def save_state(self, cmpconfigfile):
""" Save CMP Configuration state directly.
Useful if you do not want to invoke the GUI
Parameters
----------
cmpconfigfile : string
Absolute path and filename to store the CMP configuration
pickled object
"""
# check if path available
if not os.path.exists(os.path.dirname(cmpconfigfile)):
os.makedirs(os.path.abspath(os.path.dirname(cmpconfigfile)))
try:
import enthought.sweet_pickle as sp
except ImportError:
import apptools.sweet_pickle as sp
output = open(cmpconfigfile, 'wb')
# Pickle the list using the highest protocol available.
# copy object first
tmpconf = CMPGUI()
tmpconf.copy_traits(self)
sp.dump(tmpconf, output, -1)
output.close()
def save_state(self, cmpconfigfile):
""" Save CMP Configuration state directly.
Useful if you do not want to invoke the GUI
Parameters
----------
cmpconfigfile : string
Absolute path and filename to store the CMP configuration
pickled object
"""
# check if path available
if not os.path.exists(os.path.dirname(cmpconfigfile)):
os.makedirs(os.path.abspath(os.path.dirname(cmpconfigfile)))
try:
import enthought.sweet_pickle as sp
except ImportError:
import apptools.sweet_pickle as sp
output = open(cmpconfigfile, 'wb')
# Pickle the list using the highest protocol available.
# copy object first
tmpconf = CMPGUI()
tmpconf.copy_traits(self)
sp.dump(tmpconf, output, -1)
output.close()
def _save_pickle_changed(self, value):
import pickle
import enthought.sweet_pickle as sp
import os.path
from enthought.pyface.api import FileDialog, OK
wildcard = "sLORETA Converter State (*.pkl)|*.pkl|" \
"All files (*.*)|*.*"
dlg = FileDialog(wildcard=wildcard,title="Choose a File to store state",\
resizeable=False, \
default_directory=self.rootfolder,)
if dlg.open() == OK:
output = open(dlg.path, 'wb')
# Pickle the list using the highest protocol available.
sp.dump(self, output, -1)
output.close()
def save(self, path, obj):
""" Saves an object to a file. """
if not path.endswith(self.ext):
actual_path = path + self.ext
else:
actual_path = path
# Pickle the object.
f = file(actual_path, 'wb')
try:
sweet_pickle.dump(obj, f, 1)
# cPickle.dump(obj, f, 1)
# pickle.dump(obj, f, 1)
except Exception, ex:
logger.exception("Failed to pickle into file: %s, %s, object:%s" %
(path, ex, obj))
print_exc()
def save(self, path, obj):
""" Saves an object to a file. """
if not path.endswith(self.ext):
actual_path = path + self.ext
else:
actual_path = path
# Pickle the object.
f = file(actual_path, 'wb')
try:
sweet_pickle.dump(obj, f, 1)
# cPickle.dump(obj, f, 1)
# pickle.dump(obj, f, 1)
except Exception, ex:
logger.exception( "Failed to pickle into file: %s, %s, object:%s"
% (path, ex, obj))
print_exc()
def _save_pickle_changed(self, value):
import pickle
import enthought.sweet_pickle as sp
import os.path
from enthought.pyface.api import FileDialog, OK
wildcard = "sLORETA Converter State (*.pkl)|*.pkl|" \
"All files (*.*)|*.*"
dlg = FileDialog(wildcard=wildcard,title="Choose a File to store state",\
resizeable=False, \
default_directory=self.rootfolder,)
if dlg.open() == OK:
output = open(dlg.path, 'wb')
