MRG: fix issues raised by LGTM.com

.lgtm.yml

@@ -0,0 +1,13 @@
+extraction:
+  python:
+    index:
+      filters:
+        - exclude: "mne/externals/*.py"
+        - exclude: "mne/externals/*/*.py"
+  javascript:
+    index:
+      filters:
+        - exclude: "**/*.js"
+queries:
+  - exclude: py/missing-equals
+  - exclude: py/import-and-import-from

examples/visualization/plot_evoked_topomap.py

@@ -1,3 +1,4 @@
+# -*- coding: utf-8 -*-
 """
 ========================================
 Plotting topographic maps of evoked data

logo/generate_mne_logos.py

@@ -19,7 +19,6 @@
 from matplotlib.text import TextPath
 from matplotlib.patches import PathPatch
 from matplotlib.colors import LinearSegmentedColormap
-from matplotlib.transforms import Bbox
 
 # manually set values
 dpi = 72.

mne/annotations.py

@@ -8,7 +8,6 @@
 import re
 from copy import deepcopy
 from itertools import takewhile
-from collections import OrderedDict
 import collections
 
 import numpy as np
@@ -223,7 +222,7 @@ def __getitem__(self, key):
             out_keys = ('onset', 'duration', 'description', 'orig_time')
             out_vals = (self.onset[key], self.duration[key],
                         self.description[key], self.orig_time)
-            return OrderedDict(zip(out_keys, out_vals))
+            return collections.OrderedDict(zip(out_keys, out_vals))
         else:
             key = list(key) if isinstance(key, tuple) else key
             return Annotations(onset=self.onset[key],
@@ -673,7 +672,6 @@ def get_duration_from_times(t):
 
 
 def _is_iso8601(candidate_str):
-    import re
     ISO8601 = r'^\d{4}-\d{2}-\d{2}[ T]\d{2}:\d{2}:\d{2}\.\d{6}$'
     return re.compile(ISO8601).match(candidate_str) is not None
 

mne/beamformer/_dics.py

@@ -853,8 +853,6 @@ def tf_dics(epochs, forward, noise_csds, tmin, tmax, tstep, win_lengths,
                     'window %d to %d ms, in frequency range %d to %d Hz' %
                     (win_tmin * 1e3, win_tmax * 1e3, fmin, fmax)
                 )
-                win_tmin = win_tmin
-                win_tmax = win_tmax
 
                 # Calculating data CSD in current time window
                 if mode == 'fourier':

mne/bem.py

@@ -475,7 +475,6 @@ def _surfaces_to_bem(surfs, ids, sigmas, ico=None, rescale=True,
                                         len(sigmas)):
         raise ValueError('surfs, ids, and sigmas must all have the same '
                          'number of elements (1 or 3)')
-    surf = list(surfs)
     for si, surf in enumerate(surfs):
         if isinstance(surf, str):
             surfs[si] = read_surface(surf, return_dict=True)[-1]

mne/coreg.py

@@ -17,7 +17,6 @@
 from functools import reduce
 
 import numpy as np
-from numpy import dot
 
 from .io import read_fiducials, write_fiducials, read_info
 from .io.constants import FIFF
@@ -288,7 +287,7 @@ def _trans_from_params(param_info, params):
         x, y, z = params[i:i + 3]
         trans.append(scaling(x, y, z))
 
-    trans = reduce(dot, trans)
+    trans = reduce(np.dot, trans)
     return trans
 
 
@@ -362,7 +361,7 @@ def fit_matched_points(src_pts, tgt_pts, rotate=True, translate=True,
         def error(x):
             rx, ry, rz = x
             trans = rotation3d(rx, ry, rz)
-            est = dot(src_pts, trans.T)
+            est = np.dot(src_pts, trans.T)
             d = tgt_pts - est
             if weights is not None:
                 d *= weights
@@ -372,8 +371,8 @@ def error(x):
     elif param_info == (True, True, 0):
         def error(x):
             rx, ry, rz, tx, ty, tz = x
-            trans = dot(translation(tx, ty, tz), rotation(rx, ry, rz))
-            est = dot(src_pts, trans.T)[:, :3]
+            trans = np.dot(translation(tx, ty, tz), rotation(rx, ry, rz))
+            est = np.dot(src_pts, trans.T)[:, :3]
             d = tgt_pts - est
             if weights is not None:
                 d *= weights
@@ -383,9 +382,10 @@ def error(x):
     elif param_info == (True, True, 1):
         def error(x):
             rx, ry, rz, tx, ty, tz, s = x
-            trans = reduce(dot, (translation(tx, ty, tz), rotation(rx, ry, rz),
-                                 scaling(s, s, s)))
-            est = dot(src_pts, trans.T)[:, :3]
+            trans = reduce(np.dot, (translation(tx, ty, tz),
+                                    rotation(rx, ry, rz),
+                                    scaling(s, s, s)))
+            est = np.dot(src_pts, trans.T)[:, :3]
             d = tgt_pts - est
             if weights is not None:
                 d *= weights
@@ -395,9 +395,10 @@ def error(x):
     elif param_info == (True, True, 3):
         def error(x):
             rx, ry, rz, tx, ty, tz, sx, sy, sz = x
-            trans = reduce(dot, (translation(tx, ty, tz), rotation(rx, ry, rz),
-                                 scaling(sx, sy, sz)))
-            est = dot(src_pts, trans.T)[:, :3]
+            trans = reduce(np.dot, (translation(tx, ty, tz),
+                                    rotation(rx, ry, rz),
+                                    scaling(sx, sy, sz)))
+            est = np.dot(src_pts, trans.T)[:, :3]
             d = tgt_pts - est
             if weights is not None:
                 d *= weights
@@ -419,7 +420,7 @@ def error(x):
     if tol is not None:
         if not translate:
             src_pts = np.hstack((src_pts, np.ones((len(src_pts), 1))))
-        est_pts = dot(src_pts, trans.T)[:, :3]
+        est_pts = np.dot(src_pts, trans.T)[:, :3]
         err = np.sqrt(np.sum((est_pts - tgt_pts) ** 2, axis=1))
         if np.any(err > tol):
             raise RuntimeError("Error exceeds tolerance. Error = %r" % err)

