+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
'''
FIT
Non-standard Dependencies:
procplots.py (plot processing plugin)
'''
-from hooke.libhooke import WX_GOOD, ClickedPoint
+from ..libhooke import WX_GOOD, ClickedPoint
import wxversion
wxversion.select(WX_GOOD)
self.wlccurrent=None
self.wlccontact_point=None
self.wlccontact_index=None
-
+
+ def dist2fit(self):
+ '''Calculates the average distance from data to fit, scaled by the standard deviation
+ of the free cantilever area (thermal noise)
+ '''
+
+
+
def wlc_fit(self,clicked_points,xvector,yvector, pl_value, T=293, return_errors=False):
'''
Worm-like chain model fitting.
'''
#STEP 1: Prepare the vectors to apply the fit.
-
-
if pl_value is not None:
pl_value=pl_value/(10**9)
ODR STUFF
fixme: remove these comments after testing
'''
-
-
+ def dist(px,py,linex,liney):
+ distancesx=scipy.array([(px-x)**2 for x in linex])
+ minindex=np.argmin(distancesx)
+ print px, linex[0], linex[-1]
+ return (py-liney[minindex])**2
+
def f_wlc(params,x,T=T):
'''
wlc function for ODR fitting
therm=Kb*T #so we have thermal energy
return ( (therm*pii/4.0) * (((1-(x*lambd))**-2.0) - 1 + (4.0*x*lambd)) )
-
+
+
#STEP 3: plotting the fit
#obtain domain to plot the fit - from contact point to last_index plus 20 points
yfit=wlc_eval(xfit_chunk_corr_up, out.beta, pl_value,T)
yfit_down=[-y for y in yfit]
yfit_corr_down=[y+clicked_points[0].graph_coords[1] for y in yfit_down]
-
+
+
+ #calculate fit quality
+ qsum=0
+ yqeval=wlc_eval(xchunk_corr_up,out.beta,pl_value,T)
+ #we need to cut the extra from thuleindex
+ for qindex in np.arange(0,len(yqeval)):
+ qsum+=(yqeval[qindex]-ychunk_corr_up[qindex])**2
+ qstd=np.sqrt(qsum/len(ychunk_corr_up))
+
+
if return_errors:
- return fit_out, yfit_corr_down, xfit_chunk, fit_errors
+ return fit_out, yfit_corr_down, xfit_chunk, fit_errors, qstd
else:
- return fit_out, yfit_corr_down, xfit_chunk, None
-
+ return fit_out, yfit_corr_down, xfit_chunk, None, qstd
+
def fjc_fit(self,clicked_points,xvector,yvector, pl_value, T=293, return_errors=False):
'''
Freely-jointed chain function
ODR STUFF
fixme: remove these comments after testing
'''
+ def dist(px,py,linex,liney):
+ distancesx=scipy.array([(px-x)**2 for x in linex])
+ minindex=np.argmin(distancesx)
+ print minindex, px, linex[0], linex[-1]
+ return (py-liney[minindex])**2
+
def coth(z):
'''
hyperbolic cotangent
return (1/lambd)*(coth(y*(1/pii)/therm) - (therm*pii)/y)
+
#STEP 3: plotting the fit
#obtain domain to plot the fit - from contact point to last_index plus 20 points
thule_index=last_index+10
deltay=yfit_down[normalize_index]-clicked_points[0].graph_coords[1]
yfit_corr_down=[y-deltay for y in yfit_down]
-
+
+
