{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<script async src=\"https://www.googletagmanager.com/gtag/js?id=UA-59152712-8\"></script>\n",
    "<script>\n",
    "  window.dataLayer = window.dataLayer || [];\n",
    "  function gtag(){dataLayer.push(arguments);}\n",
    "  gtag('js', new Date());\n",
    "\n",
    "  gtag('config', 'UA-59152712-8');\n",
    "</script>\n",
    "\n",
    "# `WaveToyNRPy`: A simple Einstein Toolkit thorn for solving the (scalar) wave equation\n",
    "\n",
    "## Authors: Patrick Nelson, Terrence Pierre Jacques, & Zach Etienne\n",
    "### Formatting improvements courtesy Brandon Clark\n",
    "\n",
    "## This notebook outlines the construction of an Einstein Toolkit (ETK) thorn for time-evolution equations in the scalar wave equation. The thorn, similar to the ETK's `WaveToyC`, uses NRPy+ to convert SymPy expressions into C-code kernels, facilitating the time-propagation of initial data for variables $u$ and $v$. Further, the incorporation of ETK's MoL (Method of Lines) and NewRad boundary conditions allows integration of $\\partial_t u$ and $\\partial_t v$ while considering spatial boundaries.\n",
    "\n",
    "**Notebook Status:** <font color='green'><b> Validated</b></font> \n",
    "\n",
    "**Validation Notes:** As demonstrated [in the plot below](#code_validation), the numerical error converges to zero at the expected rate.\n",
    "\n",
    "### NRPy+ Source Code for this module: \n",
    "* [ScalarWave/ScalarWave_RHSs.py](../edit/ScalarWave/ScalarWave_RHSs.py) [\\[**tutorial**\\]](Tutorial-ScalarWave.ipynb) \n",
    "* [ScalarWave/InitialData_PlaneWave.py](../edit/ScalarWave/InitialData_PlaneWave.py) [\\[**tutorial**\\]](Tutorial-ScalarWave.ipynb)\n",
    "\n",
    "## Introduction:\n",
    "This tutorial notebook focuses on how to construct an Einstein Toolkit (ETK) thorn (module) that will set up the expressions for the right-hand sides of $\\partial_t u$ and $\\partial_t v$ for the scalar wave equation, as defined in the [Tutorial-ScalarWave.ipynb](Tutorial-ScalarWave.ipynb) NRPy+ tutorial notebook. In that module, we used NRPy+ to construct the SymPy expressions for these scalar wave \"time-evolution equations.\" This thorn is largely based on and should function similarly to the `WaveToyC` thorn included in the Einstein Toolkit (ETK) `CactusWave` arrangement. Furthermore, we generate the C kernels for a number of finite difference orders, so that users are free to use a given option at runtime.\n",
    "\n",
    "When interfaced properly with the ETK, this module will propagate the initial data for $u$ and $v$ defined in [IDScalarWaveNRPy](Tutorial-ETK_thorn-IDScalarWaveNRPy.ipynb) forward in time by integrating the equations for $\\partial_t u$ and $\\partial_t v$ subject to spatial boundary conditions. The time evolution itself is handled by the `MoL` (Method of Lines) thorn in the `CactusNumerical` arrangement and the boundary conditions by the `Boundary` thorn in the `CactusBase` arrangement. Specifically, we implement ETK's `NewRad` boundary condition driver, i.e. a radiation (Sommerfeld) boundary condition. \n",
    "\n",
    "Similar to the [IDScalarWaveNRPy](Tutorial-ETK_thorn-IDScalarWaveNRPy.ipynb) module, we will construct the WaveToyNRPy module in two steps.\n",
    "\n",
    "1. Call on NRPy+ to convert the SymPy expressions for the evolution equations into one C-code kernel.\n",
    "1. Write the C code and linkages to the Einstein Toolkit infrastructure (i.e., the .ccl files) to complete this Einstein Toolkit module.\n",
    "\n",
    "In this Jupyter notebook, we parallelize the code generation process for the optimized C code kernels needed for different finite difference options users may choose from at run time. Thus, this Jupyter notebook does not itself run Python code in the relevant C code generation directly. Instead\n",
    "* code blocks needed to generate the C code kernels (i.e., the C-code representations of the basic equations) are simply output to the `$ScalarWaveETKdir/WaveToyETK_C_kernels_codegen.py` file using the `%%writefile` [IPython magic command](https://ipython.readthedocs.io/en/stable/interactive/magics.html).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='toc'></a>\n",
    "\n",
    "# Table of Contents\n",
    "$$\\label{toc}$$\n",
    "\n",
    "This notebook is organized as follows\n",
    "\n",
    "1. [Step 1](#initializenrpy): Initialize needed Python/NRPy+ modules\n",
    "1. [Step 2](#scalarwave): NRPy+-generated C code kernels for solving the Scalar Wave Equation\n",
    "    1. [Step 2.a](#swrhs): Scalar Wave RHS expressions\n",
    "    1. [Step 2.b](#driver_call_codegen_funcs): Generate algorithm for calling corresponding function within `WaveToyETK_C_kernels_codegen_onepart()` to generate C code kernel \n",
    "    1. [Step 2.c](#kernel_codegen): Generate C code kernels for `WaveToyNRPy`\n",
    "        1. [Step 2.c.i](#feature_choice): Set compile-time and runtime parameters for `WaveToyNRPy`\n",
    "        1. [Step 2.c.ii](#parallel_codegen): Generate all C-code kernels for `WaveToyNRPy`, in parallel if possible\n",
    "1. [Step 3](#cclfiles): CCL files - Define how this module interacts and interfaces with the wider Einstein Toolkit infrastructure\n",
    "    1. [Step 3.a](#paramccl): `param.ccl`: specify free parameters within `WaveToyNRPy`\n",
    "    1. [Step 3.b](#interfaceccl): `interface.ccl`: define needed gridfunctions; provide keywords denoting what this thorn provides and what it should inherit from other thorns\n",
    "    1. [Step 3.c](#scheduleccl): `schedule.ccl`:schedule all functions used within `WaveToyNRPy`, specify data dependencies within said functions, and allocate memory for gridfunctions\n",
    "1. [Step 4](#cdrivers): C driver functions for ETK registration & NRPy+-generated kernels\n",
    "    1. [Step 4.a](#etkfunctions): Needed ETK functions: Banner, Symmetry registration, Parameter sanity check,  Method of Lines (`MoL`) registration, Boundary condition\n",
    "    1. [Step 4.b](#rhss) Evaluate Scalar Wave right-hand-sides (RHSs)\n",
    "    1. [Step 4.c](#makecodedefn): `make.code.defn`: List of all C driver functions needed to compile `WaveToyNRPy`\n",
    "1. [Step 5](#code_validation): Code Validation\n",
    "    1. [Step 5.a](#planewave): Monochromatic Plane Wave Convergence Tests\n",
    "    1. [Step 5.b](#gaussian): Spherical Gaussian Wave Convergence Tests\n",
    "1. [Step 6](#latex_pdf_output): Output this notebook to $\\LaTeX$-formatted PDF file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='initializenrpy'></a>\n",
    "\n",
    "# Step 1: Initialize needed Python/NRPy+ modules \\[Back to [top](#toc)\\]\n",
    "\n",
    "$$\\label{initializenrpy}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "ScalarWaveETKdir = \"ScalarWave_ETK_py_dir\"\n",
    "import cmdline_helper as cmd  # NRPy+: Multi-platform Python command-line interface\n",
    "import os, sys                # Standard Python modules for multiplatform OS-level functions\n",
    "\n",
    "cmd.mkdir(os.path.join(ScalarWaveETKdir))\n",
    "# Write an empty __init__.py file in this directory so that Python2 can load modules from it.\n",
    "with open(os.path.join(ScalarWaveETKdir, \"__init__.py\"), \"w\") as file:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting ScalarWave_ETK_py_dir/WaveToyETK_C_kernels_codegen.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile $ScalarWaveETKdir/WaveToyETK_C_kernels_codegen.py\n",
    "\n",
    "# Step 1: Import needed core NRPy+ modules\n",
    "from outputC import lhrh         # NRPy+: Core C code output module\n",
    "import finite_difference as fin  # NRPy+: Finite difference C code generation module\n",
    "import NRPy_param_funcs as par   # NRPy+: Parameter interface\n",
    "import grid as gri               # NRPy+: Functions having to do with numerical grids\n",
    "import loop as lp                # NRPy+: Generate C code loops\n",
    "import indexedexp as ixp         # NRPy+: Symbolic indexed expression (e.g., tensors, vectors, etc.) support\n",
    "import reference_metric as rfm   # NRPy+: Reference metric support\n",
    "import os, sys                   # Standard Python modules for multiplatform OS-level functions\n",
    "import time                      # Standard Python module; useful for benchmarking\n",
    "import ScalarWave.ScalarWaveCurvilinear_RHSs as swrhs\n",
    "\n",
    "def WaveToyETK_C_kernels_codegen_onepart(params=\n",
    "        \"ThornName=WaveToyNRPy,FD_order=4\"):\n",
    "    # Set default parameters\n",
    "    ThornName = \"WaveToyNRPy\"\n",
    "    FD_order = 4\n",
    "\n",
    "    import re\n",
    "    if params != \"\":\n",
    "        params2 = re.sub(\"^,\",\"\",params)\n",
    "        params = params2.strip()\n",
    "        splitstring = re.split(\"=|,\", params)\n",
    "\n",
    "#         if len(splitstring) % 2 != 0:\n",
    "#             print(\"outputC: Invalid params string: \"+params)\n",
