def CdSvsCdSe():
r = RamanSpectrum('/home/chris/Documents/DataWeiss/150521/150521_05.txt')
r = removespikes(r)
r.autobaseline((119,286), order = 0)
r.autobaseline((286,1151), order = 4, join = 'start')
r.autobaseline((1151,1560), order = 3, join = 'start')
r.smooth()
r.plot(label = 'CdSe')
r = RamanSpectrum('/home/chris/Documents/DataWeiss/150601/150601_07.txt')
c = RamanSpectrum('/home/chris/Documents/DataWeiss/150601/150601_08.txt')
r = add_RamanSpectra(r,c)
r = SPIDcorrect633(r)
r = removespikes(r)
r.autobaseline((145,1148), order = 3)
r.autobaseline((1148,1253), order = 1,join='start')
r.autobaseline((1253,2000), order = 3,join='start')
r.autobaseline((2000,3600), order = 3,join='start')
r.values[:] = r.values[:]*5
#r.smooth()
r.plot(label='CdS')
legend(['CdSe','CdS'])
return 0
def figure1():
os.chdir('/home/chris/Documents/DataWeiss/150113')
r = RamanSpectrum('15_CdSeMTP dropcast_1.txt')
s = RamanSpectrum('18_1.txt')
r = smooth(r)
s= smooth(s)
r+=100
s+=100
r.plot()
s.plot()
figure()
r = autobaseline(r,(467,1463),order=3)+200
r.plot()
s = autobaseline(s,(467,1463),order=3)+400
s.plot()
MBT = RamanSpectrum('/home/chris/Documents/DataWeiss/141014/4_methoxythiophenol_1.csv')
MBT-=min(MBT[0:2000])
MBT/=max(MBT[0:2000])/500
MBT.plot()
legend(['Ag/Hexanethiol', 'Ag/Hexanethiol + CdSeMTP', 'CdMTP ref'])
return 0
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 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 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 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 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 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 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 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 May18():
r = RamanSpectrum("/home/chris/Documents/DataWeiss/150518/150518_03b.txt")
r.autobaseline((100, 763), order=3)
r.autobaseline((763, 836), order=1, join="start")
r.autobaseline((836, 1600), order=3, join="start")
r.plot()
r = RamanSpectrum("/home/chris/Documents/DataWeiss/150518/150518_05b.txt")
r.autobaseline((100, 763), order=3)
r.autobaseline((763, 836), order=1, join="start")
r.autobaseline((836, 1650), order=3, join="start")
r.plot()
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 Apr6():
subplot(221)
a = RamanSpectrum('/home/chris/Documents/DataWeiss/150406/150406_02.txt')
a.plot(color = 'k')
a = RamanSpectrum('/home/chris/Documents/DataWeiss/150406/150406_03.txt')
a.plot(color = 'k')
a = RamanSpectrum('/home/chris/Documents/DataWeiss/150406/150406_04.txt')
a.plot(color = 'k')
ylim(1000,3500)
xlim(100,1700)
subplot(222)
a = RamanSpectrum('/home/chris/Documents/DataWeiss/150406/150406_01.txt')
a.plot(color = 'k')
xlim(2700,3100)
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)
return 0
def concentrationdependence(): ### determine best conc of dots to add to silver to get signal/fluorescend.
os.chdir('/home/chris/Documents/DataWeiss/150114')
two_x = RamanSpectrum('1_concentration 2x -highest conc_1.txt')
two_x+=1000
one_x = RamanSpectrum('2_1x conc_1.txt')
one_x+=500
_25x = RamanSpectrum('3_0_25xconc_1.txt')
_25x*=10
_25x-=1000
_0625x = RamanSpectrum('5_0_0625x conc_1.txt')
_0625x*=10
_0625x-=1500
two_x.plot()
one_x.plot()
_25x.plot()
_0625x.plot()
legend(['2x','1x','0.25x*10','0.0625x * 10'])
xlabel('Raman Shift cm$^{-1}$')
ylabel('Intensity a.u.')
