Elec_Net.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rraPsJjSyLrW"
   },
   "source": [
    "# An introduction to neural networks using Digital Rocks\n",
    "© Javier E. Santos, Bernard Chang and Maša Prodanović. 2021. Apache License 2.0.\n",
    "\n",
    "-------------------------------------------------------------------------\n",
    "Throughout this notebook we will develop and train neural networks for different petrophysical applications. We will attempt to provide the user with intuition behind how these networks work and best training practices. Let's keep in mind that this notebook is intended to be executed in a 2-hr workshop, so many important concepts will be neglected for the sake of brevity.\n",
    "\n",
    "-------------------------------------------------------------------------\n",
    "A great comprehensive resource is the convolutional neural network class (CS321N) from Stanford University. This can be streamed here: [CS321N youtube playlist link](https://www.youtube.com/playlist?list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv). \n",
    "\n",
    "Many of the concepts shown in this notebook were originally developed for the 3D ConvNet called MS-Net (https://doi.org/10.1007/s11242-021-01617-y). However, this code is new and available only to DRP IAP members."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E0XJG-D_JNJ5"
   },
   "source": [
    "## Before we begin...\n",
    "### Run the following cell to clone our workshop repo.\n",
    "The repository we will be using is a fork of https://github.com/je-santos/ms_net."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-15T17:33:14.582909Z",
     "start_time": "2021-11-15T17:33:14.486079Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "7e28a8477d8a44269dee75e830b22038",
      "ef440316e03e455abc5bd40efdc3f319",
      "205db7e1f6dc4bb785b0bb5d2f15a0cd",
      "cc4205dcdefc4a8aa225c59aa29e89f5",
      "cfa932f1d17b40f2b4bb9ed35b93fe38",
      "7186b0628897425680408231326c5384",
      "d34639c77e854169baece3a5598d8369",
      "9e54c073f11144c59924ce4a2b497037",
      "6976a429b1884c5da05cc9a9f70798e2",
      "74d3764b6a4f4af2bb459b3d80a7fef0",
      "35028c38507d4c9d9d38b426ae7b85f2"
     ]
    },
    "executionInfo": {
     "elapsed": 8739,
     "status": "ok",
     "timestamp": 1636990225683,
     "user": {
      "displayName": "Bernard C Chang",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "06189620559052393654"
     },
     "user_tz": 360
    },
    "id": "1YDIDsQpz2ZC",
    "outputId": "cdcbf320-5699-4234-ed9a-c21b2fc6d6cb"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4d01b57aa8a94d19a06fd863f7f57fc3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Install workshop packages:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The syntax of the command is incorrect.\n"
     ]
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "[WinError 2] The system cannot find the file specified: 'ms-net-workshop'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_8644/1323010095.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m   \u001b[1;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m   \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'ms-net-workshop'\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# Set working directory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m   \u001b[1;31m# Data loading package\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [WinError 2] The system cannot find the file specified: 'ms-net-workshop'"
     ]
    }
   ],
   "source": [
    "from tqdm.notebook import trange\n",
    "from tqdm import tqdm_notebook\n",
    "\n",
    "\n",
    "for i in trange(1, desc='Install workshop packages'):\n",
    "  # Our workshop repository\n",
    "  !git clone https://github.com/BC-Chang/ms-net-workshop.git &> /dev/null\n",
    "\n",
    "  import os\n",
    "  os.chdir('ms-net-workshop')  # Set working directory\n",
    "\n",
    "  # Data loading package\n",
    "  !pip install hdf5storage &> /dev/null\n",
    "\n",
    "  # Install Live Loss Plot Package\n",
    "  !pip install git+https://github.com/je-santos/livelossplot &> /dev/null\n",
    "\n",
    "  # Install Pyvista 3D Visualization\n",
    "  # !pip install pyvista\n",
    "  # !apt-get install -qq xvfb\n",
    "  # !pip install pyvista panel -q"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cl3yQVhlLXgT"
   },
   "source": [
    "### Import some packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "executionInfo": {
     "elapsed": 110,
     "status": "ok",
     "timestamp": 1636992603251,
     "user": {
      "displayName": "Bernard C Chang",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "06189620559052393654"
     },
     "user_tz": 360
    },
    "id": "lcoJ1IF9JFQm"
   },
   "outputs": [],
   "source": [
    "# Pytorch packages\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.optim import Adam, SGD\n",
    "from torch.optim.lr_scheduler import ReduceLROnPlateau# as reduceLR\n",
    "\n",
    "import numpy as np\n",
    "from hdf5storage import loadmat # Data loading\n",
    "from scipy.ndimage.morphology import distance_transform_edt  # Euclidean Distance function\n",
    "\n",
    "# Network packages\n",
    "from network import MS_Net \n",
    "from pore_utils import scale_tensor\n",
    "from network_tools import get_masks\n",
    "\n",
    "\n",
    "# Visualization packages\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg \n",
    "from matplotlib.patches import Ellipse, Polygon\n",
    "import seaborn as sns\n",
    "from IPython.display import clear_output\n",
    "import ipywidgets as widgets\n",
    "from IPython.display import display\n",
    "from livelossplot import PlotLosses\n",
    "# import pyvista\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 527,
     "status": "ok",
     "timestamp": 1636992532005,
     "user": {
      "displayName": "Bernard C Chang",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "06189620559052393654"
     },
     "user_tz": 360
    },
    "id": "HDp3IIG1W7AY",
    "outputId": "991eddd2-0c8c-4b81-a0d0-7e3f85322251"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "remote: Enumerating objects: 6, done.\u001b[K\n",
      "remote: Counting objects:  16% (1/6)\u001b[K\r",
      "remote: Counting objects:  33% (2/6)\u001b[K\r",
      "remote: Counting objects:  50% (3/6)\u001b[K\r",
      "remote: Counting objects:  66% (4/6)\u001b[K\r",
      "remote: Counting objects:  83% (5/6)\u001b[K\r",
      "remote: Counting objects: 100% (6/6)\u001b[K\r",
      "remote: Counting objects: 100% (6/6), done.\u001b[K\n",
      "remote: Compressing objects:  25% (1/4)\u001b[K\r",
      "remote: Compressing objects:  50% (2/4)\u001b[K\r",
      "remote: Compressing objects:  75% (3/4)\u001b[K\r",
      "remote: Compressing objects: 100% (4/4)\u001b[K\r",
      "remote: Compressing objects: 100% (4/4), done.\u001b[K\n",
      "Unpacking objects:  25% (1/4)   \r",
      "Unpacking objects:  50% (2/4)   \r",
      "Unpacking objects:  75% (3/4)   \r",
      "remote: Total 4 (delta 1), reused 0 (delta 0), pack-reused 0\u001b[K\n",
      "Unpacking objects: 100% (4/4)   \r",
      "Unpacking objects: 100% (4/4), done.\n",
      "From https://github.com/BC-Chang/ms-net-workshop\n",
      "   b39eebe..0435e74  main       -> origin/main\n",
      "Updating b39eebe..0435e74\n",
      "Fast-forward\n",
      " images/EmptyNeuralNet1.png | Bin \u001b[31m0\u001b[m -> \u001b[32m38647\u001b[m bytes\n",
      " 1 file changed, 0 insertions(+), 0 deletions(-)\n",
      " create mode 100644 images/EmptyNeuralNet1.png\n"
     ]
    }
   ],
   "source": [
    "!git pull"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ykErnZP_MCSR"
   },
   "source": [
    "# 1.0 Introduction\n",
    "\n",
    "Where are we?\n",
    "\n",
    "![](https://www.google.com/images/logos/google_logo_41.png) Colab\n",
    "- Google colab is an enviroment by that allows to write and execute python code directly in a browser. It doesn't require any prior configuration or software installation.\n",
    "\n",
    "    - It also provides free GPU access: Runtime -> change runtime type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 115,
     "status": "ok",
     "timestamp": 1636990573353,
     "user": {
      "displayName": "Bernard C Chang",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "06189620559052393654"
     },
     "user_tz": 360
    },
    "id": "r4oqyKjUL6ew",
    "outputId": "76d82358-dc24-427b-f197-39af0aec10c6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is a code cell. One can perform operations here: 1+3 = 4\n"
     ]
    }
   ],
   "source": [
    "print(f'This is a code cell. One can perform operations here: 1+3 = {1+3}')\n",
    "# One can you this cell with the play button or with shift+enter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dyTjCu_NPgwu"
   },
   "source": [
    "<-- To the left there is button to display the table of contents, in case anyone gets lost along the way\n",
    "\n",
    "A very common error is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 171
    },
    "executionInfo": {
     "elapsed": 76,
     "status": "error",
     "timestamp": 1636990631908,
     "user": {
      "displayName": "Bernard C Chang",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "06189620559052393654"
     },
     "user_tz": 360
    },
    "id": "j7fNkbx-PffT",
    "outputId": "3070bfd8-b7ef-4dcf-ea18-08d164c84e75"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-15-bb348eb6a236>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mNameError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'x is not defined'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: x is not defined"
     ]
    }
   ],
   "source": [
    "raise NameError('x is not defined')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1BmeFE7SPtGj"
   },
   "source": [
    "This is likely to happen when previous cells of code were not run."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0nObTaPdPw6M"
   },
   "source": [
    "## 1.1 What is a neuron?\n",
    "\n",
    "A neuron performs a mathematical operation where the input data ($x$) is multiplied by a $\\textbf{weight}$ ($w$) followed by the addition of a $\\textbf{bias}$ ($b$). These weights and biases are called $\\textbf{trainable parameters}$.\n",
    "\n",
    "$y=x*w+b$\n",
    "\n",
    "Additionally, this output can be passed through a non-linear activation function.\n",
    "\n",
    "$y_{_{NL}} = f(y)$\n",
    "\n",
    "Some examples of non-linearities commonly used in NN are the following:\n",
    "\n",
    "- $\\text{ReLU}(y)     = \\text{max}(0, y)$\n",
    "\n",
    "- $\\text{sigmoid}(y) = \\frac{1}{1 + \\text{e}^{-y}}$\n",
    "\n",
    "- $\\text{tanh}(y) = \\frac{e^x - e^{-x}}{e^x + e^{-x}}$\n",
    "\n",
    "Each function has distinct properties that can benefit certain applications."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 462,
     "referenced_widgets": [
      "c66924481bed40809f0b9a7ad1b45120",
      "b987a6d05aac4e75ad159a969ff9d2df",
      "be69999156444f629feb805bd9a4bcaf",
      "376a9f9870a942798c0490bdcb8fda21",
      "b387f05f31c9492bba4eb98dafe6491f",
      "5a95d33c5eb6449287e1f37549541f2d",
      "6896822334cb42e88db4925abaaf02a3",
      "2d00def2e94b4ff39e6d619c6068c4c9",
      "c4b71c7111664ba5b662edbfefe1a297",
      "c1ade49287704335aab335c4251d4362",
      "fd2e8d1a56964d62b11e32c3544c28a9",
      "ae284c77d9cf479285485743867b5557"
     ]
    },
    "executionInfo": {
     "elapsed": 497,
     "status": "ok",
     "timestamp": 1636992837417,
     "user": {
      "displayName": "Bernard C Chang",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "06189620559052393654"
     },
     "user_tz": 360
    },
    "id": "FdxdlRB8Q20k",
    "outputId": "95aa91e5-e802-42d9-919c-3e7dff9bf462"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c66924481bed40809f0b9a7ad1b45120",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "IntRangeSlider(value=(-10, 10), description='Input range:', max=10, min=-10)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "376a9f9870a942798c0490bdcb8fda21",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatSlider(value=1.0, description='Weight value:', max=5.0, min=-5.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6896822334cb42e88db4925abaaf02a3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatSlider(value=0.0, description='Bias value:', max=5.0, min=-5.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c1ade49287704335aab335c4251d4362",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "RadioButtons(description='Activation:', options=('linear', 'ReLU', 'sigmoid', 'tanh'), value='linear')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": [
    "## @title 1.2 A single neuron\n",
    "\n",
    "xi = widgets.IntRangeSlider()\n",
    "xi.min = -10\n",
    "xi.max =  10\n",
    "xi.value= [-10, 10]\n",
    "xi.description='Input range:'\n",
    "display(xi)\n",
    "\n",
    "\n",
    "w = widgets.FloatSlider()\n",
    "w.value= 1\n",
    "w.min = -5\n",
    "w.max =  5\n",
    "w.description='Weight value:'\n",
    "display(w)\n",
    "\n",
    "b = widgets.FloatSlider()\n",
    "b.value= 0\n",
    "b.min = -5\n",
    "b.max =  5\n",
    "b.description='Bias value:'\n",
    "display(b)\n",
    "\n",
    "act = widgets.RadioButtons()\n",
    "act.options=['linear','ReLU','sigmoid', 'tanh']\n",
    "act.description = 'Activation:'\n",
    "display(act)\n",
    "\n",
    "    \n",
    "def plot_w(*args):\n",
    "    print(f'Hi my first value is {xi.value[0]}')\n",
    "    x_t = np.linspace(xi.value[0],xi.value[1])\n",
    "    y_t = x_t*w.value+b.value\n",
    "    lim = ([-50, 50])\n",
    "    if act.value=='sigmoid':\n",
    "        y_t = 1 / (1 + np.exp(-y_t))\n",
    "        lim = ([-0.5, 1.2])\n",
    "    if act.value=='ReLU':\n",
    "        y_t = np.maximum(y_t,0)\n",
    "    if act.value=='tanh':\n",
    "        y_t = (np.exp(y_t) - np.exp(-y_t))/(np.exp(y_t)+np.exp(-y_t))\n",
    "        lim = ([-1.2, 1.2])\n",
    "\n",
    "    #plt.figure(1)\n",
    "    #plt.close()\n",
    "    clear_output(wait=True)\n",
    "    display(xi,w,b,act)\n",
    "    plt.plot(x_t, np.zeros_like(x_t), 'bo', alpha=0.6)\n",
    "    plt.plot(x_t, y_t, 'ro', alpha=0.5)\n",
    "    plt.ylim(lim)\n",
    "    plt.xlim([-10 ,10])\n",
    "    plt.legend(['input data','output of a single neuron'])\n",
    "    plt.xlabel('input')\n",
    "    plt.ylabel('output')\n",
    "    plt.show()\n",
    "\n",
    "plot_w()\n",
    "\n",
    "xi.observe(plot_w,'value')\n",
    "w.observe(plot_w,'value')\n",
    "b.observe(plot_w,'value')\n",
    "act.observe(plot_w,'value')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2mWxiCrvYbMp"
   },
   "source": [
    "The neuron is the basic building block in neural networks. Although the concept is simple, when we nest several of these, we can fit very complicated functions.\n",
    "\n",
    "$y = f(x*w_1+b_1)$ can be the output of a previous layer (one or multiple neurons)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DSfed-45Ypyn"
   },
   "source": [
    "## 1.3 How does a neuron learn?\n",
    "\n",
    "The values of the neuron's weight ($w$) and  bias ($b$) are adjusted (or trained) using the training data provided. These parameters are trained by minimizing the function:\n",
    "\n",
    "$Loss = loss\\_f(y_{pred}, y_{true})$\n",
    "\n",
    "Intuitively, this process can be seen as minimizing the error (or mismatch) of the training data with the output of the neural network. Common loss functions ($\\text{$loss\\_f$}$) for inference include mean squared error ($\\textit{MSE}$) and mean absolute error ($\\textit{MAE}$), and binary cross-entropy for segmentation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rtuaADEEbQCd"
   },
   "source": [
    "## 1.4 What happens when we use multiple neurons? \n",
    "\n",
