Tutorial-Start_to_Finish-BSSNCurvilinear-Exact_Initial_Data.ipynb

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    "\n",
    "# Start-to-Finish Example: Setting up Exact Initial Data for Einstein's Equations, in Curvilinear Coordinates\n",
    "## Authors: Brandon Clark, George Vopal, and Zach Etienne\n",
    "\n",
    "## This module sets up initial data for a specified exact solution written in terms of ADM variables, using the [ADM initial data reader/converter for BSSN-with-a-reference-metric evolutions](../edit/BSSN/ADM_Initial_Data_Reader__BSSN_Converter.py); [\\[**tutorial**\\]](Tutorial-ADM_Initial_Data_Reader__BSSN_Converter.ipynb).\n",
    "\n",
    "**Notebook Status:** <font color='green'><b> Validated </b></font>\n",
    "\n",
    "**Validation Notes:** This module has been validated, confirming that all initial data sets exhibit convergence to zero of the Hamiltonian and momentum constraints at the expected rate or better.\n",
    "\n",
    "### NRPy+ Source Code for this module:\n",
    "* [BSSN/ADM_Initial_Data_Reader__BSSN_Converter.py](../edit/BSSN/ADM_Initial_Data_Reader__BSSN_Converter.py); [\\[**tutorial**\\]](Tutorial-ADM_Initial_Data_Reader__BSSN_Converter.ipynb): Registers the C function for our \"universal\" initial data reader/converter initial_data_reader__convert_ADM_Cartesian_to_BSSN().\n",
    "* [CurviBoundaryConditions/CurviBoundaryConditions.py](../edit/CurviBoundaryConditions/CurviBoundaryConditions.py); [\\[**tutorial**\\]](Tutorial-Start_to_Finish-Curvilinear_BCs.ipynb): Registers the C function for our \"universal\" initial data reader/converter initial_data_reader__convert_ADM_Cartesian_to_BSSN(). This applies boundary conditions to BSSN quantities (including $\\lambda^i$, which is computed via finite difference derivatives and thus only defined in grid interior)\n",
    "* [BSSN/BSSN_constraints.py](../edit/BSSN/BSSN_constraints.py); [\\[**tutorial**\\]](Tutorial-BSSN_constraints.ipynb): Hamiltonian & momentum constraints in BSSN curvilinear basis/coordinates.\n",
    "\n",
    "## Introduction:\n",
    "Here we use NRPy+ to generate a C code confirming that specified *exact* initial data satisfy Einstein's equations of general relativity. The following exact initial data types are supported:\n",
    "\n",
    "* Shifted Kerr-Schild spinning black hole initial data\n",
    "* \"Static\" Trumpet black hole initial data\n",
    "* Brill-Lindquist two black hole initial data\n",
    "* UIUC black hole initial data"
   ]
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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. [Preliminaries](#prelim): The Choices for Initial Data\n",
    "    1. [Choice 1](#sks): Shifted Kerr-Schild spinning black hole initial data\n",
    "    1. [Choice 2](#st):  \"Static\" Trumpet black hole initial data\n",
    "    1. [Choice 3](#bl): Brill-Lindquist two black hole initial data\n",
    "    1. [Choice 4](#uiuc): UIUC black hole initial data\n",
    "1. [Step 1](#pickid): Specify the initial data to test\n",
    "1. [Step 2](#initializenrpy): Set core NRPy+ parameters for numerical grids and reference metric\n",
    "1. [Step 3](#adm_id): Import Black Hole ADM initial data C function from NRPy+ module\n",
    "1. [Step 4](#validate): Validating that the black hole initial data satisfy the Hamiltonian constraint\n",
    "    1. [Step 4.a](#ham_const_output): Output C code for evaluating the Hamiltonian and Momentum constraint violation\n",
    "    1. [Step 4.b](#apply_bcs): Apply singular, curvilinear coordinate boundary conditions\n",
    "    1. [Step 4.c](#enforce3metric): Enforce conformal 3-metric $\\det{\\bar{\\gamma}_{ij}}=\\det{\\hat{\\gamma}_{ij}}$ constraint\n",
    "1. [Step 5](#mainc): `Initial_Data.c`: The Main C Code\n",
    "1. [Step 6](#plot): Plotting the initial data\n",
    "1. [Step 7](#convergence): Validation: Convergence of numerical errors (Hamiltonian constraint violation) to zero\n",
    "1. [Step 8](#latex_pdf_output): Output this notebook to $\\LaTeX$-formatted PDF file"
   ]
  },
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   "source": [
    "<a id='prelim'></a>\n",
    "\n",
    "# Preliminaries: The Choices for Initial Data\n",
    "$$\\label{prelim}$$"
   ]
  },
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   "source": [
    "<a id='sks'></a>\n",
    "\n",
    "## Shifted Kerr-Schild spinning black hole initial data \\[Back to [top](#toc)\\]\n",
    "$$\\label{sks}$$\n",
    "\n",
    "Here we use NRPy+ to generate initial data for a spinning black hole.\n",
    "\n",
    "Shifted Kerr-Schild spinning black hole initial data has been <font color='green'><b> validated </b></font> to exhibit convergence to zero of both the Hamiltonian and momentum constraint violations at the expected order to the exact solution.\n",
    "\n",
    "**NRPy+ Source Code:**\n",
    "* [BSSN/ShiftedKerrSchild.py](../edit/BSSN/ShiftedKerrSchild.py); [\\[**tutorial**\\]](Tutorial-ADM_Initial_Data-ShiftedKerrSchild.ipynb)\n",
    "\n",
    "The [BSSN.ShiftedKerrSchild](../edit/BSSN/ShiftedKerrSchild.py) NRPy+ module does the following:\n",
    "\n",
    "1. Set up shifted Kerr-Schild initial data, represented by [ADM](https://en.wikipedia.org/wiki/ADM_formalism) quantities in the **Spherical basis**, as [documented here](Tutorial-ADM_Initial_Data-ShiftedKerrSchild.ipynb). \n",
    "1. Convert the exact ADM **Spherical quantities** to **BSSN quantities in the desired Curvilinear basis** (set by `reference_metric::CoordSystem`), as [documented here](Tutorial-ADM_Initial_Data-Converting_Numerical_ADM_Spherical_or_Cartesian_to_BSSNCurvilinear.ipynb).\n",
    "1. Sets up the standardized C function for setting all BSSN Curvilinear gridfunctions in a pointwise fashion, as [written here](../edit/BSSN/BSSN_ID_function_string.py), and returns the C function as a Python string."
