-
Notifications
You must be signed in to change notification settings - Fork 6
/
electrodedisplaywidget.py
1098 lines (936 loc) · 54 KB
/
electrodedisplaywidget.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
#! /usr/bin/env python
# -*- coding: utf-8 -*-
#
# Widget to select and display electrode plots on a common referential/template
#
# (c) Inserm U836 2012-2014 - Manik Bhattacharjee
#
# License GNU GPL v3
#
#
from soma.qt_gui.qt_backend import QtGui, QtCore, uic
import sys, pickle, shutil, traceback, os, json, re, numpy, csv
from brainvisa import axon
from brainvisa.configuration import neuroConfig
neuroConfig.gui = True
from brainvisa import anatomist
from soma import aims
import brainvisa.registration as registration
from locateElectrodes import createElectrode, getPlotsCenters, getPlots, getPlotsNames, createBipole
from referentialconverter import ReferentialConverter
from templatewidget import TemplateMRI, TemplateMNI
from brainvisa.data.readdiskitem import ReadDiskItem
from brainvisa.data.writediskitem import WriteDiskItem
from brainvisa.data import neuroHierarchy
from readLabels import *
from readFunctionalTractography import *
from scipy import spatial as sc_sp
from collections import OrderedDict
from locateElectrodes import natural_keys
from bipoleSEEGColors import bipoleSEEGColors
from control_ftract2 import *
import pdb
def loadElectrodeModels():
"""Load electrode models from the database"""
models = {}
rdiEM = ReadDiskItem('Electrode Model', 'Electrode Model format')
result = list (rdiEM._findValues( {}, None, False ) )
for e in result:
#WARNING if a model is available from multiple protocols, it will use only one
models[str(e.attributes()['model_name'])] = e
return models
class ElectrodeDisplayWidget(QtGui.QWidget):
def __init__(self, app=None, ana = None,dataSubjects = None):
QtGui.QWidget.__init__(self)
uic.loadUi("groupPlots.ui", self)
self.subjects = []
self.subjItems = []
self.implantations = {}
self.plotsData = {}
self.testDataSubjects = dataSubjects
self.taskCounter = 0
self.tasks = []
self.meshes = {}
self.bipolesmeshes = {}
self.dispMode = 'off'#'sphere'
self.dispParams = {'diameter':2.0}
self.transfoManager = registration.getTransformationManager()
# Get ReferentialConverter (for Talairach, AC-PC...)
self.refConv = ReferentialConverter()
self.electrodeModels = loadElectrodeModels()
self.addSelectionButton.clicked.connect(self.addSelection)
self.removeSelectionButton.clicked.connect(self.removeSelection)
self.removePlotsNotTRC.clicked.connect(self.removeNotTRC)
self.removePlotsLeftSide.clicked.connect(lambda :self.removePlotsLeftRight('Left'))
self.removePlotsRightSide.clicked.connect(lambda :self.removePlotsLeftRight('Right'))
self.addAroundButton.clicked.connect(self.selectAround)
self.selectionList.itemDoubleClicked.connect(self.updatePlotSelected)
self.generateStatsButton.clicked.connect(self.generateStatisticsContacts)
#fill the combo possibility.
loca = ['*']
parcels_namesMA= readLabels('labels/marsatlas_labels.txt')
loca.extend(list(parcels_namesMA.values()))
loca.sort()
self.AddMAparcels2SelectioncomboBox.clear()
self.AddMAparcels2SelectioncomboBox.addItems(loca)
self.AddMAparcels2SelectioncomboBox.currentIndexChanged.connect(self.AddMAparcels2selection)
self.radioButtonbothHemi.toggled.connect(self.changeBothRightDisplay)
self.radioButtonContactDisplay.toggled.connect(self.contactSEEGDisplay)
pix = QtGui.QPixmap('/home/b67-belledone/Desktop/epilepsie-manik/Logo-F-TRACT.xpm' )
anatomist.anatomist.cpp.IconDictionary.instance().addIcon('ftract_control', pix)
ad = anatomist.anatomist.cpp.ActionDictionary.instance()
#control = ensemble d'action
ad.addAction( 'fTract_Action', StimulateResults )
cd = anatomist.anatomist.cpp.ControlDictionary.instance()
cd.addControl( 'ftract_control', ControlFtract, 25 )
cm = anatomist.anatomist.cpp.ControlManager.instance()
cm.addControl('QAGLWidget3D','','ftract_control')
# Anatomist windows and objects
if ana == None:
self.a = anatomist.Anatomist('-b' )
else:
self.a = ana
layout = QtGui.QHBoxLayout( self.viewWidget )
self.axWindow = self.a.createWindow( 'Axial' )#, no_decoration=True )
self.axWindow.setParent(self.viewWidget)
layout.addWidget( self.axWindow.getInternalRep() )
self.sagWindow = self.a.createWindow( 'Sagittal' )#, no_decoration=True )
self.sagWindow.setParent(self.viewWidget)
layout.addWidget( self.sagWindow.getInternalRep() )
self.axWindow.internalRep.otherwindow = self.sagWindow
self.windows = [self.axWindow, self.sagWindow]
self.templates = {'MNI':TemplateMNI(self.a)}
self.setTemplate(self.templates['MNI'])
self.templReferential = None
self.subjectList.itemSelectionChanged.connect(self.subjectSelectionChanged)
self.electrodeList.itemSelectionChanged.connect(self.electrodeSelectionChanged)
self.selectionList.itemSelectionChanged.connect(self.selectedSelectionChanged)
self.addDisplayButton.clicked.connect(self.displayImage)
self.addMNIImageToDisplayList.clicked.connect(self.addMNIImagetoList)
self.addMNIMeshTextToDisplayList.clicked.connect(self.addMNIMeshTexttoList)
def setStatus(self, text):
self.statusLabel.setText(str(text))
def incTaskCounter(self):
self.taskCounter = self.taskCounter + 1
self.setStatus("Tasks in progress : "+str(self.taskCounter))
return self.taskCounter
def decTaskCounter(self):
self.taskCounter = self.taskCounter - 1
self.setStatus("Tasks in progress : "+str(self.taskCounter))
return self.taskCounter
def setTemplate(self, templ):
"""Set the template used as a common referential"""
