DOC Remove defaults from parameter type descriptions

doc/changes/dev/14100.other.rst

@@ -0,0 +1 @@
+Move default values out of parameter type descriptions and remove ``optional`` from docstrings, by `Daria Agafonova`_.

mne/_fiff/pick.py

@@ -944,9 +944,9 @@ def pick_channels_cov(
     ----------
     orig : Covariance
         A covariance.
-    include : list of str, (optional)
+    include : list of str
         List of channels to include (if empty, include all available).
-    exclude : list of str, (optional) | 'bads'
+    exclude : list of str | 'bads'
         Channels to exclude (if empty, do not exclude any). Defaults to 'bads'.
     %(ordered)s
     copy : bool

mne/bem.py

@@ -1443,7 +1443,7 @@ def read_bem_surfaces(
     ----------
     fname : path-like
         The name of the file containing the surfaces.
-    patch_stats : bool, optional (default False)
+    patch_stats : bool
         Calculate and add cortical patch statistics to the surfaces.
     s_id : int | None
         If int, only read and return the surface with the given ``s_id``.

mne/cov.py

@@ -606,15 +606,15 @@ def compute_raw_covariance(
         Raw data.
     tmin : float
         Beginning of time interval in seconds. Defaults to 0.
-    tmax : float | None (default None)
+    tmax : float | None
         End of time interval in seconds. If None (default), use the end of the
         recording.
-    tstep : float (default 0.2)
+    tstep : float
         Length of data chunks for artifact rejection in seconds.
         Can also be None to use a single epoch of (tmax - tmin)
         duration. This can use a lot of memory for large ``Raw``
         instances.
-    reject : dict | None (default None)
+    reject : dict | None
         Rejection parameters based on peak-to-peak amplitude.
         Valid keys are 'grad' | 'mag' | 'eeg' | 'eog' | 'ecg'.
         If reject is None then no rejection is done. Example::
@@ -625,7 +625,7 @@ def compute_raw_covariance(
                           eog=250e-6 # V (EOG channels)
                           )
 
-    flat : dict | None (default None)
+    flat : dict | None
         Rejection parameters based on flatness of signal.
         Valid keys are 'grad' | 'mag' | 'eeg' | 'eog' | 'ecg', and values
         are floats that set the minimum acceptable peak-to-peak amplitude.
@@ -637,23 +637,23 @@ def compute_raw_covariance(
         covariance or rank estimates.
 
         .. versionadded:: 1.11
-    method : str | list | None (default 'empirical')
+    method : str | list | None
         The method used for covariance estimation.
         See :func:`mne.compute_covariance`.
 
         .. versionadded:: 0.12
-    method_params : dict | None (default None)
+    method_params : dict | None
         Additional parameters to the estimation procedure.
         See :func:`mne.compute_covariance`.
 
         .. versionadded:: 0.12
-    cv : int | sklearn.model_selection object (default 3)
+    cv : int | sklearn.model_selection object
         The cross validation method. Defaults to 3, which will
         internally trigger by default :class:`sklearn.model_selection.KFold`
         with 3 splits.
 
         .. versionadded:: 0.12
-    scalings : dict | None (default None)
+    scalings : dict | None
         Defaults to ``dict(mag=1e15, grad=1e13, eeg=1e6)``.
         These defaults will scale magnetometers and gradiometers
         at the same unit.
@@ -662,7 +662,7 @@ def compute_raw_covariance(
     %(n_jobs)s
 
         .. versionadded:: 0.12
-    return_estimators : bool (default False)
+    return_estimators : bool
         Whether to return all estimators or the best. Only considered if
         method equals 'auto' or is a list of str. Defaults to False.
 
