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163 | class BayFAI(Task):
"""Optimize detector geometry using PyFAI coupled with Bayesian Optimization."""
def __init__(self, *, params: BayFAIParameters, use_mpi: bool = True) -> None:
super().__init__(params=params, use_mpi=use_mpi)
def _run(self) -> None:
start_time = time.time()
assert isinstance(self._task_parameters, BayFAIParameters)
optimizer: Union[BayFAIOpt, BayFAIOpt2]
if IS_PSANA2:
optimizer = BayFAIOpt2(
exp=self._task_parameters.lute_config.experiment,
run=int(self._task_parameters.lute_config.run),
)
else:
optimizer = BayFAIOpt(
exp=self._task_parameters.lute_config.experiment,
run=int(self._task_parameters.lute_config.run),
)
optimizer.setup(
detname=self._task_parameters.detname,
powder=self._task_parameters.powder,
smooth=self._task_parameters.preprocess,
calibrant=self._task_parameters.calibrant,
fixed=self._task_parameters.fixed,
in_file=self._task_parameters.in_file,
)
bayfai_hyperparams = {
"n_samples": self._task_parameters.bo_params.n_samples,
"n_iterations": self._task_parameters.bo_params.n_iterations,
"max_rings": self._task_parameters.bo_params.max_rings,
"Imin": optimizer.Imin,
"prior": self._task_parameters.bo_params.prior,
"beta": self._task_parameters.bo_params.beta,
"step": self._task_parameters.bo_params.step,
"seed": self._task_parameters.bo_params.seed,
}
optimizer.bayfai_opt(
center=self._task_parameters.center,
bounds=self._task_parameters.bounds,
res=self._task_parameters.resolutions,
**bayfai_hyperparams,
)
if optimizer.rank == 0:
logger.info("Optimization complete")
logger.info(f"Elapsed time: {time.time() - start_time:.2f} s")
params = optimizer.params
score = optimizer.neglog_score
distance = params[0]
cx = params[1]
cy = params[2]
logger.info(f"Detector Distance to Sample: {distance:.6f}")
logger.info(f"Beam center ({cx:.6f}, {cy:.6f})")
logger.info(
f"Rotations: \u03b8x = ({params[3]:.2e}, \u03b8y = {params[4]:.2e}, \u03b8z = {params[5]:.2e})"
)
logger.info(f"Best Score: {score:.2e}")
fig_folder = os.path.join(
self._task_parameters.lute_config.work_dir, "figs"
)
os.makedirs(fig_folder, exist_ok=True)
plot = f"{fig_folder}/bayFAI_summary_{optimizer.exp}_r{optimizer.run:0>4}_{self._task_parameters.detname}.png"
calib_detector = optimizer.update_geometry(self._task_parameters.out_file)
optimizer.upload_geometry(
self._task_parameters.out_file, self._task_parameters.detname
)
powder_plot, qs, resolutions = optimizer.create_interactive_powder()
diagnostics_plot = optimizer.create_diagnostics_panel(
detector=calib_detector,
distance=distance,
)
_ = optimizer.create_summary_plot(
detector=calib_detector,
distance=distance,
plot=plot,
)
pn.extension("matplotlib", "bokeh")
plots = pn.Row(
pn.pane.Matplotlib(diagnostics_plot, sizing_mode="fixed"),
powder_plot,
)
content = pn.Column(
pn.pane.Markdown(
"### Detector Geometry Optimization Summary",
styles={
"font-size": "2em",
"font-weight": "bold",
"text-align": "center",
"margin": "0 auto",
"display": "block",
},
),
plots,
sizing_mode="stretch_width",
)
content.save(
f"{fig_folder}/bayFAI_summary_{optimizer.exp}_r{optimizer.run:0>4}_{self._task_parameters.detname}.html",
embed=True,
)
plots = pn.Tabs(content)
self._result.summary = []
self._result.summary.append(
{
"Detector distance (m)": f"{params[0]:.6f}",
"Detector center (pix)": (
f"{cx/optimizer.detector.pixel_size:.3f}",
f"{cy/optimizer.detector.pixel_size:.3f}",
),
"Lowest q": f"{qs['closest']:.3f} \u00c5-1 | {resolutions['closest']:.3f} \u00c5",
"Highest q": f"{qs['furthest']:.3f} \u00c5-1 | {resolutions['furthest']:.3f} \u00c5 (detector corner)",
}
)
logger.info(
f">>> Lowest q : {qs['closest']:.3f} \u00c5-1 | {resolutions['closest']:.3f} \u00c5"
)
logger.info(
f">>> Highest q : {qs['furthest']:.3f} \u00c5-1 | {resolutions['furthest']:.3f} \u00c5 (detector corner)"
)
self._result.summary.append(
ElogSummaryPlots(
f"Geometry_Fit/r{self._task_parameters.lute_config.run:0>4}/{self._task_parameters.detname}",
plots,
)
)
self._result.task_status = TaskStatus.COMPLETED
|