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langmuir NMR.py
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langmuir NMR.py
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# -*- coding: utf-8 -*-
"""
Created on Thu Oct 29 16:54:57 2015
@author: chris
"""
from ramanTools.RamanSpectrum import *
from numpy import *
from matplotlib.pyplot import *
from Nov6 import *
from numpy.polynomial.polynomial import polyder
from numpy.polynomial.polynomial import polyfit as pf
import ramanTools.RamanSpectrum as rs
import UVVistools as uv
def Oct13():
a= loadtxt('/home/chris/Dropbox/DataWeiss/151013/NMR samples.csv', delimiter = ',', unpack = True, skiprows=1)
for i in range(1,6):
r = polyfit(a[0,0:100],a[i,0:100],1)
a[i]-=rs.polyeval(r,a[0])
peak = uv.findpeak(a[0],a[i],(410,420))
print peak
diameter = -0.000000066521*peak[0]**3+0.00019557*peak[0]**2-0.092352*peak[0]+13.29
print 'diam', diameter
epsilon = 21536*diameter**2.3
print 'eps', epsilon
print 'conc', 5*peak[1]/epsilon
plot(a[0],a[i])
legend(['10.5','9.5','9.3','8.6','6.8'])
return 0
def Oct17():
a= loadtxt('/home/chris/Dropbox/DataWeiss/151017/thioldots.csv', delimiter = ',', unpack = True, skiprows=1, usecols = (0,6,3,1,2,4,5))
for i in range(1,7):
r = polyfit(a[0,0:100],a[i,0:100],1)
a[i]-=rs.polyeval(r,a[0])
peak = uv.findpeak(a[0],a[i],(410,420))
#print peak
diameter = -0.000000066521*peak[0]**3+0.00019557*peak[0]**2-0.092352*peak[0]+13.29
#print 'diam', diameter
epsilon = 21536*diameter**2.3
# print 'eps', epsilon
print 'CONC', 5*peak[1]/epsilon
plot(a[0],a[i])
legend(['4.2','6.0',',6.4','7.7','9.7','11.5'])
print 'THIOL EXCHANGE'
a= loadtxt('/home/chris/Dropbox/DataWeiss/151017/thiolcapped doits.csv', delimiter = ',', unpack = True, skiprows=1, usecols = (0,1))
r = polyfit(a[0,0:100],a[1,0:100],1)
a[1]-=rs.polyeval(r,a[0])
peak = uv.findpeak(a[0],a[1],(410,420))
#print peak
diameter = -0.000000066521*peak[0]**3+0.00019557*peak[0]**2-0.092352*peak[0]+13.29
#print 'diam', diameter
epsilon = 21536*diameter**2.3
# print 'eps', epsilon
print 'CONC', 5*peak[1]/epsilon
a= loadtxt('/home/chris/Dropbox/DataWeiss/151017/thioldots.csv', delimiter = ',', unpack = True, skiprows=1, usecols = (0,3,1,2,4,5,6))
for i in range(1,7):
r = polyfit(a[0,0:100],a[i,0:100],1)
a[i]-=rs.polyeval(r,a[0])
peak = uv.findpeak(a[0],a[i],(410,420))
#print peak
diameter = -0.000000066521*peak[0]**3+0.00019557*peak[0]**2-0.092352*peak[0]+13.29
#print 'diam', diameter
epsilon = 21536*diameter**2.3
# print 'eps', epsilon
print 'CONC', 5*peak[1]/epsilon
plot(a[0],a[i])
legend(['200','400',',800','2000','3200','4000'])
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 Oct29PPATitrationUVVis():
a = loadtxt('/home/chris/Dropbox/DataWeiss/151029/151029UVVis.csv', delimiter = ',', unpack=True, skiprows=1)
for i in range(1,8):
a[i]-=a[i,0]
peak = uv.findpeak(a[0],a[i],(410,420))
print peak
plot(a[0],a[i])
diameter = -0.000000066521*peak[0]**3+0.00019557*peak[0]**2-0.092352*peak[0]+13.29
