{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "639611d7",
   "metadata": {},
   "source": [
    "# 2nd notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "b009fd9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting git+https://github.com/nndt-team/nndt.git\n",
      "  Cloning https://github.com/nndt-team/nndt.git to /tmp/pip-req-build-vxhlf_29\n",
      "  Running command git clone -q https://github.com/nndt-team/nndt.git /tmp/pip-req-build-vxhlf_29\n",
      "  Resolved https://github.com/nndt-team/nndt.git to commit c4850b38a5d82ce379a145e17fcb6a547af2c5f1\n",
      "Requirement already satisfied: anytree>=2.8.0 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from NNDT==0.0.3b1) (2.8.0)\n",
      "Requirement already satisfied: colorama>=0.4.5 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from NNDT==0.0.3b1) (0.4.5)\n",
      "Requirement already satisfied: dm-haiku>=0.0.7 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from NNDT==0.0.3b1) (0.0.8)\n",
      "Requirement already satisfied: jax>=0.3.5 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from NNDT==0.0.3b1) (0.3.17)\n",
      "Requirement already satisfied: jaxlib>=0.3.5 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from NNDT==0.0.3b1) (0.3.15)\n",
      "Requirement already satisfied: optax>=0.1.3 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from NNDT==0.0.3b1) (0.1.3)\n",
      "Requirement already satisfied: matplotlib>=3.3.0 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from NNDT==0.0.3b1) (3.5.1)\n",
      "Requirement already satisfied: numpy>=1.20 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from NNDT==0.0.3b1) (1.22.4)\n",
      "Requirement already satisfied: scikit-image>=0.13 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from NNDT==0.0.3b1) (0.19.2)\n",
      "Requirement already satisfied: vtk>=8.2 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from NNDT==0.0.3b1) (9.1.0)\n",
      "Requirement already satisfied: tqdm>=4.0.0 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from NNDT==0.0.3b1) (4.64.0)\n",
      "Requirement already satisfied: pyvista>=0.36.1 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from NNDT==0.0.3b1) (0.37.0)\n",
      "Requirement already satisfied: pykdtree>=1.3.5 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from NNDT==0.0.3b1) (1.3.6)\n",
      "Requirement already satisfied: requests>=2.28.1 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from NNDT==0.0.3b1) (2.28.1)\n",
      "Requirement already satisfied: gdown>=4.5.3 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from NNDT==0.0.3b1) (4.5.3)\n",
      "Requirement already satisfied: py7zr>=0.6 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from NNDT==0.0.3b1) (0.20.2)\n",
      "Requirement already satisfied: six>=1.9.0 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from anytree>=2.8.0->NNDT==0.0.3b1) (1.16.0)\n",
      "Requirement already satisfied: absl-py>=0.7.1 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from dm-haiku>=0.0.7->NNDT==0.0.3b1) (1.2.0)\n",
      "Requirement already satisfied: tabulate>=0.8.9 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from dm-haiku>=0.0.7->NNDT==0.0.3b1) (0.8.9)\n",
      "Requirement already satisfied: jmp>=0.0.2 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from dm-haiku>=0.0.7->NNDT==0.0.3b1) (0.0.2)\n",
      "Requirement already satisfied: beautifulsoup4 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from gdown>=4.5.3->NNDT==0.0.3b1) (4.11.1)\n",
      "Requirement already satisfied: filelock in /home/kostanew/anaconda3/lib/python3.9/site-packages (from gdown>=4.5.3->NNDT==0.0.3b1) (3.6.0)\n",
      "Requirement already satisfied: opt-einsum in /home/kostanew/anaconda3/lib/python3.9/site-packages (from jax>=0.3.5->NNDT==0.0.3b1) (3.3.0)\n",
      "Requirement already satisfied: etils[epath] in /home/kostanew/anaconda3/lib/python3.9/site-packages (from jax>=0.3.5->NNDT==0.0.3b1) (0.8.0)\n",
      "Requirement already satisfied: scipy>=1.5 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from jax>=0.3.5->NNDT==0.0.3b1) (1.7.3)\n",
      "Requirement already satisfied: typing-extensions in /home/kostanew/anaconda3/lib/python3.9/site-packages (from jax>=0.3.5->NNDT==0.0.3b1) (4.1.1)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from matplotlib>=3.3.0->NNDT==0.0.3b1) (0.11.0)\n",
      "Requirement already satisfied: packaging>=20.0 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from matplotlib>=3.3.0->NNDT==0.0.3b1) (21.3)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from matplotlib>=3.3.0->NNDT==0.0.3b1) (9.0.1)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from matplotlib>=3.3.0->NNDT==0.0.3b1) (1.3.2)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from matplotlib>=3.3.0->NNDT==0.0.3b1) (3.0.4)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from matplotlib>=3.3.0->NNDT==0.0.3b1) (4.25.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from matplotlib>=3.3.0->NNDT==0.0.3b1) (2.8.2)\n",
      "Requirement already satisfied: chex>=0.0.4 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from optax>=0.1.3->NNDT==0.0.3b1) (0.1.5)\n",
