def test_interp():
spec = np.array([[20, 0.5], [50, 1.0]])
freq = [15, 35, 60]
psdlinear = psd.interp(spec, freq, linear=True).ravel()
psdlog = psd.interp(spec, freq, linear=False).ravel()
assert np.allclose(psdlinear, [0, 0.75, 0])
assert np.allclose(psdlog, [0, 0.76352135927358911, 0])
def test_interp_nans():
Freq = np.arange(1, 10, 2)
freq = np.arange(1, 10)
PSD = (Freq**2) / 100
nan = [np.nan, np.nan]
Freq = np.hstack((Freq, nan))
PSD = np.hstack((PSD, nan))
plog = (freq**2) / 100
plog2 = psd.interp((Freq, PSD), freq, linear=False)
assert np.allclose(plog, plog2)
def test_area():
spec = np.array([
[20, 0.0053, 0.01],
[150, 0.04, 0.04],
[600, 0.04, 0.04],
[2000, 0.0036, 0.04 * 600 / 2000],
])
# the last value is to trigger the s == -1 logic in area
spec2 = spec[:, 1:]
f2 = spec[:, 0]
areas = psd.area(spec)
areas2 = psd.area((f2, spec2))
fi = np.arange(200, 20001) * 0.1
pi = psd.interp(spec, fi, linear=False)
areas3 = np.trapz(pi, fi, axis=0)
assert np.allclose(areas, areas2)
assert np.allclose(areas, areas3)
s1 = spec[:, :2]
assert np.all(psd.area(s1) == psd.area((*s1.T, )))
def test_psd2time():
spec = np.array([[20, 0.0768], [50, 0.48], [100, 0.48]])
sig, sr = psd.psd2time(spec,
ppc=10,
fstart=35,
fstop=70,
df=0.01,
winends=dict(portion=0.01))
# ensure that expand_method works:
sig2, sr2 = psd.psd2time(
spec,
ppc=10,
fstart=35,
fstop=70,
df=0.01,
winends=dict(portion=0.01),
expand_method="rescale",
)
f1, p1 = psd.psdmod(sig, sr, timeslice=f"{len(sig)}")
f2, p2 = psd.psdmod(sig2, sr2, timeslice=f"{len(sig)}")
assert np.trapz(p1, f1) < np.trapz(p2, f2)
assert_raises(
ValueError,
psd.psd2time,
spec,
ppc=10,
fstart=35,
fstop=70,
df=0.01,
winends=dict(portion=0.01),
expand_method="bad expand method",
)
assert np.allclose(700.0, sr) # 70*10
assert sig.size == 700 * 100
f, p = signal.welch(sig, sr, nperseg=sr)
pv = np.logical_and(f >= 37, f <= 68)
fi = f[pv]
psdi = p[pv]
speci = psd.interp(spec, fi).flatten()
assert abs(speci - psdi).max() < 0.05
assert abs(np.trapz(psdi, fi) - np.trapz(speci, fi)) < 0.25
spec = ([0.1, 5], [0.1, 0.1])
sig, sr = psd.psd2time(spec,
ppc=10,
fstart=0.1,
fstop=5,
df=0.2,
winends=dict(portion=0.01))
# df gets internally reset to fstart
assert np.allclose(5 * 10.0, sr)
assert sig.size == 50 * 10
f, p = signal.welch(sig, sr, nperseg=sr)
pv = np.logical_and(f >= 0.5, f <= 3.0)
fi = f[pv]
psdi = p[pv]
speci = psd.interp(spec, fi).flatten()
assert abs(speci - psdi).max() < 0.05
assert abs(np.trapz(psdi, fi) - np.trapz(speci, fi)) < 0.065
# ppc gets reset to 2 case:
spec = np.array([[0.1, 2.0], [5, 2.0]])
sig, sr = psd.psd2time(spec,
ppc=1,
fstart=0.1,
fstop=5,
df=0.2,
winends=dict(portion=0.01))
assert (np.sum(np.mean(sig**2)) - 2.0 * 4.9) / (2.0 * 4.9) < 0.1
# odd length FFT case:
spec = np.array([[0.1, 2.0], [5, 2.0]])
sig, sr, t = psd.psd2time(spec,
ppc=3,
fstart=0.2,
fstop=5,
df=0.2,
winends=dict(portion=0.01),
gettime=1)
assert np.allclose(5 * 3, sr)
assert sig.size == 15 * 5
assert (np.sum(np.mean(sig**2)) - 2.0 * 4.8) / (2.0 * 4.8) < 0.1
assert np.allclose(t, np.arange(15 * 5) / sr)