# Pickle the list using the highest protocol available.
sp.dump(self, output, -1)
output.close()
def _create_cff_changed(self, value):
import os
from os.path import join, exists, split
# Generate Connectome File
# -----
if self.convert == 'Subjects':
suffix = '_subjects'
elif self.convert == 'Statistics: CorrCoeff':
suffix = '_statistics'
elif self.convert == 'Statistics: t-Statistics':
suffix = '_tstatistics'
def removeDir(path):
import shutil
import os.path
if os.path.isdir(path):
shutil.rmtree(path, ignore_errors = True)
# create folder CFF
if not exists(join(self.rootfolder, 'CFF'+ suffix)):
os.mkdir(join(self.rootfolder, 'CFF'+ suffix))
else:
# remove folder if it exists
removeDir(join(self.rootfolder, 'CFF'+ suffix))
os.mkdir(join(self.rootfolder, 'CFF'+ suffix))
# create subfolder Networks
if not exists(join(self.rootfolder, 'CFF'+ suffix, 'Network')):
os.mkdir(join(self.rootfolder, 'CFF'+ suffix, 'Network'))
# create subfolder Volumes
if not exists(join(self.rootfolder, 'CFF'+ suffix, 'Nifti')):
os.mkdir(join(self.rootfolder, 'CFF'+ suffix, 'Nifti'))
# create electrode position network
if self.write_electrode_network:
outcontent, nodes = electrode_pos_to_graphml(filename = self.electrode_coordinates_file )
fi = open(join(self.rootfolder, 'CFF'+ suffix, 'Network', 'electrodes.graphml'), 'w')
fi.write(outcontent)
fi.close()
# create ROI segmentation volume nifti file
if not self.ROI_slor_file == '':
nim = slor2nifti(filename = self.ROI_slor_file, merge_roi = True)
filename = split(self.ROI_slor_file)[1].split('.')[0]
nim.to_filename(join(self.rootfolder, 'CFF'+ suffix, 'Nifti', filename + '.nii.gz'))
# create meta.xml
fim = open(join(self.rootfolder, 'CFF' + suffix, 'meta.xml'), 'w')
content = """<?xml version="1.0" encoding="UTF-8"?>
<viewer xmlns="http://www.connectome.ch/2009/Connectome/xmlns"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://www.connectome.ch/2009/Connectome/xmlns
connectome.xsd">
<viewer-meta version="1.0">"""
content = content + self.metainfo.get_metaxml() + """
<nr_of_networks>%s</nr_of_networks>
</viewer-meta>\n""" % str(len(self.subjects) + 1)
# add networks
# electrode
if self.write_electrode_network:
content = content + """<viewer-network name="Electrodes" src="Network/electrodes.graphml" hierarchical="0" hypergraph="0" directed="0" />\n"""
# -----
if self.convert == 'Statistics: CorrCoeff':
for mes in self.stats_measure:
#print 'Calculating measure: ', mes
for i, name in enumerate(self.stats_name):
#print 'For name: ', name
# number of external variables has to be an integer
val = int(self.stats_extvars[i])
# loop through all the independent variables and create graphml
for extvar_i in range(1, val + 1):
#print 'Independent Variable: ', extvar_i
# correct zero-padding
if val > 0:
extvar_name = '%02d' % extvar_i
elif val > 99:
extvar_name = '%03d' % extvar_i
fi = open(join(self.rootfolder, 'CFF'+ suffix, 'Network', 'statistic_'+name+'_indvar'+extvar_name+'.graphml'), 'wb')
content2 = statistics2graphml(rootfolder = self.rootfolder, nr_rois = self.nr_rois, \
name = name, measure = mes, \
extvar = extvar_name, freq_bands = self.freq_bands, \
el_cord_file = self.electrode_coordinates_file)
fi.write(content2)
fi.close()
# output network in metaxml
content = content + """<viewer-network name="%s-%s-IndVar%s" src="Network/statistic_%s_indvar%s.graphml" hierarchical="0" hypergraph="0" directed="0">""" \
% (mes, name, extvar_name, name, extvar_name)
if not self.ROI_slor_file == '':
content = content + """
<network-volume name="%s" src="Nifti/%s.nii.gz" fileformat="nifti" segmentation="true">
<description>The segmentation of the brain in Regions of Interest.</description>
</network-volume>\n""" % (filename, filename)
content = content + """</viewer-network>\n"""
elif self.convert == 'Statistics: t-Statistics':
for mes in self.stats_measure:
#print 'Calculating measure: ', mes
for i, name in enumerate(self.stats_name):
#print 'For name: ', name
fi = open(join(self.rootfolder, 'CFF'+ suffix, 'Network', 'tstatistic_'+name+'_'+mes+'.graphml'), 'wb')
content3 = tstatistics2graphml(rootfolder = self.rootfolder, nr_rois = self.nr_rois, \
name = name, measure = mes, \
freq_bands = self.freq_bands, \
el_cord_file = self.electrode_coordinates_file)
fi.write(content3)
fi.close()
# output network in metaxml
content = content + """<viewer-network name="%s-%s" src="Network/tstatistic_%s_%s.graphml" hierarchical="0" hypergraph="0" directed="0">""" \
% (name, mes, name, mes)
if not self.ROI_slor_file == '':
content = content + """
<network-volume name="%s" src="Nifti/%s.nii.gz" fileformat="nifti" segmentation="true">
