Author: bugman
Date: Tue May 20 16:17:15 2008
New Revision: 6166
URL: http://svn.gna.org/viewcvs/relax?rev=6166&view=rev
Log:
Fixed all the function docstrings to be valid epytext.
Modified:
1.3/maths_fns/chi2.py
Modified: 1.3/maths_fns/chi2.py
URL:
http://svn.gna.org/viewcvs/relax/1.3/maths_fns/chi2.py?rev=6166&r1=6165&r2=6166&view=diff
==============================================================================
--- 1.3/maths_fns/chi2.py (original)
+++ 1.3/maths_fns/chi2.py Tue May 20 16:17:15 2008
@@ -33,20 +33,22 @@
def chi2(data, back_calc_vals, errors):
"""Function to calculate the chi-squared value.
- The chi-sqared equation
- =======================
+ The chi-squared equation
+ ========================
- _n_
- \ (yi - yi(theta)) ** 2
- chi^2(theta) = > ---------------------
- /__ sigma_i ** 2
- i=1
+ The equation is::
- where:
- theta is the parameter vector.
- yi are the values of the measured data set.
- yi(theta) are the values of the back calculated data set.
- sigma_i are the values of the error set.
+ _n_
+ \ (yi - yi(theta)) ** 2
+ chi^2(theta) = > ---------------------
+ /__ sigma_i ** 2
+ i=1
+
+ where
+ - theta is the parameter vector.
+ - yi are the values of the measured data set.
+ - yi(theta) are the values of the back calculated data set.
+ - sigma_i are the values of the error set.
@param data: The vector of yi values.
@@ -70,21 +72,23 @@
def dchi2(data, back_calc_vals, back_calc_grad, errors):
"""Function to create the chi-squared gradient.
- The chi-sqared gradient
- =======================
+ The chi-squared gradient
+ ========================
- _n_
- dchi^2(theta) \ / yi - yi(theta) dyi(theta) \
- ------------- = -2 > | -------------- . ---------- |
- dthetaj /__ \ sigma_i**2 dthetaj /
- i=1
+ The equation is::
- where:
- theta is the parameter vector.
- yi are the values of the measured data set.
- yi(theta) are the values of the back calculated data set.
- dyi(theta)/dthetaj are the values of the back calculated gradient.
- sigma_i are the values of the error set.
+ _n_
+ dchi^2(theta) \ / yi - yi(theta) dyi(theta) \
+ ------------- = -2 > | -------------- . ---------- |
+ dthetaj /__ \ sigma_i**2 dthetaj /
+ i=1
+
+ where
+ - theta is the parameter vector.
+ - yi are the values of the measured data set.
+ - yi(theta) are the values of the back calculated data set.
+ - dyi(theta)/dthetaj are the values of the back calculated gradient.
+ - sigma_i are the values of the error set.
@param data: The vector of yi values.
@type data: numpy array
@@ -112,19 +116,21 @@
The chi-squared Hessian
=======================
- _n_
- d2chi^2(theta) \ 1 / dyi(theta) dyi(theta)
d2yi(theta) \
- --------------- = 2 > ---------- | ---------- . ---------- -
(yi-yi(theta)) . --------------- |
- dthetaj.dthetak /__ sigma_i**2 \ dthetaj dthetak
dthetaj.dthetak /
- i=1
+ The equation is::
- where:
- theta is the parameter vector.
- yi are the values of the measured relaxation data set.
- yi(theta) are the values of the back calculated relaxation data set.
- dyi(theta)/dthetaj are the values of the back calculated gradient.
- d2yi(theta)/dthetaj.dthetak are the values of the back calculated
Hessian.
- sigma_i are the values of the error set.
+ _n_
+ d2chi^2(theta) \ 1 / dyi(theta) dyi(theta)
d2yi(theta) \
+ --------------- = 2 > ---------- | ---------- . ---------- -
(yi-yi(theta)) . --------------- |
+ dthetaj.dthetak /__ sigma_i**2 \ dthetaj dthetak
dthetaj.dthetak /
+ i=1
+
+ where
+ - theta is the parameter vector.
+ - yi are the values of the measured relaxation data set.
+ - yi(theta) are the values of the back calculated relaxation data
set.
+ - dyi(theta)/dthetaj are the values of the back calculated gradient.
+ - d2yi(theta)/dthetaj.dthetak are the values of the back calculated
Hessian.
+ - sigma_i are the values of the error set.
@param data: The vector of yi values.
r6166 - /1.3/maths_fns/chi2.py