def run(self):
for fname in self.inputs.spmT_images:
img = nifti.load(fname)
data = np.array(img.get_data())
fdr = ENN(data.ravel())
th = fdr.threshold(0.05)
plt.figure()
ax = plt.subplot(1, 1, 1)
fdr.plot(mpaxes=ax)
plt.savefig("histogram.pdf")
active_map = data > th
thresholded_map = np.zeros(data.shape)
thresholded_map[active_map] = data[active_map]
thresholded_map = np.reshape(thresholded_map, data.shape)
new_img = nifti.Nifti1Image(thresholded_map, img.get_affine(), img.get_header())
nifti.save(new_img, 'thresholded_map.nii')
runtime = Bunch(returncode=0,
messages=None,
errmessages=None)
outputs = None
return InterfaceResult(deepcopy(self), runtime, outputs=outputs)
def run(self):
for fname in self.inputs.spmT_images:
img = nifti.load(fname)
data = np.array(img.get_data())
fdr = ENN(data.ravel())
th = fdr.threshold(0.05)
plt.figure()
ax = plt.subplot(1, 1, 1)
fdr.plot(mpaxes=ax)
plt.savefig("histogram.pdf")
active_map = data > th
thresholded_map = np.zeros(data.shape)
thresholded_map[active_map] = data[active_map]
thresholded_map = np.reshape(thresholded_map, data.shape)
new_img = nifti.Nifti1Image(thresholded_map, img.get_affine(),
img.get_header())
nifti.save(new_img, 'thresholded_map.nii')
runtime = Bunch(returncode=0, messages=None, errmessages=None)
outputs = None
return InterfaceResult(deepcopy(self), runtime, outputs=outputs)
# 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.algorithms.statistics.empirical_pvalue import NormalEmpiricalNull x = np.c_[np.random.normal(size=10000), np.random.normal(scale=4, size=10000)] enn = NormalEmpiricalNull(x) enn.threshold(verbose=True)
# 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.algorithms.statistics.empirical_pvalue import NormalEmpiricalNull
x = np.c_[np.random.normal(size=1e4),
np.random.normal(scale=4, size=1e4)]
enn = NormalEmpiricalNull(x)
enn.threshold(verbose=True)