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"<script async src=\"https://www.googletagmanager.com/gtag/js?id=UA-59152712-8\"></script>\n",
"<script>\n",
" window.dataLayer = window.dataLayer || [];\n",
" function gtag(){dataLayer.push(arguments);}\n",
" gtag('js', new Date());\n",
"\n",
" gtag('config', 'UA-59152712-8');\n",
"</script>\n",
"\n",
"# Start-to-Finish Example: Scalar Field Collapse *with regrids*\n",
"\n",
"## Authors: Leonardo Werneck & Zachariah B. Etienne\n",
"\n",
"## This module sets up spherically symmetric, time-symmetric initial data for a scalar field collapse in Spherical coordinates, as [documented in this NRPy+ module](../Tutorial-ADM_Initial_Data-ScalarField.ipynb) (the initial data is shown to satisfy the Hamiltonian constraint [in this tutorial module](../Tutorial-Start_to_Finish-BSSNCurvilinear-Setting_up_ScalarField_initial_data.ipynb)), which is then evolved forward in time. The tutorial notebook is very similar to the [tutorial notebook on gravitational collapse of a massless scalar field](../Tutorial-Start_to_Finish-BSSNCurvilinear-ScalarField_Collapse.ipynb), except that we will use *regridding* in order to adaptively increase the resolution of the run as we follow the critical solution\n",
"\n",
"<font color='red'>**WARNING:**</font> The results in this tutorial notebook (see plot [at the bottom](#visualization)) ***do not*** reproduce the ones in [Werneck *et al.* (2021)](https://arxiv.org/pdf/2106.06553.pdf). The fine-tuning of the critical solution is extremely sensitive to any changes in the code, including [round-off level disagreements](https://en.wikipedia.org/wiki/Round-off_error) which can be caused by something as simple as using different compilers. The code below chooses a different evolved conformal factor than the original *NRPyCritCol* code and while this choice of conformal factor results in a formulation of the BSSN equations which is equivalent to the one used by the original, it leads to round-off level disagreements. This ultimately changes the value of the critical amplitude, $\\eta_{*}$, and, consequently, $\\eta_{\\rm weak}$ and $\\eta_{\\rm strong}$. If you want to reproduce Fig. 3 from the paper, then we suggest using [an older version of *NRPyCritCol* code](https://github.com/leowerneck/NRPyCritCol_Ccodes), which is the one that was used to generate that plot.\n",
"\n",
"<font color='green'>**Exercise to the reader:**</font> Obtain a fine-tuning, $\\delta\\eta\\equiv 1 - \\eta_{\\rm weak}/\\eta_{\\rm strong}$, of order $10^{-10}$ or better using the code below. In order to do this you will need to add new regrids and/or adjust the existing regridding parameters. You can use the regridding times/parameters [used by the original *NRPyCritCol* code](https://github.com/leowerneck/NRPyCritCol_Ccodes) as a guide.\n",
"\n",
"### **Results from this tutorial notebook have been used in the paper [Werneck *et al.* (2021)](https://arxiv.org/pdf/2106.06553.pdf)**\n",
"\n",
"The entire algorithm is outlined below, with NRPy+-based components highlighted in <font color='green'>green</font>.\n",
"\n",
"1. Allocate memory for gridfunctions, including temporary storage for the RK4 time integration.\n",
"1. <font color='green'>Set gridfunction values to initial data.</font>\n",
"1. Evolve the system forward in time using RK4 time integration. At each RK4 substep, do the following:\n",
" 1. <font color='green'>Evaluate BSSN RHS expressions.</font>\n",
" 1. Apply singular, curvilinear coordinate boundary conditions [*a la* the SENR/NRPy+ paper](https://arxiv.org/abs/1712.07658)\n",
" 1. <font color='green'>Apply constraints on conformal 3-metric: $\\det{\\bar{\\gamma}_{ij}}=\\det{\\hat{\\gamma}_{ij}}$</font>\n",
"1. At the end of each iteration in time, output the <font color='green'>Hamiltonian constraint violation</font>.\n",
"1. Repeat above steps at two numerical resolutions to confirm convergence to zero."
]
},
{
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"metadata": {},
"source": [
"## References\n",
"\n",
"* [Akbarian & Choptuik (2015)](https://arxiv.org/pdf/1508.01614.pdf): Useful to understand the theoretical framework\n",
"* [Baumgarte (2018)](https://arxiv.org/pdf/1807.10342.pdf): Useful to understand the theoretical framework\n",
"* [Baumgarte & Shapiro's Numerical Relativity](https://books.google.com.br/books/about/Numerical_Relativity.html?id=dxU1OEinvRUC&redir_esc=y): Section 6.2.2 - Useful to understand how to solve the Hamiltonian constraint\n",
"* [Werneck *et al.* (2021)](https://arxiv.org/pdf/2106.06553.pdf): Mathematical formulation and numerical implementation of *NRPyCritCol*\n",
"\n",
"<a id='toc'></a>\n",
"\n",
"# Table of Contents\n",
"$$\\label{toc}$$\n",
"\n",
"1. [Step 1](#nrpy_core) Set core NRPy+ parameters for numerical grids and reference metric\n",
" 1. [Step 1.a](#regridding_ccode) Writing the regridding C code \n",
" 1. [Step 1.b](#cfl) Output needed C code for finding the minimum proper distance between grid points, needed for [CFL](https://en.wikipedia.org/w/index.php?title=Courant%E2%80%93Friedrichs%E2%80%93Lewy_condition&oldid=806430673)-limited timestep\n",
"1. [Step 2](#initial_data) Set up ADM initial data for the Scalar Field \n",
"1. [Step 3](#adm_id_spacetime) Convert ADM initial data to BSSN-in-curvilinear coordinates\n",
"1. [Step 4](#bssn) Output C code for BSSN spacetime evolution\n",
" 1. [Step 4.a](#bssnrhs) Set up the BSSN and ScalarField right-hand-side (RHS) expressions, and add the *rescaled* $T^{\\mu\\nu}$ source terms\n",
" 1. [Step 4.b](#hamconstraint) Output the Hamiltonian constraint\n",
" 1. [Step 4.c](#enforce3metric) Enforce conformal 3-metric $\\det{\\bar{\\gamma}_{ij}}=\\det{\\hat{\\gamma}_{ij}}$ constraint\n",
" 1. [Step 4.d](#ccodegen) Generate C code kernels for BSSN expressions, in parallel if possible\n",
" 1. [Step 4.e](#cparams_rfm_and_domainsize) Output C codes needed for declaring and setting Cparameters; also set `free_parameters.h`\n",
"1. [Step 5](#bc_functs) Set up boundary condition functions for chosen singular, curvilinear coordinate system\n",
"1. [Step 6](#main_ccode) The main C code: `ScalarFieldCollapse_Playground.c`\n",
"1. [Step 7](#visualization) Visualization: self-similar behavior of the lapse function\n",
"1. [Step 8](#output_to_pdf) Output this module as $\\LaTeX$-formatted PDF file"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='nrpy_core'></a>\n",
"\n",
"# Step 1: Set core NRPy+ parameters for numerical grids and reference metric \\[Back to [top](#toc)\\]\n",
"$$\\label{nrpy_core}$$"
]
},
{
"cell_type": "code",
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"# Step P1: Import needed NRPy+ core modules:\n",
"import shutil, os, sys # Standard Python modules for multiplatform OS-level functions\n",
"sys.path.append(\"..\")\n",
"from outputC import lhrh,outCfunction # NRPy+: Core C code output module\n",
"import NRPy_param_funcs as par # NRPy+: Parameter interface\n",
"import finite_difference as fin # NRPy+: Finite difference C code generation module\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",
"\n",
"# Step P2: Create C code output directory:\n",
"Ccodesdir = os.path.join(\"BSSN_ScalarFieldCollapse_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 = \"SinhSpherical\"\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",
"domain_size = 64\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 = 0.2 # 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.b: Set the order of spatial and temporal derivatives;\n",
"# the core data type, and the CFL factor.\n",
"# RK_method choices include: Euler, \"RK2 Heun\", \"RK2 MP\", \"RK2 Ralston\", RK3, \"RK3 Heun\", \"RK3 Ralston\",\n",
"# SSPRK3, RK4, DP5, DP5alt, CK5, DP6, L6, DP8\n",
"RK_method = \"RK4\"\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",
"CFL_FACTOR= 0.5\n",
"\n",
"# Set the lapse & shift conditions\n",
"LapseCondition = \"OnePlusLog\"\n",
"ShiftCondition = \"GammaDriving2ndOrder_Covariant\"\n",
"\n",
"# Step 3: Generate Runge-Kutta-based (RK-based) timestepping code.\n",
"# As described above the Table of Contents, this is a 3-step process:\n",
"# 3.A: Evaluate RHSs (RHS_string)\n",
"# 3.B: Apply boundary conditions (post_RHS_string, pt 1)\n",
"# 3.C: Enforce det(gammabar) = det(gammahat) constraint (post_RHS_string, pt 2)\n",
"import MoLtimestepping.C_Code_Generation as MoL\n",
"from MoLtimestepping.RK_Butcher_Table_Dictionary import Butcher_dict\n",
"RK_order = Butcher_dict[RK_method][1]\n",
"cmd.mkdir(os.path.join(Ccodesdir,\"MoLtimestepping/\"))\n",
"MoL.MoL_C_Code_Generation(RK_method,\n",
" RHS_string = \"\"\"\n",
"Ricci_eval(&rfmstruct, ¶ms, RK_INPUT_GFS, auxevol_gfs);\n",
