# spectral1.fit(Sim_mat_of_ABC_One)
# y_pred1 = spectral1.labels_.astype(np.int)
print(
'Clustering ABC according to connectivity to region 2'
)
ward2 = FeatureAgglomeration(
n_clusters=n_clusters,
affinity='euclidean',
linkage='ward')
ward2.fit(Sim_mat_of_ABC_Two)
y_pred2 = ward2.labels_.astype(np.int)
# spectral2.fit(Sim_mat_of_ABC_Two)
# y_pred2 = spectral2.labels_.astype(np.int)
y1_adj = adjacency_matrix(y_pred1)
y2_adj = adjacency_matrix(y_pred2)
reg1_ism += y1_adj
reg2_ism += y2_adj
#reg1_ism=np.asarray(reg1_ism)
#reg2_ism=np.asarray(reg2_ism)
#import pdb; pdb.set_trace()
if bootstraps == 0:
#print('No Bootstraps!')
reg1_final = reg1_ism
reg2_final = reg2_ism
reg1_pred = y_pred1
reg2_pred = y_pred2
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
######## 1.1 fs = 160 # Frequency of sampling, given by data resolution = 100 # Resolution of model (s.t. each bin has 1Hz of width) freq = 10 # Frequency of interest density = 0.2 # Density of the graph desired ###PDC # Fitting PDC models eo_pdc = ut.fit_model(eo, fs, resolution, "pdc", freq) ec_pdc = ut.fit_model(ec, fs, resolution, "pdc", freq) # Adjacency Matrices for 20% density networks ut.adjacency_matrix(eo_pdc, ut.find_threshold(eo_pdc,density), "eo_pdc_20") ut.adjacency_matrix(ec_pdc, ut.find_threshold(ec_pdc,density), "ec_pdc_20") ######## 1.2 # Fitting DTF models eo_dtf = ut.fit_model(eo, fs, resolution, "dtf", freq) ec_dtf = ut.fit_model(ec, fs, resolution, "dtf", freq) # Adjacency Matrices for 20% density networks ut.adjacency_matrix(eo_dtf, ut.find_threshold(eo_dtf,density), "eo_dtf_20") ut.adjacency_matrix(ec_dtf, ut.find_threshold(ec_dtf,density), "ec_dtf_20")
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
