def n(): ########### remove interference noise from 4_light
width = 100
r = RamanSpectrum('/home/chris/Documents/DataWeiss/150304/4_light.SPE')
lambdaa = 10**7 / (10**7 / 514.5 - array(r.index))
r.index = pandas.Float64Index(lambdaa)
r = r.iloc[90:900]
for i in range(5):
r = smooth(r)
fit = polyfit(array(r.index), r.values, 4)
r /= polyeval(fit, array(r.index))
xs = array(r.index) / 1000
def funct(x, A, m, b, phase):
return (1 + (A) * cos((x + m * x**2) * pi / b + phase))**2
guess = [0.005, 1, 0.005, 0.1]
plot(xs, funct(xs, *guess), 'k')
plot(xs, r.values)
x = scipy.optimize.curve_fit(funct, xs, r.values, guess)
print x[0]
plot(xs, funct(xs, *x[0]))
return 0
def n2(name): ########### remove interference noise from 3_light
r = RamanSpectrum(name)
lambdaa = 10**7 / (10**7 / 514.5 - array(r.index))
r.index = pandas.Float64Index(lambdaa)
r = r.iloc[300:800]
weights = zeros(r.size)
for i in range(5):
r = smooth(r)
fit = polyfit(array(r.index), r.values, 5)
r /= polyeval(fit, array(r.index))
xs = array(r.index) / 1000
plot(xs, r.values)
def funct(p):
return sum((r.values - 0.0017 * cos(700 / (xs + 4 * xs**2) + p) - 1)
**2)
bnds = ((0, 2 * pi), )
pguess = float(scipy.optimize.minimize(funct, pi, bounds=bnds)['x'])
pguess = 0
#plot(xs,0.0017*cos(600/(xs+1*xs**2) +pguess)+1 ,'r')
#def funct(x,A,m,b):return (A/1000.0)*cos(d*100/(a*x**2+b*x**2+c)+pguess)+1 #def funct(x,A,b,phase):return (1+(A)*cos(x*pi/b+phase))**2
#guess = [1.7,4,6.0]#
def funct(x, A, a, b, c):
return (A / 1000.0) * cos(
1000 / (a * x**2 + b * x + c)
) + 1 #def funct(x,A,b,phase):return (1+(A)*cos(x*pi/b+phase))**2
guess = [1.7, 3, 1, 0] #
# plot(xs,funct(xs, *guess),'k')
x = scipy.optimize.curve_fit(funct, xs, r.values, guess)
print x[0]
plot(
xs,
funct(xs, *x[0]),
label=os.path.basename(name),
color=gca().lines[-1].get_color())
print sum((r.values - funct(xs, *x[0]))**2)
legend()
return 0
def RhodBonRaman():
os.chdir('/home/chris/Documents/DataWeiss/150228')
RB1=RamanSpectrum('RhB 500sec 0_01_filter.SPE')
RB2= RamanSpectrum('RhB 500sec 0_1_filter.SPE')
RB3=RamanSpectrum('RhB 500sec full power_filter.SPE')
RBref = RamanSpectrum(pandas.Series.from_csv('/home/chris/Documents/DataWeiss/RhodamineB.csv'))
RBref.index=pandas.Float64Index(10**7/514.5-10**7/array(RBref.index))
dark = mean(RamanSpectrum('dark 50 s.SPE'))*10
RB1/=max(RB1)
RB1.plot()
RBref.plot()
return RBref
