Merge pull request #253 from soazig/soazig-Tests

Updating Test branch with noise-pca, plot function, mask

code/utils/scripts/mask_filtered_data.py

@@ -43,28 +43,15 @@ def make_mask_filtered_data(func_path, mask_path):
     # nib.save(masked_func, 'masked_' + img_name )
     return masked_func
 
-def resample_filtered_data(func_path, template_path):
-    """Resample template to filtered functional dataset
-    
-    Parameters
-    ----------
-    func_path: string
-        path of the 4D filtered data to resample
-    template_path: string
-        path to the template to apply on the data
-    
-    """
-    filtered_func = nib.load(func_path) 
-    filtered_shape = filtered_func.shape[:3]
-    template_img = nib.load(mni_fname)
-    template_data = template_img.get_data()
-    vox2vox = npl.inv(template_img.affine).dot(filtered_func.affine)
-    M, trans = nib.affines.to_matvec(vox2vox)
-    resampled = affine_transform(template_data, M, trans,
-                                 output_shape=filtered_shape)
-    froot, ext = splitext(mni_fname)
-    new_name = froot + '_2mm' + ext
-    new_img = nib.Nifti1Image(resampled, filtered_func.affine,
-                              template_img.header)
-    #nib.save(new_img, new_name) 
-    return new_img
+if __name__=='__main__':
+    project_path='../../../'
+    func_path = \
+    project_path+\
+    'data/ds005/sub001/model/model001/task001_run001.feat/filtered_func_data_mni.nii.gz'
+    img = nib.load(func_path) 
+    mask_path = project_path+'data/mni_icbm152_t1_tal_nlin_asym_09c_mask_2mm.nii'
+    masked_data = make_mask_filtered_data(func_path, mask_path)
+    new_data_path = project_path+\
+        'data/ds005/sub001/model/model001/task001_run001.feat/masked_filtered_func_data_mni.nii.gz'
+    nib.save(masked_data, new_data_path)
+    #pdb.set_trace()

code/utils/scripts/noise-pca_script.py

@@ -41,10 +41,10 @@
 # TODO: uncomment for final version
 #subject_list = [str(i) for i in range(1,17)]
 #run_list = [str(i) for i in range(1,4)]
-run_list = [str(i) for i in range(1,3)]
+run_list = [str(i) for i in range(1,2)]
 subject_list = ['1']
 
-d = path_dict['data_original'] #OR path_dict['data_filtered']
+d = path_dict['data_filtered'] #OR path_dict['data_filtered']
 
 images_paths = [('ds005' + d['type'] +'_sub' + s.zfill(3) + '_t1r' + r, \
                  data_path + 'sub%s/'%(s.zfill(3)) + d['run_path'] \

code/utils/scripts/plot_mosaic.py

@@ -6,7 +6,7 @@
 import numpy as np
 import nibabel as nib
 
-def plot_mosaic(img_data):
+def plot_mosaic(img_data, transpose=True):
     """ Return a mosaic plot for each slice of
         the 3rd dimension of img_data
 
@@ -30,19 +30,32 @@ def plot_mosaic(img_data):
     for i in range(n_rows):
         for j in range(n_cols):
 	    if z < n_slices:
+	        if transpose==True:
+		    img_data_slice = img_data[:,::-1,z].T
+		else:
+		    img_data_slice = img_data[:,::-1,z]
 	        grid_2D[i*img_data.shape[0]:(i+1)*img_data.shape[0],\
-	        j*img_data.shape[1]:(j+1)*img_data.shape[1]] = img_data[:,::-1,z].T
+	        j*img_data.shape[1]:(j+1)*img_data.shape[1]] = img_data_slice
             z += 1
     return grid_2D
 
 if __name__=='__main__':
     import matplotlib.pyplot as plt
     plt.rcParams['image.cmap'] = 'gray'
     plt.rcParams['image.interpolation'] = 'nearest'
-    img = nib.load(\
-    '../../../data/ds005/sub001/BOLD/task001_run001/bold.nii.gz') 
+    project_path='../../../'
+    #img = nib.load(\
+    #'../../../data/ds005/sub001/BOLD/task001_run001/bold.nii.gz') 
+    template = nib.load(project_path+\
+               'data/mni_icbm152_t1_tal_nlin_asym_09c_2mm.nii')
+    template_data = template.get_data()
+    img = nib.load(project_path+\
+         'data/ds005/sub001/model/model001/task001_run001.feat/' + \
+	 'masked_filtered_func_data_mni.nii.gz')
     img_data = img.get_data()
     mean_data = np.mean(img_data, axis=-1)
-    plt.imshow(plot_mosaic(mean_data), cmap='gray', alpha=1)
+    plt.title('In brain voxels - mean values')
+    plt.imshow(plot_mosaic(template_data, transpose=False), cmap='gray', alpha=1)
+    plt.imshow(plot_mosaic(mean_data, transpose=False), cmap='gray', alpha=1)
     plt.colorbar()
     plt.show()