def test_add_points():
obj = Pnoise([1e3, 1e6, 1e9], [-100, -120, -140])
obj.add_points([1e2, 1e3, 1e10], [-80, -70, -160])
assert np.all(obj.fm == [1e2, 1e3, 1e6, 1e9, 1e10])
assert np.all(obj.ldbc == [-80, -70, -120, -140, -160])
# Check deep the interpolation
obj.fm = [1e2, 1e6, 1.1e5, 7e7]
obj.fm = [1e3, 1e6, 1.1e5,7e7, 1e10]
def test__init__():
# Test __init__
fm = np.logspace(3, 9, 100)
lorentzian = Pnoise(fm, 10 * np.log10(1 / (fm * fm)), label='Lorentzian')
white = Pnoise(fm, -120 * np.ones(fm.shape), label='white')
added = white + lorentzian
added.label = "addition"
assert_almost_equal(added.ldbc[0], -60, 4)
assert_almost_equal(added.ldbc[-1], -120, 4)
ix, = np.where(fm > 1e6)
assert_almost_equal(added.ldbc[ix[0]], -117.2822, 4)
def test_integration():
fm = np.logspace(4, 8, 1000)
lorentzian = Pnoise(fm, 10 * np.log10(1 / (fm * fm)), label='Lorentzian')
white = Pnoise(fm, -120 * np.ones(fm.shape), label='white')
added = lorentzian + white
iadded_gardner = added.integrate()
iadded_trapz = added.integrate(method='trapz')
f_int = lambda fm: 2.0e-12 * fm - 2.0 / fm
i_f_int = np.sqrt(f_int(fm[-1]) - f_int(fm[0]))
assert_almost_equal(iadded_trapz, i_f_int, 5)
assert_almost_equal(iadded_trapz, i_f_int, 5)
def test_add_noise_sources():
from numpy.testing import assert_almost_equal
import matplotlib.pyplot as plt
myAnalogPLL = AnalogPLL(4, 521.8e+06, Navg=10, prescaler=2, plltype=2)
myAnalogPLL.loopcalc(1e6, 60.0, -130.0, 1e6, 0.1, 300)
myAnalogPLL.lti()
pinput = [
Pnoise([1e3, 1e6, 1e9], [-140, -140, -140], label='input', fc=48e6)
]
poutput = [
Pnoise([1e3, 1e6, 1e9], [-70, -120, -180], label='VCO', fc=480e6)
]
fm = np.logspace(4, 8, 100)
pn_total, pn_in_colored, pn_out_colored = \
myAnalogPLL.add_noise_sources(fm, pn_inputs=pinput, pn_outputs=poutput)
assert_almost_equal(pn_total.interp1d(1e8), -160, 2)
assert_almost_equal(pn_total.interp1d(1e4), -120, 2)
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_private_functions():
# test the new
fi = np.array([1e4, 1e9])
ldbc_fi = np.array([-40, -150])
slopes = np.array([-30, -20])
fm = np.logspace(3, 9, 20)
ldbc_model = Pnoise._pnoise_point_slopes(fi, ldbc_fi, slopes, fm)
func = intp.interp1d(log10(fm), ldbc_model, kind='linear')
ldbc_0 = func(log10(fi[0]))
ldbc_1 = func(log10(fi[1]))
assert_almost_equal(ldbc_0, ldbc_fi[0], 0)
assert_almost_equal(ldbc_1, ldbc_fi[1], 0)
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_private_functions():
# test the new
fi = np.array([1e4, 1e9])
ldbc_fi = np.array([-40, -150])
slopes = np.array([-30, -20])
fm = np.logspace(3, 9, 20)
ldbc_model = Pnoise._pnoise_point_slopes(fi, ldbc_fi, slopes, fm)
func = intp.interp1d(log10(fm), ldbc_model, kind='linear')
ldbc_0 = func(log10(fi[0]))
ldbc_1 = func(log10(fi[1]))
assert_almost_equal(ldbc_0, ldbc_fi[0], 0)
assert_almost_equal(ldbc_1, ldbc_fi[1], 0)
def test_add_points():
obj = Pnoise([1e3, 1e6, 1e9], [-100, -120, -140])
obj.add_points([1e2, 1e3, 1e10], [-80, -70, -160])
assert np.all(obj.fm == [1e2, 1e3, 1e6, 1e9, 1e10])
assert np.all(obj.ldbc == [-80, -70, -120, -140, -160])
# Check deep the interpolation
obj.fm = [1e2, 1e6, 1.1e5, 7e7]
obj.fm = [1e3, 1e6, 1.1e5, 7e7, 1e10]
def test_interp1d_class():
fm = np.array([1e3, 1e5, 1e7])
lorentzian = Pnoise(fm, 10 * np.log10(1 / (fm * fm)),
label='Lorentzian')
val = lorentzian.interp1d(1e6)
assert_almost_equal(val, -120, 4)
def test_fc_settler():
fm = np.array([1e3, 1e5, 1e7])
ldbc = 10 * np.log10(1 / (fm * fm))
lorentzian = Pnoise(fm, ldbc, fc=1e9, label='Lorentzian')
lorentzian.fc = 10e9
assert_almost_equal(lorentzian.ldbc, ldbc + 20*log10(10e9/1e9))
def test_fm_fc_scaling():
pnobj = Pnoise([1e4, 1e6, 1e8],[-80,-100,-120], fc=2e9)
pnobj.fc = 20e9
assert np.all(pnobj.ldbc == [-60,-80,-100])
pnobj.fm = [1e5, 1e6, 1e7]
assert np.all(pnobj.ldbc == [-70,-80,-90])
def test_at_fc():
pnobj = Pnoise([1e4, 1e6, 1e8],[-80,-100,-120], fc=2e9)
pnob2 = pnobj.at_fc(20e9)
assert np.all(pnob2.ldbc == [-60,-80,-100])
def test_fm_fc_scaling():
pnobj = Pnoise([1e4, 1e6, 1e8], [-80, -100, -120], fc=2e9)
pnobj.fc = 20e9
assert np.all(pnobj.ldbc == [-60, -80, -100])
pnobj.fm = [1e5, 1e6, 1e7]
assert np.all(pnobj.ldbc == [-70, -80, -90])
def test_at_fc():
pnobj = Pnoise([1e4, 1e6, 1e8], [-80, -100, -120], fc=2e9)
pnob2 = pnobj.at_fc(20e9)
assert np.all(pnob2.ldbc == [-60, -80, -100])
def test_fc_settler():
fm = np.array([1e3, 1e5, 1e7])
ldbc = 10 * np.log10(1 / (fm * fm))
lorentzian = Pnoise(fm, ldbc, fc=1e9, label='Lorentzian')
lorentzian.fc = 10e9
assert_almost_equal(lorentzian.ldbc, ldbc + 20 * log10(10e9 / 1e9))
def test_interp1d_class():
fm = np.array([1e3, 1e5, 1e7])
lorentzian = Pnoise(fm, 10 * np.log10(1 / (fm * fm)), label='Lorentzian')
val = lorentzian.interp1d(1e6)
assert_almost_equal(val, -120, 4)
