def apply_repro_analysis(dataset, thresholds=[3.0], method='crfx'):
"""
perform the reproducibility analysis according to the
"""
from nipy.labs.spatial_models.discrete_domain import \
grid_domain_from_binary_array
n_subj, dimx, dimy = dataset.shape
func = np.reshape(dataset, (n_subj, dimx * dimy)).T
var = np.ones((dimx * dimy, n_subj))
domain = grid_domain_from_binary_array(np.ones((dimx, dimy, 1)))
ngroups = 5
sigma = 2.0
csize = 10
niter = 5
verbose = 0
swap = False
kap, clt, pkd = [], [], []
for threshold in thresholds:
kappa, cls, pks = [], [], []
kwargs = {'threshold': threshold, 'csize': csize}
for i in range(niter):
k = voxel_reproducibility(func, var, domain, ngroups, method, swap,
verbose, **kwargs)
kappa.append(k)
cld = cluster_reproducibility(func, var, domain, ngroups, sigma,
method, swap, verbose, **kwargs)
cls.append(cld)
pk = peak_reproducibility(func, var, domain, ngroups, sigma,
method, swap, verbose, **kwargs)
pks.append(pk)
kap.append(np.array(kappa))
clt.append(np.array(cls))
pkd.append(np.array(pks))
kap = np.array(kap)
clt = np.array(clt)
pkd = np.array(pkd)
return kap, clt, pkd
def apply_repro_analysis(dataset, thresholds=[3.0], method = 'crfx'):
"""
perform the reproducibility analysis according to the
"""
from nipy.labs.spatial_models.discrete_domain import \
grid_domain_from_binary_array
n_subj, dimx, dimy = dataset.shape
func = np.reshape(dataset,(n_subj, dimx * dimy)).T
var = np.ones((dimx * dimy, n_subj))
domain = grid_domain_from_binary_array(np.ones((dimx, dimy, 1)))
ngroups = 5
sigma = 2.0
csize = 10
niter = 5
verbose = 0
swap = False
kap, clt, pkd = [], [], []
for threshold in thresholds:
kappa, cls, pks = [], [], []
kwargs = {'threshold':threshold, 'csize':csize}
for i in range(niter):
k = voxel_reproducibility(func, var, domain, ngroups,
method, swap, verbose, **kwargs)
kappa.append(k)
cld = cluster_reproducibility(func, var, domain, ngroups, sigma,
method, swap, verbose, **kwargs)
cls.append(cld)
pk = peak_reproducibility(func, var, domain, ngroups, sigma,
method, swap, verbose, **kwargs)
pks.append(pk)
kap.append(np.array(kappa))
clt.append(np.array(cls))
pkd.append(np.array(pks))
kap = np.array(kap)
clt = np.array(clt)
pkd = np.array(pkd)
return kap, clt, pkd
) from nipy.labs.spatial_models.discrete_domain import grid_domain_from_binary_array ############################################################################### # Generate the data n_subj = 105 shape = (60, 60) pos = np.array([[12, 14], [20, 20], [30, 20]]) ampli = np.array([2.5, 3.5, 3]) betas = simul.surrogate_2d_dataset(n_subj=n_subj, shape=shape, pos=pos, ampli=ampli, width=5.0) n_vox = np.prod(shape) # set the variance at 1 everywhere func = np.reshape(betas, (n_subj, n_vox)).T var = np.ones((n_vox, n_subj)) domain = grid_domain_from_binary_array(np.ones((shape[0], shape[1], 1))) ############################################################################### # Run reproducibility analysis ngroups = 10 thresholds = np.arange(0.5, 6.0, 0.5) sigma = 2.0 csize = 10 niter = 10 method = "crfx" verbose = 0 # do not use permutations swap = False
def cc_array_mask(mask, size_threshold):
"""Filter the mask to keep only connected components above a given size"""
graph = wgraph_from_coo_matrix(grid_domain_from_binary_array(mask).topology)
mask[mask] = cc_mask(graph, mask[mask], size_threshold)