@@ -911,17 +911,17 @@ def compute_covariance(
     ----------
     epochs : instance of Epochs, or list of Epochs
         The epochs.
-    keep_sample_mean : bool (default True)
+    keep_sample_mean : bool
         If False, the average response over epochs is computed for
         each event type and subtracted during the covariance
         computation. This is useful if the evoked response from a
         previous stimulus extends into the baseline period of the next.
         Note. This option is only implemented for method='empirical'.
-    tmin : float | None (default None)
+    tmin : float | None
         Start time for baseline. If None start at first sample.
-    tmax : float | None (default None)
+    tmax : float | None
         End time for baseline. If None end at last sample.
-    projs : list of Projection | None (default None)
+    projs : list of Projection | None
         List of projectors to use in covariance calculation, or None
         to indicate that the projectors from the epochs should be
         inherited. If None, then projectors from all epochs must match.
@@ -931,7 +931,7 @@ def compute_covariance(
         covariance or rank estimates.
 
         .. versionadded:: 1.11
-    method : str | list | None (default 'empirical')
+    method : str | list | None
         The method used for covariance estimation. If 'empirical' (default),
         the sample covariance will be computed. A list can be passed to
         perform estimates using multiple methods.
@@ -949,7 +949,7 @@ def compute_covariance(
         segments of data, since computation can take a long time.
 
         .. versionadded:: 0.9.0
-    method_params : dict | None (default None)
+    method_params : dict | None
         Additional parameters to the estimation procedure. Only considered if
         method is not None. Keys must correspond to the value(s) of ``method``.
         If None (default), expands to the following (with the addition of
@@ -962,16 +962,16 @@ def compute_covariance(
              'pca': {'iter_n_components': None},
              'factor_analysis': {'iter_n_components': None}}
 
-    cv : int | sklearn.model_selection object (default 3)
+    cv : int | sklearn.model_selection object
         The cross validation method. Defaults to 3, which will
         internally trigger by default :class:`sklearn.model_selection.KFold`
         with 3 splits.
-    scalings : dict | None (default None)
+    scalings : dict | None
         Defaults to ``dict(mag=1e15, grad=1e13, eeg=1e6)``.
         These defaults will scale data to roughly the same order of
         magnitude.
     %(n_jobs)s
-    return_estimators : bool (default False)
+    return_estimators : bool
         Whether to return all estimators or the best. Only considered if
         method equals 'auto' or is a list of str. Defaults to False.
     on_mismatch : str
@@ -1986,45 +1986,45 @@ def regularize(
     cov : Covariance
         The noise covariance matrix.
     %(info_not_none)s (Used to get channel types and bad channels).
-    mag : float (default 0.1)
+    mag : float
         Regularization factor for MEG magnetometers.
-    grad : float (default 0.1)
+    grad : float
         Regularization factor for MEG gradiometers. Must be the same as
         ``mag`` if data have been processed with SSS.
-    eeg : float (default 0.1)
+    eeg : float
         Regularization factor for EEG.
-    exclude : list | 'bads' (default 'bads')
+    exclude : list | 'bads'
         List of channels to mark as bad. If 'bads', bads channels
         are extracted from both info['bads'] and cov['bads'].
-    proj : bool (default True)
+    proj : bool
         Apply projections to keep rank of data.
-    seeg : float (default 0.1)
+    seeg : float
         Regularization factor for sEEG signals.
-    ecog : float (default 0.1)
+    ecog : float
         Regularization factor for ECoG signals.
-    hbo : float (default 0.1)
+    hbo : float
         Regularization factor for HBO signals.
-    hbr : float (default 0.1)
+    hbr : float
         Regularization factor for HBR signals.
-    fnirs_cw_amplitude : float (default 0.1)
+    fnirs_cw_amplitude : float
         Regularization factor for fNIRS CW raw signals.
-    fnirs_fd_ac_amplitude : float (default 0.1)
+    fnirs_fd_ac_amplitude : float
         Regularization factor for fNIRS FD AC raw signals.
-    fnirs_fd_phase : float (default 0.1)
+    fnirs_fd_phase : float
         Regularization factor for fNIRS raw phase signals.
-    fnirs_od : float (default 0.1)
+    fnirs_od : float
         Regularization factor for fNIRS optical density signals.
-    fnirs_td_gated_amplitude : float (default 0.1)
+    fnirs_td_gated_amplitude : float
         Regularization factor for fNIRS time domain gated amplitude signals.
-    fnirs_td_moments_intensity : float (default 0.1)
+    fnirs_td_moments_intensity : float
         Regularization factor for fNIRS time domain moments amplitude signals.
-    fnirs_td_moments_mean : float (default 0.1)
+    fnirs_td_moments_mean : float
         Regularization factor for fNIRS time domain moments mean signals.
-    fnirs_td_moments_variance : float (default 0.1)
+    fnirs_td_moments_variance : float
         Regularization factor for fNIRS time domain moments variance signals.
-    csd : float (default 0.1)
+    csd : float
         Regularization factor for EEG-CSD signals.
-    dbs : float (default 0.1)
+    dbs : float
         Regularization factor for DBS signals.
     %(rank_none)s
 
