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"

[INFO 08-20 19:39:12] 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 0x7f2f910aaf90>
toroidMirror01 : <blop_sim.devices.xrt.auto_element.InferredDetector object at 0x7f2f910c2ad0>
screen01 : <blop_sim.devices.xrt.auto_element.InferredDetector object at 0x7f2f910c2c10>

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)

Tiled version 0.2.15

0

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

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: str, suggestions: list[dict]) -> list[dict]:
        outcomes = []
        run = self.tiled_client[uid]

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

        # Suggestion IDs stored in start document metadata
        suggestion_ids = [suggestion["_id"] for suggestion in run.metadata["start"]["blop_suggestions"]]

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

            outcome = {
                "_id": sid,
                "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",
    experiment_type="demo",
)

# 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    750c2466-6394-4d78-a128-96d595e55981                                                                 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯



125554 rays of 500000
250323 rays of 500000
375233 rays of 500000
500330 rays of 500000
screen01
center:
[0.0, 30000.0, 1763.2506768118087]





[INFO 08-20 19:39:18] 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 08-20 19:39:18] ax.api.client: Generated new trial 0 with parameters {'big_r': 152982.843276, 'toroid_focus:toroidMirror01:r': 1162.07657} using GenerationNode CenterOfSearchSpace.
[INFO 08-20 19:39:18] ax.api.client: Generated new trial 1 with parameters {'big_r': 162539.770361, 'toroid_focus:toroidMirror01:r': 1480.124332} using GenerationNode Sobol.
[INFO 08-20 19:39:18] ax.api.client: Generated new trial 2 with parameters {'big_r': 147380.4125, 'toroid_focus:toroidMirror01:r': 855.095293} using GenerationNode Sobol.
[INFO 08-20 19:39:18] ax.api.client: Generated new trial 3 with parameters {'big_r': 140414.770171, 'toroid_focus:toroidMirror01:r': 1400.659393} using GenerationNode Sobol.
[INFO 08-20 19:39:18] ax.api.client: Generated new trial 4 with parameters {'big_r': 155500.773498, 'toroid_focus:toroidMirror01:r': 1025.632281} using GenerationNode Sobol.
[INFO 08-20 19:39:18] ax.api.client: Generated new trial 5 with parameters {'big_r': 159191.90837, 'toroid_focus:toroidMirror01:r': 1230.565489} using GenerationNode Sobol.
[INFO 08-20 19:39:18] ax.api.client: Generated new trial 6 with parameters {'big_r': 144226.640267, 'toroid_focus:toroidMirror01:r': 1105.592483} using GenerationNode Sobol.
[INFO 08-20 19:39:18] ax.api.client: Generated new trial 7 with parameters {'big_r': 151308.411518, 'toroid_focus:toroidMirror01:r': 1654.022612} using GenerationNode Sobol.
[INFO 08-20 19:39:18] ax.api.client: Generated new trial 8 with parameters {'big_r': 166112.430468, 'toroid_focus:toroidMirror01:r': 779.051528} using GenerationNode Sobol.
[INFO 08-20 19:39:18] ax.api.client: Generated new trial 9 with parameters {'big_r': 166085.379943, 'toroid_focus:toroidMirror01:r': 1310.043898} using GenerationNode Sobol.
[INFO 08-20 19:39:43] ax.api.client: Trial 4 marked COMPLETED.
[INFO 08-20 19:39:43] ax.api.client: Trial 5 marked COMPLETED.
[INFO 08-20 19:39:43] ax.api.client: Trial 1 marked COMPLETED.
[INFO 08-20 19:39:43] ax.api.client: Trial 9 marked COMPLETED.
[INFO 08-20 19:39:43] ax.api.client: Trial 8 marked COMPLETED.
[INFO 08-20 19:39:43] ax.api.client: Trial 0 marked COMPLETED.
[INFO 08-20 19:39:43] ax.api.client: Trial 0 marked COMPLETED.
[INFO 08-20 19:39:43] ax.api.client: Trial 7 marked COMPLETED.
[INFO 08-20 19:39:43] ax.api.client: Trial 2 marked COMPLETED.
[INFO 08-20 19:39:43] ax.api.client: Trial 6 marked COMPLETED.
[INFO 08-20 19:39:43] ax.api.client: Trial 3 marked COMPLETED.





