animation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.animation as animation\n",
    "from matplotlib import colors\n",
    "import csv\n",
    "\n",
    "simulation = 'rabbits'\n",
    "grids_dir = f\"examples/{simulation}/grids\"\n",
    "config_path = f\"examples/{simulation}/config/animation_config.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create frames from files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"each file is expected to contain numbers separated by space:\n",
    " - first 2 numbers are x and y dimension, respectively\n",
    " - next are x * y * 9, where each consecutive 9 numbers describe state of single cell (object and directions) \"\"\"\n",
    "\n",
    "def read_workers(grids_dir, verbose=False):\n",
    "    \"\"\" reads grids from all the workers and converts them to frames.\n",
    "    returns: map(worker_loc => frames)\"\"\"\n",
    "    workers = {}\n",
    "    for worker_dir in os.listdir(grids_dir):\n",
    "        worker_dir_path = os.path.join(grids_dir, worker_dir)\n",
    "        (x, y) = [int(c) for c in worker_dir.split(\"_\")]\n",
    "        if verbose:\n",
    "            print(f\"processing worker {(x, y)}\")\n",
    "        workers[(x,y)] = create_frames(worker_dir_path)\n",
    "    return workers\n",
    "        \n",
    "def create_frames(worker_dir, verbose=False):\n",
    "    \"\"\" converts grids saved in given directory to matplotlib frames \"\"\"\n",
    "    grids = read_grids(worker_dir, verbose)\n",
    "    color_map = read_config(config_path, verbose)\n",
    "    return grids_to_frames(grids, color_map)\n",
    "\n",
    "def read_grids(frames_dir, verbose=False):\n",
    "    \"\"\" returns: numpy array of shape (n_frames, x_size, y_size, 9)\"\"\"\n",
    "    x_size, y_size = 0, 0\n",
    "    n_frames = len(os.listdir(frames_dir))\n",
    "    frames_3d = [0 for x in range(n_frames)]\n",
    "    \n",
    "    for filename in os.listdir(frames_dir):\n",
    "        frame_nr = int(filename.split(\"_\")[1].split(\".\")[0]) - 1\n",
    "        path = os.path.join(frames_dir, filename)\n",
    "        with open(path, 'r') as f:\n",
    "            file = f.read()\n",
    "        file_int = [int(c) for c in file.split(\" \")]\n",
    "        [x_size, y_size] = file_int[:2]\n",
    "        grid = file_int[2:]\n",
    "        grid = np.array(grid).reshape((x_size,y_size,9))\n",
    "        frames_3d[frame_nr] = (grid)\n",
    "    frames_3d = np.array(frames_3d)\n",
    "    if verbose:\n",
    "        print(\"--read_grids\")\n",
    "        print(f\"found {n_frames} files\")\n",
    "        print(f\"read grid shape: {x_size} x {y_size}\")    \n",
    "    return frames_3d\n",
    "\n",
    "def read_config(config_path, verbose=False):\n",
    "    \"\"\" returns map {object number (int): color name (str)}\"\"\"\n",
    "    with open(config_path, mode='r') as config_file:\n",
    "        csv_reader = csv.DictReader(config_file, delimiter=',')\n",
    "        nr_color_map = {0: 'black'}\n",
    "        color_label_map = {'black': 'empty'}\n",
    "        for row in csv_reader:\n",
    "            nr_color_map[int(row['number'])] = row['color']\n",
    "            color_label_map[row['color']] = row['label']\n",
    "    if verbose:\n",
    "        print(\"--read_config\")\n",
    "        print(f\"color - object map: {color_label_map}\")\n",
    "        print(f\"number - color map: {nr_color_map}\")\n",
    "    return nr_color_map\n",
    "\n",
    "def grids_to_frames(grids, color_map):\n",
    "    \"\"\" transform collection of 3d grids () into 2d frames\"\"\"\n",
    "    objects, signals = to_objects_and_signals(grids)\n",
    "    objects_rgb = objects_to_colors(objects, color_map)\n",
    "    signals_rgb = signals_to_colors(signals)\n",
    "    return objects_rgb + signals_rgb\n",
    "\n",
    "def to_objects_and_signals(grids):\n",
    "    \"\"\" :param grids: collection of 3d grids, shape: (n_frames, x_size, y_size, 9)\n",
    "        :returns: \n",
    "            objects: np.array of shape  (n_frames, x_size, y_size)\n",
