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)
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