import GiRaFFEfood_NRPy_Common_Functions as gfcf # Some useful functions for GiRaFFE initial data.
import reference_metric as rfm # NRPy+: Reference metric support
import Min_Max_and_Piecewise_Expressions as noif
par.set_parval_from_str("reference_metric::CoordSystem", "Cartesian")
rfm.reference_metric()
# Step 1a: Set commonly used parameters.
thismodule = __name__
nrpyDilog = sp.Function('nrpyDilog')
from outputC import custom_functions_for_SymPy_ccode
custom_functions_for_SymPy_ccode["nrpyDilog"] = "gsl_sf_dilog"
C_SM = par.Cparameters("REAL", thismodule, ["C_SM"], 1.0)
par.initialize_param(
par.glb_param(type="bool",
module=thismodule,
parname="drop_fr",
defaultval=True))
def f_of_r(r, M):
if par.parval_from_str("drop_fr"):
return sp.sympify(0)
x = sp.sympify(2) * M / r
L = sp.sympify(0) + \
noif.coord_greater_bound(x,sp.sympify(0))*noif.coord_less_bound(x,sp.sympify(1))*nrpyDilog(x)\
+sp.Rational(1,2)*sp.log(noif.coord_greater_bound(x,sp.sympify(0))*x + noif.coord_leq_bound(x,sp.sympify(1)))\
*sp.log(noif.coord_less_bound(x,sp.sympify(1))*(sp.sympify(1)-x) + noif.coord_geq_bound(x,sp.sympify(1)))
f = r*r*(sp.sympify(2)*r-sp.sympify(3)*M)*sp.Rational(1,8)/(M**3)*L\
+(M*M+sp.sympify(3)*M*r-sp.sympify(6)*r*r)*sp.Rational(1,12)/(M*M)*sp.log(r*sp.Rational(1,2)/M)\
+sp.Rational(11,72) + M*sp.Rational(1,3)/r + r*sp.Rational(1,2)/M - r*r*sp.Rational(1,2)/(M*M)
return f
from NRPy_logo import *
#print_logo()
# Step 2: Initialize core parameter NRPy::MainModule,
# which defines the desired main module.
# E.g., scalarwave, BSSN_RHSs, BSSN_InitialData, etc.
# Contains parameter initialization, manipulation, and read-in routines
import NRPy_param_funcs as par
# Used to import needed modules dynamically
import importlib
# Initialize the MainModule parameter.
# This is the ONLY parameter initialized outside of a module!
MainModule = "scalarwave" # Default. To be overwritten later.
par.initialize_param(par.glb_param("char", "NRPy", "MainModule", MainModule))
# Step 4: Initialize NRPy+ as desired.
# Step 4a: Enter Interactive Mode if NRPy+ is run via
# `python nrpy.py`
if (len(sys.argv) == 1):
print("/* Run `python nrpy.py --help` for other command-line options */")
print("/* Entering interactive mode */\n")
# Print help message if NRPy+ is run via
# `python nrpy.py --help`
elif (len(sys.argv) == 2 and sys.argv[1] == "--help"):
print("\n \033[1m............................................\033[0m ")
print(" -={ \033[1mNRPy+ supports multiple usage modes.\033[0m }=-\n")
print(
# Author: Zachariah B. Etienne
# zachetie **at** gmail **dot* com
import NRPy_param_funcs as par # NRPy+: Parameter interface
import sympy as sp # Import SymPy, a computer algebra system written entirely in Python
from collections import namedtuple # Standard Python `collections` module: defines named tuples data structure
import os # Standard Python module for multiplatform OS-level functions
# Initialize globals related to the grid
glb_gridfcs_list = []
glb_gridfc = namedtuple('gridfunction',
'gftype name rank DIM f_infinity wavespeed')
thismodule = __name__
par.initialize_param(
par.glb_param("char", thismodule, "GridFuncMemAccess", "SENRlike"))
par.initialize_param(par.glb_param("char", thismodule, "MemAllocStyle", "210"))
par.initialize_param(par.glb_param("int", thismodule, "DIM", 3))
Nxx = par.Cparameters("int", thismodule, ["Nxx0", "Nxx1", "Nxx2"],
[64, 32, 64]) # Default to 64x32x64 grid
Nxx_plus_2NGHOSTS = par.Cparameters(
"int", thismodule,
["Nxx_plus_2NGHOSTS0", "Nxx_plus_2NGHOSTS1", "Nxx_plus_2NGHOSTS2"],
[70, 38, 70]) # Default to 64x32x64 grid w/ NGHOSTS=3
xx = par.Cparameters("REAL", thismodule, ["xx0", "xx1", "xx2"],
1e300) # These are C variables, not parameters, and
# will be overwritten; best to initialize to crazy
# number to ensure they are overwritten!
# TODO: dt = par.Cparameters("REAL", thismodule, ["dt"], 0.1)
dxx = par.Cparameters("REAL", thismodule, ["dxx0", "dxx1", "dxx2"], 0.1)
import NRPy_param_funcs as par
import re
from SIMD import expr_convert_to_SIMD_intrins
from collections import namedtuple
lhrh = namedtuple('lhrh', 'lhs rhs')
outCparams = namedtuple(
'outCparams',
'preindent includebraces declareoutputvars outCfileaccess outCverbose CSE_enable CSE_varprefix SIMD_enable SIMD_debug enable_TYPE'
)
# Parameter initialization is called once, within nrpy.py.
par.initialize_param(
par.glb_param("char", __name__, "PRECISION",
"double")) # __name__ = "outputC", this module's name.
