Demonstrating “Bring Your Own Beamline” simulation with XRT#

In this tutorial we’ll show a simulation optimization workflow by loading arbitrary XRT setups using xml/json. By the end, you should be able to go to XRT qook/glow and build your beamline from there to export and drop in to blop. Or if you are lucky enough for an XRT model to be already built for you, export to xml and load in blop.

Some Environment Setup#

note, like all other demos you need the blop_sim subpackage to run

import logging
import warnings

import matplotlib.pyplot as plt
import numpy as np
from bluesky.callbacks.best_effort import BestEffortCallback

# Import simulation devices (requires: pip install -e sim/)
from bluesky.run_engine import RunEngine
from bluesky.utils import ProgressBarManager
from bluesky_tiled_plugins import TiledWriter
from tiled.client import from_uri  # type: ignore[import-untyped]
from tiled.client.container import Container
from tiled.server import SimpleTiledServer

from blop.ax import Agent, Objective, RangeDOF
from blop.protocols import EvaluationFunction
from blop_sim.backends import XRTBackend
from blop_sim.devices.xrt import infer_detectors, infer_variables

# Suppress noisy logs from httpx and dependency deprecation warnings
logging.getLogger("httpx").setLevel(logging.WARNING)
warnings.filterwarnings("ignore", category=FutureWarning)

# Enable interactive plotting
plt.ion()

DETECTOR_STORAGE = "/tmp/blop/sim"

/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/torch/jit/_script.py:1491: FutureWarning: `torch.jit.script` is deprecated. Please switch to `torch.compile` or `torch.export`.
  warnings.warn(
[INFO 09-03 14:31:23] ax.storage.sqa_store.with_db_settings_base: Ax SQL storage initialized with SQLAlchemy 2.0.52
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/xrt/backends/raycing/sources/sybase.py:78: SyntaxWarning: invalid escape sequence '\s'
  :math:`\beta_i = \frac{\sigma_i^{2}}{\epsilon_i}`, with

fileName = r"toroid_focus.xml"
beam = XRTBackend(file=fileName)
dets = infer_detectors(beam)
motors = infer_variables(beam, filter_for=None)

created inferred variable toroid_focus:screen01:center:z of float type as value is None or auto.
                Be careful when setting this variable as the type is guessed as float by default.

A small view of the inferred motors#

for name, element in motors.items():
    print(name)
    for nm, motor in element.items():
        print(f"{nm} : {motor}")

bendingMagnet01
B0 : <InferredVariable::toroid_focus:bendingMagnet01:B0=<class 'float'>:1.0>
rho : <InferredVariable::toroid_focus:bendingMagnet01:rho=<class 'float'>:10.00692285594456>
toroidMirror01
R : <InferredVariable::toroid_focus:toroidMirror01:R=<class 'float'>:152982.84327559808>
r : <InferredVariable::toroid_focus:toroidMirror01:r=<class 'float'>:1162.0765699687756>
screen01
center:x : <InferredVariable::toroid_focus:screen01:center:x=<class 'int'>:0>
center:y : <InferredVariable::toroid_focus:screen01:center:y=<class 'int'>:30000>
center:z : <InferredVariable::toroid_focus:screen01:center:z=<class 'float'>:auto>
x:x : <InferredVariable::toroid_focus:screen01:x:x=<class 'float'>:1.0>
x:y : <InferredVariable::toroid_focus:screen01:x:y=<class 'float'>:-0.0>
x:z : <InferredVariable::toroid_focus:screen01:x:z=<class 'float'>:0.0>
z:x : <InferredVariable::toroid_focus:screen01:z:x=<class 'float'>:0.0>
z:y : <InferredVariable::toroid_focus:screen01:z:y=<class 'float'>:0.0>
z:z : <InferredVariable::toroid_focus:screen01:z:z=<class 'float'>:1.0>

Another glimpse into the inferred detectors#

for name, det in dets.items():
    print(f"{name} : {det}")

bendingMagnet01 : <blop_sim.devices.xrt.auto_element.InferredDetector object at 0x7fd05409c590>
toroidMirror01 : <blop_sim.devices.xrt.auto_element.InferredDetector object at 0x7fd054089310>
screen01 : <blop_sim.devices.xrt.auto_element.InferredDetector object at 0x7fd054089450>

Setting up an optimization#

toro_R = motors["toroidMirror01"]["R"]
toro_R.alias = "big_r"
toro_r = motors["toroidMirror01"]["r"]

screen = dets["screen01"]
screen.set_primary()


VERTICAL_BOUNDS = (toro_R.val - 15000, toro_R.val + 15000)
HORIZONTAL_BOUNDS = (toro_r.val - 500, toro_r.val + 500)
# Define DOFs using mirror radius signals
dofs = [
    RangeDOF(actuator=toro_R, bounds=VERTICAL_BOUNDS, parameter_type="float"),
    RangeDOF(actuator=toro_r, bounds=HORIZONTAL_BOUNDS, parameter_type="float"),
]

tiled_server = SimpleTiledServer()
tiled_client = from_uri(tiled_server.uri)
tiled_writer = TiledWriter(tiled_client)

RE = RunEngine({})
bec = BestEffortCallback()

# Send all metadata/data captured to the BestEffortCallback.
# RE.subscribe(bec)
RE.waiting_hook = ProgressBarManager()

tiled_client = from_uri(tiled_server.uri)
tiled_writer = TiledWriter(tiled_client)
RE.subscribe(tiled_writer)

0

# Single objective: minimize the geometric-mean FWHM
objectives = [
    Objective(name="fwhm", minimize=True),
]

from collections.abc import Hashable, Mapping, Sequence

class DetectorEvaluation(EvaluationFunction):
    def __init__(self, tiled_client: Container):
        self.tiled_client = tiled_client

    def _fwhm_from_profile(self, profile: np.ndarray) -> float:
        """Compute FWHM from a 1D marginal profile.

