{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0",
   "metadata": {},
   "source": [
    "# XRT Blop Demo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1",
   "metadata": {},
   "source": [
    "For ophyd beamline setup see: \n",
    "- https://github.com/NSLS-II/blop/blob/main/src/blop/sim/xrt_beamline.py\n",
    "- https://github.com/NSLS-II/blop/blob/main/src/blop/sim/xrt_kb_model.py\n",
    "\n",
    "The picture below displays beam from geometric source propagating through a pair of toroidal mirrors focusing the beam on screen. Simulation of a KB setup.\n",
    "\n",
    "![xrt_blop_layout_w.jpg](../_static/xrt_blop_layout_w.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from blop.utils import prepare_re_env  # noqa\n",
    "%run -i $prepare_re_env.__file__ --db-type=temp\n",
    "bec.disable_plots()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys, os\n",
    "from matplotlib import pyplot as plt\n",
    "from blop.sim.xrt_beamline import Beamline\n",
    "\n",
    "from blop import DOF, Objective, Agent\n",
    "from blop.digestion import beam_stats_digestion\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.ion()\n",
    "\n",
    "h_opt = 0\n",
    "dh = 5\n",
    "\n",
    "R1, dR1 = 40000, 10000\n",
    "R2, dR2 = 20000, 10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5",
   "metadata": {},
   "outputs": [],
   "source": [
    "beamline = Beamline(name=\"bl\")\n",
    "time.sleep(1)\n",
    "dofs = [\n",
    "    DOF(description=\"KBV R\",\n",
    "        device=beamline.kbv_dsv,\n",
    "        search_domain=(R1-dR1, R1+dR1)),\n",
    "    DOF(description=\"KBH R\",\n",
    "        device=beamline.kbh_dsh,\n",
    "        search_domain=(R2-dR2, R2+dR2)),\n",
    "\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6",
   "metadata": {},
   "outputs": [],
   "source": [
    "objectives = [\n",
    "    Objective(name=\"bl_det_sum\", \n",
    "              target=\"max\",\n",
    "              transform=\"log\",\n",
    "              trust_domain=(20, 1e12)),\n",
    "\n",
    "    Objective(name=\"bl_det_wid_x\",\n",
    "              target=\"min\",\n",
    "              transform=\"log\",\n",
    "              # trust_domain=(0, 1e12),\n",
    "              latent_groups=[(\"bl_kbh_dsh\", \"bl_kbv_dsv\")]),\n",
    "    Objective(name=\"bl_det_wid_y\",\n",
    "              target=\"min\",\n",
    "              transform=\"log\",\n",
    "              # trust_domain=(0, 1e12),\n",
    "              latent_groups=[(\"bl_kbh_dsh\", \"bl_kbv_dsv\")]),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7",
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8",
   "metadata": {},
   "outputs": [],
   "source": [
    "RE(agent.learn(\"qr\", n=16))\n",
    "RE(agent.learn(\"qei\", n=16, iterations=4))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9",
   "metadata": {},
   "outputs": [],
   "source": [
    "agent.plot_objectives(axes=(0, 1))"
   ]
  }
 ],
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