{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nbmultitask import ProcessWithLogAndControls\n",
    "from IPython.display import clear_output\n",
    "import multiprocessing as mp\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from time import sleep\n",
    "\n",
    "def fn(x):\n",
    "    while True:\n",
    "        print(x.value)\n",
    "        x.value = x.value**1.01\n",
    "        sleep(0.1)\n",
    "\n",
    "from multiprocessing import Value\n",
    "x = Value('f',2)\n",
    "task = ProcessWithLogAndControls(target=fn, args=(x,), name=\"exponential growth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#task.control_panel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.3297741413116455\n",
      "2.80667781829834\n"
     ]
    }
   ],
   "source": [
    "task.start()\n",
    "time.sleep(2)\n",
    "print(x.value)\n",
    "time.sleep(2)\n",
    "print(x.value)\n",
    "task.stop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_pure_tone(freq, duration, sample_rate=44100):\n",
    "    x = np.linspace(0, duration * freq * 2*np.pi, int(duration*sample_rate), dtype=np.float32)\n",
    "    return np.sin(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_func(selector):\n",
    "    import sounddevice as sd\n",
    "    import numpy as np\n",
    "    import time\n",
    "\n",
    "    tone1 = np.sin(np.linspace(0, 0.05 * 440 * 2*np.pi, int(0.05*44100), dtype=np.float32))\n",
    "    tone2 = np.sin(np.linspace(0, 0.05 * 880 * 2*np.pi, int(0.05*44100), dtype=np.float32))\n",
    "    ts1 = np.column_stack((tone1, tone1))\n",
    "    ts2 = np.column_stack((tone2, tone2))\n",
    "\n",
    "    sd.default.samplerate = 44100\n",
    "    stream = sd.OutputStream(samplerate=44100, channels=2, dtype='float32')\n",
    "    stream.start()\n",
    "\n",
    "    latency = 0.25\n",
    "    next_beat = time.time() + latency\n",
    "    with open('sound_log.csv', 'w') as f:\n",
    "        f.write(\"time,id\\n\")\n",
    "\n",
    "    beats = 0\n",
    "    while beats < 40:\n",
    "        t0 = time.time()\n",
    "        if t0 < next_beat:\n",
    "            time.sleep(0.0001)  # not to spin the wheels too much\n",
    "            continue\n",
    "\n",
    "        \n",
    "        with open('sound_log.csv', 'a') as f:\n",
    "            f.write(\",\".join([str(x) for x in (t0, 1)]) + \"\\n\")\n",
    "            \n",
    "        #sd.play(self.tone)  # has underrun errors, maybe try blocking?\n",
    "        if selector.value == 1:\n",
    "            stream.write(ts1)\n",
    "        else:\n",
    "            stream.write(ts2)\n",
    "            \n",
    "        beats += 1\n",
    "        next_beat += latency\n",
    "        \n",
    "    stream.stop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ALSA lib pcm.c:8526:(snd_pcm_recover) underrun occurred\n",
      "ALSA lib pcm.c:8526:(snd_pcm_recover) underrun occurred\n",
      "ALSA lib pcm.c:8526:(snd_pcm_recover) underrun occurred\n",
      "ALSA lib pcm.c:8526:(snd_pcm_recover) underrun occurred\n",
      "ALSA lib pcm.c:8526:(snd_pcm_recover) underrun occurred\n",
      "ALSA lib pcm.c:8526:(snd_pcm_recover) underrun occurred\n",
      "ALSA lib pcm.c:8526:(snd_pcm_recover) underrun occurred\n",
      "ALSA lib pcm.c:8526:(snd_pcm_recover) underrun occurred\n",
      "ALSA lib pcm.c:8526:(snd_pcm_recover) underrun occurred\n",
      "ALSA lib pcm.c:8526:(snd_pcm_recover) underrun occurred\n",
      "ALSA lib pcm.c:8526:(snd_pcm_recover) underrun occurred\n",
      "ALSA lib pcm.c:8526:(snd_pcm_recover) underrun occurred\n",
      "ALSA lib pcm.c:8526:(snd_pcm_recover) underrun occurred\n",
      "ALSA lib pcm.c:8526:(snd_pcm_recover) underrun occurred\n"
     ]
    }
   ],
   "source": [
    "selector = mp.Value('f', 0)\n",
    "#is_running = Value(c_bool, True)\n",
    "#sc = ProcessWithLogAndControls(target=sound_controller, args=(selector, is_running), name=\"sounds\")\n",
    "sc = ProcessWithLogAndControls(target=test_func, args=(selector,), name=\"foo\")\n",
    "sc.start()\n",
    "time.sleep(2)\n",
    "selector.value = 1\n",
    "time.sleep(2)\n",
    "sc.stop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.stop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = np.loadtxt('sound_log.csv', delimiter=',', skiprows=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f03b1413c10>]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.diff(ds[:, 0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_pure_tone(freq, duration, sample_rate=44100):\n",
    "    x = np.linspace(0, duration * freq * 2*np.pi, int(duration*sample_rate), dtype=np.float32)\n",
    "    return np.sin(x)\n",
    "\n",
    "def sound_controller(selector, is_running, latency=0.25):\n",
    "    \"\"\"\n",
    "    selector        mp.Value object to set the sound to be played\n",
    "    is_running      mp.Value object to stop the loop\n",
    "    \"\"\"\n",
    "    print('Loading SC..')\n",
    "    \n",
    "    tone1 = get_pure_tone(440, 0.05, 44100) * 0.5\n",
    "    tone2 = get_pure_tone(880, 0.05, 44100) * 0.5\n",
    "    silence = np.zeros(len(tone1), dtype='float32')    \n",
    "\n",
    "    sounds = {\n",
    "        0: np.column_stack((silence, silence)),\n",
    "        1: np.column_stack((tone1, tone1)),\n",
    "        2: np.column_stack((tone2, tone2))\n",
    "    }\n",
    "\n",
    "    stream = sd.OutputStream(samplerate=44100, channels=2, dtype='float32')\n",
    "    stream.start()\n",
    "    \n",
    "    print('Creating sounds..')\n",
    "        \n",
    "    next_beat = time.time() + latency\n",
    "    beat_times = collections.deque(maxlen=10000)\n",
    "\n",
    "    \n",
    "    while is_running.value:\n",
    "        print('Running loop..')\n",
    "        \n",
    "        t0 = time.time()\n",
    "        if t0 < next_beat:\n",
    "            continue\n",
    "\n",
    "        beat_times.append(t0)\n",
    "        #sd.play(self.tone)  # has underrun errors, maybe try blocking?\n",
    "        stream.write(sounds[int(selector.value)])\n",
    "        next_beat += latency\n",
    "        \n",
    "    stream.stop()\n",
    "    print('Sound controller stopped')"
   ]
  }
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