[med-svn] [python-mne] 75/376: ENH : new stats on sensors + topography plots
Yaroslav Halchenko
debian at onerussian.com
Fri Nov 27 17:22:10 UTC 2015
This is an automated email from the git hooks/post-receive script.
yoh pushed a commit to annotated tag v0.1
in repository python-mne.
commit 9a665e13efefb5c6fc51cd6e3d089ec58a87aeb7
Author: Alexandre Gramfort <alexandre.gramfort at inria.fr>
Date: Mon Feb 14 15:11:50 2011 -0500
ENH : new stats on sensors + topography plots
---
examples/stats/README.txt | 6 +
examples/stats/plot_sensor_permutation_test.py | 92 ++++++++
mne/layouts/CTF-275.lout | 276 ++++++++++++++++++++++
mne/layouts/Vectorview-all.lout | 307 +++++++++++++++++++++++++
mne/layouts/Vectorview-grad.lout | 205 +++++++++++++++++
mne/layouts/Vectorview-mag.lout | 103 +++++++++
mne/layouts/__init__.py | 1 +
mne/layouts/layout.py | 60 +++++
mne/layouts/magnesWH3600.lout | 249 ++++++++++++++++++++
9 files changed, 1299 insertions(+)
diff --git a/examples/stats/README.txt b/examples/stats/README.txt
new file mode 100644
index 0000000..b395d8f
--- /dev/null
+++ b/examples/stats/README.txt
@@ -0,0 +1,6 @@
+
+Statistics Examples
+-------------------
+
+Some examples of how to compute statistics on M/EEG data with MNE.
+
diff --git a/examples/stats/plot_sensor_permutation_test.py b/examples/stats/plot_sensor_permutation_test.py
new file mode 100644
index 0000000..dcc2484
--- /dev/null
+++ b/examples/stats/plot_sensor_permutation_test.py
@@ -0,0 +1,92 @@
+"""
+=================================
+Permutation T-test on sensor data
+=================================
+
+One tests if the signal significantly deviates from 0
+during a fixed time window of interest. Here computation
+is performed on MNE sample dataset between 40 and 60 ms.
+
+"""
+
+# Authors: Alexandre Gramfort <gramfort at nmr.mgh.harvard.edu>
+#
+# License: BSD (3-clause)
+
+print __doc__
+
+import os
+import numpy as np
+
+import mne
+from mne import fiff
+from mne.stats import permutation_t_test
+
+###############################################################################
+# Set parameters
+raw_fname = os.environ['MNE_SAMPLE_DATASET_PATH']
+raw_fname += '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
+event_fname = os.environ['MNE_SAMPLE_DATASET_PATH']
+event_fname += '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'
+event_id = 1
+tmin = -0.2
+tmax = 0.5
+
+# Setup for reading the raw data
+raw = fiff.setup_read_raw(raw_fname)
+events = mne.read_events(event_fname)
+
+# Set up pick list: MEG + STI 014 - bad channels (modify to your needs)
+include = [] # or stim channel ['STI 014']
+exclude = raw['info']['bads'] + ['MEG 2443', 'EEG 053'] # bads + 2 more
+
+# pick MEG Magnetometers
+picks = fiff.pick_types(raw['info'], meg='grad', eeg=False, stim=False,
+ include=include, exclude=exclude)
+data, times, channel_names = mne.read_epochs(raw, events, event_id,
+ tmin, tmax, picks=picks, baseline=(None, 0))
+epochs = np.array([d['epoch'] for d in data]) # as 3D matrix
+evoked_data = np.mean(epochs, axis=0) # compute evoked fields
+
+temporal_mask = np.logical_and(0.04 <= times, times <= 0.06)
+data = np.squeeze(np.mean(epochs[:, :, temporal_mask], axis=2))
+
+n_permutations = 50000
+p_values, T0, H0 = permutation_t_test(data, n_permutations)
+
+significant_sensors = picks[p_values <= 0.05]
+significant_sensors_names = [raw['info']['ch_names'][k]
+ for k in significant_sensors]
+
+print "Number of significant sensors : %d" % len(significant_sensors)
+print "Sensors names : %s" % significant_sensors_names
+
+###############################################################################
+# View location of significantly active sensors
+import pylab as pl