    "#             sys.exit(1)\n",
    "\n",
    "        parnm = []\n",
    "        value = []\n",
    "        for i in range(int(len(splitstring)/2)):\n",
    "            parnm.append(splitstring[2*i])\n",
    "            value.append(splitstring[2*i+1])\n",
    "\n",
    "        for i in range(len(parnm)):\n",
    "            parnm.append(splitstring[2*i])\n",
    "            value.append(splitstring[2*i+1])\n",
    "\n",
    "        for i in range(len(parnm)):\n",
    "            if parnm[i] == \"ThornName\":\n",
    "                ThornName = value[i]\n",
    "            elif parnm[i] == \"FD_order\":\n",
    "                FD_order = int(value[i])\n",
    "            else:\n",
    "                print(\"WaveToyETK Error: Could not parse input param: \"+parnm[i])\n",
    "                sys.exit(1)\n",
    "\n",
    "    # Set output directory for C kernels\n",
    "    outdir = os.path.join(ThornName, \"src\")  # Main C code output directory\n",
    "\n",
    "    # Set spatial dimension (must be 3 for WaveToy)\n",
    "    par.set_parval_from_str(\"grid::DIM\",3)\n",
    "\n",
    "    # Step 2: Set some core parameters, including CoordSystem MoL timestepping algorithm,\n",
    "    #                                 FD order, floating point precision, and CFL factor:\n",
    "    # Choices are: Spherical, SinhSpherical, SinhSphericalv2, Cylindrical, SinhCylindrical,\n",
    "    #              SymTP, SinhSymTP\n",
    "    # NOTE: Only CoordSystem == Cartesian makes sense here; new\n",
    "    #       boundary conditions are needed within the ETK for\n",
    "    #       Spherical, etc. coordinates.\n",
    "    CoordSystem     = \"Cartesian\"\n",
    "\n",
    "    par.set_parval_from_str(\"reference_metric::CoordSystem\",CoordSystem)\n",
    "    rfm.reference_metric() # Create ReU, ReDD needed for rescaling B-L initial data, generating WaveToy RHSs, etc.\n",
    "\n",
    "    # Set the gridfunction memory access type to ETK-like, so that finite_difference\n",
    "    #    knows how to read and write gridfunctions from/to memory.\n",
    "    par.set_parval_from_str(\"grid::GridFuncMemAccess\",\"ETK\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='scalarwave'></a>\n",
    "\n",
    "# Step 2: NRPy+-generated C code kernels for solving the Scalar Wave Equation \\[Back to [top](#toc)\\]\n",
    "\n",
    "$$\\label{scalarwave}$$\n",
    "\n",
    "<a id='swrhs'></a>\n",
    "\n",
    "## Step 2.a: Scalar Wave RHS expressions \\[Back to [top](#toc)\\]\n",
    "$$\\label{swrhs}$$\n",
    "\n",
    "Here, we simply call the [ScalarWaveCurvilinear.ScalarWaveCurvilinear_RHSs.py](ScalarWaveCurvilinear/ScalarWaveCurvilinear_RHSs.py); [\\[**tutorial**\\]](Tutorial-ScalarWaveCurvilinear.ipynb) NRPy+ Python module to generate the symbolic expressions and then output the finite-difference C code form of the equations using NRPy+'s [finite_difference](/edit/finite_difference.py) [\\[**tutorial**\\]](Tutorial-Finite_Difference_Derivatives.ipynb) C code generation module.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Appending to ScalarWave_ETK_py_dir/WaveToyETK_C_kernels_codegen.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile -a $ScalarWaveETKdir/WaveToyETK_C_kernels_codegen.py\n",
    "\n",
    "    def WaveToy_RHSs__generate_symbolic_expressions():\n",
    "        ######################################\n",
    "        # START: GENERATE SYMBOLIC EXPRESSIONS\n",
    "        print(\"Generating symbolic expressions for WaveToy RHSs...\")\n",
    "        start = time.time()\n",
    "\n",
    "        par.set_parval_from_str(\"reference_metric::enable_rfm_precompute\",\"True\")\n",
    "        par.set_parval_from_str(\"reference_metric::rfm_precompute_Ccode_outdir\",os.path.join(outdir,\"rfm_files/\"))\n",
    "\n",
    "        swrhs.ScalarWaveCurvilinear_RHSs()\n",
    "\n",
    "        end = time.time()\n",
    "        print(\"(BENCH) Finished WaveToy RHS symbolic expressions in \"+str(end-start)+\" seconds.\")\n",
    "        # END: GENERATE SYMBOLIC EXPRESSIONS\n",
    "        ######################################\n",
    "\n",
    "        # Step 2: Register uu_rhs and vv_rhs gridfunctions so\n",
    "        # they can be written to by NRPy.\n",
    "\n",
    "        uu_rhs,vv_rhs = gri.register_gridfunctions(\"AUX\",[\"uu_rhs\",\"vv_rhs\"])\n",
    "\n",
    "        WaveToy_RHSs_SymbExpressions = [\\\n",
    "                        lhrh(lhs=gri.gfaccess(\"out_gfs\",\"uu_rhs\"),rhs=swrhs.uu_rhs),\\\n",
    "                        lhrh(lhs=gri.gfaccess(\"out_gfs\",\"vv_rhs\"),rhs=swrhs.vv_rhs)]\n",
    "\n",
    "        return WaveToy_RHSs_SymbExpressions\n",
    "\n",
    "    def WaveToy_RHSs__generate_Ccode(all_RHSs_exprs_list):\n",
    "\n",
    "        print(\"Generating C code for WaveToy RHSs (FD_order=\"+str(FD_order)+\") in \"+par.parval_from_str(\"reference_metric::CoordSystem\")+\" coordinates.\")\n",
    "        start = time.time()\n",
    "\n",
    "        # Store original finite-differencing order:\n",
    "        FD_order_orig = par.parval_from_str(\"finite_difference::FD_CENTDERIVS_ORDER\")\n",
    "        # Set new finite-differencing order:\n",
    "        par.set_parval_from_str(\"finite_difference::FD_CENTDERIVS_ORDER\", FD_order)\n",
    "\n",
    "        WaveToy_RHSs_string = fin.FD_outputC(\"returnstring\",all_RHSs_exprs_list,\n",
    "                                          params=\"outCverbose=False,enable_SIMD=True\")\n",
    "\n",
    "        filename = \"WaveToy_RHSs\"+\"_FD_order_\"+str(FD_order)+\".h\"\n",
    "        with open(os.path.join(outdir,filename), \"w\") as file:\n",
    "            file.write(lp.loop([\"i2\",\"i1\",\"i0\"],[\"cctk_nghostzones[2]\",\"cctk_nghostzones[1]\",\"cctk_nghostzones[0]\"],\n",
    "           [\"cctk_lsh[2]-cctk_nghostzones[2]\",\"cctk_lsh[1]-cctk_nghostzones[1]\",\"cctk_lsh[0]-cctk_nghostzones[0]\"],\n",
    "                               [\"1\",\"1\",\"SIMD_width\"],\n",
    "                                [\"#pragma omp parallel for\",\n",
    "                                 \"#include \\\"rfm_files/rfm_struct__SIMD_outer_read2.h\\\"\",\n",
    "                                 r\"\"\"    #include \"rfm_files/rfm_struct__SIMD_outer_read1.h\"\n",
    "    #if (defined __INTEL_COMPILER && __INTEL_COMPILER_BUILD_DATE >= 20180804)\n",
    "        #pragma ivdep         // Forces Intel compiler (if Intel compiler used) to ignore certain SIMD vector dependencies\n",
    "        #pragma vector always // Forces Intel compiler (if Intel compiler used) to vectorize\n",
    "    #endif\"\"\"],\"\",\n",
    "                    \"#include \\\"rfm_files/rfm_struct__SIMD_inner_read0.h\\\"\\n\"+WaveToy_RHSs_string))\n",
    "\n",
    "        # Restore original finite-differencing order:\n",
    "        par.set_parval_from_str(\"finite_difference::FD_CENTDERIVS_ORDER\", FD_order_orig)\n",
    "\n",
    "        end = time.time()\n",
    "        print(\"(BENCH) Finished WaveToy_RHS C codegen (FD_order=\"+str(FD_order)+\") in \" + str(end - start) + \" seconds.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='driver_call_codegen_funcs'></a>\n",
    "\n",
    "## Step 2.b: Generate algorithm for calling corresponding function within `WaveToyETK_C_kernels_codegen_onepart()` to generate C code kernel \\[Back to [top](#toc)\\]\n",
    "$$\\label{driver_call_codegen_funcs}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Appending to ScalarWave_ETK_py_dir/WaveToyETK_C_kernels_codegen.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile -a $ScalarWaveETKdir/WaveToyETK_C_kernels_codegen.py\n",
    "\n",
    "    exprs  = WaveToy_RHSs__generate_symbolic_expressions()\n",
    "\n",
    "    WaveToy_RHSs__generate_Ccode(exprs)\n",
    "\n",
    "    # Store all NRPy+ environment variables to an output string so NRPy+ environment from within this subprocess can be easily restored\n",
    "    import pickle\n",
    "    # https://www.pythonforthelab.com/blog/storing-binary-data-and-serializing/\n",
    "    outstr = []\n",
    "    outstr.append(pickle.dumps(len(gri.glb_gridfcs_list)))\n",
    "    for lst in gri.glb_gridfcs_list:\n",
    "        outstr.append(pickle.dumps(lst.gftype))\n",
    "        outstr.append(pickle.dumps(lst.name))\n",
    "        outstr.append(pickle.dumps(lst.rank))\n",
    "        outstr.append(pickle.dumps(lst.DIM))\n",
    "        outstr.append(pickle.dumps(lst.f_infinity))\n",
    "        outstr.append(pickle.dumps(lst.wavespeed))\n",
    "\n",
    "    outstr.append(pickle.dumps(len(par.glb_params_list)))\n",
    "    for lst in par.glb_params_list:\n",
    "        outstr.append(pickle.dumps(lst.type))\n",
    "        outstr.append(pickle.dumps(lst.module))\n",
    "        outstr.append(pickle.dumps(lst.parname))\n",
    "        outstr.append(pickle.dumps(lst.defaultval))\n",
    "\n",
    "    outstr.append(pickle.dumps(len(par.glb_Cparams_list)))\n",