<description>The segmentation of the brain in Regions of Interest.</description>
</network-volume>\n""" % (filename, filename)
content = content + """</viewer-network>\n"""
elif self.convert == 'Subjects':
# loop through all the subjects and create graphml
for subj in self.subjects:
fi = open(join(self.rootfolder, 'CFF'+ suffix, 'Network', 'subject_'+subj+'.graphml'), 'wb')
content2 = coherences2graphml(rootfolder = self.rootfolder, nr_rois = self.nr_rois, \
subject = subj, conditions = self.conditions, \
measures = self.measures, freq_bands = self.freq_bands, \
el_cord_file = self.electrode_coordinates_file)
fi.write(content2)
fi.close()
# subjects
for subj in self.subjects:
content = content + """<viewer-network name="Subject %s" src="Network/subject_%s.graphml" hierarchical="0" hypergraph="0" directed="0">""" \
% (subj, subj)
content = content + """
<network-volume name="%s" src="Nifti/%s.nii.gz" fileformat="nifti" segmentation="true">
<description>The segmentation of the brain in Regions of Interest.</description>
</network-volume>\n""" % (filename, filename)
content = content + """</viewer-network>\n"""
# ending
content = content + "</viewer>"
fim.write(content)
fim.close()
# create .cff with Zip
import zipfile as zip
filen = self.metainfo.meta_name.replace(' ', '_')
filen = filen.lower()
mycff = zip.ZipFile(join(self.rootfolder, filen + suffix + '.cff'), mode='w')
os.chdir(join(self.rootfolder, 'CFF' + suffix))
for root, dirs, files in os.walk('.'):
for name in files:
filename = os.path.join(root, name)
# print 'Writing to Connectome File: ', filename
mycff.write(filename)
mycff.close()
message(message = 'Connectome File generated in Root Folder!', title = 'Done.')
#####
def electrode_pos_to_graphml(filename):
""" Converts the electrode position file to a network without edges """
f = open(filename, 'r')
nodes = ''
for i, line in enumerate(f):
if i == 0:
nr_of_electrodes = line
else:
lsp = line.split(' ')
x, y, z, name = float(lsp[0]), float(lsp[1]), float(lsp[2]), lsp[3].rstrip('\r\n')
nodes = nodes + """<node id="n%s">
<data key="dn_label">%s</data>
<data key="dn_position">%s,%s,%s</data>
</node>
""" % (i, name, x, y, z)
outfile = startstring % nr_of_electrodes
outfile = outfile + nodes
outfile = outfile + endstring
f.close()
return outfile, nodes
startstring = """<?xml version="1.0" encoding="UTF-8"?>
<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xmlns:xlink="http://www.w3.org/1999/xlink"
xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
http://www.connectome.ch/graphml+xlink+gifti.xsd">
<key id="dn_label" for="node" attr.name="label" attr.type="string">
<desc>The label of the electrode</desc>
</key>
<key id="dn_position" for="node" attr.name="position" attr.type="string">
<desc>The position of the electrode</desc>
</key>
<graph id="%s electrodes" edgedefault="undirected">
"""
endstring = """</graph>
</graphml>
"""
def tstatistics2graphml(rootfolder, nr_rois, name = None, \
measure = None, freq_bands = None, el_cord_file = None):
""" Converts sLORETA t-statistic files into GraphML
Naming convention: name-measure.txt
E.g. A-B-EEGLaggedConn.txt
"""
from os.path import exists, isfile, join
from numpy import zeros, array
if not exists(rootfolder):
raise Exception('Path %s does not exist.' % rootfolder)
return
outfile = startstring_coh
# write edge keys
for freq in freq_bands:
key = '<key id="%s" for="edge" attr.name="%s" attr.type="double"><default>0</default></key>\n'
outfile = outfile + key % (freq, freq)
# start graph
outfile = outfile + '<graph id="%s" edgedefault="undirected">\n' % name
# write nodes (ROIs)
# use the electrode positions preliminary
out, nodes = electrode_pos_to_graphml(filename = join(rootfolder, el_cord_file))
outfile = outfile + nodes
# write edges
file = join(rootfolder, name+'-'+measure+'.txt')
if isfile(file):
print 'Converting file %s ...' % str(file)
nr_elect, nr_mat = extract_metadata_stats(filename = file)
# number of electrodes (ROIs) from the file header must be equal as the one given in the form
assert nr_elect == nr_rois
# number of matrices must equal number of frequency bands given
assert nr_mat == len(freq_bands)
f = open(file, 'r')
# skip header
a = f.readline()
a = f.readline()
while ' = ' in a or a == '':
a = f.readline()
# create numpy array for all frequency bands
data_arr = zeros((nr_elect, nr_elect, nr_mat))
for mat_i in range(nr_mat):
for elect_i in range(nr_elect):
line = f.readline()
lsp = line.split(' ')
while lsp.count('') != 0:
lsp.remove('')
# print 'lenlsp',len(lsp)
#print nr_elect
assert len(lsp) == nr_elect
data_arr[elect_i, :, mat_i] = array(lsp)
# skip empty line
f.readline()
#print 'Done.'
