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 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 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 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 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 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 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 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 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 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 Dec15():
"""PPAcapped CdS in water Raman"""
cla()
ODPARef.plot()
OPARef.plot()
ax = subplot(111)
a = RamanSpectrum('151215/151215_04.txt')#,name='DMF 65 mM')
a.autobaseline((295,350,473,580,755,988,1188,1317,1756,1853,2296,2400,2600),join='end',order = 8,specialoption='points')
a.plot()
b=RamanSpectrum('151215/151215_02.txt')#,name='7uM PPAcapped dots in water/DMF')
b[:]/=3
b.autobaseline((295,350,473,580,755,961,1317,1756,1853,2296,2400,2600),join='end',order = 8,specialoption='points')
b.plot()
c=RamanSpectrum('151215/151215_05.txt')#,name = 'PPA')
c.autobaseline((295,350,473,580,755,961,1317,1756,1853,2296,2400,2600),join='end',order = 8,specialoption='points')
c.plot()
quickoffset(ax,rnge=(200,1600))
return 0
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 June1():
clf()
subplot(122)
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()
r= RamanSpectrum('/home/chris/Documents/DataWeiss/150601/150601_09.txt')
c = RamanSpectrum('/home/chris/Documents/DataWeiss/150601/150601_10.txt')
r = add_RamanSpectra(r,c)
r = SPIDcorrect633(r)
#r = removespikes(r)
r.autobaseline((145,1148), order = 2)
r.autobaseline((1148,1253), order = 1,join='start')
r.autobaseline((1253,1700), order = 3,join='start')
r.autobaseline((1700,3600), order = 3,join='start')
r.values[:] = r.values[:]*5
#r.smooth()
r.plot()
legend(['oleate', 'exchanged'])
subplot(121)
r = RamanSpectrum('/home/chris/Documents/DataWeiss/150529/150529_05.txt')
r.autobaseline((147,2000), order =3)
r.autobaseline((147,356), order = 1)
r.autobaseline((356,389), order = 1, join = 'start')
r.autobaseline((389,892), order = 1, join = 'start')
r.autobaseline((892,923), order = 1, join = 'start')
r.autobaseline((923,1185), order = 1, join = 'start')
r.autobaseline((1185,1211), order = 1, join = 'start')
r.autobaseline((1211,1679), order = 2, join = 'start')
r.autobaseline((1679,1702), order = 2, join = 'start')
r.autobaseline((1702,1900), order = 3, join = 'start')
r.smooth()
r.plot()
r = RamanSpectrum('/home/chris/Documents/DataWeiss/150527/150527_06.txt')
r = SPIDcorrect633(r)
r.autobaseline((98,765), order = 4)
r.autobaseline((765,839), order = 2, join = 'start')
r.autobaseline((839,1456), order = 4, join = 'start')
r.autobaseline((1456,1470), order = 2, join = 'start')
r.autobaseline((1470,1900), order = 4, join = 'start')
r.smooth()
r.plot()
legend(['exchanged', 'oleate'])
return 0
def SH():
os.chdir('/home/chris/Documents/DataWeiss/150728')
cdmbt=copy.deepcopy(CdMethylTPRef)
mbt=copy.deepcopy(MethylTPRef)
cdmbt.autobaseline((193,4000),order = 0)
mbt.autobaseline((193,4000),order = 0)
mbt[:]/=95
cdmbt[:]/=5
cdmbt.to_csv('/home/chris/Dropbox/Ken/CdMBT2.csv')
fig1 = figure(figsize=(6, 12))
A = RamanSpectrum('filesA.txt') ##450 eq
B = RamanSpectrum('filesB.txt') #200 eq MBT
C = RamanSpectrum('filesC.txt') #100 eq MBT
D = RamanSpectrum('filesD.txt') # 80 eq MBT
E = RamanSpectrum('filesE.txt') # 50 eq MBT
