def single_peak_fit(self, hist, lower_threshold, upper_threshold, mean,
zero_peak_gaussian=None):
lower_slot = 0
for slot in hist.slot_centers():
lower_slot += 1
if slot > lower_threshold: break
upper_slot = 0
for slot in hist.slot_centers():
upper_slot += 1
if slot > upper_threshold: break
x = hist.slot_centers()
y = hist.slots().as_double()
starting_gaussians = [curve_fitting.gaussian(
a=flex.max(y[lower_slot:upper_slot]), b=mean, c=3)]
# print starting_gaussians
#mamin: fit gaussian will take the maximum between starting point (lower_slot) and ending (upper_slot) as a
if zero_peak_gaussian is not None:
y -= zero_peak_gaussian(x)
if 1:
fit = curve_fitting.lbfgs_minimiser(
starting_gaussians, x[lower_slot:upper_slot], y[lower_slot:upper_slot])
sigma = abs(fit.functions[0].params[2])
if sigma < 1 or sigma > 10:
if flex.sum(y[lower_slot:upper_slot]) < 15: #mamin I changed 15 to 5
# No point wasting time attempting to fit a gaussian if there aren't any counts
#raise PixelFitError("Not enough counts to fit gaussian")
return fit
print "using cma_es:", sigma
fit = curve_fitting.cma_es_minimiser(
starting_gaussians, x[lower_slot:upper_slot], y[lower_slot:upper_slot])
else:
fit = curve_fitting.cma_es_minimiser(
starting_gaussians, x[lower_slot:upper_slot], y[lower_slot:upper_slot])
return fit
def interpolate(x, y, half_window=10):
perm = flex.sort_permutation(x)
x = x.select(perm)
y = y.select(perm)
x_all = flex.double()
y_all = flex.double()
for i in range(x.size()):
x_all.append(x[i])
y_all.append(y[i])
if i < x.size()-1 and (x[i+1] - x[i]) > 1:
window_left = min(half_window, i)
window_right = min(half_window, x.size() - i)
x_ = x[i-window_left:i+window_right]
y_ = y[i-window_left:i+window_right]
from scitbx.math import curve_fitting
# fit a 2nd order polynomial through the missing points
polynomial = curve_fitting.univariate_polynomial(1, 1)
fit = curve_fitting.lbfgs_minimiser([polynomial], x_,y_).functions[0]
missing_x = flex.double(range(int(x[i]+1), int(x[i+1])))
x_all.extend(missing_x)
y_all.extend(fit(missing_x))
perm = flex.sort_permutation(x_all)
x_all = x_all.select(perm)
y_all = y_all.select(perm)
return x_all, y_all
def single_peak_fit(self,
hist,
lower_threshold,
upper_threshold,
mean,
zero_peak_gaussian=None):
lower_slot = 0
for slot in hist.slot_centers():
lower_slot += 1
if slot > lower_threshold: break
upper_slot = 0
for slot in hist.slot_centers():
upper_slot += 1
if slot > upper_threshold: break
x = hist.slot_centers()
y = hist.slots().as_double()
starting_gaussians = [
curve_fitting.gaussian(a=flex.max(y[lower_slot:upper_slot]),
b=mean,
c=3)
]
# print starting_gaussians
#mamin: fit gaussian will take the maximum between starting point (lower_slot) and ending (upper_slot) as a
if zero_peak_gaussian is not None:
y -= zero_peak_gaussian(x)
if 1:
fit = curve_fitting.lbfgs_minimiser(starting_gaussians,
x[lower_slot:upper_slot],
y[lower_slot:upper_slot])
sigma = abs(fit.functions[0].params[2])
if sigma < 1 or sigma > 10:
if flex.sum(y[lower_slot:upper_slot]
) < 15: #mamin I changed 15 to 5
# No point wasting time attempting to fit a gaussian if there aren't any counts
#raise PixelFitError("Not enough counts to fit gaussian")
return fit
print "using cma_es:", sigma
fit = curve_fitting.cma_es_minimiser(starting_gaussians,
x[lower_slot:upper_slot],
y[lower_slot:upper_slot])
else:
fit = curve_fitting.cma_es_minimiser(starting_gaussians,
x[lower_slot:upper_slot],
y[lower_slot:upper_slot])
return fit
