def Fig2(): ##### View,filter, and average spectra of PbS dots with Methoxythiophenol in the 2300-3400 cm-1 range
subplot(121)
a = RamanTools.RamanSpectrum("/home/chris/Documents/DataWeiss/141125/7_.txt")
takeout(a)
a.plot()
b = RamanTools.RamanSpectrum("/home/chris/Documents/DataWeiss/141126/7_.txt")
b = RamanTools.FourierFilter(b, width=380)
takeout(b, centers=(456, 483), demo=True)
b.plot()
c = RamanTools.RamanSpectrum("/home/chris/Documents/DataWeiss/141126/8_.txt")
takeout(c)
c.plot()
m = RamanTools.add_RamanSpectra(a, b)
n = RamanTools.add_RamanSpectra(m, c)
legend(["1", "2", "3"])
subplot(122)
MTP = RamanTools.RamanSpectrum("/home/chris/Documents/DataWeiss/141014/4_methoxythiophenol_1.csv")
MTP -= min(MTP[0:1000])
MTP /= max(MTP[0:1000])
MTP *= 1000
MTP.plot(color="b", linewidth=3)
n.autobaseline((2300, 3400))
n /= 10
n.plot()
xlim(2300, 3400)
ylim(-500, 1500)
return 0
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 Mar31():
subplot(221)
a = RamanSpectrum('/home/chris/Documents/DataWeiss/150331/150331_02.txt')
a = autobaseline(a,(141,1700),order = 4)
a = smooth(a)+50
#b = RamanSpectrum('/home/chris/Documents/DataWeiss/150331/150331_03.txt')
#c =add_RamanSpectra(a,b)
a.plot(color = 'k')
ylim(0,200)
xlim(140,1700)
subplot(222)
a.plot(color = 'k')
xlim(2700,3100)
ylim(50,100)
subplot(223)
(RamanSpectrum('/home/chris/Documents/DataWeiss/140918/9_CdMeOTP.SPE')/10-1800).plot(color = 'b')
CdODPARef.plot(color = 'r')
xlim(100,1700)
ylim(0,5000)
subplot(224)
(RamanSpectrum('/home/chris/Documents/DataWeiss/140918/7_CdMeOTP.SPE')-40000).plot(color='b')
(CdODPARef+10000).plot(color = 'r')
xlim(2700,3100)
def spinning(): ## test of optical damage decreased by spinning
os.chdir('/home/chris/Documents/DataWeiss/150209')
figure()
a = ['1_sample not moving.SPE',
'2_sample not moving.SPE',
'3_sample not moving 5 min irradiation.SPE',
'4_sample not moving 5 min irradiation.SPE',
'5_spinning.SPE',
'6_spinning.SPE',
'7_spinning 2 min.SPE',
'8_spinning 3 min.SPE',
'9_spinning 4 min.SPE',
'10_spinning 5 min.SPE',
'11_spinning 10 min.SPE', '12_stopped spinning 0 min.SPE', '13_ 1min later.SPE', '14_ 2min later.SPE', '15_ 3min later.SPE', '16_ 4min later.SPE', '17_long scan.SPE', '18_red dots.SPE', '19.SPE']
areas = list()
for i in a[0:17]:
r = RamanSpectrum(i)
print r.name
areas.append(r.calc_area((180,220)))
plot([0,0.5,5,5.5],areas[0:4]/areas[0],'s-')
plot([0,1,2,3,4,5,10],areas[4:11]/areas[4],'s-')
plot([0,1,2,3,4],areas[11:16]/areas[11],'s-')
legend(['not spun','while spinning','stopped spinning'])
return areas
def May14():
clf()
s = RamanSpectrum("/home/chris/Documents/DataWeiss/150514/150514_12.txt")
a = RamanSpectrum("/home/chris/Documents/DataWeiss/150514/150514_13.txt")
b = RamanSpectrum("/home/chris/Documents/DataWeiss/150514/150514_14.txt")
c = add_RamanSpectra(a, b)
# c = add_RamanSpectra(s,c)
d = RamanSpectrum("/home/chris/Documents/DataWeiss/150514/150514_15.txt")
v = RamanSpectrum("/home/chris/Documents/DataWeiss/150514/150514_16.txt")
z = 0.15
e = subtract_RamanSpectra(c, d * z)
# e.smooth()
l = subtract_RamanSpectra(c, v * z)
# l.smooth()
e = RamanSpectrum(e.append(l))
e = autobaseline(e, (180, 277), order=4)
e = autobaseline(e, (277, 1700), order=4, join="start")
# j = subtract_RamanSpectra(c,RamanSpectrum(pandas.Series(f[2],f[1])))
e.plot()
# d.plot()
# v.plot()
# CdMeOTPRef.index = array(CdMeOTPRef.index)-5
(CdMeOTPRef / 120).plot()
(MeOTPRef / 240).plot()
return 0
def m():
r = RamanSpectrum('/home/chris/Documents/DataWeiss/150304/2_ light.SPE')
v = smooth(r)
for i in range(5):
v = smooth(v)
v.plot(marker='s')
r.plot()
return 0
def today():
a = "/home/chris/Documents/DataWeiss/150518/"
for i in range(1, 9):
r = RamanSpectrum(a + "150517_0" + str(i) + ".txt")
v = 10 ** 7 / array(r.index) - 10 ** 7 / 785
r = RamanSpectrum(pandas.Series(r.values, -1 * v))
r.to_csv(a + "150518_0" + str(i) + "b.txt")
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 Jan18():### data from trying out the cryostat
os.chdir('/home/chris/Documents/DataWeiss/150118')
green = RamanSpectrum('11_phonon long scan.SPE')
green.plot()
red = RamanSpectrum('12b.SPE')
s = argmin(abs(red.index-1468))
print s
print red.iloc[s-1:s+2]
red.iloc[s+1:] -= red.iloc[s+1]-red.iloc[s]
# red = _smooth(red)
red = autobaseline(red,(131,500),order = 0)
red = autobaseline(red,(500,950),order= 4)
red = autobaseline(red,(950,1520),order= 1)
red.plot()
red = RamanSpectrum('13.SPE')
s = argmin(abs(red.index-942.3))
red.iloc[s+1:] -= red.iloc[s+1]-red.iloc[s]
#red = _smooth(red)
red = autobaseline(red,(525,1612),order = 0)
#red = autobaseline(red,(500,950),order= 4)
#red = autobaseline(red,(950,1520),order= 1)
red.plot()
return 0
def Mar24(): ########### Raman of older red dots. These have polystyrene or toluene on them.
figure()
j = RamanSpectrum('/home/chris/Documents/DataWeiss/150324/NativeLigand_Red_22.txt')
k = RamanSpectrum('/home/chris/Documents/DataWeiss/150324/NativeLigand_Red_23.txt')
l = RamanSpectrum('/home/chris/Documents/DataWeiss/150324/NativeLigand_Red_26.txt')
m = add_RamanSpectra(j,k)
m=add_RamanSpectra(m,l)
m=autobaseline(m,(0,3300),order = 0)
m=smooth(m)
m.plot(label='NativeLigands')
j = RamanSpectrum('/home/chris/Documents/DataWeiss/150324/NativeLigand_Red_24.txt')
k = RamanSpectrum('/home/chris/Documents/DataWeiss/150324/NativeLigand_Red_25.txt')
l = RamanSpectrum('/home/chris/Documents/DataWeiss/150324/NativeLigand_Red_26.txt')
m = add_RamanSpectra(j,k)
m=add_RamanSpectra(m,l)
m=autobaseline(m,(0,3300),order = 0)
m=smooth(m)
m.plot(label='NativeLigands')
j = RamanSpectrum('/home/chris/Documents/DataWeiss/150324/NativeLigand_Red_28.txt')
k = RamanSpectrum('/home/chris/Documents/DataWeiss/150324/NativeLigand_Red_29.txt')
m = add_RamanSpectra(j,k)
m=autobaseline(m,(0,3300),order = 0)
m=smooth(m)
m.plot(label='NativeLigands')
ref = RamanSpectrum('/home/chris/Documents/DataWeiss/141007/Liquid sample corrected-spectrum of toluene.txt')
ref.plot(label = 'toluene')
legend()
title('Native Ligand')
figure()
j = RamanSpectrum('/home/chris/Documents/DataWeiss/150324/NativeLigand_Red_34.txt')
m=smooth(j)
m.plot(label='MeOTP')
j = RamanSpectrum('/home/chris/Documents/DataWeiss/150324/NativeLigand_Red_35.txt')
k = RamanSpectrum('/home/chris/Documents/DataWeiss/150324/NativeLigand_Red_36.txt')
m = add_RamanSpectra(j,k)
m=smooth(m)
m.plot(label='MeOTP')
title('MeOTP treated')
return 0
def May21():
a = RamanSpectrum("/home/chris/Documents/DataWeiss/150521/150521stoic_dots.CSV")
a[:] -= 0.2
n_guess = [
0.05,
0.05,
0.05,
0.1,
0.05,
0.05,
0.05,
0.05,
0.05,
0.05,
1015,
1048,
1075,
1100,
1159,
1169,
1184,
1204,
1221,
1246,
20,
20,
20,
40,
20,
40,
20,
20,
20,
20,
0,
0.0,
]
a.plot(color="k")
print a.nearest(1100)
print a.nearest(1300)
b = fitspectrum(a, (1000, 1260), "xGaussian", n_guess)
print b.params[0]
plot(b.x, b.y, "r")
print len(b.x)
print len(b.peaks)
# plot(b.x,b.peaks[0])
for p in b.peaks:
plot(b.x, p, "b")
# pass
b = (RamanSpectrum("/home/chris/Documents/DataWeiss/150520/150520_02.txt") - 500) / 10000
b.plot()
return 0
def N2(): ## test of optical damage decreased by spinning
os.chdir('/home/chris/Documents/DataWeiss/150210')
figure()
print os.listdir('.')
