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   "source": [
    "# Graviton SDK: Astrophysics & N-Body Supercomputing\n",
    "This notebook demonstrates how to run astrophysical N-body simulations using **REBOUND** and **Mercury6** on Graviton's high-density CPU Turing nodes.\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/CodeZeroLabs/graviton-examples/blob/master/graviton_sdk_astrophys_example.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Install the Graviton SDK\n",
    "!pip install graviton-sdk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import graviton as gv\n",
    "import tempfile\n",
    "import os\n",
    "\n",
    "# 2. Authenticate with Graviton\n",
    "gv.login(\"a-little-more-art-than-science\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Running a REBOUND Simulation\n",
    "REBOUND simulations use a unified JSON structure specifying simulation duration, step resolution, and orbital bodies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rebound_payload = {\n",
    "    \"t\": \"rebound\",\n",
    "    \"sim_time\": 365.25,\n",
    "    \"time_step\": 0.1,\n",
    "    \"bodies\": [\n",
    "        {\"name\": \"Sun\", \"mass\": 1.0, \"x\": 0.0, \"y\": 0.0, \"z\": 0.0},\n",
    "        {\"name\": \"Earth\", \"mass\": 3.0e-6, \"a\": 1.0, \"e\": 0.0167},\n",
    "        {\"name\": \"Mars\", \"mass\": 3.2e-7, \"a\": 1.524, \"e\": 0.0934}\n",
    "    ]\n",
    "}\n",
    "\n",
    "# Evaluate complexity and estimated credits\n",
    "eval_data = gv.evaluate(rebound_payload)\n",
    "print(f\"Job ID: {eval_data.id}\")\n",
    "print(f\"Estimated Credit Cost: {eval_data.cost:.2f}\")\n",
    "print(f\"Estimated Duration: {eval_data.estimated_time:.2f}s\")\n",
    "\n",
    "# Trigger computation on Turing swarm\n",
    "job = gv.compute(eval_data.id)\n",
    "print(f\"Job status: {job.status()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Scraping and Executing Legacy Mercury6 Datasets\n",
    "The Graviton SDK includes `gv.load_mercury(directory)` to parse legacy Fortran `param.in`, `big.in`, and `small.in` parameter files without manual conversion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create sample Mercury directory for demonstration\n",
    "sample_dir = tempfile.mkdtemp(prefix=\"mercury_demo_\")\n",
    "\n",
    "param_content = \"\"\"\n",
    ")O+_06 Integration parameters\n",
    " start time (days) = 0.0\n",
    " stop time (days) = 365.25\n",
    " timestep (days) = 1.0\n",
    "\"\"\"\n",
    "\n",
    "big_content = \"\"\"\n",
    ")O+_06 Big bodies\n",
    " style = Cartesian\n",
    " Central_Star m=1.0 r=0.005 d=1.0\n",
    " 0.0 0.0 0.0\n",
    " 0.0 0.0 0.0\n",
    " 0.0 0.0 0.0\n",
    " Planet_A m=0.001 r=0.001 d=1.0\n",
    " 1.0 0.0 0.0\n",
    " 0.0 6.28 0.0\n",
    " 0.0 0.0 0.0\n",
    "\"\"\"\n",
    "\n",
    "with open(os.path.join(sample_dir, \"param.in\"), \"w\") as f:\n",
    "    f.write(param_content)\n",
    "\n",
    "with open(os.path.join(sample_dir, \"big.in\"), \"w\") as f:\n",
    "    f.write(big_content)\n",
    "\n",
    "# Automatically load and transform the dataset\n",
    "mercury_payload = gv.load_mercury(sample_dir)\n",
    "print(\"Transformed Payload:\")\n",
    "print(mercury_payload)\n",
    "\n",
    "# Evaluate and compute\n",
    "mercury_eval = gv.evaluate(mercury_payload)\n",
    "print(f\"Mercury Job ID: {mercury_eval.id}\")\n",
    "print(f\"Cost: {mercury_eval.cost:.2f} credits\")\n",
    "\n",
    "mercury_job = gv.compute(mercury_eval.id)\n",
    "print(f\"Mercury Job provisioned: {mercury_job.job_id}\")"
   ]
  }
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