def ScalarField_Tmunu():
global T4UU
# 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)
DIM = 3
# Step 1.d: Given the chosen coordinate system, set up
# corresponding reference metric and needed
# reference metric quantities
# The following function call sets up the reference metric
# and related quantities, including rescaling matrices ReDD,
# ReU, and hatted quantities.
rfm.reference_metric()
# Step 1.e: Import all basic (unrescaled) BSSN scalars & tensors
Bq.BSSN_basic_tensors()
alpha = Bq.alpha
betaU = Bq.betaU
# Step 1.g: Define ADM quantities in terms of BSSN quantities
BtoA.ADM_in_terms_of_BSSN()
gammaDD = BtoA.gammaDD
gammaUU = BtoA.gammaUU
# Step 1.h: Define scalar field quantitites
sf_dD = ixp.declarerank1("sf_dD")
Pi = sp.Symbol("sfM", real=True)
# Step 2a: Set up \partial^{t}\varphi = Pi/alpha
sf4dU = ixp.zerorank1(DIM=4)
sf4dU[0] = Pi / alpha
# Step 2b: Set up \partial^{i}\varphi = -Pi*beta^{i}/alpha + gamma^{ij}\partial_{j}\varphi
for i in range(DIM):
sf4dU[i + 1] = -Pi * betaU[i] / alpha
for j in range(DIM):
sf4dU[i + 1] += gammaUU[i][j] * sf_dD[j]
# Step 2c: Set up \partial^{i}\varphi\partial_{i}\varphi = -Pi**2 + gamma^{ij}\partial_{i}\varphi\partial_{j}\varphi
sf4d2 = -Pi**2
for i in range(DIM):
for j in range(DIM):
sf4d2 += gammaUU[i][j] * sf_dD[i] * sf_dD[j]
# Step 3a: Setting up g^{\mu\nu}
ADMg.g4UU_ito_BSSN_or_ADM("ADM",
gammaDD=gammaDD,
betaU=betaU,
alpha=alpha,
gammaUU=gammaUU)
g4UU = ADMg.g4UU
# Step 3b: Setting up T^{\mu\nu} for a massless scalar field
T4UU = ixp.zerorank2(DIM=4)
for mu in range(4):
for nu in range(4):
T4UU[mu][nu] = sf4dU[mu] * sf4dU[nu] - g4UU[mu][nu] * sf4d2 / 2
def BSSN_source_terms_for_BSSN_RHSs(custom_T4UU=None):
global sourceterm_trK_rhs, sourceterm_a_rhsDD, sourceterm_lambda_rhsU, sourceterm_Lambdabar_rhsU
# Step 3.a: Call BSSN_source_terms_ito_T4UU to get SDD, SD, S, & rho
if custom_T4UU == "unrescaled BSSN source terms already given":
SDD = ixp.declarerank2("SDD", "sym01")
SD = ixp.declarerank1("SD")
S = sp.symbols("S", real=True)
rho = sp.symbols("rho", real=True)
else:
SDD, SD, S, rho = stress_energy_source_terms_ito_T4UU_and_ADM_or_BSSN_metricvars(
"BSSN", custom_T4UU)
PI = par.Cparameters("REAL", thismodule, ["PI"],
"3.14159265358979323846264338327950288")
alpha = sp.symbols("alpha", real=True)
# Step 3.b: trK_rhs
sourceterm_trK_rhs = 4 * PI * alpha * (rho + S)
# Step 3.c: Abar_rhsDD:
# Step 3.c.i: Compute trace-free part of S_{ij}:
import BSSN.BSSN_quantities as Bq
Bq.BSSN_basic_tensors() # Sets gammabarDD
gammabarUU, dummydet = ixp.symm_matrix_inverter3x3(
Bq.gammabarDD) # Set gammabarUU
tracefree_SDD = ixp.zerorank2()
for i in range(3):
for j in range(3):
tracefree_SDD[i][j] = SDD[i][j]
for i in range(3):
for j in range(3):
for k in range(3):
for m in range(3):
tracefree_SDD[i][j] += -sp.Rational(1, 3) * Bq.gammabarDD[
i][j] * gammabarUU[k][m] * SDD[k][m]
# Step 3.c.ii: Define exp_m4phi = e^{-4 phi}
Bq.phi_and_derivs()
# Step 3.c.iii: Evaluate stress-energy part of AbarDD's RHS
sourceterm_a_rhsDD = ixp.zerorank2()
for i in range(3):
for j in range(3):
Abar_rhsDDij = -8 * PI * alpha * Bq.exp_m4phi * tracefree_SDD[i][j]
sourceterm_a_rhsDD[i][j] = Abar_rhsDDij / rfm.ReDD[i][j]
# Step 3.d: Stress-energy part of Lambdabar_rhsU = stressenergy_Lambdabar_rhsU
sourceterm_Lambdabar_rhsU = ixp.zerorank1()
for i in range(3):
for j in range(3):
sourceterm_Lambdabar_rhsU[
i] += -16 * PI * alpha * gammabarUU[i][j] * SD[j]
sourceterm_lambda_rhsU = ixp.zerorank1()
for i in range(3):
sourceterm_lambda_rhsU[i] = sourceterm_Lambdabar_rhsU[i] / rfm.ReU[i]
def Enforce_Detgammabar_Constraint_symb_expressions():
# Set spatial dimension (must be 3 for BSSN)
DIM = 3
# Then we set the coordinate system for the numerical grid
rfm.reference_metric(
) # Create ReU, ReDD needed for rescaling B-L initial data, generating BSSN RHSs, etc.
# We will need the h_{ij} quantities defined within BSSN_RHSs
# below when we enforce the gammahat=gammabar constraint
# Step 1: All barred quantities are defined in terms of BSSN rescaled gridfunctions,
# which we declare here in case they haven't yet been declared elsewhere.
Bq.declare_BSSN_gridfunctions_if_not_declared_already()
hDD = Bq.hDD
Bq.BSSN_basic_tensors()
gammabarDD = Bq.gammabarDD
# First define the Kronecker delta:
KroneckerDeltaDD = ixp.zerorank2()
for i in range(DIM):
KroneckerDeltaDD[i][i] = sp.sympify(1)
# The detgammabar in BSSN_RHSs is set to detgammahat when BSSN_RHSs::detgbarOverdetghat_equals_one=True (default),
# so we manually compute it here:
dummygammabarUU, detgammabar = ixp.symm_matrix_inverter3x3(gammabarDD)
# Next apply the constraint enforcement equation above.
hprimeDD = ixp.zerorank2()
for i in range(DIM):
for j in range(DIM):
hprimeDD[i][j] = \
(nrpyAbs(rfm.detgammahat) / detgammabar) ** (sp.Rational(1, 3)) * (KroneckerDeltaDD[i][j] + hDD[i][j]) \
- KroneckerDeltaDD[i][j]
enforce_detg_constraint_symb_expressions = [
lhrh(lhs=gri.gfaccess("in_gfs", "hDD00"), rhs=hprimeDD[0][0]),
lhrh(lhs=gri.gfaccess("in_gfs", "hDD01"), rhs=hprimeDD[0][1]),
lhrh(lhs=gri.gfaccess("in_gfs", "hDD02"), rhs=hprimeDD[0][2]),
lhrh(lhs=gri.gfaccess("in_gfs", "hDD11"), rhs=hprimeDD[1][1]),
lhrh(lhs=gri.gfaccess("in_gfs", "hDD12"), rhs=hprimeDD[1][2]),
lhrh(lhs=gri.gfaccess("in_gfs", "hDD22"), rhs=hprimeDD[2][2])
]
return enforce_detg_constraint_symb_expressions
def setup_ADM_quantities(inputvars):
if inputvars == "ADM":
gammaDD = ixp.declarerank2("gammaDD", "sym01")
betaU = ixp.declarerank1("betaU")
alpha = sp.symbols("alpha", real=True)
elif inputvars == "BSSN":
import BSSN.ADM_in_terms_of_BSSN as AitoB
# Construct gamma_{ij} in terms of cf & gammabar_{ij}
AitoB.ADM_in_terms_of_BSSN()
gammaDD = AitoB.gammaDD
# Next construct beta^i in terms of vet^i and reference metric quantities
import BSSN.BSSN_quantities as Bq
Bq.BSSN_basic_tensors()
betaU = Bq.betaU
alpha = sp.symbols("alpha", real=True)
else:
print("inputvars = " + str(inputvars) + " not supported. Please choose ADM or BSSN.")
sys.exit(1)
return gammaDD,betaU,alpha
def Ricci__generate_symbolic_expressions():
######################################
# START: GENERATE SYMBOLIC EXPRESSIONS
starttime = print_msg_with_timing("3-Ricci tensor",
msg="Symbolic",
startstop="start")
# Evaluate 3-Ricci tensor:
import BSSN.BSSN_quantities as Bq
par.set_parval_from_str("BSSN.BSSN_quantities::LeaveRicciSymbolic",
"False")
# Register all BSSN gridfunctions if not registered already
Bq.BSSN_basic_tensors()
# Next compute Ricci tensor
Bq.RicciBar__gammabarDD_dHatD__DGammaUDD__DGammaU()
# END: GENERATE SYMBOLIC EXPRESSIONS
######################################
# Must register RbarDD as gridfunctions, as we're outputting them to gridfunctions here:
foundit = False
for i in range(len(gri.glb_gridfcs_list)):
if "RbarDD00" in gri.glb_gridfcs_list[i].name:
foundit = True
if not foundit:
ixp.register_gridfunctions_for_single_rank2("AUXEVOL", "RbarDD",
"sym01")
Ricci_SymbExpressions = [
lhrh(lhs=gri.gfaccess("auxevol_gfs", "RbarDD00"), rhs=Bq.RbarDD[0][0]),
lhrh(lhs=gri.gfaccess("auxevol_gfs", "RbarDD01"), rhs=Bq.RbarDD[0][1]),
lhrh(lhs=gri.gfaccess("auxevol_gfs", "RbarDD02"), rhs=Bq.RbarDD[0][2]),
lhrh(lhs=gri.gfaccess("auxevol_gfs", "RbarDD11"), rhs=Bq.RbarDD[1][1]),
lhrh(lhs=gri.gfaccess("auxevol_gfs", "RbarDD12"), rhs=Bq.RbarDD[1][2]),
lhrh(lhs=gri.gfaccess("auxevol_gfs", "RbarDD22"), rhs=Bq.RbarDD[2][2])
]
print_msg_with_timing("3-Ricci tensor",
msg="Symbolic",
startstop="stop",
starttime=starttime)
return Ricci_SymbExpressions
def BSSN_RHSs__generate_symbolic_expressions():
######################################
# START: GENERATE SYMBOLIC EXPRESSIONS
print("Generating symbolic expressions for BSSN RHSs...")
start = time.time()
# Enable rfm_precompute infrastructure, which results in
# BSSN RHSs that are free of transcendental functions,
# even in curvilinear coordinates, so long as
# ConformalFactor is set to "W" (default).
par.set_parval_from_str("reference_metric::enable_rfm_precompute","True")
par.set_parval_from_str("reference_metric::rfm_precompute_Ccode_outdir",os.path.join(outdir,"rfm_files/"))
# Evaluate BSSN + BSSN gauge RHSs with rfm_precompute enabled:
import BSSN.BSSN_quantities as Bq
par.set_parval_from_str("BSSN.BSSN_quantities::LeaveRicciSymbolic","True")
rhs.BSSN_RHSs()
if T4UU != None:
import BSSN.BSSN_stress_energy_source_terms as Bsest
Bsest.BSSN_source_terms_for_BSSN_RHSs(T4UU)
rhs.trK_rhs += Bsest.sourceterm_trK_rhs
for i in range(3):
# Needed for Gamma-driving shift RHSs:
rhs.Lambdabar_rhsU[i] += Bsest.sourceterm_Lambdabar_rhsU[i]
# Needed for BSSN RHSs:
rhs.lambda_rhsU[i] += Bsest.sourceterm_lambda_rhsU[i]
for j in range(3):
rhs.a_rhsDD[i][j] += Bsest.sourceterm_a_rhsDD[i][j]
gaugerhs.BSSN_gauge_RHSs()
# Add Kreiss-Oliger dissipation to the BSSN RHSs:
thismodule = "KO_Dissipation"
diss_strength = par.Cparameters("REAL", thismodule, "diss_strength", default_KO_strength)
alpha_dKOD = ixp.declarerank1("alpha_dKOD")
cf_dKOD = ixp.declarerank1("cf_dKOD")
trK_dKOD = ixp.declarerank1("trK_dKOD")
betU_dKOD = ixp.declarerank2("betU_dKOD","nosym")
vetU_dKOD = ixp.declarerank2("vetU_dKOD","nosym")
lambdaU_dKOD = ixp.declarerank2("lambdaU_dKOD","nosym")
aDD_dKOD = ixp.declarerank3("aDD_dKOD","sym01")
hDD_dKOD = ixp.declarerank3("hDD_dKOD","sym01")
for k in range(3):
gaugerhs.alpha_rhs += diss_strength*alpha_dKOD[k]*rfm.ReU[k] # ReU[k] = 1/scalefactor_orthog_funcform[k]
rhs.cf_rhs += diss_strength* cf_dKOD[k]*rfm.ReU[k] # ReU[k] = 1/scalefactor_orthog_funcform[k]
rhs.trK_rhs += diss_strength* trK_dKOD[k]*rfm.ReU[k] # ReU[k] = 1/scalefactor_orthog_funcform[k]
for i in range(3):
if "2ndOrder" in ShiftCondition:
gaugerhs.bet_rhsU[i] += diss_strength* betU_dKOD[i][k]*rfm.ReU[k] # ReU[k] = 1/scalefactor_orthog_funcform[k]
gaugerhs.vet_rhsU[i] += diss_strength* vetU_dKOD[i][k]*rfm.ReU[k] # ReU[k] = 1/scalefactor_orthog_funcform[k]
rhs.lambda_rhsU[i] += diss_strength*lambdaU_dKOD[i][k]*rfm.ReU[k] # ReU[k] = 1/scalefactor_orthog_funcform[k]
for j in range(3):
rhs.a_rhsDD[i][j] += diss_strength*aDD_dKOD[i][j][k]*rfm.ReU[k] # ReU[k] = 1/scalefactor_orthog_funcform[k]
rhs.h_rhsDD[i][j] += diss_strength*hDD_dKOD[i][j][k]*rfm.ReU[k] # ReU[k] = 1/scalefactor_orthog_funcform[k]
# We use betaU as our upwinding control vector:
Bq.BSSN_basic_tensors()
betaU = Bq.betaU
# Now that we are finished with all the rfm hatted
# quantities in generic precomputed functional
# form, let's restore them to their closed-
# form expressions.
par.set_parval_from_str("reference_metric::enable_rfm_precompute","False") # Reset to False to disable rfm_precompute.
rfm.ref_metric__hatted_quantities()
par.set_parval_from_str("BSSN.BSSN_quantities::LeaveRicciSymbolic","False")
end = time.time()
print("(BENCH) Finished BSSN RHS symbolic expressions in "+str(end-start)+" seconds.")
