def bundleFlatHierarchy(bundlefile,
hiefile,
color_palette='colors256',
anat=None):
import anatomist.api as anatomist_api
if anat == None:
anat = anatomist_api.Anatomist()
locals_var = locals()
#read all bundles names
bunlist = []
ns = dict()
exec(compile(open(bundlefile).read(), bundlefile, 'exec'), ns)
bunlist = ns['attributes']['bundles']
bundles_num = len(bunlist) / 2
labels = []
for i in range(bundles_num):
ind = i * 2
labels.append(bunlist[ind])
#create hierarchy
pl = anat.palettes()
pal = pl.find(color_palette)
color = pal.get()
colorlist = []
ind_color = 0
h = aims.Hierarchy()
h.setSyntax('hierarchy')
h['graph_syntax'] = 'RoiArg'
all_tree = aims.Tree(True, 'fold_name')
all_tree['name'] = 'all'
h.insert(all_tree)
for i in range(len(labels)):
indcol = ind_color % 256
col = color.value(indcol)
vcol = [col.red(), col.green(), col.blue()]
tree = aims.Tree(True, 'fold_name')
tree['color'] = vcol
tree['name'] = labels[i]
all_tree.insert(tree)
ind_color = ind_color + 1
#store hierarchy
aims.write(h, hiefile)
def bundleFlatHierarchyByNameList(names,
labels,
hiefile,
color_palette='colors256',
anat=None):
import anatomist.api as anatomist_api
if anat == None:
anat = anatomist_api.Anatomist()
#create hierarchy
pl = anat.palettes()
pal = pl.find(color_palette)
color = pal.get()
colorlist = []
ind_color = 0
h = aims.Hierarchy()
h.setSyntax('hierarchy')
h['graph_syntax'] = 'RoiArg'
all_tree = aims.Tree(True, 'fold_name')
all_tree['name'] = 'all'
h.insert(all_tree)
labelnum = len(names)
for i in range(labelnum):
tree = aims.Tree(True, 'fold_name')
tree['name'] = names[i]
all_tree.insert(tree)
llist = labels[i]
indcol = ind_color % 256
col = color.value(indcol)
vcol = [col.red(), col.green(), col.blue()]
tree['color'] = vcol
for bun in llist:
ntree = aims.Tree(True, 'fold_name')
ntree['name'] = bun
tree.insert(ntree)
ind_color = ind_color + 1
#store hierarchy
aims.write(h, hiefile)
def fusion2D_map_activation(
dic_maps,
template_file="/i2bm/local/spm12/canonical/single_subj_T1.nii",
dic_templates="",
threshold=3.1,
maxval=10,
orientation='Axial',
cursor=[0, 0, 0, 0],
block=False):
""" Function to merge one or many activation maps with a the
single_subj_T1.nii template of spm8. The defaults values are
"Rainbow1-fusion" palette set to minVal = 1.
Please use the interface to change values.
**Parameters :**
* dict_maps: dictionnary: {'name_fusion': map_file}
**Outputs :**
* launch Anatomist software with all activation map merged with the
template
"""
# initialize Anatomist
a = ana.Anatomist()
#load the mni referential
mni_ref = a.mniTemplateRef
liste_ref = a.getReferentials()
dic_templates_fusion = {}
if len(dic_templates) == 0:
template = a.loadObject(template_file)
#template = a.loadObject(file_name, objectName=objectName )
# load information from header for the template
template.loadReferentialFromHeader()
# load referentials
ref_template = template.getReferential()
tmp_template = 'truth for ' + os.path.basename(template_file)
liste_ref = a.getReferentials()
for ref in liste_ref:
dict_ref = ref.getInfos()
if 'name' in dict_ref.keys():
# if dict_ref['name'] == 'Talairach-MNI template-SPM':
# ref_mni_spm = ref
if dict_ref['name'].find(tmp_template) != -1:
ref_template = ref
a.loadTransformation([0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1],
ref_template, mni_ref)
#dic_templates_fusion[name_contrast] = template
dic_templates_fusion["template"] = template
if len(dic_templates) == len(dic_maps):
for name_contrast, file_name in dic_templates.items():
#template
objectName = "wanatFor_" + name_contrast
#template = a.loadObject(file_name)
template = a.loadObject(file_name, objectName=objectName )
# load information from header for the template
template.loadReferentialFromHeader()
# load referentials
ref_template = template.getReferential()
tmp_template = 'truth for ' + os.path.basename(objectName)
liste_ref = a.getReferentials()
for ref in liste_ref:
dict_ref = ref.getInfos()
if 'name' in dict_ref.keys():
# if dict_ref['name'] == 'Talairach-MNI template-SPM':
# ref_mni_spm = ref
if dict_ref['name'].find(tmp_template) != -1:
ref_template = ref
a.loadTransformation([0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1],
ref_template, mni_ref)
dic_templates_fusion[name_contrast] = template
else:
warnings.warn('The number of templates and constrasts are differents.')
fusion_list = []
window_list = []
if block:
#compute the number of columns which are needed to display in block
ncol=2
nrow=1
nimg=len(dic_maps)
print("NB IMG", nimg)
def nb_col(ncol, nrow, nimg):
ncases = ncol*nrow
while ncases < nimg :
if (ncol - nrow == 1):
ncol = ncol + 1
else :
nrow = nrow + 1
ncases = ncol*nrow
return ncol, nrow
if nimg == 1:
new_ncol = 1
else:
new_ncol, new_row = nb_col(ncol, nrow, nimg)
block=a.createWindowsBlock(new_ncol, new_row)
#loop on maps
for fusion_name, file_name in dic_maps.items():
map_activation = a.loadObject(file_name, fusion_name)
# load identity transformtion to mni_SPM
tmp_map = 'truth for ' + os.path.basename(fusion_name)
# load information from header
map_activation.loadReferentialFromHeader()
# load referentials
ref_map_activation = map_activation.getReferential()
liste_ref = a.getReferentials()
for ref in liste_ref:
dict_ref = ref.getInfos()
if 'name' in dict_ref.keys():
# if dict_ref['name'] == 'Talairach-MNI template-SPM':
# ref_mni_spm = ref
# if dict_ref['name'].find(tmp_template) != -1:
# ref_template = ref
if dict_ref['name'].find(tmp_map) != -1:
ref_map_activation= ref
a.loadTransformation([0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1],
ref_map_activation, mni_ref)
#palette and options
map_activation.setPalette("Rainbow1-fusion",
minVal=threshold,
maxVal=maxval,
absoluteMode=True)
# create the fusion object
if dic_templates_fusion.has_key("template"):
fusion_map = a.fusionObjects([map_activation,
dic_templates_fusion["template"]],
'Fusion2DMethod')
else:
fusion_map = a.fusionObjects([map_activation,
dic_templates_fusion[fusion_name]],
'Fusion2DMethod')
fusion_list.append(fusion_map)
if block:
window_list.append(a.createWindow(orientation, block=block))
else:
window_list.append(a.createWindow(orientation))
#a.linkWindows(window_list, 1)
#a.AWindowsBlock(nbCols=4,)
for w,f in zip(window_list, fusion_list) :
# show the fusion
f.setReferential(mni_ref)
w.assignReferential(mni_ref)
w.moveLinkedCursor(cursor)
a.addObjects(f, w)
#changement de position du curseur
a.execute('LinkedCursor',
object=f,
mode="linear_on_defined",
rate=0.5)
# start loop
sys.exit(app.exec_())
def fusion3D_map_activation(list_maps, template_file="", path_mesh=""):
template_file = "/neurospin/unicog/protocols/IRMf/Tests_Isa/template/avg152T1.nii"
path_mesh = "/neurospin/unicog/protocols/IRMf/Tests_Isa/template/mni152_05_brain.mesh"
# initialize Anatomist
a = ana.Anatomist()
# load the mni referential
mni_ref = a.mniTemplateRef
liste_ref = a.getReferentials()
dic_templates_fusion = {}
# load avg152_T1
template = a.loadObject(template_file)
template.loadReferentialFromHeader()
# load referentials
ref_template = template.getReferential()
tmp_template = 'truth for ' + os.path.basename(template_file)
#load the mesh and assign the T1 referential
path_mesh = "/neurospin/unicog/protocols/IRMf/Tests_Isa/template/mni152_05_brain.mesh"
template_mesh = a.loadObject(path_mesh)
template_mesh.assignReferential(ref_template)
