text: # Author: Martin Luessi <mluessi@nmr.mgh.harvard.edu>
  #
  # License: Simplified BSD
  
  import os.path as op
  import numpy as np
  from nose.tools import assert_true
  from numpy.testing import assert_array_almost_equal
  
  import mne
  from mne.datasets import sample
  from mne import fiff, read_cov, read_forward_solution
  from mne.inverse_sparse import gamma_map
  
  data_path = sample.data_path(download=False)
  fname_evoked = op.join(data_path, 'MEG', 'sample', 'sample_audvis-ave.fif')
  fname_cov = op.join(data_path, 'MEG', 'sample', 'sample_audvis-cov.fif')
  fname_fwd = op.join(data_path, 'MEG', 'sample',
                      'sample_audvis-eeg-oct-6-fwd.fif')
  
  
  @sample.requires_sample_data
  def test_gamma_map():
      """Test Gamma MAP inverse"""
      forward = read_forward_solution(fname_fwd, force_fixed=False,
                                      surf_ori=True)
      evoked = fiff.Evoked(fname_evoked, setno=0, baseline=(None, 0))
      evoked.crop(tmin=0, tmax=0.3)
  
      cov = read_cov(fname_cov)
      cov = mne.cov.regularize(cov, evoked.info)
  
      alpha = 0.2
      stc = gamma_map(evoked, forward, cov, alpha, tol=1e-5,
                      xyz_same_gamma=True, update_mode=1)
      idx = np.argmax(np.sum(stc.data ** 2, axis=1))
      assert_true(np.concatenate(stc.vertno)[idx] == 96397)
  
      stc = gamma_map(evoked, forward, cov, alpha, tol=1e-5,
                      xyz_same_gamma=False, update_mode=1)
      idx = np.argmax(np.sum(stc.data ** 2, axis=1))
      assert_true(np.concatenate(stc.vertno)[idx] == 82010)
  
      # force fixed orientation
      stc, res = gamma_map(evoked, forward, cov, alpha, tol=1e-5,
                           xyz_same_gamma=False, update_mode=2,
                           loose=None, return_residual=True)
      idx = np.argmax(np.sum(stc.data ** 2, axis=1))
      assert_true(np.concatenate(stc.vertno)[idx] == 83398)
  
      assert_array_almost_equal(evoked.times, res.times)
  