[med-svn] [python-mne] 103/353: ENH : add plot_cov function to display noise cov with and without SSPs

[Yaroslav Halchenko]

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yoh pushed a commit to tag 0.4
in repository python-mne.

commit 88e70a12f4acfa8d831fa51adc76ed3caac2346c
Author: Alexandre Gramfort <alexandre.gramfort at inria.fr>
Date:   Thu Mar 8 15:47:08 2012 +0100

    ENH : add plot_cov function to display noise cov with and without SSPs
---
 examples/plot_estimate_covariance_matrix.py   | 23 +---------
 examples/plot_read_noise_covariance_matrix.py |  3 ++
 mne/minimum_norm/inverse.py                   |  2 +
 mne/viz.py                                    | 64 ++++++++++++++++++++++++++-
 4 files changed, 70 insertions(+), 22 deletions(-)

diff --git a/examples/plot_estimate_covariance_matrix.py b/examples/plot_estimate_covariance_matrix.py
index 99bb876..556c5ed 100644
--- a/examples/plot_estimate_covariance_matrix.py
+++ b/examples/plot_estimate_covariance_matrix.py
@@ -23,26 +23,7 @@ raw = fiff.Raw(fname)
 cov = mne.compute_raw_data_covariance(raw, reject=dict(eeg=80e-6, eog=150e-6))
 print cov
 
-bads = raw.info['bads']
-sel_eeg = mne.fiff.pick_types(raw.info, meg=False, eeg=True, exclude=bads)
-sel_mag = mne.fiff.pick_types(raw.info, meg='mag', eeg=False, exclude=bads)
-sel_grad = mne.fiff.pick_types(raw.info, meg='grad', eeg=False, exclude=bads)
-idx_eeg = [cov.ch_names.index(raw.ch_names[c]) for c in sel_eeg]
-idx_mag = [cov.ch_names.index(raw.ch_names[c]) for c in sel_mag]
-idx_grad = [cov.ch_names.index(raw.ch_names[c]) for c in sel_grad]
-
 ###############################################################################
 # Show covariance
-import pylab as pl
-pl.figure(figsize=(7.3, 2.7))
-pl.subplot(1, 3, 1)
-pl.imshow(cov.data[idx_eeg][:, idx_eeg], interpolation="nearest")
-pl.title('EEG covariance')
-pl.subplot(1, 3, 2)
-pl.imshow(cov.data[idx_grad][:, idx_grad], interpolation="nearest")
-pl.title('Gradiometers')
-pl.subplot(1, 3, 3)
-pl.imshow(cov.data[idx_mag][:, idx_mag], interpolation="nearest")
-pl.title('Magnetometers')
-pl.subplots_adjust(0.04, 0.0, 0.98, 0.94, 0.2, 0.26)
-pl.show()
+mne.viz.plot_cov(cov, raw.info, exclude=raw.info['bads'], colorbar=True,
+                 proj=True)  # try setting proj to False to see the effect
diff --git a/examples/plot_read_noise_covariance_matrix.py b/examples/plot_read_noise_covariance_matrix.py
index 9efd639..9d23415 100644
--- a/examples/plot_read_noise_covariance_matrix.py
+++ b/examples/plot_read_noise_covariance_matrix.py
@@ -20,6 +20,9 @@ print cov
 
 ###############################################################################
 # Show covariance
+
+# Note: if you have the measurement info you can use mne.viz.plot_cov
+
 import pylab as pl
 pl.matshow(cov.data)
 pl.title('Noise covariance matrix (%d channels)' % cov.data.shape[0])
diff --git a/mne/minimum_norm/inverse.py b/mne/minimum_norm/inverse.py
index 2338301..c60e6a8 100644
--- a/mne/minimum_norm/inverse.py
+++ b/mne/minimum_norm/inverse.py
@@ -360,6 +360,8 @@ def prepare_inverse_operator(orig, nave, lambda2, dSPM):
                                            inv['noise_cov']['names'])
     if ncomp > 0:
         print '    Created an SSP operator (subspace dimension = %d)' % ncomp
+    else:
+        print '    The projection vectors do not apply to these channels.'
 
     #
     #   Create the whitener
diff --git a/mne/viz.py b/mne/viz.py
index 8670b2a..648c142 100644
--- a/mne/viz.py
+++ b/mne/viz.py
@@ -5,10 +5,12 @@
 #
 # License: Simplified BSD
 
+import copy
 import numpy as np
 import pylab as pl
 
-from .fiff.pick import channel_type
+from .fiff.pick import channel_type, pick_types
+from .fiff.proj import make_projector
 
 
 def plot_topo(evoked, layout):
@@ -79,6 +81,66 @@ def plot_evoked(evoked, picks=None, unit=True, show=True):
         pl.show()
 
 
+def plot_cov(cov, info, exclude=None, colorbar=True, proj=False, show=True):
+    """Plot Covariance data
+
+    Parameters
+    ----------
+    cov : instance of Covariance
+        The evoked data
+    info: dict
+        Measurement info
+    exclude : list of string
+        List of channels to exclude. If empty do not exclude any channel.
+    colorbar : bool
+        Show colorbar or not
+    proj : bool
+        Apply projections or not
+    show : bool
+        Call pylab.show() as the end or not.
+    """
+    ch_names = [n for n in cov.ch_names if not n in exclude]
+    ch_idx = [cov.ch_names.index(n) for n in ch_names]
+    info_ch_names = info['ch_names']
+    sel_eeg = pick_types(info, meg=False, eeg=True, exclude=exclude)
+    sel_mag = pick_types(info, meg='mag', eeg=False, exclude=exclude)
+    sel_grad = pick_types(info, meg='grad', eeg=False, exclude=exclude)
+    idx_eeg = [ch_names.index(info_ch_names[c]) for c in sel_eeg]
+    idx_mag = [ch_names.index(info_ch_names[c]) for c in sel_mag]
+    idx_grad = [ch_names.index(info_ch_names[c]) for c in sel_grad]
+
+    idx_names = [(idx_eeg, 'EEG covariance'),
+                 (idx_grad, 'Gradiometers'),
+                 (idx_mag, 'Magnetometers')]
+    idx_names = [(idx, name) for idx, name in idx_names if len(idx) > 0]
+
+    C = cov.data[ch_idx][:, ch_idx]
+
+    if proj:
+        projs = copy.deepcopy(info['projs'])
+
+        #   Activate the projection items
+        for p in projs:
+            p['active'] = True
+
+        P, ncomp, _ = make_projector(projs, ch_names)
+        if ncomp > 0:
+            print '    Created an SSP operator (subspace dimension = %d)' % \
+                                                                        ncomp
+            C = np.dot(P, np.dot(C, P.T))
+        else:
+            print '    The projection vectors do not apply to these channels.'
+
+    pl.figure(figsize=(2.5 * len(idx_names), 2.7))
+    for k, (idx, name) in enumerate(idx_names):
+        pl.subplot(1, len(idx_names), k + 1)
+        pl.imshow(C[idx][:, idx], interpolation="nearest")
+        pl.title(name)
+    pl.subplots_adjust(0.04, 0.0, 0.98, 0.94, 0.2, 0.26)
+    if show:
+        pl.show()
+
+
 def plot_source_estimate(src, stc, n_smooth=200, cmap='jet'):
     """Plot source estimates
     """

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