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ENH: Provide a pyFAI Geometry Optimization Task #59
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Description
This PR provides a geometry refinement task using pyFAI enhanced by Bayesian Optimization.
The code mainly originates from what has been developped in
btx/feature_pyFAI
.Checklist
OptimizePyFAIGeometryParameters
geom_opt.py
underlute.io.models
TaskParameters
object and define relevant parameters for input tasksOptimizePyFAIGeometry
taskgeom_opt.py
underlute.tasks
OptimizePyFAIGeometry
task according tobtx/feature_pyFAI
codeLCLSGeom
detector definition for Rayonix binning_report_to_executor
printsPyFAIGeometryOptimizer
MPIExecutor
PR Type:
Address issues:
Testing
Using the configuration YAML below for
mfxx49820
run 8Nota Bene
Depending on the yaml bounds input, the geometry optimizer is scanning distances 100mm around a distance guess if
bounds["dist"]
is a float, or in between two bounds ifbounds["dist"]
is a tuple. If providing a distance guess, I recommend running the task with--ntasks=102
so that a 1mm step is used between scanned distances. If providing distance bounds, set--ntasks=N+2
where N is the desired distance step size.Nota Bene Bene
This implementation depends on the the LouConreux fork of
lute
since in that version, MPI tasks are mapped by core using the special argument inlute.execution.executor
in_submit_cmd
:mpi_cmd: str = f"mpirun -np {nprocs} --map-by core"
Task can be run independently
Task can be run in workflow
Outputs
Geometry files are produced inside a directory defined as such
in_file.replace('0-end.data', f'{run}-end.data)
.Summary plots are saved in
work_dir/figs/
and posted on eLog.Logger writes the final geometry at the end of optimization.
Screenshots