[med-svn] [python-mne] 133/376: cosmit

[Yaroslav Halchenko]

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

commit 4b05bd994d90b9fad764d3a6f3acfae6bbf5d39a
Author: Alexandre Gramfort <alexandre.gramfort at inria.fr>
Date:   Mon Mar 14 16:05:09 2011 -0400

    cosmit
---
 mne/epochs.py              |  3 +++
 mne/stats/cluster_level.py |  9 +++++++--
 mne/time_frequency/tfr.py  | 10 +++++-----
 3 files changed, 15 insertions(+), 7 deletions(-)

diff --git a/mne/epochs.py b/mne/epochs.py
index 3d4bd80..6420f38 100644
--- a/mne/epochs.py
+++ b/mne/epochs.py
@@ -77,6 +77,9 @@ class Epochs(object):
         self.baseline = baseline
         self.preload = preload
 
+        if len(picks) == 0:
+            raise ValueError, "Picks cannot be empty."
+
         # Handle measurement info
         self.info = copy.copy(raw.info)
         if picks is not None:
diff --git a/mne/stats/cluster_level.py b/mne/stats/cluster_level.py
index 3a8c19e..fb87998 100644
--- a/mne/stats/cluster_level.py
+++ b/mne/stats/cluster_level.py
@@ -69,16 +69,21 @@ def permutation_cluster_test(X, stat_fun=f_oneway, threshold=1.67,
 
     For a list of 2d-arrays of data, e.g. power values, calculate some
     statistics for each timepoint (dim 1) over groups.  Do a cluster
-    analysis with permutation test for calculating corrected p-values
+    analysis with permutation test for calculating corrected p-values.
+    Randomized data are generated with random partitions of the data.
 
     Parameters
     ----------
-    X: list
+    X : list
         List of 2d-arrays containing the data, dim 1: timepoints, dim 2:
         elements of groups
     stat_fun : callable
         function called to calculate statistics, must accept 1d-arrays as
         arguments (default: scipy.stats.f_oneway)
+    threshold : float
+        The threshold for the statistic.
+    n_permutations : int
+        The number of permutations to compute.
     tail : -1 or 0 or 1 (default = 0)
         If tail is 1, the statistic is thresholded above threshold.
         If tail is -1, the statistic is thresholded below threshold.
diff --git a/mne/time_frequency/tfr.py b/mne/time_frequency/tfr.py
index 5b933e3..7f2593b 100644
--- a/mne/time_frequency/tfr.py
+++ b/mne/time_frequency/tfr.py
@@ -216,7 +216,7 @@ def single_trial_power(epochs, Fs, frequencies, use_fft=True, n_cycles=7):
     return power
 
 
-def induced_power(data, Fs, frequencies, use_fft=True, n_cycles=7,
+def induced_power(epochs, Fs, frequencies, use_fft=True, n_cycles=7,
                    n_jobs=1):
     """Compute time induced power and inter-trial phase-locking factor
 
@@ -224,7 +224,7 @@ def induced_power(data, Fs, frequencies, use_fft=True, n_cycles=7,
 
     Parameters
     ----------
-    data : array
+    epochs : array
         3D array of shape [n_epochs, n_channels, n_times]
 
     Fs : float
@@ -253,7 +253,7 @@ def induced_power(data, Fs, frequencies, use_fft=True, n_cycles=7,
         Phase locking factor in [0, 1] (Channels x Frequencies x Timepoints)
     """
     n_frequencies = len(frequencies)
-    n_epochs, n_channels, n_times = data.shape
+    n_epochs, n_channels, n_times = epochs.shape
 
     # Precompute wavelets for given frequency range to save time
     Ws = morlet(Fs, frequencies, n_cycles=n_cycles)
@@ -269,14 +269,14 @@ def induced_power(data, Fs, frequencies, use_fft=True, n_cycles=7,
         plf = np.empty((n_channels, n_frequencies, n_times), dtype=np.complex)
 
         for c in range(n_channels):
-            X = np.squeeze(data[:,c,:])
+            X = np.squeeze(epochs[:,c,:])
             psd[c], plf[c] = _time_frequency(X, Ws, use_fft)
 
     else:
         from joblib import Parallel, delayed
         psd_plf = Parallel(n_jobs=n_jobs)(
                     delayed(_time_frequency)(
-                            np.squeeze(data[:,c,:]), Ws, use_fft)
+                            np.squeeze(epochs[:,c,:]), Ws, use_fft)
                     for c in range(n_channels))
 
         psd = np.zeros((n_channels, n_frequencies, n_times))

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