{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "\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 [*Exact* ADM Spherical to BSSN Curvilinear initial data module](../edit/BSSN/ADM_Exact_Spherical_or_Cartesian_to_BSSNCurvilinear.py).\n", "\n", "**Notebook Status:** Validated \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_Exact_Spherical_or_Cartesian_to_BSSNCurvilinear.py](../edit/BSSN/ADM_Exact_Spherical_or_Cartesian_to_BSSNCurvilinear.py); [\\[**tutorial**\\]](Tutorial-ADM_Initial_Data-Converting_Exact_ADM_Spherical_or_Cartesian_to_BSSNCurvilinear.ipynb): *Exact* Spherical ADM$\\to$Curvilinear BSSN converter function\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" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\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 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" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "# Preliminaries: The Choices for Initial Data\n", "$$\\label{prelim}$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\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 validated 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." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\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 validated 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." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\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 validated 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." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "## UIUC black hole initial data \\[Back to [top](#toc)\\]\n", "$$\\label{uiuc}$$ \n", "\n", "UIUC black hole initial data has been validated 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." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\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", "* `\"UIUC\"`" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2021-03-07T17:26:00.782217Z", "iopub.status.busy": "2021-03-07T17:26:00.781148Z", "iopub.status.idle": "2021-03-07T17:26:00.784001Z", "shell.execute_reply": "2021-03-07T17:26:00.783445Z" } }, "outputs": [], "source": [ "import collections\n", "\n", "#################\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\" # \"UIUC\"\n", "\n", "\n", "dictID = {}\n", "IDmod_retfunc = collections.namedtuple('IDmod_retfunc', 'modulename functionname DestGridCoordSystem freeparams EnableMomentum')\n", "\n", "dictID['Shifted KerrSchild'] = IDmod_retfunc(\n", " modulename = \"BSSN.ShiftedKerrSchild\", functionname = \"ShiftedKerrSchild\",\n", " 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", " 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", " DestGridCoordSystem = \"SinhSpherical\",\n", " freeparams = [\"params.BH1_posn_x = 0.0; params.BH1_posn_y = 0.0; params.BH1_posn_z =+0.5;\",\n", " \"params.BH2_posn_x = 0.0; params.BH2_posn_y = 0.0; params.BH2_posn_z =-0.5;\",\n", " \"params.BH1_mass = 0.5;params.BH2_mass = 0.5;\"],\n", " EnableMomentum = False)\n", "\n", "dictID['UIUC'] = IDmod_retfunc(modulename = \"BSSN.UIUCBlackHole\", functionname = \"UIUCBlackHole\",\n", " DestGridCoordSystem = \"SinhSpherical\",\n", " freeparams = [\"params.M = 1.0;\", \"params.chi = 0.99;\"],\n", " EnableMomentum = True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\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." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2021-03-07T17:26:00.795598Z", "iopub.status.busy": "2021-03-07T17:26:00.794834Z", "iopub.status.idle": "2021-03-07T17:26:01.541492Z", "shell.execute_reply": "2021-03-07T17:26:01.540908Z" } }, "outputs": [], "source": [ "# Step P1: Import needed NRPy+ core modules:\n", "from outputC import lhrh,outC_function_dict,outCfunction # 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 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 cmdline_helper as cmd # NRPy+: Multi-platform Python command-line interface\n", "import shutil, os, sys, time # Standard Python modules for multiplatform OS-level functions, benchmarking\n", "import importlib # Standard Python module for interactive module imports\n", "\n", "# Step P2: Create C code output directory:\n", "Ccodesdir = os.path.join(\"BlackHoleID_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", "# !rm -r ScalarWaveCurvilinear_Playground_Ccodes\n", "shutil.rmtree(Ccodesdir, ignore_errors=True)\n", "# Then create a fresh directory\n", "cmd.mkdir(Ccodesdir)\n", "\n", "# Step P3: Create executable output directory:\n", "outdir = os.path.join(Ccodesdir,\"output/\")\n", "cmd.mkdir(outdir)\n", "\n", "# Step 1: Set the spatial dimension parameter\n", "# to three this time, and then read\n", "# the parameter as DIM.\n", "par.set_parval_from_str(\"grid::DIM\",3)\n", "DIM = par.parval_from_str(\"grid::DIM\")\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", "CoordSystem = dictID[initial_data_string].DestGridCoordSystem\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", "FD_order = 4 # Finite difference order: even numbers only, starting with 2. 12 is generally unstable\n", "REAL = \"double\" # Best to use double here.\n", "\n", "# Step 3: Set the coordinate system for the numerical grid\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 BSSN RHSs, etc.\n", "\n", "# Step 4: 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 5: Set the direction=2 (phi) axis to be the symmetry axis; i.e.,\n", "# axis \"2\", corresponding to the i2 direction.\n", "# This sets all spatial derivatives in the phi direction to zero.\n", "if \"Spherical\" in CoordSystem:\n", " par.set_parval_from_str(\"indexedexp::symmetry_axes\",\"2\")\n", "\n", "# Step 6: The MoLtimestepping interface is only used for memory allocation/deallocation\n", "import MoLtimestepping.C_Code_Generation as MoL\n", "from MoLtimestepping.RK_Butcher_Table_Dictionary import Butcher_dict\n", "RK_method = \"Euler\" # DOES NOT MATTER; Again MoL interface is only used for memory alloc/dealloc.\n", "RK_order = Butcher_dict[RK_method][1]\n", "cmd.mkdir(os.path.join(Ccodesdir,\"MoLtimestepping/\"))\n", "MoL.MoL_C_Code_Generation(RK_method, RHS_string = \"\", post_RHS_string = \"\",\n", " outdir = os.path.join(Ccodesdir,\"MoLtimestepping/\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "# Step 3: Import Black Hole ADM initial data C function from NRPy+ module \\[Back to [top](#toc)\\]\n", "$$\\label{adm_id}$$" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2021-03-07T17:26:01.546884Z", "iopub.status.busy": "2021-03-07T17:26:01.546256Z", "iopub.status.idle": "2021-03-07T17:26:02.708123Z", "shell.execute_reply": "2021-03-07T17:26:02.707603Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generating initial data C code required 0.6275947093963623 seconds.\n" ] } ], "source": [ "# Import Black Hole initial data\n", "import time\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() # Registers ID C function in dictionary, used below to output to file.\n", "with open(os.path.join(Ccodesdir,\"initial_data.h\"),\"w\") as file:\n", " file.write(outC_function_dict[\"initial_data\"])\n", "print(\"Generating initial data C code required \" + str(time.time() - starttime) + \" seconds.