show_aligned_trajectories.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys \n",
    "import numpy as np\n",
    "import hypertools as hyp\n",
    "from qmvpa import utils, factor\n",
    "from data_loader import load_cifar, load_mnist\n",
    "# from data_loader_local import load_data\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from config import get_log_info, get_layer_names_plt\n",
    "from dep.utils import subset_units\n",
    "\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns \n",
    "\n",
    "sns.set(style = 'white', context='paper', rc={\"lines.linewidth\": 2.5}, font_scale = 1.5)\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n",
    "\n",
    "log_root = '/tigress/qlu/logs/keras-resnet/log'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data: cifar100\n",
      "model: conv\n",
      "log epochs [ 0.  1.  2.  4.  6.  9. 14. 21. 32. 50.]\n",
      "layer selected [4, 10, 13, 16]\n"
     ]
    }
   ],
   "source": [
    "data_name = 'cifar100'\n",
    "model_name = 'conv'\n",
    "\n",
    "# load data \n",
    "_, _, _, _, _, labels, data_info = load_cifar(data_name)\n",
    "[n_classes, img_rows, img_cols, img_channels] = data_info\n",
    "n_test_egs = len(labels)\n",
    "\n",
    "# define params \n",
    "log_info_list = get_log_info(data_name, model_name)\n",
    "n_subjs, layer_selected, n_layers, log_epochs, n_log_epochs = log_info_list\n",
    "layer_names_plt = get_layer_names_plt(model_name)\n",
    "\n",
    "print('data: %s\\nmodel: %s' % (data_name, model_name))\n",
    "print('log epochs', log_epochs)\n",
    "print('layer selected', layer_selected)\n",
    "\n",
    "\n",
    "# set relevant parameters \n",
    "n_max_units = 1000\n",
    "test_size = .2 \n",
    "\n",
    "# plotting params \n",
    "temporal_window_size = 10\n",
    "save_animation = True\n",
    "n_nets_plot = 5\n",
    "\n",
    "# choose layer and epoch \n",
    "e = 32\n",
    "l = 13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Layer 13, Epoch 32\n",
      "10\n",
      "(8000, 512)\n",
      "(2000, 512)\n"
     ]
    }
   ],
   "source": [
    "ss = StandardScaler()\n",
    "\n",
    "# only test random network vs. fully trained network\n",
    "# first and last epoch \n",
    "log_epochs = [log_epochs[0], log_epochs[-1]]\n",
    "# first and last few layers\n",
    "layer_to_plt = [0, -2, -1]\n",
    "layer_selected = [layer_selected[l] for l in layer_to_plt]\n",
    "layer_names_plt = [layer_names_plt[l] for l in layer_to_plt]\n",
    "\n",
    "# e = 100\n",
    "# l = 55  \n",
    "\n",
    "# for e in log_epochs: \n",
    "#     for l in layer_selected:\n",
    "print('Layer %d, Epoch %d' % (l, e))\n",
    "# load data for all subjects \n",
    "Xs_train = []\n",
    "Xs_test = []\n",
    "for subj_id in range(n_subjs):\n",
    "    # activation log dir \n",
    "    log_dir = os.path.join(log_root, data_name, model_name, 'subj%.2d' % (subj_id))\n",
    "    acts_path = os.path.join(log_dir, 'epoch_%.3d' % e, 'activations')\n",
    "    acts_path_l = os.path.join(acts_path, 'layer_%.3d.npy' % (l))\n",
    "\n",
    "    # load activity \n",
    "    loaded_acts = np.load(acts_path_l)\n",
    "    loaded_acts = np.reshape(loaded_acts, [n_test_egs, -1])\n",
    "\n",
    "    # subset units for computational efficiency \n",
    "    n_units = np.shape(loaded_acts)[1]\n",
    "    if n_units > n_max_units: \n",
    "        loaded_acts = subset_units(loaded_acts, n_max_units)\n",
    "\n",
    "    # split to training and testing set \n",
    "    X = loaded_acts\n",
    "    X_train, X_test, y_train, y_test = train_test_split(\n",
    "        X, labels, test_size = test_size, stratify = labels, random_state=0\n",
    "    )\n",
    "    y_test_id = np.argsort(np.ravel(y_test))\n",
    "    y_test = y_test[y_test_id]\n",
    "    X_test = X_test[y_test_id,: ]    \n",
    "\n",
    "    # gather data \n",
    "    \n",
    "    X_train = ss.fit_transform(X_train.T).T\n",
    "    X_test = ss.fit_transform(X_test.T).T\n",
    "    \n",
    "    Xs_train.append(X_train.T)\n",
    "    Xs_test.append(X_test.T)\n",
    "\n",
    "print(len(Xs_train))\n",
    "print(np.shape(X_train))\n",
    "print(np.shape(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def smooth_rowwise(mat, ws):\n",
    "    n_units_ = np.shape(mat)[0]\n",
    "    smoothed_mat = np.array(\n",
    "        [utils.mov_mean(mat[u, :], ws) for u in range(n_units_)])\n",
    "    return smoothed_mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 512, 8000)\n",
      "(10, 512, 2000)\n",
      "(1991, 512)\n"
     ]
    }
   ],
   "source": [
    "\"\"\"SRM\"\"\"\n",
    "# max component, preserve rep geo\n",
    "n_components_rigid = np.shape(Xs_train)[1]\n",
    "Xs_train_s, Xs_test_s, srm, var_exp_train = factor.fit_srm(\n",
    "    Xs_train, Xs_test, n_components_rigid)\n",
    "\n",
    "print(np.shape(Xs_train_s))\n",
    "print(np.shape(Xs_test_s))\n",
    "\n",
    "# reformat to sklearn shape\n",
    "# before SRM\n",
    "# acts = Xs_test\n",
    "# after SRM\n",
    "acts = Xs_test_s\n",
    "\n",
    "# moving window smoothing        \n",
    "smoothed_acts = [smooth_rowwise(acts[s], temporal_window_size) for s in range(n_subjs)]\n",
    "# to srm/brainiak shape\n",
    "smoothed_acts = [smoothed_acts[s].T for s in range(n_subjs)]\n",
