coloured_mnist_normalised_gb.ipynb

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 "cells": [
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   "cell_type": "code",
   "execution_count": 1,
   "id": "6e0d252f",
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    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x19e4a27af30>"
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     "execution_count": 1,
     "metadata": {},
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   "source": [
    "# Datagen inspo taken from \n",
    "# https://colab.research.google.com/github/reiinakano/invariant-risk-minimization/blob/master/invariant_risk_minimization_colored_mnist.ipynb#scrollTo=sopHPgEhu4Jo\n",
    "\n",
    "%matplotlib notebook\n",
    "import os\n",
    "\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import torch\n",
    "from torch import nn\n",
    "from torchvision import datasets\n",
    "import torchvision.datasets.utils as dataset_utils\n",
    "\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "from torchmetrics import Accuracy, AUROC\n",
    "import time\n",
    "import torch.nn.functional as F\n",
    "\n",
    "import random\n",
    "\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "from sklearn.preprocessing import minmax_scale\n",
    "\n",
    "import pickle \n",
    "import seaborn as sns\n",
    "\n",
    "import scipy\n",
    "\n",
    "SEED = 1234\n",
    "DEVICE = 'cpu'\n",
    "np.random.seed(SEED)\n",
    "torch.manual_seed(SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "106d2cbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = './data/'\n",
    "if not os.path.exists(data_path):\n",
    "    os.makedirs(data_path)\n",
    "train_mnist = datasets.mnist.MNIST(data_path, train=True, download=True)\n",
    "test_mnist = datasets.mnist.MNIST(data_path, train=False, download=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4ae8d37e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# lower = 0\n",
    "# upper = 1\n",
    "# mu = 0.5\n",
    "# sigma = 0.3\n",
    "# truncnorm = scipy.stats.truncnorm((lower-mu)/sigma,(upper-mu)/sigma,loc=mu,scale=sigma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f2fb4c2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28, 1)\n",
      "255\n",
      "1.0\n",
      "107.94117647058823\n",
      "(28, 28, 3)\n",
      "323.82352941176475\n"
     ]
    }
   ],
   "source": [
    "# sample = np.array(train_mnist[0][0]).reshape((28,28,1))\n",
    "# print(sample.shape)\n",
    "# print(np.max(sample))\n",
    "# sample = sample / np.max(sample)\n",
    "# print(np.max(sample))\n",
    "# print(np.sum(sample))\n",
    "\n",
    "# sample_3 = np.concatenate([sample,sample,sample], axis=2)\n",
    "# print(sample_3.shape)\n",
    "# print(np.sum(sample_3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "738a0b0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# def scale_to_sum(arr, arrsum):\n",
    "#     return (arrsum / np.sum(arr)) * arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "76ab7366",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "107.94117647058823 107.94117647058825\n",
      "381.88470895845114\n",
      "107.94117647058823\n",
      "107.94117647058826 97.14705882352942 10.794117647058826\n",
      "107.94117647058826 107.94117647058825\n"
     ]
    }
   ],
   "source": [
    "# # arr_noisy = sample_coloured[:,:,0] + scale_to_sum(noise[:,:,0], np.sum(sample_coloured[:,:,0]))\n",
    "\n",
    "# noise_scale = 0.9\n",
    "\n",
    "\n",
    "# sample_coloured = sample_3.copy()\n",
    "# sample_coloured[:,:,1] *= 0.9\n",
    "# sample_coloured[:,:,2] *= 0.1\n",
    "# print(np.sum(sample_coloured[:,:,0]), np.sum(sample_coloured[:,:,1]) + np.sum(sample_coloured[:,:,2]))\n",
    "\n",
    "# noise = truncnorm.rvs(size=(28,28,3))\n",
    "\n",
    "# print(np.sum(noise[:,:,0]))\n",
    "\n",
    "# scaled_noise = scale_to_sum(noise[:,:,0], np.sum(sample_coloured[:,:,0]))\n",
    "\n",
    "# print(np.sum(scaled_noise))\n",
    "\n",
    "# sample_coloured[:,:,0] = (1-noise_scale) * sample_coloured[:,:,0] + noise_scale*scale_to_sum(noise[:,:,0], np.sum(sample_coloured[:,:,0]))\n",
    "# sample_coloured[:,:,1] = (1-noise_scale) * sample_coloured[:,:,1] + noise_scale*scale_to_sum(noise[:,:,1], np.sum(sample_coloured[:,:,1]))\n",
    "# sample_coloured[:,:,2] = (1-noise_scale) * sample_coloured[:,:,2] + noise_scale*scale_to_sum(noise[:,:,2], np.sum(sample_coloured[:,:,2]))\n",
    "\n",
    "# print(np.sum(sample_coloured[:,:,0]), np.sum(sample_coloured[:,:,1]), np.sum(sample_coloured[:,:,2]))\n",
    "# print(np.sum(sample_coloured[:,:,0]), np.sum(sample_coloured[:,:,1]) + np.sum(sample_coloured[:,:,2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3d42b6bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "107.94117647058825 107.94117647058826\n"
     ]
    }
   ],
   "source": [
    "# test = scale_to_sum(sample_coloured[:,:,1:], np.sum(sample_coloured[:,:,0]))\n",
    "\n",
    "# print(np.sum(sample_coloured[:,:,1:]), np.sum(sample_coloured[:,:,0]))\n",
    "# # sample_coloured[:,:,1:].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1b80bf7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rescale_values(image,max_val=1.0,min_val=0.0):\n",
    "    '''\n",
    "    image - numpy array\n",
    "    max_val/min_val - float\n",
    "    '''\n",
    "    return (image-image.min())/(image.max()-image.min())*(max_val-min_val)+min_val\n",
    "\n",
    "# Convert MNIST PIL images to 3 channel NP arrays and return separated data and label arrays\n",
    "def preprocess_data(train_mnist, test_mnist):\n",
    "    data = np.zeros((len(train_mnist)+len(test_mnist),28,28,3))\n",
    "    labels = np.zeros(len(train_mnist)+len(test_mnist))\n",
    "    \n",
    "    for i, (img, label) in enumerate(train_mnist):\n",
    "        arr = rescale_values(np.array(img)).reshape((28,28,1))\n",
    "        \n",
    "        im_arr = np.concatenate([np.array(arr),np.array(arr),np.array(arr)], axis=2)\n",
    "    \n",
    "        data[i] = im_arr\n",
    "        labels[i] = label\n",
    "        \n",
    "    for i, (img, label) in enumerate(test_mnist):\n",
    "        arr = rescale_values(np.array(img)).reshape((28,28,1))\n",
    "        \n",
    "        im_arr = np.concatenate([arr,arr,arr], axis=2)\n",
    "\n",
    "        data[len(train_mnist)+i] = im_arr\n",
    "        labels[len(train_mnist)+i] = label\n",
    "    \n",
    "    return data, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f5fb4bb4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "data, labels = preprocess_data(train_mnist, test_mnist)\n",
    "print(data.max())\n",
    "print(data.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d59a4112",
   "metadata": {},
   "outputs": [],
   "source": [
    "# this number needs to be picked s.t. 50% is divisible by 10 and 9\n",
    "n_per_class = 3600\n",
    "\n",
    "sorted_data = np.zeros((n_per_class*10,28,28,3))\n",
    "sorted_labels = np.zeros((n_per_class*10,))\n",
    "\n",
    "ind = 0\n",
    "for i in range(10):\n",
    "    class_labels = np.where(labels==i)[0][:n_per_class]\n",
    "    sorted_data[ind:ind+n_per_class] = data[class_labels]\n",
    "    sorted_labels[ind:ind+n_per_class] = labels[class_labels]\n",
    "    \n",
    "    ind += n_per_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c7c428cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Shuffled 60/20/20 split\n",
    "# split 60/40 into train and val+test, then split val and test by half\n",
    "data_splitter = StratifiedShuffleSplit(n_splits=1, test_size=0.4, random_state=SEED)\n",
    "train_indices, val_indices = list(data_splitter.split(X=sorted_data, y=sorted_labels))[0]\n",
    "\n",
    "x_train = sorted_data[train_indices]\n",
    "y_train = sorted_labels[train_indices]\n",
    "x_val_test = sorted_data[val_indices]\n",
    "y_val_test = sorted_labels[val_indices]\n",
    "\n",
    "data_splitter = StratifiedShuffleSplit(n_splits=1, test_size=0.5, random_state=SEED)\n",
    "val_indices, test_indices = list(data_splitter.split(X=x_val_test, y=y_val_test))[0]\n",
    "\n",
    "x_val = x_val_test[val_indices]\n",
    "y_val = y_val_test[val_indices]\n",
    "\n",
    "x_test = x_val_test[test_indices]\n",
    "y_test = y_val_test[test_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a76b327d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1.0, 1.0, 0.0)\n",
      "(1.0, 0.9, 0.1)\n",
      "(1.0, 0.8, 0.2)\n",
      "(1.0, 0.7, 0.3)\n",
      "(1.0, 0.6, 0.4)\n",
      "(1.0, 0.5, 0.5)\n",
      "(1.0, 0.4, 0.6)\n",
      "(1.0, 0.3, 0.7)\n",
      "(1.0, 0.2, 0.8)\n",
      "(1.0, 0.1, 0.9)\n",
      "(1.0, 0.0, 1.0)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1.0, 0.5, 0.5)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def create_colours(neutral_scalar=1.0):\n",
    "    blue = 1.0\n",
    "    green = 0.0\n",
    "    colours = []\n",
    "    for i in range(11):\n",
    "        print((neutral_scalar,np.round(blue,1),np.round(green,1)))\n",
    "        colours.append((neutral_scalar,np.round(blue,1),np.round(green,1)))\n",
    "        blue -= 0.1\n",
    "        green += 0.1\n",
    "        \n",
    "    return colours\n",
    "\n",
    "colours = create_colours()\n",
    "colours.pop(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8839dd42",
   "metadata": {},
   "outputs": [
