def update_energy(self, E_new): self.energy = E_new self.e_array = sparse.kron(np.identity(self.hamil.shape[0]), E_new).toarray() self.self_energy_l = calc.self_energy(E_new, self.hamil_l, self.t_x, self.sigma_0, self.mixing, self.iterations, self.sites) self.self_energy_r = calc.self_energy(E_new, self.hamil_r, calc.hermitian(self.t_x), self.sigma_0, self.mixing, self.iterations, self.sites) self.sigma_l = calc.construct_sigma_l(self.self_energy_l, self.n_layers) self.sigma_r = calc.construct_sigma_r(self.self_energy_r, self.n_layers) self.greens_ret = self.combine(self.hamil, self.e_array, self.sigma_l, self.sigma_r, self.sites, self.n_layers) self.greens_adv = calc.hermitian(self.greens_ret) self.cond = self.conductance(self.sigma_l,self.sigma_r,self.greens_ret,self.greens_adv)
def update_hamil_layers(self,hamil_layers,num_on_site):
self.hamil_layers = hamil_layers
self.hamil_l = hamil_layers[:num_on_site*self.sites,:num_on_site*self.sites]
self.hamil_r = hamil_layers[num_on_site*self.sites*(self.n_layers-1):,num_on_site*self.sites*(self.n_layers-1):]
self.e_array = sparse.kron(np.identity(self.hamil_layers.shape[0]), self.energy).toarray()
self.self_energy_l = calc.self_energy(self.energy, self.hamil_l, self.t_x, self.sigma_0, self.mixing, self.iterations, self.sites)
self.self_energy_r = calc.self_energy(self.energy, self.hamil_r, calc.hermitian(self.t_x), self.sigma_0, self.mixing, self.iterations, self.sites)
if np.array_equal(self.self_energy_l,self.self_energy_r):
print("equal self energies")
self.sigma_l = calc.construct_sigma_l(self.self_energy_l, self.n_layers)
self.sigma_r = calc.construct_sigma_r(self.self_energy_r, self.n_layers)
self.greens_ret = self.combine(self.hamil_layers, self.e_array, self.sigma_l, self.sigma_r, self.sites, self.n_layers)
self.greens_adv = calc.hermitian(self.greens_ret)
self.cond = self.conductance(self.sigma_l,self.sigma_r,self.greens_ret,self.greens_adv)
def update_hamil_mr(self,hamil_l_new,hamil_r_new,t_x):
self.hamil = hamil_l_new
self.n_layers = 2.
self.hamil_layers = calc.make_hamil_layers_2([hamil_l_new,hamil_r_new], t_x,self.sites)
self.hamil_r = hamil_r_new
self.hamil_l = hamil_l_new
self.e_array = sparse.kron(np.identity(self.hamil_layers.shape[0]), self.energy).toarray()
self.self_energy_l = calc.self_energy(self.energy, hamil_l_new, self.t_x, self.sigma_0, self.mixing, self.iterations, self.sites)
self.self_energy_r = calc.self_energy(self.energy, hamil_r_new, calc.hermitian(self.t_x), self.sigma_0, self.mixing, self.iterations, self.sites)
if np.array_equal(self.self_energy_l,self.self_energy_r):
print("equal self energies")
self.sigma_l = calc.construct_sigma_l(self.self_energy_l, self.n_layers)
self.sigma_r = calc.construct_sigma_r(self.self_energy_r, self.n_layers)
self.greens_ret = self.combine(self.hamil_layers, self.e_array, self.sigma_l, self.sigma_r, self.sites, self.n_layers)
self.greens_adv = calc.hermitian(self.greens_ret)
self.cond = self.conductance(self.sigma_l,self.sigma_r,self.greens_ret,self.greens_adv)
