[med-svn] [python-mne] 43/376: bug fix + doc on command line + baseline correction ...
Yaroslav Halchenko
debian at onerussian.com
Fri Nov 27 17:22:03 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 6b4485af5f8cfc50eeca842673c7875d19d8bfd5
Author: Alexandre Gramfort <alexandre.gramfort at inria.fr>
Date: Fri Jan 21 21:03:52 2011 -0500
bug fix + doc on command line + baseline correction ...
---
doc/source/contents.rst | 2 +
doc/source/tutorial.rst | 96 +++++++++++++++++++++++++++++++++++-
examples/plot_compute_mne_inverse.py | 14 +++++-
examples/plot_read_epochs.py | 10 ++--
examples/plot_read_evoked.py | 17 ++++++-
examples/plot_read_raw_data.py | 4 +-
examples/read_events.py | 4 +-
mne/epochs.py | 32 +++++++++++-
mne/fiff/evoked.py | 34 +++++++++++--
mne/fiff/raw.py | 8 +--
mne/inverse.py | 12 ++++-
11 files changed, 212 insertions(+), 21 deletions(-)
diff --git a/doc/source/contents.rst b/doc/source/contents.rst
index b2e5aa7..8ef1ff9 100644
--- a/doc/source/contents.rst
+++ b/doc/source/contents.rst
@@ -9,3 +9,5 @@ Documentation Contents
getting_started.rst
auto_examples/index.rst
+ tutorial.rst
+
diff --git a/doc/source/tutorial.rst b/doc/source/tutorial.rst
index 12be396..1b406a6 100644
--- a/doc/source/tutorial.rst
+++ b/doc/source/tutorial.rst
@@ -1,6 +1,100 @@
.. _example_config:
-TODO
+======================================
+Getting starting with MNE command line
+======================================
+
+The quick start guide shows how to run a standard processing of the
+sample data set provided with MNE. XXX add link to data set download
+
+First define your subject::
+
+ export SUBJECT=sample
+
+Build your source space::
+
+ # MRI (this is not really needed for anything)
+ mne_setup_mri --overwrite
+
+ # Source space
+ mne_setup_source_space --ico -6 --overwrite
+
+Prepare for forward computation::
+
+ # For homegenious volume conductor
+ mne_setup_forward_model --homog --surf --ico 4
+
+ # or for XXX
+ mne_setup_forward_model --surf --ico 4
+
+List your bad channels in a file. Example sample_bads.bad contains::
+
+ MEG 2443
+ EEG 053
+
+Mark bad channels::
+
+ mne_mark_bad_channels --bad sample_bads.bad sample_audvis_raw.fif
+
+Compute averaging::
+
+ mne_process_raw --raw sample_audvis_raw.fif --lowpass 40 --projoff \
+ --saveavetag -ave --ave audvis.ave
+
+Compute the noise covariance matrix::
+
+ mne_process_raw --raw sample_audvis_raw.fif --lowpass 40 --projoff \
+ --savecovtag -cov --cov audvis.cov
+
+Compute forward solution a.k.a. lead field::
+
+ # for MEG only
+ mne_do_forward_solution --mindist 5 --spacing oct-6 \
+ --meas sample_audvis_raw.fif --bem sample-5120 --megonly --overwrite \
+ --fwd sample_audvis-meg-oct-6-fwd.fif
+
+ # for EEG only
+ mne_do_forward_solution --mindist 5 --spacing oct-6 \
+ --meas sample_audvis_raw.fif --bem sample-5120-5120-5120 --eegonly \
+ --fwd sample_audvis-eeg-oct-6-fwd.fif
+
+ # for both EEG and MEG
+ mne_do_forward_solution --mindist 5 --spacing oct-6 \
+ --meas sample_audvis_raw.fif --bem sample-5120-5120-5120 \
+ --fwd sample_audvis-meg-eeg-oct-6-fwd.fif
+
+Compute MNE inverse operators::
+
+ # Note: The MEG/EEG forward solution could be used for all
+ mne_do_inverse_operator --fwd sample_audvis-meg-oct-6-fwd.fif \
+ --depth --loose 0.2 --meg
+
+ mne_do_inverse_operator --fwd sample_audvis-eeg-oct-6-fwd.fif \
+ --depth --loose 0.2 --eeg
+
+ mne_do_inverse_operator --fwd sample_audvis-meg-eeg-oct-6-fwd.fif \
+ --depth --loose 0.2 --eeg --meg
+
+Produce stc files (activation files)::
+
+ # for MEG
+ mne_make_movie --inv sample_audvis-meg-oct-6-${mod}-inv.fif \
+ --meas sample_audvis-ave.fif \
+ --tmin 0 --tmax 250 --tstep 10 --spm \
+ --smooth 5 --bmin -100 --bmax 0 --stc sample_audvis-meg
+
+ # for EEG
+ mne_make_movie --inv sample_audvis-eeg-oct-6-${mod}-inv.fif \
+ --meas sample_audvis-ave.fif \
+ --tmin 0 --tmax 250 --tstep 10 --spm \
+ --smooth 5 --bmin -100 --bmax 0 --stc sample_audvis-eeg
+
+ # for MEG and EEG combined
+ mne_make_movie --inv sample_audvis-meg-eeg-oct-6-${mod}-inv.fif \
+ --meas sample_audvis-ave.fif \
+ --tmin 0 --tmax 250 --tstep 10 --spm \
+ --smooth 5 --bmin -100 --bmax 0 --stc sample_audvis-meg-eeg
And, we're done!
