def getAverageDeltaEnergy(lowprog, highprog):
# load data
lp = I2Data.fromPath(lowprog)
lp.correctValues(useX=False)
lp.invertY()
lp.multiplyY(1e7) # from 1/nm to 1/cm
hp = I2Data.fromPath(highprog)
hp.correctValues(useX=False)
hp.invertY()
hp.multiplyY(1e7) # from 1/nm to 1/cm
# get same values for same nus
sameNus = intersect(lp.getX(), hp.getX())
deltas = []
deltas_errors = []
for nu in sameNus:
hv = hp.getByX(nu) # high value
hy = hv[1] # high y
hye = hv[3] # high error
lv = lp.getByX(nu) # low value
ly = lv[1] # low y
lye = lv[3] # low error
deltas.append(hy - ly)
deltas_errors.append(np.sqrt(hye ** 2 + lye ** 2))
return avgerrors(deltas, deltas_errors)
def calculateExcitationEnergy():
prog = 1
n = 18
m = 0
# get G'(nu = n)
absEnergy = I2Data.fromPath('../data/prog%dnl.txt' % prog)
absEnergy.correctValues(useX=False)
absEnergy.invertY()
absEnergy.multiplyY(1e7)
G = absEnergy.getByX(n)[1]
Ge = absEnergy.getByX(n)[3]
# get DeltaG from model
params = getParamsFromFittingInfo('../calc/prog%d_fit1ord.txt' % prog)
rho = getCorrelationFrom2DMatrix('../calc/prog%d_fit1ord.txt' % prog)
a = params['a']['value']
ae = params['a']['error']
b = params['b']['value']
be = params['b']['error']
dg = a - b * (2 * m + 2)
dge = np.sqrt(ae ** 2 - 4 * (1 + m) * rho * ae * be + 4 * ((1 + m) * be) ** 2)
dgn = a - b * (2 * n + 2)
dgne = np.sqrt(ae ** 2 - 4 * (1 + n) * rho * ae * be + 4 * ((1 + n) * be) ** 2)
# calculate excitation energy
E = G - n / 2 * (dg + dgn)
Ee = np.sqrt(Ge ** 2 + (dge * n) ** 2 / 4 + (dgne * n) ** 2 / 4)
# output to file
with TxtFile('../calc/ExcitationEnergy.txt', 'w') as f:
f.writeline('\t', str(E), str(Ee))
def evalHalogenLamp():
data = I2Data.fromPath('../data/03_Halogen_ngg10.txt')
c = TCanvas('c1', '', 1280, 720)
g = data.makeGraph('g', 'wavelength #lambda / nm', 'intensity / a.u.')
g.GetXaxis().SetRangeUser(415, 620)
g.GetYaxis().SetTitleOffset(1.3)
g.SetMarkerStyle(1)
g.Draw('AP')
c.Update()
c.Print('../img/halogen_lamp.pdf', 'pdf')
def makeProgTables():
name = 'prog%dnl.txt'
for i in range(1, 3 + 1):
prog = I2Data.fromPath('../data/' + name % i)
x = prog.getX()
y = prog.getY()
prog.correctValues(False)
yc = prog.getY()
yce = prog.getEY()
f = TxtFile('../src/prog%d.tex' % i, 'w')
f.write2DArrayToLatexTable(zip(x, y, yc, yce),
['$\\nu\'$', r'$\lambda_{\text{exp}}$ / nm', r'$\lambda_{\text{cor}}$ / nm', r'$s_{\lambda_{\text{cor}}}$ / nm'],
['%0.f', '%3.2f', '%3.2f', '%.8f'],
'Measured position of transmission minima in $I_2$-spectrum and corrected values of progession %d.' % i,
'tab:prog%d' % i)
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 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'])))
