class_docstr: The basic one dimensional Gamma-Gaussian-Gamma Mixture estimation
  class, where the first gamma has a negative sign, while the second
  one has a positive sign.
  
  7 parameters are used:
  - shape_n: negative gamma shape
  - scale_n: negative gamma scale
  - mean: gaussian mean
  - var: gaussian variance
  - shape_p: positive gamma shape
  - scale_p: positive gamma scale
  - mixt: array of mixture parameter
  (weights of the n-gamma,gaussian and p-gamma)
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docstr: Constructor
  
  Parameters
  -----------
  shape_n : float, optional
  scale_n: float, optional
      parameters of the nehative gamma; must be positive
  mean : float, optional
  var : float, optional
      parameters of the gaussian ; var must be positive
  shape_p : float, optional
  scale_p : float, optional
      parameters of the positive gamma; must be positive
  mixt : array of shape (3,), optional
      the mixing proportions; they should be positive and sum to 1
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code: def __init__(
      self,
      shape_n=1,
      scale_n=1,
      mean=0,
      var=1,
      shape_p=1,
      scale_p=1,
      mixt=np.array([1.0, 1.0, 1.0]) / 3,
  ):
      """Constructor
  
      Parameters
      -----------
      shape_n : float, optional
      scale_n: float, optional
          parameters of the nehative gamma; must be positive
      mean : float, optional
      var : float, optional
          parameters of the gaussian ; var must be positive
      shape_p : float, optional
      scale_p : float, optional
          parameters of the positive gamma; must be positive
      mixt : array of shape (3,), optional
          the mixing proportions; they should be positive and sum to 1
      """
      self.shape_n = shape_n
      self.scale_n = scale_n
      self.mean = mean
      self.var = var
      self.shape_p = shape_p
      self.scale_p = scale_p
      self.mixt = mixt
  
imports: import numpy
  import scipy.special
  import scipy.stats
label_desc: Use image node_id 1 to create a new GGGM object  with example: obj = GGGM(shape_n, scale_n, mean, var, shape_p, scale_p, mixt)