    "![image here](https://raw.githubusercontent.com/je-santos/MPLBM-UT/master/illustrations/playground.png)\n",
    "\n",
    "\n",
    "Let's go to: https://playground.tensorflow.org\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5gM3HZJNd8ZB"
   },
   "source": [
    "## 2.1 Training our first neural network\n",
    "We'll start with the simplest neural network architecture. A model with one neuron, which is equivalent to a linear regression:\n",
    "\n",
    "$y=x*w_1+b_1$\n",
    "\n",
    "We will use mean square error:\n",
    "\n",
    "$\\text{MSE}(y_{pred},y)=\\frac{1}{N}\\sum(y_{pred}-y)^2$\n",
    "\n",
    "and the ADAM optimizer:\n",
    "\n",
    "$\\theta_{t+1} = \\theta_{t}-LR\\frac{v_t}{\\sqrt{s_t+\\epsilon}}\\cdot g_t$\n",
    "\n",
    "Where $v_t$ and $s_t$ are the first and second gradient moments respectively  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "R5BEly1ObPG3"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YrEeDRGeOldu"
   },
   "source": [
    "## Load in data\n",
    "Single berea pore.\n",
    "\n",
    "Image size = 108 x 108 x 108"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Mbbs2RxkLPNw"
   },
   "outputs": [],
   "source": [
    "x = loadmat('./example_data/berea_pore_108.mat')['bin']\n",
    "y = loadmat('./example_data/berea_pore_108_detrended.mat')['detrended_dom']\n",
    "phi = loadmat('./example_data/berea_pore_108_elec.mat')['phi']\n",
    "linear = loadmat('./example_data/berea_pore_108_linear_trend.mat')['linear']\n",
    "edist = distance_transform_edt(x)\n",
    "edist = edist / np.amax(edist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9vF6fQmcRi9c"
   },
   "source": [
    "## Data Preprocessing\n",
    "### Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 275
    },
    "executionInfo": {
     "elapsed": 320,
     "status": "ok",
     "timestamp": 1636827940325,
     "user": {
      "displayName": "Bernard C Chang",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "06189620559052393654"
     },
     "user_tz": 360
    },
    "id": "2xicApcSIOgL",
    "outputId": "7ccbd23c-cbc8-4f67-cb4d-a7ee1645e612"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 107.5, 107.5, -0.5)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO Can we do 3D Visualization of this?\n",
    "\n",
    "plt.imshow(x[:,54,:], vmin=0, vmax=1); \n",
    "plt.colorbar()\n",
    "plt.axis('off')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 285
    },
    "executionInfo": {
     "elapsed": 576,
     "status": "ok",
     "timestamp": 1636827942877,
     "user": {
      "displayName": "Bernard C Chang",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "06189620559052393654"
     },
     "user_tz": 360
    },
    "id": "dnk2Jj9jiT10",
    "outputId": "7936012c-b94d-4e5e-de02-0bf3d9a921f9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f06220b2a90>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(edist[:,54,:]);\n",
    "plt.colorbar()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 287
    },
    "executionInfo": {
     "elapsed": 311,
     "status": "ok",
     "timestamp": 1636827945295,
     "user": {
      "displayName": "Bernard C Chang",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "06189620559052393654"
     },
     "user_tz": 360
    },
    "id": "rh0MsiDEmEiO",
    "outputId": "aaf443c4-cffc-4624-f5ef-2b5c0b90bea3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 107.5, 107.5, -0.5)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(phi[:,54,:]); plt.colorbar()\n",
    "plt.title('Electric Potential')\n",
    "plt.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 271
    },
    "executionInfo": {
     "elapsed": 438,
     "status": "ok",
     "timestamp": 1636827947969,
     "user": {
      "displayName": "Bernard C Chang",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "06189620559052393654"
     },
     "user_tz": 360
    },
    "id": "mSF9KNxulb42",
    "outputId": "a53b35e6-607d-442f-fb27-1e4d63093f8b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 107.0, 107.0, 0.0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.contourf(linear[:,54,:], levels=20); plt.colorbar()\n",
    "ax = plt.gca()\n",
    "ax.invert_yaxis()\n",
    "plt.axis('off')\n",
    "plt.axis('equal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 281
    },
    "executionInfo": {
     "elapsed": 204,
     "status": "ok",
     "timestamp": 1636827950421,
     "user": {
      "displayName": "Bernard C Chang",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "06189620559052393654"
     },
     "user_tz": 360
    },
    "id": "tzjLgxvzRh4z",
    "outputId": "78dd12cc-feed-4205-cc05-626f34c394a1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 107.5, 107.5, -0.5)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO Can we do 3D Viz w/ Pyvista?\n",
    "y = phi - linear\n",
    "plt.imshow(y[:,54,:], cmap='magma')\n",
    "plt.colorbar()\n",
    "plt.title('Detrended Electric Potential')\n",
    "plt.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9lnWuzSnT0eE"
   },
   "source": [
    "### Normalize data and compute scales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "wgqiCGY9T8rk"
   },
   "outputs": [],
   "source": [
    "# This function normalizes the given data, converts to a Torch tensor,\n",
    "# and downscales image to specified number of scales\n",
    "\n",
    "def preprocess_data(data, num_scales=2):\n",
    "  # Normalize data\n",
    "  # data = data / np.amax(data)\n",
    "  # data = (data - np.amin(data)) / (np.amax(data) - np.amin(data))\n",
    "\n",
    "  # Convert data into torch tensor\n",
    "  data = torch.as_tensor(data, dtype=torch.float32)\n",
    "  \n",
    "  # Add dimensions to torch tensor\n",
    "  data = data[None, None, :, :, :]\n",
    "\n",
    "  # Compute data on different scales\n",
    "  ds_data = []\n",
    "  ds_data.append(data)\n",
    "  for i in range( num_scales-1 ):\n",
    "    ds_data.append( scale_tensor( ds_data[-1], scale_factor=1/2, mode='nearest') )\n",
    "  \n",
    "  data = ds_data[::-1] # Returns the reversed list (smallest images first)\n",
    "\n",
    "  return data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "N3LM0FtJTuty"
   },
   "outputs": [],
   "source": [
    "num_scales = 3\n",
    "\n",
    "x_n = preprocess_data(x, num_scales)\n",
    "y_n = preprocess_data(y, num_scales)\n",
    "\n",
    "# Get masks\n",
    "masks = get_masks(x_n[-1], scales=num_scales)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5V-7mJf8VxDd"
   },
   "source": [
    "### Visualize scaled data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 428
    },
    "executionInfo": {
     "elapsed": 918,
     "status": "ok",
     "timestamp": 1636827966533,
     "user": {
      "displayName": "Bernard C Chang",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "06189620559052393654"
     },
     "user_tz": 360
    },
    "id": "pjsVHpUYVvvq",
    "outputId": "7b4f44c2-6d3d-4a4c-bd65-ce2fa2ef2ce3"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2750x2000 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(11,8), dpi=250)\n",
    "\n",
    "for i in range(num_scales):\n",
    "  plt.subplot(1,num_scales,i+1)\n",
    "  my_slice = int(y_n[-i-1].size()[-1]/2)\n",
    "  plt.imshow(y_n[-i-1][0,0,:,my_slice,:], cmap='magma')\n",
    "\n",
    "# plt.subplot(1,4,2)\n",
    "# plt.imshow(y_n[-2][0,0,:,27,:], cmap='magma')\n",
    "\n",
    "fig.subplots_adjust(bottom=0.1, top=0.9, left=0.1, right=0.8,\n",
    "                    wspace=0.5, hspace=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 298
    },
    "executionInfo": {
     "elapsed": 521,
     "status": "ok",
     "timestamp": 1636827968948,
     "user": {
      "displayName": "Bernard C Chang",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "06189620559052393654"
     },
     "user_tz": 360
    },
    "id": "CQ7DZRB8-3iQ",
    "outputId": "2648b5f0-3921-4ce9-e92f-f8cc43fe6343"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Detrended Data')"
      ]
     },
     "execution_count": 13,
     "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.subplot(1,2,1)\n",
    "\n",
    "plt.hist(y[x==1].ravel(), 50)\n",
    "plt.title('Detrended Data')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "w_fjHXxYXepb"
   },
   "source": [
    "## Set up model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 90,
     "status": "ok",
     "timestamp": 1636838065887,
     "user": {
      "displayName": "Bernard C Chang",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "06189620559052393654"
     },
     "user_tz": 360
    },
    "id": "r1rnrP0SVwRK",
    "outputId": "fd3bc3b9-3339-430e-c5f5-990be7fdf423"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filters per model: [96, 24, 6]\n"
     ]
    }
   ],
   "source": [
    "device = 'cuda'  # Include this line to train on GPUs\n",
    "\n",
    "bin_model = MS_Net(\n",
    "    num_scales = num_scales,\n",
    "    num_features = 1,\n",
    "    num_filters = 6, \n",
    "    device = device,\n",
    "    summary = False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "DdtaU3AGYzZV"
   },
   "outputs": [],
   "source": [
    "# Set up training parameters\n",
    "learning_rate = 1e-5\n",
    "epochs = 2000\n",
    "model_name = 'workshop_bin_model'\n",
    "optimizer = Adam(bin_model.parameters(), lr=learning_rate)  # Adam optimizer\n",
    "scheduler  = ReduceLROnPlateau( optimizer, verbose=True, threshold=1e-6, min_lr=1e-6, patience = 25 )  # Learning rate scheduler\n",
    "loss_f = nn.MSELoss()  # MSE loss function\n",
    "loss = 1e9  # Initialize value of loss\n",
    "best_loss = 1e10\n",
    "liveloss = PlotLosses()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "fUPQ3ig3EvIh"
   },
   "outputs": [],
   "source": [
    "# Send data to GPU\n",
    "bin_model = bin_model.to(device)\n",
    "masks = [m.to(device) for m in masks]\n",
    "x_n = [x.to(device) for x in x_n]\n",
    "y_n = [y.to(device) for y in y_n]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "aO4Zl4ZoaAOc"
   },
   "outputs": [],
   "source": [
    "def calc_loss(y_pred, y, loss_f, log):\n",
    "    \n",
    "    loss = 0\n",
    "    y_var = y[-1].var()\n",
    "    for scale, [y_hat,yi] in enumerate(zip(y_pred, y)):\n",
    "        loss_scale = loss_f(y_hat,yi)#/y_var \n",
    "        loss += loss_scale\n",
    "        prefix = f'scale_{scale}_'\n",
    "        log[prefix + 'loss'] =  loss_scale.item()\n",
    "\n",
    "    log['loss'] = loss.item()\n",
    "    \n",
    "    return loss, log"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5vbOfqsoZqui"
   },
   "source": [
    "## Training Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 275357,
     "status": "error",
     "timestamp": 1636841282951,
     "user": {
      "displayName": "Bernard C Chang",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "06189620559052393654"
     },
     "user_tz": 360
    },
    "id": "f3PyExAEZqB3",
    "outputId": "88d7e741-323c-4687-9d1a-8191323d42bb"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x1152 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss\n",
      "\ttraining         \t (min:    0.001, max:    0.004, cur:    0.001)\n",
      "lr\n",
      "\tlr               \t (min:    0.000, max:    0.000, cur:    0.000)\n",
      "scale_0_loss\n",
      "\ttraining         \t (min:    0.000, max:    0.001, cur:    0.000)\n",
      "scale_1_loss\n",
      "\ttraining         \t (min:    0.000, max:    0.001, cur:    0.000)\n",
      "scale_2_loss\n",
      "\ttraining         \t (min:    0.000, max:    0.002, cur:    0.000)\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-139-ab66a67ee4f7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0;31m# compute the model output\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0my_hat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbin_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_n\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0mloss_prev\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1100\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1101\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1103\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1104\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/content/ms-net-workshop/network.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x_list, masks)\u001b[0m\n\u001b[1;32m    150\u001b[0m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mx_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m]\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 152\u001b[0;31m             \u001b[0my_up\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscale_tensor\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mscale\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscale_factor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mmasks\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mscale\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    153\u001b[0m             \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_up\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my_up\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    154\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/content/ms-net-workshop/network_tools.py\u001b[0m in \u001b[0;36mscale_tensor\u001b[0;34m(x, scale_factor, mode)\u001b[0m\n\u001b[1;32m     48\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mrepeat\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscale_factor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m#number of repeatsx2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0max\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m                     \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrepeat_interleave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepeats\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for epoch in range(epochs):\n",
    "    logs={}\n",
    "    \n",
    "    # clear the gradients\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    # compute the model output\n",
    "    y_hat = bin_model(x_n, masks)\n",
    "\n",
    "    loss_prev = loss\n",
    "    loss, logs = calc_loss(y_hat, y_n, loss_f, logs) \n",
    "\n",
    "    # credit assignment\n",
    "    loss.backward()\n",
    "\n",
    "    # update model weights\n",
    "    optimizer.step()\n",
    "    # print('Loss: %r\\n' %loss)\n",
    "    scheduler.step(loss)\n",
    "    # Save model\n",
    "    if epoch % 100 == 0 and loss < best_loss:  # Save model every 100 epochs\n",
    "        best_loss = loss\n",
    "        print(f'Epoch: {epoch}, Best loss: {float(best_loss)}')\n",
    "    \n",
    "    logs['lr'] = optimizer.param_groups[0]['lr']\n",
    "    liveloss.update(logs)\n",
    "    liveloss.send()\n",
    "\n",
    "    #     print('Saving model...')\n",
    "    #     torch.save(net, f'{model_name}.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 849,
     "status": "ok",
     "timestamp": 1636841287738,
     "user": {
      "displayName": "Bernard C Chang",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "06189620559052393654"
     },
     "user_tz": 360
    },
    "id": "zGX9d1TRa4ji",
    "outputId": "92305ff4-dfbc-42a7-c559-a8cd3920dc8d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f0611fbe890>"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1500x900 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#%% Evaluate Model\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(5,3), dpi=300)\n",
    "\n",
    "y_n = [y.cpu() for y in y_n]\n",
    "axes[0].set_axis_off()\n",
    "im = axes[0].imshow(y_n[-1][0,0,:,54,:]*np.amax(y), vmin=-0.3, vmax=0.6, cmap='magma')\n",
    "axes[0].title.set_text('Simulation Result')\n",
    "\n",
    "y_hat = [yh.cpu() for yh in y_hat]\n",
    "pred = y_hat[-1].detach()\n",
    "\n",
    "axes[1].set_axis_off()\n",
    "im = axes[1].imshow(pred[0,0,:,54,:]*np.amax(y), vmin=-0.3, vmax=0.6, cmap='magma')\n",
