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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [],
   "source": [
    "!pip install matplotlib pandas pyarrow seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams[\"font.family\"] = \"sans\"\n",
    "plt.rcParams[\"font.size\"] = 8\n",
    "sns.set_palette('muted')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Shot Metadata\n",
    "\n",
    "This notebook contains a demonstration of plotting several of the summary statistics that accompany the shot metadata. \n",
    "\n",
    "Firstly, we're going to load all the shot data into a pandas dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "summary = pd.read_parquet('https://mastapp.site/parquet/level2/shots')\n",
    "summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary Statistics About Shots\n",
    "\n",
    "Let's look at a summary of simple counts of different shot metadata."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "fig, axes = plt.subplots(2, 2, figsize=(10, 5))\n",
    "ax1, ax2, ax3, ax4 = axes.flatten()\n",
    "\n",
    "sns.histplot(summary, y='heating', hue='campaign', multiple=\"stack\", ax=ax1)\n",
    "sns.histplot(summary, y='plasma_shape', hue='campaign', multiple=\"stack\", ax=ax2)\n",
    "sns.histplot(summary, y='current_range', hue='campaign', multiple=\"stack\", ax=ax3)\n",
    "sns.histplot(summary, y=summary.pellets.astype(str), hue='campaign', multiple=\"stack\", ax=ax4)\n",
    "\n",
    "for ax in axes.flatten():\n",
    "    ax.set_xlabel('No. Shots')\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plasma Beta ($\\beta$) v.s Confinement Time ($\\tau_E$)\n",
    "\n",
    "This plot can show how the efficiency of energy confinement varies with plasma pressure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "sns.scatterplot(summary, y='cpf_tautot_max', x='cpf_betmhd_max', hue='heating')\n",
    "plt.xlim(0, 18)\n",
    "plt.ylim(0, 1)\n",
    "plt.ylabel('Confinement time $\\\\tau_E$ (s)')\n",
    "plt.xlabel('Plasma Beta $\\\\beta$ (%)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plasma Temperature ($T_e$) vs. Plasma Density ($n_e$)\n",
    "This can reveal the relationship between temperature and density, which is critical for achieving the conditions necessary for fusion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "sns.scatterplot(summary, y='cpf_te0_ipmax', x='cpf_ne0_ipmax', hue='current_range', alpha=0.8)\n",
    "plt.xlim(0, .8e20)\n",
    "plt.ylim(0, 1750)\n",
    "plt.ylabel('Temperature $T_e$ (eV)')\n",
    "plt.xlabel('Density $n_e$ ($m^{-3}$)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plasma Current ($I_p$) vs. Confinement Time ($\\tau_E$)\n",
    "\n",
    "This can indicate how the plasma current affects the confinement time, providing insights into stability and performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "sns.scatterplot(summary, y='cpf_ip_av', x='cpf_tautot_max', hue='current_range', alpha=0.8)\n",
    "plt.xlim(0, 1)\n",
    "plt.xlabel('Confinement Time $\\\\tau_E$ (s)')\n",
    "plt.ylabel('Average Plasma Current  $I_p$ (kA)')\n",
    "plt.show()"
   ]
  }
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