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#!/usr/bin/env python3
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""
This modules launches a one-sample test on a dataset
Statistical significance is obtained using cluster-level inference
and permutation testing.
Author: Alexis Roche, Bertrand Thirion 2009-2012
"""
import numpy as np
from nibabel import Nifti1Image as Image
import nipy.labs.statistical_mapping as sm
from nipy.utils import example_data
def remake_images():
# Get group data
group_data = example_data.get_filename(
'neurospin', 'language_babies', 'offset_002.npz')
f = np.load(group_data)
data, vardata, xyz = f['mat'], f['var'], f['xyz']
dX = xyz[0].max() + 1
dY = xyz[1].max() + 1
dZ = xyz[2].max() + 1
aux = np.zeros([dX, dY, dZ])
data_images = []
vardata_images = []
mask_images = []
indices = tuple(xyz)
for i in range(data.shape[0]):
aux[indices] = data[i]
data_images.append(Image(aux.copy(), np.eye(4)))
aux[indices] = vardata[i]
vardata_images.append(Image(aux.copy(), np.eye(4)))
aux[indices] = 1
mask_images.append(aux)
return data_images, vardata_images, mask_images
data_images, vardata_images, mask_images = remake_images()
zimg, mask, nulls = sm.onesample_test(data_images,
None,
mask_images,
'wilcoxon',
permutations=1024,
cluster_forming_th=0.01)
clusters, info = sm.cluster_stats(zimg, mask, 0.01, nulls=nulls)
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