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       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    fig.rubberband_canvas.style.cursor = msg['cursor'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * https://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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1XX89RvAOs/VnS3XOVrjlm5PnWTVQp3/TgCrU5d1I1CTQJ4AttNOO0VXL3UlV3/AOifdVdP3dTbaaKNBj3L1arL+GF922WXR9xN0VTzvpO8KfeUrX4kexdIJPG3SQFqPvmhAkvY9Jw0uFSY0YNRdE92ZsM+M5x3M+Qpgaruu7OlupO4cdT7eqKvRGmTrcR4NtPS4UtHp2WefjcKdBmsaeKqf+glgWW21j62pvQoHnZP9roiuanfe6YsHMAUj+ziR7oL2CmAKI7rzpQAmK12dj184sAFMV97j38HT4FB3MLICmEJRr+/Y2PXrIoHuANj1ahCsCwt5A5juOukunibdoVJISJsUBu1FCIVEbUOTBuP6Xowmedjvx8XXoe/9nH322dEjm/oeY9r07//+79EdGQU4BTk9btaUAKZ+1913G4Y792/atGnR3U7to85v/Uw6z+muksKczjk2jNsAZL9jqXBnvx+Wth19D1PzKsDpDmh80vGhxzPV1wrCnY/laV6FFIUg1YKWt9vXuVd3brbeeuvozmXapPnzHl9aPh7AVOc6z2YFsLSLAL287Xp1PP3DP/xD7q7pPP7iC9YRwDj+undNXcdfvwEsq4j0FIweY9d4SN9NDGkigIXUG7QFgZwCeQKYrn7qKqj+WCswdU66kq+r5Rpc6KqkvcrbrQkKRxokv/HGG9GjT53fHevVdPsHVI996PGftMne7dAjA52PmWh+nUR1MtWjlBq893M13VcA05eRdcdBAzYNwu3LIayDvXOlgbYG3LpjeMMNN0SPVuhRHg0CFSLSXmxh16H+0HY0INOjPPZlKkXvgGW1Vfa6s6SBrh6bSpvso4UKKrpKbicbwNRWhQ8FFwVHBSytK+5i7+BqPn0HUlfdFfoUvBXi9cdU+6nHwzQg16BJ86lGbJjT9x0VSjXpOFAf2Ek1r5ddaPCrYKeBRdqkO3C6o6LwrAsZuqBhv5Op4KJgrf1Q+NMxp/VqP1Tr2p59rFLHjUKP7nLqoocCWOddabt9XWCCiN8+AAAgAElEQVSwd8dU59qGpniA06NlemFH52SPNT0CqTDfOWkQr/CiO+O6K6O72U0JYLrzo7AsB13U0V3U+KR/16NtCh+6S6iwqmNLTwDoOz+6s6PHTrt9L03r0ncsdZdVj5/aRw87A5j9TpzOJ/JMm9QW1YO+35T2fVndYVJ7NOmuctpFF33vV7Wm84a+26XAo7tzuqKv/tfjf6o9nY/1XTAdA/LRo5d6SkDHvoK6/l7ohS3xu4H2+NLdX100iQcwPVmhxyt7PYKoAKl9sE8h9Dr/6980v45LPWKtc5Pu+OeZ0o6/+HL2b2HW8ZdnWxx/vZXqPP7s33MdM3rEXf9fx6zqSxeTdM7sZ9KFCp37VSeq8ZAmAlhIvUFbEMgpkBXANAi0f9A1mNMf0s5JgUp3nTTpD6kdqHZrggYjGvDoj6i+IN5t8Ji2vL3bppOr7mB0TnomXwFQgwx9AVffFYhPGmzoMTQNShUwdKeoSQFM+6TvZenOngaEnZMen9PA2X7JPu1Nirp6rJASfxzNrkePm+nFDAofeuxJU78BLKutaqPaoYGh7rSkTfouiwaFnd897PUWRA1y7Rf3VY8KT/rDqzsZenRJdzPij2nJQ7WtgV18UuCRgR6r06RHpTSY1nyqIftiAN2d0zZ1x0j+dpCtgK9wpFpUnWs+3RXUAE9t0QBYwUeDZvtmxW5vvtR3q3SXQgNf+yICbV9vBtT3r7pNqhMdA50XLBTidFdDx4sCevw7bXZd9vFI/X8FkfiFFd0p1qPHCvkKX/a80JQApn1WeNejgQphnUFKg3VdyNGk7yfpO4Q6F8YnnRd1vKR90V+BVf0i4/ijh/EApgsimkfnn24Xi7Q921b9bz3m2vlynuuvvz66MKbAqHNx2qQ60UUCne9Ug2mTDVCd/6a/ETr+NADVY5I6hnTM6PiIH1+6SKcLPvEApkCmEKo7qPGXcOh40LwKjvp+qWo776RjRceh6lF29vHIzuWzjr/490S1bK+3IOq7ljpn2DeW5m0rx1+6VJ3HX6+3IOr408UG1Vy3ryektVgXmfWIss6XqmX9PQlpIoCF1Bu0BYGcAlkBLH6l3L6xsHPVGpDaK6Lxx5y6NcHe1SjynTG7Ln1XQQNPDQB1hVmD+PhkH4nSZ52PCujkqS+ia1CsR+vsF22bEsAUOPXYmbz1PaS0t6vpC8S6Qq0rfhpo6fEcPT6oOyka8OgxOg2idOdCjwbF3+ilR+f0uYKxfOzrlvsJYHnaah9tVWjR99XSJv2x1KOKumul/bKTvvuhO0n6Q2jv6qj29F0l3e2LT/p3hTztu339tP0uSHw+DU51NV0vxtDjXnrEU3WmR8j0QhJNumOgx7k63/6mgZnqOf4on77wH3/xhf7465FR3X3QYFT9p8f1dPdYoUoXJnQxQo+Q6e6DXn6jP/wabHa+JVQvftDgP+2CiN0neyxon3X3wj5+aP/d3tXQ420axMcn9Z+OMz2qqEnngfgr6RWYFTgVhHWXw05NCWD2arYeEdTjcZ2T/a6pPtcxojukGtArMCk86E6pgpls9RILBaP4pAsHMlX/x1+DHw9gqnvVti6E6C2B3e6m2bZq/Wl3oe2jvDo36JhIm/QIuY431bjarUCu+XWxLP4zFmqDvrek84W98KHgqfOKjhkdH52Po9rjSyFPddb5/T+dd1XzemlH/BFvBSgFW4XP+Is2ev3p0nlJ31NTsNPFEV0o6jb1Ov7s47fxZVXTacefalznFtWBzru9fnYk7W8Rx1+yh+o8/nRu1jlJF0b01INqWXeXdbFPNai7uzoW9Lr5Xk+C2FbrApnOnfqesC5UKTyGNhHAQusR2oNADgHXAUxv89KVJJ34FAT0Xa6ik22z/ojqER6dFPVIg06w+oOsgKL/aFCsx+DsZO+adP7hbkoAsy+C0ONKGjSl/fHQo2kaeGtKu6qufdVdF90BiX+BXQMjDfwVCHRnMP5mxX4CWJ62lglgnTWjAZIN47qqrgGn/vBqcK1gpBdVqB70fTYFqPgLNnSXQn+M9W8KQbqirqClxx4V+uwLEjSIVFDUwM6+BEV3HDTw1OOE9vErBbj4pD/4etRR/aIrrxpwq41qgx7vk7fCTrdJfaPHNPX4i/pOj9VogKFHLhUK0n7mQOvWIEeD37RH7LQt7aNCoIK6gqvqRYNyWekOtQYc9q6gtmXfUKnH8HQRRY/y6O5XPMQ3IYDp7pT6QDYySPudMN2ttHdlFDC0z52//6R60uOrOm70+Kmd7PHS+WIJ/bs9ZvVvupOjIK6Q1u33heJtrSKAdX7XTH2oO8Sa7As69B1bnVc16eLDvvvuG7Vbx4UCimpOx4Duyup8rnOuakbhTcdDPIBpMKzaVXBS4NSxpztouqCgEKu7+Pr+rUKf/U5jt+NAg2iFL10M0LGuO7t5BtBpx5+Oj153juNtiB9/uuChu+VZE8dfd6G6j79efaMLXTqu7e8K6hzZa9LfST3+re9w6oU9Oi90PvafVQsu/p0A5kKZbSBQsUBWAKv6EUQNPDQITXstdt5d00BX7e58vNAOUHRnSwNn3T3R1V5NGiDoKrbuHNjfl7Hba0oA09U8DYo7X1kdd7Pfq9Jn8e/8xOexj/7FXzRhX27S+aiflusngOVpa5lHEOP7o+/G6HExfSdEfatBpYKN/sjqiryu8qs9+sNvX8KhwGEfQ4w/02/fyqYgIRP7G1i6G6Y7PgpOGnR2/vCuvQuhx8A0UIy/Gjve1vjb7DSg1R/2PANB+z1LrUt3YLRv2i8NEBSC7CvI9e8KBboDo5c66IqvfXte2vGlRx51TOqYik8aZOjFGrprqEGu1qV91nY1gNGAWoOZzp8yaEIAs32g0KvwkTaIj3+vqvMun3Wyj/7JRY9oKtzoZTKqEYURPXrY+ZhTfFsKX7qa3uuHom1bVc+6s5t2B6zII4idj27b3/PSRR3dJdZ/Ol9wZF9qpLbr+Oq8k6pHae3xJZt4ALMXWbr91Ih+A1B3lbK+V6O789p3/bcet9TTD91+/LrX3xGFR11o0AUEHcd5ApzW13n8df6mXnybHH+9/5LXefzlGUPYl+vob4CeIug26Zyn86j6U+cK3enOukiQZ/t1zEMAq0OVdSJQs0BWANPms17CocdndGVQf8z0yFK3Z6v1SJUeY9ItfQ1YdWW1zKQBoO5w6Yqo7mJoIKOrVQpeupsRH3zau18anHe+8l6DWPtCD/2R16SBZ7cfcdS/u34Jh33Nua5AK1ilfX9L7bLfj9BjL/oDEv9xU2utAYgGIvE3+tmBs0Jq55V+3f2wLyKwA259F6/bXZu8bc3zEg4FCw32dWdKj+KlTbrLpSvr8YGjHqPU1XdbZ3oMU0HSfgdGwUwBUI/axQOt7gDKQvMp0NnvNmqgqkc2u4Vf3SnSI1W9Hg/VnSa1U7Wpqch3CfQ9MvudFfuYr30zXvwNcrqTp6u66vu8v6+ngb2+V6hHc9VGBTu91lwDVA2QdUFDwV+TvVihu89pvyOnuwM6vu1xphrp9WPsPl5Db+/69HqDngKVvuOlux962UnnXU1Z6Puk9vuu9u2b9mKF7prZ39mK16zqS5PqS8Fb30fUuabbZNuqWlUfpQWwPC/hsBdYVMcK7Hay31HSBTEFdfV3510y3QG2P8yt73mlTfb40r/ZACYTe57SRYO00GJ/akLHq+ZPm1RzOiZt+NKFt35+EkDr1jFtH3fUPtm332b9HUo7/tKW4fjLkjTRuUEXPuo4/rK3bqInInRu7PU2Q40JdB614UsXyrr9zc2zzbrnIYDVLcz6EahBIE8Aq+o19HrkTXddNKhVaOr8EnQVu6cBpAY+uhId/32kXi9tSNuuvg+gq8HdJtcBzL4QofP3qTrbp8GVvt+hSVem075XYgdL8bcP2gCWtw8Uirr9kHPetpZ5DX28nfpjqj+quqqtPtddGg2edVdHdwcUVOwPwtrldEVb349TCNJLNvSWOE3xK92y1MsGNGlArQGg5rMv5ei0krXuxsW/M2bnUV3q5Sbxt2fZH4zNY64wad/IqDsrGrzre2n6YW99P0d3lXUxQn1aJHz12rZ9oUvcp9cX3NPWlfXIlusAZn+XSRcoFAri32vrbL/ClUJW2s89aF4b1vW/7dsHbQDL06eaJ/64X+cy8bbaR03TAliR19B3/oizvSOkO73HHXdc9FtduvupoGInW9fdfi5C88WPLxvA4o9xdns7o30Nvi766AJd51Rl+NK6tQ3dedRU9vjrbCvHX3bV1338Zbdg2Q+Z63FwXdTQndTOqWnhS+0ngOXpeeZBIDCBPAEs7w8xa0Crq1rdJjvIjw/oquawg/q0t4V121bojyDqarwGivpvfc9Ng+xuk67Y606Q9iktCOhKru5o6DG5vHdIijyCWKStCiu6G5n1Q8x67EOButvjQrpDoZcD6DspCva6Wqk3vml+/X9tQ49NacBl74DZV2krjGqgrbu4+jdb67qjqsGpwo3+t+7EacCtu7aqsc5J3xGyr3LXnVkb3DSfwpdCqRx150iDUd3J1J3Fbm9w61y/femFgqUGx1qn+ln7p4CgPrXhK+vlBHmOOX2XR/ujbekOi31hTdayoT+CaM93eX5/0IbDzu+SWgMbUPP8/qF9Hbtd1v4OVy/PeFvVt71+iy/vDzHrir4uqNnJvtRIj/Mp3Om7k/HffdOdb9WBziu6YKaaTXu81h5fWq8NYAq49gU5ndu121fo00+F6BFG3YGNTzLSUw268KG7EbrY0M9jh/F12sc1yx5/ne2Ihy+Ov+5V7ev4sy3S9wH1XS5d0FLd6WmQ+KTwpccO9VisHmvXd0RDfeww3m4CWNZfJv4dgQAF8gQwXTXUyUhv2Iq/nly7Y18pr0GqXqqh7yqkTfo3nfg0abBrf7emG4nCnF5uoLs5GmDHJ73QQAPRzjdn6UdT9aiQvlyvx8WyXodv1xl6ALOv3teX5NUH9k1+3ezsYz16k54GBvZHenWlXFfcdbckq7/i6y4SwIq2VS+UUIBQGxWcbP3oS/r6vpYGfHqsUm9Ls5MGdnqEUI+I6sq5HiPUo0T646qXGWjQqAChx/1koYsCxx9/fLS4HrXSH1XVjr5/o3rR4zB6/EqhTS90UejQgFeP+ul7Y/o3DST1XSmFOtWjfWujQqwGn1pWV/x1l0ovFtAFAM0bD1+686u+0yN/unulgGwnPbqn/dIdTs2jmtTLGfQKbz1ypcfUNDjQyz50t0FXcXX1VoFOPvo+g4Jst7s1abWiMK5wFf/+mOZTOxQ0tf/dfny9W+2FHMD0eLQuZOjOaJ7fH9QdZJ2z9N+dj8DqpRsKcTo36nhSUO822fBlfwRZ82UFsM626tG/XgFMd25tP2oftS3Vpl61rxpXrWlfdPFAjyHGJ/sjzfYzhTC9ZEN1ao8De+FCda6AZX+aRPUdP760jvh3wPRIsy5IKKTqxRTx3/rSIFeGqtvO38+L3/nSca525wlfekxT+69jsvOCjS7A6K6HLlpkHX/WQvun/Ykff3oBT3yKP3bI8dd9kOPq+NPdLV1E6HzkVcecHvnVeVUXEfQ3Jn4BLB6+dMFJfyd63SEPaThHAAupN2gLAl0E9IhW/AdtFWY04NRVnvgzzhrUxQdm+uOl7wzpDoeuiOrRIp3A7AsJ9Iev1w8qK1DpO1m9fkA53mQbDNMeudEbuvT9IT1ypoGx/tBqAKL2aECqtujNZHmnPAFMwSL+XRY9gqcBrCziP3wqp/hk3wJmP1OA0ncddOKPD0YUENJ+i0nL2avLvX4rqHNfFVh0N0dXrTUI0mNF6isNrvUIjgbveX/LpEgAK9pWDWD1vRM9miJH9ZuCoga4+oOoIKDveMUHU/bHhTXIVA2odm0tWgeFHYUgBQz7A7cy1kBZNaULAhpU6kKA7l5pEGgn+zty9k2aekxK82ogqP8dn9T/ujun40KPatk3LSos6xFNHVtyj08KmdrP+PdYFBgVCrQOHXf6t/gbPLW8al3/0YBWNhpE6LfJNIBW+zVYiN/d6KwJ3W2I38nSHUhtT4/T6nP9b9Wnalj7rkGnvjuU90UF2l5WACtzPOi8pfOXJu2vfRud6ttOstO5K23SXU/1f5HfH9RFAR2XqlMZ6aKO+luPp+qukIKvzgvdjNSnuqKuSbWq84Ym+zt98XbqOLOTbatCh5ZTHelulS6c2AsqmlePteo/mr797W9HF1jsq951rtUFBN1BUm3oONM2dGFGda/6URhRm1QLmnRu0PGhdagO9LkubuhxbF0s0cUtnUt0h1d3ctUf9tixP/KsO7P24pjCrvpc7bfnIh0bOv6shf6u6M5U/JF0+91P7b++p9btopPujMXfZGd/2NwefzrPqn16lFT/0aSfilC/xO/k6eKdwqY9/rS89l3nD50zNelcpIsv8bYopMqT429Z5YZw/Om8q4u8OlZ14Vh1qb8BOl+on1S/epJEF/jik16yZH+PUn8bu11M1nlA59KQJgJYSL1BWxDoIqA3+fR665ZdTAOPzt870UlMV/71OIn+8GiQqz/WehTHvi0ubbP6To6uRukPsa5O6SpU1tQrgGnwo9ec6791h0STBpEaKGmQYF+XnbUN++95Alje75DFf+dG68/7nZnO386xbYt/0V93duyPw+bZN4UsXZ3XIEl3hzTw1JVtBbki68kbwPptqwb7GujrO4IKUno8SH88VSe66t85uNXdAD0eqP7XQE7/X2FY4VuPIep/a3CoutMfUd0V0+Bdd780KSzpjoYGXQpfetOnBlXajv0hbw1Q9VijHh20LzJR3+pKvAZhunqvSX/cZangpT/gCte626C7Rxr4agCsdmRN6hsNNHXnVseZ9klt0QDU3n2Qk/ZRAwBtT3fB1K95XyTQeUzLSbWg8Ks6lYMGzgqu8sr7e0fxfcsKYGWOhzzfU0y7YGPbp0Cimin6+4O6SKW7POpz9aeCv96mKv9eL+rRdot8Jyx+7rBtzaob3V3TuclO8fO7jiOFA91l0p1MhQm9FVbnAwUv3dlT/Woevd5d+6Ua0TGoOz+6QKRQorpWWNF3D/UdSD2Kq6CuesxT26ppDXj1fUsdrwqzWp8u4umFSTrXd74syH7Htuj+a1vx40/HkY5r/U1QONCjmrqr3HlO0TGnc1D8+FN/aDn1hc5DujvdOeWtZy3H8efm+NPbbTVGUQjTOMW+GEx3gNWHOrelhateP8Qd7/de55iseq3r3wlgdcmyXgQQQAABBBBAAAEEEECgQ4AARkkggAACCCCAAAIIIIAAAo4ECGCOoNkMAggggAACCCCAAAIIIEAAowYQQAABBBBAAAEEEEAAAUcCBDBH0GwGAQQQQAABBBBAAAEEECCAUQMIIIAAAggggAACCCCAgCMBApgjaDaDAAIIIIAAAggggAACCBDAaq4B/b7HySefHP1OhX7fQz94qt8m0Y80MiEQqgB1G2rP0K4sAWo3S4h/D1GAug2xV2gTAvUJEMDqszU33nhj9GOK+hHO/fbbL9qSfhD0jTfeiH4UUb9Kz4RAaALUbWg9QnvyClC7eaWYLyQB6jak3qAtCLgRIIDV5Pzmm29Gv0ivX63XL9hvtNFG0ZYeeeQRs8UWW5iNN97Y3HfffTVtndUi0J8AddufG0v5F6B2/fcBLSguQN0WN2MJBNogQACrqRd1RWuXXXYxX/rSl8yFF144aCsHHXSQueiii6Jg1s+jiE8++aS55pprzKRJk8waa6xR0x6w2jYLLFiwwDz22GNm2rRpZsKECct3lbptc6+3Y9+o3Xb041DbC+p2qPV4O/a3W922Y+/87gUBrCb/4447zpx++unmiiuuMHvttdegreizvffe25x77rnm8MMPL9yCc845xxx55JGFl2MBBDoFzj77bHPEEUcs/5i6pUaaIkDtNqWnaGdcgLqlHpoo0Fm3TdyH0NpMAKupR/bcc09z1VVXRY8Zbr755oO2os8mT55sDjvsMHPeeecVbsENN9xgdt1118LLsQACnQLXX399dKfWTi7q9j3mA2a0GUtnINCXwKtmvpljHjTUbl98LORJgLr1BM9mSwl0q9tSK2XhSIAAVlMh7LTTTuaWW24xc+bMiR4VjE/6bMMNNzT777+/ufTSS7u2YO7cuUb/6Zxmz57NHbCa+m2orfa2224zU6ZMWb7bLup2c7OdGTdsnaFGzf5WJPDSwHPmfnO7oXYrAmU1TgSoWyfMbKRigW51W/FmhuTqCGA1dXsVA9mTTjopeoU9EwJ1CdQxiM2qWwJYXb05NNZb50CW2h0aNeRjL6lbH+pss6wAAaysYPflCWA12VbxKBd3wGrqHFa7XKAzgLmoWwIYBVhGoNuAgNoto8qydQtQt3ULs/46BAhgdaguWycBrCbbOl9moB9snDp1ak0tZ7VDSaAzgLmoWwLYUKqw6ve124CA2q3emjVWJ0DdVmfJmtwJEMDqsyaA1WRb5+u8CWA1ddoQXG1nAHNRtwSwIVhoFe5ytwEBtVshMquqXIC6rZyUFToQIIDVh0wAq8l20aJF0cs3uv0Qs36YWb8D1s9EAOtHjWXSBDoDmIu6JYBRi2UEug0IqN0yqixbtwB1W7cw669DgABWh+qydRLA6rM1el387rvvbsaMGWP222+/aEszZ840CxcuNLNmzTJbbbVVX1sngPXFxkIpAp0BTLPUXbcEMEqxjECvAQG1W0aWZesUoG7r1GXddQkQwOqSJYDVJ/uXNWuAqzcZ3nvvvWallVYy22yzjTn11FMTvw1WpCEEsCJazNtLIC2Aaf4665YARk2WEcgaEFC7ZXRZti4B6rYuWdZbp0BW3da57bavmztgDexhAlgDOy3QJncLYHU019YtAawO3aGzTh8DAmp36NRXXXtK3dYly3rrFPBRt3XuT0jrJoCF1Bs520IAywnFbJkCBLBMImYITMDHgIAAFlgRNLA51G0DO40mGx91O1TYCWAN7GkCWAM7LdAmE8AC7Ria1VXAx4CAAEZBlhWgbssKsrwPAR9162M/fWyTAOZDveQ2CWAlAVl8uQABjGJomoCPAQEBrGlVEl57qdvw+oQWZQv4qNvsVrVjDgJYA/uRANbATgu0yQSwQDuGZnEHjBpolYCPgSwXDlpVQl52xkfdetlRDxslgHlAL7tJAlhZQZa3AgQwaqFpAj4GBAxkm1Yl4bWXug2vT2hRtoCPus1uVTvmIIA1sB8JYA3stECbTAALtGNoFnfAqIFWCfgYyHLhoFUl5GVnfNStlx31sFECmAf0spskgJUVZHnugFEDTRXwMSBgINvUagmn3dRtOH1BS/IL+Kjb/K1r9pwEsAb2HwGsgZ0WaJO5AxZox9As7oBRA60S8DGQ5cJBq0rIy874qFsvO+phowQwD+hlN0kAKyvI8twBowaaKuBjQMBAtqnVEk67qdtw+oKW5BfwUbf5W9fsOQlgDew/AlgDOy3QJnMHLNCOoVncAaMGWiXgYyDLhYNWlZCXnfFRt1521MNGCWAe0MtukgBWVpDluQNGDTRVwMeAgIFsU6slnHZTt+H0BS3JL+CjbvO3rtlzEsAa2H8EsAZ2WqBN5g5YoB1Ds7gDRg20SsDHQJYLB60qIS8746Nuveyoh40SwDygl90kAaysIMtzB4waaKqAjwEBA9mmVks47aZuw+kLWpJfwEfd5m9ds+ckgDWw/whgDey0QJvMHbBAO4ZmcQeMGmiVgI+BLBcOWlVCXnbGR9162VEPGyWAeUAvu0kCWFlBlucOGDXQVAEfAwIGsk2tlnDaTd2G0xe0JL+Aj7rN37pmz0kAa2D/DfUANnylZKeNWWN43z156PR1UpddfWRyQxutNzIx75dn/CHx2beOeGfis30/MS51O28sWpr4fMYlzyY+O/miZ/rex24LcgesctKuKxw2YkTi34a/de2+G/C7o9dLXXbJ6sl6mrDBc4l5Vz9kWOKzZ/9llcRn90++PHU7Lyx5LfH5X/3gq4nPJh31y773MW1BHwOCoR7AqN3yJUzdljcsugbqtqhYcn4fdVu+1c1YAwGsGf00qJUEsGSnEcD6K2QCWH9u/SzFYKAftTAGBAQwLh6UrV4fA1nqlrptYt2WbXNTlieANaWnYu0kgBHAqipbAlhVktnrIYBlG+WZg4FsHqVq56F2y3tSt+UNi66Bui0qFsYFr/KtbsYaCGDN6CfugMUEeASxuqIlgFVnmbUmBgNZQvn+nYFsPqcq56J2y2tSt+UNi66Bui0qRgArL5Z/DQSw/FbBzMkdsGRX8Ahif+VJAOvPrZ+lGAz0oxbGgIBHuXiUq2z1EsDKChZfnnNucbPOJXzUbflWN2MNBLBm9FNj74C9+23JL/WvMiL55f+tPzgq0RPbbrZGau+MHZ0cDHx6+7FOevKp5xYntnPPr5MvI5g2Ndme1xYmX46glT0wZ2Fincdf8HTis5/dt6DyfSSApZMO3+Q9iX8YWHXlxGd//Fiynxd+OFkPWnDcmOTnd3ww/QUXVXf0j14fnVjl6b//RLLG3v+DxGdP/TlZn5ppxrwdE/P+5rQPJD5b/Zq7Kt0dHwOCJgUwandFuYVUu9Rt79MAdUvdVvqHogErI4A1oJM6m9ikO2AEsBW9RwD7mZk6darZ3Gxnxg1Lf/NkKIcjg4EwBwNqFQNZBrJNvHhA3VK31G0of+HDaAcBLIx+KNQKAhh3wAoVTI+ZuQPGHbC4wM+4A5ZaENwB4+5t2XMuAYwARgArexS1a3kCWAP7kwBGAKuqbAlgBDACWPbRRAAjgGVXSe85CGAEMAJY2aOoXcsTwBrYnwQwAlhVZUsAI4ARwLKPJgIYASy7SghgZYx47HuF3lD/7mKZOmrSsgSwJvXWX9oaYgD70EarpUr+9IINE5+XeWOhy+5amvLOjC+eMjfRhG7f7eqc8Y/PJ1/goXn+9OqfE+v83ZOLnOzqUA9gS3ZobwEAACAASURBVKZsnup87iXnJz7fcOXkC2WcdFLBjSweWJJYYuszjkh8NuK1gVxrHv10sj614KovJF/OMXDvw7nWWWYm7iQs06N2s6sopNqlbqnbJp5zfdRt9pHdjjkIYA3sRwKYm04jgFXrHOJdBAax2X0c0iBWrfUxIKB2s+ukijnafPGAuiWAEcCqOEu0Zx0EsAb2JQHMTacRwKp1ZhBbrWe3tbV5EEsAW9HrXDzIPp5CunhAACOAEcCyj9mhNAcBrIG9TQBz02kEsGqdCWDVehLAbjNTpkxxgkrtOmE2bb54QAAjgBHA3JxHmrIVAlhTeirWTgKYm04jgFXrzCC2Wk8CGAEsrQb4/uIKFe6Ahffbi9y5zf47MNTrNluoHXMQwBrYjwQwN51GAKvWmQBWrScBjABGAOt9TA31gSznXM65ZQV83Lkt2+amLE8Aa0pPBX4HbNyaw1Ml77p048Tn649f1Yn6XQ8nX508/9XkG+KmTh6d2p43FyffEjdmymwnbXe1kaH+FsQRb39bKvXet92b+Hzf0fOcdMtXn/lwYjuPL1g78dklG1yZ2p6Xlybr9pAJ2zppu4uN+BgQhDiQpXZdVFt126Bul1lSt9XVlIs1+ahbF/sVwjYIYCH0QsE2hHgHjABWsBMDmZ0ARgALpBRzN8PHgIAAlq97uHjQ3Ym6JYDlO4rCmstH3YYlUF9rCGD12da2ZgJYPlrugGU7EcAIYNlVEtYcPgYEBLB8NUAAI4BlVQp3wLKEwvp3H+fbsATqaw0BrD7b2tZMAMtHSwDLdiKAEcCyqySsOXwMCAhg+WqAAEYAy6oUAliWUFj/7uN8G5ZAfa0hgNVnW9uaCWD5aAlg2U4EMAJYdpWENYePAQEBLF8NEMAIYFmVQgDLEgrr332cb8MSqK81BLD6bGtbc4gBrNvO7jFlbOKfdtl2TOKzX/3u9cRn533tXbkNZz+6MDHvdgf+LvHZa28sTXz2vvVHpm7nK/uuk/j8oFP/kLtNTZhxqAewbn300hc+kvinVz6RfKnL8AfXSMz3wCHfzt3133jhA4l57/lY8oUbS+a/nJhv4CMfTN3O3MOTH0/c94HcbQp9Rh8DghADGLUbeqUObh9127u/OOeGWc8+6jZMiepbRQArYHreeeeZe+65J/rPo48+agYGBszChQvNyJHpA3it+qqrrjJnnHGGeeihh8xqq61mdthhBzNjxgyz/vrrF9jy4FkJYEk6Alh/5dQtgNVZt5ub7cy4Yclw298e1LMUg4F6XIuu9Q8Dc8wr5k/Rf143r0aLb2a2NbPNzw21m65J7Ratsurnp26Lm1K3xc1cLEEAq0+ZAFbAdtiwYdHcEyZMMC+//LKZP39+zwB24YUXmoMPPtiMHz/eTJ8+PVrmsssuM6NGjTJ33323mThxYoGtr5iVAEYA66twUhZKG8TWXbcEsBUdwR2w3pX8k4Flr9ofaVY3fzaLo//0CmDUrjEMZKs6O/a/Huq2uB11W9zMxRIEsPqUCWAFbH/0ox+ZLbfc0qy99tpmypQpZtasWV0D2PPPPx8FrNGjR5sHHnjArLPOsiv+Ck/bb7+92WOPPczVV19dYOsEsF5Y3AHrq5QSdxFc1C0BjACWt1pfGHjGrGnGmVWGrWruHfiZmW9e6BrAqN1lqgxk81ZXffNRt8VtqdviZi6WIIDVp0wA69M2K4Cdf/755tBDDzWnnXaaOe644wZtZaeddjK33nqrefbZZ6MwV3TiDlhSjABWtIqWzd95B8xF3RLACGD9VGtWAKN2CWD91FXdy1C3+YQJYPmcXM9FAKtPnADWp21WANtnn33M5ZdfHj1qqLtm8UnfCTv22GPNtddea3bbbbfCLWhSAEvbuTVHrZT4+NXXki/H+M4/vjvV5m93T4bWz50wNzHvzJteKmw71BboDGAu6rYJASytDoav/ZbEx0teTNbYEzOTL9bQgr/e7uLE8lt987DEZ+uc/4uhVoa59jdrIEvtdmekdnOVWC0zUbf9s1K3/dtVtSQBrCrJ5HoIYH3aZgWwyZMnm/vuu8+8+OKLZty4cYO2ohcc7Lnnnuass84yRx11VOEWEMAIYIWLpssCnQHMRd0SwFZ0BgEsfyVnDWSpXQJY/mpyNyd12781Aax/u6qWJIBVJUkAq0wyK4BtuOGGZs6cOWbx4sVmxIgRg7Z7yy23GD2GeMIJJ5iTTz65a5vmzp1r9J/Oafbs2ebII4+sbF9cr4g7YK7Fu2+vM4C5qFsCGAGsnyMgayBL7RLA+qmrupehbvsXJoD1b1fVkgSwqiQJYJVJughgJ510Us+AVtnOOF4RAcwxeI/N1RHAsuqWAEYA6+cIcDGQpXZ5fLaf2uy1DHXbvygBrH+7qpYkgFUlSQCrTDIrgFXxOAx3wPgOWGUF22VFdTyCmFW3BDACWD91nTWQdXHOpXap3aK1S90WFVsxPwGsf7uqliSAVSVJAKtMMiuAufhCeGU7E+iKzvzK+NSWHfWZdROfz7p/QeKzjx/8aOKzpQOB7qynZvESjurhH/3O4Jfu2C08usuFiY194ckdEp89v+2yHxweNC1dUn1DG7bGrIGsi3NuUwNY3q6mdvNK5Z+Pus1v1e+c1G2/ctnLEcCyjfqdg5dw9CmXFcBcvBK5z6Y3ZjECWP1dxWvoqzdmMFC9qdaYNZB1cc4lgK3oWy4e5Ktz6jafU5m5OOeW0eu9LAGsPlsCWJ+2WQHM/ijommuuafTSDPtDzPrx5qlTpw6ZH2LukzdajABWRi/fsp0BzEXdMohlEJuvOgfPlTWQpXb7UR28DAPZ8oada6BuqzftXCN1W58xAaw+WwJYAdsZM2aYRx55JFripptuMvPmzTOf/exnzfDhw6PPvvWtbw36YeULLrjAHHLIIWb8+PFm+vTp5pVXXjEzZ840o0aNin4fbOLEiQW2vmLWpr+GPu9OE8DySvU/384772y+//3vO61bAhgBLG/Fzh14xLxmlj2S+aJ51rxpFplxZh3zknnOULt5FfPPx0A2v1WvOanbahzzroW6zStVfD4CWHGzvEsQwPJKGWPsXa9uizzxxBNmvfXWG/TPV155pdEPLz/88MNm5MiRZocddjAKchtssEGBLQ+elQDGd8D6Lp6UBV3XLQGMAJa3fu3dg1DOudQutZundqnbPErVzUMAq86yc00EsPpsCWD12da2ZgIYAayq4up8BLGq9aatx9Ytg1gGsWXqzMeAgNrlBTJlalbLUrdlBbsvTwCrz9ZH3da3N2GtmQAWVn/kas1QCWCjRq6U6nH9OZMSn39s8zUSn/314Y8lPvvxL1/JZTxUZiKAVd/Tw8eOSV3puB8OS3z+3Qk/TdbyUV9OfDb68l9W39CGrtHHgGCoBDBqt76Dgrqtz5a6rc/WR93WtzdhrZkAFlZ/5GoNAYwAlqtQcsxEAMuBVHAWBgMFwQrO7mNAQADj4kHBMk3MTt2WFey+POfc+mx91G19exPWmglgYfVHrtYQwAhguQolx0wEsBxIBWdhMFAQrODsPgYEBDACWMEyJYCVBSuwPOfcAlgFZ/Vxvi3YxMbOTgBrYNcRwAhgVZUtAawqyRXrYTBQvWl8jT4GBAQwAljZqqZuywpyB6w+we5r9lG3PvbTxzYJYD7US26TAEYAK1lCyxcngFUlSQCrXjJ9jT4GBAQwAljZ+qZuywoSwOoTJID5sCWA+VAvuc2hEsC6MW3wzlUT/3T/9zdJfDZ/wZLEZ7fdu+w3heLTvb95LXVT51/xfOLzgZJ9F9riBDB3PbLSB5M1+q/XXZRowK/fXCfx2T88OC1Zi79Kf9nHu069M7lTA+2pXAay7mrWbonaLW9O3ZY3LLoG6raoWHJ+H3VbvtXNWAMBrBn9NKiVBDACWFVlSwCrSjJ7PQwGso3yzOFjQDBU7oB186d281Rm73mo2/KGRddA3RYVI4CVF8u/BgJYfqtg5iSAEcCqKkYCWFWS2ethMJBtlGcOBrJ5lKqdh9ot70ndljcsugbqtqgYAay8WP41EMDyWwUzJwGMAFZVMRLAqpLMXg+DgWyjPHMwkM2jVO081G55T+q2vGHRNVC3RcUIYOXF8q+BAJbfKpg5CWAEsKqKkQBWlWT2ehgMZBvlmYOBbB6lauehdst7UrflDYuugbotKkYAKy+Wfw0EsPxWwcw51ANYWkdMmzI28fF3T1ov8dno1VfK3Y9fP//pxLzfu/GlxGfPvLA49zpDm5EA5rdHXvriRxIN+O8Tv5X4bOKIkbkb+r7vHZqY9z0XPZP47M+Pz829zpBmZCAbRm9Qu8X6gbot5lXX3NRtMVkfdVushc2dmwDWwL4jgCU7jQDWXyETwPpzq2opBgPFJX0MCIb6SzjSeonaLVa71G0xr7rmpm6Lyfqo22ItbO7cBLAG9h0BjABWVdkSwKqS7G89DAaKu/kYEBDAkv1E7RarXeq2mFddc1O3xWR91G2xFjZ3bgJYA/uOAEYAq6psCWBVSfa3HgYDxd18DAgIYASw4pU6eAnqtqxgNctzzi3m6KNui7WwuXMTwBrYdwQwAlhVZUsAq0qyv/UwGCju5mNAQAAjgBWvVAJYWbM6luecW0zVx/m2WAubOzcBrIF9RwDL12nvn7RaYsazjnhn4rMdthqdb4XGmO9c/UJi3lP/M/mCg6efb8aLOQhgubve2YwD22yW2NaaM55KfHbZ+jfnbtPGtx2YmHejk19OfLZkzuO51+lrRh8DAgJYvt6mdrs7Ubf5asjHXNRtWHXrowZ8bJMA5kO95DYJYPkACWDZTgSwbCPXczAY6C3OQNZ1RebfHrUb1kCWCwf5ape6Datu8/Va8+cigDWwDwlg+TqNAJbtRADLNnI9B4MBApjrmqtqe9RuWANZAli+yqZuw6rbfL3W/LkIYA3sQwJYvk4jgGU7EcCyjVzPwWCAAOa65qraHrUb1kCWAJavsqnbsOo2X681fy4CWAP7kACWr9MIYNlOBLBsI9dzMBgggLmuuaq2R+2GNZAlgOWrbOo2rLrN12vNn4sA1sA+JID132lj1xieWHjX7cakrvC7J66X+HzYsOSst97zauLDHb88p/9GOlySAOYQu8Smhq+7TmLpP+49KXWNdx17buLzlcxKic8+88ROic9e3vbFEq10syjfAXPjXNVWqN1lktRtVRXlZj3Urb+6ddPD/rdCAPPfB4VbQAArTLZ8AQLYYDsCWP+15HJJBgMrtBnIuqy88tuidv0NZLkD1n/9Urf+6rb/XmvWkgSwZvVX1FoCWP+dRgAjgPVfPf6WZDBAAPNXfeW2TO36G8gSwPqvXerWX93232vNWpIA1qz+IoCV7C8CGAGsZAl5WZzBAAHMS+FVsFFq199AlgDWfwFTt/7qtv9ea9aSBLBm9RcBrGR/EcAIYCVLyMviDAYIYF4Kr4KNUrv+BrIEsP4LmLr1V7f991qzliSANau/CGAl+4sARgArWUJeFmcwQADzUngVbJTa9TeQJYD1X8DUrb+67b/XmrUkAaxZ/UUAc9hfi+78UGJrI4YnX4P45yUDifl2PjT5FsSf3bfAYevzbYqXcORzatJcVzx1Z6K5qw9bJfHZ6wNvJj7b5bAjkstec1dQu89LOILqjkob0+bapW4rLZWgVkbdBtUdjWkMAawxXbWiobyEw02nEcCqdeZqbLWe3dbW5sGA9pmBrJs68rGVNtcudeujotxsk7p149y2rRDAGtijBDA3nUYAq9aZAFatJwHsNjNlyhQnqNSuE2bDQLZaZ+q2Ws+heM71ceHATa/53woBzH8fFG4BAawwWV8LEMD6Yuu6EIOBaj2H4mCAO2BuasjXVghg1cpzzq3Wcyiecwlg9dUQAaw+29rWTACrjXbQiglg1TozGKjWcygOBghgbmrI11YIYNXKc86t1nMonnMJYPXVEAGsPtva1kwAy0f7gUmrJWbc8+NrJT7b8r2rp65wpw+vmWtDD85ZmJhvi8/+NvHZ0uS7OnKtv86ZeAlHnbr9rXvptpslFvz9XiMTn2262dzUDVw16cZcGz79xfcl5rtjs1HJZZcuybU+VzP5GBAwkM3Xu9RudyfqNl8N+ZiLug2rbn3UgI9tEsB8qJfcJgEsHyABLNuJAJZt5HoOBgO9xRnIuq7I/NujdsMayHLhIF/tUrdh1W2+Xmv+XASwBvYhASxfpxHAsp0IYNlGrudgMEAAc11zVW2P2g1rIEsAy1fZ1G1YdZuv15o/FwGsgX1IAMvXaQSwbCcCWLaR6zkYDBDAXNdcVdujdsMayBLA8lU2dRtW3ebrtebPRQBrYB8SwPJ1GgEs24kAlm3keg4GAwQw1zVX1fao3bAGsgSwfJVN3YZVt/l6rflzEcAa2IdDPYBtNGHVRK8dtvc6ic+mTR2b+Oxtb1m5VI8vWZpc/Cd3vZL48G++8lip7bhamADmStqYYZM3TWzs0cNXSXx20TaXJj7bbuSbpRq6aGBxYvl9HpuW+GzxlGdKbcfFwnwHzIXy4G1Qu+XNqdvyhkXXQN0WFUvO76Nuy7e6GWsggOXsp6eeespcccUV5sYbbzSPPPKIef755826665rdtxxR3P88cebiRMnJtZ01VVXmTPOOMM89NBDZrXVVjM77LCDmTFjhll//fVzbjV9NgIYAaxUAcUWnjlzptl3332d1O5QvxrLYKBY1b4x8LqZZ54yL5hnzOvmVfOmWWRWMSPNGmZN86KZZ9IuHtR9zt3cbGfGDUte7Cm2Z82bm9ot1mdptTvCrGwWmzcN59xilmXmpm7L6C1blgBW3rDbGghgOW2PO+44c/rpp5tNNtnEbLfddmbMmDHm3nvvNbfeeqsZO3asueOOO8ymm664wn3hhReagw8+2IwfP95Mnz7dvPzyy+ayyy4zo0aNMnfffXdqYMvZFEMAI4DlrZWs+dZYYw1z5513OqldAhh3wLLqMf7vcwYeMk+a35lRZrQZa95qRpgR5hXzJ/Mn83w028UXX2y+8IUvLF/ExTmXALaih7h7272a02r3JfOcedXMN5xzi5wFys1LACvnRwAr79drDQSwnL7XXHNNdMdr6623HrTE2WefbY466iiz8847m5tuuin6N90d0x2x0aNHmwceeMCss86yK6YagG6//fZmjz32MFdffXXOLSdnI4ARwPounpQFXdUuAYwAVqRunxt42qxiVjVjh609aLHfDtxvnjaPmy233DK6mOXynEsAI4DlqeG02rV3ErQ859w8iuXnIYCVN+QOWHnDbmsggJW0Xbp0aRS0hg0bZhYsWBCt7fzzzzeHHnqoOe2004zunMWnnXbaKbpr9uyzz5q11x48sMjbFAIYASxvrWTNN3LkSDN8+HAntUsAI4Bl1WOef39xYJ75lbnDqHYXLlz2I+iuzrkEMAJYnhpNm8cOZDnn9itYfDkCWHGzziUIYOUNCWA1GSqArbXWWlEAmz9/frSVffbZx1x++eXR1VldpY1P+k7Ysccea6699lqz22679dWqNgawt71lRMJiv0+MS/X58l7J72Cs947kywz6wo0tdO9vXk+s4tSLky8puO72l8tuytvyeiR2xIgRTmq3jQFsxMQJib77/RfekdqfJ+39P4nPP73GC5X3/dfnTU6sc9a5H058ttald1a+bRcrtAFMtWsverk657YpgFG7Lqp1xTbsQJZzbjl36racX9GlCWBFxfLPzx2w/Fapc+rRxE996lNm2rRpyx8rnDx5srnvvvvMiy++aMaNGxwi9CXxPffc05x11lnRo4v9TAQwAlg/ddNtGVe1SwAjgFVRt48P/MY8bn5jPvrRj5rbb789WqWrcy4BbEUPcvGgWDXHH0HknFvMLj43Aax/u36WJID1o5ZvGQJYPqfUuebNm2e22GIL88ILL0Qv5LAv4dhwww3NnDlzzOLFi6O7C/HplltuMXoM8YQTTjAnn3xyz63PnTvX6D+d0+zZs82RRx5ZouXhLcodMD99svLKK5v777+/0trNqlsGsQxi+632RQNvmF+aH0dvk4u/hMPVOZfapXb7rd15A0+Zh8wvDefcfgWXLUcAK+dXdGkCWFGx/PMTwPJbDZrztddei14rf9ddd5mLLrrIHHjggcv/varBwEknnZQZ0vpsfnCLEcD8dMnRRx9tzjzzzEprN6tuGcQyiO2n2pcM/NncZ243r5iXosXjr6F3dc6ldqndfmv3LvPT6OcUOOf2I7hiGQJYOb+iSxPAiorln58Alt9q+Zz64vcnP/nJaACgwatOqPGpqsdhsu4k9NH0YBchgPnpms7fUqqidrPqlkEsg9ii1b5kYImZbX4evYJ+vJlonjZPDApgVdSt2kTt8v3ForWZNX+8djsvHOj/V1G71C11m1WH/f47AaxfuezlCGDZRoPmWLRokdl9993NzTffHN2d0qOEnZOrL4QXbLqX2dcdl3y5xvvWXy3Rlm8f867EZxuvN7LyNt/18GuJdZ75vXmp27l21rKXqsSnpQOVN8nrCjsDWJ2126TvgI1Y792Jfnl5i7cnPtv7lGU/PRGf/n7s45X36VefSb5E485/S75sQxsed8myV7MPLtwllbfJ1QqXDiwxD5hfRD++vL55rxlr1jb3m9sHBbA661b7Se3239vU7rLafbuZYJ4xTyZ+QLzO2qVuqdv+BZYtSQArK9h9eQJYAVt9p0sv0LjuuuvMMcccE/0wc9rk6pXIBZrubVYCmDf6XBvuDGB11i6DgVxdkjrTUB3ELh1Yah40d5oXzDNmgtnQvGfYB1IHBHXWLQGs/7rVktTustp9i3lb4sKBfOqsXc65/dfuUK3bTjECWP81lLUkASxL6C//vmTJErPffvuZK664whx22GHmvPPO67qk/SHmNddc0+iFGfaHmGfNmmWmTp06pH6ImQCWs8A8zdYZwOqsXQYD/XfyUBwMDAwMmIfNXWaeecq8y0wyGw3brOsV2TrrlgDWf90O1QCWVrvdBrJ11i7n3P5rdyiec9O0CGD911DWkgSwLKG//PuJJ55oTjnllOjHkw855JDod786J718wE4XXHBBNN/48ePN9OnTzSuvvGJmzpxp9Bsg+n2wiRMn5txycrYmvYaeANZ3NztZ8POf/7xZb731jIvaZTDQf5cOxcHA7wd+bZ4wvzUrm1XMO80Gy/EWmtfMs+YPRrV7ySWXOD3nNuH7izw+2/9xVtWSabUbr1vOuUlp6raq6qt2PQSwaj3jayOA5bQ94IADzKWXXtpzbl31ik9XXnml0Q8vP/zww2bkyJHRWxNnzJhhNthgxWAi5+YHzUYA60dt2TJ8ByzdzkXtEsD6r9uhGMB+PXBP9J2ZXpOLutX2qV1qt4hAKLVL3RbptcHzDsVzbpoWAaz/GspakgCWJRTgvxPA+u8UAthgu85HEPuXzV6SwUC2Ubc5GAyskPExIKB2qd3+BZYtSd32FuQOWNkKq2d5H3Vbz56Et1YCWHh9ktki3wFs3JrDE238ztcnpLZ7s42Sbzxcf/yqmftYdIZfPJh8u+FZ30++3fDmO19OrHrhopa92rAA3lAKYCPe/raEzEsXj0rVOnjirMTn+45Of1tmAe7ErIc+vW3is/svWPZdp/i09pUPJz5b+uqrZTbd2GV9DAh8BzBqt7Hlurzh1O0yCs65zaplH3XbLKH+W0sA69/O25IEsCQ9Aay/ciSAEcD6qxx/S/kYEBDAkv3NxYNixwB1SwArVjFhzO2jbsPY8/pbQQCr37jyLRDACGBVFRUBjABWVS25Wo+PAQEBjABWtr6pWwJY2RrysbyPuvWxnz62SQDzoV5ymwQwAljJElq+OAGMAFZVLblaj48BAQGMAFa2vqlbAljZGvKxvI+69bGfPrZJAPOhXnKbBDACWMkSIoD9RYDvI1RVSe7W42NAQAAjgJWtcOqWAFa2hnws76Nufeynj20SwHyol9xmXQHsrzZN3g342ufWTbR2q/cl5xu/zsol9yq5+MJFS1PXee5lzyU+/+bFzyY+e+2N9OUrb2iDV9iGO2Bv7jw50QNvHvlS4rOvT/ph4rOdVku+vKVsd85bsjB1Fdtd99XE5xv/0yOJz5bMT74opmyb2rS8jwFBXQGM2m1TZfbeF+p2mQ/n3GbVvI+6bZZQ/60lgPVv521JAhgBrKriI4ARwKqqJVfr8TEgIIBx8aBsfVO3BLCyNeRjeR9162M/fWyTAOZDveQ2CWAEsJIltHxxAhgBrKpacrUeHwMCAhgBrGx9U7cEsLI15GN5H3XrYz99bJMA5kO95DYJYASwkiVEAPuLAI/DVFVJ7tbjY0BAACOAla1w6pYAVraGfCzvo2597KePbRLAfKiX3CYBjABWsoQIYASwqkrI+Xp8DAgIYASwsoVO3RLAytaQj+V91K2P/fSxTQKYD/WS26wrgM04dHyiZV/bP/kSjiLN/+0TbyRmv/6O5EsGliwZSMz3rf+al7qp+QuWFGkC8/YQaMMjiI9esFViDx/d7YJS/X7+/A0Sy587a6fEZ8OWDEt8tvE3nkjd9pJ5yQsHpRo5RBf2MSCoK4BRu0OniKnb3n3NOTfMY8FH3YYpUX2rCGDVm9a+RgIYAayqIiOApUsyGKiqwqpfj48BAQGMiwdlK5m6JYCVrSEfy/uoWx/76WObBDAf6iW3SQAjgJUsoeWLE8AIYFXVkqv1+BgQEMAIYGXrm7olgJWtIR/L+6hbH/vpY5sEMB/qJbdJACOAlSwhAlgGIHfAqqqw6tfjY0BAACOAla1k6pYAVraGfCzvo2597KePbRLAfKiX3CYBjABWsoQIYASwqkrI+Xp8DAgIYASwsoVO3RLAytaQj+V91K2P/fSxTQKYD/WS26wrgJVsFos3UKANjyA2kJ0mlxDwMSCoK4CVYGDRhglQtw3rMJobCfio26FCTwBrYE8TwBrYaYE2mQAWaMfQrK4CPgYEBDAKsqwAJJqa9QAAG/VJREFUdVtWkOV9CPioWx/76WObBDAf6iW3SQArCcjiywUIYBRD0wR8DAgIYE2rkvDaS92G1ye0KFvAR91mt6odcxDAGtiPBLAGdlqgTSaABdoxNIs7YNRAqwR8DGS5cNCqEvKyMz7q1suOetgoAcwDetlNEsDKCrK8FSCAUQtNE/AxIGAg27QqCa+91G14fUKLsgV81G12q9oxBwGsgf1IAGtgpwXaZAJYoB1Ds7gDRg20SsDHQJYLB60qIS8746Nuveyoh40SwDygl90kAaysIMtzB4waaKqAjwEBA9mmVks47aZuw+kLWpJfwEfd5m9ds+ckgDWw/whgDey0QJvMHbBAO4ZmcQeMGmiVgI+BLBcOWlVCXnbGR9162VEPGyWAeUAvu0kCWFlBlucOGDXQVAEfAwIGsk2tlnDaTd2G0xe0JL+Aj7rN37pmz0kAa2D/EcAa2GmBNpk7YIF2DM3iDhg10CoBHwNZLhy0qoS87IyPuvWyox42SgDzgF52kwSwsoIszx0waqCpAj4GBAxkm1ot4bSbug2nL2hJfgEfdZu/dc2ekwDWwP4jgDWw0wJtMnfAAu0YmsUdMGqgVQI+BrJcOGhVCXnZGR9162VHPWyUAOYBvewmCWBlBVmeO2DUQFMFfAwIGMg2tVrCaTd1G05f0JL8Aj7qNn/rmj0nAayB/UcAa2CnBdpk7oAF2jE0iztg1ECrBHwMZLlw0KoS8rIzPurWy4562CgBzAN62U0SwMoKsjx3wKiBpgr4GBAwkG1qtYTTbuo2nL6gJfkFfNRt/tY1e04CWAP774YbbjC77rprA1tOk0MTuP76680uu+zipFm2bt9jPmBGm7FOtslG2ifwqplv5pgHDbXbvr5t8x5Rt23u3fbum4+6ba/m4D0jgDWwp8855xxz5JFHNrDlNDk0gbPPPtscccQRTppF3TphHjIboXaHTFe3akep21Z155DZGZd1O1RQCWAN7Oknn3zSXHPNNWbhwoXm61//utGBsdlmmzVwT9rd5NmzZ0dBOcT+WbBggXnsscfMtGnTzIQJE5x0hK3bSZMmRdsO1cYJRuAboXYHdxDn3MAL9i/No27T65Zzbtj1S92G3T91tY4AVpesg/Xa7yW4fJGCg91qzSbon+5diU3YZU7/pPcPLtRt2AKcc5vYP5xXmthr5dtMACtv6G0NHLTe6HNtmP5hMJCrUAKcidolgAVYlplNom4552YWSYAzULcBdoqDJhHAHCDXtQkO2rpkq1kv/cNgoJpKcr8WapcA5r7qym+RuuWcW76K3K+BunVvHsIWCWAh9EKfbeCg7RPO0WL0D4MBR6VW+WaoXQJY5UXlYIXULedcB2VW+Sao28pJG7FCAlgjuonBQBO7iZMqg4Em1q3aTO1yzm1i7VK3nHOp2yYKDM02E8Aa3O9z5841l1xyiTnggAPMeuut1+A9aWfT6Z/u/YpN2DVP/6T3Dy7UbdgCnHOb2D+cV5rYa+XbTAArb8gaEEAAAQQQQAABBBBAAIFcAgSwXEzMhAACCCCAAAIIIIAAAgiUFyCAlTdkDQgggAACCCCAAAIIIIBALgECWC4mZkIAAQQQQAABBBBAAAEEygsQwMobsgYEEEAAAQQQQAABBBBAIJcAASwXEzMhgAACCCCAAAIIIIAAAuUFCGDlDb2sQb93cvLJJ5t7773XDB8+3HzkIx8x3/zmN82HPvQhL+0Zaht96qmnzBVXXGFuvPFG88gjj5jnn3/erLvuumbHHXc0xx9/vJk4cWKC5KqrrjJnnHGGeeihh8xqq61mdthhBzNjxgyz/vrrDxk+6tZvV1O3/ftTu/3bVbEktdufInXbn1tVS1G3VUm2bz0EsAb2qQb9u+22mxkzZozZb7/9oj2YOXOmeeONN8ysWbPMlltu2cC9alaTjzvuOHP66aebTTbZxGy33XZRXygM33rrrWbs2LHmjjvuMJtuuunynbrwwgvNwQcfbMaPH2+mT59uXn75ZXPZZZeZUaNGmbvvvjs1sDVLJLu11G22Ud1zULf9CVO7/blVuRS1W1yTui1uVvUS1G3Vou1ZHwGsYX355ptvmg022MC89NJL5v777zcbbbRRtAe6C7PFFluYjTfe2Nx3330N26vmNfeaa66J7nhtvfXWgxp/9tlnm6OOOsrsvPPO5qabbor+TXfHdEds9OjR5oEHHjDrrLNO9LmuTG6//fZmjz32MFdffXXzEAq0mLotgFXjrNRtcVxqt7hZHUtQu8VUqdtiXnXNTd3WJdv89RLAGtaHuqK1yy67mC996UtGd1Xi00EHHWQuuuiiKJjxKKKfjl26dGkUtIYNG2YWLFgQNeL88883hx56qDnttNOMrobFp5122im6a/bss8+atdde20+jHWyVunWAXGIT1G13PGq3RGE5WJTaTUembh0UX4lNULcl8FqyKAGsYR1pb2fr+0d77bXXoNbrs7333tuce+655vDDD2/YnrWjuTqprrXWWlEAmz9/frRT++yzj7n88sujRw07Hw/Vd8KOPfZYc+2110aPlbZ1om7D7lnqtnv/ULvUbtgC6a2jbsPuNc65YfePi9YRwFwoV7iNPffc0+hlDnrMcPPNNx+0Zn02efJkc9hhh5nzzjuvwq2yqrwCetzgU5/6lJk2bdryxwrVJ+qbF1980YwbN27QqtSX6tOzzjorenSxrRN1G3bPUrfd+4fapXbDFkhvHXUbdq9xzg27f1y0jgDmQrnCbeiRtVtuucXMmTPHTJo0adCa9dmGG25o9t9/f3PppZdWuFVWlUdg3rx50ffwXnjhheiFHPYlHOoT9c3ixYvNiBEjBq1Kfak+PeGEE6K3WrZ1om7D7VnqtnffULvUbrgC3VtG3Ybba5xzw+0bly0jgLnUrmBbnFQrQKxhFa+99lr0Wvm77ror+h7egQceuHwrBDAThUwuHNRQeCVXSd1mA1K72UY+5qB2uXDgo+7KbpO6LSvYnuUJYA3rSx4rCK/DFi5caD75yU+a2267zZx55pnm6KOPHtRIHkE00WOWPDobVu1St/n6g9rN5+RyLmo3W5u6zTZyPQd161o87O0RwMLun0Tr+GJtWB22aNEis/vuu5ubb745eoRQjxJ2TryEw0Rvf9TvpvHymDDql7rN3w/Ubn4rF3NSu/mUqdt8Tq7mom5dSTdnOwSw5vRV1FJeLRtOh+k7XbrKeN1115ljjjkmChhpE6+hp27DqVoTfReRus3fI5xz81vVPSe1m1+Yus1vVfec1G3dws1cPwGsYf2mqyh6+Ua3H2LWDzPrd8CY6hVYsmSJ2W+//aI7OllvnbQ/xLzmmmua2bNnL/8h5lmzZpmpU6cOiR9ipm7rrce8a6du80qtmI/aLW5WxxLUbjFV6raYV11zU7d1yTZ/vQSwBvbhDTfcED32NmbMmCgEaJo5c6bR88Ua1G+11VYN3KtmNfnEE080p5xySvTjyYccckj0u1+d00knnbT8owsuuCCab/z48Wb69OnmlVdeifps1KhR0e+DTZw4sVkAfbSWuu0DreJFqNv+QKnd/tyqXIraLa5J3RY3q3oJ6rZq0fasjwDW0L7UCx/0nSO97nyllVYy22yzjTn11FMTvw3W0N0LvtkHHHBA5qv+BwYGBu3HlVdeafTDyw8//LAZOXJk9NbEGTNmmA022CD4/a2qgdRtVZL9rYe67c9NS1G7/dtVsSS1258iddufW1VLUbdVSbZvPQSw9vUpe4QAAggggAACCCCAAAKBChDAAu0YmoUAAggggAACCCCAAALtEyCAta9P2SMEEEAAAQQQQAABBBAIVIAAFmjH0CwEEEAAAQQQQAABBBBonwABrH19yh4hgAACCCCAAAIIIIBAoAIEsEA7hmYhgAACCCCAAAIIIIBA+wQIYO3rU/YIAQQQQAABBBBAAAEEAhUggAXaMTQLAQQQQAABBBBAAAEE2idAAGtfn7JHCCCAAAIIIIAAAgggEKgAASzQjqFZCCCAAAIIIIAAAggg0D4BAlj7+pQ9QgABBBBAAAEEEEAAgUAFCGCBdgzNQgABBBBAAAEEEEAAgfYJEMDa16fsEQIIIIAAAggggAACCAQqQAALtGNoFgIIIIAAAggggAACCLRPgADWvj5ljxBAAAEEEEAAAQQQQCBQAQJYoB1DsxBAAAEEEEAAAQQQQKB9AgSw9vUpe4QAAggggAACCCCAAAKBChDAAu0YmoUAAggggAACCCCAAALtEyCAta9P2SMEEEAAAQQQQAABBBAIVIAAFmjH0CwEEEAAAQQQQAABBBBonwABrH19yh4hgAACCCCAAAIIIIBAoAIEsEA7hmYhgAACCCCAAAIIIIBA+wQIYO3rU/YIAQQQQAABBBBAAAEEAhUggAXaMTQLAQQQQAABBBBAAAEE2idAAGtfn7JHCCCAAAIIIIAAAgggEKgAASzQjqFZCCCAAAIIIIAAAggg0D4BAlj7+pQ9QgABBBBAAAEEEEAAgUAFCGCBdgzNQgABBBBAAAEEEEAAgfYJEMDa16fsEQIIIIAAAggggAACCAQqQAALtGNoFgIIIIAAAggggAACCLRPgADWvj5ljxBAAAEEEEAAAQQQQCBQAQJYoB1DsxBAAAEEEEAAAQQQQKB9AgSw9vUpe4QAAggggAACCCCAAAKBChDAAu0YmoUAAggggAACCCCAAALtEyCAta9P2SMEEEAAAQQQQAABBBAIVIAAFmjH0CwEEEAAAQQQQAABBBBonwABrH19yh4hgAACCCCAAAIIIIBAoAIEsEA7hmYhgAACCCCAAAIIIIBA+wQIYO3rU/YIAQQQQAABBBBAAAEEAhUggAXaMTQLAQQQQAABBBBAAAEE2idAAGtfn7JHCCCAAAIIIIAAAgggEKgAASzQjqFZCCCAAAIIIIAAAggg0D4BAlj7+pQ9QgABBBBAAAEEEEAAgUAFCGCBdgzNQgABBBBAAAEEEEAAgfYJEMDa16fsEQIIIIAAAggggAACCAQqQAALtGNoFgIIIIAAAggggAACCLRPgADWvj5ljxBAAAEEEEAAAQQQQCBQAQJYoB1DsxBAAAEEEEAAAQQQQKB9AgSw9vUpe4QAAggggAACCCCAAAKBChDAAu0YmoUAAggggAACCCCAAALtEyCAta9P2SMEEEAAAQQQQAABBBAIVIAAFmjH0CwEEEAAAQQQQAABBBBonwABrH19yh4hgAACCCCAAAIIIIBAoAIEsEA7hmYhgAACCCCAAAIIIIBA+wQIYO3rU/YIAQQQQAABBBBAAAEEAhUggAXaMTQLAQQQQAABBBBAAAEE2idAAGtfn7JHCCCAAAIIIIAAAgggEKgAASzQjqFZCCCAAAIIIIAAAggg0D4BAlj7+pQ9QgABBBBAAAEEEEAAgUAFCGCBdgzNQgABBBBAAAEEEEAAgfYJEMDa16fsEQIIIIAAAggggAACCAQqQAALtGNoFgIIIIAAAggggAACCLRPgADWvj5ljxBAAAEEEEAAAQQQQCBQAQJYoB1DsxBAAAEEEEAAAQQQQKB9AgSw9vUpe4QAAggggAACCCCAAAKBChDAAu0YmoUAAggggAACCCCAAALtEyCAta9P2SMEEEAAAQQQQAABBBAIVIAAFmjH0CwEEEAAAQQQQAABBBBonwABrH19yh4hgAACCCCAAAIIIIBAoAIEsEA7hmYhgAACCCCAAAIIIIBA+wQIYO3rU/YIAQQQQAABBBBAAAEEAhUggAXaMTQLAQQQQAABBBBAAAEE2idAAGtfn7JHCCCAAAIIIIAAAgggEKgAASzQjqFZCCCAAAIIIIAAAggg0D4BAlj7+pQ9QgABBBBAAAEEEEAAgUAFCGCBdgzNQgABBBBAAAEEEEAAgfYJEMDa16fsEQIIIIAAAggggAACCAQqQAALtGNoFgIIIIAAAggggAACCLRPgADWvj5ljxBAAAEEEEAAAQQQQCBQAQJYoB1DsxBAAAEEEEAAAQQQQKB9AgSw9vUpe4QAAggggAACCCCAAAKBChDAAu0YmoUAAggggAACCCCAAALtEyCAta9P2SMEEEAAAQQQQAABBBAIVIAAFmjH0CwEEEAAAQQQQAABBBBonwABrH19yh4hgAACCCCAAAIIIIBAoAIEsEA7hmYhgAACCCCAAAIIIIBA+wQIYO3rU/YIAQQQQAABBBBAAAEEAhUggAXaMTQLAQQQQAABBBBAAAEE2idAAGtfn7JHCCCAAAIIIIAAAgggEKgAASzQjqFZCCCAAAIIIIAAAggg0D4BAlj7+pQ9QgABBBBAAAEEEEAAgUAFCGCBdgzNQgABBBBAAAEEEEAAgfYJEMDa16fsEQIIIIAAAggggAACCAQqQAALtGNoFgIIIIAAAggggAACCLRPgADWvj5ljxBAAAEEEEAAAQQQQCBQAQJYoB1DsxBAAAEEEEAAAQQQQKB9AgSw9vUpe4QAAggggAACCCCAAAKBChDAAu0YmoUAAv+//Tq2AQAGQiCm7L/0d5kBnbwBmAoCBAgQIECAAIGegAPW21QjAgQIECBAgAABAgRGBRyw0WHEIkCAAAECBAgQIECgJ+CA9TbViAABAgQIECBAgACBUQEHbHQYsQgQIECAAAECBAgQ6Ak4YL1NNSJAgAABAgQIECBAYFTAARsdRiwCBAgQIECAAAECBHoCDlhvU40IECBAgAABAgQIEBgVcMBGhxGLAAECBAgQIECAAIGegAPW21QjAgQIECBAgAABAgRGBRyw0WHEIkCAAAECBAgQIECgJ+CA9TbViAABAgQIECBAgACBUQEHbHQYsQgQIECAAAECBAgQ6Ak4YL1NNSJAgAABAgQIECBAYFTAARsdRiwCBAgQIECAAAECBHoCDlhvU40IECBAgAABAgQIEBgVcMBGhxGLAAECBAgQIECAAIGegAPW21QjAgQIECBAgAABAgRGBRyw0WHEIkCAAAECBAgQIECgJ+CA9TbViAABAgQIECBAgACBUQEHbHQYsQgQIECAAAECBAgQ6Ak4YL1NNSJAgAABAgQIECBAYFTAARsdRiwCBAgQIECAAAECBHoCDlhvU40IECBAgAABAgQIEBgVcMBGhxGLAAECBAgQIECAAIGegAPW21QjAgQIECBAgAABAgRGBRyw0WHEIkCAAAECBAgQIECgJ+CA9TbViAABAgQIECBAgACBUQEHbHQYsQgQIECAAAECBAgQ6Ak4YL1NNSJAgAABAgQIECBAYFTAARsdRiwCBAgQIECAAAECBHoCDlhvU40IECBAgAABAgQIEBgVcMBGhxGLAAECBAgQIECAAIGegAPW21QjAgQIECBAgAABAgRGBRyw0WHEIkCAAAECBAgQIECgJ+CA9TbViAABAgQIECBAgACBUQEHbHQYsQgQIECAAAECBAgQ6Ak4YL1NNSJAgAABAgQIECBAYFTAARsdRiwCBAgQIECAAAECBHoCDlhvU40IECBAgAABAgQIEBgVcMBGhxGLAAECBAgQIECAAIGegAPW21QjAgQIECBAgAABAgRGBRyw0WHEIkCAAAECBAgQIECgJ+CA9TbViAABAgQIECBAgACBUQEHbHQYsQgQIECAAAECBAgQ6Ak4YL1NNSJAgAABAgQIECBAYFTAARsdRiwCBAgQIECAAAECBHoCDlhvU40IECBAgAABAgQIEBgVcMBGhxGLAAECBAgQIECAAIGegAPW21QjAgQIECBAgAABAgRGBRyw0WHEIkCAAAECBAgQIECgJ+CA9TbViAABAgQIECBAgACBUQEHbHQYsQgQIECAAAECBAgQ6Ak4YL1NNSJAgAABAgQIECBAYFTAARsdRiwCBAgQIECAAAECBHoCDlhvU40IECBAgAABAgQIEBgVcMBGhxGLAAECBAgQIECAAIGegAPW21QjAgQIECBAgAABAgRGBRyw0WHEIkCAAAECBAgQIECgJ+CA9TbViAABAgQIECBAgACBUQEHbHQYsQgQIECAAAECBAgQ6Ak4YL1NNSJAgAABAgQIECBAYFTAARsdRiwCBAgQIECAAAECBHoCDlhvU40IECBAgAABAgQIEBgVcMBGhxGLAAECBAgQIECAAIGegAPW21QjAgQIECBAgAABAgRGBRyw0WHEIkCAAAECBAgQIECgJ+CA9TbViAABAgQIECBAgACBUQEHbHQYsQgQIECAAAECBAgQ6Ak4YL1NNSJAgAABAgQIECBAYFTAARsdRiwCBAgQIECAAAECBHoCDlhvU40IECBAgAABAgQIEBgVcMBGhxGLAAECBAgQIECAAIGegAPW21QjAgQIECBAgAABAgRGBRyw0WHEIkCAAAECBAgQIECgJ+CA9TbViAABAgQIECBAgACBUQEHbHQYsQgQIECAAAECBAgQ6Ak4YL1NNSJAgAABAgQIECBAYFTAARsdRiwCBAgQIECAAAECBHoCDlhvU40IECBAgAABAgQIEBgVcMBGhxGLAAECBAgQIECAAIGegAPW21QjAgQIECBAgAABAgRGBRyw0WHEIkCAAAECBAgQIECgJ+CA9TbViAABAgQIECBAgACBUQEHbHQYsQgQIECAAAECBAgQ6Ak4YL1NNSJAgAABAgQIECBAYFTAARsdRiwCBAgQIECAAAECBHoCDlhvU40IECBAgAABAgQIEBgVcMBGhxGLAAECBAgQIECAAIGegAPW21QjAgQIECBAgAABAgRGBRyw0WHEIkCAAAECBAgQIECgJ+CA9TbViAABAgQIECBAgACBUQEHbHQYsQgQIECAAAECBAgQ6Ak4YL1NNSJAgAABAgQIECBAYFTAARsdRiwCBAgQIECAAAECBHoCDlhvU40IECBAgAABAgQIEBgVcMBGhxGLAAECBAgQIECAAIGegAPW21QjAgQIECBAgAABAgRGBRyw0WHEIkCAAAECBAgQIECgJ+CA9TbViAABAgQIECBAgACBUQEHbHQYsQgQIECAAAECBAgQ6Ak4YL1NNSJAgAABAgQIECBAYFTAARsdRiwCBAgQIECAAAECBHoCDlhvU40IECBAgAABAgQIEBgVcMBGhxGLAAECBAgQIECAAIGegAPW21QjAgQIECBAgAABAgRGBRyw0WHEIkCAAAECBAgQIECgJ+CA9TbViAABAgQIECBAgACBUQEHbHQYsQgQIECAAAECBAgQ6Ak4YL1NNSJAgAABAgQIECBAYFTAARsdRiwCBAgQIECAAAECBHoCDlhvU40IECBAgAABAgQIEBgVcMBGhxGLAAECBAgQIECAAIGegAPW21QjAgQIECBAgAABAgRGBRyw0WHEIkCAAAECBAgQIECgJ+CA9TbViAABAgQIECBAgACBUQEHbHQYsQgQIECAAAECBAgQ6Ak4YL1NNSJAgAABAgQIECBAYFTAARsdRiwCBAgQIECAAAECBHoCDlhvU40IECBAgAABAgQIEBgVcMBGhxGLAAECBAgQIECAAIGegAPW21QjAgQIECBAgAABAgRGBf4BG80nFgECBAgQIECAAAECBHICL9dIIQIECBAgQIAAAQIECIwKHKqABThTBUCjAAAAAElFTkSuQmCC\" width=\"640.00004521123\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '21.588235294117652')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samp = data[0].copy()\n",
    "samp[:,:,0] *= 1.0\n",
    "samp[:,:,1] *= 0.8\n",
    "samp[:,:,2] *= 0.2\n",
    "\n",
    "\n",
    "fig, axs = plt.subplots(1,4)\n",
    "axs[0].imshow(samp)\n",
    "axs[1].imshow(samp[:,:,0])\n",
    "axs[1].set_title(np.sum(samp[:,:,0]))\n",
    "\n",
    "axs[2].imshow(samp[:,:,1])\n",
    "axs[2].set_title(np.sum(samp[:,:,1]))\n",
    "\n",
    "axs[3].imshow(samp[:,:,2])\n",
    "axs[3].set_title(np.sum(samp[:,:,2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5eac4449",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create suppressor set where 50% of samples of all classes have colour c6=(0.5,0.5,0.5), \n",
    "# and the rest are split evenly 5% for each colour [c0,...,c10]\\{c6}\n",
    "\n",
    "lower = 0\n",
    "upper = 1\n",
    "mu = 0.5\n",
    "sigma = 0.3\n",
    "truncnorm = scipy.stats.truncnorm((lower-mu)/sigma,(upper-mu)/sigma,loc=mu,scale=sigma)\n",
    "\n",
    "def scale_to_sum(arr, arrsum):\n",
    "    return (arrsum / np.sum(np.abs(arr))) * arr\n",
    "\n",
    "def create_suppressor(data, colours, c6=(1.0,0.5,0.5), noise_scale=0.5):\n",
    "    num_samples = len(data)\n",
    "    suppressor_data = np.zeros(data.shape)\n",
    "    colour_labels = np.zeros((data.shape[0],3))\n",
    "    \n",
    "    c6_chunk = data[:int(num_samples/2)]\n",
    "    c6_chunk[:,:,:,0] *= c6[0]\n",
    "    c6_chunk[:,:,:,1] *= c6[1]\n",
    "    c6_chunk[:,:,:,2] *= c6[2]\n",
    "    \n",
    "    noise = truncnorm.rvs(size=c6_chunk.shape)\n",
    "    \n",
    "#     noise[:,:,:,0] = scale_to_sum(noise[:,:,:,0], np.sum(c6_chunk[:,:,:,0]))\n",
    "\n",
    "#     noise[:,:,:,1] = scale_to_sum(noise[:,:,:,1], np.sum(noise[:,:,:,0]*0.5))\n",
    "#     noise[:,:,:,2] = scale_to_sum(noise[:,:,:,2], np.sum(noise[:,:,:,0]*0.5))\n",
    "\n",
    "    noise[:,:,:,1:] = scale_to_sum(noise[:,:,:,1:], np.sum(noise[:,:,:,0]))\n",
    "    \n",
    "    c6_chunk = (1-noise_scale)*c6_chunk + abs(noise_scale*noise)\n",
    "    colour_labels[:int(num_samples/2)] = np.array([c6]*int(num_samples/2))\n",
    "    \n",
    "    suppressor_data[:int(num_samples/2)] =  c6_chunk\n",
    "    \n",
    "    for i, data_chunk in enumerate(np.split(data[int(num_samples/2):],10,axis=0)):\n",
    "        chunk_size = len(data_chunk)\n",
    "\n",
    "        data_chunk[:,:,:,0] *= colours[i][0]\n",
    "        data_chunk[:,:,:,1] *= colours[i][1]\n",
    "        data_chunk[:,:,:,2] *= colours[i][2]\n",
    "        \n",
    "        noise = truncnorm.rvs(size=data_chunk.shape)\n",
    "        \n",
    "#         noise[:,:,:,0] = scale_to_sum(noise[:,:,:,0], np.sum(data_chunk[:,:,:,0]))\n",
    "\n",
    "#         noise[:,:,:,1] = scale_to_sum(noise[:,:,:,1], np.sum(noise[:,:,:,0]*0.5))\n",
    "#         noise[:,:,:,2] = scale_to_sum(noise[:,:,:,2], np.sum(noise[:,:,:,0]*0.5))\n",
    "        \n",
    "        noise[:,:,:,1:] = scale_to_sum(noise[:,:,:,1:], np.sum(noise[:,:,:,0]))\n",
    "\n",
    "        data_chunk = (1-noise_scale)*data_chunk + abs(noise_scale*noise)\n",
    "        \n",
    "        suppressor_data[int(num_samples/2)+chunk_size*i:int(num_samples/2)+chunk_size*(i+1)] = data_chunk\n",
    "        \n",
    "        colour_labels[int(num_samples/2)+chunk_size*i:int(num_samples/2)+chunk_size*(i+1)] = [colours[i]]*int(data_chunk.shape[0])\n",
    "    return suppressor_data, colour_labels\n",
    "\n",
    "\n",
    "# create confounder set where 50% of samples of all classes have colour c6=(0.5,0.5,0.5), \n",
    "# 25% have the class' colour and the rest are split evenly for each colour [c0,...,c10]\\{c6}\n",
    "def create_confounder(data, class_colour, colours,confounder_portion=0.25, c6=(1.0,0.5,0.5), noise_scale=0.5):\n",
    "    num_samples = len(data)\n",
    "    confounder_data = np.zeros(data.shape)\n",
    "    colour_labels = np.zeros((data.shape[0],3))\n",
    "\n",
    "    c6_chunk = data[:int(num_samples/2)]\n",
    "    c6_chunk[:,:,:,0] *= c6[0]\n",
    "    c6_chunk[:,:,:,1] *= c6[1]\n",
    "    c6_chunk[:,:,:,2] *= c6[2]\n",
    "    \n",
    "    noise = truncnorm.rvs(size=c6_chunk.shape)\n",
    "#     noise[:,:,:,0] = scale_to_sum(noise[:,:,:,0], np.sum(c6_chunk[:,:,:,0]))\n",
    "\n",
    "#     noise[:,:,:,1] = scale_to_sum(noise[:,:,:,1], np.sum(noise[:,:,:,0]*0.5))\n",
    "#     noise[:,:,:,2] = scale_to_sum(noise[:,:,:,2], np.sum(noise[:,:,:,0]*0.5))\n",
    "\n",
    "    noise[:,:,:,1:] = scale_to_sum(noise[:,:,:,1:], np.sum(noise[:,:,:,0]))\n",
    "    \n",
    "    c6_chunk = (1-noise_scale)*c6_chunk + abs(noise_scale*noise)\n",
    "\n",
    "    colour_labels[:int(num_samples/2)] = np.array([c6]*int(num_samples/2))\n",
    "    confounder_data[:int(num_samples/2)] = c6_chunk\n",
    "    \n",
    "    n_confounded = int(num_samples/2)+int(num_samples*confounder_portion) \n",
    "    \n",
    "    confounder_data[int(num_samples/2):n_confounded] = data[int(num_samples/2):n_confounded]\n",
    "\n",
    "    confounder_data[int(num_samples/2):n_confounded][:,:,:,0] *= class_colour[0]\n",
    "    confounder_data[int(num_samples/2):n_confounded][:,:,:,1] *= class_colour[1]\n",
    "    confounder_data[int(num_samples/2):n_confounded][:,:,:,2] *= class_colour[2]\n",
    "    \n",
    "    noise = truncnorm.rvs(size=confounder_data[int(num_samples/2):n_confounded].shape)\n",
    "#     noise[:,:,:,0] = scale_to_sum(noise[:,:,:,0], np.sum(confounder_data[int(num_samples/2):n_confounded][:,:,:,0]))\n",
    "    \n",
    "#     noise[:,:,:,1] = scale_to_sum(noise[:,:,:,1], np.sum(noise[:,:,:,0]*0.5))\n",
    "#     noise[:,:,:,2] = scale_to_sum(noise[:,:,:,2], np.sum(noise[:,:,:,0]*0.5))\n",
    "    \n",
    "    noise[:,:,:,1:] = scale_to_sum(noise[:,:,:,1:], np.sum(noise[:,:,:,0]))\n",
    "    \n",
    "    confounder_data[int(num_samples/2):n_confounded] = (1-noise_scale)*confounder_data[int(num_samples/2):n_confounded] + abs(noise_scale*noise)\n",
    "    \n",
    "    colour_labels[int(num_samples/2):n_confounded] = np.array([class_colour]*(n_confounded - int(num_samples/2)))\n",
    "    \n",
    "    other_colours = [x for x in colours if x != class_colour] \n",
    "    \n",
    "    for i, data_chunk in enumerate(np.split(data[n_confounded:],9,axis=0)):\n",
    "        chunk_size = len(data_chunk)\n",
    "\n",
    "        data_chunk[:,:,:,0] *= other_colours[i][0]\n",
    "        data_chunk[:,:,:,1] *= other_colours[i][1]\n",
    "        data_chunk[:,:,:,2] *= other_colours[i][2]\n",
    "        \n",
    "        noise = truncnorm.rvs(size=data_chunk.shape)\n",
    "#         noise[:,:,:,0] = scale_to_sum(noise[:,:,:,0], np.sum(data_chunk[:,:,:,0]))\n",
    "\n",
    "#         noise[:,:,:,1] = scale_to_sum(noise[:,:,:,1], np.sum(noise[:,:,:,0]*0.5))\n",
    "#         noise[:,:,:,2] = scale_to_sum(noise[:,:,:,2], np.sum(noise[:,:,:,0]*0.5))\n",
    "\n",
    "        noise[:,:,:,1:] = scale_to_sum(noise[:,:,:,1:], np.sum(noise[:,:,:,0]))\n",
    "        \n",
    "        data_chunk = (1-noise_scale)*data_chunk + abs(noise_scale*noise)\n",
    "        \n",
    "        confounder_data[n_confounded+chunk_size*i:n_confounded+chunk_size*(i+1)] = data_chunk\n",
    "        \n",
    "        colour_labels[n_confounded+chunk_size*i:n_confounded+chunk_size*(i+1)] = [other_colours[i]]*int(data_chunk.shape[0])\n",
    "        \n",
    "    return confounder_data, colour_labels\n",
    "\n",