+ #calculate fit quality
+ #creates dense y vector
+ yqeval=np.linspace(np.min(ychunk_corr_up)/2,np.max(ychunk_corr_up)*2,10*len(ychunk_corr_up))
+ #corresponding fitted x
+ xqeval=fjc_eval(yqeval,out.beta,pl_value,T)
+
+ qsum=0
+ for qindex in np.arange(0,len(ychunk_corr_up)):
+ qsum+=dist(xchunk_corr_up[qindex],ychunk_corr_up[qindex],xqeval,yqeval)
+ qstd=np.sqrt(qsum/len(ychunk_corr_up))
+
+
if return_errors:
- return fit_out, yfit_corr_down[normalize_index+1:], xfit_chunk_corr_up[normalize_index+1:], fit_errors
+ return fit_out, yfit_corr_down[normalize_index+1:], xfit_chunk_corr_up[normalize_index+1:], fit_errors, qstd
else:
- return fit_out, yfit_corr_down[normalize_index+1:], xfit_chunk_corr_up[normalize_index+1:], None
+ return fit_out, yfit_corr_down[normalize_index+1:], xfit_chunk_corr_up[normalize_index+1:], None, qstd
+
+ def efjc_fit(self,clicked_points,xvector,yvector, pl_value, T=293.0, return_errors=False):
+ '''
+ Extended Freely-jointed chain function
+ ref: F Oesterhelt, M Rief and H E Gaub, New Journal of Physics 1 (1999) 6.1–6.11
+ Please note that 2-parameter fitting (length and kl) usually does not converge, use fixed kl
+ '''
+ '''
+ clicked_points[0] is the contact point (calculated or hand-clicked)
+ clicked_points[1] and [2] are edges of chunk
+
+ '''
+ #Fixed parameters from reference
+ Kb=(1.38065e-2) #in pN.nm
+ Lp=0.358 #planar, nm
+ Lh=0.280 #helical, nm
+ Ks=150e3 #pN/nm
+
+
+ #STEP 1: Prepare the vectors to apply the fit.
+
+ #indexes of the selected chunk
+ first_index=min(clicked_points[1].index, clicked_points[2].index)
+ last_index=max(clicked_points[1].index, clicked_points[2].index)
+
+ #getting the chunk and reverting it
+ xchunk,ychunk=xvector[first_index:last_index],yvector[first_index:last_index]
+ xchunk.reverse()
+ ychunk.reverse()
+ #put contact point at zero and flip around the contact point (the fit wants a positive growth for extension and force)
+ xchunk_corr_up=[-(x-clicked_points[0].graph_coords[0]) for x in xchunk]
+ ychunk_corr_up=[-(y-clicked_points[0].graph_coords[1]) for y in ychunk]
+
+
+ #make them arrays
+ xchunk_corr_up=scipy.array(xchunk_corr_up)
+ ychunk_corr_up=scipy.array(ychunk_corr_up)
+
+ xchunk_corr_up_nm=xchunk_corr_up*1e9
+ ychunk_corr_up_pn=ychunk_corr_up*1e12
+
+
+ #STEP 2: actually do the fit
+
+ #Find furthest point of chunk and add it a bit; the fit must converge
+ #from an excess!
+ xchunk_high=max(xchunk_corr_up_nm)
+ xchunk_high+=(xchunk_high/10.0)
+
+ #Here are the linearized start parameters for the WLC.
+ #[Ns , pii=1/P]
+ #max number of monomers (all helical)for a contour length xchunk_high
+ excessNs=xchunk_high/(Lp)
+ p0=[excessNs,(1.0/(0.7))]
+ p0_plfix=[(excessNs)]