a = [ '1_under N2 0 min.SPE', '1_under N2 1 min.SPE', '3_under N2 2 min.SPE',
'4_under N2 3 min.SPE', '5_under N2 4 min.SPE', '6_under N2 5 min.SPE',
'7_under N2 6 min.SPE', '8_under N2 7 min.SPE', '9_under N2 8min.SPE',
'10_under N2 9min.SPE', '11_under N2 10min.SPE', '12_ spinning under N2 0 min.SPE',
'13_ spinning under N2 1min.SPE', '14_ spinning under N2 2min.SPE', '15_ spinning under N2 3min.SPE',
'16_ spinning under N2 4min.SPE', '17_ spinning under N2 5min.SPE',
'18_ spinning under N2 6min.SPE', '19_ spinning under N2 7min.SPE',
'20_ spinning under N2 8min.SPE', '21_ spinning under N2 9min.SPE',
'22 spinning under N2 10min.SPE', '23_after dark time.SPE',
'24_after dark time 1min.SPE', '25_ spinning in air 0 min.SPE',
'26_ spinning in air 1 min.SPE', '27_ spinning in air 2 min.SPE',
'28_ spinning in air3 min.SPE', '29_ spinning in air 4min.SPE',
'30_ spinning in air 5min.SPE', '31_ spinning in air 6 min.SPE',
'32_ spinning in air 7 min.SPE', '33_ spinning in air 8 min.SPE',
'34_ spinning in air 9 min.SPE','34_ spinning in air 10min.SPE',
'35_after 10 min dark.SPE', '36_after 10 min dark plus 1min.SPE',
'37.SPE', '38_ 30 SEC.SPE', '39_ 60s.SPE',
'40_90 s.SPE', '41_120s.SPE', '42_150s.SPE']
areas = list()
for i in a:
r = RamanSpectrum(i)
areas.append(r.calc_area((180,220)))
N2only = areas[0:11]
N2spin = areas[11:24]
airspin = areas[24:37]
airstill = areas[37:]
N2only/=N2only[0]
N2spin/=N2spin[0]
airspin/=airspin[0]
airstill/=airstill[0]
plot(N2only,'s-')
plot([0,1,2,3,4,5,6,7,8,9,10,20,21],N2spin,'s-')
plot([0,1,2,3,4,5,6,7,8,9,10,20,21],airspin,'s-')
plot(arange(0,3,0.5),airstill,'s-')
fill_between((10,20),(0,0),(1,1),color = 'y')
annotate('dark', (15,0.4),horizontalalignment = 'center', fontsize=24)
legend(['n2 only','n2 + spinning','air + spinning','air only'])
xlabel('Time (min)')
ylabel('Phonon Mode Intensity (a.u.)')
return areas
def Feb1(): ##
"""Resonance Raman of CdSe dots with PPA in water. February 1"""
clf()
a473 = RamanSpectrum('/home/chris/Dropbox/DataWeiss/160201/160201_07.txt')
a633 = RamanSpectrum('/home/chris/Dropbox/DataWeiss/160201/160201_08.txt')
a633=SPIDcorrect633(a633)
a473.autobaseline((120,700),order = 3)
a633.autobaseline((120,700),order = 3)
a473.plot()
a633.plot()
legend(['473','633'])
return 0
def smooth_phonon():
import RamanTools2
aa = RamanSpectrum('/home/chris/Documents/DataWeiss/141029/4_RubyRed in PS phonon_1.txt')
aa-=min(aa)
aa/=max(aa)
aa._smooth(aa.values,window = 'flat')
aa.plot(color = 'r')
return aa
def Oct17NMRfitting():
# a = loadtxt('/home/chris/Dropbox/DataWeiss/151020/MPAexchange on HCN_1000eq.csv',skiprows = 1, usecols = (0,1), delimiter = ',', unpack = True)
# `r = RamanSpectrum(pandas.Series(a[1],a[0]))
# w=1E-5
# g = [0.05,0.03,0.05,.03,.1,.03,.05,.03,.05,1.38,1.39,1.395,1.405,1.41,1.42,1.425,1.43,1.44,w,w,w,w,w,w,w,w,w,0,0]
# s = fitspectrum(r,(1.34,1.46), 'xGaussian', g)
# clf()
# r.plot()
# for i in s.peaks: plot(s.x,i)
# plot(s.x,s.y)
# print s.areas
# xlim(1.34,1.49)
# ylim(-0.01,0.1)
# a = loadtxt('/home/chris/Dropbox/DataWeiss/151020/MPAexchange on HCN_100eq.csv',skiprows = 