F = RamanSpectrum('filesF.txt') #25 eq MBT
G = RamanSpectrum('/home/chris/Documents/DataWeiss/150408/150408_03.txt')
G.autobaseline((2500,2700,3100,3200),order = 2, specialoption='points')
for z in [A,B,C,D,E,F]:
#z = SPIDcorrect633(z)
z.autobaseline((200,361),order = 1,join='start')
z.autobaseline((361,394),order = 2,join='start')
z.autobaseline((394,647),order = 2,join='start')
z.autobaseline((647,682),order = 0,join='start')
z.autobaseline((682,923),order = 0,join='start')
z.autobaseline((923,955),order = 0,join='start')
z.autobaseline((955,1187),order = 0,join='start')
z.autobaseline((1187,1214),order = 0,join='start')
z.autobaseline((1214,1437),order = 0,join='start')
z.autobaseline((1437,1462),order = 0,join='start')
z.autobaseline((1462,1675),order = 2,join='start')
z.autobaseline((1675,1701),order = 0,join='start')
z.autobaseline((1701,1900), order =2,join='start')
z.autobaseline((1900,2400), order =5,join='start')
z.autobaseline((2400,3200),order = 0,join='start')
z.autobaseline((3200,3600),order = 0,join='start')
# z.autobaseline((981,1013,1098,1141,1251,1491),order =3,specialoption='points', join='start')
z[:]/=50
z.smooth()
z-=z[2402]
for z in [G]:
z.autobaseline((2400,3200),order = 0)
z[:]/=50
z.smooth()
z-=z[2402]
mbt[:]+=700
mbt.set_name('mmmmmm')
A[:]*=2
A[:]+=600
B[:]+=500
C[:]+=400
D[:]+=300
E[:]+=200
F[:]+=100
G[:]*=10
for z in [mbt,A,B,C,D,E,F,G]:
z.to_csv('/home/chris/Dropbox/Ken/SHregion/'+z.name[-5]+'.csv')
mbt.plot()
A.plot()
B.plot()
C.plot()
D.plot()
E.plot()
F.plot()
G.plot()
fs = 14
anx = 2605
annotate('solid MBT',(anx,740), fontsize=fs)
annotate('450 eq MBT',(anx,620), fontsize=fs)
annotate('200 eq MBT',(anx,520), fontsize=fs)
annotate('100 eq MBT',(anx,420), fontsize=fs)
annotate('80 eq MBT',(anx,320), fontsize=fs)
annotate('50 eq MBT',(anx,220), fontsize=fs)
annotate('25 eq MBT',(anx,120), fontsize=fs)
annotate('0 eq',(anx,10), fontsize=fs)
xlim(2500,3030)
ylim(-20,1300)
return 0
def SH():
os.chdir('/home/chris/Documents/DataWeiss/150728')
cdmbt=copy.copy(CdMethylTPRef)
mbt=copy.copy(MethylTPRef)
cdmbt.autobaseline((1000,1200),order = 0)
mbt.autobaseline((1000,1200),order = 0)
mbt[:]/=95
cdmbt[:]/=5
cdmbt.to_csv('/home/chris/Dropbox/Ken/CdMBT2.csv')
fig1 = figure(figsize=(6, 12))
A = RamanSpectrum('filesA.txt') ##450 eq
B = RamanSpectrum('filesB.txt') #200 eq MBT
C = RamanSpectrum('filesC.txt') #100 eq MBT
D = RamanSpectrum('filesD.txt') # 80 eq MBT
E = RamanSpectrum('filesE.txt') # 50 eq MBT
F = RamanSpectrum('filesF.txt') #25 eq MBT
G = RamanSpectrum('/home/chris/Documents/DataWeiss/150408/150408_03.txt')
G.autobaseline((2500,2700,3100,3200),order = 2, specialoption='points')
for z in [A,B,C,D,E,F,G]:
z.autobaseline((2400,3200),order = 0)
z[:]/=50
z.smooth()
z-=z[2402]
mbt[:]+=700
mbt.set_name('mmmmmm')
A[:]*=2
A[:]+=600
B[:]+=500
C[:]+=400
D[:]+=300
E[:]+=200
F[:]+=100
G[:]*=10
for z in [mbt,A,B,C,D,E,F,G]:
#z.to_csv('/home/chris/Dropbox/Ken/SHregion/'+z.name[-5]+'.csv')
pass
mbt.plot()
A.plot()
B.plot()
C.plot()
D.plot()
E.plot()
F.plot()
G.plot()
fs = 14
anx = 2605
annotate('solid MBT',(anx,740), fontsize=fs)
annotate('450 eq MBT',(anx,620), fontsize=fs)
annotate('200 eq MBT',(anx,520), fontsize=fs)
annotate('100 eq MBT',(anx,420), fontsize=fs)
annotate('80 eq MBT',(anx,320), fontsize=fs)
annotate('50 eq MBT',(anx,220), fontsize=fs)
annotate('25 eq MBT',(anx,120), fontsize=fs)
annotate('0 eq',(anx,10), fontsize=fs)
xlim(2500,3030)
ylim(-20,1300)