      "Requirement already satisfied: toolz>=0.9.0 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from chex>=0.0.4->optax>=0.1.3->NNDT==0.0.3b1) (0.11.2)\n",
      "Requirement already satisfied: dm-tree>=0.1.5 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from chex>=0.0.4->optax>=0.1.3->NNDT==0.0.3b1) (0.1.7)\n",
      "Requirement already satisfied: multivolumefile>=0.2.3 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from py7zr>=0.6->NNDT==0.0.3b1) (0.2.3)\n",
      "Requirement already satisfied: brotli>=1.0.9 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from py7zr>=0.6->NNDT==0.0.3b1) (1.0.9)\n",
      "Requirement already satisfied: pyzstd>=0.14.4 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from py7zr>=0.6->NNDT==0.0.3b1) (0.15.3)\n",
      "Requirement already satisfied: pyppmd<1.1.0,>=0.18.1 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from py7zr>=0.6->NNDT==0.0.3b1) (1.0.0)\n",
      "Requirement already satisfied: pybcj>=0.6.0 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from py7zr>=0.6->NNDT==0.0.3b1) (1.0.1)\n",
      "Requirement already satisfied: inflate64>=0.3.1 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from py7zr>=0.6->NNDT==0.0.3b1) (0.3.1)\n",
      "Requirement already satisfied: pycryptodomex>=3.6.6 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from py7zr>=0.6->NNDT==0.0.3b1) (3.15.0)\n",
      "Requirement already satisfied: texttable in /home/kostanew/anaconda3/lib/python3.9/site-packages (from py7zr>=0.6->NNDT==0.0.3b1) (1.6.4)\n",
      "Requirement already satisfied: psutil in /home/kostanew/anaconda3/lib/python3.9/site-packages (from py7zr>=0.6->NNDT==0.0.3b1) (5.8.0)\n",
      "Requirement already satisfied: pooch in /home/kostanew/anaconda3/lib/python3.9/site-packages (from pyvista>=0.36.1->NNDT==0.0.3b1) (1.6.0)\n",
      "Requirement already satisfied: scooby>=0.5.1 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from pyvista>=0.36.1->NNDT==0.0.3b1) (0.7.0)\n",
      "Requirement already satisfied: imageio in /home/kostanew/anaconda3/lib/python3.9/site-packages (from pyvista>=0.36.1->NNDT==0.0.3b1) (2.9.0)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from requests>=2.28.1->NNDT==0.0.3b1) (1.26.9)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from requests>=2.28.1->NNDT==0.0.3b1) (3.3)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from requests>=2.28.1->NNDT==0.0.3b1) (2021.10.8)\n",
      "Requirement already satisfied: charset-normalizer<3,>=2 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from requests>=2.28.1->NNDT==0.0.3b1) (2.0.4)\n",
      "Requirement already satisfied: tifffile>=2019.7.26 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from scikit-image>=0.13->NNDT==0.0.3b1) (2021.7.2)\n",
      "Requirement already satisfied: PyWavelets>=1.1.1 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from scikit-image>=0.13->NNDT==0.0.3b1) (1.3.0)\n",
      "Requirement already satisfied: networkx>=2.2 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from scikit-image>=0.13->NNDT==0.0.3b1) (2.7.1)\n",
      "Requirement already satisfied: wslink>=1.0.4 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from vtk>=8.2->NNDT==0.0.3b1) (1.8.2)\n",
      "Requirement already satisfied: aiohttp<4 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from wslink>=1.0.4->vtk>=8.2->NNDT==0.0.3b1) (3.8.1)\n",
      "Requirement already satisfied: multidict<7.0,>=4.5 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from aiohttp<4->wslink>=1.0.4->vtk>=8.2->NNDT==0.0.3b1) (5.2.0)\n",
      "Requirement already satisfied: attrs>=17.3.0 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from aiohttp<4->wslink>=1.0.4->vtk>=8.2->NNDT==0.0.3b1) (21.4.0)\n",
      "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from aiohttp<4->wslink>=1.0.4->vtk>=8.2->NNDT==0.0.3b1) (4.0.1)\n",
      "Requirement already satisfied: aiosignal>=1.1.2 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from aiohttp<4->wslink>=1.0.4->vtk>=8.2->NNDT==0.0.3b1) (1.2.0)\n",
      "Requirement already satisfied: yarl<2.0,>=1.0 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from aiohttp<4->wslink>=1.0.4->vtk>=8.2->NNDT==0.0.3b1) (1.6.3)\n",
      "Requirement already satisfied: frozenlist>=1.1.1 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from aiohttp<4->wslink>=1.0.4->vtk>=8.2->NNDT==0.0.3b1) (1.2.0)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: soupsieve>1.2 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from beautifulsoup4->gdown>=4.5.3->NNDT==0.0.3b1) (2.3.1)\n",
      "Requirement already satisfied: zipp in /home/kostanew/anaconda3/lib/python3.9/site-packages (from etils[epath]->jax>=0.3.5->NNDT==0.0.3b1) (3.7.0)\n",
      "Requirement already satisfied: importlib_resources in /home/kostanew/anaconda3/lib/python3.9/site-packages (from etils[epath]->jax>=0.3.5->NNDT==0.0.3b1) (5.9.0)\n",
      "Requirement already satisfied: appdirs>=1.3.0 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from pooch->pyvista>=0.36.1->NNDT==0.0.3b1) (1.4.4)\n",
      "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from requests>=2.28.1->NNDT==0.0.3b1) (1.7.1)\n",
