class pclusterCommands:
- def do_pcluster(self,args):
-
- '''
- pCLUSTER
- (pcluster.py)
-
- Automatically measures peaks and extracts informations for further clustering
-
- (c)Paolo Pancaldi, Massimo Sandal 2009
- '''
- #--Custom persistent length
- pl_value=None
- for arg in args.split():
- #look for a persistent length argument.
- if 'pl=' in arg:
- pl_expression=arg.split('=')
- pl_value=float(pl_expression[1]) #actual value
- else:
- pl_value=None
-
- #configuration variables
- min_npks = self.convfilt_config['minpeaks']
- min_deviation = self.convfilt_config['mindeviation']
-
- pclust_filename=raw_input('Automeasure filename? ')
- realclust_filename=raw_input('Coordinates filename? ')
-
- f=open(pclust_filename,'w+')
- f.write('Analysis started '+time.asctime()+'\n')
- f.write('----------------------------------------\n')
- f.write('; Contour length (nm) ; Persistence length (nm) ; Max.Force (pN) ; Slope (N/m) ; Sigma contour (nm) ; Sigma persistence (nm)\n')
- f.close()
-
- f=open(realclust_filename,'w+')
- f.write('Analysis started '+time.asctime()+'\n')
- f.write('----------------------------------------\n')
- f.write('; Peak number ; Mean delta (nm) ; Median delta (nm) ; Mean force (pN) ; Median force (pN) ; First peak length (nm) ; Last peak length (nm) ; Max force (pN) ; Min force (pN) ; Max delta (nm) ; Min delta (nm)')
- f.close()
- # ------ FUNCTION ------
- def fit_interval_nm(start_index,plot,nm,backwards):
- '''
- Calculates the number of points to fit, given a fit interval in nm
- start_index: index of point
- plot: plot to use
- backwards: if true, finds a point backwards.
- '''
- whatset=1 #FIXME: should be decidable
- x_vect=plot.vectors[1][0]
-
- c=0
- i=start_index
- start=x_vect[start_index]
- maxlen=len(x_vect)
- while abs(x_vect[i]-x_vect[start_index])*(10**9) < nm:
- if i==0 or i==maxlen-1: #we reached boundaries of vector!
- return c
- if backwards:
- i-=1
- else:
- i+=1
- c+=1
- return c
-
- def plot_informations(itplot,pl_value):
- '''
- OUR VARIABLES
- contact_point.absolute_coords (2.4584142802103689e-007, -6.9647135616234017e-009)
- peak_point.absolute_coords (3.6047748250571423e-008, -7.7142802788854212e-009)
- other_fit_point.absolute_coords (4.1666139243838867e-008, -7.3759393477579707e-009)
- peak_location [510, 610, 703, 810, 915, 1103]
- peak_size [-1.2729111505202212e-009, -9.1632775347399312e-010, -8.1707438353929907e-010, -8.0335812578148904e-010, -8.7483955226387558e-010, -3.6269619757067322e-009]
- params [2.2433999931959462e-007, 3.3230248825175678e-010]
- fit_errors [6.5817195369767644e-010, 2.4415923138871498e-011]
- '''
- fit_points=int(self.config['auto_fit_points']) # number of points to fit before the peak maximum <50>
-
- T=self.config['temperature'] #temperature of the system in kelvins. By default it is 293 K. <301.0>
- cindex=self.find_contact_point() #Automatically find contact point <158, libhooke.ClickedPoint>
- contact_point=self._clickize(itplot[0].vectors[1][0], itplot[0].vectors[1][1], cindex)
- self.basepoints=[]
- base_index_0=peak_location[-1]+fit_interval_nm(peak_location[-1], itplot[0], self.config['auto_right_baseline'],False)
- self.basepoints.append(self._clickize(itplot[0].vectors[1][0],itplot[0].vectors[1][1],base_index_0))
- base_index_1=self.basepoints[0].index+fit_interval_nm(self.basepoints[0].index, itplot[0], self.config['auto_left_baseline'],False)
- self.basepoints.append(self._clickize(itplot[0].vectors[1][0],itplot[0].vectors[1][1],base_index_1))
- self.basecurrent=self.current.path
- boundaries=[self.basepoints[0].index, self.basepoints[1].index]
- boundaries.sort()
- to_average=itplot[0].vectors[1][1][boundaries[0]:boundaries[1]] #y points to average
- avg=np.mean(to_average)
- return fit_points, contact_point, pl_value, T, cindex, avg
-
