def saveData(self, path, mode='w', enc='utf-8'):
"""saves fitting info (chi^2, DoF, chi^2/DoF, paramters with values and errors, covariance and correlation matrix)
Arguments:
path -- relative path to file
mode -- write mode (usually 'w' for overwriting or 'a' for appending)
enc -- encoding (default = 'utf-8')
"""
with TxtFile.fromRelPath(path, mode) as f:
f.writeline('fitting info')
f.writeline('============')
f.writeline(TxtFile.CHISQUARE + ':\t\t' + str(self.getChisquare()))
f.writeline('DoF:\t' + str(self.getDoF()))
f.writeline(TxtFile.CHISQUARE + '/DoF:\t' + str(self.getChisquareOverDoF()))
f.writeline('\t', 'p-value:', *map(str, self.getPValue()))
f.writeline('')
f.writeline('parameters')
f.writeline('==========')
for i, param in self.params.iteritems():
f.writeline('\t', str(i), param['name'], str(param['value']), TxtFile.PM, str(param['error']))
f.writeline('')
f.writeline('covariance matrix')
f.writeline('=================')
f.writelines('\t'.join(str(j) for j in i) + '\n' for i in self._covMatrix)
f.writeline('')
f.writeline('correlation matrix')
f.writeline('==================')
f.writelines('\t'.join(str(j) for j in i) + '\n' for i in self._corrMatrix)
f.writeline()
f.close()
def evalHgPeaks():
data = I2Data.fromPath('../data/02_Hg_full_ngg11.txt')
mins = data.findExtrema(10, 425, 630, False)
mins.filterY(1300)
with TxtFile.fromRelPath('../calc/hg_lines.txt', 'w') as f:
for min in mins.points:
f.writeline(str(min[0]))
c = TCanvas('c1', '', 1280, 720)
c.SetLogy()
g = data.makeGraph('g', 'wavelength #lambda / nm', 'intensity / a.u.')
g.GetXaxis().SetRangeUser(415, 620)
g.SetMarkerStyle(1)
g.Draw('AP')
m = mins.makeGraph()
if m:
m.SetMarkerColor(2)
m.Draw('P')
c.Update()
c.Print('../img/HgPeaks.pdf', 'pdf')
def evalNaPeaks():
data = I2Data.fromPath('../data/01_Na_ngg13.txt')
mins = data.findExtrema(200, 508, 630, False)
mins.filterY(40000)
with TxtFile.fromRelPath('../calc/na_lines.txt', 'w') as f:
for min in mins.points:
f.writeline(str(min[0]))
c = TCanvas('c1', '', 1280, 720)
g = data.makeGraph('g', 'wavelength #lambda / nm', 'intensity / a.u.')
g.GetXaxis().SetRangeUser(415, 620)
g.GetYaxis().SetTitleOffset(1.2)
g.SetMarkerStyle(1)
g.Draw('AP')
m = mins.makeGraph()
if m:
m.SetMarkerColor(2)
m.Draw('P')
c.Update()
c.Print('../img/NaPeaks.pdf', 'pdf')
def saveData(self, path, mode='w', enc='utf-8'):
"""saves fitting info (chi^2, DoF, chi^2/DoF, paramters with values and errors, covariance and correlation matrix)
Arguments:
path -- relative path to file
mode -- write mode (usually 'w' for overwriting or 'a' for appending)
enc -- encoding (default = 'utf-8')
"""
with TxtFile.fromRelPath(path, mode) as f:
f.writeline('fitting info')
f.writeline('============')
f.writeline(TxtFile.CHISQUARE + ':\t\t' + str(self.getChisquare()))
f.writeline('DoF:\t' + str(self.getDoF()))
f.writeline(TxtFile.CHISQUARE + '/DoF:\t' +
str(self.getChisquareOverDoF()))
#f.writeline('\t', 'p-value:', *map(str, self.getPValue()))
f.writeline('')
f.writeline('parameters')
f.writeline('==========')
for i, param in self.params.items():
f.writeline('\t', str(i), param['name'], str(param['value']),
TxtFile.PM, str(param['error']))
f.writeline('')
f.writeline('covariance matrix')
f.writeline('=================')
if self._covMatrix:
f.writelines('\t'.join(str(j) for j in i) + '\n'
for i in self._covMatrix)
f.writeline('')
f.writeline('correlation matrix')
f.writeline('==================')
if self._corrMatrix:
f.writelines('\t'.join(str(j) for j in i) + '\n'
for i in self._corrMatrix)
f.writeline()
f.close()
def getExcitedStateOscillationConstants():
# plot spectrum
data = I2Data.fromPath('../data/04_I2_ngg10_10ms.txt')
progression = dict()
for i in range(1, 3 + 1):
progression[i] = I2Data.fromPath('../data/prog%d.txt' % i)
c = TCanvas('c', '', 1280, 720)
g = data.makeGraph('spectrum', 'wavelength #lambda / nm', 'intensity / a.u.')