    "            signals: np.array of shape  (n_frames, x_size, y_size) \n",
    "            (sum of signals in all direction for a given cell)\n",
    "     \"\"\"\n",
    "    objects = grids[:, :, :, 0]\n",
    "    signals_3d = grids[:,:,:, 1:]\n",
    "    signals_summed = np.sum(signals_3d, axis=3)\n",
    "    signals = adjust_signals(signals_summed, objects)\n",
    "    return objects, signals\n",
    "\n",
    "def adjust_signals(signals, objects):\n",
    "    \"\"\" normalize signals to fit in [0, 1] range and zero them on objects' positions \"\"\"\n",
    "    signals = (signals - np.amin(signals))\n",
    "    signals = signals/np.amax(signals)\n",
    "    return np.where(objects == 0, signals, 0)\n",
    "    \n",
    "    \n",
    "def objects_to_colors(objects, color_map):\n",
    "    \"\"\" map each object to appropriate color based on given color_map\n",
    "        :param objects: np.array of shape (n_frames, x_size, y_size)\n",
    "        :param color_map: maps number representing an object to color\n",
    "        :returns np.array of shape (n_frames, x_size, y_size, 3)\n",
    "    \"\"\"\n",
    "    shape = objects.shape\n",
    "    objects_rgb = [get_object_color(obj, color_map) for obj in objects.flatten()]\n",
    "    return np.array(objects_rgb).reshape(*shape, -1)\n",
    "\n",
    "def get_object_color(obj, color_map):\n",
    "    \" :returns 3d tuple (r, g, b), with each value being a float between 0 and 1 \"\n",
    "    color_name = color_map[obj]\n",
    "    return colors.to_rgb(color_name)\n",
    "\n",
    "def signals_to_colors(signals):\n",
    "    \"\"\" map each signal to appropriate color in gray scale, where white - no signal, black - maximum signal\n",
    "        :param signals: np.array of shape  (n_frames, x_size, y_size), of values in range [0, 1]\n",
    "        :returns: np.array of shape  (n_frames, x_size, y_size, 3)\n",
    "    \"\"\"\n",
    "    shape = signals.shape\n",
    "    signals_rgb = [np.array(colors.to_rgb('white'))*signal for signal in signals.flatten()]\n",
    "    return np.array(signals_rgb).reshape(*shape, -1)\n",
    "\n",
    "def get_signal_color(signal):\n",
    "    \" :returns 3d ARRAY [r, g, b], with each value being a float between 0 and 1 \"\n",
    "    return np.array(colors.to_rgb('white'))*signal\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def join_workers_frames(workers):\n",
    "    joined_frames = None\n",
    "    x = 1\n",
    "    while (x,1) in workers.keys():\n",
    "        joined_row = workers[(x,1)]\n",
    "        y = 2\n",
    "        while (1,y) in workers.keys():\n",
    "            joined_row = np.concatenate((joined_row, workers[(x,y)]), axis=2)\n",
    "            y +=1\n",
    "        joined_frames = joined_row if joined_frames is None else np.concatenate((joined_frames, joined_row), axis=1)\n",
    "        x += 1\n",
    "    return joined_frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "found workers: dict_keys([(1, 1), (2, 1), (1, 2), (2, 2)])\n",
      "created 26 frames of size (22, 22)\n"
     ]
    }
   ],
   "source": [
    "workers = read_workers(grids_dir)\n",
    "print(f\"found workers: {workers.keys()}\")\n",
    "frames = join_workers_frames(workers)\n",
    "(n_frames, x, y, _colors) = frames.shape\n",
    "print(f\"created {n_frames} frames of size {(x,y)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create and save animation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
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       "        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",
       "    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=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ims = []\n",
    "for frame in frames:\n",
    "    im = ax.imshow(frame, animated=True)\n",
    "    ims.append([im])\n",
    "\n",
    "ani = animation.ArtistAnimation(fig, ims, interval=500, blit=True,\n",
    "                                repeat=False)\n",
    "\n",
    "ani.save(\"movie.mp4\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mock frames - skip unless testing\n",
    "frames_3d = [\n",
    "    [\n",
