import numpy as np
import matplotlib.pyplot as plt
import scipy.sparse.linalg as spLA
import majoranaJJ.operators.sparse.qmsops as spop #sparse operators
import majoranaJJ.lattice.nbrs as nb #neighbor arrays
import majoranaJJ.lattice.shapes as shps #lattice shapes
import majoranaJJ.modules.plots as plots #plotting functions
R = 50
r = 15
ax = 10 #[A]
ay = 10 #[A]
coor = shps.donut(R, r)
NN = nb.NN_Arr(coor)
print("lattice size", coor.shape[0])
alpha = 0 #Spin-Orbit Coupling constant: [eV*A]
gammaz = 0 #Zeeman field energy contribution: [T]
delta = 0 #Superconducting Gap: [eV]
V0 = 0.0 #Amplitude of potential : [eV]
mu = 0 #Chemical Potential: [eV]
H = spop.H0(coor, ax, ay, NN)
print("H shape: ", H.shape)
num = 75 # This is the number of eigenvalues and eigenvectors you want
sigma = 0 # This is the eigenvalue we search around
which = 'LM'
eigs, vecs = spLA.eigsh(H, k=num, sigma=sigma, which=which)
import majoranaJJ.operators.sparse.qmsops as spop #sparse operators import majoranaJJ.lattice.nbrs as nb #neighbor arrays import majoranaJJ.lattice.shapes as shps #lattice shapes import majoranaJJ.modules.plots as plots #plotting functions R = 25 r = 10 coor = shps.donut(R, r) #donut coordinate array NN = nb.NN_Arr(coor) NNb = nb.Bound_Arr(coor) idx = 1 plots.lattice(idx, coor, NN=NN) plots.lattice(idx, coor, NNb=NNb)