      "Building wheels for collected packages: NNDT\n",
      "  Building wheel for NNDT (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for NNDT: filename=NNDT-0.0.3b1-py3-none-any.whl size=49113 sha256=bcd7d7d129401444fd214b6cbb8982858e19319aab9f346bdeb3be0f9c58a875\n",
      "  Stored in directory: /tmp/pip-ephem-wheel-cache-609gvw8d/wheels/48/e6/b0/eed564ae0a50dab01b1cf11f639b92028e56be7ec1205789dc\n",
      "Successfully built NNDT\n",
      "Installing collected packages: NNDT\n",
      "  Attempting uninstall: NNDT\n",
      "    Found existing installation: NNDT 0.0.3a3\n",
      "    Can't uninstall 'NNDT'. No files were found to uninstall.\n",
      "Successfully installed NNDT-0.0.3b1\n",
      "Note: you may need to restart the kernel to use updated packages.\n",
      "Requirement already satisfied: ipyvtklink in /home/kostanew/anaconda3/lib/python3.9/site-packages (0.2.3)\n",
      "Requirement already satisfied: ipywidgets~=7.7 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from ipyvtklink) (7.7.2)\n",
      "Requirement already satisfied: ipyevents>=0.8.0 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from ipyvtklink) (2.0.1)\n",
      "Requirement already satisfied: ipycanvas>=0.5.0 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from ipyvtklink) (0.13.1)\n",
      "Requirement already satisfied: pillow>=6.0 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from ipycanvas>=0.5.0->ipyvtklink) (9.0.1)\n",
      "Requirement already satisfied: numpy in /home/kostanew/anaconda3/lib/python3.9/site-packages (from ipycanvas>=0.5.0->ipyvtklink) (1.22.4)\n",
      "Requirement already satisfied: jupyterlab-widgets<3,>=1.0.0 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from ipywidgets~=7.7->ipyvtklink) (1.0.0)\n",
      "Requirement already satisfied: widgetsnbextension~=3.6.0 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from ipywidgets~=7.7->ipyvtklink) (3.6.1)\n",
      "Requirement already satisfied: ipython>=4.0.0 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from ipywidgets~=7.7->ipyvtklink) (8.2.0)\n",
      "Requirement already satisfied: ipykernel>=4.5.1 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from ipywidgets~=7.7->ipyvtklink) (6.9.1)\n",
      "Requirement already satisfied: ipython-genutils~=0.2.0 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from ipywidgets~=7.7->ipyvtklink) (0.2.0)\n",
      "Requirement already satisfied: traitlets>=4.3.1 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from ipywidgets~=7.7->ipyvtklink) (5.1.1)\n",
      "Requirement already satisfied: nest-asyncio in /home/kostanew/anaconda3/lib/python3.9/site-packages (from ipykernel>=4.5.1->ipywidgets~=7.7->ipyvtklink) (1.5.5)\n",
      "Requirement already satisfied: debugpy<2.0,>=1.0.0 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from ipykernel>=4.5.1->ipywidgets~=7.7->ipyvtklink) (1.5.1)\n",
      "Requirement already satisfied: matplotlib-inline<0.2.0,>=0.1.0 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from ipykernel>=4.5.1->ipywidgets~=7.7->ipyvtklink) (0.1.2)\n",
      "Requirement already satisfied: tornado<7.0,>=4.2 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from ipykernel>=4.5.1->ipywidgets~=7.7->ipyvtklink) (6.1)\n",
      "Requirement already satisfied: jupyter-client<8.0 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from ipykernel>=4.5.1->ipywidgets~=7.7->ipyvtklink) (6.1.12)\n",
      "Requirement already satisfied: jedi>=0.16 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from ipython>=4.0.0->ipywidgets~=7.7->ipyvtklink) (0.18.1)\n",
      "Requirement already satisfied: stack-data in /home/kostanew/anaconda3/lib/python3.9/site-packages (from ipython>=4.0.0->ipywidgets~=7.7->ipyvtklink) (0.2.0)\n",
      "Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from ipython>=4.0.0->ipywidgets~=7.7->ipyvtklink) (3.0.20)\n",
      "Requirement already satisfied: pexpect>4.3 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from ipython>=4.0.0->ipywidgets~=7.7->ipyvtklink) (4.8.0)\n",
      "Requirement already satisfied: decorator in /home/kostanew/anaconda3/lib/python3.9/site-packages (from ipython>=4.0.0->ipywidgets~=7.7->ipyvtklink) (5.1.1)\n",
      "Requirement already satisfied: pygments>=2.4.0 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from ipython>=4.0.0->ipywidgets~=7.7->ipyvtklink) (2.11.2)\n",
      "Requirement already satisfied: pickleshare in /home/kostanew/anaconda3/lib/python3.9/site-packages (from ipython>=4.0.0->ipywidgets~=7.7->ipyvtklink) (0.7.5)\n",
      "Requirement already satisfied: backcall in /home/kostanew/anaconda3/lib/python3.9/site-packages (from ipython>=4.0.0->ipywidgets~=7.7->ipyvtklink) (0.2.0)\n",
      "Requirement already satisfied: setuptools>=18.5 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from ipython>=4.0.0->ipywidgets~=7.7->ipyvtklink) (61.2.0)\n",
      "Requirement already satisfied: parso<0.9.0,>=0.8.0 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from jedi>=0.16->ipython>=4.0.0->ipywidgets~=7.7->ipyvtklink) (0.8.3)\n",
