{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<script async src=\"https://www.googletagmanager.com/gtag/js?id=UA-59152712-8\"></script>\n",
    "<script>\n",
    "  window.dataLayer = window.dataLayer || [];\n",
    "  function gtag(){dataLayer.push(arguments);}\n",
    "  gtag('js', new Date());\n",
    "\n",
    "  gtag('config', 'UA-59152712-8');\n",
    "</script>\n",
    "\n",
    "# Tutorial: Implementation of Single and Piecewise Polytropic EOSs\n",
    "\n",
    "## Author: Leo Werneck\n",
    "### Formatting improvements courtesy Brandon Clark\n",
    "\n",
    "## This notebook sets up functions to compute polytropic EOSs related quantities.\n",
    "\n",
    "**Notebook Status:** <font color='green'><b> Validated </b></font>\n",
    "\n",
    "**Validation Notes:** The functions from this notebook have been validated by being used to produce initial data that is shown to exhibit convergence to zero of the Hamiltonian constraint violation at the expected order to the exact solution (see [start-to-finish TOV notebook](Tutorial-Start_to_Finish-BSSNCurvilinear-Setting_up_TOV_initial_data.ipynb) for full test). Note that convergence at the surface of the star is lower order due to the sharp drop to zero in $T^{\\mu\\nu}$. We have also validated many results against other TOV solvers, namely one by [Joshua Faber](https://ccrg.rit.edu/~jfaber/BNSID/TOV/tov_solver.C) and one by [Joonas Nättilä](https://github.com/natj/tov). We have also reproduced portions of figure 3 in [Demorest *et al.* (2010)](https://www.nature.com/articles/nature09466) ([go to plot](#code_validation__mass_vs_radius)).\n",
    "\n",
    "### NRPy+ Source Code for this notebook: [TOV/Polytropic_EOSs.py](../edit/TOV/Polytropic_EOSs.py)\n",
    "\n",
    "[comment]: <> (Introduction: TODO)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='toc'></a>\n",
    "\n",
    "# Table of Contents\n",
    "$$\\label{toc}$$\n",
    "\n",
    "This notebook is organized as follows:\n",
    "\n",
    "0. [Step 0](#initializenrpy): **Initialize core Python/NRPy+ modules**\n",
    "1. [Step 1](#introduction): **Introduction**\n",
    "1. [Step 2](#continuity_of_pcold): **Continuity of $P_{\\rm cold}$**\n",
    "1. [Step 3](#p_poly_tab): **Computing $P_{j}$**\n",
    "1. [Step 4](#continuity_of_eps_cold): **Continuity of $\\epsilon_{\\rm cold}$**\n",
    "1. [Step 5](#eos_parameters_from_input): **Setting up EOS parameters from user input**\n",
    "    1. [Step 5.a](#eos_parameters_from_input__complete_input): *Complete set of input variables*\n",
    "    1. [Step 5.b](#eos_parameters_from_input__read_et_al_input): *\"Read et al.\" set of input variables*\n",
    "1. [Step 6](#pcold_from_rhob): **Computing $P_{\\rm cold}\\left(\\rho_{b}\\right)$**\n",
    "1. [Step 7](#rhob_and_eps_cold_from_pcold): **Computing $\\rho_{b}\\left(P_{\\rm cold}\\right)$ and $\\epsilon_{\\rm cold}\\left(\\rho_{b},P_{\\rm cold}\\right)$**\n",
    "1. [Step 8](#polytropic_index): **Determining the polytropic index**\n",
    "    1. [Step 8.a](#polytropic_index__from_rhob): *From $\\rho_{b}$*\n",
    "    1. [Step 8.b](#polytropic_index__from_pcold): *From $P_{\\rm cold}$*\n",
    "1. [Step 9](#illinoisgrmhd_parameter_file): **Generating an EOS parameter file for `IllinoisGRMHD`**\n",
    "1. [Step 10](#pcold_plot): **Visualizing $P_{\\rm cold}\\left(\\rho_{b}\\right)$**\n",
    "    1. [Step 10.a](#pcold_plot__neoseq4): *Complete input set, $n_{\\rm eos} = 4$ piecewise polytropic EOS*\n",
    "    1. [Step 10.b](#pcold_plot__neoseq7): *\"Read et al.\" input set, $n_{\\rm eos} = 7$ piecewise polytropic EOS*\n",
    "1. [Step 11](#code_validation): **Code Validation**\n",
    "    1. [Step 11.a](#code_validation__self): *Against the [TOV/Polytropic_EOSs.py](../edit/TOV/Polytropic_EOSs.py) NRPy+ module*\n",
    "    1. [Step 11.b](#code_validation__read_et_al): *Against [Read et al. (2008)](https://arxiv.org/pdf/0812.2163.pdf)*\n",
    "    1. [Step 11.c](#code_validation__joshua_faber_tov_solver): *Against [Joshua Faber's TOV solver](https://ccrg.rit.edu/~jfaber/BNSID/TOV/tov_solver.C)*\n",
    "        1. [Step 11.c.i](#code_validation__joshua_faber_tov_solver__results): Results from [Joshua Faber's TOV solver](https://ccrg.rit.edu/~jfaber/BNSID/TOV/tov_solver.C)\n",
    "        1. [Step 11.c.ii](#code_validation__joshua_faber_tov_solver__restoring_units): Restoring units to the solution of NRPy+'s TOV solver\n",
    "        1. [Step 11.c.iii](#code_validation__joshua_faber_tov_solver__validation): Validation of NRPy+'s results\n",
    "    1. [Step 11.d](#code_validation__mass_vs_radius): *Mass vs. Radius relations*\n",
    "1. [Step 12](#latex_pdf_output): **Output this module to $\\LaTeX$-formatted PDF**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='initializenrpy'></a>\n",
    "\n",
    "# Step 0: Initialize core Python/NRPy+ modules \\[Back to [top](#toc)\\]\n",
    "$$\\label{initializenrpy}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "nrpy_module_file_path = os.path.join(\"TOV\",\"Polytropic_EOSs.py\")\n",
    "validation_file_path  = os.path.join(\"TOV\",\"Polytropic_EOSs_validation.py\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing TOV/Polytropic_EOSs_validation.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile $validation_file_path\n",
    "## Polytropic EOSs Python Module\n",
    "## Author(s): Leo Werneck and Zach Etienne\n",
    "## In this NRPy+ module we set up useful \"lowlevel\" functions that compute\n",
    "## useful polytropic quantities.\n",
    "\n",
    "# Full documentation for this module may be found in the NRPy+ tutorial Jupyter notebook:\n",
    "#  Tutorial-TOV-Piecewise_Polytrope_EOSs.ipynb\n",
    "\n",
    "# Step 0: Import needed Python/NRPy+ modules\n",
    "import numpy as np                  # NumPy: A numerical methods module for Python\n",
    "import sys                          # This module is used for system related function calls\n",
    "from collections import namedtuple  # This module is used to create named tuples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='introduction'></a>\n",
    "\n",
    "# Step 1: Introduction \\[Back to [top](#toc)\\]\n",
    "$$\\label{introduction}$$\n",
    "\n",
    "In this tutorial notebook we set up many useful lowlevel functions related to polytropic EOSs. These range from functions used to compute basic EOS quantities such as the pressure as a function of baryonic density, $P_{\\rm cold}\\left(\\rho_{b}\\right)$, the baryonic density as a function of pressure, $\\rho_{b}\\left(P_{\\rm cold}\\right)$, and the specific internal energy as a function of energy density, $\\epsilon_{\\rm cold}\\left(\\rho_{b}\\right)$, to functions that are used to generate parameter files to be used by `IllinoisGRMHD`.\n",
    "\n",
    "We have taken special care to document the derivations of the expressions that are ultimately implemented. We have also validated our functions to great extent, first performing a self validation against the [Polytropic_EOSs NRPy+ notebook](#../edit/TOV/Polytropic_EOSs.py) and then validating against multiple external sources (see [Step 11](#code_validation) for more details).\n",
    "\n",
    "We also recommend the readers to look at [NRPy's TOV solver](#../edit/TOV/TOV_solver.py) \\[[**tutorial**](Tutorial-ADM_Initial_Data-TOV.ipynb)\\] and the [start-to-finish TOV initial data tutorial notebook](Tutorial-Start_to_Finish-BSSNCurvilinear-Setting_up_TOV_initial_data.ipynb), where we demonstrate that the initial data generated using the functions in this tutorial notebook exhibit convergence to zero of the Hamiltonian constraint violation at the expected order to the exact solution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='continuity_of_pcold'></a>\n",
    "\n",
    "# Step 2: Continuity of $P_{\\rm cold}$ \\[Back to [top](#toc)\\]\n",
    "$$\\label{continuity_of_pcold}$$\n",
    "\n",
    "Consider a piecewise polytrope EOS of the form\n",
    "\n",
    "$$\n",
    "\\boxed{\n",
    "P_{\\rm cold} =\n",
    "\\left\\{\n",
    "\\begin{matrix}\n",
    "K_{0}\\rho_{b}^{\\Gamma_{0}} & , & \\rho_{b} \\leq \\rho_{0}\\\\\n",
    "K_{1}\\rho_{b}^{\\Gamma_{1}} & , & \\rho_{0} \\leq \\rho_{b} \\leq \\rho_{1}\\\\\n",
    "\\vdots &  & \\vdots\\\\\n",
    "K_{j}\\rho_{b}^{\\Gamma_{j}} & , & \\rho_{j-1} \\leq \\rho_{b} \\leq \\rho_{j}\\\\\n",
    "\\vdots &  & \\vdots\\\\\n",
    "K_{N-2}\\rho_{b}^{\\Gamma_{N-2}} & , & \\rho_{N-3} \\leq \\rho_{b} \\leq \\rho_{N-2}\\\\\n",
    "K_{N-1}\\rho_{b}^{\\Gamma_{N-1}} & , & \\rho_{b} \\geq \\rho_{N-2}\n",
    "\\end{matrix}\n",
    "\\right.\n",
    "}\\ .\n",
    "$$\n",
    "\n",
    "The case of a single polytrope is given by the first EOS above, with no condition imposed on the value of $\\rho$, i.e.\n",
    "\n",
    "$$\n",
    "\\boxed{P_{\\rm cold} = K_{0}\\rho_{b}^{\\Gamma_{0}} = K\\rho_{b}^{\\Gamma}}\\ .\n",
    "$$\n",
    "\n",
    "Notice that we have the following sets of variables:\n",
    "\n",
    "$$\n",
    "\\left\\{\\underbrace{\\rho_{0},\\rho_{1},\\ldots,\\rho_{N-2}}_{N-1\\ {\\rm values}}\\right\\}\\ ;\\\n",
    "\\left\\{\\underbrace{K_{0},K_{1},\\ldots,K_{N-1}}_{N\\ {\\rm values}}\\right\\}\\ ;\\\n",
    "\\left\\{\\underbrace{\\Gamma_{0},\\Gamma_{1},\\ldots,\\Gamma_{N-1}}_{N\\ {\\rm values}}\\right\\}\\ .\n",
    "$$\n",
    "\n",
    "Also, notice that $K_{0}$ and the entire sets $\\left\\{\\rho_{0},\\rho_{1},\\ldots,\\rho_{N-1}\\right\\}$ and $\\left\\{\\Gamma_{0},\\Gamma_{1},\\ldots,\\Gamma_{N}\\right\\}$ must be specified by the user. The values of $\\left\\{K_{1},\\ldots,K_{N}\\right\\}$, on the other hand, are determined by imposing that $P_{\\rm cold}$ be continuous, i.e.\n",
    "\n",
    "$$\n",
    "P_{\\rm cold}\\left(\\rho_{0}\\right) = K_{0}\\rho_{0}^{\\Gamma_{0}} = K_{1}\\rho_{0}^{\\Gamma_{1}} \\implies\n",
    "\\boxed{K_{1} = K_{0}\\rho_{0}^{\\Gamma_{0}-\\Gamma_{1}}}\\ .\n",
    "$$\n",
    "\n",
    "Analogously,\n",
    "\n",
    "$$\n",
    "\\boxed{K_{j} = K_{j-1}\\rho_{j-1}^{\\Gamma_{j-1}-\\Gamma_{j}}\\ ,\\ j\\in\\left[1,N-1\\right]}\\ .\n",
    "$$\n",
    "\n",
    "Again, for the case of a single polytropic EOS, the set $\\left\\{\\rho_{j}\\right\\}$ is empty and $\\left\\{\\Gamma_{j},K_{j}\\right\\}\\to \\left\\{\\Gamma,K\\right\\}$.\n",
    "\n",
    "Below we implement a function to set up $\\left\\{K_{j}\\right\\}$ for both single and piecewise polytropic EOSs, based on the last boxed equation above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Appending to TOV/Polytropic_EOSs_validation.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile -a $validation_file_path\n",
    "\n",
    "\n",
    "# Function     : impose_continuity_on_P_cold()\n",
    "# Author(s)    : Leo Werneck\n",
    "# Description  : This function populates the array K_poly_tab\n",
    "#                by demanding that P_cold be everywhere continuous\n",
    "# Dependencies : none\n",
    "#\n",
    "# Inputs       : eos              - named tuple containing the following:\n",
    "#                  neos           - number of EOSs to be used (single polytrope = 1)\n",
    "#                  rho_poly_tab   - values of rho distinguish one EOS from the\n",
    "#                                   other (not required for a single polytrope)\n",
    "#                  Gamma_poly_tab - values of Gamma to be used within each EOS\n",
    "#                  K_poly_tab     - uninitialized, see output variable below\n",
    "#                  P_poly_tab     - uninitialized, see function\n",
    "#                                   compute_P_poly_tab() below\n",
    "#                K_poly_tab0      - value of K_poly_tab[0], for the first EOS\n",
    "#\n",
    "# Outputs      : eos.K_poly_tab   - values of K to be used within each EOS, determined\n",
    "#                                   by imposing that P_cold be everywhere continuous\n",
    "\n",
    "def impose_continuity_on_P_cold(eos,K_poly_tab0):\n",
    "\n",
    "    # A piecewise polytropic EOS is given by\n",
    "    # .--------------------------------------------------------------------------.\n",
    "    # |      /     K_0 * rho^(Gamma_0)     ,                rho < rho_0 ;        |\n",
    "    # |      |     K_1 * rho^(Gamma_1)     ,        rho_0 < rho < rho_1 ;        |\n",
    "    # |      |          ...                                 ...                  |\n",
    "    # | P = <      K_j * rho^(Gamma_j)     ,    rho_(j-1) < rho < rho_j ;        |\n",
    "    # |      |          ...                                 ...                  |\n",
    "    # |      | K_(n-2) * rho^(Gamma_(n-2)) , rho_(neos-3) < rho < rho_(neos-2) ; |\n",
    "    # |      \\ K_(n-1) * rho^(Gamma_(n-1)) ,                rho > rho_(neos-2) . |\n",
    "    # .--------------------------------------------------------------------------.\n",
    "    # Notice that the case of a single polytropic EOS corresponds to\n",
    "    # the first EOS in the boxed equation above, with no condition on\n",
    "    # rho. Thus we need only return K_poly_tab0.\n",
    "    eos.K_poly_tab[0] = K_poly_tab0\n",
    "    if eos.neos==1:\n",
    "        return\n",
    "\n",
    "    # For the case of a piecewise polytropic EOS, emanding that P_cold\n",
    "    # be everywhere continuous results in the relation:\n",
    "    # .-----------------------------------------------------.\n",
    "    # | K_j = K_(j-1) * rho_(j-1)^( Gamma_(j-1) - Gamma_j ) |\n",
    "    # .-----------------------------------------------------.\n",
    "    for j in range(1,eos.neos):\n",
    "        eos.K_poly_tab[j] = eos.K_poly_tab[j-1]*eos.rho_poly_tab[j-1]**(eos.Gamma_poly_tab[j-1]-eos.Gamma_poly_tab[j])\n",
    "\n",
    "    return"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='p_poly_tab'></a>\n",
    "\n",
    "# Step 3: Computing $P_{j}$ \\[Back to [top](#toc)\\]\n",
    "$$\\label{p_poly_tab}$$\n",
    "\n",
    "We now set a new set of quantities, $P_{\\rm tab}$, which are used in a similar fashion to $\\rho_{j}$, that is, to determine which EOS we should use. This quantity is defined as:\n",
    "\n",
    "$$\n",
    "\\boxed{\n",
    "P_{\\rm tab} =\n",
    "\\left\\{\n",
    "\\begin{matrix}\n",
    "P_{0} = K_{0}\\rho_{0}^{\\Gamma_{0}}& , & P \\leq P_{0} \\implies \\rho_{b} \\leq \\rho_{0}\\\\\n",
    "P_{1} = K_{1}\\rho_{1}^{\\Gamma_{1}}& , & P_{0}\\leq P\\leq P_{1} \\implies \\rho_{0} \\leq \\rho_{b} \\leq \\rho_{1}\\\\\n",
    "\\vdots & & \\vdots\\\\\n",
    "P_{j} = K_{j}\\rho_{j}^{\\Gamma_{j}}& , & P_{j-1}\\leq P\\leq P_{j} \\implies \\rho_{j-1} \\leq \\rho_{b} \\leq \\rho_{j}\\\\\n",
    "\\vdots & & \\vdots\\\\\n",
    "P_{N-2} = K_{N-2}\\rho_{N-2}^{\\Gamma_{N-2}}& , & P_{N-3}\\leq P\\leq P_{N-2} \\implies \\rho_{N-3} \\leq \\rho_{b} \\leq \\rho_{N-2}\\\\\n",
    "- & , & P \\geq P_{N-2} \\implies \\rho_{b} \\geq \\rho_{N-2}\\ .\n",
    "\\end{matrix}\n",
    "\\right.\n",
    "}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Appending to TOV/Polytropic_EOSs_validation.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile -a $validation_file_path\n",
    "\n",
    "\n",
    "# Function     : compute_P_poly_tab()\n",
    "# Author(s)    : Leo Werneck\n",
    "# Description  : This function populates the array eos.P_poly_tab,\n",
    "#                used to distinguish which EOS we are using in the\n",
    "#                case of a piecewise polytropic EOS\n",
    "# Dependencies : none\n",
    "#\n",
    "# Inputs       : eos              - named tuple containing the following:\n",
    "#                  neos           - number of EOSs to be used (single polytrope = 1)\n",
    "#                  rho_poly_tab   - values of rho used to distinguish one EOS from\n",
    "#                                   the other (not required for a single polytrope)\n",
    "#                  Gamma_poly_tab - values of Gamma to be used within each EOS\n",
    "#                  K_poly_tab     - value of K to be used within each EOS\n",
    "#                  P_poly_tab     - uninitialized, see output variable below\n",
    "#\n",
    "# Outputs      : eos.P_poly_tab   - values of P used to distinguish one EOS from\n",
    "#                                   the other (not required for a single polytrope)\n",
    "\n",
    "def compute_P_poly_tab(eos):\n",
    "\n",
    "    # We now compute the values of P_poly_tab that are used\n",
    "    # to find the appropriate polytropic index and, thus,\n",
    "    # EOS we must use.\n",
    "    # First, if we have a single polytrope EOS, we need to\n",
    "    # do nothing.\n",
    "    if eos.neos==1:\n",
    "        return\n",
    "\n",
