rename drop_epochs to drop and drop_bad_epochs to drop_bad

.gitignore

@@ -18,6 +18,7 @@
 *.egg*
 *.tmproj
 *.png
+*.dat
 .DS_Store
 events.eve
 foo-lh.label
@@ -59,5 +60,5 @@ cover
 # PyCharm
 .idea/**
 
+# sublime
 *-e
-*.dat

doc/manual/memory.rst

@@ -38,7 +38,7 @@ has been loaded with ``preload=True``. Preloading is also supported for :func:`m
 
 .. warning:: This comes with a caveat. When ``preload=False``, data rejection based on peak-to-peak thresholds is executed when the data is loaded from disk, *not* when the ``Epochs`` object is created.
 
-To explicitly reject artifacts with ``preload=False``, use the function :func:`mne.Epochs.drop_bad_epochs`.
+To explicitly reject artifacts with ``preload=False``, use the function :func:`mne.Epochs.drop_bad`.
 
 Loading data explicitly
 =======================

examples/inverse/plot_tf_lcmv.py

@@ -64,7 +64,7 @@
 # parameters passed here are used to create epochs from filtered data. However,
 # reading epochs without preloading means that bad epoch rejection is delayed
 # until later. To perform bad epoch rejection based on the reject parameter
-# passed here, run epochs.drop_bad_epochs(). This is done automatically in
+# passed here, run epochs.drop_bad(). This is done automatically in
 # tf_lcmv to reject bad epochs based on unfiltered data.
 event_id = 1
 events = mne.read_events(event_fname)

examples/plot_from_raw_to_epochs_to_evoked.py

@@ -54,7 +54,7 @@
 
 # Look at channels that caused dropped events, showing that the subject's
 # blinks were likely to blame for most epochs being dropped
-epochs.drop_bad_epochs()
+epochs.drop_bad()
 epochs.plot_drop_log(subject='sample')
 
 # Average epochs and get evoked data corresponding to the left stimulation

mne/beamformer/_lcmv.py

@@ -739,7 +739,7 @@ def tf_lcmv(epochs, forward, noise_covs, tmin, tmax, tstep, win_lengths,
     raw_picks = [raw.ch_names.index(c) for c in ch_names]
 
     # Make sure epochs.events contains only good events:
-    epochs.drop_bad_epochs()
+    epochs.drop_bad()
 
     # Multiplying by 1e3 to avoid numerical issues, e.g. 0.3 // 0.05 == 5
     n_time_steps = int(((tmax - tmin) * 1e3) // (tstep * 1e3))

mne/beamformer/tests/test_dics.py

@@ -144,7 +144,7 @@ def test_dics():
     assert_array_equal(stcs[0].data, advance_iterator(stcs_).data)
 
     # Test whether correct number of trials was returned
-    epochs.drop_bad_epochs()
+    epochs.drop_bad()
     assert_true(len(epochs.events) == len(stcs))
 
     # Average the single trial estimates

mne/beamformer/tests/test_lcmv.py

@@ -172,7 +172,7 @@ def test_lcmv():
                         reg=0.01, return_generator=True)
     assert_array_equal(stcs[0].data, advance_iterator(stcs_).data)
 
-    epochs.drop_bad_epochs()
+    epochs.drop_bad()
     assert_true(len(epochs.events) == len(stcs))
 
     # average the single trial estimates
@@ -288,7 +288,7 @@ def test_tf_lcmv():
     epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,
                         baseline=None, preload=False,
                         reject=dict(grad=4000e-13, mag=4e-12, eog=150e-6))
-    epochs.drop_bad_epochs()
+    epochs.drop_bad()
 
     freq_bins = [(4, 12), (15, 40)]
     time_windows = [(-0.1, 0.1), (0.0, 0.2)]

mne/decoding/ems.py

@@ -68,7 +68,7 @@ def compute_ems(epochs, conditions=None, picks=None, n_jobs=1, verbose=None):
     else:
         epochs = epochs[conditions]
 
