STY: PEP8

mne/beamformer/_compute_beamformer.py

@@ -125,9 +125,9 @@ def _check_proj_match(info, filters):
                                      filters['ch_names'])
     if not np.allclose(proj_data, filters['proj'],
                        atol=np.finfo(float).eps, rtol=1e-13):
-            raise ValueError('The SSP projections present in the data '
-                             'do not match the projections used when '
-                             'calculating the spatial filter.')
+        raise ValueError('The SSP projections present in the data '
+                         'do not match the projections used when '
+                         'calculating the spatial filter.')
 
 
 def _check_src_type(filters):

mne/decoding/receptive_field.py

@@ -358,8 +358,8 @@ def _check_dimensions(self, X, y, predict=False):
                 raise ValueError('X any y do not have the same n_epochs\n'
                                  '%s != %s' % (X.shape[1], y.shape[1]))
             if predict and y.shape[-1] != len(self.estimator_.coef_):
-                    raise ValueError('Number of outputs does not match'
-                                     ' estimator coefficients dimensions')
+                raise ValueError('Number of outputs does not match'
+                                 ' estimator coefficients dimensions')
         return X, y, X_dim, y_dim
 
 

mne/epochs.py

@@ -852,10 +852,10 @@ def fun(data):
             data = fun(self._data)
             assert len(self.events) == len(self._data)
             if data.shape != self._data.shape[1:]:
-                    raise RuntimeError("You passed a function that resulted "
-                                       "in data of shape {}, but it should be "
-                                       "{}.".format(data.shape,
-                                                    self._data.shape[1:]))
+                raise RuntimeError(
+                    'You passed a function that resulted n data of shape {}, '
+                    'but it should be {}.'.format(
+                        data.shape, self._data.shape[1:]))
         else:
             if mode not in {"mean", "std"}:
                 raise ValueError("If data are not preloaded, can only compute "
@@ -1499,8 +1499,9 @@ def equalize_event_counts(self, event_ids, method='mintime'):
             # 2a. for tags, find all the event_ids matched by the tags
             # 2b. for non-tag ids, just pass them directly
             # 3. do this for every input
-            event_ids = [[k for k in ids if all((tag in k.split("/")
-                         for tag in id_))]  # find ids matching all tags
+            event_ids = [[k for k in ids
+                          if all((tag in k.split("/")
+                                  for tag in id_))]  # ids matching all tags
                          if all(id__ not in ids for id__ in id_)
                          else id_  # straight pass for non-tag inputs
                          for id_ in event_ids]
@@ -2426,8 +2427,8 @@ def __init__(self, fname, proj=True, preload=True,
         for i1, i2 in zip(offsets[:-1], offsets[1:]):
             other_log = drop_log[i1:i2]
             for k, (a, b) in enumerate(zip(drop_log, other_log)):
-                    if a == ['IGNORED'] and b != ['IGNORED']:
-                        drop_log[k] = b
+                if a == ['IGNORED'] and b != ['IGNORED']:
+                    drop_log[k] = b
         drop_log = drop_log[:step]
 
         # call BaseEpochs constructor

mne/event.py

@@ -1006,7 +1006,7 @@ def __init__(self, info):  # noqa: D102
         if 'ERFversion' in self.acq_dict:
             self.compat = False  # DACQ ver >= 3.4
         elif 'ERFncateg' in self.acq_dict:  # probably DACQ < 3.4
-                self.compat = True
+            self.compat = True
         else:
             raise ValueError('Cannot parse acquisition parameters')
         dacq_vars = self._dacq_vars_compat if self.compat else self._dacq_vars

mne/io/base.py

@@ -869,7 +869,7 @@ def _parse_get_set_params(self, item):
 
         if start is None:
             start = 0
-        if (step is not None) and (step is not 1):
+        if step is not None and step != 1:
             raise ValueError('step needs to be 1 : %d given' % step)
 
         if isinstance(sel, (int, np.integer)):

mne/realtime/stim_server_client.py

@@ -302,8 +302,8 @@ def get_trigger(self, timeout=5.0, verbose=None):
 
                 # Raise timeout error
                 if current_time > (start_time + timeout):
-                        logger.info("received nothing")
-                        return None
+                    logger.info("received nothing")
+                    return None
 
                 self._sock.send("get trigger".encode('utf-8'))
                 trigger = self._sock.recv(1024)

mne/source_estimate.py

@@ -269,12 +269,12 @@ def read_source_estimate(fname, subject=None):
         else:
             raise RuntimeError('Unknown extension for file %s' % fname_arg)
 
-    if ftype is not 'volume':
+    if ftype != 'volume':
         stc_exist = [op.exists(f)
                      for f in [fname + '-rh.stc', fname + '-lh.stc']]
         w_exist = [op.exists(f)
                    for f in [fname + '-rh.w', fname + '-lh.w']]
-        if all(stc_exist) and (ftype is not 'w'):
+        if all(stc_exist) and ftype != 'w':
             ftype = 'surface'
         elif all(w_exist):
             ftype = 'w'

mne/source_space.py

@@ -2548,7 +2548,7 @@ def get_volume_labels_from_src(src, subject, subjects_dir):
         raise ValueError('No vol src space in src')
 
     if any(np.any(s['type'] != 'vol') for s in src[2:]):
-            raise ValueError('source spaces have to be of vol type')
+        raise ValueError('source spaces have to be of vol type')
 
     labels_aseg = list()
     for nr in range(2, len(src)):

