dense sisl routines

dense.py

def Skd_MK2(dh, k=(0, 0, 0)):
        dtype = np.complex128
        k = np.asarray(k, np.float64)
        k.shape = (-1,)
        # sparse matrix dimension (dh.no)
        V = zeros((len(dh), len(dh)), dtype=dtype)
        # Get the reciprocal lattice vectors dotted with k
        kr = dot(dh.rcell, k)
        # Calculate all phases
        phases = exp(-1j * dot(kr, dot(dh.cell, dh.sc.sc_off.T)))
        # Now create offsets
        offsets = - np.arange(len(phases)) * dh.no
        dia = diags(phases, offsets, shape=(dh.shape[1], dh.shape[0]), dtype=dtype).todense()
        #dia = diags(phases, offsets, shape=(dh.shape[1], dh.shape[0]), dtype=dtype).todense()
        V = (dh.tocsr(dh.S_idx)).todense().dot(dia)
        return kron(V,eye(2))
    
def Hkd_MK2(dh, k=(0, 0, 0)):
        dtype = np.complex128
        k = np.asarray(k, np.float64)
        k.shape = (-1,)
        # sparse matrix dimension (2 * dh.no)
        V = zeros((len(dh), len(dh)), dtype=dtype)
        # Get the reciprocal lattice vectors dotted with k
        kr = np.dot(dh.rcell, k)
        # Calculate all phases
        phases = exp(-1j * dot(kr, dot(dh.cell, dh.sc.sc_off.T)))
        # Now create offsets
        offsets = - arange(len(phases)) * dh.no
        dia = diags(phases, offsets, shape=(dh.shape[1], dh.shape[0]), dtype=dtype).todense()
        V[::2, ::2] = (dh.tocsr(0) + 1j * dh.tocsr(4)).todense().dot(dia)
        V[1::2, 1::2] = (dh.tocsr(1) + 1j * dh.tocsr(5)).todense().dot(dia)
        V[1::2, ::2] = (dh.tocsr(2) - 1j * dh.tocsr(3)).todense().dot(dia)
        V[::2, 1::2] = (dh.tocsr(6) + 1j * dh.tocsr(7)).todense().dot(dia)
        return V