# par.initialize_param(par.glb_param("bool", thismodule, "SIMD_enable", False))
# super fast 'uniq' function:
# f8() function from https://www.peterbe.com/plog/uniqifiers-benchmark
def superfast_uniq(seq): # Author: Dave Kirby
# Order preserving
seen = set()
return [x for x in seq if x not in seen and not seen.add(x)]
def ccode_postproc(string):
PRECISION = par.parval_from_str("PRECISION")
# In the C math library, e.g., pow(x,y) assumes x and y are doubles, and returns a double.
def unpickle_NRPy_env(NRPyEnvVars):
import pickle
# https://www.pythonforthelab.com/blog/storing-binary-data-and-serializing/
grfcs_list = []
param_list = []
Cparm_list = []
outCfunc_dict = {}
outCfuncproto_dict = {}
outCfuncoutdir_dict = {}
outCfunc_master_list = []
for WhichParamSet in NRPyEnvVars[0]:
# gridfunctions
i = 0
num_elements = pickle.loads(WhichParamSet[i])
i += 1
for lst in range(num_elements):
grfcs_list.append(
gri.glb_gridfc(gftype=pickle.loads(WhichParamSet[i + 0]),
name=pickle.loads(WhichParamSet[i + 1]),
rank=pickle.loads(WhichParamSet[i + 2]),
DIM=pickle.loads(WhichParamSet[i + 3]),
f_infinity=pickle.loads(WhichParamSet[i + 4]),
wavespeed=pickle.loads(WhichParamSet[i + 5])))
i += 6
# parameters
num_elements = pickle.loads(WhichParamSet[i])
i += 1
for lst in range(num_elements):
param_list.append(
par.glb_param(type=pickle.loads(WhichParamSet[i + 0]),
module=pickle.loads(WhichParamSet[i + 1]),
parname=pickle.loads(WhichParamSet[i + 2]),
defaultval=pickle.loads(WhichParamSet[i + 3])))
i += 4
# Cparameters
num_elements = pickle.loads(WhichParamSet[i])
i += 1
for lst in range(num_elements):
Cparm_list.append(
par.glb_Cparam(type=pickle.loads(WhichParamSet[i + 0]),
module=pickle.loads(WhichParamSet[i + 1]),
parname=pickle.loads(WhichParamSet[i + 2]),
defaultval=pickle.loads(WhichParamSet[i + 3])))
i += 4
# outC_func_dict
num_elements = pickle.loads(WhichParamSet[i])
i += 1
for lst in range(num_elements):
funcname = pickle.loads(WhichParamSet[i + 0])
funcbody = pickle.loads(WhichParamSet[i + 1])
i += 2
outCfunc_dict[funcname] = funcbody
# outC.outC_function_prototype_dict
num_elements = pickle.loads(WhichParamSet[i])
i += 1
for lst in range(num_elements):
funcname = pickle.loads(WhichParamSet[i + 0])
funcproto = pickle.loads(WhichParamSet[i + 1])
i += 2
outCfuncproto_dict[funcname] = funcproto
# outC.outC_function_outdir_dict
num_elements = pickle.loads(WhichParamSet[i])
i += 1
for lst in range(num_elements):
funcname = pickle.loads(WhichParamSet[i + 0])
funcoutdir = pickle.loads(WhichParamSet[i + 1])
i += 2
outCfuncoutdir_dict[funcname] = funcoutdir
# outC.outC_function_master_list
num_elements = pickle.loads(WhichParamSet[i])
i += 1
for lst in range(num_elements):
includes = pickle.loads(WhichParamSet[i + 0])
i += 1
prefunc = pickle.loads(WhichParamSet[i + 0])
i += 1
desc = pickle.loads(WhichParamSet[i + 0])
i += 1
c_type = pickle.loads(WhichParamSet[i + 0])
i += 1
name = pickle.loads(WhichParamSet[i + 0])
i += 1
params = pickle.loads(WhichParamSet[i + 0])
i += 1
preloop = pickle.loads(WhichParamSet[i + 0])
i += 1
body = pickle.loads(WhichParamSet[i + 0])
i += 1
loopopts = pickle.loads(WhichParamSet[i + 0])
i += 1
postloop = pickle.loads(WhichParamSet[i + 0])
i += 1
enableCparameters = pickle.loads(WhichParamSet[i + 0])
i += 1
rel_path_to_Cparams = pickle.loads(WhichParamSet[i + 0])
i += 1
# 'includes prefunc desc c_type name params preloop body loopopts postloop enableCparameters rel_path_to_Cparams append_coordsuffix'
outCfunc_master_list += [
outC.outC_function_element(
includes=includes,
prefunc=prefunc,
desc=desc,
c_type=c_type,
name=name,
params=params,
preloop=preloop,
body=body,
loopopts=loopopts,
postloop=postloop,
enableCparameters=enableCparameters,
rel_path_to_Cparams=rel_path_to_Cparams)
]
grfcs_list_uniq = []
for gf_ntuple_stored in grfcs_list:
found_gf = False
for gf_ntuple_new in grfcs_list_uniq:
if gf_ntuple_new == gf_ntuple_stored:
found_gf = True
if found_gf == False:
grfcs_list_uniq.append(gf_ntuple_stored)
param_list_uniq = []
for pr_ntuple_stored in param_list:
found_pr = False
for pr_ntuple_new in param_list_uniq:
if pr_ntuple_new == pr_ntuple_stored:
found_pr = True
if found_pr == False:
param_list_uniq.append(pr_ntuple_stored)