"rhs_eval(&rfmstruct, ¶ms, auxevol_gfs, RK_INPUT_GFS, RK_OUTPUT_GFS);\"\"\",\n",
" post_RHS_string = \"\"\"\n",
"apply_bcs_curvilinear(¶ms, &bcstruct, NUM_EVOL_GFS, evol_gf_parity, RK_OUTPUT_GFS);\n",
"enforce_detgammahat_constraint(&rfmstruct, ¶ms, RK_OUTPUT_GFS);\\n\"\"\",\n",
" outdir = os.path.join(Ccodesdir,\"MoLtimestepping/\"))\n",
"\n",
"# Step 4: 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 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 6: Copy SIMD/SIMD_intrinsics.h to $Ccodesdir/SIMD/SIMD_intrinsics.h\n",
"cmd.mkdir(os.path.join(Ccodesdir,\"SIMD\"))\n",
"shutil.copy(os.path.join(\"..\",\"SIMD\",\"SIMD_intrinsics.h\"),os.path.join(Ccodesdir,\"SIMD\"))\n",
"\n",
"# Step 7: Impose spherical symmetry by demanding that all\n",
"# derivatives in the angular directions vanish\n",
"par.set_parval_from_str(\"indexedexp::symmetry_axes\",\"12\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='regridding_ccode'></a>\n",
"\n",
"## Step 1.a: Writing the regridding C code \\[Back to [top](#toc)\\]\n",
"$$\\label{regridding_ccode}$$\n",
"\n",
"*NRPyCritCol* was designed to study critical phenomena in the context of gravitational collapse. If the scalar field's initial profile is given by, for example,\n",
"\n",
"$$\n",
"\\varphi(r) = \\eta\\exp\\left(\\frac{r^{2}}{\\sigma^{2}}\\right),\n",
"$$\n",
"\n",
"one finds that there are values of $\\eta$ that result in complete dispersal of the scalar field and values of $\\eta$ that result in gravitational collapse of the scalar field. Subcritical The result of the first case is flat spacetime, while the result of the second case is the formation of a black hole. It is then conjectured that there is a critical value of $\\eta$, denoted $\\eta_{*}$, which separates *subcritical* ($\\eta<\\eta_{*}$) runs from *supercritical* ($\\eta>\\eta_{*}$) runs.\n",
"\n",
"As we increase the level of *fine-tuning*, i.e. the closer we get to $\\eta_{*}$, the more structure we observe in our solution. The critical solution, for which $\\eta=\\eta_{*}$, is characterized by the [self-similar](https://en.wikipedia.org/wiki/Self-similarity) behavior that can be observed in spacetime and scalar fields quantity. The same solution repeats itself on ever smaller spatiotemporal scales, requiring more and more resolution in order to be resolved.\n",
"\n",
"Starting a run with very high grid resolution, which implies very small time steps (remember the [CFL condition](https://en.wikipedia.org/wiki/Courant%E2%80%93Friedrichs%E2%80%93Lewy_condition)), should allow us to resolve the structures that appear in the solution, but increases the runtime of the code to a point where studying critical phenomena becomes impractical. To remedy this, we adopt a different strategy: *regridding*.\n",
"\n",
"We begin our run with a grid of modest resolution, enough to resolve the first structures appearing during the time evolution. After some time, we define a new grid which contains higher resolution than the original one. We then interpolate our solution from the original grid onto the new, higher resolution grid, and continue the evolution using the higher resolution grid. This characterizes 1 regrid.\n",
"\n",
"Our numerical grids are defined by our coordinate system $(\\mathtt{xx0},\\mathtt{xx1},\\mathtt{xx2})$, which is related to spherical coordinates $(r,\\theta,\\phi)$ via\n",
"\n",
"$$\n",
"\\begin{aligned}\n",
"r &= \\mathcal{A}\\frac{\\sinh\\left(\\mathtt{xx0}/w\\right)}{\\sinh\\left(1/w\\right)},\\\\\n",
"\\theta &= \\mathtt{xx1},\\\\\n",
"\\phi &= \\mathtt{xx2}.\n",
"\\end{aligned}\n",
"$$\n",
"\n",
"We increase the resolution around $r\\sim0$ by doing one of two things:\n",
"\n",
"1. Decreasing the value of $w$,\n",
"2. Decreasing the value of $\\mathcal{A}$.\n",
"\n",
"Doing 1. increases the sampling density along the radial direction, while doing 2. brings the outer boundary $r_{\\rm max} = \\mathcal{A}$ closer to the center of the simulation while also increasing the resolution near the origin.\n",
"\n",
"The code below is written to interface with the regridding function and struct defined in [ScalarField/NRPyCritCol_regridding.h](ScalarField/NRPyCritCol_regridding.h). We define:\n",
"\n",
"1. The times at which regrids will take place,\n",
"2. Whether we want to change the value of $w$ (\"sinhW\") or $\\mathcal{A}$ (\"sinhA\"),\n",
"3. The new value of $w$ or $\\mathcal{A}$ on the new grid."
]
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Wrote to file 'BSSN_ScalarFieldCollapse_Ccodes/set_regrid_struct.h'\n"
]
}
],
"source": [
"# Define a named tuple to store the regrid information\n",
"from collections import namedtuple\n",
"regrid_tuple = namedtuple(\"regrid_tuple\",\"time type new_parameter_value\")\n",
"\n",
"# Set up the regridding times, types, and the new parameter value\n",
"regrid_info_list = [regrid_tuple(5.100,\"sinhW\",0.19),\n",
" regrid_tuple(5.125,\"sinhW\",0.18),\n",
" regrid_tuple(5.150,\"sinhW\",0.17),\n",
" regrid_tuple(5.175,\"sinhW\",0.16),\n",
" regrid_tuple(5.200,\"sinhW\",0.15),\n",
" regrid_tuple(5.225,\"sinhW\",0.14),\n",
" regrid_tuple(5.250,\"sinhW\",0.13),\n",
" regrid_tuple(5.275,\"sinhW\",0.12),\n",
" regrid_tuple(5.300,\"sinhW\",0.11),\n",
" regrid_tuple(5.325,\"sinhW\",0.10),\n",
" regrid_tuple(6.460,\"sinhA\",56.0),\n",
" regrid_tuple(6.465,\"sinhA\",48.0),\n",
" regrid_tuple(6.470,\"sinhA\",40.0),\n",
" regrid_tuple(6.475,\"sinhA\",32.0),\n",
" regrid_tuple(6.480,\"sinhA\",24.0),\n",
" regrid_tuple(6.485,\"sinhA\",16.0)]\n",
"\n",
"# Write the C code for it\n",
"with open(os.path.join(Ccodesdir,\"set_regrid_struct.h\"),\"w\") as file:\n",
" file.write(\"\"\" {\n",
" int num_regrids = 0;\n",
"\"\"\")\n",
" counter = 0\n",
" for regrid_params in regrid_info_list:\n",
" counter += 1\n",
" file.write(\"\"\"\n",
" // Regrid #%d\n",
" if( num_regrids < max_number_of_regrids ) {\n",
" regrid_params.regrid_time[num_regrids] = %.15e;\n",
" regrid_params.new_ampl_or_sinhW[num_regrids] = %.15e;\n",
" regrid_params.regrid_key[num_regrids] = %s;\n",
" regrid_params.regrid_counter[num_regrids] = num_regrids;\n",
" num_regrids++;\n",
" }\n",
"\"\"\"%(counter,regrid_params.time,regrid_params.new_parameter_value,\n",
" \"sinhw_regrid\" if regrid_params.type == \"sinhW\" else \"ampl_regrid\"))\n",
"\n",
" file.write(r\"\"\"\n",
" // Sanity check\n",
" if( num_regrids != max_number_of_regrids ) {\n",
" fprintf(stderr,\"ERROR: Expected %d regrids but got %d\\n\",max_number_of_regrids,num_regrids);\n",
" exit(1);\n",
" }\n",
"\n",
" // Print regridding information\n",
" for(int i=0;i<max_number_of_regrids;i++) {\n",
" printf(\"Regrid #%02d: %s, %.4lf, %.4lf\\n\",\n",
" i+1,\n",
" regrid_params.regrid_key[i] == sinhw_regrid ? \"sinhW\" : \"sinhA\",\n",
" regrid_params.regrid_time[i],\n",
" regrid_params.new_ampl_or_sinhW[i]);\n",
" }\n",
" }\n",
"\"\"\")\n",
"\n",
"print(\"Wrote to file '%s'\"%os.path.join(Ccodesdir,\"set_regrid_struct.h\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='cfl'></a>\n",
"\n",
"## Step 1.b: Output needed C code for finding the minimum proper distance between grid points, needed for [CFL](https://en.wikipedia.org/w/index.php?title=Courant%E2%80%93Friedrichs%E2%80%93Lewy_condition&oldid=806430673)-limited timestep \\[Back to [top](#toc)\\]\n",
"$$\\label{cfl}$$\n",
"\n",
"In order for our explicit-timestepping numerical solution to the scalar wave equation to be stable, it must satisfy the [CFL](https://en.wikipedia.org/w/index.php?title=Courant%E2%80%93Friedrichs%E2%80%93Lewy_condition&oldid=806430673) condition:\n",
"$$\n",
"\\Delta t \\le \\frac{\\min(ds_i)}{c},\n",
"$$\n",
"where $c$ is the wavespeed, and\n",
"$$ds_i = h_i \\Delta x^i$$ \n",
"is the proper distance between neighboring gridpoints in the $i$th direction (in 3D, there are 3 directions), $h_i$ is the $i$th reference metric scale factor, and $\\Delta x^i$ is the uniform grid spacing in the $i$th direction:"
]
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"outputs": [],
"source": [
"# Output the find_timestep() function to a C file.\n",
"rfm.out_timestep_func_to_file(os.path.join(Ccodesdir,\"find_timestep.h\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='initial_data'></a>\n",
"\n",
"# Step 2: Set up ADM initial data for the Scalar Field \\[Back to [top](#toc)\\]\n",
"$$\\label{initial_data}$$\n",
"\n",
"As documented [in the scalar field Gaussian pulse initial data NRPy+ tutorial notebook](T../Tutorial-ADM_Initial_Data-ScalarField.ipynb), we will now set up the scalar field initial data, storing the densely-sampled result to file.\n",
"\n",
"The initial data function `ScalarField_InitialData` requires `SciPy`, so let's make sure it's installed."
