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- import numpy as np
- import matplotlib.pyplot as plt
- import os
- import scipy.special.hankel2 as H2n
- c = 299792458.0
- eps_0 = 8.854187817e-12
- pi = np.pi
- verbose = 6
- r = 146.513e-9
- debug = True
- def read_data(dirname):
- data = {}
- WLs = []
- for r,d,f in os.walk(dirname):
- for fname in f:
- WLs.append(fname)
- for fname in WLs:
- fdata = np.transpose(
- np.genfromtxt(dirname+"/"+fname, delimiter=", ",skip_header=1
- ,dtype=None, encoding = None
- , converters={0: lambda s: complex(s),
- 1: lambda s: complex(s),
- 2: lambda s: complex(s.replace('i', 'j')),
- 3: lambda s: complex(s.replace('i', 'j')),
- 4: lambda s: complex(s.replace('i', 'j')),
- 5: lambda s: complex(s.replace('i', 'j')),
- 6: lambda s: complex(s.replace('i', 'j')),
- 7: lambda s: complex(s.replace('i', 'j')),
- 8: lambda s: complex(s.replace('i', 'j'))
- }
- )
- )
- data[float(fname[2:-4])]=fdata
- if debug: break
- return data
- def find_nearest(array,value):
- idx = (np.abs(array-value)).argmin()
- return array[idx],idx
- def get_WLs_idx(WLs, data):
- dist = 1
- mmedia = 1
- shift = 1
- WLs_idx = []
- for wl in WLs:
- val, idx = find_nearest(data[dist][mmedia][shift][0,:],wl*1e-9)
- WLs_idx.append(idx)
- return WLs_idx
- def analyze(data,wl):
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- lambd = wl
- omega = 2*pi*c/lambd
- eps_d = complex(1)
- eps_m = data[8,0]**2
- dip_power = data[1,0]
- z = data[0,:]
- idx_d = np.nonzero(z>1e-10)
- idx_0 = np.nonzero(np.logical_and(z<=1e-10, z>=-1e-10))
- idx_m = np.nonzero(z<-1e-10)
- z_d = z[idx_d]
- z_0 = z[idx_0]
- z_m = z[idx_m]
- if (not np.array_equal(np.hstack((z_m, z_0, z_d)), z)):
- print("ERROR! loosing z values!")
- raise
-
- Ex = data[2,:]
- Ex_m = data[2,idx_m][0]
- Ey_m = data[3,idx_m][0]
- Ez_m = data[4,idx_m][0]
- Hx_m = data[5,idx_m][0]
- Hy_m = data[6,idx_m][0]
- Hz_m = data[7,idx_m][0]
- E_m = np.array([Ex_m,Ey_m,Ez_m])
- H_m = np.array([Hx_m,Hy_m,Hz_m])
- Ex_d = data[2,idx_d][0]
- Ey_d = data[3,idx_d][0]
- Ez_d = data[4,idx_d][0]
- Hx_d = data[5,idx_d][0]
- Hy_d = data[6,idx_d][0]
- Hz_d = data[7,idx_d][0]
- E_d = np.array([Ex_d,Ey_d,Ez_d])
- H_d = np.array([Hx_d,Hy_d,Hz_d])
- k_0 = omega/c
- k_sp = k_0*np.sqrt(eps_d*eps_m/(eps_d+eps_m))
- chi_d = np.sqrt( eps_d*k_0**2 - k_sp**2 )
- chi_m = np.sqrt( eps_m*k_0**2 - k_sp**2 )
- h_sp_d = np.exp(1j*chi_d*z_d)
- e_sp_x_d = chi_d/(omega*eps_0*eps_d)*np.exp(1j*chi_d*z_d)
- e_sp_z_d = k_sp/(omega*eps_0*eps_d)*np.exp(1j*chi_d*z_d)
- h_sp_m = np.exp(1j*-chi_m*z_m)
- e_sp_x_m = -chi_m/(omega*eps_0*eps_m)*np.exp(1j*-chi_m*z_m)
- e_sp_z_m = k_sp/(omega*eps_0*eps_m)*np.exp(1j*-chi_m*z_m)
- if verbose > 5:
- print("r =",r)
- print(h_sp_m)
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- file_ext="pdf"
- dirname="bigourdan-Au-sub-Cyl-dipole-W.fsp.1D.monitor_1.results"
- def main ():
- data = read_data(dirname)
- for wl in data:
- analyze(data[wl],wl)
- main()
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