Demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dramatic-story",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing d:\\ds\\spaceml\\interactive tsne\\tsne\n",
      "Requirement already satisfied: torch in d:\\ds\\conda\\lib\\site-packages (from InteractivePlot==0.1.0) (1.8.0+cu111)\n",
      "Requirement already satisfied: matplotlib in d:\\ds\\conda\\lib\\site-packages (from InteractivePlot==0.1.0) (3.3.4)\n",
      "Requirement already satisfied: numpy in d:\\ds\\conda\\lib\\site-packages (from InteractivePlot==0.1.0) (1.20.1)\n",
      "Requirement already satisfied: tqdm in d:\\ds\\conda\\lib\\site-packages (from InteractivePlot==0.1.0) (4.59.0)\n",
      "Requirement already satisfied: torchvision in d:\\ds\\conda\\lib\\site-packages (from InteractivePlot==0.1.0) (0.9.0+cu111)\n",
      "Requirement already satisfied: sklearn in d:\\ds\\conda\\lib\\site-packages (from InteractivePlot==0.1.0) (0.0)\n",
      "Requirement already satisfied: pandas in d:\\ds\\conda\\lib\\site-packages (from InteractivePlot==0.1.0) (1.2.3)\n",
      "Requirement already satisfied: scipy in d:\\ds\\conda\\lib\\site-packages (from InteractivePlot==0.1.0) (1.6.1)\n",
      "Requirement already satisfied: Pillow in d:\\ds\\conda\\lib\\site-packages (from InteractivePlot==0.1.0) (8.1.2)\n",
      "Requirement already satisfied: tensorboardX in d:\\ds\\conda\\lib\\site-packages (from InteractivePlot==0.1.0) (2.1)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in d:\\ds\\conda\\lib\\site-packages (from matplotlib->InteractivePlot==0.1.0) (2.4.7)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in d:\\ds\\conda\\lib\\site-packages (from matplotlib->InteractivePlot==0.1.0) (2.8.1)\n",
      "Requirement already satisfied: cycler>=0.10 in d:\\ds\\conda\\lib\\site-packages (from matplotlib->InteractivePlot==0.1.0) (0.10.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in d:\\ds\\conda\\lib\\site-packages (from matplotlib->InteractivePlot==0.1.0) (1.3.1)\n",
      "Requirement already satisfied: six in d:\\ds\\conda\\lib\\site-packages (from cycler>=0.10->matplotlib->InteractivePlot==0.1.0) (1.15.0)\n",
      "Requirement already satisfied: pytz>=2017.3 in d:\\ds\\conda\\lib\\site-packages (from pandas->InteractivePlot==0.1.0) (2021.1)\n",
      "Requirement already satisfied: scikit-learn in d:\\ds\\conda\\lib\\site-packages (from sklearn->InteractivePlot==0.1.0) (0.24.1)\n",
      "Requirement already satisfied: joblib>=0.11 in d:\\ds\\conda\\lib\\site-packages (from scikit-learn->sklearn->InteractivePlot==0.1.0) (1.0.1)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in d:\\ds\\conda\\lib\\site-packages (from scikit-learn->sklearn->InteractivePlot==0.1.0) (2.1.0)\n",
      "Requirement already satisfied: protobuf>=3.8.0 in d:\\ds\\conda\\lib\\site-packages (from tensorboardX->InteractivePlot==0.1.0) (3.15.6)\n",
      "Requirement already satisfied: typing-extensions in d:\\ds\\conda\\lib\\site-packages (from torch->InteractivePlot==0.1.0) (3.7.4.3)\n",
      "Building wheels for collected packages: InteractivePlot\n",
      "  Building wheel for InteractivePlot (setup.py): started\n",
      "  Building wheel for InteractivePlot (setup.py): finished with status 'done'\n",
      "  Created wheel for InteractivePlot: filename=InteractivePlot-0.1.0-py3-none-any.whl size=9057 sha256=29882c46bb7c0f4bcdf2c537dd567a50fd2f1b17ef2722523007a41c830922e3\n",
      "  Stored in directory: C:\\Users\\tarun\\AppData\\Local\\Temp\\pip-ephem-wheel-cache-6dtbvo1f\\wheels\\04\\08\\45\\49a6495f65846849eb614fa274da20054db7415566ebc7a9f1\n",
      "Successfully built InteractivePlot\n",
      "Installing collected packages: InteractivePlot\n",
      "Successfully installed InteractivePlot-0.1.0\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "#Installs the setup.py in the current working directory.\n",
    "pip install ."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "south-democrat",
   "metadata": {},
   "source": [
    "# Interactive TSNE/UMAP Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "capable-kingdom",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "juvenile-regard",
   "metadata": {},
   "outputs": [],
   "source": [
    "import InteractivePlot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "considerable-monroe",
   "metadata": {},
   "outputs": [],
   "source": [
    "from InteractivePlot import PrepareData\n",
    "model = '../uc_merced.pt'\n",
    "data_path = '../UCMerced_LandUse/Images'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "successful-opening",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|                                                                                           | 0/33 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using CUDA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 33/33 [00:06<00:00,  4.76it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UMAP(dens_frac=0.0, dens_lambda=0.0, n_jobs=4, n_neighbors=5, random_state=42,\n",
      "     verbose=1)\n",
      "Construct fuzzy simplicial set\n",
      "Fri Mar 26 06:54:44 2021 Finding Nearest Neighbors\n",
      "Fri Mar 26 06:54:44 2021 Finished Nearest Neighbor Search\n",
      "Fri Mar 26 06:54:44 2021 Construct embedding\n",
      "\tcompleted  0  /  500 epochs\n",
      "\tcompleted  50  /  500 epochs\n",
      "\tcompleted  100  /  500 epochs\n",
      "\tcompleted  150  /  500 epochs\n",
      "\tcompleted  200  /  500 epochs\n",
      "\tcompleted  250  /  500 epochs\n",
      "\tcompleted  300  /  500 epochs\n",
      "\tcompleted  350  /  500 epochs\n",
      "\tcompleted  400  /  500 epochs\n",
      "\tcompleted  450  /  500 epochs\n",
      "Fri Mar 26 06:54:48 2021 Finished embedding\n",
      "UMAP done! Time elapsed: 5.6106276512146 seconds\n",
      "Linkage variables created.\n",
      "Filename mapping done.\n"
     ]
    }
   ],
   "source": [
    "data = PrepareData(model= model, DATA_PATH = data_path, output_size = 21, num_clusters = 21, method= 'umap')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "chicken-toyota",
   "metadata": {},
   "outputs": [],
   "source": [
    "from InteractivePlot import InteractiveAllclose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "polar-equity",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "/* 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",
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       "\n",
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       "        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",
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       "\n",
