def peak_fit(x, y): # x as etaprime mass and y as pi0mass
g = cf.gauss_fit()
g.A.set(A_fit.signal_bt_bkg(y))
g.x0.set(px0.poly(y))
g.sigma.set(pS.poly(y))
Y = g.gauss(x)
return Y
def bkg_fit(x, y): # x as etaprime mass and y as pi0mass
f = cf.gauss_fit()
f.b0.set(pB0.poly(y))
f.db0.set(0.50)
f.c0.set(pC0.poly(y))
Y = f.bt_bkg(x)
return Y
def bkg_fit(x, y): # x as etaprime mass and y as pi0mass
f = cf.gauss_fit()
f.b0.set(pB0.poly(y))
f.db0.set(0.5) # this should match f db0 value not A_fit db0 value
f.m0.set(0.87)
f.m1.set(1.05)
f.c0.set(pC0.poly(y))
Y = f.bt_bkg(x)
return Y
def fit_histo(h2d):
#define empty lists for parameters and their errors from fits
A_a, x0_a, sigma_a, c0_a, b0_a = [], [], [], [], []
for i in range(h2d.nbins_y): # looping over pi0mass bins
h = h2d.project_x(bins=[i])
M = h.bin_center
C = h.bin_content
dC = h.bin_error
mr = np.linspace(M[0], M[-1], 1000)
f = cf.gauss_fit()
# set initial parameters for fitting
f.A.set(C.max())
f.x0.set(0.956)
f.sigma.set(0.01)
f.b0.set(0.05)
f.db0.set(0.5)
f.c0.set(500)
# set bounds for the fit parameters
f.A_min.set(0.)
f.A_max.set(1e5)
f.x0_min.set(0.95)
f.x0_max.set(0.96)
f.sigma_min.set(0.008)
f.sigma_max.set(0.016)
f.c0_min.set(0.)
f.c0_max.set(1e5)
f.b0_min.set(0.00)
f.b0_max.set(0.20)
f.set_fit_list(fit=['A', 'x0', 'sigma', 'c0', 'b0'])
f.fit_gaussbt(M, C, dC) # gauss peak with bernstein bkg
A_a.append([f.A.value, f.A.err])
x0_a.append([f.x0.value, f.x0.err])
sigma_a.append([f.sigma.value, f.sigma.err])
b0_a.append([f.b0.value, f.b0.err])
c0_a.append([f.c0.value, f.c0.err])
plt.figure()
h.plot_exp()
f.plot_fit()
B.plot_line(mr, f.gauss(mr))
B.plot_line(mr, f.bt_bkg(mr))
return np.array(A_a), np.array(x0_a), np.array(sigma_a), np.array(
b0_a), np.array(c0_a)
#%% A_a, x0_a, S_a, B0_a, C0_a = fit_histo(h_epi0) #%% A_value = A_a[:, 0] A_err = A_a[:, 1] x0_value = x0_a[:, 0] x0_err = x0_a[:, 1] S_value = S_a[:, 0] S_err = S_a[:, 1] B0_value = B0_a[:, 0] B0_err = B0_a[:, 1] C0_value = C0_a[:, 0] C0_err = C0_a[:, 1] A_fit = cf.gauss_fit() A_fit.set_fit_list(fit=['A', 'x0', 'sigma', 'c0', 'b0']) # set parameters for combined gaussian linear fit A_fit.A.set(A_value.max()) A_fit.x0.set(0.1365) A_fit.sigma.set(0.004) A_fit.b0.set(0.8) A_fit.db0.set(0.1) A_fit.c0.set(50.) #set bounds for combined gaussian linear fit A_fit.A_min.set(100.) A_fit.A_max.set(3000.) A_fit.x0_min.set(0.13) A_fit.x0_max.set(0.14)
cost_etap = d['cost_etap']
etaprimephiGJ = d['etaprimephiGJ']
#%%
mep_bins = 18
mep_min = 0.86
mep_max = 1.05
h = B.histo( metap, bins = mep_bins,
range = [mep_min, mep_max] , title = 'etaprime', xlabel = "$M(\pi^{+}\pi^{-}\eta)$",
)
M = h.bin_center
C = h.bin_content
dC = h.bin_error
mr = np.linspace(M[0], M[-1], 1000)
f = cf.gauss_fit()
# set initial parameters for fitting
f.A.set(C.max())
f.x0.set(0.956)
f.sigma.set(0.01)
f.b0.set(0.05)
f.db0.set(0.5)
f.c0.set(500)
# set bounds for the fit parameters
f.A_min.set(0.); f.A_max.set(1e5)
f.x0_min.set(0.95); f.x0_max.set(0.96)
f.sigma_min.set(0.008); f.sigma_max.set(0.016)
f.c0_min.set(0.); f.c0_max.set(1e5)
f.b0_min.set(0.00); f.b0_max.set(0.20)