Comments (1)
E.g. this one
# data part
xx = np.linspace(x.min(),x.max(),100)
ff = result.eval(x=xx)
fbw1 = fit.bw(x=xx, M = result.best_values['bw1_M'],G=result.best_values['bw1_G'], amp=result.best_values['bw1_amp'] )
fbw2 = fit.bw(x=xx, M = result.best_values['bw2_M'],G=result.best_values['bw2_G'], amp=result.best_values['bw2_amp'] )
fbckg = fit.polyn(xx, result.best_values['bckg_c0'], result.best_values['bckg_c1'], result.best_values['bckg_c2'], result.best_values['bckg_c3'], result.best_values['bckg_c4'],0,0,0, result.best_values['bckg_amp'])
df=pd.DataFrame([xx,ff,fbw1,fbw2,fbckg]).T
df.columns=['x', 'fit', 'bw1','bw2', 'bkgr']
# drawing part
fig = plt.figure()
ax = fig.add_subplot()
dt, = hep.histplot(np.histogram(mass_events(kinematics.GetTracksWithPtCut(four_tracks_zq_after_all_cuts)), bins=b, range=r),ax=ax, yerr=True, xerr=0.022, color='black', histtype="errorbar",label='data')
ll = df.plot(x='x', y=['fit', 'bw1','bw2', 'bkgr'],ax=ax, style=['r-', 'g-', 'm-', 'b--'], xlabel=r'M($\pi^+\pi^-\pi^+\pi^-$) (GeV/$c^2$)', ylabel=r'Counts per 45 MeV/$c^2$')
_ = ax.legend([dt] + ll.get_children()[4:8], ['Data', 'Fit', 'Breit-Wigner', 'Breit-Wigner', 'Bkgr'])
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Related Issues (20)
- Aliases for most common visualizing scenarios HOT 1
- What is the best way to access UPC events?
- New github action for converting active ipynb to README.md with pictures HOT 1
- Generalize computations HOT 1
- ZDC energy distribution HOT 3
- Move data preparation from pandas to numpy HOT 1
- $\rho_0$ decay angle for the rest frame of $\rho_0$ HOT 1
- Dalitz plot for four pions events
- Events losses after splitting by trigger HOT 2
- Histogramming problems HOT 1
- Fit mass of resonance HOT 3
- Add information about the analysis to UPC twiki page
- Comparison runs quality
- Interference of two resonances
- Fitting with RooFit and comparing quality with lmfit
- Unbinned fitting (Likelihood)
- Comparison exp data with mc produced
- Wrap fit to a function
- change lmfit to statsmodels
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