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Merge pull request #102 from jennmald/xrt_demo
Add XRT demo to blop tutorials
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‎.github/workflows/docs.yml‎

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set -vxeo pipefail
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pip install --upgrade pip wheel
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pip install -v .[dev]
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pip install -v .[dev,xrt]
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pip list
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- name: Build Docs

‎docs/source/tutorials.rst‎

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tutorials/pareto-fronts.ipynb
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tutorials/passive-dofs.ipynb
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tutorials/kb-mirrors.ipynb
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tutorials/xrt-blop-demo.ipynb

‎docs/source/tutorials/kb-mirrors.ipynb‎

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"id": "0",
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"metadata": {},
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"source": [
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"# Hyperparameters\n",
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"# KB Mirrors\n",
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"\n",
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"This example simulates the alignment of a KB mirror endstation (with four degrees of freedom)."
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]

‎docs/source/tutorials/xrt-blop-demo.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "0",
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"metadata": {},
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"source": [
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"# XRT Blop Demo"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1",
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"metadata": {},
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"source": [
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"For ophyd beamline setup see: https://github.com/NSLS-II/blop/blob/main/src/blop/sim/xrt_beamline.py and https://github.com/NSLS-II/blop/blob/main/src/blop/sim/xrt_kb_model.py"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2",
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"metadata": {},
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"outputs": [],
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"source": [
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"from blop.utils import prepare_re_env # noqa\n",
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"%run -i $prepare_re_env.__file__ --db-type=temp\n",
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"bec.disable_plots()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3",
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys, os\n",
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"from matplotlib import pyplot as plt\n",
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"from blop.sim.xrt_beamline import Beamline\n",
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"\n",
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"from blop import DOF, Objective, Agent\n",
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"from blop.digestion import beam_stats_digestion\n",
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"import time"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4",
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.ion()\n",
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"\n",
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"h_opt = 0\n",
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"dh = 5\n",
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"\n",
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"R1, dR1 = 40000, 10000\n",
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"R2, dR2 = 20000, 10000"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5",
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"metadata": {},
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"outputs": [],
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"source": [
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"beamline = Beamline(name=\"bl\")\n",
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"time.sleep(1)\n",
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"dofs = [\n",
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" DOF(description=\"KBV R\",\n",
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" device=beamline.kbv_dsv,\n",
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" search_domain=(R1-dR1, R1+dR1)),\n",
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" DOF(description=\"KBH R\",\n",
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" device=beamline.kbh_dsh,\n",
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" search_domain=(R2-dR2, R2+dR2)),\n",
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"\n",
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"]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6",
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"metadata": {},
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"outputs": [],
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"source": [
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"objectives = [\n",
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" Objective(name=\"bl_det_sum\", \n",
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" target=\"max\",\n",
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" transform=\"log\",\n",
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" trust_domain=(20, 1e12)),\n",
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"\n",
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" Objective(name=\"bl_det_wid_x\",\n",
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" target=\"min\",\n",
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" transform=\"log\",\n",
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" # trust_domain=(0, 1e12),\n",
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" latent_groups=[(\"bl_kbh_dsh\", \"bl_kbv_dsv\")]),\n",
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" Objective(name=\"bl_det_wid_y\",\n",
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" target=\"min\",\n",
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" transform=\"log\",\n",
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" # trust_domain=(0, 1e12),\n",
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" latent_groups=[(\"bl_kbh_dsh\", \"bl_kbv_dsv\")]),\n",
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"]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7",
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"metadata": {},
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"outputs": [],
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"source": [
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"agent = Agent(\n",
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" dofs=dofs,\n",
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" objectives=objectives,\n",
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" detectors=[beamline.det],\n",
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" digestion=beam_stats_digestion,\n",
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" digestion_kwargs={\"image_key\": \"bl_det_image\"},\n",
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" verbose=True,\n",
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" db=db,\n",
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" tolerate_acquisition_errors=False,\n",
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" enforce_all_objectives_valid=True,\n",
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" train_every=3,\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8",
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"metadata": {},
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"outputs": [],
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"source": [
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"RE(agent.learn(\"qr\", n=16))\n",
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"RE(agent.learn(\"qei\", n=16, iterations=4))\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9",
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"metadata": {},
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"outputs": [],
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"source": [
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"agent.plot_objectives(axes=(0, 1))"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "xrt-blop",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.0"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎pyproject.toml‎

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"nbstripout",
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]
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adaptive = ["bluesky-adaptive"]
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xrt = ["xrt"]
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dev = [
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"pytest-codecov",

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