relax SVN archive

Added a check, that the pre- relax_time multiplied multidimensional array, equal the previous.

It does, to the sum of 1.0e-13.

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

Added the relax_time to collection of rr1rho_3d_rankN matrix collection.

This is to pre-multiply all elements with the time.

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

Added a check in lib/dispersion/ns_r1hro_2site.py, to see if the newly created multidimensional
ns matrix of rank NE][NS][NM][NO][ND][6][6], is equal to the previous [6][6] matrix.

It is.

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

Added the ns matrice, rr1rho_3d_rankN, to collect the multi dimensional 3D exchange matrix, of rank [NE][NS][NM][NO][ND][6][6].

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

Changed back from einsum to dot method, since dot method it faster for square matrixes.

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

Replaced the inner dot product with numpy einsum.

This though slows it down from 8.5 s to 13 s.
This is only as an intermediate step, how to figure to dot product inner parts of the big matrix.

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

Removed unused variables in NS CPMG 2site 3D, to clean up the code.

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

Implemeted systemtest: test_cpmg_synthetic_b14_to_ns3d_cluster

This is to cathc failures of the model, when data is clusted.

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

Moved the calculation the evolution matrix out of for loops.

The trick is that numpy.einsum allows for dot product of higher dimension:

- The the essential evolution matrix.
- This is a dot product of the outer [7][7] matrix of the Rexpo_mat and r180x_mat matrixes, which
- have the shape [NE][NS][NM][NO][ND][7][7].
- This can be achieved by using numpy einsum, and where ellipsis notation will use the last axis.
evolution_matrix_mat = einsum('...ij,...jk', Rexpo_mat, r180x_mat)
evolution_matrix_mat = einsum('...ij,...jk', evolution_matrix_mat, Rexpo_mat)
evolution_matrix_mat = einsum('...ij,...jk', evolution_matrix_mat, evolution_matrix_mat)

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

Made notation consistent for variables, using "_i" to clarify extracted data from matrix.

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