shorten strings through 88 character length

vferat · Aug 16, 2023 · aa4d672 · aa4d672
1 parent 084f15e
commit aa4d672
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Showing 23 changed files with 366 additions and 454 deletions.
diff --git a/pycrostates/cluster/_base.py b/pycrostates/cluster/_base.py
diff --git a/pycrostates/cluster/aahc.py b/pycrostates/cluster/aahc.py
@@ -156,11 +156,10 @@ def fit(
     @copy_doc(_BaseCluster.save)
     def save(self, fname: Union[str, Path]):
         super().save(fname)
-        # TODO: to be replaced by a general writer than infers the writer from
-        # the file extension.
+        # TODO: to be replaced by a general writer than infers the writer from the file
+        # extension.
         # pylint: disable=import-outside-toplevel
         from ..io.fiff import _write_cluster
-
         # pylint: enable=import-outside-toplevel
 
         _write_cluster(
@@ -250,7 +249,6 @@ def _compute_maps(
 
     # pylint: enable=too-many-locals
     # --------------------------------------------------------------------
-
     @property
     def normalize_input(self) -> bool:
         """If set, the input data is normalized along the channel dimension.

diff --git a/pycrostates/cluster/kmeans.py b/pycrostates/cluster/kmeans.py
@@ -28,14 +28,13 @@ class ModKMeans(_BaseCluster):
     ----------
     %(n_clusters)s
     n_init : int
-        Number of time the k-means algorithm is run with different centroid
-        seeds. The final result will be the run with the highest Global
-        Explained Variance (GEV).
+        Number of time the k-means algorithm is run with different centroid seeds. The
+        final result will be the run with the highest Global Explained Variance (GEV).
     max_iter : int
         Maximum number of iterations of the K-means algorithm for a single run.
     tol : float
-        Relative tolerance with regards estimate residual noise in the cluster
-        centers of two consecutive iterations to declare convergence.
+        Relative tolerance with regards estimate residual noise in the cluster centers
+        of two consecutive iterations to declare convergence.
     %(random_state)s
 
     References
@@ -151,16 +150,15 @@ def fit(
         Parameters
         ----------
         inst : Raw | Epochs | ChData
-            MNE `~mne.io.Raw`, `~mne.Epochs` or `~pycrostates.io.ChData` object
-            from which to extract :term:`cluster centers`.
+            MNE `~mne.io.Raw`, `~mne.Epochs` or `~pycrostates.io.ChData` object from
+            which to extract :term:`cluster centers`.
         picks : str | list | slice | None
-            Channels to include. Note that all channels selected must have the
-            same type. Slices and lists of integers will be interpreted as
-            channel indices. In lists, channel name strings (e.g.
-            ``['Fp1', 'Fp2']``) will pick the given channels. Can also be the
-            string values “all” to pick all channels, or “data” to pick data
-            channels. ``"eeg"`` (default) will pick all eeg channels.
-            Note that channels in ``info['bads']`` will be included if their
+            Channels to include. Note that all channels selected must have the same
+            type. Slices and lists of integers will be interpreted as channel indices.
+            In lists, channel name strings (e.g. ``['Fp1', 'Fp2']``) will pick the given
+            channels. Can also be the string values ``“all”`` to pick all channels, or
+            ``“data”`` to pick data channels. ``"eeg"`` (default) will pick all eeg
+            channels. Note that channels in ``info['bads']`` will be included if their
             names or indices are explicitly provided.
         %(tmin_raw)s
         %(tmax_raw)s
@@ -224,8 +222,8 @@ def fit(
             )
         else:
             logger.error(
-                "All the K-means run failed to converge. Please adapt the "
-                "tolerance and the maximum number of iteration."
+                "All the K-means run failed to converge. Please adapt the tolerance "
+                "and the maximum number of iteration."
             )
             self.fitted = False  # reset variables related to fit
             return  # break early
@@ -427,6 +425,6 @@ def _check_tol(tol: Union[int, float]) -> Union[int, float]:
         _check_type(tol, ("numeric",), item_name="tol")
         if tol <= 0:
             raise ValueError(
-                "The tolerance must be a positive number. Provided: '{tol}'."
+                f"The tolerance must be a positive number. Provided: '{tol}'."
             )
         return tol
diff --git a/pycrostates/cluster/utils/utils.py b/pycrostates/cluster/utils/utils.py
@@ -24,11 +24,10 @@ def _optimize_order(
 def optimize_order(inst: Cluster, template_inst: Cluster):
     """Optimize the order of cluster centers between two cluster instances.
 
-    Optimize the order of cluster centers in an instance of a clustering
-    algorithm to maximize auto-correlation, based on a template instance
-    as determined by the Hungarian algorithm.
-    The two cluster instances must have the same number of cluster centers
-    and the same polarity setting.
+    Optimize the order of cluster centers in an instance of a clustering algorithm to
+    maximize auto-correlation, based on a template instance as determined by the
+    Hungarian algorithm. The two cluster instances must have the same number of cluster
+    centers and the same polarity setting.
 
     Parameters
     ----------
@@ -40,8 +39,7 @@ def optimize_order(inst: Cluster, template_inst: Cluster):
     Returns
     -------
     order : list of int
-        The new order to apply to inst to maximize auto-correlation
-        of cluster centers.
+        The new order to apply to inst to maximize auto-correlation of cluster centers.
     """
     from .._base import _BaseCluster
 

diff --git a/pycrostates/datasets/lemon/lemon.py b/pycrostates/datasets/lemon/lemon.py
@@ -19,22 +19,20 @@
 def data_path(subject_id: str, condition: str) -> Path:
     r"""Get path to a local copy of preprocessed EEG recording from the LEMON dataset.
 
