#[h_bkg, spline_bkg, _] = get_draw_spline("../data/mca/bkg_4_1937_spectrum.mca", 0.002, 'g', 'g', "Background", ax, axins)
# subtract backround and draw
h_fe_new = subtract_bkg(h_fe, spline_fe, spline_bkg, "b", ax, axins)
h_am_new = subtract_bkg(h_am, spline_am, spline_bkg, "r", ax, axins)
# finished zoomed in sub-figure
axins.set_xlim(0, 149)
axins.set_ylim(0, 99)
#axins.set_ylim(0, 1.5)
mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5")
# spice it up and show
x = 0.05
ax.set_ylim(top=1.2 * ax.get_ylim()[1])
show_title(ax, x=x)
show_text("Dashed lines: Bsplines of original histograms", ax, y=0.85, x=x)
show_text("Full lines: Bsplined background subtracted", ax, y=0.80, x=x)
ax.set_ylabel(
"Counts for {:.0f} seconds per channel [1/s/bit]".format(time_fe))
#ax.set_ylabel("Counts per second per 4 channels [1/s/bit]")
ax.set_xlabel("Channel [bit]")
ax.legend(loc='best')
fig.show()
plt.savefig("../graphics/bkgsubtraction.pdf", format='pdf')
# used later on for calculating the number of events in gauss
binwidth = h_am_new.GetBinWidth(1)
#%%#####################################
# Fit spectra
# plot points and fit result
ax.errorbar(y=Q,
yerr=10 * Qerr,
x=mean,
xerr=5000 * mean_tot_unc,
color='r',
linestyle='None')
x0 = mean[0] - 10
xlast = mean[-1] + 10
xx = np.linspace(x0, xlast, 1000)
yy = [fit_Q.Eval(x) for x in xx]
ax.plot(xx, yy, 'b-')
# spice it up and show
show_title(ax)
show_text("Note: Channel uncertainties scaled by 5000", ax, y=0.85)
show_text(" Charge uncertainties scaled by 10", ax, y=0.80)
show_text("Fit: y = b + ax", ax, y=0.75)
show_text("b = {:.5f} ± {:.5f} ({:3.1f}σ from 0)".format(
fit_Q.GetParameter(0), fit_Q.GetParError(0),
abs(fit_Q.GetParameter(0)) / fit_Q.GetParError(0)),
ax,
y=0.70)
show_text("a = {:.7f} ± {:.7f}".format(fit_Q.GetParameter(1),
fit_Q.GetParError(1)),
ax,
y=0.65)
show_text("p( X², ndof ) = p( {:.1f}, {:d} ) = {:.1f}%".format(
fit_Q.GetChisquare(), fit_Q.GetNDF(),
fit_Q.GetProb() * 100),