    "    # For the case of a piecewise polytropic EOS, we have\n",
    "    # .---------------------------.\n",
    "    # | P_j = K_j*rho_j^(Gamma_j) |\n",
    "    # .---------------------------.\n",
    "    for j in range(eos.neos-1):\n",
    "        eos.P_poly_tab[j] = eos.K_poly_tab[j]*eos.rho_poly_tab[j]**(eos.Gamma_poly_tab[j])\n",
    "\n",
    "    return"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='continuity_of_eps_cold'></a>\n",
    "\n",
    "# Step 4: Continuity of $\\epsilon_{\\rm cold}$ \\[Back to [top](#toc)\\]\n",
    "$$\\label{continuity_of_eps_cold}$$\n",
    "\n",
    "$\\epsilon_{\\rm cold}$ is determined via\n",
    "\n",
    "$$\n",
    "\\epsilon_{\\rm cold} = \\int d\\rho\\, \\frac{P_{\\rm cold}}{\\rho^{2}} \\ ,\n",
    "$$\n",
    "\n",
    "for some integration constant $C$. **Notation alert**: in the literature, the integration constants $C_{j}$ below are usually called $\\epsilon_{j}$. We will keep them as $C_{j}$ for a clearer exposition.\n",
    "\n",
    "In the case of a piecewise polytropic EOS, we then must have\n",
    "\n",
    "$$\n",
    "\\boxed{\n",
    "\\epsilon_{\\rm cold} = \n",
    "\\left\\{\n",
    "\\begin{matrix}\n",
    "\\frac{K_{0}\\rho^{\\Gamma_{0}-1}}{\\Gamma_{0}-1} + C_{0} & , & \\rho \\leq \\rho_{0}\\\\\n",
    "\\frac{K_{1}\\rho^{\\Gamma_{1}-1}}{\\Gamma_{1}-1} + C_{1} & , & \\rho_{0} \\leq \\rho \\leq \\rho_{1}\\\\\n",
    "\\vdots &  & \\vdots\\\\\n",
    "\\frac{K_{j}\\rho^{\\Gamma_{j}-1}}{\\Gamma_{j}-1} + C_{j} & , & \\rho_{j-1} \\leq \\rho \\leq \\rho_{j}\\\\\n",
    "\\vdots &  & \\vdots\\\\\n",
    "\\frac{K_{N-2}\\rho^{\\Gamma_{N-2}-1}}{\\Gamma_{N-2}-1} + C_{N-2} & , & \\rho_{N-3} \\leq \\rho \\leq \\rho_{N-2}\\\\\n",
    "\\frac{K_{N-1}\\rho^{\\Gamma_{N-1}-1}}{\\Gamma_{N-1}-1} + C_{N-1} & , & \\rho \\geq \\rho_{N-2}\n",
    "\\end{matrix}\n",
    "\\right.\n",
    "}\\ .\n",
    "$$\n",
    "\n",
    "We fix $C_{0}$ by demanding that $\\epsilon_{\\rm cold}\\left(\\rho=0\\right) = 0$. Then, continuity of $\\epsilon_{\\rm cold}$ imposes that\n",
    "\n",
    "$$\n",
    "\\frac{K_{0}\\rho_{0}^{\\Gamma_{0}-1}}{\\Gamma_{0}-1} = \\frac{K_{1}\\rho_{0}^{\\Gamma_{1}-1}}{\\Gamma_{1}-1} + C_{1}\\implies \\boxed{C_{1} = \\frac{K_{0}\\rho_{0}^{\\Gamma_{0}-1}}{\\Gamma_{0}-1} - \\frac{K_{1}\\rho_{0}^{\\Gamma_{1}-1}}{\\Gamma_{1}-1}}\\ ,\n",
    "$$\n",
    "\n",
    "for $C_{1}$ and\n",
    "\n",
    "$$\n",
    "\\boxed{C_{j} = C_{j-1} + \\frac{K_{j-1}\\rho_{j-1}^{\\Gamma_{j-1}-1}}{\\Gamma_{j-1}-1} - \\frac{K_{j}\\rho_{j-1}^{\\Gamma_{j}-1}}{\\Gamma_{j}-1}\\ ,\\ j\\geq1}\\ ,\n",
    "$$\n",
    "\n",
    "generically. Below we implement a function to set up $\\left\\{C_{j}\\right\\}$ for both single and piecewise polytropic EOSs, based on the last boxed equation above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Appending to TOV/Polytropic_EOSs_validation.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile -a $validation_file_path\n",
    "\n",
    "\n",
    "# Function     : impose_continuity_on_eps_cold()\n",
    "# Author(s)    : Leo Werneck\n",
    "# Description  : This function populates the array eps_integ_const_tab\n",
    "#                by demanding that eps_cold be everywhere continuous\n",
    "# Dependencies : none\n",
    "#\n",
    "# Inputs       : eos                     - named tuple containing the following:\n",
    "#                  neos                  - number of EOSs to be used (single polytrope = 1)\n",
    "#                  rho_poly_tab          - values of rho distinguish one EOS from the\n",
    "#                                          other (not required for a single polytrope)\n",
    "#                  Gamma_poly_tab        - values of Gamma to be used within each EOS\n",
    "#                  K_poly_tab            - value of K to be used within each EOS\n",
    "#                  eps_integ_const_tab   - uninitialized, see output variable below\n",
    "#\n",
    "# Outputs      : eos.eps_integ_const_tab - value of C used to compute eps_cold within each EOS,\n",
    "#                                          determined by imposing that eps_cold be everywhere\n",
    "#                                          continuous\n",
    "\n",
    "def impose_continuity_on_eps_cold(eos):\n",
    "\n",
    "    # Computing eps_cold for the case of a polytropic EOS, we have\n",
    "    # .------------------------------------------------------------------------------------------------------.\n",
    "    # |        / C_0     + K_0*rho^(Gamma_0 - 1)/(Gamma_0 - 1)         ,                rho < rho_0 ;        |\n",
    "    # |        | C_1     + K_1*rho^(Gamma_1 - 1)/(Gamma_1 - 1)         ,        rho_0 < rho < rho_1 ;        |\n",
    "    # |        |                       ...                                              ...                  |\n",
    "    # | eps = <  C_j     + K_j*rho^(Gamma_j - 1)/(Gamma_j - 1)         ,    rho_(j-1) < rho < rho_j ;        |\n",
    "    # |        |                       ...                                              ...                  |\n",
    "    # |        | C_(n-2) + K_(n-2)*rho^(Gamma_(n-2)-1)/(Gamma_(n-2)-1) , rho_(neos-3) < rho < rho_(neos-2) ; |\n",
    "    # |        \\ C_(n-1) + K_(n-1)*rho^(Gamma_(n-1)-1)/(Gamma_(n-1)-1) ,                rho > rho_(neos-2) . |\n",
    "    # .------------------------------------------------------------------------------------------------------.\n",
    "    # By demanding that eps_cold(rho -> 0) = 0, we fix C_0 = 0. Thus, for\n",
    "    # a single polytrope we need only return this\n",
    "    if eos.neos==1:\n",
    "        return\n",
    "\n",
    "    # For the case of a piecewise polytropic EOS, emanding that eps_cold\n",
    "    # be everywhere continuous results in the relation:\n",
    "    # .-----------------------------------------------------------------.\n",
    "    # | C_j = C_(j-1)                                                   |\n",
    "    # |     + K_(j-1)*rho_(j-1)^( Gamma_(j-1) - 1 )/( Gamma_(j-1) - 1 ) |\n",
    "    # |     - K_(j+0)*rho_(j-1)^( Gamma_(j+0) - 1 )/( Gamma_(j+0) - 1 ) |\n",
    "    # .-----------------------------------------------------------------.\n",
    "    eos.eps_integ_const_tab[0] = 0.0\n",
    "    for j in range(1,eos.neos):\n",
    "        # Second line of the boxed equation above\n",
    "        aux_jm1 = eos.K_poly_tab[j-1]*eos.rho_poly_tab[j-1]**(eos.Gamma_poly_tab[j-1]-1.0)/(eos.Gamma_poly_tab[j-1]-1)\n",
    "\n",
    "        # Third line of the boxed equation above\n",
    "        aux_jp0 = eos.K_poly_tab[j+0]*eos.rho_poly_tab[j-1]**(eos.Gamma_poly_tab[j+0]-1.0)/(eos.Gamma_poly_tab[j+0]-1)\n",
    "\n",
    "        # Boxed equation above\n",
    "        eos.eps_integ_const_tab[j] = eos.eps_integ_const_tab[j-1] + aux_jm1 - aux_jp0\n",
    "\n",
    "    return"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eos_parameters_from_input'></a>\n",
    "\n",
    "# Step 5: Setting up EOS parameters from user input \\[Back to [top](#toc)\\]\n",
    "$$\\label{eos_parameters_from_input}$$\n",
    "\n",
    "<a id='eos_parameters_from_input__complete_input'></a>\n",
    "\n",
    "## Step 5.a: Complete set of input variables \\[Back to [top](#toc)\\]\n",
    "$$\\label{eos_parameters_from_input__complete_input}$$\n",
    "\n",
    "We now implement a driver function to set up all polytropic EOS related quantities based on the input given by the user. From the given input set:\n",
    "\n",
    "$$\n",
    "\\left\\{n_{\\rm eos}, \\rho_{j}, \\Gamma_{j}, K_{0}\\right\\}\\ ,\n",
    "$$\n",
    "\n",
    "the code returns a \"C like struct\" (a [*named tuple*](https://docs.python.org/3/library/collections.html#collections.namedtuple)) containing\n",
    "\n",
    "$$\n",
    "\\left\\{n_{\\rm eos}, \\rho_{j}, \\Gamma_{j}, K_{j}, P_{j}\\right\\}\\ .\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Appending to TOV/Polytropic_EOSs_validation.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile -a $validation_file_path\n",
    "\n",
    "\n",
    "# Function     : set_up_EOS_parameters__complete_set_of_input_variables()\n",
    "# Author(s)    : Leo Werneck\n",
    "# Description  : This function determine all polytropic related\n",
    "#                parameters from user input\n",
    "# Dependencies : impose_continuity_on_P_cold()\n",
    "#                compute_P_poly_tab()\n",
    "#\n",
    "# Inputs       : neos             - number of EOSs to be used (single polytrope = 1)\n",
    "#                rho_poly_tab     - values of rho distinguish one EOS from the\n",
    "#                                   other (not required for a single polytrope)\n",
    "#                Gamma_poly_tab   - values of Gamma to be used within each EOS\n",
    "#                K_poly_tab0      - value of K_poly_tab[0], for the first EOS\n",
    "#\n",
    "# Outputs      : eos              - named tuple containing the following:\n",
    "#                  neos           - number of EOSs to be used (single polytrope = 1)\n",
    "#                  rho_poly_tab   - values of rho used to distinguish one EOS from\n",
    "#                                   the other (not required for a single polytrope)\n",
    "#                  Gamma_poly_tab - values of Gamma to be used within each EOS\n",
    "#                  K_poly_tab     - value of K to be used within each EOS\n",
    "#                  P_poly_tab     - values of P used to distinguish one EOS from\n",
    "#                                   the other (not required for a single polytrope)\n",
    "\n",
    "def set_up_EOS_parameters__complete_set_of_input_variables(neos,rho_poly_tab,Gamma_poly_tab,K_poly_tab0):\n",
    "\n",
    "    # Error check #1: Verify if the correct number of rho_poly_tab has been given by the user\n",
    "    if (neos == 1):\n",
    "        pass\n",
    "    elif len(rho_poly_tab) != neos-1:\n",
    "        print(\"Error: neos=\"+str(neos)+\". Expected \"+str(neos-1)+\" values of rho_poly_tab, but \"+str(len(rho_poly_tab))+\" values were given.\")\n",
    "        sys.exit(1)\n",
    "\n",
    "    # Error check #2: Verify if the correct number of Gamma_poly_tab has been given by the user\n",
    "    if len(Gamma_poly_tab) != neos:\n",
    "        print(\"Error: neos=\"+str(neos)+\". Expected \"+str(neos)+\" values of Gamma_poly_tab, but \"+str(len(Gamma_poly_tab))+\" values were given.\")\n",
    "        sys.exit(1)\n",
    "\n",
    "    # Create the arrays to store the values of K_poly_tab and eps_integ_const_tab\n",
    "    K_poly_tab          = [0 for i in range(neos)]\n",
    "    P_poly_tab          = [0 for i in range(neos-1)]\n",
    "    eps_integ_const_tab = [0 for i in range(neos)]\n",
    "\n",
    "    # Create the EOS \"struct\" (named tuple)\n",
    "    eos_struct = namedtuple(\"eos_struct\",\"neos rho_poly_tab Gamma_poly_tab K_poly_tab P_poly_tab eps_integ_const_tab\")\n",
    "    eos = eos_struct(neos,rho_poly_tab,Gamma_poly_tab,K_poly_tab,P_poly_tab,eps_integ_const_tab)\n",
    "\n",
    "    # Step 1: Determine K_poly_tab. For the details, please see the implementation\n",
    "    #         of the function impose_continuity_on_P_cold() below.\n",
    "    impose_continuity_on_P_cold(eos,K_poly_tab0)\n",
    "\n",
    "    # Step 2: Determine eps_integ_const_tab. For the details, please see the\n",
    "    #         implementation of the function impose_continuity_on_eps_cold() below.\n",
    "    impose_continuity_on_eps_cold(eos)\n",
    "\n",
    "    # Step 3: Determine P_poly_tab. For the details, please see the implementation\n",
    "    #         of the function compute_P_poly_tab() below.\n",
    "    compute_P_poly_tab(eos)\n",
    "\n",
    "    return eos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eos_parameters_from_input__read_et_al_input'></a>\n",
    "\n",
    "## Step 5.b: \"Read *et al.*\" set of input variables \\[Back to [top](#toc)\\]\n",
    "$$\\label{eos_parameters_from_input__read_et_al_input}$$\n",
    "\n",
    "We now implement a driver function to set up all polytropic EOS related quantities based on the input given by the user that follows tables II and III in [Read *et al.* (2008)](https://arxiv.org/pdf/0812.2163.pdf). The input structure is the following:\n",
    "\n",
    "|  $\\rho_{j}$  | $\\Gamma_{j}$ |   $K_{j}$   |\n",
    "|--------------|--------------|-------------|\n",
    "| 2.440340e+07 |   1.58425    | 6.80110e-09 |\n",
    "| 3.78358e+11  |   1.28733    |     $-$     |\n",
    "| 2.62780e+12  |   0.62223    |     $-$     |\n",
    "|  $\\rho_{3}$  |   1.35692    |     $-$     |\n",
    "| $10^{14.7}$  | $\\Gamma_{4}$ |     $-$     |\n",
    "| $10^{15.0}$  | $\\Gamma_{5}$ |     $-$     |\n",
    "|     $-$      | $\\Gamma_{6}$ |     $-$     |\n",
    "\n",
    "The values $\\left\\{\\rho_{0},\\rho_{1},\\rho_{2},\\Gamma_{0},\\Gamma_{1},\\Gamma_{2},\\Gamma_{3},K_{0}\\right\\}$ are taken from table II in [Read *et al.* (2008)](https://arxiv.org/pdf/0812.2163.pdf) and are fixed input values. The user then needs to input $\\left\\{\\log_{10}\\left(P_{4}\\right),\\Gamma_{4},\\Gamma_{5},\\Gamma_{6}\\right\\}$, where\n",
    "\n",
    "$$\n",
    "P_{4} \\equiv K_{4}\\rho_{4}^{\\Gamma_{4}}\\ ,\n",
    "$$\n",
    "\n",
    "is, in our code's notation, the value of ${\\rm P\\_poly\\_tab[4]}$. There is a unit conversion that must be performed:\n",
    "\n",
    "$$\n",
    "\\log_{10}\\left(P_{4}\\right)\\to\\log_{10}\\left(P_{4}\\right)-2\\log_{10}\\left(c\\right)\\ ,\n",
    "$$\n",
    "\n",
    "before exponentiating the input variable to obtain $P_{4}$. After that, we must determine $\\rho_{3}$, and we now describe the equation we use to do so. Consider the EOS for $P_{4}$ above and use the continuity condition:\n",
    "\n",
    "$$\n",
    "K_{4} = K_{3}\\rho_{3}^{\\Gamma_{3}-\\Gamma_{4}}\\ ,\n",
    "$$\n",
    "\n",
    "which yields\n",
    "\n",
    "$$\n",
    "P_{4} = \\left(K_{3}\\rho_{3}^{\\Gamma_{3}-\\Gamma_{4}}\\right)\\rho_{4}^{\\Gamma_{4}}\\implies \\boxed{\\rho_{3} = \\left[\\frac{P_{4}}{K_{3}\\rho_{4}^{\\Gamma_{4}}}\\right]^{\\frac{1}{\\Gamma_{3}-\\Gamma_{4}}}}\\ .\n",
    "$$\n",
    "\n",
    "Once we have determined all the EOS quantities, we then convert them to geometrized units, where $G=1=c$. We also set\n",
    "\n",
    "$$\n",
    "\\boxed{\\rho_{\\rm nuclear} \\equiv 1\\times10^{15} \\to 1}\\ .\n",
    "$$\n",
    "\n",
    "Imposing a ratio-preserving rescaling of the baryonic density, we then have\n",
    "\n",
    "$$\n",
    "\\frac{\\rho_{j}}{\\rho_{j-1}} = \\frac{\\rho^{\\rm rescaled}_{j}}{\\rho^{\\rm rescaled}_{j-1}} \\implies\n",
    "\\boxed{\\rho^{\\rm rescaled}_{j-1} = \\left(\\frac{\\rho_{j-1}}{\\rho_{j}}\\right)\\rho^{\\rm rescaled}_{j}}\\ .\n",
    "$$\n",
    "\n",
    "Because we have $P_{\\rm cold}$ set in the same units as $\\rho_{b}$, then the ratio-preserving rescaling must also apply to it\n",
    "\n",
    "$$\n",
    "\\boxed{P^{\\rm rescaled}_{j-1} = \\left(\\frac{\\rho_{j-1}}{\\rho_{j}}\\right)P^{\\rm rescaled}_{j}}\\ ,\n",
    "$$\n",
    "\n",
    "which then allows us to determine the new values of $K_{j}$ via the standard EOS identity\n",
    "\n",
    "$$\n",
    "\\boxed{ K^{\\rm rescaled}_{j} = \\frac{P^{\\rm rescaled}_{j}}{\\left(\\rho^{\\rm rescaled}_{j}\\right)^{\\Gamma_{j}}} }\\ .\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Appending to TOV/Polytropic_EOSs_validation.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile -a $validation_file_path\n",
    "\n",
    "\n",
    "# Function     : set_up_EOS_parameters__Read_et_al_input_variables()\n",
    "# Author(s)    : Leo Werneck\n",
    "# Description  : This function determine all polytropic related\n",
    "#                parameters from user input\n",
    "# Dependencies : impose_continuity_on_P_cold()\n",
    "#                compute_P_poly_tab()\n",
    "#\n",
    "# Inputs       : neos             - number of EOSs to be used (single polytrope = 1)\n",
    "#                rho_poly_tab     - values of rho distinguish one EOS from the\n",
    "#                                   other (not required for a single polytrope)\n",
    "#                Gamma_poly_tab   - values of Gamma to be used within each EOS\n",
    "#                K_poly_tab0      - value of K_poly_tab[0], for the first EOS\n",
    "#\n",
    "# Outputs      : eos              - named tuple containing the following:\n",
    "#                  neos           - number of EOSs to be used (single polytrope = 1)\n",
    "#                  rho_poly_tab   - values of rho used to distinguish one EOS from\n",
    "#                                   the other (not required for a single polytrope)\n",
