Tutorial-TOV-Piecewise_Polytrope_EOSs.ipynb

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    "\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)"
   ]
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    "<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**"
   ]
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   "source": [
    "<a id='initializenrpy'></a>\n",
    "\n",
    "# Step 0: Initialize core Python/NRPy+ modules \\[Back to [top](#toc)\\]\n",
    "$$\\label{initializenrpy}$$"
   ]
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  {
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   "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\")"
   ]
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      "Overwriting TOV/Polytropic_EOSs_validation.py\n"
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   "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",
    "# This ensures the availability of the argument end=\"\" in the print() function.\n",
    "from __future__ import print_function\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."
   ]
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   "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."
   ]
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     "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"
   ]
  },
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   "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",
    "$$\n",
    "\\boxed{\n",
    "P_{\\rm tab} =\n",
    "\\left\\{\n",
    "\\begin{matrix}\n",
    "P_{0} = K_{0}\\rho_{0}^{\\Gamma_{0}}& , & P \\leq P_{0} \\implies \\rho_{b} \\leq \\rho_{0}\\\\\n",
    "P_{1} = K_{1}\\rho_{1}^{\\Gamma_{1}}& , & P_{0}\\leq P\\leq P_{1} \\implies \\rho_{0} \\leq \\rho_{b} \\leq \\rho_{1}\\\\\n",
    "\\vdots & & \\vdots\\\\\n",
    "P_{j} = K_{j}\\rho_{j}^{\\Gamma_{j}}& , & P_{j-1}\\leq P\\leq P_{j} \\implies \\rho_{j-1} \\leq \\rho_{b} \\leq \\rho_{j}\\\\\n",
    "\\vdots & & \\vdots\\\\\n",
    "P_{N-2} = K_{N-2}\\rho_{N-2}^{\\Gamma_{N-2}}& , & P_{N-3}\\leq P\\leq P_{N-2} \\implies \\rho_{N-3} \\leq \\rho_{b} \\leq \\rho_{N-2}\\\\\n",
    "- & , & P \\geq P_{N-2} \\implies \\rho_{b} \\geq \\rho_{N-2}\\ .\n",
    "\\end{matrix}\n",
    "\\right.\n",
    "}\n",
    "$$"
   ]
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      "Appending to TOV/Polytropic_EOSs_validation.py\n"
     ]
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   ],
   "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": {
    "execution": {
     "iopub.execute_input": "2021-03-07T17:17:12.644515Z",
     "iopub.status.busy": "2021-03-07T17:17:12.643655Z",
     "iopub.status.idle": "2021-03-07T17:17:12.646648Z",
     "shell.execute_reply": "2021-03-07T17:17:12.647148Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2021-03-07T17:17:12.653210Z",
     "iopub.status.busy": "2021-03-07T17:17:12.652258Z",
     "iopub.status.idle": "2021-03-07T17:17:12.655845Z",
     "shell.execute_reply": "2021-03-07T17:17:12.655323Z"
    }
   },
   "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",
    "Although cgs units and $G=c=M_{\\odot}=1$ units are also available, where $M_{\\dot}$ is the mass of the Sun, the default units chosen by the code are units in which $G=1=c$ and\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": {
    "execution": {
     "iopub.execute_input": "2021-03-07T17:17:12.662244Z",
     "iopub.status.busy": "2021-03-07T17:17:12.661440Z",
     "iopub.status.idle": "2021-03-07T17:17:12.664558Z",
     "shell.execute_reply": "2021-03-07T17:17:12.665080Z"
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   },
   "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,units=\"rescaledensity\"):\n",
    "\n",
    "    # Check if the input units are implemented below\n",
    "    available_units = [\"rescaledensity\",\"geometrized\",\"cgs\"]\n",
    "    if units not in available_units:\n",
    "        print(\"ERROR: unknown units \",units)\n",
    "        print(\"Available units are: \",end=\"\")\n",
    "        for unit in available_units:\n",
    "            print(unit,end=\" \")\n",
    "        print(\"\")\n",
    "        sys.exit(1)\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 = 2.997924580000000e+10 # Speed of light\n",
    "    G = 6.674299999999999e-08 # Gravitational constant\n",
    "    M = 1.988409870698051e+33 # Mass of the sun\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",
    "    if units == \"rescaledensity\":\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",
    "    elif units == \"geometrized\" :\n",
    "        # Now convert to units in which Msun = 1, G = 1, c = 1\n",
    "        csq               = c**2\n",
    "        units_of_length   = G * M / csq\n",
    "        units_of_time     = units_of_length/c\n",
    "        units_of_mass     = M\n",
    "        units_of_density  = units_of_mass / units_of_length**3\n",
    "        units_of_pressure = units_of_mass / units_of_length / units_of_time**2\n",
    "\n",
    "        for i in range(neos-1):\n",
    "            rho_poly_tab[i] /= units_of_density\n",
    "            P_poly_tab[i]   *= csq\n",
    "            P_poly_tab[i]   /= units_of_pressure\n",
    "\n",
    "    elif units == \"cgs\" :\n",
    "\n",
    "        # Restore P to cgs units\n",
    "        csq = c*c\n",
    "        for i in range(neos-1):\n",
    "            P_poly_tab[i] *= csq\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": {
    "execution": {
     "iopub.execute_input": "2021-03-07T17:17:12.670755Z",
     "iopub.status.busy": "2021-03-07T17:17:12.669939Z",
     "iopub.status.idle": "2021-03-07T17:17:12.673195Z",
     "shell.execute_reply": "2021-03-07T17:17:12.673768Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2021-03-07T17:17:12.680372Z",
     "iopub.status.busy": "2021-03-07T17:17:12.679302Z",
     "iopub.status.idle": "2021-03-07T17:17:12.683022Z",
     "shell.execute_reply": "2021-03-07T17:17:12.682495Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2021-03-07T17:17:12.688964Z",
     "iopub.status.busy": "2021-03-07T17:17:12.688097Z",
     "iopub.status.idle": "2021-03-07T17:17:12.691901Z",
     "shell.execute_reply": "2021-03-07T17:17:12.691380Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2021-03-07T17:17:12.697438Z",
     "iopub.status.busy": "2021-03-07T17:17:12.696438Z",
