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.
"""
import os
import numpy as np
from CPAC.basc import cluster_matrix_average, ndarray_to_vol
indiv_stability_set = np.asarray([np.load(ism_file) for ism_file in indiv_stability_list])
nSubjects = indiv_stability_set.shape[0]
nVoxels = indiv_stability_set.shape[1]
cluster_ids = np.unique(clusters_G)
nClusters = cluster_ids.shape[0]
cluster_voxel_scores = np.zeros((nClusters, nSubjects, nVoxels))
for i in range(nSubjects):
cluster_voxel_scores[:,i] = cluster_matrix_average(indiv_stability_set[i], clusters_G)
icvs = []
icvs_idx = 0
for k in cluster_ids:
icvs.append(ndarray_to_vol(cluster_voxel_scores[icvs_idx], roi_mask_file, roi_mask_file, 'individual_group_cluster%i_stability.nii.gz' % k))
icvs_idx += 1
return icvs
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.
"""
import os
import numpy as np
from CPAC.basc import cluster_matrix_average, ndarray_to_vol
indiv_stability_set = np.asarray([np.load(ism_file) for ism_file in indiv_stability_list])
nSubjects = indiv_stability_set.shape[0]
nVoxels = indiv_stability_set.shape[1]
cluster_ids = np.unique(clusters_G)
nClusters = cluster_ids.shape[0]
cluster_voxel_scores = np.zeros((nClusters, nSubjects, nVoxels))
for i in range(nSubjects):
cluster_voxel_scores[:,i] = cluster_matrix_average(indiv_stability_set[i], clusters_G)
icvs = []
icvs_idx = 0
for k in cluster_ids:
icvs.append(ndarray_to_vol(cluster_voxel_scores[icvs_idx], roi_mask_file, roi_mask_file, 'individual_group_cluster%i_stability.nii.gz' % k))
icvs_idx += 1
return icvs
def group_stability_matrix(indiv_stability_list, n_bootstraps, k_clusters, stratification=None):
"""
Calculate the group stability matrix of the entire dataset by bootstrapping the dataset
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_bootstraps : integer
Number of bootstrap datasets
k_clusters : integer
Number of clusters
stratification : array_like, optional
List of integer entries denoting stratums for indiv_stability_list
Returns
-------
G : array_like
Group stability matrix of shape (`V`, `V`), `V` voxels
clusters_G : array_like
Length `V` array of cluster assignments for each voxel
cluster_voxel_scores : array_like
`K` by `V` matrix of within-cluster average values for each cluster of each voxel
"""
print(('Calculating group stability matrix for', len(indiv_stability_list), 'subjects.'))
if stratification is not None:
print('Applying stratification to group dataset')
from CPAC.basc import standard_bootstrap, adjacency_matrix, cluster_timeseries, cluster_matrix_average
import numpy as np
indiv_stability_set = np.asarray([np.load(ism_file) for ism_file in indiv_stability_list])
# indiv_stability_set = indiv_stability_list
print(('Individual stability list dimensions:', indiv_stability_set.shape))
V = indiv_stability_set.shape[2]
G = np.zeros((V,V))
for bootstrap_i in range(n_bootstraps):
if stratification is not None:
strata = np.unique(stratification)
J = np.zeros((V,V))
for stratum in strata:
J += standard_bootstrap(indiv_stability_set[np.where(stratification == stratum)]).sum(0)
J /= indiv_stability_set.shape[0]
else:
J = standard_bootstrap(indiv_stability_set).mean(0)
G += adjacency_matrix(cluster_timeseries(J, k_clusters, similarity_metric = 'data')[:,np.newaxis])
G /= n_bootstraps
clusters_G = cluster_timeseries(G, k_clusters, similarity_metric = 'data')
cluster_voxel_scores = cluster_matrix_average(G, clusters_G)
# Cluster labels normally start from 0, start from 1 to provide contrast when viewing between 0 voxels
clusters_G += 1
return G, clusters_G, cluster_voxel_scores
def group_stability_matrix(indiv_stability_list, n_bootstraps, k_clusters, stratification=None):
"""
Calculate the group stability matrix of the entire dataset by bootstrapping the dataset
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_bootstraps : integer
Number of bootstrap datasets
k_clusters : integer
Number of clusters
stratification : array_like, optional
List of integer entries denoting stratums for indiv_stability_list
Returns
-------
G : array_like
Group stability matrix of shape (`V`, `V`), `V` voxels
clusters_G : array_like
Length `V` array of cluster assignments for each voxel
cluster_voxel_scores : array_like
`K` by `V` matrix of within-cluster average values for each cluster of each voxel
"""
print 'Calculating group stability matrix for', len(indiv_stability_list), 'subjects.'
if stratification is not None:
print 'Applying stratification to group dataset'
from CPAC.basc import standard_bootstrap, adjacency_matrix, cluster_timeseries, cluster_matrix_average
import numpy as np
indiv_stability_set = np.asarray([np.load(ism_file) for ism_file in indiv_stability_list])
# indiv_stability_set = indiv_stability_list
print 'Individual stability list dimensions:', indiv_stability_set.shape
V = indiv_stability_set.shape[2]
G = np.zeros((V,V))
for bootstrap_i in range(n_bootstraps):
if stratification is not None:
strata = np.unique(stratification)
J = np.zeros((V,V))
for stratum in strata:
J += standard_bootstrap(indiv_stability_set[np.where(stratification == stratum)]).sum(0)
J /= indiv_stability_set.shape[0]
else:
J = standard_bootstrap(indiv_stability_set).mean(0)
G += adjacency_matrix(cluster_timeseries(J, k_clusters, similarity_metric = 'data')[:,np.newaxis])
G /= n_bootstraps
clusters_G = cluster_timeseries(G, k_clusters, similarity_metric = 'data')
cluster_voxel_scores = cluster_matrix_average(G, clusters_G)
# Cluster labels normally start from 0, start from 1 to provide contrast when viewing between 0 voxels
clusters_G += 1
return G, clusters_G, cluster_voxel_scores
