<commit_before># -*- coding: utf-8 -*-
"""
============================================================
Source localization with equivalent current dipole (ECD) fit
============================================================

This shows how to fit a dipole :footcite:`Sarvas1987` using mne-python.

For a comparison of fits between MNE-C and mne-python, see
`this gist <https://gist.github.com/larsoner/ca55f791200fe1dc3dd2>`__.
"""

from os import path as op
import numpy as np
import matplotlib.pyplot as plt

import mne
from mne.forward import make_forward_dipole
from mne.evoked import combine_evoked
from mne.simulation import simulate_evoked

from nilearn.plotting import plot_anat
from nilearn.datasets import load_mni152_template

data_path = mne.datasets.sample.data_path()
subjects_dir = op.join(data_path, 'subjects')
fname_ave = op.join(data_path, 'MEG', 'sample', 'sample_audvis-ave.fif')
fname_cov = op.join(data_path, 'MEG', 'sample', 'sample_audvis-cov.fif')
fname_bem = op.join(subjects_dir, 'sample', 'bem', 'sample-5120-bem-sol.fif')
fname_trans = op.join(data_path, 'MEG', 'sample',
                      'sample_audvis_raw-trans.fif')
fname_surf_lh = op.join(subjects_dir, 'sample', 'surf', 'lh.white')

###############################################################################
# Let's localize the N100m (using MEG only)
evoked = mne.read_evokeds(fname_ave, condition='Right Auditory',
                          baseline=(None, 0))
evoked.pick_types(meg=True, eeg=False)
evoked_full = evoked.copy()
evoked.crop(0.07, 0.08)

# Fit a dipole
dip = mne.fit_dipole(evoked, fname_cov, fname_bem, fname_trans)[0]

# Plot the result in 3D brain with the MRI image.
dip.plot_locations(fname_trans, 'sample', subjects_dir, mode='orthoview')

###############################################################################
# Plot the result in 3D brain with the MRI image using Nilearn
# In MRI coordinates and in MNI coordinates (template brain)

trans = mne.read_trans(fname_trans)
subject = 'sample'
mni_pos = mne.head_to_mni(dip.pos, mri_head_t=trans,
                          subject=subject, subjects_dir=subjects_dir)

mri_pos = mne.head_to_mri(dip.pos, mri_head_t=trans,
                          subject=subject, subjects_dir=subjects_dir)

t1_fname = op.join(subjects_dir, subject, 'mri', 'T1.mgz')
fig_T1 = plot_anat(t1_fname, cut_coords=mri_pos[0], title='Dipole loc.')
<commit_msg>FIX: Try again [skip travis]<commit_after># -*- coding: utf-8 -*-
"""
============================================================
Source localization with equivalent current dipole (ECD) fit
============================================================

This shows how to fit a dipole :footcite:`Sarvas1987` using mne-python.

For a comparison of fits between MNE-C and mne-python, see
`this gist <https://gist.github.com/larsoner/ca55f791200fe1dc3dd2>`__.
"""

from os import path as op
import numpy as np
import matplotlib.pyplot as plt

import mne
from mne.forward import make_forward_dipole
from mne.evoked import combine_evoked
from mne.simulation import simulate_evoked

from nilearn.plotting import plot_anat
from nilearn.datasets import load_mni152_template

data_path = mne.datasets.sample.data_path()
subjects_dir = op.join(data_path, 'subjects')
fname_ave = op.join(data_path, 'MEG', 'sample', 'sample_audvis-ave.fif')
fname_cov = op.join(data_path, 'MEG', 'sample', 'sample_audvis-cov.fif')
fname_bem = op.join(subjects_dir, 'sample', 'bem', 'sample-5120-bem-sol.fif')
fname_trans = op.join(data_path, 'MEG', 'sample',
                      'sample_audvis_raw-trans.fif')
fname_surf_lh = op.join(subjects_dir, 'sample', 'surf', 'lh.white')

###############################################################################
# Let's localize the N100m (using MEG only)
evoked = mne.read_evokeds(fname_ave, condition='Right Auditory',
                          baseline=(None, 0))
evoked.pick_types(meg=True, eeg=False)
evoked_full = evoked.copy()
evoked.crop(0.07, 0.08, verbose='error')  # ignore baseline

# Fit a dipole
dip = mne.fit_dipole(evoked, fname_cov, fname_bem, fname_trans)[0]

# Plot the result in 3D brain with the MRI image.
dip.plot_locations(fname_trans, 'sample', subjects_dir, mode='orthoview')

###############################################################################
# Plot the result in 3D brain with the MRI image using Nilearn
# In MRI coordinates and in MNI coordinates (template brain)

trans = mne.read_trans(fname_trans)
subject = 'sample'
mni_pos = mne.head_to_mni(dip.pos, mri_head_t=trans,
                          subject=subject, subjects_dir=subjects_dir)

mri_pos = mne.head_to_mri(dip.pos, mri_head_t=trans,
                          subject=subject, subjects_dir=subjects_dir)

t1_fname = op.join(subjects_dir, subject, 'mri', 'T1.mgz')
fig_T1 = plot_anat(t1_fname, cut_coords=mri_pos[0], title='Dipole loc.')

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