    "print(np.shape(smoothed_acts[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
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       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
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       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
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       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
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       "        return fig.key_event(event, event['data']);\n",
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       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
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       "            fig.request_resize(ui.size.width, ui.size.height);\n",
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       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
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       "\n",
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       "\n",
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       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
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       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
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       "\n",
       "    // Add hover states to the ui-buttons\n",
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       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
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       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\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",
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       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
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       "\n",
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       "\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",
       "\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]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.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, 0, fig.canvas.width, fig.canvas.height);\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",
       "    {\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.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",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\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(\"No handler for the '\" + msg_type + \"' message type: \", msg);\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(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\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",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\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",
       "    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",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\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",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\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,\n",
       "                             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",
       "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\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"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\";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 = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\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.get(0);\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",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\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).html('<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/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<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 () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\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) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('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",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\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",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\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('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"600\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# # print some results if save\n",
    "# save_path = None\n",
    "# if save_animation: \n",
    "#     temp_plt_out_dir = '/tigress/qlu/logs/temp/hyp_nnsrm'\n",
    "#     ani_fname = '%s_%s_e%d_l%d.mp4' % (\n",
    "#         model_name.lower(), data_name.lower(), e, l)\n",
    "#     save_path = os.path.join(temp_plt_out_dir, ani_fname)\n",
    "#     print(save_path)\n",
    "\n",
    "title_text = \"\"\"\n",
    "Model: %s, Data: %s \\n epoch %.2d, layer %.2d \n",
    "\"\"\" % (model_name.upper(), data_name.upper(), e, \n",
    "       layer_names_plt[layer_selected.index(l)]) \n",
    "\n",
    "temp = hyp.plot(\n",
    "    smoothed_acts[:n_nets_plot], \n",
    "    animate=True, \n",
    "    tail_duration = 1, frame_rate=60, rotations=.15, \n",
    "    title = title_text, \n",
    "#             save_path=save_path,\n",
    "    size = (6, 5)\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}