From: devicerandom <devnull@localhost>
Date: Wed, 25 Feb 2009 16:34:08 +0000 (+0000)
Subject: (pcluster.py) fixed indentation (ok)
X-Git-Tag: 0.9.0~47
X-Git-Url: http://git.tremily.us/?a=commitdiff_plain;h=784cea290cf4cb6cd987d8556e307b67b19e05ce;p=hooke.git

(pcluster.py) fixed indentation (ok)
---

diff --git a/pcluster.py b/pcluster.py
index 272f1b4..b3de929 100644
--- a/pcluster.py
+++ b/pcluster.py
@@ -16,266 +16,255 @@ warnings.simplefilter('ignore',np.RankWarning)
 
 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