def test_triangsmooth():
data = [
3.7352e+06, 1.104e+06, 1.088e+06, 1.0695e+06, 7.1923e+05, 1.2757e+06,
1.2596e+05, 9.4364e+05, 5.8868e+05, 4.4942e+05, 6.768e+05, 4.0295e+05,
5.1843e+05, 6.2502e+05, 4.6077e+05, 1.4937e+05, 5.366e+05, 1.4942e+05,
2.4361e+05, 3.5926e+05
]
# test a smooth function. take a parabola
win_ratio = 0.04
smooth = triangsmooth(np.array(data), winlen_ratio=win_ratio)
assert all([
smooth[i] <= max(data[i - 1:i + 2])
and smooth[i] >= min(data[i - 1:i + 2])
for i in range(1,
len(data) - 1)
])
assert np.allclose(smooth,
triangsmooth0(np.array(data), win_ratio),
rtol=1e-05,
atol=1e-08,
equal_nan=True)
data = [x**2 for x in range(115)]
smooth = triangsmooth(np.array(data), winlen_ratio=win_ratio)
assert np.allclose(smooth, data, rtol=1e-03, atol=1e-08, equal_nan=True)
assert np.allclose(smooth,
triangsmooth0(np.array(data), win_ratio),
rtol=1e-05,
atol=1e-08,
equal_nan=True)
def _spectrum(trace, config):
'''Calculate the spectrum of a trace. Returns the tuple (0, df, values), where
values depends on the config dict parameters.
Does not modify the trace in-place
'''
taper_max_percentage = config['sn_spectra']['taper']['max_percentage']
taper_type = config['sn_spectra']['taper']['type']
if config['sn_spectra']['type'] == 'pow':
func = powspec # copies the trace if needed
elif config['sn_spectra']['type'] == 'amp':
func = ampspec # copies the trace if needed
else:
# raise TypeError so that if called from within main, the iteration stops
raise TypeError(
"config['sn_spectra']['type'] expects either 'pow' or 'amp'")
df_, spec_ = func(trace,
taper_max_percentage=taper_max_percentage,
taper_type=taper_type)
# if you want to implement your own smoothing, change the lines below before 'return'
# and implement your own config variables, if any
smoothing_wlen_ratio = config['sn_spectra']['smoothing_wlen_ratio']
if smoothing_wlen_ratio > 0:
spec_ = triangsmooth(spec_, winlen_ratio=smoothing_wlen_ratio)
return (0, df_, spec_)