{"action":"created","comment":{"url":"https://api.github.com/repos/mne-tools/mne-python/pulls/comments/59009116","id":59009116,"diff_hunk":"@@ -92,25 +94,31 @@\n # in csv files. The bad segments are later used to reject epochs that overlap\n # with them.\n # The file for the second run also contains some saccades. The saccades are\n-# removed by using SSP. You can view the files with your favorite text editor.\n+# removed by using SSP. We use pandas to read the data from the csv files. You\n+# can also view the files with your favorite text editor.\n \n onsets = list()\n durations = list()\n descriptions = list()\n-saccades = list()\n+saccades = np.zeros((0, 3))\n offset = raw._raw_lengths[0]\n for idx in [1, 2]:\n-    with open(op.join(data_path, 'MEG', 'bst_auditory',\n-                      'events_bad_0%s.csv' % idx), 'r') as f:\n-        reader = csv.reader(f)\n-        for row in reader:\n-            onsets.append(int(row[0]) + offset * (idx - 1))\n-            durations.append(int(row[1]))\n-            descriptions.append(row[3])\n-            if row[3] == 'saccade':  # Events for removal of saccades.\n-                sample = int(row[0]) + offset * (idx - 1)\n-                saccades.append([sample, 0, 1])\n-saccades = np.asarray(saccades)\n+    csv_fname = op.join(data_path, 'MEG', 'bst_auditory',\n+                        'events_bad_0%s.csv' % idx)\n+    df = pd.read_csv(csv_fname, header=None,\n+                     names=['onset', 'duration', 'id', 'label'])\n+    print 'Events from run %s:' % idx\n+    print 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