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



  Acquire UID  f5277af6-0a4f-4111-a3b0-8e57411a35aa



┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┓
┃ Event ┃ Suggestion ID ┃  big_r ┃ toroid_focus:toroidMirror01:r ┃    fwhm ┃
┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━┩
│     0 │             0 │ 152983 │                       1162.08 │ 43.0025 │
│     1 │             1 │ 162540 │                       1480.12 │ 63.6301 │
│     2 │             2 │ 147380 │                       855.095 │ 19.3096 │
│     3 │             3 │ 140415 │                       1400.66 │  11.449 │
│     4 │             4 │ 155501 │                       1025.63 │ 29.4513 │
│     5 │             5 │ 159192 │                       1230.57 │ 66.7957 │
│     6 │             6 │ 144227 │                       1105.59 │ 13.2713 │
│     7 │             7 │ 151308 │                       1654.02 │ 57.5428 │
│     8 │             8 │ 166112 │                       779.052 │ 56.7709 │
│     9 │             9 │ 166085 │                       1310.04 │ 61.6562 │
└───────┴───────────────┴────────┴───────────────────────────────┴─────────┘



  fwhm  min: 11.449  max: 66.7957  mean: 42.2879
  (10 pts sampled)





                                    Summary Statistics                                     
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━┓
┃ Name                          ┃ Type    ┃     Min ┃     Max ┃    Mean ┃     Std ┃ Count ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━┩
│ big_r                         │ param   │  140415 │  166112 │  154574 │ 8967.72 │    10 │
│ toroid_focus:toroidMirror01:r │ param   │ 779.052 │ 1654.02 │ 1200.29 │ 273.826 │    10 │
│ fwhm                          │ outcome │  11.449 │ 66.7957 │ 42.2879 │ 22.0101 │    10 │
└───────────────────────────────┴─────────┴─────────┴─────────┴─────────┴─────────┴───────┘



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





('750c2466-6394-4d78-a128-96d595e55981',
 'f5277af6-0a4f-4111-a3b0-8e57411a35aa')













# 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    4e87d7d4-22db-44d8-a9b3-7ec48b195c8d                                                                 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯



[INFO 08-20 19:39:48] ax.api.client: Generated new trial 10 with parameters {'big_r': 137982.843276, 'toroid_focus:toroidMirror01:r': 1271.399679} using GenerationNode MBM.
[INFO 08-20 19:39:48] ax.api.client: Generated new trial 11 with parameters {'big_r': 137982.843276, 'toroid_focus:toroidMirror01:r': 1525.031893} using GenerationNode MBM.
[INFO 08-20 19:39:48] ax.api.client: Generated new trial 12 with parameters {'big_r': 137982.843276, 'toroid_focus:toroidMirror01:r': 1003.879775} using GenerationNode MBM.
[INFO 08-20 19:39:48] ax.api.client: Generated new trial 13 with parameters {'big_r': 140960.527389, 'toroid_focus:toroidMirror01:r': 1276.291261} using GenerationNode MBM.
[INFO 08-20 19:39:48] ax.api.client: Generated new trial 14 with parameters {'big_r': 137982.843276, 'toroid_focus:toroidMirror01:r': 1369.575757} using GenerationNode MBM.
[INFO 08-20 19:40:00] ax.api.client: Trial 11 marked COMPLETED.
[INFO 08-20 19:40:00] ax.api.client: Trial 14 marked COMPLETED.
[INFO 08-20 19:40:00] ax.api.client: Trial 10 marked COMPLETED.
[INFO 08-20 19:40:00] ax.api.client: Trial 12 marked COMPLETED.
[INFO 08-20 19:40:00] ax.api.client: Trial 13 marked COMPLETED.
[INFO 08-20 19:40:05] ax.api.client: Generated new trial 15 with parameters {'big_r': 154932.039984, 'toroid_focus:toroidMirror01:r': 686.911924} using GenerationNode MBM.
[INFO 08-20 19:40:05] ax.api.client: Generated new trial 16 with parameters {'big_r': 141889.794116, 'toroid_focus:toroidMirror01:r': 1662.07657} using GenerationNode MBM.
[INFO 08-20 19:40:05] ax.api.client: Generated new trial 17 with parameters {'big_r': 139672.486537, 'toroid_focus:toroidMirror01:r': 662.07657} using GenerationNode MBM.
[INFO 08-20 19:40:05] ax.api.client: Generated new trial 18 with parameters {'big_r': 153638.691065, 'toroid_focus:toroidMirror01:r': 770.758054} using GenerationNode MBM.
[INFO 08-20 19:40:05] ax.api.client: Generated new trial 19 with parameters {'big_r': 156451.933043, 'toroid_focus:toroidMirror01:r': 662.07657} using GenerationNode MBM.
[INFO 08-20 19:40:16] ax.api.client: Trial 17 marked COMPLETED.
[INFO 08-20 19:40:16] ax.api.client: Trial 15 marked COMPLETED.
[INFO 08-20 19:40:16] ax.api.client: Trial 19 marked COMPLETED.
[INFO 08-20 19:40:16] ax.api.client: Trial 18 marked COMPLETED.
[INFO 08-20 19:40:16] ax.api.client: Trial 16 marked COMPLETED.
[INFO 08-20 19:40:19] ax.api.client: Generated new trial 20 with parameters {'big_r': 144799.915664, 'toroid_focus:toroidMirror01:r': 1662.07657} using GenerationNode MBM.
[INFO 08-20 19:40:19] ax.api.client: Generated new trial 21 with parameters {'big_r': 144847.020785, 'toroid_focus:toroidMirror01:r': 662.07657} using GenerationNode MBM.
[INFO 08-20 19:40:19] ax.api.client: Generated new trial 22 with parameters {'big_r': 142303.009638, 'toroid_focus:toroidMirror01:r': 1345.292704} using GenerationNode MBM.
[INFO 08-20 19:40:19] ax.api.client: Generated new trial 23 with parameters {'big_r': 140567.326155, 'toroid_focus:toroidMirror01:r': 1662.07657} using GenerationNode MBM.
[INFO 08-20 19:40:19] ax.api.client: Generated new trial 24 with parameters {'big_r': 143484.807198, 'toroid_focus:toroidMirror01:r': 1662.07657} using GenerationNode MBM.
[INFO 08-20 19:40:30] ax.api.client: Trial 23 marked COMPLETED.
[INFO 08-20 19:40:30] ax.api.client: Trial 24 marked COMPLETED.
[INFO 08-20 19:40:30] ax.api.client: Trial 20 marked COMPLETED.
[INFO 08-20 19:40:30] ax.api.client: Trial 21 marked COMPLETED.
[INFO 08-20 19:40:30] ax.api.client: Trial 22 marked COMPLETED.
[INFO 08-20 19:40:36] ax.api.client: Generated new trial 25 with parameters {'big_r': 141450.995074, 'toroid_focus:toroidMirror01:r': 1533.321095} using GenerationNode MBM.
[INFO 08-20 19:40:36] ax.api.client: Generated new trial 26 with parameters {'big_r': 141216.713909, 'toroid_focus:toroidMirror01:r': 1662.07657} using GenerationNode MBM.
[INFO 08-20 19:40:36] ax.api.client: Generated new trial 27 with parameters {'big_r': 142190.183547, 'toroid_focus:toroidMirror01:r': 1523.308668} using GenerationNode MBM.
[INFO 08-20 19:40:36] ax.api.client: Generated new trial 28 with parameters {'big_r': 140931.270977, 'toroid_focus:toroidMirror01:r': 1490.812908} using GenerationNode MBM.
[INFO 08-20 19:40:36] ax.api.client: Generated new trial 29 with parameters {'big_r': 144832.729017, 'toroid_focus:toroidMirror01:r': 1274.243737} using GenerationNode MBM.
[INFO 08-20 19:40:48] ax.api.client: Trial 29 marked COMPLETED.
[INFO 08-20 19:40:48] ax.api.client: Trial 25 marked COMPLETED.
[INFO 08-20 19:40:48] ax.api.client: Trial 28 marked COMPLETED.
[INFO 08-20 19:40:48] ax.api.client: Trial 26 marked COMPLETED.
[INFO 08-20 19:40:48] ax.api.client: Trial 27 marked COMPLETED.
/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/linear_operator/utils/cholesky.py:41: NumericalWarning: A not p.d., added jitter of 1.0e-08 to the diagonal
  warnings.warn(
[INFO 08-20 19:40:55] ax.api.client: Generated new trial 30 with parameters {'big_r': 142205.446461, 'toroid_focus:toroidMirror01:r': 1065.221986} using GenerationNode MBM.
[INFO 08-20 19:40:55] ax.api.client: Generated new trial 31 with parameters {'big_r': 142493.32686, 'toroid_focus:toroidMirror01:r': 973.823738} using GenerationNode MBM.
[INFO 08-20 19:40:55] ax.api.client: Generated new trial 32 with parameters {'big_r': 142032.211065, 'toroid_focus:toroidMirror01:r': 1157.999741} using GenerationNode MBM.
[INFO 08-20 19:40:55] ax.api.client: Generated new trial 33 with parameters {'big_r': 167982.843276, 'toroid_focus:toroidMirror01:r': 1662.07657} using GenerationNode MBM.
[INFO 08-20 19:40:55] ax.api.client: Generated new trial 34 with parameters {'big_r': 141563.560829, 'toroid_focus:toroidMirror01:r': 1662.07657} using GenerationNode MBM.
[INFO 08-20 19:41:06] ax.api.client: Trial 34 marked COMPLETED.
[INFO 08-20 19:41:06] ax.api.client: Trial 33 marked COMPLETED.
[INFO 08-20 19:41:06] ax.api.client: Trial 31 marked COMPLETED.
[INFO 08-20 19:41:06] ax.api.client: Trial 30 marked COMPLETED.
[INFO 08-20 19:41:06] ax.api.client: Trial 32 marked COMPLETED.