mne/cov.py

@@ -1095,9 +1095,6 @@ def _compute_covariance_auto(data, method, info, method_params, cv,
             name = method_.__name__ if callable(method_) else method_
             out[name] = dict(loglik=loglik, data=cov, estimator=estimator)
             out[name].update(runtime_info)
-        # undo scaling
-        if eigvec is not None:
-            data = np.dot(data, eigvec)
 
     return out
 

mne/datasets/hf_sef/hf_sef.py

@@ -95,5 +95,5 @@ def data_path(dataset='evoked', path=None, force_update=False,
 
         os.remove(archive)
 
-    path = _do_path_update(path, update_path, key, name)
+    _do_path_update(path, update_path, key, name)
     return destdir

mne/datasets/megsim/megsim.py

@@ -96,7 +96,7 @@ def data_path(url, path=None, force_update=False, update_path=None,
         z.close()
         destinations = [op.join(decomp_dir, f) for f in files]
 
-    path = _do_path_update(path, update_path, key, name)
+    _do_path_update(path, update_path, key, name)
     return destinations
 
 

mne/datasets/sleep_physionet/_utils.py

@@ -1,10 +1,11 @@
+# -*- coding: utf-8 -*-
 # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
 #          Joan Massich <mailsik@gmail.com>
 #
 # License: BSD Style.
 
 import os
-from os import path as op
+import os.path as op
 import numpy as np
 
 from ...utils import _fetch_file, verbose, _TempDir, _check_pandas_installed
@@ -108,7 +109,7 @@ def _update_sleep_temazepam_records(fname=TEMAZEPAM_SLEEP_RECORDS):
     data.columns.names = [None, None]
     data = (data.set_index([('Subject - age - sex', 'Age'),
                             ('Subject - age - sex', 'M1/F2')], append=True)
-                .stack(level=0).reset_index())
+            .stack(level=0).reset_index())
 
     data = data.rename(columns={('Subject - age - sex', 'Age'): 'age',
                                 ('Subject - age - sex', 'M1/F2'): 'sex',

mne/datasets/sleep_physionet/age.py

@@ -1,3 +1,4 @@
+# -*- coding: utf-8 -*-
 # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
 #          Joan Massich <mailsik@gmail.com>
 #

mne/datasets/sleep_physionet/temazepam.py

@@ -1,3 +1,4 @@
+# -*- coding: utf-8 -*-
 # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
 #          Joan Massich <mailsik@gmail.com>
 #

mne/decoding/mixin.py

@@ -35,9 +35,15 @@ def fit_transform(self, X, y=None, **fit_params):
 class EstimatorMixin(object):
     """Mixin class for estimators."""
 
-    def get_params(self):
-        """Get the estimator params."""
-        pass
+    def get_params(self, deep=True):
+        """Get the estimator params.
+
+        Parameters
+        ----------
+        deep : bool
+            Deep.
+        """
+        return
 
     def set_params(self, **params):
         """Set parameters (mimics sklearn API)."""

mne/epochs.py

@@ -675,9 +675,10 @@ def iter_evoked(self):
         self._current = 0
 
         while True:
-            out = self.next(True)
-            if out is None:
-                return  # properly signal the end of iteration
+            try:
+                out = self.__next__(True)
+            except StopIteration:
+                break
             data, event_id = out
             tmin = self.times[0]
             info = deepcopy(self.info)

mne/evoked.py

@@ -42,9 +42,9 @@
                str(FIFF.FIFFV_ASPECT_STD_ERR): 'standard_error'}
 
 
-class Evoked(ProjMixin, ContainsMixin, UpdateChannelsMixin,
-             SetChannelsMixin, InterpolationMixin, FilterMixin,
-             ToDataFrameMixin, TimeMixin, SizeMixin):
+class Evoked(ProjMixin, ContainsMixin, UpdateChannelsMixin, SetChannelsMixin,
+             InterpolationMixin, FilterMixin, ToDataFrameMixin, TimeMixin,
+             SizeMixin):
     """Evoked data.
 
     Parameters

mne/fixes.py

@@ -638,10 +638,6 @@ class BaseEstimator(object):
     @classmethod
     def _get_param_names(cls):
         """Get parameter names for the estimator"""
-        try:
-            from inspect import signature
-        except ImportError:
-            from .externals.funcsigs import signature
         # fetch the constructor or the original constructor before
         # deprecation wrapping if any
         init = getattr(cls.__init__, 'deprecated_original', cls.__init__)
@@ -651,7 +647,7 @@ def _get_param_names(cls):
 
         # introspect the constructor arguments to find the model parameters
         # to represent
-        init_signature = signature(init)
+        init_signature = inspect.signature(init)
         # Consider the constructor parameters excluding 'self'
         parameters = [p for p in init_signature.parameters.values()
                       if p.name != 'self' and p.kind != p.VAR_KEYWORD]

mne/forward/_lead_dots.py

@@ -5,7 +5,7 @@
 # License: BSD (3-clause)
 
 import os
-from os import path as op
+import os.path as op
 
 import numpy as np
 from numpy.polynomial import legendre
@@ -21,12 +21,11 @@
 def _next_legen_der(n, x, p0, p01, p0d, p0dd):
     """Compute the next Legendre polynomial and its derivatives."""
     # only good for n > 1 !
-    help_ = p0
-    helpd = p0d
-    p0 = ((2 * n - 1) * x * help_ - (n - 1) * p01) / n
-    p0d = n * help_ + x * helpd
-    p0dd = (n + 1) * helpd + x * p0dd
-    p01 = help_
+    old_p0 = p0
+    old_p0d = p0d
+    p0 = ((2 * n - 1) * x * old_p0 - (n - 1) * p01) / n
+    p0d = n * old_p0 + x * old_p0d
+    p0dd = (n + 1) * old_p0d + x * p0dd
     return p0, p0d, p0dd
 
 

mne/forward/_make_forward.py

@@ -8,7 +8,7 @@
 from copy import deepcopy
 import contextlib
 import os
-from os import path as op
+import os.path as op
 
 import numpy as np
 

mne/gui/_coreg_gui.py

@@ -391,37 +391,27 @@ def _get_head_mri_t(self):
 
     @cached_property
     def _get_processed_high_res_mri_points(self):
-        if self.grow_hair:
-            if len(self.mri.bem_high_res.surf.nn):
-                scaled_hair_dist = (1e-3 * self.grow_hair /
-                                    np.array(self.parameters[6:9]))
-                points = self.mri.bem_high_res.surf.rr.copy()
-                hair = points[:, 2] > points[:, 1]
-                points[hair] += (self.mri.bem_high_res.surf.nn[hair] *
-                                 scaled_hair_dist)
-                return points
-            else:
-                error(None, "Norms missing from bem, can't grow hair")
-                self.grow_hair = 0
-        else:
-            return self.mri.bem_high_res.surf.rr
+        return self._get_processed_mri_points('high')
 
     @cached_property
     def _get_processed_low_res_mri_points(self):
+        return self._get_processed_mri_points('low')
+
+    def _get_processed_mri_points(self, res):
+        bem = self.mri.bem_low_res if res == 'low' else self.mri.bem_high_res
         if self.grow_hair:
-            if len(self.mri.bem_low_res.surf.nn):
+            if len(bem.surf.nn):
                 scaled_hair_dist = (1e-3 * self.grow_hair /
                                     np.array(self.parameters[6:9]))
-                points = self.mri.bem_low_res.surf.rr.copy()
+                points = bem.surf.rr.copy()
                 hair = points[:, 2] > points[:, 1]
-                points[hair] += (self.mri.bem_low_res.surf.nn[hair] *
-                                 scaled_hair_dist)
+                points[hair] += bem.surf.nn[hair] * scaled_hair_dist
                 return points
             else:
                 error(None, "Norms missing from bem, can't grow hair")
                 self.grow_hair = 0
         else:
-            return self.mri.bem_low_res.surf.rr
+            return bem.surf.rr
 
     @cached_property
     def _get_mri_trans(self):

mne/gui/_fiducials_gui.py

@@ -389,7 +389,7 @@ def round_(x):
             elif i in idxs:
                 line += " (<- also MRI mesh)"
             msg.append(line)
-        logger.debug(os.linesep.join(msg))
+        logger.debug('\n'.join(msg))
 
         if self.set == 'Nasion':
             self.nasion = pt

mne/inverse_sparse/mxne_optim.py

@@ -296,8 +296,7 @@ def _mixed_norm_solver_cd(M, G, alpha, lipschitz_constant, maxit=10000,
     """Solve L21 inverse problem with coordinate descent."""
     from sklearn.linear_model.coordinate_descent import MultiTaskLasso
 
-    n_sensors, n_times = M.shape
-    n_sensors, n_sources = G.shape
+    assert M.ndim == G.ndim and M.shape[0] == G.shape[0]
 
     clf = MultiTaskLasso(alpha=alpha / len(M), tol=tol / sum_squared(M),
                          normalize=False, fit_intercept=False, max_iter=maxit,