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Developing applications on your local machine is strongly recommended as it saves on headaches with using shared OPIs and allows for robust testing on a simulated machine before launching in production.

Getting Started

• Install conda if you don't have an existing installation
• Review conda environments, the Getting Started with Conda page, and this useful conda cheatsheet
• Review this git version control intro
• Brush up on environment variables
• Run through this GitHub tutorial

Setting up GitHub

1. Make a GitHub account and record your username (personal or work email is fine)
2. Add your ssh key to your GitHub account
3. Navigate to the #comp-general Slack channel 
4. Create a SLAC GitHub access workflow (the + with the lightning bolt at the bottomWorkflow button at the top near the channel name). An admin will review and grant you access
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Installing PyDM

The basic steps can be found in the official documentation, but some quality of life improvements include:

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1. Run conda install --update-all --force-reinstall -y -c conda-forge bmad
2. Run git clone git@github.com:slaclab/lcls-lattice.git
   1. This repo can live wherever, just remember where it lives
   2. You may need to request access
3. Set the LCLS_LATTICE environment variable to wherever the repo in step 2 lives
4. Run git clone git@github.com:slaclab/simulacrum.git and remember where it lives
5. Append the path from step 4 to your python path in your environment variable file using export PYTHONPATH="${PYTHONPATH}:path/to/simulacrum/repo"Navigate to the newly cloned simulacrum repo and run pip install . 
6. Run python <service directory>/<service>.py for any service you want running

   1. This will eat a terminal tab. I strongly advise against backgrounding because you'll need to kill the process eventually, and if you background it you'll either need to remember the process ID or find it later

   2. For SRF work specifically, we typically only need the sc_rf_service so the command would be python sc_rf_service/sc_rf_service.py
      1. All other services typically need the model service running with the appropriate beam line flag (python model_service/model_service.py [lcls classic | cu_hxr | cu_spec | cu_sxr | sc_hxr | sc_sxr])

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Porting to Production

Once you're happy with whatever program you're working on and think that it's ready (and safe) to run on production, you can connect your GitHub repo

Setup

1. ssh into srv01 and activate your own profile
2. make a directory with the desired name (this isn't a clone so it doesn't make the directory for you)

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1. navigate to that directory and run git init
2. run ~/zacarias/utils/linkRepoToGithub/link_repo_to_github.sh -i and follow the prompts
3. run git pull origin main and all of the files should now be there

Updating

1. If you know where in AFS you connected your repo (setup step 4) skip to step 2 otherwise navigate to your production repository and run git remote -v to see where origin is
   1. It will look something like ssh:///[path to remote]
2. ssh into an AFS machine that has internet connectivity
3. navigate to the repo found in step 1
4. run git --bare fetch origin main:main
5. ssh to lcls-srv01 using your own profile
6. navigate back to your production repo and run git pull origin main

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