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



  Acquire UID  5d98fa52-aae4-4afe-9dac-72bd5cc2fafa



┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┓
┃ Event ┃ Suggestion ID ┃  big_r ┃ toroid_focus:toroidMirror01:r ┃    fwhm ┃
┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━┩
│     0 │            10 │ 137983 │                        1271.4 │ 17.5292 │
│     1 │            11 │ 137983 │                       1525.03 │ 17.6245 │
│     2 │            12 │ 137983 │                       1003.88 │  18.775 │
│     3 │            13 │ 140961 │                       1276.29 │ 11.6619 │
│     4 │            14 │ 137983 │                       1369.58 │ 19.0219 │
└───────┴───────────────┴────────┴───────────────────────────────┴─────────┘



  fwhm  min: 11.449  max: 66.7957  mean: 33.8328
  (15 pts sampled)



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



  Acquire UID  b27f2e46-00fb-4f7f-9ce2-ff7772118d22



┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┓
┃ Event ┃ Suggestion ID ┃  big_r ┃ toroid_focus:toroidMirror01:r ┃    fwhm ┃
┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━┩
│     0 │            15 │ 154932 │                       686.912 │ 24.0383 │
│     1 │            16 │ 141890 │                       1662.08 │  10.288 │
│     2 │            17 │ 139672 │                       662.077 │ 20.3677 │
│     3 │            18 │ 153639 │                       770.758 │ 24.9985 │
│     4 │            19 │ 156452 │                       662.077 │ 22.9503 │
└───────┴───────────────┴────────┴───────────────────────────────┴─────────┘



  fwhm  min: 10.288  max: 66.7957  mean: 30.5067
  (20 pts sampled)



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



  Acquire UID  189d9c43-7cc0-49eb-ada3-c761c02e89ee



┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┓
┃ Event ┃ Suggestion ID ┃  big_r ┃ toroid_focus:toroidMirror01:r ┃    fwhm ┃
┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━┩
│     0 │            20 │ 144800 │                       1662.08 │ 15.9071 │
│     1 │            21 │ 144847 │                       662.077 │ 19.4919 │
│     2 │            22 │ 142303 │                       1345.29 │   12.45 │
│     3 │            23 │ 140567 │                       1662.08 │ 10.4939 │
│     4 │            24 │ 143485 │                       1662.08 │ 12.1726 │
└───────┴───────────────┴────────┴───────────────────────────────┴─────────┘



  fwhm  min: 10.288  max: 66.7957  mean: 27.226
  (25 pts sampled)



