Author: bugman
Date: Fri Aug 8 11:59:09 2008
New Revision: 7120
URL: http://svn.gna.org/viewcvs/relax?rev=7120&view=rev
Log:
Modified the N_state_opt class to accept PCS data.
The __init__() docstring has been significantly expanded as well.
Modified:
branches/rdc_analysis/maths_fns/n_state_model.py
Modified: branches/rdc_analysis/maths_fns/n_state_model.py
URL:
http://svn.gna.org/viewcvs/relax/branches/rdc_analysis/maths_fns/n_state_model.py?rev=7120&r1=7119&r2=7120&view=diff
==============================================================================
--- branches/rdc_analysis/maths_fns/n_state_model.py (original)
+++ branches/rdc_analysis/maths_fns/n_state_model.py Fri Aug 8 11:59:09 2008
@@ -36,49 +36,101 @@
class N_state_opt:
"""Class containing the target function of the optimisation of the
N-state model."""
- def __init__(self, model=None, N=None, init_params=None,
full_tensors=None, red_data=None, red_errors=None, full_in_ref_frame=None,
pcs=None, pcs_errors=None, rdcs=None, rdc_errors=None, xh_vect=None,
dip_const=None, scaling_matrix=None):
+ def __init__(self, model=None, N=None, init_params=None,
full_tensors=None, red_data=None, red_errors=None, full_in_ref_frame=None,
pcs=None, pcs_errors=None, rdcs=None, rdc_errors=None, pcs_vect=None,
xh_vect=None, pcs_const=None, dip_const=None, scaling_matrix=None):
"""Set up the class instance for optimisation.
- All constant data required for the N-state model are initialised
here.
-
-
- @keyword model: The N-state model type. This can be one of
'2-domain', 'population'
- or 'fixed'.
- @type model: str
- @keyword N: The number of states.
- @type N: int
- @keyword init_params: The initial parameter values. Optimisation
must start at some
- point!
- @type init_params: numpy float64 array
- @keyword full_tensors: A list of the full alignment tensors in
matrix form.
- @type full_tensors: list of 3x3 numpy matricies
- @keyword red_data: An array of the {Sxx, Syy, Sxy, Sxz, Syz}
values for all reduced
- tensors. The format is [Sxx1, Syy1, Sxy1,
Sxz1, Syz1, Sxx2, Syy2,
- Sxy2, Sxz2, Syz2, ..., Sxxn, Syyn, Sxyn,
Sxzn, Syzn]
- @type red_data: numpy float64 array
- @keyword red_errors: An array of the {Sxx, Syy, Sxy, Sxz, Syz}
errors for all reduced
- tensors. The array format is the same as
for red_data.
- @type red_errors: numpy float64 array
- @keyword pcs: The PCS lists. The first index must
correspond to the different
- alignment media i and the second index to
the spin systems j.
- @type pcs: numpy matrix
- @keyword pcs_errors: The PCS error lists. The dimensions of this
argument are the same
- as for 'pcs'.
- @type pcs_errors: numpy matrix
- @keyword rdcs: The RDC lists. The first index must
correspond to the different
- alignment media i and the second index to
the spin systems j.
- @type rdcs: numpy matrix
- @keyword rdc_errors: The RDC error lists. The dimensions of this
argument are the same
- as for 'rdcs'.
- @type rdc_errors: numpy matrix
- @keyword xh_vect: The unit XH vector lists. The first index
must correspond to the
- spin systems and the second index to each
structure (its size being
- equal to the number of states).
- @type xh_vect: numpy matrix
- @keyword dip_const: The dipolar constants for each XH vector.
The indices correspond to
- the spin systems j.
- @scaling_matrix: The square and diagonal scaling matrix.
- @scaling_matrix: numpy rank-2 array
+ The N-state models
+ ==================
+
+ All constant data required for the N-state model are initialised
here. Depending on the
+ base data used for optimisation, different data structures can be
supplied. However a
+ number of structures must be provided for the N-state model. These
are:
+
+ - model, the type of N-state model. This can be '2-domain',
'population', or 'fixed'.
