%feature("docstring") OT::LogUniform
"LogUniform distribution.

Available constructors:
    LogUniform(*a_log=-1.0, b_log=1.0*)

Parameters
----------
a_log : float
    Lower bound in log-scale.
b_log : float, :math:`b_\ell > a_\ell`
    Upper bound in log-scale.

Notes
-----
Its probability density function is defined as:

.. math::

    f_X(x) = \frac{1}{x (b_\ell - a_\ell)},
             \quad \log(x) \in [a_\ell; b_\ell]

with :math:`a_\ell < b_\ell`.

Its first moments are:

.. math::
    :nowrap:

    \begin{eqnarray*}
        \Expect{X} & = & \frac{b - a}{b_\ell - a_\ell} \\
        \Var{X} & = & \frac{1}{2} (b - a)
                      \frac{  b (b_\ell - a_\ell - 2)
                            + a (b_\ell - a_\ell + 2)}
                           {(b_\ell - a_\ell)^2}
    \end{eqnarray*}

Examples
--------
Create a distribution:

>>> import openturns as ot
>>> distribution = ot.LogUniform(-1.0, 1.0)

Draw a sample:

>>> sample = distribution.getSample(5)"

// ---------------------------------------------------------------------

%feature("docstring") OT::LogUniform::getALog
"Accessor to the distribution's lower bound in log-scale :math:`a_\ell`.

Returns
-------
a_log : float
    Lower bound in log-scale."

// ---------------------------------------------------------------------

%feature("docstring") OT::LogUniform::getBLog
"Accessor to the distribution's upper bound in log-scale :math:`b_\ell`.

Returns
-------
b_log : float
    Upper bound in log-scale."

// ---------------------------------------------------------------------

%feature("docstring") OT::LogUniform::setALog
"Accessor to the distribution's lower bound in log-scale :math:`a_\ell`.

Parameters
----------
a_log : float, :math:`a_\ell < b_\ell`
    Lower bound in log-scale."

// ---------------------------------------------------------------------

%feature("docstring") OT::LogUniform::setBLog
"Accessor to the distribution's upper bound in log-scale :math:`b_\ell`.

Parameters
----------
b_log : float, :math:`b_\ell > a_\ell`
    Upper bound in log-scale."