print 'diam', diameter
epsilon = 21536*diameter**2.3
# print 'eps', epsilon
print 'CONC', 5*peak[1]/epsilon
legend(['vial0','vial1','vial2','vial3','vial4','vial5','vial6',])
ylabel('Absorbance')
xlabel('wavelength (nm)')
return 0
def Oct30PPATitrationUVVis():
a = loadtxt('/home/chris/Dropbox/DataWeiss/151030/151030PPAtitration.csv', delimiter = ',', unpack=True, skiprows=1)
for i in range(1,11):
a[i]-=a[i,0]
peak = uv.findpeak(a[0],a[i],(410,420))
print peak
plot(a[0],a[i])
diameter = -0.000000066521*peak[0]**3+0.00019557*peak[0]**2-0.092352*peak[0]+13.29
#print 'diam', diameter
epsilon = 21536*diameter**2.3
# print 'eps', epsilon
print 'CONC', 5*peak[1]/epsilon
legend(['vial0','vial1','vial2','vial3','vial4','vial5','vial6','vial7','vial8','vial9', 'vial10',])
ylabel('Absorbance')
xlabel('wavelength (nm)')
return 0
def peaksshift():
x = linspace(-1,2,1000)
noexchange = 1/((x-0)**2+.0004)+1/((x-1)**2+.0004)
subplot(411)
yticks()
plot(x,noexchange)
subplot(412)
plot(x,1/((x-0.25)**2+.0004))
subplot(413)
yticks()
plot(x,1/((x-0.5)**2+.0004))
subplot(414)
yticks()
plot(x,1/((x-0.75)**2+.0004))
for ax in gcf().axes:
ax.set_yticks([])
ax.set_xticks([0,0.25,0.5,0.75,1])
ax.set_xlim(-0.3,1.3)
return 0
def exampleplot():
figure()
ax1=subplot(121)
ax2=subplot(122)
c = linspace(0,10,1000)
cmaxdot = 0.2
# subplot(121)
for K in [100,1000,10000]:
K=float(K)
a = K
#def b():return -ctot0*K - K*c +K*cmaxdot+1
#def d():return ctot0+c
#
#def cfree():return (-b()+sqrt(b()**2-4*a*d()))/(2*a)
a=K
b=1+K*cmaxdot-K*c
d=-c
cfree = (-b+sqrt(b**2-4*a*d))/(2*a)
cfree2= (-b-sqrt(b**2-4*a*d))/(2*a)
r = pf(c[10:15],cfree[10:15],1)
#print r[0], (K+0.5*((1+K*cmaxdot-K*xlarge)**2+4*K*xlarge)**-0.5*(-2*K*(1+K*cmaxdot-K*xlarge)+4*K))/(2*K)
M = (r[0]-0.5)**2
A = (M-0.5)#*xlarge**2
B = (2*M-1)#*xlarge
C = M-0.5
print M,A,B,C,B**2
#print K,'estimated K=',(-B+sqrt(B**2-4*A*C))/(2*A)
print 'early slope=', cfree[0]/c[1],cfree[0]/c[1]-c[1]*r[1]
# plot(c[:10],(c[:10]-0.1)*r[1]+cfree[10]/c[10])
ax1.plot(c,cfree/c)
ax2.plot(c,cfree/c)
# plot(c,findderivative(c,cfree/c,derivorder=1)/200,'--')
#plot(c,fd(c,cfree/c),'-.')
#plot(c,K*cfree/(1+K*cfree))
# plot(c,cfree2)
legend([1,10,100,1000])
ax1.set_ylabel('$\delta_{observed}$',fontsize=24)
#ax2.set_ylabel('$\delta$',fontsize=24)
ax1.set_xlabel('c$_{total}$ (mM)', fontsize=24)
ax2.set_xlabel('c$_{total}$ (mM)', fontsize=24)
ax2.set_xlim((0,0.4))
ax2.set_xticks([0,0.1,0.2,0.3,0.4,0.5])
ax1.set_yticks([0,1])
ax2.set_yticks([])
ax1.set_yticklabels(['$\delta_{bound}$','$\delta_{free}$'],fontsize=24)
figure()
ax1=subplot(211)
ax2=subplot(212)
for K in [50]:
K=float(K)
a = K
#def b():return -ctot0*K - K*c +K*cmaxdot+1
#def d():return ctot0+c
#
#def cfree():return (-b()+sqrt(b()**2-4*a*d()))/(2*a)
a=K
b=1+K*cmaxdot-K*c
d=-c
cfree = (-b+sqrt(b**2-4*a*d))/(2*a)
ax1.plot(c,cfree/c)
ax2.plot(c,c-cfree)#K*cfree*cmaxdot/(1+K*cfree))
ax2.plot(c,cfree,'--')
ax2.plot(c,c)
legend(['bound', 'free', 'total'],loc=2)