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\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": 4, "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", "rfm.out_default_free_parameters_for_rfm(os.path.join(Ccodesdir,\"free_parameters.h\"),\n", " domain_size,sinh_width,sinhv2_const_dr,SymTP_bScale)\n", "with open(os.path.join(Ccodesdir,\"free_parameters.h\"), \"a\") as file:\n", " for line in dictID[initial_data_string].freeparams:\n", " file.write(line + \"\\n\")\n", "\n", "# Step 3.a.ii: Generate set_Nxx_dxx_invdx_params__and__xx.h:\n", "rfm.set_Nxx_dxx_invdx_params__and__xx_h(Ccodesdir)\n", "\n", "# Step 3.a.iii: Generate xx_to_Cart.h, which contains xx_to_Cart() for\n", "# (the mapping from xx->Cartesian) for the chosen\n", "# CoordSystem:\n", "rfm.xx_to_Cart_h(\"xx_to_Cart\",\"./set_Cparameters.h\",os.path.join(Ccodesdir,\"xx_to_Cart.h\"))\n", "\n", "# Step 3.a.iv: Generate declare_Cparameters_struct.h, set_Cparameters_default.h, and set_Cparameters[-SIMD].h\n", "par.generate_Cparameters_Ccodes(os.path.join(Ccodesdir))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "# Step 4: Validating that the black hole initial data satisfy the Hamiltonian constraint \\[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": "markdown", "metadata": {}, "source": [ "\n", "\n", "## Step 4.a: Output C code for evaluating the Hamiltonian and Momentum constraint violation \\[Back to [top](#toc)\\]\n", "$$\\label{ham_const_output}$$\n", "\n", "First output C code for evaluating the Hamiltonian constraint violation. For the initial data where `EnableMomentum = True` we must also output C code for evaluating the Momentum constraint violation." ] }, { "cell_type": "code", "execution_count": 5, "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": [ "Output C function Hamiltonian_constraint() to file BlackHoleID_Ccodes/Hamiltonian_constraint.h\n", "Output C function momentum_constraint() to file BlackHoleID_Ccodes/momentum_constraint.h\n" ] } ], "source": [ "import BSSN.BSSN_constraints as bssncon\n", "# Now register the Hamiltonian & momentum constraints as gridfunctions.\n", "H = gri.register_gridfunctions(\"AUX\",\"H\")\n", "MU = ixp.register_gridfunctions_for_single_rank1(\"AUX\", \"MU\")\n", "\n", "# Generate symbolic expressions for Hamiltonian & momentum constraints\n", "import BSSN.BSSN_constraints as bssncon\n", "output_H_only = True\n", "if dictID[initial_data_string].EnableMomentum:\n", " output_H_only = False\n", "bssncon.BSSN_constraints(add_T4UUmunu_source_terms=False, output_H_only=output_H_only)\n", "\n", "# Generate optimized C code for Hamiltonian constraint\n", "desc=\"Evaluate the Hamiltonian constraint\"\n", "name=\"Hamiltonian_constraint\"\n", "outCfunction(\n", " outfile = os.path.join(Ccodesdir,name+\".h\"), desc=desc, name=name,\n", " params = \"\"\"const paramstruct *restrict params, REAL *restrict xx[3],\n", " REAL *restrict in_gfs, REAL *restrict aux_gfs\"\"\",\n", " body = fin.FD_outputC(\"returnstring\",lhrh(lhs=gri.gfaccess(\"aux_gfs\", \"H\"), rhs=bssncon.H),\n", " params=\"outCverbose=False\"),\n", " loopopts = \"InteriorPoints,Read_xxs\")\n", "\n", "# Generate optimized C code for momentum constraint\n", "desc=\"Evaluate the momentum constraint\"\n", "name=\"momentum_constraint\"\n", "if dictID[initial_data_string].EnableMomentum:\n", " body = fin.FD_outputC(\"returnstring\",\n", " [lhrh(lhs=gri.gfaccess(\"aux_gfs\", \"MU0\"), rhs=bssncon.MU[0]),\n", " lhrh(lhs=gri.gfaccess(\"aux_gfs\", \"MU1\"), rhs=bssncon.MU[1]),\n", " lhrh(lhs=gri.gfaccess(\"aux_gfs\", \"MU2\"), rhs=bssncon.MU[2])],\n", " params=\"outCverbose=False\")\n", "else:\n", " body = gri.gfaccess(\"aux_gfs\", \"MU0\") + \" = 0.0;\\n\"\n", " body += gri.gfaccess(\"aux_gfs\", \"MU1\") + \" = 0.0;\\n\"\n", " body += gri.gfaccess(\"aux_gfs\", \"MU2\") + \" = 0.0;\\n\"\n", "outCfunction(\n", " outfile = os.path.join(Ccodesdir,name+\".h\"), desc=desc, name=name,\n", " params = \"\"\"const paramstruct *restrict params, REAL *restrict xx[3],\n", " REAL *restrict in_gfs, REAL *restrict aux_gfs\"\"\",\n", " body = body,\n", " loopopts = \"InteriorPoints,Read_xxs\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "## Step 4.b: Enforce conformal 3-metric $\\det{\\bar{\\gamma}_{ij}}=\\det{\\hat{\\gamma}_{ij}}$ constraint \\[Back to [top](#toc)\\]\n", "$$\\label{enforce3metric}$$\n", "\n", "Then enforce conformal 3-metric $\\det{\\bar{\\gamma}_{ij}}=\\det{\\hat{\\gamma}_{ij}}$ constraint (Eq. 53 of [Ruchlin, Etienne, and Baumgarte (2018)](https://arxiv.org/abs/1712.07658)), as [documented in the corresponding NRPy+ tutorial notebook](Tutorial-BSSN_enforcing_determinant_gammabar_equals_gammahat_constraint.ipynb)\n", "\n", "Applying curvilinear boundary conditions should affect the initial data at the outer boundary, and will in general cause the $\\det{\\bar{\\gamma}_{ij}}=\\det{\\hat{\\gamma}_{ij}}$ constraint to be violated there. Thus after we apply these boundary conditions, we must always call the routine for enforcing the $\\det{\\bar{\\gamma}_{ij}}=\\det{\\hat{\\gamma}_{ij}}$ constraint:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2021-03-07T17:26:53.004929Z", "iopub.status.busy": "2021-03-07T17:26:52.984366Z", "iopub.status.idle": "2021-03-07T17:26:53.199865Z", "shell.execute_reply": "2021-03-07T17:26:53.199262Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Output C function enforce_detgammahat_constraint() to file BlackHoleID_Ccodes/enforce_detgammahat_constraint.h\n" ] } ], "source": [ "# Set up the C function for the det(gammahat) = det(gammabar)\n", "import BSSN.Enforce_Detgammahat_Constraint as EGC\n", "enforce_detg_constraint_symb_expressions = EGC.Enforce_Detgammahat_Constraint_symb_expressions()\n", "\n", "EGC.output_Enforce_Detgammahat_Constraint_Ccode(Ccodesdir,exprs=enforce_detg_constraint_symb_expressions,\n", " Read_xxs=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "## Step 4.c: Set up boundary condition functions for chosen singular, curvilinear coordinate system \\[Back to [top](#toc)\\]\n", "$$\\label{bc_functs}$$\n", "\n", "Next apply singular, curvilinear coordinate boundary conditions [as documented in the corresponding NRPy+ tutorial notebook](Tutorial-Start_to_Finish-Curvilinear_BCs.ipynb)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2021-03-07T17:26:53.204388Z", "iopub.status.busy": "2021-03-07T17:26:53.203731Z", "iopub.status.idle": "2021-03-07T17:26:53.487051Z", "shell.execute_reply": "2021-03-07T17:26:53.486426Z" }, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Wrote to file \"BlackHoleID_Ccodes/boundary_conditions/parity_conditions_symbolic_dot_products.h\"\n", "Evolved parity: ( aDD00:4, aDD01:5, aDD02:6, aDD11:7, aDD12:8, aDD22:9,\n", " alpha:0, betU0:1, betU1:2, betU2:3, cf:0, hDD00:4, hDD01:5, hDD02:6,\n", " hDD11:7, hDD12:8, hDD22:9, lambdaU0:1, lambdaU1:2, lambdaU2:3, trK:0,\n", " vetU0:1, vetU1:2, vetU2:3 )\n", "Auxiliary parity: ( H:0, MU0:1, MU1:2, MU2:3 )\n", "\n", "Wrote to file \"BlackHoleID_Ccodes/boundary_conditions/EigenCoord_Cart_to_xx.h\"\n" ] } ], "source": [ "import CurviBoundaryConditions.CurviBoundaryConditions as cbcs\n", "cbcs.Set_up_CurviBoundaryConditions(os.path.join(Ccodesdir,\"boundary_conditions/\"),Cparamspath=os.path.join(\"../\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "# Step 5: `Initial_Data_Playground.c`: The Main C Code \\[Back to [top](#toc)\\]\n", "$$\\label{mainc}$$" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2021-03-07T17:26:53.492229Z", "iopub.status.busy": "2021-03-07T17:26:53.491616Z", "iopub.status.idle": "2021-03-07T17:26:53.494118Z", "shell.execute_reply": "2021-03-07T17:26:53.494676Z" } }, "outputs": [], "source": [ "# Part P0: Set the number of ghost cells, from NRPy+'s FD_CENTDERIVS_ORDER\n", "# set REAL=double, so that all floating point numbers are stored to at least ~16 significant digits.