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       "\n",
       "    this.context = undefined;\n",
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       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
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       "\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",
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       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
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       "\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",
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       "        '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",
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       "        'canvas'\n",
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       "    rubberband_canvas.setAttribute(\n",
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       "        '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",
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       "        if (window.ResizeObserver !== undefined) {\n",
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       "                // Chrome 84 implements new version of spec.\n",
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       "                canvas.setAttribute(\n",
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       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
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       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
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       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
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       "            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",
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       "\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",
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       "\n",
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       "        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",
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       "\n",
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       "\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",
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       "        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",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = 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",
       "            /* 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",
       "            evt.data.type = 'image/png';\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",
       "                evt.data\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 http://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",
       " * http://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.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\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, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"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.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",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['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",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\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=\"1500\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "p = InteractiveAllclose(data.cl, data.tsne_obj, data.objects, data.spd)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "armed-picnic",
   "metadata": {},
   "source": [
    "## TensorBoard Projector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "eight-taylor",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ResNet(\n",
       "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (relu): ReLU(inplace=True)\n",
       "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "  (layer1): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (layer2): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (layer3): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (layer4): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
       "  (fc): Linear(in_features=512, out_features=1000, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torchvision.models as models\n",
    "\n",
    "resnet18 = models.resnet18(pretrained=True)\n",
    "resnet18.cuda()\n",
    "resnet18.eval() # Setup for inferencing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "higher-rapid",
   "metadata": {},
   "outputs": [],
   "source": [
    "from InteractivePlot import Projector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "illegal-powell",
   "metadata": {},
   "outputs": [],
   "source": [
    "proj = Projector(model = resnet18, EXPT_NAME = 'projector_test', LOG_PATH = '.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "white-combining",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchvision import transforms\n",
    "from torchvision.datasets import ImageFolder\n",
    "import torch\n",
    "import shutil\n",
    "\n",
    "tfs = transforms.Compose([transforms.Resize((32,32)), \n",
    "                          transforms.ToTensor()\n",
    "#                           transforms.Normalize(mean=[0.485], std=[0.229])\n",
    "                         ])\n",
    "\n",
    "# MNIST_data = FashionMNIST(root = r'./FMNIST', download=True, transform=tfs)\n",
    "dataset = ImageFolder(root = '../UCMerced_LandUse/Images/', transform= tfs)\n",
    "\n",
    "data_loader = torch.utils.data.DataLoader(dataset,\n",
    "                                          batch_size= 256,\n",
    "                                          shuffle=True)\n",
    "\n",
    "batch_img, batch_labels = next(iter(data_loader))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "creative-proxy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "proj.write_embeddings(batch_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "original-sample",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([256, 1000])\n",
      "torch.Size([256, 3, 32, 32])\n",
      "TensorBoard prepared. \n",
      " Open a new console and start TensorBoard with the logdir  .\\projector_test\\TB\\projector_test\n"
     ]
    }
   ],
   "source": [
    "proj.create_tensorboard_log()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "sunset-airport",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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