g.SetMarkerStyle(1)
g.GetXaxis().SetRangeUser(505, 620)
g.SetMinimum(18000)
g.SetMaximum(49000)
myY = TGaxis()
myY.ImportAxisAttributes(g.GetYaxis())
myY.SetMaxDigits(3)
g.Draw('AL')
pg1 = progression[1].makeGraph('prog1')
pg1.SetMarkerColor(2)
pg1.Draw('P')
pg2 = progression[2].makeGraph('prog2')
pg2.SetMarkerColor(3)
pg2.Draw('P')
pg3 = progression[3].makeGraph('prog3')
pg3.SetMarkerColor(4)
pg3.Draw('P')
l = TLegend(0.6, 0.15, 0.85, 0.4)
l.AddEntry('spectrum', 'measurement', 'l')
l.AddEntry('prog1', 'first progression (#nu\'\' = 1)', 'p')
l.AddEntry('prog2', 'second progression (#nu\'\' = 2)', 'p')
l.AddEntry('prog3', 'third progression (#nu\'\' = 3)', 'p')
l.Draw()
c.Update()
c.Print('../img/I2_absorption.pdf', 'pdf')
# calculations
start = [18, 7, 9]
prog1ord = {'a': [], 'ae': [], 'b': [], 'be': []}
for i, prog in progression.iteritems():
# Calculate vacuum wavelength and create Birge-Sponer plot
prog.correctValues()
c = TCanvas('c%d' % i, '', 1280, 720)
g = makeBirgeSponer(prog, start[i - 1]).makeGraph('prog%d_bs' % i, '#nu\' + 1/2', '#Delta G (#nu\' + 1/2) / (cm^{-1})')
g.Draw('AP')
# fit 2nd-order
fit2ord = Fitter('prog%d_2ord' % i, '[0]-[1]*(2*x+2)+[2]*(3*x^2+6*x+13/4)')
fit2ord.setParam(0, 'a', 120)
fit2ord.setParam(1, 'b', 1)
fit2ord.setParam(2, 'c', 0)
fit2ord.fit(g, 4, 50)
fit2ord.saveData('../calc/prog%d_fit2ord.txt' % i, 'w')
l2 = TLegend(0.6, 0.7, 0.95, 0.95)
l2.AddEntry(0, 'Fit 2nd. order', '')
l2.AddEntry(fit2ord.function, 'y = a - b*(2*x+2) + c*(3*x^2+6*x+13/4)', 'l')
fit2ord.addParamsToLegend(l2)
l2.SetTextSize(0.03)
l2.Draw()
# fit 1st-order
fit1ord = Fitter('prog%d_1ord' % i, '[0]-[1]*(2*x+2)')
fit1ord.setParam(0, 'a', 120)
fit1ord.setParam(1, 'b', 1)
fit1ord.fit(g, 4, 50, '+')
g.GetFunction('prog%d_1ord' % i).SetLineColor(4)
fit1ord.saveData('../calc/prog%d_fit1ord.txt' % i, 'w')
prog1ord['a'].append(fit1ord.params[0]['value'])
prog1ord['ae'].append(fit1ord.params[0]['error'])
prog1ord['b'].append(fit1ord.params[1]['value'])
prog1ord['be'].append(fit1ord.params[1]['error'])
l1 = TLegend(0.125, 0.15, 0.5, 0.4)
l1.AddEntry(0, 'Fit 1st. order', '')
l1.AddEntry(g.GetFunction('prog%d_1ord' % i), 'y = a - b*(2*x+2)', 'l')
fit1ord.addParamsToLegend(l1)
l1.SetTextSize(0.03)
l1.Draw()
c.Update()
c.Print('../img/prog%d_birgesponer.pdf' % i, 'pdf')
# save vibrational constants to latex file
nus = [0, 1, 2]
f = TxtFile('../src/ExcitedStateOscillationConstants.tex', 'w')
f.write2DArrayToLatexTable(zip(nus, prog1ord['a'], prog1ord['ae'], prog1ord['b'], prog1ord['be']),
['$\\nu\'\'$', '$\omega_e\' / \\text{cm}^{-1}$', '$s_{\omega_e\'} / \\text{cm}^{-1}$',
'$\omega_e\' x_e\' / \\text{cm}^{-1}$', '$s_{\omega_e\' x_e\'} / \\text{cm}^{-1}$'],
['%0.f', '%3.1f', '%.1f', '%.3f', '%.3f'],
'Oscillation constants for first order fit of Birge-Sponer plots', 'tab:prog1ord')
