{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "33089189",
   "metadata": {},
   "source": [
    "# O$_2$scl table example for O$_2$sclpy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86cf38a6",
   "metadata": {},
   "source": [
    "See the O$_2$sclpy documentation at\n",
    "https://awsteiner.org/code/o2sclpy for more information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "aedb63ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "import o2sclpy\n",
    "import matplotlib.pyplot as plot\n",
    "import sys\n",
    "\n",
    "plots=True\n",
    "if 'pytest' in sys.modules:\n",
    "    plots=False"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b98d904b",
   "metadata": {},
   "source": [
    "Link the o2scl library:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c227db17",
   "metadata": {},
   "outputs": [],
   "source": [
    "link=o2sclpy.linker()\n",
    "link.link_o2scl()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee88f274",
   "metadata": {},
   "source": [
    "Create an HDF5 file object and open the table in O$_2$scl's data file for the Akmal, Pandharipande, and Ravenhall equation of state. The `open()` function for the `hdf_file` class is documented [here](https://awsteiner.org/code/o2sclpy/hdf.html#o2sclpy.hdf_file.open)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f02ca232",
   "metadata": {},
   "outputs": [],
   "source": [
    "hf=o2sclpy.hdf_file(link)\n",
    "hf.open(link.o2scl_settings.get_data_dir()+b'apr98.o2')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e81cc1de",
   "metadata": {},
   "source": [
    "We create a table object and specify a blank name to indicate\n",
    "that we just want to read the first table in the file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f8e78e5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "tab=o2sclpy.table(link)\n",
    "name=b''"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adecc7e1",
   "metadata": {},
   "source": [
    "Read the table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c32513ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "o2sclpy.hdf_input_table(link,hf,tab,name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be47f760",
   "metadata": {},
   "source": [
    "Close the HDF5 file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "34bb8dae",
   "metadata": {},
   "outputs": [],
   "source": [
    "hf.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32243a8b",
   "metadata": {},
   "source": [
    "We use the `cap_cout` class to capture `std::cout` to the Jupyter notebook. The `summary()` function lists the columns in the table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "14c56c8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Contents of std::cout:\n",
      "0 constants:\n",
      "3 columns: \n",
      "0. rho  1. nuc  2. neut \n",
      "13 lines of data.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cc=o2sclpy.cap_cout()\n",
    "cc.open()\n",
    "tab.summary()\n",
    "cc.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "400717b4",
   "metadata": {},
   "source": [
    "Finally, we use `matplotlib` to plot the data stored in the table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "06dd7459",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if plots:\n",
    "    plot.plot(tab['rho'],tab['nuc'])\n",
    "    plot.plot(tab['rho'],tab['neut'])\n",
    "    plot.show()"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "-all",
   "main_language": "python",
   "notebook_metadata_filter": "-all"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}