]
},
{
"cell_type": "code",
"execution_count": 4,
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"outputs": [],
"source": [
"!pip install scipy numpy > /dev/null"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next call the `ScalarField_InitialData()` function from the [ScalarField/ScalarField_InitialData.py](../edit/ScalarField/ScalarField_InitialData.py) NRPy+ module (see the [tutorial notebook](../Tutorial-ADM_Initial_Data-ScalarField.ipynb))."
]
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"name": "stdout",
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"text": [
"Generated the ADM initial data for the gravitational collapse \n",
"of a massless scalar field in Spherical coordinates.\n",
"\n",
"Type of initial condition: Scalar field: \"Gaussian\" Shell\n",
" ADM quantities: Time-symmetric\n",
" Lapse condition: Pre-collapsed\n",
"Parameters: amplitude = 0.303326061,\n",
" center = 0,\n",
" width = 1,\n",
" domain size = 70.4,\n",
" number of points = 30000,\n",
" Initial data file = BSSN_ScalarFieldCollapse_Ccodes/output/SFID_weak.txt.\n",
"\n",
"Generated the ADM initial data for the gravitational collapse \n",
"of a massless scalar field in Spherical coordinates.\n",
"\n",
"Type of initial condition: Scalar field: \"Gaussian\" Shell\n",
" ADM quantities: Time-symmetric\n",
" Lapse condition: Pre-collapsed\n",
"Parameters: amplitude = 0.303326062,\n",
" center = 0,\n",
" width = 1,\n",
" domain size = 70.4,\n",
" number of points = 30000,\n",
" Initial data file = BSSN_ScalarFieldCollapse_Ccodes/output/SFID_strong.txt.\n",
"\n",
"Output C function ID_scalarfield_ADM_quantities() to file BSSN_ScalarFieldCollapse_Ccodes/ID_scalarfield_ADM_quantities.h\n",
"Output C function ID_scalarfield_spherical() to file BSSN_ScalarFieldCollapse_Ccodes/ID_scalarfield_spherical.h\n",
"Output C function ID_scalarfield_xx0xx1xx2_to_BSSN_xx0xx1xx2() to file BSSN_ScalarFieldCollapse_Ccodes/ID_scalarfield_xx0xx1xx2_to_BSSN_xx0xx1xx2.h\n",
"Output C function ID_scalarfield() to file BSSN_ScalarFieldCollapse_Ccodes/ID_scalarfield.h\n"
]
}
],
"source": [
"# Step 2.a: Import necessary Python and NRPy+ modules\n",
"import ScalarField.ScalarField_InitialData as sfid\n",
"\n",
"# Step 2.b: Set the initial data parameters\n",
"ID_Family = \"Gaussian_pulse\"\n",
"pulse_center = 0\n",
"pulse_width = 1\n",
"Nr = 30000\n",
"rmax = domain_size*1.1\n",
"\n",
"# Step 2.c: Generate the initial data\n",
"eta_weak = 0.303326061\n",
"sfid.ScalarField_InitialData(os.path.join(outdir,\"SFID_weak.txt\"),ID_Family,\n",
" eta_weak,pulse_center,pulse_width,Nr,rmax)\n",
"eta_strong = 0.303326062\n",
"sfid.ScalarField_InitialData(os.path.join(outdir,\"SFID_strong.txt\"),ID_Family,\n",
" eta_strong,pulse_center,pulse_width,Nr,rmax)\n",
"\n",
"sfid.NRPy_param_funcs_register_C_functions_and_NRPy_basic_defines(Ccodesdir=Ccodesdir)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='adm_id_spacetime'></a>\n",
"\n",
"# Step 3: Convert ADM initial data to BSSN-in-curvilinear coordinates \\[Back to [top](#toc)\\]\n",
"$$\\label{adm_id_spacetime}$$\n",
"\n",
"This is an automated process, taken care of by [`BSSN.ADM_Numerical_Spherical_or_Cartesian_to_BSSNCurvilinear`](../edit/BSSN.ADM_Numerical_Spherical_or_Cartesian_to_BSSNCurvilinear.py), and documented [in this tutorial notebook](../Tutorial-ADM_Initial_Data-Converting_Numerical_ADM_Spherical_or_Cartesian_to_BSSNCurvilinear.ipynb)."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-15T10:12:42.472676Z",
"iopub.status.busy": "2021-06-15T10:12:42.472417Z",
"iopub.status.idle": "2021-06-15T10:12:42.905412Z",
"shell.execute_reply": "2021-06-15T10:12:42.904960Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Output C function ID_BSSN_lambdas() to file BSSN_ScalarFieldCollapse_Ccodes/ID_BSSN_lambdas.h\n",
"Output C function ID_ADM_xx0xx1xx2_to_BSSN_xx0xx1xx2__ALL_BUT_LAMBDAs() to file BSSN_ScalarFieldCollapse_Ccodes/ID_ADM_xx0xx1xx2_to_BSSN_xx0xx1xx2__ALL_BUT_LAMBDAs.h\n",
"Output C function ID_BSSN__ALL_BUT_LAMBDAs() to file BSSN_ScalarFieldCollapse_Ccodes/ID_BSSN__ALL_BUT_LAMBDAs.h\n"
]
}
],
"source": [
"import BSSN.ADM_Numerical_Spherical_or_Cartesian_to_BSSNCurvilinear as AtoBnum\n",
"AtoBnum.Convert_Spherical_or_Cartesian_ADM_to_BSSN_curvilinear(\"Spherical\",\"ID_scalarfield_ADM_quantities\",\n",
" Ccodesdir=Ccodesdir,loopopts=\"\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='bssn'></a>\n",
"\n",
"# Step 4: Output C code for BSSN spacetime evolution \\[Back to [top](#toc)\\]\n",
"$$\\label{bssn}$$\n",
"\n",
"<a id='bssnrhs'></a>\n",
"\n",
"## Step 4.a: Set up the BSSN and ScalarField right-hand-side (RHS) expressions, and add the *rescaled* $T^{\\mu\\nu}$ source terms \\[Back to [top](#toc)\\]\n",
"$$\\label{bssnrhs}$$\n",
"\n",
"`BSSN.BSSN_RHSs()` sets up the RHSs assuming a spacetime vacuum: $T^{\\mu\\nu}=0$. (This might seem weird, but remember that, for example, *spacetimes containing only single or binary black holes are vacuum spacetimes*.) Here, using the [`BSSN.BSSN_stress_energy_source_terms`](../edit/BSSN/BSSN_stress_energy_source_terms.py) ([**tutorial**](../Tutorial-BSSN_stress_energy_source_terms.ipynb)) NRPy+ module, we add the $T^{\\mu\\nu}$ source terms to these equations."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-15T10:12:42.924208Z",
"iopub.status.busy": "2021-06-15T10:12:42.922875Z",
"iopub.status.idle": "2021-06-15T10:12:48.090437Z",
"shell.execute_reply": "2021-06-15T10:12:48.089955Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generating symbolic expressions for BSSN RHSs...\n",
"(BENCH) Finished BSSN symbolic expressions in 11.03122878074646 seconds.\n"
]
}
],
"source": [
"import time\n",
"import BSSN.BSSN_RHSs as rhs\n",
"import BSSN.BSSN_gauge_RHSs as gaugerhs\n",
"par.set_parval_from_str(\"BSSN.BSSN_gauge_RHSs::LapseEvolutionOption\", LapseCondition)\n",
"par.set_parval_from_str(\"BSSN.BSSN_gauge_RHSs::ShiftEvolutionOption\", ShiftCondition)\n",
"\n",
"print(\"Generating symbolic expressions for BSSN RHSs...\")\n",
"start = time.time()\n",
"# Enable rfm_precompute infrastructure, which results in\n",
"# BSSN RHSs that are free of transcendental functions,\n",
"# even in curvilinear coordinates, so long as\n",
"# ConformalFactor is set to \"W\" (default).\n",
"cmd.mkdir(os.path.join(Ccodesdir,\"rfm_files/\"))\n",
"par.set_parval_from_str(\"reference_metric::enable_rfm_precompute\",\"True\")\n",
"par.set_parval_from_str(\"reference_metric::rfm_precompute_Ccode_outdir\",os.path.join(Ccodesdir,\"rfm_files/\"))\n",
"\n",
"# Evaluate BSSN + BSSN gauge RHSs with rfm_precompute enabled:\n",
"import BSSN.BSSN_quantities as Bq\n",
"par.set_parval_from_str(\"BSSN.BSSN_quantities::LeaveRicciSymbolic\",\"True\")\n",
"\n",
"rhs.BSSN_RHSs()\n",
"\n",
"# Evaluate the Scalar Field RHSs\n",
"import ScalarField.ScalarField_RHSs as sfrhs\n",
"sfrhs.ScalarField_RHSs()\n",
"\n",
"# Compute ScalarField T^{\\mu\\nu}\n",
"# Compute the scalar field energy-momentum tensor\n",
"import ScalarField.ScalarField_Tmunu as sfTmunu\n",
"sfTmunu.ScalarField_Tmunu()\n",
"T4UU = sfTmunu.T4UU\n",
"\n",
"import BSSN.BSSN_stress_energy_source_terms as Bsest\n",
"Bsest.BSSN_source_terms_for_BSSN_RHSs(T4UU)\n",
"rhs.trK_rhs += Bsest.sourceterm_trK_rhs\n",
"for i in range(DIM):\n",
" # Needed for Gamma-driving shift RHSs:\n",