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 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 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 OPAMBTExchange():
figure()
a = RamanSpectrum('/home/chris/Documents/DataWeiss/150707/150707_02.txt')
b = RamanSpectrum('/home/chris/Documents/DataWeiss/150707/150707_03.txt')
a[:]*=5
b[:]*=5
subplot(121)
a.plot()
CdMethylTPRef.plot()
legend(['new','old'])
r = fitspectrum(a,(1070,1110),'OneGaussian',[25000,1088,10,0,0])
plot(r.x,r.y, 'k',linewidth = 2)
r = fitspectrum(CdMethylTPRef,(1070,1110),'OneGaussian',[25000,1088,10,0,0])
plot(r.x,r.y,'r' ,linewidth = 2)
subplot(122)
b.plot()
CdMeOTPRef.plot()
legend(['new','old'])
r = fitspectrum(b,(1070,1110),'OneGaussian',[60000,1088,10,0,0])
plot(r.x,r.y,'k',linewidth = 2)
r = fitspectrum(CdMeOTPRef,(1070,1110),'OneGaussian',[6000,1088,10,0,0])
plot(r.x,r.y,'r', linewidth = 2)
figure()
June22()
c= RamanSpectrum('/home/chris/Documents/DataWeiss/150707/150707_05.txt')
c.autobaseline((200,2000),order= 4)
c[:]+=4000
c.plot()
figure()
d= RamanSpectrum('/home/chris/Documents/DataWeiss/150707/150707_06.txt')
d.autobaseline((200,2000),order= 4)
d[:]*=10
d.plot()
a.plot()
legend(['exchanged', 'reference'])
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 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 Apr8Raman_forVictor():
os.chdir('/home/chris/Documents/DataWeiss/150408')
fig = figure(figsize=(12,6))
subplot(121)
a = RamanSpectrum('150408_15.txt')
a = autobaseline(a, (200,1700),leaveout=(200,300),order=4)
a+=800
b = RamanSpectrum('150408_02.txt')
b = autobaseline(b,(200,1700),leaveout=(200,300), order = 4)
(normalize(MeOTPRef,(0,10000))*4000+2000).plot(color ='b',linewidth=2,label = 'thiophenolate reference')
a.plot(color = 'k',linewidth = 2)
b.plot(color = 'r', linewidth = 2)
ylim(-500,10000)
xlim(740,1675)
arrowprops={'width':1,'headwidth':3,'color':'k'}
ylabel('Raman Intensity (a.u.)')
xlabel('Raman Shift (cm$^{-1}$)')
annotate('C-S-H bend', (913,2830),xytext = (913,3300), xycoords = 'data',arrowprops = arrowprops,horizontalalignment='center' )
subplot(122)
e = RamanSpectrum('150408_13.txt')
e.autobaseline((2500,3600),leaveout=(200,300),order=2)
e.autobaseline((2500,2800),leaveout=(200,300), order = 1,join='end')
e+=800
f = RamanSpectrum('150408_03.txt')
f.autobaseline((2500,3600),leaveout=(200,300), order = 2)
f.autobaseline((2500,2800),leaveout=(200,300), order = 1,join='end')
(normalize(MeOTPRef,(0,10000))*4000+2000).plot(color ='b',linewidth=2)
e.plot(color = 'k',linewidth = 2, )
f.plot(color = 'r', linewidth = 2)
annotate('S-H stretch', (2560,3370),xytext = (2600,4500), xycoords = 'data',arrowprops = arrowprops,horizontalalignment='center' )
ylim(-500,10000)
xlim(2500,3200)
legend(['thiophenol reference','CdSe thiophenolate-treated','CdSe native ligand only'])
ylabel('Raman Intensity (a.u.)')