dic_templates_fusion["template"] = template_mesh
def create_palette(colors, name_palette):
palette = a.createPalette(name_palette)
palette.setColors(colors)
return palette
fusion_list = []
window_list = []
list_map_activation = []
color_list = [[255, 255, 0], [255, 153, 18], [128, 42, 42], [0, 255, 0],
[61, 145, 64], [50, 205, 50], [106, 90, 205], [19, 795, 0],
[0, 10, 12], [0, 31, 37], [255, 64, 64], [0, 0, 255],
[240, 35, 100], [74, 64, 250], [200, 150, 100]]
cpt_color = 0
#loop on maps to set correctly the referential
for file_name in list_maps:
map_activation = a.loadObject(file_name)
# load identity transformtion to mni_SPM
tmp_map = 'truth for ' + os.path.basename(file_name)
# load information from header
map_activation.loadReferentialFromHeader()
# load referentials
ref_map_activation = map_activation.getReferential()
liste_ref = a.getReferentials()
for ref in liste_ref:
dict_ref = ref.getInfos()
if 'name' in dict_ref.keys():
if dict_ref['name'].find(tmp_map) != -1:
ref_map_activation = ref
a.loadTransformation([0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1],
ref_map_activation, mni_ref)
#palette and options
name_palette = "palette_" + str(cpt_color)
colors = [255, 255, 255] * 1 + color_list[cpt_color] * 179
pal = create_palette(colors, name_palette)
cpt_color += 1
map_activation.setPalette(pal)
#list of map_activation for the Fusion2DMethod
list_map_activation.append(map_activation)
# Fusion2DMethod for all maps
fusion_map = a.fusionObjects(list_map_activation, 'Fusion2DMethod')
# for each map, create a 3DFusion
fusion_list = []
window_list = []
for map_activation in list_map_activation:
fusion_mesh = a.fusionObjects([map_activation, template_mesh],
'Fusion3DMethod')
fusion_list.append(fusion_mesh)
#now create a multitexture
fusion_multiTexture = a.fusionObjects(fusion_list,
'FusionMultiTextureMethod')
fusion_multiTexture_mesh = a.fusionObjects(
[fusion_multiTexture, template_mesh], 'FusionTexSurfMethod')
w3D_multiTexture_mesh = a.createWindow('3D')
w3D_multiTexture_mesh.assignReferential(mni_ref)
a.addObjects(fusion_multiTexture_mesh, w3D_multiTexture_mesh)
#Fusion3DMethod for all maps + T1
fusion_t1 = a.fusionObjects([fusion_map, template], 'Fusion2DMethod')
#Put the Fusion3D into a 3D window
w_ax = a.createWindow('Axial')
w_ax.assignReferential(mni_ref)
a.addObjects(fusion_t1, w_ax)
file_name = '/neurospin/unicog/protocols/IRMf/Tests_Isa/Test_nitime/test_data_antonio/ROIs_analyses/IFGorb_Pallier_2011.nii'
a = a.loadObject(file_name)
# start loop
sys.exit(app.exec_())
path_mesh = VISU_MESH
mesh = aims.read(path_mesh)
path_pre_roi = os.path.join(DIR_ROIS, subject + '_' + side + '_' + 'precentral.gii')
path_post_roi = os.path.join(DIR_ROIS, subject + '_' + side + '_' + 'postcentral.gii')
path_pre_crest = os.path.join(DIR_CRESTS, VISU_ID + '_' + 'precentral_cleaned.gii')
path_post_crest = os.path.join(DIR_CRESTS, VISU_ID + '_' + 'postcentral_cleaned.gii')
path_pre_line = os.path.join(DIR_CRESTS, VISU_ID + '_' + 'precentral_cleaned.mesh')