@@ -2375,13 +2375,13 @@ def whiten_evoked(
     noise_cov : instance of Covariance
         The noise covariance.
     %(picks_good_data)s
-    diag : bool (default False)
+    diag : bool
         If True, whiten using only the diagonal of the covariance.
     %(rank_none)s
 
         .. versionadded:: 0.18
            Support for 'info' mode.
-    scalings : dict | None (default None)
+    scalings : dict | None
         To achieve reliable rank estimation on multiple sensors,
         sensors have to be rescaled. This parameter controls the
         rescaling. If dict, it will override the

mne/decoding/_fixes.py

@@ -33,15 +33,15 @@ def validate_data(
             The estimator to validate the input for.
 
         X : {array-like, sparse matrix, dataframe} of shape \
-                (n_samples, n_features), default='no validation'
+                (n_samples, n_features) | 'no_validation'
             The input samples.
             If `'no_validation'`, no validation is performed on `X`. This is
             useful for meta-estimator which can delegate input validation to
             their underlying estimator(s). In that case `y` must be passed and
             the only accepted `check_params` are `multi_output` and
             `y_numeric`.
 
-        y : array-like of shape (n_samples,), default='no_validation'
+        y : array-like of shape (n_samples,) | None | 'no_validation'
             The targets.
 
             - If `None`, :func:`~sklearn.utils.check_array` is called on `X`. If
@@ -54,7 +54,7 @@ def validate_data(
             either :func:`~sklearn.utils.check_array` or
             :func:`~sklearn.utils.check_X_y` depending on `validate_separately`.
 
-        reset : bool, default=True
+        reset : bool
             Whether to reset the `n_features_in_` attribute.
             If False, the input will be checked for consistency with data
             provided when reset was last True.
@@ -65,7 +65,7 @@ def validate_data(
             call to `partial_fit`. All other methods that validate `X`
             should set `reset=False`.
 
-        validate_separately : False or tuple of dicts, default=False
+        validate_separately : False | tuple of dicts
             Only used if `y` is not `None`.
             If `False`, call :func:`~sklearn.utils.check_X_y`. Else, it must be a tuple
             of kwargs to be used for calling :func:`~sklearn.utils.check_array` on `X`
@@ -74,7 +74,7 @@ def validate_data(
             `estimator=self` is automatically added to these dicts to generate
             more informative error message in case of invalid input data.
 
-        skip_check_array : bool, default=False
+        skip_check_array : bool
             If `True`, `X` and `y` are unchanged and only `feature_names_in_` and
             `n_features_in_` are checked. Otherwise, :func:`~sklearn.utils.check_array`
             is called on `X` and `y`.

mne/decoding/base.py

@@ -842,9 +842,9 @@ def cross_val_multiscore(
         other cases, :class:`sklearn.model_selection.KFold` is used.
     %(n_jobs)s
     %(verbose)s
-    fit_params : dict, optional
+    fit_params : dict
         Parameters to pass to the fit method of the estimator.
-    pre_dispatch : int, or str, optional
+    pre_dispatch : int | str
         Controls the number of jobs that get dispatched during parallel
         execution. Reducing this number can be useful to avoid an
         explosion of memory consumption when more jobs get dispatched

mne/decoding/csp.py

@@ -34,28 +34,28 @@ class CSP(_GEDTransformer):
 
