Data for training the MIR framework for magnetotelluric impedance tensor reconstruction

class MagnetotelluricDataGenerate: def __init__(self) -> None: self.fres = [3.2E+02,2.4E+02,1.6E+02,1.2E+02,8.0E+01,\ 6.0E+01,4.0E+01,3.0E+01,2.0E+01,1.5E+01,\ 1.0E+01,7.5E+00,6.0E+00,4.5E+00,3.0E+00,\ 2.25E+00,1.5E+00,1.125E+00,7.5E-01,5.625E-01,\ 3.75E-01,2.8125E-01,1.875E-01,1.40625E-01,9.375E-02,\ 7.03125E-02,4.6875E-02,3.515625E-02,2.34375E-02,1.7578125E-02,\ 1.171875E-02,8.7890625E-03,5.859375E-03,4.39453125E-03,2.9296875E-03,\ 2.197265625E-03,1.46484375E-03,1.0986328125E-03,7.32421875E-04,5.4931640625E-04,\ 3.662109375E-04,2.74658203125E-04,1.8310546875E-04,1.373291015625000E-04,9.1552734375E-05,\ 6.866455078125E-05,4.57763671875E-05,3.4332275390625E-05,2.288818359375E-05,1.71661376953125E-05] self.fcount = len(self.fres) self.Zxx = [] self.Zxy = [] self.Zyx = [] self.Zyy = [] self.Tzx = [] self.Tzy = [] self.rot = 0.0 def LoadZTData(self,name): x = np.loadtxt(name, delimiter=" ", dtype="float") x = np.array(x).reshape(-1, 12, self.fcount) [self.Zxx,self.Zxy,self.Zyx,self.Zyy] = self._ExtractZ(x) [self.Tzx,self.Tzy] = self._ExtractT(x) def _ExtractZ(self,x): return x[:,0,:] + x[:,1,:] * 1j,\ x[:,2,:] + x[:,3,:] * 1j,\ x[:,4,:] + x[:,5,:] * 1j,\ x[:,6,:] + x[:,7,:] * 1j def _ExtractT(self,x): return x[:,8,:] + x[:,9,:] * 1j,\ x[:,10,:] + x[:,11,:] * 1j