+You can now get started with the `examples <auto_examples/index.html>`_.
diff --git a/examples/plot_compute_mne_inverse.py b/examples/plot_compute_mne_inverse.py
index 99e9d2c..f4aa043 100644
--- a/examples/plot_compute_mne_inverse.py
+++ b/examples/plot_compute_mne_inverse.py
@@ -23,7 +23,19 @@ snr = 3.0
lambda2 = 1.0 / snr**2
dSPM = True
-res = mne.compute_inverse(fname_data, setno, fname_inv, lambda2, dSPM)
+res = mne.compute_inverse(fname_data, setno, fname_inv, lambda2, dSPM,
+ baseline=(None, 0))
+
+# XXX : kind of ugly
+import numpy as np
+res['vertices'] = np.r_[res['inv']['src'][0]['vertno']]
+# res['vertices'] = np.r_[res['inv']['src'][0]['vertno'],
+# res['inv']['src'][1]['vertno']]
+# res['data'] = res['sol']
+res['data'] = res['sol'][:len(res['vertices'])]
+
+# Save result in stc file
+mne.write_stc('mne_dSPM_inverse-lh.stc', res)
import pylab as pl
pl.plot(res['sol'][::100,:].T)
diff --git a/examples/plot_read_epochs.py b/examples/plot_read_epochs.py
index cbf28b9..d04489d 100644
--- a/examples/plot_read_epochs.py
+++ b/examples/plot_read_epochs.py
@@ -24,9 +24,11 @@ from mne import fiff
###############################################################################
# Set parameters
raw_fname = os.environ['MNE_SAMPLE_DATASET_PATH']
-raw_fname += '/MEG/sample/sample_audvis_raw.fif'
+# raw_fname += '/MEG/sample/sample_audvis_raw.fif'
+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_raw-eve.fif'
+# event_fname += '/MEG/sample/sample_audvis_raw-eve.fif'
+event_fname += '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'
event_id = 1
tmin = -0.2
tmax = 0.5
@@ -43,7 +45,7 @@ exclude = raw['info']['bads'] + ['MEG 2443', 'EEG 053'] # bads + 2 more
meg_picks = fiff.pick_types(raw['info'], meg=True, eeg=False, stim=False,
include=include, exclude=exclude)
meg_data, times, channel_names = mne.read_epochs(raw, events, event_id,
- tmin, tmax, picks=meg_picks)
+ tmin, tmax, picks=meg_picks, baseline=(None, 0))
meg_epochs = np.array([d['epoch'] for d in meg_data]) # build 3D matrix
meg_evoked_data = np.mean(meg_epochs, axis=0) # compute evoked fields
@@ -51,7 +53,7 @@ meg_evoked_data = np.mean(meg_epochs, axis=0) # compute evoked fields
eeg_picks = fiff.pick_types(raw['info'], meg=False, eeg=True, stim=False,
include=include, exclude=exclude)
eeg_data, times, channel_names = mne.read_epochs(raw, events, event_id,
- tmin, tmax, picks=eeg_picks)
+ tmin, tmax, picks=eeg_picks, baseline=(None, 0))
eeg_epochs = np.array([d['epoch'] for d in eeg_data]) # build 3D matrix
eeg_evoked_data = np.mean(eeg_epochs, axis=0) # compute evoked potentials
diff --git a/examples/plot_read_evoked.py b/examples/plot_read_evoked.py
index d7d0c4c..f9aeba2 100644
--- a/examples/plot_read_evoked.py
+++ b/examples/plot_read_evoked.py
@@ -17,7 +17,7 @@ fname = os.environ['MNE_SAMPLE_DATASET_PATH']
fname += '/MEG/sample/sample_audvis-ave.fif'
# Reading
-data = fiff.read_evoked(fname)
+data = fiff.read_evoked(fname, baseline=(None, 0))
# Writing
fiff.write_evoked('evoked.fif', data)
@@ -26,7 +26,20 @@ fiff.write_evoked('evoked.fif', data)
# Show result
import pylab as pl
-pl.plot(data['evoked']['times'], data['evoked']['epochs'][:306,:].T)
+pl.subplot(3, 1, 1)
+pl.plot(data['evoked']['times'], data['evoked']['epochs'][0:306:3,:].T)
+pl.title('Planar Gradiometers')
+pl.xlabel('time (s)')
+pl.ylabel('MEG data (T / m)')
+pl.subplot(3, 1, 2)
+pl.plot(data['evoked']['times'], data['evoked']['epochs'][1:306:3,:].T)
+pl.title('Axial Gradiometers')
+pl.xlabel('time (s)')
+pl.ylabel('MEG data (T / m)')
