Add bloptools example notebook to documentation

docs/source/examples.rst

@@ -10,3 +10,4 @@ Examples
    notebooks/srw.ipynb
    notebooks/shadow.ipynb
    notebooks/madx.ipynb
+   notebooks/optimization.ipynb

docs/source/notebooks/optimization.ipynb

@@ -0,0 +1,166 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Optimize the beamline\n",
+    "\n",
+    "In this example, we use the `bloptools` package to optimize the beamline.\n",
+    "\n",
+    "We set up the simulation as we normally do:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from sirepo_bluesky import prepare_re_env\n",
+    "\n",
+    "%run -i $prepare_re_env.__file__\n",
+    "\n",
+    "from sirepo_bluesky.sirepo_bluesky import SirepoBluesky\n",
+    "from sirepo_bluesky.sirepo_ophyd import create_classes\n",
+    "\n",
+    "connection = SirepoBluesky(\"http://localhost:8000\")\n",
+    "\n",
+    "data, schema = connection.auth(\"shadow\", sim_id=\"00000002\")\n",
+    "classes, objects = create_classes(connection=connection)\n",
+    "globals().update(**objects)\n",
+    "\n",
+    "bec.disable_plots()\n",
+    "\n",
+    "aperture.horizontalSize.kind = \"hinted\"\n",
+    "w9.duration.kind = \"hinted\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can then instantiate an `Agent` from the `bloptools` package; because it can run any experiment defined with a Bluesky plan, we can give it any of our Sirepo components to use as degrees of freedom. We define the objectives as to maximize the flux density of the beamline."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from bloptools.bayesian import Agent, DOF, Objective\n",
+    "\n",
+    "dofs = [\n",
+    "    DOF(\n",
+    "        device=kbh.x_rot,\n",
+    "        limits=(-5e-2, +5e-2),\n",
+    "        tags=[\"kb\", \"kbv\"],\n",
+    "        units=\"deg\",\n",
+    "    ),\n",
+    "    DOF(\n",
+    "        device=kbv.x_rot,\n",
+    "        limits=(-5e-2, +5e-2),\n",
+    "        tags=[\"kb\", \"kbv\"],\n",
+    "        units=\"deg\",\n",
+    "    ),\n",
+    "]\n",
+    "\n",
+    "# composite\n",
+    "objectives = [\n",
+    "    Objective(name=\"w9_flux\", description=\"total flux\", target=\"max\", log=True),\n",
+    "    Objective(name=\"w9_fwhm_x\", description=\"horizontal FWHM\", target=\"min\", log=True),\n",
+    "    Objective(name=\"w9_fwhm_y\", description=\"vertical FWHM\", target=\"min\", log=True),\n",
+    "]\n",
+    "\n",
+    "\n",
+    "agent = Agent(\n",
+    "    dofs=dofs,\n",
+    "    dets=[w9],\n",
+    "    objectives=objectives,\n",
+    "    db=db,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We start by running a scan over a quasi-random sample of input parameters, and plot the result:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "RE(agent.learn(acq_func=\"qr\", n=16))\n",
+    "agent.plot_objectives()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We then use a more intelligent strategy (\"$q$-expected mean\") to narrow in on the optimum."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "RE(agent.learn(\"qem\", iterations=4))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see the convergence of our model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "agent.plot_history()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

requirements-dev.txt

@@ -19,3 +19,4 @@ pandoc
 sphinx
 sphinx-copybutton
 tabulate>=0.9.0
+bloptools>=0.5.0

sirepo_bluesky/sirepo_ophyd.py

@@ -336,7 +336,7 @@ def set(self, value):
         # want to make sure the crl element is updated properly when parameters are changed.
         ret.pop("state")
         # Update crl element
-        for cpt in ["absoluteFocusPosition", "focalDistance"]:
+        for cpt in ["absoluteFocusPosition", "focalDistance", "photonEnergy"]:
             getattr(self.parent, cpt).put(ret[cpt])
         return NullStatus()