[med-svn] [python-mne] 220/376: ENH : adding option to subtract_evoked in source_induced_power

[Yaroslav Halchenko]

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

commit 8bd01b8bd6b7a3a623b4b75d4d7c828ecd98047f
Author: Alexandre Gramfort <alexandre.gramfort at inria.fr>
Date:   Tue Apr 26 11:56:28 2011 -0400

    ENH : adding option to subtract_evoked in source_induced_power
---
 mne/minimum_norm/time_frequency.py | 14 +++++++++++---
 1 file changed, 11 insertions(+), 3 deletions(-)

diff --git a/mne/minimum_norm/time_frequency.py b/mne/minimum_norm/time_frequency.py
index 44710b5..d70c7b3 100644
--- a/mne/minimum_norm/time_frequency.py
+++ b/mne/minimum_norm/time_frequency.py
@@ -38,7 +38,8 @@ def _compute_power(data, K, sel, Ws, source_ori, use_fft):
 
 def source_induced_power(epochs, inverse_operator, bands, lambda2=1.0 / 9.0,
                          dSPM=True, n_cycles=5, df=1, use_fft=False,
-                         baseline=None, baseline_mode='logratio', n_jobs=1):
+                         baseline=None, baseline_mode='logratio',
+                         subtract_evoked=False, n_jobs=1):
     """Compute source space induced power
 
     Parameters
@@ -67,12 +68,15 @@ def source_induced_power(epochs, inverse_operator, bands, lambda2=1.0 / 9.0,
         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.
-    baseline_mode : None | 'logratio' | 'zscore'
+    baseline_mode: None | 'logratio' | 'zscore'
         Do baseline correction with ratio (power is divided by mean
         power during baseline) or zscore (power is divided by standard
         deviatio of power during baseline after substracting the mean,
         power = [power - mean(power_baseline)] / std(power_baseline))
-    n_jobs : int
+    subtract_evoked: bool
+        If True, the evoked component (average of all epochs) if subtracted
+        from each epochs.
+    n_jobs: int
         Number of jobs to run in parallel
     """
 
@@ -98,6 +102,10 @@ def source_induced_power(epochs, inverse_operator, bands, lambda2=1.0 / 9.0,
     #   Set up the inverse according to the parameters
     #
     epochs_data = epochs.get_data()
+
+    if subtract_evoked:  # subtract with a copy not to touch epochs
+        epochs_data = epochs_data - np.mean(epochs_data, axis=0)
+
     nave = len(epochs_data)  # XXX : can do better when no preload
 
     inv = prepare_inverse_operator(inverse_operator, nave, lambda2, dSPM)

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