# Un nom, des données (IRM ?) un identifiant de référentiel pour le refconv ?
self.template = templ
if self.template.referentialAnatomist:
self.a.assignReferential(self.template.referentialAnatomist, self.windows)
if self.template.volumes:
self.displayCombo.clear()
self.displayCombo.addItems([os.path.split(im.fullPath())[1] for im in self.template.volumes])
def displayImage(self):
try:
self.currentImage = self.a.loadObject(self.template.volumes[self.displayCombo.currentIndex()])
self.a.addObjects([self.currentImage], self.windows)
except:
print("Could not add selected image")
pdb.set_trace()
def subjectSelectionChanged(self):
"""Subject selection changed, update electrode list selection"""
selected = [str(item.text()) for item in self.subjectList.selectedItems()]
for i in range(self.electrodeList.count()):
selec = False
for s in selected:
if str(self.electrodeList.item(i).text()).startswith(s):
selec = True
break
self.electrodeList.item(i).setSelected(selec)
def electrodeSelectionChanged(self):
"""Electrode selection changed, update plot list selection"""
selected = [str(item.text()) for item in self.electrodeList.selectedItems()]
for i in range(self.plotList.count()):
selec = False
for s in selected:
if str(self.plotList.item(i).text()).startswith(s):
selec = True
break
self.plotList.item(i).setSelected(selec)
def selectedSelectionChanged(self):
"""In the selected plots list, the selected items changed -> update the view"""
# Update Anatomist selection -> select all meshes for the selected plots
g = self.a.getDefaultWindowsGroup()
g.setSelection([self.meshes[str(item.text())] for item in self.selectionList.selectedItems()])
def plotDataFromFullName(self, name):
"""Get the data from a plot using its full name (e.g. Gre_2014_DUPj : A 2)"""
(sub, elec, plot) = self.plotNameFromFullPlotName(name)
return self.plotsData[sub][elec][plot]
def fullPlotName(self, subj, elec, plot):
"""Compute fully qualified plot name from subject, electrode, plot names"""
return subj + ' : ' + elec + ' ' + plot
def plotNameFromFullPlotName(self, name):
"""Get subject, electrode, plot names from the displayed name Subject : Electrode Plot (e.g. Gre_2014_DUPj : A 2)"""
(sub, elecplot) = name.split(' : ')
(elec, plot) = elecplot.split()
return (sub, elec, plot)
def selectAround(self):
"""Find in the list of selected plots the ones near the linked cursor"""
# Get linked cursor coords (in template referential)
pTempl = list(self.a.linkCursorLastClickedPosition(self.template.referentialAnatomist).items())[:3]
# Get accepted radius
r2 = self.radiusSpin.value()**2
meshes = []
# Compute distance to all selected plots
pdb.set_trace() #need to check if need to take absolute value in x in mni
for i in range(self.selectionList.count()):
fullname = str(self.selectionList.item(i).text())
(sub, elec, plot) = self.plotNameFromFullPlotName(fullname)
coords = self.plotsData[sub][elec][plot][self.template.name][:3]
dist2 = (coords[0]-pTempl[0])**2 + (coords[1]-pTempl[1])**2 + (coords[2]-pTempl[2])**2
# Select item if in range, deselect if not
self.selectionList.item(i).setSelected(dist2 <= r2)
if fullname in self.meshes and dist2 <= r2:
meshes.append(self.meshes[fullname])
# Select them in Anatomist
g = self.a.getDefaultWindowsGroup()
g.setSelection(meshes)
def setSubjects(self, names, diskitems):
"""Sets the list of subjects (and corresponding readdiskitems in the database) for the widget"""
self.subjects = sorted(names)
self.subjItems = diskitems
self.loadImplantations()
self.plotsData = dict([(s, self.getPlotDataFromImplantation(s)) for s in self.subjects])
#remove NonType from plotsData
pat_to_remove = []
for jj,kk in self.plotsData.items():
if kk is None:
#self.plotsData.pop(jj,None)
pat_to_remove.append(jj)
for ii in range(len(pat_to_remove)):
self.plotsData.pop(pat_to_remove[ii],None)
if len(list(self.plotsData.keys())) == 0:
print("No data to show")
return
self.updateUIplots()
def addSelection(self):
"""Add the selected plots/subjects/electrodes to the selection """
current = [str(self.selectionList.item(i).text()) for i in range(self.selectionList.count())] # FIXME pas juste les selected ! Tous les items
new = [str(s.text()) for s in self.plotList.selectedItems() if str(s.text()) not in current]
# Display the new ones
meshes = []
invalid = set()
for n in new:
if self.template.name in self.plotDataFromFullName(n):
mesh = self.displaySphereAt(self.plotDataFromFullName(n)[self.template.name], self.plotDiameter(), self.template.referentialAnatomist, color=(0.0,0.9,0.1,1.0),name = n)
self.meshes[n] = mesh
meshes.append(mesh)
else:
invalid.add(n) # If there, coordinates are not available in the right template referential
if len(invalid) > 0:
print("Some plots were not added to the selection, because normalized coordinates were not available for them")
new = list(set(new) - invalid)
self.selectionList.addItems(new)
self.a.addObjects(meshes, self.windows)
def plotDiameter(self):
"""Returns the diameter of the spheres used to display plots"""
return 2.0
def displaySphereAt(self, center, diameter, referential, color, name = None):
"""Returns a spherical mesh (anatomist object) with color = [1.0,0.0,0.0,1.0] for a red, not transparent sphere"""
mesh = self.a.toAObject(aims.SurfaceGenerator.sphere(aims.Point3df(center[0], center[1], center[2]), diameter, 54))
if name is not None:
mesh.setName(name)
self.a.setMaterial(mesh, diffuse=color)#[color.redF(), color.greenF(), color.blueF(), color.alphaF()] #sortir le setMaterial(diffuse=color) et le mettre à la fin de la boucle for des fonctions qui l'appelle ?