   ]
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   "source": [
    "<a id='st'></a>\n",
    "\n",
    "## \"Static\" Trumpet black hole initial data \\[Back to [top](#toc)\\]\n",
    "$$\\label{st}$$\n",
    "\n",
    "Here we use NRPy+ to generate initial data for a single trumpet black hole ([Dennison & Baumgarte, PRD ???](https://arxiv.org/abs/??)).\n",
    "\n",
    "\"Static\" Trumpet black hole initial data has been <font color='green'><b> validated </b></font> to exhibit convergence to zero of the Hamiltonian constraint violation at the expected order to the exact solution. It was carefully ported from the [original NRPy+ code](https://bitbucket.org/zach_etienne/nrpy).\n",
    "\n",
    "**NRPy+ Source Code:**\n",
    "* [BSSN/StaticTrumpet.py](../edit/BSSN/StaticTrumpet.py); [\\[**tutorial**\\]](Tutorial-ADM_Initial_Data-StaticTrumpet.ipynb)\n",
    "\n",
    "The [BSSN.StaticTrumpet](../edit/BSSN/StaticTrumpet.py) NRPy+ module does the following:\n",
    "\n",
    "1. Set up static trumpet black hole initial data, represented by [ADM](https://en.wikipedia.org/wiki/ADM_formalism) quantities in the **Spherical basis**, as [documented here](Tutorial-ADM_Initial_Data-StaticTrumpetBlackHole.ipynb). \n",
    "1. Convert the exact ADM **Spherical quantities** to **BSSN quantities in the desired Curvilinear basis** (set by `reference_metric::CoordSystem`), as [documented here](Tutorial-ADM_Initial_Data-Converting_Numerical_ADM_Spherical_or_Cartesian_to_BSSNCurvilinear.ipynb).\n",
    "1. Sets up the standardized C function for setting all BSSN Curvilinear gridfunctions in a pointwise fashion, as [written here](../edit/BSSN/BSSN_ID_function_string.py), and returns the C function as a Python string."
   ]
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   "source": [
    "<a id='bl'></a>\n",
    "\n",
    "## Brill-Lindquist initial data \\[Back to [top](#toc)\\]\n",
    "$$\\label{bl}$$\n",
    "\n",
    "Here we use NRPy+ to generate initial data for two black holes (Brill-Lindquist, [Brill & Lindquist, Phys. Rev. 131, 471, 1963](https://journals.aps.org/pr/abstract/10.1103/PhysRev.131.471); see also Eq. 1 of [Brandt & Brügmann, arXiv:gr-qc/9711015v1](https://arxiv.org/pdf/gr-qc/9711015v1.pdf)).\n",
    "\n",
    "[//]: # \" and then we use it to generate the RHS expressions for [Method of Lines](https://reference.wolfram.com/language/tutorial/NDSolveMethodOfLines.html) time integration based on the [explicit Runge-Kutta fourth-order scheme](https://en.wikipedia.org/wiki/Runge%E2%80%93Kutta_methods) (RK4).\"\n",
    "\n",
    "Brill-Lindquist initial data has been <font color='green'><b> validated </b></font> to exhibit convergence to zero of the Hamiltonian constraint violation at the expected order to the exact solution, and all quantities have been validated against the [original SENR code](https://bitbucket.org/zach_etienne/nrpy).\n",
    "\n",
    "**NRPy+ Source Code:**\n",
    "* [BSSN/BrillLindquist.py](../edit/BSSN/BrillLindquist.py); [\\[**tutorial**\\]](Tutorial-ADM_Initial_Data-Brill-Lindquist.ipynb)\n",
    "* [BSSN/BSSN_ID_function_string.py](../edit/BSSN/BSSN_ID_function_string.py)\n",
    "\n",
    "The [BSSN.BrillLindquist](../edit/BSSN/BrillLindquist.py) NRPy+ module does the following:\n",
    "\n",
    "1. Set up Brill-Lindquist initial data [ADM](https://en.wikipedia.org/wiki/ADM_formalism) quantities in the **Cartesian basis**, as [documented here](Tutorial-ADM_Initial_Data-Brill-Lindquist.ipynb). \n",
    "1. Convert the ADM **Cartesian quantities** to **BSSN quantities in the desired Curvilinear basis** (set by `reference_metric::CoordSystem`), as [documented here](Tutorial-ADM_Initial_Data-Converting_ADMCartesian_to_BSSNCurvilinear.ipynb).\n",
    "1. Sets up the standardized C function for setting all BSSN Curvilinear gridfunctions in a pointwise fashion, as [written here](../edit/BSSN/BSSN_ID_function_string.py), and returns the C function as a Python string."
   ]
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    "<a id='uiuc'></a>\n",
    "\n",
    "## UIUC black hole initial data \\[Back to [top](#toc)\\]\n",
    "$$\\label{uiuc}$$ \n",
    "\n",
    "UIUC black hole initial data has been <font color='green'><b> validated </b></font> to exhibit convergence to zero of the Hamiltonian constraint violation at the expected order to the exact solution, and all quantities have been validated against the [original SENR code](https://bitbucket.org/zach_etienne/nrpy).\n",
    "\n",
    "**NRPy+ Source Code:**\n",
    "* [BSSN/UIUCBlackHole.py](../edit/BSSN/UIUCBlackHole.py); [\\[**tutorial**\\]](Tutorial-ADM_Initial_Data-UIUCBlackHole.ipynb)\n",
    "\n",
    "The [BSSN.UIUCBlackHole](../edit/BSSN/UIUCBlackHole.py) NRPy+ module does the following:\n",
    "\n",
    "1. Set up UIUC black hole initial data, represented by [ADM](https://en.wikipedia.org/wiki/ADM_formalism) quantities in the **Spherical basis**, as [documented here](Tutorial-ADM_Initial_Data-UIUCBlackHole.ipynb). \n",
    "1. Convert the numerical ADM **Spherical quantities** to **BSSN quantities in the desired Curvilinear basis** (set by `reference_metric::CoordSystem`), as [documented here](Tutorial-ADM_Initial_Data-Converting_Numerical_ADM_Spherical_or_Cartesian_to_BSSNCurvilinear.ipynb).\n",
    "1. Sets up the standardized C function for setting all BSSN Curvilinear gridfunctions in a pointwise fashion, as [written here](../edit/BSSN/BSSN_ID_function_string.py), and returns the C function as a Python string."
