def new_test_group_stability_matrix():
"""
Tests group_stability_matrix method. This creates a dataset of blobs varying only by additive zero-mean gaussian
noise and calculates the group stability matrix.
"""
import utils
import basc
bootstrap = 20
blobs = generate_blobs()
ism_dataset = np.zeros((5, blobs.shape[0], blobs.shape[0]))
indiv_stability_list = []
for i in range(ism_dataset.shape[0]):
ism_dataset[i] = utils.individual_stability_matrix(
blobs + 0.2 * np.random.randn(blobs.shape[0], blobs.shape[1]),
bootstrap,
3,
similarity_metric='correlation',
affinity_threshold=0.0)
f = 'ism_dataset_%i.npy' % i
indiv_stability_list.append(f)
np.save(f, ism_dataset[i])
#indiv_stability_list=ism_list
n_bootstraps = 10
n_clusters = 3
G = basc.map_group_stability(indiv_stability_list, n_bootstraps,
n_clusters)
return G
def test_individual_stability_matrix():
"""
Tests individual_stability_matrix method on three gaussian blobs.
"""
import utils
import numpy as np
import scipy as sp
desired = np.load(home + '/git_repo/PyBASC/tests/ism_test.npy')
blobs = generate_blobs()
ism = utils.individual_stability_matrix(blobs,
20,
3,
similarity_metric='correlation')
#how to use test here?
# np.corrcoef(ism.flatten(),desired.flatten())
# np.testing.assert_equal(ism,desired)
#
# corr=np.array(sp.spatial.distance.cdist(ism, desired, metric = 'correlation'))
#
assert False
def test_compare_stability_matrices():
import utils
import basc
bootstrap = 20
blobs = generate_blobs()
n_bootstraps = 10
n_clusters = 5
subjects = 20
ism_dataset = np.zeros((subjects, blobs.shape[0], blobs.shape[0]))
ism_list = []
for i in range(ism_dataset.shape[0]):
ism_dataset[i] = utils.individual_stability_matrix(
blobs + 0.2 * np.random.randn(blobs.shape[0], blobs.shape[1]),
n_bootstraps,
n_clusters,
affinity_threshold=0.0)
f = 'ism_dataset_%i.npy' % i
ism_list.append(f)
np.save(f, ism_dataset[i])
indiv_stability_list = ism_list
G = basc.map_group_stability(ism_list, n_bootstraps, n_clusters)
gsm = np.load(G)
gsm = gsm.astype("float64")
corr = []
corr = np.zeros((subjects, 1))
for j in range(ism_dataset.shape[0]):
ism = ism_dataset[j].astype("float64")
corr[j] = utils.compare_stability_matrices(gsm, ism)
meandist5 = corr.mean()
vardist5 = corr.var()
sumdist5 = corr.cumsum()
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 test_data_compress_expand():
import os
import numpy as np
import nibabel as nb
import utils
import pandas as pd
import sklearn as sk
#Setup
subject_file = home + '/git_repo/PyBASC/sample_data/sub1/Func_Quarter_Res.nii.gz'
roi_mask_file = home + '/git_repo/PyBASC/masks/LC_Quarter_Res.nii.gz'
roi2_mask_file = home + '/git_repo/PyBASC/masks/RC_Quarter_Res.nii.gz'
n_bootstraps = 100
n_clusters = 10
output_size = 20
cross_cluster = True
cbb_block_size = None
affinity_threshold = 0.5
print('Calculating individual stability matrix of:', subject_file)
data = nb.load(subject_file).get_data().astype('float32')
print('Data Loaded')
if (roi2_mask_file != None):
print('Setting up NIS')
roi_mask_file_nb = nb.load(roi_mask_file)
roi2_mask_file_nb = nb.load(roi2_mask_file)
roi_mask_nparray = nb.load(roi_mask_file).get_data().astype(
'float32').astype('bool')
roi2_mask_nparray = nb.load(roi2_mask_file).get_data().astype(
'float32').astype('bool')
roi1data = data[roi_mask_nparray]
roi2data = data[roi2_mask_nparray]
#add code that uploads the roi1data and roi2data, divides by the mean and standard deviation of the timeseries
roi1data = sk.preprocessing.normalize(roi1data, norm='l2')
roi2data = sk.preprocessing.normalize(roi2data, norm='l2')
print('Compressing data')
data_dict1 = utils.data_compression(roi1data.T, roi_mask_file_nb,
roi_mask_nparray, output_size)
Y1_compressed = data_dict1['data']
Y1_compressed = Y1_compressed.T
Y1_labels = pd.DataFrame(data_dict1['labels'])
Y1_labels = np.array(Y1_labels)
print('Y1 compressed')
print('Compressing Y2')
data_dict2 = utils.data_compression(roi2data.T, roi2_mask_file_nb,
roi2_mask_nparray, output_size)
Y2_compressed = data_dict2['data']
Y2_compressed = Y2_compressed.T
Y2_labels = pd.DataFrame(data_dict2['labels'])
print('Y2 compressed')
print('Going into ism')
ism = utils.individual_stability_matrix(Y1_compressed, n_bootstraps,
n_clusters, Y2_compressed,
cross_cluster, cbb_block_size,
affinity_threshold)
#ism=ism/n_bootstraps # was already done in ism
print('Expanding ism')
voxel_num = roi1data.shape[0]
voxel_ism = utils.expand_ism(ism, Y1_labels)
#voxel_ism=voxel_ism*100 # was already done in ism
voxel_ism = voxel_ism.astype("uint8")
def nifti_individual_stability(subject_file,
roi_mask_file,
n_bootstraps,
n_clusters,
output_size,
similarity_metric,
cross_cluster=False,
roi2_mask_file=None,
blocklength=1,
cbb_block_size=None,
affinity_threshold=0.5):
"""
Calculate the individual stability matrix for a single subject by using Circular Block Bootstrapping method
for time-series data.
