def notestPropagatePlaneRealistic(self):
label = join(test_folder, 'siesta')
eigs, kpts, EF = readInfoForSTMSimulation(label=label)
rho, variables, dh_cell = readCubeDensity(label=label)
dh = np.array(map(vectorLength, dh_cell))
band = 30
kn = 4
kappa = 1.0
rho = rho.sum(axis=0)
c, variables, domain = surfaceIntegrator(rho=rho, variables=variables)
wf, variables1, dh_cell = readCubeWavefunction(label=label, band=band, kn=kn)
kpt, wk = kpts[kn-1]
eikr = phaseFactor(kpt, variables1)
u1 = wf/eikr
u2, variables2 = propagateWaveFunction(
c=c,
kpt=kpt,
u=u1,
variables=variables,
kappa=kappa,
)
grad_u = np.array(np.gradient(u2, *dh))
index = 5
grad_uz = grad_u[2, :, :, index]
u2_plane = u2[:,:, index]
plane_z = variables2[2,0,0, index]
r_plane = variables2[:2, :,:, index]
u3, variables3 = propagateWaveFunctionPlane(
kpt=kpt,
u=u2_plane,
grad_uz=grad_uz,
variables=r_plane,
kappa=kappa,
plane_z=plane_z,
)
from matplotlib import pylab as plt
fig = plt.figure(figsize=(8, 8))
ax = plt.gca()
z = variables[2,0,0,:]
v = abs(u1).sum(axis=(0,1))
ax.semilogy(z, v)
z = variables2[2,0,0,:]
v = abs(u2).sum(axis=(0,1))
ax.semilogy(z, v)
z = variables3[2,0,0,:]
v = abs(u3).sum(axis=(0,1))
#ax.semilogy(z, v)
print abs(u2).sum(axis=(0,1))
print variables2[2,0,0,:]
plt.show()
eeeeee
def notestSaveReadWaveFunctionsTest(self):
band = 21
kn = 4
ws = 2
potential = Topographic(voltage=-2)
rho, r, dh_cell = readCubeDensity(label=test_label)
dh = np.array(map(vectorLength, dh_cell))
rho = rho.sum(axis=0)
c, variables, domain = surfaceIntegrator(rho=rho, variables=r, rho0=1e-3, DS=1)
thick_surface = vectorLength(c, axis=0) > 1e-9
take_z = thick_surface.sum(axis=0)
take_z = take_z.sum(axis=0) > 0
wf, r, dh_cell = readCubeWavefunction(label=test_label, band=band, kn=kn)
EF, eigs= readEigenvalues(label=test_label)
kpts = readKpoints(label=test_label)
energy = eigs[kn-1, band-1, 0]
we = STMWeighting(energies=[energy], potential=potential, EF=EF)
we = we[0]
kpt, wk = kpts[kn-1]
kappa = getVacuumDecayRate(energy)
eikr = phaseFactor(kpt, r)
u = wf/eikr
grad_u = np.array(np.gradient(u, *dh))
A = (c*grad_u).sum(axis=0)
B = u*c
A=A[:,:,take_z]
B=B[:,:,:,take_z]
r=r[:,:,:,take_z]
u, variables = propagateWaveFunction(
A=A,
B=B,
kpt=kpt,
r=r,
kappa=kappa,
height=10,
)
weight = we*ws*wk
Itest = weight*abs(u)**2
self.assertFalse(os.path.exists(testfile))
saveWavefunctionsToPlane(filename=testfile, label=test_label)
self.assertTrue(os.path.exists(testfile))
f = netcdf.netcdf_file(testfile, 'r')
self.assertTrue(len(f.variables.keys()) >0)
f.close()
I, r = STMFromFile(
label=join(test_folder, 'test'),
potentials=[potential],
cells=None,
)
self.assertArraysEqual(np.log(Itest[0,0]), np.log(I[0,0,0]), 5)