return 0
def June30():
"""CdOPA references and some dots"""
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)
a.autobaseline((283,1989),order = 3)
b.autobaseline((283,1989),order = 3)
c.autobaseline((600,690,826,861,900,1196,1385,1515,1657),specialoption='points',order=7)
#c[:]+=4500
#b[:]+=3000
#a[:]+=1500
c.plot()
b.plot()
a.plot()
#d.plot()
legend(['dots','CdOPA pH12', 'pH 5', 'pH1'])
a = RamanSpectrum('/home/chris/Dropbox/DataWeiss/150630/150623_2.txt')
a.autobaseline((700,1500), order = 0)
b = RamanSpectrum('/home/chris/Dropbox/DataWeiss/150630/150623_3.txt')
b.autobaseline((300,791), order =2)
b.autobaseline((791,858),order = 0,join='start')
b.autobaseline((858,2000),order =1,join='start')
b.autobaseline((400,700,954,1495,1700),specialoption='points',order=3)
#a.plot()
c = add_RamanSpectra(a,b)
b.plot()
# OPARef.plot()
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 thiophenolfits():
os.chdir('/home/chris/Documents/DataWeiss/150424')
a = RamanSpectrum('/home/chris/Documents/DataWeiss/150424/150424_01.txt')
b = RamanSpectrum('/home/chris/Documents/DataWeiss/150424/150424_04.txt')
c = RamanSpectrum('/home/chris/Documents/DataWeiss/150424/150424_10.txt')
d = RamanSpectrum('/home/chris/Documents/DataWeiss/150430/150430_01.txt')
e = RamanSpectrum('/home/chris/Documents/DataWeiss/150430/150430_03.txt')
ax1 = gca()
a = removespikes(a)
a.smooth()
a.smooth(window_len=21, window = 'SG')
a.autobaseline((68, 322),order = 4)
a.autobaseline((322, 767),order = 0,join='start')
a.autobaseline((767, 838),order = 0,join='start')
a.autobaseline((838, 1405),order = 2,join='start')
a.autobaseline((1405,1466),order = 0,join='start')
a.autobaseline((1466, 1974),order = 2,join='start')
b = removespikes(b)
b.smooth(window_len=21, window = 'SG')
b.autobaseline((68, 322),order = 4)
b.autobaseline((322, 767),order = 0,join='start')
b.autobaseline((767, 838),order = 0,join='start')
b.autobaseline((838, 1405),order = 2,join='start')
b.autobaseline((1405,1466),order = 0,join='start')
b.autobaseline((1466, 1974),order = 2,join='start')
#
c = removespikes(c)
c.smooth(window_len=21, window = 'SG')
c.autobaseline((68, 322),order = 4)
c.autobaseline((322, 767),order = 0,join='start')
c.autobaseline((767, 838),order = 0,join='start')
c.autobaseline((838, 1405),order = 2,join='start')
c.autobaseline((1405,1466),order = 0,join='start')
c.autobaseline((1466, 1974),order = 2,join='start')
d = removespikes(d)
d.smooth( window_len=21, window = 'SG')
d.autobaseline((68, 322),order = 4)
d.autobaseline((322, 767),order = 0,join='start')
d.autobaseline((767, 838),order = 0,join='start')
d.autobaseline((838, 1405),order = 2,join='start')
d.autobaseline((1405,1466),order = 0,join='start')
d.autobaseline((1466, 1974),order = 2,join='start')
e = removespikes(e)
e.smooth(window_len=21, window = 'SG')
e.autobaseline((68, 322),order = 4)
e.autobaseline((322, 767),order = 0,join='start')
e.autobaseline((767, 838),order = 0,join='start')
e.autobaseline((838, 1405),order = 2,join='start')
e.autobaseline((1405,1466),order = 0,join='start')
e.autobaseline((1466, 1974),order = 2,join='start')
a[:]+=200#ax1.lines[0].set_ydata(ax1.lines[0].get_ydata()+200)
b[:]+=500
c[:]+=800
d[:]+=1100
e[:]+=1500
a.plot(marker = 'o',markersize = 1)
b.plot(marker = 'o',markersize = 1)
c.plot(marker = 'o',markersize = 1)
d.plot(marker = 'o',markersize = 1)
e.plot(marker = 'o',markersize = 1)
ylim(0,1800)
xlim(68,1980)
a_list = list()
b_list = list()