    "    for lst in par.glb_Cparams_list:\n",
    "        outstr.append(pickle.dumps(lst.type))\n",
    "        outstr.append(pickle.dumps(lst.module))\n",
    "        outstr.append(pickle.dumps(lst.parname))\n",
    "        outstr.append(pickle.dumps(lst.defaultval))\n",
    "    return outstr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='kernel_codegen'></a>\n",
    "\n",
    "## Step 2.c: Generate C code kernels for `WaveToyNRPy` \\[Back to [top](#toc)\\]\n",
    "$$\\label{kernel_codegen}$$\n",
    "\n",
    "Here we generate the C code kernels (i.e., the C-code representation of the equations needed) for `WaveToyNRPy`.\n",
    "\n",
    "<a id='feature_choice'></a>\n",
    "\n",
    "### Step 2.c.i: Set compile-time and runtime parameters for `WaveToyNRPy`  \\[Back to [top](#toc)\\]\n",
    "$$\\label{feature_choice}$$\n",
    "\n",
    "NRPy+ is a code generation package that is designed to offer maximum flexibility *at the time of C code generation*. As a result, although NRPy+ can in principle output an infinite variety of C code kernels for solving systems of partial differential equations, generally free parameters in each kernel steerable at *runtime* are restricted to simple scalars. This leads to more optimized kernels but at the expense of flexibility in generating multiple kernels (e.g. one per finite-differencing order). Reducing the number of kernels and adding more flexibility at runtime will be a focus of future work.\n",
    "\n",
    "For now, `WaveToyNRPy` supports the following runtime options.\n",
    "\n",
    "* `WaveToyNRPy`: evolution of the Scalar Wave equation.\n",
    "    * Finite differencing of orders 2, 4, 6, and 8 via runtime parameter `FD_order`.\n",
    "\n",
    "Next, we set up the default parameter lists for `WaveToyETK_C_kernels_codegen_onepart()` for the `WaveToyNRPy` thorn. We set these parameter lists as strings to make parallelizing the code generation far easier. (It is easier to pass a list of strings than a list of function arguments to Python's `multiprocessing.Pool()`.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 2.e.i: Set compile-time and runtime parameters for WaveToy\n",
    "#         Runtime parameters for\n",
    "#         WaveToy:       FD_orders = [2,4,6,8]\n",
    "\n",
    "paramslist = []\n",
    "FD_orders = [2,4,6,8]\n",
    "WhichParamSet = 0\n",
    "ThornName = \"WaveToyNRPy\"\n",
    "for FD_order in FD_orders:\n",
    "    paramstr= \"ThornName=\"+ThornName+\",\"\n",
    "    paramstr+= \"FD_order=\"+str(FD_order)+\",\"\n",
    "    paramslist.append(paramstr)\n",
    "    WhichParamSet = WhichParamSet + 1\n",
    "\n",
    "paramslist.sort() # Sort the list alphabetically."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='parallel_codegen'></a>\n",
    "\n",
    "### Step 2.c.ii: Generate all C-code kernels for `WaveToyNRPy`, in parallel if possible \\[Back to [top](#toc)\\]\n",
    "$$\\label{parallel_codegen}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***************************************\n",
      "Starting parallel C kernel codegen...\n",
      "***************************************\n",
      "Generating symbolic expressions for WaveToy RHSs...Generating symbolic expressions for WaveToy RHSs...\n",
      "\n",
      "Generating symbolic expressions for WaveToy RHSs...\n",
      "Generating symbolic expressions for WaveToy RHSs...\n",
      "(BENCH) Finished WaveToy RHS symbolic expressions in 0.013369560241699219 seconds.\n",
      "Generating C code for WaveToy RHSs (FD_order=2) in Cartesian coordinates.\n",
      "(BENCH) Finished WaveToy RHS symbolic expressions in 0.017975807189941406 seconds.\n",
      "(BENCH) Finished WaveToy RHS symbolic expressions in 0.019555091857910156 seconds.Generating C code for WaveToy RHSs (FD_order=4) in Cartesian coordinates.\n",
      "\n",
      "Generating C code for WaveToy RHSs (FD_order=8) in Cartesian coordinates.\n",
      "(BENCH) Finished WaveToy RHS symbolic expressions in 0.02181220054626465 seconds.\n",
      "Generating C code for WaveToy RHSs (FD_order=6) in Cartesian coordinates.\n",
      "(BENCH) Finished WaveToy_RHS C codegen (FD_order=2) in 0.07765388488769531 seconds.\n",
      "(BENCH) Finished WaveToy_RHS C codegen (FD_order=4) in 0.08968853950500488 seconds.\n",
      "(BENCH) Finished WaveToy_RHS C codegen (FD_order=6) in 0.14511585235595703 seconds.\n",
      "(BENCH) Finished WaveToy_RHS C codegen (FD_order=8) in 0.1823718547821045 seconds.\n",
      "(BENCH) Finished C kernel codegen for WaveToy in 0.3171041011810303 seconds.\n"
     ]
    }
   ],
   "source": [
    "nrpy_dir_path = os.path.join(\".\")\n",
    "if nrpy_dir_path not in sys.path:\n",
    "    sys.path.append(nrpy_dir_path)\n",
    "\n",
    "# Create all output directories if they do not yet exist\n",
    "import cmdline_helper as cmd    # NRPy+: Multi-platform Python command-line interface\n",
    "for ThornName in [\"WaveToyNRPy\"]:\n",
    "    outrootdir = ThornName\n",
    "    cmd.mkdir(os.path.join(outrootdir))\n",
    "    outdir = os.path.join(outrootdir,\"src\") # Main C code output directory\n",
    "\n",
    "    # Copy SIMD/SIMD_intrinsics.h to $outdir/SIMD/SIMD_intrinsics.h, replacing\n",
    "    #   the line \"#define REAL_SIMD_ARRAY REAL\" with \"#define REAL_SIMD_ARRAY CCTK_REAL\"\n",
    "    #   (since REAL is undefined in the ETK, but CCTK_REAL takes its place)\n",
    "    cmd.mkdir(os.path.join(outdir,\"SIMD\"))\n",
    "    import fileinput\n",
    "    f = fileinput.input(os.path.join(nrpy_dir_path,\"SIMD\",\"SIMD_intrinsics.h\"))\n",
    "    with open(os.path.join(outdir,\"SIMD\",\"SIMD_intrinsics.h\"),\"w\") as outfile:\n",
    "        for line in f:\n",
    "            outfile.write(line.replace(\"#define REAL_SIMD_ARRAY REAL\", \"#define REAL_SIMD_ARRAY CCTK_REAL\"))\n",
    "\n",
    "    # Create directory for rfm_files output\n",
    "    cmd.mkdir(os.path.join(outdir,\"rfm_files\"))\n",
    "\n",
    "# Start parallel C code generation (codegen)\n",
    "# NRPyEnvVars stores the NRPy+ environment from all the subprocesses in the following\n",
    "#     parallel codegen\n",
    "NRPyEnvVars = []\n",
    "\n",
    "import time   # Standard Python module for benchmarking\n",
    "import logging\n",
    "start = time.time()\n",
    "if __name__ == \"__main__\":\n",
    "    try:\n",
    "        if os.name == 'nt':\n",
    "            # Windows & Jupyter multiprocessing do not mix, so we run in serial on Windows.\n",
    "            #  Here's why: https://stackoverflow.com/questions/45719956/python-multiprocessing-in-jupyter-on-windows-attributeerror-cant-get-attribut\n",
    "            raise Exception(\"Parallel codegen currently not available in Windows\")\n",
    "        # Step 3.d.ii: Import the multiprocessing module.\n",
    "        import multiprocessing\n",
    "        print(\"***************************************\")\n",
    "        print(\"Starting parallel C kernel codegen...\")\n",
    "        print(\"***************************************\")\n",
    "\n",
    "        # Step 3.d.iii: Define master function for parallelization.\n",
    "        #           Note that lambdifying this doesn't work in Python 3\n",
    "        def master_func(i):\n",
    "            import ScalarWave_ETK_py_dir.WaveToyETK_C_kernels_codegen as WCk\n",
    "            return WCk.WaveToyETK_C_kernels_codegen_onepart(params=paramslist[i])\n",
    "\n",
    "        # Step 3.d.iv: Evaluate list of functions in parallel if possible;\n",
    "        #           otherwise fallback to serial evaluation:\n",
    "        pool = multiprocessing.Pool() #processes=len(paramslist))\n",
    "        NRPyEnvVars.append(pool.map(master_func,range(len(paramslist))))\n",
    "        pool.terminate()\n",
    "        pool.join()\n",
    "    except:\n",
    "        logging.exception(\"Ignore this warning/backtrace if on a system in which serial codegen is necessary:\")\n",
    "        print(\"***************************************\")\n",
    "        print(\"Starting serial C kernel codegen...\")\n",
    "        print(\"(If you were running in parallel before,\")\n",
    "        print(\" this means parallel codegen failed)\")\n",
    "        print(\"***************************************\")\n",
    "        # Steps 3.d.ii-iv, alternate: As fallback, evaluate functions in serial.\n",
    "        #       This will happen on Android and Windows systems\n",
    "        import ScalarWave_ETK_py_dir.WaveToyETK_C_kernels_codegen as WCk\n",
    "        import grid as gri\n",
    "        for param in paramslist:\n",
    "            gri.glb_gridfcs_list = []\n",
    "            WCk.WaveToyETK_C_kernels_codegen_onepart(params=param)\n",
    "        NRPyEnvVars = [] # Reset NRPyEnvVars in case multiprocessing wrote to it and failed.\n",
    "# # Steps 3.d.ii-iv, alternate: As fallback, evaluate functions in serial.\n",
    "# #       This will happen on Android and Windows systems\n",