    "axes[1].title.set_text('Network Prediction')\n",
    "\n",
    "\n",
    "plt.suptitle('Detrended Electric Potential')\n",
    "fig.subplots_adjust(bottom=0.1, top=0.9, left=0.1, right=0.8,\n",
    "                    wspace=0.2, hspace=0.01)\n",
    "cb_ax = fig.add_axes([0.83, 0.1, 0.02, 0.8])\n",
    "cbar = fig.colorbar(im, cax=cb_ax)\n",
    "# plt.show()\n",
    "\n",
    "plt.figure(figsize=(8,5))\n",
    "plt.axis('off')\n",
    "im = plt.imshow(np.abs(y_n[-1][0,0,:,54,:] - pred[0,0,:,54,:]), vmin=0, vmax=0.02)#/y_n[-1][0,0,:,54,:] * 100, vmin=0, vmax=100)\n",
    "plt.title('Absolute Error')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 72,
     "status": "ok",
     "timestamp": 1636841295184,
     "user": {
      "displayName": "Bernard C Chang",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "06189620559052393654"
     },
     "user_tz": 360
    },
    "id": "9bSxVORrYzhi",
    "outputId": "22685bb7-a083-4ab2-bd14-2272f212e037"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filters per model: [96, 24, 6]\n"
     ]
    }
   ],
   "source": [
    "device = 'cuda'  # Include this line to train on GPUs\n",
    "\n",
    "edist_model = MS_Net(\n",
    "    num_scales = num_scales,\n",
    "    num_features = 1,\n",
    "    num_filters = 6, \n",
    "    device = device,\n",
    "    summary = False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "_Mqcc7zeixuJ"
   },
   "outputs": [],
   "source": [
    "# Set up training parameters\n",
    "learning_rate = 1e-3\n",
    "epochs = 1000\n",
    "model_name = 'workshop_edist_model'\n",
    "optimizer = Adam(edist_model.parameters(), lr=learning_rate)  # Adam optimizer\n",
    "scheduler  = ReduceLROnPlateau( optimizer, verbose=True, threshold=1e-6, min_lr=1e-6, patience = 25 )  # Learning rate scheduler\n",
    "loss_f = nn.MSELoss()  # MSE loss function\n",
    "loss = 1e9  # Initialize value of loss\n",
    "best_loss = 1e10\n",
    "liveloss = PlotLosses()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "b8sIYKWejMbC"
   },
   "outputs": [],
   "source": [
    "num_scales = 3\n",
    "\n",
    "edist_n = preprocess_data(edist, num_scales)\n",
    "y_n = preprocess_data(y, num_scales)\n",
    "\n",
    "# Get masks\n",
    "masks = get_masks(edist_n[-1], scales=num_scales)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "11knjUiYi_9l"
   },
   "outputs": [],
   "source": [
    "# Send data to GPU\n",
    "edist_model = edist_model.to(device)\n",
    "masks = [m.to(device) for m in masks]\n",
    "edist_n = [ed.to(device) for ed in edist_n]\n",
    "y_n = [y.to(device) for y in y_n]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 3037430,
     "status": "ok",
     "timestamp": 1636844410913,
     "user": {
      "displayName": "Bernard C Chang",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "06189620559052393654"
     },
     "user_tz": 360
    },
    "id": "JzOwEFp5i8Kk",
    "outputId": "12debc72-1cc8-4c51-ee34-30e6a74cd66f"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x1152 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss\n",
      "\ttraining         \t (min:    0.002, max:   14.115, cur:    0.002)\n",
      "lr\n",
      "\tlr               \t (min:    0.000, max:    0.001, cur:    0.000)\n",
      "scale_0_loss\n",
      "\ttraining         \t (min:    0.001, max:    6.778, cur:    0.001)\n",
      "scale_1_loss\n",
      "\ttraining         \t (min:    0.000, max:    3.057, cur:    0.000)\n",
      "scale_2_loss\n",
      "\ttraining         \t (min:    0.000, max:    4.971, cur:    0.000)\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(epochs):\n",
    "    logs={}\n",
    "    \n",
    "    # clear the gradients\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    # compute the model output\n",
    "    y_hat = edist_model(edist_n, masks)\n",
    "\n",
    "    loss_prev = loss\n",
    "    loss, logs = calc_loss(y_hat, y_n, loss_f, logs) \n",
    "\n",
    "    # credit assignment\n",
    "    loss.backward()\n",
    "\n",
    "    # update model weights\n",
    "    optimizer.step()\n",
    "    # print('Loss: %r\\n' %loss)\n",
    "    scheduler.step(loss)\n",
    "    # Save model\n",
    "    if epoch % 100 == 0 and loss < best_loss:  # Save model every 100 epochs\n",
    "        best_loss = loss\n",
    "        print(f'Epoch: {epoch}, Best loss: {float(best_loss)}')\n",
    "    logs['loss'] = loss.item()\n",
    "    logs['lr'] = scheduler.optimizer.param_groups[0]['lr']\n",
    "      \n",
    "    liveloss.update(logs)\n",
    "    liveloss.send()\n",
    "\n",
    "    #     print('Saving model...')\n",
    "    #     torch.save(net, f'{model_name}.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 34583,
     "status": "ok",
     "timestamp": 1636565828823,
     "user": {
      "displayName": "Bernard C Chang",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "06189620559052393654"
     },
     "user_tz": 360
    },
    "id": "7ev13FA60nn-",
    "outputId": "faddfd33-429b-4ae1-bd4c-baeb2e441af9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pyvista\n",
      "  Downloading pyvista-0.32.1-py3-none-any.whl (1.4 MB)\n",
      "\u001b[K     |████████████████████████████████| 1.4 MB 5.3 MB/s \n",
      "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from pyvista) (1.19.5)\n",
      "Requirement already satisfied: appdirs in /usr/local/lib/python3.7/dist-packages (from pyvista) (1.4.4)\n",
      "Collecting scooby>=0.5.1\n",
      "  Downloading scooby-0.5.7-py3-none-any.whl (13 kB)\n",
      "Requirement already satisfied: imageio in /usr/local/lib/python3.7/dist-packages (from pyvista) (2.4.1)\n",
      "Collecting vtk\n",
      "  Downloading vtk-9.1.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (88.3 MB)\n",
      "\u001b[K     |████████████████████████████████| 88.3 MB 101 kB/s \n",
      "\u001b[?25hCollecting meshio<5.0,>=4.0.3\n",
      "  Downloading meshio-4.4.6-py3-none-any.whl (158 kB)\n",
      "\u001b[K     |████████████████████████████████| 158 kB 45.3 MB/s \n",
      "\u001b[?25hRequirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from pyvista) (3.10.0.2)\n",
      "Requirement already satisfied: pillow in /usr/local/lib/python3.7/dist-packages (from pyvista) (7.1.2)\n",
      "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from meshio<5.0,>=4.0.3->pyvista) (4.8.1)\n",
      "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->meshio<5.0,>=4.0.3->pyvista) (3.6.0)\n",
      "Collecting wslink>=1.0.4\n",
      "  Downloading wslink-1.1.0-py3-none-any.whl (20 kB)\n",
      "Requirement already satisfied: matplotlib>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from vtk->pyvista) (3.2.2)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.0.0->vtk->pyvista) (2.8.2)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.0.0->vtk->pyvista) (1.3.2)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.0.0->vtk->pyvista) (2.4.7)\n",
      "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.0.0->vtk->pyvista) (0.11.0)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib>=2.0.0->vtk->pyvista) (1.15.0)\n",
      "Collecting aiohttp\n",
      "  Downloading aiohttp-3.8.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.1 MB)\n",
      "\u001b[K     |████████████████████████████████| 1.1 MB 33.8 MB/s \n",
      "\u001b[?25hRequirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->wslink>=1.0.4->vtk->pyvista) (21.2.0)\n",
      "Collecting multidict<7.0,>=4.5\n",
      "  Downloading multidict-5.2.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (160 kB)\n",
      "\u001b[K     |████████████████████████████████| 160 kB 46.8 MB/s \n",
      "\u001b[?25hCollecting aiosignal>=1.1.2\n",
      "  Downloading aiosignal-1.2.0-py3-none-any.whl (8.2 kB)\n",
      "Collecting frozenlist>=1.1.1\n",
      "  Downloading frozenlist-1.2.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (192 kB)\n",
      "\u001b[K     |████████████████████████████████| 192 kB 27.5 MB/s \n",
      "\u001b[?25hRequirement already satisfied: charset-normalizer<3.0,>=2.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->wslink>=1.0.4->vtk->pyvista) (2.0.7)\n",
      "Collecting asynctest==0.13.0\n",
      "  Downloading asynctest-0.13.0-py3-none-any.whl (26 kB)\n",
      "Collecting yarl<2.0,>=1.0\n",
      "  Downloading yarl-1.7.2-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (271 kB)\n",
      "\u001b[K     |████████████████████████████████| 271 kB 45.5 MB/s \n",
      "\u001b[?25hCollecting async-timeout<5.0,>=4.0.0a3\n",
      "  Downloading async_timeout-4.0.0-py3-none-any.whl (6.1 kB)\n",
      "Requirement already satisfied: idna>=2.0 in /usr/local/lib/python3.7/dist-packages (from yarl<2.0,>=1.0->aiohttp->wslink>=1.0.4->vtk->pyvista) (2.10)\n",
      "Installing collected packages: multidict, frozenlist, yarl, asynctest, async-timeout, aiosignal, aiohttp, wslink, vtk, scooby, meshio, pyvista\n",
      "Successfully installed aiohttp-3.8.0 aiosignal-1.2.0 async-timeout-4.0.0 asynctest-0.13.0 frozenlist-1.2.0 meshio-4.4.6 multidict-5.2.0 pyvista-0.32.1 scooby-0.5.7 vtk-9.1.0 wslink-1.1.0 yarl-1.7.2\n",
      "Collecting ipyvtklink\n",
      "  Downloading ipyvtklink-0.2.1-py3-none-any.whl (9.2 kB)\n",
      "Collecting ipycanvas>=0.5.0\n",
      "  Downloading ipycanvas-0.9.1-py2.py3-none-any.whl (246 kB)\n",
      "\u001b[K     |████████████████████████████████| 246 kB 5.3 MB/s \n",
      "\u001b[?25hCollecting ipyevents>=0.8.0\n",
      "  Downloading ipyevents-2.0.1-py2.py3-none-any.whl (130 kB)\n",
      "\u001b[K     |████████████████████████████████| 130 kB 27.1 MB/s \n",
      "\u001b[?25hRequirement already satisfied: ipywidgets in /usr/local/lib/python3.7/dist-packages (from ipyvtklink) (7.6.5)\n",
      "Requirement already satisfied: pillow>=6.0 in /usr/local/lib/python3.7/dist-packages (from ipycanvas>=0.5.0->ipyvtklink) (7.1.2)\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from ipycanvas>=0.5.0->ipyvtklink) (1.19.5)\n",
      "Requirement already satisfied: nbformat>=4.2.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets->ipyvtklink) (5.1.3)\n",
      "Requirement already satisfied: widgetsnbextension~=3.5.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets->ipyvtklink) (3.5.2)\n",
      "Requirement already satisfied: traitlets>=4.3.1 in /usr/local/lib/python3.7/dist-packages (from ipywidgets->ipyvtklink) (5.1.1)\n",
      "Requirement already satisfied: ipykernel>=4.5.1 in /usr/local/lib/python3.7/dist-packages (from ipywidgets->ipyvtklink) (4.10.1)\n",
      "Requirement already satisfied: ipython-genutils~=0.2.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets->ipyvtklink) (0.2.0)\n",
      "Requirement already satisfied: jupyterlab-widgets>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets->ipyvtklink) (1.0.2)\n",
      "Requirement already satisfied: ipython>=4.0.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets->ipyvtklink) (5.5.0)\n",
      "Requirement already satisfied: jupyter-client in /usr/local/lib/python3.7/dist-packages (from ipykernel>=4.5.1->ipywidgets->ipyvtklink) (5.3.5)\n",
      "Requirement already satisfied: tornado>=4.0 in /usr/local/lib/python3.7/dist-packages (from ipykernel>=4.5.1->ipywidgets->ipyvtklink) (5.1.1)\n",
      "Requirement already satisfied: pexpect in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipywidgets->ipyvtklink) (4.8.0)\n",
      "Requirement already satisfied: pygments in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipywidgets->ipyvtklink) (2.6.1)\n",
      "Requirement already satisfied: prompt-toolkit<2.0.0,>=1.0.4 in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipywidgets->ipyvtklink) (1.0.18)\n",
      "Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipywidgets->ipyvtklink) (57.4.0)\n",
      "Requirement already satisfied: simplegeneric>0.8 in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipywidgets->ipyvtklink) (0.8.1)\n",
      "Requirement already satisfied: decorator in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipywidgets->ipyvtklink) (4.4.2)\n",
      "Requirement already satisfied: pickleshare in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipywidgets->ipyvtklink) (0.7.5)\n",
      "Requirement already satisfied: jupyter-core in /usr/local/lib/python3.7/dist-packages (from nbformat>=4.2.0->ipywidgets->ipyvtklink) (4.9.1)\n",
      "Requirement already satisfied: jsonschema!=2.5.0,>=2.4 in /usr/local/lib/python3.7/dist-packages (from nbformat>=4.2.0->ipywidgets->ipyvtklink) (2.6.0)\n",
      "Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython>=4.0.0->ipywidgets->ipyvtklink) (0.2.5)\n",
      "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython>=4.0.0->ipywidgets->ipyvtklink) (1.15.0)\n",
      "Requirement already satisfied: notebook>=4.4.1 in /usr/local/lib/python3.7/dist-packages (from widgetsnbextension~=3.5.0->ipywidgets->ipyvtklink) (5.3.1)\n",
      "Requirement already satisfied: nbconvert in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->ipyvtklink) (5.6.1)\n",
      "Requirement already satisfied: Send2Trash in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->ipyvtklink) (1.8.0)\n",
      "Requirement already satisfied: terminado>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->ipyvtklink) (0.12.1)\n",
      "Requirement already satisfied: jinja2 in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->ipyvtklink) (2.11.3)\n",
      "Requirement already satisfied: pyzmq>=13 in /usr/local/lib/python3.7/dist-packages (from jupyter-client->ipykernel>=4.5.1->ipywidgets->ipyvtklink) (22.3.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from jupyter-client->ipykernel>=4.5.1->ipywidgets->ipyvtklink) (2.8.2)\n",
      "Requirement already satisfied: ptyprocess in /usr/local/lib/python3.7/dist-packages (from terminado>=0.8.1->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->ipyvtklink) (0.7.0)\n",
      "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->ipyvtklink) (2.0.1)\n",
      "Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->ipyvtklink) (0.8.4)\n",
      "Requirement already satisfied: defusedxml in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->ipyvtklink) (0.7.1)\n",
      "Requirement already satisfied: entrypoints>=0.2.2 in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->ipyvtklink) (0.3)\n",
      "Requirement already satisfied: bleach in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->ipyvtklink) (4.1.0)\n",
      "Requirement already satisfied: testpath in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->ipyvtklink) (0.5.0)\n",
      "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->ipyvtklink) (1.5.0)\n",
      "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->ipyvtklink) (21.2)\n",
      "Requirement already satisfied: webencodings in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->ipyvtklink) (0.5.1)\n",
      "Requirement already satisfied: pyparsing<3,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->ipyvtklink) (2.4.7)\n",
      "Installing collected packages: ipyevents, ipycanvas, ipyvtklink\n",
      "Successfully installed ipycanvas-0.9.1 ipyevents-2.0.1 ipyvtklink-0.2.1\n",
      "Selecting previously unselected package xvfb.\n",
      "(Reading database ... 155219 files and directories currently installed.)\n",
      "Preparing to unpack .../xvfb_2%3a1.19.6-1ubuntu4.9_amd64.deb ...\n",
      "Unpacking xvfb (2:1.19.6-1ubuntu4.9) ...\n",
      "Setting up xvfb (2:1.19.6-1ubuntu4.9) ...\n",
      "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n"
     ]
    }
   ],
   "source": [
    " !pip install pyvista\n",
    "# !pip install ipyvtklink\n",
    "# !pip install itkwidgets\n",
    "# # setup enviornment for interactive plotting\n",
    "!apt-get install -qq xvfb\n",
    "!pip install pyvista panel -q\n",
    "!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "executionInfo": {
