def fitTransmissionSignal(name):
data = OPData.fromPath(DIR + name + '.tab', 2)
c = TCanvas('c', '', 1280, 720)
g = data.makeGraph('g_%s' % name, 'Zeit t / s', 'Spannung der Photodiode U_{ph} / V')
prepareGraph(g, 2)
g.GetXaxis().SetRangeUser(0.004, 0.019)
g.Draw('APX')
xmin, xmax = 0.0054, 0.015
fit = Fitter('fit_%s' % name[-2:], '[0] - [1] * exp(-x/[2])')
fit.setParam(0, 'a', 0.01)
fit.setParam(1, 'b', 100)
fit.setParam(2, '#tau', 0.001)
fit.fit(g, xmin, xmax, 'M')
fit.saveData('../fit/part5/%s.txt' % name)
g.Draw('P')
l = TLegend(0.35, 0.2, 0.65, 0.525)
l.SetTextSize(0.03)
l.AddEntry(g, 'Spannung der Photodiode', 'p')
l.AddEntry(fit.function, 'Fit mit U_{ph}(t) = a - b e^{-t/#tau}', 'l')
fit.addParamsToLegend(l, [('%.6f', '%.6f'), ('%.2f', '%.2f'), ('%.6f', '%.6f')], chisquareformat='%.2f', units=['V', 'V', 's'])
l.Draw()
c.Update()
c.Print('../img/part5/%s.pdf' % name.replace('.', '-'), 'pdf')
return fit.params[2]['value'], fit.params[2]['error']
def energyGauge():
dataList = readFileToList('../calc/hg_lines.txt')
elemNames = ['Hg'] * len(dataList)
dataList += readFileToList('../calc/na_lines.txt')
elemNames += ['Na'] * (len(dataList) - len(elemNames))
litVals = readFileToList('../data/hg_litvals.txt')
litVals += readFileToList('../data/na_litvals.txt')
data = DataErrors()
for val, litval in zip(dataList, litVals):
data.addPoint(val, litval, I2Data.ERRORBIN, 0)
c = TCanvas('c', '', 1280, 720)
g = data.makeGraph('g', 'measured wavelength #lambda_{exp} / nm', 'literature value #lambda_{lit} / nm')
g.Draw('AP')
fit = Fitter('f', '[0]+[1]*x')
fit.setParam(0, 'a', 0)
fit.setParam(1, 'b', 1)
fit.fit(g, 420, 600)
fit.saveData('../calc/fit_energy_gauge.txt', 'w')
l = TLegend(0.15, 0.6, 0.5, 0.85)
l.AddEntry(fit.function, 'y = a + b*x', 'l')
fit.addParamsToLegend(l)
l.SetTextSize(0.03)
l.Draw()
c.Update()
c.Print('../img/energy_gauge.pdf', 'pdf')
def makeSigmaFit(darkTimes, sigmas):
dt, sdt = list(zip(*darkTimes))
s, ss = list(zip(*sigmas))
data = DataErrors.fromLists(dt, s, sdt, ss)
c = TCanvas('c_sigma', '', 1280, 720)
g = data.makeGraph('g_sigma', 'Dunkelzeit t_{D} / ms', 'Verschmierung #sigma / #mus')
g.Draw('APX')
fit = Fitter('fit_sigma', 'pol1(0)')
fit.setParam(0, 'a', 0)
fit.setParam(1, 'b', 1)
fit.fit(g, 0, 25)
fit.saveData('../fit/sigma.txt')
l = TLegend(0.6, 0.15, 0.85, 0.5)
l.SetTextSize(0.03)
l.AddEntry(g, 'Verschmierung #sigma', 'p')
l.AddEntry(None, 'der Fermi-Verteilung', '')
l.AddEntry(fit.function, 'Fit mit #sigma(t_{D}) = a + b t_{D}', 'l')
fit.addParamsToLegend(l, (('%.2f', '%.2f'), ('%.2f', '%.2f')), chisquareformat='%.2f', units=['#mus', '10^{-3}'])
l.Draw()
g.Draw('P')
c.Print('../img/part6/sigmaFit.pdf', 'pdf')
def main():
z, sz = 840, 40
d = [210, 106, 75]
sd = [10, 4, 4]
calc = list(map(lambda x: -20 * log10(x / z), d))
scalc = list(
map(lambda x: 20 * sqrt((x[1] / x[0])**2 + (sz / z)**2) / log(10),
zip(*[d, sd])))
data = DataErrors.fromLists(calc, [12, 18, 21], scalc, [0] * 3)
c = TCanvas('c', '', 1280, 720)
g = data.makeGraph('g', 'measured attenuation m / dB',
'nominal value n / dB')
g.Draw('AP')
fit = Fitter('fit', 'pol1(0)')
fit.setParam(0, 'a', 0)
fit.setParam(1, 'b', 1)
fit.fit(g, 11, 22)
fit.saveData('../fit/attenuator.txt')
l = TLegend(0.15, 0.6, 0.5, 0.85)
l.SetTextSize(0.03)
l.AddEntry(g, 'measured att. vs. nominal value', 'p')
l.AddEntry(fit.function, 'fit with n = a + b m', 'l')
fit.addParamsToLegend(l, (('%.2f', '%.2f'), ('%.2f', '%.2f')),
chisquareformat='%.4f',
units=['dB', ''],
lang='en')
l.Draw()
c.Update()
c.Print('../img/attenuator.pdf', 'pdf')
def fitLambda():
data = Data()
data.addPoint(500, 1.000279)
data.addPoint(540, 1.000278)
data.addPoint(600, 1.000277)
data.addPoint(682, 1.000276)
c = TCanvas('c1', '', 1280, 720)
g = data.makeGraph('g', 'wavelength #lambda / nm', 'refraction index n')
g.GetYaxis().SetLabelSize(0.03)
g.GetYaxis().SetTitleOffset(1.39)
g.Draw('AP')
fit = Fitter('f', '[0]+[1]*x')
fit.setParam(0, 'a', 2)
fit.setParam(1, 'b', -1)
fit.fit(g, 450, 700)
fit.saveData('../calc/fit_lambda.txt', 'w')
a = fit.params[0]['value']
sa = fit.params[0]['error']
b = fit.params[1]['value']
sb = fit.params[1]['error']
l = TLegend(0.4, 0.6, 0.87, 0.87)
l.AddEntry('g', 'refraction index n as a function of wavelength #lambda', 'p')
l.AddEntry(fit.function, 'fit with n(#lambda)= a+b*#lambda', 'l')
fit.addParamsToLegend(l)
l.SetTextSize(0.03)
l.Draw()
c.Update()
c.Print('../img/fit_lambda.pdf', 'pdf')
def makeBFit(darkTimes, Bs):
dt, sdt = list(zip(*darkTimes))
b, sb = list(zip(*Bs))
data = DataErrors.fromLists(dt, b, sdt, sb)
c = TCanvas('c_B', '', 1280, 720)
g = data.makeGraph('g_B', 'Dunkelzeit t_{D} / ms', 'Fitparameter B / V')
g.Draw('APX')
fit = Fitter('fit_B', '[0] + [1] * (1 - exp(-x/[2]))')
fit.function.SetNpx(1000)
fit.setParam(0, 'a', 0.1)
fit.setParam(1, 'b', 0.1)
fit.setParam(2, 'T_{R_{F}}', 6)
fit.fit(g, 0, 25)
fit.saveData('../fit/B.txt')
l = TLegend(0.55, 0.15, 0.85, 0.6)
l.SetTextSize(0.03)
l.AddEntry(g, 'Fitparameter B', 'p')
l.AddEntry(fit.function, 'Fit mit B(t_{D}) = a + b (1 - e^{-x/T_{R_{F}}})', 'l')
fit.addParamsToLegend(l, (('%.3f', '%.3f'), ('%.3f', '%.3f'), ('%.1f', '%.1f')), chisquareformat='%.2f', units=['V', 'V', 'ms'])
l.Draw()
g.Draw('P')
c.Print('../img/part6/BFit.pdf', 'pdf')
def main():
z, sz = 840, 40
d = [210, 106, 75]
sd = [10, 4, 4]
calc = list(map(lambda x:-20 * log10(x/z), d))
scalc = list(map(lambda x:20 * sqrt((x[1]/x[0]) ** 2 + (sz/z)**2) / log(10), zip(*[d, sd])))
data = DataErrors.fromLists(calc, [12, 18, 21], scalc, [0]*3)
c = TCanvas('c', '', 1280, 720)
g = data.makeGraph('g', 'measured attenuation m / dB', 'nominal value n / dB')
g.Draw('AP')
fit = Fitter('fit', 'pol1(0)')
fit.setParam(0, 'a', 0)
fit.setParam(1, 'b', 1)
fit.fit(g, 11, 22)
fit.saveData('../fit/attenuator.txt')
l = TLegend(0.15, 0.6, 0.5, 0.85)
l.SetTextSize(0.03)
l.AddEntry(g, 'measured att. vs. nominal value', 'p')
l.AddEntry(fit.function, 'fit with n = a + b m', 'l')
fit.addParamsToLegend(l, (('%.2f', '%.2f'), ('%.2f', '%.2f')), chisquareformat='%.4f', units=['dB', ''], lang='en')
l.Draw()
c.Update()
c.Print('../img/attenuator.pdf', 'pdf')
def main():
x, y, sy = zip(*loadCSVToList('../calc/part3/Co-Si_mergedbins.txt'))
data = DataErrors.fromLists(x, y, [0]*len(x), sy)
c = TCanvas('c', '', 1280, 720)
g = data.makeGraph('g', 'Kanal k', 'Counts N')
g.SetMarkerStyle(8)
g.SetMarkerSize(0.5)
g.SetLineColor(15)
g.SetLineWidth(0)
g.Draw('AP')
fit = Fitter('f', '1/(sqrt(2*pi*[2]^2))*gaus(0)')
fit.setParam(0, 'A', 50)
fit.setParam(1, 'x_{c}', 690)
fit.setParam(2, 's', 20)
fit.fit(g, 672, 712)
fit.saveData('../calc/part3/fit_Co-Si_01_mergedbins.txt', 'w')
with TxtFile('../calc/part3/fit_Co-Si_01_mergedbins_raw.txt', 'w') as f:
for key, param in fit.params.iteritems():
f.writeline('\t', *map(str, [param['value'], param['error']]))
l = TLegend(0.7, 0.5, 0.95, 0.85)
l.SetTextSize(0.0225)
l.AddEntry(g, 'Gemittelte Messwerte', 'p')
l.AddEntry(fit.function, 'Fit mit', 'l')
l.AddEntry(0, 'N(k) = #frac{A}{#sqrt{2#pi*#sigma^{2}}} exp(- #frac{1}{2} (#frac{x-x_{c}}{#sigma})^{2})', '')