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



  Acquire UID  c3ca838d-3cd2-44d6-bcbb-892a57fe4da0



┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┓
┃ Event ┃ Suggestion ID ┃  big_r ┃ toroid_focus:toroidMirror01:r ┃    fwhm ┃
┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━┩
│     0 │            25 │ 141451 │                       1533.32 │ 11.8602 │
│     1 │            26 │ 141217 │                       1662.08 │ 9.97626 │
│     2 │            27 │ 142190 │                       1523.31 │ 12.1329 │
│     3 │            28 │ 140931 │                       1490.81 │ 12.8596 │
│     4 │            29 │ 144833 │                       1274.24 │ 15.8617 │
└───────┴───────────────┴────────┴───────────────────────────────┴─────────┘



  fwhm  min: 9.97626  max: 66.7957  mean: 24.778
  (30 pts sampled)



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



  Acquire UID  06dd42ff-1334-404e-ac8a-65878466903d



┏━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┓
┃ Event ┃ Suggestion ID ┃  big_r ┃ toroid_focus:toroidMirror01:r ┃    fwhm ┃
┡━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━┩
│     0 │            30 │ 142205 │                       1065.22 │  10.713 │
│     1 │            31 │ 142493 │                       973.824 │ 12.8823 │
│     2 │            32 │ 142032 │                          1158 │ 8.53279 │
│     3 │            33 │ 167983 │                       1662.08 │ 61.5337 │
│     4 │            34 │ 141564 │                       1662.08 │ 9.95666 │
└───────┴───────────────┴────────┴───────────────────────────────┴─────────┘



  fwhm  min: 8.53279  max: 66.7957  mean: 24.1988
  (35 pts sampled)





                                    Summary Statistics                                     
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━┓
┃ Name                          ┃ Type    ┃     Min ┃     Max ┃    Mean ┃     Std ┃ Count ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━┩
│ big_r                         │ param   │  137983 │  167983 │  147089 │ 8889.91 │    35 │
│ toroid_focus:toroidMirror01:r │ param   │ 662.077 │ 1662.08 │ 1254.04 │ 341.471 │    35 │
│ fwhm                          │ outcome │ 8.53279 │ 66.7957 │ 24.1988 │ 18.4354 │    35 │
└───────────────────────────────┴─────────┴─────────┴─────────┴─────────┴─────────┴───────┘



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





('4e87d7d4-22db-44d8-a9b3-7ec48b195c8d',
 '5d98fa52-aae4-4afe-9dac-72bd5cc2fafa',
 'b27f2e46-00fb-4f7f-9ce2-ff7772118d22',
 '189d9c43-7cc0-49eb-ada3-c761c02e89ee',
 'c3ca838d-3cd2-44d6-bcbb-892a57fe4da0',
 '06dd42ff-1334-404e-ac8a-65878466903d')













_ = agent.ax_client.compute_analyses()