" rhs.Lambdabar_rhsU[i] += Bsest.sourceterm_Lambdabar_rhsU[i]\n",
" # Needed for BSSN RHSs:\n",
" rhs.lambda_rhsU[i] += Bsest.sourceterm_lambda_rhsU[i]\n",
" for j in range(DIM):\n",
" rhs.a_rhsDD[i][j] += Bsest.sourceterm_a_rhsDD[i][j]\n",
"\n",
"gaugerhs.BSSN_gauge_RHSs()\n",
"\n",
"# We use betaU as our upwinding control vector:\n",
"Bq.BSSN_basic_tensors()\n",
"betaU = Bq.betaU\n",
"\n",
"import BSSN.Enforce_Detgammahat_Constraint as EGC\n",
"enforce_detg_constraint_symb_expressions = EGC.Enforce_Detgammahat_Constraint_symb_expressions()\n",
"\n",
"# Next compute Ricci tensor\n",
"par.set_parval_from_str(\"BSSN.BSSN_quantities::LeaveRicciSymbolic\",\"False\")\n",
"Bq.RicciBar__gammabarDD_dHatD__DGammaUDD__DGammaU()\n",
"\n",
"# Now register the Hamiltonian as a gridfunction.\n",
"H = gri.register_gridfunctions(\"AUX\",\"H\")\n",
"\n",
"# Then define the Hamiltonian constraint and output the optimized C code.\n",
"import BSSN.BSSN_constraints as bssncon\n",
"bssncon.BSSN_constraints(add_T4UUmunu_source_terms=False)\n",
"Bsest.BSSN_source_terms_for_BSSN_constraints(T4UU)\n",
"bssncon.H += Bsest.sourceterm_H\n",
"\n",
"# Add Kreiss-Oliger dissipation\n",
"diss_strength = par.Cparameters(\"REAL\",\"ScalarFieldCollapse\",[\"diss_strength\"],0.1)\n",
"\n",
"alpha_dKOD = ixp.declarerank1(\"alpha_dKOD\")\n",
"cf_dKOD = ixp.declarerank1(\"cf_dKOD\")\n",
"trK_dKOD = ixp.declarerank1(\"trK_dKOD\")\n",
"sf_dKOD = ixp.declarerank1(\"sf_dKOD\")\n",
"sfM_dKOD = ixp.declarerank1(\"sfM_dKOD\")\n",
"betU_dKOD = ixp.declarerank2(\"betU_dKOD\",\"nosym\")\n",
"vetU_dKOD = ixp.declarerank2(\"vetU_dKOD\",\"nosym\")\n",
"lambdaU_dKOD = ixp.declarerank2(\"lambdaU_dKOD\",\"nosym\")\n",
"aDD_dKOD = ixp.declarerank3(\"aDD_dKOD\",\"sym01\")\n",
"hDD_dKOD = ixp.declarerank3(\"hDD_dKOD\",\"sym01\")\n",
"\n",
"for k in range(3):\n",
" gaugerhs.alpha_rhs += diss_strength*alpha_dKOD[k]*rfm.ReU[k]\n",
" rhs.cf_rhs += diss_strength* cf_dKOD[k]*rfm.ReU[k]\n",
" rhs.trK_rhs += diss_strength* trK_dKOD[k]*rfm.ReU[k]\n",
" sfrhs.sf_rhs += diss_strength* sf_dKOD[k]*rfm.ReU[k]\n",
" sfrhs.sfM_rhs += diss_strength* sfM_dKOD[k]*rfm.ReU[k]\n",
" for i in range(3):\n",
" if \"2ndOrder\" in ShiftCondition:\n",
" gaugerhs.bet_rhsU[i] += diss_strength* betU_dKOD[i][k]*rfm.ReU[k]\n",
" gaugerhs.vet_rhsU[i] += diss_strength* vetU_dKOD[i][k]*rfm.ReU[k]\n",
" rhs.lambda_rhsU[i] += diss_strength*lambdaU_dKOD[i][k]*rfm.ReU[k]\n",
" for j in range(3):\n",
" rhs.a_rhsDD[i][j] += diss_strength*aDD_dKOD[i][j][k]*rfm.ReU[k]\n",
" rhs.h_rhsDD[i][j] += diss_strength*hDD_dKOD[i][j][k]*rfm.ReU[k]\n",
"\n",
"# Now that we are finished with all the rfm hatted\n",
"# quantities in generic precomputed functional\n",
"# form, let's restore them to their closed-\n",
"# form expressions.\n",
"par.set_parval_from_str(\"reference_metric::enable_rfm_precompute\",\"False\") # Reset to False to disable rfm_precompute.\n",
"rfm.ref_metric__hatted_quantities()\n",
"end = time.time()\n",
"print(\"(BENCH) Finished BSSN symbolic expressions in \"+str(end-start)+\" seconds.\")\n",
"\n",
"def BSSN_plus_ScalarField_RHSs():\n",
" print(\"Generating C code for BSSN RHSs in \"+par.parval_from_str(\"reference_metric::CoordSystem\")+\" coordinates.\")\n",
" start = time.time()\n",
"\n",
" # Construct the left-hand sides and right-hand-side expressions for all BSSN RHSs\n",
" lhs_names = [ \"alpha\", \"cf\", \"trK\", \"sf\", \"sfM\" ]\n",
" rhs_exprs = [gaugerhs.alpha_rhs, rhs.cf_rhs, rhs.trK_rhs, sfrhs.sf_rhs, sfrhs.sfM_rhs]\n",
" for i in range(3):\n",
" lhs_names.append( \"betU\"+str(i))\n",
" rhs_exprs.append(gaugerhs.bet_rhsU[i])\n",
" lhs_names.append( \"lambdaU\"+str(i))\n",
" rhs_exprs.append(rhs.lambda_rhsU[i])\n",
" lhs_names.append( \"vetU\"+str(i))\n",
" rhs_exprs.append(gaugerhs.vet_rhsU[i])\n",
" for j in range(i,3):\n",
" lhs_names.append( \"aDD\"+str(i)+str(j))\n",
" rhs_exprs.append(rhs.a_rhsDD[i][j])\n",
" lhs_names.append( \"hDD\"+str(i)+str(j))\n",
" rhs_exprs.append(rhs.h_rhsDD[i][j])\n",
"\n",
" # Sort the lhss list alphabetically, and rhss to match.\n",
" # This ensures the RHSs are evaluated in the same order\n",
" # they're allocated in memory:\n",
" lhs_names,rhs_exprs = [list(x) for x in zip(*sorted(zip(lhs_names,rhs_exprs), key=lambda pair: pair[0]))]\n",
"\n",
" # Declare the list of lhrh's\n",
" BSSN_evol_rhss = []\n",
" for var in range(len(lhs_names)):\n",
" BSSN_evol_rhss.append(lhrh(lhs=gri.gfaccess(\"rhs_gfs\",lhs_names[var]),rhs=rhs_exprs[var]))\n",
"\n",
" # Set up the C function for the BSSN RHSs\n",
" desc=\"Evaluate the BSSN RHSs\"\n",
" name=\"rhs_eval\"\n",
" outCfunction(\n",
" outfile = os.path.join(Ccodesdir,name+\".h\"), desc=desc, name=name,\n",
" params = \"\"\"rfm_struct *restrict rfmstruct,const paramstruct *restrict params,\n",
" const REAL *restrict auxevol_gfs,const REAL *restrict in_gfs,REAL *restrict rhs_gfs\"\"\",\n",
" body = fin.FD_outputC(\"returnstring\",BSSN_evol_rhss, params=\"outCverbose=False,enable_SIMD=True\",\n",
" upwindcontrolvec=betaU),\n",
" loopopts = \"InteriorPoints,enable_SIMD,enable_rfm_precompute,pragma_on_i0\")\n",
" end = time.time()\n",
" print(\"(BENCH) Finished BSSN_RHS C codegen in \" + str(end - start) + \" seconds.\")\n",
"\n",
"def Ricci():\n",
" print(\"Generating C code for Ricci tensor in \"+par.parval_from_str(\"reference_metric::CoordSystem\")+\" coordinates.\")\n",
" start = time.time()\n",
" desc=\"Evaluate the Ricci tensor\"\n",
" name=\"Ricci_eval\"\n",
" outCfunction(\n",
" outfile = os.path.join(Ccodesdir,name+\".h\"), desc=desc, name=name,\n",
" params = \"\"\"rfm_struct *restrict rfmstruct,const paramstruct *restrict params,\n",
" const REAL *restrict in_gfs,REAL *restrict auxevol_gfs\"\"\",\n",
" body = fin.FD_outputC(\"returnstring\",\n",
" [lhrh(lhs=gri.gfaccess(\"auxevol_gfs\",\"RbarDD00\"),rhs=Bq.RbarDD[0][0]),\n",
" lhrh(lhs=gri.gfaccess(\"auxevol_gfs\",\"RbarDD01\"),rhs=Bq.RbarDD[0][1]),\n",
" lhrh(lhs=gri.gfaccess(\"auxevol_gfs\",\"RbarDD02\"),rhs=Bq.RbarDD[0][2]),\n",
" lhrh(lhs=gri.gfaccess(\"auxevol_gfs\",\"RbarDD11\"),rhs=Bq.RbarDD[1][1]),\n",
" lhrh(lhs=gri.gfaccess(\"auxevol_gfs\",\"RbarDD12\"),rhs=Bq.RbarDD[1][2]),\n",
" lhrh(lhs=gri.gfaccess(\"auxevol_gfs\",\"RbarDD22\"),rhs=Bq.RbarDD[2][2])],\n",
" params=\"outCverbose=False,enable_SIMD=True\"),\n",
" loopopts = \"InteriorPoints,enable_SIMD,enable_rfm_precompute,pragma_on_i0\")\n",
" end = time.time()\n",
" print(\"(BENCH) Finished Ricci C codegen in \" + str(end - start) + \" seconds.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='hamconstraint'></a>\n",
"\n",
"## Step 4.b: Output the Hamiltonian constraint \\[Back to [top](#toc)\\]\n",
"$$\\label{hamconstraint}$$\n",
"\n",
"Next output the C code for evaluating the Hamiltonian constraint [(**Tutorial**)](../Tutorial-BSSN_constraints.ipynb). In the absence of numerical error, this constraint should evaluate to zero. However it does not due to numerical (typically truncation and roundoff) error. We will therefore measure the Hamiltonian constraint violation to gauge the accuracy of our simulation, and, ultimately determine whether errors are dominated by numerical finite differencing (truncation) error as expected."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-15T10:12:48.094691Z",
