def test_with_points_slopes():
from copy import copy
# test the new
fi = np.array([1e4, 1e9])
ldbc_fi = np.array([-40, -150])
slopes = np.array([-30, -20])
pnoise_model = Pnoise.with_points_slopes(fi, ldbc_fi, slopes)
fm = np.logspace(3, 9, 20)
pnoise_extrapolated = copy(pnoise_model)
pnoise_extrapolated.fm = fm
def test_with_points_slopes():
from copy import copy
# test the new
fi = np.array([1e4, 1e9])
ldbc_fi = np.array([-40, -150])
slopes = np.array([-30, -20])
pnoise_model = Pnoise.with_points_slopes(fi, ldbc_fi, slopes)
fm = np.logspace(3, 9, 20)
pnoise_extrapolated = copy(pnoise_model)
pnoise_extrapolated.fm = fm
def test_generate_samples():
pnobj = Pnoise.with_points_slopes([1e5, 1e6, 1e9], [-80, -100, -120],
[-30, -20, 0])
npoints = 2**16
fs = 500e6
phi_t = pnobj.generate_samples(npoints, fs)
f, pxx = sig.welch(phi_t, fs, window='blackman', nperseg=2**8)
ldbc_noise = 10 * np.log10(pxx / 2)
pnobj.fm = f[1:]
error = np.max(np.abs((pnobj.ldbc - ldbc_noise[1:]) / pnobj.ldbc * 100))
assert error < 2.5
def test_generate_samples():
pnobj = Pnoise.with_points_slopes([1e5, 1e6, 1e9],
[-80,-100,-120],
[-30,-20,0])
npoints = 2 ** 16
fs = 500e6
phi_t = pnobj.generate_samples(npoints, fs)
f, pxx = sig.welch(phi_t, fs, window='blackman', nperseg=2**8)
ldbc_noise = 10 * np.log10(pxx / 2)
pnobj.fm = f[1:]
error = np.max(np.abs((pnobj.ldbc - ldbc_noise[1:]) / pnobj.ldbc * 100))
assert error < 2.5