# Set glb_paramsvals_list:
# Step 1: Reset all paramsvals to their defaults
# BAD IDEA: OVERWRITTEN DEFAULTS SHOULD BE KEPT.
# par.glb_paramsvals_list = []
# for parm in param_list_uniq:
# par.glb_paramsvals_list.append(parm.defaultval)
Cparm_list_uniq = []
for Cp_ntuple_stored in Cparm_list:
found_Cp = False
for Cp_ntuple_new in Cparm_list_uniq:
if Cp_ntuple_new == Cp_ntuple_stored:
found_Cp = True
if found_Cp == False:
Cparm_list_uniq.append(Cp_ntuple_stored)
gri.glb_gridfcs_list = []
par.glb_params_list = []
par.glb_Cparams_list = []
gri.glb_gridfcs_list = grfcs_list_uniq
par.glb_params_list = param_list_uniq
par.glb_Cparams_list = Cparm_list_uniq
for key, item in outCfunc_dict.items():
outC.outC_function_dict[key] = item
for key, item in outCfuncproto_dict.items():
outC.outC_function_prototype_dict[key] = item
for key, item in outCfuncoutdir_dict.items():
outC.outC_function_outdir_dict[key] = item
return outCfunc_master_list
# Sets up finite difference stencils as desired.
#
# Depends on: grid.py.
# Everything depends on outputC.py.
import grid as gri
import re
from outputC import *
from operator import itemgetter
# Step 1: Initialize free parameters for this module:
import NRPy_param_funcs as par
modulename = __name__
# Centered finite difference accuracy order
par.initialize_param(par.glb_param("INT", modulename, "FD_CENTDERIVS_ORDER",
4))
def FD_outputC(filename, sympyexpr_list, params="", upwindcontrolvec=""):
outCparams = parse_outCparams_string(params)
# Step 0.a:
# In case sympyexpr_list is a single sympy expression,
# convert it to a list with just one element:
if type(sympyexpr_list) is not list:
sympyexpr_list = [sympyexpr_list]
# Step 0.b:
# finite_difference.py takes control over outCparams.includebraces here,
# which is necessary because outputC() is called twice:
# first for the reads from main memory and finite difference
# As documented in the NRPy+ tutorial module
# Tutorial-WeylScalarsInvariants-Cartesian
# Step 1: import all needed modules from NRPy+:
import sympy as sp # SymPy: The Python computer algebra package upon which NRPy+ depends
import indexedexp as ixp # NRPy+: Symbolic indexed expression (e.g., tensors, vectors, etc.) support
import grid as gri # NRPy+: Functions having to do with numerical grids
import NRPy_param_funcs as par # NRPy+: Parameter interface
import sys # Standard Python module for multiplatform OS-level operations
# Step 2: Initialize WeylScalars parameters
thismodule = __name__
# Current option: Approx_QuasiKinnersley = choice made in Baker, Campanelli, and Lousto. PRD 65, 044001 (2002)
par.initialize_param(
par.glb_param("char *", thismodule, "TetradChoice",
"Approx_QuasiKinnersley"))
# This controls what gets output. Acceptable values are "psi4_only", "all_psis", and "all_psis_and_invariants"
par.initialize_param(
par.glb_param("char *", thismodule, "output_scalars",
"all_psis_and_invariants"))
def WeylScalars_Cartesian():
# Step 3.a: Set spatial dimension (must be 3 for BSSN)
DIM = 3
par.set_parval_from_str("grid::DIM", DIM)
# Step 3.b: declare the additional gridfunctions (i.e., functions whose values are declared
# at every grid point, either inside or outside of our SymPy expressions) needed
# for this thorn:
# * the physical metric $\gamma_{ij}$,
# Author: Maria C. Babiuc Hamilton template courtesy Zachariah B. Etienne
# babiuc **at** marshall **dot* edu
from outputC import parse_outCparams_string, outC_function_dict, outC_function_prototype_dict, outC_NRPy_basic_defines_h_dict, outC_function_master_list # NRPy+: Core C code output module
import NRPy_param_funcs as par # NRPy+: parameter interface
import sympy as sp # SymPy: The Python computer algebra package upon which NRPy+ depends
import grid as gri # NRPy+: Functions having to do with numerical grids
import os, sys # Standard Python module for multiplatform OS-level functions
from hermite_interpolator_helpers import extract_from_list_of_interp_vars__base_gfs_and_interp_ops_lists
from hermite_interpolator_helpers import generate_list_of_interp_vars_from_lhrh_sympyexpr_list
from hermite_interpolator_helpers import read_gfs_from_memory, HIparams, construct_Ccode
# Step 1: Initialize free parameters for this module:
modulename = __name__
# Hermite interpolator dimension order
par.initialize_param(par.glb_param("int", modulename, "HI_DIMENSIONS_ORDER",
3))
par.initialize_param(
par.glb_param("bool", modulename, "enable_HI_functions", False))
def HI_outputC(filename, sympyexpr_list, params="", upwindcontrolvec=""):
outCparams = parse_outCparams_string(params)
