.. testsetup:: from unittest.mock import MagicMock from typing import Any import time from bluesky.protocols import NamedMovable, Readable, Status, Hints, HasHints, HasParent from tiled.client.container import Container class AlwaysSuccessfulStatus(Status): def add_callback(self, callback) -> None: callback(self) def exception(self, timeout = 0.0): return None @property def done(self) -> bool: return True @property def success(self) -> bool: return True class ReadableSignal(Readable, HasHints, HasParent): def __init__(self, name: str) -> None: self._name = name self._value = 0.0 @property def name(self) -> str: return self._name @property def hints(self) -> Hints: return { "fields": [self._name], "dimensions": [], "gridding": "rectilinear", } @property def parent(self) -> Any | None: return None def read(self): return { self._name: { "value": self._value, "timestamp": time.time() } } def describe(self): return { self._name: { "source": self._name, "dtype": "number", "shape": [] } } class MovableSignal(ReadableSignal, NamedMovable): def __init__(self, name: str, initial_value: float = 0.0) -> None: super().__init__(name) self._value: float = initial_value def set(self, value: float) -> Status: self._value = value return AlwaysSuccessfulStatus() db = MagicMock(spec=Container) Set DOF constraints =================== This guide will show you how to set DOF constraints to refine the search space of your optimization. These constraints are evaluated by the Ax backend. See the `Ax API documentation `_ for more information. Create DOFs and an objective ---------------------------- .. testcode:: from blop import DOF, Objective motor_x = MovableSignal(name="motor_x") motor_y = MovableSignal(name="motor_y") motor_z = MovableSignal(name="motor_z") dof1 = DOF(movable=motor_x, search_domain=(0, 1000)) dof2 = DOF(movable=motor_y, search_domain=(0, 1000)) dof3 = DOF(movable=motor_z, search_domain=(0, 1000)) objective = Objective(name="objective1", target="max") Set a linear constraint ----------------------- Constraints are specified as strings that are templated and evaluated for you. .. testcode:: from blop import DOFConstraint constraint = DOFConstraint(constraint="5 * x1 + 2 * x2 <= 4 * x3", x1=motor_x, x2=motor_y, x3=motor_z) Configure an agent with DOF constraints --------------------------------------- .. testcode:: from blop.ax import Agent agent = Agent( readables=[], dofs=[dof1, dof2], objectives=[objective], db=db, dof_constraints=[constraint], )