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