# Step 0.a:
# In case sympyexpr_list is a single sympy expression,
# convert it to a list with just one element.
# This enables the rest of the routine to assume
# sympyexpr_list is indeed a list.
if not isinstance(sympyexpr_list, list):
sympyexpr_list = [sympyexpr_list]
# indexedexp.py: functions related to indexed expressions,
# including e.g., tensors and pseudotensors:
# Step 1: Load needed modules
import NRPy_param_funcs as par # NRPy+: Parameter interface
import grid as gri # NRPy+: Functions having to do with numerical grids
import functional as func # NRPy+: Python toolkit for functional programming
import sympy as sp # SymPy: The Python computer algebra package upon which NRPy+ depends
import sys # Standard Python module for multiplatform OS-level functions
import re # Standard Python module for regular expressions
thismodule = __name__
par.initialize_param(par.glb_param("char", thismodule, "symmetry_axes", ""))
def declare_indexedexp(rank, symbol=None, symmetry=None, dimension=None):
""" Generate an indexed expression of specified rank and dimension
>>> ixp = declare_indexedexp(rank=2, symbol='M', dimension=3, symmetry='sym01')
>>> assert func.pipe(ixp, lambda x: func.repeat(func.flatten, x, 1), set, len) == 6
>>> ixp = declare_indexedexp(rank=3, symbol='M', dimension=3, symmetry='sym01')
>>> assert len(set(func.repeat(func.flatten, ixp, 2))) == 18
>>> ixp = declare_indexedexp(rank=3, symbol='M', dimension=3, symmetry='sym02')
>>> assert len(set(func.repeat(func.flatten, ixp, 2))) == 18
>>> ixp = declare_indexedexp(rank=3, symbol='M', dimension=3, symmetry='sym12')
>>> assert len(set(func.repeat(func.flatten, ixp, 2))) == 18
>>> ixp = declare_indexedexp(rank=3, symbol='M', dimension=3, symmetry='sym012')
>>> assert len(set(func.repeat(func.flatten, ixp, 2))) == 10
# THIS MODULE: ../Tutorial-VacuumMaxwell_Flat_ID.ipynb
# Step P1: Import needed NRPy+ core modules:
import NRPy_param_funcs as par # NRPy+: Parameter interface
import sympy as sp # SymPy: The Python computer algebra package upon which NRPy+ depends
import indexedexp as ixp # NRPy+: Symbolic indexed expression (e.g., tensors, vectors, etc.) support
import reference_metric as rfm # NRPy+: Reference metric support
import sys # Standard Python module for multiplatform OS-level functions
from Maxwell.CommonParams import amp, lam, time # NRPy+: Common parameters for all VacuumMaxwell modules (defines amp, lam, and time)
# The name of this module ("InitialData") is given by __name__:
thismodule = __name__
# define parameter for which system to use
par.initialize_param(
par.glb_param("char", thismodule, "System_to_use", "System_I"))
# Initial data for toroidal dipole field
def Toroidal():
system = par.parval_from_str(thismodule + "::System_to_use")
DIM = par.parval_from_str("grid::DIM")
dst_basis = par.parval_from_str("reference_metric::CoordSystem")
# Set coordinate system to Cartesian
par.set_parval_from_str("reference_metric::CoordSystem", "Cartesian")
rfm.reference_metric()
global AidU, EidU, psi_ID
num_elements = pickle.loads(WhichParamSet[i])
i += 1
for lst in range(num_elements):
grfcs_list.append(
gri.glb_gridfc(gftype=pickle.loads(WhichParamSet[i + 0]),
name=pickle.loads(WhichParamSet[i + 1]),
rank=pickle.loads(WhichParamSet[i + 2]),
DIM=pickle.loads(WhichParamSet[i + 3])))
i += 4
# parameters
num_elements = pickle.loads(WhichParamSet[i])
i += 1
for lst in range(num_elements):
param_list.append(
par.glb_param(type=pickle.loads(WhichParamSet[i + 0]),
module=pickle.loads(WhichParamSet[i + 1]),
parname=pickle.loads(WhichParamSet[i + 2]),
defaultval=pickle.loads(WhichParamSet[i + 3])))
i += 4
# Cparameters
num_elements = pickle.loads(WhichParamSet[i])
i += 1
for lst in range(num_elements):
Cparm_list.append(
par.glb_Cparam(type=pickle.loads(WhichParamSet[i + 0]),
module=pickle.loads(WhichParamSet[i + 1]),
parname=pickle.loads(WhichParamSet[i + 2]),
defaultval=pickle.loads(WhichParamSet[i + 3])))
i += 4
grfcs_list_uniq = []
for gf_ntuple_stored in grfcs_list:
