{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0",
   "metadata": {},
   "source": [
    "# KB Mirrors\n",
    "\n",
    "This example simulates the alignment of a KB mirror endstation (with four degrees of freedom)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from blop.utils import prepare_re_env  # noqa\n",
    "\n",
    "%run -i $prepare_re_env.__file__ --db-type=temp\n",
    "bec.disable_plots()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from blop.sim import Beamline\n",
    "\n",
    "beamline = Beamline(name=\"bl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from blop import DOF, Agent, Objective\n",
    "from blop.digestion import beam_stats_digestion\n",
    "\n",
    "dofs = [\n",
    "    DOF(description=\"KBV downstream\", device=beamline.kbv_dsv, search_domain=(-5.0, 5.0)),\n",
    "    DOF(description=\"KBV upstream\", device=beamline.kbv_usv, search_domain=(-5.0, 5.0)),\n",
    "    DOF(description=\"KBH downstream\", device=beamline.kbh_dsh, search_domain=(-5.0, 5.0)),\n",
    "    DOF(description=\"KBH upstream\", device=beamline.kbh_ush, search_domain=(-5.0, 5.0)),\n",
    "]\n",
    "\n",
    "objectives = [\n",
    "    Objective(name=\"bl_det_sum\", target=\"max\", transform=\"log\", trust_domain=(200, np.inf)),\n",
    "    Objective(name=\"bl_det_wid_x\", target=\"min\", transform=\"log\", latent_groups=[(\"bl_kbh_dsh\", \"bl_kbh_ush\")]),\n",
    "    Objective(name=\"bl_det_wid_y\", target=\"min\", transform=\"log\", latent_groups=[(\"bl_kbv_dsv\", \"bl_kbv_usv\")]),\n",
    "]\n",
    "\n",
    "agent = Agent(\n",
    "    dofs=dofs,\n",
    "    objectives=objectives,\n",
    "    detectors=[beamline.det],\n",
    "    digestion=beam_stats_digestion,\n",
    "    digestion_kwargs={\"image_key\": \"bl_det_image\"},\n",
    "    verbose=True,\n",
    "    db=db,\n",
    "    tolerate_acquisition_errors=False,\n",
    "    enforce_all_objectives_valid=True,\n",
    "    train_every=3,\n",
    ")\n",
    "\n",
    "(uid,) = RE(agent.learn(\"qr\", n=32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4",
   "metadata": {},
   "outputs": [],
   "source": [
    "RE(agent.learn(\"qei\", n=4, iterations=4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.imshow(agent.best.bl_det_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6",
   "metadata": {},
   "outputs": [],
   "source": [
    "agent.plot_objectives(axes=(2, 3))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "blop-dev",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}