else:
#print 'No file %s existing.' % file
return
# now loop over bigarray and write the the edges
for i in range(0, nr_rois):
for j in range(i, nr_rois):
if i == j:
# skipping self-loops
continue
outfile = outfile + '<edge id="e%s_%s" source="n%s" target="n%s">\n' \
% (str(i+1), str(j+1), str(i+1), str(j+1))
for freq_i, frequency_label in enumerate(freq_bands):
# loop over all frequency bands
label, value = frequency_label, data_arr[i,j,freq_i]
if value == 0.0:
continue
# convert value (float) to truncated string
value = value
outfile = outfile + ' <data key="%s">%s</data>\n'\
% (label, str(value))
outfile = outfile + '</edge>\n'
outfile = outfile + endstring
return outfile
def statistics2graphml(rootfolder, nr_rois, name = None, extvar = None, \
measure = None, freq_bands = None, el_cord_file = None):
""" Converts sLORETA statistic files (correlations) into GraphML
"""
from os.path import exists, isfile, join
from numpy import zeros, array
if not exists(rootfolder):
raise Exception('Path %s does not exist.' % rootfolder)
return
outfile = startstring_coh
# write edge keys
for freq in freq_bands:
key = '<key id="%s" for="edge" attr.name="%s" attr.type="double"><default>0</default></key>\n'
outfile = outfile + key % (freq, freq)
# start graph
outfile = outfile + '<graph id="%s" edgedefault="undirected">\n' % name
# write nodes (ROIs)
# use the electrode positions preliminary
out, nodes = electrode_pos_to_graphml(filename = join(rootfolder, el_cord_file))
outfile = outfile + nodes
# write edges
file = join(rootfolder, name+'-'+measure+'-IndVar'+extvar+'.txt')
if isfile(file):
#print 'Converting file %s ...' % str(file)
nr_elect, nr_mat = extract_metadata_stats(filename = file)
# number of electrodes (ROIs) from the file header must be equal as the one given in the form
assert nr_elect == nr_rois
# number of matrices must equal number of frequency bands given
assert nr_mat == len(freq_bands)
f = open(file, 'r')
# skip header
a = f.readline()
a = f.readline()
while ' = ' in a or a == '':
a = f.readline()
# create numpy array for all frequency bands
data_arr = zeros((nr_elect, nr_elect, nr_mat))
for mat_i in range(nr_mat):
for elect_i in range(nr_elect):
line = f.readline()
lsp = line.split(' ')
while lsp.count('') != 0:
lsp.remove('')
assert len(lsp) == nr_elect
data_arr[elect_i, :, mat_i] = array(lsp)
# skip empty line
f.readline()
#print 'Done.'
else:
#print 'No file %s existing.' % file
return
# now loop over bigarray and write the the edges
for i in range(0, nr_rois):
for j in range(i, nr_rois):
if i == j:
# skipping self-loops
continue
outfile = outfile + '<edge id="e%s_%s" source="n%s" target="n%s">\n' \
% (str(i+1), str(j+1), str(i+1), str(j+1))
for freq_i, frequency_label in enumerate(freq_bands):
# loop over all frequency bands
label, value = frequency_label, data_arr[i,j,freq_i]
if value == 0.0:
continue
# convert value (float) to truncated string
value = value
outfile = outfile + ' <data key="%s">%s</data>\n'\
% (label, str(value))
outfile = outfile + '</edge>\n'
outfile = outfile + endstring
return outfile
def coherences2graphml(rootfolder, nr_rois, subject = None, conditions = None, \
measures = None, freq_bands = None, el_cord_file = None):
""" Given a proper folder structure, all subjects, conditions and measures are
searched and converted to GraphML files for all frequency bands.