        Finds the half-maximum crossing points with sub-pixel interpolation.
        Returns a large value if the beam is too dim or fills the entire detector.
        """
        peak = profile.max()
        if peak == 0:
            return float(len(profile))  # No signal — return detector width as penalty

        half_max = peak / 2.0
        above = profile >= half_max
        if not above.any():
            return float(len(profile))

        indices = np.where(above)[0]
        left_idx = indices[0]
        right_idx = indices[-1]

        # Sub-pixel interpolation at left crossing
        if left_idx > 0:
            left = left_idx - 1 + (half_max - profile[left_idx - 1]) / (profile[left_idx] - profile[left_idx - 1])
        else:
            left = 0.0

        # Sub-pixel interpolation at right crossing
        if right_idx < len(profile) - 1:
            right = right_idx + (half_max - profile[right_idx]) / (profile[right_idx + 1] - profile[right_idx])
        else:
            right = float(len(profile) - 1)

        return right - left

    def _compute_stats(self, image: np.ndarray) -> tuple[float, float]:
        """Compute FWHM and integrated intensity from a beam image.

        Returns
        -------
        fwhm : float
            Geometric mean of the horizontal and vertical FWHM (in pixels).
        intensity : float
            Total integrated intensity (sum of all pixel values).
        """
        gray = image.squeeze().astype(np.float64)
        if gray.ndim == 3:
            gray = gray.mean(axis=-1)

        # Integrated intensity (total flux on detector)
        intensity = gray.sum()

        if intensity == 0:
            return 400.0, 0.0  # No beam — return max FWHM penalty

        # Marginal profiles: project onto each axis
        x_profile = gray.sum(axis=0)  # sum along Y rows -> X profile
        y_profile = gray.sum(axis=1)  # sum along X cols -> Y profile

        fwhm_x = self._fwhm_from_profile(x_profile)
        fwhm_y = self._fwhm_from_profile(y_profile)

        # Geometric mean FWHM — targets a small, round spot
        fwhm = np.sqrt(fwhm_x * fwhm_y)

        return float(fwhm), float(intensity)

    def __call__(self, uid: Hashable, suggestions: Sequence[Mapping]) -> Sequence[Mapping]:
        if not isinstance(uid, str):
            raise TypeError(f"DetectorEvaluation requires a Bluesky run UID string, got {uid!r}")
        outcomes = []
        run = self.tiled_client[uid]

        # Read beam images from detector
        images = run[f"primary/{screen.name}"].read()

        # These IDs, not positions in suggestions, are ordered to match acquired images.
        acquisition_order = run.metadata["start"]["blop_acquisition_order"]

        # Compute statistics from each image
        for idx, suggestion_id in enumerate(acquisition_order):
            image = images[idx]
            fwhm, intensity = self._compute_stats(image)

            outcome = {
                "_id": suggestion_id,
                "fwhm": fwhm,
                # "intensity": intensity,
            }
            outcomes.append(outcome)
        return outcomes

agent = Agent(
    sensors=[screen],
    dofs=dofs,
    objectives=objectives,
    evaluation_function=DetectorEvaluation(tiled_client),
    name="xrt-blop-demo",
    description="A demo of the Blop agent with XRT simulated beamline",
)

# Run 1 iteration with a batch of 10 points for initial exploration
RE(agent.optimize(1, n_points=10))



╭───────────────────────────────────────────────── Optimization ──────────────────────────────────────────────────╮
│ Optimizer  AxOptimizer                                                                                          │
│ Actuators  big_r, toroid_focus:toroidMirror01:r                                                                 │
│ Sensors    screen01                                                                                             │
│ Iterations 1  Points/iter 10                                                                                    │
│ Run UID    26478513-b015-4e64-a635-f88374beb077                                                                 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯



124782 rays of 500000
250311 rays of 500000
375175 rays of 500000
500490 rays of 500000
screen01
center:
[0.0, 30000.0, 1763.260675414056]





[INFO 09-03 14:31:28] ax.api.client: GenerationStrategy(name='Center+Sobol+MBM:fast', nodes=[CenterGenerationNode(next_node_name='Sobol', use_existing_trials_for_initialization=True), GenerationNode(name='Sobol', generator_specs=[GeneratorSpec(generator_enum=Sobol, generator_key_override=None)], transition_criteria=[MinTrials(transition_to='MBM'), MinTrials(transition_to='MBM')], suggested_experiment_status=ExperimentStatus.INITIALIZATION, pausing_criteria=[MaxTrialsAwaitingData(threshold=5)]), GenerationNode(name='MBM', generator_specs=[GeneratorSpec(generator_enum=BoTorch, generator_key_override=None)], transition_criteria=None, suggested_experiment_status=ExperimentStatus.OPTIMIZATION, pausing_criteria=None)]) chosen based on user input and problem structure.
[INFO 09-03 14:31:28] ax.api.client: Generated new trial 0 with parameters {'big_r': 152982.843276, 'toroid_focus:toroidMirror01:r': 1162.07657} using GenerationNode CenterOfSearchSpace.
[INFO 09-03 14:31:28] ax.api.client: Generated new trial 1 with parameters {'big_r': 164710.902329, 'toroid_focus:toroidMirror01:r': 800.432093} using GenerationNode Sobol.
[INFO 09-03 14:31:28] ax.api.client: Generated new trial 2 with parameters {'big_r': 148659.170987, 'toroid_focus:toroidMirror01:r': 1556.533148} using GenerationNode Sobol.
[INFO 09-03 14:31:28] ax.api.client: Generated new trial 3 with parameters {'big_r': 141657.244963, 'toroid_focus:toroidMirror01:r': 1121.838348} using GenerationNode Sobol.
[INFO 09-03 14:31:28] ax.api.client: Generated new trial 4 with parameters {'big_r': 156771.418051, 'toroid_focus:toroidMirror01:r': 1357.918187} using GenerationNode Sobol.
[INFO 09-03 14:31:28] ax.api.client: Generated new trial 5 with parameters {'big_r': 154536.708118, 'toroid_focus:toroidMirror01:r': 1022.251129} using GenerationNode Sobol.
[INFO 09-03 14:31:28] ax.api.client: Generated new trial 6 with parameters {'big_r': 143877.421347, 'toroid_focus:toroidMirror01:r': 1270.051208} using GenerationNode Sobol.
[INFO 09-03 14:31:28] ax.api.client: Generated new trial 7 with parameters {'big_r': 151816.904144, 'toroid_focus:toroidMirror01:r': 704.767817} using GenerationNode Sobol.
[INFO 09-03 14:31:28] ax.api.client: Generated new trial 8 with parameters {'big_r': 161538.6347, 'toroid_focus:toroidMirror01:r': 1464.773748} using GenerationNode Sobol.
[INFO 09-03 14:31:28] ax.api.client: Generated new trial 9 with parameters {'big_r': 163406.317885, 'toroid_focus:toroidMirror01:r': 1071.989694} using GenerationNode Sobol.
[INFO 09-03 14:31:55] ax.api.client: Trial 0 marked COMPLETED.
[INFO 09-03 14:31:55] ax.api.client: Trial 7 marked COMPLETED.
[INFO 09-03 14:31:55] ax.api.client: Trial 5 marked COMPLETED.
[INFO 09-03 14:31:55] ax.api.client: Trial 4 marked COMPLETED.
[INFO 09-03 14:31:55] ax.api.client: Trial 8 marked COMPLETED.
[INFO 09-03 14:31:55] ax.api.client: Trial 9 marked COMPLETED.
[INFO 09-03 14:31:55] ax.api.client: Trial 1 marked COMPLETED.
[INFO 09-03 14:31:55] ax.api.client: Trial 2 marked COMPLETED.
[INFO 09-03 14:31:55] ax.api.client: Trial 6 marked COMPLETED.
[INFO 09-03 14:31:55] ax.api.client: Trial 3 marked COMPLETED.