    "#                  Gamma_poly_tab - values of Gamma to be used within each EOS\n",
    "#                  K_poly_tab     - value of K to be used within each EOS\n",
    "#                  P_poly_tab     - values of P used to distinguish one EOS from\n",
    "#                                   the other (not required for a single polytrope)\n",
    "\n",
    "def set_up_EOS_parameters__Read_et_al_input_variables(EOSname):\n",
    "\n",
    "    # Set up the number of polytropic EOSs, which is\n",
    "    # fixed at seven for this type of input\n",
    "    neos = 7\n",
    "\n",
    "    # Set up input from table II of Read et al. (2008),\n",
    "    # and from the legend in FIG. 3.\n",
    "    # Source: https://arxiv.org/pdf/0812.2163.pdf\n",
    "    # .--------------.---------.-------------.-----.\n",
    "    # |    rho_j     | Gamma_j |     K_j     | P_j |\n",
    "    # .--------------.---------.-------------.-----.\n",
    "    # | 2.440340e+07 | 1.58425 | 6.80110e-09 | P_0 |\n",
    "    # | 3.78358e+11  | 1.28733 |     K_1     | P_1 |\n",
    "    # | 2.62780e+12  | 0.62223 |     K_2     | P_2 |\n",
    "    # |    rho_3     | 1.35692 |     K_3     | P_3 |\n",
    "    # |  10^(14.7)   | Gamma_4 |     K_4     | P_4 |\n",
    "    # |  10^(15.0)   | Gamma_5 |     K_5     | P_5 |\n",
    "    # |       -      | Gamma_6 |     K_6     |  -  |\n",
    "    # .--------------.---------.-------------.-----.\n",
    "    #\n",
    "    # Load up the NRPy+ dictionary containing information about the Read et al.\n",
    "    # EOS tables (table III in Read et al. - https://arxiv.org/pdf/0812.2163.pdf)\n",
    "    import TOV.Piecewise_Polytrope__dict as PPdict\n",
    "    log_of_p4 = PPdict.EOS_Read_et_al_dict[EOSname].log_of_p4\n",
    "    Gamma4    = PPdict.EOS_Read_et_al_dict[EOSname].Gamma4\n",
    "    Gamma5    = PPdict.EOS_Read_et_al_dict[EOSname].Gamma5\n",
    "    Gamma6    = PPdict.EOS_Read_et_al_dict[EOSname].Gamma6\n",
    "\n",
    "    # Set up the speed of light and change the units of the input pressure\n",
    "    c = 2.9979e10 # cm/s -- cgs units\n",
    "    log_of_p4 -= 2.0*np.log10(c)\n",
    "\n",
    "    # Set up tabulated polytropic values following the table above\n",
    "    # and the user input. All quantities which are still unknown are\n",
    "    # set to absurd values to make sure they are overwritten\n",
    "    rho_poly_tab   = [2.440340e+07, 3.78358e+11, 2.62780e+12, -1e30  , 10**(14.7)     , 10**(15.0)]\n",
    "    P_poly_tab     = [-1e30       , -1e30      , -1e30      , -1e30  , 10**(log_of_p4), -1e30     ]\n",
    "    Gamma_poly_tab = [1.58425     , 1.28733    , 0.62223    , 1.35692, Gamma4         , Gamma5, Gamma6]\n",
    "    K_poly_tab     = [6.80110e-09 , -1e30      , -1e30      , -1e30  , -1e30          , -1e30  , -1e30]\n",
    "\n",
    "    # Compute {K_1,K_2,K_3}, using\n",
    "    # .-----------------------------------------------------.\n",
    "    # | K_j = K_(j-1) * rho_(j-1)^( Gamma_(j-1) - Gamma_j ) |\n",
    "    # .-----------------------------------------------------.\n",
    "    for j in range(1,4):\n",
    "        K_poly_tab[j] = K_poly_tab[j-1] * rho_poly_tab[j-1]**(Gamma_poly_tab[j-1] - Gamma_poly_tab[j])\n",
    "\n",
    "    # Compute {P_0,P_1,P_2}, using\n",
    "    # .-------------------------------.\n",
    "    # | P_j = K_j * rho_j^( Gamma_j ) |\n",
    "    # .-------------------------------.\n",
    "    for j in range(3):\n",
    "        P_poly_tab[j] = K_poly_tab[j] * rho_poly_tab[j]**(Gamma_poly_tab[j])\n",
    "\n",
    "\n",
    "    # Set up auxiliary variables for the evaluation of rho_3\n",
    "    P4          = P_poly_tab[4]\n",
    "    K3          = K_poly_tab[3]\n",
    "    rho4_p_Gam4 = rho_poly_tab[4]**(Gamma_poly_tab[4])\n",
    "    G3m4        = Gamma_poly_tab[3] - Gamma_poly_tab[4]\n",
    "\n",
    "    # Compute rho_3 using\n",
    "    # .----------------------------------------------------------------------.\n",
    "    # | rho_3 = ( P_4 /( K_3 * rho_4^(Gamma_4) ) )^(1.0/(Gamma_3 - Gamma_4)) |\n",
    "    # .----------------------------------------------------------------------.\n",
    "    rho_poly_tab[3] = ( P4/(K3 * rho4_p_Gam4) )**(1.0/G3m4)\n",
    "\n",
    "    # Compute {P_3,P_4,P_5} and {K_4,K_5,K_6}\n",
    "    for j in range(3,neos-1):\n",
    "        P_poly_tab[j]   = K_poly_tab[j] * rho_poly_tab[j]**(Gamma_poly_tab[j])\n",
    "        K_poly_tab[j+1] = K_poly_tab[j] * rho_poly_tab[j]**(Gamma_poly_tab[j] - Gamma_poly_tab[j+1])\n",
    "\n",
    "    # We impose a \"ratio preserving rescaling\" of rhob:\n",
    "    #\n",
    "    # rhob_rescaled[j] / rhob[j] = rhob_rescaled[j-1] / rhob[j-1]\n",
    "    #\n",
    "    # which implies the relation\n",
    "    # .-------------------------------------------------------------.\n",
    "    # | rhob_rescaled[j-1] = (rhob[j-1]/rhob[j]) * rhob_rescaled[j] |\n",
    "    # .-------------------------------------------------------------.\n",
    "    # after setting rhob_nuclear_rescaled = 1\n",
    "    rhob_rescaled  = [1.0 for i in range(neos-1)]\n",
    "    for j in range(neos-2,0,-1):\n",
    "        rhob_rescaled[j-1] = (rho_poly_tab[j-1]/rho_poly_tab[j]) * rhob_rescaled[j]\n",
    "\n",
    "    # Now because the values of P and rho given by Read et al. are already\n",
    "    # in the same units, namely (g/cm^3), the ratio P/rho should be invariant\n",
    "    # under this rescalling procedure. Therefore\n",
    "    # .---------------------------------------------------------------------.\n",
    "    # | P_rescaled[j] = (rhob_rescaled[j]/rhob_readetal[j]) * P_readetal[j] |\n",
    "    # .---------------------------------------------------------------------.\n",
    "    P_rescaled = [0.0 for i in range(neos-1)]\n",
    "    for j in range(neos-1):\n",
    "        P_rescaled[j] = (rhob_rescaled[j]/rho_poly_tab[j]) * P_poly_tab[j]\n",
    "\n",
    "    rho_poly_tab = rhob_rescaled\n",
    "    P_poly_tab   = P_rescaled\n",
    "\n",
    "    # Demanding that the pressure be everywhere continuous then imposes\n",
    "    # .-------------------------------------------------------------------------------------------.\n",
    "    # | K_dimensionless[j-1] = K_dimensionless[j]/rhob_dimensionless[j-1]^(Gamma[j-1] - Gamma[j]) |\n",
    "    # .-------------------------------------------------------------------------------------------.\n",
    "    K_poly_tab[0] = P_poly_tab[0]/rho_poly_tab[0]**(Gamma_poly_tab[0])\n",
    "    for j in range(1,neos):\n",
    "        K_poly_tab[j] = K_poly_tab[j-1]*rho_poly_tab[j-1]**(Gamma_poly_tab[j-1]-Gamma_poly_tab[j])\n",
    "\n",
    "    # Allocate memory for the integration constants of eps_cold\n",
    "    eps_integ_const_tab = [0 for i in range(neos)]\n",
    "\n",
    "    # Create the EOS \"struct\" (named tuple)\n",
    "    eos_struct = namedtuple(\"eos_struct\",\"neos rho_poly_tab Gamma_poly_tab K_poly_tab P_poly_tab eps_integ_const_tab\")\n",
    "    eos = eos_struct(neos,rho_poly_tab,Gamma_poly_tab,K_poly_tab,P_poly_tab,eps_integ_const_tab)\n",
    "\n",
    "    # Populate the integration constants of eps_cold\n",
    "    impose_continuity_on_eps_cold(eos)\n",
    "\n",
    "    return eos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='pcold_from_rhob'></a>\n",
    "\n",
    "# Step 6: Computing $P_{\\rm cold}\\left(\\rho_{b}\\right)$ \\[Back to [top](#toc)\\]\n",
    "$$\\label{pcold_from_rhob}$$\n",
    "\n",
    "Then, let us compute $P_{\\rm cold}$ for a polytropic EOS:\n",
    "\n",
    "$$\n",
    "\\boxed{\n",
    "P_{\\rm cold} =\n",
    "\\left\\{\n",
    "\\begin{matrix}\n",
    "K_{0}\\rho_{b}^{\\Gamma_{0}} & , & \\rho_{b} \\leq \\rho_{0}\\\\\n",
    "K_{1}\\rho_{b}^{\\Gamma_{1}} & , & \\rho_{0} \\leq \\rho_{b} \\leq \\rho_{1}\\\\\n",
    "\\vdots &  & \\vdots\\\\\n",
    "K_{j}\\rho_{b}^{\\Gamma_{j}} & , & \\rho_{j-1} \\leq \\rho_{b} \\leq \\rho_{j}\\\\\n",
    "\\vdots &  & \\vdots\\\\\n",
    "K_{N-2}\\rho_{b}^{\\Gamma_{N-2}} & , & \\rho_{N-3} \\leq \\rho_{b} \\leq \\rho_{N-2}\\\\\n",
    "K_{N-1}\\rho_{b}^{\\Gamma_{N-1}} & , & \\rho_{b} \\geq \\rho_{N-2}\n",
    "\\end{matrix}\n",
    "\\right.\n",
    "}\\ .\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Appending to TOV/Polytropic_EOSs_validation.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile -a $validation_file_path\n",
    "\n",
    "\n",
    "# Function     : Polytrope_EOS__compute_P_cold_from_rhob()\n",
    "# Author(s)    : Leo Werneck\n",
    "# Description  : This function computes P_cold for a polytropic EOS\n",
    "# Dependencies : polytropic_index_from_rhob()\n",
    "#\n",
    "# Inputs       : eos              - named tuple containing the following:\n",
    "#                  neos           - number of EOSs to be used (single polytrope = 1)\n",
    "#                  rho_poly_tab   - values of rho distinguish one EOS from the\n",
    "#                                   other (not required for a single polytrope)\n",
    "#                  Gamma_poly_tab - values of Gamma to be used within each EOS\n",
    "#                  K_poly_tab     - value of K to be used within each EOS\n",
    "#                  P_poly_tab     - values of P used to distinguish one EOS from\n",
    "#                                   the other (not required for a single polytrope)\n",
    "#                rho_baryon       - the value of rho for which we want to\n",
    "#                                   compute P_cold\n",
    "#\n",
    "# Outputs      : P_cold           - for a single or piecewise polytropic EOS\n",
    "\n",
    "def Polytrope_EOS__compute_P_cold_from_rhob(eos, rho_baryon):\n",
    "\n",
    "    # Compute the polytropic index from rho_baryon\n",
    "    j = polytropic_index_from_rhob(eos, rho_baryon)\n",
    "\n",
    "    # Return the value of P_cold for a polytropic EOS\n",
    "    # .--------------------------------.\n",
    "    # | P_cold = K_j * rho_b^(Gamma_j) |\n",
    "    # .--------------------------------.\n",
    "    return eos.K_poly_tab[j]*rho_baryon**eos.Gamma_poly_tab[j]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='rhob_and_eps_cold_from_pcold'></a>\n",
    "\n",
    "# Step 7: Computing $\\rho_{b}\\left(P_{\\rm cold}\\right)$ and $\\epsilon_{\\rm cold}\\left(\\rho_{b},P_{\\rm cold}\\right)$ \\[Back to [top](#toc)\\]\n",
    "$$\\label{rhob_and_eps_cold_from_pcold}$$\n",
    "\n",
    "Then, let us compute $\\rho_{b}$ as a function of $P_{\\rm cold}\\equiv P$ for a polytropic EOS:\n",
    "\n",
    "$$\n",
    "\\boxed{\n",
    "\\rho_{b} =\n",
    "\\left\\{\n",
    "\\begin{matrix}\n",
    "\\left(\\frac{P}{K_{0}}\\right)^{1/\\Gamma_{0}} & , & P \\leq P_{0}\\\\\n",
    "\\left(\\frac{P}{K_{1}}\\right)^{1/\\Gamma_{1}} & , & P_{0} \\leq P \\leq P_{1}\\\\\n",
    "\\vdots &  & \\vdots\\\\\n",
    "\\left(\\frac{P}{K_{j}}\\right)^{1/\\Gamma_{j}} & , & P_{j-1} \\leq P \\leq P_{j}\\\\\n",
    "\\vdots &  & \\vdots\\\\\n",
    "\\left(\\frac{P}{K_{N-2}}\\right)^{1/\\Gamma_{N-2}} & , & P_{N-3} \\leq P \\leq P_{N-2}\\\\\n",
    "\\left(\\frac{P}{K_{N-1}}\\right)^{1/\\Gamma_{N-1}} & , & P \\geq P_{N-2}\n",
    "\\end{matrix}\n",
    "\\right.\n",
    "}\\ .\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Appending to TOV/Polytropic_EOSs_validation.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile -a $validation_file_path\n",
    "\n",
    "\n",
    "# Function     : Polytrope_EOS__compute_rhob_from_P_cold()\n",
    "# Author(s)    : Leo Werneck\n",
    "# Description  : This function computes rho_b for a polytropic EOS\n",
    "# Dependencies : polytropic_index_from_P()\n",
    "#\n",
    "# Inputs       : eos              - named tuple containing the following:\n",
    "#                  neos           - number of EOSs to be used (single polytrope = 1)\n",
    "#                  rho_poly_tab   - values of rho distinguish one EOS from the\n",
    "#                                   other (not required for a single polytrope)\n",
    "#                  Gamma_poly_tab - values of Gamma to be used within each EOS\n",
    "#                  K_poly_tab     - value of K to be used within each EOS\n",
    "#                  P_poly_tab     - values of P used to distinguish one EOS from\n",
    "#                                   the other (not required for a single polytrope)\n",
    "#                P                - the value of P for which we want to\n",
    "#                                   compute rho_b\n",
    "#\n",
    "# Outputs      : rho_baryon       - for a single or piecewise polytropic EOS\n",
    "\n",
    "def Polytrope_EOS__compute_rhob_from_P_cold(eos,P):\n",
    "\n",
    "    # Compute the polytropic index from P\n",
    "    j = polytropic_index_from_P(eos,P)\n",
    "\n",
    "    # Return the value of rho_b for a polytropic EOS\n",
    "    # .----------------------------------.\n",
    "    # | rho_b = (P_cold/K_j)^(1/Gamma_j) |\n",
    "    # .----------------------------------.\n",
    "    return (P/eos.K_poly_tab[j])**(1.0/eos.Gamma_poly_tab[j])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we compute $\\epsilon_{\\rm cold}$:\n",
    "\n",
    "$$\n",
    "\\boxed{\\epsilon_{\\rm cold} = \\frac{P_{\\rm cold}}{\\rho_{b}\\left(\\Gamma_{\\rm poly}-1\\right)}}\\ .\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Appending to TOV/Polytropic_EOSs_validation.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile -a $validation_file_path\n",
    "\n",
    "\n",
    "# Function     : Polytrope_EOS__compute_eps_cold_from_rhob()\n",
    "# Author(s)    : Leo Werneck\n",
    "# Description  : This function computes eps_cold for a polytropic EOS\n",
    "# Dependencies : polytropic_index_from_rhob()\n",
    "#\n",
    "# Inputs       : eos              - named tuple containing the following:\n",
    "#                  neos           - number of EOSs to be used (single polytrope = 1)\n",
    "#                  rho_poly_tab   - values of rho distinguish one EOS from the\n",
    "#                                   other (not required for a single polytrope)\n",
    "#                  Gamma_poly_tab - values of Gamma to be used within each EOS\n",
    "#                  K_poly_tab     - value of K to be used within each EOS\n",
    "#                  P_poly_tab     - values of P used to distinguish one EOS from\n",
    "#                                   the other (not required for a single polytrope)\n",
    "#                rho_baryon       - the value of rho for which we want to\n",
    "#                                   compute P_cold\n",
    "#\n",
    "# Outputs      : eps_cold         - for a single or piecewise polytropic EOS\n",
    "\n",
    "def Polytrope_EOS__compute_eps_cold_from_rhob(eos, rho_baryon):\n",
    "\n",
    "    if rho_baryon == 0.0:\n",
    "        return 0.0\n",
    "\n",
    "    # Compute the polytropic index from rho_baryon\n",
    "    j = polytropic_index_from_rhob(eos, rho_baryon)\n",
    "\n",
    "    # Compute P_cold\n",
    "    P_cold = Polytrope_EOS__compute_P_cold_from_rhob(eos, rho_baryon)\n",
    "\n",
    "    # Return the value of P_cold for a polytropic EOS\n",
    "    # .----------------------------------------------.\n",
    "    # | eps_cold = C_j + P_cold/( rhob*(Gamma_j-1) ) |\n",
    "    # .----------------------------------------------.\n",
    "    return ( eos.eps_integ_const_tab[j] + P_cold/(rho_baryon*(eos.Gamma_poly_tab[j] - 1.0)) )\n",
    "\n",
    "# Function     : Polytrope_EOS__compute_rhob_and_eps_cold_from_P_cold()\n",
    "# Author(s)    : Leo Werneck\n",
    "# Description  : This function computes rho_b and eps_cold for a polytropic EOS\n",
    "# Dependencies : polytropic_index_from_P()\n",
    "#                Polytrope_EOS__compute_rhob_from_P_cold()\n",
    "#\n",
    "# Inputs       : eos              - named tuple containing the following:\n",
    "#                  neos           - number of EOSs to be used (single polytrope = 1)\n",
    "#                  rho_poly_tab   - values of rho distinguish one EOS from the\n",
    "#                                   other (not required for a single polytrope)\n",
    "#                  Gamma_poly_tab - values of Gamma to be used within each EOS\n",
    "#                  K_poly_tab     - value of K to be used within each EOS\n",
    "#                  P_poly_tab     - values of P used to distinguish one EOS from\n",
    "#                                   the other (not required for a single polytrope)\n",
    "#                P                - the value of P for which we want to\n",
    "#                                   compute rho_b\n",
    "#\n",