-    Get path to a local copy of preprocessed EEG recording from the
-    mind-brain-body dataset of MRI, EEG, cognition, emotion, and peripheral
-    physiology in young and old adults\ :footcite:p:`babayan_mind-brain-body_2019`.
-    If there is no local copy of the recording, this function will fetch it
-    from the online repository and store it. The default location is
-    ``~/pycrostates_data``.
+    Get path to a local copy of preprocessed EEG recording from the mind-brain-body
+    dataset of MRI, EEG, cognition, emotion, and peripheral physiology in young and old
+    adults\ :footcite:p:`babayan_mind-brain-body_2019`. If there is no local copy of the
+    recording, this function will fetch it from the online repository and store it. The
+    default location is ``~/pycrostates_data``.
 
     Parameters
     ----------
     subject_id : str
         The subject id to use. For example ``'010276'``.
-        The list of available subjects can be found
-        on this `FTP server <https://ftp.gwdg.de/pub/misc/MPI-Leipzig_Mind-Brain-Body-LEMON/EEG_MPILMBB_LEMON/EEG_Raw_BIDS_ID>`_.
+        The list of available subjects can be found on this
+        `FTP server <https://ftp.gwdg.de/pub/misc/MPI-Leipzig_Mind-Brain-Body-LEMON/EEG_MPILMBB_LEMON/EEG_Raw_BIDS_ID>`_.
     condition : str
-        Can be ``'EO'`` for eyes open condition or ``'EC'`` for eyes closed
-        condition.
+        Can be ``'EO'`` for eyes open condition or ``'EC'`` for eyes closed condition.
 
     Returns
     -------
@@ -51,8 +49,8 @@ def data_path(subject_id: str, condition: str) -> Path:
     - ``pip install pymatreader``
     - ``conda install -c conda-forge pymatreader``
 
-    Note that an environment created via the MNE installers includes
-    ``pymatreader`` by default.
+    Note that an environment created via the MNE installers includes ``pymatreader`` by
+    default.
 
     References
     ----------
@@ -65,7 +63,7 @@ def data_path(subject_id: str, condition: str) -> Path:
     config = get_config()
     fetcher = pooch.create(
         path=config["PREPROCESSED_LEMON_DATASET_PATH"],
-        base_url="https://ftp.gwdg.de/pub/misc/MPI-Leipzig_Mind-Brain-Body-LEMON/EEG_MPILMBB_LEMON/EEG_Preprocessed_BIDS_ID/EEG_Preprocessed/",  # noqa,
+        base_url="https://ftp.gwdg.de/pub/misc/MPI-Leipzig_Mind-Brain-Body-LEMON/EEG_MPILMBB_LEMON/EEG_Preprocessed_BIDS_ID/EEG_Preprocessed/",  # noqa: E501
         version=None,
         registry=None,
     )
@@ -86,8 +84,8 @@ def data_path(subject_id: str, condition: str) -> Path:
 def standardize(raw: BaseRaw):
     """Standardize :class:`~mne.io.Raw` from the lemon dataset.
 
-    This function will interpolate missing channels from the standard setup,
-    then reorder channels and finally reference to a common average.
+    This function will interpolate missing channels from the standard setup, then
+    reorder channels and finally reference to a common average.
 
     Parameters
     ----------
@@ -102,8 +100,8 @@ def standardize(raw: BaseRaw):
     Notes
     -----
     If you don't want to interpolate missing channels, you can use
-    :func:`mne.channels.equalize_channels` instead to have the same electrodes
-    across different recordings.
+    :func:`mne.channels.equalize_channels` instead to have the same electrodes across
+    different recordings.
     """
     raw = raw.copy()
     # fmt: off

diff --git a/pycrostates/io/ch_data.py b/pycrostates/io/ch_data.py
@@ -16,17 +16,17 @@
 class ChData(CHData, ChannelsMixin, ContainsMixin, MontageMixin):
     """ChData stores atemporal data with its spatial information.
 
-    ChData is similar to a raw instance where temporality has been removed.
-    Only the spatial information, stored as a `~pycrostates.io.ChInfo` is
+    `~pycrostates.io.ChData` is similar to a raw instance where temporality has been
+    removed. Only the spatial information, stored as a `~pycrostates.io.ChInfo` is
     retained.
 
     Parameters
     ----------
     data : array
         Data array of shape ``(n_channels, n_samples)``.
     info : mne.Info | ChInfo
-        Atemporal measurement information. If a `mne.Info` is provided, it is
-        converted to a `~pycrostates.io.ChInfo`.
+        Atemporal measurement information. If a `mne.Info` is provided, it is converted
+        to a `~pycrostates.io.ChInfo`.
     """
 
     def __init__(self, data: NDArray[float], info: Union[Info, CHInfo]):
@@ -36,9 +36,8 @@ def __init__(self, data: NDArray[float], info: Union[Info, CHInfo]):
         _check_type(info, (Info, ChInfo), "info")
         if data.ndim != 2:
             raise ValueError(
-                "Argument 'data' should be a 2D array "
-                "(n_channels, n_samples). The provided array "
-                f"shape is {data.shape} which has {data.ndim} "
+                "Argument 'data' should be a 2D array (n_channels, n_samples). The "
+                f"provided array shape is {data.shape} which has {data.ndim} "
                 "dimensions."
             )
         if not len(info["ch_names"]) == data.shape[0]:
@@ -118,8 +117,8 @@ def pick(self, picks, exclude="bads"):
         ----------
         %(picks_all)s
         exclude : list | str
-            Set of channels to exclude, only used when picking based on
-            types (e.g., ``exclude="bads"`` when ``picks="meg"``).
+            Set of channels to exclude, only used when picking based on types (e.g.,
+            ``exclude="bads"`` when ``picks="meg"``).
 
         Returns
         -------