found_gf = False
# Depends on: outputC.py and grid.py.
# Author: Zachariah B. Etienne
# zachetie **at** gmail **dot* com
from outputC import * # NRPy+: Core C code output module
import grid as gri # NRPy+: Functions having to do with numerical grids
import sys # Standard Python module for multiplatform OS-level functions
from operator import itemgetter
# Step 1: Initialize free parameters for this module:
import NRPy_param_funcs as par
modulename = __name__
# Centered finite difference accuracy order
par.initialize_param(par.glb_param("int", modulename, "FD_CENTDERIVS_ORDER",
4))
par.initialize_param(
par.glb_param("int", modulename, "FD_KO_ORDER__CENTDERIVS_PLUS", 2))
def FD_outputC(filename, sympyexpr_list, params="", upwindcontrolvec=""):
outCparams = parse_outCparams_string(params)
# Step 0.a:
# In case sympyexpr_list is a single sympy expression,
# convert it to a list with just one element:
if type(sympyexpr_list) is not list:
sympyexpr_list = [sympyexpr_list]
# Step 0.b:
# finite_difference.py takes control over outCparams.includebraces here,
# Author: Zachariah B. Etienne
# zachetie **at** gmail **dot* com
# Step 1: Import all needed modules from NRPy+:
import sympy as sp # SymPy: The Python computer algebra package upon which NRPy+ depends
import NRPy_param_funcs as par # NRPy+: Parameter interface
import indexedexp as ixp # NRPy+: Symbolic indexed expression (e.g., tensors, vectors, etc.) support
import reference_metric as rfm # NRPy+: Reference metric support
import BSSN.BSSN_quantities as Bq # NRPy+: Computes useful BSSN quantities
import BSSN.BSSN_RHSs as Brhs # NRPy+: Constructs BSSN right-hand-side expressions
import sys # Standard Python modules for multiplatform OS-level functions
# Step 1.a: Declare/initialize parameters for this module
thismodule = __name__
par.initialize_param(
par.glb_param("char", thismodule, "LapseEvolutionOption", "OnePlusLog"))
par.initialize_param(
par.glb_param("char", thismodule, "ShiftEvolutionOption",
"GammaDriving2ndOrder_Covariant"))
def BSSN_gauge_RHSs():
# Step 1.d: Set spatial dimension (must be 3 for BSSN, as BSSN is
# a 3+1-dimensional decomposition of the general
# relativistic field equations)
DIM = 3
# Step 1.e: Given the chosen coordinate system, set up
# corresponding reference metric and needed
# reference metric quantities
# The following function call sets up the reference metric
# orthogonal coordinate scale factor
# (positive root of diagonal metric components),
# 2) xxCart[]: Cartesian coordinate (x,y,z)
# 3) xxSph[]: Spherical coordinate (r,theta,phi)
#
import time
import sympy as sp
import NRPy_param_funcs as par
import indexedexp as ixp
import grid as gri
# Step 0a: Initialize parameters
thismodule = __name__
par.initialize_param(par.glb_param("char", thismodule, "CoordSystem", "Spherical"))
# Step 0b: Declare global variables
xx = gri.xx
xxCart = ixp.zerorank1(DIM=4) # Must be set in terms of xx[]s
Cart_to_xx = ixp.zerorank1(DIM=4) # Must be set in terms of xx[]s
Cartx,Carty,Cartz = sp.symbols("Cartx Carty Cartz", real=True)
Cart = [Cartx,Carty,Cartz]
xxSph = ixp.zerorank1(DIM=4) # Must be set in terms of xx[]s
scalefactor_orthog = ixp.zerorank1(DIM=4) # Must be set in terms of xx[]s
have_already_called_reference_metric_function = False
def reference_metric():
global have_already_called_reference_metric_function # setting to global enables other modules to see updated value.
have_already_called_reference_metric_function = True
import NRPy_param_funcs as par
from collections import namedtuple
import sympy as sp
# grid.py: functions & parameters related to numerical grids:
# functions: Automatic loop output, output C code needed for gridfunction memory I/O, gridfunction registration
# Initialize globals related to the grid
glb_gridfcs_list = []
glb_gridfc = namedtuple('gridfunction', 'gftype name')
thismodule = __name__
par.initialize_param(
par.glb_param("char", thismodule, "GridFuncMemAccess", "SENRlike"))
par.initialize_param(par.glb_param("char", thismodule, "MemAllocStyle", "210"))
par.initialize_param(par.glb_param("INT", thismodule, "DIM", 3))
par.initialize_param(
par.glb_param("INT", thismodule, "Nx[DIM]", "SetAtCRuntime"))
xx = par.Cparameters("REALARRAY", thismodule, ["xx0", "xx1", "xx2", "xx3"])
def variable_type(var):
var_is_gf = False
for gf in range(len(glb_gridfcs_list)):
if str(var) == glb_gridfcs_list[gf].name:
var_is_gf = True
var_is_parameter = False
for paramname in range(len(par.glb_params_list)):
if str(var) == par.glb_params_list[paramname].parname:
var_is_parameter = True
import loop as lp # NRPy+: Generate C code loops
import indexedexp as ixp # NRPy+: Symbolic indexed expression (e.g., tensors, vectors, etc.) support
import reference_metric as rfm # NRPy+: Reference metric support
import cmdline_helper as cmd # NRPy+: Multi-platform Python command-line interface
thismodule = __name__
# There are several C parameters that we will need in this module:
M_PI = par.Cparameters("#define",thismodule,["M_PI"], "")
GAMMA_SPEED_LIMIT = par.Cparameters("REAL",thismodule,"GAMMA_SPEED_LIMIT",10.0) # Default value based on
# IllinoisGRMHD.
# GiRaFFE default = 2000.0
# There are three fixes in this module; we might not always want to do all (or even any!) of them.
# So, we initialize some NRPy+ parameters to control this behavior
par.initialize_param(par.glb_param(type="bool", module=thismodule, parname="enforce_orthogonality_StildeD_BtildeU", defaultval=True))
par.initialize_param(par.glb_param(type="bool", module=thismodule, parname="enforce_speed_limit_StildeD", defaultval=True))
par.initialize_param(par.glb_param(type="bool", module=thismodule, parname="enforce_current_sheet_prescription", defaultval=True))
def GiRaFFE_NRPy_C2P(StildeD,BU,gammaDD,gammaUU,gammadet,betaU,alpha):
sqrtgammadet = sp.sqrt(gammadet)
BtildeU = ixp.zerorank1()
for i in range(3):
# \tilde{B}^i = B^i \sqrt{\gamma}
BtildeU[i] = sqrtgammadet*BU[i]
BtildeD = ixp.zerorank1()
for i in range(3):
for j in range(3):
BtildeD[j] += gammaDD[i][j]*BtildeU[i]
