#!/usr/bin/env python
# -*- coding: utf-8 -*-
+from ..libhooke import WX_GOOD, ClickedPoint, config_file_path
from mdp import pca
-from libhooke import WX_GOOD, ClickedPoint
import wxversion
wxversion.select(WX_GOOD)
from wx import PostEvent
import copy
import os.path
import time
-import libhookecurve as lhc
import pylab as pyl
+from .. import libhookecurve as lhc
+
+
import warnings
warnings.simplefilter('ignore',np.RankWarning)
-class pclusterCommands:
-
+class pclusterCommands(object):
+
def _plug_init(self):
self.clustplot1=None
self.clustplot2=None
Automatically measures peaks and extracts informations for further clustering
(c)Paolo Pancaldi, Massimo Sandal 2009
'''
- if self.config['hookedir'][0]=='/':
- slash='/' #a Unix or Unix-like system
- else:
- slash='\\'
blindw = str(self.convfilt_config['blindwindow'])
pclus_dir = "pCluster_blind"+blindw+"_"+time.strftime("%Y%m%d_%H%M")
- self.my_work_dir = os.getcwd()+slash+pclus_dir+slash
+ self.my_work_dir = os.path.join(os.getcwd(), pclus_dir)
self.my_curr_dir = os.path.basename(os.getcwd())
os.mkdir(self.my_work_dir)
-
+
#--Custom persistent length
pl_value=None
for arg in args.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 = "automeasure_"+self.my_curr_dir+"_blind"+blindw+".txt" #raw_input('Automeasure filename? ')
realclust_filename = "coordinate_"+self.my_curr_dir+"_blind"+blindw+".txt" #raw_input('Coordinates filename? ')
peackforce_filename = "peakforce_"+self.my_curr_dir+"_blind"+blindw+".txt" #raw_input('Peacks and Forces filename? ')
-
+
f=open(self.my_work_dir+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(self.my_work_dir+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) ; Peaks Diff\n')
f.close()
-
+
f=open(self.my_work_dir+peackforce_filename,'w+')
f.write('Analysis started '+time.asctime()+'\n')
f.write('----------------------------------------\n')
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(itplot[0]) #Automatically find contact point <158, libhooke.ClickedPoint>
contact_point=self._clickize(itplot[0].vectors[1][0], itplot[0].vectors[1][1], cindex)
def features_peaks(itplot, peak, fit_points, contact_point, pl_value, T, cindex, avg):
'''
- calculate informations for each peak and add they in
+ calculate informations for each peak and add they in
c_lengths, p_lengths, sigma_c_lengths, sigma_p_lengths, forces, slopes
'''
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)
#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)
+ slopes.append(slope)
'''
c_leng=params[0]*(1.0e+9)
sigma_c_leng=fit_errors[0]*(1.0e+9)
itplot[0]=flatten(itplot[0], item, customvalue=1)
try:
peak_location,peak_size=self.exec_has_peaks(item,min_deviation)
- except:
+ except:
#We have troubles with exec_has_peaks (bad curve, whatever).
#Print info and go to next cycle.
print 'Cannot process ',item.path
- continue
+ continue
if len(peak_location)==0:
print 'No peaks!'
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
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 'sigma p (nm)',sigma_p_lengths
print 'forces (pN)',forces
print 'slopes (N/m)',slopes
-
+
'''
write automeasure text file
'''
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()
-
+
peak_number=len(c_lengths)
-
+
'''
write peackforce text file
'''
peackforce_info = peackforce_info + ' ; ' + str(c_lengths[i]) + ' ; ' + str(forces[i])
f.write(' ; '+str(peak_number)+peackforce_info+'\n')
f.close()
-
+
'''
calculate clustering coordinates
'''
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)
-
+
delta_stdev=np.std(deltas)
forces_stdev=np.std(forces[:-1])
-
+
peaks_diff=(last_peak_cl-first_peak_cl)/peak_number
-
+
print 'Coordinates'
print 'Peaks',peak_number
print 'Mean delta',delta_mean
print 'Delta stdev',delta_stdev
print 'Forces stdev',forces_stdev
print 'Peaks difference',peaks_diff
-
+
'''
write clustering coordinates
'''
' ; '+str(peaks_diff)+ # 12
'\n')
f.close()
-
+
# start PCA
self.do_pca(pclus_dir+"/"+realclust_filename)
-
-
+
+
def do_pca(self,args):
'''
PCA -> "pca gaeta_coor_blind50.txt 1,3,6"
Automatically measures pca from coordinates filename and shows two interactives plots
- With the second argument (arbitrary) you can select the columns and the multiplier factor
+ With the second argument (arbitrary) you can select the columns and the multiplier factor
to use for the pca (for es "1,3*50,6,8x10,9"). Dont use spaces. "*" or "x" are the same thing.