+pl.subplot(3, 1, 3)
+pl.plot(data['evoked']['times'], data['evoked']['epochs'][2:306:3,:].T)
+pl.title('Magnetometers')
pl.xlabel('time (s)')
pl.ylabel('MEG data (T)')
+pl.subplots_adjust(0.175, 0.08, 0.94, 0.94, 0.2, 0.63)
pl.show()
diff --git a/examples/plot_read_raw_data.py b/examples/plot_read_raw_data.py
index 8074f84..b0305fa 100644
--- a/examples/plot_read_raw_data.py
+++ b/examples/plot_read_raw_data.py
@@ -17,7 +17,9 @@ fname += '/MEG/sample/sample_audvis_raw.fif'
raw = fiff.setup_read_raw(fname)
-meg_channels_idx = fiff.pick_types(raw['info'], meg=True)
+exclude = ['MEG 2443', 'EEG 053'] # bad channels
+
+meg_channels_idx = fiff.pick_types(raw['info'], meg=True, exclude=exclude)
meg_channels_idx = meg_channels_idx[:5] # take 5 first
start, stop = raw.time_to_index(100, 115) # 100 s to 115 s data segment
diff --git a/examples/read_events.py b/examples/read_events.py
index 211c3c9..fcd941f 100644
--- a/examples/read_events.py
+++ b/examples/read_events.py
@@ -9,9 +9,11 @@ Reading an event file
print __doc__
+import os
import mne
-fname = 'MNE-sample-data/MEG/sample/sample_audvis_raw-eve.fif'
+fname = os.environ['MNE_SAMPLE_DATASET_PATH']
+fname += '/MEG/sample/sample_audvis_raw-eve.fif'
# Reading events
events = mne.read_events(fname)
diff --git a/mne/epochs.py b/mne/epochs.py
index ca3b9ac..010b2c1 100644
--- a/mne/epochs.py
+++ b/mne/epochs.py
@@ -8,7 +8,7 @@ import fiff
def read_epochs(raw, events, event_id, tmin, tmax, picks=None,
- keep_comp=False, dest_comp=0):
+ keep_comp=False, dest_comp=0, baseline=None):
"""Read epochs from a raw dataset
Parameters
@@ -28,6 +28,18 @@ def read_epochs(raw, events, event_id, tmin, tmax, picks=None,
tmax : float
End time after event
+ keep_comp : boolean
+ Apply CTF gradient compensation
+
+ baseline: None (default) or tuple of length 2
+ The time interval to apply baseline correction.
+ If None do not apply it. If baseline is (a, b)
+ the interval is between "a (s)" and "b (s)".
+ If a is None the beginning of the data is used
+ and if b is None then b is set to the end of the interval.
+ If baseline is equal ot (None, None) all the time
+ interval is used.
+
Returns
-------
data : list of epochs
@@ -147,6 +159,23 @@ def read_epochs(raw, events, event_id, tmin, tmax, picks=None,
times = np.arange(start - event_samp, stop - event_samp,
dtype=np.float) / sfreq
+ # Run baseline correction
+ if baseline is not None:
+ print "Applying baseline correction ..."
+ bmin = baseline[0]
+ bmax = baseline[1]
+ if bmin is None:
+ imin = 0
+ else:
+ imin = int(np.where(times >= bmin)[0][0])
+ if bmax is None:
+ imax = len(times)
+ else:
+ imax = int(np.where(times <= bmax)[0][-1]) + 1
+ epoch -= np.mean(epoch[:, imin:imax], axis=1)[:,None]
+ else:
+ print "No baseline correction applied..."
+
d = dict()
d['epoch'] = epoch
d['event'] = event_id
@@ -154,6 +183,7 @@ def read_epochs(raw, events, event_id, tmin, tmax, picks=None,
d['tmax'] = (float(stop) - float(raw['first_samp'])) / sfreq
data.append(d)
+
print 'Read %d epochs, %d samples each.' % (len(data),
data[0]['epoch'].shape[1])
diff --git a/mne/fiff/evoked.py b/mne/fiff/evoked.py
index f45272c..fb7611a 100644
--- a/mne/fiff/evoked.py
+++ b/mne/fiff/evoked.py
@@ -12,7 +12,7 @@ from .tree import dir_tree_find
from .meas_info import read_meas_info
-def read_evoked(fname, setno=0):
+def read_evoked(fname, setno=0, baseline=None):
"""Read an evoked dataset
Parameters
@@ -24,6 +24,15 @@ def read_evoked(fname, setno=0):
The index of the evoked dataset to read. FIF
file can contain multiple datasets.
