old_contents: r"""
  Lorentz (Ornstein-Zernicke Model)
  
  Definition
  ----------
  
  The Ornstein-Zernicke model is defined by
  
  .. math:: I(q)=\frac{\text{scale}}{1+(qL)^2}+\text{background}
  
  The parameter $L$ is the screening length *cor_length*.
  
  For 2D data the scattering intensity is calculated in the same way as 1D,
  where the $q$ vector is defined as
  
  .. math:: q=\sqrt{q_x^2 + q_y^2}
  
  .. figure:: img/lorentz_1d.jpg
  
      1D plot using the default values (w/200 data point).
  
  References
  ----------
  
  L.S. Qrnstein and F. Zernike, *Proc. Acad. Sci. Amsterdam* 17, 793 (1914), and
  *Z. Phys.* 19, 134 (1918), and 27, 761 {1926); referred to as QZ.
  """
  
  from numpy import inf
  
  name = "lorentz"
  title = "Ornstein-Zernicke correlation length model"
  description = """
  Model that evaluates a Lorentz (Ornstein-Zernicke) model.
          
  I(q) = scale/( 1 + (q*L)^2 ) + bkd
          
  The model has three parameters: 
      length     =  screening Length
      scale  =  scale factor
      background    =  incoherent background
  """
  category = "shape-independent"
  
  #             ["name", "units", default, [lower, upper], "type","description"],
  parameters = [["cor_length", "Ang", 50.0, [0, inf], "", "Screening length"],]
  
  Iq = """
      double denominator = 1 + (q*cor_length)*(q*cor_length);
      return 1/denominator;
  """
  
  Iqxy = """
      return Iq(sqrt(qx*qx + qy*qy), cor_length);
      """
  
  # parameters for demo
  demo = dict(scale=1.0,background=0.0,cor_length=50.0)
  
  # For testing against the old sasview models, include the converted parameter
  # names and the target sasview model name.
  oldname = 'LorentzModel'
  oldpars = dict(cor_length='length')
  
  # parameters for unit tests
  tests = [
           [{'cor_length' : 250},0.01,0.137931]
           ]
new_contents: r"""
  Lorentz (Ornstein-Zernicke Model)
  
  Definition
  ----------
  
  The Ornstein-Zernicke model is defined by
  
  .. math:: I(q)=\frac{\text{scale}}{1+(qL)^2}+\text{background}
  
  The parameter $L$ is the screening length *cor_length*.
  
  For 2D data the scattering intensity is calculated in the same way as 1D,
  where the $q$ vector is defined as
  
  .. math:: q=\sqrt{q_x^2 + q_y^2}
  
  .. figure:: img/lorentz_1d.jpg
  
      1D plot using the default values (w/200 data point).
  
  References
  ----------
  
  L.S. Qrnstein and F. Zernike, *Proc. Acad. Sci. Amsterdam* 17, 793 (1914), and
  *Z. Phys.* 19, 134 (1918), and 27, 761 {1926); referred to as QZ.
  """
  
  from numpy import inf
  
  name = "lorentz"
  title = "Ornstein-Zernicke correlation length model"
  description = """
  Model that evaluates a Lorentz (Ornstein-Zernicke) model.
          
  I(q) = scale/( 1 + (q*L)^2 ) + bkd
          
  The model has three parameters: 
      length     =  screening Length
      scale  =  scale factor
      background    =  incoherent background
  """
  category = "shape-independent"
  
  #             ["name", "units", default, [lower, upper], "type","description"],
  parameters = [["cor_length", "Ang", 50.0, [0, inf], "", "Screening length"],]
  
  Iq = """
      double denominator = 1 + (q*cor_length)*(q*cor_length);
      return 1/denominator;
  """
  
  Iqxy = """
      return Iq(sqrt(qx*qx + qy*qy), cor_length);
      """
  
  # parameters for demo
  demo = dict(scale=1.0, background=0.0, cor_length=50.0)
  
  # For testing against the old sasview models, include the converted parameter
  # names and the target sasview model name.
  oldname = 'LorentzModel'
  oldpars = dict(cor_length='length')
  
  # parameters for unit tests
  tests = [[{'cor_length' : 250},0.01,0.137931]]
  
subject: Fix pylint