Difference between revisions of "Relax 1.0.2"

From relax wiki
Jump to navigation Jump to search
(→‎Description: Math mode conversion.)
(→‎Description: More math mode conversions.)
Line 9: Line 9:
  
 
<math>
 
<math>
     D_{iso} = \frac{1}/{3} \left( D_x + D_y + D_z \right),
+
     D_{iso} = \frac{1}{3} \left( D_x + D_y + D_z \right),
 
</math>
 
</math>
* D<sub>a</sub> = D<sub>z</sub> - 1/2(D<sub>x</sub> + D<sub>y</sub>),
+
<math>
* D<sub>r</sub> = D<sub>y</sub> - D<sub>x</sub> / 2D<sub>a</sub>.
+
    D_a = D_z - \frac{1}{2} \left( D_x + D_y \right) ,
 +
</math>
 +
<math>
 +
    D_r = D_y - \frac{D_x}{2D_a} .
 +
</math>
 +
 
  
 
Numerous bugs in the old correlation time, weights, and direction cosine code have been fixed.  A few of these bugs had been fixed previously but those fixes were somehow lost.  Two major problems in the calculation of the Hessian of the ellipsoid direction cosines have been fixed and the ellipsoid diffusion tensor now minimises orders of magnitude faster.
 
Numerous bugs in the old correlation time, weights, and direction cosine code have been fixed.  A few of these bugs had been fixed previously but those fixes were somehow lost.  Two major problems in the calculation of the Hessian of the ellipsoid direction cosines have been fixed and the ellipsoid diffusion tensor now minimises orders of magnitude faster.

Revision as of 10:12, 7 September 2014


Description

This release corresponds to many changes to the Brownian rotational diffusion tensor.

It includes the shift to the geometric parameters:

[math] D_{iso} = \frac{1}{3} \left( D_x + D_y + D_z \right), [/math] [math] D_a = D_z - \frac{1}{2} \left( D_x + D_y \right) , [/math] [math] D_r = D_y - \frac{D_x}{2D_a} . [/math]


Numerous bugs in the old correlation time, weights, and direction cosine code have been fixed. A few of these bugs had been fixed previously but those fixes were somehow lost. Two major problems in the calculation of the Hessian of the ellipsoid direction cosines have been fixed and the ellipsoid diffusion tensor now minimises orders of magnitude faster.


See also