-    epochs.drop_bad_epochs()
+    epochs.drop_bad()
 
     if len(conditions) != 2:
         raise ValueError('Currently this function expects exactly 2 '

mne/epochs.py

@@ -43,6 +43,7 @@
                   plot_epochs_image, plot_topo_image_epochs)
 from .utils import (check_fname, logger, verbose, _check_type_picks,
                     _time_mask, check_random_state, object_hash, warn)
+from .utils import deprecated
 from .externals.six import iteritems, string_types
 from .externals.six.moves import zip
 
@@ -1070,9 +1071,18 @@ def plot_topo_image(self, layout=None, sigma=0., vmin=None, vmax=None,
             border=border, fig_facecolor=fig_facecolor, font_color=font_color,
             show=show)
 
+    @deprecated('drop_bad_epochs method has been renamed drop_bad. '
+                'drop_bad_epochs method will be removed in 0.14')
     def drop_bad_epochs(self, reject='existing', flat='existing'):
         """Drop bad epochs without retaining the epochs data.
 
+        .. Warning:: This method has been renamed. See drop_bad
+        """
+        return self.drop_bad(reject, flat)
+
+    def drop_bad(self, reject='existing', flat='existing'):
+        """Drop bad epochs without retaining the epochs data.
+
         Should be used before slicing operations.
 
         .. Warning:: Operation is slow since all epochs have to be read from
@@ -1164,7 +1174,7 @@ def plot_drop_log(self, threshold=0, n_max_plot=20, subject='Unknown',
         if not self._bad_dropped:
             raise ValueError("You cannot use plot_drop_log since bad "
                              "epochs have not yet been dropped. "
-                             "Use epochs.drop_bad_epochs().")
+                             "Use epochs.drop_bad().")
 
         from .viz import plot_drop_log
         return plot_drop_log(self.drop_log, threshold, n_max_plot, subject,
@@ -1233,16 +1243,26 @@ def plot_image(self, picks=None, sigma=0., vmin=None,
                                  cmap=cmap, fig=fig,
                                  overlay_times=overlay_times)
 
+    @deprecated('drop_bad method has been renamed drop_bad. '
+                'drop_bad method will be removed in 0.14')
     @verbose
     def drop_epochs(self, indices, reason='USER', verbose=None):
         """Drop epochs based on indices or boolean mask
 
+        Deprecated: See the drop method.
+        """
+        return self.drop(indices, reason, verbose)
+
+    @verbose
+    def drop(self, indices, reason='USER', verbose=None):
+        """Drop epochs based on indices or boolean mask
+
         Note that the indices refer to the current set of undropped epochs
         rather than the complete set of dropped and undropped epochs.
         They are therefore not necessarily consistent with any external indices
         (e.g., behavioral logs). To drop epochs based on external criteria,
         do not use the preload=True flag when constructing an Epochs object,
-        and call this method before calling the drop_bad_epochs method.
+        and call this method before calling the drop_bad method.
 
         Parameters
         ----------
@@ -1340,7 +1360,7 @@ def _get_data(self, out=True, verbose=None):
                 data[idx] = epoch_out
         else:
             # bads need to be dropped, this might occur after a preload
-            # e.g., when calling drop_bad_epochs w/new params
+            # e.g., when calling drop_bad w/new params
             good_idx = []
             n_out = 0
             assert n_events == len(self.selection)
@@ -1408,7 +1428,7 @@ def __len__(self):
             raise RuntimeError('Since bad epochs have not been dropped, the '
                                'length of the Epochs is not known. Load the '
                                'Epochs with preload=True, or call '
-                               'Epochs.drop_bad_epochs(). To find the number '
+                               'Epochs.drop_bad(). To find the number '
                                'of events in the Epochs, use '
                                'len(Epochs.events).')
         return len(self.events)
@@ -1682,7 +1702,7 @@ def save(self, fname, split_size='2GB'):
 
         # to know the length accurately. The get_data() call would drop
         # bad epochs anyway
-        self.drop_bad_epochs()
+        self.drop_bad()
         total_size = self[0].get_data().nbytes * len(self)
         n_parts = int(np.ceil(total_size / float(split_size)))
         epoch_idxs = np.array_split(np.arange(len(self)), n_parts)
@@ -1756,7 +1776,7 @@ def equalize_event_counts(self, event_ids, method='mintime', copy=True):
         if len(event_ids) == 0:
             raise ValueError('event_ids must have at least one element')
         if not epochs._bad_dropped:
-            epochs.drop_bad_epochs()
+            epochs.drop_bad()
         # figure out how to equalize
         eq_inds = list()
 