    "# for param in paramslist:\n",
    "#     import grid as gri\n",
    "#     gri.glb_gridfcs_list = []\n",
    "#     import ScalarWave_ETK_py_dir.WaveToyETK_C_kernels_codegen as WCk\n",
    "#     NRPyEnvVars.append(WCk.WaveToyETK_C_kernels_codegen_onepart(params=param))\n",
    "\n",
    "print(\"(BENCH) Finished C kernel codegen for WaveToy in \"+str(time.time()-start)+\" seconds.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store all NRPy+ environment variables to file so NRPy+ environment from within this subprocess can be easily restored\n",
    "import pickle                   # Standard Python module for converting arbitrary data structures to a uniform format.\n",
    "import grid as gri              # NRPy+: Functions having to do with numerical grids\n",
    "import NRPy_param_funcs as par  # NRPy+: Parameter interface\n",
    "\n",
    "if len(NRPyEnvVars) > 0:\n",
    "    # https://www.pythonforthelab.com/blog/storing-binary-data-and-serializing/\n",
    "    grfcs_list = []\n",
    "    param_list = []\n",
    "    Cparm_list = []\n",
    "\n",
    "    for WhichParamSet in NRPyEnvVars[0]:\n",
    "        # gridfunctions\n",
    "        i=0\n",
    "        # print(\"Length of WhichParamSet:\",str(len(WhichParamSet)))\n",
    "        num_elements = pickle.loads(WhichParamSet[i]); i+=1\n",
    "        for lst in range(num_elements):\n",
    "            grfcs_list.append(gri.glb_gridfc(gftype    =pickle.loads(WhichParamSet[i+0]),\n",
    "                                             name      =pickle.loads(WhichParamSet[i+1]),\n",
    "                                             rank      =pickle.loads(WhichParamSet[i+2]),\n",
    "                                             DIM       =pickle.loads(WhichParamSet[i+3]),\n",
    "                                             f_infinity=pickle.loads(WhichParamSet[i+4]),\n",
    "                                             wavespeed =pickle.loads(WhichParamSet[i+5]))) ; i+=6\n",
    "        # parameters\n",
    "        num_elements = pickle.loads(WhichParamSet[i]); i+=1\n",
    "        for lst in range(num_elements):\n",
    "            param_list.append(par.glb_param(type      =pickle.loads(WhichParamSet[i+0]),\n",
    "                                            module    =pickle.loads(WhichParamSet[i+1]),\n",
    "                                            parname   =pickle.loads(WhichParamSet[i+2]),\n",
    "                                            defaultval=pickle.loads(WhichParamSet[i+3]))) ; i+=4\n",
    "        # Cparameters\n",
    "        num_elements = pickle.loads(WhichParamSet[i]); i+=1\n",
    "        for lst in range(num_elements):\n",
    "            Cparm_list.append(par.glb_Cparam(type      =pickle.loads(WhichParamSet[i+0]),\n",
    "                                             module    =pickle.loads(WhichParamSet[i+1]),\n",
    "                                             parname   =pickle.loads(WhichParamSet[i+2]),\n",
    "                                             defaultval=pickle.loads(WhichParamSet[i+3]))) ; i+=4\n",
    "    grfcs_list_uniq = []\n",
    "    for gf_ntuple_stored in grfcs_list:\n",
    "        found_gf = False\n",
    "        for gf_ntuple_new in grfcs_list_uniq:\n",
    "            if gf_ntuple_new == gf_ntuple_stored:\n",
    "                found_gf = True\n",
    "        if found_gf == False:\n",
    "            grfcs_list_uniq.append(gf_ntuple_stored)\n",
    "\n",
    "    param_list_uniq = []\n",
    "    for pr_ntuple_stored in param_list:\n",
    "        found_pr = False\n",
    "        for pr_ntuple_new in param_list_uniq:\n",
    "            if pr_ntuple_new == pr_ntuple_stored:\n",
    "                found_pr = True\n",
    "        if found_pr == False:\n",
    "            param_list_uniq.append(pr_ntuple_stored)\n",
    "\n",
    "    # Set glb_paramsvals_list:\n",
    "    # Step 1: Reset all paramsvals to their defaults\n",
    "    par.glb_paramsvals_list = []\n",
    "    for parm in param_list_uniq:\n",
    "        par.glb_paramsvals_list.append(parm.defaultval)\n",
    "\n",
    "    Cparm_list_uniq = []\n",
    "    for Cp_ntuple_stored in Cparm_list:\n",
    "        found_Cp = False\n",
    "        for Cp_ntuple_new in Cparm_list_uniq:\n",
    "            if Cp_ntuple_new == Cp_ntuple_stored:\n",
    "                found_Cp = True\n",
    "        if found_Cp == False:\n",
    "            Cparm_list_uniq.append(Cp_ntuple_stored)\n",
    "\n",
    "    gri.glb_gridfcs_list = []\n",
    "    par.glb_params_list  = []\n",
    "    par.glb_Cparams_list = []\n",
    "\n",
    "    gri.glb_gridfcs_list = grfcs_list_uniq\n",
    "    par.glb_params_list  = param_list_uniq\n",
    "    par.glb_Cparams_list = Cparm_list_uniq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='cclfiles'></a>\n",
    "\n",
    "# Step 3: ETK `ccl` file generation \\[Back to [top](#toc)\\]\n",
    "$$\\label{cclfiles}$$\n",
    "\n",
    "<a id='paramccl'></a>\n",
    "\n",
    "## Step 3.a: `param.ccl`: specify free parameters within `WaveToyNRPy` \\[Back to [top](#toc)\\]\n",
    "$$\\label{paramccl}$$\n",
    "\n",
    "All parameters necessary for the computation of the Scalar Wave right-hand side (RHS) expressions are registered within NRPy+; we use this information to automatically generate `param.ccl`. NRPy+ also specifies default values for each parameter. \n",
    "\n",
    "More information on `param.ccl` syntax can be found in the  [official Einstein Toolkit documentation](https://einsteintoolkit.org/usersguide/UsersGuide.html#x1-184000D2.3)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def keep_param__return_type(paramtuple):\n",
    "    keep_param = True # We'll not set some parameters in param.ccl;\n",
    "                      #   e.g., those that should be #define'd like M_PI.\n",
    "    typestring = \"\"\n",
    "    # Separate thorns within the ETK take care of grid/coordinate parameters;\n",
    "    #   thus we ignore NRPy+ grid/coordinate parameters:\n",
    "    if paramtuple.module in ('grid', 'reference_metric'):\n",
    "        keep_param = False\n",
    "\n",
    "    partype = paramtuple.type\n",
    "    if partype == \"bool\":\n",
    "        typestring += \"BOOLEAN \"\n",
    "    elif partype == \"REAL\":\n",
    "        if paramtuple.defaultval != 1e300: # 1e300 is a magic value indicating that the C parameter should be mutable\n",
    "            typestring += \"CCTK_REAL \"\n",
    "        else:\n",
    "            keep_param = False\n",
    "    elif partype == \"int\":\n",
    "        typestring += \"CCTK_INT \"\n",
    "    elif partype == \"#define\":\n",
    "        keep_param = False\n",
    "    elif partype == \"char\":\n",
    "        # FIXME: char/string parameter types should in principle be supported\n",
    "        print(\"Error: parameter \"+paramtuple.module+\"::\"+paramtuple.parname+\n",
    "              \" has unsupported type: \\\"\"+ paramtuple.type + \"\\\"\")\n",
    "        sys.exit(1)\n",
    "    else:\n",
    "        print(\"Error: parameter \"+paramtuple.module+\"::\"+paramtuple.parname+\n",
    "              \" has unsupported type: \\\"\"+ paramtuple.type + \"\\\"\")\n",
    "        sys.exit(1)\n",
    "    return keep_param, typestring\n",
    "\n",
    "with open(os.path.join(outrootdir,\"param.ccl\"), \"w\") as file:\n",
    "    file.write(\"\"\"\n",
    "# This param.ccl file was automatically generated by NRPy+.\n",
    "#   You are advised against modifying it directly.\n",
    "\n",
    "restricted:\n",
    "CCTK_REAL wavespeed \"The speed at which the wave propagates\"\n",
    "{\n",
    " *:* :: \"Wavespeed as a multiple of c\"\n",
    "} 1.0\n",
    "\n",
    "restricted:\n",
    "\n",
    "CCTK_INT FD_order \"Finite-differencing order\"\n",
    "{\\n\"\"\")\n",
    "    FDorders = []\n",
    "    for _root, _dirs, files in os.walk(os.path.join(ThornName,\"src\")): # _root,_dirs unused.\n",
    "        for Cfilename in files:\n",
    "            if (\".h\" in Cfilename) and (\"RHSs\" in Cfilename) and (\"FD\" in Cfilename) and (\"intrinsics\" not in Cfilename):\n",
    "                array = Cfilename.replace(\".\",\"_\").split(\"_\")\n",
    "                FDorders.append(int(array[-2]))\n",
    "    FDorders.sort()\n",
    "    for order in FDorders:\n",
    "        file.write(\" \"+str(order)+\":\"+str(order)+\"   :: \\\"finite-differencing order = \"+str(order)+\"\\\"\\n\")\n",
    "    FDorder_default = 4\n",
    "    if FDorder_default not in FDorders:\n",
    "        print(\"WARNING: 4th-order FD kernel was not output!?! Changing default FD order to \"+str(FDorders[0]))\n",
    "        FDorder_default = FDorders[0]\n",
    "    file.write(\"} \"+str(FDorder_default)+ \"\\n\\n\") # choose 4th order by default, consistent with ML_WaveToy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='interfaceccl'></a>\n",
    "\n",