return mask
def group_reproducibility_metrics(mask_images,
contrast_images,
variance_images,
thresholds,
ngroups,
method,
cluster_threshold=10,
number_of_samples=10,
sigma=6.,
do_clusters=True,
do_voxels=True,
do_peaks=True,
swap=False):
"""
Main function to perform reproducibility analysis, including nifti1 io
Parameters
----------
threshold: list or 1-d array,
the thresholds to be tested
Returns
-------
cluster_rep_results: dictionary,
results of cluster-level reproducibility analysis
voxel_rep_results: dictionary,
results of voxel-level reproducibility analysis
peak_rep_results: dictionary,
results of peak-level reproducibility analysis
"""
from nibabel import load
from ..mask import intersect_masks
if ((len(variance_images) == 0) & (method is not 'crfx')):
raise ValueError('Variance images are necessary')
nsubj = len(contrast_images)
# compute the group mask
affine = get_affine(load(mask_images[0]))
mask = intersect_masks(mask_images, threshold=0) > 0
domain = grid_domain_from_binary_array(mask, affine)
# read the data
group_con = []
group_var = []
for s in range(nsubj):
group_con.append(load(contrast_images[s]).get_data()[mask])
if len(variance_images) > 0:
group_var.append(load(variance_images[s]).get_data()[mask])
group_con = np.squeeze(np.array(group_con)).T
group_con[np.isnan(group_con)] = 0
if len(variance_images) > 0:
group_var = np.squeeze(np.array(group_var)).T
group_var[np.isnan(group_var)] = 0
group_var = np.maximum(group_var, 1.e-15)
# perform the analysis
voxel_rep_results = {}
cluster_rep_results = {}
peak_rep_results = {}
for ng in ngroups:
if do_voxels:
voxel_rep_results.update({ng: {}})
if do_clusters:
cluster_rep_results.update({ng: {}})
if do_peaks:
peak_rep_results.update({ng: {}})
for th in thresholds:
kappa = []
cls = []
pk = []
kwargs = {'threshold': th, 'csize': cluster_threshold}
for i in range(number_of_samples):
if do_voxels:
kappa.append(
voxel_reproducibility(group_con, group_var, domain, ng,
method, swap, **kwargs))
if do_clusters:
cls.append(
cluster_reproducibility(group_con, group_var, domain,
ng, sigma, method, swap,
**kwargs))
if do_peaks:
pk.append(
peak_reproducibility(group_con, group_var, domain, ng,
sigma, method, swap, **kwargs))
if do_voxels:
voxel_rep_results[ng].update({th: np.array(kappa)})
if do_clusters:
cluster_rep_results[ng].update({th: np.array(cls)})
if do_peaks:
peak_rep_results[ng].update({th: np.array(cls)})
return voxel_rep_results, cluster_rep_results, peak_rep_results
# step 1: generate some synthetic data
n_subj = 10
shape = (60, 60)
pos = 3 * np.array([[6, 7],
[10, 10],
[15, 10]])
ampli = np.array([5, 7, 6])
sjitter = 6.0
dataset = simul.surrogate_2d_dataset(n_subj=n_subj, shape=shape, pos=pos,
ampli=ampli, width=10.0)
# dataset represents 2D activation images from n_subj subjects,
# step 2 : prepare all the information for the parcellation
nbparcel = 10
ldata = np.reshape(dataset, (n_subj, np.prod(shape), 1))
domain = dom.grid_domain_from_binary_array(np.ones(shape))
# step 3 : run the algorithm
Pa = hp.hparcel(domain, ldata, nbparcel, mu=3.0)
# note: play with mu to change the 'stiffness of the parcellation'
# step 4: look at the results
Label = np.array([np.reshape(Pa.individual_labels[:, s], shape)
for s in range(n_subj)])
import matplotlib.pylab as mp
mp.figure(figsize=(8, 4))
mp.title('Input data')