path_post_line = os.path.join(DIR_CRESTS, VISU_ID + '_' + 'postcentral_cleaned.mesh')
df = pd.read_csv(PPFM)
pp_pre_index = int(df.loc[(df['Subject'] == int(subject)) & (df['Hemisphere'] == side), 'PP_Pre_Index_Brain'])
pp_post_index = int(df.loc[(df['Subject'] == int(subject)) & (df['Hemisphere'] == side), 'PP_Post_Index_Brain'])
pp_pre_sphere = index_to_sphere(mesh, pp_pre_index)
pp_post_sphere = index_to_sphere(mesh, pp_post_index)
a = anatomist.Anatomist()
# remove anatomist cursor in all windows
a.config()['linkedCursor'] = 0
# custom palettes
orange_palette = a.createPalette('PreCG')
orange_palette.setColors(colors=orange_colors)
purple_palette = a.createPalette('PoCG')
purple_palette.setColors(colors=purple_colors)
# create a windows for each sub-component of the figure
mesh_with_cs = a.createWindow('3D')
cs_landmarks = a.createWindow('3D')
cs_rois = a.createWindow('3D')
raw_tractogram = a.createWindow('3D')
filtered_tractogram = a.createWindow('3D')
def bundleFlatHierarchyByToken(bundlefile,
hiefile,
token_index=0,
delimiter='_',
color_palette='colors256',
anat=None):
import anatomist.api as anatomist_api
if anat == None:
anat = anatomist_api.Anatomist()
#read all bundles names
bunlist = []
ns = dict()
exec(compile(open(bundlefile).read(), bundlefile, 'exec'), ns)
bunlist = ns['attributes']['bundles']
bundles_num = len(bunlist) / 2
labels = []
for i in range(bundles_num):
ind = i * 2
labels.append(bunlist[ind])
#create groups
groups = dict()
for lab in labels:
group = lab.split(delimiter)[token_index]
if (group in groups) == False:
groups[group] = []
gr = groups[group]
gr.append(lab)
#create hierarchy
pl = anat.palettes()
pal = pl.find(color_palette)
color = pal.get()
colorlist = []
ind_color = 0
h = aims.Hierarchy()
h.setSyntax('hierarchy')
h['graph_syntax'] = 'RoiArg'
all_tree = aims.Tree(True, 'fold_name')
all_tree['name'] = 'all'
h.insert(all_tree)
for key in list(groups.keys()):
tree = aims.Tree(True, 'fold_name')
tree['name'] = key
all_tree.insert(tree)
group = groups[key]
for bun in group:
indcol = ind_color % 256
col = color.value(indcol)
vcol = [col.red(), col.green(), col.blue()]
ntree = aims.Tree(True, 'fold_name')
ntree['color'] = vcol
ntree['name'] = bun
tree.insert(ntree)
ind_color = ind_color + 1
#store hierarchy
aims.write(h, hiefile)
def do_mesh_cluster_rendering(
title,
clust_mesh_file,
clust_texture_file,
brain_mesh_file,
anat_file,
palette_file="palette_signed_values_blackcenter",
#a = None,
check=False,
verbose=True):
""" Vizualization of signed stattistic map or weigths.
Parameters
----------
clust_mesh_file : str (mandatory)
a mesh file of interest.
clust_texture_file : str (mandatory)
an IMAGE from which to extract texture (for the mesh file).
brain_mesh_file : str (mandatory)
a mesh file of the underlying neuroanatomy.
anat_file : str (mandatory)
an IMAGE of the underlying neuroanatomy.
check : bool (default False)
manual check of the input parameters.
verbose : bool (default True)
print to stdout the function prototype.