     "elapsed": 875,
     "status": "ok",
     "timestamp": 1636760354326,
     "user": {
      "displayName": "Bernard C Chang",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "06189620559052393654"
     },
     "user_tz": 360
    },
    "id": "nyK_psfYFF4T",
    "outputId": "9061309a-44c3-485c-cf22-a611e98459b8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fb7a59f8550>"
      ]
     },
     "execution_count": 122,
     "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": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 17,
     "referenced_widgets": [
      "782b2f205cbe49caaccfe76b5a9205da"
     ]
    },
    "executionInfo": {
     "elapsed": 695,
     "status": "ok",
     "timestamp": 1636565911889,
     "user": {
      "displayName": "Bernard C Chang",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "06189620559052393654"
     },
     "user_tz": 360
    },
    "id": "f8LJF0690jLc",
    "outputId": "569463b4-bf55-46e9-8ef1-5743a36d427a"
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\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",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\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, js_modules, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "    if (js_modules == null) js_modules = [];\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.length === 0 && js_modules.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 + js_modules.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() {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < css_urls.length; i++) {\n",
       "      var url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\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",
       "    var skip = [];\n",
       "    if (window.requirejs) {\n",
       "      window.requirejs.config({'packages': {}, 'paths': {'vtk': 'https://unpkg.com/vtk.js@14.16.4/dist/vtk'}, 'shim': {'vtk': {'exports': 'vtk'}}});\n",
       "      \n",
       "      require([\"vtk\"], function() {\n",
       "\twindow. = \n",
       "      })\n",
       "      \n",
       "    }\n",
       "    if (((window['vtk'] !== undefined) && (!(window['vtk'] instanceof HTMLElement))) || window.requirejs) {\n",
       "      var urls = ['https://unpkg.com/vtk.js@14.16.4/dist/vtk.js'];\n",
       "      for (var i = 0; i < urls.length; i++) {\n",
       "        skip.push(urls[i])\n",
       "      }\n",
       "    }\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      if (skip.indexOf(url) >= 0) { on_load(); continue; }\n",
       "      var element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "    for (var i = 0; i < js_modules.length; i++) {\n",
       "      var url = js_modules[i];\n",
       "      if (skip.indexOf(url) >= 0) { on_load(); continue; }\n",
       "      var element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      element.type = \"module\";\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "    if (!js_urls.length && !js_modules.length) {\n",
       "      on_load()\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",
       "  var js_urls = [\"https://unpkg.com/vtk.js@14.16.4/dist/vtk.js\", \"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\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/panel.min.js\"];\n",
       "  var js_modules = [];\n",
       "  var css_urls = [\"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/card.css\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/widgets.css\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/json.css\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/dataframe.css\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/alerts.css\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/loading.css\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/markdown.css\"];\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      inject_raw_css(\"\\n    .bk.pn-loading.arcs:before {\\n      background-image: url(\\\"data:image/svg+xml;base64,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\\\")\\n    }\\n    \");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    function(Bokeh) {} // ensure no trailing comma for IE\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\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",
       "    }\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, js_modules, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.holoviews_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  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\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, js_modules, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n    if (js_modules == null) js_modules = [];\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.length === 0 && js_modules.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 + js_modules.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() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\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    var skip = [];\n    if (window.requirejs) {\n      window.requirejs.config({'packages': {}, 'paths': {'vtk': 'https://unpkg.com/vtk.js@14.16.4/dist/vtk'}, 'shim': {'vtk': {'exports': 'vtk'}}});\n      \n      require([\"vtk\"], function() {\n\twindow. = \n      })\n      \n    }\n    if (((window['vtk'] !== undefined) && (!(window['vtk'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://unpkg.com/vtk.js@14.16.4/dist/vtk.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      if (skip.indexOf(url) >= 0) { on_load(); continue; }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (var i = 0; i < js_modules.length; i++) {\n      var url = js_modules[i];\n      if (skip.indexOf(url) >= 0) { on_load(); continue; }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\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  var js_urls = [\"https://unpkg.com/vtk.js@14.16.4/dist/vtk.js\", \"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\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/panel.min.js\"];\n  var js_modules = [];\n  var css_urls = [\"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/card.css\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/widgets.css\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/json.css\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/dataframe.css\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/alerts.css\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/loading.css\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/markdown.css\"];\n  var inline_js = [\n    function(Bokeh) {\n      inject_raw_css(\"\\n    .bk.pn-loading.arcs:before {\\n      background-image: url(\\\"data:image/svg+xml;base64,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\\\")\\n    }\\n    \");\n    },\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\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    }\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, js_modules, 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": {
      "application/javascript": [
       "\n",
       "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n",
       "  window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n",
       "}\n",
       "\n",
       "\n",
       "    function JupyterCommManager() {\n",
       "    }\n",
       "\n",
       "    JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n",
       "      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n",
       "        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n",
       "        comm_manager.register_target(comm_id, function(comm) {\n",
       "          comm.on_msg(msg_handler);\n",
       "        });\n",
       "      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n",
       "        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n",
       "          comm.onMsg = msg_handler;\n",
       "        });\n",
       "      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n",
       "        google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n",
       "          var messages = comm.messages[Symbol.asyncIterator]();\n",
       "          function processIteratorResult(result) {\n",
       "            var message = result.value;\n",
       "            console.log(message)\n",
       "            var content = {data: message.data, comm_id};\n",
       "            var buffers = []\n",
       "            for (var buffer of message.buffers || []) {\n",
       "              buffers.push(new DataView(buffer))\n",
       "            }\n",
       "            var metadata = message.metadata || {};\n",
       "            var msg = {content, buffers, metadata}\n",
       "            msg_handler(msg);\n",
       "            return messages.next().then(processIteratorResult);\n",
       "          }\n",
       "          return messages.next().then(processIteratorResult);\n",
       "        })\n",
       "      }\n",
       "    }\n",
       "\n",
       "    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n",
       "      if (comm_id in window.PyViz.comms) {\n",
       "        return window.PyViz.comms[comm_id];\n",
       "      } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n",
       "        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n",
       "        var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n",
       "        if (msg_handler) {\n",
       "          comm.on_msg(msg_handler);\n",
       "        }\n",
       "      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n",
       "        var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n",
       "        comm.open();\n",
       "        if (msg_handler) {\n",
       "          comm.onMsg = msg_handler;\n",
       "        }\n",
       "      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n",
       "        var comm_promise = google.colab.kernel.comms.open(comm_id)\n",
       "        comm_promise.then((comm) => {\n",
       "          window.PyViz.comms[comm_id] = comm;\n",
       "          if (msg_handler) {\n",
       "            var messages = comm.messages[Symbol.asyncIterator]();\n",
       "            function processIteratorResult(result) {\n",
       "              var message = result.value;\n",
       "              var content = {data: message.data};\n",
       "              var metadata = message.metadata || {comm_id};\n",
       "              var msg = {content, metadata}\n",
       "              msg_handler(msg);\n",
       "              return messages.next().then(processIteratorResult);\n",
       "            }\n",
       "            return messages.next().then(processIteratorResult);\n",
       "          }\n",
       "        }) \n",
       "        var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n",
       "          return comm_promise.then((comm) => {\n",
       "            comm.send(data, metadata, buffers, disposeOnDone);\n",
       "          });\n",
       "        };\n",
       "        var comm = {\n",
       "          send: sendClosure\n",
       "        };\n",
       "      }\n",
       "      window.PyViz.comms[comm_id] = comm;\n",
       "      return comm;\n",
       "    }\n",
       "    window.PyViz.comm_manager = new JupyterCommManager();\n",
       "    \n",
       "\n",
       "\n",
       "var JS_MIME_TYPE = 'application/javascript';\n",
       "var HTML_MIME_TYPE = 'text/html';\n",
       "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n",
       "var CLASS_NAME = 'output';\n",
       "\n",
       "/**\n",
       " * Render data to the DOM node\n",
       " */\n",
       "function render(props, node) {\n",
       "  var div = document.createElement(\"div\");\n",
       "  var script = document.createElement(\"script\");\n",
       "  node.appendChild(div);\n",
       "  node.appendChild(script);\n",
       "}\n",
       "\n",
       "/**\n",
       " * Handle when a new output is added\n",
       " */\n",
       "function handle_add_output(event, handle) {\n",
       "  var output_area = handle.output_area;\n",
       "  var output = handle.output;\n",
       "  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "    return\n",
       "  }\n",
       "  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "  if (id !== undefined) {\n",
       "    var nchildren = toinsert.length;\n",
       "    var html_node = toinsert[nchildren-1].children[0];\n",
       "    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "    var scripts = [];\n",
       "    var nodelist = html_node.querySelectorAll(\"script\");\n",
       "    for (var i in nodelist) {\n",
       "      if (nodelist.hasOwnProperty(i)) {\n",
       "        scripts.push(nodelist[i])\n",
       "      }\n",
       "    }\n",
       "\n",
       "    scripts.forEach( function (oldScript) {\n",
       "      var newScript = document.createElement(\"script\");\n",
       "      var attrs = [];\n",
       "      var nodemap = oldScript.attributes;\n",
       "      for (var j in nodemap) {\n",
       "        if (nodemap.hasOwnProperty(j)) {\n",
       "          attrs.push(nodemap[j])\n",
       "        }\n",
       "      }\n",
       "      attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n",
       "      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n",
       "      oldScript.parentNode.replaceChild(newScript, oldScript);\n",
       "    });\n",
       "    if (JS_MIME_TYPE in output.data) {\n",
       "      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n",
       "    }\n",
       "    output_area._hv_plot_id = id;\n",
       "    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n",
       "      window.PyViz.plot_index[id] = Bokeh.index[id];\n",
       "    } else {\n",
       "      window.PyViz.plot_index[id] = null;\n",
       "    }\n",
       "  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "    var bk_div = document.createElement(\"div\");\n",
       "    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "    var script_attrs = bk_div.children[0].attributes;\n",
       "    for (var i = 0; i < script_attrs.length; i++) {\n",
       "      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "    }\n",
       "    // store reference to server id on output_area\n",
       "    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "  }\n",
       "}\n",
       "\n",
       "/**\n",
       " * Handle when an output is cleared or removed\n",
       " */\n",
       "function handle_clear_output(event, handle) {\n",
       "  var id = handle.cell.output_area._hv_plot_id;\n",
       "  var server_id = handle.cell.output_area._bokeh_server_id;\n",
       "  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n",
       "  var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n",
       "  if (server_id !== null) {\n",
       "    comm.send({event_type: 'server_delete', 'id': server_id});\n",
       "    return;\n",
       "  } else if (comm !== null) {\n",
       "    comm.send({event_type: 'delete', 'id': id});\n",
       "  }\n",
       "  delete PyViz.plot_index[id];\n",
       "  if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n",
       "    var doc = window.Bokeh.index[id].model.document\n",
       "    doc.clear();\n",
       "    const i = window.Bokeh.documents.indexOf(doc);\n",
       "    if (i > -1) {\n",
       "      window.Bokeh.documents.splice(i, 1);\n",
       "    }\n",
       "  }\n",
       "}\n",
       "\n",
       "/**\n",
       " * Handle kernel restart event\n",
       " */\n",
       "function handle_kernel_cleanup(event, handle) {\n",
       "  delete PyViz.comms[\"hv-extension-comm\"];\n",
       "  window.PyViz.plot_index = {}\n",
       "}\n",
       "\n",
       "/**\n",
       " * Handle update_display_data messages\n",
       " */\n",
       "function handle_update_output(event, handle) {\n",
       "  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n",
       "  handle_add_output(event, handle)\n",