xlabel('Raman Shift (cm$^{-1}$)')
# savetxt()
return 0
def May29b():
r = RamanSpectrum('/home/chris/Documents/DataWeiss/150521/150521_05.txt') ## stoichiometric ODPA capped CdSe
r = removespikes(r)
r.autobaseline((119,286), order = 0)
r.autobaseline((286,1151), order = 4, join = 'start')
r.autobaseline((1151,1489), order = 3, join = 'start')
r.smooth()
r.plot()
r = RamanSpectrum('/home/chris/Documents/DataWeiss/150408/150408_02.txt') # Cd enriched CdSe
r = removespikes(r)
r.autobaseline((272,1746), order = 2)
#r.autobaseline((1151,1489), order = 3, join = 'start')
r.plot()
r = RamanSpectrum('/home/chris/Documents/DataWeiss/150516/150516_08.txt')
r = removespikes(r)
r.autobaseline((272,1746), order = 2)
r.smooth()
r.plot()
legend(['stoic', 'rich apr8', 'rich may16'])
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 Mar26():
a = RamanSpectrum('/home/chris/Documents/DataWeiss/150326/orangedot-nativeligand_20.txt')
print type(a)
a.smooth()
a.autobaseline((400,520),order =0)
a.autobaseline((520,1756),order = 4)
a.values[:]*=10
a.plot(label = '633 nm')
a = RamanSpectrum('/home/chris/Documents/DataWeiss/150326/orangedot-nativeligand_21.txt')
a = smooth(a)
a = autobaseline(a,(2482,3600),4)
a*=10
a.plot(label = '633 nm')
b = CdODPARef-2597
b.plot(label = 'reference')
a = RamanSpectrum('/home/chris/Documents/DataWeiss/150326/orangedot-nativeligand_10.txt')
a = smooth(a)
a-=161
a*=25
a.plot(label = '785 nm')
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 May7():
figure()
a = RamanSpectrum('/home/chris/Documents/DataWeiss/150507/150507_01.txt')
a.normalize()
a.plot()
ics('/home/chris/Orca/CdTP_bridge/CdTP_bridgeDFT.out',normalize = True)
title('thiophenol')
figure()
a = RamanSpectrum('/home/chris/Documents/DataWeiss/150507/150507_03.txt')
a.normalize()
a.plot()
i = RamanSpectrum('/home/chris/Documents/DataWeiss/150508/150508_02.txt')
i[:]/=1200
i.smooth()
i.autobaseline((70,450),leaveout=(70,340),order = 4)
i.autobaseline((450,1650),order = 2, join='start')
i.plot()
ics('/home/chris/Orca/CdClTP/CdClTP.out',normalize = True,labelpeaks = False)
figure()
a = RamanSpectrum('/home/chris/Documents/DataWeiss/150507/150507_06.txt') ## bromocomplex
a.autobaseline((70,450),leaveout=(70,340),order = 4)
a.autobaseline((450,1650),order = 2, join='start')
a.normalize()
a.plot()
i = RamanSpectrum('/home/chris/Documents/DataWeiss/150508/150508_08.txt') ## bromo on dots
i[:]/=1200
i.smooth()
i.autobaseline((70,450),leaveout=(70,340),order = 4)
i.autobaseline((450,1650),order = 2, join='start')
i.plot()
ics('/home/chris/Orca/CdBrTP/CdBrTP.out',normalize = True,labelpeaks = False)
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 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 June22():
"""Nice spectra of CdOPA and dotsOPA"""
d = RamanSpectrum('/home/chris/Dropbox/DataWeiss/150622/150622_06.txt') ### pH 1
a = RamanSpectrum('/home/chris/Dropbox/DataWeiss/150622/150622_07.txt') ### pH 5
b = RamanSpectrum('/home/chris/Dropbox/DataWeiss/150622/150622_08.txt') ### pH 12
c= RamanSpectrum('/home/chris/Dropbox/DataWeiss/150612/150612_01_CdSe.txt') ## dots
d.autobaseline((283,1989),order = 3)
d.autobaseline((284,408,577,701,826,1147,1380,1588,1849,1976),join='start',order = 9,specialoption='points')
a.autobaseline((283,1989),order = 3)
a.autobaseline((284,577,701,826,1147,1380,1588,1849,1976),join='start',order = 9,specialoption='points')
b.autobaseline((283,1989),order = 3)
b.autobaseline((284,577,701,826,1147,1380,1588,1849,1976),join='start',order = 9,specialoption='points')
c= RamanSpectrum('/home/chris/Dropbox/DataWeiss/150612/150612_01_CdSe.txt') ## dots
c.autobaseline((911,1196,1385,1515,1800),specialoption='points',order=7,join='start')
c.autobaseline((280,600,690,826,861,911),specialoption='points', order = 5,join='end')
c.autobaseline((281,630), order = 4, join = 'end')
c.autobaseline((1492,1800), order = 5, join = 'start')
c[:]*=3
c[:]+=5000
b[:]+=4000
a[:]+=1300
c.plot()
b.plot()
a.plot()
d.plot()
ylim(0,10000)
xlim(400,1800)
legend(['dots','CdOPA pH12', 'pH 5', 'pH1'])
tosave = transpose([d.index,d.values,a.values,b.values])
savetxt('/home/chris/Desktop/emily/CdOPARaman.csv', tosave, header = 'pH1, pH5, pH12',delimiter = ',')
c.to_csv('/home/chris/Desktop/emily/DotsOPARaman.csv')
ylabel('Intensity (a.u.)')
xlabel('Raman Shift (cm$^{-1}$)')
# savefig('/home/chris/Dropbox/GroupmeetingJuly9_2015/dotsandrefs.png', dpi=256)
return 0