"iopub.status.busy": "2021-06-15T10:12:48.094159Z",
"iopub.status.idle": "2021-06-15T10:12:48.096356Z",
"shell.execute_reply": "2021-06-15T10:12:48.095814Z"
}
},
"outputs": [],
"source": [
"def Hamiltonian():\n",
" start = time.time()\n",
" print(\"Generating optimized C code for Hamiltonian constraint. May take a while, depending on CoordSystem.\")\n",
" # Set up the C function for the Hamiltonian RHS\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 = \"\"\"rfm_struct *restrict rfmstruct,const paramstruct *restrict params,\n",
" REAL *restrict in_gfs, REAL *restrict auxevol_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,enable_rfm_precompute,pragma_on_i0\")\n",
"\n",
" end = time.time()\n",
" print(\"(BENCH) Finished Hamiltonian C codegen in \" + str(end - start) + \" seconds.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='enforce3metric'></a>\n",
"\n",
"## Step 4.c: 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": 9,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-15T10:12:48.099864Z",
"iopub.status.busy": "2021-06-15T10:12:48.099331Z",
"iopub.status.idle": "2021-06-15T10:12:48.101178Z",
"shell.execute_reply": "2021-06-15T10:12:48.100767Z"
}
},
"outputs": [],
"source": [
"def gammadet():\n",
" start = time.time()\n",
" print(\"Generating optimized C code for gamma constraint. May take a while, depending on CoordSystem.\")\n",
"\n",
" # Set up the C function for the det(gammahat) = det(gammabar)\n",
" EGC.output_Enforce_Detgammahat_Constraint_Ccode(Ccodesdir,exprs=enforce_detg_constraint_symb_expressions)\n",
" end = time.time()\n",
" print(\"(BENCH) Finished gamma constraint C codegen in \" + str(end - start) + \" seconds.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='ccodegen'></a>\n",
"\n",
"## Step 4.d: Generate C code kernels for BSSN expressions, in parallel if possible \\[Back to [top](#toc)\\]\n",
"$$\\label{ccodegen}$$"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-15T10:12:48.104958Z",
"iopub.status.busy": "2021-06-15T10:12:48.104565Z",
"iopub.status.idle": "2021-06-15T10:13:12.087955Z",
"shell.execute_reply": "2021-06-15T10:13:12.088357Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generating optimized C code for Hamiltonian constraint. May take a while, depending on CoordSystem.Generating C code for Ricci tensor in SinhSpherical coordinates.Generating C code for BSSN RHSs in SinhSpherical coordinates.Generating optimized C code for gamma constraint. May take a while, depending on CoordSystem.\n",
"\n",
"\n",
"\n",
"Output C function enforce_detgammahat_constraint() to file BSSN_ScalarFieldCollapse_Ccodes/enforce_detgammahat_constraint.h\n",
"(BENCH) Finished gamma constraint C codegen in 0.2685582637786865 seconds.\n",
"Output C function Ricci_eval() to file BSSN_ScalarFieldCollapse_Ccodes/Ricci_eval.h\n",
"(BENCH) Finished Ricci C codegen in 24.411128044128418 seconds.\n",
"Output C function rhs_eval() to file BSSN_ScalarFieldCollapse_Ccodes/rhs_eval.h\n",
"(BENCH) Finished BSSN_RHS C codegen in 25.715099811553955 seconds.\n",
"Output C function Hamiltonian_constraint() to file BSSN_ScalarFieldCollapse_Ccodes/Hamiltonian_constraint.h\n",
"(BENCH) Finished Hamiltonian C codegen in 43.34551215171814 seconds.\n"
]
}
],
"source": [
"# Step 4.d: C code kernel generation\n",
"# Step 4.d.i: Create a list of functions we wish to evaluate in parallel\n",
"funcs = [BSSN_plus_ScalarField_RHSs,Ricci,Hamiltonian,gammadet]\n",
"\n",
"try:\n",
" if os.name == 'nt':\n",
" # It's a mess to get working in Windows, so we don't bother. :/\n",
" # https://medium.com/@grvsinghal/speed-up-your-python-code-using-multiprocessing-on-windows-and-jupyter-or-ipython-2714b49d6fac\n",
" raise Exception(\"Parallel codegen currently not available in Windows\")\n",
" # Step 4.d.ii: Import the multiprocessing module.\n",
" import multiprocess as multiprocessing\n",
"\n",
" # Step 4.d.iii: Define master function for parallelization.\n",
" # Note that lambdifying this doesn't work in Python 3\n",
" def master_func(arg):\n",
" funcs[arg]()\n",
"\n",
" # Step 4.d.iv: Evaluate list of functions in parallel if possible;\n",
" # otherwise fallback to serial evaluation:\n",
" pool = multiprocessing.Pool()\n",
" pool.map(master_func,range(len(funcs)))\n",
"except:\n",
" # Steps 4.d.iii-4.d.v, alternate: As fallback, evaluate functions in serial.\n",
" for func in funcs:\n",
" func()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='cparams_rfm_and_domainsize'></a>\n",
"\n",
"## Step 4.e: 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": 11,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-15T10:13:12.093427Z",
"iopub.status.busy": "2021-06-15T10:13:12.093016Z",
"iopub.status.idle": "2021-06-15T10:13:12.178095Z",
"shell.execute_reply": "2021-06-15T10:13:12.178546Z"
}
},
"outputs": [],
"source": [
"# Step 4.e.i: Generate declare_Cparameters_struct.h, set_Cparameters_default.h, and set_Cparameters[-SIMD].h\n",
"par.generate_Cparameters_Ccodes(os.path.join(Ccodesdir))\n",
"\n",
"# Step 4.e.ii: 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",
"\n",
"# Step 4.e.iii: Generate set_Nxx_dxx_invdx_params__and__xx.h:\n",
"rfm.set_Nxx_dxx_invdx_params__and__xx_h(Ccodesdir)\n",
"\n",
"# Step 4.e.iv: 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 4.e.v: 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": [
"<a id='bc_functs'></a>\n",
"\n",
"# Step 5: 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": 12,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-15T10:13:12.181581Z",
"iopub.status.busy": "2021-06-15T10:13:12.181073Z",
"iopub.status.idle": "2021-06-15T10:13:12.317547Z",
"shell.execute_reply": "2021-06-15T10:13:12.317011Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wrote to file \"BSSN_ScalarFieldCollapse_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, sf:0,\n",
" sfM:0, trK:0, vetU0:1, vetU1:2, vetU2:3 )\n",
"Auxiliary parity: ( H:0 )\n",
"AuxEvol parity: ( RbarDD00:4, RbarDD01:5, RbarDD02:6, RbarDD11:7,\n",
" RbarDD12:8, RbarDD22:9 )\n",
"Wrote to file \"BSSN_ScalarFieldCollapse_Ccodes/boundary_conditions/EigenCoord_Cart_to_xx.h\"\n"
]
}
],
"source": [
"import CurviBoundaryConditions.CurviBoundaryConditions as cbcs\n",
"cmd.mkdir(os.path.join(Ccodesdir,\"boundary_conditions\"))\n",
"cbcs.Set_up_CurviBoundaryConditions(os.path.join(Ccodesdir,\"boundary_conditions/\"),Cparamspath=os.path.join(\"../\"),enable_copy_of_static_Ccodes=False)\n",
"\n",
"# Manually copy the static files required by boundary conditions\n",
"for file in [\"apply_bcs_curvilinear.h\",\"BCs_data_structs.h\",\"bcstruct_freemem.h\",\"CurviBC_include_Cfunctions.h\",\n",
" \"driver_bcstruct.h\",\"set_bcstruct.h\",\"set_up__bc_gz_map_and_parity_condns.h\"]:\n",
" shutil.copy(os.path.join(\"..\",\"CurviBoundaryConditions\",\"boundary_conditions\",file),\n",
" os.path.join(Ccodesdir,\"boundary_conditions\"))\n",
"\n",
"with open(os.path.join(Ccodesdir,\"boundary_conditions\",\"CurviBC_include_Cfunctions.h\"),\"a\") as file:\n",
" file.write(\"\\n#include \\\"apply_bcs_curvilinear.h\\\"\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='main_ccode'></a>\n",
"\n",
"# Step 6: The main C code: `ScalarFieldCollapse_Playground.c` \\[Back to [top](#toc)\\]\n",
"$$\\label{main_ccode}$$"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-15T10:13:12.320911Z",