[ERROR 08-20 19:41:09] ax.core.experiment: Encountered ValueError Data to attach is empty. while attaching results. Proceeding and returning results fetched without attaching.
[ERROR 08-20 19:41:09] ax.core.experiment: Encountered ValueError Data to attach is empty. while attaching results. Proceeding and returning results fetched without attaching.
[ERROR 08-20 19:41:09] ax.core.experiment: Encountered ValueError Data to attach is empty. while attaching results. Proceeding and returning results fetched without attaching.
[ERROR 08-20 19:41:09] ax.core.experiment: Encountered ValueError Data to attach is empty. while attaching results. Proceeding and returning results fetched without attaching.
[ERROR 08-20 19:41:09] ax.core.experiment: Encountered ValueError Data to attach is empty. while attaching results. Proceeding and returning results fetched without attaching.
[ERROR 08-20 19:41:09] ax.core.experiment: Encountered ValueError Data to attach is empty. while attaching results. Proceeding and returning results fetched without attaching.
[ERROR 08-20 19:41:09] ax.core.experiment: Encountered ValueError Data to attach is empty. while attaching results. Proceeding and returning results fetched without attaching.
[ERROR 08-20 19:41:09] ax.analysis.analysis: Failed to compute TransferLearningAnalysis
[ERROR 08-20 19:41:09] ax.analysis.analysis: Traceback (most recent call last):
  File "/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/analysis/analysis.py", line 115, in compute_result
    card = self.compute(
        experiment=experiment,
        generation_strategy=generation_strategy,
        adapter=adapter,
    )
  File "/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/analysis/healthcheck/transfer_learning_analysis.py", line 104, in compute
    transferable_experiments = identify_transferable_experiments(
        search_space=experiment.search_space,
    ...<4 lines>...
        experiment_name=experiment.name,
    )
  File "/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/storage/sqa_store/load.py", line 839, in identify_transferable_experiments
    experiments_search_spaces = _query_historical_experiments_given_parameters(
        parameter_names=list(search_space.parameters.keys()),
        experiment_types=experiment_types,
        config=config,
    )
  File "/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/storage/sqa_store/load.py", line 759, in _query_historical_experiments_given_parameters
    with session_scope() as session:
         ~~~~~~~~~~~~~^^
  File "/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/contextlib.py", line 141, in __enter__
    return next(self.gen)
  File "/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/storage/sqa_store/db.py", line 287, in session_scope
    session = get_session()
  File "/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/storage/sqa_store/db.py", line 263, in get_session
    init_engine_and_session_factory()
    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
  File "/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/storage/sqa_store/db.py", line 191, in init_engine_and_session_factory
    raise ValueError("Must specify either `url` or `creator`.")
ValueError: Must specify either `url` or `creator`.









    

        
        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
      32
      32_0
      COMPLETED
      MBM
      8.532785
      142032.211065
      1157.999741
    

  


    








    

        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
      43.002534
      152982.843276
      1162.076570
    

    

      1
      1
      1_0
      COMPLETED
      Sobol
      63.630135
      162539.770361
      1480.124332
    

    

      2
      2
      2_0
      COMPLETED
      Sobol
      19.309558
      147380.412500
      855.095293
    

    

      3
      3
      3_0
      COMPLETED
      Sobol
      11.448977
      140414.770171
      1400.659393
    

    

      4
      4
      4_0
      COMPLETED
      Sobol
      29.451328
      155500.773498
      1025.632281
    

    

      5
      5
      5_0
      COMPLETED
      Sobol
      66.795688
      159191.908370
      1230.565489
    

    

      6
      6
      6_0
      COMPLETED
      Sobol
      13.271337
      144226.640267
      1105.592483
    

    

      7
      7
      7_0
      COMPLETED
      Sobol
      57.542820
      151308.411518
      1654.022612
    

    

      8
      8
      8_0
      COMPLETED
      Sobol
      56.770914
      166112.430468
      779.051528
    

    

      9
      9
      9_0
      COMPLETED
      Sobol
      61.656183
      166085.379943
      1310.043898
    

    

      10
      10
      10_0
      COMPLETED
      MBM
      17.529180
      137982.843276
      1271.399679
    

    

      11
      11
      11_0
      COMPLETED
      MBM
      17.624497
      137982.843276
      1525.031893
    

    

      12
      12
      12_0
      COMPLETED
      MBM
      18.775045
      137982.843276
      1003.879775
    

    

      13
      13
      13_0
      COMPLETED
      MBM
      11.661922
      140960.527389
      1276.291261
    

    

      14
      14
      14_0
      COMPLETED
      MBM
      19.021939
      137982.843276
      1369.575757
    

    

      15
      15
      15_0
      COMPLETED
      MBM
      24.038316
      154932.039984
      686.911924
    

    

      16
      16
      16_0
      COMPLETED
      MBM
      10.288022
      141889.794116
      1662.076570
    

    

      17
      17
      17_0
      COMPLETED
      MBM
      20.367675
      139672.486537
      662.076570
    

    

      18
      18
      18_0
      COMPLETED
      MBM
      24.998488
      153638.691065
      770.758054
    

    