Without second argument it reads pca_config.txt file
(c)Paolo Pancaldi, Massimo Sandal 2009
'''
-
+
# reads the columns of pca
- if self.config['hookedir'][0]=='/':
- slash='/' #a Unix or Unix-like system
- else:
- slash='\\'
- self.my_hooke_dir = self.config['hookedir']+slash
- #self.my_work_dir = os.getcwd()+slash+"pCluster_"+time.strftime("%Y%m%d_%H%M")+slash
- #self.my_curr_dir = os.path.basename(os.getcwd())
- conf=open(self.my_hooke_dir+"pca_config.txt")
+ conf=open(config_file_path("pca_config.txt"), 'r')
config = conf.readlines()
conf.close()
-
+
self.plot_myCoord = [] # tiene le coordinate prese direttamente dal file creato con pCluster
self.plot_origCoord = [] # tiene le coordinate solo delle colonne scelte e moltiplicate per i valori scelti
self.plot_pcaCoord = [] # tiene le due colonne della pca
self.plot_NewPcaCoord = [] # tiene le due colonne della pca filtrate per densita
self.plot_NewPcaCoordTr=[] # tiene le due colonne della pca trasposta filtrate per densita
plot_path_temp = ""
-
- # prende in inpunt un arg (nome del file)
+
+ # prende in inpunt un arg (nome del file)
# e il secondo le colonne su cui lavorare (e' arbitrario, riceve x es "1,2,3")
arg = args.split(" ")
if arg[0]==args:
else:
file_name=arg[0]
config[0] = arg[1]
-
+
# creo l'array "plot_myCoord" con tutte le coordinate dei plots
# e l'array plot_paths con tutti i percorsi dei plots
nPlotTot = -3 #tolgo le prime 3 righe iniziali del file
if row[0]==" " and row.find('nan')==-1 and row.find("-1.#IND")==-1:
row = row[row.index(";",2)+2:].split(" ; ") # non considero la prima colonna col #picchi
row = [float(i) for i in row]
-
+
#0:Mean delta, 1:Median delta, 2:Mean force, 3:Median force, 4:First peak length, 5:Last peak length
#6:Max delta 7:Min delta 8:Max force 9:Min force 10:Std delta 11:Std force
if (row[0]<500 and row[1]<500 and row[2]<500 and row[3]<500 and row[4]<500 and row[5]<500 and row[6]<500 and row[7]<500 and row[8]<500 and row[9]<500 and row[10]<500 and row[11]<500):
self.plot_myCoord.append(row)
self.plot_paths.append(plot_path_temp)
f.close()
-
- # creo l'array con alcune colonne e pure moltiplicate
+
+ # creo l'array con alcune colonne e pure moltiplicate
for row in self.plot_myCoord:
res=[]
for cols in config[0].split(","):
molt = 1
res.append(row[col]*molt)
self.plot_origCoord.append(res)
-
+
# array convert, calculate PCA, transpose
self.plot_origCoord = np.array(self.plot_origCoord,dtype='float')
#print self.plot_origCoord.shape
self.plot_pcaCoordTr = np.transpose(self.plot_pcaCoord)
pca_X=np.array(self.plot_pcaCoordTr[0],dtype='float')
pca_Y=np.array(self.plot_pcaCoordTr[1],dtype='float')
-
+
'''
# Start section of testing with good plots # 4 TESTING!
Xsyn_1=[]
- Ysyn_1=[]
+ Ysyn_1=[]
Xgb1_1=[]
Ygb1_1=[]
Xbad_1=[]
goodnames=goodnamefile.readlines()
nPlotGood = len(goodnames)-2 #tolgo prima e ultima riga
goodnames=[i.split()[0] for i in goodnames[1:]]
-
+
for index in range(len(self.plot_paths)):
if self.plot_paths[index][:-1] in goodnames:
Xsyn_1.append(pca_X[index])
Ybad_1.append(pca_Y[index])