f.close()
# calculate weighted average for fit 1st- order
with TxtFile.fromRelPath('../calc/ExcitedStateOscillationConstants.txt', 'w') as f:
f.writeline('\t', *map(lambda x: str(x), avgerrors(prog1ord['a'], prog1ord['ae'])))
f.writeline('\t', *map(lambda x: str(x), avgerrors(prog1ord['b'], prog1ord['be'])))
def makeMassFit():
# config files
# file = [mass, path]
files = []
files.append([2.0123, "../data/11_K_m9_3200_t420.txt", 420])
files.append([2.0123, "../data/11b_K_m9_3200_t420.txt", 420])
files.append([1.9047, "../data/13_K_m8_3200_t420.txt", 420])
files.append([1.6812, "../data/15_K_m7_3200_t420.txt", 420])
files.append([1.4827, "../data/17_K_m6_3200_t420.txt", 420])
files.append([1.2952, "../data/19_K_m5_3200_t480.txt", 480])
files.append([1.0993, "../data/21_K_m4_3200_t480.txt", 480])
files.append([0.8086, "../data/23_K_m3_3200_t540.txt", 540])
files.append([0.6954, "../data/25_K_m2_3200_t540.txt", 540])
files.append([0.5007, "../data/27_K_m1_3200_t660.txt", 660])
files.append([0.3030, "../data/29_K_m0_3200_t780.txt", 780])
u = 0.760
tu = 50
d = DataErrors()
for file in files:
n = readSingleEntryFile(file[1])
d.addPoint(file[0], n - u, 0.001, np.sqrt(n / file[2] + u / tu))
d.saveDataToLaTeX(['Masse $m /$g', '$s_m /$g', 'Z\"ahlrate $n / (1/\\text{s})$', '$s_n / (1/\\text{s})$'],
['%.3f', '%.3f', '%.3f', '%.3f'],
'Z\"ahlraten von \\kalium\,f\"ur verschiedene Massen mit Fehlern', 'tab:data:kalium', '../src/data_kalium.tex', 'w')
c = TCanvas('c2', '', 800, 600)
g = d.makeGraph('g', 'Masse m / g', 'Z#ddot{a}hlrate n / (1/s)')
g.SetMaximum(6)
g.SetMinimum(2)
g.Draw('AP')
fit = Fitter('f', '[0]*(1-exp(-[1]*x))')
fit.setParam(0, 'a')
fit.setParam(1, 'b')
fit.fit(g, 0.1, 2.5)
fit.saveData('../fit/kalium.txt')
a = fit.params[0]['value']
sa = fit.params[0]['error']
b = fit.params[1]['value']
sb = fit.params[1]['error']
l = TLegend(0.4, 0.15, 0.85, 0.5)
l.AddEntry('g', '{}^{40} Kalium ohne Untergrund', 'p') # TODO with error bar? (options +'e')
l.AddEntry(fit.function, 'Fit mit n(m)=a(1-exp(-b*m))', 'l')
l.AddEntry(0, '#chi^{2} / DoF : %f' % fit.getChisquareOverDoF(), '')
l.AddEntry(0, 'Paramter:', '')
l.AddEntry(0, 'a: %e #pm %e' % (a, sa), '')
l.AddEntry(0, 'b: %e #pm %e' % (b, sb), '')
l.SetTextSize(0.03)
l.Draw()
NA = 6.02214129e23
hrel = 0.000118
mrel = 39.0983 + 35.45
f = 1.29
rho = fit.getCorrMatrixElem(1, 0)
thalf = (np.log(2) * NA * hrel * f) / (1.12 * mrel * 2 * a * b) / (3600 * 24 * 365.242)
sthalf = thalf * np.sqrt((sa / a) ** 2 + (sb / b) ** 2 + 2 * rho * (sa / a) * (sb / b))
with TxtFile.fromRelPath('../fit/kalium.txt', 'a') as f:
f.writeline()
f.writeline('calculations')
f.writeline('============')
f.writeline('\t', 'half-life of Kalium:', '%e' % thalf, TxtFile.PM, '%e' % sthalf)
c.Update()
c.Print('../img/Kalium40_Massenabhaengigkeit.pdf')