────────────────────────────────────────── Iteration 1 / 1  (10 points) ───────────────────────────────────────────



  Acquire UID  eb036af4-9fe7-4ddb-9b22-f863cbfb51a5



┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┓
┃ Event ┃ Suggestion ID ┃  big_r ┃ toroid_focus:toroidMirror01:r ┃    fwhm ┃
┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━┩
│     0 │             0 │ 152983 │                       1162.08 │ 40.4277 │
│     1 │             1 │ 164711 │                       800.432 │ 57.9716 │
│     2 │             2 │ 148659 │                       1556.53 │ 32.6747 │
│     3 │             3 │ 141657 │                       1121.84 │ 11.9509 │
│     4 │             4 │ 156771 │                       1357.92 │ 83.1127 │
│     5 │             5 │ 154537 │                       1022.25 │ 30.5616 │
│     6 │             6 │ 143877 │                       1270.05 │   13.54 │
│     7 │             7 │ 151817 │                       704.768 │ 26.8324 │
│     8 │             8 │ 161539 │                       1464.77 │ 63.8953 │
│     9 │             9 │ 163406 │                       1071.99 │ 59.9384 │
└───────┴───────────────┴────────┴───────────────────────────────┴─────────┘



  fwhm  min: 11.9509  max: 83.1127  mean: 42.0905
  (10 pts sampled)





                                    Summary Statistics                                     
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━┓
┃ Name                          ┃ Type    ┃     Min ┃     Max ┃    Mean ┃     Std ┃ Count ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━┩
│ big_r                         │ param   │  141657 │  164711 │  153996 │ 7867.92 │    10 │
│ toroid_focus:toroidMirror01:r │ param   │ 704.768 │ 1556.53 │ 1153.26 │  271.96 │    10 │
│ fwhm                          │ outcome │ 11.9509 │ 83.1127 │ 42.0905 │ 23.3564 │    10 │
└───────────────────────────────┴─────────┴─────────┴─────────┴─────────┴─────────┴───────┘



────────────────────────────────────────────── Optimization Complete ──────────────────────────────────────────────





('26478513-b015-4e64-a635-f88374beb077',
 'eb036af4-9fe7-4ddb-9b22-f863cbfb51a5')













# Run more iterations
RE(agent.optimize(5, n_points=5))











╭───────────────────────────────────────────────── Optimization ──────────────────────────────────────────────────╮
│ Optimizer  AxOptimizer                                                                                          │
│ Actuators  big_r, toroid_focus:toroidMirror01:r                                                                 │
│ Sensors    screen01                                                                                             │
│ Iterations 5 more (1 completed, 6 total)  Points/iter 5                                                         │
│ Run UID    7ef2b0d0-c123-4d50-b838-e97544a96522                                                                 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯



[INFO 09-03 14:31:59] ax.api.client: Generated new trial 10 with parameters {'big_r': 146009.169628, 'toroid_focus:toroidMirror01:r': 1015.726556} using GenerationNode MBM.
[INFO 09-03 14:31:59] ax.api.client: Generated new trial 11 with parameters {'big_r': 138748.174084, 'toroid_focus:toroidMirror01:r': 1300.360682} using GenerationNode MBM.
[INFO 09-03 14:31:59] ax.api.client: Generated new trial 12 with parameters {'big_r': 137982.843276, 'toroid_focus:toroidMirror01:r': 803.921368} using GenerationNode MBM.
[INFO 09-03 14:31:59] ax.api.client: Generated new trial 13 with parameters {'big_r': 141822.619333, 'toroid_focus:toroidMirror01:r': 1662.07657} using GenerationNode MBM.
[INFO 09-03 14:31:59] ax.api.client: Generated new trial 14 with parameters {'big_r': 167982.843276, 'toroid_focus:toroidMirror01:r': 1662.07657} using GenerationNode MBM.
[INFO 09-03 14:32:12] ax.api.client: Trial 12 marked COMPLETED.
[INFO 09-03 14:32:12] ax.api.client: Trial 11 marked COMPLETED.
[INFO 09-03 14:32:12] ax.api.client: Trial 13 marked COMPLETED.
[INFO 09-03 14:32:12] ax.api.client: Trial 10 marked COMPLETED.
[INFO 09-03 14:32:12] ax.api.client: Trial 14 marked COMPLETED.
[INFO 09-03 14:32:16] ax.api.client: Generated new trial 15 with parameters {'big_r': 137982.843276, 'toroid_focus:toroidMirror01:r': 1662.07657} using GenerationNode MBM.
[INFO 09-03 14:32:16] ax.api.client: Generated new trial 16 with parameters {'big_r': 142648.037445, 'toroid_focus:toroidMirror01:r': 1508.462386} using GenerationNode MBM.
[INFO 09-03 14:32:16] ax.api.client: Generated new trial 17 with parameters {'big_r': 142841.862454, 'toroid_focus:toroidMirror01:r': 662.07657} using GenerationNode MBM.
[INFO 09-03 14:32:16] ax.api.client: Generated new trial 18 with parameters {'big_r': 137982.843276, 'toroid_focus:toroidMirror01:r': 1064.468576} using GenerationNode MBM.
[INFO 09-03 14:32:16] ax.api.client: Generated new trial 19 with parameters {'big_r': 144237.846246, 'toroid_focus:toroidMirror01:r': 1662.07657} using GenerationNode MBM.
[INFO 09-03 14:32:28] ax.api.client: Trial 19 marked COMPLETED.
[INFO 09-03 14:32:28] ax.api.client: Trial 17 marked COMPLETED.
[INFO 09-03 14:32:28] ax.api.client: Trial 16 marked COMPLETED.
[INFO 09-03 14:32:28] ax.api.client: Trial 15 marked COMPLETED.
[INFO 09-03 14:32:28] ax.api.client: Trial 18 marked COMPLETED.
[INFO 09-03 14:32:34] ax.api.client: Generated new trial 20 with parameters {'big_r': 142248.383551, 'toroid_focus:toroidMirror01:r': 903.61824} using GenerationNode MBM.
[INFO 09-03 14:32:34] ax.api.client: Generated new trial 21 with parameters {'big_r': 157223.961105, 'toroid_focus:toroidMirror01:r': 662.07657} using GenerationNode MBM.
[INFO 09-03 14:32:34] ax.api.client: Generated new trial 22 with parameters {'big_r': 141218.768654, 'toroid_focus:toroidMirror01:r': 1460.107855} using GenerationNode MBM.
[INFO 09-03 14:32:34] ax.api.client: Generated new trial 23 with parameters {'big_r': 149713.004093, 'toroid_focus:toroidMirror01:r': 934.665514} using GenerationNode MBM.
[INFO 09-03 14:32:34] ax.api.client: Generated new trial 24 with parameters {'big_r': 143376.18587, 'toroid_focus:toroidMirror01:r': 919.800778} using GenerationNode MBM.
[INFO 09-03 14:32:47] ax.api.client: Trial 22 marked COMPLETED.
[INFO 09-03 14:32:47] ax.api.client: Trial 20 marked COMPLETED.
[INFO 09-03 14:32:47] ax.api.client: Trial 24 marked COMPLETED.
[INFO 09-03 14:32:47] ax.api.client: Trial 23 marked COMPLETED.
[INFO 09-03 14:32:47] ax.api.client: Trial 21 marked COMPLETED.
[INFO 09-03 14:32:56] ax.api.client: Generated new trial 25 with parameters {'big_r': 141662.224899, 'toroid_focus:toroidMirror01:r': 1551.108959} using GenerationNode MBM.
[INFO 09-03 14:32:56] ax.api.client: Generated new trial 26 with parameters {'big_r': 141723.527309, 'toroid_focus:toroidMirror01:r': 1317.159832} using GenerationNode MBM.
[INFO 09-03 14:32:56] ax.api.client: Generated new trial 27 with parameters {'big_r': 143319.708005, 'toroid_focus:toroidMirror01:r': 1077.255209} using GenerationNode MBM.
[INFO 09-03 14:32:56] ax.api.client: Generated new trial 28 with parameters {'big_r': 140990.940954, 'toroid_focus:toroidMirror01:r': 1528.240861} using GenerationNode MBM.
[INFO 09-03 14:32:56] ax.api.client: Generated new trial 29 with parameters {'big_r': 142567.866192, 'toroid_focus:toroidMirror01:r': 1662.07657} using GenerationNode MBM.
[INFO 09-03 14:33:08] ax.api.client: Trial 27 marked COMPLETED.
[INFO 09-03 14:33:08] ax.api.client: Trial 29 marked COMPLETED.
[INFO 09-03 14:33:08] ax.api.client: Trial 26 marked COMPLETED.
[INFO 09-03 14:33:08] ax.api.client: Trial 25 marked COMPLETED.
[INFO 09-03 14:33:08] ax.api.client: Trial 28 marked COMPLETED.
[INFO 09-03 14:33:14] ax.api.client: Generated new trial 30 with parameters {'big_r': 141108.685216, 'toroid_focus:toroidMirror01:r': 1314.512846} using GenerationNode MBM.
[INFO 09-03 14:33:14] ax.api.client: Generated new trial 31 with parameters {'big_r': 141588.180113, 'toroid_focus:toroidMirror01:r': 1425.876782} using GenerationNode MBM.
[INFO 09-03 14:33:14] ax.api.client: Generated new trial 32 with parameters {'big_r': 146266.130773, 'toroid_focus:toroidMirror01:r': 773.492559} using GenerationNode MBM.
[INFO 09-03 14:33:14] ax.api.client: Generated new trial 33 with parameters {'big_r': 140904.149291, 'toroid_focus:toroidMirror01:r': 1115.624952} using GenerationNode MBM.
[INFO 09-03 14:33:14] ax.api.client: Generated new trial 34 with parameters {'big_r': 140822.578331, 'toroid_focus:toroidMirror01:r': 1420.085883} using GenerationNode MBM.
[INFO 09-03 14:33:26] ax.api.client: Trial 34 marked COMPLETED.
[INFO 09-03 14:33:26] ax.api.client: Trial 33 marked COMPLETED.
[INFO 09-03 14:33:26] ax.api.client: Trial 30 marked COMPLETED.
[INFO 09-03 14:33:26] ax.api.client: Trial 31 marked COMPLETED.
[INFO 09-03 14:33:26] ax.api.client: Trial 32 marked COMPLETED.





