r24302 - /branches/disp_spin_speed/lib/dispersion/ns_cpmg_2site_3d.py -- June 25, 2014 - 02:14

Author: tlinnet
Date: Wed Jun 25 02:14:50 2014
New Revision: 24302

URL: http://svn.gna.org/viewcvs/relax?rev=24302&view=rev
Log:
Tried to implement using lib.linear_algebra.matrix_power.square_matrix_power 
instead of matrix_power from numpy in NS CPMG 2site 3d.

Strangely, then systemtest:
test_hansen_cpmg_data_to_ns_cpmg_2site_3D_full

starts to fail!

This is very weird.

Task #7807 (https://gna.org/task/index.php?7807): Speed-up of dispersion 
models for Clustered analysis.

Modified:
    branches/disp_spin_speed/lib/dispersion/ns_cpmg_2site_3d.py

Modified: branches/disp_spin_speed/lib/dispersion/ns_cpmg_2site_3d.py
URL: 
http://svn.gna.org/viewcvs/relax/branches/disp_spin_speed/lib/dispersion/ns_cpmg_2site_3d.py?rev=24302&r1=24301&r2=24302&view=diff
==============================================================================
--- branches/disp_spin_speed/lib/dispersion/ns_cpmg_2site_3d.py (original)
+++ branches/disp_spin_speed/lib/dispersion/ns_cpmg_2site_3d.py Wed Jun 25 
02:14:50 2014
@@ -62,6 +62,7 @@
 # relax module imports.
 from lib.float import isNaN
 from lib.dispersion.matrix_exponential import 
matrix_exponential_rank_NE_NS_NM_NO_ND_x_x
+from lib.linear_algebra.matrix_power import square_matrix_power
 
 # Repetitive calculations (to speed up calculations).
 m_r10a = array([
@@ -332,23 +333,15 @@
                 # This matrix is a propagator that will evolve the 
magnetization with the matrix R for a delay tcp.
                 evolution_matrix_T_i = evolution_matrix_T_mat[0, si, mi, 0, 
di]
 
-                # If the looping over the number of CPMG elements is given 
by the index l, and the initial magnetization has
-                # been formed, then the number of times for propagation of 
magnetization is l = power_si_mi_di-1.
-                # If the magnetization matrix "Mint" has the index 
Mint_(i,k) and the evolution matrix has the index Evol_(k,j), i=1, k=7, j=7
-                # then the dot product is given by: Sum_{k=1}^{k} Mint_(1,k) 
* Evol_(k,j) = D_(1, j).
-                # The numpy einsum formula for this would be: einsum('ik,kj 
-> ij', Mint, Evol)
-                # 
-                # Following evolution will be: Sum_{k=1}^{k} D_(1, j) * 
Evol_(k,j) = Mint_(1,k) * Evol_(k,j) * Evol_(k,j).
-                # We can then realize, that the evolution matrix can be 
raised to the power l. Evol^l.
-
                 # Get which power to raise the matrix to.
                 l = power_si_mi_di-1
 
                 # Raise the square evolution matrix to the power l.
                 evolution_matrix_T_pwer_i = 
matrix_power(evolution_matrix_T_i, l)
-
-                #Mint_T_i = dot(Mint_T_i, evolution_matrix_T_pwer_i)
-                Mint_T_i = einsum('ik,kj -> ij', Mint_T_i, 
evolution_matrix_T_pwer_i)
+                #evolution_matrix_T_pwer_i = 
square_matrix_power(evolution_matrix_T_i, l).real
+
+                Mint_T_i = dot(Mint_T_i, evolution_matrix_T_pwer_i)
+                #Mint_T_i = einsum('ik,kj -> ij', Mint_T_i, 
evolution_matrix_T_pwer_i)
 
                 # The next lines calculate the R2eff using a two-point 
approximation, i.e. assuming that the decay is mono-exponential.
                 Mx = Mint_T_i[0][1] / pA