r25423 - /trunk/specific_analyses/relax_disp/estimate_r2eff.py -- August 29, 2014 - 11:50

Author: tlinnet
Date: Fri Aug 29 11:50:23 2014
New Revision: 25423

URL: http://svn.gna.org/viewcvs/relax?rev=25423&view=rev
Log:
Implemented the direct Jacobian in Python, to be independent of C-code in 
development fase.

task #7822(https://gna.org/task/index.php?7822): Implement user function to 
estimate R2eff and associated errors for exponential curve fitting.

Modified:
    trunk/specific_analyses/relax_disp/estimate_r2eff.py

Modified: trunk/specific_analyses/relax_disp/estimate_r2eff.py
URL: 
http://svn.gna.org/viewcvs/relax/trunk/specific_analyses/relax_disp/estimate_r2eff.py?rev=25423&r1=25422&r2=25423&view=diff
==============================================================================
--- trunk/specific_analyses/relax_disp/estimate_r2eff.py        (original)
+++ trunk/specific_analyses/relax_disp/estimate_r2eff.py        Fri Aug 29 
11:50:23 2014
@@ -59,6 +59,38 @@
     from scipy.optimize import leastsq
 
 
+def func_exp_grad(params=None, times=None, values=None, errors=None):
+    """The gradient (Jacobian matrix) of func_exp for Co-variance 
calculation.
+
+    @param params:  The vector of parameter values.
+    @type params:   numpy rank-1 float array
+    @keyword times: The time points.
+    @type times:    numpy array
+    @param values:  The measured values.
+    @type values:   numpy array
+    @param errors:  The standard deviation of the measured intensity values 
per time point.
+    @type errors:   numpy array
+    @return:        The Jacobian matrix with 'm' rows of function 
derivatives per 'n' columns of parameters.
+    @rtype:         numpy array
+    """
+
+    # Unpack the parameter values.
+    r2eff = params[0]
+    i0 = params[1]
+
+    # Make partial derivative, with respect to r2eff.
+    d_exp_d_r2eff = -i0 * times * exp(-r2eff * times)
+
+    # Make partial derivative, with respect to i0.
+    d_exp_d_i0 = exp(-r2eff * times)
+
+    # Define Jacobian as m rows with function derivatives and n columns of 
parameters.
+    jacobian_matrix_exp = transpose(array( [d_exp_d_r2eff , d_exp_d_i0] ) )
+
+    # Return Jacobian matrix.
+    return jacobian_matrix_exp
+
+
 def func_exp_chi2_grad(params=None, times=None, values=None, errors=None):
     """Return the gradient (Jacobian matrix) of func_exp_chi2() for 
exponential fit .
 
@@ -168,7 +200,8 @@
                 weights = ones(errors.shape)
             else:
                 # Calculate the direct exponential Jacobian matrix from C 
code.
-                jacobian_matrix_exp = transpose(asarray( jacobian(params) ) )
+                #jacobian_matrix_exp = transpose(asarray( jacobian(params) ) 
)
+                jacobian_matrix_exp = func_exp_grad(params=params, 
times=times, values=values, errors=errors)
                 weights = 1. / errors**2
 
             # Get the co-variance
@@ -527,38 +560,6 @@
 
         # Return
         return Kw
-
-
-    def func_exp_grad(self, params=None, times=None, values=None, 
errors=None):
-        """The gradient (Jacobian matrix) of func_exp for Co-variance 
calculation.
-
-        @param params:  The vector of parameter values.
-        @type params:   numpy rank-1 float array
-        @keyword times: The time points.
-        @type times:    numpy array
-        @param values:  The measured values.
-        @type values:   numpy array
-        @param errors:  The standard deviation of the measured intensity 
values per time point.
-        @type errors:   numpy array
-        @return:        The Jacobian matrix with 'm' rows of function 
derivatives per 'n' columns of parameters.
-        @rtype:         numpy array
-        """
-
-        # Unpack the parameter values.
-        r2eff = params[0]
-        i0 = params[1]
-
-        # Make partial derivative, with respect to r2eff.
-        d_exp_d_r2eff = -i0 * times * exp(-r2eff * times)
-
-        # Make partial derivative, with respect to i0.
-        d_exp_d_i0 = exp(-r2eff * times)
-
-        # Define Jacobian as m rows with function derivatives and n columns 
of parameters.
-        jacobian_matrix_exp = transpose(array( [d_exp_d_r2eff , d_exp_d_i0] 
) )
-
-        # Return Jacobian matrix.
-        return jacobian_matrix_exp
 
 
     def func_exp_chi2(self, params=None, times=None, values=None, 
errors=None):
@@ -985,7 +986,7 @@
             jacobian_matrix_exp = func_exp_chi2_grad(params=param_vector, 
times=E.times, values=E.values, errors=E.errors)
         else:
             # Use the direct Jacobian.
-            jacobian_matrix_exp = E.func_exp_grad(params=param_vector, 
times=E.times, values=E.values, errors=E.errors)
+            jacobian_matrix_exp = func_exp_grad(params=param_vector, 
times=E.times, values=E.values, errors=E.errors)
 
     # Get the co-variance
     if E.chi2_jacobian: