AI_Tunes_Generate_Music.ipynb

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    "colab": {
      "name": "AI-Tunes Generate Music",
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        "<a href=\"https://colab.research.google.com/github/JosephBless/Deep-Learning-Colab/blob/main/AI_Tunes_Generate_Music.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
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        "id": "PGD-rwhuWOOx"
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      "source": [
        "# **AI-Tunes: Creating New Songs with Artificial Intelligence**\n",
        "### **fine-tuned OpenAI's GPT-3 to generate music with a global structure**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Qe6fbeWTc3J"
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      "source": [
        "#@title **Initalize the System**\n",
        "#@markdown Hover over play button and hit the Run cell.</br>\n",
        "#@markdown HIt takes about 3 minutes to complete the setup.\n",
        "!git clone https://github.com/robgon-art/music-geometry-eval\n",
        "!pip install openai\n",
        "\n",
        "import sys\n",
        "sys.path.append('/content/music-geometry-eval/music_geometry_eval')\n",
        "\n",
        "import music_geometry_eval\n",
        "import openai\n",
        "import music21\n",
        "import numpy as np\n",
        "from collections.abc import Iterable\n",
        "\n",
        "!gsutil -q -m cp -r gs://magentadata/models/music_transformer/primers/* /content/\n",
        "!gsutil -q -m cp gs://magentadata/soundfonts/Yamaha-C5-Salamander-JNv5.1.sf2 /content/\n",
        "!apt-get update -qq && apt-get install -qq libfluidsynth1 build-essential libasound2-dev libjack-dev\n",
        "!pip install magenta\n",
        "!pip install pyfluidsynth\n",
        "import note_seq\n",
        "SF2_PATH = '/content/Yamaha-C5-Salamander-JNv5.1.sf2'\n",
        "SAMPLE_RATE = 16000\n",
        "CMM_mean = 2.2715\n",
        "CMM_std = 0.4831\n",
        "LM_mean = 2.0305\n",
        "LM_std = 0.5386\n",
        "CENT_mean = 0.3042\n",
        "CENT_std = 0.0891"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DXqi6YhFXpCn"
      },
      "source": [
        "**Specify Your OpenAI API Key**</br>\n",
        "This Colab only works if you have an account with OpenAI.</br>\n",
        "If you don't have an account you can sign up here https://openai.com/blog/openai-api/"
      ]
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    {
      "cell_type": "code",
      "metadata": {
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      "source": [
        "openai.api_key = \"<Your OpenAI API Key>\""
      ],
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      "source": [
        "#@title **Generate a new Song Title and Band Name**\n",
        "#@markdown This data will be used to prompt the AI-Tunes system to create the song.\n",
        "response = openai.Completion.create(\n",
        "  engine=\"davinci-instruct-beta\",\n",
        "  prompt=\"Create a new song title a new band name. Be creative!\\n\\nBand name: The Execs\\nSong title: Company Meeting\\n###\\n\\nBand name: The One Chords\\nSong title: Moving Down to Burlington\\n###\",\n",
        "  temperature=0.7,\n",
        "  max_tokens=64,\n",
        "  top_p=1,\n",
        "  frequency_penalty=0.5,\n",
        "  presence_penalty=0.5,\n",
        "  stop=[\"###\"]\n",
        ")\n",
        "\n",
        "# print(response)\n",
        "\n",
        "song_metadata = response[\"choices\"][0][\"text\"].strip()\n",
        "lines = song_metadata.split(\"\\n\")\n",
        "generated_metadata = {}\n",
        "song_title = \"Our Random Song\"\n",
        "band_name = \"Some Random Band\"\n",
        "for line in lines:\n",
        "  parts = line.split(\":\")\n",
        "  if len(parts) == 2:\n",
        "    if \"song title\" in parts[0].lower() and len(parts[1]) > 0:\n",
        "      song_title = parts[1].strip()\n",
        "    if \"band name\" in parts[0].lower() and len(parts[1]) > 0:\n",
        "      band_name = parts[1].strip()\n",
        "\n",
        "print(\"Song Title:\", song_title)\n",
        "print(\"Band Name :\", band_name)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Song Title: I'm in Love with My Best Friend\n",
            "Band Name : The Last Chance\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hol2mChWjzrr",
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        "cellView": "form",
        "outputId": "b0982ef4-8ca5-4378-ef87-5912894d9f82"
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      "source": [
        "#@title **Generate Songs**\n",