    "## Step 3.b: `interface.ccl`: define needed gridfunctions; provide keywords denoting what this thorn provides and what it should inherit from other thorns \\[Back to [top](#toc)\\]\n",
    "$$\\label{interfaceccl}$$\n",
    "\n",
    "`interface.ccl` declares all gridfunctions and determines how `WaveToyNRPy` interacts with other Einstein Toolkit thorns.\n",
    "\n",
    "The [official Einstein Toolkit documentation](https://einsteintoolkit.org/usersguide/UsersGuide.html#x1-179000D2.2) defines what must/should be included in an `interface.ccl` file. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "evol_gfs_list    = []\n",
    "for i in range(len(gri.glb_gridfcs_list)):\n",
    "    if gri.glb_gridfcs_list[i].gftype == \"EVOL\":\n",
    "        evol_gfs_list.append(   gri.glb_gridfcs_list[i].name+\"GF\")\n",
    "\n",
    "# NRPy+'s finite-difference code generator assumes gridfunctions\n",
    "#    are alphabetized; not sorting may result in unnecessary\n",
    "#    cache misses.\n",
    "evol_gfs_list.sort()\n",
    "\n",
    "with open(os.path.join(outrootdir,\"interface.ccl\"), \"w\") as file:\n",
    "    file.write(\"\"\"\n",
    "# With \"implements\", we give our thorn its unique name.\n",
    "implements: WaveToyNRPy\n",
    "\n",
    "# By \"inheriting\" other thorns, we tell the Toolkit that we\n",
    "#   will rely on variables/function that exist within those\n",
    "#   functions.\n",
    "inherits: Boundary grid MethodofLines\n",
    "\n",
    "# Needed functions and #include's:\n",
    "USES INCLUDE: Symmetry.h\n",
    "USES INCLUDE: Boundary.h\n",
    "\n",
    "# Needed Method of Lines function\n",
    "CCTK_INT FUNCTION MoLRegisterEvolvedGroup(CCTK_INT IN EvolvedIndex,                                           CCTK_INT IN RHSIndex)\n",
    "REQUIRES FUNCTION MoLRegisterEvolvedGroup\n",
    "\n",
    "# Needed Boundary Conditions function\n",
    "CCTK_INT FUNCTION GetBoundarySpecification(CCTK_INT IN size, CCTK_INT OUT ARRAY nboundaryzones, CCTK_INT OUT ARRAY is_internal, CCTK_INT OUT ARRAY is_staggered, CCTK_INT OUT ARRAY shiftout)\n",
    "USES FUNCTION GetBoundarySpecification\n",
    "\n",
    "CCTK_INT FUNCTION SymmetryTableHandleForGrid(CCTK_POINTER_TO_CONST IN cctkGH)\n",
    "USES FUNCTION SymmetryTableHandleForGrid\n",
    "\n",
    "CCTK_INT FUNCTION Boundary_SelectVarForBC(CCTK_POINTER_TO_CONST IN GH, CCTK_INT IN faces, CCTK_INT IN boundary_width, CCTK_INT IN table_handle, CCTK_STRING IN var_name, CCTK_STRING IN bc_name)\n",
    "USES FUNCTION Boundary_SelectVarForBC\n",
    "\n",
    "# Needed for EinsteinEvolve/NewRad outer boundary condition driver:\n",
    "CCTK_INT FUNCTION                         \\\n",
    "    NewRad_Apply                          \\\n",
    "        (CCTK_POINTER_TO_CONST IN cctkGH, \\\n",
    "         CCTK_REAL ARRAY IN var,          \\\n",
    "         CCTK_REAL ARRAY INOUT rhs,       \\\n",
    "         CCTK_REAL IN var0,               \\\n",
    "         CCTK_REAL IN v0,                 \\\n",
    "         CCTK_INT IN radpower)\n",
    "REQUIRES FUNCTION NewRad_Apply\n",
    "\n",
    "# Tell the Toolkit that we want all gridfunctions\n",
    "#    to be visible to other thorns by using\n",
    "#    the keyword \"public\". Note that declaring these\n",
    "#    gridfunctions *does not* allocate memory for them;\n",
    "#    that is done by the schedule.ccl file.\n",
    "\n",
    "# FIXME: add info for symmetry conditions:\n",
    "#    https://einsteintoolkit.org/thornguide/CactusBase/SymBase/documentation.html\n",
    "\n",
    "\"\"\")\n",
    "\n",
    "# Next we declare gridfunctions based on their corresponding gridfunction groups as registered within NRPy+\n",
    "\n",
    "    def output_list_of_gfs(gfs_list,description=\"User did not provide description\"):\n",
    "        gfsstr = \"    \"\n",
    "        for i in range(len(gfs_list)):\n",
    "            gfsstr += gfs_list[i]\n",
    "            if i != len(gfs_list)-1:\n",
    "                gfsstr += \",\" # This is a comma-separated list of gridfunctions\n",
    "            else:\n",
    "                gfsstr += \"\\n} \\\"\"+description+\"\\\"\\n\\n\"\n",
    "        return gfsstr\n",
    "    # First EVOL type:\n",
    "    file.write(\"public:\\n\")\n",
    "    file.write(\"cctk_real scalar_fields type = GF Timelevels=3 tags=\\'tensortypealias=\\\"Scalar\\\"\\'\\n{\\n\")\n",
    "    file.write(output_list_of_gfs(evol_gfs_list,\"The evolved scalar fields\"))\n",
    "    # Second EVOL right-hand-sides\n",
    "    file.write(\"public:\\n\")\n",
    "    file.write(\"cctk_real scalar_fields_rhs type = GF Timelevels=3 tags=\\'tensortypealias=\\\"Scalar\\\"\\'\\n{\\n\")\n",
    "    rhs_list = []\n",
    "    for gf in evol_gfs_list:\n",
    "        rhs_list.append(gf.replace(\"GF\",\"\")+\"_rhsGF\")\n",
    "    file.write(output_list_of_gfs(rhs_list,\"The rhs of the scalar fields\"))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='scheduleccl'></a>\n",
    "\n",
    "## Step 3.c: `schedule.ccl`: schedule all functions used within `WaveToyNRPy`, specify data dependencies within said functions, and allocate memory for gridfunctions \\[Back to [top](#toc)\\]\n",
    "$$\\label{scheduleccl}$$\n",
    "\n",
    "Official documentation on constructing ETK `schedule.ccl` files is found [here](https://einsteintoolkit.org/usersguide/UsersGuide.html#x1-187000D2.4)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(os.path.join(outrootdir,\"schedule.ccl\"), \"w\") as file:\n",
    "    file.write(\"\"\"\n",
    "# Allocate storage for all 2 gridfunction groups used in WaveToyNRPy\n",
    "STORAGE: scalar_fields_rhs[3]\n",
    "STORAGE: scalar_fields[3]\n",
    "\n",
    "# The following scheduler is based on Lean/LeanBSSNMoL/schedule.ccl\n",
    "\n",
    "schedule WaveToyNRPy_Banner at STARTUP\n",
    "{\n",
    "  LANG: C\n",
    "  OPTIONS: meta\n",
    "} \"Output ASCII art banner\"\n",
    "\n",
    "schedule WaveToyNRPy_Symmetry_registration at BASEGRID\n",
    "{\n",
    "  LANG: C\n",
    "  OPTIONS: Global\n",
    "} \"Register symmetries, the CartGrid3D way.\"\n",
    "\n",
    "schedule WaveToyNRPy_zero_rhss at BASEGRID after WaveToyNRPy_Symmetry_registration\n",
    "{\n",
    "  LANG: C\n",
    "} \"Idea from Lean: set all rhs functions to zero to prevent spurious nans\"\n",
    "\n",
    "\n",
    "schedule GROUP ApplyBCs as WaveToyNRPy_ApplyBCs at CCTK_INITIAL\n",
    "{\n",
    "} \"Apply boundary conditions\"\n",
    "\n",
    "\n",
    "# MoL: registration\n",
    "\n",
    "schedule WaveToyNRPy_MoL_registration in MoL_Register\n",
    "{\n",
    "  LANG: C\n",
    "  OPTIONS: META\n",
    "} \"Register variables for MoL\"\n",
    "\n",
    "\n",
    "# MoL: compute RHSs, etc\n",
    "\n",
    "schedule WaveToyNRPy_set_rhs as WaveToy_Evolution IN MoL_CalcRHS\n",
    "{\n",
    "  LANG: C\n",
    "  READS: uuGF(Everywhere)\n",
    "  READS: vvGF(Everywhere)\n",
    "  WRITES: uu_rhsGF(Interior)\n",
    "  WRITES: vv_rhsGF(Interior)\n",
    "} \"Evolution of 3D wave equation\"\n",
    "\n",
    "schedule WaveToyNRPy_NewRad in MoL_CalcRHS after WaveToyNRPy_RHS\n",
    "{\n",
    "  LANG: C\n",
    "} \"NewRad boundary conditions, scheduled right after RHS eval.\"\n",
    "\n",
    "schedule WaveToyNRPy_BoundaryConditions_register_evolved_gfs in MoL_PostStep\n",
    "{\n",
    "  LANG: C\n",
    "  OPTIONS: LEVEL\n",
    "  SYNC: scalar_fields\n",
    "} \"Apply boundary conditions and perform AMR+interprocessor synchronization\"\n",
    "\n",
    "schedule GROUP ApplyBCs as WaveToyNRPy_ApplyBCs in MoL_PostStep after WaveToyNRPy_BoundaryConditions_register_evolved_gfs\n",
    "{\n",
    "} \"Group for applying boundary conditions\"\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='cdrivers'></a>\n",
    "\n",
    "# Step 4: C driver functions for ETK registration & NRPy+-generated kernels \\[Back to [top](#toc)\\]\n",
    "$$\\label{cdrivers}$$\n",
    "\n",
    "Now that we have constructed the basic C code kernels and the needed Einstein Toolkit `ccl` files, we next write the driver functions for registering `WaveToyNRPy` within the Toolkit and the C code kernels. Each of these driver functions is called directly from [`schedule.ccl`](#scheduleccl).\n",
    "\n",
    "<a id='etkfunctions'></a>\n",
    "## Step 4.a: Needed ETK functions: Banner, Symmetry registration, Parameter sanity check,  Method of Lines (`MoL`) registration, Boundary condition \\[Back to [top](#toc)\\]\n",
    "$$\\label{etkfunctions}$$\n",
    "\n",
    "### To-do: Parameter sanity check function. E.g., error should be thrown if `cctk_nghostzones[]` is set too small for the chosen finite-differencing order within NRPy+."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "make_code_defn_list = []\n",
    "def append_to_make_code_defn_list(filename):\n",
    "    if filename not in make_code_defn_list:\n",
    "        make_code_defn_list.append(filename)\n",
    "    return os.path.join(outdir,filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First the ETK banner code, proudly showing the NRPy+ banner\n",