+
+# load sensor layout
+from mne.layouts import Layout
+layout = Layout('Vectorview-grad')
+
+# Extract mask and indices of active sensors in layout
+idx_of_sensors = [layout.names.index(name)
+ for name in significant_sensors_names
+ if name in layout.names]
+mask_significant_sensors = np.zeros(len(layout.pos), dtype=np.bool)
+mask_significant_sensors[idx_of_sensors] = True
+mask_non_significant_sensors = mask_significant_sensors == False
+
+# plot it
+pl.figure(facecolor='k')
+pl.axis('off')
+pl.axis('tight')
+pl.scatter(layout.pos[mask_significant_sensors, 0],
+ layout.pos[mask_significant_sensors, 1], s=50, c='r')
+pl.scatter(layout.pos[mask_non_significant_sensors, 0],
+ layout.pos[mask_non_significant_sensors, 1], c='w')
+title = 'MNE sample data (Left auditory between 40 and 60 ms)'
+pl.figtext(0.03, 0.93, title, color='w', fontsize=18)
+pl.show()
+pl.show()
+
diff --git a/mne/layouts/CTF-275.lout b/mne/layouts/CTF-275.lout
new file mode 100644
index 0000000..53d924c
--- /dev/null
+++ b/mne/layouts/CTF-275.lout
@@ -0,0 +1,276 @@
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diff --git a/mne/layouts/Vectorview-all.lout b/mne/layouts/Vectorview-all.lout
new file mode 100644
index 0000000..b6395fb
--- /dev/null
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diff --git a/mne/layouts/Vectorview-grad.lout b/mne/layouts/Vectorview-grad.lout
new file mode 100644
index 0000000..1f133a1
--- /dev/null
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diff --git a/mne/layouts/Vectorview-mag.lout b/mne/layouts/Vectorview-mag.lout
new file mode 100644
index 0000000..c5f4c60
--- /dev/null
+++ b/mne/layouts/Vectorview-mag.lout
@@ -0,0 +1,103 @@
+-50.000000 50.000000 -50.000000 38.000000
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diff --git a/mne/layouts/__init__.py b/mne/layouts/__init__.py
new file mode 100644
index 0000000..481ce9b
--- /dev/null
+++ b/mne/layouts/__init__.py
@@ -0,0 +1 @@
+from .layout import Layout
\ No newline at end of file
diff --git a/mne/layouts/layout.py b/mne/layouts/layout.py
new file mode 100644
index 0000000..e73bba4
--- /dev/null
+++ b/mne/layouts/layout.py
@@ -0,0 +1,60 @@
+import os.path as op
+
+import numpy as np
+import pylab as pl
+
+
+class Layout(object):
+ """Sensor layouts"""
+ def __init__(self, kind='Vectorview-all'):
+ """
+ kind : 'Vectorview-all' | 'CTF-275' | 'Vectorview-grad' | 'Vectorview-mag'
+ """
+ lout_fname = op.join(op.dirname(__file__), kind + '.lout')
+
+ f = open(lout_fname)
+ f.readline() # skip first line
+
+ names = []
+ pos = []
+
+ for line in f:
+ _, x, y, dx, dy, chkind, nb = line.split()
+ name = chkind + ' ' + nb
+ pos.append(np.array([x, y, dx, dy], dtype=np.float))
+ names.append(name)
+
+ pos = np.array(pos)
+ pos[:, 0] -= np.min(pos[:, 0])
+ pos[:, 1] -= np.min(pos[:, 1])
+
+ scaling = max(np.max(pos[:, 0]), np.max(pos[:, 1]))
+ pos /= scaling
+ pos[:,:2] += 0.03
+ pos[:,:2] *= 0.97 / 1.03
+ pos[:,2:] *= 0.94
+
+ f.close()
+
+ self.kind = kind
+ self.pos = pos
+ self.names = names
+
+if __name__ == '__main__':
+
+ layout = Layout()
+
+ import pylab as pl
+ pl.rcParams['axes.edgecolor'] = 'w'
+ pl.close('all')
+ pl.figure(facecolor='k', )
+
+ for i in range(5):
+ # for i in range(len(pos)):
+ ax = pl.axes(layout.pos[i], axisbg='k')
+ ax.plot(np.random.randn(3), 'w')
+ pl.xticks([], ())
+ pl.yticks([], ())
+ pl.gca().grid(color='w')
+
+ pl.show()
diff --git a/mne/layouts/magnesWH3600.lout b/mne/layouts/magnesWH3600.lout
new file mode 100644
index 0000000..577e953
--- /dev/null
+++ b/mne/layouts/magnesWH3600.lout
@@ -0,0 +1,249 @@
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--
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