{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Animating slices  of a human head  MRI scan, using Plotly FigureWidget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go\n",
    "import numpy as np\n",
    "from skimage import io"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the volume data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "volume = (io.imread(\"https://s3.amazonaws.com/assets.datacamp.com/blog_assets/attention-mri.tif\")).T\n",
    "volume.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_slices = volume.shape[0]\n",
    "r, c = volume[0].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the height of the first slice to be visualized:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "height = (volume.shape[0]-1) / 10\n",
    "grid = np.linspace(0, height, n_slices)\n",
    "slice_step = grid[1] - grid[0]\n",
    "slice_step"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The normalized slice values are color-mapped to the bone colorscale (the usual colorscale for medical images)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pl_bone=[[0.0, 'rgb(0, 0, 0)'],\n",
    "         [0.05, 'rgb(10, 10, 14)'],\n",
    "         [0.1, 'rgb(21, 21, 30)'],\n",
    "         [0.15, 'rgb(33, 33, 46)'],\n",
    "         [0.2, 'rgb(44, 44, 62)'],\n",
    "         [0.25, 'rgb(56, 55, 77)'],\n",
    "         [0.3, 'rgb(66, 66, 92)'],\n",
    "         [0.35, 'rgb(77, 77, 108)'],\n",
    "         [0.4, 'rgb(89, 92, 121)'],\n",
    "         [0.45, 'rgb(100, 107, 132)'],\n",
    "         [0.5, 'rgb(112, 123, 143)'],\n",
    "         [0.55, 'rgb(122, 137, 154)'],\n",
    "         [0.6, 'rgb(133, 153, 165)'],\n",
    "         [0.65, 'rgb(145, 169, 177)'],\n",
    "         [0.7, 'rgb(156, 184, 188)'],\n",
    "         [0.75, 'rgb(168, 199, 199)'],\n",
    "         [0.8, 'rgb(185, 210, 210)'],\n",
    "         [0.85, 'rgb(203, 221, 221)'],\n",
    "         [0.9, 'rgb(220, 233, 233)'],\n",
    "         [0.95, 'rgb(238, 244, 244)'],\n",
    "         [1.0, 'rgb(255, 255, 255)']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_slice = dict(type='surface',\n",
    "                     z=height*np.ones((r,c)),\n",
    "                     surfacecolor=np.flipud(volume[-1]),\n",
    "                     colorscale=pl_bone,\n",
    "                     showscale=False\n",
    "                     )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the plot layout:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "layout3d = dict(title='Head Scanning', \n",
    "                font=dict(family='Balto'),\n",
    "                width=650,\n",
    "                height=650,\n",
    "                scene=dict(zaxis=dict(range=[-0.1, 6.8], autorange=False)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fw = go.FigureWidget(data=[initial_slice], layout=layout3d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ipywidgets as iw\n",
    "slider = iw.IntSlider(value=0, min=0, \n",
    "                      max=n_slices-1, step=1, \n",
    "                      description='slice #')\n",
    "slider.layout = dict(margin='1px 80px 40px 5px', width='400px')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def slice_changed(change):\n",
    "    k=slider.value\n",
    "    fw.data[0].update(z=(height-slice_step*k)*np.ones((r,c)),\n",
    "                      surfacecolor=np.flipud(volume[-1-k]))\n",
    "        \n",
    "\n",
    "slider.observe(slice_changed, 'value')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#help(iw.Play)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "play_button = iw.Play(value=0, min=0, max=n_slices-1,\n",
    "                      step=1, interval=40)\n",
    "play_button.layout = dict(margin='1px 10px 50px 100px')\n",
    "iw.jslink((play_button, 'value'), (slider, 'value'))\n",
    "iw.VBox([fw,  iw.HBox([play_button, slider])])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Animation without slider (watch the above cell):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for k in range(n_slices):\n",
    "    with fw.batch_animate(duration=10, easing='cubic-in-out'):\n",
    "        fw.data[0].update(z=(height-k*slice_step)*np.ones((r,c)),\n",
    "                          surfacecolor=np.flipud(volume[-1-k]))\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that the latter animation (without slider) is faster."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a much faster animation  see this gif file created from the fig generated in the notebook  `scanning-the-head-classic.ipynb`:"
   ]
  }
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