Returns
-------
None
"""
#FunctionSummary(check, verbose)
global ANATOMIST
global VIEWS
global IMAGE
# instance of anatomist
if ANATOMIST is None:
ANATOMIST = anatomist.Anatomist()
# Add new SnapshotControl button
pix = QtGui.QPixmap('control.xpm')
anatomist.cpp.IconDictionary.instance().addIcon('Snap', pix)
ad = anatomist.cpp.ActionDictionary.instance()
ad.addAction('SnapshotAction', lambda: SnapshotAction())
cd = anatomist.cpp.ControlDictionary.instance()
cd.addControl('Snap', lambda: SnapshotControl(), 25)
cm = anatomist.cpp.ControlManager.instance()
cm.addControl('QAGLWidget3D', '', 'Snap')
a = ANATOMIST
# ------------
# load objects
# ------------
clust_mesh = a.loadObject(clust_mesh_file)
brain_mesh = a.loadObject(brain_mesh_file)
clust_texture = a.loadObject(clust_texture_file)
IMAGE = aims.Reader().read(clust_texture_file)
image_dim = IMAGE.header()['volume_dimension'].arraydata()[:3]
"""
cd /home/ed203246/mega/data/mescog/wmh_patterns/summary/cluster_mesh/tvl1l20001
from soma import aims
clust_texture_file = "clust_values.nii.gz"
IMAGE = aims.Reader().read(clust_texture_file)
image_dim = IMAGE.header()['volume_dimension'].arraydata()[:3]
trm_ima_mm2ref_mm = aims.Motion(ima.header()['transformations'][0])
trm_xyz_to_mm = ima_get_trm_xyz_to_mm(ima)
min_mm = trm_ima_mm2ref_mm.transform([0, 0, 0])
#a=ima.header()['volume_dimension']
ima_dim_xyz = ima.header()['volume_dimension'].arraydata()[:3]
max_mm = trm_ima_mm2ref_mm.transform(ima_dim_xyz)
trm_ima_mm2ref_mm = aims.Motion(ima.header()['transformations'][0])
ima_voxel_size = ima.header()['voxel_size'][:3]
xyz_to_mm(trm_ima_mm2ref_mm, ima_voxel_size)
"""
#print clust_texture.header()#['volume_dimension']
a_anat = a.loadObject(anat_file)
# mesh option
material = a.Material(diffuse=[0.8, 0.8, 0.8, 0.6])
brain_mesh.setMaterial(material)
# change palette
#palette_file = get_sample_data("brainvisa_palette").edouard
# bv_rgb_dir = os.path.join(os.environ["HOME"], ".anatomist", "rgb")
# if not os.path.isdir(bv_rgb_dir):
# os.makedirs(bv_rgb_dir)
# bv_rgb_file = os.path.join(bv_rgb_dir,
# os.path.basename(palette_file))
# if not os.path.isfile(bv_rgb_file):
# shutil.copyfile(palette_file, bv_rgb_file)
palette = a.getPalette(palette_file)
clust_texture.setPalette(palette, minVal=-10, maxVal=10, absoluteMode=True)
# view object
block = a.createWindowsBlock(2)
windows = list()
objects = list()
# --------------------------------
# 3D view = fusion 3D + brain_mesh
# --------------------------------
win3d = a.createWindow("3D", block=block)
# orientation front left
Q_rot = [np.cos(np.pi / 8.), np.sin(np.pi / 8.), 0, 0]
fusion3d = a.fusionObjects([clust_mesh, clust_texture], "Fusion3DMethod")
win3d.addObjects([fusion3d, brain_mesh])
a.execute("Fusion3DParams",
object=fusion3d,
step=0.1,
depth=5.,
sumbethod="max",
method="line_internal")
#define point of view
Q1 = win3d.getInfos()["view_quaternion"] # current point of view
Q = product_quaternion(Q1, Q_rot)
a.execute("Camera", windows=[win3d], view_quaternion=Q)
windows.append(win3d)
objects.append(fusion3d)
VIEWS[(title, "3d")] = [win3d, fusion3d]