self.a.assignReferential(referential, mesh)
return mesh
def removeSelection(self):
"""Remove plots from the selected list"""
removable = [str(s.text()) for s in self.selectionList.selectedItems()]
meshes = [self.meshes[r] for r in removable if r in self.meshes]
for r in removable:
if r in self.meshes:
del self.meshes[r]
self.a.removeObjects(meshes, self.windows)
self.a.deleteObjects(meshes)
# Reverse loop to remove from the bottom (avoids messing up the index)
for idx in reversed(list(range(self.selectionList.count()))):
if str(self.selectionList.item(idx).text()) in removable:
self.selectionList.takeItem(idx)
def removeNotTRC(self):
"""Remove plots which are not registered in the TRC"""
all_items=[str(self.selectionList.item(i).text()) for i in range(self.selectionList.count())]
#on remplace les ' par des p dans all_items
all_items = [all_items[x].replace("'","p") for x in range(len(all_items))]
full_list_trc=[]
for subj in self.subjects:
#check if exist TRC in DB for this subject
rdi = ReadDiskItem('Raw SEEG recording', 'EEG TRC format' )
di = rdi.findValue({'subject':subj})
if di is not None:
data_micromed = neo.MicromedIO(filename = str(di)).read_segment() #all data
taille=len(data_micromed.analogsignals)
##Normalisation name (between analogsignals' name and plots' name)
number=['01', '02', '03', '04', '05', '06', '07', '08', '09']
noms=[]
for i in range(taille):
name=data_micromed.analogsignals[i].name
name=name.upper()
if name[len(name)-2:] in number:
name=name[:len(name)-2]+name[len(name)-1:]
setattr(data_micromed.analogsignals[i],'name',name)
noms+=[data_micromed.analogsignals[i].name]
noms_remade= [subj + " : " + re.findall('\S+(?<![\d_])',noms[x])[0] + " Plot"+re.findall('\d+',noms[x])[0] for x in range(len(noms)) if len(re.findall('\d+',noms[x])) > 0]
#on remplace les ' par des p dans noms_remade
[full_list_trc.append(noms_remade[x].replace("'","p")) for x in range(len(noms_remade))]
to_keep=[all_items[x] for x in range(len(all_items)) for y in range(len(full_list_trc)) if all_items[x]==full_list_trc[y]]
to_remove=list(set(all_items)-set(to_keep))
meshes = [self.meshes[r] for r in to_remove if r in self.meshes]
for r in to_remove:
if r in self.meshes:
del self.meshes[r]
self.a.removeObjects(meshes, self.windows)
self.a.deleteObjects(meshes)
# Reverse loop to remove from the bottom (avoids messing up the index)
for idx in reversed(list(range(self.selectionList.count()))):
if str(self.selectionList.item(idx).text()) in to_remove:
self.selectionList.takeItem(idx)
def removePlotsLeftRight(self, side):
all_items=[str(self.selectionList.item(i).text()) for i in range(self.selectionList.count())]
to_remove = []
for ii in all_items:
MNI_pos = self.plotDataFromFullName(ii)['MNI']
if side == 'Left':
if MNI_pos[0] >= 0:
to_remove.append(ii)
elif side == 'Right':
if MNI_pos[0] <=0:
to_remove.append(ii)
meshes = [self.meshes[r] for r in to_remove if r in self.meshes]
for r in to_remove:
if r in self.meshes:
del self.meshes[r]
self.a.removeObjects(meshes,self.windows)
self.a.deleteObjects(meshes)
for idx in reversed(list(range(self.selectionList.count()))):
if str(self.selectionList.item(idx).text()) in to_remove:
self.selectionList.takeItem(idx)
def updateUIplots(self):
self.subjectList.clear()
self.subjectList.addItems(self.subjects)
allElecs = sorted([s + ' : ' + el for s in list(self.plotsData.keys()) for el in list(self.plotsData[s].keys())])
self.electrodeList.clear()
self.electrodeList.addItems(allElecs)
allPlots = sorted([self.fullPlotName(s, el, pl) for s in list(self.plotsData.keys()) for el in list(self.plotsData[s].keys()) for pl in list(self.plotsData[s][el].keys()) ])
self.plotList.clear()
self.plotList.addItems(allPlots)
def t1pre2ScannerBased(self, subject):
""" Returns a triplet of Anatomist objects (native T1pre referential, scanner-base T1pre referential, Transformation from T1pre referential to T1pre Scanner-Based referential) """
rdi = ReadDiskItem('Transformation to Scanner Based Referential', 'Transformation matrix', exactType=True,\
requiredAttributes={'modality':'t1mri', 'subject':subject})
allTransf = list (rdi._findValues( {}, None, False ) )
for trsf in allTransf:
if trsf.attributes()['acquisition'].startswith('T1pre'):
print(repr(trsf.attributes()))
srcrDiskItem = self.transfoManager.referential( trsf.attributes()['source_referential'] )
srcr = self.a.createReferential(srcrDiskItem)
dstrDiskItem = self.transfoManager.referential(trsf.attributes()['destination_referential'])
self.t1pre2ScannerBasedId = trsf.attributes()['destination_referential']
dstr = self.a.createReferential(dstrDiskItem)
return (srcr, dstr, self.a.loadTransformation(trsf.fullPath(), srcr, dstr))
return None
def loadImplantations(self):
self.implantations = dict([(s,self.loadImplantation(rdi)) for s,rdi in zip(self.subjects, self.subjItems)])
def loadImplantation(self, rdiSuj):
rdi = ReadDiskItem( 'Electrode implantation', 'Electrode Implantation format')
impl = rdi.findValue(rdiSuj)
if not impl:
print("Cannot find implantation for %s"%rdiSuj.attributes()['subject'])
return {}
if (os.path.exists(str(impl))):
filein = open(str(impl), 'rb')
try:
dic = json.loads(filein.read())
except:
filein.close()
filein = open(str(impl), 'rb')
dic = pickle.load(filein)
filein.close()
#we load eleclabel now if exist
rdi_eleclabel = ReadDiskItem('Electrodes Labels','Electrode Label Format')