- def features_peaks(itplot, peak, fit_points, contact_point, pl_value, T, cindex, avg):
- '''
- calculate informations for each peak and add they in
- c_lengths, p_lengths, sigma_c_lengths, sigma_p_lengths, forces, slopes
- '''
- c_leng=None
- p_leng=None
- sigma_c_leng=None
- sigma_p_leng=None
- force=None
- slope=None
-
- delta_force=10
- slope_span=int(self.config['auto_slope_span'])
-
- peak_point=self._clickize(itplot[0].vectors[1][0],itplot[0].vectors[1][1],peak)
- other_fit_point=self._clickize(itplot[0].vectors[1][0],itplot[0].vectors[1][1],peak-fit_points)
-
- points=[contact_point, peak_point, other_fit_point]
-
- params, yfit, xfit, fit_errors = self.wlc_fit(points, itplot[0].vectors[1][0], itplot[0].vectors[1][1], pl_value, T, return_errors=True)
-
- #Measure forces
- delta_to_measure=itplot[0].vectors[1][1][peak-delta_force:peak+delta_force]
- y=min(delta_to_measure)
- #Measure slopes
- slope=self.linefit_between(peak-slope_span,peak)[0]
- #check fitted data and, if right, add peak to the measurement
- if len(params)==1: #if we did choose 1-value fit
- p_leng=pl_value
- c_leng=params[0]*(1.0e+9)
- sigma_p_lengths=0
- sigma_c_lengths=fit_errors[0]*(1.0e+9)
- force = abs(y-avg)*(1.0e+12)
- else: #2-value fit
- p_leng=params[1]*(1.0e+9)
- #check if persistent length makes sense. otherwise, discard peak.
- if p_leng>self.config['auto_min_p'] and p_leng<self.config['auto_max_p']:
- '''
- p_lengths.append(p_leng)
- c_lengths.append(params[0]*(1.0e+9))
- sigma_c_lengths.append(fit_errors[0]*(1.0e+9))
- sigma_p_lengths.append(fit_errors[1]*(1.0e+9))
- forces.append(abs(y-avg)*(1.0e+12))
- slopes.append(slope)
- '''
- c_leng=params[0]*(1.0e+9)
- sigma_c_leng=fit_errors[0]*(1.0e+9)
- sigma_p_leng=fit_errors[1]*(1.0e+9)
- force=abs(y-avg)*(1.0e+12)
-
- else:
- p_leng=None
- slope=None
- #return c_lengths, p_lengths, sigma_c_lengths, sigma_p_lengths, forces, slopes
- return c_leng, p_leng, sigma_c_leng, sigma_p_leng, force, slope
+ def do_pcluster(self,args):
+ '''
+ pCLUSTER
+ (pcluster.py)
+ Automatically measures peaks and extracts informations for further clustering
+ (c)Paolo Pancaldi, Massimo Sandal 2009
+ '''
+ #--Custom persistent length
+ pl_value=None
+ for arg in args.split():
+ #look for a persistent length argument.
+ if 'pl=' in arg:
+ pl_expression=arg.split('=')
+ pl_value=float(pl_expression[1]) #actual value
+ else:
+ pl_value=None
+
+ #configuration variables
+ min_npks = self.convfilt_config['minpeaks']
+ min_deviation = self.convfilt_config['mindeviation']
+
+ pclust_filename=raw_input('Automeasure filename? ')
+ realclust_filename=raw_input('Coordinates filename? ')
+
+ f=open(pclust_filename,'w+')
+ f.write('Analysis started '+time.asctime()+'\n')
+ f.write('----------------------------------------\n')
+ f.write('; Contour length (nm) ; Persistence length (nm) ; Max.Force (pN) ; Slope (N/m) ; Sigma contour (nm) ; Sigma persistence (nm)\n')
+ f.close()
+
+ f=open(realclust_filename,'w+')
+ f.write('Analysis started '+time.asctime()+'\n')
+ f.write('----------------------------------------\n')
+ f.write('; Peak number ; Mean delta (nm) ; Median delta (nm) ; Mean force (pN) ; Median force (pN) ; First peak length (nm) ; Last peak length (nm) ; Max force (pN) ; Min force (pN) ; Max delta (nm) ; Min delta (nm)\n')
+ f.close()
+ # ------ FUNCTION ------
+ def fit_interval_nm(start_index,plot,nm,backwards):
+ '''
+ Calculates the number of points to fit, given a fit interval in nm
+ start_index: index of point
+ plot: plot to use
+ backwards: if true, finds a point backwards.
+ '''
+ whatset=1 #FIXME: should be decidable
+ x_vect=plot.vectors[1][0]
+
+ c=0
+ i=start_index
+ start=x_vect[start_index]
+ maxlen=len(x_vect)
+ while abs(x_vect[i]-x_vect[start_index])*(10**9) < nm:
+ if i==0 or i==maxlen-1: #we reached boundaries of vector!