\n", "\n", "with open(os.path.join(Ccodesdir,\"Initial_Data_Playground_REAL__NGHOSTS.h\"), \"w\") as file:\n", " file.write(\"\"\"\n", "// Part P0.a: Set the number of ghost cells, from NRPy+'s FD_CENTDERIVS_ORDER\n", "#define NGHOSTS \"\"\"+str(int(par.parval_from_str(\"finite_difference::FD_CENTDERIVS_ORDER\")/2)+1)+\"\"\"\\n\n", "// Part P0.b: Set the numerical precision (REAL) to double, ensuring all floating point\n", "// numbers are stored to at least ~16 significant digits\n", "#define REAL double\\n\"\"\")\n", " if \"Cartesian\" in CoordSystem:\n", " file.write(\"const int CARTESIANLIKE=1;\\n\")\n", " else:\n", " file.write(\"const int CARTESIANLIKE=0;\\n\")\n", " if \"Cylindrical\" in CoordSystem:\n", " file.write(\"const int CYLINDRICALLIKE=1;\\n\")\n", " else:\n", " file.write(\"const int CYLINDRICALLIKE=0;\\n\")" ] }, { "cell_type": "code", "execution_count": 9, "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": [ { "name": "stdout", "output_type": "stream", "text": [ "Writing BlackHoleID_Ccodes//Initial_Data_Playground.c\n" ] } ], "source": [ "%%writefile $Ccodesdir/Initial_Data_Playground.c\n", "\n", "// Step P0: Define REAL and NGHOSTS. This header is generated by NRPy+.\n", "#include \"Initial_Data_Playground_REAL__NGHOSTS.h\"\n", "\n", "#include \"declare_Cparameters_struct.h\"\n", "\n", "// Step P1: Import needed header files\n", "#include \"stdio.h\"\n", "#include \"stdlib.h\"\n", "#include \"math.h\"\n", "#ifndef M_PI\n", "#define M_PI 3.141592653589793238462643383279502884L\n", "#endif\n", "#ifndef M_SQRT1_2\n", "#define M_SQRT1_2 0.707106781186547524400844362104849039L\n", "#endif\n", "\n", "// Step P2: Declare the IDX4S(gf,i,j,k) macro, which enables us to store 4-dimensions of\n", "// data in a 1D array. In this case, consecutive values of \"i\"\n", "// (all other indices held to a fixed value) are consecutive in memory, where\n", "// consecutive values of \"j\" (fixing all other indices) are separated by\n", "// Nxx_plus_2NGHOSTS0 elements in memory. Similarly, consecutive values of\n", "// \"k\" are separated by Nxx_plus_2NGHOSTS0*Nxx_plus_2NGHOSTS1 in memory, etc.\n", "#define IDX4S(g,i,j,k) \\\n", "( (i) + Nxx_plus_2NGHOSTS0 * ( (j) + Nxx_plus_2NGHOSTS1 * ( (k) + Nxx_plus_2NGHOSTS2 * (g) ) ) )\n", "#define IDX4ptS(g,idx) ( (idx) + (Nxx_plus_2NGHOSTS0*Nxx_plus_2NGHOSTS1*Nxx_plus_2NGHOSTS2) * (g) )\n", "#define IDX3S(i,j,k) ( (i) + Nxx_plus_2NGHOSTS0 * ( (j) + Nxx_plus_2NGHOSTS1 * ( (k) ) ) )\n", "#define LOOP_REGION(i0min,i0max, i1min,i1max, i2min,i2max) \\\n", " for(int i2=i2min;i2Cartesian via\n", "// {xx[0][i0],xx[1][i1],xx[2][i2]}->{xCart[0],xCart[1],xCart[2]}\n", "#include \"xx_to_Cart.h\"\n", "\n", "// Step P5: Defines set_Nxx_dxx_invdx_params__and__xx(const int EigenCoord, const int Nxx[3],\n", "// paramstruct *restrict params, REAL *restrict xx[3]),\n", "// which sets params Nxx,Nxx_plus_2NGHOSTS,dxx,invdx, and xx[] for\n", "// the chosen Eigen-CoordSystem if EigenCoord==1, or\n", "// CoordSystem if EigenCoord==0.\n", "#include \"set_Nxx_dxx_invdx_params__and__xx.h\"\n", "\n", "// Step P6: Include basic functions needed to impose curvilinear\n", "// parity and boundary conditions.\n", "#include \"boundary_conditions/CurviBC_include_Cfunctions.h\"\n", "\n", "// Step P8: Include function for enforcing detgammabar constraint.\n", "#include \"enforce_detgammahat_constraint.h\"\n", "\n", "// Step P10: Declare function necessary for setting up the initial data.\n", "// Step P10.a: Define BSSN_ID() for BrillLindquist initial data\n", "\n", "// Step P10.b: Set the generic driver function for setting up BSSN initial data\n", "#include \"initial_data.h\"\n", "\n", "// Step P11: Declare function for evaluating Hamiltonian constraint (diagnostic)\n", "#include \"Hamiltonian_constraint.h\"\n", "#include \"momentum_constraint.h\"\n", "\n", "// 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: Start the timer, for keeping track of how fast the simulation is progressing.