diff --git a/esigmapy/python_codes/__init__.py b/esigmapy/python_codes/__init__.py new file mode 100644 index 0000000..203562b --- /dev/null +++ b/esigmapy/python_codes/__init__.py @@ -0,0 +1 @@ +# __init__.py \ No newline at end of file diff --git a/esigmapy/python_codes/esigma_go_terms.py b/esigmapy/python_codes/esigma_go_terms.py new file mode 100644 index 0000000..5b37bf3 --- /dev/null +++ b/esigmapy/python_codes/esigma_go_terms.py @@ -0,0 +1,3234 @@ +# Translated from ESIGMA_GO_Terms.c by Samanwaya Mukherjee, 2026 + +import numpy as np +from dataclasses import dataclass + +# Storing constants +M_PI = np.pi +M_PI2 = M_PI ** 2 +LOG2 = np.log(2) +LOG3 = np.log(3) + +#---------------------------------------------------- +def rect(r: float, phi: float) -> complex: + """Convert polar coordinates to rectangular form.""" + return r * np.exp(1j * phi) +#---------------------------------------------------- + +@dataclass +class CommonVars: + """ + A dataclass to hold common variables for GO term calculations. + xp5: float + logx: float + b0: float + r0: float + logb0: float + logr0: float + delta: float + """ + xp5: float + logx: float + b0: float + r0: float + logb0: float + logr0: float + delta: float +#---------------------------------------------------- + +H_GO_LM_FUNCS = {} +H_QC_LM_FUNCS = {} +def register_hgo_lm(l, m): + """ + Decorator to register a GO waveform mode function for a given (l, m). + + This decorator adds the decorated function to the global HLM_FUNCS + registry, using the tuple (l, m) as the key. It enables clean and + scalable dispatch of mode-specific functions without requiring a + large manual lookup table or switch-case logic. + + Parameters + ---------- + l : int + Spherical harmonic index (typically 2 ≤ l ≤ 8). + m : int + Azimuthal index (-l ≤ m ≤ l, excluding m = 0 if unused). + + Returns + ------- + decorator : callable + A decorator that takes a function and registers it in HLM_FUNCS. + + Notes + ----- + - The decorated function should have a signature compatible with: + func(params, pno, ...) + where `pno` is the PN order. + - If a function is already registered for the same (l, m), it may be + overwritten unless explicitly guarded against. + + Examples + -------- + >>> @register_hlm(2, 2) + ... def hl_2_m_2(params, pno): + ... return 0j + + >>> H_GO_LM_FUNCS[(2, 2)] is hGO_2_m_2 + True + """ + def decorator(func): + H_GO_LM_FUNCS[(l, m)] = func + return func + return decorator + +def register_hqc_lm(l, m): + """Similar decorator for QC functions""" + def decorator(func): + H_QC_LM_FUNCS[(l, m)] = func + return func + return decorator + +############ l = 2 ############### + +# H22 + +@register_hgo_lm(2, 2) +def hGO_2_m_2(total_mass: float, eta: float, r: float, rDOT: float, + PhiDOT: float, vpnorder: int, S1z: float, S2z: float, + x: float, params: CommonVars) -> complex: + + # For black holes kappa and lambda is 1 + kappa1 = 1.0 + kappa2 = 1.0 + lambda1 = 1.0 + lambda2 = 1.0 + + # delta = np.sqrt(1 - 4 * eta) + delta = params.delta + + combination_a = (PhiDOT * r + complex(0, 1) * rDOT) + combination_a3 = combination_a * combination_a * combination_a + combination_a4 = combination_a3 * combination_a + combination_a5 = combination_a4 * combination_a + + combination_b = (PhiDOT * r - complex(0, 1) * rDOT) + combination_b2 = combination_b * combination_b + combination_b3 = combination_b2 * combination_b + + rDOT2 = rDOT * rDOT + rDOT3 = rDOT2 * rDOT + rDOT4 = rDOT3 * rDOT + rDOT5 = rDOT4 * rDOT + rDOT6 = rDOT5 * rDOT + + total_mass2 = total_mass * total_mass + total_mass3 = total_mass2 * total_mass + total_mass4 = total_mass3 * total_mass + + PhiDOT2 = PhiDOT * PhiDOT + PhiDOT3 = PhiDOT2 * PhiDOT + PhiDOT4 = PhiDOT3 * PhiDOT + PhiDOT5 = PhiDOT4 * PhiDOT + PhiDOT6 = PhiDOT5 * PhiDOT + + r2 = r * r + r3 = r2 * r + r4 = r3 * r + r5 = r4 * r + r6 = r5 * r + + eta2 = eta * eta + eta3 = eta2 * eta + + S1z2 = S1z * S1z + S1z3 = S1z2 * S1z + + S2z2 = S2z * S2z + S2z3 = S2z2 * S2z + + if vpnorder == 0: + return (total_mass / r + PhiDOT2 * r2 + + complex(0, 2) * PhiDOT * r * rDOT - rDOT2) + + elif vpnorder == 2: + return ((21 * total_mass2 * (-10 + eta) - + 27 * (-1 + 3 * eta) * r2 * combination_b * combination_a3 + + total_mass * r * + ((11 + 156 * eta) * PhiDOT2 * r2 + + complex(0, 10) * (5 + 27 * eta) * PhiDOT * r * rDOT - + 3 * (15 + 32 * eta) * rDOT2)) / + (42. * r2)) + + elif vpnorder == 3: + return ((total_mass2 * (complex(0, -1) * rDOT * + ((3 + 3 * delta - 8 * eta) * S1z + + (3 - 3 * delta - 8 * eta) * S2z) + + PhiDOT * r * + ((-3 - 3 * delta + 5 * eta) * S1z + + (-3 + 3 * delta + 5 * eta) * S2z))) / + (3. * r2)) + # (<--This is the general orbit term) + # (This is the quasi-circular limit of the general orbit term-->) + # - ((-4 * ((1 + delta - eta) * S1z + S2z - (delta + eta) * S2z) * params.x2p5) / 3.) + + # ((-4 * (S1z + delta * S1z + S2z - delta * S2z - eta * (S1z + S2z)) * params.x2p5) / 3.) + # (<--This is Quentins quasi-circular term) + + elif vpnorder == 4: + return ((6 * total_mass3 * (3028 + 1267 * eta + 158 * eta2) + + 9 * (83 - 589 * eta + 1111 * eta2) * r3 * + combination_b2 * combination_a4 + + total_mass2 * r * + ((-11891 - 36575 * eta + 13133 * eta2) * PhiDOT2 * + r2 + + complex(0, 8) * (-773 - 3767 * eta + 2852 * eta2) * + PhiDOT * r * rDOT - + 6 * (-619 + 2789 * eta + 934 * eta2) * rDOT2) - + 3 * total_mass * r2 * + (2 * (-835 - 19 * eta + 2995 * eta2) * PhiDOT4 * + r4 + + complex(0, 6) * (-433 - 721 * eta + 1703 * eta2) * + PhiDOT3 * r3 * rDOT + + 6 * (-33 + 1014 * eta + 232 * eta2) * PhiDOT2 * + r2 * rDOT2 + + complex(0, 4) * (-863 + 1462 * eta + 2954 * eta2) * + PhiDOT * r * rDOT3 - + 3 * (-557 + 664 * eta + 1712 * eta2) * rDOT4)) / + (1512. * r3) + + (3 * total_mass3 * + (S1z * (4 * eta * S2z + (1 + delta - 2 * eta) * S1z * kappa1) - + (-1 + delta + 2 * eta) * S2z2 * kappa2)) / + (4. * r3)) + # This is where circular limit terms added and subtracted + # - ((kappa1 * (1 + delta - 2 * eta) * S1z2 + S2z * (4 * eta * S1z - kappa2 * (-1 + delta + 2 * eta) * S2z)) * params.x3) + + # ((kappa1 * (1 + delta - 2 * eta) * S1z2 + S2z * (4 * eta * S1z - kappa2 * (-1 + delta + 2 * eta) * S2z)) * params.x3) + + elif vpnorder == 5: + return ((total_mass2 * eta * + (2 * total_mass * (complex(0, -702) * PhiDOT * r + rDOT) + + 3 * r * + (complex(0, -316) * PhiDOT3 * r3 - + 847 * PhiDOT2 * r2 * rDOT + + complex(0, 184) * PhiDOT * r * rDOT2 - + 122 * rDOT3))) / + (105. * r3) + # Henry et al. QC spin terms + # + ((2*(56*delta*eta*(-S1z + S2z) + 101*eta*(S1z + S2z) + 132*eta2*(S1z + S2z) - 80*(S1z + delta*S1z + S2z - delta*S2z))*params.x3p5)/63.) + + + # Henry et al. ecc spin terms + ((total_mass2 * + (total_mass * + ((238 + delta * (238 - 141 * eta) + eta * (-181 + 474 * eta)) * + PhiDOT * r * S1z + + complex(0, 8) * + (55 + delta * (55 - 19 * eta) + + 2 * eta * (-50 + 43 * eta)) * + rDOT * S1z + + (238 + delta * (-238 + 141 * eta) + eta * (-181 + 474 * eta)) * + PhiDOT * r * S2z + + complex(0, 8) * + (55 + delta * (-55 + 19 * eta) + + 2 * eta * (-50 + 43 * eta)) * + rDOT * S2z) - + r * (PhiDOT * r * rDOT2 * + (-((18 * (1 + delta) + 5 * (-63 + 55 * delta) * eta + + 188 * eta2) * + S1z) + + (18 * (-1 + delta) + 5 * (63 + 55 * delta) * eta - + 188 * eta2) * + S2z) - + complex(0, 2) * rDOT3 * + ((-27 * (1 + delta) + 6 * (5 + 7 * delta) * eta - + 4 * eta2) * + S1z + + (-27 + 27 * delta + 30 * eta - 42 * delta * eta - + 4 * eta2) * + S2z) + + complex(0, 2) * PhiDOT2 * r2 * rDOT * + ((51 + 88 * eta * (-3 + 5 * eta) + + delta * (51 + 62 * eta)) * + S1z + + (51 + 88 * eta * (-3 + 5 * eta) - + delta * (51 + 62 * eta)) * + S2z) + + PhiDOT3 * r3 * + ((120 * (1 + delta) + (-483 + 83 * delta) * eta + + 234 * eta2) * + S1z + + (120 + 3 * eta * (-161 + 78 * eta) - + delta * (120 + 83 * eta)) * + S2z)))) / + (84. * r3))) + + elif vpnorder == 6: + return ((4 * total_mass4 * + (-8203424 + 2180250 * eta2 + 592600 * eta3 + + 15 * eta * (-5503804 + 142065 * M_PI2)) - + 2700 * (-507 + 6101 * eta - 25050 * eta2 + 34525 * eta3) * + r4 * combination_b3 * combination_a5 + + total_mass3 * r * + (PhiDOT2 * + (337510808 - 198882000 * eta2 + + 56294600 * eta3 + + eta * (183074880 - 6392925 * M_PI2)) * + r2 + + complex(0, 110) * PhiDOT * + (-5498800 - 785120 * eta2 + 909200 * eta3 + + 3 * eta * (-1849216 + 38745 * M_PI2)) * + r * rDOT + + 2 * + (51172744 - 94929000 * eta2 - 5092400 * eta3 + + 45 * eta * (2794864 + 142065 * M_PI2)) * + rDOT2) - + 20 * total_mass2 * r2 * + ((-986439 + 1873255 * eta - 9961400 * eta2 + + 6704345 * eta3) * + PhiDOT4 * r4 + + complex(0, 4) * + (-273687 - 978610 * eta - 4599055 * eta2 + + 2783005 * eta3) * + PhiDOT3 * r3 * rDOT + + (-181719 + 19395325 * eta + 8237980 * eta2 + + 2612735 * eta3) * + PhiDOT2 * r2 * rDOT2 + + complex(0, 8) * + (-234312 + 1541140 * eta + 1230325 * eta2 + + 1828625 * eta3) * + PhiDOT * r * rDOT3 - + 3 * + (-370268 + 1085140 * eta + 2004715 * eta2 + + 1810425 * eta3) * + rDOT4) + + 300 * total_mass * r3 * + (4 * + (12203 - 36427 * eta - 27334 * eta2 + + 149187 * eta3) * + PhiDOT6 * r6 + + complex(0, 2) * + (44093 - 68279 * eta - 295346 * eta2 + + 541693 * eta3) * + PhiDOT5 * r5 * rDOT + + 2 * + (27432 - 202474 * eta + 247505 * eta2 + + 394771 * eta3) * + PhiDOT4 * r4 * rDOT2 + + complex(0, 2) * + (97069 - 383990 * eta - 8741 * eta2 + + 1264800 * eta3) * + PhiDOT3 * r3 * rDOT3 + + (-42811 + 53992 * eta + 309136 * eta2 - + 470840 * eta3) * + PhiDOT2 * r2 * rDOT4 + + complex(0, 2) * + (51699 - 252256 * eta + 131150 * eta2 + + 681160 * eta3) * + PhiDOT * r * rDOT5 - + 3 * + (16743 - 75104 * eta + 26920 * eta2 + + 207200 * eta3) * + rDOT6)) / + (3.3264e6 * r4) + # Henry et al. QC spin terms + # + (((4*(1 + delta)*(-7 + 9*kappa1) - 7*(9 + 17*delta)*eta - 9*(15 + 7*delta)*kappa1*eta + 12*(7 - 17*kappa1)*eta2)*S1z2 + 2*S1z*(complex(0,-42)*(1 + delta - 2*eta) - 84*(1 + delta - eta)*M_PI + eta*(-271 + 288*eta)*S2z) + S2z*(12*(7 - 17*kappa2)*eta2*S2z + 4*(-1 + delta)*(complex(0,21) + 42*M_PI + 7*S2z - 9*kappa2*S2z) + eta*(168*(complex(0,1) + M_PI) + 7*delta*(17 + 9*kappa2)*S2z - 9*(7 + 15*kappa2)*S2z)))*params.x4)/63. + + + # Henry et al. ecc spin terms + (-0.005952380952380952 * + (total_mass3 * + (2 * total_mass * + (S1z * (complex(0, 14) * (1 + delta - 2 * eta) + + 42 * (1 + delta - 2 * eta) * eta * S1z + + kappa1 * + (438 + delta * (438 + 103 * eta) + + eta * (-773 + 108 * eta)) * + S1z) + + 2 * + (complex(0, -7) * (-1 + delta + 2 * eta) + + (995 - 192 * eta) * eta * S1z) * + S2z - + (42 * eta * (-1 + delta + 2 * eta) + + kappa2 * (-438 + (773 - 108 * eta) * eta + + delta * (438 + 103 * eta))) * + S2z2) + + r * (rDOT2 * + (complex(0, 56) * (1 + delta - 2 * eta) * S1z + + (-56 * (1 + delta - 2 * eta) * eta + + kappa1 * (291 * (1 + delta) - + 2 * (445 + 154 * delta) * eta + + 24 * eta2)) * + S1z2 + + 4 * eta * (-3 + 44 * eta) * S1z * S2z + + S2z * (complex(0, -56) * (-1 + delta + 2 * eta) + + 56 * eta * (-1 + delta + 2 * eta) * S2z + + kappa2 * + (291 - 890 * eta + 24 * eta2 + + delta * (-291 + 308 * eta)) * + S2z)) + + PhiDOT2 * r2 * + (complex(0, 196) * (1 + delta - 2 * eta) * S1z + + (56 * eta * (7 + 7 * delta + eta) + + kappa1 * (-153 * (1 + delta) - + 2 * (62 + 215 * delta) * eta + + 804 * eta2)) * + S1z2 + + 8 * (60 - 187 * eta) * eta * S1z * S2z + + S2z * (complex(0, -196) * (-1 + delta + 2 * eta) + + 56 * eta * (7 - 7 * delta + eta) * S2z + + kappa2 * + (-153 + 4 * eta * (-31 + 201 * eta) + + delta * (153 + 430 * eta)) * + S2z)) + + 2 * PhiDOT * r * rDOT * + (complex(0, -1) * + (117 * (1 + delta) * kappa1 - + 434 * (1 + delta) * eta + + (-23 + 211 * delta) * kappa1 * eta + + 2 * (14 - 195 * kappa1) * eta2) * + S1z2 + + 4 * S1z * + (35 * (1 + delta - 2 * eta) + + complex(0, 1) * (9 - 209 * eta) * eta * S2z) + + S2z * (-140 * (-1 + delta + 2 * eta) + + complex(0, 1) * + (-14 * eta * (-31 + 31 * delta + 2 * eta) + + kappa2 * (117 * (-1 + delta) + + (23 + 211 * delta) * eta + + 390 * eta2)) * + S2z))))) / + r4)) + # + Henry et al. QC spinning hereditary terms + # (((-8 * M_PI * ((1 + delta - eta) * S1z + S2z - (delta + eta) * S2z) * params.x4) / 3.)) + + elif vpnorder == 7: + return ( + # Henry et al QC spin terms + # ((3318*eta3*(S1z + S2z) + eta*(-504*((7 + delta)*kappa1 - 3*(3 + delta)*lambda1)*S1z3 - 1008*S1z2*(3*kappa1*M_PI - 3*(1 + delta)*S2z + 2*(1 + delta)*kappa1*S2z) + S1z*(17387 + 20761*delta + 1008*S2z*(6*M_PI + (-1 + delta)*(-3 + 2*kappa2)*S2z)) + S2z*(17387 - 20761*delta + 504*S2z*(-6*kappa2*M_PI + (-7 + delta)*kappa2*S2z - 3*(-3 + delta)*lambda2*S2z))) + 2*(2809*(1 + delta)*S1z + 756*(1 + delta)*kappa1*M_PI*S1z2 + 756*(1 + delta)*(kappa1 - lambda1)*S1z3 - (-1 + delta)*S2z*(2809 + 756*S2z*(-(lambda2*S2z) + kappa2*(M_PI + S2z)))) - 2*eta2*(708*delta*(-S1z + S2z) + (S1z + S2z)*(4427 + 1008*(kappa1*S1z2 + S2z*(-2*S1z + kappa2*S2z)))))*params.x4p5)/756. + # + + # Henry et al. ecc+spin terms + ((total_mass2 * + (-3 * total_mass * r * + (complex(0, -16) * rDOT3 * + (complex(0, 12) * eta * (-16703 + 4427 * eta) + + 35 * eta * + (4578 + eta * (-4288 + 765 * eta) + + delta * (3748 + 802 * eta)) * + S1z + + 35 * + (delta * (942 - 2 * eta * (1874 + 401 * eta)) + + eta * (4578 + eta * (-4288 + 765 * eta))) * + S2z - + 32970 * (S1z + delta * S1z + S2z)) + + 4 * PhiDOT * r * rDOT2 * + (-338520 * eta3 * (S1z + S2z) + + 48930 * (S1z + delta * S1z + S2z - delta * S2z) + + eta * (complex(0, 3420696) - + 35 * (14154 + 21167 * delta) * S1z - + 495390 * S2z + 740845 * delta * S2z) + + eta2 * (complex(0, -612336) + + 245 * (3566 - 1565 * delta) * S1z + + 245 * (3566 + 1565 * delta) * S2z)) + + PhiDOT3 * r3 * + (2515380 * eta3 * (S1z + S2z) - + 5 * eta * + (complex(0, 1859936) + + 7 * (82329 + 37061 * delta) * S1z + + 7 * (82329 - 37061 * delta) * S2z) - + 128100 * (S1z + delta * S1z + S2z - delta * S2z) + + 4 * eta2 * + (complex(0, 381348) + + 35 * (-18505 + 1777 * delta) * S1z - + 35 * (18505 + 1777 * delta) * S2z)) + + complex(0, 8) * PhiDOT2 * r2 * rDOT * + (779100 * eta3 * (S1z + S2z) + + 5 * eta * + (complex(0, 828806) + + 7 * (4839 + 5971 * delta) * S1z + + 7 * (4839 - 5971 * delta) * S2z) - + 62475 * (S1z + delta * S1z + S2z - delta * S2z) + + eta2 * (complex(0, -976002) + + 35 * (-29599 + 3109 * delta) * S1z - + 35 * (29599 + 3109 * delta) * S2z))) + + 3 * r2 * + (complex(0, 4) * PhiDOT2 * r2 * rDOT3 * + (complex(0, 2) * (65451 - 350563 * eta) * eta + + 105 * eta * + (6020 + delta * (3114 + 411 * eta) + + eta * (-4513 + 9136 * eta)) * + S1z + + 105 * + (-3 * delta * (-408 + eta * (1038 + 137 * eta)) + + eta * (6020 + eta * (-4513 + 9136 * eta))) * + S2z - + 128520 * (S1z + delta * S1z + S2z)) + + PhiDOT * r * rDOT4 * + (complex(0, -128) * eta * (2487 + 18334 * eta) - + 105 * eta * + (8689 + 8 * eta * (-687 + 1402 * eta) + + delta * (2143 + 8212 * eta)) * + S1z + + 105 * + (eta * (-8689 + 8 * (687 - 1402 * eta) * eta) + + delta * (-1470 + eta * (2143 + 8212 * eta))) * + S2z + + 154350 * (S1z + delta * S1z + S2z)) - + complex(0, 6) * rDOT5 * + (-11760 * eta3 * (S1z + S2z) + + 4 * eta2 * + (complex(0, 57338) + + 35 * (569 + 301 * delta) * S1z + + 35 * (569 - 301 * delta) * S2z) - + 16 * eta * + (complex(0, -2517) + 35 * (72 + 71 * delta) * S1z + + 35 * (72 - 71 * delta) * S2z) + + 8715 * (S1z + delta * S1z + S2z - delta * S2z)) + + PhiDOT5 * r5 * + (2263380 * eta3 * (S1z + S2z) + + 3 * eta * + (complex(0, 653432) + + 35 * (13283 + 7839 * delta) * S1z + + 35 * (13283 - 7839 * delta) * S2z) - + 219240 * (S1z + delta * S1z + S2z - delta * S2z) + + 4 * eta2 * + (complex(0, -291268) + + 105 * (-7669 + 165 * delta) * S1z - + 105 * (7669 + 165 * delta) * S2z)) + + complex(0, 14) * PhiDOT4 * r4 * rDOT * + (385170 * eta3 * (S1z + S2z) + + 15 * eta * + (complex(0, 16914) + (8633 + 2267 * delta) * S1z + + 8633 * S2z - 2267 * delta * S2z) - + 18630 * (S1z + delta * S1z + S2z - delta * S2z) + + 2 * eta2 * + (complex(0, -67904) + + 15 * (-13932 + 679 * delta) * S1z - + 15 * (13932 + 679 * delta) * S2z)) + + 6 * PhiDOT3 * r3 * rDOT2 * + (-6720 * eta3 * (S1z + S2z) + + 5 * eta * + (complex(0, -241704) + + 7 * (4377 + 3083 * delta) * S1z + + 7 * (4377 - 3083 * delta) * S2z) - + 36960 * (S1z + delta * S1z + S2z - delta * S2z) + + eta2 * (complex(0, -364384) + + 35 * (5059 - 4753 * delta) * S1z + + 35 * (5059 + 4753 * delta) * S2z))) + + 14 * total_mass2 * + (complex(0, 30) * rDOT * + (15280 * eta3 * (S1z + S2z) - + 4 * eta2 * + (complex(0, -111) + 3562 * S1z + 682 * delta * S1z + + 945 * kappa1 * S1z3 + + (3562 - 682 * delta + + 945 * (-2 + kappa1) * S1z2) * + S2z + + 945 * (-2 + kappa2) * S1z * S2z2 + + 945 * kappa2 * S2z3) + + eta * (complex(0, -27520) + + 378 * + (5 * (1 + delta) * kappa1 + + 3 * (3 + delta) * lambda1) * + S1z3 + + (29749 - 13605 * delta) * S2z - + 1512 * (1 + delta) * kappa1 * S1z2 * S2z - + 378 * + (5 * (-1 + delta) * kappa2 + + 3 * (-3 + delta) * lambda2) * + S2z3 + + S1z * (29749 + 13605 * delta + + 1512 * (-1 + delta) * kappa2 * + S2z2)) + + 2 * (-8009 * (1 + delta) * S1z - + 567 * (1 + delta) * lambda1 * S1z3 + + (-1 + delta) * S2z * + (8009 + 567 * lambda2 * S2z2))) + + PhiDOT * r * + (285840 * eta3 * (S1z + S2z) - + 30 * eta2 * + (complex(0, 11504) + 1890 * kappa1 * S1z3 + + 23090 * S2z - 1823 * delta * S2z + + 1890 * (-2 + kappa1) * S1z2 * S2z + + 1890 * kappa2 * S2z3 + + S1z * (23090 + 1823 * delta + + 1890 * (-2 + kappa2) * S2z2)) + + 30 * (689 * (1 + delta) * S1z - + 1134 * (1 + delta) * lambda1 * S1z3 + + (-1 + delta) * S2z * + (-689 + 1134 * lambda2 * S2z2)) + + 2 * eta * + (complex(0, 415432) - 66840 * S2z + + 15 * (8 * (-557 + 1342 * delta) * S1z + + 189 * + (5 * (1 + delta) * kappa1 + + 6 * (3 + delta) * lambda1) * + S1z3 - + 10736 * delta * S2z - + 2457 * (1 + delta) * kappa1 * S1z2 * + S2z + + 2457 * (-1 + delta) * kappa2 * S1z * + S2z2 - + 189 * + (5 * (-1 + delta) * kappa2 + + 6 * (-3 + delta) * lambda2) * + S2z3)))))) / + (317520. * r4)) + # + Henry et al. QC spinning hereditary terms + # (2 * M_PI * (kappa1 * (1 + delta - 2 * eta) * S1z2 + S2z * (4 * eta * S1z - kappa2 * (-1 + delta + 2 * eta) * S2z)) * params.x4p5) + ) + + else: + return complex(0, 0) + +@register_hqc_lm(2, 2) +def hQC_2_m_2(total_mass: float, eta: float, vpnorder: int, x: float, S1z: float, S2z: float, + params: CommonVars) -> complex: + + EulerGamma = 0.5772156649015329 + x0 = 0.4375079683656479 + # b0 = 2 * total_mass / np.exp(0.5) + # r0 = b0 + kappa1 = 1.0 # for black holes kappa and lambda is 1 + kappa2 = 1.0 + # delta = np.sqrt(1 - 4 * eta) + xp5 = params.xp5 + logx = params.logx + logb0 = params.logb0 + logr0 = params.logr0 + delta = params.delta + # b0 = params.b0 + # r0 = params.r0 + x2p5 = x * x * xp5 + x3p5 = x * x2p5 + x4p5 = x * x3p5 + x4 = x*x*x*x + x5 = x*x4 + + eta2 = eta * eta + + S1z2 = S1z * S1z + + S2z2 = S2z * S2z + + # keeping only the hereditary terms + # if vpnorder == 0: + # return (2 * x) + + # elif vpnorder == 2: + # return ((-5.095238095238095 + (55 * eta) / 21.) * x2) + + if vpnorder == 3: + # return (4 * M_PI * x2p5) + return (complex(0, 0.6666666666666666) * x2p5 * (-11 + 12 * EulerGamma - complex(0, 6) * M_PI + 24 * LOG2 + 12 * logb0 + 18 * logx)) + + # elif vpnorder == 4: + # return ((-2.874338624338624 - (1069 * eta) / 108. + (2047 * eta2) / 756.) * x3) + + elif vpnorder == 5: + # return ((complex(0,-48)*eta - (214*M_PI)/21. + (68*eta*M_PI)/21.)*x3p5) + # return ((2 * (-107 + 34 * eta) * M_PI * x3p5) / 21.) # This is old implementation + return (complex(0, 0.015873015873015872) * (-107 + 34 * eta) * x3p5 * (-11 + 12 * EulerGamma - complex(0, 6) * M_PI + 24 * LOG2 + 12 * logb0 + 18 * logx)) + + elif vpnorder == 6: + # return ((x4 * (-27392 * EulerGamma + M_PI * (complex(0, 13696) + 35 * (64 + 41 * eta) * M_PI) - 13696 * np.log(16 * x))) / 1680.) # This is the old implementation. + + return (x4 * (-45.216145124716554 + (456 * EulerGamma) / 35. - 16 * EulerGamma * EulerGamma - + complex(0, 6.514285714285714) * M_PI + complex(0, 16) * EulerGamma * M_PI + (4 * M_PI2) / 3. + + complex(0, 4.888888888888889) * S1z - complex(0, 5.333333333333333) * EulerGamma * S1z - + complex(0, 4.888888888888889) * eta * S1z + complex(0, 5.333333333333333) * EulerGamma * eta * S1z - + (8 * M_PI * S1z) / 3. + (8 * eta * M_PI * S1z) / 3. + complex(0, 4.888888888888889) * S2z - + complex(0, 5.333333333333333) * EulerGamma * S2z - complex(0, 4.888888888888889) * eta * S2z + + complex(0, 5.333333333333333) * EulerGamma * eta * S2z - (8 * M_PI * S2z) / 3. + (8 * eta * M_PI * S2z) / 3. + + (912 * LOG2) / 35. - 64 * EulerGamma * LOG2 + complex(0, 32) * M_PI * LOG2 - + complex(0, 10.666666666666666) * S1z * LOG2 + complex(0, 10.666666666666666) * eta * S1z * LOG2 - + complex(0, 10.666666666666666) * S2z * LOG2 + complex(0, 10.666666666666666) * eta * S2z * LOG2 - + 64 * LOG2 * LOG2 + (88 * logb0) / 3. - 32 * EulerGamma * logb0 + + complex(0, 16) * M_PI * logb0 - complex(0, 5.333333333333333) * S1z * logb0 + + complex(0, 5.333333333333333) * eta * S1z * logb0 - complex(0, 5.333333333333333) * S2z * logb0 + + complex(0, 5.333333333333333) * eta * S2z * logb0 - 64 * LOG2 * logb0 - + 16 * logb0 * logb0 - (1712 * logr0) / 105. + (684 * logx) / 35. - + 48 * EulerGamma * logx + complex(0, 24) * M_PI * logx - complex(0, 8) * S1z * logx + + complex(0, 8) * eta * S1z * logx - complex(0, 8) * S2z * logx + complex(0, 8) * eta * S2z * logx - + 96 * LOG2 * logx - 48 * logb0 * logx - 36 * logx * logx - + complex(0, 0.4444444444444444) * delta * (S1z - S2z) * (-11 + 12 * EulerGamma - complex(0, 6) * M_PI + + 24 * LOG2 + 12 * logb0 + 18 * logx))) + + elif vpnorder == 7: + # return (((-2173 - 4990 * eta + 1120 * eta2) * M_PI * x4p5) / 378.) # This is the old implementation. + return (complex(0, 0.0004409171075837742) * x4p5 * (23903 - 26076 * EulerGamma + 57452 * eta - + 59880 * EulerGamma * eta - 18728 * eta2 + 13440 * EulerGamma * eta2 + + complex(0, 13038) * M_PI + complex(0, 29940) * eta * M_PI - complex(0, 6720) * eta2 * M_PI - + 8316 * kappa1 * S1z2 - 8316 * delta * kappa1 * S1z2 + 9072 * EulerGamma * kappa1 * S1z2 + + 9072 * delta * EulerGamma * kappa1 * S1z2 + 16632 * kappa1 * eta * S1z2 - + 18144 * EulerGamma * kappa1 * eta * S1z2 - complex(0, 4536) * kappa1 * M_PI * S1z2 - + complex(0, 4536) * delta * kappa1 * M_PI * S1z2 + complex(0, 9072) * kappa1 * eta * M_PI * S1z2 - + 33264 * eta * S1z * S2z + 36288 * EulerGamma * eta * S1z * S2z - complex(0, 18144) * eta * M_PI * S1z * S2z - + 8316 * kappa2 * S2z2 + 8316 * delta * kappa2 * S2z2 + 9072 * EulerGamma * kappa2 * S2z2 - + 9072 * delta * EulerGamma * kappa2 * S2z2 + 16632 * kappa2 * eta * S2z2 - + 18144 * EulerGamma * kappa2 * eta * S2z2 - complex(0, 4536) * kappa2 * M_PI * S2z2 + + complex(0, 4536) * delta * kappa2 * M_PI * S2z2 + complex(0, 9072) * kappa2 * eta * M_PI * S2z2 - + 52152 * LOG2 - 119760 * eta * LOG2 + 26880 * eta2 * LOG2 + + 18144 * kappa1 * S1z2 * LOG2 + 18144 * delta * kappa1 * S1z2 * LOG2 - + 36288 * kappa1 * eta * S1z2 * LOG2 + 72576 * eta * S1z * S2z * LOG2 + + 18144 * kappa2 * S2z2 * LOG2 - 18144 * delta * kappa2 * S2z2 * LOG2 - + 36288 * kappa2 * eta * S2z2 * LOG2 + 12 * (-2173 - 4990 * eta + 1120 * eta2 + + 756 * kappa1 * (1 + delta - 2 * eta) * S1z2 + 3024 * eta * S1z * S2z - + 756 * kappa2 * (-1 + delta + 2 * eta) * S2z2) * logb0 + 18 * (-2173 - 4990 * eta + + 1120 * eta2 + 756 * kappa1 * (1 + delta - 2 * eta) * S1z2 + 3024 * eta * S1z * S2z - + 756 * kappa2 * (-1 + delta + 2 * eta) * S2z2) * logx)) + + # 4PN non-spinning quasi-circular (2,2) mode has been obtained from Blanchet + # et al. arXiv:2304.11185. This is written in terms of phi. Please refer to file shared by Quentin. + + elif vpnorder == 8: + return (-1.3109423540262542e-11 * (x5 * (29059430400 * EulerGamma * EulerGamma * (-107 + 13 * eta) + + 12 * (628830397253 + 1854914893791 * eta + 421984442880 * LOG2) + 415134720 * EulerGamma * (6099 - 40277 * eta + complex(0, 70) * (107 - 13 * eta) * M_PI + 280 * (-107 + 13 * eta) * LOG2) + + 35 * (-28 * eta2 * (5385456111 + 5 * eta * (-163158374 + 26251249 * eta)) + + 135135 * (54784 + 5 * eta * (1951 + 6560 * eta)) * M_PI2 - 955450349568 * eta * LOG2 + + 3321077760 * (-107 + 13 * eta) * LOG2 * LOG2 - complex(0, 5930496) * M_PI * (6099 - 74773 * eta + 280 * (-107 + 13 * eta) * LOG2)) - 5700491596800 * np.log(total_mass) + + 6966444925440 * logx + 69189120 * (420 * (-107 + 13 * eta) * logb0 * logb0 + + 420 * (-107 + 13 * eta) * np.log(total_mass) * np.log(total_mass) - 14 * np.log(total_mass) * (-11803 * eta + + 60 * EulerGamma * (-107 + 13 * eta) + complex(0, 30) * (107 - 13 * eta) * M_PI + + 120 * (-107 + 13 * eta) * LOG2 + 90 * (-107 + 13 * eta) * logx) + 14 * logb0 * (5885 - 6420 * EulerGamma - 11803 * eta + 780 * EulerGamma * eta + complex(0, 3210) * M_PI - + complex(0, 390) * eta * M_PI + 120 * (-107 + 13 * eta) * LOG2 + (6420 - 780 * eta) * np.log(total_mass) + + 90 * (-107 + 13 * eta) * logx) + 5 * logx * (-74149 * eta + 252 * EulerGamma * (-107 + 13 * eta) - + complex(0, 126) * (-107 + 13 * eta) * M_PI + 504 * (-107 + 13 * eta) * LOG2 + + 189 * (-107 + 13 * eta) * logx) + 84672 * eta * np.log(x0))))) + + # return ((x5 * (276756480 * EulerGamma * (11449 + 19105 * eta) - 12 * (846557506853 + 1008017482431 * eta) + 35 * (28 * eta2 * (5385456111 + 5 * eta * (-163158374 + 26251249 * eta)) - complex(0, 3953664) * (11449 + 109657 * eta) * M_PI - 135135 * (54784 + 5 * eta * (1951 + 6560 * eta)) * M_PI2) + 138378240 * (11449 + 19105 * eta) * np.log(16 * x))) / 7.62810048e10) + + else: + return complex(0, 0) + +# H21 + +@register_hgo_lm(2, 1) +def hGO_2_m_1( + total_mass: float, + eta: float, + r: float, + rDOT: float, + PhiDOT: float, + vpnorder: int, + S1z: float, + S2z: float, + x: float, + params: CommonVars + ) -> complex: + + delta = params.delta + kappa1 = 1.0 + kappa2 = 1.0 + # r0 = 2 * total_mass / np.exp(0.5) + r0 = params.r0 + + rDOT2 = rDOT * rDOT + rDOT3 = rDOT2 * rDOT + rDOT4 = rDOT3 * rDOT + rDOT5 = rDOT4 * rDOT + rDOT6 = rDOT5 * rDOT + + total_mass2 = total_mass * total_mass + total_mass3 = total_mass2 * total_mass + + PhiDOT2 = PhiDOT * PhiDOT + PhiDOT3 = PhiDOT2 * PhiDOT + PhiDOT4 = PhiDOT3 * PhiDOT + PhiDOT5 = PhiDOT4 * PhiDOT + PhiDOT6 = PhiDOT5 * PhiDOT + + r2 = r * r + r3 = r2 * r + r4 = r3 * r + r5 = r4 * r + r6 = r5 * r + + eta2 = eta * eta + eta3 = eta2 * eta + + S1z2 = S1z * S1z + S1z3 = S1z2 * S1z + + S2z2 = S2z * S2z + + if vpnorder == 1: + return 0.6666666666666666j * delta * total_mass * PhiDOT + + elif vpnorder == 2: + return ( + (-0.5j * total_mass2 * + ((1 + delta) * S1z + (-1 + delta) * S2z)) + / r2 + ) + + elif vpnorder == 3: + return ( + (0.023809523809523808j * delta * total_mass * PhiDOT * + (4 * total_mass * (-9 + 11 * eta) + + r * ((19 - 24 * eta) * PhiDOT2 * r2 + + 2j * (83 + 2 * eta) * PhiDOT * r * rDOT + + 2 * (-33 + 10 * eta) * rDOT2))) + / r + ) + + elif vpnorder == 4: + return ( + (0.011904761904761904j * total_mass2 * + (2 * total_mass * + ((77 + 59 * eta + 11 * delta * (7 + eta)) * S1z + + (-77 - 59 * eta + 11 * delta * (7 + eta)) * S2z) + + r * (-2j * PhiDOT * r * rDOT * + (147 * (1 + delta) * S1z + (-83 + 13 * delta) * eta * S1z + + 147 * (-1 + delta) * S2z + (83 + 13 * delta) * eta * S2z) + + rDOT2 * + ((105 * (1 + delta) - 4 * (13 + 15 * delta) * eta) * S1z + + (-105 + 15 * delta * (7 - 4 * eta) + 52 * eta) * S2z) + + 4 * PhiDOT2 * r2 * + ((-21 - 21 * delta + 66 * eta + 4 * delta * eta) * S1z + + (21 - 21 * delta - 66 * eta + 4 * delta * eta) * S2z)))) + / r3 + ) + + elif vpnorder == 5: + term1 = ( + (0.0013227513227513227j * delta * total_mass * PhiDOT * + (10 * total_mass2 * (31 - 205 * eta + 111 * eta2) - + 2 * total_mass * r * + ((-197 + 5 * eta + 660 * eta2) * PhiDOT2 * r2 + + 1j * (-3167 - 5278 * eta + 201 * eta2) * PhiDOT * r * rDOT + + 8 * (202 + 587 * eta - 177 * eta2) * rDOT2) + + 3 * r2 * + ((152 - 692 * eta + 333 * eta2) * PhiDOT4 * r4 + + 2j * (308 - 1607 * eta + 111 * eta2) * PhiDOT3 * r3 * rDOT - + 3 * (75 - 560 * eta + 68 * eta2) * PhiDOT2 * r2 * rDOT2 - + 2j * (-265 + 526 * eta + 18 * eta2) * PhiDOT * r * rDOT3 + + (-241 + 550 * eta - 264 * eta2) * rDOT4))) + / r2 + ) + + # Henry et al. ecc spin terms + term2 = ( + (total_mass3 * + (-4 * rDOT * + (kappa1 * (1 + delta - 2 * eta) * S1z2 + + kappa2 * (-1 + delta + 2 * eta) * S2z2) - + 1j * PhiDOT * r * + ((-((1 + delta) * (9 + kappa1)) + + 2 * (9 + (4 + 3 * delta) * kappa1) * eta) * S1z2 - + 12 * delta * eta * S1z * S2z + + (9 + kappa2 - 2 * (9 + 4 * kappa2) * eta + + delta * (-9 - kappa2 + 6 * kappa2 * eta)) * S2z2))) + / (6.0 * r3) + ) + + return term1 + term2 + + elif vpnorder == 6: + term1 = ( + (delta * total_mass2 * eta * PhiDOT * + (total_mass * (195 * PhiDOT * r - 946j * rDOT) + + 9 * r * + (270 * PhiDOT3 * r3 - + 483j * PhiDOT2 * r2 * rDOT - + 580 * PhiDOT * r * rDOT2 + + 42j * rDOT3))) + / (315.0 * r2) + ) + + # Henry et al. ecc spin terms + term2 = ( + 0.00033068783068783067j * total_mass2 * + (3 * r2 * + (4j * PhiDOT * r * rDOT3 * + ((-315 * (1 + delta) + 2 * (251 + 463 * delta) * eta + + 4 * (15 + delta) * eta2) * S1z + + (315 - 315 * delta - 502 * eta + 926 * delta * eta + + 4 * (-15 + delta) * eta2) * S2z) + + 12 * PhiDOT2 * r2 * rDOT2 * + ((189 * (1 + delta) - 2 * (521 + 293 * delta) * eta + + 7 * (55 + 23 * delta) * eta2) * S1z + + (189 * (-1 + delta) + 2 * (521 - 293 * delta) * eta + + 7 * (-55 + 23 * delta) * eta2) * S2z) + + rDOT4 * + ((567 * (1 + delta) - 16 * (77 + 64 * delta) * eta + + 8 * (177 + 173 * delta) * eta2) * S1z + + (567 * (-1 + delta) + 16 * (77 - 64 * delta) * eta + + 8 * (-177 + 173 * delta) * eta2) * S2z) - + 4j * PhiDOT3 * r3 * rDOT * + ((936 * (1 + delta) - 5 * (979 + 215 * delta) * eta + + 2 * (1353 + 293 * delta) * eta2) * S1z + + (936 * (-1 + delta) + 5 * (979 - 215 * delta) * eta + + 2 * (-1353 + 293 * delta) * eta2) * S2z) + + 4 * PhiDOT4 * r4 * + ((-252 * (1 + delta) + (1315 + 857 * delta) * eta + + 4 * (-285 + 43 * delta) * eta2) * S1z + + (252 + 5 * eta * (-263 + 228 * eta) + + delta * (-252 + eta * (857 + 172 * eta))) * S2z)) - + 2 * total_mass * r * + (-1j * PhiDOT * r * rDOT * + ((2043 * (1 + delta) + (37 + 2597 * delta) * eta + + (10635 + 139 * delta) * eta2) * S1z + + (2043 * (-1 + delta) + (-37 + 2597 * delta) * eta + + (-10635 + 139 * delta) * eta2) * S2z) + + PhiDOT2 * r2 * + ((-765 - eta * (667 + 7773 * eta) + + delta * (-765 + 7 * eta * (-533 + 245 * eta))) * S1z + + (765 + eta * (667 + 7773 * eta) + + delta * (-765 + 7 * eta * (-533 + 245 * eta))) * S2z) + + 4 * rDOT2 * + ((-234 * (1 + delta) - 4 * (560 + 901 * delta) * eta + + (483 + 1111 * delta) * eta2) * S1z + + (234 + 7 * (320 - 69 * eta) * eta + + delta * (-234 + eta * (-3604 + 1111 * eta))) * S2z)) + + 2 * total_mass2 * + (1134 * kappa1 * (-1 - delta + (3 + delta) * eta) * S1z3 + + 1134 * (1 + delta) * (-2 + kappa1) * eta * S1z2 * S2z + + S1z * + (-5661 - 5661 * delta - 17156 * eta - 9172 * delta * eta + + 231 * eta2 + 775 * delta * eta2 + + 1134 * (-1 + delta) * (-2 + kappa2) * eta * S2z2) + + S2z * (5661 - 5661 * delta + 17156 * eta - 9172 * delta * eta - + 231 * eta2 + 775 * delta * eta2 + + 1134 * kappa2 * (1 - delta + (-3 + delta) * eta) * + S2z2))) + ) / r4 + + + return term1 + term2 + + elif vpnorder == 7: + + spin_terms = ( + -23760 * total_mass3 * + (PhiDOT3 * r4 * + (-12j * (-35 + 107 * eta) * S1z + + 5 * (126 - 462 * eta + 80 * eta2 + + kappa1 * (-2 - 79 * eta + 153 * eta2)) * S1z2 + + S2z * (-420j + 1284j * eta + + 10 * (-63 + kappa2) * S2z + + 5 * (462 + 79 * kappa2) * eta * S2z - + 5 * (80 + 153 * kappa2) * eta2 * S2z)) - + 1j * PhiDOT2 * r3 * rDOT * + (-24j * (-35 + 202 * eta) * S1z + + 5 * (-14 * eta * (3 + 58 * eta) + + kappa1 * (-409 + 1096 * eta + 6 * eta2)) * S1z2 + + S2z * (-840j + 2045 * kappa2 * S2z + + 10 * (406 - 3 * kappa2) * eta2 * S2z + + eta * (4848j + 210 * S2z - 5480 * kappa2 * S2z))) - + 4j * r * rDOT3 * + (3j * (26 + 57 * eta) * S1z + + 5 * (4 * eta2 + + kappa1 * (-7 + 49 * eta + 15 * eta2)) * S1z2 - + S2z * (78j - 35 * kappa2 * S2z + + 5 * (4 + 15 * kappa2) * eta2 * S2z + + eta * (171j + 245 * kappa2 * S2z))) + + 2j * total_mass * rDOT * + (-4j * (-78 + 769 * eta) * S1z + + 5 * ((14 - 59 * eta) * eta + + kappa1 * (-70 - 77 * eta + 18 * eta2)) * S1z2 + + S2z * (-312j + 350 * kappa2 * S2z + + 5 * (59 - 18 * kappa2) * eta2 * S2z + + eta * (3076j - 70 * S2z + 385 * kappa2 * S2z))) + + PhiDOT * r2 * rDOT2 * + (6j * (-62 + 219 * eta) * S1z - + 5 * (189 - 756 * eta + 388 * eta2 + + kappa1 * (98 - 266 * eta + 276 * eta2)) * S1z2 + + S2z * (372j + 945 * S2z + 490 * kappa2 * S2z + + 20 * (97 + 69 * kappa2) * eta2 * S2z - + 2 * eta * (657j + 35 * (54 + 19 * kappa2) * S2z))) + + total_mass * PhiDOT * r * + (8j * (-61 + 480 * eta) * S1z + + 5 * (-392 + 448 * eta + 474 * eta2 + + kappa1 * (-11 + 150 * eta + 58 * eta2)) * S1z2 - + S2z * (-488j - 5 * (392 + 11 * kappa2) * S2z + + 10 * (237 + 29 * kappa2) * eta2 * S2z + + 10 * eta * (384j + (224 + 75 * kappa2) * S2z)))) + ) + + orbital_terms = ( + delta * total_mass * + (-240 * total_mass * PhiDOT * r3 * + ((197936 - 139360 * eta - 367105 * eta2 + + 253245 * eta3) * PhiDOT4 * r4 + + 1j * (279236 - 483940 * eta - 2817805 * eta2 + + 459180 * eta3) * PhiDOT3 * r3 * rDOT - + 6 * (38627 + 89295 * eta - 492740 * eta2 + + 75975 * eta3) * PhiDOT2 * r2 * rDOT2 - + 1j * (-731008 + 2287930 * eta + 981060 * eta2 + + 10275 * eta3) * PhiDOT * r * rDOT3 + + (-327667 + 436705 * eta + 659790 * eta2 - + 438255 * eta3) * rDOT4) + + 900 * PhiDOT * r4 * + (2 * (-2594 + 27609 * eta - 74032 * eta2 + + 25974 * eta3) * PhiDOT6 * r6 + + 4j * (-5730 + 58833 * eta - 137842 * eta2 + + 17123 * eta3) * PhiDOT5 * r5 * rDOT + + 2 * (-114 - 41622 * eta + 147569 * eta2 + + 4196 * eta3) * PhiDOT4 * r4 * rDOT2 + + 4j * (-9554 + 70788 * eta - 156227 * eta2 + + 5810 * eta3) * PhiDOT3 * r3 * rDOT3 + + (17619 - 138450 * eta + 322600 * eta2 - + 80816 * eta3) * PhiDOT2 * r2 * rDOT4 - + 2j * (8793 - 52230 * eta + 69340 * eta2 + + 2536 * eta3) * PhiDOT * r * rDOT5 + + 2 * (3957 - 24534 * eta + 42584 * eta2 - + 20800 * eta3) * rDOT6) - + 2 * total_mass3 * + (-23760j * rDOT * + (5 * (eta * (-14 + 31 * eta) + 7 * kappa1 * (10 + 31 * eta)) * S1z2 + + 2 * S1z * (-156j + 155 * eta2 * S2z + + 2 * eta * (613j + 390 * S2z)) + + S2z * (-312j + 350 * kappa2 * S2z + + 155 * eta2 * S2z + + eta * (2452j + 35 * (-2 + 31 * kappa2) * S2z))) + + PhiDOT * r * + (8946400 * eta3 - + 8 * (6991786 + 724680j * S1z + + 7425 * (392 + 11 * kappa1) * S1z2 + + 724680j * S2z + + 7425 * (392 + 11 * kappa2) * S2z2) - + 3600 * eta2 * + (-628 + 33 * (-19 + 92 * kappa1) * S1z2 - + 7326 * S1z * S2z + + 33 * (-19 + 92 * kappa2) * S2z2) + + 15 * eta * + (994455 * M_PI2 + + 8 * (-2249485 + + 7920 * (-21 + 8 * kappa1) * S1z2 + + 283536j * S2z + + 7920 * (-21 + 8 * kappa2) * S2z2 - + 1584 * S1z * (-179j + 170 * S2z))))) + + 3 * total_mass2 * r * + (31680j * rDOT3 * + (5 * (4 * eta2 + 7 * kappa1 * (-1 + 5 * eta)) * S1z2 + + S1z * (78j + 40 * eta2 * S2z + + eta * (327j + 420 * S2z)) + + S2z * (78j - 35 * kappa2 * S2z + + 20 * eta2 * S2z + + eta * (327j + 175 * kappa2 * S2z))) - + 22 * PhiDOT * r * rDOT2 * + (2553200 * eta3 - + 24 * (268267 + 5580j * S1z + + 525 * (27 + 14 * kappa1) * S1z2 + + 5580j * S2z + + 525 * (27 + 14 * kappa2) * S2z2) - + 200 * eta2 * + (39445 + 72 * (-4 + 21 * kappa1) * S1z2 - + 3600 * S1z * S2z + + 72 * (-4 + 21 * kappa2) * S2z2) + + 25 * eta * + (23247 * M_PI2 + + 8 * (-69259 + 1026j * S1z + + 126 * (27 + 5 * kappa1) * S1z2 + + 1026j * S2z + + 126 * (27 + 5 * kappa2) * S2z2))) + + PhiDOT3 * r3 * + (10071200 * eta3 + + 96 * (-421183 - 34650j * S1z + + 825 * (-63 + kappa1) * S1z2 - + 34650j * S2z + + 825 * (-63 + kappa2) * S2z2) - + 400 * eta2 * + (64177 + 792 * (-5 + 6 * kappa1) * S1z2 - + 17424 * S1z * S2z + + 792 * (-5 + 6 * kappa2) * S2z2) + + 15 * eta * + (426195 * M_PI2 + + 8 * (-509635 + + 330 * (210 + 83 * kappa1) * S1z2 + + 29304j * S2z + + 330 * (210 + 83 * kappa2) * S2z2 - + 792 * S1z * (-37j + 70 * S2z)))) - + 2j * PhiDOT2 * r2 * rDOT * + (-8330400 * eta3 + + 8 * (-2810116 - 415800j * S1z + + 1012275 * kappa1 * S1z2 - + 415800j * S2z + + 1012275 * kappa2 * S2z2) + + 4800 * eta2 * + (13411 + 33 * (19 + 12 * kappa1) * S1z2 + + 462 * S1z * S2z + + 33 * (19 + 12 * kappa2) * S2z2) + + 5 * eta * + (1278585 * M_PI2 - + 8 * (5139685 + + 990 * (-21 + 139 * kappa1) * S1z2 - + 313632j * S2z + + 990 * (-21 + 139 * kappa2) * S2z2 - + 3564 * S1z * (88j + 185 * S2z)))))) + ) + + log_term = ( + -13559040 * delta * total_mass3 * PhiDOT * r * + (2 * total_mass - 3 * PhiDOT2 * r3 + + 6j * PhiDOT * r2 * rDOT + + 6 * r * rDOT2) * + np.log(r / r0) + ) + + return ( + -1.0020843354176688e-7j * + (spin_terms + orbital_terms + log_term) + ) / r4 + + else: + return complex(0.0, 0.0) + +@register_hqc_lm(2, 1) +def hQC_2_m_1( + total_mass: float, + eta: float, + vpnorder: int, + x: float, + S1z: float, + S2z: float, + params:CommonVars + ) -> complex: + + # delta: float = np.sqrt(1.0 - 4.0 * eta) + EulerGamma: float = 0.5772156649015329 + # b0: float = 2.0 * total_mass / np.exp(0.5) + # r0: float = b0 + + delta = params.delta + xp5 = params.xp5 + logx = params.logx + logb0 = params.logb0 + logr0 = params.logr0 + + x3p5 = x**3 * xp5 + x4p5 = x**4 * xp5 + x4 = x**4 + x3 = x**3 + + # Common log terms to simplify the messy 4PN-7PN expressions + log_term_base = (-7.0 + 6.0 * EulerGamma - 3j * M_PI + 6*LOG2 + 6.0 * logb0 + 9.0 * logx) + + if vpnorder == 4: + return (-2.0 * delta * x3 * log_term_base) / 9.0 + + elif vpnorder == 5: + spin_factor = ((1.0 + delta) * S1z + (-1.0 + delta) * S2z) + return (spin_factor * x3p5 * log_term_base) / 6.0 + + elif vpnorder == 6: + return -0.007936507936507936 * (delta * (-17.0 + 6.0 * eta) * x4 * log_term_base) + + elif vpnorder == 7: + # Heavily nested 3.5PN (order 7) term + term1 = (x4p5 * ( + -98 * S1z + 84 * EulerGamma * S1z + 3017 * eta * S1z - 2586 * EulerGamma * eta * S1z - + 42j * M_PI * S1z + 1293j * eta * M_PI * S1z + 98 * S2z - 84 * EulerGamma * S2z - + 3017 * eta * S2z + 2586 * EulerGamma * eta * S2z + 42j * M_PI * S2z - 1293j * eta * M_PI * S2z + + 84 * S1z * LOG2 - 2586 * eta * S1z * LOG2 - 84 * S2z * LOG2 + 2586 * eta * S2z * LOG2 + + 84 * S1z * logb0 - 2586 * eta * S1z * logb0 - 84 * S2z * logb0 + 2586 * eta * S2z * logb0 + + 126 * S1z * logx - 3879 * eta * S1z * logx - 126 * S2z * logx + 3879 * eta * S2z * logx + + 252 * delta * ( + 1.5875434618291762j + 1.7523809523809524j * EulerGamma - 1.3333333333333333j * EulerGamma**2 + + (92 * M_PI) / 105.0 - (4 * EulerGamma * M_PI) / 3.0 + 0.1111111111111111j * M_PI**2 - + (7 * S1z) / 18.0 + (EulerGamma * S1z) / 3.0 + (29 * eta * S1z) / 12.0 - (29 * EulerGamma * eta * S1z) / 14.0 - + 0.16666666666666666j * M_PI * S1z + 1.0357142857142858j * eta * M_PI * S1z - + (7 * S2z) / 18.0 + (EulerGamma * S2z) / 3.0 + (29 * eta * S2z) / 12.0 - (29 * EulerGamma * eta * S2z) / 14.0 - + 0.16666666666666666j * M_PI * S2z + 1.0357142857142858j * eta * M_PI * S2z + + 1.7523809523809524j * LOG2 - 2.6666666666666665j * EulerGamma * LOG2 - + (4 * M_PI * LOG2) / 3.0 + (S1z * LOG2) / 3.0 - (29 * eta * S1z * LOG2) / 14.0 + + (S2z * LOG2) / 3.0 - (29 * eta * S2z * LOG2) / 14.0 - 1.3333333333333333j * LOG2**2 - + 1.3333333333333333j * logb0**2 - 1.3587301587301588j * logr0 + + (logb0 * (392j - 336j * EulerGamma - 168 * M_PI + 42 * S1z - 261 * eta * S1z + 42 * S2z - 261 * eta * S2z - 336j * LOG2 - 504j * logx)) / 126.0 + + 2.6285714285714286j * logx - 4j * EulerGamma * logx - 2 * M_PI * logx + + (S1z * logx) / 2.0 - (87 * eta * S1z * logx) / 28.0 + (S2z * logx) / 2.0 - + (87 * eta * S2z * logx) / 28.0 - 4j * LOG2 * logx - 3j * logx**2 + ) + )) / 252.0 + return term1 + + else: + return complex(0.0, 0.0) + +############ l = 3 ############### + +# H33 + +@register_hgo_lm(3, 3) +def hGO_3_m_3( + total_mass: float, + eta: float, + r: float, + rDOT: float, + PhiDOT: float, + vpnorder: int, + S1z: float, + S2z: float, + x: float, + params:CommonVars + ) -> complex: + kappa1 = 1.0 + kappa2 = 1.0 + r0 = params.r0 + + delta = params.delta + + combination_a = 1j * PhiDOT * r - rDOT + combination_a3 = combination_a ** 3 + + combination_b = PhiDOT * r + 1j * rDOT + combination_b2 = combination_b ** 2 + combination_b4 = combination_b2 ** 2 + combination_b6 = combination_b ** 6 + + combination_c = PhiDOT * r - 1j * rDOT + combination_c2 = combination_c ** 2 + + combination_d = -1j * PhiDOT * r + rDOT + combination_d5 = combination_d ** 5 + + combination_e = 1j * PhiDOT * r + rDOT + combination_e3 = combination_e ** 3 + + rDOT2 = rDOT * rDOT + rDOT3 = rDOT2 * rDOT + rDOT4 = rDOT3 * rDOT + rDOT5 = rDOT4 * rDOT + rDOT6 = rDOT5 * rDOT + rDOT7 = rDOT6 * rDOT + + total_mass2 = total_mass * total_mass + total_mass3 = total_mass2 * total_mass + total_mass4 = total_mass3 * total_mass + + PhiDOT2 = PhiDOT * PhiDOT + PhiDOT3 = PhiDOT2 * PhiDOT + PhiDOT4 = PhiDOT3 * PhiDOT + PhiDOT5 = PhiDOT4 * PhiDOT + PhiDOT6 = PhiDOT5 * PhiDOT + PhiDOT7 = PhiDOT6 * PhiDOT + + r2 = r * r + r3 = r2 * r + r4 = r3 * r + r5 = r4 * r + r6 = r5 * r + r7 = r6 * r + + eta2 = eta * eta + eta3 = eta2 * eta + + S1z2 = S1z * S1z + + S2z2 = S2z * S2z + + + if vpnorder == 1: + return ( + (np.sqrt(0.11904761904761904) * delta * + (2 * r * combination_a3 + + total_mass * (-7j * PhiDOT * r + 4 * rDOT))) + / (2.0 * r) + ) + + elif vpnorder == 3: + return ( + (np.sqrt(0.11904761904761904) * delta * + (6 * (-5 + 19 * eta) * r2 * combination_b4 * + (1j * PhiDOT * r + rDOT) + + 2 * total_mass2 * + (-3j * (-101 + 43 * eta) * PhiDOT * r + + (-109 + 86 * eta) * rDOT) + + 3 * total_mass * r * + (-12j * (1 + 4 * eta) * PhiDOT3 * r3 + + 6 * (14 + 31 * eta) * PhiDOT2 * r2 * rDOT + + 3j * (33 + 62 * eta) * PhiDOT * r * rDOT2 - + 4 * (8 + 17 * eta) * rDOT3))) + / (36.0 * r2) + ) + + elif vpnorder == 4: + return ( + (-0.125j * np.sqrt(0.11904761904761904) * total_mass2 * + (4 * total_mass * (-1 + 5 * eta) * ((1 + delta) * S1z + (-1 + delta) * S2z) + + r * (2 * rDOT2 * + (6 * (1 + delta) * S1z - 5 * (5 + 3 * delta) * eta * S1z + + (-6 + delta * (6 - 15 * eta) + 25 * eta) * S2z) + + PhiDOT2 * r2 * + (-24 * (1 + delta) * S1z + (119 + 33 * delta) * eta * S1z + + (24 - 119 * eta + 3 * delta * (-8 + 11 * eta)) * S2z) + + 2j * PhiDOT * r * rDOT * + (-18 * (1 + delta) * S1z + (77 + 39 * delta) * eta * S1z + + (18 - 77 * eta + 3 * delta * (-6 + 13 * eta)) * S2z)))) + / r3 + ) + + elif vpnorder == 5: + term1 = ( + delta * + (30 * (183 - 1579 * eta + 3387 * eta2) * r3 * + combination_c2 * combination_d5 + + 10 * total_mass3 * + (-1j * (26473 - 27451 * eta + 9921 * eta2) * PhiDOT * r + + 4 * (623 - 732 * eta + 1913 * eta2) * rDOT) + + 2 * total_mass2 * r * + (-11j * (-5353 - 13493 * eta + 4671 * eta2) * PhiDOT3 * r3 + + (-75243 - 142713 * eta + 192821 * eta2) * PhiDOT2 * r2 * rDOT + + 220j * (-256 + 781 * eta + 840 * eta2) * PhiDOT * r * rDOT2 - + 10 * (-756 + 8238 * eta + 7357 * eta2) * rDOT3) + + 3 * total_mass * r2 * + (2j * (-7633 + 9137 * eta + 28911 * eta2) * PhiDOT5 * r5 - + 4 * (-8149 + 1576 * eta + 43533 * eta2) * PhiDOT4 * r4 * rDOT - + 2j * (-9297 - 19517 * eta + 64839 * eta2) * PhiDOT3 * r3 * rDOT2 - + 32 * (-1288 + 3667 * eta + 4056 * eta2) * PhiDOT2 * r2 * rDOT3 - + 5j * (-9851 + 17954 * eta + 40968 * eta2) * PhiDOT * r * rDOT4 + + 20 * (-771 + 1126 * eta + 3616 * eta2) * rDOT5)) + / (1584.0 * np.sqrt(210) * r3) + ) + + # Henry et al. ecc spin terms + term2 = ( + 0.125j * np.sqrt(1.0714285714285714) * total_mass3 * + (7 * PhiDOT * r + 2j * rDOT) * + (kappa1 * (-1 - delta + 2 * (2 + delta) * eta) * S1z2 + + S2z * (-4 * delta * eta * S1z + + kappa2 * (1 - delta + 2 * (-2 + delta) * eta) * S2z)) + / r3 + ) + + return term1 + term2 + + elif vpnorder == 6: + term1 = ( + -(delta * total_mass2 * eta * + (668 * total_mass2 + + 2 * total_mass * r * + (4081 * PhiDOT2 * r2 + + 297j * PhiDOT * r * rDOT - 452 * rDOT2) + + 5 * r2 * + (1329 * PhiDOT4 * r4 - + 2926j * PhiDOT3 * r3 * rDOT - + 384 * PhiDOT2 * r2 * rDOT2 - + 408j * PhiDOT * r * rDOT3 + + 200 * rDOT4))) + / (36.0 * np.sqrt(210) * r4) + ) + + # Henry et al. ecc spin terms + term2 = ( + -0.006944444444444444j * total_mass2 * + (10 * total_mass2 * + ((252 * (1 + delta) - (1277 + 1279 * delta) * eta + + 8 * (12 + 47 * delta) * eta2) * S1z + + (252 * (-1 + delta) + (1277 - 1279 * delta) * eta + + 8 * (-12 + 47 * delta) * eta2) * S2z) + + 2 * total_mass * r * + (2 * PhiDOT2 * r2 * + ((1320 * (1 + delta) - 2 * (4469 + 211 * delta) * eta + + (8709 + 2777 * delta) * eta2) * S1z + + (1320 * (-1 + delta) + 8938 * eta - 422 * delta * eta + + (-8709 + 2777 * delta) * eta2) * S2z) + + 3j * PhiDOT * r * rDOT * + ((2000 * (1 + delta) - (9147 + 3173 * delta) * eta + + (8911 + 5273 * delta) * eta2) * S1z + + (2000 * (-1 + delta) + (9147 - 3173 * delta) * eta + + (-8911 + 5273 * delta) * eta2) * S2z) + + 10 * rDOT2 * + ((-105 * (1 + delta) + (541 + 77 * delta) * eta - + 2 * (462 + 247 * delta) * eta2) * S1z + + (105 + eta * (-541 + 924 * eta) + + delta * (-105 + (77 - 494 * eta) * eta)) * S2z)) - + 3 * r2 * + (-3 * PhiDOT2 * r2 * rDOT2 * + ((480 * (1 + delta) - (1711 + 1889 * delta) * eta + + 2 * (-1161 + 757 * delta) * eta2) * S1z + + (480 * (-1 + delta) + (1711 - 1889 * delta) * eta + + 2 * (1161 + 757 * delta) * eta2) * S2z) + + 2 * PhiDOT4 * r4 * + ((350 * (1 + delta) - 4 * (404 + 461 * delta) * eta + + (883 + 769 * delta) * eta2) * S1z + + (350 * (-1 + delta) + 4 * (404 - 461 * delta) * eta + + (-883 + 769 * delta) * eta2) * S2z) + + 2j * PhiDOT3 * r3 * rDOT * + ((660 * (1 + delta) - (4061 + 2899 * delta) * eta + + (2643 + 4789 * delta) * eta2) * S1z + + (660 * (-1 + delta) + (4061 - 2899 * delta) * eta + + (-2643 + 4789 * delta) * eta2) * S2z) + + 10 * rDOT4 * + ((-30 * (1 + delta) + (187 + 101 * delta) * eta - + 2 * (159 + 61 * delta) * eta2) * S1z + + (30 + eta * (-187 + 318 * eta) + + delta * (-30 + (101 - 122 * eta) * eta)) * S2z) + + 2j * PhiDOT * r * rDOT3 * + ((90 + eta * (-1321 + 5118 * eta) + + delta * (90 + eta * (-319 + 714 * eta))) * S1z + + (-90 + (1321 - 5118 * eta) * eta + + delta * (90 + eta * (-319 + 714 * eta))) * S2z))) + / (np.sqrt(210) * r4) + ) + + return term1 + term2 + + elif vpnorder == 7: + M_PI2 = M_PI ** 2 + + spin_terms = ( + 504504 * total_mass3 * + (2 * total_mass * + (rDOT * + (S1z * (108 - 498 * eta + + 5j * (-24 - 5 * kappa1 + 3 * (76 + kappa1) * eta + + 4 * (-111 + 13 * kappa1) * eta2) * S1z) + + 6 * (-18 + 83 * eta) * S2z - + 5j * (-24 - 5 * kappa2 + 3 * (76 + kappa2) * eta + + 4 * (-111 + 13 * kappa2) * eta2) * S2z2) + + PhiDOT * r * + (S1z * (-3j * (-99 + 581 * eta) + + 5 * (-24 + 399 * kappa1 + 48 * (7 - 19 * eta) * eta + + kappa1 * eta * (-1629 + 188 * eta)) * S1z) + + 3j * (-99 + 581 * eta) * S2z - + 5 * (-24 + 399 * kappa2 + 48 * (7 - 19 * eta) * eta + + kappa2 * eta * (-1629 + 188 * eta)) * S2z2)) + + r * (3 * PhiDOT * r * rDOT2 * + (S1z * (216j + 545 * kappa1 * S1z + + 40 * (45 + 8 * kappa1) * eta2 * S1z - + 30 * eta * (50j + 20 * S1z + 73 * kappa1 * S1z)) + + 12j * (-18 + 125 * eta) * S2z - + 5 * (109 * kappa2 - 6 * (20 + 73 * kappa2) * eta + + 8 * (45 + 8 * kappa2) * eta2) * S2z2) + + 2 * rDOT3 * + (S1z * (-54 + 145j * kappa1 * S1z - + 30j * eta * (11j + 2 * eta * S1z + + kappa1 * (17 + 8 * eta) * S1z)) + + 6 * (9 - 55 * eta) * S2z + + 5j * (12 * eta2 + + kappa2 * (-29 + 6 * eta * (17 + 8 * eta))) * S2z2) + + 6 * PhiDOT2 * r2 * rDOT * + (S1z * (297 - 465 * eta + + 5j * (6 * (20 - 87 * eta) * eta + + kappa1 * (-50 + 3 * eta * (47 + 76 * eta))) * S1z) + + 3 * (-99 + 155 * eta) * S2z - + 5j * (6 * (20 - 87 * eta) * eta + + kappa2 * (-50 + 3 * eta * (47 + 76 * eta))) * S2z2) + + PhiDOT3 * r3 * + (3j * (531 - 1295 * eta) * S1z + + 10 * (-33 * kappa1 + 6 * (30 + 13 * kappa1) * eta + + 4 * (-96 + 67 * kappa1) * eta2) * S1z2 + + S2z * (-1593j + 330 * kappa2 * S2z + + 5 * eta * (777j - + 4 * (90 + 39 * kappa2 - 192 * eta + + 134 * kappa2 * eta) * S2z))))) + ) + + orbital_terms = ( + delta * + (-17640 * (-4083 + eta * (58311 + eta * (-269240 + 405617 * eta))) * + r4 * combination_b6 * combination_e3 + + 168 * total_mass2 * r2 * + (1j * (-7508635 + 7 * eta * (-1318438 + eta * (-10231834 + 9667755 * eta))) * + PhiDOT5 * r5 + + 7 * (1235591 + eta * (884445 + (23935218 - 26913443 * eta) * eta)) * + PhiDOT4 * r4 * rDOT - + 1j * (8961149 + 7 * eta * (-31755709 + eta * (-11134798 + 22187331 * eta))) * + PhiDOT3 * r3 * rDOT2 - + (-36806435 + 7 * eta * (33178545 + eta * (24565078 + 22873537 * eta))) * + PhiDOT2 * r2 * rDOT3 - + 5j * (-7761899 + 7 * eta * (2892563 + 5998602 * eta + 7493619 * eta2)) * + PhiDOT * r * rDOT4 + + 5 * (-2422057 + 7 * eta * (501045 + eta * (2033141 + 2771816 * eta))) * + rDOT5) + + 1764 * total_mass * r3 * + (-2j * (239087 + eta * (-1206515 + eta * (422631 + 3979375 * eta))) * + PhiDOT7 * r7 + + 2 * (621284 + eta * (-2279907 + 2 * eta * (-1180187 + 5876531 * eta))) * + PhiDOT6 * r6 * rDOT + + 2j * (39270 + eta * (1235486 - 5319747 * eta + 4406349 * eta2)) * + PhiDOT5 * r5 * rDOT2 + + 8 * (349111 + 4 * eta * (-519370 + 33 * eta * (10939 + 42635 * eta))) * + PhiDOT4 * r4 * rDOT3 + + 2j * (1212607 + 3 * eta * (-2012698 - 67827 * eta + 7955628 * eta2)) * + PhiDOT3 * r3 * rDOT4 + + 4 * (201135 + 2 * eta * (-773107 + eta * (1214819 + 1157652 * eta))) * + PhiDOT2 * r2 * rDOT5 + + 5j * (333969 + 2 * eta * (-981471 + 4 * eta * (154039 + 750016 * eta))) * + PhiDOT * r * rDOT6 - + 40 * (13245 + 2 * eta * (-37005 + eta * (14251 + 130160 * eta))) * + rDOT7) + + 2 * total_mass4 * + (4 * rDOT * + (269279500 * eta3 + + 2 * (-174108226 + + 63063 * S1z * (108j + 5 * (24 + 5 * kappa1) * S1z) + + 63063 * S2z * (108j + 5 * (24 + 5 * kappa2) * S2z)) - + 21 * eta * + (103100846 + 1846845 * M_PI2 + 1693692j * S2z - + 6006 * (S1z * (-282j + 5 * (-180 + 7 * kappa1) * S1z) - + 980 * S1z * S2z + + 5 * (-180 + 7 * kappa2) * S2z2)) - + 2940 * eta2 * + (-122855 + + 4719 * ((-6 + 7 * kappa1) * S1z2 - + 26 * S1z * S2z + + (-6 + 7 * kappa2) * S2z2))) + + 1j * PhiDOT * r * + (-1176172480 * eta3 + + 8 * (-74084729 + + 189189 * S1z * (99j + 5 * (-8 + 133 * kappa1) * S1z) + + 189189 * S2z * (99j + 5 * (-8 + 133 * kappa2) * S2z)) + + 35280 * eta2 * + (56255 + + 429 * ((-22 + 65 * kappa1) * S1z2 - + 174 * S1z * S2z + + (-22 + 65 * kappa2) * S2z2)) - + 147 * eta * + (-65012788 + 4485195 * M_PI2 + 3943368j * S2z + + 10296 * (S1z * (383j + 5 * (-96 + 277 * kappa1) * S1z) - + 3220 * S1z * S2z + + 5 * (-96 + 277 * kappa2) * S2z2)))) + + total_mass3 * r * + (-12j * PhiDOT * r * rDOT2 * + (-1035895280 * eta3 - + 2 * (-547993687 + + 63063 * S1z * (216j + 545 * kappa1 * S1z) + + 63063 * S2z * (216j + 545 * kappa2 * S2z)) + + 77 * eta * + (42451610 + 1511055 * M_PI2 + 1749384j * S2z + + 6552 * (S1z * (267j + 25 * (6 + 11 * kappa1) * S1z) - + 5 * S1z * S2z + + 25 * (6 + 11 * kappa2) * S2z2)) + + 490 * eta2 * + (-5802767 + + 5148 * ((-6 + 23 * kappa1) * S1z2 - + 58 * S1z * S2z + + (-6 + 23 * kappa2) * S2z2))) + + 4 * rDOT3 * + (-1359334480 * eta3 - + 4 * (-150254558 + + 63063 * S1z * (54j + 145 * kappa1 * S1z) + + 63063 * S2z * (54j + 145 * kappa2 * S2z)) + + 231 * eta * + (8490448 + 503685 * M_PI2 + 242424j * S2z + + 2184 * (S1z * (111j + 110 * kappa1 * S1z) + + 70 * S1z * S2z + + 110 * kappa2 * S2z2)) + + 11760 * eta2 * + (-312980 + + 429 * ((3 + 25 * kappa1) * S1z2 - + 44 * S1z * S2z + + (3 + 25 * kappa2) * S2z2))) + + 6 * PhiDOT2 * r2 * rDOT * + (2368900688 * eta3 + + 8 * (-812986529 + + 63063 * S1z * (297j + 250 * kappa1 * S1z) + + 63063 * S2z * (297j + 250 * kappa2 * S2z)) - + 1176 * eta2 * + (2423171 + + 4290 * ((-3 + 41 * kappa1) * S1z2 - + 88 * S1z * S2z + + (-3 + 41 * kappa2) * S2z2)) + + 539 * eta * + (-24139772 + 647595 * M_PI2 + 120744j * S2z - + 936 * (S1z * (-129j + 600 * S1z + 205 * kappa1 * S1z) - + 460 * S1z * S2z + + 5 * (120 + 41 * kappa2) * S2z2))) + + 1j * PhiDOT3 * r3 * + (-4538040136 * eta3 - + 88 * (259018351 + + 17199 * S1z * (-531j + 110 * kappa1 * S1z) + + 17199 * S2z * (-531j + 110 * kappa2 * S2z)) + + 2352 * eta2 * + (7332973 + + 12870 * ((5 + 23 * kappa1) * S1z2 - + 36 * S1z * S2z + + (5 + 23 * kappa2) * S2z2)) + + 21 * eta * + (49864815 * M_PI2 + + 8 * (-88128538 - 2099097j * S2z + + 9009 * (S1z * (-233j + 40 * (15 + kappa1) * S1z) + + 360 * S1z * S2z + + 40 * (15 + kappa2) * S2z2)))))) + ) + + log_term = ( + 74954880 * delta * total_mass3 * + (22j * total_mass * PhiDOT * r + + 59j * PhiDOT3 * r4 + 8 * total_mass * rDOT + + 66 * PhiDOT2 * r3 * rDOT + + 24j * PhiDOT * r2 * rDOT2 - + 4 * r * rDOT3) * + np.log(r / r0) + ) + + return ( + 1j * (spin_terms + orbital_terms + log_term) + / (2.4216192e7 * np.sqrt(210) * r4) + ) + + else: + return complex(0, 0) + +@register_hqc_lm(3, 3) +def hQC_3_m_3( + total_mass: float, + eta: float, + vpnorder: int, + x: float, + S1z: float, + S2z: float, + params: CommonVars, + ) -> complex: + + delta: float = params.delta + EulerGamma: float = 0.5772156649015329 + + xp5 = params.xp5 + + x3 = x**3 + x4 = x**4 + x4p5 = x4*xp5 + + logx = params.logx + logb0 = params.logb0 + logr0 = params.logr0 + + if vpnorder == 4: + return ( + (3.0 * np.sqrt(0.04285714285714286) * delta * x3 * ( + -97.0 + 60.0 * EulerGamma - 30.0j * M_PI + + 60.0 * LOG2 + 60.0 * LOG3 + + 60.0 * logb0 + 90.0 * logx + )) / 4.0 + ) + + elif vpnorder == 6: + numerator_6 = (delta * x4 * ( + 188568.0 - 116640.0 * EulerGamma - 70537.0 * eta + 43740.0 * EulerGamma * eta + + 58320.0j * M_PI - 21870.0j * eta * M_PI - + 116640.0 * LOG2 + 43740.0 * eta * LOG2 - + 116640.0 * LOG3 + 43740.0 * eta * LOG3 + + 14580.0 * (-8.0 + 3.0 * eta) * logb0 + + 21870.0 * (-8.0 + 3.0 * eta) * logx + )) + return numerator_6 / (216.0 * np.sqrt(210.0)) + + elif vpnorder == 7: + # Logarithmic expansion for the 3.5PN (order 7) term + # log_b0_term handles the final nested log(b0) block + log_b0_term = 15876.0 * logb0 * ( + -194.0j * delta + 120.0j * delta * EulerGamma + 60.0 * delta * M_PI + + 5.0 * (-4.0 - 4.0 * delta + 19.0 * eta + 5.0 * delta * eta) * S1z + + 20.0 * S2z - 20.0 * delta * S2z - 95.0 * eta * S2z + 25.0 * delta * eta * S2z + + 120.0j * delta * LOG2 + 120.0j * delta * LOG3 + + 180.0j * delta * logx + ) + + numerator_7 = (x4p5 * ( + 1434564.0j * delta - 2490264.0j * delta * EulerGamma + 952560.0j * delta * EulerGamma**2 - + 1245132.0 * delta * M_PI + 952560.0 * delta * EulerGamma * M_PI - + 79380.0j * delta * (M_PI**2) + 513324.0 * S1z + 513324.0 * delta * S1z - + 317520.0 * EulerGamma * S1z - 317520.0 * delta * EulerGamma * S1z - + 2439857.0 * eta * S1z - 643223.0 * delta * eta * S1z + 1508220.0 * EulerGamma * eta * S1z + + 396900.0 * delta * EulerGamma * eta * S1z + 158760.0j * M_PI * S1z + 158760.0j * delta * M_PI * S1z - + 754110.0j * eta * M_PI * S1z - 198450.0j * delta * eta * M_PI * S1z - + 513324.0 * S2z + 513324.0 * delta * S2z + 317520.0 * EulerGamma * S2z - + 317520.0 * delta * EulerGamma * S2z + 2439857.0 * eta * S2z - 643223.0 * delta * eta * S2z - + 1508220.0 * EulerGamma * eta * S2z + 396900.0 * delta * EulerGamma * eta * S2z - + 158760.0j * M_PI * S2z + 158760.0j * delta * M_PI * S2z + + 754110.0j * eta * M_PI * S2z - 198450.0j * delta * eta * M_PI * S2z - + 2490264.0j * delta * LOG2 + 1905120.0j * delta * EulerGamma * LOG2 + + 952560.0 * delta * M_PI * LOG2 - 317520.0 * S1z * LOG2 - + 317520.0 * delta * S1z * LOG2 + 1508220.0 * eta * S1z * LOG2 + + 396900.0 * delta * eta * S1z * LOG2 + 317520.0 * S2z * LOG2 - + 317520.0 * delta * S2z * LOG2 - 1508220.0 * eta * S2z * LOG2 + + 396900.0 * delta * eta * S2z * LOG2 + 952560.0j * delta * (LOG2**2) - + 2490264.0j * delta * LOG3 + 1905120.0j * delta * EulerGamma * LOG3 + + 952560.0 * delta * M_PI * LOG3 - 317520.0 * S1z * LOG3 - + 317520.0 * delta * S1z * LOG3 + 1508220.0 * eta * S1z * LOG3 + + 396900.0 * delta * eta * S1z * LOG3 + 317520.0 * S2z * LOG3 - + 317520.0 * delta * S2z * LOG3 - 1508220.0 * eta * S2z * LOG3 + + 396900.0 * delta * eta * S2z * LOG3 + 1905120.0j * delta * LOG2 * LOG3 + + 952560.0j * delta * (LOG3**2) + 952560.0j * delta * (logb0**2) + + 589680.0j * delta * logr0 - 3735396.0j * delta * logx + + 2857680.0j * delta * EulerGamma * logx + 1428840.0 * delta * M_PI * logx - + 476280.0 * S1z * logx - 476280.0 * delta * S1z * logx + + 2262330.0 * eta * S1z * logx + 595350.0 * delta * eta * S1z * logx + + 476280.0 * S2z * logx - 476280.0 * delta * S2z * logx - + 2262330.0 * eta * S2z * logx + 595350.0 * delta * eta * S2z * logx + + 2857680.0j * delta * LOG2 * logx + + 2857680.0j * delta * LOG3 * logx + + 2143260.0j * delta * (logx**2) + + log_b0_term + )) + return numerator_7 / (2352.0 * np.sqrt(210.0)) + + else: + return complex(0, 0) + +# H32 + +@register_hgo_lm(3, 2) +def hGO_3_m_2( + total_mass: float, + eta: float, + r: float, + rDOT: float, + PhiDOT: float, + vpnorder: int, + S1z: float, + S2z: float, + x: float, + params: CommonVars, + ) -> complex: + delta = params.delta + kappa1 = 1.0 + kappa2 = 1.0 + + r2 = r * r + r3 = r2 * r + r4 = r3 * r + r5 = r4 * r + + eta2 = eta * eta + eta3 = eta2 * eta + + S1z2 = S1z * S1z + S1z3 = S1z2 * S1z + + S2z2 = S2z * S2z + S2z3 = S2z2 * S2z + + PhiDOT2 = PhiDOT * PhiDOT + PhiDOT3 = PhiDOT2 * PhiDOT + PhiDOT4 = PhiDOT3 * PhiDOT + PhiDOT5 = PhiDOT4 * PhiDOT + + rDOT2 = rDOT * rDOT + rDOT3 = rDOT2 * rDOT + rDOT4 = rDOT3 * rDOT + rDOT5 = rDOT4 * rDOT + + total_mass2 = total_mass * total_mass + total_mass3 = total_mass2 * total_mass + + if vpnorder == 2: + return ( + -(np.sqrt(0.7142857142857143) * total_mass * (-1 + 3 * eta) * PhiDOT * + (4 * PhiDOT * r + 1j * rDOT)) + / 6.0 + ) + + elif vpnorder == 3: + return ( + (np.sqrt(0.7142857142857143) * total_mass2 * eta * + (4 * PhiDOT * r + 1j * rDOT) * (S1z + S2z)) + / (3.0 * r2) + ) + + elif vpnorder == 4: + return ( + -(total_mass * PhiDOT * + (2 * total_mass * + ((167 - 925 * eta + 1615 * eta2) * PhiDOT * r + + 5j * (-82 + 239 * eta + 55 * eta2) * rDOT) - + 3 * r * + (2 * (-13 - 25 * eta + 355 * eta2) * PhiDOT3 * r3 - + 60j * (-8 + 25 * eta + eta2) * PhiDOT2 * r2 * rDOT + + 12 * (-23 + 70 * eta + 10 * eta2) * PhiDOT * r * rDOT2 + + 5j * (-13 + 38 * eta + 10 * eta2) * rDOT3))) + / (108.0 * np.sqrt(35) * r) + ) + + elif vpnorder == 5: + term1 = ( + (total_mass2 * eta * PhiDOT * + (7j * total_mass + + r * (49j * PhiDOT2 * r2 + + 90 * PhiDOT * r * rDOT - 6j * rDOT2))) + / (4.0 * np.sqrt(35) * r2) + ) + + # Henry et al. ecc spin terms + term2 = ( + (np.sqrt(0.7142857142857143) * total_mass2 * + (2j * total_mass * rDOT * + ((-12 + eta * (97 + 4 * eta) + delta * (-12 + 5 * eta)) * S1z + + (-12 + delta * (12 - 5 * eta) + eta * (97 + 4 * eta)) * S2z) + + 4 * total_mass * PhiDOT * r * + (-((12 + delta * (12 - 23 * eta) + eta * (53 + 8 * eta)) * S1z) - + (12 + eta * (53 + 8 * eta) + delta * (-12 + 23 * eta)) * S2z) - + 3 * r * + (16 * eta2 * PhiDOT * r * + (4 * PhiDOT2 * r2 - + 2j * PhiDOT * r * rDOT + rDOT2) * + (S1z + S2z) + + 30j * PhiDOT2 * r2 * rDOT * + (S1z + delta * S1z + S2z - delta * S2z) + + eta * (-4 * PhiDOT3 * r3 * + ((5 + 17 * delta) * S1z + (5 - 17 * delta) * S2z) - + 1j * PhiDOT2 * r2 * rDOT * + ((189 + 17 * delta) * S1z + (189 - 17 * delta) * S2z) + + 20 * PhiDOT * r * rDOT2 * + (-((-3 + delta) * S1z) + (3 + delta) * S2z) - + 4j * rDOT3 * + ((-4 + delta) * S1z - (4 + delta) * S2z))))) + / (72.0 * r3) + ) + + return term1 + term2 + + elif vpnorder == 6: + term1 = ( + -(total_mass * PhiDOT * + (4 * total_mass2 * + (2 * (5377 + 6438 * eta - 79866 * eta2 + 37348 * eta3) * + PhiDOT * r - + 5j * (-4115 + 18399 * eta - 20276 * eta2 + 7 * eta3) * + rDOT) - + 4 * total_mass * r * + ((4599 - 15737 * eta + 36259 * eta2 + 108563 * eta3) * + PhiDOT3 * r3 - + 1j * (-34053 + 59698 * eta + 192949 * eta2 + 16193 * eta3) * + PhiDOT2 * r2 * rDOT + + (-59058 + 77983 * eta + 322468 * eta2 - 4264 * eta3) * + PhiDOT * r * rDOT2 + + 5j * (-3387 + 8518 * eta + 8968 * eta2 + 884 * eta3) * + rDOT3) + + 3 * r2 * + (4 * (-710 + 3892 * eta - 10655 * eta2 + 24000 * eta3) * + PhiDOT5 * r5 + + 11j * (-1484 + 11693 * eta - 25006 * eta2 + 428 * eta3) * + PhiDOT4 * r4 * rDOT + + 4 * (4161 - 25618 * eta + 29489 * eta2 + 22078 * eta3) * + PhiDOT3 * r3 * rDOT2 + + 44j * (-151 + 1067 * eta - 2419 * eta2 + 57 * eta3) * + PhiDOT2 * r2 * rDOT3 + + 4 * (2041 - 11680 * eta + 19334 * eta2 + 3368 * eta3) * + PhiDOT * r * rDOT4 + + 5j * (477 - 2624 * eta + 3862 * eta2 + 1160 * eta3) * + rDOT5))) + / (4752.0 * np.sqrt(35) * r2) + ) + + # Henry et al. ecc spin terms + term2 = ( + (np.sqrt(0.7142857142857143) * total_mass3 * + (2 * total_mass * + (2j * (1 + delta - 2 * eta) * S1z + + eta * ((1 + delta) * (-6 + kappa1) + 12 * eta) * S1z2 + + 8 * eta * (-1 + 3 * eta) * S1z * S2z + + S2z * (-2j * (-1 + delta + 2 * eta) + + eta * (-6 - delta * (-6 + kappa2) + kappa2 + 12 * eta) * S2z)) + + r * (4 * rDOT2 * + (2j * (1 + delta - 2 * eta) * S1z + + eta * ((1 + delta) * (-2 + kappa1) + 4 * eta) * S1z2 + + 8 * eta2 * S1z * S2z + + S2z * (-2j * (-1 + delta + 2 * eta) + + eta * (-2 - delta * (-2 + kappa2) + kappa2 + 4 * eta) * S2z)) + + 2 * PhiDOT2 * r2 * + (14j * (1 + delta - 2 * eta) * S1z + + (6 * (1 + delta) * kappa1 - + (26 + 23 * kappa1 + delta * (26 + 11 * kappa1)) * eta + + 4 * (1 + 9 * kappa1) * eta2) * S1z2 - + 64 * eta2 * S1z * S2z + + S2z * (-14j * (-1 + delta + 2 * eta) + + 2 * eta * (-13 + 13 * delta + 2 * eta) * S2z + + kappa2 * (6 + delta * (-6 + 11 * eta) + + eta * (-23 + 36 * eta)) * S2z)) + + PhiDOT * r * rDOT * + (40 * (1 + delta - 2 * eta) * S1z - + 1j * (8 * eta * (-2 - 2 * delta + eta) + + kappa1 * (3 + delta * (3 + 11 * eta) + + eta * (5 + 18 * eta))) * S1z2 + + 20j * (-3 + eta) * eta * S1z * S2z + + S2z * (-40 * (-1 + delta + 2 * eta) - + 1j * (8 * eta * (-2 + 2 * delta + eta) + + kappa2 * (3 - delta * (3 + 11 * eta) + + eta * (5 + 18 * eta))) * S2z))))) + / (24.0 * r4) + ) + + return term1 + term2 + + elif vpnorder == 7: + return ( + -0.000014029180695847363 * + (total_mass2 * + (3 * r2 * + (-120 * eta3 * + (3565 * PhiDOT5 * r5 + + 2321j * PhiDOT4 * r4 * rDOT + + 8244 * PhiDOT3 * r3 * rDOT2 - + 869j * PhiDOT2 * r2 * rDOT3 - + 56 * PhiDOT * r * rDOT4 - + 120j * rDOT5) * + (S1z + S2z) + + 2475 * PhiDOT2 * r2 * + (6 * PhiDOT3 * r3 + + 77j * PhiDOT2 * r2 * rDOT - + 72 * PhiDOT * r * rDOT2 + + 6j * rDOT3) * + (S1z + delta * S1z + S2z - delta * S2z) - + 3 * eta * + (22j * PhiDOT4 * r4 * rDOT * + (36322j + 5 * (2993 + 3893 * delta) * S1z + + 5 * (2993 - 3893 * delta) * S2z) - + 25j * rDOT5 * + ((1053 + 443 * delta) * S1z + (1053 - 443 * delta) * S2z) + + 44j * PhiDOT2 * r2 * rDOT3 * + (-5444j + 5 * (1424 + 849 * delta) * S1z + + 7120 * S2z - 4245 * delta * S2z) - + 20 * PhiDOT * r * rDOT4 * + (-1782j + (5963 + 2969 * delta) * S1z + + 5963 * S2z - 2969 * delta * S2z) + + 4 * PhiDOT5 * r5 * + (-86889j + 10 * (2063 + 225 * delta) * S1z + + 20630 * S2z - 2250 * delta * S2z) + + 4 * PhiDOT3 * r3 * rDOT2 * + (234861j + 40 * (-1824 + 97 * delta) * S1z - + 40 * (1824 + 97 * delta) * S2z)) + + 2 * eta2 * + (PhiDOT5 * r5 * + (-1549757j + 300 * (1448 + 1311 * delta) * S1z + + 300 * (1448 - 1311 * delta) * S2z) + + 11j * PhiDOT4 * r4 * rDOT * + (329548j + 15 * (4113 + 1411 * delta) * S1z + + 61695 * S2z - 21165 * delta * S2z) + + 22j * PhiDOT2 * r2 * rDOT3 * + (-23971j + 15 * (3829 + 243 * delta) * S1z + + 57435 * S2z - 3645 * delta * S2z) + + 150j * rDOT5 * + ((-503 + 92 * delta) * S1z - (503 + 92 * delta) * S2z) + + 10 * PhiDOT * r * rDOT4 * + (4565j + 6 * (-6327 + 991 * delta) * S1z - + 6 * (6327 + 991 * delta) * S2z) + + 21 * PhiDOT3 * r3 * rDOT2 * + (161403j + 10 * (-1897 + 1471 * delta) * S1z - + 10 * (1897 + 1471 * delta) * S2z))) - + 6 * total_mass * r * + (60 * eta3 * + (2417 * PhiDOT3 * r3 + + 7258j * PhiDOT2 * r2 * rDOT - + 4381 * PhiDOT * r * rDOT2 + + 480j * rDOT3) * + (S1z + S2z) - + 165 * + (1161 * PhiDOT3 * r3 - + 536j * PhiDOT2 * r2 * rDOT - + 2412 * PhiDOT * r * rDOT2 - + 270j * rDOT3) * + (S1z + delta * S1z + S2z - delta * S2z) + + 2 * eta * + (1j * PhiDOT2 * r2 * rDOT * + (1015784j + 5 * (30849 + 88721 * delta) * S1z + + 5 * (30849 - 88721 * delta) * S2z) + + PhiDOT3 * r3 * + (-173371j + 5 * (61569 + 10789 * delta) * S1z + + 307845 * S2z - 53945 * delta * S2z) - + 5 * PhiDOT * r * rDOT2 * + (-115368j + (177417 + 52307 * delta) * S1z + + 177417 * S2z - 52307 * delta * S2z) + + 100j * rDOT3 * + ((-1545 + 181 * delta) * S1z - (1545 + 181 * delta) * S2z)) + + eta2 * + (20 * PhiDOT * r * rDOT2 * + (-11187j - 48074 * S1z + 11057 * delta * S1z - + 48074 * S2z - 11057 * delta * S2z) + + 725j * rDOT3 * + (-73 * S1z + 31 * delta * S1z - 73 * S2z - 31 * delta * S2z) + + PhiDOT3 * r3 * + (603141j - 543040 * S1z + 404620 * delta * S1z - + 20 * (27152 + 20231 * delta) * S2z) + + 1j * PhiDOT2 * r2 * rDOT * + (-1798104j - 648485 * S1z + 105755 * delta * S1z - + 5 * (129697 + 21151 * delta) * S2z))) + + 10 * total_mass2 * + (24 * eta3 * + (6981 * PhiDOT * r + 1600j * rDOT) * + (S1z + S2z) - + 66 * (2027 * PhiDOT * r + 380j * rDOT) * + (S1z + delta * S1z + S2z - delta * S2z) + + 30j * eta * rDOT * + (297 * (1 + delta) * kappa1 * S1z3 + + 297 * (1 + delta) * kappa1 * S1z2 * S2z + + S1z * (17261 - 1641 * delta - + 297 * (-1 + delta) * kappa2 * S2z2) + + S2z * (17261 + 1641 * delta - + 297 * (-1 + delta) * kappa2 * S2z2)) + + 8 * eta * PhiDOT * r * + (-7315j - + 4455 * (1 + delta) * kappa1 * S1z3 + + 3 * (3881 - 7757 * delta) * S2z - + 4455 * (1 + delta) * kappa1 * S1z2 * S2z + + 4455 * (-1 + delta) * kappa2 * S2z3 + + 3 * S1z * + (3881 + 7757 * delta + + 1485 * (-1 + delta) * kappa2 * S2z2)) + + 3 * eta2 * + (-5j * rDOT * + (S1z * (18793 + 223 * delta + 1188 * kappa1 * S1z2) + + (18793 - 223 * delta + 1188 * (-2 + kappa1) * S1z2) * S2z + + 1188 * (-2 + kappa2) * S1z * S2z2 + + 1188 * kappa2 * S2z3) + + 4 * PhiDOT * r * + (-4939j - 23359 * S1z - 5563 * delta * S1z + + 5940 * kappa1 * S1z3 + + (-23359 + 5563 * delta + + 5940 * (-2 + kappa1) * S1z2) * S2z + + 5940 * (-2 + kappa2) * S1z * S2z2 + + 5940 * kappa2 * S2z3))))) + / (np.sqrt(35) * r4) + ) + + else: + return complex(0.0, 0.0) + +@register_hqc_lm(3, 2) +def hQC_3_m_2( + total_mass: float, + eta: float, + vpnorder: int, + x: float, + S1z: float, + S2z: float, + params: CommonVars + ) -> complex: + """ + Translates the hQC_3_m_2 mode from C to Python using NumPy. + 'params' contains pre-computed powers of x (x3p5, x4, x4p5) and eta (eta2). + """ + # Using np.exp for real-valued constants is fine + logb0 = params.logb0 + logx = params.logx + EulerGamma: float = 0.5772156649015329 + + xp5 = params.xp5 + x3 = x**3 + x4 = x*x3 + x3p5 = x3*xp5 + x4p5 = x4*xp5 + + eta2 = eta*eta + + + # Pre-calculating the shared log term using np.log + common_log_term: complex = ( + -10.0 + 6.0 * EulerGamma - 3.0j * M_PI + + np.log(4096.0) + 6.0 * logb0 + 9.0 * logx + ) + + if vpnorder == 5: + return ( + -0.4444444444444444j * np.sqrt(0.7142857142857143) * (-1.0 + 3.0 * eta) * x3p5 * common_log_term + ) + + elif vpnorder == 6: + return ( + 0.8888888888888888j * np.sqrt(0.7142857142857143) * eta * (S1z + S2z) * x4 * common_log_term + ) + + elif vpnorder == 7: + numerator = ( + -0.024691358024691357j * (193.0 - 680.0 * eta + 230.0 * eta2) * x4p5 * common_log_term + ) + return numerator / np.sqrt(35.0) + + else: + return complex(0, 0) + +# H31 + +@register_hgo_lm(3, 1) +def hGO_3_m_1( + total_mass: float, + eta: float, + r: float, + rDOT: float, + PhiDOT: float, + vpnorder: int, + S1z: float, + S2z: float, + x: float, + params: CommonVars, + ) -> complex: + delta = params.delta + kappa1 = 1.0 + kappa2 = 1.0 + r0 = params.r0 + + combination_a = PhiDOT * r + 1j * rDOT + combination_a2 = combination_a ** 2 + combination_a3 = combination_a ** 3 + combination_a4 = combination_a ** 4 + combination_a5 = combination_a ** 5 + + combination_b = PhiDOT * r - 1j * rDOT + combination_b2 = combination_b ** 2 + combination_b3 = combination_b ** 3 + combination_b4 = combination_b ** 4 + + rDOT2 = rDOT * rDOT + rDOT3 = rDOT2 * rDOT + rDOT4 = rDOT3 * rDOT + rDOT5 = rDOT4 * rDOT + rDOT6 = rDOT5 * rDOT + rDOT7 = rDOT6 * rDOT + + total_mass2 = total_mass * total_mass + total_mass3 = total_mass2 * total_mass + total_mass4 = total_mass3 * total_mass + + PhiDOT2 = PhiDOT * PhiDOT + PhiDOT3 = PhiDOT2 * PhiDOT + PhiDOT4 = PhiDOT3 * PhiDOT + PhiDOT5 = PhiDOT4 * PhiDOT + PhiDOT6 = PhiDOT5 * PhiDOT + PhiDOT7 = PhiDOT6 * PhiDOT + + r2 = r * r + r3 = r2 * r + r4 = r3 * r + r5 = r4 * r + r6 = r5 * r + r7 = r6 * r + + eta2 = eta * eta + eta3 = eta2 * eta + + S1z2 = S1z * S1z + + S2z2 = S2z * S2z + + if vpnorder == 1: + return ( + delta * (total_mass * (7j * PhiDOT * r - 12 * rDOT) - + 6j * r * combination_b * combination_a2) + / (6.0 * np.sqrt(14) * r) + ) + + elif vpnorder == 3: + return ( + delta * + (6j * (-5 + 19 * eta) * r2 * combination_b2 * combination_a3 + + 2 * total_mass2 * + (1j * (-101 + 43 * eta) * PhiDOT * r + + (109 - 86 * eta) * rDOT) + + 3 * total_mass * r * + (-4j * (-9 + 14 * eta) * PhiDOT3 * r3 + + 6 * (2 + 9 * eta) * PhiDOT2 * r2 * rDOT - + 1j * (33 + 62 * eta) * PhiDOT * r * rDOT2 + + 4 * (8 + 17 * eta) * rDOT3)) + / (36.0 * np.sqrt(14) * r2) + ) + + elif vpnorder == 4: + return ( + 0.041666666666666664j * total_mass2 * + (4 * total_mass * (-1 + 5 * eta) * ((1 + delta) * S1z + (-1 + delta) * S2z) - + r * (2 * rDOT2 * + (-6 * (1 + delta) * S1z + 5 * (5 + 3 * delta) * eta * S1z + + 6 * S2z - 6 * delta * S2z + + 5 * (-5 + 3 * delta) * eta * S2z) + + PhiDOT2 * r2 * + ((24 + 24 * delta - 87 * eta + 31 * delta * eta) * S1z + + (-24 + 24 * delta + 87 * eta + 31 * delta * eta) * S2z) + + 2j * PhiDOT * r * rDOT * + ((6 + 6 * delta - 31 * eta + 35 * delta * eta) * S1z + + (-6 + 6 * delta + 31 * eta + 35 * delta * eta) * S2z))) + / (np.sqrt(14) * r3) + ) + + elif vpnorder == 5: + term1 = ( + delta * + (-18j * (183 - 1579 * eta + 3387 * eta2) * r3 * + combination_b3 * combination_a4 + + 2 * total_mass3 * + (1j * (26473 - 27451 * eta + 9921 * eta2) * PhiDOT * r - + 12 * (623 - 732 * eta + 1913 * eta2) * rDOT) + + 2 * total_mass2 * r * + (-1j * (-8641 - 59189 * eta + 31959 * eta2) * PhiDOT3 * r3 + + (-32635 - 29345 * eta + 29541 * eta2) * PhiDOT2 * r2 * rDOT - + 44j * (-256 + 781 * eta + 840 * eta2) * PhiDOT * r * rDOT2 + + 6 * (-756 + 8238 * eta + 7357 * eta2) * rDOT3) + + 3 * total_mass * r2 * + (2j * (-2479 - 4505 * eta + 16785 * eta2) * PhiDOT5 * r5 + + 4 * (817 + 1220 * eta - 7449 * eta2) * PhiDOT4 * r4 * rDOT + + 6j * (-1679 + 1469 * eta + 12233 * eta2) * PhiDOT3 * r3 * rDOT2 - + 32 * (-460 + 421 * eta + 2514 * eta2) * PhiDOT2 * r2 * rDOT3 + + 1j * (-9851 + 17954 * eta + 40968 * eta2) * PhiDOT * r * rDOT4 - + 12 * (-771 + 1126 * eta + 3616 * eta2) * rDOT5)) + / (4752.0 * np.sqrt(14) * r3) + ) + + # Henry et al. ecc spin terms + term2 = ( + total_mass3 * + (kappa1 * + (-1j * (-13 - 13 * delta + 68 * eta + 42 * delta * eta) * PhiDOT * r - + 34 * (1 + delta) * rDOT + 4 * (26 + 9 * delta) * eta * rDOT) * + S1z2 + + S2z * (12 * delta * eta * (7j * PhiDOT * r - 6 * rDOT) * S1z + + kappa2 * + (-1j * (13 - 13 * delta - 68 * eta + 42 * delta * eta) * PhiDOT * r + + 2 * (17 - 17 * delta - 52 * eta + 18 * delta * eta) * rDOT) * + S2z)) + / (24.0 * np.sqrt(14) * r3) + ) + + return term1 + term2 + + elif vpnorder == 6: + term1 = ( + delta * total_mass2 * eta * + (668 * total_mass2 - + 2 * total_mass * r * + (727 * PhiDOT2 * r2 - + 99j * PhiDOT * r * rDOT + 452 * rDOT2) + + r2 * + (-499 * PhiDOT4 * r4 + + 1534j * PhiDOT3 * r3 * rDOT + + 3072 * PhiDOT2 * r2 * rDOT2 - + 680j * PhiDOT * r * rDOT3 + + 1000 * rDOT4)) + / (180.0 * np.sqrt(14) * r4) + ) + + # Henry et al. ecc spin terms + term2 = ( + 0.0023148148148148147j * total_mass2 * + (2 * total_mass2 * + ((252 * (1 + delta) - (1277 + 1279 * delta) * eta + + 8 * (12 + 47 * delta) * eta2) * S1z + + (252 * (-1 + delta) + (1277 - 1279 * delta) * eta + + 8 * (-12 + 47 * delta) * eta2) * S2z) + + 3 * r2 * + (2 * rDOT4 * + ((30 + eta * (-187 + 318 * eta) + + delta * (30 + eta * (-101 + 122 * eta))) * S1z + + (-30 + (187 - 318 * eta) * eta + + delta * (30 + eta * (-101 + 122 * eta))) * S2z) + + 2 * PhiDOT4 * r4 * + ((90 - eta * (28 + 579 * eta) + + delta * (90 + eta * (-800 + 551 * eta))) * S1z + + (-90 + eta * (28 + 579 * eta) + + delta * (90 + eta * (-800 + 551 * eta))) * S2z) + + 2j * PhiDOT * r * rDOT3 * + ((186 - eta * (745 + 354 * eta) + + delta * (186 + eta * (-191 + 554 * eta))) * S1z + + (-186 + eta * (745 + 354 * eta) + + delta * (186 + eta * (-191 + 554 * eta))) * S2z) + + 3 * PhiDOT2 * r2 * rDOT2 * + ((32 + eta * (-451 + 230 * eta) + + delta * (32 + eta * (691 + 626 * eta))) * S1z + + (-32 + (451 - 230 * eta) * eta + + delta * (32 + eta * (691 + 626 * eta))) * S2z) + + 2j * PhiDOT3 * r3 * rDOT * + ((-12 + eta * (-341 + 315 * eta) + + delta * (-12 + eta * (-91 + 1213 * eta))) * S1z + + (12 + (341 - 315 * eta) * eta + + delta * (-12 + eta * (-91 + 1213 * eta))) * S2z)) - + 2 * total_mass * r * + (2 * PhiDOT2 * r2 * + ((-312 * (1 + delta) + 2 * (827 - 923 * delta) * eta + + 5 * (-201 + 131 * delta) * eta2) * S1z + + (-312 * (-1 + delta) - 2 * (827 + 923 * delta) * eta + + 5 * (201 + 131 * delta) * eta2) * S2z) + + 2 * rDOT2 * + ((105 + eta * (-541 + 924 * eta) + + delta * (105 + eta * (-77 + 494 * eta))) * S1z + + (-105 + (541 - 924 * eta) * eta + + delta * (105 + eta * (-77 + 494 * eta))) * S2z) + + 1j * PhiDOT * r * rDOT * + ((1104 - 7 * eta * (439 + 597 * eta) + + delta * (1104 + eta * (-3071 + 3083 * eta))) * S1z + + (-1104 + 7 * eta * (439 + 597 * eta) + + delta * (1104 + eta * (-3071 + 3083 * eta))) * S2z))) + / (np.sqrt(14) * r4) + ) + + return term1 + term2 + + elif vpnorder == 7: + M_PI2 = M_PI ** 2 + + spin_terms = ( + 1513512 * total_mass3 * + (2 * total_mass * + (PhiDOT * r * + (1j * (-97 + 631 * eta) * S1z + + 5 * (8 + 16 * eta * (-7 + 15 * eta) + + 3 * kappa1 * (-39 + eta * (149 + 4 * eta))) * S1z2 + + S2z * (97j - 631j * eta - + 5 * (8 + 16 * eta * (-7 + 15 * eta) + + 3 * kappa2 * (-39 + eta * (149 + 4 * eta))) * S2z)) + + rDOT * + (2 * (-18 + 83 * eta) * S1z - + 5j * (-4 * (6 + eta * (-25 + 7 * eta)) + + kappa1 * (155 + eta * (-373 + 164 * eta))) * S1z2 + + S2z * (36 - 166 * eta + + 5j * (-4 * (6 + eta * (-25 + 7 * eta)) + + kappa2 * (155 + eta * (-373 + 164 * eta))) * S2z))) + + r * (2 * rDOT3 * + (S1z * (18 - 110 * eta - + 5j * (69 * kappa1 - 214 * kappa1 * eta + + 4 * (5 + 4 * kappa1) * eta2) * S1z) + + 2 * (-9 + 55 * eta) * S2z + + 5j * (69 * kappa2 - 214 * kappa2 * eta + + 4 * (5 + 4 * kappa2) * eta2) * S2z2) + + PhiDOT3 * r3 * + (S1z * (255j - 1403j * eta + + 10 * (28 * (3 - 8 * eta) * eta + + kappa1 * (51 + 2 * eta * (-91 + 118 * eta))) * S1z) + + 1j * (-255 + 1403 * eta) * S2z - + 10 * (28 * (3 - 8 * eta) * eta + + kappa2 * (51 + 2 * eta * (-91 + 118 * eta))) * S2z2) + + 2 * PhiDOT2 * r2 * rDOT * + (S1z * (255 - 1079 * eta + + 5j * (2 * (60 - 361 * eta) * eta + + kappa1 * (6 + eta * (-47 + 164 * eta))) * S1z) + + (-255 + 1079 * eta) * S2z - + 5j * (2 * (60 - 361 * eta) * eta + + kappa2 * (6 + eta * (-47 + 164 * eta))) * S2z2) + + PhiDOT * r * rDOT2 * + (4j * (-114 + 781 * eta) * S1z + + 5 * (-213 * kappa1 - 72 * eta + 278 * kappa1 * eta + + 8 * (7 + 44 * kappa1) * eta2) * S1z2 + + S2z * (456j + 1065 * kappa2 * S2z + + 2 * eta * (-1562j - 5 * (-36 + 139 * kappa2 + + 4 * (7 + 44 * kappa2) * eta) * S2z))))) + ) + + orbital_terms = ( + delta * + (52920 * (-4083 + eta * (58311 + eta * (-269240 + 405617 * eta))) * + r4 * combination_b4 * combination_a5 + + 840 * total_mass2 * r2 * + ((-2555489 + 7 * eta * (820078 + eta * (-6623390 + 4948497 * eta))) * + PhiDOT5 * r5 + + 1j * (3537631 + 7 * eta * (-2817653 + eta * (-7052042 + 4017147 * eta))) * + PhiDOT4 * r4 * rDOT + + 3 * (-1428997 + 7 * eta * (-1230747 + eta * (-237418 + 4061717 * eta))) * + PhiDOT3 * r3 * rDOT2 + + 1j * (-5153011 + 7 * eta * (-2375327 + 9 * eta * (218846 + 1640185 * eta))) * + PhiDOT2 * r2 * rDOT3 + + (-7761899 + 7 * eta * (2892563 + 5998602 * eta + 7493619 * eta2)) * + PhiDOT * r * rDOT4 + + 3j * (-2422057 + 7 * eta * (501045 + eta * (2033141 + 2771816 * eta))) * + rDOT5) - + 8820 * total_mass * r3 * + (2 * (111737 + eta * (-366573 + eta * (-618923 + 2278593 * eta))) * + PhiDOT7 * r7 + + 2j * (101844 + eta * (-273675 - 871630 * eta + 2069774 * eta2)) * + PhiDOT6 * r6 * rDOT + + 2 * (341322 + eta * (-1429938 + eta * (-1206083 + 7690681 * eta))) * + PhiDOT5 * r5 * rDOT2 + + 8j * (90241 + 2 * eta * (-206022 + eta * (-62113 + 1003558 * eta))) * + PhiDOT4 * r4 * rDOT3 + + 2 * (410547 + eta * (-2269686 + eta * (762091 + 8400052 * eta))) * + PhiDOT3 * r3 * rDOT4 + + 4j * (217935 + 2 * eta * (-573699 + 5 * eta * (18671 + 445748 * eta))) * + PhiDOT2 * r2 * rDOT5 + + (333969 + 2 * eta * (-981471 + 4 * eta * (154039 + 750016 * eta))) * + PhiDOT * r * rDOT6 + + 24j * (13245 + 2 * eta * (-37005 + eta * (14251 + 130160 * eta))) * + rDOT7) + + 2 * total_mass4 * + (-4178597424j * rDOT + + 84j * rDOT * + (38468500 * eta3 + + 648648j * (S1z + S2z) - + 90090 * ((-24 + 155 * kappa1) * S1z2 + + (-24 + 155 * kappa2) * S2z2) - + 420 * eta2 * + (-122855 + + 3003 * ((2 + 11 * kappa1) * S1z2 - + 18 * S1z * S2z + + (2 + 11 * kappa2) * S2z2)) + + 3 * eta * + (-103100846 - 1846845 * M_PI2 - + 564564j * S2z + + 6006 * (S1z * (-94j + 5 * (-52 + 63 * kappa1) * S1z) - + 20 * S1z * S2z + + 5 * (-52 + 63 * kappa2) * S2z2))) + + PhiDOT * r * + (1176172480 * eta3 + + 8 * (74084729 - + 189189 * S1z * (97j + 5 * (-8 + 117 * kappa1) * S1z) - + 189189 * S2z * (97j + 5 * (-8 + 117 * kappa2) * S2z)) - + 176400 * eta2 * + (11251 + + 429 * ((2 + 13 * kappa1) * S1z2 - + 22 * S1z * S2z + + (2 + 13 * kappa2) * S2z2)) + + 147 * eta * + (-65012788 + 4485195 * M_PI2 + + 4499352j * S2z + + 10296 * (S1z * (437j + 15 * (-32 + 71 * kappa1) * S1z) - + 3860 * S1z * S2z + + 15 * (-32 + 71 * kappa2) * S2z2)))) - + 3 * total_mass3 * r * + (-4j * rDOT3 * + (601018232 - 1359334480 * eta3 - + 756756 * S1z * (6j + 115 * kappa1 * S1z) - + 756756 * S2z * (6j + 115 * kappa2 * S2z) + + 231 * eta * + (8490448 + 503685 * M_PI2 + + 80808j * S2z + + 2184 * (S1z * (37j + 190 * kappa1 * S1z) + + 70 * S1z * S2z + + 190 * kappa2 * S2z2)) + + 58800 * eta2 * + (-62596 + + 429 * ((-1 + 5 * kappa1) * S1z2 - + 12 * S1z * S2z + + (-1 + 5 * kappa2) * S2z2))) - + 14j * PhiDOT2 * r2 * rDOT * + (-229522160 * eta3 + + 8 * (48303859 + + 135135 * S1z * (-17j + 2 * kappa1 * S1z) + + 135135 * S2z * (-17j + 2 * kappa2 * S2z)) + + 2520 * eta2 * + (100913 + + 286 * ((-31 + 5 * kappa1) * S1z2 - + 72 * S1z * S2z + + (-31 + 5 * kappa2) * S2z2)) + + 7 * eta * + (125038052 + 2374515 * M_PI2 + + 5858424j * S2z - + 10296 * (S1z * (-569j + 25 * (-24 + 7 * kappa1) * S1z) + + 700 * S1z * S2z + + 25 * (-24 + 7 * kappa2) * S2z2))) + + 4 * PhiDOT * r * rDOT2 * + (-1095987374 + 1035895280 * eta3 + + 378378 * S1z * (152j + 355 * kappa1 * S1z) + + 378378 * S2z * (152j + 355 * kappa2 * S2z) - + 490 * eta2 * + (-5802767 + + 5148 * ((2 + 23 * kappa1) * S1z2 - + 42 * S1z * S2z + + (2 + 23 * kappa2) * S2z2)) - + 77 * eta * + (42451610 + 1511055 * M_PI2 + + 3623256j * S2z - + 6552 * (S1z * (-553j + 5 * (18 + 37 * kappa1) * S1z) + + 965 * S1z * S2z + + 5 * (18 + 37 * kappa2) * S2z2))) + + 7 * PhiDOT3 * r3 * + (512893080 * eta3 - + 136 * (-2089567 + + 135135 * S1z * (1j + 2 * kappa1 * S1z) + + 135135 * S2z * (1j + 2 * kappa2 * S2z)) - + 560 * eta2 * + (2457671 + + 2574 * ((11 + 53 * kappa1) * S1z2 - + 84 * S1z * S2z + + (11 + 53 * kappa2) * S2z2)) + + 3 * eta * + (16621605 * M_PI2 + + 8 * (27468722 + + 2681679j * S2z + + 3003 * (S1z * (893j - 840 * S1z + 800 * kappa1 * S1z) - + 3160 * S1z * S2z + + 40 * (-21 + 20 * kappa2) * S2z2)))))) + ) + + log_term = ( + 74954880 * delta * total_mass3 * + (total_mass * (-22j * PhiDOT * r - 24 * rDOT) + + 3 * r * + (7j * PhiDOT3 * r3 + + 14 * PhiDOT2 * r2 * rDOT - + 8j * PhiDOT * r * rDOT2 + + 4 * rDOT3)) * + np.log(r / r0) + ) + + return ( + 1j * (spin_terms + orbital_terms + log_term) + / (3.6324288e8 * np.sqrt(14) * r4) + ) + + else: + return complex(0, 0) + +@register_hqc_lm(3, 1) +def hQC_3_m_1( + mass: float, + eta: float, + vpnorder: int, + x: float, + S1z: float, + S2z: float, + params: CommonVars + ) -> complex: + + delta = params.delta + EulerGamma = 0.5772156649015329 + logx = params.logx + logb0 = params.logb0 + logr0 = params.logr0 + + x2 = x*x + x3 = x2*x + x4 = x3*x + xp5 = params.xp5 + x4p5 = x4*xp5 + + if vpnorder == 4: + return ( + -0.005555555555555556 * ( + delta * x3 * ( + -97.0 + 60.0 * EulerGamma - 30.0j * M_PI + + 60.0 * LOG2 + 60.0 * logb0 + 90.0 * logx + ) + ) / np.sqrt(14.0) + ) + + elif vpnorder == 6: + return ( + (delta * x4 * ( + -1552.0 + 960.0 * EulerGamma - 6487.0 * eta + 420.0 * EulerGamma * eta - + 480.0j * M_PI - 210.0j * eta * M_PI + 960.0 * LOG2 + + 420.0 * eta * LOG2 + 60.0 * (16.0 + 7.0 * eta) * logb0 + + 90.0 * (16.0 + 7.0 * eta) * logx + )) / (1080.0 * np.sqrt(14.0)) + ) + + elif vpnorder == 7: + numerator_7 = ( + -9.44822373393802e-6 * ( + x4p5 * ( + 53132.0j * delta - 92232.0j * delta * EulerGamma + 35280.0j * delta * EulerGamma**2 - + 46116.0 * delta * M_PI + 35280.0 * delta * EulerGamma * M_PI - 2940.0j * delta * M_PI2 + + 57036.0 * S1z + 57036.0 * delta * S1z - 35280.0 * EulerGamma * S1z - 35280.0 * delta * EulerGamma * S1z - + 114513.0 * eta * S1z - 143031.0 * delta * eta * S1z + 97020.0 * EulerGamma * eta * S1z + + 114660.0 * delta * EulerGamma * eta * S1z + 17640.0j * M_PI * S1z + 17640.0j * delta * M_PI * S1z - + 48510.0j * eta * M_PI * S1z - 57330.0j * delta * eta * M_PI * S1z - 57036.0 * S2z + + 57036.0 * delta * S2z + 35280.0 * EulerGamma * S2z - 35280.0 * delta * EulerGamma * S2z + + 114513.0 * eta * S2z - 143031.0 * delta * eta * S2z - 97020.0 * EulerGamma * eta * S2z + + 114660.0 * delta * EulerGamma * eta * S2z - 17640.0j * M_PI * S2z + 17640.0j * delta * M_PI * S2z + + 48510.0j * eta * M_PI * S2z - 57330.0j * delta * eta * M_PI * S2z - 92232.0j * delta * LOG2 + + 70560.0j * delta * EulerGamma * LOG2 + 35280.0 * delta * M_PI * LOG2 - + 35280.0 * S1z * LOG2 - 35280.0 * delta * S1z * LOG2 + 97020.0 * eta * S1z * LOG2 + + 114660.0 * delta * eta * S1z * LOG2 + 35280.0 * S2z * LOG2 - 35280.0 * delta * S2z * LOG2 - + 97020.0 * eta * S2z * LOG2 + 114660.0 * delta * eta * S2z * LOG2 + + 35280.0j * delta * LOG2**2 - 2490264.0j * delta * LOG3 + + 1905120.0j * delta * EulerGamma * LOG3 + 952560.0 * delta * M_PI * LOG3 - + 317520.0 * S1z * LOG3 - 317520.0 * delta * S1z * LOG3 + 1508220.0 * eta * S1z * LOG3 + + 396900.0 * delta * eta * S1z * LOG3 + 317520.0 * S2z * LOG3 - 317520.0 * delta * S2z * LOG3 - + 1508220.0 * eta * S2z * LOG3 + 396900.0 * delta * eta * S2z * LOG3 + + 1905120.0j * delta * LOG2 * LOG3 + 952560.0j * delta * LOG3**2 + + 35280.0j * delta * logb0**2 + 21840.0j * delta * logr0 - 138348.0j * delta * logx + + 105840.0j * delta * EulerGamma * logx + 52920.0 * delta * M_PI * logx - 52920.0 * S1z * logx - + 52920.0 * delta * S1z * logx + 145530.0 * eta * S1z * logx + 171990.0 * delta * eta * S1z * logx + + 52920.0 * S2z * logx - 52920.0 * delta * S2z * logx - 145530.0 * eta * S2z * logx + + 171990.0 * delta * eta * S2z * logx + 105840.0j * delta * LOG2 * logx + + 79380.0j * delta * logx**2 + + 588.0 * logb0 * ( + -194.0j * delta + 120.0j * delta * EulerGamma + 60.0 * delta * M_PI + + 15.0 * (-4.0 - 4.0 * delta + 11.0 * eta + 13.0 * delta * eta) * S1z + + 60.0 * S2z - 60.0 * delta * S2z - 165.0 * eta * S2z + 195.0 * delta * eta * S2z + + 120.0j * delta * LOG2 + 180.0j * delta * logx + ) + ) + ) + ) + return numerator_7 / np.sqrt(14.0) + + else: + return complex(0, 0) + +############ l = 4 ############### + +# H44 + +@register_hgo_lm(4, 4) +def hGO_4_m_4( + total_mass: float, + eta: float, + r: float, + rDOT: float, + PhiDOT: float, + vpnorder: int, + S1z: float, + S2z: float, + x: float, + params: CommonVars, + ) -> complex: + delta = params.delta + kappa1 = 1.0 + kappa2 = 1.0 + + combination_a = PhiDOT * r + 1j * rDOT + combination_a4 = combination_a ** 4 + combination_a5 = combination_a ** 5 + combination_a6 = combination_a ** 6 + + combination_b = PhiDOT * r - 1j * rDOT + combination_b2 = combination_b ** 2 + + total_mass2 = total_mass * total_mass + total_mass3 = total_mass2 * total_mass + total_mass4 = total_mass3 * total_mass + + rDOT2 = rDOT * rDOT + rDOT3 = rDOT2 * rDOT + rDOT4 = rDOT3 * rDOT + rDOT5 = rDOT4 * rDOT + rDOT6 = rDOT5 * rDOT + + PhiDOT2 = PhiDOT * PhiDOT + PhiDOT3 = PhiDOT2 * PhiDOT + PhiDOT4 = PhiDOT3 * PhiDOT + PhiDOT5 = PhiDOT4 * PhiDOT + PhiDOT6 = PhiDOT5 * PhiDOT + + r2 = r * r + r3 = r2 * r + r4 = r3 * r + r5 = r4 * r + r6 = r5 * r + + eta2 = eta * eta + eta3 = eta2 * eta + + S1z2 = S1z * S1z + + if vpnorder == 2: + return ( + np.sqrt(0.7142857142857143) * (-1 + 3 * eta) * + (7 * total_mass2 + 6 * r2 * combination_a4 + + 3 * total_mass * r * + (17 * PhiDOT2 * r2 + + 18j * PhiDOT * r * rDOT - 6 * rDOT2)) + / (36.0 * r2) + ) + + elif vpnorder == 4: + return ( + (40 * total_mass3 * (314 - 987 * eta + 195 * eta2) - + 60 * (23 - 159 * eta + 291 * eta2) * r3 * + combination_b * combination_a5 + + total_mass2 * r * + ((53143 - 199660 * eta + 127500 * eta2) * PhiDOT2 * r2 + + 24j * (967 - 4615 * eta + 5935 * eta2) * PhiDOT * r * rDOT - + 10 * (290 - 2033 * eta + 4365 * eta2) * rDOT2) - + 3 * total_mass * r2 * + ((613 - 920 * eta + 6420 * eta2) * PhiDOT4 * r4 - + 8j * (-976 + 1745 * eta + 3150 * eta2) * PhiDOT3 * r3 * rDOT + + 2 * (-6141 + 8980 * eta + 31500 * eta2) * PhiDOT2 * r2 * rDOT2 + + 4j * (-1853 + 1730 * eta + 13230 * eta2) * PhiDOT * r * rDOT3 - + 20 * (-83 + 30 * eta + 762 * eta2) * rDOT4)) + / (1584.0 * np.sqrt(35) * r3) + ) + + elif vpnorder == 5: + term1 = ( + total_mass2 * eta * + (6 * total_mass * (-43j * PhiDOT * r + 9 * rDOT) + + r * (-734j * PhiDOT3 * r3 + + 129 * PhiDOT2 * r2 * rDOT + + 156j * PhiDOT * r * rDOT2 - + 26 * rDOT3)) + / (24.0 * np.sqrt(35) * r3) + ) + + # Henry et al. ecc spin terms + term2 = ( + total_mass2 * + (-3j * PhiDOT2 * r3 * rDOT * + ((-250 + 1221 * eta - 1512 * eta2 + + delta * (-250 + 849 * eta)) * S1z + + (-250 + delta * (250 - 849 * eta) + 1221 * eta - + 1512 * eta2) * S2z) - + 2 * total_mass * PhiDOT * r * + ((-130 + 757 * eta - 1224 * eta2 + + delta * (-130 + 513 * eta)) * S1z + + (-130 + delta * (130 - 513 * eta) + 757 * eta - + 1224 * eta2) * S2z) - + 2j * total_mass * rDOT * + ((-100 + 577 * eta - 864 * eta2 + + delta * (-100 + 333 * eta)) * S1z + + (-100 + delta * (100 - 333 * eta) + 577 * eta - + 864 * eta2) * S2z) - + 6 * PhiDOT3 * r4 * + ((-65 + 263 * eta - 291 * eta2 + + delta * (-65 + 282 * eta)) * S1z + + (-65 + delta * (65 - 282 * eta) + 263 * eta - + 291 * eta2) * S2z) + + 12 * PhiDOT * r2 * rDOT2 * + ((-40 + 201 * eta - 252 * eta2 + + delta * (-40 + 129 * eta)) * S1z + + (-40 + delta * (40 - 129 * eta) + 201 * eta - + 252 * eta2) * S2z) + + 6j * r * rDOT3 * + ((-20 + 107 * eta - 144 * eta2 + + delta * (-20 + 63 * eta)) * S1z + + (-20 + delta * (20 - 63 * eta) + 107 * eta - + 144 * eta2) * S2z)) + / (72.0 * np.sqrt(35) * r3) + ) + + return term1 + term2 + + elif vpnorder == 6: + term1 = ( + (10 * total_mass4 * + (-4477296 + 12734393 * eta - 6895 * eta2 + 1043805 * eta3) + + 3150 * (-367 + 4337 * eta - 17462 * eta2 + 23577 * eta3) * + r4 * combination_b2 * combination_a6 + + 2 * total_mass3 * r * + ((-36967579 + 245501977 * eta - 459916170 * eta2 + + 150200680 * eta3) * PhiDOT2 * r2 + + 4j * (7571073 - 10780154 * eta - 56898800 * eta2 + + 43665510 * eta3) * PhiDOT * r * rDOT - + 10 * (1283609 - 5800627 * eta + 3725295 * eta2 + + 4771935 * eta3) * rDOT2) - + total_mass2 * r2 * + ((-28258134 + 3245207 * eta + 144051250 * eta2 + + 136991820 * eta3) * PhiDOT4 * r4 - + 24j * (2371982 - 7733376 * eta - 7948185 * eta2 + + 9074870 * eta3) * PhiDOT3 * r3 * rDOT + + 7 * (6557973 - 50558069 * eta + 59901380 * eta2 + + 104752320 * eta3) * PhiDOT2 * r2 * rDOT2 + + 168j * (52044 - 1084807 * eta + 1849450 * eta2 + + 4171730 * eta3) * PhiDOT * r * rDOT3 - + 35 * (1083 - 1246819 * eta + 2524240 * eta2 + + 5995845 * eta3) * rDOT4) - + 105 * total_mass * r3 * + ((116396 - 551405 * eta + 560658 * eta2 + + 293036 * eta3) * PhiDOT6 * r6 + + 2j * (158192 - 670661 * eta + 177718 * eta2 + + 2163976 * eta3) * PhiDOT5 * r5 * rDOT + + (-393665 + 1322392 * eta + 1589680 * eta2 - + 8622660 * eta3) * PhiDOT4 * r4 * rDOT2 - + 8j * (-23048 + 209397 * eta - 487057 * eta2 + + 260396 * eta3) * PhiDOT3 * r3 * rDOT3 - + (630647 - 3391000 * eta + 2501958 * eta2 + + 7664096 * eta3) * PhiDOT2 * r2 * rDOT4 - + 2j * (218975 - 1037408 * eta + 148970 * eta2 + + 3699480 * eta3) * PhiDOT * r * rDOT5 + + 10 * (10233 - 44864 * eta - 13050 * eta2 + + 203280 * eta3) * rDOT6)) + / (1.44144e6 * np.sqrt(35) * r4) + ) + + # Henry et al. ecc spin terms + term2 = ( + np.sqrt(0.7142857142857143) * total_mass3 * (-1 + 3 * eta) * + (12 * total_mass + + r * (53 * PhiDOT2 * r2 + + 26j * PhiDOT * r * rDOT - 8 * rDOT2)) * + (kappa1 * (1 + delta - 2 * eta) * S1z2 + + S2z * (4 * eta * S1z + kappa2 * S2z - delta * kappa2 * S2z - + 2 * kappa2 * eta * S2z)) + / (48.0 * r4) + ) + + return term1 + term2 + + elif vpnorder == 7: + # Henry et al. ecc+spin terms + return ( + total_mass2 * + (14 * total_mass2 * + (120 * eta3 * + (24635 * PhiDOT * r + 18657j * rDOT) * + (S1z + S2z) - + 60 * (10039 * PhiDOT * r + 7706j * rDOT) * + (S1z + delta * S1z + S2z - delta * S2z) + + 5 * eta * + (PhiDOT * r * + (448616j + 703833 * S1z + 505635 * delta * S1z + + 703833 * S2z - 505635 * delta * S2z) + + 4j * rDOT * + (4175j + 123114 * S1z + 76938 * delta * S1z + + 123114 * S2z - 76938 * delta * S2z)) - + 6 * eta2 * + (2 * PhiDOT * r * + (217374j + 549175 * S1z + 61075 * delta * S1z + + 549175 * S2z - 61075 * delta * S2z) + + 5j * rDOT * + (9861j + 132241 * S1z + 18825 * delta * S1z + + 132241 * S2z - 18825 * delta * S2z))) - + 3 * r2 * + (1680 * eta3 * + (2833 * PhiDOT5 * r5 + + 18796j * PhiDOT4 * r4 * rDOT - + 13185 * PhiDOT3 * r3 * rDOT2 + + 1186j * PhiDOT2 * r2 * rDOT3 - + 5863 * PhiDOT * r * rDOT4 - + 2100j * rDOT5) * + (S1z + S2z) - + 1050 * + (454 * PhiDOT5 * r5 + + 1195j * PhiDOT4 * r4 * rDOT - + 1950 * PhiDOT3 * r3 * rDOT2 - + 442j * PhiDOT2 * r2 * rDOT3 - + 384 * PhiDOT * r * rDOT4 - + 184j * rDOT5) * + (S1z + delta * S1z + S2z - delta * S2z) - + 6 * eta2 * + (2 * PhiDOT5 * r5 * + (-2459811j + 35 * (26517 + 10223 * delta) * S1z + + (928095 - 357805 * delta) * S2z) - + 10j * rDOT5 * + (35291j + 28 * (2183 + 1155 * delta) * S1z + + (61124 - 32340 * delta) * S2z) + + 1j * PhiDOT2 * r2 * rDOT3 * + (917901j + 1120 * (-191 + 1616 * delta) * S1z - + 1120 * (191 + 1616 * delta) * S2z) + + 5 * PhiDOT3 * r3 * rDOT2 * + (85426j + 7 * (-148363 + 4365 * delta) * S1z - + 7 * (148363 + 4365 * delta) * S2z) - + 4 * PhiDOT * r * rDOT4 * + (280067j + 70 * (5844 + 4817 * delta) * S1z - + 70 * (-5844 + 4817 * delta) * S2z) + + 1j * PhiDOT4 * r4 * rDOT * + (10375501j + 70 * (65831 + 22871 * delta) * S1z - + 70 * (-65831 + 22871 * delta) * S2z)) + + eta * (-40j * rDOT5 * + (12203j + 42 * (843 + 656 * delta) * S1z - + 42 * (-843 + 656 * delta) * S2z) + + 4j * PhiDOT2 * r2 * rDOT3 * + (-266071j + 210 * (-2279 + 1010 * delta) * S1z - + 210 * (2279 + 1010 * delta) * S2z) - + 16 * PhiDOT * r * rDOT4 * + (58753j + 105 * (2030 + 1947 * delta) * S1z - + 105 * (-2030 + 1947 * delta) * S2z) + + PhiDOT5 * r5 * + (-8997592j + 105 * (39959 + 15835 * delta) * S1z - + 105 * (-39959 + 15835 * delta) * S2z) - + 10 * PhiDOT3 * r3 * rDOT2 * + (254228j + 21 * (65293 + 34551 * delta) * S1z - + 21 * (-65293 + 34551 * delta) * S2z) + + 2j * PhiDOT4 * r4 * rDOT * + (12351083j + 105 * (43193 + 37913 * delta) * S1z - + 105 * (-43193 + 37913 * delta) * S2z))) + + total_mass * r * + (2520 * eta3 * + (8036 * PhiDOT3 * r3 + + 41814j * PhiDOT2 * r2 * rDOT - + 30537 * PhiDOT * r * rDOT2 - + 9064j * rDOT3) * + (S1z + S2z) - + 210 * + (11849 * PhiDOT3 * r3 + + 31868j * PhiDOT2 * r2 * rDOT + + 3572 * PhiDOT * r * rDOT2 + + 1508j * rDOT3) * + (S1z + delta * S1z + S2z - delta * S2z) - + 12 * eta2 * + (1j * PhiDOT2 * r2 * rDOT * + (-1150397j + 35 * (154765 + 157449 * delta) * S1z + + 5416775 * S2z - 5510715 * delta * S2z) - + PhiDOT * r * rDOT2 * + (2306552j + 35 * (4931 + 110733 * delta) * S1z + + 172585 * S2z - 3875655 * delta * S2z) + + PhiDOT3 * r3 * + (12461121j + 35 * (18331 + 87381 * delta) * S1z + + 641585 * S2z - 3058335 * delta * S2z) - + 25j * rDOT3 * + (36676j + 35 * (137 + 1053 * delta) * S1z + + 4795 * S2z - 36855 * delta * S2z)) + + eta * (-200j * rDOT3 * + (-2501j + 42 * (-308 + 51 * delta) * S1z - + 42 * (308 + 51 * delta) * S2z) + + 2 * PhiDOT * r * rDOT2 * + (-1951984j - 105 * (-37907 + 14661 * delta) * S1z + + 105 * (37907 + 14661 * delta) * S2z) + + 8j * PhiDOT2 * r2 * rDOT * + (-10005028j + 105 * (40991 + 31689 * delta) * S1z - + 105 * (-40991 + 31689 * delta) * S2z) + + PhiDOT3 * r3 * + (88418488j + 105 * (57793 + 266391 * delta) * S1z - + 105 * (-57793 + 266391 * delta) * S2z)))) + / (332640.0 * np.sqrt(35) * r4) + ) + + else: + return complex(0, 0) + +@register_hqc_lm(4, 4) +def hQC_4_m_4( + mass: float, + eta: float, + vpnorder: int, + x: float, + S1z: float, + S2z: float, + params: CommonVars + ) -> complex: + EulerGamma: float = 0.5772156649015329 + logb0 = params.logb0 + logx = params.logx + + eta2 = eta * eta + + x2 = x*x + x3 = x2*x + x4 = x3*x + xp5 = params.xp5 + x3p5 = x3*xp5 + x4p5 = x4*xp5 + + if vpnorder == 5: + # Pre-factor: Complex(0, 0.07407407407407407) / sqrt(35) + return ( + (0.07407407407407407j * x3p5 * ( + 1888.0 - 960.0 * EulerGamma - 5661.0 * eta + 2880.0 * EulerGamma * eta + + 480.0j * M_PI - 1440.0j * eta * M_PI - 2880.0 * LOG2 + + 8640.0 * eta * LOG2 + 960.0 * (-1.0 + 3.0 * eta) * logb0 + + 1440.0 * (-1.0 + 3.0 * eta) * logx + )) / np.sqrt(35.0) + ) + + elif vpnorder == 7: + # Pre-factor: Complex(0, 1.0020843354176687e-6) / sqrt(35) + return ( + (1.0020843354176687e-6j * x4p5 * ( + -752360448.0 + 382556160.0 * EulerGamma + 2620364605.0 * eta - + 1333248000.0 * EulerGamma * eta - 898312500.0 * eta2 + + 458035200.0 * EulerGamma * eta2 - 191278080.0j * M_PI + + 666624000.0j * eta * M_PI - 229017600.0j * eta2 * M_PI + + 1147668480.0 * LOG2 - 3999744000.0 * eta * LOG2 + + 1374105600.0 * eta2 * LOG2 + + 215040.0 * (1779.0 - 6200.0 * eta + 2130.0 * eta2) * logb0 + + 322560.0 * (1779.0 - 6200.0 * eta + 2130.0 * eta2) * logx + )) / np.sqrt(35.0) + ) + + else: + return complex(0, 0) + +# H43 + +@register_hgo_lm(4, 3) +def hGO_4_m_3( + total_mass: float, + eta: float, + r: float, + rDOT: float, + PhiDOT: float, + vpnorder: int, + S1z: float, + S2z: float, + x: float, + params: CommonVars, + ) -> complex: + delta = params.delta + kappa1 = 1.0 + kappa2 = 1.0 + + rDOT2 = rDOT * rDOT + rDOT3 = rDOT2 * rDOT + rDOT4 = rDOT3 * rDOT + rDOT5 = rDOT4 * rDOT + rDOT6 = rDOT5 * rDOT + + total_mass2 = total_mass * total_mass + total_mass3 = total_mass2 * total_mass + + PhiDOT2 = PhiDOT * PhiDOT + PhiDOT3 = PhiDOT2 * PhiDOT + PhiDOT4 = PhiDOT3 * PhiDOT + PhiDOT5 = PhiDOT4 * PhiDOT + PhiDOT6 = PhiDOT5 * PhiDOT + + r2 = r * r + r3 = r2 * r + r4 = r3 * r + r5 = r4 * r + r6 = r5 * r + + eta2 = eta * eta + eta3 = eta2 * eta + + S1z2 = S1z * S1z + + S2z2 = S2z * S2z + + if vpnorder == 3: + return ( + 0.16666666666666666j * delta * total_mass * (-1 + 2 * eta) * PhiDOT * + (4 * total_mass + + r * (23 * PhiDOT2 * r2 + + 10j * PhiDOT * r * rDOT - 2 * rDOT2)) + / (np.sqrt(70) * r) + ) + + elif vpnorder == 4: + return ( + -0.041666666666666664j * np.sqrt(0.35714285714285715) * + total_mass2 * eta * + (4 * total_mass + + r * (23 * PhiDOT2 * r2 + + 10j * PhiDOT * r * rDOT - 2 * rDOT2)) * + ((-1 + delta) * S1z + S2z + delta * S2z) + / r3 + ) + + elif vpnorder == 5: + return ( + 0.0012626262626262627j * delta * total_mass * PhiDOT * + (2 * total_mass2 * (972 - 2293 * eta + 1398 * eta2) + + 2 * total_mass * r * + ((1788 - 9077 * eta + 13416 * eta2) * PhiDOT2 * r2 + + 3j * (-2796 + 5299 * eta + 1622 * eta2) * PhiDOT * r * rDOT - + 2 * (-1200 + 2545 * eta + 162 * eta2) * rDOT2) - + 3 * r2 * + ((-524 - 489 * eta + 6392 * eta2) * PhiDOT4 * r4 + + 4j * (796 - 1864 * eta + 133 * eta2) * PhiDOT3 * r3 * rDOT + + 42 * (-51 + 94 * eta + 56 * eta2) * PhiDOT2 * r2 * rDOT2 + + 4j * (-229 + 366 * eta + 358 * eta2) * PhiDOT * r * rDOT3 - + 4 * (-43 + 62 * eta + 80 * eta2) * rDOT4)) + / (np.sqrt(70) * r2) + ) + + elif vpnorder == 6: + term1 = ( + delta * total_mass2 * eta * PhiDOT * + (6 * total_mass * (181 * PhiDOT * r - 89j * rDOT) + + r * (4847 * PhiDOT3 * r3 - + 7338j * PhiDOT2 * r2 * rDOT - + 408 * PhiDOT * r * rDOT2 + + 112j * rDOT3)) + / (180.0 * np.sqrt(70) * r2) + ) + + # Henry et al. ecc spin terms + term2 = ( + -0.0006313131313131314j * total_mass2 * + (2 * total_mass2 * + ((-440 + 6801 * eta - 1428 * eta2 + + delta * (-440 - 3193 * eta + 300 * eta2)) * S1z + + (440 - 6801 * eta + 1428 * eta2 + + delta * (-440 - 3193 * eta + 300 * eta2)) * S2z) - + 2 * total_mass * r * + (-3j * PhiDOT * r * rDOT * + (delta * (-1320 + 9093 * eta + 59 * eta2) * S1z - + 5 * (264 - 311 * eta + 823 * eta2) * S1z + + delta * (-1320 + 9093 * eta + 59 * eta2) * S2z + + 5 * (264 - 311 * eta + 823 * eta2) * S2z) - + 2 * rDOT2 * + ((220 + 1659 * eta - 1512 * eta2 + + delta * (220 - 3067 * eta + 240 * eta2)) * S1z + + (-220 - 1659 * eta + 1512 * eta2 + + delta * (220 - 3067 * eta + 240 * eta2)) * S2z) + + 2 * PhiDOT2 * r2 * + ((1826 - 19530 * eta + 20145 * eta2 + + delta * (1826 + 1534 * eta + 567 * eta2)) * S1z + + (-1826 + 19530 * eta - 20145 * eta2 + + delta * (1826 + 1534 * eta + 567 * eta2)) * S2z)) - + 3 * r2 * + (3080j * PhiDOT3 * r3 * rDOT * + ((1 + delta) * S1z + (-1 + delta) * S2z) + + 2 * eta2 * + (129 * PhiDOT2 * r2 * rDOT2 * + (41 * S1z + 23 * delta * S1z - 41 * S2z + 23 * delta * S2z) - + 2 * rDOT4 * + (149 * S1z + 75 * delta * S1z - 149 * S2z + 75 * delta * S2z) + + 2j * PhiDOT * r * rDOT3 * + (925 * S1z + 491 * delta * S1z - 925 * S2z + 491 * delta * S2z) - + 1j * PhiDOT3 * r3 * rDOT * + (-2105 * S1z + 753 * delta * S1z + 2105 * S2z + 753 * delta * S2z) + + PhiDOT4 * r4 * + (11617 * S1z + 4847 * delta * S1z - 11617 * S2z + 4847 * delta * S2z)) + + eta * (16 * PhiDOT4 * r4 * + (-413 * S1z + 127 * delta * S1z + 413 * S2z + 127 * delta * S2z) - + 2 * rDOT4 * + (-301 * S1z + 213 * delta * S1z + 301 * S2z + 213 * delta * S2z) + + 2j * PhiDOT * r * rDOT3 * + (-1625 * S1z + 1009 * delta * S1z + 1625 * S2z + 1009 * delta * S2z) + + 3 * PhiDOT2 * r2 * rDOT2 * + (-2587 * S1z + 1267 * delta * S1z + 2587 * S2z + 1267 * delta * S2z) - + 2j * PhiDOT3 * r3 * rDOT * + (3635 * S1z + 7981 * delta * S1z - 3635 * S2z + 7981 * delta * S2z)))) + / (np.sqrt(70) * r4) + ) + + return term1 + term2 + + elif vpnorder == 7: + # Henry et al. ecc+spin terms + spin_terms = ( + 5005 * total_mass2 * + (-24 * PhiDOT * r2 * rDOT2 * + (1j * (-11 + 48 * eta) * S1z + + 6 * (-5 + 4 * kappa1) * eta2 * S1z2 + + S2z * (11j - 48j * eta - + 6 * (-5 + 4 * kappa2) * eta2 * S2z)) + + 4 * r * rDOT3 * + ((-11 + 93 * eta) * S1z - + 6j * (-5 + 4 * kappa1) * eta2 * S1z2 + + S2z * (11 - 93 * eta + + 6j * (-5 + 4 * kappa2) * eta2 * S2z)) + + 4 * total_mass * rDOT * + ((22 - 111 * eta) * S1z + + 6j * (15 + 8 * kappa1) * eta2 * S1z2 + + S2z * (-22 + 111 * eta - + 6j * (15 + 8 * kappa2) * eta2 * S2z)) + + 6j * PhiDOT2 * r3 * rDOT * + (1j * (-121 + 963 * eta) * S1z + + 6 * (-55 * eta2 + + kappa1 * (-5 + 30 * eta + 4 * eta2)) * S1z2 + + S2z * (121j + 30 * kappa2 * S2z - + 6 * (-55 + 4 * kappa2) * eta2 * S2z - + 9 * eta * (107j + 20 * kappa2 * S2z))) + + 2 * total_mass * PhiDOT * r * + (-1j * (-121 + 633 * eta) * S1z + + 6 * (180 * eta2 + + kappa1 * (9 - 54 * eta + 28 * eta2)) * S1z2 - + S2z * (121j + 54 * kappa2 * S2z + + 24 * (45 + 7 * kappa2) * eta2 * S2z - + 3 * eta * (211j + 108 * kappa2 * S2z))) + + PhiDOT3 * r4 * + (-1j * (-649 + 4767 * eta) * S1z + + 6 * (820 * eta2 + + kappa1 * (75 - 450 * eta + 364 * eta2)) * S1z2 - + S2z * (649j + 450 * kappa2 * S2z + + 24 * (205 + 91 * kappa2) * eta2 * S2z - + 3 * eta * (1589j + 900 * kappa2 * S2z)))) + ) + + orbital_terms = ( + delta * + (2 * total_mass * PhiDOT * r3 * + (10 * (234744 - 1010534 * eta + 1024443 * eta2 + + 5451096 * eta3) * PhiDOT4 * r4 - + 3j * (-2426804 + 1512854 * eta + 4994115 * eta2 + + 610960 * eta3) * PhiDOT3 * r3 * rDOT + + (-30341028 + 23936528 * eta + 89326545 * eta2 + + 19329660 * eta3) * PhiDOT2 * r2 * rDOT2 + + 21j * (-668008 + 803028 * eta + 1908955 * eta2 + + 540370 * eta3) * PhiDOT * r * rDOT3 - + 14 * (-172143 + 155683 * eta + 680580 * eta2 + + 111840 * eta3) * rDOT4) - + 105 * PhiDOT * r4 * + ((-8280 + 24681 * eta - 