+ return c
+ if backwards:
+ i-=1
+ else:
+ i+=1
+ c+=1
+ return c
-
- # ------ PROGRAM -------
- c=0
- for item in self.current_list:
- c+=1
- item.identify(self.drivers)
- itplot=item.curve.default_plots()
- try:
- peak_location,peak_size=self.exec_has_peaks(item,min_deviation)
- except:
- #We have troubles with exec_has_peaks (bad curve, whatever).
- #Print info and go to next cycle.
- print 'Cannot process ',item.path
- continue
-
- if len(peak_location)==0:
- continue
-
- fit_points, contact_point, pl_value, T, cindex, avg = plot_informations(itplot,pl_value)
- print '\n\nCurve',item.path, 'is',c,'of',len(self.current_list),': found '+str(len(peak_location))+' peaks.'
-
- #initialize output data vectors
- c_lengths=[]
- p_lengths=[]
- sigma_c_lengths=[]
- sigma_p_lengths=[]
- forces=[]
- slopes=[]
-
- #loop each peak of my curve
- for peak in peak_location:
- c_leng, p_leng, sigma_c_leng, sigma_p_leng, force, slope = features_peaks(itplot, peak, fit_points, contact_point, pl_value, T, cindex, avg)
- for var, vector in zip([c_leng, p_leng, sigma_c_leng, sigma_p_leng, force, slope],[c_lengths, p_lengths, sigma_c_lengths, sigma_p_lengths, forces, slopes]):
- if var is not None:
- vector.append(var)
-
- #FIXME: We need a dictionary here...
- allvects=[c_lengths, p_lengths, sigma_c_lengths, sigma_p_lengths, forces, slopes]
- for vect in allvects:
- if len(vect)==0:
- for i in range(len(c_lengths)):
- vect.append(0)
-
- print 'Measurements for all peaks detected:'
- print 'contour (nm)', c_lengths
- print 'sigma contour (nm)',sigma_c_lengths
- print 'p (nm)',p_lengths
- print 'sigma p (nm)',sigma_p_lengths
- print 'forces (pN)',forces
- print 'slopes (N/m)',slopes
-
- '''
- write automeasure text file
- '''
- print 'Saving automatic measurement...'
- f=open(pclust_filename,'a+')
-
- f.write(item.path+'\n')
- for i in range(len(c_lengths)):
- f.write(' ; '+str(c_lengths[i])+' ; '+str(p_lengths[i])+' ; '+str(forces[i])+' ; '+str(slopes[i])+' ; '+str(sigma_c_lengths[i])+' ; '+str(sigma_p_lengths[i])+'\n')
- f.close()
-
- '''
- calculate clustering coordinates
- '''
- peak_number=len(c_lengths)
-
- if peak_number > 1:
-
- deltas=[]
- for i in range(len(c_lengths)-1):
- deltas.append(c_lengths[i+1]-c_lengths[i])
-
- delta_mean=np.mean(deltas)
- delta_median=np.median(deltas)
-
- force_mean=np.mean(forces)
- force_median=np.median(forces)
-
- first_peak_cl=c_lengths[0]
- last_peak_cl=c_lengths[-1]
-
- max_force=max(forces[:-1])
- min_force=min(forces)
-
- max_delta=max(deltas)
- min_delta=min(deltas)
-
- print 'Coordinates'
- print 'Peaks',peak_number
- print 'Mean delta',delta_mean
- print 'Median delta',delta_median
- print 'Mean force',force_mean
- print 'Median force',force_median
- print 'First peak',first_peak_cl
- print 'Last peak',last_peak_cl
- print 'Max force',max_force
- print 'Min force',min_force
- print 'Max delta',max_delta
- print 'Min delta',min_delta
-
- '''
- write clustering coordinates
- '''
-
- f=open(realclust_filename,'a+')
- f.write(item.path+'\n')
- f.write(' ; '+str(peak_number)+' ; '+str(delta_mean)+' ; '+str(delta_median)+' ; '+str(force_mean)+' ; '+str(force_median)+' ; '+str(first_peak_cl)+' ; '+str(last_peak_cl)+ ' ; '+str(max_force)+' ; '
- +str(min_force)+' ; '+str(max_delta)+' ; '+str(min_delta)+ '\n')
- f.close()
- else:
- pass
-
-
-
\ No newline at end of file
+ def plot_informations(itplot,pl_value):
+ '''
+ OUR VARIABLES
+ contact_point.absolute_coords (2.4584142802103689e-007, -6.9647135616234017e-009)
+ peak_point.absolute_coords (3.6047748250571423e-008, -7.7142802788854212e-009)
+ other_fit_point.absolute_coords (4.1666139243838867e-008, -7.3759393477579707e-009)
+ peak_location [510, 610, 703, 810, 915, 1103]
