def better_bumps(model):
zin=[]
zout=[]
chis=[]
dzs=[]
nllfs=[]
# for zs in np.arange(.05,.45,.005):
# #print("zs", zs)
# model.atomListModel.atomModels[0].z.value = zs
# model.update()
# schi=model.nllf()
# nllfs.append(schi)
# zin.append(zs)
xpeaks = findmin(zin,nllfs,10)
print('xpeak', xpeaks)
# these are experimental values, shortcut
model.atomListModel.atomModels[0].z.value = .35973
model.atomListModel.atomModels[5].x.value = .07347
model.atomListModel.atomModels[5].y.value = .07347
model.update()
problem = bumps.FitProblem(model)
result = fitters.fit(problem, method='lm')
for p, v in zip(problem._parameters, result.dx):
p.dx = v
return result.x, result.dx, problem.chisq(), problem._parameters
def fit(model):
#Create a problem from the model with bumps, then fit and solve it
problem = bumps.FitProblem(model)
monitor = fitter.StepMonitor(problem, open("sxtalFitMonitor.txt","w"))
fitted = fitter.LevenbergMarquardtFit(problem)
x, dx = fitted.solve(monitors=[monitor])
return x, dx, problem.chisq()
def graphError():
cell = Mod.makeCell(crystalCell, spaceGroup.xtalSystem)
m = S.Model(tt, sfs2, backg, wavelength, spaceGroup, cell,
[atomList], exclusions,
scale=0.06298,hkls=refList, error=error, extinction=[0.0001054])
m.atomListModel.atomModels[0].z.range(0,0.5)
z = 0
xval = []
y = []
while (z < 0.5):
#Set a range on the x value of the first atom in the model
m.atomListModel.atomModels[0].z.value = z
m.atomListModel.atomModels[0].z.range(0, 0.5)
problem = bumps.FitProblem(m)
# monitor = fitter.StepMonitor(problem, open("sxtalFitMonitor.txt","w"))
fitted = fitter.LevenbergMarquardtFit(problem)
x, dx = fitted.solve()
xval.append(x[0])
y.append(problem.chisq())
print(x, problem.chisq())
z += 0.005
fig = plt.figure()#
mpl.pyplot.scatter(xval, yval)
mpl.pyplot.xlabel("Pr z coordinate")
mpl.pyplot.ylabel("X2 value")
fig.savefig('/mnt/storage/prnio_chisq_vals_optcfl_lm.png')
def fit(self, model):
#Create a problem from the model with bumps,
#then fit and solve it
problem = bumps.FitProblem(model)
fitted = fitter.LevenbergMarquardtFit(problem)
x, dx = fitted.solve()
return x, dx, problem.chisq()
def fit(model):
#Create a problem from the model with bumps,
#then fit and solve it
problem = bumps.FitProblem(model)
fitted = fitter.LevenbergMarquardtFit(problem)
x, dx = fitted.solve()
#return:
# x: parameter values
# dx: uncertainty on parameters
# chi squared value for model
return x, dx, problem.chisq()
def fit(model):
print("Fitting problem...")
#Crate a problem from the model with bumps,
#then fit and solve it
problem = bumps.FitProblem(model)
print(problem.labels())
fitted = fitter.MPFit(problem)
x, dx = fitted.solve()
print(problem.nllf())
problem.model_update()
model.update()
print(problem.nllf())
return x, dx
def fitFullModel():
cell = Mod.makeCell(crystalCell, spaceGroup.xtalSystem)
#Define a model
m = S.Model(tt, sfs2, backg, wavelength, spaceGroup, cell,
[atomList], exclusions,
scale=0.06298,hkls=refList, error=error, extinction=[0.0001054])
m.u.range(0,2)
m.zero.pm(0.1)
m.v.range(-2,0)
m.w.range(0,2)
m.eta.range(0,1)
m.scale.range(0,10)
m.base.pm(250)
for atomModel in m.atomListModel.atomModels:
atomModel.x.pm(0.1)
atomModel.z.pm(0.1)
if (atomModel.atom.multip == atomModel.sgmultip):
# atom lies on a general position
atomModel.x.pm(0.1)
atomModel.y.pm(0.1)
atomModel.z.pm(0.1)
m.atomListModel.atomModels[0].z.value = 0.3
m.atomListModel.atomModels[0].z.range(0,0.5)
problem = bumps.FitProblem(m)
fitted = fitter.SimplexFit(problem)
x, dx = fitted.solve(steps=50)
problem.model_update()
print(problem.summarize())
print(x, dx, problem.chisq())
Mq.scale.range(0.1, 40)
Mq.center.range(-0.1, 0.1)
Mq.A0.range(0, 0.05)
Mq.A1.range(0, 1)
Mq.hwhm1.range(0, 0.5)
Mq.hwhm2.range(0, 3)
# Q-independent parameters
if i == 0:
QA0 = Mq.A0
else:
Mq.A0 = QA0
M.append(Mq)
problem = bmp.FitProblem(M)
# Preview of the settings
print('Initial chisq', problem.chisq_str())
problem.plot()
result = fit(problem, method='lm', steps=100, verbose=True)
problem.plot()
# Print chi**2 and parameters' values after fit
print("final chisq", problem.chisq_str())
for k, v, dv in zip(problem.labels(), result.x, result.dx):
print(k, ":", format_uncertainty(v, dv))
model.b5.range(20, 50) model.a5.range(-2, 2) model.b6.range(20, 50) model.a6.range(-2, 2) model.b7.range(20, 50) model.a7.range(-2, 2) model.b8.range(20, 50) model.a8.range(-2, 2) model.b9.range(20, 50) model.a9.range(-2, 2) model.b10.range(20, 50) model.a10.range(-2, 2) model.b11.range(20, 50) model.a11.range(-2, 2) model.b12.range(20, 50) model.a12.range(-2, 2) model.b13.range(20, 50) model.a13.range(-2, 2) model.b14.range(20, 50) model.a14.range(-2, 2) model.b15.range(20, 50) model.a15.range(-2, 2) model.b16.range(20, 50) model.a16.range(-2, 2) model.b17.range(20, 50) model.a17.range(-2, 2) # model.center.range(41.5, 44.5) # model.fwhm.range(0, 3) problem = bumps.FitProblem(model)