b28f80346270231ff2ef253af7ef4c5cd37f0916,plot_localizer_mass_univariate.py,,,#,26

Before Change

pvals_bonferroni[np.isnan(pvals_bonferroni)] = 1
pvals_bonferroni[pvals_bonferroni > 1] = 1
neg_log_pvals_bonferroni = -np.log10(pvals_bonferroni)
neg_log_pvals_bonferroni_unmasked = nifti_masker.inverse_transform(
    neg_log_pvals_bonferroni).get_data()

////// Visualization //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import ImageGrid

// Use the structural image of one subject as a background image
resampled_anat = resample_img(
    dataset_files.anat[0],
    target_affine=nibabel.load(dataset_files.cmaps[0]).get_affine(),
    target_shape=neg_log_pvals_bonferroni_unmasked.shape)
structural_data = resampled_anat.get_data()

// Various plotting parameters
picked_slice = 35  // plotted slice
vmin = -np.log10(0.1)  // 10% corrected

After Change

    target_affine=structural_img.get_affine(),
    target_shape=structural_img.shape,
    interpolation="nearest")
neg_log_pvals_bonferroni_resampled = resample_img(
    neg_log_pvals_bonferroni_unmasked,
    target_affine=structural_img.get_affine(),
    target_shape=structural_img.shape,
    interpolation="nearest")

// Various plotting parameters
picked_slice = 110  // plotted slice
vmin = -np.log10(0.1)  // 10% corrected

Instances