    "# Outputs      : rho_baryon       - for a single or piecewise polytropic EOS\n",
    "\n",
    "def Polytrope_EOS__compute_rhob_and_eps_cold_from_P_cold(eos,P):\n",
    "\n",
    "    # Compute the polytropic index from P and set Gamma\n",
    "    j     = polytropic_index_from_P(eos,P)\n",
    "    Gamma = eos.Gamma_poly_tab[j]\n",
    "    # Compute the value of rho_b for a polytropic EOS\n",
    "    # .----------------------------------.\n",
    "    # | rho_b = (P_cold/K_j)^(1/Gamma_j) |\n",
    "    # .----------------------------------.\n",
    "    rho_b = (P/eos.K_poly_tab[j])**(1.0/Gamma)\n",
    "\n",
    "    return rho_b, Polytrope_EOS__compute_eps_cold_from_rhob(eos, rho_b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='polytropic_index'></a>\n",
    "\n",
    "# Step 8: Determining the polytropic index \\[Back to [top](#toc)\\]\n",
    "$$\\label{polytropic_index}$$\n",
    "\n",
    "<a id='polytropic_index__from_rhob'></a>\n",
    "\n",
    "## Step 8.a: From $\\rho_{b}$ \\[Back to [top](#toc)\\]\n",
    "$$\\label{polytropic_index__from_rhob}$$\n",
    "\n",
    "The function below determines the polytropic index from a given value $\\rho_{b} = \\rho_{\\rm in}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Appending to TOV/Polytropic_EOSs_validation.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile -a $validation_file_path\n",
    "\n",
    "\n",
    "# Function     : polytropic_index_from_rhob()\n",
    "# Author(s)    : Leo Werneck and Zach Etienne\n",
    "# Description  : This function computes P_cold for a polytropic EOS\n",
    "# Dependencies : none\n",
    "#\n",
    "# Input(s)     : eos              - named tuple containing the following:\n",
    "#                  neos           - number of EOSs to be used (single polytrope = 1)\n",
    "#                  rho_poly_tab   - values of rho distinguish one EOS from the\n",
    "#                                   other (not required for a single polytrope)\n",
    "#                  Gamma_poly_tab - values of Gamma to be used within each EOS\n",
    "#                  K_poly_tab     - value of K to be used within each EOS\n",
    "#                  P_poly_tab     - values of P used to distinguish one EOS from\n",
    "#                                   the other (not required for a single polytrope)\n",
    "#                rho_in           - value of rho for which we compute the\n",
    "#                                   polytropic index\n",
    "#\n",
    "# Output(s)    : polytropic index computed from rho_in\n",
    "\n",
    "def polytropic_index_from_rhob(eos, rho_in):\n",
    "\n",
    "    # Returns the value of the polytropic index based on rho_in\n",
    "    polytropic_index = 0\n",
    "    if not (eos.neos==1):\n",
    "        for j in range(eos.neos-1):\n",
    "            polytropic_index += (rho_in > eos.rho_poly_tab[j])\n",
    "\n",
    "    return polytropic_index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='polytropic_index__from_pcold'></a>\n",
    "\n",
    "## Step 8.b: From $P_{\\rm cold}$ \\[Back to [top](#toc)\\]\n",
    "$$\\label{polytropic_index__from_pcold}$$\n",
    "\n",
    "The function below determines the polytropic index from a given value $P_{\\rm cold} = P$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Appending to TOV/Polytropic_EOSs_validation.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile -a $validation_file_path\n",
    "\n",
    "\n",
    "# Function     : polytropic_index_from_P()\n",
    "# Author(s)    : Leo Werneck and Zach Etienne\n",
    "# Description  : This function computes P_cold for a polytropic EOS\n",
    "# Dependencies : none\n",
    "#\n",
    "# Input(s)     : eos              - named tuple containing the following:\n",
    "#                  neos           - number of EOSs to be used (single polytrope = 1)\n",
    "#                  rho_poly_tab   - values of rho distinguish one EOS from the\n",
    "#                                   other (not required for a single polytrope)\n",
    "#                  Gamma_poly_tab - values of Gamma to be used within each EOS\n",
    "#                  K_poly_tab     - value of K to be used within each EOS\n",
    "#                  P_poly_tab     - values of P used to distinguish one EOS from\n",
    "#                                   the other (not required for a single polytrope)\n",
    "#                P_in             - value of P for which we compute the\n",
    "#                                   polytropic index\n",
    "#\n",
    "# Output(s)    : polytropic index computed from P_in\n",
    "\n",
    "def polytropic_index_from_P(eos, P_in):\n",
    "\n",
    "    # Returns the value of the polytropic index based on P_in\n",
    "    polytropic_index = 0\n",
    "    if not (eos.neos==1):\n",
    "        for j in range(eos.neos-1):\n",
    "            polytropic_index += (P_in > eos.P_poly_tab[j])\n",
    "\n",
    "    return polytropic_index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='illinoisgrmhd_parameter_file'></a>\n",
    "\n",
    "# Step 9: Generating an EOS parameter file for `IllinoisGRMHD` \\[Back to [top](#toc)\\]\n",
    "$$\\label{illinoisgrmhd_parameter_file}$$\n",
    "\n",
    "We now generate a parameter file for `IllinoisGRMHD` from the EOS parameters we have just determined."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Appending to TOV/Polytropic_EOSs_validation.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile -a $validation_file_path\n",
    "\n",
    "\n",
    "# Function     : generate_IllinoisGRMHD_EOS_parameter_file()\n",
    "# Author(s)    : Leo Werneck and Zach Etienne\n",
    "# Description  : This function computes P_cold for a polytropic EOS\n",
    "# Dependencies : none\n",
    "#\n",
    "# Input(s)     : eos              - named tuple containing the following:\n",
    "#                  neos           - number of EOSs to be used (single polytrope = 1)\n",
    "#                  rho_poly_tab   - values of rho distinguish one EOS from the\n",
    "#                                   other (not required for a single polytrope)\n",
    "#                  Gamma_poly_tab - values of Gamma to be used within each EOS\n",
    "#                  K_poly_tab     - value of K to be used within each EOS\n",
    "#                  P_poly_tab     - values of P used to distinguish one EOS from\n",
    "#                                   the other (not required for a single polytrope)\n",
    "# Output(s)    : parameter file to be used by IllinoisGRMHD\n",
    "\n",
    "def generate_IllinoisGRMHD_EOS_parameter_file(EOSname,outfilename, \\\n",
    "                                              Gamma_thermal=None,  \\\n",
    "                                              EOS_struct=None, \\\n",
    "                                              tau_atmosphere=4.876083025795607e-12, \\\n",
    "                                              rho_atmosphere=1.292852735094440e-10, \\\n",
    "                                              K_single_polytrope=1.0, \\\n",
    "                                              Gamma_single_polytrope=2.0):\n",
    "\n",
    "    with open(outfilename,\"w\") as file:\n",
    "        file.write(\"\"\"\n",
    "#vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv\n",
    "#\n",
    "#.-------------------------------------------------------------------------.\n",
    "#| IllinoisGRMHD Equation of State (EOS) parameter file Generated by NRPy+ |\n",
    "#|-------------------------------------------------------------------------|\n",
    "#|         This section of the parameter file has been generated by        |\n",
    "#|                 the TOV/Polytropic_EOSs.py NRPy+ module                 |\n",
    "#|-------------------------------------------------------------------------|\n",
    "#|    Recommended reading: Tutorial-TOV-Piecewise_Polytrope_EOSs.ipynb     |\n",
    "#|-------------------------------------------------------------------------|\n",
    "#| NRPy+ repositoryon github: https://github.com/zachetienne/nrpytutorial/ |\n",
    "#|-------------------------------------------------------------------------|\n",
    "#| *Warning*: it is highly recommended not to change this section manually |\n",
    "#.-------------------------------------------------------------------------.\n",
    "\"\"\")\n",
    "    if EOSname == \"single\":\n",
    "        with open(outfilename,\"a\") as file:\n",
    "            file.write(\"\"\"#\n",
    "#.-------------------------------.\n",
    "#|  EOS Type: Single Polytrope   |\n",
    "#.-------------------------------.\n",
    "#| Required inputs:              |\n",
    "#|   - K_single_polytrope        |\n",
    "#|   - Gamma_single_polytrope    |\n",
    "#|   - tau_atmosphere            |\n",
    "#|   - rho_atmosphere            |\n",
    "#|                               |\n",
    "#| IllinoisGRMHD parameters set: |\n",
    "#|   - neos                      |\n",
    "#|   - K_ppoly_tab0              |\n",
    "#|   - rho_ppoly_tab_in[0]       |\n",
    "#|   - Gamma_ppoly_tab_in[0]     |\n",
    "#|   - Gamma_th                  |\n",
    "#|   - tau_atm                   |\n",
    "#|   - rho_b_atm                 |\n",
    "#|                               |\n",
    "#| NRPyPlusTOVID parameters set: |\n",
    "#|   - rho_atmosphere            |\n",
    "#|   - Gamma_atmosphere          |\n",
    "#|   - K_atmosphere              |\n",
    "#|-------------------------------|\n",
    "#| For single polytropes, we     |\n",
    "#| always assume:                |\n",
    "#| Gamma_th = Gamma_poly_tab     |\n",
    "#.-------------------------------.\n",
    "#\n",
    "# Set up initial data file name\n",
    "NRPyPlusTOVID::TOV_filename = \"outputTOVpolytrope.txt\"\n",
    "\n",
    "# Set the number of EOSs to 1 (single polytrope)\n",
    "IllinoisGRMHD::neos = 1\n",
    "\n",
    "# Set atmospheric value of tau\n",
    "IllinoisGRMHD::tau_atm = %.15e\n",
    "\n",
    "# Set atmospheric value of rho\n",
    "IllinoisGRMHD::rho_b_atm      = %.15e\n",
    "NRPyPlusTOVID::rho_atmosphere = %.15e\n",
    "\n",
    "# Set K_ppoly_tab0 and K_atmosphere\n",
    "IllinoisGRMHD::K_ppoly_tab0 = %.15e\n",
    "NRPyPlusTOVID::K_atmosphere = %.15e\n",
    "\n",
    "# Set Gamma_ppoly_tab_in[0]  and Gamma_atmosphere\n",
    "IllinoisGRMHD::Gamma_ppoly_tab_in[0] = %.15e\n",
    "NRPyPlusTOVID::Gamma_atmosphere      = %.15e\n",
    "\n",
    "# Set Gamma_thermal\n",
    "# (must be the same as Gamma_ppoly_tab for a single polytrope)\n",
    "IllinoisGRMHD::Gamma_th = %.15e\n",
    "\n",
    "# Set rho_ppoly_tab_in[0] to zero\n",
    "# (for a single polytrope this value is not used)\n",
    "IllinoisGRMHD::rho_ppoly_tab_in[0] = 0.0\n",
    "\n",
    "#.----------------------.\n",
    "#| EOS_omni parameters: |\n",
    "#|  - n_pieces          |\n",
    "#|  - hybrid_k0         |\n",
    "#|  - hybrid_gamma[0]   |\n",
    "#|  - hybrid_gamma_th   |\n",
    "#.----------------------.\n",
    "# Set up the number of polytropic EOSs.\n",
    "EOS_omni::neos = 1\n",
    "\n",
    "# Set hybrid_k0 to K_ppoly_tab0\n",
    "EOS_omni::hybrid_k0 = %.15e\n",
    "\n",
    "# Set hybrid_gamma to Gamma_ppoly_tab_in\n",
    "EOS_omni::hybrid_gamma[0] = %.15e\n",
    "\n",
    "# Set hybrid_gamma_th to Gamma_th\n",
    "EOS_omni::hybrid_gamma_th = %.15e\n",
    "\n",
    "#.--------------------------------.\n",
    "#| End of NRPy+ generated section |\n",
    "#.--------------------------------.\n",
    "#\n",
    "#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
    "\"\"\"%(tau_atmosphere,          # sets IllinoisGRMHD::tau_atm\n",
    "     rho_atmosphere,          # sets IllinoisGRMHD::rho_b_atm\n",
    "     rho_atmosphere,          # sets NRPyPlusTOVID::rho_atmosphere\n",
    "     K_single_polytrope,      # sets IllinoisGRMHD::K_ppoly_tab0\n",
    "     K_single_polytrope,      # sets NRPyPlusTOVID::K_atmosphere\n",
    "     Gamma_single_polytrope,  # sets IllinoisGRMHD::Gamma_ppoly_tab_in[0]\n",
    "     Gamma_single_polytrope,  # sets NRPyPlusTOVID::Gamma_atmosphere\n",
    "     Gamma_single_polytrope,  # sets IllinoisGRMHD::Gamma_th\n",
    "     K_single_polytrope,      # sets EOS_omni::hybrid_k0\n",
    "     Gamma_single_polytrope,  # sets EOS_omni::hybrid_gamma[0]\n",
    "     Gamma_single_polytrope)) # sets EOS_omni::hybrid_gamma_th\n",
    "\n",
    "    elif EOSname == \"piecewise\":\n",
    "        if EOS_struct is None:  # Use \"is None\" instead of \"==None\", as the former is more correct.\n",
    "            print(\"Error: Please set the EOS named tuple. Usage:\")\n",
    "            print(\"generate_IllinoisGRMHD_EOS_parameter_file(\\\"piecewise\\\",outfilename,Gamma_thermal=Gamma_th,EOS_struct=eos_named_tuple)\")\n",
    "            sys.exit(1)\n",
    "\n",
    "        if Gamma_thermal is None:  # Use \"is None\" instead of \"==None\", as the former is more correct.\n",
    "            print(\"Error: Please set Gamma_thermal. Usage:\")\n",
    "            print(\"generate_IllinoisGRMHD_EOS_parameter_file(\\\"piecewise\\\",outfilename,Gamma_thermal=Gamma_th,EOS_struct=eos_named_tuple)\")\n",
    "            sys.exit(1)\n",
    "\n",
    "        atm_index  = polytropic_index_from_rhob(EOS_struct,rho_atmosphere)\n",
    "        Gamma_atm  = EOS_struct.Gamma_poly_tab[atm_index]\n",
    "        Kpoly_atm  = EOS_struct.K_poly_tab[atm_index]\n",
    "        IDfilename = \"outputTOVpolytrope-\"+EOSname+\".txt\"\n",
    "\n",
    "        with open(outfilename,\"a\") as file:\n",
    "            file.write(\"\"\"#\n",
    "#.---------------------------------------.\n",
    "#| EOS Type: Generic Piecewise Polytrope |\n",
    "#.---------------------------------------.\n",
    "#| Required parameters:                  |\n",
    "#|  - EOS_struct                         |\n",
    "#|  - Gamma_thermal                      |\n",
    "#|  - tau_atmosphere                     |\n",
    "#|  - rho_atmosphere                     |\n",
    "#|                                       |\n",
    "#| IllinoisGRMHD parameters set:         |\n",
    "#|  - neos                               |\n",
    "#|  - K_ppoly_tab0                       |\n",
    "#|  - rho_ppoly_tab_in[j]   0<=j<=neos-2 |\n",
    "#|  - Gamma_ppoly_tab_in[j] 0<=j<=neos-1 |\n",
    "#|  - Gamma_th                           |\n",
    "#|  - tau_atm                            |\n",
    "#|  - rho_b_atm                          |\n",
    "#.---------------------------------------.\n",
    "#| NRPyPlusTOVID parameters set:         |\n",
    "#|  - rho_atmosphere                     |\n",
    "#|  - Gamma_atmosphere                   |\n",
    "#|  - K_atmosphere                       |\n",
    "#.---------------------------------------.\n",
    "#| EOS_omni parameters set:              |\n",
    "#|  - n_pieces                           |\n",
    "#|  - hybrid_k0                          |\n",
    "#|  - hybrid_rho[j]   0<=j<=neos-2       |\n",
    "#|  - hybrid_gamma[j] 0<=j<=neos-1       |\n",
    "#|  - hybrid_gamma_th                    |\n",
    "#.---------------------------------------.\n",
    "#\n",
    "# Set up initial data file name\n",
    "NRPyPlusTOVID::TOV_filename = \\\"%s\\\"\n",
    "\n",
    "# Set up the number of polytropic EOSs.\n",
    "IllinoisGRMHD::neos = %d\n",
    "\n",
    "# Set atmospheric value of tau\n",
    "IllinoisGRMHD::tau_atm = %.15e\n",
    "\n",
    "# Set K_ppoly_tab0 and K_atmosphere\n",
    "IllinoisGRMHD::K_ppoly_tab0 = %.15e\n",
    "NRPyPlusTOVID::K_atmosphere = %.15e\n",
    "\n",
    "# Set atmospheric value of rho\n",
    "IllinoisGRMHD::rho_b_atm      = %.15e\n",
    "NRPyPlusTOVID::rho_atmosphere = %.15e\n",
    "\n",
    "# Set rho_ppoly_tab_in\"\"\" %(IDfilename,EOS_struct.neos,tau_atmosphere,EOS_struct.K_poly_tab[0],Kpoly_atm,rho_atmosphere,rho_atmosphere))\n",
    "            for j in range(EOS_struct.neos-1):\n",
    "                file.write(\"\"\"\n",
    "IllinoisGRMHD::rho_ppoly_tab_in[%d] = %.15e\"\"\" %(j,EOS_struct.rho_poly_tab[j]))\n",
    "            file.write(\"\"\"\n",
    "\n",
    "# Set Gamma_atmosphere and Gamma_ppoly_tab_in\n",
    "NRPyPlusTOVID::Gamma_atmosphere      = %.15e\"\"\" %(Gamma_atm))\n",
    "            for j in range(EOS_struct.neos):\n",
    "                file.write(\"\"\"\n",
    "IllinoisGRMHD::Gamma_ppoly_tab_in[%d] = %.15e\"\"\" %(j,EOS_struct.Gamma_poly_tab[j]))\n",
    "            file.write(\"\"\"\n",
    "\n",
    "# Set Gamma_th\n",
    "IllinoisGRMHD::Gamma_th = %.15e\n",
    "\n",
    "#.---------------------------------.\n",
