Repository: scipy/scipy
File: scipy/stats/_distribution_infrastructure.py (Python)

Code before:
    ...     def cdf(self, x, *, a, b):
    ...         return (np.cbrt(x) - a) / (b - a)
    >>>
    >>> MyUniformCube = stats.make_distribution(MyUniformCube())
    >>> X = MyUniformCube(a=-2, b=2)
    >>> Y = stats.Uniform(a=-2, b=2)**3
    >>> X.support()
    (-8.0, 8.0)
    >>> np.isclose(X.cdf(2.1), Y.cdf(2.1))
    np.True_

    Create a custom distribution with multiple parameterizations. Here we create a
    custom version of the beta distribution that has an alternative parameterization
    in terms of the mean ``mu`` and a dispersion parameter ``nu``.

    >>> class MyBeta:
    ...     @property
    ...     def __make_distribution_version__(self):
    ...         return "1.16.0"
    ...
    ...     @property
    ...     def parameters(self):
    ...         return (
    ...             {"a": (0, np.inf), "b": (0, np.inf)},
    ...             {"mu": (0, 1), "nu": (0, np.inf)},
    ...         )
    ...
    ...     def process_parameters(self, a=None, b=None, mu=None, nu=None):
    ...         if a is not None and b is not None and mu is None and nu is None:
    ...             nu = a + b
    ...             mu = a / nu
    ...         else:
    ...             a = mu * nu
    ...             b = nu - a
    ...         return {"a": a, "b": b, "mu": mu, "nu": nu}
    ...
    ...     @property
    ...     def support(self):
    ...         return {'endpoints': (0, 1)}
    ...
    ...     def pdf(self, x, a, b, mu, nu):
    ...         return special._ufuncs._beta_pdf(x, a, b)
    ...
    ...     def cdf(self, x, a, b, mu, nu):
    ...         return special.betainc(a, b, x)
    >>>
    >>> MyBeta = stats.make_distribution(MyBeta())
    >>> X = MyBeta(a=2.0, b=2.0)
    >>> Y = MyBeta(mu=0.5, nu=4.0)
    >>> np.isclose(X.pdf(0.3), Y.pdf(0.3))
    np.True_

Review comment (suggestion):
Sorry, I can't help myself. To the user, I think two lines will look simpler than four, even if it's mostly whitespace.
```suggestion
    ...         return ({"a": (0, np.inf), "b": (0, np.inf)},
    ...                 {"mu": (0, 1), "nu": (0, np.inf)})
```

Code after:
    ...     def cdf(self, x, *, a, b):
    ...         return (np.cbrt(x) - a) / (b - a)
    >>>
    >>> MyUniformCube = stats.make_distribution(MyUniformCube())
    >>> X = MyUniformCube(a=-2, b=2)
    >>> Y = stats.Uniform(a=-2, b=2)**3
    >>> X.support()
    (-8.0, 8.0)
    >>> np.isclose(X.cdf(2.1), Y.cdf(2.1))
    np.True_

    Create a custom distribution with multiple parameterizations. Here we create a
    custom version of the beta distribution that has an alternative parameterization
    in terms of the mean ``mu`` and a dispersion parameter ``nu``.

    >>> class MyBeta:
    ...     @property
    ...     def __make_distribution_version__(self):
    ...         return "1.16.0"
    ...
    ...     @property
    ...     def parameters(self):
    ...         return ({"a": (0, np.inf), "b": (0, np.inf)},
    ...                 {"mu": (0, 1), "nu": (0, np.inf)})
    ...
    ...     def process_parameters(self, a=None, b=None, mu=None, nu=None):
    ...         if a is not None and b is not None:
    ...             nu = a + b
    ...             mu = a / nu
    ...         else:
    ...             a = mu * nu
    ...             b = nu - a
    ...         return dict(a=a, b=b, mu=mu, nu=nu)
    ...
    ...     @property
    ...     def support(self):
    ...         return {'endpoints': (0, 1)}
    ...
    ...     def pdf(self, x, a, b, mu, nu):
    ...         return special._ufuncs._beta_pdf(x, a, b)
    ...
    ...     def cdf(self, x, a, b, mu, nu):
    ...         return special.betainc(a, b, x)
    >>>
    >>> MyBeta = stats.make_distribution(MyBeta())
    >>> X = MyBeta(a=2.0, b=2.0)
    >>> Y = MyBeta(mu=0.5, nu=4.0)
    >>> np.isclose(X.pdf(0.3), Y.pdf(0.3))
    np.True_

    """