Overview

This page summarizes the design & implementation of the interface between the live FACET-II controls system and the FACET2E Bmad model. The infrastructure consists of two layers:

• BmadLiveModel: a python class responsible for watching the accelerator and updating a live instance of PyTao based on extant machine settings
• The live model PVA service: a process that runs it's own BmadLiveModel and publishes data to table PVs

BmadLiveModel

The goal of this class is to provide access to an instance of Tao that can view/manipulate the FACET2 Bmad model with settings that match those of the actual production accelerator.

The BmadLiveModel python class loads the FACET2E lattice and runs a local copy of pytao.Tao

This object connects to the production controls system via EPICS Channel Access, and connects callback functions to each live quantity (PV) of interest. When live quantities change, these callbacks will either (a) submit update instructions to a shared queue or (b) request a recalculation of the live momentum profile by the LEM-watcher thread. Additionally, a second daemon thread (model-update) will periodically empty the queue and execute all the scheduled update commands and then update the BmadLiveModel.live and .LEM data structures.

This class makes use of both async functions (by way of PV callbacks), and thread-based parallelism for controls system monitoring, processing of update requests and data structure changes to optimize performance and minimize delays between changes being broadcast over the network and being mirrored in the model. There are three major concurrent features:

Device Monitoring

Real-time controls-system monitoring is accomplished using PV callback functions.

• RF-related PVs (i.e. klystron ENLDS, phases etc) will request a LEM update, which will trigger the LEM-watcher to recalculate the live momentum profile
• Other PVs (magnet BACTs etc) will directly directly submit the update of a given parameter to a shared queue. To do so, each function must:
  
  • convert the new value into Bmad units (i.e. from kGm → T, GeV → eV etc)
  • submit a tuple of (element name, attribute, value) to the command queue

PyTao updates

model-update

This thread is responsible for executing all the submitted update requests using Tao, and to updating derived quantities (BLEM) and python data structures.

1. empty the command queue of (approximately*) all submitted updates
2. run all the set ele commands, then re-calculate lattice parameters
3. update the live.p0c, live.e_tot, live.twiss, live.quads, live.bends, live.rf, live.LEM data structures

LEM

LEM (LINAC energy management) is a set of algorithms responsible for estimating the beam momentum profile and calculating the corresponding lattice quad settings. This code only calculates the momentum profile, errors and magnet settings. Actually setting magnets in the accelerator will be handled by a LEM server GUI (coming soon...).

• BmadLiveModel will monitors the klystron complement and bend magnet settings to calculate the live momentum profile of the beam
• Periodically, the server will use the live momentum profile to calculate energy errors and magnet settings, which are published to a table PV

Detailed overview of LEM implementation: LEM_notes-3.pdf

LEM-watcher

This is the second daemon thread spawned by BmadLiveModel and is responsible for calculating the live beam momentum profile as described below, and for submitting update requests for cavity voltages and phases to the command queue.

PVA model server

The principal use case for the BmadLiveModel is running a real-time live model server. This server runs its own BmadLiveModel, and then publishes live model data to scalar and table PVs accessible on the controls system via EPICS PVAccess. The code for the live model itself is relatively simple, and is derived from the lcls_live_model server.

Access to the live Bmad model server (for twiss/rmat data), is mediated by the python meme service

Implementation: F2_live_model code

The code for all the infrastructure described in this document lives together. Source code can be viewed on GitHub, and API documentation is available online.

• SLAC network master repository: /afs/slac/g/cd/swe/git/repos/slac/FACET/F2_live_model.git
• Production deployment: /usr/local/tools/python/F2_live_model
• GitHub mirror: F2_live_model.git

BmadLiveModel Usage

API documentation: f2-live-model.readthedocs.io/

The BmadLiveModel operates in three distinct modes, depending on the input flags at construction:

f2m = BmadLiveModel(design_only=True)

f2m = BmadLiveModel(instanced=True)
f2m.refresh_all() 
# --> your code here

f2m = BmadLiveModel()
f2m.start()
# --> your code here
f2m.stop

with BmadLiveModel() as f2m:
    # --> your code here

Model server data description

Additionally, the service will publish scalar PVs of the estimated linac amplitudes/chirps/fudges by calculated by LEM. Their names are as follows:

• BMAD:SYS0:1:FACET2E:LEM:<XX>_FUDGE
• BMAD:SYS0:1:FACET2E:LEM:<XX>_AMPL
• BMAD:SYS0:1:FACET2E:LEM:<XX>_CHIRP

where <XX> is a "LEM region", either L0, L1, L2 or L3, for a total of an additional 12 PVs. (ex: BMAD:SYS0:1:FACET2E:LEM:L2_FUDGE)

Dependencies

• numpy, spicy
• pyEPICS
• p4p
• Bmad + Tao
• Pytao
• facet2-lattice
• The live model needs to run on the production network (primarily on facet-srv02)

Source file descriptions