import pylab import glob from nibabel import load, save, Nifti1Image from retino.angular_analysis import load_texture, save_texture, angular_maps from nipy.modalities.fmri.design_matrix import make_dmtx from nipy.modalities.fmri.glm import GeneralLinearModel, data_scaling from nipy.labs import compute_mask_files from config_look_loc import make_paths # ----------------------------------------------------------- # --------- Set the paths ----------------------------------- #----------------------------------------------------------- paths = make_paths() subjects = paths.keys()[:1] result_dir = 'analysis' # choose volume-based or surface-based analysis sides = ['left', 'right'] #[False] # # False: volume-based analysis # left: left hemisphere # right: right hemisphere # --------------------------------------------------------- # -------- General data-related Information --------------- # --------------------------------------------------------- tr = 2.4 nb_frames = 166 frametimes = np.arange(nb_frames) * tr
""" Visualization of retinotopy results Author: Bertrand Thirion, 2012 """ import numpy as np import os.path as op import enthought.mayavi.mlab as mlab from nibabel.gifti import read from retino.visu_mlab import plot_retino_image from config_look_loc import make_paths paths = make_paths() for subject in paths.keys(): print subject func_path = op.join(paths[subject]['base'], paths[subject]['acquisition'], 'analysis') # set all the paths ltex_path = op.join(func_path, 'left_phase_wedge.gii') rtex_path = op.join(func_path, 'right_phase_wedge.gii') lmesh_path_inflated = paths[subject]['left_inflated'] lcurv_path = op.join(op.dirname(lmesh_path_inflated), 'lh.avg_curv.gii') rmesh_path_inflated = paths[subject]['right_inflated'] rcurv_path = op.join(op.dirname(lmesh_path_inflated), 'rh.avg_curv.gii') lmask_path = op.join(func_path, 'left_mask.gii') rmask_path = op.join(func_path, 'right_mask.gii')
from nibabel import load, save, Nifti1Image from nipy.modalities.fmri.design_matrix import make_dmtx from nipy.modalities.fmri.experimental_paradigm import BlockParadigm from nipy.modalities.fmri.glm import GeneralLinearModel, data_scaling from nipy.labs import compute_mask_files from retino.angular_analysis import ( load_texture, save_texture, cc_array_mask, cc_mesh_mask, phase_unwrapping) from config_look_loc import make_paths # ----------------------------------------------------------- # --------- Set the paths ----------------------------------- #----------------------------------------------------------- paths = make_paths() subjects = paths.keys() result_dir = 'block' # choose volume-based or surface-based analysis sides = ['left', 'right'] # [False] # # False: volume-based analysis # left: left hemisphere # right: right hemisphere # --------------------------------------------------------- # -------- General data-related Information --------------- # --------------------------------------------------------- tr = 2.4 nb_frames = 166 frametimes = np.arange(nb_frames) * tr