l.AddEntry(0, '', '')
fit.addParamsToLegend(l, chisquareformat='%.2f')
l.Draw()
c.Update()
c.Print('../img/part3/Co-Si_01_mergedbins.pdf', 'pdf')
def evalPedestal():
name = 'pedestal'
data = MyonData.fromPath('../data/%s.TKA' % name)
data.convertToCountrate()
c = TCanvas('c_ped', '', 1280, 720)
g = data.makeGraph('g_ped', 'channel c', 'countrate n / (1/s)')
g.SetLineColor(1)
g.SetLineWidth(1)
g.GetXaxis().SetRangeUser(0, 20)
g.Draw('APX')
fit = Fitter('fit_%s' % name, 'gaus(0)')
fit.setParam(0, 'A', 30)
fit.setParam(1, 'x', 6)
fit.setParam(2, '#sigma', 3)
fit.setParamLimits(2, 0, 100)
fit.fit(g, 3.5, 10.5)
fit.saveData('../fit/%s.txt' % name)
l = TLegend(0.55, 0.6, 0.85, 0.85)
l.SetTextSize(0.03)
l.AddEntry(g, 'pedestal', 'p')
l.AddEntry(fit.function, 'fit with n(c) = A gaus(c; x, #sigma)', 'l')
fit.addParamsToLegend(l, (('%.2f', '%.2f'), ('%.3f', '%.3f'), ('%.3f', '%.3f')), chisquareformat='%.2f', units=('1/s', '', ''), lang='en')
l.Draw()
g.Draw('P')
c.Update()
c.Print('../img/%s.pdf' % name, 'pdf')
return (fit.params[1]['value'], fit.params[1]['error'])
def fitX0(period):
# get data and make graph
data = X0Data.fromPath('../data/T_%dms.txt' % period)
c = TCanvas('c_%d' % period, '', 1280, 720)
g = data.makeGraph('g_%d' % period, 'Differenzenfrequenz #Delta#nu / kHz', 'Auftreffpunkt x_{0}\' / mm')
g.Draw('AP')
# axis cross
vline = TLine(0, 33, 0, 60)
vline.Draw()
# fit
fit = Fitter('fit_%d' % period, 'pol1(0)')
fit.setParam(0, 'x_{m}', 47)
fit.setParam(1, 'm')
fit.fit(g, min(data.getX()) - 3, max(data.getX()) + 3)
fit.saveData('../calc/fit_T_%dms.txt' % period, 'w')
# legend
l = TLegend(0.15, 0.65, 0.425, 0.85)
l.SetTextSize(0.03)
l.AddEntry(g, 'Messreihe bei T = %d ms' % period, 'p')
l.AddEntry(fit.function, 'Fit mit x_{0}\'(#Delta#nu) = x_{m} + m * #Delta#nu', 'l')
fit.addParamsToLegend(l, [('%.3f', '%.3f'), ('%.4f', '%.4f')], chisquareformat='%.2f')
l.Draw()
# print
c.Update()
c.Print('../img/fit_T_%dms.pdf' % period, 'pdf')
return [(fit.params[0]['value'], fit.params[0]['error']), (fit.params[1]['value'], fit.params[1]['error'])]
def compareSpectrum(prefix, spectrum, litvals):
xlist = list(zip(*spectrum))[0]
sxlist = list(zip(*spectrum))[1]
compData = DataErrors.fromLists(xlist, litvals, sxlist, [0] * len(litvals))
c = TCanvas('c_%s_compspectrum' % prefix, '', 1280, 720)
g = compData.makeGraph('g_%s_compspectrum' % prefix,
'experimentell bestimmte HFS-Aufspaltung #Delta#nu^{exp}_{%s} / GHz' % prefix,
'theoretische HFS-Aufspaltung #Delta#nu^{theo} / GHz')
g.Draw('AP')
fit = Fitter('fit_%s_compspectum' % prefix, 'pol1(0)')
fit.setParam(0, 'a_{%s}' % prefix, 0)
fit.setParam(1, 'b_{%s}' % prefix, 1)
fit.fit(g, compData.getMinX() - 0.5, compData.getMaxX() + 0.5)
if prefix == "up":
l = TLegend(0.15, 0.6, 0.45, 0.85)
else:
l = TLegend(0.15, 0.6, 0.5, 0.85)
l.SetTextSize(0.03)
l.AddEntry(g, 'Spektrum', 'p')
l.AddEntry(fit.function, 'Fit mit #Delta#nu^{theo} = a_{%s} + b_{%s} #Delta#nu^{exp}_{%s}' % (prefix, prefix, prefix), 'l')
fit.addParamsToLegend(l, [('%.2f', '%.2f'), ('%.3f', '%.3f')], chisquareformat='%.2f', units=['GHz', ''])
l.Draw()
c.Update()
if not DEBUG:
c.Print('../img/part2/%s-spectrum.pdf' % prefix, 'pdf')
def fitT(x0):
# get data and make graph
data = TData.fromPath('../data/x0_%dmm.txt' % x0)
c = TCanvas('c_%d' % x0, '', 1280, 720)
g = data.makeGraph('g_%d' % x0, 'Kreisfrequenz #omega / (rad/ms)', 'Differenzenfrequenz #Delta#nu / kHz')
g.Draw('AP')
# fit
fit = Fitter('fit_%d' % x0, 'pol1(0)')
fit.setParam(0, 'a')
fit.setParam(1, 'b')
fit.fit(g, min(data.getX()) - 3, max(data.getX()) + 3)
fit.saveData('../calc/fit_x0_%dmm.txt' % x0, 'w')
# legend
l = TLegend(0.15, 0.625, 0.425, 0.85)
l.SetTextSize(0.03)
l.AddEntry(g, 'Messreihe bei x_{0}\' = %d mm' % x0, 'p')
l.AddEntry(fit.function, 'Fit mit #Delta#nu (#omega) = a + b*#omega', 'l')
fit.addParamsToLegend(l, [('%.1f', '%.1f'), ('%.0f', '%.0f')], chisquareformat='%.2f')
l.Draw()
# print
c.Update()
c.Print('../img/fit_x0_%dmm.pdf' % x0, 'pdf')
return [(fit.params[0]['value'], fit.params[0]['error']), (fit.params[1]['value'], fit.params[1]['error'])]
def evalPedestal():
name = 'pedestal'
data = MyonData.fromPath('../data/%s.TKA' % name)
data.convertToCountrate()
c = TCanvas('c_ped', '', 1280, 720)
g = data.makeGraph('g_ped', 'channel c', 'countrate n / (1/s)')
g.SetLineColor(1)
g.SetLineWidth(1)
g.GetXaxis().SetRangeUser(0, 20)
g.Draw('APX')
fit = Fitter('fit_%s' % name, 'gaus(0)')
fit.setParam(0, 'A', 30)
fit.setParam(1, 'x', 6)
fit.setParam(2, '#sigma', 3)
fit.setParamLimits(2, 0, 100)
fit.fit(g, 3.5, 10.5)
fit.saveData('../fit/%s.txt' % name)
l = TLegend(0.55, 0.6, 0.85, 0.85)
l.SetTextSize(0.03)
l.AddEntry(g, 'pedestal', 'p')
l.AddEntry(fit.function, 'fit with n(c) = A gaus(c; x, #sigma)', 'l')
fit.addParamsToLegend(l, (('%.2f', '%.2f'), ('%.3f', '%.3f'),
('%.3f', '%.3f')),
chisquareformat='%.2f',
units=('1/s', '', ''),
lang='en')
l.Draw()
g.Draw('P')
c.Update()
c.Print('../img/%s.pdf' % name, 'pdf')
return (fit.params[1]['value'], fit.params[1]['error'])
def fitTransmissionSignal(name):
data = OPData.fromPath(DIR + name + '.tab', 2)
c = TCanvas('c', '', 1280, 720)
g = data.makeGraph('g_%s' % name, 'Zeit t / s',
'Spannung der Photodiode U_{ph} / V')
prepareGraph(g, 2)
g.GetXaxis().SetRangeUser(0.004, 0.019)
g.Draw('APX')
xmin, xmax = 0.0054, 0.015
fit = Fitter('fit_%s' % name[-2:], '[0] - [1] * exp(-x/[2])')
fit.setParam(0, 'a', 0.01)
fit.setParam(1, 'b', 100)
fit.setParam(2, '#tau', 0.001)
fit.fit(g, xmin, xmax, 'M')
fit.saveData('../fit/part5/%s.txt' % name)
g.Draw('P')
l = TLegend(0.35, 0.2, 0.65, 0.525)
l.SetTextSize(0.03)
l.AddEntry(g, 'Spannung der Photodiode', 'p')
l.AddEntry(fit.function, 'Fit mit U_{ph}(t) = a - b e^{-t/#tau}', 'l')
fit.addParamsToLegend(l, [('%.6f', '%.6f'), ('%.2f', '%.2f'),
('%.6f', '%.6f')],
chisquareformat='%.2f',
units=['V', 'V', 's'])
l.Draw()
c.Update()
c.Print('../img/part5/%s.pdf' % name.replace('.', '-'), 'pdf')
return fit.params[2]['value'], fit.params[2]['error']
def fitA(amps, times):
listx, listsx = zip(*times)
listy, listsy = zip(*amps)
slaser_rel = 0.05
listsy = list(listsy)
for i, y in enumerate(listy):
listsy[i] = y * np.sqrt(listsy[i] ** 2 + slaser_rel ** 2)
data = DataErrors.fromLists(listx, listy, listsx, listsy)
c = TCanvas('cA', '', 1280, 720)
g = data.makeGraph('A', 'Zeit t / s', 'Amplitude A / V')
g.GetYaxis().SetTitleOffset(1.2)
g.Draw('AP')
fit = Fitter('A', '[0]*exp(-(x)/[1])+[2]')
fit.function.SetNpx(1000)
fit.setParam(0, 'C', 5e-8)
fit.setParam(1, '#tau_{n}', 45e-6)
fit.setParam(2, 'a', 0)
fit.fit(g, 7.5e-6, 18e-6)
fit.saveData('../calc/part2/volt_fit_A.txt')
l = TLegend(0.625, 0.625, 0.85, 0.85)
l.SetTextSize(0.03)
l.AddEntry('A', 'Messung', 'p')
l.AddEntry(fit.function, 'Fit mit A(t) = C*e^{- #frac{t}{#tau_{n}}} + a', 'l')
fit.addParamsToLegend(l, [('%.2e', '%.1e'), ('%.2e', '%.1e'), ('%.2e', '%.1e')], chisquareformat='%.2f')
l.Draw()
if PRINTGRAPHS or True:
c.Update()
c.Print('../img/part2/volt_fitA.pdf', 'pdf')
c.SetLogy()
c.Update()
c.Print('../img/part2/volt_fitA_log.pdf', 'pdf')
