def test_onesample(self):
data, vardata, XYZ = make_data()
# rfx calibration
P = PT.permutation_test_onesample(data, XYZ, ndraws=ndraws)
c = [(P.random_Tvalues[P.ndraws*(0.95)],None),(P.random_Tvalues[P.ndraws*(0.5)],10)]
r = np.ones(data.shape[1],int)
r[data.shape[1]/2:] *= 10
#p_values, cluster_results, region_results = P.calibrate(nperms=100, clusters=c, regions=[r])
# mfx calibration
P = PT.permutation_test_onesample(data, XYZ, vardata=vardata, stat_id="student_mfx", ndraws=ndraws)
p_values, cluster_results, region_results = P.calibrate(nperms=nperms, clusters=c, regions=[r])
def onesample_test(data_images, vardata_images, mask_images, stat_id,
permutations=0, cluster_forming_th=0.01):
"""
Helper function for permutation-based mass univariate onesample
group analysis.
"""
# Prepare arrays
data, vardata, xyz, mask = prepare_arrays(data_images, vardata_images,
mask_images)
# Create one-sample permutation test instance
ptest = permutation_test_onesample(data, xyz, vardata=vardata,
stat_id=stat_id)
# Compute z-map image
zmap = np.zeros(data_images[0].get_shape()).squeeze()
zmap[list(xyz)] = ptest.zscore()
zimg = Image(zmap, data_images[0].get_affine())
# Compute mask image
maskimg = Image(mask, data_images[0].get_affine())
# Multiple comparisons
if permutations <= 0:
return zimg, maskimg
else:
# Cluster definition: (threshold, diameter)
cluster_def = (ptest.height_threshold(cluster_forming_th), None)
# Calibration
voxel_res, cluster_res, region_res = \
ptest.calibrate(nperms=permutations, clusters=[cluster_def])
nulls = {}
nulls['zmax'] = ptest.zscore(voxel_res['perm_maxT_values'])
nulls['s'] = cluster_res[0]['perm_size_values']
nulls['smax'] = cluster_res[0]['perm_maxsize_values']
# Return z-map image, mask image and dictionary of null distribution
# for cluster sizes (s), max cluster size (smax) and max z-score (zmax)
return zimg, maskimg, nulls
XYZ = np.array(np.where(mask==0))
p = XYZ.shape[1]
data = np.random.randn(n,p)
I = np.where(np.square(XYZ - XYZ.max(axis=1).reshape(-1,1)/2).sum(axis=0) <= r**2 )[0]
data[:, I] += signal
vardata = np.random.randn(n,p)**2
if axis==1:
data = data.transpose()
vardata = vardata.transpose()
return data, vardata, XYZ
################################################################################
# Example for using permutation_test_onesample class
data, vardata, XYZ = make_data()
# rfx calibration
P = PT.permutation_test_onesample(data,XYZ)
# clusters definition (height threshold, max diameter)
c = [(P.random_Tvalues[P.ndraws * (0.95)], None),
(P.random_Tvalues[P.ndraws * (0.5)], 10)]
# regions definition (label vector)
r = np.ones(data.shape[1], int)
r[data.shape[1]/2.:] *= 10
voxel_results, cluster_results, region_results = \
P.calibrate(nperms=100, clusters=c, regions=[r])
# mfx calibration
P = PT.permutation_test_onesample(data, XYZ, vardata=vardata,
stat_id="student_mfx")
voxel_results, cluster_results, region_results = \
P.calibrate(nperms=100, clusters=c, regions=[r])
################################################################################
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
import numpy as np
from nipy.neurospin.group.permutation_test import permutation_test_onesample
# Get group data
f = np.load('data/offset_002.npz')
data, vardata, xyz = f['mat'], f['var'], f['xyz']
# Create one-sample permutation test instance
ptest = permutation_test_onesample(data, xyz, stat_id='wilcoxon')
# Cluster definition: (threshold, diameter)
# Note that a list of definitions can be passed to ptest.calibrate
cluster_def = (ptest.height_threshold(0.01), None)
print cluster_def
# Multiple calibration
# To get accurate pvalues, don't pass nperms (default is 1e4)
# Yet it will take longer to run
voxel_res, cluster_res, region_res = ptest.calibrate(nperms=100, clusters=[cluster_def])
# Simulated Zmax values for FWER correction
simu_zmax = ptest.zscore(voxel_res['perm_maxT_values'])
# Output regions
clusters = cluster_res[0] ## This is a list because several cluster definitions can be accepted
sizes = clusters['size_values']
clusters_Pcorr = clusters['size_Corr_p_values']