"iopub.status.busy": "2021-06-15T10:13:12.320506Z",
"iopub.status.idle": "2021-06-15T10:13:12.322151Z",
"shell.execute_reply": "2021-06-15T10:13:12.322504Z"
}
},
"outputs": [],
"source": [
"# Part P0: Define REAL, set the number of ghost cells NGHOSTS (from NRPy+'s FD_CENTDERIVS_ORDER),\n",
"# and set the CFL_FACTOR (which can be overwritten at the command line)\n",
"\n",
"with open(os.path.join(Ccodesdir,\"ScalarFieldCollapse_Playground_REAL__NGHOSTS__CFL_FACTOR.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(FD_order/2)+1)+\"\"\"\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 \"\"\"+REAL+\"\"\"\n",
"// Part P0.c: Set the number of ghost cells, from NRPy+'s FD_CENTDERIVS_ORDER\n",
"REAL CFL_FACTOR = \"\"\"+str(CFL_FACTOR)+\"\"\"; // Set the CFL Factor. Can be overwritten at command line.\\n\"\"\")\n",
"\n",
"files = [\"NRPyCritCol_regridding.h\",\"ScalarField_output_central_values.h\"]\n",
"for file in files:\n",
" shutil.copyfile(os.path.join(file),os.path.join(Ccodesdir,file))\n",
"\n",
"outfile = os.path.join(Ccodesdir,\"rfm_files\",\"rfm_struct__define-pointer.h\")\n",
"shutil.copyfile(os.path.join(Ccodesdir,\"rfm_files\",\"rfm_struct__define.h\"),\n",
" outfile)\n",
"\n",
"with open(outfile,\"r\") as file:\n",
" file_contents = file.read()\n",
"\n",
"with open(outfile,\"w\") as file:\n",
" file.write(file_contents.replace(\"rfmstruct.\",\"rfmstruct->\"))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-15T10:13:12.328132Z",
"iopub.status.busy": "2021-06-15T10:13:12.327658Z",
"iopub.status.idle": "2021-06-15T10:13:12.329926Z",
"shell.execute_reply": "2021-06-15T10:13:12.329574Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Writing BSSN_ScalarFieldCollapse_Ccodes/ScalarFieldCollapse_Playground.c\n"
]
}
],
"source": [
"%%writefile $Ccodesdir/ScalarFieldCollapse_Playground.c\n",
"\n",
"// Step P0: Define REAL and NGHOSTS; and declare CFL_FACTOR. This header is generated in NRPy+.\n",
"#include \"ScalarFieldCollapse_Playground_REAL__NGHOSTS__CFL_FACTOR.h\"\n",
"\n",
"#include \"rfm_files/rfm_struct__declare.h\"\n",
"\n",
"#include \"declare_Cparameters_struct.h\"\n",
"\n",
"// All SIMD intrinsics used in SIMD-enabled C code loops are defined here:\n",
"#include \"SIMD/SIMD_intrinsics.h\"\n",
"\n",
"// Step P1: Import needed header files\n",
"#include \"stdio.h\"\n",
"#include \"stdlib.h\"\n",
"#include \"math.h\"\n",
"#include \"time.h\"\n",
"#include \"stdint.h\" // Needed for Windows GCC 6.x compatibility\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",
"#define wavespeed 1.0 // Set CFL-based \"wavespeed\" to 1.0.\n",
"#define alpha_threshold (2e-3) // Value below which we rule gravitational collapse has happened\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;i2<i2max;i2++) for(int i1=i1min;i1<i1max;i1++) for(int i0=i0min;i0<i0max;i0++)\n",
"#define LOOP_ALL_GFS_GPS(ii) _Pragma(\"omp parallel for\") \\\n",
" for(int (ii)=0;(ii)<Nxx_plus_2NGHOSTS_tot*NUM_EVOL_GFS;(ii)++)\n",
"\n",
"// Step P3: Set UUGF and VVGF macros, as well as xx_to_Cart()\n",
"#include \"boundary_conditions/gridfunction_defines.h\"\n",
"\n",
"// Step P4: Set xx_to_Cart(const paramstruct *restrict params,\n",
"// REAL *restrict xx[3],\n",
"// const int i0,const int i1,const int i2,\n",
"// REAL xCart[3]),\n",
"// which maps xx->Cartesian 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 P7: Implement the algorithm for upwinding.\n",
"// *NOTE*: This upwinding is backwards from\n",
"// usual upwinding algorithms, because the\n",
"// upwinding control vector in BSSN (the shift)\n",
"// acts like a *negative* velocity.\n",
"//#define UPWIND_ALG(UpwindVecU) UpwindVecU > 0.0 ? 1.0 : 0.0\n",
"\n",
"// Step P8: Include function for enforcing detgammabar constraint.\n",
"#include \"enforce_detgammahat_constraint.h\"\n",
"\n",
"// Step P9: Find the CFL-constrained timestep\n",
"#include \"find_timestep.h\"\n",
"\n",
"// Step P10: Declare initial data input struct:\n",
"// stores data from initial data solver,\n",
"// so they can be put on the numerical grid.\n",
"typedef struct __ID_inputs {\n",
" int interp_stencil_size;\n",
" int numlines_in_file;\n",
" REAL *r_arr,*sf_arr,*psi4_arr,*alpha_arr;\n",
"} ID_inputs;\n",
"\n",
"// Part P11: Declare all functions for setting up ScalarField initial data.\n",
"/* Routines to interpolate the ScalarField solution and convert to ADM & T^{munu}: */\n",
"#include \"../ScalarField_interp.h\"\n",
"#include \"ID_scalarfield_ADM_quantities.h\"\n",
"#include \"ID_scalarfield_spherical.h\"\n",
"#include \"ID_scalarfield_xx0xx1xx2_to_BSSN_xx0xx1xx2.h\"\n",
"#include \"ID_scalarfield.h\"\n",
"\n",
"/* Next perform the basis conversion and compute all needed BSSN quantities */\n",
"#include \"ID_ADM_xx0xx1xx2_to_BSSN_xx0xx1xx2__ALL_BUT_LAMBDAs.h\"\n",
"#include \"ID_BSSN__ALL_BUT_LAMBDAs.h\"\n",
"#include \"ID_BSSN_lambdas.h\"\n",
"\n",
"// Step P12: Set the generic driver function for setting up BSSN initial data\n",
"void initial_data(const paramstruct *restrict params,const bc_struct *restrict bcstruct,\n",
" const rfm_struct *restrict rfmstruct,\n",
" REAL *restrict xx[3], REAL *restrict auxevol_gfs, REAL *restrict in_gfs) {\n",
"#include \"set_Cparameters.h\"\n",
"\n",
" // Step 1: Set up ScalarField initial data\n",
" // Step 1.a: Read ScalarField initial data from data file\n",
" // Open the data file:\n",
" char filename[100];\n",
" sprintf(filename,\"./SFID.txt\");\n",
" FILE *fp = fopen(filename, \"r\");\n",
" if (fp == NULL) {\n",
" fprintf(stderr,\"ERROR: could not open file %s\\n\",filename);\n",
" exit(1);\n",
" }\n",
" // Count the number of lines in the data file:\n",
" int numlines_in_file = count_num_lines_in_file(fp);\n",
" // Allocate space for all data arrays:\n",
" REAL *r_arr = (REAL *)malloc(sizeof(REAL)*numlines_in_file);\n",
" REAL *sf_arr = (REAL *)malloc(sizeof(REAL)*numlines_in_file);\n",
" REAL *psi4_arr = (REAL *)malloc(sizeof(REAL)*numlines_in_file);\n",
" REAL *alpha_arr = (REAL *)malloc(sizeof(REAL)*numlines_in_file);\n",
"\n",
" // Read from the data file, filling in arrays\n",
" // read_datafile__set_arrays() may be found in ScalarField/ScalarField_interp.h\n",
" if(read_datafile__set_arrays(fp,r_arr,sf_arr,psi4_arr,alpha_arr) == 1) {\n",
" fprintf(stderr,\"ERROR WHEN READING FILE %s!\\n\",filename);\n",
" exit(1);\n",
" }\n",
" fclose(fp);\n",
"\n",
" const int interp_stencil_size = 12;\n",
" ID_inputs SF_in;\n",
" SF_in.interp_stencil_size = interp_stencil_size;\n",
" SF_in.numlines_in_file = numlines_in_file;\n",
" SF_in.r_arr = r_arr;\n",
" SF_in.sf_arr = sf_arr;\n",
" SF_in.psi4_arr = psi4_arr;\n",
" SF_in.alpha_arr = alpha_arr;\n",
"\n",
" // Step 1.b: Interpolate data from data file to set BSSN gridfunctions\n",
" ID_scalarfield(params,xx,SF_in, in_gfs);\n",
" ID_BSSN__ALL_BUT_LAMBDAs(params,xx,SF_in, in_gfs);\n",
" apply_bcs_curvilinear(params, bcstruct, NUM_EVOL_GFS, evol_gf_parity, in_gfs);\n",
" enforce_detgammahat_constraint(rfmstruct, params, in_gfs);\n",
" ID_BSSN_lambdas(params, xx, in_gfs);\n",
" apply_bcs_curvilinear(params, bcstruct, NUM_EVOL_GFS, evol_gf_parity, in_gfs);\n",
" enforce_detgammahat_constraint(rfmstruct, params, in_gfs);\n",
"\n",
" free(r_arr);\n",
" free(sf_arr);\n",
" free(psi4_arr);\n",
" free(alpha_arr);\n",
"}\n",
"\n",