       "}\n",
       "\n",
       "function register_renderer(events, OutputArea) {\n",
       "  function append_mime(data, metadata, element) {\n",
       "    // create a DOM node to render to\n",
       "    var toinsert = this.create_output_subarea(\n",
       "    metadata,\n",
       "    CLASS_NAME,\n",
       "    EXEC_MIME_TYPE\n",
       "    );\n",
       "    this.keyboard_manager.register_events(toinsert);\n",
       "    // Render to node\n",
       "    var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "    render(props, toinsert[0]);\n",
       "    element.append(toinsert);\n",
       "    return toinsert\n",
       "  }\n",
       "\n",
       "  events.on('output_added.OutputArea', handle_add_output);\n",
       "  events.on('output_updated.OutputArea', handle_update_output);\n",
       "  events.on('clear_output.CodeCell', handle_clear_output);\n",
       "  events.on('delete.Cell', handle_clear_output);\n",
       "  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n",
       "\n",
       "  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "    safe: true,\n",
       "    index: 0\n",
       "  });\n",
       "}\n",
       "\n",
       "if (window.Jupyter !== undefined) {\n",
       "  try {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  } catch(err) {\n",
       "  }\n",
       "}\n"
      ],
      "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n  window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n    function JupyterCommManager() {\n    }\n\n    JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        comm_manager.register_target(comm_id, function(comm) {\n          comm.on_msg(msg_handler);\n        });\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n          comm.onMsg = msg_handler;\n        });\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n          var messages = comm.messages[Symbol.asyncIterator]();\n          function processIteratorResult(result) {\n            var message = result.value;\n            console.log(message)\n            var content = {data: message.data, comm_id};\n            var buffers = []\n            for (var buffer of message.buffers || []) {\n              buffers.push(new DataView(buffer))\n            }\n            var metadata = message.metadata || {};\n            var msg = {content, buffers, metadata}\n            msg_handler(msg);\n            return messages.next().then(processIteratorResult);\n          }\n          return messages.next().then(processIteratorResult);\n        })\n      }\n    }\n\n    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n      if (comm_id in window.PyViz.comms) {\n        return window.PyViz.comms[comm_id];\n      } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n        if (msg_handler) {\n          comm.on_msg(msg_handler);\n        }\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n        comm.open();\n        if (msg_handler) {\n          comm.onMsg = msg_handler;\n        }\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        var comm_promise = google.colab.kernel.comms.open(comm_id)\n        comm_promise.then((comm) => {\n          window.PyViz.comms[comm_id] = comm;\n          if (msg_handler) {\n            var messages = comm.messages[Symbol.asyncIterator]();\n            function processIteratorResult(result) {\n              var message = result.value;\n              var content = {data: message.data};\n              var metadata = message.metadata || {comm_id};\n              var msg = {content, metadata}\n              msg_handler(msg);\n              return messages.next().then(processIteratorResult);\n            }\n            return messages.next().then(processIteratorResult);\n          }\n        }) \n        var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n          return comm_promise.then((comm) => {\n            comm.send(data, metadata, buffers, disposeOnDone);\n          });\n        };\n        var comm = {\n          send: sendClosure\n        };\n      }\n      window.PyViz.comms[comm_id] = comm;\n      return comm;\n    }\n    window.PyViz.comm_manager = new JupyterCommManager();\n    \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n  var div = document.createElement(\"div\");\n  var script = document.createElement(\"script\");\n  node.appendChild(div);\n  node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n  var output_area = handle.output_area;\n  var output = handle.output;\n  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n    return\n  }\n  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n  if (id !== undefined) {\n    var nchildren = toinsert.length;\n    var html_node = toinsert[nchildren-1].children[0];\n    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n    var scripts = [];\n    var nodelist = html_node.querySelectorAll(\"script\");\n    for (var i in nodelist) {\n      if (nodelist.hasOwnProperty(i)) {\n        scripts.push(nodelist[i])\n      }\n    }\n\n    scripts.forEach( function (oldScript) {\n      var newScript = document.createElement(\"script\");\n      var attrs = [];\n      var nodemap = oldScript.attributes;\n      for (var j in nodemap) {\n        if (nodemap.hasOwnProperty(j)) {\n          attrs.push(nodemap[j])\n        }\n      }\n      attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n      oldScript.parentNode.replaceChild(newScript, oldScript);\n    });\n    if (JS_MIME_TYPE in output.data) {\n      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n    }\n    output_area._hv_plot_id = id;\n    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n      window.PyViz.plot_index[id] = Bokeh.index[id];\n    } else {\n      window.PyViz.plot_index[id] = null;\n    }\n  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n    var bk_div = document.createElement(\"div\");\n    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n    var script_attrs = bk_div.children[0].attributes;\n    for (var i = 0; i < script_attrs.length; i++) {\n      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n    }\n    // store reference to server id on output_area\n    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n  }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n  var id = handle.cell.output_area._hv_plot_id;\n  var server_id = handle.cell.output_area._bokeh_server_id;\n  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n  var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n  if (server_id !== null) {\n    comm.send({event_type: 'server_delete', 'id': server_id});\n    return;\n  } else if (comm !== null) {\n    comm.send({event_type: 'delete', 'id': id});\n  }\n  delete PyViz.plot_index[id];\n  if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n    var doc = window.Bokeh.index[id].model.document\n    doc.clear();\n    const i = window.Bokeh.documents.indexOf(doc);\n    if (i > -1) {\n      window.Bokeh.documents.splice(i, 1);\n    }\n  }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n  delete PyViz.comms[\"hv-extension-comm\"];\n  window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n  handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n  function append_mime(data, metadata, element) {\n    // create a DOM node to render to\n    var toinsert = this.create_output_subarea(\n    metadata,\n    CLASS_NAME,\n    EXEC_MIME_TYPE\n    );\n    this.keyboard_manager.register_events(toinsert);\n    // Render to node\n    var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n    render(props, toinsert[0]);\n    element.append(toinsert);\n    return toinsert\n  }\n\n  events.on('output_added.OutputArea', handle_add_output);\n  events.on('output_updated.OutputArea', handle_update_output);\n  events.on('clear_output.CodeCell', handle_clear_output);\n  events.on('delete.Cell', handle_clear_output);\n  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n    safe: true,\n    index: 0\n  });\n}\n\nif (window.Jupyter !== undefined) {\n  try {\n    var events = require('base/js/events');\n    var OutputArea = require('notebook/js/outputarea').OutputArea;\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  } catch(err) {\n  }\n}\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\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",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\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, js_modules, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "    if (js_modules == null) js_modules = [];\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.length === 0 && js_modules.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 + js_modules.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() {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < css_urls.length; i++) {\n",
       "      var url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\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",
       "    var skip = [];\n",
       "    if (window.requirejs) {\n",
       "      window.requirejs.config({'packages': {}, 'paths': {'vtk': 'https://unpkg.com/vtk.js@14.16.4/dist/vtk'}, 'shim': {'vtk': {'exports': 'vtk'}}});\n",
       "      \n",
       "      require([\"vtk\"], function() {\n",
       "\twindow. = \n",
       "      })\n",
       "      \n",
       "    }\n",
       "    if (((window['vtk'] !== undefined) && (!(window['vtk'] instanceof HTMLElement))) || window.requirejs) {\n",
       "      var urls = ['https://unpkg.com/vtk.js@14.16.4/dist/vtk.js'];\n",
       "      for (var i = 0; i < urls.length; i++) {\n",
       "        skip.push(urls[i])\n",
       "      }\n",
       "    }\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      if (skip.indexOf(url) >= 0) { on_load(); continue; }\n",
       "      var element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "    for (var i = 0; i < js_modules.length; i++) {\n",
       "      var url = js_modules[i];\n",
       "      if (skip.indexOf(url) >= 0) { on_load(); continue; }\n",
       "      var element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      element.type = \"module\";\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "    if (!js_urls.length && !js_modules.length) {\n",
       "      on_load()\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",
       "  var js_urls = [\"https://unpkg.com/vtk.js@14.16.4/dist/vtk.js\", \"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\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/panel.min.js\"];\n",
       "  var js_modules = [];\n",
       "  var css_urls = [\"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/card.css\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/widgets.css\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/json.css\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/dataframe.css\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/alerts.css\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/loading.css\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/markdown.css\"];\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      inject_raw_css(\"\\n    .bk.pn-loading.arcs:before {\\n      background-image: url(\\\"data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHhtbG5zOnhsaW5rPSJodHRwOi8vd3d3LnczLm9yZy8xOTk5L3hsaW5rIiBzdHlsZT0ibWFyZ2luOiBhdXRvOyBiYWNrZ3JvdW5kOiBub25lOyBkaXNwbGF5OiBibG9jazsgc2hhcGUtcmVuZGVyaW5nOiBhdXRvOyIgdmlld0JveD0iMCAwIDEwMCAxMDAiIHByZXNlcnZlQXNwZWN0UmF0aW89InhNaWRZTWlkIj4gIDxjaXJjbGUgY3g9IjUwIiBjeT0iNTAiIHI9IjMyIiBzdHJva2Utd2lkdGg9IjgiIHN0cm9rZT0iI2MzYzNjMyIgc3Ryb2tlLWRhc2hhcnJheT0iNTAuMjY1NDgyNDU3NDM2NjkgNTAuMjY1NDgyNDU3NDM2NjkiIGZpbGw9Im5vbmUiIHN0cm9rZS1saW5lY2FwPSJyb3VuZCI+ICAgIDxhbmltYXRlVHJhbnNmb3JtIGF0dHJpYnV0ZU5hbWU9InRyYW5zZm9ybSIgdHlwZT0icm90YXRlIiByZXBlYXRDb3VudD0iaW5kZWZpbml0ZSIgZHVyPSIxcyIga2V5VGltZXM9IjA7MSIgdmFsdWVzPSIwIDUwIDUwOzM2MCA1MCA1MCI+PC9hbmltYXRlVHJhbnNmb3JtPiAgPC9jaXJjbGU+PC9zdmc+\\\")\\n    }\\n    \");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    function(Bokeh) {} // ensure no trailing comma for IE\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\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",
       "    }\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, js_modules, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.holoviews_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  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\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, js_modules, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n    if (js_modules == null) js_modules = [];\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.length === 0 && js_modules.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 + js_modules.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() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\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    var skip = [];\n    if (window.requirejs) {\n      window.requirejs.config({'packages': {}, 'paths': {'vtk': 'https://unpkg.com/vtk.js@14.16.4/dist/vtk'}, 'shim': {'vtk': {'exports': 'vtk'}}});\n      \n      require([\"vtk\"], function() {\n\twindow. = \n      })\n      \n    }\n    if (((window['vtk'] !== undefined) && (!(window['vtk'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://unpkg.com/vtk.js@14.16.4/dist/vtk.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      if (skip.indexOf(url) >= 0) { on_load(); continue; }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (var i = 0; i < js_modules.length; i++) {\n      var url = js_modules[i];\n      if (skip.indexOf(url) >= 0) { on_load(); continue; }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\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  var js_urls = [\"https://unpkg.com/vtk.js@14.16.4/dist/vtk.js\", \"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\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/panel.min.js\"];\n  var js_modules = [];\n  var css_urls = [\"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/card.css\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/widgets.css\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/json.css\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/dataframe.css\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/alerts.css\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/loading.css\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/markdown.css\"];\n  var inline_js = [\n    function(Bokeh) {\n      inject_raw_css(\"\\n    .bk.pn-loading.arcs:before {\\n      background-image: url(\\\"data:image/svg+xml;base64,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\\\")\\n    }\\n    \");\n    },\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\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    }\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, js_modules, 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": {
      "application/javascript": [
       "\n",
       "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n",
       "  window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n",
       "}\n",
       "\n",
       "\n",
       "    function JupyterCommManager() {\n",
       "    }\n",
       "\n",
       "    JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n",
       "      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n",
       "        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n",
       "        comm_manager.register_target(comm_id, function(comm) {\n",
       "          comm.on_msg(msg_handler);\n",
       "        });\n",
       "      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n",
       "        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n",
       "          comm.onMsg = msg_handler;\n",
       "        });\n",
       "      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n",
       "        google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n",
       "          var messages = comm.messages[Symbol.asyncIterator]();\n",