─────────────────────────────────────────── Iteration 2 / 6  (5 points) ───────────────────────────────────────────



  Acquire UID  b2bf0259-8709-48c6-9274-6249494b14c3



┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┓
┃ Event ┃ Suggestion ID ┃  big_r ┃ toroid_focus:toroidMirror01:r ┃    fwhm ┃
┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━┩
│     0 │            10 │ 146009 │                       1015.73 │  14.065 │
│     1 │            11 │ 138748 │                       1300.36 │ 15.0393 │
│     2 │            12 │ 137983 │                       803.921 │ 20.0548 │
│     3 │            13 │ 141823 │                       1662.08 │   12.15 │
│     4 │            14 │ 167983 │                       1662.08 │ 60.6699 │
└───────┴───────────────┴────────┴───────────────────────────────┴─────────┘



  fwhm  min: 11.9509  max: 83.1127  mean: 36.1923
  (15 pts sampled)



─────────────────────────────────────────── Iteration 3 / 6  (5 points) ───────────────────────────────────────────



  Acquire UID  88754285-e9b6-4b2b-ae91-d3abac5af205



┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┓
┃ Event ┃ Suggestion ID ┃  big_r ┃ toroid_focus:toroidMirror01:r ┃    fwhm ┃
┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━┩
│     0 │            15 │ 137983 │                       1662.08 │ 16.9881 │
│     1 │            16 │ 142648 │                       1508.46 │ 12.3308 │
│     2 │            17 │ 142842 │                       662.077 │ 15.5513 │
│     3 │            18 │ 137983 │                       1064.47 │ 17.4857 │
│     4 │            19 │ 144238 │                       1662.08 │ 13.5515 │
└───────┴───────────────┴────────┴───────────────────────────────┴─────────┘



  fwhm  min: 11.9509  max: 83.1127  mean: 30.9396
  (20 pts sampled)



─────────────────────────────────────────── Iteration 4 / 6  (5 points) ───────────────────────────────────────────



  Acquire UID  e4e2ba97-31bf-4733-a864-27f0099b2197



┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┓
┃ Event ┃ Suggestion ID ┃  big_r ┃ toroid_focus:toroidMirror01:r ┃    fwhm ┃
┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━┩
│     0 │            20 │ 142248 │                       903.618 │ 12.2424 │
│     1 │            21 │ 157224 │                       662.077 │  23.579 │
│     2 │            22 │ 141219 │                       1460.11 │ 10.5651 │
│     3 │            23 │ 149713 │                       934.666 │ 17.6522 │
│     4 │            24 │ 143376 │                       919.801 │ 13.9519 │
└───────┴───────────────┴────────┴───────────────────────────────┴─────────┘



  fwhm  min: 10.5651  max: 83.1127  mean: 27.8713
  (25 pts sampled)



─────────────────────────────────────────── Iteration 5 / 6  (5 points) ───────────────────────────────────────────



  Acquire UID  1972a798-2d8e-4356-a3fd-fc1135f395a3



┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┓
┃ Event ┃ Suggestion ID ┃  big_r ┃ toroid_focus:toroidMirror01:r ┃    fwhm ┃
┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━┩
│     0 │            25 │ 141662 │                       1551.11 │ 11.6272 │
│     1 │            26 │ 141724 │                       1317.16 │ 10.8039 │
│     2 │            27 │ 143320 │                       1077.26 │ 12.3882 │
│     3 │            28 │ 140991 │                       1528.24 │ 10.6474 │
│     4 │            29 │ 142568 │                       1662.08 │ 12.0653 │
└───────┴───────────────┴────────┴───────────────────────────────┴─────────┘



  fwhm  min: 10.5651  max: 83.1127  mean: 25.1438
  (30 pts sampled)



─────────────────────────────────────────── Iteration 6 / 6  (5 points) ───────────────────────────────────────────



  Acquire UID  7dce8c05-4b73-42b9-ac58-6f475545bb9d



┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┓
┃ Event ┃ Suggestion ID ┃  big_r ┃ toroid_focus:toroidMirror01:r ┃    fwhm ┃
┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━┩
│     0 │            30 │ 141109 │                       1314.51 │ 10.3436 │
│     1 │            31 │ 141588 │                       1425.88 │ 11.0249 │
│     2 │            32 │ 146266 │                       773.493 │ 20.6537 │
│     3 │            33 │ 140904 │                       1115.62 │ 12.3213 │
│     4 │            34 │ 140823 │                       1420.09 │ 10.5196 │
└───────┴───────────────┴────────┴───────────────────────────────┴─────────┘



  fwhm  min: 10.3436  max: 83.1127  mean: 23.4051
  (35 pts sampled)





                                    Summary Statistics                                     
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━┓
┃ Name                          ┃ Type    ┃     Min ┃     Max ┃    Mean ┃     Std ┃ Count ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━┩
│ big_r                         │ param   │  137983 │  167983 │  146655 │  8247.7 │    35 │
│ toroid_focus:toroidMirror01:r │ param   │ 662.077 │ 1662.08 │ 1217.19 │ 321.014 │    35 │
│ fwhm                          │ outcome │ 10.3436 │ 83.1127 │ 23.4051 │ 18.9311 │    35 │
└───────────────────────────────┴─────────┴─────────┴─────────┴─────────┴─────────┴───────┘



────────────────────────────────────────────── Optimization Complete ──────────────────────────────────────────────





('7ef2b0d0-c123-4d50-b838-e97544a96522',
 'b2bf0259-8709-48c6-9274-6249494b14c3',
 '88754285-e9b6-4b2b-ae91-d3abac5af205',
 'e4e2ba97-31bf-4733-a864-27f0099b2197',
 '1972a798-2d8e-4356-a3fd-fc1135f395a3',
 '7dce8c05-4b73-42b9-ac58-6f475545bb9d')













_ = agent.ax_client.compute_analyses()









[ERROR 09-03 14:33:29] ax.core.experiment: Encountered ValueError Data to attach is empty. while attaching results. Proceeding and returning results fetched without attaching.
[ERROR 09-03 14:33:29] ax.core.experiment: Encountered ValueError Data to attach is empty. while attaching results. Proceeding and returning results fetched without attaching.
[ERROR 09-03 14:33:29] ax.core.experiment: Encountered ValueError Data to attach is empty. while attaching results. Proceeding and returning results fetched without attaching.
[ERROR 09-03 14:33:29] ax.core.experiment: Encountered ValueError Data to attach is empty. while attaching results. Proceeding and returning results fetched without attaching.
[ERROR 09-03 14:33:29] ax.core.experiment: Encountered ValueError Data to attach is empty. while attaching results. Proceeding and returning results fetched without attaching.
[ERROR 09-03 14:33:29] ax.core.experiment: Encountered ValueError Data to attach is empty. while attaching results. Proceeding and returning results fetched without attaching.
[ERROR 09-03 14:33:29] ax.core.experiment: Encountered ValueError Data to attach is empty. while attaching results. Proceeding and returning results fetched without attaching.