      19
      19
      19_0
      COMPLETED
      MBM
      22.950316
      156451.933043
      662.076570
    

    

      20
      20
      20_0
      COMPLETED
      MBM
      15.907144
      144799.915664
      1662.076570
    

    

      21
      21
      21_0
      COMPLETED
      MBM
      19.491861
      144847.020785
      662.076570
    

    

      22
      22
      22_0
      COMPLETED
      MBM
      12.449982
      142303.009638
      1345.292704
    

    

      23
      23
      23_0
      COMPLETED
      MBM
      10.493899
      140567.326155
      1662.076570
    

    

      24
      24
      24_0
      COMPLETED
      MBM
      12.172618
      143484.807198
      1662.076570
    

    

      25
      25
      25_0
      COMPLETED
      MBM
      11.860185
      141450.995074
      1533.321095
    

    

      26
      26
      26_0
      COMPLETED
      MBM
      9.976261
      141216.713909
      1662.076570
    

    

      27
      27
      27_0
      COMPLETED
      MBM
      12.132904
      142190.183547
      1523.308668
    

    

      28
      28
      28_0
      COMPLETED
      MBM
      12.859569
      140931.270977
      1490.812908
    

    

      29
      29
      29_0
      COMPLETED
      MBM
      15.861714
      144832.729017
      1274.243737
    

    

      30
      30
      30_0
      COMPLETED
      MBM
      10.713027
      142205.446461
      1065.221986
    

    

      31
      31
      31_0
      COMPLETED
      MBM
      12.882335
      142493.326860
      973.823738
    

    

      32
      32
      32_0
      COMPLETED
      MBM
      8.532785
      142032.211065
      1157.999741
    

    

      33
      33
      33_0
      COMPLETED
      MBM
      61.533662
      167982.843276
      1662.076570
    

    

      34
      34
      34_0
      COMPLETED
      MBM
      9.956656
      141563.560829
      1662.076570
    

  


    








    








    

        
        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 26_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 26_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 26_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 = 0.89)
        
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 80.16%** from `43.00` in arm `'0_0'` to `8.53` in arm `'32_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 80.16%** from `43.00` in arm `'0_0'` to `8.53` in arm `'32_0'`.
    

  


    








    

        TransferLearningAnalysis Error
        
ValueError: Must specify either `url` or `creator`.

        
    

    

        
Traceback (most recent call last):
  File "/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/analysis/analysis.py", line 115, in compute_result
    card = self.compute(
        experiment=experiment,
        generation_strategy=generation_strategy,
        adapter=adapter,
    )
  File "/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/analysis/healthcheck/transfer_learning_analysis.py", line 104, in compute
    transferable_experiments = identify_transferable_experiments(
        search_space=experiment.search_space,
    ...<4 lines>...
        experiment_name=experiment.name,
    )
  File "/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/storage/sqa_store/load.py", line 839, in identify_transferable_experiments
    experiments_search_spaces = _query_historical_experiments_given_parameters(
        parameter_names=list(search_space.parameters.keys()),
        experiment_types=experiment_types,
        config=config,
    )
  File "/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/storage/sqa_store/load.py", line 759, in _query_historical_experiments_given_parameters
    with session_scope() as session:
         ~~~~~~~~~~~~~^^
  File "/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/contextlib.py", line 141, in __enter__
    return next(self.gen)
  File "/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/storage/sqa_store/db.py", line 287, in session_scope
    session = get_session()
  File "/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/storage/sqa_store/db.py", line 263, in get_session
    init_engine_and_session_factory()
    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
  File "/home/runner/work/blop/blop/.pixi/envs/docs/lib/python3.13/site-packages/ax/storage/sqa_store/db.py", line 191, in init_engine_and_session_factory
    raise ValueError("Must specify either `url` or `creator`.")
ValueError: Must specify either `url` or `creator`.


