def output_maps(self, nifti_filepath, threshold=0.01, two_tail=True, threshold_type="pvalue"):
print "Normalizing X matrix..."
Xnorm = simple_normalize(self.data.X)
print "Classifying with linear svm..."
clf = self.fit_linearsvc(Xnorm, self.data.Y)
print "Thresholding and dumping coefficients to file..."
self.coefs = clf.coef_[0]
if threshold_type == "pvalue":
thresholded_coefs = threshold_by_pvalue(self.coefs, threshold, two_tail=two_tail)
elif threshold_type == "raw_percentage":
thresholded_coefs = threshold_by_rawrange(self.coefs, threshold, two_tail=two_tail)
self.nifti.output_nifti_thrumask(
thresholded_coefs,
self.data.trial_mask,
self.data.mask_shape,
len(self.data.selected_trs),
self.data.raw_affine,
nifti_filepath,
)
self.nifti.convert_to_afni(nifti_filepath, nifti_filepath[:-4])
self.nifti.adwarp_to_template_talairach(
nifti_filepath[:-4] + "+orig", None, self.data.talairach_template_path, self.data.dxyz, overwrite=True
)
def output_maps(self, X, Y, time_points, nifti_filepath, threshold=0.01,
two_tail=True, verbose=True):
if not nifti_filepath.endswith('.nii'):
nifti_filepath = nifti_filepath+'.nii'
if verbose:
print 'fitting to output...'
clf = self.fit_linearsvc(X, Y)
self.coefs = clf.coef_[0]
thresholded_coefs = threshold_by_pvalue(self.coefs, threshold, two_tail=two_tail)
if verbose:
print 'reshaping the coefs to original brain shape...'
unmasked = self.data.unmask_Xcoefs(thresholded_coefs, time_points, verbose=verbose)
if verbose:
print 'saving nifti to filename:', nifti_filepath
self.data.save_unmasked_coefs(unmasked, nifti_filepath)
def output_maps(self, X, Y, time_points, nifti_filepath,
threshold=0.01, two_tail=True, verbose=True):
if not nifti_filepath.endswith('.nii'):
nifti_filepath = nifti_filepath+'.nii'
if verbose:
print 'fitting prior to output...'
pls = self.pls_train(X, Y, verbose=verbose)
coefs = pls.coefs
thresh_coefs = threshold_by_pvalue(coefs, threshold, two_tail=two_tail)
if verbose:
print 'reshaping the coefs to the original brain shape...'
unmasked = self.data.unmask_Xcoefs(thresh_coefs, time_points, verbose=verbose)
if verbose:
print 'saving nifti to filename: ', nifti_filepath
self.data.save_unmasked_coefs(unmasked, nifti_filepath)