       "          function processIteratorResult(result) {\n",
       "            var message = result.value;\n",
       "            console.log(message)\n",
       "            var content = {data: message.data, comm_id};\n",
       "            var buffers = []\n",
       "            for (var buffer of message.buffers || []) {\n",
       "              buffers.push(new DataView(buffer))\n",
       "            }\n",
       "            var metadata = message.metadata || {};\n",
       "            var msg = {content, buffers, metadata}\n",
       "            msg_handler(msg);\n",
       "            return messages.next().then(processIteratorResult);\n",
       "          }\n",
       "          return messages.next().then(processIteratorResult);\n",
       "        })\n",
       "      }\n",
       "    }\n",
       "\n",
       "    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n",
       "      if (comm_id in window.PyViz.comms) {\n",
       "        return window.PyViz.comms[comm_id];\n",
       "      } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n",
       "        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n",
       "        var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n",
       "        if (msg_handler) {\n",
       "          comm.on_msg(msg_handler);\n",
       "        }\n",
       "      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n",
       "        var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n",
       "        comm.open();\n",
       "        if (msg_handler) {\n",
       "          comm.onMsg = msg_handler;\n",
       "        }\n",
       "      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n",
       "        var comm_promise = google.colab.kernel.comms.open(comm_id)\n",
       "        comm_promise.then((comm) => {\n",
       "          window.PyViz.comms[comm_id] = comm;\n",
       "          if (msg_handler) {\n",
       "            var messages = comm.messages[Symbol.asyncIterator]();\n",
       "            function processIteratorResult(result) {\n",
       "              var message = result.value;\n",
       "              var content = {data: message.data};\n",
       "              var metadata = message.metadata || {comm_id};\n",
       "              var msg = {content, metadata}\n",
       "              msg_handler(msg);\n",
       "              return messages.next().then(processIteratorResult);\n",
       "            }\n",
       "            return messages.next().then(processIteratorResult);\n",
       "          }\n",
       "        }) \n",
       "        var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n",
       "          return comm_promise.then((comm) => {\n",
       "            comm.send(data, metadata, buffers, disposeOnDone);\n",
       "          });\n",
       "        };\n",
       "        var comm = {\n",
       "          send: sendClosure\n",
       "        };\n",
       "      }\n",
       "      window.PyViz.comms[comm_id] = comm;\n",
       "      return comm;\n",
       "    }\n",
       "    window.PyViz.comm_manager = new JupyterCommManager();\n",
       "    \n",
       "\n",
       "\n",
       "var JS_MIME_TYPE = 'application/javascript';\n",
       "var HTML_MIME_TYPE = 'text/html';\n",
       "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n",
       "var CLASS_NAME = 'output';\n",
       "\n",
       "/**\n",
       " * Render data to the DOM node\n",
       " */\n",
       "function render(props, node) {\n",
       "  var div = document.createElement(\"div\");\n",
       "  var script = document.createElement(\"script\");\n",
       "  node.appendChild(div);\n",
       "  node.appendChild(script);\n",
       "}\n",
       "\n",
       "/**\n",
       " * Handle when a new output is added\n",
       " */\n",
       "function handle_add_output(event, handle) {\n",
       "  var output_area = handle.output_area;\n",
       "  var output = handle.output;\n",
       "  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "    return\n",
       "  }\n",
       "  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "  if (id !== undefined) {\n",
       "    var nchildren = toinsert.length;\n",
       "    var html_node = toinsert[nchildren-1].children[0];\n",
       "    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "    var scripts = [];\n",
       "    var nodelist = html_node.querySelectorAll(\"script\");\n",
       "    for (var i in nodelist) {\n",
       "      if (nodelist.hasOwnProperty(i)) {\n",
       "        scripts.push(nodelist[i])\n",
       "      }\n",
       "    }\n",
       "\n",
       "    scripts.forEach( function (oldScript) {\n",
       "      var newScript = document.createElement(\"script\");\n",
       "      var attrs = [];\n",
       "      var nodemap = oldScript.attributes;\n",
       "      for (var j in nodemap) {\n",
       "        if (nodemap.hasOwnProperty(j)) {\n",
       "          attrs.push(nodemap[j])\n",
       "        }\n",
       "      }\n",
       "      attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n",
       "      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n",
       "      oldScript.parentNode.replaceChild(newScript, oldScript);\n",
       "    });\n",
       "    if (JS_MIME_TYPE in output.data) {\n",
       "      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n",
       "    }\n",
       "    output_area._hv_plot_id = id;\n",
       "    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n",
       "      window.PyViz.plot_index[id] = Bokeh.index[id];\n",
       "    } else {\n",
       "      window.PyViz.plot_index[id] = null;\n",
       "    }\n",
       "  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "    var bk_div = document.createElement(\"div\");\n",
       "    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "    var script_attrs = bk_div.children[0].attributes;\n",
       "    for (var i = 0; i < script_attrs.length; i++) {\n",
       "      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "    }\n",
       "    // store reference to server id on output_area\n",
       "    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "  }\n",
       "}\n",
       "\n",
       "/**\n",
       " * Handle when an output is cleared or removed\n",
       " */\n",
       "function handle_clear_output(event, handle) {\n",
       "  var id = handle.cell.output_area._hv_plot_id;\n",
       "  var server_id = handle.cell.output_area._bokeh_server_id;\n",
       "  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n",
       "  var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n",
       "  if (server_id !== null) {\n",
       "    comm.send({event_type: 'server_delete', 'id': server_id});\n",
       "    return;\n",
       "  } else if (comm !== null) {\n",
       "    comm.send({event_type: 'delete', 'id': id});\n",
       "  }\n",
       "  delete PyViz.plot_index[id];\n",
       "  if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n",
       "    var doc = window.Bokeh.index[id].model.document\n",
       "    doc.clear();\n",
       "    const i = window.Bokeh.documents.indexOf(doc);\n",
       "    if (i > -1) {\n",
       "      window.Bokeh.documents.splice(i, 1);\n",
       "    }\n",
       "  }\n",
       "}\n",
       "\n",
       "/**\n",
       " * Handle kernel restart event\n",
       " */\n",
       "function handle_kernel_cleanup(event, handle) {\n",
       "  delete PyViz.comms[\"hv-extension-comm\"];\n",
       "  window.PyViz.plot_index = {}\n",
       "}\n",
       "\n",
       "/**\n",
       " * Handle update_display_data messages\n",
       " */\n",
       "function handle_update_output(event, handle) {\n",
       "  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n",
       "  handle_add_output(event, handle)\n",
       "}\n",
       "\n",
       "function register_renderer(events, OutputArea) {\n",
       "  function append_mime(data, metadata, element) {\n",
       "    // create a DOM node to render to\n",
       "    var toinsert = this.create_output_subarea(\n",
       "    metadata,\n",
       "    CLASS_NAME,\n",
       "    EXEC_MIME_TYPE\n",
       "    );\n",
       "    this.keyboard_manager.register_events(toinsert);\n",
       "    // Render to node\n",
       "    var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "    render(props, toinsert[0]);\n",
       "    element.append(toinsert);\n",
       "    return toinsert\n",
       "  }\n",
       "\n",
       "  events.on('output_added.OutputArea', handle_add_output);\n",
       "  events.on('output_updated.OutputArea', handle_update_output);\n",
       "  events.on('clear_output.CodeCell', handle_clear_output);\n",
       "  events.on('delete.Cell', handle_clear_output);\n",
       "  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n",
       "\n",
       "  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "    safe: true,\n",
       "    index: 0\n",
       "  });\n",
       "}\n",
       "\n",
       "if (window.Jupyter !== undefined) {\n",
       "  try {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  } catch(err) {\n",
       "  }\n",
       "}\n"
      ],
      "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n  window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n    function JupyterCommManager() {\n    }\n\n    JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        comm_manager.register_target(comm_id, function(comm) {\n          comm.on_msg(msg_handler);\n        });\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n          comm.onMsg = msg_handler;\n        });\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n          var messages = comm.messages[Symbol.asyncIterator]();\n          function processIteratorResult(result) {\n            var message = result.value;\n            console.log(message)\n            var content = {data: message.data, comm_id};\n            var buffers = []\n            for (var buffer of message.buffers || []) {\n              buffers.push(new DataView(buffer))\n            }\n            var metadata = message.metadata || {};\n            var msg = {content, buffers, metadata}\n            msg_handler(msg);\n            return messages.next().then(processIteratorResult);\n          }\n          return messages.next().then(processIteratorResult);\n        })\n      }\n    }\n\n    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n      if (comm_id in window.PyViz.comms) {\n        return window.PyViz.comms[comm_id];\n      } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n        if (msg_handler) {\n          comm.on_msg(msg_handler);\n        }\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n        comm.open();\n        if (msg_handler) {\n          comm.onMsg = msg_handler;\n        }\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        var comm_promise = google.colab.kernel.comms.open(comm_id)\n        comm_promise.then((comm) => {\n          window.PyViz.comms[comm_id] = comm;\n          if (msg_handler) {\n            var messages = comm.messages[Symbol.asyncIterator]();\n            function processIteratorResult(result) {\n              var message = result.value;\n              var content = {data: message.data};\n              var metadata = message.metadata || {comm_id};\n              var msg = {content, metadata}\n              msg_handler(msg);\n              return messages.next().then(processIteratorResult);\n            }\n            return messages.next().then(processIteratorResult);\n          }\n        }) \n        var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n          return comm_promise.then((comm) => {\n            comm.send(data, metadata, buffers, disposeOnDone);\n          });\n        };\n        var comm = {\n          send: sendClosure\n        };\n      }\n      window.PyViz.comms[comm_id] = comm;\n      return comm;\n    }\n    window.PyViz.comm_manager = new JupyterCommManager();\n    \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n  var div = document.createElement(\"div\");\n  var script = document.createElement(\"script\");\n  node.appendChild(div);\n  node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n  var output_area = handle.output_area;\n  var output = handle.output;\n  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n    return\n  }\n  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n  if (id !== undefined) {\n    var nchildren = toinsert.length;\n    var html_node = toinsert[nchildren-1].children[0];\n    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n    var scripts = [];\n    var nodelist = html_node.querySelectorAll(\"script\");\n    for (var i in nodelist) {\n      if (nodelist.hasOwnProperty(i)) {\n        scripts.push(nodelist[i])\n      }\n    }\n\n    scripts.forEach( function (oldScript) {\n      var newScript = document.createElement(\"script\");\n      var attrs = [];\n      var nodemap = oldScript.attributes;\n      for (var j in nodemap) {\n        if (nodemap.hasOwnProperty(j)) {\n          attrs.push(nodemap[j])\n        }\n      }\n      attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n      oldScript.parentNode.replaceChild(newScript, oldScript);\n    });\n    if (JS_MIME_TYPE in output.data) {\n      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n    }\n    output_area._hv_plot_id = id;\n    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n      window.PyViz.plot_index[id] = Bokeh.index[id];\n    } else {\n      window.PyViz.plot_index[id] = null;\n    }\n  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n    var bk_div = document.createElement(\"div\");\n    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n    var script_attrs = bk_div.children[0].attributes;\n    for (var i = 0; i < script_attrs.length; i++) {\n      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n    }\n    // store reference to server id on output_area\n    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n  }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n  var id = handle.cell.output_area._hv_plot_id;\n  var server_id = handle.cell.output_area._bokeh_server_id;\n  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n  var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n  if (server_id !== null) {\n    comm.send({event_type: 'server_delete', 'id': server_id});\n    return;\n  } else if (comm !== null) {\n    comm.send({event_type: 'delete', 'id': id});\n  }\n  delete PyViz.plot_index[id];\n  if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n    var doc = window.Bokeh.index[id].model.document\n    doc.clear();\n    const i = window.Bokeh.documents.indexOf(doc);\n    if (i > -1) {\n      window.Bokeh.documents.splice(i, 1);\n    }\n  }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n  delete PyViz.comms[\"hv-extension-comm\"];\n  window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n  handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n  function append_mime(data, metadata, element) {\n    // create a DOM node to render to\n    var toinsert = this.create_output_subarea(\n    metadata,\n    CLASS_NAME,\n    EXEC_MIME_TYPE\n    );\n    this.keyboard_manager.register_events(toinsert);\n    // Render to node\n    var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n    render(props, toinsert[0]);\n    element.append(toinsert);\n    return toinsert\n  }\n\n  events.on('output_added.OutputArea', handle_add_output);\n  events.on('output_updated.OutputArea', handle_update_output);\n  events.on('clear_output.CodeCell', handle_clear_output);\n  events.on('delete.Cell', handle_clear_output);\n  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n    safe: true,\n    index: 0\n  });\n}\n\nif (window.Jupyter !== undefined) {\n  try {\n    var events = require('base/js/events');\n    var OutputArea = require('notebook/js/outputarea').OutputArea;\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  } catch(err) {\n  }\n}\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "782b2f205cbe49caaccfe76b5a9205da",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "ViewInteractiveWidget(height=768, layout=Layout(height='auto', width='100%'), width=1024)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "os.system('/usr/bin/Xvfb :99 -screen 0 1024x768x24 &')\n",