c_list = list()
d_list = list()
e_list = list()
for w in (541,628,742,1574):
z = fitspectrum(a,(w-30,w+30),'OneGaussian', [300, w,50, 0,50])
if z ==-1:
print 'fit awry'
else:
a_list.append(z[0])
plot(z[1], z[2])
z = fitspectrum(a,(1045,1130),'TwoGaussian', [225, 1067,20,250, 1095,20, -1,50])
if z ==-1:
print 'fit awry'
else:
a_list.append(z[0])
plot(z[1], z[2])
###############################################################
for w in (623,639,793,1086,1598):
z = fitspectrum(b,(w-30,w+30),'OneGaussian', [300, w,50, 0,850])
if z ==-1:
print 'fit awry'
else:
b_list.append(z[0])
plot(z[1], z[2])
###############################################################
for w in (144,206,499,623,636,795,1088,1178,1280,1591):
z= fitspectrum(c,(w-30,w+30),'OneGaussian', [300, w,50, 0,1150])
if z ==-1:
print 'fit awry'
else:
c_list.append(z[0])
plot(z[1], z[2])
###############################################################
for w in (88,122,262,496,538,628,724,1066,1087,1177,1561):
z= fitspectrum(d,(w-30,w+30),'OneGaussian', [300, w,50, 0,1550])
if z ==-1:
print 'fit awry'
else:
d_list.append(z[0])
plot(z[1], z[2])
###############################################################
for w in (242,631,813,1083,1157,1588):
z= fitspectrum(e,(w-30,w+30),'OneGaussian', [300, w,50, 0,50])
if z ==-1:
print 'fit awry'
else:
e_list.append(z[0])
plot(z[1], z[2])
f = open('/home/chris/Documents/DataWeiss/150430/thiophenolsonDots.txt', 'w')
f.write('\n\nchloro\n\n')
for i in a_list:
f.write(str(i[0])+'\n')
f.write('\n\ncmethyl\n\n')
for i in b_list:
f.write(str(i[0])+'\n')
f.write('\n\nmethoxy\n\n')
for i in c_list:
f.write(str(i[0])+'\n')
f.write('\n\ncbromo\n\n')
for i in d_list:
f.write(str(i[0])+'\n')
f.write('\n\ncfluoro\n\n')
for i in e_list:
f.write(str(i[0])+'\n')
f.close()
# fitspectrum(spectrum, rnge, func, guess)
return (a_list,b_list,c_list,d_list,e_list)
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 July1phosphonicacidtreated():
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
a.autobaseline((283,1989),order = 3)
b.autobaseline((283,1989),order = 3)
c.autobaseline((600,690,826,861,900,1196,1385,1515,1657),specialoption='points',order=7)
c[:]+=4500
b[:]+=3000
a[:]+=1500
c.plot()
b.plot()
a.plot()
c= RamanSpectrum('/home/chris/Dropbox/DataWeiss/150701/files6phosphonate.txt' )
c.autobaseline((283,1989),order = 3)
c[:]*=3
c.plot()
a = RamanSpectrum('/home/chris/Dropbox/DataWeiss/150630/150623_2.txt')
a.autobaseline((700,1500), order = 0)
b = RamanSpectrum('/home/chris/Dropbox/DataWeiss/150630/150623_3.txt')
b.autobaseline((300,791), order =2)
b.autobaseline((791,858),order = 0,join='start')
b.autobaseline((858,2000),order =1,join='start')
b.autobaseline((400,700,954,1495,1700),specialoption='points',order=3)
c = add_RamanSpectra(a,b)
x = RamanSpectrum('/home/chris/Dropbox/DataWeiss/150623/150623_4.txt')
b.plot()
OPARef.plot()
x.plot()
legend(['dots','CdOPA pH12', 'pH 5','phosJuuly1', 'phosJune23', 'OPA ref','june23'])
return 0
def July1():
ratiolist = list()
native= RamanSpectrum('/home/chris/Dropbox/DataWeiss/150612/150612_01_CdSe.txt') ###### Native ligand only
native[:]/=2
native=removespikes(native)
native.autobaseline((600,690,826,861,900,1196,1385,1515,1657),specialoption='points',order=7)
native.smooth()
a = RamanSpectrum('/home/chris/Documents/DataWeiss/150701/files1.txt')
b= RamanSpectrum('/home/chris/Documents/DataWeiss/150701/files2.txt')
c= RamanSpectrum('/home/chris/Documents/DataWeiss/150701/files3.txt')
