Author: bugman Date: Sat Nov 29 10:26:15 2014 New Revision: 26830 URL: http://svn.gna.org/viewcvs/relax?rev=26830&view=rev Log: Renamed all of the relaxation curve-fitting target functions. This includes all of the C functions which are model specific, by appending '_exp' to the current names to now be func_exp, dfunc_exp, d2func_exp, jacobian_exp, and jacobian_chi2_exp. And all of the Relax_fit_opt target function class *_wrapper() methods to *_exp(). The target function class is now only aliasing the *_exp() methods when the model is set to 'exp'. Modified: trunk/target_functions/relax_fit.c trunk/target_functions/relax_fit_wrapper.py Modified: trunk/target_functions/relax_fit.c URL: http://svn.gna.org/viewcvs/relax/trunk/target_functions/relax_fit.c?rev=26830&r1=26829&r2=26830&view=diff ============================================================================== --- trunk/target_functions/relax_fit.c (original) +++ trunk/target_functions/relax_fit.c Sat Nov 29 10:26:15 2014 @@ -102,8 +102,8 @@ } static PyObject * -func(PyObject *self, PyObject *args) { - /* Target function for calculating and returning the chi-squared value. +func_exp(PyObject *self, PyObject *args) { + /* Target function for the two parameter exponential for calculating and returning the chi-squared value. * * Firstly the back calculated intensities are generated, then the chi-squared statistic is * calculated. @@ -128,8 +128,8 @@ static PyObject * -dfunc(PyObject *self, PyObject *args) { - /* Target function for calculating and returning the chi-squared gradient. +dfunc_exp(PyObject *self, PyObject *args) { + /* Target function for the two parameter exponential for calculating and returning the chi-squared gradient. * */ @@ -167,8 +167,8 @@ } static PyObject * -d2func(PyObject *self, PyObject *args) { - /* Target function for calculating and returning the chi-squared Hessian. +d2func_exp(PyObject *self, PyObject *args) { + /* Target function for the two parameter exponential for calculating and returning the chi-squared Hessian. * */ @@ -234,8 +234,8 @@ static PyObject * -jacobian(PyObject *self, PyObject *args) { - /* Return the Jacobian as a Python list of lists. */ +jacobian_exp(PyObject *self, PyObject *args) { + /* Return the Jacobian for the two parameter exponential as a Python list of lists. */ /* Declarations. */ PyObject *params_arg; @@ -271,8 +271,8 @@ static PyObject * -jacobian_chi2(PyObject *self, PyObject *args) { - /* Return the Jacobian as a Python list of lists. +jacobian_chi2_exp(PyObject *self, PyObject *args) { + /* Return the Jacobian for the two parameter exponential as a Python list of lists. The Jacobian ============ @@ -345,35 +345,35 @@ METH_VARARGS | METH_KEYWORDS, "Set up the module in preparation for calls to the target function." }, { - "func", - func, - METH_VARARGS, - "Target function for calculating and returning the chi-squared value.\n\nFirstly the back calculated intensities are generated, then the chi-squared statistic is calculated." - }, { - "dfunc", - dfunc, - METH_VARARGS, - "Target function for calculating and returning the chi-squared gradient." - }, { - "d2func", - d2func, - METH_VARARGS, - "Target function for calculating and returning the chi-squared Hessian." + "func_exp", + func_exp, + METH_VARARGS, + "Target function for the two parameter exponential for calculating and returning the chi-squared value.\n\nFirstly the back calculated intensities are generated, then the chi-squared statistic is calculated." + }, { + "dfunc_exp", + dfunc_exp, + METH_VARARGS, + "Target function for the two parameter exponential for calculating and returning the chi-squared gradient." + }, { + "d2func_exp", + d2func_exp, + METH_VARARGS, + "Target function for the two parameter exponential for calculating and returning the chi-squared Hessian." }, { "back_calc_I", back_calc_I, METH_VARARGS, "Return the back calculated peak intensities as a Python list." }, { - "jacobian", - jacobian, - METH_VARARGS, - "Return the Jacobian matrix as a Python list." - }, { - "jacobian_chi2", - jacobian_chi2, - METH_VARARGS, - "Return the Jacobian matrix of the chi-squared function as a Python list." + "jacobian_exp", + jacobian_exp, + METH_VARARGS, + "Return the Jacobian matrix for the two parameter exponential as a Python list." + }, { + "jacobian_chi2_exp", + jacobian_chi2_exp, + METH_VARARGS, + "Return the Jacobian matrix of the chi-squared function for the two parameter exponential as a Python list." }, {NULL, NULL, 0, NULL} /* Sentinel. */ }; Modified: trunk/target_functions/relax_fit_wrapper.py URL: http://svn.gna.org/viewcvs/relax/trunk/target_functions/relax_fit_wrapper.py?rev=26830&r1=26829&r2=26830&view=diff ============================================================================== --- trunk/target_functions/relax_fit_wrapper.py (original) +++ trunk/target_functions/relax_fit_wrapper.py Sat Nov 29 10:26:15 2014 @@ -32,7 +32,7 @@ # C modules. if C_module_exp_fn: - from target_functions.relax_fit import jacobian, jacobian_chi2, setup, func, dfunc, d2func, back_calc_I + from target_functions.relax_fit import jacobian_exp, jacobian_chi2_exp, setup, func_exp, dfunc_exp, d2func_exp, back_calc_I class Relax_fit_opt: @@ -62,13 +62,15 @@ 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. - self.func = self.func_wrapper - self.dfunc = self.dfunc_wrapper - self.d2func = self.d2func_wrapper + if model == 'exp': + self.func = self.func_exp + self.dfunc = self.dfunc_exp + self.d2func = self.d2func_exp # Alias the Jacobian C functions. - self.jacobian = jacobian - self.jacobian_chi2 = jacobian_chi2 + if model == 'exp': + self.jacobian = jacobian_exp + self.jacobian_chi2 = jacobian_chi2_exp def back_calc_data(self): @@ -82,7 +84,7 @@ return back_calc_I() - def func_wrapper(self, params): + def func_exp(self, params): """Wrapper function for the C module, for converting numpy arrays. @param params: The parameter array from the minimisation code. @@ -96,13 +98,13 @@ params = params.tolist() # Call the C code. - chi2 = func(params) + chi2 = func_exp(params) # Return the chi2 value. return nan_to_num(chi2) - def dfunc_wrapper(self, params): + def dfunc_exp(self, params): """Wrapper function for the C module, for converting numpy arrays. @param params: The parameter array from the minimisation code. @@ -116,13 +118,13 @@ params = params.tolist() # Call the C code. - dchi2 = dfunc(params) + dchi2 = dfunc_exp(params) # Return the chi2 gradient as a numpy array. return array(dchi2, float64) - def d2func_wrapper(self, params): + def d2func_exp(self, params): """Wrapper function for the C module, for converting numpy arrays. @param params: The parameter array from the minimisation code. @@ -136,7 +138,7 @@ params = params.tolist() # Call the C code. - d2chi2 = d2func(params) + d2chi2 = d2func_exp(params) # Return the chi2 Hessian as a numpy array. return array(d2chi2, float64)