# and related quantities, including rescaling matrices ReDD,
# ReU, and hatted quantities.
# DO NOT CALL IN STANDALONE PYTHON MODULE
# rfm.reference_metric()
# Step 1.c: Set spatial dimension (must be 3 for BSSN, as BSSN is
# a 3+1-dimensional decomposition of the general
# relativistic field equations)
# DO NOT CALL IN STANDALONE PYTHON MODULE
# DIM = 3
# par.set_parval_from_str("grid::DIM",DIM)
# Step 1.d: Declare/initialize parameters for this module
thismodule = __name__
par.initialize_param(
par.glb_param("char", thismodule, "EvolvedConformalFactor_cf", "W"))
par.initialize_param(
par.glb_param("bool", thismodule, "detgbarOverdetghat_equals_one", "True"))
par.initialize_param(
par.glb_param("bool", thismodule, "LeaveRicciSymbolic", "False"))
def declare_BSSN_gridfunctions_if_not_declared_already():
# Step 2: Register all needed BSSN gridfunctions.
# Declare as globals all variables that may be
# used outside this function
global hDD, aDD, lambdaU, vetU, betU, trK, cf, alpha
# Check to see if this function has already been called.
# If so, do not register the gridfunctions again!
# This module provides functions that declare and define useful BSSN quantities
# Author: Zachariah B. Etienne
# zachetie **at** gmail **dot* com
# Step 1.a: Import all needed modules from NRPy+:
import NRPy_param_funcs as par
import sympy as sp
import indexedexp as ixp
import grid as gri
import reference_metric as rfm
# Step 1.b: Declare/initialize parameters for this module
thismodule = __name__
par.initialize_param(par.glb_param("char", thismodule, "ConformalFactor", "W"))
par.initialize_param(par.glb_param("bool", thismodule, "detgbarOverdetghat_equals_one", "True"))
def declare_BSSN_gridfunctions_if_not_declared_already():
# Step 2: Register all needed BSSN gridfunctions.
# Step 2.a: Declare as globals all variables that may be
# used outside this function
global hDD,aDD,lambdaU,vetU,betU,trK,cf,alpha
# Step 2.a: First check to see if this function has already been called.
# If so, do not register the gridfunctions again!
for i in range(len(gri.glb_gridfcs_list)):
if "hDD00" in gri.glb_gridfcs_list[i].name:
hDD = ixp.declarerank2("hDD", "sym01")
aDD = ixp.declarerank2("aDD", "sym01")
lambdaU = ixp.declarerank1("lambdaU")
from outputC import *
import loop
import reference_metric as rfm
par.set_parval_from_str("reference_metric::CoordSystem", "Cartesian")
rfm.reference_metric()
# Step 1a: Set commonly used parameters.
thismodule = "GiRaFFEfood_HO"
# Set the spatial dimension parameter to 3.
par.set_parval_from_str("grid::DIM", 3)
DIM = par.parval_from_str("grid::DIM")
# Create a parameter to control the initial data choice. For now, this will only have Exact Wald as an option.
par.initialize_param(
par.glb_param("char", thismodule, "IDchoice", "Exact_Wald"))
# Step 1b: Set needed Cparameters
M = par.Cparameters("REAL", thismodule, ["M"],
1.0) # The mass of the black hole
M_PI = par.Cparameters("#define", thismodule, ["M_PI"], "") # pi, 3.141592...
KerrSchild_radial_shift = par.Cparameters("REAL", thismodule,
"KerrSchild_radial_shift",
0.4) # Default value for ExactWald
def GiRaFFEfood_HO_Exact_Wald():
# <a id='step2'></a>
#
# ### Step 2: Set the vectors A and E in Spherical coordinates
# Authors: Zachariah B. Etienne
# (zachetie **at** gmail **dot* com),
# and Patrick Nelson
# Step 1.a: import all needed modules from NRPy+:
import sympy as sp # SymPy: The Python computer algebra package upon which NRPy+ depends
import NRPy_param_funcs as par # NRPy+: Parameter interface
import indexedexp as ixp # NRPy+: Symbolic indexed expression (e.g., tensors, vectors, etc.) support
import reference_metric as rfm # NRPy+: Reference metric support
import sys # Standard Python modules for multiplatform OS-level functions
# Step 1.b: Initialize TetradChoice parameter
thismodule = __name__
# Current option: QuasiKinnersley = choice made in Baker, Campanelli, and Lousto. PRD 65, 044001 (2002)
par.initialize_param(
par.glb_param("char", thismodule, "TetradChoice", "QuasiKinnersley"))
par.initialize_param(
par.glb_param("char", thismodule, "UseCorrectUnitNormal",
"False")) # False = consistent with WeylScal4 ETK thorn.
def Psi4_tetrads():
global l4U, n4U, mre4U, mim4U
# Step 1.c: Check if tetrad choice is implemented:
if par.parval_from_str(thismodule + "::TetradChoice") != "QuasiKinnersley":
print("ERROR: " + thismodule + "::TetradChoice = " +
par.parval_from_str("TetradChoice") + " currently unsupported!")