d= RamanSpectrum('/home/chris/Documents/DataWeiss/150701/files4.txt')
e= RamanSpectrum('/home/chris/Documents/DataWeiss/150701/files5.txt')
#c = removespikes(c)
#c = removespikes(c)
correct = zeros(e.values.shape)
for z in [a,b,c,d,e]:
y = deepcopy(z)
y.smooth()
y.smooth()
y.smooth()
correct+= y/z
correct/=5
ax1=figure().add_subplot(111)
mbt = CdMethylTPRef.copy()
mbt[:]/=10
for z in [a,b,c,d,e]:
z[:]*=correct
z=removespikes(z)
z.autobaseline((109,500),order=3, join='start')
z.autobaseline((500,725),order=2, join='start')
z.autobaseline((725,795),order=1, join='start')
z.autobaseline((795,1363),order=2, join='start')
z.autobaseline((1363,1430),order = 1, join = 'start')
z.autobaseline((1430,1930),order = 4, join='start')
z.autobaseline((200,555,613,764,1141,1321,1565,1700,1920),specialoption='points',order=7)
z.smooth()
mbt[:]+=3000
a[:]+=1000
b[:]+=600
c[:]+=400
d[:]+=200
e[:]-=200
native-=500
lw = 2
mbt.plot(linewidth = lw)
guess = [50,100,100,1065,1080,1085,7,7,7,0,z[1110]]
for z in [a,b,c,d,e]:
print z.name
r = fitspectrum(z,(1050,1110),'ThreeGaussian',[50,100,500,1065,1078,1085,15,15,15,0,z[1110]] ) #r = fitspectrum(z,(1070,1110),'TwoGaussian',guess )
ratio = r.areas[1]/r.areas[2]
if z is a:
r = fitspectrum(z,(1070,1110),'TwoGaussian',[100,1000,1078,1085,15,15,0,z[1110]] ) #r = fitspectrum(z,(1070,1110),'TwoGaussian',guess )
ratio = r.areas[0]/r.areas[1]
ratiolist.append(ratio)
z.plot(linewidth = lw)
print r.params[0]
for p in r.peaks:
ax1.plot(r.x,p,color = 'k',linewidth = 2)
plot(r.x,r.y, color = 'k', linewidth = lw)
native.plot(linewidth = lw)
xlim(555,1700)
ylim(-500,3000)
#legend(['mbt solid','2035eq','713eq','502eq','80eq','58eq','native'])
ax2=figure().add_subplot(111)
print ratiolist
ax2.plot([2035,713,502,80,58],ratiolist,'rs-')
return 0
def May29():
r = RamanSpectrum('/home/chris/Documents/DataWeiss/150529/150529_05.txt') ### exchanged CdS dots with phosphonic acid (octadecyl)
r.autobaseline((147,1678), order =3)
r.autobaseline((147,356), order = 1)
r.autobaseline((356,389), order = 1, join = 'start')
r.autobaseline((389,892), order = 1, join = 'start')
r.autobaseline((892,923), order = 1, join = 'start')
r.autobaseline((923,1185), order = 1, join = 'start')
r.autobaseline((1185,1211), order = 1, join = 'start')
r.autobaseline((1211,1678), order = 1, join = 'start')
r.smooth()
r.plot()
r = RamanSpectrum('/home/chris/Documents/DataWeiss/150527/150527_06.txt') ### native ligands CdS dots
r.autobaseline((98,764), order = 1)
r.autobaseline((764,839), order = 1, join = 'start')
r.autobaseline((839,1700), order = 2, join = 'start')
r.smooth()
r.plot()
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()
legend(['exchanged', 'oleate', 'stoich','rich'])
ylim(-100,2000)
return 0
def DMFWash():
clf()
ax1 = gca()
a = RamanSpectrum('/home/chris/Documents/DataWeiss/150709/150709_03.txt')#### sample A washed with DMF
b = RamanSpectrum('/home/chris/Documents/DataWeiss/150709/150709_04.txt')# sa,[;e B washed with DMF]
c = RamanSpectrum('/home/chris/Documents/DataWeiss/150709/150709_05.txt')##sample C washed with DMF
d = RamanSpectrum('/home/chris/Documents/DataWeiss/150709/150709_06.txt')##sample C washed with DMF using 50x close up objective
e= RamanSpectrum('/home/chris/Documents/DataWeiss/150709/150709_07.txt')##sample E washed with DMF
for z in [a,b,c]:
z.autobaseline((200,725),order=3, join='start')
z.autobaseline((725,800),order=0, join='start')