    "def create_no_col(data, c6=(1.0,0.5,0.5), noise_scale=0.5):\n",
    "    no_col_data = np.zeros((data.shape))\n",
    "    \n",
    "    c6_chunk = data\n",
    "    c6_chunk[:,:,:,0] *= c6[0]\n",
    "    c6_chunk[:,:,:,1] *= c6[1]\n",
    "    c6_chunk[:,:,:,2] *= c6[2]\n",
    "    \n",
    "    noise = truncnorm.rvs(size=data.shape)\n",
    "    \n",
    "#     noise[:,:,:,1] = scale_to_sum(noise[:,:,:,1], np.sum(noise[:,:,:,0]*0.5))\n",
    "#     noise[:,:,:,2] = scale_to_sum(noise[:,:,:,2], np.sum(noise[:,:,:,0]*0.5))\n",
    "\n",
    "    noise[:,:,:,1:] = scale_to_sum(noise[:,:,:,1:], np.sum(noise[:,:,:,0]))\n",
    "    \n",
    "    c6_chunk = (1-noise_scale)*c6_chunk + abs(noise_scale*noise)\n",
    "    \n",
    "    return c6_chunk\n",
    "    \n",
    "    \n",
    "#     no_col_train[train_inds] = (1.0-noise_scale)*(x_train[train_inds].copy()) + abs(noise_scale*truncnorm.rvs(size=no_col_train[train_inds].shape))\n",
    "#     no_col_val[val_inds] = (1.0-noise_scale)*(x_val[val_inds].copy()) + abs(noise_scale*truncnorm.rvs(size=no_col_val[val_inds].shape))\n",
    "#     no_col_test[test_inds] = (1.0-noise_scale)*(x_test[test_inds].copy()) + abs(noise_scale*truncnorm.rvs(size=no_col_test[test_inds].shape))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "aef555f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def scale_to_bound(row, scale):\n",
    "    # scale = bound / np.max(np.abs(row))\n",
    "    return row * scale\n",
    "\n",
    "noise_scale = 0.75\n",
    "confounder_portion = 0.4\n",
    "\n",
    "conf_train = np.zeros(x_train.shape)\n",
    "conf_val = np.zeros(x_val.shape)\n",
    "conf_test = np.zeros(x_test.shape)\n",
    "\n",
    "sup_train = np.zeros(x_train.shape)\n",
    "sup_val = np.zeros(x_val.shape)\n",
    "sup_test = np.zeros(x_test.shape)\n",
    "\n",
    "no_col_train = np.zeros(x_train.shape)\n",
    "no_col_val = np.zeros(x_val.shape)\n",
    "no_col_test = np.zeros(x_test.shape)\n",
    "\n",
    "conf_train_colours = np.zeros((x_train.shape[0], 3))\n",
    "conf_val_colours = np.zeros((x_val.shape[0], 3))\n",
    "conf_test_colours = np.zeros((x_test.shape[0], 3))\n",
    "\n",
    "sup_train_colours = np.zeros((x_train.shape[0], 3))\n",
    "sup_val_colours = np.zeros((x_val.shape[0], 3))\n",
    "sup_test_colours = np.zeros((x_test.shape[0], 3))\n",
    "\n",
    "train_ind = 0\n",
    "val_ind = 0\n",
    "\n",
    "for i in range(10):\n",
    "    train_inds = np.where(y_train==i)[0]\n",
    "    val_inds = np.where(y_val==i)[0]\n",
    "    test_inds = np.where(y_test==i)[0]  \n",
    "\n",
    "    conf_train[train_inds], conf_train_colours[train_inds] = create_confounder(x_train[train_inds].copy(), colours[i], colours, confounder_portion=confounder_portion, noise_scale=noise_scale)\n",
    "    conf_val[val_inds], conf_val_colours[val_inds] = create_confounder(x_val[val_inds].copy(), colours[i], colours, confounder_portion=confounder_portion, noise_scale=noise_scale)\n",
    "    conf_test[test_inds], conf_test_colours[test_inds] = create_confounder(x_test[test_inds].copy(), colours[i], colours, confounder_portion=confounder_portion, noise_scale=noise_scale)\n",
    "    \n",
    "    sup_train[train_inds], sup_train_colours[train_inds] = create_suppressor(x_train[train_inds].copy(), colours, noise_scale=noise_scale)\n",
    "    sup_val[val_inds], sup_val_colours[val_inds] = create_suppressor(x_val[val_inds].copy(), colours, noise_scale=noise_scale)\n",
    "    sup_test[test_inds], sup_test_colours[test_inds] = create_suppressor(x_test[test_inds].copy(), colours, noise_scale=noise_scale)  \n",
    "\n",
    "    no_col_train[train_inds] = create_no_col(x_train[train_inds].copy(), noise_scale=noise_scale)\n",
    "    no_col_val[val_inds] = create_no_col(x_val[val_inds].copy(), noise_scale=noise_scale)\n",
    "    no_col_test[test_inds] = create_no_col(x_test[test_inds].copy(), noise_scale=noise_scale)\n",
    "\n",
    "#     no_col_train[train_inds] = (1.0-noise_scale)*(x_train[train_inds].copy()) + abs(noise_scale*truncnorm.rvs(size=no_col_train[train_inds].shape))\n",
    "#     no_col_val[val_inds] = (1.0-noise_scale)*(x_val[val_inds].copy()) + abs(noise_scale*truncnorm.rvs(size=no_col_val[val_inds].shape))\n",
    "#     no_col_test[test_inds] = (1.0-noise_scale)*(x_test[test_inds].copy()) + abs(noise_scale*truncnorm.rvs(size=no_col_test[test_inds].shape))\n",
    "\n",
    "\n",
    "scale = 1 / np.max(np.abs(np.vstack([conf_train, conf_val, conf_test])))\n",
    "\n",
    "conf_train = np.apply_along_axis(scale_to_bound, 1, conf_train, scale)\n",
    "conf_val = np.apply_along_axis(scale_to_bound, 1, conf_val, scale)\n",
    "conf_test = np.apply_along_axis(scale_to_bound, 1, conf_test, scale)\n",
    "\n",
    "##############\n",
    "\n",
    "scale = 1 / np.max(np.abs(np.vstack([sup_train, sup_val, sup_test])))\n",
    "\n",
    "sup_train = np.apply_along_axis(scale_to_bound, 1, sup_train, scale)\n",
    "sup_val = np.apply_along_axis(scale_to_bound, 1, sup_val, scale)\n",
    "sup_test = np.apply_along_axis(scale_to_bound, 1, sup_test, scale)\n",
    "\n",
    "##############\n",
    "\n",
    "scale = 1 / np.max(np.abs(np.vstack([no_col_train, no_col_val, no_col_test])))\n",
    "\n",
    "no_col_train = np.apply_along_axis(scale_to_bound, 1, no_col_train, scale)\n",
    "no_col_val = np.apply_along_axis(scale_to_bound, 1, no_col_val, scale)\n",
    "no_col_test = np.apply_along_axis(scale_to_bound, 1, no_col_test, scale)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f3366c50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6910031.44854736 6910031.448547358\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(conf_train[:,:,:,0]), np.sum(conf_train[:,:,:,1]) + np.sum(conf_train[:,:,:,2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1557de36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6908947.732961149 6908947.732961152\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(no_col_train[:,:,:,0]), np.sum(no_col_train[:,:,:,1]) + np.sum(no_col_train[:,:,:,2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a0ef3380",
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_class_data(x_conf, x_sup, x_no_col, y, class_ind=0, n_cols=10):\n",
    "    class_inds = np.where(y==class_ind)[0]\n",
    "    \n",
    "    sample = random.sample(list(class_inds), n_cols)\n",
    "    fig, axs = plt.subplots(3,n_cols, figsize=(10,4))\n",
    "    \n",
    "    dataset_labels = ['Confounder', 'Suppressor', 'No\\nColour']\n",
    "    \n",
    "    for i, dataset in enumerate([x_conf, x_sup, x_no_col]):\n",
    "        for j in range(n_cols):\n",
    "            if i == 0:\n",
    "                axs[i,j].set_title(f'Sample\\n{sample[j]}')\n",
    "            axs[i,j].imshow(dataset[sample[j]])\n",
    "            axs[i,j].set_xticks([])\n",
    "            axs[i,j].set_yticks([])\n",
    "            \n",
    "        axs[i,0].set_ylabel(dataset_labels[i])\n",
    "            \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ed80b7c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_device_pixel_ratio', {\n",
       "                device_pixel_ratio: fig.ratio,\n",
       "            });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
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       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
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       "        for (var i = 0; i < nentries; i++) {\n",
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       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    fig.rubberband_canvas.style.cursor = msg['cursor'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * https://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"1000.0000264909539\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_class_data(conf_train, sup_train, no_col_train, y_train, class_ind=0, n_cols=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "1d069248",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle as pkl\n",
    "\n",
    "conf_save = [(conf_train, y_train, conf_train_colours), (conf_val, y_val, conf_val_colours), (conf_test, y_test, conf_test_colours)]\n",
    "sup_save = [(sup_train, y_train, sup_train_colours), (sup_val, y_val, sup_val_colours), (sup_test, y_test, sup_test_colours)]\n",
    "no_col_save = [(no_col_train, y_train), (no_col_val, y_val), (no_col_test, y_test)]\n",
    "\n",
    "\n",
    "with open('mnist_conf_data.pkl', 'wb') as f:\n",
    "    pkl.dump(conf_save, f)\n",
    "    \n",
    "with open('mnist_sup_data.pkl', 'wb') as f:\n",
    "    pkl.dump(sup_save, f)\n",
    "    \n",
    "with open('mnist_no_col_data.pkl', 'wb') as f:\n",
    "    pkl.dump(no_col_save, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6452747f",
   "metadata": {},
   "source": [
    "## Model Training\n",
    "Following the implementation of Anders et. al (2022): https://www.sciencedirect.com/science/article/pii/S1566253521001573\n",
    "and https://colab.research.google.com/github/reiinakano/invariant-risk-minimization/blob/master/invariant_risk_minimization_colored_mnist.ipynb#scrollTo=9hYJRewnv80x\n",
    "\n",
    "\n",
    "All models on colored MNIST in Sections 3.2 and 4.1 are trained\n",
    "using the AdaDelta algorithm with a learning rate of 1.0, which is\n",
    "multiplied by 0.7 after each epoch, for 10 epochs. The a posteriori ClArC\n",
    "is trained for 10 epochs on top of the baseline model, which has also\n",
    "been trained for 10 epochs. The network consists of 2 convolutional\n",
    "layers, followed by a max-pooling, and finally 2 fully-connected layers.\n",
    "Dropout is used after the max pooling and after the first fully-connected\n",
    "layer, with 25 percent and 50 percent dropout probabilities respectively. ReLU activations follow all fully-connected layers except the\n",
    "final one. The model used for 5.1 is trained with SGD, with a learning\n",