sys.exit(1)
# Step 1.d: Given the chosen coordinate system, set up
def unpickle_NRPy_env(NRPyEnvVars):
import pickle
# https://www.pythonforthelab.com/blog/storing-binary-data-and-serializing/
grfcs_list = []
param_list = []
Cparm_list = []
outCfunc_dict = {}
outCfuncproto_dict = {}
outCfuncoutdir_dict = {}
for WhichParamSet in NRPyEnvVars[0]:
# gridfunctions
i = 0
num_elements = pickle.loads(WhichParamSet[i])
i += 1
for lst in range(num_elements):
grfcs_list.append(
gri.glb_gridfc(gftype=pickle.loads(WhichParamSet[i + 0]),
name=pickle.loads(WhichParamSet[i + 1]),
rank=pickle.loads(WhichParamSet[i + 2]),
DIM=pickle.loads(WhichParamSet[i + 3])))
i += 4
# parameters
num_elements = pickle.loads(WhichParamSet[i])
i += 1
for lst in range(num_elements):
param_list.append(
par.glb_param(type=pickle.loads(WhichParamSet[i + 0]),
module=pickle.loads(WhichParamSet[i + 1]),
parname=pickle.loads(WhichParamSet[i + 2]),
defaultval=pickle.loads(WhichParamSet[i + 3])))
i += 4
# Cparameters
num_elements = pickle.loads(WhichParamSet[i])
i += 1
for lst in range(num_elements):
Cparm_list.append(
par.glb_Cparam(type=pickle.loads(WhichParamSet[i + 0]),
module=pickle.loads(WhichParamSet[i + 1]),
parname=pickle.loads(WhichParamSet[i + 2]),
defaultval=pickle.loads(WhichParamSet[i + 3])))
i += 4
# outC_func_dict
num_elements = pickle.loads(WhichParamSet[i])
i += 1
for lst in range(num_elements):
funcname = pickle.loads(WhichParamSet[i + 0])
funcbody = pickle.loads(WhichParamSet[i + 1])
i += 2
outCfunc_dict[funcname] = funcbody
# outC_function_prototype_dict
num_elements = pickle.loads(WhichParamSet[i])
i += 1
for lst in range(num_elements):
funcname = pickle.loads(WhichParamSet[i + 0])
funcproto = pickle.loads(WhichParamSet[i + 1])
i += 2
outCfuncproto_dict[funcname] = funcproto
# outC_function_outdir_dict
num_elements = pickle.loads(WhichParamSet[i])
i += 1
for lst in range(num_elements):
funcname = pickle.loads(WhichParamSet[i + 0])
funcoutdir = pickle.loads(WhichParamSet[i + 1])
i += 2
outCfuncoutdir_dict[funcname] = funcoutdir
grfcs_list_uniq = []
for gf_ntuple_stored in grfcs_list:
found_gf = False
for gf_ntuple_new in grfcs_list_uniq:
if gf_ntuple_new == gf_ntuple_stored:
found_gf = True
if found_gf == False:
grfcs_list_uniq.append(gf_ntuple_stored)
param_list_uniq = []
for pr_ntuple_stored in param_list:
found_pr = False
for pr_ntuple_new in param_list_uniq:
if pr_ntuple_new == pr_ntuple_stored:
found_pr = True
if found_pr == False:
param_list_uniq.append(pr_ntuple_stored)
# Set glb_paramsvals_list:
# Step 1: Reset all paramsvals to their defaults
# BAD IDEA: OVERWRITTEN DEFAULTS SHOULD BE KEPT.
# par.glb_paramsvals_list = []
# for parm in param_list_uniq:
# par.glb_paramsvals_list.append(parm.defaultval)
Cparm_list_uniq = []
for Cp_ntuple_stored in Cparm_list:
found_Cp = False
for Cp_ntuple_new in Cparm_list_uniq:
if Cp_ntuple_new == Cp_ntuple_stored:
found_Cp = True
if found_Cp == False:
Cparm_list_uniq.append(Cp_ntuple_stored)
gri.glb_gridfcs_list = []
par.glb_params_list = []
par.glb_Cparams_list = []
gri.glb_gridfcs_list = grfcs_list_uniq
par.glb_params_list = param_list_uniq
par.glb_Cparams_list = Cparm_list_uniq
for key, item in outCfunc_dict.items():
outC_function_dict[key] = item
for key, item in outCfuncproto_dict.items():
outC_function_prototype_dict[key] = item
for key, item in outCfuncoutdir_dict.items():
outC_function_outdir_dict[key] = item
# (zachetie **at** gmail **dot* com),
# and Patrick Nelson
# Step 1.a: import all needed modules from NRPy+:
import sympy as sp
import NRPy_param_funcs as par
import indexedexp as ixp
import grid as gri
import finite_difference as fin
import reference_metric as rfm
# Step 1.b: Initialize TetradChoice parameter
thismodule = __name__
# Current option: QuasiKinnersley = choice made in Baker, Campanelli, and Lousto. PRD 65, 044001 (2002)
par.initialize_param(
par.glb_param("char", thismodule, "TetradChoice", "QuasiKinnersley"))
par.initialize_param(
par.glb_param("char", thismodule, "UseCorrectUnitNormal", "False"))
def v_vectorDU(v1, v2, v3, i, a):
if i == 0:
return v1[a]
elif i == 1:
return v2[a]
elif i == 2:
return v3[a]
else:
print("ERROR: unknown vector!")
