def test_fourier_spectra_with_motion():
record_path = TEST_DATA_DIR
record_filename = 'test_motion_dt0p01.txt'
motion_dt = 0.01
rec = np.loadtxt(record_path + record_filename)
rec2 = np.zeros(2 ** 13)
rec2[:len(rec)] = rec
acc_signal = AccSignal(-rec, motion_dt)
nfreq = len(acc_signal.fa_spectrum)
test_filename = 'test_motion_true_fourier_spectra.csv'
data = np.loadtxt(record_path + test_filename, skiprows=1, delimiter=",")
freqs = data[:nfreq - 1, 0]
fa = data[:nfreq - 1, 1]
phase = data[:nfreq - 1, 2]
fa_eqsig = abs(acc_signal.fa_spectrum)
freq_eqsig = acc_signal.fa_frequencies
org_phases = np.angle(acc_signal.fa_spectrum)
ss_phases = np.angle(np.fft.rfft(rec2))[:len(org_phases)] + 0.0001
for i in range(10):
print(phase[i], acc_signal.fa_spectrum[i + 1], org_phases[i + 1])
assert ct.isclose(freqs[0], freq_eqsig[1], rel_tol=0.001), freqs[0]
assert ct.isclose(freqs[20], freq_eqsig[21], rel_tol=0.0001)
assert ct.isclose(freqs[-1], freq_eqsig[-1], rel_tol=0.001)
for i in range(len(fa)):
assert ct.isclose(fa[i], fa_eqsig[i + 1], abs_tol=0.00001), i
def test_butterpass():
record_path = TEST_DATA_DIR
record_filename = 'noise_test_1.txt'
rec = load_test_record_from_file(record_path, record_filename)
ssq_org = np.sum(rec.values ** 2)
x = np.linspace(0, 1.0, rec.npts)
rec.add_series(0.2 * x - 0.5 * x ** 2)
ssq_w_linear = np.sum(rec.values ** 2)
rec.butter_pass([0.2, 25])
ssq_corrected = np.sum(rec.values ** 2)
assert ssq_org != ssq_w_linear
assert ct.isclose(ssq_org, 31854.72888, rel_tol=0.0001)
assert ct.isclose(ssq_corrected, 10663.4862479, rel_tol=0.0001)
def test_duration_stats():
record_path = TEST_DATA_DIR
record_filename = 'noise_test_1.txt'
rec = load_test_record_from_file(record_path, record_filename, scale=9.8)
rec.generate_duration_stats()
assert ct.isclose(rec.t_595, 18.03)
def test_butterpass():
record_path = TEST_DATA_DIR
record_filename = 'test_motion_dt0p01.txt'
motion_step = 0.01
rec = np.loadtxt(record_path + record_filename)
acc_signal = Signal(rec, motion_step)
ssq_org = np.sum(acc_signal.values ** 2)
x = np.linspace(0, 1.0, acc_signal.npts)
acc_signal.add_series(0.2 * x - 0.5 * x ** 2)
ssq_w_linear = np.sum(acc_signal.values ** 2)
acc_signal.butter_pass([0.2, 25])
ssq_corrected = np.sum(acc_signal.values ** 2)
assert ssq_org != ssq_w_linear
assert ct.isclose(ssq_org, 395.9361125, rel_tol=0.0001)
assert ct.isclose(ssq_corrected, 393.7198723, rel_tol=0.0001)
def test_remove_polyfit_1():
record_path = TEST_DATA_DIR
record_filename = 'test_motion_dt0p01.txt'
motion_step = 0.01
rec = np.loadtxt(record_path + record_filename)
acc_signal = Signal(rec, motion_step)
# Remove any trend
acc_signal.remove_poly(poly_fit=1)
ssq_cleaned = np.sum(acc_signal.values ** 2)
# Add a trend
acc_signal.add_series(np.linspace(0, 0.2, acc_signal.npts))
ssq_w_linear = np.sum(acc_signal.values ** 2)
# remove the trend
acc_signal.remove_poly(poly_fit=1)
ssq_corrected = np.sum(acc_signal.values ** 2)
assert not ct.isclose(ssq_cleaned, ssq_w_linear)
assert ct.isclose(ssq_cleaned, ssq_corrected)
def test_fourier_spectra_stable_against_aliasing():
record_path = TEST_DATA_DIR
record_filename = 'test_motion_dt0p01.txt'
motion_step = 0.01
rec = np.loadtxt(record_path + record_filename)
rec2 = np.zeros(2 ** 13)
rec2[:len(rec)] = rec
org_signal = AccSignal(rec, motion_step)
extended_signal = AccSignal(rec2, motion_step)
rec_split = []
for i in range(int(len(rec2) / 2)):
rec_split.append(rec2[i * 2])
acc_split = AccSignal(rec_split, motion_step * 2)
org_fa = abs(org_signal.fa_spectrum)
split_fa = abs(acc_split.fa_spectrum)
ext_fa = abs(extended_signal.fa_spectrum)
org_freq = abs(org_signal.fa_frequencies)
split_freq = abs(acc_split.fa_frequencies)
ext_freq = abs(extended_signal.fa_frequencies)
for i in range(len(org_signal.fa_spectrum)):
if i > 1830:
abs_tol = 0.03
else:
abs_tol = 0.02
assert ct.isclose(org_freq[i], ext_freq[i])
assert ct.isclose(org_fa[i], ext_fa[i])
if i < 2048:
assert ct.isclose(org_freq[i], split_freq[i])
assert ct.isclose(org_fa[i], split_fa[i], abs_tol=abs_tol), i
def test_remove_polyfit_1():
record_path = TEST_DATA_DIR
record_filename = 'noise_test_1.txt'
rec = load_test_record_from_file(record_path, record_filename)
rec.remove_poly(poly_fit=1)
ssq_org = np.sum(rec.values ** 2)
rec.add_series(np.linspace(0, 0.2, rec.npts))
ssq_w_linear = np.sum(rec.values ** 2)
rec.remove_poly(poly_fit=1)
ssq_corrected = np.sum(rec.values ** 2)
assert ssq_org != ssq_w_linear
assert ct.isclose(ssq_org, ssq_corrected)