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        "description": ".. image:: https://circleci.com/gh/bids-standard/pybv.svg?style=svg\n   :target: https://circleci.com/gh/bids-standard/pybv\n   :alt: CircleCI\n\n.. image:: https://codecov.io/gh/bids-standard/pybv/branch/master/graph/badge.svg\n   :target: https://codecov.io/gh/bids-standard/pybv\n   :alt: codecov\n\n.. image:: https://badge.fury.io/py/pybv.svg\n   :target: https://badge.fury.io/py/pybv\n   :alt: pypi\n\n.. image:: https://pepy.tech/badge/pybv\n   :target: https://pepy.tech/project/pybv\n   :alt: Downloads\n\n.. image:: https://readthedocs.org/projects/pybv/badge/?version=stable\n   :target: https://pybv.readthedocs.io/en/stable/?badge=stable\n   :alt: Documentation Status\n\n====\npybv\n====\n\n``pybv`` is a lightweight exporter to the BrainVision data format. It is meant\nfor use with electrophysiology datasets stored in the\n`Brain Imaging Data Structure <https://bids.neuroimaging.io>`_.\n\n\nThe documentation can be found under the following links:\n\n- for the `stable release <https://pybv.rtfd.io/en/stable/>`_\n- for the `latest (development) version <https://pybv.rtfd.io/en/latest/>`_\n\nAbout the BrainVision data format\n=================================\n\nBrainVision is the name of a file format commonly used for storing\nelectrophysiology data. Originally, it was put forward by the\ncompany `Brain Products <https://www.brainproducts.com>`_, however the\nsimplicity of the format has allowed for a diversity of tools reading from and\nwriting to the format.\n\nThe format consists of three separate files:\n\n1. A text header file (``.vhdr``) containing meta data\n2. A text marker file (``.vmrk``) containing information about events in the\n   data\n3. A binary data file (``.eeg``) containing the voltage values of the EEG\n\nBoth text files are based on the\n`Microsoft Windows INI format <https://en.wikipedia.org/wiki/INI_file>`_\nconsisting of:\n\n- sections marked as ``[square brackets]``\n- comments marked as ``; comment``\n- key-value pairs marked as ``key=value``\n\nA documentation for core BrainVision file format is provided by Brain Products.\nYou can `view the specification <https://www.brainproducts.com/productdetails.php?id=21&tab=5>`_\nas hosted by Brain Products.\n\n\nInstallation\n============\n``pybv``'s only dependency is ``numpy``. However, we currently recommend that\nyou install MNE-Python for reading BrainVision data. See their instructions\n`here <https://www.martinos.org/mne/stable/install_mne_python.html>`_.\n\nAfter you have a working installation of MNE-Python (or only ``numpy`` if you\ndon't want to read data and only write it), you can install ``pybv`` through\nthe following: ``pip install -U pybv``\n\nContributing\n============\nThe development of ``pybv`` is taking place on\n`Github <https://github.com/bids-standard/pybv>`_.\n\nFor more information, please see\n`CONTRIBUTING.md <https://github.com/bids-standard/pybv/blob/master/CONTRIBUTING.md>`_\n\nUsage\n=====\n\nWriting BrainVision files\n-------------------------\n\nThe primary functionality provided by ``pybv`` is the ``write_brainvision``\nfunction. This writes a numpy array of data and provided metadata into a\ncollection of BrainVision files on disk.\n\n.. code-block:: python\n\n    from pybv import write_brainvision\n\n    # for further parameters see our API documentation in the docs\n    write_brainvision(data, sfreq, ch_names, fname, tmpdir, events,\n                      resolution=1e-6, fmt='binary_float32')\n\nReading BrainVision files\n-------------------------\n\nCurrently, pybv recommends using\n`MNE-Python <https://martinos.org/mne/stable/index.html>`_\nfor reading BrainVision files written with ``pybv``. This results in a\nnearly-round-trip conversion of the data (up to the numerical precision you\nspecify in the ``resolution`` parameter).\n\nHere is an example of the MNE code required to read in BrainVision data:\n\n.. code-block:: python\n\n    import mne\n\n    # Import the BrainVision data into an MNE Raw object\n    raw = mne.io.read_raw_brainvision('tmp/test.vhdr', preload=True)\n\n    # Read in the event information as MNE annotations\n    annot = mne.read_annotations('tmp/test.vmrk')\n\n    # Add the annotations to our raw object so we can use them with the data\n    raw.set_annotations(annot)\n\n    # Reconstruct the original events from our Raw object\n    events, event_ids = mne.events_from_annotations(raw)\n\nAcknowledgements\n================\n\nThis package was originally adapted from the\n`Philistine package <https://gitlab.com/palday/philistine>`_ by\n`palday <https://palday.bitbucket.io/>`_. It copies much of the BrainVision\nexporting code, removes the dependence on MNE, and focuses the code around\nBrainVision I/O.\n\n\n",
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