      "Requirement already satisfied: pyzmq>=13 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from jupyter-client<8.0->ipykernel>=4.5.1->ipywidgets~=7.7->ipyvtklink) (22.3.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from jupyter-client<8.0->ipykernel>=4.5.1->ipywidgets~=7.7->ipyvtklink) (2.8.2)\n",
      "Requirement already satisfied: jupyter-core>=4.6.0 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from jupyter-client<8.0->ipykernel>=4.5.1->ipywidgets~=7.7->ipyvtklink) (4.9.2)\n",
      "Requirement already satisfied: ptyprocess>=0.5 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from pexpect>4.3->ipython>=4.0.0->ipywidgets~=7.7->ipyvtklink) (0.7.0)\n",
      "Requirement already satisfied: wcwidth in /home/kostanew/anaconda3/lib/python3.9/site-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython>=4.0.0->ipywidgets~=7.7->ipyvtklink) (0.2.5)\n",
      "Requirement already satisfied: six>=1.5 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from python-dateutil>=2.1->jupyter-client<8.0->ipykernel>=4.5.1->ipywidgets~=7.7->ipyvtklink) (1.16.0)\n",
      "Requirement already satisfied: notebook>=4.4.1 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from widgetsnbextension~=3.6.0->ipywidgets~=7.7->ipyvtklink) (6.4.8)\n",
      "Requirement already satisfied: Send2Trash>=1.8.0 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets~=7.7->ipyvtklink) (1.8.0)\n",
      "Requirement already satisfied: nbconvert in /home/kostanew/anaconda3/lib/python3.9/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets~=7.7->ipyvtklink) (6.4.4)\n",
      "Requirement already satisfied: jinja2 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets~=7.7->ipyvtklink) (2.11.3)\n",
      "Requirement already satisfied: nbformat in /home/kostanew/anaconda3/lib/python3.9/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets~=7.7->ipyvtklink) (5.3.0)\n",
      "Requirement already satisfied: prometheus-client in /home/kostanew/anaconda3/lib/python3.9/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets~=7.7->ipyvtklink) (0.13.1)\n",
      "Requirement already satisfied: argon2-cffi in /home/kostanew/anaconda3/lib/python3.9/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets~=7.7->ipyvtklink) (21.3.0)\n",
      "Requirement already satisfied: terminado>=0.8.3 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets~=7.7->ipyvtklink) (0.13.1)\n",
      "Requirement already satisfied: argon2-cffi-bindings in /home/kostanew/anaconda3/lib/python3.9/site-packages (from argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets~=7.7->ipyvtklink) (21.2.0)\n",
      "Requirement already satisfied: cffi>=1.0.1 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets~=7.7->ipyvtklink) (1.15.0)\n",
      "Requirement already satisfied: pycparser in /home/kostanew/anaconda3/lib/python3.9/site-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets~=7.7->ipyvtklink) (2.21)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: MarkupSafe>=0.23 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from jinja2->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets~=7.7->ipyvtklink) (2.0.1)\n",
      "Requirement already satisfied: testpath in /home/kostanew/anaconda3/lib/python3.9/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets~=7.7->ipyvtklink) (0.5.0)\n",
      "Requirement already satisfied: bleach in /home/kostanew/anaconda3/lib/python3.9/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets~=7.7->ipyvtklink) (4.1.0)\n",
      "Requirement already satisfied: entrypoints>=0.2.2 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets~=7.7->ipyvtklink) (0.4)\n",
      "Requirement already satisfied: mistune<2,>=0.8.1 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets~=7.7->ipyvtklink) (0.8.4)\n",
      "Requirement already satisfied: pandocfilters>=1.4.1 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets~=7.7->ipyvtklink) (1.5.0)\n",
      "Requirement already satisfied: defusedxml in /home/kostanew/anaconda3/lib/python3.9/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets~=7.7->ipyvtklink) (0.7.1)\n",
      "Requirement already satisfied: nbclient<0.6.0,>=0.5.0 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets~=7.7->ipyvtklink) (0.5.13)\n",
      "Requirement already satisfied: beautifulsoup4 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets~=7.7->ipyvtklink) (4.11.1)\n",
      "Requirement already satisfied: jupyterlab-pygments in /home/kostanew/anaconda3/lib/python3.9/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets~=7.7->ipyvtklink) (0.1.2)\n",
      "Requirement already satisfied: jsonschema>=2.6 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from nbformat->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets~=7.7->ipyvtklink) (4.4.0)\n",
      "Requirement already satisfied: fastjsonschema in /home/kostanew/anaconda3/lib/python3.9/site-packages (from nbformat->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets~=7.7->ipyvtklink) (2.15.1)\n",
      "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from jsonschema>=2.6->nbformat->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets~=7.7->ipyvtklink) (0.18.0)\n",
