latest shared results 2024-02-26
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@@ -1,8 +1,6 @@
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import click
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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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@@ -45,32 +43,11 @@ def compute_manual(save: bool):
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beta_op = sc.operators.constant_polynomial_dispersion(params.beta2_coefficients, w_c)
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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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def nonlinear(spec, _):
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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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@@ -86,7 +63,7 @@ def compute_manual(save: bool):
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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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for i, _ 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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