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Introduction

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.

Prerequisites

It is recommended that you already have working conda installation and have beginner's knowledge of conda environments.

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Getting Started

• Install conda if you don't have an existing installation
• Review conda environments, the Getting Started with Conda page

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• , and this useful conda cheatsheet
• Review this git version control intro
• Brush up on environment variables

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 (Workflow 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:

• Setting an alias in your native system to launch the pydm conda environment
  • For example, my ~/.zshrc (my Mac's default shell is zsh for some reason) has the line alias pydmenv="conda activate pydm-environment"
• Learning how to save environment variables in your conda environment (note that you need to scroll down for Mac/linux)
  • That internal file will need the export QT_MAC_WANTS_LAYER=1 from  and export PYDM_DEFAULT_PROTOCOL=ca from the installation page
  • I personally also added an alias to launch designer to save keystrokes (alias designer='open $CONDA_PREFIX/bin/Designer.app')

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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. 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])

Installing LCLS Tools

A shared library used by most (if not all) SRF software

1. Run git clone git@github.com:slaclab/lcls-tools.git
2. Navigate to that repo and run pip install --upgrade .

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)
3. navigate to that directory and run git init
4. run ~/zacarias/utils/linkRepoToGithub/link_repo_to_github.sh -i and follow the prompts
5. 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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