+
+ def dist(px,py,linex,liney):
+ distancesx=scipy.array([(px-x)**2 for x in linex])
+ minindex=np.argmin(distancesx)
+ return (py-liney[minindex])**2
+
+ def deltaG(f):
+ dG0=12.4242 #3kt in pN.nm
+ dL=0.078 #planar-helical
+ return dG0-f*dL
+
+ def Lfactor(f,T=T):
+ Lp=0.358 #planar, nm
+ Lh=0.280 #helical, nm
+ Kb=(1.38065e-2)
+ therm=Kb*T
+ dG=deltaG(f)
+
+ return Lp/(np.exp(dG/therm)+1)+Lh/(np.exp(-dG/therm)+1)
+
+ def coth(z):
+ '''
+ hyperbolic cotangent
+ '''
+ return 1.0/np.tanh(z)
+
+ def x_efjc(params,f,T=T,Ks=Ks):
+ '''
+ efjc function for ODR fitting
+ '''
+
+ Ns=params[0]
+ invkl=params[1]
+ Kb=(1.38065e-2)
+ therm=Kb*T
+ beta=(f/therm)/invkl
+
+ x=Ns*Lfactor(f)*(coth(beta)-1.0/beta)+Ns*f/Ks
+ return x
+
+ def x_efjc_plfix(params,f,kl_value=pl_value,T=T,Ks=Ks):
+ '''
+ efjc function for ODR fitting
+ '''
+
+ Ns=params
+ invkl=1.0/kl_value
+ Kb=(1.38065e-2)
+ therm=Kb*T
+ beta=(f/therm)/invkl
+
+ x=Ns*Lfactor(f)*(coth(beta)-1.0/beta)+Ns*f/Ks
+ return x
+
+ #make the ODR fit
+ realdata=scipy.odr.RealData(ychunk_corr_up_pn,xchunk_corr_up_nm)
+ if pl_value:
+ model=scipy.odr.Model(x_efjc_plfix)
+ o = scipy.odr.ODR(realdata, model, p0_plfix)
+ else:
+ print 'WARNING eFJC fit with free pl sometimes does not converge'
+ model=scipy.odr.Model(x_efjc)
+ o = scipy.odr.ODR(realdata, model, p0)
+
+ o.set_job(fit_type=2)
+ out=o.run()
+
+
+ Ns=out.beta[0]
+ Lc=Ns*Lp*1e-9
+ if len(out.beta)>1:
+ kfit=1e-9/out.beta[1]
+ kfitnm=1/out.beta[1]
+ else:
+ kfit=1e-9*pl_value
+ kfitnm=pl_value
+
+ fit_out=[Lc, kfit]
+
+ #Calculate fit errors from output standard deviations.
+ fit_errors=[]
+ fit_errors.append(out.sd_beta[0]*Lp*1e-9)
+ if len(out.beta)>1:
+ fit_errors.append(1e9*out.sd_beta[1]*kfit**2)
+
+
+
+ def efjc_eval(y,params,pl_value,T=T,Lfactor=Lfactor,Ks=Ks):
+ '''
+ Evaluates the eFJC function
+ '''
+ if not pl_value:
+ Ns, invkl = params
+ else:
+ Ns = params
+
+ if pl_value:
+ invkl=1.0/pl_value
+
+ Kb=(1.38065e-2) #boltzmann constant
+ therm=Kb*T #so we have thermal energy
+ beta=(y/therm)/invkl
+ x= Ns*Lfactor(y)*(coth(beta)-1.0/beta)+Ns*y/Ks
+
+ return x
+
+ #STEP 3: plotting the fit
+ #obtain domain to plot the fit - from contact point to last_index plus 20 points
+ thule_index=last_index+10
+ if thule_index > len(xvector): #for rare cases in which we fit something at the END of whole curve.
+ thule_index = len(xvector)
+ #reverse etc. the domain
+ ychunk=yvector[clicked_points[0].index:thule_index]
+ if len(ychunk)>0:
+ y_evalchunk=np.linspace(min(ychunk),max(ychunk),100)
+ else:
+ #Empty y-chunk. It happens whenever we set the contact point after a recognized peak,
+ #or other buggy situations. Kludge to live with it now...
