-#!/usr/bin/env python
+# Copyright (C) 2008-2010 Alberto Gomez-Casado
+# Fabrizio Benedetti
+# Massimo Sandal <devicerandom@gmail.com>
+# W. Trevor King <wking@drexel.edu>
+#
+# This file is part of Hooke.
+#
+# Hooke is free software: you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation, either
+# version 3 of the License, or (at your option) any later version.
+#
+# Hooke is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public
+# License along with Hooke. If not, see
+# <http://www.gnu.org/licenses/>.
-'''
-FIT
-
-Force spectroscopy curves basic fitting plugin.
-Licensed under the GNU GPL version 2
+"""Force spectroscopy curves basic fitting plugin.
Non-standard Dependencies:
procplots.py (plot processing plugin)
-'''
-from libhooke import WX_GOOD, ClickedPoint
+"""
+
+from ..libhooke import WX_GOOD, ClickedPoint
+
import wxversion
wxversion.select(WX_GOOD)
#from wx import PostEvent
#from wx.lib.newevent import NewEvent
import scipy
import scipy.odr
+import scipy.stats
import numpy as np
import copy
import Queue
events_from_fit=Queue.Queue() #GUI ---> CLI COMMUNICATION
-class fitCommands:
-
+class fitCommands(object):
+
def _plug_init(self):
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):
'''
The function is the simple polynomial worm-like chain as proposed by C.Bustamante, J.F.Marko, E.D.Siggia
and S.Smith (Science. 1994 Sep 9;265(5178):1599-600.)
'''
-
+
'''
clicked_points[0] = contact point (calculated or hand-clicked)
clicked_points[1] and [2] are edges of chunk
'''
-
+
#STEP 1: Prepare the vectors to apply the fit.
-
-
if pl_value is not None:
pl_value=pl_value/(10**9)
-
+
#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()
+ 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)
-
-
+
+
#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)
xchunk_high+=(xchunk_high/10)
-
+
#Here are the linearized start parameters for the WLC.
#[lambd=1/Lo , pii=1/P]
-
+
p0=[(1/xchunk_high),(1/(3.5e-10))]
p0_plfix=[(1/xchunk_high)]
'''
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):
'''
therm=Kb*T
y=(therm*pii/4.0) * (((1-(x*lambd))**-2) - 1 + (4*x*lambd))
return y
-
+
def f_wlc_plfix(params,x,pl_value=pl_value,T=T):
'''
wlc function for ODR fitting
therm=Kb*T
y=(therm*pii/4.0) * (((1-(x*lambd))**-2) - 1 + (4*x*lambd))
return y
-
+
#make the ODR fit
realdata=scipy.odr.RealData(xchunk_corr_up,ychunk_corr_up)
if pl_value:
else:
model=scipy.odr.Model(f_wlc)
o = scipy.odr.ODR(realdata, model, p0)
-
+
o.set_job(fit_type=2)
out=o.run()
fit_out=[(1/i) for i in out.beta]
-
+
#Calculate fit errors from output standard deviations.
#We must propagate the error because we fit the *inverse* parameters!
#The error = (error of the inverse)*(value**2)
for sd,value in zip(out.sd_beta, fit_out):
err_real=sd*(value**2)
fit_errors.append(err_real)
-
- def wlc_eval(x,params,pl_value,T):
+
+ def wlc_eval(x,params,pl_value,T):
'''
Evaluates the WLC function
'''
lambd, pii = params
else:
lambd = params
-
+
if pl_value:
pii=1/pl_value
-
+
Kb=(1.38065e-23) #boltzmann constant
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
+ #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.
xfit_chunk.reverse()
xfit_chunk_corr_up=[-(x-clicked_points[0].graph_coords[0]) for x in xfit_chunk]
xfit_chunk_corr_up=scipy.array(xfit_chunk_corr_up)
-
+
#the fitted curve: reflip, re-uncorrect
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):
'''
clicked_points[0] is the contact point (calculated or hand-clicked)
clicked_points[1] and [2] are edges of chunk
'''
-
+
#STEP 1: Prepare the vectors to apply the fit.
if pl_value is not None:
pl_value=pl_value/(10**9)
-
+
#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()
+ 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)
-
-
+
+
#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)
xchunk_high+=(xchunk_high/10)
-
+
#Here are the linearized start parameters for the WLC.
