# Computing 

## Computing environments

Standard Linux environment:

````
Linux saresc-cons-04.psi.ch 3.10.0-1160.45.1.el7.x86_64 #1 SMP Fri Sep 24 10:17:16 UTC 2021 x86_64 x86_64 x86_64 GNU/Linux

NAME="Red Hat Enterprise Linux Server"
VERSION="7.9 (Maipo)"
````

Some basic commands:
* `module list` : lists currently loaded modules in the environment
* `module avail`: lists available modules
* `module load <modulename>` : loads the given module into the current shell (it is directly active)

## Space
There is a large number of directories, default locations and quotas for the different computing tasks at SwissFEL. 

For large experimental data the allocated space for the respective p-group should be used. E.g. under `/gpfs/photonics/swissfel/res/cristallina-staff/pxxxxx`, here ra.psi.ch does not have write access (regardless of the user) but can read the data. 

To check the currently available space:
`quota -h` and `df -h`. 


## File permsissions
We should all be part of the unx-cristall group. Please make sure permissions are set at least `chmod g+rw,` so that everyone within the group can access and modify documents. Only set more restrictive permissions when necessary. (Alternatively `chmod g=u` to set the permissions equal to user permissions).


## Anaconda
For all python-based applications and analysis scripts we use use virtual environments administrated using conda/mamba. 

### Activate base environment
To bootstrap the base environment as a starting point use `source /sf/cristallina/applications/conda/envs/miniconda/bin/activate`. With a base environment now loaded we can initialize our shell using `conda init` so this environment is available at the next login. 

### Cristallina environments
All conda environments for cristallina are located at `/sf/cristallina/applications/conda/envs`. Their respective descriptions in `/sf/cristallina/applications/conda/env_specs`, which is also tracked in git.psi.ch.

The main environments are: 
 * slic (`/sf/cristallina/applications/conda/envs/slic`)
 * analysis (`/sf/cristallina/applications/conda/envs/analysis`)
 
for instrument control and data analysis. We are trying to track recent upstream packages but please only update if you ensure a working state afterwards and document the changes.

To be able to use the shortcut for activation, e.g. `conda activate slic` the environment directory needs to be added to your `.condarc` file as follows:

````
envs_dirs:
  - /sf/cristallina/applications/conda/envs
````

A backup environment is currently located at: 
````
/gpfs/photonics/swissfel/res/cristallina-staff/p19739/backup/cristallina2
````
if all else fails.


### Updating environments

The cleanest way to install and update environments is via their .yml specification. After editing the specification run for example:

`mamba env update --file cristallina_diagnostics.yml --prune`

to bring the environment to the most up-to-date state according to .yml file. 

This has the advantage that we can always reproduce this environment. If manually installed packages become uninstalled their dependencies remain. Additionally we lose track of these manual installations.

## Getting git
The `git` installed on the cristallina consoles is from 2015, so we can use conda to provide us with an up-to-date version. E.g. with `miniconda` activated simply install using `conda install git`. 

# sf_datafiles directly from github
- pip:
  - "git+https://github.com/paulscherrerinstitute/sf_datafiles@master"

sf_data is installed directly from github (using python ensures that the activated environment site-packages is used):

python -m pip install git+https://github.com/paulscherrerinstitute/sf_datafiles@master

## Git repository settings

To allow shared access to a git repository folder:
````
git config --get core.sharedRepository
````

# Remote access (how can users get to RA)
## Through hop.psi.ch (nicer way)
Connect to PSI network for data access
`ssh -L user_name@hop.psi.ch user_name@ra.psi.ch -L5000:jupytera.psi.ch:443`

after that, just open the browser and type:
`https://localhost:5000/hub/login`

## Through NoMachine
see https://gitlab.psi.ch/maloja/docs/-/wikis/python/Getting-started 

# Setting up the cluster:
This is a short summary of the steps to be followed to access the experimental data and the compute resources available for Cristallina users.

###  Set up the enviroments paths
Once on ra (ssh user_name@ra.psi.ch as described above), create a symbolic link to the bash file:
`ln -s /sf/cristallina/bin/setting_up_ra.sh ~`

and run it
`sh setting_up_ra.sh`

This should set up the working python enviroments. For the analysis, please use the 'analysis' enviroment, which is set as a default one. 

### Running own jupyter instance
Once on ra, copy the bash file:
`cp /sf/cristallina/bin/jupyter_on_ra.sh ~`
and run it.