ax1.set_ylabel('$\delta_{observed}$',fontsize=24)
ax2.set_xlabel('c$_{total}$ (mM)', fontsize=24)
ax2.set_ylabel('c (mM)',fontsize=24)
#ax2.set_xticks([0,0.1,0.2,0.3,0.4,0.5])
ax1.set_yticks([0,1])
ax1.set_xlim(0,1)
ax2.set_xlim(0,1)
#ax1.set_ylim(-0.1,1.1)
ax2.set_ylim(0,1)
ax1.set_yticklabels(['$\delta_{bound}$','$\delta_{free}$'],fontsize=24)
# subplot(122)
# for cinit in [0,0.1,0.2,0.5,1]:
# a = K
# #def b():return -ctot0*K - K*c +K*cmaxdot+1
# #def d():return ctot0+c
# #
# #def cfree():return (-b()+sqrt(b()**2-4*a*d()))/(2*a)
# ctot = cinit+c
# a=K
# b=1+K*cmaxdot-K*ctot
# d=-ctot
#
# cfree = (-b+sqrt(b**2-4*a*d))/(2*a)
#
# plot(c,cfree/ctot)
# # plot(c,cfree2)
# legend([0,0.1,0.2,0.5,1])
return 0
def datafit():
def peakwalk(cadded,K,c_init,cmaxdot,deltadots,deltafree):
c = cadded+c_init
a=K
b=1+K*cmaxdot-K*c
d=-c
return (deltafree-deltadots)*(-b+sqrt(b**2-4*a*d))/(2*a*c)+ deltadots +sin(200*int(c_init<0))
def peakwalkfixinit(cadded,K,cmaxdot,deltadots,deltafree):
c_init=200
c = cadded+c_init
a=K
b=1+K*cmaxdot-K*c
d=-c
return (deltafree-deltadots)*(-b+sqrt(b**2-4*a*d))/(2*a*c)+ deltadots +sin(200*int(c_init<0))
def peakwalkfixdeltas(cadded,K,c_init,cmaxdot):
c_init=200
c = cadded+c_init
a=K
b=1+K*cmaxdot-K*c
d=-c
return (20.95-19.53)*(-b+sqrt(b**2-4*a*d))/(2*a*c)+ 19.53 +sin(200*int(c_init<0))
def peakwalksimple(cadded,c_init,cmaxdot,deltadots,deltafree):
return (deltafree-deltadots)*(cadded+c_init-cmaxdot)/(cadded+c_init)+ deltadots
def peakwalksimplefixinit(cadded,cmaxdot,deltadots):
c_init = 600
return (20.96-deltadots)*(cadded+c_init-cmaxdot)/(cadded+c_init)+ deltadots
figure()
usefunction =peakwalk#(cadded,K,c_init,cmaxdot) peakwalksimplefixinit
guess =[1,1100,200,21,18]
xs = array([0,86,128,171,214,428,891,1644,1645,2041,2381,3833,7079]) ### added amount of PPA in mM
ys = array([19.61, 19.67, 19.71, 19.74, 19.73, 19.82, 20.02,20.43, 20.44, 20.52, 20.55, 20.75,20.96])
fit = scipy.optimize.curve_fit(usefunction,xs,ys,guess)
print fit[0]#,fit[1]#np.sqrt(np.diag(fit[1]))
xsOct30=array([0,40,79,197,408,812,1574,2372,3899,7992])
ysOct30 = array([19.67,19.55,19.63,19.76,19.9,20.03,20.36,20.48,20.64,20.85])
fitOct30 = scipy.optimize.curve_fit(usefunction,xsOct30,ysOct30,guess)
print fitOct30[0]#,fitOct30[1]#np.sqrt(np.diag(fitOct30[1]))
#print 'cmax as determined from slope=', cmax,'. equivalents per dot =', cmax/0.0024
#print 'K=',fit[0][0]
#print 'Initial conc PPA total (mM)=',fit[0][1]
#print 'maximum concentration on dots=', fit[0][2],'. equivalents per dot =', fit[0][2]/0.0024
#print 'chemical shift of PPA on dots=', fit[0][3]
f = fit[0][1]
fit[0][1]=0
fOct30 = fitOct30[0][1]
fitOct30[0][1]=0
fitxs = arange(0,10000,1)
plot(fitxs,usefunction(fitxs,*fit[0]))
plot(xs+f,ys,'s')
plot(fitxs,usefunction(fitxs,*fitOct30[0]))
#f_free = (usefunction(fitxs,*fitOct30[0])-fitOct30[0][3])/(fitOct30[0][2]-fitOct30[0][3])
#f_bound = 1-f_free
plot(xsOct30+fOct30,ysOct30,'s')
xlabel('Total PPA ($\mu$M)')
ylabel('chemical shift (ppm)')
return 0