Parameters
----------
subject_file : string
Nifti file of a subject
roi_mask_file : string
Region of interest (this method is too computationally intensive to perform on a whole-brain volume)
n_bootstraps : integer
Number of bootstraps
n_clusters : integer
Number of clusters
cbb_block_size : integer, optional
Size of the time-series block when performing circular block bootstrap
affinity_threshold : float, optional
Minimum threshold for similarity matrix based on correlation to create an edge
Returns
-------
ism : array_like
Individual stability matrix of shape (`V`, `V`), `V` voxels
"""
import os
import numpy as np
import nibabel as nb
import utils
import pandas as pd
import sklearn as sk
from sklearn import preprocessing
print('Calculating individual stability matrix of:', subject_file)
data = nb.load(subject_file).get_data().astype('float32')
#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')
# roi_np_mask_file = os.path.join(os.getcwd(), 'mask_nparray.npy')
# np.save(roi_np_mask_file, roi_mask_nparray)
# import pdb; pdb.set_trace()
roi1data = data[roi_mask_nparray]
roi1data = sk.preprocessing.normalize(roi1data, norm='l2')
data_dict1 = utils.data_compression(roi1data.T, roi_mask_file_nb,
roi_mask_nparray, output_size)
Y1_compressed = data_dict1['data']
#Y1_compressed = Y1_compressed.T
Y1_labels = pd.DataFrame(data_dict1['labels'])
Y1_labels = np.array(Y1_labels)
#import pdb;pdb.set_trace()
if (roi2_mask_file != None):
roi2_mask_file_nb = nb.load(roi2_mask_file)
roi2_mask_nparray = nb.load(roi2_mask_file).get_data().astype(
'float32').astype('bool')
roi2data = data[roi2_mask_nparray]
roi2data = sk.preprocessing.normalize(roi2data, norm='l2')
#print( 'Compressing Y2')
output_size2 = output_size + 5
data_dict2 = utils.data_compression(roi2data.T, roi2_mask_file_nb,
roi2_mask_nparray, output_size2)
Y2_compressed = data_dict2['data']
Y2_labels = pd.DataFrame(data_dict2['labels'])
Y2_labels = np.array(Y2_labels)
print('Going into ism')
#import pdb;pdb.set_trace()
ism = utils.individual_stability_matrix(Y1_compressed,
roi_mask_nparray, n_bootstraps,
n_clusters, similarity_metric,
Y2_compressed, cross_cluster,
cbb_block_size, blocklength,
affinity_threshold)
#import pdb; pdb.set_trace()
#ism=ism/n_bootstraps # was already done in ism
print('Expanding ism')
voxel_num = roi1data.shape[0]
voxel_ism = utils.expand_ism(ism, Y1_labels)
#voxel_ism=voxel_ism*100 # was already done in ism
voxel_ism = voxel_ism.astype("uint8")
ism_file = os.path.join(os.getcwd(), 'individual_stability_matrix.npy')
np.save(ism_file, voxel_ism)
#print ('Saving individual stability matrix %s for %s' % (ism_file, subject_file))
return ism_file
else:
print('debug2')
#Y2_compressed=None
#import pdb; pdb.set_trace()
Y2 = None
ism = utils.individual_stability_matrix(Y1_compressed,
roi_mask_nparray, n_bootstraps,
n_clusters, similarity_metric,
Y2, cross_cluster,
cbb_block_size, blocklength,
affinity_threshold)
#ism=ism/n_bootstraps # was already done in ism
print('debug3')
print(Y1_labels)
print('expanding ism')
voxel_num = roi1data.shape[0]
voxel_ism = utils.expand_ism(ism, Y1_labels)
print('debug3b')
#voxel_ism=voxel_ism*100 # was already done in ism
voxel_ism = voxel_ism.astype("uint8")
ism_file = os.path.join(os.getcwd(), 'individual_stability_matrix.npy')
np.save(ism_file, voxel_ism)
print('debug4')
#print( 'Saving individual stability matrix %s for %s' % (ism_file, subject_file))
return ism_file
return ism_file