1, usecols = (0,1), delimiter = ',', unpack = True)
# r = RamanSpectrum(pandas.Series(a[1],a[0]))
# r.name = ''
# w=1E-5
# a = 0.04
# g = [a,a,a,a,a,a,a,a,a,a,a,1.38,1.385,1.392,1.398,1.405,1.41,1.412,1.42,1.43,1.435,1.44,w,w,w,w,w,w,w,w,w,w,w,0,0]
# # s = fitspectrum(r,(1.34,1.46), 'xGaussian', g)
#
# r.plot()
# for i in s.peaks: plot(s.x,i)
# plot(s.x,s.y)
# print s.areas
# xlim(1.34,1.49)
# ylim(-0.01,0.1)
a = loadtxt('/home/chris/Dropbox/DataWeiss/151020/MPAexchange on HCN_100eq.csv',skiprows = 1, usecols = (0,1), delimiter = ',', unpack = True)
r = RamanSpectrum(pandas.Series(a[1],a[0]))
r.name = ''
w=1E-5
a = 0.04
g = [a,0.05,a,a,a,a,a,a,a,a,a,1.725,1.73,1.738,1.743,1.745,1.75,1.755,1.765,1.77,1.78,1.785,w,w,w,w,w,w,w,w,w,w,w,0,0]
s = fitspectrum(r,(1.70,1.80), 'xGaussian', g)
clf()
r.plot()
for i in s.peaks: plot(s.x,i)
plot(s.x,s.y)
print s.params[0]
xlim(1.70,1.80)
ylim(-0.01,0.1)
return s.areas
def Oct17():
a= loadtxt('/home/chris/Dropbox/DataWeiss/151017/thioldots.csv', delimiter = ',', unpack = True, skiprows=1, usecols = (0,6,3,1,2,4,5))
for i in range(1,7):
r = polyfit(a[0,0:100],a[i,0:100],1)
a[i]-=rs.polyeval(r,a[0])
peak = uv.findpeak(a[0],a[i],(410,420))
#print peak
diameter = -0.000000066521*peak[0]**3+0.00019557*peak[0]**2-0.092352*peak[0]+13.29
#print 'diam', diameter
epsilon = 21536*diameter**2.3
# print 'eps', epsilon
print 'CONC', 5*peak[1]/epsilon
plot(a[0],a[i])
legend(['4.2','6.0',',6.4','7.7','9.7','11.5'])
print 'THIOL EXCHANGE'
a= loadtxt('/home/chris/Dropbox/DataWeiss/151017/thiolcapped doits.csv', delimiter = ',', unpack = True, skiprows=1, usecols = (0,1))
r = polyfit(a[0,0:100],a[1,0:100],1)
a[1]-=rs.polyeval(r,a[0])
peak = uv.findpeak(a[0],a[1],(410,420))
#print peak
diameter = -0.000000066521*peak[0]**3+0.00019557*peak[0]**2-0.092352*peak[0]+13.29
#print 'diam', diameter
epsilon = 21536*diameter**2.3
# print 'eps', epsilon
print 'CONC', 5*peak[1]/epsilon
a= loadtxt('/home/chris/Dropbox/DataWeiss/151017/thioldots.csv', delimiter = ',', unpack = True, skiprows=1, usecols = (0,3,1,2,4,5,6))
for i in range(1,7):
r = polyfit(a[0,0:100],a[i,0:100],1)
a[i]-=rs.polyeval(r,a[0])
peak = uv.findpeak(a[0],a[i],(410,420))
#print peak
diameter = -0.000000066521*peak[0]**3+0.00019557*peak[0]**2-0.092352*peak[0]+13.29
#print 'diam', diameter
epsilon = 21536*diameter**2.3
# print 'eps', epsilon
print 'CONC', 5*peak[1]/epsilon
plot(a[0],a[i])
legend(['200','400',',800','2000','3200','4000'])
return 0
def May16():
c = RamanSpectrum("/home/chris/Documents/DataWeiss/150516/150516_08.txt")
c.values[:] *= 3
c = autobaseline(c, (300, 1700), order=6)
c.smooth()
c.plot(label="Cd-enriched")
a = fitspectrum(
c,
(900, 1150),
"SixGaussian",
[200, 200, 200, 200, 200, 200, 950, 990, 1026, 1064, 1087, 1115, 10, 10, 10, 10, 10, 10, 1, -100],
)
plot(a[1], a[2], linewidth=3, label="Cdenriched fit")
# a = RamanSpectrum('/home/chris/Documents/DataWeiss/150516/150516_08.txt')
# b = RamanSpectrum('/home/chris/Documents/DataWeiss/150516/150516_07.txt')
# c = add_RamanSpectra(a,b)
#
# c = autobaseline(c,(300,1700),order = 4)
# c.smooth()
# c.plot(label='stoichiometric')
#
# CdMeOTPRef.index = array(CdMeOTPRef.index)-5
# (CdMeOTPRef/120).plot()
# (MeOTPRef/240).plot()
a = RamanSpectrum("/home/chris/Documents/DataWeiss/150516/150516_01.txt")