\n", "// Step 3: Integrate the initial data forward in time using the chosen RK-like Method of\n", "// Lines timestepping algorithm, and output periodic simulation diagnostics\n", "// Step 3.a: Output 2D data file periodically, for visualization\n", "// Step 3.b: Step forward one timestep (t -> t+dt) in time using\n", "// chosen RK-like MoL timestepping algorithm\n", "// Step 3.c: If t=t_final, output conformal factor & Hamiltonian\n", "// constraint violation to 2D data file\n", "// Step 3.d: Progress indicator printing to stderr\n", "// Step 4: Free all allocated memory\n", "int main(int argc, const char *argv[]) {\n", " paramstruct params;\n", "#include \"set_Cparameters_default.h\"\n", "\n", " // Step 0a: 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 0b: Set up numerical 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 0c: Set free parameters, overwriting Cparameters defaults\n", " // by hand or with command-line input, as desired.\n", "#include \"free_parameters.h\"\n", "\n", " // Step 0d: Uniform coordinate grids are stored to *xx[3]\n", " REAL *xx[3];\n", " // Step 0d.i: Set bcstruct\n", " bc_struct bcstruct;\n", " {\n", " int EigenCoord = 1;\n", " // Step 0d.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, ¶ms, xx);\n", " // Step 0d.iii: Set Nxx_plus_2NGHOSTS_tot\n", "#include \"set_Cparameters-nopointer.h\"\n", " const int Nxx_plus_2NGHOSTS_tot = Nxx_plus_2NGHOSTS0*Nxx_plus_2NGHOSTS1*Nxx_plus_2NGHOSTS2;\n", " // Step 0e: Find ghostzone mappings; set up bcstruct\n", "#include \"boundary_conditions/driver_bcstruct.h\"\n", " // Step 0e.i: Free allocated space for xx[][] array\n", " for(int i=0;i<3;i++) free(xx[i]);\n", " }\n", "\n", " // Step 0f: 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", " int EigenCoord = 0;\n", " set_Nxx_dxx_invdx_params__and__xx(EigenCoord, Nxx, ¶ms, xx);\n", "\n", " // Step 0g: Set all C parameters \"blah\" for params.blah, including\n", " // Nxx_plus_2NGHOSTS0 = params.Nxx_plus_2NGHOSTS0, etc.\n", "#include \"set_Cparameters-nopointer.h\"\n", " const int Nxx_plus_2NGHOSTS_tot = Nxx_plus_2NGHOSTS0*Nxx_plus_2NGHOSTS1*Nxx_plus_2NGHOSTS2;\n", "\n", " // Step 0j: Error out if the number of auxiliary gridfunctions outnumber evolved gridfunctions.\n", " // This is a limitation of the RK method. You are always welcome to declare & allocate\n", " // additional gridfunctions by hand.\n", " if(NUM_AUX_GFS > NUM_EVOL_GFS) {\n", " fprintf(stderr,\"Error: NUM_AUX_GFS > NUM_EVOL_GFS. Either reduce the number of auxiliary gridfunctions,\\n\");\n", " fprintf(stderr,\" or allocate (malloc) by hand storage for *diagnostic_output_gfs. \\n\");\n", " exit(1);\n", " }\n", "\n", " // Step 0k: Allocate memory for gridfunctions\n", "#include \"MoLtimestepping/RK_Allocate_Memory.h\"\n", " REAL *restrict auxevol_gfs = (REAL *)malloc(sizeof(REAL) * NUM_AUXEVOL_GFS * Nxx_plus_2NGHOSTS_tot);\n", "\n", " // Step 1: Set up initial data to an exact solution\n", " initial_data(¶ms, xx, y_n_gfs);\n", "\n", " // Step 1b: Apply boundary conditions, as initial data\n", " // are sometimes ill-defined in ghost zones.\n", " // E.g., spherical initial data might not be\n", " // properly defined at points where r=-1.\n", " apply_bcs_curvilinear(¶ms, &bcstruct, NUM_EVOL_GFS,evol_gf_parity, y_n_gfs);\n", " enforce_detgammahat_constraint(¶ms, xx, y_n_gfs);\n", "\n", " // Evaluate Hamiltonian & momentum constraint violations\n", " Hamiltonian_constraint(¶ms, xx, y_n_gfs, diagnostic_output_gfs);\n", " momentum_constraint( ¶ms, xx, y_n_gfs, diagnostic_output_gfs);\n", "\n", " /* Step 2: 2D output: Output conformal factor (CFGF) and constraint violations (HGF, MU0GF, MU1GF, MU2GF). */\n", " const int i0MIN=NGHOSTS; // In spherical, r=Delta r/2.