    

        
        Overview of the Entire Optimization Process 
        
        

        This analysis provides an overview of the entire optimization process. It includes visualizations of the results obtained so far, insights into the parameter and metric relationships learned by the Ax model, diagnostics such as model fit, and health checks to assess the overall health of the experiment.
        

    

    

        



    

        
        Results Analysis
        
        

        Result Analyses provide a high-level overview of the results of the optimization process so far with respect to the metrics specified in experiment design.
        

    

    

        



    

        
        Metric Effects: Predicted and observed effects for all arms in the experiment
        
        

        These pair of plots visualize the metric effects for each arm, with the Ax model predictions on the left and the raw observed data on the right. The predicted effects apply shrinkage for noise and adjust for non-stationarity in the data, so they are more representative of the reproducible effects that will manifest in a long-term validation experiment. 
        

    

    

        



    

        
        Metric Effects Pair for fwhm
        
        

        
        

    

    

        



    



    

        Modeled Arm Effects on fwhm
        
Modeled effects on fwhm. This plot visualizes predictions of the true metric changes for each arm based on Ax's model. This is the expected delta you would expect if you (re-)ran that arm. This plot helps in anticipating the outcomes and performance of arms based on the model's predictions. Note, flat predictions across arms indicate that the model predicts that there is no effect, meaning if you were to re-run the experiment, the delta you would see would be small and fall within the confidence interval indicated in the plot.

        
    

    

        




    
                            
                    




    








    

        Observed Arm Effects on fwhm
        
Observed effects on fwhm. This plot visualizes the effects from previously-run arms on a specific metric, providing insights into their performance. This plot allows one to compare and contrast the effectiveness of different arms, highlighting which configurations have yielded the most favorable outcomes. 

        
    

    

        




    
                            
                    




    








    




    








    



    

        Utility Progression
        
Shows the best fwhm value achieved so far across completed trials (objective is to minimize). The x-axis shows trial index. Only completed or early-stopped trials with complete metric data are included, so there may be gaps if some trials failed, were abandoned, or have incomplete data. The y-axis shows cumulative best utility. Only improvements are plotted, so flat segments indicate trials that didn't surpass the previous best. Infeasible trials (violating outcome constraints) don't contribute to the improvements.

        
    

    

        




    
                            
                    




    








    

        Best Trial for Experiment
        
Displays the trial with the best objective value based on raw observations. This reflects actual measured performance during execution. This trial achieved the optimal objective value and represents the recommended configuration for your optimization goal. Only considering COMPLETED trials.

        
    

    

        

  
    

      
      trial_index
      arm_name
      trial_status
      generation_node
      fwhm
      big_r
      toroid_focus:toroidMirror01:r
    

  
  
    

      0
      30
      30_0
      COMPLETED
      MBM
      10.343617
      141108.685216
      1314.512846
    

  


    








    

        Summary for xrt-blop-demo
        
High-level summary of the `Trial`-s in this `Experiment`

        
    

    

        

  
    

      
      trial_index
      arm_name
      trial_status
      generation_node
      fwhm
      big_r
      toroid_focus:toroidMirror01:r
    

  
  
    

      0
      0
      0_0
      COMPLETED
      CenterOfSearchSpace
      40.427716
      152982.843276
      1162.076570
    

    

      1
      1
      1_0
      COMPLETED
      Sobol
      57.971580
      164710.902329
      800.432093
    

    

      2
      2
      2_0
      COMPLETED
      Sobol
      32.674736
      148659.170987
      1556.533148
    

    

      3
      3
      3_0
      COMPLETED
      Sobol
      11.950935
      141657.244963
      1121.838348
    

    

      4
      4
      4_0
      COMPLETED
      Sobol
      83.112749
      156771.418051
      1357.918187
    

    

      5
      5
      5_0
      COMPLETED
      Sobol
      30.561611
      154536.708118
      1022.251129
    

    

      6
      6
      6_0
      COMPLETED
      Sobol
      13.539995
      143877.421347
      1270.051208
    

    

      7
      7
      7_0
      COMPLETED
      Sobol
      26.832404
      151816.904144
      704.767817
    

    

      8
      8
      8_0
      COMPLETED
      Sobol
      63.895277
      161538.634700
      1464.773748
    

    

      9
      9
      9_0
      COMPLETED
      Sobol
      59.938416
      163406.317885
      1071.989694
    

    

      10
      10
      10_0
      COMPLETED
      MBM
      14.065019
      146009.169628
      1015.726556
    

    

      11
      11
      11_0
      COMPLETED
      MBM
      15.039348
      138748.174084
      1300.360682
    

    

      12
      12
      12_0
      COMPLETED
      MBM
      20.054826
      137982.843276
      803.921368
    

    

      13
      13
      13_0
      COMPLETED
      MBM
      12.150049
      141822.619333
      1662.076570
    

    

      14
      14
      14_0
      COMPLETED
      MBM
      60.669861
      167982.843276
      1662.076570
    

    

      15
      15
      15_0
      COMPLETED
      MBM
      16.988110
      137982.843276
      1662.076570
    

    

      16
      16
      16_0
      COMPLETED
      MBM
      12.330771
      142648.037445
      1508.462386
    

    

      17
      17
      17_0
      COMPLETED
      MBM
      15.551321
      142841.862454
      662.076570
    

    

      18
      18
      18_0
      COMPLETED
      MBM
      17.485660
      137982.843276
      1064.468576
    

    

      19
      19
      19_0
      COMPLETED
      MBM
      13.551492
      144237.846246
      1662.076570
    

    

      20
      20
      20_0
      COMPLETED
      MBM
      12.242423
      142248.383551
      903.618240
    

    

      21
      21
      21_0
      COMPLETED
      MBM
      23.578967
      157223.961105
      662.076570
    

    

      22
      22
      22_0
      COMPLETED
      MBM
      10.565070
      141218.768654
      1460.107855
    

    

      23
      23
      23_0
      COMPLETED
      MBM
      17.652154
      149713.004093
      934.665514
    

    

      24
      24
      24_0
      COMPLETED
      MBM
      13.951852
      143376.185870
      919.800778
    

    

      25
      25
      25_0
      COMPLETED
      MBM
      11.627205
      141662.224899
      1551.108959
    

    