If allinone is True, all the files must be stored only in the root folder, but
subjects, conditions, measures and frequency_bands have to be given according
to the file name convention.
Example:
Files like VP07R1-ROIlaggedCoh.txt with the classical frequency bands amounts to calling
(number of ROIs and number of frequency_bands is according to sLORETA selections)
rootfolder = '/', nr_rois, subjects = 'VP07', conditions = ['R1'], \
measures = ['ROIlaggedCoh'], freq_band['alpha1', 'alpha2'], el_cord_file = 'List19e.sxyz'
"""
from os.path import exists, isfile, join
from numpy import zeros, array
if not exists(rootfolder):
raise Exception('Path %s does not exist.' % rootfolder)
return
outfile = startstring_coh
# write edge keys
for cond in conditions:
for meas in measures:
for freq in freq_bands:
key = '<key id="%s-%s-%s" for="edge" attr.name="%s.%s.%s" attr.type="double"><default>0</default></key>\n'
outfile = outfile + key % (cond, meas, freq, cond, meas, freq)
# start graph
outfile = outfile + '<graph id="%s" edgedefault="undirected">\n' % subject
# write nodes (ROIs)
# use the electrode positions preliminary
out, nodes = electrode_pos_to_graphml(filename = join(rootfolder, el_cord_file))
outfile = outfile + nodes
# write edges
bigarray = {}
for cond in conditions:
for meas in measures:
file = join(rootfolder, subject+cond+'-'+meas+'.txt')
if isfile(file):
#print 'Converting file %s ...' % str(file)
nr_elect, nr_mat = extract_metadata(filename = file)
# number of electrodes (ROIs) from the file header must be equal as the one given in the form
assert nr_elect == nr_rois
# number of matrices must equal number of frequency bands given
assert nr_mat == len(freq_bands)
f = open(file, 'r')
# skip header
a = f.readline()
while ' = ' in a or 'File name' in a or a == '':
a = f.readline()
# create numpy array for all frequency bands
data_arr = zeros((nr_elect, nr_elect, nr_mat))
for mat_i in range(nr_mat):
#print 'Matrix: ', str(mat_i)
for elect_i in range(nr_elect):
#print 'ROI:', str(elect_i)
line = f.readline()
lsp = line.split(' ')
try:
lsp.remove('') # remove first empty character
except ValueError:
break
assert len(lsp) == nr_elect
data_arr[elect_i, :, mat_i] = array(lsp)
#print 'Done.'
bigarray[cond + '-' + meas] = data_arr
# now store data_arr
# now loop over bigarray and write the the edges
for i in range(0, nr_rois):
for j in range(i, nr_rois):
if i == j:
# skipping self-loops
continue
outfile = outfile + '<edge id="e%s_%s" source="n%s" target="n%s">\n' \
% (str(i+1), str(j+1), str(i+1), str(j+1))
for k, v in bigarray.items():
# number of matrices has to be the same as the number of frequency bands
assert v.shape[2] == len(freq_bands)
for freq_i, frequency_label in enumerate(freq_bands):
# loop over all frequency bands
label, value = k+'-'+frequency_label, v[i,j,freq_i]
if value == 0.0:
continue
# convert value (float) to truncated string
value = value
outfile = outfile + ' <data key="%s">%s</data>\n'\
% (label, str(value))
outfile = outfile + '</edge>\n'
outfile = outfile + endstring
return outfile
startstring_coh = """<?xml version="1.0" encoding="UTF-8"?>
<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xmlns:xlink="http://www.w3.org/1999/xlink"
xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
http://www.connectome.ch/graphml+xlink+gifti.xsd">
<key id="dn_label" for="node" attr.name="label" attr.type="string">
<desc>The label of the ROI</desc>
</key>
<key id="dn_position" for="node" attr.name="position" attr.type="string">
<desc>The position of the ROI</desc>
</key>
"""
def extract_metadata_stats(filename):
""" Extract metadata for statistics file, namely number of electrodes and nr of matrices
and band type """
f = open(filename, 'r')
for line in f:
lsp = line.split(' = ')
if len(lsp) == 1:
continue
if 'Number of regions of interest' in lsp[1]:
nr_elect = int(lsp[0])
elif 'Number of matrices' in lsp[1]:
nr_mat = int(lsp[0])
f.close()
return nr_elect, nr_mat
