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* Write a 'hypothetical' relax script to perform a basic analysis (this will of course not work until the steps below have been completed).
* Calculate by hand what some of the results should be.
* Add the data to the test_suite directories and create a system/unit test which runs the script and checks the hand-calculated values to machine precision (~1e<sup>-15</sup>).
== Data input ==
* This takes the input data and implements functions, gradients and Hessians for the chi-squared optimisation function for the analysis.
* The first and second partial derivatives of the equations should be calculated by hand (computer algebra system (CAS) software such as maxima, mathematica, etc. can do this easily). The availability of gradients and Hessians opens up the possibility to use Newton optimisation which, speed- and precision-wise, is well worth the effort! It also allows for the use of the Method of Multipliers (or Augmented Lagrangian) constraint algorithm which, if parameter constraints are required, e.g. 0 <= ≤ {{:S2 <= }} ≤ 1, is an incredible algorithm.
== Result visualisation ==
== GUI implementation ==
* This is not at all essential, but many students developers see this as the "icing on the cake" ;)
== Documentation ==
* New relax users often ask for a demo data set to test out the software with. This is totally unnecessary and is only needed to sooth the uncertainty of the software user.
* Simply bundle a data set and a functional relax script.
* I have recently created There is a git repository for this (e.g. at https://sourceforge.net/p/nmr-relax/relax-demo/ci/master/tree/).
It is essential that step 1) is completed first. Then the developer knows that their task is complete once the test passes. Most of this development would be based on mimicking what is already in place in relax. There are many different analysis types contributed by many different people, and development by copying/mimicking is highly recommended. It is also good to have complete test coverage added at step 8), as this ensures that the analysis will be robust and never break as relax is further developed. That future-proofs the code. I have uploaded the relax git repositories to SourceForge, github, gitlab, and bitbucket (and hopefully soon to Savannah) so that the software should survive for decades to come, independent of my presence.
== Speed ==
Note that a lot of time can be spent on improving the isolated target function code, after implementation. Troels did this for relaxation dispersion in relax, allowing relax to likely be the fastest implementation in the field for this analysis. This requires a lot of numerical tricks to take advantage of the BLAS and LAPACK routines found in the Python numpy package, converting the target functions to operate on matrices as much as possible. Speed comparisons are difficult, as the optimisation in relax is far more precise than any of the other software (precision and quality is often dropped to speed up the software). But I have the source code for many of the dispersion analysis softwares, or I directly asked the developers, and I think that only relax uses these routines. Note that the use of BLAS and LAPACK routines has orders of magnitude greater impact on software optimisation speed than the choice of programming language (Python vs. C), but that the target functions in relax can later be converted from Python to C for additional, though less impressive, speed ups.
== See also ==
[[Category:Tutorials]]