\n", " const int i1mid=Nxx_plus_2NGHOSTS1/2;\n", " const int i2mid=Nxx_plus_2NGHOSTS2/2;\n", " int i0min=NGHOSTS,i0max=Nxx_plus_2NGHOSTS0-NGHOSTS,\n", " i1min=NGHOSTS,i1max=Nxx_plus_2NGHOSTS1-NGHOSTS,\n", " i2min=NGHOSTS,i2max=Nxx_plus_2NGHOSTS2-NGHOSTS;\n", " if(CARTESIANLIKE || CYLINDRICALLIKE) {\n", " i2min=i2mid; i2max=i2mid+1;\n", " } else { // Assume spherical-like grids\n", " i1min=i1mid; i1max=i1mid+1;\n", " }\n", " LOOP_REGION(i0min,i0max, i1min,i1max, i2min,i2max) {\n", " REAL xCart[3];\n", " xx_to_Cart(¶ms, xx, i0,i1,i2, xCart);\n", " int idx = IDX3S(i0,i1,i2);\n", " printf(\"%e %e %e %e %e %e %e %e\\n\",xCart[0],xCart[1],xCart[2], 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", " }\n", " // Step 4: Free all allocated memory\n", "#include \"boundary_conditions/bcstruct_freemem.h\"\n", "#include \"MoLtimestepping/RK_Free_Memory.h\"\n", " free(auxevol_gfs);\n", " for(int i=0;i<3;i++) free(xx[i]);\n", "\n", " return 0;\n", "}" ] }, { "cell_type": "code", "execution_count": 10, "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": [ "Compiling executable...\n", "(EXEC): Executing `gcc -std=gnu99 -Ofast -fopenmp -march=native -funroll-loops BlackHoleID_Ccodes/Initial_Data_Playground.c -o Initial_Data_Playground -lm`...\n", "(BENCH): Finished executing in 3.4092857837677 seconds.\n", "Finished compilation.\n", "(EXEC): Executing `taskset -c 1,3,5,7,9,11,13,15 ./Initial_Data_Playground 96 96 96`...\n", "(BENCH): Finished executing in 0.4053025245666504 seconds.\n", "(EXEC): Executing `taskset -c 1,3,5,7,9,11,13,15 ./Initial_Data_Playground 48 48 48`...\n", "(BENCH): Finished executing in 0.2041778564453125 seconds.\n" ] } ], "source": [ "import cmdline_helper as cmd\n", "\n", "cmd.C_compile(os.path.join(Ccodesdir,\"Initial_Data_Playground.c\"), \"Initial_Data_Playground\")\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(\"Initial_Data_Playground\", args_output[0], args_output[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\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. Hence, 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": 11, "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" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: scipy in /home/zetienne/jup310/lib/python3.10/site-packages (1.8.0.dev0+1970.9a657fb)\r\n", "Requirement already satisfied: numpy>=1.17.3 in /home/zetienne/jup310/lib/python3.10/site-packages (from scipy) (1.21.4)\r\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": 12, "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": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "from scipy.interpolate import griddata\n", "from pylab import savefig\n", "import matplotlib.pyplot as plt\n", "import matplotlib.cm as cm\n", "from IPython.display import Image\n", "\n", "x96,y96,z96,valuesCF96,valuesHam96,valuesmomr96,valuesmomtheta96,valuesmomphi96 = np.loadtxt('out96.txt').T #Transposed for easier unpacking\n", "\n", "\n", "pl_xmin = -3.\n", "pl_xmax = +3.\n", "pl_ymin = -3.\n", "pl_ymax = +3.\n", "\n", "grid_x, grid_y = np.mgrid[pl_xmin:pl_xmax:100j, pl_ymin:pl_ymax:100j]\n", "points96 = np.zeros((len(x96), 2))\n", "for i in range(len(x96)):\n", " points96[i][0] = x96[i]\n", " points96[i][1] = y96[i]\n", "\n", "grid96 = griddata(points96, valuesCF96, (grid_x, grid_y), method='nearest')\n", "grid96cub = griddata(points96, valuesCF96, (grid_x, grid_y), method='cubic')\n", "\n", "plt.clf()\n", "plt.title(\"Initial Data\")\n", "plt.xlabel(\"x/M\")\n", "plt.ylabel(\"y/M\")\n", "\n", "# fig, ax = plt.subplots()\n", "#ax.plot(grid96cub.T, extent=(pl_xmin,pl_xmax, pl_ymin,pl_ymax))\n", "plt.imshow(grid96.T, extent=(pl_xmin,pl_xmax, pl_ymin,pl_ymax))\n", "savefig(\"ID.png\")\n", "plt.close()\n", "Image(\"ID.png\")\n", "# # interpolation='nearest', cmap=cm.gist_rainbow)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\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": 