    "import NRPy_logo as logo\n",
    "\n",
    "with open(append_to_make_code_defn_list(\"Banner.c\"),\"w\") as file:\n",
    "    file.write(\"\"\"\n",
    "#include <stdio.h>\n",
    "\n",
    "void WaveToyNRPy_Banner()\n",
    "{\n",
    "    \"\"\")\n",
    "    logostr = logo.print_logo(print_to_stdout=False)\n",
    "    file.write(\"printf(\\\"WaveToyNRPy: another Einstein Toolkit thorn generated by\\\\n\\\");\\n\")\n",
    "    for line in logostr.splitlines():\n",
    "        file.write(\"    printf(\\\"\"+line+\"\\\\n\\\");\\n\")\n",
    "    file.write(\"}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Next register symmetries\n",
    "with open(append_to_make_code_defn_list(\"Symmetry_registration_oldCartGrid3D.c\"),\"w\") as file:\n",
    "    file.write(\"\"\"\n",
    "#include \"cctk.h\"\n",
    "#include \"cctk_Arguments.h\"\n",
    "#include \"cctk_Parameters.h\"\n",
    "#include \"Symmetry.h\"\n",
    "\n",
    "void WaveToyNRPy_Symmetry_registration(CCTK_ARGUMENTS)\n",
    "{\n",
    "  DECLARE_CCTK_ARGUMENTS;\n",
    "  DECLARE_CCTK_PARAMETERS;\n",
    "\n",
    "  // Stores gridfunction parity across x=0, y=0, and z=0 planes, respectively\n",
    "  int sym[3];\n",
    "\n",
    "  // Next register parities for each gridfunction based on its name\n",
    "  //    (to ensure this algorithm is robust, gridfunctions with integers\n",
    "  //     in their base names are forbidden in NRPy+).\n",
    "\"\"\")\n",
    "    full_gfs_list = []\n",
    "    full_gfs_list.extend(evol_gfs_list)\n",
    "    for gf in full_gfs_list:\n",
    "        file.write(\"\"\"\n",
    "  // Default to scalar symmetry:\n",
    "  sym[0] = 1; sym[1] = 1; sym[2] = 1;\n",
    "  // Now modify sym[0], sym[1], and/or sym[2] as needed\n",
    "  //    to account for gridfunction parity across\n",
    "  //    x=0, y=0, and/or z=0 planes, respectively\n",
    "\"\"\")\n",
    "        # If gridfunction name does not end in a digit, by NRPy+ syntax, it must be a scalar\n",
    "        if gf[len(gf) - 1].isdigit() == False:\n",
    "            pass # Scalar = default\n",
    "        elif len(gf) > 2:\n",
    "            # Rank-1 indexed expression (e.g., vector)\n",
    "            if gf[len(gf) - 2].isdigit() == False:\n",
    "                if int(gf[-1]) > 2:\n",
    "                    print(\"Error: Found invalid gridfunction name: \"+gf)\n",
    "                    sys.exit(1)\n",
    "                symidx = gf[-1]\n",
    "                file.write(\"  sym[\"+symidx+\"] = -1;\\n\")\n",
    "            # Rank-2 indexed expression\n",
    "            elif gf[len(gf) - 2].isdigit() == True:\n",
    "                if len(gf) > 3 and gf[len(gf) - 3].isdigit() == True:\n",
    "                    print(\"Error: Found a Rank-3 or above gridfunction: \"+gf+\", which is at the moment unsupported.\")\n",
    "                    print(\"It should be easy to support this if desired.\")\n",
    "                    sys.exit(1)\n",
    "                symidx0 = gf[-2]\n",
    "                file.write(\"  sym[\"+symidx0+\"] *= -1;\\n\")\n",
    "                symidx1 = gf[-1]\n",
    "                file.write(\"  sym[\"+symidx1+\"] *= -1;\\n\")\n",
    "        else:\n",
    "            print(\"Don't know how you got this far with a gridfunction named \"+gf+\", but I'll take no more of this nonsense.\")\n",
    "            print(\"   Please follow best-practices and rename your gridfunction to be more descriptive\")\n",
    "            sys.exit(1)\n",
    "        file.write(\"  SetCartSymVN(cctkGH, sym, \\\"WaveToyNRPy::\"+gf+\"\\\");\\n\")\n",
    "    file.write(\"}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Next register symmetries\n",
    "import loop as lp                # NRPy+: Generate C code loops\n",
    "with open(append_to_make_code_defn_list(\"zero_rhss.c\"),\"w\") as file:\n",
    "    file.write(\"\"\"\n",
    "#include \"cctk.h\"\n",
    "#include \"cctk_Arguments.h\"\n",
    "#include \"cctk_Parameters.h\"\n",
    "#include \"Symmetry.h\"\n",
    "\n",
    "void WaveToyNRPy_zero_rhss(CCTK_ARGUMENTS)\n",
    "{\n",
    "  DECLARE_CCTK_ARGUMENTS;\n",
    "  DECLARE_CCTK_PARAMETERS;\n",
    "\"\"\")\n",
    "    set_rhss_to_zero = \"\"\n",
    "    for gf in rhs_list:\n",
    "        set_rhss_to_zero += gf+\"[CCTK_GFINDEX3D(cctkGH,i0,i1,i2)] = 0.0;\\n\"\n",
    "\n",
    "    file.write(lp.loop([\"i2\",\"i1\",\"i0\"],[\"0\", \"0\", \"0\"],\n",
    "                       [\"cctk_lsh[2]\",\"cctk_lsh[1]\",\"cctk_lsh[0]\"],\n",
    "                       [\"1\",\"1\",\"1\"],\n",
    "                        [\"#pragma omp parallel for\",\"\",\"\",],\"\",set_rhss_to_zero))\n",
    "    file.write(\"}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Next registration with the Method of Lines thorn\n",
    "with open(append_to_make_code_defn_list(\"MoL_registration.c\"),\"w\") as file:\n",
    "    file.write(\"\"\"\n",
    "//--------------------------------------------------------------------------\n",
    "// Register with the Method of Lines time stepper\n",
    "// (MoL thorn, found in arrangements/CactusBase/MoL)\n",
    "// MoL documentation located in arrangements/CactusBase/MoL/doc\n",
    "//--------------------------------------------------------------------------\n",
    "#include <stdio.h>\n",
    "\n",
    "#include \"cctk.h\"\n",
    "#include \"cctk_Parameters.h\"\n",
    "#include \"cctk_Arguments.h\"\n",
    "\n",
    "#include \"Symmetry.h\"\n",
    "\n",
    "void WaveToyNRPy_MoL_registration(CCTK_ARGUMENTS)\n",
    "{\n",
    "  DECLARE_CCTK_ARGUMENTS;\n",
    "  DECLARE_CCTK_PARAMETERS;\n",
    "\n",
    "  CCTK_INT ierr = 0, group, rhs;\n",
    "\n",
    "  // Register evolution & RHS gridfunction groups with MoL, so it knows\n",
    "\n",
    "  group = CCTK_GroupIndex(\"WaveToyNRPy::scalar_fields\");\n",
    "  rhs = CCTK_GroupIndex(\"WaveToyNRPy::scalar_fields_rhs\");\n",
    "  ierr += MoLRegisterEvolvedGroup(group, rhs);\n",
    "\n",
    "  if (ierr) CCTK_ERROR(\"Problems registering with MoL\");\n",
    "}\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Next register with the boundary conditions thorns.\n",
    "# PART 1: Set BC type to \"none\" for all variables\n",
    "# Since we choose NewRad boundary conditions, we must register all\n",
    "#   gridfunctions to have boundary type \"none\". This is because\n",
    "#   NewRad is seen by the rest of the Toolkit as a modification to the\n",
    "#   RHSs.\n",
    "\n",
    "# This code is based on Kranc's McLachlan/ML_BSSN/src/Boundaries.cc code.\n",
    "with open(append_to_make_code_defn_list(\"BoundaryConditions_register_evolved_gfs.c\"),\"w\") as file:\n",
    "    file.write(\"\"\"\n",
    "#include \"cctk.h\"\n",
    "#include \"cctk_Arguments.h\"\n",
    "#include \"cctk_Parameters.h\"\n",
    "#include \"cctk_Faces.h\"\n",
    "#include \"util_Table.h\"\n",
    "#include \"Symmetry.h\"\n",
    "\n",
    "// Set `none` boundary conditions on RHSs, as these are set via NewRad.\n",
    "void WaveToyNRPy_BoundaryConditions_register_evolved_gfs(CCTK_ARGUMENTS)\n",
    "{\n",
    "  DECLARE_CCTK_ARGUMENTS;\n",
    "  DECLARE_CCTK_PARAMETERS;\n",
    "\n",
    "  CCTK_INT ierr CCTK_ATTRIBUTE_UNUSED = 0;\n",
    "\"\"\")\n",
    "    for gf in evol_gfs_list:\n",
    "        file.write(\"\"\"\n",
    "  ierr = Boundary_SelectVarForBC(cctkGH, CCTK_ALL_FACES, 1, -1, \"WaveToyNRPy::\"\"\"+gf+\"\"\"\", \"none\");\n",
    "  if (ierr < 0) CCTK_ERROR(\"Failed to register BC for WaveToyNRPy::\"\"\"+gf+\"\"\"!\");\n",
    "\n",
    "\"\"\")\n",
    "    file.write(\"}\")\n",
    "\n",
    "# PART 2: Set C code for calling NewRad BCs\n",
    "#   As explained in lean_public/LeanBSSNMoL/src/calc_bssn_rhs.F90,\n",
    "#   the function NewRad_Apply takes the following arguments:\n",
    "#   NewRad_Apply(cctkGH, var, rhs, var0, v0, radpower),\n",
    "#     which implement the boundary condition:\n",
    "#       var  =  var_at_infinite_r + u(r-var_char_speed*t)/r^var_radpower\n",
    "#  Obviously for var_radpower>0, var_at_infinite_r is the value of\n",
    "#    the variable at r->infinity. var_char_speed is the propagation\n",
    "#    speed at the outer boundary, and var_radpower is the radial\n",
    "#    falloff rate.\n",
    "\n",
    "with open(append_to_make_code_defn_list(\"BoundaryCondition_NewRad.c\"),\"w\") as file:\n",
    "    file.write(\"\"\"\n",
    "#include <math.h>\n",
    "\n",
    "#include \"cctk.h\"\n",
    "#include \"cctk_Arguments.h\"\n",
    "#include \"cctk_Parameters.h\"\n",
    "\n",
    "void WaveToyNRPy_NewRad(CCTK_ARGUMENTS) {\n",
    "  DECLARE_CCTK_ARGUMENTS;\n",
    "  DECLARE_CCTK_PARAMETERS;\n",
    "\n",
    "  const CCTK_REAL uu_at_infinite_r = 1.0;\n",
    "  const CCTK_REAL vv_at_infinite_r = 0.0;\n",
    "  const CCTK_REAL var_char_speed    = 1.0;\n",
    "  const CCTK_REAL var_radpower      = 3.0;\n",
