def test_nlse_loss():
"""Check that the loss is applied correctly in the NLSE."""
# create a 1 meter fiber with 3.01 dB (50%) loss per meter:
loss = 3.0102999566
fiber = lf.Fiber(length=1, loss_dB_per_m=loss)
pulse = lf.Pulse(epp=100e-12) # create pulse
results = lf.NLSE(pulse, fiber, print_status=False)
assert np.isclose(pulse.epp * 0.5, results.pulse_out.epp, rtol=1e-11)
def test_fiber():
fiber = lf.Fiber(1, center_wl_nm=1550)
def disp(z):
return [1]
def myfunc(z):
return 1
fiber.set_dispersion_function(disp)
fiber.set_gamma_function(myfunc)
fiber.set_alpha_function(myfunc)
pulse = lf.Pulse(pulse_type='sech')
assert np.all(np.isfinite(fiber.get_B(pulse, z=0.5)))
assert fiber.get_gamma(z=0.5) == 1
assert fiber.get_alpha(z=0.5) == 1
def test_nlse_spectrum():
"""Check for reasonable agreement of NLSE output with old PyNLO results."""
pulse = lf.Pulse(pulse_type='sech',
fwhm_ps=0.050,
time_window_ps=7,
npts=2**12,
center_wavelength_nm=1550.0,
epp=50e-12)
fiber1 = lf.Fiber(length=8e-3,
center_wl_nm=1550.0,
gamma_W_m=1,
dispersion=(-0.12, 0, 5e-6))
results = lf.NLSE(pulse,
fiber1,
raman=True,
shock=True,
nsaves=100,
atol=1e-5,
rtol=1e-5,
integrator='lsoda',
print_status=False)
z, f, t, AW, AT = results.get_results()
dB = 10 * np.log10(np.abs(AW[-1])**2)
path = os.path.split(os.path.realpath(__file__))[0]
f_prev, dB_prev = np.loadtxt(path + '/nlse_output.txt',
delimiter=',',
unpack=True,
skiprows=1)
# this is probably overly stringent because we are on a dB scale
np.testing.assert_allclose(dB, dB_prev, rtol=1e-4, atol=0)
# test that the wavelengths function works:
z, new_wls, t, AW_wls, AT = results.get_results_wavelength()
assert np.all(np.isfinite(AW_wls))
pulse = lf.Pulse(pulse_type='sech',
power=1,
fwhm_ps=FWHM,
center_wavelength_nm=pulseWL,
time_window_ps=Window,
GDD=GDD,
TOD=TOD,
npts=Points,
frep_MHz=100,
power_is_avg=False,
epp=EPP)
# create the fiber!
fiber1 = lf.Fiber(Length * 1e-3,
center_wl_nm=pulseWL,
dispersion=(beta2 * 1e-3, beta3 * 1e-3, beta4 * 1e-3),
gamma_W_m=Gamma * 1e-3,
loss_dB_per_m=Alpha * 100)
t_start = time.time()
results = lf.NLSE(pulse,
fiber1,
raman=Raman,
shock=Steep,
nsaves=Steps,
atol=1e-5,
rtol=1e-5,
integrator='lsoda',
reload_fiber=False)
z, f, t, AW, AT = results.get_results()
"""Runs a simple example of NLSE pulse propagation."""
import laserfun as lf
p = lf.Pulse(pulse_type='sech', fwhm_ps=0.05, epp=50e-12,
center_wavelength_nm=1550, time_window_ps=7)
f = lf.Fiber(length=0.010, center_wl_nm=1550, dispersion=(-0.12, 0, 5e-6),
gamma_W_m=1)
results = lf.NLSE(p, f, print_status=False)
if __name__ == '__main__': # make plots if we're not running tests
results.plot()
import matplotlib.pyplot as plt
p = lf.Pulse(pulse_type='sech',
power=10000,
npts=2**13,
fwhm_ps=0.0284 * 1.76,
center_wavelength_nm=835,
time_window_ps=12.5)
# betas = [beta2, beta3, ...] in units [ps^2/m, ps^3/m ...]
betas = [
-11.830e-3, 8.1038e-5, -9.5205e-8, 2.0737e-10, -5.3943e-13, 1.3486e-15,
-2.5495e-18, 3.0524e-21, -1.7140e-24
]
f = lf.Fiber(length=0.15, center_wl_nm=835, dispersion=betas, gamma_W_m=0.11)
r = lf.NLSE(p, f, nsaves=200, raman=True)
z, new_wls, t, AW, AT = r.get_results_wavelength(wmin=400,
wmax=1350,
wn=400,
datatype='dB')
fig, axs = plt.subplots(1, 2, figsize=(8, 5), tight_layout='True')
axs[0].imshow(AW,
aspect='auto',
origin='lower',
extent=(new_wls.min(), new_wls.max(), z.min(), z.max()),
clim=(np.max(AW) - 40, np.max(AW)),
cmap='jet')
Gammaf = 2000 # nonlinearity (1/(W km))
Alphaf = 2 # loss (dB/cm)
Length = 10 # length in mm
fibWL = pulseWL # Center WL of fiber (nm)
Raman = True # Enable Raman effect?
Steep = True # Enable self steepening?
# create the pulse
pulse = lf.Pulse(pulse_type='sech', fwhm_ps=FWHM, center_wavelength_nm=pulseWL,
time_window_ps=Window, GDD=GDD, TOD=TOD, npts=Points, epp=EPP)
# create the fiber!
fiber1 = lf.Fiber(Length * 1e-3, center_wl_nm=pulseWL)
def dispersion_function(z):
"""Set the dispersion."""
frac = z/(Length*1e-3)
beta2 = (1-frac) * beta2i + frac * beta2f
beta3 = (1-frac) * beta3i + frac * beta3f
beta4 = (1-frac) * beta4i + frac * beta4f
return (beta2*1e-3, beta3*1e-3, beta4*1e-3)
def gamma_function(z):
"""Set the gamma."""
frac = z/(Length*1e-3)
gamma = 1e-3*((1-frac)*Gammai + frac*Gammaf)
