def fit_hist(hist, fit_options="LR"):
# I: fit using integral of func (not value @ centre, better for rapid function)
# L: Loglikelihood (better @ low stats)
# V: verbose mode
# E: better errors
# M: More, improve fit results (looks for alt local minima)
# R: fit to function range
def get_fit_goodness(func):
return (func.GetChisquare()/func.GetNDF())
initial_settings = get_initial_settings(hist)
fit_func = get_func_with_named_initialised_param("f_"+hist.GetTitle(), hist,
func_fmt, initial_settings,
l_bound, u_bound)
fit_func.SetNpx(10000) # number of points for drawing the function
hist.Fit(fit_func, fit_options)
# best_t = t = initial_settings[2][1](hist)
# d=0.05
# min_goodness = goodness = get_fit_goodness(fit_func)
# while (goodness)>3.0:
# t+=d
# fit_func.SetParameter(2,t)
# hist.Fit(fit_func, fit_options)
# goodness = get_fit_goodness(fit_func)
# if goodness < min_goodness:
# min_goodness=goodness
# best_t = t
# if t > (initial_settings[2][1](hist)+ d*100):
# print "breaking fit loop, best fit {} for t={}".format(min_goodness, best_t)
# break
hist.fit_func = fit_func
hist.fit_param = get_fit_parameters(fit_func)
def fit_hist(hist, fit_options="LIMER"):
def get_fit_goodness(func):
return (func.GetChisquare()/func.GetNDF())
initial_settings = get_initial_settings(hist)
fit_func = get_func_with_named_initialised_param("f_"+hist.GetTitle(), hist,
func_fmt, initial_settings,
l_bound, u_bound)
fit_func.SetNpx(100000)
hist.Fit(fit_func, fit_options)
hist.fit_func = fit_func
hist.fit_param = get_fit_parameters(fit_func)
def fit_histogram(hist, func_fmt, initial_settings, func_name, fit_options="MER",img_name=""):
func = get_func_with_named_initialised_param(func_name, hist, func_fmt, initial_settings)
if img_name:
canvas = make_canvas(img_name, resize=True)
hist.Draw()
hist.Fit(func, fit_options)
canvas.Update()
canvas.SaveAs(img_name+".eps")
canvas.SaveAs(img_name+".svg")
else:
hist.Fit(func, fit_options+"N")
hist.func = func
hist.fit_param = get_fit_parameters(func)