coco_pipe.io.utils
==================

.. py:module:: coco_pipe.io.utils

.. autoapi-nested-parse::

   IO Utilities
   ============

   Helper functions for IO operations.



Attributes
----------

.. autoapisummary::

   coco_pipe.io.utils.logger
   coco_pipe.io.utils.mne
   coco_pipe.io.utils.BIDSPath
   coco_pipe.io.utils.read_raw_bids


Functions
---------

.. autoapisummary::

   coco_pipe.io.utils._get_mne
   coco_pipe.io.utils._get_bids_path
   coco_pipe.io.utils._get_read_raw_bids
   coco_pipe.io.utils.row_quality_score
   coco_pipe.io.utils.make_strata
   coco_pipe.io.utils.sample_indices
   coco_pipe.io.utils.split_column
   coco_pipe.io.utils.read_bids_entry
   coco_pipe.io.utils.load_participants_tsv
   coco_pipe.io.utils.detect_subjects
   coco_pipe.io.utils.detect_sessions
   coco_pipe.io.utils.detect_runs
   coco_pipe.io.utils.smart_reader
   coco_pipe.io.utils.default_id_extractor


Module Contents
---------------

.. py:data:: logger

.. py:data:: mne
   :value: None


.. py:data:: BIDSPath
   :value: None


.. py:data:: read_raw_bids
   :value: None


.. py:function:: _get_mne()

   Lazily import MNE so base IO structures stay lightweight.


.. py:function:: _get_bids_path()

   Lazily import mne-bids' ``BIDSPath``.


.. py:function:: _get_read_raw_bids()

   Lazily import the MNE-BIDS raw reader.


.. py:function:: row_quality_score(df: pandas.DataFrame, exclude_cols: Optional[List[str]] = None, count_zero: bool = True) -> pandas.Series

   Calculate a 'badness' score for each row (NaNs + Infs + Zeros).
   Lower is better.


.. py:function:: make_strata(df: pandas.DataFrame, covariates: List[str], n_bins: int = 5, binning: str = 'quantile') -> pandas.Series

   Create a single stratification label from multiple covariates.
   Numeric covariates are binned.


.. py:function:: sample_indices(df: pandas.DataFrame, target: str, size_map: Dict[Any, int], rng, replace: bool, prefer_clean: bool, exclude: List[str]) -> pandas.Index

   Sample indices for each class based on size_map.


.. py:function:: split_column(name: str, sep: str, reverse: bool) -> Tuple[str, str]

   Split a column into (unit, feature) using `sep` and `reverse`.


.. py:function:: read_bids_entry(bids_path: Any, is_pre_epoched: bool, is_evoked: bool, mode: str, window_length: Optional[float], stride: Optional[float], event_id: Optional[Union[Dict[str, int], str, List[str]]] = None, tmin: float = -0.2, tmax: float = 0.5, baseline: Optional[Tuple[Optional[float], Optional[float]]] = None) -> Tuple[numpy.ndarray, numpy.ndarray, List[str], float, Optional[numpy.ndarray]]

.. py:function:: load_participants_tsv(root: pathlib.Path) -> Dict[str, Dict[str, Any]]

   Reads participants.tsv and returns dict: {sub_id: {col: val, ...}}.


.. py:function:: detect_subjects(root: pathlib.Path) -> List[str]

.. py:function:: detect_sessions(root: pathlib.Path, subject: str) -> List[str]

.. py:function:: detect_runs(root: pathlib.Path, subject: str, session: Optional[str] = None, task: Optional[str] = None, datatype: str = 'eeg') -> List[str]

   Detect available runs for a given subject/session/task.


.. py:function:: smart_reader(path: pathlib.Path) -> Any

.. py:function:: default_id_extractor(path: pathlib.Path) -> str