def test_cluster_matrix_average():
import utils
import basc
import matplotlib.pyplot as plt
roi_mask_nparray = 'empty'
blobs = generate_blobs()
n_clusters = 3
similarity_metric = 'correlation'
ism = utils.individual_stability_matrix(blobs, 100, n_clusters,
similarity_metric)
y_predict = utils.cluster_timeseries(blobs,
roi_mask_nparray,
n_clusters,
similarity_metric,
affinity_threshold=0.0,
neighbors=10)
cluster_voxel_scores, K_mask = utils.cluster_matrix_average(ism, y_predict)
plt.imshow(K_mask)
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
def ism_nifti(roi_mask_file, n_clusters, out_dir): #NEED TO CHANGE THIS SCRIPT TO:
#APPLY GROUP LEVEL CLUSTER LABELS TO INDIVIDUAL LEVELS
#
#EXTRACT VOXELWISE STABILITY INFO FOR THAT CLUSTER ACROSS ALL PARTICIPANTS
#USE KMASK TO CREATE THAT CLUSTER INFORMATION
#
#Loop over all ISMs,
# load ISM,
# loop over all clusters,
# perform calculation,
# add calculation to running tabs.
# #Calculate mean at each voxel and CV-
# output a voxelwise mean stability and CV Nifti map for each cluster.
"""
Calculate the individual 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 ism cluster stability maps
"""
import utils
import basc
import numpy as np
import os
from pathlib import Path
#*ACTION - FIGURE OUT IF CAN BE ADDED TO BASC WORKFLOW, OR DIFFERENT WORKFLOW?
#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)
subdirs_all = [x[1] for x in os.walk(ismdir)]
subdirs=subdirs_all[0]
roi_mask_nparray = nb.load(roi_mask_file).get_data().astype('float32').astype('bool')
for subdir in subdirs:
os.chdir(ismdir + subdir)
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')
end_file = Path(ism_cluster_voxel_scores_file)
if end_file.exists():
return
else:
ism=np.load(ismdir + subdir + '/individual_stability_matrix.npy')
clusters_ism = utils.cluster_timeseries(ism, roi_mask_nparray, n_clusters, similarity_metric = 'correlation', affinity_threshold=0.0, cluster_method='ward')
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)
def individual_stability_matrix(Y1, roi_mask_nparray, n_bootstraps, n_clusters, similarity_metric, Y2=None, cross_cluster=False, cbb_block_size = None, blocklength=1, affinity_threshold = 0.5):
"""
Calculate the individual stability matrix of a single subject by bootstrapping their time-series
Parameters
----------
Y1 : array_like
A matrix of shape (`V`, `N`) with `V` voxels `N` timepoints
Y2 : array_like
A matrix of shape (`V`, `N`) with `V` voxels `N` timepoints
For Cross-cluster solutions- this will be the matrix by which Y1 is clustered
n_bootstraps : integer
Number of bootstrap samples
k_clusters : integer
Number of clusters
cbb_block_size : integer, optional
Block size to use for the Circular Block Bootstrap algorithm
affinity_threshold : float, optional
Minimum threshold for similarity matrix based on correlation to create an edge
Returns
-------
S : array_like
A matrix of shape (`V1`, `V1`), each element v1_{ij} representing the stability of the adjacency of voxel i with voxel j
"""
import utils
import time
import numpy as np
#print("Calculating Individual Stability Matrix")
ismtime=time.time()
if affinity_threshold < 0.0:
raise ValueError('affinity_threshold %d must be non-negative value' % affinity_threshold)
N1 = Y1.shape[0]
V1 = Y1.shape[1]
#import pdb; pdb.set_trace()
print('N1',N1)
print('V1',V1)
print(int(np.sqrt(N1)))
print('block size is- ', cbb_block_size)
temp_block_size = int(np.sqrt(N1))