        "#@markdown The sysystem will generate five versions of the song and show the statistics for tonal quality.</br>\n",
        "#@markdown Lower temperature values will create more repetive melodies.</br>\n",
        "#@markdown Higher values will create more random melodies (and occasional ABC parsing errors.)\n",
        "temperature = 0.95 #@param {type:\"slider\", min:0.0, max:1.2, step:0.01}\n",
        "\n",
        "prompt = \"X: 1 $ T: \" + song_title + \" $ C: \" + band_name + \" $ <song>\"\n",
        "print(\"prompt\", prompt)\n",
        "print()\n",
        "\n",
        "songs_with_scores = []\n",
        "score_arr = np.empty((0), np.float32)\n",
        "\n",
        "for i in range(5):\n",
        "  print(\"\\nGenerating Song Version\", i)\n",
        "  response = openai.Completion.create(\n",
        "      model=\"curie:ft-user-j0julqovorjakyuyt3kv3zci-2021-08-24-11-42-59\",\n",
        "      prompt=prompt,\n",
        "      stop = \" $ <end>\",\n",
        "      temperature=temperature,\n",
        "      top_p=1.0,\n",
        "      frequency_penalty=0.0,\n",
        "      presence_penalty=0.0,\n",
        "      max_tokens = 1000)\n",
        "\n",
        "  # print(response)\n",
        "  # print()\n",
        "\n",
        "  formatted_prompt = \"X: 1 $ T: \" + song_title + \" $ C: \" + band_name + \" $ L: 1/4 $ M: 4/4 $ K: C $ V: 1 treble\"\n",
        "  formatted_prompt = formatted_prompt.replace(\" $ \", \"\\n\")\n",
        "  formatted_prompt = formatted_prompt.replace(\"<song>\", \"\").strip()\n",
        "\n",
        "  formatted_song = response[\"choices\"][0][\"text\"].strip()\n",
        "  formatted_song = formatted_song.replace('`', '\"')\n",
        "  formatted_song = formatted_song.replace(\" $ \", \"\\n\")\n",
        "  new_song = formatted_prompt + \"\\n\" + formatted_song\n",
        "  # print(new_song)\n",
        "  with open(\"new_song.abc\", \"w\") as new_song_file:\n",
        "    new_song_file.write(new_song)\n",
        "\n",
        "  song = music21.converter.parse(\"new_song.abc\")\n",
        "\n",
        "  part = song.parts[0]\n",
        "  chord_end = song.highestTime\n",
        "  for pi in reversed(range(len(part))):\n",
        "    p = part[pi]\n",
        "    for ni in reversed(range(len(p))):\n",
        "      n = p[ni]\n",
        "      if type(n) == music21.harmony.ChordSymbol:\n",
        "        chord_start = p.offset + n.offset\n",
        "        n.duration.quarterLength = chord_end - chord_start\n",
        "        n.volume = music21.volume.Volume(velocity=48)\n",
        "        chord_end = chord_start\n",
        "      elif type(n) == music21.note.Note:\n",
        "        n.volume = music21.volume.Volume(velocity=64)\n",
        "  file_name = \"song\" + str(i).zfill(2) + \".mid\"\n",
        "  song.write('midi', fp=file_name)\n",
        "\n",
        "  part = song.parts[0]\n",
        "  note_array = []\n",
        "\n",
        "  for p in part:\n",
        "    if isinstance(p, Iterable):\n",
        "      for n in p:\n",
        "        if type(n) == music21.note.Note:\n",
        "          note_array.append([int(n.pitch.ps), int(n.quarterLength*4+0.5)])\n",
        "\n",
        "  CMM = music_geometry_eval.calculate_time_supported_conjunct_melodic_motion(note_array)\n",
        "  LM = music_geometry_eval.calculate_time_supported_limited_macroharmony(note_array, span_size=32)\n",
        "  CENT = music_geometry_eval.calculate_time_supported_centricity(note_array, span_size=32)\n",
        "\n",
        "  print(\"  CMM :\", round(CMM, 4))\n",
        "  print(\"  LM  :\", round(LM, 4))\n",
        "  print(\"  CENT:\", round(CENT, 4))\n",
        "\n",
        "  norm_cmm = (CMM - CMM_mean) / CMM_std\n",
        "  norm_lm = (LM - LM_mean) / LM_std\n",
        "  norm_cent = (CENT - CENT_mean) / CMM_std\n",
        "  norm_score_squared = norm_cmm * norm_cmm + norm_lm * norm_lm + norm_cent * norm_cent\n",
        "  print(\"  NDM :\", round(norm_score_squared, 4))\n",
        "  score_arr = np.append(score_arr, norm_score_squared)\n",
        "  songs_with_scores.append([norm_score_squared, file_name])\n",
        "\n",
        "print(\"\\nResults:\")\n",
        "songs_with_scores.sort()\n",
        "for i, pair in enumerate(songs_with_scores):\n",
        "  print(\"Version: \" + str(i) + \", Score: \" + str(round(pair[0], 4)) + \", File: \", pair[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "prompt X: 1 $ T: I'm in Love with My Best Friend $ C: The Last Chance $ <song>\n",
            "\n",
            "\n",
            "Generating Song Version 0\n",
            "  CMM : 1.4471\n",