    "        [[1,2, 2],[3,4, 4]],\n",
    "        [[5,6, 6], [7,8, 8]]\n",
    "    ], \n",
    "    \n",
    "    [\n",
    "        [[10,20, 20],[30,40, 40]],\n",
    "        [[50,60, 60], [70,80, 80]]\n",
    "    ], \n",
    "            ]\n",
    "frames_3d = np.array(frames_3d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[[0.        , 0.        , 1.        ],\n",
       "         [0.66666667, 0.66666667, 0.66666667]],\n",
       "\n",
       "        [[1.        , 0.        , 0.        ],\n",
       "         [1.        , 1.        , 1.        ]]],\n",
       "\n",
       "\n",
       "       [[[0.        , 0.        , 1.        ],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       "\n",
       "        [[1.        , 1.        , 1.        ],\n",
       "         [1.        , 0.        , 0.        ]]],\n",
       "\n",
       "\n",
       "       [[[0.        , 0.        , 1.        ],\n",
       "         [1.        , 0.        , 0.        ]],\n",
       "\n",
       "        [[0.26666667, 0.26666667, 0.26666667],\n",
       "         [0.4       , 0.4       , 0.4       ]]],\n",
       "\n",
       "\n",
       "       [[[0.        , 0.        , 1.        ],\n",
       "         [1.        , 0.        , 0.        ]],\n",
       "\n",
       "        [[1.        , 1.        , 1.        ],\n",
       "         [0.        , 0.        , 1.        ]]]])"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "objects = [\n",
    "    [[1,0],[2,0]],\n",
    "    [[1,0],[0,2]],\n",
    "    [[1,2],[0,0]],\n",
    "    [[1,2],[0,1]],\n",
    "]\n",
    "objects = np.array(objects)\n",
    "signals = [\n",
    "    [[10,10],\n",
    "     [10,20]],\n",
    "    [[15,-10],\n",
    "     [20,20]],\n",
    "    [[10,20],\n",
    "     [-2,2]],\n",
    "    [[20,2],\n",
    "     [20,10]],\n",
    "]\n",
    "signals = np.array(signals)\n",
    "# TODO read color map\n",
    "color_map = {0: 'black', 1: 'blue', 2: 'red'}\n",
    "\n",
    "def normalize_signals(signals, objects):\n",
    "    signals = (signals - np.amin(signals))\n",
    "    signals = signals/np.amax(signals)\n",
    "    return np.where(objects == 0, signals, 0)\n",
    "    \n",
    "def to_objects_and_signals(frames_3d):\n",
    "    objects = frames_3d[:, :, :, 0]\n",
    "    signals_3d = frames_3d[:,:,:, 1:]\n",
    "    signals_sum = np.sum(signals_3d, axis=3)\n",
    "    signals_norm = normalize_signals(signals_sum, objects)\n",
    "    return objects, signals_norm\n",
    "\n",
    "def get_object_color(x, color_map):\n",
    "    color_name = color_map[x]\n",
    "    return colors.to_rgb(color_name)\n",
    "    \n",
    "def objects_to_colors(objects, color_map):\n",
    "    shape = objects.shape\n",
    "    objects_rgb = [get_object_color(obj, color_map) for obj in objects.flatten()]\n",
    "    return np.array(objects_rgb).reshape(*shape, -1)\n",
    "\n",
    "def signals_to_colors(signals):\n",
    "    shape = signals.shape\n",
    "    signals_rgb = [np.array(colors.to_rgb('white'))*signal for signal in signals.flatten()]\n",
    "    return np.array(signals_rgb).reshape(*shape, -1)\n",
    "\n",
    "def grids_to_frames(grids):\n",
    "    objects, signals = to_objects_and_signals(grids)\n",
    "    objects_rgb = objects_to_colors(objects, color_map)\n",
    "    signals_rgb = signals_to_colors(signals)\n",
    "    objects_rgb+signals_rgb\n",
    "    \n",
    "# def combine(objects, signals):\n",
    "#     (frames, rows, columns, _colors_dim) = objects.shape\n",
    "#     combined = objects\n",
    "#     for frame in range(frames):\n",
    "#         for row in range(rows):\n",
    "#             for col in range(columns):\n",
    "#                 if not np.any(objects[frame,row, col]):\n",
    "#                     combined[frame,row,col] = signals[frame,row,col]\n",
    "#     combined\n",
    "        \n",
    "#     return np.where()\n",
    "    \n",
    "o = objects_to_colors(objects, color_map)\n",
    "signals = normalize_signals(signals,objects)\n",
    "s = signals_to_colors(signals)\n",
    "s+o"
   ]
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}