hist([data1,data2], histtype='step', bins=100); # <codecell> #note here that they share the same sigma g1 = rename(gaussian, ['x','mu1','sigma']) g2 = rename(gaussian, ['x','mu2','sigma']) print describe(g1) print describe(g2) # <codecell> #make two likelihood and them up ulh1 = UnbinnedLH(g1,data1) ulh2 = UnbinnedLH(g2,data2) sim = SimultaneousFit(ulh1,ulh2) print describe(sim) #note the sigma merge # <codecell> sim.draw(args=(0.5, 1.5, 10.5)) # <codecell> m = Minuit(sim, mu1=0.5, sigma=1.5, mu2=10.5) # <codecell> m.migrad(); # <codecell>
hist([data1,data2], histtype='step', bins=100); # <codecell> #note here that they share the same sigma g1 = rename(gaussian, ['x','mu1','sigma']) g2 = rename(gaussian, ['x','mu2','sigma']) print describe(g1) print describe(g2) # <codecell> #make two likelihood and them up ulh1 = UnbinnedLH(g1,data1) ulh2 = UnbinnedLH(g2,data2) sim = SimultaneousFit(ulh1,ulh2) print describe(sim) #note the sigma merge # <codecell> sim.draw(args=(0.5, 1.5, 10.5)) # <codecell> m = Minuit(sim, mu1=0.5, sigma=1.5, mu2=10.5) # <codecell> m.migrad(); # <codecell>
ext_bkg_pdf = Extended(nrm_bkg_pdf, extname='Ncomb_%d' % bin)
ext_sig_pdf = Extended(rename(gaussian, ['x', 'm%d' % bin, "sigma%d" % bin]), extname='Nsig_%d' % bin)
tot_pdf = AddPdf(ext_bkg_pdf, ext_sig_pdf)
print('pdf: {}'.format(describe(tot_pdf)))
return tot_pdf
fit_range = (2900, 3300)
mod_1 = model(fit_range, 1)
lik_1 = UnbinnedLH(mod_1, tot_m, extended=True)
mod_2 = model(fit_range, 2)
lik_2 = UnbinnedLH(mod_2, tot_u, extended=True)
sim_lik = SimultaneousFit(lik_1, lik_2)
describe(sim_lik)
pars = dict(l1=0.002, Ncomb_1=1000, m1=3100, sigma1=10, Nsig_1=1000, l2=0.002, Ncomb_2=1000, m2=3100, sigma2=10,
Nsig_2=1000)
minuit = Minuit(sim_lik, pedantic=False, print_level=0, **pars)
# In[8]:
if do_probfit:
start = time.time()
minuit.migrad()
time_probfit = time.time() - start
print("starting zfit")
from matplotlib import pyplot as plt
from numpy.random import randn, seed
seed(0)
width = 2.
data1 = randn(1000)*width + 1
data2 = randn(1000)*width + 2
#two gaussian with shared width
pdf1 = rename(gaussian, ('x', 'mu_1', 'sigma'))
pdf2 = rename(gaussian, ('x', 'mu_2', 'sigma'))
lh1 = UnbinnedLH(pdf1, data1)
lh2 = UnbinnedLH(pdf2, data2)
simlh = SimultaneousFit(lh1, lh2)
m = Minuit(simlh, mu_1=1.2, mu_2=2.2, sigma=1.5)
plt.figure(figsize=(8, 3))
plt.subplot(211)
simlh.draw(m)
plt.suptitle('Before')
m.migrad() # fit
plt.figure(figsize=(8, 3))
plt.subplot(212)
simlh.draw(m)
plt.suptitle('After')
from matplotlib import pyplot as plt
from numpy.random import randn, seed
seed(0)
width = 2.
data1 = randn(1000) * width + 1
data2 = randn(1000) * width + 2
#two gaussian with shared width
pdf1 = rename(gaussian, ('x', 'mu_1', 'sigma'))
pdf2 = rename(gaussian, ('x', 'mu_2', 'sigma'))
lh1 = UnbinnedLH(pdf1, data1)
lh2 = UnbinnedLH(pdf2, data2)
simlh = SimultaneousFit(lh1, lh2)
m = Minuit(simlh, mu_1=1.2, mu_2=2.2, sigma=1.5)
plt.figure(figsize=(8, 3))
plt.subplot(211)
simlh.draw(m)
plt.suptitle('Before')
m.migrad() # fit
plt.figure(figsize=(8, 3))
plt.subplot(212)
simlh.draw(m)
plt.suptitle('After')
