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 5c7c9935e4fefe775b4a4e4adc24fa8b7589a97b Author: Alexandre Gramfort <[email protected]> Date: Tue Mar 8 18:06:47 2011 -0500 bug fix due to new Evoked class + pyflakes --- examples/plot_compute_mne_inverse.py | 1 - examples/plot_estimate_covariance_matrix.py | 1 - examples/plot_read_forward.py | 1 - examples/plot_read_noise_covariance_matrix.py | 1 - examples/plot_read_stc.py | 1 - examples/plot_topography.py | 7 ++-- examples/plot_whiten_forward_solution.py | 1 - examples/plot_whitened_evoked_data.py | 21 +++++----- examples/read_events.py | 1 - examples/read_inverse.py | 1 - mne/cov.py | 55 +++++++++++++-------------- mne/fiff/tests/test_raw.py | 8 +--- mne/layouts/layout.py | 2 +- mne/stats/tests/test_permutations.py | 1 - mne/tests/test_bem_surfaces.py | 2 +- mne/tests/test_cov.py | 1 - mne/tests/test_epochs.py | 1 - mne/tests/test_event.py | 1 - mne/tests/test_forward.py | 4 +- mne/tests/test_inverse.py | 3 +- mne/tests/test_stc.py | 1 - mne/viz.py | 10 ++--- 22 files changed, 51 insertions(+), 74 deletions(-) diff --git a/examples/plot_compute_mne_inverse.py b/examples/plot_compute_mne_inverse.py index ab30d7a..7cfb025 100644 --- a/examples/plot_compute_mne_inverse.py +++ b/examples/plot_compute_mne_inverse.py @@ -14,7 +14,6 @@ and stores the solution in stc files for visualisation. print __doc__ -import os import numpy as np import pylab as pl import mne diff --git a/examples/plot_estimate_covariance_matrix.py b/examples/plot_estimate_covariance_matrix.py index 66a92d3..65c62bc 100644 --- a/examples/plot_estimate_covariance_matrix.py +++ b/examples/plot_estimate_covariance_matrix.py @@ -10,7 +10,6 @@ Estimate covariance matrix from a raw FIF file print __doc__ -import os import mne from mne import fiff from mne.datasets import sample diff --git a/examples/plot_read_forward.py b/examples/plot_read_forward.py index 797f8a9..24c2144 100644 --- a/examples/plot_read_forward.py +++ b/examples/plot_read_forward.py @@ -9,7 +9,6 @@ Reading a forward operator a.k.a. lead field matrix print __doc__ -import os import mne from mne.datasets import sample data_path = sample.data_path('.') diff --git a/examples/plot_read_noise_covariance_matrix.py b/examples/plot_read_noise_covariance_matrix.py index 2be0830..8e9b0ab 100644 --- a/examples/plot_read_noise_covariance_matrix.py +++ b/examples/plot_read_noise_covariance_matrix.py @@ -9,7 +9,6 @@ Reading/Writing a noise covariance matrix print __doc__ -import os import mne from mne.datasets import sample diff --git a/examples/plot_read_stc.py b/examples/plot_read_stc.py index d8b7778..374c614 100644 --- a/examples/plot_read_stc.py +++ b/examples/plot_read_stc.py @@ -12,7 +12,6 @@ reconstructions print __doc__ -import os import numpy as np import mne from mne.datasets import sample diff --git a/examples/plot_topography.py b/examples/plot_topography.py index 939ec8e..7df851a 100644 --- a/examples/plot_topography.py +++ b/examples/plot_topography.py @@ -11,7 +11,6 @@ Plot topographies for MEG sensors print __doc__ -import os import pylab as pl from mne import fiff @@ -23,13 +22,13 @@ data_path = sample.data_path('.') fname = data_path + '/MEG/sample/sample_audvis-ave.fif' # Reading -data = fiff.read_evoked(fname, setno=0, baseline=(None, 0)) +evoked = fiff.read_evoked(fname, setno=0, baseline=(None, 0)) layout = Layout('Vectorview-all') ############################################################################### # Show topography -plot_topo(data, layout) -title = 'MNE sample data (condition : %s)' % data['evoked']['comment'] +plot_topo(evoked, layout) +title = 'MNE sample data (condition : %s)' % evoked.comment pl.figtext(0.03, 0.93, title, color='w', fontsize=18) pl.show() diff --git a/examples/plot_whiten_forward_solution.py b/examples/plot_whiten_forward_solution.py index 4899da8..828240e 100644 --- a/examples/plot_whiten_forward_solution.py +++ b/examples/plot_whiten_forward_solution.py @@ -9,7 +9,6 @@ Whiten a forward operator with a noise covariance matrix print __doc__ -import os import mne from mne import fiff from mne.datasets import sample diff --git a/examples/plot_whitened_evoked_data.py b/examples/plot_whitened_evoked_data.py index 1228f77..af01bbc 100644 --- a/examples/plot_whitened_evoked_data.py +++ b/examples/plot_whitened_evoked_data.py @@ -10,7 +10,6 @@ Whiten evoked data using a noise covariance matrix print __doc__ -import os import mne from mne import fiff from mne.datasets import sample @@ -20,35 +19,33 @@ fname = data_path + '/MEG/sample/sample_audvis-ave.fif' cov_fname = data_path + '/MEG/sample/sample_audvis-cov.fif' # Reading -ave = fiff.read_evoked(fname, setno=0, baseline=(None, 0)) +evoked = fiff.read_evoked(fname, setno=0, baseline=(None, 0)) cov = mne.Covariance() cov.load(cov_fname) -ave_whiten, W = cov.whiten_evoked(ave) +evoked_whiten, W = cov.whiten_evoked(evoked) -bads = ave_whiten['info']['bads'] -ind_meg_grad = fiff.pick_types(ave['info'], meg='grad', exclude=bads) -ind_meg_mag = fiff.pick_types(ave['info'], meg='mag', exclude=bads) -ind_eeg = fiff.pick_types(ave['info'], meg=False, eeg=True, exclude=bads) +bads = evoked_whiten.info['bads'] +ind_meg_grad = fiff.pick_types(evoked.info, meg='grad', exclude=bads) +ind_meg_mag = fiff.pick_types(evoked.info, meg='mag', exclude=bads) +ind_eeg = fiff.pick_types(evoked.info, meg=False, eeg=True, exclude=bads) ############################################################################### # Show result import pylab as pl pl.clf() pl.subplot(3, 1, 1) -pl.plot(ave['evoked']['times'], - ave_whiten['evoked']['epochs'][ind_meg_grad,:].T) +pl.plot(evoked.times, evoked_whiten.data[ind_meg_grad,:].T) pl.title('MEG Planar Gradiometers') pl.xlabel('time (s)') pl.ylabel('MEG data') pl.subplot(3, 1, 2) -pl.plot(ave['evoked']['times'], - ave_whiten['evoked']['epochs'][ind_meg_mag,:].T) +pl.plot(evoked.times, evoked_whiten.data[ind_meg_mag,:].T) pl.title('MEG Magnetometers') pl.xlabel('time (s)') pl.ylabel('MEG data') pl.subplot(3, 1, 3) -pl.plot(ave['evoked']['times'], ave_whiten['evoked']['epochs'][ind_eeg,:].T) +pl.plot(evoked.times, evoked_whiten.data[ind_eeg,:].T) pl.title('EEG') pl.xlabel('time (s)') pl.ylabel('EEG data') diff --git a/examples/read_events.py b/examples/read_events.py index cc3fc1f..2e3fbb3 100644 --- a/examples/read_events.py +++ b/examples/read_events.py @@ -9,7 +9,6 @@ Reading an event file print __doc__ -import os import mne from mne.datasets import sample diff --git a/examples/read_inverse.py b/examples/read_inverse.py index 025832a..6e5bcbd 100644 --- a/examples/read_inverse.py +++ b/examples/read_inverse.py @@ -27,7 +27,6 @@ print "Number of channels: %s" % inv['nchan'] # Show result # 3D source space -import numpy as np lh_points = inv['src'][0]['rr'] lh_faces = inv['src'][0]['use_tris'] rh_points = inv['src'][1]['rr'] diff --git a/mne/cov.py b/mne/cov.py index 5fc4826..7c9ca10 100644 --- a/mne/cov.py +++ b/mne/cov.py @@ -91,7 +91,7 @@ class Covariance(object): for ii in ind: data[ind,ind] += reg - def whiten_evoked(self, ave, eps=0.2): + def whiten_evoked(self, evoked, eps=0.2): """Whiten an evoked data file The whitening matrix is estimated and then multiplied to data. @@ -100,14 +100,14 @@ class Covariance(object): Parameters ---------- - ave : evoked data - A evoked data set read with fiff.read_evoked + evoked : Evoked object + A evoked data set eps : float The regularization factor used. Returns ------- - ave : evoked data + evoked_whiten : Evoked object Evoked data set after whitening. W : array of shape [n_channels, n_channels] The whitening matrix @@ -118,18 +118,18 @@ class Covariance(object): # Add (eps x identity matrix) to magnetometers only. # This is based on the mean magnetometer variance like MNE C-code does it. - mag_ind = pick_types(ave['info'], meg='mag', eeg=False, stim=False) - mag_names = [ave['info']['chs'][k]['ch_name'] for k in mag_ind] + mag_ind = pick_types(evoked.info, meg='mag', eeg=False, stim=False) + mag_names = [evoked.info['chs'][k]['ch_name'] for k in mag_ind] self._regularize(data, variances, mag_names, eps) # Add (eps x identity matrix) to gradiometers only. - grad_ind = pick_types(ave['info'], meg='grad', eeg=False, stim=False) - grad_names = [ave['info']['chs'][k]['ch_name'] for k in grad_ind] + grad_ind = pick_types(evoked.info, meg='grad', eeg=False, stim=False) + grad_names = [evoked.info['chs'][k]['ch_name'] for k in grad_ind] self._regularize(data, variances, grad_names, eps) # Add (eps x identity matrix) to eeg only. - eeg_ind = pick_types(ave['info'], meg=False, eeg=True, stim=False) - eeg_names = [ave['info']['chs'][k]['ch_name'] for k in eeg_ind] + eeg_ind = pick_types(evoked.info, meg=False, eeg=True, stim=False) + eeg_names = [evoked.info['chs'][k]['ch_name'] for k in eeg_ind] self._regularize(data, variances, eeg_names, eps) d, V = linalg.eigh(data) # Compute eigen value decomposition. @@ -141,18 +141,17 @@ class Covariance(object): W = d[:,None] * V.T # Get all channel indices - n_channels = len(ave['info']['chs']) - ave_ch_names = [ave['info']['chs'][k]['ch_name'] + n_channels = len(evoked.info['chs']) + ave_ch_names = [evoked.info['chs'][k]['ch_name'] for k in range(n_channels)] ind = [ave_ch_names.index(name) for name in self._cov['names']] - ave_whiten = copy.copy(ave) - ave_whiten['evoked']['epochs'][ind] = np.dot(W, - ave['evoked']['epochs'][ind]) + evoked_whiten = copy.copy(evoked) + evoked_whiten.data[ind] = np.dot(W, evoked.data[ind]) - return ave_whiten, W + return evoked_whiten, W - def whiten_evoked_and_forward(self, ave, fwd, eps=0.2): + def whiten_evoked_and_forward(self, evoked, fwd, eps=0.2): """Whiten an evoked data set and a forward solution The whitening matrix is estimated and then multiplied to @@ -162,8 +161,8 @@ class Covariance(object): Parameters ---------- - ave : evoked data - A evoked data set read with fiff.read_evoked + evoked : Evoked object + A evoked data set fwd : forward data A forward solution read with mne.read_forward eps : float @@ -171,17 +170,17 @@ class Covariance(object): Returns ------- - ave : evoked data - A evoked data set read with fiff.read_evoked - fwd : evoked data - Forward solution after whitening. + ave : Evoked object + The whitened evoked data set + fwd : dict + The whitened forward solution. W : array of shape [n_channels, n_channels] The whitening matrix """ # handle evoked - ave_whiten, W = self.whiten_evoked(ave, eps=eps) + evoked_whiten, W = self.whiten_evoked(evoked, eps=eps) - ave_ch_names = [ch['ch_name'] for ch in ave_whiten['info']['chs']] + evoked_ch_names = [ch['ch_name'] for ch in evoked_whiten.info['chs']] # handle forward (keep channels in covariance matrix) fwd_whiten = copy.copy(fwd) @@ -194,10 +193,10 @@ class Covariance(object): fwd_whiten['chs'] = [fwd_whiten['chs'][k] for k in ind] # keep in forward the channels in the evoked dataset - fwd_whiten = pick_channels_forward(fwd, include=ave_ch_names, - exclude=ave['info']['bads']) + fwd_whiten = pick_channels_forward(fwd, include=evoked_ch_names, + exclude=evoked.info['bads']) - return ave_whiten, fwd_whiten, W + return evoked_whiten, fwd_whiten, W def __repr__(self): s = "kind : %s" % self.kind diff --git a/mne/fiff/tests/test_raw.py b/mne/fiff/tests/test_raw.py index 9f5851c..9cf74b3 100644 --- a/mne/fiff/tests/test_raw.py +++ b/mne/fiff/tests/test_raw.py @@ -1,12 +1,8 @@ -import os import os.path as op -from numpy.testing import assert_array_almost_equal, assert_equal - -from math import ceil -from .. import Raw, read_raw_segment_times, pick_types, \ - start_writing_raw, write_raw_buffer, finish_writing_raw +# from numpy.testing import assert_array_almost_equal, assert_equal +from .. import Raw, pick_types fname = op.join(op.dirname(__file__), 'data', 'test_raw.fif') diff --git a/mne/layouts/layout.py b/mne/layouts/layout.py index d205e38..c9b33ca 100644 --- a/mne/layouts/layout.py +++ b/mne/layouts/layout.py @@ -1,11 +1,11 @@ import os.path as op import numpy as np -import pylab as pl class Layout(object): """Sensor layouts""" + def __init__(self, kind='Vectorview-all', path=None): """ Parameters diff --git a/mne/stats/tests/test_permutations.py b/mne/stats/tests/test_permutations.py index 768dbf8..805b9dd 100644 --- a/mne/stats/tests/test_permutations.py +++ b/mne/stats/tests/test_permutations.py @@ -2,7 +2,6 @@ import numpy as np from numpy.testing import assert_array_equal, assert_almost_equal from scipy import stats -import mne from ..permutations import permutation_t_test diff --git a/mne/tests/test_bem_surfaces.py b/mne/tests/test_bem_surfaces.py index 59e3c26..7400776 100644 --- a/mne/tests/test_bem_surfaces.py +++ b/mne/tests/test_bem_surfaces.py @@ -1,6 +1,6 @@ import os.path as op -from numpy.testing import assert_array_almost_equal +# from numpy.testing import assert_array_almost_equal import mne from mne.datasets import sample diff --git a/mne/tests/test_cov.py b/mne/tests/test_cov.py index ac86811..3613fa3 100644 --- a/mne/tests/test_cov.py +++ b/mne/tests/test_cov.py @@ -1,4 +1,3 @@ -import os import os.path as op from numpy.testing import assert_array_almost_equal diff --git a/mne/tests/test_epochs.py b/mne/tests/test_epochs.py index a335fe6..3760b3f 100644 --- a/mne/tests/test_epochs.py +++ b/mne/tests/test_epochs.py @@ -2,7 +2,6 @@ # # License: BSD (3-clause) -import os import os.path as op import mne diff --git a/mne/tests/test_event.py b/mne/tests/test_event.py index d51496a..3e9680b 100644 --- a/mne/tests/test_event.py +++ b/mne/tests/test_event.py @@ -1,4 +1,3 @@ -import os import os.path as op from numpy.testing import assert_array_almost_equal diff --git a/mne/tests/test_forward.py b/mne/tests/test_forward.py index 5bf55ba..15e3006 100644 --- a/mne/tests/test_forward.py +++ b/mne/tests/test_forward.py @@ -1,7 +1,6 @@ -import os import os.path as op -from numpy.testing import assert_array_almost_equal, assert_equal +# from numpy.testing import assert_array_almost_equal, assert_equal import mne from mne.datasets import sample @@ -16,3 +15,4 @@ def test_io_forward(): fwd = mne.read_forward_solution(fname) fwd = mne.read_forward_solution(fname, force_fixed=True) leadfield = fwd['sol']['data'] + # XXX : test something diff --git a/mne/tests/test_inverse.py b/mne/tests/test_inverse.py index 840e02c..09ce352 100644 --- a/mne/tests/test_inverse.py +++ b/mne/tests/test_inverse.py @@ -1,8 +1,7 @@ -import os import os.path as op import numpy as np -from numpy.testing import assert_array_almost_equal, assert_equal +# from numpy.testing import assert_array_almost_equal, assert_equal import mne from mne.datasets import sample diff --git a/mne/tests/test_stc.py b/mne/tests/test_stc.py index 8eece25..611abee 100644 --- a/mne/tests/test_stc.py +++ b/mne/tests/test_stc.py @@ -1,4 +1,3 @@ -import os import os.path as op from numpy.testing import assert_array_almost_equal diff --git a/mne/viz.py b/mne/viz.py index 1763c46..4265a00 100644 --- a/mne/viz.py +++ b/mne/viz.py @@ -8,12 +8,12 @@ import pylab as pl -def plot_topo(data, layout): +def plot_topo(evoked, layout): """Plot 2D topographies """ - ch_names = data['info']['ch_names'] - times = data['evoked']['times'] - epochs = data['evoked']['epochs'] + ch_names = evoked.info['ch_names'] + times = evoked.times + data = evoked.data pl.rcParams['axes.edgecolor'] = 'w' pl.figure(facecolor='k') @@ -21,7 +21,7 @@ def plot_topo(data, layout): if name in ch_names: idx = ch_names.index(name) ax = pl.axes(layout.pos[idx], axisbg='k') - ax.plot(times, epochs[idx,:], 'w') + ax.plot(times, data[idx,:], 'w') pl.xticks([], ()) pl.yticks([], ()) -- Alioth's /usr/local/bin/git-commit-notice on /srv/git.debian.org/git/debian-med/python-mne.git _______________________________________________ debian-med-commit mailing list [email protected] http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/debian-med-commit
