def gsm_nifti(roi_mask_file, n_clusters, out_dir):
import CPAC.basc.utils as utils
import basc
import numpy as np
import os
#Individual subject ISM to NIFTI and individual
#Inputs Subject ISM, ROIFile,
#for i in range(nSubjects):
gsmdir=out_dir + '/workflow_output/basc_workflow_runner/basc/join_group_stability/'
os.chdir(gsmdir)
gsm=np.load(gsmdir + '/group_stability_matrix.npy')
clusters_gsm = utils.cluster_timeseries(gsm, n_clusters, similarity_metric = 'correlation', affinity_threshold=0.0)
clusters_gsm = clusters_gsm+1
#niftifilename = gsmdir +'/gsm_clust.nii.gz'
#clusters_gsm_file = gsmdir +'/clusters_gsm.npy'
#Saving Individual Level Cluster Solution
# ndarray_to_vol(clusters_gsm, roi_mask_file, roi_mask_file, niftifilename)
# np.save(clusters_gsm_file, clusters_gsm)
cluster_ids = np.unique(clusters_gsm)
nClusters = cluster_ids.shape[0]
nVoxels = clusters_gsm.shape[0]
gsm_cluster_voxel_scores = np.zeros((nClusters, nVoxels))
k_mask=np.zeros((nVoxels, nVoxels))
gsm_cluster_voxel_scores[:,:], k_mask[:,:] = utils.cluster_matrix_average(gsm, clusters_gsm)
gsm_cluster_voxel_scores=gsm_cluster_voxel_scores.astype("uint8")
grp_cluster_stability=[]
grp_cluster_INSTABILITY=[]
grp_cluster_stability_Diff=[]
grp_cluster_stability_file = os.path.join(os.getcwd(), 'grp_cluster_stability.npy')
grp_cluster_INSTABILITY_file = os.path.join(os.getcwd(), 'grp_cluster_INSTABILITY.npy')
grp_cluster_stability_Diff_file = os.path.join(os.getcwd(), 'grp_cluster_stability_Diff.npy')
gsm_cluster_voxel_scores_file = os.path.join(os.getcwd(), 'gsm_cluster_voxel_scores.npy')
for k in cluster_ids:
grp_cluster_stability.append(gsm_cluster_voxel_scores[(k-1),clusters_gsm==k].mean())
grp_cluster_INSTABILITY.append(gsm_cluster_voxel_scores[(k-1),clusters_gsm!=k].mean())
A, B = basc.ndarray_to_vol(gsm_cluster_voxel_scores[k-1,:], roi_mask_file, roi_mask_file, 'gsm_single_cluster%i_stability.nii.gz' % k)
grp_cluster_stability=np.asarray(grp_cluster_stability)
grp_cluster_INSTABILITY=np.asarray(grp_cluster_INSTABILITY)
grp_cluster_stability_Diff=grp_cluster_stability-grp_cluster_INSTABILITY
np.save(grp_cluster_stability_file, grp_cluster_stability)
np.save(grp_cluster_INSTABILITY_file, grp_cluster_INSTABILITY)
np.save(grp_cluster_stability_Diff_file, grp_cluster_stability_Diff)
np.save(gsm_cluster_voxel_scores_file, gsm_cluster_voxel_scores)
return
def create_group_cluster_maps(gsm_file, clusters_G_file, roi_mask_file):
"""
Loops through every row of cluster_voxel_scores and creates nifti files
Parameters
----------
gsm_file- a cluster number by voxel measure of the stability
of group level solutions.
clusters_G_file- taken from igcm output
roi_mask_file- file you want to transform the data back into.