    "os.environ['DISPLAY'] = ':99'\n",
    "\n",
    "import panel as pn\n",
    "pn.extension('vtk')\n",
    "\n",
    "\n",
    "\n",
    "### new cell simple plot ###\n",
    "pn.extension('vtk')  # this needs to be at the top of each cell for some reason\n",
    "import pyvista as pv\n",
    "sphere = pv.Sphere()\n",
    "sphere.plot(color='w', notebook=True,  interactive=True, off_screen=False)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 17,
     "referenced_widgets": [
      "91237ca9516349e79cae8e0524b4063f"
     ]
    },
    "executionInfo": {
     "elapsed": 1759,
     "status": "ok",
     "timestamp": 1636564801239,
     "user": {
      "displayName": "Bernard C Chang",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "06189620559052393654"
     },
     "user_tz": 360
    },
    "id": "j0aproAM2eAu",
    "outputId": "02ac3e17-fab1-4e34-dc2e-cbb0f973d6e8"
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\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",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\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, js_modules, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "    if (js_modules == null) js_modules = [];\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.length === 0 && js_modules.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 + js_modules.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() {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < css_urls.length; i++) {\n",
       "      var url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\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",
       "    var skip = [];\n",
       "    if (window.requirejs) {\n",
       "      window.requirejs.config({'packages': {}, 'paths': {'vtk': 'https://unpkg.com/vtk.js@14.16.4/dist/vtk'}, 'shim': {'vtk': {'exports': 'vtk'}}});\n",
       "      \n",
       "      require([\"vtk\"], function() {\n",
       "\twindow. = \n",
       "      })\n",
       "      \n",
       "    }\n",
       "    if (((window['vtk'] !== undefined) && (!(window['vtk'] instanceof HTMLElement))) || window.requirejs) {\n",
       "      var urls = ['https://unpkg.com/vtk.js@14.16.4/dist/vtk.js'];\n",
       "      for (var i = 0; i < urls.length; i++) {\n",
       "        skip.push(urls[i])\n",
       "      }\n",
       "    }\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      if (skip.indexOf(url) >= 0) { on_load(); continue; }\n",
       "      var element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "    for (var i = 0; i < js_modules.length; i++) {\n",
       "      var url = js_modules[i];\n",
       "      if (skip.indexOf(url) >= 0) { on_load(); continue; }\n",
       "      var element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      element.type = \"module\";\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "    if (!js_urls.length && !js_modules.length) {\n",
       "      on_load()\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",
       "  var js_urls = [\"https://unpkg.com/vtk.js@14.16.4/dist/vtk.js\", \"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\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/panel.min.js\"];\n",
       "  var js_modules = [];\n",
       "  var css_urls = [\"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/widgets.css\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/markdown.css\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/dataframe.css\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/card.css\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/alerts.css\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/json.css\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/loading.css\"];\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      inject_raw_css(\"\\n    .bk.pn-loading.arcs:before {\\n      background-image: url(\\\"data:image/svg+xml;base64,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\\\")\\n    }\\n    \");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    function(Bokeh) {} // ensure no trailing comma for IE\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\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",
       "    }\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, js_modules, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.holoviews_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  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\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, js_modules, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n    if (js_modules == null) js_modules = [];\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.length === 0 && js_modules.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 + js_modules.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() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\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    var skip = [];\n    if (window.requirejs) {\n      window.requirejs.config({'packages': {}, 'paths': {'vtk': 'https://unpkg.com/vtk.js@14.16.4/dist/vtk'}, 'shim': {'vtk': {'exports': 'vtk'}}});\n      \n      require([\"vtk\"], function() {\n\twindow. = \n      })\n      \n    }\n    if (((window['vtk'] !== undefined) && (!(window['vtk'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://unpkg.com/vtk.js@14.16.4/dist/vtk.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      if (skip.indexOf(url) >= 0) { on_load(); continue; }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (var i = 0; i < js_modules.length; i++) {\n      var url = js_modules[i];\n      if (skip.indexOf(url) >= 0) { on_load(); continue; }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\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  var js_urls = [\"https://unpkg.com/vtk.js@14.16.4/dist/vtk.js\", \"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\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/panel.min.js\"];\n  var js_modules = [];\n  var css_urls = [\"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/widgets.css\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/markdown.css\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/dataframe.css\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/card.css\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/alerts.css\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/json.css\", \"https://unpkg.com/@holoviz/panel@0.12.1/dist/css/loading.css\"];\n  var inline_js = [\n    function(Bokeh) {\n      inject_raw_css(\"\\n    .bk.pn-loading.arcs:before {\\n      background-image: url(\\\"data:image/svg+xml;base64,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\\\")\\n    }\\n    \");\n    },\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\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    }\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, js_modules, 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": {
      "application/javascript": [
       "\n",
       "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n",
       "  window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n",
       "}\n",
       "\n",
       "\n",
       "    function JupyterCommManager() {\n",
       "    }\n",
       "\n",
       "    JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n",
       "      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n",
       "        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n",
       "        comm_manager.register_target(comm_id, function(comm) {\n",
       "          comm.on_msg(msg_handler);\n",
       "        });\n",
       "      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n",
       "        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n",
       "          comm.onMsg = msg_handler;\n",
       "        });\n",
       "      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n",
       "        google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n",
       "          var messages = comm.messages[Symbol.asyncIterator]();\n",
       "          function processIteratorResult(result) {\n",
       "            var message = result.value;\n",
       "            console.log(message)\n",
       "            var content = {data: message.data, comm_id};\n",
       "            var buffers = []\n",
       "            for (var buffer of message.buffers || []) {\n",
       "              buffers.push(new DataView(buffer))\n",
       "            }\n",
       "            var metadata = message.metadata || {};\n",
       "            var msg = {content, buffers, metadata}\n",
       "            msg_handler(msg);\n",
       "            return messages.next().then(processIteratorResult);\n",
       "          }\n",
       "          return messages.next().then(processIteratorResult);\n",
       "        })\n",
       "      }\n",
       "    }\n",
       "\n",
       "    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n",
       "      if (comm_id in window.PyViz.comms) {\n",
       "        return window.PyViz.comms[comm_id];\n",
       "      } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n",
       "        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n",
       "        var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n",
       "        if (msg_handler) {\n",
       "          comm.on_msg(msg_handler);\n",
       "        }\n",
       "      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n",
       "        var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n",
       "        comm.open();\n",
       "        if (msg_handler) {\n",
       "          comm.onMsg = msg_handler;\n",
       "        }\n",
       "      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n",
       "        var comm_promise = google.colab.kernel.comms.open(comm_id)\n",
       "        comm_promise.then((comm) => {\n",
       "          window.PyViz.comms[comm_id] = comm;\n",
       "          if (msg_handler) {\n",
       "            var messages = comm.messages[Symbol.asyncIterator]();\n",
       "            function processIteratorResult(result) {\n",
       "              var message = result.value;\n",
       "              var content = {data: message.data};\n",
       "              var metadata = message.metadata || {comm_id};\n",
       "              var msg = {content, metadata}\n",
       "              msg_handler(msg);\n",
       "              return messages.next().then(processIteratorResult);\n",
       "            }\n",
       "            return messages.next().then(processIteratorResult);\n",
       "          }\n",
       "        }) \n",
       "        var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n",
       "          return comm_promise.then((comm) => {\n",
       "            comm.send(data, metadata, buffers, disposeOnDone);\n",
       "          });\n",
       "        };\n",
       "        var comm = {\n",
       "          send: sendClosure\n",
       "        };\n",
       "      }\n",
       "      window.PyViz.comms[comm_id] = comm;\n",
       "      return comm;\n",
       "    }\n",
       "    window.PyViz.comm_manager = new JupyterCommManager();\n",
       "    \n",
       "\n",
       "\n",
       "var JS_MIME_TYPE = 'application/javascript';\n",
       "var HTML_MIME_TYPE = 'text/html';\n",
       "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n",
       "var CLASS_NAME = 'output';\n",
       "\n",
       "/**\n",
       " * Render data to the DOM node\n",
       " */\n",
       "function render(props, node) {\n",
       "  var div = document.createElement(\"div\");\n",
       "  var script = document.createElement(\"script\");\n",
       "  node.appendChild(div);\n",
       "  node.appendChild(script);\n",
       "}\n",
       "\n",
       "/**\n",
       " * Handle when a new output is added\n",
       " */\n",
       "function handle_add_output(event, handle) {\n",
       "  var output_area = handle.output_area;\n",
       "  var output = handle.output;\n",
       "  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "    return\n",
       "  }\n",
       "  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "  if (id !== undefined) {\n",
       "    var nchildren = toinsert.length;\n",
       "    var html_node = toinsert[nchildren-1].children[0];\n",
       "    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "    var scripts = [];\n",
       "    var nodelist = html_node.querySelectorAll(\"script\");\n",
       "    for (var i in nodelist) {\n",
       "      if (nodelist.hasOwnProperty(i)) {\n",
       "        scripts.push(nodelist[i])\n",
       "      }\n",
       "    }\n",
       "\n",
       "    scripts.forEach( function (oldScript) {\n",
       "      var newScript = document.createElement(\"script\");\n",
       "      var attrs = [];\n",
       "      var nodemap = oldScript.attributes;\n",
       "      for (var j in nodemap) {\n",
       "        if (nodemap.hasOwnProperty(j)) {\n",
       "          attrs.push(nodemap[j])\n",
       "        }\n",
       "      }\n",
       "      attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n",
       "      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n",
       "      oldScript.parentNode.replaceChild(newScript, oldScript);\n",
       "    });\n",
       "    if (JS_MIME_TYPE in output.data) {\n",
       "      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n",
       "    }\n",
       "    output_area._hv_plot_id = id;\n",
       "    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n",
       "      window.PyViz.plot_index[id] = Bokeh.index[id];\n",
       "    } else {\n",
       "      window.PyViz.plot_index[id] = null;\n",
       "    }\n",
       "  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "    var bk_div = document.createElement(\"div\");\n",
       "    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "    var script_attrs = bk_div.children[0].attributes;\n",
       "    for (var i = 0; i < script_attrs.length; i++) {\n",
       "      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "    }\n",
       "    // store reference to server id on output_area\n",
       "    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "  }\n",
       "}\n",
       "\n",
       "/**\n",
       " * Handle when an output is cleared or removed\n",
       " */\n",
       "function handle_clear_output(event, handle) {\n",
       "  var id = handle.cell.output_area._hv_plot_id;\n",
       "  var server_id = handle.cell.output_area._bokeh_server_id;\n",
       "  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n",
       "  var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n",
       "  if (server_id !== null) {\n",
       "    comm.send({event_type: 'server_delete', 'id': server_id});\n",
       "    return;\n",
       "  } else if (comm !== null) {\n",
       "    comm.send({event_type: 'delete', 'id': id});\n",
       "  }\n",
       "  delete PyViz.plot_index[id];\n",
       "  if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n",
       "    var doc = window.Bokeh.index[id].model.document\n",
       "    doc.clear();\n",
       "    const i = window.Bokeh.documents.indexOf(doc);\n",
       "    if (i > -1) {\n",
       "      window.Bokeh.documents.splice(i, 1);\n",
       "    }\n",
       "  }\n",
       "}\n",
       "\n",
       "/**\n",
       " * Handle kernel restart event\n",
       " */\n",
       "function handle_kernel_cleanup(event, handle) {\n",
       "  delete PyViz.comms[\"hv-extension-comm\"];\n",
       "  window.PyViz.plot_index = {}\n",