      26
      26
      26_0
      COMPLETED
      MBM
      10.803885
      141723.527309
      1317.159832
    

    

      27
      27
      27_0
      COMPLETED
      MBM
      12.388242
      143319.708005
      1077.255209
    

    

      28
      28
      28_0
      COMPLETED
      MBM
      10.647417
      140990.940954
      1528.240861
    

    

      29
      29
      29_0
      COMPLETED
      MBM
      12.065270
      142567.866192
      1662.076570
    

    

      30
      30
      30_0
      COMPLETED
      MBM
      10.343617
      141108.685216
      1314.512846
    

    

      31
      31
      31_0
      COMPLETED
      MBM
      11.024912
      141588.180113
      1425.876782
    

    

      32
      32
      32_0
      COMPLETED
      MBM
      20.653706
      146266.130773
      773.492559
    

    

      33
      33
      33_0
      COMPLETED
      MBM
      12.321307
      140904.149291
      1115.624952
    

    

      34
      34
      34_0
      COMPLETED
      MBM
      10.519576
      140822.578331
      1420.085883
    

  


    








    








    

        
        Insights Analysis
        
        

        Insight Analyses display information to help understand the underlying experiment i.e parameter and metric relationships learned by the Ax model.Use this information to better understand your experiment space and users.
        

    

    

        



    

        
        Top Surfaces Analysis: Parameter sensitivity, slice, and contour plots
        
        

        The top surfaces analysis displays three analyses in one. First, it shows parameter sensitivities, which shows the sensitivity of the metrics in the experiment to the most important parameters. Subsetting to only the most important parameters, it then shows slice plots and contour plots for each metric in the experiment, displaying the relationship between the metric and the most important parameters. 
        

    

    

        



    



    

        Sensitivity Analysis for fwhm
        
Understand how each parameter affects fwhm according to a second-order sensitivity analysis.

        
    

    

        




    
                            
                    




    












    

        
        Slice Plots: Metric effects by parameter value
        
        

        These plots show the relationship between a metric and a parameter. They show the predicted values of the metric on the y-axis as a function of the parameter on the x-axis while keeping all other parameters fixed at their status_quo value (if available), best trial value, or the center of the search space.
        

    

    

        



    



    

        fwhm vs. big_r
        
The slice plot provides a one-dimensional view of predicted outcomes for fwhm as a function of a single parameter, while keeping all other parameters fixed at their best trial value (Arm 22_0). This visualization helps in understanding the sensitivity and impact of changes in the selected parameter on the predicted metric outcomes.

        
    

    

        




    
                            
                    




    








    

        fwhm vs. toroid_focus:toroidMirror01:r
        
The slice plot provides a one-dimensional view of predicted outcomes for fwhm as a function of a single parameter, while keeping all other parameters fixed at their best trial value (Arm 22_0). This visualization helps in understanding the sensitivity and impact of changes in the selected parameter on the predicted metric outcomes.

        
    

    

        




    
                            
                    




    








    








    

        
        Contour Plots: Metric effects by parameter values
        
        

        These plots show the relationship between a metric and two parameters. They show the predicted values of the metric (indicated by color) as a function of the two parameters on the x- and y-axes while keeping all other parameters fixed at their status_quo value (if available), best trial value, or the center of the search space.
        

    

    

        



    



    

        fwhm (Mean) vs. big_r, toroid_focus:toroidMirror01:r
        
The contour plot visualizes the predicted outcomes for fwhm across a two-dimensional parameter space, with other parameters held fixed at their best trial value (Arm 22_0). This plot helps in identifying regions of optimal performance and understanding how changes in the selected parameters influence the predicted outcomes. Contour lines represent levels of constant predicted values, providing insights into the gradient and potential optima within the parameter space.

        
    

    

        




    
                            
                    




    








    




    




    








    

        
        Diagnostic Analysis
        
        

        Diagnostic Analyses provide information about the optimization process and the quality of the model fit. You can use this information to understand if the experimental design should be adjusted to improve optimization quality.
        

    

    

        



    

        
        Cross Validation: Assessing model fit
        
        

        Cross-validation plots display the model fit for each metric in the experiment. The model is trained on a subset of the data and then predicts the outcome for the remaining subset. The plots show the predicted outcome for the validation set on the y-axis against its actual value on the x-axis. Points that align closely with the dotted diagonal line indicate a strong model fit, signifying accurate predictions. Additionally, the plots include confidence intervals that provide insight into the noise in observations and the uncertainty in model predictions. 

NOTE: A horizontal, flat line of predictions indicates that the model has not picked up on sufficient signal in the data, and instead is just predicting the mean.
        

    

    

        



    



    

        Cross Validation for fwhm (R² = 0.94)
        
The cross-validation plot displays the model fit for each metric in the experiment. It employs a leave-one-out approach, where the model is trained on all data except one sample, which is used for validation. The plot shows the predicted outcome for the validation set on the y-axis against its actual value on the x-axis. Points that align closely with the dotted diagonal line indicate a strong model fit, signifying accurate predictions. Additionally, the plot includes 95% confidence intervals that provide insight into the noise in observations and the uncertainty in model predictions. A horizontal, flat line of predictions indicates that the model has not picked up on sufficient signal in the data, and instead is just predicting the mean.

        
    

    

        




    
                            
                    




    








    

        Summary of model fits
        
R² (coefficient of determination) measures how well the model predicts each metric. Higher values indicate better model fit. Metrics with R² >= 0.1 are highlighted in green.

        
    

    

        




    
                            
                    




    








    








    



    

        Generation Strategy Graph
        
GenerationStrategy: Center+Sobol+MBM:fast

Visualize the structure of a GenerationStrategy as a directed graph. Each node
represents a GenerationNode in the strategy, and edges represent transitions
between nodes based on TransitionCriterion. Edge labels show the criterion
class names that trigger the transition.


