{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Slice animation in a MRI volumetric data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A volumetric data set read from a  tif image is  sliced, and succesive  slices are displayed by Plotly animation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'4.2.0'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import plotly\n",
    "plotly.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from skimage import io\n",
    "import plotly.graph_objects as go"
   ]
  },
  {
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "volume.shape"
   ]
  },
  {
   "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": [
    "r, c = volume[0].shape\n",
    "n_slices = volume.shape[0]\n",
    "height = (volume.shape[0]-1) / 10\n",
    "grid = np.linspace(0, height, n_slices)\n",
    "slice_step = grid[1] - grid[0]\n",
    "height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_slice = go.Surface(\n",
    "                     z=height*np.ones((r,c)),\n",
    "                     surfacecolor=np.flipud(volume[-1]),\n",
    "                     colorscale=pl_bone,\n",
    "                     showscale=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "frames = [go.Frame(data=[dict(type='surface',\n",
    "                          z=(height-k*slice_step)*np.ones((r,c)),\n",
    "                          surfacecolor=np.flipud(volume[-1-k]))],\n",
    "                          name=f'frame{k+1}') for k in range(1, n_slices)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sliders = [dict(steps = [dict(method= 'animate',\n",
    "                              args= [[f'frame{k+1}'],                           \n",
    "                              dict(mode= 'immediate',\n",
    "                                   frame= dict(duration=40, redraw= True),\n",
    "                                   transition=dict(duration= 0))\n",
    "                                 ],\n",
    "                              label=f'{k+1}'\n",
    "                             ) for k in range(n_slices)], \n",
    "                active=17,\n",
    "                transition= dict(duration= 0 ),\n",
    "                x=0, # slider starting position  \n",
    "                y=0, \n",
    "                currentvalue=dict(font=dict(size=12), \n",
    "                                  prefix='slice: ', \n",
    "                                  visible=True, \n",
    "                                  xanchor= 'center'\n",
    "                                 ),  \n",
    "               len=1.0) #slider length\n",
    "           ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "layout3d = dict(title_text='Head Scanning', title_x=0.5,\n",
    "                width=600,\n",
    "                height=600,\n",
    "                scene_zaxis_range= [-0.1, 6.8],\n",
    "                sliders=sliders,\n",
    "            )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = go.Figure(data=[initial_slice], layout=layout3d, frames=frames)\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode,  iplot\n",
    "init_notebook_mode(connected=True)\n",
    "iplot(fig) #wait a few seconds  until the plot is displayed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Moving the slider to right or left we can inspect each slice."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The gif file created from the above animation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src='head-scanning.gif'>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%html\n",
    "<img src='head-scanning.gif'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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