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 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 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 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 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 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 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 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 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 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 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)