def finishFit(self, X): """Call when fitting done""" d = self.datasets[self.dsindex] model = X.getResult()['model'] fitdata = Fitting.makeFitData(model, X.variables, X.getError()) self.E.setFitData(d, fitdata) self.replot() self.stop = False return
def fitElwellSchellman(self,E=None, d=None, xy=None,transwidth=50, invert=False,show=True,figname=None): """Fit entire raw data simultaneously to the three main thermodynamic parameters using Elwell/Schellman method""" if E !=None: ek = E.getDataset(d) x,y,a, xerr,yerr = ek.getAll() elif xy!=None: x,y = xy else: return if invert == True: y = [max(y)-i for i in y[:]] f=plt.figure(figsize=(10,5)) ax=f.add_subplot(121) p=ax.plot(x,y,'o',alpha=0.5) ax.set_xlabel('T');ax.set_xlabel('mdeg') ax.set_title('raw data') x1,y1,x,y = self.transformCD(x,y,transwidth,ax) t=[];dg=[] R=8.3144e-3 for T,fu in zip(x,y): if fu>=1 or fu<=0: continue K = fu/(1-fu) deltaGt = -R * T * math.log(K) dg.append(deltaGt) t.append(T) ax1=f.add_subplot(122) p=ax1.plot(t,dg,'x',mew=2,color='black') ax1.set_xlabel('T'); ax1.set_ylabel('dG(T)') ax.set_title('stability curve') A,X=Fitting.doFit(expdata=zip(t,dg),model='schellman',grad=1e-9,conv=1e-9) fity = X.getFitLine(t) p=ax1.plot(t,fity,'r',lw=2) fd=X.getFitDict() self.drawParams(ax1,fd) deltaH=fd['deltaH']; deltacp=fd['deltacp']; Tm=fd['Tm'] f.suptitle("Method 2 - deltaH: %2.2f deltaCp: %2.2e Tm: %2.2f" %(deltaH,deltacp,Tm),size=18) if show == True: self.showTkFigure(f) if figname == None: figname = d figname = figname.replace('.','_') fname = figname+'m1'+'.png' f.savefig(fname,dpi=300) print 'plot saved to %s' %os.path.abspath(fname) if E!=None: fdata = Fitting.makeFitData(X.name,vrs=X.variables) E.insertDataset(xydata=[t,dg], newname=d+'_vanthoff2',replace=True,fit=fdata) #E.saveProject() return deltaH, Tm, deltacp
def fitDifferentialCurve(self, E=None, d=None, xy=None,smooth=0, invert=False,show=True,figname=None): """Derive differential denaturation curve and fit to get deltaH We smooth the unfolding curve and then differentiate and finally fit to a 3 parameter equation. See http://www.ncbi.nlm.nih.gov/pubmed/10933511""" if E !=None: ek = E.getDataset(d) x,y,a, xerr,yerr = ek.getAll() elif xy!=None: x,y = xy else: return if invert == True: y = [max(y)-i for i in y[:]] leg=[]; lines=[] f=plt.figure(figsize=(10,5)) ax=f.add_subplot(121) p=ax.plot(x,y,'x',color='black',mew=3,alpha=0.5) leg.append(p); lines.append('original') #smooth if smooth == 0: smooth=int(len(x)/15.0) s=self.smoothListGaussian(y,smooth) p=ax.plot(x[:len(s)-1],s[:-1],lw=3) leg.append(p); lines.append('smoothed') ax.set_title("original data") ax.set_xlabel('T') ax1=f.add_subplot(122) #differentiate dx,ds = self.differentiate(x[:len(s)],s) #ds = [i/max(ds) for i in ds] ds = [i*10 for i in ds] cw=csv.writer(open('diffcd.csv','w')) for row in zip(dx,ds): cw.writerow(row) p=ax1.plot(dx,ds,'-',lw=1.5,alpha=0.7,color='black') leg.append(p); lines.append('differential') ax1.set_title("differential denaturation") ax1.set_xlabel('T'); ax1.set_ylabel('dsignal/dT') A,X=Fitting.doFit(expdata=zip(dx,ds),model='diffDenaturation',grad=1e-9,conv=1e-10) fity = X.getFitLine(dx) p=ax1.plot(dx,fity,'r',lw=2) leg.append(p); lines.append('fit') t=X.getFitDict() self.drawParams(ax1,t) dHkcal=t['deltaH']/4.184 f.suptitle('Method 3 - deltaH: %2.2f kJ/mol (%2.2f kcal) Tm: %2.2f' %(t['deltaH'],dHkcal,t['Tm']),size=18) ax.legend(leg,lines,loc='best',prop=FontProperties(size="smaller")) #f.subplots_adjust(hspace=0.8) if show == True: self.showTkFigure(f) if figname != None: figname = figname.replace('.','_') f.savefig(figname+'m3',dpi=300) plt.close() if E!=None: fdata = Fitting.makeFitData(X.name,vrs=X.variables) E.insertDataset(xydata=[dx,ds], newname=d+'_diff',replace=True,fit=fdata) #E.saveProject() return t['deltaH'],t['Tm']
def fitVantHoff(self, E=None, d=None, xy=None, transwidth=80, invert=False, show=True, figname=None): """Derive fraction unfolded, get K and fit to Van't Hoff. see http://www.jbc.org/content/277/43/40717.full or http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2144003/ """ if E != None: if not d in E.datasets: print 'no such dataset, %s' %d print 'available datasets:', E.datasets return ek = E.getDataset(d) x,y = ek.getxySorted() elif xy!=None: x,y = xy if 'Convert Celsius-Kelvin' in self.conversions.getcurselection(): x = [i+273 for i in x] if invert == True: y = [max(y)-i for i in y[:]] f=plt.figure(figsize=(18,6)) ax=f.add_subplot(131) p=ax.plot(x,y,'o',alpha=0.6) ax.set_xlabel('T(K)'); ax.set_ylabel('mdeg') ax.set_title('raw data') x1,y1,x,y = self.transformCD(x,y,transwidth,ax) cw=csv.writer(open('frac_unfolded_'+d+'.csv','w')) cw.writerow(['temp','frac']) for i in zip(x1,y1): cw.writerow(i) #derive lnK vs 1/T t=[]; k=[] for T,fu in zip(x,y): if fu>=1 or fu<=0: continue K = fu/(1-fu) klog = math.log(K) k.append(klog) t.append(1/T) if len(t)<2: return None, None, None ax=f.add_subplot(132) p=ax.plot(x1,y1,'o',color='g',alpha=0.6) ax.set_xlabel('T(K)'); ax.set_ylabel('fu') ax.set_title('fraction unfolded') ax=f.add_subplot(133) p=ax.plot(t,k,'x',mew=2,color='black') ax.set_xlabel('1/T')#(r'$1/T ($K^-1)$') ax.set_ylabel('ln K') formatter = matplotlib.ticker.ScalarFormatter() formatter.set_scientific(True) formatter.set_powerlimits((0,0)) ax.xaxis.set_major_formatter(formatter) for l in ax.get_xticklabels(): l.set_rotation(30) #fit this van't hoff plot A,X=Fitting.doFit(expdata=zip(t,k),model='Linear') fitk = X.getFitLine(t) p=ax.plot(t,fitk,'r',lw=2) fd=X.getFitDict() #self.drawParams(ax,fd) #slope is deltaH/R/1000 in kJ deltaH = -fd['a']*self.R/1000 deltaS = fd['b']*self.R/1000 f.suptitle("Method 1 - deltaH: %2.2f deltaS: %2.2f" %(deltaH,deltaS),size=18) f.subplots_adjust(bottom=0.15,top=0.85) if show==True: self.showTkFigure(f) if figname == None: figname = d figname = figname.replace('.','_') fname = figname+'m1'+'.png' f.savefig(fname,dpi=300) print 'plot saved to %s' %os.path.abspath(fname) #plt.close() if E!=None: fdata = Fitting.makeFitData(X.name,vrs=X.variables) E.insertDataset(xydata=[t,k], newname=d+'_vanthoff',replace=True,fit=fdata) #E.saveProject() return deltaH, deltaS, ax