            "  LM  : 2.8857\n",
            "  CENT: 0.5428\n",
            "  NDM : 5.6776\n",
            "\n",
            "Generating Song Version 1\n",
            "  CMM : 2.6371\n",
            "  LM  : 2.5648\n",
            "  CENT: 0.3101\n",
            "  NDM : 1.557\n",
            "\n",
            "Generating Song Version 2\n",
            "  CMM : 2.2919\n",
            "  LM  : 2.2661\n",
            "  CENT: 0.3403\n",
            "  NDM : 0.1986\n",
            "\n",
            "Generating Song Version 3\n",
            "  CMM : 2.1721\n",
            "  LM  : 2.3629\n",
            "  CENT: 0.432\n",
            "  NDM : 0.4931\n",
            "\n",
            "Generating Song Version 4\n",
            "  CMM : 2.631\n",
            "  LM  : 1.9726\n",
            "  CENT: 0.3174\n",
            "  NDM : 0.5659\n",
            "\n",
            "Results:\n",
            "Version: 0, Score: 0.1986, File:  song02.mid\n",
            "Version: 1, Score: 0.4931, File:  song03.mid\n",
            "Version: 2, Score: 0.5659, File:  song04.mid\n",
            "Version: 3, Score: 1.557, File:  song01.mid\n",
            "Version: 4, Score: 5.6776, File:  song00.mid\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
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        "id": "paPkHWNIIWE1",
        "colab": {
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        "cellView": "form",
        "outputId": "b7fdc1cd-9d84-4ae4-874e-015ad7a41fcb"
      },
      "source": [
        "#@title **Choose a Song**\n",
        "#@markdown Choose a version of the song to play.\n",
        "import copy\n",
        "\n",
        "version_number = 0 #@param {type:\"slider\", min:0, max:5, step:1}\n",
        "tempo_bpm = 120 #@param {type:\"slider\", min:60, max:240, step:1}\n",
        "tempo_scale = 120.0/tempo_bpm\n",
        "\n",
        "melody_ns = note_seq.midi_file_to_sequence_proto(songs_with_scores[version_number][1])\n",
        "\n",
        "melody2_ns = copy.deepcopy(melody_ns)\n",
        "melody2_ns.tempos[0].qpm = tempo_bpm\n",
        "\n",
        "for n in melody2_ns.notes:\n",
        "  n.start_time *= tempo_scale\n",
        "  n.end_time *= tempo_scale\n",
        "\n",
        "print(\"\\nSong Title: \" + song_title)\n",
        "print(\"Version:   \", version_number)\n",
        "print(\"Band Name: \", band_name)\n",
        "print(\"NDM Score: \", round(songs_with_scores[version_number][0], 4))\n",
        "print(\"MIDI File: \", songs_with_scores[version_number][1], \"\\n\")\n",
        "\n",
        "note_seq.play_sequence(\n",
        "  melody2_ns,\n",
        "  synth=note_seq.fluidsynth, sample_rate=SAMPLE_RATE, sf2_path=SF2_PATH)\n",
        "note_seq.plot_sequence(melody2_ns)\n",
        "\n",
        "# from bokeh.plotting import show\n",
        "# fig = note_seq.plot_sequence(melody2_ns, show_figure = False)\n",
        "# fig.width = 500\n",
        "# fig.height = 500\n",
        "# fig.toolbar.logo = None\n",
        "# show(fig)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Song Title: I'm in Love with My Best Friend\n",
            "Version:    0\n",
            "Band Name:  The Last Chance\n",
            "NDM Score:  0.1986\n",
            "MIDI File:  song02.mid \n",
            "\n"
          ]
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              "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
              "      }\n",
              "    } else if (Date.now() < root._bokeh_timeout) {\n",
              "      setTimeout(display_loaded, 100)\n",
              "    }\n",
              "  }\n",
              "\n",
              "\n",
              "  function run_callbacks() {\n",
              "    try {\n",
              "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
              "        if (callback != null)\n",
              "          callback();\n",
              "      });\n",
              "    } finally {\n",
              "      delete root._bokeh_onload_callbacks\n",
              "    }\n",
              "    console.debug(\"Bokeh: all callbacks have finished\");\n",
              "  }\n",
              "\n",
              "  function load_libs(css_urls, js_urls, callback) {\n",
              "    if (css_urls == null) css_urls = [];\n",
              "    if (js_urls == null) js_urls = [];\n",
              "\n",
              "    root._bokeh_onload_callbacks.push(callback);\n",
              "    if (root._bokeh_is_loading > 0) {\n",
              "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
              "      return null;\n",
              "    }\n",
              "    if (js_urls == null || js_urls.length === 0) {\n",
