Author: bugman
Date: Mon Dec 1 19:27:12 2014
New Revision: 26879
URL: http://svn.gna.org/viewcvs/relax?rev=26879&view=rev
Log:
Deletion of the Python relax_fit_wrapper target function module.
Removed:
branches/relax_fit_c_class/target_functions/relax_fit_wrapper.py
Removed: branches/relax_fit_c_class/target_functions/relax_fit_wrapper.py
URL:
http://svn.gna.org/viewcvs/relax/branches/relax_fit_c_class/target_functions/relax_fit_wrapper.py?rev=26878&view=auto
==============================================================================
--- branches/relax_fit_c_class/target_functions/relax_fit_wrapper.py
(original)
+++ branches/relax_fit_c_class/target_functions/relax_fit_wrapper.py
(removed)
@@ -1,278 +0,0 @@
-###############################################################################
-#
#
-# Copyright (C) 2004-2014 Edward d'Auvergne
#
-# Copyright (C) 2014 Troels E. Linnet
#
-#
#
-# This file is part of the program relax (http://www.nmr-relax.com).
#
-#
#
-# This program is free software: you can redistribute it and/or modify
#
-# it under the terms of the GNU General Public License as published by
#
-# the Free Software Foundation, either version 3 of the License, or
#
-# (at your option) any later version.
#
-#
#
-# This program is distributed in the hope that it will be useful,
#
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
#
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
#
-# GNU General Public License for more details.
#
-#
#
-# You should have received a copy of the GNU General Public License
#
-# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
-#
#
-###############################################################################
-
-# Module docstring.
-"""The R1 and R2 exponential relaxation curve fitting optimisation
functions."""
-
-# Python module imports.
-from numpy import array, float64, ndarray, nan_to_num
-
-# relax module imports.
-from dep_check import C_module_exp_fn
-from specific_analyses.relax_fit.parameters import assemble_param_vector
-
-# C modules.
-if C_module_exp_fn:
- from target_functions.relax_fit import back_calc_I, d2func_exp,
d2func_inv, d2func_sat, dfunc_exp, dfunc_inv, dfunc_sat, func_exp, func_inv,
func_sat, jacobian_chi2_exp, jacobian_chi2_inv, jacobian_chi2_sat,
jacobian_exp, jacobian_inv, jacobian_sat, setup
-
-
-class Relax_fit_opt:
- """The exponential curve-fitting Python to C wrapper target function
class."""
-
- def __init__(self, model=None, num_params=None, values=None,
errors=None, relax_times=None, scaling_matrix=None):
- """Set up the target function class and alias the target functions.
-
- @keyword model: The exponential curve type. This can be
'exp' for the standard two parameter exponential curve, 'inv' for the
inversion recovery experiment, and 'sat' for the saturation recovery
experiment.
- @type model: str
- @keyword num_params: The number of parameters in the model.
- @type num_params: int
- @keyword values: The peak intensities.
- @type values: list of float
- @keyword errors: The peak intensity errors.
- @type errors: list of float
- @keyword relax_times: The list of relaxation times.
- @type relax_times: list of float
- @keyword scaling_matrix: The scaling matrix in a diagonalised
list form.
- @type scaling_matrix: list of float
- """
-
- # Store the args.
- self.model = model
-
- # Initialise the C code.
- setup(num_params=num_params, num_times=len(relax_times),
values=values, sd=errors, relax_times=relax_times,
scaling_matrix=scaling_matrix)
-
- # Alias the target functions.
- if model == 'exp':
- self.func = self.func_exp
- self.dfunc = self.dfunc_exp
- self.d2func = self.d2func_exp
- elif model == 'inv':
- self.func = self.func_inv
- self.dfunc = self.dfunc_inv
- self.d2func = self.d2func_inv
- elif model == 'sat':
- self.func = self.func_sat
- self.dfunc = self.dfunc_sat
- self.d2func = self.d2func_sat
-
- # Alias the Jacobian C functions.
- if model == 'exp':
- self.jacobian = jacobian_exp
- self.jacobian_chi2 = jacobian_chi2_exp
- elif model == 'inv':
- self.jacobian = jacobian_inv
- self.jacobian_chi2 = jacobian_chi2_inv
- elif model == 'sat':
- self.jacobian = jacobian_sat
- self.jacobian_chi2 = jacobian_chi2_sat
-
-
- def back_calc_data(self):
- """Return the back-calculated data from the C code.
-
- @return: The back-calculated peak intensities.
- @rtype: list of float
- """
-
- # Return the data.
- return back_calc_I()
-
-
- def func_exp(self, params):
- """Wrapper function for the C module, for converting numpy arrays.
-
- @param params: The parameter array from the minimisation code.
