def compute_log_power(data,
frequencies=np.logspace(np.log10(3), np.log10(180), 8),
epsilon=1.0e-4,
buffer=2.0):
wf = MorletWaveletFilterCpp(time_series=data.T,
freqs=frequencies,
output='power',
cpus=25)
pow_wavelet, phase_wavelet = wf.filter()
pow_wavelet = pow_wavelet.remove_buffer(duration=buffer)
pow_wavelet = np.log10(pow_wavelet + epsilon)
pow_wavelet_avg = pow_wavelet.mean(axis=2)
return pow_wavelet_avg
def test_wavelets_python_cpp(self):
from ptsa.data.filters import MorletWaveletFilterCpp
print('hello')
wf = MorletWaveletFilter(
time_series=self.base_eegs,
freqs=np.logspace(np.log10(3), np.log10(180), 8),
output='power',
)
pow_wavelet, phase_wavelet = wf.filter()
wf_cpp = MorletWaveletFilterCpp(time_series=self.base_eegs,
freqs=np.logspace(
np.log10(3), np.log10(180), 8),
output='power',
cpus=4)
pow_wavelet_cpp, phase_wavelet_cpp = wf_cpp.filter()
decimal = 2
freq_num = 0
from scipy.stats import describe
desc_cpp = describe(pow_wavelet_cpp[freq_num, :, :, 500:-500])
desc_py = describe(pow_wavelet[freq_num, :, :, 500:-500])
try:
assert_array_almost_equal(
(pow_wavelet_cpp[freq_num, :, :, 500:-500] -
pow_wavelet[freq_num, :, :, 500:-500]) /
pow_wavelet_cpp[freq_num, :, :, 500:-500],
np.zeros_like(pow_wavelet_cpp[freq_num, :, :, 500:-500]),
decimal=decimal)
except AssertionError:
print(
'WARNING: Cpp and Python wavelets are not within 1%. Will try weaker test '
)
mean_min = np.min((desc_cpp.mean - desc_py.mean) / desc_cpp.mean)
mean_max = np.max((desc_cpp.mean - desc_py.mean) / desc_cpp.mean)
print('mean_max=', mean_max)
print('mean_min=', mean_min)
self.assertTrue(np.abs(mean_max) < 0.05)
self.assertTrue(np.abs(mean_min) < 0.05)
def test_wavelets_python_cpp(self):
from ptsa.data.filters import MorletWaveletFilterCpp
print('hello')
wf = MorletWaveletFilter(time_series=self.base_eegs,
freqs=np.logspace(np.log10(3), np.log10(180), 8),
output='power',
)
pow_wavelet, phase_wavelet = wf.filter()
wf_cpp = MorletWaveletFilterCpp(time_series=self.base_eegs,
freqs=np.logspace(np.log10(3), np.log10(180), 8),
output='power',
cpus=4
)
pow_wavelet_cpp, phase_wavelet_cpp = wf_cpp.filter()
decimal = 2
freq_num=0
from scipy.stats import describe
desc_cpp = describe(pow_wavelet_cpp[freq_num,:,:,500:-500])
desc_py = describe(pow_wavelet[freq_num,:,:,500:-500])
try:
assert_array_almost_equal(
(pow_wavelet_cpp[freq_num,:,:,500:-500]-pow_wavelet[freq_num,:,:,500:-500])/pow_wavelet_cpp[freq_num,:,:,500:-500],
np.zeros_like(pow_wavelet_cpp[freq_num,:,:,500:-500]), decimal=decimal)
except AssertionError:
print('WARNING: Cpp and Python wavelets are not within 1%. Will try weaker test ')
mean_min = np.min((desc_cpp.mean-desc_py.mean)/desc_cpp.mean)
mean_max = np.max((desc_cpp.mean-desc_py.mean)/desc_cpp.mean)
print('mean_max=',mean_max)
print('mean_min=',mean_min)
self.assertTrue(np.abs(mean_max)<0.05)
self.assertTrue(np.abs(mean_min)<0.05)
def test_wavelets_multicore(self):
from ptsa.data.filters import MorletWaveletFilterCpp
print 'hello'
print sys.path
wf_cpp_multi = MorletWaveletFilterCpp(time_series=self.base_eegs,
freqs=np.logspace(np.log10(3), np.log10(180), 8),
output='both',
cpus=8
)
pow_wavelet_multi, phase_wavelet_multi = wf_cpp_multi.filter()
wf_cpp = MorletWaveletFilterCpp(time_series=self.base_eegs,
freqs=np.logspace(np.log10(3), np.log10(180), 8),
output='both',
cpus=1
)
pow_wavelet, phase_wavelet= wf_cpp.filter()
assert_array_equal(pow_wavelet_multi,pow_wavelet)
assert_array_equal(phase_wavelet_multi,phase_wavelet)
def test_wavelets_multicore(self):
from ptsa.data.filters import MorletWaveletFilterCpp
print('hello')
print(sys.path)
wf_cpp_multi = MorletWaveletFilterCpp(time_series=self.base_eegs,
freqs=np.logspace(
np.log10(3), np.log10(180),
8),
output='both',
cpus=8)
pow_wavelet_multi, phase_wavelet_multi = wf_cpp_multi.filter()
wf_cpp = MorletWaveletFilterCpp(time_series=self.base_eegs,
freqs=np.logspace(
np.log10(3), np.log10(180), 8),
output='both',
cpus=1)
pow_wavelet, phase_wavelet = wf_cpp.filter()
assert_array_equal(pow_wavelet_multi, pow_wavelet)
assert_array_equal(phase_wavelet_multi, phase_wavelet)
use_reref_eeg=True,
common_root='data').read()
words = events[events.type == 'WORD']
# select all available channels
eegfile = words[0].eegfile
eegfile_reref = str.split(str(eegfile), '/')
day = eegfile_reref[-1]
eegfile_reref = '/'.join(eegfile_reref[0:-1])
channels = glob.glob(eegfile_reref + '/' + day + "*.[0-9]*")
channels = np.array([str.split(x, '.')[-1] for x in channels])
#channels = np.array(['{:03}'.format(x) for x in range(0,132)])
eeg = EEGReader(events=words, channels=channels, start_time=0.3,
end_time=1.6).read()
b_filter = ButterworthFilter(time_series=eeg, order=4, freq_range=[58, 62])
eeg_filtered = b_filter.filter()
eeg_buffered = eeg_filtered.add_mirror_buffer(1.3)
eeg_buffered.data = np.ascontiguousarray(eeg_buffered.data)
import time
pt = time.time()
freqs = np.logspace(np.log10(3), np.log10(180), 50)
pow_ev, _ = MorletWaveletFilterCpp(time_series=eeg_buffered,
freqs=freqs[0:1],
output='power',
cpus=20,
verbose=False).filter()
run_time = time.time() - pt