def fitSigma(sigs, times):
listx, listsx = zip(*times)
listy, listsy = zip(*sigs)
listy = map(abs, listy) # fits can yield negative sigma, because it only occurse to
data = DataErrors.fromLists(listx, listy, listsx, listsy)
c = TCanvas('cSigma', '', 1280, 720)
g = data.makeGraph('Sigma', 'Zeit t / s', 'Standardabweichung #sigma / cm')
g.Draw('AP')
fit = Fitter('fitS', 'sqrt(2*[0]*(x + [1]))')
fit.setParam(0, 'D_{n}', 100)
fit.setParam(1, 't_{0}', 0)
fit.fit(g, 1e-6, 21e-6)
fit.saveData('../calc/part2/dist_fit_sigma.txt')
l = TLegend(0.15, 0.625, 0.45, 0.85)
l.SetTextSize(0.03)
l.AddEntry('Sigma', 'Messung', 'p')
l.AddEntry(fit.function, 'Fit mit #sigma(t) = #sqrt{2*D_{n}*(t + t_{0})}', 'l')
fit.addParamsToLegend(l, [('%.1f', '%.1f'), ('%.2e', '%.2e')], chisquareformat='%.2f')
l.Draw()
if PRINTGRAPHS or True:
c.Update()
c.Print('../img/part2/dist_fitSigma.pdf', 'pdf')
def fitSpectrum(detector, element, params, logy=True):
data = P3SemiCon.fromPath('../data/part3/%s-%s.mca' % (element, detector))
printTotalSpectrum(data, element, detector, logy)
fitresults = []
for i, peak in enumerate(params):
c = TCanvas('cpeakl_%s-%s_%d' % (element, detector, i), '', 1280, 720)
g = data.makeGraph('g%s-%s_%d' % (element, detector, i), 'Kanal k', 'Counts N')
prepareGraph(g)
g.GetXaxis().SetRangeUser(peak[2][0], peak[2][1])
g.SetMinimum(peak[3][0])
g.SetMaximum(peak[3][1])
g.Draw('AP')
fit = None
paramnames = []
if len(peak[0]) == 5:
fit = Fitter('fit%d' % i, 'pol1(0) + 1/(sqrt(2*pi*[4]^2))*gaus(2)')
paramnames = ['a', 'b', 'A', 'k_{c}', 's']
elif len(peak[0]) == 4:
fit = Fitter('fit%d' % i, '[0] + 1/(sqrt(2*pi*[3]^2))*gaus(1)')
paramnames = ['a', 'A', 'k_{c}', 's']
elif len(peak[0]) == 3:
fit = Fitter('fit%d' % i, '1/(sqrt(2*pi*[2]^2))*gaus(0)')
paramnames = ['A', 'k_{c}', 's']
l = None
if len(peak[0]) > 0:
for j, param in enumerate(peak[0]):
fit.setParam(j, paramnames[j], param)
fit.fit(g, *peak[1])
fitname = ''
if len(peak[0]) == 5:
fitname = 'N(k) = a + b*k + #frac{A}{#sqrt{2#pi*#sigma^{2}}} exp(- #frac{1}{2} (#frac{k-k_{c}}{#sigma})^{2})'
elif len(peak[0]) == 4:
fitname = 'N(k) = a + #frac{A}{#sqrt{2#pi*#sigma^{2}}} exp(- #frac{1}{2} (#frac{k-k_{c}}{#sigma})^{2})'
elif len(peak[0]) == 3:
fitname = 'N(k) = #frac{A}{#sqrt{2#pi*#sigma^{2}}} exp(- #frac{1}{2} (#frac{k-k_{c}}{#sigma})^{2})'
fit.saveData('../calc/part3/fit_%s-%s_%02d.txt' % (element, detector, i), 'w')
results = []
for j, param in fit.params.iteritems():
results.append((param['value'], param['error']))
fitresults.append(results)
# legend
l = TLegend(0.675, 0.5, 0.995, 0.85)
l.SetTextSize(0.025)
l.AddEntry(g, 'Messwerte', 'p')
l.AddEntry(fit.function, 'Fit mit', 'l')
l.AddEntry(0, fitname, '')
l.AddEntry(0, '', '')
fit.addParamsToLegend(l, chisquareformat='%.2f')
l.Draw()
c.Update()
if PRINTGRAPHS:
c.Print('../img/part3/%s-%s_%02d.pdf' % (element, detector, i), 'pdf')
return fitresults
def makeCSGraphUnderground(ctype, startGammaEE=None):
data = loadCrossSection(ctype)
c = TCanvas('c_%s' % ctype, '', 1280, 720)
g = data.makeGraph('g_%s' % ctype, '#sqrt{s} / GeV', '#sigma / nb')
g.Draw('AP')
if not startGammaEE:
fit = Fitter(
'fit_%s' % ctype,
'[0] + [1] * x + 12*pi / [2]^2 * x^2 * [3]^2 / ((x^2-[2]^2)^2 + x^4 * [4]^2 / [2]^2) * 0.3894e6'
)
fitfuncstring = "a + b#sqrt{s} + #frac{12#pi}{M_{Z}^{2}} #frac{s #Gamma_{%s}^{2}}{(s-M_{Z}^{2})^{2} + s^{2} #Gamma_{Z}^{2} / M_{Z}^{2}}" % ctype[
0]
else:
fit = Fitter(
'fit_%s' % ctype,
'[0] + [1] * x + 12*pi / [2]^2 * x^2 * [3] * [5] / ((x^2-[2]^2)^2 + x^4 * [4]^2 / [2]^2) * 0.3894e6'
)
fitfuncstring = "a + b#sqrt{s} + #frac{12#pi}{M_{Z}^{2}} #frac{s #Gamma_{e} #Gamma_{%s}}{(s-M_{Z}^{2})^{2} + s^{2} #Gamma_{Z}^{2} / M_{Z}^{2}}" % ctype[
0]
fit.setParam(0, 'a', 0)
fit.setParam(1, 'b', 0)
fit.setParam(2, 'M_{Z}', 91.2)
fit.setParam(4, '#Gamma_{Z}', 2.5)
if not startGammaEE:
fit.setParam(3, '#Gamma_{%s}' % ctype[0], 0.08)
else:
fit.setParam(3, '#Gamma_{e}', startGammaEE, True)
fit.setParam(5, '#Gamma_{%s}' % ctype[0], 2)
fit.fit(g, 88, 94)
fit.saveData('../fit/crosssections_%s.txt' % ctype)
l = TLegend(0.625, 0.575, 0.98, 0.98)
l.SetTextSize(0.03)
l.AddEntry(g, "%s Wirkungsquerschnitte" % ctype, 'p')
l.AddEntry(fit.function, "Fit mit #sigma(s) = ", 'l')
l.AddEntry(None, "", '')
l.AddEntry(None, fitfuncstring, '')
l.AddEntry(None, "", '')
if not startGammaEE:
fit.addParamsToLegend(
l, [('%.3f', '%.3f'), ('%.3f', '%.3f'), ('%.3f', '%.3f'),
('%.3f', '%.3f'), ('%.3f', '%.3f')],
chisquareformat='%f',
units=['nb', 'nb/GeV', 'GeV / c^{2}', 'GeV', 'GeV'])
else:
fit.addParamsToLegend(
l, [('%.3f', '%.3f'), ('%.3f', '%.3f'), ('%.3f', '%.3f'), '%.4f',
('%.3f', '%.3f'), ('%.3f', '%.3f')],
chisquareformat='%f',
units=['nb', 'nb/GeV', 'GeV/c^{2}', 'GeV', 'GeV', 'GeV'])
l.Draw()
c.Update()
c.Print('../img/crosssections_%s.pdf' % ctype, 'pdf')
return fit.params[3]['value']
def main():
snu = ERRORS["nu"] # TODO get error
sB = ERRORS["B"]
sgyrorel = 0
files = ["H", "Glycol", "Teflon"]
for file in files:
datalist = loadCSVToList("../data/03-%s.txt" % file)
if len(datalist) == 1:
B, nu = datalist[0]
gyro, sgyro = calcGyro(nu, snu, B, sB)
if not sgyrorel == 0:
sgyro = gyro * sgyrorel
with TxtFile("../calc/%s.txt" % file, "w") as f:
f.writeline("\t", "gyro", *map(str, (gyro, sgyro)))
f.writeline("\t", "mu", *map(str, calcMu(gyro, sgyro)))
f.writeline("\t", "gI", *map(str, calcNucGFactor(gyro, sgyro)))
else:
x, y = zip(*datalist)
sx = [0] * len(x)
sy = [snu] * len(y)
data = DataErrors.fromLists(x, y, sx, sy)
data.setXErrorAbs(sB)
c = TCanvas("c%s" % file, "", 1280, 720)
g = data.makeGraph("g%s" % file, "Magnetfeld B / mT", "Resonanzfrequenz #nu / MHz")
g.Draw("AP")
fit = Fitter("fit%s" % file, "[0]*x")
fit.setParam(0, "m", 0.002)
fit.fit(g, datalist[0][0] * 0.95, datalist[-1][0] * 1.05)
fit.saveData("../calc/fit-%s.txt" % file, "w")
l = TLegend(0.15, 0.60, 0.475, 0.85)
l.SetTextSize(0.03)
l.AddEntry(g, "Messdaten", "p")
l.AddEntry(fit.function, "Fit mit #nu(B) = m*B", "l")
l.AddEntry(0, "", "")
fit.addParamsToLegend(
l,
[("%.5f", "%.5f"), ("%.2f", "%.2f")],
chisquareformat="%.2f",
advancedchi=True,
units=["MHz / mT", "MHz"],
)
l.Draw()
gyro = 2 * np.pi * fit.params[0]["value"] * 1e9 # in Hz / T
sgyro = 2 * np.pi * fit.params[0]["error"] * 1e9 # in Hz / T
sgyrorel = sgyro / gyro
with TxtFile("../calc/%s.txt" % file, "w") as f:
f.writeline("\t", "gyro", *map(str, (gyro, sgyro)))
f.writeline("\t", "sgyrorel", str(sgyrorel))
f.writeline("\t", "mu", *map(str, calcMu(gyro, sgyro)))
f.writeline("\t", "gI", *map(str, calcNucGFactor(gyro, sgyro)))
c.Update()
c.Print("../img/03-%s.pdf" % file, "pdf")
def evalDiode():
datalist = loadCSVToList('../data/part1/Kennlinie.txt')
data = DataErrors()
U0 = datalist[0][1]
sU0 = 0.05 + 0.01 * U0
for I, u in datalist:
U = u - U0
su = 5 + 0.01 * u
sU = sqrt(su**2 + sU0**2)
data.addPoint(I, U, 0.1, sU)
xmin, xmax = 53, 71.5
c = TCanvas('c_diode', '', 1280, 720)
g = data.makeGraph('g_diode', "Laserstrom I_{L} / mA",
"Photodiodenspannung U_{ph} / mV")
g.GetXaxis().SetRangeUser(-5, 90)
g.SetMinimum(-50)
g.SetMaximum(1400)
g.Draw('APX')