b = RamanSpectrum("/home/chris/Documents/DataWeiss/150516/150516_02.txt")
c = RamanSpectrum("/home/chris/Documents/DataWeiss/150516/150516_03.txt") * 4
d = RamanSpectrum("/home/chris/Documents/DataWeiss/150516/150516_05.txt")
e = RamanSpectrum("/home/chris/Documents/DataWeiss/150516/150516_06.txt")
a = add_RamanSpectra(a, b)
a = add_RamanSpectra(a, c)
a = add_RamanSpectra(a, d)
a = add_RamanSpectra(a, e)
a.values[:] /= 10
a.plot(label="pieces")
# ics('/home/chris/Orca/Successful/CdMeOTP/CdMeOTP.out')
# ics('/home/chris/Orca/CdTP_bridge/CdTP_bridgeDFT.out',color='r')
# a= fitspectrum(a,(900,1150),'SixGaussian', [200,200,200,200,200,200,950,990,1026,1064,1087,1115,10,10,10,10,10,10,1,-100])
# plot(a[1],a[2],linewidth =3,label= 'piecesfit')
return 0
def May20():
a = RamanSpectrum("/home/chris/Documents/DataWeiss/150520/150520_02.txt")
# def xGaussian(x,*guess):
# numpeaks = (len(guess)-2)/3
# y = guess[-2]*x/1000+guess[-1]
# for i in range(numpeaks):
# y+= guess[i]*exp(-(x-guess[i+numpeaks])**2/guess[i+2*numpeaks])
# return y
n_guess = [
100,
100,
100,
100,
100,
100,
100,
100,
720,
790,
785,
811,
820,
848,
865,
885,
10,
10,
10,
10,
10,
10,
10,
10,
0,
600,
]
a.plot()
b = fitspectrum(a, (700, 908), "xGaussian", n_guess)
plot(b[1], b[2])
return 0
def Apr15():
def gauss(x,A,G,m,b):return A*exp(-(1090-x)**2/G)+m*x+b
a = RamanSpectrum('/home/chris/Documents/DataWeiss/150415/150415_15.txt')
a = SPIDcorrect(a)
noise = calc_noise(a,(900,1000))
start = argmin(abs(1065-array(a.index)))
end = argmin(abs(1115-array(a.index)))
x = array(a.index[start:end])
y = a.values[start:end]
guess = [10,15,-1,y[0]+1000]
peak = scipy.optimize.curve_fit(gauss, x, y, guess)
print peak[0]
a.plot(color = 'k')
#plot(x,gauss(x,*peak[0]))
#
print 'signal to noise =', sqrt(peak[0][0]/noise)
ylim(27000,37000)
ylabel('Raman Intensity (a.u.)')
xlabel('Raman Shift (cm$^{-1}$)')
ax3 = gcf().add_axes((0.6,0.6,0.25,0.25))
a.plot(color='k', ax = ax3)
ax3.annotate('S/N: '+str(1.66), (1080, 28700), textcoords = 'data', size = 18)
ax3.set_ylim(27500,29300)
ax3.set_xlim(900,1200)
return 0
def calculate_enchancement():
global r,s
#### Calc SERS enhancement on
os.chdir('/home/chris/Documents/DataWeiss/150109')
r = RamanSpectrum('1_MeTOP roughened Ag_1.txt')
s = RamanSpectrum('2_MeOTP smooth silver_1.txt')
r.autobaseline((500,1750),order=0)
s.autobaseline((500,1750),order=0)
on_roughened = calc_area(r,(1050,1130))*100 #### multiply, because used filter 0.01
on_smooth = calc_area(s,(1050,1130))
print 'hormalized area on roughened substrate =', on_roughened
print 'hormalized area on roughened substrate =', on_smooth
r.plot(color = 'r')
s.plot(color = 'k')
legend(['roughened', 'smooth (x100)'],loc=2)
annotate('x100', (0.6,0.7), xycoords = 'axes fraction', size = 24,color = 'k')
print 'Approximate surface enhancement =',on_roughened/on_smooth
xlabel('Raman Shift cm$^{-1}$')
ylabel('Intensity a.u.')