impl_label = rdi_eleclabel.findValue(rdiSuj)
if not impl_label:
print(("Cannot find implantation label for %s"%rdiSuj.attributes()['subject']))
pass
else:
if (os.path.exists(str(impl_label))):
filein = open(str(impl_label),"rb")
try:
dic2 = json.loads(filein.read())
except:
filein.close()
filein.open(str(impl_label),"rb")
dic2 = pickle.load(filein)
filein.close()
dic.update({'label':dic2['plots_label']})
return dic
def saveImplantation(self, subj, rdiSubj):
wdi = WriteDiskItem( 'Electrode implantation', 'Electrode Implantation format')
impl = wdi.findValue(rdiSubj)
if impl is None:
print("Could not find electrode implantation file to save to (%s) !"%subj)
return
try:
#import pdb; pdb.set_trace()
fileout = open(impl.fullPath()+'.temporary', 'wb')
content = self.implantations[subj]
content['plotsData-timestamp'] = content['timestamp']
content['plotsData'] = self.plotsData[subj]
fileout.write(json.dumps(content))
#pickle.dump(content, fileout)
fileout.close()
#to modify to json
shutil.move(impl.fullPath()+'.temporary', impl.fullPath())
neuroHierarchy.databases.insertDiskItem( impl, update=True )
except:
print("Exception while writing implantation file for %s"%subj)
traceback.print_exc(file=sys.stdout)
return
def getPlotDataFromImplantation(self, subj):
els = self.subjectElectrodes(subj)
#self.addElectrode(e['name'], e['model'], e['target'], e['entry'], refId)
if 'plotsData' in self.implantations[subj]:
if self.implantations[subj]['plotsData-timestamp'] == self.implantations[subj]['timestamp']:
print("Using pre-recorded plots coordinates for %s"%subj)
return self.implantations[subj]['plotsData']
else:
print("PlotsData timestamp was invalid for %s"%subj)
res = {}
for e in els:
res[e['name']] = self.getPlotsFromElectrode(e, subj)
print(subj)
if "plotsMNI" in list(self.implantations[subj].keys()):
info_plotsMNI = dict(self.implantations[subj]['plotsMNI'])
for kk,vv in res.items():
for ll,ww in res[kk].items():
ww.update({"MNI":info_plotsMNI[kk+"%02d"%int(ll[4:])]})
if "label" in list(self.implantations[subj].keys()):
try:
ww.update({"label":self.implantations[subj]["label"][kk+"%02d"%int(ll[4:])]})
except:
pdb.set_trace()
else:
print("Error MNI Coordinates")
QtGui.QMessageBox.warning(self, "Error", "MNI coordinates haven't been generated for electrode contacts of Subject: {}\nThey have to be generated using locateElectrodes".format(subj))
return
return res
def getPlotsFromElectrode(self, el, subj):
print("Creating electrode model for %s"%subj)
traceback.print_exc(file=sys.stdout)
(nativeRef, sbRef, t1pre2ScannerBased) = self.t1pre2ScannerBased(subj)
(newRef, transf, elecModel) = createElectrode(el['target'], el['entry'], nativeRef, ana=self.a, model = self.electrodeModels[str(el['model'])].fullPath(), dispMode = self.dispMode, dispParams = self.dispParams)
plots = getPlots(elecModel)
pNames = getPlotsNames(elecModel)
return dict([(n, {'internal':plots[n]['center'], 'native':list(transf.transform(plots[n]['center'])), 'Scanner-based':list(t1pre2ScannerBased.transform(transf.transform(plots[n]['center'])))}) for n in pNames])
def getSubjectImplantation(self, subj):
"""Returns electrode implantation data for the subject (dictionary from the elecimplant file)"""
return self.implantations[subj]
def subjectElectrodes(self, subj):
"""Returns the list available electrodes for the subject"""
impl = self.getSubjectImplantation(subj)
if 'electrodes' in impl:
return impl['electrodes']
return []
def subjectElectrodesNames(self, subj):
"""Returns the list of names of available electrodes for the subject"""
return [el['name'] for el in self.subjectElectrodes()]
def subjectPlot(self, subj, electrode):
"""Returns the list of plots of the chosen electrode for the subject"""
impl = self.getSubjectImplantation(subj)
if 'electrodes' in impl:
els = [e for e in impl['electrodes'] if e['name'] == electrode]
if len(els) > 0:
els[0] # (e['name'], e['model'], e['target'], e['entry'], refId)
return None
def getPlotsCoordinates(self, subj, referential = None, electrode = None, plot = None):
"""Returns the coordinates of the centers of all plots of the subject, or only for the given electrodes/plots
If referential is None, coordinates are returned in the native referential (T1pre of the subject)
"""
return None
def updatePlotSelected(self, item = None):
try:
if item is not None:
xyz = self.plotDataFromFullName(str(item.text()))[self.template.name]
# setLinkedCursor uses window referential : must apply transform before setting the position
self.windows[0].moveLinkedCursor(xyz)
else:
print("Error moving the cursor to the contact2")
except Exception as e:
print("Error moving the cursor to the contact")
#pdb.set_trace()
def addMNIImagetoList(self,path_fichier = None):
if path_fichier is None or path_fichier is False:
fichier = QtGui.QFileDialog.getOpenFileName(self, "Opening file: ", "", "(*.nii *.gii *.img *.nii.gz)")
elif not os.path.isfile(path_fichier):
fichier = QtGui.QFileDialog.getOpenFileName(self, "Opening file: ", "", "(*.nii *.gii *.img *.nii.gz)")
else:
fichier = path_fichier
image_mni_ref = self.a.loadObject(self.template.volumes[0])
self.currentImage = self.a.loadObject(str(fichier))
self.a.execute('LoadReferentialFromHeader', objects=[image_mni_ref,self.currentImage])
all_trans = self.a.getTransformations()
trans_from_vols = []