   ]
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    "<a id='pickid'></a>\n",
    "\n",
    "# Step 1: Specify the initial data to test \\[Back to [top](#toc)\\]\n",
    "$$\\label{pickid}$$\n",
    "\n",
    "Here you have a choice for which initial data you would like to import and test for convergence. The following is a list of the currently compatible `initial_data_string` options for you to choose from.\n",
    "\n",
    "* `\"Shifted KerrSchild\"`\n",
    "* `\"Static Trumpet\"`\n",
    "* `\"Brill-Lindquist\"`\n",
    "* `\"UIUCBlackHole\"`"
   ]
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    "#################\n",
    "# For the User: Choose initial data, default is Shifted KerrSchild.\n",
    "#               You are also encouraged to adjust any of the\n",
    "#               DestGridCoordSystem, freeparams, or EnableMomentum parameters!\n",
    "#               NOTE: Only DestGridCoordSystem == Spherical or SinhSpherical\n",
    "#                     currently work out of the box; additional modifications\n",
    "#                     will likely be necessary for other CoordSystems.\n",
    "#################\n",
    "initial_data_string = \"Shifted KerrSchild\" # \"UIUCBlackHole\""
   ]
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    "import collections\n",
    "\n",
    "dictID = {}\n",
    "IDmod_retfunc = collections.namedtuple('IDmod_retfunc', 'modulename functionname OrigCoordSystem DestGridCoordSystem freeparams EnableMomentum')\n",
    "\n",
    "dictID['Shifted KerrSchild']  = IDmod_retfunc(\n",
    "    modulename = \"BSSN.ShiftedKerrSchild\", functionname = \"ShiftedKerrSchild\",\n",
    "    OrigCoordSystem = \"Spherical\", DestGridCoordSystem = \"Spherical\",\n",
    "    freeparams = [\"params.M   = 1.0;\", \"params.a   = 0.9;\", \"params.r0 = 1.0;\"],\n",
    "    EnableMomentum = True)\n",
    "\n",
    "dictID['Static Trumpet'] = IDmod_retfunc(\n",
    "    modulename = \"BSSN.StaticTrumpet\", functionname = \"StaticTrumpet\",\n",
    "    OrigCoordSystem = \"Spherical\", DestGridCoordSystem = \"Spherical\",\n",
    "    freeparams = [\"params.M = 1.0;\"],\n",
    "    EnableMomentum = False)\n",
    "\n",
    "dictID['Brill-Lindquist'] = IDmod_retfunc(\n",
    "    modulename = \"BSSN.BrillLindquist\", functionname = \"BrillLindquist\",\n",
    "    OrigCoordSystem = \"Cartesian\", DestGridCoordSystem = \"SinhSpherical\",\n",
    "    freeparams = [\"params.BH1_posn_x =+1.0; params.BH1_posn_y = 0.0; params.BH1_posn_z = 0.0;\",\n",
    "                  \"params.BH2_posn_x =-1.0; params.BH2_posn_y = 0.0; params.BH2_posn_z = 0.0;\",\n",
    "                  \"params.BH1_mass = 0.5;params.BH2_mass = 0.5;\"],\n",
    "    EnableMomentum = False)\n",
    "\n",
    "dictID['UIUCBlackHole'] = IDmod_retfunc(modulename = \"BSSN.UIUCBlackHole\", functionname = \"UIUCBlackHole\",\n",
    "    OrigCoordSystem = \"Spherical\", DestGridCoordSystem = \"SinhSpherical\",\n",
    "    freeparams = [\"params.M = 1.0;\", \"params.chi = 0.99;\"],\n",
    "    EnableMomentum = True)"
   ]
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    "<a id='initializenrpy'></a>\n",
    "\n",
    "# Step 2: Set up the needed NRPy+ infrastructure and declare core gridfunctions \\[Back to [top](#toc)\\]\n",
    "$$\\label{initializenrpy}$$\n",
    "\n",
    "We will import the core modules of NRPy that we will need and specify the main gridfunctions we will need."
   ]
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    "# Step P1: Import needed NRPy+ core modules:\n",
    "from outputC import add_to_Cfunction_dict # 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 reference_metric as rfm   # NRPy+: Reference metric support\n",
    "import cmdline_helper as cmd     # NRPy+: Multi-platform Python command-line interface\n",
    "import shutil, os, time     # Standard Python modules for multiplatform OS-level functions, benchmarking\n",
    "import diagnostics_generic.process_2D_data as plot2D  # NRPy+: analysis of output data\n",
    "import diagnostics_generic.output_yz_or_xy_plane as planar_diags  # NRPy+: C code for generating output data\n",
    "\n",
    "# Step P2: Create C code output directory:\n",
    "Ccodesrootdir = os.path.join(\"Exact_InitialDataPlayground_Ccodes\")\n",
    "# First remove C code output directory if it exists\n",
    "# Courtesy https://stackoverflow.com/questions/303200/how-do-i-remove-delete-a-folder-that-is-not-empty\n",
    "shutil.rmtree(Ccodesrootdir, ignore_errors=True)\n",
    "# Then create a fresh directory\n",
    "cmd.mkdir(Ccodesrootdir)\n",
    "\n",
    "# Step P3: Create executable output directory:\n",
    "outdir = os.path.join(Ccodesrootdir, \"output\")\n",
    "cmd.mkdir(outdir)\n",
    "\n",
    "# Step 1.c: Enable \"FD functions\". In other words, all finite-difference stencils\n",
    "#         will be output as inlined static functions. This is essential for\n",
    "#         compiling highly complex FD kernels with using certain versions of GCC;\n",
    "#         GCC 10-ish will choke on BSSN FD kernels at high FD order, sometimes\n",
    "#         taking *hours* to compile. Unaffected GCC versions compile these kernels\n",
    "#         in seconds. FD functions do not slow the code performance, but do add\n",
    "#         another header file to the C source tree.\n",
    "# With gcc 7.5.0, enable_FD_functions=True decreases performance by 10%\n",
    "enable_FD_functions = False\n",
    "\n",
    "# Step 2: Set some core parameters, including coord system, FD order, and floating point precision\n",
    "# Choices are: Spherical, SinhSpherical, SinhSphericalv2, Cylindrical, SinhCylindrical,\n",
    "#              SymTP, SinhSymTP\n",
    "CoordSystem     = \"Spherical\"\n",
    "par.set_parval_from_str(\"reference_metric::CoordSystem\", CoordSystem)\n",
    "rfm.reference_metric()\n",
    "\n",
    "# Step 2.a: Set defaults for Coordinate system parameters.\n",
    "#           These are perhaps the most commonly adjusted parameters,\n",
    "#           so we enable modifications at this high level.\n",
    "\n",
    "# domain_size sets the default value for:\n",
    "#   * Spherical's params.RMAX\n",
    "#   * SinhSpherical*'s params.AMAX\n",
    "#   * Cartesians*'s -params.{x,y,z}min & .{x,y,z}max\n",
    "#   * Cylindrical's -params.ZMIN & .{Z,RHO}MAX\n",
    "#   * SinhCylindrical's params.AMPL{RHO,Z}\n",
    "#   * *SymTP's params.AMAX\n",
    "domain_size     = 3.0\n",
    "\n",
    "# sinh_width sets the default value for:\n",
    "#   * SinhSpherical's params.SINHW\n",
    "#   * SinhCylindrical's params.SINHW{RHO,Z}\n",
    "#   * SinhSymTP's params.SINHWAA\n",
    "sinh_width      = 0.4 # If Sinh* coordinates chosen\n",
    "\n",
    "# sinhv2_const_dr sets the default value for:\n",
    "#   * SinhSphericalv2's params.const_dr\n",
    "#   * SinhCylindricalv2's params.const_d{rho,z}\n",
    "sinhv2_const_dr = 0.05# If Sinh*v2 coordinates chosen\n",
    "\n",
    "# SymTP_bScale sets the default value for:\n",
    "#   * SinhSymTP's params.bScale\n",
    "SymTP_bScale    = 0.5 # If SymTP chosen\n",
    "\n",
    "# Step 2.c: Set the order of spatial derivatives and\n",
    "#           the core data type\n",
    "FD_order  = 4            # Finite difference order: even numbers only, starting with 2. 12 is generally unstable\n",
    "REAL      = \"double\"     # Best to use double here."