exit(1)
# Author: Zachariah B. Etienne
# zachetie **at** gmail **dot* com
from outputC import parse_outCparams_string, outC_function_dict, outC_function_prototype_dict, outC_NRPy_basic_defines_h_dict, outC_function_master_list # NRPy+: Core C code output module
import NRPy_param_funcs as par # NRPy+: parameter interface
import sympy as sp # SymPy: The Python computer algebra package upon which NRPy+ depends
import grid as gri # NRPy+: Functions having to do with numerical grids
import os, sys # Standard Python module for multiplatform OS-level functions
from finite_difference_helpers import extract_from_list_of_deriv_vars__base_gfs_and_deriv_ops_lists
from finite_difference_helpers import generate_list_of_deriv_vars_from_lhrh_sympyexpr_list
from finite_difference_helpers import read_gfs_from_memory, FDparams, construct_Ccode
# Step 1: Initialize free parameters for this module:
modulename = __name__
# Centered finite difference accuracy order
par.initialize_param(par.glb_param("int", modulename, "FD_CENTDERIVS_ORDER", 4))
par.initialize_param(par.glb_param("bool", modulename, "enable_FD_functions", False))
par.initialize_param(par.glb_param("int", modulename, "FD_KO_ORDER__CENTDERIVS_PLUS", 2))
def FD_outputC(filename, sympyexpr_list, params="", upwindcontrolvec=""):
outCparams = parse_outCparams_string(params)
# Step 0.a:
# In case sympyexpr_list is a single sympy expression,
# convert it to a list with just one element.
# This enables the rest of the routine to assume
# sympyexpr_list is indeed a list.
if not isinstance(sympyexpr_list, list):
sympyexpr_list = [sympyexpr_list]
# Step 0.b:
import NRPy_param_funcs as par # NRPy+: Parameter interface
import indexedexp as ixp # NRPy+: Symbolic indexed expression (e.g., tensors, vectors, etc.) support
import grid as gri # NRPy+: Functions having to do with numerical grids
import sympy as sp # SymPy: The Python computer algebra package upon which NRPy+ depends
par.initialize_param(
par.glb_param("char", __name__, "System_to_use", "System_II"))
def MaxwellCartesian_Evol():
#Step 0: Set the spatial dimension parameter to 3.
par.set_parval_from_str("grid::DIM", 3)
DIM = par.parval_from_str("grid::DIM")
# Step 1: Set the finite differencing order to 4.
# (not needed here)
# par.set_parval_from_str("finite_difference::FD_CENTDERIVS_ORDER", 4)
# Step 2: Register gridfunctions that are needed as input.
_psi = gri.register_gridfunctions(
"EVOL", ["psi"]) # lgtm [py/unused-local-variable]
# Step 3a: Declare the rank-1 indexed expressions E_{i}, A_{i},
# and \partial_{i} \psi. Derivative variables like these
# must have an underscore in them, so the finite
# difference module can parse the variable name properly.
ED = ixp.register_gridfunctions_for_single_rank1("EVOL", "ED")
AD = ixp.register_gridfunctions_for_single_rank1("EVOL", "AD")
psi_dD = ixp.declarerank1("psi_dD")
## Step 3b: Declare the conformal metric tensor and its first
import NRPy_param_funcs as par
import indexedexp as ixp
import grid as gri
import finite_difference as fin
import reference_metric as rfm
rfm.reference_metric()
from outputC import *
import BSSN_RHSs as bssn
import sympy as sp
# Step 1: Initialize WeylScalar parameters
thismodule = __name__
# Use proper names for Tetrad Choices. If no name given (hunt the literature), then use the literature reference as the name.
TetradChoice = par.initialize_param(
par.glb_param("char", thismodule, "TetradChoice",
"Approx_QuasiKinnersley"))
# Why are these needed?
# xorig = par.initialize_param(par.glb_param("REAL", thismodule, "xorig", "0.0"))
# yorig = par.initialize_param(par.glb_param("REAL", thismodule, "yorig", "0.0"))
# zorig = par.initialize_param(par.glb_param("REAL", thismodule, "zorig", "0.0"))
# offset = par.initialize_param(par.glb_param("REAL", thismodule, "offset", "1.0e-15"))
# Step 2: Define the Levi-Civita symbol, used with tetrads <- better description needed.
def define_LeviCivitaSymbol(DIM=-1):
if DIM == -1:
DIM = par.parval_from_str("DIM")
LeviCivitaSymbol = ixp.zerorank3()
# THIS MODULE: ../Tutorial-VacuumMaxwell_Flat_ID.ipynb
# Step P1: Import needed NRPy+ core modules:
import NRPy_param_funcs as par # NRPy+: Parameter interface
import sympy as sp # SymPy: The Python computer algebra package upon which NRPy+ depends
import indexedexp as ixp # NRPy+: Symbolic indexed expression (e.g., tensors, vectors, etc.) support
import reference_metric as rfm # NRPy+: Reference metric support
import sys # Standard Python module for multiplatform OS-level functions
from Maxwell.CommonParams import amp, lam, time # NRPy+: Common parameters for all VacuumMaxwell modules (defines amp, lam, and time)
# The name of this module ("InitialData") is given by __name__:
thismodule = __name__
# define parameter for which system to use
par.initialize_param(par.glb_param("char",thismodule,"System_to_use","System_I"))
# Initial data for toroidal dipole field
def Toroidal():
system = par.parval_from_str(thismodule+"::System_to_use")
DIM = par.parval_from_str("grid::DIM")
dst_basis = par.parval_from_str("reference_metric::CoordSystem")
# Set coordinate system to Cartesian
par.set_parval_from_str("reference_metric::CoordSystem","Cartesian")
rfm.reference_metric()
global AidU, EidU, psi_ID
x = rfm.xx_to_Cart[0]