madminer/sampling/parameters.py
def random_morphing_points(n_thetas, priors):
    """
    Utility function to be used as input to various SampleAugmenter functions, specifying random parameter points
    sampled from a prior in a morphing setup.

    Parameters
    ----------
    n_thetas : int
        Number of parameter points to be sampled

    priors : list of tuples
        Priors for each parameter is characterized by a tuple of the form `(prior_shape, prior_param_0, prior_param_1)`.
        Currently, the supported prior_shapes are `flat`, in which case the two other parameters are the lower and upper
        bound of the flat prior, and `gaussian`, in which case they are the mean and standard deviation of a Gaussian.

    Returns
    -------
    output : tuple
        Input to various SampleAugmenter functions

    """
    return "random_morphing_points", (n_thetas, priors)

    Utility function to be used as input to various SampleAugmenter functions, specifying random parameter points
    sampled from a prior in a morphing setup.

    Parameters
    ----------
    n_thetas : int
        Number of parameter points to be sampled

    priors : list of tuples
        Priors for each parameter is characterized by a tuple of the form `(prior_shape, prior_param_0, prior_param_1)`.
        Currently, the supported prior_shapes are `flat`, in which case the two other parameters are the lower and upper
        bound of the flat prior, and `gaussian`, in which case they are the mean and standard deviation of a Gaussian.

    Returns
    -------
    output : tuple
        Input to various SampleAugmenter functions

    
Utility function to be used as input to various SampleAugmenter functions, specifying random parameter points
sampled from a prior in a morphing setup.

Parameters

n_thetas : int
Number of parameter points to be sampled

priors : list of tuples
Priors for each parameter is characterized by a tuple of the form `(prior_shape, prior_param_0, prior_param_1)`.
Currently, the supported prior_shapes are `flat`, in which case the two other parameters are the lower and upper
bound of the flat prior, and `gaussian`, in which case they are the mean and standard deviation of a Gaussian.

Returns

output : tuple
Input to various SampleAugmenter functions
Utility function to be used as input to various SampleAugmenter functions specifying random parameter points sampled from a prior in a morphing setup . Parameters n_thetas : int Number of parameter points to be sampled priors : list of tuples Priors for each parameter is characterized by a tuple of the form ` ( prior_shape prior_param_0 prior_param_1 ) ` . Currently the supported prior_shapes are ` flat ` in which case the two other parameters are the lower and upper bound of the flat prior and ` gaussian ` in which case they are the mean and standard deviation of a Gaussian . Returns output : tuple Input to various SampleAugmenter functions
def random_morphing_points(n_thetas, priors):
    return "random_morphing_points", (n_thetas, priors)
def random_morphing_points ( n_thetas , priors ) : return "random_morphing_points" , ( n_thetas , priors )
Utility function to be used as input to various SampleAugmenter functions, specifying random parameter points
sampled from a prior in a morphing setup.
Utility function to be used as input to various SampleAugmenter functions specifying random parameter points sampled from a prior in a morphing setup .
"""
    Utility function to be used as input to various SampleAugmenter functions, specifying random parameter points
    sampled from a prior in a morphing setup.

    Parameters
    ----------
    n_thetas : int
        Number of parameter points to be sampled

    priors : list of tuples
        Priors for each parameter is characterized by a tuple of the form `(prior_shape, prior_param_0, prior_param_1)`.
        Currently, the supported prior_shapes are `flat`, in which case the two other parameters are the lower and upper
        bound of the flat prior, and `gaussian`, in which case they are the mean and standard deviation of a Gaussian.

    Returns
    -------
    output : tuple
        Input to various SampleAugmenter functions

    """
n_thetas
priors