+ peak_size [-1.2729111505202212e-009, -9.1632775347399312e-010, -8.1707438353929907e-010, -8.0335812578148904e-010, -8.7483955226387558e-010, -3.6269619757067322e-009]
+ params [2.2433999931959462e-007, 3.3230248825175678e-010]
+ fit_errors [6.5817195369767644e-010, 2.4415923138871498e-011]
+ '''
+ fit_points=int(self.config['auto_fit_points']) # number of points to fit before the peak maximum <50>
+
+ T=self.config['temperature'] #temperature of the system in kelvins. By default it is 293 K. <301.0>
+ cindex=self.find_contact_point() #Automatically find contact point <158, libhooke.ClickedPoint>
+ contact_point=self._clickize(itplot[0].vectors[1][0], itplot[0].vectors[1][1], cindex)
+ self.basepoints=[]
+ base_index_0=peak_location[-1]+fit_interval_nm(peak_location[-1], itplot[0], self.config['auto_right_baseline'],False)
+ self.basepoints.append(self._clickize(itplot[0].vectors[1][0],itplot[0].vectors[1][1],base_index_0))
+ base_index_1=self.basepoints[0].index+fit_interval_nm(self.basepoints[0].index, itplot[0], self.config['auto_left_baseline'],False)
+ self.basepoints.append(self._clickize(itplot[0].vectors[1][0],itplot[0].vectors[1][1],base_index_1))
+ self.basecurrent=self.current.path
+ boundaries=[self.basepoints[0].index, self.basepoints[1].index]
+ boundaries.sort()
+ to_average=itplot[0].vectors[1][1][boundaries[0]:boundaries[1]] #y points to average
+ avg=np.mean(to_average)
+ return fit_points, contact_point, pl_value, T, cindex, avg
+
+ def features_peaks(itplot, peak, fit_points, contact_point, pl_value, T, cindex, avg):
+ '''
+ calculate informations for each peak and add they in
+ c_lengths, p_lengths, sigma_c_lengths, sigma_p_lengths, forces, slopes
+ '''
+ c_leng=None
+ p_leng=None
+ sigma_c_leng=None
+ sigma_p_leng=None
+ force=None
+ slope=None
+
+ delta_force=10
+ slope_span=int(self.config['auto_slope_span'])
+
+ peak_point=self._clickize(itplot[0].vectors[1][0],itplot[0].vectors[1][1],peak)
+ other_fit_point=self._clickize(itplot[0].vectors[1][0],itplot[0].vectors[1][1],peak-fit_points)
+
+ points=[contact_point, peak_point, other_fit_point]
+
+ params, yfit, xfit, fit_errors = self.wlc_fit(points, itplot[0].vectors[1][0], itplot[0].vectors[1][1], pl_value, T, return_errors=True)
+
+ #Measure forces
+ delta_to_measure=itplot[0].vectors[1][1][peak-delta_force:peak+delta_force]
+ y=min(delta_to_measure)
+ #Measure slopes
+ slope=self.linefit_between(peak-slope_span,peak)[0]
+ #check fitted data and, if right, add peak to the measurement
+ if len(params)==1: #if we did choose 1-value fit
+ p_leng=pl_value
+ c_leng=params[0]*(1.0e+9)
+ sigma_p_lengths=0
+ sigma_c_lengths=fit_errors[0]*(1.0e+9)
+ force = abs(y-avg)*(1.0e+12)
+ else: #2-value fit
+ p_leng=params[1]*(1.0e+9)
+ #check if persistent length makes sense. otherwise, discard peak.
+ if p_leng>self.config['auto_min_p'] and p_leng<self.config['auto_max_p']:
+ '''
+ p_lengths.append(p_leng)
+ c_lengths.append(params[0]*(1.0e+9))
+ sigma_c_lengths.append(fit_errors[0]*(1.0e+9))
+ sigma_p_lengths.append(fit_errors[1]*(1.0e+9))
+ forces.append(abs(y-avg)*(1.0e+12))
+ slopes.append(slope)
+ '''
+ c_leng=params[0]*(1.0e+9)
+ sigma_c_leng=fit_errors[0]*(1.0e+9)
+ sigma_p_leng=fit_errors[1]*(1.0e+9)
+ force=abs(y-avg)*(1.0e+12)
+ else:
+ p_leng=None
+ slope=None
+ #return c_lengths, p_lengths, sigma_c_lengths, sigma_p_lengths, forces, slopes
+ return c_leng, p_leng, sigma_c_leng, sigma_p_leng, force, slope
+
+
+ # ------ PROGRAM -------
+ c=0
+ for item in self.current_list:
+ c+=1
+ item.identify(self.drivers)
+ itplot=item.curve.default_plots()
+ try:
+ peak_location,peak_size=self.exec_has_peaks(item,min_deviation)
+ except:
+ #We have troubles with exec_has_peaks (bad curve, whatever).
