Adding simulations into the NMRlipids databank

Stepwise instructions to add simulation into the NMRlipids databank, run the basic analyses and perform automatic quality evaluation are given here. The first three steps are a minimum requirements to add a simulation. The first three steps can be performed using graphical GitHub interface. To run the basic analyses and quality evaluation from steps 4 forward, you need to create a local fork of the NMRlipids databank git.

Add trajectory and topology (tpr for Gromacs, pdb or corresponding to other programs) file into a Zenodo repository. If you want to use other repository than Zenodo, please do not hesitate to open an GitHub issue on this.
Create an info.yaml file containing the essential information on your simulation by filling the template. For instructions, see User input and content of README.yaml files and examples. Mapping files are described in Universal molecule and atom names and are available from here .
Save the created info.yaml file into a new directory with the next free integer into Scripts/BuildDatabank/info_files/ folder in the NMRlipids databank git and make a pull request to the main branch. You can stop here or continue to create `README.yaml` file in step 4.
Before continuing, clone a repo on your computer and perform the following steps locally before making a pull request to the main branch. To create the README.yaml file for the databank, return to the Databank/Scripts/BuildDatabank/ folder in the NMRlipids databank git and run
```
python3 AddData.py -f {path to the info.yaml file that you created}
```
For available command line arguments, you can run
```
python3 AddData.py --help
```
After this is finished, you should see a new folder in Data/simulations which contains the README.yaml file of your system. After successfull creation of README.yaml file, run
```
python createIDs.py
```
to add the ID number of the simulation into the README.yaml file. Commit the created README.yaml file into the git.

To perform basic analyses for the added system(s), go to Databank/Scripts/AnalyzeDatabank/ folder and run

./calcProperties.sh

After this, you should see the results in the same folder where README.yaml is located. The results can be added into the get by running

git add ../../Data/Simulations/*/*/*/*/apl.json
git add ../../Data/Simulations/*/*/*/*/*OrderParameters.json
git add ../../Data/Simulations/*/*/*/*/FormFactor.json
git add ../../Data/Simulations/*/*/*/*/TotalDensity.json
git add ../../Data/Simulations/*/*/*/*/thickness.json
git add ../../Data/Simulations/*/*/*/*/eq_times.json

For the quality evaluation against experiments, the simulation needs to be first connected to the corresponding experimental data (if available) by running searchDATABANK.py in Scripts/BuildDatabank folder. This will add the EXPERIMENT dictionary into the README.yaml files. This dictionary defines the location of related experimental data in Data/experiments folder. Then the quality evaluation can be then done by running the QualityEvaluation.py
```
python searchDATABANK.py
python QualityEvaluation.py
```
The resulting qualities can be then added into the git by running
```
git add ../../Data/Simulations/*/*/*/*/README.yaml
git add ../../Data/Simulations/*/*/*/*/*OrderParameters_quality.json
git add ../../Data/Simulations/*/*/*/*/FormFactorQuality.json
git add ../../Data/Simulations/*/*/*/*/*FragmentQuality.json
git add ../../Data/Simulations/*/*/*/*/SYSTEM_quality.json
```
To create rankings of simulations based on their quality against experiments and to store the results in folder Data/Ranking, run
```
python makeRanking.py
```
Finally, commit the added data into your fork and make a pull request into the main branch.