def update_hamil_2(self, hamil_layers, N, n_layers, no_mag_leads=False,hamil_sub=[]): #self.hamil = hamil_new self.n_layers = n_layers self.hamil_layers = hamil_layers #self.hamil_sep = np.hsplit(np.vsplit(hamil_layers, N),N) if not no_mag_leads: self.hamil_r = np.hsplit(np.vsplit(hamil_layers, self.n_layers)[-1],self.n_layers)[-1] self.hamil_l = np.hsplit(np.vsplit(hamil_layers, self.n_layers)[0],self.n_layers)[0] else: self.hamil_r = hamil_sub self.hamil_l = hamil_sub self.e_array = sparse.kron(np.identity(hamil_layers.shape[0]), self.energy).toarray() self.self_energy_l = calc.self_energy(self.energy, self.hamil_l, self.t_x, self.sigma_0, self.mixing, self.iterations, self.sites) self.self_energy_r = calc.self_energy(self.energy, self.hamil_r, calc.hermitian(self.t_x), self.sigma_0, self.mixing, self.iterations, self.sites) self.sigma_l = calc.construct_sigma_l(self.self_energy_l, n_layers) self.sigma_r = calc.construct_sigma_r(self.self_energy_r, n_layers) self.greens_ret = self.combine(self.hamil_layers, self.e_array, self.sigma_l, self.sigma_r, self.sites, 1) self.greens_adv = calc.hermitian(self.greens_ret) self.cond = self.conductance(self.sigma_l,self.sigma_r,self.greens_ret,self.greens_adv)
def __init__(self,E,h,t_y,sigma_0,a,iterations,N, n_layers, t_x): """ The Constructor class takes the inputs: int E -> energy int h -> value of Hamiltonian at each atomic site np.complex_ sigma_0 -> starting value for sigma convergence function int a -> mixing coefficient for sigma convergence int iterations -> number of iterations in sigma convergence loop int N -> number of sites in supercell int n_layers -> number of layers (supercells) in system int t_y -> width of supercell (in y-direction) int t_x -> hopping matrix for transport direction (x-direction) """ self.runtimes = [] #hamil_time_i = time.time() self.hamil = calc.hamiltonian(h,t_y,N) #hamil_time_o = time.time() self.hamil_layers = self.hamil self.hamil_l = self.hamil self.hamil_r = self.hamil self.t_y = t_y self.energy = E self.e_array = sparse.kron(np.identity(self.hamil.shape[0]), self.energy).toarray() self.sites = N self.n_layers = n_layers self.sigma_0 = sigma_0 self.iterations = iterations self.mixing = a self.t_x = t_x #selfe_time_i = time.time() self.self_energy_l = calc.self_energy(self.energy, self.hamil_l, t_x, sigma_0, a, iterations, N) self.self_energy_r = calc.self_energy(self.energy, self.hamil_r, calc.hermitian(t_x), sigma_0, a, iterations, N) self.sigma_l = calc.construct_sigma_l(self.self_energy_l, self.n_layers) self.sigma_r = calc.construct_sigma_r(self.self_energy_r, self.n_layers) selfe_time_o = time.time() #greens_time_i = time.time() self.greens_ret = self.combine(self.hamil, self.e_array, self.sigma_l, self.sigma_r, self.sites, self.n_layers) self.greens_adv = calc.hermitian(self.greens_ret) greens_time_o = time.time() #cond_time_i = time.time() self.cond = self.conductance(self.sigma_l,self.sigma_r,self.greens_ret,self.greens_adv)
def expand_y(self, t_y,E, k_y): """ Expands the conductance function in the y-direction using a Fourier Transform. t_y is the value assigned to the y-direction connection between layers. """ if t_y == 0: return self.cond else: t_y_array = np.zeros((self.sites,self.sites)) t_y_array[0,self.sites-1] = 1.0 t_y_array = sparse.kron(t_y_array,t_y) t_y_array_dag = calc.hermitian(t_y_array) exponent = (1j)*(k_y) hamil_y = self.hamil + np.multiply(t_y_array, np.exp(exponent)).toarray() + np.multiply(t_y_array_dag, np.exp((-1)*exponent)).toarray() self_energy_y = calc.self_energy(self.energy, hamil_y, t_y, self.sigma_0, self.mixing, self.iterations, self.sites) sigma_l_y = calc.construct_sigma_l(self_energy_y, self.n_layers) sigma_r_y = calc.construct_sigma_r(self_energy_y, self.n_layers) e_array_y = sparse.kron(np.identity(hamil_y.shape[0]), E).toarray() greens_ret_y = self.combine(hamil_y, e_array_y, sigma_l_y, sigma_r_y, self.sites, self.n_layers) greens_adv_y = calc.hermitian(greens_ret_y) cond_y = self.conductance(sigma_l_y,sigma_r_y,greens_ret_y,greens_adv_y) return cond_y