   ]
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    "# Step 5: Set the finite differencing order to FD_order (set above).\n",
    "par.set_parval_from_str(\"finite_difference::FD_CENTDERIVS_ORDER\", FD_order)\n",
    "\n",
    "# Step 7: Set finite_difference::enable_FD_functions appropriately. Defaults to False\n",
    "if enable_FD_functions:\n",
    "    par.set_parval_from_str(\"finite_difference::enable_FD_functions\", enable_FD_functions)\n",
    "\n",
    "rfm.register_C_functions(enable_rfm_precompute=False, use_unit_wavespeed_for_find_timestep=True)\n",
    "rfm.register_NRPy_basic_defines(enable_rfm_precompute=False)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='adm_id'></a>\n",
    "\n",
    "# Step 3: Import Black Hole ADM initial data C function from NRPy+ module \\[Back to [top](#toc)\\]\n",
    "$$\\label{adm_id}$$"
   ]
  },
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   "source": [
    "# Import Black Hole initial data\n",
    "import time\n",
    "import importlib\n",
    "starttime = time.time()\n",
    "IDmodule = importlib.import_module(dictID[initial_data_string].modulename)\n",
    "IDfunc = getattr(IDmodule, dictID[initial_data_string].functionname)\n",
    "IDfunc()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we register the C function for our \"universal\" initial data reader/converter `initial_data_reader__convert_ADM_spherical_to_BSSN()`, provided by [`BSSN.Initial_Data_Reader__BSSN_Converter`](../edit/BSSN.Initial_Data_Reader__BSSN_Converter.py) ([documentation](Tutorial-ADM_Initial_Data_Reader__BSSN_Converter.ipynb))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import BSSN.ADM_Initial_Data_Reader__BSSN_Converter as IDread\n",
    "\n",
    "IDread.add_to_Cfunction_dict_exact_ADM_ID_function(dictID[initial_data_string].functionname,\n",
    "                                                   dictID[initial_data_string].OrigCoordSystem,\n",
    "                                                   IDmodule.alpha, IDmodule.betaU, IDmodule.BU,\n",
    "                                                   IDmodule.gammaDD, IDmodule.KDD)\n",
    "IDread.add_to_Cfunction_dict_initial_data_reader__convert_ADM_Sph_or_Cart_to_BSSN(input_Coord=dictID[initial_data_string].OrigCoordSystem,\n",
    "                                                                                  include_T4UU=False)\n",
    "\n",
    "IDread.register_NRPy_basic_defines(include_T4UU=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, register boundary conditions, so that $\\lambda^i$ can be set everywhere. \n",
    "These boundary conditions are documented [in this NRPy+ tutorial notebook](Tutorial-Start_to_Finish-Curvilinear_BCs.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evolved gridfunction \"aDD00\" has parity type 4.\n",
      "Evolved gridfunction \"aDD01\" has parity type 5.\n",
      "Evolved gridfunction \"aDD02\" has parity type 6.\n",
      "Evolved gridfunction \"aDD11\" has parity type 7.\n",
      "Evolved gridfunction \"aDD12\" has parity type 8.\n",
      "Evolved gridfunction \"aDD22\" has parity type 9.\n",
      "Evolved gridfunction \"alpha\" has parity type 0.\n",
      "Evolved gridfunction \"betU0\" has parity type 1.\n",
      "Evolved gridfunction \"betU1\" has parity type 2.\n",
      "Evolved gridfunction \"betU2\" has parity type 3.\n",
      "Evolved gridfunction \"cf\" has parity type 0.\n",
      "Evolved gridfunction \"hDD00\" has parity type 4.\n",
      "Evolved gridfunction \"hDD01\" has parity type 5.\n",
      "Evolved gridfunction \"hDD02\" has parity type 6.\n",
      "Evolved gridfunction \"hDD11\" has parity type 7.\n",
      "Evolved gridfunction \"hDD12\" has parity type 8.\n",
      "Evolved gridfunction \"hDD22\" has parity type 9.\n",
      "Evolved gridfunction \"lambdaU0\" has parity type 1.\n",
      "Evolved gridfunction \"lambdaU1\" has parity type 2.\n",
      "Evolved gridfunction \"lambdaU2\" has parity type 3.\n",
      "Evolved gridfunction \"trK\" has parity type 0.\n",
      "Evolved gridfunction \"vetU0\" has parity type 1.\n",
      "Evolved gridfunction \"vetU1\" has parity type 2.\n",
      "Evolved gridfunction \"vetU2\" has parity type 3.\n"
     ]
    }
   ],
   "source": [
    "import CurviBoundaryConditions.CurviBoundaryConditions as CBC\n",
    "CBC.CurviBoundaryConditions_register_NRPy_basic_defines()\n",
    "CBC.add_to_Cfunction_dict_bcstruct_set_up()\n",
    "CBC.add_to_Cfunction_dict_apply_bcs_inner_only()\n",
    "CBC.add_to_Cfunction_dict_apply_bcs_outerextrap_and_inner()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='cparams_rfm_and_domainsize'></a>\n",
    "\n",
    "## Step 3.a: Output C codes needed for declaring and setting Cparameters; also set `free_parameters.h` \\[Back to [top](#toc)\\]\n",
    "$$\\label{cparams_rfm_and_domainsize}$$\n",
    "\n",
    "Based on declared NRPy+ Cparameters, first we generate `declare_Cparameters_struct.h`, `set_Cparameters_default.h`, and `set_Cparameters[-SIMD].h`.\n",
    "\n",
    "Then we output `free_parameters.h`, which sets initial data parameters, as well as grid domain & reference metric parameters, applying `domain_size` and `sinh_width`/`SymTP_bScale` (if applicable) as set above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-07T17:26:02.713697Z",
     "iopub.status.busy": "2021-03-07T17:26:02.713089Z",
     "iopub.status.idle": "2021-03-07T17:26:02.864383Z",
     "shell.execute_reply": "2021-03-07T17:26:02.864948Z"
    }
   },
   "outputs": [],
   "source": [
    "# Step 3.a.i: Set free_parameters.h\n",
    "# Output to $Ccodesdir/free_parameters.h reference metric parameters based on generic\n",
    "#    domain_size,sinh_width,sinhv2_const_dr,SymTP_bScale,\n",
    "#    parameters set above.\n",
    "\n",
    "outstr = \"\"\n",
    "for line in dictID[initial_data_string].freeparams:\n",
    "    outstr += line + \"\\n\"\n",
    "outstr += rfm.out_default_free_parameters_for_rfm(\"returnstring\",\n",
    "                                                  domain_size,sinh_width,sinhv2_const_dr,SymTP_bScale)\n",