      "Requirement already satisfied: attrs>=17.4.0 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from jsonschema>=2.6->nbformat->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets~=7.7->ipyvtklink) (21.4.0)\n",
      "Requirement already satisfied: soupsieve>1.2 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from beautifulsoup4->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets~=7.7->ipyvtklink) (2.3.1)\n",
      "Requirement already satisfied: packaging in /home/kostanew/anaconda3/lib/python3.9/site-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets~=7.7->ipyvtklink) (21.3)\n",
      "Requirement already satisfied: webencodings in /home/kostanew/anaconda3/lib/python3.9/site-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets~=7.7->ipyvtklink) (0.5.1)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /home/kostanew/anaconda3/lib/python3.9/site-packages (from packaging->bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets~=7.7->ipyvtklink) (3.0.4)\n",
      "Requirement already satisfied: pure-eval in /home/kostanew/anaconda3/lib/python3.9/site-packages (from stack-data->ipython>=4.0.0->ipywidgets~=7.7->ipyvtklink) (0.2.2)\n",
      "Requirement already satisfied: asttokens in /home/kostanew/anaconda3/lib/python3.9/site-packages (from stack-data->ipython>=4.0.0->ipywidgets~=7.7->ipyvtklink) (2.0.5)\n",
      "Requirement already satisfied: executing in /home/kostanew/anaconda3/lib/python3.9/site-packages (from stack-data->ipython>=4.0.0->ipywidgets~=7.7->ipyvtklink) (0.8.3)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install git+https://github.com/nndt-team/nndt.git\n",
    "%pip install ipyvtklink"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f9af7179",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.0.3b1'"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nndt\n",
    "import nndt.space2 as spc\n",
    "from nndt.datasets import ACDC\n",
    "\n",
    "nndt.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d4c64b81",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyvista as pv\n",
    "import jax.numpy as jnp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6befcaba",
   "metadata": {},
   "source": [
    "`nndt.init_colab()` turns on interactive plots in google collaboratory. This string is not necessary in py-scripts or jupyter notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "520bab5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# nndt.init_colab()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3170e4fe",
   "metadata": {},
   "source": [
    "Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "849d7681",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading...\n",
      "From: https://drive.google.com/uc?export=download&id=1UzC2WPkjMQSxzI5sj1rMT47URuZbQhYb\n",
      "To: /home/kostanew/PycharmProjects/nndt/tutorials/.datasets/ACDC_5/temp.7z\n",
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 9.01M/9.01M [00:01<00:00, 4.74MB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading complete\n"
     ]
    }
   ],
   "source": [
    "ACDC().load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "5fa32acf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mS:space\u001b[90m 0.0.3b1\u001b[39m\n",
      "├── \u001b[32mO3D:patient009\u001b[37m ((-0.85, -0.81, -0.87), (0.85, 0.81, 0.87))\u001b[39m\n",
      "│   ├── \u001b[32mFS:colored_obj\u001b[37m mesh_obj^ ./.datasets/ACDC_5/patient009/colored.obj\u001b[39m\n",
      "│   ├── \u001b[32mFS:sdf_npy\u001b[37m sdt^ ./.datasets/ACDC_5/patient009/sdf.npy\u001b[39m\n",
      "│   ├── \u001b[31mTR:transform\u001b[37m shift_and_scale\u001b[39m\n",
      "│   ├── \u001b[33mMS:mesh\u001b[39m\n",
      "│   ├── \u001b[33mMS:mesh_colors\u001b[39m\n",
      "│   ├── \u001b[33mMS:sampling\u001b[39m\n",
      "│   ├── \u001b[33mMS:sdt\u001b[39m\n",
      "│   ├── \u001b[33mMS:train_task\u001b[39m\n",
      "│   ├── \u001b[39mplot(default, filepath=None)\u001b[39m\n",
      "│   ├── \u001b[39mprint(default|source|full)\u001b[39m\n",
      "│   ├── \u001b[39msampling_eachN_from_mesh(count=N, step=M, shift=K) -> (ns_ind[N], ns_xyz[N])\u001b[39m\n",
      "│   ├── \u001b[39msampling_grid(spacing=(D,H,W)) -> ns_xyz[D,H,W,3]\u001b[39m\n",
      "│   ├── \u001b[39msampling_grid_with_noise(key, spacing=(D,H,W), sigma) -> ns_xyz[N,3]\u001b[39m\n",
      "│   ├── \u001b[39msampling_uniform(key, N) -> ns_xyz[N,3]\u001b[39m\n",
      "│   ├── \u001b[39msave_mesh(filepath, {name, array})\u001b[39m\n",
      "│   ├── \u001b[39msurface_ind2rgba(ind[..,1]) -> rgba[..,4]\u001b[39m\n",
      "│   ├── \u001b[39msurface_ind2xyz(ind[..,1]) -> ns_xyz[..,3]\u001b[39m\n",
      "│   ├── \u001b[39msurface_rgba() -> xyz[N,4]\u001b[39m\n",
      "│   ├── \u001b[39msurface_xyz() -> xyz[N,3]\u001b[39m\n",
      "│   ├── \u001b[39msurface_xyz2ind(ns_xyz[..,3]) -> ns_dist[..,1], ns_ind[..,1]\u001b[39m\n",
      "│   ├── \u001b[39msurface_xyz2localsdt(ns_xyz[3], spacing=(D,H,W), scale=1.) -> ns_xyz[D,H,W,3], ns_localsdt[D,H,W,1]\u001b[39m\n",
      "│   ├── \u001b[39msurface_xyz2rgba(ns_xyz[..,3]) -> rgba[..,4]\u001b[39m\n",
      "│   ├── \u001b[39msurface_xyz2sdt(ns_xyz[..,3]) -> ns_sdt[..,1]\u001b[39m\n",