Returns
-------
Creates NIFTI files for all the gsm file for the group across all clusters
"""
import numpy as np
import basc
import utils
group_stability_mat = np.asarray([np.load(gsm_file)])
group_stability_set = group_stability_mat[0]
#nSubjects = indiv_stability_set.shape[0]
nVoxels = group_stability_set.shape[1]
clusters_G = np.load(clusters_G_file)
group_cluster_stability = []
cluster_ids = np.unique(clusters_G)
nClusters = cluster_ids.shape[0]
print("igcm debug 2")
# cluster_voxel_scores = np.zeros((nSubjects,nClusters, nVoxels))
# k_mask=np.zeros((nSubjects,nVoxels, nVoxels))
group_cluster_voxel_scores = np.zeros((nClusters, nVoxels))
k_mask = np.zeros((nVoxels, nVoxels))
#for i in range(nSubjects):
group_cluster_voxel_scores[:, :], k_mask[:, :] = utils.cluster_matrix_average(
group_stability_set, clusters_G)
for i in cluster_ids:
group_cluster_stability.append(
group_cluster_voxel_scores[(i - 1), clusters_G == i].mean())
for k in cluster_ids:
print(
'k equals \n\n', k, '\n\n'
) #Loops through every row of cluster_voxel_scores and creates nifti files
print('clustervoxelscores equals \n\n',
group_cluster_voxel_scores[k - 1, :], '\n\n')
A, B = basc.ndarray_to_vol(
group_cluster_voxel_scores[k - 1, :], roi_mask_file, roi_mask_file,
'group_level_cluster%i_stability.nii.gz' % k)
print('Output A equals', A, '\n\n')
return
def test_ndarray_to_vol():
import basc
import nibabel as nb
subject_file = home + '/git_repo/PyBASC/sample_data/sub1/Func_Quarter_Res.nii.gz'
subject_file = home + '/git_repo/PyBASC/sample_data/test.nii.gz'
data = nb.load(subject_file).get_data().astype('float32')
roi_mask_file = home + '/git_repo/PyBASC/masks/LC_Quarter_Res.nii.gz'
print('Data Loaded')
roi_mask_file_nb = nb.load(roi_mask_file)
roi_mask_nparray = nb.load(roi_mask_file).get_data().astype(
'float32').astype('bool')
roi1data = data[roi_mask_nparray]
data_array = roi1data
sample_file = subject_file
filename = home + '/git_repo/PyBASC/sample_data/ndarray_to_vol_test.nii.gz'
basc.ndarray_to_vol(data_array, roi_mask_file, roi_mask_file, filename)
def save_igcm_nifti(cluster_voxel_scores_file, clusters_G_file, roi_mask_file):
"""
Loops through every row of cluster_voxel_scores and creates nifti files
Parameters
----------
cluster_voxel_scores_file- a cluster number by voxel measure of the stability
of group level solutions at the individual level.
clusters_G_file- taken from igcm output
roi_mask_file- file you want to transform the data back into.
Returns
-------
Creates NIFTI files for all the igcm files for each participant across all clusters
"""
import numpy as np
import basc
cluster_voxel_scores = np.load(cluster_voxel_scores_file)
clusters_G = np.load(clusters_G_file)
cluster_ids = np.unique(clusters_G)
icvs_idx = 0
A = []
B = []
#import pdb; pdb.set_trace()
for k in cluster_ids:
print(
'k equals \n\n', k, '\n\n'
) #Loops through every row of cluster_voxel_scores and creates nifti files
print('clustervoxelscores equals \n\n', cluster_voxel_scores[k - 1, :],
'\n\n')
A, B = basc.ndarray_to_vol(
cluster_voxel_scores[k - 1, :], roi_mask_file, roi_mask_file,
'individual_group_cluster%i_stability.nii.gz' % k)
print('Output A equals', A, '\n\n')
def ism_nifti(roi_mask_file, n_clusters, out_dir):
import utils
import basc
import numpy as np
import os
#Individual subject ISM to NIFTI and individual
#Inputs Subject ISM, ROIFile,
#for i in range(nSubjects):
ismdir = out_dir + '/workflow_output/basc_workflow_runner/basc/individual_stability_matrices/mapflow/'
os.chdir(ismdir)
os.chdir(
out_dir +
'/workflow_output/basc_workflow_runner/basc/individual_stability_matrices/mapflow/'
)
subdirs_all = [x[1] for x in os.walk(ismdir)]
subdirs = subdirs_all[0]