for s in range(n_subj):
mp.subplot(2, 5, s + 1)
mp.imshow(dataset[s], interpolation='nearest')
def group_reproducibility_metrics(
mask_images, contrast_images, variance_images, thresholds, ngroups,
method, cluster_threshold=10, number_of_samples=10, sigma=6.,
do_clusters=True, do_voxels=True, do_peaks=True, swap=False):
"""
Main function to perform reproducibility analysis, including nifti1 io
Parameters
----------
threshold: list or 1-d array,
the thresholds to be tested
Returns
-------
cluster_rep_results: dictionary,
results of cluster-level reproducibility analysi
voxel_rep_results: dictionary,
results of voxel-level reproducibility analysis
peak_rep_results: dictionary,
results of peak-level reproducibility analysis
"""
from nibabel import load
from ..mask import intersect_masks
if ((len(variance_images) == 0) & (method is not 'crfx')):
raise ValueError('Variance images are necessary')
nsubj = len(contrast_images)
# compute the group mask
affine = load(mask_images[0]).get_affine()
mask = intersect_masks(mask_images, threshold=0) > 0
domain = grid_domain_from_binary_array(mask, affine)
# read the data
group_con = []
group_var = []
for s in range(nsubj):
group_con.append(load(contrast_images[s]).get_data()[mask])
if len(variance_images) > 0:
group_var.append(load(variance_images[s]).get_data()[mask])
group_con = np.squeeze(np.array(group_con)).T
group_con[np.isnan(group_con)] = 0
if len(variance_images) > 0:
group_var = np.squeeze(np.array(group_var)).T
group_var[np.isnan(group_var)] = 0
group_var = np.maximum(group_var, 1.e-15)
# perform the analysis
voxel_rep_results = {}
cluster_rep_results = {}
peak_rep_results = {}
for ng in ngroups:
if do_voxels:
voxel_rep_results.update({ng: {}})
if do_clusters:
cluster_rep_results.update({ng: {}})
if do_peaks:
peak_rep_results.update({ng: {}})
for th in thresholds:
kappa = []
cls = []
pk = []
kwargs = {'threshold': th, 'csize': cluster_threshold}
for i in range(number_of_samples):
if do_voxels:
kappa.append(voxel_reproducibility(
group_con, group_var, domain, ng, method, swap,
**kwargs))
if do_clusters:
cls.append(cluster_reproducibility(
group_con, group_var, domain, ng, sigma, method,
swap, **kwargs))
if do_peaks:
pk.append(peak_reproducibility(
group_con, group_var, domain, ng, sigma, method,
swap, **kwargs))
if do_voxels:
voxel_rep_results[ng].update({th: np.array(kappa)})
if do_clusters:
cluster_rep_results[ng].update({th: np.array(cls)})
if do_peaks:
peak_rep_results[ng].update({th: np.array(cls)})
return voxel_rep_results, cluster_rep_results, peak_rep_results
###############################################################################
# Generate the data
n_subj = 105
shape = (60, 60)
pos = np.array([[12, 14],
[20, 20],
[30, 20]])
ampli = np.array([2.5, 3.5, 3])
betas = simul.surrogate_2d_dataset(n_subj=n_subj, shape=shape, pos=pos,
ampli=ampli, width=5.0)
n_vox = np.prod(shape)
# set the variance at 1 everywhere
func = np.reshape(betas, (n_subj, n_vox)).T
var = np.ones((n_vox, n_subj))
domain = grid_domain_from_binary_array(np.ones((shape[0], shape[1], 1)))
###############################################################################
# Run reproducibility analysis
ngroups = 10
thresholds = np.arange(.5, 6., .5)
sigma = 2.0
csize = 10
niter = 10
method = 'crfx'
verbose = 0
# do not use permutations
swap = False
