def create_normal(mu=0., sigma=1., mu_err=0.,
                  sigma_err=1., seed=None, **kwargs):
    """Generate a data with magnitudes that follows a Gaussian
     distribution. Also their errors are gaussian.

    Parameters
    ----------

    mu : float (default=0)
        Mean of the gaussian distribution of magnitudes
    sigma : float (default=1)
        Standar deviation of the gaussian distribution of magnitude errors
    mu_err : float (default=0)
        Mean of the gaussian distribution of magnitudes
    sigma_err : float (default=1)
        Standar deviation of the gaussian distribution of magnitude errorrs
    seed : {None, int, array_like}, optional
        Random seed used to initialize the pseudo-random number generator.
        Can be any integer between 0 and 2**32 - 1 inclusive, an
        array (or other sequence) of such integers, or None (the default).
        If seed is None, then RandomState will try to read data from
        /dev/urandom (or the Windows analogue) if available or seed from
        the clock otherwise.
    kwargs : optional
        extra arguments for create_random.

    Returns
    -------

    data
        A Data object with a random lightcurves.

    Examples
    --------

    .. code-block:: pycon

        >>> ds = create_normal(0, 1, 0, .0008, seed=42)
        >>> ds
        Data(id=None, ds_name='feets-synthetic', bands=('B', 'V'))
        >>> ds.data.B
        LightCurve(time[10000], magnitude[10000], error[10000])
        >>> ds.data.B.time
        array([  0.00000000e+00,   1.00010001e-04,   2.00020002e-04, ...,
                 9.99799980e-01,   9.99899990e-01,   1.00000000e+00])

    """

    random = np.random.RandomState(seed)
    return create_random(
        magf=random.normal, magf_params={"loc": mu, "scale": sigma},
        errf=random.normal, errf_params={"loc": mu_err, "scale": sigma_err},
        **kwargs)
def create_normal ( mu = 0. , sigma = 1. , mu_err = 0. , sigma_err = 1. , seed = None , ** kwargs ) : random = np . random . RandomState ( seed ) return create_random ( magf = random . normal , magf_params = { "loc" : mu , "scale" : sigma } , errf = random . normal , errf_params = { "loc" : mu_err , "scale" : sigma_err } , ** kwargs )
Generate a data with magnitudes that follows a Gaussian
     distribution. Also their errors are gaussian.

    Parameters
    ----------

    mu : float (default=0)
        Mean of the gaussian distribution of magnitudes
    sigma : float (default=1)
        Standar deviation of the gaussian distribution of magnitude errors
    mu_err : float (default=0)
        Mean of the gaussian distribution of magnitudes
    sigma_err : float (default=1)
        Standar deviation of the gaussian distribution of magnitude errorrs
    seed : {None, int, array_like}, optional
        Random seed used to initialize the pseudo-random number generator.
        Can be any integer between 0 and 2**32 - 1 inclusive, an
        array (or other sequence) of such integers, or None (the default).
        If seed is None, then RandomState will try to read data from
        /dev/urandom (or the Windows analogue) if available or seed from
        the clock otherwise.
    kwargs : optional
        extra arguments for create_random.

    Returns
    -------

    data
        A Data object with a random lightcurves.

    Examples
    --------

    .. code-block:: pycon

        >>> ds = create_normal(0, 1, 0, .0008, seed=42)
        >>> ds
        Data(id=None, ds_name='feets-synthetic', bands=('B', 'V'))
        >>> ds.data.B
        LightCurve(time[10000], magnitude[10000], error[10000])
        >>> ds.data.B.time
        array([  0.00000000e+00,   1.00010001e-04,   2.00020002e-04, ...,
                 9.99799980e-01,   9.99899990e-01,   1.00000000e+00])