for subdir in subdirs:
os.chdir(ismdir + subdir)
ism = np.load(ismdir + subdir + '/individual_stability_matrix.npy')
clusters_ism = utils.cluster_timeseries(
ism,
n_clusters,
similarity_metric='correlation',
affinity_threshold=0.0)
clusters_ism = clusters_ism + 1
niftifilename = ismdir + subdir + '/ism_clust.nii.gz'
clusters_ism_file = ismdir + subdir + '/clusters_ism.npy'
#Saving Individual Level Cluster Solution
ndarray_to_vol(clusters_ism, roi_mask_file, roi_mask_file,
niftifilename)
np.save(clusters_ism_file, clusters_ism)
cluster_ids = np.unique(clusters_ism)
nClusters = cluster_ids.shape[0]
nVoxels = clusters_ism.shape[0]
ism_cluster_voxel_scores = np.zeros((nClusters, nVoxels))
k_mask = np.zeros((nVoxels, nVoxels))
ism_cluster_voxel_scores[:, :], k_mask[:, :] = utils.cluster_matrix_average(
ism, clusters_ism)
ism_cluster_voxel_scores = ism_cluster_voxel_scores.astype("uint8")
ind_cluster_stability = []
ind_cluster_INSTABILITY = []
ind_cluster_stability_Diff = []
ind_cluster_stability_file = os.path.join(os.getcwd(),
'ind_cluster_stability.npy')
ind_cluster_INSTABILITY_file = os.path.join(
os.getcwd(), 'ind_cluster_INSTABILITY.npy')
ind_cluster_stability_Diff_file = os.path.join(
os.getcwd(), 'ind_cluster_stability_Diff.npy')
ism_cluster_voxel_scores_file = os.path.join(
os.getcwd(), 'ism_cluster_voxel_scores.npy')
os.chdir(ismdir + '/' + subdir)
for k in cluster_ids:
ind_cluster_stability.append(
ism_cluster_voxel_scores[(k - 1), clusters_ism == k].mean())
ind_cluster_INSTABILITY.append(
ism_cluster_voxel_scores[(k - 1), clusters_ism != k].mean())
A, B = basc.ndarray_to_vol(
ism_cluster_voxel_scores[k - 1, :], roi_mask_file,
roi_mask_file, 'ism_single_cluster%i_stability.nii.gz' % k)
ind_cluster_stability = np.asarray(ind_cluster_stability)
ind_cluster_INSTABILITY = np.asarray(ind_cluster_INSTABILITY)
ind_cluster_stability_Diff = ind_cluster_stability - ind_cluster_INSTABILITY
np.save(ind_cluster_stability_file, ind_cluster_stability)
np.save(ind_cluster_INSTABILITY_file, ind_cluster_INSTABILITY)
np.save(ind_cluster_stability_Diff_file, ind_cluster_stability_Diff)
np.save(ism_cluster_voxel_scores_file, ism_cluster_voxel_scores)
return
grp_cluster_stability=[]
grp_cluster_INSTABILITY=[]
grp_cluster_stability_Diff=[]
grp_cluster_stability_file = os.path.join(os.getcwd(), 'grp_cluster_stability.npy')
grp_cluster_INSTABILITY_file = os.path.join(os.getcwd(), 'grp_cluster_INSTABILITY.npy')
grp_cluster_stability_Diff_file = os.path.join(os.getcwd(), 'grp_cluster_stability_Diff.npy')
gsm_cluster_voxel_scores_file = os.path.join(os.getcwd(), 'gsm_cluster_voxel_scores.npy')
for k in cluster_ids:
grp_cluster_stability.append(gsm_cluster_voxel_scores[(k-1),clusters_gsm==k].mean())
grp_cluster_INSTABILITY.append(gsm_cluster_voxel_scores[(k-1),clusters_gsm!=k].mean())
A, B = basc.ndarray_to_vol(gsm_cluster_voxel_scores[k-1,:], roi_mask_file, roi_mask_file, 'gsm_single_cluster%i_stability.nii.gz' % k)
grp_cluster_stability=np.asarray(grp_cluster_stability)
grp_cluster_INSTABILITY=np.asarray(grp_cluster_INSTABILITY)
grp_cluster_stability_Diff=grp_cluster_stability-grp_cluster_INSTABILITY
np.save(grp_cluster_stability_file, grp_cluster_stability)
np.save(grp_cluster_INSTABILITY_file, grp_cluster_INSTABILITY)
np.save(grp_cluster_stability_Diff_file, grp_cluster_stability_Diff)
np.save(gsm_cluster_voxel_scores_file, gsm_cluster_voxel_scores)
#%%
def set_style():
# This sets reasonable defaults for font size for
# a figure that will go in a paper
sns.set_context("paper")
def gsm_nifti(roi_mask_file, n_clusters, out_dir):
"""
Calculate the group level stability and instability maps for each of the group clusters.