      "│   ├── \u001b[39mtrain_task_sdt2sdf(filename, **kwargs)\u001b[39m\n",
      "│   ├── \u001b[39mtransform_sdt_ns2ps(ns_sdt[..]) -> ps_sdt[..]\u001b[39m\n",
      "│   ├── \u001b[39mtransform_sdt_ps2ns(ps_sdt[..]) -> ns_sdt[..]\u001b[39m\n",
      "│   ├── \u001b[39mtransform_xyz_ns2ps(ns_xyz[..,3]) -> ps_xyz[..,3]\u001b[39m\n",
      "│   ├── \u001b[39mtransform_xyz_ps2ns(ps_xyz[..,3]) -> ns_xyz[..,3]\u001b[39m\n",
      "│   └── \u001b[39munload_from_memory()\u001b[39m\n",
      "├── \u001b[32mO3D:patient029\u001b[37m ((-0.85, -0.88, -0.93), (0.85, 0.88, 0.93))\u001b[39m\n",
      "│   ├── \u001b[32mFS:colored_obj\u001b[37m mesh_obj^ ./.datasets/ACDC_5/patient029/colored.obj\u001b[39m\n",
      "│   ├── \u001b[32mFS:sdf_npy\u001b[37m sdt^ ./.datasets/ACDC_5/patient029/sdf.npy\u001b[39m\n",
      "│   ├── \u001b[31mTR:transform\u001b[37m shift_and_scale\u001b[39m\n",
      "│   ├── \u001b[33mMS:mesh\u001b[39m\n",
      "│   ├── \u001b[33mMS:mesh_colors\u001b[39m\n",
      "│   ├── \u001b[33mMS:sampling\u001b[39m\n",
      "│   ├── \u001b[33mMS:sdt\u001b[39m\n",
      "│   ├── \u001b[33mMS:train_task\u001b[39m\n",
      "│   ├── \u001b[39mplot(default, filepath=None)\u001b[39m\n",
      "│   ├── \u001b[39mprint(default|source|full)\u001b[39m\n",
      "│   ├── \u001b[39msampling_eachN_from_mesh(count=N, step=M, shift=K) -> (ns_ind[N], ns_xyz[N])\u001b[39m\n",
      "│   ├── \u001b[39msampling_grid(spacing=(D,H,W)) -> ns_xyz[D,H,W,3]\u001b[39m\n",
      "│   ├── \u001b[39msampling_grid_with_noise(key, spacing=(D,H,W), sigma) -> ns_xyz[N,3]\u001b[39m\n",
      "│   ├── \u001b[39msampling_uniform(key, N) -> ns_xyz[N,3]\u001b[39m\n",
      "│   ├── \u001b[39msave_mesh(filepath, {name, array})\u001b[39m\n",
      "│   ├── \u001b[39msurface_ind2rgba(ind[..,1]) -> rgba[..,4]\u001b[39m\n",
      "│   ├── \u001b[39msurface_ind2xyz(ind[..,1]) -> ns_xyz[..,3]\u001b[39m\n",
      "│   ├── \u001b[39msurface_rgba() -> xyz[N,4]\u001b[39m\n",
      "│   ├── \u001b[39msurface_xyz() -> xyz[N,3]\u001b[39m\n",
      "│   ├── \u001b[39msurface_xyz2ind(ns_xyz[..,3]) -> ns_dist[..,1], ns_ind[..,1]\u001b[39m\n",
      "│   ├── \u001b[39msurface_xyz2localsdt(ns_xyz[3], spacing=(D,H,W), scale=1.) -> ns_xyz[D,H,W,3], ns_localsdt[D,H,W,1]\u001b[39m\n",
      "│   ├── \u001b[39msurface_xyz2rgba(ns_xyz[..,3]) -> rgba[..,4]\u001b[39m\n",
      "│   ├── \u001b[39msurface_xyz2sdt(ns_xyz[..,3]) -> ns_sdt[..,1]\u001b[39m\n",
      "│   ├── \u001b[39mtrain_task_sdt2sdf(filename, **kwargs)\u001b[39m\n",
      "│   ├── \u001b[39mtransform_sdt_ns2ps(ns_sdt[..]) -> ps_sdt[..]\u001b[39m\n",
      "│   ├── \u001b[39mtransform_sdt_ps2ns(ps_sdt[..]) -> ns_sdt[..]\u001b[39m\n",
      "│   ├── \u001b[39mtransform_xyz_ns2ps(ns_xyz[..,3]) -> ps_xyz[..,3]\u001b[39m\n",
      "│   ├── \u001b[39mtransform_xyz_ps2ns(ps_xyz[..,3]) -> ns_xyz[..,3]\u001b[39m\n",
      "│   └── \u001b[39munload_from_memory()\u001b[39m\n",
      "├── \u001b[32mO3D:patient049\u001b[37m ((-0.86, -0.83, -0.60), (0.86, 0.83, 0.60))\u001b[39m\n",
      "│   ├── \u001b[32mFS:colored_obj\u001b[37m mesh_obj^ ./.datasets/ACDC_5/patient049/colored.obj\u001b[39m\n",
      "│   ├── \u001b[32mFS:sdf_npy\u001b[37m sdt^ ./.datasets/ACDC_5/patient049/sdf.npy\u001b[39m\n",
      "│   ├── \u001b[31mTR:transform\u001b[37m shift_and_scale\u001b[39m\n",
      "│   ├── \u001b[33mMS:mesh\u001b[39m\n",
      "│   ├── \u001b[33mMS:mesh_colors\u001b[39m\n",
      "│   ├── \u001b[33mMS:sampling\u001b[39m\n",
      "│   ├── \u001b[33mMS:sdt\u001b[39m\n",
      "│   ├── \u001b[33mMS:train_task\u001b[39m\n",
      "│   ├── \u001b[39mplot(default, filepath=None)\u001b[39m\n",
      "│   ├── \u001b[39mprint(default|source|full)\u001b[39m\n",
      "│   ├── \u001b[39msampling_eachN_from_mesh(count=N, step=M, shift=K) -> (ns_ind[N], ns_xyz[N])\u001b[39m\n",
      "│   ├── \u001b[39msampling_grid(spacing=(D,H,W)) -> ns_xyz[D,H,W,3]\u001b[39m\n",
      "│   ├── \u001b[39msampling_grid_with_noise(key, spacing=(D,H,W), sigma) -> ns_xyz[N,3]\u001b[39m\n",
      "│   ├── \u001b[39msampling_uniform(key, N) -> ns_xyz[N,3]\u001b[39m\n",
      "│   ├── \u001b[39msave_mesh(filepath, {name, array})\u001b[39m\n",
      "│   ├── \u001b[39msurface_ind2rgba(ind[..,1]) -> rgba[..,4]\u001b[39m\n",
      "│   ├── \u001b[39msurface_ind2xyz(ind[..,1]) -> ns_xyz[..,3]\u001b[39m\n",
      "│   ├── \u001b[39msurface_rgba() -> xyz[N,4]\u001b[39m\n",
      "│   ├── \u001b[39msurface_xyz() -> xyz[N,3]\u001b[39m\n",
      "│   ├── \u001b[39msurface_xyz2ind(ns_xyz[..,3]) -> ns_dist[..,1], ns_ind[..,1]\u001b[39m\n",
      "│   ├── \u001b[39msurface_xyz2localsdt(ns_xyz[3], spacing=(D,H,W), scale=1.) -> ns_xyz[D,H,W,3], ns_localsdt[D,H,W,1]\u001b[39m\n",
      "│   ├── \u001b[39msurface_xyz2rgba(ns_xyz[..,3]) -> rgba[..,4]\u001b[39m\n",
      "│   ├── \u001b[39msurface_xyz2sdt(ns_xyz[..,3]) -> ns_sdt[..,1]\u001b[39m\n",
      "│   ├── \u001b[39mtrain_task_sdt2sdf(filename, **kwargs)\u001b[39m\n",
      "│   ├── \u001b[39mtransform_sdt_ns2ps(ns_sdt[..]) -> ps_sdt[..]\u001b[39m\n",
      "│   ├── \u001b[39mtransform_sdt_ps2ns(ps_sdt[..]) -> ns_sdt[..]\u001b[39m\n",
      "│   ├── \u001b[39mtransform_xyz_ns2ps(ns_xyz[..,3]) -> ps_xyz[..,3]\u001b[39m\n",
