[med-svn] [python-mne] 301/353: ENH : add MxNE example of sample data

[Yaroslav Halchenko]

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yoh pushed a commit to tag 0.4
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commit 4014c120b96fd4371d5a5f550c341a1736819a45
Author: Alexandre Gramfort <alexandre.gramfort at inria.fr>
Date:   Tue Jul 17 22:32:41 2012 +0200

    ENH : add MxNE example of sample data
---
 examples/inverse/plot_mixed_norm_L21_inverse.py | 69 +++++++++++++++++++++++++
 1 file changed, 69 insertions(+)

diff --git a/examples/inverse/plot_mixed_norm_L21_inverse.py b/examples/inverse/plot_mixed_norm_L21_inverse.py
new file mode 100644
index 0000000..c60668d
--- /dev/null
+++ b/examples/inverse/plot_mixed_norm_L21_inverse.py
@@ -0,0 +1,69 @@
+"""
+===============================================
+Compute sparse solver based on L1/L2 mixed norm
+===============================================
+
+See
+Gramfort A., Kowalski M. and Hamalainen, M,
+Mixed-norm estimates for the M/EEG inverse problem using accelerated
+gradient methods, Physics in Medicine and Biology, 2012
+"""
+# Author: Alexandre Gramfort <gramfort at nmr.mgh.harvard.edu>
+#
+# License: BSD (3-clause)
+
+print __doc__
+
+import mne
+from mne import fiff
+from mne.datasets import sample
+from mne.mixed_norm import mixed_norm
+from mne.minimum_norm import make_inverse_operator, apply_inverse
+from mne.viz import plot_sparse_source_estimates, plot_evoked
+
+data_path = sample.data_path('..')
+fwd_fname = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'
+ave_fname = data_path + '/MEG/sample/sample_audvis-ave.fif'
+cov_fname = data_path + '/MEG/sample/sample_audvis-cov.fif'
+
+# Read noise covariance matrix
+cov = mne.read_cov(cov_fname)
+# Handling average file
+setno = 0
+evoked = fiff.read_evoked(ave_fname, setno=setno, baseline=(None, 0))
+evoked.crop(tmin=0, tmax=0.3)
+# Handling forward solution
+forward = mne.read_forward_solution(fwd_fname, force_fixed=True,
+                                    surf_ori=True)
+
+cov = mne.cov.regularize(cov, evoked.info)
+
+import pylab as pl
+pl.figure(-2)
+ylim = dict(eeg=[-10, 10], grad=[-400, 400], mag=[-600, 600])
+plot_evoked(evoked, ylim=ylim, proj=True)
+
+###############################################################################
+# Run solver
+alpha = 70  # regularization parameter between 0 and 100 (100 is high)
+loose, depth = 0.2, 0.9  # loose orientation & depth weighting
+
+# Compute dSPM solution to be used as weights in MxNE
+inverse_operator = make_inverse_operator(evoked.info, forward, cov,
+                                         loose=loose, depth=depth)
+stc_dspm = apply_inverse(evoked, inverse_operator, lambda2=1. / 9., dSPM=True)
+
+# Compute MxNE inverse solution
+stc, residual = mixed_norm(evoked, forward, cov, alpha, loose=loose,
+                 depth=depth, maxit=3000, tol=1e-4, active_set_size=10,
+                 debias=True, weights=stc_dspm, weights_min=8.,
+                 return_residual=True)
+
+pl.figure(-3)
+plot_evoked(residual, ylim=ylim, proj=True)
+
+###############################################################################
+# View in 2D and 3D ("glass" brain like 3D plot)
+plot_sparse_source_estimates(forward['src'], stc, bgcolor=(1, 1, 1),
+                             opacity=0.1, fig_name="MxNE (cond %s)" % setno,
+                             fig_number=setno)

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