relax SVN archive

Reordered the initialization structure of the special numpy arrays.

This was done in the init part of the target function of relaxation dispersion.

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

Replaced the loop structure in target function of TAP03 with numpy arrays.

This makes the model faster.

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

Made dw in unit tests of TAP03 be of numpy array.

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

Converted TAP03 model to use multi dimensional numpy arrays.

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

Documentation fix for description of input arrays to lib functions.

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

Moved the expansion of frqs out of for loops.

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

For inv_relax_times, expanded one axis, and tiled up to NR spins, before reshaping and blowing up to full structure.

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

Moved the multi dimensional expansion of inv_relax_times out of for loop.

This can be done for all structures, which does not have missing points.

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

Moved the packing of errors and values out of for loop in the __init__ class of target function.

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

Moved the expansion of the R1 structure out of the for loops.

This is to speed-up the __init__ of the class of the target function.

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