              "      run_callbacks();\n",
              "      return null;\n",
              "    }\n",
              "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
              "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
              "\n",
              "    function on_load() {\n",
              "      root._bokeh_is_loading--;\n",
              "      if (root._bokeh_is_loading === 0) {\n",
              "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
              "        run_callbacks()\n",
              "      }\n",
              "    }\n",
              "\n",
              "    function on_error(url) {\n",
              "      console.error(\"failed to load \" + url);\n",
              "    }\n",
              "\n",
              "    for (let i = 0; i < css_urls.length; i++) {\n",
              "      const url = css_urls[i];\n",
              "      const element = document.createElement(\"link\");\n",
              "      element.onload = on_load;\n",
              "      element.onerror = on_error.bind(null, url);\n",
              "      element.rel = \"stylesheet\";\n",
              "      element.type = \"text/css\";\n",
              "      element.href = url;\n",
              "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
              "      document.body.appendChild(element);\n",
              "    }\n",
              "\n",
              "    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.3.3.min.js\": \"dM3QQsP+wXdHg42wTqW85BjZQdLNNIXqlPw/BgKoExPmTG7ZLML4EGqLMfqHT6ON\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.3.3.min.js\": \"8x57I4YuIfu8XyZfFo0XVr2WAT8EK4rh/uDe3wF7YuW2FNUSNEpJbsPaB1nJ2fz2\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.3.3.min.js\": \"3QTqdz9LyAm2i0sG5XTePsHec3UHWwVsrOL68SYRoAXsafvfAyqtQ+h440+qIBhS\"};\n",
              "\n",
              "    for (let i = 0; i < js_urls.length; i++) {\n",
              "      const url = js_urls[i];\n",
              "      const element = document.createElement('script');\n",
              "      element.onload = on_load;\n",
              "      element.onerror = on_error.bind(null, url);\n",
              "      element.async = false;\n",
              "      element.src = url;\n",
              "      if (url in hashes) {\n",
              "        element.crossOrigin = \"anonymous\";\n",
              "        element.integrity = \"sha384-\" + hashes[url];\n",
              "      }\n",
              "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
              "      document.head.appendChild(element);\n",
              "    }\n",
              "  };\n",
              "\n",
              "  function inject_raw_css(css) {\n",
              "    const element = document.createElement(\"style\");\n",
              "    element.appendChild(document.createTextNode(css));\n",
              "    document.body.appendChild(element);\n",
              "  }\n",
              "\n",
              "  \n",
              "  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.3.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.3.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.3.3.min.js\"];\n",
              "  var css_urls = [];\n",
              "  \n",
              "\n",
              "  var inline_js = [\n",
              "    function(Bokeh) {\n",
              "      Bokeh.set_log_level(\"info\");\n",
              "    },\n",
              "    function(Bokeh) {\n",
              "    \n",
              "    \n",
              "    }\n",
              "  ];\n",
              "\n",
              "  function run_inline_js() {\n",
              "    \n",
              "    if (root.Bokeh !== undefined || force === true) {\n",
              "      \n",
              "    for (var i = 0; i < inline_js.length; i++) {\n",
              "      inline_js[i].call(root, root.Bokeh);\n",
              "    }\n",
              "    } else if (Date.now() < root._bokeh_timeout) {\n",
              "      setTimeout(run_inline_js, 100);\n",
              "    } else if (!root._bokeh_failed_load) {\n",
              "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
              "      root._bokeh_failed_load = true;\n",
              "    } else if (force !== true) {\n",
              "      var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n",
              "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
              "    }\n",
              "\n",
              "  }\n",
              "\n",
              "  if (root._bokeh_is_loading === 0) {\n",
              "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