z.autobaseline((800,1427),order=2, join='start')
z.autobaseline((1427,1435),order=0, join='start')
z.autobaseline((1435,2000),order=0, join='start')
z[:]-=z[1700]
z.plot()
d.autobaseline((520,1250),order = 3)
e.autobaseline((520,1250),order = 3)
d.plot()
e.plot()
xlim(900,1200)
ax1=figure().add_subplot(111)
ratiolist = list()
# native= RamanSpectrum('/home/chris/Dropbox/DataWeiss/150612/150612_01_CdSe.txt') ###### Native ligand only
# native[:]/=2
# native=removespikes(native)
# native.autobaseline((600,690,826,861,900,1196,1385,1515,1657),specialoption='points',order=7)
# native.smooth()
#
a = RamanSpectrum('/home/chris/Documents/DataWeiss/150701/files1.txt')
b= RamanSpectrum('/home/chris/Documents/DataWeiss/150701/files2.txt')
c_unwashed= RamanSpectrum('/home/chris/Documents/DataWeiss/150701/files3.txt')
d_unwashed= RamanSpectrum('/home/chris/Documents/DataWeiss/150701/files4.txt')
e_unwashed= RamanSpectrum('/home/chris/Documents/DataWeiss/150701/files5.txt')
correct = zeros(e_unwashed.values.shape)
for z in [a,b,c_unwashed,d_unwashed,e_unwashed]:
y = deepcopy(z)
y.smooth()
y.smooth()
y.smooth()
correct+= y/z
correct/=5
for z in [c_unwashed,e_unwashed]:
z[:]*=correct
z=removespikes(z)
z.autobaseline((109,500),order=3, join='start')
z.autobaseline((500,725),order=2, join='start')
z.autobaseline((725,795),order=1, join='start')
z.autobaseline((795,1363),order=2, join='start')
z.autobaseline((1363,1430),order = 1, join = 'start')
z.autobaseline((1430,1930),order = 4, join='start')
z.autobaseline((200,555,613,764,1141,1321,1565,1700,1920),specialoption='points',order=7)
z.smooth()
lw = 2
c_unwashed[:]*=3
guess = [300,1500,1078,1085,15,15,0,0]
r = fitspectrum(d,(1070,1110),'TwoGaussian',guess ) #r = fitspectrum(z,(1070,1110),'TwoGaussian',guess )
ratio = r.areas[0]/r.areas[1]
ratiolist.append(ratio)
d.plot(linewidth = lw)
print r.params[0]
for p in r.peaks:
ax1.plot(r.x,p,color = 'k',linewidth = 2)
plot(r.x,r.y, color = 'k', linewidth = lw)
r = fitspectrum(c_unwashed,(1070,1110),'TwoGaussian', guess) #r = fitspectrum(z,(1070,1110),'TwoGaussian',guess )
ratio = r.areas[0]/r.areas[1]
ratiolist.append(ratio)
c_unwashed.plot(linewidth = lw)
print r.params[0]
for p in r.peaks:
ax1.plot(r.x,p,color = 'k',linewidth = 2)
plot(r.x,r.y, color = 'k', linewidth = lw)
e[:]+=2000
e_unwashed[:]+=2000
e.smooth()
guess = [300,1500,1078,1085,15,15,0,2000]
r = fitspectrum(e,(1050,1110),'ThreeGaussian', [100,300,1500,1065,1078,1085,15,15,15,0,2000] ) #r = fitspectrum(z,(1070,1110),'TwoGaussian',guess )
ratio = r.areas[1]/r.areas[2]
ratiolist.append(ratio)
e.plot(linewidth = lw)
print r.params[0]
for p in r.peaks:
ax1.plot(r.x,p,color = 'k',linewidth = 2)
plot(r.x,r.y, color = 'k', linewidth = lw)
r = fitspectrum(e_unwashed,(1070,1110),'TwoGaussian',guess) #r = fitspectrum(z,(1070,1110),'TwoGaussian',guess )
ratio = r.areas[0]/r.areas[1]
ratiolist.append(ratio)
e_unwashed.plot(linewidth = lw)
print r.params[0]
for p in r.peaks:
ax1.plot(r.x,p,color = 'k',linewidth = 2)
plot(r.x,r.y, color = 'k', linewidth = lw)
legend(['c-washed1', 'c-unwashed','e-washed', 'eunwashed'])
print ratiolist
return 0
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
def MBTSeries():
"""methylbenzenethiol exchanged CdSe quantum dots on June12"""
clf()
ax1 = gca()
chdefarea=array([])
thiolarea=array([])
native= RamanSpectrum('/home/chris/Dropbox/DataWeiss/150612/150612_01_CdSe.txt') ###### Native ligand only
native[:]/=2