mne/tests/test_chpi.py

@@ -283,7 +283,7 @@ def test_simulate_calculate_chpi_positions():
 
     info['hpi_subsystem'] = hpi_subsystem
     for l, freq in enumerate(coil_freq):
-            info['hpi_meas'][0]['hpi_coils'][l]['coil_freq'] = freq
+        info['hpi_meas'][0]['hpi_coils'][l]['coil_freq'] = freq
     picks = pick_types(info, meg=True, stim=True, eeg=False, exclude=[])
     info['sfreq'] = 100.  # this will speed it up a lot
     info = pick_info(info, picks)

mne/time_frequency/csd.py

@@ -1050,7 +1050,7 @@ def _prepare_csd_array(X, sfreq, t0, tmin, tmax, fmin=None, fmax=None):
     if tmin is None:
         tmin = times.min()
     if tmax <= tmin:
-            raise ValueError('tmax must be larger than tmin')
+        raise ValueError('tmax must be larger than tmin')
     if tmin < times[0] - tstep:
         raise ValueError('tmin should be larger than the smallest data time '
                          'point')

mne/viz/epochs.py

@@ -1961,8 +1961,8 @@ def _plot_histogram(params):
         color = colors[types[idx]]
         rej = None
         if epochs.reject is not None and types[idx] in epochs.reject.keys():
-                rej = epochs.reject[types[idx]] * scalings[types[idx]]
-                rng = [0., rej * 1.1]
+            rej = epochs.reject[types[idx]] * scalings[types[idx]]
+            rng = [0., rej * 1.1]
         else:
             rng = None
         plt.hist(data[idx] * scalings[types[idx]], bins=100, color=color,

mne/viz/evoked.py

@@ -246,7 +246,7 @@ def _plot_evoked(evoked, picks, exclude, unit, show, ylim, proj, xlim, hline,
         for im in ims:
             im.set_clim(min, max)
         figs = [ax.get_figure() for ax in axes.values()]
-        if len(set(figs)) is 1:
+        if len(set(figs)) == 1:
             return figs[0]
         else:
             return figs

mne/viz/topo.py

@@ -420,7 +420,7 @@ def _rm_cursor(event):
     plt.connect('axes_leave_event', _rm_cursor)
 
     _setup_ax_spines(ax, vline, tmin, tmax)
-    ax.figure.set_facecolor('k' if hvline_color is 'w' else 'w')
+    ax.figure.set_facecolor('k' if hvline_color == 'w' else 'w')
     ax.spines['bottom'].set_color(hvline_color)
     ax.spines['left'].set_color(hvline_color)
     ax.tick_params(axis='x', colors=hvline_color, which='both')
@@ -719,7 +719,7 @@ def _plot_evoked_topo(evoked, layout=None, layout_scale=0.945, color=None,
     if ylim is None:
         # find maxima over all evoked data for each channel pick
         ymaxes = np.array([max(np.abs(e.data[t]).max() for e in evoked)
-                          for t in picks])
+                           for t in picks])
         ylim_ = (-ymaxes, ymaxes)
     elif isinstance(ylim, dict):
         ylim_ = _handle_default('ylim', ylim)

mne/viz/topomap.py

@@ -35,7 +35,7 @@ def _prepare_topo_plot(inst, ch_type, layout):
     """Prepare topo plot."""
     info = copy.deepcopy(inst if isinstance(inst, Info) else inst.info)
 
-    if layout is None and ch_type is not 'eeg':
+    if layout is None and ch_type != 'eeg':
         from ..channels import find_layout
         layout = find_layout(info)  # XXX : why not passing ch_type???
     elif layout == 'auto':

mne/viz/utils.py

@@ -2573,7 +2573,7 @@ def _set_title_multiple_electrodes(title, combine, ch_names, max_chans=6,
             combine = combine[0].upper() + combine[1:]
             title = "{} of {} {}".format(
                 combine, len(ch_names), ch_type)
-        elif len(ch_names) > max_chans and combine is not "gfp":
+        elif len(ch_names) > max_chans and combine != "gfp":
             warn("More than {} channels, truncating title ...".format(
                 max_chans))
             title += ", ...\n({} of {} {})".format(
@@ -2659,7 +2659,7 @@ def _plot_masked_image(ax, data, times, mask=None, picks=None, yvals=None,
         warn("With matplotlib version 2.1.0, lines may not show up in "
              "`AverageTFR.plot_joint`. Upgrade to a more recent version.")
 
-    if yscale is "log":  # pcolormesh for log scale
+    if yscale == "log":  # pcolormesh for log scale
         # compute bounds between time samples
         time_diff = np.diff(times) / 2. if len(times) > 1 else [0.0005]
         time_lims = np.concatenate([[times[0] - time_diff[0]], times[:-1] +
@@ -2709,10 +2709,10 @@ def _plot_masked_image(ax, data, times, mask=None, picks=None, yvals=None,
             im = ax.imshow(data, cmap=cmap, **im_args)
 
         if draw_contour and np.unique(mask).size == 2:
-                big_mask = np.kron(mask, np.ones((10, 10)))
-                ax.contour(big_mask, colors=["k"], extent=extent,
-                           linewidths=[.75], corner_mask=False,
-                           antialiased=False, levels=[.5])
+            big_mask = np.kron(mask, np.ones((10, 10)))
+            ax.contour(big_mask, colors=["k"], extent=extent,
+                       linewidths=[.75], corner_mask=False,
+                       antialiased=False, levels=[.5])
         time_lims = times[[0, -1]]
         ylim = yvals[0], yvals[-1] + 1