    "#| EOS_omni parameters:            |\n",
    "#|  - n_pieces                     |\n",
    "#|  - hybrid_k0                    |\n",
    "#|  - hybrid_rho[j]   0<=j<=neos-2 |\n",
    "#|  - hybrid_gamma[j] 0<=j<=neos-1 |\n",
    "#|  - hybrid_gamma_th              |\n",
    "#.---------------------------------.\n",
    "# Set up the number of polytropic EOSs.\n",
    "EOS_omni::neos = %d\n",
    "\n",
    "# Set hybrid_k0 to K_ppoly_tab0\n",
    "EOS_omni::hybrid_k0 = %.15e\n",
    "\n",
    "# Set hybrid_rho to rho_ppoly_tab_in\"\"\" %(Gamma_thermal,EOS_struct.neos,EOS_struct.K_poly_tab[0]))\n",
    "            for j in range(EOS_struct.neos-1):\n",
    "                file.write(\"\"\"\n",
    "EOS_omni::hybrid_rho[%d] = %.15e\"\"\" %(j,EOS_struct.rho_poly_tab[j]))\n",
    "            file.write(\"\"\"\n",
    "\n",
    "# Set hybrid_gamma to Gamma_ppoly_tab_in\"\"\")\n",
    "            for j in range(EOS_struct.neos):\n",
    "                file.write(\"\"\"\n",
    "EOS_omni::hybrid_gamma[%d] = %.15e\"\"\" %(j,EOS_struct.Gamma_poly_tab[j]))\n",
    "            file.write(\"\"\"\n",
    "\n",
    "# Set hybrid_gamma_th to Gamma_th\n",
    "EOS_omni::hybrid_gamma_th = %.15e\n",
    "\n",
    "#.--------------------------------.\n",
    "#| End of NRPy+ generated section |\n",
    "#.--------------------------------.\n",
    "#\n",
    "#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\"\"\" %(Gamma_thermal))\n",
    "\n",
    "    else:\n",
    "        import TOV.Piecewise_Polytrope__dict\n",
    "        if EOSname not in TOV.Piecewise_Polytrope__dict.EOS_Read_et_al_dict:\n",
    "            print(\"ERROR: Unknown EOS name \"+EOSname)\n",
    "            sys.exit(1)\n",
    "\n",
    "        if Gamma_thermal is None: # Use \"is None\" instead of \"==None\", as the former is more correct.\n",
    "            print(\"Error: Please set Gamma_thermal. Usage:\")\n",
    "            print(\"generate_IllinoisGRMHD_EOS_parameter_file(EOSname,outfilename,Gamma_thermal=None)\")\n",
    "            sys.exit(1)\n",
    "\n",
    "        eos        = set_up_EOS_parameters__Read_et_al_input_variables(EOSname)\n",
    "        atm_index  = polytropic_index_from_rhob(eos,rho_atmosphere)\n",
    "        Gamma_atm  = EOS_struct.Gamma_poly_tab[atm_index]\n",
    "        Kpoly_atm  = EOS_struct.K_poly_tab[atm_index]\n",
    "        IDfilename = \"outputTOVpolytrope-\"+EOSname+\".txt\"\n",
    "\n",
    "        # This is done for cosmetic purposes, so that parameter files\n",
    "        # of different EOS names all look the same.\n",
    "        largest_name_in_EOS_table = 6\n",
    "        if len(EOSname)==largest_name_in_EOS_table:\n",
    "            pass\n",
    "        else:\n",
    "            for k in range(largest_name_in_EOS_table - len(EOSname)):\n",
    "                EOSname += \" \"\n",
    "\n",
    "        with open(outfilename,\"a\") as file:\n",
    "            file.write(\"\"\"#\n",
    "#.---------------------------------------.\n",
    "#|     EOS Type: Piecewise Polytrope     |\n",
    "#.---------------------------------------.\n",
    "#|           EOS name: \"\"\"+EOSname+\"\"\"            |\n",
    "#.---------------------------------------.\n",
    "#|  Reference: Table II and III in       |\n",
    "#|    Read et al. PRD 79,124032 (2009)   |\n",
    "#|  https://arxiv.org/pdf/0812.2163.pdf  |\n",
    "#.---------------------------------------.\n",
    "#| Note that while we use the values in  |\n",
    "#| Read et al. (2009), we write them in  |\n",
    "#| geometrized units where G = 1 = c. We |\n",
    "#| also normalize the nuclear density to |\n",
    "#| unity.                                |\n",
    "#| You can read more about this in the   |\n",
    "#| following NRPy+ tutorial module:      |\n",
    "#| Tutorial-TOV-Piecewise_Polytrope_EOSs |\n",
    "#.---------------------------------------.\n",
    "#| Required inputs:                      |\n",
    "#|  - EOS name                           |\n",
    "#|  - Gamma_thermal                      |\n",
    "#.---------------------------------------.\n",
    "#| IllinoisGRMHD parameters:             |\n",
    "#|  - neos                               |\n",
    "#|  - K_ppoly_tab0                       |\n",
    "#|  - rho_ppoly_tab_in[j]   0<=j<=neos-2 |\n",
    "#|  - Gamma_ppoly_tab_in[j] 0<=j<=neos-1 |\n",
    "#|  - Gamma_th                           |\n",
    "#|  - tau_atm                            |\n",
    "#|  - rho_b_atm                          |\n",
    "#.---------------------------------------.\n",
    "# Set up the number of polytropic EOSs.\n",
    "IllinoisGRMHD::neos = %d\n",
    "\n",
    "# Set atmospheric value of tau\n",
    "IllinoisGRMHD::tau_atm = %.15e\n",
    "\n",
    "# Set K_ppoly_tab0\n",
    "IllinoisGRMHD::K_ppoly_tab0 = %.15e\n",
    "\n",
    "# Set atmospheric value of rho\n",
    "IllinoisGRMHD::rho_b_atm = %.15e\n",
    "\n",
    "# Set rho_ppoly_tab_in\"\"\" %(EOS_struct.neos,tau_atmosphere,EOS_struct.K_poly_tab[0],rho_atmosphere))\n",
    "            for j in range(EOS_struct.neos-1):\n",
    "                file.write(\"\"\"\n",
    "IllinoisGRMHD::rho_ppoly_tab_in[%d] = %.15e\"\"\" %(j,EOS_struct.rho_poly_tab[j]))\n",
    "            file.write(\"\"\"\n",
    "\n",
    "# Set Gamma_ppoly_tab_in\"\"\")\n",
    "            for j in range(EOS_struct.neos):\n",
    "                file.write(\"\"\"\n",
    "IllinoisGRMHD::Gamma_ppoly_tab_in[%d] = %.15e\"\"\" %(j,EOS_struct.Gamma_poly_tab[j]))\n",
    "            file.write(\"\"\"\n",
    "\n",
    "# Set Gamma_th\n",
    "IllinoisGRMHD::Gamma_th = %.15e\n",
    "\n",
    "#.---------------------------.\n",
    "#| NRPyPlusTOVID parameters: |\n",
    "#|  - TOV_filename           |\n",
    "#|  - rho_atmosphere         |\n",
    "#|  - Gamma_atmosphere       |\n",
    "#|  - K_atmosphere           |\n",
    "#.---------------------------.\n",
    "# Set up initial data file name\n",
    "NRPyPlusTOVID::TOV_filename = \\\"%s\\\"\n",
    "\n",
    "# Set atmospheric value of rho\n",
    "NRPyPlusTOVID::rho_atmosphere = %.15e\n",
    "\n",
    "# Set Gamma_atmosphere\n",
    "NRPyPlusTOVID::Gamma_atmosphere = %.15e\n",
    "\n",
    "# Set K_atmosphere\n",
    "NRPyPlusTOVID::K_atmosphere = %.15e\n",
    "\n",
    "#.---------------------------------.\n",
    "#| EOS_omni parameters:            |\n",
    "#|  - n_pieces                     |\n",
    "#|  - hybrid_k0                    |\n",
    "#|  - hybrid_rho[j]   0<=j<=neos-2 |\n",
    "#|  - hybrid_gamma[j] 0<=j<=neos-1 |\n",
    "#|  - hybrid_gamma_th              |\n",
    "#.---------------------------------.\n",
    "# Set up the number of polytropic EOSs.\n",
    "EOS_omni::neos = %d\n",
    "\n",
    "# Set hybrid_k0 to K_ppoly_tab0\n",
    "EOS_omni::hybrid_k0 = %.15e\n",
    "\n",
    "# Set hybrid_rho to rho_ppoly_tab_in\"\"\" %(Gamma_thermal,IDfilename,rho_atmosphere,Gamma_atm,Kpoly_atm,EOS_struct.neos,EOS_struct.K_poly_tab[0]))\n",
    "            for j in range(EOS_struct.neos-1):\n",
    "                file.write(\"\"\"\n",
    "EOS_omni::hybrid_rho[%d] = %.15e\"\"\" %(j,EOS_struct.rho_poly_tab[j]))\n",
    "            file.write(\"\"\"\n",
    "\n",
    "# Set hybrid_gamma to Gamma_ppoly_tab_in\"\"\")\n",
    "            for j in range(EOS_struct.neos):\n",
    "                file.write(\"\"\"\n",
    "EOS_omni::hybrid_gamma[%d] = %.15e\"\"\" %(j,EOS_struct.Gamma_poly_tab[j]))\n",
    "            file.write(\"\"\"\n",
    "\n",
    "# Set hybrid_gamma_th to Gamma_th\n",
    "EOS_omni::hybrid_gamma_th = %.15e\n",
    "\n",
    "#.--------------------------------.\n",
    "#| End of NRPy+ generated section |\n",
    "#.--------------------------------.\n",
    "#\n",
    "#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\"\"\" %(Gamma_thermal))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='pcold_plot'></a>\n",
    "\n",
    "# Step 10: Visualizing $P_{\\rm cold}\\left(\\rho_{b}\\right)$ \\[Back to [top](#toc)\\]\n",
    "$$\\label{pcold_plot}$$\n",
    "\n",
    "Now let us visualize our results by plotting $P_{\\rm cold}$ as a function of $\\rho_{b}$. We will make plots that cover the entire range of $\\left\\{\\rho_{j}\\right\\}$, so that we can visualize whether or not all of our EOSs are being used. We will also differenciate each EOS by color, ranging from colder regions (blue) to hotter regions (red).\n",
    "\n",
    "<a id='pcold_plot__neoseq4'></a>\n",
    "\n",
    "## Step 10.a: Complete input set, $n_{\\rm eos} = 4$ piecewise polytropic EOS \\[Back to [top](#toc)\\]\n",
    "$$\\label{pcold_plot__neoseq4}$$\n",
    "\n",
    "First let us generate our polytropic data from a complete input set $\\left\\{\\rho_{0},\\rho_{1},\\rho_{2},\\Gamma_{0},\\Gamma_{1},\\Gamma_{2},\\Gamma_{3},K_{0}\\right\\}$, using the values from table II in [Read *et al.* (2008)](https://arxiv.org/pdf/0812.2163.pdf), then generate our plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import TOV.Polytropic_EOSs_validation as poly_validation\n",
    "# Number of equation of states\n",
    "neos = 4\n",
    "\n",
    "# User input #1: The values of rho_b that distinguish one EOS from the other\n",
    "#                Values taken from Table II of J.C. Read et al. (2008)\n",
    "#                       https://arxiv.org/pdf/0812.2163.pdf\n",
    "rho_poly_tab = [2.44034e+07,3.78358e+11,2.62780e+12]\n",
    "\n",
    "# User input #2: The values of Gamma to be used within each EOS\n",
    "#                Values taken from Table II of J.C. Read et al. (2008)\n",
    "#                       https://arxiv.org/pdf/0812.2163.pdf\n",
    "Gamma_poly_tab = [1.58425,1.28733,0.62223,1.35692]\n",
    "\n",
    "# User input #3: The value K_0, to be used within the *first* EOS. The other\n",
    "#                values of K_j are determined by imposing that P be everywhere\n",
    "#                continuous\n",
    "#                Value taken from Table II of J.C. Read et al. (2008)\n",
    "#                       https://arxiv.org/pdf/0812.2163.pdf\n",
    "K_poly_tab0 = 6.80110e-09\n",
    "\n",
    "# Set up the EOS \"struct\" (named tuple)\n",
    "eos = poly_validation.set_up_EOS_parameters__complete_set_of_input_variables(neos,rho_poly_tab,Gamma_poly_tab,K_poly_tab0)\n",
    "\n",
    "############\n",
    "# PLOTTING #\n",
    "############\n",
    "# Step 0: Import needed Python/NRPy+ modules\n",
    "import numpy as np                  # This module is used for math functions\n",
    "import matplotlib.pyplot as plt     # This module is used for plotting\n",
    "import matplotlib.patheffects as pe # This module is used to draw a border around our plotting curve\n",
    "from IPython.display import Image   # This module is used to display saved images\n",
    "# First set the number of points in the plot\n",
    "n_plot = 1000\n",
    "\n",
    "# Then split the plot by the number of EOSs used in the code\n",
    "rho_b    = [0 for i in range(eos.neos)]\n",
    "P_cold   = [[0 for j in range(int(n_plot/eos.neos))] for i in range(eos.neos)]\n",
    "eps_cold = [[0 for j in range(int(n_plot/eos.neos))] for i in range(eos.neos)]\n",
    "\n",
    "# Then set the plotting limits for each region\n",
    "lim = [eos.rho_poly_tab[0]/50.0,eos.rho_poly_tab[0],eos.rho_poly_tab[1],eos.rho_poly_tab[2],eos.rho_poly_tab[2]*30.0]\n",
    "\n",
    "# Then populate the rho_b arrays\n",
    "for j in range(eos.neos):\n",
    "    rho_b[j] = np.linspace(lim[j],lim[j+1],int(n_plot/eos.neos))\n",
    "\n",
    "# Finally, populate the P array\n",
    "for i in range(eos.neos):\n",
    "    for j in range(int(n_plot/4)):\n",
    "        P_cold[i][j]   = poly_validation.Polytrope_EOS__compute_P_cold_from_rhob(eos, rho_b[i][j])\n",
    "        eps_cold[i][j] = poly_validation.Polytrope_EOS__compute_eps_cold_from_rhob(eos, rho_b[i][j])\n",
    "\n",
    "# We will draw a border around our plot curve so that it\n",
    "# is easier to distinguish between the EOSs. Source:\n",
    "# https://stackoverflow.com/questions/12729529/can-i-give-a-border-outline-to-a-line-in-matplotlib-plot-function\n",
    "colors = ['blue','lime','orange','red']\n",
    "fig = plt.figure(figsize=(12,5))\n",
    "nplots = 2\n",
    "ax = [0 for j in range(nplots)]\n",
    "ax[0] = fig.add_subplot(121)\n",
    "ax[1] = fig.add_subplot(122)\n",
    "\n",
    "titles = [r\"$\\log_{10}\\left(P_{\\rm cold}\\right)\\times\\log_{10}\\left(\\rho_{b}\\right)$\", \\\n",
    "          r\"$\\log_{10}\\left(\\epsilon_{\\rm cold}\\right)\\times\\log_{10}\\left(\\rho_{b}\\right)$\"]\n",
    "\n",
    "xlabels = [r\"$\\log_{10}\\left(\\rho_{b}\\right)$\", \\\n",
    "           r\"$\\log_{10}\\left(\\rho_{b}\\right)$\"]\n",
    "\n",
    "ylabels = [r\"$\\log_{10}\\left(P_{\\rm cold}\\right)$\", \\\n",
    "           r\"$\\log_{10}\\left(\\epsilon_{\\rm cold}\\right)$\"]\n",
    "for i in range(eos.neos):\n",
    "    label=r'EOS #'+str(i+1)+r', parameters: $\\left(\\Gamma_'+str(i)+r',K_'+str(i)+r'\\right)$'\n",
    "    ax[0].plot(np.log10(rho_b[i]),np.log10(P_cold[i]),label=label,c=colors[i],lw=3, path_effects=[pe.Stroke(linewidth=5,foreground='k'),pe.Normal()])\n",
    "    ax[1].plot(np.log10(rho_b[i]),np.log10(eps_cold[i]),label=label,c=colors[i],lw=3, path_effects=[pe.Stroke(linewidth=5,foreground='k'),pe.Normal()])\n",
    "\n",
    "for j in range(nplots):\n",
    "    ax[j].legend()\n",
    "    ax[j].grid()\n",
    "    ax[j].set_title(titles[j],fontsize=14)\n",
    "    ax[j].set_xlabel(xlabels[j],fontsize=14)\n",
    "    ax[j].set_ylabel(ylabels[j],fontsize=14)\n",
    "\n",
    "outfilename = \"P_and_eps_cold_x_rho_b__piecewise_polytrope_neoseq4.png\"\n",
    "plt.savefig(outfilename)\n",
    "plt.close(fig)\n",
    "Image(outfilename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='pcold_plot__neoseq7'></a>\n",
    "\n",
    "## Step 10.b: \"Read et al.\" input set, $n_{\\rm eos} = 7$ piecewise polytropic EOS \\[Back to [top](#toc)\\]\n",
    "$$\\label{pcold_plot__neoseq7}$$\n",
    "\n",
    "Now let us generate our polytropic data from the \"Read *et al.*\" set of input variables, namely $\\left\\{\\log_{10}\\left(P_{4}\\right),\\Gamma_{4},\\Gamma_{5},\\Gamma_{6}\\right\\}$, using the values from table III in [Read *et al.