parameters: (x, tol=1e-12, linearSpread=0)
return_statement: return np.sign(x) * (np.log10(1 + np.abs(x/tol))+linearSpread/2)
docstring: Symmetric bi-logarithmic transformation (as used in matplotlib).
  
      Transforms a signed values in a logarithmic way preserving the sign.
      All absolute values below a certain threshold are treated zero or linearly
      distributed (if linearSpread>0).
  
      .. math::
  
          f(x) = sign(x) * (log10(1 + abs(x)/tol) + s/2)
  
      Parameters
      ----------
      x : iterable
          array to be transformed
      tol : float [None]
          tolerance for minimum values to be treated zero (or linear)
      linearSpread : float
          define how wide linear transformation is done (0-not, 1-one decade)
  
      Returns
      -------
      y : np.array
          transformed array of same size
  
      See Also
      --------
      pygimli.math.symlogInv
docstring_summary: Symmetric bi-logarithmic transformation (as used in matplotlib).
docstring_tokens: Symmetric bi - logarithmic transformation ( as used in matplotlib ) .
function: def symlog(x, tol=1e-12, linearSpread=0):
      """Symmetric bi-logarithmic transformation (as used in matplotlib).
  
      Transforms a signed values in a logarithmic way preserving the sign.
      All absolute values below a certain threshold are treated zero or linearly
      distributed (if linearSpread>0).
  
      .. math::
  
          f(x) = sign(x) * (log10(1 + abs(x)/tol) + s/2)
  
      Parameters
      ----------
      x : iterable
          array to be transformed
      tol : float [None]
          tolerance for minimum values to be treated zero (or linear)
      linearSpread : float
          define how wide linear transformation is done (0-not, 1-one decade)
  
      Returns
      -------
      y : np.array
          transformed array of same size
  
      See Also
      --------
      pygimli.math.symlogInv
      """
      if tol is None:
          tol = np.min(np.abs(x))
  
      return np.sign(x) * (np.log10(1 + np.abs(x/tol))+linearSpread/2)
function_tokens: def symlog ( x , tol = 1e-12 , linearSpread = 0 ) : if tol is None : tol = np . min ( np . abs ( x ) ) return np . sign ( x ) * ( np . log10 ( 1 + np . abs ( x / tol ) ) + linearSpread / 2 )