return 0
def May21b():
clf()
a = RamanSpectrum("/home/chris/Documents/DataWeiss/150520/150520_02.txt")
a[:] -= 0.2
n_guess = [
0.05,
0.05,
0.05,
0.05,
0.05,
0.05,
0.05,
1068,
1100,
1169,
1184,
1204,
1221,
1246,
20,
20,
20,
20,
20,
20,
20,
0,
0.21,
]
a.plot(color="k")
CdODPARef.autobaseline((200, 1700), order=1)
CdODPARef.plot()
# b = RamanSpectrum('/home/chris/Documents/DataWeiss/150408/150408_02.txt')
# b = autobaseline(b,(200,1700),leaveout=(200,300), order = 4)
# b.plot()
return 0
def processxymap():
os.chdir('/home/chris/Documents/DataWeiss/150114')
averagespectrum = RamanSpectrum('10_maybemap_1.txt')
phononarea = array([])
fluorescenceat1600=array([])
fullspectrum = ndarray((1000,))
for f in os.listdir('.'):
if '10_maybemap' in f:
if 'SPE' in f:
continue
elif f == 'maybemap_1.txt':
continue
elif f == 'maybemap.txt':
continue
else:
r = RamanSpectrum(f)
phononarea = append(phononarea,r.calc_area((200,230)))
fluorescenceat1600 = append(fluorescenceat1600,r.values[-1]-min(r.values))
averagespectrum+=r
averagespectrum = _smooth(averagespectrum)
figure()
subplot(221)
hist(fluorescenceat1600,bins=range(0,200,20))
hist(phononarea,bins=range(0,200,20),color='r')
xticks(range(0,200,20))
subplot(223)
averagespectrum.plot()
return phononarea
def Jan22():### data from trying out the cryostat
from scipy import optimize
os.chdir('/home/chris/Documents/DataWeiss/150121')
red = RamanSpectrum('17.SPE')
red = normalize(red,(0,1600))
red.plot(label='647 nm')
green = RamanSpectrum('14.SPE')
green=normalize(green,(0,1600))
green.plot(label = '514 nm')
cdRef=normalize(CdMeOTPRef,(0,1600))
cdRef.plot(label = 'CdMeOTP Reference')
mtpref = normalize(MeOTPRef,(0,1600))
mtpref.plot(label = 'MeOTP Reference')
xlim(1050,1150)
legend()
def singlegauss(x, A1, x1, c1):return A1*exp(-(x-x1)**2/(2*c1**2))
def doublegauss(x, A1, x1, c1,A2,x2,c2):return A1*exp(-(x-x1)**2/(2*c1**2)) +A2*exp(-(x-x2)**2/(2*c2**2))
green = autobaseline(green, (1050,1150), order = 0)
print argmin(abs(green.index-1100))
x = array(green.index[620:680])
y = array(green.values[620:680])
print x
r = list(optimize.curve_fit(singlegauss,x,y,[1,1087,20])[0])
figure()
plot(x,y,'s')
plot(x,singlegauss(x,*r),'k')
print r
r = list(optimize.curve_fit(doublegauss,x,y,[1,1080,10,1,1090,10])[0])
plot(x,doublegauss(x,*r),'r')
plot(x,singlegauss(x,r[0],r[1],r[2]),'k.')
plot(x,singlegauss(x,*r[3:6]),'k.')
print r
xlabel('Raman Shift cm$^{-1}$')
ylabel('Intensity a.u.')
return 0
def Oct13():
a= loadtxt('/home/chris/Dropbox/DataWeiss/151013/NMR samples.csv', delimiter = ',', unpack = True, skiprows=1)
for i in range(1,6):
r = polyfit(a[0,0:100],a[i,0:100],1)
a[i]-=rs.polyeval(r,a[0])
peak = uv.findpeak(a[0],a[i],(410,420))
print peak
diameter = -0.000000066521*peak[0]**3+0.00019557*peak[0]**2-0.092352*peak[0]+13.29
print 'diam', diameter
epsilon = 21536*diameter**2.3
print 'eps', epsilon
print 'conc', 5*peak[1]/epsilon
plot(a[0],a[i])
legend(['10.5','9.5','9.3','8.6','6.8'])
return 0
def CdMBTinDMF():
clf()
a = RamanSpectrum('/home/chris/Documents/DataWeiss/150709/150709_01.txt')#### DMFonly
b = RamanSpectrum('/home/chris/Documents/DataWeiss/150709/150709_02.txt')#### 510mg DMF with 200 mgCdMBT
a.autobaseline((523,935,1336,1780),order = 3,specialoption='points')
b.autobaseline((523,935,1336,1780),order = 3,specialoption='points')
a[:]*=4720
a[:]/=6256
c = RamanSpectrum(b-a)
c.plot()
r = fitspectrum(c,(1070,1105),'xVoigt',[10000,1088,15,6,0,0])
plot(r.x,r.y,'s-',linewidth=2)
for i in r.peaks:
plot(r.x,i)
print r.areas
print r.params[0][2:4]
CdMethylTPRef.plot()
# def difference(c): return sum((b[200:1700]-c*a[200:1700])**2)
# r = minimize(difference,[1])
return r.params
def Fig1(show_vib_numbers = True): ### reference spectra of methylbenzenethiol
figure(figsize = (6,6))
MBT = copy(MeOTPRef)
MBT-=min(MBT[0:2000])
MBT/=max(MBT[0:2000])
CdMBT = copy(CdMeOTPRef)
CdMBT.index = array(CdMBT.index)-3
CdMBT-=min(CdMBT[0:2000])
CdMBT/=max(CdMBT[0:2000])
a = RamanSpectrum('/home/chris/Documents/DataWeiss/150408/150408_15.txt')
a.autobaseline((268,440,723,915,1200,1391,1505,1680),specialoption='points', order = 4)
a.smooth(window_len=11,window = 'SG')
a[:]/=3000
MBT.plot(color = 'b',linewidth = 2)
CdMBT.plot(color = 'k',linewidth = 2)
a.plot(color = 'r',linewidth = 2)
xlim(500,1675)
ylim(0,1.5)
ylabel('Intensity (a.u.)')