"// Step P11: Declare function for evaluating Hamiltonian constraint (diagnostic)\n",
"#include \"Hamiltonian_constraint.h\"\n",
"\n",
"// Step P12: Declare rhs_eval function, which evaluates BSSN RHSs\n",
"#include \"rhs_eval.h\"\n",
"\n",
"// Step P13: Declare Ricci_eval function, which evaluates Ricci tensor\n",
"#include \"Ricci_eval.h\"\n",
"\n",
"#define max_number_of_regrids (16)\n",
"#include \"ScalarField_output_central_values.h\"\n",
"#include \"NRPyCritCol_regridding.h\"\n",
"\n",
"REAL rho_max = 0.0;\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 1D 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 && argc != 5) || atoi(argv[1]) < NGHOSTS || atoi(argv[2]) < 2 || atoi(argv[3]) < 2 /* FIXME; allow for axisymmetric sims */) {\n",
" fprintf(stderr,\"Error: Expected three command-line arguments: ./ScalarFieldCollapse_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",
" if(argc == 5) {\n",
" CFL_FACTOR = strtod(argv[4],NULL);\n",
" if(CFL_FACTOR > 0.5 && atoi(argv[3])!=2) {\n",
" fprintf(stderr,\"WARNING: CFL_FACTOR was set to %e, which is > 0.5.\\n\",CFL_FACTOR);\n",
" fprintf(stderr,\" This will generally only be stable if the simulation is purely axisymmetric\\n\");\n",
" fprintf(stderr,\" However, Nx2 was set to %d>2, which implies a non-axisymmetric simulation\\n\",atoi(argv[3]));\n",
" }\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",
" // Start with no KO dissipation\n",
" params.diss_strength = 0.0;\n",
" params.eta = 0.5/0.218; // 0.5/M_ADM\n",
"\n",
" // Set up regridding struct\n",
" NRPyCritCol_regrid_params_struct regrid_params;\n",
"#include \"set_regrid_struct.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 0h: Time coordinate parameters\n",
" REAL t_final = 6.6; /* Final time is set so that at t=t_final,\n",
" * data at the origin have not been corrupted\n",
" * by the approximate outer boundary condition */\n",
"\n",
" // Step 0i: Set timestep based on smallest proper distance between gridpoints and CFL factor\n",
" REAL dt = find_timestep(¶ms, xx);\n",
" //fprintf(stderr,\"# Timestep set to = %e\\n\",(double)dt);\n",
" int N_final = (int)(t_final / dt + 0.5); // The number of points in time.\n",
" // Add 0.5 to account for C rounding down\n",
" // typecasts to integers.\n",
" int output_every_N = 20;//(int)((REAL)N_final/800.0);\n",
" if(output_every_N == 0) output_every_N = 1;\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 0l: Set up precomputed reference metric arrays\n",
" // Step 0l.i: Allocate space for precomputed reference metric arrays.\n",
"#include \"rfm_files/rfm_struct__malloc.h\"\n",
"\n",
" // Step 0l.ii: Define precomputed reference metric arrays.\n",
" {\n",
" #include \"set_Cparameters-nopointer.h\"\n",
" #include \"rfm_files/rfm_struct__define.h\"\n",
" }\n",
"\n",
" // Step 1: Set up initial data to an exact solution\n",
" initial_data(¶ms,&bcstruct, &rfmstruct, xx, auxevol_gfs, 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(&rfmstruct, ¶ms, y_n_gfs);\n",
"\n",
" // Step 2: Start the timer, for keeping track of how fast the simulation is progressing.\n",
"#ifdef __linux__ // Use high-precision timer in Linux.\n",
" struct timespec start, end;\n",
" clock_gettime(CLOCK_REALTIME, &start);\n",
"#else // Resort to low-resolution, standards-compliant timer in non-Linux OSs\n",
" // http://www.cplusplus.com/reference/ctime/time/\n",
" time_t start_timer,end_timer;\n",
" time(&start_timer); // Resolution of one second...\n",
"#endif\n",
"\n",
" int number_of_regrids_performed = 0;\n",
" REAL t = 0.0;\n",
"\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",
" for(int n=0;n<=N_final;n++) { // Main loop to progress forward in time.\n",
"\n",
" // Step 3.a: Step forward one timestep (t -> t+dt) in time using\n",
" // chosen RK-like MoL timestepping algorithm\n",
"#include \"MoLtimestepping/RK_MoL.h\" \n",
" t += dt;\n",
" \n",
" // Step 3.b: Output central values\n",
" int lapse_collapsed = output_central_values(t,¶ms,y_n_gfs);\n",
" if( lapse_collapsed ) break;\n",
" \n",
" // Step 3.c: Check if it's time to regrid\n",
" if( (t > regrid_params.regrid_time[number_of_regrids_performed]) &&\n",
" (number_of_regrids_performed == regrid_params.regrid_counter[number_of_regrids_performed]) &&\n",
" (number_of_regrids_performed < max_number_of_regrids) ) {\n",
" regrid(regrid_params.regrid_key[number_of_regrids_performed],\n",
" n,t,\n",
" regrid_params.new_ampl_or_sinhW[number_of_regrids_performed],\n",
" &bcstruct,&rfmstruct,¶ms,\n",
" &N_final,&t_final,&dt,\n",
" xx,diagnostic_output_gfs,y_n_gfs);\n",
" number_of_regrids_performed++;\n",
"\n",
" // Turn on KO dissipation\n",
" params.diss_strength = 1.0;\n",
" }\n",
"\n",
" // Step 3.d: Progress indicator printing to stderr\n",
" // Step 3.d.i: Measure average time per iteration\n",
"#ifdef __linux__ // Use high-precision timer in Linux.\n",
" clock_gettime(CLOCK_REALTIME, &end);\n",
" const long long unsigned int time_in_ns = 1000000000L * (end.tv_sec - start.tv_sec) + end.tv_nsec - start.tv_nsec;\n",
"#else // Resort to low-resolution, standards-compliant timer in non-Linux OSs\n",
" time(&end_timer); // Resolution of one second...\n",
" REAL time_in_ns = difftime(end_timer,start_timer)*1.0e9+0.5; // Round up to avoid divide-by-zero.\n",
"#endif\n",
" const REAL s_per_iteration_avg = ((REAL)time_in_ns / (REAL)n) / 1.0e9;\n",
"\n",
" const int iterations_remaining = N_final - n;\n",
" const REAL time_remaining_in_mins = s_per_iteration_avg * (REAL)iterations_remaining / 60.0;\n",
"\n",
" const REAL num_RHS_pt_evals = (REAL)(Nxx[0]*Nxx[1]*Nxx[2]) * 4.0 * (REAL)n; // 4 RHS evals per gridpoint for RK4\n",
" const REAL RHS_pt_evals_per_sec = num_RHS_pt_evals / ((REAL)time_in_ns / 1.0e9);\n",
" \n",
"\n",
" // Step 3.d.ii: Output simulation progress to stderr\n",
" if(n%10 == 0) {\n",
" fprintf(stderr,\"%c[2K\", 27); // Clear the line\n",
" fprintf(stderr,\"It: %d t=%.2f dt=%.2e | %.1f%%; ETA %.0f s | t/h %.2f | gp/s %.2e\\r\", // \\r is carriage return, move cursor to the beginning of the line\n",
" n, t, (double)dt, (double)(100.0 * (REAL)n / (REAL)N_final),\n",
" (double)time_remaining_in_mins*60, (double)(dt * 3600.0 / s_per_iteration_avg), (double)RHS_pt_evals_per_sec);\n",
" fflush(stderr); // Flush the stderr buffer\n",
" } // End progress indicator if(n % 10 == 0)\n",
" } // End main loop to progress forward in time.\n",
" fprintf(stderr,\"\\n\"); // Clear the final line of output from progress indicator.\n",
"\n",
" // Step 4: Free all allocated memory\n",
"#include \"rfm_files/rfm_struct__freemem.h\"\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": 15,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-15T10:13:12.335088Z",
"iopub.status.busy": "2021-06-15T10:13:12.332699Z",
"iopub.status.idle": "2021-06-15T10:13:18.809062Z",
"shell.execute_reply": "2021-06-15T10:13:18.809526Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Now compiling, should take ~10 seconds...\n",
"\n",
"Compiling executable...\n",
"(EXEC): Executing `gcc -std=gnu99 -Ofast -fopenmp -march=native -funroll-loops BSSN_ScalarFieldCollapse_Ccodes/ScalarFieldCollapse_Playground.c -o BSSN_ScalarFieldCollapse_Ccodes/output/ScalarFieldCollapse_Playground -lm`...\n",
"(BENCH): Finished executing in 6.10278582572937 seconds.\n",
"Finished compilation.\n",
