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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.

Getting Started

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 bottom). An admin will review and grant you access

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 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')

Installing Simulacrum

These steps should all be done in your pydm conda environment unless you don't need/intend to use PyDM

  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
  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"
  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])

Module Not Found

Sometimes running a service for the first time will generate "module not found" errors. If that is the case, simply pip install the missing module and try again

Runtime Error: Empty SharedPV

If you get a SharedPV runtime error when running the model service, it's likely that the newer p4p version isn't backward compatible. Try running pip install p4p==4.0.0


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 .


If there are sudden unexplained errors in your program, it is possible that one of these libraries was updated. In most cases, all you'll need is a git pull from the relevant repo. LCLS Tools specifically also needs the pip upgrade


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