# y=0 line
line = TLine(-5, 0, 90, 0)
line.SetLineColor(OPData.CH2ECOLOR)
line.Draw()
data.filterX(xmin, xmax)
g2 = data.makeGraph('g_diode_2', "Laserstrom I_{L} / mA",
"Photodiodenspannung U_{ph} / mV")
g2.SetMarkerColor(OPData.CH1COLOR)
g2.SetLineColor(OPData.CH1COLOR)
fit = Fitter('fit_diode', '[0] * (x-[1])')
fit.function.SetNpx(1000)
fit.setParam(0, 'a', 1)
fit.setParam(1, 'I_{th}', 50)
fit.fit(g2, 40, 77)
fit.saveData('../fit/part1/kennlinie.txt')
l = TLegend(0.15, 0.55, 0.4, 0.85)
l.SetTextSize(0.03)
l.AddEntry(g, 'Laserdiodenkennlinie', 'p')
l.AddEntry(g2, 'Ausschnitt zum Fitten', 'p')
l.AddEntry(fit.function, 'Fit mit U_{ph} = a (I_{ L} - I_{ th} )', 'l')
fit.addParamsToLegend(l, (('%.1f', '%.1f'), ('%.2f', '%.2f')),
chisquareformat='%.2f',
units=['mV/mA', 'mA'])
l.Draw()
g.Draw('P')
g2.Draw('P')
c.Update()
c.Print('../img/part1/diodenkennlinie.pdf', 'pdf')
def makeCSGraph(ctype, startGammaEE=None):
data = loadCrossSection(ctype)
c = TCanvas('c_%s' % ctype, '', 1280, 720)
g = data.makeGraph('g_%s' % ctype, '#sqrt{s} / GeV', '#sigma / nb')
g.Draw('AP')
if not startGammaEE:
fit = Fitter('fit_%s' % ctype, '(12*pi / [0]^2) * (x^2 * [1]^2) / ((x^2-[0]^2)^2 + x^4 * [2]^2 / [0]^2) * 0.3894*10^6') # GeV^-2 = 0.3894 mb
fitfuncstring = "#frac{12#pi}{M_{Z}^{2}} #frac{s #Gamma_{%s}^{2}}{(s-M_{Z}^{2})^{2} + s^{2} #Gamma_{Z}^{2} / M_{Z}^{2}}" % ctype[0]
else:
fit = Fitter('fit_%s' % ctype, '(12*pi / [0]^2) * (x^2 * [1] * [2]) / ((x^2-[0]^2)^2 + x^4 * [3]^2 / [0]^2) * 0.3894*10^6')
fitfuncstring = "#frac{12#pi}{M_{Z}^{2}} #frac{s #Gamma_{m} #Gamma_{%s}}{(s-M_{Z}^{2})^{2} + s^{2} #Gamma_{Z}^{2} / M_{Z}^{2}}" % ctype[0]
fit.setParam(0, 'M_{Z}', 91.2)
fit.setParamLimits(0, 0, 1000)
if not startGammaEE:
fit.setParam(1, '#Gamma_{%s}' % ctype[0], 0.08)
fit.setParamLimits(1, 0, 1000)
fit.setParam(2, '#Gamma_{Z}', 2.5)
fit.setParamLimits(2, 0, 1000)
else:
fit.setParam(1, '#Gamma_{m}', startGammaEE, True)
fit.setParam(2, '#Gamma_{%s}' % ctype[0], 1.5)
fit.setParam(3, '#Gamma_{Z}', 2.5)
fit.setParamLimits(2, 0, 1000)
fit.fit(g, 88, 94, '')
fit.saveData('../fit/crosssections_%s.txt' % ctype)
l = TLegend(0.625, 0.6, 0.98, 0.975)
l.SetTextSize(0.03)
l.AddEntry(g, "%s Wirkungsquerschnitte" % ctype, 'p')
l.AddEntry(fit.function, "Fit mit #sigma(s) = %s" % fitfuncstring, 'l')
l.AddEntry(None, "", '')
if not startGammaEE:
fit.addParamsToLegend(l, [('%.3f', '%.3f'), ('%.4f', '%.4f'), ('%.3f', '%.3f')], chisquareformat='%f', units=['GeV / c^{2}', 'GeV', 'GeV'])
else:
fit.addParamsToLegend(l, [('%.3f', '%.3f'), '%.4f', ('%.3f', '%.3f'), ('%.3f', '%.3f')], chisquareformat='%f', units=['GeV/c^{2}', 'GeV', 'GeV', 'GeV'])
l.Draw()
c.Update()
if not DEBUG:
c.Print('../img/crosssections_%s.pdf' % ctype, 'pdf')
if not startGammaEE:
result = [(fit.params[0]['value'], fit.params[0]['error']),
(fit.params[1]['value'], fit.params[1]['error']),
(fit.params[2]['value'], fit.params[2]['error'])]
else:
result = [(fit.params[0]['value'], fit.params[0]['error']),
(fit.params[2]['value'], fit.params[2]['error']),
(fit.params[3]['value'], fit.params[3]['error'])]
return result
def evalEnergyCalibration(peaks, percents):
maxenergy = 1.95 * 0.87 * 84
smaxenergy = maxenergy * sqrt((0.05 / 1.95)**2 + (0.01 / 0.87)**2 +
(5 / 84)**2)
channels = list(list(zip(*peaks))[0])
schannels = list(list(zip(*peaks))[1])
energies = list(map(lambda x: x / 100 * maxenergy, percents))
senergies = list(map(lambda x: x / 100 * smaxenergy, percents))
print(energies)
print(senergies)
with TxtFile('../src/tab_energycalibration.tex', 'w') as f:
f.write2DArrayToLatexTable(
list(zip(*[percents, channels, schannels, energies, senergies])),
['\% energy', '$c$', '$s_c$', '$E$ / MeV', '$s_E$ / MeV'],
['%d', '%.3f', '%.3f', '%.1f', '%.1f'],
"Channels of fitted peaks and their theoretical energy for the energy calibration.",
"tab:ecal")
data = DataErrors.fromLists(channels, energies, schannels, senergies)
c = TCanvas('c_energycalibration', '', 1280, 720)
g = data.makeGraph('g_energycalibration', 'channel c', 'energy E / MeV')
g.Draw('AP')
fit = Fitter('fit_energycalibration', 'pol1(0)')
fit.function.SetNpx(1000)
fit.setParam(0, 'a', 0)
fit.setParam(1, 'b', 1 / 3)
fit.fit(g, 0, max(channels) + 5)
fit.saveData('../fit/energyCalibration.txt')
l = TLegend(0.15, 0.6, 0.575, 0.85)
l.SetTextSize(0.03)
l.AddEntry(g, 'Peaks of flight through spectra / pedestal', 'p')
l.AddEntry(fit.function, 'fit with E(c) = a + b * c', 'l')
fit.addParamsToLegend(l, (('%.2f', '%.2f'), ('%.3f', '%.3f')),
chisquareformat='%.2f',
units=('MeV', 'MeV / channel'),
lang='en')
l.Draw()
c.Update()
c.Print('../img/energyCalibration.pdf', 'pdf')
with TxtFile('../calc/energyCalibration.txt', 'w') as f:
f.writeline('\t', str(fit.params[0]['value']),
str(fit.params[0]['error']))
f.writeline('\t', str(fit.params[1]['value']),
str(fit.params[1]['error']))
f.writeline('\t', str(fit.getCovMatrixElem(0, 1)))
def printGraph(datas, phi, name='', fit=False):
"""make graph with measured taus for one specific angle phi
Arguments:
datas -- datalists (sorted by series in dictonary)
phi -- angle
name -- additional name for file name
fit -- if true fit data with linear model (default=False)
"""
# setup canvas and legend
c = TCanvas('c_%d' % phi, '', 1280, 720)
if fit:
l = TLegend(0.6, 0.15, 0.85, 0.5)
else:
l = TLegend(0.65, 0.15, 0.85, 0.35)
l.SetTextSize(0.03)
# make and draw graphs, graphs are organized by TMultiGraph
graphs = TMultiGraph()
for s, datalist in datas.iteritems():
data = DataErrors.fromLists(*zip(*datalist))
g = data.makeGraph('g_%d_%d' % (phi, s))
g.SetMarkerColor(seriescolors[s])
g.SetLineColor(seriescolors[s])
l.AddEntry(g, serieslabels[s], 'p')
graphs.Add(g)
graphs.Draw('AP')
gPad.Update()
setMultiGraphTitle(graphs, 'Druck p / mPa', '#tau / ns')
# fit data with linear fit
if fit:
fit = Fitter('fit_%d' % phi, 'pol1(0)')
fit.function.SetNpx(1000)
fit.setParam(0, '#tau_{0}', 119)
fit.setParam(1, 'm', 0.5)
fit.fit(graphs, 0, 225, 'M')
fit.saveData('../calc/fit_tau_%02d%s.txt' % (phi, name), 'w')
l.AddEntry(fit.function, 'Fit mit #tau(p) = #tau_{0} + m * p', 'l')
fit.addParamsToLegend(l, [('%.1f', '%.1f'), ('%.2f', '%.2f')], chisquareformat='%.2f',
advancedchi=True, units=['ns', 'ns / mPa'])
# draw legend and print canvas to file
l.Draw()
c.Update()
c.Print('../img/taus_%02d%s.pdf' % (phi, name), 'pdf')
# return required fit parameter
if fit:
return fit.params[0]['value'], fit.params[0]['error']
def evalDiode():
datalist = loadCSVToList('../data/part1/Kennlinie.txt')
data = DataErrors()
U0 = datalist[0][1]
sU0 = 0.05 + 0.01 * U0
for I, u in datalist:
U = u - U0
su = 5 + 0.01 * u
sU = sqrt(su ** 2 + sU0 ** 2)
data.addPoint(I, U, 0.1, sU)
xmin, xmax = 53, 71.5
c = TCanvas('c_diode', '', 1280, 720)
g = data.makeGraph('g_diode', "Laserstrom I_{L} / mA", "Photodiodenspannung U_{ph} / mV")
g.GetXaxis().SetRangeUser(-5, 90)
g.SetMinimum(-50)
g.SetMaximum(1400)
g.Draw('APX')
# y=0 line
line = TLine(-5, 0, 90, 0)
line.SetLineColor(OPData.CH2ECOLOR)
line.Draw()
data.filterX(xmin, xmax)
