fix memory useage

Author: alexrockhill

mne/evoked.py

@@ -24,6 +24,7 @@
                     _check_pandas_installed, _check_pandas_index_arguments,
                     _convert_times, _scale_dataframe_data, _check_time_format,
                     _check_preload, _check_fname)
+from .utils.mixin import _check_decim
 from .viz import (plot_evoked, plot_evoked_topomap, plot_evoked_field,
                   plot_evoked_image, plot_evoked_topo)
 from .viz.evoked import plot_evoked_white, plot_evoked_joint
@@ -860,30 +861,6 @@ def to_data_frame(self, picks=None, index=None,
         return df
 
 
-def _check_decim(info, decim, offset):
-    """Check decimation parameters."""
-    if decim < 1 or decim != int(decim):
-        raise ValueError('decim must be an integer > 0')
-    decim = int(decim)
-    new_sfreq = info['sfreq'] / float(decim)
-    lowpass = info['lowpass']
-    if decim > 1 and lowpass is None:
-        warn('The measurement information indicates data is not low-pass '
-             'filtered. The decim=%i parameter will result in a sampling '
-             'frequency of %g Hz, which can cause aliasing artifacts.'
-             % (decim, new_sfreq))
-    elif decim > 1 and new_sfreq < 3 * lowpass:
-        warn('The measurement information indicates a low-pass frequency '
-             'of %g Hz. The decim=%i parameter will result in a sampling '
-             'frequency of %g Hz, which can cause aliasing artifacts.'
-             % (lowpass, decim, new_sfreq))  # > 50% nyquist lim
-    offset = int(offset)
-    if not 0 <= offset < decim:
-        raise ValueError('decim must be at least 0 and less than %s, got '
-                         '%s' % (decim, offset))
-    return decim, offset, new_sfreq
-
-
 @fill_doc
 class EvokedArray(Evoked):
     """Evoked object from numpy array.

mne/utils/mixin.py

@@ -423,6 +423,31 @@ def metadata(self, metadata, verbose=None):
         self._metadata = metadata
 
 
+def _check_decim(info, decim, offset, check_filter=True):
+    """Check decimation parameters."""
+    if decim < 1 or decim != int(decim):
+        raise ValueError('decim must be an integer > 0')
+    decim = int(decim)
+    new_sfreq = info['sfreq'] / float(decim)
+    offset = int(offset)
+    if not 0 <= offset < decim:
+        raise ValueError('decim must be at least 0 and less than %s, got '
+                         '%s' % (decim, offset))
+    if check_filter:
+        lowpass = info['lowpass']
+        if decim > 1 and lowpass is None:
+            warn('The measurement information indicates data is not low-pass '
+                 'filtered. The decim=%i parameter will result in a sampling '
+                 'frequency of %g Hz, which can cause aliasing artifacts.'
+                 % (decim, new_sfreq))
+        elif decim > 1 and new_sfreq < 3 * lowpass:
+            warn('The measurement information indicates a low-pass frequency '
+                 'of %g Hz. The decim=%i parameter will result in a sampling '
+                 'frequency of %g Hz, which can cause aliasing artifacts.'
+                 % (lowpass, decim, new_sfreq))  # > 50% nyquist lim
+    return decim, offset, new_sfreq
+
+
 class EpochsTimesMixin(object):
     """Class to handle times, tmin, tmax and decimation for epochs."""
 
@@ -481,8 +506,11 @@ def decimate(self, decim, offset=0, verbose=None):
         ----------
         .. footbibliography::
         """
-        from ..evoked import _check_decim
-        decim, offset, new_sfreq = _check_decim(self.info, decim, offset)
+        # if epochs have frequencies, they are not in time (EpochsTFR)
+        # and so do not need to be checked whether they have been
+        # appropriately filtered to avoid aliasing
+        decim, offset, new_sfreq = _check_decim(
+            self.info, decim, offset, check_filter=hasattr(self, 'freqs'))
         start_idx = int(round(-self._raw_times[0] * (self.info['sfreq'] *
                                                      self._decim)))
         self._decim *= decim

tutorials/inverse/55_tfr_stc.py

@@ -189,6 +189,9 @@
 # Now, we can apply the filters and compute a source time-course estimate
 # for each frequency.
 
+# decimate for speed in this tutorial, you probably shouldn't decimate
+#  so much in a real analysis
+epochs_tfr.decimate(decim=20)
 stc = mne.beamformer.apply_lcmv_epochs_tfr(epochs_tfr, filters)
 del filters