126 lines
3.4 KiB
Python
126 lines
3.4 KiB
Python
import colorcet as cc
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import matplotlib.pyplot as plt
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import numpy as np
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from plotapp import PlotApp
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import scgenerator as sc
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params = sc.Parameters(
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wavelength=835e-9,
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width=50e-15,
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peak_power=10e3,
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repetition_rate=20e6,
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shape="sech",
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# fiber
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length=15e-2,
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raman_type="measured",
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beta2_coefficients=[
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-11.830e-27,
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8.1038e-41,
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-9.5205e-56,
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2.0737e-70,
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-5.3943e-85,
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1.3486e-99,
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-2.5495e-114,
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3.0524e-129,
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-1.7140e-144,
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],
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gamma=0.11,
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# simulation
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tolerated_error=1e-8,
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wavelength_window=[300e-9, 1500e-9],
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t_num=8192,
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quantum_noise=False,
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z_num=128,
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)
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def compute_manual():
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spec0 = params.compute("spec_0")
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w_c, w0, gamma = params.compute("w_c", "w0", "gamma")
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p = params.compile()
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print(p.dt)
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beta_op = sc.operators.constant_polynomial_dispersion(
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params.beta2_coefficients, w_c, params.compute("dispersion_ind")
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)
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linear = sc.operators.envelope_linear_operator(
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beta_op,
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# sc.operators.constant_quantity(0),
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sc.operators.constant_quantity(0),
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)
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# with PlotApp() as app:
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# o = np.argsort(p.l)
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# sax = app["spectrum"]
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# sax.set_line_data("beta2", p.l[o], beta_op(0)[o].imag)
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# return
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# spm = sc.operators.envelope_spm(0)
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# plt.plot(p.l, sc.abs2(p.spec_0))
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# plt.plot(p.l, sc.abs2(np.fft.fft(spm(np.fft.ifft(p.spec_0), 0))), ls=":")
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# plt.xlim(100e-9, 1500e-9)
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# plt.show()
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# return
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# nonlinear = sc.operators.envelope_nonlinear_operator(
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# gamma_op=sc.operators.constant_quantity(params.gamma),
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# ss_op=sc.operators.constant_quantity(w_c / w0),
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# spm_op=sc.operators.envelope_spm(0),
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# raman_op=sc.operators.no_op_time(params.t_num),
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# )
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hr_w = params.compute("hr_w")
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def nonlinear(spec, z):
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field = np.fft.ifft(spec)
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field2 = sc.abs2(field)
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fr = 0.18
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return (
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-1j
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* gamma
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* (1 + w_c / w0)
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* np.fft.fft(field * ((1 - fr) * field2 + fr * np.fft.ifft(hr_w * np.fft.fft(field2))))
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)
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above0 = p.l > 0
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linear_arr = np.zeros(p.t_num, dtype=complex)
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w_power_fact = np.array(
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[sc.math.power_fact(w_c[above0], k) for k in range(2, len(p.beta2_coefficients) + 2)]
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)
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for i, wn in reversed(list(enumerate(w_power_fact))):
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print(i, p.beta2_coefficients[i])
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linear_arr[above0] += p.beta2_coefficients[i] * sc.math.power_fact(w_c[above0], i + 2)
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linear_arr *= -1j
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def linear(_):
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return linear_arr
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prop = sc.propagation("examples/dudley_manual.zip", params)
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z = []
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for i, (spec, stat) in enumerate(
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sc.solve43(spec0, linear, nonlinear, params.length, 1e-6, 1e-6, 0.9, h_const=20e-6)
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):
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if i % 20:
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continue
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print(stat, end="\r")
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prop.append(spec)
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z.append(stat["z"])
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def compute_auto():
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sc.compute(params, True, "examples/dudley2006")
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def plot():
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prop = sc.propagation("examples/dudley_manual.zip")
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newprop = sc.propagation("examples/dudley2006.zip")
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fig, (top, bot) = plt.subplots(2, 2)
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z = np.linspace(0, prop.parameters.length, len(prop))
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sc.plotting.summary_plot(prop[:], z, axes=top)
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sc.plotting.summary_plot(newprop[:], newprop.parameters.z_targets, axes=bot)
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plt.show()
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if __name__ == "__main__":
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# compute_auto()
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plot()
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