g2 = data.makeGraph('g_diode_2', "Laserstrom I_{L} / mA", "Photodiodenspannung U_{ph} / mV")
g2.SetMarkerColor(OPData.CH1COLOR)
g2.SetLineColor(OPData.CH1COLOR)
fit = Fitter('fit_diode', '[0] * (x-[1])')
fit.function.SetNpx(1000)
fit.setParam(0, 'a', 1)
fit.setParam(1, 'I_{th}', 50)
fit.fit(g2, 40, 77)
fit.saveData('../fit/part1/kennlinie.txt')
l = TLegend(0.15, 0.55, 0.4, 0.85)
l.SetTextSize(0.03)
l.AddEntry(g, 'Laserdiodenkennlinie', 'p')
l.AddEntry(g2, 'Ausschnitt zum Fitten', 'p')
l.AddEntry(fit.function, 'Fit mit U_{ph} = a (I_{ L} - I_{ th} )', 'l')
fit.addParamsToLegend(l, (('%.1f', '%.1f'), ('%.2f', '%.2f')), chisquareformat='%.2f', units=['mV/mA', 'mA'])
l.Draw()
g.Draw('P')
g2.Draw('P')
c.Update()
c.Print('../img/part1/diodenkennlinie.pdf', 'pdf')
def makeGraph(channel):
data = MyonData.fromPath('../data/energieaufloesung_%s.TKA' % channel)
data.convertToCountrate()
c = TCanvas('c_%s' % channel, '', 1280, 720)
g = data.makeGraph('g%s' % channel, 'channel c', 'countrate n / (1/s)')
prepareGraph(g)
g.GetXaxis().SetRangeUser(0, 700)
g.Draw('APX')
ch = int(channel)
if ch < 100:
xmin, xmax = ch - 25, ch + 25
elif ch < 120:
xmin, xmax = ch - 50, ch + 40
elif ch < 200:
xmin, xmax = ch - 50, ch + 50
elif ch < 600:
xmin, xmax = 0, 700
else:
xmin, xmax = 0, ch + 45
fit = Fitter('fit_%s' % channel, '[0] + gaus(1)')
fit.function.SetNpx(1000)
fit.setParam(0, 'b', 0) # offset
fit.setParam(1, 'A', data.getMaxY()) # amplitude
fit.setParam(2, 'x', ch) # channel
fit.setParam(3, '#sigma', 50) # sigma
#fit.setParamLimits(0, 0, 1000)
fit.fit(g, xmin, xmax)
fit.saveData('../fit/energieaufloesung_%s.txt' % channel)
if ch > 350:
l = TLegend(0.15, 0.5, 0.45, 0.85)
else:
l = TLegend(0.55, 0.5, 0.85, 0.85)
l.SetTextSize(0.03)
l.AddEntry(g, "measurement", 'p')
l.AddEntry(fit.function, "fit with n(c) =", 'l')
l.AddEntry(None, "b + A gaus(c; x, #sigma)", '')
fit.addParamsToLegend(l, (('%.4f', '%.4f'), ('%.2f', '%.2f'), ('%.2f', '%.2f'), ('%.2f', '%.2f'),),
chisquareformat='%.2f', units=['1/s', '1/s', '', ''], lang='en')
l.Draw()
g.Draw('P')
c.Update()
c.Print('../img/energieaufloesung_%s.pdf' % channel, 'pdf')
return (fit.params[2]['value'], fit.params[2]['error'], abs(fit.params[3]['value']), fit.params[3]['error']) # abs(sigma)
def makePeakFreqGraph(peaks, name):
xlist = list(list(zip(*peaks))[0])
sxlist = list(list(zip(*peaks))[1])
ylist = list(map(lambda i: i * 9.924, range(len(peaks))))
sylist = list(map(lambda i: i * 0.03, range(len(peaks))))
direction = name.split('-')[0]
tabledata = list(zip(*[list(range(1, len(xlist) + 1)), list(map(lambda x:x * 1000, xlist)), list(map(lambda x:x * 1000, sxlist)),
ylist, sylist]))
with TxtFile('../src/tab_part2_etalonfreqs_%s.tex' % direction, 'w') as f:
f.write2DArrayToLatexTable(tabledata,
["i", r"$x_i$ / ms", r"$0.2 \cdot s_i$ / ms", r"$\nu_i$ / GHz", r"$s_{\nu_i}$ / GHz"],
["%d", "%.3f", "%.3f", "%.2f", "%.2f"],
"Zentren $x_i$ der gefitteten Cauchy-Funktionen mit Fehler aus den " +
"Breiteparametern $s_i$ und Frequenzdifferenzen zum ersten Peak. ",
"tab:etalon:calib:%s" % direction)
etalonData = DataErrors.fromLists(xlist, ylist, sxlist, sylist)
etalonData.multiplyX(1000)
c = TCanvas('c_pf_' + name, '', 1280, 720)
g = etalonData.makeGraph('g_pf_' + name, 'Zeit t / ms', 'Frequenzabstand #Delta#nu / GHz')
g.SetMinimum(etalonData.getMinY() - 5)
g.SetMaximum(etalonData.getMaxY() + 5)
g.Draw('AP')
fit = Fitter('fit_pf_' + name, 'pol1(0)')
fit.setParam(0, 'a')
fit.setParam(1, 'r')
xmin, xmax = etalonData.getMinX(), etalonData.getMaxX()
deltax = (xmax - xmin) / 10
fit.fit(g, xmin - deltax, xmax + deltax)
fit.saveData('../fit/part2/%s-etalon_calibration.txt' % name)
if fit.params[1]['value'] < 0:
l = TLegend(0.575, 0.6, 0.85, 0.85)
else:
l = TLegend(0.15, 0.6, 0.425, 0.85)
l.SetTextSize(0.03)
l.AddEntry(g, 'Etalonpeaks', 'p')
l.AddEntry(fit.function, 'Fit mit #Delta#nu = a + r * t', 'l')
fit.addParamsToLegend(l, [('%.1f', '%.1f'), ('%.2f', '%.2f')], chisquareformat='%.2f', units=('GHz', 'GHz/ms'))
l.Draw()
c.Update()
if not DEBUG:
c.Print('../img/part2/%s-etalon_calibration.pdf' % name, 'pdf')
return (fit.params[1]['value'], fit.params[1]['error'])
def makeFranzenFit(n, plotParams):
data = OPData.fromPath(DIR + '%02d.tab' % n, 1)
xmin, xmax = plotParams[:2]
fitparams = plotParams[2:]
c = TCanvas('c_%d' % n, '', 1280, 720)
g = data.makeGraph('g_%d' % n, 'Zeit t / s', 'U_{ph} / V')
prepareGraph(g, 2)
g.GetXaxis().SetRangeUser(xmin, xmax)
g.Draw('APX')
fit = Fitter('fit%d' % n, franzenFunc, (xmin, xmax, 6))
fit.function.SetNpx(1000)
paramnames = ["A", "u", "#mu", "#sigma", "B", "#lambda"]
for i in range(6):
fit.setParam(i, paramnames[i], fitparams[3 * i])
fit.setParamLimits(i, fitparams[3 * i + 1], fitparams[3 * i + 2])
print(fitparams[3 * i], fitparams[3 * i + 1], fitparams[3 * i + 2])
fit.fit(g, xmin, xmax)
fit.saveData('../fit/part6/%02d.txt' % n)
fermi = TF1('func', fermiFunc, xmin, xmax, 5)
fermi.SetNpx(1000)
fermi.SetLineColor(getRootColor(0))
fermi.SetLineWidth(1)
for i in range(5):
fermi.SetParameter(i, fit.params[i]['value'])
fermi.Draw('SAME')
g.Draw('P')
l = TLegend(0.4, 0.15, 0.85, 0.65)
l.SetTextSize(0.03)
l.AddEntry(g, "Spannung Photodiode U_{ph}", 'l')
l.AddEntry(fit.function, 'Fit mit U_{ph}(t) = U_{F}(t; A, u, #mu, #sigma) + U_{E}(t; #mu, B, #lambda)', 'l')
l.AddEntry(fermi, 'Fermi-Verteilung', 'l')
fit.addParamsToLegend(l, [('%.4f', '%.4f'), ('%.4f', '%.4f'), ('%.5f', '%.5f'), ('%.2f', '%.2f'), ('%.4f', '%.4f'), ('%.3f', '%.3f')],
chisquareformat='%.2f', units=["V", "V", "ms", "#mus", "V", "1/ms"])
l.Draw()
c.Update()
c.Print('../img/part6/%02d.pdf' % n, 'pdf')
result = []
exportvals = [2, 3, 4] # mu, sigma, B
for e in exportvals:
result.append((fit.params[e]['value'], fit.params[e]['error']))
return result
def evalFlythroughSpectrum(name, xmin, xmax):
data = MyonData.fromPath('../data/%s.TKA' % name)
data.convertToCountrate()
c = TCanvas('c_%s' % name, '', 1280, 720)
g = data.makeGraph('g_%s' % name, 'channel c', 'countrate n / (1/s)')
prepareGraph(g)
g.GetXaxis().SetRangeUser(0, 700)
g.GetYaxis().SetTitleOffset(1.2)
g.Draw('APX') # don't draw error bars => fit function in front
maxY = data.getByY(data.getMaxY())[0]
area = g.Integral(xmin, xmax)
print('start fitting %s' % name)
t1 = datetime.datetime.now()
fit = Fitter('fit_%s' % name, langaufun, (xmin, xmax, 4))
fit.setParam(0, 's', 1)
fit.setParam(1, 'm', maxY)
fit.setParam(2, 'A', area)
fit.setParam(3, '#sigma', 30)
fit.fit(g, xmin, xmax, 'RBO')
t2 = datetime.datetime.now()
delta = t2 - t1
print('fitted in %d s' % int(delta.total_seconds()))
fit.saveData('../fit/%s.txt' % name)
if not name == "energiekalibration_100":
l = TLegend(0.5, 0.55, 0.85, 0.85)
else:
l = TLegend(0.15, 0.55, 0.5, 0.85)
l.SetTextSize(0.03)
l.AddEntry(g, 'flight through with %s%% energy' % name.split('_')[1], 'p')
l.AddEntry(fit.function, 'fit with n(c) =', 'l')
l.AddEntry(None, '(landau(m, s) * gaus(0, #sigma))(c)', '')
fit.addParamsToLegend(l, (('%.2f', '%.2f'), ('%.2f', '%.2f'),
('%.3f', '%.3f'), ('%.2f', '%.2f')),
chisquareformat='%.2f',
lang='en')
l.Draw()
g.Draw('P') # redraw points with error bars