    "rate of 0.001 for 5 epochs. The architecture, however, is the same as\n",
    "for the other colored MNIST models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b5631e99",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(3, 20, 5, 1)\n",
    "        self.conv2 = nn.Conv2d(20, 50, 5, 1)\n",
    "        self.fc1 = nn.Linear(4 * 4 * 50, 500)\n",
    "        self.fc2 = nn.Linear(500, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.conv1(x))\n",
    "        x = F.max_pool2d(x, 2, 2)\n",
    "        x = F.relu(self.conv2(x))\n",
    "        x = F.max_pool2d(x, 2, 2)\n",
    "        x = x.view(-1, 4 * 4 * 50)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        logits = self.fc2(x)\n",
    "        return logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a3a5d77d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(lr,epochs,train_loader,val_loader,name_model,momentum=0.9,weight_decay=1e-3,num_models=10):\n",
    "    metric = AUROC(task='multiclass', num_classes=10) \n",
    "    softmax = nn.Softmax(dim=1)\n",
    "    \n",
    "    training_accuracy = Accuracy(task='multiclass', num_classes=10).to(DEVICE)\n",
    "    val_accuracy = Accuracy(task='multiclass', num_classes=10).to(DEVICE)\n",
    "\n",
    "    torch.manual_seed(SEED)\n",
    "\n",
    "    trained_models = []\n",
    "    training_logs = []\n",
    "\n",
    "    for training_ind in range(num_models):\n",
    "        cnn_conf=Net()\n",
    "        cnn_conf.to(DEVICE)\n",
    "        criterion = nn.CrossEntropyLoss()\n",
    "        \n",
    "        # Anders et. al uses mainly AdaDelta but also SGD in one section\n",
    "#         optimizer = optim.SGD(cnn_conf.parameters(), lr=lr, momentum=momentum, weight_decay=weight_decay)\n",
    "        optimizer = optim.Adadelta(cnn_conf.parameters(), lr=lr, weight_decay=weight_decay)\n",
    "\n",
    "\n",
    "        loss_list_train=[]\n",
    "        loss_list_val=[]\n",
    "        acc_list_train=[]\n",
    "        acc_list_val=[]\n",
    "        \n",
    "\n",
    "        min_loss = 100000000\n",
    "    #     last_loss = 1000000000\n",
    "\n",
    "        for epoch in range(epochs):  # loop over the dataset multiple times\n",
    "            print(f' epoch {epoch+1} in {epochs}')\n",
    "            t0=time.time()\n",
    "            epoch_loss = 0.0\n",
    "            epoch_loss_val= 0.0\n",
    "            batch_acc=[]\n",
    "            batch_acc_val=[]\n",
    "\n",
    "            auroc_train=[]\n",
    "            auroc_val=[]\n",
    "\n",
    "            pred_train=[]\n",
    "            pred_val=[]\n",
    "            true_labels_train=[]\n",
    "            true_labels_val=[]\n",
    "\n",
    "            for i, data in enumerate(train_loader):\n",
    "                inputs, labels = data\n",
    "\n",
    "                inputs = inputs.to(DEVICE,dtype=torch.float)\n",
    "                labels = labels.type(torch.LongTensor)\n",
    "                labels=labels.to(DEVICE)\n",
    "\n",
    "                # zero the parameter gradients\n",
    "                optimizer.zero_grad()\n",
    "\n",
    "                # forward + backward + optimize\n",
    "                outputs = cnn_conf(inputs).squeeze()\n",
    "                _, predicted = torch.max(outputs.data,1)\n",
    "                loss = criterion(outputs, labels)\n",
    "                loss.backward()\n",
    "                optimizer.step()\n",
    "\n",
    "                epoch_loss += loss.item()        \n",
    "                batch_acc.append(training_accuracy(predicted, labels).item())\n",
    "\n",
    "#                 print(softmax(outputs)[:,1].detach().numpy().shape)\n",
    "#                 pred_train=np.concatenate((pred_train, softmax(outputs)[:,1].detach().numpy()))\n",
    "#                 true_labels_train=np.concatenate((true_labels_train, labels.cpu().numpy()))\n",
    "                \n",
    "                auroc_train.append(metric(outputs,labels.to(torch.int32)))\n",
    "\n",
    "            auroc_train = sum(auroc_train)/len(auroc_train)\n",
    "\n",
    "            for i_v, data_val in enumerate(val_loader):\n",
    "                inputs_val, labels_val = data_val\n",
    "\n",
    "                inputs_val = inputs_val.to(DEVICE,dtype=torch.float)\n",
    "                labels_val = labels_val.type(torch.LongTensor)\n",
    "                labels_val=labels_val.to(DEVICE)\n",
    "\n",
    "                outputs_val = cnn_conf(inputs_val).squeeze()\n",
    "                _, predicted_val = torch.max(outputs_val.data, 1)\n",
    "\n",
    "                loss_val = criterion(outputs_val, labels_val)\n",
    "\n",
    "                epoch_loss_val += loss_val.item()        \n",
    "                batch_acc_val.append(val_accuracy(predicted_val, labels_val).item())\n",
    "\n",
    "                auroc_val.append(metric(outputs_val,labels_val.to(torch.int32)))\n",
    "\n",
    "#                 pred_val=np.concatenate((pred_val, softmax(outputs_val)[:,1].cpu().detach().numpy()))\n",
    "#                 true_labels_val=np.concatenate((true_labels_val, labels_val.cpu().detach().numpy()))\n",
    "            auroc_val = sum(auroc_val)/len(auroc_val)\n",
    "\n",
    "            if epoch_loss_val < min_loss:\n",
    "                min_loss = epoch_loss_val\n",
    "                save_model = type(cnn_conf)() # get a new instance\n",
    "                save_model.load_state_dict(cnn_conf.state_dict()) # copy weights and stuff\n",
    "\n",
    "                torch.save(save_model.state_dict(), f'{str(name_model)}_{training_ind}.pt')\n",
    "\n",
    "#             auroc_val=metric(torch.tensor(pred_val),torch.tensor(true_labels_val).to(torch.int32))\n",
    "\n",
    "            epoch_acc=sum(batch_acc)/len(batch_acc)\n",
    "            epoch_acc_val=sum(batch_acc_val)/len(batch_acc_val)\n",
    "\n",
    "            print(f'epoch train loss: {epoch_loss} | epoch train acc {epoch_acc} | AUROC: {auroc_train}')\n",
    "            print(f'epoch val loss: {epoch_loss_val} | epoch val acc {epoch_acc_val} | AUROC: {auroc_val}')\n",
    "            print(f'time elapsed: {round(time.time()-t0,2)} s')\n",
    "\n",
    "            loss_list_train.append(epoch_loss)\n",
    "            loss_list_val.append(epoch_loss_val)\n",
    "            acc_list_train.append(epoch_acc)\n",
    "            acc_list_val.append(epoch_acc_val)\n",
    "\n",
    "            epoch_loss = 0.0\n",
    "            epoch_loss_val=0.0\n",
    "\n",
    "        print('Finished Training')\n",
    "        print()\n",
    "        print('\\t \\t *******************')\n",
    "        print()\n",
    "        \n",
    "        torch.save(save_model.state_dict(), f'{str(name_model)}_{training_ind}.pt')\n",
    "\n",
    "        trained_models.append(save_model)\n",
    "        training_logs.append([acc_list_train, loss_list_train, acc_list_val, loss_list_val])\n",
    "#     plt.plot(loss_list_train)\n",
    "#     plt.plot(loss_list_val)\n",
    "#     plt.plot([i*100 for i in acc_list_train])\n",
    "#     plt.plot([i*100 for i in acc_list_va])\n",
    "    return trained_models, [acc_list_train, loss_list_train, acc_list_val, loss_list_val]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9415ca55",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loader = DataLoader(TensorDataset(torch.tensor(conf_train.transpose(0,3,1,2)), torch.tensor(y_train)), batch_size=1000, shuffle=True)\n",
    "val_loader = DataLoader(TensorDataset(torch.tensor(conf_val.transpose(0,3,1,2)), torch.tensor(y_val)), batch_size=1000, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4d40a193",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = 0.005\n",
    "epochs = 100\n",
    "num_models=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1857af32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " epoch 1 in 100\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\1\\ipykernel_17276\\1339579275.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtrain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlr\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mepochs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mval_loader\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'mnist_conf'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmomentum\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.9\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mweight_decay\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1e-3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnum_models\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnum_models\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\1\\ipykernel_17276\\377032434.py\u001b[0m in \u001b[0;36mtrain\u001b[1;34m(lr, epochs, train_loader, val_loader, name_model, momentum, weight_decay, num_models)\u001b[0m\n\u001b[0;32m     57\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     58\u001b[0m                 \u001b[1;31m# forward + backward + optimize\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 59\u001b[1;33m                 \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcnn_conf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     60\u001b[0m                 \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpredicted\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     61\u001b[0m                 \u001b[0mloss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.virtualenvs\\debugging_xai-9luXJGIR\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m   1192\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[0;32m   1193\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[1;32m-> 1194\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1195\u001b[0m         \u001b[1;31m# Do not call functions when jit is used\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1196\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\1\\ipykernel_17276\\1189412014.