def nonlinear_fit():
global Plot1
global Plot2
ds = Plot1.ds
if len(ds) == 0:
print 'Error: no curve to fit in Plot2'
return
for d in ds:
if d.title == 'nonlinear_fitting':
Plot1.remove_dataset(d)
d0 = ds[0]
d1 = d0 / 180 * math.pi
axis = d1.axes[0].__copy__()
axis[:] = (3.5238 * 3.5238 / axis).sqrt() * 2
d1.set_axes([axis])
Plot2.set_dataset(d1)
function = '2*asin(k1/x[0])+k2'
fitting = UndefinedFitting(function, 'Energy')
fitting.set_histogram(d1)
m = 1.67e-27
h = 6.63e-34
eV = 1.6e-19
pl = 1.0e10
l = h * pl * 100 / math.sqrt(linear_slope.value * eV * 20 * m)
fitting.set_param('k1', l)
fitting.set_param('k2', 0)
res = fitting.fit()
res.var[:] = 0
res.title = 'nonlinear_fitting'
Plot2.add_dataset(res)
lambda_fit.value = fitting.k1
two_theta_off.value = fitting.k2 * 180 / math.pi
print 'lambda = ' + str(fitting.k1)
print 'meV = ' + str((h * pl / fitting.k1 / m) ** 2 * m * 500 / eV)
print 'two-theta_offset = ' + str(fitting.k2 * 180 / math.pi)
def nonlinear_fit():
global Plot3
ds3 = Dataset(peaks * -1, axes=[twod])
Plot3.set_dataset(ds3)
ds = Plot3.ds
if len(ds) == 0:
slog('Error: no curve to fit in Plot3')
return
for d in ds:
if d.title == 'nonlinear_fit':
Plot3.remove_dataset(d)
d3 = ds[0]
cur = d3 * math.pi / 180
function = '2*asin(k1/x[0])+k2'
fitting = UndefinedFitting(function, 'Energy')
fitting.set_histogram(cur)
m = 1.67492861e-027
h = 6.626068e-034
eV = 1.60217646e-019
pl = 1.0e10
l = h * pl * 100 / math.sqrt(linear_slope.value * eV * 20 * m)
if lambda_fit.value == 0 and two_theta_off.value == 0:
fitting.set_param('k1', l)
fitting.set_param('k2', 0)
else:
fitting.set_param('k1', lambda_fit.value)
# fitting.set_param('k2', two_theta_off.value)
fitting.fitter.setResolutionMultiple(10)
# fitting.fitter.getRawFitter().fitParameterSettings("k1").setStepSize(0.00001);
# fitting.fitter.getRawFitter().fitParameterSettings("k2").setStepSize(0.00001);
for i in xrange(5):
res = fitting.fit()
lambda_fit.value = fitting.k1
two_theta_off.value = fitting.k2 * 180 / math.pi
fit_quality.value = fitting.fitter.getQuality()
fitting = UndefinedFitting(function, 'Energy')
fitting.set_histogram(cur)
fitting.set_param('k1', lambda_fit.value)
res.var[:] = 0
res.title = 'nonlinear_fit'
res = res * 180 / math.pi
Plot3.add_dataset(res)
m1_new.value = math.asin(lambda_fit.value / 2 / PG002d) * 180 / math.pi
m2_new.value = 2 * m1_new.value
slog('Chi2 = ' + str(fit_quality.value))
slog('lambda = ' + str(lambda_fit.value))
slog('meV = ' + str((h * pl / lambda_fit.value / m) ** 2 * m * 500 / eV))
slog('two-theta_offset = ' + str(two_theta_off.value * 180 / math.pi))
def nonlinear_fit():
global Plot2
ds = Plot2.ds
if len(ds) == 0:
print 'Error: no curve to fit in Plot2'
return
for d in ds:
if d.title == 'fitting':
Plot2.remove_dataset(d)
d0 = ds[0]
function = '2*asin(k1/x[0])+k2'
fitting = UndefinedFitting(function, 'Energy')
fitting.set_histogram(d0)
res = fitting.fit()
res.var[:] = 0
res.title = 'fitting'
Plot2.add_dataset(res)
print fitting.params
def nonlinear_fit():
global Plot3
valid = []
vaxes = []
for i in xrange(peak_res.size):
if not Double.isNaN(peak_res[i]):
valid.append(peak_res[i] * -1)
vaxes.append(twod[i])
if len(valid) == 0:
slog('Error: there is not any available peak.')
return
ds3 = Dataset(valid, axes=[vaxes])
Plot3.set_dataset(ds3)
ds = Plot3.ds
if len(ds) == 0:
print 'Error: no curve to fit in Plot3'
return
for d in ds:
if d.title == 'nonlinear_fit':
Plot3.remove_dataset(d)
d3 = ds[0]
cur = d3 * math.pi / 180
function = '2*asin(k1/x[0])+k2'
fitting = UndefinedFitting(function, 'Energy')
fitting.set_histogram(cur)
m = 1.67492861e-027
h = 6.626068e-034
eV = 1.60217646e-019
pl = 1.0e10
l = h * pl * 100 / math.sqrt(linear_slope.value * eV * 20 * m)
# if lambda_fit.value == 0 and s2_offset.value == 0:
# fitting.set_param('k1', 0)
## fitting.set_param('k2', 0)
# else:
# fitting.set_param('k1', lambda_fit.value)
# fitting.set_param('k2', s2_offset.value)
try:
v_lmd = lmd.value
except:
v_lmd = 0
fitting.set_param('k1', v_lmd)
fitting.set_param('k2', 0)
fitting.fitter.setResolutionMultiple(10)
# fitting.fitter.getRawFitter().fitParameterSettings("k1").setStepSize(0.00001);
# fitting.fitter.getRawFitter().fitParameterSettings("k2").setStepSize(0.00001);
for i in xrange(5):
res = fitting.fit()
lambda_fit.value = fitting.k1
s2_offset.value = fitting.k2 * 180 / math.pi
fit_quality.value = fitting.fitter.getQuality()
fitting = UndefinedFitting(function, 'Energy')
fitting.set_histogram(cur)
fitting.set_param('k1', lambda_fit.value)
res.var[:] = 0
res.title = 'nonlinear_fit'
res = res * 180 / math.pi
Plot3.add_dataset(res)
# lambda_fit.value = fitting.k1
# s2_offset.value = fitting.k2 * 180 / math.pi
# fit_quality.value = fitting.fitter.getQuality()
m1_new.value = math.asin(lambda_fit.value / 2 / D_space) * 180 / math.pi
m2_new.value = 2 * m1_new.value
slog('Chi2 = ' + str(fit_quality.value))
slog('lambda = ' + str(lambda_fit.value))
slog('meV = ' + str((h * pl / lambda_fit.value / m) ** 2 * m * 500 / eV))
slog('two-theta_offset = ' + str(s2_offset.value))