c.Update()
c.Print('../img/%s.pdf' % name, 'pdf')
return ((fit.params[1]['value'], fit.params[1]['error']),
(fit.params[3]['value'], fit.params[3]['error']))
def evalTaus(taus, filters):
data = DataErrors()
table = []
for key, (tau, stau) in taus.items():
invtau = 1 / tau
sinvtau = stau / (tau**2)
int, sint = filters[key]
data.addPoint(int, invtau, sint, sinvtau)
table.append([int * 100, sint * 100, tau * 1000, stau * 1000])
table.sort(key=lambda x: x[0], reverse=True)
# make table
with TxtFile('../src/tab_part5_taus.tex', 'w') as f:
f.write2DArrayToLatexTable(
table, [
r'$I_\text{mess}$ / \%', r'$s_{I_\text{mess}}$ / \%',
r'$\tau$ / ms', r'$s_\tau$ / ms'
], ['%.2f', '%.2f', '%.3f', '%.3f'],
r'Orientierungszeiten $\tau$ des Rubidiumensembles bei verschiedenen Pumpintensitäten $I_{\text{mess}}$.',
'tab:deh:fitres')
# make fit
c = TCanvas('c_taus', '', 1280, 720)
g = data.makeGraph('g_taus', r'relative Intensitaet I_{mess}',
'inverse Orientierungszeit #tau^{ -1} / s^{-1}')
g.Draw('APX')
fit = Fitter('fit_taus', '[0]*x + 1/[1]')
fit.setParam(0, '#alpha', 1)
fit.setParam(1, 'T_{R}', 1)
fit.fit(g, 0, 1.1)
fit.saveData('../fit/part5/taufit.txt')
l = TLegend(0.55, 0.15, 0.85, 0.5)
l.SetTextSize(0.03)
l.AddEntry(g, 'Inverse Orientierungszeit #tau^{ -1}', 'p')
l.AddEntry(fit.function,
'Fit mit #tau^{ -1} (I) = #alpha I + #frac{1}{T_{R}}', 'l')
fit.addParamsToLegend(l, [('%.0f', '%.0f'), ('%.5f', '%.5f')],
chisquareformat='%.2f',
units=['s^{-1}', 's'])
l.Draw()
g.Draw('P')
c.Print('../img/part5/taufit.pdf', 'pdf')
def evalFlythroughSpectrum(name, xmin, xmax):
data = MyonData.fromPath('../data/%s.TKA' % name)
data.convertToCountrate()
c = TCanvas('c_%s' % name, '', 1280, 720)
g = data.makeGraph('g_%s' % name, 'channel c', 'countrate n / (1/s)')
prepareGraph(g)
g.GetXaxis().SetRangeUser(0, 700)
g.GetYaxis().SetTitleOffset(1.2)
g.Draw('APX') # don't draw error bars => fit function in front
maxY = data.getByY(data.getMaxY())[0]
area = g.Integral(xmin, xmax)
print('start fitting %s' % name)
t1 = datetime.datetime.now()
fit = Fitter('fit_%s' % name, langaufun, (xmin, xmax, 4))
fit.setParam(0, 's', 1)
fit.setParam(1, 'm', maxY)
fit.setParam(2, 'A', area)
fit.setParam(3, '#sigma', 30)
fit.fit(g, xmin, xmax, 'RBO')
t2 = datetime.datetime.now()
delta = t2 - t1
print('fitted in %d s' % int(delta.total_seconds()))
fit.saveData('../fit/%s.txt' % name)
if not name == "energiekalibration_100":
l = TLegend(0.5, 0.55, 0.85, 0.85)
else:
l = TLegend(0.15, 0.55, 0.5, 0.85)
l.SetTextSize(0.03)
l.AddEntry(g, 'flight through with %s%% energy' % name.split('_')[1], 'p')
l.AddEntry(fit.function, 'fit with n(c) =', 'l')
l.AddEntry(None, '(landau(m, s) * gaus(0, #sigma))(c)', '')
fit.addParamsToLegend(l, (('%.2f', '%.2f'), ('%.2f', '%.2f'), ('%.3f', '%.3f'), ('%.2f', '%.2f')), chisquareformat='%.2f', lang='en')
l.Draw()
g.Draw('P') # redraw points with error bars
c.Update()
c.Print('../img/%s.pdf' % name, 'pdf')
return ((fit.params[1]['value'], fit.params[1]['error']), (fit.params[3]['value'], fit.params[3]['error']))
def fitXc(volts, times, d, sd):
listx, listsx = zip(*times)
listy = []
listsy = []
for u, su in volts: # convert u to 1/u
y = 1. / u
sy = su / (u ** 2)
listy.append(y)
listsy.append(sy)
data = DataErrors.fromLists(listx, listy, listsx, listsy)
c = TCanvas('cXc', '', 1280, 720)
g = data.makeGraph('xc', 'Zeit t / s', 'reziproke Treibspannung 1/U_{T} / (1/V)')
g.GetYaxis().SetTitleOffset(1.25)
g.Draw('AP')
fit = Fitter('fitXc', '1/[0] + [1]*x')
fit.setParam(0, 'U_{0}', -150)
fit.setParam(1, 'm', 3e-3)
fit.fit(g, 8e-6, 18e-6)
fit.saveData('../calc/part2/volt_fit_xc.txt')
l = TLegend(0.15, 0.55, 0.4, 0.85)
l.SetTextSize(0.03)
l.AddEntry('xc', 'Messung', 'p')
l.AddEntry(fit.function, 'Fit mit #frac{1}{U_{T}} = m*t + #frac{1}{U_{0}}', 'l')
l.AddEntry(0, '', '')
fit.addParamsToLegend(l, [('%.0f', '%.0f'), ('%.0f', '%.0f')], chisquareformat='%.2f')
l.Draw()
if PRINTGRAPHS or True:
c.Update()
c.Print('../img/part2/volt_fitXc.pdf', 'pdf')
# calculate mu
m, sm = fit.params[1]['value'], fit.params[1]['error']
l = 30
mu = m * l * d / 100 # /100 -> from mm in cm
smu = mu * np.sqrt((sm / m) ** 2 + (sd / d) ** 2)
with TxtFile('../calc/part2/volt_mu.txt', 'w') as f:
f.writeline('\t', str(mu), str(smu))
return mu, smu
def evalEnergyCalibration(peaks, percents):
maxenergy = 1.95 * 0.87 * 84
smaxenergy = maxenergy * sqrt((0.05/1.95) ** 2 + (0.01/0.87) ** 2 + (5/84) ** 2 )
channels = list(list(zip(*peaks))[0])
schannels = list(list(zip(*peaks))[1])
energies = list(map(lambda x:x/100*maxenergy, percents))
senergies = list(map(lambda x:x/100*smaxenergy, percents))
print(energies)
print(senergies)
with TxtFile('../src/tab_energycalibration.tex', 'w') as f:
f.write2DArrayToLatexTable(list(zip(*[percents, channels, schannels, energies, senergies])),
['\% energy', '$c$', '$s_c$', '$E$ / MeV', '$s_E$ / MeV'],
['%d', '%.3f', '%.3f', '%.1f', '%.1f'],
"Channels of fitted peaks and their theoretical energy for the energy calibration.", "tab:ecal")
data = DataErrors.fromLists(channels, energies, schannels, senergies)
c = TCanvas('c_energycalibration', '', 1280, 720)
g = data.makeGraph('g_energycalibration', 'channel c', 'energy E / MeV')
g.Draw('AP')
fit = Fitter('fit_energycalibration', 'pol1(0)')
fit.function.SetNpx(1000)
fit.setParam(0, 'a', 0)
fit.setParam(1, 'b', 1/3)
fit.fit(g, 0, max(channels) + 5)
fit.saveData('../fit/energyCalibration.txt')
l = TLegend(0.15, 0.6, 0.575, 0.85)
l.SetTextSize(0.03)
l.AddEntry(g, 'Peaks of flight through spectra / pedestal', 'p')
l.AddEntry(fit.function, 'fit with E(c) = a + b * c', 'l')
fit.addParamsToLegend(l, (('%.2f', '%.2f'), ('%.3f', '%.3f')), chisquareformat='%.2f', units=('MeV', 'MeV / channel'), lang='en')
l.Draw()
c.Update()
c.Print('../img/energyCalibration.pdf', 'pdf')
with TxtFile('../calc/energyCalibration.txt', 'w') as f:
f.writeline('\t', str(fit.params[0]['value']), str(fit.params[0]['error']))
f.writeline('\t', str(fit.params[1]['value']), str(fit.params[1]['error']))
f.writeline('\t', str(fit.getCovMatrixElem(0, 1)))
def makeCSGraphUnderground(ctype, startGammaEE=None):
data = loadCrossSection(ctype)
c = TCanvas('c_%s' % ctype, '', 1280, 720)
g = data.makeGraph('g_%s' % ctype, '#sqrt{s} / GeV', '#sigma / nb')
g.Draw('AP')
if not startGammaEE:
fit = Fitter('fit_%s' % ctype, '[0] + [1] * x + 12*pi / [2]^2 * x^2 * [3]^2 / ((x^2-[2]^2)^2 + x^4 * [4]^2 / [2]^2) * 0.3894e6')
fitfuncstring = "a + b#sqrt{s} + #frac{12#pi}{M_{Z}^{2}} #frac{s #Gamma_{%s}^{2}}{(s-M_{Z}^{2})^{2} + s^{2} #Gamma_{Z}^{2} / M_{Z}^{2}}" % ctype[0]
else:
fit = Fitter('fit_%s' % ctype, '[0] + [1] * x + 12*pi / [2]^2 * x^2 * [3] * [5] / ((x^2-[2]^2)^2 + x^4 * [4]^2 / [2]^2) * 0.3894e6')
fitfuncstring = "a + b#sqrt{s} + #frac{12#pi}{M_{Z}^{2}} #frac{s #Gamma_{e} #Gamma_{%s}}{(s-M_{Z}^{2})^{2} + s^{2} #Gamma_{Z}^{2} / M_{Z}^{2}}" % ctype[0]
fit.setParam(0, 'a', 0)
fit.setParam(1, 'b', 0)
fit.setParam(2, 'M_{Z}', 91.2)
fit.setParam(4, '#Gamma_{Z}', 2.5)
if not startGammaEE:
fit.setParam(3, '#Gamma_{%s}' % ctype[0], 0.08)
else:
