From: devicerandom Date: Thu, 13 Nov 2008 15:55:00 +0000 (+0000) Subject: (fit.py , autopeak.py) wlc fitting now uses ODR algorithms. wlc command returns stand... X-Git-Tag: 0.9.0~62 X-Git-Url: http://git.tremily.us/?a=commitdiff_plain;h=8b88d5bd5e7a9c92f868e99d3a5d83eca1df39b1;p=hooke.git (fit.py , autopeak.py) wlc fitting now uses ODR algorithms. wlc command returns standard deviations of parameters --- diff --git a/autopeak.py b/autopeak.py index 7961313..da96fcf 100644 --- a/autopeak.py +++ b/autopeak.py @@ -217,7 +217,7 @@ class autopeakCommands: if abs(peak_point.index-other_fit_point.index) < 2: continue - params, yfit, xfit = self.wlc_fit(points, displayed_plot.vectors[1][0], displayed_plot.vectors[1][1], pl_value, T) + params, yfit, xfit, fit_errors = self.wlc_fit(points, displayed_plot.vectors[1][0], displayed_plot.vectors[1][1], pl_value, T) #Measure forces diff --git a/fit.py b/fit.py index c65ce44..bced87e 100755 --- a/fit.py +++ b/fit.py @@ -15,6 +15,7 @@ wxversion.select(WX_GOOD) #from wx import PostEvent #from wx.lib.newevent import NewEvent import scipy +import scipy.odr import numpy as np import copy import Queue @@ -79,21 +80,54 @@ class fitCommands: p0=[(1/xchunk_high),(1/(3.5e-10))] p0_plfix=[(1/xchunk_high)] - - def residuals(params,y,x,T): + ''' + ODR STUFF + fixme: remove these comments after testing + ''' + + + def f_wlc(params,x,T=T): ''' - Calculates the residuals of the fit + wlc function for ODR fitting ''' - lambd, pii=params + lambd,pii=params + Kb=(1.38065e-23) + 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 + ''' + lambd=params + pii=1/pl_value Kb=(1.38065e-23) - #T=293 therm=Kb*T + y=(therm*pii/4.0) * (((1-(x*lambd))**-2) - 1 + (4*x*lambd)) + return y - err = y-( (therm*pii/4) * (((1-(x*lambd))**-2) - 1 + (4*x*lambd)) ) + #make the ODR fit + realdata=scipy.odr.RealData(xchunk_corr_up,ychunk_corr_up) + if pl_value: + model=scipy.odr.Model(f_wlc_plfix) + o = scipy.odr.ODR(realdata, model, p0_plfix) + 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) + fit_errors=[] + for sd,value in zip(out.sd_beta, fit_out): + err_real=sd*(value**2) + fit_errors.append(err_real) - return err - def wlc_eval(x,params,pl_value,T): ''' Evaluates the WLC function @@ -111,29 +145,9 @@ class fitCommands: therm=Kb*T #so we have thermal energy return ( (therm*pii/4.0) * (((1-(x*lambd))**-2.0) - 1 + (4.0*x*lambd)) ) - - def residuals_plfix(params, y, x, pii, T): - ''' - Calculates the residuals of the fit, if we have the persistent length from an external source - ''' - lambd=params - Kb=(1.38065e-23) - therm=Kb*T - - err = y-( (therm*pii/4) * (((1-(x*lambd))**-2) - 1 + (4*x*lambd)) ) - - return err - - #make the fit! and obtain params - if pl_value: - plsq=scipy.optimize.leastsq(residuals_plfix, p0_plfix, args=(ychunk_corr_up,xchunk_corr_up,1/pl_value,T), full_output=1) - else: - plsq=scipy.optimize.leastsq(residuals, p0, args=(ychunk_corr_up,xchunk_corr_up,T),full_output=1) - - #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. @@ -145,30 +159,14 @@ class fitCommands: xfit_chunk_corr_up=scipy.array(xfit_chunk_corr_up) #the fitted curve: reflip, re-uncorrect - yfit=wlc_eval(xfit_chunk_corr_up, plsq[0],pl_value,T) + 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] - #get out true fit paramers - fit_out=plsq[0] - fit_cov=plsq[1] - - try: - fit_out=[(1.0/x) for x in fit_out] - except TypeError: #if we fit only 1 parameter, we have a float and not a list in output. - fit_out=[(1.0/fit_out)] - - fit_errors=[] - for i in range(len(fit_cov[0])): - sqerr=scipy.sqrt(fit_cov[i,i]) - err_real=sqerr*(fit_out[i]**2) - fit_errors.append(err_real) - - if return_errors: return fit_out, yfit_corr_down, xfit_chunk, fit_errors else: - return fit_out, yfit_corr_down, xfit_chunk + return fit_out, yfit_corr_down, xfit_chunk, None def do_wlc(self,args): @@ -248,8 +246,8 @@ class fitCommands: points=self._measure_N_points(N=2, whatset=1) points=[contact_point]+points try: - params, yfit, xfit, fit_cov = self.wlc_fit(points, displayed_plot.vectors[1][0], displayed_plot.vectors[1][1],pl_value,T, return_errors=True ) - except SyntaxError: + 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 ) + except: print 'Fit not possible. Probably wrong interval -did you click two *different* points?' return @@ -261,7 +259,13 @@ class fitCommands: to_dump='persistent '+self.current.path+' '+str(params[1]*(1.0e+9))+' nm' self.outlet.push(to_dump) - print 'Errors', fit_cov[0]*10**9 , fit_cov[1]*10**9 + 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 (persistent length)', fit_nm[1] + + #add the clicked points in the final PlotObject clickvector_x, clickvector_y=[], [] for item in points: