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MRG: Add links for parameters (#5840)
* ENH: Add links for parameters * FIX: Heisenbug
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‎.circleci/config.yml

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@@ -56,6 +56,7 @@ jobs:
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- run:
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name: Get Anaconda running
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# pip install --user --upgrade --progress-bar off https://api.github.com/repos/larsoner/numpydoc/zipball/xref-param-type
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command: |
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wget -q http://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh;
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chmod +x ~/miniconda.sh;

‎doc/_static/style.css

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@@ -284,3 +284,10 @@ h4.list-group-item-heading {
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.skinnytable thead {
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border-bottom: 1px solid black;
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}
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/* Unify color of refs and classes in parameter lists */
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span.classifier a span.xref {
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color: #2c3e50;
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}
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span.classifier a code.docutils.literal.notranslate {
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color: #2c3e50;
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}

‎doc/conf.py

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@@ -20,9 +20,7 @@
2020

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import sphinx_gallery
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from sphinx_gallery.sorting import FileNameSortKey
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import sphinx_bootstrap_theme
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from numpydoc import numpydoc, docscrape # noqa
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import sphinx_fontawesome
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from numpydoc import docscrape
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import mne
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from mne.utils import linkcode_resolve # noqa, analysis:ignore
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@@ -45,9 +43,6 @@
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# If your documentation needs a minimal Sphinx version, state it here.
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needs_sphinx = '1.5'
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# XXX This hack defines what extra methods numpydoc will document
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docscrape.ClassDoc.extra_public_methods = mne.utils._doc_special_members
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# Add any Sphinx extension module names here, as strings. They can be
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# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom ones.
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@@ -64,6 +59,7 @@
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'sphinx_fontawesome',
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'numpydoc',
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'gen_commands',
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'sphinx_bootstrap_theme',
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]
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linkcheck_ignore = [
@@ -176,9 +172,6 @@
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],
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}
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# Add any paths that contain custom themes here, relative to this directory.
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html_theme_path = sphinx_bootstrap_theme.get_html_theme_path()
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# The name for this set of Sphinx documents. If None, it defaults to
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# "<project> v<release> documentation".
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#html_title = None
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'dipy': ('http://nipy.org/dipy', None),
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}
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##############################################################################
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# sphinx-gallery
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examples_dirs = ['../examples', '../tutorials']
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gallery_dirs = ['auto_examples', 'auto_tutorials']
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@@ -386,4 +382,113 @@ def reset_warnings(gallery_conf, fname):
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'within_subsection_order': FileNameSortKey,
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}
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##############################################################################
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# numpydoc
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# XXX This hack defines what extra methods numpydoc will document
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docscrape.ClassDoc.extra_public_methods = mne.utils._doc_special_members
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numpydoc_class_members_toctree = False
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numpydoc_xref_param_type = True
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numpydoc_xref_aliases = {
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'None': ':data:`python:None`',
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'bool': ':ref:`bool <python:bltin-boolean-values>`',
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'boolean': ':ref:`bool <python:bltin-boolean-values>`',
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'True': ':data:`python:True`',
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'False': ':data:`python:False`',
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'list': ':class:`python:list`',
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'tuple': ':class:`python:tuple`',
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'str': ':class:`python:str`',
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'string': ':class:`python:str`',
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'dict': ':class:`python:dict`',
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'float': ':class:`python:float`',
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'int': ':class:`python:int`',
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'callable': ':func:`callable <python:callable>`',
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'iterable': ':term:`python:iterable`',
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'contextmanager': ':func:`python:contextlib.contextmanager`',
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'namedtuple': ':func:`python:collections.namedtuple`',
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'generator': ':term:`python:generator`',
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# NumPy
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'array': '~numpy.ndarray',
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'ndarray': '~numpy.ndarray',
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'np.ndarray': '~numpy.ndarray',
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'array-like': ':term:`numpy:array_like`',