# Stop section of testing with good plots # 4 TESTING!
'''
-
+
# print first plot
clustplot1=lhc.PlotObject()
clustplot1.add_set(pca_X,pca_Y)
clustplot1.destination=0
self._send_plot([clustplot1])
self.clustplot1=clustplot1
-
+
# density and filer estimation
kernel = sp.stats.kde.gaussian_kde(sp.c_[pca_X,pca_Y].T)
tallest = 0
axis_X = np.linspace(xmin,xmax,num=100)
axis_Y = np.linspace(ymin,ymax,num=100)
'''
-
+
# density filtering:
# tramite "kernel.evaluate" trovo lo score (altezza) di ogni coordinata e decido se mantenerla o no
filtered_pca_X = []
filtered_PcaCoordTr.append(filtered_pca_X)
filtered_PcaCoordTr.append(filtered_pca_Y)
filtered_PcaCoord = np.transpose(filtered_PcaCoordTr)
-
+
# creo i due array "plot_FiltOrigCoord" e "plot_FiltPaths" contenenti solo i dati filtrati con alta densita
for index in range(len(self.plot_pcaCoord)):
if self.plot_pcaCoord[index] in filtered_PcaCoord:
self.plot_FiltOrigCoord.append(self.plot_myCoord[index])
self.plot_FiltPaths.append(self.plot_paths[index])
-
+
'''
# START PCA#2: USELESS!!!
-
+
# creo l array con alcune colonne e pure moltiplicate
temp_coord = []
for row in self.plot_FiltOrigCoord:
res.append(row[col]*molt)
temp_coord.append(res)
self.plot_FiltOrigCoord = temp_coord
-
+
# ricalcolo la PCA: array convert, calculate PCA, transpose
self.plot_FiltOrigCoord = np.array(self.plot_FiltOrigCoord,dtype='float')
#print self.plot_FiltOrigCoord.shape
self.plot_NewPcaCoordTr = np.transpose(self.plot_NewPcaCoord)
pca_X2=np.array(self.plot_NewPcaCoordTr[0],dtype='float')
pca_Y2=np.array(self.plot_NewPcaCoordTr[1],dtype='float')
-
+
# Start section of testing with good plots # 4 TESTING!
Xsyn_2=[]
Ysyn_2=[]
else:
Xbad_2.append(pca_X2[index])
Ybad_2.append(pca_Y2[index])
-
+
# print second plot
clustplot2=lhc.PlotObject()
#clustplot2.add_set(pca_X2,pca_Y2)
self._send_plot([clustplot2])
self.clustplot2=clustplot2
'''
-
+
# PRINT density plot
clustplot2=lhc.PlotObject()
clustplot2.add_set(filtered_pca_X,filtered_pca_Y)
clustplot2.destination=1
self._send_plot([clustplot2])
self.clustplot2=clustplot2
-
+
# printing results
config_pca1 = config[0].replace("*", "x").rstrip("\n")
config_pca2 = config[2].replace("*", "x").rstrip("\n")
print "Piu alta: ", tallest
#print "- FILTRO 3: 2'PCA:"+config_pca2
print ""
-
+
# -- exporting coordinates and plot of PCA in debug mode! --
if config[3].find("true")!=-1:
#1' PCA: save plot and build coordinate s file
f.write (" ; " + str(cel))
f.write ("\n")
f.close()
-
+
# pCLUSTER SAVING!!!
import shutil
pcl_name = file_name.replace("coordinate_", "goodplots_").replace('.txt','_'+config_pca1+'_'+str(my_filter).replace(".",","))
f.write (myfile+"\n")
shutil.copy2(myfile, pcl_name)
f.close()
-
-
+
+
def do_multipca(self,args):
'''
MULTIPCA -> "multipca gaeta_coor_blind50.txt 3"
- Automatically multiply the column suggest in second argument for value between 1-100 (step of 2),
+ Automatically multiply the column suggest in second argument for value between 1-100 (step of 2),
measures pca from coordinates filename and save the png plots.
(c)Paolo Pancaldi, Massimo Sandal 2009
'''
for j in range(1, 13):
if i!=j:
self.do_pca(file_name + " " + str(i) + "," + str(j))
-
+
def do_triplepca(self,args):
'''
TRIPLEPCA -> "triplepca gaeta_coor_blind50.txt"
'''
It returns id, coordinates and file name of a clicked dot on a PCA graphic
'''
-
+
self._send_plot([self.clustplot1]) #quick workaround for BAD problems in the GUI
print 'Click point'
point = self._measure_N_points(N=1, whatset=0)
print "coord: " + str(dot_coord)
self.do_genlist(str(plot_file))
#self.do_jump(str(plot_file))
-
+
# indea iniziata e messa da parte...
def do_peakforce(self, args):
'''
Automatically measures peack and force plots
(c)Paolo Pancaldi, Massimo Sandal 2009
'''
-
+
# prende in inpunt un arg (nome del file)
file_name=args
f=open(file_name)
-
+
# scrivo un file temp
g = open('_prove.txt','w')
-
+
plot_file = '';
rows = f.readlines()
for row in rows:
g.write(writeme)
g.close()
f.close()
-
-
\ No newline at end of file
projects / hooke.git / blobdiff