#native=removespikes(native)
native.autobaseline((911,1196,1385,1515,1800),join='start',specialoption='points',order=7)
native.autobaseline((600,690,826,861,911),specialoption='points', order = 5,join='end')
eightyfour= RamanSpectrum('/home/chris/Dropbox/DataWeiss/150623/150623_7.txt') ###### 84 eq
eightyfour[:]/=2
#eightyfour=removespikes(eightyfour)
eightyfour.autobaseline((764,838),order = 0,join='start')
eightyfour.autobaseline((838,2000),order = 1,join='start')
eightyfour.autobaseline((600,690,826,861,900,1196,1385,1515,1657),specialoption='points',order=7)
# eightyfour.smooth()
sixhundredforty= RamanSpectrum('/home/chris/Dropbox/DataWeiss/150617/150617_01.txt') ###### 640 eq MBT
#sixhundredforty=removespikes(sixhundredforty)
sixhundredforty.autobaseline((803,861),order=1, join='start')
sixhundredforty.autobaseline((861,1254),order = 2, join='start')
sixhundredforty.autobaseline((1254,1515),order = 4, join = 'start')
sixhundredforty.autobaseline((1515,2000),order = 3, join = 'start')
sixhundredforty.autobaseline((555,613,764,1141,1321,1565,1652),specialoption='points',order=6)
#sixhundredforty.smooth()
thirtytwo=RamanSpectrum('/home/chris/Dropbox/DataWeiss/150623/150623_10.txt') ###### 32 equivalents
#thirtytwo=removespikes(thirtytwo)
thirtytwo.autobaseline((300,862),order = 1,join='start')
thirtytwo.autobaseline((786,862),order = 0,join='start')
thirtytwo.autobaseline((862,1425),order = 2,join='start')
thirtytwo.autobaseline((1425,1439),order = 1,join='start')
thirtytwo.autobaseline((1439,2000),order = 2,join='start')
x = RamanSpectrum('/home/chris/Dropbox/DataWeiss/150623/150623_12.txt')
#x= removespikes(x)
x.autobaseline((740,1441),order = 1)
thirtytwo=add_RamanSpectra(thirtytwo,x)
thirtytwo.autobaseline((740,764,1052,1141,1321,1425,1441),specialoption='points',order=2)
# thirtytwo.smooth()
oneseventynine=RamanSpectrum('/home/chris/Dropbox/DataWeiss/150623/150623_16.txt') ###### 179 equivalents
# oneseventynine=removespikes(oneseventynine)
oneseventynine.autobaseline((300,793),order = 1,join='start')
oneseventynine.autobaseline((793,862),order = 1,join='start')
oneseventynine.autobaseline((862,1460),order = 1,join='start')
oneseventynine.autobaseline((1460,1486),order = 1,join='start')
oneseventynine.autobaseline((1486,2000),order = 1,join='start')
oneseventynine.autobaseline((740,764,1052,1141,1321,1425,1441,1700),specialoption='points',order=2)
#oneseventynine.smooth()
mbt = CdMethylTPRef.copy()
mbt[:]/=10
lw = 2
thirtytwo[:]+=200
eightyfour[:]+=700
oneseventynine[:]+=1000
sixhundredforty[:]+=1550
mbt[:]+=1950
chtwistarea=array([native.calc_area((1285,1332)),thirtytwo.calc_area((1285,1332)),eightyfour.calc_area((1285,1332)),oneseventynine.calc_area((1285,1332)),sixhundredforty.calc_area((1285,1332)),mbt.calc_area((1285,1332))])
chdefarea=array([native.calc_area((1413,1475)),thirtytwo.calc_area((1413,1475)),eightyfour.calc_area((1413,1475)),oneseventynine.calc_area((1413,1475)),sixhundredforty.calc_area((1413,1475)),mbt.calc_area((1413,1475))])
thiolarea1=array([native.calc_area((1587,1611)),thirtytwo.calc_area((1587,1611)),eightyfour.calc_area((1587,1611)),oneseventynine.calc_area((1587,1611)),sixhundredforty.calc_area((1587,1611)),mbt.calc_area((1587,1611))])
fits1 = list()
fits2 = list()
a = [thirtytwo, eightyfour, oneseventynine, sixhundredforty, mbt]
a.reverse()
for i in a:
i.plot(linewidth=lw,axes=ax1)
native.plot(linewidth = lw,axes=ax1)
ax1.set_ylabel('Intensity (a.u.)')
