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   "source": [
    "# Elementary Cellular Automata [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/maxencefaldor/cax/blob/main/examples/10_elementary.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Installation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You will need Python 3.12 or later, and a working JAX installation. For example, you can install JAX with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install -U \"jax[cuda]\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, install CAX from PyPi:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install -U \"cax[examples]\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import jax.numpy as jnp\n",
    "import mediapy\n",
    "from flax import nnx\n",
    "\n",
    "from cax.cs.elementary import Elementary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 0\n",
    "\n",
    "num_steps = 512\n",
    "spatial_dims = (1_024,)\n",
    "wolfram_code_int = 110  # Rule 110\n",
    "\n",
    "rngs = nnx.Rngs(seed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instantiate system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "wolfram_code = Elementary.wolfram_code_from_rule_number(wolfram_code_int)\n",
    "wolfram_code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cs = Elementary(wolfram_code=wolfram_code, rngs=rngs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sample initial state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sample_state():\n",
    "\t\"\"\"Sample a state with a single active cell.\"\"\"\n",
    "\tstate = jnp.zeros((*spatial_dims, 1))\n",
    "\treturn state.at[spatial_dims[0] // 2].set(1.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_init = sample_state()\n",
    "state_final = cs(state_init, num_steps=num_steps, sow=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "intermediates = nnx.pop(cs, nnx.Intermediate)\n",
    "states = intermediates.state[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "states = jnp.concatenate([state_init[None], states])\n",
    "frame = cs.render(states)\n",
    "\n",
    "mediapy.show_image(frame)"
   ]
  }
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