151973 * eta2 + + 624074 * eta3) * PhiDOT6 * r6 + + 2j * (-32208 + 248485 * eta - 524074 * eta2 + + 24546 * eta3) * PhiDOT5 * r5 * rDOT + + 2 * (48924 - 239802 * eta + 137447 * eta2 + + 358156 * eta3) * PhiDOT4 * r4 * rDOT2 + + 4j * (174 + 24488 * eta - 102039 * eta2 + + 44882 * eta3) * PhiDOT3 * r3 * rDOT3 + + 3 * (10455 - 56490 * eta + 84504 * eta2 + + 54016 * eta3) * PhiDOT2 * r2 * rDOT4 + + 2j * (11175 - 52698 * eta + 57436 * eta2 + + 60808 * eta3) * PhiDOT * r * rDOT5 - + 6 * (829 - 3726 * eta + 3480 * eta2 + + 4640 * eta3) * rDOT6) + + total_mass2 * r * + (20020 * rDOT3 * (S1z + S2z) * + (-11 + 71 * eta + 30j * eta2 * (S1z + S2z)) - + 52 * PhiDOT * r * rDOT2 * + (129150 * eta3 + + 7 * eta * (31961 + 8580j * S1z + 8580j * S2z) - + 3j * (32671j + 8470 * S1z + 8470 * S2z) - + 35 * eta2 * + (33313 + 1980 * S1z2 + 3960 * S1z * S2z + 1980 * S2z2)) + + PhiDOT3 * r3 * + (-10566168 - 70869960 * eta3 + + 3248245j * S1z + 2252250 * kappa1 * S1z2 + + 3248245j * S2z + 2252250 * kappa2 * S2z2 - + 7 * eta * + (4818166 + 2480335j * S1z + 1287000 * kappa1 * S1z2 + + 2480335j * S2z + 1287000 * kappa2 * S2z2) + + 3850 * eta2 * + (38873 + 78 * (7 + 30 * kappa1) * S1z2 - + 3588 * S1z * S2z + + 78 * (7 + 30 * kappa2) * S2z2)) - + 2j * PhiDOT2 * r2 * rDOT * + (6531280 * eta3 + + 3 * (5382288 + 605605j * S1z + 150150 * kappa1 * S1z2 + + 605605j * S2z + 150150 * kappa2 * S2z2) + + 210 * eta2 * + (322144 + 2145 * (1 + 4 * kappa1) * S1z2 - + 12870 * S1z * S2z + + 2145 * (1 + 4 * kappa2) * S2z2) - + 21 * eta * + (2826484 + 85800 * kappa1 * S1z2 + + 515515j * S2z + 85800 * kappa2 * S2z2 - + 5005 * S1z * (-103j + 60 * S2z)))) - + 26 * total_mass3 * + (770 * rDOT * + (-90j * eta2 * S1z2 + + S2z * (-22 + 67 * eta - 90j * eta2 * S2z) + + S1z * (-22 + eta * (67 + 300j * S2z) - 180j * eta2 * S2z)) + + PhiDOT * r * + (-38076 + 174720 * eta3 - + 46585j * S1z - 20790 * kappa1 * S1z2 - + 46585j * S2z - 20790 * kappa2 * S2z2 - + 1260 * eta2 * + (158 + 33 * (5 + 2 * kappa1) * S1z2 + + 198 * S1z * S2z + + 33 * (5 + 2 * kappa2) * S2z2) + + 7 * eta * + (6188 + 11880 * kappa1 * S1z2 + + 21505j * S2z + 11880 * kappa2 * S2z2 + + 55 * S1z * (391j + 744 * S2z))))) + ) + + return ( + -1.3875013875013875e-6j * total_mass * + (spin_terms + orbital_terms) + / (np.sqrt(70) * r4) + ) + + else: + return complex(0.0, 0.0) + + +def generate_hlm(l: int, m: int, total_mass: float, eta: float, r: float, rDOT: float, Phi: float, + PhiDOT: float, R: float, vpnorder: int, S1z: float, + S2z: float, x: float, params: CommonVars) -> complex: + if vpnorder < 0 or vpnorder > 8: + raise ValueError(f"Error in hl_{l}_m_{m}: Input PN order parameter should be between [0, 8].") + + else: + # Calculate the leading amplitude coefficient + amplitude = (4 * total_mass * eta * np.sqrt(M_PI / 5.0)) / R + + GO_func = H_GO_LM_FUNCS.get((l, abs(m))) + QC_func = H_QC_LM_FUNCS.get((l, abs(m))) + + # Sum the Generalized Orbital (GO) and Quasi-Circular (QC) terms + waveform_modes = ( + GO_func(total_mass, eta, r, rDOT, PhiDOT, vpnorder, S1z, S2z, x, params) + + QC_func(total_mass, eta, vpnorder, x, S1z, S2z, params) + ) + if m < 0: + amplitude = (-1)**l * amplitude # Apply the (-1)^l factor for negative m modes + waveform_modes = waveform_modes.conjugate() + + # cpolar(r, theta) in C is equivalent to rect(r, theta) in Python + phase_factor = rect(1.0, -m * Phi) + + return amplitude * waveform_modes * phase_factor + +def hlmGOresult(l: int, m: int, total_mass: float, eta: float, r: float, rDOT: float, Phi: float, + PhiDOT: float, R: float, vpnorder: int, S1z: float, S2z: float, x: float + ) -> complex: + + if not (0 <= vpnorder <= 8): + raise ValueError("PN order must be between 0 and 8") + + if not (2 <= l <= 8): + raise ValueError("l must be between 2 and 8") + + if not (-l <= m <= l): + raise ValueError(f"m must be between {-l} and {l}") + + # Modes with m = 0 are zero in your C code + if m == 0: + return 0j + + b0 = 2 * total_mass / np.exp(0.5) + r0 = b0 + params = CommonVars( + xp5=np.sqrt(x), + logx=np.log(x), + b0 = b0, + r0 = r0, + logb0=np.log(b0), + logr0=np.log(r0), + delta=np.sqrt(1 - 4*eta), + ) + hlm = 0j + + for pno in range(vpnorder, -1, -1): + hlm += generate_hlm(l,m,total_mass, eta, r, rDOT, Phi, PhiDOT, R, pno, S1z, S2z, x, params) + + return hlm \ No newline at end of file diff --git a/esigmapy/python_codes/esigma_pn_inspiral.py b/esigmapy/python_codes/esigma_pn_inspiral.py new file mode 100644 index 0000000..b17b8af --- /dev/null +++ b/esigmapy/python_codes/esigma_pn_inspiral.py @@ -0,0 +1,2972 @@ +""" +esigma_pn_inspiral.py +==================== +Python translation of ESIGMA_PNInspiral.c + +Translated by Samanwaya Mukherjee, 2026 + +References: + - T. Damour, A. Gopakumar, and B. Iyer (PRD 70 064028), Eqs. 71a, 71b + - K. G. Arun, Luc Blanchet, Bala R. Iyer and Siddharta Sinha, arXiv:0908.3854 + - I. Hinder, F. Herrmann, P. Laguna and D. Shoemaker, arXiv:0806.1037, Eqs. A35, A36 + - Huerta et al. + +Notes +----- +* REAL8 -> float (Python float is already double precision) +* M_PI -> np.pi +* M_PI2 -> np.pi**2 (macro used in original) +* M_PI3 -> np.pi**3 +* LAL_GAMMA -> EULER_GAMMA constant defined below +* GSL_SUCCESS / GSL_FAILURE -> 0 / 1 (returned from ode function) +* XLAL_REAL8_FAIL_NAN -> float('nan') +* static functions -> module-level functions (no class needed) +* The ODE dispatcher (eccentric_x_model_odes) is translated to return + (dxdt, dedt, dldt, dphidt) directly – the caller should use + scipy.integrate.ode or solve_ivp with this as the rhs. +""" + +import numpy as np + +# ------ Constants -------------------------------------------------- +EULER_GAMMA = np.euler_gamma # Euler–Mascheroni constant +LOG2 = np.log(2.) +LOG3 = np.log(3.) +LOG4 = np.log(4.) +LOG5 = np.log(5.) +REAL8_FAIL_NAN = float('nan') + +# ============ Utility functions ========================================= + +def SymMassRatio(q: float) -> float: + """Symmetric mass ratio from mass ratio q (can be >1 or <1).""" + return q / (1.0 + q)**2 + + +def SmallMassRatio(eta: float) -> float: + """q<1 mass ratio from symmetric mass ratio eta.""" + return (1.0 - 2.0*eta - np.sqrt(1.0 - 4.0*eta)) / (2.0*eta) + +def polygamma(n: int, z: complex, mp_dps=30): + # Look up the polygamma definition in LALSimESIGMA.c, line number 490 + + """ + General polygamma wrapper: + n = 0 → digamma (ψ) + n > 0 → polygamma (ψ^(n)) via mpmath + + Supports complex z. + + Parameters + ---------- + n : int + Order of the polygamma function (n >= 0) + z : float or complex + Evaluation point + mp_dps : int + Decimal precision for mpmath + + Returns + ------- + complex + """ + if n < 0: + raise ValueError("n must be >= 0") + + # Use SciPy for digamma (fast, supports complex) + if n == 0: + from scipy.special import digamma + return digamma(z) + + import mpmath as mp + # Use mpmath for higher orders (complex-safe) + mp.mp.dps = mp_dps + z_mp = mp.mpc(z.real, z.imag) if isinstance(z, complex) else mp.mpf(z) + return complex(mp.polygamma(n, z_mp)) + + +# ------ Eccentric enhancement factors ------------------------------- + +def phi_e(e: float) -> float: + e2 = e * e + ef = 1.0 - e2 + e4 = e2 * e2 + e6 = e4 * e2 + e8 = e6 * e2 + e10 = e8 * e2 + e12 = e10 * e2 + num = (1.0 + + (18970894028.0 * e2) / 2649026657.0 + + (157473274.0 * e4) / 30734301.0 + + (48176523.0 * e6) / 177473701.0 + + (9293260.0 * e8) / 3542508891.0 + - (5034498.0 * e10) / 7491716851.0 + + (428340.0 * e12) / 9958749469.0) + den = ef**5 + return num / den + + +def psi_e(e: float) -> float: + e2 = e * e + ef = 1.0 - e2 + e4 = e2 * e2 + e6 = e4 * e2 + num = 1.0 - (185.0 * e2) / 21.0 - (3733.0 * e4) / 99.0 - (1423.0 * e6) / 104.0 + den = ef**6 + return num / den + + +def zed_e(e: float) -> float: + e2 = e * e + ef = 1.0 - e2 + e4 = e2 * e2 + e6 = e4 * e2 + num = 1.0 + (2095.0 * e2) / 143.0 + (1590.0 * e4) / 59.0 + (977.0 * e6) / 113.0 + den = ef**6 + return num / den + + +def kappa_e(e: float) -> float: + e2 = e * e + ef = 1.0 - e2 + e4 = e2 * e2 + e6 = e4 * e2 + e8 = e6 * e2 + e10 = e8 * e2 + num = (1.0 + + (1497.0 * e2) / 79.0 + + (7021.0 * e4) / 143.0 + + (997.0 * e6) / 98.0 + + (463.0 * e8) / 51.0 + - (3829.0 * e10) / 120.0) + den = ef**7 + return num / den + + +def phi_e_tilde(e: float) -> float: + e2 = e * e + ef = 1.0 - e2 + e4 = e2 * e2 + e6 = e4 * e2 + e8 = e6 * e2 + e10 = e8 * e2 + num = (1.0 + + (413137256.0 * e2) / 136292703.0 + + (37570495.0 * e4) / 98143337.0 + - (2640201.0 * e6) / 993226448.0 + - (4679700.0 * e8) / 6316712563.0 + - (328675.0 * e10) / 8674876481.0) + den = ef**3 * np.sqrt(ef) + return num / den + + +def psi_e_tilde(e: float) -> float: + e2 = e * e + ef = 1.0 - e2 + e4 = e2 * e2 + e6 = e4 * e2 + e8 = e6 * e2 + e10 = e8 * e2 + num = (1.0 + - (2022.0 * e2) / 305.0 + - (249.0 * e4) / 26.0 + - (193.0 * e6) / 239.0 + + (23.0 * e8) / 43.0 + - (102.0 * e10) / 463.0) + den = ef**4 * np.sqrt(ef) + return num / den + + +def zed_e_tilde(e: float) -> float: + e2 = e * e + ef = 1.0 - e2 + e4 = e2 * e2 + e6 = e4 * e2 + e8 = e6 * e2 + num = (1.0 + + (1563.0 * e2) / 194.0 + + (1142.0 * e4) / 193.0 + + (123.0 * e6) / 281.0 + - (27.0 * e8) / 328.0) + den = ef**4 * np.sqrt(ef) + return num / den + + +def kappa_e_tilde(e: float) -> float: + e2 = e * e + ef = 1.0 - e2 + e4 = e2 * e2 + e6 = e4 * e2 + e8 = e6 * e2 + e10 = e8 * e2 + num = (1.0 + + (1789.0 * e2) / 167.0 + + (5391.0 * e4) / 340.0 + + (2150.0 * e6) / 219.0 + - (1007.0 * e8) / 320.0 + + (2588.0 * e10) / 189.0) + den = ef**5 + return num / den + + +# ---------- Derived eccentricity functions ---------------------------------- + +def phi_e_rad(e: float) -> float: + ef = 1.0 - e * e + sqef = np.sqrt(ef) + e2 = e * e + pre = 192.0 * sqef / (985.0 * e2) + return pre * (sqef * phi_e(e) - phi_e_tilde(e)) + + +def psi_e_rad(e: float) -> float: + ef = 1.0 - e * e + sqef = np.sqrt(ef) + e2 = e * e + pf1 = 18816.0 / (55691.0 * e2 * sqef) + pf2 = 16382.0 * sqef / (55691.0 * e2) + b1 = sqef * (1.0 - (11.0 / 7.0) * e2) * phi_e(e) - (1.0 - (3.0 / 7.0) * e2) * phi_e_tilde(e) + b2 = sqef * psi_e(e) - psi_e_tilde(e) + return pf1 * b1 + pf2 * b2 + + +def zed_e_rad(e: float) -> float: + ef = 1.0 - e * e + sqef = np.sqrt(ef) + e2 = e * e + pf1 = 924.0 / (19067.0 * e2 * sqef) + pf2 = 12243.0 * sqef / (76268.0 * e2) + b1 = -ef * sqef * phi_e(e) + (1.0 - (5.0 / 11.0) * e2) * phi_e_tilde(e) + b2 = sqef * zed_e(e) - zed_e_tilde(e) + return pf1 * b1 + pf2 * b2 + + +def kappa_e_rad(e: float) -> float: + ef = 1.0 - e * e + sqef = np.sqrt(ef) + e2 = e * e + den = 769.0 / 96.0 - 3059665.0 * LOG2 / 700566.0 + 8190315.0 * LOG3 / 1868176.0 + pf = sqef / e2 + b1 = sqef * kappa_e(e) - kappa_e_tilde(e) + return pf * b1 / den + + +def f_e(e: float) -> float: + ef = 1.0 - e * e + e2 = e * e + e4 = e2 * e2 + e6 = e4 * e2 + e8 = e6 * e2 + denom = np.sqrt(ef) * ef**6 + num = 1.0 + 85.0 * e2 / 6.0 + 5171.0 * e4 / 192.0 + 1751.0 * e6 / 192.0 + 297.0 * e8 / 1024.0 + return num / denom + + +def capital_f_e(e: float) -> float: + ef = 1.0 - e * e + e2 = e * e + e4 = e2 * e2 + e6 = e4 * e2 + denom = np.sqrt(ef) * ef**5 + num = 1.0 + 2782.0 * e2 / 769.0 + 10721.0 * e4 / 6152.0 + 1719.0 * e6 / 24608.0 + return num / denom + + +def psi_n(e: float) -> float: + ef = 1.0 - e * e + e2 = e * e + pf1 = 1344.0 * (7.0 - 5.0 * e2) / (17599.0 * ef) + pf2 = 8191.0 / 17599.0 + return pf1 * phi_e(e) + pf2 * psi_e(e) + + +def zed_n(e: float) -> float: + pf1 = 583.0 / 567.0 + pf2 = 16.0 / 567.0 + return pf1 * zed_e(e) - pf2 * phi_e(e) + + +# ========= x_dot (dx/dt) terms ======================================== + +## --------- 0PN terms ------------ + +def x_dot_0pn(e: float, eta: float) -> float: + """Eq. (A26)""" + e0 = 2.0 * eta * 96.0 / 15.0 + if abs(e) < 1e-12: return e0 + e2 = e * e + e4 = e2 * e2 + ef = 1.0 - e2 + num = 2.0 * eta * (37.0 * e4 + 292.0 * e2 + 96.0) + den = 15.0 * ef**3 * np.sqrt(ef) + return (num / den) + +## --------- 1PN terms ------------ + +def x_dot_1pn(e: float, eta: float) -> float: + """Eq. (A27)""" + e0 = -eta * (2972.0 / 105.0 + 176.0 * eta / 5.0) + if abs(e) < 1e-12: return e0 + e2 = e * e + e4 = e2 * e2 + e6 = e4 * e2 + ef = 1.0 - e2 + t0 = 11888.0 + 14784.0 * eta + t2 = e2 * (-87720.0 + 159600.0 * eta) + t4 = e4 * (-171038.0 + 141708.0 * eta) + t6 = e6 * (-11717.0 + 8288.0 * eta) + den = 420.0 * ef**4 * np.sqrt(ef) + num = -eta * (t0 + t2 + t4 + t6) + return (num / den) + +## --------- 1.5PN terms ------------ + +def x_dot_1_5_pn(e: float, eta: float, m1: float, m2: float, + S1z: float, S2z: float) -> float: + """1.5PN spin-orbit term (Klein et al. arXiv:1801.08542, Eq. C1a)""" + + M = m1 + m2 + if abs(e) < 1e-12: + pre = 64.0 * eta / 5.0 + return pre * (-1.0/12.0 * (113*m1*m1*S1z + 113*m2*m2*S2z + 75*m1*m2*(S1z+S2z)) / M**2) + e2 = e * e + e4 = e2 * e2 + e6 = e4 * e2 + ef = 1.0 - e2 + ef5 = ef*ef*ef*ef*ef + M4 = M*M*M*M + + return -( + m1 * m2 * ( + (5424 + 27608*e2 + 16694*e4 + 585*e6) * m1*m1 * S1z + + (5424 + 27608*e2 + 16694*e4 + 585*e6) * m2*m2 * S2z + + 3 * (1200 + 6976*e2 + 4886*e4 + 207*e6) * m1*m2 * (S1z + S2z) + ) + ) / (45.0 * ef5 * M4) + + + +def x_dot_hereditary_1_5(e: float, eta: float, x: float) -> float: + """Eq. (A28)""" + pre = eta * x*x*x*x*x*x * np.sqrt(x) + if abs(e) > 1e-12: + return pre * 256.0 * np.pi * phi_e(e) / 5.0 + else: + return pre * 256.0 * np.pi / 5.0 + +## --------- 2PN terms ------------ + +def x_dot_2pn(e: float, eta: float, x: float) -> float: + """Eq. (A29)""" + + + eta2 = eta * eta + + # Small-e limit + e0_lim = eta * (68206.0 / 2835.0 + + 27322.0 * eta / 315.0 + + 1888.0 * eta2 / 45.0) + + if abs(e) < 1e-12: return e0_lim + + else: + e2 = e * e + e4 = e2 * e2 + e6 = e4 * e2 + e8 = e4 * e4 + + ef = 1.0 - e2 + sqrt_ef = np.sqrt(ef) + + den = 45360.0 * ef**5 * sqrt_ef + + e0_term = -360224.0 + 4514976.0 * eta + 1903104.0 * eta2 + e2_term = e2 * (-92846560.0 + 15464736.0 * eta + 61282032.0 * eta2) + e4_term = e4 * (783768.0 - 207204264.0 * eta + 166506060.0 * eta2) + e6_term = e6 * (83424402.0 - 123108426.0 * eta + 64828848.0 * eta2) + e8_term = e8 * (3523113.0 - 3259980.0 * eta + 1964256.0 * eta2) + + rt_e0_term = 1451520.0 - 580608.0 * eta + rt_e2_term = e2 * (64532160.0 - 25812864.0 * eta) + rt_e4_term = e4 * (66316320.0 - 26526528.0 * eta) + rt_e6_term = e6 * (2646000.0 - 1058400.0 * eta) + + num = eta * ( + e0_term + e2_term + e4_term + e6_term + e8_term + + sqrt_ef * (rt_e0_term + rt_e2_term + rt_e4_term + rt_e6_term) + ) + + return num / den + +def x_dot_2pn_SS(e: float, eta: float, m1: float, m2: float, + S1z: float, S2z: float) -> float: + """2PN spin-spin term (Quentin Henry et al., arXiv:2308.13606)""" + kappa1 = 1.0 + kappa2 = 1.0 + + M2 = (m1 + m2)**2 + pre = 64.0 * eta / 5.0 + if abs(e) < 1e-12: + return pre * ( + ((1 + 80*kappa1)*m1*m1*S1z*S1z + + 158*m1*m2*S1z*S2z + + (1 + 80*kappa2)*m2*m2*S2z*S2z) + ) / (16.0 * M2) + + else: + e2 = e * e + e4 = e2 * e2 + e6 = e4 * e2 + ef = 1.0 - e2 + ef_sqrt = np.sqrt(ef) + ef_55 = ef**4 * ef * ef_sqrt + M4 = M2**2 + return ( + m1 * m2 * ( + (48*(1+80*kappa1) + 3*e6*(9+236*kappa1) + + 8*e2*(57+2692*kappa1) + 2*e4*(207+7472*kappa1)) * m1*m1 * S1z*S1z + + 2*(3792 + 21080*e2 + 14530*e4 + 681*e6) * m1*m2 * S1z*S2z + + (48*(1+80*kappa2) + 3*e6*(9+236*kappa2) + + 8*e2*(57+2692*kappa2) + 2*e4*(207+7472*kappa2)) * m2*m2 * S2z*S2z + ) + ) / (60.0 * ef_55 * M4) + + +## --------- 2.5PN terms ------------ + +def x_dot_hereditary_2_5(e: float, eta: float, x: float) -> float: + """See Huerta et al.""" + + pre = eta * x**7 * np.sqrt(x) + + if abs(e) > 1e-12: + ef = 1.0 - e*e + ef2 = ef * ef + e2 = e * e + b1 = 256.0 * np.pi * phi_e(e) / ef + b2 = (-17599.0 * np.pi * psi_n(e) / 35.0 + - 2268.0 * eta * np.pi * zed_n(e) / 5.0 + - 788.0 * e2 * np.pi * phi_e_rad(e) / ef2) + return pre * (b1 + 2.0*b2/3.0) + else: + b1e0 = 256.0 * np.pi + b2e0 = -17599.0 * np.pi / 35.0 - 2268.0 * eta * np.pi / 5.0 + return pre * (b1e0 + 2.0*b2e0/3.0) + +def x_dot_2_5pn_SO(e: float, eta: float, m1: float, m2: float, + S1z: float, S2z: float) -> float: + """2.5PN spin-orbit (Quentin Henry et al., arXiv:2308.13606)""" + + M2 = (m1 + m2)**2 + M4 = M2 * M2 + pre = 64.0 * eta / 5.0 + + if abs(e) > 1e-12: + e2 = e * e + e4 = e2 * e2 + e6 = e4 * e2 + e8 = e6 * e2 + ef = 1.0 - e2 + ef_sqrt = np.sqrt(ef) + M6 = M4 * M2 + return ( + -0.0000992063492063492 * + m1 * m2 * ( + (4008832 + 808515*e8 + + 896*e4*(126373 + 26748*ef_sqrt) + + 16*e6*(2581907 + 30576*ef_sqrt) + + 384*e2*(111403 + 61264*ef_sqrt)) * m1**4 * S1z + + (4008832 + 808515*e8 + + 896*e4*(126373 + 26748*ef_sqrt) + + 16*e6*(2581907 + 30576*ef_sqrt) + + 384*e2*(111403 + 61264*ef_sqrt)) * m2**4 * S2z + + (1962112 + 1803645*e8 + + 32*e6*(2542879 + 26754*ef_sqrt) + + 896*e4*(202075 + 46809*ef_sqrt) + + 768*e2*(51189 + 53606*ef_sqrt)) * m1*m1*m2*m2 * (S1z + S2z) + + m1**3 * m2 * ( + (3029504 + 1807155*e8 + + 64*e6*(1314169 + 16562*ef_sqrt) + + 128*e2*(340325 + 398216*ef_sqrt) + + 112*e4*(1732273 + 463632*ef_sqrt)) * S1z + + 3 * (414208 + 281775*e8 + + 112*e6*(103639 + 728*ef_sqrt) + + 336*e4*(77531 + 11888*ef_sqrt) + + 128*e2*(55999 + 30632*ef_sqrt)) * S2z + ) + + m1 * m2**3 * ( + 3 * (414208 + 281775*e8 + + 112*e6*(103639 + 728*ef_sqrt) + + 336*e4*(77531 + 11888*ef_sqrt) + + 128*e2*(55999 + 30632*ef_sqrt)) * S1z + + (3029504 + 1807155*e8 + + 64*e6*(1314169 + 16562*ef_sqrt) + + 128*e2*(340325 + 398216*ef_sqrt) + + 112*e4*(1732273 + 463632*ef_sqrt)) * S2z + ) + ) / ((-1 + e2)**6 * M6) + ) + else: + return pre * ( + -0.000992063492063492 * + (31319*m1**4*S1z + 31319*m2**4*S2z + + 15329*m1*m1*m2*m2*(S1z + S2z) + + 4*m1**3*m2*(5917*S1z + 2427*S2z) + + 4*m1*m2**3*(2427*S1z + 5917*S2z)) / M4 + ) + +def x_dot_2_5pn_SF(e: float, eta: float, S1z: float) -> float: + """ + This piece comes from the horizon flux. + """ + pre_factor = 64. * eta / 5. + + # Pre-calculating the e=0 case + # Note: s1z**3 is used for readability; s1z * s1z * s1z is slightly faster + x_2_5pn_SF_e0 = pre_factor * (-504. * S1z - 1512. * S1z**3) / 2016. + + # Logic preserved: currently returns the same value for all e + if np.abs(e) > 1e-12: + x_2_5pn_SF = x_2_5pn_SF_e0 + else: + x_2_5pn_SF = x_2_5pn_SF_e0 + + return x_2_5pn_SF + +## --------- 3PN terms ------------ + +def x_dot_hereditary_3(e: float, eta: float, x: float) -> float: + """See Huerta et al.""" + pi2 = np.pi * np.pi + pre = eta * x**8 + + if abs(e) > 1e-12: + x_3_term = (64.0 * + (-116761.0 * kappa_e(e) + + (19600.0*pi2 - 59920.0*EULER_GAMMA - 59920.0*LOG4 - 89880.0*np.log(x)) * f_e(e)) + ) / 18375.0 + return pre * x_3_term + else: + x_3_term_e0 = (64.0 * + (-59920.0*EULER_GAMMA - 116761.0 + 19600.0*pi2 - 59920.0*LOG4 - 89880.0*np.log(x)) + ) / 18375.0 + return pre * x_3_term_e0 + +def x_dot_3pn(e: float, eta: float, x: float) -> float: + """3PN term (Huerta et al.)""" + + + eta2 = eta * eta + pi2 = np.pi * np.pi + + # Small-e limit + e0_lim = eta * ( + 426247111.0 / 222750.0 + - 56198689.0 * eta / 17010.0 + + 541.0 * eta2 / 70.0 + - 2242.0 * eta2 * eta / 81.0 + + 1804.0 * eta * pi2 / 15.0 + + 109568.0 * np.log(x) / 525.0 + ) + + if abs(e)<1e-12: return e0_lim + + e2 = e * e + e4 = e2 * e2 + e6 = e4 * e2 + e8 = e4 * e4 + e10 = e8 * e2 + + ef = 1.0 - e2 + + sqrt_ef = np.sqrt(ef) + + den = 598752000.0 * ef**7 + + bit_1 = -25.0 * e10 * ( + 81.0 * (99269280.0 - 33332681.0 * sqrt_ef) + + 176.0 * eta * ( + 9.0 * (-5132796.0 + 1874543.0 * sqrt_ef) + + 4.0 * eta * ( + 3582684.0 - 2962791.0 * sqrt_ef + + 2320640.0 * sqrt_ef * eta + ) + ) + ) + + bit_2 = -128.0 * ( + 3950984268.0 - 12902173599.0 * sqrt_ef + + 275.0 * eta * ( + -1066392.0 + 57265081.0 * sqrt_ef + + 81.0 * (-17696.0 + 16073.0 * sqrt_ef) * eta + + 470820.0 * sqrt_ef * eta2 + - 46494.0 * (-1.0 + 45.0 * sqrt_ef) * pi2 + ) + ) + + bit_3 = -32.0 * e2 * ( + -18.0 * (2603019496.0 + 19904214811.0 * sqrt_ef) + + 55.0 * eta * ( + 8147179440.0 - 5387647438.0 * sqrt_ef + + 270.0 * (-6909392.0 + 9657701.0 * sqrt_ef) * eta + + 901169500.0 * sqrt_ef * eta2 + - 193725.0 * (229.0 + 237.0 * sqrt_ef) * pi2 + ) + ) + + bit_4 = -8.0 * e4 * ( + -6.0 * (312191560692.0 + 8654689873.0 * sqrt_ef) + + 55.0 * eta * ( + 42004763280.0 - 88628306866.0 * sqrt_ef + - 1350.0 * (8601376.0 + 1306589.0 * sqrt_ef) * eta + + 23638717900.0 * sqrt_ef * eta2 + + 891135.0 * (-2.0 + 627.0 * sqrt_ef) * pi2 + ) + ) + + bit_5 = -2.0 * e8 * ( + 4351589277552.0 - 1595548875627.0 * sqrt_ef + + 550.0 * eta * ( + 432.0 * (6368264.0 - 10627167.0 * sqrt_ef) * eta + + 2201124800.0 * sqrt_ef * eta2 + + 9.0 * ( + 8.0 * (-134041982.0 + 65136045.0 * sqrt_ef) + + 861.0 * (14.0 + 891.0 * sqrt_ef) * pi2 + ) + ) + ) + + bit_6 = -12.0 * e6 * ( + 589550775792.0 - 6005081022.0 * sqrt_ef + + 55.0 * eta * ( + 90.0 * (90130656.0 - 311841025.0 * sqrt_ef) * eta + + 17925404000.0 * sqrt_ef * eta2 + + 3.0 * ( + -2.0 * (5546517920.0 + 383583403.0 * sqrt_ef) + + 4305.0 * (9046.0 + 19113.0 * sqrt_ef) * pi2 + ) + ) + ) + + bit_7 = -40677120.0 * sqrt_ef * ( + 3072.0 + 43520.0 * e2 + 82736.0 * e4 + + 28016.0 * e6 + 891.0 * e8 + ) * ( + LOG2 - np.log((1.0 / ef) + (1.0 / sqrt_ef)) - np.log(x) + ) + + num = eta * (bit_1 + bit_2 + bit_3 + bit_4 + bit_5 + bit_6 + bit_7) + + return num / den + +def x_dot_3pn_SO(e: float, eta: float, m1: float, m2: float, + S1z: float, S2z: float) -> float: + """3PN spin-orbit (Quentin Henry et al., arXiv:2308.13606)""" + + M2 = (m1 + m2)**2 + M4 = M2 * M2 + pre = 64.0 * eta / 5.0 + + if abs(e) > 1e-12: + e2 = e * e + e4 = e2 * e2 + e6 = e4 * e2 + e8 = e6 * e2 + ef = 1.0 - e2 + ef_sqrt = np.sqrt(ef) + ef_65 = ef**6 * ef_sqrt + return ( + -9.645061728395062e-6 * + m1 * m2 * np.pi * ( + (49766400 + 528887808*e2 + 814424832*e4 + 213166272*e6 + 3911917*e8) * m1*m1*S1z + + (49766400 + 528887808*e2 + 814424832*e4 + 213166272*e6 + 3911917*e8) * m2*m2*S2z + + 3 * (11132928 + 133936128*e2 + 232455168*e4 + 67616624*e6 + 1479919*e8) * m1*m2*(S1z+S2z) + ) / (ef_65 * M4) + ) + else: + return pre * ( + -1.0/6.0 * np.pi * + (225*m1*m1*S1z + 225*m2*m2*S2z + 151*m1*m2*(S1z+S2z)) / M2 + ) + +def x_dot_3pn_SS(e: float, eta: float, + m1: float, m2: float, + S1z: float, S2z: float) -> float: + """3PN spin-spin. Computed using inputs from energy and + flux expressions. See Blanchet liv. rev. + + Bohe et al. arXiv: 1501.01529 + Marsat et al. arXiv: 1411.4118""" + + kappa1 = 1.0 + kappa2 = 1.0 + + pre_factor = 64.0 * eta / 5.0 + + M = m1 + m2 + M2 = M * M + M4 = M2 * M2 + + # --- Small-e fallback (better with tolerance) --- + if abs(e) < 1e-12: + return pre_factor * ( + ( + (36995 + 16358 * kappa1) * m1**4 * S1z * S1z + + (36995 + 16358 * kappa2) * m2**4 * S2z * S2z + + m1**3 * m2 * S1z * + ((34377 + 5864 * kappa1) * S1z + 59554 * S2z) + + m1 * m2**3 * S2z * + (59554 * S1z + (34377 + 5864 * kappa2) * S2z) + + 3 * m1**2 * m2**2 * + ((1841 + 1318 * kappa1) * S1z * S1z + + 35498 * S1z * S2z + + (1841 + 1318 * kappa2) * S2z * S2z) + ) / (672.0 * M4) + ) + e2 = e * e + e4 = e2 * e2 + e6 = e4 * e2 + e8 = e6 * e2 + + e_fact = 1.0 - e2 + e_fact_sqrt = np.sqrt(e_fact) + + e_fact_65 = (e_fact**6) * e_fact_sqrt + M6 = M4 * M2 + + # --- Main expression --- + term1 = ( + 27 * e8 * (15141 + 109852 * kappa1) + + 8 * e4 * ( + 22676605 + 3044160 * e_fact_sqrt + + 32290722 * kappa1 + + 5327280 * e_fact_sqrt * kappa1 + ) + + 84 * e6 * ( + 437079 + 448 * e_fact_sqrt + + 14 * (89253 + 56 * e_fact_sqrt) * kappa1 + ) + - 576 * ( + -37891 + 896 * e_fact_sqrt + + 2 * (-8963 + 784 * e_fact_sqrt) * kappa1 + ) + + 224 * e2 * ( + 633619 + 107616 * e_fact_sqrt + + 42 * (10029 + 4484 * e_fact_sqrt) * kappa1 + ) + ) + + term2 = term1 # symmetric except kappa → kappa2 + # replace kappa1 with kappa2 + term2 = term2 + (kappa2 - kappa1) * ( + 27 * e8 * 109852 + + 8 * e4 * (32290722 + 5327280 * e_fact_sqrt) + + 84 * e6 * (14 * (89253 + 56 * e_fact_sqrt)) + - 576 * (2 * (-8963 + 784 * e_fact_sqrt)) + + 224 * e2 * (42 * (10029 + 4484 * e_fact_sqrt)) + ) + + part1 = term1 * m1**4 * S1z * S1z + part2 = term2 * m2**4 * S2z * S2z + + # Mixed terms (kept explicit for clarity) + mix_common = ( + 521613 * e8 + + 96 * (31457 - 1680 * e_fact_sqrt) + + 294 * e6 * (72619 + 40 * e_fact_sqrt) + + 112 * e2 * (247661 + 67260 * e_fact_sqrt) + + e4 * (60534556 + 7610400 * e_fact_sqrt) + ) + + part3 = 6 * m1**3 * m2 * S1z * ( + ( + 3 * e8 * (47103 + 202177 * kappa1) + - 96 * (-34713 + 336 * e_fact_sqrt - 8440 * kappa1 + + 2576 * e_fact_sqrt * kappa1) + + 112 * e2 * ( + 278677 + 13452 * e_fact_sqrt + + 53216 * kappa1 + + 103132 * e_fact_sqrt * kappa1 + ) + + 14 * e6 * ( + 772539 + 168 * e_fact_sqrt + + 4 * (369781 + 322 * e_fact_sqrt) * kappa1 + ) + + 4 * e4 * ( + 7 * (1655621 + 54360 * e_fact_sqrt) + + 460 * (22397 + 6342 * e_fact_sqrt) * kappa1 + ) + ) * S1z + + 2 * mix_common * S2z + ) + + part4 = 6 * m1 * m2**3 * S2z * ( + 2 * mix_common * S1z + + ( + 3 * e8 * (47103 + 202177 * kappa2) + - 96 * (-34713 + 336 * e_fact_sqrt - 8440 * kappa2 + + 2576 * e_fact_sqrt * kappa2) + + 112 * e2 * ( + 278677 + 13452 * e_fact_sqrt + + 53216 * kappa2 + + 103132 * e_fact_sqrt * kappa2 + ) + + 14 * e6 * ( + 772539 + 168 * e_fact_sqrt + + 4 * (369781 + 322 * e_fact_sqrt) * kappa2 + ) + + 4 * e4 * ( + 7 * (1655621 + 54360 * e_fact_sqrt) + + 460 * (22397 + 6342 * e_fact_sqrt) * kappa2 + ) + ) * S2z + ) + + part5 = m1**2 * m2**2 * ( + 9 * ( + -86016 * e_fact_sqrt * kappa1 + + 192 * (1729 + 112 * e_fact_sqrt + 1766 * kappa1) + + e8 * (58359 + 325902 * kappa1) + + 28 * e6 * ( + 121449 - 56 * e_fact_sqrt + + (388890 + 224 * e_fact_sqrt) * kappa1 + ) + + 224 * e2 * ( + 31367 - 4484 * e_fact_sqrt + + 2 * (12189 + 8968 * e_fact_sqrt) * kappa1 + ) + + 8 * e4 * ( + 1541617 - 126840 * e_fact_sqrt + + 6 * (482187 + 84560 * e_fact_sqrt) * kappa1 + ) + ) * S1z * S1z + + 8 * ( + 8135280 + 1021401 * e8 - 467712 * e_fact_sqrt + + 147 * e6 * (305971 + 232 * e_fact_sqrt) + + 56 * e2 * (1084885 + 390108 * e_fact_sqrt) + + 2 * e4 * (65163991 + 11035080 * e_fact_sqrt) + ) * S1z * S2z + + 9 * ( + -86016 * e_fact_sqrt * kappa2 + + 192 * (1729 + 112 * e_fact_sqrt + 1766 * kappa2) + + e8 * (58359 + 325902 * kappa2) + + 28 * e6 * ( + 121449 - 56 * e_fact_sqrt + + (388890 + 224 * e_fact_sqrt) * kappa2 + ) + + 224 * e2 * ( + 31367 - 4484 * e_fact_sqrt + + 2 * (12189 + 8968 * e_fact_sqrt) * kappa2 + ) + + 8 * e4 * ( + 1541617 - 126840 * e_fact_sqrt + + 6 * (482187 + 84560 * e_fact_sqrt) * kappa2 + ) + ) * S2z * S2z + ) + + numerator = m1 * m2 * (part1 + part2 + part3 + part4 + part5) + denominator = 30240.0 * e_fact_65 * M6 + + return numerator / denominator + +## --------- 3.5PN terms ------------ + +def x_dot_3_5pnSO(e: float, eta: float, m1: float, m2: float, + S1z: float, S2z: float) -> float: + """3.5PN spin-orbit""" + pre = 64.0 * eta / 5.0 + M = m1 + m2 + val_e0 = pre * ( + -0.00005511463844797178 * + (3127800*m1**6*S1z + 3127800*m2**6*S2z + + 4914306*m1**3*m2**3*(S1z+S2z) + + m1**5*m2*(6542338*S1z + 1195759*S2z) + + m1**4*m2**2*(6694579*S1z + 3284422*S2z) + + m1*m2**5*(1195759*S1z + 6542338*S2z) + + m1**2*m2**4*(3284422*S1z + 6694579*S2z)) + / M**6 + ) + val_e = val_e0 #placeholder for e-dependent expression + return val_e if abs(e) > 1e-12 else val_e0 + +def x_dot_3_5pn_SS(e: float, eta: float, m1: float, m2: float, + S1z: float, S2z: float) -> float: + """3.5PN spin-spin""" + kappa1 = 1.0 + kappa2 = 1.0 + pre = 64.0 * eta / 5.0 + M2 = (m1 + m2)**2 + val_e0 = pre * ( + np.pi * ((1 + 160*kappa1)*m1*m1*S1z*S1z + + 318*m1*m2*S1z*S2z + + (1 + 160*kappa2)*m2*m2*S2z*S2z) + ) / (8.0 * M2) + val_e = val_e0 #placeholder for e-dependent expression + return val_e if abs(e) > 1e-12 else val_e0 + +def x_dot_3_5pn_cubicSpin(e: float, eta: float, m1: float, m2: float, + S1z: float, S2z: float) -> float: + """3.5PN cubic-in-spin""" + kappa1 = 1.0; kappa2 = 1.0 + lambda1 = 1.0; lambda2 = 1.0 + pre = 64.0 * eta / 5.0 + M = m1 + m2 + val_e0 = pre * ( + -0.020833333333333332 * + (2*(15+1016*kappa1+528*lambda1) * m1**4 * S1z**3 + + 2*(15+1016*kappa2+528*lambda2) * m2**4 * S2z**3 + + m1**3*m2 * S1z*S1z * ((21+536*kappa1+1056*lambda1)*S1z + (4033+3712*kappa1)*S2z) + + 12*m1*m1*m2*m2 * S1z*S2z * ((89+434*kappa1)*S1z + (89+434*kappa2)*S2z) + + m1*m2**3 * S2z*S2z * ((4033+3712*kappa2)*S1z + (21+536*kappa2+1056*lambda2)*S2z)) + / M**4 + ) + val_e = val_e0 #placeholder for e-dependent expression + return val_e if abs(e) > 1e-12 else val_e0 + +def x_dot_3_5_pn(e: float, eta: float) -> float: + """3.5PN non-spinning""" + val_e0 = (64.0 * eta * np.pi * + (-4415.0/4032.0 + 358675.0*eta/6048.0 + 91495.0*eta*eta/1512.0) + / 5.0) + val_e = val_e0 #placeholder for e-dependent expression + return val_e if abs(e) > 1e-12 else val_e0 + +def x_dot_3_5pn_SF(e: float, eta: float, S1z: float) -> float: + """ + This piece comes from the horizon flux (3.5PN term). + """ + pre_factor: float = 64. * eta / 5. + + x_3_5pn_SF_e0: float = pre_factor * ((-16632. * S1z - 38556. * S1z**3) / 12096.) + + if np.abs(e) > 1e-12: + x_3_5pn_SF = x_3_5pn_SF_e0 + else: + x_3_5pn_SF = x_3_5pn_SF_e0 + + return x_3_5pn_SF + +## --------- 4PN and 4.5PN terms ------------ + +def x_dot_4pn(e: float, eta: float, x: float) -> float: + """4PN non-spinning (uses e=0 limit)""" + euler = EULER_GAMMA + pre = 64.0 * eta / 5.0 + eta2 = eta * eta + val_e0 = pre * ( + (3959271176713 - 20643291551545*eta) / 2.54270016e10 + + eta2 * (2016887396 + 21*eta*(-1909807 + 49518*eta)) / 1.306368e6 - + 896*eta*euler/3.0 + + (124741 + 734620*eta)*euler/4410.0 - + 361*np.pi**2/126.0 - + eta*(1472377 + 928158*eta)*np.pi**2/16128.0 + + 127751*LOG2/1470.0 - 47385*LOG3/1568.0 + + eta*(-850042*LOG2/2205.0 + 47385*LOG3/392.0) + + (124741 - 582500*eta)*np.log(x)/8820.0 + ) + val_e = val_e0 #placeholder for e-dependent expression + return val_e if abs(e) > 1e-12 else val_e0 + +def x_dot_4pnSO(e: float, eta: float, m1: float, m2: float, + S1z: float, S2z: float) -> float: + """4PN spin-orbit (uses e=0 limit)""" + pre = 64.0 * eta / 5.0 + M = m1 + m2 + val_e0 = pre * ( + -0.000496031746031746 * + np.pi * (307708*m1**4*S1z + 307708*m2**4*S2z + + 93121*m1*m1*m2*m2*(S1z+S2z) + + m1*m2**3*(119880*S1z + 75131*S2z) + + m1**3*m2*(75131*S1z + 119880*S2z)) / M**4 + ) + val_e = val_e0 #placeholder for e-dependent expression + return val_e if abs(e) > 1e-12 else val_e0 + +def x_dot_4pn_SF(e: float, eta: float, S1z: float) -> float: + """ + 4PN Self-Force term from the horizon flux. + Note: Python uses 'j' for the imaginary unit. Complex(0, 2) in C is 2j in Python. + """ + pre_factor = 64. * eta / 5. + S1z2 = S1z * S1z + S1z3 = S1z2 * S1z + S1z4 = S1z3 * S1z + # Common denominator used in the PolyGamma arguments + denom = np.sqrt(1.0 - S1z2) + + # gsl_sf_psi_n(0, z) is the digamma function + # Complex(0, 2) * S1z is 2j * S1z in Python + PolyGammaFunc01 = polygamma(0, 3.0 - (2j * S1z) / denom) + PolyGammaFunc02 = polygamma(0, 3.0 + (2j * S1z) / denom) + + inner_term = ( + -1 + 15 * S1z2 + 42 * S1z4 + + denom + + 13 * S1z2 * denom + + 6 * S1z4 * denom + + 2j * PolyGammaFunc01 * (S1z + 3 * S1z3) - + 2j * PolyGammaFunc02 * (S1z + 3 * S1z3) + ) + + # Calculate the e=0 case + x_4pn_SF_e0_complex = pre_factor * (inner_term / 2.0) + + # If the original C code returns REAL8, it likely implicitly casts to real or + # the imaginary parts cancel out. We use .real to ensure a float return. + x_4pn_SF_e0: float = float(x_4pn_SF_e0_complex.real) + + # Maintaining the structure of your original C logic + if np.abs(e) > 1e-12: + x_4pn_SF = x_4pn_SF_e0 + else: + x_4pn_SF = x_4pn_SF_e0 + + return x_4pn_SF + +def x_dot_4pnSS(e: float, eta: float, m1: float, m2: float, + S1z: float, S2z: float) -> float: + """4PN spin-spin (uses e=0 limit)""" + kappa1 = 1.0; kappa2 = 1.0 + pre = 64.0 * eta / 5.0 + M = m1 + m2 + val_e0 = pre * ( + ((41400957 + 10676336*kappa1)*m1**6*S1z*S1z + + (41400957 + 10676336*kappa2)*m2**6*S2z*S2z + + m1**5*m2*S1z*(5*(16862889+4890568*kappa1)*S1z + 53648974*S2z) + + m1*m2**5*S2z*(53648974*S1z + 5*(16862889+4890568*kappa2)*S2z) + + m1**4*m2**2*((82037757+30833184*kappa1)*S1z*S1z + 168293278*S1z*S2z + (8950581+4778168*kappa2)*S2z*S2z) + + m1**2*m2**4*((8950581+4778168*kappa1)*S1z*S1z + 168293278*S1z*S2z + 3*(27345919+10277728*kappa2)*S2z*S2z) + + m1**3*m2**3*((43557309+18675776*kappa1)*S1z*S1z + 226571670*S1z*S2z + (43557309+18675776*kappa2)*S2z*S2z)) + / (72576.0 * M**6) + ) + val_e = val_e0 #placeholder for e-dependent expression + return val_e if abs(e) > 1e-12 else val_e0 + +def x_dot_4_5_pn(e: float, eta: float, x: float) -> float: + """4.5PN non-spinning (uses e=0 limit)""" + euler = EULER_GAMMA + pre = 64.0 * eta / 5.0 + val_e0 = pre * ( + 451*eta*np.pi**3/12.0 - + np.pi * (700*eta*(3098001198 + eta*(525268513 + 289286988*eta)) + + 145786798080*euler + + 3*(-343801320119 + 97191198720*LOG2)) / 2.2353408e9 - + 3424*np.pi*np.log(x)/105.0 + ) + val_e = val_e0 #placeholder for e-dependent expression + return val_e if abs(e) > 1e-12 else val_e0 + + +# Self-Force (SF) higher-order terms in addition to x_dot + +def dxdt_4pn(x: float, eta: float) -> float: + """4PN contribution to dx/dt""" + + x2 = x * x + x3 = x2 * x + x4 = x3 * x + x5 = x4 * x + + eta2 = eta * eta + eta3 = eta2 * eta + eta4 = eta2 * eta2 + + pi2 = np.pi * np.pi + euler = EULER_GAMMA + + pre_fact = 64.0 * x5 * eta / 5.0 + + bit_4pn = ( + x4 * ( + -( + (-891.0 + 477.0 * eta - 11.0 * eta2) + * (-4.928461199294532 + (9271.0 * eta) / 504.0 + (65.0 * eta2) / 18.0) + ) / 36.0 + + + (5.0 * (-3.7113095238095237 - (35.0 * eta) / 12.0) + * (19683.0 - 66375.0 * eta + 1188.0 * eta2 + 19.0 * eta3 + + 2214.0 * eta * pi2) + ) / 648.0 + + - (-3.0 - eta / 3.0) * ( + 95.10839000836025 + - (134543.0 * eta) / 7776.0 + - (94403.0 * eta2) / 3024.0 + - (775.0 * eta3) / 324.0 + - (1712.0 * euler) / 105.0 + + (16.0 * pi2) / 3.0 + + (41.0 * eta * pi2) / 48.0 + - (3424.0 * LOG2) / 105.0 + - (856.0 * np.log(x)) / 105.0 + ) + + + 2.0 * ( + -101.65745990813167 + + (232597.0 * euler) / 4410.0 + - (1369.0 * pi2) / 126.0 + + (39931.0 * LOG2) / 294.0 + - (47385.0 * LOG3) / 1568.0 + + (232597.0 * np.log(x)) / 8820.0 + ) + + + 0.071630658436214 * ( + 12774.514362657092 + - 39782.929590804066 * eta + + 346.61235705608044 * eta2 + + 2.068222621184919 * eta3 + + eta4 + - 4169.536804308797 * eta * np.log(x) + ) + ) + ) / 2.0 + + return pre_fact * bit_4pn + +########################################################################## +# dx_dt_4_5pn, dx_dt_5pn, dx_dt_5_5pn, dx_dt_6pn are not implemented here, +# as they are not needed for ESIGMAHMv1. +# (Implemented in the original C file, line number 2981 - 3468) +########################################################################## + + + +# ================ e_dot (de/dt) terms ================================== + +## ---------- 0PN ------------- + +def e_dot_0pn(e: float, eta: float) -> float: + """Eq. (A31)""" + e2 = e * e + ef = 1.0 - e2 + if abs(e) < 1e-12: + return 0.0 + num = -e * eta * (121.0*e2 + 304.0) + den = 15.0 * ef**2 * np.sqrt(ef) + return num / den + +## ---------- 1PN ------------- + +def e_dot_1pn(e: float, eta: float) -> float: + """Eq. (A32)""" + if abs(e) < 1e-12: + return 0.0 + e2 = e * e + e4 = e2 * e2 + ef = 1.0 - e2 + t0 = 8.0 * (28588.0*eta + 8451.0) + t2 = 12.0 * (54271.0*eta - 59834.0) * e2 + t4 = (93184.0*eta - 125361.0) * e4 + pre = e * eta / (2520.0 * ef**3 * np.sqrt(ef)) + return pre * (t0 + t2 + t4) + +## ---------- 1.5PN ------------- + +def e_dot_1_5pn_SO(e: float, m1: float, m2: float, + S1z: float, S2z: float) -> float: + """1.5PN SO eccentricity (Klein et al. arXiv:1801.08542, Eq. C1b)""" + if abs(e) < 1e-12: + return 0.0 + e2 = e * e + e4 = e2 * e2 + ef = 1.0 - e2 + ef4 = ef*ef*ef*ef + e_pre = e / ef4 + pre = e_pre * ((m1*m2) / (90.0 *(m1 + m2) * (m1 + m2) * (m1 + m2) * (m1 + m2))) + return pre * ( + (19688 + 28256*e2 + 2367*e4)*m1*m1*S1z + + (19688 + 28256*e2 + 2367*e4)*m2*m2*S2z + + 3*(4344 + 8090*e2 + 835*e4)*m1*m2*(S1z+S2z)) + +def e_rad_hereditary_1_5(e: float, eta: float, x: float) -> float: + if abs(e) < 1e-12: + return 0.0 + pre = 32.0 * eta * e * x**4 * x*np.sqrt(x) / 5.0 + return pre * (-985.0 * np.pi * phi_e_rad(e) / 48.0) + +## ---------- 2PN ------------- + +def e_dot_2pn(e: float, eta: float) -> float: + + if abs(e) < 1e-12: + return 0.0 + + eta_pow_2 = eta * eta + e_pow_2 = e * e + e_pow_4 = e_pow_2 * e_pow_2 + e_pow_6 = e_pow_4 * e_pow_2 + e_fact = 1.0 - e_pow_2 + + zero_term = ( + -952397.0 / 1890.0 + + 5937.0 * eta / 14.0 + + 752.0 * eta_pow_2 / 5.0 + ) + + e_2_term = e_pow_2 * ( + -3113989.0 / 2520.0 + - 388419.0 * eta / 280.0 + + 64433.0 * eta_pow_2 / 40.0 + ) + + e_4_term = e_pow_4 * ( + 4656611.0 / 3024.0 + - 13057267.0 * eta / 5040.0 + + 127411.0 * eta_pow_2 / 90.0 + ) + + e_6_term = e_pow_6 * ( + 420727.0 / 3360.0 + - 362071.0 * eta / 2520.0 + + 821.0 * eta_pow_2 / 9.0 + ) + + zero_rt = 1336.0 / 3.0 - 2672.0 * eta / 15.0 + + e_2_rt = e_pow_2 * (2321.0 / 2.0 - 2321.0 * eta / 5.0) + + e_4_rt = e_pow_4 * (565.0 / 6.0 - 113.0 * eta / 3.0) + + + pre_factor = -e * eta / (e_fact**4 * np.sqrt(e_fact)) + e_2_pn = pre_factor * ( + zero_term + + e_2_term + + e_4_term + + e_6_term + + np.sqrt(e_fact) * (zero_rt + e_2_rt + e_4_rt) + ) + + return e_2_pn + + +def e_dot_2pn_SS(e: float, m1: float, m2: float, + S1z: float, S2z: float) -> float: + """2PN SS eccentricity (Quentin Henry et al., arXiv:2308.13606v1)""" + if abs(e) < 1e-12: + return 0.0 + kappa1 = 1.0; kappa2 = 1.0 + e2 = e * e + e4 = e2 * e2 + ef = 1.0 - e2 + ef_45 = ef**4 * np.sqrt(ef) + M2 = (m1+m2)**2 + M4 = M2*M2 + return ( + -0.008333333333333333 * + e * m1 * m2 * ( + (45*(8+12*e2+e4) + 4*(3752+5950*e2+555*e4)*kappa1)*m1*m1*S1z*S1z + + 2*(14648+23260*e2+2175*e4)*m1*m2*S1z*S2z + + (45*(8+12*e2+e4) + 4*(3752+5950*e2+555*e4)*kappa2)*m2*m2*S2z*S2z + ) / (ef_45 * M4) + ) + +## ---------- 2.5PN ------------- + +def e_dot_2_5pn_SO(e: float, m1: float, m2: float, S1z: float, S2z: float) -> float: + if abs(e) < 1e-12: + return 0.0 + + e_2 = e * e + e_4 = e_2 * e_2 + e_6 = e_4 * e_2 + + e_fact = 1.0 - e_2 + e_fact_2 = e_fact * e_fact + e_fact_sqrt = np.sqrt(e_fact) + e_fact_55 = e_fact_2 * e_fact_2 * e_fact * e_fact_sqrt + + M = m1 + m2 + M_fact_2 = M * M + M_fact_4 = M_fact_2 * M_fact_2 + M_fact_6 = M_fact_4 * M_fact_2 + + common_factor = e * m1 * m2 + + term_A = ( + 3 * ( + 192 * (82880 + 36083 * e_fact_sqrt) + + 168 * e_4 * (-211648 + 487675 * e_fact_sqrt) + + 3 * e_6 * (-560896 + 1037433 * e_fact_sqrt) + + 16 * e_2 * (1332912 + 6885449 * e_fact_sqrt) + ) + ) + + term_B = ( + 3 * ( + 27847680 - 9476416 * e_fact_sqrt + + 7 * e_6 * (-420672 + 929363 * e_fact_sqrt) + + 24 * e_4 * (-2592688 + 6508535 * e_fact_sqrt) + + 16 * e_2 * (2332596 + 9517267 * e_fact_sqrt) + ) + ) + + term_C1 = ( + 128 * (808080 - 259453 * e_fact_sqrt) + + 3 * e_6 * (-3645824 + 6571731 * e_fact_sqrt) + + 48 * e_2 * (2887976 + 10533923 * e_fact_sqrt) + + 32 * e_4 * (-7222488 + 15232045 * e_fact_sqrt) + ) + + term_C2 = ( + 9 + * ( + 206464 * e_fact_sqrt + + 21830032 * e_2 * e_fact_sqrt + + 22413824 * e_4 * e_fact_sqrt + + 1071519 * e_6 * e_fact_sqrt + - 896 * (1 - e) * (1 + e) * (2960 + 6927 * e_2 + 313 * e_4) + ) + ) + + term_D1 = ( + 9 + * ( + 128 * (20720 + 1613 * e_fact_sqrt) + + 256 * e_4 * (-23149 + 87554 * e_fact_sqrt) + + 112 * e_2 * (31736 + 194911 * e_fact_sqrt) + + e_6 * (-280448 + 1071519 * e_fact_sqrt) + ) + ) + + term_D2 = term_C1 + + numerator = ( + common_factor + * ( + term_A * (m1**4) * S1z + + term_A * (m2**4) * S2z + + term_B * (m1**2) * (m2**2) * (S1z + S2z) + + (m1**3) * m2 * (term_C1 * S1z + term_C2 * S2z) + + m1 * (m2**3) * (term_D1 * S1z + term_D2 * S2z) + ) + ) + + result = numerator / (60480.0 * e_fact_55 * M_fact_6) + + return result + +def e_rad_hereditary_2_5(e: float, eta: float, x: float) -> float: + if abs(e) < 1e-12: + return 0.0 + pre = 32.0 * eta * e * x**4 * x**2 * np.sqrt(x) / 5.0 + a2 = (55691.0 * psi_e_rad(e) / 1344.0 + 19067.0 * eta * zed_e_rad(e) / 126.0) * np.pi + return pre * a2 + +## --------- 3PN ------------- + +def e_rad_hereditary_3(e: float, eta: float, x: float) -> float: + if abs(e) < 1e-12: + return 0.0 + pre = 32.0 * eta * e * x**7 / 5.0 + x32 = x * np.sqrt(x) + a3 = (89789209.0/352800.0 - 87419.0*LOG2/630.0 + 78003.0*LOG3/560.0) * kappa_e_rad(e) + a4 = (-769.0/96.0) * (16.0*np.pi**2/3.0 - 1712.0*EULER_GAMMA/105.0 - 1712.0*np.log(4.0*x32)/105.0) * capital_f_e(e) + return pre * (a3 + a4) + +def e_dot_3pn(e: float, eta: float, x: float) -> float: + if abs(e) < 1e-12: + return 0.0 + + eta_pow_2 = eta * eta + eta_pow_3 = eta_pow_2 * eta + + e_pow_2 = e * e + e_pow_4 = e_pow_2 * e_pow_2 + e_pow_6 = e_pow_4 * e_pow_2 + e_pow_8 = e_pow_6 * e_pow_2 + + e_fact = 1.0 - e_pow_2 + sqrt_e_fact = np.sqrt(e_fact) + + pi_pow_2 = np.pi * np.pi + + zero_term = ( + 7742634967.0 / 891000.0 + + (43386337.0 / 113400.0 + 1017.0 * pi_pow_2 / 10.0) * eta + - 4148897.0 * eta_pow_2 / 2520.0 + - 61001.0 * eta_pow_3 / 486.0 + ) + + e_2_term = e_pow_2 * ( + 6556829759.0 / 891000.0 + + (770214901.0 / 25200.0 - 15727.0 * pi_pow_2 / 192.0) * eta + - 80915371.0 * eta_pow_2 / 15120.0 + - 86910509.0 * eta_pow_3 / 19440.0 + ) + + e_4_term = e_pow_4 * ( + -17072216761.0 / 2376000.0 + + (8799500893.0 / 907200.0 - 295559.0 * pi_pow_2 / 1920.0) * eta + + 351962207.0 * eta_pow_2 / 20160.0 + - 2223241.0 * eta_pow_3 / 180.0 + ) + + e_6_term = e_pow_6 * ( + 17657772379.0 / 3696000.0 + + (-91818931.0 / 10080.0 - 6519.0 * pi_pow_2 / 640.0) * eta + + 2495471.0 * eta_pow_2 / 252.0 + - 11792069.0 * eta_pow_3 / 2430.0 + ) + + e_8_term = e_pow_8 * ( + 302322169.0 / 1774080.0 + - 1921387.0 * eta / 10080.0 + + 41179.0 * eta_pow_2 / 216.0 + - 193396.0 * eta_pow_3 / 1215.0 + ) + + zero_rt = ( + -22713049.0 / 15750.0 + + (-5526991.0 / 945.0 + 8323.0 * pi_pow_2 / 180.0) * eta + + 54332.0 * eta_pow_2 / 45.0 + ) + + e_2_rt = e_pow_2 * ( + 89395687.0 / 7875.0 + + (-38295557.0 / 1260.0 + 94177.0 * pi_pow_2 / 960.0) * eta + + 681989.0 * eta_pow_2 / 90.0 + ) + + e_4_rt = e_pow_4 * ( + 5321445613.0 / 378000.0 + + (-26478311.0 / 1512.0 + 2501.0 * pi_pow_2 / 2880.0) * eta + + 225106.0 * eta_pow_2 / 45.0 + ) + + e_6_rt = e_pow_6 * ( + 186961.0 / 336.0 + - 289691.0 * eta / 504.0 + + 3197.0 * eta_pow_2 / 18.0 + ) + + free_term = 730168.0 / (23625.0 * (1.0 + sqrt_e_fact)) + + log_term = ( + (304.0 / 15.0) + * ( + 82283.0 / 1995.0 + + 297674.0 * e_pow_2 / 1995.0 + + 1147147.0 * e_pow_4 / 15960.0 + + 61311.0 * e_pow_6 / 21280.0 + ) + * np.log(x * (1.0 + sqrt_e_fact) / (2.0 * e_fact)) + ) + + pre_factor = -e * eta / (e_fact**5 * sqrt_e_fact) + + return pre_factor * ( + zero_term + + e_2_term + + e_4_term + + e_6_term + + e_8_term + + sqrt_e_fact * (zero_rt + e_2_rt + e_4_rt + e_6_rt) + + free_term + + log_term + ) + +def e_dot_3pn_SO(e: float, m1: float, m2: float, + S1z: float, S2z: float) -> float: + """3PN SO eccentricity (Quentin Henry et al., arXiv:2308.13606v1)""" + if abs(e) < 1e-12: + return 0.0 + e2 = e * e + e4 = e2 * e2 + e6 = e4 * e2 + ef = 1.0 - e2 + ef_sqrt = np.sqrt(ef) + ef_55 = ef**4 * ef * ef_sqrt + M2 = (m1+m2)**2 + M4 = M2*M2 + return ( + e * m1 * m2 * np.pi * ( + (64622592 + 238783104*e2 + 96887280*e4 + 2313613*e6)*m1*m1*S1z + + (64622592 + 238783104*e2 + 96887280*e4 + 2313613*e6)*m2*m2*S2z + + 24*(1744704 + 8150400*e2 + 3941409*e4 + 122714*e6)*m1*m2*(S1z+S2z) + ) + ) / (51840.0 * ef_55 * M4) + +def e_dot_3pn_SS(e: float, m1: float, m2: float, S1z: float, S2z: float) -> float: + if abs(e) < 1e-12: + return 0.0 + + kappa1 = 1.0 + kappa2 = 1.0 + + e_2 = e * e + e_4 = e_2 * e_2 + e_6 = e_4 * e_2 + + e_fact = 1.0 - e_2 + e_fact_2 = e_fact * e_fact + e_fact_sqrt = np.sqrt(e_fact) + e_fact_55 = e_fact_2 * e_fact_2 * e_fact * e_fact_sqrt + + M = m1 + m2 + M_fact_2 = M * M + M_fact_4 = M_fact_2 * M_fact_2 + M_fact_6 = M_fact_4 * M_fact_2 + + prefactor_const = -0.000016534391534391536 + + term = ( + e * m1 * m2 + * ( + # S1z^2 sector + ( + 27 * e_6 * (53837 + 368940 * kappa1) + + 12 * e_4 * ( + 6350925 + 27328 * e_fact_sqrt + 16634634 * kappa1 + 47824 * e_fact_sqrt * kappa1 + ) + + 32 * ( + 2226847 + 545664 * e_fact_sqrt + 538758 * kappa1 + 954912 * e_fact_sqrt * kappa1 + ) + + 32 * e_2 * ( + 7292761 + 1157688 * e_fact_sqrt + + 9 * (847619 + 225106 * e_fact_sqrt) * kappa1 + ) + ) * m1**4 * S1z * S1z + + # S2z^2 sector + + ( + 27 * e_6 * (53837 + 368940 * kappa2) + + 12 * e_4 * ( + 6350925 + 27328 * e_fact_sqrt + 16634634 * kappa2 + 47824 * e_fact_sqrt * kappa2 + ) + + 32 * ( + 2226847 + 545664 * e_fact_sqrt + 538758 * kappa2 + 954912 * e_fact_sqrt * kappa2 + ) + + 32 * e_2 * ( + 7292761 + 1157688 * e_fact_sqrt + + 9 * (847619 + 225106 * e_fact_sqrt) * kappa2 + ) + ) * m2**4 * S2z * S2z + + # m1^3 m2 S1z sector + + 6 * m1**3 * m2 * S1z * ( + ( + 9 * e_6 * (55293 + 221219 * kappa1) + + 2 * e_4 * ( + 11036963 + 10248 * e_fact_sqrt + 19572200 * kappa1 + + 78568 * e_fact_sqrt * kappa1 + ) + + 16 * ( + 798819 + 68208 * e_fact_sqrt - 336460 * kappa1 + + 522928 * e_fact_sqrt * kappa1 + ) + + 8 * e_2 * ( + 7063231 + 289422 * e_fact_sqrt + + 6 * (698816 + 369817 * e_fact_sqrt) * kappa1 + ) + ) * S1z + + + 2 * ( + 1818549 * e_6 + + 240 * (31249 + 22736 * e_fact_sqrt) + + 2 * e_4 * (20863999 + 51240 * e_fact_sqrt) + + 8 * e_2 * (7764719 + 1447110 * e_fact_sqrt) + ) * S2z + ) + + # m1 m2^3 S2z sector + + 6 * m1 * m2**3 * S2z * ( + 2 * ( + 1818549 * e_6 + + 240 * (31249 + 22736 * e_fact_sqrt) + + 2 * e_4 * (20863999 + 51240 * e_fact_sqrt) + + 8 * e_2 * (7764719 + 1447110 * e_fact_sqrt) + ) * S1z + + + ( + 9 * e_6 * (55293 + 221219 * kappa2) + + 2 * e_4 * ( + 11036963 + 10248 * e_fact_sqrt + 19572200 * kappa2 + + 78568 * e_fact_sqrt * kappa2 + ) + + 16 * ( + 798819 + 68208 * e_fact_sqrt - 336460 * kappa2 + + 522928 * e_fact_sqrt * kappa2 + ) + + 8 * e_2 * ( + 7063231 + 289422 * e_fact_sqrt + + 6 * (698816 + 369817 * e_fact_sqrt) * kappa2 + ) + ) * S2z + ) + + # m1^2 m2^2 sector + + m1**2 * m2**2 * ( + 9 * ( + 3 * e_6 * (63301 + 361786 * kappa1) + + 4 * e_4 * ( + 1667883 - 3416 * e_fact_sqrt + 5133138 * kappa1 + + 13664 * e_fact_sqrt * kappa1 + ) + + 32 * ( + 69895 - 22736 * e_fact_sqrt - 37046 * kappa1 + + 90944 * e_fact_sqrt * kappa1 + ) + + 32 * e_2 * ( + 439124 - 48237 * e_fact_sqrt + 616472 * kappa1 + + 192948 * e_fact_sqrt * kappa1 + ) + ) * S1z * S1z + + + 8 * ( + 3553929 * e_6 + + 3 * e_4 * (29583761 + 99064 * e_fact_sqrt) + + 116 * e_2 * (1176325 + 289422 * e_fact_sqrt) + + 8 * (2057131 + 1978032 * e_fact_sqrt) + ) * S1z * S2z + + + 9 * ( + 3 * e_6 * (63301 + 361786 * kappa2) + + 4 * e_4 * ( + 1667883 - 3416 * e_fact_sqrt + 5133138 * kappa2 + + 13664 * e_fact_sqrt * kappa2 + ) + + 32 * ( + 69895 - 22736 * e_fact_sqrt - 37046 * kappa2 + + 90944 * e_fact_sqrt * kappa2 + ) + + 32 * e_2 * ( + 439124 - 48237 * e_fact_sqrt + 616472 * kappa2 + + 192948 * e_fact_sqrt * kappa2 + ) + ) * S2z * S2z + ) + ) + ) + + return prefactor_const * term / (e_fact_55 * M_fact_6) + +## ---------- 3.5PN ------------- + +def e_dot_3_5pn(e: float, eta: float) -> float: + """3.5PN eccentricity — zero.""" + return 0.0 + + +# ================= l_dot (dl/dt) terms =================================== + +## ---------- 1PN ------------- + +def l_dot_1pn(e: float, eta: float) -> float: + """Eq. (A2)""" + return 3.0 / (e*e - 1.0) + +## ---------- 1.5PN ------------- + +def l_dot_1_5pn_SO(e: float, m1: float, m2: float, + S1z: float, S2z: float) -> float: + """SO correction (Klein et al. arXiv:1801.08542, Eq. B1e/B2e)""" + e2 = e * e + ef = 1.0 - e2 + pf = 1.0 / (ef * np.sqrt(ef) * (m1+m2)**2) + return pf * (4*m1*m1*S1z + 4*m2*m2*S2z + 3*m1*m2*(S1z+S2z)) + +## ---------- 2PN ------------- + +def l_dot_2pn_SS(e: float, m1: float, m2: float, + S1z: float, S2z: float) -> float: + """SS correction (Klein et al. arXiv:1801.08542, Eq. B1e/B2e)""" + kappa1 = 1.0; kappa2 = 1.0 + return ( + -3.0 * (m1*S1z*(2*m2*S2z + m1*S1z*kappa1) + m2*m2*S2z*S2z*kappa2) + ) / (2.0 * (-1 + e**2)**2 * (m1+m2)**2) + +def l_dot_2pn(e: float, eta: float) -> float: + """Eq. (A3)""" + ef = 1.0 - e*e + ef2 = ef*ef + return ((26.0*eta - 51.0)*e*e + 28.0*eta - 18.0) / (4.0 * ef2) + +## ---------- 2.5PN ------------- + +def l_dot_2_5pn_SO(e: float, m1: float, m2: float, + S1z: float, S2z: float) -> float: + """2.5PN SO (Quentin Henry et al., arXiv:2308.13606v1)""" + e2 = e * e + ef = 1.0 - e2 + ef2 = ef * ef + ef_sqrt = np.sqrt(ef) + ef_25 = ef2 * ef_sqrt + M2 = (m1+m2)**2 + M4 = M2*M2 + return ( + (20*m1**4*(S1z + 3*e2*S1z) + + 20*(1+3*e2)*m2**4*S2z + + (5+129*e2)*m1*m1*m2*m2*(S1z+S2z) + + m1**3*m2*((23+137*e2)*S1z + 45*e2*S2z) + + m1*m2**3*(23*S2z + e2*(45*S1z+137*S2z))) + ) / (2.0 * ef_25 * M4) + +## ---------- 3PN ------------- + +def l_dot_3pn(e: float, eta: float) -> float: + """Eq. (A4)""" + ef = 1.0 - e*e + ef3 = ef*ef*ef + pre = -1.0 / (128.0 * np.sqrt(ef) * ef3) + eta2 = eta*eta + pi2 = np.pi*np.pi + e2 = e*e + e4 = e2*e2 + t0 = 1920.0 - 768.0*eta + t2 = (1920.0 - 768.0*eta)*e2 + t4 = (1536.0*eta - 3840.0)*e4 + r0 = 896.0*eta2 - 14624.0*eta + 492.0*pi2*eta - 192.0 + r2 = (5120.0*eta2 + 123.0*pi2*eta - 17856.0*eta + 8544.0)*e2 + r4 = (1040.0*eta2 - 1760.0*eta + 2496.0)*e4 + return pre * (t0 + t2 + t4 + np.sqrt(ef)*(r0 + r2 + r4)) + + +def l_dot_3pn_SS(e: float, m1: float, m2: float, + S1z: float, S2z: float) -> float: + """3PN SS (Quentin Henry et al., arXiv:2308.13606v1)""" + kappa1 = 1.0; kappa2 = 1.0 + e2 = e * e + M2 = (m1+m2)**2 + M4 = M2*M2 + return ( + (m1*m1*((-8+42*kappa1+6*e2*(4+13*kappa1))*m1*m1 + + (-60+50*kappa1+11*e2*(3+11*kappa1))*m1*m2 + + 3*(-14+6*kappa1+3*e2*(1+8*kappa1))*m2*m2) * S1z*S1z + + 2*m1*m2*((6+93*e2)*m1*m1 + (12+157*e2)*m1*m2 + 3*(2+31*e2)*m2*m2)*S1z*S2z + + m2*m2*(3*(-14+6*kappa2+3*e2*(1+8*kappa2))*m1*m1 + + (-60+50*kappa2+11*e2*(3+11*kappa2))*m1*m2 + + 2*(-4+21*kappa2+3*e2*(4+13*kappa2))*m2*m2)*S2z*S2z) + ) / (4.0 * (-1+e2)**3 * M4) + + +# =========== phi_dot (dphi/dt) terms ===================================================== + +def cosu_factor(e: float, u: float) -> float: + return e * np.cos(u) - 1.0 + +## --------- 0PN ------------- + +def phi_dot_0pn(e: float, eta: float, u: float) -> float: + """Eq. (A11)""" + cf = cosu_factor(e, u) + return np.sqrt(1.0 - e*e) / (cf * cf) + +## --------- 1PN ------------- + +def phi_dot_1pn(e: float, eta: float, u: float) -> float: + """Eq. (A12)""" + cf = cosu_factor(e, u) + return -(e*(eta - 4.0)*(e - np.cos(u))) / (np.sqrt(1.0 - e*e) * cf**3) + +## --------- 1.5PN ------------- + +def phi_dot_1_5_pnSO_ecc(e: float, m1: float, m2: float, + S1z: float, S2z: float, u: float) -> float: + """1.5PN SO phi_dot (Klein et al. arXiv:1801.08542, Eq. B1/B2)""" + if abs(e) < 1e-12: + return 0.0 + cf = -1.0 + e * np.cos(u) + return (2*e*(m1*S1z + m2*S2z)*(e - np.cos(u))) / ((-1+e*e)*(m1+m2)*cf**3) + +## --------- 2PN ------------- + +def phi_dot_2pn(e: float, eta: float, u: float) -> float: + """Eq. (A13)""" + cf = cosu_factor(e, u) + cf5 = cf**5 + e2 = e*e; e3=e2*e; e4=e2*e2; e5=e4*e; e6=e4*e2 + ef = 1.0 - e2 + eta2 = eta*eta + cu = np.cos(u); cu2=cu*cu; cu3=cu2*cu + + t0 = 90.0 - 36.0*eta + t2 = (-2.0*eta2 + 50.0*eta + 75.0)*e2 + t4 = (20.0*eta2 - 26.0*eta - 60.0)*e4 + t6 = (-12.0*eta - 18.0)*eta*e6 + c1 = ((-eta2+97.0*eta+12.0)*e5 + (-16.0*eta2-74.0*eta-81.0)*e3 + (-eta2+67.0*eta-246.0)*e)*cu + c2 = ((17.0*eta2-17.0*eta+48.0)*e6 + (-4.0*eta2-38.0*eta+153.0)*e4 + (5.0*eta2-35.0*eta+114.0)*e2)*cu2 + c3 = ((-14.0*eta2+8.0*eta-147.0)*e5 + (8.0*eta2+22.0*eta+42.0)*e3)*cu3 + + r0 = (180.0-72.0*eta)*e2 + 36.0*eta - 90.0 + rc1 = ((144.0*eta-360.0)*e3 + (90.0-36.0*eta)*e)*cu + rc2 = ((180.0-72.0*eta)*e4 + (90.0-36.0*eta)*e2)*cu2 + rc3 = e3*(36.0*eta-90.0)*cu3 + + pre = 1.0 / (12.0 * np.sqrt(ef) * ef * cf5) + return pre * (t0+t2+t4+t6+c1+c2+c3 + np.sqrt(ef)*(r0+rc1+rc2+rc3)) + + +def phi_dot_2_pnSS_ecc(e: float, m1: float, m2: float, + S1z: float, S2z: float, u: float) -> float: + """2PN SS phi_dot (Klein et al. arXiv:1801.08542, Eq. B1/B2)""" + if abs(e) < 1e-12: + return 0.0 + kappa1 = 1.0; kappa2 = 1.0 + return ( + e * (kappa2*m2*m2*S2z*S2z + m1*S1z*(kappa1*m1*S1z + 2*m2*S2z)) * + (e - np.cos(u)) + ) / ((1-e**2)**1.5 * (m1+m2)**2 * (-1+e*np.cos(u))**3) + +## --------- 2.5PN ------------- + +def phi_dot_2_5pn_SO(e: float, m1: float, m2: float, S1z: float, S2z: float, u: float) -> float: + e_2 = e * e + e_3 = e_2 * e + e_4 = e_2 * e_2 + e_6 = e_4 * e_2 + + e_fact = 1.0 - e_2 + e_fact_sqrt = np.sqrt(e_fact) + + M = m1 + m2 + M_fact_2 = M * M + M_fact_4 = M_fact_2 * M_fact_2 + + cos_u = np.cos(u) + + numerator = ( + # (m1 - m2)(m1 + m2)(...) + (m1 - m2) * M * ( + 24 * (m1 * m2 - 3 * M_fact_2) + - 6 * e_6 * (m1 * m2 - 2 * M_fact_2) + + 18 * e_4 * (3 * m1 * m2 - 2 * M_fact_2) + - e_2 * (31 * m1 * m2 + 56 * M_fact_2) + ) * (S1z - S2z) + + # symmetric spin part + + ( + e_2 * ( + 50 * m1*m1*m2*m2 - 57 * m1*m2*M_fact_2 - 56 * M_fact_4 + ) + + 6 * e_6 * ( + 2 * m1*m1*m2*m2 - 3 * m1*m2*M_fact_2 + 2 * M_fact_4 + ) + - 6 * e_4 * ( + 8 * m1*m1*m2*m2 - 27 * m1*m2*M_fact_2 + 6 * M_fact_4 + ) + - 12 * ( + m1*m1*m2*m2 - 8 * m1*m2*M_fact_2 + 6 * M_fact_4 + ) + ) * (S1z + S2z) + + # cos(u) term + - e * ( + -8 * (56 + 49 * e_2 + 9 * e_4) * m1**4 * S1z + -8 * (56 + 49 * e_2 + 9 * e_4) * m2**4 * S2z + + 4 * (-217 - 200 * e_2 + 9 * e_4) * m1*m1*m2*m2 * (S1z + S2z) + - 2 * m1**3 * m2 * ( + (530 + 496 * e_2 + 6 * e_4) * S1z + + 9 * (14 + 13 * e_2) * S2z + ) + - 2 * m1 * m2**3 * ( + 9 * (14 + 13 * e_2) * S1z + + 2 * (265 + 248 * e_2 + 3 * e_4) * S2z + ) + ) * cos_u + + # cos^2(u) term + + e_2 * ( + -8 * (2 + e_2) * (29 + 9 * e_2) * m1**4 * S1z + -8 * (2 + e_2) * (29 + 9 * e_2) * m2**4 * S2z + -8 * (2 + e_2) * (47 + 21 * e_2) * m1*m1*m2*m2 * (S1z + S2z) + -2 * m1**3 * m2 * ( + (514 + 440 * e_2 + 78 * e_4) * S1z + + 9 * (2 + e_2) * (5 + 4 * e_2) * S2z + ) + -2 * m1 * m2**3 * ( + 9 * (2 + e_2) * (5 + 4 * e_2) * S1z + + 2 * (257 + 220 * e_2 + 39 * e_4) * S2z + ) + ) * (cos_u ** 2) + + # cos^3(u) term + - e_3 * ( + -8 * (17 + 21 * e_2) * m1**4 * S1z + -8 * (17 + 21 * e_2) * m2**4 * S2z + -8 * (11 + 57 * e_2) * m1*m1*m2*m2 * (S1z + S2z) + -2 * m1**3 * m2 * ( + 4 * (29 + 57 * e_2) * S1z - 6 * S2z + 87 * e_2 * S2z + ) + -2 * m1 * m2**3 * ( + (-6 + 87 * e_2) * S1z + 4 * (29 + 57 * e_2) * S2z + ) + ) * (cos_u ** 3) + + # sqrt(e_fact) term + - 12 * e_fact_sqrt * ( + -12 * m1**4 * S1z + -12 * m2**4 * S2z + -21 * m1*m1*m2*m2 * (S1z + S2z) + -2 * m1**3 * m2 * (13 * S1z + 3 * S2z) + -2 * m1 * m2**3 * (3 * S1z + 13 * S2z) + ) * ((-1 + e * cos_u) ** 2) * (1 - 2 * e_2 + e * cos_u) + ) + + denominator = ( + 12.0 + * ((-1 + e_2) ** 2) + * M_fact_4 + * ((-1 + e * cos_u) ** 5) + ) + + return numerator / denominator + + +## --------- 3PN ------------- + +def phi_dot_3pn(e: float, eta: float, u: float) -> float: + u_factor = cosu_factor(e, u) + u_factor_pow_7 = u_factor ** 7 + pi_pow_2 = np.pi ** 2 + eta_pow_2 = eta ** 2 + eta_pow_3 = eta ** 3 + e_pow_2 = e ** 2 + e_fact = 1.0 - e_pow_2 + e_pow_3 = e ** 3 + e_pow_4 = e ** 4 + e_pow_5 = e ** 5 + e_pow_6 = e ** 6 + e_pow_7 = e ** 7 + e_pow_8 = e ** 8 + e_pow_9 = e ** 9 + e_pow_10 = e ** 10 + e_factor = 1.0 - e_pow_2 + cos_u = np.cos(u) + cos_u_pow_2 = cos_u ** 2 + cos_u_pow_3 = cos_u ** 3 + cos_u_pow_4 = cos_u ** 4 + cos_u_pow_5 = cos_u ** 5 + + pre_factor = 1.0 / (13440.0 * np.sqrt(e_factor) * e_factor ** 2 * u_factor_pow_7) + + e_0_term = 67200.0 * eta_pow_2 - 761600.0 * eta + 8610.0 * eta * pi_pow_2 + 201600.0 + e_2_term = (4480.0 * eta_pow_3 - 412160.0 * eta_pow_2 + - 30135.0 * pi_pow_2 * eta + 553008.0 * eta + 342720.0) * e_pow_2 + e_4_term = (-52640.0 * eta_pow_3 + 516880.0 * eta_pow_2 + + 68880.0 * pi_pow_2 * eta - 1916048.0 * eta + 262080.0) * e_pow_4 + e_6_term = (84000.0 * eta_pow_3 - 190400.0 * eta_pow_2 + - 17220.0 * pi_pow_2 * eta - 50048.0 * eta - 241920.0) * e_pow_6 + e_8_term = (-52640.0 * eta_pow_2 - 13440.0 * eta + 483280.0) * eta * e_pow_8 + e_10_term = (10080.0 * eta_pow_2 + 40320.0 * eta - 15120.0) * eta * e_pow_10 + + cosu_1 = ( + (-2240.0 * eta_pow_3 - 168000.0 * eta_pow_2 - 424480.0 * eta) * e_pow_9 + + (28560.0 * eta_pow_3 + 242480.0 * eta_pow_2 + 34440.0 * pi_pow_2 * eta + - 1340224.0 * eta + 725760.0) * e_pow_7 + + (-33040.0 * eta_pow_3 - 754880.0 * eta_pow_2 - 172200.0 * pi_pow_2 * eta + + 5458480.0 * eta - 221760.0) * e_pow_5 + + (40880.0 * eta_pow_3 + 738640.0 * eta_pow_2 + 30135.0 * pi_pow_2 * eta + + 1554048.0 * eta - 2936640.0) * e_pow_3 + + (-560.0 * eta_pow_3 - 100240.0 * eta_pow_2 - 43050.0 * pi_pow_2 * eta + + 3284816.0 * eta - 389760.0) * e + ) * cos_u + + cosu_2 = ( + (4480.0 * eta_pow_3 - 20160.0 * eta_pow_2 + 16800.0 * eta) * e_pow_10 + + (3920.0 * eta_pow_3 + 475440.0 * eta_pow_2 - 17220.0 * pi_pow_2 * eta + + 831952.0 * eta - 7257600.0) * e_pow_8 + + (-75600.0 * eta_pow_3 + 96880.0 * eta_pow_2 + 154980.0 * pi_pow_2 * eta + - 3249488.0 * eta - 685440.0) * e_pow_6 + + (5040.0 * eta_pow_3 - 659120.0 * eta_pow_2 + 25830.0 * pi_pow_2 * eta + - 7356624.0 * eta + 6948480.0) * e_pow_4 + + (-5040.0 * eta_pow_3 + 190960.0 * eta_pow_2 + 137760.0 * pi_pow_2 * eta + - 7307920.0 * eta + 107520.0) * e_pow_2 + ) * cos_u_pow_2 + + cosu_3 = ( + (560.0 * eta_pow_3 - 137200.0 * eta_pow_2 + 388640.0 * eta + 241920.0) * e_pow_9 + + (30800.0 * eta_pow_3 - 264880.0 * eta_pow_2 - 68880.0 * pi_pow_2 * eta + + 624128.0 * eta + 766080.0) * e_pow_7 + + (66640.0 * eta_pow_3 + 612080.0 * eta_pow_2 - 8610.0 * pi_pow_2 * eta + + 6666080.0 * eta - 6652800.0) * e_pow_5 + + (-30800.0 * eta_pow_3 - 294000.0 * eta_pow_2 - 223860.0 * pi_pow_2 * eta + + 9386432.0 * eta) * e_pow_3 + ) * cos_u_pow_3 + + cosu_4 = ( + (-16240.0 * eta_pow_3 + 12880.0 * eta_pow_2 + 18480.0 * eta) * e_pow_10 + + (16240.0 * eta_pow_3 - 91840.0 * eta_pow_2 + 17220.0 * pi_pow_2 * eta + - 652192.0 * eta + 100800.0) * e_pow_8 + + (-55440.0 * eta_pow_3 + 34160.0 * eta_pow_2 - 30135.0 * pi_pow_2 * eta + - 2185040.0 * eta + 2493120.0) * e_pow_6 + + (21480.0 * eta_pow_3 + 86800.0 * eta_pow_2 + 163590.0 * pi_pow_2 * eta + - 5713888.0 * eta + 228480.0) * e_pow_4 + ) * cos_u_pow_4 + + cosu_5 = ( + (13440.0 * eta_pow_3 + 94640.0 * eta_pow_2 - 113680.0 * eta - 221760.0) * e_pow_9 + + (-11200.0 * eta_pow_3 - 112000.0 * eta_pow_2 + 12915.0 * pi_pow_2 * eta + + 692928.0 * eta - 194880.0) * e_pow_7 + + (4480.0 * eta_pow_3 + 8960.0 * eta_pow_2 - 43050.0 * pi_pow_2 * eta + + 1127280.0 * eta - 147840.0) * e_pow_5 + ) * cos_u_pow_5 + + rt_zero = ( + -67200.0 * eta_pow_2 + 761600.0 * eta + + e_pow_4 * (40320.0 * eta_pow_2 + 309120.0 * eta - 672000.0) + + e_pow_2 * (208320.0 * eta_pow_2 + 17220.0 * pi_pow_2 * eta + - 2289280.0 * eta + 1680000.0) + - 8610.0 * pi_pow_2 * eta - 201600.0 + ) + + rt_cosu_1 = ( + (-282240.0 * eta_pow_2 - 450240.0 * eta + 1478400.0) * e_pow_5 + + (-719040.0 * eta_pow_2 - 68880.0 * pi_pow_2 * eta + + 8128960.0 * eta - 5040000.0) * e_pow_3 + + (94080.0 * eta_pow_2 + 25830.0 * pi_pow_2 * eta + - 1585920.0 * eta - 470400.0) * e + ) * cos_u + + rt_cosu_2 = ( + (604800.0 * eta_pow_2 - 504000.0 * eta - 403200.0) * e_pow_6 + + (1034880.0 * eta_pow_2 + 103320.0 * pi_pow_2 * eta + - 11195520.0 * eta + 5779200.0) * e_pow_4 + + (174720.0 * eta_pow_2 - 17220.0 * pi_pow_2 * eta + - 486080.0 * eta + 2688000.0) * e_pow_2 + ) * cos_u_pow_2 + + rt_cosu_3 = ( + (-524160.0 * eta_pow_2 + 1122240.0 * eta - 940800.0) * e_pow_7 + + (-873600.0 * eta_pow_2 - 68880.0 * pi_pow_2 * eta + + 7705600.0 * eta - 3897600.0) * e_pow_5 + + (-416640.0 * eta_pow_2 - 17220.0 * pi_pow_2 * eta + + 3357760.0 * eta - 3225600.0) * e_pow_3 + ) * cos_u_pow_3 + + rt_cosu_4 = ( + (161280.0 * eta_pow_2 - 477120.0 * eta + 537600.0) * e_pow_8 + + (477120.0 * eta_pow_2 + 17220.0 * pi_pow_2 * eta + - 2894080.0 * eta + 2217600.0) * e_pow_6 + + (268800.0 * eta_pow_2 + 25830.0 * pi_pow_2 * eta + - 2721600.0 * eta + 1276800.0) * e_pow_4 + ) * cos_u_pow_4 + + rt_cosu_5 = ( + (-127680.0 * eta_pow_2 + 544320.0 * eta - 739200.0) * e_pow_7 + + (-53760.0 * eta_pow_2 - 8610.0 * pi_pow_2 * eta + + 674240.0 * eta - 67200.0) * e_pow_5 + ) * cos_u_pow_5 + + return pre_factor * ( + e_0_term + e_2_term + e_4_term + e_6_term + e_8_term + e_10_term + + cosu_1 + cosu_2 + cosu_3 + cosu_4 + cosu_5 + + np.sqrt(e_fact) * ( + rt_zero + rt_cosu_1 + rt_cosu_2 + rt_cosu_3 + rt_cosu_4 + rt_cosu_5 + ) + ) + + +def phi_dot_3pn_SS(e: float, m1: float, m2: float, S1z: float, S2z: float, u: float) -> float: + """Quentin Henry et al terms, arXiv:2308.13606v1""" + kappa1 = 1.0 + kappa2 = 1.0 + e_2 = e * e + e_3 = e_2 * e + e_4 = e_2 * e_2 + e_6 = e_4 * e_2 + e_fact = 1.0 - e_2 + e_fact_sqrt = np.sqrt(e_fact) + M_fact_2 = (m1 + m2) ** 2 + M_fact_4 = M_fact_2 * M_fact_2 + cos_u = np.cos(u) + cos_u_2 = cos_u * cos_u + cos_u_3 = cos_u_2 * cos_u + + # --- constant terms (no cos(u) power) --- + s1z2_m1_4 = 6 * ( + 4 * e_6 * e_fact_sqrt * kappa1 + - 2 * (-1 + e_fact_sqrt) * (4 + 7 * kappa1) + - e_2 * (24 + 20 * e_fact_sqrt + 42 * kappa1 + 19 * e_fact_sqrt * kappa1) + - 4 * e_4 * (-4 + (-7 + 3 * e_fact_sqrt) * kappa1) + ) * m1**4 * S1z * S1z + + s2z2_m2_4 = 6 * ( + 4 * e_6 * e_fact_sqrt * kappa2 + - 2 * (-1 + e_fact_sqrt) * (4 + 7 * kappa2) + - e_2 * (24 + 20 * e_fact_sqrt + 42 * kappa2 + 19 * e_fact_sqrt * kappa2) + - 4 * e_4 * (-4 + (-7 + 3 * e_fact_sqrt) * kappa2) + ) * m2**4 * S2z * S2z + + cross_m1_3_m2 = m1**3 * m2 * S1z * ( + ( + 48 * e_6 * e_fact_sqrt * (-1 + kappa1) + - 6 * (-1 + e_fact_sqrt) * (3 + 23 * kappa1) + - 4 * e_4 * (-9 - 60 * e_fact_sqrt - 69 * kappa1 + 32 * e_fact_sqrt * kappa1) + - e_2 * (54 + 297 * e_fact_sqrt + 414 * kappa1 + 145 * e_fact_sqrt * kappa1) + ) * S1z + + 6 * ( + 60 * e_4 + 4 * e_6 * e_fact_sqrt - 30 * (-1 + e_fact_sqrt) + - e_2 * (90 + 67 * e_fact_sqrt) + ) * S2z + ) + + cross_m1_m2_3 = m1 * m2**3 * S2z * ( + 6 * ( + 60 * e_4 + 4 * e_6 * e_fact_sqrt - 30 * (-1 + e_fact_sqrt) + - e_2 * (90 + 67 * e_fact_sqrt) + ) * S1z + + ( + 48 * e_6 * e_fact_sqrt * (-1 + kappa2) + - 6 * (-1 + e_fact_sqrt) * (3 + 23 * kappa2) + - 4 * e_4 * (-9 - 60 * e_fact_sqrt - 69 * kappa2 + 32 * e_fact_sqrt * kappa2) + - e_2 * (54 + 297 * e_fact_sqrt + 414 * kappa2 + 145 * e_fact_sqrt * kappa2) + ) * S2z + ) + + cross_m1_2_m2_2 = m1**2 * m2**2 * ( + 3 * ( + 4 * e_6 * e_fact_sqrt * (-4 + 3 * kappa1) + - 6 * (-1 + e_fact_sqrt) * (-1 + 4 * kappa1) + - e_2 * (-18 + 55 * e_fact_sqrt + 4 * (18 + e_fact_sqrt) * kappa1) + + e_4 * (-12 + 68 * e_fact_sqrt - 8 * (-6 + 5 * e_fact_sqrt) * kappa1) + ) * S1z * S1z + + 2 * ( + 12 * e_6 * e_fact_sqrt - 174 * (-1 + e_fact_sqrt) + + 4 * e_4 * (87 + 7 * e_fact_sqrt) + - e_2 * (522 + 409 * e_fact_sqrt) + ) * S1z * S2z + + 3 * ( + 4 * e_6 * e_fact_sqrt * (-4 + 3 * kappa2) + - 6 * (-1 + e_fact_sqrt) * (-1 + 4 * kappa2) + - e_2 * (-18 + 55 * e_fact_sqrt + 4 * (18 + e_fact_sqrt) * kappa2) + + e_4 * (-12 + 68 * e_fact_sqrt - 8 * (-6 + 5 * e_fact_sqrt) * kappa2) + ) * S2z * S2z + ) + + term_const = s1z2_m1_4 + s2z2_m2_4 + cross_m1_3_m2 + cross_m1_m2_3 + cross_m1_2_m2_2 + + # --- cos(u) terms --- + cosu_m1_4_S1z2 = 6 * ( + -8 + 40 * e_fact_sqrt + 2 * (-7 + 25 * e_fact_sqrt) * kappa1 + + 4 * e_4 * (-8 + (-14 + 3 * e_fact_sqrt) * kappa1) + + e_2 * (40 + 44 * e_fact_sqrt + (70 + 61 * e_fact_sqrt) * kappa1) + ) * m1**4 * S1z * S1z + + cosu_m2_4_S2z2 = 6 * ( + -8 + 40 * e_fact_sqrt + 2 * (-7 + 25 * e_fact_sqrt) * kappa2 + + 4 * e_4 * (-8 + (-14 + 3 * e_fact_sqrt) * kappa2) + + e_2 * (40 + 44 * e_fact_sqrt + (70 + 61 * e_fact_sqrt) * kappa2) + ) * m2**4 * S2z * S2z + + cosu_m1_3_m2 = m1**3 * m2 * S1z * ( + ( + -18 + 246 * e_fact_sqrt + 46 * (-3 + 10 * e_fact_sqrt) * kappa1 + + 2 * e_4 * (-36 - 60 * e_fact_sqrt - 276 * kappa1 + 47 * e_fact_sqrt * kappa1) + + e_2 * (90 + 243 * e_fact_sqrt + (690 + 535 * e_fact_sqrt) * kappa1) + ) * S1z + + 18 * ( + -10 + 42 * e_fact_sqrt + 4 * e_4 * (-10 + e_fact_sqrt) + + e_2 * (50 + 47 * e_fact_sqrt) + ) * S2z + ) + + cosu_m1_m2_3 = m1 * m2**3 * S2z * ( + 18 * ( + -10 + 42 * e_fact_sqrt + 4 * e_4 * (-10 + e_fact_sqrt) + + e_2 * (50 + 47 * e_fact_sqrt) + ) * S1z + + ( + -18 + 246 * e_fact_sqrt + 46 * (-3 + 10 * e_fact_sqrt) * kappa2 + + 2 * e_4 * (-36 - 60 * e_fact_sqrt - 276 * kappa2 + 47 * e_fact_sqrt * kappa2) + + e_2 * (90 + 243 * e_fact_sqrt + (690 + 535 * e_fact_sqrt) * kappa2) + ) * S2z + ) + + cosu_m1_2_m2_2 = m1**2 * m2**2 * ( + 3 * ( + 6 + 14 * e_fact_sqrt + 8 * (-3 + 8 * e_fact_sqrt) * kappa1 + + 4 * e_4 * (6 - 7 * e_fact_sqrt + 8 * (-3 + e_fact_sqrt) * kappa1) + + e_2 * (5 * (-6 + e_fact_sqrt) + 24 * (5 + 3 * e_fact_sqrt) * kappa1) + ) * S1z * S1z + + 2 * ( + -174 + 760 * e_fact_sqrt + + e_4 * (-696 + 34 * e_fact_sqrt) + + e_2 * (870 + 835 * e_fact_sqrt) + ) * S1z * S2z + + 3 * ( + 6 + 14 * e_fact_sqrt + 8 * (-3 + 8 * e_fact_sqrt) * kappa2 + + 4 * e_4 * (6 - 7 * e_fact_sqrt + 8 * (-3 + e_fact_sqrt) * kappa2) + + e_2 * (5 * (-6 + e_fact_sqrt) + 24 * (5 + 3 * e_fact_sqrt) * kappa2) + ) * S2z * S2z + ) + + term_cosu = e * ( + cosu_m1_4_S1z2 + cosu_m2_4_S2z2 + cosu_m1_3_m2 + cosu_m1_m2_3 + cosu_m1_2_m2_2 + ) * cos_u + + # --- cos(u)^2 terms --- + cosu2_m1_4_S1z2 = 6 * ( + 8 + 56 * e_fact_sqrt + 14 * kappa1 + 58 * e_fact_sqrt * kappa1 + + 4 * e_4 * (-4 - 7 * kappa1 + 3 * e_fact_sqrt * kappa1) + + e_2 * (8 + 28 * e_fact_sqrt + 14 * kappa1 + 53 * e_fact_sqrt * kappa1) + ) * m1**4 * S1z * S1z + + cosu2_m2_4_S2z2 = 6 * ( + 8 + 56 * e_fact_sqrt + 14 * kappa2 + 58 * e_fact_sqrt * kappa2 + + 4 * e_4 * (-4 - 7 * kappa2 + 3 * e_fact_sqrt * kappa2) + + e_2 * (8 + 28 * e_fact_sqrt + 14 * kappa2 + 53 * e_fact_sqrt * kappa2) + ) * m2**4 * S2z * S2z + + cosu2_m1_3_m2 = m1**3 * m2 * S1z * ( + ( + 2 * e_4 * (-18 - 12 * e_fact_sqrt - 138 * kappa1 + 55 * e_fact_sqrt * kappa1) + + 2 * (9 + 111 * e_fact_sqrt + 69 * kappa1 + 262 * e_fact_sqrt * kappa1) + + e_2 * (18 + 171 * e_fact_sqrt + 138 * kappa1 + 455 * e_fact_sqrt * kappa1) + ) * S1z + + 18 * ( + 10 + 46 * e_fact_sqrt + + 4 * e_4 * (-5 + 2 * e_fact_sqrt) + + e_2 * (10 + 39 * e_fact_sqrt) + ) * S2z + ) + + cosu2_m1_m2_3 = m1 * m2**3 * S2z * ( + 18 * ( + 10 + 46 * e_fact_sqrt + + 4 * e_4 * (-5 + 2 * e_fact_sqrt) + + e_2 * (10 + 39 * e_fact_sqrt) + ) * S1z + + ( + 2 * e_4 * (-18 - 12 * e_fact_sqrt - 138 * kappa2 + 55 * e_fact_sqrt * kappa2) + + 2 * (9 + 111 * e_fact_sqrt + 69 * kappa2 + 262 * e_fact_sqrt * kappa2) + + e_2 * (18 + 171 * e_fact_sqrt + 138 * kappa2 + 455 * e_fact_sqrt * kappa2) + ) * S2z + ) + + cosu2_m1_2_m2_2 = m1**2 * m2**2 * ( + 3 * ( + -6 - 14 * e_fact_sqrt + 24 * kappa1 + 68 * e_fact_sqrt * kappa1 + + 4 * e_4 * (3 - 2 * e_fact_sqrt - 12 * kappa1 + 7 * e_fact_sqrt * kappa1) + + e_2 * (-6 + 13 * e_fact_sqrt + 24 * kappa1 + 72 * e_fact_sqrt * kappa1) + ) * S1z * S1z + + 2 * ( + 174 + 872 * e_fact_sqrt + + e_4 * (-348 + 98 * e_fact_sqrt) + + e_2 * (174 + 659 * e_fact_sqrt) + ) * S1z * S2z + + 3 * ( + -6 - 14 * e_fact_sqrt + 24 * kappa2 + 68 * e_fact_sqrt * kappa2 + + 4 * e_4 * (3 - 2 * e_fact_sqrt - 12 * kappa2 + 7 * e_fact_sqrt * kappa2) + + e_2 * (-6 + 13 * e_fact_sqrt + 24 * kappa2 + 72 * e_fact_sqrt * kappa2) + ) * S2z * S2z + ) + + term_cosu2 = -e_2 * ( + cosu2_m1_4_S1z2 + cosu2_m2_4_S2z2 + cosu2_m1_3_m2 + cosu2_m1_m2_3 + cosu2_m1_2_m2_2 + ) * cos_u_2 + + # --- cos(u)^3 terms --- + cosu3_m1_4_S1z2 = 6 * ( + 8 + 24 * e_fact_sqrt + 2 * (7 + 9 * e_fact_sqrt) * kappa1 + + e_2 * (-8 + 4 * e_fact_sqrt - 14 * kappa1 + 23 * e_fact_sqrt * kappa1) + ) * m1**4 * S1z * S1z + + cosu3_m2_4_S2z2 = 6 * ( + 8 + 24 * e_fact_sqrt + 2 * (7 + 9 * e_fact_sqrt) * kappa2 + + e_2 * (-8 + 4 * e_fact_sqrt - 14 * kappa2 + 23 * e_fact_sqrt * kappa2) + ) * m2**4 * S2z * S2z + + cosu3_m1_3_m2 = m1**3 * m2 * S1z * ( + ( + 18 + 42 * e_fact_sqrt + 2 * (69 + 77 * e_fact_sqrt) * kappa1 + + e_2 * (-18 + 81 * e_fact_sqrt - 138 * kappa1 + 209 * e_fact_sqrt * kappa1) + ) * S1z + + 6 * ( + 30 + 38 * e_fact_sqrt + 5 * e_2 * (-6 + 11 * e_fact_sqrt) + ) * S2z + ) + + cosu3_m1_m2_3 = m1 * m2**3 * S2z * ( + 6 * ( + 30 + 38 * e_fact_sqrt + 5 * e_2 * (-6 + 11 * e_fact_sqrt) + ) * S1z + + ( + 18 + 42 * e_fact_sqrt + 2 * (69 + 77 * e_fact_sqrt) * kappa2 + + e_2 * (-18 + 81 * e_fact_sqrt - 138 * kappa2 + 209 * e_fact_sqrt * kappa2) + ) * S2z + ) + + cosu3_m1_2_m2_2 = m1**2 * m2**2 * ( + 3 * ( + -6 - 18 * e_fact_sqrt + 8 * (3 + 2 * e_fact_sqrt) * kappa1 + + e_2 * (6 + 15 * e_fact_sqrt + 8 * (-3 + 5 * e_fact_sqrt) * kappa1) + ) * S1z * S1z + + 2 * ( + 174 + 274 * e_fact_sqrt + e_2 * (-174 + 269 * e_fact_sqrt) + ) * S1z * S2z + + 3 * ( + -6 - 18 * e_fact_sqrt + 8 * (3 + 2 * e_fact_sqrt) * kappa2 + + e_2 * (6 + 15 * e_fact_sqrt + 8 * (-3 + 5 * e_fact_sqrt) * kappa2) + ) * S2z * S2z + ) + + term_cosu3 = e_3 * ( + cosu3_m1_4_S1z2 + cosu3_m2_4_S2z2 + cosu3_m1_3_m2 + cosu3_m1_m2_3 + cosu3_m1_2_m2_2 + ) * cos_u_3 + + denominator = 12.0 * (e_2 - 1.0) ** 3 * M_fact_4 * (1.0 - e * cos_u) ** 5 + + phi_3pn_SS = (term_const + term_cosu + term_cosu2 + term_cosu3) / denominator + + return phi_3pn_SS + +## ------- 4PN & 4.5PN--------------- +def phi_dot_4pn_SS(e: float, m1: float, m2: float, + S1z: float, S2z: float) -> float: + """4PN SS phi_dot — returns 0 (same as active code in original).""" + return 0.0 + + +def phi_dot_4_5_pn(e: float, eta: float, x: float) -> float: + """4.5PN phi_dot — returns 0.""" + return 0.0 + + +# ================ Relative separation ====================================== + +## ---------- 0PN -------------------- + +def rel_sep_0pn(e: float, u: float) -> float: + return 1.0 - e * np.cos(u) + +## ---------- 1PN -------------------- + +def rel_sep_1pn(e: float, u: float, eta: float) -> float: + ef = 1.0 - e*e + b1 = 2.0*(1.0 - e*np.cos(u)) / ef + b2 = (-18.0 + 2.0*eta - e*(6.0 - 7.0*eta)*np.cos(u)) / 6.0 + return b1 + b2 + +## ---------- 1.5PN -------------------- + +def rel_sep_1_5pn(e: float, u: float, m1: float, m2: float, + S1z: float, S2z: float) -> float: + """1.5PN SO (Klein et al. arXiv:1801.08542, Eq. B1a/B2a/B2b)""" + e2 = e*e + ef = 1.0 - e2 + M = m1 + m2 + return ( + -1.0/3.0 * + (2*m1*m1*(S1z + 3*e2*S1z) + + 2*(1+3*e2)*m2*m2*S2z + + 3*(1+e2)*m1*m2*(S1z+S2z) - + 2*e*(4*m1*m1*S1z + 4*m2*m2*S2z + 3*m1*m2*(S1z+S2z))*np.cos(u)) + / (ef**1.5 * M**2) + ) + +## ---------- 2PN -------------------- + +def rel_sep_2pn(e: float, u: float, eta: float) -> float: + eta2 = eta*eta + e2 = e*e + ef = 1.0 - e2 + n1 = (-48.0 + 28.0*eta + e2*(-51.0+26.0*eta))*(-1.0+e*np.cos(u)) + d1 = 6.0*ef**2 + n2 = (72.0*(-4.0+7.0*eta) + + 36.0*np.sqrt(ef)*(-5.0+2.0*eta)*(2.0+e*np.cos(u)) + + ef*(72.0+30.0*eta+8.0*eta2 + e*(-72.0+7*(33.0-5.0*eta)*eta)*np.cos(u))) + d2 = 72.0*ef + return n1/d1 + n2/d2 + + +def rel_sep_2pnSS(e: float, u: float, m1: float, m2: float, + S1z: float, S2z: float) -> float: + """2PN SS relative separation""" + kappa1 = 1.0; kappa2 = 1.0 + return ( + (m1*S1z*(2*m2*S2z + m1*S1z*kappa1) + m2*m2*S2z*S2z*kappa2) * + (1 + e*e - 2*e*np.cos(u)) + ) / (2.0 * (-1+e**2)**2 * (m1+m2)**2) + +## ---------- 2.5PN -------------------- + +def rel_sep_2_5pn_SO(e: float, u: float, m1: float, m2: float, + S1z: float, S2z: float) -> float: + """2.5PN SO relative separation.""" + e2 = e*e; e4=e2*e2 + ef = 1.0-e2; ef_sqrt=np.sqrt(ef) + M2 = (m1+m2)**2; M4=M2*M2 + return ( + (2*(-1+e2)**2 * + (-12*m1**4*S1z - 12*m2**4*S2z - + 21*m1*m1*m2*m2*(S1z+S2z) - + 2*m1**3*m2*(13*S1z+3*S2z) - + 2*m1*m2**3*(3*S1z+13*S2z)) * (2+e*np.cos(u)) + + 2*ef_sqrt * + (12*(2+7*e2+e4)*m1**4*S1z + + 12*(2+7*e2+e4)*m2**4*S2z + + 2*(22+85*e2+10*e4)*m1*m1*m2*m2*(S1z+S2z) + + m1**3*m2*(2*(28+96*e2+11*e4)*S1z + 3*(4+19*e2+2*e4)*S2z) + + m1*m2**3*(3*(4+19*e2+2*e4)*S1z + 2*(28+96*e2+11*e4)*S2z) - + e*(60*(1+e2)*m1**4*S1z + 60*(1+e2)*m2**4*S2z + + 3*(35+43*e2)*m1*m1*m2*m2*(S1z+S2z) + + m1**3*m2*(133*S1z+137*e2*S1z+30*S2z+45*e2*S2z) + + m1*m2**3*(30*S1z+45*e2*S1z+133*S2z+137*e2*S2z))*np.cos(u))) + ) / (6.0 * (-1+e2)**3 * M4) + +## ---------- 3PN -------------------- + +def rel_sep_3pn(e: float, u: float, eta: float) -> float: + pi_pow_2 = np.pi * np.pi + + e_factor = 1.0 - e * e + eta_pow_2 = eta * eta + e_pow_2 = e * e + e_pow_4 = e_pow_2 * e_pow_2 + + def pow7_2(x): + return x ** 3.5 + + term1 = ( + (-665280.0 * eta_pow_2 + 1753920.0 * eta - 1814400.0) * e_pow_4 + + ( + (725760.0 * eta_pow_2 - 77490.0 * pi_pow_2 + 5523840.0) * eta + - 3628800.0 + ) * e_pow_2 + + ( + (544320.0 * eta_pow_2 + 154980.0 * pi_pow_2 - 14132160.0) * eta + + 7257600.0 + ) + ) * e_pow_2 + + term2 = -604800.0 * eta_pow_2 + 6854400.0 * eta + + term3_cos = ( + ( + (302400.0 * eta_pow_2 - 1254960.0 * eta + 453600.0) * e_pow_4 + + ( + (-1542240.0 * eta_pow_2 - 38745.0 * pi_pow_2 + 6980400.0) * eta + - 453600.0 + ) * e_pow_2 + + ( + (2177280.0 * eta_pow_2 + 77490.0 * pi_pow_2 - 12373200.0) * eta + + 4989600.0 + ) + ) * e * e_pow_2 + + ( + (-937440.0 * eta_pow_2 - 37845.0 * pi_pow_2 + 6647760.0) * eta + - 4989600.0 + ) * e + ) * np.cos(u) + + term4_sqrt = np.sqrt(e_factor) * ( + ( + ( + (-4480.0 * eta_pow_2 - 25200.0 * eta + 22680.0) * eta + - 120960.0 + ) * e_pow_4 + + ( + 13440.0 * eta_pow_2 * eta + + 4404960.0 * eta * eta + + 116235.0 * pi_pow_2 + - 12718296.0 * eta + + 5261760.0 + ) * e_pow_2 + + ( + (-13440.0 * eta_pow_2 + 2242800.0 * eta + 348705.0 * pi_pow_2 + - 19225080.0) * eta + + 1614160.0 + ) + ) * e_pow_2 + + ( + 4480.0 * eta_pow_2 + 45360.0 * eta - 8600904.0 + ) * eta + + ( + ( + ( + (-6860.0 * eta_pow_2 - 550620.0 * eta - 986580.0) * eta + + 120960.0 + ) * e_pow_4 + + ( + (20580.0 * eta_pow_2 - 2458260.0 * eta + 3458700.0) * eta + - 2358720.0 + ) * e_pow_2 + + ( + (-20580.0 * eta_pow_2 - 3539340.0 * eta + - 116235.0 * pi_pow_2 + 20173860.0) * eta + - 16148160.0 + ) + ) * e * e_pow_2 + + ( + (6860.0 * eta_pow_2 - 1220940.0 * eta + + 464940.0 * pi_pow_2 + 17875620.0) * eta + - 417440.0 + ) * e + ) * np.cos(u) + + 116235.0 * pi_pow_2 * eta + + 1814400.0 + ) + + term5 = -77490.0 * pi_pow_2 * eta - 1814400.0 + + numerator = term1 + term2 + term3_cos + term4_sqrt + term5 + + denominator = 181440.0 * pow7_2(e_factor) + + return numerator / denominator + +def rel_sep_3pn_SS(e: float, u: float, m1: float, m2: float, S1z: float, S2z: float) -> float: + kappa1 = 1.0 + kappa2 = 1.0 + e_2 = e * e + e_4 = e_2 * e_2 + e_fact = 1.0 - e_2 + e_fact_sqrt = np.sqrt(e_fact) + e_fact_35 = e_fact_sqrt * e_fact * e_fact * e_fact + M_fact_2 = (m1 + m2) * (m1 + m2) + M_fact_4 = M_fact_2 * M_fact_2 + cos_u = np.cos(u) + e_fact_m1 = (-1 + e_2) ** 2 # i.e. (e^2 - 1)^2, used for pow(-1+e_2, 2) + + # --- constant terms --- + m1_4_S1z2 = ( + -48 + 40 * e_fact_sqrt - 84 * kappa1 + 99 * e_fact_sqrt * kappa1 + + 6 * e_2 * (16 + 28 * e_fact_sqrt + 28 * kappa1 + 51 * e_fact_sqrt * kappa1) + + e_4 * (-48 + (-84 + 45 * e_fact_sqrt) * kappa1) + ) * m1**4 * S1z * S1z + + m2_4_S2z2 = ( + -48 + 40 * e_fact_sqrt - 84 * kappa2 + 99 * e_fact_sqrt * kappa2 + + 6 * e_2 * (16 + 28 * e_fact_sqrt + 28 * kappa2 + 51 * e_fact_sqrt * kappa2) + + e_4 * (-48 + (-84 + 45 * e_fact_sqrt) * kappa2) + ) * m2**4 * S2z * S2z + + m1_3_m2 = 3 * m1**3 * m2 * S1z * ( + ( + -6 + 4 * e_fact_sqrt - 46 * kappa1 + 48 * e_fact_sqrt * kappa1 + + e_4 * (-6 - 6 * e_fact_sqrt - 46 * kappa1 + 25 * e_fact_sqrt * kappa1) + + e_2 * (12 + 55 * e_fact_sqrt + 92 * kappa1 + 158 * e_fact_sqrt * kappa1) + ) * S1z + + 2 * ( + -30 + 29 * e_fact_sqrt + + 5 * e_2 * (12 + 25 * e_fact_sqrt + 3 * e_2 * (-2 + e_fact_sqrt)) + ) * S2z + ) + + m1_m2_3 = 3 * m1 * m2**3 * S2z * ( + 2 * ( + -30 + 29 * e_fact_sqrt + + 5 * e_2 * (12 + 25 * e_fact_sqrt + 3 * e_2 * (-2 + e_fact_sqrt)) + ) * S1z + + ( + -6 + 4 * e_fact_sqrt - 46 * kappa2 + 48 * e_fact_sqrt * kappa2 + + e_4 * (-6 - 6 * e_fact_sqrt - 46 * kappa2 + 25 * e_fact_sqrt * kappa2) + + e_2 * (12 + 55 * e_fact_sqrt + 92 * kappa2 + 158 * e_fact_sqrt * kappa2) + ) * S2z + ) + + m1_2_m2_2 = m1**2 * m2**2 * ( + 9 * ( + 2 - 2 * e_fact_sqrt - 8 * kappa1 + 6 * e_fact_sqrt * kappa1 + + e_4 * (2 - 2 * e_fact_sqrt - 8 * kappa1 + 6 * e_fact_sqrt * kappa1) + + e_2 * (-4 + 3 * e_fact_sqrt + 4 * (4 + 7 * e_fact_sqrt) * kappa1) + ) * S1z * S1z + + 2 * ( + -174 + 175 * e_fact_sqrt + + 348 * e_2 * (1 + 2 * e_fact_sqrt) + + 6 * e_4 * (-29 + 11 * e_fact_sqrt) + ) * S1z * S2z + + 9 * ( + 2 - 2 * e_fact_sqrt - 8 * kappa2 + 6 * e_fact_sqrt * kappa2 + + e_4 * (2 - 2 * e_fact_sqrt - 8 * kappa2 + 6 * e_fact_sqrt * kappa2) + + e_2 * (-4 + 3 * e_fact_sqrt + 4 * (4 + 7 * e_fact_sqrt) * kappa2) + ) * S2z * S2z + ) + + term_const = -(m1_4_S1z2 + m2_4_S2z2 + m1_3_m2 + m1_m2_3 + m1_2_m2_2) + + # --- cos(u) terms --- + cosu_m1_4_S1z2 = 2 * ( + 12 + 68 * e_fact_sqrt + 3 * (7 + 36 * e_fact_sqrt) * kappa1 + + 3 * e_4 * (4 + 7 * kappa1) + + 3 * e_2 * (-8 + 12 * e_fact_sqrt - 14 * kappa1 + 39 * e_fact_sqrt * kappa1) + ) * m1**4 * S1z * S1z + + cosu_m2_4_S2z2 = 2 * ( + 12 + 68 * e_fact_sqrt + 3 * (7 + 36 * e_fact_sqrt) * kappa2 + + 3 * e_4 * (4 + 7 * kappa2) + + 3 * e_2 * (-8 + 12 * e_fact_sqrt - 14 * kappa2 + 39 * e_fact_sqrt * kappa2) + ) * m2**4 * S2z * S2z + + cosu_m1_3_m2 = 3 * m1**3 * m2 * S1z * ( + ( + 20 * e_fact_sqrt + 33 * e_2 * e_fact_sqrt + + 110 * e_fact_sqrt * kappa1 + 121 * e_2 * e_fact_sqrt * kappa1 + + e_fact_m1 * (3 + 23 * kappa1) + ) * S1z + + (152 * e_fact_sqrt + 186 * e_2 * e_fact_sqrt + 30 * e_fact_m1) * S2z + ) + + cosu_m1_m2_3 = 3 * m1 * m2**3 * S2z * ( + (152 * e_fact_sqrt + 186 * e_2 * e_fact_sqrt + 30 * e_fact_m1) * S1z + + ( + 20 * e_fact_sqrt + 33 * e_2 * e_fact_sqrt + + 110 * e_fact_sqrt * kappa2 + 121 * e_2 * e_fact_sqrt * kappa2 + + e_fact_m1 * (3 + 23 * kappa2) + ) * S2z + ) + + cosu_m1_2_m2_2 = m1**2 * m2**2 * ( + 9 * ( + e_4 * (-1 + 4 * kappa1) + + (1 + 4 * e_fact_sqrt) * (-1 + 4 * kappa1) + + e_2 * (2 + 3 * e_fact_sqrt - 8 * kappa1 + 24 * e_fact_sqrt * kappa1) + ) * S1z * S1z + + 2 * ( + 87 + 87 * e_4 + 466 * e_fact_sqrt + + 3 * e_2 * (-58 + 157 * e_fact_sqrt) + ) * S1z * S2z + + 9 * ( + e_4 * (-1 + 4 * kappa2) + + (1 + 4 * e_fact_sqrt) * (-1 + 4 * kappa2) + + e_2 * (2 + 3 * e_fact_sqrt - 8 * kappa2 + 24 * e_fact_sqrt * kappa2) + ) * S2z * S2z + ) + + term_cosu = e * ( + cosu_m1_4_S1z2 + cosu_m2_4_S2z2 + cosu_m1_3_m2 + cosu_m1_m2_3 + cosu_m1_2_m2_2 + ) * cos_u + + r_3pn_SS = -0.05555555555555555 * (term_const + term_cosu) / (e_fact_35 * M_fact_4) + + return r_3pn_SS + +def separation( + u: float, + eta: float, + x: float, + e: float, + m1: float, + m2: float, + S1z: float, + S2z: float, + ) -> float: + """Relative separation r(u) at 3PN order, including spin effects.""" +# 3PN accurate + sqx = np.sqrt(x) + return ((1.0 / x) * rel_sep_0pn(e, u) + rel_sep_1pn(e, u, eta) + + rel_sep_1_5pn(e, u, m1, m2, S1z, S2z) * sqx + + rel_sep_2pnSS(e, u, m1, m2, S1z, S2z) * x + + rel_sep_2pn(e, u, eta) * x + + rel_sep_2_5pn_SO(e, u, m1, m2, S1z, S2z) * x * sqx + + rel_sep_3pn(e, u, eta) * x * x + + rel_sep_3pn_SS(e, u, m1, m2, S1z, S2z) * x * x) + + +# ================== ODE right-hand-side dispatchers ========================== + +def dx_dt(radiation_pn_order: int, + eta: float, m1: float, m2: float, S1z: float, S2z: float, + x: float, e: float) -> float: + + x2 = x * x + x3 = x2 * x + x5 = x3*x2 + sqx = np.sqrt(x) + xsqx = x * sqx + x2sqx = x2 * sqx + x3sqx = x3 * sqx + + # ------------------------- + # Instantaneous PN part + # ------------------------- + inst = x_dot_0pn(e, eta) + + if radiation_pn_order >= 2: + inst += x_dot_1pn(e, eta) * x + + if radiation_pn_order >= 3: + inst += x_dot_1_5_pn(e, eta, m1, m2, S1z, S2z) * xsqx + + if radiation_pn_order >= 4: + inst += x_dot_2pn(e, eta, x) * x2 + inst += x_dot_2pn_SS(e, eta, m1, m2, S1z, S2z) * x2 + + if radiation_pn_order >= 5: + inst += x_dot_2_5pn_SO(e, eta, m1, m2, S1z, S2z) * x2sqx + inst += x_dot_2_5pn_SF(e, eta, S1z) * x2sqx + + if radiation_pn_order >= 6: + inst += x_dot_3pn(e, eta, x) * x3 + inst += x_dot_3pn_SO(e, eta, m1, m2, S1z, S2z) * x3 + inst += x_dot_3pn_SS(e, eta, m1, m2, S1z, S2z) * x3 + + if radiation_pn_order >= 7: + inst += x_dot_3_5pnSO(e, eta, m1, m2, S1z, S2z) * x3sqx + inst += x_dot_3_5_pn(e, eta) * x3sqx + inst += x_dot_3_5pn_SS(e, eta, m1, m2, S1z, S2z) * x3sqx + inst += x_dot_3_5pn_cubicSpin(e, eta, m1, m2, S1z, S2z) * x3sqx + inst += x_dot_3_5pn_SF(e, eta, S1z) * x3sqx + + if radiation_pn_order >= 8: + inst += ( + x_dot_4pn(e, eta, x) + + x_dot_4pnSO(e, eta, m1, m2, S1z, S2z) + + x_dot_4pnSS(e, eta, m1, m2, S1z, S2z) + ) * (x2 * x2) + inst += x_dot_4pn_SF(e, eta, S1z) * (x2 * x2) + + if radiation_pn_order >= 9: + inst += x_dot_4_5_pn(e, eta, x) * (x2 * x2) * sqx + + if radiation_pn_order >= 10: + inst += 0.0 #dxdt_5pn(x, eta), not implemented + + if radiation_pn_order >= 11: + inst += 0.0 #dxdt_5_5pn(x, eta), not implemented + + if radiation_pn_order >= 12: + inst += 0.0 #dxdt_6pn(x, eta), not implemented + + # multiply ONLY instantaneous part + result = inst * x5 + + # ------------------------- + # Hereditary part (NO x^5) + # ------------------------- + if radiation_pn_order >= 3: + result += x_dot_hereditary_1_5(e, eta, x) + + if radiation_pn_order >= 5: + result += x_dot_hereditary_2_5(e, eta, x) + + if radiation_pn_order >= 6: + result += x_dot_hereditary_3(e, eta, x) + + return result + + +def de_dt(radiation_pn_order: int, + eta: float, m1: float, m2: float, S1z: float, S2z: float, + x: float, e: float) -> float: + + x2 = x * x + x3 = x2 * x + x4 = x2 * x2 + sqx = np.sqrt(x) + xsqx = x * sqx + x2sqx = x2 * sqx + x3sqx = x3 * sqx + + # ------------------------- + # Instantaneous PN part + # ------------------------- + inst = e_dot_0pn(e, eta) + + if radiation_pn_order >= 1: + inst += e_dot_1pn(e, eta) * x + + if radiation_pn_order >= 3: + inst += e_dot_1_5pn_SO(e, m1, m2, S1z, S2z) * xsqx + + if radiation_pn_order >= 4: + inst += e_dot_2pn(e, eta) * x2 + inst += e_dot_2pn_SS(e, m1, m2, S1z, S2z) * x2 + + if radiation_pn_order >= 5: + inst += e_dot_2_5pn_SO(e, m1, m2, S1z, S2z) * x2sqx + + if radiation_pn_order >= 6: + inst += e_dot_3pn(e, eta, x) * x3 + inst += e_dot_3pn_SO(e, m1, m2, S1z, S2z) * x3 + inst += e_dot_3pn_SS(e, m1, m2, S1z, S2z) * x3 + + if radiation_pn_order >= 7: + inst += e_dot_3_5pn(e, eta) * x3sqx + + if radiation_pn_order >= 8: + inst += 0.0 # placeholder for higher order terms, not implemented + + if radiation_pn_order >= 9: + inst += 0.0 # placeholder for higher order terms, not implemented + + if radiation_pn_order >= 10: + inst += 0.0 # placeholder for higher order terms, not implemented + + if radiation_pn_order >= 11: + inst += 0.0 # placeholder for higher order terms, not implemented + + if radiation_pn_order >= 12: + inst += 0.0 # placeholder for higher order terms, not implemented + + # multiply only instantaneous part + result = inst * x4 + + # ------------------------- + # Hereditary part (NOT multiplied by x^4) + # ------------------------- + if radiation_pn_order >= 3: + result += e_rad_hereditary_1_5(e, eta, x) + + if radiation_pn_order >= 5: + result += e_rad_hereditary_2_5(e, eta, x) + + if radiation_pn_order >= 6: + result += e_rad_hereditary_3(e, eta, x) + + return result + + +def dl_dt(eta: float, m1: float, m2: float, S1z: float, S2z: float, + x: float, e: float) -> float: + """dl/dt — 3PN accurate with spin corrections.""" + x32 = np.sqrt(x) * x + return ( + (1.0 + + x * l_dot_1pn(e, eta) + + x32 * l_dot_1_5pn_SO(e, m1, m2, S1z, S2z) + + x*x * l_dot_2pn(e, eta) + + x*x * l_dot_2pn_SS(e, m1, m2, S1z, S2z) + + l_dot_2_5pn_SO(e, m1, m2, S1z, S2z)*x32*x + + x**3 * l_dot_3pn(e, eta) + + x**3 * l_dot_3pn_SS(e, m1, m2, S1z, S2z)) * x32 + ) + + +def dphi_dt(u: float, eta: float, m1: float, m2: float, S1z: float, S2z: float, + x: float, e: float) -> float: + """dphi/dt — 4PN accurate.""" + x32 = np.sqrt(x) * x + return ( + (phi_dot_0pn(e, eta, u) + + x * phi_dot_1pn(e, eta, u) + + x32 * phi_dot_1_5_pnSO_ecc(e, m1, m2, S1z, S2z, u) + + x*x * phi_dot_2_pnSS_ecc(e, m1, m2, S1z, S2z, u) + + x*x * phi_dot_2pn(e, eta, u) + + x32*x * phi_dot_2_5pn_SO(e, m1, m2, S1z, S2z, u) + + x**3 * phi_dot_3pn(e, eta, u) + + x32*x32 * phi_dot_3pn_SS(e, m1, m2, S1z, S2z, u) + + phi_dot_4pn_SS(e, m1, m2, S1z, S2z)*x32*x*x32 + + phi_dot_4_5_pn(e, eta, x)*x32**3) * x32 + ) + + +# ================== Kepler equation solvers ========================== + +def pow1_3(x): return x ** (1.0/3.0) +def pow3(x): return x * x * x +def pow5(x): return x * x * x * x * x + +def mikkola_finder(eccentricity, mean_anomaly): + """ + Solves Kepler's equation using Mikkola's method for an initial guess. + """ + # Range reduction of mean_anomaly to [-pi, pi] + while mean_anomaly > np.pi: + mean_anomaly -= 2 * np.pi + while mean_anomaly < - np.pi: + mean_anomaly += 2 * np.pi + + # Compute the sign of l + sgn_mean_anomaly = 1.0 if mean_anomaly >= 0.0 else -1.0 + mean_anomaly *= sgn_mean_anomaly + + # compute alpha and beta of Mikkola Eq. (9a) + a = (1.0 - eccentricity) / (4.0 * eccentricity + 0.5) + b = (0.5 * mean_anomaly) / (4.0 * eccentricity + 0.5) + + # compute the sign of beta needed in Eq. (9b) + sgn_b = 1.0 if b >= 0.0 else -1.0 + + # Mikkola Eq. (9b) + z = pow1_3(b + sgn_b * np.sqrt(b * b + a * a * a)) + + # Mikkola Eq. (9c) + s = z - a / z + + # add the correction given in Mikkola Eq. (7) + s = s - 0.078 * pow5(s) / (1.0 + eccentricity) + + # finally Mikkola Eq. (8) gives u + ecc_anomaly = mean_anomaly + eccentricity * (3.0 * s - 4.0 * pow3(s)) + + # correct the sign of u + return ecc_anomaly * sgn_mean_anomaly + + +def pn_kepler_equation(eta, x, e, l): + """ + 3PN accurate Kepler equation solver using Newton's method. + """ + mean_anom_negative = False + u = 0.0 + tol = 1.0e-12 + + if l == 0: + return u + + # range reduction of the l + while l > np.pi: + l -= 2 * np.pi + while l < - np.pi: + l += 2 * np.pi + + # solve in the positive part of the orbit + if l < 0.0: + l = -l + mean_anom_negative = True + + newt_thresh = tol * abs(1.0 - e) + + # use mikkola for a guess + u = mikkola_finder(e, l) + + # high eccentricity case + if (e > 0.8) and (l < np.pi / 3.0): + trial = l / abs(1.0 - e) + if trial * trial > 6.0 * abs(1.0 - e): + # cubic term is dominant + if l < np.pi: + trial = pow1_3(6.0 * l) + u = trial + + # iterate using Newton's method to get solution + if e < 1.0: + newt_err = u - e * np.sin(u) - l + while abs(newt_err) > newt_thresh: + u -= newt_err / (1.0 - e * np.cos(u)) + newt_err = u - e * np.sin(u) - l + + return -u if mean_anom_negative else u + +# ================== ODE system for eccentric models ========================== + +def eccentric_x_model_odes(t, y, params): + """ + ODE system for eccentric gravitational wave models. + y = [x, e, l, phi] + """ + # Assuming params is an object or dictionary + eta = params.eta + m1 = params.m1 + m2 = params.m2 + S1z = params.S1z + S2z = params.S2z + radiation_pn_order = params.radiation_pn_order + + # Input variables + x, e, l = y[0], y[1], y[2] + + # Calculate eccentric anomaly + u = pn_kepler_equation(eta, x, e, l) + + dydt = [0.0, 0.0, 0.0, 0.0] + + if abs(e) > 1e-12: + dydt[0] = dx_dt(radiation_pn_order, eta, m1, m2, S1z, S2z, x, e) + dydt[1] = de_dt(radiation_pn_order, eta, m1, m2, S1z, S2z, x, e) + dydt[2] = dl_dt(eta, m1, m2, S1z, S2z, x, e) + dydt[3] = dphi_dt(u, eta, m1, m2, S1z, S2z, x, e) + else: + # zero eccentricity limit (arXiv:0909.0066) + dydt[0] = dx_dt(radiation_pn_order, eta, m1, m2, S1z, S2z, x, e) + dydt[1] = de_dt(radiation_pn_order, eta, m1, m2, S1z, S2z, x, e) + dydt[2] = dl_dt(eta, m1, m2, S1z, S2z, x, e) + dydt[3] = x * np.sqrt(x) + + # Check for NaN (equivalent to XLAL_REAL8_FAIL_NAN) + if np.isnan(dydt[0]) or np.isnan(dydt[1]): + # Raise an exception or return a specific error code + raise ValueError("ODE derivative calculated as NaN") + + return dydt + diff --git a/esigmapy/python_codes/esigma_pn_main.py b/esigmapy/python_codes/esigma_pn_main.py new file mode 100644 index 0000000..49f0c55 --- /dev/null +++ b/esigmapy/python_codes/esigma_pn_main.py @@ -0,0 +1,421 @@ +# Translated from LALSimESIGMA.c by Samanwaya Mukherjee, 2026 + +import numpy as np +from scipy.integrate import solve_ivp +from scipy.interpolate import CubicSpline +import math +from .esigma_pn_inspiral import * +from .esigma_go_terms import * +import lal + +# Constants (LAL equivalents) +LAL_PI = lal.PI +LAL_MTSUN_SI = lal.MTSUN_SI #4.925491025543576e-6, Solar mass in seconds +RadiationPNOrderDefault = 8 # Default radiation reaction PN order (3PN) + +import os +# from dataclasses import dataclass, field + +# ------------------------------------------------------------------ # +# Constants / defaults (set these to match your C macros) +# ------------------------------------------------------------------ # +L_MIN = 2 +L_MAX = 4 +ONLY_LeqM_MODES = False +ModePNOrderDefault = 8 +LAL_MRSUN_SI = 1.476625061404649e3 # solar mass in metres + +from dataclasses import dataclass + +@dataclass +class Params: + eta: float + radiation_pn_order: int + m1: float + m2: float + S1z: float + S2z: float + +# ------------------------------------------------------------------ # +# 1. compute_mode_from_dynamics +# ------------------------------------------------------------------ # +def compute_mode_from_dynamics( + l: int, + m: int, + x_vec: np.ndarray, # PN expansion parameter (length N) + phi_vec: np.ndarray, # orbital phase + phi_dot_vec: np.ndarray, # d(phi)/dt + r_vec: np.ndarray, # orbital separation + r_dot_vec: np.ndarray, # d(r)/dt + mass1: float, + mass2: float, + S1z: float, + S2z: float, + R: float, # source distance (m) + vpnorder: int, +) -> np.ndarray: # complex128, length N + """ + Compute the (l, m) spin-weighted spherical harmonic mode h_lm + at every time step from the orbital dynamics arrays. + + Mirrors the static C function compute_mode_from_dynamics(). + """ + total_mass = mass1 + mass2 + eta = (mass1 * mass2) / total_mass**2 + + # kv = _build_kepler_vars(eta, total_mass, S1z, S2z) + + length = len(x_vec) + h_lm = np.zeros(length, dtype=complex) + + for i in range(length): + # populate_kepler_params( + # kv, + # e=0.0, + # x=x_vec[i], + # r=r_vec[i] * total_mass, + # r_dot=r_dot_vec[i], + # phi_dot=phi_dot_vec[i] / total_mass, + # ) + h_lm[i] = ( + hlmGOresult( + l, m, total_mass, eta, + r_vec[i] * total_mass, + r_dot_vec[i], + phi_vec[i], + phi_dot_vec[i] / total_mass, + R, vpnorder, S1z, S2z, + x_vec[i], + ) + * LAL_MRSUN_SI + ) + + return h_lm + + +# ------------------------------------------------------------------ # +# 2. compute_strain_from_dynamics +# ------------------------------------------------------------------ # +def compute_strain_from_dynamics( + x_vec: np.ndarray, + phi_vec: np.ndarray, + phi_dot_vec: np.ndarray, + r_vec: np.ndarray, + r_dot_vec: np.ndarray, + mass1: float, + mass2: float, + S1z: float, + S2z: float, + euler_iota: float, + euler_beta: float, + R: float, + vpnorder: int, +) -> tuple[np.ndarray, np.ndarray]: + """ + Sum all (l, m) modes weighted by spin-weighted spherical harmonics + to produce the + and × strain polarizations. + + Mirrors the static C function compute_strain_from_dynamics(). + """ + length = len(x_vec) + h_plus = np.zeros(length, dtype=float) + h_cross = np.zeros(length, dtype=float) + + for ell in range(L_MIN, L_MAX + 1): + for em in range(-ell, ell + 1): + + if ONLY_LeqM_MODES and ell != abs(em): + continue + + hlm = compute_mode_from_dynamics( + ell, em, + x_vec, phi_vec, phi_dot_vec, r_vec, r_dot_vec, + mass1, mass2, S1z, S2z, R, vpnorder, + ) + + ylm = lal.SpinWeightedSphericalHarmonic( + euler_iota, euler_beta, -2, ell, em + ) + + hlm_times_ylm = hlm * ylm + h_plus += hlm_times_ylm.real + h_cross -= hlm_times_ylm.imag + + return h_plus, h_cross + + +# ------------------------------------------------------------------ # +# 3. XLALSimInspiralesigmaModeFromDynamics (public) +# ------------------------------------------------------------------ # +def inspiral_esigma_mode_from_dynamics( + l: int, + m: int, + t_vector: np.ndarray, + x_vector: np.ndarray, + phi_vector: np.ndarray, + phi_dot_vector: np.ndarray, + r_vector: np.ndarray, + r_dot_vector: np.ndarray, + mass1: float, + mass2: float, + S1z: float, + S2z: float, + R: float, +) -> np.ndarray: # complex128 + """ + Public wrapper: compute a single (l, m) waveform mode from dynamics. + + Note: in the C version this modifies t_vec, r_vec, phi_dot_vec in place + by scaling by total_mass. Here we keep the arrays immutable and do the + scaling inside compute_mode_from_dynamics (matches the C end result). + """ + mode_pn_order = int(os.environ.get("ModePNOrder", ModePNOrderDefault)) + + return compute_mode_from_dynamics( + l, m, + x_vector, phi_vector, phi_dot_vector, + r_vector, r_dot_vector, + mass1, mass2, S1z, S2z, R, mode_pn_order, + ) + + +# ------------------------------------------------------------------ # +# 4. XLALSimInspiralesigmaStrainFromDynamics (public) +# ------------------------------------------------------------------ # +def esigma_strain_from_dynamics( + t_vector: np.ndarray, + x_vector: np.ndarray, + phi_vector: np.ndarray, + phi_dot_vector: np.ndarray, + r_vector: np.ndarray, + r_dot_vector: np.ndarray, + mass1: float, + mass2: float, + S1z: float, + S2z: float, + euler_iota: float, + euler_beta: float, + R: float, +) -> tuple[np.ndarray, np.ndarray]: + """ + Public wrapper: compute h+ and hx polarizations from dynamics arrays. + Returns (h_plus, h_cross) as 1-D float64 arrays. + """ + mode_pn_order = int(os.environ.get("ModePNOrder", ModePNOrderDefault)) + + return compute_strain_from_dynamics( + x_vector, phi_vector, phi_dot_vector, + r_vector, r_dot_vector, + mass1, mass2, S1z, S2z, + euler_iota, euler_beta, R, mode_pn_order, + ) + + +# ------------------------------------------------------------------ # +# 5. x_model_eccbbh_inspiral_waveform (internal, called by esigma) +# ------------------------------------------------------------------ # +def x_model_eccbbh_inspiral_waveform( + mass1: float, # solar masses + mass2: float, + S1z: float, + S2z: float, + e_init: float, + f_gw_init: float, # Hz + distance: float, # metres + mean_anom_init: float, + ode_eps: float, + euler_iota: float, + euler_beta: float, + sampling_rate: float, # Hz +) -> tuple[np.ndarray, np.ndarray]: + """ + Drive the full esigma inspiral: + 1. integrate orbital dynamics, + 2. project onto polarizations. + + Returns (h_plus, h_cross) as float64 arrays. + """ + # --- Step 1: orbital dynamics ----------------------------------- # + dyn = inspiral_esigma_dynamics( + mass1, mass2, S1z, S2z, + e_init, f_gw_init, mean_anom_init, + ode_eps, sampling_rate, + ) + + # --- Step 2: strain from dynamics ------------------------------- # + h_plus, h_cross = esigma_strain_from_dynamics( + dyn["time_evol"], + dyn["x_evol"], + dyn["phi_evol"], + dyn["phi_dot_evol"], + dyn["r_evol"], + dyn["r_dot_evol"], + mass1, mass2, S1z, S2z, + euler_iota, euler_beta, distance, + ) + + return h_plus, h_cross + +def inspiral_esigma_dynamics( + mass1, # mass1 in solar mass + mass2, # mass2 in solar mass + S1z, # z-component of spin of companion 1 + S2z, # z-component of spin of companion 2 + e_init, # initial eccentricity + f_gw_init, # initial GW frequency + mean_anom_init, # initial mean anomaly + ode_eps, # tolerance (relative) + sampling_rate, # sample rate in Hz +): + """ + Compute ESIGMA orbital dynamics via ODE integration, then interpolate + to a uniform time grid. + + Returns a dict with keys: + time_evol, x_evol, eccentricity_evol, mean_ano_evol, + phi_evol, phi_dot_evol, r_evol, r_dot_evol + Each value is a 1D numpy array sampled at 1/sampling_rate intervals. + """ + + # ------------------------------------------------------------------ # + # Input validation + # ------------------------------------------------------------------ # + if not (0.0 <= e_init < 1.0): + raise ValueError("Invalid eccentricity, must be in range [0, 1)") + + # ------------------------------------------------------------------ # + # Mass / PN bookkeeping + # ------------------------------------------------------------------ # + total_mass = mass1 + mass2 + reduced_mass = mass1 * mass2 / total_mass + eta = reduced_mass / total_mass # symmetric mass ratio + + omega_init = LAL_PI * f_gw_init * LAL_MTSUN_SI + x_init = (total_mass * omega_init) ** (2.0 / 3.0) + + # PN / radiation order (mirror the C env-var logic; default hardcoded) + import os + rad_pn_order = int(os.environ.get("RadiationPNOrder", RadiationPNOrderDefault)) + params = Params( + eta=eta, + radiation_pn_order=rad_pn_order, + m1=mass1, + m2=mass2, + S1z=S1z, + S2z=S2z, + ) + + # ------------------------------------------------------------------ # + # Termination condition: ISCO + # ------------------------------------------------------------------ # + TRANS = float(os.environ.get("InspiralEndRadius", 4.0)) + f_gw_isco = 1.0 / (TRANS * math.sqrt(TRANS) * LAL_PI * total_mass) + x_final = (LAL_PI * total_mass * f_gw_isco) ** (2.0 / 3.0) + + # ------------------------------------------------------------------ # + # Time step in geometric units + # ------------------------------------------------------------------ # + dt_sec = 1.0 / sampling_rate # seconds + dt = dt_sec / (total_mass * LAL_MTSUN_SI) # geometric (M) + + # ------------------------------------------------------------------ # + # Initial conditions y = [x, e, l (mean anomaly), phi] + # ------------------------------------------------------------------ # + + y0 = np.array([x_init, e_init, mean_anom_init, 0.0]) + + # Define the ODE system as a function of (t, y) + def rhs(t, y): + return eccentric_x_model_odes(t, y, params) + + MAX_SAMPLES = 2048 * 16384 # maximum number of samples to prevent infinite loops; adjust as needed + + #=========== ODE solver using solve_ivp (LSODA)=========================# + + #--- define the termination condition as an event function ---# + def isco_event(t, y): + return y[0] - x_final # stop when x >= x_final + + isco_event.terminal = True + isco_event.direction = 1 + t_max = MAX_SAMPLES * dt + #----------------------------------- + sol = solve_ivp( + rhs, + (0.0, t_max), # large upper bound; event will stop earlier + y0, + method="LSODA", + rtol=ode_eps, + atol=1e-25, + max_step=dt, + events=isco_event, + ) + t_arr = sol.t + y_arr = sol.y.T # shape (N, 4) + + # NaN / Inf guard (equivalent to your check) + bad_number = False + if not np.all(np.isfinite(y_arr)): + bad_number = True + + # Unpack variables + x_arr = y_arr[:, 0] + e_arr = y_arr[:, 1] + l_arr = y_arr[:, 2] + phi_arr = y_arr[:, 3] + + u_arr = np.empty_like(x_arr) + r_arr = np.empty_like(x_arr) + + for i, (x, e, l) in enumerate(zip(x_arr, e_arr, l_arr)): + ui = pn_kepler_equation(eta, x, e, l) + ri = separation(ui, eta, x, e, mass1, mass2, S1z, S2z) + u_arr[i] = ui + r_arr[i] = ri + + final_i = len(t_arr) + if final_i < 4: + raise RuntimeError("Integration produced fewer than 4 points; cannot interpolate.") + elif bad_number: + raise ValueError("Infinity or nan encountered!") + + # ------------------------------------------------------------------ # + # Uniform-grid interpolation + # ------------------------------------------------------------------ # + t_final = t_arr[-1] + Length = int(math.ceil(t_final / dt)) + + if Length < 2: + raise RuntimeError("Output length < 2; waveform too short.") + + uniform_t = np.arange(Length) * dt # [0, dt, 2·dt, …] + + def interp_uniform(t_raw, y_raw): + cs = CubicSpline(t_raw, y_raw) + return cs(uniform_t) + + def interp_deriv_uniform(t_raw, y_raw): + cs = CubicSpline(t_raw, y_raw) + return cs(uniform_t, 1) # first derivative + + uniform_x = interp_uniform(t_arr, x_arr) + uniform_phi = interp_uniform(t_arr, phi_arr) + uniform_phi_dot = interp_deriv_uniform(t_arr, phi_arr) + uniform_r = interp_uniform(t_arr, r_arr) + uniform_r_dot = interp_deriv_uniform(t_arr, r_arr) + uniform_e = interp_uniform(t_arr, e_arr) + uniform_l = interp_uniform(t_arr, l_arr) + + # Convert time back to seconds for the caller + uniform_t_sec = uniform_t * (total_mass * LAL_MTSUN_SI) + + return { + "time_evol": uniform_t_sec, + "x_evol": uniform_x, + "eccentricity_evol": uniform_e, + "mean_ano_evol": uniform_l, + "phi_evol": uniform_phi, + "phi_dot_evol": uniform_phi_dot, + "r_evol": uniform_r, + "r_dot_evol": uniform_r_dot, + } \ No newline at end of file diff --git a/esigmapy/python_codes/generator_python.py b/esigmapy/python_codes/generator_python.py new file mode 100644 index 0000000..5552a1d --- /dev/null +++ b/esigmapy/python_codes/generator_python.py @@ -0,0 +1,1041 @@ +# Written by Samanwaya Mukherjee (2026), based on generator.py in esigmapy +# +"""Functions specific to generating ESIGMA waveforms""" + +# from __future__ import absolute_import + +import numpy as np +import time +import esigmapy +import lal +import lalsimulation as ls +import pycbc.types as pt +from ..utils import f_ISCO_spin +from .esigma_pn_main import * +from ..generator import _get_transition_frequency_window + +ECCENTRICITY_LEVEL_ISCO_WARNING = 0.02 +ECCENTRICITY_LEVEL_ISCO_ERROR = 0.1 + + +def eccentricity_at_extremum_frequency_py( + mass1, + mass2, + spin1z, + spin2z, + e0, + l0, + f_lower, + sample_rate, + f_extremum, + extremum="periastron", + show_figures=False, + verbose=False, +): + """ """ + if extremum.lower() not in ["periastron", "apastron"]: + raise IOError("Allowed values for extremum: periastron, apastron") + + def find_x_for_y(x, y, y0): + return x[abs(y - y0).argmin()] + + itime = time.perf_counter() + retval = inspiral_esigma_dynamics( + mass1, mass2, spin1z, spin2z, e0, f_lower, l0, 1e-12, sample_rate, + ) + # t, x, e, l, phi, phidot, r, rdot = retval[:8] + + t = np.asarray(retval["time_evol"]) + x = np.asarray(retval["x_evol"]) + e = np.asarray(retval["eccentricity_evol"]) + l = np.asarray(retval["mean_ano_evol"]) + phi = np.asarray(retval["phi_evol"]) + phidot = np.asarray(retval["phi_dot_evol"]) + r = np.asarray(retval["r_evol"]) + rdot = np.asarray(retval["r_dot_evol"]) + + t *= lal.MTSUN_SI + + if verbose: + print(f"Orbital evolution took: {time.perf_counter() - itime} seconds") + + omega = pt.TimeSeries(phidot, delta_t=t[1] - t[0]) + if extremum == "periastron": + ( + extremum_frequencies_times, + extremum_frequencies, + ) = esigmapy.utils.get_peak_freqs(omega) + else: + ( + extremum_frequencies_times, + extremum_frequencies, + ) = esigmapy.utils.get_peak_freqs(-1 * omega) + extremum_frequencies *= -1 + + piMf_extremum = f_extremum * lal.PI * (mass1 + mass2) * lal.MTSUN_SI + time_at_sensitive_freq = find_x_for_y( + extremum_frequencies_times, extremum_frequencies, piMf_extremum + ) + idx_e0 = abs(t - time_at_sensitive_freq).argmin() + e0 = e[idx_e0] + + if show_figures: + import matplotlib.pyplot as plt + + fig, (ax1) = plt.subplots(1, 1, figsize=(10, 5), sharex=True) + ax2 = ax1 + ax1.plot( + extremum_frequencies_times, + extremum_frequencies, + "o", + markersize=1, + label="extrema", + ) + ax1.plot(t, x**1.5, "--", label="x ** {3/2}") + ax1.plot(t, phidot, lw=0.5, label="omega") + + ax1.axhline(piMf_extremum, c="c", lw=1) + ax1.axvline(time_at_sensitive_freq, c="r", lw=1) + + ax1.set_xlabel("Time (s)") + ax1.set_ylabel("Orbital angular velocity") + + ax = plt.twinx(ax2) + ax.plot(t, e, label="eccentricity", lw=1) + ax.axhline(e0, c="k", lw=1) + ax.set_xlim(ax1.get_xlim()) + + ax1.legend(loc="center left") + ax.legend(loc="upper center") + + return e0, fig + + return e0 + + +def eccentricity_at_reference_frequency_py( + mass1, + mass2, + spin1z, + spin2z, + e0, + l0, + f_lower, + sample_rate, + f_reference, + show_figures=False, + verbose=False, +): + """ """ + itime = time.perf_counter() + retval = inspiral_esigma_dynamics( + mass1, mass2, spin1z, spin2z, e0, f_lower, l0, 1e-12, sample_rate, + ) + # t, x, e, l, phi, phidot, r, rdot = retval[:8] + + t = np.asarray(retval["time_evol"]) + x = np.asarray(retval["x_evol"]) + e = np.asarray(retval["eccentricity_evol"]) + l = np.asarray(retval["mean_ano_evol"]) + phi = np.asarray(retval["phi_evol"]) + phidot = np.asarray(retval["phi_dot_evol"]) + r = np.asarray(retval["r_evol"]) + rdot = np.asarray(retval["r_dot_evol"]) + + t *= lal.MTSUN_SI + + if verbose: + print(f"Orbital evolution took: {time.perf_counter() - itime} seconds") + + x_reference = (np.pi * (mass1 + mass2) * lal.MTSUN_SI * f_reference) ** (2.0 / 3.0) + + idx_e0 = abs(x - x_reference).argmin() + e0 = e[idx_e0] + + if show_figures: + import matplotlib.pyplot as plt + + fig, (ax) = plt.subplots(1, 1, figsize=(10, 5), sharex=True) + ax.plot(t, x**1.5, "--", label="x ** {3/2}") + ax.plot(t, phidot, lw=0.5, label="omega") + ax.axhline(x_reference**1.5, color="c", lw=1) + ax.axvline(t[idx_e0], color="r", lw=1) + ax.set_xlabel("Time (s)") + ax.set_ylabel("Orbital angular velocity") + + ax2 = plt.twinx(ax) + ax2.plot(t, e, label="eccentricity", lw=1) + ax2.axhline(e0, c="k", lw=1) + ax2.set_xlim(ax.get_xlim()) + + ax.legend(loc="center left") + ax2.legend(loc="upper center") + return e0, fig + + return e0 + + +def get_inspiral_esigma_modes_py( + mass1, + mass2, + f_lower, + delta_t, + spin1z=0.0, + spin2z=0.0, + eccentricity=0.0, + mean_anomaly=0.0, + distance=1.0, + f_ref=None, + modes_to_use=[(2, 2), (3, 3), (4, 4)], + include_conjugate_modes=True, + return_orbital_params=False, + return_pycbc_timeseries=True, + verbose=False, +): + """ + Returns inspiral ESIGMA GW modes + + Parameters: + ----------- + mass1, mass2 -- Binary's component masses (in solar masses) + f_lower -- Starting frequency of the waveform (in Hz) + f_ref -- Reference frequency at which to define the waveform + parameters. + We require f_ref <= f_lower. f_ref = f_lower by default. + delta_t -- Waveform's time grid-spacing (in s) + spin1z, spin2z -- z-components of component dimensionless spins (lies in [0,1)) + eccentricity -- Initial eccentricity + mean_anomaly -- Mean anomaly of the periastron (in rad) + distance -- Luminosity distance to the binary (in Mpc) + modes_to_use -- GW modes to use. List of tuples (l, |m|) + include_conjugate_modes -- If True, (l, -|m|) modes are included as well + return_orbital_params -- If True, returns the orbital evolution of all the orbital elements. + Can also be a list of orbital variable names to return only those + specific variables. Available orbital variables are: + ['x', 'e', 'l', 'phi', 'phidot', 'r', 'rdot'] + return_pycbc_timeseries -- If True, returns data in the form of PyCBC timeseries. + True by default. + verbose -- Verbosity flag + + Returns: + -------- + t -- Time grid (in seconds). + Returned only if return_pycbc_timeseries=False + orbital_var_dict -- Dictionary of evolution of orbital elements. + Returned only if "return_orbital_params" is specified + modes -- Dictionary of GW modes + """ + + if return_orbital_params: + all_orbital_var_names = ["x", "e", "l", "phi", "phidot", "r", "rdot"] + if return_orbital_params != True: + for name in return_orbital_params: + if name not in all_orbital_var_names: + raise Exception( + f"{name} is not a valid orbital variable name. Available orbital variable names are: {all_orbital_var_names}." + ) + + # Calculating the orbital variables, depending on user input. + # Use of `f_ref` is activated + f_start = f_lower + if f_ref is None: + f_start = f_lower + f_ref = f_lower + itime = time.perf_counter() + elif f_ref > f_lower: + # Calculating new orbital variables + # itime = time.perf_counter() + # retval = ls.SimInspiralESIGMADynamicsBackwardInTime( + # mass1, + # mass2, + # spin1z, + # spin2z, + # eccentricity, + # f_ref, + # f_lower, + # mean_anomaly, + # 1e-12, + # 1 / delta_t, + # ) + # t, x, e, l, phi, phidot, r, rdot = retval[:8] + # eccentricity = e[-1] + # mean_anomaly = l[-1] + # f_start = f_lower + raise ValueError('fref > flow case is currently not supported. Please set f_ref <= f_lower.') + elif f_ref < f_lower: + itime = time.perf_counter() + f_start = f_ref + + retval = inspiral_esigma_dynamics( + mass1, + mass2, + spin1z, + spin2z, + eccentricity, + f_start, + mean_anomaly, + 1e-12, + 1 / delta_t, + ) + + if f_ref < f_lower: + x = np.asarray(retval["x_evol"]) + ref_idx = np.searchsorted( + (x ** 1.5) / ((mass1 + mass2) * lal.MTSUN_SI * np.pi), + f_lower, + ) + + for key in retval: + retval[key] = np.asarray(retval[key])[ref_idx:] + + # t, x, e, l, phi, phidot, r, rdot = retval[:8] + t = np.asarray(retval["time_evol"]) + x = np.asarray(retval["x_evol"]) + e = np.asarray(retval["eccentricity_evol"]) + l = np.asarray(retval["mean_ano_evol"]) + phi = np.asarray(retval["phi_evol"]) + phidot = np.asarray(retval["phi_dot_evol"]) + r = np.asarray(retval["r_evol"]) + rdot = np.asarray(retval["r_dot_evol"]) + + t *= ( + mass1 + mass2 + ) * lal.MTSUN_SI # Time from geometrized units to seconds + + if verbose: + print(f"Orbital evolution took: {time.perf_counter() - itime} seconds") + + # Include conjugate modes in the mode list + if include_conjugate_modes: + for el, em in modes_to_use: + if (el, -em) not in modes_to_use: + modes_to_use.append((el, -em)) + + itime = time.perf_counter() + modes = {} + distance *= 1.0e6 * lal.PC_SI # Mpc to SI conversion + for el, em in modes_to_use: + modes[(el, em)] = inspiral_esigma_mode_from_dynamics( + el, + em, + t, + x, + phi, + phidot, + r, + rdot, + mass1, + mass2, + spin1z, + spin2z, + distance, + ) + + if return_pycbc_timeseries: + modes = { + k: pt.TimeSeries( + modes[k], + delta_t=delta_t, + epoch=-delta_t * (len(modes[k]) - 1), + ) + for k in modes + } + else: + modes = {k: np.asarray(modes[k]) for k in modes} + + if verbose: + print(f"Modes generation took: {time.perf_counter() - itime} seconds") + + if return_orbital_params: + orbital_var_dict = {} + if return_orbital_params == True: + return_orbital_params = all_orbital_var_names + + if return_pycbc_timeseries: + for name in return_orbital_params: + exec( + f"orbital_var_dict['{name}'] = pt.TimeSeries({name}, delta_t=delta_t, epoch=-delta_t * len({name}))" + ) + return orbital_var_dict, modes + + for name in return_orbital_params: + exec(f"orbital_var_dict['{name}'] = {name}") + return (t - t[-1]), orbital_var_dict, modes + + if return_pycbc_timeseries: + return modes + return (t - t[-1]), modes + + +def get_inspiral_esigma_waveform_py( + mass1, + mass2, + f_lower, + delta_t, + spin1z=0.0, + spin2z=0.0, + eccentricity=0.0, + mean_anomaly=0.0, + inclination=0.0, + coa_phase=0.0, + distance=1.0, + f_ref=None, + modes_to_use=[(2, 2), (3, 3), (4, 4)], + return_orbital_params=False, + return_pycbc_timeseries=True, + verbose=False, + **kwargs, +): + """ + Returns inspiral ESIGMA GW polarizations + + Parameters: + ----------- + mass1, mass2 -- Binary's component masses (in solar masses) + f_lower -- Starting frequency of the waveform (in Hz) + f_ref -- Reference frequency at which to define the waveform + parameters. + We require f_ref <= f_lower. f_ref = f_lower by default. + delta_t -- Waveform's time grid-spacing (in s) + spin1z, spin2z -- z-components of component dimensionless spins (lies in [0,1)) + eccentricity -- Initial eccentricity + mean_anomaly -- Mean anomaly of the periastron (in rad) + inclination -- Inclination (in rad), defined as the angle between the orbital + angular momentum L and the line-of-sight + coa_phase -- Coalesence phase of the binary (in rad) + distance -- Luminosity distance to the binary (in Mpc) + modes_to_use -- GW modes to use. List of tuples (l, |m|) + return_orbital_params -- If True, returns the orbital evolution of all the orbital elements (in + geometrized units). Can also be a list of orbital variable names to return + only those specific variables. Available orbital variables names are: + ['x', 'e', 'l', 'phi', 'phidot', 'r', 'rdot'] + return_pycbc_timeseries -- If True, returns data in the form of PyCBC timeseries. + True by default + verbose -- Verbosity level. Available values are: 0, 1, 2 + + Returns: + -------- + t -- Time grid (in seconds). + Returned only if return_pycbc_timeseries=False + orbital_var_dict -- Dictionary of evolution of orbital elements. + Returned only if "return_orbital_params" is specified + hp, hc -- Plus and cross GW polarizations + """ + + retval = get_inspiral_esigma_modes_py( + mass1=mass1, + mass2=mass2, + spin1z=spin1z, + spin2z=spin2z, + eccentricity=eccentricity, + mean_anomaly=mean_anomaly, + distance=distance, + f_ref=f_ref, + f_lower=f_lower, + delta_t=delta_t, + modes_to_use=modes_to_use, + include_conjugate_modes=True, # Always include conjugate modes while generating polarizations + return_orbital_params=return_orbital_params, + verbose=verbose, + return_pycbc_timeseries=False, + ) + + if return_orbital_params: + t, orbital_var_dict, modes_inspiral = retval + else: + t, modes_inspiral = retval + + hp, hc = esigmapy.utils.get_polarizations_from_multipoles( + modes_inspiral, + inclination=inclination, + coa_phase=np.pi / 2 - coa_phase, + verbose=verbose, + ) + + if return_pycbc_timeseries: + hp = pt.TimeSeries(hp, delta_t=delta_t, epoch=-delta_t * (len(hp) - 1)) + hc = pt.TimeSeries(hc, delta_t=delta_t, epoch=-delta_t * (len(hc) - 1)) + + if return_orbital_params: + if return_pycbc_timeseries: + for name in orbital_var_dict: + exec( + f"orbital_var_dict['{name}'] = pt.TimeSeries(orbital_var_dict['{name}'], delta_t=delta_t, epoch=-delta_t * (len(orbital_var_dict['{name}'])-1))" + ) + return orbital_var_dict, hp, hc + return t, orbital_var_dict, hp, hc + + if return_pycbc_timeseries: + return hp, hc + return t, hp, hc + + +def get_imr_esigma_modes_py( + mass1, + mass2, + f_lower, + delta_t, + spin1z=0.0, + spin2z=0.0, + eccentricity=0.0, + mean_anomaly=None, + coa_phase=None, + distance=1.0, + f_ref=None, + modes_to_use=[(2, 2), (3, 3), (4, 4)], + mode_to_align_by=(2, 2), + include_conjugate_modes=True, + f_mr_transition=None, + f_window_mr_transition=None, + num_hyb_orbits=0.25, + blend_using_avg_orbital_frequency=True, + blend_aligning_merger_to_inspiral=True, + keep_f_mr_transition_at_center=False, + merger_ringdown_approximant="NRSur7dq4", + return_hybridization_info=False, + return_orbital_params=False, + failsafe=True, + verbose=False, +): + """ + Returns IMR GW modes constructed using ESIGMA for inspiral and + NRSur7dq4/SEOBNRv4PHM for merger-ringdown + + Parameters: + ----------- + mass1, mass2 -- Binary's component masses (in solar masses) + f_lower -- Starting frequency of the waveform (in Hz) + f_ref -- Reference frequency at which to define the + waveform parameters. + We require f_ref <= f_lower. + f_ref = f_lower by default. + delta_t -- Waveform's time grid-spacing (in s) + spin1z, spin2z -- z-components of component dimensionless + spins (lies in [0,1)) + eccentricity -- Initial eccentricity + mean_anomaly -- Mean anomaly of the periastron (radians) + coa_phase -- Coalesence phase of the binary (in rad) + distance -- Luminosity distance to the binary (in Mpc) + modes_to_use -- GW modes to use. List of tuples (l, |m|) + mode_to_align_by -- GW mode to use to align inspiral and merger + in phase and time + include_conjugate_modes -- If True, (l, -|m|) modes are included as + well + f_mr_transition -- Inspiral to merger transition GW frequency + (Hz). Should correspond to the mode + specified by `mode_to_align_by`. + Defaults to the minimum of the Kerr and + Schwarzschild ISCO frequency equivalent + for the mode `mode_to_align_by`. + f_window_mr_transition -- Hybridization frequency window (in Hz). + Should correspond to the mode specified by + `mode_to_align_by`. + Disabled by the default value (None). In + such a case, the hybridization proceeds + over a window of `num_hyb_orbits` orbital + cycles (1 orbital cycle ~ 2 GW cycles) + that ends at the frequency value given by + `f_mr_transition`. + Also see `keep_f_mr_transition_at_center` + to choose the position of `f_mr_transition` + within this window. + num_hyb_orbits -- number of orbital cycles to blend over. + Only used if f_window_mr_transition is not + specified. + blend_using_avg_orbital_frequency -- If True, the orbit averaged + frequency during the inspiral is used to + blend modes, instead of the modes' + frequency. + blend_aligning_merger_to_inspiral -- (default: False) If True, the + merger-ringdown mode would be phase aligned + to the inspiral + If False, the inspiral is phase aligned + Note: specify the desired + keep_f_mr_transition_at_center -- If True, `f_mr_transition` is kept at + the center of the hybridization window. + Otherwise, it's kept at the end of the + window (default). + merger_ringdown_approximant -- Choose merger-ringdown model. Tested + choices: [NRSur7dq4, SEOBNRv4PHM] + return_hybridization_info -- If True, returns hybridization related data + return_orbital_params -- If True, returns the orbital evolution of + all the orbital elements (in + geometrized units). Can also be a list of + orbital variable names to return + only those specific variables. Available + orbital variables names are: + ['x', 'e', 'l', 'phi', 'phidot', 'r', 'rdot']. + Note that these are available only for the + inspiral portion of the waveform! + failsafe -- If True, we make reasonable choices for the + user, if the inputs to this method lead + into exceptions. + verbose -- Verbosity level. Available values are: 0, 1, 2 + + Returns: + -------- + modes_imr -- Dictionary of IMR GW modes (PyCBC TimeSeries) + orbital_var_dict -- Dictionary of evolution of orbital elements. + Returned only if the flag `return_orbital_params` + is set + retval -- Hybridization related data. Returned only if the + flag `return_hybridization_info` is set + """ + if not hasattr(ls, merger_ringdown_approximant): + raise IOError( + """We cannot generate individual modes for {merger_ringdown_approximant}. + Try one of: [NRSur7dq4, SEOBNRv4PHM]""" + ) + if (mean_anomaly is None) and (coa_phase is None): + raise IOError( + f"""Please specify one of the phase angles, either of + `mean_anomaly` or `coa_phase`.""" + ) + if blend_aligning_merger_to_inspiral and (mean_anomaly is None): + raise IOError( + f"""If you want to attach ESIGMA inspiral to merger, by + phase shifting merger to inspiral, please specify the + phase angle `mean_anomaly`""" + ) + if (not blend_aligning_merger_to_inspiral) and (coa_phase is None): + raise IOError( + f"""If you want to attach ESIGMA inspiral to merger, by + phase shifting inspiral to merger, please specify the + phase angle `coa_phase`""" + ) + if mean_anomaly is None: + mean_anomaly = 0 + if coa_phase is None: + coa_phase = 0 + + available_inspiral_orbital_params = ["x", "e", "l", "phi", "phidot", "r", "rdot"] + if return_orbital_params == True: + return_orbital_params = available_inspiral_orbital_params + return_orbital_params_user = set(return_orbital_params) + elif ( + isinstance(return_orbital_params, list) + or isinstance(return_orbital_params, set) + or isinstance(return_orbital_params, tuple) + ): + return_orbital_params_user = return_orbital_params.copy() + return_orbital_params_user = set(return_orbital_params_user).intersection( + set(available_inspiral_orbital_params) + ) + if return_orbital_params_user != set(return_orbital_params): + print( + f"""Warning: You requested the following list of orbital +parameters to be returned: {return_orbital_params}, but we reduce it to +{return_orbital_params_user} as we only have the evolution of the following +parameters available with us: {available_inspiral_orbital_params}. + """ + ) + elif not return_orbital_params: + return_orbital_params = [] + return_orbital_params_user = False + + return_orbital_params = set(return_orbital_params) + return_orbital_params = return_orbital_params.union( + set(["e"]) + ) # "e" needed necessarily for hybridization error printing + + if failsafe or (verbose > 1): + return_orbital_params = return_orbital_params.union(set(["phidot"])) + + # If the user does not provide the width of hybridization window (in terms + # of orbital frequency) over which the inspiral should transition to + # merger-ringdown, we switch schemes and blend over `num_hyb_orbits` + # orbits instead. + if f_window_mr_transition is None: + # These will be used for figuring out the hybridization window + return_orbital_params = return_orbital_params.union(set(["phi", "phidot"])) + if blend_using_avg_orbital_frequency: + return_orbital_params = return_orbital_params.union(set(["x"])) + + _, mode_to_align_by_em = mode_to_align_by + + # If the user does not provide the orbital frequency at which the inspiral + # should transition to merger-ringdown, we use sensible defaults here. + if f_mr_transition is None: + # Kerr ISCO frequency + f_Kerr = f_ISCO_spin(mass1, mass2, spin1z, spin2z) + # Schwarzschild ISCO frequency + f_Schwarz = 6.0**-1.5 / (mass1 + mass2) / lal.MTSUN_SI / lal.PI + f_mr_transition = min(f_Kerr, f_Schwarz) * (mode_to_align_by_em / 2) + + retval = get_inspiral_esigma_modes_py( + mass1=mass1, + mass2=mass2, + spin1z=spin1z, + spin2z=spin2z, + eccentricity=eccentricity, + mean_anomaly=mean_anomaly, + distance=distance, + f_lower=f_lower, + f_ref=f_ref, + delta_t=delta_t, + modes_to_use=modes_to_use, + include_conjugate_modes=include_conjugate_modes, + return_orbital_params=list(return_orbital_params), + return_pycbc_timeseries=False, + verbose=verbose, + ) + + # Retrieve modes, orbital phase and frequency from the returned list + modes_inspiral_numpy = retval[-1] + if mode_to_align_by not in modes_inspiral_numpy: + raise RuntimeError( + f"""The inspiral modes do not contain the primary +desired {mode_to_align_by} multipole. It currently holds only the following: +{modes_inspiral_numpy.keys()}""" + ) + + orbital_eccentricity = retval[-2]["e"] + # Throw error if eccentricity at the end of inspiral is definitely unsafe + if orbital_eccentricity[-1] > ECCENTRICITY_LEVEL_ISCO_ERROR: + raise IOError( + f"""ERROR: You entered a very large initial eccentricity +{eccentricity}. The orbital eccentricity at the end of inspiral was +{orbital_eccentricity[-1]}. The merger-ringdown attachment with a +quasicircular will be questionable.""" + ) + # Warn user if eccentricity at the end of inspiral is potentially unsafe + if orbital_eccentricity[-1] > ECCENTRICITY_LEVEL_ISCO_WARNING and verbose: + print( + f"""WARNING: You entered a very large initial eccentricity +{eccentricity}. The orbital eccentricity at the end of inspiral was +{orbital_eccentricity[-1]}. The merger-ringdown attachment with a quasicircular +model might be affected.""" + ) + + if (f_window_mr_transition is None) or failsafe or (verbose > 1): + if blend_using_avg_orbital_frequency: + orbital_frequency = ( + retval[-2]["x"] ** 1.5 / ((mass1 + mass2) * lal.MTSUN_SI) / (2 * np.pi) + ) + else: + orbital_frequency = ( + retval[-2]["phidot"] / ((mass1 + mass2) * lal.MTSUN_SI) / (2 * np.pi) + ) + + if return_orbital_params_user: + orbital_vars_dict = { + key: pt.TimeSeries(retval[-2][key], delta_t=delta_t, epoch=retval[0][0]) + for key in return_orbital_params_user + } + + # DEBUG + if verbose > 5: + mode_phase = esigmapy.blend.compute_phase( + modes_inspiral_numpy[mode_to_align_by] + ) + mode_frequency = esigmapy.blend.compute_frequency(mode_phase, delta_t) + print( + f"""DEBUG: Orbital freq at end of inspiral is {orbital_frequency[-1]}Hz, +mode-{mode_to_align_by} freq at the end of inspiral is {mode_frequency[-1]}Hz, max and min +mode-{mode_to_align_by} frequencies are {np.max(mode_frequency)}Hz and +{np.min(mode_frequency)}Hz, and the transition frequency (of {mode_to_align_by}-mode) +requested is {f_mr_transition}Hz, which should be less than the maximum freq of +{mode_to_align_by}-mode: {mode_frequency.max()}Hz.""" + ) + return ( + modes_inspiral_numpy, + mode_phase, + mode_frequency, + orbital_frequency, + orbital_eccentricity, + orbital_vars_dict, + ) + + # In case the user-specified transition frequency is too high, and they + # requested failsafe mode, we reset it to a reasonable value. + if failsafe: + mode_phase = esigmapy.blend.compute_phase( + modes_inspiral_numpy[mode_to_align_by] + ) + mode_frequency = esigmapy.blend.compute_frequency(mode_phase, delta_t) + if mode_frequency.max() < f_mr_transition: + if verbose: + print( + f"""FAILSAFE: Maximum orbital freq during inspiral is +{orbital_frequency.max()}Hz, and max frequency of {mode_to_align_by}-mode is +{mode_frequency.max()}Hz, so we are resetting transition frequency from +{f_mr_transition}Hz to {mode_frequency.max()}Hz.""" + ) + f_mr_transition = mode_frequency.max() + + # If the user does not provide the width of hybridization window ( + # `f_window_mr_transition`) over which the inspiral should transition to + # merger-ringdown, we switch schemes and blend over `num_hyb_orbits` + # orbits instead. + if f_window_mr_transition is None: + f_window_mr_transition = ( + _get_transition_frequency_window( + retval[-2]["phi"], + orbital_frequency, + delta_t, + f_mr_transition / mode_to_align_by_em, + num_hyb_orbits, + keep_f_mr_transition_at_center, + blend_using_avg_orbital_frequency, + failsafe=failsafe, + verbose=verbose, + ) + * mode_to_align_by_em + ) + + # This is done to make use of the same hybridization code, that actually + # assumes f_mr_transition to be at window's midpoint, to keep the + # hybridization window's end at f_mr_transition + if not keep_f_mr_transition_at_center: + f_mr_transition -= f_window_mr_transition / 2.0 + + # Generate NR surrogate waveform that will be our merger-ringdown, starting + # from a frequency = 90% of + max_retries = 20 + f_lower_mr = (f_mr_transition - f_window_mr_transition / 2) * ( + 1.8 / mode_to_align_by_em + ) + for _ in range(max_retries): + try: + if verbose: + print(f"Generating MR waveform from {f_lower_mr}Hz...") + hlm_mr = ls.SimInspiralChooseTDModes( + coa_phase, # phiRef + delta_t, # deltaT + mass1 * lal.MSUN_SI, + mass2 * lal.MSUN_SI, + 0, # spin1x + 0, # spin1y + spin1z, + 0, # spin2x + 0, # spin2y + spin2z, + f_lower_mr, # f_min + f_lower_mr, # f_ref + distance * lal.PC_SI * 1.0e6, + None, # LALpars + 4, # lmax + getattr(ls, merger_ringdown_approximant), + ) + break + except: + f_lower_mr *= 0.8 + continue + # Extracting only the modes we need + modes_to_use = list(modes_inspiral_numpy.keys()) + modes_mr_numpy = {} + while hlm_mr is not None: + key = (hlm_mr.l, hlm_mr.m) + if key in modes_to_use: + modes_mr_numpy[key] = hlm_mr.mode.data.data + hlm_mr = hlm_mr.next + + try: + retval = esigmapy.blend.blend_modes( + modes_inspiral_numpy, + modes_mr_numpy, + orbital_frequency, + f_mr_transition, + frq_width=f_window_mr_transition, + delta_t=delta_t, + modes_to_blend=modes_to_use, + mode_to_align_by=mode_to_align_by, + blend_using_avg_orbital_frequency=blend_using_avg_orbital_frequency, + blend_aligning_merger_to_inspiral=blend_aligning_merger_to_inspiral, + include_conjugate_modes=include_conjugate_modes, + verbose=verbose, + ) + except Exception as exc: + print( + f"""Inspiral + MergerRingdown attachment failed. It's very likely +that you entered a very large initial eccentricity {eccentricity}. The orbital +eccentricity at the end of inspiral was {orbital_eccentricity[-1]} + """ + ) + raise exc + modes_imr_numpy = retval[0] + + # Align modes at peak of (2, 2) mode + if mode_to_align_by not in modes_imr_numpy: + mode_to_align_by = list(modes_imr_numpy.keys())[0] + idx_peak = abs(modes_imr_numpy[mode_to_align_by]).argmax() + t_peak = idx_peak * delta_t + + itime = time.perf_counter() + modes_imr = {} + for el, em in modes_imr_numpy: + modes_imr[(el, em)] = pt.TimeSeries( + modes_imr_numpy[(el, em)], delta_t=delta_t, epoch=-1 * t_peak + ) + if verbose > 4: + print( + "Time taken to store in pycbc.TimeSeries is {} secs".format( + time.perf_counter() - itime + ) + ) + + if verbose: + print("blended.") + + if return_hybridization_info and return_orbital_params_user: + return modes_imr, orbital_vars_dict, retval + elif return_orbital_params_user: + return modes_imr, orbital_vars_dict + elif return_hybridization_info: + return modes_imr, retval + return modes_imr + + +def get_imr_esigma_waveform_py( + mass1, + mass2, + f_lower, + delta_t, + f_ref=None, + spin1z=0.0, + spin2z=0.0, + eccentricity=0.0, + mean_anomaly=0.0, + coa_phase=0.0, + inclination=0.0, + distance=1.0, + modes_to_use=[(2, 2), (3, 3), (4, 4)], + mode_to_align_by=(2, 2), + f_mr_transition=None, + f_window_mr_transition=None, + num_hyb_orbits=0.25, + blend_using_avg_orbital_frequency=True, + blend_aligning_merger_to_inspiral=True, + keep_f_mr_transition_at_center=False, + merger_ringdown_approximant="NRSur7dq4", + return_hybridization_info=False, + return_orbital_params=False, + failsafe=True, + verbose=False, + **kwargs, +): + """ + Returns IMR GW polarizations constructed using IMR ESIGMA modes + + Parameters: + ----------- + mass1, mass2 -- Binary's component masses (in solar masses) + f_lower -- Starting frequency of the waveform (in Hz) + f_ref -- Reference frequency at which to define the + waveform parameters. We require that + `f_ref <= f_lower`. + `f_ref = f_lower` by default. + delta_t -- Waveform's time grid-spacing (in s) + spin1z, spin2z -- z-components of component dimensionless + spins (lies in [0,1)) + eccentricity -- Initial eccentricity + mean_anomaly -- Mean anomaly of the periastron (in rad) + inclination -- Inclination (in rad), defined as the angle + between the orbital angular momentum L and + the line-of-sight + coa_phase -- Coalesence phase of the binary (in rad) + distance -- Luminosity distance to the binary (in Mpc) + modes_to_use -- GW modes to use. List of tuples (l, |m|) + mode_to_align_by -- GW mode to use to align inspiral and merger + in phase and time + f_mr_transition -- Inspiral to merger transition GW frequency + (Hz). + Defaults to the minimum of the Kerr and + Schwarzschild ISCO frequency + f_window_mr_transition -- Hybridization frequency window (in Hz). + Disabled by the default value (None). In + such a case, the hybridization proceeds + over a window of `num_hyb_orbits` orbital + cycles (1 orbital cycle ~ 2 GW cycles) + that ends at the frequency value given by + `f_mr_transition`. + Also see `keep_f_mr_transition_at_center` + to choose the position of `f_mr_transition` + within this window. + num_hyb_orbits -- number of orbital cycles to blend over. + Only used if f_window_mr_transition is not + specified. + blend_using_avg_orbital_frequency -- If True, the orbit averaged + frequency during the inspiral is used to + blend modes, instead of the modes' + frequency. + keep_f_mr_transition_at_center -- If True, `f_mr_transition` is kept at + the center of the hybridization window. + Otherwise, it's kept at the end of the + window (default). + merger_ringdown_approximant -- Choose merger-ringdown model. Tested + choices: [NRSur7dq4, SEOBNRv4PHM] + return_hybridization_info -- If True, returns hybridization related data + return_orbital_params -- If True, returns the orbital evolution of + all the orbital elements (in + geometrized units). Can also be a list of + orbital variable names to return + only those specific variables. Available + orbital variables names are: + ['x', 'e', 'l', 'phi', 'phidot', 'r', 'rdot']. + Note that these are available only for the + inspiral portion of the waveform! + failsafe -- If True, we make reasonable choices for the + user, if the inputs to this method lead + into exceptions. + verbose -- Verbosity level. Available values are: 0, 1, 2 + + Returns: + -------- + hp, hc -- Plus and cross IMR GW polarizations PyCBC TimeSeries + orbital_vars_dict -- Dictionary of evolution of orbital elements. + Returned only if return_orbital_params is specified + retval -- Hybridization related data. + Returned only if return_hybridization_info is True + """ + + retval = get_imr_esigma_modes_py( + mass1=mass1, + mass2=mass2, + spin1z=spin1z, + spin2z=spin2z, + eccentricity=eccentricity, + mean_anomaly=mean_anomaly, + coa_phase=coa_phase, + distance=distance, + f_lower=f_lower, + f_ref=f_ref, + delta_t=delta_t, + modes_to_use=modes_to_use, + mode_to_align_by=mode_to_align_by, + include_conjugate_modes=True, # Always include conjugate modes while generating polarizations + f_mr_transition=f_mr_transition, + f_window_mr_transition=f_window_mr_transition, + num_hyb_orbits=num_hyb_orbits, + blend_using_avg_orbital_frequency=blend_using_avg_orbital_frequency, + blend_aligning_merger_to_inspiral=blend_aligning_merger_to_inspiral, + keep_f_mr_transition_at_center=keep_f_mr_transition_at_center, + merger_ringdown_approximant=merger_ringdown_approximant, + return_hybridization_info=return_hybridization_info, + return_orbital_params=return_orbital_params, + failsafe=failsafe, + verbose=verbose, + ) + if return_hybridization_info and return_orbital_params: + modes_imr, orbital_vars_dict, retval = retval + elif return_hybridization_info: + modes_imr, retval = retval + elif return_orbital_params: + modes_imr, orbital_vars_dict = retval + else: + modes_imr = retval + + hp, hc = esigmapy.utils.get_polarizations_from_multipoles( + modes_imr, + inclination=inclination, + coa_phase=np.pi / 2 - coa_phase, + verbose=verbose, + ) + + if return_hybridization_info and return_orbital_params: + return hp, hc, orbital_vars_dict, retval + elif return_hybridization_info: + return hp, hc, retval + elif return_orbital_params: + return hp, hc, orbital_vars_dict + return hp, hc diff --git a/notebooks/ESIGMA_tutorial.ipynb b/notebooks/ESIGMA_tutorial.ipynb index c634378..83ec9a1 100644 --- a/notebooks/ESIGMA_tutorial.ipynb +++ b/notebooks/ESIGMA_tutorial.ipynb @@ -19,6 +19,36 @@ "id": "108225e1", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/samanwaya/esigmapy-dev/esigmapy/esigmapy/generator.py:10: UserWarning: Wswiglal-redir-stdio:\n", + "\n", + "SWIGLAL standard output/error redirection is enabled in IPython.\n", + "This may lead to performance penalties. To disable locally, use:\n", + "\n", + "with lal.no_swig_redirect_standard_output_error():\n", + " ...\n", + "\n", + "To disable globally, use:\n", + "\n", + "lal.swig_redirect_standard_output_error(False)\n", + "\n", + "Note however that this will likely lead to error messages from\n", + "LAL functions being either misdirected or lost when called from\n", + "Jupyter notebooks.