def setUpClass(cls):
yb_number_density = 3e25
core_r = 5e-6
background_loss = 0
length = 3
pump_cladding_r = 50e-6
core_to_cladding_ratio = core_r / pump_cladding_r
core_na = 0.12
tolerance = 1e-5
cls.input_signal_power = 0.4
cls.input_pump_power = 47.2
fiber = YbDopedDoubleCladFiber(length, core_r, yb_number_density,
background_loss, core_na,
core_to_cladding_ratio)
pump_wavelengths = np.linspace(910, 950, 11) * 1e-9
cls.gains = []
init_guess_array = None
for pump_wl in pump_wavelengths:
simulation = SteadyStateSimulation(fiber)
simulation.add_forward_signal(wl=1030e-9,
input_power=cls.input_signal_power,
mode=GaussianMode(
mfd=2 * 4.8e-6,
core_radius=fiber.core_radius),
channel_id='signal')
simulation.add_backward_pump(wl=pump_wl,
input_power=cls.input_pump_power)
if init_guess_array is not None:
simulation.set_guess_array(init_guess_array)
result = simulation.run(tol=tolerance)
init_guess_array = result.powers
result_dict = result.make_result_dict()
signal_gain = result_dict['signal']['gain']
cls.gains.append(signal_gain)
def setUpClass(cls):
Yb_number_density = 3e25
core_r = 5e-6
background_loss = 0
length = 3
pump_cladding_r = 50e-6
core_to_cladding_ratio = core_r / pump_cladding_r
core_NA = 0.12
tolerance = 1e-5
cls.input_signal_power = 0.4
cls.input_pump_power = 47.2
fiber = YbDopedDoubleCladFiber(length, core_r, Yb_number_density,
background_loss, core_NA,
core_to_cladding_ratio)
pump_wavelengths = np.linspace(910, 950, 11) * 1e-9
cls.gains = []
init_guess_array = None
for pump_wl in pump_wavelengths:
simulation = SteadyStateSimulation()
simulation.fiber = fiber
simulation.add_cw_signal(wl=1030e-9,
power=cls.input_signal_power,
mode_shape_parameters={
'functional_form': 'gaussian',
'mode_diameter': 2 * 4.8e-6
})
simulation.add_backward_pump(wl=pump_wl,
power=cls.input_pump_power)
if init_guess_array is not None:
simulation.set_guess_array(init_guess_array)
result = simulation.run(tol=tolerance)
init_guess_array = result.powers
result_dict = result.make_result_dict()
signal_gain = result_dict['forward_signal']['gain'][0]
cls.gains.append(signal_gain)