+ #Print info and go to next cycle.
+ print 'Cannot process ',item.path
+ continue
+
+ if len(peak_location)==0:
+ continue
+
+ fit_points, contact_point, pl_value, T, cindex, avg = plot_informations(itplot,pl_value)
+ print '\n\nCurve',item.path, 'is',c,'of',len(self.current_list),': found '+str(len(peak_location))+' peaks.'
+
+ #initialize output data vectors
+ c_lengths=[]
+ p_lengths=[]
+ sigma_c_lengths=[]
+ sigma_p_lengths=[]
+ forces=[]
+ slopes=[]
+
+ #loop each peak of my curve
+ for peak in peak_location:
+ c_leng, p_leng, sigma_c_leng, sigma_p_leng, force, slope = features_peaks(itplot, peak, fit_points, contact_point, pl_value, T, cindex, avg)
+ for var, vector in zip([c_leng, p_leng, sigma_c_leng, sigma_p_leng, force, slope],[c_lengths, p_lengths, sigma_c_lengths, sigma_p_lengths, forces, slopes]):
+ if var is not None:
+ vector.append(var)
+
+ #FIXME: We need a dictionary here...
+ allvects=[c_lengths, p_lengths, sigma_c_lengths, sigma_p_lengths, forces, slopes]
+ for vect in allvects:
+ if len(vect)==0:
+ for i in range(len(c_lengths)):
+ vect.append(0)
+
+ print 'Measurements for all peaks detected:'
+ print 'contour (nm)', c_lengths
+ print 'sigma contour (nm)',sigma_c_lengths
+ print 'p (nm)',p_lengths
+ print 'sigma p (nm)',sigma_p_lengths
+ print 'forces (pN)',forces
+ print 'slopes (N/m)',slopes
+
+ '''
+ write automeasure text file
+ '''
+ print 'Saving automatic measurement...'
+ f=open(pclust_filename,'a+')
+ f.write(item.path+'\n')
+ for i in range(len(c_lengths)):
+ f.write(' ; '+str(c_lengths[i])+' ; '+str(p_lengths[i])+' ; '+str(forces[i])+' ; '+str(slopes[i])+' ; '+str(sigma_c_lengths[i])+' ; '+str(sigma_p_lengths[i])+'\n')
+ f.close()
+
+ '''
+ calculate clustering coordinates
+ '''
+ peak_number=len(c_lengths)
+ if peak_number > 1:
+ deltas=[]
+ for i in range(len(c_lengths)-1):
+ deltas.append(c_lengths[i+1]-c_lengths[i])
+
+ delta_mean=np.mean(deltas)
+ delta_median=np.median(deltas)
+
+ force_mean=np.mean(forces)
+ force_median=np.median(forces)
+
+ first_peak_cl=c_lengths[0]
+ last_peak_cl=c_lengths[-1]
+
+ max_force=max(forces[:-1])
+ min_force=min(forces)
+
+ max_delta=max(deltas)
+ min_delta=min(deltas)
+
+ print 'Coordinates'
+ print 'Peaks',peak_number
+ print 'Mean delta',delta_mean
+ print 'Median delta',delta_median
+ print 'Mean force',force_mean
+ print 'Median force',force_median
+ print 'First peak',first_peak_cl
+ print 'Last peak',last_peak_cl
+ print 'Max force',max_force
+ print 'Min force',min_force
+ print 'Max delta',max_delta
+ print 'Min delta',min_delta
+
+ '''
+ write clustering coordinates
+ '''
+ f=open(realclust_filename,'a+')
+ f.write(item.path+'\n')
+ f.write(' ; '+str(peak_number)+' ; '+str(delta_mean)+' ; '+str(delta_median)+' ; '+str(force_mean)+' ; '+str(force_median)+' ; '+str(first_peak_cl)+' ; '+str(last_peak_cl)+ ' ; '+str(max_force)+' ; '
+ +str(min_force)+' ; '+str(max_delta)+' ; '+str(min_delta)+ '\n')
+ f.close()
+ else:
+ pass
projects / hooke.git / commitdiff