        
    

    

        
b'\n\n\n\n\n\nGenerationStrategy\n\n\n\nCenterOfSearchSpace\n\nCenterOfSearchSpace\n()\n\n\n\nSobol\n\nSobol\n\n\n\nCenterOfSearchSpace->Sobol\n\n\nAutoTransitionAfterGen\n\n\n\nMBM\n\nMBM\n(BoTorch)\n\n\n\nSobol->MBM\n\n\nMinTrials(5)\nMinTrials(2)\n\n\n\n'

    








    








    

        
        Health Checks
        
        

        Comprehensive health checks designed to identify potential issues in the Ax experiment. These checks cover areas such as metric fetching, search space configuration, and candidate generation, with the aim of flagging areas where user intervention may be necessary to ensure the experiment's robustness and success.
        

    

    

        



    



    

        Baseline Improvement Healthcheck
        
All 1 objective(s) improved over baseline.

**Metric `fwhm` improved 74.41%** from `40.43` in arm `'0_0'` to `10.34` in arm `'30_0'`.

**Note:** Using the first trial's first arm ('0_0') as the baseline since no explicit baseline was provided.

        
    

    

        

  
    

      
      Metric
      Status
      Details
    

  
  
    

      0
      fwhm
      Improved
      **Metric `fwhm` improved 74.41%** from `40.43` in arm `'0_0'` to `10.34` in arm `'30_0'`.
    

  


    








    




    












agent.ax_client.summarize()













  
    

      
      trial_index
      arm_name
      trial_status
      generation_node
      fwhm
      big_r
      toroid_focus:toroidMirror01:r
    

  
  
    

      0
      0
      0_0
      COMPLETED
      CenterOfSearchSpace
      40.427716
      152982.843276
      1162.076570
    

    

      1
      1
      1_0
      COMPLETED
      Sobol
      57.971580
      164710.902329
      800.432093
    

    

      2
      2
      2_0
      COMPLETED
      Sobol
      32.674736
      148659.170987
      1556.533148
    

    

      3
      3
      3_0
      COMPLETED
      Sobol
      11.950935
      141657.244963
      1121.838348
    

    

      4
      4
      4_0
      COMPLETED
      Sobol
      83.112749
      156771.418051
      1357.918187
    

    

      5
      5
      5_0
      COMPLETED
      Sobol
      30.561611
      154536.708118
      1022.251129
    

    

      6
      6
      6_0
      COMPLETED
      Sobol
      13.539995
      143877.421347
      1270.051208
    

    

      7
      7
      7_0
      COMPLETED
      Sobol
      26.832404
      151816.904144
      704.767817
    

    

      8
      8
      8_0
      COMPLETED
      Sobol
      63.895277
      161538.634700
      1464.773748
    

    

      9
      9
      9_0
      COMPLETED
      Sobol
      59.938416
      163406.317885
      1071.989694
    

    

      10
      10
      10_0
      COMPLETED
      MBM
      14.065019
      146009.169628
      1015.726556
    

    

      11
      11
      11_0
      COMPLETED
      MBM
      15.039348
      138748.174084
      1300.360682
    

    

      12
      12
      12_0
      COMPLETED
      MBM
      20.054826
      137982.843276
      803.921368
    

    

      13
      13
      13_0
      COMPLETED
      MBM
      12.150049
      141822.619333
      1662.076570
    

    

      14
      14
      14_0
      COMPLETED
      MBM
      60.669861
      167982.843276
      1662.076570
    

    

      15
      15
      15_0
      COMPLETED
      MBM
      16.988110
      137982.843276
      1662.076570
    

    

      16
      16
      16_0
      COMPLETED
      MBM
      12.330771
      142648.037445
      1508.462386
    

    

      17
      17
      17_0
      COMPLETED
      MBM
      15.551321
      142841.862454
      662.076570
    

    

      18
      18
      18_0
      COMPLETED
      MBM
      17.485660
      137982.843276
      1064.468576
    

    

      19
      19
      19_0
      COMPLETED
      MBM
      13.551492
      144237.846246
      1662.076570
    

    

      20
      20
      20_0
      COMPLETED
      MBM
      12.242423
      142248.383551
      903.618240
    

    

      21
      21
      21_0
      COMPLETED
      MBM
      23.578967
      157223.961105
      662.076570
    

    

      22
      22
      22_0
      COMPLETED
      MBM
      10.565070
      141218.768654
      1460.107855
    

    

      23
      23
      23_0
      COMPLETED
      MBM
      17.652154
      149713.004093
      934.665514
    

    

      24
      24
      24_0
      COMPLETED
      MBM
      13.951852
      143376.185870
      919.800778
    

    

      25
      25
      25_0
      COMPLETED
      MBM
      11.627205
      141662.224899
      1551.108959
    

    

      26
      26
      26_0
      COMPLETED
      MBM
      10.803885
      141723.527309
      1317.159832
    

    

      27
      27
      27_0
      COMPLETED
      MBM
      12.388242
      143319.708005
      1077.255209
    

    

      28
      28
      28_0
      COMPLETED
      MBM
      10.647417
      140990.940954
      1528.240861
    

    

      29
      29
      29_0
      COMPLETED
      MBM
      12.065270
      142567.866192
      1662.076570
    

    

      30
      30
      30_0
      COMPLETED
      MBM
      10.343617
      141108.685216
      1314.512846
    

    

      31
      31
      31_0
      COMPLETED
      MBM
      11.024912
      141588.180113
      1425.876782
    

    

      32
      32
      32_0
      COMPLETED
      MBM
      20.653706
      146266.130773
      773.492559
    

    

      33
      33
      33_0
      COMPLETED
      MBM
      12.321307
      140904.149291
      1115.624952
    

    

      34
      34
      34_0
      COMPLETED
      MBM
      10.519576
      140822.578331
      1420.085883
    

  











optimal_parameters, metrics, _, _ = agent.ax_client.get_best_parameterization(use_model_predictions=False)
optimal_parameters









{'big_r': 141108.68521567257,
 'toroid_focus:toroidMirror01:r': 1314.5128457729918}













from bluesky.plans import list_scan

uid = RE(
    list_scan(
        [screen],
        toro_r,
        [optimal_parameters[toro_r.name]],
        toro_R,
        [optimal_parameters[toro_R.name]],
    )
)













image = tiled_client[uid[0]][f"primary/{screen.name}"].read().squeeze()
plt.imshow(image)
plt.colorbar()
plt.title("Optimized toroid Mirror Beam")
plt.show()








../_images/0af752f73d114429c64f1548f24b01840a5b2b027adfa7f5e94dad0ecdc4b78d.png