@@ -1810,7 +1830,7 @@ def equalize_event_counts(self, event_ids, method='mintime', copy=True):
         indices = _get_drop_indices(event_times, method)
         # need to re-index indices
         indices = np.concatenate([e[idx] for e, idx in zip(eq_inds, indices)])
-        epochs.drop_epochs(indices, reason='EQUALIZED_COUNT')
+        epochs.drop(indices, reason='EQUALIZED_COUNT')
         # actually remove the indices
         return epochs, indices
 
@@ -1964,7 +1984,7 @@ class Epochs(_BaseEpochs):
         'NO_DATA' or 'TOO_SHORT' if epoch didn't contain enough data;
         names of channels that exceeded the amplitude threshold;
         'EQUALIZED_COUNTS' (see equalize_event_counts);
-        or 'USER' for user-defined reasons (see drop_epochs).
+        or 'USER' for user-defined reasons (see drop method).
     verbose : bool, str, int, or None
         See above.
 
@@ -2148,7 +2168,7 @@ def __init__(self, data, info, events, tmin=0, event_id=None,
         for ii, e in enumerate(self._data):
             # This is safe without assignment b/c there is no decim
             self._detrend_offset_decim(e)
-        self.drop_bad_epochs()
+        self.drop_bad()
 
 
 def combine_event_ids(epochs, old_event_ids, new_event_id, copy=True):
@@ -2242,11 +2262,11 @@ def equalize_epoch_counts(epochs_list, method='mintime'):
     # make sure bad epochs are dropped
     for e in epochs_list:
         if not e._bad_dropped:
-            e.drop_bad_epochs()
+            e.drop_bad()
     event_times = [e.events[:, 0] for e in epochs_list]
     indices = _get_drop_indices(event_times, method)
     for e, inds in zip(epochs_list, indices):
-        e.drop_epochs(inds, reason='EQUALIZED_COUNT')
+        e.drop(inds, reason='EQUALIZED_COUNT')
 
 
 def _get_drop_indices(event_times, method):
@@ -2604,7 +2624,7 @@ def __init__(self, fname, proj=True, add_eeg_ref=True, preload=True,
             name=name, proj=proj, add_eeg_ref=add_eeg_ref,
             preload_at_end=False, on_missing='ignore', selection=selection,
             drop_log=drop_log, verbose=verbose)
-        # use the private property instead of drop_bad_epochs so that epochs
+        # use the private property instead of drop_bad so that epochs
         # are not all read from disk for preload=False
         self._bad_dropped = True
 
@@ -2804,7 +2824,7 @@ def _finish_concat(info, data, events, event_id, tmin, tmax, baseline,
                       baseline=baseline, add_eeg_ref=False,
                       selection=selection, drop_log=drop_log,
                       proj=False, on_missing='ignore', verbose=verbose)
-    out.drop_bad_epochs()
+    out.drop_bad()
     return out
 
 

mne/minimum_norm/tests/test_time_frequency.py

@@ -135,7 +135,7 @@ def test_source_psd_epochs():
                     baseline=(None, 0), reject=reject)
 
     # only look at one epoch
-    epochs.drop_bad_epochs()
+    epochs.drop_bad()
     one_epochs = epochs[:1]
 
     inv = prepare_inverse_operator(inverse_operator, nave=1,

mne/preprocessing/ssp.py

@@ -185,7 +185,7 @@ def _compute_exg_proj(mode, raw, raw_event, tmin, tmax,
     epochs = Epochs(raw, events, None, tmin, tmax, baseline=None, preload=True,
                     picks=picks, reject=reject, flat=flat, proj=True)
 
-    epochs.drop_bad_epochs()
+    epochs.drop_bad()
     if epochs.events.shape[0] < 1:
         warn('No good epochs found, returning None for projs')
         return None, events