# END: GENERATE SYMBOLIC EXPRESSIONS
######################################
BSSN_RHSs_SymbExpressions = [lhrh(lhs=gri.gfaccess("rhs_gfs","aDD00"), rhs=rhs.a_rhsDD[0][0]),
lhrh(lhs=gri.gfaccess("rhs_gfs","aDD01"), rhs=rhs.a_rhsDD[0][1]),
lhrh(lhs=gri.gfaccess("rhs_gfs","aDD02"), rhs=rhs.a_rhsDD[0][2]),
lhrh(lhs=gri.gfaccess("rhs_gfs","aDD11"), rhs=rhs.a_rhsDD[1][1]),
lhrh(lhs=gri.gfaccess("rhs_gfs","aDD12"), rhs=rhs.a_rhsDD[1][2]),
lhrh(lhs=gri.gfaccess("rhs_gfs","aDD22"), rhs=rhs.a_rhsDD[2][2]),
lhrh(lhs=gri.gfaccess("rhs_gfs","alpha"), rhs=gaugerhs.alpha_rhs),
lhrh(lhs=gri.gfaccess("rhs_gfs","betU0"), rhs=gaugerhs.bet_rhsU[0]),
lhrh(lhs=gri.gfaccess("rhs_gfs","betU1"), rhs=gaugerhs.bet_rhsU[1]),
lhrh(lhs=gri.gfaccess("rhs_gfs","betU2"), rhs=gaugerhs.bet_rhsU[2]),
lhrh(lhs=gri.gfaccess("rhs_gfs","cf"), rhs=rhs.cf_rhs),
lhrh(lhs=gri.gfaccess("rhs_gfs","hDD00"), rhs=rhs.h_rhsDD[0][0]),
lhrh(lhs=gri.gfaccess("rhs_gfs","hDD01") ,rhs=rhs.h_rhsDD[0][1]),
lhrh(lhs=gri.gfaccess("rhs_gfs","hDD02"), rhs=rhs.h_rhsDD[0][2]),
lhrh(lhs=gri.gfaccess("rhs_gfs","hDD11"), rhs=rhs.h_rhsDD[1][1]),
lhrh(lhs=gri.gfaccess("rhs_gfs","hDD12"), rhs=rhs.h_rhsDD[1][2]),
lhrh(lhs=gri.gfaccess("rhs_gfs","hDD22"), rhs=rhs.h_rhsDD[2][2]),
lhrh(lhs=gri.gfaccess("rhs_gfs","lambdaU0"),rhs=rhs.lambda_rhsU[0]),
lhrh(lhs=gri.gfaccess("rhs_gfs","lambdaU1"),rhs=rhs.lambda_rhsU[1]),
lhrh(lhs=gri.gfaccess("rhs_gfs","lambdaU2"),rhs=rhs.lambda_rhsU[2]),
lhrh(lhs=gri.gfaccess("rhs_gfs","trK"), rhs=rhs.trK_rhs),
lhrh(lhs=gri.gfaccess("rhs_gfs","vetU0"), rhs=gaugerhs.vet_rhsU[0]),
lhrh(lhs=gri.gfaccess("rhs_gfs","vetU1"), rhs=gaugerhs.vet_rhsU[1]),
lhrh(lhs=gri.gfaccess("rhs_gfs","vetU2"), rhs=gaugerhs.vet_rhsU[2]) ]
return [betaU,BSSN_RHSs_SymbExpressions]
def ADM_in_terms_of_BSSN():
global gammaDD, gammaDDdD, gammaDDdDD, gammaUU, detgamma, GammaUDD, KDD, KDDdD
# Step 1.c: Given the chosen coordinate system, set up
# corresponding reference metric and needed
# reference metric quantities
# The following function call sets up the reference metric
# and related quantities, including rescaling matrices ReDD,
# ReU, and hatted quantities.
rfm.reference_metric()
# 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: Import all basic (unrescaled) BSSN scalars & tensors
import BSSN.BSSN_quantities as Bq
Bq.BSSN_basic_tensors()
gammabarDD = Bq.gammabarDD
cf = Bq.cf
AbarDD = Bq.AbarDD
trK = Bq.trK
Bq.gammabar__inverse_and_derivs()
gammabarDD_dD = Bq.gammabarDD_dD
gammabarDD_dDD = Bq.gammabarDD_dDD
Bq.AbarUU_AbarUD_trAbar_AbarDD_dD()
AbarDD_dD = Bq.AbarDD_dD
# Step 2: The ADM three-metric gammaDD and its
# derivatives in terms of BSSN quantities.
gammaDD = ixp.zerorank2()
exp4phi = sp.sympify(0)
if par.parval_from_str("EvolvedConformalFactor_cf") == "phi":
exp4phi = sp.exp(4 * cf)
elif par.parval_from_str("EvolvedConformalFactor_cf") == "chi":
exp4phi = (1 / cf)
elif par.parval_from_str("EvolvedConformalFactor_cf") == "W":
exp4phi = (1 / cf ** 2)
else:
print("Error EvolvedConformalFactor_cf type = \"" + par.parval_from_str("EvolvedConformalFactor_cf") + "\" unknown.")
sys.exit(1)
for i in range(DIM):
for j in range(DIM):
gammaDD[i][j] = exp4phi * gammabarDD[i][j]
# Step 2.a: Derivatives of $e^{4\phi}$
phidD = ixp.zerorank1()
phidDD = ixp.zerorank2()
cf_dD = ixp.declarerank1("cf_dD")
cf_dDD = ixp.declarerank2("cf_dDD","sym01")
if par.parval_from_str("EvolvedConformalFactor_cf") == "phi":
for i in range(DIM):
phidD[i] = cf_dD[i]
for j in range(DIM):
phidDD[i][j] = cf_dDD[i][j]
elif par.parval_from_str("EvolvedConformalFactor_cf") == "chi":
for i in range(DIM):
phidD[i] = -sp.Rational(1,4)*exp4phi*cf_dD[i]
for j in range(DIM):
phidDD[i][j] = sp.Rational(1,4)*( exp4phi**2*cf_dD[i]*cf_dD[j] - exp4phi*cf_dDD[i][j] )
elif par.parval_from_str("EvolvedConformalFactor_cf") == "W":
exp2phi = (1 / cf)
for i in range(DIM):
phidD[i] = -sp.Rational(1,2)*exp2phi*cf_dD[i]
for j in range(DIM):
phidDD[i][j] = sp.Rational(1,2)*( exp4phi*cf_dD[i]*cf_dD[j] - exp2phi*cf_dDD[i][j] )
else:
print("Error EvolvedConformalFactor_cf type = \""+par.parval_from_str("EvolvedConformalFactor_cf")+"\" unknown.")
sys.exit(1)
exp4phidD = ixp.zerorank1()
exp4phidDD = ixp.zerorank2()
for i in range(DIM):
exp4phidD[i] = 4*exp4phi*phidD[i]
for j in range(DIM):
exp4phidDD[i][j] = 16*exp4phi*phidD[i]*phidD[j] + 4*exp4phi*phidDD[i][j]
# Step 2.b: Derivatives of gammaDD, the ADM three-metric
gammaDDdD = ixp.zerorank3()
gammaDDdDD = ixp.zerorank4()
for i in range(DIM):
for j in range(DIM):
for k in range(DIM):
gammaDDdD[i][j][k] = exp4phidD[k] * gammabarDD[i][j] + exp4phi * gammabarDD_dD[i][j][k]
for l in range(DIM):
gammaDDdDD[i][j][k][l] = exp4phidDD[k][l] * gammabarDD[i][j] + \
exp4phidD[k] * gammabarDD_dD[i][j][l] + \
exp4phidD[l] * gammabarDD_dD[i][j][k] + \
exp4phi * gammabarDD_dDD[i][j][k][l]
# Step 2.c: 3-Christoffel symbols associated with ADM 3-metric gammaDD
# Step 2.c.i: First compute the inverse 3-metric gammaUU:
gammaUU, detgamma = ixp.symm_matrix_inverter3x3(gammaDD)
GammaUDD = ixp.zerorank3()
for i in range(DIM):
for j in range(DIM):
for k in range(DIM):
for l in range(DIM):
GammaUDD[i][j][k] += sp.Rational(1,2)*gammaUU[i][l]* \
(gammaDDdD[l][j][k] + gammaDDdD[l][k][j] - gammaDDdD[j][k][l])
# Step 3: Define ADM extrinsic curvature KDD and
# its first spatial derivatives KDDdD
# in terms of BSSN quantities
KDD = ixp.zerorank2()
for i in range(DIM):
for j in range(DIM):
KDD[i][j] = exp4phi * AbarDD[i][j] + sp.Rational(1, 3) * gammaDD[i][j] * trK
KDDdD = ixp.zerorank3()
trK_dD = ixp.declarerank1("trK_dD")
for i in range(DIM):
for j in range(DIM):
for k in range(DIM):
KDDdD[i][j][k] = exp4phidD[k] * AbarDD[i][j] + exp4phi * AbarDD_dD[i][j][k] + \
sp.Rational(1, 3) * (gammaDDdD[i][j][k] * trK + gammaDD[i][j] * trK_dD[k])
def BSSN_constraints(add_T4UUmunu_source_terms=False):
# Step 1.a: 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.b: Given the chosen coordinate system, set up
# corresponding reference metric and needed
# reference metric quantities
# The following function call sets up the reference metric
# and related quantities, including rescaling matrices ReDD,
# ReU, and hatted quantities.
rfm.reference_metric()
# Step 2: Hamiltonian constraint.
# First declare all needed variables
Bq.declare_BSSN_gridfunctions_if_not_declared_already() # Sets trK
Bq.BSSN_basic_tensors() # Sets AbarDD
Bq.gammabar__inverse_and_derivs() # Sets gammabarUU
Bq.AbarUU_AbarUD_trAbar_AbarDD_dD() # Sets AbarUU and AbarDD_dD
Bq.RicciBar__gammabarDD_dHatD__DGammaUDD__DGammaU() # Sets RbarDD
Bq.phi_and_derivs() # Sets phi_dBarD & phi_dBarDD
###############################
###############################
# HAMILTONIAN CONSTRAINT
###############################
###############################
# Term 1: 2/3 K^2
global H
H = sp.Rational(2, 3) * Bq.trK**2
# Term 2: -A_{ij} A^{ij}
for i in range(DIM):
for j in range(DIM):
H += -Bq.AbarDD[i][j] * Bq.AbarUU[i][j]
# Term 3a: trace(Rbar)
Rbartrace = sp.sympify(0)
for i in range(DIM):
for j in range(DIM):
Rbartrace += Bq.gammabarUU[i][j] * Bq.RbarDD[i][j]
# Term 3b: -8 \bar{\gamma}^{ij} \bar{D}_i \phi \bar{D}_j \phi = -8*phi_dBar_times_phi_dBar
# Term 3c: -8 \bar{\gamma}^{ij} \bar{D}_i \bar{D}_j \phi = -8*phi_dBarDD_contraction
phi_dBar_times_phi_dBar = sp.sympify(0) # Term 3b
phi_dBarDD_contraction = sp.sympify(0) # Term 3c
for i in range(DIM):
for j in range(DIM):
phi_dBar_times_phi_dBar += Bq.gammabarUU[i][j] * Bq.phi_dBarD[
i] * Bq.phi_dBarD[j]
phi_dBarDD_contraction += Bq.gammabarUU[i][j] * Bq.phi_dBarDD[i][j]
# Add Term 3:
H += Bq.exp_m4phi * (Rbartrace - 8 *
(phi_dBar_times_phi_dBar + phi_dBarDD_contraction))
if add_T4UUmunu_source_terms:
M_PI = par.Cparameters("#define", thismodule, "M_PI",
"") # M_PI is pi as defined in C
BTmunu.define_BSSN_T4UUmunu_rescaled_source_terms()
rho = BTmunu.rho
H += -16 * M_PI * rho
# FIXME: ADD T4UUmunu SOURCE TERMS TO MOMENTUM CONSTRAINT!
# Step 3: M^i, the momentum constraint
###############################
###############################
# MOMENTUM CONSTRAINT
###############################
###############################
# SEE Tutorial-BSSN_constraints.ipynb for full documentation.
global MU
MU = ixp.zerorank1()
# Term 2: 6 A^{ij} \partial_j \phi:
for i in range(DIM):
for j in range(DIM):
MU[i] += 6 * Bq.AbarUU[i][j] * Bq.phi_dD[j]
# Term 3: -2/3 \bar{\gamma}^{ij} K_{,j}
trK_dD = ixp.declarerank1(
"trK_dD") # Not defined in BSSN_RHSs; only trK_dupD is defined there.
for i in range(DIM):
for j in range(DIM):
MU[i] += -sp.Rational(2, 3) * Bq.gammabarUU[i][j] * trK_dD[j]
# First define aDD_dD:
aDD_dD = ixp.declarerank3("aDD_dD", "sym01")
# Then evaluate the conformal covariant derivative \bar{D}_j \bar{A}_{lm}
AbarDD_dBarD = ixp.zerorank3()
for i in range(DIM):
for j in range(DIM):
for k in range(DIM):
AbarDD_dBarD[i][j][k] = Bq.AbarDD_dD[i][j][k]
for l in range(DIM):
AbarDD_dBarD[i][j][
k] += -Bq.GammabarUDD[l][k][i] * Bq.AbarDD[l][j]
AbarDD_dBarD[i][j][
k] += -Bq.GammabarUDD[l][k][j] * Bq.AbarDD[i][l]
# Term 1: Contract twice with the metric to make \bar{D}_{j} \bar{A}^{ij}
for i in range(DIM):
for j in range(DIM):
for k in range(DIM):
for l in range(DIM):
MU[i] += Bq.gammabarUU[i][k] * Bq.gammabarUU[j][
l] * AbarDD_dBarD[k][l][j]
# Finally, we multiply by e^{-4 phi} and rescale the momentum constraint:
for i in range(DIM):
MU[i] *= Bq.exp_m4phi / rfm.ReU[i]
def output_Enforce_Detgammabar_Constraint_Ccode():
# Set spatial dimension (must be 3 for BSSN)
DIM = 3
# Then we set the coordinate system for the numerical grid
rfm.reference_metric(
) # Create ReU, ReDD needed for rescaling B-L initial data, generating BSSN RHSs, etc.
# We will need the h_{ij} quantities defined within BSSN_RHSs
# below when we enforce the gammahat=gammabar constraint
# Step 1: All barred quantities are defined in terms of BSSN rescaled gridfunctions,
# which we declare here in case they haven't yet been declared elsewhere.
Bq.declare_BSSN_gridfunctions_if_not_declared_already()
hDD = Bq.hDD
Bq.BSSN_basic_tensors()
gammabarDD = Bq.gammabarDD
# First define the Kronecker delta:
KroneckerDeltaDD = ixp.zerorank2()
for i in range(DIM):
KroneckerDeltaDD[i][i] = sp.sympify(1)
# The detgammabar in BSSN_RHSs is set to detgammahat when BSSN_RHSs::detgbarOverdetghat_equals_one=True (default),
# so we manually compute it here:
dummygammabarUU, detgammabar = ixp.symm_matrix_inverter3x3(gammabarDD)
# Next apply the constraint enforcement equation above.
hprimeDD = ixp.zerorank2()
for i in range(DIM):
for j in range(DIM):
hprimeDD[i][j] = \
(sp.Abs(rfm.detgammahat) / detgammabar) ** (sp.Rational(1, 3)) * (KroneckerDeltaDD[i][j] + hDD[i][j]) \
- KroneckerDeltaDD[i][j]
enforce_detg_constraint_vars = [ \
lhrh(lhs=gri.gfaccess("in_gfs", "hDD00"), rhs=hprimeDD[0][0]),
lhrh(lhs=gri.gfaccess("in_gfs", "hDD01"), rhs=hprimeDD[0][1]),
lhrh(lhs=gri.gfaccess("in_gfs", "hDD02"), rhs=hprimeDD[0][2]),
lhrh(lhs=gri.gfaccess("in_gfs", "hDD11"), rhs=hprimeDD[1][1]),
lhrh(lhs=gri.gfaccess("in_gfs", "hDD12"), rhs=hprimeDD[1][2]),
lhrh(lhs=gri.gfaccess("in_gfs", "hDD22"), rhs=hprimeDD[2][2])]
enforce_gammadet_string = fin.FD_outputC(
"returnstring",
enforce_detg_constraint_vars,
params="outCverbose=False,preindent=0,includebraces=False")
with open("BSSN/enforce_detgammabar_constraint.h", "w") as file:
indent = " "
file.write(
"void enforce_detgammabar_constraint(const int Nxx_plus_2NGHOSTS[3],REAL *xx[3], REAL *in_gfs) {\n\n"
)
file.write(
lp.loop(["i2", "i1", "i0"], ["0", "0", "0"], [
"Nxx_plus_2NGHOSTS[2]", "Nxx_plus_2NGHOSTS[1]",
"Nxx_plus_2NGHOSTS[0]"
], ["1", "1", "1"], [
"#pragma omp parallel for", " const REAL xx2 = xx[2][i2];",
" const REAL xx1 = xx[1][i1];"
], "", "const REAL xx0 = xx[0][i0];\n" + enforce_gammadet_string))
file.write("}\n")
print(
"Output C implementation of det(gammabar) constraint to file BSSN/enforce_detgammabar_constraint.h"
)