      "│   ├── \u001b[39mtransform_xyz_ps2ns(ps_xyz[..,3]) -> ns_xyz[..,3]\u001b[39m\n",
      "│   └── \u001b[39munload_from_memory()\u001b[39m\n",
      "├── \u001b[32mO3D:patient069\u001b[37m ((-0.68, -0.58, -0.58), (0.68, 0.58, 0.58))\u001b[39m\n",
      "│   ├── \u001b[32mFS:colored_obj\u001b[37m mesh_obj^ ./.datasets/ACDC_5/patient069/colored.obj\u001b[39m\n",
      "│   ├── \u001b[32mFS:sdf_npy\u001b[37m sdt^ ./.datasets/ACDC_5/patient069/sdf.npy\u001b[39m\n",
      "│   ├── \u001b[31mTR:transform\u001b[37m shift_and_scale\u001b[39m\n",
      "│   ├── \u001b[33mMS:mesh\u001b[39m\n",
      "│   ├── \u001b[33mMS:mesh_colors\u001b[39m\n",
      "│   ├── \u001b[33mMS:sampling\u001b[39m\n",
      "│   ├── \u001b[33mMS:sdt\u001b[39m\n",
      "│   ├── \u001b[33mMS:train_task\u001b[39m\n",
      "│   ├── \u001b[39mplot(default, filepath=None)\u001b[39m\n",
      "│   ├── \u001b[39mprint(default|source|full)\u001b[39m\n",
      "│   ├── \u001b[39msampling_eachN_from_mesh(count=N, step=M, shift=K) -> (ns_ind[N], ns_xyz[N])\u001b[39m\n",
      "│   ├── \u001b[39msampling_grid(spacing=(D,H,W)) -> ns_xyz[D,H,W,3]\u001b[39m\n",
      "│   ├── \u001b[39msampling_grid_with_noise(key, spacing=(D,H,W), sigma) -> ns_xyz[N,3]\u001b[39m\n",
      "│   ├── \u001b[39msampling_uniform(key, N) -> ns_xyz[N,3]\u001b[39m\n",
      "│   ├── \u001b[39msave_mesh(filepath, {name, array})\u001b[39m\n",
      "│   ├── \u001b[39msurface_ind2rgba(ind[..,1]) -> rgba[..,4]\u001b[39m\n",
      "│   ├── \u001b[39msurface_ind2xyz(ind[..,1]) -> ns_xyz[..,3]\u001b[39m\n",
      "│   ├── \u001b[39msurface_rgba() -> xyz[N,4]\u001b[39m\n",
      "│   ├── \u001b[39msurface_xyz() -> xyz[N,3]\u001b[39m\n",
      "│   ├── \u001b[39msurface_xyz2ind(ns_xyz[..,3]) -> ns_dist[..,1], ns_ind[..,1]\u001b[39m\n",
      "│   ├── \u001b[39msurface_xyz2localsdt(ns_xyz[3], spacing=(D,H,W), scale=1.) -> ns_xyz[D,H,W,3], ns_localsdt[D,H,W,1]\u001b[39m\n",
      "│   ├── \u001b[39msurface_xyz2rgba(ns_xyz[..,3]) -> rgba[..,4]\u001b[39m\n",
      "│   ├── \u001b[39msurface_xyz2sdt(ns_xyz[..,3]) -> ns_sdt[..,1]\u001b[39m\n",
      "│   ├── \u001b[39mtrain_task_sdt2sdf(filename, **kwargs)\u001b[39m\n",
      "│   ├── \u001b[39mtransform_sdt_ns2ps(ns_sdt[..]) -> ps_sdt[..]\u001b[39m\n",
      "│   ├── \u001b[39mtransform_sdt_ps2ns(ps_sdt[..]) -> ns_sdt[..]\u001b[39m\n",
      "│   ├── \u001b[39mtransform_xyz_ns2ps(ns_xyz[..,3]) -> ps_xyz[..,3]\u001b[39m\n",
      "│   ├── \u001b[39mtransform_xyz_ps2ns(ps_xyz[..,3]) -> ns_xyz[..,3]\u001b[39m\n",
      "│   └── \u001b[39munload_from_memory()\u001b[39m\n",
      "├── \u001b[32mO3D:patient089\u001b[37m ((-0.83, -0.72, -0.55), (0.83, 0.72, 0.55))\u001b[39m\n",
      "│   ├── \u001b[32mFS:colored_obj\u001b[37m mesh_obj^ ./.datasets/ACDC_5/patient089/colored.obj\u001b[39m\n",
      "│   ├── \u001b[32mFS:sdf_npy\u001b[37m sdt^ ./.datasets/ACDC_5/patient089/sdf.npy\u001b[39m\n",
      "│   ├── \u001b[31mTR:transform\u001b[37m shift_and_scale\u001b[39m\n",
      "│   ├── \u001b[33mMS:mesh\u001b[39m\n",
      "│   ├── \u001b[33mMS:mesh_colors\u001b[39m\n",
      "│   ├── \u001b[33mMS:sampling\u001b[39m\n",
      "│   ├── \u001b[33mMS:sdt\u001b[39m\n",
      "│   ├── \u001b[33mMS:train_task\u001b[39m\n",
      "│   ├── \u001b[39mplot(default, filepath=None)\u001b[39m\n",
      "│   ├── \u001b[39mprint(default|source|full)\u001b[39m\n",
      "│   ├── \u001b[39msampling_eachN_from_mesh(count=N, step=M, shift=K) -> (ns_ind[N], ns_xyz[N])\u001b[39m\n",
      "│   ├── \u001b[39msampling_grid(spacing=(D,H,W)) -> ns_xyz[D,H,W,3]\u001b[39m\n",
      "│   ├── \u001b[39msampling_grid_with_noise(key, spacing=(D,H,W), sigma) -> ns_xyz[N,3]\u001b[39m\n",
      "│   ├── \u001b[39msampling_uniform(key, N) -> ns_xyz[N,3]\u001b[39m\n",
      "│   ├── \u001b[39msave_mesh(filepath, {name, array})\u001b[39m\n",
      "│   ├── \u001b[39msurface_ind2rgba(ind[..,1]) -> rgba[..,4]\u001b[39m\n",
      "│   ├── \u001b[39msurface_ind2xyz(ind[..,1]) -> ns_xyz[..,3]\u001b[39m\n",
      "│   ├── \u001b[39msurface_rgba() -> xyz[N,4]\u001b[39m\n",
      "│   ├── \u001b[39msurface_xyz() -> xyz[N,3]\u001b[39m\n",
      "│   ├── \u001b[39msurface_xyz2ind(ns_xyz[..,3]) -> ns_dist[..,1], ns_ind[..,1]\u001b[39m\n",
      "│   ├── \u001b[39msurface_xyz2localsdt(ns_xyz[3], spacing=(D,H,W), scale=1.) -> ns_xyz[D,H,W,3], ns_localsdt[D,H,W,1]\u001b[39m\n",
      "│   ├── \u001b[39msurface_xyz2rgba(ns_xyz[..,3]) -> rgba[..,4]\u001b[39m\n",
      "│   ├── \u001b[39msurface_xyz2sdt(ns_xyz[..,3]) -> ns_sdt[..,1]\u001b[39m\n",
      "│   ├── \u001b[39mtrain_task_sdt2sdf(filename, **kwargs)\u001b[39m\n",
      "│   ├── \u001b[39mtransform_sdt_ns2ps(ns_sdt[..]) -> ps_sdt[..]\u001b[39m\n",
      "│   ├── \u001b[39mtransform_sdt_ps2ns(ps_sdt[..]) -> ns_sdt[..]\u001b[39m\n",
      "│   ├── \u001b[39mtransform_xyz_ns2ps(ns_xyz[..,3]) -> ps_xyz[..,3]\u001b[39m\n",
      "│   ├── \u001b[39mtransform_xyz_ps2ns(ps_xyz[..,3]) -> ns_xyz[..,3]\u001b[39m\n",
      "│   └── \u001b[39munload_from_memory()\u001b[39m\n",
      "├── \u001b[33mMS:sampling\u001b[39m\n",
      "├── \u001b[39minit()\u001b[39m\n",
      "├── \u001b[39mplot(default, filepath=None)\u001b[39m\n",
