FIX: Spelling and newer PySurfer (#5216)

ignore_words.txt

@@ -6,6 +6,8 @@ buss
 dur
 sinc
 wan
+reord
+iff
 # for tempita
 delimeters
 delimeter

mne/forward/forward.py

@@ -410,14 +410,14 @@ def read_forward_solution(fname, include=(), exclude=(), verbose=None):
     Forward solutions, which are derived from an original forward solution with
     free orientation, are always stored on disk as forward solution with free
     orientation in X/Y/Z RAS coordinates. To apply any transformation to the
-    forward operator (surface orientation, fixed orienation) please apply
+    forward operator (surface orientation, fixed orientation) please apply
     :func:`convert_forward_solution` after reading the forward solution with
     :func:`read_forward_solution`.
 
     Forward solutions, which are derived from an original forward solution with
     fixed orientation, are stored on disk as forward solution with fixed
     surface-based orientations. Please note that the transformation to
-    surface-based, fixed orienation cannot be reverted after loading the
+    surface-based, fixed orientation cannot be reverted after loading the
     forward solution with :func:`read_forward_solution`.
     """
     check_fname(fname, 'forward', ('-fwd.fif', '-fwd.fif.gz',
@@ -746,14 +746,14 @@ def write_forward_solution(fname, fwd, overwrite=False, verbose=None):
     Forward solutions, which are derived from an original forward solution with
     free orientation, are always stored on disk as forward solution with free
     orientation in X/Y/Z RAS coordinates. Transformations (surface orientation,
-    fixed orienation) will be reverted. To reapply any transformation to the
+    fixed orientation) will be reverted. To reapply any transformation to the
     forward operator please apply :func:`convert_forward_solution` after
     reading the forward solution with :func:`read_forward_solution`.
 
     Forward solutions, which are derived from an original forward solution with
     fixed orientation, are stored on disk as forward solution with fixed
     surface-based orientations. Please note that the transformation to
-    surface-based, fixed orienation cannot be reverted after loading the
+    surface-based, fixed orientation cannot be reverted after loading the
     forward solution with :func:`read_forward_solution`.
     """
     check_fname(fname, 'forward', ('-fwd.fif', '-fwd.fif.gz',

mne/preprocessing/tests/test_eeglab_infomax.py

@@ -145,7 +145,7 @@ def test_mne_python_vs_eeglab():
 
             w_change_eeglab = 1e-7 if N > 32 else 1e-6
 
-            # Call mne_python infomax version using the following sintax
+            # Call mne_python infomax version using the following syntax
             # to obtain the same result than eeglab version
             unmixing = infomax(
                 Y.T, extended=use_extended, random_state=random_state,

mne/time_frequency/_stockwell.py

@@ -7,7 +7,7 @@ from copy import deepcopy
 import math
 import numpy as np
 from scipy import fftpack
-# XXX explore cuda optimazation at some point.
+# XXX explore cuda optimization at some point.
 
 from ..io.pick import pick_types, pick_info
 from ..utils import verbose, warn

mne/viz/_3d.py

@@ -1662,7 +1662,7 @@ def plot_source_estimates(stc, subject=None, surface='inflated', hemi='lh',
 
     title = subject if len(hemis) > 1 else '%s - %s' % (subject, hemis[0])
     with warnings.catch_warnings(record=True):  # traits warnings
-        brain = Brain(subject, hemi=hemi, surf=surface, curv=True,
+        brain = Brain(subject, hemi=hemi, surf=surface,
                       title=title, cortex=cortex, size=size,
                       background=background, foreground=foreground,
                       figure=figure, subjects_dir=subjects_dir,
@@ -1861,7 +1861,7 @@ def plot_vector_source_estimates(stc, subject=None, hemi='lh', colormap='auto',
 
     title = subject if len(hemis) > 1 else '%s - %s' % (subject, hemis[0])
     with warnings.catch_warnings(record=True):  # traits warnings
-        brain = Brain(subject, hemi=hemi, surf='white', curv=True,
+        brain = Brain(subject, hemi=hemi, surf='white',
                       title=title, cortex=cortex, size=size,
                       background=background, foreground=foreground,
                       figure=figure, subjects_dir=subjects_dir,
@@ -2398,15 +2398,15 @@ def _plot_dipole(ax, data, points, idx, dipole, gridx, gridy, ori, coord_frame,
         colors[idx] = 'r'
         size = np.repeat(5, len(points))
         size[idx] = 20
-        visibles = range(len(points))
+        visible = np.arange(len(points))
     else:
         colors = 'r'
         size = 20
-        visibles = idx
+        visible = idx
 
     offset = np.min(gridx)
-    ax.scatter(xs=xyz[visibles, 0], ys=xyz[visibles, 1],
-               zs=xyz[visibles, 2], zorder=2, s=size, facecolor=colors)
+    ax.scatter(xs=xyz[visible, 0], ys=xyz[visible, 1],
+               zs=xyz[visible, 2], zorder=2, s=size, facecolor=colors)
     xx = np.linspace(offset, xyz[idx, 0], xidx)
     yy = np.linspace(offset, xyz[idx, 1], yidx)
     zz = np.linspace(offset, xyz[idx, 2], zidx)

mne/viz/tests/test_3d.py

@@ -411,12 +411,15 @@ def test_plot_vec_source_estimates():
     data = np.random.RandomState(0).rand(n_verts, 3, n_time)
     stc = VectorSourceEstimate(data, vertices, 1, 1)
 
-    with warnings.catch_warnings(record=True):
+    with warnings.catch_warnings(record=True) as w:
         warnings.simplefilter('always')
         stc.plot('sample', subjects_dir=subjects_dir)
+        assert len(w) == 0  # not using deprecated params
+
         with pytest.raises(ValueError, match='use pos_lims'):
             stc.plot('sample', subjects_dir=subjects_dir,
                      clim=dict(pos_lims=[1, 2, 3]))
+        assert len(w) == 0
 
 
 run_tests_if_main()

tutorials/plot_background_filtering.py

@@ -736,7 +736,7 @@ def baseline_plot(x):
 baseline_plot(x)
 
 ###############################################################################
-# In respose, Maess *et al.* 2016 [11]_ note that these simulations do not
+# In response, Maess *et al.* 2016 [11]_ note that these simulations do not
 # address cases of pre-stimulus activity that is shared across conditions, as
 # applying baseline correction will effectively copy the topology outside the
 # baseline period. We can see this if we give our signal ``x`` with some

tutorials/plot_dipole_fit.py

@@ -56,7 +56,7 @@ best_time = dip.times[best_idx]
 print('Highest GOF %0.1f%% at t=%0.1f ms with confidence volume %0.1f cm^3'
       % (dip.gof[best_idx], best_time * 1000,
          dip.conf['vol'][best_idx] * 100 ** 3))
-# rememeber to create a subplot for the colorbar
+# remember to create a subplot for the colorbar
 fig, axes = plt.subplots(nrows=1, ncols=4, figsize=[10., 3.4])
 vmin, vmax = -400, 400  # make sure each plot has same colour range