[med-svn] [python-mne] 265/376: cleanup manual + some examples

[Yaroslav Halchenko]

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yoh pushed a commit to annotated tag v0.1
in repository python-mne.

commit 811c2db42f1c2fbbdfb25e5a0cd2aa3f866bff94
Author: Alexandre Gramfort <alexandre.gramfort at inria.fr>
Date:   Wed May 25 14:49:04 2011 -0400

    cleanup manual + some examples
---
 doc/source/manual/browse.rst                       |  6 ++-
 examples/inverse/plot_compute_mne_inverse.py       |  6 ---
 ...py => plot_compute_mne_inverse_raw_in_label.py} |  0
 examples/inverse/plot_minimum_norm_estimate.py     | 54 ----------------------
 .../plot_source_space_time_frequency.py            |  1 -
 5 files changed, 4 insertions(+), 63 deletions(-)

diff --git a/doc/source/manual/browse.rst b/doc/source/manual/browse.rst
index f93cd6a..37f1e44 100755
--- a/doc/source/manual/browse.rst
+++ b/doc/source/manual/browse.rst
@@ -1986,7 +1986,8 @@ in this section.
 
     Works similarly to ignore except that a mask specifies the trigger channel
     bits to be included. For example, to look at trigger input lines
-    one to three only, ignoring others, specify ``mask 7`` (INLINE_EQUATION)
+    one to three only, ignoring others, specify ``mask 7``
+    (:math:`2^0 + 2^1 + 2^2 = 7`).
 
 **prevevent <*number*>**
 
@@ -2264,7 +2265,8 @@ epochs to be included in the estimation of the covariance matrix.
 
     Works similarly to ignore except that a mask specifies the trigger channel
     bits to be included. For example, to look at trigger input lines
-    one to three only, ignoring others, specify ``mask 7`` (INLINE_EQUATION)
+    one to three only, ignoring others, specify ``mask 7``
+    (:math:`2^0 + 2^1 + 2^2 = 7`).
 
 **delay <*time / s*>**
 
diff --git a/examples/inverse/plot_compute_mne_inverse.py b/examples/inverse/plot_compute_mne_inverse.py
index 141475b..7813f7c 100644
--- a/examples/inverse/plot_compute_mne_inverse.py
+++ b/examples/inverse/plot_compute_mne_inverse.py
@@ -14,13 +14,10 @@ and stores the solution in stc files for visualisation.
 
 print __doc__
 
-import numpy as np
 import pylab as pl
-import mne
 from mne.datasets import sample
 from mne.fiff import Evoked
 from mne.minimum_norm import apply_inverse, read_inverse_operator
-from mne.viz import plot_source_estimate
 
 
 data_path = sample.data_path('..')
@@ -48,6 +45,3 @@ pl.plot(1e3 * stc.times, stc.data[::100, :].T)
 pl.xlabel('time (ms)')
 pl.ylabel('dSPM value')
 pl.show()
-
-# View in 3D
-plot_source_estimate(inverse_operator['src'], stc)
diff --git a/examples/inverse/plot_minimum_norm_raw_data_in_label.py b/examples/inverse/plot_compute_mne_inverse_raw_in_label.py
similarity index 100%
rename from examples/inverse/plot_minimum_norm_raw_data_in_label.py
rename to examples/inverse/plot_compute_mne_inverse_raw_in_label.py
diff --git a/examples/inverse/plot_minimum_norm_estimate.py b/examples/inverse/plot_minimum_norm_estimate.py
deleted file mode 100644
index 7c16887..0000000
--- a/examples/inverse/plot_minimum_norm_estimate.py
+++ /dev/null
@@ -1,54 +0,0 @@
-"""
-================================================
-Compute MNE-dSPM inverse solution on evoked data
-================================================
-
-Compute dSPM inverse solution on MNE evoked dataset
-and stores the solution in stc files for visualisation.
-
-"""
-
-# Author: Alexandre Gramfort <gramfort at nmr.mgh.harvard.edu>
-#
-# License: BSD (3-clause)
-
-print __doc__
-
-import pylab as pl
-import mne
-from mne.datasets import sample
-from mne.fiff import Evoked
-from mne.minimum_norm import minimum_norm
-
-data_path = sample.data_path('..')
-fname_fwd = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'
-fname_cov = data_path + '/MEG/sample/sample_audvis-cov.fif'
-fname_evoked = data_path + '/MEG/sample/sample_audvis-ave.fif'
-
-setno = 0
-snr = 3.0
-lambda2 = 1.0 / snr ** 2
-dSPM = True
-
-# Load data
-evoked = Evoked(fname_evoked, setno=setno, baseline=(None, 0))
-forward = mne.read_forward_solution(fname_fwd)
-noise_cov = mne.Covariance(fname_cov)
-
-# Compute whitener from noise covariance matrix
-whitener = noise_cov.get_whitener(evoked.info, mag_reg=0.1,
-                                  grad_reg=0.1, eeg_reg=0.1, pca=True)
-# Compute inverse solution
-stc = minimum_norm(evoked, forward, whitener, orientation='loose',
-                             method='dspm', snr=3, loose=0.2)
-
-# Save result in stc files
-stc.save('mne_dSPM_inverse')
-
-###############################################################################
-# View activation time-series
-pl.close('all')
-pl.plot(1e3 * stc.times, stc.data[::100, :].T)
-pl.xlabel('time (ms)')
-pl.ylabel('dSPM value')
-pl.show()
diff --git a/examples/time_frequency/plot_source_space_time_frequency.py b/examples/time_frequency/plot_source_space_time_frequency.py
index b7412d8..b5bbcc4 100644
--- a/examples/time_frequency/plot_source_space_time_frequency.py
+++ b/examples/time_frequency/plot_source_space_time_frequency.py
@@ -18,7 +18,6 @@ import mne
 from mne import fiff
 from mne.datasets import sample
 from mne.minimum_norm import read_inverse_operator, source_induced_power
-from mne.viz import plot_source_estimate
 
 ###############################################################################
 # Set parameters

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