      "├── \u001b[39mpreload(identity|shift_and_scale|to_cube, scale, keep_in_memory=True)\u001b[39m\n",
      "├── \u001b[39mprint(default|source|full)\u001b[39m\n",
      "├── \u001b[39msampling_grid(spacing=(D,H,W)) -> ns_xyz[D,H,W,3]\u001b[39m\n",
      "├── \u001b[39msampling_grid_with_noise(key, spacing=(D,H,W), sigma) -> ns_xyz[N,3]\u001b[39m\n",
      "├── \u001b[39msampling_uniform(key, N) -> ns_xyz[N,3]\u001b[39m\n",
      "├── \u001b[39msave_space_to_file(filepath)\u001b[39m\n",
      "├── \u001b[39mto_json()\u001b[39m\n",
      "└── \u001b[39munload_from_memory()\u001b[39m\n"
     ]
    }
   ],
   "source": [
    "space = spc.load_from_path(\"./.datasets/ACDC_5\")\n",
    "space.preload(\"shift_and_scale\", ns_padding=(0.1, 0.1, 0.1))\n",
    "print(space.print())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13afc0fc",
   "metadata": {},
   "source": [
    "Examples for `save_3D_slices` with data arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "0d86eafa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((32, 32, 32, 3), (32, 32, 32))"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cube_xyz = space.patient009.sampling_grid((32, 32, 32))\n",
    "cube_std = space.patient009.surface_xyz2sdt(cube_xyz)\n",
    "cube_std = cube_std[:, :, :, 0]\n",
    "cube_xyz.shape, cube_std.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "db68e65f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 648x648 with 18 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nndt.save_3D_slices(cube_std, \"./viz1.png\", include_boundary=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "b7a2fdad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 648x648 with 18 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nndt.save_3D_slices(\n",
    "    cube_std, \"./viz1.png\", levels=(), level_colors=(), include_boundary=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea94fc5c",
   "metadata": {},
   "source": [
    "Examples for `save_3D_slices` with RGB/RGBA colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "420c0107",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32, 32, 32, 4)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cube_rgba = space.patient009.surface_xyz2rgba(cube_xyz)\n",
    "cube_rgba = cube_rgba\n",
    "cube_rgba.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "92ce2db4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x648 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nndt.save_3D_slices(cube_rgba, include_boundary=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93433d94",
   "metadata": {},
   "source": [
    "Examples for `save_sdt_as_obj`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "589a7ab1",
   "metadata": {},
   "outputs": [],
   "source": [
    "nndt.save_sdt_as_obj(cube_std, \"./model.obj\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "8db18509",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8e845b25655d42769f44f7449b337e50",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "ViewInteractiveWidget(height=768, layout=Layout(height='auto', width='100%'), width=1024)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mesh = pv.read(\"./model.obj\")\n",
    "cpos = mesh.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b06b8107",
   "metadata": {},
   "source": [
    "Load data and put values into `.vtp` output using `.save_mesh()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "619ce77f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2502, 2502, 2502)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vertex = space.patient009.surface_xyz()\n",
    "vertex_rgba = space.patient009.surface_rgba()\n",
    "vertex_from_full_sampling = space.patient009.sampling_eachN_from_mesh(len(vertex), 1, 0)\n",
    "len(vertex), len(vertex_rgba), len(vertex_from_full_sampling[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "4d11557f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DeviceArray(True, dtype=bool)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jnp.allclose(vertex, vertex_from_full_sampling[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a0b1998b",
   "metadata": {},
   "outputs": [],
   "source": [
    "space.patient009.save_mesh(\n",
    "    \"./output.vtp\",\n",
    "    {\n",
    "        \"red\": vertex_rgba[:, 0],\n",
    "        \"green\": vertex_rgba[:, 1],\n",
    "        \"blue\": vertex_rgba[:, 2],\n",
    "        \"alpha\": vertex_rgba[:, 2],\n",
    "        \"x\": vertex[:, 0],\n",
    "        \"y\": vertex[:, 1],\n",
    "        \"z\": vertex[:, 2],\n",
    "        \"target_class\": jnp.argmax(vertex_rgba[:, :3], axis=1),\n",
    "    },\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e1d108e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "447fc705ae5341b2bd4754a918bfe4bf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "ViewInteractiveWidget(height=768, layout=Layout(height='auto', width='100%'), width=1024)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mesh = pv.read(\"./output.vtp\")\n",
    "cpos = mesh.plot(scalars=\"target_class\", show_edges=True)"
   ]
  }
 ],
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