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m     11\u001b[0m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mF\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax_pool2d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mF\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconv2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mF\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax_pool2d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     14\u001b[0m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mview\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m4\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;36m4\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;36m50\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mF\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfc1\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.virtualenvs\\debugging_xai-9luXJGIR\\lib\\site-packages\\torch\\_jit_internal.py\u001b[0m in \u001b[0;36mfn\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    483\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mif_true\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    484\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 485\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mif_false\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    486\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    487\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mif_true\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mif_false\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.virtualenvs\\debugging_xai-9luXJGIR\\lib\\site-packages\\torch\\nn\\functional.py\u001b[0m in \u001b[0;36m_max_pool2d\u001b[1;34m(input, kernel_size, stride, padding, dilation, ceil_mode, return_indices)\u001b[0m\n\u001b[0;32m    780\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mstride\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    781\u001b[0m         \u001b[0mstride\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjit\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mannotate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mList\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 782\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax_pool2d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkernel_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstride\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpadding\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdilation\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mceil_mode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    783\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    784\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "train(lr,epochs,train_loader,val_loader,'mnist_conf',momentum=0.9,weight_decay=1e-3,num_models=num_models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59fffd1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loader = DataLoader(TensorDataset(torch.tensor(sup_train.transpose(0,3,1,2)), torch.tensor(y_train)), batch_size=1000, shuffle=True)\n",
    "val_loader = DataLoader(TensorDataset(torch.tensor(sup_val.transpose(0,3,1,2)), torch.tensor(y_val)), batch_size=1000, shuffle=True)\n",
    "\n",
    "train(lr,epochs,train_loader,val_loader,'mnist_sup',momentum=0.9,weight_decay=1e-3,num_models=num_models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7bf2bac",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loader = DataLoader(TensorDataset(torch.tensor(no_col_train.transpose(0,3,1,2)), torch.tensor(y_train)), batch_size=1000, shuffle=True)\n",
    "val_loader = DataLoader(TensorDataset(torch.tensor(no_col_val.transpose(0,3,1,2)), torch.tensor(y_val)), batch_size=1000, shuffle=True)\n",
    "\n",
    "train(lr,epochs,train_loader,val_loader,'mnist_no_col',momentum=0.9,weight_decay=1e-3,num_models=num_models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12cef5c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_trained(path, device='cpu'):\n",
    "    model = Net()\n",
    "    model.load_state_dict(torch.load(path,map_location=device))\n",
    "    return model\n",
    "\n",
    "def print_AUC(loader, model_ind=0, device='cpu'):\n",
    "\n",
    "    cnn_conf=load_trained(f'./mnist_conf_{model_ind}.pt').to(device)\n",
    "    cnn_sup=load_trained(f'./mnist_sup_{model_ind}.pt').to(device)\n",
    "    cnn_no=load_trained(f'./mnist_no_col_{model_ind}.pt').to(device)\n",
    "    \n",
    "    #models -> cnn_no; cnn_sup; cnn_conf\n",
    "\n",
    "    softmax = nn.Softmax(dim=1)\n",
    "    metric = AUROC(num_classes=10, task='multiclass')\n",
    "\n",
    "    model_conf_test_conf_pred=np.array([])\n",
    "    model_sup_test_conf_pred=np.array([])\n",
    "    model_no_test_conf_pred=np.array([])\n",
    "    true_labels=np.array([])\n",
    "    \n",
    "    model_conf_auroc_test = []\n",
    "    model_sup_auroc_test = []\n",
    "    model_no_col_auroc_test = []\n",
    "\n",
    "    for i_v, data_val in enumerate(loader):\n",
    "        inputs_val, labels_val = data_val\n",
    "        inputs_val = inputs_val.to(DEVICE,dtype=torch.float)\n",
    "        labels_val = labels_val.type(torch.LongTensor)\n",
    "        labels_val=labels_val.to(DEVICE)\n",
    "\n",
    "        outputs_conf = cnn_conf(inputs_val).squeeze()\n",
    "        outputs_sup = cnn_sup(inputs_val).squeeze()\n",
    "        outputs_no = cnn_no(inputs_val).squeeze()\n",
    "\n",
    "        out_pred_conf=softmax(outputs_conf)\n",
    "        out_pred_sup=softmax(outputs_sup)\n",
    "        out_pred_no=softmax(outputs_no)\n",
    "\n",
    "#         model_conf_test_conf_pred=np.concatenate((model_conf_test_conf_pred, out_pred_conf[:,1].cpu().detach().numpy()))\n",
    "#         model_sup_test_conf_pred=np.concatenate((model_sup_test_conf_pred, out_pred_sup[:,1].cpu().detach().numpy()))\n",
    "#         model_no_test_conf_pred=np.concatenate((model_no_test_conf_pred, out_pred_no[:,1].cpu().detach().numpy()))\n",
    "\n",
    "#         print(labels_val.shape)\n",
    "#         print(model_conf_test_conf_pred)\n",
    "\n",
    "        model_conf_auroc_test.append(metric(out_pred_conf, labels_val))\n",
    "        model_sup_auroc_test.append(metric(out_pred_sup, labels_val))\n",
    "        model_no_col_auroc_test.append(metric(out_pred_no, labels_val))\n",
    "        \n",
    "#         true_labels=np.concatenate((true_labels, labels_val.cpu().numpy()))\n",
    "\n",
    "    print('model: confounder', sum(model_conf_auroc_test)/len(model_conf_auroc_test))\n",
    "    print('model: suppressor',sum(model_sup_auroc_test)/len(model_sup_auroc_test))\n",
    "    print('model: no colour',sum(model_no_col_auroc_test)/len(model_no_col_auroc_test))\n",
    "    print()\n",
    "\n",
    "    l=[sum(model_conf_auroc_test)/len(model_conf_auroc_test),\n",
    "       sum(model_sup_auroc_test)/len(model_sup_auroc_test),\n",
    "       sum(model_no_col_auroc_test)/len(model_no_col_auroc_test)]\n",
    "    return l\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "baad3499",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle as pkl\n",
    "with open('mnist_conf_data.pkl', 'rb') as f:\n",
    "    conf_data = pkl.load(f)\n",
    "    \n",
    "with open('mnist_sup_data.pkl', 'rb') as f:\n",
    "    sup_data = pkl.load(f)\n",
    "    \n",
    "with open('mnist_no_col_data.pkl', 'rb') as f:\n",
    "    no_col_data = pkl.load(f)\n",
    "\n",
    "batch_size = 64\n",
    "\n",
    "confounder_test_loader = DataLoader(TensorDataset(torch.tensor(conf_data[2][0].transpose(0,3,1,2)), torch.tensor(conf_data[2][1])),batch_size=batch_size, shuffle=False)\n",
    "suppressor_test_loader = DataLoader(TensorDataset(torch.tensor(sup_data[2][0].transpose(0,3,1,2)), torch.tensor(sup_data[2][1])),batch_size=batch_size, shuffle=False)\n",
    "no_col_test_loader = DataLoader(TensorDataset(torch.tensor(no_col_data[2][0].transpose(0,3,1,2)), torch.tensor(no_col_data[2][1])),batch_size=batch_size, shuffle=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c4b2f38",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size=64\n",
    "# confounder_test_loader = DataLoader(TensorDataset(torch.tensor(conf_test.transpose(0,3,1,2)), torch.tensor(y_test)),batch_size=batch_size, shuffle=False)\n",
    "# supressor_test_loader = DataLoader(TensorDataset(torch.tensor(sup_test.transpose(0,3,1,2)), torch.tensor(y_test)),batch_size=batch_size, shuffle=False)\n",
    "# no_col_test_loader = DataLoader(TensorDataset(torch.tensor(no_col_test.transpose(0,3,1,2)), torch.tensor(y_test)),batch_size=batch_size, shuffle=False)\n",
    "\n",
    "confounder_data_results = []\n",
    "suppressor_data_results = []\n",
    "no_col_data_results = []\n",
    "\n",
    "for i in range(1):\n",
    "    print(f'MODEL {i}')\n",
    "    print('confounder data:')\n",
    "    confounder_data_results.append(print_AUC(confounder_test_loader, i))\n",
    "    print()\n",
    "    print('suppressor data:')\n",
    "    suppressor_data_results.append(print_AUC(suppressor_test_loader, i))\n",
    "    print()\n",
    "    print('no colour data:')\n",
    "    no_col_data_results.append(print_AUC(no_col_test_loader, i))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a135d8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# current problem\n",
    "# scaling channel noise by sum of that channel will colour background in accordance with pattern colour\n",
    "# alternative: scale channel noise according to another factor, i.e. sum(channel_0) = 2*sum(channel_1 or _2)\n",
    "# key aim: keep total mass in red = green + blue\n",
    "# another problem: we have the c6_chunk = (1.0, 1.0, 1.0) where r = g + b; should we just make this 1.0, 0.5, 0.5? it's "
   ]
  }
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