def test_freq_by_time_consistent():
"""
Confirm consistency in beta bandpass filter results on a neural signal
"""
# Load data
data_idx = 1
x = _load_example_data(data_idx=data_idx)
Fs = 1000
f_range = (13, 30)
# Load ground truth frequency time series
i_f_true = np.load(os.path.dirname(neurodsp.__file__) + '/tests/data/sample_data_'+str(data_idx)+'_i_f.npy')
# Compute frequency time series
i_f = neurodsp.freq_by_time(x, Fs, f_range)
# Compute difference between current and past filtered signals
signal_diff = i_f[~np.isnan(i_f)] - i_f_true[~np.isnan(i_f_true)]
assert np.allclose(np.sum(np.abs(signal_diff)), 0, atol=10 ** -5)
def test_NaN_in_x():
"""
Assure that time-resolved timefrequency functions do not return all NaN
if one of the elements in the input array is NaN.
Do this by replacing edge artifacts with NaN for a lowpass filter
"""
# Generate a low-pass filtered signal with NaNs
x = np.random.randn(10000)
Fs = 1000
x = neurodsp.filter(x, Fs, 'lowpass', f_lo=50)
# Compute phase, amp, and freq time series
f_range = (4, 8)
pha = neurodsp.phase_by_time(x, Fs, f_range)
amp = neurodsp.amp_by_time(x, Fs, f_range)
i_f = neurodsp.freq_by_time(x, Fs, f_range)
assert len(pha[~np.isnan(pha)]) > 0
assert len(amp[~np.isnan(amp)]) > 0
assert len(i_f[~np.isnan(i_f)]) > 0
def test_timefreq_consistent():
"""
Confirm consistency in estimation of instantaneous phase, amp, and frequency
with computations in previous versions
"""
# Load data
data_idx = 1
x = _load_example_data(data_idx=data_idx)
Fs = 1000
f_range = (13, 30)
# Load ground truth phase time series
pha_true = np.load(
os.path.dirname(neurodsp.__file__) + '/tests/data/sample_data_' +
str(data_idx) + '_pha.npy')
# Load ground truth amplitude time series
amp_true = np.load(
os.path.dirname(neurodsp.__file__) + '/tests/data/sample_data_' +
str(data_idx) + '_amp.npy')
# Load ground truth frequency time series
i_f_true = np.load(
os.path.dirname(neurodsp.__file__) + '/tests/data/sample_data_' +
str(data_idx) + '_i_f.npy')
# Compute phase time series
pha = neurodsp.phase_by_time(x, Fs, f_range)
# Compute amplitude time series
amp = neurodsp.amp_by_time(x, Fs, f_range)
# Compute frequency time series
i_f = neurodsp.freq_by_time(x, Fs, f_range)
# Compute difference between current and past signals
assert np.allclose(np.sum(np.abs(pha - pha_true)), 0, atol=10**-5)
assert np.allclose(np.sum(np.abs(amp - amp_true)), 0, atol=10**-5)
assert np.allclose(np.sum(
np.abs(i_f[~np.isnan(i_f)] - i_f_true[~np.isnan(i_f_true)])),
0,
atol=10**-5)