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
# and related quantities, including rescaling matrices ReDD,
# ReU, and hatted quantities.
rfm.reference_metric()
# Step 1.f: Define needed BSSN quantities:
# Declare scalars & tensors (in terms of rescaled BSSN quantities)
Bq.BSSN_basic_tensors()
Bq.betaU_derivs()
# Declare BSSN_RHSs (excluding the time evolution equations for the gauge conditions)
Brhs.BSSN_RHSs()
# Step 2.a: The 1+log lapse condition:
# \partial_t \alpha = \beta^i \alpha_{,i} - 2*\alpha*K
# First import expressions from BSSN_quantities
cf = Bq.cf
trK = Bq.trK
alpha = Bq.alpha
betaU = Bq.betaU
# Implement the 1+log lapse condition
global alpha_rhs
alpha_rhs = sp.sympify(0)
if par.parval_from_str(thismodule +
"::LapseEvolutionOption") == "OnePlusLog":
alpha_rhs = -2 * alpha * trK
alpha_dupD = ixp.declarerank1("alpha_dupD")
for i in range(DIM):
alpha_rhs += betaU[i] * alpha_dupD[i]
# Implement the harmonic slicing lapse condition
elif par.parval_from_str(thismodule +
"::LapseEvolutionOption") == "HarmonicSlicing":
if par.parval_from_str("BSSN.BSSN_quantities::ConformalFactor") == "W":
alpha_rhs = -3 * cf**(-4) * Brhs.cf_rhs
elif par.parval_from_str(
"BSSN.BSSN_quantities::ConformalFactor") == "phi":
alpha_rhs = 6 * sp.exp(6 * cf) * Brhs.cf_rhs
else:
print(
"Error LapseEvolutionOption==HarmonicSlicing unsupported for ConformalFactor!=(W or phi)"
)
exit(1)
# Step 2.c: Frozen lapse
# \partial_t \alpha = 0
elif par.parval_from_str(thismodule +
"::LapseEvolutionOption") == "Frozen":
alpha_rhs = sp.sympify(0)
else:
print("Error: " + thismodule + "::LapseEvolutionOption == " +
par.parval_from_str(thismodule + "::LapseEvolutionOption") +
" not supported!")
exit(1)
# Step 3.a: Set \partial_t \beta^i
# First import expressions from BSSN_quantities
BU = Bq.BU
betU = Bq.betU
betaU_dupD = Bq.betaU_dupD
# Define needed quantities
beta_rhsU = ixp.zerorank1()
B_rhsU = ixp.zerorank1()
if par.parval_from_str(
thismodule +
"::ShiftEvolutionOption") == "GammaDriving2ndOrder_NoCovariant":
# Step 3.a.i: Compute right-hand side of beta^i
# * \partial_t \beta^i = \beta^j \beta^i_{,j} + B^i
for i in range(DIM):
beta_rhsU[i] += BU[i]
for j in range(DIM):
beta_rhsU[i] += betaU[j] * betaU_dupD[i][j]
# Compute right-hand side of B^i:
eta = par.Cparameters("REAL", thismodule, ["eta"])
# Step 3.a.ii: Compute right-hand side of B^i
# * \partial_t B^i = \beta^j B^i_{,j} + 3/4 * \partial_0 \Lambda^i - eta B^i
# Step 15b: Define BU_dupD, in terms of derivative of rescaled variable \bet^i
BU_dupD = ixp.zerorank2()
betU_dupD = ixp.declarerank2("betU_dupD", "nosym")
for i in range(DIM):
for j in range(DIM):
BU_dupD[i][j] = betU_dupD[i][j] * rfm.ReU[i] + betU[
i] * rfm.ReUdD[i][j]
# Step 15c: Compute \partial_0 \bar{\Lambda}^i = (\partial_t - \beta^i \partial_i) \bar{\Lambda}^j
Lambdabar_partial0 = ixp.zerorank1()
for i in range(DIM):
Lambdabar_partial0[i] = Brhs.Lambdabar_rhsU[i]
for i in range(DIM):
for j in range(DIM):
Lambdabar_partial0[j] += -betaU[i] * Brhs.LambdabarU_dupD[j][i]
# Step 15d: Evaluate RHS of B^i:
for i in range(DIM):
B_rhsU[i] += sp.Rational(3,
4) * Lambdabar_partial0[i] - eta * BU[i]
for j in range(DIM):
B_rhsU[i] += betaU[j] * BU_dupD[i][j]
if par.parval_from_str(
thismodule +
"::ShiftEvolutionOption") == "GammaDriving2ndOrder_Covariant":
# Step 14 Option 2: \partial_t \beta^i = \left[\beta^j \bar{D}_j \beta^i\right] + B^{i}
# First we need GammabarUDD, defined in Bq.gammabar__inverse_and_derivs()
Bq.gammabar__inverse_and_derivs()
GammabarUDD = Bq.GammabarUDD
# Then compute right-hand side:
# Term 1: \beta^j \beta^i_{,j}
for i in range(DIM):
for j in range(DIM):
beta_rhsU[i] += betaU[j] * betaU_dupD[i][j]
# Term 2: \beta^j \bar{\Gamma}^i_{mj} \beta^m
for i in range(DIM):
for j in range(DIM):
for m in range(DIM):
beta_rhsU[i] += betaU[j] * GammabarUDD[i][m][j] * betaU[m]
# Term 3: B^i
for i in range(DIM):
beta_rhsU[i] += BU[i]
if par.parval_from_str(
thismodule +
"::ShiftEvolutionOption") == "GammaDriving2ndOrder_Covariant":
# Step 15: Covariant option:
# \partial_t B^i = \beta^j \bar{D}_j B^i
# + \frac{3}{4} ( \partial_t \bar{\Lambda}^{i} - \beta^j \bar{D}_j \bar{\Lambda}^{i} )
# - \eta B^{i}
# = \beta^j B^i_{,j} + \beta^j \bar{\Gamma}^i_{mj} B^m
# + \frac{3}{4}[ \partial_t \bar{\Lambda}^{i}
# - \beta^j (\bar{\Lambda}^i_{,j} + \bar{\Gamma}^i_{mj} \bar{\Lambda}^m)]
# - \eta B^{i}
# Term 1, part a: First compute B^i_{,j} using upwinded derivative
BU_dupD = ixp.zerorank2()
betU_dupD = ixp.declarerank2("betU_dupD", "nosym")
for i in range(DIM):
for j in range(DIM):
BU_dupD[i][j] = betU_dupD[i][j] * rfm.ReU[i] + betU[
i] * rfm.ReUdD[i][j]
# Term 1: \beta^j B^i_{,j}
for i in range(DIM):
for j in range(DIM):
B_rhsU[i] += betaU[j] * BU_dupD[i][j]
# Term 2: \beta^j \bar{\Gamma}^i_{mj} B^m
for i in range(DIM):
for j in range(DIM):
for m in range(DIM):
B_rhsU[i] += betaU[j] * GammabarUDD[i][m][j] * BU[m]
# Term 3: \frac{3}{4}\partial_t \bar{\Lambda}^{i}
for i in range(DIM):
B_rhsU[i] += sp.Rational(3, 4) * Brhs.Lambdabar_rhsU[i]
# Term 4: -\frac{3}{4}\beta^j \bar{\Lambda}^i_{,j}
for i in range(DIM):
for j in range(DIM):
B_rhsU[i] += -sp.Rational(
3, 4) * betaU[j] * Brhs.LambdabarU_dupD[i][j]
# Term 5: -\frac{3}{4}\beta^j \bar{\Gamma}^i_{mj} \bar{\Lambda}^m
for i in range(DIM):
for j in range(DIM):
for m in range(DIM):
B_rhsU[i] += -sp.Rational(3, 4) * betaU[j] * GammabarUDD[
i][m][j] * Bq.LambdabarU[m]
# Term 6: - \eta B^i
# eta is a free parameter; we declare it here:
eta = par.Cparameters("REAL", thismodule, ["eta"])
for i in range(DIM):
B_rhsU[i] += -eta * BU[i]
# Step 4: Rescale the BSSN gauge RHS quantities so that the evolved
# variables may remain smooth across coord singularities
global vet_rhsU, bet_rhsU
vet_rhsU = ixp.zerorank1()
bet_rhsU = ixp.zerorank1()
for i in range(DIM):
vet_rhsU[i] = beta_rhsU[i] / rfm.ReU[i]
bet_rhsU[i] = B_rhsU[i] / rfm.ReU[i]
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
# corresponding reference metric and needed
# reference metric quantities
# The following function call sets up the reference metric
# and related quantities, including rescaling matrices ReDD,
# ReU, and hatted quantities.
rfm.reference_metric()
# Step 1.e: 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.f: Import all ADM quantities as written in terms of BSSN quantities
import BSSN.ADM_in_terms_of_BSSN as AB
AB.ADM_in_terms_of_BSSN()
# Step 2.a: Declare the Cartesian x,y,z in terms of
# xx0,xx1,xx2.
x = rfm.xxCart[0]
y = rfm.xxCart[1]
z = rfm.xxCart[2]
# Step 2.b: Declare detgamma and gammaUU from
# BSSN.ADM_in_terms_of_BSSN;
# simplify detgamma & gammaUU expressions,
# which expedites Psi4 codegen.
detgamma = sp.simplify(AB.detgamma)
gammaUU = ixp.zerorank2()
for i in range(DIM):
for j in range(DIM):
gammaUU[i][j] = sp.simplify(AB.gammaUU[i][j])
# Step 2.c: Define v1U and v2U
v1UCart = [-y, x, sp.sympify(0)]
v2UCart = [x, y, z]
# Step 2.d: Construct the Jacobian d x_Cart^i / d xx^j
Jac_dUCart_dDrfmUD = ixp.zerorank2()
for i in range(DIM):
for j in range(DIM):
Jac_dUCart_dDrfmUD[i][j] = sp.simplify(
sp.diff(rfm.xxCart[i], rfm.xx[j]))
# Step 2.e: Invert above Jacobian to get needed d xx^j / d x_Cart^i
Jac_dUrfm_dDCartUD, dummyDET = ixp.generic_matrix_inverter3x3(
Jac_dUCart_dDrfmUD)
# Step 2.e.i: Simplify expressions for d xx^j / d x_Cart^i:
for i in range(DIM):
for j in range(DIM):
Jac_dUrfm_dDCartUD[i][j] = sp.simplify(Jac_dUrfm_dDCartUD[i][j])
# Step 2.f: Transform v1U and v2U from the Cartesian to the xx^i basis
v1U = ixp.zerorank1()
v2U = ixp.zerorank1()
for i in range(DIM):
for j in range(DIM):
v1U[i] += Jac_dUrfm_dDCartUD[i][j] * v1UCart[j]
v2U[i] += Jac_dUrfm_dDCartUD[i][j] * v2UCart[j]
# Step 2.g: Define v3U
v3U = ixp.zerorank1()
LeviCivitaSymbolDDD = ixp.LeviCivitaSymbol_dim3_rank3()
for a in range(DIM):
for b in range(DIM):
for c in range(DIM):
for d in range(DIM):
v3U[a] += sp.sqrt(detgamma) * gammaUU[a][
d] * LeviCivitaSymbolDDD[d][b][c] * v1U[b] * v2U[c]
# Step 2.g.i: Simplify expressions for v1U,v2U,v3U. This greatly expedites the C code generation (~10x faster)
# Drat. Simplification with certain versions of SymPy & coord systems results in a hang. Let's just
# evaluate the expressions so the most trivial optimizations can be performed.
for a in range(DIM):
v1U[a] = v1U[a].doit() # sp.simplify(v1U[a])
v2U[a] = v2U[a].doit() # sp.simplify(v2U[a])
v3U[a] = v3U[a].doit() # sp.simplify(v3U[a])
# Step 2.h: Define omega_{ij}
omegaDD = ixp.zerorank2()
gammaDD = AB.gammaDD
def v_vectorDU(v1U, v2U, v3U, i, a):
if i == 0:
return v1U[a]
if i == 1:
return v2U[a]
if i == 2:
return v3U[a]
print("ERROR: unknown vector!")
sys.exit(1)
def update_omega(omegaDD, i, j, v1U, v2U, v3U, gammaDD):
omegaDD[i][j] = sp.sympify(0)
for a in range(DIM):
for b in range(DIM):
omegaDD[i][j] += v_vectorDU(v1U, v2U, v3U, i, a) * v_vectorDU(
v1U, v2U, v3U, j, b) * gammaDD[a][b]
# Step 2.i: Define e^a_i. Note that:
# omegaDD[0][0] = \omega_{11} above;
# omegaDD[1][1] = \omega_{22} above, etc.
# First e_1^a: Orthogonalize & normalize:
e1U = ixp.zerorank1()
update_omega(omegaDD, 0, 0, v1U, v2U, v3U, gammaDD)
for a in range(DIM):
e1U[a] = v1U[a] / sp.sqrt(omegaDD[0][0])
# Next e_2^a: First orthogonalize:
e2U = ixp.zerorank1()
update_omega(omegaDD, 0, 1, e1U, v2U, v3U, gammaDD)
for a in range(DIM):
e2U[a] = (v2U[a] - omegaDD[0][1] * e1U[a])
# Then normalize:
update_omega(omegaDD, 1, 1, e1U, e2U, v3U, gammaDD)
for a in range(DIM):
e2U[a] /= sp.sqrt(omegaDD[1][1])
# Next e_3^a: First orthogonalize:
e3U = ixp.zerorank1()
update_omega(omegaDD, 0, 2, e1U, e2U, v3U, gammaDD)
update_omega(omegaDD, 1, 2, e1U, e2U, v3U, gammaDD)
for a in range(DIM):
e3U[a] = (v3U[a] - omegaDD[0][2] * e1U[a] - omegaDD[1][2] * e2U[a])
# Then normalize:
update_omega(omegaDD, 2, 2, e1U, e2U, e3U, gammaDD)
for a in range(DIM):
e3U[a] /= sp.sqrt(omegaDD[2][2])
# Step 2.j: Construct l^mu, n^mu, and m^mu, based on r^mu, theta^mu, phi^mu, and u^mu:
r4U = ixp.zerorank1(DIM=4)
u4U = ixp.zerorank1(DIM=4)
theta4U = ixp.zerorank1(DIM=4)
phi4U = ixp.zerorank1(DIM=4)
for a in range(DIM):
r4U[a + 1] = e2U[a]
theta4U[a + 1] = e3U[a]
phi4U[a + 1] = e1U[a]
# FIXME? assumes alpha=1, beta^i = 0
if par.parval_from_str(thismodule + "::UseCorrectUnitNormal") == "False":
u4U[0] = 1
else:
# Eq. 2.116 in Baumgarte & Shapiro:
# n^mu = {1/alpha, -beta^i/alpha}. Note that n_mu = {alpha,0}, so n^mu n_mu = -1.
import BSSN.BSSN_quantities as Bq
Bq.declare_BSSN_gridfunctions_if_not_declared_already()
Bq.BSSN_basic_tensors()
u4U[0] = 1 / Bq.alpha
for i in range(DIM):
u4U[i + 1] = -Bq.betaU[i] / Bq.alpha
l4U = ixp.zerorank1(DIM=4)
n4U = ixp.zerorank1(DIM=4)
mre4U = ixp.zerorank1(DIM=4)
mim4U = ixp.zerorank1(DIM=4)
# M_SQRT1_2 = 1 / sqrt(2) (defined in math.h on Linux)
M_SQRT1_2 = par.Cparameters("#define", thismodule, "M_SQRT1_2", "")
isqrt2 = M_SQRT1_2 #1/sp.sqrt(2) <- SymPy drops precision to 15 sig. digits in unit tests
for mu in range(4):
l4U[mu] = isqrt2 * (u4U[mu] + r4U[mu])
n4U[mu] = isqrt2 * (u4U[mu] - r4U[mu])
mre4U[mu] = isqrt2 * theta4U[mu]
mim4U[mu] = isqrt2 * phi4U[mu]
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
# and related quantities, including rescaling matrices ReDD,
# ReU, and hatted quantities.
rfm.reference_metric()
# Step 1.f: Define needed BSSN quantities:
# Declare scalars & tensors (in terms of rescaled BSSN quantities)
Bq.BSSN_basic_tensors()
Bq.betaU_derivs()
# Declare BSSN_RHSs (excluding the time evolution equations for the gauge conditions),
# if they haven't already been declared.
if Brhs.have_already_called_BSSN_RHSs_function == False:
print(
"BSSN_gauge_RHSs() Error: You must call BSSN_RHSs() before calling BSSN_gauge_RHSs()."