#[lambd=1/Lo , pii=1/P]
-
+
p0=[(1/xchunk_high),(1/(3.5e-10))]
p0_plfix=[(1/xchunk_high)]
'''
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 (np.exp(2*z)+1)/(np.exp(2*z)-1)
-
+
def x_fjc(params,f,T=T):
'''
fjc function for ODR fitting
lambd,pii=params
Kb=(1.38065e-23)
therm=Kb*T
-
+
#x=(therm*pii/4.0) * (((1-(x*lambd))**-2) - 1 + (4*x*lambd))
x=(1/lambd)*(coth(f*(1/pii)/therm) - (therm*pii)/f)
return x
-
+
def x_fjc_plfix(params,f,pl_value=pl_value,T=T):
'''
fjc function for ODR fitting
#y=(therm*pii/4.0) * (((1-(x*lambd))**-2) - 1 + (4*x*lambd))
x=(1/lambd)*(coth(f*(1/pii)/therm) - (therm*pii)/f)
return x
-
+
#make the ODR fit
realdata=scipy.odr.RealData(ychunk_corr_up,xchunk_corr_up)
if pl_value:
else:
model=scipy.odr.Model(x_fjc)
o = scipy.odr.ODR(realdata, model, p0)
-
+
o.set_job(fit_type=2)
out=o.run()
fit_out=[(1/i) for i in out.beta]
-
+
#Calculate fit errors from output standard deviations.
#We must propagate the error because we fit the *inverse* parameters!
#The error = (error of the inverse)*(value**2)
for sd,value in zip(out.sd_beta, fit_out):
err_real=sd*(value**2)
fit_errors.append(err_real)
-
- def fjc_eval(y,params,pl_value,T):
+
+ def fjc_eval(y,params,pl_value,T):
'''
Evaluates the WLC function
'''
lambd, pii = params
else:
lambd = params
-
+
if pl_value:
pii=1/pl_value
-
+
Kb=(1.38065e-23) #boltzmann constant
therm=Kb*T #so we have thermal energy
#return ( (therm*pii/4.0) * (((1-(x*lambd))**-2.0) - 1 + (4.0*x*lambd)) )
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
+ 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=fjc_eval(yfit_corr_down, out.beta, pl_value,T)
+ 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)/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, qstd
+ else:
+ 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
yfit_corr_down=scipy.array(yfit_corr_down)
#the fitted curve: reflip, re-uncorrect
- xfit=fjc_eval(yfit_corr_down, out.beta, pl_value,T)
+ 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]
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
+ 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 do_wlc(self,args):
'''
WLC
(fit.py plugin)
-
+
See the fit command
'''
self.do_fit(args)
-
+
def do_fjc(self,args):
'''
FJC
(fit.py plugin)
-
+
See the fit command
'''
self.do_fit(args)
-
+
def do_fit(self,args):
'''
FIT
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 WLC, the function is the simple polynomial worm-like chain as proposed by
- C.Bustamante, J.F.Marko, E.D.Siggia and S.Smith (Science. 1994
+
+ For WLC, the function is the simple polynomial worm-like chain as proposed by
+ C.Bustamante, J.F.Marko, E.D.Siggia and S.Smith (Science. 1994
Sep 9;265(5178):1599-600.)
-
- For FJC, ref:
+
+ 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,
- the fit will be a 2-variable
+ pl=[value] : Use a fixed persistent length (WLC) or Kuhn length (FJC) for the fit. If pl is not given,
+ the fit will be a 2-variable
fit. DO NOT put spaces between 'pl', '=' and the value.
- The value must be in nanometers.
-
+ The value must be in nanometers.
+
t=[value] : Use a user-defined temperature. The value must be in
kelvins; by default it is 293 K.
DO NOT put spaces between 't', '=' and the value.
-
- noauto : allows for clicking the contact point by
+
+ noauto : allows for clicking the contact point by
hand (otherwise it is automatically estimated) the first time.
If subsequent measurements are made, the same contact point
clicked is used
-
+
reclick : redefines by hand the contact point, if noauto has been used before
but the user is unsatisfied of the previously choosen contact point.
---------
if ('t=' in arg[0:2]) or ('T=' in arg[0:2]):
t_expression=arg.split('=')
T=float(t_expression[1])
-
+
#use the currently displayed plot for the fit
displayed_plot=self._get_displayed_plot()
-
+
#handle contact point arguments correctly
if 'reclick' in args.split():
print 'Click contact point'
contact_point.absolute_coords=displayed_plot.vectors[1][0][cindex], displayed_plot.vectors[1][1][cindex]
contact_point.find_graph_coords(displayed_plot.vectors[1][0], displayed_plot.vectors[1][1])
contact_point.is_marker=True
-
+
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 'Fit not possible. Probably wrong interval -did you click two *different* points?'
return
-
+
#FIXME: print "Kuhn length" for FJC
print 'Fit function:',self.config['fit_function']
print 'Contour length: ',params[0]*(1.0e+9),' nm'
print name_of_charlength+': ',params[1]*(1.0e+9),' nm'
to_dump='persistent '+self.current.path+' '+str(params[1]*(1.0e+9))+' nm'
self.outlet.push(to_dump)
-
+
if fit_errors:
fit_nm=[i*(10**9) for i in fit_errors]
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
for item in points:
clickvector_x.append(item.graph_coords[0])
clickvector_y.append(item.graph_coords[1])
-
+
#create a custom PlotObject to gracefully plot the fit along the curves
-
+
fitplot=copy.deepcopy(displayed_plot)
fitplot.add_set(xfit,yfit)
fitplot.add_set(clickvector_x,clickvector_y)
-
+
#FIXME: this colour/styles stuff must be solved at the root!
if fitplot.styles==[]:
fitplot.styles=[None,None,None,'scatter']
else:
fitplot.styles+=[None,'scatter']
-
+
if fitplot.colors==[]:
fitplot.colors=[None,None,None,None]
else:
fitplot.colors+=[None,None]
-
+
self._send_plot([fitplot])
-
-
+
+
#----------
-
-
+
+
def find_contact_point(self,plot=False):
'''
Finds the contact point on the curve.