"(BENCH) Finished in 6.122713088989258 seconds.\n",
"\n",
"/Users/werneck/Codes/nrpytutorial/ScalarField/BSSN_ScalarFieldCollapse_Ccodes/output\n",
"(EXEC): Executing `./ScalarFieldCollapse_Playground 320 2 2 0.5`...\n",
"\u001b[2KIt: 24300 t=6.60 dt=1.78e-05 | 100.0%; ETA 0 s | t/h 5.98 | gp/s 4.77e+05557e+14\n",
"(BENCH): Finished executing in 261.22367572784424 seconds.\n",
"(EXEC): Executing `./ScalarFieldCollapse_Playground 320 2 2 0.5`...\n",
"\u001b[2KIt: 23280 t=6.58 dt=1.78e-05 | 95.8%; ETA 11 s | t/h 6.05 | gp/s 4.83e+05514e+14\n",
"(BENCH): Finished executing in 247.43084621429443 seconds.\n",
"(BENCH) Finished in 508.7111668586731 seconds.\n",
"\n"
]
}
],
"source": [
"import os\n",
"import time\n",
"import cmdline_helper as cmd\n",
"\n",
"print(\"Now compiling, should take ~10 seconds...\\n\")\n",
"start = time.time()\n",
"cmd.C_compile(os.path.join(Ccodesdir,\"ScalarFieldCollapse_Playground.c\"),\n",
" os.path.join(outdir,\"ScalarFieldCollapse_Playground\"),compile_mode=\"optimized\")\n",
"end = time.time()\n",
"print(\"(BENCH) Finished in \"+str(end-start)+\" seconds.\\n\")\n",
"\n",
"# Change to output directory\n",
"os.chdir(outdir)\n",
"# Clean up existing output files\n",
"cmd.delete_existing_files(\"out*.txt\")\n",
"cmd.delete_existing_files(\"out*.png\")\n",
"# Run executable\n",
"\n",
"print(os.getcwd())\n",
"start = time.time()\n",
"\n",
"shutil.copyfile(\"SFID_weak.txt\",\"SFID.txt\")\n",
"cmd.Execute(\"ScalarFieldCollapse_Playground\", \"320 2 2 \"+str(CFL_FACTOR),\"out320.txt\")\n",
"shutil.copyfile(\"out_central_values.dat\",\"out_weak.dat\")\n",
"os.remove(\"out_central_values.dat\")\n",
"\n",
"shutil.copyfile(\"SFID_strong.txt\",\"SFID.txt\")\n",
"cmd.Execute(\"ScalarFieldCollapse_Playground\", \"320 2 2 \"+str(CFL_FACTOR),\"out320.txt\")\n",
"shutil.copyfile(\"out_central_values.dat\",\"out_strong.dat\")\n",
"os.remove(\"out_central_values.dat\")\n",
"\n",
"end = time.time()\n",
"print(\"(BENCH) Finished in \"+str(end-start)+\" seconds.\\n\")\n",
"\n",
"# Return to root directory\n",
"os.chdir(os.path.join(\"../../\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='visualization'></a>\n",
"\n",
"# Step 7: Visualization: self-similar behavior of the lapse function \\[Back to [top](#toc)\\]\n",
"$$\\label{visualization}$$\n",
"\n",
"We now plot the values of the lapse at the origin, $\\alpha_{\\rm central}$, as a function of coordinate time $t$."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.patches import ConnectionPatch\n",
"from matplotlib.patches import Rectangle\n",
"from IPython.display import Image\n",
"\n",
"def draw_rectangle_and_connecting_lines(ax1_in,ax2_in,rec_left,rec_right,rec_bot,rec_top,connect):\n",
"\n",
" # Draw rectangle\n",
" con = Rectangle((rec_left,rec_bot),rec_right-rec_left,rec_top-rec_bot,edgecolor='black',facecolor='none',lw=0.5,ls='--')\n",
" ax1_in.add_artist(con)\n",
"\n",
" # Draw connecting lines from plot 1 to plot 2\n",
" if connect == 'bottom_to_top':\n",
" xys_1 = [(rec_left,rec_top),(rec_right,rec_top)]\n",
" xys_2 = [(rec_left,rec_bot),(rec_right,rec_bot)]\n",
" for i in range(2):\n",
" con = ConnectionPatch(xyA=xys_1[i], xyB=xys_2[i], coordsA=\"data\", coordsB=\"data\",\n",
" axesA=ax2_in, axesB=ax1_in,color=\"black\",ls='--',lw=0.5)\n",
" ax1_in.add_artist(con)\n",
" elif connect == 'left_to_right':\n",
" xys_1 = [(rec_left,rec_top),(rec_left,rec_bot)]\n",
" xys_2 = [(rec_right,rec_top),(rec_right,rec_bot)]\n",
" for i in range(2):\n",
" con = ConnectionPatch(xyA=xys_1[i], xyB=xys_2[i], coordsA=\"data\", coordsB=\"data\",\n",
" axesA=ax1_in, axesB=ax2_in,color=\"black\",ls='--',lw=0.5)\n",
" ax1_in.add_artist(con)\n",
"\n",
"# Output file name\n",
"outfile = \"lapse_self_similarity.png\"\n",
"\n",
"# Load weak data\n",
"t_w,alp_w,sf_w = np.loadtxt(os.path.join(Ccodesdir,\"output\",\"out_weak.dat\")).T\n",
"\n",
"# Load strong data\n",
"t_s,alp_s,sf_s = np.loadtxt(os.path.join(Ccodesdir,\"output\",\"out_strong.dat\")).T\n",
"\n",
"fig = plt.figure()\n",
"\n",
"linewidth = 1\n",
"color_w = 'blue'\n",
"color_s = 'orange'\n",
"\n",
"# Top panel\n",
"ax1 = plt.subplot(2, 1, 1)\n",
"plt.grid(ls=':')\n",
"plt.plot(t_w,alp_w,lw=linewidth,c=color_w,label=r\"$\\eta_{\\rm weak} = 0.303326061$\")\n",
"plt.plot(t_s,alp_s,lw=linewidth,c=color_s,ls='--',label=r\"$\\eta_{\\rm strong} = 0.303326062$\")\n",
"plt.legend(loc=2,markerfirst=False)\n",
"plt.xlim(-0.4,7.4)\n",
"plt.ylim(-0.2,1.2)\n",
"plt.xticks([0,1,2,3,4,5,6,7],['0','1','2','3','4','5','6','7'])\n",
"plt.yticks([0,0.2,0.4,0.6,0.8,1],['0.0','0.2','0.4','0.6','0.8','1.0'])\n",
"\n",
"# Bottom right panel\n",
"xl2 = [+5.6,+6.8]\n",
"yl2 = [-0.1,+0.9]\n",
"ax2 = plt.subplot(2, 2, 4)\n",
"plt.grid(ls=':')\n",
"plt.plot(t_w,alp_w,lw=linewidth,c=color_w)\n",
"plt.plot(t_s,alp_s,lw=linewidth,c=color_s,ls='--')\n",
"plt.xlim(xl2[0],xl2[1])\n",
"plt.ylim(yl2[0],yl2[1])\n",
"plt.xticks([5.8,6.2,6.6],['5.9','6.2','6.5'])\n",
"plt.yticks([0,0.4,0.8],['0.0','0.4','0.8'])\n",
"\n",
"# Bottom left panel\n",
"xl3 = [+6.51,+6.61]\n",
"yl3 = [-0.05,+0.55]\n",
"ax3 = plt.subplot(2, 2, 3)\n",
"plt.grid(ls=':')\n",
"plt.plot(t_w,alp_w,lw=linewidth,c=color_w)\n",
"plt.plot(t_s,alp_s,lw=linewidth,c=color_s,ls='--')\n",
"plt.xlim(xl3[0],xl3[1])\n",
"plt.ylim(yl3[0],yl3[1])\n",
"plt.xticks([6.52,6.56,6.6],['6.54','6.56','6.58'])\n",
"plt.yticks([0,0.25,0.5],['0.00','0.25','0.50'])\n",
"\n",
"draw_rectangle_and_connecting_lines(ax1,ax2,xl2[0],xl2[1],yl2[0],yl2[1],'bottom_to_top')\n",
"draw_rectangle_and_connecting_lines(ax2,ax3,xl3[0],xl3[1],yl3[0],yl3[1],'left_to_right')\n",
"\n",
"# Set labels\n",
"fig.text(0.5, 0.03, r\"$t$\", ha='center', va='center')\n",
"fig.text(0.03, 0.5, r\"$\\alpha_{\\rm central}$\", ha='center', va='center', rotation='vertical')\n",
"\n",
"plt.savefig(outfile,dpi=300,bbox_inches='tight',facecolor='white')\n",
"plt.close(fig)\n",
"\n",
"Image(outfile)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='output_to_pdf'></a>\n",
"\n",
"# Step 8: Output this module as $\\LaTeX$-formatted PDF file \\[Back to [top](#toc)\\]\n",
"$$\\label{output_to_pdf}$$\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-ScalarField_Collapse.pdf](Tutorial-Start_to_Finish-BSSNCurvilinear-ScalarField_Collapse.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": 17,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-15T10:13:37.841111Z",
"iopub.status.busy": "2021-06-15T10:13:37.840831Z",
"iopub.status.idle": "2021-06-15T10:13:40.961139Z",
"shell.execute_reply": "2021-06-15T10:13:40.960676Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Created Tutorial-Start_to_Finish-BSSNCurvilinear-\n",
" ScalarField_Collapse_with_regrids.tex, and compiled LaTeX file to PDF\n",
" file Tutorial-Start_to_Finish-BSSNCurvilinear-\n",
" ScalarField_Collapse_with_regrids.pdf\n"
]
}
],
"source": [
"import cmdline_helper as cmd # NRPy+: Multi-platform Python command-line interface\n",
"shutil.copy(os.path.join(\"..\",\"latex_nrpy_style.tplx\"),\"latex_nrpy_style.tplx\")\n",
"cmd.output_Jupyter_notebook_to_LaTeXed_PDF(\"Tutorial-Start_to_Finish-BSSNCurvilinear-ScalarField_Collapse_with_regrids\")\n",
"os.remove(\"latex_nrpy_style.tplx\")"
]
}
],
"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": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}