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
npoints = 20
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)
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=914e-9, power=cls.input_pump_power)
simulation.set_number_of_nodes(npoints)
cls.result = simulation.run(tol=tolerance)
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
npoints = 20
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)
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))
simulation.add_backward_pump(wl=914e-9,
input_power=cls.input_pump_power)
simulation.set_number_of_nodes(npoints)
cls.result = simulation.run(tol=tolerance)
def test_steady_state(self):
steady_state_simulation = SteadyStateSimulation(self.fiber)
steady_state_simulation.add_forward_signal(
wl=self.signal_wl,
input_power=self.signal_power,
loss=self.signal_loss)
steady_state_simulation.add_forward_pump(wl=self.pump_wl,
input_power=self.pump_power)
steady_state_simulation.add_backward_pump(wl=self.pump_wl,
input_power=self.pump_power,
loss=self.bw_pump_loss)
steady_state_result = steady_state_simulation.run(tol=1e-5)
ss_losses = steady_state_result.powers_at_fiber_end() / np.array(
[self.signal_power, self.pump_power, self.pump_power])
self.assertTrue(
np.allclose(to_db(ss_losses),
self.expected_losses,
atol=1e-3,
rtol=1e-5))
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)
def test_steady_state_python_and_cpp_single_ring(self):
steady_state_simulation = SteadyStateSimulation(self.fiber)
steady_state_simulation.add_forward_signal(wl=self.signal_wl, input_power=self.signal_power)
steady_state_simulation.add_backward_pump(wl=self.pump_wl, input_power=self.pump_power / 2)
steady_state_simulation.add_forward_pump(wl=self.pump_wl, input_power=self.pump_power / 2)
steady_state_simulation.add_ase(wl_start=1020e-9, wl_end=1040e-9, n_bins=3)
steady_state_result = steady_state_simulation.run(tol=1e-5)
dynamic_simulation = DynamicSimulation(self.time_steps, self.fiber)
dynamic_simulation.add_forward_signal(wl=self.signal_wl, input_power=self.signal_power)
dynamic_simulation.add_backward_pump(wl=self.pump_wl, input_power=self.pump_power / 2)
dynamic_simulation.add_forward_pump(wl=self.pump_wl, input_power=self.pump_power / 2)
dynamic_simulation.add_ase(wl_start=1020e-9, wl_end=1040e-9, n_bins=3)
dynamic_simulation.use_cpp_backend()
cpp_result = dynamic_simulation.run(z_nodes=self.z_nodes, dt=self.steady_state_dt, stop_at_steady_state=True)
dynamic_simulation.use_python_backend()
python_result = dynamic_simulation.run(z_nodes=self.z_nodes, dt=self.steady_state_dt, stop_at_steady_state=True)
dynamic_simulation.use_pythran_backend()
pythran_result = dynamic_simulation.run(z_nodes=self.z_nodes, dt=self.steady_state_dt,
stop_at_steady_state=True)
dynamic_simulation.use_numba_backend()
numba_result = dynamic_simulation.run(z_nodes=self.z_nodes, dt=self.steady_state_dt, stop_at_steady_state=True)
steady_state_output_powers = steady_state_result.powers_at_fiber_end()
cpp_output_powers = cpp_result.powers_at_fiber_end()
python_output_powers = python_result.powers_at_fiber_end()
pythran_output_powers = pythran_result.powers_at_fiber_end()
numba_output_powers = numba_result.powers_at_fiber_end()
self.assertTrue(np.allclose(steady_state_output_powers, cpp_output_powers, rtol=1e-3))
self.assertTrue(np.allclose(cpp_output_powers, python_output_powers, rtol=1e-6))
self.assertTrue(np.allclose(pythran_output_powers, python_output_powers, rtol=1e-6))
self.assertTrue(np.allclose(numba_output_powers, python_output_powers, rtol=1e-6))