def fitDifferentialCurve(self, E=None, d=None, xy=None, smooth=0, invert=False, show=True, figname=None): """Derive differential denaturation curve and fit to get deltaH We smooth the unfolding curve and then differentiate and finally fit to a 3 parameter equation. See http://www.ncbi.nlm.nih.gov/pubmed/10933511""" if E != None: ek = E.getDataset(d) x, y, a, xerr, yerr = ek.getAll() elif xy != None: x, y = xy else: return if invert == True: y = [max(y) - i for i in y[:]] leg = [] lines = [] f = plt.figure(figsize=(10, 5)) ax = f.add_subplot(121) p = ax.plot(x, y, 'x', color='black', mew=3, alpha=0.5) leg.append(p) lines.append('original') #smooth if smooth == 0: smooth = int(len(x) / 15.0) s = self.smoothListGaussian(y, smooth) p = ax.plot(x[:len(s) - 1], s[:-1], lw=3) leg.append(p) lines.append('smoothed') ax.set_title("original data") ax.set_xlabel('T') ax1 = f.add_subplot(122) #differentiate dx, ds = self.differentiate(x[:len(s)], s) #ds = [i/max(ds) for i in ds] ds = [i * 10 for i in ds] cw = csv.writer(open('diffcd.csv', 'w')) for row in zip(dx, ds): cw.writerow(row) p = ax1.plot(dx, ds, '-', lw=1.5, alpha=0.7, color='black') leg.append(p) lines.append('differential') ax1.set_title("differential denaturation") ax1.set_xlabel('T') ax1.set_ylabel('dsignal/dT') A, X = Fitting.doFit(expdata=zip(dx, ds), model='diffDenaturation', grad=1e-9, conv=1e-10) fity = X.getFitLine(dx) p = ax1.plot(dx, fity, 'r', lw=2) leg.append(p) lines.append('fit') t = X.getFitDict() self.drawParams(ax1, t) dHkcal = t['deltaH'] / 4.184 f.suptitle('Method 3 - deltaH: %2.2f kJ/mol (%2.2f kcal) Tm: %2.2f' % (t['deltaH'], dHkcal, t['Tm']), size=18) ax.legend(leg, lines, loc='best', prop=FontProperties(size="smaller")) #f.subplots_adjust(hspace=0.8) if show == True: self.showTkFigure(f) if figname != None: figname = figname.replace('.', '_') f.savefig(figname + 'm3', dpi=300) plt.close() if E != None: fdata = Fitting.makeFitData(X.name, vrs=X.variables) E.insertDataset(xydata=[dx, ds], newname=d + '_diff', replace=True, fit=fdata) #E.saveProject() return t['deltaH'], t['Tm']
def fitElwellSchellman(self, E=None, d=None, xy=None, transwidth=50, invert=False, show=True, figname=None): """Fit entire raw data simultaneously to the three main thermodynamic parameters using Elwell/Schellman method""" if E != None: ek = E.getDataset(d) x, y, a, xerr, yerr = ek.getAll() elif xy != None: x, y = xy else: return if invert == True: y = [max(y) - i for i in y[:]] f = plt.figure(figsize=(10, 5)) ax = f.add_subplot(121) p = ax.plot(x, y, 'o', alpha=0.5) ax.set_xlabel('T') ax.set_xlabel('mdeg') ax.set_title('raw data') x1, y1, x, y = self.transformCD(x, y, transwidth, ax) t = [] dg = [] R = 8.3144e-3 for T, fu in zip(x, y): if fu >= 1 or fu <= 0: continue K = fu / (1 - fu) deltaGt = -R * T * math.log(K) dg.append(deltaGt) t.append(T) ax1 = f.add_subplot(122) p = ax1.plot(t, dg, 'x', mew=2, color='black') ax1.set_xlabel('T') ax1.set_ylabel('dG(T)') ax.set_title('stability curve') A, X = Fitting.doFit(expdata=zip(t, dg), model='schellman', grad=1e-9, conv=1e-9) fity = X.getFitLine(t) p = ax1.plot(t, fity, 'r', lw=2) fd = X.getFitDict() self.drawParams(ax1, fd) deltaH = fd['deltaH'] deltacp = fd['deltacp'] Tm = fd['Tm'] f.suptitle("Method 2 - deltaH: %2.2f deltaCp: %2.2e Tm: %2.2f" % (deltaH, deltacp, Tm), size=18) if show == True: self.showTkFigure(f) if figname == None: figname = d figname = figname.replace('.', '_') fname = figname + 'm1' + '.png' f.savefig(fname, dpi=300) print 'plot saved to %s' % os.path.abspath(fname) if E != None: fdata = Fitting.makeFitData(X.name, vrs=X.variables) E.insertDataset(xydata=[t, dg], newname=d + '_vanthoff2', replace=True, fit=fdata) #E.saveProject() return deltaH, Tm, deltacp
def fitVantHoff(self, E=None, d=None, xy=None, transwidth=80, invert=False, show=True, figname=None): """Derive fraction unfolded, get K and fit to Van't Hoff. see http://www.jbc.org/content/277/43/40717.full or http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2144003/ """ if E != None: if not d in E.datasets: print 'no such dataset, %s' % d print 'available datasets:', E.datasets return ek = E.getDataset(d) x, y = ek.getxySorted() elif xy != None: x, y = xy if 'Convert Celsius-Kelvin' in self.conversions.getcurselection(): x = [i + 273 for i in x] if invert == True: y = [max(y) - i for i in y[:]] f = plt.figure(figsize=(18, 6)) ax = f.add_subplot(131) p = ax.plot(x, y, 'o', alpha=0.6) ax.set_xlabel('T(K)') ax.set_ylabel('mdeg') ax.set_title('raw data') x1, y1, x, y = self.transformCD(x, y, transwidth, ax) cw = csv.writer(open('frac_unfolded_' + d + '.csv', 'w')) cw.writerow(['temp', 'frac']) for i in zip(x1, y1): cw.writerow(i) #derive lnK vs 1/T t = [] k = [] for T, fu in zip(x, y): if fu >= 1 or fu <= 0: continue K = fu / (1 - fu) klog = math.log(K) k.append(klog) t.append(1 / T) if len(t) < 2: return None, None, None ax = f.add_subplot(132) p = ax.plot(x1, y1, 'o', color='g', alpha=0.6) ax.set_xlabel('T(K)') ax.set_ylabel('fu') ax.set_title('fraction unfolded') ax = f.add_subplot(133) p = ax.plot(t, k, 'x', mew=2, color='black') ax.set_xlabel('1/T') #(r'$1/T ($K^-1)$') ax.set_ylabel('ln K') formatter = matplotlib.ticker.ScalarFormatter() formatter.set_scientific(True) formatter.set_powerlimits((0, 0)) ax.xaxis.set_major_formatter(formatter) for l in ax.get_xticklabels(): l.set_rotation(30) #fit this van't hoff plot A, X = Fitting.doFit(expdata=zip(t, k), model='Linear') fitk = X.getFitLine(t) p = ax.plot(t, fitk, 'r', lw=2) fd = X.getFitDict() #self.drawParams(ax,fd) #slope is deltaH/R/1000 in kJ deltaH = -fd['a'] * self.R / 1000 deltaS = fd['b'] * self.R / 1000 f.suptitle("Method 1 - deltaH: %2.2f deltaS: %2.2f" % (deltaH, deltaS), size=18) f.subplots_adjust(bottom=0.15, top=0.85) if show == True: self.showTkFigure(f) if figname == None: figname = d figname = figname.replace('.', '_') fname = figname + 'm1' + '.png' f.savefig(fname, dpi=300) print 'plot saved to %s' % os.path.abspath(fname) #plt.close() if E != None: fdata = Fitting.makeFitData(X.name, vrs=X.variables) E.insertDataset(xydata=[t, k], newname=d + '_vanthoff', replace=True, fit=fdata) #E.saveProject() return deltaH, deltaS, ax