ``l_freq > h_freq``: band-stop filter
* ``l_freq is not None and h_freq is None``: high-pass filter
* ``l_freq is None and h_freq is not None``: low-pass filter
If n_jobs > 1, more memory is required as "len(picks) * n_times"
additional time points need to be temporarily stored in memory.
raw.info['lowpass'] and raw.info['highpass'] are only updated
with picks=None.
Parameters
----------
l_freq : float | None
Low cut-off frequency in Hz. If None the data are only low-passed.
h_freq : float | None
High cut-off frequency in Hz. If None the data are only
high-passed.
picks : array-like of int | None
Indices of channels to filter. If None only the data (MEG/EEG)
channels will be filtered.
filter_length : str (Default: '10s') | int | None
Length of the filter to use. If None or "len(x) < filter_length",
the filter length used is len(x). Otherwise, if int, overlap-add
filtering with a filter of the specified length in samples) is
used (faster for long signals). If str, a human-readable time in
units of "s" or "ms" (e.g., "10s" or "5500ms") will be converted
to the shortest power-of-two length at least that duration.
Not used for 'iir' filters.
l_trans_bandwidth : float
Width of the transition band at the low cut-off frequency in Hz
(high pass or cutoff 1 in bandpass). Not used if 'order' is
specified in iir_params.
h_trans_bandwidth : float
Width of the transition band at the high cut-off frequency in Hz
(low pass or cutoff 2 in bandpass). Not used if 'order' is
specified in iir_params.
n_jobs : int | str
Number of jobs to run in parallel. Can be 'cuda' if scikits.cuda
is installed properly, CUDA is initialized, and method='fft'.
method : str
'fft' will use overlap-add FIR filtering, 'iir' will use IIR
forward-backward filtering (via filtfilt).
iir_params : dict | None
Dictionary of parameters to use for IIR filtering.
See mne.filter.construct_iir_filter for details. If iir_params
is None and method="iir", 4th order Butterworth will be used.
verbose : bool, str, int, or None
If not None, override default verbose level (see mne.verbose).
Defaults to raw.verbose.
See Also
--------
mne.Epochs.savgol_filter
"""
fname='ec_rest_before_tsss_mc_rsl.fif'
raw = Raw(fname, preload=False)
raw.preload_data() # data becomes numpy.float64
if verbose is None:
verbose = raw.verbose
fs = float(raw.info['sfreq'])
if l_freq == 0:
l_freq = None
if h_freq is not None and h_freq > (fs / 2.):
h_freq = None
if l_freq is not None and not isinstance(l_freq, float):
l_freq = float(l_freq)
if h_freq is not None and not isinstance(h_freq, float):
h_freq = float(h_freq)
if not raw.preload:
raise RuntimeError('Raw data needs to be preloaded to filter. Use '
'preload=True (or string) in the constructor.')
if picks is None:
if 'ICA ' in ','.join(raw.ch_names):
pick_parameters = dict(misc=True, ref_meg=False)
else:
pick_parameters = dict(meg=True, eeg=True, ref_meg=False)
picks = pick_types(raw.info, exclude=[], **pick_parameters)
# let's be safe.
if len(picks) < 1:
raise RuntimeError('Could not find any valid channels for '
'your Raw object. Please contact the '
'MNE-Python developers.')
# update info if filter is applied to all data channels,
# and it's not a band-stop filter
if h_freq is not None:
if (l_freq is None or l_freq < h_freq) and \
(raw.info["lowpass"] is None or
h_freq < raw.info['lowpass']):
raw.info['lowpass'] = h_freq
if l_freq is not None:
if (h_freq is None or l_freq < h_freq) and \
(raw.info["highpass"] is None or
l_freq > raw.info['highpass']):
raw.info['highpass'] = l_freq
if l_freq is None and h_freq is not None:
low_pass_filter(raw._data, fs, h_freq,
filter_length=filter_length,
trans_bandwidth=h_trans_bandwidth, method=method,
iir_params=iir_params, picks=picks, n_jobs=n_jobs,
copy=False)
if l_freq is not None and h_freq is None:
high_pass_filter(raw._data, fs, l_freq,
filter_length=filter_length,
trans_bandwidth=l_trans_bandwidth, method=method,
iir_params=iir_params, picks=picks, n_jobs=n_jobs,
copy=False)
if l_freq is not None and h_freq is not None:
if l_freq < h_freq:
raw._data = band_pass_filter(
raw._data, fs, l_freq, h_freq,
filter_length=filter_length,
l_trans_bandwidth=l_trans_bandwidth,
h_trans_bandwidth=h_trans_bandwidth,
method=method, iir_params=iir_params, picks=picks,
n_jobs=n_jobs, copy=False)
else:
raw._data = band_stop_filter(
raw._data, fs, h_freq, l_freq,
filter_length=filter_length,
l_trans_bandwidth=h_trans_bandwidth,
h_trans_bandwidth=l_trans_bandwidth, method=method,
iir_params=iir_params, picks=picks, n_jobs=n_jobs,
copy=False)
