"""Tutorial module with data for docs and quick testing."""
import numpy as np
import xarray as xr


def generate_mcmc_like_dataset(seed=None):
    """Generate a Dataset with multiple variables, some with dimensions from mcmc sampling.

    Parameters
    ----------
    seed : int or sequence of int, optional
        The random seed used to initialize :func:`numpy.random.default_rng`.

    Examples
    --------
    The dataset generated is the following:

    .. jupyter-execute::

        from xarray_einstats import tutorial
        tutorial.generate_mcmc_like_dataset(3)

    Notes
    -----
    This function is not part of the public API and is designed for use in our documentation.
    In addition to generating the data, it also sets ``display_expand_data=False`` to
    avoid taking too much virtual space with examples.

    """
    xr.set_options(display_expand_data=False)
    rng = np.random.default_rng(seed)
    ds = xr.Dataset(
        {
            "x_plot": (["plot_dim"], np.linspace(0, 10, 20)),
            "mu": (["chain", "draw", "team"], rng.exponential(size=(4, 10, 6))),
            "sigma": (["chain", "draw"], rng.exponential(size=(4, 10))),
            "score": (["chain", "draw", "match"], rng.poisson(size=(4, 10, 12))),
        },
        coords={"team": list("abcdef"), "chain": np.arange(4), "draw": np.arange(10)},
    )
    return ds


def generate_matrices_dataarray(seed=None):
    """Generate a 4d DataArray representing a batch of matrices.

    Parameters
    ----------
    seed : int or sequence of int, optional
        The random seed used to initialize :func:`numpy.random.default_rng`.

    Examples
    --------
    The dataset generated is the following:

    .. jupyter-execute::

        from xarray_einstats import tutorial
        tutorial.generate_matrices_dataarray(5)

    Notes
    -----
    This function is not part of the public API and is designed for use in our documentation.
    In addition to generating the data, it also sets ``display_expand_data=False`` to
    avoid taking too much virtual space with examples.

    """
    xr.set_options(display_expand_data=False)
    rng = np.random.default_rng(seed)
    return xr.DataArray(
        rng.exponential(size=(10, 3, 4, 4)), dims=["batch", "experiment", "dim", "dim2"]
    )

"""Tutorial module with data for docs and quick testing."""
import numpy as np
import xarray as xr


def generate_mcmc_like_dataset(seed=None):
    """Generate a Dataset with multiple variables, some with dimensions from mcmc sampling.

    Parameters
    ----------
    seed : int or sequence of int, optional
        The random seed used to initialize :func:`numpy.random.default_rng`.

    Examples
    --------
    The dataset generated is the following:

    .. jupyter-execute::

        from xarray_einstats import tutorial
        tutorial.generate_mcmc_like_dataset(3)

    Notes
    -----
    This function is not part of the public API and is designed for use in our documentation.
    In addition to generating the data, it also sets ``display_expand_data=False`` to
    avoid taking too much virtual space with examples.

    """
    xr.set_options(display_expand_data=False)
    rng = np.random.default_rng(seed)
    ds = xr.Dataset(
        {
            "x_plot": (["plot_dim"], np.linspace(0, 10, 20)),
            "mu": (["chain", "draw", "team"], rng.exponential(size=(4, 10, 6))),
            "sigma": (["chain", "draw"], rng.exponential(size=(4, 10))),
            "score": (["chain", "draw", "match"], rng.poisson(size=(4, 10, 12))),
        },
        coords={"team": list("abcdef"), "chain": np.arange(4), "draw": np.arange(10)},
    )
    return ds


def generate_matrices_dataarray(seed=None):
    """Generate a 4d DataArray representing a batch of matrices.

    Parameters
    ----------
    seed : int or sequence of int, optional
        The random seed used to initialize :func:`numpy.random.default_rng`.

    Examples
    --------
    The dataset generated is the following:

    .. jupyter-execute::

        from xarray_einstats import tutorial
        tutorial.generate_matrices_dataarray(5)

    Notes
    -----
    This function is not part of the public API and is designed for use in our documentation.
    In addition to generating the data, it also sets ``display_expand_data=False`` to
    avoid taking too much virtual space with examples.

    """
    xr.set_options(display_expand_data=False)
    rng = np.random.default_rng(seed)
    return xr.DataArray(
        rng.exponential(size=(10, 3, 4, 4)), dims=["batch", "experiment", "dim", "dim2"]
    )

Tutorial module with data for docs and quick testing. Generate a Dataset with multiple variables, some with dimensions from mcmc sampling.      Parameters     ----------     seed : int or sequence of int, optional         The random seed used to initialize :func:`numpy.random.default_rng`.      Examples     --------     The dataset generated is the following:      .. jupyter-execute::          from xarray_einstats import tutorial         tutorial.generate_mcmc_like_dataset(3)      Notes     -----     This function is not part of the public API and is designed for use in our documentation.     In addition to generating the data, it also sets ``display_expand_data=False`` to     avoid taking too much virtual space with examples. Generate a 4d DataArray representing a batch of matrices.      Parameters     ----------     seed : int or sequence of int, optional         The random seed used to initialize :func:`numpy.random.default_rng`.      Examples     --------     The dataset generated is the following:      .. jupyter-execute::          from xarray_einstats import tutorial         tutorial.generate_matrices_dataarray(5)      Notes     -----     This function is not part of the public API and is designed for use in our documentation.     In addition to generating the data, it also sets ``display_expand_data=False`` to     avoid taking too much virtual space with examples.