     "iopub.status.idle": "2021-03-07T17:17:12.700099Z",
     "shell.execute_reply": "2021-03-07T17:17:12.700741Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2021-03-07T17:17:12.707741Z",
     "iopub.status.busy": "2021-03-07T17:17:12.706739Z",
     "iopub.status.idle": "2021-03-07T17:17:12.710018Z",
     "shell.execute_reply": "2021-03-07T17:17:12.710511Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2021-03-07T17:17:12.718732Z",
     "iopub.status.busy": "2021-03-07T17:17:12.717104Z",
     "iopub.status.idle": "2021-03-07T17:17:12.722247Z",
     "shell.execute_reply": "2021-03-07T17:17:12.722976Z"
    }
   },
   "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::n_pieces = 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::n_pieces = %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)): # _k is unused.\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::n_pieces = %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"
   ]
  },
  {
   "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": {
    "execution": {
     "iopub.execute_input": "2021-03-07T17:17:12.741976Z",
     "iopub.status.busy": "2021-03-07T17:17:12.741289Z",
     "iopub.status.idle": "2021-03-07T17:17:13.734225Z",
     "shell.execute_reply": "2021-03-07T17:17:13.734810Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "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                  # NumPy: A numerical methods module for Python\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": {
    "execution": {
     "iopub.execute_input": "2021-03-07T17:17:13.751410Z",
     "iopub.status.busy": "2021-03-07T17:17:13.742774Z",
     "iopub.status.idle": "2021-03-07T17:17:15.262385Z",
     "shell.execute_reply": "2021-03-07T17:17:15.263260Z"
    }
   },
   "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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4+VjHL9tYxyciIjJNUVFRonYdAA017OMsgFRJLCREzj6GRERE+sEJhERERMb05ptAYKCxsyATEBsbqzJQQB3VqlWDh4eHSvzy5cuitp2dHRo1aiQ7v8IKVkAs7NKlSyoDDwDgs88+Ew08AIDo6Gj4+vrC1dUVnTt3Rrt27eDt7Q13d3eVlT8N4a233hKt6FpY7969kZmZCQD4/PPPEVjEz+v48eNx4cIFAMC7776LIUOGFNlXpUqVdJPw/7dnzx7RzV4AGDZsmE6voUsFgw66du2K7du3q9yUtre3x8iRIxEUFARfX19hldgHDx7g888/x6JFi4rte8mSJWjfvr3KCryFKRQKeHh4YPHixejduze6d++Oly9fAgBmzZql8c38ghvD48aNw9y5c4u8XseOHZGbmyuaQLhw4UL4+fkZNNeCQQezZ8/GhAkTVL7u7OyMRYsW4cWLF1i9ejWA/Jvb77zzDi5dugR7e3vs2bMHfn5+Kuc2btwYf/75J7y8vHDp0iUA+f9nu3fvRq9evYrM5+nTpxg8eLBwM9/MzAwrVqzAiBEjijy+cuXKCA8PR7du3RAYGIhHjx4BAKZNm4ahQ4eKBkIAEAY1FXzfBVxcXNCxY8cir2GMPIuj7murQGRkJFJT/y1BDxkyBGvWrCm2fwcHBwQHByM4OBgzZ87E3bt31cpL316+fImEhASh7erqCmtrayNmVDqdOXNG1G7cuDGGDRuGtWvXiuKBgYHYtGmTSU9QaNKkCfbs2SO0z507x4EHRERkdGlpaVi4cKEoNghAXRl9zQFQeF8TS0vLIj+vExGRkbGGT4Wwjs86vqZYx/+XKdfxCyaSGTNX1vHzsY6fj3X8so11fCIiItMUGRkpasvZNzBS0m7UqBFq164tOyciIiJd4wRCIiIiIhMwceJEWef17NkT27dvV4kXvgkFANWrV1dZvVKuqlWrwszMTLSSp/R6BXx9ffHll1/i22+/VflaYmIiVqxYgRUrVgDIv/Hs7e2NwMBAdOnSBV5eXjrJ93VcXFzg4uKiEr98+bIw6AAA/vvf/8LV1VV0THZ2Nm7cuCG0Bw4cqNGNTblSU1MxatQoUaxXr17w9vbW+7W14eLigo0bN5a4om3Dhg2xdu1a/Oc//xFiq1evxsyZM+Ho6FjkOUXdEC9Jp06dMGnSJMyYMQMAcOLECcTHx6Np06Ya9dO+fXvMmTNHo3NeN3lQX7m+8847rx2MPGPGDKxZs0b42S4YSDBnzpwSn+MKFSrgyy+/RFhYmBD7888/ix14sHTpUjx48EBo/9///V+xN/MLa9q0KX755ReEhoYCAF69eoWFCxcKz42uGTNPTV5bV65cEbXHjBmj9nWsrKxQv359tY/Xp9u3b4t+r7CILo904MGpU6dw6tQpUeyDDz7AggULSnwvNgU1a9YUtaWDiYiIiIxh8eLFSE9PF9pmAD6T0c9TAEsksaFDh/IzEBERkYljHZ91fE2wji9W2ur4mmAd/1+s4xeNdXwqjHV8IiIi05Obm4uDBw+KYnImEEZJ2sHBcnohIiLSH8MvDUdEREREeicdCFDcDVs5FAoFKlasWOL1Cps+fTrmz58PGxubEvvNzMxEVFQUpk2bhjZt2qB58+ZYtWqV6EaUIUVHRwuP3dzcVAYdAPk3dJ4/fw4AMDc3F1ZM1ae8vDwMHjxYtNqoo6MjFixYoPdra+uLL76Ag4PDa4976623REW0Fy9e4LffftNpLoMHDxa1//77b437mD59us4G9JRE21wVCgWmT5/+2uNq1qypMuDH1dVVrZvt3bt3F608Lr35WSA3Nxc//fST0K5bt65Gu6x07dpVtHr6li1b1D5XE8bOU5PX1osXL0RtXd5MrlevHpRKpehfvXr1dNZ/YXfu3BG1ixoQRq939uzZEr8+b948LFmyxOQHHQCqrwHpa4SIiMjQnj9/rrKzRB8AjWX09ROAwnucmJmZITw8XIvsiIiIqDRiHV97rOPrHuv48rCO/y/W8VWxjk+FsY5PRERkes6cOYOnT5+KYiEa9pEN4IgkFhKiaS9ERET6xQmERERERGVQRkaGqG1nZ6fT/qX9Sa8n9cknn+Dq1asYM2aM2oMgLl68iPfeew/e3t5ITEyUnatchQceBAYGFnnMoUOHhMetWrVSGZChD5MmTcKff/4pii1btgx16tTR+7W1YWlpiQEDBqh9/NChQ0Vt6Upf2nJzcxO1i7tRXpzq1asbbKUwbXP18PBAo0aN1Dq2efPmonbv3r3V2jHR3t5edEP69u3bRR537tw53L9/X2gPGDBA4xugnTt3Fh5fvnwZjx8/1uh8dRgzT01fW9LVXdetW6f2uaYkLS1N1La3tzdSJqWb9P1BOoAlPj7ekOloRfoakL5GiIiIDO3nn3/Go0ePRLGpMvp5BmC+JNa/f380bNhQbmpERERUSrGOrz3W8XWLdXz5WMcXYx1fjHV8Kox1fCIiItMTGRkpajcGUEvDPo4hv/5fWFBQkBZZERER6R4nEBIRERGZgOjoaJXVIdX5t3379iL7k64O++yZtEShHWl/6qxGW7t2bSxatAgPHz7Ezp07MX78eHh5ecHKyqrE82JjY+Ht7Y3r169rlbOmCg8qKK6gU/iYgIAAvee0YMECzJkzRxQLDw/HO++8o/dra8vDwwOVK1dW+3jpYI8TJ06odd6JEycwdepUdO3aFfXr10flypVhaWkJhUIh+mdtbS06T9Ob115eXlqvWmyoXD09PdU+1tnZWdRu3bq1rHPT09OLPObIEfF6a9KVktVRt25dUfvSpUsa9/E6xsxT09dWcHCwaHDI3LlzMWbMGNy4cUPtPkxBwSrwBWxtbY2USdHS09Nx6NAhREREYODAgWjUqBHMzMyEn9Nbt24ZO0XcuXMHKSkpQtvKykpll6Tly5dj8eLFWl/LEM9HhQoVRG1df5YhIiLSxKtXr/Djjz+KYv8B0Krow0u0HECKJDZlyhSZmRERvZ5SqcTNmzexc+dOLF68GDNnzkRERARWrVqFI0eOICsry9gpEpUarOMXj3X8f7GO/3qs46tiHV9zrOMbHuv42mMdn4iIyDRFRUWJ2nKWIYmStN98802Vz+5ERETGZmHsBIiIiIhI96Q3eHW52p9SqVS5qajJDWVra2t0794d3bt3B5A/EDMuLg4xMTH466+/sG/fPrx69Up0TnJyMt5++23ExsaqtYqqti5evIjk5GShXdTAg9zcXMTExAhtfQ882LBhA8aNGyeKDRs2DLNmzdLrdXVFuiLu69StWxcVK1YUXmuJiYlQKpXF3pA9cuQIPvroI5w/f15Wfk+fPtXoeOlqwpowdK5Vq1ZV+1jpjT6557548aLIY6Q33/v37692/8VJTU3Vug8pY+ap6WurTp06GDFiBFasWCHElixZgiVLlsDT0xMdO3aEv78/fHx8NHqvNjalUmnsFEQCAgJw9uxZY6dRIumqxc2aNcPYsWMRFxeHn3/+WYiPHTsWzZo10+r3liGeD1N7DRARUfm2fv163LlzRxSTs/vgKwARkli3bt3g4eEhNzUioiI9efIE27dvx969exEVFVXiJAZLS0uEhoZi3LhxBplYQ0T/Yh1fO6zj6x7r+P9iHf9frOOrYh0/n6nVcFnHF2Mdn4iISD0vX75UWZwiREY/kZJ2SIicXoiIiPSLOxASERERlUHSm0vJyck6K+A/evQIeXl5JV5PE1ZWVvD09MQnn3yCnTt34v79+5g0aZLKAINz585h48aNsq+jiejoaOHxG2+8gdq1a6scc/r0aWRkZAAAzMzM4Ovrq7d8du3ahaFDh4r+D/v06YOVK1dqvXquochZVavw6yovL6/Y1XCXLVuGgIAA2TfygfyCoCYqVqwo6zrLly83eK42Njayr6XNuUUpvKqqruhyYFUBY+Yp57W1YMECYWciz9wAACAASURBVDBXYbGxsfj+++8RGhqKKlWqoFWrVpg6dSouXryo8TX0TTroxdR2wCj8/uvo6IjAwEDUqFHDiBmpkg4EaNUqf0+kRYsWoW3btkI8JycHffv2RWJiouxrGeL5kA5gsrOz02n/RERE6srNzVUZ8O0LwE9GX2sA3JPEpk6VMxWRiKh4H374IWrUqIERI0bg999/f+0OSNnZ2di+fTsCAwMxdOjQYusvRKR7rONrh3V83WMdP58xcmUdX3Os4xse6/jaYx2fiIjI9Bw/flz0O80MQNH72xfvOYDjklhwsJx9DImIiPSLOxASERERlUGNGzcWtTMzM3H16lU0atRI676lKyMCgLu7u9b9FnB2dsYPP/wAf39/9OrVC7m5ucLX1q1bh0GDBunkOoGBgTh06NBrj7t+/fprb+7n5eUVO/hi9erVGDZsmJwUAeQPgujXrx9ycnKEWKdOnfDbb78ZZBVnXZHeVFSH9CZTZmYmHB0dRbHo6GiMHj1adBPMwsICvr6+aNu2LVxdXVGtWjXY2NjA2tpadG6nTp00zqmApaWlxuccOXIEH374ocFzNSWarrqsDulAKF0wZp5yXls2NjbYsWMHNm7ciB9++KHIFWWVSiXOnj2Ls2fP4rvvvkNoaCjmzZuHBg0aaHw9fXBychK1CwZ2mYoRI0agatWq8PLyQoMGDaBQKBAYGIgHDx4YOzWB9Pfzm2++CSB/x4AtW7bAy8tLyPfx48fo2bMnYmJiZN3QN8TzkZmZKWpL3/+JiIgMZevWrbhy5YooJmfKXy6AHySxwMBAtGvXTm5qRERFOn78uMquYABgbm4OFxcXVK9eHdnZ2UhMTFQZJL927VpcvnwZkZGRsLe3N1TKROUW6/ivxzq+YbGOb7xcTQnr+K/HOn4+1vE1xzo+ERGR6YmMFO8d2BKApsvvHAFQuBpnYWEBf39/LTMjIiLSPU4gJCIiIiqDfHx8YG5uLrppf+rUKZ0MPIiNjRW1XVxc4ObmpnW/Ut26dcPQoUOxatUqIXb06FGdX8eUHT9+HD169BCt4Nm+fXts27YNVlZWRsxMc8+fP9f4nGfPnonaRQ1cmzBhguhGfmhoKJYuXVrkatOFabr6ry588cUXpSZXfZEOQJk1axY8PT216rNZs2ZanV+U0pJnYQqFAgMHDsTAgQMRHx+PAwcO4ODBgzh69GiRu03s3r0bhw8fxu7du+HnJ2f/HN2qU6eOqJ2UlGSkTIr2ySefGDuF15IOPChYuRgAatWqhc2bNyM4OFgYSHzu3DkMHz4cv//+u8bXMsTzcf/+fVG7bt26er8mERGRlFKpxMyZM0WxVgC6yOjrdwDXJDHuPkhE+ubk5ISwsDCEhobCz88PDg4Owtdyc3Nx5MgRTJs2DUeOHBHiJ06cwLBhw7B582ZjpExUrrCOXzawjl+26vjh4eGlJld9KS318dKSZ2Gs4+sX6/hirOMTERGpRzqBMEROH5J227ZtuTgXERGZJE4gJCIiIiqD7O3t0bJlS5w+fVqIbdmyBWFhYVr3vWXLFlG7Q4cOWvdZnP79+4sGHmRmZiItLa1crF54/vx5dOnSRbRyY6tWrbBnzx5Zq0waW1E3Pl8nNTVVeGxmZoaKFSuKvn7lyhXRjbbmzZtj69atag3KKNy3IVy7dg3nz58X2qacqz5VqVJF1HZzc0PHjh2NlE3xSkuexWnatCmaNm2KsWPHQqlU4vLly9i/fz82b94sGsCVkZGBvn374vr160YvXtepUwdmZmbCCs937941aj6lzZMnT5CYmCi0FQoFWrZsKTqmQ4cOWLBgAT744AMhtmnTJsyYMQNffPGFwXJVl3TgQb169YyTCBERlWt79+5V2RViisy+vpO0vby8StVnTCIqXerVq4cvvvgCYWFhsLW1LfIYc3NzBAYGIjo6GmPGjMHy5cuFr23ZsgXR0dEICgoyVMpE5RLr+KUf6/hlr45fWnLVp9JSHy8teRaHdfzyh3V8IiIi05OZmYnjx4+LYsEy+omStIOD5fRCRESkf5xASERERFRGvf3226KBB3/88QceP36sckNNExcuXBD1CQB9+vSR3d/rFHWT4fnz5zoZeBAREYEnT56oxPfu3YuIiAgAQO3atbF69WqVYy5fvoyPP/5YaO/YsUNlpdMCclYrTUhIQKdOnUT5ubu7Y9++faV20MWFCxc0Oj4xMRHp6elC29XVFQqFQnTMsWPHRO33339f7RWdL168qFE+2jp16pSobcq56pN0lfNr16R7sJiG0pKnOhQKBdzd3eHu7o6xY8fiyJEj6NOnjzAYKDk5Gb/++itGjx5t1Dytra3RuHFjXLp0CQBw+/ZtZGVlwcbGxqh5lRbSVYvfeOMN0c4iBUaNGoXY2FisWLFCiE2bNg0eHh7o0aOH3vPUxOXLl0VtDw8PI2VCRETlmXT3wcYA3pbRzx8A4iSxqVOnqvyNQ0SkC9988w06deqkdt3B3NwcixcvxunTp0X1i5UrV3ICIZEBsI5fMtbxDYt1fNbxgdJTHy8teaqDdfzygXV8IiIi03PkyBHk5OQIbUsA/hr28QTAGUksJETOPoZERET6xwmERERExiRZxZ4MqBw89yNHjsS3336LrKwsAEB2djZmzJiBefPmye5z2rRponatWrXQr18/rfIsybNnz1Rizs7OOunb09OzyPimTZuExwEBAUWuVpqQkCA8btKkiU5v1iQmJqJjx45ITk4WYm5ubjhw4ACqVq2qs+sYWlxcHFJTU1G5cmW1jj906JCo7e3trXLMw4cPRe3GjRurnU9UlHT9L/0q/P8JmHau+iQdeBgVFYWpU6fq5VpmZmaitlKpVPtcQ+ZpaH5+fpg1axbef/99IXb06FGjDzwA8t+XCwYe5ObmIj4+Hq1btzZyVqWDdGekN998s9hjFy5ciAsXLuCff/4BkP+zMXjwYBw7dgxNmzbVa56aiIv7d5qFk5MTGjRoYMRsiIioPDpy5Iho1wcAmAzArOjDSzRT0nZ3d0fPnj3lpkZEVKLQ0FCNzzE3N0d4eDj69+8vxPbt26fLtEqvclBHNmnl4PlnHb9krOMbFuv4rOMDrOObAtbxyybW8YmIiExPZGSkqN0WgKZ7yUcDyCvUtrW1hY+Pj5aZERER6QcnEBIRERnT+PHGzoDKsCpVqmDIkCFYvny5EFu4cCHefffdYm+6l2TXrl3Ytm2bKPbxxx/DwkJ/HylPnjwpateoUUPt1V7lOnz4sPDYz8+vyGOOHDkiPPb313TtqeIlJSUhJCQEd+/eFWK1atVCZGQkatWqpbPrGEN2djY2btyIMWPGqHX8mjVrRO2AgACVY6Q3kl+9eqVW3y9fvsSqVavUOlZXSlOu+uTt7Y1KlSoJq3JHRUUhPj5eLzc77ezEZd3nz5+rfa4h8zSGDh06iNoFqxgbm5+fH9atWye0Y2NjOfBATdKVi1u1alXssVZWVtiyZQs8PT2RlJQEAMjIyEDPnj1x4sQJVKpUSa+5quPhw4ei34XF/T4mIiLSJ+nug3UBDJbRTzSAY5LYlClTVAbKEhEZm/Rzd0pKCp4/f17sbl3lBmv4pGes48vDOr5+sI5fenLVJ9bxTQPr+GUP6/hERESmR7oQSLCcPiRtX19fWFtby86JiIhIn3iHmoiIiKgMmzlzJlxcXIR2bm4uevbsiZs3b2rUz5kzZzB4sHioZMuWLTG+hAE0jx8/xoYNG5CXl1fsMSV59eoVfvrpJ1HsrbfektWXuh49eoTLly8LbUMOPEhNTUWnTp1w/fp1IVa1alUcOHAAbm5uOrmGsc2YMQMZGRmvPW7fvn2iIp2trS0GDhyoclyNGjVEbenuIMX58ssvVVY91rfq1auL2qacqz5ZWlpi3LhxQlupVGLUqFHIzs7W+bUqVqwIc3Nzoa3J+54h8zQG6UADU7jRDKi+xxceCEYlkw48KGnlYgBwcXHBli1bRIP5rl27hgEDBiA3N1cvOWpC+n+v79//REREUqdPn8bevXtFsYkALGX0Jd19sF69ehgwYIDc1IiI9Kaovw3T0tKMkAlR+cM6vmZYx9cv1vH/Zcq56hPr+KaBdfyyh3V8IiIi05KSkqKyQ3CIjH4iJe2QEDm9EBERGQYnEBIRERGVYc7OzlizZg0UCoUQu3fvHoKCgtS+obNr1y507txZNGDIxsYG69evL3EV4czMTAwaNAgtWrTAunXr8OLFC7XzzsrKwuDBg3Hx4kVRfMiQIWr3IUfhAQXOzs5wd3dXOebGjRu4f/++0NbFaooZGRn4z3/+I/p+nZycsH///iJz0NatW7egUChE/27duqXz60glJSVhwIABJd68vXbtmsr/89ChQ+Hk5KRybPv27UXtpUuX4tq1ayXmsGzZMsyePVuDrHXD29tb1DblXPVt7NixooEYR48eRd++fTUalPjs2TMsWLAAP//8c7HHWFpaolGjRkL77NmzooE9ppKntr788kusW7cOOTk5ah2vVCoREREhihW3mr2h3ytcXV3RrFkzoR0dHa23axmbLp/brKws0aA5oOSViwu0a9cOixYtEsX279+PSZMmycpDlw4ePChqd+3a1TiJEBFRufXdd9+J2tUAvC+jn5MA/pLEwsPDYWkpZyoiEZF+3bt3TyXm7OxshEyIyh/W8TXDOr5+sY7/L1POVd9Yx9ct1vFLJ9bxS8Y6PhERlXYHDx4U7UJeAYCPhn3cB3BZEgsOlrOPIRERkWFYGDsBIiKi8oQDLkyXsf9vYmNj1b5pJFWtWjV4eHgU+/VOnTrh+++/R3h4uBBLTExEYGAgBg0ahGHDhiEwMFB0TnZ2Nnbv3o1ffvkF27ZtE33N3Nwcq1evFt0cKkl8fDzeffddfPjhh+jfvz86d+4MX19f0YrKBe7fv4/t27fjxx9/VLkB06dPH70XWQoPxvD19RUN2ChQeHCCq6sr6tatq/V1e/TogZMnT4pin376KR4/foy//pIONy2Zp6enyaxCWpirqysSExOxZ88etGvXDhEREfD39xee42fPnmHDhg2YPHkynjx5IpxXvXp1zJwp3bMjX4MGDdCuXTv8888/APIHcPj7+2P+/Pno3bs3LCz+/XPn3LlzmDlzJn7//XcAgLu7Oy5duqSvb1dF/fr10aZNG+H/2ZRz1TdHR0ds2rQJISEhwiCUnTt3olmzZhg/fjz69etX5M/VnTt3cPz4cWzfvh1//PEH0tPT8dVXX5V4rc6dOwvPXW5uLvz9/TFy5Eh4eHjA3t5e9DPerFkz0fuSIfPURlxcHGbMmIEJEyagT58+6NmzJ9q0aaPyey0vLw9///03vvnmG9H7SoUKFRAWFqa3/DQVFhaGzz//HED+QLlTp07By8vLyFmZtri4ONFqw9WqVSvyd2xR3n//fcTGxmLp0qVCbO7cuWjVqhXeffddneeqDqVSiZ07dwrtdu3alZkV/ImIqHS4fPkytmzZIoqNA2Aroy/pXzLVq1fH8OHD5aZGRKRXhWteQH4tp6RJR2WVsevEVDJj//+wjp+Pdfx/sY6fryzV8X18fHDs2DGTz1XfWMfXLdbxiXV8IiIi0xMZKd470BeAppWwKEnbyckJrVu31iYtIiIiveIEQiIiIgOSripHVGDixImyz+3Zsye2b99e4jGTJk2CnZ0dxo4dKwxwUCqVWLduHdatWwdbW1vUrFkTjo6OSElJwYMHD/Dy5UuVfuzs7PDrr7+id+/eGueZnp6OlStXYuXKlQDyB3tUqVIFTk5OyMrKQlJSEpKTk4s8t23btli9erXG19RU4YEHxa1IXHjggb+/v06uK12hEQCmTZsmq6/o6GiVgSSmYMiQITh27BgOHDiA2NhYBAYGwtnZGa6ursjKysLNmzdVVre2trbGunXrShxIMXv2bAQGBgo3hpOSktC/f3/Y29ujYcOGMDMzw927d/Hw4UPhHDs7O6xfv97gRbtvv/0W3bt3LxW56pufnx/Wrl2L4cOHIysrC0D+TeaJEydi4sSJcHFxQbVq1WBtbY20tDQkJyeLBqSoa8yYMVi2bJlwjfv37+Prr78u8tjVq1dj2LBhRslTF5KTk7F06VLhBrKLiwuqVKkCOzs7PHv2DDdv3kRmZqbKeREREahVq5ah0y3WoEGD8MUXXwgr/W3dupUDD17jzJkzorY6qxYXtmDBAsTFxSEmJkaIjRw5Eo0bN1ZZdd0QTp06hbt37wrtwYMHGzwHIiIq377//nvRqsNOAD6U0U88gB2S2IQJE2BjY6NFdkRE+rNq1SpRu7zuIMIaPpWEdXzW8aVYxy97dfwffvhBNBnNlHPVN9bxdY91/PKLdXwiIiLTI51AGCKnD0k7MDAQ5ubmsnMiIiLSNzNjJ0BEREREhjFmzBj8/fffePPNN1W+9uLFC1y/fh2nT59GYmJikYMOAgICEBsbq/agA3t7e7Rs2bLYr6ekpCAhIQHHjx/HuXPnihx0YGZmhg8++AAHDhxAxYoV1bquXOnp6Th37pzQNuTAA0MruIFawMLCAvb29nq9ppmZGTZt2gRfX18hlpKSgtOnTyM+Pl5l0IGDgwM2b96Mjh07lthv+/btsWLFClhaWorimZmZOHPmDGJjY0U38itVqoRdu3ZpfGNOF9q2bYtly5aVilwNYcCAATh69CgaNWqk8rWkpCScO3cOJ06cQEJCQpE3883NzVGzZs0Sr9GoUSP8+uuvWr2+DZGnPiQlJSEuLg7Hjh1DXFycyqADW1tbLF26FB988IHBcyuJq6uraJDqhg0bkJeXZ8SM9EOX78PSgQdF/Z4viaWlJbZs2SIagJKVlYXevXsjKSlJVk7a+PXXX4XHDg4OHHhAREQGlZiYiHXr1oliHwKQ89fodwCUhdpOTk4m99mLiKjAnj17RBNyAKgMziciw2Adv2Ss47OOr2+lKVdDYB1fv1jHN22s4xePdXwiIirt7t69iytXrohiwTL6ke5AGBwspxciIiLD4Q6EREREROVImzZtcPr0