     Parameters
     ----------
-    n_components : int (default 4)
+    n_components : int
         The number of components to decompose M/EEG signals. This number should
         be set by cross-validation.
-    reg : float | str | None (default None)
+    reg : float | str | None
         If not None (same as ``'empirical'``, default), allow regularization
         for covariance estimation. If float (between 0 and 1), shrinkage is
         used. For str values, ``reg`` will be passed as ``method`` to
         :func:`mne.compute_covariance`.
-    log : None | bool (default None)
+    log : None | bool
         If ``transform_into`` equals ``'average_power'`` and ``log`` is None or
         True, then apply a log transform to standardize features, else features
         are z-scored. If ``transform_into`` is ``'csp_space'``, ``log`` must be
         None.
-    cov_est : 'concat' | 'epoch' (default 'concat')
+    cov_est : 'concat' | 'epoch'
         If ``'concat'``, covariance matrices are estimated on concatenated
         epochs for each class. If ``'epoch'``, covariance matrices are
         estimated on each epoch separately and then averaged over each class.
-    transform_into : 'average_power' | 'csp_space' (default 'average_power')
+    transform_into : 'average_power' | 'csp_space'
         If 'average_power' then ``self.transform`` will return the average
         power of each spatial filter. If ``'csp_space'``, ``self.transform``
         will return the data in CSP space.
-    norm_trace : bool (default False)
+    norm_trace : bool
         Normalize class covariance by its trace. Trace normalization is a step
         of the original CSP algorithm :footcite:`KolesEtAl1990` to eliminate
         magnitude variations in the EEG between individuals. It is not applied
@@ -89,7 +89,7 @@ class CSP(_GEDTransformer):
     %(rank_none)s
 
         .. versionadded:: 0.17
-    component_order : 'mutual_info' | 'alternate' (default 'mutual_info')
+    component_order : 'mutual_info' | 'alternate'
         If ``'mutual_info'`` order components by decreasing mutual information
         (in the two-class case this uses a simplification which orders
         components by decreasing absolute deviation of the eigenvalues from 0.5
@@ -611,9 +611,9 @@ def _ajd_pham(X, eps=1e-6, max_iter=15):
     ----------
     X : ndarray, shape (n_epochs, n_channels, n_channels)
         A set of covariance matrices to diagonalize.
-    eps : float, default 1e-6
+    eps : float
         The tolerance for stopping criterion.
-    max_iter : int, default 1000
+    max_iter : int
         The maximum number of iteration to reach convergence.
 
     Returns
@@ -705,13 +705,13 @@ class SPoC(CSP):
     ----------
     n_components : int
         The number of components to decompose M/EEG signals.
-    reg : float | str | None (default None)
+    reg : float | str | None
         If not None (same as ``'empirical'``, default), allow
         regularization for covariance estimation.
         If float, shrinkage is used (0 <= shrinkage <= 1).
         For str options, ``reg`` will be passed to ``method`` to
         :func:`mne.compute_covariance`.
-    log : None | bool (default None)
+    log : None | bool
         If transform_into == 'average_power' and log is None or True, then
         applies a log transform to standardize the features, else the features
         are z-scored. If transform_into == 'csp_space', then log must be None.

mne/decoding/ems.py

@@ -131,13 +131,13 @@ def compute_ems(
     ----------
     epochs : instance of mne.Epochs
         The epochs.
-    conditions : list of str | None, default None
+    conditions : list of str | None
         If a list of strings, strings must match the epochs.event_id's key as
         well as the number of conditions supported by the objective_function.
         If None keys in epochs.event_id are used.
     %(picks_good_data)s
     %(n_jobs)s
-    cv : cross-validation object | str | None, default LeaveOneOut
+    cv : cross-validation object | str | None
         The cross-validation scheme.
     %(verbose)s