tm=registration.getTransformationManager()
tm.referential(registration.talairachMNIReferentialId)
for vol in (self.currentImage, image_mni_ref):
trans_from_vol = [t for t in all_trans if t.source() == vol.referential and not t.isGenerated()]
# hope trans_from_vol1 contains just one transform
# but if there are several, try to select the one going to
# scanner-based
if len(trans_from_vol) > 1:
trans_from_vol_filt = [t for t in trans_from_vol if t.destination().header()['name'].startswith('Scanner-based anatomical coordinates')]
if len(trans_from_vol_filt) == 1:
trans_from_vol = trans_from_vol_filt
if len(trans_from_vol) == 0:
raise RuntimeError('could not find a non-ambiguous transform')
elif len(trans_from_vol) > 1:
print("There is more than one available transformation ... we take the first one and pray")
trans_from_vol[0] = trans_from_vol_filt[0]
trans_from_vols.append(trans_from_vol)
trans_from_vol1, trans_from_vol2 = trans_from_vols
self.template.volumes.append(str(fichier))
self.a.execute('LoadTransformation',origin=trans_from_vol1[0].destination(),destination=trans_from_vol2[0].destination(),matrix=[0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1])
self.a.addObjects([self.currentImage,], self.windows)
self.displayCombo.addItems([os.path.split(str(fichier))[1]])
def addMNIMeshTexttoList(self):
#ask for a texture gii or a functionalTractography file
texture_info = QtGui.QMessageBox(self)
texture_info.setText("Choose the type of texture format (gii or csv to generate the gii)")
texture_info.setWindowTitle("texture format")
gii_button = texture_info.addButton(QtGui.QPushButton('.gii'),QtGui.QMessageBox.AcceptRole)
csvfuncTract_button =texture_info.addButton(QtGui.QPushButton('.csv functionalTractography'),QtGui.QMessageBox.AcceptRole)
#center_seg.setWindowModality(QtCore.Qt.NonModal)
texture_info.show()
texture_info.exec_()
#reply = texture_info.buttonRole(texture_info.clickedButton())
if str(texture_info.clickedButton().text())=='.gii':
print("texture already gii generated")
fichierTexture = QtGui.QFileDialog.getOpenFileName(self, "Opening texture (corresponding to the mesh): ", "", "(*.gii)")
pdb.set_trace()
elif str(texture_info.clickedButton().text())=='.csv functionalTractography':
print("have to generate the gii texture from the csv data, functionalTractography csv model")
fichierCSV = QtGui.QFileDialog.getOpenFileName(self, "Opening functional tractography data: ", "", "(*.csv)")
if not os.path.isfile(fichierCSV):
print("the file doesn't exist")
full_data = readFunctionalTractography(fichierCSV)
#ask where to save the data
path_to_save = QtGui.QFileDialog.getExistingDirectory(self,'Directory to save the mesh')
BrodmannParcels = aims.read('MNI_Atlases/rbrodmann.nii')
BrodmannParcelsArrayData = BrodmannParcels.arraydata()
left_white = aims.read('MNI_Brainvisa/t1mri/T1pre_1900-1-3/default_analysis/segmentation/mesh/Gre_2016_MNI1_Lwhite.gii')
right_white = aims.read('MNI_Brainvisa/t1mri/T1pre_1900-1-3/default_analysis/segmentation/mesh/Gre_2016_MNI1_Rwhite.gii')
list_remove = set(['Patient', 'Atlas'])
list_condi_max = set(['ValueNb','PeakDelayMed','PeakDelaySTD','Probability'])
list_condi_present = set(full_data.keys())
#condi_intersect = list(list_condi_max & list_condi_present)
condi_intersect = sorted(list(list_condi_present-list_remove), key=lambda s: s.lower())
nb_time = len(condi_intersect)
orderTexture = dict([(i,condi_intersect[i]) for i in range(len(condi_intersect))]) #{0:'ValueNb',1:'PeakDelayMed',2:'PeakDelaySTD',3:'Probability'}
if full_data['Atlas'] == 'MarsAtlas':
#read the marsAtlas parcellation
left_MA = aims.read('MNI_Brainvisa/t1mri/T1pre_1900-1-3/default_analysis/segmentation/mesh/surface_analysis/Gre_2016_MNI1_Lwhite_parcels_marsAtlas.gii')
right_MA = aims.read('MNI_Brainvisa/t1mri/T1pre_1900-1-3/default_analysis/segmentation/mesh/surface_analysis/Gre_2016_MNI1_Rwhite_parcels_marsAtlas.gii')
aims.write(left_white,str(path_to_save) + os.path.sep + 'left_white.gii')
aims.write(right_white,str(path_to_save) + os.path.sep + 'right_white.gii')
try:
os.mkdir(str(path_to_save)+os.path.sep+'Texture')
except:
pass
#faire un test si all au lieu des noms de parcels.
for i_parcels_stimulated in list(full_data[orderTexture[0]].keys()):
if len( full_data[orderTexture[0]][i_parcels_stimulated]) > 0:
new_TimeSurfTextLeft = aims.TimeTexture('FLOAT')
new_TimeSurfTextRight = aims.TimeTexture('FLOAT')
for i in range(nb_time): #assign the value
textnowLeft = new_TimeSurfTextLeft[i]
textnowRight = new_TimeSurfTextRight[i]
textnowLeft.reserve(len(left_white.vertex(0))) #left_white.vertex(0)))
textnowRight.reserve(len(right_white.vertex(0)))
marsatlas_label = readLabels('labels/marsatlas_labels.txt')
#gauche #control lateral lorsqu'étude contro/ipsi
for iter_vert in range(len(left_white.vertex(0))):
#marsatlas_label[left_MA[0].arraydata()[iter_vert]]
#if isinstance(full_data[orderTexture[i]]['L_VCcm'][marsatlas_label[left_MA[0].arraydata()[iter_vert]]], (str, unicode)):
#join right and left or not
#for now we assume that we are using marsatlas
actual_marsatlas_parcels = []
if i_parcels_stimulated.startswith('L_') or i_parcels_stimulated.startswith('R_'):
try:
actual_marsatlas_parcels = [marsatlas_label[left_MA[0].arraydata()[iter_vert]]]
except:
pass #faudrait mieux mettre si c'est == 0 alors c'est un vertex qui n'a pas de correspondance marsatlas.