    "with open(os.path.join(Ccodesrootdir,\"free_parameters.h\"),\"w\") as file:\n",
    "    file.write(outstr.replace(\"params.\", \"griddata.params.\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='validate'></a>\n",
    "\n",
    "# Step 4: Validating that the black hole initial data satisfy the BSSN Hamiltonian and momentum constraints \\[Back to [top](#toc)\\]\n",
    "$$\\label{validate}$$\n",
    "\n",
    "We will validate that the black hole initial data satisfy the Hamiltonian constraint, modulo numerical finite differencing error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-07T17:26:02.874787Z",
     "iopub.status.busy": "2021-03-07T17:26:02.874146Z",
     "iopub.status.idle": "2021-03-07T17:26:52.979967Z",
     "shell.execute_reply": "2021-03-07T17:26:52.979321Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating symbolic expressions for 3-Ricci tensor (Spherical coords)...\n",
      "Finished generating symbolic expressions for 3-Ricci tensor (Spherical coords) in 0.4 seconds. Next up: C codegen...\n",
      "Generating C code for 3-Ricci tensor (FD order=4) (Spherical coords)...\n",
      "Finished generating C code for 3-Ricci tensor (FD order=4) (Spherical coords) in 7.7 seconds.\n",
      "Generating symbolic expressions for BSSN constraints (Spherical coords)...\n",
      "Finished generating symbolic expressions for BSSN constraints (Spherical coords) in 0.5 seconds. Next up: C codegen...\n",
      "Generating C code for BSSN constraints (FD order=4) (Spherical coords)...\n",
      "Finished generating C code for BSSN constraints (FD order=4) (Spherical coords) in 2.2 seconds.\n"
     ]
    }
   ],
   "source": [
    "import BSSN.BSSN_Ccodegen_library as BCl\n",
    "_ignore = BCl.add_Ricci_eval_to_Cfunction_dict(includes=[\"NRPy_basic_defines.h\"], rel_path_to_Cparams=os.path.join(\".\"),\n",
    "                                               enable_rfm_precompute=False, enable_golden_kernels=False, enable_SIMD=False,\n",
    "                                               enable_split_for_optimizations_doesnt_help=False, OMP_pragma_on=\"i2\")\n",
    "\n",
    "_ignore = BCl.add_BSSN_constraints_to_Cfunction_dict(includes=[\"NRPy_basic_defines.h\"],\n",
    "                                                     rel_path_to_Cparams=os.path.join(\".\"), output_H_only=False,\n",
    "                                                     enable_rfm_precompute=False, enable_SIMD=False,\n",
    "                                                     leave_Ricci_symbolic=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='mainc'></a>\n",
    "\n",
    "# Step 5: `Initial_Data_Playground.c`: The Main C Code \\[Back to [top](#toc)\\]\n",
    "$$\\label{mainc}$$\n",
    "\n",
    "First some diagnostics, which will output data at points closest to the xy plane."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_of_outputs = [\"y_n_gfs[IDX4ptS(CFGF,idx)]\",\n",
    "                   \"log10(fabs(diagnostic_output_gfs[IDX4ptS(HGF,idx)]))\",\n",
    "                   \"log10(fabs(diagnostic_output_gfs[IDX4ptS(MU0GF,idx)])+1e-15)\",\n",
    "                   \"log10(fabs(diagnostic_output_gfs[IDX4ptS(MU1GF,idx)])+1e-15)\",\n",
    "                   \"log10(fabs(diagnostic_output_gfs[IDX4ptS(MU2GF,idx)])+1e-15)\"]\n",
    "planar_diags.add_to_Cfunction_dict__plane_diagnostics(plane=\"xy\", include_ghosts=False,\n",
    "                                                      list_of_outputs=list_of_outputs, num_sig_figs=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-07T17:26:53.503457Z",
     "iopub.status.busy": "2021-03-07T17:26:53.502303Z",
     "iopub.status.idle": "2021-03-07T17:26:53.505635Z",
     "shell.execute_reply": "2021-03-07T17:26:53.506091Z"
    }
   },
   "outputs": [],
   "source": [
    "def add_to_Cfunction_dict_main__Exact_Initial_Data_Playground():\n",
    "    includes = [\"NRPy_basic_defines.h\", \"NRPy_function_prototypes.h\", \"time.h\"]\n",
    "    desc = \"\"\"// main() function:\n",
    "// Step 0: Read command-line input, set up grid structure, allocate memory for gridfunctions, set up coordinates\n",
    "// Step 1: Set up initial data to an exact solution\n",
    "// Step 2: Output data on xy plane to file.\n",
    "// Step 3: Free all allocated memory\n",
    "\"\"\"\n",
    "    c_type = \"int\"\n",
    "    name = \"main\"\n",
    "    params = \"int argc, const char *argv[]\"\n",
    "    body = r\"\"\"  griddata_struct griddata;\n",
    "  set_Cparameters_to_default(&griddata.params);\n",
    "\n",
    "  // Step 0.a: Set free parameters, overwriting Cparameters defaults\n",
    "  //          by hand or with command-line input, as desired.\n",
    "#include \"free_parameters.h\"\n",
    "\n",
    "  // Step 0.b: Read command-line input, error out if nonconformant\n",
    "  if((argc != 4) || atoi(argv[1]) < NGHOSTS || atoi(argv[2]) < NGHOSTS || atoi(argv[3]) < 2 /* FIXME; allow for axisymmetric sims */) {\n",
    "    fprintf(stderr,\"Error: Expected three command-line arguments: ./BrillLindquist_Playground Nx0 Nx1 Nx2,\\n\");\n",
    "    fprintf(stderr,\"where Nx[0,1,2] is the number of grid points in the 0, 1, and 2 directions.\\n\");\n",
    "    fprintf(stderr,\"Nx[] MUST BE larger than NGHOSTS (= %d)\\n\",NGHOSTS);\n",
    "    exit(1);\n",
    "  }\n",
    "  // Step 0.c: Check grid structure, first in space...\n",
    "  const int Nxx[3] = { atoi(argv[1]), atoi(argv[2]), atoi(argv[3]) };\n",
    "  if(Nxx[0]%2 != 0 || Nxx[1]%2 != 0 || Nxx[2]%2 != 0) {\n",
    "    fprintf(stderr,\"Error: Cannot guarantee a proper cell-centered grid if number of grid cells not set to even number.\\n\");\n",
    "    fprintf(stderr,\"       For example, in case of angular directions, proper symmetry zones will not exist.\\n\");\n",
    "    exit(1);\n",
    "  }\n",
    "\n",
    "  // Step 0.d: Uniform coordinate grids are stored to *xx[3]\n",
    "  // Step 0.d.i: Set bcstruct\n",
    "  {\n",
    "    int EigenCoord;\n",
    "    EigenCoord = 1;\n",
    "    // Step 0.d.ii: Call set_Nxx_dxx_invdx_params__and__xx(), which sets\n",
    "    //             params Nxx,Nxx_plus_2NGHOSTS,dxx,invdx, and xx[] for the\n",