aWzfvh0rV67EX3/9hVevXhV7fIUKFdC1a1d88MEHCAnRbK2lKlWq4OzZs7hx4wZ27NiB6OhoHD16VK1VPV1cXNC/f3+89957aNGihUbXlSsmJka4uVWhQoUiV4pNTk4WFZBK68CDv//+W9R+9913UaVKFb1f19HREVFRUYiIiEBERAQeP36scoyVlRV69eqFiIgI1K5dW61+hw4disaNG2Py5Mkqg90K2NjYICwsDDNmzICLi4tW34c2hgwZAnd391KRqyF4enoiPj4eGzZswOLFi3Hy5Enk5uYWe7y1tTU6dOiArl27IiwsTK3np2/fvujQoQPWrl2L6OhoxMfH48mTJ3j27JloZxlj5ynXihUr0LNnT+zevRtHjx4VDV4pSuXKldG/f3989tlncHV11Vte2hg3bhx2794NIH8Q/4EDB/DWW28ZOSvd0uX7sLYDDwCgevXq2Lp1K/z9/YXBh/fv30efPn1w8OBBWFtby8pNUy9fvsT69euF9vDhw/U+8JCIiKiw2bNnC7v9AEAFAONk9HMTwEZJ7JNPPoGDg4MW2RER6UdqaipGjRolivXq1UvnO5kkJyfj0aNHGp1z7do1UTszMxPp6emyc8jNzRX+njczMxNiCoVCdp9E6lIqlSXWkwpr3bo1Tp48iR07duDnn39GZGTka+v4Xbp0wciRI4U6vrrXqlSpEmJjY3Hjxg3s3LkTBw8eRExMjNp1/H79+mH48OFCHV/d68p1+PBhUR2/ZcuWKteU1vE7dOig97w0Vfj9qDhHjx4VtQcPHoxKlSrp9HuR9pWXlwd7e3scOHAAc+bMwdy5c4ut4/fs2RM//vgjateurVZOgwcPRoMGDTBlyhTRBM/CbGxsMHDgQEyfPh0uLi4q/b7u56io7+d1uUm/npuba7K5Fj5W2pe650pr4uqc9+abbyIuLg6//fYblixZglOnTr22Pt6+fXt06dIFAwcOLPL5kerduzd8fHzw66+/4uDBg7h06VKRdfySnid95qnN/xcALF26FN27d8eePXsQExOjVh2/X79+CA8Ph6urq0avpYKYvt/3Pv74Y1Edf+/evUIdv6ifq+LoO9fCrx9Nr6XL9+HTp0+L2h4eHhr3U6VKFWzatAlBQUGiOn7v3r0RFRWlVh1fm+ejgLSOP2zYMNjZ2Wn9/5ibmyvkV/A+l5GRwV2c/r9nz56V2CYizfHnqnwr+BxToBIATfcTvwbgtiTWtm1brWpXpRl/hoiISgeFUt0Rk0RERGXIxYsX0bx5c6F94cIFNGvWTHZ/OTk5uHr1qijWsGFDWFhwrj6ZthcvXiA2NhZXr17Fw4cPkZGRAWtra1SrVg0eHh7w8vKClZWVzq6nVCqRmJiIK1eu4Pbt20hLS8OLFy9QoUIFODg4oGbNmmjZsqXaE8dInsGDBws3diwtLZGQkAA3NzedXuPgwYMICgoS2l999RW+/vproZ2Tk4N//vkHcXFxePLkCSpWrIjatWsjKCgITk5Osq9769YtxMTEICkpCS9fvoSTkxMaN26M9u3bo0KFCtp8S7Lk5uYiIyNDaDs4OAg3ukwtV1OQlpaGY8eO4f79+3j8+DGys7Ph4OCAatWqoUmTJmjcuDFsbGyMnaZJ53nz5k0kJCQgMTERaWlpePXqFezt7VG1alW0aNECTZs2LRWfT7y8vBAbGwsA6NGjB3bs2AGg5J8pQwsMDMShQ4cA5D/v9erVU/tcQ7wPG5o2z0eBtWvXYujQoQB0/7zw83rx0tPTER0dLbSDgoI4cZNIS/y5Kp0ePnyIevXqiXYYGAtgnoy+RgNYWqhtZ2eHxMREODs7a5ll+VTwM3X79m188sknQlzbeh4R5Q/M7datG/78808h5ujoiLi4ONSpU0en1/r666/xzTffaNXHggULULduXdnnKxQKVK1aFQCESd0uLi4clEwm78WLF8JifY8fP8arV6+EOn6DBg3g4eGh8zr+nTt3cO3aNdy9exfp6enIysqCra0t7O3tUaNGDTRv3ly0CxLp3siRI7Fp0yYA+XWSkydP6nxBsKNHj6J79+5Ce/Lkyfjss8+Edk5ODk6cOIH4+Hg8ffoUFStWRM2aNeHn5wdHR0fZ1719+zaOHTuGhw8f4uXLl3B0dETDhg3h7e1tcrXx0pSroaSlpeHUqVN48OABUlJShPp4lSpV0LBhQzRs2NBk6vimmmdiYiKuXr2KO3fuID09HdnZ2bCzs4OzszOaNm2KJk2alIq6ZVBQEM6ePQsA6NKlCzZs2GDkjFR169YNMTExAIBz585p9FnSEO/DhqbN81Fg48aNGD16NADdPi+5ubnCbooF94EePXqk9mKgREREmpg/f77oPk5vAFs17GM5gMJLclWqVAmrVq0q9wtVsY5PRGTaTL/aQERERER6Y2trC19fX/j6+hpkUoZCoUC9evVkTSog3SlcBBs2bJhRJq1YWFjAz88Pfn5+Ou23NL2+SlOuhuLo6Fgqdpoz5Tzd3NxK/UQ0AJg+fTpCQ0MBAH/88QcSEhLQuHFjI2elO6bwPmyKZs+eLTwePnw4nxciIjKoefPmiSYPWgGYKKOfBwBWS2KjRo3i5EEiMkmTJk0STR4EgGXLlul88iARacfW1hbt2rVDu3btDHI9hUKBunXrajVhl7RXeOersLAwo0xasbCwQPv27dG+fXud9luaXl+lKVdDcXR0FHY7NWWmnKerq2upn4gGAFOmTME777wDANi7dy+uXr2Khg0bGjkr3TGF92FT9NNPPwmP+bwQEVFppFQqcf78eVEsWEY/kZJ2ixYtyv3kQSIiMn1mxk6AiIiIiIgM5/Lly7h//z4AwMrKCl988YWRMyIiUtW1a1d06NABQH4B/4cffjByRrrD9+Gi7d69G3FxcQAAGxsbPi9ERGRQT58+xaJFi0SxdwHUltFXBICXhdpWVlaYMGGCFtkREenHggULMGfOHFEsPDxcGARORETGc+XKFWEXJn6eJCJT1blzZ7Rt2xZAfh1/wYIFRs5Id/g+XLR9+/YhPj4eQH4df+JEOUsvERERGdf9+/eRkpIiislZdiJa0vbw8JCdExERkaFwB0IiIiIionIkKipKePzee+9x5VwiMlnz5s1D27ZtkZeXh7Vr12Ly5Ml44403jJLLtWvXRKsNA8CDBw+Ex5s3b0aVKlWEtr29Pfr27VtkX2XhfViXzweQP7jkyy+/FNoTJ07kjidERGRQixYtQkZGhtA2B/CZjH6eAFgqiQ0fPhw1a9bUIjsiIt3bsGEDxo0bJ4oNGzYMs2bN0ts1x4wZg379+ml0zrVr19CrVy+h7e3tDXd3d9k55Obm4t69ewAAM7P8dXbt7e1hYcFb5lR65Obm4vnz50K7QoUKMDc3N2JGpA8nTpwQHo8YMQJNmzbVy3VsbW1FbSsrKzg4OOjlWqaKP1NE2pk/fz7at2+PvLw8bNy4EVOnTsUbb7xhlJ+ra9euISYmRhR7/Pix8Hjv3r0qdeu33367yL4M9T6sT7p8PoD8Ov73338vtD/99FOtPptL5eTkCJ/RC34XNWnShO/J/9+zZ89Er0tvb2/Y2dkZMSOi0o8/V+XXzz//LGq7AND0N9p5AI8ksZEjR5brnXmlP1NERGSaeDeEiIiIiKgciY7OXwPL2toaU6dONXI2RETF8/LywogRI7By5Urk5OTg888/x8aNG42Sy9GjRzF8+PBivz5p0iRR29XVtdgJc2XhfViXzwcA/O9//8OZM2cAALVr18aUKVN0kygREZEanj17hnnz5olifQE0kNHXAgCZhdpmZmYIDw/XIjsiIt3btWsXhg4dCqVSKcT69OmDlStXQqFQ6O261apVQ7Vq1bTqw97eHhUrVpR9fk5OjsoAZHNzcw5KplKNr+Gy6dChQwDy60eff/653v6Ppf2amZmV+9cTf6aINNO2Km332QAAIABJREFUbVtRHX/atGkqdXxD/Vz9888/eO+994r9+uTJk0VtV1dX9O/fv8hjDfU+rE+6fD4AYOPGjaI6vq6fF6VSKfw9UtCvg4MDF/sohp2dnVZ/GxGRKv5clR9///23qB0so49ISbt+/fpo0aKF7JyIiIgMhX9hERERERGVI5s2bTJ2CkREaluxYgVWrFghtHNzc42YjW7wfVjVgAEDMGDAAGOnQURE5dTKlStFK/ADgJwp/pnIn0BY2MCBA1G/fn25qRER6Vx0dDT69euHnJwcIdapUyf89ttvpXJQNBFRWcX6ERGVJqzjlw+s4xMRUVmQl5cnLPhbIERGP9IJhCEhcnohIiIyPDNjJ0BEREREREREZMqGDRsGpVKp9r9bt24ZO2W94vNBRERlxcuXL/Hjjz+KYqEAPGT0tQxAqiT22WefycyMiEj3jh8/jh49eiArK0uItW/fHtu2bYOVlZURMyMiIiIi0h7r1mJ8PoiIiFSdP38eKSkpopimOxDmADgsiQUHy9nHkIiIyPA4gZCIiIiIiIiIiIiIiMqdX3/9Fffu3RPFPpfRz0sAcySxnj17onnz5nJTIyLSqfPnz6NLly7IzMwUYq1atcKePXtgZ2dnxMyIiIiIiIiIiIgMIzJSvHfgGwBcNezjJIAMSSwoKEiLrIiIiAyHEwiJiIiIiIiIiIiIiKhcycnJwaxZs0SxAADtZPT1C4D7ktiUKVPkJUZEpGMJCQno1KkTnjx5IsTc3d2xb98+ODo6GjEzIiIiIiIiIiIiw4mKihK15ewbGCVpN2/eHNWrV5edExERkSFZGDsBIiIiIiIqewIDA6FUKo2dBhERERERUZE2b96M69evi2JTZfSTC+AHSSwkJARt27aVmxoRkc4kJiaiY8eOSE5OFmJubm44cOAAqlatasTMiIjIFLCOT0RERERE5UV2djYOHz4sioXI6CdS0g4JkdMLERGRcXAHQiIiIiIiIiIiIiIiKjeUSiVmzpwpinkB6Cyjr40AbkhiU6fKmYpIRKRbSUlJCAkJwd27d4VYrVq1EBkZiVq1ahkxMyIiIiIiIiIiIsM6ceIEMjMzhbYCmu9AmAXgb0mMEwiJiKg04QRCIiIiIiIiIiIiIiIqN3bv3o24uDhRbIrMvmZJ2m3btkVQUJDM3oiIdCM1NRWdOnUS7bRatWpVHDhwAG5ubkbMjIiIiIiIiIiIyPCioqJE7eYAqmrYRwyAl4XaZmZm8Pf31zIzIiIiw+EEQiIiIiIiIiIiIiIiKheUSiX+7//+TxRzB9BHRl87AFyQxKZOnQqFQiEzOyIi7WVkZOA///kPLl68KMScnJywf/9+uLu7GzEzIiIiIiIiIiIi44iMjBS15ewbGClpt2nTBo6OjrJzIiIiMjQLYydARERERERERERERERkCIcOHcKxY8dEsc9k9jVT0m7evDm6desmszciIt3o0aMHTp48KYp9+umnePz4Mf766y+N+vL09ESlSpV0mR4REREREREREZFBPX/+HP/8848oJmcCYZSkHRwcLDsnIiIiY+AEQiIiIiIiIiIiIiIiKhdmzhRP+6sHIExGP38BOCGJTZkyBWZmZvISIyLSkYMHD6rEpk2bJquv6OhoBAYGapcQERERERERERGREcXExODVq1dC2wJAgIZ9pAE4JYmFhMiZhkhERGQ8vJNNRERERERERERERERl3smTJ3HgwAFRbBLkrbQo3X2wfv366N+/v9zUiIiIiIiIiIiIiIhIDyIjI0VtLwAOGvZxCEBuoba1tTXat2+vZWZERESGxQmERERERERERERERERU5n333Xeidg0AI2T0cwxAtCQ2efJkWFjImYpIRERERERERERERET6EhUVJWoHy+lD0m7fvj1sbW1l50RERGQMvJtNRERERERERERERERlWnx8PLZt2yaKjQdgI6Mv6e6DLi4uGDp0qNzUiIh0SqlUGjsFIiIiIiIiIiIik/D06VPExsaKYiEy+omUtENC5PRCRERkXNyBkIiIiIiIiIiIiIiIyrRZs2aJ2pUAjJbRTxyAXZLYxIkTYW1tLTMzIiIiIiIiIiIiIiLSh0OHDiEvL09o2wBor2EfyQAuSGKcQEhERKURJxASEREREREREREREVGZdfPmTWzYsEEU+wiAg4y+vgNQeG+vypUrY+TIkVpkR0RERERERERERERE+hAZKd47sD3yJxFqIkrSdnBwgJeXlzZpERERGQUnEBIRERERERERERERUZn1448/Ijc3V2jbARgro5/rAH6XxMaOHQt7e3stsiMiIiIiIiIiIiIiIn2QTiCUs29gpKQdEBAACwsL2TkREREZCycQEhERERERERERERFRmZSUlIRVq1aJYiMBOMvo63sAuYXa9vb2+Oijj7TIjoiIiIiIiIiIiIiI9OHBgweIj48XxYJl9CPdgTA4WE4vRERExscJhEREREREREREREREVCbNnTsXL1++FNpWACbK6Oc+gDWS2OjRo1G5cmUtsiMiIiIiIiIiIiIiIn2IihJP/asIoI2GfdwCcEMSCwmRs48hERGR8XECIRERERERERERERERlTmpqalYsmSJKDYUQE0Zfc0G8KpQ29raGuPHj9ciOyIiIiIiIiIiIiIi0hfpBMIAAOYa9hEpaVetWhXNmzfXJi0iIiKj4QRCIiIiIiIiIiIiIiIqcxYuXIjMzEyhbQ5gsox+UgAsl8RGjBgBFxcXLbIjIiIiIiIiIiIiIiJ9iYwUT/8LltFHlKQdFBQEMzNOvyAiotKJv8GIiIiIiIiIiIiIiKhMyczMxPz580Wx/gDekNHXfADPCrXNzc0xadIkLbIjIiIiIiIiIiIiIiJ9uXHjBm7duiWKhcjoRzqBMCRETi9ERESmgRMIiYiIiIiIiIiIiIioTFm+fDlSU1OFtgLAFBn9ZABYKImFhYXBzc1Ni+yI6P+xd99RUV1rG8CfAelFEcWIImCMNWIURVQQBFvEhiXBbmyJ5saSxB5NNF57ixF77zdRo0ZNLIA9NhRjQRQLakARQYqAUub7g48TzgxletHntxZrzd5zzj4vwzTe3YiIiIiIiIiIiIi0JTxcPPXPCUBDJdu4CeCpTF1AgCr7GBIRERmGcvoOgIiISieVSvHw4UNcv34dT548wcuXL2FhYQEHBwd88MEHaNasGSwtLfUdJhERERERERERkUF4/fo1Fi5cKKrrDOUHBwDASgApMnWTJk1SMTIiIiIiIiIiIiIiItK2sLAwUbmNCm3I7j5Yo0YNvP/++yrHREREpG+cQEhEZIBSUlKwb98+/PnnnwgPD0dSUlKJx5qZmSEoKAhjx46Fn5+fTuJzc3NDXFycyudHRETA399fcwERERERERERERH9v82bNyMhIUFUN1WFdrIBLJGpCw4ORv369VUNjYiIiIiIiIiIiIiItEgqlcrtQBioQjthMuXAwEBIJBKV4yIiItI3E30HQEREYl9++SXee+89DBkyBL/88kupkwcBICcnB/v27YO/vz8GDRqEtLQ0HUVKRERERERERERkWHJzczFv3jxRXRsAzVVoawOApzJ1kydPVjEyIiIiIiIiIiIiIiLStps3byIxMVFUF6BkG3kATsrUBQQo2woREZFh4Q6EREQG5sKFC3jz5o1cvampKapWrYoqVaogJycHcXFxSE1NFR2zZcsW3L59G2FhYbC1tdVVyERERERERERERAbhl19+wf3790V1U1RoJxfAApm6du3aoVmzZqqGRkREREREREREREREWhYWJt470BXA+0q2cQXAS5k6TiAkIiJjxwmEREQGrEKFCujbty+CgoLg6+sLOzs74b68vDycPn0a06dPx+nTp4X6ixcvYvDgwdi9e7dOYqxSpQq2bdum1DmNGjXSUjRERERERERERPSuys/Px5w5c0R1XgDaqtDWDgAPZeqmTFFlKiIREREREREREREREelKeHi4qByoQhthMuV69erB2dlZ5ZiIiIgMAScQEhEZIDc3N3z33Xfo27cvrKysij3G1NQU/v7+iIiIwKhRo7BmzRrhvj179iAiIgJt2rTReqyWlpZo21aVYVhERERERERERESac/DgQdy4cUNUN1nFtubKlFu0aAE/Pz8VWyMiIiIiIiIiIiIiIm3Lzc3FiRMnRHWq7BsYLlPm7oNERPQ2MNF3AEREJDZjxgzExMRg6NChJU4eLMrU1BQrVqxA06ZNRfXr1q3TVohEREREREREREQGRSqV4r///a+orgGA7iq0tRdAtEzdlClTIJFIVIyOiIiIiIiIiIiIiIi0LTIyEmlpaaI6Zaf+vQFwRqYuMFCVfQyJiIgMCycQEhEZmKCgIJibmyt1jqmpKSZMmCCqO3LkiCbDIiIiIiLSueHDh0MikUAikaB37976Doe0ZNeuXcLf2cXFBZmZmfoOiYiIjFBERAQuXrwoqpukYltzZMoeHh4ICgpSsTUiIiIiIiKitxfz+O8G5vGJiMhYhIeL9w6sB6Cqkm38BSCrSFkikcDPz0/NyIiIiPSvnL4DICIizfD19RWVX7x4gczMTFhbW+spIiIyBtnZ2bhy5Qru3r2LZ8+eIS0tDZaWlqhcuTIaNWoET09PmJmZafy6WVlZwnVTUlLw6tUrWFlZwd7eHjVq1MD777+PmjVrwsTk3Vzv4s2bN7h9+zYePnyIf/75B+np6cjJyYG9vT0cHR3h4eGBevXqwdTUVN+hEhFpzeXLl7FhwwYAQLly5eR2FKK3x6effop58+YhKioKT548wdy5czFz5kx9h0VEREZm9uzZorI7gD4qtHMUwGWZusmTJ3P3QSIiItIb5vENE/P4RETM479LmMcnIiJjERYWJiqrsm9gmEy5SZMmqFixosoxERERGQpOICQieks4ODjI1aWmpnICIZGBcnNzQ1xcnEba+u2339C9e3eFj5dKpThw4ADWrVuHY8eO4fXr1yUea2Njg6CgIHz++ecICAhQK87C665atQrHjx9Hbm5uqcfb2dnB09MTfn5++Pjjj9GsWTO9DUSIj49HtWrVhPK1a9fg4eEhd1yNGjXw+PFjAMD27dvRt29fha+xe/duHD9+HGfPnsXt27fLfHzKly+PPn36YMyYMahbt67C1yEiMhZjx45Ffn4+AGDgwIGoXbs28vLySjxek5+tUqlUI+3oS0pKilwHRkpKCipUqKBwG9HR0ejWrRvu3r0r1JmYmGD27NmYOHGixmIFClZs/PHHH9GlSxcAwIIFCzB8+HC4uLho9DpERPT2unDhgtyggAkAVBmqPVumXKtWLe6gQERE9I5iHp95fFnM4xMRiTGPrzrm8YmIiDQvOzsbZ8+eFdVpYgJhYKAqrRARERmed3MpOCKit9A///wjV+fo6KiHSIjIkF25cgXNmjVD9+7dcfDgwVIHHQDAq1ev8MsvvyAwMBCBgYGIjY1V6bpxcXFo27Ytunfvjj///LPMTnUASE9Px4kTJzBjxgx4e3vj999/V+namlA0uVShQgV8+OGHcsfExcUJgw4AwMfHR6lrjB07FqtXr8aNGzcUenxSU1OxatUqeHh44IcffjD6TjIiTXv48CEkEonwM3jwYH2HREo4fPiw8N4rkUgwYcIEPUdkXKKiokRld3d3pQYdHDp0CN7e3qJBB/b29jhw4IDGBx0UCgoKEj5fs7OzuVI1EREpZc6cOaJyVQCfqdDOOQAnZeomTpzIXWOIiIhIp5jHVw3z+ETGh3l848Y8vnqYxyciItK8v/76C9nZ2ULZBIC/km1kALgkU6fuYj1ERESGghMIiYjeEqdPnxaVXV1dYW5urrPrJyUlISoqCqdOnUJUVBQeP37MTjAiA7NmzRp4e3sjMjJS7j4rKyvUrFkTTZo0gYuLS7HvH+Hh4WjcuDEOHDig1HXv37+PVq1aITw8XO4+c3Nz1KlTB15eXmjUqBFcXFxKXJ1Yn+8pRQcetGjRotgYi74PV69eHTVq1FD7upaWlqhduzaaNWsGT09PuLq6QiKRiI7JycnBjBkzMGzYMLWvR0RkKKZPny7c7tq1K+rUqaPHaIzP1atXReXGjRsrfO68efPQtWtXpKWlCXW1atXC+fPnERQUpLEYZUkkEnz77bdCecOGDXj48KHWrkdERG+PGzduYP/+/aK6rwFYqNCW7O6D1apVw4ABA1QNjYiIiEhpzOOrjnl8IiLdYh5fPczjExERaV5YmHjvwCYAFJ+eX+AUgJwiZTMzM6UXnyEiIjJU5fQdABERacaGDRtE5U6dOunkuomJiahfvz6io6Pl7qtYsSJ8fX3Rt29f9OzZk6u1E5Vi4cKFaNSokUrnKnLekiVL8PXXX4vqJBIJ+vXrh0GDBqFNmzYAClYLBoA3b97gr7/+wqZNm7Bv3z7hnIyMDPTo0QO7du1Cr169yrxuTk4OunTpItoltfC6n3/+Oby9vVGunPgraUZGBiIjI/HHH39g9+7duHfvXpnX0baiAw9KSgoVHXjQqlUrla7j7OyMoKAgtG7dGi1atIC7u7vcIIeUlBTs3r0bM2fOxJMnT4T6DRs2wMfHB599pso+G0REhuP48eOiQXIjR45UqR11PluNnSoDD7KzszFs2DBs375dVN+uXTv873//g4ODg0ZjLM6nn36KcePGISUlBTk5OVi6dCmWLl2q9esSEZFxmzt3rqhcEcAXKrRzDcAhmbpvv/0WFhaqTEUkIiKitxHz+P9iHp95fCJ6tzGPrz7m8YmIiDRPdlEcVfYNDJMpe3t7w8bGRuWYiIiIDAknEBIRvQUOHz6MU6dOieoGDx6sk2tnZWUVO3kQAJKTk7F//37s378f77//PtavXw8/Pz+dxEVkbDw9PeHv76+VtsPDw/HNN9+I6mrUqIGtW7eidevWQl1eXp5w29zcHJ07d0a3bt3w+++/Y8iQIUhKShKOGzhwID788EPUrVu31GuvWrUKt27dEsqWlpbYs2dPqZOcbW1t4efnBz8/P8ydOxcnT57EkiVL9DYJOTMzE1FRUUJZWwMPDh8+jIYNG8qtTCzLwcEBw4cPR69evdC2bVtcuXJFuG/q1KkYNGhQias/ExEZg59++km47erqinbt2qnUjjY/Ww2dsgMP4uPj0b17d1y6dElUP3r0aCxevFhnn8GWlpbo168fli9fDgDYuHEjfvzxR9jZ2enk+kREZHzu37+PnTt3iuq+AmCrQltzZMqOjo4YPny4qqERERHRW4h5/H8xj888PhG925jHVx/z+ERERJqVlpaGixcviuoCVWgnXKYcGKhKK0RERIaJEwiJiIxccnIyPv/8c1Fd9+7d4eXlpaeIinfv3j0EBgZi0aJFGDNmjEbbTkxMxPPnz5U6JzY2VlTOyMhAWlqayjHk5eUJnbaFHX55eXlldiASFSr6HNKk5ORkDBw4EFKpVKhzdnbG8ePHUbNmTdE1Za9fWO7UqRMOHz6Mdu3aITU1FUDB5OG+ffvi7NmzMDc3L/H6mzdvFpWnTZuGDh06KPW7+vj4CJ392niMynLu3Dnk5uYCKBiQ4enpKRdHUlISbt++LZS9vb2VjrVBgwbIz89X+Hh7e3ts3rwZHh4ewt83ISEBp06dgq+vr1LXJu0p6XVF2if7WEulUj7+RiAuLg6HDx8Wyn369BH97ZR5TWnrs9XQZWdnIyYmRlTn4eFR4mNx4cIF9OrVCwkJCUKdubk5QkNDhdXwdfk4Fh14kJaWhq1bt8r9v6OKvLw84fOy8PM2PT2du6QDePXqVallIlIeX1e6M2vWLNH/UbYARqvQzl0Av8rUjRw5Enl5eWrli0gz+BoiIqK3XUl5/IiICNSsWVOhNrp06YIjR44gICBALo9//vx5pfL433//famTB4tTOJlQXy5cuCDK4xfXTyqbx1dlAqGHh4dSxzs4OGDbtm1o0KCBKI9/9uxZ5vGJyGjJ5vH79evHSdFKKi6PX9oEwgsXLiA4OFguj79y5UoMGTJEa3GWZODAgaI8/vbt2/HFF1/oPA4iIqKiTp8+LV50B0DxS8uU7AWAazJ1nEBIRERvE04gJCIyYvn5+ejfvz+ePHki1JUvXx7Lli3T+rXt7e3RsWNHdOjQAY0aNYK7uzvs7e2RmZmJ+Ph4nDt3Dhs3bsSZM2eEc/Ly8jBu3DhUqVIFISEhGotlxYoVmDFjhlptXLx4EU+fPlX5fIlEgsqVKwOAsLJaRkYGByRTiWQnimVlZSE9PV3j15k4cSL++ecfoWxqaort27ejcuXKZV4vMzNTuP3BBx9g1apV6NOnj1B39epVzJ8/v8RJwSkpKYiMjBTKJiYmCAkJ0crvqU0nTpwQbjdq1Ag5OTnIyckRHXP8+HGh89/W1hY1a9bUye9ZvXp1fPTRR6IVKqOiovDRRx9p/dqkmqKvK9Iu2UHGOTk5Rvf+8y7atGmT6DOyQ4cOpf7dir6mdPXZauiuXLkiDJgDgMqVK8POzq7Yx2LXrl0YO3YsXr9+LTp+y5Yt8Pb21svjV6dOHTg7OyM+Ph4AsGXLFvTt21ftdvPy8kQTB4GCTqSigzOpgOzKnESkPr6utCM5ORlbt24V1X0BoKIKbc0DUPSbhJWVFerWrYuIiAg1IiQiIiJSzNSpU+Xy+AcOHFB48mChJk2aYNu2bejSpYtQd/XqVSxduhQTJkwo9pzk5GS5PL4x7sJ89uxZ4banpycsLS3ljjlz5owoj9+oUSOdxFavXj14enri8uXLQl10dDQnEBKR0dqxY4coH9+jRw89RmOcrl+/LsrjOzk5wdnZudhjt2zZghEjRojy+E5OTti7d69Kk+E1oWnTpqhevbowVmnbtm2cQEhERHoXFhYmKnsDsFayjQgARXuPra2tDW4jDyIiInVwAiERkREbP348/vjjD1Hd6tWr4eLiotXrLliwAB9//DFsbW3l7rO3t4e9vT3q1q2LIUOG4LfffsOQIUPw8uVLAAW7/wwdOhT+/v547733tBqnIfLy8kJycrK+w6BiVKxY8a0b1JmcnIxdu3aJ6oYNG6by5LKOHTsiKCgIhw4dEurWrFmDL7/8EuXKyX+tLBz4X8jR0REVK6oylFO/zp8/L9z29vYu9pi//vpLuO3p6anTycNubm6iCYQvXrzQ2bXVkZubi0uXLiE6OhopKSmws7ND9erV0apVK5QvX16ttuPi4hAdHY0nT54gLS0N5cqVg4ODA1xcXNC0adNiP7804datW7hz5w6ePXuGjIwMODk5ISQkBGZmZgYX65UrV3Dv3j0kJCTAxMQE7u7u8PHxKfOxz87Oxvnz53Hnzh2kp6ejQoUKqF27Nlq0aFHs+4Cy7t69ixs3biApKQlpaWlwcHDAe++9hxYtWsDBwUHt9jVF13Eq+9xKT0/H33//jdjYWKSmpuLNmzewsrJChQoVUKNGDdSpUwdOTk4aj1MTdu/eLdx2dnbmhGgV/P3336JycSvj5+fn4/vvvxdWCC567Pbt21G9enWtxlgaiUSCjz/+GOvXrwdQMOnm0aNHqFGjht5iIiIiw7R//37RYDsLAN+o0M4TAFtl6krKexER0duvbt26RpNfexc5OjqKdpB7G7x48UJuB8Avv/wSnp6eKrXXuXNndO/eHfv27RPqfv75Z3z99dfF5u+KTlwEgEqVKsHR0VGla+tT0QmEPj7F7zFx+vRp4Xbz5s11msd///33RRMIk5KSdHZtdeTm5uL8+fO4ceMGkpOTYW9vDxcXF/j7+6udx3/w4AFu3ryJR48eITU1FeXKlUPFihXh6uoKb29vrX0fv3HjBqKjo5GQkICMjAxUrlwZ3bt3LzWPr69YL126hLt37+Kff/6BiYkJ3n//fbRp00ahPP6ZM2cQHR2N9PR0ODg4oG7duvD19dVIHj8mJgbXrl3D8+fPkZqaiooVK8LZ2Rk+Pj4G1Q+o6zhln1tVqlTBwIEDS83jX716FTExMXj58iVev34Na2trODg4wM3NDfXr10eVKlU0Hqcm7NixQ7hdrVo1lT+z3mVF+3aB4ncfzM/Px4QJE7Bo0SK5Y/fv36/1cUGlkUgk6Nq1K1asWAEAOHfuHOLi4uDq6qq3mIiIiGQnEKqyb2CYTLl169YwNzdXOSYiIiJDwwmERERGatmyZVi8eLGobsKECfj000+1fu3evXsrfGxwcDCqVq2KgIAAZGVlASjYKea///0vfv75Z22FaLCSk5M5+IB0ZtOmTcLrDgDMzMwwfvx4tdqcPHmyaAJhfHw89u/fj549e8odm5GRISrn5eWpdW19yM/Px6VLl4SyIhMImzdvrvW4iiq62iQAtTvtNeXMmTOila4nTpyISZMmIScnB6GhoQgNDS12kISFhQWCgoLw448/lrjSpqzs7GwcPXoUv//+O06fPo1nz56VeKypqSn8/Pwwbty4EgeSKPP75ObmYsOGDdi4cWOxg5e6du0q+pvoM9a8vDysW7cOa9euxb179+TOs7a2xtChQzFlyhS5FbrT09OxcOFCbNy4sdgdySpVqoTp06djwIABCsdZKCMjA6Ghodi5cyfi4uKKPcbU1BQtWrTA5MmT0bJly1Lb8/DwwOPHj+Xqd+7ciZ07d5Z4XmhoaKm7nGk6zkKaem4BwLVr17Bw4UIcPXoUb968KfW6rq6uaN++PYYMGYK6desqFKu2PXr0CNHR0UJZmec9/Ut2AmHDhg1F5dTUVAwdOlSuAyU4OBjLly+HtbWy6zBqnq+vrzCBUCqV4ujRoxg2bJieoyIiIkOSlpaGI0eOiOo+A6DKUlULART95mRubo6uXbuqER0RERmzFy9eGM3EHno7rFmzRi6PP23aNLXanDlzpmgC4ZMnT7B7926EhITIHSub6zPWPH7RHL0iEwh1vWNTdna2qFyhQgWdXr8kJ06cQJs2bYTy999/jx9++AE5OTlYvHgxFi1ahOfPn8udZ2FhgeDgYCyXL0Q0AAAgAElEQVRcuBDVqlVT6FrZ2dk4dOgQ9u7di/DwcDx9+rTEY01NTREYGIjJkyfD399f7d8nNzcXK1euxOrVq3Hz5k2589q3by+Xx9dXrHl5eQgNDcXy5ctx9+5dufOsra3x5ZdfYubMmcXm8WfNmoVVq1YhLS1N7tzKlStjzpw5GDp0qMJxFsrIyMCiRYuwefNmPHjwoNhjTE1N4evri5kzZ5a5w6abm1uxefbNmzfLTaouauPGjRg8eLDO4iyk6nOrZ8+ecq/3K1euYNasWTh06FCZeXx3d3cEBQVh5MiRqF+/vkKxaltcXBxu3LghlIs+LqS4siYQpqamIiQkBH/++aeo/pNPPsHGjRsNIo/fpk0bYQKhVCrFoUOHMGrUKD1HRURE76rnz5/L9ZNrYgJhYKAqrRARERkuTiAkIjJCO3bswNixY0V1gwcPxty5c/UUUem8vb0xYcIEzJgxQ6jbsWMHfvrpJ5iYmKjd/qhRo5Sa1AgAsbGx6N69u1D28vJCvXr1VI4hLy9PWKW18HeytbWVW8VRIpGofA3SLolEAjs7O51dT/a5b2VlpfHrHz58WFTu3Lkz3NzcSjw+Ly8PmZmZQtna2lpuBV5vb280adIEV65cEer++OOPYjvrZCd/JScn49mzZ6hVq5YSv4X2bd68WeHO0n79+pV5zPz58zF//ny5+tatWyM8PFzp+EojlUoRFRUlqmvZsqVOn8slsbKyEpXNzc2Rl5eH7t2749y5cyWe9/r1a+zduxfHjx/H9u3b8fHHH5d5rbZt2yIyMlKhuPLy8hAeHo7w8HCMGjUKixcvVmjF3eJ+n9zcXHTv3l20unVZ5wUFBYleP7qK1cTEBL1798bx48dLPC8zMxM///wzrl69ij/++ENo5969e+jcuXOxgxUKJSUlYfTo0YiNjZVb4KA0Bw8exIgRI5CYmFjqcXl5eThz5gyCgoIwfPhw/PzzzyU+Fqp+t7C0tCzxtaONOAup+tyytbUVxTtv3jxMmzYN+fn5pV6vUFxcHNauXYtKlSqhWbNmxR7z8OFDuffs2NjYUj9L1CH73hAYGCj3Nynts0oXn63G4NatW6Jy8+bNhcchJiYGwcHBuHPnjnC/RCLB999/j++++06ncZamffv2ovLJkycxbtw4tdrMzc0VniOFj0fdunV1utuAoXr16pVoJ24vLy/Y2NjoMSIi48fXlfbNmTNHNAi7HIAJKrSTBGCtTN2gQYMQHBysRnSkabKvKSIiorfJnj17ROUuXbqgUqVKarXZsGFDuTz+nj17ip1AKDux5cWLF4iNjTW4PP6mTZvw2WefKXRst27dyjxm5syZmDlzply9n58fTpw4oWx4pZJKpaKFCgEY9G5dL1++ROfOnUvNTb5+/Rq7du3C4cOHsWvXLoXy+D4+Pkrl8Y8ePYqjR4/iyy+/xNKlS1XeOS8lJQVdu3bFmTNnFD5HX7G+evUKwcHBOHbsWInHZGZmYsGCBTh//jyOHDkiyuN//PHHpebxnz9/jmHDhuHGjRtYsmSJwnEdPHgQQ4cOVSg/fuLECbRu3RojRoxAaGioRnY8VJSu41TluTV37lxMnTpV4Tz+gwcPsHz5cpQvXx6zZs0q9piHDx/C3d1d7jxt5fFlF/Px8/PTynXedrL9u0UnEMbExKBr165yefwZM2aovciAJrVu3VpU/vPPPzmBkIiI9CYiIkJUtgXgpWQbTwDIfpsOCAhQIyoiIiLDwwmERERG5uDBgxg0aBCkUqlQ16NHD6xbt86gJ6eNGTMGs2bNElYuTU5OxuXLl+Hlpey/avKcnJzg5OSkVhu2trawt7dX+fzc3Fy5wcempqYckGxk9Pn30vTzJSMjA9euXRPV9erVS6lrlBRTz549RQMPzp07V+xxH3zwASwtLUWDOqdMmYLdu3cb1PuVJiYyK0IikWj8ObZ+/XrEx8cL5bp166JFixYG8fgW97uGhISIJgg5OjqiRo0ayM7OxoMHD0TPlbS0NPTu3RuHDx8uc/VS2dWbgYIJrBUrVoStrS1evXqFR48eITU1VXTMihUrkJWVhQ0bNij9++Tn5yM4OFg0iMLBwQEuLi4ACiZnpaamyp0nu2OkLmKVSCTo16+faPKgs7MznJ2dkZmZiZiYGNHK4mfOnMHXX3+NNWvWIDExEe3atcOjR4+Ettzd3eHo6IgXL17g/v37omstW7YMXl5eCk22XbNmDUaNGiW3qrm1tTVcXV1hZ2eH5ORk3L9/X9SZvnbtWiQmJuK3337T6HPdxMSk2OettuNU57lVeO769esxdepUubbt7Ozg5uYGGxsbZGVlITk5GU+ePBF9jyzp9y4utsI6bX1eyg5K8vLyKvNapcXzLn4Xy8/Px/Xr10V1np6eMDU1xZEjRxASEoKXL18K99na2mLr1q2ihTUMQdWqVeHi4iLsJnrmzBm1/5ZSqVR4LRa2ZWdnp9NBTMbCxsZGrf+NiEgeX1ealZ6ejtWrV4vqQgC4F394qZYCyCxSLleuHKZOncq/FxEREelEcXn8nj17aqRt2Tx+SZPBatasKZfHnzhxosHl8Y3Zhg0b5PL4mugf1Yb8/Hz07t1b9HwpK4/fo0cPrebxQ0NDkZmZqVBuXFZubq7cBK/icq2GEKtUKkVISIho8mBpefzTp09jzJgxQh4/ICBA4Tz+0qVL0bRpU63m8desWYNnz55pPI9vKHGq8txav349Jk+eLNeWInl8Q1J0N1cAaNq0qZ4iMV75+flyOyQVTiA0pjy+k5OTKI8v+9wgIiLSpbAw8d6BvgDMlG1Dpuzg4ICPPvpInbCIiIgMDkdJEREZkYiICPTu3Ru5ublCXbt27bBz506DHxzt4OCAJk2aiFbYjImJMdgOMp1ZAoD/Z+pHFAD1NrIxaBcuXBC9VwCaW9FWtp2EhAQ8ePBAbnVLS0tLBAYG4tChQ0Ld3r17ERgYiB9//BGtWrXSSDzvqs2bN4tWcTQxMcHy5csNdlDHli1bEBcXBwBo0qQJFi1aBD8/PyHejIwM7NixAxMnThQ6xbKzs9G3b19ER0fLrYQtq0aNGujduzc6deqEpk2byg34lUqluH79OlatWoU1a9YIncgbN25Ely5dlN5hZN26dXj27BmAgh0QZ8yYAW9vb2FCqlQqRVhYGKysrPDmzRu9xrplyxY8fPgQANCnTx9MmzZNtOvuixcvMH36dKxYsUL0+3311VcYP348Hj16BEtLS0yYMAEjR47Ee++9JxwXExODESNG4NSpU0Ldt99+i08++QRmZiWnY8PCwjBy5EhRR32XLl3wzTffoFWrVqIJPcnJyVi3bh1mzZqF9PR0AMD+/fsxf/58TJw4Ua7t7du3IysrC8+ePUP//v2F+vbt22P8+PElxtSgQQOdxlkSZZ5bQMGk1AkTxPvt9OzZE5MnT0aTJk3k3hPS09Nx6dIlHD58GNu2bVM4Ll0ouqq3qampWrtDv6tiYmJEOzTa2dmhVq1aWLRoESZOnCgaQOPu7o79+/ejYcOG+gi1TA0bNhQGHrx8+dIgdz8gIiL9WL16NVJSUoSyBID8EMyypQEIlanr168fXF1d1YiOiIjeOszh6xfz+CpjHt8wMI8vZoh5/KNHj8LS0lLvsTKPX4B5/ALM47/dmMcnIiLSvPDwcFFZlX0Dw2XKbdq00dmi9ERERLrCCYREREbiwoUL6Nq1q2jFw5YtW+K3336Dubm5HiNTnIuLi2gC4fPnz/UYjYH4CIC/voMgQxAZGSk3UEARTk5O8PDwkKu/ffu2qGxjY4PatWurHF9RhSsgFhUdHS038AAAJk2aJBp4ABRMhvbx8YGrqyvat2+PFi1awMvLC/Xq1dNL4qVDhw6iFV2LCg4ORkZGBgBg6tSp8Pf3lztm3LhxuHHjBgBgwIABGDhwYLFtOTg4KBXXnTt3hNViASAnJwcpKSm4ceMG9u/fj1u3bgn3mZubY82aNQgMDFTqGrpUOOigU6dO2Ldvn1yntK2tLUaMGIE2bdrAx8cHiYmJAICnT59i6tSpCA2VHdr7r5UrV6Jly5alTqaXSCTw8PDAihUrEBwcjC5dugi7Ac6dO1fpzvzCjuGxY8diyZIlxV6vbdu2yMvLE00gXL58OXx9fXUaa+Ggg4ULF+Kbb76Ru9/R0RGhoaHIysrCxo0bARR0bn/66aeIjo6Gra0tDh8+DF9fX7lz69Spgz/++ANNmzZFdHQ0gIK/2aFDh0pcCfXly5fo37+/0JlvYmKCtWvXYsiQIcUeX7FiRUyYMAGdO3eGv7+/8P1h+vTpGDRokGggBABhUFPh712oatWqaNu2bbHX0EecJVH0uVUoLCwMycnJQnngwIHYvHlzie3b2dkhICAAAQEBmD17Np48eaJQXNr2+vVrxMTECGVXV1dYWFjoMSLjdPXqVVG5Tp06GDx4MLZs2SKq9/f3x6+//opKlSrpMjyl1K1bF4cPHxbK165d48ADIiJCdnY2Fi1aJKrrBqC+Cm2tAPCySFkikWDSpElqREdERG8l5vCpCObxmceXxTz+25HHL5xIps9YmccvwDx+Aebx327M4xMREWnWo0ePEBsbK6pT5b8u2R0IDfl/NyIiIlVxAiERkRH4+++/8fHHHwudX0BBx9/hw4dhY2Ojx8iUI9vJlJOTo6dIiAzPt99+q9J53bp1w759++Tqi3ZCAUCVKlU0tqJt5cqVYWJiIlrJU/Z6hXx8fDBt2jT8+OOPcvfFxcVh7dq1WLt2LYCCjmcvLy/4+/vj448/RtOmTTUSb1mqVq2KqlWrytXfvn1b9L47fPhwuZ0ocnJycP/+faHcp08fpTo2S7NixQr89NNPpR4jkUjQsWNHzJkzB40aNdLIdbWpatWq2LVrV6kr2n7wwQfYsmULOnbsKNRt3LgRs2fPRvny5Ys9p7gO8dK0a9cO48ePx6xZswAAFy9exK1bt1C/vnLDjlu2bInFixcrdU5Zkwe1Feunn35a7KCDombNmoXNmzcLr+3CgQSLFy8u9TG2trbGtGnT0LdvX6Hujz/+KHHgwapVq/D06VOh/N///rfEzvyi6tevj02bNiEoKAgA8ObNGyxfvlx4bDRNn3Eq89y6c+eOqFx0RfOymJubo2bNmgofr02PHj0Sfa5Ur15dj9EYL9mBB5cvX8bly5dFdV988QWWLVtW6nuxIXB2dhaVZQcTERHRu2nTpk2i72gAMEWFdrJQsKFUUT179kTdunVVDY2IiIjeAczjM48vi3n8tzePrwzm8f/FPH7xmMenopjHJyIi0izZ3QcdAcgvqVO6GAD/yNRxAiEREb2NuLcuEZGBi4mJQbt27ZCSkiLU1atXD0eOHCmxA8ZQyQ7wqly5sp4iIXr7yQ4E0OT7hUQigb29fanXK2rmzJn46aefYGlpWWq7GRkZCA8Px/Tp09GsWTN8+OGH2LBhg6gjSpciIiKE2+7u7nKDDoCCDp3MzEwAgKmpqbBiqq707t0bU6dONYpBBwDw3Xffwc7OrszjOnTogICAAKGclZWFnTt3ajSW/v37i8rnzp1Tuo2ZM2dqbEBPadSNVSKRYObMmWUe5+zsLDfgx9XVVaHO9i5duohWHpft/CyUl5eHn3/+WSjXqFGjzAERRXXq1Em0evqePXsUPlcZ+o5TmedWVlaWqKzJzmQ3NzdIpVLRj5ubm8baL+rx48eicnEDwqhsUVFRpd6/dOlSrFy50uAHHQDyzwHZ5wgREb17cnNzMW/ePFFdWwDNVGhrPYBEmbrJkyerGBkRERGRapjHVx/z+JrHPL5qmMf/F/P48pjHp6KYxyciItKssDDx3oH+KrQRLlN2dnZG7dq1VQ2JiIjIYHECIRGRAYuLi0Pbtm2RmPjvkCZ3d3ccO3bM6CbfvX79GpcuXRLVubi46Ckaordfenq6qKzp3Upl25O9nqzRo0fj7t27GDVqlMKDIG7evImhQ4fCy8sLcXFxKseqqqIDD/z9/Ys95uTJk8Ltxo0byw3I0LZffvkFPj4+aN26NWJjY3V6bWWZmZkhJCRE4eMHDRokKp84cUKj8bi7u4vKJXWUl6RKlSqiwRHapG6sHh4eCic2P/zwQ1E5ODhYoR0TbW1tRR3Sjx49Kva4a9euIT4+XiiHhIQo3QHavn174fbt27eRlJSk1PmK0Gecyj63ZFd33bZtm8LnGpLU1FRR2dbWVk+RGDfZ9wfZASy3bt3SZThqkX0OyD5HiIjo3bNz5065lexV2X0wB8ACmbqOHTuiSZMmKkZGREREpBrm8dXHPL5mMY+vOubxxZjHF2Men4piHp+IiEhzpFKp3ARCVfYNDJMpBwYG6mQhEiIiIl3jBEIiIgOVkJCAwMBAPHnyRKirVq0awsLCUK1aNT1Gpppdu3YJq3sCgIWFhc5X+CQyZBEREXKrQyrys2/fvmLbk10d9tWrVxqNV7Y9RVajrV69OkJDQ/Hs2TMcOHAA48aNQ9OmTWFubl7qeZGRkfDy8sK9e/fUillZRQcVtGnTpsxj/Pz8NHr9pUuXiv7WmZmZePz4MQ4ePIihQ4fCyspKOPb06dNo1qwZLl++rNEYNMnDwwMVK1ZU+HjZwR4XL15U6LyLFy9iypQp6NSpE2rWrImKFSvCzMwMEolE9GNhYSE6T9nO66ZNm6qdLNRVrJ6engof6+joKCorM4i66LlpaWnFHnP69GlRWXalZEXUqFFDVI6Ojla6jbLoM05ln1sBAQGiwSFLlizBqFGjcP/+fYXbMARFvycCEL3HGYJ79+4hNDQUn376KerVqwc7OzuYm5vDyckJ/v7+mD17Np49e6bXGB8/fowXL14IZXNzcyxZskR0zJo1a7BixQq1r6WLx8Pa2lpU1vR3GSIiMi75+fmYM2eOqK4FgOL/UyvddgCyw2SnTFFlKiIRERG9a5jHLxnz+MzjA8zjqxMr8/jKYx5f95jHVx/z+ERERJoVExODhIQEUZ0qS4hEyJR1tRAJERGRrpXTdwBERCQvOTkZ7dq1E3WyVa5cGceOHZNbvdAYPH36FFOnThXVtW/fXi6ZSESaI9vBq8nV/qRSqVynojIdyhYWFujSpQu6dOkCAHjz5g2uX7+Os2fP4vjx4zhy5AjevHkjOicxMRE9e/ZEZGSkQquoquvmzZui3V+LG3iQl5eHs2fPCmVNDzyQZWVlherVq6N69eoICgrCpEmT0Lt3b0RFRQEAXr58ie7du+PGjRuoUKGCVmNRheyKuGWpUaMG7O3thedaXFwcpFJpiR2yp0+fxn/+8x/8/fffKsX38uVLpY5X5/NY17Eqs2ux7GezqudmZWUVe4xs5/snn3yicPslSU5OVrsNWfqMU9nnlouLC4YMGYK1a9cKdStXrsTKlSvh6emJtm3bonXr1vD29lbqvVrfpFKpvkMQDB48GJs3by72vufPn+PkyZM4efIk5s2bh9DQUPTv31/HERaQXbW4QYMGGDNmDK5fv47169cL9WPGjEGDBg1U/tzS1eNhSM8BIiLSv/3798t9R5usQjv5AObJ1Pn4+MDX11fV0IiIiIhUxjy+epjH1zzm8f/FPP6/mMeXxzx+AUPK4TKPL8Y8PhERvStkdx+sBqCOkm1cBSD7LTAwUJV9DImIiAwfdyAkIjIw6enp6NixI27evCnUVahQAUePHkW9evU0eq2HDx/KrY748OHDEo9PSEjA999/j5SUFKWu0bFjR/zzzz9CnUQiwQ8//KBG5ERUFtnOpcTERI0l8J8/f478/PxSr6cMc3NzeHp6YvTo0Thw4ADi4+Mxfvx4uQEG165dw65du1S+jjIiIv5dW+r9999H9erV5Y65cuUK0tPTAQAmJibw8fHRSWyFatWqhWPHjsHFxUWo++eff7BgwQKdxqEo2RVxFVH0eZWfn1/iarirV6+Gn5+fyh35APD69Wuljre3t1fpOmvWrNF5rJaWlipfS51zi1N0VVVN0eTAqkL6jFOV59ayZcuEwVxFRUZGYt68eQgKCkKlSpXQuHFjTJkyRfQ901DIDnrJzs7WUyTyCncEt7GxQUhICNasWYOTJ0/iypUr2L9/P/r37w+JRIK0tDQMHDgQ//vf//QSZ+FAtEKNGzcGAISGhqJ58+ZCfW5uLnr16oW4uDiVrqOrx0N2AJONjY1K7RARkfGTSqWYPXu2qM4DgPy3n7LtBXBbpo67DxIREZG+MI+vHubxNY95/AL6iJV5fOUxj697zOOrj3l8IiIizQoPDxeVVZn2FyZT/uCDD0T/wxEREb1NuAMhEZGB6dq1Ky5duiSq+/rrr5GUlITjx48r1ZanpyccHBw0Ftvr168xc+ZMLF68GF27dkXPnj3h7e0NZ2dnuWNjY2OxadMmLF++XC7RPmbMGDRp0kRjcRGRvDp1xOspZWRk4O7du6hdu7babcuujAhAoxOcHR0dMX/+fLRu3Rrdu3dHXl6ecN+2bdvQr18/jVzH398fJ0+eLPO4e/fulbhabqH8/PwSB19s3LgRgwcPViXEMlWqVAkzZszAkCFDhLpNmzbhv//9r1aupw5Vdp2V7WTKyMhA+fLlRXUREREYOXKkaGBNuXLl4OPjg+bNm8PV1RVOTk6wtLSEhYWF6Nx27dopHVMhMzMzpc85ffo0vvzyS53HakiUXXVZEbIDoTRBn3Gq8tyytLTE/v37sWvXLsyfP1+uAxooGHgfFRWFqKgozJkzB0FBQVi6dClq1aql9PW0QXbF9cKBXYbA2dkZS5cuxdChQ2Frayu6r3HjxujatSuCgoLQp08fSKVS/Oc//0GXLl10vtu27OfzRx99BKBgx4A9e/agadOmePr0KQAgKSkJ3bp1w9mzZ5Xu0NfV45GRkSEqy77/ExHRu+P48eO4fPmyqG6Sim3NkSl/9NFH6Nixo4qtEREREamHefyyMY+vW8zj6y9WQ8I8ftmYxy/APL7ymMcnIiLSnLy8PNHCMgAQoEI74TLlgABVWiEiIjIOnEBIRGRgTpw4IVc3ffp0ldqKiIiAv7+/egEVIyMjAzt27MCOHTsAFHQSOjk5wd7eHllZWUhISMDz58+LPbd3795YtGiRxmMiIjFvb2+YmpqKOu0vX76skYEHkZGRonLVqlXh7u6udruyOnfujEGDBmHDhg1C3ZkzZzR+HWMXHByMoUOHCp3Z8fHxiIuLg6urq54jE8vMzFT6nFevXonKsh1cAPDNN9+IOvKDgoKwatWqYlebLkrZ1X814bvvvjOaWLVFtuNx7ty58PT0VKvNBg0aqHV+cYwlzqIkEgn69OmDPn364NatWzh27BhOnDiBM2fOICkpSe74Q4cO4dSpUzh06BB8fX21GpsiZFfwS0hI0FMk8rZs2VLmMSEhIfjf//6Hffv2CQt/dO3aVQfR/Ut24EHhysUAUK1aNezevRsBAQF48+YNgIIdAT777DP88ssvSl1HV49HfHy8qFyjRg2lzicioreH7O6DtQB8okI7fwK4IlM3ZcqUMgeaExEREWkL8/jvDubxjSePP2HCBKOJVVuMJT9uLHEWxTy+9jCPL8Y8PhERvQuioqKQkpIiqlN2B8IcAKdk6gIDVdnHkIiIyDhwAiEREantxYsXePHiRanHWFhYYPbs2Rg3bhwHZhHpgK2tLRo1aoQrV/4dHrlnzx707dtX7bb37NkjKrdq1UrtNkvyySefiAYeZGRkIDU1lasXFlGhQgVUrFhR9D789OlTgxt4UFzHZ1mSk5OF2yYmJrC3txfdf+fOHVFH24cffoi9e/fC3NxcqbZ1ITY2Fn///bdQNuRYtalSpUqisru7O9q2baunaEpmLHGWpH79+qhfvz7GjBkDqVSK27dv4+jRo9i9e7doAFd6ejp69eqFe/fuFTuwR5dcXFxgYmIirPD85MkTvcajisDAQOzbtw9AwfuTLqWkpCAuLk4oSyQSNGrUSHRMq1atsGzZMnzxxRdC3a+//opZs2bhu+++03hM6j4esgMP3NzcNBEWEREZmXPnzskttjUBgKkKbc2WKdeuXRs9evRQMTIiIiIi9TGP/+5gHt948vjGEqs2GUt+3FjiLAnz+PrBPL4Y8/hERGTMwsPFewfWBlD6sh/yLgB4JVPXpk0bNaIiIiIybJxASERECqtSpQp++uknRERE4K+//sKzZ8/KPMfV1RUDBgzAyJEj4ezsrIMoiahQz549RQMPfv/9dyQlJcl1qCnjxo0bojYBaHXAZXGdDJmZmRoZeLBo0SK5lagA4M8//xR2Sq1evTo2btwod8zt27fx1VdfCeX9+/fLrXRaSNurlRbHzMxM59csy40bN5Q6Pi4uDmlpaULZ1dVVbgL6+fPnReVhw4Yp1JEPADdv3lQqHnVdvnxZVDbkWLVJdpXz2NhYPUVSOmOJUxESiQT16tVDvXr1MGbMGJw+fRo9evQQBgMlJiZi69atGDlypF7jtLCwQJ06dRAdHQ0AePToEbKzs2FpaanXuJRRuCIwAJiaqjKtQXWyqxa///77sLOzkzvu888/R2RkJNauXSvUTZ8+HR4eHhpfaVndx+P27duisoeHh9oxERGR8ZkzZ46oXA3AIBXaOf3/P0VNmjRJ55/ZRERERLKYxy8d8/i6xTw+8/iA8eTHjSVORTCPrzvM44sxj09ERMYsLCxMVA5QoY1wmXKjRo3U+n+ciIjI0HECIRGRgZFKpTq7lpubm1LXs7KywujRozF69GgAQEJCAmJiYvDo0SMkJSUhMzMT5ubmcHBwgJOTE5o1a8ZJg2WJ0ncA77B34LEfMWIEfvzxR2RnZwMAcnJyMGvWLCxdulTlNqdPny4qV6tWDb1791YrztK8eiW7zhPg6OiokbY9PT2Lrf/111+F235+fsWuVhoTEyPcrlu3rsY7a5SRnp4ut7JtlSpV9BRNya5fv47k5GRUrFhRoeNPnjwpKnt5eckdIzuRvU6dOgrHI7sSmbYlJtBs6vAAACAASURBVCaKyoYcqzbJrtQWHh6OKVOmaOVaJiYmorIy33l0Gaeu+fr6Yu7cuRg2bJhQd+bMGb0PPAAK3pcLBx7k5eXh1q1baNKkiZ6jUlxERIRwW9eDzqKixF9sPvrooxKPXb58OW7cuIG//voLQMFro3///jh//jzq16+vsZjUfTyuX78u3K5QoQJq1aqlkbiIiMh4XLt2DQcPHhTVfQNAseG7YrK7D7q4uKBfv36qhkZERO+SdyCPbNDegcefefzSMY+vW8zjM48PMI9vCJjH1x7m8cWYxyciImP15s0bnD4tXjYwUIV2wmTKgYGqtEJERGQ8OIGQiIhUVrVqVVStWlXfYRi3cfoOgN5mlSpVwsCBA7FmzRqhbvny5RgwYECJne6lOXjwIH777TdR3VdffYVy5bT3lfLSpUui8nvvvafwaq+qOnXqlHDb19e32GOKJqFat26t1XjKcujQIVGHauXKlQ3yvTknJwe7du3CqFGjFDp+8+bNorKfn5/cMbIdyUVXySzN69evsWHDBoWO1RRjilWbvLy84ODgIKwaHh4ejlu3bmm0s7OQjY2NqJyZmanwubqMUx9atWolKheuYqxvvr6+2LZtm1COjIw0moEHly9fxh9//AGgYFCe7OAVbZNdubhx48YlHmtubo49e/bA09MTCQkJAAoGsXXr1g0XL16Eg4OD2vGo+3g8e/YMT548EcolfR4TEdHbbe7cuaJyJQAjVGjnKoA/ZerGjx+v9f8tiYjoLcEcPmkZ8/iqYR5fO5jHN55YtYl5fMPAPL7mMY8vxjw+EREZswsXLoi+O0oAKPvJngngvExdQIAq+xgSEREZD5OyDyEiIiIiYzV79mxRJ3ReXh66deuGBw8eKNXO1atX0b9/f1Fdo0aNMG5cySNokpKSsGPHDuTn5ysX9P978+YNfv75Z1Fdhw4dVGpLUc+fP8ft27eFsqEPPMjKysL3338vquvcubPciq2GYtasWUhPTy/zuCNHjohW67WyskKfPn3kjnvvvfdE5TNnzigUx7Rp0+RWPdY22dWkDTlWbTIzM8PYsWOFslQqxeeff46cnByNX8ve3h6mpqZCWZn3PV3GqQ+yAw000dGsCbLv8UUHghmyjIwMDB48GHl5eQCAOXPmwMzMTKcxyA48KG3lYqBgIZA9e/aIBvPFxsYiJCRE+D1UpYnHQ/Zvr+3PfyIiMjx3797FL7/8IqobDcCm+MNLJbv7YOXKlTF06FBVQyMiIiLSOObxlcM8vnYxj/8vQ45Vm5jHNwzM42sW8/hizOMTEZGxCwsT7x3YCICjkm2cAVB0yZBy5crpffEZIiIibTPMjCQRERERaYSjoyM2b94MiUQi1P3zzz9o06aNwh06Bw8eRPv27ZGamirUWVpaYvv27aWuIpyRkYF+/fqhYcOG2LZtG7KyshSOOzs7G/3798fNmzdF9QMHDlS4DVUUHVDg6OiIevXqyR1z//59xMfHC2VNrKY4YcIEuVWay5KcnIyuXbvizp07Qp2pqWmpg0EKPXz4EBKJRPTz8OFDZcNWWkJCAkJCQkrtvI2NjZX7Ow8aNAgVKlSQO7Zly5ai8qpVqxAbG1tqDKtXr8bChQuViFozvLy8RGVDjlXbxowZIxqIcebMGfTq1Uv0HlOWV69eYdmyZVi/fn2Jx5iZmaF27dpCOSoqCvfu3TO4ONU1bdo0bNu2Dbm5uQodL5VKsWjRIlFdSavZ6/q9wtXVFQ0aNBDKERERWruWpuTn56Nfv37C51VISAgGDBhQ5nmafGyzs7NFg+aA0lcuLtSiRQuEhoaK6o4ePYrx48erFAeg+uMh68SJE6Jyp06dVI6JiIiM0/z580WD2O0AfKVCOzEA9srUjRs3DtbW1mpER0RERKRZzOMrh3l87WIe/1+GHKu2MY+vWczj6xfz+GLM4xMR0dug6GImABCoQhthMmUvLy/Y2dmpHBMREZExKKfvAIiIiN4ljo7KrnVDuqLvv01kZKTCnUaynJyc4OHhUeL97dq1w7x58zBhwgShLi4uDv7+/ujXrx8GDx4Mf39/0Tk5OTk4dOgQNm3ahN9++010n6mpKTZu3CjqHCrNrVu3MGDAAHz55Zf45JNP0L59e/j4+IhWVC4UHx+Pffv2YcGCBXIdMD169EBAQIBC11RV0cEYPj4+ogEbhYoOTnB1dUWNGjXUvu7Ro0exYMECeHl54dNPP0VAQAAaNGggt9KjVCpFTEwMfv31Vyxbtkxu9dFx48ahYcOGasejDa6uroiLi8Phw4fRokULLFq0CK1btxYe41evXmHHjh2YOHEiUlJShPOqVKmC2bNl9wwpUKtWLbRo0QJ//fUXACA9PR2tW7fGTz/9hODgYJQr9++/O9euXcPs2bOFXUzq1auH6Ohobf26cmrWrIlmzZoJA0wMOVZtK1++PH799VcEBgYKg1AOHDiABg0aYNy4cejdu3exr6vHjx/jwoUL2LdvH37//XekpaXJrdwtq3379sJjl5eXh9atW2PEiBHw8PCAra2t6DXeoEED0fuSLuNUx/Xr1zFr1ix888036NGjB7p164ZmzZrJfa7l5+fj3LlzmDFjBo4fPy7UW1tbo2/fvlqLT1l9+/bF1KlTARQMlLt8+TKaNm2q56iKJ5VKMXz4cBw4cAAA0Lx5c6xbt07ncVy/fl202rCTk1Oxn7HFGTZsGCIjI7Fq1SqhbsmSJWjcuLHSAwY09XhIpVKhDaBggIS7u7vS7RARkfF68uQJNm/eLKobCUB+KHLZ5gIoupeOvb09Ro0apUZ0RET0ttN3nphKp++/D/P4BZjHZx7/bc7je3t74/z58wYfq7Yxj69ZzOPrD/P4YszjExHR2+DVq1fCd/ZCqvwXGi5T1vb/skRERIaAEwiJiIh0SHZVOaJC3377rcrnduvWDfv27Sv1mPHjx8PGxgZjxowRBjhIpVJs27YN27Ztg5WVFZydnVG+fHm8ePECT58+xevXr+XasbGxwdatWxEcHKx0nGlpaVi3bp3QCeHo6IhKlSqhQoUKyM7ORkJCAhITE4s9t3nz5ti4caPS11RW0YEHJa1IXHTgQevWrTV6/YsXL+LixYsAAHNzc1SrVg0VKlSAubk50tPT8fjxY6Snpxd77qBBgzBv3jyNxqNJAwcOxPnz53Hs2DFERkbC398fjo6OcHV1RXZ2Nh48eCC3urWFhQW2bdsGBweHEttduHAh/P39hY7hhIQEfPLJJ7C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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "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": {
    "execution": {
     "iopub.execute_input": "2021-03-07T17:17:15.271765Z",
     "iopub.status.busy": "2021-03-07T17:17:15.270676Z",
     "iopub.status.idle": "2021-03-07T17:17:15.274264Z",
     "shell.execute_reply": "2021-03-07T17:17:15.275090Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: PASSED!\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": {
    "execution": {
     "iopub.execute_input": "2021-03-07T17:17:15.286657Z",
     "iopub.status.busy": "2021-03-07T17:17:15.285379Z",
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     "shell.execute_reply": "2021-03-07T17:17:15.291459Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2021-03-07T17:17:15.299479Z",
     "iopub.status.busy": "2021-03-07T17:17:15.298426Z",
     "iopub.status.idle": "2021-03-07T17:17:15.306694Z",
     "shell.execute_reply": "2021-03-07T17:17:15.306106Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2021-03-07T17:17:15.313588Z",
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     "shell.execute_reply": "2021-03-07T17:17:15.315031Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2021-03-07T17:17:15.326945Z",
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     "shell.execute_reply": "2021-03-07T17:17:18.630019Z"
    }
   },
   "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 = 7 |\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 = 6 | 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": {
    "execution": {
     "iopub.execute_input": "2021-03-07T17:17:18.642516Z",
     "iopub.status.busy": "2021-03-07T17:17:18.641628Z",
     "iopub.status.idle": "2021-03-07T17:17:18.644723Z",
     "shell.execute_reply": "2021-03-07T17:17:18.645405Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2021-03-07T17:17:19.522513Z",
     "iopub.status.busy": "2021-03-07T17:17:19.291760Z",
     "iopub.status.idle": "2021-03-07T17:18:19.235340Z",
     "shell.execute_reply": "2021-03-07T17:18:19.235859Z"
    }
   },
   "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: 60.58 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": {
    "execution": {
     "iopub.execute_input": "2021-03-07T17:18:19.275112Z",
     "iopub.status.busy": "2021-03-07T17:18:19.265493Z",
     "iopub.status.idle": "2021-03-07T17:18:19.928885Z",
     "shell.execute_reply": "2021-03-07T17:18:19.929359Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "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": {
    "execution": {
     "iopub.execute_input": "2021-03-07T17:18:19.933757Z",
     "iopub.status.busy": "2021-03-07T17:18:19.933060Z",
     "iopub.status.idle": "2021-03-07T17:18:29.647399Z",
     "shell.execute_reply": "2021-03-07T17:18:29.647940Z"
    }
   },
   "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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