Hyedryn · Dany546 · Nov 7, 2021 · Nov 8, 2021 · Nov 9, 2021 · Nov 10, 2021
diff --git a/docs/LICENSE b/docs/LICENSE
diff --git a/docs/Makefile b/docs/Makefile
diff --git a/docs/conf.py b/docs/conf.py
diff --git a/docs/contributing.rst b/docs/contributing.rst
diff --git a/docs/elikopy.rst b/docs/elikopy.rst
diff --git a/docs/elikopy_project.rst b/docs/elikopy_project.rst
diff --git a/docs/elikopy_usage.rst b/docs/elikopy_usage.rst
diff --git a/docs/examples.rst b/docs/examples.rst
diff --git a/docs/favicon.png b/docs/favicon.png
diff --git a/docs/index.rst b/docs/index.rst
diff --git a/docs/installation.rst b/docs/installation.rst
diff --git a/docs/introduction.rst b/docs/introduction.rst
diff --git a/docs/license.rst b/docs/license.rst
diff --git a/docs/logo.png b/docs/logo.png
diff --git a/docs/make.bat b/docs/make.bat
diff --git a/docs/metrics.rst b/docs/metrics.rst
diff --git a/docs/other_utils.rst b/docs/other_utils.rst
diff --git a/docs/pictures/APvsT1.jpg b/docs/pictures/APvsT1.jpg
diff --git a/docs/pictures/T1_pipeline_steps.jpg b/docs/pictures/T1_pipeline_steps.jpg
diff --git a/docs/pictures/folder_structure.jpg b/docs/pictures/folder_structure.jpg
diff --git a/docs/pictures/pipeline_steps.jpg b/docs/pictures/pipeline_steps.jpg
diff --git a/docs/pictures/preproc_bet.jpg b/docs/pictures/preproc_bet.jpg
diff --git a/docs/pictures/preproc_gibbs.jpg b/docs/pictures/preproc_gibbs.jpg
diff --git a/docs/pictures/preproc_mppca.jpg b/docs/pictures/preproc_mppca.jpg
diff --git a/docs/pictures/preprocessing.PNG b/docs/pictures/preprocessing.PNG
diff --git a/docs/pictures/provisoire.png b/docs/pictures/provisoire.png
diff --git a/docs/pictures/root.png b/docs/pictures/root.png
diff --git a/docs/pictures/root_reverse.png b/docs/pictures/root_reverse.png
diff --git a/docs/preprocessing_T1.rst b/docs/preprocessing_T1.rst
diff --git a/docs/preprocessing_dmri.rst b/docs/preprocessing_dmri.rst
diff --git a/docs/stats.rst b/docs/stats.rst
diff --git a/elikopy/core.py b/elikopy/core.py
diff --git a/elikopy/individual_subject_processing.py b/elikopy/individual_subject_processing.py
diff --git a/elikopy/utils.py b/elikopy/utils.py
@@ -575,6 +575,9 @@ def synb0DisCo(folder_path,topuppath,patient_path,starting_step=None,topup=True,
     import torch.nn.functional as F
     import torch.optim as optim
 
+    if starting_step == "None":
+        starting_step = None
+
     assert starting_step in (None, "Registration", "Inference", "Apply", "topup")
 
     synb0path = topuppath + "/synb0-DisCo"
@@ -678,7 +681,7 @@ def synb0DisCo(folder_path,topuppath,patient_path,starting_step=None,topup=True,
             model_path = glob.glob(model_path)[0]
             # Get model
             model = UNet3D(2, 1).to(device)
-            model.load_state_dict(torch.load(model_path))
+            model.load_state_dict(torch.load(model_path,map_location=device))
 
             # Inference
             step3_log.write("[SynB0DISCO] " + datetime.datetime.now().strftime("%d.%b %Y %H:%M:%S") + ": Beginning of inference " + str(i) +"\n\n")
@@ -822,7 +825,7 @@ def inference(T1_path, b0_d_path, model, device):
     return img_model
 
 
-def regall_FA(folder_path, grp1, grp2, starting_state=None, registration_type="-T", postreg_type="-S", prestats_treshold=0.2, core_count=1):
+def regall_FA(folder_path, grp1, grp2, starting_state=None, registration_type="-T", postreg_type="-S", prestats_treshold=0.2, core_count=1,patient_list=None):
     """ Register all the subjects Fractional Anisotropy into a common space, skeletonisedd and non skeletonised. This is performed based on TBSS of FSL.
     It is mandatory to have performed DTI prior to regall_FA.
 