Create Nifti files for each individual cluster's stability map
Parameters
----------
roi_mask_file: the mask of the region to calculate stability for.
n_clusters: the number of clusters calculated
out_dir: the directory to output the saved nifti images
Returns
-------
Creates NIFTI files for all the gsm cluster stability maps
"""
import utils
import basc
import numpy as np
import os
#*ACTION - FIGURE OUT IF CAN BE ADDED TO BASC WORKFLOW, OR DIFFERENT WORKFLOW?
#Individual subject ISM to NIFTI and individual
#Inputs Subject ISM, ROIFile,
roi_mask_nparray = nb.load(roi_mask_file).get_data().astype(
'float32').astype('bool')
#for i in range(nSubjects):
gsmdir = out_dir + '/workflow_output/basc_workflow_runner/basc/join_group_stability/'
os.chdir(gsmdir)
gsm = np.load(gsmdir + '/group_stability_matrix.npy')
clusters_gsm = utils.cluster_timeseries(gsm,
roi_mask_nparray,
n_clusters,
similarity_metric='correlation',
affinity_threshold=0.0,
cluster_method='ward')
clusters_gsm = clusters_gsm + 1
#niftifilename = gsmdir +'/gsm_clust.nii.gz'
#clusters_gsm_file = gsmdir +'/clusters_gsm.npy'
#Saving Individual Level Cluster Solution
# ndarray_to_vol(clusters_gsm, roi_mask_file, roi_mask_file, niftifilename)
# np.save(clusters_gsm_file, clusters_gsm)
cluster_ids = np.unique(clusters_gsm)
nClusters = cluster_ids.shape[0]
nVoxels = clusters_gsm.shape[0]
gsm_cluster_voxel_scores = np.zeros((nClusters, nVoxels))
k_mask = np.zeros((nVoxels, nVoxels))
gsm_cluster_voxel_scores[:, :], k_mask[:, :] = utils.cluster_matrix_average(
gsm, clusters_gsm)
gsm_cluster_voxel_scores = gsm_cluster_voxel_scores.astype("uint8")
grp_cluster_stability = []
grp_cluster_INSTABILITY = []
grp_cluster_stability_Diff = []
grp_cluster_stability_file = os.path.join(os.getcwd(),
'grp_cluster_stability.npy')
grp_cluster_INSTABILITY_file = os.path.join(os.getcwd(),
'grp_cluster_INSTABILITY.npy')
grp_cluster_stability_Diff_file = os.path.join(
os.getcwd(), 'grp_cluster_stability_Diff.npy')
gsm_cluster_voxel_scores_file = os.path.join(
os.getcwd(), 'gsm_cluster_voxel_scores.npy')
for k in cluster_ids:
grp_cluster_stability.append(
gsm_cluster_voxel_scores[(k - 1), clusters_gsm == k].mean())
grp_cluster_INSTABILITY.append(
gsm_cluster_voxel_scores[(k - 1), clusters_gsm != k].mean())
A, B = basc.ndarray_to_vol(gsm_cluster_voxel_scores[k - 1, :],
roi_mask_file, roi_mask_file,
'gsm_single_cluster%i_stability.nii.gz' % k)
grp_cluster_stability = np.asarray(grp_cluster_stability)
grp_cluster_INSTABILITY = np.asarray(grp_cluster_INSTABILITY)
grp_cluster_stability_Diff = grp_cluster_stability - grp_cluster_INSTABILITY
np.save(grp_cluster_stability_file, grp_cluster_stability)
np.save(grp_cluster_INSTABILITY_file, grp_cluster_INSTABILITY)
np.save(grp_cluster_stability_Diff_file, grp_cluster_stability_Diff)
np.save(gsm_cluster_voxel_scores_file, gsm_cluster_voxel_scores)
return
def individual_group_clustered_maps(indiv_stability_list, clusters_G,
roi_mask_file):
"""
Calculate the individual stability maps of each subject based on the group stability clustering solution.