xlabel('Raman shift (cm$^{-1}$)')
####Assignments
assignmentfontsize = 10
# annotate('Ring bending',(635,0.2), xycoords = 'data',horizontalalignment = 'center',verticalalignment = 'bottom',color = 'k',size = assignmentfontsize,rotation ='vertical')
annotate('Ring bending',(647,0.38), xycoords = 'data',horizontalalignment = 'center',verticalalignment = 'bottom',color = 'k',size = assignmentfontsize,rotation ='vertical')
annotate('Ring stretching',(1105,1.05), xycoords = 'data',horizontalalignment = 'center',verticalalignment = 'bottom',color = 'k',size = assignmentfontsize,rotation ='horizontal')
annotate('Ring stretching',(806,1.1), xycoords = 'data',horizontalalignment = 'center',verticalalignment = 'bottom',color = 'k',size = assignmentfontsize,rotation ='horizontal')
annotate('CSH bending ',(914,0.25), xycoords = 'data',horizontalalignment = 'center',verticalalignment = 'bottom',color = 'k',size = assignmentfontsize,rotation ='vertical')
# annotate('CH bending ',(1190,0.33), xycoords = 'data',horizontalalignment = 'center',verticalalignment = 'bottom',color = 'k',size = assignmentfontsize,rotation ='vertical')
#annotate('Ring stretching',(1300,0.5), xycoords = 'data',horizontalalignment = 'center',verticalalignment = 'bottom',color = 'k',size = assignmentfontsize,rotation ='vertical')
# annotate('CH bending',(1382,0.1), xycoords = 'data',horizontalalignment = 'center',verticalalignment = 'bottom',color = 'k',size = assignmentfontsize,rotation ='vertical')
annotate('CC ring stretching',(1607,0.5), xycoords = 'data',horizontalalignment = 'center',verticalalignment = 'bottom',color = 'k',size = assignmentfontsize,rotation ='vertical')
legend(['MTP', 'CdMTP$_2$', 'QDs-MTP'])
matplotlib.pyplot.tight_layout()
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
def Feb10():
figure()
a = RamanSpectrum('/home/chris/Documents/DataWeiss/150210/43_ long scan.SPE')
adding = pandas.Series([NaN]*len(arange(300,1500,0.5)),arange(300,1500,0.5))
d=a.append(adding)
d = d.interpolate(method='index')
d = d[arange(300,1500,0.5)]
e = FourierFilter(d,width = 1100)
e.plot()
b = RamanSpectrum('/home/chris/Documents/DataWeiss/150210/44.SPE')
c=b+a
a.autobaseline((300,1600),order = 4)
#a = smooth(a,window_len=9)
b.autobaseline((300,1600),order = 4)
#b=smooth(b,window_len=9)
#a.plot()
#b.plot()
c= autobaseline(c,(300,1600),order = 4)
c = smooth(c, window_len=9)
#c.plot()
legend(['a','d','e'])
return d
def Apr8Raman():
os.chdir('/home/chris/Documents/DataWeiss/150408')
fig = figure()
a = RamanSpectrum('150408_15.txt')
a.autobaseline((268,440,723,915,1200,1391,1505,1680),specialoption='points', order = 4)
a.smooth(window_len=11,window = 'SG')
#a+=800
b = RamanSpectrum('150408_02.txt')
#b = autobaseline(b,(200,1700),leaveout=(200,300), order = 4)
b.autobaseline((268,440,723,915,1200,1391,1505,1680),specialoption='points', order = 4)
b.smooth(window_len=11,window = 'SG')
(normalize(MeOTPRef,(0,10000))*4000+1000).plot(color ='b',linewidth=2)
a.plot(color = 'k',linewidth = 2)
b.plot(color = 'r', linewidth = 2)
ylim(-500,6000)
xlim(200,1675)
legend(['MeOTP ref', 'MeOTP treated','Native ligand only'])
ylabel('Raman Intensity (a.u.)')