* (2008)](https://arxiv.org/pdf/0812.2163.pdf), then generate our plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pp58McatWrZx2rrxs165dhjjtxIODBw+qUaNGhkkHHh4eGj16tL766qscMelAklq2bGmIrSdJAACQnx0/flyLFy/O8LmtW7eqY8eOqlu3rhYvXuzy35sBAID9qOPbhjq+61DHd1+u1PGzhzo+dfzchDo+AAB5V3h4uCFOSEhQeHi43nzzTTVp0kSlS5dW+/bt9dFHHykqKkrJycluyhQAkB+xhB0AALeaKukBdyeRT+2SZN8FstwkNjZWu3fvNrR16dLFIWN36dJFO3bssMQbN27MsF/VqlXl4+OjhIQES9vw4cO1bNkyeXh4OCQX5B5JSUnq1q2b4f1SunRpVa5cWQkJCTp27JjhvXLt2jV17txZ33///R13L017XKoKFSqoVKlS8vX11fXr13XixAnDJBJJmjFjhuLi4jRv3jy7f57bt2+rffv22rBhg6WtZMmS8vf3l5SyA6v1+dyVa3Jysrp3765ffvnFcM4KFSooLi5OBw8eNOwsvn79eg0ePFhz5szRuXPnFBoaqhMnTkhKuUAZGBio0qVL6+LFizp69KjhXNOmTVODBg3Us2fPO+Y1Z84cDRo0KN2u5kWKFFFAQID8/Px06dIlHT16VElJSYbjzp49q2+//dYlnyWuztPMe+vzzz/XiBEj0o3l5+enKlWqqGjRooqPj9elS5d06tSpHFuATrsLvCQ1aNDATZnkXklJSfrzzz8NbakTD3766Sd1795dV65csTzn6+urL7/8Uh07dnRpnndStmxZ+fv76+TJk5LSvzcAAMjPPvzwwzveGWjv3r3q1auXRo0apeHDh6tPnz7p7k4CALAVNXz3oo5vFnV8mEEd3325UsfPHur4rkMdP/uo4wMAkHfFx8dr06ZNWfa5du2aVq9erdWrV0tK+U4NCQlRWFiYQkNDVbVqVf4/DgBwGhYQAgDgVg9ICnZ3EsiD/vjjj3Q7ZgYFBTlkbOtxzpw5o2PHjikwMNDQ7uPjo7CwMK1Zs8bS9s033ygsLEzvv/++mjVr5pB8kDssXLhQ0dHRkqT69etr8uTJatWqlaXoFRsbqyVLlmj48OGWi2IJCQnq0aOH9u/fn24nbGuVK1dWt27d1KZNGzVo0EDFihUzPJ+cnKyoqCjNmjVLc+bMsVxEnj9/vtq1a6dOnTrZ9fN89tlnOnv2rCTp4Ycf1nvvvacmTZrI09PTcr7w8HAVLlw43S6yrs514cKFlt1mn3rqKY0aNUq1atWyPH/x4kW9/fbb+vTTTw0/30svvaTXX39dJ06ckI+Pj4YNG6aBAwfq7rvvtvQ7ePCg+vXrp99//93SNnToUD3xxBMqWLBgpjmFh4dr4MCBhgv17dq102uvvaZmzZqpQIF//6t66dIlffbZZxozZoxiYmIkSStXrtTEiRM1fPjwdGMvXrxY8fHxOnv2rHr16mVpb926tV5//fVMc6pTp45L88yMPe8tKWW372HDhhnG6NKli0aMGKH69eunKyzHxMRo27Zt+v7777Vo0SKb83KFyMhIy2MvLy/D+xS2OXjwoOLi4iyxn5+fqlevrsmTJ2v48OGGCTSBgYFauXKl7r//fnekekf333+/ZeLBlStXdPjwYVWvXt3NWQEA4F7//POPPv/8c0Pbw5KOSTqSQf9jx45pwIABeu+99/Taa6+pf//+8vX1dUWqAJCHUMOH81DHR06TH+v4P//8c4abbVDHp46fFer4Kajjm0MdHwCAvGvTpk26ceOGXcecO3dOX331lb766itJUkBAgGUxYWhoqMqXL++MVAEA+RQLCAEAAHKAyMjIdBMFbFG2bFnVrVs3XfuBAwcMcdGiRVWjRg3T+aWVugNiWvv370838UCS3njjDcPEA0lau3atmjdvroCAALVu3VpNmzZVo0aNVKtWLcuFNVd69NFHDTu6ptWpUyfFxsZKkt58800FBwen6zNkyBDt2bNHkvT000+rd+/eGY5VsmRJxyScS6VOOmjTpo1WrFiR7qK0r6+v+vXrp5CQEDVv3lznzp2TlDJB9s0339SMGTMyHXvmzJl66KGH5OXllWkfDw8P1a1bV59++qk6deqkdu3aWYp248ePt/tifuqF4VdeeUVTp07N8HwPP/ywEhMTDQsIp0+frhYtWrg019RJB5MmTdJrr72W7vnSpUtrxowZio+P1/z58yWlXNx+8skntX//fvn6+ur7779XixYt0h1bs2ZN/fDDD2rQoIH2798vKeXvbM2aNZnuhHrlyhX16tXLcjHf09NTc+fO1XPPPZdh/1KlSmnYsGF6/PHHFRwcrPPnz0uS3n77bfXp08cwEUKSZVJT6s+dqnz58nr44YczPIc78syMre+tVOHh4bp06ZIl7t27txYsWJDp+H5+fpZC89ixY3Xq1Cmb8nK2Gzdu6ODBg5Y4ICBAhQoVcmNGudPOnTsNcc2aNfXMM89o4cKFhvbg4GB9/fXXKlOmjCvTs8u9996r77//3hLv3r2biQcAgHxv6tSphskH3pIWSConaamk8ZL2ZHDcmTNnNHToUI0dO1Yvv/yyXnrpJZUqVcolOQMAkJdQx6eOn9fl1zp+6kIyd+ZKHT8FdfwU1PHzNur4AADkXREREYb4QUlvS4qQFC5pnw1jREdHa968eZY7mz/wwAOKjIx0y//FAQB5DwsIAQAAcoChQ4eaOq5Dhw5asWJFuva0F6EkqVy5cul2rzTrrrvukqenp2EnT+vzpWrevLlGjRql999/P91z0dHRmjt3rubOnSsp5cJzo0aNFBwcrMcee0wNGjRwSL53Ur58+Qx3azpw4IBl0oEk9e3bVwEBAYY+t27d0tGjRy3xU089ZdeFzfymfPnyWrp0aZY72t5zzz1auHCh/t//+3+Wtvnz52vs2LEqXrx4hsdkdEE8K4888ohef/11jRkzRpK0detW7du3T7Vr17ZrnIceekhTpkyx65g7LR50Vq5PPvlkhpMO0hozZowWLFhg+bedOpFgypQpWb7GRYoU0ahRo9SjRw9L2w8//JDpxINZs2bpn3/+scQffPBBphfz06pdu7a++OILtW3bVpJ08+ZNTZ8+3fLaOJo787TnvfXXX38Z4kGDBtl8Hm9vb1WtWtXm/s504sQJw/dKpUqV3JhN7mU98WD79u3avn27oW3AgAH6+OOPs/wszgkqVKhgiK0nEwEAkN9cunTJcLcRSeojKfUbs+f//VkpaZykPzIZ491339WkSZM0YMAAvfrqq+xeDACAHajjU8fPD6jjuy9X6vj2o47vWtTxHYM6PgAAeVd4eLghflRSx//7I0n/KGUxYeqCwuM2jFm8eHEWDwIAHIZvFAAAgDzIeiJAZhdszfDw8FCxYsWyPF9ao0eP1kcffSQfH58sx42NjVVERITefvttNWzYUPfdd5/mzZtnuBDlSmvXrrU8DgwMTDfpQEq5oBMXFydJ8vLysuyYioy99dZb8vPzu2O/Rx99VKGhoZY4Pj5e//3vfx2aS69evQzxpk2b7B5j9OjRDpvQk5Xs5urh4aHRo0ffsV+FChXSTfgJCAiw6WJ7u3btDAVL64ufqRITE/XJJ59Y4sqVK99xQkRabdq0Meyevnz5cpuPtYe787TnvRUfH2+IHXkxuUqVKkpOTjb8qVKlisPGT+vkyZOGmIns5uzatSvL56dNm6aZM2fm+EkHUvr3gPV7BACA/Gb69OmGyfFekoZn0K+DpC2SfpUUmsHzUsr/vydNmqTAwEANHDhQp0+fdni+AADgzqjjZx91fMejjm8Odfx/UcdPjzo+0qKODwBA3nT16lVt27bN0BZm1eduST0kfSbpmKQjkuZK6i6pXCbjhoVZjwIAgHksIAQAAMiDYmJiDHHRokUdOr71eNbns/byyy/r0KFDGjRokM2TIPbu3avnn39ejRo1UnR0tOlczUo78SA4ODjDPr/99pvl8YMPPphuQgb+VbBgQXXv3t3m/n369DHE69atc2g+gYGBhjizC+WZKVeunGFyhDNlN9e6deuqRo0aNvW97777DHGnTp1sumOir6+v4YL0iRMnMuy3e/duw+Tk7t27230BtHXr1pbHBw4c0IULF+w63hbuzNPe95b17q6LFi2y+dic5OrVq4bY19fXTZnkbtafD9YTWPbt2+fKdLLF+j1g/R4BACA/iY2N1UcffWRoe0JStSyOCVPKDsZbJLWXlNG01hs3bmju3Lm6ceOGo1IFAAB2oI6ffdTxHYs6vnnU8Y2o4xtRx0da1PEBAMibfv/9d8PmOoUk3Wn7mqqSXpD0X/17d0Jrrvo/DQAgf2ABIQAAQA6wdu3adLtD2vJnxYoVGY5nvTvs9evXHZqv9Xi27EZbqVIlzZgxQ2fPntWqVas0ZMgQNWjQQN7e3lkeFxkZqUaNGunIkSPZytleaScVhISE3LFPq1atnJ5Tbla3bl2VKlXK5v7Wkz22bt1q03Fbt27VyJEj1aZNG1WtWlWlSpVSwYIF5eHhYfhTqFAhw3H2Xrxu0KBBtnctdlWuQUFBNvctXbq0Ia5fv76pY69du5Zhn/Xr1xti652SbVG5cmVDvH//frvHuBN35mnveys0NNQwOWTq1KkaNGiQjh49avMYOUHqLvCpChcu7KZMsnblyhWNHTtWDRs2VOnSpVWkSBFVr15dffv2VWRkpFtzO3nypC5evGiJvb29NXXqVEOfOXPm6NNPP3XYOZ35ehQpUsQQO/p3GQAAcpM5c+YY7hjkIWmEjcc2lrRS0p+SumTwfI8ePdJNdgYAABmjjp856vh5A3V89+VKHd9+1PFdjzp+9lHHBwAg74qIMC7/ayrJ3t+WzlvFvr6+atSoUXbSAgDAoIC7EwAAAIDjWV/gdeRuf8nJyekuKtpzQblQoUJq166d2rVrJ0m6efOmoqKitHHjRv3666/66aefdPPmTcMx586dU5cuXRQZGWnTLqrZtXfvXp07d84SZzTxIDExURs3brTETDzImvWOuHdSuXJlFStWzPJei46OVnJycqYXZNevX6///Oc/+vPPP03ld+XKFbv6Z2eCratzveuuu2zua32hz+yx8fHxGfaxvvj+xBNP2Dx+ZtJOpHYUd+Zp73vL399fzz33nObOnWtpmzlzpmbOnKmgoCA9/PDDatmypZo0aWLXZ7W7JScnuzuFdLZu3aouXbro1KlThvYjR47oyJEjmj9/vt555x2NGjXKLflZ71pcp04dDR48WFFRUfr8888t7YMHD1adOnWy/b3l7NcjJ74HAABwhxs3bmjSpEmGtscl3W/nOPcpZTHhcqv2N954w3xyAAAgW6jjZw91fMejjv8v6vj/oo6fHnX8FDmxhksd34g6PgAArhMeHm6Iw8yMYRW3bNnS7rtsAwCQFe5ACAAAkAdZX1w6d+6cwwr458+fV1JSUpbns4e3t7eCgoL08ssva9WqVTp9+rRef/31dBMMdu/eraVLl5o+jz3Wrl1reVytWjVVqlQpXZ8dO3YoJiZGkuTp6anmzZu7JLfcynpHXFukfV8lJSVluhvu7Nmz1apVK9MX8qWUSbn2KFasmKnzzJkzx+W5+vj4mD5Xdo7NSNpdVR3FkROrUrkzTzPvrY8//tgymSutyMhITZgwQW3btlWZMmX04IMPauTIkdq7d6/d53A260kvCQkJbsokY0ePHlXbtm116tQpeXh4qH///vr111+1ZcsWffTRRypfvrwSExP19ttv65NPPnFLjrt27TLEDz74oCRpxowZaty4saX99u3b6tq1q6Kjo02fyxWvh/UEpqJFi5rOFwCA3GzBggU6c+aMoe1NE+PckDTFqq1Tp06qXbu22dQAAEA2UcfPHur4jkcdP4U7cqWObz/q+K5HHT/7qOMDAJA3nTt3TlFRUYa2UBPjRFjFoaFmRgEAIHPcgRAAACAPqlmzpiGOjY3VoUOHVKNGjWyPbb0zoiTVqlUr2+OmKl26tCZOnKiWLVuqY8eOSkxMtDy3aNEi9ezZ0yHnCQ4O1m+//XbHfkeOHMl0t9xUSUlJmU6+mD9/vp555hkzKeYp1hcVbWF9kSk2NlbFixc3tK1du1YDBw40TKwpUKCAmjdvrsaNGysgIEBly5aVj4+PChUqZDj2kUcesTunVGZ2+Fq/fr1efPFFl+eak9i767ItrCdCOYI78zTz3vLx8dHKlSu1dOlSTZw4Md0FaCllF9hdu3Zp165dGjdunNq2batp06apevXqdp/PGUqUKGGIUyd25RSvvvqqLly4IEmaNWuW+vXrZ3mucePG6tSpk4KCgnT+/Hm98cYb6tKliypUqODSHK2/nx944AFJKXcMWL58uRo0aKB//vlHknThwgV16NBBGzduNHVB3xWvR2xsrCG2/vwHACA/uH37tiZMmGBoC1HKnQTtNU/SP1ZtI0aMMJkZAABwBOr4d0Yd37Wo47sv15yEOv6dUcdPQR3fftTxAQDIm9JucCNJfpIa2TnGCUmHrdrCwszcxxAAgMyxgBAAACAPatKkiby8vAwX7bdv3+6QiQeRkZGGuHz58goMDMz2uNYef/xx9enTR/PmzbO0bdiwweHngWvExcXZfcz169cNsa+vb7o+r732muFCftu2bTVr1qwMd5tOy97dfx3hrbfeyjW5Oov1BJTx48crKCgoW2PWqVMnW8dnJLfkmZaHh4eeeuopPfXUU9q3b59++eUXrVu3Ths2bLBcIE5rzZo1+v3337VmzRq1aNHCqbnZwt/f3xBb32XHnfbt26eVK1dKkpo3b264yJ7K399fY8eOVd++fRUXF6ePPvoo3WR/Z7OeeJC6c7EkVaxYUcuWLVNoaKhu3rwpKeWOAM8++6z+97//2XUeV70ep0+fNsSVK1e263gAAPKC//3vfzp69KihbaSJcW5LmmjV9sgjj6hhw4ZmUwMAAA5AHR85DXV8adiwYbkmV2fJLfXx3JJnWtTxnYc6vhF1fAAAXCs8PNwQt5D9CzTCreLSpUurbt262UkLAIB0WEAIAACQB/n6+qpevXrasWOHpW358uXq0aNHtsdevny5IW7WrFm2x8zME088YZh4EBsbq6tXr7J7U7I3lwAAIABJREFUYS6U0YXPO7l06ZLlsaenp4oVK2Z4/q+//jJcaLvvvvv0zTffyNvb266xXeHw4cP6888/LXFOztWZypQpY4gDAwP18MMPuymbzOWWPDNTu3Zt1a5dW4MHD1ZycrIOHDign3/+WcuWLTNM4IqJiVHXrl115MiRDCf2uJK/v788PT0tOzyfOnXKrfmktWzZMsvjvn37ZtqvZ8+eGjx4sOLi4rRs2TKXTjy4fPmyoqOjLbGHh4fq1atn6NOsWTN9/PHHGjBggKXt66+/1pgxY/TWW2/ZfC5XvR7WEw+qVKli1/EAAOR2SUlJGjdunKGtkSQzv5UukXTcqm3kSDNLEQEAgCNRx0dOQx3/cK7J1ZlyS308t+SZGer4jkUd34g6PgAArhUREWGIzdw3MMIqDgkJkaenp+mcAADICAsIAQBwq13uTiAfy/uvfZcuXQwTD1avXq0LFy6ku6Bmjz179hjGlKTOnTubHu9OMrrIEBcX55CJB5MnT9bly5fTtf/444+aPHmyJKlSpUqaP39+uj4HDhzQSy+9ZIlXrlyZbqfTVM7erTS32LNnj139o6Ojde3aNUscEBAgDw8PQ58tW7YY4hdeeMGmC/mStHfvXrvyya7t27cb4pycqzNZ73J++PBhN2WStdySpy08PDxUq1Yt1apVS4MHD9b69evVuXNny2Sgc+fO6csvv9TAgQPdmmehQoVUs2ZN7d+/X5J04sQJJSQkyMfHx615SdJvv/1meRwaGpppv8KFC6tJkyaKiIjQ0aNHdfLkyXQ7MjuL9a7F1apVk5+fX7p+/fv3V2RkpObOnWtpe/vtt1W3bl21b9/epnO56vU4cOCAIWZ3RQBAfvPdd9+l+3/UCJNjjbeKmzZtqlatWpkcDQDym7xfR87Z8v7rTx0/a9TxXYs6PnV8KffUx3NLnragjp991PGNqOMDAOA60dHROnLkiKEt82/fzFkvIMzqOxwAALNYQAgAgFsNcXcCyMP69eun999/XwkJCZKkW7duacyYMZo2bZrpMd9++21DXLFiRXXr1i1beWbl+vXr6dpKly7tkLGDgoIybP/6668tj1u1apXhbqUHDx60PL733nttvliTn0VFRenSpUsqVaqUTf3TXtiSpEaNGqXrc/bsWUNcs2ZNm/Ox3v3L2c6dO2eIc3KuzhQSEmKIIyIinHbnE+ud2JKTk20+1pV5ulqLFi00fvx4vfDCC5a2DRs2uH3igZTyuZw68SAxMVH79u1T/fr13ZzVv5N/ihUrpkqVKmXZt3bt2pZ/s/v27XPZxINdu4wTKh944IFM+06fPl179uzR5s2bJaX82+jVq5e2bNmi2rVr3/Fcrno9oqKiLI9LlCih6tWr23wsAAC5XXJysj744ANDWx1JHU2M9Y2k/VZtI0eOTDexGwCQGWr4cC7q+Fmjju9a1PGp40vU8XMC6vj2o45vRB0fAADXsf6/QBlJmX/LZ+yApNNWbWFhZu5jCABA1ri3LQAAQB5VpkwZ9e7d29A2ffp0RUZGmhrvu+++07fffmtoe+mll1SggPP2pNi2bZshvvvuu23e7dWs33//3fK4RYsWGfZZv3695XHLli2dmk9ecevWLS1dutTm/gsWLDDEGd0dw/pC8s2bN20a+8aNG5o3b57NuThCbsrVmRo1aqSSJUta4oiICO3bt88p5ypatKghjouLs/lYV+bpDs2aNTPEqbsYu5v1Z67Z7ytHunHjhmWSky0XzdP2iY6Odlpe1qx3Ln7wwQcz7evt7a3ly5erfPnylraYmBh16NAhwx3903LV63H27FmdOnXKEmf2fQwAQF61du1abd261dD2hsmxxlnFdevWVdu2bU2OBgAAHI06vjnU8Z2DOn7uydWZqOPnDNTxbUcd34g6PgAArhUeHm6IQzLpl+UYVnHFihV1zz33mM4JAIDMsIAQAAAgDxs7dqzhwkJiYqI6dOigY8eO2TXOzp071atXL0NbvXr1NGRI5jtwX7hwQUuWLFFSUpJ9Sf+fmzdv6pNPPjG0Pfroo6bGstX58+d14MABS8zEA8caM2aMYmJi7tjvp59+MuzQVbhwYT311FPp+t19992GeMOGDTblMWrUqHS7HjtbuXLlDHFOztWZChYsqFdeecUSJycnq3///rp165bDz1WsWDF5eXlZYns+91yZpztYTzRIO8nCnaw/49NOBHOXtJ9Zvr6+d+zv5+eX4bHOZj3xIKudiyWpfPnyWr58uWEy3+HDh9W9e3clJiZmepyrXg/rv3tnf/8DAJDTjB071hAHSkr/P6I7+1nSdqu2ESNGcPdBAAByGOr49qGO71zU8f+Vk3N1Jur4OQN1fNtRxzeijg8AgOskJyenW0AYamIc6/uZh4WFUccHADiF87aZAwAA6ZQuXdrdKSATefXvpnTp0lqwYIEeffRRy86pf//9t0JCQrRw4UKbLpp/9913evbZZ3X16lVLm4+PjxYvXpzlLsKxsbHq2bOnPvjgA40YMUJdunRR4cKFbco7ISFBvXv31t69ew3t1jsxO1raCQWlS5dWrVq10vU5evSoTp8+bYlz426Kx48fV2BgoKHt2LFjqlKlilPPe+bMGXXv3l0rVqxQwYIFM+xz+PDhdH/Pffr0UYkSJdL1feihhwzxrFmz1K9fP1WvXj3THGbPnq1JkyaZyD57GjVqZIhzcq7ONnjwYH366aeWCRUbNmxQ165dtXDhQhUvXtymMa5fv67PP/9cRYsW1fPPP59hn4IFC6pGjRrav3+/JGnXrl06cuSIqlWrlqPyzK5Ro0apZs2a6t69u007yScnJ2vy5MmGtqCgoAz7uvqzIiAgQHXq1LF89q9du9Yp57FHfHy85bEtO+cXKlQow2OtOfK1TUhIMEyak7LeuThV06ZNNWPGDPXt29fS9vPPP+v111/XlClTMjzGWa+HtXXr1hniNm3a2HwsAAC53R9//JFuwsEwSV4Zd8/SWKu4evXq6tatm9nUACBfyKt14rwir/79UMe3D3X8Kk49L3X8f+XkXJ2NOr5jUcd3Lur4RtTxAQBwnQMHDuiff/4xtIXZOUaSpHVWbWFh9o4CAIBtWEAIAIALWReFgVSRkZG6ffu2qWPLli2runXrZvr8I488ogkTJmjYsGGWtujoaAUHB6tnz5565plnFBwcbDjm1q1bWrNmjb744gt9++23hue8vLw0f/581alTx6b89u3bp6efflovvviinnjiCbVu3VrNmzc37Kic6vTp01qxYoU+/PBDHT9+3PBc586dFRpqZp8m26XdKbF58+YZ7uaUdnJCQECAKleu7LDzb9y4McMLM7t37zbECQkJ+vXXXzMco0KFCqpdu7bDcnKUgIAARUdH6/vvv1fTpk01efJktWzZ0vIaX79+XUuWLNHw4cN1+fJly3HlypVLd/eNVNWrV1fTpk21efNmSSm7YrZs2VIfffSROnXqZLgIu3v3bo0dO1b/+9//JEm1atWyXJB2hapVq6phw4batm1bjs/V2YoXL66vv/5aYWFhlp2AV61apTp16mjIkCHq1q1bhv+uTp48qT/++EMrVqzQ6tWrde3aNb3zzjtZnqt169aW1y4xMVEtW7ZUv379VLduXfn6+hr+jdepU8fwueTKPLMjKipKY8aM0WuvvabOnTurQ4cOatiwYboJdUlJSdq0aZPee+89w+dHkSJF1KNHD6flZ68ePXrozTfflJQyUW779u1q0KCB2/JJO2Hu5s2bd+x/48aNDI91pqioKMNuw2XLls3wOzYjL7zwgiIjIzVr1ixL29SpU/Xggw/q6aefTtffFa9HcnKyVq1aZYmbNm2abpIGAAB52bhx4wxxeUnPmhhnk6TfrNqGDx9uuLsHACA9avjICnX8FNTxqePn5Tp+kyZNtGXLlhyfq7NRx3cs6vjORR3fiDo+AACuY70ZoL+ke+wcY5ekS1Ztzv5/NQAg/2IBIQAAQA4wdOhQ08d26NBBK1asyLLP66+/rqJFi2rw4MGWCQ7JyclatGiRFi1apMKFC6tChQoqXry4Ll68qH/++cdwsSBV0aJF9eWXX6pTp05253nt2jV99tln+uyzzySl7AxcpkwZlShRQgkJCTpz5ozOnTuX4bGNGzfW/Pnz7T6nvdJOPMhsR+K0Ew9s2fnZHj179lR0dPQd+509e1aPPPJIhs/16dNHX3zxhUPzcoTevXtry5Yt+uWXXxQZGang4GCVLl1aAQEBSkhI0LFjx9JNuihUqJAWLVqkkiVLZjrupEmTFBwcbLkwfObMGT3xxBPy9fXVPffcI09PT506dcqy+6yU8j5evHix6tev75wfNhPvv/++2rVrlytydbYWLVpo4cKFevbZZ5WQkCAp5SLz0KFDNXToUJUvX15ly5ZVoUKFdPXqVZ07d84wIcVWgwYN0uzZsy3nOH36tN59990M+86fP1/PPPOMW/J0hHPnzmnWrFmWC8jly5dXmTJlVLRoUV2/fl3Hjh1TbGxsuuMmT56sihUrujrdTPXs2VNvvfWWZbf9b775xq0TD/z8/CyPM3r9rKXtk/ZYZ9q5c6chtmXX4rQ+/vhjRUVFaePGjZa2fv36qWbNmul2XXfF67F9+3adOnXKEvfq1cum4wAAyAv27NmjlStXGtpelVQo4+5Zsp6+XbFixQwnFgIAANtRx6eOL1HHz+t1/IkTJxoWo+XkXJ2NOr7jUcd3Dur41PEBAHCXiIgIQ2xm2V+4VVyjRg1VqlTJdE4AAGTF090JAAAAwDUGDRqkTZs26YEHHkj3XHx8vI4cOaIdO3YoOjo6w0kHrVq1UmRkpM2TDnx9fVWvXr1Mn7948aIOHjyoP/74Q7t3785w0oGnp6cGDBigX375RcWKFbPpvGZdu3bNsEOwOyYeuErqBdRUBQoUkK+vr1PP6enpqa+//lrNmze3tF28eFE7duzQvn370k068PPz07Jly/Twww9nOe5DDz2kuXPnqmDBgob22NhY7dy5U5GRkYYL+SVLltR3331n94U5R2jcuLFmz56dK3J1he7du2vDhg2qUaNGuufOnDmj3bt3a+vWrTp48GCGF/O9vLxUoUKFLM9Ro0YNffnll9l6f7siT2c4c+aMoqKitGXLFkVFRaW7SFy4cGHNmjVLAwYMcHluWQkICFCbNm0s8ZIlS5SUlOS2fAoVKqSyZctKStmV+k5OnDhheZzVzvaO/By2nniQ0fd8VgoWLKjly5cbJqAkJCSoU6dOOnPmjKGvs16PtL788kvLYz8/PyYeAADylfHjxxviUpLM/La2W9Iaq7ahQ4eqUCEzSxEBAIArUcfPGnV86vjOlptydQXq+M5FHd8xqONTxwcAwB0SExO1du1aQ5uZBYQRVjF3HwQAOBMLCAEAAPKRhg0baseOHfrmm2/Upk0beXt7Z9m/SJEi6tq1q3799VetW7dONWvWtPlcZcqU0a5du3TkyBFNmTJF7dq1y3IH2rTKly+vwYMHa9euXZo5c6ZLdn/cuHGj5eJWkSJFMtwp9ty5c/rrr78scW6deLBp0yZD/PTTT6tMmTJOP2/x4sUVERGhcePGZXo+b29vPfHEE9q3b58ef/xxm8bt06ePfv/99yz/Pnx8fPTcc89p7969Cg4ONpO+Q/Tu3TvX5OoKQUFB2rdvnxYuXKgmTZrIy8sry/6FChVSaGioJk2apJMnT6pfv353PEfXrl31119/afz48Xr00Ufl7+8vX19feXh45Kg8zZo7d67mzZunLl26qFy5cnfsX6pUKQ0YMED79+9X//79nZZXdrzyyiuWx9HR0frll1/cmI1Up04dSSkT1NLuqJuRffv2pTsuI478HM7uxANJKleunL755hvDooLTp0+rc+fO6SYjOuP1SHXjxg0tXrzYEj/77LNOn3gIAEBOcfToUf33v/81tL0kyczUxHFWcenSpdW3b1+zqQEAABejjp856vjU8V0hN+XqCtTxs486vvNRx6eODwCAq+3atUtXrlwxtIXZOcYtSeut2sLC7B0FAADbeSQnJye7OwkAAFxt7969uu+++yzxnj17bCqGZub27ds6dOiQoe2ee+5RgQIFTI8JuEJ8fLwiIyN16NAhnT17VjExMZZdCevWrasGDRrccXKCPZKTkxUdHa2//vpLJ06c0NWrVxUfH68iRYrIz89PFSpUUL169VSpUiWHnRPp9erVy3Jhp2DBgjp48KACAwMdeo5169YpJCTEEr/zzjt69913LfHt27e1efNmRUVF6fLlyypWrJgqVaqkkJAQlShRwvR5jx8/ro0bN+rMmTO6ceOGSpQooZo1a+qhhx5SkSJFsvMjmZKYmKiYmBhL7OfnZ7londNyzQmuXr2qLVu26PTp07pw4YJu3bolPz8/lS1bVvfee69q1qwpHx8fd6eZo/M8duyYDh48qOjoaF29elU3b96Ur6+v7rrrLt1///2qXbt2rvj9pEGDBoqMjJQktW/fXitXrpSU9b8pZ3nvvfcsn18LFy7U008/nWG/+Ph4lSlTRnFxcQoMDNTRo0czHdMVn8PO4ozXI9XChQvVp08fSY5/Xfh9PXPXrl0z7I4ZEhLChA8gm/h3BTMGDBig2bNnW2JfSdFKuQuhPQ5JuldS2ns/vP/++3rrrbeynaO7pP6bOnHihF5++WVLe3breQByPur4QArq+PkTdXzXuFO9MSflmlPk5Pp4bsmTOr7jUcc3yo11fH5Xzxr1RsDx+HeF7Jo4caKGDx9uiWtIOmjnGOslWW9bcv78eZdsHuNo1PEBIHfgf1gAAAD5WOHChdW8eXM1b97cJRdzPDw8VKVKFVWpUsWh48I+aYugzzzzjFsudhUoUEAtWrRQixYtHDpubnp/5aZcXaV48eJ69NFH3Z3GHeXkPAMDA3PNBeysjB49Wm3btpUkrV69WgcPHrRr93xH6tq1q+VC+9y5czO90L5kyRLFxcVZjslKTvgcNssZr0eqSZMmWR4/++yzuep1AQAgO06fPq358+cb2vrL/sWDkjRBxsWDfn5+evHFF7ORHQAAcDfq+PlTTqgfUcfPXbm6Sk6uj6eVk/Okju941PGNqOMDAOB8ERERhtjMfQMjrOIHHnggVy4eBADkHp7uTgAAAACA6xw4cECnT5+WJHl7e+fqu1AAyLvatGmjZs2aSUrZ9X7ixIluy6VOnTpq166dJGn9+vWaM2dOuj4nT57UyJEjJaVM6hs8eHCm4+X2z2FHvx6p1qxZo6ioKEmSj49PrntdAADIjilTpujmzZuWuJCk10yMc0rSl1ZtgwYNUsmSJbORHQAAAFwtt9ePAOQP1PFzLur4AAA4182bN7V+/XpDm5kFhOFWcWhoqOmcAACwBQsIAQAAgHwk7Q5Yzz//vCpXruzGbAAgc9OmTZOnZ0rZYuHChfrrr7/clsuUKVNUqlTKPYAGDBiggQMHKiIiQlu3btX06dPVuHFjnTt3TpI0duxYVaxYMdOx8sLnsCNfDyllcsmoUaMs8dChQ+Xv7++8HwAAgBzk4sWLmjVrlqHtGUnlTYw1SdLNNLGPj4+GDBliPjkAAAC4RV6oHwHIH6jj51zU8QEAcJ4tW7ZY7uIrSR6SQuwcI07SH1ZtYWFmliECAGA7FhACAAAA+cjatWslSYUKFbLsKgkAOVGDBg303HPPSZJu376tN9980225VK9eXWvWrFGFChWUnJysWbNmKSwsTI0bN9ZLL72kM2fOyNPTU++8845eeeWVLMfKC5/Djnw9JOmrr77Szp07JUmVKlXSiBEjnP0jAACQY3zyySe6fv26JfaSNNzEOBckzbVqe/7551WuXLlsZAcAAAB3yAv1IwD5A3X8nIs6PgAAzpN2swFJekBSKTvHWC/jhoAFChRQixYtspkZAABZK+DuBAAAAAC4ztdff+3uFADAZnPnztXcuf9OhU9MTHRbLk2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AAAAAAAAAAAAAAIAhgRAAAAAAAAAAAAAAAAAAhgRCAAAAAAAAAAAAAAAAABgSCAEAAAAAAAAAAAAAAABgSCAEAAAAAAAAAAAAAAAAgCGBEAAAAAAAAAAAAAAAAACGBEIAAAAAAAAAAAAAAAAAGBIIAQAAAAAAAAAAAAAAAGBIIAQAAAAAAAAAAAAAAACAIYEQAAAAAAAAAAAAAAAAAIYEQgAAAAAAAAAAAAAAAAAYEggBAAAAAAAAAAAAAAAAYEggBAAAAAAAAAAAAAAAAIAhgRAAAAAAAAAAAAAAAAAAhgRCAAAAAAAAAAAAAAAAABgSCAEAAAAAAAAAAAAAAABgKE1vREGBDjnDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set up the EOS \"struct\" (named tuple)\n",
    "eos = poly_validation.set_up_EOS_parameters__Read_et_al_input_variables('APR4')\n",
    "\n",
    "############\n",
    "# PLOTTING #\n",
    "############\n",
    "# First set the number of points in the plot\n",
    "n_plot = 1000\n",
    "\n",
    "# Then split the plot by the number of EOSs used in the code\n",
    "rho_b    = [0 for i in range(eos.neos)]\n",
    "P_cold   = [[0 for j in range(int(n_plot/eos.neos))] for i in range(eos.neos)]\n",
    "eps_cold = [[0 for j in range(int(n_plot/eos.neos))] for i in range(eos.neos)]\n",
    "\n",
    "# Then set the plotting limits for each region\n",
    "lim = [eos.rho_poly_tab[0]/50.0,\n",
    "       eos.rho_poly_tab[0],\n",
    "       eos.rho_poly_tab[1],\n",
    "       eos.rho_poly_tab[2],\n",
    "       eos.rho_poly_tab[3],\n",
    "       eos.rho_poly_tab[4],\n",
    "       eos.rho_poly_tab[5],\n",
    "       eos.rho_poly_tab[5]*30.0]\n",
    "\n",
    "# Then populate the rho_b arrays\n",
    "for j in range(eos.neos):\n",
    "    rho_b[j] = np.linspace(lim[j],lim[j+1],int(n_plot/eos.neos))\n",
    "\n",
    "# Finally, populate the P array\n",
    "for i in range(eos.neos):\n",
    "    for j in range(int(n_plot/eos.neos)):\n",
    "        P_cold[i][j]   = poly_validation.Polytrope_EOS__compute_P_cold_from_rhob(eos, rho_b[i][j])\n",
    "        eps_cold[i][j] = poly_validation.Polytrope_EOS__compute_eps_cold_from_rhob(eos, rho_b[i][j])\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "# We will use the following package to draw a border around\n",
    "# our plot curve so that it is easier to distinguish between\n",
    "# the EOSs. Source:\n",
    "# https://stackoverflow.com/questions/12729529/can-i-give-a-border-outline-to-a-line-in-matplotlib-plot-function\n",
    "import matplotlib.patheffects as pe\n",
    "\n",
    "fig = plt.figure(dpi=300,figsize=(12,6))\n",
    "nplots = int(2)\n",
    "ax = [0 for j in range(nplots)]\n",
    "ax[0] = fig.add_subplot(121)\n",
    "ax[1] = fig.add_subplot(122)\n",
    "\n",
    "colors = ['blue','cyan','lime','yellow','peachpuff','darkorange','red']\n",
    "\n",
    "titles = [r\"$\\log_{10}\\left(P_{\\rm cold}\\right)\\times\\log_{10}\\left(\\rho_{b}\\right)$\", \\\n",
    "          r\"$\\log_{10}\\left(\\epsilon_{\\rm cold}\\right)\\times\\log_{10}\\left(\\rho_{b}\\right)$\"]\n",
    "\n",
    "xlabels = [r\"$\\log_{10}\\left(\\rho_{b}\\right)$\", \\\n",
    "           r\"$\\log_{10}\\left(\\rho_{b}\\right)$\"]\n",
    "\n",
    "ylabels = [r\"$\\log_{10}\\left(P\\right)$\", \\\n",
    "           r\"$\\log_{10}\\left(\\epsilon\\right)$\"]\n",
    "\n",
    "for i in range(eos.neos):\n",
    "    label=r'EOS #'+str(i+1)+r', parameters: $\\left(\\Gamma_'+str(i)+r',K_'+str(i)+r'\\right)$'\n",
    "    ax[0].plot(np.log10(rho_b[i]),np.log10(P_cold[i]),label=label,c=colors[i],lw=3, path_effects=[pe.Stroke(linewidth=5,foreground='k'),pe.Normal()])\n",
    "    ax[1].plot(np.log10(rho_b[i]),np.log10(eps_cold[i]),label=label,c=colors[i],lw=3, path_effects=[pe.Stroke(linewidth=5,foreground='k'),pe.Normal()])\n",
    "\n",
    "for j in range(nplots):\n",
    "    ax[j].legend()\n",
    "    ax[j].grid()\n",
    "    #ax[j].set_title(titles[j],fontsize=14)\n",
    "    ax[j].set_xlabel(xlabels[j],fontsize=14)\n",
    "    ax[j].set_ylabel(ylabels[j],fontsize=14)\n",
    "\n",
    "outfilename = \"P_and_eps_cold_x_rho_b__piecewise_polytrope_neoseq7.png\"\n",
    "plt.savefig(outfilename,dpi=300)\n",
