Python RamanSpectrum.smooth方法代碼示例

# 需要導入模塊: from ramanTools import RamanSpectrum [as 別名]
# 或者: from ramanTools.RamanSpectrum import smooth [as 別名]
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

# 需要導入模塊: from ramanTools import RamanSpectrum [as 別名]
# 或者: from ramanTools.RamanSpectrum import smooth [as 別名]
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

# 需要導入模塊: from ramanTools import RamanSpectrum [as 別名]
# 或者: from ramanTools.RamanSpectrum import smooth [as 別名]
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

# 需要導入模塊: from ramanTools import RamanSpectrum [as 別名]
# 或者: from ramanTools.RamanSpectrum import smooth [as 別名]
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

# 需要導入模塊: from ramanTools import RamanSpectrum [as 別名]
# 或者: from ramanTools.RamanSpectrum import smooth [as 別名]
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

# 需要導入模塊: from ramanTools import RamanSpectrum [as 別名]
# 或者: from ramanTools.RamanSpectrum import smooth [as 別名]
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

# 需要導入模塊: from ramanTools import RamanSpectrum [as 別名]
# 或者: from ramanTools.RamanSpectrum import smooth [as 別名]
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

# 需要導入模塊: from ramanTools import RamanSpectrum [as 別名]
# 或者: from ramanTools.RamanSpectrum import smooth [as 別名]
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

# 需要導入模塊: from ramanTools import RamanSpectrum [as 別名]
# 或者: from ramanTools.RamanSpectrum import smooth [as 別名]
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

# 需要導入模塊: from ramanTools import RamanSpectrum [as 別名]
# 或者: from ramanTools.RamanSpectrum import smooth [as 別名]
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]

#.........這裏部分代碼省略.........

# 需要導入模塊: from ramanTools import RamanSpectrum [as 別名]
# 或者: from ramanTools.RamanSpectrum import smooth [as 別名]
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

# 需要導入模塊: from ramanTools import RamanSpectrum [as 別名]
# 或者: from ramanTools.RamanSpectrum import smooth [as 別名]
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])  
    
    ###############################################################    
#.........這裏部分代碼省略.........