elif i_parcels_stimulated == 'All':
try:
actual_marsatlas_parcels = [marsatlas_label[left_MA[0].arraydata()[iter_vert]]]
except:
pass #faudrait mieux mettre si c'est == 0 alors c'est un vertex qui n'a pas de correspondance marsatlas.
else:
try:
actual_marsatlas_parcels = [[marsatlas_label[left_MA[0].arraydata()[iter_vert]]][0][2:]]
except:
pass
try:
if left_MA[0].arraydata()[iter_vert] == 0:
textnowLeft.append(-4)
else:
if list(full_data[orderTexture[i]][i_parcels_stimulated].keys())[0].startswith('i_') or list(full_data[orderTexture[i]][i_parcels_stimulated].keys())[0].startswith('c_'):
if full_data[orderTexture[i]][i_parcels_stimulated]['c_'+actual_marsatlas_parcels[0]] == 'NaN':
textnowLeft.append(-4)
else:
textnowLeft.append(float(full_data[orderTexture[i]][i_parcels_stimulated]['c_'+actual_marsatlas_parcels[0]]))
else:
if full_data[orderTexture[i]][i_parcels_stimulated][actual_marsatlas_parcels[0]] == 'NaN':
textnowLeft.append(-4)
else:
textnowLeft.append(float(full_data[orderTexture[i]][i_parcels_stimulated][actual_marsatlas_parcels[0]]))
except:
textnowLeft.append(-4)
#puis droite #ipsi lateral lorsqu'étude contro_ipsi
for iter_vert in range(len(right_white.vertex(0))):
actual_marsatlas_parcels = []
if i_parcels_stimulated.startswith('L_') or i_parcels_stimulated.startswith('R_'):
try:
actual_marsatlas_parcels = [marsatlas_label[right_MA[0].arraydata()[iter_vert]]]
except:
pass
elif i_parcels_stimulated == 'All':
try:
actual_marsatlas_parcels = [marsatlas_label[right_MA[0].arraydata()[iter_vert]]]
except:
pass #faudrait mieux mettre si c'est == 0 alors c'est un vertex qui n'a pas de correspondance marsatlas.
else:
try:
actual_marsatlas_parcels = [[marsatlas_label[right_MA[0].arraydata()[iter_vert]]][0][2:]]
except:
pass
try:
if right_MA[0].arraydata()[iter_vert] == 0:
textnowRight.append(-4)
else:
if list(full_data[orderTexture[i]][i_parcels_stimulated].keys())[0].startswith('i_') or list(full_data[orderTexture[i]][i_parcels_stimulated].keys())[0].startswith('c_'):
if full_data[orderTexture[i]][i_parcels_stimulated]['i_'+actual_marsatlas_parcels[0]] == 'NaN':
textnowRight.append(-4)
else:
textnowRight.append(float(full_data[orderTexture[i]][i_parcels_stimulated]['i_'+actual_marsatlas_parcels[0]]))
else:
if full_data[orderTexture[i]][i_parcels_stimulated][actual_marsatlas_parcels[0]] == 'NaN':
textnowRight.append(-4)
else:
textnowRight.append(float(full_data[orderTexture[i]][i_parcels_stimulated][actual_marsatlas_parcels[0]]))
except:
textnowRight.append(-4)
aims.write(new_TimeSurfTextLeft,str(path_to_save) + os.path.sep + 'Texture' + os.path.sep + ('%s_left.gii')%i_parcels_stimulated)
aims.write(new_TimeSurfTextRight,str(path_to_save) + os.path.sep + 'Texture' + os.path.sep + ('%s_right.gii')%i_parcels_stimulated)
else:
print((('No Data for %s')%i_parcels_stimulated))
obj1 = self.a.loadObject('MNI_Brainvisa/t1mri/T1pre_1900-1-3/default_analysis/segmentation/mesh/Gre_2016_MNI1_Lwhite.gii')
obj2 = self.a.loadObject('MNI_Brainvisa/t1mri/T1pre_1900-1-3/default_analysis/segmentation/mesh/surface_analysis/Gre_2016_MNI1_Lwhite_parcels_marsAtlas.gii')
obj1.loadReferentialFromHeader()
obj2.setPalette(palette = 'marsatlas')
MarsAtlas_fusion_obj = self.a.fusionObjects([obj1, obj2], method='FusionTexSurfMethod')
self.a.addObjects(MarsAtlas_fusion_obj,self.axWindow)
self.currentImage = [obj1,obj2]
if full_data['Atlas'] == 'Brodmann':
voxel_size_T1 = [BrodmannParcels.getVoxelSize()[0], BrodmannParcels.getVoxelSize()[1], BrodmannParcels.getVoxelSize()[2], 1.0]
sizeOutputnii = BrodmannParcels.getSize().list()
sizeOutputnii[-1] = len(list(orderTexture.keys()))
for i_parcels_stimulated in list(full_data[orderTexture[0]].keys()):
#di.setMinf('ColorPalette','Blue-Red-fusion')
volToGenerate = aims.Volume(*sizeOutputnii,dtype = 'float')
volToGenerate.header()['voxel_size']=voxel_size_T1
volToGenerate.fill(-4)
for i_texture in list(orderTexture.keys()):
for i_parcels_result in list(full_data[orderTexture[i_texture]][i_parcels_stimulated].keys()):
#fait chier le droite gauche
if full_data[orderTexture[0]][i_parcels_stimulated][i_parcels_result]=='NaN':
volToGenerate.arraydata()[numpy.where(BrodmannParcelsArrayData==float(i_parcels_result))]=-4
else:
volToGenerate.arraydata()[numpy.where(BrodmannParcelsArrayData==float(i_parcels_result))]=full_data[orderTexture[i_texture]][i_parcels_stimulated][i_parcels_result]
aims.write(volToGenerate,str(path_to_save)+os.path.sep+'%s.nii'%(str(int(float(i_parcels_stimulated)))))
print("done")
pdb.set_trace()
def doubleClickedFunctionalTractography(self):
pdb.set_trace()
def generateStatisticsContacts(self):
#get selected contacts
current = [str(self.selectionList.item(i).text()) for i in range(self.selectionList.count())]
#il me faut un dictionnaire avec toutes les parcels mars atlas et un dictionnaire avec toutes les parcels freesurfer possible.
