old_contents: from scipy import stats
  
  from . import process_args
  
  @process_args.greco_deco
  def uniform(low=0, high=1):
      """ Create a frozen uniform distribution
  
      Parameters
      ----------
      low, high : floats, optional
          Lower and upper limits of the distribution.
  
      Returns
      -------
      scipy.stats.uniform
  
      """
  
      return stats.uniform(**process_args.uniform(low=low, high=high))
  
  
  @process_args.greco_deco
  def normal(mu=0, sigma=1):
      """ Create a frozen normal distribution
  
      Parameters
      ----------
      mu : float
          The mean (location) of the distribution.
      sigma : float
          The standard deviation (scale) of the distribution.
  
      Returns
      -------
      scipy.stats.normal
  
      """
  
      return stats.norm(**process_args.normal(mu=mu, sigma=sigma))
  
  
  @process_args.greco_deco
  def lognormal(mu=0, sigma=1, offset=0):
      """ Create a frozen lognormal distribution
  
      Parameters
      ----------
      mu : float
          The mean (location) of the underlying normal distribution.
      sigma : float
          The standard deviation (scale) of the underlying normal
          distribution.
      offset : float, optional (default = 0)
          Lateral translation parameter availabe in scipy's implementation
          of the distribution. Highly recommended to leave this at zero.
  
      Returns
      -------
      scipy.stats.lognormal
  
      """
  
      return stats.lognorm(**process_args.lognormal(mu=mu, sigma=sigma, offset=offset))
  
  
  @process_args.greco_deco
  def beta(alpha, beta):
      """ Create a frozen beta distribution
  
      Parameters
      ----------
      alpha, beta : floats
  
      Returns
      -------
      scipy.stats.beta
  
      """
  
      return stats.beta(**process_args.beta(alpha=alpha, beta=beta))
  
  
  @process_args.greco_deco
  def chi_squared(k):
      """ Create a frozen chi_squared distribution
  
      Parameters
      ----------
      k : float
  
      Returns
      -------
      scipy.stats.chi_squared
  
      """
  
      return stats.chi2(**process_args.chi_squared(k=k))
  
  
  @process_args.greco_deco
  def pareto(alpha):
      """ Create a frozen pareto distribution
  
      Parameters
      ----------
      alpha : float
  
      Returns
      -------
      scipy.stats.pareto
  
      """
  
      return stats.pareto(**process_args.pareto(alpha=alpha))
  
  
  @process_args.greco_deco
  def gamma(k, theta):
      """ Create a frozen gamma distribution
  
      Parameters
      ----------
      k, theta : floats
  
      Returns
      -------
      scipy.stats.gamma
  
      """
  
      return stats.gamma(**process_args.gamma(k=k, theta=theta))
  
new_contents: from scipy import stats
  
  from . import process_args
  
  
  @process_args.greco_deco
  def uniform(low=0, high=1):
      """
      Create a frozen uniform distribution.
  
      Parameters
      ----------
      low, high : floats, optional
          Lower and upper limits of the distribution.
  
      Returns
      -------
      scipy.stats.uniform
  
      """
  
      return stats.uniform(**process_args.uniform(low=low, high=high))
  
  
  @process_args.greco_deco
  def normal(mu=0, sigma=1):
      """
      Create a frozen normal distribution.
  
      Parameters
      ----------
      mu : float
          The mean (location) of the distribution.
      sigma : float
          The standard deviation (scale) of the distribution.
  
      Returns
      -------
      scipy.stats.normal
  
      """
  
      return stats.norm(**process_args.normal(mu=mu, sigma=sigma))
  
  
  @process_args.greco_deco
  def lognormal(mu=0, sigma=1, offset=0):
      """
      Create a frozen lognormal distribution.
  
      Parameters
      ----------
      mu : float
          The mean (location) of the underlying normal distribution.
      sigma : float
          The standard deviation (scale) of the underlying normal
          distribution.
      offset : float, optional (default = 0)
          Lateral translation parameter availabe in scipy's implementation
          of the distribution. Highly recommended to leave this at zero.
  
      Returns
      -------
      scipy.stats.lognormal
  
      """
  
      return stats.lognorm(**process_args.lognormal(mu=mu, sigma=sigma, offset=offset))
  
  
  @process_args.greco_deco
  def beta(alpha, beta):
      """
      Create a frozen beta distribution.
  
      Parameters
      ----------
      alpha, beta : float
          Shape parameters for the distribution.
  
      Returns
      -------
      scipy.stats.beta
  
      """
  
      return stats.beta(**process_args.beta(alpha=alpha, beta=beta))
  
  
  @process_args.greco_deco
  def chi_squared(k):
      """
      Create a frozen chi_squared distribution.
  
      Parameters
      ----------
      k : float
          Shape parameter for the distribution.
  
      Returns
      -------
      scipy.stats.chi_squared
  
      """
  
      return stats.chi2(**process_args.chi_squared(k=k))
  
  
  @process_args.greco_deco
  def pareto(alpha):
      """
      Create a frozen pareto distribution
  
      Parameters
      ----------
      alpha : float
          Shape parameter for the distribution.
  
      Returns
      -------
      scipy.stats.pareto
  
      """
  
      return stats.pareto(**process_args.pareto(alpha=alpha))
  
  
  @process_args.greco_deco
  def gamma(k, theta):
      """
      Create a frozen gamma distribution.
  
      Parameters
      ----------
      k, theta : float
          Shape parameters for the distribution.
  
      Returns
      -------
      scipy.stats.gamma
  
      """
  
      return stats.gamma(**process_args.gamma(k=k, theta=theta))
  
subject: format docstrings