)
sys.exit(1)
# Step 2: Lapse conditions
LapseEvolOption = par.parval_from_str(thismodule +
"::LapseEvolutionOption")
# Step 2.a: The 1+log lapse condition:
# \partial_t \alpha = \beta^i \alpha_{,i} - 2*\alpha*K
# First import expressions from BSSN_quantities
cf = Bq.cf
trK = Bq.trK
alpha = Bq.alpha
betaU = Bq.betaU
# Implement the 1+log lapse condition
global alpha_rhs
alpha_rhs = sp.sympify(0)
if LapseEvolOption == "OnePlusLog":
alpha_rhs = -2 * alpha * trK
alpha_dupD = ixp.declarerank1("alpha_dupD")
for i in range(DIM):
alpha_rhs += betaU[i] * alpha_dupD[i]
# Step 2.b: Implement the harmonic slicing lapse condition
elif LapseEvolOption == "HarmonicSlicing":
if par.parval_from_str(
"BSSN.BSSN_quantities::EvolvedConformalFactor_cf") == "W":
alpha_rhs = -3 * cf**(-4) * Brhs.cf_rhs
elif par.parval_from_str(
"BSSN.BSSN_quantities::EvolvedConformalFactor_cf") == "phi":
alpha_rhs = 6 * sp.exp(6 * cf) * Brhs.cf_rhs
else:
print(
"Error LapseEvolutionOption==HarmonicSlicing unsupported for EvolvedConformalFactor_cf!=(W or phi)"
)
sys.exit(1)
# Step 2.c: Frozen lapse
# \partial_t \alpha = 0
elif LapseEvolOption == "Frozen":
alpha_rhs = sp.sympify(0)
else:
print("Error: " + thismodule + "::LapseEvolutionOption == " +
LapseEvolOption + " not supported!")
sys.exit(1)
# Step 3.a: Set \partial_t \beta^i
# First check that ShiftEvolutionOption parameter choice is supported.
ShiftEvolOption = par.parval_from_str(thismodule +
"::ShiftEvolutionOption")
if ShiftEvolOption != "Frozen" and \
ShiftEvolOption != "GammaDriving2ndOrder_NoCovariant" and \
ShiftEvolOption != "GammaDriving2ndOrder_Covariant" and \
ShiftEvolOption != "GammaDriving2ndOrder_Covariant__Hatted" and \
ShiftEvolOption != "GammaDriving1stOrder_Covariant" and \
ShiftEvolOption != "GammaDriving1stOrder_Covariant__Hatted":
print("Error: ShiftEvolutionOption == " + ShiftEvolOption +
" unsupported!")
sys.exit(1)
# Next import expressions from BSSN_quantities
BU = Bq.BU
betU = Bq.betU
betaU_dupD = Bq.betaU_dupD
# Define needed quantities
beta_rhsU = ixp.zerorank1()
B_rhsU = ixp.zerorank1()
# In the case of Frozen shift condition, we
# explicitly set the betaU and BU RHS's to zero
# instead of relying on the ixp.zerorank1()'s above,
# for safety.
if ShiftEvolOption == "Frozen":
for i in range(DIM):
beta_rhsU[i] = sp.sympify(0)
BU[i] = sp.sympify(0)
if ShiftEvolOption == "GammaDriving2ndOrder_NoCovariant":
# Step 3.a.i: Compute right-hand side of beta^i
# * \partial_t \beta^i = \beta^j \beta^i_{,j} + B^i
for i in range(DIM):
beta_rhsU[i] += BU[i]
for j in range(DIM):
beta_rhsU[i] += betaU[j] * betaU_dupD[i][j]
# Compute right-hand side of B^i:
eta = par.Cparameters("REAL", thismodule, ["eta"], 2.0)
# Step 3.a.ii: Compute right-hand side of B^i
# * \partial_t B^i = \beta^j B^i_{,j} + 3/4 * \partial_0 \Lambda^i - eta B^i
# Step 3.a.iii: Define BU_dupD, in terms of derivative of rescaled variable \bet^i
BU_dupD = ixp.zerorank2()
betU_dupD = ixp.declarerank2("betU_dupD", "nosym")
for i in range(DIM):
for j in range(DIM):
BU_dupD[i][j] = betU_dupD[i][j] * rfm.ReU[i] + betU[
i] * rfm.ReUdD[i][j]
# Step 3.a.iv: Compute \partial_0 \bar{\Lambda}^i = (\partial_t - \beta^i \partial_i) \bar{\Lambda}^j
Lambdabar_partial0 = ixp.zerorank1()
for i in range(DIM):
Lambdabar_partial0[i] = Brhs.Lambdabar_rhsU[i]
for i in range(DIM):
for j in range(DIM):
Lambdabar_partial0[j] += -betaU[i] * Brhs.LambdabarU_dupD[j][i]
# Step 3.a.v: Evaluate RHS of B^i:
for i in range(DIM):
B_rhsU[i] += sp.Rational(3,
4) * Lambdabar_partial0[i] - eta * BU[i]
for j in range(DIM):
B_rhsU[i] += betaU[j] * BU_dupD[i][j]
# Step 3.b: The right-hand side of the \partial_t \beta^i equation
if "GammaDriving2ndOrder_Covariant" in ShiftEvolOption:
# Step 3.b Option 2: \partial_t \beta^i = \left[\beta^j \bar{D}_j \beta^i\right] + B^{i}
# First we need GammabarUDD, defined in Bq.gammabar__inverse_and_derivs()
Bq.gammabar__inverse_and_derivs()
ConnectionUDD = Bq.GammabarUDD
# If instead we wish to use the Hatted covariant derivative, we replace
# ConnectionUDD with GammahatUDD:
if ShiftEvolOption == "GammaDriving2ndOrder_Covariant__Hatted":
ConnectionUDD = rfm.GammahatUDD
# Then compute right-hand side:
# Term 1: \beta^j \beta^i_{,j}
for i in range(DIM):
for j in range(DIM):
beta_rhsU[i] += betaU[j] * betaU_dupD[i][j]
# Term 2: \beta^j \bar{\Gamma}^i_{mj} \beta^m
for i in range(DIM):
for j in range(DIM):
for m in range(DIM):
beta_rhsU[
i] += betaU[j] * ConnectionUDD[i][m][j] * betaU[m]
# Term 3: B^i
for i in range(DIM):
beta_rhsU[i] += BU[i]
if "GammaDriving2ndOrder_Covariant" in ShiftEvolOption:
ConnectionUDD = Bq.GammabarUDD
# If instead we wish to use the Hatted covariant derivative, we replace
# ConnectionUDD with GammahatUDD:
if ShiftEvolOption == "GammaDriving2ndOrder_Covariant__Hatted":
ConnectionUDD = rfm.GammahatUDD
# Step 3.c: Covariant option:
# \partial_t B^i = \beta^j \bar{D}_j B^i
# + \frac{3}{4} ( \partial_t \bar{\Lambda}^{i} - \beta^j \bar{D}_j \bar{\Lambda}^{i} )
# - \eta B^{i}
# = \beta^j B^i_{,j} + \beta^j \bar{\Gamma}^i_{mj} B^m
# + \frac{3}{4}[ \partial_t \bar{\Lambda}^{i}
# - \beta^j (\bar{\Lambda}^i_{,j} + \bar{\Gamma}^i_{mj} \bar{\Lambda}^m)]
# - \eta B^{i}
# Term 1, part a: First compute B^i_{,j} using upwinded derivative
BU_dupD = ixp.zerorank2()
betU_dupD = ixp.declarerank2("betU_dupD", "nosym")
for i in range(DIM):
for j in range(DIM):
BU_dupD[i][j] = betU_dupD[i][j] * rfm.ReU[i] + betU[
i] * rfm.ReUdD[i][j]
# Term 1: \beta^j B^i_{,j}
for i in range(DIM):
for j in range(DIM):
B_rhsU[i] += betaU[j] * BU_dupD[i][j]
# Term 2: \beta^j \bar{\Gamma}^i_{mj} B^m
for i in range(DIM):
for j in range(DIM):
for m in range(DIM):
B_rhsU[i] += betaU[j] * ConnectionUDD[i][m][j] * BU[m]
# Term 3: \frac{3}{4}\partial_t \bar{\Lambda}^{i}
for i in range(DIM):
B_rhsU[i] += sp.Rational(3, 4) * Brhs.Lambdabar_rhsU[i]
# Term 4: -\frac{3}{4}\beta^j \bar{\Lambda}^i_{,j}
for i in range(DIM):
for j in range(DIM):
B_rhsU[i] += -sp.Rational(
3, 4) * betaU[j] * Brhs.LambdabarU_dupD[i][j]
# Term 5: -\frac{3}{4}\beta^j \bar{\Gamma}^i_{mj} \bar{\Lambda}^m
for i in range(DIM):
for j in range(DIM):
for m in range(DIM):
B_rhsU[i] += -sp.Rational(3, 4) * betaU[j] * ConnectionUDD[
i][m][j] * Bq.LambdabarU[m]
# Term 6: - \eta B^i
# eta is a free parameter; we declare it here:
eta = par.Cparameters("REAL", thismodule, ["eta"], 2.0)
for i in range(DIM):
B_rhsU[i] += -eta * BU[i]
if "GammaDriving1stOrder_Covariant" in ShiftEvolOption:
# Step 3.c: \partial_t \beta^i = \left[\beta^j \bar{D}_j \beta^i\right] + 3/4 Lambdabar^i - eta*beta^i
# First set \partial_t B^i = 0:
B_rhsU = ixp.zerorank1() # \partial_t B^i = 0
# Second, set \partial_t beta^i RHS:
# Compute covariant advection term:
# We need GammabarUDD, defined in Bq.gammabar__inverse_and_derivs()
Bq.gammabar__inverse_and_derivs()
ConnectionUDD = Bq.GammabarUDD
# If instead we wish to use the Hatted covariant derivative, we replace
# ConnectionUDD with GammahatUDD:
if ShiftEvolOption == "GammaDriving1stOrder_Covariant__Hatted":
ConnectionUDD = rfm.GammahatUDD
# Term 1: \beta^j \beta^i_{,j}
for i in range(DIM):
for j in range(DIM):
beta_rhsU[i] += betaU[j] * betaU_dupD[i][j]
# Term 2: \beta^j \bar{\Gamma}^i_{mj} \beta^m
for i in range(DIM):
for j in range(DIM):
for m in range(DIM):
beta_rhsU[
i] += betaU[j] * ConnectionUDD[i][m][j] * betaU[m]
# Term 3: 3/4 Lambdabar^i - eta*beta^i
eta = par.Cparameters("REAL", thismodule, ["eta"], 2.0)
for i in range(DIM):
beta_rhsU[i] += sp.Rational(3,
4) * Bq.LambdabarU[i] - eta * betaU[i]
# Step 4: Rescale the BSSN gauge RHS quantities so that the evolved
# variables may remain smooth across coord singularities
global vet_rhsU, bet_rhsU
vet_rhsU = ixp.zerorank1()
bet_rhsU = ixp.zerorank1()
for i in range(DIM):
vet_rhsU[i] = beta_rhsU[i] / rfm.ReU[i]
bet_rhsU[i] = B_rhsU[i] / rfm.ReU[i]
def BSSN_RHSs__generate_symbolic_expressions(
LapseCondition="OnePlusLog",
ShiftCondition="GammaDriving2ndOrder_Covariant",
enable_KreissOliger_dissipation=True,
enable_stress_energy_source_terms=False,
leave_Ricci_symbolic=True):
######################################
# START: GENERATE SYMBOLIC EXPRESSIONS
starttime = print_msg_with_timing("BSSN_RHSs",
msg="Symbolic",
startstop="start")
# Returns None if enable_stress_energy_source_terms==False; otherwise returns symb expressions for T4UU
T4UU = register_stress_energy_source_terms_return_T4UU(
enable_stress_energy_source_terms)
# Evaluate BSSN RHSs:
import BSSN.BSSN_quantities as Bq
par.set_parval_from_str("BSSN.BSSN_quantities::LeaveRicciSymbolic",
str(leave_Ricci_symbolic))
rhs.BSSN_RHSs()
if enable_stress_energy_source_terms:
import BSSN.BSSN_stress_energy_source_terms as Bsest
Bsest.BSSN_source_terms_for_BSSN_RHSs(T4UU)
rhs.trK_rhs += Bsest.sourceterm_trK_rhs
for i in range(3):
# Needed for Gamma-driving shift RHSs:
rhs.Lambdabar_rhsU[i] += Bsest.sourceterm_Lambdabar_rhsU[i]
# Needed for BSSN RHSs:
rhs.lambda_rhsU[i] += Bsest.sourceterm_lambda_rhsU[i]
for j in range(3):
rhs.a_rhsDD[i][j] += Bsest.sourceterm_a_rhsDD[i][j]
par.set_parval_from_str("BSSN.BSSN_gauge_RHSs::LapseEvolutionOption",
LapseCondition)
par.set_parval_from_str("BSSN.BSSN_gauge_RHSs::ShiftEvolutionOption",
ShiftCondition)
gaugerhs.BSSN_gauge_RHSs() # Can depend on above RHSs
# Restore BSSN.BSSN_quantities::LeaveRicciSymbolic to False
par.set_parval_from_str("BSSN.BSSN_quantities::LeaveRicciSymbolic",
"False")
# Add Kreiss-Oliger dissipation to the BSSN RHSs:
if enable_KreissOliger_dissipation:
thismodule = "KO_Dissipation"
diss_strength = par.Cparameters(
"REAL", thismodule, "diss_strength", 0.1
) # *Bq.cf # *Bq.cf*Bq.cf*Bq.cf # cf**1 is found better than cf**4 over the long term.