# -------------------------------------
# Slices view = three VIEWS offusion 2D
# -------------------------------------
# fusion 2D
fusion2d = a.fusionObjects([a_anat, clust_texture], "Fusion2DMethod")
a.execute("Fusion2DMethod", object=fusion2d)
# change 2D fusion settings
a.execute("TexturingParams",
texture_index=1,
objects=[
fusion2d,
],
mode="Geometric") #, rate=0.1)
##############
# Coronal view
win_coronal = a.createWindow("3D", block=block)
print "win_coronal", win_coronal
# Fusion cut mesh
fusionmesh_coronal = a.fusionObjects([brain_mesh, fusion2d],
"FusionCutMeshMethod")
a.execute("FusionCutMeshMethod", object=fusionmesh_coronal)
fusionmesh_coronal.addInWindows(win_coronal)
# Coronal view
# Slice
#sclice_coronal(fusionmesh_coronal, -90)
sclice_coronal(fusionmesh_coronal, int(-(image_dim / 2)[1]))
# Store
windows.append(win_coronal)
#a.execute
objects.append([fusion2d, fusionmesh_coronal])
VIEWS[(title, "coronal")] = [win_coronal, fusionmesh_coronal]
##############
# Axial view
win_axial = a.createWindow("3D", block=block)
print "win_axial", win_axial, win_axial.getInfos()
# Fusion cut mesh
fusionmesh_axial = a.fusionObjects([brain_mesh, fusion2d],
"FusionCutMeshMethod")
a.execute("FusionCutMeshMethod", object=fusionmesh_axial)
fusionmesh_axial.addInWindows(win_axial)
# Axial view, orientation front top left
Q_rot = [np.cos(np.pi / 8.), np.sin(np.pi / 8.), 0, np.sin(np.pi / 8.)]
Q1 = win_axial.getInfos()["view_quaternion"] # current point of view
Q = product_quaternion(Q1, Q_rot)
a.execute("Camera", windows=[win_axial], view_quaternion=Q, zoom=0.8)
# Slice
sclice_axial(fusionmesh_axial, int(-(image_dim / 2)[2]))
# Store
windows.append(win_axial)
#a.execute
objects.append([fusion2d, fusionmesh_axial])
VIEWS[(title, "axial")] = [win_axial, fusionmesh_axial]
##############
#Sagital view
win_sagital = a.createWindow("3D", block=block)
print "win_sagital", win_sagital
# Fusion cut mesh
fusionmesh_sagital = a.fusionObjects([brain_mesh, fusion2d],
"FusionCutMeshMethod")
a.execute("FusionCutMeshMethod", object=fusionmesh_sagital)
fusionmesh_sagital.addInWindows(win_sagital)
# Sagital view: rotation -Pi/4 --> orientation right
Q_rot = [np.sqrt(2) / 2., -np.sqrt(2) / 2., 0, 0]
Q1 = win_sagital.getInfos()["view_quaternion"] # current point of view
Q = product_quaternion(Q1, Q_rot)
a.execute("Camera", windows=[win_sagital], view_quaternion=Q, zoom=0.9)
# Slice
sclice_sagital(fusionmesh_sagital, int(-(image_dim / 2)[0]))
# Store
windows.append(win_sagital)
#a.execute
objects.append([fusion2d, fusionmesh_sagital])
VIEWS[(title, "sagital")] = [win_sagital, fusionmesh_sagital]
# Global windows info
try:
block.widgetProxy().widget.setWindowTitle(str(title))
except:
print "could not set name"
return [
clust_mesh, brain_mesh, clust_texture, a_anat, material, palette,
block, windows, objects
]
def fusion2D_map_activation(
dic_maps,
template_file="/i2bm/local/spm8/canonical/single_subj_T1.nii",
dic_templates=""):
""" Function to merge one or many activation maps with a the
single_subj_T1.nii template of spm8. The defaults values are
"Rainbow1-fusion" palette set to minVal = 1.