xlabel('Raman Shift (cm$^{-1}$)')
figure()
title('Washing')
a = RamanSpectrum('150408_11.txt')
b = RamanSpectrum('150408_02.txt')
a = autobaseline(a, (200,1700),leaveout=(200,300),order=4)
b = autobaseline(b,(200,1700),leaveout=(200,300), order = 4)
(normalize(ODPARef,(0,10000))*4000+2000).plot(color ='b',linewidth=2, label='ODPA Ref')
a.plot(color = 'r',label='washed 5x')
b.plot(color = 'k',label='washed 4x')
#a= fitspectrum(b,(900,1150),'SixGaussian', [200,200,200,200,200,200,950,990,1026,1064,1087,1115,10,10,10,10,10,10,1,-100])
#plot(a[1],a[2],linewidth =3,label='fit')
legend()
ylabel('Raman Intensity (a.u.)')
xlabel('Raman Shift (cm$^{-1}$)')
return 0
def Fig2(show_vib_numbers = False): ### reference spectra of methoxythiphenol
figure(figsize = (6,6))
a= loadtxt('/home/chris/Documents/DataWeiss/141014/4_methoxythiophenol_1.csv',
delimiter = ',',
unpack = True)
MBT = pandas.Series(a[1],a[0])
MBT-=min(MBT[0:2000])
MBT/=max(MBT[0:2000])
#a = loadtxt('/home/chris/Documents/DataWeiss/140918/9_MeOTP_1.txt',
# delimiter = ',',
# unpack = True)
CdMBT = RamanSpectrum('/home/chris/Documents/DataWeiss/140918/9_CdMeOTP_1.txt')
CdMBT-=min(CdMBT[200:1000])
CdMBT/=max(CdMBT[500:2000])
subplot(211)
MBT.plot(color = 'b')
xlim(500,1700)
ylim(-0.01,1.2)
ylabel('Intensity (a.u.)')
xlabel('Raman shift (cm$^{-1}$)')
annotate('MTP',(0.01,0.9), xycoords = 'axes fraction',
color = 'k',
horizontalalignment = 'left',
size = 12)
legend(['expt', 'calc'])
####Assignments
assignmentfontsize = 10
annotate('Ring bending',(647,0.25), xycoords = 'data',horizontalalignment = 'center',verticalalignment = 'bottom',color = 'k',size = assignmentfontsize,rotation ='vertical')
annotate('Ring stretching',(1098,0.8), xytext=(1070,1.05), xycoords = 'data',horizontalalignment = 'center',verticalalignment = 'bottom',color = 'k',size = assignmentfontsize,rotation ='horizontal',arrowprops = {'width':2,'color':'k','headwidth':5})
annotate('Ring stretching',(805,1.02), xycoords = 'data',horizontalalignment = 'center',verticalalignment = 'bottom',color = 'k',size = assignmentfontsize,rotation ='horizontal')
annotate('CSH bending ',(914,0.25), xycoords = 'data',horizontalalignment = 'center',verticalalignment = 'bottom',color = 'k',size = assignmentfontsize,rotation ='vertical')
annotate('CH bending ',(1190,0.35), xycoords = 'data',horizontalalignment = 'center',verticalalignment = 'bottom',color = 'k',size = assignmentfontsize,rotation ='vertical')
annotate('Ring stretching',(1245,0.15), xycoords = 'data',horizontalalignment = 'center',verticalalignment = 'bottom',color = 'k',size = assignmentfontsize,rotation ='vertical')
#annotate('CH bending',(1382,0.1), xycoords = 'data',horizontalalignment = 'center',verticalalignment = 'bottom',color = 'k',size = assignmentfontsize,rotation ='vertical')
annotate('CC ring stretching',(1590,0.65),xytext=(1557,0.75), xycoords = 'data',horizontalalignment = 'center',verticalalignment = 'bottom',color = 'k',size = assignmentfontsize,rotation ='horizontal',arrowprops = {'width':2,'color':'k','headwidth':5})
subplot(212)
CdMBT.plot(color = 'k')
ics('/home/chris/Orca/Successful/CdMeOTP/CdMeOTP.out',normalize=True,color = 'r', labelpeaks = False)
xlim(500,1700)
ylim(-0.01,1.6)
ylabel('Intensity (a.u.)')
xlabel('Raman shift (cm$^{-1}$)')
annotate('Cd(MTP)$_2$',(0.01,0.9), xycoords = 'axes fraction',
color = 'k',
horizontalalignment = 'left',
size = 12)
####Assignments
#annotate('CS$_{\\nu}$',(639,0.38), xycoords = 'data',horizontalalignment = 'center',color = 'k',size = assignmentfontsize)
#annotate('$\phi$CC$_{\\nu}$',(1088,1.05), xycoords = 'data',horizontalalignment = 'center',color = 'k',size = assignmentfontsize)
#annotate('Ring CH$_\delta,oop$ ',(796,0.6), xycoords = 'data',horizontalalignment = 'center',color = 'k',size = assignmentfontsize)
#annotate('$\phi$CC$_{\\nu}$ ',(1600,0.45), xycoords = 'data',horizontalalignment = 'center',color = 'k',size = assignmentfontsize)
matplotlib.pyplot.tight_layout()
return 0