@@ -853,8 +856,9 @@ def regall_FA(folder_path, grp1, grp2, starting_state=None, registration_type="-
 
     # open the subject and is_control lists
     dest_success = folder_path + "/subjects/subj_list.json"
-    with open(dest_success, 'r') as f:
-        patient_list = json.load(f)
+    if patient_list==None:
+      with open(dest_success, 'r') as f:
+          patient_list = json.load(f)
     dest_subj_type = folder_path + "/subjects/subj_type.json"
     with open(dest_subj_type, 'r') as f:
         subj_type = json.load(f)
@@ -977,7 +981,7 @@ def regall_FA(folder_path, grp1, grp2, starting_state=None, registration_type="-
     registration_log.flush()
 
 
-def regall(folder_path, grp1, grp2, core_count=1 ,metrics_dic={'_noddi_odi':'noddi','_mf_fvf_tot':'mf','_diamond_kappa':'diamond'}):
+def regall(folder_path, grp1, grp2, patient_list=None, core_count=1 ,metrics_dic={'_noddi_odi':'noddi','_mf_fvf_tot':'mf','_diamond_kappa':'diamond'}):
     """ Register all the subjects diffusion metrics specified in the argument metrics_dic into a common space using the transformation computed for the FA with the regall_FA function. This is performed based on TBSS of FSL.
     It is mandatory to have performed regall_FA prior to regall.
 
@@ -1004,8 +1008,9 @@ def regall(folder_path, grp1, grp2, core_count=1 ,metrics_dic={'_noddi_odi':'nod
 
     # open the subject and is_control lists
     dest_success = folder_path + "/subjects/subj_list.json"
-    with open(dest_success, 'r') as f:
-        patient_list = json.load(f)
+    if patient_list in (None,'None'):
+      with open(dest_success, 'r') as f:
+          patient_list = json.load(f)
     dest_subj_type = folder_path + "/subjects/subj_type.json"
     with open(dest_subj_type, 'r') as f:
         subj_type = json.load(f)
@@ -1169,25 +1174,39 @@ def randomise_all(folder_path,randomise_numberofpermutation=5000,skeletonised=Tr
             xmlName = [atlas_path + "/MNI.xml", atlas_path + "/HarvardOxford-Cortical.xml", atlas_path + "/HarvardOxford-Subcortical.xml", atlas_path + "/JHU-tracts.xml"]
             atlases = [atlas_path + "/MNI/MNI-prob-1mm.nii.gz", atlas_path + "/HarvardOxford/HarvardOxford-cort-prob-1mm.nii.gz", atlas_path + "/HarvardOxford/HarvardOxford-sub-prob-1mm.nii.gz", atlas_path + "/JHU/JHU-ICBM-tracts-prob-1mm.nii.gz"]
             name = ["MNI", "HarvardCortical", "HarvardSubcortical", "JHUWhiteMatterTractography"]
+            csv_or_xml = ["xml","xml","xml","xml"]
             if additional_atlases:
                 xmlName = xmlName + list(map(list, zip(*list(additional_atlases.values()))))[0]
                 atlases = atlases + list(map(list, zip(*list(additional_atlases.values()))))[1]
+                csv_or_xml = csv_or_xml + list(map(list, zip(*list(additional_atlases.values()))))[2]
                 name = name + list(additional_atlases.keys())
             # open the data
             data, data_affine = load_nifti(outputdir + '/stats/all_' + key + '.nii.gz')
 
             for iteration in range(len(atlases)):
-
-                # read the labels in xml file
-                x = etree.parse(xmlName[iteration])
-                labels = []
-                for elem in x.iter():
-                    if elem.tag == 'label':
-                        labels.append([elem.attrib['index'], elem.text])
-                labels = np.array(labels)[:, 1]
-
+
+                csv = csv_or_xml[iteration]=='csv'
+                if csv:
+                    # read the labels in csv file
+                    labels = pd.read_csv(xmlName[iteration],header=None,names=['index','Area'])
+                    labels = labels['Area'].values
+                else:
+                    # read the labels in xml file
+                    x = etree.parse(xmlName[iteration])
+                    labels = []
+                    for elem in x.iter():
+                        if elem.tag == 'label':
+                            labels.append([elem.attrib['index'], elem.text])
+                    labels = np.array(labels)[:, 1]
+
                 # open the atlas
                 atlas, atlas_affine = load_nifti(atlases[iteration])
+                if csv:
+                    atlas_stack = np.zeros((np.shape(atlas)[0],np.shape(atlas)[1],np.shape(atlas)[2],int(len(np.unique(atlas.flatten())[1:]))))
+                    for ivalue, value in enumerate(np.unique(atlas.flatten())[1:]): 
+                        atlas_stack[..., int(ivalue-1)] = atlas==value  
+                    # define a 3D image for each label and stack them together    
+                    atlas = atlas_stack  
 
                 matrix = np.zeros((np.shape(data)[-1], np.shape(atlas)[-1]))
                 for i in range(np.shape(atlas)[-1]):