def _reflamp(self):
#calc_q = self.probe.calc_Q
#return calc_q,calc_q
key = 'calc_r'
if key not in self._cache:
slabs = self._render_slabs()
w = slabs.w
rho,irho = slabs.rho, slabs.irho
sigma = slabs.sigma
#sigma = slabs.sigma
calc_q = self.probe.calc_Q
#print("calc Q", self.probe.calc_Q)
if slabs.ismagnetic:
rhoM, thetaM = slabs.rhoM, slabs.thetaM
Aguide = self.probe.Aguide
calc_r = reflmag(-calc_q/2, depth=w, rho=rho[0], irho=irho[0],
rhoM=rhoM, thetaM=thetaM, Aguide=Aguide)
else:
calc_r = reflamp(-calc_q/2, depth=w, rho=rho, irho=irho,
sigma=sigma)
if False and numpy.isnan(calc_r).any():
print("w %s",w)
print("rho",rho)
print("irho",irho)
print("sigma",sigma)
print("kz",self.probe.calc_Q/2)
print("R",abs(calc_r**2))
pars = parameter.unique(self.parameters())
fitted = parameter.varying(pars)
print(parameter.summarize(fitted))
print("===")
self._cache[key] = calc_q,calc_r
#if numpy.isnan(calc_q).any(): print("calc_Q contains NaN")
#if numpy.isnan(calc_r).any(): print("calc_r contains NaN")
return self._cache[key]
def _reflamp(self):
#calc_q = self.probe.calc_Q
#return calc_q, calc_q
key = 'calc_r'
if key not in self._cache:
slabs = self._render_slabs()
w = slabs.w
rho, irho = slabs.rho, slabs.irho
sigma = slabs.sigma
#sigma = slabs.sigma
calc_q = self.probe.calc_Q
#print("calc Q", self.probe.calc_Q)
if slabs.ismagnetic:
rhoM, thetaM = slabs.rhoM, slabs.thetaM
Aguide = self.probe.Aguide.value
H = self.probe.H.value
calc_r = reflmag(-calc_q / 2,
depth=w,
rho=rho[0],
irho=irho[0],
rhoM=rhoM,
thetaM=thetaM,
Aguide=Aguide,
H=H,
sigma=sigma)
else:
calc_r = reflamp(-calc_q / 2,
depth=w,
rho=rho,
irho=irho,
sigma=sigma)
if False and np.isnan(calc_r).any():
print("w", w)
print("rho", rho)
print("irho", irho)
if slabs.ismagnetic:
print("rhoM", rhoM)
print("thetaM", thetaM)
print("Aguide", Aguide, "H", H)
print("sigma", sigma)
print("kz", self.probe.calc_Q / 2)
print("R", abs(np.asarray(calc_r)**2))
pars = parameter.unique(self.parameters())
fitted = parameter.varying(pars)
print(parameter.summarize(fitted))
print("===")
self._cache[key] = calc_q, calc_r
#if np.isnan(calc_q).any(): print("calc_Q contains NaN")
#if np.isnan(calc_r).any(): print("calc_r contains NaN")
return self._cache[key]
def fit_all(M, pmp=20):
"""
Set all non-zero parameters to fitted parameters inside the model.
"""
# Exclude unlikely fitting parameters
exclude = set((M.sample[0].thickness,
M.sample[-1].thickness,
M.sample[-1].interface,
M.probe.back_absorption,
))
if M.probe.intensity.value == 1:
exclude.add(M.probe.intensity)
if M.probe.background.value < 2e-10:
exclude.add(M.probe.background.value)
# Fit everything else using a range of +/- pmp %
for p in parameter.unique(M.parameters()):
if p in exclude: continue
if p.value != 0: p.pmp(pmp)
def fit_all(M, pmp=20):
"""
Set all non-zero parameters to fitted parameters inside the model.
"""
# Exclude unlikely fitting parameters
exclude = set((
M.sample[0].thickness,
M.sample[-1].thickness,
M.sample[-1].interface,
M.probe.back_absorption,
))
if M.probe.intensity.value == 1:
exclude.add(M.probe.intensity)
if M.probe.background.value < 2e-10:
exclude.add(M.probe.background.value)
# Fit everything else using a range of +/- pmp %
for p in parameter.unique(M.parameters()):
if p in exclude: continue
if p.value != 0: p.pmp(pmp)