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
+
+ 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
-
+ 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()
+ 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):
'''
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
-
+ 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)
+ 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]
+ 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)
+ 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
+ #Measure forces
delta_to_measure=itplot[0].vectors[1][1][peak-delta_force:peak+delta_force]
y=min(delta_to_measure)
#Measure slopes
#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)
+ 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)
-
+ '''
+ 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
+ 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
+ return c_leng, p_leng, sigma_c_leng, sigma_p_leng, force, slope
# ------ PROGRAM -------
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
-
+ 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.'
#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)
-
+ 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 '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
-
-
+ '''
+ 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
projects / hooke.git / commitdiff