alpha_dKOD = ixp.declarerank1("alpha_dKOD")
cf_dKOD = ixp.declarerank1("cf_dKOD")
trK_dKOD = ixp.declarerank1("trK_dKOD")
betU_dKOD = ixp.declarerank2("betU_dKOD", "nosym")
vetU_dKOD = ixp.declarerank2("vetU_dKOD", "nosym")
lambdaU_dKOD = ixp.declarerank2("lambdaU_dKOD", "nosym")
aDD_dKOD = ixp.declarerank3("aDD_dKOD", "sym01")
hDD_dKOD = ixp.declarerank3("hDD_dKOD", "sym01")
for k in range(3):
gaugerhs.alpha_rhs += diss_strength * alpha_dKOD[k] * rfm.ReU[
k] # ReU[k] = 1/scalefactor_orthog_funcform[k]
rhs.cf_rhs += diss_strength * cf_dKOD[k] * rfm.ReU[
k] # ReU[k] = 1/scalefactor_orthog_funcform[k]
rhs.trK_rhs += diss_strength * trK_dKOD[k] * rfm.ReU[
k] # ReU[k] = 1/scalefactor_orthog_funcform[k]
for i in range(3):
if "2ndOrder" in ShiftCondition:
gaugerhs.bet_rhsU[
i] += diss_strength * betU_dKOD[i][k] * rfm.ReU[
k] # ReU[k] = 1/scalefactor_orthog_funcform[k]
gaugerhs.vet_rhsU[
i] += diss_strength * vetU_dKOD[i][k] * rfm.ReU[
k] # ReU[k] = 1/scalefactor_orthog_funcform[k]
rhs.lambda_rhsU[
i] += diss_strength * lambdaU_dKOD[i][k] * rfm.ReU[
k] # ReU[k] = 1/scalefactor_orthog_funcform[k]
for j in range(3):
rhs.a_rhsDD[i][
j] += diss_strength * aDD_dKOD[i][j][k] * rfm.ReU[
k] # ReU[k] = 1/scalefactor_orthog_funcform[k]
rhs.h_rhsDD[i][
j] += diss_strength * hDD_dKOD[i][j][k] * rfm.ReU[
k] # ReU[k] = 1/scalefactor_orthog_funcform[k]
# We use betaU as our upwinding control vector:
Bq.BSSN_basic_tensors()
betaU = Bq.betaU
# END: GENERATE SYMBOLIC EXPRESSIONS
######################################
lhs_names = ["alpha", "cf", "trK"]
rhs_exprs = [gaugerhs.alpha_rhs, rhs.cf_rhs, rhs.trK_rhs]
for i in range(3):
lhs_names.append("betU" + str(i))
rhs_exprs.append(gaugerhs.bet_rhsU[i])
lhs_names.append("lambdaU" + str(i))
rhs_exprs.append(rhs.lambda_rhsU[i])
lhs_names.append("vetU" + str(i))
rhs_exprs.append(gaugerhs.vet_rhsU[i])
for j in range(i, 3):
lhs_names.append("aDD" + str(i) + str(j))
rhs_exprs.append(rhs.a_rhsDD[i][j])
lhs_names.append("hDD" + str(i) + str(j))
rhs_exprs.append(rhs.h_rhsDD[i][j])
# Sort the lhss list alphabetically, and rhss to match.
# This ensures the RHSs are evaluated in the same order
# they're allocated in memory:
lhs_names, rhs_exprs = [
list(x) for x in zip(
*sorted(zip(lhs_names, rhs_exprs), key=lambda pair: pair[0]))
]
# Declare the list of lhrh's
BSSN_RHSs_SymbExpressions = []
for var in range(len(lhs_names)):
BSSN_RHSs_SymbExpressions.append(
lhrh(lhs=gri.gfaccess("rhs_gfs", lhs_names[var]),
rhs=rhs_exprs[var]))
print_msg_with_timing("BSSN_RHSs",
msg="Symbolic",
startstop="stop",
starttime=starttime)
return [betaU, BSSN_RHSs_SymbExpressions]
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!")
exit(1)
# Step 1.d: Given the chosen coordinate system, set up
# corresponding reference metric and needed
# reference metric quantities
# The following function call sets up the reference metric
# and related quantities, including rescaling matrices ReDD,
# ReU, and hatted quantities.
rfm.reference_metric()
# Step 1.e: 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.f: Import all ADM quantities as written in terms of BSSN quantities
import BSSN.ADM_in_terms_of_BSSN as AB
AB.ADM_in_terms_of_BSSN()
# Step 2.a: Declare the Cartesian x,y,z as input parameters
# and v_1^a, v_2^a, and v_3^a tetrads,
# as well as detgamma and gammaUU from
# BSSN.ADM_in_terms_of_BSSN
x, y, z = par.Cparameters("REAL", thismodule, ["x", "y", "z"])
v1UCart = ixp.zerorank1()
v2UCart = ixp.zerorank1()
detgamma = AB.detgamma
gammaUU = AB.gammaUU
# Step 2.b: Define v1U and v2U
v1UCart = [-y, x, sp.sympify(0)]
v2UCart = [x, y, z]
# Step 2.c: Construct the Jacobian d x_Cart^i / d xx^j
Jac_dUCart_dDrfmUD = ixp.zerorank2()
for i in range(DIM):
for j in range(DIM):
Jac_dUCart_dDrfmUD[i][j] = sp.diff(rfm.xxCart[i], rfm.xx[j])
# Step 2.d: Invert above Jacobian to get needed d xx^j / d x_Cart^i
Jac_dUrfm_dDCartUD, dummyDET = ixp.generic_matrix_inverter3x3(
Jac_dUCart_dDrfmUD)
# Step 2.e: Transform v1U and v2U from the Cartesian to the xx^i basis
v1U = ixp.zerorank1()
v2U = ixp.zerorank1()
for i in range(DIM):
for j in range(DIM):
v1U[i] += Jac_dUrfm_dDCartUD[i][j] * v1UCart[j]
v2U[i] += Jac_dUrfm_dDCartUD[i][j] * v2UCart[j]
# Step 2.f: Define the rank-3 version of the Levi-Civita symbol. Amongst
# other uses, this is needed for the construction of the approximate
# quasi-Kinnersley tetrad.
def define_LeviCivitaSymbol_rank3(DIM=-1):
if DIM == -1:
DIM = par.parval_from_str("DIM")
LeviCivitaSymbol = ixp.zerorank3()
for i in range(DIM):
for j in range(DIM):
for k in range(DIM):
# From https://codegolf.stackexchange.com/questions/160359/levi-civita-symbol :
LeviCivitaSymbol[i][j][k] = (i - j) * (j - k) * (k - i) / 2
return LeviCivitaSymbol
# Step 2.g: Define v3U
v3U = ixp.zerorank1()
LeviCivitaSymbolDDD = define_LeviCivitaSymbol_rank3(DIM=3)
for a in range(DIM):
for b in range(DIM):
for c in range(DIM):
for d in range(DIM):
v3U[a] += sp.sqrt(detgamma) * gammaUU[a][
d] * LeviCivitaSymbolDDD[d][b][c] * v1U[b] * v2U[c]
# Step 2.h: Define omega_{ij}
omegaDD = ixp.zerorank2()
gammaDD = AB.gammaDD
def v_vectorDU(v1U, v2U, v3U, i, a):
if i == 0:
return v1U[a]
elif i == 1:
return v2U[a]
elif i == 2:
return v3U[a]
else:
print("ERROR: unknown vector!")
exit(1)
def update_omega(omegaDD, v1U, v2U, v3U, gammaDD):
for i in range(DIM):
for j in range(DIM):
omegaDD[i][j] = sp.sympify(0)
for i in range(DIM):
for j in range(DIM):
for a in range(DIM):
for b in range(DIM):
omegaDD[i][j] += v_vectorDU(
v1U, v2U, v3U, i, a) * v_vectorDU(
v1U, v2U, v3U, j, b) * gammaDD[a][b]
# Step 2.i: Define e^a_i. Note that:
# omegaDD[0][0] = \omega_{11} above;
# omegaDD[1][1] = \omega_{22} above, etc.
e1U = ixp.zerorank1()
e2U = ixp.zerorank1()
e3U = ixp.zerorank1()
update_omega(omegaDD, v1U, v2U, v3U, gammaDD)
for a in range(DIM):
e1U[a] = v1U[a] / sp.sqrt(omegaDD[0][0])
update_omega(omegaDD, e1U, v2U, v3U, gammaDD)
for a in range(DIM):
e2U[a] = (v2U[a] - omegaDD[0][1] * e1U[a]) / sp.sqrt(omegaDD[1][1])
update_omega(omegaDD, e1U, e2U, v3U, gammaDD)
for a in range(DIM):
e3U[a] = (v3U[a] - omegaDD[0][2] * e1U[a] -
omegaDD[1][2] * e2U[a]) / sp.sqrt(omegaDD[2][2])
# Step 2.j: Construct l^mu, n^mu, and m^mu, based on r^mu, theta^mu, phi^mu, and u^mu:
r4U = ixp.zerorank1(DIM=4)
u4U = ixp.zerorank1(DIM=4)
theta4U = ixp.zerorank1(DIM=4)
phi4U = ixp.zerorank1(DIM=4)
for a in range(DIM):
r4U[a + 1] = e2U[a]
theta4U[a + 1] = e3U[a]
phi4U[a + 1] = e1U[a]
# FIXME? assumes alpha=1, beta^i = 0
if par.parval_from_str(thismodule + "::UseCorrectUnitNormal") == "False":
u4U[0] = 1
else:
# Eq. 2.116 in Baumgarte & Shapiro:
# n^mu = {1/alpha, -beta^i/alpha}. Note that n_mu = {alpha,0}, so n^mu n_mu = -1.
import BSSN.BSSN_quantities as Bq
Bq.declare_BSSN_gridfunctions_if_not_declared_already()
Bq.BSSN_basic_tensors()
u4U[0] = 1 / Bq.alpha
for i in range(DIM):
u4U[i + 1] = -Bq.betaU[i] / Bq.alpha
l4U = ixp.zerorank1(DIM=4)
n4U = ixp.zerorank1(DIM=4)
mre4U = ixp.zerorank1(DIM=4)
mim4U = ixp.zerorank1(DIM=4)
isqrt2 = 1 / sp.sqrt(2)
for mu in range(4):
l4U[mu] = isqrt2 * (u4U[mu] + r4U[mu])
n4U[mu] = isqrt2 * (u4U[mu] - r4U[mu])
mre4U[mu] = isqrt2 * theta4U[mu]
mim4U[mu] = isqrt2 * phi4U[mu]
def driver_C_codes(Csrcdict,
ThornName,
rhs_list,
evol_gfs_list,
aux_gfs_list,
auxevol_gfs_list,
LapseCondition="OnePlusLog",
enable_stress_energy_source_terms=False):
# First the ETK banner code, proudly showing the NRPy+ banner
import NRPy_logo as logo
outstr = """
#include <stdio.h>
void BaikalETK_Banner()
{
"""
logostr = logo.print_logo(print_to_stdout=False)
outstr += "printf(\"BaikalETK: another Einstein Toolkit thorn generated by\\n\");\n"
for line in logostr.splitlines():
outstr += " printf(\"" + line + "\\n\");\n"
outstr += "}\n"
# Finally add C code string to dictionaries (Python dictionaries are immutable)
# Add C code string to dictionary (Python dictionaries are immutable)
Csrcdict[append_to_make_code_defn_list("Banner.c")] = outstr.replace(
"BaikalETK", ThornName)
# Then the RegisterSlicing() function, needed for other ETK thorns
outstr = """
#include "cctk.h"
#include "Slicing.h"
int BaikalETK_RegisterSlicing (void)
{
Einstein_RegisterSlicing ("BaikalETK");
return 0;
}"""
# Add C code string to dictionary (Python dictionaries are immutable)
Csrcdict[append_to_make_code_defn_list(
"RegisterSlicing.c")] = outstr.replace("BaikalETK", ThornName)
# Next BaikalETK_Symmetry_registration(): Register symmetries
full_gfs_list = []
full_gfs_list.extend(evol_gfs_list)
full_gfs_list.extend(auxevol_gfs_list)
full_gfs_list.extend(aux_gfs_list)
outstr = """
#include "cctk.h"
#include "cctk_Arguments.h"
#include "cctk_Parameters.h"
#include "Symmetry.h"
void BaikalETK_Symmetry_registration(CCTK_ARGUMENTS)
{
DECLARE_CCTK_ARGUMENTS;
DECLARE_CCTK_PARAMETERS;
// Stores gridfunction parity across x=0, y=0, and z=0 planes, respectively
int sym[3];
// Next register parities for each gridfunction based on its name
// (to ensure this algorithm is robust, gridfunctions with integers
// in their base names are forbidden in NRPy+).
"""
outstr += ""
for gf in full_gfs_list:
# Do not add T4UU gridfunctions if enable_stress_energy_source_terms==False:
if not (enable_stress_energy_source_terms == False and "T4UU" in gf):
outstr += """
// Default to scalar symmetry:
sym[0] = 1; sym[1] = 1; sym[2] = 1;
// Now modify sym[0], sym[1], and/or sym[2] as needed
// to account for gridfunction parity across
// x=0, y=0, and/or z=0 planes, respectively
"""
# If gridfunction name does not end in a digit, by NRPy+ syntax, it must be a scalar
if gf[len(gf) - 1].isdigit() == False:
pass # Scalar = default
elif len(gf) > 2:
# Rank-1 indexed expression (e.g., vector)
if gf[len(gf) - 2].isdigit() == False:
if int(gf[-1]) > 2:
print("Error: Found invalid gridfunction name: " + gf)
sys.exit(1)
symidx = gf[-1]
outstr += " sym[" + symidx + "] = -1;\n"
# Rank-2 indexed expression
elif gf[len(gf) - 2].isdigit() == True:
if len(gf) > 3 and gf[len(gf) - 3].isdigit() == True:
print("Error: Found a Rank-3 or above gridfunction: " +
gf + ", which is at the moment unsupported.")
print("It should be easy to support this if desired.")
sys.exit(1)
symidx0 = gf[-2]
outstr += " sym[" + symidx0 + "] *= -1;\n"
symidx1 = gf[-1]
outstr += " sym[" + symidx1 + "] *= -1;\n"
else:
print(
"Don't know how you got this far with a gridfunction named "
+ gf + ", but I'll take no more of this nonsense.")
print(
" Please follow best-practices and rename your gridfunction to be more descriptive"
)
sys.exit(1)
outstr += " SetCartSymVN(cctkGH, sym, \"BaikalETK::" + gf + "\");\n"
outstr += "}\n"
# Add C code string to dictionary (Python dictionaries are immutable)
Csrcdict[append_to_make_code_defn_list("Symmetry_registration_oldCartGrid3D.c")] = \
outstr.replace("BaikalETK",ThornName)
# Next set RHSs to zero
outstr = """
#include "cctk.h"
#include "cctk_Arguments.h"
#include "cctk_Parameters.h"
#include "Symmetry.h"
void BaikalETK_zero_rhss(CCTK_ARGUMENTS)
{
DECLARE_CCTK_ARGUMENTS;
DECLARE_CCTK_PARAMETERS;
"""
set_rhss_to_zero = ""
for gf in rhs_list:
set_rhss_to_zero += gf + "[CCTK_GFINDEX3D(cctkGH,i0,i1,i2)] = 0.0;\n"
outstr += lp.loop(["i2", "i1", "i0"], ["0", "0", "0"],
["cctk_lsh[2]", "cctk_lsh[1]", "cctk_lsh[0]"],
["1", "1", "1"], [
"#pragma omp parallel for",
"",
"",
], "", set_rhss_to_zero)
outstr += "}\n"
# Add C code string to dictionary (Python dictionaries are immutable)
Csrcdict[append_to_make_code_defn_list("zero_rhss.c")] = outstr.replace(
"BaikalETK", ThornName)
# Next registration with the Method of Lines thorn
outstr = """
//--------------------------------------------------------------------------
// Register with the Method of Lines time stepper
// (MoL thorn, found in arrangements/CactusBase/MoL)
// MoL documentation located in arrangements/CactusBase/MoL/doc
//--------------------------------------------------------------------------
#include <stdio.h>
#include "cctk.h"
#include "cctk_Parameters.h"
#include "cctk_Arguments.h"
#include "Symmetry.h"
void BaikalETK_MoL_registration(CCTK_ARGUMENTS)
{
DECLARE_CCTK_ARGUMENTS;
DECLARE_CCTK_PARAMETERS;
CCTK_INT ierr = 0, group, rhs;
// Register evolution & RHS gridfunction groups with MoL, so it knows
group = CCTK_GroupIndex("BaikalETK::evol_variables");
rhs = CCTK_GroupIndex("BaikalETK::evol_variables_rhs");
ierr += MoLRegisterEvolvedGroup(group, rhs);
if (ierr) CCTK_ERROR("Problems registering with MoL");
}
"""
# Add C code string to dictionary (Python dictionaries are immutable)
Csrcdict[append_to_make_code_defn_list(
"MoL_registration.c")] = outstr.replace("BaikalETK", ThornName)
# Next register with the boundary conditions thorns.
# PART 1: Set BC type to "none" for all variables
# Since we choose NewRad boundary conditions, we must register all
# gridfunctions to have boundary type "none". This is because
# NewRad is seen by the rest of the Toolkit as a modification to the
# RHSs.
# This code is based on Kranc's McLachlan/ML_BSSN/src/Boundaries.cc code.
outstr = """
#include "cctk.h"
#include "cctk_Arguments.h"
#include "cctk_Parameters.h"
#include "cctk_Faces.h"
#include "util_Table.h"
#include "Symmetry.h"
// Set `none` boundary conditions on BSSN RHSs, as these are set via NewRad.