ax1.set_xlabel('Raman shift (cm$^{-1}$')
legend(['solid', '640eq','179', '84eq','32eq','0'])
ax1.set_xlim(500,1800)
ax1.set_ylim(0,10000)
ax2 = figure().add_subplot(111)
for i in [thirtytwo, eightyfour, oneseventynine, sixhundredforty]:
guess = [100,500,500,1065,1080,1085,7, 7,7,0,i[1100]]
r = fitspectrum(i,(1050,1105), 'xGaussian', guess)
for p in r.peaks:
ax1.plot(r.x, p,'k', linewidth = 2)
fits1.append(r.areas[1]/r.areas[2])
ax2.plot([32,84,179,640],fits1,'rs-', label='1')
return None
def Jan6(): ### Raman spectra of PPA exchanged dots at different points in exchange
clf()
a = RamanSpectrum('/home/chris/Dropbox/DataWeiss/160106/160106_03.txt')
b = RamanSpectrum('/home/chris/Dropbox/DataWeiss/160106/160106_05.txt')
c = RamanSpectrum('/home/chris/Dropbox/DataWeiss/160106/160106_06.txt')
d = RamanSpectrum('/home/chris/Dropbox/DataWeiss/160106/160106_07.txt')
e = RamanSpectrum('/home/chris/Dropbox/DataWeiss/160106/160106_09.txt')
f = RamanSpectrum('/home/chris/Dropbox/DataWeiss/160106/160106_10.txt')
a = removespikes(a)
a.autobaseline((200,1800), order = 5)
a.autobaseline((1800,2100,2700,3200,3600), specialoption='points',order = 3,join='start')
b.autobaseline((200,1800), order = 5)
b.autobaseline((1800,2100,2700,3200,3600), specialoption='points',order = 3,join='start')
b[:]+=10000
c.autobaseline((200,1800), order = 5)
c.autobaseline((1800,2100,2700,3200,3600), specialoption='points',order = 3,join='start')
c[:]+=20000
d.autobaseline((200,1800), order = 5)
d.autobaseline((1800,2100,2700,3200,3600), specialoption='points',order = 3,join='start')
d[:]+=30000
e.autobaseline((200,1800), order = 5)
e.autobaseline((1800,2100,2700,3200,3600), specialoption='points',order = 3,join='start')
e[:]+=40000
f.autobaseline((200,1800), order = 5)
f.autobaseline((1800,2100,2700,3200,3600), specialoption='points',order = 3,join='start')
f[:]+=50000
a.plot(color = 'k')
b.plot(color = 'k')
c.plot(color = 'k')
d.plot(color = 'k')
e.plot(color = 'k')
f.plot(color = 'k')
OPAdots = RamanSpectrum('/home/chris/Dropbox/DataWeiss/150612/150612_01_CdSe.txt') ## dots
OPAdots= RamanSpectrum('/home/chris/Dropbox/DataWeiss/150612/150612_01_CdSe.txt') ## dots
OPAdots.autobaseline((911,1196,1385,1515,1800),specialoption='points',order=7,join='start')
OPAdots.autobaseline((280,600,690,826,861,911),specialoption='points', order = 5,join='end')
OPAdots.autobaseline((281,630), order = 4, join = 'end')
OPAdots.autobaseline((1492,1800), order = 5, join = 'start')
OPAdots[:]*=3
OPAdots[:]+=35000
# OPAdots.plot()
DMF = RamanSpectrum('/home/chris/Dropbox/DataWeiss/151215/151215_04.txt')
DMF.autobaseline((300,1800), order =3)
DMF[:]+=21000
#DMF.plot()
#(CdOPARef*3+35000).plot(color = 'b')
return 0
