artiq/artiq/examples/artiq_ipython_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "\n",
    "import os\n",
    "import logging\n",
    "import time\n",
    "import asyncio\n",
    "import datetime\n",
    "import glob\n",
    "from pprint import pprint\n",
    "\n",
    "import numpy as np\n",
    "np.set_printoptions(precision=3)\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn\n",
    "seaborn.set_style(\"whitegrid\")\n",
    "import pandas as pd\n",
    "import h5py\n",
    "\n",
    "from sipyco.pc_rpc import (Client, AsyncioClient,\n",
    "                           BestEffortClient, AutoTarget)\n",
    "from artiq.master.databases import DeviceDB\n",
    "from artiq.master.worker_db import DeviceManager"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# let's assume artiq_master and artiq_ctlmgr are already running\n",
    "# then move to a location where we have our artiq setup\n",
    "os.chdir(os.path.expanduser(\"~/work/nist/artiq/run\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# we can directly use the artiq controller infrastructure\n",
    "# and access any artiq device\n",
    "\n",
    "# we can have artiq prepare that connection for us:\n",
    "ddb = DeviceDB(\"device_db.py\")\n",
    "devmgr = DeviceManager(ddb)\n",
    "lda = devmgr.get(\"lda\")\n",
    "lda.set_attenuation(42)\n",
    "assert lda.get_attenuation() == 42\n",
    "\n",
    "# ... or we can wire it up ourselves if you know where it is\n",
    "assert ddb.get(\"lda\", resolve_alias=True)[\"host\"] == \"::1\"\n",
    "assert ddb.get(\"lda\", resolve_alias=True)[\"port\"] == 3253\n",
    "\n",
    "# there are different Client types tailored to different use cases:\n",
    "\n",
    "# synchronous\n",
    "lda = Client(\"::1\", 3253)\n",
    "assert lda.get_attenuation() == 42\n",
    "\n",
    "# asyncio\n",
    "lda = AsyncioClient()\n",
    "async def test_lda():\n",
    "    await lda.connect_rpc(\"::1\", 3253, AutoTarget)\n",
    "    return await lda.get_attenuation()\n",
    "assert asyncio.get_event_loop().run_until_complete(test_lda()) == 42\n",
    "\n",
    "# best effort\n",
    "lda = BestEffortClient(\"::1\", 3253, AutoTarget)\n",
    "assert lda.get_attenuation() == 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "current schedule\n",
      "{}\n",
      "experiments:\n",
      "['ex/',\n",
      " 'test_analyzer.py',\n",
      " 'notebook_test.py',\n",
      " 'speed_benchmark.py',\n",
      " 'histograms.py',\n",
      " 'arguments_demo.py',\n",
      " '.git/',\n",
      " '__pycache__/',\n",
      " 'flopping_f_simulation.py',\n",
      " 'test_crash.py',\n",
      " 'run_forever.py',\n",
      " 'transport.py',\n",
      " 'pdq2_simple.py']\n"
     ]
    }
   ],
   "source": [
    "# let's connect to the master\n",
    "\n",
    "schedule, exps, datasets = [\n",
    "    Client(\"::1\", 3251, i) for i in\n",
    "    \"schedule experiment_db dataset_db\".split()]\n",
    "\n",
    "print(\"current schedule\")\n",
    "pprint(schedule.get_status())\n",
    "print(\"experiments:\")\n",
    "pprint(exps.list_directory(\"repository\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "current schedule\n",
      "{4722: {'due_date': None,\n",
      "        'expid': {'arguments': {'F0': 1500, 'noise_amplitude': 0.3},\n",
      "                  'class_name': 'FloppingF',\n",
      "                  'file': 'repository/flopping_f_simulation.py',\n",
      "                  'log_level': 30},\n",
      "        'flush': False,\n",
      "        'pipeline': 'main',\n",
      "        'priority': 0,\n",
      "        'repo_msg': None,\n",
      "        'status': 'preparing'}}\n"
     ]
    }
   ],
   "source": [
    "# we can submit experiments to be run\n",
    "\n",
    "expid = dict(\n",
    "    file=\"repository/flopping_f_simulation.py\",\n",
    "    class_name=\"FloppingF\",\n",
    "    log_level=logging.WARNING,\n",
    "    arguments=dict(\n",
    "        F0=1500,\n",
    "        noise_amplitude=.3,\n",
    "    ),\n",
    ")\n",
    "if not schedule.get_status():\n",
    "    rid = schedule.submit(pipeline_name=\"main\", expid=expid,\n",
    "        priority=0, due_date=None, flush=False)\n",
    "print(\"current schedule\")\n",
    "pprint(schedule.get_status())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# wait for experiment to finish\n",
    "# this can be written nicer by subscribing and reacting to scheduler changes\n",
    "while rid in schedule.get_status():\n",
    "    time.sleep(.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "flopping_f: 1499.944285221012\n"
     ]
    },
    {
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       "<matplotlib.figure.Figure at 0x7fc5187c8668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# now that the experiment has completed, we can get the\n",
    "# current value of the (live) dataset and plot it\n",
    "# had we done this earlier, the dataset would have been incomplete\n",
    "fig, ax = plt.subplots()\n",
    "d = datasets.get(\"flopping_f_brightness\")\n",
    "ax.plot(d)\n",
    "print(\"flopping_f:\", datasets.get(\"flopping_freq\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# this is how you would clear all pipelines\n",
    "for i in schedule.get_status():\n",
    "    schedule.delete(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "available datasets ['artiq_version', 'flopping_f_brightness']\n"
     ]
    }
   ],
   "source": [
    "# we can easily find and use the data that was saved as part\n",
    "# of the experiment\n",
    "\n",
    "t = datetime.datetime.now()\n",
    "f = os.path.join(\n",
    "    \"results\", t.strftime(\"%Y-%m-%d\"), #t.strftime(\"%H-%M\"),\n",
    "    \"*\", \"{:09d}-FloppingF.h5\".format(rid))\n",
    "\n",
    "# we would usually like to use pandas but our data does not have\n",
    "# the metadata pandas want\n",
    "#d = pd.HDFStore(glob.glob(f)[0])\n",
    "\n",
    "with h5py.File(glob.glob(f)[0]) as f:\n",
    "    print(\"available datasets\", list(f))\n",
    "    assert np.allclose(f[\"datasets/flopping_f_brightness\"], d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting repository/notebook_test.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile repository/notebook_test.py\n",
    "\n",
    "# we can also write experiments in the notebook and submit them\n",
    "# we don't have submit-by-content yet (and there would be questions\n",
    "# about other modules that would need to be imported) so we just export\n",
    "# this cell and submit it by filename\n",
    "\n",
    "from artiq.experiment import *\n",
    "\n",
    "class Hello(EnvExperiment):\n",
    "    def build(self):\n",
    "        pass\n",
    "    \n",
    "    def run(self):\n",
    "        print(\"Hello world!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4724\n"
     ]
    }
   ],
   "source": [
    "expid = dict(\n",
    "    file=\"repository/notebook_test.py\",\n",
    "    class_name=\"Hello\",\n",
    "    log_level=logging.WARNING,\n",
    "    arguments=dict(),\n",
    ")\n",
    "rid = schedule.submit(pipeline_name=\"misc\", expid=expid,\n",
    "    priority=1, due_date=None, flush=False)\n",
    "print(rid)\n",
    "# on the master you should see the message."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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