def test_csd_fourier():
"""Test computing cross-spectral density using short-term Fourier."""
epochs = _generate_coherence_data()
sfreq = epochs.info['sfreq']
_test_fourier_multitaper_parameters(epochs, csd_fourier, csd_array_fourier)
# Compute CSDs using various parameters
times = [(None, None), (1, 9)]
as_arrays = [False, True]
parameters = product(times, as_arrays)
for (tmin, tmax), as_array in parameters:
if as_array:
csd = csd_array_fourier(epochs.get_data(),
sfreq,
epochs.tmin,
fmin=9,
fmax=23,
tmin=tmin,
tmax=tmax,
ch_names=epochs.ch_names)
else:
csd = csd_fourier(epochs, fmin=9, fmax=23, tmin=tmin, tmax=tmax)
if tmin is None and tmax is None:
assert csd.tmin == 0 and csd.tmax == 9.98
else:
assert csd.tmin == tmin and csd.tmax == tmax
csd = csd.mean([9.9, 14.9, 21.9], [10.1, 15.1, 22.1])
_test_csd_matrix(csd)
# For the next test, generate a simple sine wave with a known power
times = np.arange(20 * sfreq) / sfreq # 20 seconds of signal
signal = np.sin(2 * np.pi * 10 * times)[None, None, :] # 10 Hz wave
signal_power_per_sample = sum_squared(signal) / len(times)
# Power per sample should not depend on time window length
for tmax in [12, 18]:
t_mask = (times <= tmax)
n_samples = sum(t_mask)
# Power per sample should not depend on number of FFT points
for add_n_fft in [0, 30]:
n_fft = n_samples + add_n_fft
csd = csd_array_fourier(signal, sfreq, tmax=tmax,
n_fft=n_fft).sum().get_data()
first_samp = csd[0, 0]
fourier_power_per_sample = np.abs(first_samp) * sfreq / n_fft
assert abs(signal_power_per_sample -
fourier_power_per_sample) < 0.001
def test_csd_fourier():
"""Test computing cross-spectral density using short-term Fourier."""
epochs = _generate_coherence_data()
sfreq = epochs.info['sfreq']
_test_fourier_multitaper_parameters(epochs, csd_fourier, csd_array_fourier)
# Compute CSDs using various parameters
times = [(None, None), (1, 9)]
as_arrays = [False, True]
parameters = product(times, as_arrays)
for (tmin, tmax), as_array in parameters:
if as_array:
csd = csd_array_fourier(epochs.get_data(), sfreq, epochs.tmin,
fmin=9, fmax=23, tmin=tmin, tmax=tmax,
ch_names=epochs.ch_names)
else:
csd = csd_fourier(epochs, fmin=9, fmax=23, tmin=tmin, tmax=tmax)
if tmin is None and tmax is None:
assert csd.tmin == 0 and csd.tmax == 9.98
else:
assert csd.tmin == tmin and csd.tmax == tmax
csd = csd.mean([9.9, 14.9, 21.9], [10.1, 15.1, 22.1])
_test_csd_matrix(csd)
# For the next test, generate a simple sine wave with a known power
times = np.arange(20 * sfreq) / sfreq # 20 seconds of signal
signal = np.sin(2 * np.pi * 10 * times)[None, None, :] # 10 Hz wave
signal_power_per_sample = sum_squared(signal) / len(times)
# Power per sample should not depend on time window length
for tmax in [12, 18]:
t_mask = (times <= tmax)
n_samples = sum(t_mask)
# Power per sample should not depend on number of FFT points
for add_n_fft in [0, 30]:
n_fft = n_samples + add_n_fft
csd = csd_array_fourier(signal, sfreq, tmax=tmax,
n_fft=n_fft).sum().get_data()
first_samp = csd[0, 0]
fourier_power_per_sample = np.abs(first_samp) * sfreq / n_fft
assert abs(signal_power_per_sample -
fourier_power_per_sample) < 0.001