    "    //             chosen Eigen-CoordSystem.\n",
    "    set_Nxx_dxx_invdx_params__and__xx(EigenCoord, Nxx, &griddata.params, griddata.xx);\n",
    "    // Step 0.e: Find ghostzone mappings; set up bcstruct\n",
    "    bcstruct_set_up(&griddata.params, griddata.xx, &griddata.bcstruct);\n",
    "    // Step 0.e.i: Free allocated space for xx[][] array\n",
    "    for(int i=0;i<3;i++) free(griddata.xx[i]);\n",
    "\n",
    "    // Step 0.f: Call set_Nxx_dxx_invdx_params__and__xx(), which sets\n",
    "    //          params Nxx,Nxx_plus_2NGHOSTS,dxx,invdx, and xx[] for the\n",
    "    //          chosen (non-Eigen) CoordSystem.\n",
    "    EigenCoord = 0;\n",
    "    set_Nxx_dxx_invdx_params__and__xx(EigenCoord, Nxx, &griddata.params, griddata.xx);\n",
    "  }\n",
    "\n",
    "  // Step 0.j: Allocate memory for y_n_gfs gridfunctions\n",
    "  const int Nxx_plus_2NGHOSTS0 = griddata.params.Nxx_plus_2NGHOSTS0;\n",
    "  const int Nxx_plus_2NGHOSTS1 = griddata.params.Nxx_plus_2NGHOSTS1;\n",
    "  const int Nxx_plus_2NGHOSTS2 = griddata.params.Nxx_plus_2NGHOSTS2;\n",
    "  const int grid_size = Nxx_plus_2NGHOSTS0*Nxx_plus_2NGHOSTS1*Nxx_plus_2NGHOSTS2;\n",
    "  griddata.gridfuncs.y_n_gfs = (REAL *restrict)malloc(sizeof(REAL)*grid_size*NUM_EVOL_GFS);\n",
    "\n",
    "  // Step 0.l: Set up initial data to an exact solution\n",
    "  ID_persist_struct ID_persist;\n",
    "  initial_data_reader__convert_ADM_\"\"\"+dictID[initial_data_string].OrigCoordSystem+r\"\"\"_to_BSSN(&griddata, &ID_persist, \"\"\"+dictID[initial_data_string].functionname+r\"\"\");\n",
    "\n",
    "  // Step 0.m: Apply boundary conditions, as the BSSN\n",
    "  //           quantity lambda^i, defined using finite-\n",
    "  //           difference derivatives, is undefined in\n",
    "  //           ghost zones.\n",
    "  apply_bcs_outerextrap_and_inner(&griddata.params, &griddata.bcstruct, griddata.gridfuncs.y_n_gfs);\n",
    "\n",
    "  griddata.gridfuncs.auxevol_gfs = (REAL *restrict)malloc(sizeof(REAL)*grid_size*NUM_AUXEVOL_GFS);\n",
    "  REAL *restrict aux_gfs = (REAL *restrict)malloc(sizeof(REAL)*grid_size*NUM_AUX_GFS);\n",
    "\n",
    "  // To simplify the expressions somewhat, we compute & store the Ricci tensor separately\n",
    "  //    from the BSSN constraints.\n",
    "  Ricci_eval(&griddata.params, griddata.xx, griddata.gridfuncs.y_n_gfs, griddata.gridfuncs.auxevol_gfs);\n",
    "  BSSN_constraints(&griddata.params, griddata.xx, griddata.gridfuncs.y_n_gfs, griddata.gridfuncs.auxevol_gfs, aux_gfs);\n",
    "\n",
    "  xy_plane_diagnostics(&griddata, griddata.gridfuncs.y_n_gfs, aux_gfs);\n",
    "\n",
    "  // Step 4: Free all allocated memory\n",
    "  free(griddata.gridfuncs.y_n_gfs);\n",
    "  free(griddata.gridfuncs.auxevol_gfs);\n",
    "  free(aux_gfs);\n",
    "  for(int i=0;i<3;i++) free(griddata.xx[i]);\n",
    "\n",
    "  return 0;\n",
    "\"\"\"\n",
    "    add_to_Cfunction_dict(\n",
    "        includes=includes,\n",
    "        desc=desc,\n",
    "        c_type=c_type, name=name, params=params,\n",
    "        body=body,\n",
    "        rel_path_to_Cparams=os.path.join(\".\"), enableCparameters=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "add_to_Cfunction_dict_main__Exact_Initial_Data_Playground()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import outputC as outC\n",
    "outC.outputC_register_C_functions_and_NRPy_basic_defines()  # #define M_PI, etc.\n",
    "\n",
    "###\n",
    "# Set up NRPy_basic_defines.h:\n",
    "outC.outC_NRPy_basic_defines_h_dict[\"MoL\"] = \"\"\"\n",
    "typedef struct __MoL_gridfunctions_struct__ {\n",
    "  REAL *restrict y_n_gfs;\n",
    "  REAL *restrict auxevol_gfs;\n",
    "} MoL_gridfunctions_struct;\n",
    "\"\"\"\n",
    "par.register_NRPy_basic_defines()  # add `paramstruct params` to griddata struct.\n",
    "list_of_extras_in_griddata_struct = [\"MoL_gridfunctions_struct gridfuncs;\"]\n",
    "\n",
    "# #define IDX3S(), etc.\n",
    "gri.register_C_functions_and_NRPy_basic_defines(list_of_extras_in_griddata_struct=list_of_extras_in_griddata_struct)  # #define IDX3S(), etc.\n",
    "###\n",
    "\n",
    "# Declare paramstruct, register set_Cparameters_to_default(),\n",
    "#   and output declare_Cparameters_struct.h and set_Cparameters[].h:\n",
    "outC.NRPy_param_funcs_register_C_functions_and_NRPy_basic_defines(os.path.join(Ccodesrootdir))\n",
    "\n",
    "fin.register_C_functions_and_NRPy_basic_defines(NGHOSTS_account_for_onezone_upwind=False,\n",
    "                                                enable_SIMD=False)  # #define NGHOSTS, and UPWIND() macro if SIMD disabled\n",
    "\n",
    "# Output functions for computing all finite-difference stencils.\n",
    "#   Must be called after defining all functions depending on FD stencils.\n",
    "if enable_FD_functions:\n",
    "    fin.output_finite_difference_functions_h(path=Ccodesrootdir)\n",
    "\n",
    "# Call this last: Set up NRPy_basic_defines.h and NRPy_function_prototypes.h.\n",
    "outC.construct_NRPy_basic_defines_h(Ccodesrootdir, enable_SIMD=False)\n",
    "outC.construct_NRPy_function_prototypes_h(Ccodesrootdir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-07T17:26:53.514348Z",
     "iopub.status.busy": "2021-03-07T17:26:53.513670Z",
     "iopub.status.idle": "2021-03-07T17:26:58.238439Z",
     "shell.execute_reply": "2021-03-07T17:26:58.239495Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(EXEC): Executing `make -j18`...\n",
      "(BENCH): Finished executing in 2.20 seconds.\n",
      "Finished compilation.\n",
      "(EXEC): Executing `taskset -c 1,3,5,7,9,11,13,15 ./Exact_Initial_Data_Playground 96 96 96`...\n",
      "(BENCH): Finished executing in 0.40 seconds.\n",
      "(EXEC): Executing `taskset -c 1,3,5,7,9,11,13,15 ./Exact_Initial_Data_Playground 48 48 48`...\n",
      "(BENCH): Finished executing in 0.20 seconds.\n"
     ]
    }
   ],
   "source": [
    "import cmdline_helper as cmd\n",
    "cmd.new_C_compile(Ccodesrootdir, \"Exact_Initial_Data_Playground\",\n",
