Learner-parallel-plotter.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adaptive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import holoviews as hv\n",
    "from holoviews.streams import Stream, param\n",
    "hv.notebook_extension('bokeh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import adalearner\n",
    "from time import sleep\n",
    "from random import randint\n",
    "from functools import partial\n",
    "import ipyparallel\n",
    "import concurrent.futures\n",
    "import importlib\n",
    "importlib.reload(adalearner)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import asyncio\n",
    "from ipykernel.eventloops import register_integration\n",
    "\n",
    "@register_integration('asyncio')\n",
    "def loop_asyncio(kernel):\n",
    "    '''Start a kernel with asyncio event loop support.'''\n",
    "    loop = asyncio.get_event_loop()\n",
    "\n",
    "    def kernel_handler():\n",
    "        loop.call_soon(kernel.do_one_iteration)\n",
    "        loop.call_later(kernel._poll_interval, kernel_handler)\n",
    "\n",
    "    loop.call_soon(kernel_handler)\n",
    "    try:\n",
    "        if not loop.is_running():\n",
    "            loop.run_forever()\n",
    "    finally:\n",
    "        loop.run_until_complete(loop.shutdown_asyncgens())\n",
    "        loop.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%gui asyncio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def func(x, wait=True):\n",
    "    \"\"\"Function with a sharp peak on a smooth background\"\"\"\n",
    "    import numpy as np\n",
    "    from time import sleep\n",
    "    x = np.asarray(x)\n",
    "    a = 0.001\n",
    "    if wait:\n",
    "        sleep(np.random.randint(1, 3))\n",
    "    return x + a**2/(a**2 + (x)**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parallel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learner = adalearner.Learner1D(func, client=ipyparallel.Client())\n",
    "\n",
    "learner.add_point(-1, func(-1))\n",
    "learner.add_point(1, func(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "learner.start()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_stream = Stream.define('data', data=param.ObjectSelector(default=dict()))\n",
    "dm = hv.DynamicMap(learner.plot, streams=[data_stream()])\n",
    "dm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "async def monitor(delay=1):\n",
    "    while True:\n",
    "        dm.event(data=learner.data)\n",
    "        await asyncio.sleep(delay)\n",
    "        \n",
    "monitor_task = learner.ioloop.create_task(monitor())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learner.task.print_stack()"
   ]
  }
 ],
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