    "plt.close(fig)\n",
    "Image(outfilename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='code_validation'></a>\n",
    "\n",
    "# Step 11: Code validation \\[Back to [top](#toc)\\]\n",
    "$$\\label{code_validation}$$\n",
    "\n",
    "We will now begin our validation tests. We will need input parameters to validate our results from this tutorial module against:\n",
    "1. [Step 10.a](#code_validation__self): The [Polytropic EOSs_Python_Module](../edit/TOV/Polytropic_EOSs.py);\n",
    "2. [Step 10.b](#code_validation__read_et_al): The parameter reference, [Read *et al.* (2008)](https://arxiv.org/pdf/0812.2163.pdf);\n",
    "3. [Step 10.c](#code_validation__joshua_faber_tov_solver): Results from [Joshua Faber's TOV solver](https://ccrg.rit.edu/~jfaber/BNSID/TOV/tov_solver.C);\n",
    "4. [Step 10.d](#code_validation__mass_vs_radius): The neutron star's mass vs radius plots from [Demorest *et al.* (2010)](https://www.nature.com/articles/nature09466) and [Joonas Nättilä's TOV solver](https://github.com/natj/tov/blob/master/mr.pdf)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='code_validation__self'></a>\n",
    "\n",
    "## Step 11.a: Against the [TOV/Polytropic_EOSs.py](../edit/TOV/Polytropic_EOSs.py) NRPy+ module. \\[Back to [top](#toc)\\]\n",
    "$$\\label{code_validation__self}$$\n",
    "\n",
    "We start by validating the results obtained in this tutorial module with the results obtained from the [TOV/Polytropic_EOSs.py](../edit/TOV/Polytropic_EOSs.py) NRPy+ module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation may have failed. Check for non-whitespace differences below:\n",
      "--- trusted\n",
      "+++ new\n",
      "@@ -7,7 +7,7 @@\n",
      " #  Tutorial-TOV-Piecewise_Polytrope_EOSs.ipynb\n",
      " \n",
      " # Step 0: Import needed Python/NRPy+ modules\n",
      "-import numpy as np                  # This module is used for math functions\n",
      "+import numpy as np                  # NumPy: A numerical methods module for Python\n",
      " import sys                          # This module is used for system related function calls\n",
      " from collections import namedtuple  # This module is used to create named tuples\n",
      " \n",
      "@@ -680,12 +680,12 @@\n",
      "      Gamma_single_polytrope)) # sets EOS_omni::hybrid_gamma_th\n",
      " \n",
      "     elif EOSname == \"piecewise\":\n",
      "-        if EOS_struct is None: # Use \"is None\" instead of \"==None\", as the former is more correct.\n",
      "+        if EOS_struct is None:  # Use \"is None\" instead of \"==None\", as the former is more correct.\n",
      "             print(\"Error: Please set the EOS named tuple. Usage:\")\n",
      "             print(\"generate_IllinoisGRMHD_EOS_parameter_file(\\\"piecewise\\\",outfilename,Gamma_thermal=Gamma_th,EOS_struct=eos_named_tuple)\")\n",
      "             sys.exit(1)\n",
      " \n",
      "-        if Gamma_thermal is None: # Use \"is None\" instead of \"==None\", as the former is more correct.\n",
      "+        if Gamma_thermal is None:  # Use \"is None\" instead of \"==None\", as the former is more correct.\n",
      "             print(\"Error: Please set Gamma_thermal. Usage:\")\n",
      "             print(\"generate_IllinoisGRMHD_EOS_parameter_file(\\\"piecewise\\\",outfilename,Gamma_thermal=Gamma_th,EOS_struct=eos_named_tuple)\")\n",
      "             sys.exit(1)\n",
      "@@ -819,7 +819,7 @@\n",
      "         if len(EOSname)==largest_name_in_EOS_table:\n",
      "             pass\n",
      "         else:\n",
      "-            for _k in range(largest_name_in_EOS_table - len(EOSname)): # _k is unused.\n",
      "+            for k in range(largest_name_in_EOS_table - len(EOSname)):\n",
      "                 EOSname += \" \"\n",
      " \n",
      "         with open(outfilename,\"a\") as file:\n",
      "@@ -935,3 +935,4 @@\n",
      " #.--------------------------------.\n",
      " #\n",
      " #^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\"\"\" %(Gamma_thermal))\n",
      "+\n",
      "<generator object unified_diff at 0x7fd7b89e7d60>\n"
     ]
    }
   ],
   "source": [
    "###################\n",
    "# SELF VALIDATION #\n",
    "###################\n",
    "#\n",
    "# Here we validate the results obtained using the function\n",
    "# in this tutorial notebook with the Polytrope_EOS_Python_Module\n",
    "# NRPy+ module.\n",
    "#\n",
    "# We start by setting up a validation function, so that we don't\n",
    "# have to duplicate code while performing this validation.\n",
    "import filecmp,sys\n",
    "if filecmp.cmp(nrpy_module_file_path,validation_file_path):\n",
    "    print(\"Validation: PASSED!\")\n",
    "else:\n",
    "    print(\"Validation may have failed. Check for non-whitespace differences below:\")\n",
    "    # https://stackoverflow.com/questions/19120489/compare-two-files-report-difference-in-python\n",
    "    import difflib\n",
    "    with open(nrpy_module_file_path, 'r') as trusted:\n",
    "        with open(validation_file_path, 'r') as new:\n",
    "            diff = difflib.unified_diff(\n",
    "                trusted.readlines(),\n",
    "                new.readlines(),\n",
    "                fromfile='trusted',\n",
    "                tofile='new',\n",
    "            )\n",
    "            for line in diff:\n",
    "                sys.stdout.write(line)\n",
    "            print(diff)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='code_validation__read_et_al'></a>\n",
    "\n",
    "## Step 11.b: Against [Read *et al.* (2008)](https://arxiv.org/pdf/0812.2163.pdf) \\[Back to [top](#toc)\\]\n",
    "$$\\label{code_validation__read_et_al}$$\n",
    "\n",
    "We now want to test the functions we have implemented above. In order for us to work with realistic values (i.e. values actually used by researchers), we will implement a simple test using the values from [Table II in J.C. Read *et al.* (2008)](https://arxiv.org/pdf/0812.2163.pdf):\n",
    "\n",
    "| $\\rho_{i}$ | $\\Gamma_{i}$ | $K_{\\rm expected}$ |\n",
    "|------------|--------------|--------------------|\n",
    "|2.44034e+07 |   1.58425    |    6.80110e-09     |\n",
    "|3.78358e+11 |   1.28733    |    1.06186e-06     |\n",
    "|2.62780e+12 |   0.62223    |    5.32697e+01     |\n",
    "|    $-$     |   1.35692    |    3.99874e-08     |\n",
    "\n",
    "First, we have $n_{\\rm eos} = 4$. Then, giving our function the values\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\left\\{\\rho_{j}\\right\\}   &= \\left\\{\\text{2.44034e+07},\\text{3.78358e+11},\\text{2.62780e+12}\\right\\}\\ ,\\\\\n",
    "\\left\\{\\Gamma_{j}\\right\\} &= \\left\\{{\\rm 1.58425},{\\rm 1.28733},{\\rm 0.62223},{\\rm 1.35692}\\right\\}\\ ,\\\\\n",
    "K_{0} &= \\text{6.80110e-09}\\ ,\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "we expect to obtain the values\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "K_{1} &= \\text{1.06186e-06}\\ ,\\\\\n",
    "K_{2} &= \\text{5.32697e+01}\\ ,\\\\\n",
    "K_{3} &= \\text{3.99874e-08}\\ .\n",
    "\\end{align}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Significant digits of agreement - Input Kpoly_0: inf\n",
      "Significant digits of agreement - Input Kpoly_1:   5\n",
      "Significant digits of agreement - Input Kpoly_2:   4\n",
      "Significant digits of agreement - Input Kpoly_3:   4\n"
     ]
    }
   ],
   "source": [
    "# Number of equation of states\n",
    "neos = 4\n",
    "\n",
    "# User input #1: The values of rho_b that distinguish one EOS from the other\n",
    "#                Values taken from Table II of J.C. Read et al. (2008)\n",
    "#                       https://arxiv.org/pdf/0812.2163.pdf\n",
    "rho_poly_tab = [2.44034e+07,3.78358e+11,2.62780e+12]\n",
    "\n",
    "# User input #2: The values of Gamma to be used within each EOS\n",
    "#                Values taken from Table II of J.C. Read et al. (2008)\n",
    "#                       https://arxiv.org/pdf/0812.2163.pdf\n",
    "Gamma_poly_tab = [1.58425,1.28733,0.62223,1.35692]\n",
    "\n",
    "# User input #3: The value K_0, to be used within the *first* EOS. The other\n",
    "#                values of K_j are determined by imposing that P be everywhere\n",
    "#                continuous\n",
    "#                Value taken from Table II of J.C. Read et al. (2008)\n",
    "#                       https://arxiv.org/pdf/0812.2163.pdf\n",
    "K_poly_tab0 = 6.80110e-09\n",
    "\n",
    "# Set up the EOS \"struct\" (named tuple)\n",
    "import TOV.Polytropic_EOSs as poly\n",
    "eos = poly.set_up_EOS_parameters__complete_set_of_input_variables(neos,rho_poly_tab,Gamma_poly_tab,K_poly_tab0)\n",
    "\n",
    "# Function to compute the relative error between a and b\n",
    "def rel_error(a,b):\n",
    "    if a!=0:\n",
    "        return np.fabs(a-b)/np.fabs(a)\n",
    "    elif b!=0:\n",
    "        return np.fabs(a-b)/np.fabs(b)\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "# Function to compute significant digits of agreement between a and b\n",
    "def significant_digits_of_agreement(a,b):\n",
    "    return round(np.fabs(np.log10(np.fabs(rel_error(a,b)))))\n",
    "\n",
    "# Compute significant digits of agreement between our results and\n",
    "# Read et al. (2008) (https://arxiv.org/pdf/0812.2163.pdf)\n",
    "Kpoly_expected = [K_poly_tab0,1.06186e-06,5.32697e+01,3.99874e-08]\n",
    "for i in range(eos.neos):\n",
    "    if rel_error(Kpoly_expected[i],eos.K_poly_tab[i])==0:\n",
    "        print(\"Significant digits of agreement - Input Kpoly_%d: inf\" %i)\n",
    "    else:\n",
    "        print(\"Significant digits of agreement - Input Kpoly_%d: %3d\" %(i,significant_digits_of_agreement(Kpoly_expected[i],eos.K_poly_tab[i])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='code_validation__joshua_faber_tov_solver'></a>\n",
    "\n",
    "## Step 11.c: Against [Joshua Faber's TOV solver](https://ccrg.rit.edu/~jfaber/BNSID/TOV/tov_solver.C) \\[Back to [top](#toc)\\]\n",
    "$$\\label{code_validation__joshua_faber_tov_solver}$$\n",
    "\n",
    "<a id='code_validation__joshua_faber_tov_solver__results'></a>\n",
    "\n",
    "### Step 11.c.i: Results from [Joshua Faber's TOV solver](https://ccrg.rit.edu/~jfaber/BNSID/TOV/tov_solver.C) \\[Back to [top](#toc)\\]\n",
    "$$\\label{code_validation__joshua_faber_tov_solver__results}$$\n",
    "\n",
    "We now present here results obtained using [Joshua Faber's TOV solver](https://ccrg.rit.edu/~jfaber/BNSID/TOV/tov_solver.C):\n",
    "\n",
    "| EOS Name | Central Density (g/cm$^3$) | Mass (solar masses) |      Radius (km)     | M/R (adimensional)  |\n",
    "|----------|----------------------------|---------------------|----------------------|---------------------|\n",
    "| **SLy**  |     $2.0\\times10^{15}$     | $2.048399851801091$ | $ 9.985183794999273$ | $0.302924806758338$ |\n",
    "| **APR3** |     $2.0\\times10^{15}$     | $2.324551590827044$ | $10.049001815713444$ | $0.341579997979191$ |\n",
    "| **APR4** |     $2.0\\times10^{15}$     | $2.185879526276314$ | $ 9.770197442859468$ | $0.330368817625631$ |\n",
    "| **MS1**  |     $2.0\\times10^{15}$     | $2.612634529283015$ | $12.438863435136328$ | $0.310151673994438$ |\n",
    "| **ALF2** |     $2.0\\times10^{15}$     | $1.958593084362050$ | $10.923811531250033$ | $0.264756228030622$ |\n",
    "\n",
    "\n",
    "We store these results into a dictionary, so that it is simple to compare these results with the ones obtained from NRPy+'s TOV solver."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up a named tuple to store the TOV solution. Store:\n",
    "#\n",
    "# - The EOS name from Read et al. (SLy, APR1, MS1b, etc...)\n",
    "# - The central density used to obtain the TOV solution\n",
    "# - The value of the neutron star mass, in solar masses\n",
    "# - The value of the neutron star radius, in kilometers\n",
    "# - The dimensionless compactness, M/R\n",
    "from collections import namedtuple  # This module is used to create named tuples\n",
    "TOV_solver_results = namedtuple(\"TOV_Solution\",\"EOSname rho_central M R C\")\n",
    "\n",
    "# Set up the dictionary with the results of\n",
    "# asorted EOSs obtained using Faber's TOV solver:\n",
    "# https://ccrg.rit.edu/~jfaber/BNSID/TOV/tov_solver.C\n",
    "rho_central = 2.0e15\n",
    "Faber_TOV_solver_dict = {}\n",
    "Faber_TOV_solver_dict[\"SLy\"]  = (TOV_solver_results(\"SLy\" ,rho_central,2.048399851801091, 9.985183794999273,0.302924806758338))\n",
    "Faber_TOV_solver_dict[\"APR3\"] = (TOV_solver_results(\"APR3\",rho_central,2.324551590827044,10.049001815713444,0.341579997979191))\n",
    "Faber_TOV_solver_dict[\"APR4\"] = (TOV_solver_results(\"APR4\",rho_central,2.185879526276314, 9.770197442859468,0.330368817625631))\n",
    "Faber_TOV_solver_dict[\"MS1\"]  = (TOV_solver_results(\"MS1\" ,rho_central,2.612634529283015,12.438863435136328,0.310151673994438))\n",
    "Faber_TOV_solver_dict[\"ALF2\"] = (TOV_solver_results(\"ALF2\",rho_central,1.958593084362050,10.923811531250033,0.264756228030622))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='code_validation__joshua_faber_tov_solver__restoring_units'></a>\n",
    "\n",
    "### Step 11.c.ii: Restoring units to the solution of NRPy+'s TOV solver \\[Back to [top](#toc)\\]\n",
    "$$\\label{code_validation__joshua_faber_tov_solver__restoring_units}$$\n",
    "\n",
    "It is important to note, however, that while Faber outputs the Neutron star's (NS) mass in solar masses and its radius in kilometers, our code outputs them in geometrized units where $G=1=c$. Thus, we must restore units to our results before we are able to compare the results.\n",
    "\n",
    "Our code is written in such a way that\n",
    "\n",
    "$$\n",
    "1 \\text{ in code units} \\leftrightarrow 1\\times10^{15}\\,{\\rm g/cm^{3}}\\ ,\n",
    "$$\n",
    "\n",
    "so that, because $G=1=c$, $\\rho$ has units of inverse length squared (i.e. $\\rm cm^{-2}$), while $M$ and $R$ both have units of length (i.e $\\rm cm$). Thus, to restore units, we reintroduce (in cgs units)\n",
    "\n",
    "\\begin{align}\n",
    "G &= 6.67408\\times10^{-8}\\,\\frac{\\rm cm^{3}}{\\rm g\\cdot s^{2}}\\ ,\\\\\n",
    "c &= 2.99792\\times10^{10}\\,\\frac{\\rm cm}{\\rm s}\\ ,\n",
    "\\end{align}\n",
    "\n",
    "and the mass of the Sun\n",
    "\n",
    "\\begin{align}\n",
    "M_{\\odot} = 1.989\\times10^{33}\\,{\\rm g}\\ .\n",
    "\\end{align}\n",
    "\n",
    "Consider the notation $\\left[A\\right]$ to represent the *units* of the quantity $A$. In other words,\n",
    "\n",
    "\\begin{align}\n",
    "\\left[G\\right] &= \\frac{\\rm cm^{3}}{\\rm g\\cdot s^{2}}\\ ,\\\\\n",
    "\\left[c\\right] &=\\frac{\\rm cm}{\\rm s}\\ ,\\\\\n",
    "\\left[M_{\\odot}\\right] &= {\\rm g}\\ .\n",
    "\\end{align}\n",
    "\n",
    "Then, to restore the units of $M$, which currently has units of length, we need to find a combination of $G$ and $c$ that will convert that to units of mass again. Consider\n",
    "\n",
    "$$\n",
    "\\left[\\frac{G}{c^{2}}\\right]\n",
    "= \\left(\\frac{\\rm cm^{3}}{\\rm g\\cdot s^{2}}\\right)\\left(\\frac{\\rm cm}{\\rm s}\\right)^{-2}\n",
    "= \\left(\\frac{\\rm cm^{3}}{\\rm g\\cdot s^{2}}\\right)\\left(\\frac{\\rm s^{2}}{\\rm cm^{2}}\\right)\n",
    "= \\frac{\\rm cm}{\\rm g}\\ .\n",
    "$$\n",
    "\n",
    "Thus, to convert our code's mass to grams, we have\n",
    "\n",
    "$$\n",
    "\\boxed{M_{\\rm grams} = \\left(\\frac{c^{2}}{G}\\right)M_{\\rm code\\ units}}\\ .\n",
    "$$\n",
    "\n",
    "To obtain the mass of the NS in solar masses we simply divide the NS mass by the mass of the Sun\n",
    "\n",
    "$$\n",
    "\\boxed{M_{\\rm solar\\ masses} = \\frac{M_{\\rm grams}}{M_{\\odot}}}\\ .\n",
    "$$\n",
    "\n",
    "To obtain our basic unit of length, we first notice that one code unit can be converted to $\\rm cm^{2}$ by doing\n",
    "\n",
    "$$\n",
    "\\left(1\\text{ in code units}\\right)\\times\\left(\\frac{G}{c^{2}}\\right) = \\left(\\frac{G}{c^{2}}\\right)\\times10^{15}\\,{\\rm cm^{-2}}\\ .\n",
    "$$\n",
    "\n",
    "Thus we can obtain the relation\n",
    "\n",
    "$$\n",
    "\\left[\\left(1\\text{ in code units}\\right)\\times\\left(\\frac{G}{c^{2}}\\right)\\right]^{-1/2} = \\sqrt{\\left(\\frac{c^{2}}{G}\\right)\\times10^{-15}}\\,{\\rm cm}\\ .\n",
    "$$\n",
    "\n",
    "Finally, we can convert $R_{\\rm code\\ units}$ to kilometers by considering $1\\,{\\rm cm} = 1\\times10^{-5}\\,{\\rm km}$, which yields\n",
    "\n",
    "$$\n",