dict_marsatlas = {}
dict_freesurfer = {}
dict_dispersion_MA = {}
dict_dispersion_FS = {}
parcels_names = readLabels('labels/marsatlas_labels.txt')
freesurfer_parcel_names = readLabels('labels/freesurfer_labels.txt')
missing_marsatlas = []
missing_freesurfer = []
all_patients = []
dict_MNI_PatientName = {}
for ii in list(parcels_names.values()):
dict_marsatlas.update({ii:[]})
dict_dispersion_MA.update({ii:{}})
for ii in list(freesurfer_parcel_names.values()):
dict_freesurfer.update({ii:[]})
dict_dispersion_FS.update({ii:{}})
#je parcours toutes les "current", je regarde leur parcels et j'ajoute la position mni à la list de cette parcels.
for ii in current:
(sub, elec, plot) = self.plotNameFromFullPlotName(ii)
dataplot = self.plotDataFromFullName(ii)
if 'MarsAtlas' in list(dataplot['label'].keys()):
if dataplot['label']['MarsAtlas'][1] != 'not in a mars atlas parcel':
dict_marsatlas[dataplot['label']['MarsAtlas'][1]].append(dataplot['MNI'])
else:
#signaler que certains patients n'ont pas marsatlas de généré et que ça va "fausser" les résultats
#print("plot %s without marsAtlas parcellation estimated"%(ii))
if sub not in missing_marsatlas:
missing_marsatlas.append(sub)
if 'Freesurfer' in list(dataplot['label'].keys()):
if dataplot['label']['Freesurfer'][1] != 'not in a freesurfer parcel':
try:
dict_freesurfer[dataplot['label']['Freesurfer'][1]].append(dataplot['MNI'])
except:
print(sub)
print("probleme avec ce patient")
pass
else:
#signaler que certains patient n'ont pas freesurfer de généré et que ça va "fausser" les résultats
#print("plot %s without FreeSurfer parcellation estimated"%(ii))
if sub not in missing_freesurfer:
missing_freesurfer.append(sub)
if sub not in all_patients:
all_patients.append(sub)
dict_MNI_PatientName.update({str(dataplot['MNI']):sub})
#now I calculate the dispersion per parcels: (and I'ld like to normalized it but don't know how)
for iter_MA in list(dict_marsatlas.keys()):
points_array = numpy.array(dict_marsatlas[iter_MA])
#array_median = repmat(numpy.median(points_array,axis=0),len(points_array),1)
#diff = points_array - array_median
#list_dist_median = sc_sp.distance.cdist([numpy.median(points_array,axis=0)],points_array)
if len(points_array)>1:
dict_dispersion_MA[iter_MA].update({'nb contact':len(dict_marsatlas[iter_MA]),'average point':numpy.mean(points_array,axis=0),'median point':numpy.median(points_array,axis=0)})
found_outlier = self.is_outlier(points_array,thresh = 3)
pos_outlier = numpy.where(found_outlier==True)
if len(pos_outlier[0]) == 0:
dict_dispersion_MA[iter_MA].update({'outlier position':None})
else:
#[dict_MNI_PatientName[str(points_array[pos_outlier[0]][i].tolist())] for i in range(len(points_array[pos_outlier[0]]))]
dict_dispersion_MA[iter_MA].update({'outlier position':points_array[pos_outlier[0]]})
dict_dispersion_MA[iter_MA].update({'outlier name':[dict_MNI_PatientName[str(points_array[pos_outlier[0]][i].tolist())] for i in range(len(points_array[pos_outlier[0]]))]})
elif len(points_array)==1:
dict_dispersion_MA[iter_MA].update({'nb contact': 1,'average point':numpy.mean(points_array,axis=0),'median point':numpy.median(points_array,axis=0)})
else:
dict_dispersion_MA[iter_MA].update({'nb contact': 0})
for iter_FS in list(dict_freesurfer.keys()):
points_array = numpy.array(dict_freesurfer[iter_FS])
#array_median = repmat(numpy.median(points_array,axis=0),len(points_array),1)
#diff = points_array - array_median
#list_dist_median = sc_sp.distance.cdist([numpy.median(points_array,axis=0)],points_array)
if len(points_array)>1:
dict_dispersion_FS[iter_FS].update({'nb contact':len(dict_freesurfer[iter_FS]),'average point':numpy.mean(points_array,axis=0),'median point':numpy.median(points_array,axis=0)})
found_outlier = self.is_outlier(points_array,thresh = 3)
pos_outlier = numpy.where(found_outlier==True)
if len(pos_outlier[0]) == 0:
dict_dispersion_FS[iter_FS].update({'outlier position':None})
else:
dict_dispersion_FS[iter_FS].update({'outlier position':points_array[pos_outlier[0]]})
dict_dispersion_FS[iter_FS].update({'outlier name':[dict_MNI_PatientName[str(points_array[pos_outlier[0]][i].tolist())] for i in range(len(points_array[pos_outlier[0]]))]})
elif len(points_array)==1:
dict_dispersion_FS[iter_FS].update({'nb contact':1,'average point':numpy.mean(points_array,axis=0),'median point':numpy.median(points_array,axis=0)})
else:
dict_dispersion_FS[iter_FS].update({'nb contact':0})
#ecrire le tout dans un csv
fileName = QtGui.QFileDialog.getSaveFileName(self, 'Dialog Title', '/', '*.csv') #str(QtGui.QFileDialog.getExistingDirectory(self, "Select Directory"))
fileName=str(fileName)
testcsv = fileName.split('.')