              "    run_inline_js();\n",
              "  } else {\n",
              "    load_libs(css_urls, js_urls, function() {\n",
              "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
              "      run_inline_js();\n",
              "    });\n",
              "  }\n",
              "}(window));"
            ],
            "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(null);\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.3.3.min.js\": \"dM3QQsP+wXdHg42wTqW85BjZQdLNNIXqlPw/BgKoExPmTG7ZLML4EGqLMfqHT6ON\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.3.3.min.js\": \"8x57I4YuIfu8XyZfFo0XVr2WAT8EK4rh/uDe3wF7YuW2FNUSNEpJbsPaB1nJ2fz2\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.3.3.min.js\": \"3QTqdz9LyAm2i0sG5XTePsHec3UHWwVsrOL68SYRoAXsafvfAyqtQ+h440+qIBhS\"};\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      if (url in hashes) {\n        element.crossOrigin = \"anonymous\";\n        element.integrity = \"sha384-\" + hashes[url];\n      }\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  \n  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.3.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.3.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.3.3.min.js\"];\n  var css_urls = [];\n  \n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (var i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "\n",
              "\n",
              "\n",
              "\n",
              "\n",
              "  <div class=\"bk-root\" id=\"81fce8f5-a203-425d-8fb9-70c5152d7114\" data-root-id=\"1002\"></div>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "(function(root) {\n",
              "  function embed_document(root) {\n",
              "    \n",
              "  var docs_json = {\"97214897-aac1-47cf-b7dd-f27613b4fd97\":{\"defs\":[],\"roots\":{\"references\":[{\"attributes\":{\"below\":[{\"id\":\"1011\"}],\"center\":[{\"id\":\"1014\"},{\"id\":\"1018\"}],\"height\":200,\"left\":[{\"id\":\"1015\"}],\"renderers\":[{\"id\":\"1038\"}],\"title\":{\"id\":\"1040\"},\"toolbar\":{\"id\":\"1025\"},\"width\":500,\"x_range\":{\"id\":\"1003\"},\"x_scale\":{\"id\":\"1007\"},\"y_range\":{\"id\":\"1005\"},\"y_scale\":{\"id\":\"1009\"}},\"id\":\"1002\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"bottom\":{\"field\":\"bottom\"},\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"value\":\"#d53e4f\"},\"left\":{\"field\":\"start_time\"},\"right\":{\"field\":\"end_time\"},\"top\":{\"field\":\"top\"}},\"id\":\"1036\",\"type\":\"Quad\"},{\"attributes\":{\"axis_label\":\"time 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,48,48,64,64,64,48,48,48,48,64,64,64,48,48,48,48,64,64,48,48,48,48,64,64,48,48,48,48,64,48,48,48,48,64,48,48,48,48,64,64,48,48,48,48,64,64,48,48,48,48,64,48,48,48,48,64,64,64,64,48,48,48,48,48,64,48,48,48,48,64,64,64,48,48,48,48,64,64,64,48,48,48,48,64,64,64,48,48,48,48,64,64,64,64,48,48,48,48,48,64,48,48,48,48,64,64,48,48,48,48,48,48,48,64,64,48,48,48,48,64,64,48,48,48,48,64,64,48,48,48,48,64,48,48,48,48,64,48,48,48,48,64,48,48,48,64,64,48,48,48,48]},\"selected\":{\"id\":\"1048\"},\"selection_policy\":{\"id\":\"1049\"}},\"id\":\"1034\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"1040\",\"type\":\"Title\"},{\"attributes\":{},\"id\":\"1009\",\"type\":\"LinearScale\"}],\"root_ids\":[\"1002\"]},\"title\":\"Bokeh Application\",\"version\":\"2.3.3\"}};\n",
              "  var render_items = [{\"docid\":\"97214897-aac1-47cf-b7dd-f27613b4fd97\",\"root_ids\":[\"1002\"],\"roots\":{\"1002\":\"81fce8f5-a203-425d-8fb9-70c5152d7114\"}}];\n",
              "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
              "\n",
              "  }\n",
              "  if (root.Bokeh !== undefined) {\n",
              "    embed_document(root);\n",
              "  } else {\n",
              "    var attempts = 0;\n",
              "    var timer = setInterval(function(root) {\n",
              "      if (root.Bokeh !== undefined) {\n",
              "        clearInterval(timer);\n",
              "        embed_document(root);\n",
              "      } else {\n",
              "        attempts++;\n",
              "        if (attempts > 100) {\n",
              "          clearInterval(timer);\n",
              "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
              "        }\n",
              "      }\n",
              "    }, 10, root)\n",
              "  }\n",
              "})(window);"
            ],
            "application/vnd.bokehjs_exec.v0+json": ""
          },
          "metadata": {
            "application/vnd.bokehjs_exec.v0+json": {
              "id": "1002"
            }
          }
        }
      ]
    }
  ]
}