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'array_like': ':term:`numpy:array_like`',
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'scalar': ':ref:`scalar <numpy:arrays.scalars>`',
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'RandomState': '~numpy.random.RandomState',
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'np.random.RandomState': '~numpy.random.RandomState',
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'np.inf': ':data:`~numpy.inf`',
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# 'numpy': ':mod:`numpy`',
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# Matplotlib
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'colormap': ':doc:`colormap <matplotlib:tutorials/colors/colormaps>`',
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'color': ':doc:`color <matplotlib:api/colors_api>`',
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'collection': ':doc:`collections <matplotlib:api/collections_api>`',
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'Axes': '~matplotlib.axes.Axes',
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'Figure': '~matplotlib.figure.Figure',
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'Axes3D': '~mpl_toolkits.mplot3d.axes3d.Axes3D',
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# Mayavi
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'mayavi.mlab.Figure': 'mayavi.core.api.Scene',
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'mlab.Figure': 'mayavi.core.api.Scene',
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# sklearn
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'LeaveOneOut': '~sklearn.model_selection.LeaveOneOut',
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'sklearn.model_selection': ':mod:`sklearn.model_selection`',
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# nibabel
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'Nifti1Image': '~nibabel.nifti1.Nifti1Image',
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'Nifti2Image': '~nibabel.nifti2.Nifti2Image',
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# MNE
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'Label': '~mne.Label', 'Forward': '~mne.Forward', 'Evoked': '~mne.Evoked',
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'Info': '~mne.Info', 'SourceSpaces': '~mne.SourceSpaces',
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'Epochs': '~mne.Epochs', 'Layout': '~mne.channels.Layout',
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'EvokedArray': '~mne.EvokedArray', 'BiHemiLabel': '~mne.BiHemiLabel',
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'AverageTFR': '~mne.time_frequency.AverageTFR',
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'EpochsTFR': '~mne.time_frequency.EpochsTFR',
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'Raw': '~mne.io.Raw', 'ICA': '~mne.preprocessing.ICA',
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'Covariance': '~mne.Covariance', 'Annotations': '~mne.Annotations',
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'Montage': '~mne.channels.Montage',
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'DigMontage': '~mne.channels.DigMontage',
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'VectorSourceEstimate': '~mne.VectorSourceEstimate',
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'VolSourceEstimate': '~mne.VolSourceEstimate',
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'MixedSourceEstimate': '~mne.MixedSourceEstimate',
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'SourceEstimate': '~mne.SourceEstimate', 'Projection': '~mne.Projection',
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'ConductorModel': '~mne.bem.ConductorModel',
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'Dipole': '~mne.Dipole', 'DipoleFixed': '~mne.DipoleFixed',
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'InverseOperator': '~mne.minimum_norm.InverseOperator',
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'CrossSpectralDensity': '~mne.time_frequency.CrossSpectralDensity',
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'RtEpochs': '~mne.realtime.RtEpochs',
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'SourceMorph': '~mne.SourceMorph',
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'Xdawn': '~mne.preprocessing.Xdawn',
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'Report': '~mne.Report', 'Forward': '~mne.Forward',
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'TimeDelayingRidge': '~mne.decoding.TimeDelayingRidge',
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'Vectorizer': '~mne.decoding.Vectorizer',
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'UnsupervisedSpatialFilter': '~mne.decoding.UnsupervisedSpatialFilter',
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'TemporalFilter': '~mne.decoding.TemporalFilter',
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'Scaler': '~mne.decoding.Scaler', 'SPoC': '~mne.decoding.SPoC',
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'PSDEstimator': '~mne.decoding.PSDEstimator',
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'LinearModel': '~mne.decoding.LinearModel',
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'FilterEstimator': '~mne.decoding.FilterEstimator',
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'EMS': '~mne.decoding.EMS', 'CSP': '~mne.decoding.CSP',
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'Beamformer': '~mne.beamformer.Beamformer',
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}
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numpydoc_xref_ignore = {
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# words
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'instance', 'instances', 'of', 'default', 'shape', 'or',
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'with', 'length', 'pair', 'matplotlib', 'optional', 'kwargs', 'in',
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'dtype', 'object', 'self.verbose',
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# shapes
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'n_vertices', 'n_faces', 'n_channels', 'm', 'n', 'n_events', 'n_colors',
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'n_times', 'obj', 'n_chan', 'n_epochs', 'n_picks', 'n_ch_groups',
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'n_dipoles', 'n_ica_components', 'n_pos', 'n_node_names', 'n_tapers',
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'n_signals', 'n_step', 'n_freqs', 'wsize', 'Tx', 'M', 'N', 'p', 'q',
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'n_observations', 'n_regressors', 'n_cols', 'n_frequencies', 'n_tests',
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'n_samples', 'n_permutations', 'nchan', 'n_points', 'n_features',
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'n_parts', 'n_features_new', 'n_components', 'n_labels', 'n_events_in',
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'n_splits', 'n_scores', 'n_outputs', 'n_trials', 'n_estimators', 'n_tasks',
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'nd_features', 'n_classes', 'n_targets', 'n_slices', 'n_hpi', 'n_fids',
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'n_elp', 'n_pts', 'n_tris',
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# Undocumented (on purpose)
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'RawKIT', 'RawEximia', 'RawEGI', 'RawEEGLAB', 'RawEDF', 'RawCTF', 'RawBTi',
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'RawBrainVision',
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# sklearn subclasses
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'mapping', 'to', 'any',
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# unlinkable
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'mayavi.mlab.pipeline.surface',
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}

‎mne/annotations.py

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@@ -37,7 +37,7 @@ class Annotations(object):
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Array of strings containing description for each annotation. If a
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string, all the annotations are given the same description. To reject
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epochs, use description starting with keyword 'bad'. See example above.
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orig_time : float | int | instance of datetime | array of int | None | str
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orig_time : float | int | instance of datetime.datetime | array of int | None | str
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A POSIX Timestamp, datetime or an array containing the timestamp as the
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first element and microseconds as the second element. Determines the
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starting time of annotation acquisition. If None (default),
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----------
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raw : instance of Raw
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The raw data for which Annotations are defined.
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event_id : dict | Callable | None
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event_id : dict | callable | None
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Dictionary of string keys and integer values as used in mne.Epochs
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to map annotation descriptions to integer event codes. Only the
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keys present will be mapped and the annotations with other descriptions

‎mne/beamformer/_dics.py

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@@ -641,11 +641,11 @@ def tf_dics(epochs, forward, noise_csds, tmin, tmax, tstep, win_lengths,
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tf source grid. Defaults to False.
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mode : 'fourier' | 'multitaper' | 'cwt_morlet'
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Spectrum estimation mode. Defaults to 'fourier'.
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freq_bins : list of tuples of float
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freq_bins : list of tuple of float
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Start and end point of frequency bins of interest.
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Only used in 'multitaper' or 'fourier' mode. For 'cwt_morlet' mode, use
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the ``frequencies`` parameter instead.
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frequencies : list of float | list of lists of float
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frequencies : list of float | list of list of float
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The frequencies to compute the source power for. If you want to compute
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the average power for multiple frequency bins, specify a list of
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lists: each list containing the frequencies for the corresponding bin.

‎mne/beamformer/_lcmv.py

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win_lengths : list of float
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Time window lengths in seconds. One time window length should be
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provided for each frequency bin.
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freq_bins : list of tuples of float
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freq_bins : list of tuple of float
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Start and end point of frequency bins of interest.
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subtract_evoked : bool
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If True, subtract the averaged evoked response prior to computing the

‎mne/channels/channels.py

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show_names : bool | array of str
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Whether to display all channel names. If an array, only the channel
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names in the array are shown. Defaults to False.
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ch_groups : 'position' | array of shape (ch_groups, picks) | None
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ch_groups : 'position' | array of shape (n_ch_groups, n_picks) | None
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Channel groups for coloring the sensors. If None (default), default
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coloring scheme is used. If 'position', the sensors are divided
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into 8 regions. See ``order`` kwarg of :func:`mne.viz.plot_raw`. If
@@ -556,7 +556,7 @@ def plot_sensors(self, kind='topomap', ch_type=None, title=None,
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Returns
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-------
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fig : instance of matplotlib figure
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fig : instance of Figure
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Figure containing the sensor topography.
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selection : list
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A list of selected channels. Only returned if ``kind=='select'``.
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Returns
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-------
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ch_connectivity : scipy.sparse matrix, shape (n_channels, n_channels)
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ch_connectivity : scipy.sparse.csr_matrix, shape (n_channels, n_channels)
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The connectivity matrix.
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ch_names : list
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The list of channel names present in connectivity matrix.
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Returns
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-------
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ch_connectivity : scipy.sparse matrix, shape (n_channels, n_channels)
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ch_connectivity : scipy.sparse.csr_matrix, shape (n_channels, n_channels)
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The connectivity matrix.
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ch_names : list
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The list of channel names present in connectivity matrix.
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Parameters
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info : instance of info
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info : instance of Info
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Where to obtain the channel names from. The picks will
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be in relation to the position in `info["ch_names"]`. If possible, this
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‎mne/channels/layout.py

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Returns
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fig : instance of matplotlib figure
104+
fig : instance of matplotlib.figure.Figure
105105
Figure containing the sensor topography.
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107107
Notes
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914914
Parameters
915915
----------
916-
xy : ndarray (N x 2)
916+
xy : ndarray, shape (N, 2)
917917
The xy coordinates of sensor locations.
918918
w : float
919919
The width of each sensor's axis (between 0 and 1)
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ch_indices : list
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Index of each channel - must be a collection of unique integers,
930930
one index per channel.
931-
name : string
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name : str
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The name of this layout type.
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bg_image : str | ndarray
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The image over which sensor axes will be plotted. Either a path to an

‎mne/connectivity/effective.py

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e.g., data = [(arr_0, stc_0), (arr_1, stc_1), (arr_2, stc_2)],
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corresponds to 3 epochs, and arr_* could be an array with the same
5151
number of time points as stc_*.
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indices : tuple of arrays | None
52+
indices : tuple of array | None
5353
Two arrays with indices of connections for which to compute
5454
connectivity. If None, all connections are computed.
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sfreq : float
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The sampling frequency.
5757
mode : str
5858
Spectrum estimation mode can be either: 'multitaper', 'fourier', or
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'cwt_morlet'.
60-
fmin : float | tuple of floats
60+
fmin : float | tuple of float
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The lower frequency of interest. Multiple bands are defined using
6262
a tuple, e.g., (8., 20.) for two bands with 8Hz and 20Hz lower freq.
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If None the frequency corresponding to an epoch length of 5 cycles
6464
is used.
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fmax : float | tuple of floats
65+
fmax : float | tuple of float
6666
The upper frequency of interest. Multiple bands are dedined using
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a tuple, e.g. (13., 30.) for two band with 13Hz and 30Hz upper freq.
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tmin : float | None

‎mne/connectivity/spectral.py

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649649
or a list/generator of SourceEstimate or VolSourceEstimate objects.
650650
method : string | list of string
651651
Connectivity measure(s) to compute.
652-
indices : tuple of arrays | None
652+
indices : tuple of array | None
653653
Two arrays with indices of connections for which to compute
654654
connectivity. If None, all connections are computed.
655655
sfreq : float
656656
The sampling frequency.
657657
mode : str
658658
Spectrum estimation mode can be either: 'multitaper', 'fourier', or
659659
'cwt_morlet'.
660-
fmin : float | tuple of floats
660+
fmin : float | tuple of float
661661
The lower frequency of interest. Multiple bands are defined using
662662
a tuple, e.g., (8., 20.) for two bands with 8Hz and 20Hz lower freq.
663663
If None the frequency corresponding to an epoch length of 5 cycles
664664
is used.
665-
fmax : float | tuple of floats
665+
fmax : float | tuple of float
666666
The upper frequency of interest. Multiple bands are dedined using
667667
a tuple, e.g. (13., 30.) for two band with 13Hz and 30Hz upper freq.
668668
fskip : int
@@ -707,7 +707,7 @@ def spectral_connectivity(data, method='coh', indices=None, sfreq=2 * np.pi,
707707
708708
Returns
709709
-------
710-
con : array | list of arrays
710+
con : array | list of array
711711
Computed connectivity measure(s). The shape of each array is either
712712
(n_signals, n_signals, n_freqs) mode: 'multitaper' or 'fourier'
713713
(n_signals, n_signals, n_freqs, n_times) mode: 'cwt_morlet'

‎mne/connectivity/utils.py

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Returns
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-------
31-
indices : tuple of arrays
31+
indices : tuple of array
3232
The indices parameter used for connectivity computation.
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"""
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# make them arrays

‎mne/cov.py

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@@ -374,7 +374,7 @@ def compute_raw_covariance(raw, tmin=0, tmax=None, tstep=0.2, reject=None,
374374
See :func:`mne.compute_covariance`.
375375
376376
.. versionadded:: 0.12
377-
cv : int | sklearn model_selection object (default 3)
377+
cv : int | sklearn.model_selection object (default 3)
378378
The cross validation method. Defaults to 3, which will
379379
internally trigger by default :class:`sklearn.model_selection.KFold`
380380
with 3 splits.
@@ -605,7 +605,7 @@ def compute_covariance(epochs, keep_sample_mean=True, tmin=None, tmax=None,
605605
606606
Parameters
607607
----------
608-
epochs : instance of Epochs, or a list of Epochs objects
608+
epochs : instance of Epochs, or list of Epochs
609609
The epochs.
610610
keep_sample_mean : bool (default True)
611611
If False, the average response over epochs is computed for
@@ -651,7 +651,7 @@ def compute_covariance(epochs, keep_sample_mean=True, tmin=None, tmax=None,
651651
'pca': {'iter_n_components': None},
652652
'factor_analysis': {'iter_n_components': None}}
653653
654-
cv : int | sklearn model_selection object (default 3)
654+
cv : int | sklearn.model_selection object (default 3)
655655
The cross validation method. Defaults to 3, which will
656656
internally trigger by default :class:`sklearn.model_selection.KFold`
657657
with 3 splits.
@@ -1532,7 +1532,7 @@ def regularize(cov, info, mag=0.1, grad=0.1, eeg=0.1, exclude='bads',
15321532
exclude : list | 'bads' (default 'bads')
15331533
List of channels to mark as bad. If 'bads', bads channels
15341534
are extracted from both info['bads'] and cov['bads'].
1535-
proj : bool (default true)
1535+
proj : bool (default True)
15361536
Apply projections to keep rank of data.
15371537
seeg : float (default 0.1)
15381538
Regularization factor for sEEG signals.

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