\n", + "\n", + "To suppress this warning, use:\n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\", \"Wswiglal-redir-stdio\")\n", + "import lal\n", + "\n", + " import lal\n", + "PyCBC.libutils: pkg-config call failed, setting NO_PKGCONFIG=1\n" + ] + }, { "name": "stdout", "output_type": "stream", diff --git a/notebooks/esigma_python.ipynb b/notebooks/esigma_python.ipynb new file mode 100644 index 0000000..2f992cb --- /dev/null +++ b/notebooks/esigma_python.ipynb @@ -0,0 +1,1144 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "e42eaa22-6e2d-42a6-9c30-a1193d252492", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No version information file '.version' found\n" + ] + } + ], + "source": [ + "import warnings\n", + "warnings.filterwarnings(\"ignore\", \"Wswiglal-redir-stdio\")\n", + "import lal\n", + "import lalsimulation as lalsim\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import esigmapy\n", + "from esigmapy.python_codes import generator_python as genpy\n", + "from esigmapy.python_codes import esigma_pn_main as espn" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b962168c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "radiation PN order is 8\n", + "mode PN order is 4\n", + ":/home/samanwaya/nrsur7dq4\n" + ] + } + ], + "source": [ + "import os\n", + "\n", + "os.environ[\"RadiationPNOrder\"] = '8'\n", + "os.environ[\"ModePNOrder\"] = '4'\n", + "\n", + "x=os.environ.get(\"RadiationPNOrder\")\n", + "y=os.environ.get(\"ModePNOrder\")\n", + "\n", + "print('radiation PN order is ' + ('not set' if x is None else str(int(x))))\n", + "print('mode PN order is ' + ('not set' if y is None else str(int(y))))\n", + "\n", + "y=os.environ.get(\"LAL_DATA_PATH\")\n", + "print('Poof!' if y is None else y)" + ] + }, + { + "cell_type": "markdown", + "id": "d0784669", + "metadata": {}, + "source": [ + "## Set the parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "4ec5f8c9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Modes to include: [(2, -2), (2, -1), (2, 1), (2, 2), (3, -3), (3, -2), (3, -1), (3, 1), (3, 2), (3, 3), (4, 4), (4, -4)]\n" + ] + } + ], + "source": [ + "m1 = 8.0 # masses (in solar masses)\n", + "m2 = 48.0\n", + "spin1z = -0.8 # dimensionless spins\n", + "spin2z = -0.8\n", + "eccentricity = 0.4 # starting eccentricity\n", + "mean_anomaly = 60 * np.pi / 180.0 # starting mean anomaly\n", + "\n", + "# Creating modes list\n", + "modes_to_use = []\n", + "for l in [2,3]:\n", + " for m in range(-l,l+1):\n", + " if m==0: continue\n", + " modes_to_use.append((l,m))\n", + "modes_to_use.extend([(4,4),(4,-4),]) \n", + "\n", + "print('Modes to include:',modes_to_use)\n", + "#-------------------\n", + "\n", + "distance = 400.0 # source luminosity distance (in Mpc)\n", + "inclination = 30 * np.pi / 180.0 # orbital inclination with line-of-sight\n", + "\n", + "f_low = 20.0 # starting frequency (in Hz)\n", + "delta_t = 1 / 2**10 # time grid-spacing (in s)\n", + "\n", + "# storing factors to take to geometric units if needed\n", + "strain_fac = (distance*1e6*lal.PC_SI/lal.C_SI)/((m1+m2)*lal.MTSUN_SI)\n", + "time_fac = 1/((m1+m2)*lal.MTSUN_SI)" + ] + }, + { + "cell_type": "markdown", + "id": "d4289df1-f13d-474e-a7f4-e8f355f283c4", + "metadata": {}, + "source": [ + "## ============== Extracting modes ====================" + ] + }, + { + "cell_type": "markdown", + "id": "ff51d368", + "metadata": {}, + "source": [ + "### Inspiral ESIGMA modes" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a666270a-c644-473b-8559-57df6e16217c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================ Python ================\n", + "Orbital evolution took: 0.8314332690006268 seconds\n", + "Modes generation took: 0.4021174190002057 seconds\n", + "================= C =================\n", + "Orbital evolution took: 0.017237385000044014 seconds\n", + "Modes generation took: 0.05197061299986672 seconds\n", + "532\n", + "532\n" + ] + } + ], + "source": [ + "print('================ Python ================')\n", + "inspiral_modes_py = genpy.get_inspiral_esigma_modes_py(\n", + " mass1=m1,\n", + " mass2=m2,\n", + " spin1z=spin1z,\n", + " spin2z=spin2z,\n", + " eccentricity=eccentricity,\n", + " mean_anomaly=mean_anomaly,\n", + " distance=distance,\n", + " # inclination=inclination,\n", + " f_lower=f_low,\n", + " delta_t=delta_t,\n", + " modes_to_use=modes_to_use,\n", + " verbose=True\n", + ")\n", + "\n", + "print('================= C =================')\n", + "inspiral_modes_c = esigmapy.get_inspiral_esigma_modes(\n", + " mass1=m1,\n", + " mass2=m2,\n", + " spin1z=spin1z,\n", + " spin2z=spin2z,\n", + " eccentricity=eccentricity,\n", + " mean_anomaly=mean_anomaly,\n", + " distance=distance,\n", + " # inclination=inclination,\n", + " f_lower=f_low,\n", + " delta_t=delta_t,\n", + " modes_to_use=modes_to_use,\n", + " verbose=True\n", + ")\n", + "print(len(inspiral_modes_c[modes_to_use[0]]))\n", + "print(len(inspiral_modes_py[modes_to_use[0]]))\n", + "for mode in modes_to_use: \n", + " inspiral_modes_c[mode] = inspiral_modes_c[mode].copy()[1:-2] \n", + " inspiral_modes_py[mode] = inspiral_modes_py[mode].copy()[1:-2] " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "9fc2cdd6", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "529\n", + "529\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_mode = (2,2)\n", + "\n", + "hlm_py_real_inspiral = inspiral_modes_py[plot_mode].real()\n", + "hlm_c_real_inspiral = inspiral_modes_c[plot_mode].real() \n", + "\n", + "print(len(hlm_py_real_inspiral))\n", + "print(len(hlm_c_real_inspiral))\n", + "\n", + "plt.figure(figsize=(10, 4))\n", + "plt.title(\n", + " rf\"\"\"Inspiral-ESIGMA\n", + " ==============================\n", + " $m_1={m1} M_\\odot, m_2={m2} M_\\odot$, \n", + " $\\chi_{{1z}}={spin1z}, \\chi_{{2z}}={spin2z}$, \n", + " $e_0={eccentricity}, l_0={mean_anomaly:.2f}, f_{{\\rm{{low}}}}={f_low}\\rm{{Hz}}$\n", + " $d_L={distance}\\rm{{Mpc}}, \\iota={inclination:.2f}$\"\"\"\n", + ")\n", + "\n", + "plt.plot(hlm_py_real_inspiral.sample_times,hlm_py_real_inspiral,label='Python')\n", + "plt.plot(hlm_c_real_inspiral.sample_times,hlm_c_real_inspiral,label='C',ls='--')\n", + "\n", + "plt.xlabel(r\"$t (s)$\")\n", + "plt.ylabel(f\"Re [h {plot_mode}]\")\n", + "plt.legend()\n", + "plt.grid()\n", + "# plt.xlim(0.7,0.74)\n", + "# plt.ylim(-1e-21,1e-21)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "### Plotting difference ##\n", + "\n", + "eps = 1e-30\n", + "abs_diff = np.abs(inspiral_modes_py[plot_mode]-inspiral_modes_c[plot_mode])\n", + "rel_diff = abs_diff / np.maximum(np.abs(inspiral_modes_c[plot_mode]), eps)\n", + "\n", + "plt.figure(figsize=(10, 4))\n", + "plt.plot(hlm_py_real_inspiral.sample_times,rel_diff)\n", + "plt.xlabel(r\"$t (s)$\")\n", + "plt.ylabel('relative difference')\n", + "plt.grid()\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "\n", + "plt.figure(figsize=(10, 4))\n", + "plt.semilogy(hlm_py_real_inspiral.sample_times,abs_diff)\n", + "plt.xlabel(r\"$t (s)$\")\n", + "plt.ylabel('absolute difference')\n", + "plt.grid()\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "09f56085-0fa8-422f-ae6b-87171ffd0b19", + "metadata": {}, + "source": [ + "### Match between individual modes" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d58e9bd0-9bab-4d13-9ce0-faa44199ca7f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mode | Mismatch (%)\n", + "-------------------------\n", + "(2, -2) | 0.1834 <-- more than 0.1%\n", + "(2, -1) | 0.0443\n", + "(2, 1) | 0.0443\n", + "(2, 2) | 0.1834 <--\n", + "(3, -3) | 0.4470 <--\n", + "(3, -2) | 0.1482 <--\n", + "(3, -1) | 0.0442\n", + "(3, 1) | 0.0442\n", + "(3, 2) | 0.1482 <--\n", + "(3, 3) | 0.4470 <--\n", + "(4, 4) | 0.5126 <--\n", + "(4, -4) | 0.5126 <--\n" + ] + } + ], + "source": [ + "from pycbc.filter import match\n", + "from pycbc.psd import aLIGOZeroDetHighPower\n", + "\n", + "f_low = 20.5\n", + "print(f'{\"Mode\":<10} | {\"Mismatch (%)\":>12}')\n", + "print('-' * 25)\n", + "flag = None\n", + "\n", + "for mode in modes_to_use:\n", + " hlm_py = inspiral_modes_py[mode].real()\n", + " hlm_c = inspiral_modes_c[mode].real()\n", + " \n", + " tlen = max(len(hlm_c), len(hlm_py))\n", + " hlm_c.resize(tlen)\n", + " hlm_py.resize(tlen)\n", + " \n", + " delta_f = 1.0 / hlm_c.duration\n", + " flen = tlen//2 + 1\n", + " psd = aLIGOZeroDetHighPower(flen, delta_f, f_low)\n", + " \n", + " match1, _ = match(hlm_c, hlm_py, psd=psd, low_frequency_cutoff=f_low)\n", + " mm = (1 - match1) * 100\n", + " if mm > 0.1:\n", + " flag = \" <-- more than 0.1%\" if flag is None else \" <--\"\n", + " else: flag = \"\"\n", + " print(f'{str(mode):<10} | {mm:>12.4f}{flag}')" + ] + }, + { + "cell_type": "markdown", + "id": "4d79b9f9", + "metadata": {}, + "source": [ + "### IMR ESIGMA modes" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "311c00be", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================ Python ================\n", + "Orbital evolution took: 0.7828852089996872 seconds\n", + "Modes generation took: 0.4152848880003148 seconds\n", + "WARNING: You entered a very large initial eccentricity\n", + "0.4. The orbital eccentricity at the end of inspiral was\n", + "0.057552566100066385. The merger-ringdown attachment with a quasicircular\n", + "model might be affected.\n", + "Generating MR waveform from 25.6427517572802Hz...\n", + "Hybridizing the following modes: [(2, -2), (2, -1), (2, 1), (2, 2), (3, -3), (3, -2), (3, -1), (3, 1), (3, 2), (3, 3), (4, 4), (4, -4)]\n", + "By aligning (2, 2) mode\n", + "..and inheriting the phase/time shifts for alignment of [(2, -2), (2, -1), (2, 1), (3, -3), (3, -2), (3, -1), (3, 1), (3, 2), (3, 3), (4, 4), (4, -4), (2, 2)] modes\n", + "INSPIRAL mode (2, -2) goes from -98.6923745720908Hz to 41.73513908706739Hz\n", + "MERGER mode (2, -2) goes from -309.28896999794586Hz to 14.006232958042581Hz\n", + "INSPIRAL mode (2, -1) goes from -64.97997906689893Hz to 12.248984120619063Hz\n", + "MERGER mode (2, -1) goes from -259.1904730953254Hz to 175.9402732557564Hz\n", + "INSPIRAL mode (2, 1) goes from -12.248984120619063Hz to 64.97997906689893Hz\n", + "MERGER mode (2, 1) goes from -309.3549765915733Hz to 221.6333159685051Hz\n", + "INSPIRAL mode (2, 2) goes from -41.73513908706739Hz to 98.6923745720908Hz\n", + "MERGER mode (2, 2) goes from 25.616644976646672Hz to 440.6711678762227Hz\n", + "INSPIRAL mode (3, -3) goes from -147.6000260934237Hz to 39.50973121448475Hz\n", + "MERGER mode (3, -3) goes from -359.75734688506583Hz to 476.3621395254231Hz\n", + "INSPIRAL mode (3, -2) goes from -149.52320648331312Hz to 28.811501111388914Hz\n", + "MERGER mode (3, -2) goes from -439.26082476234194Hz to 242.23805983344295Hz\n", + "INSPIRAL mode (3, -1) goes from -108.74454476314538Hz to 208.57214010137864Hz\n", + "MERGER mode (3, -1) goes from -394.3408541630987Hz to 252.46331171534231Hz\n", + "INSPIRAL mode (3, 1) goes from -208.5721401013786Hz to 108.74454476314597Hz\n", + "MERGER mode (3, 1) goes from -156.9946520864311Hz to 400.04416765085347Hz\n", + "INSPIRAL mode (3, 2) goes from -28.811501111388903Hz to 149.52320648331542Hz\n", + "MERGER mode (3, 2) goes from -72.51190416024517Hz to 440.76134814838804Hz\n", + "INSPIRAL mode (3, 3) goes from -39.50973121448479Hz to 147.6000260934237Hz\n", + "MERGER mode (3, 3) goes from -445.8522505642973Hz to 437.2803421243881Hz\n", + "INSPIRAL mode (4, 4) goes from -65.77765625935193Hz to 272.71362547077956Hz\n", + "MERGER mode (4, 4) goes from -163.05823361227974Hz to 459.0304799482537Hz\n", + "INSPIRAL mode (4, -4) goes from -272.71362547077956Hz to 65.77765625935193Hz\n", + "MERGER mode (4, -4) goes from -474.79448268252094Hz to 397.6711150503594Hz\n", + "blended.\n", + "================= C =================\n", + "Orbital evolution took: 0.03317919799974334 seconds\n", + "Modes generation took: 0.07433556900014082 seconds\n", + "WARNING: You entered a very large initial eccentricity\n", + "0.4. The orbital eccentricity at the end of inspiral was\n", + "0.058448599740953164. The merger-ringdown attachment with a quasicircular\n", + "model might be affected.\n", + "Generating MR waveform from 25.644575955959233Hz...\n", + "Hybridizing the following modes: [(2, -2), (2, -1), (2, 1), (2, 2), (3, -3), (3, -2), (3, -1), (3, 1), (3, 2), (3, 3), (4, 4), (4, -4)]\n", + "By aligning (2, 2) mode\n", + "..and inheriting the phase/time shifts for alignment of [(2, -2), (2, -1), (2, 1), (3, -3), (3, -2), (3, -1), (3, 1), (3, 2), (3, 3), (4, 4), (4, -4), (2, 2)] modes\n", + "INSPIRAL mode (2, -2) goes from -96.76196496360744Hz to -9.438739507933441Hz\n", + "MERGER mode (2, -2) goes from -251.0687028266488Hz to 12.966069881700337Hz\n", + "INSPIRAL mode (2, -1) goes from -62.02379567141938Hz to -4.393818338795352Hz\n", + "MERGER mode (2, -1) goes from -254.9292482800305Hz to 327.87649538971766Hz\n", + "INSPIRAL mode (2, 1) goes from 4.393818338795352Hz to 62.02379567141938Hz\n", + "MERGER mode (2, 1) goes from -313.63739087416826Hz to 221.7175658913996Hz\n", + "INSPIRAL mode (2, 2) goes from 9.438739507933368Hz to 96.76196496360744Hz\n", + "MERGER mode (2, 2) goes from -110.08789072952271Hz to 237.35295131831893Hz\n", + "INSPIRAL mode (3, -3) goes from -145.21410110217633Hz to -13.51471181013238Hz\n", + "MERGER mode (3, -3) goes from -358.3278926596189Hz to 225.35579870164025Hz\n", + "INSPIRAL mode (3, -2) goes from -144.55046914567717Hz to -9.262444099344108Hz\n", + "MERGER mode (3, -2) goes from -391.8867990338296Hz to 309.743824820241Hz\n", + "INSPIRAL mode (3, -1) goes from -120.222675009537Hz to -8.500360572408773Hz\n", + "MERGER mode (3, -1) goes from -412.5287985185308Hz to 216.6842377326586Hz\n", + "INSPIRAL mode (3, 1) goes from 8.500360572408773Hz to 120.222675009537Hz\n", + "MERGER mode (3, 1) goes from -187.96224455265502Hz to 407.47762764811296Hz\n", + "INSPIRAL mode (3, 2) goes from 9.262444099344108Hz to 144.55046914567717Hz\n", + "MERGER mode (3, 2) goes from -63.88312655739305Hz to 349.354524990372Hz\n", + "INSPIRAL mode (3, 3) goes from 13.51471181013238Hz to 145.21410110217633Hz\n", + "MERGER mode (3, 3) goes from -228.5430307886252Hz to 435.0758303356852Hz\n", + "INSPIRAL mode (4, 4) goes from 18.954061747847653Hz to 268.708448543125Hz\n", + "MERGER mode (4, 4) goes from -142.27116424365914Hz to 460.95503496021666Hz\n", + "INSPIRAL mode (4, -4) goes from -268.708448543125Hz to -18.954061747847653Hz\n", + "MERGER mode (4, -4) goes from -478.2305532730522Hz to 405.83311177341966Hz\n", + "blended.\n" + ] + } + ], + "source": [ + "print('================ Python ================')\n", + "imr_modes_py = genpy.get_imr_esigma_modes_py(\n", + " mass1=m1,\n", + " mass2=m2,\n", + " spin1z=spin1z,\n", + " spin2z=spin2z,\n", + " eccentricity=eccentricity,\n", + " mean_anomaly=mean_anomaly,\n", + " distance=distance,\n", + " # inclination=inclination,\n", + " f_lower=f_low,\n", + " delta_t=delta_t,\n", + " modes_to_use=modes_to_use,\n", + " mode_to_align_by=plot_mode,\n", + " verbose=True\n", + ")\n", + "\n", + "print('================= C =================')\n", + "imr_modes_c = esigmapy.get_imr_esigma_modes(\n", + " mass1=m1,\n", + " mass2=m2,\n", + " spin1z=spin1z,\n", + " spin2z=spin2z,\n", + " eccentricity=eccentricity,\n", + " mean_anomaly=mean_anomaly,\n", + " distance=distance,\n", + " # inclination=inclination,\n", + " f_lower=f_low,\n", + " delta_t=delta_t,\n", + " modes_to_use=modes_to_use,\n", + " mode_to_align_by=plot_mode,\n", + " verbose=True\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "05fba2cf", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hp_py_real_imr = imr_modes_py[plot_mode].real()\n", + "hp_c_real_imr = imr_modes_c[plot_mode].real()\n", + "plt.figure(figsize=(10, 4))\n", + "plt.title(\n", + " rf\"\"\"IMR-ESIGMA\n", + " ==============================\n", + " $m_1={m1} M_\\odot, m_2={m2} M_\\odot$, \n", + " $\\chi_{{1z}}={spin1z}, \\chi_{{2z}}={spin2z}$, \n", + " $e_0={eccentricity}, l_0={mean_anomaly:.2f}, f_{{\\rm{{low}}}}={f_low}\\rm{{Hz}}$\n", + " $d_L={distance}\\rm{{Mpc}}, \\iota={inclination:.2f}$\"\"\"\n", + ")\n", + "# hp.plot(label=r\"$h_+$\")\n", + "# hc.plot(label=r\"$h_\\times$\")\n", + "\n", + "plt.plot(hp_py_real_imr.sample_times,hp_py_real_imr,label='Python')\n", + "plt.plot(hp_c_real_imr.sample_times,hp_c_real_imr,label='C',ls='--')\n", + "\n", + "plt.xlabel(r\"$t (s)$\")\n", + "plt.ylabel(f\"Re [h {plot_mode}]\")\n", + "plt.legend()\n", + "plt.grid()\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b445d311", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true, + "source_hidden": true + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dict_keys([(2, -2), (2, -1), (2, 1), (2, 2), (3, -3), (3, -2), (3, -1), (3, 1), (3, 2), (3, 3), (4, 4), (4, -4)])\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(imr_modes_py.keys())\n", + "hp22_imr = imr_modes_py[plot_mode].real()\n", + "hc22_imr = imr_modes_py[plot_mode].imag()\n", + "plt.figure(figsize=(10, 4))\n", + "plt.title(\n", + " rf\"\"\"IMR-ESIGMA (Python)\n", + " ==============================\n", + " $m_1={m1} M_\\odot, m_2={m2} M_\\odot, \\chi_{{1z}}={spin1z}, \\chi_{{2z}}={spin2z}$ \n", + " $e_0={eccentricity}, l_0={mean_anomaly:.2f}, f_{{\\rm{{low}}}}={f_low}\\rm{{Hz}}$\n", + " $d_L={distance}\\rm{{Mpc}}, \\iota={inclination:.2f}$\"\"\"\n", + ")\n", + "# hp.plot(label=r\"$h_+$\")\n", + "# hc.plot(label=r\"$h_\\times$\")\n", + "\n", + "plt.plot(hp22_imr.sample_times,hp22_imr,label=r\"Re [$h_{22}$]\")\n", + "plt.plot(hc22_imr.sample_times,hc22_imr,label=r\"Im [$h_{22}$]\")\n", + "\n", + "plt.xlabel(r\"$t (s)$\")\n", + "plt.ylabel(r\"$h$\")\n", + "plt.legend()\n", + "plt.grid()\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "19faf573-1338-4d47-8080-580a00f7e45a", + "metadata": {}, + "source": [ + "## ============== Extracting waveform =================" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "e950c6ea-b5a3-49cb-aa9e-f154615fa72d", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================ Python ================\n", + "Orbital evolution took: 0.7828921499994976 seconds\n", + "Modes generation took: 0.3700421319999805 seconds\n", + "WARNING: You entered a very large initial eccentricity\n", + "0.4. The orbital eccentricity at the end of inspiral was\n", + "0.057552566100066385. The merger-ringdown attachment with a quasicircular\n", + "model might be affected.\n", + "Generating MR waveform from 25.6427517572802Hz...\n", + "Hybridizing the following modes: [(2, -2), (2, -1), (2, 1), (2, 2), (3, -3), (3, -2), (3, -1), (3, 1), (3, 2), (3, 3), (4, 4), (4, -4)]\n", + "By aligning (2, 2) mode\n", + "..and inheriting the phase/time shifts for alignment of [(2, -2), (2, -1), (2, 1), (3, -3), (3, -2), (3, -1), (3, 1), (3, 2), (3, 3), (4, 4), (4, -4), (2, 2)] modes\n", + "INSPIRAL mode (2, -2) goes from -98.6923745720908Hz to 41.73513908706739Hz\n", + "MERGER mode (2, -2) goes from -309.28896999794586Hz to 14.006232958042581Hz\n", + "INSPIRAL mode (2, -1) goes from -64.97997906689893Hz to 12.248984120619063Hz\n", + "MERGER mode (2, -1) goes from -259.1904730953254Hz to 175.9402732557564Hz\n", + "INSPIRAL mode (2, 1) goes from -12.248984120619063Hz to 64.97997906689893Hz\n", + "MERGER mode (2, 1) goes from -309.3549765915733Hz to 221.6333159685051Hz\n", + "INSPIRAL mode (2, 2) goes from -41.73513908706739Hz to 98.6923745720908Hz\n", + "MERGER mode (2, 2) goes from 25.616644976646672Hz to 440.6711678762227Hz\n", + "INSPIRAL mode (3, -3) goes from -147.6000260934237Hz to 39.50973121448475Hz\n", + "MERGER mode (3, -3) goes from -359.75734688506583Hz to 476.3621395254231Hz\n", + "INSPIRAL mode (3, -2) goes from -149.52320648331312Hz to 28.811501111388914Hz\n", + "MERGER mode (3, -2) goes from -439.26082476234194Hz to 242.23805983344295Hz\n", + "INSPIRAL mode (3, -1) goes from -108.74454476314538Hz to 208.57214010137864Hz\n", + "MERGER mode (3, -1) goes from -394.3408541630987Hz to 252.46331171534231Hz\n", + "INSPIRAL mode (3, 1) goes from -208.5721401013786Hz to 108.74454476314597Hz\n", + "MERGER mode (3, 1) goes from -156.9946520864311Hz to 400.04416765085347Hz\n", + "INSPIRAL mode (3, 2) goes from -28.811501111388903Hz to 149.52320648331542Hz\n", + "MERGER mode (3, 2) goes from -72.51190416024517Hz to 440.76134814838804Hz\n", + "INSPIRAL mode (3, 3) goes from -39.50973121448479Hz to 147.6000260934237Hz\n", + "MERGER mode (3, 3) goes from -445.8522505642973Hz to 437.2803421243881Hz\n", + "INSPIRAL mode (4, 4) goes from -65.77765625935193Hz to 272.71362547077956Hz\n", + "MERGER mode (4, 4) goes from -163.05823361227974Hz to 459.0304799482537Hz\n", + "INSPIRAL mode (4, -4) goes from -272.71362547077956Hz to 65.77765625935193Hz\n", + "MERGER mode (4, -4) goes from -474.79448268252094Hz to 397.6711150503594Hz\n", + "blended.\n", + "================= C =================\n", + "Orbital evolution took: 0.016270807000182685 seconds\n", + "Modes generation took: 0.047177468999507255 seconds\n", + "WARNING: You entered a very large initial eccentricity\n", + "0.4. The orbital eccentricity at the end of inspiral was\n", + "0.058448599740953164. The merger-ringdown attachment with a quasicircular\n", + "model might be affected.\n", + "Generating MR waveform from 25.644575955959233Hz...\n", + "Hybridizing the following modes: [(2, -2), (2, -1), (2, 1), (2, 2), (3, -3), (3, -2), (3, -1), (3, 1), (3, 2), (3, 3), (4, 4), (4, -4)]\n", + "By aligning (2, 2) mode\n", + "..and inheriting the phase/time shifts for alignment of [(2, -2), (2, -1), (2, 1), (3, -3), (3, -2), (3, -1), (3, 1), (3, 2), (3, 3), (4, 4), (4, -4), (2, 2)] modes\n", + "INSPIRAL mode (2, -2) goes from -96.76196496360744Hz to -9.438739507933441Hz\n", + "MERGER mode (2, -2) goes from -251.0687028266488Hz to 12.966069881700337Hz\n", + "INSPIRAL mode (2, -1) goes from -62.02379567141938Hz to -4.393818338795352Hz\n", + "MERGER mode (2, -1) goes from -254.9292482800305Hz to 327.87649538971766Hz\n", + "INSPIRAL mode (2, 1) goes from 4.393818338795352Hz to 62.02379567141938Hz\n", + "MERGER mode (2, 1) goes from -313.63739087416826Hz to 221.7175658913996Hz\n", + "INSPIRAL mode (2, 2) goes from 9.438739507933368Hz to 96.76196496360744Hz\n", + "MERGER mode (2, 2) goes from -110.08789072952271Hz to 237.35295131831893Hz\n", + "INSPIRAL mode (3, -3) goes from -145.21410110217633Hz to -13.51471181013238Hz\n", + "MERGER mode (3, -3) goes from -358.3278926596189Hz to 225.35579870164025Hz\n", + "INSPIRAL mode (3, -2) goes from -144.55046914567717Hz to -9.262444099344108Hz\n", + "MERGER mode (3, -2) goes from -391.8867990338296Hz to 309.743824820241Hz\n", + "INSPIRAL mode (3, -1) goes from -120.222675009537Hz to -8.500360572408773Hz\n", + "MERGER mode (3, -1) goes from -412.5287985185308Hz to 216.6842377326586Hz\n", + "INSPIRAL mode (3, 1) goes from 8.500360572408773Hz to 120.222675009537Hz\n", + "MERGER mode (3, 1) goes from -187.96224455265502Hz to 407.47762764811296Hz\n", + "INSPIRAL mode (3, 2) goes from 9.262444099344108Hz to 144.55046914567717Hz\n", + "MERGER mode (3, 2) goes from -63.88312655739305Hz to 349.354524990372Hz\n", + "INSPIRAL mode (3, 3) goes from 13.51471181013238Hz to 145.21410110217633Hz\n", + "MERGER mode (3, 3) goes from -228.5430307886252Hz to 435.0758303356852Hz\n", + "INSPIRAL mode (4, 4) goes from 18.954061747847653Hz to 268.708448543125Hz\n", + "MERGER mode (4, 4) goes from -142.27116424365914Hz to 460.95503496021666Hz\n", + "INSPIRAL mode (4, -4) goes from -268.708448543125Hz to -18.954061747847653Hz\n", + "MERGER mode (4, -4) goes from -478.2305532730522Hz to 405.83311177341966Hz\n", + "blended.\n" + ] + } + ], + "source": [ + "print('================ Python ================')\n", + "hp_py, hc_py = genpy.get_imr_esigma_waveform_py(\n", + " mass1=m1,\n", + " mass2=m2,\n", + " spin1z=spin1z,\n", + " spin2z=spin2z,\n", + " eccentricity=eccentricity,\n", + " mean_anomaly=mean_anomaly,\n", + " distance=distance,\n", + " # inclination=inclination,\n", + " f_lower=f_low,\n", + " delta_t=delta_t,\n", + " modes_to_use=modes_to_use,\n", + " verbose=1\n", + ")\n", + "\n", + "print('================= C =================')\n", + "hp_c, hc_c = esigmapy.get_imr_esigma_waveform(\n", + " mass1=m1,\n", + " mass2=m2,\n", + " spin1z=spin1z,\n", + " spin2z=spin2z,\n", + " eccentricity=eccentricity,\n", + " mean_anomaly=mean_anomaly,\n", + " distance=distance,\n", + " # inclination=inclination,\n", + " f_lower=f_low,\n", + " delta_t=delta_t,\n", + " modes_to_use=modes_to_use,\n", + " verbose=1\n", + ")\n", + "\n", + "## adjust arrays\n", + "hp_py, hc_py = hp_py[3:-2], hc_py[3:-2]\n", + "hp_c, hc_c = hp_c[2:-2], hc_c[2:-2]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "04ba5692-e543-4c24-9110-7406324cee82", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "529\n", + "529\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(len(hp_py))\n", + "print(len(hp_c))\n", + "\n", + "hp_py = hp_py.copy()\n", + "hp_c = hp_c.copy()\n", + "\n", + "plt.figure(figsize=(10, 4))\n", + "plt.title(\n", + " rf\"\"\"Inspiral-ESIGMA\n", + " ==============================\n", + " $m_1={m1} M_\\odot, m_2={m2} M_\\odot$, \n", + " $\\chi_{{1z}}={spin1z}, \\chi_{{2z}}={spin2z}$, \n", + " $e_0={eccentricity}, l_0={mean_anomaly:.2f}, f_{{\\rm{{low}}}}={f_low}\\rm{{Hz}}$\n", + " $d_L={distance}\\rm{{Mpc}}, \\iota={inclination:.2f}$\"\"\"\n", + ")\n", + "\n", + "hp_py.plot(label=r\"$h_+$ (python)\")\n", + "hc_py.plot(label=r\"$h_\\times$ (python)\")\n", + "\n", + "hp_c.plot(label=r\"$h_+$ (C)\",ls='--')\n", + "hc_c.plot(label=r\"$h_\\times$ (C)\",ls='--')\n", + "\n", + "# print(len(hp_py))\n", + "# print(len(hp_c))\n", + "# a = hp_py-hp_c\n", + "\n", + "plt.xlabel(r\"$t (s)$\")\n", + "plt.ylabel(\"strain\")\n", + "plt.legend(ncol=4,loc='lower left')\n", + "plt.grid()\n", + "# plt.xlim(0.7,0.74)\n", + "# plt.ylim(-1e-21,1e-21)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "### Plotting difference ##\n", + "\n", + "eps = 1e-30\n", + "hp_py.start_time = hp_c.start_time\n", + "abs_diff = np.abs(hp_py-hp_c)\n", + "rel_diff = abs_diff / np.maximum(np.abs(hp_c), eps)\n", + "\n", + "plt.figure(figsize=(10, 4))\n", + "plt.semilogy(hp_py.sample_times,rel_diff)\n", + "plt.xlabel(r\"$t (s)$\")\n", + "plt.ylabel('relative difference')\n", + "plt.grid()\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "\n", + "plt.figure(figsize=(10, 4))\n", + "plt.semilogy(hp_py.sample_times,abs_diff)\n", + "plt.xlabel(r\"$t (s)$\")\n", + "plt.ylabel('absolute difference')\n", + "plt.grid()\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f8212777-6614-4ece-b493-e7978c2bab28", + "metadata": {}, + "source": [ + "### Match between waveforms" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "ac35d7b2-0dea-410f-8f59-dfb2c6227345", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The mismatch is: 0.2074%\n" + ] + } + ], + "source": [ + "from pycbc.filter import match\n", + "from pycbc.psd import aLIGOZeroDetHighPower\n", + "\n", + "f_low = 20.\n", + "\n", + "tlen = max(len(hp_c), len(hp_py))\n", + "hp_c.resize(tlen)\n", + "hp_py.resize(tlen)\n", + "\n", + "# Generate the aLIGO ZDHP PSD\n", + "delta_f = 1.0 / hp_c.duration\n", + "flen = tlen//2 + 1\n", + "psd = aLIGOZeroDetHighPower(flen, delta_f, f_low)\n", + "\n", + "# Note: This takes a while the first time as an FFT plan is generated\n", + "# subsequent calls are much faster.\n", + "match1, _ = match(hp_c, hp_py, psd=psd, low_frequency_cutoff=f_low)\n", + "mm = (1-match1)*100\n", + "print(f'The mismatch is: {mm:.4f}%')" + ] + }, + { + "cell_type": "markdown", + "id": "bf734c70", + "metadata": {}, + "source": [ + "## ============== Extracting orbital variables ============" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "ca70e4cd-94b4-4ef9-bf3c-6054f4e724c3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dict_keys(['time_evol', 'x_evol', 'eccentricity_evol', 'mean_ano_evol', 'phi_evol', 'phi_dot_evol', 'r_evol', 'r_dot_evol'])\n" + ] + } + ], + "source": [ + "# orb_params_list = [\"x\", \"e\", \"l\", \"r\", \"rdot\", \"phi\", \"phidot\"]\n", + "\n", + "out_c = lalsim.SimInspiralESIGMADynamics(m1,m2,spin1z,spin2z,eccentricity,f_low,mean_anomaly,1e-12,1/delta_t)\n", + "\n", + "time_c = out_c[0].data.data\n", + "x_c = out_c[1].data.data\n", + "e_c = out_c[2].data.data\n", + "meanan_c = out_c[3].data.data\n", + "phi_c = out_c[4].data.data\n", + "phidot_c = out_c[5].data.data\n", + "r_c = out_c[6].data.data\n", + "rdot_c = out_c[7].data.data\n", + "\n", + "out_py = espn.inspiral_esigma_dynamics(m1,m2,spin1z,spin2z,eccentricity,f_low,mean_anomaly,1e-12,1/delta_t,)\n", + "print(out_py.keys())\n", + "time_py = out_py['time_evol']/(m1+m2)/lal.MTSUN_SI\n", + "x_py = out_py['x_evol']\n", + "e_py = out_py['eccentricity_evol']\n", + "meanan_py = out_py['mean_ano_evol']\n", + "phi_py = out_py['phi_evol']\n", + "phidot_py = out_py['phi_dot_evol']\n", + "r_py = out_py['r_evol']\n", + "rdot_py = out_py['r_dot_evol']" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "73c89a53-73d7-4b80-a8df-52a9be1e6443", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_14518/1397554071.py:56: RuntimeWarning: invalid value encountered in divide\n", + " reldiff_phi = (phi_c-phi_py)/phi_c\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig,ax = plt.subplots(1,2,figsize=(15,5))\n", + "ax[0].plot(time_py,e_py,label='python')\n", + "ax[0].plot(time_c,e_c,ls='--',label='C')\n", + "ax[0].set_xlabel('$t (M)$',fontsize=13)\n", + "ax[0].set_ylabel('$e (t)$',fontsize=13)\n", + "ax[0].legend(), ax[0].grid()\n", + "\n", + "reldiff_e = (e_c-e_py)/e_c\n", + "ax[1].semilogy(time_py,np.abs(reldiff_e))\n", + "ax[1].set_xlabel('$t (M)$',fontsize=13)\n", + "ax[1].set_ylabel('|rel diff|',fontsize=13)\n", + "ax[1].grid()\n", + "plt.show()\n", + "\n", + "###---------------\n", + "\n", + "fig,ax = plt.subplots(1,2,figsize=(15,5))\n", + "ax[0].plot(time_py,x_py,label='python')\n", + "ax[0].plot(time_c,x_c,ls='--',label='C')\n", + "ax[0].set_xlabel('$t (M)$',fontsize=13)\n", + "ax[0].set_ylabel('$x (t)$',fontsize=13)\n", + "ax[0].legend(), ax[0].grid()\n", + "\n", + "reldiff_x = (x_c-x_py)/x_c\n", + "ax[1].semilogy(time_py,np.abs(reldiff_x))\n", + "ax[1].set_xlabel('$t (M)$',fontsize=13)\n", + "ax[1].set_ylabel('|rel diff|',fontsize=13)\n", + "ax[1].grid()\n", + "plt.show()\n", + "\n", + "###----------------\n", + "\n", + "fig,ax = plt.subplots(1,2,figsize=(15,5))\n", + "ax[0].plot(time_py,meanan_py,label='python')\n", + "ax[0].plot(time_c,meanan_c,ls='--',label='C')\n", + "ax[0].set_xlabel('$t (M)$',fontsize=13)\n", + "ax[0].set_ylabel('$l (t)$',fontsize=13)\n", + "ax[0].legend(), ax[0].grid()\n", + "\n", + "reldiff_meanan = (meanan_c-meanan_py)/meanan_c\n", + "ax[1].semilogy(time_py,np.abs(reldiff_meanan))\n", + "ax[1].set_xlabel('$t (M)$',fontsize=13)\n", + "ax[1].set_ylabel('|rel diff|',fontsize=13)\n", + "ax[1].grid()\n", + "plt.show()\n", + "\n", + "###----------------\n", + "\n", + "fig,ax = plt.subplots(1,2,figsize=(15,5))\n", + "ax[0].plot(time_py,phi_py,label='python')\n", + "ax[0].plot(time_c,phi_c,ls='--',label='C')\n", + "ax[0].set_xlabel('$t (M)$',fontsize=13)\n", + "ax[0].set_ylabel('$\\\\phi (t)$',fontsize=13)\n", + "ax[0].legend(), ax[0].grid()\n", + "\n", + "reldiff_phi = (phi_c-phi_py)/phi_c\n", + "ax[1].semilogy(time_py,np.abs(reldiff_phi))\n", + "ax[1].set_xlabel('$t (M)$',fontsize=13)\n", + "ax[1].set_ylabel('|rel diff|',fontsize=13)\n", + "ax[1].grid()\n", + "plt.show()\n", + "\n", + "###----------------\n", + "\n", + "fig,ax = plt.subplots(1,2,figsize=(15,5))\n", + "ax[0].plot(time_py,phidot_py,label='python')\n", + "ax[0].plot(time_c,phidot_c,ls='--',label='C')\n", + "ax[0].set_xlabel('$t (M)$',fontsize=13)\n", + "ax[0].set_ylabel('$\\\\dot{\\\\phi} (t)$',fontsize=13)\n", + "ax[0].legend(), ax[0].grid()\n", + "\n", + "reldiff_phidot = (phidot_c-phidot_py)/phidot_c\n", + "ax[1].semilogy(time_py,np.abs(reldiff_phidot))\n", + "ax[1].set_xlabel('$t (M)$',fontsize=13)\n", + "ax[1].set_ylabel('|rel diff|',fontsize=13)\n", + "ax[1].grid()\n", + "plt.show()\n", + "\n", + "###----------------\n", + "\n", + "fig,ax = plt.subplots(1,2,figsize=(15,5))\n", + "ax[0].plot(time_py,r_py,label='python')\n", + "ax[0].plot(time_c,r_c,ls='--',label='C')\n", + "ax[0].set_xlabel('$t (M)$',fontsize=13)\n", + "ax[0].set_ylabel('$r (t)$',fontsize=13)\n", + "ax[0].legend(), ax[0].grid()\n", + "\n", + "reldiff_r = (r_c-r_py)/r_c\n", + "ax[1].semilogy(time_py,np.abs(reldiff_r))\n", + "ax[1].set_xlabel('$t (M)$',fontsize=13)\n", + "ax[1].set_ylabel('|rel diff|',fontsize=13)\n", + "ax[1].grid()\n", + "plt.show()\n", + "\n", + "###----------------\n", + "\n", + "fig,ax = plt.subplots(1,2,figsize=(15,5))\n", + "ax[0].plot(time_py,rdot_py,label='python')\n", + "ax[0].plot(time_c,rdot_c,ls='--',label='C')\n", + "ax[0].set_xlabel('$t (M)$',fontsize=13)\n", + "ax[0].set_ylabel('$\\\\dot{r} (t)$',fontsize=13)\n", + "ax[0].legend(), ax[0].grid()\n", + "\n", + "reldiff_rdot = (rdot_c-rdot_py)/rdot_c\n", + "ax[1].semilogy(time_py,np.abs(reldiff_rdot))\n", + "ax[1].set_xlabel('$t (M)$',fontsize=13)\n", + "ax[1].set_ylabel('|rel diff|',fontsize=13)\n", + "ax[1].grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6cf3d53c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "esigma-dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}