+ - N, the number of states (or structures).
+ - init_params, the initial parameter values.
+ - scaling_matrix, the matrix used for parameter scaling during
optimisation.
+
+
+ 2-domain N-state model
+ ----------------------
+
+ If the model type is set to '2-domain', then the following data
structures should be
+ supplied:
+
+ - full_tensors, the alignment tensors in matrix form.
+ - red_data, the alignment tensors in 5D form in a rank-1 array.
+ - red_errors, the alignment tensor errors in 5D form in a rank-1
array. This data is
+ not obligatory.
+ - full_in_ref_frame, an array of flags specifying if the tensor
in the reference frame
+ is the full or reduced tensor.
+
+
+ PCS base data
+ -------------
+
+ If pseudocontact shift data is to be used for optimisation, then the
following should be
+ supplied:
+
+ - pcs, the pseudocontact shifts.
+ - pcs_errors, the optional pseudocontact shift error values.
+ - pcs_vect, the unit vectors connecting the paramagnetic centre
(the electron magnetic
+ dipole) to the nuclear spin centre.
+ - pcs_const, the pseudocontact shift constants.
+
+
+ RDC base data
+ -------------
+
+ If residual dipolar coupling data is to be used for optimisation,
then the following should
+ be supplied:
+
+ - rdcs, the residual dipolar couplings.
+ - rdc_errors, the optional residual dipolar coupling errors.
+ - xh_vect, the heteronucleus to proton unit vectors.
+ - dip_const, the dipolar contants.
+
+
+ @keyword model: The N-state model type. This can be one
of '2-domain',
+ 'population' or 'fixed'.
+ @type model: str
+ @keyword N: The number of states.
+ @type N: int
+ @keyword init_params: The initial parameter values.
Optimisation must start at some
+ point!
+ @type init_params: numpy float64 array
+ @keyword full_tensors: A list of the full alignment tensors in
matrix form.
+ @type full_tensors: list of 3x3 numpy matricies
+ @keyword red_data: An array of the {Sxx, Syy, Sxy, Sxz,
Syz} values for all reduced
+ tensors. The format is [Sxx1, Syy1,
Sxy1, Sxz1, Syz1, Sxx2,
+ Syy2, Sxy2, Sxz2, Syz2, ..., Sxxn, Syyn,
Sxyn, Sxzn, Syzn]
+ @type red_data: numpy float64 array
+ @keyword red_errors: An array of the {Sxx, Syy, Sxy, Sxz,
Syz} errors for all reduced
+ tensors. The array format is the same
as for red_data.
+ @type red_errors: numpy float64 array
+ @keyword full_in_ref_frame: An array of flags specifying if the
tensor in the reference
+ frame is the full or reduced tensor.
+ @type full_in_ref_frame: numpy rank-1 array
+ @keyword pcs: The PCS lists. The first index must
correspond to the different
+ alignment media i and the second index
to the spin systems j.
+ @type pcs: numpy matrix
+ @keyword pcs_errors: The PCS error lists. The dimensions of
this argument are the
+ same as for 'pcs'.
+ @type pcs_errors: numpy matrix
+ @keyword rdcs: The RDC lists. The first index must
correspond to the different
+ alignment media i and the second index
to the spin systems j.
+ @type rdcs: numpy matrix
+ @keyword rdc_errors: The RDC error lists. The dimensions of
this argument are the
+ same as for 'rdcs'.
+ @type rdc_errors: numpy matrix
+ @keyword xh_vect: The unit XH vector lists. The first
index must correspond to
+ the spin systems and the second index to
each structure (its
+ size being equal to the number of
states).
+ @type xh_vect: numpy matrix
+ @keyword dip_const: The dipolar constants for each XH
vector. The indices
+ correspond to the spin systems j.
+ @scaling_matrix: The square and diagonal scaling matrix.
+ @scaling_matrix: numpy rank-2 array
"""
# Store the data inside the class instance namespace.
r7120 - /branches/rdc_analysis/maths_fns/n_state_model.py