fit.setParam(3, '#Gamma_{e}', startGammaEE, True)
fit.setParam(5, '#Gamma_{%s}' % ctype[0], 2)
fit.fit(g, 88, 94)
fit.saveData('../fit/crosssections_%s.txt' % ctype)
l = TLegend(0.625, 0.575, 0.98, 0.98)
l.SetTextSize(0.03)
l.AddEntry(g, "%s Wirkungsquerschnitte" % ctype, 'p')
l.AddEntry(fit.function, "Fit mit #sigma(s) = ", 'l')
l.AddEntry(None, "", '')
l.AddEntry(None, fitfuncstring, '')
l.AddEntry(None, "", '')
if not startGammaEE:
fit.addParamsToLegend(l, [('%.3f', '%.3f'), ('%.3f', '%.3f'), ('%.3f', '%.3f'), ('%.3f', '%.3f'), ('%.3f', '%.3f')], chisquareformat='%f', units=['nb', 'nb/GeV', 'GeV / c^{2}', 'GeV', 'GeV'])
else:
fit.addParamsToLegend(l, [('%.3f', '%.3f'), ('%.3f', '%.3f'), ('%.3f', '%.3f'), '%.4f', ('%.3f', '%.3f'), ('%.3f', '%.3f')], chisquareformat='%f', units=['nb', 'nb/GeV', 'GeV/c^{2}', 'GeV', 'GeV', 'GeV'])
l.Draw()
c.Update()
c.Print('../img/crosssections_%s.pdf' % ctype, 'pdf')
return fit.params[3]['value']
def stFit(data, energie, xmin, xmax, binsize):
datacut = data.cut(CUTS[0][1]) # ee-cut
c = TCanvas('c', '', 1280, 720)
hist = datacut.makeHistogramm('hist_%f' % energie, 'cos_thet', binsize, -1, 1)
setHistTitle(hist, "cos #Theta", "Anzahl der Ereignisse N")
hist.Draw()
fit = Fitter('f', '[0] * (1 + x^2) + [1] * (1-x)^(-2)')
fit.setParam(0, 's', 100)
fit.setParam(1, 't', 10)
fit.fit(hist, xmin, xmax)
fit.saveData('../fit/s_t_fit_%.2f.txt' % energie)
sfunc = TF1('sfunc', '[0]*(1+x^2)', xmin, xmax)
sfunc.SetParameter(0, fit.params[0]['value'])
sfunc.SetLineWidth(1)
sfunc.SetLineColor(3)
sfunc.Draw('same')
tfunc = TF1('tfunc', '[0]*(1-x)^(-2)', xmin, xmax)
tfunc.SetParameter(0, fit.params[1]['value'])
tfunc.SetLineWidth(1)
tfunc.SetLineColor(4)
tfunc.Draw('same')
l = TLegend(0.15, 0.5, 0.55, 0.85)
l.SetTextSize(0.03)
l.AddEntry(hist, "Echte Daten (#sqrt{s} = %.2f GeV) mit ee-cut" % energie, 'l')
l.AddEntry(fit.function, 'Fit mit N(#Theta) = s (1 + cos^{2}#Theta) + t (1 - cos#Theta)^{-2}', 'l')
fit.addParamsToLegend(l, [('%.2f', '%.2f'), ('%.2f', '%.2f')], chisquareformat='%.2f')
l.AddEntry(sfunc, 's (1 + cos^{2}#Theta)', 'l')
l.AddEntry(tfunc, 't (1 - cos#Theta)^{-2}', 'l')
l.Draw()
c.Update()
s = '%.2f' % energie
if not DEBUG:
c.Print('../img/s_t_fit_%s.pdf' % s.replace('.', '-'), 'pdf')
return ((fit.params[0]["value"], fit.params[0]["error"]), (fit.params[1]["value"], fit.params[1]["error"]))
def makeFermiKuriePlot():
data = MyonData.fromPath('../data/betaspektrum.TKA')
data.convertToCountrate()
data.convertChannelToEnergy()
data = calcFermiKuriePlot(data)
c = TCanvas('c', '', 1280, 720)
g = data.makeGraph('g', 'energy E / MeV', '#sqrt{n/E^{2}}')
prepareGraph(g)
g.GetXaxis().SetRangeUser(0, 150)
g.SetMaximum(0.004)
g.GetYaxis().SetTitleOffset(1.3)
g.Draw('APX')
g.Draw('P')
c.Update()
c.Print('../img/betaspectrum_FermiKurie.pdf', 'pdf')
g.GetXaxis().SetRangeUser(0, 80)
g.SetMaximum(3e-3)
fit = Fitter('fit', '[0]*(x-[1])*1e-6')
fit.setParam(0, 'a', -0.3)
fit.setParam(1, 'b', 55)
fit.fit(g, 23, 45)
fit.saveData('../fit/FermiKurie.txt')
l = TLegend(0.65, 0.6, 0.85, 0.85)
l.SetTextSize(0.03)
l.AddEntry(g, 'Fermi-Kurie plot', 'p')
l.AddEntry(fit.function, 'fit with a(E-b)', 'l')
fit.addParamsToLegend(l, (('%.2f', '%.2f'), ('%.2f', '%.2f')),
chisquareformat='%.2f',
units=["10^{-6}", ""],
lang='en')
l.Draw()
c.Update()
c.Print('../img/betaspectrum_FermiKurie_fit.pdf', 'pdf')
return fit.params[1]["value"], fit.params[1]["error"]
def makeEnergyGauge(detector, peaks, litvals):
x, sx = zip(*peaks)
data = DataErrors.fromLists(x, litvals, sx, [0] * len(x))
c = TCanvas('c_eg_%s' % detector, '', 1280, 720)
g = data.makeGraph('g_eg_%s' % detector, 'Kanal k', 'Energie E / eV')
g.Draw('AP')
fit = Fitter('f_eg_%s' % detector, 'pol1(0)')
fit.setParam(0, 'a', 0)
fit.setParam(1, 'b')
fit.fit(g, x[0] - 20, x[-1] + 20)
fit.saveData('../calc/part3/energygauge_%s.txt' % detector, 'w')
l = TLegend(0.15, 0.6, 0.4, 0.85)
l.AddEntry(g, 'Messwerte', 'p')
l.AddEntry(fit.function, 'Fit mit E(k) = a + b*k', 'l')
fit.addParamsToLegend(l, [('%.2f', '%.2f'), ('%.4f', '%.4f')], chisquareformat='%.2f')
l.Draw()
c.Update()
if PRINTGRAPHS:
c.Print('../img/part3/energygauge_%s.pdf' % detector, 'pdf')
def evalDistance(n, params):
xmin, xmax = params[1:]
deltax = abs(xmax - xmin) * 0.1
data = P2SemiCon.fromPath('../data/part2/length/ALL%04.d/F%04dCH1.CSV' % (n, n))
g = data.makeGraph('g%d' % n, 'Zeit t / s', 'Spannung U / V')
c = TCanvas('c%d' % n, '', 1280, 720)
g.SetMarkerStyle(1)
g.GetXaxis().SetRangeUser(xmin - deltax, xmax + deltax)
closex = min(getByCloseX(data, xmin)[1], getByCloseX(data, xmax)[1])
if closex > 0:
ymin = closex * 0.95
else:
ymin = closex * 1.1
g.SetMinimum(ymin)
g.GetYaxis().SetTitleOffset(1.2)
g.Draw('APX')
fit = Fitter('f', 'pol1(0) + 1/(sqrt(2*pi*[4]^2))*gaus(2)')
fit.function.SetNpx(1000)
paramname = ['a', 'b', 'A', 't_{c}', '#sigma']
for i, param in enumerate(params[0]):
fit.setParam(i, paramname[i], param)
fit.fit(g, xmin, xmax)
fit.saveData('../calc/part2/dist%02d.txt' % n, 'w')
l = TLegend(0.625, 0.6, 0.99, 0.99)
l.SetTextSize(0.03)
l.AddEntry(g, 'Messung', 'p')
l.AddEntry(fit.function, 'Fit mit U(t) = a + b*t + #frac{A}{#sqrt{2#pi*#sigma^{2}}} e^{- #frac{1}{2} (#frac{t - t_{c}}{#sigma})^{2}}', 'l')
l.AddEntry(0, '', '')
fit.addParamsToLegend(l, [('%.2e', '%.2e')] * len(params[0]), chisquareformat='%.2f')
l.Draw()
if PRINTGRAPHS:
c.Update()
c.Print('../img/part2/dist%02d.pdf' % n, 'pdf')
return data, fit.params, fit.getCorrMatrix(), params[1], params[2]
def main():
times = [2.4, 4.5, 6.25, 8.6]
times_error = [0.02] * len(times)
channels = [113, 223, 315.5, 441]
channels_error = [0.5] * len(times)
with TxtFile('../src/tab_timecalibration.tex', 'w') as f:
f.write2DArrayToLatexTable(list(zip(*[times, times_error, channels, channels_error])),
["$t$ / \\textmu s", "$s_t$ / \\textmu s", "$c$", "$s_c$"],
["%.2f", "%.2f", "%.1f", "%.1f"],
"Measured times and channels with errors for the time calibration.", "tab:tcal")
data = DataErrors.fromLists(channels, times, channels_error, times_error)
c = TCanvas('c', '', 1280, 720)
g = data.makeGraph('g', 'channel c', 'time t / #mus')
g.Draw('APX')
fit = Fitter('fit', 'pol1(0)')
fit.setParam(0, 'a', 0)
fit.setParam(1, 'b', 50)
fit.fit(g, 100, 450)
fit.saveData('../fit/timeCalibration.txt')
l = TLegend(0.15, 0.6, 0.5, 0.85)
l.SetTextSize(0.03)
l.AddEntry(g, 'measurement', 'p')
l.AddEntry(fit.function, 'fit with t(c) = a + b * c', 'l')
fit.addParamsToLegend(l, (('%.2f', '%.2f'), ('%.5f', '%.5f')), chisquareformat='%.2f', units=('#mus', '#mus/channel'), lang='en')
l.Draw()
g.Draw('P')
c.Update()
c.Print('../img/timeCalibration.pdf', 'pdf')
with TxtFile('../calc/timeCalibration.txt', 'w') as f:
f.writeline('\t', str(fit.params[0]['value']), str(fit.params[0]['error']))
f.writeline('\t', str(fit.params[1]['value']), str(fit.params[1]['error']))
f.writeline('\t', str(fit.getCovMatrixElem(0, 1)))
def fitXc(dists, times):
listx, listsx = zip(*times)
listy, listsy = zip(*dists)
data = DataErrors.fromLists(listx, listy, listsx, listsy)
c = TCanvas('cXc', '', 1280, 720)