13, "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": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "RefData=[valuesHam96,valuesmomr96,valuesmomtheta96,valuesmomphi96]\n", "SubTitles=[\"\\mathcal{H}\",'\\mathcal{M}^r',r\"\\mathcal{M}^{\\theta}\",\"\\mathcal{M}^{\\phi}\"]\n", "axN = [] #this will let us automate the subplots in the loop that follows\n", "grid96N = [] #we need to calculate the grid96 data for each constraint for use later\n", "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", "\n", "if dictID[initial_data_string].EnableMomentum == False:\n", " num_plots = 1\n", "\n", "\n", "for p in range(num_plots):\n", " grid96 = griddata(points96, RefData[p], (grid_x, grid_y), method='nearest')\n", " grid96N.append(grid96)\n", " grid96cub = griddata(points96, RefData[p], (grid_x, grid_y), method='cubic')\n", "\n", " #fig, axes = plt.subplots(nrows=2, ncols=2, sharex=True, sharey=True)\n", "\n", "\n", " #Generate the subplot for the each constraint\n", " ax = fig.add_subplot(221+p)\n", " axN.append(ax) # Grid of 2x2\n", "\n", " axN[p].set_xlabel('x/M')\n", " axN[p].set_ylabel('y/M')\n", " axN[p].set_title('$96^3$ Numerical Err.: $log_{10}|'+SubTitles[p]+'|$')\n", "\n", " fig96cub = plt.imshow(grid96cub.T, extent=(pl_xmin,pl_xmax, pl_ymin,pl_ymax))\n", " cb = plt.colorbar(fig96cub)\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": 14, "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": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "x48,y48,z48,valuesCF48,valuesHam48,valuesmomr48,valuesmomtheta48,valuesmomphi48 = np.loadtxt('out48.txt').T #Transposed for easier unpacking\n", "points48 = np.zeros((len(x48), 2))\n", "for i in range(len(x48)):\n", " points48[i][0] = x48[i]\n", " points48[i][1] = y48[i]\n", "\n", "RefData=[valuesHam48,valuesmomr48,valuesmomtheta48,valuesmomphi48]\n", "SubTitles=[\"\\mathcal{H}\",'\\mathcal{M}^r',r\"\\mathcal{M}^{\\theta}\",\"\\mathcal{M}^{\\phi}\"]\n", "if \"Cartesian\" in CoordSystem:\n", " SubTitles=[\"\\mathcal{H}\",'\\mathcal{M}^x',r\"\\mathcal{M}^{y}\",\"\\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", " grid48 = griddata(points48, RefData[p], (grid_x, grid_y), method='nearest')\n", " griddiff_48_minus_96 = np.zeros((100,100))\n", " griddiff_48_minus_96_1darray = np.zeros(100*100)\n", " gridx_1darray_yeq0 = np.zeros(100)\n", " grid48_1darray_yeq0 = np.zeros(100)\n", " grid96_1darray_yeq0 = np.zeros(100)\n", " count = 0\n", " for i in range(100):\n", " for j in range(100):\n", " griddiff_48_minus_96[i][j] = grid48[i][j] - grid96N[p][i][j]\n", " griddiff_48_minus_96_1darray[count] = griddiff_48_minus_96[i][j]\n", " if j==49:\n", " gridx_1darray_yeq0[i] = grid_x[i][j]\n", " grid48_1darray_yeq0[i] = grid48[i][j] + np.log10((48./96.)**4)\n", " grid96_1darray_yeq0[i] = grid96N[p][i][j]\n", " count = count + 1\n", "\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 $'+SubTitles[p]+'$')\n", " axN[p].set_xlabel(\"x/M\")\n", " axN[p].set_ylabel(\"$log_{10}$(Relative Error)\")\n", "\n", " ax.plot(gridx_1darray_yeq0, grid96_1darray_yeq0, 'k-', label='Nr=96')\n", " ax.plot(gridx_1darray_yeq0, grid48_1darray_yeq0, '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": [ "\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-Setting_up_Exact_Initial_Data.pdf](Tutorial-Start_to_Finish-BSSNCurvilinear-Setting_up_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": 15, "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": [ "Created Tutorial-Start_to_Finish-BSSNCurvilinear-\n", " Setting_up_Exact_Initial_Data.tex, and compiled LaTeX file to PDF file\n", " Tutorial-Start_to_Finish-BSSNCurvilinear-\n", " Setting_up_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-Setting_up_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": "3.10.0" } }, "nbformat": 4, "nbformat_minor": 2 }