+ baseline: None (default) or tuple of length 2
+ The time interval to apply baseline correction.
+ If None do not apply it. If baseline is (a, b)
+ the interval is between "a (s)" and "b (s)".
+ If a is None the beginning of the data is used
+ and if b is None then b is set to the end of the interval.
+ If baseline is equal ot (None, None) all the time
+ interval is used.
+
Returns
-------
data: dict
@@ -52,7 +61,6 @@ def read_evoked(fname, setno=0):
raise ValueError, 'Could not find evoked data'
# Identify the aspects
- #
naspect = 0
is_smsh = None
sets = []
@@ -209,13 +217,31 @@ def read_evoked(fname, setno=0):
cals = np.array([info['chs'][k].cal for k in range(info['nchan'])])
all_data = np.dot(np.diag(cals.ravel()), all_data)
+ times = np.arange(first, last+1, dtype=np.float) / info['sfreq']
+
+ # Run baseline correction
+ if baseline is not None:
+ print "Applying baseline correction ..."
+ bmin = baseline[0]
+ bmax = baseline[1]
+ if bmin is None:
+ imin = 0
+ else:
+ imin = int(np.where(times >= bmin)[0][0])
+ if bmax is None:
+ imax = len(times)
+ else:
+ imax = int(np.where(times <= bmax)[0][-1]) + 1
+ all_data -= np.mean(all_data[:, imin:imax], axis=1)[:,None]
+ else:
+ print "No baseline correction applied..."
+
# Put it all together
data = dict(info=info, evoked=dict(aspect_kind=aspect_kind,
is_smsh=is_smsh[setno],
nave=nave, first=first,
last=last, comment=comment,
- times=np.arange(first, last+1,
- dtype=np.float) / info['sfreq'],
+ times=times,
epochs=all_data))
fid.close()
diff --git a/mne/fiff/raw.py b/mne/fiff/raw.py
index a62ae86..100dbe7 100644
--- a/mne/fiff/raw.py
+++ b/mne/fiff/raw.py
@@ -76,14 +76,14 @@ def setup_read_raw(fname, allow_maxshield=False):
# Locate the data of interest
raw = dir_tree_find(meas, FIFF.FIFFB_RAW_DATA)
- if raw is None:
+ if len(raw) == 0:
raw = dir_tree_find(meas, FIFF.FIFFB_CONTINUOUS_DATA)
if allow_maxshield:
raw = dir_tree_find(meas, FIFF.FIFFB_SMSH_RAW_DATA)
- if raw is None:
+ if len(raw) == 0:
raise ValueError, 'No raw data in %s' % fname
else:
- if raw is None:
+ if len(raw) == 0:
raise ValueError, 'No raw data in %s' % fname
if len(raw) == 1:
@@ -618,7 +618,7 @@ def _make_compensator(info, kind):
def make_compensator(info, from_, to, exclude_comp_chs=False):
- """
+ """ XXX : bug !!! 2 make_compensator functions
%
% [comp] = mne_make_compensator(info,from,to,exclude_comp_chs)
%
diff --git a/mne/inverse.py b/mne/inverse.py
index 6886ee4..ffb6665 100644
--- a/mne/inverse.py
+++ b/mne/inverse.py
@@ -402,7 +402,7 @@ def prepare_inverse_operator(orig, nave, lambda2, dSPM):
def compute_inverse(fname_data, setno, fname_inv, lambda2, dSPM=True,
- nave=None):
+ nave=None, baseline=None):
"""Compute inverse solution
Computes a L2-norm inverse solution
@@ -424,6 +424,14 @@ def compute_inverse(fname_data, setno, fname_inv, lambda2, dSPM=True,
The regularization parameter
dSPM: bool
do dSPM ?
+ baseline: None (default) or tuple of length 2
+ The time interval to apply baseline correction.
+ If None do not apply it. If baseline is (a, b)
+ the interval is between "a ms" and "b ms".
+ If a is None the beginning of the data is used
+ and if b is None then b is set to the end of the interval.
+ If baseline is equal ot (None, None) all the time
+ interval is used.
Returns
-------
@@ -434,7 +442,7 @@ def compute_inverse(fname_data, setno, fname_inv, lambda2, dSPM=True,
#
# Read the data first
#
- data = read_evoked(fname_data, setno)
+ data = read_evoked(fname_data, setno, baseline=baseline)
#
# Then the inverse operator
#
--
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