    "\n",
    "  NewRad_Apply(cctkGH, uuGF, uu_rhsGF, uu_at_infinite_r, var_char_speed, var_radpower);\n",
    "  NewRad_Apply(cctkGH, vvGF, vv_rhsGF, vv_at_infinite_r, var_char_speed, var_radpower);\n",
    "}\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='rhss'></a>\n",
    "\n",
    "## Step 4.b: Evaluate Scalar Wave right-hand-sides (RHSs) \\[Back to [top](#toc)\\]\n",
    "$$\\label{rhss}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "###########################\n",
    "# WaveToy_RHSs\n",
    "###########################\n",
    "common_includes = \"\"\"\n",
    "#include <math.h>\n",
    "#include \"cctk.h\"\n",
    "#include \"cctk_Arguments.h\"\n",
    "#include \"cctk_Parameters.h\"\n",
    "#include \"SIMD/SIMD_intrinsics.h\"\n",
    "\n",
    "\"\"\"\n",
    "common_preloop = \"\"\"\n",
    "DECLARE_CCTK_ARGUMENTS;\n",
    "const CCTK_REAL NOSIMDinvdx0 = 1.0/CCTK_DELTA_SPACE(0);\n",
    "const REAL_SIMD_ARRAY invdx0 = ConstSIMD(NOSIMDinvdx0);\n",
    "const CCTK_REAL NOSIMDinvdx1 = 1.0/CCTK_DELTA_SPACE(1);\n",
    "const REAL_SIMD_ARRAY invdx1 = ConstSIMD(NOSIMDinvdx1);\n",
    "const CCTK_REAL NOSIMDinvdx2 = 1.0/CCTK_DELTA_SPACE(2);\n",
    "const REAL_SIMD_ARRAY invdx2 = ConstSIMD(NOSIMDinvdx2);\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = os.path.join(ThornName,\"src\")\n",
    "WaveToyETK_src_filelist = []\n",
    "for _root, _dirs, files in os.walk(path):  # _root, _dirs unused.\n",
    "    for filename in files:\n",
    "        WaveToyETK_src_filelist.append(filename)\n",
    "WaveToyETK_src_filelist.sort() # Sort the list in place.\n",
    "\n",
    "WaveToy_FD_orders_output = []\n",
    "for filename in WaveToyETK_src_filelist:\n",
    "    if \"WaveToy_RHSs_\" in filename:\n",
    "        array = filename.replace(\".\",\"_\").split(\"_\")\n",
    "        FDorder =  int(array[-2])\n",
    "        if FDorder not in WaveToy_FD_orders_output:\n",
    "            WaveToy_FD_orders_output.append(FDorder)\n",
    "WaveToy_FD_orders_output.sort()\n",
    "\n",
    "###########################\n",
    "# Output WaveToy RHSs driver function\n",
    "outstr = common_includes\n",
    "for filename in WaveToyETK_src_filelist:\n",
    "    if (\"WaveToy_RHSs_\" in filename) and (\".h\" in filename):\n",
    "        outstr += \"\"\"extern void \"\"\"+filename.replace(\".h\", \"(CCTK_ARGUMENTS);\") + \"\\n\"\n",
    "\n",
    "outstr += \"\"\"\n",
    "void WaveToyNRPy_set_rhs(CCTK_ARGUMENTS) {\n",
    "DECLARE_CCTK_ARGUMENTS;\n",
    "\n",
    "const CCTK_INT *FD_order = CCTK_ParameterGet(\"FD_order\",\"WaveToyNRPy\",NULL);\n",
    "\n",
    "\"\"\"\n",
    "for filename in WaveToyETK_src_filelist:\n",
    "    if (\"WaveToy_RHSs_\" in filename) and (\".h\" in filename):\n",
    "        array = filename.replace(\".\", \"_\").split(\"_\")\n",
    "        outstr += \"    if(*FD_order == \" + str(array[-2]) + \") {\\n\"\n",
    "        outstr += \"        \" +filename.replace(\".h\", \"(CCTK_PASS_CTOC);\") + \"\\n\"\n",
    "        outstr += \"    }\\n\"\n",
    "outstr += \"} // END FUNCTION\\n\"\n",
    "# Add C code string to dictionary (Python dictionaries are immutable)\n",
    "with open(append_to_make_code_defn_list(\"ScalarWave_RHSs.c\"),\"w\") as file:\n",
    "    file.write(outstr)\n",
    "\n",
    "def SIMD_declare_C_params():\n",
    "    SIMD_declare_C_params_str = \"\"\n",
    "    for i in range(len(par.glb_Cparams_list)):\n",
    "        # keep_param is a boolean indicating whether we should accept or reject\n",
    "        #    the parameter. singleparstring will contain the string indicating\n",
    "        #    the variable type.\n",
    "        keep_param, singleparstring = keep_param__return_type(par.glb_Cparams_list[i])\n",
    "\n",
    "        if (keep_param) and (\"CCTK_REAL\" in singleparstring):\n",
    "            parname = par.glb_Cparams_list[i].parname\n",
    "            SIMD_declare_C_params_str += \"    const \"+singleparstring + \"*NOSIMD\"+parname+\\\n",
    "                                         \" = CCTK_ParameterGet(\\\"\"+parname+\"\\\",\\\"WaveToyNRPy\\\",NULL);\\n\"\n",
    "            SIMD_declare_C_params_str += \"    const REAL_SIMD_ARRAY \"+parname+\" = ConstSIMD(*NOSIMD\"+parname+\");\\n\"\n",
    "    return SIMD_declare_C_params_str\n",
    "\n",
    "# # Create functions for the largest C kernels (WaveToy RHSs and Ricci) and output\n",
    "# #    the .h files to .c files with function wrappers; delete original .h files\n",
    "path = os.path.join(ThornName, \"src\")\n",
    "for filename in WaveToyETK_src_filelist:\n",
    "    if (\"WaveToy_RHSs_\" in filename) and (\".h\" in filename):\n",
    "        outstr = common_includes + \"void \"+filename.replace(\".h\",\"\")+\"(CCTK_ARGUMENTS) {\\n\"\n",
    "        outstr += common_preloop+SIMD_declare_C_params()\n",
    "        with open(os.path.join(path,filename), \"r\") as currfile:\n",
    "            outstr += currfile.read()\n",
    "         # Now that we've inserted the contents of the kernel into this file,\n",
    "        #     we delete the file containing the kernel\n",
    "        os.remove(os.path.join(path,filename))\n",
    "        outstr += \"} // END FUNCTION\\n\"\n",
    "        # Add C code string to dictionary (Python dictionaries are immutable)\n",
    "        with open(append_to_make_code_defn_list(filename.replace(\".h\",\".c\")),\"w\") as file:\n",
    "            file.write(outstr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='makecodedefn'></a>\n",
    "\n",
    "## Step 4.b: `make.code.defn`: List of all C driver functions needed to compile `WaveToyNRPy` \\[Back to [top](#toc)\\]\n",
    "$$\\label{makecodedefn}$$\n",
    "\n",
    "When constructing each C code driver function above, we called the `append_to_make_code_defn_list()` function, which built a list of each C code driver file. We'll now add each of those files to the `make.code.defn` file, used by the Einstein Toolkit's build system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(os.path.join(outdir,\"make.code.defn\"), \"w\") as file:\n",
    "    file.write(\"\"\"\n",
    "# Main make.code.defn file for thorn WaveToyNRPy\n",
    "\n",
    "# Source files in this directory\n",
    "SRCS =\"\"\")\n",
    "    filestring = \"\"\n",
    "    for i in range(len(make_code_defn_list)):\n",
    "        filestring += \"      \"+make_code_defn_list[i]\n",
    "        if i != len(make_code_defn_list)-1:\n",
    "            filestring += \" \\\\\\n\"\n",
    "        else:\n",
    "            filestring += \"\\n\"\n",
    "    file.write(filestring)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='code_validation'></a>\n",
    "\n",
    "# Step 5: Code Validation, Convergence Tests \\[Back to [top](#toc)\\]\n",
    "$$\\label{code_validation}$$\n",
    "\n",
    "We have performed a number of convergence tests with $\\text{WaveToyNRPy}$ and $\\text{IDScalarWaveNRPy}$ within the ETK, which are presented below. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='planewave'></a>\n",
    "\n",
    "## Step 5.a: Monochromatic Plane Wave Convergence Tests \\[Back to [top](#toc)\\]\n",
    "$$\\label{planewave}$$\n",
    "\n",
    "**Three-dimensional scalar wave equation code tests, adopting fourth-order finite differencing, coupled to RK4 method-of-lines for time integration**\n",
    "\n",
    "Inside the directory *`WaveToyNRPy/example_parfiles/4thOrder_ConvergenceTests/`* are the files used for this convergence test.\n",
    "1. **planewave_along_3D_diagonal\\*.par** : ETK parameter files needed for performing the 3D tests. These parameter files set up a sinusoidal plane wave propagating along the x=y=z diagonal of a 3D numerical grid that extends from -12.8 to +12.8 along the x-, y-, and z-axis (in units of $\\omega=k=c=1$). The parameter files are identical, except one has grid resolution that is twice as high (so the errors should drop in the higher resolution case by a factor of $2^4$, since we adopt fourth-order-convergent timestepping and spatial finite differencing.)\n",
    "1. **runscript.sh** : runs the cactus executable (assumed to be named *cactus_etilgrmhd-FD4*) for all of the above parameter files.\n",
    "    1. **convert_IOASCII_1D_to_gnuplot.sh** : used by **runscript.sh** to convert the 1D output from the execution into a format that [gnuplot](http://gnuplot.info/) can recognize.\n",
    "1. **gnuplot_script** : script for creating code validation convergence plots with [gnuplot](http://gnuplot.info/).\n",
    "\n",
    "**Fourth-order code validation test results**\n",
    "\n",
    "The plot below shows the discrepancy between numerical and exact solutions to the scalar wave equation at two different resolutions: dashed is low resolution ($\\Delta x_{\\rm low}=0.4$) and solid is high resolution ($\\Delta x_{\\rm high}=0.2$). Since this test adopts fourth-order finite differencing for spatial derivatives and fourth-order Runge-Kutta (RK4) for timestepping, we would expect this error to drop by a factor of approximately $(\\Delta x_{\\rm low}/\\Delta x_{\\rm high})^4 = (0.4/0.2)^4 = 2^4=16$ when going from low to high resolution, and after rescaling the error in the low-resolution case, we see that indeed it overlaps the low-resolution result quite nicely, confirming fourth-order convergence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 20,