- @type params: numpy array
- @return: The function value generated by the C module.
- @rtype: float
- """
-
- # Convert if necessary.
- if isinstance(params, ndarray):
- params = params.tolist()
-
- # Call the C code.
- chi2 = func_exp(params)
-
- # Return the chi2 value.
- return nan_to_num(chi2)
-
-
- def func_inv(self, params):
- """Wrapper function for the C module, for converting numpy arrays.
-
- @param params: The parameter array from the minimisation code.
- @type params: numpy array
- @return: The function value generated by the C module.
- @rtype: float
- """
-
- # Convert if necessary.
- if isinstance(params, ndarray):
- params = params.tolist()
-
- # Call the C code.
- chi2 = func_inv(params)
-
- # Return the chi2 value.
- return nan_to_num(chi2)
-
-
- def func_sat(self, params):
- """Wrapper function for the C module, for converting numpy arrays.
-
- @param params: The parameter array from the minimisation code.
- @type params: numpy array
- @return: The function value generated by the C module.
- @rtype: float
- """
-
- # Convert if necessary.
- if isinstance(params, ndarray):
- params = params.tolist()
-
- # Call the C code.
- chi2 = func_sat(params)
-
- # Return the chi2 value.
- return nan_to_num(chi2)
-
-
- def dfunc_exp(self, params):
- """Wrapper function for the C module, for converting numpy arrays.
-
- @param params: The parameter array from the minimisation code.
- @type params: numpy array
- @return: The gradient generated by the C module converted to
numpy format.
- @rtype: numpy float64 array
- """
-
- # Convert if necessary.
- if isinstance(params, ndarray):
- params = params.tolist()
-
- # Call the C code.
- dchi2 = dfunc_exp(params)
-
- # Return the chi2 gradient as a numpy array.
- return array(dchi2, float64)
-
-
- def dfunc_inv(self, params):
- """Wrapper function for the C module, for converting numpy arrays.
-
- @param params: The parameter array from the minimisation code.
- @type params: numpy array
- @return: The gradient generated by the C module converted to
numpy format.
- @rtype: numpy float64 array
- """
-
- # Convert if necessary.
- if isinstance(params, ndarray):
- params = params.tolist()
-
- # Call the C code.
- dchi2 = dfunc_inv(params)
-
- # Return the chi2 gradient as a numpy array.
- return array(dchi2, float64)
-
-
- def dfunc_sat(self, params):
- """Wrapper function for the C module, for converting numpy arrays.
-
- @param params: The parameter array from the minimisation code.
- @type params: numpy array
- @return: The gradient generated by the C module converted to
numpy format.
- @rtype: numpy float64 array
- """
-
- # Convert if necessary.
- if isinstance(params, ndarray):
- params = params.tolist()
-
- # Call the C code.
- dchi2 = dfunc_sat(params)
-
- # Return the chi2 gradient as a numpy array.
- return array(dchi2, float64)
-
-
- def d2func_exp(self, params):
- """Wrapper function for the C module, for converting numpy arrays.
-
- @param params: The parameter array from the minimisation code.
- @type params: numpy array
- @return: The Hessian generated by the C module converted to
numpy format.
- @rtype: numpy float64 rank-2 array
- """
-
- # Convert if necessary.
- if isinstance(params, ndarray):
- params = params.tolist()
-
- # Call the C code.
- d2chi2 = d2func_exp(params)
-
- # Return the chi2 Hessian as a numpy array.
- return array(d2chi2, float64)
-
-
- def d2func_inv(self, params):
- """Wrapper function for the C module, for converting numpy arrays.
-
- @param params: The parameter array from the minimisation code.
- @type params: numpy array
- @return: The Hessian generated by the C module converted to
numpy format.
- @rtype: numpy float64 rank-2 array
- """
-
- # Convert if necessary.
- if isinstance(params, ndarray):
- params = params.tolist()
-
- # Call the C code.
- d2chi2 = d2func_inv(params)
-
- # Return the chi2 Hessian as a numpy array.
- return array(d2chi2, float64)
-
-
- def d2func_sat(self, params):
- """Wrapper function for the C module, for converting numpy arrays.
-
- @param params: The parameter array from the minimisation code.
- @type params: numpy array
- @return: The Hessian generated by the C module converted to
numpy format.
- @rtype: numpy float64 rank-2 array
- """
-
- # Convert if necessary.
- if isinstance(params, ndarray):
- params = params.tolist()
-
- # Call the C code.
- d2chi2 = d2func_sat(params)
-
- # Return the chi2 Hessian as a numpy array.
- return array(d2chi2, float64)
r26879 - /branches/relax_fit_c_class/target_functions/relax_fit_wrapper.py