    "                  uses_free_parameters_h=True, compiler_opt_option=\"fast\") # fastdebug or debug also supported\n",
    "\n",
    "# Change to output directory\n",
    "os.chdir(Ccodesrootdir)\n",
    "\n",
    "cmd.delete_existing_files(\"out*.txt\")\n",
    "cmd.delete_existing_files(\"out*.png\")\n",
    "args_output_list = [[\"96 96 96\", \"out96.txt\"], [\"48 48 48\", \"out48.txt\"]]\n",
    "for args_output in args_output_list:\n",
    "    cmd.Execute(\"Exact_Initial_Data_Playground\", args_output[0], args_output[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='plot'></a>\n",
    "\n",
    "# Step 6: Plotting the  initial data \\[Back to [top](#toc)\\]\n",
    "$$\\label{plot}$$\n",
    "\n",
    "Here we plot the evolved conformal factor of these initial data on a 2D grid, such that darker colors imply stronger gravitational fields. For example in the case of `Brill-Lindquist` initial data, we see the black hole(s) centered at $x/M=\\pm 1$, where $M$ is an arbitrary mass scale (conventionally the [ADM mass](https://en.wikipedia.org/w/index.php?title=ADM_formalism&oldid=846335453) is chosen), and our formulation of Einstein's equations adopt $G=c=1$ [geometrized units](https://en.wikipedia.org/w/index.php?title=Geometrized_unit_system&oldid=861682626)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-07T17:26:58.246517Z",
     "iopub.status.busy": "2021-03-07T17:26:58.245319Z",
     "iopub.status.idle": "2021-03-07T17:26:59.303363Z",
     "shell.execute_reply": "2021-03-07T17:26:59.304812Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scipy in /home/zetienne/jup311/lib/python3.11/site-packages (1.9.3)\n",
      "Requirement already satisfied: numpy<1.26.0,>=1.18.5 in /home/zetienne/jup311/lib/python3.11/site-packages (from scipy) (1.24.0)\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# First install scipy if it's not yet installed. This will have no effect if it's already installed.\n",
    "!pip install scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-07T17:26:59.319297Z",
     "iopub.status.busy": "2021-03-07T17:26:59.318022Z",
     "iopub.status.idle": "2021-03-07T17:27:00.173168Z",
     "shell.execute_reply": "2021-03-07T17:27:00.173635Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.interpolate import griddata\n",
    "from pylab import savefig\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-07T17:26:59.319297Z",
     "iopub.status.busy": "2021-03-07T17:26:59.318022Z",
     "iopub.status.idle": "2021-03-07T17:27:00.173168Z",
     "shell.execute_reply": "2021-03-07T17:27:00.173635Z"
    }
   },
   "outputs": [],
   "source": [
    "xy_extent=domain_size\n",
    "# Data are in format x,y,z, CF,Ham,mom0,mom1,mom2\n",
    "output_grid_data = []\n",
    "for i in [3, 4, 5, 6, 7]:\n",
    "    output_grid_x, output_grid_y, output_grid_data_i = \\\n",
    "        plot2D.generate_uniform_2D_grid('out96.txt', 0,1,i, [-xy_extent,xy_extent], [-xy_extent,xy_extent])\n",
    "    output_grid_data += [output_grid_data_i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-07T17:26:59.319297Z",
     "iopub.status.busy": "2021-03-07T17:26:59.318022Z",
     "iopub.status.idle": "2021-03-07T17:27:00.173168Z",
     "shell.execute_reply": "2021-03-07T17:27:00.173635Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.clf()\n",
    "plt.title(r\"Initial Data, conformal factor $W$\")\n",
    "plt.xlabel(r\"$x/M$\")\n",
    "plt.ylabel(r\"$y/M$\")\n",
    "\n",
    "plt.imshow(output_grid_data[0], extent=(-xy_extent,xy_extent, -xy_extent,xy_extent))\n",
    "savefig(\"ID.png\")\n",
    "plt.close()\n",
    "Image(\"ID.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='convergence'></a>\n",
    "\n",
    "# Step 7: Validation: Convergence of numerical errors (Hamiltonian & momentum constraint violations) to zero \\[Back to [top](#toc)\\]\n",
    "$$\\label{convergence}$$\n",
    "\n",
    "**Special thanks to George Vopal for creating the following plotting script.**\n",
    "\n",
    "The equations behind these initial data solve Einstein's equations exactly, at a single instant in time. One reflection of this solution is that the Hamiltonian constraint violation should be exactly zero in the initial data. \n",
    "\n",
    "However, when evaluated on numerical grids, the Hamiltonian constraint violation will *not* generally evaluate to zero due to the associated numerical derivatives not being exact. However, these numerical derivatives (finite difference derivatives in this case) should *converge* to the exact derivatives as the density of numerical sampling points approaches infinity.\n",
    "\n",
    "In this case, all of our finite difference derivatives agree with the exact solution, with an error term that drops with the uniform gridspacing to the fourth power: $\\left(\\Delta x^i\\right)^4$. \n",
    "\n",
    "Here, as in the [Start-to-Finish Scalar Wave (Cartesian grids) NRPy+ tutorial](Tutorial-Start_to_Finish-ScalarWave.ipynb) and the [Start-to-Finish Scalar Wave (curvilinear grids) NRPy+ tutorial](Tutorial-Start_to_Finish-ScalarWaveCurvilinear.ipynb) we confirm this convergence.\n",
    "\n",
    "First, let's take a look at what the numerical error looks like on the x-y plane at a given numerical resolution, plotting $\\log_{10}|H|$, where $H$ is the Hamiltonian constraint violation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-07T17:27:00.184231Z",
     "iopub.status.busy": "2021-03-07T17:27:00.183383Z",
     "iopub.status.idle": "2021-03-07T17:27:02.672580Z",
     "shell.execute_reply": "2021-03-07T17:27:02.673112Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "\n",
    "# We want to create four plots. One for the Hamiltonian, and three for the momentum\n",
    "# constraints (r,th,ph)\n",
    "# Define the size of the overall figure\n",
    "fig = plt.figure(figsize=(12,12)) # 8 in x 8 in\n",
    "\n",
    "num_plots = 4\n",
    "Labels=[r\"W\",  r\"\\mathcal{H}\",r\"\\mathcal{M}^r\",r\"\\mathcal{M}^{\\theta}\",r\"\\mathcal{M}^{\\phi}\"]\n",
    "if \"Cartesian\" in CoordSystem:\n",
    "    Labels=[r\"W\",  r\"\\mathcal{H}\",r\"\\mathcal{M}^x\",r\"\\mathcal{M}^y\",r\"\\mathcal{M}^z\"]\n",