g = data.makeGraph('xc', 'Zeit t / s', 'x_{c} / mm')
g.SetLineWidth(0)
g.Draw('AP')
fit = Fitter('fitXc', 'pol1(0)')
fit.setParam(0, 'x_{0}', 1)
fit.setParam(1, 'm', 0.5e-6)
fit.fit(g, 1e-6, 23e-6)
fit.saveData('../calc/part2/dist_fit_xc.txt')
l = TLegend(0.15, 0.625, 0.4, 0.85)
l.SetTextSize(0.03)
l.AddEntry('xc', 'Messung', 'p')
l.AddEntry(fit.function, 'Fit mit x_{c}(t) = x_{0}+m*t', 'l')
l.AddEntry(0, '', '')
fit.addParamsToLegend(l, [('%.3f', '%.3f'), ('%.2e', '%.2e')], chisquareformat='%.2f')
l.Draw()
if PRINTGRAPHS or True:
c.Update()
c.Print('../img/part2/dist_fitXc.pdf', 'pdf')
# calculate mu
m, sm = fit.params[1]['value'], fit.params[1]['error']
U, sU = 48.8, P2SemiCon.UERROR
l = 30
mu = m * l / U / 100 # /100 -> from mm in cm
smu = mu * np.sqrt((sm / m) ** 2 + (sU / U) ** 2)
with TxtFile('../calc/part2/dist_mu.txt', 'w') as f:
f.writeline('\t', str(mu), str(smu))
return mu, smu
def evalAngleDependency():
datalist = loadCSVToList(DIR + 'Winkelabh.txt')
data = DataErrors()
strel = 0.01
sI = 1
for t2, t1, n, phi in datalist:
f, sf = calcPrecissionFreq(t2, t1, n)
data.addPoint(phi, f, 0.5, sf)
c = TCanvas('c_phi', '', 1280, 720)
g = data.makeGraph('g_phi', 'Rotation des Strahlengangs #varphi / #circ',
'Praezessionsfrequenz f / kHz')
g.GetXaxis().SetRangeUser(-15, 15)
g.SetMinimum(0)
g.Draw('APX')
fit = Fitter('fit_phi', '[0] * abs(sin((x-[1])*pi/180))')
fit.function.SetNpx(1000)
fit.setParam(0, '#beta', 1)
fit.setParam(1, '#phi_{0}', 0)
fit.fit(g, -15, 15)
fit.saveData('../fit/part4/winkel.txt')
g.Draw('P')
l = TLegend(0.7, 0.15, 0.95, 0.5)
l.SetTextSize(0.03)
l.AddEntry(g, 'Praezessionsfrequenz', 'p')
l.AddEntry(fit.function, 'Fit mit f = #beta |sin(#varphi + #varphi_{0})|',
'l')
fit.addParamsToLegend(l, (('%.1f', '%.1f'), ('%.2f', '%.2f')),
chisquareformat='%.2f',
units=['kHz/#muT', '#circ'])
l.Draw()
c.Update()
c.Print('../img/part4/winkel.pdf', 'pdf')
def makeFBAFit(energy, data, binsize):
cutdata = data.cut(CUTS[1][1])
c = TCanvas('c_%f' % energy, '', 1280, 720)
hist = cutdata.makeHistogramm('hist_%f' % energy, 'cos_thet', binsize, -1,
1)
hist.Draw()
if energy in [90.22720, 91.23223, 91.97109]:
#fit = Fitter('fit_%d' % round(energy*100), "pi * (1/128.9)^2 / (2 * %f) * ([0]*(1+x^2) + 2 * [1] * x)" % (energy ** 2))
fit = Fitter('fit_%d' % round(energy * 100),
"[0]*(1+x^2) + 2 * [1] * x")
fit.setParam(0, 'F_{1}', 1e10)
fit.setParam(1, 'F_{2}', -4e8)
fit.fit(hist, -0.9, 0.9, "M")
l = TLegend(0.65, 0.75, 0.98, 0.98)
l.SetTextSize(0.03)
l.AddEntry(hist, 'daten_1 (mit mm-cut)', 'l')
l.AddEntry(fit.function, 'Fit mit F_{1} (1 + x^{2}) + 2 F_{2} x', 'l')
fit.addParamsToLegend(l, (('%.3f', '%.3f'), ('%.3f', '%.3f')),
chisquareformat='%.2f')
l.Draw()
F1, sF1 = fit.params[0]["value"], fit.params[0]["error"]
F2, sF2 = fit.params[1]["value"], fit.params[1]["error"]
A = 3 / 4 * F2 / F1
sA = abs(A) * sqrt((sF1 / F1)**2 + (sF2 / F2)**2)
sinW = 1 / 4 * (1 - sqrt(abs(A) / 3))
ssinW = sA / (8 * sqrt(3 * abs(A)))
res = A, sA, sinW, ssinW
else:
res = None
c.Update()
s = '%.5f' % energy
c.Print("../img/FBA_%.5f.pdf" % s.replace('.', '-'), 'pdf')
return res
def evalSpinPrecission(name):
datalist = loadCSVToList(DIR + name + '.txt')
data = DataErrors()
sI = 1
for t2, t1, n, I in datalist:
f, sf = calcPrecissionFreq(t2, t1, n)
B, sB = inductorIToB(4, I * 1e-3, sI * 1e-3)
data.addPoint(B * 1e6, f, sB * 1e6, sf)
if len(name) == 4:
xmin, xmax = 0, 50
else:
xmin, xmax = 0, 80
c = TCanvas('c_%s' % name, '', 1280, 720)
g = data.makeGraph('g_%s' % name,
'Zusaetzliches Vertikalfeld B_{S, v} / #muT',
'Praezessionsfrequenz f / kHz')
g.GetXaxis().SetRangeUser(xmin, xmax)
g.Draw('APX')
fit1 = Fitter('fit1_%s' % name, '[0] * abs([1]-x)')
fit1.function.SetNpx(1000)
fit1.setParam(0, '#alpha', 1)
fit1.setParam(1, 'B_{E, v}', 40)
fit1.fit(g, xmin, xmax)
fit1.saveData('../fit/part4/fit1_%s' % name)
fit2 = Fitter('fit1_%s' % name, '[0] * (abs([1]-x) + [2])')
fit2.function.SetNpx(1000)
fit2.function.SetLineColor(3)
fit2.setParam(0, '#alpha', 1)
fit2.setParam(1, 'B_{E, v}', 40)
fit2.setParam(2, 'B_{E,h,s}', 20)
fit2.setParamLimits(2, 0, 100)
fit2.fit(g, xmin, xmax, '+')
fit2.saveData('../fit/part4/fit2_%s' % name)
g.Draw('P')
if len(name) == 4:
l = TLegend(0.6, 0.4, 0.975, 0.95)
else:
l = TLegend(0.325, 0.475, 0.725, 0.99)
l.SetTextSize(0.03)
l.AddEntry(g, 'Praezessionsfrequenzen', 'p')
l.AddEntry(fit1.function,
'Fit mit f_{1}(B_{S, v}) = #alpha |B_{E, v} - B_{S, v}|', 'l')
fit1.addParamsToLegend(l, (('%.2f', '%.2f'), ('%.2f', '%.2f')),
chisquareformat='%.2f',
units=['kHz/#muT', '#muT'])
l.AddEntry(
fit2.function,
'Fit mit f_{2}(B_{S, v}) = #alpha (|B_{E, v} - B_{S, v}| + B_{E,h,s})',
'l')
fit2.addParamsToLegend(l, (('%.2f', '%.2f'), ('%.2f', '%.2f'),
('%.2f', '%.2f')),
chisquareformat='%.2f',
units=['kHz/#muT', '#muT', '#muT'])
l.Draw()
c.Update()
c.Print('../img/part4/%s.pdf' % name, 'pdf')
def makeCSGraph(ctype, startGammaEE=None):
data = loadCrossSection(ctype)
c = TCanvas('c_%s' % ctype, '', 1280, 720)
g = data.makeGraph('g_%s' % ctype, '#sqrt{s} / GeV', '#sigma / nb')
g.Draw('AP')
if not startGammaEE:
fit = Fitter(
'fit_%s' % ctype,
'(12*pi / [0]^2) * (x^2 * [1]^2) / ((x^2-[0]^2)^2 + x^4 * [2]^2 / [0]^2) * 0.3894*10^6'
) # GeV^-2 = 0.3894 mb
fitfuncstring = "#frac{12#pi}{M_{Z}^{2}} #frac{s #Gamma_{%s}^{2}}{(s-M_{Z}^{2})^{2} + s^{2} #Gamma_{Z}^{2} / M_{Z}^{2}}" % ctype[
0]
else:
fit = Fitter(
'fit_%s' % ctype,
'(12*pi / [0]^2) * (x^2 * [1] * [2]) / ((x^2-[0]^2)^2 + x^4 * [3]^2 / [0]^2) * 0.3894*10^6'
)
fitfuncstring = "#frac{12#pi}{M_{Z}^{2}} #frac{s #Gamma_{m} #Gamma_{%s}}{(s-M_{Z}^{2})^{2} + s^{2} #Gamma_{Z}^{2} / M_{Z}^{2}}" % ctype[
0]
fit.setParam(0, 'M_{Z}', 91.2)
fit.setParamLimits(0, 0, 1000)
if not startGammaEE:
fit.setParam(1, '#Gamma_{%s}' % ctype[0], 0.08)
fit.setParamLimits(1, 0, 1000)
fit.setParam(2, '#Gamma_{Z}', 2.5)
fit.setParamLimits(2, 0, 1000)
else:
fit.setParam(1, '#Gamma_{m}', startGammaEE, True)
fit.setParam(2, '#Gamma_{%s}' % ctype[0], 1.5)
fit.setParam(3, '#Gamma_{Z}', 2.5)
fit.setParamLimits(2, 0, 1000)
fit.fit(g, 88, 94, '')
fit.saveData('../fit/crosssections_%s.txt' % ctype)
l = TLegend(0.625, 0.6, 0.98, 0.975)
l.SetTextSize(0.03)
l.AddEntry(g, "%s Wirkungsquerschnitte" % ctype, 'p')
l.AddEntry(fit.function, "Fit mit #sigma(s) = %s" % fitfuncstring, 'l')
l.AddEntry(None, "", '')
if not startGammaEE:
fit.addParamsToLegend(l, [('%.3f', '%.3f'), ('%.4f', '%.4f'),
('%.3f', '%.3f')],
chisquareformat='%f',
units=['GeV / c^{2}', 'GeV', 'GeV'])
else:
fit.addParamsToLegend(l, [('%.3f', '%.3f'), '%.4f', ('%.3f', '%.3f'),
('%.3f', '%.3f')],
chisquareformat='%f',
units=['GeV/c^{2}', 'GeV', 'GeV', 'GeV'])
l.Draw()
c.Update()
if not DEBUG:
c.Print('../img/crosssections_%s.pdf' % ctype, 'pdf')
if not startGammaEE:
result = [(fit.params[0]['value'], fit.params[0]['error']),
(fit.params[1]['value'], fit.params[1]['error']),
(fit.params[2]['value'], fit.params[2]['error'])]
else:
result = [(fit.params[0]['value'], fit.params[0]['error']),
(fit.params[2]['value'], fit.params[2]['error']),
(fit.params[3]['value'], fit.params[3]['error'])]
return result