void BaikalETK_BoundaryConditions_evolved_gfs(CCTK_ARGUMENTS)
{
DECLARE_CCTK_ARGUMENTS;
DECLARE_CCTK_PARAMETERS;
CCTK_INT ierr CCTK_ATTRIBUTE_UNUSED = 0;
"""
for gf in evol_gfs_list:
outstr += """
ierr = Boundary_SelectVarForBC(cctkGH, CCTK_ALL_FACES, 1, -1, "BaikalETK::""" + gf + """", "none");
if (ierr < 0) CCTK_ERROR("Failed to register BC for BaikalETK::""" + gf + """!");
"""
outstr += """
}
// Set `flat` boundary conditions on BSSN constraints, similar to what Lean does.
void BaikalETK_BoundaryConditions_aux_gfs(CCTK_ARGUMENTS) {
DECLARE_CCTK_ARGUMENTS;
DECLARE_CCTK_PARAMETERS;
CCTK_INT ierr CCTK_ATTRIBUTE_UNUSED = 0;
"""
for gf in aux_gfs_list:
outstr += """
ierr = Boundary_SelectVarForBC(cctkGH, CCTK_ALL_FACES, cctk_nghostzones[0], -1, "BaikalETK::""" + gf + """", "flat");
if (ierr < 0) CCTK_ERROR("Failed to register BC for BaikalETK::""" + gf + """!");
"""
outstr += "}\n"
# Add C code string to dictionary (Python dictionaries are immutable)
Csrcdict[append_to_make_code_defn_list(
"BoundaryConditions.c")] = outstr.replace("BaikalETK", ThornName)
# PART 2: Set C code for calling NewRad BCs
# As explained in lean_public/LeanBSSNMoL/src/calc_bssn_rhs.F90,
# the function NewRad_Apply takes the following arguments:
# NewRad_Apply(cctkGH, var, rhs, var0, v0, radpower),
# which implement the boundary condition:
# var = var_at_infinite_r + u(r-var_char_speed*t)/r^var_radpower
# Obviously for var_radpower>0, var_at_infinite_r is the value of
# the variable at r->infinity. var_char_speed is the propagation
# speed at the outer boundary, and var_radpower is the radial
# falloff rate.
outstr = """
#include <math.h>
#include "cctk.h"
#include "cctk_Arguments.h"
#include "cctk_Parameters.h"
void BaikalETK_NewRad(CCTK_ARGUMENTS) {
DECLARE_CCTK_ARGUMENTS;
DECLARE_CCTK_PARAMETERS;
"""
for gf in evol_gfs_list:
var_at_infinite_r = "0.0"
var_char_speed = "1.0"
var_radpower = "1.0"
if gf == "alpha":
var_at_infinite_r = "1.0"
if LapseCondition == "OnePlusLog":
var_char_speed = "sqrt(2.0)"
else:
pass # 1.0 (default) is fine
if "aDD" in gf or "trK" in gf: # consistent with Lean code.
var_radpower = "2.0"
outstr += " NewRad_Apply(cctkGH, " + gf + ", " + gf.replace(
"GF", ""
) + "_rhsGF, " + var_at_infinite_r + ", " + var_char_speed + ", " + var_radpower + ");\n"
outstr += "}\n"
# Add C code string to dictionary (Python dictionaries are immutable)
Csrcdict[append_to_make_code_defn_list(
"BoundaryCondition_NewRad.c")] = outstr.replace(
"BaikalETK", ThornName)
# First we convert from ADM to BSSN, as is required to convert initial data
# (given using) ADM quantities, to the BSSN evolved variables
import BSSN.ADM_Numerical_Spherical_or_Cartesian_to_BSSNCurvilinear as atob
IDhDD,IDaDD,IDtrK,IDvetU,IDbetU,IDalpha,IDcf,IDlambdaU = \
atob.Convert_Spherical_or_Cartesian_ADM_to_BSSN_curvilinear("Cartesian","DoNotOutputADMInputFunction",os.path.join(ThornName,"src"))
# Store the original list of registered gridfunctions; we'll want to unregister
# all the *SphorCart* gridfunctions after we're finished with them below.
orig_glb_gridfcs_list = []
for gf in gri.glb_gridfcs_list:
orig_glb_gridfcs_list.append(gf)
alphaSphorCart = gri.register_gridfunctions("AUXEVOL", "alphaSphorCart")
betaSphorCartU = ixp.register_gridfunctions_for_single_rank1(
"AUXEVOL", "betaSphorCartU")
BSphorCartU = ixp.register_gridfunctions_for_single_rank1(
"AUXEVOL", "BSphorCartU")
gammaSphorCartDD = ixp.register_gridfunctions_for_single_rank2(
"AUXEVOL", "gammaSphorCartDD", "sym01")
KSphorCartDD = ixp.register_gridfunctions_for_single_rank2(
"AUXEVOL", "KSphorCartDD", "sym01")
# ADM to BSSN conversion, used for converting ADM initial data into a form readable by this thorn.
# ADM to BSSN, Part 1: Set up function call and pointers to ADM gridfunctions
outstr = """
#include <math.h>
#include "cctk.h"
#include "cctk_Arguments.h"
#include "cctk_Parameters.h"
void BaikalETK_ADM_to_BSSN(CCTK_ARGUMENTS) {
DECLARE_CCTK_ARGUMENTS;
DECLARE_CCTK_PARAMETERS;
CCTK_REAL *alphaSphorCartGF = alp;
"""
# It's ugly if we output code in the following ordering, so we'll first
# output to a string and then sort the string to beautify the code a bit.
outstrtmp = []
for i in range(3):
outstrtmp.append(" CCTK_REAL *betaSphorCartU" + str(i) +
"GF = beta" + chr(ord('x') + i) + ";\n")
outstrtmp.append(" CCTK_REAL *BSphorCartU" + str(i) +
"GF = dtbeta" + chr(ord('x') + i) + ";\n")
for j in range(i, 3):
outstrtmp.append(" CCTK_REAL *gammaSphorCartDD" + str(i) +
str(j) + "GF = g" + chr(ord('x') + i) +
chr(ord('x') + j) + ";\n")
outstrtmp.append(" CCTK_REAL *KSphorCartDD" + str(i) + str(j) +
"GF = k" + chr(ord('x') + i) + chr(ord('x') + j) +
";\n")
outstrtmp.sort()
for line in outstrtmp:
outstr += line
# ADM to BSSN, Part 2: Set up ADM to BSSN conversions for BSSN gridfunctions that do not require
# finite-difference derivatives (i.e., all gridfunctions except lambda^i (=Gamma^i
# in non-covariant BSSN)):
# h_{ij}, a_{ij}, trK, vet^i=beta^i,bet^i=B^i, cf (conformal factor), and alpha
all_but_lambdaU_expressions = [
lhrh(lhs=gri.gfaccess("in_gfs", "hDD00"), rhs=IDhDD[0][0]),
lhrh(lhs=gri.gfaccess("in_gfs", "hDD01"), rhs=IDhDD[0][1]),
lhrh(lhs=gri.gfaccess("in_gfs", "hDD02"), rhs=IDhDD[0][2]),
lhrh(lhs=gri.gfaccess("in_gfs", "hDD11"), rhs=IDhDD[1][1]),
lhrh(lhs=gri.gfaccess("in_gfs", "hDD12"), rhs=IDhDD[1][2]),
lhrh(lhs=gri.gfaccess("in_gfs", "hDD22"), rhs=IDhDD[2][2]),
lhrh(lhs=gri.gfaccess("in_gfs", "aDD00"), rhs=IDaDD[0][0]),
lhrh(lhs=gri.gfaccess("in_gfs", "aDD01"), rhs=IDaDD[0][1]),
lhrh(lhs=gri.gfaccess("in_gfs", "aDD02"), rhs=IDaDD[0][2]),
lhrh(lhs=gri.gfaccess("in_gfs", "aDD11"), rhs=IDaDD[1][1]),
lhrh(lhs=gri.gfaccess("in_gfs", "aDD12"), rhs=IDaDD[1][2]),
lhrh(lhs=gri.gfaccess("in_gfs", "aDD22"), rhs=IDaDD[2][2]),
lhrh(lhs=gri.gfaccess("in_gfs", "trK"), rhs=IDtrK),
lhrh(lhs=gri.gfaccess("in_gfs", "vetU0"), rhs=IDvetU[0]),
lhrh(lhs=gri.gfaccess("in_gfs", "vetU1"), rhs=IDvetU[1]),
lhrh(lhs=gri.gfaccess("in_gfs", "vetU2"), rhs=IDvetU[2]),
lhrh(lhs=gri.gfaccess("in_gfs", "betU0"), rhs=IDbetU[0]),
lhrh(lhs=gri.gfaccess("in_gfs", "betU1"), rhs=IDbetU[1]),
lhrh(lhs=gri.gfaccess("in_gfs", "betU2"), rhs=IDbetU[2]),
lhrh(lhs=gri.gfaccess("in_gfs", "alpha"), rhs=IDalpha),
lhrh(lhs=gri.gfaccess("in_gfs", "cf"), rhs=IDcf)
]
outCparams = "preindent=1,outCfileaccess=a,outCverbose=False,includebraces=False"
all_but_lambdaU_outC = fin.FD_outputC("returnstring",
all_but_lambdaU_expressions,
outCparams)
outstr += lp.loop(["i2", "i1", "i0"], ["0", "0", "0"],
["cctk_lsh[2]", "cctk_lsh[1]", "cctk_lsh[0]"],
["1", "1", "1"], ["#pragma omp parallel for", "", ""],
" ", all_but_lambdaU_outC)
# ADM to BSSN, Part 3: Set up ADM to BSSN conversions for BSSN gridfunctions defined from
# finite-difference derivatives: lambda^i, which is Gamma^i in non-covariant BSSN):
outstr += """
const CCTK_REAL invdx0 = 1.0/CCTK_DELTA_SPACE(0);
const CCTK_REAL invdx1 = 1.0/CCTK_DELTA_SPACE(1);
const CCTK_REAL invdx2 = 1.0/CCTK_DELTA_SPACE(2);
"""
path = os.path.join(ThornName, "src")
BSSN_RHS_FD_orders_output = []
for root, dirs, files in os.walk(path):
for file in files:
if "BSSN_RHSs_FD_order" in file:
array = file.replace(".", "_").split("_")
BSSN_RHS_FD_orders_output.append(int(array[4]))
for current_FD_order in BSSN_RHS_FD_orders_output:
# Store original finite-differencing order:
orig_FD_order = par.parval_from_str(
"finite_difference::FD_CENTDERIVS_ORDER")
# Set new finite-differencing order:
par.set_parval_from_str("finite_difference::FD_CENTDERIVS_ORDER",
current_FD_order)
outCparams = "preindent=1,outCfileaccess=a,outCverbose=False,includebraces=False"
lambdaU_expressions = [
lhrh(lhs=gri.gfaccess("in_gfs", "lambdaU0"), rhs=IDlambdaU[0]),
lhrh(lhs=gri.gfaccess("in_gfs", "lambdaU1"), rhs=IDlambdaU[1]),
lhrh(lhs=gri.gfaccess("in_gfs", "lambdaU2"), rhs=IDlambdaU[2])
]
lambdaU_expressions_FDout = fin.FD_outputC("returnstring",
lambdaU_expressions,
outCparams)
lambdafile = "ADM_to_BSSN__compute_lambdaU_FD_order_" + str(
current_FD_order) + ".h"
with open(os.path.join(ThornName, "src", lambdafile), "w") as file:
file.write(
lp.loop(["i2", "i1", "i0"], [
"cctk_nghostzones[2]", "cctk_nghostzones[1]",
"cctk_nghostzones[0]"
], [
"cctk_lsh[2]-cctk_nghostzones[2]",
"cctk_lsh[1]-cctk_nghostzones[1]",
"cctk_lsh[0]-cctk_nghostzones[0]"
], ["1", "1", "1"], ["#pragma omp parallel for", "", ""], "",
lambdaU_expressions_FDout))
outstr += " if(FD_order == " + str(current_FD_order) + ") {\n"
outstr += " #include \"" + lambdafile + "\"\n"
outstr += " }\n"
# Restore original FD order
par.set_parval_from_str("finite_difference::FD_CENTDERIVS_ORDER",
orig_FD_order)
outstr += """
ExtrapolateGammas(cctkGH,lambdaU0GF);
ExtrapolateGammas(cctkGH,lambdaU1GF);
ExtrapolateGammas(cctkGH,lambdaU2GF);
}
"""
# Unregister the *SphorCartGF's.
gri.glb_gridfcs_list = orig_glb_gridfcs_list
# Add C code string to dictionary (Python dictionaries are immutable)
Csrcdict[append_to_make_code_defn_list("ADM_to_BSSN.c")] = outstr.replace(
"BaikalETK", ThornName)
import BSSN.ADM_in_terms_of_BSSN as btoa
import BSSN.BSSN_quantities as Bq
btoa.ADM_in_terms_of_BSSN()
Bq.BSSN_basic_tensors() # Gives us betaU & BU
outstr = """
#include <math.h>
#include "cctk.h"
#include "cctk_Arguments.h"
#include "cctk_Parameters.h"
void BaikalETK_BSSN_to_ADM(CCTK_ARGUMENTS) {
DECLARE_CCTK_ARGUMENTS;
DECLARE_CCTK_PARAMETERS;
"""
btoa_lhrh = []
for i in range(3):
for j in range(i, 3):
btoa_lhrh.append(
lhrh(lhs="g" + chr(ord('x') + i) + chr(ord('x') + j) +
"[CCTK_GFINDEX3D(cctkGH,i0,i1,i2)]",
rhs=btoa.gammaDD[i][j]))
for i in range(3):
for j in range(i, 3):
btoa_lhrh.append(
lhrh(lhs="k" + chr(ord('x') + i) + chr(ord('x') + j) +
"[CCTK_GFINDEX3D(cctkGH,i0,i1,i2)]",
rhs=btoa.KDD[i][j]))
btoa_lhrh.append(
lhrh(lhs="alp[CCTK_GFINDEX3D(cctkGH,i0,i1,i2)]", rhs=Bq.alpha))
for i in range(3):
btoa_lhrh.append(
lhrh(lhs="beta" + chr(ord('x') + i) +
"[CCTK_GFINDEX3D(cctkGH,i0,i1,i2)]",
rhs=Bq.betaU[i]))
for i in range(3):
btoa_lhrh.append(
lhrh(lhs="dtbeta" + chr(ord('x') + i) +
"[CCTK_GFINDEX3D(cctkGH,i0,i1,i2)]",
rhs=Bq.BU[i]))
outCparams = "preindent=1,outCfileaccess=a,outCverbose=False,includebraces=False"
bssn_to_adm_Ccode = fin.FD_outputC("returnstring", btoa_lhrh, outCparams)
outstr += lp.loop(["i2", "i1", "i0"], ["0", "0", "0"],
["cctk_lsh[2]", "cctk_lsh[1]", "cctk_lsh[0]"],
["1", "1", "1"], ["#pragma omp parallel for", "", ""],
"", bssn_to_adm_Ccode)
outstr += "}\n"
# Add C code string to dictionary (Python dictionaries are immutable)
Csrcdict[append_to_make_code_defn_list("BSSN_to_ADM.c")] = outstr.replace(
"BaikalETK", ThornName)
# Next, the driver for computing the Ricci tensor:
outstr = """
#include <math.h>
#include "SIMD/SIMD_intrinsics.h"
#include "cctk.h"
#include "cctk_Arguments.h"
#include "cctk_Parameters.h"
void BaikalETK_driver_pt1_BSSN_Ricci(CCTK_ARGUMENTS) {
DECLARE_CCTK_ARGUMENTS;
const CCTK_INT *FD_order = CCTK_ParameterGet("FD_order","BaikalETK",NULL);
const CCTK_REAL NOSIMDinvdx0 = 1.0/CCTK_DELTA_SPACE(0);
const REAL_SIMD_ARRAY invdx0 = ConstSIMD(NOSIMDinvdx0);
const CCTK_REAL NOSIMDinvdx1 = 1.0/CCTK_DELTA_SPACE(1);
const REAL_SIMD_ARRAY invdx1 = ConstSIMD(NOSIMDinvdx1);
const CCTK_REAL NOSIMDinvdx2 = 1.0/CCTK_DELTA_SPACE(2);
const REAL_SIMD_ARRAY invdx2 = ConstSIMD(NOSIMDinvdx2);
"""
path = os.path.join(ThornName, "src")
for root, dirs, files in os.walk(path):
for file in files:
if "BSSN_Ricci_FD_order" in file:
array = file.replace(".", "_").split("_")
outstr += " if(*FD_order == " + str(array[4]) + ") {\n"
outstr += " #include \"" + file + "\"\n"
outstr += " }\n"
outstr += "}\n"
# Add C code string to dictionary (Python dictionaries are immutable)
Csrcdict[append_to_make_code_defn_list(
"driver_pt1_BSSN_Ricci.c")] = outstr.replace("BaikalETK", ThornName)
def SIMD_declare_C_params():
SIMD_declare_C_params_str = ""
for i in range(len(par.glb_Cparams_list)):