       "}\n",
       "\n",
       "/**\n",
       " * Handle update_display_data messages\n",
       " */\n",
       "function handle_update_output(event, handle) {\n",
       "  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n",
       "  handle_add_output(event, handle)\n",
       "}\n",
       "\n",
       "function register_renderer(events, OutputArea) {\n",
       "  function append_mime(data, metadata, element) {\n",
       "    // create a DOM node to render to\n",
       "    var toinsert = this.create_output_subarea(\n",
       "    metadata,\n",
       "    CLASS_NAME,\n",
       "    EXEC_MIME_TYPE\n",
       "    );\n",
       "    this.keyboard_manager.register_events(toinsert);\n",
       "    // Render to node\n",
       "    var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "    render(props, toinsert[0]);\n",
       "    element.append(toinsert);\n",
       "    return toinsert\n",
       "  }\n",
       "\n",
       "  events.on('output_added.OutputArea', handle_add_output);\n",
       "  events.on('output_updated.OutputArea', handle_update_output);\n",
       "  events.on('clear_output.CodeCell', handle_clear_output);\n",
       "  events.on('delete.Cell', handle_clear_output);\n",
       "  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n",
       "\n",
       "  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "    safe: true,\n",
       "    index: 0\n",
       "  });\n",
       "}\n",
       "\n",
       "if (window.Jupyter !== undefined) {\n",
       "  try {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  } catch(err) {\n",
       "  }\n",
       "}\n"
      ],
      "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n  window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n    function JupyterCommManager() {\n    }\n\n    JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        comm_manager.register_target(comm_id, function(comm) {\n          comm.on_msg(msg_handler);\n        });\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n          comm.onMsg = msg_handler;\n        });\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n          var messages = comm.messages[Symbol.asyncIterator]();\n          function processIteratorResult(result) {\n            var message = result.value;\n            console.log(message)\n            var content = {data: message.data, comm_id};\n            var buffers = []\n            for (var buffer of message.buffers || []) {\n              buffers.push(new DataView(buffer))\n            }\n            var metadata = message.metadata || {};\n            var msg = {content, buffers, metadata}\n            msg_handler(msg);\n            return messages.next().then(processIteratorResult);\n          }\n          return messages.next().then(processIteratorResult);\n        })\n      }\n    }\n\n    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n      if (comm_id in window.PyViz.comms) {\n        return window.PyViz.comms[comm_id];\n      } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n        if (msg_handler) {\n          comm.on_msg(msg_handler);\n        }\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n        comm.open();\n        if (msg_handler) {\n          comm.onMsg = msg_handler;\n        }\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        var comm_promise = google.colab.kernel.comms.open(comm_id)\n        comm_promise.then((comm) => {\n          window.PyViz.comms[comm_id] = comm;\n          if (msg_handler) {\n            var messages = comm.messages[Symbol.asyncIterator]();\n            function processIteratorResult(result) {\n              var message = result.value;\n              var content = {data: message.data};\n              var metadata = message.metadata || {comm_id};\n              var msg = {content, metadata}\n              msg_handler(msg);\n              return messages.next().then(processIteratorResult);\n            }\n            return messages.next().then(processIteratorResult);\n          }\n        }) \n        var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n          return comm_promise.then((comm) => {\n            comm.send(data, metadata, buffers, disposeOnDone);\n          });\n        };\n        var comm = {\n          send: sendClosure\n        };\n      }\n      window.PyViz.comms[comm_id] = comm;\n      return comm;\n    }\n    window.PyViz.comm_manager = new JupyterCommManager();\n    \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n  var div = document.createElement(\"div\");\n  var script = document.createElement(\"script\");\n  node.appendChild(div);\n  node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n  var output_area = handle.output_area;\n  var output = handle.output;\n  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n    return\n  }\n  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n  if (id !== undefined) {\n    var nchildren = toinsert.length;\n    var html_node = toinsert[nchildren-1].children[0];\n    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n    var scripts = [];\n    var nodelist = html_node.querySelectorAll(\"script\");\n    for (var i in nodelist) {\n      if (nodelist.hasOwnProperty(i)) {\n        scripts.push(nodelist[i])\n      }\n    }\n\n    scripts.forEach( function (oldScript) {\n      var newScript = document.createElement(\"script\");\n      var attrs = [];\n      var nodemap = oldScript.attributes;\n      for (var j in nodemap) {\n        if (nodemap.hasOwnProperty(j)) {\n          attrs.push(nodemap[j])\n        }\n      }\n      attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n      oldScript.parentNode.replaceChild(newScript, oldScript);\n    });\n    if (JS_MIME_TYPE in output.data) {\n      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n    }\n    output_area._hv_plot_id = id;\n    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n      window.PyViz.plot_index[id] = Bokeh.index[id];\n    } else {\n      window.PyViz.plot_index[id] = null;\n    }\n  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n    var bk_div = document.createElement(\"div\");\n    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n    var script_attrs = bk_div.children[0].attributes;\n    for (var i = 0; i < script_attrs.length; i++) {\n      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n    }\n    // store reference to server id on output_area\n    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n  }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n  var id = handle.cell.output_area._hv_plot_id;\n  var server_id = handle.cell.output_area._bokeh_server_id;\n  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n  var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n  if (server_id !== null) {\n    comm.send({event_type: 'server_delete', 'id': server_id});\n    return;\n  } else if (comm !== null) {\n    comm.send({event_type: 'delete', 'id': id});\n  }\n  delete PyViz.plot_index[id];\n  if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n    var doc = window.Bokeh.index[id].model.document\n    doc.clear();\n    const i = window.Bokeh.documents.indexOf(doc);\n    if (i > -1) {\n      window.Bokeh.documents.splice(i, 1);\n    }\n  }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n  delete PyViz.comms[\"hv-extension-comm\"];\n  window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n  handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n  function append_mime(data, metadata, element) {\n    // create a DOM node to render to\n    var toinsert = this.create_output_subarea(\n    metadata,\n    CLASS_NAME,\n    EXEC_MIME_TYPE\n    );\n    this.keyboard_manager.register_events(toinsert);\n    // Render to node\n    var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n    render(props, toinsert[0]);\n    element.append(toinsert);\n    return toinsert\n  }\n\n  events.on('output_added.OutputArea', handle_add_output);\n  events.on('output_updated.OutputArea', handle_update_output);\n  events.on('clear_output.CodeCell', handle_clear_output);\n  events.on('delete.Cell', handle_clear_output);\n  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n    safe: true,\n    index: 0\n  });\n}\n\nif (window.Jupyter !== undefined) {\n  try {\n    var events = require('base/js/events');\n    var OutputArea = require('notebook/js/outputarea').OutputArea;\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  } catch(err) {\n  }\n}\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "91237ca9516349e79cae8e0524b4063f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "ViewInteractiveWidget(height=768, layout=Layout(height='auto', width='100%'), width=1024)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "PnYejBeO22-h"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3Ffmz5d84cyd"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "authorship_tag": "ABX9TyNvwUXEbCa4KsCSkGucg5mt",
   "collapsed_sections": [],
   "name": "Elec_Net.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.11"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "205db7e1f6dc4bb785b0bb5d2f15a0cd": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_d34639c77e854169baece3a5598d8369",
      "placeholder": "​",
      "style": "IPY_MODEL_7186b0628897425680408231326c5384",
      "value": "Install workshop packages: 100%"
     }
    },
    "2d00def2e94b4ff39e6d619c6068c4c9": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "SliderStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "SliderStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": "",
      "handle_color": null
     }
    },
    "35028c38507d4c9d9d38b426ae7b85f2": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "376a9f9870a942798c0490bdcb8fda21": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatSliderModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatSliderModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "FloatSliderView",
      "continuous_update": true,
      "description": "Weight value:",
      "description_tooltip": null,
      "disabled": false,
      "layout": "IPY_MODEL_5a95d33c5eb6449287e1f37549541f2d",
      "max": 5,
      "min": -5,
      "orientation": "horizontal",
      "readout": true,
      "readout_format": ".2f",
      "step": 0.1,
      "style": "IPY_MODEL_b387f05f31c9492bba4eb98dafe6491f",
      "value": 1
     }
    },
    "5a95d33c5eb6449287e1f37549541f2d": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "6896822334cb42e88db4925abaaf02a3": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatSliderModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatSliderModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "FloatSliderView",
      "continuous_update": true,
      "description": "Bias value:",
      "description_tooltip": null,
      "disabled": false,
      "layout": "IPY_MODEL_c4b71c7111664ba5b662edbfefe1a297",
      "max": 5,
      "min": -5,
      "orientation": "horizontal",
      "readout": true,
      "readout_format": ".2f",
      "step": 0.1,
      "style": "IPY_MODEL_2d00def2e94b4ff39e6d619c6068c4c9",
      "value": 0
     }
    },
    "6976a429b1884c5da05cc9a9f70798e2": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "7186b0628897425680408231326c5384": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "74d3764b6a4f4af2bb459b3d80a7fef0": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "782b2f205cbe49caaccfe76b5a9205da": {
     "model_module": "ipycanvas",
     "model_module_version": "^0.9.0",
     "model_name": "CanvasModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "ipycanvas",
      "_model_module_version": "^0.9.0",
      "_model_name": "CanvasModel",
      "_send_client_ready_event": true,
      "_view_count": null,
      "_view_module": "ipycanvas",
      "_view_module_version": "^0.9.0",
      "_view_name": "CanvasView",
      "height": 768,
      "image_data": null,
      "layout": "IPY_MODEL_5e44ffc0332c4711814cfd5897d69220",
      "sync_image_data": false,
      "width": 1024
     }
    },
    "7e28a8477d8a44269dee75e830b22038": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_205db7e1f6dc4bb785b0bb5d2f15a0cd",
       "IPY_MODEL_cc4205dcdefc4a8aa225c59aa29e89f5",
       "IPY_MODEL_cfa932f1d17b40f2b4bb9ed35b93fe38"
      ],
      "layout": "IPY_MODEL_ef440316e03e455abc5bd40efdc3f319"
     }
    },
    "91237ca9516349e79cae8e0524b4063f": {
     "model_module": "ipycanvas",
     "model_module_version": "^0.9.0",
     "model_name": "CanvasModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "ipycanvas",
      "_model_module_version": "^0.9.0",
      "_model_name": "CanvasModel",
      "_send_client_ready_event": true,
      "_view_count": null,
      "_view_module": "ipycanvas",
      "_view_module_version": "^0.9.0",
      "_view_name": "CanvasView",
      "height": 768,
      "image_data": null,
      "layout": "IPY_MODEL_784a4e19ec694e718b94026b059f96f5",
      "sync_image_data": false,
      "width": 1024
     }
    },
    "9e54c073f11144c59924ce4a2b497037": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "ae284c77d9cf479285485743867b5557": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "b387f05f31c9492bba4eb98dafe6491f": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "SliderStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "SliderStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": "",
      "handle_color": null
     }
    },
    "b987a6d05aac4e75ad159a969ff9d2df": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "SliderStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "SliderStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": "",
      "handle_color": null
     }
    },
    "be69999156444f629feb805bd9a4bcaf": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "c1ade49287704335aab335c4251d4362": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "RadioButtonsModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "RadioButtonsModel",
      "_options_labels": [
       "linear",
       "ReLU",
       "sigmoid",
       "tanh"
      ],
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "RadioButtonsView",
      "description": "Activation:",
      "description_tooltip": null,
      "disabled": false,
      "index": 0,
      "layout": "IPY_MODEL_ae284c77d9cf479285485743867b5557",
      "style": "IPY_MODEL_fd2e8d1a56964d62b11e32c3544c28a9"
     }
    },
    "c4b71c7111664ba5b662edbfefe1a297": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "c66924481bed40809f0b9a7ad1b45120": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "IntRangeSliderModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "IntRangeSliderModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "IntRangeSliderView",
      "continuous_update": true,
      "description": "Input range:",
      "description_tooltip": null,
      "disabled": false,
      "layout": "IPY_MODEL_be69999156444f629feb805bd9a4bcaf",
      "max": 10,
      "min": -10,
      "orientation": "horizontal",
      "readout": true,
      "readout_format": "d",
      "step": 1,
      "style": "IPY_MODEL_b987a6d05aac4e75ad159a969ff9d2df",
      "value": [
       -10,
       10
      ]
     }
    },
    "cc4205dcdefc4a8aa225c59aa29e89f5": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_6976a429b1884c5da05cc9a9f70798e2",
      "max": 1,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_9e54c073f11144c59924ce4a2b497037",
      "value": 1
     }
    },
    "cfa932f1d17b40f2b4bb9ed35b93fe38": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_35028c38507d4c9d9d38b426ae7b85f2",
      "placeholder": "​",
      "style": "IPY_MODEL_74d3764b6a4f4af2bb459b3d80a7fef0",
      "value": " 1/1 [00:08&lt;00:00,  8.65s/it]"
     }
    },
    "d34639c77e854169baece3a5598d8369": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "ef440316e03e455abc5bd40efdc3f319": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "fd2e8d1a56964d62b11e32c3544c28a9": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}