+ ychunk=yvector[:thule_index]
+ y_evalchunk=np.linspace(min(ychunk),max(ychunk),100)
+
+ yfit_down=[-y for y in y_evalchunk]
+ yfit_corr_down=[y+clicked_points[0].graph_coords[1] for y in yfit_down]
+ yfit_corr_down=scipy.array(yfit_corr_down)
+
+ #the fitted curve: reflip, re-uncorrect
+ xfit=efjc_eval(1e12*yfit_corr_down, out.beta, pl_value,T)*1e-9
+ xfit=list(xfit)
+ xfit.reverse()
+ xfit_chunk_corr_up=[-(x-clicked_points[0].graph_coords[0]) for x in xfit]
+
+ #xfit_chunk_corr_up=scipy.array(xfit_chunk_corr_up)
+ #deltay=yfit_down[0]-yvector[clicked_points[0].index]
+
+ #This is a terrible, terrible kludge to find the point where it should normalize (and from where it should plot)
+ xxxdists=[]
+ for index in scipy.arange(1,len(xfit_chunk_corr_up),1):
+ xxxdists.append((clicked_points[0].graph_coords[0]-xfit_chunk_corr_up[index])**2)
+ normalize_index=xxxdists.index(min(xxxdists))
+ #End of kludge
+
+ deltay=yfit_down[normalize_index]-clicked_points[0].graph_coords[1]
+ yfit_corr_down=[y-deltay for y in yfit_down]
+
+ #calculate fit quality
+ #creates dense y vector
+ yqeval=np.linspace(np.min(ychunk_corr_up_pn)/2,np.max(ychunk_corr_up_pn)*2,10*len(ychunk_corr_up_pn))
+ #corresponding fitted x
+ xqeval=efjc_eval(yqeval,out.beta,pl_value)
+
+ qsum=0
+ for qindex in np.arange(0,len(ychunk_corr_up_pn)):
+ qsum+=dist(xchunk_corr_up_nm[qindex],ychunk_corr_up_pn[qindex],xqeval,yqeval)
+ qstd=1e-12*np.sqrt(qsum/len(ychunk_corr_up_pn))
+
+ if return_errors:
+ return fit_out, yfit_corr_down[normalize_index+1:], xfit_chunk_corr_up[normalize_index+1:], fit_errors, qstd
+ else:
+ return fit_out, yfit_corr_down[normalize_index+1:], xfit_chunk_corr_up[normalize_index+1:], None, qstd
+
+
+
+
def do_wlc(self,args):
'''
WLC
First you have to click a contact point.
Then you have to click the two edges of the data you want to fit.
-
+
+ Fit quality compares the distance to the fit with the thermal noise (a good fit should be close to 1)
+
The fit function depends on the fit_function variable. You can set it with the command
"set fit_function wlc" or "set fit_function fjc" depending on the function you prefer.
For FJC, ref:
C.Ray and B.B. Akhremitchev; http://www.chem.duke.edu/~boris/research/force_spectroscopy/fit_efjc.pdf
+
+ For eFJC, ref:
+ F Oesterhelt, M Rief and H E Gaub, New Journal of Physics 1 (1999) 6.1–6.11 (section 4.2)
+ NOTE: use fixed pl for better results.
Arguments:
pl=[value] : Use a fixed persistent length (WLC) or Kuhn length (FJC) for the fit. If pl is not given,
print 'Click edges of chunk'
points=self._measure_N_points(N=2, whatset=1)
points=[contact_point]+points
+
try:
if self.config['fit_function']=='wlc':
- params, yfit, xfit, fit_errors = self.wlc_fit(points, displayed_plot.vectors[1][0], displayed_plot.vectors[1][1],pl_value,T, return_errors=True )
+ params, yfit, xfit, fit_errors,qstd = self.wlc_fit(points, displayed_plot.vectors[1][0], displayed_plot.vectors[1][1],pl_value,T, return_errors=True )
name_of_charlength='Persistent length'
elif self.config['fit_function']=='fjc':
- params, yfit, xfit, fit_errors = self.fjc_fit(points, displayed_plot.vectors[1][0], displayed_plot.vectors[1][1],pl_value,T, return_errors=True )
+ params, yfit, xfit, fit_errors,qstd = self.fjc_fit(points, displayed_plot.vectors[1][0], displayed_plot.vectors[1][1],pl_value,T, return_errors=True )
name_of_charlength='Kuhn length'
+ elif self.config['fit_function']=='efjc':
+ params, yfit, xfit, fit_errors,qstd = self.efjc_fit(points, displayed_plot.vectors[1][0], displayed_plot.vectors[1][1],pl_value,T, return_errors=True )
+ name_of_charlength='Kuhn length (e)'
else:
print 'No recognized fit function defined!'
- print 'Set your fit function to wlc or fjc.'
+ print 'Set your fit function to wlc, fjc or efjc.'
return
except:
print 'Standard deviation (contour length)', fit_nm[0]
if len(fit_nm)>1:
print 'Standard deviation ('+name_of_charlength+')', fit_nm[1]
-
-
+
+ print 'Fit quality: '+str(qstd/np.std(displayed_plot.vectors[1][1][-20:-1]))
+
+
#add the clicked points in the final PlotObject
clickvector_x, clickvector_y=[], []
for item in points:
projects / hooke.git / blobdiff