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- #!/usr/bin/env python3
- # -*- coding: UTF-8 -*-
- from matplotlib import pyplot as plt
- import cmath
- from evalMie import *
- import numpy as np
- min_lim = 0.4
- max_lim = 0.75
- # mat = scipy.io.loadmat('d-parameters/rs=4.mat')
- # omega_ratio = mat['omegav'][0]
- # d_perp = mat['dperp'][0]*10
- from_disk = np.loadtxt('rs4-d_perp_interpolated.txt')
- omega_ratio = from_disk[0, :]
- d_perp = from_disk[1, :] + 1j*from_disk[2, :]
- c = 299792458 # m/s
- h_reduced = 6.5821e-16 # eV s
- omega_p = 5.9 # eV
- gamma = 0.1 # eV
- eps_d = 1
- def eps_m(omega):
- return 1 - omega_p * omega_p / (omega*omega + 1j*omega*gamma)
- Rs = [2.5, 5, 10, 25] # nm
- y_min = [1e-2, 1e-2, 1e-1, 1e-1]
- y_max = [1e1, 5e1, 5e1, 5e1]
- # for om_rat in omega_ratio:
- for fig in range(len(Rs)):
- R = Rs[fig]
- Qext = []
- Qext_mie = []
- om_rat_plot = []
- x_in = []
- m_in = []
- d_perp_in = []
- for i in range(len(omega_ratio)):
- om_rat = omega_ratio[i]
- if om_rat < min_lim or om_rat > max_lim:
- continue
- omega = om_rat*omega_p
- m = cmath.sqrt(eps_m(omega))
- x = (omega/c) * R * 1e-9/h_reduced
- x_in.append(x)
- m_in.append(m)
- om_rat_plot.append(om_rat)
- d_perp_in.append(d_perp[i])
- Qext_mie, _ = eval_mie_spectrum(x_in, m_in)
- Qext, _ = eval_mesomie_spectrum(x_in, m_in, R, d_perp_in)
- print(Qext)
- plt.figure(fig)
- plt.plot(om_rat_plot, Qext_mie, label='classic', color='gray', lw=4)
- plt.plot(om_rat_plot, Qext, label='non-classic', color='red', lw=4)
- # plt.plot(om_rat_plot, Qext, label="R="+str(R), lw=1)
- plt.legend()
- plt.yscale('log')
- plt.xlim((0.4, 0.75))
- plt.ylim((y_min[fig], y_max[fig]))
- plt.title("R="+str(R))
- plt.show()
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