# keep_param is a boolean indicating whether we should accept or reject
# the parameter. singleparstring will contain the string indicating
# the variable type.
keep_param, singleparstring = ccl.keep_param__return_type(
par.glb_Cparams_list[i])
if (keep_param) and ("CCTK_REAL" in singleparstring):
parname = par.glb_Cparams_list[i].parname
SIMD_declare_C_params_str += " const "+singleparstring + "*NOSIMD"+parname+\
" = CCTK_ParameterGet(\""+parname+"\",\"BaikalETK\",NULL);\n"
SIMD_declare_C_params_str += " const REAL_SIMD_ARRAY " + parname + " = ConstSIMD(*NOSIMD" + parname + ");\n"
return SIMD_declare_C_params_str
# Next, the driver for computing the BSSN RHSs:
outstr = """
#include <math.h>
#include "SIMD/SIMD_intrinsics.h"
#include "cctk.h"
#include "cctk_Arguments.h"
#include "cctk_Parameters.h"
void BaikalETK_driver_pt2_BSSN_RHSs(CCTK_ARGUMENTS) {
DECLARE_CCTK_ARGUMENTS;
const CCTK_INT *FD_order = CCTK_ParameterGet("FD_order","BaikalETK",NULL);
const CCTK_REAL NOSIMDinvdx0 = 1.0/CCTK_DELTA_SPACE(0);
const REAL_SIMD_ARRAY invdx0 = ConstSIMD(NOSIMDinvdx0);
const CCTK_REAL NOSIMDinvdx1 = 1.0/CCTK_DELTA_SPACE(1);
const REAL_SIMD_ARRAY invdx1 = ConstSIMD(NOSIMDinvdx1);
const CCTK_REAL NOSIMDinvdx2 = 1.0/CCTK_DELTA_SPACE(2);
const REAL_SIMD_ARRAY invdx2 = ConstSIMD(NOSIMDinvdx2);
""" + SIMD_declare_C_params()
path = os.path.join(ThornName, "src")
for root, dirs, files in os.walk(path):
for file in files:
if "BSSN_RHSs_FD_order" in file:
array = file.replace(".", "_").split("_")
outstr += " if(*FD_order == " + str(array[4]) + ") {\n"
outstr += " #include \"" + file + "\"\n"
outstr += " }\n"
outstr += "}\n"
# Add C code string to dictionary (Python dictionaries are immutable)
Csrcdict[append_to_make_code_defn_list(
"driver_pt2_BSSN_RHSs.c")] = outstr.replace("BaikalETK", ThornName)
# Next, the driver for enforcing detgammabar = detgammahat constraint:
outstr = """
#include <math.h>
#include "cctk.h"
#include "cctk_Arguments.h"
#include "cctk_Parameters.h"
void BaikalETK_enforce_detgammabar_constraint(CCTK_ARGUMENTS) {
DECLARE_CCTK_ARGUMENTS;
DECLARE_CCTK_PARAMETERS;
"""
path = os.path.join(ThornName, "src")
for root, dirs, files in os.walk(path):
for file in files:
if "enforcedetgammabar_constraint_FD_order" in file:
array = file.replace(".", "_").split("_")
outstr += " if(FD_order == " + str(array[4]) + ") {\n"
outstr += " #include \"" + file + "\"\n"
outstr += " }\n"
outstr += "}\n"
# Add C code string to dictionary (Python dictionaries are immutable)
Csrcdict[append_to_make_code_defn_list("driver_enforcedetgammabar_constraint.c")] = \
outstr.replace("BaikalETK",ThornName)
# Next, the driver for computing the BSSN Hamiltonian & momentum constraints
outstr = """
#include <math.h>
#include "cctk.h"
#include "cctk_Arguments.h"
#include "cctk_Parameters.h"
void BaikalETK_BSSN_constraints(CCTK_ARGUMENTS) {
DECLARE_CCTK_ARGUMENTS;
DECLARE_CCTK_PARAMETERS;
const CCTK_REAL invdx0 = 1.0/CCTK_DELTA_SPACE(0);
const CCTK_REAL invdx1 = 1.0/CCTK_DELTA_SPACE(1);
const CCTK_REAL invdx2 = 1.0/CCTK_DELTA_SPACE(2);
"""
path = os.path.join(ThornName, "src")
for root, dirs, files in os.walk(path):
for file in files:
if "BSSN_constraints_FD_order" in file:
array = file.replace(".", "_").split("_")
outstr += " if(FD_order == " + str(array[4]) + ") {\n"
outstr += " #include \"" + file + "\"\n"
outstr += " }\n"
outstr += "}\n"
# Add C code string to dictionary (Python dictionaries are immutable)
Csrcdict[append_to_make_code_defn_list(
"driver_BSSN_constraints.c")] = outstr.replace("BaikalETK", ThornName)
if enable_stress_energy_source_terms == True:
# Declare T4DD as a set of gridfunctions. These won't
# actually appear in interface.ccl, as interface.ccl
# was set above. Thus before calling the code output
# by FD_outputC(), we'll have to set pointers
# to the actual gridfunctions they reference.
# (In this case the eTab's.)
T4DD = ixp.register_gridfunctions_for_single_rank2("AUXEVOL",
"T4DD",
"sym01",
DIM=4)
import BSSN.ADMBSSN_tofrom_4metric as AB4m
AB4m.g4UU_ito_BSSN_or_ADM("BSSN")
T4UUraised = ixp.zerorank2(DIM=4)
for mu in range(4):
for nu in range(4):
for delta in range(4):
for gamma in range(4):
T4UUraised[mu][nu] += AB4m.g4UU[mu][delta] * AB4m.g4UU[
nu][gamma] * T4DD[delta][gamma]
T4UU_expressions = [
lhrh(lhs=gri.gfaccess("in_gfs", "T4UU00"), rhs=T4UUraised[0][0]),
lhrh(lhs=gri.gfaccess("in_gfs", "T4UU01"), rhs=T4UUraised[0][1]),
lhrh(lhs=gri.gfaccess("in_gfs", "T4UU02"), rhs=T4UUraised[0][2]),
lhrh(lhs=gri.gfaccess("in_gfs", "T4UU03"), rhs=T4UUraised[0][3]),
lhrh(lhs=gri.gfaccess("in_gfs", "T4UU11"), rhs=T4UUraised[1][1]),
lhrh(lhs=gri.gfaccess("in_gfs", "T4UU12"), rhs=T4UUraised[1][2]),
lhrh(lhs=gri.gfaccess("in_gfs", "T4UU13"), rhs=T4UUraised[1][3]),
lhrh(lhs=gri.gfaccess("in_gfs", "T4UU22"), rhs=T4UUraised[2][2]),
lhrh(lhs=gri.gfaccess("in_gfs", "T4UU23"), rhs=T4UUraised[2][3]),
lhrh(lhs=gri.gfaccess("in_gfs", "T4UU33"), rhs=T4UUraised[3][3])
]
outCparams = "outCverbose=False,includebraces=False,preindent=2,SIMD_enable=True"
T4UUstr = fin.FD_outputC("returnstring", T4UU_expressions, outCparams)
T4UUstr_loop = lp.loop(["i2", "i1", "i0"], ["0", "0", "0"],
["cctk_lsh[2]", "cctk_lsh[1]", "cctk_lsh[0]"],
["1", "1", "SIMD_width"],
["#pragma omp parallel for", "", ""], "",
T4UUstr)
outstr = """
#include <math.h>
#include "cctk.h"
#include "cctk_Arguments.h"
#include "cctk_Parameters.h"
#include "SIMD/SIMD_intrinsics.h"
void BaikalETK_driver_BSSN_T4UU(CCTK_ARGUMENTS) {
DECLARE_CCTK_ARGUMENTS;
DECLARE_CCTK_PARAMETERS;
const CCTK_REAL *restrict T4DD00GF = eTtt;
const CCTK_REAL *restrict T4DD01GF = eTtx;
const CCTK_REAL *restrict T4DD02GF = eTty;
const CCTK_REAL *restrict T4DD03GF = eTtz;
const CCTK_REAL *restrict T4DD11GF = eTxx;
const CCTK_REAL *restrict T4DD12GF = eTxy;
const CCTK_REAL *restrict T4DD13GF = eTxz;
const CCTK_REAL *restrict T4DD22GF = eTyy;
const CCTK_REAL *restrict T4DD23GF = eTyz;
const CCTK_REAL *restrict T4DD33GF = eTzz;
""" + T4UUstr_loop + """
}\n"""
# Add C code string to dictionary (Python dictionaries are immutable)
Csrcdict[append_to_make_code_defn_list(
"driver_BSSN_T4UU.c")] = outstr.replace("BaikalETK", ThornName)
def BSSN_RHSs():
# Step 1.c: Given the chosen coordinate system, set up
# corresponding reference metric and needed
# reference metric quantities
# The following function call sets up the reference metric
# and related quantities, including rescaling matrices ReDD,
# ReU, and hatted quantities.
rfm.reference_metric()
global have_already_called_BSSN_RHSs_function # setting to global enables other modules to see updated value.
have_already_called_BSSN_RHSs_function = True
# 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: Import all basic (unrescaled) BSSN scalars & tensors
import BSSN.BSSN_quantities as Bq
Bq.BSSN_basic_tensors()
gammabarDD = Bq.gammabarDD
AbarDD = Bq.AbarDD
LambdabarU = Bq.LambdabarU
trK = Bq.trK
alpha = Bq.alpha
betaU = Bq.betaU
# Step 1.f: Import all neeeded rescaled BSSN tensors:
aDD = Bq.aDD
cf = Bq.cf
lambdaU = Bq.lambdaU
# Step 2.a.i: Import derivative expressions for betaU defined in the BSSN.BSSN_quantities module:
Bq.betaU_derivs()
betaU_dD = Bq.betaU_dD
betaU_dDD = Bq.betaU_dDD
# Step 2.a.ii: Import derivative expression for gammabarDD
Bq.gammabar__inverse_and_derivs()
gammabarDD_dupD = Bq.gammabarDD_dupD
# Step 2.a.iii: First term of \partial_t \bar{\gamma}_{i j} right-hand side:
# \beta^k \bar{\gamma}_{ij,k} + \beta^k_{,i} \bar{\gamma}_{kj} + \beta^k_{,j} \bar{\gamma}_{ik}
gammabar_rhsDD = ixp.zerorank2()
for i in range(DIM):
for j in range(DIM):
for k in range(DIM):
gammabar_rhsDD[i][j] += betaU[k] * gammabarDD_dupD[i][j][k] + betaU_dD[k][i] * gammabarDD[k][j] \
+ betaU_dD[k][j] * gammabarDD[i][k]
# Step 2.b.i: First import \bar{A}_{ij} = AbarDD[i][j], and its contraction trAbar = \bar{A}^k_k
# from BSSN.BSSN_quantities
Bq.AbarUU_AbarUD_trAbar_AbarDD_dD()
trAbar = Bq.trAbar
# Step 2.b.ii: Import detgammabar quantities from BSSN.BSSN_quantities:
Bq.detgammabar_and_derivs()
detgammabar = Bq.detgammabar
detgammabar_dD = Bq.detgammabar_dD
# Step 2.b.ii: Compute the contraction \bar{D}_k \beta^k = \beta^k_{,k} + \frac{\beta^k \bar{\gamma}_{,k}}{2 \bar{\gamma}}
Dbarbetacontraction = sp.sympify(0)
for k in range(DIM):
Dbarbetacontraction += betaU_dD[k][
k] + betaU[k] * detgammabar_dD[k] / (2 * detgammabar)
# Step 2.b.iii: Second term of \partial_t \bar{\gamma}_{i j} right-hand side:
# \frac{2}{3} \bar{\gamma}_{i j} \left (\alpha \bar{A}_{k}^{k} - \bar{D}_{k} \beta^{k}\right )
for i in range(DIM):
for j in range(DIM):
gammabar_rhsDD[i][j] += sp.Rational(2, 3) * gammabarDD[i][j] * (
alpha * trAbar - Dbarbetacontraction)
# Step 2.c: Third term of \partial_t \bar{\gamma}_{i j} right-hand side:
# -2 \alpha \bar{A}_{ij}
for i in range(DIM):
for j in range(DIM):
gammabar_rhsDD[i][j] += -2 * alpha * AbarDD[i][j]
# Step 3.a: First term of \partial_t \bar{A}_{i j}:
# \beta^k \partial_k \bar{A}_{ij} + \partial_i \beta^k \bar{A}_{kj} + \partial_j \beta^k \bar{A}_{ik}
# First define AbarDD_dupD:
AbarDD_dupD = Bq.AbarDD_dupD # From Bq.AbarUU_AbarUD_trAbar_AbarDD_dD()
Abar_rhsDD = ixp.zerorank2()
for i in range(DIM):
for j in range(DIM):
for k in range(DIM):
Abar_rhsDD[i][j] += betaU[k] * AbarDD_dupD[i][j][k] + betaU_dD[k][i] * AbarDD[k][j] \
+ betaU_dD[k][j] * AbarDD[i][k]
# Step 3.b: Second term of \partial_t \bar{A}_{i j}:
# - (2/3) \bar{A}_{i j} \bar{D}_{k} \beta^{k} - 2 \alpha \bar{A}_{i k} {\bar{A}^{k}}_{j} + \alpha \bar{A}_{i j} K
gammabarUU = Bq.gammabarUU # From Bq.gammabar__inverse_and_derivs()
AbarUD = Bq.AbarUD # From Bq.AbarUU_AbarUD_trAbar()
for i in range(DIM):
for j in range(DIM):
Abar_rhsDD[i][j] += -sp.Rational(2, 3) * AbarDD[i][
j] * Dbarbetacontraction + alpha * AbarDD[i][j] * trK
for k in range(DIM):
Abar_rhsDD[i][j] += -2 * alpha * AbarDD[i][k] * AbarUD[k][j]
# Step 3.c.i: Define partial derivatives of \phi in terms of evolved quantity "cf":
Bq.phi_and_derivs()
phi_dD = Bq.phi_dD
phi_dupD = Bq.phi_dupD
phi_dDD = Bq.phi_dDD
exp_m4phi = Bq.exp_m4phi
phi_dBarD = Bq.phi_dBarD # phi_dBarD = Dbar_i phi = phi_dD (since phi is a scalar)
phi_dBarDD = Bq.phi_dBarDD # phi_dBarDD = Dbar_i Dbar_j phi (covariant derivative)
# Step 3.c.ii: Define RbarDD
Bq.RicciBar__gammabarDD_dHatD__DGammaUDD__DGammaU()
RbarDD = Bq.RbarDD
# Step 3.c.iii: Define first and second derivatives of \alpha, as well as
# \bar{D}_i \bar{D}_j \alpha, which is defined just like phi
alpha_dD = ixp.declarerank1("alpha_dD")
alpha_dDD = ixp.declarerank2("alpha_dDD", "sym01")
alpha_dBarD = alpha_dD
alpha_dBarDD = ixp.zerorank2()
GammabarUDD = Bq.GammabarUDD # Defined in Bq.gammabar__inverse_and_derivs()
for i in range(DIM):
for j in range(DIM):
alpha_dBarDD[i][j] = alpha_dDD[i][j]
for k in range(DIM):
alpha_dBarDD[i][j] += -GammabarUDD[k][i][j] * alpha_dD[k]
# Step 3.c.iv: Define the terms in curly braces:
curlybrackettermsDD = ixp.zerorank2()
for i in range(DIM):
for j in range(DIM):
curlybrackettermsDD[i][j] = -2 * alpha * phi_dBarDD[i][j] + 4 * alpha * phi_dBarD[i] * phi_dBarD[j] \
+ 2 * alpha_dBarD[i] * phi_dBarD[j] \
+ 2 * alpha_dBarD[j] * phi_dBarD[i] \
- alpha_dBarDD[i][j] + alpha * RbarDD[i][j]
# Step 3.c.v: Compute the trace:
curlybracketterms_trace = sp.sympify(0)
for i in range(DIM):
for j in range(DIM):
curlybracketterms_trace += gammabarUU[i][j] * curlybrackettermsDD[
i][j]
# Step 3.c.vi: Third and final term of Abar_rhsDD[i][j]:
for i in range(DIM):
for j in range(DIM):
Abar_rhsDD[i][j] += exp_m4phi * (
curlybrackettermsDD[i][j] -
sp.Rational(1, 3) * gammabarDD[i][j] * curlybracketterms_trace)
# Step 4: Right-hand side of conformal factor variable "cf". Supported
# options include: cf=phi, cf=W=e^(-2*phi) (default), and cf=chi=e^(-4*phi)
# \partial_t phi = \left[\beta^k \partial_k \phi \right] <- TERM 1
# + \frac{1}{6} \left (\bar{D}_{k} \beta^{k} - \alpha K \right ) <- TERM 2
global cf_rhs
cf_rhs = sp.Rational(1, 6) * (Dbarbetacontraction - alpha * trK) # Term 2
for k in range(DIM):