if len(testcsv)>0:
if testcsv[1] != 'csv':
print("error, the extension should be .csv")
return
else:
fileName = fileName + '.csv'
with open(fileName, 'w') as csvfile:
writer = csv.writer(csvfile, delimiter='\t')
writer.writerow(['Group Analysis'])
listwrite_allpatients = ['Patients']
for ii in all_patients:
listwrite_allpatients.append(ii)
writer.writerow(listwrite_allpatients) #writer.writerow([u'Patients',all_patients])
listwrite_missingMA = ['missing marsAtlas info for the following patients (analysis done without their data):']
for ii in missing_marsatlas:
listwrite_missingMA.append(ii)
writer.writerow(listwrite_missingMA)#writer.writerow([u'missing marsAtlas info for the following patients (analysis done without their data):',missing_marsatlas])
listwrite_missingFS = ['missing FreeSurfer info for the following patients (analysis done without their data):']
for ii in missing_freesurfer:
listwrite_missingFS.append(ii)
writer.writerow(listwrite_missingFS)
#writer.writerow([u'missing FreeSurfer info for the following patients (analysis done without their data):',missing_freesurfer])
writer.writerow(['MarsAtlas parcellation analysis'])
writer.writerow(['parcel name','nb contact', 'average point', 'median point', 'outliers positions','patient names of the outliers'])
dictMA_sorted_tmp = OrderedDict(sorted(dict_dispersion_MA.items()))
for kk,vv in dictMA_sorted_tmp.items():
if vv['nb contact']>0:
listwrite = [kk]
listwrite.append(vv['nb contact'])
if 'average point' in list(vv.keys()):
listwrite.append([float(format(vv['average point'][i],'.3f')) for i in range(3)])
listwrite.append([float(format(vv['median point'][i],'.3f')) for i in range(3)])
if 'outlier position' in list(vv.keys()):
if vv['outlier position'] is not None:
listwrite.append(vv['outlier position'])
listwrite.append(vv['outlier name'])
writer.writerow(listwrite)
#writer.writerow(dict_dispersion_MA)
writer.writerow([])
writer.writerow(['Freesurfer parcellation analysis'])
writer.writerow(['parcel name','nb contact', 'average point', 'median point', 'outliers positions','patient names of the outliers'])
dictFS_sorted_tmp = OrderedDict(sorted(dict_dispersion_FS.items()))
for kk,vv in dictFS_sorted_tmp.items():
if vv['nb contact']>0:
listwrite = [kk]
listwrite.append(vv['nb contact'])
if 'average point' in list(vv.keys()):
listwrite.append([float(format(vv['average point'][i],'.3f')) for i in range(3)])
listwrite.append([float(format(vv['median point'][i],'.3f')) for i in range(3)])
if 'outlier position' in list(vv.keys()):
if vv['outlier position'] is not None:
listwrite.append(vv['outlier position'])
listwrite.append(vv['outlier name'])
writer.writerow(listwrite)
print("csv done")
def is_outlier(self,list_points, thresh=3):
#to be changed to the MAD
if len(list_points.shape) == 1:
list_points = list_points[:,None]
median = numpy.median(list_points, axis=0)
diff = numpy.sum((list_points - median)**2, axis=-1)
diff = numpy.sqrt(diff)
med_abs_deviation = numpy.median(diff)
modified_z_score = 0.6745 * diff / med_abs_deviation
return modified_z_score > thresh
def AddMAparcels2selection(self):
print("select contacts according to marsatlas parcels")
fullPlot_List = [str(self.plotList.item(idx).text()) for idx in range(self.plotList.count())]
parcels_names = readLabels('labels/marsatlas_labels.txt')
dict_plotMA = {}
for ii in list(parcels_names.values()):
dict_plotMA.update({ii:[]})
for ii in fullPlot_List:
(sub, elec, plot) = self.plotNameFromFullPlotName(ii)
dataplot = self.plotDataFromFullName(ii)
if 'MarsAtlas' in list(dataplot['label'].keys()):
if dataplot['label']['MarsAtlas'][1] != 'not in a mars atlas parcel':
dict_plotMA[dataplot['label']['MarsAtlas'][1]].append(ii)
if str(self.AddMAparcels2SelectioncomboBox.currentText()) =='*':
print('unselect all')
for i in range(self.plotList.count()):
selec = False
self.plotList.item(i).setSelected(selec)
else:
list_plot2select= dict_plotMA[str(self.AddMAparcels2SelectioncomboBox.currentText())]
for i in range(self.plotList.count()):
selec = False
if str(self.plotList.item(i).text()) in list_plot2select:
selec = True
self.plotList.item(i).setSelected(selec)
def changeBothRightDisplay(self):
all_items=[str(self.selectionList.item(i).text()) for i in range(self.selectionList.count())]
refBothHemi = self.template.referentialAnatomist
newRef = self.a.createReferential()
transf = self.a.createTransformation([0,0,0,-1,0,0,0,1,0,0,0,1], origin = newRef, destination = refBothHemi)
meshesLeft = []
for ii in all_items:
MNI_pos = self.plotDataFromFullName(ii)['MNI']
if MNI_pos[0] >=0:
#on fait classique
pass
elif MNI_pos[0] < 0:
if self.radioButtonbothHemi.isChecked():
meshesLeft.append(ii)
self.a.assignReferential(refBothHemi,self.meshes[ii])