-
+
The current algorithm (thanks to Francesco Musiani, francesco.musiani@unibo.it and Massimo Sandal) is:
- take care of the PicoForce trigger bug - exclude retraction portions with too high standard deviation
- fit the second half of the retraction curve to a line
- if the fit is not almost horizontal, take a smaller chunk and repeat
- otherwise, we have something horizontal
- so take the average of horizontal points and use it as a baseline
-
+
Then, start from the rise of the retraction curve and look at the first point below the
baseline.
-
+
FIXME: should be moved, probably to generalvclamp.py
'''
-
+
if not plot:
plot=self.plots[0]
-
+
outplot=self.subtract_curves(1)
xret=outplot.vectors[1][0]
ydiff=outplot.vectors[1][1]
yext=plot.vectors[0][1]
xret2=plot.vectors[1][0]
yret=plot.vectors[1][1]
-
+
#taking care of the picoforce trigger bug: we exclude portions of the curve that have too much
#standard deviation. yes, a lot of magic is here.
monster=True
else: #move away from the monster
monlength-=int(len(xret)/50)
finalength-=int(len(xret)/50)
-
-
+
+
#take half of the thing
endlength=int(len(xret)/2)
-
+
ok=False
-
+
while not ok:
xchunk=yext[endlength:monlength]
ychunk=yext[endlength:monlength]
if (abs(regr[1]) > 0.1) and ( endlength < len(xret)-int(len(xret)/6) ) :
endlength+=10
else:
- ok=True
-
-
+ ok=True
+
+
ymean=np.mean(ychunk) #baseline
-
+
index=0
point = ymean+1
-
+
#find the first point below the calculated baseline
while point > ymean:
try:
point=yret[index]
- index+=1
+ index+=1
except IndexError:
#The algorithm didn't find anything below the baseline! It should NEVER happen
- index=0
+ index=0
return index
-
+
return index
-
-
-
+
+
+
def find_contact_point2(self, debug=False):
'''
TO BE DEVELOPED IN THE FUTURE
Finds the contact point on the curve.
-
+
FIXME: should be moved, probably to generalvclamp.py
'''
-
+
#raw_plot=self.current.curve.default_plots()[0]
raw_plot=self.plots[0]
'''xext=self.plots[0].vectors[0][0]
yext=raw_plot.vectors[0][1]
xret2=raw_plot.vectors[1][0]
yret=raw_plot.vectors[1][1]
-
+
first_point=[xext[0], yext[0]]
last_point=[xext[-1], yext[-1]]
-
+
#regr=scipy.polyfit(first_point, last_point,1)[0:2]
diffx=abs(first_point[0]-last_point[0])
diffy=abs(first_point[1]-last_point[1])
-
+
#using polyfit results in numerical errors. good old algebra.
a=diffy/diffx
b=first_point[1]-(a*first_point[0])
baseline=scipy.polyval((a,b), xext)
-
+
ysub=[item-basitem for item,basitem in zip(yext,baseline)]
-
+
contact=ysub.index(min(ysub))
-
+
return xext,ysub,contact
-
+
#now, exploit a ClickedPoint instance to calculate index...
dummy=ClickedPoint()
dummy.absolute_coords=(x_intercept,y_intercept)
dummy.find_graph_coords(xret2,yret)
-
+
if debug:
return dummy.index, regr, regr_contact
else:
return dummy.index
-
-
+
+
def x_do_contact(self,args):
'''
DEBUG COMMAND to be activated in the future
'''
xext,ysub,contact=self.find_contact_point2(debug=True)
-
+
contact_plot=self.plots[0]
contact_plot.add_set(xext,ysub)
contact_plot.add_set([xext[contact]],[self.plots[0].vectors[0][1][contact]])
contact_plot.styles=[None,None,None,'scatter']
self._send_plot([contact_plot])
return
-
-
+
+
index,regr,regr_contact=self.find_contact_point2(debug=True)
print regr
print regr_contact
#nc_line=[(item*regr[0])+regr[1] for item in x_nc]
nc_line=scipy.polyval(regr,xret)
c_line=scipy.polyval(regr_contact,xret)
-
-
+
+
contact_plot=self.current.curve.default_plots()[0]
contact_plot.add_set(xret, nc_line)
contact_plot.add_set(xret, c_line)
#contact_plot.styles.append(None)
contact_plot.destination=1
self._send_plot([contact_plot])
-
\ No newline at end of file
projects / hooke.git / blobdiff