cbb_block_size = int(temp_block_size * blocklength)
# if(cbb_block_size is None):
# cbb_block_size = int(np.sqrt(N1))
print('block size now is- ', cbb_block_size)
S = np.zeros((V1, V1))
#import pdb;pdb.set_trace()
if (cross_cluster is True):
for bootstrap_i in range(n_bootstraps):
N2 = Y2.shape[1]
temp_block_size2 = int(np.sqrt(N2))
cbb_block_size2 = int(temp_block_size2 * blocklength)
if (bootstrap_i==1):
Y_b1=Y1
Y_b2=Y2
else:
Y_b1, block_mask = utils.timeseries_bootstrap(Y1, cbb_block_size)
Y_b2 = Y2[block_mask.astype('int'), :]
#import pdb;pdb.set_trace()
#tseries[block_mask.astype('int'), :]
#import pdb; pdb.set_trace()
#SPATIAL CONSTRAINT EXPERIMENT#
roi_mask_nparray='empty'
#SPATIAL CONSTRAINT EXPERIMENT#
# if spatial_constraint==true:
# roi_mask_nparray='empty'
# else:
# roi_mask_nparray=roi_mask_nparray
#import pdb; pdb.set_trace()
S += utils.adjacency_matrix(utils.cross_cluster_timeseries(Y_b1, Y_b2, roi_mask_nparray, n_clusters, similarity_metric = similarity_metric, affinity_threshold= affinity_threshold, cluster_method='ward'))
S /= n_bootstraps
S=S*100
S=S.astype("uint8")
#print('ISM calculation took', (time.time() - ismtime), ' seconds')
else:
for bootstrap_i in range(n_bootstraps):
print('ismcalc1')
print('block size', cbb_block_size)
#import pdb; pdb.set_trace()
if (bootstrap_i==1):
Y_b1=Y1
Y_b2=Y2
else:
Y_b1, block_mask = utils.timeseries_bootstrap(Y1, cbb_block_size)
print('ismcalc2')
#import pdb;pdb.set_trace()
#SPATIAL CONSTRAINT EXPERIMENT#
roi_mask_nparray='empty'
#SPATIAL CONSTRAINT EXPERIMENT#
S += utils.adjacency_matrix(utils.cluster_timeseries(Y_b1, roi_mask_nparray, n_clusters, similarity_metric = similarity_metric, affinity_threshold = affinity_threshold, cluster_method='ward')[:,np.newaxis])
print('S shape0', S.shape[0])
print('S shape1', S.shape[1])
print('ismcalc3')
S /= n_bootstraps
print('ismcalc4')
S=S*100
S=S.astype("uint8")
#print('ISM calculation took', (time.time() - ismtime), ' seconds')
return S
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 ism_nifti(roi_mask_file, n_clusters, out_dir):
"""
Calculate the individual 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 ism cluster stability maps
"""
import utils
import basc
import numpy as np
import os
from pathlib import Path
#*ACTION - FIGURE OUT IF CAN BE ADDED TO BASC WORKFLOW, OR DIFFERENT WORKFLOW?
#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)
subdirs_all = [x[1] for x in os.walk(ismdir)]
subdirs = subdirs_all[0]
roi_mask_nparray = nb.load(roi_mask_file).get_data().astype(
'float32').astype('bool')
for subdir in subdirs:
os.chdir(ismdir + subdir)
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')
end_file = Path(ism_cluster_voxel_scores_file)
if end_file.exists():
return
else:
ism = np.load(ismdir + subdir + '/individual_stability_matrix.npy')
clusters_ism = utils.cluster_timeseries(
ism,
roi_mask_nparray,
n_clusters,
similarity_metric='correlation',
affinity_threshold=0.0,
cluster_method='ward')
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
def join_group_stability(indiv_stability_list, group_stability_list,
n_bootstraps, n_clusters, roi_mask_file):
"""
Merges the group stability maps for all and compares to all individual stability maps
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
group_stability_list : list of strings
A length `N` list of file paths to numpy matrices of shape (`V`, `V`), `N` subjects, `V` voxels
n_bootstraps : array_like
Number of bootstraps to join and average.
