relax SVN archive

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.

Fix for forgetting to multiply frqs to power 2.

This was found by inspecting all print out before and after implementation.

New implementation of DPL94 now passes all system and unit tests.

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

Trying to move some of the structures into its own part.

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

First try to speed up model DPL94.

This has not succeded, since systemtest: Relax_disp.test_dpl94_data_to_dpl94 stiÃll fails.

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

Fix for import missing error in lib function dpl94.

Needed to import numpy any() function.

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

Replacing math domain checking in model DPL94, with masked array replacement.

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

Speed up and simplifications for the vector calculations used for the PCS numerical integration.

This has a minimal effect on the total speed as the target function calc_vectors() method is not the
major bottleneck - the slowest part is the quasi-random numerical integration. However the changes
may be useful for speeding up the integration later on.

The 3D pivot point, average domain rotation pivot, and paramagnetic centre position arrays are now
converted into rank-2 arrays in __init__() where the first dimension corresponds to the spin. Each
element is a copy of the 3D array.

These are then used for the calculation of the pivot to atom vectors, eliminating the looping over
spins. The numpy add() and subtract() ufuncs are used together with the out argument for speed and
to avoid temporary data structure creation and deletion. The end result is that the calculated
vector structure is transposed, so the first dimension are the spins.

The changes required minor updates to a number of system tests. The target functions themselves had
to be modified so that the pivot is converted to the larger structure when optimised, or aliased.

Simplification and clean up of the RDC and PCS flags in the frame order target functions.

The per-alignment flags have been removed and replaced by a global flag for all data. This
accidentally fixes a bug when only RDCs are present, as the calc_vectors() method was being called
when it should not have been.