{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ipyvolume as ipv\n",
    "import numpy as np\n",
    "from matplotlib.pyplot import cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# download the dataset\n",
    "!wget -q https://www.dropbox.com/s/eqht79b7j4jqit2/petct.npz?dl=1 -O petct.npz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We show a CT scan and overlay the PET scan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_scan = {k: v.swapaxes(0, 1)[::-1] for k,v in np.load('petct.npz').items()}\n",
    "print(list(full_scan.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "table_ct = cm.gray_r(np.linspace(0, 1, 255))\n",
    "table_ct[:50, 3] = 0 # make the lower values transparent\n",
    "table_ct[50:, 3] = np.linspace(0, 0.05, table_ct[50:].shape[0])\n",
    "tf_ct = ipv.TransferFunction(rgba=table_ct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "ct_vol = ipv.quickvolshow(full_scan['ct_data'], \n",
    "                          tf=tf_ct, lighting=False, \n",
    "                          data_min=-1000, data_max=1000)\n",
    "ct_vol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Zoom\n",
    "Zoom in by clicking the magnifying icon, or keep the alt/option key pressed. After zooming in, the higher resolution verion cutout will be displayed. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multivolume rendering\n",
    "Since version 0.5, ipyvolume supports multivolume rendering, so we can render two volumetric datasets at the same time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "table_pet = cm.hot(np.linspace(0, 1, 255))\n",
    "table_pet[:50, 3] = 0 # make the lower values transparent\n",
    "table_pet[50:, 3] = np.linspace(0, 1, table_pet[50:].shape[0])\n",
    "tf_pet = ipv.TransferFunction(rgba=table_pet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pet_vol = ipv.volshow(full_scan['pet_data'], \n",
    "            tf=tf_pet, \n",
    "            data_min=0, \n",
    "            data_max=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pet_vol.rendering_method='MAX_INTENSITY'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "table_lab = np.array([\n",
    "    [0,0,0,0],\n",
    "    [0,1,0,1]\n",
    "])\n",
    "tf_lab = ipv.TransferFunction(rgba=table_lab)\n",
    "lab_vol = ipv.volshow(full_scan['label_data']>0, \n",
    "            tf=tf_lab, \n",
    "            data_min=0, \n",
    "            data_max=1)"
   ]
  }
 ],
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