| 
  | __init__(self,
        model=None,
        init_params=None,
        full_tensors=None,
        full_in_ref_frame=None,
        rdcs=None,
        rdc_errors=None,
        rdc_weights=None,
        rdc_vect=None,
        dip_const=None,
        pcs=None,
        pcs_errors=None,
        pcs_weights=None,
        atomic_pos=None,
        temp=None,
        frq=None,
        paramag_centre=array([0., 0., 0.]),
        scaling_matrix=None,
        sobol_max_points=200,
        sobol_oversample=100,
        com=None,
        ave_pos_pivot=array([0., 0., 0.]),
        pivot=None,
        pivot_opt=False,
        quad_int=False)
    (Constructor)
 | source code |  Set up the target functions for the Frame Order theories. 
    Parameters:
        model(str) - The name of the Frame Order model.init_params(numpy float64 array) - The initial parameter values.full_tensors(numpy nx5D, rank-1 float64 array) - An array of the {Axx, Ayy, Axy, Axz, Ayz} values for all full 
          alignment tensors.  The format is [Axx1, Ayy1, Axy1, Axz1, Ayz1, 
          Axx2, Ayy2, Axy2, Axz2, Ayz2, ..., Axxn, Ayyn, Axyn, Axzn, Ayzn].full_in_ref_frame(numpy rank-1 array) - An array of flags specifying if the tensor in the reference frame
          is the full or reduced tensor.rdcs(numpy rank-2 array) - The RDC lists.  The first index must correspond to the different 
          alignment media i and the second index to the spin systems j.rdc_errors(numpy rank-2 array) - The RDC error lists.  The dimensions of this argument are the 
          same as for 'rdcs'.rdc_weights(numpy rank-2 array) - The RDC weight lists.  The dimensions of this argument are the 
          same as for 'rdcs'.rdc_vect(numpy rank-2 array) - The unit XH vector lists corresponding to the RDC values.  The 
          first index must correspond to the spin systems and the second 
          index to the x, y, z elements.dip_const(numpy rank-1 array) - The dipolar constants for each RDC.  The indices correspond to 
          the spin systems j.pcs(numpy rank-2 array) - The PCS lists.  The first index must correspond to the different 
          alignment media i and the second index to the spin systems j.pcs_errors(numpy rank-2 array) - The PCS error lists.  The dimensions of this argument are the 
          same as for 'pcs'.pcs_weights(numpy rank-2 array) - The PCS weight lists.  The dimensions of this argument are the 
          same as for 'pcs'.atomic_pos(numpy rank-3 array) - The atomic positions of all spins for the PCS and PRE data.  The 
          first index is the spin systems j and the second is the structure
          or state c.temp(numpy rank-1 array) - The temperature of each PCS data set.frq(numpy rank-1 array) - The frequency of each PCS data set.paramag_centre(numpy rank-1, 3D array or rank-2, Nx3 array) - The paramagnetic centre position (or positions).scaling_matrix(numpy rank-2 array) - The square and diagonal scaling matrix.sobol_max_points(int) - The maximum number of Sobol' points to use for the numerical PCS 
          integration technique.sobol_oversample(int) - The oversampling factor Ov used for the total number of points N 
          * Ov * 10**M, where N is the maximum number of Sobol' points and 
          M is the number of dimensions or torsion-tilt angles for the 
          system.com(numpy 3D rank-1 array) - The centre of mass of the system.  This is used for defining the 
          rotor model systems.ave_pos_pivot(numpy 3D rank-1 array) - The pivot point to rotate all atoms about to the average domain 
          position.  In most cases this will be the centre of mass of the 
          moving domain.  This pivot is shifted by the translation vector.pivot(numpy rank-1, 3D array or None) - The pivot point for the ball-and-socket joint motion.  This is 
          needed if PCS or PRE values are used.pivot_opt(bool) - A flag which if True will allow the pivot point of the motion to 
          be optimised.quad_int(bool) - A flag which if True will perform high precision numerical 
          integration via the scipy.integrate quad(), dblquad() and 
          tplquad() integration methods rather than the rough quasi-random 
          numerical integration. |