Parameters
----------
indiv_stability_list : list of strings
A length `N` list of file paths to numpy matrices of shape (`V`, `V`), `N` subjects, `V` voxels
clusters_G : array_like
Length `V` array of cluster assignments for each voxel
Returns
-------
individual_cluster_voxel_scores : list of strings
A length `N` list of nifti files of the individual group clustered stability maps for each cluster. Temporal
dimension of each file corresponds to each subject.
"""
##*ACTION - CLEAN UP COMMENTED OUT CODE
import os
import numpy as np
import utils
import basc
print('starting igcm')
#import pdb; pdb.set_trace()
print("igcm debug 1")
# *REMOVE LINE* indiv_stability_set = np.asarray([np.load(ism_file) for ism_file in indiv_stability_list])
indiv_stability_mat = np.asarray([np.load(indiv_stability_list)])
indiv_stability_set = indiv_stability_mat[0]
#*REMOVE LINE*nSubjects = indiv_stability_set.shape[0]
nVoxels = indiv_stability_set.shape[1]
cluster_ids = np.unique(clusters_G)
nClusters = cluster_ids.shape[0]
print("igcm debug 2")
# *REMOVE LINE*cluster_voxel_scores = np.zeros((nSubjects,nClusters, nVoxels))
# *REMOVE LINE*k_mask=np.zeros((nSubjects,nVoxels, nVoxels))
cluster_voxel_scores = np.zeros((nClusters, nVoxels))
k_mask = np.zeros((nVoxels, nVoxels))
#*REMOVE LINE*for i in range(nSubjects):
cluster_voxel_scores[:, :], k_mask[:, :] = utils.cluster_matrix_average(
indiv_stability_set, clusters_G)
#*REMOVE LINE*clust5[0,clusters_g==1].mean()
print("igcm debug 3")
#*REMOVE LINE*import pdb; pdb.set_trace()
#*REMOVE LINE*cluster_voxel_scores[0,clusters_g==1].mean()
ind_group_cluster_stability = []
icvs = []
icvs = np.asarray(1)
icvs_idx = 0
print("igcm debug 4")
#*REMOVE LINE*ind_group_cluster_stability.append(cluster_voxel_scores[(k-1),clusters_G==k].mean())
#*REMOVE LINE*icvs_idx += 1
for i in cluster_ids:
ind_group_cluster_stability.append(
cluster_voxel_scores[(i - 1), clusters_G == i].mean())
print("igcm debug 5")
ind_group_cluster_stability = np.array(ind_group_cluster_stability)
ind_group_cluster_stability = np.array([1, 2, 3, 4, 5])
#print( 'saving files: icvs')
icvs_file = os.path.join(os.getcwd(), 'icvs.npy')
#*REMOVE LINE*np.save(icvs_file, icvs)
print('saving files: cluster_voxel_scores')
cluster_voxel_scores = cluster_voxel_scores.astype("uint8")
cluster_voxel_scores_file = os.path.join(os.getcwd(),
'cluster_voxel_scores.npy')
#*REMOVE LINE*np.save(cluster_voxel_scores_file, cluster_voxel_scores)
#REMOVE
#import pdb; pdb.set_trace()
#np.save(individualized_group_clusters_img_file, img)
#return img_file, img
#For each column, which row has the largest number?