    








    




    












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
      43.002534
      152982.843276
      1162.076570
    

    

      1
      1
      1_0
      COMPLETED
      Sobol
      63.630135
      162539.770361
      1480.124332
    

    

      2
      2
      2_0
      COMPLETED
      Sobol
      19.309558
      147380.412500
      855.095293
    

    

      3
      3
      3_0
      COMPLETED
      Sobol
      11.448977
      140414.770171
      1400.659393
    

    

      4
      4
      4_0
      COMPLETED
      Sobol
      29.451328
      155500.773498
      1025.632281
    

    

      5
      5
      5_0
      COMPLETED
      Sobol
      66.795688
      159191.908370
      1230.565489
    

    

      6
      6
      6_0
      COMPLETED
      Sobol
      13.271337
      144226.640267
      1105.592483
    

    

      7
      7
      7_0
      COMPLETED
      Sobol
      57.542820
      151308.411518
      1654.022612
    

    

      8
      8
      8_0
      COMPLETED
      Sobol
      56.770914
      166112.430468
      779.051528
    

    

      9
      9
      9_0
      COMPLETED
      Sobol
      61.656183
      166085.379943
      1310.043898
    

    

      10
      10
      10_0
      COMPLETED
      MBM
      17.529180
      137982.843276
      1271.399679
    

    

      11
      11
      11_0
      COMPLETED
      MBM
      17.624497
      137982.843276
      1525.031893
    

    

      12
      12
      12_0
      COMPLETED
      MBM
      18.775045
      137982.843276
      1003.879775
    

    

      13
      13
      13_0
      COMPLETED
      MBM
      11.661922
      140960.527389
      1276.291261
    

    

      14
      14
      14_0
      COMPLETED
      MBM
      19.021939
      137982.843276
      1369.575757
    

    

      15
      15
      15_0
      COMPLETED
      MBM
      24.038316
      154932.039984
      686.911924
    

    

      16
      16
      16_0
      COMPLETED
      MBM
      10.288022
      141889.794116
      1662.076570
    

    

      17
      17
      17_0
      COMPLETED
      MBM
      20.367675
      139672.486537
      662.076570
    

    

      18
      18
      18_0
      COMPLETED
      MBM
      24.998488
      153638.691065
      770.758054
    

    

      19
      19
      19_0
      COMPLETED
      MBM
      22.950316
      156451.933043
      662.076570
    

    

      20
      20
      20_0
      COMPLETED
      MBM
      15.907144
      144799.915664
      1662.076570
    

    

      21
      21
      21_0
      COMPLETED
      MBM
      19.491861
      144847.020785
      662.076570
    

    

      22
      22
      22_0
      COMPLETED
      MBM
      12.449982
      142303.009638
      1345.292704
    

    

      23
      23
      23_0
      COMPLETED
      MBM
      10.493899
      140567.326155
      1662.076570
    

    

      24
      24
      24_0
      COMPLETED
      MBM
      12.172618
      143484.807198
      1662.076570
    

    

      25
      25
      25_0
      COMPLETED
      MBM
      11.860185
      141450.995074
      1533.321095
    

    

      26
      26
      26_0
      COMPLETED
      MBM
      9.976261
      141216.713909
      1662.076570
    

    

      27
      27
      27_0
      COMPLETED
      MBM
      12.132904
      142190.183547
      1523.308668
    

    

      28
      28
      28_0
      COMPLETED
      MBM
      12.859569
      140931.270977
      1490.812908
    

    

      29
      29
      29_0
      COMPLETED
      MBM
      15.861714
      144832.729017
      1274.243737
    

    

      30
      30
      30_0
      COMPLETED
      MBM
      10.713027
      142205.446461
      1065.221986
    

    

      31
      31
      31_0
      COMPLETED
      MBM
      12.882335
      142493.326860
      973.823738
    

    

      32
      32
      32_0
      COMPLETED
      MBM
      8.532785
      142032.211065
      1157.999741
    

    

      33
      33
      33_0
      COMPLETED
      MBM
      61.533662
      167982.843276
      1662.076570
    

    

      34
      34
      34_0
      COMPLETED
      MBM
      9.956656
      141563.560829
      1662.076570
    

  











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









{'big_r': 142032.2110654863,
 'toroid_focus:toroidMirror01:r': 1157.999740821632}













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/1a407918d5f74defdd7295afe84b73d5ef16691a5631a990242e77a3f169be45.png