     "metadata": {
      "image/png": {
       "height": 500,
       "width": 500
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(\"./WaveToyNRPy/example_parfiles/4thOrder_ConvergenceTests/convergence-RK4-FD4-3D.png\", width=500, height=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Three-dimensional scalar wave equation code tests for monochromatic plane wave initial data adopting *eighth*-order finite differencing, coupled to *RK8* method-of-lines for time integration\n",
    "\n",
    "Inside the directory *`WaveToyNRPy/example_parfiles/8thOrder_ConvergenceTests/`* are the files used for this convergence test:\n",
    "1. **planewave_along_3D_diagonal\\*.par** : ETK parameter files needed for performing the 3D tests. These parameter files set up a sinusoidal plane wave propagating along the x=y=z diagonal of a 3D numerical grid that extends from -13.6 to +13.6 along the x-, y-, and z-axis (in units of $\\omega=k=c=1$). The parameter files are identical, except one has grid resolution that is twice as high (so the errors should drop in the higher resolution case by a factor of $2^8$, since we adopt eighth-order-convergent timestepping and spatial finite differencing.)\n",
    "1. **runscript.sh** : runs the cactus executable (assumed to be named *cactus_etilgrmhd-FD8*) for all of the above parameter files.\n",
    "    1. **convert_IOASCII_1D_to_gnuplot.sh** : used by **runscript.sh** to convert the 1D output from the execution into a format that [gnuplot](http://gnuplot.info/) can recognize.\n",
    "1. **gnuplot_script** : script for creating code validation convergence plots with [gnuplot](http://gnuplot.info/).\n",
    "\n",
    "**Eighth-order code validation test results**\n",
    "\n",
    "The plot below shows the discrepancy between numerical and exact solutions to the scalar wave equation at two different resolutions: dashed is low resolution ($\\Delta x_{\\rm low}=0.4$) and solid is high resolution ($\\Delta x_{\\rm high}=0.2$). Since this test adopts **eighth**-order finite differencing for spatial derivatives and **eighth**-order Runge-Kutta (RK8) for timestepping, we would expect this error to drop by a factor of approximately $(\\Delta x_{\\rm low}/\\Delta x_{\\rm high})^8 = (0.4/0.2)^8 = 2^8=256$ when going from low to high resolution, and after rescaling the error in the low-resolution case, we see that indeed it overlaps the low-resolution result quite nicely, confirming eighth-order convergence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 21,
     "metadata": {
      "image/png": {
       "height": 500,
       "width": 500
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"WaveToyNRPy/example_parfiles/8thOrder_ConvergenceTests/convergence-RK8-FD8-3D.png\", width=500, height=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The NewRad boundary condition driver allows the wave within our numerical domain to propagate out through the boundary, with minimal reflections. Shown below is the full extent of the x-axis of our grid, [-13.6,13.6], and the wave amplitude at different times. We observe indeed that there are minimal reflections as the wave propagates to the right. Note the features on the left side of the wave that originate from the left boundary. These features are the resullt of the incorrect assumption that our wave propagates radially, as opposed to just along a single diagonal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 22,
     "metadata": {
      "image/png": {
       "height": 500,
       "width": 500
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"WaveToyNRPy/example_parfiles/8thOrder_ConvergenceTests/wavetimes.png\", width=500, height=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='gaussian'></a>\n",
    "\n",
    "## Step 5.b: Spherical Gaussian Wave Convergence Tests \\[Back to [top](#toc)\\]\n",
    "$$\\label{gaussian}$$\n",
    "\n",
    "**Three-dimensional scalar wave equation code tests, adopting fourth-order finite differencing, coupled to RK4 method-of-lines for time integration**\n",
    "\n",
    "Inside the directory *`WaveToyNRPy/example_parfiles/4thOrder_ConvergenceTests/`* are the files used for this convergence test:\n",
    "**spherical_gaussian\\*.par**: ETK parameter files needed for performing the 3D tests. These parameter files set up a spherical Gaussian wave propagating in a 3D numerical grid that extends from -13.0 to +13.0 along the x-, y-, and z-axis (in units of $c=1$). The parameter files are identical, except one has grid resolution that is twice as high (so the errors should drop in the higher resolution case by a factor of $2^4$, since we adopt eighth-order-convergent timestepping and spatial finite differencing.)\n",
    "\n",
    "**Fourth-order code validation test results**\n",
    "\n",
    "The plot below shows the discrepancy between numerical and exact solutions to the scalar wave equation at two different resolutions: dashed is low resolution ($\\Delta x_{\\rm low}=0.4$) and solid is high resolution ($\\Delta x_{\\rm high}=0.2$). Since this test adopts **fourth**-order finite differencing for spatial derivatives and **fourth**-order Runge-Kutta (RK4) for timestepping, we would expect this error to drop by a factor of approximately $(\\Delta x_{\\rm low}/\\Delta x_{\\rm high})^4 = (0.4/0.2)^4 = 2^4=16$ when going from low to high resolution, and after rescaling the error in the low-resolution case, we see that indeed it overlaps the low-resolution result quite nicely, confirming fourth-order convergence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 23,
     "metadata": {
      "image/png": {
       "height": 500,
       "width": 500
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"WaveToyNRPy/example_parfiles/4thOrder_ConvergenceTests/convergence-RK4-FD4-spherical_gaussian.png\", width=500, height=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Three-dimensional scalar wave equation code tests for spherical Gaussian initial data, adopting *eighth*-order finite differencing, coupled to *RK8* method-of-lines for time integration\n",
    "\n",
    "Inside the directory *`WaveToyNRPy/example_parfiles/8thOrder_ConvergenceTests/`* are the files used for this convergence test:\n",
    "**spherical_gaussian\\*.par**: ETK parameter files needed for performing the 3D tests. These parameter files set up a spherical Gaussian wave propagating in a 3D numerical grid that extends from -13.8 to +13.8 along the x-, y-, and z-axis (in units of $c=1$). The parameter files are identical, except one has grid resolution that is twice as high (so the errors should drop in the higher resolution case by a factor of $2^8$, since we adopt eighth-order-convergent timestepping and spatial finite differencing.)\n",
    "\n",
    "**Eighth-order code validation test results**\n",
    "\n",
    "The plot below shows the discrepancy between numerical and exact solutions to the scalar wave equation at two different resolutions: dashed is low resolution ($\\Delta x_{\\rm low}=0.4$) and solid is high resolution ($\\Delta x_{\\rm high}=0.2$). Since this test adopts **eighth**-order finite differencing for spatial derivatives and **eighth**-order Runge-Kutta (RK8) for timestepping, we would expect this error to drop by a factor of approximately $(\\Delta x_{\\rm low}/\\Delta x_{\\rm high})^8 = (0.4/0.2)^8 = 2^8=256$ when going from low to high resolution, and after rescaling the error in the low-resolution case, we see that indeed it overlaps the low-resolution result quite nicely, confirming eighth-order convergence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 24,
     "metadata": {
      "image/png": {
       "height": 500,
       "width": 500
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"WaveToyNRPy/example_parfiles/8thOrder_ConvergenceTests/convergence-RK8-FD8-spherical_gaussian.png\", width=500, height=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='latex_pdf_output'></a>\n",
    "\n",
    "# Step 6: Output this notebook 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-ETK_thorn-WaveToyNRPy.pdf](Tutorial-ETK_thorn-WaveToyNRPy.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": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created Tutorial-ETK_thorn-WaveToyNRPy.tex, and compiled LaTeX file to PDF\n",
      "    file Tutorial-ETK_thorn-WaveToyNRPy.pdf\n"
     ]
    }
   ],
   "source": [
    "import cmdline_helper as cmd    # NRPy+: Multi-platform Python command-line interface\n",
    "cmd.output_Jupyter_notebook_to_LaTeXed_PDF(\"Tutorial-ETK_thorn-WaveToyNRPy\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}