# Creates a single row with voxel labels.
#print( 'saving files: k_mask')
k_mask = k_mask.astype("bool_")
k_mask_file = os.path.join(os.getcwd(), 'k_mask.npy')
#*REMOVE LINE*np.save(k_mask_file, k_mask)
#REMOVE
#print( 'saving files: ind_group_cluster_stability')
#ind_group_cluster_stability=ind_group_cluster_stability.astype("uint8")
ind_group_cluster_stability_file = os.path.join(
os.getcwd(), 'ind_group_cluster_stability.npy')
np.save(ind_group_cluster_stability_file, ind_group_cluster_stability)
#import pdb; pdb.set_trace()
Individualized_Group_Cluster = np.argmax(cluster_voxel_scores, axis=0) + 1
individualized_group_clusters_img_file, img = basc.ndarray_to_vol(
Individualized_Group_Cluster, roi_mask_file, roi_mask_file,
os.path.join(os.getcwd(), 'IndividualizedGroupClusters.nii.gz'))
img.to_filename(individualized_group_clusters_img_file)
return icvs_file, cluster_voxel_scores_file, k_mask_file, ind_group_cluster_stability_file, individualized_group_clusters_img_file #icvs, cluster_voxel_scores, k_mask
data_array2 = SimVisual
roi_mask_file2 = '/Users/aki.nikolaidis/git_repo/PyBASC/masks/Yeo7_3mmMasks/Yeo_1_3mm.nii.gz'
sample_file2 = '/Users/aki.nikolaidis/git_repo/PyBASC/masks/Yeo7_3mmMasks/Yeo_1_3mm.nii.gz'
filename2 = '/Users/aki.nikolaidis/git_repo/PyBASC/SimData4/SimVisual_' + 'sub_' + str(
subs) + 'corrstrength_' + str(
corrstrength) + 'noise_' + str(noiselevel) + '.nii.gz'
data_array3 = SimMotor
roi_mask_file3 = '/Users/aki.nikolaidis/git_repo/PyBASC/masks/Yeo7_3mmMasks/Yeo_2_3mm.nii.gz'
sample_file3 = '/Users/aki.nikolaidis/git_repo/PyBASC/masks/Yeo7_3mmMasks/Yeo_2_3mm.nii.gz'
filename3 = '/Users/aki.nikolaidis/git_repo/PyBASC/SimData4/SimMotor_' + 'sub_' + str(
subs) + 'corrstrength_' + str(
corrstrength) + 'noise_' + str(noiselevel) + '.nii.gz'
#write Regions to nifti file
basc.ndarray_to_vol(data_array_right, roi_mask_file_right,
sample_file_right, filename_right)
basc.ndarray_to_vol(data_array_thal, roi_mask_file_thal,
sample_file_thal, filename_thal)
basc.ndarray_to_vol(data_array_left, roi_mask_file_left,
sample_file_left, filename_left)
#write Region One to nifti file
basc.ndarray_to_vol(data_array2, roi_mask_file2, sample_file2,
filename2)
#
basc.ndarray_to_vol(data_array3, roi_mask_file3, sample_file3,
filename3)
niftiadditionfile = 'fslmaths ' + filename_right + ' -add ' + filename_thal + ' -add ' + filename_left + ' -add ' + filename2 + ' -add ' + filename3 + ' /Users/aki.nikolaidis/git_repo/PyBASC/SimData4/sub_' + str(
subs) + 'corr_' + str(corrstrength) + '_noise_' + str(
noiselevel) + '_TRs_' + str(n) + '.nii.gz'
