:py:mod:`sovabids.datasets`
===========================

.. py:module:: sovabids.datasets

.. autoapi-nested-parse::

   Module with dataset utilities.



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


Functions
~~~~~~~~~

.. autoapisummary::

   sovabids.datasets.lemon_prepare
   sovabids.datasets.lemon_bidscoin_prepare
   sovabids.datasets.make_dummy_dataset
   sovabids.datasets.get_dummy_raw
   sovabids.datasets.save_dummy_vhdr
   sovabids.datasets.save_dummy_cnt
   sovabids.datasets.replace_brainvision_filename



.. py:function:: lemon_prepare()

   Download and prepare a few files of the LEMON dataset.

   .. rubric:: Notes

   See the `LEMON dataset <http://fcon_1000.projects.nitrc.org/indi/retro/MPI_LEMON.html>`_ .


.. py:function:: lemon_bidscoin_prepare(src_path)

   Download and prepare a few files of the LEMON dataset to be used with BIDSCOIN.

   :param src_path: The path where the BIDSCOIN-ready LEMON files will be
   :type src_path: str

   .. seealso:: :obj:`datasets.lemon_prepare`


.. py:function:: make_dummy_dataset(EXAMPLE, PATTERN='T%task%/S%session%/sub%subject%_%acquisition%_%run%', DATASET='DUMMY', NSUBS=2, NSESSIONS=2, NTASKS=2, NACQS=2, NRUNS=2, PREFIXES={'subject': 'SU', 'session': 'SE', 'task': 'TA', 'acquisition': 'AC', 'run': 'RU'}, ROOT=None)

   Create a dummy dataset given some parameters.

   :param EXAMPLE: Path of the file to replicate as each file in the dummy dataset.
                   If a list, it is assumed each item is a file. All of these items are replicated.
   :type EXAMPLE: str,PathLike|list , required
   :param PATTERN: The pattern in placeholder notation using the following fields:
                   %dataset%, %task%, %session%, %subject%, %run%, %acquisition%
   :type PATTERN: str, optional
   :param DATASET: Name of the dataset.
   :type DATASET: str, optional
   :param NSUBS: Number of subjects.
   :type NSUBS: int, optional
   :param NSESSIONS: Number of sessions.
   :type NSESSIONS: int, optional
   :param NTASKS: Number of tasks.
   :type NTASKS: int, optional
   :param NACQS: Number of acquisitions.
   :type NACQS: int, optional
   :param NRUNS: Number of runs.
   :type NRUNS: int, optional
   :param PREFIXES: Dictionary with the following keys:'subject', 'session', 'task' and 'acquisition'.
                    The values are the corresponding prefix. RUN is not present because it has to be a number.
   :type PREFIXES: dict, optional
   :param ROOT: Path where the files will be generated.
                If None, the _data subdir will be used.
   :type ROOT: str, optional


.. py:function:: get_dummy_raw(NCHANNELS=5, SFREQ=200, STOP=10, NUMEVENTS=10)

   Create a dummy MNE Raw file given some parameters.

   :param NCHANNELS: Number of channels.
   :type NCHANNELS: int, optional
   :param SFREQ: Sampling frequency of the data.
   :type SFREQ: float, optional
   :param STOP: Time duration of the data in seconds.
   :type STOP: float, optional
   :param NUMEVENTS: Number of events along the duration.
   :type NUMEVENTS: int, optional


.. py:function:: save_dummy_vhdr(fpath, dummy_args={})

   Save a dummy vhdr file.

   :param fpath: Path where to save the file.
   :type fpath: str, required
   :param kwargs: Dictionary with the arguments of the get_dummy_raw function.
   :type kwargs: dict, optional

   :returns: * *List with the Paths of the desired vhdr file, if those were succesfully created,*
             * *None otherwise.*


.. py:function:: save_dummy_cnt(fpath)

   Save a dummy cnt file.

   :param fpath: Path where to save the file.
   :type fpath: str, required

   :returns: * *Path of the desired file if the file was succesfully created,*
             * *None otherwise.*


.. py:function:: replace_brainvision_filename(fpath, newname)