cf_rhs += betaU[k] * phi_dupD[k] # Term 1
# Next multiply to convert phi_rhs to cf_rhs.
if par.parval_from_str(
"BSSN.BSSN_quantities::EvolvedConformalFactor_cf") == "phi":
pass # do nothing; cf_rhs = phi_rhs
elif par.parval_from_str(
"BSSN.BSSN_quantities::EvolvedConformalFactor_cf") == "W":
cf_rhs *= -2 * cf # cf_rhs = -2*cf*phi_rhs
elif par.parval_from_str(
"BSSN.BSSN_quantities::EvolvedConformalFactor_cf") == "chi":
cf_rhs *= -4 * cf # cf_rhs = -4*cf*phi_rhs
else:
print("Error: EvolvedConformalFactor_cf == " + par.parval_from_str(
"BSSN.BSSN_quantities::EvolvedConformalFactor_cf") +
" unsupported!")
exit(1)
# Step 5: right-hand side of trK (trace of extrinsic curvature):
# \partial_t K = \beta^k \partial_k K <- TERM 1
# + \frac{1}{3} \alpha K^{2} <- TERM 2
# + \alpha \bar{A}_{i j} \bar{A}^{i j} <- TERM 3
# - - e^{-4 \phi} (\bar{D}_{i} \bar{D}^{i} \alpha + 2 \bar{D}^{i} \alpha \bar{D}_{i} \phi ) <- TERM 4
global trK_rhs
# TERM 2:
trK_rhs = sp.Rational(1, 3) * alpha * trK * trK
trK_dupD = ixp.declarerank1("trK_dupD")
for i in range(DIM):
# TERM 1:
trK_rhs += betaU[i] * trK_dupD[i]
for i in range(DIM):
for j in range(DIM):
# TERM 4:
trK_rhs += -exp_m4phi * gammabarUU[i][j] * (
alpha_dBarDD[i][j] + 2 * alpha_dBarD[j] * phi_dBarD[i])
AbarUU = Bq.AbarUU # From Bq.AbarUU_AbarUD_trAbar()
for i in range(DIM):
for j in range(DIM):
# TERM 3:
trK_rhs += alpha * AbarDD[i][j] * AbarUU[i][j]
# Step 6: right-hand side of \partial_t \bar{\Lambda}^i:
# \partial_t \bar{\Lambda}^i = \beta^k \partial_k \bar{\Lambda}^i - \partial_k \beta^i \bar{\Lambda}^k <- TERM 1
# + \bar{\gamma}^{j k} \hat{D}_{j} \hat{D}_{k} \beta^{i} <- TERM 2
# + \frac{2}{3} \Delta^{i} \bar{D}_{j} \beta^{j} <- TERM 3
# + \frac{1}{3} \bar{D}^{i} \bar{D}_{j} \beta^{j} <- TERM 4
# - 2 \bar{A}^{i j} (\partial_{j} \alpha - 6 \partial_{j} \phi) <- TERM 5
# + 2 \alpha \bar{A}^{j k} \Delta_{j k}^{i} <- TERM 6
# - \frac{4}{3} \alpha \bar{\gamma}^{i j} \partial_{j} K <- TERM 7
# Step 6.a: Term 1 of \partial_t \bar{\Lambda}^i: \beta^k \partial_k \bar{\Lambda}^i - \partial_k \beta^i \bar{\Lambda}^k
# First we declare \bar{\Lambda}^i and \bar{\Lambda}^i_{,j} in terms of \lambda^i and \lambda^i_{,j}
global LambdabarU_dupD # Used on the RHS of the Gamma-driving shift conditions
LambdabarU_dupD = ixp.zerorank2()
lambdaU_dupD = ixp.declarerank2("lambdaU_dupD", "nosym")
for i in range(DIM):
for j in range(DIM):
LambdabarU_dupD[i][j] = lambdaU_dupD[i][j] * rfm.ReU[i] + lambdaU[
i] * rfm.ReUdD[i][j]
global Lambdabar_rhsU # Used on the RHS of the Gamma-driving shift conditions
Lambdabar_rhsU = ixp.zerorank1()
for i in range(DIM):
for k in range(DIM):
Lambdabar_rhsU[i] += betaU[k] * LambdabarU_dupD[i][k] - betaU_dD[
i][k] * LambdabarU[k] # Term 1
# Step 6.b: Term 2 of \partial_t \bar{\Lambda}^i = \bar{\gamma}^{jk} (Term 2a + Term 2b + Term 2c)
# Term 2a: \bar{\gamma}^{jk} \beta^i_{,kj}
Term2aUDD = ixp.zerorank3()
for i in range(DIM):
for j in range(DIM):
for k in range(DIM):
Term2aUDD[i][j][k] += betaU_dDD[i][k][j]
# Term 2b: \hat{\Gamma}^i_{mk,j} \beta^m + \hat{\Gamma}^i_{mk} \beta^m_{,j}
# + \hat{\Gamma}^i_{dj}\beta^d_{,k} - \hat{\Gamma}^d_{kj} \beta^i_{,d}
Term2bUDD = ixp.zerorank3()
for i in range(DIM):
for j in range(DIM):
for k in range(DIM):
for m in range(DIM):
Term2bUDD[i][j][k] += rfm.GammahatUDDdD[i][m][k][j] * betaU[m] \
+ rfm.GammahatUDD[i][m][k] * betaU_dD[m][j] \
+ rfm.GammahatUDD[i][m][j] * betaU_dD[m][k] \
- rfm.GammahatUDD[m][k][j] * betaU_dD[i][m]
# Term 2c: \hat{\Gamma}^i_{dj}\hat{\Gamma}^d_{mk} \beta^m - \hat{\Gamma}^d_{kj} \hat{\Gamma}^i_{md} \beta^m
Term2cUDD = ixp.zerorank3()
for i in range(DIM):
for j in range(DIM):
for k in range(DIM):
for m in range(DIM):
for d in range(DIM):
Term2cUDD[i][j][k] += (rfm.GammahatUDD[i][d][j] * rfm.GammahatUDD[d][m][k] \
- rfm.GammahatUDD[d][k][j] * rfm.GammahatUDD[i][m][d]) * betaU[m]
Lambdabar_rhsUpieceU = ixp.zerorank1()
# Put it all together to get Term 2:
for i in range(DIM):
for j in range(DIM):
for k in range(DIM):
Lambdabar_rhsU[i] += gammabarUU[j][k] * (Term2aUDD[i][j][k] +
Term2bUDD[i][j][k] +
Term2cUDD[i][j][k])
Lambdabar_rhsUpieceU[i] += gammabarUU[j][k] * (
Term2aUDD[i][j][k] + Term2bUDD[i][j][k] +
Term2cUDD[i][j][k])
# Step 6.c: Term 3 of \partial_t \bar{\Lambda}^i:
# \frac{2}{3} \Delta^{i} \bar{D}_{j} \beta^{j}
DGammaU = Bq.DGammaU # From Bq.RicciBar__gammabarDD_dHatD__DGammaUDD__DGammaU()
for i in range(DIM):
Lambdabar_rhsU[i] += sp.Rational(
2, 3) * DGammaU[i] * Dbarbetacontraction # Term 3
# Step 6.d: Term 4 of \partial_t \bar{\Lambda}^i:
# \frac{1}{3} \bar{D}^{i} \bar{D}_{j} \beta^{j}
detgammabar_dDD = Bq.detgammabar_dDD # From Bq.detgammabar_and_derivs()
Dbarbetacontraction_dBarD = ixp.zerorank1()
for k in range(DIM):
for m in range(DIM):
Dbarbetacontraction_dBarD[m] += betaU_dDD[k][k][m] + \
(betaU_dD[k][m] * detgammabar_dD[k] +
betaU[k] * detgammabar_dDD[k][m]) / (2 * detgammabar) \
- betaU[k] * detgammabar_dD[k] * detgammabar_dD[m] / (
2 * detgammabar * detgammabar)
for i in range(DIM):
for m in range(DIM):
Lambdabar_rhsU[i] += sp.Rational(
1, 3) * gammabarUU[i][m] * Dbarbetacontraction_dBarD[m]
# Step 6.e: Term 5 of \partial_t \bar{\Lambda}^i:
# - 2 \bar{A}^{i j} (\partial_{j} \alpha - 6 \alpha \partial_{j} \phi)
for i in range(DIM):
for j in range(DIM):
Lambdabar_rhsU[i] += -2 * AbarUU[i][j] * (alpha_dD[j] -
6 * alpha * phi_dD[j])
# Step 6.f: Term 6 of \partial_t \bar{\Lambda}^i:
# 2 \alpha \bar{A}^{j k} \Delta^{i}_{j k}
DGammaUDD = Bq.DGammaUDD # From RicciBar__gammabarDD_dHatD__DGammaUDD__DGammaU()
for i in range(DIM):
for j in range(DIM):
for k in range(DIM):
Lambdabar_rhsU[
i] += 2 * alpha * AbarUU[j][k] * DGammaUDD[i][j][k]
# Step 6.g: Term 7 of \partial_t \bar{\Lambda}^i:
# -\frac{4}{3} \alpha \bar{\gamma}^{i j} \partial_{j} K
trK_dD = ixp.declarerank1("trK_dD")
for i in range(DIM):
for j in range(DIM):
Lambdabar_rhsU[i] += -sp.Rational(
4, 3) * alpha * gammabarUU[i][j] * trK_dD[j]
# Step 7: Rescale the RHS quantities so that the evolved
# variables are smooth across coord singularities
global h_rhsDD, a_rhsDD, lambda_rhsU
h_rhsDD = ixp.zerorank2()
a_rhsDD = ixp.zerorank2()
lambda_rhsU = ixp.zerorank1()
for i in range(DIM):
lambda_rhsU[i] = Lambdabar_rhsU[i] / rfm.ReU[i]
for j in range(DIM):
h_rhsDD[i][j] = gammabar_rhsDD[i][j] / rfm.ReDD[i][j]
a_rhsDD[i][j] = Abar_rhsDD[i][j] / rfm.ReDD[i][j]
def ScalarField_RHSs():
# Step B.4: 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 B.5: Import all basic (unrescaled) BSSN scalars & tensors
Bq.BSSN_basic_tensors()
trK = Bq.trK
alpha = Bq.alpha
betaU = Bq.betaU
Bq.gammabar__inverse_and_derivs()
gammabarUU = Bq.gammabarUU
global sf_rhs, sfM_rhs
# Step B.5.a: Declare grid functions for varphi and Pi
sf, sfM = sfgfs.declare_scalar_field_gridfunctions_if_not_declared_already(
)
# Step 2.a: Add Term 1 to sf_rhs: -alpha*Pi
sf_rhs = -alpha * sfM
# Step 2.b: Add Term 2 to sf_rhs: beta^{i}\partial_{i}\varphi
sf_dupD = ixp.declarerank1("sf_dupD")
for i in range(DIM):
sf_rhs += betaU[i] * sf_dupD[i]
# Step 3a: Add Term 1 to sfM_rhs: alpha * K * Pi
sfM_rhs = alpha * trK * sfM
# Step 3b: Add Term 2 to sfM_rhs: beta^{i}\partial_{i}Pi
sfM_dupD = ixp.declarerank1("sfM_dupD")
for i in range(DIM):
sfM_rhs += betaU[i] * sfM_dupD[i]
# Step 3c: Adding Term 3 to sfM_rhs
# Step 3c.i: Term 3a: gammabar^{ij}\partial_{i}\alpha\partial_{j}\varphi
alpha_dD = ixp.declarerank1("alpha_dD")
sf_dD = ixp.declarerank1("sf_dD")
sfMrhsTerm3 = sp.sympify(0)
for i in range(DIM):
for j in range(DIM):
sfMrhsTerm3 += -gammabarUU[i][j] * alpha_dD[i] * sf_dD[j]
# Step 3c.ii: Term 3b: \alpha*gammabar^{ij}\partial_{i}\partial_{j}\varphi
sf_dDD = ixp.declarerank2("sf_dDD", "sym01")
for i in range(DIM):
for j in range(DIM):
sfMrhsTerm3 += -alpha * gammabarUU[i][j] * sf_dDD[i][j]
# Step 3c.iii: Term 3c: 2*alpha*gammabar^{ij}\partial_{j}\varphi\partial_{i}\phi
Bq.phi_and_derivs(
) # sets exp^{-4phi} = exp_m4phi and \partial_{i}phi = phi_dD[i]
for i in range(DIM):
for j in range(DIM):
sfMrhsTerm3 += -2 * alpha * gammabarUU[i][j] * sf_dD[
j] * Bq.phi_dD[i]
# Step 3c.iv: Multiplying Term 3 by e^{-4phi} and adding it to sfM_rhs
sfMrhsTerm3 *= Bq.exp_m4phi
sfM_rhs += sfMrhsTerm3
# Step 3d: Adding Term 4 to sfM_rhs
# Step 3d.i: Term 4a: \alpha \bar\Lambda^{i}\partial_{i}\varphi
LambdabarU = Bq.LambdabarU
sfMrhsTerm4 = sp.sympify(0)
for i in range(DIM):
sfMrhsTerm4 += alpha * LambdabarU[i] * sf_dD[i]
# Step 3d.ii: Evaluating \bar\gamma^{ij}\hat\Gamma^{k}_{ij}
GammahatUDD = rfm.GammahatUDD
gammabarGammahatContractionU = ixp.zerorank1()
for k in range(DIM):
for i in range(DIM):
for j in range(DIM):
gammabarGammahatContractionU[
k] += gammabarUU[i][j] * GammahatUDD[k][i][j]
# Step 3d.iii: Term 4b: \alpha \bar\gamma^{ij}\hat\Gamma^{k}_{ij}\partial_{k}\varphi
for i in range(DIM):
sfMrhsTerm4 += alpha * gammabarGammahatContractionU[i] * sf_dD[i]
# Step 3d.iii: Multplying Term 4 by e^{-4phi} and adding it to sfM_rhs
sfMrhsTerm4 *= Bq.exp_m4phi
sfM_rhs += sfMrhsTerm4
return