Please use the interface to change values.
**Parameters :**
* dict_maps: dictionnary: {'name_fusion': map_file}
**Outputs :**
* launch Anatomist software with all activation map merged with the
template
"""
# initialize Anatomist
a = ana.Anatomist()
#load the mni referential
mni_ref = a.mniTemplateRef
liste_ref = a.getReferentials()
dic_templates_fusion = {}
if len(dic_templates) == 0:
template = a.loadObject(template_file)
#template = a.loadObject(file_name, objectName=objectName )
# load information from header for the template
template.loadReferentialFromHeader()
# load referentials
ref_template = template.getReferential()
tmp_template = 'truth for ' + os.path.basename(template_file)
liste_ref = a.getReferentials()
for ref in liste_ref:
dict_ref = ref.getInfos()
if 'name' in dict_ref.keys():
# if dict_ref['name'] == 'Talairach-MNI template-SPM':
# ref_mni_spm = ref
if dict_ref['name'].find(tmp_template) != -1:
ref_template = ref
a.loadTransformation([0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1],
ref_template, mni_ref)
#dic_templates_fusion[name_contrast] = template
dic_templates_fusion["template"] = template
if len(dic_templates) == len(dic_maps):
for name_contrast, file_name in dic_templates.items():
#template
objectName = "wanatFor_" + name_contrast
#template = a.loadObject(file_name)
template = a.loadObject(file_name, objectName=objectName)
# load information from header for the template
template.loadReferentialFromHeader()
# load referentials
ref_template = template.getReferential()
tmp_template = 'truth for ' + os.path.basename(objectName)
liste_ref = a.getReferentials()
for ref in liste_ref:
dict_ref = ref.getInfos()
if 'name' in dict_ref.keys():
# if dict_ref['name'] == 'Talairach-MNI template-SPM':
# ref_mni_spm = ref
if dict_ref['name'].find(tmp_template) != -1:
ref_template = ref
a.loadTransformation([0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1],
ref_template, mni_ref)
dic_templates_fusion[name_contrast] = template
else:
warnings.warn('The number of templates and constrasts are differents.')
fusion_list = []
window_list = []
#loop on maps
for fusion_name, file_name in dic_maps.items():
map_activation = a.loadObject(file_name, fusion_name)
# load identity transformtion to mni_SPM
tmp_map = 'truth for ' + os.path.basename(fusion_name)
# load information from header
map_activation.loadReferentialFromHeader()
# load referentials
ref_map_activation = map_activation.getReferential()
liste_ref = a.getReferentials()
for ref in liste_ref:
dict_ref = ref.getInfos()
if 'name' in dict_ref.keys():
# if dict_ref['name'] == 'Talairach-MNI template-SPM':
# ref_mni_spm = ref
# if dict_ref['name'].find(tmp_template) != -1:
# ref_template = ref
if dict_ref['name'].find(tmp_map) != -1:
ref_map_activation = ref
a.loadTransformation([0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1],
ref_map_activation, mni_ref)
#palette and options
map_activation.setPalette("Rainbow1-fusion",
minVal=1,
absoluteMode=True)
# create the fusion object
if dic_templates_fusion.has_key("template"):
fusion_map = a.fusionObjects(
[map_activation, dic_templates_fusion["template"]],
'Fusion2DMethod')
else:
fusion_map = a.fusionObjects(
[map_activation, dic_templates_fusion[fusion_name]],
'Fusion2DMethod')
fusion_list.append(fusion_map)
window_list.append(a.createWindow('Axial'))
for w, f in zip(window_list, fusion_list):
# show the fusion
w.assignReferential(mni_ref)
a.addObjects(f, w)
#changement de position du curseur
a.execute('LinkedCursor',
object=f,
mode="linear_on_defined",
rate=0.5,
position=[100, 100, 100, 0])
# start loop
sys.exit(app.exec_())