    "\\boxed{R_{\\rm km} = \\left[10^{-5}\\sqrt{\\left(\\frac{c^{2}}{G}\\right)\\times10^{-15}}\\right]R_{\\rm code\\ units}}\\ .\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Unit conversion: From our code's units to dimensionful quantities\n",
    "#\n",
    "# Set basic physical constants\n",
    "def convert_M_to_solar_masses_and_R_to_km(M,R):\n",
    "    Msun = 1.98900e+33 # g                    # Mass of the sun\n",
    "    c    = 2.99792e+10 # cm s^(-1)            # Speed of light\n",
    "    G    = 6.67408e-08 # cm^(3) g^(-1) s^(-2) # Gravitational constant\n",
    "\n",
    "    # Set our code's units\n",
    "    one_code_unit_in_one_over_cm2 = 1.0e+15 * G / c**2\n",
    "    one_code_unit_in_cm = 1.0/one_code_unit_in_one_over_cm2**0.5\n",
    "    one_code_unit_in_km = one_code_unit_in_cm*1e-5\n",
    "\n",
    "    # Convert R and M to numpy arrays, so that we can\n",
    "    # manipulate all entries with a single line of code\n",
    "    M = np.array(M)\n",
    "    R = np.array(R)\n",
    "\n",
    "    # Convert mass to solar masses\n",
    "    M_in_g = M * one_code_unit_in_cm * c**2/G\n",
    "    M_in_solar_masses = M_in_g/Msun\n",
    "\n",
    "    # Convert code radius to km\n",
    "    R_in_km = R*one_code_unit_in_km\n",
    "\n",
    "    return M_in_solar_masses, R_in_km"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='code_validation__joshua_faber_tov_solver__validation'></a>\n",
    "\n",
    "### Step 11.c.iii: Validation of NRPy+'s results \\[Back to [top](#toc)\\]\n",
    "$$\\label{code_validation__joshua_faber_tov_solver__validation}$$\n",
    "\n",
    "Finally, we use [NRPy+'s TOV solver](../edit/TOV/TOV_solver.py) \\[[**Tutorial**](Tutorial-ADM_Initial_Data-TOV.ipynb)\\] to obtain solutions to the same EOSs we have used in Josh Faber's TOV solver. We then show the relative errors between our solutions and Faber's."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found M and R for the EOS: SLy\n",
      "Found M and R for the EOS: APR3\n",
      "Found M and R for the EOS: APR4\n",
      "Found M and R for the EOS: MS1\n",
      "Found M and R for the EOS: ALF2\n",
      ".---------------------------------.----------.------------.-----------------.\n",
      "|   Validation of the EOS: \u001b[38;5;20mSLy\u001b[0m    |----------|------------|-----------------|\n",
      "| \u001b[38;5;160mSignificant digits of agreement\u001b[0m | Mass = 4 | Radius = 5 | Compactness = 6 |\n",
      ".---------------------------------.----------.------------.-----------------.\n",
      "|   Validation of the EOS: \u001b[38;5;20mAPR3\u001b[0m   |----------|------------|-----------------|\n",
      "| \u001b[38;5;160mSignificant digits of agreement\u001b[0m | Mass = 4 | Radius = 5 | Compactness = 6 |\n",
      ".---------------------------------.----------.------------.-----------------.\n",
      "|   Validation of the EOS: \u001b[38;5;20mAPR4\u001b[0m   |----------|------------|-----------------|\n",
      "| \u001b[38;5;160mSignificant digits of agreement\u001b[0m | Mass = 4 | Radius = 5 | Compactness = 6 |\n",
      ".---------------------------------.----------.------------.-----------------.\n",
      "|   Validation of the EOS: \u001b[38;5;20mMS1\u001b[0m    |----------|------------|-----------------|\n",
      "| \u001b[38;5;160mSignificant digits of agreement\u001b[0m | Mass = 4 | Radius = 5 | Compactness = 6 |\n",
      ".---------------------------------.----------.------------.-----------------.\n",
      "|   Validation of the EOS: \u001b[38;5;20mALF2\u001b[0m   |----------|------------|-----------------|\n",
      "| \u001b[38;5;160mSignificant digits of agreement\u001b[0m | Mass = 4 | Radius = 5 | Compactness = 6 |\n",
      ".---------------------------------.----------.------------.-----------------.\n"
     ]
    }
   ],
   "source": [
    "# Load the NRPy+ TOV solver module\n",
    "import TOV.TOV_Solver as TOV\n",
    "\n",
    "# Set up the central density\n",
    "rho_central = 2.0\n",
    "\n",
    "# Set up which EOSs we want to solve for\n",
    "EOSnames = [\"SLy\",\"APR3\",\"APR4\",\"MS1\",\"ALF2\"]\n",
    "\n",
    "# Initialize the dictionary of NRPy+ TOV solutions\n",
    "NRPy_TOV_solver_dict = {}\n",
    "\n",
    "# Populate the dictionary\n",
    "for EOSname in EOSnames:\n",
    "    # Set up the EOS based on the EOSname\n",
    "    eos = poly.set_up_EOS_parameters__Read_et_al_input_variables(EOSname)\n",
    "\n",
    "    # Compute the mass and radius of the NS for the given EOS and central density\n",
    "    Mass, RadiusSchw, Radius_iso = TOV.TOV_Solver(eos,rho_baryon_central=rho_central,verbose=False,return_M_RSchw_and_Riso=True,accuracy=\"medium\",no_output_File=True)\n",
    "\n",
    "    # Convert the mass and radius to the same units as Josh's code\n",
    "    M,RSchw = convert_M_to_solar_masses_and_R_to_km(Mass, RadiusSchw)\n",
    "\n",
    "    # Add the solution to our dictionary\n",
    "    NRPy_TOV_solver_dict[EOSname] = (TOV_solver_results(EOSname ,rho_central,M,RSchw,Mass/RadiusSchw))\n",
    "    print(\"Found M and R for the EOS: \"+EOSname)\n",
    "\n",
    "# Print with colors\n",
    "# Reference #1: https://stackoverflow.com/questions/16816013/is-it-possible-to-print-using-different-colors-in-ipythons-notebook\n",
    "# Reference #2: https://en.wikipedia.org/wiki/ANSI_escape_code\n",
    "def print_red(text):\n",
    "    return \"\\x1b[38;5;160m\"+text+\"\\x1b[0m\"\n",
    "\n",
    "def print_blue(text):\n",
    "    return \"\\x1b[38;5;20m\"+text+\"\\x1b[0m\"\n",
    "\n",
    "# Print out significant digits of agreement between Josh's and\n",
    "# NRPy+'s solutions, for the mass, radius, and compactness of\n",
    "# the Neutron stars obtained using different PPEOSs\n",
    "print(\".---------------------------------.----------.------------.-----------------.\")\n",
    "for EOSname in NRPy_TOV_solver_dict:\n",
    "    if len(EOSname)==4:\n",
    "        print(\"|   Validation of the EOS: \"+print_blue(EOSname)+\"   |----------|------------|-----------------|\")\n",
    "    elif len(EOSname)==3:\n",
    "        print(\"|   Validation of the EOS: \"+print_blue(EOSname)+\"    |----------|------------|-----------------|\")\n",
    "    sig_digits_M = significant_digits_of_agreement(NRPy_TOV_solver_dict[EOSname].M,Faber_TOV_solver_dict[EOSname].M)\n",
    "    sig_digits_R = significant_digits_of_agreement(NRPy_TOV_solver_dict[EOSname].R,Faber_TOV_solver_dict[EOSname].R)\n",
    "    sig_digits_C = significant_digits_of_agreement(NRPy_TOV_solver_dict[EOSname].C,Faber_TOV_solver_dict[EOSname].C)\n",
    "    print(\"| \"+print_red(\"Significant digits of agreement\")+\" | Mass = %d | Radius = %d | Compactness = %d |\" %(sig_digits_M,sig_digits_R,sig_digits_C))\n",
    "    print(\".---------------------------------.----------.------------.-----------------.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='code_validation__mass_vs_radius'></a>\n",
    "\n",
    "## Step 11.d: Mass vs. Radius relations \\[Back to [top](#toc)\\]\n",
    "$$\\label{code_validation__mass_vs_radius}$$\n",
    "\n",
    "We now subject our functions to a very stringent test, where we aim to determine the relationship between the mass and radius of Neutron stars subject to different piecewise polytropic EOS parameters.\n",
    "\n",
    "A similar, yet more complete, plot to the one we are generating is [Figure 3 in Demorest *et al.* (2010)](https://www.nature.com/articles/nature09466) (notice that the figure can be accessed by going into the \"Figure\" tab on the right).\n",
    "\n",
    "Another plot which is identical to ours can be found by looking at the results of [Joonas Nättilä's TOV solver](https://github.com/natj/tov/blob/master/mr.pdf).\n",
    "\n",
    "Let us start by defining a simple loop that computes a set of pairs $\\left\\{M,R\\right\\}$ for different values of $\\rho_{\\rm central}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import TOV.TOV_Solver as TOV\n",
    "\n",
    "def M_of_R(EOSname,rho_central=3e-1,factor=1.1,max_iterations=300,accuracy=\"verylow\",integrator_type=\"default\"):\n",
    "\n",
    "    M = []\n",
    "    R = []\n",
    "    eos = poly.set_up_EOS_parameters__Read_et_al_input_variables(EOSname)\n",
    "    Mass_TOV, RSchw_TOV, Riso_TOV = TOV.TOV_Solver(eos,rho_baryon_central=rho_central,verbose=False,return_M_RSchw_and_Riso=True,no_output_File=True)\n",
    "    M.append(Mass_TOV)\n",
    "    R.append(RSchw_TOV)\n",
    "    rho_central *= factor\n",
    "\n",
    "    for j in range(1,max_iterations):\n",
    "        Mass_TOV, RSchw_TOV, Riso_TOV = TOV.TOV_Solver(eos,rho_baryon_central=rho_central,verbose=False,return_M_RSchw_and_Riso=True,accuracy=accuracy,integrator_type=integrator_type,no_output_File=True)\n",
    "        if(Mass_TOV >= M[j-1]):\n",
    "            M.append(Mass_TOV)\n",
    "            R.append(RSchw_TOV)\n",
    "            rho_central *= factor\n",
    "\n",
    "        else:\n",
    "            # This snippet is just meant to make all EOS names\n",
    "            # have length = max_EOS_length. This is just for\n",
    "            # cosmetic purposes on our print statements.\n",
    "            max_EOSname_length = 4\n",
    "            if len(EOSname) == max_EOSname_length:\n",
    "                pass\n",
    "            else:\n",
    "                for i in range(max_EOSname_length - len(EOSname)):\n",
    "                    EOSname += \" \"\n",
    "\n",
    "            print(EOSname+\": DONE after \"+str(j)+\" iterations!\")\n",
    "            break\n",
    "\n",
    "    M, R = convert_M_to_solar_masses_and_R_to_km(M,R)\n",
    "    return M, R"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we compute different sets of pairs $\\left\\{M,R\\right\\}$ for different EOSs, as given by table III in [Read *et al.* (2008)](https://arxiv.org/pdf/0812.2163.pdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WFF1: DONE after 20 iterations!\n",
      "WFF2: DONE after 21 iterations!\n",
      "APR3: DONE after 19 iterations!\n",
      "APR4: DONE after 20 iterations!\n",
      "SLy : DONE after 22 iterations!\n",
      "ENG : DONE after 21 iterations!\n",
      "H4  : DONE after 23 iterations!\n",
      "ALF2: DONE after 20 iterations!\n",
      "MS1 : DONE after 19 iterations!\n",
      "MS1b: DONE after 19 iterations!\n",
      "MPA1: DONE after 22 iterations!\n",
      "(BENCH) Finished generating plot data. Time elapsed: 40.72 seconds.\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "# Generate the plot data\n",
    "# Warning: This low resolution setting should take between\n",
    "#          220 and 280 seconds to run.\n",
    "#          For a higher resolution plot, change the factor\n",
    "#          value from 1.10 to 1.03. Keep in mind that this\n",
    "#          will slow down the code even further.\n",
    "start = time.time()\n",
    "factor = 1.10\n",
    "max_it = 300\n",
    "M_WFF1, R_WFF1 = M_of_R(\"WFF1\",rho_central=3.5e-1,factor=factor,max_iterations=max_it)\n",
    "M_WFF2, R_WFF2 = M_of_R(\"WFF2\",rho_central=3.0e-1,factor=factor,max_iterations=max_it)\n",
    "M_APR3, R_APR3 = M_of_R(\"APR3\",rho_central=3.0e-1,factor=factor,max_iterations=max_it)\n",
    "M_APR4, R_APR4 = M_of_R(\"APR4\",rho_central=3.0e-1,factor=factor,max_iterations=max_it)\n",
    "M_SLy,  R_SLy  = M_of_R(\"SLy\" ,rho_central=2.7e-1,factor=factor,max_iterations=max_it)\n",
    "M_ENG,  R_ENG  = M_of_R(\"ENG\" ,rho_central=2.7e-1,factor=factor,max_iterations=max_it)\n",
    "M_H4,   R_H4   = M_of_R(\"H4\"  ,rho_central=1.9e-1,factor=factor,max_iterations=max_it)\n",
    "M_ALF2, R_ALF2 = M_of_R(\"ALF2\",rho_central=2.7e-1,factor=factor,max_iterations=max_it)\n",
    "M_MS1,  R_MS1  = M_of_R(\"MS1\" ,rho_central=2.1e-1,factor=factor,max_iterations=max_it)\n",
    "M_MS1b, R_MS1b = M_of_R(\"MS1b\",rho_central=2.1e-1,factor=factor,max_iterations=max_it)\n",
    "M_MPA1, R_MPA1 = M_of_R(\"MPA1\",rho_central=2.1e-1,factor=factor,max_iterations=max_it)\n",
    "end = time.time()\n",
    "print(\"(BENCH) Finished generating plot data. Time elapsed: %.2lf seconds.\"%(end-start))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we generate the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plotting different PPEOS Mass vs Radius relations\n",
    "#\n",
    "# Set up basic plot parameters\n",
    "fig = plt.figure(dpi=300)\n",
    "# plt.title(r\"Piecewise Polytrope EOS $-$ Mass vs Radius\")\n",
    "plt.xlabel(r\"$R$ (km)\")\n",
    "plt.ylabel(r\"$M$ (solar masses)\")\n",
    "plt.xlim(9,16)\n",
    "plt.ylim(0,3)\n",
    "plt.minorticks_on()\n",
    "plt.grid(which='major',lw=0.5)\n",
    "plt.grid(which='minor',ls=\":\",lw=0.5)\n",
    "\n",
    "# Plot the WFF1 EOS and its legend\n",
    "color = 'k'\n",
    "plt.plot(R_WFF1,M_WFF1,color=color)\n",
    "plt.text(9.8,0.3,\"WFF1\",color=color)\n",
    "\n",
    "# Plot the WFF2 EOS and its legend\n",
    "plt.plot(R_WFF2,M_WFF2,'k')\n",
    "plt.text(10.5,0.7,\"WFF2\",color='k')\n",
    "plt_pt = int(np.ceil(0.3*len(R_WFF2)))\n",
    "plt.plot(np.linspace(10.8,R_WFF2[plt_pt],10),np.linspace(0.65,M_WFF2[plt_pt],10),'k',lw=0.75)\n",
    "\n",
    "# Plot the APR4 EOS and its legend\n",
    "plt.plot(R_APR4,M_APR4,'magenta')\n",
    "plt.text(10.5,1,\"APR4\",color='magenta')\n",
    "plt_pt = int(np.ceil(0.6*len(R_APR4)))\n",
    "plt.plot(np.linspace(10.8,R_APR4[plt_pt],10),np.linspace(1.15,M_APR4[plt_pt],10),'magenta',lw=0.75)\n",
    "\n",
    "# Plot the SLy EOS and its legend\n",
    "plt.plot(R_SLy ,M_SLy,'b')\n",
    "plt.text(11.4,1,\"SLy\",color='b')\n",
    "\n",
    "# Plot the ENG EOS and its legend\n",
    "plt.plot(R_ENG ,M_ENG,color='k')\n",
    "plt.text(9.8,2.4,\"ENG\",color='k')\n",
    "plt_pt = int(np.ceil(0.85*len(R_ENG)))\n",
    "plt.plot(np.linspace(10.1,R_ENG[plt_pt],10),np.linspace(2.35,M_ENG[plt_pt],10),'k',lw=0.75)\n",
    "\n",
    "# Plot the APR3 EOS and its legend\n",
    "plt.plot(R_APR3,M_APR3,color='k')\n",
    "plt.text(10.3,2.6,\"APR3\",color='k')\n",
    "plt_pt = int(np.ceil(0.8*len(R_APR3)))\n",
    "plt.plot(np.linspace(10.6,R_APR3[plt_pt],10),np.linspace(2.55,M_APR3[plt_pt],10),'k',lw=0.75)\n",
    "\n",
    "# Plot the MPA1 EOS and its legend\n",
    "plt.plot(R_MPA1,M_MPA1,color='k')\n",
    "plt.text(11.3,2.6,\"MPA1\",color='k')\n",
    "plt_pt = int(np.ceil(0.85*len(R_MPA1)))\n",
    "plt.plot(np.linspace(11.6,R_MPA1[plt_pt],10),np.linspace(2.55,M_MPA1[plt_pt],10),'k',lw=0.75)\n",
    "\n",
    "# Plot the AFL2 EOS and its legend\n",
    "plt.plot(R_ALF2,M_ALF2,color='r')\n",
    "plt.text(12.6,0.9,\"ALF2\",color='r')\n",
    "\n",
    "# Plot the MS1b EOS and its legend\n",
    "plt.plot(R_MS1b,M_MS1b,color='k')\n",
    "plt.text(13.8,2.2,\"MS1b\",color='k')\n",
    "plt_pt = int(np.ceil(0.5*len(R_MS1b)))\n",
    "plt.plot(np.linspace(14.2,R_MS1b[plt_pt],10),np.linspace(2.15,M_MS1b[plt_pt],10),'k',lw=0.75)\n",
    "\n",
    "# Plot the H4 EOS and its legend\n",
    "plt.plot(R_H4  ,M_H4,color='g')\n",
    "plt.text(13.5,1.3,\"H4\",color='g')\n",
    "\n",
    "# Plot the MS1 EOS and its legend\n",
    "plt.plot(R_MS1 ,M_MS1,color='k')\n",
    "plt.text(15,2.2,\"MS1\",color='k')\n",
    "plt_pt = int(np.ceil(0.5*len(R_MS1)))\n",
    "plt.plot(np.linspace(15.2,R_MS1[plt_pt],10),np.linspace(2.15,M_MS1[plt_pt],10),'k',lw=0.75)\n",
    "\n",
    "# Save the plot as a PNG picture\n",
    "filename = \"piecewise_polytrope__MvsR.png\"\n",
    "plt.savefig(filename)\n",
    "\n",
    "# Close the plot and import the image for display\n",
    "plt.close(fig)\n",
    "Image(filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='latex_pdf_output'></a>\n",
    "\n",
    "# Step 12: Output this module to $\\LaTeX$-formatted PDF file \\[Back to [top](#toc)\\]\n",
    "$$\\label{latex_pdf_output}$$\n",
    "\n",
    "The following code cell converts this Jupyter notebook into a proper, clickable $\\LaTeX$-formatted PDF file. After the cell is successfully run, the generated PDF may be found in the root NRPy+ tutorial directory, with filename\n",
    "[Tutorial-TOV-Piecewise_Polytrope_EOSs](Tutorial-TOV-Piecewise_Polytrope_EOSs.pdf) (Note that clicking on this link may not work; you may need to open the PDF file through another means.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created Tutorial-TOV-Piecewise_Polytrope_EOSs.tex, and compiled LaTeX file\n",
      "    to PDF file Tutorial-TOV-Piecewise_Polytrope_EOSs.pdf\n"
     ]
    }
   ],
   "source": [
    "import cmdline_helper as cmd    # NRPy+: Multi-platform Python command-line interface\n",
    "cmd.output_Jupyter_notebook_to_LaTeXed_PDF(\"Tutorial-TOV-Piecewise_Polytrope_EOSs\")"
   ]
  }
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