n_clusters : array_like
number of clusters extrated from adjacency matrx
Returns
-------
G_file : numpy array
The group stability matrix for a single bootstrap repitition
"""
import os
import numpy as np
import nibabel as nb
import utils
print("starting join group stability")
group_stability_set = np.asarray(
[np.load(G_file) for G_file in group_stability_list])
gsm = group_stability_set.sum(axis=0)
G = gsm / int(n_bootstraps)
G = G * 100
G = G.astype("uint8")
#SPATIAL CONSTRAINT EXPERIMENT#
#roi_mask_nparray='empty'
#SPATIAL CONSTRAINT EXPERIMENT#
print('calculating clusters_G')
#import pdb; pdb.set_trace()
roi_mask_nparray = nb.load(roi_mask_file).get_data().astype(
'float32').astype('bool')
clusters_G = utils.cluster_timeseries(G,
roi_mask_nparray,
n_clusters,
similarity_metric='correlation',
affinity_threshold=0.0,
cluster_method='ward')
#APPLY THIS METHOD TO THE INDIVIDUAL LEVEL CLUSTER
print('calculating cluster_voxel scores')
#print( '1')
# Cluster labels normally start from 0, start from 1 to provide contrast when viewing between 0 voxels
clusters_G += 1
clusters_G = clusters_G.astype("uint8")
indiv_stability_set = np.asarray(
[np.load(ism_file) for ism_file in indiv_stability_list])
#print( '2')
ism_gsm_corr = np.zeros(len(indiv_stability_list))
for i in range(0, len(indiv_stability_list)):
#import pdb;pdb.set_trace()
ism_gsm_corr[i] = utils.compare_stability_matrices(
indiv_stability_set[i], G)
#print( '3')
print('saving files: G')
print(G)
gsm_file = os.path.join(os.getcwd(), 'group_stability_matrix.npy')
np.save(gsm_file, G)
print('saving files: clusters_G')
clusters_G_file = os.path.join(os.getcwd(), 'clusters_G.npy')
np.save(clusters_G_file, clusters_G)
print('saving files: ism_gsm_corr')
ism_gsm_corr_file = os.path.join(os.getcwd(), 'ism_gsm_corr.npy')
np.save(ism_gsm_corr_file, ism_gsm_corr)
return G, clusters_G, ism_gsm_corr, gsm_file, clusters_G_file, ism_gsm_corr_file
def map_group_stability(indiv_stability_list, n_clusters, bootstrap_list,
roi_mask_file):
"""
Calculate the group stability maps for each group-level bootstrap
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
n_clusters : array_like
number of clusters extrated from adjacency matrx
Returns
-------
G_file : numpy array
The group stability matrix for a single bootstrap repitition
"""
import os
import numpy as np
import nibabel as nb
import utils
print('Calculating group stability matrix for', len(indiv_stability_list),
'subjects.')
#import pdb; pdb.set_trace()
indiv_stability_set = np.asarray(
[np.load(ism_file) for ism_file in indiv_stability_list])
#print( 'Group stability list dimensions:', indiv_stability_set.shape )
V = indiv_stability_set.shape[2]
G = np.zeros((V, V))
if (bootstrap_list == 1):
J = indiv_stability_set.mean(0)
else:
J = utils.standard_bootstrap(indiv_stability_set).mean(0)
roi_mask_nparray = nb.load(roi_mask_file).get_data().astype(
'float32').astype('bool')
J = J.astype("uint8")
#SPATIAL CONSTRAINT EXPERIMENT#
#roi_mask_nparray='empty'
#SPATIAL CONSTRAINT EXPERIMENT#
#print( 'calculating adjacency matrix')
G = utils.adjacency_matrix(
utils.cluster_timeseries(J,
roi_mask_nparray,
n_clusters,
similarity_metric='correlation',
affinity_threshold=0.0,
cluster_method='ward')[:, np.newaxis])
#print("finished calculating group stability matrix")
G = G.astype("uint8")
G_file = os.path.join(os.getcwd(), 'group_stability_matrix.npy')
np.save(G_file, G)
print('Saving group stability matrix %s' % (G_file))
return G_file
def gsm_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):
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
