def load_pig_central_venous_pressure(return_X_y=False):
    r"""Load and return the PigCVP dataset.

    In the test set, a class is represented by four examples, the second and
    third 2000 data points of the before time series and the second and third
    2000 data points of the after time series. Data created by Mathieu
    Guillame-Bert et al. Data edited by Shaghayegh Gharghabi and Eamonn Keogh.

    ================   ==============
    Training samples              104
    Test samples                  208
    Timestamps                   2000
    Classes                        52
    ================   ==============

    Parameters
    ----------
    return_X_y : bool (default = False)
        If True, return
        ``(data_train, data_test, target_train, target_test)`` instead of a
        Bunch object.

    Returns
    -------
    data : Bunch
        Dictionary-like object, with attributes:

        data_train : array of floats
            The time series in the training set.
        data_test : array of floats
            The time series in the test set.
        target_train : array of integers
            The classification labels in the training set.
        target_test : array of integers
            The classification labels in the test set.
        DESCR : str
            The full description of the dataset.
        url : str
            The url of the dataset.

    (data_train, data_test, target_train, target_test) : tuple if ``return_X_y`` is True

    References
    ----------
    .. [1] M. Guillame-Bert and A. Dubrawski, "Classification of Time Sequences
           using Graphs of Temporal Constraints". Journal of Machine Learning
           Research, 2017.

    .. [2] `UCR archive entry for the PigCVP dataset
           <http://www.timeseriesclassification.com/description.php?Dataset=PigCVP>`_

    Examples
    --------
    >>> from pyts.datasets import load_pig_central_venous_pressure
    >>> bunch = load_pig_central_venous_pressure()
    >>> bunch.data_train.shape
    (104, 2000)
    >>> X_train, X_test, y_train, y_test = load_pig_central_venous_pressure(
    ...    return_X_y=True)
    >>> X_train.shape
    (104, 2000)

    """  # noqa: E501
    return _load_dataset('PigCVP', 'UCR', return_X_y)
Load and return the PigCVP dataset.

    In the test set, a class is represented by four examples, the second and
    third 2000 data points of the before time series and the second and third
    2000 data points of the after time series. Data created by Mathieu
    Guillame-Bert et al. Data edited by Shaghayegh Gharghabi and Eamonn Keogh.

    ================   ==============
    Training samples              104
    Test samples                  208
    Timestamps                   2000
    Classes                        52
    ================   ==============

    Parameters
    ----------
    return_X_y : bool (default = False)
        If True, return
        ``(data_train, data_test, target_train, target_test)`` instead of a
        Bunch object.

    Returns
    -------
    data : Bunch
        Dictionary-like object, with attributes:

        data_train : array of floats
            The time series in the training set.
        data_test : array of floats
            The time series in the test set.
        target_train : array of integers
            The classification labels in the training set.
        target_test : array of integers
            The classification labels in the test set.
        DESCR : str
            The full description of the dataset.
        url : str
            The url of the dataset.

    (data_train, data_test, target_train, target_test) : tuple if ``return_X_y`` is True

    References
    ----------
    .. [1] M. Guillame-Bert and A. Dubrawski, "Classification of Time Sequences
           using Graphs of Temporal Constraints". Journal of Machine Learning
           Research, 2017.

    .. [2] `UCR archive entry for the PigCVP dataset
           <http://www.timeseriesclassification.com/description.php?Dataset=PigCVP>`_

    Examples
    --------
    >>> from pyts.datasets import load_pig_central_venous_pressure
    >>> bunch = load_pig_central_venous_pressure()
    >>> bunch.data_train.shape
    (104, 2000)
    >>> X_train, X_test, y_train, y_test = load_pig_central_venous_pressure(
    ...    return_X_y=True)
    >>> X_train.shape
    (104, 2000)

    
pyts/datasets/load.py