    "\n",
    "data_idx=[    0,                1,               2,               3,               4]\n",
    "\n",
    "plotlist = [1, 2, 3, 4]\n",
    "\n",
    "if dictID[initial_data_string].EnableMomentum == False:\n",
    "    plotlist = [1]\n",
    "\n",
    "axN = []  # initialize axis/plot array.\n",
    "for i in plotlist:\n",
    "    whichplot = i-1\n",
    "    #Generate the subplot for the each constraint\n",
    "    ax = fig.add_subplot(221+whichplot)\n",
    "    axN.append(ax) # Grid of 2x2\n",
    "\n",
    "    axN[whichplot].set_xlabel(r'$x/M$')\n",
    "    axN[whichplot].set_ylabel(r'$y/M$')\n",
    "    axN[whichplot].set_title(r\"$96^3$ Numerical Err.: $log_{10}|\"+Labels[i]+r\"|$\")\n",
    "\n",
    "    figure = plt.imshow(output_grid_data[i], extent=(-xy_extent,xy_extent, -xy_extent,xy_extent))\n",
    "    cb = plt.colorbar(figure)\n",
    "\n",
    "# Adjust the spacing between plots\n",
    "plt.tight_layout(pad=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we set up the same initial data but on a lower-resolution, $48^3$ grid. Since the constraint violation (numerical error associated with the fourth-order-accurate, finite-difference derivatives) should converge to zero with the uniform gridspacing to the fourth power: $\\left(\\Delta x^i\\right)^4$, we expect the constraint violation will increase (relative to the $96^3$ grid) by a factor of $\\left(96/48\\right)^4$. Here we demonstrate that indeed this order of convergence is observed as expected. I.e., at all points *except* at the points immediately surrounding the coordinate center of the black hole (due to the spatial slice excising the physical singularity at this point through [the puncture method](http://gr.physics.ncsu.edu/UMD_June09.pdf)) exhibit numerical errors that drop as $\\left(\\Delta x^i\\right)^4$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-07T17:27:02.687927Z",
     "iopub.status.busy": "2021-03-07T17:27:02.686384Z",
     "iopub.status.idle": "2021-03-07T17:27:03.882216Z",
     "shell.execute_reply": "2021-03-07T17:27:03.882720Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot settings\n",
    "x_extent=domain_size      # plot from -x_extent to +x_extent\n",
    "sample_numpts_x = 100     # number of points to plot\n",
    "interp_method = \"linear\"  # Could be linear (recommended), nearest (don't use; gridpoints are off-axis), or cubic\n",
    "\n",
    "# Data are in format x,y,z, CF,Ham,mom0,mom1,mom2\n",
    "output_1D_grid_data48 = []\n",
    "output_1D_grid_data96 = []\n",
    "for i in [4, 5, 6, 7]:\n",
    "    output_grid_x96, output_1D_grid_data48_i = \\\n",
    "        plot2D.extract_1D_slice_from_2D_data('out48.txt', 0.0,\n",
    "                                      0,1,i, [-x_extent, x_extent], sample_numpts_x=sample_numpts_x,\n",
    "                                      interp_method=interp_method)\n",
    "    output_grid_x48, output_1D_grid_data96_i = \\\n",
    "        plot2D.extract_1D_slice_from_2D_data('out96.txt', 0.0,\n",
    "                                      0,1,i, [-x_extent, x_extent], sample_numpts_x=sample_numpts_x,\n",
    "                                      interp_method=interp_method)\n",
    "    output_1D_grid_data48 += [output_1D_grid_data48_i]\n",
    "    output_1D_grid_data96 += [output_1D_grid_data96_i]\n",
    "\n",
    "\n",
    "PlotTitles=[r\"\\mathcal{H}\",r\"\\mathcal{M}^r\",r\"\\mathcal{M}^{\\theta}\",r\"\\mathcal{M}^{\\phi}\"]\n",
    "if \"Cartesian\" in CoordSystem:\n",
    "    PlotTitles=[r\"\\mathcal{H}\",r\"\\mathcal{M}^x\",r\"\\mathcal{M}^{y}\",r\"\\mathcal{M}^{z}\"]\n",
    "\n",
    "axN = []\n",
    "plt.clf()\n",
    "\n",
    "# We want to create four plots. One for the Hamiltonian, and three for the momentum\n",
    "# constrains (r,th,ph)\n",
    "# Define the size of the overall figure\n",
    "fig = plt.figure(figsize=(12,12)) # 8 in x 8 in\n",
    "\n",
    "for p in range(num_plots): #loop to cycle through our constraints and plot the data\n",
    "    #Generate the subplot for the each constraint\n",
    "    ax = fig.add_subplot(221+p)\n",
    "    axN.append(ax) # Grid of 2x2\n",
    "    axN[p].set_title('Plot Demonstrating $4^{th}$-Order Convergence of $'+PlotTitles[p]+'$')\n",
    "    axN[p].set_xlabel(r\"$x/M$\")\n",
    "    axN[p].set_ylabel(\"$log_{10}$(Relative Error)\")\n",
    "\n",
    "    ax.plot(output_grid_x96, output_1D_grid_data96[p], 'k-', label='Nr=96')\n",
    "    ax.plot(output_grid_x48, output_1D_grid_data48[p] + 4*np.log10(48./96.), 'k--', label='Nr=48, mult by (48/96)^4')\n",
    "    ax.set_ylim([-15.2,4.5])\n",
    "\n",
    "    legend = ax.legend(loc='lower right', shadow=True, fontsize='x-large')\n",
    "    legend.get_frame().set_facecolor('C1')\n",
    "\n",
    "# Adjust the spacing between plots\n",
    "plt.tight_layout(pad=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='latex_pdf_output'></a>\n",
    "\n",
    "# Step 7: 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-Start_to_Finish-BSSNCurvilinear-Exact_Initial_Data.pdf](Tutorial-Start_to_Finish-BSSNCurvilinear-Exact_Initial_Data.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": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-07T17:27:03.892072Z",
     "iopub.status.busy": "2021-03-07T17:27:03.890989Z",
     "iopub.status.idle": "2021-03-07T17:27:09.005565Z",
     "shell.execute_reply": "2021-03-07T17:27:09.004655Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NbConvertApp] WARNING | pattern 'Tutorial-Start_to_Finish-BSSNCurvilinear-Exact_Initial_Data.ipynb' matched no files\n",
      "Created Tutorial-Start_to_Finish-BSSNCurvilinear-Exact_Initial_Data.tex,\n",
      "    and compiled LaTeX file to PDF file Tutorial-Start_to_Finish-\n",
      "    BSSNCurvilinear-Exact_Initial_Data.pdf\n"
     ]
    }
   ],
   "source": [
    "import cmdline_helper as cmd    # NRPy+: Multi-platform Python command-line interface\n",
    "cmd.output_Jupyter_notebook_to_LaTeXed_PDF(\"Tutorial-Start_to_Finish-BSSNCurvilinear-Exact_Initial_Data\")"
   ]
  }
 ],
 "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": 2
}