def calculate_residuals(self, parameters):
ma = ModelAdapter(self.sample_model)
ma.update_model_from_params(parameters)
layers = [self.sample_model.incoming_media
] + self.sample_model.layers + [self.sample_model.substrate]
sublayers = generateSublayers(layers)[0]
chi_array = []
for ds in self.data_model.datasets:
if ds.R is not None and len(ds.R) > 1:
q = ds.Q / 2.
r = reflection(q, sublayers)
pol_eff = np.ones(len(q), dtype=np.complex128)
an_eff = np.ones(len(q), dtype=np.complex128)
rr = np.real(
spin_av(r, ds.pol_Polarizer, ds.pol_Analyzer, pol_eff,
an_eff))
sigma = ds.sigmaQ
rrr = resolut(
rr, q, sigma, 4
) * self.data_model.theory_factor + self.data_model.background
chi_array.append(
(rrr / self.data_model.experiment_factor - ds.R) / ds.E)
chi = np.array(chi_array).ravel()
self.chiSquaredChanged.emit((chi**2).mean())
return chi
def test_reference_results(self):
paramfile = open(os.path.join(os.path.dirname(__file__),'data/refl_par.dat'),'r')
n_monte_carlo = int(paramfile.readline())
formalism = int(paramfile.readline())
res_mode = int(paramfile.readline())
n_of_outputs = int(paramfile.readline())
pol_vecs = np.array([float(value) for value in paramfile.readline().strip().split()]).reshape(6,3)
an_vecs = np.array([float(value) for value in paramfile.readline().strip().split()]).reshape(6,3)
pol_fun = [int(value) for value in paramfile.readline().split()]
norm_factor = [int(value) for value in paramfile.readline().split()]
maxwell = int(paramfile.readline())
glance_angle = int(paramfile.readline())
background = float(paramfile.readline())
percentage = float(paramfile.readline())
nlayers1 = int(paramfile.readline())
substrate_tmp = [float(value) for value in paramfile.readline().split()]
substrate=Layer()
substrate.nsld = complex(substrate_tmp[0],substrate_tmp[1])
NC = float(paramfile.readline())
layers = [Layer(name='Incoming media')]
for i in range(nlayers1):
l = Layer()
l.thickness = float(paramfile.readline())
nsld_tmp = [float(value) for value in paramfile.readline().split()]
l.nsld = complex(nsld_tmp[0], nsld_tmp[1])
msld_xyz = np.array([float(value) for value in paramfile.readline().split()])
l.msld.rho = np.sqrt(msld_xyz.dot(msld_xyz))
l.msld.phi= np.degrees(np.arctan2(msld_xyz[1],msld_xyz[0]))
l.msld.theta=0
if l.msld.rho.value!=0:
l.msld.theta=np.degrees(np.arccos(np.nan_to_num(msld_xyz[2]/l.msld.rho.value)))
l.NC = float(paramfile.readline())
layers.append(l)
paramfile.close()
q, dq = np.loadtxt(os.path.join(os.path.dirname(__file__),'data/refl_q_dq.dat'),unpack=True)
inc_moment = q / 2.0
pol_eff = np.ones(len(q), dtype=np.complex128)
an_eff = np.ones(len(q), dtype=np.complex128)
layers.append(substrate)
R = reflection.reflection(inc_moment, layers)
for k in range(n_of_outputs):
RR = reflection.spin_av(R, pol_vecs[k], an_vecs[k], pol_eff, an_eff)
RR = np.real(RR)
for res_mode in range(1,3):
RRr = reflection.resolut(RR, q, dq, res_mode)
RRr = RRr * norm_factor[k] + background
reference_values = np.loadtxt(os.path.join(os.path.dirname(__file__),'data/res_mode'+str(res_mode)+'refl'+str(k+1)+'.dat'), unpack=True)
assert_array_almost_equal(reference_values, RRr)
class Testchi3_137(unittest.TestCase):
Q = np.loadtxt(os.path.join(os.path.dirname(__file__),'data/chi3_137/q.dat'),unpack=True)
inc_moment = Q / 2.0
sigma = res_chi3_137(Q)
substrate = Layer()
substrate.nsld = complex(3.6214e-006, 0.0)
layer_info = np.loadtxt(os.path.join(os.path.dirname(__file__), 'data/chi3_137/profile_sublayers.dat'))
layer_info = layer_info[:-1]
layers = [Layer(name='Incoming media')]
for line in layer_info:
l = Layer()
l.thickness = line[1]
l.nsld = complex(line[2], line[3])
msld = [line[4], line[5], line[6]]
#l.msld = [msld[0]*np.sin(msld[2])*np.cos(msld[1]), msld[0]*np.sin(msld[2])*np.sin(msld[1]), msld[0]*np.cos(msld[2])]
l.msld.rho=msld[0]
l.msld.theta=msld[2]
l.msld.phi=msld[1]
l.NC = 0.0
layers.append(l)
layers.append(substrate)
R = reflection.reflection(inc_moment, layers)
pol_vecs = [[0.98,0.0,0.0], [-0.98,0.0,0.0], [0.98,0.0,0.0], [0.0,0.0,0.0], [0.0,0.0,0.0], [0.0,0.0,0.0]]
an_vecs = [[0.98,0.0,0.0], [-0.98,0.0,0.0], [-0.98,0.0,0.0], [0.0,0.0,0.0], [0.0,0.0,0.0], [0.0,0.0,0.0]]
norm_factor=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
background=1.2e-05;
pol_eff = np.ones(len(Q), dtype = np.complex128)
an_eff = np.ones(len(Q), dtype = np.complex128)
n_of_outputs = 3
for k in range(n_of_outputs):
RR = reflection.spin_av(R, pol_vecs[k], an_vecs[k], pol_eff, an_eff)
RR = np.real(RR)
RRr = reflection.resolut(RR, Q, sigma, 3)
RRr = RRr * norm_factor[k] + background
fig,ax = plt.subplots()
ax.semilogy(Q,RRr,label='calculation')
#plt.xlim([0.0,0.06])
#plt.ylim([1.0e-4,10.0])
reference_values = np.loadtxt(os.path.join(os.path.dirname(__file__),'data/chi3_137/rtheory'+str(k+1)+'.dat'), unpack=True)
assert_array_almost_equal(RRr,reference_values,2)
ax.semilogy(Q, reference_values, label='reference')
ax.set_xlabel('Momentum Transfer $\AA^{-1}$')
ax.set_ylabel('Reflectivity')
ax.set_title('Polarization ({},{},{}), Analysis({},{},{})'.format(pol_vecs[k][0],pol_vecs[k][1],pol_vecs[k][2],an_vecs[k][0],an_vecs[k][1],an_vecs[k][2]))
ax.legend()
fig.savefig('chi3_137_'+str(k+1)+'.pdf')
plt.close()
def residual_with_adapter(params, q, r, dr, model_adapter):
"""
Compute residuals
"""
model_adapter.update_model_with_params(params)
model_layers = model_adapter.generate_sub_layers()
substrate = np.complex(2.07e-006, 0.0)
pol_eff = np.ones(len(q), dtype=np.complex128)
an_eff = np.ones(len(q), dtype=np.complex128)
# Why do we need to divide by 2?
R = reflection.reflection(q / 2, model_layers[1:-1], substrate)
RR = reflection.spin_av(R, [1, 0, 0], [1, 0, 0], pol_eff, an_eff)
RR = np.real(RR)
return (RR - r) / dr